Boris Bukovec, Faculty of Organisation Studies in Novo mesto, Slovenia Alois Paulin, Technical University Vienna, Austria Juraj Marušiak, Slovak Academy of Science, Slovakia Mario Ianniello, Udine University, Italy Anisoara Popa, Danubius University, Romania Raluca Viman-Miller, University of North Georgia, Georgia, USA Anna Kołomycew, Rzeszów University, Poland Jurgita Mikolaityte, Siauliai University, Lithuania Patricia Kaplanova, Faculty of Organisation Studies in Novo mesto, Slovenia Laura Davidel, Univeristy of Lorraine, France Ana Železnik, Ljubljana University, Slovenia Marko Vulić, Information Technology School - ITS ComTrade, Serbia Vita Jukneviciene, Siauliai University, Lithuania Mitja Durnik, Ljubljana University, Slovenia Anca-Olga Andronic - Spiru Haret University, Romunija Razvan-Lucian Andronic - Spiru Haret University, Romunija Tine Bertoncel - Faculty of Organisation Studies in Novo mesto, Slovenia   © C O P Y R I G H T F A K U L T E T A Z A O R G A N I Z A C I J S K E Š T U D I J E V N O V E M M E S T U . F A C U L T Y O F O R G A N I S A T I O N S T U D I E S . V S E P R A V I C E Z A D R Ž A N E . A L L R I G H T S R E S E R V E D . A N N M A R I E G O R E N C Z O R A N UREDNIŠKI ODBOR / EDITORIAL BOARD Naslov uredništva / Editorial address: Fakulteta za organizacijske študije v Novem mestu Ulica talcev 3 8000 Novo mesto, Slovenija ISSN 2463-9281 GLAVNI IN ODGOVORNI UREDNIK / EDITOR IN CHIEF Izid publikacije je finančno podprla ARRS iz naslova razpisa za sofinanciranje domačih znanstvenih periodičnih publikacij. The journal is subsidised by the Slovenian Research Agency. Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 61–84. * Korespondenčni avtor / Correspondence author 61 Prejeto: 8. marec 2021; revidirano: 30. april 2021; sprejeto: 24. maj 2021. / Received: 8th March 2020; revised: 30th April 2021; accepted: 24th May 2021. DOI: 10.37886/ip.2021.020 Intergenerational Cooperation, Learning and Knowledge-Sharing in the Workplace Danijela Brečko* MLC Ljubljana - Faculty for Management and Law, Tržaška 207, 1000 Ljubljana, Slovenia e-mail: danijela.brecko@sofos.si Abstract Background and Originality: The article focuses on organizations that face the challenge of es- tablishing a working environment adapted to the characteristics of different generations of em- ployees. Each of the generations in the workplace must be motivated to work, cooperate and share knowledge among co-workers of different ages. Many research studies have been done on motiva- tion in the workplace, but we have not found the research on the impact of different learning forms to motivate different generations to cooperate and exchange knowledge at the workplace, either on a Slovenian or global scale. Method: In this study, we examine the following two issues: If different approaches are needed to motivate different generations of employees to cooperate, and whether different generations differ in the desired ways of acquiring and sharing knowledge, using Piktialis and Greenes (2008) cate- gorization of learning and knowledge-sharing forms at work. In the critical assessment of motiva- tion for intergenerational cooperation and knowledge-sharing, we used a quantitative research method. The survey was conducted on a random sample among employees in a selected organiza- tion with 2,000 staff, with 334 responding to the survey. Results: The results showed that for Generation Z it is least important that they to share their knowledge and work experience with colleagues from other generations and that the younger gen- erations (Y and Z) are less suited to knowledge-sharing through storytelling (examples from prac- tice, comparisons, summarizing experiences) and summaries of key knowledge (from conversa- tions, interviews, conferences). Society: The concept of intergenerational cooperation in the workplace includes knowledge- sharing among staff, as well as a shift from knowledge-sharing to co-creating knowledge. We be- lieve this shift is of key importance for further development of human capital as well as knowledge accumulation in the organization. Therefore, co-creating knowledge should represent the future ambitions of every organization and research's communities. Limitations / further research: Research limitations and suggestions for further research. A selective sample should be taken into account as this research only included one organization and individuals from this particular organization that were motivated to participate. The small sample of generation Z should also be mentioned. The main limitation of this research was the failure to consider an individual's characteristic, organizational climate and communication pattern among different departments. At the same time, the focal organization operates in different geo- graphical locations, as this can play an important role in intergenerational cooperation and knowledge-sharing. Herefore, each organization needs to determine the preferred form of knowledge-sharing in each specific environment and choose a form that suits both the employees who provide information and those who receive it. This is also an area of further research, thus the influence of organizational climate and culture on the process of intergenerational cooperation and knowledge exchange. Keywords: generations at work, intergenerational cooperation, forms of intergenerational learning, knowledge-sharing, transfer knowledge at work. Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 61–84. 61 1 Introduction The presence of employees of different ages in the workplace is an important source of organ- ization success in terms of mutual cooperation and knowledge-sharing. However, both the organization and the employees must recognize this resource as an opportunity and not as an obstacle. Therefore, the organization and employees need to understand and respect diversity and difference according to age and generational affiliation. Only in this way can each em- ployee realize their potential and contribute to the entire organization's success. Organizations are thus forced to engage in a number of activities with age-diverse employees. They must rationally and effectively integrate the needs and abilities of employees of differ- ent ages into managing their work and increasing the work efficiency of both older and younger employees, which is inextricably linked to the constant need to spread learning and exchange knowledge. Although learning in organizations mainly takes place as individual learning, it occurs with the mutual influences and connections that employees have with each other (Rozman & Kovač, 2012). This means that employees learn individually, but not in iso- lation, and must be connected with other employees in the firm. In this way, knowledge is transferred throughout the organization, leading to greater efficiency, creativity and innova- tion. An individual employee represents the smallest link in organizations, and relations among people are formed during work processes. Therefore, work processes in organizations can only be effective if collaborative relationships are established among employees. Knowledge is an important component of modern organizations; therefore, it is especially important to establish cooperation and connections among employees of different ages and encourage learning and knowledge-sharing. It is thus necessary to create an environment that will be adapted to different generations' characteristics so that each of the generations in the workplace can be motivated to work and participate in achieving the firm's goals. Many or- ganizations are looking for ways to improve this process, with various measures designed depending on which age group or generation of employees they are intended for, as some can be intended for all employees and some only for a certain generation. As Bjursell (2019, p. 217) notes, "Although the ability to learn remains throughout one's life, one may change how one participates in education or educational activities." The article focuses on the issue of organizations that face the challenge of establishing a working environment that will be adapted to the characteristics of different generations of employees. Each of the generations in the workplace must be motivated to work, cooperate and share knowledge among co-workers of different ages. In this study, we examine the following two issues: If different approaches are needed to mo- tivate different generations of employees to cooperate, and whether different generations dif- fer in the desired ways of acquiring and sharing knowledge, using Piktialis and Greenes (2008) categorization of learning and knowledge-sharing forms at work. Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 61–84. 62 The concept of intergenerational cooperation in the workplace includes knowledge-sharing among staff, as well as a shift from knowledge-sharing to co-creating knowledge. We believe this shift is of key importance for further development of human capital as well as knowledge accumulation in the organization. Therefore, the area of co-creating knowledge should repre- sent the future ambitions of every organization and research communities. 2 Theoretical Framework It is necessary to define the concept of a generation and that of age in the work environment. From the point of view of the present article, age is the umbrella construct, which includes all changes related to age that an individual experiences during aging. Age-related changes do not occur equally in all people, and there are considerable differences between individuals. Some 50-year-old employees feel young and able to work, while others at the same age feel exhausted and unmotivated to continue in their careers. Therefore, the chronological age of the individual is insufficient to explain the differences in work motivation and employee behaviour. The individual's motivation for work is influenced not so much by the individual's perception of their chronological age, but by the perception of their future: in terms of whether they see this in the organization and are thus open to new work challenges. As people age, they go through various changes during their working lives, such as changes in personality, needs, impulses, intelligence, physical abilities, (working) memory, work experience, emotional reg- ulation and social perceptions (Bal, Kooij, & Rousseau, 2018, p. 13). It is impossible to find an unambiguous answer to how old an individual employee should be to be considered an "older employee" in theory and practice. In the literature, it is possible to recognize the con- sensus that employees in the age group of 50 or 55 fall into this category (Bal, Kooij, & Rousseau, 2018, p. 17). On the other hand, generations are groups of individuals born in a particular historical period, in a particular area, and share important life historical events of major social dimensions that are most representatives of the generation directly confronted with during their personal de- velopment. Generations in society change approximately every twenty years or so, where some deviations from the years of birth emerge from the literature (Dolot, 2018, p. 44, Speer, 2011, p. 15). Each generation is divided into three to seven annual subgroups, based on the first wave, core, and last wave (Tolbize, 2008, p. 1). Due to the importance of the issue – the impact on the economy, the labour market, corporate strategies – intergenerational differences and the emergence of new generations have become the subject of research throughout the developed world. Age management and intergenerational differences are increasingly issues in all organ- izations (May, 2015). Although these various authors identify different generational year continuums (e.g., specific Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 61–84. 63 authors conclude generation Y in 1995, others in 1996, and so forth), the following recent generations have been identified (Tolbize, 2008, p. 2): • Traditionalists (silent generation, veterans), born between 1922 and 1945, who uncon- ditionally value authority and a hierarchical managerial approach; • Baby-boom Generation (baby boomers, children of prosperity), born 1946 to 1964, de- fined as the workaholic generation; • Generation X, born 1965 to 1980, respect authority and believe that a work-life balance is needed; • Generation Y (millennials), born from 1981 to 1995 or 1996, who grew up in prosperity and rapid technological development; • Generation Z, which also has many other names, such as iGeneration, Gen Tech, Online Generation, Facebook Generation, and Generation C (Dolot, 2018, p. 45), born approximately 1995 to 2010, which naturally communicates and works in both real and virtual worlds and loves change; • Generation Alpha, a new generation that already follows Generation Z and was born af- ter 2010 as children of millennials, living in a world of digital technology, and has not yet entered the labour market (McCrindle, 2019). Generations have different learning styles that depend on whether the matter is learned in- volves hard or soft skills. Hard skills are closely related to knowledge, such as the knowledge of laws, theories, regulations, and procedures, while soft skills are closely related to relation- ships and include communication, negotiation, leadership, teamwork, innovation, and creativ- ity. The hard skills are easier to observe, learn and measure than the soft ones, as the latter is less tangible and also more challenging to quantify and develop (European Commission, 2011, p. 9). Tolbize (2008, p. 14) notes that while Generation X and younger want to learn soft and hard skills at work, the Baby-boom Generation prefers the classic classroom envi- ronment for learning hard skills, while soft ones are happier learning while working. Learning in a group is the second most popular learning method for older employees, but it is less pop- ular among the younger generations. Younger generations have highlighted the use of as- sessment and feedback as one of the most desirable learning methods, while the opposite is found for older employees. Knowledge as the accumulated knowing and understanding of facts, rules, laws, and experi- ence is the foundation for achieving a competitive advantage. Knowledge in an organization can be divided into the following (Rozman & Kovač, 2012, p. 317): • knowledge of the individual as a result of employee learning, • group knowledge as the knowledge of employees connected in a group, • knowledge of the organization as knowledge of all the employees in the organization, • knowledge generated as a result of connections among organizations. Important characteristics of knowledge in an organization (Rozman & Kovač, 2012, p. 347) include the level of general knowledge, enabling the organization to achieve greater efficien- Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 61–84. 64 cy and effectiveness, and the level of specific knowledge that cannot be found elsewhere and is challenging to imitate. Based on this definition of skills, employees in organizations can be divided into four groups: • related external collaborators who have little general and much specific knowledge, • employees with key knowledge who have a lot of general and specific knowledge, • contract employees with little general and specific knowledge, and • traditional employees who have little general and much specific knowledge. The organization needs to deal with both external and internal employees. It is necessary to establish long-term cooperation with external ones and form working groups of internal and external collaborators. To maintain or expand group knowledge (i.e., the knowledge of em- ployees connected in a group) and the organization's knowledge (i.e. the knowledge of all the employees in the organization), it is important to establish cooperation among employees, which naturally includes intergenerational cooperation. Knowledge-sharing among employees as a form of cooperation is important in creating the competitive advantages of an organization (Jiacheng, Lu & Francesco, 2010). It encompasses behaviours that facilitate sharing the knowledge an individual has acquired or established within the organization (Hsu, 2006). In order to enhance the acquisition of knowledge and knowledge-sharing, it is important what form of learning or training the organization chooses. The acquisition of knowledge in organizations takes place in various organized ways, both through direct personal participation and in the form of e-learning, as summarized by Brečko (2018, p. 8): • Course: several consecutive meetings, usually with a few daily breaks to reflect on what has been learned. • Seminar: one or more daily meetings involving the one-way presentation of information. • Consultation: one or more daily meetings to discuss a specific topic. • Problem conference: one or more daily meetings where one problem is discussed from several angles, usually with the participation of experts. • Symposium: A gathering where experts discuss and consult on a specific topic. • Workshop: one or more daily interactive meetings, with an emphasis on training. • College: a short problem meeting to find ideas or solutions. • Lecture: an informative presentation of a certain thing, novelties. • Educational meeting: a meeting with the purpose of exchanging knowledge and experi- ence among participants. • Working meeting: a meeting with the purpose of producing a specific product. • Consultations: a professional conversation about something, with counselling on a spe- cific problem. • Coaching: a special form of learning by asking questions that lead an individual or group to self-awareness and the solution to a problem • Mentoring: a process in which an experienced individual assists a less experienced person in professional and personal development. The mentor guides the mentee with Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 61–84. 65 advice, suggestions and explanations. A mentor is a trusted person who transfers his knowledge and experience to the mentee. In the mentoring process., the mentor follows a mentoring program that clearly defines the goals of mentoring and the criteria for evaluating the entire mentoring process. Mentors must also be appropriately trained for their work. Piktialis and Greenes (2008, p. 25 - 61) note some other learning and knowledge-sharing forms at work, which we also address in our research: • Blog or weblog: a record on web pages or web portals, also on an organization's intranet. • Circles: forms of organized socializing of employees, during which knowledge is transferred among people who have the same profession or field of work. • Sending messages: the transfer knowledge between employees by sending messages to each other in real time, such as: email, Skype, SMS, and so on. • Records: records of information or knowledge in books or online. • Conversations: a conversation or unstructured interview between a person possessing knowledge and a person who asks for certain information. • Summaries: extracts of key knowledge from conversations, interviews, conferences. • Notes: notes made by individuals themselves for their own purpose to record certain information, lesson summaries. • Guided workshops: workshops led by a specific person, during which the transfer of knowledge between workshop participants is accelerated. • Mentoring: mutual cooperation between a person with advanced knowledge (mentor) and a novice, with a focus on career advancement for both. It is intended for the extensive transfer of knowledge among employees arising from different but related content, generations or departments. • Employee assistance: meetings or workshops where employees share their experience and knowledge with colleagues who have asked for help in relation to a specific work challenge. • Podcast: a way of transferring knowledge to a broader audience via audio or video media. The listener or viewer downloads audio or video recording from a specific medium (e.g. a website) and then played back. • Retrospective: a meeting of a team that takes place immediately after a certain event, and at which the team members make summaries of essential, newly acquired information or knowledge. • Storytelling: a form of knowledge transfer that is generally used to share and acquire specific expertise among employees from different backgrounds. It is considered to be one of the oldest forms of complex information transfer. For example: concrete examples from practice, comparisons, summarizing experiences. • Wikis: websites (including on an intranet, a organization's internal website) where anyone can create and edit content. It is a fast way to create, share and transfer group knowledge in a quickly accessible way. Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 61–84. 66 There are thus many different ways of learning and transferring knowledge in the workplace, and organizations must understand the learning style of each generation so they can adapt the ways and techniques of learning or knowledge transfer. Only with the right choice of learning methods will the members of an individual generation be motivated for learning and knowledge-sharing at work. With this research, we wanted to examine the following two questions: • Research question 1: Are different approaches needed to motivate different generations of employees to collaborate? • Research question 2: Do different generations of employees differ in their desired way of acquiring and transferring knowledge? 3 Method In studying intergenerational collaboration and knowledge-sharing in Organisation X we used a quantitative research method, which was carried out using a random sample of the employees, where the desired sample was 10 % of all the staff at Organisation X. Individual variables (nominal, ordinal and interval measurement scales) were analyzed. The question- naire was hosted on the website www.1ka.si. The survey was conducted from 24 November 2019 to 16 December 2019. The research was performed using the snowball method. We sent the questionnaire to 21 people employed in the selected organization (in leading positions) and asked them to pass it to their subordinates, taking into account the generational diversity of the workplace. The persons to whom we sent a hyperlink to the questionnaire were selected from our directory (105 persons) with a random 20 % sample (random selection, as all units had the same proba- bility of selection). A total of 394 people responded to the survey, and we received 334 ques- tionnaires with at least one question completed, giving a positive response rate of 85 %. A total of 255 surveys were completed in full (i.e. 76 % of all questionnaires with at least an answer). See also Figure 1. Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 61–84. 67 Figure 1. The theoretical model of research We undertook critical assessments of intergenerational cooperation and knowledge-sharing in an organization with over 2,000 employees from four generations and a large share of older staff. The selected organization, called Organisation X in this study, was founded in Slovenia. According to its size, it is classified as a large organization. It is engaged in gainful activity, being a Slovenian provider of technological solutions. The technological industry, by its very nature, is changing, developing and adapting to new technologies and the growing demands of users (Organisation Annual Report X, 2018, p. 134) At the end of 2018, the focal organization had over 2,000 employees, and the average age of these was 44.8 years. Just over 500 employees were over 51, representing 23 % of all staff. In the age structure of older employees, the majority were between 51 and 55 (64.1 %), followed by employees over 55 and up to 60 (32 %), with just 3.9 % over 60 (internal material of Organisation X, 2018, 2019). The number of respondents by generation is satisfactory in terms of the number or share of representatives of each generation in the primary population. Most respondents are from Generation X (the average age of this generation, born between 1965 and 1980, is 46.5 years), which roughly coincides with the average age in the organization, which is 44.8 years. We, Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 61–84. 68 therefore, estimated that the structure of respondents aligns with the age structure of employees in the organization. In the statistical analysis of the survey, we considered the relatively large sample, with more than 334 individuals, all born between 1946 and 2010, who completed the questionnaire. In the analysis of the questionnaires, we used the statistical software package SPSS (Statisti- cal Package for the Social Sciences). The first question asked the respondents about which generation they belong to and then asked for their opinions on what motivates them to cooperate with other employees and which form of learning they prefer to accumulate and share knowledge within the organization. In studying intergenerational and knowledge-sharing in Company X, we designed the questionnaire for the research study only. We started from the already existing practice of knowledge transfer in the organization, using an observation method (focus groups) that we performed with 150 employees. Moderated focus group meetings were held in the company from 10 to 25 September 2019, primarily for preparation of the company's strategy. We formed a total of six focus groups, each with 25 participants. The sample included approximately the same number of employees from all departments. Moderators introduced to the participants the importance of cooperation and knowledge transfer in the workplace and the modern knowledge-transfer methods. They then quantita- tively checked the participants' perceptions in the following areas: motivation for cooperation and collaboration with colleagues at work, attitudes towards knowledge-sharing, and current and desired method for knowledge-sharing in the workplace. Moderators checked the percep- tions of the participants with four pre-designed questions:  What does cooperation with co-workers means to you?  In what concrete ways do you connect and cooperate with co-workers at work?  What kind of knowledge-transfer practices do you already use at work?  What kind of (new) methods of knowledge transfer in the workplace would you like to use in the future? Your ideas? Each question was written separately on a poster, and the participants spontaneously wrote down their answers. Focus groups were moderated according to the principle of the open space method, which means that each participant in the focus group wrote down the answer to those questions to which they wanted or felt they could contribute an answer. For example, for the questions - What kind of knowledge-transfer practices do you already use at work? We obtained a total of 107 spontaneous responses. Those that were very similar in content were combined into one answer. Based on the focus group results, we prepared the questionnaire to assess the motivation for cooperation and knowledge transfer in a broader population. We did not conduct a pilot test of Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 61–84. 69 the questionnaire, as we conducted pre-testing that was performed on the population of one company with common elements of organizational culture. Pre-testing was performed in two parts. In the first part, we discussed the structure of the questionnaire and the formulation of the questions in a small group of human resources experts who were preparing a new strategy for intergenerational cooperation and learning in company X. In the second part, we surveyed eight employees (two from each generation). We asked the respondents the meaning of each question and asked them to say out loud the course of thinking and then to answer the ques- tion. Based on the results of the pre-testing, we made some final corrections to the question- naire. 4 Results 4.1 Basic and surveyed population Figure 2 shows the distribution of responses concerning "Which generation do you belong to according to your year of birth?", which shows that 207 (62 %) respondents are representa- tives of Generation X, 54 (16 %) of Generation Y, 50 (15 %) of the Baby-boom Generation, and 23 (7 %) of Generation Z. Figure 2. Distribution of responses on which generation according to the year of birth? Figure 3 shows the distribution of respondents in correlation with the primary population. Figure 3. The distribution of respondents in the primary population 4.2 Research question 1: Are different approaches needed to motivate different genera- tions of employees to collaborate? In the first research question, Are different approaches needed to motivate different genera- tions of employees to collaborate? We were interested in which aspects of motivation have statistically significant differences between individual generations. We analyzed the trans- formed variables (1 = very unmotivated, to 4 = very motivated) and used the ANOVA (analy- sis of variance) test to compare the averages of several independent samples (see Table 1). Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 61–84. 70 Table 1. ANOVA on for "To what extent does the statement apply to you…?" Variable Sum of Squares df Mean Square (MS) F Sig. I believe that mutual advice and the sharing of ideas and information about work are an important part of my responsibility Between Groups 1.393 3 .464 1.615 .186 Within Groups 72.739 253 .288 Total 74.132 256 I do the work faster and more efficiently on my own than in a team of co-workers Between Groups 1.409 3 .470 .873 .456 Within Groups 136.140 253 .538 Total 137.549 256 If I help a co-worker get the job done faster, I benefit from it myself Between Groups .997 3 .332 .603 .614 Within Groups 139.408 253 .551 Total 140.405 256 It is important to me to share my work ex- perience and knowledge with co-workers Between Groups 2.383 3 .794 2.932 .034 Within Groups 68.536 253 .271 Total 70.918 256 Personal contact with co-workers is the most desirable way to transfer knowledge and experience for me Between Groups .260 3 .087 .268 .849 Within Groups 81.599 252 .324 Total 81.859 255 It is important to me that I have relation- ships with my colleagues in which I can openly share my feelings about our work Between Groups 1.382 3 .461 1.604 .189 Within Groups 72.649 253 .287 Total 74.031 256 When I encounter a problem at work, I turn to my colleagues for advice or knowledge Between Groups .531 3 .177 .593 .620 Within Groups 75.277 252 .299 Total 75.809 255 From Table 1, we can see that statistically significant differences between the average of gen- erations occur only in the statement "It is important to share my work experience and knowledge with co-workers" with Mean Square (MS) between groups =, 794; F = 2.932 and Sig. = 0.34. For the other statements, there are no differences between the averages. We did not detect significant differences. We conclude that different motivational tools between gen- erations are not required, as they all have a very similar impact. ANOVA gives us an answer as to whether the averages between the groups are statistically significantly different, but it does not tell us which groups are those where the differences occur, so we checked this with post-hoc tests. In Table 2, we used a post-hoc test to analyze which generations contribute the most to the differences in the variable "It is important for me to share my work experience and knowledge with my colleagues". We find that Generation Z contributes the most to the differences, which is significantly more unmotivated for these variables than the other generations (MD of Baby Boom = -.458; to Generation X= -.384 and Generation Y = -.381), while there are no signifi- cant differences in this statement between the other three generations. We conclude that the same motivational tools regarding the sharing of work experience and knowledge among co- workers have the least effect on Generation Z, while the impacts on the other three genera- tions are very similar. Although we did not detect significant differences between the averag- es of all groups in the other statements, we perceive some partial differences between the Ba- by-boom and Generation Y averages in the statement "I believe that mutual advice and trans- Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 61–84. 71 fer of ideas and information about work is an important part of my responsibility" ( The baby- boom generation feels more motivated on average) and between Generation Y and Z averages in the statement "It's important for me to have a relationship with co-workers in which I can openly share my feelings about our work" Generation Z feels on average less motivated). From the post-hoc test results (see Table 2), we can conclude that for Generation Z, it is least important to share their work experience and knowledge with co-workers. Representatives of Generation Z use technical devices practically all the time and have access to a large amount of information due to the World Wide Web; their technical and linguistic knowledge is at a high level. They are independent, self-sufficient and find it difficult to accept authority. They know how to find the correct information and resources. Perhaps this is why they do not feel motivated to share their knowledge and work experience with others. It is also possible to interpret that the youngest generation in the selected company does not yet have enough work experience and knowledge to feel competent to share it with others. Table 2. Post-hoc tests for "To what extent does the statement apply to you…?" Dependent Variable (I) Which gener- ation do you belong to ac- cording to the year of your birth? (J) Which generation do you belong to according to the year of your birth? Mean Differ- ence (MD) (I-J) Std. Er- ror Sig. 95 % Confidence Interval Lower Bound Upper Bound I believe that mutu- al advice and shar- ing of ideas and information about work are an im- portant part of my responsibility Baby-boom Generation X .136 .097 .160 -.05 .33 Generation Y .263* .119 .029 .03 .50 Generation Z .125 .164 .447 -.20 .45 Generation X Baby-boom -.136 .097 .160 -.33 .05 Generation Y .126 .092 .171 -.05 .31 Generation Z -.012 .145 .936 -.30 .27 Generation Y Baby-boom -.263* .119 .029 -.50 -.03 Generation X -.126 .092 .171 -.31 .05 Generation Z -.138 .161 .392 -.45 .18 Generation Z Baby-boom -.125 .164 .447 -.45 .20 Generation X .012 .145 .936 -.27 .30 Generation Y .138 .161 .392 -.18 .45 I do the work faster and more efficiently on my own than in a team of co- workers Baby-boom Generation X -.053 .132 .688 -.31 .21 Generation Y -.185 .163 .257 -.51 .14 Generation Z .139 .224 .536 -.30 .58 Generation X Baby-boom .053 .132 .688 -.21 .31 Generation Y -.132 .126 .294 -.38 .12 Generation Z .192 .198 .334 -.20 .58 Generation Y Baby-boom .185 .163 .257 -.14 .51 Generation X .132 .126 .294 -.12 .38 Generation Z .324 .220 .142 -.11 .76 Generation Z Baby-boom -.139 .224 .536 -.58 .30 Generation X -.192 .198 .334 -.58 .20 Generation Y -.324 .220 .142 -.76 .11 If I help a co- worker get the job done faster, I bene- fit from it myself Baby-boom Generation X .097 .134 .470 -.17 .36 Generation Y .196 .165 .236 -.13 .52 Generation Z .223 .226 .326 -.22 .67 Generation X Baby-boom -.097 .134 .470 -.36 .17 Generation Y .100 .127 .436 -.15 .35 Generation Z .126 .200 .530 -.27 .52 Generation Y Baby-boom -.196 .165 .236 -.52 .13 Generation X -.100 .127 .436 -.35 .15 Generation Z .026 .223 .906 -.41 .46 “to be continued” Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 61–84. 72 Generation Z Baby-boom -.223 .226 .326 -.67 .22 Generation X -.126 .200 .530 -.52 .27 Generation Y -.026 .223 .906 -.46 .41 It is important to me to share my work experience and knowledge with co-workers Baby-boom Generation X .074 .094 .431 -.11 .26 Generation Y .076 .116 .510 -.15 .30 Generation Z .458* .159 .004 .15 .77 Generation X Baby-boom -.074 .094 .431 -.26 .11 Generation Y .002 .089 .978 -.17 .18 Generation Z .384* .141 .007 .11 .66 Generation Y Baby-boom -.076 .116 .510 -.30 .15 Generation X -.002 .089 .978 -.18 .17 Generation Z .381* .156 .015 .07 .69 Generation Z Baby-boom -.458* .159 .004 -.77 -.15 Generation X -.384* .141 .007 -.66 -.11 Generation Y -.381* .156 .015 -.69 -.07 Personal contact with co-workers is the most desirable way to transfer knowledge and experience for me Baby-boom Generation X -.053 .103 .609 -.25 .15 Generation Y .006 .127 .965 -.24 .26 Generation Z .047 .174 .785 -.29 .39 Generation X Baby-boom .053 .103 .609 -.15 .25 Generation Y .058 .098 .553 -.13 .25 Generation Z .100 .154 .516 -.20 .40 Generation Y Baby-boom -.006 .127 .965 -.26 .24 Generation X -.058 .098 .553 -.25 .13 Generation Z .042 .171 .806 -.29 .38 Generation Z Baby-boom -.047 .174 .785 -.39 .29 Generation X -.100 .154 .516 -.40 .20 Generation Y -.042 .171 .806 -.38 .29 It is important to me that I have relationships with my colleagues in which I can openly share my feelings about our work. Baby-boom Generation X .057 .097 .554 -.13 .25 Generation Y -.026 .119 .830 -.26 .21 Generation Z .312 .163 .057 -.01 .63 Generation X Baby-boom -.057 .097 .554 -.25 .13 Generation Y -.083 .092 .368 -.26 .10 Generation Z .255 .145 .079 -.03 .54 Generation Y Baby-boom .026 .119 .830 -.21 .26 Generation X .083 .092 .368 -.10 .26 Generation Z .338* .161 .036 .02 .65 Generation Z Baby-boom -.312 .163 .057 -.63 .01 Generation X -.255 .145 .079 -.54 .03 Generation Y -.338* .161 .036 -.65 -.02 When I encounter a problem at work, I turn to my col- leagues for advice or knowledge Baby-boom Generation X .105 .099 .287 -.09 .30 Generation Y .024 .122 .845 -.22 .26 Generation Z .139 .167 .406 -.19 .47 Generation X Baby-boom -.105 .099 .287 -.30 .09 Generation Y -.081 .094 .387 -.27 .10 Generation Z .033 .148 .821 -.26 .32 Generation Y Baby-boom -.024 .122 .845 -.26 .22 Generation X .081 .094 .387 -.10 .27 Generation Z .115 .164 .485 -.21 .44 Generation Z Baby-boom -.139 .167 .406 -.47 .19 Generation X -.033 .148 .821 -.32 .26 Generation Y -.115 .164 .485 -.44 .21 * The mean difference (MD) is significant at the 0.05 level. Table 3 shows the ANOVA results for the transformed variables in terms of motivation to participate by different generations. The average values of all four generations were higher than 3 for all questions, and thus all generations were at least slightly motivated, except for the item Regarding achievement at work, where they ranged between 2 and 3 (with all four generations somewhere between unmotivated and motivated). When we study the results of “continued” Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 61–84. 73 ANOVA, we find that at a 5 % risk level, we cannot reject the zero assumption that the arith- metic means between the groups are the same or that there are no statistically significant dif- ferences between the average motivations among the generations. Therefore, we conclude that the motivational tools examined in this study work very similarly on all four generations. We can presume that it is least important for Generation Z to share their experience and knowledge with co-workers. Generation Z uses digital devices practically all the time and has access to a large amount of information on the Internet, and thus their technical and language knowledge is very high. It is also possible to interpret the results showing that the youngest generation in Organisation X does not yet have enough work experience and knowledge to feel competent to share it with others. It is also a significant fact that there is a minimal share of Generation Z respondents in the survey, so the results are not statistically significant. How- ever, because there is a minimal number of members of Generation Z in the focal organiza- tion, a significantly larger sample of this group would not be possible. Table 3. ANOVA for motivation to collaborate Sum of Squares df Mean Square F Sig. My idea of communicating about goals and priorities at work is Between Groups 3.944 3 1.315 1.271 .285 Within Groups 261.776 253 1.035 Total 265.720 256 We meet in person with colleagues with whom we are involved in a joint work process Between Groups 2.076 3 .692 .602 .614 Within Groups 290.858 253 1.150 Total 292.934 256 In relation to other co-workers Between Groups .904 3 .301 .546 .651 Within Groups 139.641 253 .552 Total 140.545 256 In case of disagreements and conflicts between co-workers, I deal with the situation as follows Between Groups 2.565 3 .855 1.645 .179 Within Groups 130.993 252 .520 Total 133.559 255 Regarding my achievements at work Between Groups 2.233 3 .744 .376 .770 Within Groups 498.388 252 1.978 Total 500.621 255 Regarding trust in the workplace Between Groups 1.007 3 .336 .560 .642 Within Groups 151.608 253 .599 Total 152.615 256 My idea of sharing information with other co-workers is Between Groups .108 3 .036 .156 .926 Within Groups 58.126 253 .230 Total 58.233 256 Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 61–84. 74 4.2 Research question 2: Do different generations of employees differ in the desired way of acquiring and sharing knowledge? To answer this research question, we wanted to determine which favoured ways of acquiring and sharing knowledge showed statistically significant differences among the four genera- tions. Table 4 shows the ANOVA results for the given variables regarding the desired form of knowledge-sharing among employees, while Table 5 shows the results of post-hoc tests. Table 4. ANOVA for Various forms of knowledge-sharing between co-workers, indicate to what extent these suit to you Sum of Squares df Mean Square F Sig. Blog Between Groups 3.131 3 1.044 .692 .557 Within Groups 375.311 249 1.507 Total 378.443 252 Circles Between Groups 6.013 3 2.004 1.506 .214 Within Groups 332.747 250 1.331 Total 338.760 253 Sending messages Between Groups 7.633 3 2.544 1.759 .155 Within Groups 361.568 250 1.446 Total 369.201 253 Records Between Groups 1.817 3 .606 .596 .618 Within Groups 253.841 250 1.015 Total 255.657 253 Talking Between Groups 5.114 3 1.705 1.867 .136 Within Groups 228.335 250 .913 Total 233.449 253 Summaries Between Groups 8.352 3 2.784 3.235 .023 Within Groups 215.113 250 .860 Total 223.465 253 Notes Between Groups Within Groups Total 6.618 296.473 303.091 3 250 253 2.206 1.186 1.860 .137 Guided workshops Between Groups 3.723 3 1.241 1.536 .206 Within Groups 201.978 250 .808 Total 205.701 253 Mentoring Between Groups 2.806 3 .935 .963 .411 Within Groups 242.741 250 .971 Total 245.547 253 Employee assistance Between Groups 2.680 3 .893 1.262 .288 Within Groups 177.068 250 .708 Total 179.748 253 Podcast Between Groups 7.453 3 2.484 2.085 .103 “to be continued” Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 61–84. 75 Within Groups 297.874 250 1.191 Total 305.327 253 Retrospective Between Groups .868 3 .289 .295 .829 Within Groups 245.545 250 .982 Total 246.413 253 Storytelling Between Groups 16.060 3 5.353 5.740 .001 Within Groups 233.153 250 .933 Total 249.213 253 Wikis Between Groups 6.811 3 2.270 1.715 .164 Within Groups 330.910 250 1.324 Total 337.720 253 There are statistically significant differences between the averages of the generations in the Extracts (MS = 2.784; F = 3.235; Sig. = .023) and Storytetlling (MS = 5.353; F = 5.704; Sig. = .001) while in the other possibilities there are no differences between the averages. Using post-hoc tests, we analyze which generations contribute the most to the differences in the variables Extracts and Storytelling. We find that statistically significant differences be- tween the average values of the variable Extracts occur between Baby-boom generation and Generation Y (MD = .477); between Baby-boom generation and Generation Z (MD = .763); between Generation X and Generation Z (MD = .522). Average values of the variable Ex- tracts fall from Baby-boom generation to Generation Z through Generations X and Y; there- fore, we conclude that the younger the generation, the less suitable it is on average for the transfer of knowledge through extracts of key knowledge from conversations, interviews, conferences. Statistically significant differences between the average values of the variable storytelling are between all pairs of generations, except between Generation X and Y, where these differences are not detectable (MD = .124). Also, in this variable, the average values fall from Baby- boom generation to Generation Z, so we conclude that the younger the generation, the less it corresponds to the average knowledge transfer through storytelling, with statistically signifi- cant differences in the 5 % risk level between Generation X and Y cannot be detected. Story- telling is used as a form of knowledge transfer for specific expertise among employees from different backgrounds. It should be pointed out that this is one of the oldest forms of infor- mation transfer - but for younger generations, this method of knowledge transfer may already be obsolete. Younger generations demand information that is fast, accurate, consistent, and unwilling to listen to long stories. Based on arithmetic means for all generations, we conclude that on average respondents are less suited to the transfer of knowledge for “podcast” (Mean = 2,74); "blog" or "web blog”, (Mean = 2,88) and most suitable for the “help of a colleague “(Mean = 4,03); “conversations” (Mean = 3,87; guided workshops (Mean = 3,84) and mentoring (Mean = 3,81). Respondents “continued” Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 61–84. 76 prefer personal forms of knowledge transfer to non-personal ones. Table 5. Post-hoc tests for the variables of various forms of knowledge-sharing between co-workers indicate to what extent it suits to you Dependent Variable (I) Which genera- tion do you belong to accord- ing to the year of your birth (J) Which genera- tion do you belong to according to the year of your birth Mean Differ- ence (I- J) Std. Error Sig. 95 % Confidence Interval Lower Bound Upper Bound Blog Baby-boom Generation X -.199 .224 .376 -.64 .24 Generation Y -.396 .277 .154 -.94 .15 Generation Z -.258 .376 .494 -1.00 .48 Generation X Baby-boom .199 .224 .376 -.24 .64 Generation Y -.197 .213 .355 -.62 .22 Generation Z -.059 .332 .859 -.71 .59 Generation Y Baby-boom .396 .277 .154 -.15 .94 Generation X .197 .213 .355 -.22 .62 Generation Z .138 .369 .709 -.59 .87 Generation Z Baby-boom .258 .376 .494 -.48 1.00 Generation X .059 .332 .859 -.59 .71 Generation Y -.138 .369 .709 -.87 .59 Circles Baby-boom Generation X -.215 .208 .303 -.63 .20 Generation Y -.526* .258 .043 -1.04 -.02 Generation Z -.093 .352 .792 -.79 .60 Generation X Baby-boom .215 .208 .303 -.20 .63 Generation Y -.311 .200 .121 -.71 .08 Generation Z .122 .312 .696 -.49 .74 Generation Y Baby-boom .526* .258 .043 .02 1.04 Generation X .311 .200 .121 -.08 .71 Generation Z .433 .347 .213 -.25 1.12 Generation Z Baby-boom .093 .352 .792 -.60 .79 Generation X -.122 .312 .696 -.74 .49 Generation Y -.433 .347 .213 -1.12 .25 Sending messages Baby-boom Generation X .286 .217 .189 -.14 .71 Generation Y -.048 .269 .860 -.58 .48 Generation Z -.233 .367 .525 -.96 .49 Sending messages Generation X Baby-boom -.286 .217 .189 -.71 .14 Generation Y -.334 .209 .111 -.74 .08 Generation Z -.519 .325 .111 -1.16 .12 Generation Y Baby-boom .048 .269 .860 -.48 .58 Generation X .334 .209 .111 -.08 .74 Generation Z -.186 .362 .608 -.90 .53 Generation Z Baby-boom .233 .367 .525 -.49 .96 Generation X .519 .325 .111 -.12 1.16 Generation Y .186 .362 .608 -.53 .90 Records Baby-boom Generation X .228 .182 .211 -.13 .59 Generation Y .140 .226 .534 -.30 .58 Generation Z .074 .307 .811 -.53 .68 Generation X Baby-boom -.228 .182 .211 -.59 .13 Generation Y -.088 .175 .615 -.43 .26 Generation Z -.155 .272 .570 -.69 .38 Generation Y Baby-boom -.140 .226 .534 -.58 .30 Generation X .088 .175 .615 -.26 .43 Generation Z -.067 .303 .826 -.66 .53 Generation Z Baby-boom -.074 .307 .811 -.68 .53 Generation X .155 .272 .570 -.38 .69 Generation Y .067 .303 .826 -.53 .66 Talking Baby-boom Generation X -.163 .173 .347 -.50 .18 Generation Y -.287 .214 .181 -.71 .13 Generation Z .337 .291 .249 -.24 .91 Generation X Baby-boom .163 .173 .347 -.18 .50 Generation Y -.124 .166 .454 -.45 .20 Generation Z .499 .258 .054 -.01 1.01 Generation Y Baby-boom .287 .214 .181 -.13 .71 “to be continued” Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 61–84. 77 Generation X .124 .166 .454 -.20 .45 Generation Z .624* .287 .031 .06 1.19 Talking Generation Z Baby-boom -.337 .291 .249 -.91 .24 Generation X -.499 .258 .054 -1.01 .01 Generation Y -.624* .287 .031 -1.19 -.06 Summaries Baby-boom Generation X .241 .167 .151 -.09 .57 Generation Y .477* .208 .022 .07 .89 Generation Z .763* .283 .007 .21 1.32 Generation X Baby-boom -.241 .167 .151 -.57 .09 Generation Y .236 .161 .143 -.08 .55 Generation Z .522* .251 .038 .03 1.02 Generation Y Baby-boom -.477* .208 .022 -.89 -.07 Generation X -.236 .161 .143 -.55 .08 Generation Z .286 .279 .307 -.26 .84 Generation Z Baby-boom -.763* .283 .007 -1.32 -.21 Generation X -.522* .251 .038 -1.02 -.03 Generation Y -.286 .279 .307 -.84 .26 Notes Baby-boom Generation X .191 .197 .332 -.20 .58 Generation Y .199 .244 .415 -.28 .68 Generation Z -.458 .332 .169 -1.11 .20 Generation X Baby-boom -.191 .197 .332 -.58 .20 Generation Y .008 .189 .966 -.36 .38 Generation Z -.649* .294 .028 -1.23 -.07 Generation Y Baby-boom -.199 .244 .415 -.68 .28 Generation X -.008 .189 .966 -.38 .36 Generation Z -.657* .328 .046 -1.30 -.01 Generation Z Baby-boom .458 .332 .169 -.20 1.11 Generation X .649* .294 .028 .07 1.23 Generation Y .657* .328 .046 .01 1.30 Guided workshops Baby-boom Generation X .189 .162 .246 -.13 .51 Generation Y .048 .201 .813 -.35 .44 Generation Z .533 .274 .053 -.01 1.07 Generation X Baby-boom -.189 .162 .246 -.51 .13 Generation Y -.141 .156 .367 -.45 .17 Generation Z .345 .243 .157 -.13 .82 Generation Y Baby-boom -.048 .201 .813 -.44 .35 Generation X .141 .156 .367 -.17 .45 Generation Z .486 .270 .074 -.05 1.02 Generation Z Baby-boom -.533 .274 .053 -1.07 .01 Generation X -.345 .243 .157 -.82 .13 Generation Y -.486 .270 .074 -1.02 .05 Mentoring Baby-boom Generation X .016 .178 .929 -.33 .37 Generation Y -.234 .221 .289 -.67 .20 Generation Z .189 .300 .529 -.40 .78 Generation X Baby-boom -.016 .178 .929 -.37 .33 Generation Y -.250 .171 .145 -.59 .09 Generation Z .174 .266 .515 -.35 .70 Generation Y Baby-boom .234 .221 .289 -.20 .67 Generation X .250 .171 .145 -.09 .59 Generation Z .424 .296 .154 -.16 1.01 Generation Z Baby-boom -.189 .300 .529 -.78 .40 Generation X -.174 .266 .515 -.70 .35 Generation Y -.424 .296 .154 -1.01 .16 Employee assistance Baby-boom Generation X -.014 .152 .928 -.31 .29 Generation Y -.288 .188 .127 -.66 .08 Generation Z -.026 .257 .918 -.53 .48 Employee assistance Generation X Baby-boom .014 .152 .928 -.29 .31 Generation Y -.274 .146 .061 -.56 .01 Generation Z -.013 .227 .956 -.46 .44 Generation Y Baby-boom .288 .188 .127 -.08 .66 Generation X .274 .146 .061 -.01 .56 Generation Z .262 .253 .302 -.24 .76 Generation Z Baby-boom .026 .257 .918 -.48 .53 Generation X .013 .227 .956 -.44 .46 Generation Y -.262 .253 .302 -.76 .24 Podcast Baby-boom Generation X .063 .197 .748 -.32 .45 Generation Y .511* .244 .037 .03 .99 Generation Z .135 .333 .685 -.52 .79 Generation X Baby-boom -.063 .197 .748 -.45 .32 “to be continued” “continued” Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 61–84. 78 Generation Y .448* .189 .019 .07 .82 Generation Z .072 .295 .808 -.51 .65 Generation Y Baby-boom -.511* .244 .037 -.99 -.03 Generation X -.448* .189 .019 -.82 -.07 Generation Z -.376 .328 .253 -1.02 .27 Generation Z Baby-boom -.135 .333 .685 -.79 .52 Generation X -.072 .295 .808 -.65 .51 Generation Y .376 .328 .253 -.27 1.02 Retrospective Baby-boom Generation X .029 .179 .872 -.32 .38 Generation Y .086 .222 .697 -.35 .52 Generation Z .258 .302 .394 -.34 .85 Generation X Baby-boom -.029 .179 .872 -.38 .32 Generation Y .058 .172 .738 -.28 .40 Generation Z .229 .268 .393 -.30 .76 Generation Y Baby-boom -.086 .222 .697 -.52 .35 Generation X -.058 .172 .738 -.40 .28 Generation Z .171 .298 .566 -.42 .76 Retrospective Generation Z Baby-boom -.258 .302 .394 -.85 .34 Generation X -.229 .268 .393 -.76 .30 Generation Y -.171 .298 .566 -.76 .42 Storytelling Baby-boom Generation X .352* .174 .044 .01 .70 Generation Y .476* .216 .029 .05 .90 Generation Z 1.200* .294 .000 .62 1.78 Generation X Baby-boom -.352* .174 .044 -.70 -.01 Generation Y .124 .168 .460 -.21 .45 Generation Z .848* .261 .001 .33 1.36 Generation Y Baby-boom -.476* .216 .029 -.90 -.05 Generation X -.124 .168 .460 -.45 .21 Generation Z .724* .290 .013 .15 1.30 Generation Z Baby-boom -1.200* .294 .000 -1.78 -.62 Generation X -.848* .261 .001 -1.36 -.33 Generation Y -.724* .290 .013 -1.30 -.15 Wikis Baby-boom Generation X -.003 .208 .989 -.41 .41 Generation Y -.397 .258 .124 -.90 .11 Generation Z -.388 .351 .270 -1.08 .30 Generation X Baby-boom .003 .208 .989 -.41 .41 Generation Y -.394* .200 .049 -.79 .00 Generation Z -.385 .311 .217 -1.00 .23 Generation Y Baby-boom .397 .258 .124 -.11 .90 Generation X .394* .200 .049 .00 .79 Generation Z .010 .346 .978 -.67 .69 Generation Z Baby-boom .388 .351 .270 -.30 1.08 Generation X .385 .311 .217 -.23 1.00 Generation Y -.010 .346 .978 -.69 .67 * The mean difference (MD) is significant at the 0.05 level. 5 Discussion In research question 1, we analyzed the importance of cooperation between colleagues, where we asked respondents to assess (1) the extent to which mutual advice and transfer of ideas and information about work represent an important part of the responsibility of an individual em- ployee; (2) the extent to which they do the work faster and more efficiently on their own than in a team of colleagues; (3) the extent to which helping an employee to get the job done faster also benefits of individuals; (4) the extent to which it is important to share work experience and knowledge with colleagues; (5) the extent to which personal contact with colleagues is a desirable way of transferring knowledge and experience; and (6) the extent to which an individual needs to have a relationship with co-workers in which he or she can openly share his or her feelings about working together. With the statement "To what extent the statement applies to you" have been measured on a “continued” Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 61–84. 79 scale between 1 and 5 (1 ... not true at all, 5 ... absolutely true). The median of the respond- ents' scores were between 3 and 5, and the arithmetic means were between 2.96 and 4.49. All but one of the variables (i.e., I do the work faster and more efficiently on my own than in a team of co-workers) have a negative asymmetry coefficient, indicating some asymmetry to the left compared to the normal distribution. Flattening coefficients are positive for all but one variable (I do the work faster and more efficiently on my own than in a team of co-workers), suggesting more pointed distributions compared to the normal distribution. Respondents, on average, agreed with all the statements to a greater extent, except for the variable I do the work faster and more efficiently on my own than in the team of co-workers, where they agreed only to a moderate extent. This finding is not surprising, as all variables, except the one mentioned, contained claims about the importance of relationships and mutual cooperation at work, while the mentioned variable advocates the opposite, more individual approach. Research question 1:Are different approaches needed to motivate different generations of employees to collaborate? Here we found statistically significant differences between the generation averages for the statement: It is important for me to share my work experience and knowledge with co-workers. In contrast, in other statements, there were no significant differ- ences between the averages. Where characteristic differences did not occur, we concluded that different motivational tools for different generations are not required, as they have a very sim- ilar impact on all ages. We used post-hoc tests to analyze which generations contributed the most to the differences concerning the responses to the statement. It is important for me to share my work experience and knowledge with my colleagues. We found that Generation Z, which is significantly less motivated than the other generations, contributed the most to the differences, while we did not detect any significant differences for this statement for the other three generations. We conclude that the same motivational tools regarding the sharing of ex- perience and knowledge among co-workers had the least effect on Generation Z, while the impacts on the other three generations were very similar. Although we did not detect signifi- cant differences between the averages of all groups in the other statements, we perceived some partial differences by averages between the Baby-boom Generation and Generation Y for the statement I believe that mutual advice and transfer of ideas and information about work is an important part of my responsibility (for which the Baby-boom Generation feels more motivated on average), and between Generations Y and Z for the statement It is im- portant to me to have a relationship with co-workers in which I can openly share my feelings about our work (for which Generation Z feels less motivated on average). We especially want to highlight the aspect of motivating individual employees for coopera- tion, where respondents answered very bimodally about how they wanted to highlight their achievements at work: about the same percentage of respondents did not want their achieve- ments to be publicly announced in the organization (39 %), and those who found it important that co-workers became acquainted with their achievements (48 %). Since the results showed Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 61–84. 80 no statistically significant differences between the generations on this issue, we conclude that the motivational tool of praise of the employee by the employer for all four generations works very similarly. This means that praise requires a very individual approach. Certain employees did not want their achievements to be exposed, and public praise could mean demotivation. Meanwhile, it was important for others to become acquainted with them and that there were criteria for defining achievement, as achievement affects an organization's common goals, and therefore public non-recognition of praise would be demotivating. Research question 2: Do different generations of employees differ in the desired way of ac- quiring and sharing knowledge? There were statistically significant differences between the averages of the generations in Summaries and Storytelling, while there are no differences between the averages for the other possibilities. We found that statistically significant differences between the average values for Summaries occured between the Baby-boom Generation and Generation Y, the Baby-boom Generation and Generation Z, and between Generations X and Z. The average values for Summaries fell from the Baby-boom Generation to Generation Z, so we concluded that the younger a generation was, the less it enjoyed knowledge-sharing with regard to Summaries. Statistically significant differences between the mean values for storytelling existed between all generations, except between Generations X and Y, where these differences were not detectable. Moreover, the average values fell from the Baby-boom Generation to Generation Z, so we concluded that the younger the generation, the less interested in knowledge-sharing through storytelling. Based on the arithmetic means for all generations, we concluded that the respondents were on average less suited for knowledge-sharing via podcasts or blogs and most suited for learning and sharing knowledge based on help from colleagues, conversations, guided workshops and mentoring. The results also indicated that the respondents prefer personal forms of knowledge-sharing to non-personal ones. It should be pointed out that this research had certain limitations as that the conclusions based on the results are also limited. Selective sample should be taken into account as this research only included one organization and individuals from this particular organization motivated to participate, so the sample is not representative of the Slovenian working population educational and gender structure. The small sample of generation Z should also be mentioned, as it means there were limitations in the conclusions of statistical analyses, as the sample of generation Z did not represent the statistically robust group. The main limitation of this re- search was the failure to consider an individual's characteristic, organizational climate, and communication pattern among different departments. At the same time, the focal organization operates in different geographical locations, as this can play an important role in intergenera- tional cooperation and knowledge-sharing. Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 61–84. 81 6 Conclusion In this article, we examined intergenerational cooperation and knowledge-sharing at work. It is of great importance for organizations to encourage the continuous learning of employees and knowledge-sharing among them. We have categorized various forms of training that organizations can use to share knowledge among employees. We performed a critical analysis of the favoured approaches of different generations for cooperation and knowledge-sharing at Organisation X, with more than 2,000 employees. This organization deals with technological solutions intended for both business and private customers and is therefore necessarily engaged in a market with constant and rapid changes. To answer the research question of whether different approaches are needed to motivate different generations of employees to collaborate, we found that it is least important for Gen- eration Z to share their work experience and knowledge with co-workers. However for all of the following four generations gave positive responses: that mutual advice and transfer of ideas and information about work is an important part of the employee's responsibility, that work is done faster in a team, that helping a co-worker brings benefits, that a personal contact is a desirable form of knowledge-sharing, and that it is good to have relationships and be able to turn to colleagues to share advice and knowledge. Concerning whether different generations of employees differ in their desired way of acquir- ing and transferring knowledge, the results showed that podcasts or blogs for knowledge- sharing were seen as less appropriate by the respondents, who prefer peer support, interviews, guided workshops, and mentoring. The respondents thus prefer personal forms of knowledge- sharing to non-personal ones. The results for all the selected forms of knowledge-sharing in the focal organization were roughly the same across the generations, except for Summaries and Storytelling, where we found that the younger the generation, the less they were interested in knowledge-sharing in these ways. 'With age diversity increasing in society, organization face the challenge of reconstructing the learning processes' (Prelog & Ismagilova & Boštjančič, 2019, p. 64) and encouraging the knowledge-sharing among the different generations. Therefore, each organization needs to determine the preferred form of knowledge-sharing in each specific environment and choose a form that suits both the employees who provide information and those who receive it. The concept of intergenerational cooperation in the workplace includes knowledge-sharing among staff and a shift from knowledge-sharing to co-creating knowledge. We believe this shift is of key importance for further development of human capital as well as knowledge accumulation in the organization. Therefore, the area of co-creating knowledge should represent the future ambitions of every organization and research's communities. Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 61–84. 82 References 1. Annual Report of Company X. (2016). Retrieved from website of Company X 2. Annual Report of Company X. (2017). Retrieved from website of Company X 3. Annual Report of Company X. (2018). 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International journal of business administration. Retrieved from http://dx.doi.org/10.5430/ijba.v4n1p23 *** Danijela Brečko, has been the Executive Director at Sofos, the Institute for Knowledge Management and Talent Development, since 2014. She finished her PhD at the University of Ljubljana (Faculty of Arts), where she pre- viously obtained a master's degree in adult personal development. She is Assistant Professor for Management at MLC Ljubljana - Faculty of Management and Law and Assistant Professor of Education and Human Resources Management at Doba Faculty. Her research focuses mainly on Leadership and Human Resources management. She participates in international research projects with the researchers from EMCC Global, Atiner from Greece, Institute for HRM, Prague… Since 2016 she has been leading the national project Organizational Energy, where she co-ordinates 16 development partners. ORCID ID: https://orcid.org/0000-0003-0221-606X. *** Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 61–84. 84 Povzetek: Medgeneracijsko sodelovanje, učenje in izmenjava znanja na delovnem mestu Ozadje in izvirnost: Članek se osredotoča na vprašanje organizacij, ki se soočajo z izzivom vzpostavitve delovnega okolja, ki bo prilagojeno značilnostim različnih generacij zaposlenih. Vsa- ka generacija na delovnem mestu mora biti motivirana za delo, sodelovanje in izmenjavo znanja med sodelavci različnih starosti. O motivaciji na delovnem mestu je bilo opravljenih veliko ra- ziskav, vendar nismo zasledili raziskave o vplivu različnih učnih oblik, da bi motivirali različne generacije za sodelovanje in izmenjavo znanja na delovnem mestu, ne v slovenskem ne v svetov- nem merilu. Metoda: V tej študiji preučujemo dve raziskovalni vprašanji: Ali so potrebni različni pristopi za motiviranje različnih generacij zaposlenih k sodelovanju in ali se različne generacije razlikujejo v želenih načinih pridobivanja in izmenjave znanja. Pri raziskovanju smo se naslonili na Piktialis in Greenes (2008) kategorizacijo oblik učenja in izmenjave znanja pri delu. Pri kritični oceni moti- vacije za medgeneracijsko sodelovanje in izmenjavo znanja smo uporabili kvantitativno ra- ziskovalno metodo. Raziskava je bila izvedena na naključnem vzorcu med zaposlenimi v izbranem podjetju z 2.000 zaposlenimi, na anketo pa se je odzvalo 334. Rezultati: Rezultati so pokazali, da je za predstavnike generacije Z najmanj pomembno, da svoje znanje in delovne izkušnje delijo s kolegi iz drugih generacij in da je za mlajše generacije (Y in Z) pri izmenjavi znanja manj primerno uporabljati pripovedovanje zgodb (primeri iz prakse, primer- jave, povzemanje izkušenj) in povzetke ključnih znanj (iz pogovorov, intervjujev, konferenc). Družba: Koncept medgeneracijskega sodelovanja na delovnem mestu vključuje izmenjavo znanja med zaposlenimi in prehod od izmenjave znanja k soustvarjanju znanja. Verjamemo, da je ta premik ključnega pomena za nadaljnji razvoj človeškega kapitala in tudi za potrebno akumulacijo znanja v organizaciji. Področje soustvarjanja znanja bi torej moralo predstavljati prihodnje ambi- cije vsake organizacije, pa tudi raziskovalnih skupnosti. Omejitve / nadaljnje raziskovanje: Upoštevati je treba selektivni vzorec, saj je raziskava vključevala samo eno organizacijo in posa- meznike iz te organizacije, ki so bili motivirani za sodelovanje. Omeniti je treba tudi majhen vzorec generacije Z. Glavna omejitev te raziskave je bila nezmožnost upoštevanja specifične or- ganizacijske klime in komunikacijskega vzorca posameznika med različnimi oddelki, medtem ko podjetje deluje na različnih geografskih lokacijah, saj lahko to igra pomembno vlogo pri medgen- eracijskem sodelovanju in izmenjavi znanja. Zato je ključno, da vsako podjetje v vsakem določenem okolju določi prednostno obliko izmenjave znanja in izbere obliko, ki ustreza tako zaposlenim, ki informacije posredujejo, kot tistim, ki jih prejmejo. To pa je tudi področje nadaljnjega raziskovanja; torej vpliv organizacijske klime in kulture na proces medgeneracijskega sodelovanja in izmenjave znanja. Ključne besede: generacije pri delu, medgeneracijsko sodelovanje, oblike medgeneracijskega učenja, delitev znanja, izmenjava znanja pri delu. Copyright (c) Danijela BREČKO Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 85-109. * Korespondenčni avtor / Correspondence author 85 Prejeto: 22. januar 2021; revidirano: 11. februar 2021; sprejeto: 16. februar 2021. / Received: 22nd January 2021; revised: 11th February 2021; accepted: 16th February 2021. DOI: 10.37886/ip.2021.021 Position of Slovenian High-Growth Firms within the European Context Stojan Črv* ITW Appliance Components d.o.o., Poljubinj 89E, 5220 Tolmin, Slovenija stojan.crv@gmail.com Abstract Purpose and Originality: This research aimed to present the position of Slovenian high-growth firms (HGFs) or gazelles in the European area and compare Slovenian high-growth firms with their European counterparts in terms of their growth rates. This research is based on a systematic review of high-growth firms and their position in Slovenia and Europe. The comparison of data on Slovenian high-growth firms and those in the European area enabled us to position the Slovenian high-growth firms within the broader European context. Method: The survey is based on a systematic review of the Financial Times newspaper ranking of the 1000 high-growth firms in Europe and the Dnevnik publishing house ranking of the 500 high- growth firms in Slovenia, as well as on data obtained from the statistical office of the European Union and Slovenia. Using the Minitab software, we verified the normality of the distribution of the considered data, performed the transformation of non-normal distributed data and calculated the Pearson correlation coefficient »r« between the individual considered factors, based on which we then confirmed the hypotheses. The distribution of data was shown with Pareto and bar charts. Results: The research showed the position of Slovenian high-growth firms in the European area. According to the research findings, it is evident that both Slovenian and European high-growth firms are mostly smaller organizations. The biggest share of high-growth firms is in Germany, while the biggest share of such firms in Slovenia is in the Osrednjeslovenska (Central Slovenia) region. The growth rate of high-growth Slovenian and European firms is comparable. The level of sales revenues and the number of employees do not show any impact on the companies' growth rates, while gross domestic product, on the other hand, contributes positively to their growth rate. By comparing the Slovenian and European high-growth firms considered in the survey, we found that the European firms show a higher growth rate than their Slovenian counterparts, while they are comparable in sales revenues. Society: The research enables better visibility of high-growth firms among the general public. The wider society evaluates the impact, operation and social responsibility of high-growth firms in a given environment. Last but not least, a well-informed public can positively contribute to the success of high-growth firms. Limitations / further research: This research is limited to comparing Slovenian high-growth firms with those in the European area based on two HGF rankings. Based on the fact that Slovenia is a small country in the common European area, it would be appropriate to conduct a survey or comparison with smaller European countries and to position Slovenian high-growth firms within this group. The other possibility would be to use a greater number of representative rankings and repeat the existing survey. Keywords: high-growth firms, gazelles, Slovenia, Europe, companies' performance indicators, growth rate. Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 85-109. 86 1 Introduction There has been a growing interest in high-growth firms lately (Hölzl, 2014, p. 199). Different factors and their impact on companies' (in)ability to cope with rapidly changing environment are widely researched and discussed, especially in periods of greater economic fluctuations or even in times of major crises. Successful companies thus attract public interest, but the real interest and attention are given to high-growth firms (Krasniqi & Desai, 2016, p. 1075). This is due to the widespread opinion that fast-growth firms significantly contribute to creating jobs and have a positive impact on economic growth Henrekson & Johansson, 2009, str. 227). According to an increasing body of literature on this subject, the vast majority of high-growth firms, also known as gazelles, are small and young businesses (Pereira & Temouri, 2018, p.11-14). Among the potential positive factors of these firms' growth and entrepreneurial dynamics are the research and development activity, innovation and organization flexibility, which are the principal modes of operation and which enable them to respond promptly to external and internal changes as well as to structural economic changes (Segarra-Blasco, Teruel & Jové-Llopis, 2018, p. 661-662). However, high-growth firms are not immune to institutional, social, cultural and economic characteristics of the environment in which they originate (Giner, Santa-María, & Fuster, 2016, str. 75). Therefore, these factors have a direct or indirect impact on these firms. Nevertheless, regardless of the environment in which they originate, they all operate in common markets, either in the European Union or global markets. Slovenian high-growth firms, operating also in the EU market, are compared with one another on Slovenia's relatively small geographic territory. The comparisons have been made since 2001 by the Dnevnik publishing house within the framework of the Gazelle project. This raises the question of Slovenian high-growth firms' position within the European context where the comparison of the fastest growing firms is made by the Financial Times newspaper. The research allows for a comparison of a list of high-growth firms in Slovenia with that of their counterparts elsewhere in Europe. Furthermore, Slovenia and Europe's comparison provides Slovenian high-growth firms and those with such aspirations an insight into a wider economic area and their position therein. The research aims to stimulate Slovenian high- growth firms to implement continuous improvement processes and thus, vigorously enhance their competitiveness and performance. 2 Theoretical framework There is a constant public interest in successful companies. The increased interest in high- growth firms has been driven by their remarkable ability to create new jobs (Brown & Mawson, 2016, p. 207). The two authors conducted research among Scottish high-growth firms. They observe that high-growth firms adopt more aggressive forms of international expansion when compared with other firms. Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 85-109. 87 However, high-growth firms also differ in many other aspects, such as their geographical location and industry in which they operate. In this regard, high-growth firms are the subject of many studies examining individual factors that are at work in these firms. Henrekson and Johansson (2009, p. 240) undertook a detailed analysis of the existing studies on high-growth firms and found that these studies differ in their definitions of gazelles, measures of growth and time periods over which growth was measured. Moreover, high-growth firms also differ in size, industry affiliation, age and geographic location. High-growth firms exist in all industries but are still somewhat overrepresented in service industries. The authors also note that a certain proportion of high-growth firms generate a disproportionately large share of new jobs compared to non-high-growth firms. They also state that size is not a factor that would significantly impact the growth of a firm. It seems that the factor of newness is more important than size. Different studies deal with the analysis of factors that have an impact on the rapid growth of firms. One of these studies was conducted by Pereira and Temouri (2018, p.11-14) on a sample of firms from 11 European emerging countries. The survey results suggest that there is a negative correlation between a firm's age and the likelihood of it becoming a high-growth firm. This means that younger firms are more likely to become high-growth firms. The authors note that smaller firms are those that are most likely to become high-growth firms. Regarding institutional factors, the authors emphasize that an improvement in an institutional environment positively impacts the likelihood of firms becoming HGFs. Reduction of bureaucracy and its greater efficiency seem to have a larger positive impact as compared with corruption and investment climate. A study covering 179,970 firms from nine European countries, which was conducted by Weinblat (2018, p. 33), indicates that among the most important variables affecting a firm's growth are the number of employees and the firm's age. The growth rate differs between firms. Characteristics of Slovenian firms with different types of growth were examined by Tajnikar, Ponikvar and Bonča (2016, p. 43-45). Their findings show that firms' rapid growth is based on the better use of existing capacities and the adjustment of the volume of capacity to changing market conditions. Such firms are mainly export-oriented. The majority of HGFs are in manufacturing (HGF stands for a high-growth firm). Authors point out that fast growth can only last a short while because different companies emerge as generators of fast growth in different years. As regards the life cycle, it could be said that younger companies start growing by investing in production capacity and employing workers, whereas older companies try to utilize their existing capacity better. The study also shows the negative side of fast growth, which may be reflected in the company's reduced liquidity and indebtedness. The fact that HGFs do not grow in the same way was also established by Delmar, Davidsson and Gartner (2003, p. 211) who conducted an analysis of Swedish firms. They found that different forms of growth are measured with different growth measures. Nevertheless, the authors note that a firm's growth is systematically related to the firm's age, size and industry affiliation. Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 85-109. 88 When it comes to successful long-term operation of a company or instead the life cycle of HGFs, some researchers focus their attention on the duration of the period of rapid growth. In relation thereto, Hölzl (2014, p. 225-226) examined the factor of the survival of companies in Austria in the period of three to nine years after becoming HGFs. As is evident from the research results, the mere fact that a company is a high-growth firm does not increase the likelihood of its survival compared to other companies. Moreover, the majority of HGFs are not able to repeat this exceptional growth. The classification of a company as a high-growth firm is probably related to the large one-off project of the company's expansion. As was already mentioned in the introduction, HGFs also differ in terms of their geographic location. This diversity also includes the factor of the state as the institution that sets the rules and thus helps shape the factors affecting the high growth of firms. Krasniqi and Desai (2016, p. 1075) conducted a research of the role of state institutional factors in HGFs in twenty-six transition countries, including Slovenia, and discovered that the interaction between formal and informal institutions positively influences HGFs. They further found that formal institutions in fast-reforming transition economies discourage HGFs, while informal institutions in slow-reforming transition economies encourage them. Nowadays, institutions stimulate companies through various mechanisms to move towards the so-called green way of working. This is where the ecological awareness and orientation of HGFs, as well as their use and utilization of the so-called green technologies, comes into focus. The role of ecological or rather green technologies in 5,498 manufacturing firms in Italy was analyzed by Leoncini, Marzucchi, Montresor, Rentocchini and Rizzo (2019, p. 900). The study confirmed the important role of green technologies in fostering the firms' growth, as measured by employment growth. By using green technologies, firms enter green markets more easily, while on the other hand they optimize their internal operations by decreasing production costs due to greater resource efficiency and possibility of recycling these resources. All this contributes to a higher growth of firms. There are many studies that emphasize the leading role of HGFs in job creation. Of course, these jobs vary in terms of their complexity, affecting the need for employment of people with different skills and levels of education. In her study of Danish firms, Eklund (2020, p. 700- 701) establishes that the correlation between a firm's high growth and highly educated employees' share is rather important. Moreover, a firm's high growth is facilitated by its organizational as well as human capital. The author also concludes that innovation capabilities can significantly contribute to a firm's high growth. It is the innovations that positively impact a firm's growth and are actually a synonym for its long-term existence and performance. In addition to green technologies, innovations are those that today receive a lot of attention and incentives. The role of innovation in HGFs in EU countries was studied by Segarra-Blasco, Teruel and Jové-Llopis (2018, p. 655-659). The countries were classified into three clusters (core countries, Mediterranean countries and new EU member states, including Slovenia). The key finding of the research is that there is a Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 85-109. 89 positive and highly significant correlation between investments in R&D and the likelihood of introducing innovations. In terms of size, the study notes that medium and larger firms show a larger propensity to innovate than small firms. Moreover, a firm is more likely to be innovative if it is an export-oriented firm and one that cooperates with other firms within a group because this provides them with greater support in carrying out innovative activities. This is especially evident in the Mediterranean and new EU member states. While institutional cooperation seems to be more important for core country firms, Mediterranean firms rely more on market cooperation. Cooperation with other firms that belong to the group positively affects new EU member states' ability to innovate. Access to public funds, including EU subsidies, is also a variable that positively impacts innovation and significantly affects the likelihood of a firm being an innovative firm and thus also a high-growth firm. Innovation as an impact factor was also emphasized by Brüderl and Preisendörfer (2000, p. 66-67) who mainly focused on employment effects and the growth potential of newly established businesses in Upper Bavaria, Germany. The authors claim that job creation can be attributed to a small number of fast-growing firms, while a firm's size and innovation are a strong predictor of rapid growth. Slovenian HGFs operate in a domestic market and, above all, in external or international markets because they are mostly export-oriented. This is the competitive environment in which they operate and in which they compare themselves with other foreign HGFs. Although the growth rate is based on the positive change in sales value within a specific period of time, the absolute value of sales in the examined body of literature is not identified as an independent factor that would positively impact growth rates of HGFs. On the other hand, the number of employees is identified as a significant factor affecting a firm's growth rate. Finally, it should be noted that the geographical locations of HGFs or the environments in which they operate differ greatly. Moreover, access to public funds and subsidies as well as the economic power of the country in which HGFs are located cannot be ignored because all these are factors that contribute to innovation and result in faster growth of firms. Our research aimed to examine these statements. Therefore, a number of hypotheses were formulated that relate to correlations between the characteristics of HGFs in Slovenia and those in other European countries. Hypothesis 1: »The growth rate of Slovenian HGFs is comparable to that of other European HGFs.« Hypothesis 2: »The number of employees does not impact a firm's rapid growth.« Hypothesis 3: »Sales revenues do not impact a firm's rapid growth.« Hypothesis 4: »The level of gross domestic product has a positive impact on the number of HGFs.« Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 85-109. 90 3 Method The Financial Times newspaper uses its own method of selecting HGFs in Europe by means of which it compiles the FT 1000 ranking featuring 1000 fastest growing companies in Europe. A significant indicator on the basis of which companies make a list is the firms' growth rates in the last three years. The selection committee conducts an extensive inquiry of companies and their data with the purpose of making the ranking as complete and representative as possible, but the ranking is not ideal as some companies do not want to make their figures public or do not participate for other reasons. Companies that wish to participate may apply via the website of Financial Times or company Statista. In addition, through research in company databases and other public sources, Statista identifies the companies in Europe that could be potential candidates for the FT 1000 ranking. These companies are then invited to participate in the competition by post, email and telephone. Companies that wished to be listed in the ranking for 2020 had to meet the following criteria:  revenue of at least € 100,000 generated in 2015,  revenue of at least € 1,500,000 generated in 2018,  the company is independent and not a subsidiary or branch office of another company,  the revenue growth between 2015 and 2018 was primarily organic and  if a company is listed on a stock exchange, its share price has not fallen by 50 % or more since 2018. The countries that were eligible to participate in the competition for the fastest growing companies in Europe in 2020 were as follows: Austria, Belgium, Bosnia & Herzegovina, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Monaco, Netherlands, Norway, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, Switzerland and the United Kingdom. The minimum average growth rate required to be included in the FT 1000 ranking for 2020 was 38.4 per cent. The Dnevnik publishing house performs the selection of HGFs or gazelles in Slovenia. The selection is based on the firms' growth in the last six consecutive financial years with the purpose of promoting companies that are focused on long-term growth. The selection is conducted in five steps. In the first step, company Bisnode analyzes financial data and compiles a ranking of the fastest-growing companies in six Slovenian statistical regions defined in the Dnevnik methodology. These regions are the following: Dolenjsko-Posavska (Lower Carniola-Sava), Dravsko-Pomurska (Drava-Mura), Gorenjska (Upper Carniola), Osrednjeslovenska (Central Slovenia), Primorsko-Notranjska (Littoral-Inner Carniola) and Savinjsko-Zasavska (Savinja-Central Sava). Companies can make the regional list if they have a minimum of 15 employees, net revenues from sales of € 400,000 in the selected year, if they operate profitably and have exhibited the highest revenue growth in the last five years. In addition to the listed criteria, companies' ranking also depends on their credit rating, profit, personal incomes, management efficiency, and values of sustainable growth. Members of the Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 85-109. 91 selection committee then have interviews with the firms' owners and managing directors through which they establish some other attributive factors, such as the level of internal communication, corporate social responsibility, sales orientation and target markets, level of development and innovation, possible patent protections as well as the implementation of the firms' vision, strategy and goals. In the third step, the committee takes into consideration all firms identified in the first two steps and nominates three of them for the title of the fastest- growing firm or gazelle within its region. Innovative companies and those operating internationally are given precedence. The selection committee then selects the first, second and third fastest growing firm within each region. The regional winner is then eligible to compete on a national level and list the fastest growing firms in the country. In the final stage, three fastest growing firms in Slovenia are selected and presented with the Golden Gazelle, Silver Gazelle and Bronze Gazelle awards based on their economic performance, growth rate and positioning in the wider environment. A comparison between the methods of the Financial Times and Dnevnik shows differences between the two. The ranking compiled by Financial Times is based on the growth rate in the preceding four consecutive financial years, while the Dnevnik ranking takes into consideration the growth rate in the preceding six consecutive financial years. The method of entering the ranking also differs. While companies cannot apply to enter the Dnevnik ranking by themselves, this is possible with the Financial Times ranking. The Dnevnik method of ranking the 500 fastest growing companies in Slovenia does not change in a given year, while the entry conditions according to the Financial Times method are adjusted every time a new ranking of 1000 fastest growing European countries is compiled. However, both rankings include data on the growth rate, sales revenues, and the number of employees of each firm listed in the ranking. Our research was conducted on the basis of the Financial Times ranking of Europe's 1000 fastest growing companies and the Dnevnik ranking of Slovenian gazelles or the 500 fastest growing companies in Slovenia. Data for our research was taken from these two rankings, namely for the first 500 HGFs from each ranking. The research model (Figure 1) illustrates the main steps to verify the set hypotheses and the positioning of Slovenian HGFs within the European context based thereupon. Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 85-109. 92 Figure 1. Research model. Figure 2 shows factors considered in our research and the set hypotheses. Figure 2. Factors considered in Slovenian and European HGFs, and the set hypotheses. The Financial Times ranking illustrates the growth rates of the 1000 fastest growing companies in Europe in a four-year period from 2015 to 2018 inclusive, while the Dnevnik ranking of the 500 fastest growing companies in Slovenia illustrates the growth rates for a six- year period, from 2013 to 2018 inclusive. The survey includes data on sales revenues and number of employees for 2018, as they are considered in both rankings. Because the Dnevnik Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 85-109. 93 ranking lists 500 fastest growing companies, we only considered data for the first 500 companies listed in the Financial Times ranking. We thus made sure that the sample size was equal. Comparing the above-mentioned data for the same factors ensured the validity of the method of our research. Data on gross domestic product for 2018 was obtained from the European Union's statistical office (Eurostat) and that of the Republic of Slovenia. When selecting the fastest growing companies, both Financial Times and Dnevnik verify the data's reliability and validity according to their methods. Moreover, both newspaper companies have been compiling the ranking of the fastest-growing companies for several years, thereby enabling the analysis of the trends of the rapid growth of companies and the measuring suitability of the selection methods. The same applies to the statistical offices of the European Union and the Republic of Slovenia. By making this information publicly available, they all guarantee their reliability and credibility. Distribution of data that illustrate the number of HGFs in individual countries of the European Union and regions of Slovenia is shown by Pareto and bar charts. Prior to statistical analysis of data by means of the Minitab software, the normality of the distribution of the considered data was verified. Non-normal distributed data was transformed with Johnson transformation. Normal distributed data obtained in this manner was then applied to verify the hypotheses. Correlations between the companies' growth rates, sales revenues and the number of employees, as well as between gross domestic product and the number of HGFs were examined by means of the Pearson correlation coefficients »r«. 4 Results 4.1 HGFs in Slovenia regions and European countries According to the Companies Act of the Republic of Slovenia (ZGD-1), companies are classified as micro, small, medium-sized and large companies. This classification also takes into consideration the number of employees, namely:  a micro company: up to 10 employees,  a small company: from 11 up to 50 employees,  a medium-sized company: from 51 up to 250 employees and  a large company: more than 250 employees. In view thereof, European HGFs from the Financial Times ranking for 2018 and Slovenian HGFs from the Dnevnik ranking for 2018 were distributed by size (Figure 3). Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 85-109. 94 16% 47% 30% 6% 0% 76% 20% 4% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% micro small medium large sh ar e company size EU companies SLO companies Figure 3. European and Slovenian HGFs distributed by size. Among the considered companies, small companies' share is the highest, followed by medium-sized, micro and large companies. The Dnevnik ranking for 2018 lists HGFs from six Slovenian regions (Figure 4). Figure 4. Number of high-growth firms by individual Slovenian regions. Slovenian regions are illustrated with bar charts, while the sum of Pareto shares is shown with a line. There is a significantly higher number of HGFs in the Osrednjeslovenska (Central Slovenia) region, followed by the Dravsko-Pomurska (Drava-Mura) region, while the number of HGFs in other regions is very similar. The Financial Times ranking for 2018 lists HGFs from 27 European countries (Figure 5). Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 85-109. 95 Figure 5. Number of high-growth firms by individual European countries. Figure 5 shows European countries illustrated with bar charts, while the sum of Pareto shares is shown with a line. Germany and the United Kingdom stand out as the two leading countries; Italy and France follow them closely, while Spain comes in fifth place. Other countries constitute the lower part of the ranking. 4.2 Comparison of growth rates of HGFs in Slovenia and elsewhere in Europe Correlations between various factors were examined by means of the Pearson correlation coefficients »r«. The critical value of the Pearson correlation coefficient for 500 high-growth firms (N = 500) at a level of confidence of 95% (ɑ = 0.05) is at 0.0877. The same critical value was considered in all the correlation calculations that follow. A possible correlation between the growth rates of Slovenian firms listed in the Dnevnik ranking for 2018 and those listed in the Financial Times ranking for 2018 is shown in Table 1. Table 1. The Pearson coefficient values for the correlation between the growth rates of Slovenian and European companies. Growth rate EU Growth rate SLO r = 0.997 Confidence interval (CI) = 0.997; 0.998 p < 0.001 Standard deviation = 3.022 The correlation coefficient has a significant positive value indicating a complete correlation between the growth rates. The first thirty European HGFs exhibit significantly higher growth rates than Slovenian HGFs, after which the growth rates gradually converge (Figure 6). Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 85-109. 96 0 50 100 150 200 250 300 350 400 1 50 99 148 197 246 295 344 393 442 491 gr ow th ra te No. of companies EU companies SLO companies Figure 6. Comparison of growth rates of HGFs in Slovenia and elsewhere in Europe. The matrix plot of growth rates is shown in Figure 7. Figure 7. Matrix plot of growth rates of HGFs in Slovenia and elsewhere in Europe. The matrix plot of growth rates illustrates a complete correlation between the growth rates of Slovenian and European companies as well as a significant positive value of the Pearson correlation coefficient (Table 1). Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 85-109. 97 4.3 Correlation between sales revenues, number of employees and growth rate The correlation between sales revenues and the growth rate and the number of employees and the growth rate of Slovenian HGFs is shown in Table 2. Table 2. The Pearson coefficient values for correlation between sales revenues, number of employees and growth rates in Slovenian HGFs. Slovenia Growth rate Sales revenues r = 0.071 Confidence interval (CI) = -0.017; 0.158 p < 0.112 Standard deviation = 81.39 Number of employees r = 0.029 Confidence interval (CI) = -0.059; 0.116 p < 0.524 Standard deviation = 184.12 No correlation between sales revenues and growth rates in Slovenian HGFs was identified. The same applies to the number of employees and the growth rate. Correlations between sales revenues and growth rates as well as between the number of employees and growth rates of European HGFs are shown in Table 3. Table 3. The Pearson coefficient values for correlation between sales revenues, number of employees and growth rates in European HGFs. Europe Growth rate Sales revenues r = 0.193 Confidence interval (CI) = 0.108; 0.276 p < 0.001 Standard deviation = 51.81 Number of employees r = 0.078 Confidence interval (CI) = -0.010; 0.165 p < 0.081 Standard deviation = 245.50 There is a weak correlation between sales revenues and growth rates in European HGFs, while no correlation was identified between the number of employees and growth rates. Sales revenues of Slovenian HGFs are comparable to those of European HGFs (Figure 8). Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 85-109. 98 0 200 400 600 800 1000 1200 1400 1600 1800 1 50 99 148 197 246 295 344 393 442 491 sa le s r ev en ue s No. of companies EU companies SLO companies Figure 8. Comparison of sales revenues of HGFs in Slovenia and elsewhere in Europe. 4.4 Correlation between gross domestic product and the number of HGFs Gross domestic product (henceforth GDP) is often used to compare different regions or countries' development levels. These development levels may be reflected in a higher number of HGFs. A correlation between gross domestic product and the number of HGFs in Slovenia (Table 4) and elsewhere in Europe (Table 5) was thus explored. Table 4. The Pearson coefficient values for the correlation between the gross domestic product and the number of HGFs in Slovenia. Slovenia Number of companies GDP r = 0.954 Confidence interval (CI) = 0.631; 0.995 p < 0.003 Standard deviation = 4,861 There is a strong correlation between the gross domestic product and the number of HGFs in Slovenia. Table 5. The Pearson coefficient values for the correlation between the gross domestic product and the number of HGFs in Europe. Europe Number of companies GDP r = 0.770 Confidence interval (CI) = 0.552; 0.890 p < 0.001 Standard deviation = 872,803 As regards the European companies, the correlation between the two considered factors is also strong. Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 85-109. 99 5 Discussion Slovenian and European HGFs studied in the research were mostly small companies with up to 50 employees (see Figure 3). The proportion of such companies in Slovenia is at 76 %, while in Europe it is 47 %. They are followed by medium-sized companies with up to 250 employees, constituting 20 % of HGFs in Slovenia and 30 % in Europe. In Slovenia, there are no micro-companies with up to 10 employees among HGFs, while the share of such companies elsewhere in Europe is at 16 %. Large companies with over 250 employees come last. The percentage of such companies in Slovenia (4 %) is similar to that in Europe (6 %). The findings of our research are consistent with those of some other studies, noting that the share of small companies among HGFs is rather significant (Henrekson & Johansson, 2009, p. 240) because the likelihood of small companies becoming HGFs is higher (Pereira & Temouri, 2018, p. 12). As regards the number of HGFs in Slovenia, the Osrednjeslovenska (Central Slovenia) region comes first, followed by the Dravsko-Pomurska (Drava-Mura) region. The number of HGFs in these two regions is higher than elsewhere in Slovenia. Compared with the Osrednjeslovenska (Central Slovenia) region, the share of HGFs in the Dolenjsko-Posavska (Lower Carniola-Sava) region, which is in the third place, is lower by as much as 65 %. These regions are then followed by the Primorsko-Notranjska (Littoral-Inner Carniola), Savinjsko- Zasavska (Savinja-Central Sava) and Gorenjska (Upper Carniola) region (see Figure 4). The number of HGFs in these last four regions is very similar. In more developed regions, conditions for the creation and development of HGFs seem to be better. Other studies (Tajnikar, Ponikvar & Bonča, 2016, p. 43) also found that the number of HGFs is the highest in the most developed Slovenian regions. Among the 27 studied European countries, the highest number of HGFs listed in the Financial Times FT 1000 ranking is in Germany followed by the United Kingdom, Italy, France, Spain and others (see Figure 5). The fifth country in the ranking is Spain, with a proportion of HGFs smaller by 59 % than that in Germany, which is in the first place. Central and Eastern European countries are not to be found among the leading countries in the ranking. For example, Poland comes in sixth place, while the Czech Republic is in ninth. The share of HGFs in Slovenia is at 0.4 %, ranking the country in the 24th place among the 27 studied countries. It probably comes as no surprise that in Germany, the economic superpower, the number of HGFs is the highest. The more competitive the country is, the better the conditions for creating companies, particularly smaller ones, resulting in a higher number of HGFs (Pereira & Temouri, 2018, p. 13). In the study, we calculated the Pearson correlation coefficients for predefined factors (see Chapter 4) by means of which the set hypotheses listed in the following chapter were confirmed or refuted. Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 85-109. 100 Hypothesis 1: »The growth rate of Slovenian HGFs is comparable to that of other European HGFs.« The calculated Pearson coefficient value for correlation between the growth rate of HGFs in the Dnevnik ranking for Slovenia and the growth rate of HGFs in the Financial Times FT 1000 ranking for Europe has a significant positive value (r = 0.997). Although studies of other authors emphasize differences in measures of growth (Henrekson & Johansson, 2009, p. 240) or growth rate measurement methods (Delmar, Davidsson & Gartner, 2003, p. 211), our research confirms that there is a complete correlation between the studied growth rates (Table 1). Based on the calculation, it can be established that Hypothesis 1 was confirmed by our research. Hypothesis 2: »The number of employees does not impact a firm's rapid growth.« The calculated correlation coefficient between the number of employees in Slovenian HGFs listed in the Dnevnik ranking and these companies' growth rate is low (r = 0.029). There is an insignificant correlation between the two studied factors (Table 2). The calculated correlation coefficient between the number of employees in European HGFs listed in the Financial Times FT 1000 ranking and these companies' growth rate is also low (r = 0.078). Also in this case, the correlation between the number of employees and the growth rate is insignificant (Table 3). Although Weinblat (2018, p. 33) finds in his research that a company's growth rate is affected by the number of employees, our research did not establish any correlation between the two factors. Based on the calculation of both correlation coefficients, it can be established that Hypothesis 2 was confirmed by our research. Hypothesis 3: »Sales revenues do not impact a firm's rapid growth.« A potential correlation between sales revenues and growth rates of HGFs listed in both rankings that were studied in our research was determined on the basis of the calculated correlation coefficient. The correlation coefficient for Slovenian HGFs (r = 0.071) shows that there is an insignificant correlation between the two factors (Table 2). In view of the calculated correlation coefficient for European HGFs (r = 0.193), it was established that there is a weak correlation between the two factors (Table 3). Sales revenues do not impact the rapid growth of Slovenian HGFs, while, based on the statistically significant correlation coefficient, sales revenues in European HGFs seem to have a slight effect on their rapid growth. Based on the calculation of both correlation coefficients, Hypothesis 3 can only be partially confirmed. Hypothesis 4: »The level of the gross domestic product has a positive impact on the number of HGFs.« The calculated correlation coefficient between the gross domestic product of individual Slovenian regions studied in the research and the number of HGFs in those regions (r = 0.954) confirms a very strong correlation between these two factors (Table 4). Moreover, the Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 85-109. 101 correlation coefficient between the gross domestic product of individual European countries and the number of HGFs in those countries (r = 0.770) confirms a strong correlation between the two (Table 5). Based on the calculation of both correlation coefficients, it can be established that Hypothesis 4 was confirmed by our research. The Pearson correlation coefficients calculated in the research have demonstrated the existence or non-existence of correlations between the examined factors. On the one hand, the calculated correlation coefficients are low, while on the other, they are very high which means that correlations between individual factors are weak, insignificant or strong. No medium or moderate correlations were established in our research, making the confirmation of hypotheses easier. For our research, the critical value of the Pearson correlation coefficient was at 0.0877. At the beginning of our research, we were interested in the position of Slovenian HGFs within a wider European context and their position in respect of the comparison of Slovenia and other European countries in terms of their growth. By confirming Hypothesis 1, it was established that growth rates of Slovenian and European HGFs are comparable as there is a complete correlation between the studied growth rates (Table 1). Growth rates are shown in Figure 7. There are two Slovenian companies among the 500 fastest growing companies in Europe listed in the Financial Times FT 1000 ranking, namely in the 92nd and 284th place. Both companies are classified as micro companies having less than ten employees. These two companies were not listed in the Dnevnik ranking of the 500 fastest growing companies in Slovenia. This can be attributed to different entry conditions for both rankings (see Chapter 3). Based on the studied growth rates, we positioned Slovenian HGFs within the context of other European HGFs as well as within the wider European area. For this purpose, we merged the Financial Times FT 1000 and Dnevnik rankings and arranged HGFs in order by their growth rates. Although other authors also acknowledge that growth rates are calculated using different methods (Delmar, Davidsson, & Gartner, 2001, p. 32), the only difference in our study is the time component. The Financial Times FT 1000 ranking period is two years longer than that of the Dnevnik ranking. Nevertheless, both calculations are representative, taking into consideration four or rather six consecutive financial years. Moreover, when merging the two rankings, we took into consideration the fact that, based on the calculated Pearson correlation coefficient between the growth rate of HGFs in the Dnevnik ranking for Slovenia and that of HGFs in the Financial Times FT 1000 for Europe, there was a complete correlation between the two (Table 1). HGFs in the merged ranking were thus ranked by their growth rates from the highest to the lowest (Appendix). The merged ranking represents the first 608 HGFs since from here on out follows only Slovenian HGFs. As regards the growth rates for 2018, the first place was taken Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 85-109. 102 by a company from the United Kingdom having a growth rate of 37.463 %. The highest- ranked Slovenian HGF was the golden gazelle for 2018 positioned in the 16th place with a growth rate of 5.288 %. The second highest-ranked Slovenian HGF or the silver gazelle was in the 46th place with a growth rate of 2.377 %, while the third HGF or the bronze gazelle was in the 48th place with a growth rate of 2.270 % (see Appendix). In view of sales revenues in 2018, the position of Slovenian HGFs among their European counterparts is better. The first place in the merged ranking was thus taken by a Slovenian company with sales revenues of EUR 1,771 million, followed by a Czech company with revenues of EUR 1,079 million. The third and fourth places also belonged to Slovenian companies with sales revenues of EUR 221 million and EUR 203 million respectively. There are as many as ten Slovenian companies among the top twenty HGFs. When compared with growth rates (Figure 6), sales revenues of Slovenian HGFs are much more comparable to those of European HGFs (Figure 8). 6 Conclusion The research was based on the analysis of HGFs in Slovenia and elsewhere in Europe, and comparisons between them. The results of our research show that the studied HGFs are mostly small businesses, which is also consistent with the findings of authors Henrekson and Johansson (2009, p. 240) who note that the biggest share of HGFs is made up of small companies. It was also established that most HGFs in Slovenia are located in the Osrednjeslovenska (Central Slovenia) region. It is namely a fact that the majority of HGFs are concentrated in the most developed regions of Slovenia, such as the Osrednjeslovenska (Central Slovenia) region (Tajnikar, Ponikvar & Bonča, 2016, p. 43). Our research shows that Germany stands out among the European countries with the highest number of HGFs, followed by the United Kingdom, Italy, France and Spain. Conditions for establishing smaller companies are better in more competitive countries (Pereira & Temouri, 2018, p. 13), resulting in a higher number of HGFs. Our research confirmed that growth rates of Slovenian and European HGFs are comparable since the calculated value of the Pearson correlation coefficient (Table 1) indicates that there is a complete correlation between the studied growth rates. It is also noteworthy that the number of employees and the amount of sales revenues do not affect the rapid growth of a company, although Weinblat (2018, p. 33) argues that the number of employees also impacts the rapid growth of companies. Our research shows that the growth rate is positively affected by the level of gross domestic product. A comparison of all considered HGFs in our research showed that the first three Slovenian HGFs or rather the golden, silver and bronze gazelle rank between the 16th and the 48th place among the European HGFs. When it comes to growth rates, European HGFs exhibit higher growth rates than the best Slovenian HGFs. However, in terms of sales revenues, Slovenian HGFs are fully comparable with their European counterparts. There are a number of studies (Brown & Mawson, 2016, p. 207; Hölzl, 2014, p. 225-226; Krasniqi & Desai, 2016, p. 1; Leoncini, Marzucchi, Montresor, Rentocchini & Rizzo, 2019, p. Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 85-109. 103 900) that explore HGFs in individual countries or communities of countries. No scientific research was found that would compare Slovenian HGFs with larger territories or global markets and that would show positive effects in terms of facilitating competitiveness, growth and potential collaboration with other companies. Our research is based on a systematic review of HGFs in Slovenia and their counterparts elsewhere in Europe, which is a much greater territory to explore. Based on the comparison of data on Slovenian HGFs and their European counterparts, Slovenian HGFs were positioned within the wider European context and thus internationalized. We believe that our approach to the research is applied rarely, if at all. Our research helps Slovenian HGFs gain insight into their position among other European HGFs in terms of their growth rates and sales revenues. Companies can use such comparisons to plan or upgrade their business strategies or draw up their business plans to improve their business operations. Moreover, other fast-growing companies that are likely to become HGFs can use the data to compare themselves with their counterparts and to analyze the environment they are entering. The purpose of our research is also to increase the visibility of HGFs among the general public. The wider society evaluates the impact of HGFs, their attitude towards the stakeholders and corporate social responsibility towards the environment in which they operate. Better visibility of HGFs on the one hand and a well-informed public familiar with the activities within these HGFs on the other can have a positive impact on the companies' performance. The research that we conducted was limited to only two representative rankings of HGFs – the Financial Times FT 1000 ranking of European HGFs and the Dnevnik ranking of Slovenian HGFs – and five factors, namely the growth rate, sales revenues, number of employees, number of companies by individual countries and gross domestic product. These two rankings provided us with enough data to confirm the hypotheses in our research and to position Slovenian HGFs within the wider European context. In view of the small geographical size of Slovenia, our research was limited to the European territory. Considering the scope of our research, further research studies that include a greater number of comparable rankings of HGFs and data of national as well as European statistical offices would be recommended. Such research studies could also consider a more significant number of factors than those studied in our research. Research comparing Slovenian HGFs with firms from neighbouring countries or countries comparable in size to Slovenia (e.g. the Czech Republic, Slovakia or Croatia) would also be interesting. Such studies could provide suggestions for the harmonization of the so-called entry conditions for HGFs to be listed in various rankings as well as for the implementation of uniform criteria for the calculation of individual factors. All this would contribute to an easier and more transparent implementation of studies, classification and comparisons in the future. Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 85-109. 104 References 1. Brown, R., Mawson, S. (2016). The geography of job creation in high growth firms: the implications of 'growing abroad'. 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Pridobljeno na http://pisrs.si/Pis.web/pregledPredpisa?id=ZAKO4291# Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 85-109. 105 Appendices: Common ranking of the fastest growing firms The Dnevnik ranking of the 500 Slovenian and the Financial Times ranking of the 500 European HGFs were merged into a new common ranking which represents the first 609 HGFs by their growth rates in 2018 from the highest to the lowest. After 609th place there are only Slovenian HGFs and therefore are not listed in the following ranking. Slovenian companies are listed in bold italics. High-Growth Firm Country Growth rate High-Growth Firm Country Growth rate 1 OakNorth Bank United Kingdom 37463% 71 LVS Brokers Finland 1742% 2 Wolt Enterprises Finland 15642% 72 Dall Energy Denmark 1741% 3 Bolt Technology Estonia 12960% 73 Mediforma, d. o. o. Slovenia 1730% 4 Elements Global Services Spain 10233% 74 Sneakers & Jackets France 1716% 5 Les Eco-Isolateurs France 10078% 75 Viralize Italy 1710% 6 Gismart United Kingdom 8788% 76 Ruehe Healthcare Germany 1692% 7 Enens France 8603% 77 Laundryheap United Kingdom 1663% 8 Qred Företagslån Sweden 7301% 78 COMATCH Germany 1638% 9 Mamma 2.0 Italy 7110% 79 NoviCap United Kingdom 1635% 10 In Touch Networks United Kingdom 6325% 80 Elvie United Kingdom 1623% 11 Farmermobil Germany 6056% 81 GPA Innova (Dlyte) Spain 1611% 12 Holidu Germany 6031% 82 Lumenaza Germany 1604% 13 Feiniko Handelsgesellschaft Germany 5854% 83 Skills RH France 1557% 14 Franco Fresco Germany 5487% 84 ENPIRE Poland 1530% 15 MessengerPeople Germany 5378% 85 PALS E HIJOS IMPORT Spain 1513% 16 Milšped, d. o. o. Slovenia 5228% 86 Mrs Wordsmith United Kingdom 1501% 17 Foot District Spain 5129% 87 Nexolub Spain 1486% 18 Deelit Ventures Netherlands 5116% 88 INNOVI Recherche France 1475% 19 ID Finance Investments Spain 5019% 89 Parkster Sweden 1463% 20 getAir Germany 4706% 90 Bending Spoons Italy 1450% 21 PastBook Netherlands 4623% 91 ComplyAdvantage United Kingdom 1449% 22 Supermercato24 Italy 4567% 92 MAKING SCIENCE Spain 1430% 23 Donatella Germany 4495% 93 Società Gestioni Lavori Italy 1430% 24 Futureplay Finland 4410% 94 Ooni United Kingdom 1428% 25 Jobandtalent Spain 4093% 95 Ubiquicom Italy 1410% 26 Jungle Creations United Kingdom 3900% 96 Flight Club Darts United Kingdom 1408% 27 # sinob Germany 3614% 97 Narajan Slovenia 1408% 28 Global-e Online United Kingdom 3567% 98 Studapart France 1373% 29 Rinah Italy 3425% 99 Tylia Invest France 1359% 30 Domator24.com Poland 3419% 100 Enesco Italy 1359% 31 UPLINK Network Germany 3417% 101 ConceptIF Pro Germany 1357% 32 Trouva United Kingdom 3353% 102 Carcamovil Spain 1354% 33 STA PORTAGE (H2S) France 3271% 103 Tingopol Estonia 1350% 34 SoftwareHut Poland 3090% 104 Samy Road Spain 1347% 35 Beattie Passive Group United Kingdom 3027% 105 Grupo Grl (Élite Diseños) Spain 1317% 36 Fensterblick Germany 2926% 106 GAP Vehicle Hire United Kingdom 1314% 37 Eskimoz France 2900% 107 OTA Insight United Kingdom 1312% 38 Indie Campers Portugal 2878% 108 Priomold Germany 1277% 39 Zonneplan Netherlands 2807% 109 Nurole United Kingdom 1276% 40 ITP Nord Germany 2778% 110 Cru Wine United Kingdom 1271% 41 Agriconomie France 2697% 111 InnovaMaxx Germany 1245% 42 Bettzeit Germany 2662% 112 Signaturit Solutions Spain 1227% 43 Blue Motor Finance United Kingdom 2586% 113 P&W Bau Germany 1226% 44 Vidsy Media United Kingdom 2528% 114 Total Tiles United Kingdom 1212% 45 Darktrace United Kingdom 2446% 115 E paper Italy 1211% 46 Tekoma Marguč, d. o. o. Slovenia 2377% 116 Kiwi.com Czech Republic 1189% 47 Sorted Group United Kingdom 2300% 117 Oradian Croatia 1167% 48 Aparati, d. o. o. Slovenia 2270% 118 Egarri France 1167% 49 FOSTEC & Company Germany 2250% 119 GOVECS Germany 1157% 50 Stillfront Group Sweden 2222% 120 Stayincortina Italy 1155% 51 Tappx Spain 2157% 121 DMS Germany 1135% 52 Payment Assist United Kingdom 2140% 122 We Can Do HR Poland 1132% 53 Workcapital Spain 2126% 123 Univers Auto France 1130% 54 Yoyo Wallet United Kingdom 2103% 124 Crossflow Payments United Kingdom 1127% 55 TMT International Italy 2096% 125 Quantion Spain 1117% 56 CBE, d. o. o. Slovenia 2068% 126 Democom Italy 1110% 57 SourceBreaker United Kingdom 2021% 127 RTB House Poland 1108% 58 Glassbox Digital UK United Kingdom 2020% 128 FirstPhone Hungary 1102% 59 LittleBIG Connection France 1990% 129 YouLoveWords France 1097% 60 Hostmaker United Kingdom 1962% 130 Landbay Partners United Kingdom 1089% 61 Golden Bees France 1958% 131 Verocaffè Italia Italy 1088% 62 Chez Nestor France 1951% 132 Soorce Germany 1085% 63 Solnet Green Energy Finland 1926% 133 ISO Spaces South West United Kingdom 1084% 64 Osiway France 1909% 134 Voxpopme United Kingdom 1080% 65 Nexumstp Italy 1898% 135 Vadeca Facility Services Portugal 1074% 66 MeP Trans Estonia 1848% 136 Superprof France 1072% 67 Bluwalk Portugal 1811% 137 Typeform Spain 1059% 68 Discover Car Hire Latvia 1780% 138 Dipe Concept France 1043% 69 Missoma United Kingdom 1780% 139 Fitbox Germany 1042% 70 Grapevine (The Insiders) Netherlands 1757% 140 MF-CT, d. o. o. Slovenia 1037% Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 85-109. 106 High-Growth Firm Country Growth rate High-Growth Firm Country Growth rate 141 Prodigi Group United Kingdom 1026% 226 DataValue Consulting France 760% 142 Pekarna Kaučič, d. o. o. Slovenia 1025% 227 Seargin Poland 760% 143 TBT Italy 1023% 228 A.N.NE Germany 759% 144 Biochem Organics Romania 1020% 229 Fram Skandinavien Sweden 755% 145 Malagris Lithuania 1016% 230 Versoprobo Italy 752% 146 Eltim, d. o. o. Slovenia 1012% 231 Proficio Marketing Czech Republic 750% 147 Applifting Czech Republic 1011% 232 Reverse Media Group United Kingdom 734% 148 MTM Ruhrzinn Germany 1011% 233 Grabyo United Kingdom 729% 149 Rezatec United Kingdom 1009% 234 Housekeep United Kingdom 727% 150 ACCENTA Music Germany 1009% 235 Powerspace France 727% 151 Facephi Biometria Spain 1000% 236 DUSAPRO Immobilien Germany 719% 152 Mia Italy 993% 237 REMA-Solutions Germany 715% 153 eila Consulting Germany 992% 238 Eciglogística Spain 711% 154 Ankerkraut Germany 978% 239 Forsyth Barnes United Kingdom 706% 155 Cornexim Hungary 977% 240 Gute Marken Online Germany 706% 156 Adsmurai Spain 974% 241 New Wave Capital United Kingdom 703% 157 2E Vertriebs-GmbH Germany 973% 242 My Portage France 703% 158 Beer52 United Kingdom 964% 243 Born Stahlbau Germany 698% 159 Circle Imperium Netherlands 963% 244 tecRacer Consulting Germany 697% 160 Widget Brain Holding Netherlands 963% 245 Medical Net Italy 696% 161 Velotransport Italy 961% 246 Eksist, d. o. o. Slovenia 695% 162 Dani AFC, d. o. o. Slovenia 956% 247 Midstream Lighting United Kingdom 693% 163 Technobell, d. o. o. Koper Slovenia 955% 248 Budišin, d. o. o. Slovenia 693% 164 FIDUCIM France 953% 249 Good one Lithuania 691% 165 Matsmart in Scandinavia Sweden 946% 250 Lapelle Italy 685% 166 Moluna Germany 945% 251 Goap, d. o. o. Slovenia 682% 167 Metis Germany 937% 252 Eesti Metsameister Estonia 681% 168 CP Group Germany 934% 253 Cookies Factory Italy 681% 169 Ogury United Kingdom 931% 254 MC2 Technologies France 675% 170 Elements Talent Consultancy United Kingdom 931% 255 Stavební Interiérové Systémy Czech Republic 672% 171 Miyagi Italy 923% 256 cosos Germany 670% 172 ARES France 918% 257 HalalBooking United Kingdom 669% 173 Q Agency Croatia 917% 258 Idento France 668% 174 Binary Subject Portugal 912% 259 T-WATT Czech Republic 668% 175 Königskinder Immobilien Germany 904% 260 DrDoctor United Kingdom 665% 176 Macropix Italy 903% 261 SKILLSDAY France 665% 177 New Systems HandelsgmbH Austria 902% 262 Sonarworks Latvia 663% 178 Rojen Commerce Romania 901% 263 griep Baulogistik Germany 662% 179 YourParkingSpace United Kingdom 901% 264 checkout.com United Kingdom 662% 180 Infratech Bau Germany 900% 265 ZeitWerk Personal Germany 661% 181 n Dreams United Kingdom 898% 266 Tresorit Hungary 657% 182 Nova Cartotecnica Roberto.s Italy 898% 267 Oxwork France 657% 183 Geneplanet, d. o. o. Slovenia 884% 268 STEPS France 657% 184 Neuhoff Massivbau Germany 868% 269 Virtus Lab Poland 654% 185 HR XPERIENCE Germany 863% 270 Aquis Exchange United Kingdom 653% 186 Ogrodosfera Poland 857% 271 Eco Pro France 652% 187 Velvet Media Italia Italy 857% 272 Maxxi Engineering Italy 648% 188 Sendcloud Netherlands 853% 273 Alpha Real Estate Holding Germany 647% 189 Ds Glass Italy 852% 274 Kumulus Vape France 646% 190 Liikennevirta Finland 850% 275 Nuevo Conseil et Formation France 645% 191 MetaMinds Romania 845% 276 Recambios Endado Spain 645% 192 Prestige Car Center Finland 840% 277 SCA Investments (Gousto) United Kingdom 637% 193 Eland Oil & Gas United Kingdom 835% 278 Nu'Art Events Italy 637% 194 InTradeLog Hungary 829% 279 Noonic Italy 635% 195 Gecko Labs United Kingdom 826% 280 Brodynt Global Spain 634% 196 Riedl CNC, d. o. o. Slovenia 824% 281 Dolly Noire Italy 634% 197 Silxo United Kingdom 822% 282 Ideria France 634% 198 SOLSOL Czech Republic 820% 283 Simples Análises Portugal 627% 199 Ministry of Programming Bosnia and Herz. 819% 284 Hyper Recruitment Solutions United Kingdom 626% 200 Ramge Software Distribution Germany 819% 285 Italianway Italy 624% 201 Genius Sports Group United Kingdom 814% 286 Rosano Dry Fruit Italy 622% 202 ITP TECHNOLOGIE France 813% 287 Gruppo Del Barba Consulting Italy 621% 203 Letisan (RocketBaby) Italy 813% 288 Oni Foods Overseas Spain 618% 204 Štolfa GP, d. o. o. Slovenia 812% 289 Žaga Pogorelc, d. o. o. Slovenia 618% 205 Santigado Portugal 809% 290 WeWATCH Security Service Germany 612% 206 Kombitex Germany 808% 291 SuperAwesome United Kingdom 610% 207 Tower Hamlets GP Care Group United Kingdom 801% 292 Vmway Italy 609% 208 Mega tekstil, d. o. o. Slovenia 800% 293 CLICKLEARN Aps Denmark 603% 209 DEFAMA Deutsche Fachmarkt Germany 800% 294 Dexatel Estonia 601% 210 Etno Cafe Poland 800% 295 BXR IndustrieService Germany 601% 211 Lockwood Publishing United Kingdom 800% 296 City Shop West Kiosk Germany 600% 212 Plan Communications Holding United Kingdom 798% 297 Heltti Finland 600% 213 Gipin, d. o. o. Slovenia 798% 298 Zero Italy 599% 214 Globalwork Italy 792% 299 BKE Eisenbahn-Service Germany 599% 215 Parlem Telecom Spain 791% 300 Naïo Technologies France 594% 216 Anteco Systems (AnyTech365) Spain 791% 301 Decor Leader Italy 593% 217 Praesto, d. o. o. Slovenia 782% 302 Nutrisslim, d. o. o. Slovenia 593% 218 Xtream Codes Bulgaria 781% 303 Standal Spain 592% 219 Mobile Wave Solutions Bulgaria 780% 304 Mytho Italy 587% 220 Watt And Volt Greece 776% 305 Motius Germany 586% 221 84codes Sweden 775% 306 Rumi Storitve Slovenia 586% 222 VH24 Germany 773% 307 Herrero Brigantina Spain 586% 223 Sagles, d. o. o. Slovenia 771% 308 Lorefice & Ponzio Italy 586% 224 JBR Capital United Kingdom 770% 309 Qaiware Bulgaria 582% 225 PlasmaPro Estonia 769% 310 SIMWERT Germany 580% Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 85-109. 107 High-Growth Firm Country Growth rate High-Growth Firm Country Growth rate 311 507, d. o. o. Slovenia 579% 396 Feroles, d. o. o. Slovenia 473% 312 CogBooks United Kingdom 577% 397 A..E. Genc Warenhandels Germany 473% 313 Sierra Paper Spain 575% 398 Prohit, d. o. o. Slovenia 473% 314 INDOS Financial United Kingdom 571% 399 truu wasserweik Germany 471% 315 Ecosupply Portugal 568% 400 Kolinpharma Italy 471% 316 Streetec Germany 568% 401 Mabotrans, d. o. o. Slovenia 471% 317 Buzzoole Italy 562% 402 GSM Telecom Products Norway 470% 318 Cye Energia Spain 562% 403 Reech France 470% 319 Monterosa Productions United Kingdom 560% 404 Mercur Commerce, d. o. o. Slovenia 468% 320 Treves, d. o. o. Slovenia 558% 405 Neo Systems France 467% 321 PTM Germany 556% 406 Fountain Partnership United Kingdom 466% 322 SZM, d. o. o. Slovenia 552% 407 FUGU France 465% 323 Mapri, d. o. o. Slovenia 550% 408 Iplan Gestión Integral Spain 464% 324 Šeruga VAC, d. o. o. Slovenia 549% 409 Mojstrovina, d. o. o. Slovenia 464% 325 Gioielleria Duca Italy 547% 410 New adVentures Portugal 463% 326 Epidemic Sound Sweden 547% 411 Pulsar Photonics Germany 463% 327 TECHPROS Norway 547% 412 Podium Engineering Italy 462% 328 gohenry United Kingdom 543% 413 Vizolution United Kingdom 462% 329 Northmill Group Sweden 543% 414 Azuri Technologies United Kingdom 460% 330 LBBZ Germany 543% 415 Bitset, d. o. o. Slovenia 460% 331 Solar Clim - BF Cote d'azur France 542% 416 BigChange United Kingdom 459% 332 Assetz Capital United Kingdom 541% 417 ADay's March Shirts & Staples Sweden 456% 333 Maxpay Malta 541% 418 Intito Finland 456% 334 HiveMQ Germany 537% 419 Le Slip France 456% 335 All Good Poland 537% 420 Home Instead Germany 455% 336 freestyle Germany 537% 421 Schneider Financial Solutions United Kingdom 454% 337 New Truck Italy 536% 422 ARCESI Occitanie France 452% 338 Scribbr Netherlands 532% 423 CSHARK Poland 452% 339 Heart Apartments Italy 531% 424 Fill Up Média France 449% 340 GpasPlus France 530% 425 Bunker Partner Estonia 447% 341 Pharazon (PHZ Full Stack) Finland 529% 426 Holla Online Finland 446% 342 Personalhaus Bielefeld Germany 529% 427 lengoo Germany 446% 343 CLAREO France 529% 428 ELS Express Logistik Service Germany 443% 344 Comal Impianti Italy 527% 429 GROUPE INOVEFA France 441% 345 C-Astral, d. o. o. Slovenia 526% 430 Eco CO2 France 441% 346 SPG Steiner Germany 524% 431 Pan-Jan, d. o. o. Slovenia 439% 347 Velocity Outlet United Kingdom 522% 432 Torsion Group United Kingdom 433% 348 Laser Wire Solutions United Kingdom 520% 433 ETYO France 433% 349 CO.NET Germany 520% 434 Fb.M. Italy 431% 350 Phenofarm Italy 519% 435 Farmacosmo Italy 431% 351 AlterBoutique France 519% 436 Famoco France 430% 352 myElefant France 519% 437 Da.Dif Consulting Italy 430% 353 Senergica Italy 518% 438 B-MB, d. o. o. Slovenia 429% 354 Global Control 5 Poland 517% 439 Durophan Handelsgesellschaft Germany 428% 355 VIPCON Germany 516% 440 Sl Frais Distribution France 428% 356 innercircle Germany 515% 441 A.B.C., d. o. o. Slovenia 427% 357 Vialterra Infraestructuras Spain 515% 442 Quickcom Italy 427% 358 Alprem oprema, d. o. o. Slovenia 515% 443 Sonet plus, d. o. o. Slovenia 427% 359 Burgar-teh, d. o. o. Slovenia 514% 444 MPPM, d. o. o. Slovenia 426% 360 Transmission United Kingdom 513% 445 Lämpö-Valli Finland 426% 361 Zelená pošta Slovakia 512% 446 Bauer Energiekonzepte Germany 426% 362 Rhenus logistika, d. o. o. Slovenia 509% 447 T1 Solution Czech Republic 424% 363 Trans and Trade 1 Bulgaria 509% 448 Super, d. o. o. Slovenia 424% 364 SILKHOM France 508% 449 Tavan Tiefbau Germany 422% 365 Dato avtomatizacija, d. o. o. Slovenia 508% 450 Links, d. o. o. Slovenia 422% 366 LIT Tranzit, d. o. o. Slovenia 507% 451 SOS sejemske rešitve, d. o. o. Slovenia 421% 367 Rocksteady Music School United Kingdom 505% 452 Workable Software United Kingdom 420% 368 Botron Software Solutions Bulgaria 503% 453 Lestro-Ledinek, d. o. o. Slovenia 420% 369 Kariera, d. o. o. Slovenia 502% 454 eSMART Technologies Switzerland 419% 370 CSG (Greencell) Poland 502% 455 Tropic Skincare United Kingdom 418% 371 JSC Medium Group Lithuania 500% 456 cerascreen Germany 417% 372 WED2B United Kingdom 500% 457 Dein Stellplatz Germany 417% 373 Xcede Recruitment United Kingdom 497% 458 Lazer Lamps United Kingdom 416% 374 LEAD Consult Bulgaria 494% 459 Awinsi Technologies France 416% 375 Cloudi-Fi France 494% 460 Nanushka International Hungary 415% 376 Pergola, d. o. o. Slovenia 492% 461 Energía, Innovación y Desarrollo F. Spain 415% 377 C2A France 492% 462 Branchspace United Kingdom 413% 378 Fast-Up Partners France 489% 463 B-Open Solutions Italy 413% 379 AFR-IX Telecom Spain 489% 464 UAB Incorpus Lithuania 412% 380 StillKom Norway 489% 465 Aconity3D Germany 412% 381 raumweltenheiss Germany 487% 466 Bittnet Systems Romania 412% 382 MyPostcard.com Germany 485% 467 nanosun Czech Republic 411% 383 Arpilabe France 485% 468 PR, d. o. o. Slovenia 411% 384 I.C.E. Italy 484% 469 Genba Digital United Kingdom 409% 385 Alternativa Impianti Italy 483% 470 GoodCall Czech Republic 409% 386 Impera, d. o. o. Slovenia 483% 471 Dairylac Spain 409% 387 NowaLed Poland 482% 472 ON ZE AIR France 409% 388 Rosenbauer, d. o. o. Slovenia 480% 473 Faniani skupina, d. o. o. Slovenia 409% 389 bizforward Germany 477% 474 Pomorska Platforma Pracy Poland 409% 390 Globalis Viagens e Eventos Corp. Portugal 476% 475 Ekipa2, d. o. o. Slovenia 408% 391 Forever Entertainment Poland 475% 476 Financial Consulting Lab Italy 408% 392 Cablex Plastik, d. o. o. Slovenia 475% 477 L - Inox, d. o. o. Slovenia 408% 393 Exacaster Lithuania 474% 478 MOORE France 408% 394 Rotra, trgovina, d. o. o. Slovenia 474% 479 Treesystem Italy 408% 395 Adrenaline X-Treme Adv. Group Italy 473% 480 Audensiel France 407% Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 85-109. 108 High-Growth Firm Country Growth rate High-Growth Firm Country Growth rate 481 Tips, d. o. o. Slovenia 407% 545 Varovanje Galekom, d. o. o. Slovenia 370% 482 Simes, d. o. o. Slovenia 406% 546 WILLIE BEAMEN France 369% 483 Basemark Finland 405% 547 Kompas Novo mesto, d. o. o. Slovenia 369% 484 Tegar, d. o. o. Slovenia 404% 548 Interstar, d. o. o. Slovenia 368% 485 Lesnina MG oprema, d. d. Slovenia 402% 549 Arhel, d. o. o. Slovenia 368% 486 Synapsy Italy 402% 550 Sitem Motori Elettrici Italy 366% 487 Enerdat - S, d. o. o. Slovenia 401% 551 Fekra Consulting France 366% 488 OBIZ CONCEPT France 401% 552 Emark Slovakia 366% 489 Edubroker Poland 401% 553 Te-Tis Testen, d. o. o. Slovenia 365% 490 Werksräder24 Germany 400% 554 Aluvar, d. o. o. Slovenia 365% 491 Upgrade Italy 399% 555 Resgreen Europe Spain 365% 492 Conditus, d. o. o. Slovenia 398% 556 Aplast, d. o. o. Slovenia 364% 493 Edilbiangi Italy 398% 557 IS Prime United Kingdom 363% 494 KBS Group Germany 398% 558 Almac Italy 363% 495 BAM GmbH Germany 398% 559 GPS Germany 363% 496 Fokus Premium Poland 397% 560 Communis Projektbau Germany 362% 497 D' agencija, d. o. o. Slovenia 396% 561 Novastar Greece 361% 498 VanMoof Global Holding Netherlands 394% 562 Ledinek Engineering, d. o. o. Slovenia 361% 499 Stiltz United Kingdom 392% 563 ITFS Poland 361% 500 Stilles, d. o. o. Slovenia 392% 564 Don Don, d. o. o. Slovenia 361% 501 Haberkorn, d. o. o. Slovenia 392% 565 Avtoservis Koper, d. o. o. Slovenia 360% 502 AerFin United Kingdom 391% 566 ContactEngine United Kingdom 360% 503 France Bières France 390% 567 Peter Kenkel Germany 360% 504 STEBU Germany 390% 568 hedgehog lab United Kingdom 359% 505 Microblink Croatia 390% 569 Innocv Solutions Spain 358% 506 Omnia Group Italy 389% 570 Victus, d. o. o. Slovenia 358% 507 Versandmanufaktur Germany 388% 571 Sgerm, d. o. o. Slovenia 357% 508 Autovia Italy 388% 572 Alpha FX Group United Kingdom 357% 509 MC Conseil France 387% 573 Castel Italy 356% 510 Intellienergy Italy 387% 574 mVISE Germany 356% 511 Younited Credit France 387% 575 Resal, d. o. o. Slovenia 355% 512 Unify Communications United Kingdom 386% 576 Mežnar, d. o. o. Slovenia 354% 513 Readly International Sweden 385% 577 Thiga France 354% 514 Fering fam, d. o. o. Slovenia 385% 578 Škerjanc, d. o. o. Slovenia 353% 515 Regius, d. o. o. Slovenia 384% 579 Virs, d. o. o. Slovenia 353% 516 Zuum, d. o. o. Slovenia 384% 580 Pekrul ProjektPartner Germany 353% 517 Mizarstvo Košak, d. o. o. Slovenia 383% 581 Solid World, d. o. o. Slovenia 352% 518 Go Concept France 383% 582 We Exhibit Italy 352% 519 Dermaroller Germany 383% 583 Willy Stadler, d. o. o. Slovenia 350% 520 Axopar Boats Finland 383% 584 Prima Solutions France 350% 521 The Currency Cloud Group United Kingdom 382% 585 GomSpace Group Sweden 350% 522 E-Novia Italy 381% 586 Tarlogic Security Spain 349% 523 KEROC Norway 381% 587 Signor Prestito Italy 348% 524 DCMN Germany 381% 588 Berus, d. o. o. Slovenia 348% 525 MBS vzdrževanje, d. o. o. Slovenia 380% 589 Vaukan, d. o. o. Slovenia 348% 526 Liko, d. d. Slovenia 379% 590 Kiddus Accesorios Spain 348% 527 FPTP France 379% 591 Lipar, d. o. o. Slovenia 347% 528 IGW Italy 379% 592 RadarServices Smart IT-Security Austria 347% 529 Coatransporti, d. o. o. Slovenia 378% 593 A-sprint, d. o. o. Slovenia 346% 530 T-Momo, d. o. o. Slovenia 378% 594 Darson, d. o. o. Slovenia 346% 531 Bulchicken JSC Bulgaria 377% 595 Ni-Mi, d. o. o. Slovenia 346% 532 halsdorfer + ingenieure projekt Germany 377% 596 SGS Facility Management Germany 345% 533 Norma Meccanica Italy 376% 597 MyChauffage.com France 344% 534 Catalyx Switzerland 376% 598 CCHG (VPZ) United Kingdom 344% 535 Joannes Distribution France 374% 599 Fincite Germany 344% 536 Mediterranean Food Solutions France 374% 600 Kalit, d. o. o. Slovenia 344% 537 Ino, d. o. o. Slovenia 373% 601 Cerámicas Anoru Spain 344% 538 Solis Straža, d. o. o. Slovenia 373% 602 Kleine Riesen Nord Germany 344% 539 Dorssen hrm, d. O. O. Slovenia 372% 603 Protekt Dolenjska, d. o. o. Slovenia 343% 540 Infinum Croatia 372% 604 Euro Grad, d. o. o. Slovenia 343% 541 R exy Czech Republic 372% 605 OKK, d. o. o. Slovenia 343% 542 WKND Latvia 371% 606 MDT & T, d. o. o. Slovenia 343% 543 3fs, d. o. o. Slovenia 371% 607 Kyo Electric Spain 342% 544 Aura Italy 370% 608 Ergos Technology Spain 342% *** Stojan Črv is a doctoral student of Quality Management at the Faculty of Organization Studies in Novo mesto. He has a Master's Degree in Business Sciences and a BSc in Mechanical Engineering. He has also completed the Six Sigma Black Belt training. Mr Črv has been working in the field of quality for more than 20 years and is the quality manager at industrial enterprise ITW Appliance Components d.o.o., which is a part of the American multinational corporation ITW. He is also an external or leading auditor of the quality management system according to ISO 9001 standard, of the environmental management system according to ISO 14001 standard as well as of the occupational health and safety management system according to ISO 45001 standard. *** Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 85-109. 109 Povzetek: Položaj slovenskih hitro rastočih podjetij v evropskem prostoru Namen in izvirnost: Namen te raziskave je bil predstaviti položaj slovenskih hitro rastočih podjetij oziroma gazel v evropskem prostoru in izvesti primerjavo slovenskih hitro rastočih podjetij z evropskimi z vidika stopnje rasti. Raziskava temelji na sistematičnem pregledu hitro rastočih podjetij in njihovem položaju v Sloveniji in Evropi. Prav primerjava podatkov o slovenskih hitro rastočih podjetij in tistih v evropskem prostoru omogoča umestitev slovenskih hitro rastočih podjetij v širši evropski prostor. Metoda: Raziskava temelji na sistematičnem pregledu lestvice časopisne hiše Financial Times 1.000 najhitreje rastočih podjetij v Evropi in lestvice časopisne hiše Dnevnik 500 najhitreje rastočih podjetij v Sloveniji ter podatkih, pridobljenih iz statističnega urada Evropske unije in Slovenije. Z uporabo programske opreme Minitab smo preverili normalnost porazdelitve obravnavanih podatkov, izvedli transformacijo nenormalno porazdeljenih podatkov ter izračunali Pearsonov koeficient korelacije »r« med posameznimi obravnavanimi faktorji, na podlagi katerih smo nato izvedli potrditev postavljenih hipotez. Distribucijo podatkov smo prikazali s pareto in stolpčnimi grafikoni. Rezultati: Raziskava je pokazala položaj slovenskih hitro rastočih podjetij v evropskem prostoru. Glede na ugotovitve raziskave je razvidno, da so tako slovenska kot evropska hitro rastoča podjetja v večini manjše organizacije. Največ hitro rastočih podjetij v evropskem prostoru je v Nemčiji, medtem ko je največ takih podjetij v Sloveniji v Osrednjeslovenski regiji. Stopnja rasti hitro rastočih slovenskih in evropskih podjetij je primerljiva, višina prihodkov od prodaje in število zaposlenih ne izkazujeta vpliva na stopnjo rasti podjetja, bruto domači proizvod pa na drugi strani pozitivno prispeva k stopnji rasti. S primerjavo v raziskavi obravnavanih slovenskih in evropskih hitro rastočih podjetij smo ugotovili, da evropska izkazujejo višjo stopnjo rasti kot slovenska, iz vidika prihodkov od prodaje pa so med seboj primerljiva. Družba: Raziskava omogoča boljšo prepoznavnost hitro rastočih podjetij v družbi. Iz tega naslova lahko širša družba oceni vpliv, delovanje in socialno odgovornost hitro rastočih podjetij v določenem okolju. Nenazadnje lahko tudi boljša informiranost družbe pozitivno prispeva k uspešnosti hitro rastočih podjetij. Omejitve/nadaljnje raziskovanje: Raziskava je omejena na primerjavo slovenskih hitro rastočih podjetij s tistimi v evropskem prostoru na podlagi dveh lestvic hitro rastočih podjetij. Glede na dejstvo, da je Slovenija v skupnem evropskem prostoru majhna država, bi bilo primerno izvesti raziskavo oziroma primerjavo z manjšimi evropskimi državami in umestiti slovenska hitro rastoča podjetja v tej skupini, ali pa uporabiti več reprezentativnih lestvic in obstoječo raziskavo ponoviti. Ključne besede: hitro rastoča podjetja, gazele, Slovenija, Evropa, kazalniki podjetij, stopnja rasti. Copyright (c) Stojan ČRV Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 110-128. * Korespondenčni avtor / Correspondence author 110 Prejeto: 15. februar 2021; revidirano: 11. februar 2021; sprejeto: 26. februar 2021. / Received: 15th February 2021; revised: 11th February 2021; accepted: 26th February 2021. DOI: 10.37886/ip.2021.022 Soodvisnost med velikostjo občine in vloženimi sredstvi v investicijske projekte Simon E. Pantar* Fakulteta za organizacijske študije v Novem mestu, Ulica talcev 3, 8000 Novo mesto, Slovenija simone_pantar@yahoo.com, simon.erri.pantar@fos-unm.si Povzetek: Ozadje in izvirnost: Namen raziskave je ugotoviti koliko sredstev na prebivalca vlagajo posamezne občine v investicijske projekte in kako je to odvisno od velikosti občine. Cilj raziskave je ugotoviti soodvisnost med vloženimi sredstvi na prebivalca v investicijske projekte ter velikostjo občine. Originalnost raziskave je v tem, da na enem mestu vsebuje podatke o odobrenih sredstvih iz državnega proračuna za izvedbo projektov po posameznih občinah. Raziskava lahko tudi služi posameznih županom, kot orientacija koliko sredstev so uspeli pridobiti v ostalih občinah, na podlagi različnih kriterijev. Metoda: Izvedena je bila kvantitativna raziskava iz podatkov, katera je bila na najdena na spletu. Rezultati: Z izvedeno raziskavo smo ugotovili, da v splošnem večje občine, po številu prebivalcev, pridobivajo več sredstev, iz državnega proračuna, kot manjše občine. Hipoteza, da se v večjih občinah vlaga več sredstev v projekte na prebivalca, kot v manjših občinah ni bila potrjena. Po raziskavi se v občine s prebivalci, do 1000 prebivalcev, celo več vlaga, kot v večje občine. Družba: Rezultati raziskave lahko imajo vpliv na družbo, ker bo lahko župan določene občine preveril kaj vpliva na pridobivanje sredstev in bo to lahko uporabil pri sprejemanju svojih bodočih odločitev. Raziskava je lahko v pomoč in ideja za nadaljnje raziskave. Omejitve/nadaljnje raziskovanje: Omejitve pri raziskavi so bile v tem, da ni bilo vseh podatkov o projektih na enem mestu (na spletni strani Transparency International Slovenia). Težava je bila odpravljena tako, da so bili podatki poiskani po različnih uradnih listih. Predlog za nadaljnje raziskovanje je, da se v raziskavo vključi in primerja tudi ostale kazalnike, kot so vložena sredstva za investicije na število zaposlenih, na prihodek podjetij, na povprečno neto plačo, na površino občine. Prav tako je predlog, da se v analizo vključi tudi projekte izvedene iz EU sredstev. Ključne besede: projekt, projektni management, investicija, finančna sredstva, državni proračun. 1 Uvod Vsaka občina potrebuje za svoje delovanje finančna sredstva, ki jih vlaga tudi v investicijske projekte, kateri so potrebni za delovanje in razvoj občine. Financiranje občin določa Zakon o financiranju občin (ZFO-1). »Zakon o financiranju občin ureja enak sistem financiranja za vse občine. Občine so upravičene do prihodkov iz treh virov: lastni viri, transferni prihodki iz državnega proračuna in sredstva EU, tretji vir je zadolževanje. Navedeni zakon ureja tudi sofinanciranje investicij, ki jih občine načrtujejo v svojih načrtih razvojnih programov in zanje zagotavljajo sredstva, s sredstvi državnega proračuna.« (Vlada Republike Slovenije) V Sloveniji imamo velik delež majhnih občin, kjer živi celo manj kot 2000 prebivalcev. Po eni strani je v takšnih občinah prisotna večja pripadnost in preglednost nad delovanjem Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 110-128. 111 občine, po drugi strani pa se pojavljajo tudi določeni problemi, ki so povezani predvsem s financiranjem. S to raziskavo želimo preveriti ravno to, ali se v večjih občinah dejansko vlaga več sredstev na prebivalca v investicijske projekte, kot v manjših občinah. Namen raziskave je ugotoviti koliko sredstev na prebivalca vlagajo posamezne občine v investicijske projekte in kako je to odvisno od velikosti občine. Cilj raziskave je dokazati, da se več sredstev na prebivalca v investicijske projekte vlaga v večjih občinah. 2 Teoretična izhodišča 2.1 Teorija o projektih, projektnem vodenju in investicijah Uporaba projektnega vodenja kot poslovnega procesa sega že dolgo nazaj. Številni verjamejo, da je pri gradnji egiptovskih piramid mnogim pomagala uporaba preprostih načel vodenja projektov. (Hall, 2012, str. 130) Različni avtorji imajo različna mnenja o tem kdo je začetnik in kdaj se je začelo sodobno vodenje projektov. Večina avtorjev si je enotna, da se je pomemben dogodek zgodil leta 1917, ko je Henry L. Gantt izumil Ganttovo karto. Ta grafikon spremlja napredek nalog in dodeljevanje virov skozi čas in je osrednje orodje za vizualizacijo napredka projekta pri uporabi programske opreme za upravljanje projektov (Hall, 2012, str. 131). Kwak trdi, da se je začetek sodobnega vodenja projektov začel med 1900 in 1950 leti. V tem obdobju se je vodenje projektov iz obrtnega sistema preoblikovalo v upravo za človeške odnose. Takrat so boljši prometni in telekomunikacijski sistemi začeli tudi omogočati večjo mobilnost in hitrejšo komunikacijo. (Seymour & Hussein, 2014, str. 235) S tehnološkim razvojem se s pomočjo boljšega usposabljanja za upravljanje projektov, objavo informacij o najboljših praksah in boljšo programsko podporo olajšuje vodenje projektov. Po drugi strani pa ugotavljajo tudi trende, ki otežujejo vodenje projektov. To povzroča: povečana konkurenca, krajši življenjski cikli izdelkov in storitev, strožji proračuni, neznane in bolj zapletene aplikacije, globalno porazdeljene in večkulturne projektne skupine. (Hall, 2012, str. 140) Tradicionalni projekti so po svoji naravi pogosto dolgotrajni, sodobni projekti pa so lahko veliko krajši, zlasti za nove izdelke in storitve. Zaradi kratkih življenjskih ciklov izdelkov, na primer v industriji zabavne elektronike, lahko zamuda pri dokončanju projekta pomeni, da je izdelek preprosto že nekonkurenčen. (Hall, 2012, str. 131) Za razlikovanje med projektom in projektnim vodenjem je treba razviti ločeni opredelitvi obeh pojmov. Projekt lahko štejemo za doseganje določenega cilja, ki vključuje vrsto dejavnosti in nalog, kateri porabljajo vire. Izpolniti ga je treba v določeni specifikaciji, z določenimi datumi začetka in konca. V nasprotju s tem je projektno vodenje mogoče opredeliti kot postopek nadzora doseganja ciljev projekta. (Munns & Bjeirmi, 1996, str. 81) Po definiciji PMI je projektni management proces, s katerim se projekti definirajo, načrtujejo, spremljajo, nadzorujejo in izvajajo, tako da se uresničijo dogovorjene koristi (Project Management Institute 2006, str. 151). Za primerjavo poglejmo na primer definicijo PMI, ki trdi, da je projekt običajno opredeljen kot »začasno prizadevanje za ustvarjanje edinstvenega Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 110-128. 112 izdelka ali storitve« (Project Management Institute 2008, str. 442). Klastorin pa trdi, da je projekt dobro opredeljen sklop nalog, katere je potrebno v celoti izvesti, da dosežemo cilj projekta (Klastorin, 2004, str. 78). Poznamo še več definicij, ki jih zagovarjajo različni avtorji. Na splošno lahko rečemo, da se projekt izvaja za koristne spremembe in ima tri bistvene lastnosti, kot jih navajata Turner in Müller (Turner & Müller, 2003, str. 1): • je edinstven, ker se vsak projekt razlikuje od vsakega predhodnega oz. kasnejšega, • izvaja se z novimi postopki: noben projekt pred ali po njem ne bo uporabljal popolnoma enakega pristopa, • je prehoden: ima začetek in konec. Posebna lastnost projektov je tudi, da se lahko številne naloge izvajajo sočasno oz. paralelno. Obstajajo tudi prednostna razmerja med določenimi nalogami, kar pomeni, da se mora določena naloga končati pred začetkom naslednje. V primerjavi s številnimi poslovnimi procesi se zdi upravljanje projektov še posebej težko, tako s teoretičnega kot tudi s praktičnega vidika. S teoretičnega vidika je temeljni problem načrtovanje razporejanja virov. S praktičnega vidika sta standardna cilja pri vodenju projektov proračunska vrednost in pravočasno dokončanje projekta. (Hall, 2012, str. 129) Za projekte so značilni tudi naslednji trije pritiski (Turner & Müller, 2003, str. 1): • Projekti so izpostavljeni negotovosti: ne moremo biti prepričani, da bodo naši načrti prinesli zahtevane rezultate projekta ali želene koristne spremembe. • Ustvarijo potrebo po integraciji: virov za izvedbo projekta, med različnimi deli projekta in projekta v posel. • Izvajajo se ob nujnosti: doseči želene rezultate v želenih časovnih okvirih. De Wit in drugi avtorji razlikujejo med uspešnim projektom (merjeno glede na splošne cilje projekta) in uspešnim vodenjem projekta (merjeno glede na stroške, čas in kakovost). Pomembno je tudi drugo razlikovanje - to je razlika med merili uspeha (ukrepi, na podlagi katerih se bo ocenjeval uspeh ali neuspeh projekta) in dejavniki uspeha (tisti vložki v sistem upravljanja, ki neposredno ali posredno vodijo do uspeha projekta). (Cooke-Davies, 2002, str. 185) Za vedno večji pomen vodenja projektov kot poslovnega procesa je več razlogov (Hall, 2012, str. 131): • Vodenje projektov učinkovito nadzoruje spremembe in omogoča organizacijam, da uvedejo nove izdelke, procese in programe. • Projekti postajajo vse bolj zapleteni, zato jih je težje nadzirati brez formalne strukture upravljanja. • Pojavljajo se projekti z bistveno drugačnimi značilnostmi. • Vodenje projektov pomaga večfunkcionalnim skupinam, da postanejo učinkovitejši. • Podjetja uporabljajo projektno vodenje za razvoj in preizkušanje svojih prihodnjih vodij. Morris in Hugh trdita, da je uspeh projekta odvisen od naslednjih dejavnikov (Munns & Bjeirmi, 1996, str. 82): Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 110-128. 113 • realen cilj, • tekmovanje, • zadovoljstvo strank, • določen cilj, • donosnost, • tretje osebe, • razpoložljivost na trgu, • postopek izvajanja, • zaznana vrednost projekta. Vodenje projektov igra vlogo pri uspehu projekta, vendar na to vlogo vplivajo številni drugi dejavniki, ki niso pod neposrednim nadzorom vodje projekta. Ti dejavniki so (Munns & Bjeirmi, 1996, str. 82): • neustrezna osnova za projekt, • napačna oseba kot vodja projekta, • najvišje vodstvo ne podpira projekta, • neustrezno opredeljene naloge, • pomanjkanje tehnik vodenja projektov, • napačno uporabljene tehnike upravljanja, • zaprtje projekta ni načrtovano, • pomanjkanje zavezanosti projektu. Investicija je sredstvo ali predmet, pridobljen z namenom ustvarjanja dohodka ali apreciacije. Apreciacija se nanaša na povečanje vrednosti sredstva sčasoma. Ko posameznik blago kupi kot naložbo, njegov namen ni zaužiti blago, temveč ga v prihodnosti uporabiti za ustvarjanje bogastva. Naložba se vedno nanaša na današnje izdatke nekega sredstva - časa, denarja ali truda - v upanju, da bo v prihodnosti večji izplačilo od tistega, kar je bilo prvotno vloženo. (Investopedia) Naložbeni projekt je podroben predlog porabe likvidnih virov s ciljem sprejeti ukrepe, ki bodo vodili do prihodnjih dobičkov (Radzhabov & Rustamov, 2019, str. 95). Nabor metod in ustrezna merila, ki se uporabljajo za oceno ekonomske učinkovitosti naložb in investicijskih projektov, lahko razdelimo v tri skupine, odvisno od načina upoštevanja časovnega dejavnika pri izvedbi investicijskih stroškov in pridobivanju donosnega investicijskega toka (Danylyshyn et al., 2019, str. 2696): • dinamične - modeli diskontiranih denarnih tokov, • statični - modeli, ki predvidevajo uporabo pri izračunih računovodskih podatkov o investicijskih stroških in dohodkih brez diskontiranja skozi čas, • alternativni ali nestandardni - modeli, ki upoštevajo omejitve in slabosti drugih dveh skupin metod.. Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 110-128. 114 2.2 Hipoteza Pojavljajo se očitki, da se v manjše občine vlaga manj sredstev za investicijske projete na prebivalca, kot v večjih občinah. Te isti trdijo, da je to problem, ki zaradi slabše finančne podpore, manjšim občinam onemogoča ustrezen razvoj. S to raziskavo želimo preveriti ravno to ali se v večjih občinah, iz državnega proračuna, dejansko vlaga več sredstev na prebivalca, v investicijske projekte, kot v manjših občinah. Hipoteza: V večjih občinah se vlaga več sredstev na prebivalca v investicijske projekte, z državnega proračuna, kot v manjših občinah. 3 Metoda Izvedli smo kvantitativno raziskavo iz podatkov, katere smo našli na spletu. Podatke smo dobili iz baze statističnih podatkov občinskih projektov v Sloveniji financiranih iz državnega proračuna, na spletni strani Transparency International Slovenia (TI Slovenia). TI Slovenia je nevladna in neprofitna organizacija s statusom v javnem interesu pri Ministrstvu za javno upravo, ki je del mednarodne mreže Transparency International. Ker so podatki predstavljeni na tej spletni strani nepopolni, smo preostale podatke poiskali v različnih Uradnih listih Republike Slovenije. Statistične podatke o naseljenosti posameznih občin smo črpali iz spletne strani Statističnega urada Republike Slovenije. V teoretičnem delu raziskave je bil uporabljen sistematični pregled znanstvene in strokovne literature s področja projektnega vodenja. Značilnost izbrane metode temelji na zbiranju, pregledovanju in analiziranju obstoječe literature. Pri predpostavki, da obstaja literatura na temo projektnega vodenja smo najprej, po različnih bazah (ERIC, Scopus, SSCI, ProQuest), poiskali članke s primernimi naslovi. Pogoj je bil, da je bil članek objavljen v indeksiranih revijah. Slika 1. Model raziskave Podatke smo obdelali s pomočjo programa MS Excel. Rezultate smo prikazali v obliki grafov in tabel ter jih tudi komentirali. Višina sredstev odobrenih projektov po občinah Višina sredstev projektov/prebivalca po občinah Hipoteza Število prebivalcev po občinah Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 110-128. 115 Baza podatkov je pripravljena na osnovi podatkov iz Uradnih listov Republike Slovenije. Poleg tega smo manjkajoče podatke sami poiskali, nekatere pa tudi vzorčno preverili v različnih uradnih listih, zato menimo, da so podatki zanesljivi. 4 Rezultati V tabeli 1 so predstavljeni podatki vrednosti projektov po velikosti občin. Velikosti občin so razdeljene po kategorijah oz. po številu prebivalcev, ker je v Sloveniji kar 212 občin. Te so predstavljene v tabeli 3. Poleg tega so v tabeli predstavljeni podatki za različna obdobja, in sicer za obdobja: 1998-2014, 2015-2018, 2019-2029 in za celotno obdobje od leta 1998 do leta 2029. Leto 2029 je navedeno zato, ker se nekateri projekti, ki so že odobreni in se tudi že odvijajo, predvideni z zaključkom v letu 2029. Tabela 1. Analiza vrednosti projektov po občinah Št. preb. Vrednost projektov 1998-2014 2015-2018 2019-2029 1998-2029 do 1000 4.230.250 1.572.211 1.833.857 7.636.317 1000 - 2000 3.925.311 2.503.279 1.757.882 8.186.471 2000 - 3000 2.888.256 1.889.117 4.142.071 8.919.444 3000 - 4000 5.741.068 5.082.842 2.035.389 12.859.300 4000 - 5000 5.159.411 5.287.888 11.321.675 21.768.974 5000 - 6000 11.593.566 5.446.400 3.693.622 20.733.587 6000 - 7000 16.994.627 9.621.394 8.091.617 34.707.638 7000 - 10000 15.068.291 5.623.512 25.882.033 46.573.836 10000-15000 17.526.197 10.790.002 17.553.546 45.869.745 15000-30000 39.056.655 30.692.025 11.080.768 80.829.448 30000-50000 133.069.813 49.109.112 47.625.237 229.804.161 50000 in več 306.343.300 308.027.203 84.791.169 699.161.672 Za bolj nazorno predstavitev vrednosti odobrenih sredstev, glede na velikost občin po številu prebivalcev, so za celotno obdobje, od leta 1998 do 2029, vrednosti predstavljene v grafični obliki (glej sliko 2). Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 110-128. 116 Slika 2. Vrednosti projektov glede velikost občin po številu prebivalcev Tabela 2. Analiza vrednosti projektov na število prebivalcev po občinah Št. prebivalcev Vrednost projektov/ št. prebivalcev 1998-2014 2015-2018 2019-2029 1998-2029 do 1000 522 830 344 609 1000 - 2000 139 383 105 208 2000 - 3000 69 193 140 133 3000 - 4000 101 388 54 169 4000 - 5000 70 299 228 189 5000 - 6000 123 250 63 150 6000 - 7000 155 378 114 215 7000 - 10000 110 170 280 197 10000-15000 84 219 135 145 15000-30000 114 356 50 165 30000-50000 216 293 111 223 50000 in več 132 562 61 230 V tabeli 2 so prikazani že podatki, ki bodo ključni pri potrjevanju hipoteze. Kategorije občin so razdeljene isto, kot v tabeli 1, ki je bila že obrazložen, zato ne bomo ponavljali. V tabeli 2 so s sivim odtenkom obarvane celice s šestimi najvišjimi vrednostmi oz. s poudarjeno pisavo najvišji dve vrednosti. Vrednosti so predstavljene v EUR/prebivalca, pri čemer je navedena povprečna vrednost na leto, za posamezno obdobje. Prav tako so podatki predstavljeni v grafični obliki, na sliki 3. Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 110-128. 117 Slika 3. Vrednosti projektov glede na število prebivalcev po občinah V tabeli 3 je predstavljeno katere občine spadajo v posamezne kategorije. Tabela 3. Kategorije občin Število prebivalcev Občine do 1000 Hodoš, Osilnica, Solčava, Kobilje, Jezersko, Kostel, Dobje 1000 - 2000 Ribnica na Pohorju, Sveti Andraž v Sl. Goricah, Razkrižje, Dobrovnik, Veržej, Žetale, Bistrica ob Sotli, Trnovska vas, Šalovci, Velika Polana, Luče, Zavrč, Bloke, Kuzma, Odranci, Tabor, Cankova, Podlehnik, Loški Potok, Središče ob Dravi, Gornji Petrovci, Sveti Tomaž 2000 - 3000 Makole, Grad, Cerkvenjak, Sveta Trojica v Sl. Goricah, Sveti Jurij v Sl. Goricah, Sodražica, Dobrna, Vitanje, Sveta Ana, Rečica ob Savinji, Podvelka, Cirkulane, Juršinci, Kostanjevica na Krki, Gornji Grad, Ljubno, Benedikt, Nazarje, Destrnik, Vransko, Mirna, Vuzenica, Gorje, Sveti Jurij ob Ščavnici, Dornava, Lovrenc na Pohorju, Šentrupert, Mirna Peč 3000 - 4000 Horjul, Kozje, Rogašovci, Bovec, Mokronog - Trebelno, Rogatec, Turnišče, Ankaran, Šmartno ob Paki, Črna na Koroškem, Škocjan, Šmarješke Toplice, Muta, Podčetrtek, Dolenjske Toplice, Apače, Križevci, Komen, Mežica, Log - Dragomer, Preddvor, Loška dolina, Dobrepolje, Semič, Hajdina, Straža, Trzin, Črenšovci, Tišina 4000 - 5000 Markovci, Starše, Majšperk, Kobarid, Gorišnica, Oplotnica, Mozirje, Radeče, Divača, Renče - Vogrsko, Velike Lašče, Žirovnica, Poljčane, Selnica ob Dravi, Hrpelje - Kozina, Borovnica, Mislinja, Štore, Cerkno, Žužemberk, Kungota, Žiri, Vodice, Miren - Kostanjevica 5000 - 6000 Radenci, Bohinj, Prebold, Kranjska Gora, Kanal, Naklo, Moravče, Braslovče, Videm, Šmartno pri Litiji, Brda, Vipava, Moravske Toplice, Lukovica, Puconci 6000 - 7000 Pivka, Radlje ob Dravi, Polzela, Šempeter - Vrtojba, Dol pri Ljubljani, Komenda, Kidričevo, Zreče, Železniki, Prevalje, Miklavž na Dravskem polju, Duplek 7000 - 10000 Ruše, Šentjernej, Pesnica, Rače - Fram, Gorenja vas - Poljane, Ig, Cerklje na Gorenjskem, Dobrova - Polhov Gradec, Bled, Beltinci, Mengeš, Šentilj, Metlika, Gornja Radgona, Lenart, Šoštanj, Šenčur, Vojnik, Dravograd, Hrastnik, Ribnica 10000-15000 Šmarje pri Jelšah, Lendava, Tolmin, Rogaška Slatina, Ljutomer, Ravne na Koroškem, Škofljica, Cerknica, Idrija, Hoče - Slivnica, Ormož, Brezovica, Trebnje, Laško, Ilirska Bistrica, Sežana, Logatec, Črnomelj, Tržič, Slovenske Konjice 15000-30000 Litija, Kočevje, Trbovlje, Postojna, Izola, Zagorje ob Savi, Slovenj Gradec, Medvode, Ivančna Gorica, Vrhnika, Sevnica, Piran, Murska Sobota, Radovljica, Šentjur, Ajdovščina, Jesenice, Grosuplje, Žalec, Škofja Loka, Ptuj, Brežice, Slovenska Bistrica, Krško, Kamnik 30000-50000 Nova Gorica, Velenje, Domžale, Novo mesto, Celje 50000 in več Koper, Kranj, Maribor, Ljubljana Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 110-128. 118 5 Razprava Kot že navedeno so v tabeli oz. na sliki 1 predstavljeni podatki o višini odobrenih sredstev za projekte, po posameznih kategorijah velikosti občin. Podatki so predstavljeni za različna časovna obdobja. Če se osredotočimo na kategorijo celotnega obdobja, za katerega so zbrani podatki, se pravi od leta 1998 do leta 2029, lahko takoj opazimo, da z velikostjo občine rastejo tudi odobrena sredstva. Rezultat je definitivno pričakovan. Bolj pomembni podatki oz. rezultati, za našo raziskavo, so predstavljeni v tabeli oz. na sliki 2. Ti rezultati že prikazujejo koliko sredstev so občine prejele na prebivalca, po posameznih obdobjih. Za podrobnejši komentar rezultatov, najprej zopet poglejmo našo hipotezo, ki se glasi: »V večjih občinah se vlaga več sredstev z državnega proračuna na prebivalca, v investicijske projekte, kot v manjših občinah.« Iz podatkov v tabeli 2 je takoj razvidno, da ne velja trditev oz. navedena hipoteza. Glede na našo hipotezo bi moralo biti s sivim odtenkom obarvanih zadnjih šest celic in poudarjeni vrednosti zadnjih dveh celic. Takoj vidimo, da temu ni tako. Lahko rečemo, da podatki prikazujejo ravno nasprotno. Kategorija občin z velikostjo do 1000 prebivalcev, v vseh posameznih obdobjih, vsebuje najvišje vrednosti sredstev projektov na število prebivalcev. Če iz analize izključimo kategorijo najmanjših občin, ki najbolj odstopa iz vseh kategorij, vidimo, da so odobrena sredstva po ostalih kategorijah približno enaka in bistveno ne odstopajo oz. se spreminjajo glede na velikost občin. Morda je v kategoriji od 1998 – 2029 (vsi projekti v analizi) moč zaznati, da malce odstopajo zadnji dve kategoriji po velikosti občin, če seveda ne upoštevamo kategorijo tistih najmanjših občin. Če bi podrobneje pogledali podatke za vse občine, bi lahko videli, da se tudi znotraj posameznih kategorij podatki oz. odobrena sredstva zelo razlikujejo. Za primer poglejmo kategoriji najmanjših in največjih občin, ki sta predstavljeni v tabelah 4 in 5. Tabela 4. Analiza vrednosti projektov s številom prebivalcev do 1000 ljudi Občina Površ. (km2) Št. preb. Št. pr. Vrednost projektov Vred. proj./ št. prebiv. 1998- 2029 1998-2014 2015- 2018 2019- 2029 1998-2029 1998- 2014 2015- 2018 2019- 2029 1998- 2029 Hodoš 18 358 8 78.284 498.593 0 576.877 12 347 0 94 Osilnica 36 366 13 14.178.001 3.032.000 2.830.000 20.040.001 2.230 2.077 703 1.970 Solčava 103 517 15 3.917.175 5.386.285 8.467.000 17.770.460 445 2.590 1.489 1.400 Kobilje 20 545 9 63.093 27.108 0 90.201 6 12 0 7 Jezersko 69 635 5 5.270.782 952.972 980.000 7.203.754 480 380 140 401 Kostel 56 643 11 3.778.165 945.000 560.000 5.283.165 344 365 79 303 Dobje 18 965 6 2.326.248 163.515 0 2.489.763 140 42 0 86 V tabeli 4 so predstavljeni podatki za kategorijo najmanjših občin, s številom prebivalcev do 1000. V prvem stolpcu so navedene občine, nato podatek o številu prebivalcev, vrednosti odobrenih sredstev za posamezna obdobja in vrednosti projektov na prebivalca, prav tako po posameznih obdobjih. Kot vidimo iz podatkov, odstopata občini Osilnica in Solčava, katerih vrednosti so bistveno višje od povprečnih vrednosti. Če bi podatke teh dveh občin odstranili, Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 110-128. 119 bi bile pridobljene vrednosti primerljive s povprečnimi vrednostmi ostalih občin v tej kategoriji. Podrobneje sem preveril še podatke oz. projekte v občinah Osilnica in Solčava, ki povišujejo vrednost odobrenih sredstev. V primeru občine Osilnica je to povzročil projekt »Vzpostavitev nove prometne povezave Kočevska Reka - Osilnica.«, ki je v celotnem obdobju od 1998-2029 prispeval kar 16.811.195,53 EUR. Vrednost preostalih sredstev, v navedenem obdobju, znaša 3.228.805,62 EUR. V občini Solčava je situacija podobna. Tudi v tem primeru je oz. bo za cestne povezave namenjeno kar 12.992.509,57 EUR od skupno 17.770.460,23 EUR. Če bi navedene projekte izločili iz analize, bi bila slika povsem drugačna. Tabela 5. Analiza vrednosti projektov s številom prebivalcev višjim od 30000 ljudi Občina Površ. (km2) Št. preb. Št. pr. Vrednost projektov Vred. proj./ št. prebiv. 1998- 2029 1998-2014 2015-2018 2019-2029 1998-2029 1998- 2014 2015- 2018 2019- 2029 1998- 2029 Nova Gorica 280 31.932 61 79.585.068 22.329.214 21.680.000 123.594.281 147 176 62 143 Velenje 84 33.506 31 21.980.053 7.683.251 11.952.000 41.615.304 39 58 32 46 Domžale 72 36.429 23 6.427.643 9.502.488 0 15.930.131 11 66 0 22 Novo mesto 236 37.280 49 505.179.354 71.093.972 142.532.000 718.805.327 821 483 348 662 Celje 95 49.602 61 52.176.944 134.936.633 61.962.183 249.075.761 63 681 114 242 Koper 311 52.540 75 203.967.902 214.652.229 26.075.011 444.695.142 221 1.035 45 392 Kranj 151 56.715 35 43.837.279 66.231.519 24.870.000 134.938.798 46 297 40 111 Maribor 148 112.095 106 188.029.215 68.522.535 136.941.909 393.493.658 99 155 111 129 Ljubljana 275 294.113 341 789.538.806 882.702.529 151.277.755 1.823.519.089 162 761 47 288 Iz tabele 5 je podobna situacija vidna, kot smo opazili že v prejšnjem primeru, in sicer, da se podatki med občinami razlikujejo oz. odstopajo od ostalih. V tem primeru takoj opazimo odstopanje občin Domžale in Velenje, ki sta bistveno pod povprečjem ostalih občin. Iz podatkov glede površine občin je sicer tudi razvidno, da sta manjši od ostalih občin. Za podrobnejšo analizo bi morali vključiti še druge faktorje, kar je morda priložnost za nadaljnje raziskave. Z raziskavo smo dokazali, da: • z velikostjo občine po številu prebivalcev, naraščajo tudi odobrena sredstva za projekte, • najmanjše občine po številu prebivalcev (kategorija do 1000 prebivalcev) prejemajo največ sredstev za projekte na število prebivalcev in hipoteza, da so najmanjše občine najbolj prikrajšane, ne velja. Iz podatkov v tabeli 2 je takoj razvidno, da ne velja trditev oz. navedena hipoteza. Glede na to, da hipoteza ni bila potrjena, menimo, da je neupravičeno pritoževanje manjših občin, da so prikrajšane pri financiranju iz državnega proračuna. Rezultati analiz, v določenih manjših občinah, prikazujejo ravno nasprotno sliko. Iz pridobljenih podatkov se odpirajo še nadaljnje možnosti za raziskave, ki so predstavljene v poglavju 6. Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 110-128. 120 6 Zaključek Z izvedeno raziskavo smo ugotovili, da v splošnem večje občine, po številu prebivalcev, pridobivajo več sredstev, iz državnega proračuna, kot manjše občine. Gledano z vidika odobrenih sredstev na število prebivalcev smo z raziskavo ugotovili, da je kategorija najmanjših občin (do 1000 prebivalcev) na nek način celo privilegirana. Iz rezultatov je namreč razvidno, da so v vseh časovnih kategorijah imeli bistveno največ odobrenih sredstev na število prebivalcev. V tej kategoriji odstopata občini Osilnica in Solčava, ki sta višino sredstev povečala predvsem na račun izgradnje novih cest. Tudi v kategoriji največjih občin, je razvidno, da imamo odstopanja. Od povprečja ostalih občin sta odstopali najmanjši po površini, Domžale in Velenje. Z raziskavo smo prišli do zaključka, da navedene hipoteze ne moremo potrditi ter da ostaja še veliko možnosti za nadaljnje raziskave. Pridobljeni podatki iz raziskovalne naloge so dostopni na internetu, kar pa ne pomeni da niso uporabni. Podatki iz spletne strani Transparency International Slovenia so dopolnjeni iz podatkov raznih uradnih listov in predstavljeni v obliki, ki doslej ni bila na voljo. Rezultati raziskave lahko imajo vpliv na družbo, ker bo lahko župan določene občine preveril kaj vpliva na pridobivanje sredstev in bo to lahko uporabil pri sprejemanju svojih bodočih odločitev. Raziskava je lahko v pomoč in ideja za nadaljnje raziskave. Omejitve pri raziskavi so bile v tem, da ni bilo vseh podatkov o projektih na enem mestu (na spletni strani Transparency International Slovenia). Težavo smo odpravil tako, da smo podatke poiskali po različnih uradnih listih. Predlog za nadaljnje raziskovanje je, da se v raziskavo vključi in primerja tudi ostale kazalnike, kot so vložena sredstva za investicije na število zaposlenih, na prihodek podjetij, na povprečno neto plačo, na površino občine. Prav tako je predlog, da se v analizo vključi tudi projekte izvedene iz EU sredstev. Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 110-128. 121 Reference 1. Cooke-Davies, T. (2002). The “real” success factors on projects. International Journal of Project Management, 20(3), 185–190. 2. Danylyshyn, B., Bondarenko, S., Malanchuk, M., Kucherenko, K., Pylypiv, V., & Usachenko, O. (2019). Method of real options in managing investment projects. International Journal of Innovative Technology and Exploring Engineering, 8(10), 2696-2699. 3. Hall, N. G. (2012). Project management: Recent developments and research opportunities. Journal of Systems Science and Systems Engineering, 21(2), 129–143. 4. Investopedia. (2020, 27. december). Pridobljeno na https://www.investopedia.com/terms/i/investment.asp 5. Klastorin, T.D. (2004). Project Management: Tools and Trade-Offs, 1st edition. Wiley, Hoboken, NJ 6. Munns, A., & Bjeirmi, B. (1996). The role of project management in achieving project success. International Journal of Project Management, 14(2), 81–87. 7. Project Management Institute, & American National Standards Institute. (2008). A guide to the project management body of knowledge. Project Management Institute. 8. Project Management Institute, & American National Standards Institute. (2006). A guide to the project management body of knowledge. Project Management Institute. 9. Radzhabov, O., & Rustamov, U. (2019). Classification of investment projects. Bridge to science: Research works, 95. 10. Seymour, T., & Hussein, S. (2014). The history of project management. International Journal of Management & Information Systems (IJMIS), 18(4), 233-240. 11. Statistični urad Republike Slovenije. (2020, 6. december). Pridobljeno na https://www.stat.si/obcine 12. Transparency International Slovenia. (2020, 6. december). Pridobljeno na http://www.transparency.si/ 13. Turner, J. R., & Müller, R. (2003). On the nature of the project as a temporary organization. International Journal of Project Management, 21(1), 1–8. 14. Vlada Republike Slovenije. (2020, 27. december). Pridobljeno na https://www.gov.si/teme/financiranje-obcin/ *** Simon E. Pantar je diplomiral na Fakulteti za elektrotehniko s področja zagotavljanja kakovosti. Zaposlen je na delovnem mestu vodje projektov. Vodi projekte v matični družbi in odvisnem podjetju v Ruski federaciji, kjer je bil pred tem zaposlen kot vodja tehnične službe. V odvisnem podjetju je odgovarjal za tehnično področje in uredil postopke na ostalih povezanih področjih. Pred tem je delal na delovnem mestu tehnologa v službi za meroslovje, kjer je uvedel mnoge postopke kalibracij na področju mase in sodeloval v mednarodnih interkomparacijah. Bil je mentor študentom in novozaposlenim ter izvajal izobraževanja sodelavcev na področju tehtanja. Za uspehe na področju inovacij, razvoja kalibracijskih metod ter mentorstva je v letu 2007 prejel nagrado občine Novo mesto na raziskovalnem področju. Prejel je tudi nagrado v sklopu projekta naj-sodelavec. *** Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 110-123. 122 Abstract: Correlation Between the Size of the Municipality and the Funds Invested in Investment Projects Background and Originality: The purpose of the research is to determine how much funds per capita individual municipalities invest in investment projects and how this depends of the size of the municipality. The goal of the research is to determine the correlation between the funds invested per capita in investment projects and the size of the municipality. The originality of the research lies in the fact that it contains in one place data on approved funds from the state budget for the implementation of projects by individual municipalities. Research can also serve individual mayors as an orientation of how much fundings they have managed to obtain in other municipalities, based on different criteria. Method: We conducted a quantitative research from the data which we found on the internet. The datas we obtained from statistical database of municipal projects in Slovenia financed from the state budget, on the website Transparency International Slovenia. Transparency International Slovenia is a non-governmental and non-profit organization with a status in the public interest at the Ministry of Public Administration, which is part of the international network Transparency International. Because the data presented on this website are incomplete, we searched for the remaining data in various Official Gazettes of the Republic of Slovenia. Statistical datas of the population of individual municipalities we drawn from the website of the Statistical Office of the Republic of Slovenia. The data we processed in MS Excel. The results we showed in graphs and tables, and they are also commented. The database is prepared on the basis of data from the Official Gazettes of the Republic of Slovenia. In addition, we searched for the missing data ourselves, and also checked some of them in various official gazettes, so we believe that the data are reliable. Results: With the performed study we found that in general larger municipalities, by the number of inhabitants, gaining more resources from the state budget as smaller municipalities. Seen from the perspective of the approved funds, to the number of inhabitants, was with the survey founds that the category of the smallest municipalities (up to 1000 inhabitants ), in a way, even privileged. From the results it is evident that in all time categories had substantially the most approved funds to the number of inhabitants. In this category differ municipality Osilnica and Solčava, which are the amount of funds increased mainly on account of the construction of new roads. Even in the category of the largest municipalities, it is clear that we have deviations. From the average of other municipalities have departed the smallest at the surface, Domžale and Velenje. With the research we came to the conclusion that the stated hypothesis cannot be confirmed and that there is still a lot of space for further research. Given that the hypothesis has not been confirmed, we believe that it is unjustified for smaller municipalities to complain that they are deprived of funding from the state budget. The results of the analyzes, in certain smaller municipalities, show just the opposite picture. From the obtained data, we have further possibilities for research. The data obtained from the research are available on the Internet, which does not mean that they are not useful. Data from Web pages Transparency International Slovenia are updated from the data of various official gazettes and presented in a form, which has not been available so far. Society: The results of the survey can have an impact on society because the mayor of a particular municipality will be able to check what influences fundraising and will be able to use this in making his or her future decisions. Research can be helpful and an idea for further research. Limitations / further research: The limitations of the research were that not all data of projects were in one place (on the website Transparency International Slovenia). The problem was solved by searching the data in various official gazettes. The proposal for further research is to include in the research and compare other indicators, such as invested funds for investments in the number of employees, company income, average net salary, the area of the municipality. It is also a proposal to include projects derived from EU funds in the analysis. Keywords: project, project management, investition, financial resourses, state budget. Izzivi prihodnosti / Challenges of the Future, Članek / Article Maj / May 2021, leto / year 6, številka / number 2, str. / pp. 110-123. 123 Copyright (c) Simon E. PANTAR Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.