Abstract

General Background: Employee performance in higher education institutions is a critical determinant of administrative effectiveness and institutional success. Specific Background: However, the complex interplay between work climate, employee competence, work experience, and performance remains underexplored, particularly among non-academic staff. Knowledge Gap: Existing literature rarely integrates these variables within a single analytical framework, especially in the Indonesian higher education context. Aims: This study aims to examine the causal relationships among work climate, employee competence, work experience, and employee performance among administrative staff at Universitas Pattimura Ambon (UNPATTI) using a mixed-methods approach. Results: Findings indicate that work experience significantly influences both competence (path coefficient = 0.704) and work climate (0.652), but has a negligible direct effect on performance (–0.015). Competence emerges as the strongest predictor of performance (0.819), while work climate exerts only a minor direct effect (0.026). Novelty: The integration of Structural Equation Modeling (SEM) with qualitative insights provides a comprehensive understanding of mediating effects, revealing competence as a pivotal mechanism linking experience and climate to performance. Implications: These results inform strategic human resource management practices in higher education, emphasizing targeted professional development, mentorship, and the cultivation of a supportive work climate to enhance staff performance and institutional sustainability.

Highlights:

1. Work climate and competence affect performance in higher education administration.
2. Mixed-methods with SEM show competence mediates experience's impact on performance.
3. Prioritize HR strategies: mentorship, training, and supportive work environment.

Keyword: Human Resource Management, Employee Competence, Work Climate, Higher Education Administration, Structural Equation Modeling

Introduction

Human Resource Management (HRM) is a cornerstone of organizational effectiveness, encompassing a comprehensive framework for managing employee relations and optimizing workforce productivity. HRM is not merely an administrative function; it is a strategic enabler that integrates decision-making processes across recruitment, training, development, and performance evaluation [1], [2]. It aims to align individual capabilities with organizational goals, promoting a synergy that enhances both employee satisfaction and institutional efficiency.

One of the fundamental principles of HRM is the alignment of employee competence with job requirements, encapsulated in the philosophy of "the right man on the right job" [3]. Competence is a multifaceted construct that includes technical skills, behavioral attributes, and the ability to adapt to dynamic work environments. When effectively harnessed, competence becomes a driving force for organizational success, as it ensures that tasks are performed with precision, innovation, and accountability.

In the context of higher education institutions, HRM assumes an even more critical role. Universities, as centers of knowledge creation and dissemination, rely heavily on the effectiveness of their human resources, including academic staff, administrative personnel, and support teams. HRM in this setting is intricately linked to the achievement of the tri-dharma of higher education—education, research, and community service [4]. Effective HRM practices in universities not only enhance operational efficiency but also contribute to institutional reputation, research outputs, and student satisfaction.

Established in 1959, Universitas Pattimura Ambon (UNPATTI) stands as a beacon of higher education in Eastern Indonesia. With nine faculties, over 1,151 lecturers, and a student body exceeding 23,000, the university has positioned itself as a key player in the region’s educational landscape. Complementing its academic staff are approximately 450 administrative employees, whose roles are pivotal in supporting the university’s academic and operational functions.

However, UNPATTI faces a series of HRM challenges that threaten to undermine its potential. Over the past decade, the administrative staff at UNPATTI has struggled to adapt to increasing demands brought about by internal reforms and external pressures, such as the Ministry of Education’s Kampus Merdeka program. Issues such as chronic absenteeism, failure to meet deadlines, and inadequate task completion have become increasingly prevalent. Such inefficiencies are not merely isolated incidents; they have systemic implications, affecting service delivery to faculty, students, and the wider community [5]. The root causes of these challenges are multifaceted, often tied to mismatches between job requirements and employee competencies. Inadequate educational qualifications further exacerbate the problem, limiting employees’ ability to innovate and respond to the dynamic needs of the university. The absence of a structured performance management system compounds the issue, making it difficult to assess and address performance gaps effectively.

The interplay between competence, education, and performance is well-documented in HRM literature. Competence, defined as the combination of skills, knowledge, and attitudes required to perform a job effectively, is a critical determinant of employee performance [3]. Educational qualifications, while distinct from competence, provide a foundational framework for skill acquisition and cognitive development. Together, these factors form the basis for employee effectiveness, influencing outcomes such as task completion, problem-solving, and decision-making [6]. Performance management systems play a vital role in bridging the gap between individual capabilities and organizational expectations. These systems encompass a range of practices, from goal setting and performance appraisal to feedback and developmental interventions. In the context of higher education, performance management must account for the unique challenges of academic and administrative roles, integrating quantitative and qualitative metrics to provide a holistic view of employee contributions [7].

While previous studies have explored the relationship between competence and performance, few have examined this dynamic within the unique context of higher education institutions. Research on HRM practices in universities often focuses on academic staff, neglecting the critical role of administrative personnel. Moreover, existing studies tend to adopt a single-variable approach, failing to capture the nuanced interactions between competence, education, and performance. This study addresses these gaps by employing an integrated framework that combines empirical and comparative methodologies. By focusing on administrative staff at UNPATTI, it provides a novel perspective on HRM challenges in higher education. The study’s dual emphasis on competence and education offers a comprehensive understanding of their collective impact on performance, paving the way for targeted interventions and policy recommendations.

The primary objective of this study is to examine the influence of employee competence and educational attainment on the performance of administrative staff at Universitas Pattimura Ambon. Specific research questions include: 1) What is the relationship between employee competence and performance in the context of UNPATTI?, 2) How does educational attainment influence administrative performance at UNPATTI?, 3) What interventions can be implemented to enhance employee effectiveness and support institutional goals?

The study adopts a quantitative approach, utilizing structured surveys and performance data to analyze the relationship between competence, education, and performance. Empirical methods are employed to gather primary data from administrative staff across various departments, including academic services, registration, and student affairs. A comparative analysis is conducted to identify performance variations among employees with different competency levels and educational backgrounds. Structural Equation Modeling (SEM) serves as the primary analytical tool, offering a robust framework for examining complex relationships between variables. The integration of quantitative data with comparative insights ensures a comprehensive understanding of HRM dynamics at UNPATTI.

The findings of this study have far-reaching implications for HRM practices at Universitas Pattimura Ambon. By identifying key performance drivers, the research provides actionable insights for enhancing employee effectiveness. Specifically, it informs the development of targeted training programs, competency-based recruitment strategies, and performance appraisal systems.

Beyond its immediate application at UNPATTI, the study contributes to the broader discourse on HRM in higher education. It highlights the need for context-specific HRM practices that address the unique challenges of university settings. The research also underscores the importance of integrating competence and education as dual pillars of performance management, offering a replicable framework for other institutions. As UNPATTI strives to achieve its vision of becoming a world-class university by 2030, the insights from this study serve as a foundation for continuous improvement.

Future research could expand the scope to include longitudinal studies that track the impact of HRM interventions over time. Comparative studies involving multiple universities would further enrich the understanding of HRM dynamics in higher education, providing a broader basis for policy recommendations. HRM is an indispensable component of organizational success, particularly in complex environments such as higher education. By examining the relationship between competence, education, and performance, this study sheds light on critical HRM challenges at Universitas Pattimura Ambon. The findings not only address immediate performance gaps but also contribute to the university’s long-term strategic goals, reinforcing its commitment to excellence in education, research, and community service.

Methods

This study adopted a mixed-methods approach, integrating descriptive and inferential statistical techniques to analyze the relationship between employee competence, work experience, educational attainment, and performance among administrative staff at Universitas Pattimura Ambon. By combining descriptive analysis and Structural Equation Modeling (SEM), the research aimed to provide a comprehensive understanding of the variables while exploring causal relationships [8], [9]. The research design utilized descriptive analysis to summarize data through percentages, means, medians, modes, and frequency distributions. These techniques were employed to describe the conditions of the study variables, such as competence, educational level, and work experience, and present the findings in tabular formats for clarity. SEM was selected for its robust capabilities in estimating complex models, including factor analysis, path analysis, and multiple regression [10]. This combination of methods ensured both detailed descriptive insights and a rigorous examination of inter-variable relationships.

The population for this study included administrative staff from five key departments: academic services, registration and statistics, infrastructure management, planning and budgeting, and human resources. A total of 30 respondents were selected using purposive sampling to ensure representation from all relevant units. This sampling technique was deemed appropriate for targeting individuals with specific roles and expertise, aligning with the study's objectives [11]. Data collection was conducted using three primary methods: Focus Group Discussions (FGDs), structured interviews, and questionnaires. FGDs were organized to gather qualitative insights into challenges and performance drivers, fostering interactive discussions that uncovered nuanced perspectives [12]. Structured interviews provided in-depth accounts of individual experiences, competencies, and educational backgrounds, adding contextual richness to the data. Additionally, structured questionnaires employing a 5-point Likert scale were distributed to measure perceptions of competence, work experience, and performance. Likert scales were particularly effective in capturing attitudinal data with reliability and validity [13].

The analysis process was carried out in two stages. First, descriptive statistics were used to summarize demographic data and variable conditions through percentage distributions, means, and frequency tables, offering a foundational understanding of trends in the dataset [14]. Second, SEM was utilized to test hypotheses and examine the structural relationships between independent variables (competence, work experience, and education) and the dependent variable (employee performance). The SEM process included Confirmatory Factor Analysis (CFA) to validate measurement models, path analysis to assess direct and indirect effects, and model fit assessments using indices such as CFI, TLI, RMSEA, and Chi-square statistics [15]. All analyses were conducted using AMOS software for Windows, a widely recognized tool for SEM applications [16]. The questionnaire was meticulously designed based on an extensive review of HRM literature, ensuring alignment with the study’s objectives. Each construct was operationalized using multiple items, with responses recorded on a Likert scale ranging from 1 (“strongly disagree”) to 5 (“strongly agree”), allowing for a nuanced measurement of perceptions [17]. Ethical considerations were central to the study’s design and implementation. Approval was obtained from the university’s research ethics committee, and informed consent was secured from all participants, ensuring voluntary participation. Data confidentiality and anonymity were upheld throughout the research process to maintain ethical integrity.

To ensure data quality, reliability and validity tests were conducted. Cronbach’s alpha values exceeding 0.70 confirmed the internal consistency of constructs [18]. Construct validity was assessed through CFA, and convergent and discriminant validity were evaluated using Average Variance Extracted (AVE) and Maximum Shared Variance (MSV) criteria [19]. While this study provides valuable insights into HRM practices at Universitas Pattimura Ambon, it is limited by the small sample size and focus on a single institution. Future research should consider expanding the scope to include multiple universities and larger respondent pools to enhance generalizability.

Results and Discussions

Model Fit

The researchers compared various indices between the Saturated Model and the Estimated Model. These indices were utilized to evaluate how well the developed model aligns with the observed data. Fit indices such as NFI and Chi-Square indicate that the model fits the data reasonably well. However, the high SRMR value suggests a significant discrepancy between the observed and predicted covariances. Additionally, d_ULS and d_G indices require further analysis for more accurate interpretation. Overall, while there are positive indicators, the model may need revisions or improvements to achieve optimal fit.

SRMR (Standardized Root Mean Square Residual) reflects the average discrepancy between the observed and predicted covariances, normalized into standard form. The SRMR values range from 0 to 1, with lower values indicating better model fit. The SRMR for the Estimated Model is 0.155. Typically, SRMR values below 0.08 suggest a good fit [20]; thus, the 0.155 value indicates suboptimal model fit requiring adjustments. d_ULS (Squared Euclidean Distance) measures the Euclidean distance between the observed and predicted covariance matrices. Smaller values indicate better model fit. The d_ULS value for the Estimated Model is 109.022. As there is no standard threshold for d_ULS, its value is best compared with other models or evaluated through bootstrap testing for significance [21]. Similarly, d_G (Geodesic Distance) evaluates the geodesic distance between the observed and predicted covariance matrices on a positive definite manifold. Like d_ULS, smaller values suggest better model fit, but further analysis, such as bootstrap tests, is required for interpretation [22]. Chi-Square assesses the difference between observed and predicted covariance matrices, with smaller values indicating better model fit. The Chi-Square value of 0.626 for the Estimated Model is very low, suggesting good fit. However, the Chi-Square test is sensitive to sample size, as even minor discrepancies can become statistically significant in large samples [23]. NFI (Normed Fit Index) compares the Estimated Model to a null model and ranges from 0 to 1, with higher values indicating better fit. The NFI of 0.904 exceeds the threshold of 0.90, typically considered indicative of good fit [24], suggesting that the model fits well according to this index.

Construct Validity and Reliability

Construct validity and reliability were assessed using four key metrics: Cronbach’s Alpha, rho_A, Composite Reliability, and Average Variance Extracted (AVE).

The Work Climate construct achieved a Cronbach’s Alpha of 0.804 and rho_A of 0.863, indicating good internal consistency. Composite Reliability of 0.825 and AVE of 0.789 confirm adequate convergent validity [25]. Employee Performance demonstrated high reliability, with a Cronbach’s Alpha of 0.901 and rho_A of 0.925, as well as Composite Reliability of 0.914 and AVE of 0.704, all surpassing recommended thresholds [26]. Employee Competence yielded the highest reliability scores across all metrics, with Cronbach’s Alpha at 0.927, rho_A at 0.937, Composite Reliability at 0.936, and AVE at 0.716, reflecting strong reliability and validity [27]. Work Experience also demonstrated robust results with Cronbach’s Alpha of 0.922, rho_A of 0.927, Composite Reliability of 0.930, and AVE of 0.705, consistent with good measurement standards [28], [23]. Overall, all constructs met the necessary criteria, ensuring the instruments used were reliable and valid..

Factor Loadings

Factor loadings were analyzed to assess the contribution of individual items to their respective latent constructs.

Table Factor Loadings in here

Items for the Work Climate construct displayed high factor loadings ranging from 0.673 to 0.839, indicating strong construct validity [25]. Similarly, Employee Competence demonstrated adequate loadings, though certain items like KK_11 (0.529) and KK_14 (0.520) fell below the preferred threshold of 0.6, warranting further attention [29]. Employee Performance mostly exhibited factor loadings above 0.6, with exceptions like KP_11 (0.502), which may marginally affect construct reliability [30]. In contrast, all items within the Work Experience construct exceeded the 0.5 threshold, contributing significantly to the construct [26]. These results affirm the instruments’ capacity to accurately represent latent constructs, though items with lower factor loadings should be evaluated further to enhance validity and reliability [23].

Path Analysis

Path analysis revealed the causal relationships among the study variables, including Work Climate, Employee Performance, Employee Competence, and Work Experience.

Figure 1. Structural Equation Model of Work Experience , Work Climate , Employee Competence and Employee Performance

The Multifaceted Relationship Between Work Experience and Work Climate

The significant positive relationship between Work Experience and Work Climate, with a path coefficient of 0.652, highlights the essential role of employee tenure in shaping workplace perceptions. Employees with extensive experience often demonstrate a deeper understanding of organizational structures, norms, and culture, enabling them to adapt and contribute positively to the work environment. This aligns with the theory of organizational socialization, where long-serving employees internalize and reinforce workplace values, creating an environment conducive to collaboration and productivity [31]. Further, experienced employees often act as informal leaders, fostering cohesion and guiding newer employees, which enhances the collective perception of the work climate. Research also suggests that experienced employees play pivotal roles in shaping team dynamics and organizational culture [32]. The positive work climate created through experience-induced adaptation benefits the organization by enhancing job satisfaction and reducing turnover [33]. This finding also resonates with psychological theories of employee engagement, which posit that experienced employees, through their familiarity with workplace norms and reduced uncertainty, are better positioned to navigate organizational complexities [34]. By fostering a supportive work climate, experienced employees contribute to a culture of trust and collaboration that transcends individual roles.

The Strength of Work Experience in Developing Employee Competence

The robust relationship between Work Experience and Employee Competence (path coefficient: 0.704) underscores the importance of experiential learning in organizational contexts. Work experience, as a cumulative process, allows employees to develop technical, procedural, and interpersonal skills essential for job performance. This aligns with Kolb’s Experiential Learning Theory, which emphasizes that experience serves as a primary driver of knowledge and competence acquisition [35]. Longitudinal studies in organizational psychology have consistently demonstrated that job-specific experience directly enhances competence through practice and reflection [36]. For example, employees who have navigated complex job challenges over time develop problem-solving skills and contextual expertise, making them invaluable to organizational success [37]. The higher competence levels observed in experienced employees reflect their ability to integrate theoretical knowledge with practical applications, a trait that is increasingly vital in dynamic workplace environments [38]. The implications of this finding are profound. Organizations must recognize the dual role of experience in fostering not only expertise but also a sense of professional identity and confidence among employees. These traits are crucial for promoting innovation and resilience in organizational systems [39].

The Negligible Direct Effect of Work Experience on Employee Performance

The unexpected finding of a negligible direct effect of Work Experience on Employee Performance (-0.015) challenges conventional assumptions about the linear impact of tenure on output. While experience enhances competence, its direct translation to performance appears contingent on mediating factors, such as job design, resource availability, and employee motivation [40]. This result may be attributed to the phenomenon of "tenure plateau," where long-tenured employees may become less engaged or innovative, relying instead on routine approaches to problem-solving. Although experience contributes to expertise, it may not necessarily lead to sustained performance improvements without external stimuli or growth opportunities [41]. Moreover, performance outcomes are often shaped by a complex interplay of individual and environmental factors, such as leadership support, team dynamics, and recognition systems [42]. In this study, the mediating influence of Employee Competence suggests that organizations must focus on leveraging experiential learning to foster actionable skills and behaviors that drive performance.

The Limited Direct Impact of Work Climate on Employee Performance

The marginal direct effect of Work Climate on Employee Performance (0.026) reflects the nuanced role of workplace environment in shaping employee outcomes. While work climate influences perceptions and behaviors, its impact on performance is often indirect, mediated by factors like job satisfaction, organizational commitment, and engagement [43]. The limited direct effect observed in this study aligns with findings that a positive work climate acts as an enabler rather than a determinant of performance [44]. For example, a collaborative and supportive environment may boost motivation and reduce stress, but actual task execution depends on individual competencies and external incentives. Additionally, perceptions of work climate vary based on personal expectations and experiences. As noted by Schneider et al., the organizational climate is a shared perception, but its impact on individuals depends on alignment with organizational goals [43].

The Overwhelming Influence of Employee Competence on Employee Performance

The strong direct effect of Employee Competence on Employee Performance (0.819) underscores the centrality of skills and abilities in determining workplace outcomes. This finding aligns with competency-based theories of performance, which assert that employees equipped with the necessary knowledge and skills are better positioned to meet job demands [45]. Competence drives performance by enhancing efficiency, adaptability, and initiative. It enables employees to complete tasks more accurately and respond to changing job requirements [46]. Competence also boosts confidence and proactive behavior, encouraging employees to contribute more meaningfully to organizational goals [38]. This also reflects the Resource-Based View (RBV) of strategic management, which identifies employee competence as a key source of competitive advantage [47].

The Mediating Role of Employee Competence in Performance Dynamics

The mediating role of Employee Competence between Work Experience and Employee Performance highlights the transformative potential of skill development. While experience provides the foundation, competence translates learning into outcomes [36]. This is consistent with findings that competence is the key conduit through which broader predictors like experience influence performance [48]. For instance, an experienced but unskilled employee may struggle, whereas competence enhances the impact of experience through effective behavior. This underscores the importance of training programs, mentorships, and competency-based assessments in HRM. Targeted interventions can convert experience into performance gains, building a capable and resilient workforce [42].

Implications of Findings in Higher Education

These findings offer valuable insights for HRM in higher education. Universities must foster a work climate that leverages the expertise of experienced staff to support institutional culture and academic excellence [32]. Policies such as mentorship, recognition programs, and collaborative governance enhance climate and engagement. The strong link between experience and competence highlights the need for experiential learning opportunities across departments [35], [36]. For academic staff, research, teaching, and community engagement build competence, while administrative staff benefit from cross-functional projects and reflective practices. The negligible direct effect of experience on performance signals the need for motivation and job enrichment strategies [41]. Institutions should design structured development pathways that enhance competence as a mediator of performance. Finally, the limited direct influence of work climate emphasizes the importance of leadership, engagement, and institutional alignment to sustain high performance [43], [44].

Conclusion

This study underscores the intricate dynamics between Work Climate, Employee Competence, Work Experience, and Employee Performance within the context of higher education. The findings reveal that while Work Experience significantly enhances Competence and Work Climate, its direct impact on performance is minimal, highlighting the critical mediating role of Competence. The strong influence of Competence on Performance emphasizes the necessity for universities to prioritize targeted skill development, mentorship programs, and competency-based management frameworks. Moreover, fostering a supportive Work Climate indirectly bolsters performance by enhancing engagement and job satisfaction. These insights call for strategic investments in human resource development, emphasizing experiential learning, professional growth opportunities, and transformative leadership to drive excellence in teaching, research, and administration in higher education institutions.

References

1. S. L. Albrecht, A. B. Bakker, J. A. Gruman, W. H. Macey, and A. M. Saks, “Employee engagement, human resource management practices and competitive advantage: An integrated approach,” J. Organ. Eff.: People Perform., vol. 2, no. 1, pp. 7–35, 2015, doi: 10.1108/JOEPP-08-2014-0042.
2. D. P. Lepak, H. Liao, Y. Chung, and E. E. Harden, “A conceptual review of human resource management systems in strategic human resource management research,” Res. Pers. Hum. Resour. Manag., vol. 25, pp. 217–271, 2006, doi: 10.1016/S0742-7301(06)25006-0.
3. R. E. Boyatzis, “Competencies in the 21st century,” J. Manag. Dev., vol. 27, no. 1, pp. 5–12, 2008, doi: 10.1108/02621710810840730.
4. R. Deem, “Globalisation, new managerialism, academic capitalism and entrepreneurialism in universities: Is the local dimension still important?,” Comp. Educ., vol. 37, no. 1, pp. 7–20, 2001, doi: 10.1080/03050060020020408.
5. B. Afsar and Y. F. Badir, “Workplace spirituality, perceived organizational support, and innovative work behavior: The mediating effects of person-organization fit,” J. Workplace Learn., vol. 29, no. 2, pp. 95–109, 2017, doi: 10.1108/JWL-11-2015-0086.
6. M. Armstrong and S. Taylor, Armstrong’s Handbook of Human Resource Management Practice, 15th ed. London: Kogan Page, 2020.
7. J. Purcell, N. Kinnie, S. Hutchinson, B. Rayton, and J. Swart, Understanding the People and Performance Link: Unlocking the Black Box. London: CIPD, 2003.
8. R. B. Kline, Principles and Practice of Structural Equation Modeling, 4th ed. New York: Guilford Press, 2015.
9. J. F. Hair, W. C. Black, B. J. Babin, and R. E. Anderson, Multivariate Data Analysis, 8th ed. Upper Saddle River, NJ: Pearson, 2017.
10. P. M. Bentler, “Comparative fit indexes in structural models,” Psychol. Bull., vol. 107, no. 2, pp. 238–246, 1990, doi: 10.1037/0033-2909.107.2.238.
11. J. W. Creswell and V. L. Plano Clark, Designing and Conducting Mixed Methods Research, 3rd ed. Thousand Oaks, CA: SAGE, 2018.
12. D. L. Morgan, Focus Groups as Qualitative Research, 2nd ed. Thousand Oaks, CA: SAGE, 1997.
13. R. Likert, “A technique for the measurement of attitudes,” Arch. Psychol., vol. 22, no. 140, pp. 1–55, 1932.
14. J. Pallant, SPSS Survival Manual, 7th ed. New York: Routledge, 2020.
15. L. T. Hu and P. M. Bentler, “Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives,” Struct. Equ. Model., vol. 6, no. 1, pp. 1–55, 1999, doi: 10.1080/10705519909540118.
16. J. L. Arbuckle, AMOS 25.0 User’s Guide. Chicago, IL: IBM SPSS, 2017.
17. J. J. Vaske, Survey Research and Analysis: Applications in Parks, Recreation and Human Dimensions. State College, PA: Venture Publishing, 2008.
18. J. C. Nunnally and I. H. Bernstein, Psychometric Theory, 3rd ed. New York: McGraw-Hill, 1994.
19. C. Fornell and D. F. Larcker, “Evaluating structural equation models with unobservable variables and measurement error,” J. Market. Res., vol. 18, no. 1, pp. 39–50, 1981, doi: 10.2307/3151312.
20. L. T. Hu and P. M. Bentler, op. cit. [15].
21. T. K. Dijkstra and J. Henseler, “Consistent partial least squares path modeling,” MIS Q., vol. 39, no. 2, pp. 297–316, 2015, doi: 10.25300/MISQ/2015/39.2.02.
22. J. Henseler, C. M. Ringle, and M. Sarstedt, “A new criterion for assessing discriminant validity in variance-based structural equation modeling,” J. Acad. Mark. Sci., vol. 43, no. 1, pp. 115–135, 2014.
23. R. B. Kline, op. cit. [8].
24. P. M. Bentler and D. G. Bonett, “Significance tests and goodness of fit in the analysis of covariance structures,” Psychol. Bull., vol. 88, no. 3, pp. 588–606, 1980.
25. J. F. Hair, W. C. Black, B. J. Babin, and R. E. Anderson, op. cit. [9].
26. C. Fornell and D. F. Larcker, op. cit. [19].
27. J. C. Nunnally, op. cit. [18].
28. R. P. Bagozzi and Y. Yi, “On the evaluation of structural equation models,” J. Acad. Market. Sci., vol. 16, no. 1, pp. 74–94, 1988.
29. J. P. Stevens, Applied Multivariate Statistics for the Social Sciences, 5th ed. New York: Routledge, 2009.
30. B. M. Byrne, Structural Equation Modeling with AMOS: Basic Concepts, Applications, and Programming, 2nd ed. New York: Routledge, 2010.
31. J. Van Maanen and E. H. Schein, “Toward a theory of organizational socialization,” Res. Organ. Behav., vol. 1, pp. 209–264, 1979.
32. C. Ostroff, A. J. Kinicki, and M. M. Tamkins, “Organizational culture and climate,” in Handbook of Psychology, vol. 12, Borman, Ilgen, & Klimoski, Eds. Hoboken, NJ: Wiley, 2003, pp. 565–593.
33. B. Afsar and Y. F. Badir, op. cit. [5].
34. A. B. Bakker and E. Demerouti, “The job demands–resources model: State of the art,” J. Manag. Psychol., vol. 22, no. 3, pp. 309–328, 2007, doi: 10.1108/02683940710733115.
35. D. A. Kolb, Experiential Learning: Experience as the Source of Learning and Development. Englewood Cliffs, NJ: Prentice Hall, 1984.
36. M. Eraut, op. cit. [36].
37. L. M. Spencer and S. M. Spencer, Competence at Work: Models for Superior Performance. New York: Wiley, 1993.
38. M. Armstrong and S. Taylor, op. cit. [6].
39. E. Cameron and M. Green, Making Sense of Change Management, 5th ed. London: Kogan Page, 2019.
40. R. M. Baron and D. A. Kenny, “The moderator–mediator variable distinction in social psychological research,” J. Pers. Soc. Psychol., vol. 51, no. 6, pp. 1173–1182, 1986.
41. T. W. H. Ng and D. C. Feldman, “The relationships of age with job attitudes: A meta-analysis,” Pers. Psychol., vol. 63, no. 3, pp. 677–718, 2010, doi: 10.1111/j.1744-6570.2010.01184.x.
42. J. Hayes, Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach. New York: Guilford Press, 2013.
43. B. Schneider, M. G. Ehrhart, and W. H. Macey, “Organizational climate and culture,” Annu. Rev. Psychol., vol. 64, no. 1, pp. 361–388, 2013, doi: 10.1146/annurev-psych-113011-143809.
44. J. R. Schermerhorn, R. N. Osborn, and J. G. Hunt, Organizational Behavior, 11th ed. Hoboken, NJ: Wiley, 2010.