کاربرد مدل معادلات ساختاری (SEM) در تحلیل بهرهوری منابع انسانی؛ مطالعه موردی سازمان بهزیستی
محورهای موضوعی : مدیریتالناز کریمی 1 , رسول داودی 2 , محمد رضا کرمیپور شمس آبادی 3
1 - دانشجوی دکتری مدیریت آموزشی، واحد زنجان، دانشگاه آزاد اسلامی، زنجان، ایران.
2 - دانشیار گروه مدیریت آموزشی، واحد زنجان، دانشگاه آزاد اسلامی، زنجان، ایران.
3 - استادیارگروه مدیریت آموزشی، واحد زنجان، دانشگاه آزاد اسلامی، زنجان، ایران.
کلید واژه: بهرهوری منابع انسانی, مدل معادلات ساختاری, تحلیل عاملی تأییدی, سازمان بهزیستی, شاخصهای برازش.,
چکیده مقاله :
هدف این پژوهش، تحلیل ساختار بهرهوری منابع انسانی در سازمان بهزیستی از طریق مدلسازی معادلات ساختاری (SEM) است. بهرهوری منابع انسانی بهعنوان یکی از مهمترین عوامل موفقیت سازمانها، تحت تأثیر متغیرهای متعدد و مرتبطی قرار دارد که تحلیل همزمان آنها نیازمند بهکارگیری روشهای آماری پیشرفته است. پژوهش حاضر با رویکرد کمی و به روش توصیفی ـ همبستگی انجام شد. جامعه آماری شامل کارکنان سازمان بهزیستی استان تهران در سال ۱۳۹۷ بود که از میان آنها ۳۲۰ نفر با روش نمونهگیری خوشهای انتخاب شدند. ابزار گردآوری دادهها دو پرسشنامه محققساخته شامل مؤلفههای بهرهوری (کارایی، اثربخشی، تعهد، همکاری و حل مسئله) و عوامل مؤثر (توانایی، درک نقش، فرهنگ سازمانی، ساختار، انگیزش، سبک رهبری و...) بود. برای تجزیه و تحلیل دادهها از نرمافزارهای SPSS و LISREL و مدلسازی معادلات ساختاری استفاده شد. نتایج تحلیل عاملی تأییدی (CFA) و مدل ساختاری نهایی نشان داد که مدل مفهومی ارائهشده دارای برازش مطلوبی است و روابط عِلّی بین مؤلفهها و عوامل تأثیرگذار معنادار هستند. شاخصهای برازش نظیر RMSEA، CFI و χ²/df تأییدکننده اعتبار مدل میباشند. بر این اساس، روش SEM نه تنها توانست ساختار پیچیده بهرهوری منابع انسانی را بهخوبی تبیین کند، بلکه الگویی برای تحلیل و بهبود بهرهوری در سایر نهادهای دولتی نیز فراهم ساخت.
Application of Structural Equation Modeling (SEM) in Analyzing Human Resource Productivity; A Case Study of the Welfare Organization
Introduction
In today’s dynamic and highly competitive environment, organizations are increasingly reliant on the effective utilization of their most vital asset: human resources. Human resource productivity (HRP), encompassing efficiency, effectiveness, organizational commitment, collaboration, and problem-solving, has emerged as a central determinant of institutional success. However, HRP is not a static or linear concept; it is shaped by a complex interplay of psychological, organizational, and environmental variables. In the context of Iranian public service institutions—such as the Welfare Organization—understanding and improving HRP requires a multidimensional and context-sensitive approach.
This study seeks to analyze the structure of HRP through Structural Equation Modeling (SEM), enabling simultaneous assessment of observed and latent variables and their causal relationships. The research adopts the ACHIEVE model (Hersey & Goldsmith, 1980) as a theoretical foundation and integrates additional dimensions relevant to Iran’s public sector realities, including organizational culture, leadership styles, and work environment.
Although SEM has been widely applied in private sector studies, its application within welfare and service-oriented organizations remains limited, particularly in developing countries. This research fills that gap by developing and validating a localized conceptual model of HRP within the Welfare Organization of Tehran Province. The model's design enables a nuanced understanding of the direct and indirect effects of key variables, and it holds promise for broader application across public sector institutions seeking to enhance workforce productivity.
Methodology
This study adopts a quantitative, correlational-descriptive approach using a cross-sectional survey design. The target population included all employees of the Welfare Organization in Tehran Province in 2018. A multi-stage cluster sampling method was employed, resulting in a final sample of 320 respondents, which meets SEM analysis requirements.
Data were collected using a researcher-developed questionnaire comprising 88 items. The questionnaire included two main sections: 20 items measuring HRP across four dimensions (efficiency, effectiveness, commitment, collaboration/problem-solving), and 68 items assessing ten influencing factors (ability, role clarity, organizational culture, structure, leadership style, motivation, physical environment, empowerment, competitiveness, and creativity). All items were measured using a five-point Likert scale.
Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) were performed using LISREL software. Reliability was confirmed through Cronbach’s alpha and Composite Reliability (CR), all exceeding 0.70. Model fit indices including RMSEA, CFI, GFI, and χ²/df were used to validate the conceptual model. Ethical principles were observed throughout, ensuring anonymity and voluntary participation.
Discussion
1. Structural Dimensions of HRP
The research confirmed HRP as a multidimensional construct encompassing four interrelated components: efficiency, effectiveness, organizational commitment, and collaboration/problem-solving. This structure aligns with the ACHIEVE model and previous studies (e.g., Ulrich & Lake, 2009), indicating its robustness in both theory and practice.
2. Key Predictors of Productivity
Among the ten influencing variables, three emerged as the most significant direct predictors of HRP: motivation, leadership style, and empowerment. These variables had the highest standardized coefficients in the SEM model.
Motivation: Employee motivation was identified as the primary catalyst for productive behavior. This finding echoes Herzberg’s two-factor theory and emphasizes the importance of both intrinsic and extrinsic motivators, including recognition, job satisfaction, and advancement opportunities.
Leadership Style: Participatory and supportive leadership styles were positively correlated with higher HRP scores. Leaders who foster open communication and delegate authority contribute to increased employee commitment and collaboration.
Empowerment: Empowerment was not only a motivational enhancer but also a developmental tool. It influenced HRP through increased autonomy, ownership, and opportunities for growth, supporting Bandura’s (1986) theory of self-efficacy and Spreitzer’s (1995) work on psychological empowerment.
3. Mediating and Indirect Factors
Organizational culture, structure, and the physical work environment played important indirect roles in enhancing productivity. While not as impactful individually, these elements acted as mediators that facilitated the influence of more dominant factors like leadership and motivation.
Organizational Culture: A cohesive, value-based culture fosters a positive climate that supports collaboration and innovation. The study found that cultural alignment significantly enhanced employees’ sense of belonging and responsibility.
Organizational Structure: Flexible, decentralized structures were linked to improved information flow, decision-making autonomy, and innovative behavior—all of which are conducive to HRP.
Physical Environment: Though often overlooked, the quality of the workplace—lighting, noise, layout—was shown to influence employee morale and effectiveness.
4. Contextual and Moderating Elements
Less direct but still noteworthy were factors such as role clarity, creativity, and competitiveness. While their direct impact on HRP was modest, they significantly contributed to internal motivation and job engagement.
Role Clarity: Employees with clear expectations and defined roles reported higher job satisfaction and lower burnout levels.
Creativity: Opportunities for creative input fostered a sense of psychological ownership and engagement, indirectly improving productivity.
Competitiveness: A moderate level of internal competition encouraged goal orientation and high performance without triggering counterproductive stress.
5. Model Fit and Implications
The final SEM model exhibited strong fit indices: RMSEA (0.067), CFI (0.98), and GFI (0.93), all within acceptable thresholds. This validates the conceptual model and demonstrates its applicability to public service institutions with complex operational dynamics. The model serves not only as an analytical tool but also as a strategic framework for HR policy design.
Conclusion
This study provides a comprehensive framework for understanding and enhancing human resource productivity in public welfare organizations. By applying Structural Equation Modeling, the research confirms the multifaceted nature of HRP and highlights the central role of motivation, leadership, and empowerment as direct drivers. The influence of organizational culture, structure, and environmental factors is also underscored as important enablers.
The study's contribution lies in its integration of theory, statistical modeling, and practical application within a culturally specific context. The developed model offers a diagnostic tool that can be adapted to other public service institutions aiming to optimize their workforce performance.
For policymakers and HR managers, the findings suggest that boosting productivity requires coordinated efforts at multiple levels—individual, organizational, and structural. Interventions should focus on leadership development, empowerment programs, cultural alignment, and environmental improvements.
Ultimately, the research demonstrates that public sector productivity is not solely a function of resources or structure, but a product of systemic alignment between human capabilities, leadership strategies, and institutional design. Future studies may expand on this model by incorporating longitudinal data or testing its applicability across different governmental agencies.
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