International Journal of Innovation Management and Organizational Behavior (IJIMOB)
ISSN: 3041-8992 Director in Charge: Sepehr Khajeh Naeini Editor-in-chief: Dr. Yus Nugraha Owner: KMAN Research Institute Publisher: KMAN Publication Inc. (KMANPUB) Email: ijimob@kmanpub.com / journalimobs@gmail.com Open Access: YES International Journal of Innovation Management and Organizational Behavior (IJIMOB) is a scientific open access peer-reviewed journal. The primary purpose of the International Journal of Innovation Management and Organizational Behavior (IJIMOB) is to publish scholarly research articles in the fields of Management and Organizational Behavior. As an official journal of the KMANPUB, the IJIMOB is recognized as an instrument for projecting and supporting the goals and objectives of this organization, which include scholarly research and the free exchange of ideas. IJIMOB appreciates original articles, review articles, short-papers, and conceptual papers research on all aspects of Innovation Management and Organizational Behavior. About the Publisher Publisher: KMAN Publication Inc. Publisher Office: Unit 5‑10825 Yonge St, Richmond Hill, Ontario, Canada, L4C 3E3 https://journals.kmanpub.com Email: kmanpu@kmanpub.com Telegram: +1 (647) 656‑4401 | WhatsApp: +1 (437) 429‑3507 Germany Office: Dratelnstrasse, Wohnung A60, 21119 Hamburg, Germany (+4915207720487)
Predicting Employee Innovative Work Behavior from Psychological Safety and Proactive Personality Using Machine Learning Models
Objective: This study aims to predict employee innovative work behavior by evaluating the distinct and interactive contributions of psychological safety and proactive personality through the application of advanced machine learning algorithms. Methods and Materials: A cross-sectional, quantitative design was utilized, drawing a stratified random sample of full-time employees from various dynamic corporate sectors across Indonesia. Data were collected via self-administered, translated questionnaires utilizing established Likert-scale instruments. To evaluate the predictive relationships, the dataset was partitioned into an training and testing split. Four machine learning models—Multiple Linear Regression, Support Vector Regression, Random Forest Regressor, and Gradient Boosting Regressor—were trained and evaluated using five-fold cross-validation, while SHapley Additive exPlanations (SHAP) values were computed to interpret feature importance and non-linear interactions. Findings: The non-linear ensemble models outperformed traditional linear approaches, with the Gradient Boosting Regressor demonstrating the highest predictive accuracy on the testing set ( , , ). Feature importance analysis using SHAP values identified proactive personality as the dominant predictor of innovative work behavior (mean absolute SHAP value = ), closely followed by psychological safety (mean absolute SHAP value = ). Furthermore, the dependency plots revealed a critical non-linear interaction, indicating that high levels of proactive personality only consistently translate into innovative behavior when a baseline threshold of psychological safety is present in the work environment. Conclusion: Organizations must concurrently hire for proactive traits and cultivate psychologically safe climates, as both are fundamentally intertwined in actualizing and predicting employee innovation.
Clustering Innovation Behaviors Using Machine Learning: Roles of Future Time Perspective and Proactive Motivation
Objective: This study aimed to identify distinct profiles of employee innovation behaviors using unsupervised machine learning and to evaluate the predictive roles of future time perspective and proactive motivation in determining membership within these empirically derived behavioral clusters. Methods and Materials: A quantitative, cross-sectional design was utilized to collect data from a purposive sample of professionals employed in various knowledge-intensive sectors across Spain. Participants completed validated self-report questionnaires assessing three core dimensions of innovation behavior (idea generation, idea promotion, and idea realization), alongside measures of future time perspective and proactive motivation. The analytical framework employed a K-means clustering algorithm to partition the multidimensional innovation scores into distinct profiles, followed by multinomial logistic regression models to evaluate the predictive capacity of the psychological variables in determining cluster categorization. Findings: The K-means clustering analysis ( ) successfully identified three distinct behavioral profiles: “Passive Innovators” ( , ), “Moderate Innovators” ( , ), and “High Innovators” ( , ). The multinomial logistic regression model demonstrated robust predictive power (Nagelkerke ). A one-unit increase in future time perspective significantly increased the odds of an individual belonging to the High Innovators cluster rather than the Passive Innovators cluster ( , ). Furthermore, proactive motivation emerged as an exceptionally strong differentiator; a one-unit increase in proactive motivation drastically elevated the likelihood of being classified as a High Innovator compared to the passive reference group ( , ). Conclusion: Fostering a future-oriented mindset and cultivating intrinsic proactive motivation are critical psychological catalysts for transforming passive employees into highly engaged innovators capable of driving ideas from initial conception to full realization.
Predicting Employee Creativity Trajectories Through Longitudinal Deep Learning Models of Motivation and Job Design
Objective: The objective of this study was to predict long-term employee creativity trajectories by developing a longitudinal deep learning model integrating evolving motivational states and job design characteristics. Methods and Materials: This longitudinal study followed 611 full-time employees from knowledge-intensive organizations in Spain across four measurement waves over 18 months. Repeated measures of intrinsic and extrinsic motivation, psychological empowerment, and job design characteristics (including autonomy, task significance, skill variety, and feedback quality) were collected using validated instruments. Employee creativity was assessed using a multi-source composite index incorporating self-ratings, supervisor evaluations, and peer nominations. Long Short-Term Memory (LSTM) neural networks were employed to model non-linear temporal relationships and predict individual creativity trajectories. Model performance was evaluated using root mean square error, mean absolute error, explained variance, and trajectory similarity indices. Feature importance was examined using attention mechanisms and SHAP values, and unsupervised clustering was applied to identify distinct creativity development profiles. Findings: The deep learning model explained 76.1% of variance in unseen creativity trajectories. Intrinsic motivation (β_importance = 0.31, p < .001) and job autonomy (β_importance = 0.24, p < .001) emerged as the strongest predictors of sustained creative growth. Four statistically distinct creativity trajectories were identified: accelerated growth (34.5%), stable growth (41.2%), stagnant (16.7%), and declining (7.6%). Employees in the accelerated growth cluster demonstrated significantly higher increases in intrinsic motivation and job autonomy over time (p < .001), whereas the stagnant and declining clusters exhibited significant decreases in psychological empowerment and increases in role overload (p < .01). Conclusion: Employee creativity is a dynamic developmental capability shaped by continuous interactions between motivation and job design, and longitudinal deep learning provides a powerful framework for predicting creative growth and identifying intervention targets.
Using Random Forests to Predict Team Creativity from Psychological Diversity and Emotional Intelligence
Objective: The objective of this study was to deploy a Random Forest machine learning algorithm to evaluate the predictive power and complex non-linear interactions of psychological diversity and team emotional intelligence on collective team creativity. Methods and Materials: A cross-sectional quantitative research design was employed, collecting data from employees nested within established work teams in the Greek corporate sector. Team creativity was assessed via supervisor ratings, while emotional intelligence and psychological diversity (measured as the standard deviation of Big Five personality traits within teams) were self-reported and statistically aggregated to the team level. Data analysis utilized Random Forest regression, comparing its predictive performance against traditional Multiple Linear Regression using an training set ( teams) and a testing set ( teams), with hyperparameters optimized via grid search cross-validation. Findings: The Random Forest model significantly outperformed Multiple Linear Regression in predicting team creativity on the testing set ( , , versus , , ). Variable importance analysis revealed that Team Emotional Intelligence was the paramount predictor (Importance Score , , ). This was followed by psychological diversity in Openness (Importance Score , , ) and Extraversion (Importance Score , , ), which both positively correlated with creative output. Conversely, diversity in Conscientiousness (Importance Score , , ) demonstrated a negative impact on team creativity. Conclusion: High aggregate emotional intelligence and specific deep-level personality diversities interact in highly non-linear patterns to drive team innovation, underscoring the necessity of advanced ensemble learning techniques for accurate organizational and behavioral modeling.
Unsupervised Learning of Organizational Learning Patterns: The Roles of Knowledge Sharing and Cognitive Flexibility
Objective: The objective of this study was to autonomously identify latent patterns of organizational learning among corporate professionals and examine the differentiating roles of knowledge sharing and cognitive flexibility within these profiles. Methods and Materials: A descriptive, quantitative cross-sectional design was employed, collecting data from a sample of corporate professionals in Egypt via a validated, self-administered online questionnaire. The analytical framework utilized unsupervised machine learning, specifically Principal Component Analysis for dimensionality reduction and K-means clustering to extract distinct learning profiles. Subsequently, Multivariate Analysis of Variance (MANOVA) and Tukey’s post-hoc tests were conducted to evaluate how knowledge sharing and cognitive flexibility differed across the autonomously generated clusters. Findings: The K-means clustering algorithm identified an optimal three-cluster solution ( ): a Passive Learning Profile ( , ), an Adaptive Learning Profile ( , ), and a Proactive Learning Profile ( , ). The MANOVA results revealed a highly significant omnibus effect across the clusters, Wilks’ , . Univariate and post-hoc analyses demonstrated that individuals in the Proactive Learning Profile exhibited significantly higher levels of knowledge sharing ( ) and cognitive flexibility ( ) compared to those in the Adaptive Profile ( and , respectively; ) and the Passive Profile ( and , respectively; ). Conclusion: Fostering a highly proactive and generative organizational learning environment fundamentally depends on deliberately cultivating both interpersonal knowledge exchange networks and individual cognitive adaptability.
Modeling Innovation Adoption through Reinforcement Learning: The Influence of Risk Perception and Change Readiness
Objective: The objective of this study was to elucidate the temporal dynamics of organizational innovation adoption by integrating empirical psychological metrics of risk perception and change readiness with a computational reinforcement learning framework. Methods and Materials: This study utilized a cross-sectional design, sampling mid-to-senior level managers from the corporate sector in South Africa. Primary data regarding individual risk perception, baseline change readiness, and behavioral adoption intent were collected utilizing a structured, validated online questionnaire. The data analysis phase employed a novel hybrid methodology: inferential statistics via Structural Equation Modeling (SEM) were integrated with a mathematical Reinforcement Learning (RL) architecture. Specifically, a -learning algorithm was developed where the empirical psychometric scores directly parameterized the internal variables of synthetic agents, mapping readiness to the learning rate ( ) and risk perception to the environmental penalty weight ( ) and temporal discount factor ( ). Findings: The empirical baseline analysis revealed a strong positive correlation between change readiness and the final innovation adoption rate ( ), while risk perception exerted a severe suppressive effect on the ultimate probability of adoption ( ). When mapped to the computational simulation, the fully optimized -learning model significantly outperformed static predictive models, successfully accounting for of the variance in actual innovation adoption trajectories ( ). The algorithmic simulation accurately demonstrated that agents with high change readiness exhibited an accelerated , quickly escalating their expected utility for adoption, whereas high risk perception effectively delayed transition by mathematically magnifying the operational penalty ( ) and minimizing the perceived future value of the new technology ( ). Conclusion: The findings provides a highly accurate, dynamic computational model that predicts the temporal evolution of human behavioral adaptation during organizational technological transitions.
The Effects of Work Meaningfulness and Autonomy on Radically Innovative Behavior: A Neural Network Approach
Objective: The primary objective of this study was to utilize an advanced artificial neural network approach to investigate the complex, non-linear predictive effects of work meaningfulness and job autonomy on radically innovative behavior among professional employees. Methods and Materials: A quantitative, cross-sectional design was employed, utilizing a purposive sample of Polish professionals operating within the high-technology, engineering, and manufacturing sectors. Data were gathered using adapted, standardized psychometric instruments measured on a five-point Likert scale, with all measures demonstrating robust internal reliability (Cronbach’s ). To accurately model intricate, non-linear behavioral relationships without relying on restrictive parametric assumptions, the data were analyzed using a Multilayer Perceptron artificial neural network. The dataset was systematically partitioned into training ( ), testing ( ), and holdout ( ) subsets to rigorously validate the computational model’s predictive accuracy and isolate predictor strength via sensitivity analysis. Findings: The descriptive analysis confirmed that the sample ( ) reported significant positive correlations among all variables ( ). The artificial neural network demonstrated excellent model fit and explanatory power, successfully accounting for approximately of the total variance in radically innovative behavior ( ). The normalized sensitivity analysis revealed that job autonomy is the paramount predictor of radical innovation, yielding an absolute importance of and a normalized importance of . Work meaningfulness emerged as a highly critical secondary predictor, demonstrating an absolute importance of and a normalized importance of . Conclusion: Structural job autonomy is the absolute foundational prerequisite for enabling radically innovative behavior, while the cultivation of work meaningfulness provides the indispensable psychological drive and intrinsic resilience required to sustain these complex, paradigm-shifting efforts.
Explainable AI Models of Organizational Creativity: Influences of Inclusive Leadership and Team Psychological Safety
Objective: The primary objective of this study was to utilize an explainable artificial intelligence (XAI) machine learning framework to predict organizational creativity and mathematically elucidate the complex, non-linear interactions between inclusive leadership and team psychological safety. Methods and Materials: A quantitative, cross-sectional predictive design was employed, utilizing a multi-stage stratified random sample of employees nested within work teams across the IT, finance, and advanced manufacturing sectors in Indonesia. Data were collected via structured digital questionnaires measuring inclusive leadership (Cronbach’s ), team psychological safety (Cronbach’s ), and organizational creativity (Cronbach’s ) using -point Likert scales. Data analysis moved beyond traditional linear models by employing the Extreme Gradient Boosting (XGBoost) algorithm for prediction, coupled with SHapley Additive exPlanations (SHAP) to quantify global and local feature importance and map non-linear synergistic effects. Findings: The XGBoost model demonstrated robust predictive accuracy ( , ), significantly outperforming traditional multiple linear regression ( ). SHAP analysis identified team psychological safety as the paramount global predictor of creativity (Mean ), followed by inclusive leadership (Mean ). Crucially, SHAP dependence plots revealed a distinct non-linear threshold: inclusive leadership only yielded a positive, synergistic impact on organizational creativity when team psychological safety scores exceeded approximately on a -point scale, with veteran employees showing the highest sensitivity to these dynamics. Conclusion: Cultivating a baseline threshold of team psychological safety is a mandatory structural prerequisite that must be mathematically satisfied before inclusive leadership behaviors can effectively catalyze organizational creativity.
Developing a Qualitative Framework for Sharing Managerial Experiences Using a Phenomenological Approach (Case Study: Islamic Azad University)
Objective: The present study was conducted with the aim of explaining a conceptual model for sharing managerial experiences based on the lived experiences of middle managers at Islamic Azad Universities in Semnan Province. Methods and Materials: In terms of purpose, this research is applied, and in terms of nature, it is qualitative with an exploratory approach, employing a phenomenological method. The participants consisted of middle managers from Islamic Azad Universities in Semnan Province, who were selected through purposive sampling. Data saturation was achieved after conducting 17 semi-structured interviews. The data were analyzed using Colaizzi’s seven-step phenomenological method. Findings: The results of the data analysis led to the extraction of 128 initial codes (indicators), which, after processes of comparison and integration, were organized into 27 sub-themes and 7 main themes. The main themes included: the nature and characteristics of managerial experience (4 sub-themes, 20 indicators), managers’ perceptions and attitudes toward experience sharing (4 sub-themes, 20 indicators), individual factors influencing the sharing of managerial experiences (3 sub-themes, 12 indicators), organizational contexts and requirements (4 sub-themes, 20 indicators), operational mechanisms for sharing managerial experiences (4 sub-themes, 17 indicators), challenges and barriers to sharing managerial experiences (4 sub-themes, 19 indicators), and the outcomes of sharing managerial experiences (4 sub-themes, 20 indicators). Conclusion: The findings indicated that sharing managerial experiences is a multidimensional and context-dependent phenomenon influenced by the interaction of individual, organizational, and cultural factors. Its effective realization requires the provision of appropriate structural contexts, the strengthening of managers’ motivation and professional trust, and the design of both formal and informal mechanisms for experience transfer. The results of this study can serve as a scientific basis for policymaking, improving organizational learning systems, and enhancing managerial effectiveness in Islamic Azad Universities.
Copyright (c) 2025 Saba Asri (Author); Narges Shariatmadari (Corresponding Author); Maryam AfzalKhani (Author)
Validation of the Citizenship Education Model in Enhancing Civic Culture in the Cultural and Artistic Organization of Tehran Municipality
Objective: The objective of this study was to validate a citizenship education model for enhancing civic culture within the Cultural and Artistic Organization of Tehran Municipality. Methods and Materials: This study employed an applied, quantitative approach using a descriptive–survey design. The statistical population consisted of 1,857 employees and experts of the Cultural and Artistic Organization of Tehran Municipality, from which a sample of 384 participants was selected through simple random sampling based on Cochran’s formula at a 95% confidence level. Data were collected using a researcher-developed questionnaire derived from the qualitative phase and grounded theory components, designed on a five-point Likert scale. Content validity was confirmed by expert review, and reliability was established using Cronbach’s alpha (α > 0.70). Data analysis was conducted using descriptive and inferential statistics, and structural equation modeling (SEM) based on the Partial Least Squares (PLS) approach was applied to test the conceptual model and examine relationships among variables. Findings: The results indicated that causal factors, including heterogeneous socio-cultural context (β = -0.337), cultural diversity (β = -0.255), neglect of citizenship rights (β = -0.377), erosion of social capital (β = -0.293), and inefficiency of educational programs (β = -0.274), had significant negative effects on civic culture. Contextual conditions such as access to demographic data (β = 0.351) and the background of citizenship education programs (β = 0.509) positively influenced the implementation of strategies. Intervening factors (β = 0.281) significantly affected the effectiveness of strategies. Furthermore, strategies including localized content design (β = 0.379), interactive and multimedia methods (β = 0.285), local instructor networks (β = 0.314), peer support groups (β = 0.346), participatory events (β = 0.382), and monitoring and evaluation (β = 0.385) demonstrated significant positive effects on outcomes, including civic culture, social capital, civic participation, and urban quality of life. Conclusion: The validated model demonstrates that effective citizenship education, supported by appropriate contextual conditions and strategic interventions, can significantly enhance civic culture, strengthen social capital, and improve civic participation and urban quality of life, providing a robust framework for policy and practice in urban cultural organizations.
Cultural and Artistic Organization of Tehran Municipality
Copyright (c) 2025 Ali Haji Mollarabi (Author); Zolfaghar Rashidi (Corresponding Author); Rozita Sepehrnia (Author)
Machine Learning–Based Discovery of Hidden Behavioral Profiles Underlying High-Performing Innovation Teams
Objective: The objective of this study was to identify latent behavioral profiles underlying innovation teams and examine their predictive relationship with innovation performance using advanced machine learning techniques. Methods and Materials: This quantitative cross-sectional study was conducted among 574 employees from 84 formally designated innovation teams across technology, engineering, biotechnology, finance, and advanced manufacturing organizations in Singapore. Data were collected using validated multi-item instruments measuring collaborative orientation, psychological safety, knowledge sharing, intrinsic motivation, proactive problem solving, learning orientation, adaptive flexibility, creative self-efficacy, and collective efficacy. Team innovation performance was assessed through both leader-rated evaluations and objective performance indicators. A multi-phase machine learning pipeline was implemented, including data normalization, dimensionality reduction, unsupervised clustering using Gaussian mixture models, and supervised prediction using Random Forest and XGBoost algorithms. Model robustness was evaluated through five-fold cross-validation, out-of-sample testing, and feature importance analysis using SHAP values. Findings: Unsupervised learning revealed four statistically distinct behavioral profiles that significantly differed in innovation performance. XGBoost achieved the highest predictive accuracy (93%), with strong discriminative power (AUC = 0.96). The Synergistic Innovators profile demonstrated significantly higher innovation outcomes than all other profiles (p < .001). Feature importance analysis identified intrinsic motivation, collaborative orientation, psychological safety, proactive problem solving, and knowledge sharing as the strongest predictors of innovation performance. Conclusion: The findings demonstrate that innovation performance emerges from coherent behavioral configurations rather than isolated behavioral factors, and that machine learning provides a powerful framework for uncovering and predicting these hidden structures within organizational teams.
Copyright (c) 2025 Gabriela Torres (Author); Fatima Zahra Benali (Corresponding Author); Amina Al-Mansoori (Author)
Applying Machine Learning to Examine Psychological Ownership and Work Passion in Innovation Processes
Objective: This study aims to apply supervised machine learning algorithms to evaluate the non-linear predictive power of psychological ownership and work passion on employee engagement in organizational innovation processes. Methods and Materials: A quantitative, cross-sectional design was employed, utilizing a self-administered questionnaire to collect data from a sample of Moroccan professionals. To capture complex, non-linear interactions without the constraints of traditional linear models, the data was analyzed using advanced supervised machine learning algorithms, specifically Random Forest, Support Vector Machine (SVM), and Gradient Boosting Regressor. Findings: The Gradient Boosting regressor demonstrated the highest predictive accuracy, successfully explaining of the variance in innovation process engagement ( , ). Feature importance analysis identified self-efficacy as the most critical predictor, accounting for of the variance, followed closely by harmonious work passion ( ), belongingness ( ), and identity ( ). Obsessive work passion contributed to the model and exhibited a non-linear threshold effect, indicating that excessive obsession diminishes innovative output, while demographic variables (age, tenure) collectively contributed less than to the predictive power. Conclusion: Cultivating intrinsic psychological states—specifically high self-efficacy and harmonious passion—is significantly more critical for driving organizational innovation than demographic factors, highlighting the need for compassionate, autonomy-supportive human resource strategies.
Copyright (c) 2026 Mikko Lahtinen (Author); Youssef El Amrani (Corresponding Author); Adrian Tan Wei Ming (Author)
A Machine‑Learning Approach to Employee Change Innovation: Roles of Adaptability and Job Crafting
Objective: The objective of this study is to employ an advanced machine-learning approach to precisely predict employee change innovation by computationally delineating the complex, non-linear predictive roles of psychological adaptability and multidimensional job crafting behaviors. Methods and Materials: A cross-sectional quantitative design was utilized, surveying full-time professionals in Turkey experiencing organizational change, selected via purposeful and snowball sampling. Data were collected using validated self-report digital questionnaires evaluating adaptability, job crafting, and change innovation (Cronbach’s ). The dataset was split into an training set ( ) and a testing set ( ). An advanced machine learning pipeline evaluated Support Vector Regression (SVR), Random Forest (RF), and Gradient Boosting Machine (GBM) algorithms, optimizing hyperparameters via 10-fold cross-validation. Findings: The GBM model demonstrated the highest predictive accuracy on the unseen test set ( , , ), outperforming both RF ( ) and SVR ( ). Feature importance analysis extracted from the GBM revealed that the job crafting dimension of “increasing structural job resources” was the paramount predictor ( ), followed closely by the adaptability dimension of “confidence” ( ). “Increasing challenging job demands” ( ) and “control” ( ) were also substantial drivers. Conversely, traditional demographic variables such as organizational tenure ( ) and age ( ) provided minimal predictive utility. Conclusion: Proactive resource-seeking and psychological confidence are the fundamental micro-level drivers of employee innovation during organizational transitions, highlighting the superior capacity of machine learning to map complex, non-linear behavioral dynamics.
Copyright (c) 2026 Zhang Wei (Author); Selin Karaca (Corresponding Author); Priyanka Deshmukh (Author)
Development of a Social Value Framework Based on Strategic Management with a Stakeholder Theory Approach Using Qualitative Thematic Analysis
Objective: The objective of this study was to develop and conceptualize an integrated framework for social value grounded in strategic management principles through the lens of stakeholder theory. Methods and Materials: This research was conducted as an exploratory qualitative study within an interpretive paradigm using thematic analysis as the primary methodological approach. The study population consisted of academic and professional experts in accounting, financial reporting, sustainability standards, and corporate governance, selected through purposive and snowball sampling. Data were collected through documentary analysis of relevant scholarly texts and semi-structured interviews with fourteen experts until theoretical saturation was achieved. Interviews were audio-recorded, transcribed verbatim, and analyzed following the systematic procedure of thematic analysis and thematic network construction. Trustworthiness was ensured through prolonged engagement, member clarification during interviews, researcher triangulation, and inter-coder agreement assessment. The analysis aimed to identify patterns of meaning leading to the extraction of basic, organizing, and global themes forming the conceptual structure of the proposed framework. Findings: The inferential analysis revealed a coherent multidimensional structure of social value consisting of three interrelated global themes: strategic requirements of social value, social value perception, and social value implementation structures. These dimensions collectively demonstrate that social value operates as a strategic organizational capability rather than a peripheral ethical activity. The findings indicate that effective integration of social value depends on alignment between strategic decision-making, stakeholder management mechanisms, performance perception processes, and organizational execution systems. The thematic relationships further suggest that organizations generate sustainable social value when governance structures, performance evaluation practices, and resource allocation mechanisms are systematically aligned with stakeholder expectations and long-term strategic objectives. Conclusion: The study concludes that embedding social value within strategic management requires a holistic organizational architecture that integrates stakeholder engagement, strategic governance, perception management, and operational execution mechanisms. The proposed framework offers a theoretically grounded and practically applicable model for transforming social value from a normative concept into an institutionalized strategic process capable of enhancing organizational legitimacy, sustainability performance, and stakeholder trust.
Copyright (c) 2026 Amirhossein Abedi (Author); Khadijeh Ebrahimi Kahrizsangi (Corresponding Author); Mahnam Molaei (Author)
An Analytical Approach to the Function of Internal Auditors’ Job Well-Being in Auditor Professional Maturity
Objective: The objective of this study was to develop and validate a comprehensive model of internal auditors’ job well-being and examine its effect on auditor professional maturity. Methods and Materials: This research adopted a mixed-methods design combining qualitative and quantitative approaches. In the qualitative phase, thematic analysis was conducted through semi-structured interviews with expert internal auditors and academic specialists to identify the underlying dimensions of internal auditors’ job well-being. Extracted themes were categorized into overarching, organizing, and basic themes, forming the conceptual framework of the model. In the quantitative phase, data were collected using a researcher-developed questionnaire measuring job well-being dimensions and a standardized instrument assessing auditor professional maturity. The statistical population consisted of internal auditors, and sampling was conducted using purposive selection in the qualitative phase and survey-based sampling in the quantitative phase. The Delphi technique was employed to confirm content validity and expert consensus regarding identified components. Measurement model reliability and validity were evaluated using confirmatory factor analysis, Cronbach’s alpha, composite reliability, convergent validity (AVE), and discriminant validity (Fornell–Larcker criterion). Structural Equation Modeling based on the Partial Least Squares (PLS-SEM) approach was applied to test the research hypothesis and assess model fit. Findings: The structural model demonstrated satisfactory measurement and structural fit indices. Results indicated that internal auditors’ job well-being has a positive and statistically significant effect on auditor professional maturity (β = 0.564, T = 20.226). The model explained approximately 52.9% of the variance in professional maturity. All dimensions of job well-being—including professional satisfaction, growth and development opportunities, social support and communication, psychological and physical health, and enhancement of audit process quality—were validated and significantly contributed to the latent construct of job well-being. Reliability, convergent validity, discriminant validity, and predictive relevance indices confirmed the robustness of the proposed model. Conclusion: The findings indicate that internal auditors’ job well-being functions as a fundamental driver of professional maturity by strengthening psychological readiness, ethical judgment, professional competence, and organizational engagement. Promoting well-being-oriented organizational environments can enhance audit effectiveness, reinforce professional independence, and improve governance quality. The study highlights the strategic importance of integrating human-centered well-being policies into professional auditing frameworks to achieve sustainable professional development.
Occupational well-being of internal auditors
Copyright (c) 2026 Nooshin Dinyarian (Author); Javad Ramezani (Corresponding Author); Ali Fallah, Mehdi Khalilpour (Author)
Identification of the Fundamental Dimensions of Corporate Entrepreneurship and Corporate Governance: A Delphi Study in Entrepreneurial Companies
Objective: The present study aimed to identify and conceptualize the fundamental dimensions and components of corporate entrepreneurship and corporate governance within entrepreneurial companies through an exploratory qualitative approach. Methods and Materials: This research employed a qualitative exploratory design using the classical Delphi method to develop a contextualized conceptual framework. The study population consisted of senior managers and academic experts with extensive experience in entrepreneurial firms, selected through purposive and snowball sampling techniques. Data collection was conducted in two iterative Delphi rounds involving 12 experts. In the first round, an initial list of factors derived from literature review and preliminary qualitative exploration was evaluated and expanded by panel members. In the second round, participants reassessed the refined factors after receiving structured feedback, including median scores and response ranges. Consensus among experts was assessed using Kendall’s coefficient of concordance (W), and statistical analyses were performed using SPSS Version 22. Qualitative content analysis with inductive coding was applied to organize concepts into subcategories, core dimensions, and an integrated conceptual model describing the interaction between corporate entrepreneurship and corporate governance. Findings: The findings indicated that corporate entrepreneurship is structured around five interrelated dimensions: human resource prerequisites, managerial competencies, external opportunities, internationalization infrastructure, and market orientation. Simultaneously, corporate governance was conceptualized through five principal dimensions: corporate accountability, information transparency and reliability, board effectiveness, corporate responsibility, and company member competencies. The statistically significant Kendall’s coefficient confirmed expert consensus regarding the identified dimensions. Results suggest that entrepreneurial effectiveness depends on supportive governance mechanisms, while governance performance is strengthened by entrepreneurial capabilities, demonstrating a mutually reinforcing relationship between the two constructs. Conclusion: The study concludes that effective organizational performance in entrepreneurial companies emerges from the integrated alignment of corporate entrepreneurship and corporate governance systems. Governance structures provide accountability, transparency, and strategic control, whereas corporate entrepreneurship promotes innovation, adaptability, and competitive renewal. The proposed multidimensional framework offers a comprehensive roadmap for understanding organizational dynamics and supports the development of measurement tools, managerial strategies, and future empirical research aimed at enhancing sustainable competitiveness.
Copyright (c) 2025 Shahriar Fatehi, Jahangir Yadollahi Farsi, Seyed Mostafa Razavi (Author)
Predictive Modeling of Innovation Failure Risk from Organizational Stress, Workload Distribution, and Team Conflict Using Machine Learning Classification
Objective: The objective of this study was to develop and validate a machine learning–based predictive model for estimating innovation failure risk using organizational stress, workload distribution, and team conflict as primary predictors. Methods and Materials: This quantitative cross-sectional study was conducted among 612 full-time employees from innovation-driven organizations in Malaysia. Data were collected using standardized survey instruments measuring organizational stress, workload distribution, team conflict, and perceived innovation failure risk. After psychometric validation, the dataset underwent preprocessing including normalization, outlier detection, and feature engineering. Innovation failure risk was converted into a binary classification outcome. Multiple machine learning classifiers were trained and compared, including logistic regression, support vector machines, random forest, gradient boosting, and extreme gradient boosting. Hyperparameter optimization and nested cross-validation were applied to ensure model stability and generalizability. Findings: The XGBoost classifier achieved the highest predictive performance with an accuracy of 94%, precision of 93%, recall of 92%, F1-score of 92%, and AUC of 0.97, significantly outperforming all baseline models. Feature importance analysis revealed that emotional exhaustion and task overload were the strongest predictors of innovation failure risk, followed by relationship conflict and resource imbalance. The final model demonstrated high sensitivity for detecting high-risk innovation cases, confirming the robustness and reliability of the proposed predictive framework. Conclusion: The findings demonstrate that innovation failure risk is strongly driven by human-centered organizational factors and can be accurately predicted using advanced machine learning models. The proposed framework provides organizations with a powerful early-warning system for preventing innovation breakdowns and strengthening innovation sustainability through proactive management of psychological and structural risk factors.
Feature Importance Analysis of Organizational Citizenship Behaviors in Driving Product Innovation Performance via XGBoost Models
Objective: The objective of this study was to identify and quantify the relative importance of organizational citizenship behavior dimensions in predicting product innovation performance using an explainable machine learning framework. Methods and Materials: This study employed a quantitative cross-sectional design with data collected from 412 employees working in medium and large organizations. Organizational citizenship behaviors were measured across five dimensions including conscientiousness, civic virtue, courtesy, altruism, and sportsmanship, while product innovation performance was assessed using multidimensional performance indicators. Data were analyzed using the XGBoost algorithm with hyperparameter optimization and five-fold cross-validation. Feature importance was examined using both gain-based metrics and SHAP (SHapley Additive exPlanations) values to ensure transparent and robust interpretation of predictor contributions. Model robustness was evaluated through comparison with Random Forest and Elastic Net models. Findings: The XGBoost model demonstrated strong predictive performance, explaining 68.4% of the variance in product innovation performance. Feature importance analysis revealed that conscientiousness was the most influential predictor, followed by civic virtue, courtesy, altruism, and sportsmanship. Conscientiousness exhibited the highest SHAP contribution, indicating its dominant role in driving innovation outcomes. Comparative analysis showed that XGBoost significantly outperformed Random Forest and Elastic Net across all evaluation metrics, confirming its superior capacity to capture nonlinear behavioral effects on innovation performance. Conclusion: The findings confirm that organizational citizenship behaviors constitute critical behavioral drivers of product innovation performance and that advanced machine learning models provide superior analytical precision for understanding their complex influence. The study offers strong empirical evidence supporting the strategic integration of citizenship behavior development into organizational innovation policies.
Predicting Open Innovation Success from Trust Networks, Communication Density, and Collaborative Behaviors Using Graph Neural Networks
Objective: The objective of this study was to develop and validate a graph neural network model for predicting open innovation success based on trust networks, communication density, and collaborative behaviors in Nigerian organizations. Methods and Materials: This study employed a quantitative, cross-sectional research design involving 487 professionals from innovation-intensive organizations in Nigeria across the technology, manufacturing, finance, telecommunications, and energy sectors. Data were collected using validated instruments measuring inter-organizational trust, communication density, and collaborative behaviors, combined with objective network data extracted from organizational collaboration platforms. Multilayer networks were constructed in which nodes represented individuals and edges represented trust relations, communication ties, and collaborative interactions. A graph neural network architecture integrating graph convolutional and attention mechanisms was trained to predict open innovation success. Model performance was evaluated using root mean squared error, mean absolute error, and explained variance, and results were compared against baseline machine learning models. Findings: The graph neural network demonstrated strong predictive performance (R² = 0.82, RMSE = 0.31, MAE = 0.24), significantly outperforming gradient boosting (R² = 0.64), random forest (R² = 0.61), and support vector regression (R² = 0.58). Explainability analysis revealed that trust networks exerted the strongest influence on innovation success (importance weight = 0.41), followed by collaborative behaviors (0.30) and communication density (0.29). Multilevel network analysis showed that team-level structures had the largest standardized effect on innovation success (β = 0.44, p < 0.001), followed by individual-level (β = 0.36, p < 0.001) and organizational-level networks (β = 0.28, p < 0.001). Conclusion: The findings demonstrate that open innovation success is primarily driven by the structure and quality of relational networks, and that graph neural networks provide a powerful and superior framework for modeling innovation outcomes within complex organizational ecosystems.
Modeling Organizational Ambidexterity through Ensemble Learning: Behavioral and Structural Predictors of Exploratory and Exploitative Innovation
Objective: The objective of this study was to model organizational ambidexterity by applying ensemble machine learning techniques to identify the behavioral and structural predictors of exploratory and exploitative innovation. Methods and Materials: This explanatory study employed a cross-sectional design involving 487 middle- and senior-level managers from medium and large organizations across major industries in Chile. Data were collected using validated instruments measuring leadership cognitive flexibility, learning orientation, psychological safety, risk tolerance, cross-functional integration, decentralization, resource flexibility, knowledge-sharing systems, and dual innovation outcomes. The analytical framework integrated traditional statistical validation with an ensemble learning architecture composed of Random Forest, Gradient Boosting, XGBoost, and Support Vector Regression models. Model training applied stratified sampling, five-fold cross-validation, and hyperparameter optimization, while performance was evaluated using R², RMSE, MAE, and explained variance. Explainable AI techniques based on SHAP were employed to interpret nonlinear relationships and predictor contributions. Findings: The ensemble model demonstrated superior predictive performance for both exploratory innovation (R² = 0.81, RMSE = 0.25) and exploitative innovation (R² = 0.84, RMSE = 0.22), significantly outperforming individual machine learning algorithms. Leadership cognitive flexibility and learning orientation emerged as the strongest predictors of exploratory innovation, whereas cross-functional integration and structural decentralization exerted the greatest influence on exploitative innovation. Psychological safety, risk tolerance, knowledge sharing, and resource flexibility contributed significantly to both innovation dimensions, with SHAP analysis revealing asymmetric and nonlinear interaction effects across predictors. Conclusion: The results confirm that organizational ambidexterity is a systemic, nonlinear phenomenon driven by the dynamic interaction of behavioral and structural factors and that ensemble learning provides a powerful methodological approach for modeling this complexity, offering both theoretical advancement and practical guidance for innovation management.
Machine Learning Identification of Innovation Bottlenecks: A Behavioral Analytics Approach Using Gradient Boosting Models
Objective: The objective of this study was to develop and validate a machine learning–based behavioral analytics framework for identifying organizational innovation bottlenecks through the interaction of leadership, psychological, and behavioral factors. Methods and Materials: This study employed a cross-sectional explanatory design involving 547 employees and middle-level managers from diverse organizations in Georgia. Data were collected using validated behavioral, psychological, and organizational measures capturing resistance to change, psychological safety, leadership support, communication friction, knowledge sharing, learning orientation, and innovation outcomes. Organizational performance indicators were integrated with survey data to enhance behavioral signal extraction. Gradient boosting algorithms (XGBoost, LightGBM, CatBoost) and an optimized ensemble model were implemented using five-fold cross-validation and Bayesian hyperparameter tuning. Feature engineering and explainable artificial intelligence techniques (SHAP values) were applied to uncover the relative importance and interaction effects of predictors. Findings: The ensemble model demonstrated strong predictive performance (R² = 0.846, RMSE = 0.387), explaining nearly 85% of the variance in innovation bottleneck intensity. Resistance to change was the strongest positive predictor, while leadership support for innovation, psychological safety, knowledge sharing quality, and proactive behavior significantly reduced bottleneck severity. Communication friction and excessive process formalization amplified innovation constraints. Behavioral segmentation revealed four distinct innovation profiles, with “Resistant Traditionalists” exhibiting the highest bottleneck levels and “Adaptive Innovators” the lowest. Conclusion: The findings confirm that innovation bottlenecks are systemic behavioral–organizational phenomena emerging from complex non-linear interactions among leadership dynamics, employee psychology, communication structures, and organizational culture.
A Deep Learning Framework for Modeling the Impact of Transformational Leadership and Organizational Climate on Continuous Innovation Capability
Objective: The objective of this study was to develop and validate a deep learning model capable of explaining and predicting the combined effects of transformational leadership and organizational climate on continuous innovation capability within organizational settings. Methods and Materials: The study employed a quantitative cross-sectional design using data collected from 684 full-time employees and managers working in medium-to-large organizations in Germany. Transformational leadership was measured using the Multifactor Leadership Questionnaire, organizational climate was assessed through a standardized organizational climate scale, and continuous innovation capability was captured using a multidimensional innovation capability instrument. After establishing construct validity and reliability through confirmatory factor analysis, the dataset was analyzed using a deep neural network implemented with TensorFlow and Keras. Data were split into training, validation, and test sets (70–15–15). Model optimization employed Bayesian hyperparameter tuning, and performance was evaluated using mean squared error, root mean squared error, coefficient of determination, and prediction accuracy. Model interpretability was enhanced using SHAP analysis and permutation feature importance techniques. Findings: Inferential results demonstrated strong predictive performance of the deep learning model (R² = .89 training, .87 validation, .86 test; prediction accuracy = 90.4% on test set). Transformational leadership and organizational climate jointly explained a substantial proportion of variance in continuous innovation capability. Feature importance analysis revealed inspirational motivation and innovation-oriented climate as the most influential predictors, followed by intellectual stimulation and supportive climate. The deep neural network significantly outperformed random forest, gradient boosting, and multiple regression models. Conclusion: The findings confirm that continuous innovation capability is governed by complex, non-linear interactions between transformational leadership and organizational climate and that deep learning provides a superior analytical framework for modeling these dynamics, offering substantial theoretical and practical implications for innovation management.
Investigating the Impact of Organizational Learning and Innovation on Sustainable Human Resource Development in Hospitals of Kermanshah
Objective: The objective of this study was to examine the impact of organizational learning and organizational innovation on sustainable human resource development in hospitals of Kermanshah using a structural equation modeling approach. Methods and Materials: This applied study employed a descriptive–correlational design using structural equation modeling to investigate the relationships among organizational learning, innovation, and sustainable human resource development. The statistical population consisted of all employees working in hospitals affiliated with Kermanshah University of Medical Sciences and the Social Security Organization in 2023 (N = 2,560). Using Cochran’s formula and stratified random sampling with proportional allocation, 328 valid questionnaires were collected and analyzed. Data were gathered using a researcher-developed questionnaire consisting of measures for empowerment and skill development, workforce diversity and equity, social development participation, sustainable quality of work life improvement, sustainable human resource development, environmental commitment and training, sustainable learning and innovation, and green human resource governance and leadership. The questionnaire used a five-point Likert scale and demonstrated acceptable validity and reliability. Data were analyzed using SPSS version 26 and SmartPLS version 4, including confirmatory factor analysis and structural equation modeling to evaluate the measurement and structural models. Findings: The results of structural equation modeling indicated that all components of organizational learning and innovation had significant positive effects on sustainable human resource development (p = 0.001). Sustainable human resource development showed the strongest effect (β = 0.888, t = 8.490), followed by sustainable learning and innovation (β = 0.685, t = 6.854), green human resource governance and leadership (β = 0.655, t = 7.403), and sustainable quality of work life improvement (β = 0.638, t = 6.497). Social development participation (β = 0.597, t = 7.008), environmental commitment and training (β = 0.467, t = 5.801), workforce diversity and equity (β = 0.444, t = 5.684), and empowerment and skill development (β = 0.421, t = 5.647) also demonstrated significant positive effects. The model explained meaningful variance in sustainable human resource development, confirming the validity of the proposed structural model. Conclusion: The findings demonstrate that organizational learning and innovation play critical roles in enhancing sustainable human resource development in hospital settings. Learning-oriented practices, innovation-driven initiatives, sustainability-focused leadership, and workforce empowerment contribute significantly to the development of sustainable human resource systems. Hospitals that invest in organizational learning, promote innovation, and implement sustainability-oriented human resource strategies are better positioned to enhance workforce capabilities, improve organizational performance, and ensure long-term sustainability.
Copyright (c) 2025 Saeed Khani (Author); Abbas Babaeinejad (Corresponding Author); Hamid Reza Mollaei, Saeed Sayadi (Author)
Threshold Effects of Gross Fixed Capital Formation in the Relationship Between Healthcare Infrastructure and Life Expectancy Index in Iran
Objective: The present study aimed to investigate the threshold effects of gross fixed capital formation on the relationship between healthcare infrastructure and the life expectancy index in Iran. Methods and Materials: This applied and descriptive–analytical study employed an ex post facto design using annual time-series data for Iran covering the period 1986–2023. The empirical analysis was conducted using EViews software. To examine nonlinear dynamics and potential regime-switching behavior, a first-order Logistic Smooth Transition Regression (LSTR1) model was estimated, with gross fixed capital formation serving as the transition variable. Prior to model estimation, Phillips–Perron unit root tests were applied to assess stationarity, and the Johansen–Juselius cointegration approach was used to test for long-run equilibrium relationships among the variables. The model incorporated healthcare infrastructure indicators, including hospital beds, physicians, nurses and midwives, public and private health sector investment, labor force participation, population growth, and literacy rate. Diagnostic tests were performed to assess autocorrelation, heteroskedasticity, parameter stability, and remaining nonlinearity. Findings: The results indicate that all variables are integrated of order one and cointegrated in the long run. The linearity test rejects the null hypothesis of linearity, confirming the appropriateness of the LSTR1 specification. Gross fixed capital formation exhibits a statistically significant threshold value of −0.211, dividing the model into two distinct regimes. In the nonlinear (high-capital) regime, the coefficients of gross fixed capital formation, hospital beds, physicians, nurses and midwives, and both public and private health sector investment are positive and statistically significant, with greater magnitudes compared to the low-capital regime. Diagnostic tests confirm the absence of autocorrelation and heteroskedasticity, and parameter constancy tests support regime-dependent coefficient variation. The adjusted R² of 0.88 indicates strong explanatory power. Conclusion: The findings demonstrate that the effectiveness of healthcare infrastructure in improving life expectancy in Iran is conditional upon the level of gross fixed capital formation. When capital accumulation surpasses a critical threshold, the impact of healthcare infrastructure intensifies significantly, highlighting the importance of sustained macroeconomic investment in enhancing health outcomes.
Smooth Transition Autoregressive (STAR) model
Copyright (c) 2025 Ali Atashak Maman (Author); Lotfali Agheli (Corresponding Author); Gholamreza Abbasi (Author)
Predicting Purchase Intention Based on Salesperson Behavior, Social Utility, Psychological Utility, and Economic Utility
Objective: The objective of this study was to predict consumers’ purchase intention based on salesperson behavior, economic utility, social utility, and psychological utility. Methods and Materials: This applied study employed a descriptive–correlational design and was conducted using a cross-sectional survey method. The statistical population consisted of consumers who completed online questionnaires during May and June 2025. A total of 377 valid questionnaires were analyzed after excluding incomplete responses. Data collection instruments included researcher-developed questionnaires measuring purchase intention, salesperson behavior, economic utility, social utility, and psychological utility, all assessed on five-point Likert scales. Content validity was confirmed by subject-matter experts, and reliability was evaluated using Cronbach’s alpha. Data analysis was carried out using SPSS version 26, applying Pearson correlation analysis and stepwise multiple regression to examine the predictive relationships among variables. Findings: Inferential analyses indicated that purchase intention was positively and significantly correlated with salesperson behavior, economic utility, social utility, and psychological utility (p ≤ .01). Stepwise multiple regression analysis showed that economic utility entered the regression model in the first step and explained 24% of the variance in purchase intention (p = .001). In the second step, social utility entered the model and, together with economic utility, explained 27% of the variance in purchase intention (p = .001). Psychological utility and salesperson behavior did not retain significant predictive power in the final regression model. Conclusion: The findings demonstrate that consumers’ purchase intention is primarily driven by economic utility and, to a lesser extent, social utility, highlighting the dominant role of cost–benefit evaluations and social value considerations in purchase decision-making. These results suggest that while psychological benefits and salesperson behavior are important correlates, their influence on purchase intention may be indirect and mediated through perceived value dimensions.
Copyright (c) 2025 Fatemeh Sadat Derakhshan (Author); Ali Mahdad (Corresponding Author)
Artificial Intelligence in Strategic Management: A Quantitative Validation of the Proposed Framework in Iraqi Public Universities
Objective: This study aimed to empirically examine and validate the impact of artificial intelligence on strategic management, with particular emphasis on strategy formulation, strategy implementation, and strategic control. Methods and Materials: The study adopted a quantitative, applied–developmental research design grounded in a descriptive–analytical approach. Data were collected using a structured questionnaire administered to senior managers and decision-makers within the target organizational context. The measurement instrument encompassed validated indicators of artificial intelligence capabilities and the three core dimensions of strategic management. Reliability and validity were assessed using Cronbach’s alpha, composite reliability, average variance extracted, exploratory factor analysis, and confirmatory factor analysis. Structural relationships among variables were tested using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS software, supported by inferential statistical analysis to evaluate correlations and causal paths among constructs. Findings: The inferential results revealed a statistically significant and positive relationship between artificial intelligence and overall strategic management. Structural model analysis demonstrated that artificial intelligence exerted a significant direct effect on strategic management, explaining a substantial proportion of its variance. At the dimensional level, artificial intelligence showed significant positive effects on strategy formulation, strategy implementation, and strategic control, with the strongest effect observed on strategic control. Conclusion: The findings confirm that artificial intelligence functions as a critical strategic enabler that strengthens the effectiveness of strategic management by enhancing analytical rigor in formulation, operational efficiency in implementation, and real-time oversight in control.
Copyright (c) 2025 Hassan Fadhel Saleh Al-Thabhawee (Author); Nasrin Razi (Corresponding Author); Mohammad Fariyabi (Author)
Presenting a Model of Managers’ Psychological Capability in Public Organizations
Objective: This study aimed to identify and develop a comprehensive model of managers’ psychological capability in public organizations. Methods and Materials: This study employed a qualitative research design using thematic analysis with an inductive approach. Data were collected through semi-structured interviews with managers and employees working in public organizations. Sampling was conducted purposively and continued until theoretical saturation was achieved, which occurred after 22 interviews. The interview data were transcribed verbatim and analyzed systematically through open coding, categorization of basic themes, abstraction into organizing themes, and integration into global themes, following established procedures for inductive thematic analysis to ensure analytical rigor and conceptual coherence. Findings: The analysis resulted in the identification of 94 basic themes, which were clustered into 17 organizing themes and ultimately integrated into four global dimensions of managers’ psychological capability. These dimensions included: (1) the capability of inner regulatory capacity and emotion management, (2) the capability of cognitive adaptability and mental–social resilience, (3) the capability of personality integration and moral–psychological maturity, and (4) the capability of social skills and effective interaction. The findings indicate that managers’ psychological capability is a multidimensional and integrative construct that encompasses emotional regulation, cognitive flexibility, ethical coherence, resilience, and relational effectiveness, all of which jointly shape managerial functioning in public organizations. Conclusion: The study concludes that managers’ psychological capability represents a holistic psychological framework that underpins effective leadership in public organizations and extends beyond isolated constructs such as empowerment or psychological safety. By offering an empirically grounded and context-sensitive model, the findings provide a robust foundation for leadership development, managerial selection, and organizational policy aimed at enhancing psychological effectiveness and sustainable performance in the public sector.
Copyright (c) 2025 Majid Dadkhah Tehrani (Author); Akbar Etebarian (Corresponding Author); Mehrdad Sadeghi Dehcheshmeh, Mehraban Hadi Peykani (Author)
Designing a Strategic Human Resource Management Model in the Era of Digital Transformation with Emphasis on Artificial Intelligence Applications: A Mixed-Methods Study
Objective: The present study aimed to design and validate a strategic human resource management model aligned with digital transformation requirements by examining the role of artificial intelligence applications in enhancing organizational human capital management. Methods and Materials: This research adopted a sequential exploratory mixed-methods design conducted in two phases. In the qualitative phase, a meta-synthesis based on the seven-step Sandelowski and Barroso method was performed to analyze 95 scholarly articles published between 2000 and 2025, complemented by semi-structured interviews with 28 experts including senior HR managers, digital transformation consultants, and university scholars selected through purposive sampling until theoretical saturation was achieved. Thematic analysis was used to identify key dimensions and components of strategic HR transformation. In the quantitative phase, a researcher-developed questionnaire consisting of 42 items derived from qualitative findings was distributed among 384 human resource managers and specialists from organizations operating in information technology, financial, manufacturing, and service sectors in 2025 using stratified random sampling. Reliability and validity of the instrument were confirmed through expert review and internal consistency measures. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) in SmartPLS 4, complemented by Interpretive Structural Modeling (ISM) and MICMAC analysis to determine hierarchical relationships among model components. Findings: Structural model results demonstrated that transformation infrastructure significantly predicted strategic AI applications (β = .68, p < .001), while digital organizational culture exerted a strong positive effect (β = .54, p < .001). Ethical challenges showed a significant negative influence on AI implementation (β = −.35, p < .001). Artificial intelligence applications significantly enhanced talent acquisition (β = .71), talent development (β = .66), talent retention (β = .63), and HR utilization effectiveness (β = .60). The model explained 62% of the variance in AI applications and achieved strong overall goodness of fit (GOF = 0.624), indicating robust explanatory and predictive power. Conclusion: The findings confirm that artificial intelligence serves as a strategic enabler of human resource transformation when supported by technological infrastructure, digital organizational culture, and ethical governance mechanisms. The proposed model provides an integrated framework that balances algorithmic efficiency with human-centered management and demonstrates that successful digital HR transformation requires coordinated investment in technology, organizational learning, and responsible AI practices to achieve sustainable competitive advantage.
Copyright (c) 2026 Abbas Babaeinejad (Corresponding Author); Seyed Hossein Shoghi, Seyed Hasan Shoghi (Author)
A Phenomenological Study of the Lived Experiences of Female School Principals in Their Relationships with Teachers
Objective: The objective of this study was to explore and describe the lived experiences of married female principals of girls’ upper secondary schools in their professional relationships with teachers. Methods and Materials: This qualitative study employed a descriptive phenomenological design to capture the essence of participants’ lived experiences. The research population consisted of married female principals working in public girls’ upper secondary schools across the five educational districts of Tabriz. Participants were selected through purposive and theoretical sampling based on defined criteria, including a minimum of five years of managerial experience, at least a master’s degree, and having at least one child. Data were collected through semi-structured, in-depth interviews conducted until theoretical saturation was achieved with twelve participants. Interviews were audio-recorded, transcribed verbatim, and analyzed using Colaizzi’s seven-step phenomenological method. Credibility and trustworthiness were ensured through prolonged engagement, member checking, careful transcription review, and consistency in coding and theme development. Findings: Data analysis led to the extraction of three main categories reflecting the principals’ lived experiences: low job motivation among teachers, the necessity of effective communication and mutual understanding, and teachers’ personal problems affecting professional functioning. These categories encompassed experiences related to reduced responsibility, task avoidance, limited participation in school administration, resistance to professional updating, unprofessional behaviors, and the impact of family- and health-related issues such as childbirth and frequent absences. Conclusion: The findings indicate that the leadership experiences of female school principals are deeply relational and shaped by ongoing efforts to balance empathy, accountability, and organizational effectiveness. Understanding these lived experiences can inform more context-sensitive leadership development and support strategies in secondary education.
Copyright (c) 2025 Shahin Jabalhang (Author); Sadegh Maleki Avarsin (Corresponding Author); Jahangir Yari Haj Ataloo (Author)
The Effect of Organizational Well-Being on Organizational Flourishing among Employees of Selected Martial Arts Federations: The Mediating Role of Psychological Flexibility
Objective: This study aimed to examine the effect of organizational well-being on organizational flourishing among employees of selected martial arts federations with the mediating role of psychological flexibility. Methods and Materials: The present research employed a descriptive–correlational design using structural equation modeling. The statistical population consisted of 337 employees from the martial arts, judo, wushu, karate, and taekwondo federations, from which 175 participants were selected through proportionate stratified random sampling. Data were collected using the Organizational Well-Being Questionnaire, the Organizational Flourishing Questionnaire, and the Psychological Flexibility Questionnaire. Data analysis was conducted using correlation analysis, multiple regression, and structural equation modeling with PLS and SPSS software. Findings: The results indicated that organizational well-being significantly predicted organizational flourishing (β = 0.460, t = 5.766, p < .001) and psychological flexibility (β = 0.584, t = 3.156, p < .001). Psychological flexibility also significantly predicted organizational flourishing (β = 0.496, t = 6.600, p < .001). Mediation analysis revealed that psychological flexibility significantly mediated the relationship between organizational well-being and organizational flourishing, as the direct effect of organizational well-being on organizational flourishing decreased after inclusion of the mediator while the indirect effect became significant. Organizational well-being explained 21.16% of the variance in organizational flourishing and 34.10% of the variance in psychological flexibility, while psychological flexibility explained 24.60% of the variance in organizational flourishing. Conclusion: The findings demonstrate that organizational well-being enhances organizational flourishing both directly and indirectly by strengthening employees’ psychological flexibility, highlighting psychological flexibility as a core psychological mechanism in sustainable organizational development within sport federations.
Copyright (c) 2025 Mohammad hadi Allah Gholizadeh Haghi (Author); Robab Mokhtari Yoznab (Corresponding Author); Mehrdad Moharramzadeh , Masoud Imanzadeh (Author)