Journal of Management, Informatics and Human Resources Volume 59, Issue 1, February 2026 ISSN 1318-5454 Revija za management, informatiko in kadre Organizacija (Journal of Management, Informatics and Organizacija (Revija za management, informatiko in Human Resources) is an interdisciplinary peer-reviewed človeške vire) je interdisciplinarna recenzirana revija, ki journal which is open to contributions of high quality, from objavlja visoko kakovostne prispevke z vseh vidikov, ki so any perspective relevant to the organizational phenomena. pomembni za organizacijske procese in strukture. Revija je zasnovana tako, da spodbuja zanimanje za The journal is designed to encourage interest in all matters različne vidike v zvezi z organizacijskimi vedami in je relating to organizational sciences and is intended to ap- namenjena tako akademski kot strokovni skupnosti. peal to both the academic and professional community. In Revija objavlja izvirne članke, ki spodbujajo empirično, particular, journal publishes original articles that advance teoretično in metodološko razumevanje teorij in konceptov the empirical, theoretical, and methodological understand- managementa in organizacije. Pozdravljamo tudi ing of the theories and concepts of management and or- prispevke iz drugih znanstvenih disciplin, ki spodbujajo ganization. The journal welcomes contributions from other nove koncepte v organizacijski teoriji in praksi. Objavljamo scientific disciplines that encourage new conceptualiza- članke, ki analizirajo organiziranost z različnih vidikov, so tions in organizational theory and management practice. usmerjeni na organizacije različnih velikosti in iz različnih sektorjev, na enote, ki sestavljajo organizacije, in na mreže, We welcome different perspectives of analysis, including v katere so organizacije vpete. the organizations of various sizes and from various branch- es, units that constitute organizations, and the networks in Teme so pokrivajo predvsem naslednja področja: which organizations are embedded. • organizacijska teorija, upravljanje, razvoj in Topics are drawn, but not limited to the following areas: organizacijsko vedenje; • organizational theory, management, development, • management človeških virov (kot so organizacija in and organizational behaviour; razvoj zaposlenih, vodenje, ustvarjanje vrednosti s pomočjo človeških virov, organizacijski pojavi na • human resources management (such as organization delovnem mestu itd.); & employee development, leadership, value creation through HRM, workplace phenomena etc.); • vodstveni in podjetniški vidiki izobraževanja; • managerial and entrepreneurial aspects of education; • poslovni informacijski sistemi (kot so digitalno poslovanje, sistemi za podporo odločanju, poslovna • business information systems (such as digital business, analitika itd.); decision support systems, business analytics etc.); • podjetniški inženiring (npr. organizacijsko oblikovanje, • enterprise engineering (e.g., organizational upravljanje poslovnih procesov, paradigme design, business process management, enterprise preoblikovanja podjetij itd.); transformation paradigms etc.); • članki, ki analizirajo organizacijsko uspešnost in • papers that analyse and seek to improve organizational prizadevanja za izboljšanje le-te. performance. Organizacija, Volume 59, Issue 1 February 2026 Contents 1/2026 RESEARCH PAPERS 3 Ivan MATIĆ, How Academic Context Shapes Students’ Ana JURAS, Ethical Behaviour: New Evidence from a Marina LOVRINČEVIĆ Transitional Society 22 Dušan GOŠNIK, Analysis of Key Impact Factors in New Mateja JERMAN Methods Implementation in Organisations: A Change Management Perspective 34 Slavka SILBERG, Relationships between Personal Luděk STEHLÍK, Characteristics, Job Satisfaction and Tomáš SOLLÁR, Organisational Behaviour of Work Team Michal SILBERG, Members and the Role of Organisational Jana MATOŠKOVÁ Agility Maturity 55 Agne SNEIDERIENE, Identifying Marketing Tools to Promote the Marius ZAMKAUSKAS Development of the Knowledge Economy: The Case of Lithuania 71 Oleksandr DOROKHOV, Development of a Methodology Based Kadri UKRAINSKI, on Fuzzy Logic for Solving the Problem Hanna KANEP, of Evaluating a Startup Team Under Liudmyla DOROKHOVA Uncertainty REVIEW ARTICLE 95 Marjeta MAROLT, Overview of Research on Higher Danijela JAKŠIĆ Education Teachers’ Involvement in Learning Analytics Editorial office: University of Maribor, Faculty of Organizational Sciences, Založba Moderna Organizacija, Kidriceva 55a, 4000 Kranj, Slovenia Tel.: +386-4-2374297, E-mail: organizacija.fov@um.si, URL: https://organizacija.fov.um.si. 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Organizacija, Volume 59 Issue 1, February 2026 EDITOR / UREDNIK EDITORIAL BOARD / Nataša Petrović UREDNIŠKI ODBOR REVIJE University of Belgrade, Serbia Maja Meško University of Maribor, Faculty of Hossein Arsham, Tetyana Pimonenko, Organizational Sciences, Slovenia University of Baltimore, USA Sumy State University, Balatsky Academic and Scientific Institute of Franc Čuš, Finance, Economics and Management, CO-EDITORS / SOUREDNIKI University of Maribor, Slovenia Ukraine Petr Doucek Sasha M. Dekleva Hans Puxbaum, Prague University of Economics, DePaul University, School of Vienna University of Technology, Faculty of Informatics and Statistics, Accountancy and MIS, Chicago, USA Austria Czech Republic Vlado Dimovski, Vladislav Rajkovič, Matjaž Maletič University of Ljubljana, Slovenia University of Maribor, Slovenia University of Maribor, Faculty of Organizational Sciences, Slovenia Daniel C. Ganster, Gábor Rekettye, Colorado State University, USA University of Pécs, Hungary Jože Zupančič University of Maribor, Faculty of Jože Gričar, Henk G. Sol, Organizational Sciences, Slovenia University of Maribor, Slovenia Faculty of Economics and Business, University of Groningen, Netherlands Włodzimierz Sroka Werner Jammernegg WSB University, Department of Viena University of Economics and Eugene Semenkin Management, Dąbrowa Górnicza, Business Administration, Austria Reshetnev Siberian State University of Poland Science and Technology, Krasnoyarsk, Marius Alexander Janson, Russian Federation University of Missouri-St. Louis, USA Velimir Srića, Stefan Klein, University of Zagreb, Croatia University of Münster, Germany Paula Swatman, Aleksandar Marković, University of Tasmania, Australia University of Belgrade, Serbia Brian Timney, Hermann Maurer, Western University, Graz University of Technology, Graz, London, Ontario, Canada Austria Maurice Yolles, Matjaž Mulej, Liverpool John Moores University, UK University of Maribor, Slovenia Douglas R. Vogel, Valentinas Navickas, City University of Hong Kong, China Kaunas University of Technology, Lithuania Gerhard Wilhelm Weber, Poznan University of Technology, Ota Novotny, Poland University of Economics, Prague, Czech Republic Anna Lucyna Wziątek-Staśko, Jagiellonian University in Kraków, Milan Pagon, Poland Independent University, Bangladesh (IUB), Dhaka, Bangladesh Yvonne Ziegler, Frankfurt University of Applied Björn Paape, Sciences, Germany RWTH-Technical University Aachen, Germany Hans-Dieter Zimmermann, Eastern Switzerland University of Matjaž Perc Applied Sciences (OST), St. Gallen, University of Maribor, Slovenia Switzerland Dušan Petrač, NASA, Jet Propulsion Laboratory, California Institute of Technology, USA 2 Organizacija, Volume 59 Research Paper Issue 1, February 2026 DOI: 10.2478/orga-2026-0001 How Academic Context Shapes Students’ Ethical Behaviour: New Evidence from a Transitional Society Ivan MATIĆ, Ana JURAS, Marina LOVRINČEVIĆ University of Split, Faculty of Economics, Business and Tourism, Split, Croatia, imatic@efst.hr, ajuras@efst.hr, mlovrinc@efst.hr Background/Purpose: An ongoing problem of the ethical transgression of students poses a fundamental threat to the functioning of higher education institutions and translates to their behaviour in the future workplace. The aim of the paper is to examine the relationship between the academic context in higher education institutions in a transi- tional society and students’ ethical behaviour. Methods: Two-source empirical research was conducted using samples of 235 students and 112 faculty and ad- ministrative staff from 12 higher education institutions in Croatia. Data on the ethical infrastructure of higher educa- tion institutions and the ethical behaviour of students and employees were collected from both groups. Descriptive statistics were used to provide insights into various aspects of the academic context and the characteristics of stu- dents’ and employees’ ethical behaviour. Multiple regression analyses were conducted to examine the relationships between the academic context and the ethical behaviour of students and employees. Results: Perceptions of students and employees differed on a number of aspects of the academic context in their higher education institutions, while the formal ethical framework, individual-level ethics, and witnessing and sanc- tioning of unethical behaviour are found to be the factors that play a role in shaping students’ ethical behaviour. Students enrolled in natural sciences-related programmes are less susceptible to the effects of academic context than those studying programmes in other scientific fields. Conclusion: The current state of academic context at higher education institutions in a typical transitional society leaves considerable room for improvement in developing ethical infrastructure and promoting a culture of academic integrity and ethical values. Translating the ‘words’ into ‘actions’ at both organisational and individual levels is a primary goal for these institutions to establish an effective ethical framework and culture, and to be perceived as ethical by their stakeholders. Keywords: Academic dishonesty, Organisational ethics, Higher education institutions, Students, Faculty, Croatia 1 Introduction ucation institutions (HEIs), many of which have emerged even at leading HEIs, clearly demonstrate the aforemen- In the 21st century, ethical transgression is an ongo- tioned problem (Cronan et al., 2018). The recent rise of ing problem in higher education (Rothman, 2017), and the artificial intelligence (AI), with all its benefits for teachers, lack of student academic integrity is regarded as a serious researchers, and students, has also created numerous eth- threat to the fundamental function of educational institu- ics-related challenges for HEIs, such as academic integrity tions (MacLeod & Eaton, 2020). A number of scandals versus academic misconduct and ethical dilemmas (e.g. involving students’ ethical behaviour (EB) at higher ed- cheating and plagiarism), data privacy, transparency, ac- 1 Received: 12th March 2025; Accepted: 19th November 2025 3 Organizacija, Volume 59 Research Paper Issue 1, February 2026 countability and security, equity in access to AI, responsi- countries (Hendy & Montargot, 2019; Hendy et al., 2021), ble use of AI, etc. (Dabis & Csáki, 2024), further exacer- especially in transitional countries, although it has been an bating ‘the ethical transgression problem’. In a globalised academic research topic for years, further justifying the world marked by rapid technological advancements and need for studies such as the one presented in this paper. increasing social complexity, where students’ motivation To achieve the stated research objective, the paper and interest in science are constantly declining, HEIs face is structured as follows. The second section of the paper an even greater challenge of ‘getting through to students’ provides a brief theoretical background for conducting the on the topics of morals, values, and ethics within the ac- empirical research. The third section presents the meth- ademic environment (Chowdhury, 2016). As a result, odological approach used and the main characteristics of ethics in universities, faculties, and schools has become the two research samples. The next section focuses on the a burdensome issue for institutional leaders and a subject main findings of the empirical research. The final sections of considerable scholarly attention. Research findings in of the paper discuss the key findings and outline the main academic contexts indicate that cheating has been steadily conclusions derived from the research. increasing over decades, accompanied by a decline in indi- viduals’ perceived severity of dishonest behaviour, result- ing in an academic environment where student cheating 2 Theoretical background is a highly persistent and pervasive behaviour (Farnese et al., 2011; Park, 2017; Hendy & Montargot, 2019; Cheng The role of higher education in developing young et al., 2021). Following the ethical violations that made experts in terms of ethics and ethical values has attract- headlines (Gupta et al., 2011), HEIs are recognising the ed considerable attention from researchers, largely due need to develop an ethical organisational culture by set- to numerous corporate scandals and reports of UB in the ting formal ethical standards and ensuring that leaders and workplace (Deshpande, Joseph & Berry, 2012; Ballantine employees act ethically, with the ultimate aim of reduc- et al., 2018). Given the assumption that students who are ing the incidence of unethical behaviour (UB) among both dishonest in their studies will transfer this behaviour to students and employees (Elliot et al., 2013). To foster a their future workplaces (Rakovski & Levy, 2007; Hendy culture of academic honesty, HEIs invest significant re- & Montargot, 2019), HEIs, with their societal role and sources in emphasizing EB, establishing clear frameworks potential to educate exceptionally innovative profession- for acceptable and unacceptable behaviour in an academ- als and tomorrow’s leaders across all industrial fields, re- ic context, and in developing policies to guide the con- search, and politics (Momete, 2019; Cheng et al., 2021), duct of students and faculty (MacLeod & Eaton, 2020). have a critical responsibility to prepare students to address Ethical infrastructure in HEIs—a combination of ethical ethical issues in the workplace appropriately. In address- culture, climate, rules, codes of ethics, and EB-related pro- ing academic dishonesty, which results from a complex grammes, rewards, and sanctions (Treviño et al., 2014)— interplay between individual and situational factors, HEIs needs to move from ‘words’ to ‘actions’ at both individual must respond with an equally complex approach to pro- and organisational levels for HEIs and their students to be moting academic honesty and a culture of integrity (Ste- perceived as ethical (Credo et al., 2010). phens, 2015, p. 2). In this sense, Prisacariu & Shah (2016, Following the above, this study explores the role of the p. 161) emphasise that “ethics and moral values are a vir- academic context in shaping students’ EB in a transition- tue to a high-quality university, where corporate and aca- al society, particularly the dimension related to studying, demic governance framework articulates ethical standards namely academic honesty. The study has several objec- in teaching, research, and all other activities”. To produce tives: honest, reliable, and trustworthy graduates, HEIs must de- (1) to examine the current state of the academic con- velop and sustain a culture of integrity and ethical values text in HEIs in a transitional society; to socialise and develop their employees to adhere to the (2) to identify key predictors from the HEI’s academic proclaimed ethical values (Momete, 2019). This is par- context that play a role in shaping faculty’s EB; ticularly important as the long-standing values of HEIs, (3) to identify key predictors from the HEI’s academic namely fairness and impartiality, are in many cases being context that play a role in shaping students’ EB; and superseded or even replaced by UB and individual inter- (4) to examine the nature of the identified relationships. ests. The arrival of the AI era, with its beneficial tools and Despite the multitude of different approaches, strate- unprecedented opportunities for teachers, researchers, and gies, initiatives, and actions implemented by HEIs, based students, has also generated a range of ethical concerns on the results of many related studies, the students’ UB at if AI tools are not used correctly, further amplifying the HEIs persists, further reinforcing the need for new research challenges faced by HEIs (Ocen et al., 2025) and high- and contributions on the topic of students’ UB and its pre- lighting the need for specially designed AI ethics-focused dictors (Cronan et al., 2018). Moreover, students’ UB has regulatory frameworks and their effective implementation only been modestly researched at the national level in EU (Usher et al., 2025). All this requires the management of 4 Organizacija, Volume 59 Research Paper Issue 1, February 2026 HEIs to formulate and implement comprehensive strate- monitoring and maintenance of systems to detect mis- gies, mechanisms, and initiatives to establish organisation- conduct, and failure to investigate reported cases of aca- al ethics and promote ethical conduct (Heyneman, 2011; demic misconduct), and (3) students (plagiarism, contract Elliot et al., 2013). cheating, purchasing customised essays from freelance Organisational ethics manifests itself in the ethical in- writers, using portable high-tech devices to communicate frastructure of HEI in the form of culture and climate, code with accomplices, and obtaining examination questions of ethics, programmes, rewards, and sanctions (Treviño et in advance (Finchilescu & Cooper, 2018)). Due to the AI al., 2014), and can be regarded as a factor that enables ex- revolution and the widespread availability of AI tools, ceptional, strong ethical conditions in organisational life the previously discussed UB practices are gaining further (Bright et al., 2014 in McLeod et al., 2016). For HEIs to be momentum, and new forms of ethically questionable ac- perceived as ethical, organisational ethics in these institu- tions have emerged, such as fabrication and falsification tions must move from ‘words’ to ‘actions’ at both the indi- of data and plagiarism through the use of AI, dilemmas vidual and organisational levels (Credo et al., 2010), which in maintaining ethical standards in research (e.g. lack of includes the behaviours of employees (managers, faculty, transparency when using AI tools in research, use of algo- and administrative staff) and the behaviours of students rithms in data processing and results generation leading to as the final outputs of HEIs (Moore, 2006; Mirshekary & wrong interpretations, and avoidance of accountability due Lawrence, 2009). In addition to the ethical framework they to ‘black box’ decision-making), privacy issues related to encounter, students’ attitudes towards ethics and UB can data collection and use by AI, irresponsible use of AI by be influenced by what their professors teach about ethics ignoring its limitations, algorithmic bias in hiring process- and by observing how their professors and fellow students es, etc. (Dabis & Csáki, 2024; Ocen et al., 2025; Usher et act in ethically challenging situations (Robie & Kidwell, al., 2025). 2003). Therefore, it is essential for HEIs to ensure the ef- When examining students’ EB, relevant studies have fectiveness of their ethical infrastructure by achieving con- addressed a wide range of possible predictors, mainly fo- gruence between ‘words’ (formal policies, programmes, cusing on predictors of the intention to behave unethically initiatives, …) and the behaviours demonstrated at both the and/or predictors of actual UB (Cronan et al., 2021). Re- organisational and individual employee levels (‘actions’). garding predictors of students’ EB, three general groups The congruence between individual employee ethics and have a significant influence: individual factors, contextual organisational ethics is the main catalyst for the effective- factors, and HEIs’ ethics-related programmes and policies ness of HEIs in shaping desirable responses of individuals (McCabe et al., 2001). Hendy et al. (2021) take a similar (employees and students) to ethical dilemmas (Liedtka, approach, dividing the predictors into two broad catego- 1989, in Elango et al., 2010). ries: individual (demographic, cognitive, and non-cogni- Most relevant studies have identified academic UB as tive variables) and situational or contextual variables (such manifesting through cheating and plagiarism, labelling it as the existence of a code of ethics at the HEI, disciplinary as academic dishonesty (Zopiatis & Krambia-Kapardis, actions or penalties for UB, faculty attitudes towards stu- 2008; Cronan et al., 2018; Hendy et al., 2021). Academ- dents’ UB, and a supportive learning environment). Cheng ic dishonesty, or UB, in contrast to academic integrity, is et al. (2021) identify individual variables (demographic primarily perceived as academic cheating to gain unlaw- factors, individual differences, attitudes, etc.), organisa- ful advantage – a conscious effort to use proscribed data tional variables (punishment policies regarding UB, hon- and/or resources in examinations (e.g. copying another our code, etc.), and national-level variables (national cul- student’s answers) or written work (e.g. plagiarising an- ture, study level, etc.) as influential predictors of students’ other person’s research or reports) submitted for academ- EB. From the categorizations, it is evident that a specific ic credit or publication (Chapman et al., 2004; Hayes & set of predictors, labelled as contextual, plays a significant Introna, 2005 in Mirshekary & Lawrence, 2009; Cheng role in shaping students’ EB. As emphasised in its main et al., 2021). Academic dishonesty is multifaceted and objectives, this study focuses on investigating contextu- encompasses a wide range of harmful behaviours, includ- al predictors of students’ EB and considers the academic ing cheating, plagiarism, and fraudulent excuses (Yazici context category to reflect elements of the HEI’s ethical et al., 2011, in Kuntz & Butler, 2014, p. 478). According infrastructure in terms of ‘words’ and ‘actions’. In this to Davies (2023), in HEIs, UB can be demonstrated by (1) sense, the study focuses on the HEI’s ethics-related strate- academics or researchers (predatory publishing, overpub- gies, initiatives, programmes, and policies (words, formal lishing using the same dataset, questionable practices in framework) and their manifestations in the form of an eth- student supervision, hiring practices, and inappropriate ical culture and climate and an ethics-promoting learning interpersonal relationships (Christensen Hughes & Eaton, environment (actions, effective implementation of the for- 2022)), (2) administrative staff (questionable practices in mal ethical framework). Formal mechanisms such as the admission rules, questionable accuracy and honesty in re- existence of a code of ethics, the embeddedness of ethics cording results and checking module credits, questionable into the HEI’s mission and strategy, the existence of formal 5 Organizacija, Volume 59 Research Paper Issue 1, February 2026 job positions and/or bodies and courses dealing with ethi- Valentine & Fleischman (2004), Weber (2006), and Lau cal issues, and manifestations such as HEI management’s et al. (2012). The student questionnaire included seven EB and its commitment to implementing ethics, the EB of demographic-related questions and 24 closed-type ques- faculty and fellow students, the HEI’s sanctioning meas- tions on a 5-point Likert scale, investigating the students’ ures for employees or students when faced with UB, etc., perceptions of the academic context at their HEI, as well are among the predictors of students’ EB investigated in as self-assessment of their own EB. The employee ques- this study. tionnaire included seven demographic-related questions and 26 closed-ended questions on a 5-point Likert scale, 3 Methodology exploring the employees’ perceptions of the academic con- text and students’ EB at their HEI, as well as self-assess- ment of their own EB. Results of Cronbach’s alpha tests To provide answers to the main research questions of for internal consistency reliability, conducted on our data the study, we used a questionnaire as our research instru- (Table 1), confirmed good to excellent reliability and va- ment and conducted our research at HEIs in the Republic of lidity of the scales used in our research instruments (Hair Croatia during 2022, at the end of the COVID-19 pandem- et al., 2019b). ic and just before the widespread adoption of AI tools in After three rounds of emails with questionnaires and higher education. We initially contacted the management reminders, sent by designated contact persons at HEIs to of 20 targeted HEIs to obtain their consent to participate their employees and students, we collected 235 correctly in the research and to request their operational assistance completed questionnaires from students (1.9% response in distributing our questionnaires to their employees and rate) and 112 correctly completed questionnaires from em- students. After receiving their positive responses, we con- ployees (5.6% response rate). All statistical analyses were ducted our research at 12 faculties and university depart- conducted using SPSS Statistics 23.0 software, and, as a ments in the Republic of Croatia by sending two separate rule of thumb, we used a threshold of p < .10 to determine questionnaires in Google Forms format to the designated statistical significance (Aguinis et al., 2010). contact person in the management of each participating The student research sample (Figure 1) consisted main- HEI. These individuals were responsible for further dis- ly of undergraduate students (77%) enrolled in university seminating the questionnaires to the e-mail addresses of study programmes (77%), of whom more than two-thirds their students, teachers and administrative staff. The HEIs (69%) were female. As expected, compared to the usu- included in the study cover all major scientific fields, al student population structure, 81% of the sample were which was one of the sampling criteria, employ approxi- younger than 23, particularly those aged 20 and 21. First- mately 2,000 academic and non-academic staff, and have and second-year students made up 63% of the sample. The around 18,000 enrolled students. We used a combination natural and interdisciplinary sciences, compared to the of non-probability and probability sampling strategies: we other three scientific fields, were significantly less repre- targeted a specific number of HEIs based on convenience sented in the sample (7% and 13%, respectively) and on a criteria, while the entire population of employees and stu- scale where 2 is the minimum and 5 is the maximum pass- dents at the selected HEIs had an equal chance of being in- ing grade, about half of the students surveyed (51%) had cluded in the sample, i.e., a probability sampling strategy. mediocre to high GPAs, while more than a third achieved Taking into account the characteristics, perspectives, very high to excellent results during their studies (36%). and ability of each target group of respondents to provide Similar to the student sample, female employees sig- informed opinions, we developed two separate, partially nificantly dominated the employee research sample, com- overlapping questionnaires (Appendix 1) based on the prising 72% (Figure 1). Nearly 60% of the sample were contributions of Hunt et al. (1989), Treviño et al. (1998), Table 1: Internal consistency reliability statistics (Cronbach’s alpha) for measurement scales Scale Sample Number of items Cronbach’s alpha Interpretation Formally embedded ethical mechanisms Student 6 .736 Good at HEI Employee 7 .722 Good Current ethics-related environment at Student 17 .738 Good HEI Employee 18 .928 Excellent The importance of ethics to the respon- Student 1 n/a n/a dent Employee 1 n/a n/a 6 Organizacija, Volume 59 Research Paper Issue 1, February 2026 Figure 1: Characteristics of research samples Note. SSH stands for Social Sciences and Humanities, while Middle/low (T&S) refers to middle and low management positions in teaching and science jobs. middle-aged employees aged between 36 and 55 (58%), veyed, the promotion and implementation of ethical rules, while the length of service was relatively balanced, with a norms, behaviour, and culture are at a moderate level, and slightly higher proportion of new employees having up to respondents are moderately aware of the ethical activities, 5 years of service (32%). Employees from HEIs specialis- norms, and culture of their HEI. Compared to students, ing in medicine and biotechnology, and interdisciplinary staff are more aware (p = .003) and perceive the HEI’s sciences were the most represented in the sample (35% commitment to promoting ethical rules, norms, behaviour, and 34%, respectively), which differs slightly from the stu- and culture as greater (p = .011). As expected, compared dent sample, where students studying at HEIs specialising to students, employees are significantly more familiar with in social sciences and humanities and technical sciences the existence of a Code of Ethics, stand-alone courses, eth- were most represented (26% and 30%, respectively). The ics-related topics in syllabuses, and formal units and dedi- research sample was relatively balanced in terms of job cated individuals involved in the implementation of ethics type, with 55% of employees working in teaching and re- at their HEI (p ≤ .001). According to employees, a Code of search, and the remaining 45% in administration. As ex- Ethics is predominantly present at HEIs, while stand-alone pected, a smaller proportion of all employees in the sample courses, ethics-related topics in curricula, and formal posi- held management positions (32%). The structure of teach- tions concerned with the implementation of ethics are only ing and research jobs by academic rank was relatively bal- moderately present. In addition, the students mentioned anced, with each academic rank represented between 16% were moderately exposed to ethics and ethical dilemmas and 31%. in lecture and exercise topics. Extracurricular content fo- cusing on ethics is a problematic area according to both 4 Results students and employees, with students being significantly more critical of this content than employees (p < .001). 4.1 Descriptive statistics The descriptive statistics provided interesting and somewhat concerning insights (Table 2). At the HEIs sur- 7 Organizacija, Volume 59 Research Paper Issue 1, February 2026 8 Table 2: Descriptive statistics of the academic context and students’ and employees’ ethical behaviour (both samples) Organizacija, Volume 59 Research Paper Issue 1, February 2026 Employees perceive UB as having a low to moderate while their responses when confronted with UB by stu- presence among students and a low presence among em- dents (e.g., cheating in examinations) still leave significant ployees, while students perceive UB as having a low to room for improvement. moderate presence among students and a fairly low pres- ence among employees. Students perceive study-related 4.2 The relationship between academic UB as far more present among students compared to other UB, that is, at a moderate versus quite low level. Similarly, context and the ethical behaviour of students perceive UB, albeit at a fairly low level, as slight- employees and students ly more present among faculty than among administrative staff. The perceptions of UB by students and employees 4.2.1 Exploratory factor analyses differ between the two groups of respondents (p = .074, p = .006), although both groups perceive the frequency To identify latent factors within a given set of items of sanctioning UB among both students and employees related to perceived academic context, we conducted two as low (p = .802, p = .576). It is interesting to note that exploratory factor analyses, one for the student sample and both groups of respondents generally perceive faculty EB one for the employee sample, both using principal compo- as high (p = .631), while employees perceive students’ be- nent analysis and varimax rotation (Tables 3 and 4). Given haviour as more in line with proclaimed ethical rules and the number of items and respondents in each sample (stu- norms – at a moderate to high level – compared to stu- dent sample: 19 items, 235 respondents; employee sample: dents’ perception of their own behaviour, which they rate 12 items, 112 respondents), a minimum factor loading cri- as low to moderate (p = .015). According to employees, the terion of .35 was established (Hair et al., 2019a). faculty’s contribution to the development of ethics among The exploratory factor analyses were valid and relia- students during classes and the implementation of ethi- ble according to various rules of thumb, such as having 10 cal standards and norms in relation to students are high, subjects per variable and an absolute sample size above while students rate this contribution and implementation 100. For both exploratory factor analyses, the results in- somewhat lower, at a moderate to high level (p = .067, dicate a good appropriateness of the data for factor anal- p = .024). The importance of ethics for the respondent is ysis (MSA > .60) and that the chosen data reduction tech- significantly higher for employees, who perceive ethics as nique can meaningfully compress the data (p < .05) (Field, very important, compared to students, who consider ethics 2024). For the student sample, the factor solution yielded only moderately important (p < .001). The results regard- five factors from the scale, accounting for 61.95% of the ing students’ tendency to cheat on examinations and their variance in the data. For the employee sample, the factor tendency to report cheating by other students are concern- solution yielded three factors from the scale, accounting ing. Students stated that they have rarely cheated on exam- for 62.89% of the variance in the data. The communalities inations during their studies, have cheated occasionally in in both factor models showed that all items contributed elective and extracurricular courses, and have little or no significantly to measuring the underlying factors, with R2 tendency to report cheating by fellow students. > .40 and average communalities of .70 (MacCallum et al., Similar to the ten questions directed only at students, 1999, 2001), while all factor loadings were above the cut- nine questions were also directed solely at employees, as off value of .40 (S7 = .395) (Stevens, 2012). they address aspects of the academic context on which We named each specific factor according to the nature students cannot provide informed opinions. According to of the items loaded (e.g. Individual-level ethics, Formal employees, the ethical values of HEIs are embedded in ethical framework, Ethics implementation in practice, their vision, mission, and strategy at a high level, while etc.). Estimates of the reliability of the yielded subscales the behaviour of HEI management regarding the imple- (factors) indicate that all have acceptable to excellent reli- mentation of ethical norms and standards is at a low to ability (and internal consistency), as all Cronbach’s alpha moderate level. Specifically, HEI management is not clear- values were in the .60–.90 range (Gliem & Gliem, 2003; ly associated with high ethical standards and behaviours, Hair et al., 2019b), especially when considering that low- their stance of zero tolerance towards UB is not clear, and er Cronbach’s alpha values (around .50) can be expected they are not strict in sanctioning UB among HEI employ- for scales with a small number of items (< 10) (Pallant, ees. Additionally, there is a certain degree of favouritism 2020). Due to the assumption of multiple-item correlation, towards individuals by HEI management and only modest estimates of internal consistency reliability do not apply to recognition of exemplary EB by Individuals. Thus, em- the Factor 2 and Factor 3 subscales in the factor solution ployees feel to some extent that they need to compromise for the employee sample (Hair et al., 2019b), as these sub- their ethical values and principles to be successful at their scales contain two or fewer items. HEI. Finally, faculty adherence to ethical standards in con- ducting scientific research and publishing is at a high level, 9 Organizacija, Volume 59 Research Paper Issue 1, February 2026 Table 3: Results of the exploratory factor analysis for the academic context (student sample) KMO Significance of Extraction method Rotation method coefficient Bartlett’s test Principal Component Analysis Varimax .785 .000 Item Factor loading 1 1 2 3 4 5 Factor 1: Individual-level ethics S16 .69 S17 .57 .55 S18 .71 .35 S19 .71 S24 .69 Factor 2: Ethics-related content (extracurricular/curricular) S3 .89 S4 .89 S5 .48 Factor 3: Sanctioning UB S9 .73 S10 .82 S13 .77 Factor 4: Witnessing UB S7 .40 S8 .77 S11 .81 S12 .77 Factor 5: Formal ethical framework S1 .75 S2 .63 S6 .42 .52 S14 .36 .64 N=235 Eigenvalues 4.698 2.639 1.903 1.384 1.146 % of Variance 24.726 13.889 10.017 7.282 6.032 % of Cumulative 24.726 38.616 48.632 55.914 61.946 Cronbach alpha .774 .751 .750 .637 .683 Note. Factor loadings above .35 are shown in the table, with the highest factor loadings shown in bold. 10 Organizacija, Volume 59 Research Paper Issue 1, February 2026 Table 4: Results of the exploratory factor analysis for the academic context (employee sample) Significance of Rotation KMO Extraction method method coefficient Bartlett’s test Principal Component Analysis Varimax .871 .000 Factor loadings Item 1 2 3 Factor 1: Ethics implementation in practice E1 .62 .50 E2 .69 .43 E3 .80 E6 .76 E9 .45 E10 .82 E18 .70 E19 .63 E20 .81 Factor 2: Ethics-related regulations and implementation mechanisms E4 .83 E5 .71 Factor 3: Personal attitude on ethics E26 .96 N=112 Eigenvalues 5.279 1.225 1.043 % of Variance 43.995 10.206 8.693 % of Cumulative 43.995 54.201 62.894 Cronbach alpha .891 n/a n/a Note. Factor loadings above .35 are shown in the table, with the highest factor loadings shown in bold. 4.2.2 Multiple regression analyses EB as the response variable, and yielded academic con- text-related factors and control variables as predictors. To identify key predictors from the HEI’s academic Two multiple regression models were statistically signif- context that play a role in shaping students’ and faculty’s icant (Table 5). The first significant regression model (R2 EB, and to examine the nature of identified relationships, = .359, F(10, 224) = 12.533, p < .001), explaining 36% of we conducted multiple linear regression analyses, testing the variance in the response variable, revealed that four of the relationships between the yielded academic context-re- the five academic context-related factors significantly pre- lated factors and specific aspects of students’ and employ- dict students’ awareness of the HEI’s ethical infrastructure. ees’ EB. To ensure more valid results, we included five Specifically, individual-level ethics, ethics-related content control variables in every regression model. For the stu- (extracurricular/curricular), witnessing UB, and formal dent sample, the control variables were the student’s gen- ethical framework significantly predict students’ aware- der, age, type and level of study programme, and GPA. ness of the HEI’s ethical infrastructure, whereas sanction- For the employee sample, the control variables were the ing UB and the five control variables do not. Similarly, the employee’s gender, age, length of service, job type, and second significant regression model (R2 = .291, F(10, 224) managerial rank. = 9.210, p < .001), which explained 29% of the variance in For the student sample, we tested five multiple regres- the response variable, found that four of the five academic sion models, each with a specific aspect of the student’s context-related factors significantly predict students’ EB in 11 Organizacija, Volume 59 Research Paper Issue 1, February 2026 general. Individual-level ethics, sanctioning UB, witness- general depends (R2 = .504, F(10,59) = 4.676, p < .001) on ing UB, and a formal ethical framework significantly pre- ethics at the individual level, the experience of UB, and the dict students’ EB in general, whereas ethics-related con- formal ethical framework. tent (extracurricular/curricular) and three control variables For the employee sample, three multiple regression do not. Notably, the level of the study programme the stu- models were tested, and all were found to be statistically dent was attending and the student’s GPA also significantly significant (Table 6). In each regression model, one aspect predict the student’s EB in general. of employees’ and students’ EB was treated as the response When considering the scientific fields to which the stu- variable, while the three academic context-related factors dents’ study programmes belong, natural sciences students (employee sample) and control variables were used as pre- were found to be less susceptible to the effects of academic dictors. The first regression model (R2 = .352, F(8, 103) = context compared to students in other fields, both in terms 6.999, p < .001), which explained 35% of the variance in of their EB in general and their awareness of the HEI’s the response variable, revealed that ethics implementation ethical infrastructure (Appendix 2). Neither of the two re- in practice and ethics-related rules and implementation gression models for the natural sciences subsample was mechanisms significantly predict employees’ awareness statistically significant (R2 = .440, F(10,19) = 1.490, p = of the HEI’s ethical infrastructure. The second regression .218; R2 = .364, F(10,19) = 1.090, p = .416). In contrast, model (R2 = .494, F(8, 103) = 12.568, p < .001), which all six multiple regression models focusing on the other explained 49% of the variance in the response variable, three scientific field-related subsamples were statistically found that all three yielded academic context-related fac- significant, explaining 26% to 50% of the variance in the tors significantly predict faculty’s EB in general. The third response variables. In this regard, medicine and biotech- regression model (R2 = .302, F(8, 103) = 5.558, p < .001), nology sciences students are the most susceptible to the ef- which explained 30% of the variance in the response var- fects of academic context, with regression models explain- iable, found that ethics implementation in practice and ing 47% and 50% of the variance in the response variables. personal attitudes towards ethics significantly predict stu- Specifically, these students’ awareness of the HEI’s ethical dents’ EB in general. Finally, the regression analyses in infrastructure depends (R2 = .473, F(10,46) = 4.128, p < relation to five scientific fields, previously conducted for .001) on ethics-related content (extracurricular/curricular) the student sample, could not be applied to the employee and the formal ethical framework, whereas students’ EB in sample due to the small sample size of 112 respondents. Table 5: Multiple linear regression analysis predicting students’ awareness of the higher education institution’s ethical infra- structure and students’ ethical behaviour in general (student sample) S15 S23 Variable/ Factor 95% CI 95% CI Est. SE LL UL p Est. SE LL UL p Gender -.03 .15 -.33 .27 .835 -.02 .14 -.28 .25 .913 Age .06 .07 -.08 .19 .419 .04 .06 -.08 .17 .476 SP type .28 .17 -.05 .61 .100 -.22 .15 -.52 .07 .140 SP level -.29 .20 -.68 .10 .144 -.29 .18 -.64 .06 .099 GPA -.04 .06 -.16 .07 .444 -.11 .05 -.21 -.01 .028 S-F1 .14 .07 .01 .28 .034 .39 .06 .28 .51 .000 S-F2 .16 .07 .01 .30 .032 .09 .07 -.04 .21 .195 S-F3 .10 .07 -.03 .23 .143 .26 .06 .14 .38 .000 S-F4 -.16 .07 -.29 -.02 .021 .15 .06 .04 .27 .011 S-F5 .68 .07 .54 .81 .000 .25 .06 .13 .37 .000 Model Constant = 3.261, F(10,224) = Constant = 4.345, F(10,224) = 12.533, p < .001, R2 9.210, p < .001, = .359. R2 = .291. N=235 Note. Note. S-F1 to S-F5 represent the five yielded factors in the student sample. SP stands for study programme, Est. for Estimate, SE for Standard error, CI for Confidence interval, LL for Lower limit, and UL for Upper limit. 12 Organizacija, Volume 59 Research Paper Issue 1, February 2026 Table 6: Multiple linear regression analysis predicting faculty’s awareness of their own higher education institution’s ethical infrastructure, and their own and students’ ethical behaviour in general (employee sample) E25 E24 E17 Variable/ Factor 95% CI 95% CI 95% CI Est. SE LL UL p Est. SE LL UL p Est. SE LL UL p Gender .12 .19 -.26 .50 .536 .05 .17 -.28 .39 .749 .01 .16 -.31 .32 .971 Age -.01 .09 -.19 .17 .884 .06 .08 -.10 .22 .464 .01 .07 -.14 .15 .923 Service length .15 .08 -.01 .32 .070 -.08 .07 -.23 .07 .287 .04 .07 -.10 .17 .615 Job type -.06 .08 -.21 .09 .465 -.15 .07 -.28 -.02 .028 -.02 .06 -.14 .11 .779 Mgmnt -.16 .09 -.34 .03 .095 .06 .08 -.11 .22 .516 -.05 .08 -.21 .10 .502 rank E-F1 .26 .08 .10 .42 .002 .63 .07 .48 .77 .000 .39 .07 .26 .53 .000 E-F2 .36 .09 .10 .53 .000 .15 .08 .00 .30 .044 .06 .07 -.08 .20 .363 E-F3 .02 .08 -.15 .18 .836 .27 .07 .12 .42 .000 .15 .07 .01 .29 .031 Model Constant = 3.776, Constant = 4.137, Constant = 3.756, F(8,103) = 6.999, p < .001, F(8,103) = 12.568, p < .001, F(8,103) = 5.558, p < .001, N=112 R2 = .352. R2 = .494. R2 = .302. Note. E-F1 to E-F3 represent the three yielded factors in the employee sample. Est. stands for Estimate, SE for Standard error, CI for Confi- dence interval, LL for Lower limit, and UL for Upper limit. 5 Discussion of respondents, faculty still display some UB and are rela- tively passive when confronted with UB by students. The In this study, we examined the current state of the aca- frequency of sanctioning UB among both students and em- demic context at HEIs in the transitional society of Croa- ployees is low according to both groups of respondents, tia. The main findings indicate that, apart from the formal highlighting the problem of translating ‘words’ into ‘ac- embeddedness of ethical values in their vision, mission, tions’ and achieving congruence on ethics-related values and strategy, the existence of a Code of ethics, stand-alone and behaviours at all levels in HEIs. Furthermore, both courses, ethical topics in syllabuses, and formal units and groups of respondents report that UB among students at dedicated individuals dealing with the implementation of HEIs is low to moderate. Students occasionally cheat in ethics, these HEIs still have significant work to do in de- examinations, occasionally cheat in extracurricular activ- veloping and implementing an ethical infrastructure and, ities, and very rarely, if ever, report observed cheating by ultimately, in shaping the desired responses of their em- fellow students. These students are only moderately aware ployees and students in ethically challenging situations. of the HEI’s commitment to promoting and implementing The exemplary behaviour of HEIs’ management and their ethics-related regulations, activities, norms, and culture, commitment to implementing declared ethical values and as well as the existing ethical infrastructure. Both groups norms is problematic. HEI’s management is not clearly as- of respondents have similar perceptions of the academic sociated with high ethical standards and behaviour, does context in HEIs, with students being more critical of their not demonstrate zero tolerance for UB, is not strict in sanc- peers’ EB and less critical of the EB of HEIs’ staff. tioning UB among HEI’s employees, rewards exemplary The bivariate and multivariate analyses conducted in EB only modestly, and shows a degree of favouritism to- the study provided valuable insights into the predictors wards certain individuals. These issues, evident at the or- of students’ and faculty’s EB when it comes to academic ganisational level, are also reflected at the individual level. context. For example, students enrolled in natural scienc- Although faculty’s EB (contribution to the development of es-related programmes are less susceptible to the effects ethics among students during classes, implementation of of academic context regarding their EB and their aware- ethical standards and norms in relation to students, com- ness of HEI’s ethical infrastructure, compared to students pliance with ethical standards in scientific work, and EB in medicine and biotechnology sciences, who are the most in general) is reported to be at a high level by both groups susceptible to these effects. According to students, their 13 Organizacija, Volume 59 Research Paper Issue 1, February 2026 awareness of HEI’s ethical activities, norms, and values effective implementation of ethical infrastructure in HEIs depends on their personal attitudes towards ethics, the – moving from ‘words’ to ‘actions’ – highlighted by the formal ethical framework established by the HEI, and the results of this study, aligns with the conclusions empha- ethics-related engagement and behaviour of HEI employ- sised in studies such as Treviño et al. (2014) and MacLeod ees, especially faculty members, with whom students in- & Eaton (2020). In this sense, faculty members’ person- teract most frequently. In this regard, HEI’s commitment al attitudes towards ethics play an important role in their to implementing ethical values, norms, and culture; the overall EB, including their behaviour towards students and existence of a Code of Ethics; the existence of formal their treatment of students’ UB when confronted with such units and dedicated individuals responsible for enforcing behaviour (McCabe et al., 2001; Vehviläinen et al., 2018 ethical rules and norms; listening to ethics-related courses in Davis, 2023; MacLeod & Eaton, 2020). The findings and topics; the availability of extracurricular ethics-related of the study regarding the predictors of students’ EB are content; and faculty members’ commitment to and empha- consistent with influential research such as McCabe et al. sis on ethics during classes; as well as their application of (2001), Simkin & McLeod (2010), Cronan et al. (2018), ethical standards in interactions with students, all signifi- and Cheng et al. (2021), who found that individual and cantly increase students’ awareness of HEI’s ethical activi- contextual factors – particularly students’ attitudes towards ties, norms, and values. Conversely, students’ awareness of ethics, faculty’s and peers’ behaviour, and the formal set- the ethical activities, norms, and values of HEI decreases ting of the HEI – have a significant impact on students’ when they observe UB among their peers and HEI staff. EB. A puzzling result is the positive effect of students’ ex- Similarly, staff perceive the availability of ethics-related posure to UB (from fellow students and HEI employees) regulations and mechanisms as a significant predictor of on students’ EB, which contradicts the long-held view in students’ awareness of HEI’s ethical activities, norms, and the relevant literature (Bandura, 1988; Robie & Kidwell, values, along with the commitment to and concrete imple- 2003; Sisti, 2007). mentation of proclaimed ethical values, norms, and regula- tions by HEI employees at all levels in cases of ethical vi- olations by their peers. These two predictors, together with 6 Conclusion personal attitudes towards ethics, are important in shaping faculty members’ EB, which, as mentioned earlier, is par- This study, conducted on two different samples – stu- ticularly important for students’ awareness of HEI’s ethical dents and employees of 12 HEIs in Croatia – has achieved activities, norms, and values, as well as their own EB. its main objectives. We have gained insights into the cur- When it comes to students’ EB, in addition to the for- rent state of the academic context in a typical transitional mal ethical framework and individual-level ethics, the fact society, revealing that there is still considerable room for that students have witnessed UB by fellow students and improvement in HEIs regarding the development of eth- HEI employees, as well as HEI’s actions in sanctioning ical infrastructure and the promotion of a culture of aca- those behaviours, is also an influential predictor. Accord- demic integrity and ethical values. In this sense, translating ing to students, all these predictors positively contribute ‘words’ into ‘actions’ at both organisational and individual to their EB. Consistent with these perceptions, HEI em- levels is a primary goal for HEIs to establish an effective ployees view students’ personal attitudes towards ethics, ethical framework and culture, and to be perceived as eth- along and the commitment to and concrete implementa- ical by their stakeholders. tion of proclaimed ethical values, norms, and regulations A fundamental prerequisite for HEI management to by HEI employees at all levels when addressing ethical ensure the effective translation of words into actions is to violations by their peers, as predictors that positively im- have well-written and adopted ‘words’ and clearly defined pact students’ EB. and kick-started ‘levers and wheels’ of a formal ethical The findings of the study regarding the academic framework. Specifically, management at HEIs in transi- context in HEIs within a transitional society provide em- tional societies must first ensure that all formal mecha- pirical support for studies such as Farnese et al. (2011), nisms enabling the implementation of an ethical culture Park (2017), Hendy & Montagrot (2019), and Cheng et and the values of academic integrity – such as the embed- al. (2021), which found that academic dishonesty or UB dedness of ethics and ethical values into HEI’s strategic is common and widespread among students. The results documents, the existence of a Code of Ethics, the estab- obtained also highlight the importance of an ethical in- lishment of units or roles dedicated to the effective imple- frastructure for the behaviour of staff and students when mentation of the ethical framework, etc. are in place. The faced with ethical dilemmas. In this regard, a clear and designed ethical framework at HEI and the high expecta- strong ethical climate and effectively implemented appro- tions for employees and students regarding EB must be priate ethical strategies and policies have a positive impact intensively promoted by the institution and further empha- on student and employee attitudes towards ethics and their sized by HEI management’s uncompromising decisions, EB (Birtch & Chiang, 2013). The identified problem of actions, and personal conduct. The strict application of the 14 Organizacija, Volume 59 Research Paper Issue 1, February 2026 principle of ‘leading by example’ by HEI management in Notably, the relatively small size of the two samples is a implementing the ethical framework and culture – demon- significant limitation. Conducting research with larger strating to employees and students that HEI management samples and at a greater number of HEIs would yield more exemplifies high ethical standards and maintains zero tol- valid results. The study was cross-sectional and included erance for study- and research-related UB, favouritism, respondents from study programmes in different scientif- discrimination, and similar misconduct – is a cornerstone ic fields, with independent and dependent variables tested of building an ethical culture. Strict and prompt sanction- at the same time, making it difficult to identify causali- ing of reported UB at HEI, and ensuring that employees ty. Adopting a longitudinal research method instead of a and students are well aware of the UB-related sanction- cross-sectional one, and/or focusing on respondents from ing processes, outcomes, and consequences for offenders, study programmes within a single scientific field, would sends a powerful message to potential violators. Conduct- also enhance the validity of the results. Additionally, the ing regular ethics-related education and training for em- same respondents provided answers for both the independ- ployees on the importance of ethics in performing their ent and dependent variables in the research, potentially professional duties, particularly in their interactions with leading to a common-method bias, while self-assessment students, as well as providing a rich and diverse range of of one’s own ethics-related behaviour is another potential ethics-related content for students, are additional pillars in limitation of this study. These two limitations are partially fostering a culture of academic integrity at HEI. Further- mitigated by the use of two-source data collection. more, with the ‘start of the AI era’, the development and The study’s limitations suggest several methodolo- effective implementation of AI ethics-focused regulatory gy-driven recommendations for future research, such as frameworks at HEIs has become a pressing challenge for conducting similar studies with larger samples, focusing HEI management in transitional societies, essential for on respondents from a single scientific field, adopting a building and maintaining a culture and values of academ- longitudinal research method design for clearer identifica- ic integrity. Finally, management at HEIs in transitional tion of causal implications, or using qualitative approaches societies should not be content with merely meeting the to account for respondents providing answers on both in- expected or legally prescribed minimum standards regard- dependent and dependent variables and self-assessing their ing ethics and academic integrity, but should strive to lead own behaviour. Additionally, we recommend conducting in the implementation of ethical culture and values, espe- comparative studies across institutions, countries, or edu- cially given the fragile reputation of many HEIs in certain cational systems to uncover globally relevant approaches transitional countries. to building effective ethical frameworks and cultures at Supporting the practical implications and recommen- HEIs in transitional societies, while also highlighting lo- dations outlined above, the study results have shown that cally effective practices tailored to specific cultural and students’ EB depends on the existing formal ethical frame- institutional needs (Usher et al., 2025). Furthermore, stud- works at HEIs, on the emphasis placed on ethics in teach- ies examining moderating and/or mediating variables in ing by faculty and on their EB, on students witnessing the relationship between an HEI’s ethical framework and UB by fellow students and HEIs employees, and finally culture and students’ and/or faculty’s EB, arising from in- on the measures taken by institutions to sanction these be- dividual variables (demographic factors, individual differ- haviours. As one of the main predictors of students’ EB, ences, attitudes, etc.) and national-level variables (national faculty’s EB depends on ethics-related regulations and culture, study level, etc.) (Cheng et al., 2021), or studies mechanisms in HEIs, as well as on the commitment to further exploring the interdynamics between faculty and and concrete implementation of proclaimed ethical values, students’ EB, would be welcomed in the relevant litera- norms, and regulations by HEIs’ staff at all levels when ture. Finally, as this study was conducted as one of the last ethical violations occur among colleagues. In shaping stu- just before the widespread use of AI in higher education, dents’ EB, those in medical and biotechnology study pro- repeating this or a similar study in the AI era – by examin- grammes are most susceptible to the effects of academic ing the same HEIs or similar samples of respondents and context elements, while students in natural science-related within the same or a similar national context – to assess programmes are most resistant to such effects. how HEIs’ ethics-related practices have changed, espe- The study provides new and valuable insights into our cially in response to the AI era and rapidly evolving tech- understanding of the problem of academic (dis)honesty nology behind it, would be a valuable contribution to the and the role that various elements of the academic context relevant literature (Dabis & Csáki, 2024). play in such behaviour. 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Managing ethics in higher education: https://doi.org/10.1186/s40594-025-00567-x Implementing a code or embedding virtue? Business Zopiatis, A. A., & Krambia-Kapardis, M. (2008). Eth- Ethics: A European Review, 15(4), 407–418. ical behaviour of tertiary education students in Cy- https://doi.org/10.1111/j.1467-8608.2006.00462.x prus. Journal of Business Ethics, 81, 647–663. Ocen, S., Elasu, J., Aarakit, S. M., & Olupot, C. (2025). Ar- https://doi.org/10.1007/s10551-007-9538-6. tificial intelligence in higher education institutions: review of innovations, opportunities and challenges. Frontiers in Education, 10, 1530247. https://doi. Ivan Matić, PhD, is a full professor at the Department org/10.3389/feduc.2025.1530247 of Management, Faculty of Economics, Business and Pallant, J. (2020). SPSS survival manual: A step by step Tourism, University of Split, Croatia. His research guide to data analysis using IBM SPSS (7th ed.). interests include organisation design, leadership and London, United Kingdom: Routledge. https://doi. project management skills, and organisational learning. org/10.4324/9781003117452 He is the author of numerous original scientific articles, Park, C. (2017). In other (people’s) words: Plagia- and several monographs and university textbooks, rism by university students - literature and les- a programme committee member of international sons. Academic Ethics, 525–542. https://doi. conferences, and a reviewer for several high-impact org/10.4324/9781315263465 journals. He also participates in research projects and consulting work. Prisacariu, A., & Shah, M. (2016). Defining the quality of higher education around ethics and moral values. Quality in Higher Education, 22(2), 152–166. Ana Juras, PhD, is an assistant professor at the https://doi.org/10.1080/13538322.2016.1201931 Department of Management, Faculty of Economics, Rakovski, C. C., & Levy, E. S. (2007). Academic dishon- Business and Tourism, University of Split, Croatia, esty: Perceptions of business students. College Student where she teaches courses on entrepreneurship Journal, 41(2), 466-482. and business ethics. Her primary research interests Robie, C., & Kidwell, R. E. (2003). The “ethical” profes- are organisational complexity, leadership and sor and the undergraduate student: Current percep- entrepreneurial skills, and business ethics. She is tions of moral behavior among business school facul- the author of numerous original scientific papers, ty. Journal of Academic Ethics, 1, 153–173. https://doi. participates in research projects, and serves as a org/10.1023/B:JAET.0000006893.06303.41 reviewer for several scientific journals. Rothman, P. (2017). Ethics in Higher Education: A study of the perceived ethical climate of administrators and faculty at a higher education institution. Education Marina Lovrinčević, PhD, is a full professor of Doctoral. Paper 287. management at the University of Split, Croatia. She Simkin, M. G., & McLeod, A. (2010). Why do college stu- is the head of the postgraduate study programme in dents cheat? Journal of Business Ethics, 94, 441–453. Business Economics, and her teaching and research https://doi.org/10.1007/s10551-009-0275-x interests focus on management, entrepreneurship, Sisti, D. A. (2007). How do high school students justify in- strategic management, and corporate governance. ternet plagiarism? Ethics & Behavior, 17(3), 215–231. She has authored 60 scientific papers and chapters in handbooks and monographs, and has participated in https://doi.org/10.1080/10508420701519163 numerous scientific and professional projects. She has Stephens, J. M. (2015). Creating cultures of integrity: A been recognized multiple times for her work, including multi-level intervention model for promoting academic receiving the University of Split Plaque. 17 Organizacija, Volume 59 Research Paper Issue 1, February 2026 Kako akademski kontekst oblikuje etično vedenje študentov: Nova spoznanja iz tranzicijske družbe Ozadje in namen: Nenehni problem etičnih kršitev med študenti predstavlja temeljno grožnjo delovanju visokošol- skih ustanov in se prenaša v njihovo vedenje na prihodnjem delovnem mestu. Namen prispevka je preučiti povezavo med akademskim kontekstom v visokošolskih ustanovah tranzicijske družbe in etičnim vedenjem študentov. Metode: Izvedena je bila empirična raziskava na vzorcu 235 študentov ter 112 visokošolskih učiteljev in admini- strativnih delavcev iz 12 visokošolskih ustanov na Hrvaškem. Podatki o etični infrastrukturi visokošolskih ustanov ter etičnem vedenju študentov in zaposlenih so bili zbrani pri obeh skupinah. Za analizo smo uporabili deskriptivno statistiko za vpogled v značilnosti akademskega konteksta in vedenja ter večkratne regresijske analize za preučitev povezav med kontekstom in etičnim vedenjem. Rezultati: Percepcije študentov in zaposlenih se razlikujejo glede številnih vidikov akademskega konteksta. Ključni dejavniki, ki vplivajo na etično vedenje študentov, so formalni etični okvir, individualna raven etike ter zaznavanje in sankcioniranje neetičnega vedenja. Študenti naravoslovnih programov so manj dovzetni za vpliv akademskega konteksta kot študenti drugih znanstvenih področij. Zaključek: Trenutno stanje akademskega konteksta v visokošolskih ustanovah tranzicijske družbe kaže na veliko možnosti za izboljšave pri razvoju etične infrastrukture in spodbujanju kulture akademske integritete ter etičnih vrednot. Ključni izziv je prenos deklarativnih načel v dejansko prakso na organizacijski in individualni ravni, da bi ustanove vzpostavile učinkovit etični okvir in kulturo ter bile prepoznane kot etične med deležniki. Ključne besede: Akademska nepoštenost, Organizacijska etika, Visokošolske ustanove, Študenti, Visokošolski uči- telji, Hrvaška 18 Organizacija, Volume 59 Research Paper Issue 1, February 2026 Appendix 1: Organisational ethics at higher education institutions – questionnaires used in the research Student questionnaire on organisational ethics at HEI – Full item list S1 Rate your HEI’s commitment to promoting ethical regulations and norms and to creating an ethical culture (e.g., announcements on the official website, promotional events, promotional lectures, etc.). S2 Does your HEI has adopted Code of ethics? S3 In your study programme, did you listen until now, or will you listen in the upcoming semesters a stand-alone course focusing on ethics or business ethics? S4 Until now, did your professors covered ethics-related topics in their lectures, exercises, or case studies, or reflected on the lectured topics from an ethical point of view? S5 At your HEI, are there opportunities outside the classroom for students to attend ethics-related content and events such as lectures, workshops, seminars, etc.? S6 At your HEI, are there established formal bodies (e.g. centres, offices, committees, commissions, student commis- sioners) responsible for implementing ethical regulations, decisions, and norms? S7 Until now, did you witnessed unethical behaviour by your fellow students, such as cheating on exams, cheating on assignments, plagiarism, free-riding on group assignments, etc. S8 Until now, did you witnessed any other non-study-related unethical behaviour by your fellow students, such as dis- crimination, belittling, harassment, sexual harassment, etc.? S9 To your knowledge, were the just mentioned study-related unethical behaviours of your fellow students (such as cheating on exams, plagiarism, etc.) reported and sanctioned by the competent bodies of the HEI in an appropriate manner? S10 To your knowledge, were the just mentioned other, non-study-related unethical behaviours of your fellow students (such as discrimination, harassment, etc.) reported and sanctioned by the competent bodies of the HEI in an appro- priate manner? S11 Until now, did you witnessed unethical behaviour by your professors (including assistants, lecturers, etc.), such as discrimination, belittling, harassment, sexual harassment, or similar actions? S12 Until now, did you witnessed unethical behaviour by the administrative or support staff at your HEI (Office of the Registrar, Library, etc.), such as discrimination, belittling, harassment, sexual harassment, or similar conduct? S13 To your knowledge, were the aforementioned unethical behaviours of HEI employees (professors and/or administra- tive staff) reported and sanctioned by the competent bodies of the HEI in an appropriate manner? S14 Rate your HEI management’s commitment to implementing ethical regulations and norms, and to creating an ethical culture. S15 Rate your familiarity with the key elements of the ethical system, actions, and ethical culture of your HEI. S16 Rate your faculty’s contributions during classes to raising awareness of and developing ethical values in students. S17 Rate your faculty’s tendency to include ethics and ethical dilemmas in lecture or exercise topics. S18 Rate your faculty’s adherence to ethical regulations and standards in their relations with students (e.g. communica- tion, evaluation of intended learning outcomes, etc.). S19 Rate your faculty’s overall ethical behaviour and adherence to ethical regulations and standards. S20 How frequently have you cheated on examinations during your studies so far? S21 How frequently have you cheated in elective and extracurricular courses so far in your studies? S22 How likely are you to report cheating by other students in examinations to the competent bodies of your HEI? S23 Rate your fellow students’ overall ethical behaviour, that is, their adherence to ethical regulations and standards. S24 How important are ethics, ethical values, norms, and rules to you personally? 19 Organizacija, Volume 59 Research Paper Issue 1, February 2026 Employee questionnaire on organisational ethics at HEI – Full item list E1 Rate your HEI’s commitment to promoting ethical regulations and norms and to creating an ethical culture (e.g., announcements on the official website, promotional events, promotional lectures, etc.). E2 Assess the extent to which ethics and ethical values are integrated into your HEI’s strategic documents (such as vision, mission, strategy, policies, etc.). E3 Rate your HEI management’s commitment to implementing ethical regulations and norms, and to creating an ethical culture. E4 Does your HEI has adopted Code of ethics? E5 At your HEI, are there established formal bodies (e.g. centres, offices, committees, commissions, student commis- sioners) responsible for implementing ethical regulations, decisions, and norms? E6 Rate your HEI management’s tolerance of unethical behaviour. E7 Does management at your HEI favour certain individuals? E8 Does management at your HEI reward individuals who demonstrate integrity, honour, and other desirable ethical traits in their behaviour and work? E9 Is it necessary to compromise your own ethical values and principles in order to succeed at your HEI? E10 Would you rate your HEI management as a synonym for high ethical standards and behaviour? E11 Do your HEI’s study programmes include courses focusing on ethics or business ethics? E12 To your knowledge, do the professors at your HEI cover ethics-related topics in their lectures, exercises, or case stud- ies, or reflect on the lectured topics from an ethical point of view? E13 Based on your knowledge, rate your HEI faculty’s contributions during classes to raising awareness of and developing ethical values in students. E14 At your HEI, are there opportunities outside the classroom for students to attend ethics-related content and events such as lectures, workshops, seminars, etc.? E15 In your everyday work, did you witnessed unethical behaviour by HEI students, such as cheating on exams, cheating on assignments, plagiarism, free-riding on group assignments, discrimination, belittling, harassment, sexual harass- ment, etc.? E16 To your knowledge, were the aforementioned unethical behaviours of HEI students (such as cheating in exams, pla- giarism, discrimination, belittling, harassment, etc.) reported and sanctioned by the competent bodies of the HEI in an appropriate manner? E17 Rate HEI students’ overall ethical behaviour, that is, their adherence to ethical regulations and standards. E18 In your everyday work, did you witnessed unethical behaviour by HEI employees (professors, administrative and sup- port staff), such as discrimination, belittling, harassment, sexual harassment, questionable practices in the evaluation of intended learning outcomes, questionable communication with students, etc.? E19 To your knowledge, were the aforementioned unethical behaviours of HEI employees (such as discrimination, belittling, harassment, sexual harassment, questionable practices in the evaluation of intended learning outcomes, questionable communication with students, etc.) reported and sanctioned by the competent bodies of the HEI in an appropriate manner? E20 Rate the strictness with which the HEI sanctions the unethical behaviour of employees. E21 Based on your knowledge, rate your HEI faculty’s adherence to ethical regulations and standards in their relations with students (e.g. communication, evaluation of intended learning outcomes, etc.). E22 Based on your knowledge, how do HEI faculty react when they catch students engaging in unethical behaviour? E23 Based on your knowledge, rate your HEI faculty’s adherence to ethical regulations and standards in conducting scien- tific research and publishing. E24 Based on your knowledge, rate your faculty’s overall ethical behaviour and adherence to ethical regulations and standards. E25 Rate your familiarity with the key elements of the ethical system, actions, and ethical culture of your HEI. E26 How important are ethics, ethical values, norms, and rules to you personally? 20 Organizacija, Volume 59 Research Paper Issue 1, February 2026 Appendix 2: Multiple linear regression analyses predicting students’ awareness of the higher education institution’s ethical infrastructure (student sub-samples) 21 Organizacija, Volume 59 Research Paper Issue 1, February 2026 DOI: 10.2478/orga-2026-0002 Analysis of Key Impact Factors in New Methods Implementation in Organisations: A Change Management Perspective Dušan GOŠNIK, Mateja JERMAN University of Primorska, Faculty of Management, Koper, Slovenia, dusan.gosnik@fm.upr.si, mateja.jerman@fm.upr.si Background/Purpose: This paper examines the key factors for successful change implementation in organisations, management qualities, and the most common barriers to change implementation. The main change we focus on is implementing new work methods in the organisation, such as Six Sigma, Lean, Lean Six Sigma, Kaizen, and similar methods. Methods: The latest findings from the literature about change management, key success factors and barriers to change implementation are presented. An empirical study of 55 organisations from Slovenia is presented. An online questionnaire was used to gather data. Descriptive statistics were used to analyse the data. The research questions concerned the key factors influencing the successful implementation of organisational changes, the qualities neces- sary for organisational leaders, and the most common barriers to successful implementation. Results: The key factors for successful change implementation are strongly connected to cultural and human-re- lated factors, such as top management and employee involvement. Choosing the right leaders and communicating effectively about the implementation of change are key success factors. Key barriers identified include ineffective means of communication and employee habits and mindsets that do not support change. The most important quality of a manager who is leading organisational change is respect for other parties in the change management process, such as employees. Conclusion: Understanding key success factors and the barriers to implementing change in organisations can im- prove change management practices. The findings contribute to a better understanding of change management in the implementation of new methods in organisations and deliver theoretical and practical implications. Keywords: Change, Management, Method, Implementation, Success 1 Introduction Raval et al., 2021; De Koeijer et al., 2024). A lack of in- formation about which factors critically impact success To achieve better organisational performance, organi- can reduce the success rate of these initiatives and lead to sations implement various work methods. The implemen- poor business performance, as asserted by Vashishth et al. tation of new work methods in organisations often fails, (2024) and Gošnik (2024). As reported by Gastelum-Acos- and this can be attributed to a poor understanding of key ta et al. (2024), Lameijer et al. (2024), Paneerselvam et success factors (Gijo & Antony, 2013; Hirzel et al., 2017; al. (2025), Al Owad et al. (2025), Bagherian et al. (2025), 1 Received: 5th June 2025; Accepted: 8th December 2025 22 Organizacija, Volume 59 Research Paper Issue 1, February 2026 Furterer et al. (2025), many key success factors pertain to requires the study of various areas of potential key suc- human-related issues and management-related aspects, cess factors and barriers on different organisational levels, such as poor involvement of top management in the pro- especially those related to employees and management, as cess of change and a lack of top management support. On suggested by the literature review (Kumar Samanta et al., the other hand, success also depends upon the specific fac- 2024; Gastelum-Acosta et al., 2024; Al Owad et al., 2025; tors known as barriers (Raval et al., 2018). Barriers can Bagherian et al., 2025; Citybabu &Yamini, 2025; Furterer diminish the positive effects of success factors and lead et al., 2025). On the other hand, changes such as the imple- to difficulties, such as lower efficiency and performance. mentation of new technologies, e.g., blockchain, artificial Therefore, studying the most common barriers to success- intelligence, digitalisation, robotisation and others, call ful new method implementation in organisations can in- into question the validity of the existing findings. form key success factors and improve the overall success Therefore, the authors of this study have focused on of new method implementation. studying key success factors, the necessary qualities of The previous findings of Albliwi et al. (2014), Gijo & managers, and the most common barriers to the successful Antony (2013), Yadav et al. (2018), and Raval et al. (2021) implementation of changes in organisations in today’s rap- reveal a strong relationship between the most common idly changing business environment. Our study provides a barriers, such as employee-related barriers, and the suc- review of the latest literature on change management and cess of implementing new methods. On the other hand, the the implementation of organisational changes. We present latest findings of Kumar Samanta et al. (2024), Samanta the results of an empirical study conducted in Slovenia. et al. (2024); Gastelum-Acosta et al. (2024), Al Owad Due to the lack of studies in this field, this research extends et al. (2025), Bagherian et al. (2025); Citybabu &Yam- existing findings on the implementation of organisational ini (2025), and Furterer et al. (2025) reveal that the most change and adds unique value for researchers and practi- common barriers to implementation of changes in organ- tioners. isations relate to a poorly defined vision of the planned The paper is structured as follows: (1) The theoreti- change, a lack of clear communication and the absence cal background is presented, including the latest findings of long-term planning for success. Change management about change management, key success factors and the has become a critical issue for the successful implemen- most common barriers to change implementation in organ- tation of changes in organisations. Well-known models of isations; (2) Methodology chapter; (3) Results, analysis change management, such as Kotter’s eight-step model, and findings of the empirical study; (4) Discussion with can be helpful (Kotter, 1995, 1997, 2014). The simplicity limitations, implications and possible further research, and and generality of this model make it convenient for use. (5) References. However, this model does not provide specific answers to questions such as what the key success factors are or which qualities of leaders’ support change most, especially 2 Theoretical backgrounds regarding the implementation of new organisational work methods in today’s business environment. This attracted 2.1 Change management our attention and guided our research. Additionally, there has been a lack of studies on key Change aims to renew an organisation’s capabili- success factors in organisational change that specifically ties to remain competitive (Pop et al., 2023). The theory address the successful implementation of new organisa- of change explains how changes should be managed to tional methods. Based on previous findings (Will 2015; achieve success (Kotter 1995, 1997, 2014). Changes often Idogawa et al., 2023; Kilkelly, 2024) about the importance cause interruptions to existing processes, create barriers of top management engagement in change implementation to implementation, require effort, a project management in organisations, it is clear that there has also been a lack of approach, and additional resources, and must be managed studies regarding the specific organisation leader qualities with a change management approach. New technologies necessary for the successful implementation of changes in and their implementation also increase the urgency for a the organisation. This was identified as our research gap. change management approach in organisations. To achieve The main research problem we address is to identify success, the use of a change management approach within which factors most impact the successful implementation an organisation must play a key role. of changes in organisations. Our paper aims to increase To achieve a competitive advantage, companies imple- the success rate of change management initiatives in or- ment various new methodologies, such as Six Sigma and ganisations. Lean, as well as combinations such as Lean Six Sigma, In summary, change management can help organisa- Kaizen, and other process-improvement methods. Imple- tions overcome implementation challenges and increase mentation of these methods can be considered as changes the success rate of change. Studying the most common and requires a change management approach. A change barriers to new method implementation in organisations management approach helps us to achieve agility and 23 Organizacija, Volume 59 Research Paper Issue 1, February 2026 speed (Kotter, 2014). management. Change management can be defined as a multidisci- To improve change management initiatives in organ- plinary discipline which navigates organisational change. isations, we need to study the key factors that drive suc- It focuses on strategies, business processes and specific cessful change implementation. Specifically, regarding the tools for managing changes (Monferdini & Bottani, 2024). implementation of methods such as Six Sigma, Lean, Lean Change management explains how an organisation should Six Sigma, Kaizen, and others, which are key success fac- move from its existing state to a desired (future) state tors, we can overcome common barriers and improve our (Hussain et al., 2018). Successful change management re- success rate in change implementation. quires a focus on meeting the expectations of internal and external stakeholders. A company’s ability to successfully 2.2 Success factors in change adopt changes is strongly related to its flexibility and sur- vival; it impacts customer experiences and the organisa- implementation in organisations tion’s competitiveness (Monferdini & Bottani, 2024; Vash- ishth et al., 2024; Al Owad et al., 2025). Successful change Success depends on specific factors known as success management demands a strong commitment to business factors. Success factors can be identified in a narrow area strategy and management support (Vashishth et al., 2024). that has a key impact on success (Gošnik, 2024). Analysis On the other hand, an inappropriate approach to change in of key success factors helps us identify which of them have an organisation can result in resistance to change, lower the greatest impact on success and on direct decision-mak- efficiency, lower performance of business processes, and ing. Key success factors also guide what needs to be done thus lower company performance (Idogawa et al., 2023; in an organisation to achieve better business performance. Trzeciak, 2024). In relation to the aim of this study and its defined research There are many common errors which are made by questions, we specifically identify the key success fac- managers in organisations, including poorly presented ur- tors in implementing organisational change. In this case, gency for change, lack of involvement of key employees changes refer to the implementation of new work meth- in change plan development, a missing vision of change, ods in organisations, e.g., the Six Sigma method, the Lean and a lack of communication, which are strongly related method, the Lean Six Sigma method, Kaizen, and others. to the well-established Kotter’s eight step change model These methods improve business processes and organisa- (Kotter 1995, 1997; 2014; Al Owad et al., 2025). Kotter tional performance (Gijo & Antony, 2013; Lameijer et al., also reveals the importance of connecting strategy, skills 2024; Vashishth et al., 2024; Paneerselvam et al., 2025). and structures to achieve a successful implementation of To achieve better organisational performance, organi- changes (Kotter, 2014). sations implement various work methods. The implemen- Kotter (1997) has recognised the importance of moti- tation of these new methods in organisations often fails. vation for change as one of the key priorities in an organ- Failures in implementation can be related to a lack of un- isation, as explained in the early, fundamental eight-step derstanding of key success factors (Gijo & Antony, 2013; model of change (Kotter, 1995). The findings of Badge et Hirzel et al., 2017; Raval et al., 2021; De Koeijer et al., al. (2010) show that change management plays a key role 2024) and poor management support for new method im- in the introduction of new projects. The findings of Will plementations, as reported in the latest findings of Gošnik (2015) reveal the relationship among managers, employ- (2024) and others (Lameijer et al., 2024; Paneerselvam et ees, and the implementation of change. Their interactions al., 2025; Al Owad et al., 2025; Marič et al., 2025). are identified as a key factor for successful change imple- The potential benefits for organisations of knowing mentation in organisations. On the other hand, Pollack & that many researchers have identified the key success Algeo (2016) identify key success factors for change im- factors. Raval et al. (2021) reveal strategic, organisation- plementation and state that these factors are strongly re- al and project-related benefits for organisations. Gošnik lated to the project manager leading the change. Kilkelly (2024) reports that knowledge of critical success factors (2024) reveals that change initiatives are more likely to leads to better overall organisational performance. Vash- succeed when they are properly managed by competitive ishth et al. (2024) and Raval et al. (2021) emphasise the leaders who are empowered with knowledge of change importance of key success factors in managing resistance management. The findings of Idogawa et al. (2023) show to change in organisations. Lameijer et al. (2024) reveal the relationship between top management support and that knowledge of key success factors underscores the im- change management. Idogawa et al. (2023) state that top portance of selected areas, such as employee and manage- management support and technological competencies pos- ment engagement, for better performance. Knowing the itively affect change management. On the other hand, Kala key success factors can improve general decision-making Kamdjoug (2024) elaborates that IT plays a key role in in organisations (Gošnik, 2024), enhance communication human resources change management. Trzeciak (2024) between the operational level and top management (Raval also illustrates the significance of IT in relation to change et al., 2021), and improve the success rate of implementing 24 Organizacija, Volume 59 Research Paper Issue 1, February 2026 new technologies in organisations (Singh & Rathi, 2021). methods can result in barriers to implementation, increase Setting priorities in organisations based on key success implementation costs, prolong implementation time, and factors leads us to lower development costs and improved lead to less successful implementation or potential failure business efficiency (Lameijer et al., 2024). (Lameijer et al., 2024). Research into specific success factors in implementing new methods (Six Sigma, Lean, Lean Six Sigma) shows 2.3 Barriers to change implementation in that employees and top management often play key roles. Raval et al. (2021) suggest that a wide range of success organisations factors should be included in research on key success fac- tors, including strategic, operational, cultural, financial, For the successful implementation of new work meth- customer, and supplier aspects. Specifically, De Koeijer et ods, it is necessary to know not only the key success factors al. (2024) reveal that soft aspects, characterised by an ori- (what to focus on) but also the most critical barriers (what entation to people during the implementation of new meth- to avoid) to change or new method implementation. Ac- ods, are a key success factor. A similar finding is supported cording to Raval et al. (2018), success also depends upon by Vashishth et al. (2024), who report that employee mo- the specific factors known as barriers. Barriers cause an tivation to adopt new methods is a critical success factor. organisation difficulty, e.g., barriers interrupt the existing On the other hand, De Koeijer et al. (2024) also high- process. Overcoming barriers requires additional effort, light the importance of hard factors, which are charac- additional time, prolonged processes, the use of additional terised by a result and performance-oriented approach. resources, increased costs, re-directing our focus, and de- Employee empowerment, employee commitment, and creasing or nullifying the positive impact of key success participation have been identified as key success factors factors, resulting in decreased performance. This demands in Lean Six Sigma implementation, as shown by Hirzel the timely identification of barriers and the development of et al. (2017), Gošnik (2024), and Vashishth et al. (2024). appropriate responses prior to implementing new methods The findings of Lameijer et al. (2024) show that employee (Singh & Rathi, 2021). Barriers are usually presented as education, proper information flow, communication and the opposite of success factors. If we know the barriers, we managerial involvement present key success factors. On can potentially transform them into success factors (Yadav the other hand, Singh & Rathi (2021) reveal that effective et al., 2018). The prompt identification of barriers can help employee training and education in certain methods (Six an organisation to overcome difficulties, improve change Sigma, Lean, Lean Six Sigma, Kaizen, and others) lead to management, and improve performance. the successful implementation of these methods. The same The latest studies on the most common barriers to im- finding is supported by Singh & Rathi (2021), who report plementing new methodologies in organisations reveal the that proper training provides deep insight into employees, following. The research of Albliwi et al. (2014) reveals that leading to success. improper project team selection presents a key barrier to Bhat et al. (2023) argue that studying key success successful Lean Six Sigma implementation. According to factors requires a holistic, multilevel approach. The find- Gijo & Antony (2013), a lack of proper training was identi- ings of Francescatto et al. (2023) reveal the most critical fied as a significant barrier to implementing new methods. success factors, including leadership and management in- Yadav et al. (2018) reveal that resistance to culture change volvement and employee training and education. On the presents a key barrier to success. According to Raval et other hand, Samanta et al. (2024) highlight the importance al. (2021), ineffective communication and employee re- of organisational culture in adopting changes within an sistance to change are key barriers to success in organisa- organisation, supported by top management commitment. tions. People, processes and technology-related issues are Top management commitment has also been reported as key areas that present barriers to success in organisations, a key success factor in the research of Gastelum-Acosta as reported in the latest findings of Kumar Samanta et al. et al. (2024). Kumar et al. (2024) report that companies (2024) and Citybabu & Yamini (2025). should focus on strategy-related key success factors and Overcoming these barriers requires a shared vision for supplier-based success factors. Bagherian et al. (2025) planned change, clear communication, and long-term plan- have identified leadership support as a key success factor. ning for success (Al Owad et al., 2025). Gastelum-Acosta The key findings of Furterer et al. (2025) support the pre- et al. (2024), Furterer et al. (2025), and Bagherian et al. vious findings of Paneerselvam et al. (2025) that leader- (2025) also suggest that leadership skills and competenc- ship plays a key role in the successful implementation of es play key roles in successfully overcoming barriers to organisational change. On the other hand, Paneerselvam implementing new methods (such as Lean Six Sigma) in et al. (2025) highlight employee factors, such as the effec- organisations. Singh & Rathi (2021) highlight the impor- tiveness of education and training, as key success factors. tance of studying not only the most common barriers to In summary, regarding key success factors, a lack of success but also their interference, such as cross-barrier understanding of those related to implementing specific influences. 25 Organizacija, Volume 59 Research Paper Issue 1, February 2026 3 Methodology agerial methods in organisations, e.g., Six Sigma, Lean, Lean Six Sigma, Kaizen, EFQM, Project management Methodologically, the research was carried out in the and similar, which was explained at the beginning of the following steps: after the literature review chapter, we de- questionnaire. The snowball sampling method was used to fined a research gap, presented change management theo- gather data. ry, and developed research questions. The next steps were Participants joined our research voluntarily and anony- data gathering and analysis, followed by the presentation mously. Participants in our research were asked to rank the and discussion of results. importance of each factor/statement regarding implemen- The main research questions (RQ) in this research are: tation changes in organisations on a 5-point Likert scale. RQ (1): What are the key influencing factors for the Data from 55 organisations was gathered in June 2024. successful implementation of changes in an organisation? This comprised our research sample. Descriptive statistics RQ (2): What are the necessary qualities of leaders in were used to analyse the data. Average values and standard organisations for the successful implementation of organ- deviations were calculated collectively for the entire col- isational changes? lected sample of 55 organisations included in this research. RQ (3): What are the barriers to the successful imple- The results are presented as tables (Tables 1, 2, 3) and fig- mentation of organisational changes? ures (Figures 1, 2, 3). To answer our research questions, we have reviewed the latest literature and findings from various authors on 4 Results change management practices in organisations. Further on, we focused especially on the implementation of new The results from the sample of 55 organisations (N) managerial methods in organisations, e.g., Six Sigma, show that the majority are middle-sized organisations Lean, Lean Six Sigma, Kaizen, Project management, and (34.55%), followed by small (25.45%), micro (20.00%), similar methods. Findings from the literature formed the and large (20.00%)—Table 1. basis for developing the questionnaire. In accordance with Our sample, in total, is well-balanced and includes the preliminary literature review, we included in the ques- organisations of all sizes and across various industries, tionnaire many aspects and factors suggested to impact the such as the services and production sectors. The majority successful implementation of organisational changes, such of organisations in our sample operates in the mechanical as strategic, organisational, cultural, financial, customer, engineering industry (61.82%), followed by organisations and supplier factors. For each aspect, we developed fac- in the chemical industry (9.09%), automotive industry tors/statements which were later included in our question- (5.45%), telecommunications (5.45%), transportation in- naire. For data collection, we used a quantitative method cluding railways (5.45%), energetics (5.45%), electro in- with an online questionnaire, similar to that used by Raval dustry (3.64%) and pharmacy (3.64%)—table 2. et al. (2021). This approach enabled us to conduct a com- A well-balanced sample, from an organisational and parative analysis with similar research carried out in this industry point of view, gives us more representative results field. regarding the importance of the further results presented in The target population of our research comprised prac- Figures 1, 2 and 3. Due to the relatively small sample size titioners and employees in various organisations with ex- in our research (N=55), the subsequent tables and figures perience in the field of implementation of organisational present aggregate results for all organisations. changes, especially the implementation of various man- Table 1: Size of the organisation Size of organisation No. of organisations % of organisations in the sample in the sample Micro (1-9 employees) 11 20.00% Small (10-49 employees) 14 25.45% Middle (50-249 employees) 19 34.55% Large (250 or more employees) 11 20.00% TOTAL 55 100.00% 26 Organizacija, Volume 59 Research Paper Issue 1, February 2026 Table 2: Activity of the organisation Size of organisation No. of organisations % of organisations in the sample in the sample Mechanical engineering 34 61.82% Chemical industry 5 9.09% Automotive industry 3 5.45% Services-telecommunications 3 5.45% Services-railways 3 5.45% Services-energetics 3 5.45% Electro industry 2 3.64% Pharmacy 2 3.64% TOTAL 55 100.00% Table 3: Position of the respondents Position No. of organisations % of organisations in the sample in the sample Business owner 2 3.64% Director or head of a business unit 5 9.09% Project manager 9 16.36% Project team member 39 70.91% TOTAL 55 100.00% Note. SA-strategic aspect, OA-organisational aspect, CA-cultural aspects, FA-financial aspects, AC-aspect of customer, AS-as- pect of supplier; N - sample, Likert scale (1-5): 1-not important factor, 2…,3…, 4…, 5-highly important factor, Avg.-Average Value, St.Dev.-Standard Deviation Figure 1: Influence factors for the successful implementation of changes in an organisation 27 Organizacija, Volume 59 Research Paper Issue 1, February 2026 Table 3 presents a profile of the respondents includ- ment change” (4.78 out of 5), was identified as the most ed in our research. As shown in Table 3, the majority of important factor in implementing change in organisations. respondents are employed as project team members, such The second most important factor was identified as the cul- as technologists, quality managers, developers, mainte- tural aspect (CA) defined by the factor “Effective commu- nance engineers (70.91%), followed by project managers nication” (4.62 out of 5), followed by strategic aspect (SA) (16.36%), directors of business units (9.09%) and business defined by the factor “Support of top management” (4.58 owners (3.64%). Utilising participants from different posi- out of 5) and financial aspect (FA) defined by the factor tions within organisations gives us a better overall view of “Tracking and controlling of goals” (4.40 out of 5). At the the importance and indicates the relevance of the factors bottom of the list are factors such as “Working closely with presented in Figures 1, 2 and 3. suppliers”, “Appropriate infrastructure” and “Appropriate Figure 1 shows the results regarding the most impor- budget for change”, which all scored 4.25 out of 5 on a tant factors in change implementation in organisations. 5-stage Likert scale. Analysis shows the importance of selected aspects, such as The results are in accordance with the previous findings strategic (SA), organisational (OA), cultural (CA), finan- of Gošnik (2024); Gastelum-Acosta et al. (2024); Lamei- cial (FA), customer (AC) and supplier (AS), in the imple- jer et al. (2024); Paneerselvam et al. (2025); Al Owad et mentation of change in organisations. Within each aspect, al. (2025); Bagherian et al. (2025); Furterer et al. (2025), we have used many specific statements which present an who also highlighted the importance of human aspects in influence factor. The level of agreement with each state- the successful implementation of changes in relation to the ment, as indicated on a Likert scale calibrated from 1 to 5, implementation of methods such as Six Sigma, Lean or was a measure of the importance of each factor. The results Lean Six Sigma. and relative importance of the influence factors are shown As the findings of Raval et al. (2021), Idogawa et al. in Figure 1. (2023), and Kilkelly (2024) suggest the importance of Selected cultural aspects (CA) and strategic aspects managers for successful change implementation in or- (SA), followed by financial aspects (FA), were identified ganisations, we have researched the qualities required of as the most important factors for the successful implemen- a leader to implement changes within organisations suc- tation of changes in an organisation. cessfully. The results and relative position of our empirical As shown in Figure 1, the cultural aspect (CA), de- study about the importance of factors relating to leadership fined by the factor “Choosing the right leaders to imple- qualities are shown in Figure 2. Note. N - sample, Likert scale (1-5): 1-not important factor, 2…,3…, 4…, 5-highly important factor, Avg.-Average Value, St.Dev.-Standard Deviation Figure 2: Necessary qualities of a leader for the successful implementation of changes in the organisation 28 Organizacija, Volume 59 Research Paper Issue 1, February 2026 Note. N - sample, Likert scale (1-5): 1- low barrier, 2…,3…, 4…, 5 – very high barrier, Avg.-Average Value, St.Dev.-Standard Deviation Figure 3: Barriers to the successful implementation of changes in the organisation As presented in Figure 2, the most important quali- which scored 4.20 out of 5 on a 5 stage Likert scale. At the ty of the manager leading the organisational change im- bottom of the list are the barriers deemed relatively less plementation project is identified as the factor “Respects important, such as: “Too high risk of disruption”, which others”, which scored 4.80 out of 5, followed by “Is able scored 3.96 out of 5, followed by “We do not have pre-de- to solve problems”, which scored 4.73 out of 5 and “Is veloped plans”, which scored 4.02 out of 5 and “The scale motivator”, which scored 4.71 out of 5 on a 5 stage Likert of planned change is too big”, which scored 4.00 out of 5. scale. At the bottom of the list are the factors considered The top-ranking barrier, “Ineffective way of communica- least important, such as “Is representative of the custom- tion”, can be related to managers leading the change, as it ers’ interests”, which scored 4.13 on a 5-point Likert scale, stems from Figure 2, where the factor “Manager respects followed by “Is result-oriented”, which scored 4.35 out of others” was identified as the most important quality re- 5 and “Is a positive thinker”, which scored 4.56 out of 5 quired of managers for successful change implementation on a 5-point Likert scale. The most important factor, “Re- in an organisation (Figure 2). The findings are also consist- spects others”, is shown to have the lowest St.Dev. (0.45), ent with those of Raval et al. (2021). which shows, on average, the highest congruency among 5 Discussion all the participants evaluating this factor. These findings This paper reveals the most important factors for the resonate with those of Raval et al. (2021) and others, such successful implementation of changes in an organisation. as Idogawa et al. (2023); Kilkelly (2024). Regarding our A literature study reveals the latest findings on change third research question about key barriers to the successful management and its relationship to the successful imple- implementation of changes in the organisation, we present mentation of organisational changes, such as the introduc- the results in Figure 3. tion of new managerial methods, e.g., Six Sigma, Lean, The results compiled in Figure 3 show that the most Lean Six Sigma, Project management, Kaizen, EFQM. important barrier to successful change implementation in The results of our study contribute to an understanding organisations is identified as “Ineffective way of commu- of the key influential factors for successful change im- nication”, which scored 4.49 out of 5, followed by “Em- plementation in organisations, including the most impor- ployee habits and mindsets do not support change”, which tant managerial qualities that support implementation and scored 4.33 out of 5, and “Key employees resist change”, overcome barriers. 29 Organizacija, Volume 59 Research Paper Issue 1, February 2026 Three main research questions (RQ) were studied: RQ managerial methods such as Six Sigma, Lean, Lean Six (1): What are the key influence factors for the successful Sigma, Project management, Kaizen, and EFQM. The re- implementation of changes in an organisation? RQ (2): sults of this research will provide researchers with a bet- What are the necessary qualities of leaders in organisations ter insight into and understanding of which factors impact for the successful implementation of organisational chang- successful change management in organisations. Addi- es? RQ (2): What are the barriers to the successful imple- tionally, this research establishes the feasibility of similar mentation of organisational changes? In our research, we comparative studies in this field and enhances knowledge have included various aspects such as strategic (SA), or- of change management. ganisational (OA), cultural (CA), financial (FA), customer On the other hand, this study offers some practical (AC) and supplier (AS) aspects and thus related factors implications as well. The findings of our empirical study which in the literature research were identified as having a can help managers in organisations by highlighting the potential impact on the success of change implementation key factors that contribute to the success of change im- in organisations. plementation projects across organisations of various siz- The results concerning key influence factors for the es and industries. The results can help managers in their successful implementation of changes in an organisation decision-making by directing them to focus on the key show that a cultural aspect (CA), such as “Choosing the impact factors that support the implementation of change right leaders to implement change”, is followed by a sec- initiatives and, consequently, improve organisational per- ond cultural aspect (CA) defined by the factor “Effective formance. communication”, as shown in Figure 1. These results are Based on the results, it is important to consider the lim- also supported by the previous findings of Raval et al. itations of this study. This study presents results regard- (2021). ing the impact factors for the successful implementation Our results (Figure 2) show that the most important of changes in organisations, especially the introduction of quality of the manager leading the change implementa- new managerial methods, e.g., Six Sigma, Lean, Lean Six tion project in the organisation is “Respects others”, fol- Sigma, Project management, Kaizen, and EFQM. Success lowed by “Is able to solve problems”. The results show is influenced by many factors, which unfortunately can- that the highest barriers to the successful implementation not all be included in a single research project at the same of changes in organisations (Figure 3) are “Ineffective way time. So, in our study, we include only a limited number of communication” and “Employee habits and mindsets do of pre-selected aspects and thus related success factors not support change”. which, in the literature, were suggested to have an impact The results also show that the most important barriers on the successful implementation of changes in an organ- to successful change implementation in organisations are isation. Our research is limited to a sample of 55 organi- “Ineffective way of communication” and “Employee hab- sations (of varying sizes and industries) in Slovenia. From its and mindsets do not support change”. These two factors a methodological point of view, the limitation is reflected are also strongly related to the cultural and human-related in the use of an online questionnaire for quantitative data aspects and are supported by the previous findings of De collection and descriptive statistics for data analysis. Koeijer et al. (2024) and Gastelum-Acosta et al. (2024). Future research in this field could include a larger It can be concluded that the cultural and human-related sample size, comparative studies across different types of aspects play a key role in the successful implementation organisations and industries, comparative studies across of changes in an organisation, as similarly identified in similar economies, longitudinal studies, and the use of the findings of Yadav et al. (2018), Raval et al. (2021), De other research methods. Koeijer et al. (2024), and Gastelum-Acosta et al. (2024). Overall, this article contributes to the theoretical un- The results of our empirical study must be interpreted derstanding of change management in the implementation with caution. The results of this study depend strongly on of new work methods in organisations and highlights the the relatively small sample size (N=55) included in this theoretical and practical implications for researchers and research. On the other hand, our sample does not depend managers/practitioners. on a single company size or profile. However, it includes organisations of various sizes (Table 1), various industries (Table 2), and various participant positions (Table 3). So, References our well-balanced sample provides additional value and contributes to our findings, yielding more representative Al Owad, A., Yadav, N., Kumar, V., Swarnakar, V., Jayakr- results. ishna, K., Haridy, S., Yadav, V. (2025). Integrated Lean The theoretical implication of this research is an over- Six Sigma and Kotter change management framework view of the latest literature in the field of change man- for emergency healthcare services in Saudi Arabia. agement, especially in relation to successful change im- Benchmarking. 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Analyzing critical suc- Production Planning and Control, 29(3), 185–201. doi: cess factors in Lean Six Sigma training. Internation- 10.1080/09537287.2017.1402134 al Journal of Productivity and Performance Manage- ment, 74(4), 1400–1424. https://doi.org/10.1108/ IJPPM-11-2023-0627 PhD Dušan Gošnik is an Associate Professor and Pollack, J., & Algeo, C. (2016). Project managers’ and researcher in the field of Management. He is employed change managers’ contribution to success. Internation- at the Department of Management at the Faculty of al Journal of Managing Projects in Business, 9(2), 451– Management in Koper, Slovenia. His research areas 465. https://doi.org/10.1108/IJMPB-09-2015-0085 include project management, business process Pop, B.O., Popescu, C., & Gabor, M.R. (2023). Process management, lean management, and strategic and product change management as a predictor and management. For more than 9 years, he has been innovative solution for company performance: a case employed as a project manager in a large international study on the optimization process in the automotive company, and for 15 years as a university lecturer and industry. Applied System Innovation, 6(5), 75, doi: researcher. 10.3390/asi6050075 Raval, S.J., Kant, R. & Shankar, R. (2018). Lean six sig- PhD Mateja Jerman is an Associate Professor of ma implementation: modelling the interaction among Accounting at the Faculty of Management, University the enablers. Production Planning and Control, 29(12), of Primorska, Slovenia. She has been affiliated with the 1010–1029. Doi: 10.1080/09537287.2018.1495773 faculty since 2007. Her research focuses on financial Raval, S.J., Kant, R. & Shankar, R. (2021). Analyzing the reporting, with a particular emphasis on disclosure critical success factors influencing Lean Six Sigma practices, accounting for intangibles, and earnings implementation: fuzzy DEMATEL approach. Journal management. She is the author and co-author of of Modelling in Management, 16(2), 728–764. https:// several scientific and professional papers, as well as of doi.org/10.1108/JM2-07-2019-0155 accounting textbooks. Samanta, M., Virmani. N., Kumar Singh, R., Nadimul Haque. S., & Jamshed, M. (2024). Analysis of criti- cal success factors for successful integration of Lean Six Sigma and Industry 4.0 for organisational excel- lence. The TQM Journal, 36(1), 208–243. https://doi. org/10.1108/TQM-07-2022-0215 Singh, M. & Rathi, R. (2021). Investigation and modelling of lean six sigma barriers in small and medium-sized industries using hybrid ISM-SEM approach. Inter- national Journal of Lean Six Sigma, 12(6), 1115– 1145. https://doi.org/10.1108/IJLSS-09-2020-0146 Trzeciak, M. (2024). Factors of success in the change man- agement process of IT programs. Journal of Organiza- 32 Organizacija, Volume 59 Research Paper Issue 1, February 2026 Analiza ključnih faktorjev vpliva pri uvajanju novih metod v organizacijah: vidik managementa sprememb Namen raziskave je preučiti uključne dejavnike za uspešno uvajanje sprememb v organizacijah, katere lastnosti managerjev najbolje podpirajo uvedbo sprememb in katere so najpogostejše ovire pri uvajanju sprememb v orga- nizacijah. Glavna sprememba, na katero se osredotočamo, je uvedba novih metod dela v organizaciji, kot so Šest Sigma, Vitko poslovanje, Vitka šest sigma, Kaizen in podobne metode. Metode: Predstavljene so najnovejše ugotovitve iz literature o upravljanju sprememb, ključnih dejavnikih uspeha in ovirah pri uvajanju sprememb. Predstavljena je empirična študija o na vzorcu 55 organizacij iz Slovenije. Za zbiranje podatkov je bil uporabljen spletni vprašalnik. Za analizo podatkov je bila uporabljena opisna statistika. Rezultati kažejo, da so ključni dejavniki za uspešno izvajanje sprememb močno povezani s kulturo in ostalimi člo- veškimi dejavniki, kot je podpora vršnega managementa in primerna vključenost zaposlenih. Izbira pravih vodij in učinkovita komunikacija pri uvajanju sprememb sta bila zaznana ključni dejavnik uspeha. Ugotovljeno je, da so ključ- ne ovire pri uvajanju sprememb neučinkovit način komunikacije ter to, da obstoječe navade in miselnost zaposlenih ne podpirajo sprememb. Ugotovitve te raziskave prispevajo k boljšemu razumevanju uvajanja sprememb ter prinašajo teoretične in praktič- ne implikacije, kot je uspešnejše uvajanje sprememb v organizacije. Ključne besede: Change, Management, Method, Implementation, Success 33 Organizacija, Volume 59 Research Paper Issue 1, February 2026 DOI: 10.2478/orga-2026-0003 Relationships between Personal Characteristics, Job Satisfaction and Organisational Behaviour of Work Team Members and the Role of Organisational Agility Maturity Slavka SILBERG1*, Luděk STEHLÍK2, Tomáš SOLLÁR3, Michal SILBERG2, Jana MATOŠKOVÁ4 1 Palacký University Olomouc, Faculty of Arts, Department of Psychology, Olomouc, Czech Republic, slavka.silberg@upol.cz 2 Independent Scholar, ludek.stehlik@gmail.com, michal.silberg@gmail.com 3 Constantine the Philosopher University in Nitra, Faculty of Social Sciences and Health Care, Nitra, Slovakia, tsollar@ukf.sk 4 Tomas Bata University in Zlín, Faculty of Management and Economics, Zlín, Czech Republic, matoskova@utb.cz Introduction & Purpose: The aim of this study is to analyse the relationships between professional personal com- petencies, organisational agility, job satisfaction, and organisational citizenship behaviour in work teams, as these factors can influence organisational performance and competitiveness. Methodology: The cross-sectional study included a sample of 25 teams (N = 135) from various economic sectors in Slovakia. We conducted multilevel correlation and regression analyses, factor analyses, and structural modelling. Results: The multilevel correlation analysis showed positive correlations with job satisfaction for all scales of the Bochum Inventory of Personality (ranging from 0.097 to 0.406), 10 of which were statistically significant. The results indicate that job competencies predict job satisfaction, and that job satisfaction correlates positively with employees’ organisational citizenship behaviour. However, the moderating effect of organisational agility on the relationship between job competencies and job satisfaction could not be demonstrated. We found a statistically significant posi- tive relationship between the maturity level of agility and job satisfaction. We discuss possible causes, highlight the limitations, and suggest implications. Conclusion: Appropriate professional skills and a people-centred approach are key to long-term success in a com- petitive environment and, along with organisational agility, can contribute to employee job satisfaction. Keywords: Job satisfaction, OCB, Organisational agility, Professional personal competencies, Team effectiveness 1 Introduction predictable, turbulent changes require responsiveness, im- provisation, inventiveness, agility, organisational stability, Confrontation with a constantly changing environment endurance, and resilience to maintain market positions in is becoming the new norm. Technological progress and un- global competition and meet stakeholders’ needs on time. 1 Received: 12th December 2024; Accepted: 18th December 2025 34 Organizacija, Volume 59 Research Paper Issue 1, February 2026 This creates new challenges that increase demands on competence–satisfaction link becomes stronger when the jobs and skills. Organisational agility and effective team- team’s agility level is high (resource-gain interaction). Job work are therefore among the current topics of interest to satisfaction, in turn, is a proximal motivational outcome researchers and practitioners, but it is also important to that predicts discretionary, extra-role actions such as OCB consider the broader context of the organisation/team (e.g. (Organ & Ryan, 1995; Podsakoff et al., 2000). Hence, the Sherehiy  Karwowski, 2014 De; ;nning, 2018 De ; et JD-R model predicts a mediated pathway from resources al., 2018 Russ 2021 Peter; mann  Zacher, 2022 Ri- to satisfaction to OCB. etze ;  Zacher, 2022, etc.). Organisations are increasingly The aim of this paper is to test the relationships for- aware of the need to develop the human capital and skills mulated in a proposed theoretical model and their fit with of their employees, not only to deliver work proactively, the data and extend existing knowledge and examines but also to engage beyond formal requirements, adopt an these JD–R-derived relationships in Slovak work teams. entrepreneurial approach, respond quickly to unpredict- Specifically, we test whether (i) job related professional able and complex changes, and innovate. Skills such as competencies (personal resources) and (ii) organisational creativity, critical thinking, teamwork, initiative, and prob- agility maturity (job resources) each positively predict job lem-solving are playing an increasingly important role in satisfaction, (iii) and agility moderates the competence– managing complexity and change in society (European satisfaction relationship. Commission, 2018; Stareček et al., 2023). Dissatisfac- tion at work can lead to alienation from work, increased turnover intentions, and negatively affect life satisfaction, 2 Literature Review and vice versa (Demerouti et al., 2000; Loewe et al., 2014; Rastogi et al., 2018). From a Job Demands-Resources 2.1 Job-related professional personal (JD–R) perspective (Bakker & Demerouti, 2007), this en- competences and job satisfaction vironment creates high job demands (e.g., time pressure, role ambiguity) that can erode well-being unless they are Spector (1997) defines job satisfaction as “the overall offset by adequate job and personal resources. Two re- feeling of individuals about various aspects of their jobs”. source streams are particularly relevant for knowledge-in- Job satisfaction is a psychological concept reflecting ad- tensive teams: aptation to work, its characteristics and attributes; it is a Personal resources - stable individual characteristics complex, individually variable, largely conditioned, yet that help employees cope with demands (e.g., self-effica- universal phenomenon (Kollárik, Sollárová et al., 2004). cy, conscientiousness, emotional stability). In the JD–R Job satisfaction can be viewed as a psychological state model, these are termed personal resources and are known and a dynamic process influenced by employees’ individ- to predict job satisfaction and extra-role behaviours (De- ual preferences. Hackman and Oldham (1976) argue that merouti et al., 2020). In this study, we operationalise per- job satisfaction is directly affected by job characteristics sonal resources using the Bochum Inventory of Person- and reflects the extent to which the job meets a person’s ality (BIP), which yields five factors: Conscientiousness, expectations. Locke (1995) adds that job satisfaction also Teamness, Drive, Leadership, and Composure, providing partly depends on how well tasks align with a person’s a comprehensive measure of personal resources. long-term goals, which affects self-esteem, and on which Job resources - aspects of the work environment that types of work experience are most memorable. In some are instrumental, motivational, and developmental (e.g., studies, job satisfaction is considered not in terms of need autonomy, feedback, supportive leadership). Organisation- fulfilment, but rather in terms of work attitude and its as- al agility encompasses these qualities: an agile organisation pects (Grüneberg, 1979; Spector, 1997). It results from provides flexible structures, rapid decision-making, col- the combined effect of the interrelationship between em- laborative norms, and opportunities for continuous learn- ployees’ attitudes, the structure of the organisation’s value ing (Wendler, 2014; Petermann & Zacher, 2022). Within system, and leadership style. This study focuses on overall the JD–R framework, agility can be regarded as a job re- job satisfaction. source that should enhance job satisfaction and, through Studies (Abun et al., 2022; Fekonja et al., 2022) show the satisfaction-mediated pathway, stimulate organisation- that job satisfaction depends not only on the organisa- al citizenship behaviour (OCB). According to the JD–R tional environment but also on factors such as individual model, resources can act additively (each resource inde- competencies, career and promotion opportunities, val- pendently increases satisfaction) and interactively (high ue alignment with supervisors and the organisation (Ko, job resources amplify the positive effect of personal re- 2012; Gorenak et al., 2020), organisational culture, and sources). This leads to three testable propositions for work leadership style (Saleem, 2015; Specchia et al., 2021). teams: (a) Personal resources to job satisfaction (main Employees who possess the competencies required for effect of job-related competencies). (b) Job resources to their roles are more likely to be satisfied with their work, job satisfaction (main effect of agility). (c) Interaction: the which leads to improved performance and organisational 35 Organizacija, Volume 59 Research Paper Issue 1, February 2026 outcomes (Yan et al., 2022). Self-esteem, locus of control, skills, the more satisfied they are with their job (Glisson self-efficacy, and various personality traits have also been & Durick, 1988). Several studies have confirmed that job shown to be important predictors of job satisfaction (Judge satisfaction mediates the relationship between work com- et al., 1997) and to increase knowledge sharing within the petencies and OCB (Chiu & Chen, 2005), and between or- organisation (Matošková et al., 2022; Stoyanova et al., ganisational justice and OCB (Saifi & Shahzad, 2017), as 2024). Numerous studies in various settings show that well as significantly influencing OCB (Fitrio et al., 2019). competence-related personal resources predict job satis- Ryan and Deci’s (2000) Self-Determination Theory (SDT) faction and highlight the significant influence of person- states that people’s levels of motivation and engagement ality traits. The Big Five personality traits, particularly can vary depending on their social environment, which can conscientiousness, agreeableness, and emotional stability, increase self-motivation and promote positive psycholog- are consistently associated with higher job satisfaction ical development according to three innate psychological (Ariyabuddhiphongs & Marican, 2015; Bui, 2017; Judge needs: autonomy, social belonging and support, and the & Ilies, 2002; Steel et al., 2019; Yan et al., 2022), where- experience of competence, especially through feedback. as neuroticism has a negative impact (Kang & Malvaso, Agile collaboration reinforces these natural needs, particu- 2023; Peltokorpi & Sekiguchi, 2023). In JD-R terminol- larly by promoting essential job resources such as autono- ogy, personal resources are stable psychological charac- my, teamwork and task feedback (Rietze & Zacher, 2022). teristics that help individuals manage demands, mobilise Furthermore, job crafting - the proactive reshaping of tasks energy, and sustain motivation (Bakker & Demerouti, and relational boundaries - mediates the relationship be- 2007). Various dispositional factors (e.g., agreeableness, tween personality traits and job performance. Employees conscientiousness, positive and negative affectivity) pre- who actively shape their job roles tend to experience high- dispose people to certain orientations towards colleagues er job satisfaction and performance (Peral & Geldenhuys, and managers, which may increase the likelihood that they 2020). Psychological safety and proactive personality experience treatment they perceive as satisfying, support- traits improve job satisfaction and performance. Employ- ive, fair, and deserving of commitment (Organ & Ryan, ees who feel safe to express themselves are more proac- 1995). A study by Franěk and Večeřa (2008) reports pos- tive, resulting in greater job satisfaction (Kuo et al., 2019; itive correlations between agreeableness, emotional sta- Shi & Cao, 2022). In line with JD-R, we therefore expect bility, openness, self-efficacy, and overall job satisfaction. that higher scores on the BIP competency factors (espe- In particular, agreeableness and openness foster positive cially Conscientiousness and Teamness) will be associated social interactions and collaborative environments, which with greater job satisfaction. are associated with higher satisfaction. Other studies have H1: Employees’ professional competencies predict job found positive associations between conscientiousness and satisfaction. job satisfaction (Furnham et al., 2009), as well as between conscientiousness and OCB-C (Lapierre & Hackett, 2010; 2.2 Organisational and workforce agility Topino et al., 2021). Conscientious employees tend to be organised, diligent, and goal-oriented, which enhances the perceived fit between job demands and personal capaci- Agility is the proactive ability of an organisation and ties and, in turn, increases job satisfaction (Furnham et al., its employees to use resources to exploit a competitive 2009). According to the research results of Thomas et al. advantage or avert a threat. The agile approach has both (2004), personality has neither a direct influence on satis- supporters and critics. Petermann and Zacher (2022) state faction nor a moderating influence on the relationship be- that workforce agility is defined in various ways, theories tween job characteristics and job satisfaction. The results and conceptualisations. Organisational agility is a readi- of the Steel et al. (2019) study show that the Big Five per- ness that enables rapid changes of direction and early rec- sonality traits account for about 10% of the variance in job ognition and exploitation of opportunities as they arise. satisfaction, which in turn accounts for 13% of the vari- Agility is a philosophy based on fast-moving, self-direct- ance in life satisfaction. The results also support the finding ed, project-based, innovative teams. Organisational agility that dispositions influence perceptions of life satisfaction, maturity is essential for leveraging personality traits to en- which in turn influence the more specific subset of job sat- hance job satisfaction and performance. Agility enables or- isfaction. These findings are important for researchers and ganisations to adapt quickly, creating environments where practitioners, as they suggest that information is lost when diverse personalities can thrive (Marchalina et al., 2021). personality aspects are overlooked and that training and Companies that have successfully adopted an agile ap- interventions in the workplace could improve perceptions proach and agile methods include Mercedes-Benz (Acker- of satisfaction among individuals who tend to have lower mann et al., 2021), John Deere, Spotify, LinkedIn, Netflix, subjective well-being. Studies confirm that job competen- Amazon, Facebook, Bosch, Google, Nestlé, IBM, 3M and cies are positively related to job satisfaction (Ko, 2012; many others (Rigby et al., 2020). The agile approach orig- Riyanto et al., 2023), and the more employees use their inates from the Agile Manifesto, written by 17 visionaries 36 Organizacija, Volume 59 Research Paper Issue 1, February 2026 for software development (Beck et al., 2001), whose prin- software developers, effect sizes were stronger in other ciples and core values aimed to improve processes in the occupations, such as manufacturing, healthcare, and logis- IT sector. These principles have since been adopted and tics. The studies showed a positive correlation between successfully implemented in other areas, such as educa- innovative behaviour, organisational agility, and job sat- tion and HR management. Agile practices are designed to isfaction (Omidvar et al., 2021); confirmed a significant overcome obstacles and problems caused by rapid change, positive impact of OCB on organisational agility (Vahdati enabling teams to respond quickly and effectively through et al., 2014; Moradi et al., 2017; Zakeri et al., 2018); and efficient information flow and decision-making processes demonstrated that collaborative management positively (Sherehiy & Karwowski, 2014). affects organisational agility and employee empowerment, In a survey conducted by Deloitte (McDowell et al., with a significant effect on organisational agility (Nouri & 2017) involving over 10,000 HR and service managers Mousavi, 2020). A study by Petermann and Zacher (2022) across 140 countries, 94% of respondents stated that agili- shows that workforce agility is associated with innovation ty and collaboration are critical to their organisation’s suc- performance, organisational citizenship behaviour, task cess. Only 19% of business executives consider traditional performance, and job satisfaction. Organisations with high work models the best way to create value for workers and agility can better leverage diverse personality traits to en- organisations, while 85% believe work must be organised hance employee satisfaction and performance (Marchalina more agilely to adapt quickly to market changes (Cantrell et al., 2021). Sherehiy and Karwowski (2014) also found et al., 2022). However, the agile transformation of organ- that autonomy in the workplace is a key factor in predict- isational processes takes time and is often complex and ing workforce agility. In addition, members of agile teams challenging (Naslund & Kale, 2020). The goal is to boost experience a higher level of psychological empowerment innovation and productivity by shifting from the tradition- due to frequent communication within the team, support, al hierarchical organisational structure to a more efficient, and increased team accountability (Malik et al., 2021). flexible, flatter, and agile structure that encourages and Job resources are organisational, supervisory, or task-level empowers employees (Campanelli & Parreiras, 2015) to characteristics that reduce job demands, stimulate growth, increase speed and improve problem-solving. Tripp et al. and fulfil basic psychological needs (autonomy, compe- (2016) found a direct correlation between the use of agile tence, relatedness) (Deci & Ryan, 2000). Organisational development practises and job satisfaction. Information agility embodies these resources: agile structures provide transfer and knowledge sharing in dynamic, uncertain, and high autonomy, rapid feedback loops, transparent commu- highly demanding environments – both among employees nication, and empowering leadership (Rigby et al., 2020; and across the organisation – along with empowerment Wendler, 2014). Empirical meta-analyses show that agile and intrinsic motivation, drive activities that achieve inno- practices are positively related to job satisfaction and psy- vative results (Malik et al., 2021; Stoyanova et al., 2024). chological empowerment (Koch et al., 2023; Tripp et al., Regarding conceptual aspects, the systematic literature 2016). From a JD-R perspective, agility should therefore review (Tessarini Junior & Saltorato, 2021) found that an function as a job resource that directly enhances satisfac- agile workforce comprises four interrelated and interde- tion. pendent dimensions: proactivity, flexibility and adaptabil- H2: Organisational agility moderates the relationship ity, resilience, and competence. These characteristics can between professional competencies and job satisfaction. be fostered through strategies in the following areas: a) learning and training, b) forms of work organisation, c) 2.3 Job satisfaction and OCB human resource management (HRM), and d) culture and organisational structure. The authors (Koch et al., 2023) of the pre-registered meta-analysis (k = 41 independent Job satisfaction and OCB are well-researched con- studies, N = 73,825) focused specifically on the affec- structs and valued outcomes in organisational practise. It tive, behavioural and cognitive outcomes of agile project is generally assumed that there is a reciprocal relationship; management. For the affective outcomes of job satisfac- that is, job satisfaction correlates positively with Organi- tion, affective strain, and organisational commitment, the sational Citizenship Behaviour (OCB), and several studies effect sizes were generally small. Medium-to-large effect have confirmed a significant influence of job satisfaction sizes were observed for the behavioural outcomes of per- on OCB (Dewi et al., 2021; Fitrio et al., 2019; LePine et formance and innovative behaviour. The meta-analysis al., 2002; Organ & Ryan, 1995; Urbini et al., 2023; Whit- shows a medium effect on the cognitive outcome of psy- man et al., 2010). Job satisfaction significantly influences chological empowerment. The analysis of the moderating organisational behaviour, affecting turnover intentions and effects of contextual factors was also notable, particularly organisational commitment. Satisfied employees are more regarding team size, which showed no significant mod- likely to demonstrate OCB and have lower turnover inten- erating effect, and occupational groups, which showed a tions (Bakotic, 2016; Chordiya et al., 2019). Some authors significant moderating effect. Compared to studies with (Colquitt et al., 2011; Podsakoff et al., 2000) define OCB 37 Organizacija, Volume 59 Research Paper Issue 1, February 2026 as voluntary activities by employees that may or may not H5: Organisational agility moderates the relationship be rewarded. It is discretionary (self-initiated) behaviour between professional competence and job satisfaction, and beyond the call of duty, not part of the employee’s formal is manifested in employees’ job satisfaction and positive job requirements, and contributes psychologically and so- organisational behaviour. cially to improving the overall quality of the work environ- ment and working conditions (Robins & Judge, 2017). Da- 2.4 Objectives lal (2005) and Spector, Bauer, and Fox (2010) identify two types of OCB: those that benefit the organisation (OCB-O) and those that are employee-centred (OCB-P), which help The aim of the study is to test the relationships pro- others solve work-related problems. Researchers (Dewi et posed in a theoretical model between professional person- al., 2021) confirmed that psychological empowerment and al competencies, organisational agility, job satisfaction, job satisfaction enhance employees’ OCB, and that there and organisational citizenship behaviour, and to assess is a disparity between employees’ personalities and their their consistency with the data. Based on an analysis of work behaviour. They recommend that adequate attention literary sources, a theoretical model (Figure 1) is proposed be given to employees’ job-related personalities and abil- in which occupational personality traits predict employee ities, as these influence OCB. A core JD R proposition is job satisfaction and organisational citizenship behaviour. that job resources can enhance the positive impact of per- Organisational agility moderates the relationship between sonal resources (the resource gain hypothesis). Empirical professional competencies and job satisfaction, as well as studies have shown that high autonomy strengthens the re- the relationships among organisational agility, job satisfac- lationship between self-efficacy and satisfaction (Demer- tion, and OCB. The model comprises several sub-models, outi et al., 2020) and that supportive climates amplify the with relationships first tested using two variables, then effect of conscientiousness on engagement (Bakker & De- with the addition of the third and fourth variables. merouti, 2017). In the present context, teams with great- Although the JD R model has been widely applied er agility should enable competent employees to convert to stress , strain, and performance outcomes, few studies their personal strengths into higher satisfaction. Converse- have examined agility as a job resource alongside person- ly, in low-agility teams, the same competencies may be al competency factors, particularly in non-IT, multi-sector underutilised, weakening the competence satisfaction link. work teams. Furthermore, the moderating role of agility H3: Employee job satisfaction predicts OCB. in competence satisfaction relationships remains under- Job Satisfaction as a Mediator of OCB explored, and the mediating pathway to OCB has rarely Within the JD-R framework, job satisfaction is a prox- been investigated within a unified JD R framework. This imal motivational outcome that drives higher-order behav- research addresses these gaps by (1) measuring a broad iours such as OCB and performance. Satisfied employees range of competency dimensions, (2) assessing organisa- experience greater affective commitment and discretionary tional agility maturity at the team level, and (3) applying energy, which translate into voluntary, extra-role actions multilevel modelling to capture cross-level interactions that benefit the organisation (Organ & Ryan, 1995; Pod- and mediation. In summary, the JD R perspective predicts sakoff et al., 2000). Meta-analyses confirm a robust pos- that both personal competencies and organisational agili- itive link between satisfaction and OCB (LePine et al., ty independently increase job satisfaction, that agility en- 2002; Whitman et al., 2010). Accordingly, we expect job hances the competence satisfaction relationship, and that satisfaction to mediate the effects of both personal and job satisfaction transmits these resource effects into higher resources on OCB. OCB. The following sections describe the methods used to H4: There is a positive relationship between organisa- test these propositions. tional agility and employee OCB. Figure 1: Theoretical model 38 Organizacija, Volume 59 Research Paper Issue 1, February 2026 Figure 2: Distribution of teams according to the employer’s main sector 3 Method In the first part, socio-demographic data of the respond- ents were collected (gender, age, highest level of education Regarding the research objectives, research question, achieved, role in the team, number of years, and sector in and hypotheses, we chose a quantitative, descriptive, cor- which they work in the current organisation). Each team relational-comparative, cross-sectional research design. and its individual members were coded according to team The research was based on the results of a self-adminis- membership. The battery includes the Bochum Inventory tered paper-and-pencil questionnaire. of Personality (BIP; Hossiep & Paschen, in Džuka, 2007), Sample the DMPS T303 (Kollárik et al., 1988) to measure job sat- The research study used an opportunistic sample of isfaction, and two questionnaires adapted for the Slovak 135 respondents, consisting of employees from Slovak population using a back-translation procedure (Beaton work teams aged over 19. Number of teams: 25, Team size: et al., 2000): the Organizational Agility Maturity Model M = 5.4, SD = 2.27, Q25 = 4, MED = 5, Q75 = 6, MIN (Wendler, 2014) and the Organizational Citizenship Be- = 3, MAX = 11, Gender: 33 males (24.4%), 102 females havior Checklist (OCB C; Spector & Fox, 2011). (75.6%), Education: 1x high school diploma without Mat- Measures ura (0.7%), 29x Abitur (21.5%), 6x Secondary school with Professional job-related personal competencies. abitur (4.4%), 16x Bachelor´s degree (11.9%), 73x Mas- The Bochum Inventory of Personality (Hossiep & Pas- ter´s degree (54.1%), 10x Doctor´s degree (7.4%), Age: M chen, 1998, in Džuka, 2007) was used to assess 14 scales = 40.8, SD = 9.83, Q25 = 34, MED = 39, Q75 = 48, MIN (210 experience-related items) across four dimensions = 24, MAX = 64, Team roles: 22 team leaders (16.3%), of work behaviour, measuring respondents’ occupational 113 team members (83.7%), Years with the company: M = personality assumptions. The response scale was a 6-point 8.59, SD = 8.62, Q25 = 3, MED = 5, Q75 = 10.5, MIN = 0, forced-choice scale (from “strongly agree” to “strongly MAX = 34. A high or postgraduate degree was reported by disagree”), with the median value omitted to prevent indif- 73% of respondents, an intermediate degree by 26%, and a ferent responses. Respondents selected the statement that lower secondary school diploma by 1%. In terms of length best applied to them. The questionnaire includes a compre- of employment, the sample included both new entrants and hensive set of scales measuring professional personality long-serving employees, with up to 34 years of experience. characteristics relevant to the topic under investigation and By employer, the most represented sectors were finance demonstrates good psychometric properties (reliability in (30%), including banking and insurance, public and gov- the German sample, N = 9,139, with Cronbach’s α ranging ernment administration (21%), and education (16%). The from 0.74 to 0.91, and in the Slovak sample, N = 373, with overall sector breakdown is shown in Figure 2. values from 0.58 to 0.83) (Džuka, 2007). 39 Organizacija, Volume 59 Research Paper Issue 1, February 2026 Organisational citizenship behaviour we used the programming language R for statistical calcu- The organisational behaviour of the research sample lations and data visualisation (R Core Team, 2020). Due to was assessed using a 20-item questionnaire adapted for the the nature of the data and the cross-sectional design, the re- Slovak population (Spector & Fox, 2011). The question- lationships tested are not strictly causal but follow the tra- naire measures the frequency of organisational citizenship dition of approaches that use regression analysis methods. behaviour among employees. Respondents indicated on a 5-point Likert scale (from never to daily) how often they perform these activities at their current workplace. The to- 4 Results tal score reflects the overall level of OCB: the higher the score, the higher the OCB. According to the authors, the 4.1 Hypothesis test questionnaire has good psychometric properties, with in- ternal consistency ranging from 0.75 to 0.92 and test-retest Employees’ professional competencies predict job sat- reliability from 0.77 to 0.89. isfaction (H1) Organisational agility maturity We conducted a multilevel Pearson product-moment The battery also included the OAM questionnaire on correlation analysis, using team membership as the group- organisational agility (Wendler, 2014), which measures ing variable, to examine the relationship between scores six scales and was adapted for the Slovak population. The on all 14 BIP scales and scores on the DMPS T-303 test. questionnaire covers key aspects of organisational agility For all BIP scales, the point estimates of the correlation and has a Cronbach’s α between 0.90 and 0.96 (Wendler, coefficients are positive, ranging from 0.097 to 0.406, with 2014). 10 statistically significant at the alpha level of 0.05 (Table Job satisfaction 1). To measure job satisfaction, we used the 18-item DMPS Organisational agility moderates the relationship T-303 questionnaire (Kollárik et al., 1988). Respondents between professional competencies and job satisfaction selected one option for each statement on a 5-point Likert (H2) scale (from “strongly agree” to “strongly disagree”), indi- Given the large number of scales in both the BIP and cating their relationship to their current job. The reliability the Organisational Agility Maturity (OAM) test, our first calculated from the collected data shows a Cronbach’s al- step was to reduce the dimensionality of our data for anal- pha coefficient α = 0.895. ysis. For the OAM, we used a simple sum of the scores Procedure from all six OAM scales, as the scales are highly positively We conducted descriptive analysis to provide an over- intercorrelated (0.38-0.7), and exploratory factor analysis view of the distribution of the variables analysed. In addi- (EFA) showed that one- and two-factor solutions explain tion, we used multilevel regression and multilevel correla- more than 50% and 100% of the overall variability, respec- tion analyses to examine relationships between variables tively. For the BIP, we identified a well-fitted and mean- in data with a hierarchical structure, as well as factor anal- ingful five-factor EFA solution, which explained 100% of ysis and structural modelling methods. For data analysis, the overall variability. Instead of using the 14 original BIP Table 1: Correlation between BIP and job satisfaction (DMPS) BIP_scale r CI95L CI95H t_value p BIP_Team orientation 0.406 0.254 0.538 5.117 ˂.001 BIP_Conscientiousness 0.396 0.243 0.529 4.971 ˂.001 BIP_Achievement Motivation 0.331 0.172 0.474 4.051 ˂.001 BIP_Openness to Contact 0.317 0.157 0.461 3.856 ˂.001 BIP_Sociability 0.303 0.141 0.449 3.661 ˂.001 BIP_Social Sensitivity 0.302 0.140 0.448 3.651 ˂.001 BIP_Action Orientation 0.270 0.106 0.420 3.233 .002 BIP_Working under Pressure 0.252 0.087 0.404 3.009 .003 BIP_Leadership Motivation 0.211 0.043 0.367 2.487 .014 BIP_Power Motivation 0.189 0.020 0.347 2.216 .028 BIP_Assertiveness 0.158 -0.011 0.318 1.845 .067 BIP_Self-Confidence 0.147 -0.022 0.309 1.718 .088 BIP_Flexibility 0.120 -0.050 0.283 1.391 .167 BIP_Emotional Stability 0.097 -0.073 0.262 1.130 .261 40 Organizacija, Volume 59 Research Paper Issue 1, February 2026 scales, we used the five resulting factor values in subse- CI [0.108, 0.422], t = 3.263, p = 0.001). quent analyses (see Table 2 below for the interpretation of Positive relationship between organisational agility the identified factors based on the factor loadings for the and OCB (H4) original BIP scales). We conducted a multilevel Pearson product-moment After variable reduction, we provide a multilevel re- correlation analysis, using team membership as the group- gression analysis for each competency factor, including an ing variable, to examine the relationship between scores interaction term between competency and agility, as well on all six scales of the Organizational Agility Maturity as a random intercept effect. For all competency factors, Test (OAM) and overall OCB-C test scores. The analyses the estimated interaction terms were not statistically sig- showed that, except for one OAM scale (RZ - Manage- nificant at the 0.05 alpha level (Table 3). ment of Change), all OAM scales were positively corre- Employee job satisfaction predicts OCB (H3) lated with organisational citizenship behaviour. However, We used a multilevel Pearson product-moment corre- none of the correlations was statistically significant at the lation, with team membership as the grouping variable, alpha level of 0.05 (see Table 4 below for details). There- to analyse the relationship between scores on the DMPS fore, we have not found sufficient evidence to support a T-303 test and overall scores on the OCB-C test. The anal- positive relationship between organisational agility and ysis showed a positive correlation between job satisfaction organisational citizenship behaviour. and organisational citizenship behaviour (r = 0.272, 95% Table 2: The five EFA solution factors according to the original scales Competency factor Original scales are most strongly associated with a particular competency factor. Composure Emotional Stability, Working under Pressure, Self-Confidence, Flexibility, Action Orientation Drive Achievement Motivation, Power Motivation, Leadership Motivation, Flexibility Teamness Sociability, Social Sensitivity, Team Orientation, Openness to Contact Leadership Openness to Contact, Sociability, Self-Confidence, Leadership Motivation Conscientiousness Conscientiousness Table 3: Moderating effect of organisational agility on the relationship between professional competencies and job satisfaction interaction_term estimate std. error CI95L CI95H t statistic p_value Composure: Agility_Maturity -0.006 0.028 -0.062 0.049 -0.219 0.827 Drive: Agility_Maturity -0.005 0.027 -0.058 0.049 -0.180 0.857 Teamness: Agility_Maturity 0.016 0.029 -0.041 0.073 0.553 0.581 Leadership: Agility_Maturity -0.056 0.032 -0.118 0.007 -1.734 0.085 Conscientiousness: Agility_Maturity -0.010 0.032 -0.071 0.052 -0.305 0.761 Table 4: Correlation of the OAM scales with organisational citizenship behaviour Agility_scale Pearson_r CI95L CI95H t_value p_value AH (Agile Values) 0.124 -0.046 0.287 1.445 0.151 Tech (Technology) 0.119 -0.051 0.282 1.381 0.170 PS (Workforce) 0.163 -0.007 0.323 1.900 0.060 RZ (Management of Change) -0.123 -0.286 0.047 -1.434 0.154 KK (Collaboration and Cooperation) 0.085 -0.085 0.250 0.982 0.328 FS (Flexible Structures) 0.104 -0.066 0.268 1.209 0.229 41 Organizacija, Volume 59 Research Paper Issue 1, February 2026 Organisational agility moderates the relationship =0.978, RMSEA = 0.172; between professional competence and job satisfaction, • Leadership: χ2 (df = 3) = 10.205, p = 0.017, CFI which is manifested in the job satisfaction and positive =0.987, RMSEA = 0.133; organisational behaviour of employees (H5) • Conscientiousness: χ2 (df = 3) = 5.818, p = 0.12, To test this hypothesis, we conducted a multilevel CFI =0.995, RMSEA = 0.083. structural equation modelling (SEM) for each competen- The plots below, showing the standardised regression cy factor (Composure, Drive, Teamness, Leadership, and coefficients for each individual SEM model, present a mul- Consciousness), whose structure corresponds to the nested tilevel structural equation model (SEM) for each compe- structure of the data and the hypothesised relationships be- tency factor: Composure, Drive, Teamness, Leadership, tween the constructs under investigation (e.g. 1) organisa- and Consciousness. The models have two levels – with- tional agility maturity and professional competencies have in (1) and between (2) teams – to account for the nested a direct influence on job satisfaction, 2) organisational structure of the data. At the first level, the theoretical struc- agility maturity moderates the direct influence of profes- ture proposed by our research hypotheses is as follows: sional competencies on job satisfaction, 3) job satisfaction 1) organisational agility maturity (OAM) and professional has a direct influence on organisational citizenship behav- competencies directly influence job satisfaction (DMPS); iour and 4) organisational agility maturity has a direct in- 2) organisational agility maturity (OAM) moderates the fluence on OCB. Given the relatively small sample size, direct influence of professional competencies on job sat- we had to simplify the nested structure of the models in isfaction (DMPS); 3) job satisfaction (DMPS) directly order to fit these models. Therefore, we considered only influences organisational citizenship behaviour (OCB-C); the relationships between the DMPS, OCB-C, and OAM and 4) organisational agility maturity directly influences variables at the second level. The random part of the fit- OCB. The values provided correspond to the estimated ted two-level models includes only random intercepts. For standardised regression coefficients and the variability of model fitting, we used data with reduced dimensionality, the individual variables. The second level of the model in- as described earlier. All models, except those for Teamness cludes only the direct influence of the maturity level of and Leadership, showed satisfactory or at least marginally organisational agility (OAM) on job satisfaction (DMPS), acceptable fit statistics: the direct influence of job satisfaction (DMPS) on organ- • Composure: χ2 (df = 3) = 0.672, p = 0.880, CFI isational citizenship behaviour (OCB-C), and the direct =1, RMSEA = 0; influence of organisational agility maturity on OCB. The • Drive: χ2 (df = 3) = 4.186, p = 0.242, CFI =0.998, random part of the fitted two-level models includes only RMSEA = 0.054; random intercepts. • Teamness: χ2 (df = 3) = 14.956, p = 0.002, CFI Figure 3: Plot of the individual SEM model of the competency factor Composure within and between teams according to the relationships hypothesised in the proposed theoretical model 42 Organizacija, Volume 59 Research Paper Issue 1, February 2026 Figure 4: Plot of the individual SEM model of the competency factor Consciousness within and between teams according to the relationships hypothesised in the proposed theoretical model Figure 5: Plot of the individual SEM model of the competency factor Drive within and between teams according to the relation- ships hypothesised in the proposed theoretical model Figure 6: Plot of the individual SEM model of the Leadership competency factor within and between teams according to the relationships hypothesised in the proposed theoretical model 43 Organizacija, Volume 59 Research Paper Issue 1, February 2026 Figure 7: Plot of the individual SEM model of the competency factor Teamness within and between teams according to the relationships hypothesised in the proposed theoretical model Table 5: Gender vs. OAM, OCB-C and DMPS Gender vs. OAM Gen- Scale n M SD Q25 MED Q75 MIN MAX der female AH 102 21.87 5.08 18 22 25 10 33 male AH 33 22.7 6.58 18 22 26 9 35 female FS 102 16.21 3.98 14 16.5 19 7 25 male FS 33 16.12 3.6 14 16 18 6 25 female KK 102 30.06 5.58 26 30.5 34 17 45 male KK 33 30.52 6.62 27 30 36 15 42 female PS 102 39.52 6.52 35 39 44 21 55 male PS 33 37.21 7.3 30 39 42 25 54 female RZ 102 22.69 4.77 20 22 26 11 33 male RZ 33 22.97 6 18 22 27 11 35 Gender vs. OCB-C Gender n M SD Q25 MED Q75 MIN MAX female 102 56.25 12.1 47 56.5 64.75 29 87 male 33 52.03 11.28 44 50 57 23 78 Gender vs. DMPS Gender n M SD Q25 MED Q75 MIN MAX female 102 13.21 8.45 7 13.5 19 -9 31 male 33 12.27 10.63 7 15 21 -10 26 The fitted models showed similar results, specifical- statistically non-significant relationship between Agility ly a statistically significant positive relationship between Maturity (OAM) and organisational citizenship behaviour job satisfaction (DMPS) and organisational citizenship (OCB-C), and a statistically non-significant relationship behaviour (OCB-C), as well as between Agility Maturi- between professional competencies and job satisfaction ty (OAM) and job satisfaction (DMPS). There was also (DMPS). Overall, the results of the analysis do not support a statistically non-significant interaction between pro- the original conceptual model of relationships between the fessional competencies and Agility Maturity (OAM), a constructs of interest. 44 Organizacija, Volume 59 Research Paper Issue 1, February 2026 4.2 Exploratory analysis fluence organisational agility (OAM), organisational citi- zenship behaviour (OCB-C), and job satisfaction (DMPS), In addition, we investigated the extent to which gender, as some authors (Saremi & Rezeghi, 2015; Bowling et al., age, education, and length of employment in the organisa- 2005) have confirmed differences in job satisfaction levels tion play a role, and whether these personal variables in- in their studies. Except for some scales, the exploratory analyses did not find any larger differences. Table 6: Education vs. OAM, OCB-C and DMPS Education vs. OAM Education Scale n M SD Q25 MED Q75 MIN MAX Higher AH 99 22.21 5.4 18 22 26 9 34 Lower AH 36 21.69 5.73 18 22 25 10 35 Higher FS 99 16.33 3.74 14 16 19 6 25 Lower FS 36 15.78 4.26 12.75 16 18 7 25 Higher KK 99 29.51 5.9 26 29 33 15 45 Lower KK 36 32 5.28 29.75 32 36 19 42 Higher PS 99 38.74 7.15 33.5 39 44 21 55 Lower PS 36 39.56 5.6 36 38.5 43 30 54 Higher RZ 99 22.91 4.95 20 22 27 11 34 Lower RZ 36 22.33 5.46 19.5 22.5 25 11 35 Education vs. OCB-C Education n M SD Q25 MED Q75 MIN MAX Higher 99 53.77 11.59 45 52 61.5 23 80 Lower 36 59.22 12.37 49.75 60.5 67.75 29 87 Education vs. DMPS Education n M SD Q25 MED Q75 MIN MAX Higher 99 13.49 9 7 14 21 -10 31 Lower 36 11.56 8.96 6.75 13.5 18 -6 26 Table 7: Age vs. OAM, OCB-C and DMPS Scale Regression coeffi- Person correlation cient coefficient Age vs. OAM AH (Agile Values) -0.103 -0.184 TECH (Technology) -0.09 -0.175 PS (Workforce) 0.026 0.037 RZ (Management of Change) -0.031 -0.059 KK (Collaboration and Cooperation) 0.054 0.091 FS (Flexible Structures) -0.016 -0.040 Age vs. OCB-C OCB-C 0.227 0.184 Age vs. DMPS DMPS 0.035 0.038 45 Organizacija, Volume 59 Research Paper Issue 1, February 2026 Table 8: Years at the company vs. OAM, OCB-C and DMPS Scale Regression coeffi- Person correlation cient coefficient AH (Agile Values) 0.027 0.043 TECH (Technology) -0.013 -0.022 PS (Workforce) 0.050 0.064 RZ (Management of Change) -0.045 -0.076 KK (Collaboration and Cooperation) 0.107 0.158 FS (Flexible Structures) 0.032 0.070 Years at the company vs. OCB-C OCB-C 0.338 0.243 Years at the company vs. DMPS DMPS -0.061 -0.058 4.2.1 Gender vs. OAM, OCB-C, DMPS lation between age and the OCB-C scales (rp = 0.07 (95% CI [-0.10, 0.24]). The estimated correlation coefficient be- 4.2.2 Education vs. OAM, OCB-C, DMPS tween the age of the respondents and scores on the DMPS, which measures job satisfaction, is around zero (rp = 0.02 (95% CI [-0.15, 0.18]). The length of time working at the Note that higher education is defined as education in company shows a positive relationship with the respond- one of the following categories: Bachelor’s, Master’s, or ents’ OCB-C (rp = 0.23 (95% CI [-0.06, 0.38]) and has no PhD. Lower education is defined as education in one of connection with the DMPS. The scoring of respondents by the following categories: high school, with or without a gender across the scales of the OAM questionnaire, us- diploma, or college. ing the mean, was very similar, with the most significant difference in the Workforce factor (about 2 points). In the 4.2.3 Age vs. OAM, OCB-C, DMPS relationship between highest educational attainment and OAM, respondents scored quite similarly; the intervals are 4.2.4 Years at the company relatively wide, include 0, and do not swing significant- ly toward the positive or negative side. Across the OAM scales, the correlation with age is not significant (correla- The exploratory analyses find some differences in job tion coefficients are less than 0.2). The correlation is very satisfaction, organisational behaviour, or organisational weak, except for the Workforce and Collaboration and agility attributable to personal variables. The results show Cooperation scales, where it is stronger. The correlation that men scored -2.62 lower on OCB-C than women (95% between years of service in the organisation and scoring CI [-7.29, 2.05]). The assessment of job satisfaction by on the OAM questionnaire suggests essentially zero effect gender using mean scores shows that respondents scored across the OAM scales, except for the Agile Values and very similarly (women about 2 points higher than men). Collaboration and Cooperation scales, where the data sug- The estimated difference: men scored 1.84 points lower than women (95% CI [-5.41, 1.74]). Respondents with a gest weak positive and negative correlations, respectively. lower highest educational qualification scored higher on average on OCB-C (by more than 6 points). The estimated 5 Discussion difference between the groups for the OCB variable is 4.15 points (95% CI: [-0.644, 8.95]) in favour of respondents The confirmed effect of professional personal com- with lower levels of education. The relationship between petencies – especially Teamness and Conscientiousness the highest educational qualification and the respondents’ – on job satisfaction supports the JD R view of personal job satisfaction shows that the respondents with a low- resources as relatively stable characteristics that help in- er educational qualification score -1.85 points (95% CI dividuals manage demands, mobilise energy, and sustain [-5.50, 1.80]), which is lower than the group with a higher motivation (Bakker & Demerouti, 2007). This is consistent educational qualification. There is a small positive corre- with evidence that conscientiousness, agreeableness, and 46 Organizacija, Volume 59 Research Paper Issue 1, February 2026 emotional stability are robust predictors of job satisfac- ness and competence, providing a plausible mechanism for tion (Judge & Ilies, 2002; Steel et al., 2019; Yan et al., their link to both satisfaction and OCB. 2022), and that self-efficacy and related traits are positive- In contrast, the absence of a significant moderating ly linked to satisfaction (Franěk & Večeřa, 2008; Gkolia effect of organisational agility on the competence–satis- et al., 2014). Team-oriented and conscientious employees faction link challenges the common assumption in some are better able to coordinate with others, structure their JD R applications that job and personal resources will work, and achieve a good person–job fit (Furnham et al., reliably interact synergistically (Demerouti et al., 2020). 2009; Wang et al., 2021), so these competencies function Instead, our results suggest largely additive effects: profes- as internal resources that support satisfaction even under sional competencies and agility maturity each contribute demanding conditions. This aligns with JD 2.0/3.0 ex- independently to job satisfaction, with a small, statisti- tensions, which explicitly treat personality and job-relat- cally fragile interaction. This pattern is compatible with ed competencies as personal resources that feed into the meta-analytic work indicating that resource interaction ef- motivational process leading to positive outcomes such as fects in JD-R are typically weaker and less consistent than satisfaction and performance (Bakker & Demerouti, 2017; the main effects of resources (Bakker & Demerouti, 2017; Demerouti et al., 2020). Steel et al., 2019). The tentative negative interaction for Consistent with previous findings, the strongest corre- the Leadership competence factor can be interpreted as a lations with job satisfaction in our data were for Teamness potential “resource loss” effect: for individuals whose key and Conscientiousness, suggesting a tendency towards co- resource is a strong leadership identity and aspiration for operation, shared performance, and performance-oriented hierarchical influence, highly agile, flat structures may of- self-management (Furnham et al., 2009; Judge & Ilies, fer fewer clear leadership opportunities (Naslund & Kale, 2002; Unterreiner et al., 2013). Prior research shows that 2020; Rigby et al., 2020), thereby dampening the other- such traits foster positive social interactions, perceived wise positive relationship between that competence and support, and achievement motivation, which in turn in- satisfaction. crease job satisfaction and professional success (Huo & The weak and largely non-significant association be- Jiang, 2021; Sheng et al., 2010; Walter & Bruch, 2008). tween agility maturity and OCB further suggests that agil- Our results extend this literature by showing that team-re- ity, as a job resource, primarily enhances satisfaction but lated competencies are also strongly tied to OCB-C, sup- does not automatically lead to extra-role behaviour with- porting the idea, already suggested by Organ and Ryan out additional, more immediate social resources, such as (1995) and Topino et al. (2021), that resources fostering empowerment, trust, and psychological safety. This aligns high-quality social exchange and psychological safety with findings that agile practices have small to medium ef- encourage discretionary, cooperative behaviour in teams. fects on satisfaction and psychological empowerment, but From a JD-R perspective, this is consistent with the notion stronger effects on performance and innovative behaviour that personal resources shape how employees experience (Koch et al., 2023; Tripp et al., 2016), and that OCB is par- and utilise job resources, ultimately influencing extra-role ticularly sensitive to relational climates and empowerment behaviour through their impact on motivational states (Dewi et al., 2021; Malik et al., 2021). In our data, OCB is (Bakker & Demerouti, 2007; Demerouti et al., 2020). more strongly associated with team-related competences The positive relationship between job satisfaction and than with agility maturity itself, highlighting the central OCB in our data is also consistent with prior meta-analyt- role of team-level relational resources – such as coopera- ic evidence that satisfaction is a robust predictor of OCB tion, trust, and mutual support (Marks et al., 2001; Piccoli (Fitrio et al., 2019; LePine et al., 2002; Organ & Ryan, et al., 2004; Stoyanova et al., 2024) – for discretionary co- 1995; Whitman et al., 2010). Within the JD R framework, operative behaviour. job satisfaction is a proximal motivational outcome that Taken together, the results support an interpretation drives higher-order behaviours such as OCB and perfor- in which personal job-related competencies and organisa- mance (Bakker & Demerouti, 2017). Our findings are con- tional agility maturity both function as resources within sistent with this: satisfaction appears to act as a common the JD-R framework, contributing to a shared motivational channel for both personal resources (competencies) and pathway via job satisfaction. Rather than demonstrating job resources (agility maturity), which then feed into OCB. strong resource–resource interactions, the data indicate This mirrors earlier work showing that job and personal parallel, additive effects and highlight satisfaction as the resources often predict OCB indirectly via engagement or key mechanism through which these resources are con- satisfaction rather than only through direct paths (Dewi et verted into OCB and other positive behaviours (Bakker al., 2021; Podsakoff et al., 2000). It also fits Self Deter- & Demerouti, 2007; Organ & Ryan, 1995). Future JD-R- mination Theory (Ryan & Deci, 2000), which posits that based research could build on this by explicitly modelling when needs for autonomy, competence, and relatedness multi-step paths from personal and job resources to satis- are met, employees internalise organisational goals and faction or engagement to OCB, and by testing under which voluntarily exceed formal role requirements; in our case, conditions agile structures are experienced as resource Teamness and Conscientiousness seem to support related- 47 Organizacija, Volume 59 Research Paper Issue 1, February 2026 gains versus resource losses for different competence contribute independently to job satisfaction. Second, the profiles, especially for employees with strong leadership analysis clarifies the pathway from personal competen- aspirations (Petermann & Zacher, 2022; Sherehiy & Kar- cies to discretionary behaviour: job satisfaction mediates wowski, 2014). the relationship between competence and OCB. In other words, satisfied employees are more likely to go beyond 6 Conclusion and implications their prescribed or formal duties. Third, the absence of a direct link between agility and OCB indicates that agility alone does not automatically generate extra-role actions; This paper aims to analyse the relationships between additional mechanisms, such as psychological safety or professional personal competencies, organisational agility empowerment, appear necessary to translate an agile en- maturity, job satisfaction, and organisational citizenship vironment into higher OCB. Finally, the tentative negative behaviour. The data collected supported only some aspects interaction observed for the leadership competency factor of the proposed model, with discrepancies most evident in suggests a possible misfit between hierarchical aspirations the moderating effect of organisational agility on the re- and flat agile structures, opening a new line of inquiry into lationship between job competencies and job satisfaction. how leadership identity aligns with organisational design. The paper explains how personal professional skills and From a practical perspective, the findings suggest sev- the organisation’s agility maturity can influence employ- eral actions for managers. Human resources and learning ees’ job satisfaction and organisational behaviour in work and development professionals should focus on develop- teams, thereby expanding the existing body of knowledge. ing team-oriented skills such as collaboration, communi- Organisational agility is crucial for organisations, so it is cation, and conscientious work habits, as these competen- important to leverage the benefits of OCB and foster to- cies are most strongly associated with job satisfaction and, getherness, cohesion, and a mutually supportive approach. through satisfaction, enhance OCB. Agile coaches and Even small improvements in collaboration (e.g., towards leaders should embed practices that increase autonomy, teamwork, regular communication and feedback) can lead provide rapid feedback, and reinforce a sense of purpose, to significant changes in design thinking and innovation for example, through daily stand-ups and sprint retrospec- that not only outperform the competition but also increase tives. These practices increase satisfaction regardless of employee engagement, job satisfaction, and a sense of be- employees’ competence levels. Senior leadership should longing. In summary, employees’ professional competen- consider offering dual-track career paths that allow techni- cies and agile organisational practices play an important cally skilled employees to advance without taking on tra- role in predicting job satisfaction. From a management ditional managerial roles, thereby mitigating the negative perspective, it is important to set priorities, consider the de- interaction observed for the leadership factor. Performance gree of autonomy (e.g. in decision-making, work organisa- management systems would benefit from pairing agile tion, and methods) according to the context and resources, metrics with explicit recognition programmes for OCB, communicate value and the broader mission and impact, such as peer-nominated awards or collaboration points, to support authenticity, actively encourage divergent think- bridge the gap between an agile culture and discretionary ing, promote self-organised teamwork, and share knowl- behaviours. Finally, organisations should treat investments edge. The manager’s role is to create and shape a culture of in competence development and agile maturity as com- trust and support, acting as a servant leader and facilitator. plementary rather than substitutive, recognising that each Organisations that invest in training and developing em- contributes uniquely to employee well-being and overall ployees’ skills and agility foster a supportive, people-cen- effectiveness. tred environment with open communication, psycholog- At the policy level, workforce development initiatives ical safety, learning, and empowerment, harnessing the – such as those funded by European Union programmes potential of individuals and teams. Such organisations are – should allocate resources to both upskilling and support- more likely to achieve higher job satisfaction and stronger ing the adoption of an agile approach in small and medi- team relationships, which can positively influence overall um-sized enterprises. Occupational health and safety reg- performance, competitiveness, and loyalty. ulations could be expanded to include psychosocial risk The study adds several layers to existing theory. First, assessments that explicitly measure the combined pres- it confirms that the Job Demands Resources (JDR) mod- ence of personal resources (competence) and organisation- el can accommodate two distinct streams of resources: al resources (agility) as protective factors against burnout personal resources in the form of professional compe- and turnover. tencies, and job resources in the form of organisational Our study shows that personal job-related competen- agility, each of which independently increases job satis- cies and agile environments are distinct yet essential driv- faction. This finding adds to evidence that resource effects ers of job satisfaction, which, in turn, channels well-be- in JD R are often primarily additive rather than strongly ing into OCB. Although agility does not moderate the interactive, suggesting that competence and agility each competence–satisfaction link, it provides an independent 48 Organizacija, Volume 59 Research Paper Issue 1, February 2026 pathway to satisfaction that, when combined with empow- The research also identifies several avenues for future erment and recognition, indirectly enhances OCB. Prac- investigation. Further studies should examine additional titioners should adopt a dual strategy: develop teamwork variables within the organisational environment to deepen and conscientiousness skills while fostering agile cultures understanding of the impact of organisational agility and that provide autonomy, rapid feedback, mastery, and lead- broaden perspectives. Longitudinal designs are needed to ership opportunities. Longitudinal research to disentangle trace how agility initially enhances psychological empow- the subcomponents of agility will clarify how organisa- erment, subsequently improves satisfaction, and ultimately tions translate maturity into sustained extra-role perfor- drives OCB. Disaggregating the agility construct into its mance. constituent dimensions (such as agile values versus change management capabilities) may reveal differential modera- 7 Limitations and future research tion effects that were obscured when the scales were com- bined. Experimental interventions, such as randomised rollouts of agile boot camps, could establish causal links Due to non-random sample selection, gender imbal- between specific agile practices and the outcomes meas- ance, and the study’s self-reported cross-sectional design, ured here. Expanding the sample to include a wider range we cannot generalise the research results to the entire pop- of cultural contexts, more balanced gender and education ulation. Motivation to participate in the survey may be in- distributions, and larger participant numbers would in- fluenced by social desirability, deliberate response distor- crease statistical power to detect interaction effects. Final- tion, a lack of self-criticism or introspection, issues with ly, qualitative interviews with leaders who feel constrained double negatives, and the ambiguity or vagueness of some by flat agile structures could clarify why leadership com- questions or statements. The respondents’ concentration petence sometimes conflicts with agility and inform the may have decreased due to the large number of items in design of dual-track career paths. the questionnaire battery (298), which could have led to Acknowledgements: This research was supported by fluctuations in their psychological state and increased frus- the Czech Ministry of Education, Youth and Sports for tration, potentially affecting the validity of their responses. specific research under Grant IGA_FF_2023_001 and is To minimise the negative effects of common method bias based on work from COST Action CA22120 LeverAge, and false internal consistency, we allowed respondents to supported by COST (European Cooperation in Science complete individual questionnaires over multiple days, and Technology). ensuring that the questionnaires measuring the predictors were separate from those measuring the dependent varia- ble and could be completed at a location of their choice. 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PLoS One, the relationship between the capabilities of workers 16(7). https://doi.org/10.1371/journal.pone.0252275. in different age groups, teamwork effectiveness, Unterreiner, CH., Jeppesen, H. J., & Jønsson, T. S. (2013). organisational behaviour, job satisfaction, the release Different forms of job satisfaction: Does job satisfac- and utilisation of human potential through systemic tion mean satisfied employees? Psyke & Logos, 34(2), L&D, workplace design, and job resources. She is also 398-419, https://doi.org/10.7146/pl.v34i2.16628 involved in several interdisciplinary research networks Urbini, F., Chirumbolo, A., Caracuzzo, E., & Callea, and has more than 20 years of professional and practical experience in management, team building, A. (2023). Job satisfaction and OCBs: what’s new? organisational development and the search for The mediating role of organizational identification, In- transformative, holistic solutions in a rapidly changing ternational Journal of Organizational Analysis, 31(5), and competitive environment. 1748-1762. https://doi.org/10.1108/IJOA-07-2021- 2865 Vahdati, H., Nejad, S.H.M., & Mousavi, S.A. (2014). The PhDr. Luděk Stehlík, Ph.D., is a psychologist and achievement of organizational agility through the or- people & data scientist with extensive experience ganizational citizenship behavior and the intellectual using data and analytics to optimise organisational capital. Indian Journal of Scientific Research, 4(3), processes, including employee selection, learning and 51-58. Walter, F., & Bruch, H. (2008). The positive group affect 53 Organizacija, Volume 59 Research Paper Issue 1, February 2026 development, workforce planning, team communication, ageing workforce, the implementation of emerging collaboration, and change management. He is also digital technologies in the workplace, and human– a former research and teaching assistant at the AI interactions, examined from interdisciplinary Department of Psychology at Charles University perspectives and informed by his practical experience. in Prague, where he focused on the psychology of He is also involved in several research networks and reasoning, cognitive psychometrics, computational projects, as well as in the development and validation modelling of cognitive functions, and statistical data of new psychological assessment tools. analysis. Doc. Ing. Jana Matošková, PhD, is Vice-Dean Prof. PhDr. Tomáš Sollár, PhD., is Dean of the Faculty for Internal Relations and Quality at the Faculty of of Social Sciences and Health Care at Constantine the Management and Economics, Tomas Bata University Philosopher University in Nitra. His research focuses in Zlín, where she has been Associate Professor on psychometrics and the development and application of Economics and Management since 2021. Her of measurement instruments in personality psychology research focuses on human resources, leadership, and across various contexts, including work psychology, knowledge sharing, particularly on how HRM practices, health psychology, cognitive psychology, and career communication tools, and organisational culture counselling. He has extensive teaching experience support innovation and organisational performance. and is currently a full professor. As a researcher, he She is the principal investigator of several internal has participated in national and international projects research projects at TBU focused on knowledge- as both investigator and principal investigator. He oriented perspectives of organisations, the facilitation has contributed to the development and validation of knowledge sharing, and enhancing organisational of several questionnaires, rating scales, and tests in performance through human capital management. these fields. In practice, he applies the Person-Centred She has also contributed to GA ČR, TAČR, COST, and Approach (PCA) in various non-therapeutic settings, OP PIK grant projects in the creative economy, smart including industrial and organisational psychology. factories, and new workspaces. She is an author and co-author of numerous articles in international peer- reviewed journals and monographs on knowledge Ing. Bc. Michal Silberg, MBA, MSc, graduated sharing, high-commitment HR practices, employer in Human Resource Management, Information branding, cluster managers’ competences, and the Security Management, and Law. His research impacts of telework and coworking spaces on work and addresses contemporary organisational challenges, work–life balance. including leader–follower dynamics, teamwork, the Povezanosti med osebnimi značilnostmi, zadovoljstvom pri delu in organizacijskim vedenjem članov delov- nih skupin ter vloga zrelosti organizacijske agilnosti Uvod in namen: Cilj raziskave je analizirati povezanosti med strokovnimi osebnimi kompetencami, organizacijsko agilnostjo, zadovoljstvom pri delu ter organizacijskim državljanskim vedenjem v delovnih skupinah, saj lahko ti de- javniki pomembno vplivajo na organizacijsko uspešnost in konkurenčnost. Metodologija: V prerečno raziskavo je bilo vključenih 25 delovnih skupin (N = 135) iz različnih gospodarskih sek- torjev na Slovaškem. Izvedli smo večnivojske korelacijske in regresijske analize, faktorske analize ter strukturno modeliranje. Rezultati: Večnivojska korelacijska analiza je pokazala pozitivne povezave med zadovoljstvom pri delu in vsemi lestvicami Bochumskega osebnostnega inventarja (v razponu od 0,097 do 0,406), pri čemer je bilo deset korelacij statistično značilnih. Rezultati kažejo, da delovne kompetence napovedujejo zadovoljstvo pri delu, zadovoljstvo pa je pozitivno povezano z organizacijskim državljanskim vedenjem zaposlenih. Vendar pa moderirajočega učinka organizacijske agilnosti na odnos med kompetencami in zadovoljstvom nismo uspeli dokazati. Ugotovili smo stati- stično značilno pozitivno povezavo med stopnjo zrelosti agilnosti in zadovoljstvom pri delu. Razpravljamo o možnih vzrokih, poudarimo omejitve ter predstavimo praktične implikacije. Sklep: Ustrezne strokovne veščine in pristop, usmerjen v ljudi, sta ključna za dolgoročni uspeh v konkurenčnem okolju ter lahko skupaj z organizacijsko agilnostjo prispevata k večjemu zadovoljstvu zaposlenih pri delu. Ključne besede: Zadovoljstvo pri delu, Organizacijsko državljansko vedenje (OCB), Organizacijska agilnost, Stro- kovne osebne kompetence, Učinkovitost skupin 54 Organizacija, Volume 59 Research Paper Issue 1, February 2026 DOI: 10.2478/orga-2026-0004 Identifying Marketing Tools to Promote the Development of the Knowledge Economy: The Case of Lithuania Agne SNEIDERIENE, Marius ZAMKAUSKAS Klaipeda University, Klaipeda, Lithuania, agne.sneideriene@ku.lt, mzamkauskas@gmail.com Background/Purpose: The research aims to identify marketing tools that promote the development of the knowl- edge economy and to design a development model grounded in the research findings. This paper analyses the theoretical aspects of non-profit, social, and political marketing, introduces the concept of self-segmentation, and proposes a strategy to transform society’s readiness to foster the values of the knowledge economy. Methods: The research was carried out in two successive stages. The first stage consisted of three separate sub-studies: a case study, quantitative descriptive research, and qualitative exploratory research. The second stage encompassed designing the model based on the findings. Results: After conducting the case study, secondary impact factors of the tools were identified, influencing the choice of tools or the formation of their set. Following the study of the survey results, groups of high-priority market- ing tools were distinguished. Based on the results of the expert evaluation, a set of the most commonly proposed marketing tools was compiled. As a result of the conducted research, a coherent five-stage model for promoting the development of the knowledge economy was created. Conclusion: The model reflects key ideas: the choice of marketing tools is determined by the readiness and open- ness of the society to accept ideas; the success of dissemination is determined by inter-institutional cooperation; effective dissemination requires identification of the target consumer audience; and a set of marketing tools is de- signed on the basis of the results of continuous data analysis. Keywords: Knowledge economy, Marketing tools, Development model, Societal readiness, Lithuania 1 Introduction preconditions characteristic of its emergence into a chain and, following the principles of sequence and cyclicity, The opportunities for the development of the knowl- creating an environment for its dissemination and perva- edge economy are relevant to almost all economies in the siveness. Consequently, this paper addresses the problem of selecting marketing tools for promoting the develop- world. However, it is likely that, particularly in the case ment of the knowledge economy. Previous studies have of Lithuania, insufficient attention is paid to the integrat- examined individual elements of the knowledge economy ed coordination of actions among the participants of the or the aspects of the mechanisms of its functioning, for knowledge economy. This is highly evident at the institu- example, Melnikas (2016) suggested the importance of in- tional level. The authors are convinced that the importance tellectual potential in determining society’s ability to fos- of collective actions is reflected in the fundamental princi- ter the values of the knowledge-based society; Mazur and ples of the knowledge economy, where, as a phenomenon, Malkowski (2021) developed this idea further by analysing the knowledge economy consists of combining groups of the country’s socio-economic policies and public mindset 1 Received: 27th May 2025; Accepted: 12th December 2025 55 Organizacija, Volume 59 Research Paper Issue 1, February 2026 as key factors for progress. The individual elements of the a sense of security, community recognition, contributions knowledge economy were examined more closely by Ad- to society’s well-being, the creation of a sustainable envi- amczewski (2016), who presented in detail the challenges ronment for future generations, and, in some cases, direct and opportunities of information and communication tech- material benefits. nologies; Vovk and Braga, in their paper of 2017, argued Miller (2010) and Andreasen (2012) argue that often the advantages of fostering the values of the knowledge non-profit activities are based on changing people’s be- economy; Tudzarovska-Gjorgjievska (2014) analysed the haviour for social good, or as Shah and George (2021, p. challenges in adapting the values of the knowledge econ- 572) put it, “the ultimate objective of an NPO is usually to omy. A study on the individual elements of the knowledge make a ‘positive difference” which often involves intan- economy through the lens of marketing was conducted gible performance outcomes. It is not necessary to limit by Bolisani and Scarso (2012), in which the researchers oneself to behavioural changes; rather, one should evaluate presented their vision for interpreting the 4Ps marketing positive changes in well-being; therefore, the authors add mix in the context of the knowledge economy. Thus, while the dimensions of change: attitudinal and behavioural. To there is no deficiency of research on the expression of the clarify the distinction between the financial objectives of knowledge economy, there is a noticeable lack of research marketing for profit organisations and the social welfare on examining the very development opportunities or pre- objectives of marketing for non-profit organisations, the senting the applied solutions for the implementation of concepts of social marketing and political marketing are development; moreover, most of the research is limited to identified (Duralia, 2017; Stan & Epuran, 2019; Ștefan et the expression of one of the elements of the knowledge al., 2020). Social marketing, as part of the marketing of economy when examining narrowly focused problems. a non-profit organisation, makes sense when it is used to Consequently, the study has aimed to develop and propose persuade individuals or groups to change their behaviour an instrument for the formation of knowledge economy for personal and/or social benefits (Andreasen, 2012). The development strategies and to design a model of market- result of a successful campaign is the solution to the pri- ing tools to promote the development of the knowledge mary objective (problem), preferably a positive behaviour economy. change. Meanwhile, when offering more traditional goods and services, it is worth using the elements of political 2 Literature review marketing. Jha (2008) explains the need to distinguish the political argument and highlights the responsibility of political institutions to formulate sustainable policies for 2.1 The importance of marketing in the social and economic change, as the pursuit of profit must activities of non-profit organisations not motivate their activity. If this happens, the institution is discredited, and the social interest is sabotaged. Hence, Many non-profit organisations follow the approach of the desired result here is the exchange of mutual benefits, entities that dedicate their entire activity to solving specific where a politician, their team, and the party, by proposing societal problems or phenomena. The same applies to the and implementing the idea, gain the trust and support of marketing of non-profit organisations and the marketing the voter (consumer) (e.g., victory in the next elections), solutions they use. The activities of non-profit organisa- and fulfil their obligations expressed through a positive tions are often identified with brand promotion, and it is change in the welfare of the individual and society. believed that this merely reinforces a non-profit image in The discussed expression of marketing for a non-profit the public eye. According to Andreasen (2012), non-profit organisation through social and political marketing is re- marketing does not include simple commercial marketing flected in the scheme (Figure 1) depicting the interaction concepts but rather more complex cases, with the conse- among the marketing elements of a non-profit organisa- quent statement that commercial marketing is a narrower tion. application of non-profit marketing. Figure 1 demonstrates that an organisation plays an Authors draw attention to the importance of the “ex- intermediate role, that of an enabler of products (or ser- change of offers” element in the marketing of a non-profit vices), by linking the available resources to the recipients organisation. The process is relatively simple in business: who need them. This scheme also reflects the operational an individual exchanges money for products or services dimensions needed to organise activities: of perceived value. A balance of perceived values regu- 1. Planning. The process needed to ensure a clear se- lates the process. Meanwhile, the services and products of quence of actions to achieve the strategic objectives. non-profit organisations are often more abstract, and their 2. Positioning. Non-profit organisations face the chal- values are much more challenging to perceive objectively. lenge of convincing consumers and other stakeholder Each transaction, therefore, brings a non-profit organisa- groups that the need for their products (or services) exists tion closer to achieving its specific mission. At the same in the first place. time, participants can receive numerous benefits, such as 3. Communication. It is divided into several directions, 56 Organizacija, Volume 59 Research Paper Issue 1, February 2026 addressing the needs of stakeholder groups while consid- (Vukasović, 2020). Two groups of variables are usually ering each group’s role in the operational process. distinguished: 4. Resources. Expressed in two values: funds—various 1. Descriptive variables: geographic, demographic, so- forms of tangible and intangible assets, and time—the ob- cioeconomic, psychographic; ject of exchange among stakeholder groups such as volun- 2. Behavioural variables: benefits sought, desired us- teers, participants, and end consumers. age, purchasing patterns, loyalty, participation in the de- It should be noted that Figure 1 is a stylised reference cision-making and dissemination process, brand attitude, to commercial marketing to emphasise that this form of sensitivity to marketing mix elements. marketing is the main supplier of the traditional market- Daniel et al. (2015) present the following typical steps ing principles. In other words, all tools and solutions have in the process of market segmentation: been and are adapted from the theory and practice of com- 1. Establishing criteria (variables) for the division of mercial marketing. Figure 1 also reflects the principle of the overall market into homogeneous sub-markets; marketing as a process: the process is carried out until the 2. Extracting data based on the established criteria; objectives are achieved. 3. Dividing the total market with the help of the data The foundation of activities of a non-profit organisa- obtained. tion is built upon the needs of the stakeholder groups (their 4. Selection of sub-markets for the segment-related ap- needs may vary from group to group), the organisation’s plication of processes; mission (the idea for which it exists), communication de- 5. Performing segment-oriented marketing activities in cisions (consumers do not know what social benefits they selected markets. need), and the perception of value (only perceived bene- According to the researchers, the process is relatively fits are considered valuable). The process of accumulating simple and easy to apply. However, Canhoto et al. (2013) added value is cyclical and requires constant evaluation note that academic research often focuses on the ideal con- and calibration, following the principle that the potential ditions for segmentation (frequently referred to as labo- value tends to become essential. ratory environments), whether it concerns the very base, the model, or the method. Authors emphasise that seg- 2.2 Solutions for developing instruments mentation under real-world conditions is far from smooth for market segmentation (this idea is supported by Scheuffelen et al., 2019). Hence, practical guidelines for the segmentation process lack sol- id empirical support. Similarly, although there are many The segmentation process involves extracting a seg- normative guidelines for segment selection, research on ment defined by available and interesting variables Figure 1: The interaction of marketing elements of a non-profit organisation Source: own elaboration 57 Organizacija, Volume 59 Research Paper Issue 1, February 2026 Figure 2: Conceptual model of market segmentation Source: prepared by the authors, based on research by Boyd et al. (2012) how organisations actually select segments successfully is The proposed conceptual framework comprises a still lacking. three-stage process. In the first stage, information about Taking into account the challenges associated with the the essence of the product, service, or idea is provided to application of the segmentation process—the volume of the potential consumer population. The researchers believe segments, the set of criteria, the impact of the environ- that the selected parameters should help identify the appro- ment, the value relevance of segments, the relative benefits priate segments more accurately. The parameters for pre- of the proposal—the concept of segmentation proposed by senting information include: variety (detailed or special- Boyd et al. (2012) has been used. This process is called the ised), depth (abstract or complex), frequency (how often “self-segmentation process”. The concept is based on re- the information is presented), and reach (in specific places search that has found a tendency for people to interact with or at specific times). The parameters can both limit and those who are similar to themselves across various param- encourage consumer engagement. In this way, part of the eters or parameter sets. A similar idea was expressed by population influenced by the provided information is “pu- Scheuffelen et al. (2019) in their research on the respons- rified”, meaning that a segment based on the parameters es of different market groups to various online marketing closer to the study than the primary random population of tools, i.e., groups are formed based on their responses to subjects is automatically distinguished. The second stage the marketing tool, not vice versa. From a philosophical is the traditional segmentation based on the selected de- point of view, Madeira et al. (2015) consider that the effect scriptive and behavioural variable sets. The final stage— of the phenomenon of belonging to a group is the need the assessment and selection of a target group—is advisory to belong, to be part of a group, of not being unique and in nature. therefore isolated, i.e. the “unique individual” has to find (and may naturally develop) common aspects with other 2.3 Transforming society’s readiness to individuals in order to integrate, be part of a group. The a priori or “common-sense” segmentation approach is foster the values of the knowledge mentioned by Dolnicar and Leisch (2014), who recall that economy some marketing managers used to choose a single mar- ket segmentation criterion and then apply a complex set of As early as 1996, the Organisation for Economic variables. The insights from the previously discussed au- Co-operation and Development (OECD) defined a knowl- thors are combined into a conceptual segmentation model edge economy as “… economies which are directly based (Figure 2). on the production, distribution and use of knowledge and 58 Organizacija, Volume 59 Research Paper Issue 1, February 2026 information” (OECD, 1996, p. 7). This is reflected in forming the preconditions for developing the knowledge countries’ economies through rising investment in high economy requires a transformation of society to foster the technology, high-tech industries, workforce qualifica- values of the knowledge society. The proposed concept of tions, and related productivity. According to Mazur and the levels of readiness is presented in Figure 3. Malkowski (2021), the foundation of such an economy The initial level of perception is limited to the dis- is “the application of human know-how in all production semination of information about the values of knowledge; processes”, with an emphasis on the visible and measura- it answers the question “What is it?”. The use of educa- ble part of the process of the knowledge economy. An im- tional marketing solutions (e.g., speeches by authorities, portant concept is presented by Tudzarovska-Gjorgjievska presentations, general information disseminated in media, (2014), who recites the main idea of Romer and Gross- etc.). At the next level of projective perception, tools are man’s “theory of growth”, where researchers classified selected to reflect the necessity and benefits of implement- education, skills training and knowledge as “non-rival” ing the knowledge economy; they answer the question goods which could “diffuse knowledge” through the econ- “Who needs it?”. Values are expressed through tools that omy, thereby enhancing competitive and entrepreneurial reflect benefits (e.g., expert opinions and forecasts, media incentives, which in turn could create new employment coverage and debates, presentations of success stories, opportunities in dynamic sectors and companies, as well thematic seminars, public conferences, etc.). The final lev- as increasing productivity and fostering economic growth. el—the level of application—involves solutions to specific However, the author (Tudzarovska-Gjorgjievska) empha- proposals through active engagement, therefore, a mature, sises that political and societal openness is essential for prepared and informed society is needed to perceive their successful transformation towards a knowledge economy. value; this level answers the question “How to do it?” or Mazur and Malkowski (2021) support the idea, adding “What to do?”, accordingly, measures are selected that in- that technological and organisational progress depends on volve the implementation itself (e.g. promotion of upskill- a country’s socio-economic policies and societal mindset ing / re-skilling courses, promotion of training, dissemi- changes. The researcher’s insights suggest that knowledge nation of initiatives for change in the education system, is a commodity of the knowledge economy, produced, penetration and development of information and commu- disseminated, and consumed within this environment. Its nication technologies, etc.). The scheme implements the leading enablers are the political system and society, and approach of selecting marketing solutions that depend on its primary consumers are businesses and the same soci- society’s readiness. ety (Wierzbicka, 2018, expresses a similar idea). Hence, Figure 3: Transforming societal readiness to foster the values of the knowledge economy Source: own elaboration 59 Organizacija, Volume 59 Research Paper Issue 1, February 2026 Figure 4: Methodological scheme for identifying marketing tools Source: own elaboration 3 Methodology 2. Identification of measures to promote the knowl- edge-based economy’s development through a ranking Two research tasks have been set: to identify the ex- method. The ranking is based on the frequency with which pression of marketing tools in different environments of the same tools are used in individual cases, thereby high- the knowledge economy (stage one) and to systematise lighting marketing solutions that have led to their devel- marketing tools to promote the development of the knowl- opment. edge economy (stage two). The tasks are united by a re- Study parameters: search purpose: designing a marketing tool model to pro- a) Justification of the country selection for the study. mote the development of the knowledge economy (Figure The country selected for the study is the USA. The choice 4). has been based on: a high score in the development of The first stage involved preparing for the research stud- the knowledge economy (referring to the ranking results ies, conducting them, organising the collected information, in the Global Knowledge Index (GKI), the USA has been analysing the data, assessing the implications of the pro- among the leading countries for the past 5 years); among posed hypotheses and defended statements, and generat- the countries considered, the USA is distinguished by a ing summary conclusions. The second stage encompassed freer and more accessible data; English language is used designing the model based on the research findings from for information presentation and accumulation (English is the first stage. given priority over other languages to reduce the risk of The first sub-study. Case study: misinterpretation); The study aims to identify „good practice“ marketing b) The tools used to search and select information: tools used in the country to promote the development of Web browser Firefox, search engine Google; Web browser the knowledge economy. Edge, search engine Bing; Artificial intelligence ChatGPT Objectives of the study: (used to search for primary sources of information); 1. To identify selected country politicians, non-prof- c) The study’s time frame covers the period since the it organisations, and non-government organisations that active launch of knowledge economy programmes and have contributed to developing the knowledge economy in development objectives in the USA and the emergence of the chosen country; active attempts to inform the public about the knowledge 2. To identify the marketing tools used by selected economy’s elements, benefits, and prospects. Thus, the country politicians, non-profit organisations, and non-gov- study’s starting point is assumed to be the beginning of Al ernment organisations to promote the values of the knowl- Gore’s vice presidency in 1993. edge economy; The second sub-study. Survey: 3. Rank the identified tools, highlighting the predomi- The study aims to identify opportunities to promote the nant marketing solutions. development of Lithuania’s knowledge economy. Type of research. Qualitative descriptive research. Objectives of the study: Research method: 1. To determine the Lithuanian population’s readiness 1. Search and selection of information available in the to foster the knowledge economy’s values; public domain; 2. To identify marketing tools to promote the develop- 60 Organizacija, Volume 59 Research Paper Issue 1, February 2026 ment of the knowledge economy in Lithuania. the problems of the knowledge economy development. Type of research. Quantitative descriptive research; Objectives of the study: Study method: 1. To identify the problems and shortcomings of the a) To present the characteristics of respondents’ an- development of the knowledge economy in Lithuania. swers, a percentage analysis has been employed. 2. To identify marketing solutions for the implementa- b) Cronbach‘s alpha coefficient was used to assess in- tion of the knowledge economy development in Lithuania. ternal consistency; Type of research. Qualitative exploratory research; b) To test the hypotheses, the X2 (Chi) test has been Research method. The methodology of the expert used; study was based on the Delphi method, in which a series c) The Spearman correlation calculation method was of steps summarising and synthesising were used to iden- used to determine the strength of statistically significant tify the dominant results most consistent with the views correlations. of the whole group of experts. The Rain of Thoughts ap- d) Methodology for determining the level of readiness proach has been used to stimulate proposal generation for to foster knowledge economy values: marketing measures. The experts have been involved in a 1) The questions have been grouped into clusters cor- multi-stage exploratory activity: responding to the elements of the knowledge economy 1. In the first stage of evaluation, the experts were giv- (economic and social context; human resources, innova- en a questionnaire of questions relating to each aspect of tion system; ICT; operational and environmental context). the knowledge economy. 2) Value units have been assigned to the answer choic- 2. The researchers summarised the results and com- es, where the lowest value is assigned to the low readiness piled a summary of the prevailing opinions. value and the highest value to the high readiness value (e.g. 3. In a second reflective phase, the experts were pre- in a question with three answer choices, the first choice is sented with the summary drawn up in the first phase. assigned zero value units, the third choice is assigned two 4. The researchers summarised the results of the ex- value units, etc.); perts‘ reflections and suggestions for marketing solutions 3) results have been aggregated; mean and median val- returned to them and compiled a summary of recommend- ues have been calculated; data have been normalised on a ed marketing tools. scale from 0 to 6 value units; Study parameters: 4) The resulting data have been distributed across three a) Search for experts: Experts from institutions direct- levels of public readiness, with the initial perception level ly related to the development of the knowledge economy assigned data values ranging from 0 to 2.99 value units, the in Lithuania have been invited to participate. The experts projective perception levels assigned values ranging from might include: government representatives, representa- 2.00 to 3.99 value units, and the applied perception level tives of the education sector, representatives of scientific assigned values ranging from 4.00 to 5.99 value units. research and experimental development activities, repre- Study parameters: sentatives of institutions engaged in the development of a) The general population (statistical population) has information and communication technologies, and repre- included the adult population of Lithuania (age group of sentatives of activities promoting the development of in- 18 years and older): 2348613 (source: Lithuanian Official novative business. Statistics Portal (accessed December 14, 2023)); b) The method of selecting experts is random expert b) The sampling method – simple random sampling; selection. Randomness has been emphasised in the free- c) Method of information collection. Respondents dom of choice regarding participation, subject to the con- were asked to fill in a pre-prepared questionnaire. The dition of matching subject-matter competences. questionnaire was designed using the Qualtrics platform. c) Topic orientation – experts must be familiar, di- d) A sample representing the general population should rectly or indirectly through professional activity, with one comprise at least 400 respondents, as calculated using the or more aspects of the knowledge creation process in the Paniotto formula. More than 1000 questionnaires have knowledge economy; been received, of which 410 have met the eligibility cri- d) Method of collecting information. Multi-stage dis- teria (the most common reason for non-eligibility was in- cussion by means of a questionnaire with open-ended complete answers). questions and statements; e) Survey data processing. The data, graphs, and tables e) Processing of the survey data. The data, graphs, and were processed and prepared in Microsoft Excel. Statisti- tables were processed and prepared in Microsoft Excel, cal indicators were prepared using the electronic platform while the descriptive part of the study was prepared in Mi- DATAtab. The descriptive part of the study was prepared crosoft Word. in Microsoft Word. Model creation: The third sub-study. Expert study: The study aims to structure the marketing tools and The study aims to identify marketing tools to address create a model to promote the development of the knowl- 61 Organizacija, Volume 59 Research Paper Issue 1, February 2026 edge economy. the “best practices” of marketing tools applied in a country Research method. The model is designed by synthe- to promote the development of the knowledge economy. sising the results of the analysis from the previous stage: The ChatGPT instrument has been used to search for 1. The process of synthesising the results has been car- US politicians who contributed to the spread of the idea ried out in five stages corresponding to the components of of the knowledge economy. The selection’s reliability was the conceptual knowledge economy marketing complex. 72%; of the 18 candidates proposed for the research, 13 2. Tools have been identified and included in relation were confirmed to have contact with the knowledge econ- to the cause-and-effect sequence of actions accompanying omy. The proposals of the candidates who were not con- the logical implementation of the conceptual marketing firmed were largely wrong due to their active involvement complex; in climate change issues and/or support for changes to the 3. The sequence of implementation actions has been general U.S. economy. However, the content was not relat- expressed in the formulation of questions: ed to the context of the knowledge economy. Consequent- a) What is the object of development? ly, 13 USA politicians who actively promoted the idea of b) Who will disseminate the intended object? the knowledge economy were selected for further research. c) What is the development position of the dissemina- Also, the ChatGPT instrument suggested US non-profit tion object? and non-governmental organisations that have contributed d) What measures could lead to a change of position? to the dissemination of the knowledge economy. The accu- e) What selection decisions could contribute to the ef- racy of the selection was 58%: almost every second can- fectiveness of the measures? didate suggested was falsely associated with organisations f) What is the outcome of the model? actively contributing to the dissemination of the knowl- Criteria for the study: edge economy in the USA. Two factors of misattribution a) The analysis is only carried out after all three can be identified: the pursuit of commercial activities as sub-studies planned in the previous stage of research are the organisation’s main objective, or the exclusive genera- completed; tion of the resources needed to develop the elements of the b) The proposed development tools must be realistical- knowledge economy. Certainly, in a general sense, they all ly implementable. impact the development of the elements of the knowledge economy, but this does not mean they are associated with 4 Results disseminating the very idea of the knowledge economy; therefore, they were not suitable for the study. The analysis of the research results has enabled the 4.1 The evaluation of the case study identification of the marketing tools most commonly used results by USA politicians, non-profits, and non-governmental or- ganisations to spread the ideas of the knowledge economy The case study has assumed that by examining the de- to their voters, consumers, and later, the public. The results velopment practices of the knowledge economy already have been consolidated by identifying the secondary im- implemented in other countries, it is possible to identify pact factors applied in selecting tools (Figure 5) and com- the tools that have contributed to their success. Based on piling a summary of recommended tools (Table 1). this assumption, the task of this study has been to identify Figure 5: Secondary impact factors of the selection of tools Source: own elaboration 62 Organizacija, Volume 59 Research Paper Issue 1, February 2026 Table 1: Summary of recommended tools for disseminating the idea of the knowledge economy A group of participants in No. the dissemination of the Priority content by idea of the knowledge Secondary impact factors of tools the knowledge econ- economy omy element Reputation Accessibility Identification Dissemination tool 1 Politicians Internal media External media Personal webpage Human resources 2 NPO and NGO Events Social network Organisation’s Operational and en- platforms webpage vironmental context Source: own elaboration Secondary impact factors are recommended when for- tool for identity-building for organisation, reflecting the mulating strategies for disseminating the idea of the knowl- direction, goals, and objectives of the organisation (cur- edge economy and selecting alternative sets of marketing rently it is the most important information asset of an or- tools. That is, when selecting dissemination channels, the ganisation). When forming the content of information, the optimal combination would be: a dissemination tool that following prioritised elements of the knowledge economy ensures the legitimacy and validity of information, in oth- are distinguished: human resources, operational and envi- er words, reputation (thus making the content attractive to ronmental context (although we claim that the knowledge large and respectable participants of information dissemi- economy consists of five elements, the research neverthe- nation); a dissemination tool that guarantees accessibility less has shown that participants involved in idea dissem- and popularity by orienting to larger masses of consumers; ination paid most attention to information covering the a dissemination tool that plays the role of the carrier of the development of human capital: education, self-education, main idea and defines the identity of the participant. career opportunities, retraining, etc.), and the involvement In Table 1, the recommended tools are grouped by the of business in the implementation of innovations in its ac- secondary impact factors of the tool and by their attribution tivities (availability of innovative solutions and ensuring a to the dissemination participants group. It has been deter- favourable environment for their implementation). mined that the recommended set of tools for disseminating It can also be argued that a case study based on the the idea of the knowledge economy for politicians would principle of “best practice” can help objectively identify be the following: internal media—official government or the marketing tools that have contributed to the develop- national media channels (government websites, etc., to ment of the knowledge economy and prepare recommen- give reputational weight to the disseminated information dations for their application. and attract the attention of the mainstream media); exter- nal media—well-known and popular channels for mass 4.2 The evaluation of the survey results dissemination (usually broadcasting information of essen- tial content to attract the attention of the broadest possi- ble audience); a politician’s personal webpage—open, Researchers (Chlivickas & Melnikas, 2016; Melnikas, accessible, reflecting specific activities and ideas, reliable, 2014; Żelazny, 2015) have highlighted that society is both informative, and revealing the politician’s main vision the initiator and the main consumer of the knowledge (dissemination of information associated with the politi- economy, but the process is active and productive only cian through easily accessible and targeted content). The if participants are aware of its importance and place in recommended set of tools for organisations would include: their daily activities. Naturally, this statement raises re- events—public forums, happenings, conferences, fairs, search-worthy questions: how does society perceive the etc. that attract respectable participants in the dissemina- values of the knowledge economy, and are there unex- tion of expert information (over time, reputational weight plored opportunities to promote readiness to adapt to these is created as the international audience notes events); values? Based on the statement, the task of this study is social network platforms—for mass dissemination of in- to identify opportunities to promote the development of formation (there is no reason not to take advantage of the Lithuania’s knowledge economy. dissemination opportunities offered by social networks at a Figure 6 shows the results of the evaluation of the Lith- relatively low cost); the organisation’s webpage—the key uanian population’s readiness to foster the values of the knowledge economy. 63 Organizacija, Volume 59 Research Paper Issue 1, February 2026 Figure 6: The level of readiness of the Lithuanian population to foster the values of the knowledge economy Source: own elaboration Figure 7: Summary of tools recommended to disseminate the idea of the knowledge economy Source: own elaboration The readiness of the Lithuanian population to foster economic and social context group, it can be seen that the the values of the knowledge economy has been assessed respondents’ views on economic issues were quite posi- by identifying levels of perception and recommending tive, closer to the level of applied perception. As a result, appropriate marketing tools for each group of knowledge their willingness to accept new ideas and challenges is cor- economy elements and the overall environment. As can respondingly more favourable. The results of the human be seen, the results lie between the hypothetical perfect resources group are mixed. Respondents’ views on higher maximum and minimum. Moreover, this section is divided education in Lithuania were rather negative, but they were into three levels of perception, corresponding to society’s well aware of the purpose and benefits of lifelong learning. readiness. It should be noted that the mean value indicates The evaluation of answers about the group of innovations a more accurate position, while the median value more ac- system reveals that respondents demonstrated good aware- curately reflects the distribution of choices toward some ness about the importance of scientific research and the extreme level. Evaluating the results of questions in the consequences of an inappropriate development strategy. 64 Organizacija, Volume 59 Research Paper Issue 1, February 2026 Answers to questions about the group of ICT demonstrate mation will be disseminated?” “Which information search that respondents’ involvement in electronic service solu- channels do potential consumers use?” and “What tools do tions was at a fairly high level, suggesting that a larger potential consumers use to exchange information?” Based proportion of respondents tend to foster the values of the on the results of the previous stages, marketing tools meet- information society and are more open to innovations. The ing the characteristics of the target audience are selected: results of the questions in the group of elements of the for information dissemination: passive engagement tools operational and environmental context show that respond- (various forms of advertising) or active engagement tools ents were well aware of the opportunities and prospects (events, personal conversation or email); form of informa- for creating innovative businesses, but expressed a lack of tional impact: electronic tools (a webpage, social account, confidence in their ability to create new businesses. The e-mail) or contact tools (personal consultation, open day, total score of the responses has revealed that the level of fair); tools for information exchange: traditional tools perception of the knowledge economy was above average (phone calls, e-mail and text messages) or modern tools and close to the upper limit of the applied perception level. (social media, instant messaging, video chat). Consequent- The purpose of the additional question was to determine ly, given the target audience, the maximum possible set of the respondents’ motivation for showing interest in the ide- impact tools is selected. as of the knowledge economy. The results show that 59% During the survey, the following aspects limiting its of respondents were inclined to learn the definition of the efficiency were identified: insufficient distribution of re- knowledge economy. Consequently, using the concept of spondents across age and income groups. self-segmentation, it can be argued that more than 50% of target consumers are open to ideas and proposals of the 4.3 The evaluation of the expert study knowledge economy. Based on the analysis of survey results, a summary of results the formation of marketing tools for decisions to develop the knowledge economy has been compiled (Figure 7). As defined by the researchers (Jucevičienė & Šajeva, In the first stage of formation, the impact of targeted 2008; Kriščiūnas & Daugelienė, 2006), the knowledge tools is determined based on the target audience’s level economy is the process of acquiring, creating, and dissem- of readiness. The purpose of tools at the initial level of inating knowledge, expressed through the development perception is educational; the purpose of tools at the pro- and growth of the overall economy and the dissemination jective level of perception is to reflect benefits; and tools of innovations. However, society is heterogeneous; thus, at the applied level of perception are shaped as specific such processes should be initiated by relevant institutions propositional solutions. In the second stage, depending on and business entities. In this statement, research questions the target audience, the influencing factors are taken into can be noted: how do Lithuanian institutions participate account, with different types of motivators and incentives in disseminating the values of the knowledge economy in being more relevant to various consumer groups. In the society, what role does business play in supporting these third stage, the justification for content formation to influ- efforts, and what unexploited dissemination instruments ence the target audience is determined, taking into account can promote the development of the knowledge economy. the characteristics of the environment in which the impact These questions formed the basis for the formulation of the tools are implemented. In the fourth stage, the target au- task of this study: to identify the marketing tools to address dience’s priority communication channels are identified the problems of knowledge economy development. by answering the questions “How and what kind of infor- Figure 8: Summary of marketing tools for achieving knowledge economy development objectives Source: own elaboration 65 Organizacija, Volume 59 Research Paper Issue 1, February 2026 Figure 8 presents a summary of marketing tools com- government, and society, community meetings, alternative piled from the analysis of expert study results to achieve electronic forms of collaboration, etc.) were proposed as the objectives of knowledge economy development. alternatives to educational activities, emphasising the ne- A summary of the tools and solutions generated by cessity of collaboration. Alternative decision-making tools experts, taking into account the identified problems and included research, surveys, studies, and strategy formula- weaknesses of the knowledge economy, is provided. tions - proposals that more closely reflect the importance Based on the results of the expert study, two objectives of planning the development of the knowledge economy, are set for the development of the knowledge economy: which directly impacts all subsequent choices of tools. disseminating the idea of the knowledge economy and In the course of the expert study, the following aspects implementing the knowledge economy. In the first case, limiting the usefulness of the research results have been the activity is limited to the dissemination of information identified: about each element to familiarise the target consumer with 1. Due to the small number of respondents, the research the benefits of the knowledge economy, thereby chang- results may lack the number of opinions that are necessary ing attitudes into ones more favourable to the develop- to confirm objectivity. The recommended number of re- ment and the readiness of society to accept the dissemi- spondents should be at least 10 experts. nated values. In the second case, the activity is organised 2. Due to lower activity and productivity in the sec- to encourage the target consumer to take action (change ond session, it is recommended that a revised survey be behaviour and habits, undertake new activities and initi- designed that combines closed-ended questions with open- atives, engage in educational activities, etc.). It is worth text fields. The original survey included only open-text noting that experts were more inclined to recommend fields. educational tools to address the identified problems and weaknesses of the knowledge economy; only in the hu- 4.4 The model of marketing tools to man resources group was a higher proportion of the tools solution-oriented suggestions. Experts believe that most of promote the development of the the objectives of knowledge economy development can be knowledge economy achieved through promotional campaigns that disseminate examples of best practices. To encourage decision-making Three sub-studies have been conducted to design a that supports the values of the knowledge economy, ex- model of marketing tools to promote the knowledge econ- perts suggested using training, seminars, and conferences omy. Based on the results analysis of each sub-study, sum- that combine educational and skill-development activities maries of the peculiarities of the establishment and selec- with initiatives needed throughout the education chain tion of promotion tools identified in the respective studies (learning programs, modules, studies, non-formal learn- have been concluded. Their synthesis is expressed in the ing solutions, etc.). Events and various discussion tools designed model of marketing tools to promote the knowl- (discussion events among representatives of business, edge economy. The model is presented in Figure 9. Figure 9: The model of marketing tools to promote the development of the knowledge economy Source: own elaboration 66 Organizacija, Volume 59 Research Paper Issue 1, February 2026 The implementation of the marketing tools model as a segmented group comprising individuals, communi- to promote the development of the knowledge economy ties, businesses, organisations, sectors, or combinations. proceeds in five stages that correspond to the components Based on the results of the exploratory research, the fac- of the conceptual marketing mix of the knowledge econ- tors motivating the target group and the prioritised content omy. The conceptual nature of the proposed complex is are identified, and a target consumer profile is created. It is expressed as an adaptation required by the complexity used in later stages. Thus, at this stage, taking into account of applying the traditional complex in the context of the the identified level of society’s readiness and the consumer knowledge economy, which encompasses the specificities profile, the projection of the idea’s value (perceived bene- of quality and value interpretations, as well as the percep- fit) is maximised. tion of the phenomenon. The fundamental change is the In the fourth stage, a group of tools appropriate to the replacement of the elements of product and price by the idea, impact objective, and target consumer profile is se- elements of idea and value, in terms of their perception and lected. By form of activity, tools can be: tools for passive experience only in perspective, where the idea is a prop- engagement (e.g., various forms of advertising), which osition, as in this case the promotion of the values of the do not require the consumer’s direct involvement in the knowledge economy (e.g. the idea of developing human process of information dissemination; and tools for active capital potential or the cultural transformation of society engagement (e.g., training, personal conversation), which through the implementation of the values of the knowledge require direct participation. The delivery tools can be elec- society), and the value is communicated as a promise or tronic or remote (e.g., a web page, a social account), or an example (e.g. a change in the well-being of society, or contact or physical (e.g., a personal consultation, an open a case of a good experience). The remaining elements – day). The grouping of the forms of activity and presenta- dissemination and diffusion, promotion and actors – are tion in the model is based on the conducted research; how- closer to the context of the elements of a traditional mar- ever, there may be exceptions in individual cases. keting mix, but taking into account the specificities of the In the fifth stage, the secondary impact factors–repu- knowledge economy as a marketing object, i.e. the choice tation, accessibility, and identification–of tools are taken of the channels of dissemination and diffusion, the tools of into account. Although these impact factors are considered promotion, and the actors who will disseminate the idea. in creating differentiated sets of tools, they should also be In the first stage, the idea of the knowledge economy is taken into account when selecting individual tools for idea identified. It may encompass the phenomenon as a whole dissemination — i.e., evaluating reputation (the ability to or a specific group of elements of the knowledge economy. attract public media), accessibility (to achieve mass dis- It is noted that the result of the identification of the idea semination), and identity (to substantiate the value of the may be any objective corresponding to the expression of disseminated idea). This stage considers additional promo- the knowledge economy: an idea, decision, activity, vision, tional opportunities to ensure the idea reaches the target product, service, promise, etc. Also, in this stage, through consumer. exploratory research, the level (educational, projective, or The model includes a control mechanism: periodic applied) of society’s readiness to accept the ideas of the activity is recommended. The development strategy of knowledge economy is determined. Depending on the pur- the knowledge economy defines a monitoring strategy to sued strategic objectives, targeted exploratory research can ensure the effectiveness of the tools. Exploratory research be conducted to assess the level of readiness for a particu- enables the identification of high-priority communication lar idea, or the general level when an idea is at the concep- tools to help implement monitoring more effectively. tual stage of development. The marketing tools model to promote the development In the second stage, the role of participants is deter- of the knowledge economy ensures an effective selection mined. This can involve an individual politician, a group of marketing tools based on the needs and opportunities of politicians, an organisation, or public authorities. The of the target consumer. The model accounts for a dynam- model is a stylised representation of interactions among ic environment and rapidly changing consumer priorities, participants, highlighting the need for cooperation. Politi- thereby emphasising its universal applicability. It can be cians, NPOs, and NGOs are involved in disseminating the argued that the successful development of the knowledge idea, while public authorities provide the necessary instru- economy is influenced by a data-driven, systematic pro- ments (various forms of funding, legislation, legislative cess for selecting marketing tools. projects, institutional structures, etc.). In the third stage, the society’s readiness level to fos- ter the values of the knowledge economy is essential. The 5 Discussion impact depends directly on it. To change attitudes, the pro- posed tools have an educational impact, while to influence While many forms and tools can be used to express the behaviour, the chosen tools encourage participants to take idea of the knowledge economy and to disseminate it, in action. At the same stage, the target consumer is identified planning its implementation, we recommend taking into 67 Organizacija, Volume 59 Research Paper Issue 1, February 2026 account the principles of the cyclical nature of the knowl- 6 Conclusions edge economy and the strict sequencing of the knowledge creation chain. This helps avoid unexploited and neglect- Through a case study, we have identified the secondary ed elements of the knowledge economy, thereby disrupt- impacts of the tools on the choice of tools or the compo- ing the development of the knowledge economy, which sition of their set: reputation, accessibility, and identifica- requires consistency. By emphasising the importance of tion. The tools most commonly used by US politicians are inter-institutional cooperation, we recommend creating an internal media (reputation factor), external media (accessi- environment that fosters it by establishing dedicated units bility factor), and personal websites (identification factor). to build and foster inter-institutional relations. To increase The tools used by NPOs and NGOs to disseminate their business involvement, we recommend prioritising initi- ideas consisted of events (reputation factor), social me- atives that promote co-creation. We found that the study dia platforms (accessibility factor), and the organisation’s respondents had a fairly good understanding of the values webpage (identification factor). After analysing the sur- of the knowledge economy, but still had doubts about its vey results, we have identified the groups of high-priority successful implementation. Therefore, solutions are need- marketing tools: for passive engagement: various forms of ed to shape new attitudes and raise awareness to influence advertising; for active engagement: events, personal con- established norms, beliefs, prejudices, stereotypes, and a versation, an e-mail; for electronic (remote) engagement: lack of information. In the model, we note that the dis- a website, social account, an e-mail; contact engagement: semination of any idea must begin with understanding personal consultation, open day, fair; for traditional en- the target audience by identifying its needs, expectations, gagement: phone calls, e-mails, text messages; modern readiness to learn, and openness to new suggestions. We engagement: social media, instant messaging, video chat. argue that only marketing tools selected based on contin- In the same study, we have confirmed that differentiation uous data can be applied to strategic decisions to promote of marketing tools according to demographic indicators of the development of the knowledge economy. gender, age, income, and education is not purposeful in In our study, we observed a lack of publicity among preparing the development strategy of the knowledge econ- Lithuanian politicians when they declared their ideas, omy. Based on the results of the expert evaluation, we have compared with the active involvement of US politicians. identified the most commonly proposed marketing tools: We recommend that public figures in Lithuania use the educational and attitude-changing, advertising campaigns, marketing tools highlighted in the study to disseminate and dissemination of best practices; decision-making and ideas, proposals, promises, initiatives, and other activities behaviour-changing: seminars, training, conferences, and aimed at influencing public opinion. It is also within the changes in the education system. In the same study, we will of politicians and political organisations to establish have confirmed that inter-institutional cooperation is key and coordinate institutes for inter-institutional coopera- to the successful development of the knowledge economy. tion. Businesses have the opportunity to engage in active After synthesising the results of three sub-studies, we idea dissemination. We have identified effective cases of have designed a five-stage marketing tool model to pro- cooperation; thus, politicians, political organisations, and mote the development of the knowledge economy. The non-profit and non-governmental organisations could con- process of idea dissemination begins with formulating the tribute more significantly to developing ideas by using idea and assessing public readiness to accept the knowl- businesses’ platforms, resources, and infrastructure. We edge economy’s ideas. The role of participants involved in see a continuation of the present study in the form of a the dissemination process is identified (politicians, NPOs, national strategy for developing the knowledge economy, and NGOs are engaged in the dissemination of the idea, i.e., guidelines for implementing the established precon- while government institutions initiate the necessary instru- ditions. We also recommend other possible directions for ments for this), and the possibilities for their cooperation research continuation: the applicability of the development are determined. Depending on the level of public readiness, model in a commercial environment, the dissemination of the objective of the impact is chosen: to change attitudes or ideas through social and public projects, and the adapta- behaviour towards the proposed idea. The target audience tion of the model and concepts to objectives implemented is identified, and its profile is developed based on motivat- in international contexts. ing factors and content justification. Combining the results However, we must emphasise that the research con- of the actions listed above, an appropriate set of tools is ducted reflects the development opportunities in Lithua- selected (passive or active engagement and electronic (re- nia. Therefore, the presented concepts and findings should mote) or contact (physical) forms of presentation). Final- be applied within the context of the target country’s ex- ly, the secondary impact factors of the tools (reputation, isting conditions. These include the political regime, the accessibility, and identity) are taken into account to max- openness of society, the state of the education system, the imise the effectiveness of the set. We recommend imple- supportive environment for development, and the level of menting a control mechanism to monitor the effectiveness technological development. 68 Organizacija, Volume 59 Research Paper Issue 1, February 2026 of the tools and make timely corrections, using indicators Dynamical Systems. GESTÃO.Org: Revista Eletrôni- that correspond to the areas of impact of the disseminated ca de Gestão Organizacional, 13(1), 71–78. 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Marketing Segmentation: Your Role For Diversity in 10.26493/1854-4231.15.29-41. 69 Organizacija, Volume 59 Research Paper Issue 1, February 2026 Wierzbicka, W. (2018). Information infrastructure as a pil- and ethical business practices. She has authored lar of the knowledge-based economy - an analysis of and co-authored over 20 scientific publications in regional differentiation in Poland. Equilibrium (1689- international and national journals and has participated 765X), 13(1), 123–139. 10.24136/eq.2018.007. in various international conferences. She contributes Żelazny, R. (2015). Information society and knowledge to international projects related to sustainability issues economy - essence and key relationships. Journal of and promotes responsible business practices through Economics and Management, 20(2), 5–22. Retrieved education and research. She is a technical editor for the from http://cejsh.icm.edu.pl/cejsh/element/bwme- journal “Regional Formation and Development Studies.” ta1.element.desklight-9606cbb2-3b20-43c4-b2b4- ORCID: 0000-0002-3774-3153 85a471577e3c. Marius Zamkauskas is a PhD student at the Agne Sneideriene holds a PhD in Economics and Department of Management and Economics, Faculty Social Sciences and serves as an Associate Professor of Social Sciences and Humanities, Klaipeda at the Department of Management and Economics, University. He holds a Master’s degree in Marketing Faculty of Social Sciences and Humanities, Klaipeda and a Bachelor’s degree in Economics from the same University. Her research interests include regional faculty. His research interests include the knowledge and economic growth, the knowledge economy, economy, particularly its development and application corporate social responsibility and sustainability within organisational and societal contexts, as well as disclosure, the green/blue economy, environmental knowledge management and organisational resilience. economics, climate change impacts, greenwashing, ORCID: 0009-0008-5658-497X Identifikacija marketinških orodij za spodbujanje razvoja ekonomije znanja: primer Litve Ozadje/Namen: Namen raziskave je identificirati marketinška orodja, ki spodbujajo razvoj ekonomije znanja, ter oblikovati razvojni model, utemeljen na empiričnih rezultatih. Prispevek analizira teoretične vidike neprofitnega, so- cialnega in političnega marketinga, uvaja koncept samo-segmentacije ter predlaga strategijo za krepitev družbene pripravljenosti na uveljavljanje vrednot ekonomije znanja. Metode: Raziskava je bila izvedena v dveh zaporednih fazah. Prva faza je zajemala tri delne študije: študijo primera, kvantitativno deskriptivno raziskavo in kvalitativno eksploratorno raziskavo. Druga faza je vključevala oblikovanje razvojnega modela na podlagi ugotovitev iz prve faze. Rezultati: Študija primera je omogočila identifikacijo sekundarnih dejavnikov vpliva, ki določajo izbor posameznih orodij ali oblikovanje njihovega celostnega nabora. Analiza anketnih podatkov je razkrila skupine najpomembnejših marketinških orodij. Na podlagi ekspertnih ocen je bil oblikovan nabor najpogosteje predlaganih orodij. Celovita sin- teza rezultatov je privedla do oblikovanja usklajenega petstopenjskega modela za spodbujanje razvoja ekonomije znanja. Zaključek: Predlagani model odraža ključna spoznanja: izbira marketinških orodij je pogojena s pripravljenostjo in odprtostjo družbe za sprejemanje novih idej; uspešnost diseminacije je odvisna od medinstitucionalnega sodelova- nja; učinkovita diseminacija zahteva jasno opredeljeno ciljno publiko; nabor marketinških orodij pa mora temeljiti na rezultatih nenehne analize podatkov. Ključne besede: Ekonomija znanja, Marketinška orodja, Razvojni model, Družbena pripravljenost, Litva. 70 Organizacija, Volume 59 Research Paper Issue 1, February 2026 DOI: 10.2478/orga-2026-0006 Development of a Methodology Based on Fuzzy Logic for Solving the Problem of Evaluating a Startup Team Under Uncertainty Oleksandr DOROKHOV, Kadri UKRAINSKI, Hanna KANEP, Liudmyla DOROKHOVA School of Economics and Business Administration, University of Tartu, Tartu, Estonia, oleksandr.dorokhov@ut.ee, kadri.ukrainski@ut.ee, hanna.kanep@ern.ee, liudmyla.dorokhova@ut.ee Aim/Purpose: The purpose of the article is to develop a methodology for evaluating startup teams and to create a corresponding computer model based on multicriteria analysis and fuzzy-logic decision-making. Particular attention is paid to determining both qualitative and quantitative characteristics of the team, and obtaining a generalized inte- gral assessment of the startup team under uncertainty. Design/methodology/approach: An integrated evaluation method is proposed that combines the principles of the fuzzy set approach and expert evaluation and is implemented as a fuzzy inference system in MATLAB. The devel- oped model used different initial characteristics of the startup team as input parameters. For this, formulas were identified, described, and utilized to calculate the values of these evaluation parameters. The set of linguistic vari- ables and a system of rules for processing fuzzy data were defined. Literature data, expert and investor assess- ments, and case studies of real startup projects served as the empirical basis for the study. Findings: The results demonstrate that the proposed approach enables a fairly objective and comprehensive as- sessment of a startup team’s quality, considering multiple assessment criteria, their interrelationships, and the com- bination of qualitative and quantitative input data, all within the context of significant uncertainty. The methodology ensures the objectivity and repeatability of the assessment, making it a valuable decision-support tool for various situations and participants within the startup community. Research implications/limitations: The study is limited by the amount of data on real startup teams for model ver- ification, which leaves much to be desired, as well as the need for further empirical substantiation and adjustment of the fuzzy model as a whole, including formulas for input parameters, linguistic variables, and decision rules, based on expert opinions. Possible areas for further research include adapting the method to different stages of startup development, taking into account their field of activity, size, and other specific features, and enabling more accurate model adjustment across various practical cases. Originality/value/contribution: The article’s originality lies in integrating fuzzy logic with multicriteria analysis to assess the human factor in startups. A useful contribution involves creating a practice-oriented tool that enhances the accuracy and reliability of team analysis, which is essential for startups themselves, business angels, venture funds, accelerators, and other participants in the startup community. Keywords: Startup team evaluation, Fuzzy evaluation methodology, Team scoring model 1 Received: 20th August 2025; Accepted: 20th January 2026 71 Organizacija, Volume 59 Research Paper Issue 1, February 2026 1 Introduction velopment, when the product is not yet complete and the business model is still evolving, the team becomes the pri- 1.1 Place of characteristics of quality, mary asset that determines the project’s success. Different players in the startup ecosystem, including investors, ac- level, and team strength in the celerators, government funds, corporate partners, potential overall startup assessment employees, analysts, and even end users, evaluate the team based on their specific goals and risks. Assessing the quality and level of a startup team is a For investors, including venture funds, business an- core element of venture expertise and the strategic anal- gels, and corporate venture arms, the startup team is often ysis of innovative projects. In contexts marked by high considered a key factor in investment decisions. According uncertainty, limited revenue stability, and dynamic market to Silva et al. (2024) and Li et al. (2024), because startups conditions, Berman et al. (2024) suggest that team-related frequently undergo strategic or product changes, the capa- factors can be particularly influential in determining ear- bilities of the team’s human capital—such as adaptability, ly-stage startup outcomes. learning capacity, execution, and scalability—may strong- The startup team constitutes the project’s core human ly influence outcomes. Investors tend to consider profes- capital, encompassing competencies, experience, motiva- sional experience, entrepreneurial orientation, leadership tion, and managerial capabilities. In this regard, Knight et skills, and the potential for effective team synergy. al. (2020) and McCarthy et al. (2023) note that a team’s Evaluators often consider whether a startup team can capacity to adapt to external changes, the quality of mana- execute its strategy, withstand market and investor pres- gerial decision-making, and its ability to scale growth are sures, and maintain the potential for future financing closely associated with the degree of team balance, mem- rounds. According to Berna et al. (2024), accelerators bers’ professional qualifications, entrepreneurial mindset, and incubators - tasked with distributing limited resourc- and effectiveness of internal interaction. es among many applicants - tend to concentrate on team Empirical research in the field of entrepreneurship characteristics, including readiness for intensive work, confirms that high-quality teams have a higher resistance openness to feedback, rapid hypothesis testing, and full to failure, reach the product-market fit stage faster, use participation by all team members. resources more efficiently, and attract investments more The successful completion of the acceleration program often. Moreover, investors and acceleration programs sys- largely depends not on the idea itself, but on the quality of tematically include team assessment as one of the main the team and its ability to quickly learn and make informed factors when selecting startups, since even a strong idea decisions. loses practical value if poorly implemented. State and quasi-state support institutions, including Key parameters commonly used to assess the level of a innovation funds and development agencies, often assess startup team include the presence of relevant industry ex- whether a startup team uses allocated resources responsi- perience, managerial and technical competencies, the de- bly, advances the project toward commercialization, and gree of motivation and involvement, role distribution and contributes to technological and economic value. Schulte functional balance, leadership and strategic vision, as well and Birkenmeier (2024) suggest that these evaluations as prior collaboration history and performance indicators, tend to emphasize the team’s relevant experience, mana- as outlined by González et al. (2024). gerial maturity, and prior implementation record. Together, these characteristics enable us to draw con- Corporate partners involved in open innovation, pi- clusions about the team’s capacity to cope with growth loting, and technology integration programs evaluate the challenges, overcome crises, and interact effectively with team as a potential solution provider. In the context of external stakeholders. Thus, assessing the level of a start- corporate standards and reliability requirements, process up team is not a secondary element of analysis, but rather maturity, technological competence, transparent commu- a critical tool for predicting the success of an innovative nication, and the ability to flexibly adapt become critical. enterprise, especially in the phase before achieving a sus- The level of trust within the team becomes crucial for the tainable business model and profitability. successful launch of joint projects. According to Aryadita et al. (2023), prospective co-founders and key employees 1.2 The importance of assessing the tend to consider the quality of the team and its leadership when deciding whether to join a startup. Issues of compat- startup team quality for participants ibility, trust, shared values, and confidence in the project’s in the startup ecosystem future directly depend on the strength and stability of the existing team. A high level of competence and cohesion Evaluating the quality and caliber of a startup team acts as a marker of reliability and prospects. is a crucial aspect of analysis for all key stakeholders in For analysts, market researchers, and specialized me- the startup ecosystem. In the early stages of a startup’s de- dia, the team also plays a key role. In the early stages, the 72 Organizacija, Volume 59 Research Paper Issue 1, February 2026 founders’ personalities become the focus and shape per- mutual trust, established communication channels, and ceptions of the startup as a stable or, conversely, a weak a history of effective collaboration, all of which may in- player in the market. Charisma, reputation, history of pre- fluence the team’s level of synergy and internal resilience vious projects, and the ability to convincingly convey the under conditions of uncertainty and high pressure. For ex- vision are key factors that attract media and expert interest. ample, Berman et al. (2024) seem to indicate that strong Even end users, especially in the B2B segment, build trust leadership and strategic vision may be regarded as rele- in the product through interactions with the team. Flexibil- vant aspects of team effectiveness, encompassing the ca- ity, professionalism, openness to feedback, and speed of pacity of one or more team members not only to formulate response become criteria of reliability, especially when the long-term goals but also to communicate them externally, product itself is still unstable and in need of improvement. potentially encouraging both the team and relevant stake- The team is perceived as a guarantee of development, sup- holders to pursue desired outcomes. Execution ability, ex- port, and long-term value. pressed in the speed and quality of task implementation, is The assessment of a startup team’s quality and level is also a key indicator. In particular, the assessment focuses widely regarded as a universal and cross-cutting element on metrics such as time-to-market for the Minimum Viable of analysis within the startup ecosystem. Assenova and Product, the number of product iterations before entering Chang (2023) and Blume and Hsueh (2023) suggest that the market, and the alignment of actual actions with set such evaluations can influence the decisions of a range goals under limited resources. of stakeholders, from investors to potential team mem- A key factor in a team’s resilience is often the level of bers. In addition, Esen et al. (2023) and Mueller (2024) motivation and involvement of its members. Knight et al. note that team assessment often serves as an indicator of a (2020) note that this aspect can be reflected in personal project’s viability, its capacity for growth and adaptation, commitment to the project idea, equity among team mem- and its ability to interact effectively with the environment. bers, the average work intensity (e.g., hours per week), and The team is not merely a group of individuals executing the willingness to participate in the project over the long an idea; it is a system-forming element, essential for sus- term. High motivation may be positively associated with taining progress under conditions of high uncertainty and the team’s endurance and its potential to navigate crisis competition. phases during development. Finally, considerable attention is paid to the team’s 1.3 Standard Components and Variables ability to attract external resources, including investments. for Startup-Team Evaluation Here, the charisma of leaders, public speaking and negoti- ation skills, and the ability to build trust with investors and partners are crucial. Media coverage and reputational capi- The assessment of a startup team, drawing on insights tal are also considered additional indicators of the project’s from the scientific and venture literature, may be under- potential investment attractiveness. Thus, the combined stood as comprising a set of interrelated components, each assessment of the listed characteristics allows us to form reflecting critical aspects of entrepreneurial potential. a holistic view of the team’s potential as a key factor in Takas et al. (2025) and Jáki et al. (2022) appear to indicate the success of a startup in the early stages of its life cycle. that these components can serve as a useful framework for evaluating the team’s readiness and capabilities. Among these components, the level of professional competence 1.4 Methods of Teams evaluation and relevant experience of team members appears to be particularly important. This may include entrepreneurial Scientific and applied literature on startup evaluation experience, such as involvement in prior projects—both emphasizes that the team is one of the most critical success successful and unsuccessful—as well as technical, indus- factors, especially in the early stages of project develop- try-specific, and managerial skills, ideally complemented ment. In this regard, several methodological approaches by suitable educational qualifications. to assessing the quality and potential of startup teams have An equally important variable is the team structure in been developed and applied. These methods differ in their terms of functional diversification and balance. Success- structure, depth of analysis, applicability to various stag- ful startups are typically built around teams with comple- es of startup development, level of subjectivity, and asso- mentary competencies, ranging from product development ciated costs. Therefore, it is advisable to consider some and marketing to financial and strategic management, with well-known approaches, indicating their advantages and clearly defined roles (leadership, execution, analytics). limitations. Such internal complementarity enables prompt, flexible Expert (qualitative) evaluation is perhaps the most task implementation. common in venture practice. It is based on the subjective Particular attention is often given to the quality of team conclusions of experienced investors, mentors, consult- dynamics, including the ability to interact productively, ants, or accelerator managers. The evaluation is based on 73 Organizacija, Volume 59 Research Paper Issue 1, February 2026 interviews, personal meetings, observation of team inter- ity tests, conflict potential analysis, and leadership charac- actions, analysis of participants’ resumes and portfolios, teristics are often used. In this case, the advantages include and general “intuition” regarding the team’s potential. a deep understanding of internal interactions, the ability Typically, the focus is on parameters such as professional to identify potential risks (such as conflicts and demoti- and entrepreneurial experience, level of competence, team vation), and assistance in selecting and forming effective integrity and cohesion, motivation, communication skills, teams. At the same time, the disadvantages include high and management potential. labor intensity and demanding qualifications of specialists, The advantages of this approach may include high possible resistance from participants (unwillingness to “be flexibility and the ability to adapt to the specifics of a giv- tested”), and not always correct application outside the en project, taking into account contextual and individual coaching context. factors while drawing on the expert’s practical experience. Recently, the analysis of digital traces and behavioural At the same time, Franke et al. (2008) noted potential analytics (Digital Footprint & Behavioral Data Analysis) limitations, including considerable subjectivity, limited has been increasingly used. Modern technologies enable reproducibility and transparency, and the possibility of the analysis of startup teams’ digital activity. As discussed cognitive or behavioral distortions influencing the expert’s by Howison et al. (2019), such analysis may include indi- judgment. cators such as the number of GitHub commits, the frequen- Another approach to startup evaluation involves stand- cy of Slack communication, activity on LinkedIn, partic- ardized checklists and assessment matrices, commonly re- ipation in professional communities, and the use of task ferred to as structured frameworks, as noted by Antunes management systems such as Jira or Trello. This type of et al. (2021). Tools such as the Team Assessment Matrix, data can be used to indirectly assess levels of involvement, the Founder VC Fit Matrix, venture fund scorecards, and discipline, process transparency, and professional activity. methods that adapt elements of SWOT analysis to human Among the advantages of this approach are the potential capital could provide a structured framework for incorpo- for automation and scalability, its suitability for continu- rating specific parameters and scales, potentially formaliz- ous monitoring, and its reliance on observed behavioral ing and systematizing the evaluation process. patterns. At the same time, this method is associated with Their advantages include standardized procedures, the ethical and legal considerations, including issues related ability to compare multiple teams, ease of use, and docu- to personal data collection, as well as limitations of inter- mentation. Among the disadvantages are the superficiality pretation, since digital activity does not necessarily reflect of the analysis in complex cases and limited sensitivity to the quality of decision-making. In addition, its effective “soft” factors (for example, emotional intelligence, cha- application may require supplementary tools and analyt- risma). Sometimes the formal approach can ignore hidden ical resources. dynamics. Finally, combined methods are used. Modern practice Behavioral and operational indicators are also used. uses mixed models that integrate qualitative and quanti- These approaches often emphasize objective, quantitative- tative approaches, expert judgment, and standardized as- ly measurable indicators, with examples of metrics includ- sessment tools. Such models are described in the literature ing the speed of bringing an MVP to market, the number by Wei (2025) and include multi-criteria team assessment of product iterations before launch, the volume of team frameworks, internal scoring systems within venture member involvement (including ownership shares in the funds, and acceleration-focused due diligence structures. company), weekly working hours, resistance to deadlines, Their advantages include greater assessment validity, the and flexibility in adapting the strategy, which may be con- ability to cross-check data, and flexibility and adaptability. sidered as highlighted by Ven et al. (2023). However, this requires careful validation and coordination Among the advantages are high objectivity, the ability of methodological approaches. It is also noted that they are to track dynamics over time, and convenience for analyt- time and resource-intensive and require a fairly high level ical reporting and decision-making. Among the disadvan- of analytical culture. tages are inapplicability at the pre-seed stage, when data is Thus, assessing startup teams is a complex, multi-level missing, the risk of overlooking qualitative characteristics, task that requires a combination of quantitative and qual- and the potential to erroneously account for formal activity itative methods. There is no universal method suitable for rather than real efficiency. all types of startups and development stages. However, the The psychometric and sociometric approach, also use of combined approaches, with a focus on the context known as Team Psychology Diagnostics, may be a relevant of the project, the specifics of the industry, and the objec- tool for evaluating startup teams, as noted by Fukuzaki and tives of the analysis, allows us to significantly increase the Iwata (2024). This approach uses methods for assessing reliability and usefulness of the data obtained, which is the team’s role structure (e.g., Belbin Team Roles), person- especially important in investment selection, acceleration, ality profiles (MBTI, Big Five), motivational drivers, and and strategic planning for the development of innovative group dynamics. Behavioural interviews, team compatibil- projects. 74 Organizacija, Volume 59 Research Paper Issue 1, February 2026 So, the main gap in research, which the presented 2 Methodology methodology aims to address, is the underdeveloped meth- ods and models for multi-criteria assessment of the quality of startup teams in conditions of unclear, insufficient, or 2.1 Research subsequences vague information about them. The structure of the work on the article reflects the tasks 1.5 The goals of the study set and is presented in Figure 1. It presents an overview of the methodology developed in this study, in the form of a workflow diagram. The diagram outlines four sequential The Research presented below had three main aims phases, each comprising distinct functions or steps. and objectives. The subsequent sub-sections provide a definition and First, to study the state of the issue of evaluating a concise explanation of the specific modeling techniques startup team and to identify the main criteria, parameters, designed and applied within the scope of this research. and initial variables for such evaluation. Second, to determine possible mathematical formulas and their components for obtaining numerical measure- 2.2 Fuzzy approaches for evaluation of ments of the initial evaluation parameters and to discuss startup teams their elements, as well as to develop a structure that allows integrating the initial parameters into a common model for Recently, fuzzy methods have been increasingly used further consistent obtaining of a numerical value for a gen- to evaluate startup teams, as demonstrated by Kumar et eralized, integral evaluation. al. (2023), Lin et al. (2021), and Bandurin (2023). Fuzzy And third, to analyze the feasibility of using fuzzy logic enables the analysis of non-formalizable, subjective, modeling, specifically fuzzy interference systems, to cre- and qualitative characteristics such as motivation, synergy, ate such a model and then develop a basic system using leadership qualities, and team compatibility. Several key specialized software. fuzzy approaches used for team evaluation can be iden- Thus, the aim of the article is to address the research tified. problem of developing and formalizing a methodology for One of the most common approaches is Fuzzy Mul- evaluating startup teams using fuzzy logic and to construct ti-Criteria Decision Making, which extends classical meth- a corresponding computational model. ods (AHP, TOPSIS, ELECTRE) by incorporating fuzzy values, as described by Bölükbaş et al. (2025), Puzović 1.6 Brief structure of the article et al. (2023), and Kyrylych & Povstenko (2023). These methods are suitable when expert judgments are expressed in linguistic rather than numerical form. The manuscript consists of four main parts. In the Fuzzy AHP is widely used to determine the weights first section, a general introduction, the motivation for the of team evaluation criteria based on linguistic expert as- study, and a literature review background are provided. In sessments such as “high experience,” “moderate involve- the second part, methodological approaches used to eval- ment,” or “low conflict.” Triangular or trapezoidal fuzzy uate startup teams are identified, presented, and described. numbers are typically used in the models. FAHP effective- The following section presents the main results described ly captures uncertainty and subjectivity in expert opinions, in the paper. Firstly, this involves structuring the parame- although it requires careful specification of membership ters for evaluating a startup team and determining the feasi- functions, as shown by Salehzadeh & Ziaeian (2024) and bility of obtaining numerical estimates for each. Secondly, Alharairi et al. (2025). this is a structured multi-criteria computer model for such Another method is Fuzzy TOPSIS, which evaluates evaluation built in MATLAB. Also, numerical examples startup teams based on their closeness to fuzzy “ideal” and of calculations are given. Finally, the fourth and last sec- “worst” solutions. This approach, argued by Afful-Dadzie tion of the manuscript discusses the directions for the next & Afful-Dadzie (2016), incorporates fuzzy assessments investigation, additional research perspectives, theoretical (e.g., “moderate coordination” or “high flexibility”) and contributions, practical implications, and the limitations of scores teams on criteria such as reliability, communication, the developed approach and the model. experience, and motivation. Fuzzy Rule-Based Systems represent a different class of approaches. They rely on expert-defined IF–THEN rules, such as IF “experience” is high AND “coherence” is medium THEN “team quality” is high. Its components in- clude a rule base, membership functions, and an inference engine, as shown in Dogan & Avvad (2025) and Mikulić et al. (2021). These systems are applicable for simulation 75 Organizacija, Volume 59 Research Paper Issue 1, February 2026 Figure 1: A workflow diagram for the research methodology used in the paper Source: Composed by the authors modeling and automated decision-making within acceler- over time. ation platforms, venture CRM tools, etc. An extension of classical fuzzy is Type-2 Fuzzy Log- Fuzzy Cognitive Maps (FCM) constitute another ic, which explicitly accounts for uncertainty in the mem- widely used methodology, as confirmed by Tchupo et al. bership functions themselves. This approach, according (2020). FCMs enable modeling causal relationships among to Mittal et al. (2020), is relevant in situations where ex- team-related factors, such as motivation, leadership, expe- pert judgments are inconsistent or contradictory and the rience, involvement, and charisma, and how they influence boundaries of concepts such as “team synergy” are diffi- one another and overall team effectiveness. In this meth- cult to define. Its practical application remains limited due od, team qualities are represented as nodes, while causal to high computational complexity, and holds promise for links between them are assigned fuzzy weights provided startup analysis involving heterogeneous or conflicting by experts. This approach enables the analysis of team dy- data sources. namics and the potential for strengthening or degradation 76 Organizacija, Volume 59 Research Paper Issue 1, February 2026 Figure 2: Model’s development steps Source: Composed by the authors The Fuzzy Delphi Method combines the traditional judgment and linguistic assessments, making them espe- Delphi technique with fuzzy aggregation mechanisms to cially valuable in venture decision-making contexts. achieve consensus among experts. As shown in Moham- madi & Shafiee (2021), Lianto (2023), and Alnoor et al. 2.3 An application of a fuzzy interference (2022), it is useful for evaluating startup teams using as- sessments from mentors, investors, or advisors, especially system for multi-criteria evaluation when formal performance indicators are unavailable. of startup teams Finally, fuzzy logic is often combined with fuzzy clustering, such as Fuzzy C-Means, to classify and group To address the challenge of multi-criteria evaluation startup teams, which is illustrated in Dang et al. (2021) for a startup team, given heterogeneous initial data and and Semerci et al. (2017). This allows teams to belong to subjective expert assessments of their relative importance, multiple clusters with different degrees of membership, fa- we will employ a fuzzy-logic-based approach. The meth- cilitating the identification of hybrid team types and asso- odology for constructing such or similar systems for ap- ciated strengths or risks. Overall, fuzzy-based approaches plied use is widely described in the literature, for example, provide effective tools for evaluating startup teams under in Varshney & Torra (2023), Furizal et al. (2024), and Saa- conditions of uncertainty, subjectivity, and limited data. for example, intchi (2024). However, it is also appropriate Their main advantage lies in their ability to model human to briefly consider, describe, and comment on some key 77 Organizacija, Volume 59 Research Paper Issue 1, February 2026 points related to our model. A3 (for three input variables, fragment). Thus, we will use input linguistic variables (corre- The general development sequence for the fuzzy mod- sponding to certain initial characteristics used for evalu- el is illustrated in Figure 2. ation) containing three membership functions. The mem- bership functions will be triangular. Additionally, if it is necessary to specify an interval of the input variable with- 3 Results in which the result remains constant, trapezoid functions should be used, as shown in Figure A1. 3.1 Structure of multicriteria fuzzy logic- This figure illustrates the set, parameters, and type of based inference systems for team membership functions for all input variables of the mod- evaluations el, as well as potential changes to membership functions of input parameters when necessary to introduce greater fuzziness, softness, or a specific range of initial data that The tree for calculating the final overall assessment of does not alter the result. The same applies to the member- a startup team, based on elementary initial criteria, is pre- ship functions of the intermediate and resulting variables. sented in Figure 3. In the model, for all variables, input data, intermediate re- sults, and final results (including their possible, maximum, 3.2 Explanation of parameters and minimum values) are presented in the range of 0-100. During construction, we will use two variants of ele- As described earlier, the model we propose consists of mentary fuzzy blocks: those with 2 and 3 input variables. seven main groups of elements, each with numerical val- For each block, it is necessary to set the decision rules ues used to obtain the final, generalized assessment. There- (rules) of fuzzy logical inference. Accordingly, the type of fore, we will consider approaches to obtaining numerical the rules themselves and their graphical interpretation are assessments of the primary components of the startup team presented in Figure A2 (for two input variables) and Figure assessment model. Figure 3: Input parameter composition tree for multi-criteria startup team evaluation Source: Author’s elaborations 78 Organizacija, Volume 59 Research Paper Issue 1, February 2026 Team Experience & Competence. Table B1 contains dinated actions, consistent decision-making, and efficient formulas for calculating the components included in its task execution. Early-stage startups operate in dynamic en- composition. When evaluating startups, team experience vironments with tight deadlines, requiring strong commu- and competence are among the most critical factors influ- nication, mutual trust, and collective adaptability. Teams encing long-term success. Even with an innovative idea with these qualities tend to manage stress more effectively, and a scalable business model, a startup’s performance resolve conflicts faster, and maintain higher productivity. ultimately depends on the team’s ability to execute strat- Team synergy arises when collaborative results ex- egy, adapt to changing conditions, and manage resources ceed the sum of individual contributions. This is achieved efficiently. Industry-specific experience allows founders through clearly defined and complementary roles, respect to better understand market dynamics, customer needs, for each member’s expertise, and alignment around shared and competitive environments. Teams with strong domain strategic goals. Synergy enhances creativity, supports in- knowledge tend to set realistic goals, develop well-founded novative problem-solving, and accelerates product de- strategies, and respond more effectively to market shifts. velopment and market entry. Additionally, strong team The competence of founders and key team members dynamics are viewed favorably by investors, as they re- directly affects the quality of managerial decisions, includ- duce operational and managerial risks. Research indicates ing strategic planning, operational execution, fundraising, that poor teamwork, unresolved conflicts, and leadership and growth management. Empirical evidence from ven- disputes are common causes of startup failure, even when ture capital practice shows that startups led by experienced products are competitive. Therefore, the Teamwork & Syn- and skilled teams are more likely to reach market entry, ergy is an essential element of startup evaluation, reflect- secure additional funding rounds, and achieve sustainable ing a team’s capacity for effective execution, adaptability, financial results. In addition, teams with a proven track re- and sustainable growth in competitive environments. cord of successful ventures inspire greater investor confi- Leadership & Vision. This criterion plays a central dence, reducing perceived risk and signaling the ability to role in startup evaluation, as it reflects not only the team’s operate under uncertainty. Therefore, the Team Experience strategic direction but also its ability to achieve long-term & Competence represents a core element of startup eval- goals amid high uncertainty and a rapidly changing exter- uation, significantly influencing investment attractiveness, nal environment. The included components are described scalability potential, and long-term resilience. in Table B4. Skills Diversity & Balance. Table B2 lists the com- Effective leadership and a clear vision are key driv- ponents of its composition. When evaluating startup po- ers of sustainable growth, organizational cohesion, and tential, the diversity and balance of skills within the team innovation in startups. Leadership strongly influences are crucial in determining the likelihood of success. This company culture, strategic decision-making, and resource criterion reflects the degree of functional complementari- allocation. In early development stages, an adaptable and ty among team members and directly affects the startup’s goal-oriented leader is essential for motivating the team, ability to address the challenges of early-stage develop- managing crises, and navigating uncertainty. A clear and ment. realistic vision supports strategic planning and market Startups operate in environments characterized by positioning by defining long-term objectives and aligning high uncertainty and limited resources, requiring compe- short-term actions with the chosen business model. Start- tence across multiple domains, including product devel- ups without a well-defined vision often face strategic in- opment, technology, marketing, sales, legal, and finance. consistency and inefficient resource use. Moreover, strong Teams with diverse skill sets can allocate responsibilities leadership and a coherent vision enhance trust among more effectively, reduce reliance on external support, and investors, partners, and customers. Empirical evidence accelerate execution. In addition, a balanced skill compo- shows that startups with capable leadership and strategic sition improves decision-making by integrating different clarity are more likely to scale successfully, attract invest- perspectives. Empirical studies indicate that functionally ment, and achieve sustainable growth. Consequently, the diverse teams demonstrate higher innovation levels and Leadership & Vision represents a core element of startup stronger market sustainability. Consequently, the Skills evaluation, combining strategic direction with managerial Diversity & Balance is a key element of startup evalua- effectiveness and risk mitigation. tion, influencing problem-solving capacity, scalability, and Execution & Productivity. Table B5 shows the com- long-term growth potential. ponents for this group. In evaluating startups, this criteri- Teamwork & Synergy. This group includes the input on is crucial, as it reflects the team’s ability to transform variables shown in Table B3. When evaluating startups, ideas into concrete actions and achieve operational goals this criterion deserves special attention, as it is a key in- under time and resource constraints. It captures not only dicator of organizational effectiveness and long-term sus- strategic intent but also disciplined and systematic imple- tainability under conditions of uncertainty and limited mentation typical of successful early-stage ventures. Key resources. Effective internal collaboration ensures coor- indicators include MVP time-to-market and the number of 79 Organizacija, Volume 59 Research Paper Issue 1, February 2026 iterations before launch. A short MVP development cycle cision-making, greater flexibility, and greater resilience to signals strong coordination, rapid decision-making, and a stress, which helps achieve product-market fit more quick- clear focus on essential market needs. At the same time, ly. Ownership stakes, working hours, and long-term com- multiple pre-launch iterations demonstrate effective hy- mitment further reinforce responsibility and dedication. pothesis testing, user feedback integration, and continuous Investors often view energy, proactiveness, and focus improvement. as indicators of strong entrepreneurial potential. Converse- High productivity is evident in the team’s ability to ly, low engagement, divided attention, or weak commit- reach meaningful milestones despite uncertainty. This ment may signal a high risk of early abandonment. There- includes regular releases, adherence to timelines, timely fore, this criterion serves as a crucial measure of a team’s resolution of blockers, and strategic adjustments based on internal strength, resilience, and ability to sustain effective external feedback. Empirical evidence shows that startups work under pressure while continuously improving and with consistent execution and operational efficiency are adapting in a high-uncertainty environment. more likely to overcome early-stage challenges and secure follow-on funding. Thus, Execution & Productivity serve 3.3 Model creation in MATLAB as strong indicators of scalability, adaptability, and long- term growth potential in competitive markets. Charisma &Attracting Investment. It is described Based on the above-described input data and the tree by the variables presented in Table B6. When evaluat- representing the composition’s structure, a corresponding ing a startup team, founders’ charisma and ability to at- model was built to provide an integrated assessment of the tract investment are crucial, especially in the early stages, startup team’s performance. It should be noted that MAT- when external funding often determines viability. Though LAB was used under the University of Tartu license, but largely behavioral and subjective, this criterion signifi- this licensing requirement is not a limitation, since a com- cantly influences pre-seed and seed-stage success. A char- pletely identical model can be built in the same way using ismatic founder can clearly convey an inspiring vision the freely available SCILAB software. and effectively communicate the startup’s value proposi- The model has 26 input variables (initial evaluation tion to investors, customers, and partners. In competitive criteria) and 14 intermediate variables, distributed across fundraising environments, qualities such as confidence, 18 elementary fuzzy blocks (11 with 2 and 7 with 3 in- persuasiveness, and emotional intelligence often become put variables). It is not practical to use more than three decisive. input variables for an elementary fuzzy block, since this The ability to attract investment also reflects the team’s dramatically increases the number of fuzzy decision rules social capital, including professional networks, experience that need to be specified in such a case. with accelerators or venture funds, and the capacity to Intermediate fuzzy blocks and their variables in the build trust-based, long-term relationships. This enhanc- model are additional and are used to sequentially combine es the likelihood of securing initial funding, follow-on estimates (input variables) according to the decision tree rounds, strategic partnerships, and market expansion. Em- structure (estimation). Accordingly, intermediate output pirical evidence shows that charismatic leaders drive high- variables serve as inputs for the next level of the model. er team engagement, lower turnover, and greater loyalty In general, if necessary, several output variables can be from early adopters. Therefore, the Charisma & Ability to used to obtain a generalized, integrated estimate not only Attract Investment is a key indicator of entrepreneurial po- for all 26 input variables, but also for individual (interest- tential and the team’s capacity to communicate the value ing) groups of input data or their specific combinations. of their innovation in high-risk, low-trust environments. For visual control of the correctness of the deci- Motivation &Engagement. This group includes the sion-making rules set for each elementary fuzzy block, variables listed in Table B7. For a startup team, motiva- fuzzy inference surfaces can be used. They demonstrate tion and engagement are key criteria for sustainable de- the dependence of the result on the input variables and velopment. They reflect the team’s readiness to overcome should have a form similar to that shown in Figure 7, challenges and maintain focus, creativity, and productiv- monotonic and smooth, without sharp jumps, dips, mini- ity in the face of uncertainty and limited resources. High ma, or maxima. motivation underpins entrepreneurial persistence, enabling As mentioned above, the model uses triangular mem- the team to endure failures, iterate quickly, and adapt to bership functions and decision rules corresponding to the changing circumstances. Intrinsic motivation indicates case “input parameter value is greater, result value is great- personal commitment to the startup’s mission and a drive er”. to create value beyond profit. In cases where the situation (the dependence between Engagement is expressed through active participation input and output variables) is opposite for some input pa- in key processes and accountability for outcomes. Highly rameters (“input parameter is greater - result is less”), the engaged teams demonstrate greater coherence, faster de- model should be minimally reconfigured, changing only 80 Organizacija, Volume 59 Research Paper Issue 1, February 2026 the designations for the linguistic terms of the correspond- second, by average values; and in the third, by maximum ing input variable. In this case, all other components of values (worst-average-best approach). Also, if this is due the model, and, most importantly, the decision rules them- and justified by the circumstances, it is possible to select selves, remain unchanged. different options for different startups, even when compar- In the form presented, the model for all variables uses ing them simultaneously. a 100-point scale. Therefore, if some variables have other The general view of the complete model in MATLAB dimensions, value boundaries, and units of measurement, is shown in Figure 4. there are two possible ways to adjust the model. The first option is to pre-match the numerical value of the input var- 3.4 Results of numerical calculations iable with the expert assessment, which is then used in the model. The second option is to fine-tune the model itself by replacing the numerical values of the expert version For example, consider applying this model to evaluate with the existing numerical values (boundaries and point teams at four startups. The initial data and all calculation characteristics of the membership functions) for the corre- results for them are presented in Table 1. The fill colors in sponding variable in the model. the table correspond to the colors of the tree parts shown In practice, it is possible that the values (or estimates) earlier in Figure 3. of some of the input parameters of the assessment are In the general case, the numerical calculations can missing (or cannot be used for some reason). In this case, include searching, determining, or preliminary formulaic the calculation (without changing the model itself) can be calculation of the values of the initial data (input parame- performed using three options: pessimistic, average, or op- ters for evaluation); preparing an Excel file with input data timistic. for exchanging information with MATLAB, calculations In the first option, the missing data are replaced by the in the model directly in MATLAB, and loading the results minimum values for unknown (unused) input data; in the back into the Excel file. Figure 4: The full model in MATLAB and the control surface (top right) for the 2-input case Source: Developed by the authors 81 Organizacija, Volume 59 Research Paper Issue 1, February 2026 Table 1: Input data, intermediate, and final results of calculations, case of experts variable names Startup1 Startup2 Startup3 Startup4 in the calculation tree in the model Input variables Industry Experience EI 85 60 70 35 Startup Experience ES 80 55 65 30 Successful Exits XS 70 40 30 10 Education Level Score DL 75 65 80 50 Participation in Accelerators AG 65 50 45 20 Time……..in the current startup LC 75 60 55 35 Time……..on other projects LO 65 55 60 30 Team Synergy Index SI 80 65 60 40 Separation of roles SR 85 70 65 45 Management tools MT 75 60 55 35 Specialists Ratio SR 80 65 75 55 Sales & Marketing Specialists Ratio MR 75 60 40 25 Management & Finance Specialists Ratio FR 70 55 45 30 Number of key technologies owned by the team KT 85 65 90 50 Number of issued MVPs NP 80 60 70 40 Startup Leadership Experience LS 80 55 60 30 Experience in management positions LM 85 65 55 35 Vision & Strategy Clarity VS 90 70 65 45 Reputation and connections RC 85 60 55 30 Effectiveness of interaction with investors SN 80 55 50 25 Startup Promotion SP 85 65 45 30 Participants with shares in the company PC 75 70 65 50 Number of working hours WH 80 75 85 70 Readiness for lõng-term work LW 90 80 75 60 Time to market for MVP TP 75 65 80 50 Number of product iterations before launch NI 70 60 75 40 Intermediate results variables EI+ES+XS EI+ES+XS 77 53 56 24 DL+AG DL+AG 71 58 60 35 Team Experience & Competence ET 74 58 61 29 SR+MR+FR SR+MR+FR 71 60 60 30 KT+NP KT+NP 80 70 65 40 Skills Diversity & Balance BT 75 67 63 32 Teamwork Duration (LC+LO) TD 74 64 51 37 Team Synergy (SI+SR) TS 80 66 79 44 Teamwork & Synergy ST 76 68 65 37 Leadership Experience (LS+LM) LE 80 62 60 30 82 Organizacija, Volume 59 Research Paper Issue 1, February 2026 Table 1: Input data, intermediate, and final results of calculations, case of experts (continues) variable names Startup1 Startup2 Startup3 Startup4 in the calculation tree in the model Input variables Leadership & Vision LV 82 69 66 36 Charisma &Attracting Investment CA 79 64 50 27 Motivation & Engagement ME 79 75 74 62 ET+ST ET+ST 74 65 66 28 BT+LV BT+LV 78 71 69 33 Execution & Productivity EP 73 66 76 43 CA+ME+EP CA+ME+EP 76 71 67 44 Final result variable Total Team score SCORE 76 70 68 34 Source: Calculated by the authors This table presents the results of the preliminary ex- teams. Startup 1 demonstrates consistently high values pert assessment of all input parameters on a 100-point across most input and intermediate variables, resulting in scale. Individual numerical estimates can also be used the highest integrated overall team score. Startups 2 and 3 with minimal adaptation of the corresponding elementary form the middle group, with relatively balanced profiles, fuzzy blocks in the model. While the basic, initial model while Startup 2 shows stronger motivation and engage- assumes that each input parameter is assessed by experts ment indicators. However, Startup 3 demonstrates stronger on a 100-point scale, the table also presents possible other execution and performance indicators, including mastery units of measurement for individual parameters. The corre- of technology and iteration dynamics. Startup 4 demon- sponding, more precise fine-tuning of the model is carried strates the lowest values across both input and aggregate out during the review and preliminary preparation of spe- indicators, resulting in a significantly lower overall team cific input data, taking into account the task’s specifics and score. Overall, the comparison shows that high scores on the overall content of the corresponding database. In the intermediate dimensions such as leadership, skill balance, absence of data for any parameter, the worst-average-best and teamwork are associated with higher integrated team approach can be used. In this case, the missing values in scores. the input parameters are replaced during calculations with A more detailed analysis reveals that Startup 1 demon- the minimum, average, or maximum value, respectively. strates particularly high scores in leadership, strategic A preliminary look at the results shows that, both for vision, team experience, and skill diversity. Startup 2 the overall integrated assessment and for the individual has a relatively balanced team profile, highlighting mo- evaluation components and their groups, it is possible, if tivation and engagement, as well as teamwork and lead- necessary, to conduct a more in-depth analysis of specific ership, resulting in a fairly good overall score. Startup 3 aspects of a startup team. Overall, it is worth noting that has strengths primarily in execution and performance, in- Startup1 represents the strongest and most experienced cluding mastery of key technologies, work intensity, and team among those considered and demonstrates relative- product iteration dynamics, while leadership and external ly high investment attractiveness. Startup2 appears to be a engagement exhibit more moderate values, resulting in an balanced, average, and “typical” startup. Startup3 is tech- intermediate overall score. Finally, Startup 4 demonstrates nically strong; however, it performs comparatively weakly low values across most input and aggregate metrics, in- in business development, leadership, and investment-relat- cluding experience, leadership, and synergy, resulting in ed areas. Finally, Startup4 represents an early-stage start- a significantly lower overall team score within this group. up, a less mature project associated with relatively high The proposed methodology and model for evaluating and sensitive risks. startup teams based on a combination of the above indi- cators are highly useful for analyzing their development 4 Discussion potential. In the early stages, the team represents a key source of human capital and largely determines a startup’s ability to formulate and implement strategy, adapt to envi- The results in Table 1 demonstrate significant differ- ronmental changes, and navigate highly uncertain environ- ences in the structure and overall strength of the startup ments. Characteristics such as industry and entrepreneurial 83 Organizacija, Volume 59 Research Paper Issue 1, February 2026 experience, leadership qualities, balance of competencies, can be used to identify the team’s strengths and weakness- motivation, and work intensity are closely linked to the es, adjust development strategies, and inform decisions quality of decision-making, the speed of product develop- regarding support, acceleration, or funding of startup pro- ment, and the efficiency of resource utilization. jects. These results can be used both by teams for self-as- From a practical perspective, an integrated team as- sessment and by other participants in the entrepreneurial sessment enables us to identify differences among startups ecosystem for evaluating competitors and startups. in terms of investment attractiveness and long-term sus- Considering the presented model, it is worth noting its tainability. Higher values for the aggregate indicators are methodological novelty. Despite a significant number of generally associated with a greater likelihood of attracting publications devoted to evaluating startups, the methods, external financing, as strong teams reduce the risks related approaches, and practical tools for multi-criteria evalu- to market and product uncertainty for investors. Further- ation of the quality and level of startup teams proposed more, teams characterized by strong synergy, clear role in them require further development. In this context, the assignments, and high engagement tend to progress more use of the proposed approaches of fuzzy logic, the de- quickly through the MVP development, hypothesis testing, velopment of the corresponding model, and its comput- and scaling stages, thereby accelerating time-to-market er implementation in specialized software are a strong and increasing the project’s competitiveness. methodological approach, and at the same time, a highly Thus, using a structured team assessment model not sought-after practical tool for the integral evaluation of the only ensures a more objective and comparable assessment level and quality of startup teams, necessary for all partici- of startups but also serves as a basis for management and pants (components) of the startup community. investment decisions. The results of such an assessment Table 2: Model advantages and improvement directions Advantages of the approach and the model Directions of model development Direct and relatively simple, without intermediate transformations, Search, definition, construction, use of mathematical formulas, normalizations, etc., combination in calculations of input data pre- and elementary subparameters for more accurate numerical sented in different units of measurement and different scales, with calculation of the values of the input parameters of the model different min-max boundaries Taking into account the inaccuracy and underdetermination of de- Study of the structure and possibilities of reducing the number cision-making rules (construction of integral assessments) by using of decision-making rules with a large number of input variables the appropriate type of membership functions, the type and com- of the elementary fuzzy block of the model, and without notice- bination of individual rules from the rule base able deterioration of the results (small deviation of the results in the case of reducing the number of rules compared to the standard set of rules) Availability of opportunities and tools for adapting the initial, gen- Research on the dependence of results on the forms and types eral model, taking into account the scope of the startup’s activity, of membership functions in elementary fuzzy blocks of the mod- its stage, existing specific requirements, and conditions for evalu- el of different levels, the possibility of using additional weight ating the team. parameters both in the combination of input variables and for a set of decision-making rules Obtaining the desired, calculated, resulting numerical generalized Construction of a methodology for presenting input data in case estimate for any type, composition, and combination of input data. of their incompleteness (absence, unknown, weak certainty of some) under various scenarios (pessimistic, average, optimistic) regarding missing values Use of valid and reliable numerical calculation algorithms provided Mastering the capabilities of the software product itself for by a specialized, strong theory-based, and verified software checking the consistency of rules and the validity of the parame- ters of linguistic terms of membership functions Possibility of fragmentation and/or composition of fuzzy models Developing approaches to model management to adapt it to a for cases of different amounts of initial data (parameters) and re- wide range of cases – disabling certain variables or branches of quired output results the model, excluding some rules, and adding additional output variables Communicability with Excel for exchanging input/output data and Study of the possibility of creating user interfaces for the model, scalability of the fuzzy model by creating an installable and then both directly in MATLAB and in Excel, as an add-on; determina- executable application independent of the creation environment tion of the features and possibilities of creating installations and (MATLAB) running applications for computers without MATLAB Source: Developed by the authors 84 Organizacija, Volume 59 Research Paper Issue 1, February 2026 The developed model enables the generation of an in- Table 2 presents the main advantages of the described tegrated assessment of a startup team and tracking its dy- model, as well as the prospects for its further development namics as the initial data changes. Firstly, it enables con- and directions for improvement. tinuous team monitoring, which is important for analyzing the impact of actual changes in certain parameters over 4.2 Implications for Practice time. Secondly, similar to simulation modeling approach- es, it allows numerical and computational experiments to determine the necessary changes in certain parameters to The proposed methodology and the developed com- achieve the desired level for the team as a whole or for its puter model provide valuable information for various par- aggregated components in its overall assessment. Further- ticipants in the startup ecosystem. For investors, it allows more, the relative importance of selected factors in calcu- for a more objective assessment of team potential, iden- lating the overall assessment can vary, given the practical tification of strengths and weaknesses, and prediction of realities of assessing startup teams at different stages, in- the startup’s ability to adapt to market changes and man- dustries, and other specific conditions. age risks, thereby increasing the likelihood of a successful investment. For accelerators and business incubators, the model helps rank applications, select teams with the high- 4.1 Theoretical Contributions est potential for accelerated growth and effective resource use, and monitor progress during the acceleration process. The theoretical component of the presented model in- For startup teams, the model provides structured feedback, volves systematizing the elementary components of the highlights areas for improvement, and enables adjustments initial (input) parameters (criteria) for evaluating a startup to team formation strategies, role distribution, and compe- team, structuring them, and generalizing them by creating tency development. Thus, the tool contributes to increased a common tree to obtain an integrated assessment. transparency, objectivity, and systematic decision-making, The possibilities of calculating numerical assessments enhancing the chances of successful project development. for initial assessment parameters are also generalized; the corresponding formulas are proposed, along with their ex- 4.3 Limitations planations and descriptions. The methodology for creating elements and the struc- ture of a fuzzy model for the practical calculation of an in- Table 3 highlights the model’s existing shortcomings tegral assessment are also proposed. The application of the and describes the potential to eliminate and overcome model’s components, as well as the steps and stages of its them. computer implementation, are substantiated and described. Table 3: Model disadvantages and ways to overcome them Model’s shortcomings and weaknesses Ways to overcome limitations Complexity and privacy issues in obtaining data on real Use of open databases, accessible state and other public statis- startup teams, as well as extreme boundary values of input tical information, materials from startup incubators, accelera- parameters to fine-tune the model for specific conditions tors, and other components of the startup environment A significant number of input parameters and other data, Creation of flexible fuzzy evaluation models, with the possibility which are required for preliminary calculations of their val- of partial disabling of parameters, variable decision rules, and ues using formulas flexible structure The need for precise and adequate adjustment of deci- Using expert assessments and fine-tuning of models using inter- sion-making rules nal tools of the development environment for fuzzy inference systems Necessity of use of licensed software (MATLAB, Excel) Development of a model similar in functionality and appear- ance using free software (SciLab, OpenOffice, R language) The impossibility of carrying out calculations for end users Creation of independent applications in the form of executable who do not have specialized programs in the environment files installed on the user’s computer, and not requiring, in addi- in which the computer model was developed tion to Windows, any other additional software Source: Developed by the authors 85 Organizacija, Volume 59 Research Paper Issue 1, February 2026 4.4 Directions of Next Investigation and At the same time, the proposed model can be directly Further Research Perspective applied in real-world decision-making processes, such as preliminary startup screening by investors and accelera- Further development of the proposed model and meth- tors, ranking applications for acceleration and incubation odological approach may include several directions. programs, and internal team diagnostics to identify areas This is, first and foremost, the clarification of the in- for development. Thus, the article provides a methodolog- itial, elementary, and individual criteria for evaluating ical foundation for the practical application of the model, startup teams. In this case, both unification and detailing while future research may focus on its validation using of these criteria are possible. specific real-world cases and on expanding the empirical Another important issue here is the possibilities (and base. corresponding formulas) for obtaining numerical values of The startups used in the calculation examples are based input parameter assessments. On the other hand, if expert on real projects that participated in the CDL Estonia pro- assessments are based on indirect information or survey gram. Approximately 80% of the initial characteristics responses, then the digitalization of such assessments also and team structures correspond to real startup data, while requires appropriate methods and their practical testing. certain parameters were partially aggregated, adjusted, or Another important point is adapting the general, com- modified to comply with confidentiality requirements or prehensive decision tree to various practical situations, due to the absence of complete information for some indi- such as the actual assessment of a specific startup, while cators. This approach preserves the realism and practical accounting for its maturity level, line of business, and oth- relevance of the illustrative examples without violating er specific conditions. ethical or legal constraints on using data on specific teams. Fine, accurate adjustment of the decision tree should Thus, moving on to the final conclusions, the following enable the control of the importance of certain compo- can be emphasized. Evaluating the startup team is a highly nents, i.e., utilize their weights, and also easily combine relevant and in-demand task for all participants, compo- numerical, expert linguistic, and binary assessments with- nents, and structures within the startup community and the out internal restructuring of the model, simply by manag- broader entrepreneurial ecosystem. The complexity of its ing additional external parameters of its adjustment. solution stems from poorly defined, insufficiently formal- Special attention should be paid to clarifying the type ized input data and strong subjectivity in evaluation, which of membership functions, which requires accumulating a can be overcome by using fuzzy-logic-based approaches. base of numerical calculations for various startup teams The article proposes a methodology that includes pre- and subsequently analyzing the results. defined values for a set of individual evaluation parameters, The model can also be supplemented with additional their composition into a decision tree with corresponding output variables that specify partial assessments of the rules, and the calculation of a general integral numerical startup team in the desired context, with the corresponding assessment using a fuzzy inference system. This approach subset of input data. In this case, some decision-making is implemented in a MATLAB computational model. rules can also change; that is, new rules can be added to the The calculations performed demonstrated the model’s new set of rules and/or some existing rules can be removed potential practical application, as well as the directions from the initial set. for its development, refinement, and adaptation to various practical tasks of evaluating startup teams. The described methodological approach and its model implementation 5 Conclusion are powerful tools, useful both for practical application and the practical assessment of startup teams in the con- It should be noted that the empirical basis for devel- text of making various management, financial, and other oping the model structure, selecting input parameters, and decisions. At the same time, they provide opportunities to configuring the fuzzy inference rules was derived from ag- develop and study scientific problems related to startup as- gregated data from more than 60 startups that participated sessment, both in general and in specific conditions and in the CDL Estonia program, as well as expert assessments applications. from investors and mentors involved in this accelerator. The calculation examples presented in the article are Acknowledgements intended to demonstrate the process of model construction and operation, the logic of indicator aggregation, and the interpretation of integrated results. The use of aggregat- This work of K. Ukrainski, O. Dorokhov, and H. Kanep ed data for the analyzed startups ensures the protection of was supported by the project „Increasing the knowledge confidential information related to real teams, which is a intensity of Ida-Viru entrepreneurship“, co-funded by the standard practice in studies focused on human capital as- European Union. sessment and venture evaluation. 86 Organizacija, Volume 59 Research Paper Issue 1, February 2026 References nal Conference on Artificial Intelligence and Big Data (ICAIBD 2021), 541–544. https://doi.org/10.1109/IC- AIBD51990.2021.9459054 Afful-Dadzie, E., & Afful-Dadzie, A. (2016). A deci- Dogan, O., & Avvad, H. (2025). A fuzzy rule-based de- sion-making model for selecting start-up businesses cision support in process mining: Turning diagnostics in a government venture capital scheme using intui- into prescriptions. Applied Sciences, 15(23), 12402. tionistic fuzzy TOPSIS. 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A hybrid fuzzy MCDM approach to open inno- vation partner evaluation. Mathematics, 11(14), 3168. Kadri Ukrainski is a Professor in Research and https://doi.org/10.3390/math11143168 Innovation Policy and Head of the Department of Public Economics and Policy, University of Tartu, Estonia. She Saatchi, R. (2024). Fuzzy logic concepts, developments earned her PhD from the Faculty of Economics and and implementation. Information, 15(10), 656. https:// Business Administration, University of Tartu, Estonia. doi.org/10.3390/info15100656 Her research focuses on science and innovation Salehzadeh, R., & Ziaeian, M. (2024). Decision making 88 Organizacija, Volume 59 Research Paper Issue 1, February 2026 policy, startups, high- and deep-tech technologies, and research focuses on science and educational policy, the opportunities that public-sector agents, such as startups and entrepreneurial education, and these universities, ministries, SOEs, and agencies, have to processes in Estonia and abroad. facilitate these processes in societies. Liudmyla Dorokhova is a Visiting Professor at the Hanna Kanep is a Researcher at the Department Department of Marketing, University of Tartu, Estonia. of Public Economics and Policy, University of Tartu, She earned her PhD in Pharmacy from the National Estonia. Also, she is the Secretary General at the University of Pharmacy, Ukraine. Her research focuses Estonian Rectors’ Council. She earned her master’s on medical and healthcare startups, consumer behavior degree from the Faculty of Economics and Business and choice, and decision-making models. Administration, University of Tartu, Estonia. Her Razvoj metodologije, temelječe na mehki logiki, za reševanje problema ocenjevanja startup ekipe v pogojih negotovosti Namen/cilj: Namen članka je razviti metodologijo za ocenjevanje startup ekip ter oblikovati ustrezen računalniški model, ki temelji na večkriterijski analizi in odločanju z uporabo mehke logike. Posebna pozornost je namenjena opredelitvi tako kvalitativnih kot kvantitativnih značilnosti ekipe ter pridobitvi posplošene integralne ocene startup ekipe v pogojih negotovosti. Zasnova/metodologija/pristop: Predlagana je integrirana metoda ocenjevanja, ki združuje načela pristopa meh- kih množic in ekspertnega vrednotenja ter je implementirana kot sistem mehke sklepanja v okolju MATLAB. Razviti model uporablja različne začetne značilnosti startup ekipe kot vhodne parametre. V ta namen so bile identificirane, opisane in uporabljene formule za izračun vrednosti teh ocenjevalnih parametrov. Opredeljen je bil nabor jezikovnih spremenljivk ter sistem pravil za obdelavo mehkih podatkov. Empirično osnovo raziskave predstavljajo podatki iz literature, ocene strokovnjakov in investitorjev ter študije primerov dejanskih startup projektov. Ugotovitve: Rezultati kažejo, da predlagani pristop omogoča razmeroma objektivno in celovito oceno kakovosti startup ekipe, saj upošteva več ocenjevalnih meril, njihove medsebojne povezanosti ter kombinacijo kvalitativnih in kvantitativnih vhodnih podatkov v razmerah izrazite negotovosti. Metodologija zagotavlja objektivnost in ponovljivost ocenjevanja, zaradi česar predstavlja uporabno orodje za podporo odločanju v različnih situacijah in za različne udeležence v startup okolju. Raziskovalne implikacije/omejitve: Raziskavo omejuje omejena razpoložljivost podatkov o dejanskih startup eki- pah za preverjanje modela, kar pušča precej prostora za izboljšave, ter potreba po nadaljnji empirični utemeljitvi in prilagoditvi celotnega mehko-logičnega modela, vključno s formulami vhodnih parametrov, jezikovnimi spremenljiv- kami in pravili odločanja na podlagi strokovnih mnenj. Možna nadaljnja raziskovalna področja vključujejo prilagoditev metode različnim fazam razvoja startupov, upoštevanje področja delovanja, velikosti in drugih specifičnih značilnosti ter natančnejšo prilagoditev modela v različnih praktičnih primerih. Izvirnost/vrednost/prispevek: Izvirnost članka se kaže v integraciji mehke logike in večkriterijske analize za oce- njevanje človeškega dejavnika v startupih. Pomemben prispevek predstavlja razvoj praktično usmerjenega orodja, ki povečuje natančnost in zanesljivost analize ekip, kar je bistvenega pomena za startupe, poslovne angele, sklade tveganega kapitala, pospeševalnike ter druge deležnike v startup skupnosti. Ključne besede: Ocenjevanje startup ekip, Metodologija mehke ocene, Model točkovanja ekip 89 Organizacija, Volume 59 Research Paper Issue 1, February 2026 Appendix A Figure 5: Membership functions: triangular (left) and trapezoid (right) Source: Developed by the authors Figure 6: Decision rules set for the case of 2 input variables, and graphical interpretation of the interaction of decision-making rules and results Source: Developed by the authors 90 Organizacija, Volume 59 Research Paper Issue 1, February 2026 Figure 7: Decision rules set for the case of 3 input variables, and graphical interpretation of the interaction of decision-making rules and results Source: Developed by the authors 91 Organizacija, Volume 59 Research Paper Issue 1, February 2026 Appendix B Table 4: Calculations for the Team Experience & Competence group Source: Developed by the authors 92 Organizacija, Volume 59 Research Paper Issue 1, February 2026 Table 5: Calculations for Skills Diversity & Balance group Source: Developed by the authors Table 6: Calculations for the Teamwork & Synergy group Source: Developed by the authors Source: Developed by the authors 93 Organizacija, Volume 59 Research Paper Issue 1, February 2026 Table 7: Calculations for the Leadership & Vision group Source: Developed by the authors Table 8: Calculations for the Execution & Productivity group Source: Developed by the authors Table 9: Calculations for the Charisma &Attracting Investment group Source: Developed by the authors Table 9: Calculations for the Motivation & Engagement group Source: Developed by the authors 94 Organizacija, Volume 59 Review Article Issue 1, February 2026 DOI: 10.2478/orga-2026-0005 Overview of Research on Higher Education Teachers’ Involvement in Learning Analytics Marjeta MAROLT¹, Danijela JAKŠIĆ² 1 University of Maribor, Faculty of Organizational Sciences, Kranj, Slovenia, marjeta.marolt@um.si 2 University of Rijeka, Faculty of Informatics and Digital Technologies, Rijeka, Croatia, danijela.jaksic@inf.uniri.hr Background and purpose: This systematic literature review focuses on the use of learning analytics among higher education teachers, who play a key role in collecting, analysing, and interpreting data. Empirical studies from the period between 2011 and 2024 were analysed to understand the role of teachers in learning analytics and the an- tecedents and outcomes of its use. Methods: A systematic literature review was conducted to reduce research bias and ensure repeatability. The relevant articles identified were analysed in two phases, first with a descriptive analysis and then with an in-depth qualitative synthesis. Results: The literature review reveals two predominant trends in how higher education teachers use learning ana- lytics. The first focuses on the use of learning analytics technologies to solve specific problems, while the second considers learning analytics in the context of broader pedagogical practices of teaching and learning. The paper also discusses antecedents and outcomes of the use of learning analytics among higher education teachers, highlights gaps in existing research, and suggests further research directions in this field. Conclusion: This paper provides an overview of recent literature on the use of learning analytics among higher education teachers. The findings clarify the role of teachers in the use of learning analytics and provide insights into the antecedents and outcomes of its use that are also relevant to other stakeholders and decision-makers in higher education. Keywords: Learning analytics, Higher education, Teachers’ perspective, Systematic literature review 1 Introduction understanding the learning process by integrating diverse information from different data sources, such as eye-track- Learning analytics (LA), which involves collecting ing and digital tracing data, and enhancing the capabilities and analysing data about learners to improve educational of data processing and modelling techniques, such as data outcomes, was initially a niche interest of a few research- mining, machine learning, and deep learning, to uncover ers. Due to the rise of online learning, the availability of hidden patterns in this extensive data (Pei et al., 2021; rich learning data and rapid technological changes, LA Sghir et al., 2023) and support the optimisation of learning is now widely used in all educational settings, including environments. schools, higher education, workplace training, and infor- The history of LA can be traced back to the pre-digital mal learning environments (Ferguson et al., 2019; Hernán- era, when teachers assessed and evaluated courses main- dez-de-Menéndez et al., 2022). One of the recent trends is ly by collecting data from learners, initially by examining multimodal LA, which offers a breakthrough approach to student performance (quantitative data) and later also by 1 Received: 30th July 2024; Accepted: 17th January 2026 95 Organizacija, Volume 59 Review Article Issue 1, February 2026 examining the frequency of student behaviour based on 2020) and are often neglected in the literature, yet their observations they made during their lectures. In the 1950s, perspectives and practices are frequently underrepresented the introduction of programmed instruction using teaching in the existing literature (Hernández-de-Menéndez et al., machines was a significant advance, which led to the de- 2022). As teachers play an important role in interpreting velopment of computer-assisted instruction systems such LA data to improve student achievement and curriculum as Programmed Logic for Automatic Teaching Operations design, their first-hand experience and understanding of in the 1960s. At the same time, adaptive computer-assist- classroom dynamics are invaluable in applying insights ed instruction began to develop, which was customised to from LA tools. the individual learner, as the work of Richard Chatham At- The main objective of this review is to highlight a topic kinson shows. The 1980s saw the emergence of intelligent that has gained importance as the share of the education tutoring systems that used artificial intelligence to pro- market has increased significantly in recent years due to vide personalised learning experiences. The rise of online the growing awareness and understanding of LA and artifi- learning in the 1990s, facilitated by learning management cial intelligence. It is also expected to grow at a compound systems such as Blackboard and Moodle, further revolu- annual growth rate of 10.49% during the forecast period tionised data collection and pedagogical practices. The from 2022 to 2027 (Statista, 2024). In this context, the pa- term “Educational Data Mining” (EDM) gained promi- per proceeds as follows. The second section describes the nence in 2011, focusing on analysing educational data to methodology used in this study. The third section provides improve learning outcomes. The founding of the Society an overview of studies on journals, publication distribu- for Learning Analytics Research (SoLAR) in 2011 and the tions, and investigated regions. In section four, we summa- first international conference on Learning Analytics and rise our findings through the lens of the ADO framework. Knowledge (LAK) in 2015 marked the formal recognition In the final section, we make concluding remarks and out- of LA (Ye, 2022). Since then, the number of publications line future research directions. in this area has increased exponentially. From a research perspective, the majority of published studies focus either on the use of LA in higher education, 2 Methodology the challenges involved or the outcomes of using LA. These studies consider different stakeholders in LA, usu- To address the research gaps, a systematic literature ally focusing on students, teachers or managers or a com- review was conducted. The main aim of this research is to bination of these (Mahmoud et al., 2020). According to systematise and summarise the empirical research in this Hernández-de-Menéndez et al. (2022), few primarily focus area published between 2011 and 2024. Before the system- on improving the teaching process or addressing academic atic review, we conducted a broad search using the terms issues. Furthermore, the existing domain-based literature “learning analytics*” AND “higher education” to famil- reviews rarely or only to some extent rely on established iarise ourselves with the scope of the literature, identify frameworks to guide the review of a domain, which, ac- main keywords and develop the exclusion criteria. To en- cording to Paul et al. (2021), helps authors maximise clar- sure minimal researcher bias and support reproducibility, a ity and coverage. For example, the interrogative frame- transparent procedure proposed by Paul et al. (2021) was work consisting of what, why, where, when, who, and how adapted. (5W1H) (Lim, 2020) has only been used to some extent To maintain a high standard for the studies analysed, in recent literature reviews (Banihashem et al., 2023; de only empirical, peer-reviewed academic research was in- Oliveira et al., 2021; Foster & Francis, 2020). Foster & cluded. Scopus was chosen as the search engine as it covers Francis (2020) and de Oliveira et al. (2021) only addressed a broader range of subject areas and categories compared the where, how, and what questions, while Banihashem et to WOS, allowing researchers to more effectively find the al. (2023) neglected the where and when questions. Al- journals most relevant to their area of enquiry (Paul et al., though one of the first domain-based systematic literature 2021), and it has the largest abstract database (Schotten et reviews (Nunn et al., 2016) provided an overview of LA al., 2017). Based on the main keywords identified (see Ta- issues in higher education by covering the antecedents, de- ble 1), we conducted our search in May 2024. The search cisions, and outcomes of the Antecedents, Decisions, and resulted in 634 entries. Outcomes (ADO) framework proposed by Paul & Benito The screening of the articles was carried out in two (2018), it only included literature published up to 2016. As stages, as applying the ADO framework to all 634 articles significant gaps remain, this study examines empirical and would have been too time-consuming. In the first stage, theoretical studies published after 2011 to understand the the titles and abstracts were examined to decide whether antecedents, decisions, and outcomes of LA in higher ed- the articles should be considered for further analysis. The ucation. In particular, the review focuses on teaching staff, articles that (1) did not focus on teachers’ perspective, (2) as the adoption of LA at the institutional level remains focused only on the specific use of LA – case study, (3) limited. Despite this, many teachers independently attempt were conceptual or review articles, and (4) focused on the to implement LA within their courses (Gedrimiene et al., design or evaluation of LA tools rather than their use were 96 Organizacija, Volume 59 Review Article Issue 1, February 2026 excluded from the list. In the second screening phase, the In the second stage, an in-depth qualitative synthesis of full text of 51 articles was screened, and the ADO frame- the included studies was conducted. This process involved work (Paul & Benito, 2018) was used to code articles in dividing the studies into ADO categories, analysing the terms of antecedents, decisions, and outcomes. In this pu- results within each ADO category and synthesising the re- rification phase, 16 papers were excluded as they focused sults of all studies (Petticrew & Roberts, 2006). more on design analytics or mainly reflected the perspec- To obtain a theoretically meaningful, robust classifica- tive of students or institutions. tion of antecedents, decisions, and outcomes of LA use, we Before the evaluation, the snowball technique was used used an inductive-deductive approach to content analysis to identify additional relevant studies by examining the (Tranfield et al., 2003). First, an inductive approach was reference sections of each selected paper. This approach applied to identify antecedents, decisions, and outcomes led to the inclusion of 4 more articles, bringing the total of LA use from teachers’ perspectives, reported in previous number of relevant articles used for further analysis to 39. studies. Then, a theory-based approach was employed to To understand LA antecedents, decisions, and out- synthesise them into meaningful constructs. comes in higher education from the teaching staff perspec- tive, we used a two-stage analysis (Tranfield et al., 2003) to obtain relevant information for our review: (1) general 3 Overview of included studies characteristics of the included studies, (2) teachers’ use of LA at the course/classroom level, (3) antecedents specif- First, a descriptive summary of the characteristics of ic to teachers’ LA use, (4) the outcomes of teachers’ LA the included studies is provided. This summary includes use. In the first phase, some of the data used for the de- aspects such as the date of publication, the regions ana- scriptive analysis were exported from the Scopus search lysed and the journals in which the studies were published. results, while the remaining data were collected manually. Table 1: Results following the adapted SPAR-4-SLR protocol Stage Sub-stage Criterion Action Domain Learning analytics in higher education: the teacher perspective. Identification Source type English peer-reviewed papers in academic journals. Source quality Scopus Search mechanism Scopus search engine Search period From 2011, when SoLAR was established. Acquisition “learning analytics” AND (“higher education” OR “tertiary educa- Search keywords tion” OR “University” OR “College” OR “Faculty”) AND (“educator” OR “teacher” OR “class” OR “course”) Total number of articles 634 First purifica- Articles excluded 583 tion Articles included 51 Organization Organization framework ADO Second purifi- Articles excluded 16 cation Articles included 35 Identification of addition- Articles included (snow- al relevant ball technique) 4 studies Analysis method descriptive and content Evaluation Agenda proposal method best practices, gaps Reporting Reporting conventions Summarization (visualisations) and discussion 97 Assessing Arranging Assembling Organizacija, Volume 59 Review Article Issue 1, February 2026 Figure 1 illustrates the distribution of publications by North America and Oceania (see Figure 2). Of the seven year. Up to three papers on LA adoption and use of teach- international studies, five collected data from South Amer- ing at the course level were published up to 2018, with no ica, North America and Oceania. At the national level, publications in 2014 and 2017. Since 2018, the number of most studies were conducted in Australia. Australia was publications has increased, reaching nine in 2020. There- also involved in international studies, with countries such after, however, the number of publications has declined. as Chile, Canada and the United States indicating that their The declining number of publications in recent years can researchers were actively collaborating with their coun- be attributed to the emerging trend towards big data, where terparts in the Americas. While several studies have been researchers have shifted their focus to individual projects conducted in Europe, particularly in the United Kingdom that use specific predictive analyses. and Spain, our research did not find comprehensive inter- Teachers’ perspectives on the adoption and use of LA national studies across multiple European countries. were mainly analysed in European countries, followed by Figure 1: Distribution of publications per year Figure 2: Investigated regions 98 Organizacija, Volume 59 Review Article Issue 1, February 2026 Figure 3: Articles distributed over journals Figure 4: Synthesis of findings on teachers’ use of LA 99 Organizacija, Volume 59 Review Article Issue 1, February 2026 The distribution of publication journals (see Figure 3) lic data, etc. There are several methods for collecting data indicates that the research in this area is spread across a throughout the course. The most convenient way is to use large number of journals. Journal of Learning Analytics, LMS features, such as digital fingerprint (connection time, British Journal of Educational Technology, Technology, access and use of resources provided by the teacher, par- Knowledge and Learning, and Australian Journal of Ed- ticipation in forums, …) (Cobo-Rendon et al., 2021; Cui et ucational Technology are somewhat more strongly repre- al., 2020; Deng et al., 2019), assessment results, and quiz sented in this area. 26 journals featured only one article. results (Cui et al., 2020; Deng et al., 2019; Dietz-Uhler Most of these 26 journals belong to the fields of education, & Hurn, 2013). More advanced approaches utilise Web information technology, and engineering. 2.0 tools, including social media and virtual environments (Cambruzzi et al., 2015), as well as modern devices such as 4 Synthesis of findings eye trackers and smartphones (Saar et al., 2017). Data can also be collected after the course has ended. Post-course data collection can include metrics such as resource utili- 4.1 Teachers’ use of learning analytics sation data, student evaluations, grades, pass rates, failure rates, and retention rates. This data provides valuable in- Existing studies investigate LA either from a solu- sight into the effectiveness of the course and the overall tion-oriented perspective, focusing on specific technol- student learning experience. However, several issues with ogies and their applications, or from a process-oriented data collection have been reported. Firstly, the data is often perspective, emphasising broader teaching and learning available but not readily accessible (Hilliger et al., 2020). practices. To summarise these findings, a theory-based ap- In addition, advanced methods of data collection require proach was employed to develop a model that links the expensive equipment, significant human resources, and process-oriented LA perspective with data sources and an- raise ethical concerns (Bellini et al., 2019; Godínez et al., alytical tools, providing a coherent structure to support the 2024; Thoma et al., 2020; van Leeuwen, 2019). pedagogical use of LA (see Figure 4). The use of various data collection tools generates di- LA implementation represents a complex, multi-stake- verse types of data, including structured, unstructured, holder endeavour aimed at supporting educators in ad- visual, non-visual, historical, and real-time data. The vol- dressing questions related to student learning (Tsai & ume of this diverse data continues to grow rapidly and Gašević, 2017). While institutional support is important, should ideally, but not necessarily, be accessible in one teachers remain central actors, as they directly apply LA place for analysis. While individual teachers with suffi- insights to improve teaching practices and learning out- cient knowledge and skills can use various methods and comes (Naujokaitienė et al., 2020). Accordingly, several tools to extract data from different sources, such as student frameworks have been proposed to assist teachers in struc- data, LMS, assessment tools, etc., and load it into a single turing course objectives and planning learning activities repository (Alachiotis et al., 2019), this task is best per- supported by LA. However, as noted by Kaliisa, Kluge, formed at the institutional level. At this level, it is essential et al. (2022), existing frameworks reflect diverse learning not only to establish infrastructure for big data but also theories and pedagogical assumptions, suggesting that to implement comprehensive data governance policies LA can serve different pedagogical purposes to varying (Godínez et al., 2024; Thoma et al., 2020). This approach extents. Some frameworks focus on supporting teachers provides a more comprehensive and coherent view of the in collecting, representing, analysing, interpreting, and data. Before various analyses can be carried out, datasets acting upon LA outputs (Arthars et al., 2019; Bakharia et must first be cleaned and prepared from their raw state. al., 2016; Wise & Jung, 2019), while others emphasise the Microsoft Excel and Google Sheets are the most accessible mapping of learner characteristics and course-related data tools, with a handful of languages available for those with derived from learning management systems (LMS) and programming skills, Python being considered the most other sources (Gunn et al., 2017). Taken together, these suitable (Slater et al., 2017). Once the data is properly pre- perspectives highlight the need for an integrated and flexi- pared, the next step is to perform the various analyses. For ble framework that supports pedagogical decision-making the basic descriptive and diagnostic analyses, the reporting while accommodating different teaching contexts and uses functions in LMS and other educational tools can be used. of learning analytics. For creating advanced reports, various LMS plugins or By properly structuring the course objectives and ac- other tools such as Microsoft Excel and Power BI can be tivities, teachers can determine in advance what data about used to create a dashboard that visualises the frequency of the students is useful to them and when they want to col- login, clickstream pattern, time spent in an online learning lect it (van Leeuwen, 2019). Some data can be collected environment, and students’ assessment scores and ranking before the course begins, such as demographic data, cohort compared to their peers (Deng et al., 2019). However, to size, etc. This data can be collected from student infor- fully understand how students interact with the course, mation systems, learning platforms, questionnaires, pub- their engagement, motivation, performance, and weak- 100 Organizacija, Volume 59 Review Article Issue 1, February 2026 nesses, specialised tools are required. These tools include Godínez et al., 2024; Herodotou et al., 2021; Hilliger et al., those for text mining and social network analysis that can 2020; Kaliisa, Mørch, et al., 2022; Muljana & Luo, 2021; process specific types of data (Kaliisa, Mørch, et al., 2022; Muñoz-Merino et al., 2023; Olney et al., 2021; Thoma et Muñoz-Merino et al., 2023; Nguyen et al., 2020). The in- al., 2020; West et al., 2016) identifies several antecedents formation obtained from the descriptive analysis serves as that influence the extent to which LA tools are adopted and the basis for the predictive analysis (Cobos & Ruiz-Gar- used effectively in educational institutions. cia, 2021). Tools such as RapidMiner, Orange, and Weka Several prerequisites are often mentioned in the litera- provide a wide range of algorithms (e.g., artificial neural ture to facilitate the effective use of LA. These include ap- networks, random forest, gradient boosting, decision tree) propriate policies regulating data access, the availability of and modelling frameworks to model student performance suitable data analytic tools, training on the use of LA, and and predict the likelihood of course dropout or failure. Pre- sufficient information on the use of LA. Distance learning scriptive analytics then builds on predictive analytics, pro- facilitates the collection of sensitive data and the profiling viding various possibilities for interventions in the student of students to predict and identify learning behaviour. The learning process. Open University (OU) has been instrumental in addressing Using interactive visual interfaces and dashboards, these concerns by developing the first policy on the ethical teachers can monitor students’ progress and engagement use of LA in 2014, which has influenced global standards (Cambruzzi et al., 2015; Cohen, 2018) as well as antici- (Bart et al., 2020). Despite this progress, teachers and stu- pate potential performance issues. By knowing the course dents are often unaware of the specifics of data collection activities, they can make appropriate interventions and and consent processes, emphasising the need for greater improvements (Naujokaitienė et al., 2020). For informed transparency and data governance. Ethical principles, such and timely interventions, teachers can use frameworks as transparency, student control over data, security, and such as the Analytics4Action Framework (Rienties et al., accountability, are essential for ensuring the protection of 2016), which guides the selection of appropriate interven- user data (Godínez et al., 2024). The growing volume of tions. Alternatively, instead of relying solely on existing educational data requires a balance between the use of data frameworks, teachers can leverage prescriptive analytics and the protection of student privacy (Bellini et al., 2019). to determine an optimal course of action (Susnjak, 2024). This balance requires teachers to be well-informed about Integrating these approaches allows for continuous mon- their responsibilities and the legal implications of using itoring, iterative course improvement, and proactive sup- student data. Therefore, institutions must foster a culture port, ultimately enhancing both teaching effectiveness and of privacy and data literacy to effectively address these student learning experiences. challenges (West et al., 2016). LA could serve as a crucial reflective tool to improve Many studies emphasise the potential of LA tools, teaching practise and enable adaptive rather than reactive but their successful use depends heavily on the support teaching approaches (Kaliisa, Mørch, et al., 2022). Ac- of teachers (Godínez et al., 2024). Teachers are the main cording to Wise & Jung (2019), LA can not only lead to actors who access and interpret LA data, draw conclusions simple course adjustments, but teachers can also reflect on about student performance, support them and improve their teaching practises and course design, which affects curriculum design. However, the transition to deep and the continuity of the LA cycle. personalised learning requires major changes in the educa- tional infrastructure (de Freitas et al., 2015). A responsive 4.2 Antecedent specific to the teacher’s and flexible infrastructure is needed to support dynamic LA use and adaptable learning contexts, as Gibson (2012) sug- gests. In addition, teachers need access to appropriate tools and resources to effectively integrate LA into their practise Teachers are increasingly recognising the value of (Bart et al., 2020; Dollinger et al., 2019; Mahmoud et al., LA as they discover its potential to improve educational 2022; Muljana & Luo, 2021). Despite technological ad- outcomes. After initial scepticism and unfamiliarity, LA vancements, many universities continue to face challenges tools are now gaining traction due to case studies (Alachi- in integrating data to gain a comprehensive understand- otis et al., 2019; De Laet et al., 2020; Deng et al., 2019; ing of educational processes and service quality. This em- Mahmoud et al., 2020; Qazdar et al., 2023) and other re- phasises the need for institutions to become more flexible search highlighting their positive effects (Bamiah et al., and build partnerships with different stakeholders that will 2018; Cobos & Ruiz-Garcia, 2021; Kaliisa, Mørch, et al., help them implement LA (de Freitas et al., 2015). 2022; Naujokaitienė et al., 2020; West et al., 2016), which LA in education faces major challenges due to the emphasise their positive effects. The literature (Bamiah shortage of qualified staff and the high cost of recruit- et al., 2018; Bart et al., 2020; Bellini et al., 2019; Cam- ing professionals (Bamiah et al., 2018). Teachers, as key bruzzi et al., 2015; de Freitas et al., 2015; De Laet et al., stakeholders in the effective use of LA, need professional 2020; Dietz-Uhler & Hurn, 2013; Dollinger et al., 2019; development to improve their data literacy and technical 101 Organizacija, Volume 59 Review Article Issue 1, February 2026 skills. Studies have shown that teachers who do not en- into their classroom practise. Support from colleagues and gage in continuous professional development often strug- administrators can have a significant impact on whether gle to interpret and act on data insights (Bart et al., 2020). teachers adopt institutional educational tools. When teach- For example, even after initial training, teachers reported ers feel supported and encouraged by colleagues and su- difficulties in understanding the functions of dashboards pervisors, they are more willing to experiment with and and interpreting data (Herodotou et al., 2021). To address adopt new technologies, including LA tools (Bart et al., these issues, institutions need to invest in continuous and 2020; De Laet et al., 2020; Kaliisa, Mørch, et al., 2022; practical training programmes. These programmes should Muljana & Luo, 2021). The organisational culture of a include case studies and good LA practises to improve university can influence the willingness of teachers to use teacher understanding and promote usage (Muñoz-Meri- LA. In environments that foster innovation, encourage no et al., 2023; West et al., 2016). In addition, informal the use of technology, and maintain a positive attitude to- professional development opportunities such as online wards change, teachers are more inclined to use LA tools courses and social media groups can provide teachers with (Mahmoud et al., 2022). Additionally, participation in pro- flexible learning opportunities to improve their LA skills fessional learning communities, where teachers can share (Muljana & Luo, 2021; West et al., 2018). their experiences, challenges, and successes with LA, can A well-developed infrastructure for LA, clear guide- have a positive impact on the adoption of these tools. Pro- lines for data management and targeted professional devel- fessional learning communities provide a support network opment seem to be the most important prerequisites for the for collaborative problem solving and make the use of LA effective use of LA. However, these are not the only pre- less challenging (Muljana & Luo, 2021; West et al., 2018). requisites, as the existing literature also emphasises other The perceived risks have a significant impact on teach- factors such as performance expectancy, effort expectancy, ers’ acceptance and use of LA in education. These risks social effect, and perceived risks. A recent study emphasis- include concerns about privacy and security as well as the es that teachers who find LA useful for their teaching are potential misuse of student data (Bamiah et al., 2018; Di- more likely to engage with these technologies and use them etz-Uhler & Hurn, 2013; Dollinger et al., 2019; Godínez effectively (Herodotou et al., 2021). For example, Masiel- et al., 2024). Research shows that teachers are wary of the lo et al. (2024) highlight that LA dashboards are designed ethical implications and negative consequences of using to improve teachers’ pedagogical decisions by providing LA tools, which can affect their willingness to integrate actionable insights based on student data. When teachers these technologies into their teaching practise (Bart et al., understand and expect these tools to have a positive im- 2020). In addition, issues such as the accuracy and reliabil- pact on their learning outcomes, their willingness to adopt ity of analytics-based findings also contribute to teachers’ and integrate LA into their practise increases. Similarly, scepticism and hesitation (Kaliisa, Mørch, et al., 2022). research by Mohseni et al. (2023) shows that co-designing To summarise, several models are particularly relevant user-friendly dashboards tailored to teachers’ needs can in- to the study of LA adoption (Cobo-Rendon et al., 2021; crease their expected performance. By involving teachers Herodotou et al., 2021; Kaliisa, Mørch, et al., 2022; Mul- in the design process, dashboards are more likely to meet jana & Luo, 2021; Olney et al., 2021). These models are their practical needs, increasing their confidence in using primarily grounded on the Theory of Diffusion of Inno- LA tools to support their teaching. vations and examine the role of innovation, communica- If teachers feel that using LA tools requires minimal tion, social systems and time in the context of adoption effort, they are more likely to use them. Mohseni et al. (Rogers, 1995). In contrast, the Technology Acceptance (2023) emphasise that the design and usability of LA dash- Model (TAM) (Davis, 1989) and the Universal Technolo- boards play a key role in shaping teachers’ effort expectan- gy Adoption and Use Theory (UTAUT) (Venkatesh et al., cy. Their study shows that teachers are more likely to use 2003) focus on individual characteristics that influence the LA tools when dashboards are intuitive and user-friendly. adoption of specific technologies, such as perceptions, be- Similarly, Herodotou et al. (2021) indicated that the per- liefs, and attitudes. In addition, the Concerns-Based Model ceived ease of use of LA tools has a direct impact on teach- of Acceptance (CBAM) addresses faculty concerns in the ers’ engagement with these tools. If teachers perceive LA context of P-20 education (Hall, 1979), while the Academ- tools as intuitive and easy, they are more likely to integrate ic Resistance Model emphasises the critical importance them consistently into their lessons. Additionally, teachers’ of examining staff responses to change over time and un- attitudes towards technology and their previous experi- derstanding their reactions to change initiatives that occur ence with digital tools also influence their expected effort. from the bottom up (Piderit, 2000). Those who are more comfortable with technology tend to perceive LA tools as less intimidating and easier to use. A supportive environment, organisational culture, and participation in professional learning communities promote teachers’ willingness and ability to integrate LA 102 Organizacija, Volume 59 Review Article Issue 1, February 2026 4.3 Outcomes of teachers’ LA use 5 Discussion From a teacher’s perspective, LA can change teaching This literature review reveals a decline in publications methods, course recommendations, and improve the over- dealing with the use of LA by teachers. This decline appears all educational experience through data-driven insights to be related to the advent of big data, as recent publica- and personalised learning paths. A key benefit is the ability tions have shifted to advanced LA projects. These projects to tailor courses to the individual learner needs, increas- often utilise machine learning and deep learning models to ing engagement and performance (Alachiotis et al., 2019; predict key academic outcomes beyond the course level. Cobos & Ruiz-Garcia, 2021; Ifenthaler et al., 2018; Q. In these contexts, teachers are usually only mentioned as Nguyen et al., 2018; West et al., 2018). For example, by stakeholders and primarily as users of pre-built dashboards analysing learners’ past behaviour and performance pat- that help identify specific learning issues and predict out- terns, teachers can predict future performance and identify comes (Herodotou et al., 2021). Teachers’ perspectives on at-risk learners and thus implement timely interventions the use of LA have been studied in Europe, North America, to reduce dropout rates (Bart et al., 2020; Dietz-Uhler & and Oceania, with Australia playing a significant role in Hurn, 2013; Kaliisa, Mørch, et al., 2022; West et al., 2016). promoting international research collaboration and con- Teachers also use LA to monitor absences in real time tributing notable findings. Therefore, institutions and re- and improve attendance, as demonstrated by the use of searchers in the field of LA should seek opportunities for dashboards and big data analytics at Purdue University international research collaboration to promote innovation (Bamiah et al., 2018). Additionally, teachers at the Univer- and improve educational practise globally (Kurniati et al., sity of Wollongong employ visual analytics to understand 2022). By pooling resources and knowledge, internation- student interactions in discussion forums, which in turn al partnerships can advance the development and appli- enables them to tailor teaching strategies to student needs cation of LA in different educational contexts, enriching (Daniel & Butson, 2013). This holistic data use supports the overall understanding and effectiveness of LA tools. In more informed decisions regarding course content and addition, researchers should continue to draw on knowl- teaching methods, fostering an environment that promotes edge, methods and perspectives from various disciplines to active learning and student retention (West et al., 2016). It deepen the understanding of LA. This approach can help also reduces the time spent identifying students who need address complex educational challenges by integrating dif- support and engaging in follow-up communication (Hon- ferent perspectives and methods (Ouhaichi et al., 2023). son et al., 2024). The content analysis revealed that most of the articles Various institutions have started to use LA for specif- in our sample used a descriptive research approach. For ic purposes. For example, Northern Arizona University example, Dollinger et al. (2019) show the use of the LA uses the Performance Grading System (GPS) to identify tool over six years at several Australian universities, and students at risk of dropping out, while Austin Peay State Cobos & Ruiz-Garcia (2021) present the effectiveness of University’s Degree Compass system predicts student per- weekly feedback on learner interaction and performance formance and makes study recommendations. Similarly, in a learning system. Another dominant approach is the- Purdue University uses Course Signals to examine behav- ory-based research. Different theories explain different iour and identify at-risk students (Bamiah et al., 2018). aspects of LA, such as the LA cycle (Arthars et al., 2019; These implementations demonstrate the potential of LA to Wise & Jung, 2019) and the acceptance and use of tech- improve teaching effectiveness and ultimately contribute nology (Cobos & Ruiz-Garcia, 2021; Herodotou et al., to better educational outcomes (Deng et al., 2019). 2021; Muljana & Luo, 2021; Olney et al., 2021). Although Overall, implementing LA at the course level lays the theoretical developments in this area are evident, inter- foundation for continuous improvement across the insti- pretative methods of data collection (e.g., interviews, fo- tution. By using data to enhance personalisation, early in- cus groups) are most commonly used (Bart et al., 2020; tervention, instructional quality, resource allocation, and Godínez et al, 2024; Honson et al., 2024; Kaliisa, Mørch, support services, institutions can achieve better student et al., 2022; Naujokaitienė et al., 2020), followed by ex- outcomes, higher retention and graduation rates, and an periment (Cagliero et al., 2019; Cambruzzi et al., 2015; overall improved performance and reputation (Tsai et al., Deng et al., 2019) and survey (Cobo-Rendon et al., 2021; 2020). This approach also reduces the time and effort re- West et al., 2018). Only a few studies used mixed meth- quired to identify students in need of support and to coor- ods for data collection (De Laet et al., 2020; Herodotou et dinate appropriate interventions, allowing staff to focus on al., 2021; Hilliger et al., 2020), highlighting the need for proactive, data-informed decision-making across courses more frequent use of approaches that combine qualitative and programs. Moreover, it supports the adaptation of cur- and quantitative methods to gain a deeper understanding of ricula to evolving industry demands driven by digital tech- complex learning environments. nologies, fostering more agile and responsive higher edu- To systematically summarise the solution-oriented and cation institutions (Honson et al., 2024; West et al., 2018). process-oriented findings, an LA cycle was adapted from 103 Organizacija, Volume 59 Review Article Issue 1, February 2026 the existing literature (Arthars et al., 2019; Drachsler & to effectively enhance student achievement and inform Kalz, 2016; Ndukwe & Daniel, 2020). The first step in this curriculum design (Godínez et al., 2024; Herodotou et al., cycle is the structuring of course objectives and the plan- 2021). The successful integration of LA requires a respon- ning of activities for the use of LA tools, for which various sive educational infrastructure and a supportive organisa- frameworks have been developed to support teachers. De- tional culture that encourages innovation and the use of spite the variety of frameworks available, more compre- technology (Mahmoud et al., 2022; West et al., 2018). In hensive guidelines on which framework or combination of addition, concerns about privacy, security, and the accu- frameworks to use are needed, along with clear instruc- racy and reliability of LA findings affect teachers’ accept- tions on specific actions to take and their timing (Kaliisa, ance and use of these tools (Dollinger et al., 2019; Godínez Kluge, et al., 2022). Properly structuring course objectives et al., 2024; Kaliisa, Mørch, et al., 2022). To utilise LA and activities enables teachers to determine useful data effectively, institutions need to invest in robust infrastruc- and optimal collection times, which is the second step in ture, clear data management guidelines, and targeted pro- this cycle. During this process, ethics must be prioritised fessional development that addresses both technical and (Bamiah et al., 2018; Bellini et al., 2019; Dollinger et al., ethical aspects of LA use (Bamiah et al, 2018; Bart et al, 2019). The various data collected should ideally be organ- 2020; Mahmoud et al, 2022; West et al, 2018). Overall, ised and managed in a way that meets the needs and in- the inclusion of models such as TAM, UTAUT, and edu- terests of the teachers. Although experienced teachers can cation-focused adoption models such as CBAM can help perform this task, it is more effective when implemented to promote wider and more effective adoption of LA in at an organisational level (Godínez et al., 2024; Thoma et educational institutions by exploring the decision points of al., 2020). Although not mandatory, this step provides a individuals at the interface between the organisation and better overview of the available data, facilitating subse- the technological innovation. As predominantly qualitative quent data manipulation and analysis. Before conducting methodological approaches were utilised when building analyses, data sets must be cleaned and prepared using on the theoretical lenses of these models to gain a more accessible tools such as Microsoft Excel or programming comprehensive understanding, future research should aim languages like Python (Slater et al., 2017). Basic descrip- to collect quantitative data from a large cohort of teachers tive and diagnostic analyses can be performed using the to explore how factors such as age, gender, experience, LMS reporting features, while more advanced reports and and social influence affect adoption. dashboards can be created using plug-ins and tools such as LA tools are gaining acceptance due to case studies Microsoft Excel and Power BI (Deng et al., 2019). Predic- and research highlighting their positive effects (Alachio- tive and prescriptive analytics, on the other hand, require tis et al., 2019; Bamiah et al., 2018; Cobos & Ruiz-Gar- more specialised tools and advanced knowledge to make cia, 2021; De Laet et al., 2020; Deng et al., 2019; Kaliisa, predictions and informed interventions. The easiest way Mørch, et al., 2022; Mahmoud et al., 2020; Naujokaitienė for teachers to use predictive and prescriptive analytics is et al., 2020; Qazdar et al., 2023; West et al., 2016). Regard- through interactive dashboards provided by their institu- less, there is a need for more case studies that demonstrate tions. However, the availability of these tools is limited, clear performance outcomes. These case studies should and many teachers indicated that they are unsure how to highlight successful implementations of LA and provide use them effectively (Herodotou et al., 2021). Alternative- well-documented approaches and practical insights that ly, teachers with limited LA experience can use frame- can facilitate the adoption of LA at individual and insti- works to help select the most appropriate interventions tutional levels. To obtain generalisable and transferable (Kaliisa, Kluge, et al., 2022). Regardless of the method, results, future LA research must also place a stronger teachers need to systematically engage with LA to make methodological focus on large-scale, longitudinal, and informed decisions during the course and to reflect on and experimental research designs. Future studies should also continuously improve their teaching practises. As such, explore the development of teacher-centred dashboards LA can serve as an important tool for proactive pedagog- and the long-term effects of LA on student learning and re- ical approaches. tention. Moreover, research examining cross-cultural and Teachers are increasingly recognising the value of LA international contexts could reveal how local practices and in significantly improving educational outcomes. Howev- organisational cultures shape the adoption and use of LA. er, there are several factors that influence the adoption of The research gaps identified in this review offer multiple LA tools (Bamiah et al., 2018; Bart et al., 2020; Bellini opportunities for further research. et al., 2019; De Laet et al., 2020; Dollinger et al., 2019; Godínez et al., 2024; Herodotou et al., 2021; Hilliger et al., 2020; Muljana & Luo, 2021; Muñoz-Merino et al., 6 Conclusion and outlook 2023; Olney et al., 2021; Thoma et al., 2020). As primary interpreters of LA data, teachers need access to suitable Building an integrated LA system that effectively tools, resources, and ongoing professional development meets the needs of higher education institutions is a vision 104 Organizacija, Volume 59 Review Article Issue 1, February 2026 shared by many education stakeholders around the world. 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Newly Proposed Student Performance In- Tsai, Y.-S., & Gasevic, D. (2017). Learning analytics in dicators Based on Learning Analytics for Continuous higher education --- challenges and policies: A re- Monitoring in Learning Management Systems. Inter- view of eight learning analytics policies. Proceed- 108 Organizacija, Volume 59 Review Article Issue 1, February 2026 ings of the Seventh International Learning Analyt- sian Journal of Educational Technology, 34(3), Article ics & Knowledge Conference, 233–242. https://doi. 3. https://doi.org/10.14742/ajet.3836 org/10.1145/3027385.3027400 Wise, A. F., & Jung, Y. (2019). Teaching with Analyt- Tsai, Y.-S., Rates, D., Moreno-Marcos, P. M., Muñoz-Me- ics: Towards a Situated Model of Instructional Deci- rino, P. J., Jivet, I., Scheffel, M., Drachsler, H., Delga- sion-Making. Journal of Learning Analytics, 6(2), Ar- do Kloos, C., & Gašević, D. (2020). Learning analytics ticle 2. https://doi.org/10.18608/jla.2019.62.4 in European higher education—Trends and barriers. Ye, D. (2022). The History and Development of Learning Computers & Education, 155, 103933. https://doi. Analytics in Learning, Design, & Technology Field. org/10.1016/j.compedu.2020.103933 TechTrends, 66(4), 607–615. https://doi.org/10.1007/ van Leeuwen, A. (2019). Teachers’ perceptions of the usa- s11528-022-00720-1 bility of learning analytics reports in a flipped univer- sity course: When and how does information become Marjeta Marolt is an associate professor at the actionable knowledge? Educational Technology Re- Department of Informatics, Faculty of Organizational search and Development, 67(5), 1043–1064. https:// Sciences, University of Maribor, Slovenia. Her research doi.org/10.1007/s11423-018-09639-y interests include business model innovation, digital Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. transformation, social media, customer relationship (2003). User Acceptance of Information Technology: management, business intelligence, and learning Toward a Unified View. MIS Quarterly, 27(3), 425– analytics. 478. https://doi.org/10.2307/30036540 West, D., Huijser, H., Heath, D., Lizzio, A., Toohey, D., Miles, C., Searle, B., & Bronnimann, J. (2016). Higher Danijela Jakšić is an associate professor at the Faculty Education Teachers’ Experiences with Learning Ana- of Informatics and Digital Technologies, University of lytics in Relation to Student Retention. Australasian Rijeka, Croatia. Her research and professional interests Journal of Educational Technology, 32(5), Article 5. include database design and information systems https://doi.org/10.14742/ajet.3435 development, data analytics and business intelligence, West, D., Tasir, Z., Luzeckyj, A., Na, K. S., Toohey, D., the application of information and communication Abdullah, Z., Searle, B., Jumaat, N. F., & Price, R. technologies in education, as well as the use of digital (2018). Learning analytics experience among academ- marketing and social media in education. ics in Australia and Malaysia: A comparison. Australa- Pregled raziskav o vključevanju visokošolskih učiteljev v učno analitiko Ozadje in namen: Sistematični pregled literature se osredotoča na uporabo učne analitike med visokošolskimi uči- telji, ki imajo ključno vlogo pri zbiranju, analizi in interpretaciji podatkov. Za razumevanje vloge učiteljev v učni anali- tiki ter dejavnikov in učinkov njene uporabe so analizirane empirične raziskave iz obdobja med letoma 2011 in 2024. Metode: Z namenom zmanjšanja raziskovalne pristranskosti in zagotavljanja ponovljivosti je bil izveden sistematični pregled literature. Identificirani relevantni članki so bili analizirani v dveh fazah, najprej z opisno analizo, nato pa s poglobljeno kvalitativno sintezo. Rezultati: Pregled literature razkriva dva prevladujoča trenda v načinu uporabe učne analitike med visokošolski- mi učitelji. Prvi se osredotoča na uporabo tehnologij učne analitike za reševanje konkretnih problemov, drugi pa obravnava učno analitiko v okviru širših pedagoških praks poučevanja in učenja. Prispevek poleg tega obravnava dejavnike in učinke uporabe učne analitike med visokošolskimi učitelji ter izpostavlja vrzeli v obstoječih raziskavah in predlaga nadaljnje raziskovalne smernice na tem področju. Zaključek: Prispevek ponuja pregled novejše literature na področju uporabe učne analitike med visokošolskimi učitelji. Ugotovitve pojasnjujejo vlogo učiteljev pri uporabi učne analitike ter podajajo vpogled v dejavnike in učinke njene uporabe, ki so relevantni tudi za druge deležnike in odločevalce v visokem šolstvu. 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Organizacija is covered by the following services: Cabell’s Directory, CEJSH (The Central European Journal of Social Sciences and Humanities), Celdes, Clarivate Analytics - Emerging Sources Citation Index (ESCI), CNPIEC, Die Elektronische Zeitschriftenbibliothek, DOAJ, EBSCO - TOC Premier, EBSCO Discovery Service, ECONIS, Ergonomics Abstracts, ERIH PLUS, Google Scholar, Inspec, International Abstracts in Operations Research, J-Gate, Microsoft Academic Search, Naviga (Softweco), Primo Central (ExLibris), ProQuest - Advanced Polymers Abstracts, ProQuest - Aluminium Industry Abstracts, ProQuest - Ceramic Abstracts/World Ceramics Abstracts, ProQuest - Composites Industry Abstracts, ProQuest - Computer and Information Systems Abstracts, ProQuest - Corrosion Abstracts, ProQuest - Electronics and Communications Abstracts, ProQuest - Engineered Materials Abstracts, ProQuest - Mechanical & Transportation Engineering Abstracts, ProQuest - METADEX (Metals Abstracts), ProQuest - Sociological Abstracts, ProQuest - Solid State and Superconductivity Abstracts, Research Papers in Economics (RePEc), SCOPUS, Summon (Serials Solutions/ProQuest), TDOne (TDNet), TEMA Technik und Management, WorldCat (OCLC) CONTENTS - 1/2026 Ivan MATIĆ, Ana JURAS, Marina LOVRINČEVIĆ 3 How Academic Context Shapes Students’ Ethical Behaviour: New Evidence from a Transitional Society Dušan GOŠNIK, Mateja JERMAN 22 Analysis of Key Impact Factors in New Methods Implementation in Organisations: A Change Management Perspective Slavka SILBERG, Luděk STEHLÍK, Tomáš SOLLÁR, Michal SILBERG, Jana MATOŠKOVÁ 34 Relationships between Personal Characteristics, Job Satisfaction and Organisational Behaviour of Work Team Members and the Role of Organisational Agility Maturity Agne SNEIDERIENE, Marius ZAMKAUSKAS 55 Identifying Marketing Tools to Promote the Development of the Knowledge Economy: The Case of Lithuania Oleksandr DOROKHOV, Kadri UKRAINSKI, Hanna KANEP, Liudmyla DOROKHOVA 71 Development of a Methodology Based on Fuzzy Logic for Solving the Problem of Evaluating a Startup Team Under Uncertainty Marjeta MAROLT, Danijela JAKŠIĆ 95 Overview of Research on Higher Education Teachers’ Involvement in Learning Analytics