72 Organizacija, V olume 57 Issue 1, February 2024 Research Papers 1 Received: 20th June 2023; Accepted: 19th December 2023 An Examination of Work Conditions and Well-Being of Slovene Train Drivers Danica MURKO 1 , Sarwar KHAWAJA 2 , Fayyaz Hussain QURESHI 2 1 Ministry of Infrastructure, Slovenia, danica.murko@gov.si 2 Oxford Business College, 65 George Street, Oxford, United Kingdom, sarwar.khawaja@oxfordbusinesscollege.ac.uk, Fayyaz.qureshi@oxfordbusinesscollege.ac.uk Background and purpose: While the occupation of a train driver can be likened to other transportation professions like truck or bus drivers, it is essential to note that there are distinct hazards exclusive to this role that have a notable impact on the mental and physical well-being of train drivers. The study aims to define personal characteristics, work organisation and work characteristics, professional development and work in general in connection with risk factors among employees who perform the work tasks of train drivers in railway transport. Methodology: The study on train drivers in Slovenia was conducted with 179 participants, representing 13.3% of the total population of train drivers. The sample was predominantly male and varied in age, most hailing from the Podravska region. The OPSA digital tool was used to analyse risk factors and gauge psychosocial stress across 17 areas, using a questionnaire split into two sections. Data was collected through online and physical surveys, with voluntary and anonymous participation. Results: The study found that the personal characteristics of train drivers do not significantly impact their percep - tion of workplace workload. While professional development factors negatively influenced workload perception, the impact was not statistically significant. However, general work characteristics strongly impact how train drivers perceive their workload. These findings suggest that interventions should focus on modifying general work charac - teristics to improve train drivers’ work conditions. Conclusions: These findings have important implications for the railway industry. They suggest that interventions aimed at improving the work conditions of train drivers should focus on modifying general work characteristics rather than targeting personal characteristics or professional development factors. Future research should explore these relationships and develop strategies to mitigate the identified risk factors. Keywords: Work conditions, Well-being, Train drivers, Psychosocial risk factors DOI: 10.2478/orga-2024-0005 1 Introduction Human error has been identified as the primary cause of traffic accidents in studies (e.g., Edkins & Pollock, 1997; Wilde & Stinson, 1983; Chang & Ju, 2008). This is also true for railway traffic. Train drivers operate in a de- manding environment requiring high concentration, skill, and resilience. Their work conditions can significantly im- pact safety, stress levels, and sleep patterns. For instance, the work hours and physical work environment have been identified as significant contributors to workload (Keck- lund et al., 1999). Moreover, the profession often involves shift work and long hours, leading to sleep disorders and fatigue (Samerei et al., 2020). In addition to the physical demands, train drivers also face psychological challenges. The responsibility for the safety of passengers and goods can lead to high stress levels (Kecklund et al., 1999). Fur- 73 Organizacija, V olume 57 Issue 1, February 2024 Research Papers thermore, the solitary nature of the job can contribute to feelings of isolation and impact mental health (Samerei et al., 2020). Professional development is another crucial aspect of a train driver’s work life. The rapidly evolving technology in the railway industry necessitates continuous learning and adaptation. However, the high-stress environ- ment and demanding work schedules can make it challeng- ing for drivers to pursue further training and development (Olsson, Lidestam & Thorslund, 2021). The physical work environment of train drivers is also unique and can signifi- cantly impact their performance. For example, the cab en- vironment is crucial. Ergonomic facilities that reduce strain and stress are essential, especially considering drivers tend to work long hours in the same position. The design of the cab and drivers’ attitudes towards it have been assessed in various studies. System design-related factors such as the position of running signals, visibility of different signal types, and platform location about the travelling direction can influence the propagation of driver-related incidents. Train drivers often work full-time; some work more than 40 hours per week. This can lead to fatigue and stress, impacting their performance and health. Railroad work- ers typically need several months of on-the-job training. This training often includes understanding and adapting to the physical work environment (Rjabovs et al., 2015). Understanding these aspects is crucial for improving the work conditions of train drivers and enhancing their over- all well-being. However, there is no consensual agreement around a single definition of well-being (Qureshi et al., 2022). Generally, well-being can be defined as consider- ing life positively and feeling good (Diener, Suh & Oishi, 1997; Veenhoven, 2008). Well-being, a vital construct ex- pounded in positive psychology, is a state of overall men- tal and physical health, strength, resilience and fitness to function well at work and personally (Qureshi et al., 2022; NHS, 2023). Personal well-being, life satisfaction, and overall health are vital for work (Sokić, Qureshi & Kha- waja, 2021). Risk factors contributing to health issues among train drivers are multifaceted. They include long working hours, shift work, exposure to traumatic incidents, and workplace violence (Carnall et al., 2022). Specific to train drivers, factors such as rest and sleep schedules, workload, auto- mation levels, and use of mobile devices can lead to central nervous fatigue and cognitive distraction. These factors can result in loss of concentration, slow reaction times, and dangerous driving behaviour (Sajid et al., 2008). Train driving, being a safety-critical job as defined in relevant regional legislation, requires the driver to work calmly, rested, and adequately trained, as outlined in the Rulebook on special health conditions for obtaining and maintaining the validity of a train driver’s license. Peters and O’Conner (1988) emphasise specific skills required of train drivers, including remembering and summarising information, anticipating and assessing the influence of various factors affecting train operation, reacting quickly, controlling events, and maintaining concentration. Train drivers are exposed to specific psychosocial risks. Risks include both those arising from the nature of the job: train drivers’ work is primarily sedentary, and electromagnet- ic waves and vibrations from the locomotive running on tracks negatively impact their health and well-being, as well as personal risks: stress, illnesses, psychological consequences of traumatic events, private life, and more (Wilson et al., 2017). An important component of the psy- chosocial work environment is influencing and controlling one’s work environment. The work of a train driver, i.e., train management and related activities, is strictly regulat- ed and governed by several regulations and is also condi- tioned by technical conditions and instructions (Doroga & Baban, 2013). In recent years, there has been an increase in research aimed at understanding the factors that influence the health of train drivers. The initial studies on risk factors can be traced back to the 1970s (Sussman & Ofsevit, 1976). It is worth noting that research focusing on the unique aspects of the train driver profession experienced a slowdown dur- ing the 1980s and early 1990s. However, new technical and system discoveries, operational reorganisations, effi- ciency enhancement efforts, and the need for reliable and safe railway traffic development, particularly post-2000, have spurred various research programs. These programs delve into the factors affecting train drivers’ work and deepen the understanding of human factors, their inter- relationships, and their specificities in railway transport. Behavioural observation research involving train drivers began in the early 1970s. Sussman and Ofsevit (1976) ob- served that train drivers process a substantial amount of diverse information while operating a train, a finding that was corroborated by later studies (Naweed et al., 2018; Hamilton & Clarke, 2005). This information processing was found to be significantly more extensive than previ- ously thought. Other studies have also explored specific risk factors (Wang et al., 2021; Wickens, 2002). Recent research has made several significant findings regarding the health of train drivers. A study by Olsson, Lidestam, and Thorslund (2021) found that many excep- tional cases are generally insufficiently practised during the internship of train drivers and, therefore, should be practised in simulators. The study also found that experi- enced and novice drivers prioritise safety over efficiency. Research by Naweed (2014) showed positive reductions in some coronary heart disease indicators, such as systolic blood pressure, total cholesterol, and smoking levels, in train drivers over several years. However, the proportions of drivers who are obese or have diabetes or pre-diabetes have all increased significantly over time. Naweed et al. (2017, pp. 264-273) state that “sleep patterns, diet, occu- pational stress, workplace ergonomics, fitness motivation, and family or social life conflicts influence the health of 74 Organizacija, V olume 57 Issue 1, February 2024 Research Papers train drivers.” This study, conducted in Australia, con- cludes, “In the field of occupational health, the organisa- tion of work, the ergonomics of workplaces must be ade- quately addressed, and employees must be guided towards healthy lifestyle behaviour”. The study highlights issues in organisational culture, such as communication, inadequate organisational support, and existing social norms. Barriers to work planning included fatigue, stimulant dependence, and unsettled family life. Regulatory frameworks of a healthy lifestyle included the study participants’ eating and exercise habits or patterns. Other studies (Lavrič, 2017) identify the significant impact of traumatic events on train drivers’ psychological health and the potential for devel- oping cancer due to pathogens (Verma et al., 2003) among the more common risks associated with this profession. According to Doroga and Baban (2013), the driver’s job also includes gastrointestinal problems, stress-related car- diological problems, musculoskeletal pain due to forced posture, hearing damage, and degenerative diseases of the spine caused by train vibrations. Zoer, Sluiter, and Frings- Dresen (2014) note that the job demands for train drivers include high emotional and mental stress, limited autono- my, and prudence. Given the necessity for complete concentration in train driving and related tasks, it is understandable that men- tal and physical health issues can pose significant risks in such an environment. The increasing capacity of traction means and the growing complexity of railway systems add to the tasks performed by train drivers. Cognitive and per- ceptual abilities are dominant, as noted by Tichon (2007). Even a minor error by a train driver, potentially caused by poor health or lack of concentration, can have severe consequences, endangering lives and health and causing substantial material damage. For this reason, in this study, we define personal characteristics, work organisation and work characteristics, professional development and work in general in connection with risk factors among employ- ees who perform the work tasks of train drivers in railway transport. We also pinpoint crucial risk factors for develop- ing health issues among train drivers. Methodology Sample Data for the study was gathered from respondents who were invited to participate with assistance from the Human Resources Department of Slovene Railways, the Repre- sentative Trade Union of Train Drivers, and the Service for Sustainable Mobility and Transport Policy at the Ministry of Infrastructure. These organisations provided the email addresses, and we sent a link to the online survey. The total number of train drivers in the Republic of Slovenia is 1,341. We received 179 completed survey questionnaires, representing 13.3% of the total population of train drivers. All 179 participants who completed the questionnaire are part of the occupational group of train drivers. The sample is predominantly male, with 99.4% male and 0.6% female participants. The age distribution of the survey participants is as fol- lows: 39.7% are between 41 and 50 years old, 25.7% are between 51 and 65 years old, 24.0% are between 31 and 40 years old, and 10.1% are between 18 and 30 years old. There were no respondents older than 65 years. In terms of regional residence, the most significant number of participants hail from the Podravska region (20.7%), followed by the Primorsko-notranjska region (13.4%), and then the Savinjska (10.6%), Gorenjska (10.6%), and Obalno-kraška regions (10.1%). The Pomur- ska (0.6%), Goriška (1.7%), and Zasavska (5.0%) regions have the most miniature representation in the sample. Two-thirds of the participants (67.4%) are employed in the region where they reside, while 32.6% are employed outside their region of residence. Instrument Risk factors are analysed using the OPSA digital tool for managing psychosocial risks and absenteeism, with the approval of the Research Centre of the Slovenian Acade- my of Sciences and Arts (ZRC SAZU). This self-assess- ment questionnaire is a validated instrument that gauges the psychosocial stress of employees across 17 distinct areas. The analysis allows for developing specific, target- ed strategies for managing psychosocial risks based on the measured situation (Šprah & Dolenc, 2014). The questionnaire was split into two sections. The first, or fundamental section, contained questions about the re- spondents’ socio-demographic characteristics and health status. The second section comprised a self-assessment questionnaire with 130 statements about the respondent’s work and organisational characteristics. Respondents evaluated the degree to which individual statements and descriptions applied to them using a five- point scale, where 1 signifies ‘I do not agree at all/does not apply to me’ and 5 signifies ‘I strongly agree/applies to me’. Procedure We employed two data collection methods. We gath- ered data via an online questionnaire and physical surveys handed to respondents, which were later manually con- verted to electronic format. We electronically collected the data using the 1.ka ap- plication. We designed an online questionnaire identical in content to the physical questionnaire filled out by the re- spondents. We emailed the link to the online questionnaire to 73.2% of the respondents, while the remainder received 75 Organizacija, V olume 57 Issue 1, February 2024 Research Papers paper-and-pen versions. Participation in the research was voluntary and anonymous for all participants. Results In the beginning, we checked the reliability of the measuring instrument using the Cronbach alpha coeffi- cient, which checks the internal consistency of the meas- uring instrument. Reliability means that we would get similar results if we measured again. We can discuss suffi- cient reliability when the coefficient value exceeds α = 0.7 (Field, 2009). Based on the results, we find that almost all sets of in- dicators, based on which the merging into standard varia- bles took place, are appropriately reliable. The Cronbach coefficient exceeds the recommended value α = 0.7. In constructing the risk factor model emphasising managing the psychosocial risks of train drivers, we used two multivariate statistical methods - the structural equa- tion model and regression analysis (Appendix 1). We initially constructed an SEM model using AMOS 26. This model included all variable relationships, such as correlations, influences, and errors. The model’s general assumptions included sufficient sample size, numerical and normally distributed variables, complete data or appropri- ate handling of missing data, and a theoretical model that served as the foundation for the baseline model. We sub- stituted missing values in the model with average values, ensuring that each variable had at most 5 per cent missing values. The imputation process was conducted using IBM SPSS 27. We examined the recommended modifications for the model to fit the data optimally; following these recommendations (termed “modification indices” - free in- stead of constrained), we fine-tuned the model to align the parameters as closely as possible with the suggested val- ues. The factor weight with the strongest association with the latent variable was fixed and assigned a value of 1. Table 1: Reliability of the measuring instrument Theoretical constructs Cronbach Alpha OPSA profile Employee’s family circumstances (5 statements) 0,783 Interpersonal relationships at work (7 statements) 0,802 Strains as a result of socio-demographic circumstances (8 statements) 0,824 Personality characteristics (10 statements) 0,824 Career development (10 statements) 0.837 Organisational culture (11 statements) 0.914 Organisational structure (7 statements) 0.733 Attitude to work (9 statements) 0.744 Content of work (6 statements) 0.660 Supervision (4 statements) 0.542 Care for oneself (8 statements) 0.776 Psychophysical health status (5 statements) 0.733 Separation of private life and work (9 statements) 0.716 Workload, speed of work (9 statements) 0.712 Working environment and work equipment, physical strains (11 statements) 0.745 Role and responsibility in the organisation (8 statements) 0.589 Work schedule (6 statements) 0.721 76 Organizacija, V olume 57 Issue 1, February 2024 Research Papers Figure 1: Standardised structural model without considering modifications We formulated and tested the following hypotheses: H1: The personal characteristics of train drivers (OZ) exert a statistically significant positive impact on the perception of workplace workload (DEL_OBR). This hypothesis was evaluated using a linear structural model. The independent variable in the model is a latent variable gauged through statements associated with the personal attributes of train drivers. The dependent variable is the directly measured variable of workload/workflow speed. The structural model results indicate no statistically signif- icant influence of the independent variable on the depend- ent. This leads us to reject the hypothesis that train drivers’ personal characteristics positively affect workplace work- load perception (beta = –0.05, p = 0.759). H2: Factors related to professional development (PR) have a statistically significant positive impact on the per- ception of workplace workload (DEL_OBR). This hypoth- esis was also evaluated using a linear structural model. The independent variable is a latent variable measured through statements associated with the professional development factors of train drivers. The dependent variable is the di- rectly measured variable of workload/workflow speed. The hypothesis posited that professional development factors have a statistically significant positive effect on workplace workload perception, which was found to be marginally statistically significant. The structural model demonstrated a weak positive influence of this independent variable on the dependent (beta = 0.282, p = 0.077). However, we re- ject the hypothesis as the risk exceeds 5%. H3: General work characteristics (D) have a statisti- cally significant positive impact on workplace workload perception (DEL_OBR). This hypothesis was evaluated using a linear structural model. The independent variable is a latent variable measured through statements related to the general work characteristics of train drivers. The dependent variable is the directly measured variable of 77 Organizacija, V olume 57 Issue 1, February 2024 Research Papers Figure 2: A standardised structural model with modifications taken into account workload/workflow speed. We confirmed the hypothesis that general work characteristics have a statistically signif- icant positive impact on workplace workload perception. Statistical analysis revealed a strong positive influence of this independent variable on the dependent (beta = 0.865, p < 0.05). Discussion This study has significantly contributed to understand- ing the workload of train drivers, focusing on the influence of personal characteristics, career development, and work in general. The findings suggest that the factor of work, which includes self-assessment of the work environment and physical burdens, has the most statistically significant influence on the perception of workload. Compared to re- cent studies by Balfe et al. (2017), who presented a method to extract train driver task loads from downloads of on- train-data records, our approach aligns with their study’s focus on the work environment and physical burdens as significant factors influencing workload. However, our study extends this by integrating personality characteris- tics and career development constructs, providing a more comprehensive understanding of the factors influencing train driver workload. Another study conducted an in- depth analysis of job satisfaction and perceived workload among subway train conductors. While this study focused on a different subset of railway workers, it underscores the importance of understanding workload and its impact on job satisfaction, a relevant aspect to consider in future re- search (Gottwald & Lejsková, 2023). 78 Organizacija, V olume 57 Issue 1, February 2024 Research Papers Human factors are essential in the safe and orderly con- duct of railway traffic. Employees must be professionally trained, especially those whose jobs directly affect safe, orderly, and economical railway traffic. The main factors of railway traffic safety and quality of railway services are technical means with their functional characteristics and workers who directly participate in the operation of railway traffic and railway services (Balfe et al., 2017). Changing technology in railway traffic significantly affects the tech - nology change, which is changing with new knowledge and staff. Safe railway traffic and quality of railway ser - vices require new techniques, technology, and knowledge, for which modern forms of education are needed. The conceptual model of risk factor integration in train driv- ers and the “evaluated model” represents a new version of the model of integration of constructs “personality charac- teristics”, “career development”, and “work in general”, which influence the perception of “workload”. In studying the choice of model for a specific organisation, we found that the degree of knowledge of the organisation as a busi- ness system also strongly influences the prevalence of the model, depending on its activity, which certainly applies to the system of organisation, such as Slovenian Railways. This research underscores the importance of focusing on general work characteristics to improve train drivers’ work conditions and manage individual risk factors effectively. Conclusions The study provides valuable insights into the workload of train drivers, highlighting the importance of general work characteristics over personal characteristics and pro- fessional development factors. However, it is important to acknowledge the limitations of this research. Firstly, the study was conducted in Slovenia with a sample represent- ing 13.3% of the total population of train drivers. While this provides a good starting point, the findings may not be generalisable to train drivers in other countries or regions due to cultural, infrastructural, and regulatory differenc- es. Secondly, the study utilised the OPSA digital tool to analyse risk factors and gauge psychosocial stress. While this tool is effective, the reliance on self-reported data may introduce bias. Future studies could consider incorporat- ing objective measures or observational data to comple- ment self-reported data. Looking ahead, future research should continue to explore the relationships identified in this study. Specifically, more in-depth studies could be conducted to understand how different aspects of general work characteristics impact the workload of train drivers. Research could also investigate effective strategies for modifying these work characteristics to improve condi- tions for train drivers. 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K., & Frings-Dresen, M. H. (2014). Psychological work characteristics, psychological workload and associated psychological and cognitive requirements of train drivers. Ergonomics, 57(10), 1473-1487. doi: 10.1080/00140139.2014.938130 Danica Murko, PhD, works at the Ministry of Infrastructure. She is a PhD candidate at the Faculty of Management. Her research interests include the areas of management and human resources management. Sarwar Khawaja is the Chairman of Business Development at Oxford Business College, located at 65 George Street, Oxford, United Kingdom. He holds an MBA and an LLM degree. His role involves overseeing the development and growth strategies of the business. Fayyaz Hussain Qureshi is the Head of Research at Oxford Business College, located at 65 George Street, Oxford, United Kingdom. He has a diverse educational background in Economics, Journalism, Botany, 80 Organizacija, V olume 57 Issue 1, February 2024 Research Papers Zoology, Chemistry, English Literature, Marketing, Finance, and Internet Technologies. He also holds a Doctorate in Marketing and a Postgraduate Diploma in Organisations Knowledge. In addition to his role at Oxford Business College, he is a Postgraduate Research (PGR) Doctoral Supervisor at the University of Wales Trinity Saint David (UWTSD). 81 Organizacija, V olume 57 Issue 1, February 2024 Research Papers Appendix 1 SEM MODEL Computation of degrees of freedom (Default model) Number of distinct sample moments: 253 Number of distinct parameters to be estimated: 87 Degrees of freedom (253–87): 166 Result (Default model) Minimum was achieved Chi-square = 351.134 Degrees of freedom = 166 Probability level = 0,000 Estimates (Group number 1 – Default model) Scalar Estimates (Group number 1 – Default model) Maximum Likelihood Estimates Regression Weights: (Group number 1 – Default model) Estimate S.E. C.R. P Label PR6 <––– PR 1.226 .099 12.361 *** W2 PR4 <––– PR 1.000 PR4 <––– PR6 .250 .054 4.597 *** PR1 <––– PR 1.533 .107 14.316 *** W6 D2 <––– D 1.000 D1 <––– D 1.176 .215 5.470 *** W17 D2 <––– PR4 –.098 .091 –1.086 .278 PR7 <––– PR 1.122 .096 11.722 *** W1 PR5 <––– PR 1.197 .109 10.957 *** W3 PR3 <––– PR 1.153 .109 10.611 *** W4 PR2 <––– PR 1.276 .101 12.671 *** W5 OZ7 <––– OZ 1.229 .112 10.950 *** W7 OZ6 <––– OZ 1.037 .098 10.613 *** W8 OZ5 <––– OZ 1.025 .097 10.575 *** W9 OZ4 <––– OZ 1.169 .107 10.917 *** W10 OZ3 <––– OZ 1.000 OZ2 <––– OZ 1.280 .120 10.657 *** W11 OZ1 <––– OZ 1.465 .144 10.177 *** W12 D6 <––– D .575 .154 3.735 *** W13 D5 <––– D 1.439 .268 5.367 *** W14 D4 <––– D 1.331 .224 5.937 *** W15 D3 <––– D .829 .173 4.795 *** W16 OZ8 <––– OZ 1.274 .129 9.868 *** W18 DEL_OBR <––– PR .282 .159 1.771 .077 W19 DEL_OBR <––– OZ –.050 .162 –.306 .759 W20 DEL_OBR <––– D .865 .227 3.818 *** W21 OZ1 <––– D2 –.214 .061 –3.486 *** OZ3 <––– D1 .126 .053 2.354 .019 D6 <––– PR1 .636 .061 10.345 *** Standardised Regression Weights: (Group number 1 – Default model) 82 Organizacija, V olume 57 Issue 1, February 2024 Research Papers Estimate PR6 <––– PR .752 PR4 <––– PR .566 PR4 <––– PR6 .230 PR1 <––– PR .886 D2 <––– D .601 D1 <––– D .719 D2 <––– PR4 –.110 PR7 <––– PR .762 PR5 <––– PR .698 PR3 <––– PR .705 PR2 <––– PR .810 OZ7 <––– OZ .838 OZ6 <––– OZ .812 OZ5 <––– OZ .808 OZ4 <––– OZ .834 OZ3 <––– OZ .744 OZ2 <––– OZ .816 OZ1 <––– OZ .853 D6 <––– D .294 D5 <––– D .814 D4 <––– D 1.001 D3 <––– D .568 OZ8 <––– OZ .757 DEL_OBR <––– PR .196 DEL_OBR <––– OZ –.038 DEL_OBR <––– D .571 OZ1 <––– D2 –.180 OZ3 <––– D1 .134 D6 <––– PR1 .592 Covariances: (Group number 1 – Default model) Estimate S.E. C.R. P Label OZ <––> D .192 .041 4.746 *** C1 PR <––> OZ .217 .025 8.806 *** C2 PR <––> D .186 .035 5.250 *** C3 e4 <––> PR .061 .010 6.308 *** e7 <––> D –.025 .007 –3.302 *** e20 <––> PR –.024 .009 –2.797 .005 e19 <––> D .017 .012 1.365 .172 e19 <––> PR .028 .017 1.703 .088 e7 <––> e19 –.109 .022 –4.877 *** e10 <––> e20 –.074 .015 –4.987 *** e1 <––> e5 .098 .016 6.121 *** e18 <––> e7 –.035 .011 –3.111 .002 e11 <––> e7 .040 .011 3.541 *** e9 <––> e21 .077 .018 4.147 *** e6 <––> e7 .065 .015 4.491 *** e3 <––> e2 .139 .024 5.691 *** e1 <––> D .022 .008 2.705 .007 83 Organizacija, V olume 57 Issue 1, February 2024 Research Papers Estimate S.E. C.R. P Label e1 <––> e22 .102 .014 7.059 *** e1 <––> e18 .068 .015 4.482 *** e18 <––> D .022 .010 2.125 .034 e18 <––> e22 .089 .018 4.940 *** e16 <––> D –.018 .018 –1.009 .313 e16 <––> e17 –.050 .016 –3.124 .002 e10 <––> e14 .047 .017 2.779 .005 e6 <––> e19 –.126 .023 –5.408 *** e5 <––> e15 .067 .017 4.041 *** e17 <––> e4 .032 .007 4.424 *** e16 <––> e19 –.020 .027 –.739 .460 e16 <––> e4 –.035 .016 –2.211 .027 e1 <––> e16 .026 .016 1.649 .099 e1 <––> e3 –.023 .011 –2.072 .038 e3 <––> e9 .045 .014 3.272 .001 e21 <––> e19 –.046 .021 –2.136 .033 e18 <––> e20 .040 .016 2.468 .014 e15 <––> e2 .040 .015 2.632 .008 e1 <––> e6 .032 .009 3.527 *** e15 <––> e4 .095 .015 6.555 *** Continues Correlations: (Group number 1 – Default model) Estimate OZ <--> D .847 PR <--> OZ .908 PR <--> D .892 e4 <--> PR .356 e7 <--> D –.147 e20 <--> PR –.103 e19 <--> D .063 e19 <--> PR .100 e7 <--> e19 –.481 e10 <--> e20 –.384 e1 <--> e5 .401 e18 <--> e7 –.186 e11 <--> e7 .271 e9 <--> e21 .358 e6 <--> e7 .401 e3 <--> e2 .478 e1 <--> D .110 e1 <--> e22 .486 e1 <--> e18 .304 e18 <--> D .097 e18 <--> e22 .380 e16 <--> D –.081 e10 <--> e14 .231 e6 <--> e19 –.482 84 Organizacija, V olume 57 Issue 1, February 2024 Research Papers Estimate e5 <--> e15 .252 e16 <--> e19 –.065 e16 <--> e4 –.185 e1 <--> e16 .112 e1 <--> e3 –.090 e3 <--> e9 .205 e21 <--> e19 –.134 e18 <--> e20 .156 e15 <--> e2 .162 e1 <--> e6 .168 e15 <--> e4 .532 Variances: (Group number 1 – Default model) Continues Estimate S.E. C.R. P Label PR .220 .022 10.160 *** V1 OZ .260 .047 5.506 *** V2 D .198 .069 2.867 .004 V3 e2 .254 .028 9.021 *** e4 .135 .018 7.572 *** e7 .142 .019 7.551 *** e19 .364 .039 9.460 *** e20 .257 .027 9.454 *** e22 .220 .022 10.160 *** V1 e1 .200 .020 10.043 *** e3 .332 .035 9.584 *** e5 .296 .032 9.391 *** e6 .188 .021 9.159 *** e8 .166 .020 8.212 *** e9 .145 .017 8.499 *** e10 .146 .017 8.492 *** e11 .156 .019 8.238 *** e12 .145 .017 8.480 *** e13 .214 .025 8.408 *** e14 .287 .034 8.333 *** e15 .237 .025 9.652 *** e16 .262 .044 5.907 *** e17 –.001 .009 –.075 .941 e18 .251 .026 9.477 *** e21 .315 .036 8.737 *** Squared Multiple Correlations: (Group number 1 – Default model) 85 Organizacija, V olume 57 Issue 1, February 2024 Research Papers Estimate PR6 .565 PR4 .803 D1 .516 D2 .335 PR1 .785 DEL_OBR .516 OZ8 .573 D3 .407 D4 1.002 D5 .578 D6 .688 OZ1 .626 OZ2 .666 OZ3 .692 OZ4 .695 OZ5 .652 OZ6 .659 OZ7 .703 PR2 .656 PR3 .497 PR5 .487 PR7 .581 Model Fit Summary CMIN Model NPAR CMIN DF P CMIN/DF Default model 87 351.134 166 .000 2.115 Saturated model 253 .000 0 Independence model 22 4380.793 231 .000 18.964 RMR, GFI Model RMR GFI AGFI PGFI Default model .065 .849 .769 .557 Saturated model .000 1.000 Independence model .360 .115 .031 .105 Baseline Comparisons Model NFI Delta1 RFI rho1 IFI Delta2 TLI rho2 CFI Default model .920 .888 .956 .938 .955 Saturated model 1.000 1.000 1.000 Independence model .000 .000 .000 .000 .000 86 Organizacija, V olume 57 Issue 1, February 2024 Research Papers Parsimony-Adjusted Measures Model PRATIO PNFI PCFI Default model .719 .661 .687 Saturated model .000 .000 .000 Independence model 1.000 .000 .000 NCP Model NCP LO 90 HI 90 Default model 185.134 135.101 242.924 Saturated model .000 .000 .000 Independence model 4149.793 3938.498 4368.363 FMIN Model FMIN F0 LO 90 HI 90 Default model 1.973 1.040 .759 1.365 Saturated model .000 .000 .000 .000 Independence model 24.611 23.313 22.126 24.541 RMSEA Model RMSEA LO 90 HI 90 PCLOSE Default model .079 .068 .091 .000 Independence model .318 .309 .326 .000 AIC Model AIC BCC BIC CAIC Default model 525.134 550.953 802.437 889.437 Saturated model 506.000 581.084 1312.409 1565.409 Independence model 4424.793 4431.322 4494.915 4516.915 ECVI Model ECVI LO 90 HI 90 MECVI Default model 2.950 2.669 3.275 3.095 Saturated model 2.843 2.843 2.843 3.265 Independence model 24.858 23.671 26.086 24.895 HOELTER Model HOELTER .05 HOELTER .01 Default model 100 108 Independence model 11 12