International Journal of Innovation Management and Organizational Behavior (IJIMOB)
type: channel
description: 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)
publisher: KMAN Publication Inc. (KMANPUB)
language: en
publication name: International Journal of Innovation Management and Organizational Behavior (IJIMOB)
issn: 3041-8992
items: Exploring the Mediating Role of Green Transformational Leadership in the Relationship between Corporate Social Responsibility and Environmental Performance
items: Analysis of the Dimensions of Gender Justice in the Process of Appointing Managers: A Case Study in the Welfare Organization of Fars Province
items: Designing a Model of Organizational Authenticity, Its Antecedents and Consequences at Kerman University of Medical Sciences
items: A Structural Model Based on the Relationship Between Auditors’ Dark Personality Traits and Judgment Regarding Key Audit Matters
items: The Impact of Artificial Intelligence Use on Students’ Innovative Behavior and Well-Being: The Mediating Role of Digital Literacy and the Moderating Role of Happiness
items: Forecasting Organizational Innovation Performance Through Long Short-Term Memory (LSTM) Networks and Strategic Capability Indicators
items: Predicting Employee Turnover Using Explainable Machine Learning Models: A Comparative Study of XGBoost, LightGBM, CatBoost, and Deep Neural Networks
items: Strategic Foresight Capability, Dynamic Capabilities, Organizational Resilience, Innovation Speed, and Firm Performance: Testing a Structural Equation Model
items: Ethical Leadership, Psychological Safety, Knowledge Sharing, Employee Creativity, and Organizational Innovation Performance: A Structural Equation Modeling Analysis
items: Artificial Intelligence Readiness, Digital Transformation Capability, Organizational Agility, Innovation Ambidexterity, and Sustainable Competitive Advantage: A Structural Equation Modeling Approach
items: Predicting Employee Engagement Through Extreme Gradient Boosting (XGBoost): An Explainable AI Approach
items: XGBoost-Based Prediction of Innovative Work Behavior Using Organizational Climate and Leadership Variables
items: Designing an Appropriate Model of Managers’ Decision-Making Styles by Considering Managers’ Spiritual Intelligence and Employees’ Organizational Citizenship Behavior
items: Developing a Performance-Based Educational Leadership Competency Development Model with a Futures Studies Approach: A Systematic Meta-Synthesis of International Studies
items: The Effect of Similarity Bias on Auditor Professional Judgment with Emphasis on the Moderating Role of Self-Esteem
items: Predicting Employee Innovative Work Behavior from Psychological Safety and Proactive Personality Using Machine Learning Models
items: Clustering Innovation Behaviors Using Machine Learning: Roles of Future Time Perspective and Proactive Motivation
items: Predicting Employee Creativity Trajectories Through Longitudinal Deep Learning Models of Motivation and Job Design
items: Using Random Forests to Predict Team Creativity from Psychological Diversity and Emotional Intelligence
items: Unsupervised Learning of Organizational Learning Patterns: The Roles of Knowledge Sharing and Cognitive Flexibility
items: Modeling Innovation Adoption through Reinforcement Learning: The Influence of Risk Perception and Change Readiness
items: The Effects of Work Meaningfulness and Autonomy on Radically Innovative Behavior: A Neural Network Approach
items: Explainable AI Models of Organizational Creativity: Influences of Inclusive Leadership and Team Psychological Safety
items: Developing a Qualitative Framework for Sharing Managerial Experiences Using a Phenomenological Approach (Case Study: Islamic Azad University)
items: Machine Learning–Based Discovery of Hidden Behavioral Profiles Underlying High-Performing Innovation Teams
items: Applying Machine Learning to Examine Psychological Ownership and Work Passion in Innovation Processes
items: A Machine‑Learning Approach to Employee Change Innovation: Roles of Adaptability and Job Crafting
items: Development of a Comprehensive Continuous Auditing Model for Risk Management in Iranian Commercial Banks
items: Contextual Procedures Framework of Career Plateauing among Accountants in the Health Insurance Organization: A Multi-Faceted Grounded Theory Approach
items: Development of an Organizational Compassion Model Based on Employee Behavior in Service-Oriented Organizations: A Content Analysis Approach

Exploring the Mediating Role of Green Transformational Leadership in the Relationship between Corporate Social Responsibility and Environmental Performance
type: item
description: Objective: The present study aimed to investigate the mediating role of green transformational leadership in the relationship between corporate social responsibility (CSR) and environmental performance among companies listed on the Tehran Stock Exchange. Methods and Materials: This applied research employed a descriptive–correlational survey design. The statistical population consisted of financial managers, heads of accounting departments, and senior accounting experts working in companies listed on the Tehran Stock Exchange in 2025. Using Cochran’s formula and a stratified random sampling approach, 395 valid questionnaires were collected and analyzed. Standardized instruments were used to measure corporate social responsibility, green transformational leadership, and environmental performance. Data analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM) through SmartPLS 3 software. Reliability, convergent validity, discriminant validity, and overall model fit were assessed prior to hypothesis testing. The results confirmed satisfactory psychometric properties and a strong model fit (GOF = 0.672), indicating the adequacy of the measurement and structural models. Findings: The structural model results demonstrated that corporate social responsibility had a significant positive effect on environmental performance (β = 0.414, t = 9.370, p < 0.001). Corporate social responsibility also exerted a significant positive effect on green transformational leadership (β = 0.739, t = 29.310, p < 0.001). Furthermore, green transformational leadership significantly enhanced environmental performance (β = 0.584, t = 13.958, p < 0.001). The findings additionally confirmed the mediating role of green transformational leadership in the relationship between corporate social responsibility and environmental performance, indicating that CSR contributes to superior environmental outcomes both directly and indirectly through the development of green trans…
subject: Articles
  topic: section
subject: Corporate Social Responsibility
  topic: keywords
subject: Green Transformational Leadership
  topic: keywords
subject: Environmental Performance
  topic: keywords
subject: Sustainability Performance
  topic: keywords
subject: Sustainable Management
  topic: keywords
creator: Mohammad Barati
creator: Seyed Rasool Masoumi
creator: Abbas Ramezanzadeh Zeidi
rights: Copyright (c) 2025 Mohammad Barati (Author); Seyed Rasool Masoumi (Corresponding Author); Abbas Ramezanzadeh Zeidi (Author)
date: 2026-09-01
publication date: 2026-09-01
starting page: 1
ending page: 14
doi: 10.61838/

Analysis of the Dimensions of Gender Justice in the Process of Appointing Managers: A Case Study in the Welfare Organization of Fars Province
type: item
description: Objective: This study aimed to analyze the dimensions of gender justice in the process of appointing managers in the Welfare Organization of Fars Province and to develop a qualitative model of fair and merit-based managerial appointment. Methods and Materials: This study was conducted using a qualitative research design based on the grounded theory approach. The research participants included 15 managers, experts, and informed organizational actors in the Welfare Organization of Fars Province who had direct knowledge or practical experience regarding managerial appointment, promotion procedures, organizational justice, and gender-related challenges in administrative advancement. Participants were selected through purposive sampling, and data collection continued until theoretical saturation was achieved. Data were collected through semi-structured interviews focused on managerial appointment criteria, gender justice in promotion, organizational barriers, supportive policies, and contextual conditions affecting women’s access to leadership positions. The interview data were transcribed and analyzed through open, axial, and selective coding. Findings: The inferential analysis of the qualitative data showed that managerial competency indicators formed the central phenomenon of the extracted model. These indicators included leadership skills, decision-making ability, specialized background, and professional experience. Organizational justice and gender justice were identified as causal conditions influencing fair appointment. Organizational justice consisted of distributive, procedural, and interactional justice, while gender justice included equal opportunities for promotion, removal of mental and structural barriers for women, gender-oriented policies and supports in appointments, and employees’ perception of gender discrimination. Contextual and environmental factors, including organizational culture, attitudes of senior managers, higher-level laws and regulations, a…
subject: Articles
  topic: section
subject: Gender justice
  topic: keywords
subject: managerial appointment
  topic: keywords
subject: managerial competency
  topic: keywords
subject: organizational justice
  topic: keywords
subject: women’s leadership
  topic: keywords
subject: Welfare Organization
  topic: keywords
creator: Ali Jamshidi
creator: Abdolkhalegh Gholami Chenarestan Olya
creator: Ali Pirzad
rights: Copyright (c) 2025 Ali Jamshidi (Author); Abdolkhalegh Gholami Chenarestan Olya (Corresponding Author); Ali Pirzad (Author)
date: 2026-09-01
publication date: 2026-09-01
starting page: 1
ending page: 15
doi: 10.61838/

Designing a Model of Organizational Authenticity, Its Antecedents and Consequences at Kerman University of Medical Sciences
type: item
description: 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…
subject: Articles
  topic: section
subject: Organizational Authenticity
  topic: keywords
subject: Authentic Leadership
  topic: keywords
subject: Value-Based Organizational Culture
  topic: keywords
subject: Organizational Trust
  topic: keywords
subject: Thematic Analysis.
  topic: keywords
creator: Afsaneh Emamipour
creator: Mohammad Jalal Kamali
creator: Zahra Anjom Shoae
creator: Sanjar Salajqeh
rights: Copyright (c) 2025 Afsaneh Emamipour (Author); Mohammad Jalal Kamali (Corresponding Author); Zahra Anjom Shoae, Sanjar Salajqeh (Author)
date: 2026-09-01
publication date: 2026-09-01
starting page: 1
ending page: 13
doi: 10.61838/

A Structural Model Based on the Relationship Between Auditors’ Dark Personality Traits and Judgment Regarding Key Audit Matters
type: item
description: 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 auditor…
subject: Articles
  topic: section
subject: dark personality traits
  topic: keywords
subject: key audit matters
  topic: keywords
subject: auditor judgment
  topic: keywords
creator: Seyyed Mohammad Hosseini
creator: Javad Ramezani
creator: Azadeh Kiapour
creator: Seyed Javad Ebrahimian
rights: Copyright (c) 2025 Seyyed Mohammad Hosseini (Author); Javad Ramezani (Corresponding Author); Azadeh Kiapour, Seyed Javad Ebrahimian (Author)
date: 2026-09-01
publication date: 2026-09-01
starting page: 1
ending page: 11
doi: 10.61838/

The Impact of Artificial Intelligence Use on Students’ Innovative Behavior and Well-Being: The Mediating Role of Digital Literacy and the Moderating Role of Happiness
type: item
description: 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 sugge…
subject: Articles
  topic: section
subject: artificial intelligence
  topic: keywords
subject: innovative behavior
  topic: keywords
subject: student well-being
  topic: keywords
subject: digital literacy
  topic: keywords
subject: happiness
  topic: keywords
creator: Samereh Abbasi Bibalan
creator: Samira Pali
rights: Copyright (c) 2025 Samereh Abbasi Bibalan (Author); Samira Pali (Corresponding Author)
date: 2026-07-01
publication date: 2026-07-01
starting page: 1
ending page: 13
doi: 10.61838/

Forecasting Organizational Innovation Performance Through Long Short-Term Memory (LSTM) Networks and Strategic Capability Indicators
type: item
description: Objective: The objective of this study was to forecast organizational innovation performance using Long Short-Term Memory (LSTM) networks based on strategic capability indicators and to evaluate the predictive contribution of key organizational capabilities to future innovation outcomes. Methods and Materials: This quantitative longitudinal predictive study was conducted among 312 organizations operating across multiple industries in Canada, including manufacturing, information technology, healthcare, financial services, telecommunications, and professional services. Data were collected from 1,248 senior managers and executives using standardized instruments measuring strategic flexibility, technological capability, organizational learning capability, knowledge management capability, market sensing capability, innovation capability, absorptive capacity, resource integration capability, and leadership capability. In addition, longitudinal organizational performance records covering five consecutive years were obtained from organizational databases and annual reports. Data preprocessing procedures included normalization, outlier treatment, and temporal sequence generation. The dataset was divided into training (70%), validation (15%), and testing (15%) subsets. Organizational innovation performance was forecasted using a Long Short-Term Memory neural network. Comparative analyses were conducted using Linear Regression, Support Vector Regression, Random Forest, XGBoost, and Multilayer Perceptron models. Model performance was evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²). SHapley Additive exPlanations (SHAP) analysis was employed to determine predictor importance. Findings: The results revealed significant positive correlations between all strategic capability indicators and organizational innovation performance (p < .01). Technological capability demonstrated t…
subject: Articles
  topic: section
subject: Organizational Innovation Performance
  topic: keywords
subject: Long Short-Term Memory Network
  topic: keywords
subject: LSTM
  topic: keywords
subject: Strategic Capabilities
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Innovation Forecasting
  topic: keywords
creator: Priyanka Deshmukh
creator: Marc-André Gagnon
rights: Copyright (c) 2026 Priyanka Deshmukh (Author); Marc-André Gagnon (Corresponding Author)
date: 2026-05-01
publication date: 2026-05-01
starting page: 1
ending page: 13
doi: 10.61838/kman.ijimob.5463

Predicting Employee Turnover Using Explainable Machine Learning Models: A Comparative Study of XGBoost, LightGBM, CatBoost, and Deep Neural Networks
type: item
description: Objective: This study aimed to develop and compare explainable machine learning models for employee turnover prediction using XGBoost, LightGBM, CatBoost, and Deep Neural Networks and to identify the most influential predictors of turnover risk through explainable artificial intelligence techniques. Methods and Materials: This quantitative predictive analytics study was conducted using a dataset of 4,862 employees from 37 organizations across multiple industries in Canada. Employee demographic, organizational, behavioral, performance-related, and psychological variables were collected from human resource information systems and validated survey instruments. The dataset was preprocessed through normalization, feature engineering, categorical encoding, and missing-value imputation procedures. Four machine learning algorithms, including XGBoost, LightGBM, CatBoost, and Deep Neural Networks, were developed and optimized using grid search and Bayesian hyperparameter tuning. The dataset was divided into training, validation, and testing subsets using stratified sampling. Model performance was evaluated using accuracy, precision, recall, F1-score, area under the receiver operating characteristic curve (AUC-ROC), area under the precision-recall curve (AUC-PR), Matthews correlation coefficient, and balanced accuracy. Explainability was achieved through SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), and partial dependence analyses. Findings: The comparative analysis revealed that all machine learning models achieved strong predictive performance; however, CatBoost outperformed the other algorithms, achieving an accuracy of 91.9%, an F1-score of 0.878, and an AUC-ROC value of 0.958. XGBoost demonstrated the second-highest performance with an accuracy of 91.3% and an AUC-ROC of 0.951, followed by LightGBM (accuracy = 90.6%, AUC-ROC = 0.944) and the Deep Neural Network (accuracy = 89.7%, AUC-ROC = 0.936). ROC curve analyses confirme…
subject: Articles
  topic: section
subject: Employee Turnover Prediction
  topic: keywords
subject: Explainable Artificial Intelligence
  topic: keywords
subject: Human Resource Analytics
  topic: keywords
subject: Machine Learning
  topic: keywords
creator: Gabriela Torres
creator: Priya Nair
creator: Mikko Lahtinen
rights: Copyright (c) 2026 Gabriela Torres (Author); Priya Nair (Corresponding Author); Mikko Lahtinen (Author)
date: 2026-05-01
publication date: 2026-05-01
starting page: 1
ending page: 13
doi: 10.61838/kman.ijimob.5454

Strategic Foresight Capability, Dynamic Capabilities, Organizational Resilience, Innovation Speed, and Firm Performance: Testing a Structural Equation Model
type: item
description: Objective: The purpose of this study was to examine the direct and indirect relationships among strategic foresight capability, dynamic capabilities, organizational resilience, innovation speed, and firm performance through a comprehensive structural equation model among organizations operating in Canada. Methods and Materials: This study employed a quantitative, cross-sectional design using structural equation modeling (SEM). The research population consisted of managers, executives, innovation specialists, and strategic planning professionals employed in medium-sized and large organizations across Canada. Using a stratified random sampling approach, data were collected from 542 participants representing manufacturing, technology, financial services, healthcare, logistics, and professional service sectors. Data were gathered using standardized instruments measuring strategic foresight capability, dynamic capabilities, organizational resilience, innovation speed, and firm performance. The psychometric properties of the instruments were evaluated through confirmatory factor analysis, composite reliability, and convergent validity assessments. Data analysis was conducted using SPSS 29 and AMOS 29. The measurement and structural models were assessed using maximum likelihood estimation, bootstrap procedures, and multiple model-fit indices, including χ²/df, CFI, TLI, IFI, GFI, RMSEA, and SRMR. Findings: The measurement model demonstrated excellent fit to the data (χ²/df = 2.31, CFI = .957, TLI = .952, IFI = .957, GFI = .924, RMSEA = .049, SRMR = .041). Structural equation modeling revealed that strategic foresight capability positively influenced dynamic capabilities (β = .73, p < .001), organizational resilience (β = .41, p < .001), and firm performance (β = .18, p < .001). Dynamic capabilities significantly affected organizational resilience (β = .48, p < .001) and innovation speed (β = .56, p < .001). Organizational resilience exerted a positive effect on innovation s…
subject: Articles
  topic: section
subject: Strategic foresight capability
  topic: keywords
subject: dynamic capabilities
  topic: keywords
subject: organizational resilience
  topic: keywords
subject: innovation speed
  topic: keywords
subject: firm performance
  topic: keywords
creator: Katarzyna Nowak
creator: Oliver McKenzie
creator: Dimitrios Papadakis
rights: Copyright (c) 2026 Katarzyna Nowak (Author); Oliver McKenzie (Corresponding Author); Dimitrios Papadakis (Author)
date: 2026-05-01
publication date: 2026-05-01
starting page: 1
ending page: 13
doi: 10.61838/kman.ijimob.5452

Ethical Leadership, Psychological Safety, Knowledge Sharing, Employee Creativity, and Organizational Innovation Performance: A Structural Equation Modeling Analysis
type: item
description: Objective: This study aimed to examine the direct and indirect relationships among ethical leadership, psychological safety, knowledge sharing, employee creativity, and organizational innovation performance and to test a structural model explaining how ethical leadership contributes to innovation outcomes through psychological and behavioral mechanisms. Methods and Materials: This quantitative cross-sectional study was conducted among 548 employees working in medium-sized and large organizations across multiple industries in Canada. Data were collected using standardized questionnaires measuring ethical leadership, psychological safety, knowledge sharing, employee creativity, and organizational innovation performance. The reliability and validity of all measurement instruments were confirmed through Cronbach’s alpha, composite reliability, average variance extracted, and confirmatory factor analysis. Structural equation modeling (SEM) using AMOS 29 was employed to evaluate the measurement and structural models. Model fit was assessed using multiple fit indices, including χ²/df, CFI, TLI, IFI, GFI, AGFI, RMSEA, and SRMR. Direct and indirect effects were examined using bootstrap analysis with 5,000 resamples. Findings: The results demonstrated that ethical leadership had significant positive effects on psychological safety (β = 0.62, p < .001), knowledge sharing (β = 0.29, p < .001), and employee creativity (β = 0.17, p < .001). Psychological safety significantly predicted knowledge sharing (β = 0.47, p < .001) and employee creativity (β = 0.31, p < .001). Knowledge sharing positively influenced employee creativity (β = 0.39, p < .001) and organizational innovation performance (β = 0.18, p < .001). Employee creativity exhibited the strongest direct effect on organizational innovation performance (β = 0.68, p < .001). The structural model demonstrated excellent fit (χ²/df = 2.37, CFI = 0.958, TLI = 0.953, RMSEA = 0.050). Bootstrap analyses confirmed significant indirec…
subject: Articles
  topic: section
subject: Ethical leadership
  topic: keywords
subject: Psychological safety
  topic: keywords
subject: Knowledge sharing
  topic: keywords
subject: Employee creativity
  topic: keywords
subject: Organizational innovation performance
  topic: keywords
subject: Structural equation modeling
  topic: keywords
subject: Innovation management
  topic: keywords
subject: Organizational behavior
  topic: keywords
creator: Youssef El Amrani
creator: Nadine Beaulieu
rights: Copyright (c) 2026 Youssef El Amrani (Author); Nadine Beaulieu (Corresponding Author)
date: 2026-05-01
publication date: 2026-05-01
starting page: 1
ending page: 12
doi: 10.61838/kman.ijimob.5438

Artificial Intelligence Readiness, Digital Transformation Capability, Organizational Agility, Innovation Ambidexterity, and Sustainable Competitive Advantage: A Structural Equation Modeling Approach
type: item
description: Objective: This study aimed to examine the structural relationships among artificial intelligence readiness, digital transformation capability, organizational agility, innovation ambidexterity, and sustainable competitive advantage and to evaluate the direct and indirect pathways through which these organizational capabilities contribute to long-term competitive success. Methods and Materials: This quantitative study employed a cross-sectional survey design and Structural Equation Modeling (SEM) to test the proposed conceptual framework. The study population consisted of managers, executives, digital transformation specialists, and innovation professionals employed in medium-sized and large organizations across Canada. Using a stratified random sampling approach, data were collected from 587 participants through a structured questionnaire measuring artificial intelligence readiness, digital transformation capability, organizational agility, innovation ambidexterity, and sustainable competitive advantage. The psychometric properties of the measurement model were assessed using Confirmatory Factor Analysis (CFA), composite reliability, and convergent and discriminant validity tests. Structural relationships were examined using maximum likelihood estimation in AMOS 26, while model adequacy was evaluated through multiple goodness-of-fit indices. Findings: The measurement model demonstrated excellent fit to the data (χ²/df = 2.41, CFI = 0.957, TLI = 0.951, IFI = 0.958, GFI = 0.921, AGFI = 0.903, RMSEA = 0.049, SRMR = 0.042). Artificial intelligence readiness exerted significant positive effects on digital transformation capability (β = 0.68, p < .001) and organizational agility (β = 0.24, p < .001). Digital transformation capability positively influenced organizational agility (β = 0.53, p < .001) and innovation ambidexterity (β = 0.41, p < .001). Organizational agility significantly enhanced innovation ambidexterity (β = 0.47, p < .001) and sustainable competitive advan…
subject: Articles
  topic: section
subject: Artificial Intelligence Readiness
  topic: keywords
subject: Digital Transformation Capability
  topic: keywords
subject: Organizational Agility
  topic: keywords
subject: Innovation Ambidexterity
  topic: keywords
subject: Sustainable Competitive Advantage
  topic: keywords
creator: Gabriela Torres
creator: Nathaniel Brown
creator: Katarzyna Nowak
rights: Copyright (c) 2026 Gabriela Torres (Author); Nathaniel Brown (Corresponding Author); Katarzyna Nowak (Author)
date: 2026-05-01
publication date: 2026-05-01
starting page: 1
ending page: 13
doi: 10.61838/kman.ijimob.5436

Predicting Employee Engagement Through Extreme Gradient Boosting (XGBoost): An Explainable AI Approach
type: item
description: 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 organizat…
subject: Articles
  topic: section
subject: Employee Engagement
  topic: keywords
subject: Extreme Gradient Boosting
  topic: keywords
subject: XGBoost
  topic: keywords
subject: Explainable Artificial Intelligence
  topic: keywords
subject: SHAP
  topic: keywords
subject: Human Resource Analytics
  topic: keywords
creator: Mohammad El-Sayed
creator: Lerato Nkosi
creator: Amina Al-Mansoori
rights: Copyright (c) 2026 Mohammad El-Sayed (Author); Lerato Nkosi (Corresponding Author); Amina Al-Mansoori (Author)
date: 2026-05-01
publication date: 2026-05-01
starting page: 1
ending page: 14
doi: 10.61838/kman.ijimob.5426

XGBoost-Based Prediction of Innovative Work Behavior Using Organizational Climate and Leadership Variables
type: item
description: 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%, indicati…
subject: Articles
  topic: section
subject: Innovative Work Behavior
  topic: keywords
subject: XGBoost
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Organizational Climate
  topic: keywords
subject: Transformational Leadership
  topic: keywords
subject: Leadership Variables
  topic: keywords
subject: Innovation Climate
  topic: keywords
creator: Takumi Nakamura
creator: Dewi Kartika Sari
creator: Dimitrios Papadakis
rights: Copyright (c) 2026 Takumi Nakamura (Author); Dewi Kartika Sari (Corresponding Author); Dimitrios Papadakis (Author)
date: 2026-05-01
publication date: 2026-05-01
starting page: 1
ending page: 13
doi: 10.61838/kman.ijimob.5416

Designing an Appropriate Model of Managers’ Decision-Making Styles by Considering Managers’ Spiritual Intelligence and Employees’ Organizational Citizenship Behavior
type: item
description: 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 …
subject: Articles
  topic: section
subject: Decision-Making Styles
  topic: keywords
subject: Spiritual Intelligence
  topic: keywords
subject: Organizational Citizenship Behavior
  topic: keywords
subject: Managers
  topic: keywords
creator: Mosayeb Nazeri
creator: Mohammad Jalal Kamali
creator: Ali Hamzehee
rights: Copyright (c) 2025 Mosayeb Nazeri (Author); Mohammad Jalal Kamali (Corresponding Author); Ali Hamzehee (Author)
date: 2026-07-01
publication date: 2026-07-01
starting page: 1
ending page: 13
doi: 10.61838/

Developing a Performance-Based Educational Leadership Competency Development Model with a Futures Studies Approach: A Systematic Meta-Synthesis of International Studies
type: item
description: 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 e…
subject: Articles
  topic: section
subject: Educational leadership on system performance
  topic: keywords
subject: educational leaders' competence
  topic: keywords
subject: meta-synthesis
  topic: keywords
subject: futures studies
  topic: keywords
creator: Maryam Nazarizadeh Kermani
creator: Abbas Abbaspour
creator: Hamid Rahimian
creator: Morteza Taheri
rights: Copyright (c) 2025 Maryam Nazarizadeh Kermani (Author); Abbas Abbaspour (Corresponding Author); Hamid Rahimian, Morteza Taheri (Author)
date: 2026-07-01
publication date: 2026-07-01
starting page: 1
ending page: 13
doi: 10.61838/

The Effect of Similarity Bias on Auditor Professional Judgment with Emphasis on the Moderating Role of Self-Esteem
type: item
description: 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…
subject: Articles
  topic: section
subject: Similarity bias
  topic: keywords
subject: self-esteem
  topic: keywords
subject: auditor professional judgment
  topic: keywords
subject: cognitive bias
  topic: keywords
subject: audit quality
  topic: keywords
subject: behavioral auditing
  topic: keywords
creator: Ebrahim Enayatpour Shiadeh
creator: Ali Akbar Ramezani
creator: Saeid Ahmad Khosravi
rights: Copyright (c) 2025 Ebrahim Enayatpour Shiadeh (Author); Ali Akbar Ramezani (Corresponding Author); Saeid Ahmad Khosravi (Author)
date: 2026-07-01
publication date: 2026-07-01
starting page: 1
ending page: 12
doi: 10.61838/

Predicting Employee Innovative Work Behavior from Psychological Safety and Proactive Personality Using Machine Learning Models
type: item
description: 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.
subject: Articles
  topic: section
subject: Innovative Work Behavior
  topic: keywords
subject: Psychological Safety
  topic: keywords
subject: Proactive Personality
  topic: keywords
creator: Selin Karaca
creator: Dewi Kartika Sari
creator: Julien Moreau
rights: Copyright (c) 2026
date: 2026-05-01
publication date: 2026-05-01
starting page: 1
ending page: 11
doi: 10.61838/kman.ijimob.5284

Clustering Innovation Behaviors Using Machine Learning: Roles of Future Time Perspective and Proactive Motivation
type: item
description: 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 concep…
subject: Articles
  topic: section
subject: Innovation Behaviors
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: K-means Clustering
  topic: keywords
subject: Future Time Perspective
  topic: keywords
subject: Proactive Motivation
  topic: keywords
subject: Organizational Behavior
  topic: keywords
creator: Henrik Solberg
creator: Laura Sánchez Romero
creator: Julien Moreau
rights: Copyright (c) 2026
date: 2026-04-01
publication date: 2026-04-01
starting page: 1
ending page: 10
doi: 10.61838/kman.ijimob.5283

Predicting Employee Creativity Trajectories Through Longitudinal Deep Learning Models of Motivation and Job Design
type: item
description: 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 …
subject: Articles
  topic: section
subject: Employee creativity
  topic: keywords
subject: longitudinal modeling
  topic: keywords
subject: deep learning
  topic: keywords
subject: motivation
  topic: keywords
subject: job design
  topic: keywords
subject: organizational innovation
  topic: keywords
creator: Carlos Méndez Alvarez
creator: Arvind Narayanan
rights: Copyright (c) 2026
date: 2026-01-01
publication date: 2026-01-01
starting page: 1
ending page: 10
doi: 10.61838/kman.ijimob.5277

Using Random Forests to Predict Team Creativity from Psychological Diversity and Emotional Intelligence
type: item
description: 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.
subject: Articles
  topic: section
subject: Team Creativity
  topic: keywords
subject: Emotional Intelligence
  topic: keywords
subject: Psychological Diversity
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Personality Psychology
  topic: keywords
creator: Mustafa Al-Hadithi
creator: Dimitrios Papadakis
rights: Copyright (c) 2026
date: 2026-04-01
publication date: 2026-04-01
starting page: 1
ending page: 12
doi: 10.61838/kman.ijimob.5276

Unsupervised Learning of Organizational Learning Patterns: The Roles of Knowledge Sharing and Cognitive Flexibility
type: item
description: 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.
subject: Articles
  topic: section
subject: Organizational Learning
  topic: keywords
subject: Knowledge Sharing
  topic: keywords
subject: Cognitive Flexibility
  topic: keywords
subject: Unsupervised Machine Learning
  topic: keywords
creator: Takumi Nakamura
creator: Mohammad El-Sayed
rights: Copyright (c) 2026
date: 2026-05-01
publication date: 2026-05-01
starting page: 1
ending page: 13
doi: 10.61838/kman.ijimob.5275

Modeling Innovation Adoption through Reinforcement Learning: The Influence of Risk Perception and Change Readiness
type: item
description: 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 mode…
subject: Articles
  topic: section
subject: Innovation Adoption
  topic: keywords
subject: Reinforcement Learning
  topic: keywords
subject: Change Readiness
  topic: keywords
subject: Risk Perception
  topic: keywords
creator: Henrik Solberg
creator: Lerato Nkosi
creator: Laura Sánchez Romero
rights: Copyright (c) 2026
date: 2026-05-01
publication date: 2026-05-01
starting page: 1
ending page: 11
doi: 10.61838/kman.ijimob.5272

The Effects of Work Meaningfulness and Autonomy on Radically Innovative Behavior: A Neural Network Approach
type: item
description: 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 …
subject: Articles
  topic: section
subject: Radically Innovative Behavior
  topic: keywords
subject: Work Meaningfulness
  topic: keywords
subject: Job Autonomy
  topic: keywords
creator: Jonathan R Miller
creator: Katarzyna Nowak
creator: Farah Nadia Zulkifli
rights: Copyright (c) 2026
date: 2026-04-01
publication date: 2026-04-01
starting page: 1
ending page: 11
doi: 10.61838/kman.ijimob.5271

Explainable AI Models of Organizational Creativity: Influences of Inclusive Leadership and Team Psychological Safety
type: item
description: 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.
subject: Articles
  topic: section
subject: Organizational Creativity
  topic: keywords
subject: Inclusive Leadership
  topic: keywords
subject: Team Psychological Safety
  topic: keywords
creator: Eleanor Hughes
creator: Rizky Pratama
rights: Copyright (c) 2026
date: 2026-01-01
publication date: 2026-01-01
starting page: 1
ending page: 11
doi: 10.61838/kman.ijimob.5270

Developing a Qualitative Framework for Sharing Managerial Experiences Using a Phenomenological Approach (Case Study: Islamic Azad University)
type: item
description: 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 thi…
subject: Articles
  topic: section
subject: managerial experiences
  topic: keywords
subject: experience sharing
  topic: keywords
subject: phenomenology
  topic: keywords
subject: middle managers
  topic: keywords
subject: Islamic Azad University
  topic: keywords
creator: Saba Asri
creator: Narges Shariatmadari
creator: Maryam AfzalKhani
rights: Copyright (c) 2025 Saba Asri (Author); Narges Shariatmadari (Corresponding Author); Maryam AfzalKhani (Author)
date: 2026-05-01
publication date: 2026-05-01
starting page: 1
ending page: 15
doi: 10.61838/kman.ijimob.5280

Machine Learning–Based Discovery of Hidden Behavioral Profiles Underlying High-Performing Innovation Teams
type: item
description: 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…
subject: Articles
  topic: section
subject: Innovation teams
  topic: keywords
subject: behavioral profiles
  topic: keywords
subject: machine learning
  topic: keywords
subject: organizational behavior
  topic: keywords
subject: innovation performance
  topic: keywords
subject: XGBoost
  topic: keywords
subject: psychological safety
  topic: keywords
subject: team dynamics
  topic: keywords
creator: Gabriela Torres
creator: Fatima Zahra Benali
creator: Amina Al-Mansoori
rights: Copyright (c) 2025 Gabriela Torres (Author); Fatima Zahra Benali (Corresponding Author); Amina Al-Mansoori (Author)
date: 2026-05-01
publication date: 2026-05-01
starting page: 1
ending page: 9
doi: 10.61838/kman.ijimob.5258

Applying Machine Learning to Examine Psychological Ownership and Work Passion in Innovation Processes
type: item
description: 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.
subject: Articles
  topic: section
subject: Machine Learning
  topic: keywords
subject: Psychological Ownership
  topic: keywords
subject: Work Passion
  topic: keywords
subject: Innovation Processes
  topic: keywords
creator: Mikko Lahtinen
creator: Youssef El Amrani
creator: Adrian Tan Wei Ming
rights: Copyright (c) 2026 Mikko Lahtinen (Author); Youssef El Amrani (Corresponding Author); Adrian Tan Wei Ming (Author)
date: 2026-04-01
publication date: 2026-04-01
starting page: 1
ending page: 11
doi: 10.61838/kman.ijimob.5257

A Machine‑Learning Approach to Employee Change Innovation: Roles of Adaptability and Job Crafting
type: item
description: 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.
subject: Articles
  topic: section
subject: Machine Learning
  topic: keywords
subject: Employee Change Innovation
  topic: keywords
subject: Adaptability
  topic: keywords
subject: Job Crafting
  topic: keywords
subject: Gradient Boosting Machine
  topic: keywords
subject: Organizational Change
  topic: keywords
creator: Zhang Wei
creator: Selin Karaca
creator: Priyanka Deshmukh
rights: Copyright (c) 2026 Zhang Wei (Author); Selin Karaca (Corresponding Author); Priyanka Deshmukh (Author)
date: 2026-01-01
publication date: 2026-01-01
starting page: 1
ending page: 11
doi: 10.61838/kman.ijimob.5256

Development of a Comprehensive Continuous Auditing Model for Risk Management in Iranian Commercial Banks
type: item
description: Objective: The present study aimed to develop a comprehensive continuous auditing model with a focus on risk management in Iranian commercial banks. Methods and Materials: This study employed a mixed-methods design (qualitative–quantitative). In the qualitative phase, the grounded theory approach based on Anselm Strauss and Juliet Corbin was utilized, while the quantitative phase complemented the analysis. The statistical population in the qualitative section consisted of 22 banking experts, including managers and heads of internal audit departments from Tejarat, Mellat, and Saderat banks, as well as academic specialists, who were selected through snowball sampling. In the quantitative section, the sample size was determined purposively and included 146 senior managers from the aforementioned banks in East Azerbaijan Province. Data collection tools included structured interviews (qualitative) and a questionnaire developed based on the qualitative model (quantitative). Data analysis in the qualitative phase was conducted using three-stage coding (open, axial, and selective coding), and in the quantitative phase through structural equation modeling (SEM) using SmartPLS version 3.1.1. Findings: Qualitative analysis led to the development of a six-dimensional conceptual model, including causal conditions (risk identification and assessment, controls, and assurance), contextual conditions (infrastructure, human resources, and culture), intervening conditions (complexity, rate of change, data, security, and cost), the core phenomenon (continuous auditing with a focus on risk management), strategies (managerial, technological, and structural), and outcomes (risk reduction and managerial development). The results of the quantitative phase confirmed a good model fit and revealed significant relationships among the model dimensions; in particular, a strong and statistically significant relationship was observed between causal conditions and the core phenomenon, as well as bet…
subject: Articles
  topic: section
subject: Continuous auditing
  topic: keywords
subject: risk management
  topic: keywords
subject: Iranian commercial banks
  topic: keywords
subject: grounded theory
  topic: keywords
subject: structural equation modeling
  topic: keywords
creator: Elyas Akbari
creator: Zahra Karimi Takanloo
creator: Sajad Naghdi
creator: Jafar Haghighat
rights: Copyright (c) 2026 Elyas Akbari (Author); Zahra Karimi Takanloo (Corresponding Author); Sajad Naghdi, Jafar Haghighat (Author)
date: 2026-05-01
publication date: 2026-05-01
starting page: 1
ending page: 12
doi: 10.61838/kman.ijimob.5217

Contextual Procedures Framework of Career Plateauing among Accountants in the Health Insurance Organization: A Multi-Faceted Grounded Theory Approach
type: item
description: Objective: The present study aimed to develop a grounded theoretical model explaining the functional factors, contextual conditions, strategic responses, and consequences of career plateauing among accountants working in the Health Insurance Organization. Methods and Materials: This research adopted a qualitative exploratory design based on a multi-faceted grounded theory approach. The statistical population consisted of experienced financial managers and professional accountants employed within the Health Insurance Organization. Participants were selected using purposive and theoretical sampling to ensure rich experiential data. Data were collected through in-depth semi-structured interviews conducted during 2025, continuing until theoretical saturation was achieved. Data analysis followed systematic grounded theory procedures including open coding, focused coding, axial coding, and selective coding. Constant comparative analysis was applied to identify patterns, relationships, and conceptual categories. The rigor and credibility of findings were ensured through memo writing, continuous comparison, participant validation, and theoretical integration. The final analytical outcome was the construction of a paradigmatic model linking causal conditions, contextual and intervening factors, strategies, the central phenomenon, and resulting organizational outcomes. Findings: The results indicated that career plateauing among accountants is primarily driven by organizational financial pressures, blocked promotion pathways, and declining intrinsic motivation. Contextual conditions such as inflexible hierarchical structures, passive organizational culture, and weaknesses in professional training systems were found to sustain plateau experiences. Intervening factors—including financial technology transformations, complex regulatory changes, and conflicting stakeholder expectations—intensified professional stagnation while simultaneously creating opportunities for role transfo…
subject: Articles
  topic: section
subject: Career plateauing
  topic: keywords
subject: structural factors of plateauing
  topic: keywords
subject: decreased valuation of specialization
  topic: keywords
subject: health insurance
  topic: keywords
creator: Safiyeh Deylami
creator: Alireza Matoufi
creator: Ali Khozein
creator: Mansour Garkaz
rights: Copyright (c) 2026 Safiyeh Deylami (Author); Alireza Matoufi (Corresponding Author); Ali Khozein, Mansour Garkaz (Author)
date: 2026-05-01
publication date: 2026-05-01
starting page: 1
ending page: 13
doi: 10.61838/kman.ijimob.5100

Development of an Organizational Compassion Model Based on Employee Behavior in Service-Oriented Organizations: A Content Analysis Approach
type: item
description: Objective: The purpose of this study is to present an organizational compassion model based on employee behavior in service-oriented organizations. Methods and Materials: This research is descriptive-analytical in terms of data collection methods and inductive-deductive in terms of research approach. Semi-structured interviews were conducted with 19 experts. Data saturation was observed after the 17th interview; however, interviews continued up to the 19th to ensure complete saturation of the data. The interviews were semi-structured, with questions pertaining to the levels and components of the conceptual model. All interviews were recorded, and after each interview, data analysis was conducted to establish the model. The qualitative data analysis process involved coding. Initially, the content of all interviews was transcribed, followed by open coding. The collected interview data were transcribed onto paper, and line-by-line and segment-by-segment analysis was performed to generate open codes. After editing, the open codes were direct quotations from the texts or, in some cases, verbatim excerpts. Findings: From the analysis of 19 interviews, a total of 378 initial codes were extracted. After analyzing the interview texts, 55 selective codes and 12 sub-themes were identified. In the final stage, through examination of the organizing themes, six main themes or overarching themes were identified: Organizational Perception and Human Sensitivity; Compassionate Management Response; Organizational Action and Intervention; Culture and Institutionalization of Compassion; Collective Compassion and Interpersonal Relationships; and Behavioral and Psychological Outcomes. Conclusion: This study establishes a comprehensive organizational compassion model for service-oriented entities, demonstrating how structured compassion practices enhance employee well-being, reduce burnout, and elevate service quality.
subject: Articles
  topic: section
subject: Organizational Compassion
  topic: keywords
subject: Employee Behavior
  topic: keywords
subject: Service-Oriented Organizations
  topic: keywords
creator: Zahra Asgarpour Gazafroudi
creator: Samira Pali
creator: Narges Saeidian Khorasgani
rights: Copyright (c) 2026 Zahra Asgarpour Gazafroudi (Author); Samira Pali (Corresponding Author); Narges Saeidian Khorasgani (Author)
date: 2026-05-01
publication date: 2026-05-01
starting page: 1
ending page: 12
doi: 10.61838/kman.ijimob.5139