International Journal of Innovation Management and Organizational Behavior (IJIMOB)

Objective: The present study was conducted with the aim of developing a model of organizational authenticity and identifying its antecedents and consequences at Kerman University of Medical Sciences.

Methods and Materials: This study was applied-developmental in terms of purpose and qualitative in terms of research approach. Data were collected through a review of the relevant literature, upstream policy documents, and semi-structured interviews with 15 experts and managers from Kerman University of Medical Sciences. Participants were selected using purposive sampling, and interviews continued until theoretical saturation was achieved. Data were analyzed using thematic analysis with the assistance of MAXQDA 2018 software.

Findings: The results of the data analysis led to the identification of a set of antecedents and consequences of organizational authenticity. The antecedents of organizational authenticity included authentic leadership, value-based organizational culture, organizational justice and trust, psychological safety, employees’ individual authenticity, and participation and empowerment. Furthermore, the most significant consequences of organizational authenticity included affective commitment, reduced turnover intention and absenteeism, public trust and organizational reputation, organizational citizenship behavior, intrinsic motivation, employee well-being, and creativity and innovation. The findings indicated that organizational authenticity is a multidimensional and dynamic phenomenon that emerges from the interaction of individual, managerial, and cultural factors and generates extensive outcomes for employees, organizations, and stakeholders.

Conclusion: The findings suggest that organizational authenticity in medical universities is not merely an identity-related characteristic; rather, it represents a strategic mechanism for strengthening trust, enhancing organizational cohesion, and improving performance. The proposed model can assist managers and policymakers within the healthcare system in designing management interventions grounded in authentic organizational values and in promoting organizational effectiveness.

Objective: The present study aimed to investigate the effect of auditors’ dark personality traits on professional judgment regarding Key Audit Matters (KAMs).

Methods and Materials: This study was applied in terms of purpose and descriptive–correlational in terms of methodology. The statistical population consisted of all certified public accountants who were members of the Iranian Association of Certified Public Accountants and the Audit Organization in 2026. Based on Cochran’s formula, the estimated sample size was 384 participants; however, due to incomplete questionnaire returns, 237 usable questionnaires were ultimately collected through convenience and snowball sampling methods. Data were gathered using the Dark Personality Traits Questionnaire developed by Jones and Paulhus (2014) and a questionnaire assessing judgment regarding Key Audit Matters adapted from prior auditing studies. Data analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM). Reliability and validity of the instruments were confirmed through Cronbach’s alpha, composite reliability, Average Variance Extracted (AVE), and confirmatory factor analysis.

Findings: The results indicated that all factor loadings exceeded the acceptable threshold of 0.40 and all t-values were greater than 1.96, confirming the convergent validity of the measurement model. Cronbach’s alpha and composite reliability coefficients for all constructs exceeded 0.90, demonstrating strong internal consistency. The coefficient of determination (R²) for auditors’ judgment regarding KAMs was 0.572, indicating that dark personality traits explained 57.2% of the variance in professional judgment. Furthermore, the Goodness-of-Fit (GOF) index was 0.613, confirming strong overall model fit. Hypothesis testing revealed that dark personality traits had a significant negative effect on auditors’ judgment regarding KAMs (β = -0.552, t = 3.622, p < 0.001).

Conclusion: The findings demonstrate that auditors’ dark personality traits significantly impair professional judgment regarding Key Audit Matters. The results highlight the importance of behavioral and psychological factors in audit quality and suggest that personality characteristics may influence auditors’ objectivity, skepticism, and ethical decision-making processes. The study contributes to the interdisciplinary literature linking auditing and psychology and provides practical implications for audit firms and regulatory bodies in identifying behavioral risks and strengthening professional judgment quality through appropriate control and training mechanisms.

Objective: The present study aimed to investigate the effect of artificial intelligence use on students’ innovative behavior and well-being through the mediating role of digital literacy and the moderating role of happiness.

Methods and Materials: This study was applied research in terms of purpose and descriptive-survey in terms of methodology. The statistical population consisted of all male and female lower and upper secondary school students in Langarud during the 2025–2026 academic year. Based on the Krejcie and Morgan table, 367 students were selected through stratified random sampling. Data collection instruments included the Innovative Behavior Questionnaire by Ng and Lucianetti (2016), Student Well-Being Questionnaire by Zheng et al. (2015), Artificial Intelligence Use Questionnaire by Long and Magerko (2020), Digital Literacy Questionnaire by Ibrahim et al. (2024), and the Happiness Questionnaire by Hills and Argyle (2002). Data analysis was conducted using descriptive statistics, the Kolmogorov–Smirnov test, and structural equation modeling (SEM) through SmartPLS software at a 95% confidence level.

Finding: The results indicated that artificial intelligence use had a positive and significant effect on innovative behavior (β = 0.357, t = 6.165), digital literacy (β = 0.541, t = 14.222), and student well-being (β = 0.387, t = 6.984). In addition, digital literacy positively and significantly affected innovative behavior (β = 0.245, t = 4.786) and student well-being (β = 0.301, t = 5.339). The findings further demonstrated that digital literacy mediated the relationship between artificial intelligence use and both innovative behavior and student well-being. Moreover, happiness significantly moderated the relationship between artificial intelligence use and innovative behavior (β = 0.113, t = 2.298), indicating that higher levels of happiness strengthened the positive effect of artificial intelligence use on innovative behavior.

Conclusion: The findings suggest that artificial intelligence use can improve students’ innovative behavior and well-being when accompanied by higher levels of digital literacy and happiness. Digital literacy plays a crucial role in facilitating the positive outcomes of AI-based educational environments, while happiness strengthens students’ innovative responses to technology use.

Objective: This study aimed to predict employee engagement using Extreme Gradient Boosting (XGBoost) and Explainable Artificial Intelligence (XAI) techniques while identifying the relative importance of psychological, organizational, leadership, and demographic factors influencing employee engagement among employees in South African organizations.

Methods and Materials: This quantitative cross-sectional study was conducted among 1,248 employees working in diverse South African organizations across multiple industries. Data were collected using standardized instruments measuring employee engagement, psychological empowerment, perceived organizational support, job satisfaction, psychological safety, and transformational leadership, alongside demographic and organizational variables. Following data preprocessing, feature engineering, and missing value treatment, the dataset was divided into training and testing subsets using an 80:20 ratio. Extreme Gradient Boosting (XGBoost) served as the primary predictive model and was compared with Multiple Linear Regression, Decision Tree Regression, Support Vector Regression, and Random Forest models. Model performance was evaluated using coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Explainable Artificial Intelligence was implemented using SHapley Additive exPlanations (SHAP) to determine feature importance and interpret model predictions.

Findings: The results demonstrated that XGBoost outperformed all competing models, achieving the highest predictive accuracy (R² = 0.902) and the lowest prediction errors (RMSE = 0.276, MAE = 0.198, and MAPE = 4.88%). Random Forest exhibited the second-highest predictive performance, while Multiple Linear Regression produced the weakest results. SHAP analysis revealed that psychological empowerment was the most influential predictor of employee engagement, followed by job satisfaction, perceived organizational support, transformational leadership, and psychological safety. Additional contributors included performance ratings, training participation, organizational tenure, and workload balance. The explainability analysis further indicated that higher levels of these organizational and psychological resources consistently generated positive effects on engagement predictions, whereas demographic characteristics exhibited comparatively limited predictive influence.

Conclusion: The findings demonstrate that employee engagement can be predicted with high accuracy using XGBoost and explainable artificial intelligence techniques. Psychological empowerment, job satisfaction, organizational support, transformational leadership, and psychological safety emerged as the most critical drivers of engagement. The integration of predictive analytics and explainable AI provides organizations with a powerful evidence-based framework for understanding, forecasting, and enhancing employee engagement while supporting strategic human resource decision-making.

Objective: The present study aimed to develop and evaluate an XGBoost-based machine learning model for predicting innovative work behavior using organizational climate and leadership variables among employees working in Indonesian organizations.

Methods and Materials: This study employed a cross-sectional predictive research design involving 1,248 employees from multiple Indonesian organizations representing manufacturing, information technology, telecommunications, financial services, and public administration sectors. Data were collected using standardized measures of innovative work behavior, organizational climate, and leadership. Demographic and occupational variables were also included as supplementary predictors. Data preprocessing procedures included missing-value treatment, standardization, and quality screening. The dataset was divided into training (80%) and testing (20%) subsets. Extreme Gradient Boosting (XGBoost) was utilized to develop the predictive model, while hyperparameter optimization was performed using five-fold cross-validation and grid-search procedures. Model performance was evaluated using the coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). SHapley Additive exPlanations (SHAP) analysis was applied to identify and interpret the relative importance of predictor variables.

Findings: Inferential analyses revealed significant positive correlations between innovative work behavior and organizational climate (r = .71, p < .01), transformational leadership (r = .68, p < .01), transactional leadership (r = .41, p < .01), and organizational tenure (r = .19, p < .01). The XGBoost model demonstrated strong predictive performance, explaining 88.7% of the variance in innovative work behavior in the training dataset and 84.2% in the testing dataset (R² = .842). The model achieved low prediction errors, with testing RMSE = 0.287, MAE = 0.223, and MAPE = 7.46%, indicating excellent predictive accuracy and generalizability. SHAP analysis identified transformational leadership as the most influential predictor, followed by innovation and flexibility climate, supervisory support, autonomy, communication quality, organizational integration, contingent reward leadership, educational level, organizational tenure, and organizational size. The results demonstrated that leadership and organizational climate variables accounted for the majority of predictive power within the model.

Objective: The present study aimed to design and test an appropriate model of managers’ decision-making styles based on managers’ spiritual intelligence and employees’ organizational citizenship behavior among managers and employees of Islamic Azad University in Sistan and Baluchestan Province.

Methods and Materials: This study was conducted using an applied, descriptive-correlational design with a structural equation modeling approach. The statistical population included all managers and employees of Islamic Azad University in Sistan and Baluchestan Province during the 2020–2021 academic year. Sampling was performed using a stratified sampling method. Based on Cochran’s formula, 146 employees were selected from among 300 employees and 57 managers were selected from among 70 managers, resulting in a total sample of 203 participants. Data were collected using the General Decision-Making Style Questionnaire, the Spiritual Intelligence Self-Report Inventory, and the Organizational Citizenship Behavior Questionnaire. The reliability and validity of the instruments were confirmed through Cronbach’s alpha coefficients, composite reliability, and confirmatory factor analysis. Data analysis was conducted using descriptive statistics, Pearson correlation analysis, and structural equation modeling through SPSS and AMOS software.

Findings:  The results demonstrated that spiritual intelligence had a significant positive effect on rational decision-making style (β = 0.61, p = 0.001) and intuitive decision-making style (β = 0.37, p = 0.001), while it had a significant negative effect on avoidant decision-making style (β = -0.46, p = 0.001). Organizational citizenship behavior also positively influenced rational decision-making style (β = 0.42, p = 0.001) and negatively affected avoidant decision-making style (β = -0.34, p = 0.001). Furthermore, spiritual intelligence significantly predicted organizational citizenship behavior (β = 0.66, p = 0.001).

Conclusion:  The findings of the present study indicated that spiritual intelligence and organizational citizenship behavior play substantial roles in shaping managers’ decision-making styles within higher education institutions.

Objective: The present study aimed to develop a comprehensive model of performance-based educational leadership competencies using a futures studies approach through the systematic meta-synthesis of international studies published between 2015 and 2025.

Methods and Materials: This study employed a qualitative meta-synthesis approach based on the interpretive framework of Noblit and Hare (1988) and the seven-step procedure of Sandelowski and Barroso. Scientific databases including Scopus, ERIC, Google Scholar, SID, Magiran, ScienceDirect, and Noormags were systematically searched using keywords related to educational leadership competencies, performance-based leadership, educational management, and competency development. Initially, 145 studies published between 2015 and 2025 were identified. After applying inclusion and exclusion criteria and conducting title, abstract, and full-text screening, 35 valid qualitative and mixed-method studies were selected for final analysis. Open coding, thematic synthesis, and axial categorization were utilized to extract conceptual categories and formulate the final competency model.

Findings: The findings revealed that performance-based educational leadership competencies possess a multidimensional and interdisciplinary structure organized into three major levels: causal, executive, and contextual. At the causal level, macro-leadership, policy-oriented transformation, analytical leadership, and data-driven decision-making emerged as the primary drivers of educational transformation. At the executive level, transformational and innovative leadership, learner-oriented leadership, organizational learning, and human resource empowerment were identified as core dimensions of leadership performance. At the contextual level, higher education governance, organizational culture, decentralization, and supportive policy environments were recognized as structural foundations for leadership effectiveness.

Conclusion: The results indicate that effective educational leadership in contemporary educational systems requires an integrated combination of transformational, analytical, learner-centered, and performance-based competencies.

Objective: The present study aimed to investigate the effect of similarity bias on auditors’ professional judgment and to examine the moderating role of self-esteem in this relationship.

Methods and Materials: This study was applied research in terms of purpose and descriptive-survey research in terms of data collection method. The statistical population consisted of auditors employed by the Audit Organization and audit firms affiliated with the Iranian Association of Certified Public Accountants. Based on the Krejcie and Morgan sampling table, 139 auditors were selected as the study sample. Data were collected using standardized questionnaires related to similarity bias, auditor professional judgment, and self-esteem. Similarity bias was examined through two questionnaires distributed with a one-month interval, differing only in the similarity-bias scenario. Auditor professional judgment was measured using the Jenkins and Haynes questionnaire utilized in the study by Puspa (2008), while self-esteem was measured using the Rosenberg Self-Esteem Scale. Data analysis was performed using descriptive and inferential statistics, including Cronbach’s alpha reliability analysis, AVE and HTMT validity tests, Kolmogorov–Smirnov normality testing, and structural equation modeling for hypothesis testing.

Findings: The findings demonstrated that similarity bias had a significant negative effect on auditors’ professional judgment (β = -0.361, t = 4.574, p < 0.001), indicating that increased similarity bias reduces the quality and objectivity of professional judgment. In contrast, self-esteem had a significant positive effect on auditor professional judgment (β = 0.271, t = 3.435, p = 0.001), suggesting that auditors with higher self-esteem demonstrate more accurate and confident professional judgments. However, the moderating effect of self-esteem on the relationship between similarity bias and auditor professional judgment was not statistically significant (β = -0.038, t = 0.822, p = 0.411). Therefore, the moderating hypothesis was rejected.

Conclusion: The results indicate that cognitive biases, particularly similarity bias, can significantly impair auditors’ professional judgment and potentially threaten audit quality and objectivity. At the same time, self-esteem contributes positively to auditors’ decision-making quality and professional confidence.