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: 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: 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: Validation of the Citizenship Education Model in Enhancing Civic Culture in the Cultural and Artistic Organization of Tehran Municipality
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
items: Development of a Social Value Framework Based on Strategic Management with a Stakeholder Theory Approach Using Qualitative Thematic Analysis
items: An Analytical Approach to the Function of Internal Auditors’ Job Well-Being in Auditor Professional Maturity
items: Identification of the Fundamental Dimensions of Corporate Entrepreneurship and Corporate Governance: A Delphi Study in Entrepreneurial Companies
items: Predictive Modeling of Innovation Failure Risk from Organizational Stress, Workload Distribution, and Team Conflict Using Machine Learning Classification
items: Feature Importance Analysis of Organizational Citizenship Behaviors in Driving Product Innovation Performance via XGBoost Models
items: Predicting Open Innovation Success from Trust Networks, Communication Density, and Collaborative Behaviors Using Graph Neural Networks
items: Modeling Organizational Ambidexterity through Ensemble Learning: Behavioral and Structural Predictors of Exploratory and Exploitative Innovation
items: Machine Learning Identification of Innovation Bottlenecks: A Behavioral Analytics Approach Using Gradient Boosting Models
items: A Deep Learning Framework for Modeling the Impact of Transformational Leadership and Organizational Climate on Continuous Innovation Capability
items: Investigating the Impact of Organizational Learning and Innovation on Sustainable Human Resource Development in Hospitals of Kermanshah

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/

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/

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/

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/

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/

Validation of the Citizenship Education Model in Enhancing Civic Culture in the Cultural and Artistic Organization of Tehran Municipality
type: item
description: Objective: The objective of this study was to validate a citizenship education model for enhancing civic culture within the Cultural and Artistic Organization of Tehran Municipality. Methods and Materials: This study employed an applied, quantitative approach using a descriptive–survey design. The statistical population consisted of 1,857 employees and experts of the Cultural and Artistic Organization of Tehran Municipality, from which a sample of 384 participants was selected through simple random sampling based on Cochran’s formula at a 95% confidence level. Data were collected using a researcher-developed questionnaire derived from the qualitative phase and grounded theory components, designed on a five-point Likert scale. Content validity was confirmed by expert review, and reliability was established using Cronbach’s alpha (α > 0.70). Data analysis was conducted using descriptive and inferential statistics, and structural equation modeling (SEM) based on the Partial Least Squares (PLS) approach was applied to test the conceptual model and examine relationships among variables. Findings: The results indicated that causal factors, including heterogeneous socio-cultural context (β = -0.337), cultural diversity (β = -0.255), neglect of citizenship rights (β = -0.377), erosion of social capital (β = -0.293), and inefficiency of educational programs (β = -0.274), had significant negative effects on civic culture. Contextual conditions such as access to demographic data (β = 0.351) and the background of citizenship education programs (β = 0.509) positively influenced the implementation of strategies. Intervening factors (β = 0.281) significantly affected the effectiveness of strategies. Furthermore, strategies including localized content design (β = 0.379), interactive and multimedia methods (β = 0.285), local instructor networks (β = 0.314), peer support groups (β = 0.346), participatory events (β = 0.382), and monitoring and evaluation (β = 0.385) demonstrated signi…
subject: Articles
  topic: section
subject: Citizenship Education
  topic: keywords
subject: Civic Culture
  topic: keywords
subject: Structural Equation Modeling
  topic: keywords
subject: Cultural and Artistic Organization of Tehran Municipality
  topic: keywords
subject: Social Capital
  topic: keywords
creator: Ali Haji Mollarabi
creator: Zolfaghar Rashidi
creator: Rozita Sepehrnia
rights: Copyright (c) 2025 Ali Haji Mollarabi (Author); Zolfaghar Rashidi (Corresponding Author); Rozita Sepehrnia (Author)
date: 2026-05-01
publication date: 2026-05-01
starting page: 1
ending page: 10
doi: 10.61838/

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/

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/

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/

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/

Development of a Social Value Framework Based on Strategic Management with a Stakeholder Theory Approach Using Qualitative Thematic Analysis
type: item
description: Objective: The objective of this study was to develop and conceptualize an integrated framework for social value grounded in strategic management principles through the lens of stakeholder theory. Methods and Materials: This research was conducted as an exploratory qualitative study within an interpretive paradigm using thematic analysis as the primary methodological approach. The study population consisted of academic and professional experts in accounting, financial reporting, sustainability standards, and corporate governance, selected through purposive and snowball sampling. Data were collected through documentary analysis of relevant scholarly texts and semi-structured interviews with fourteen experts until theoretical saturation was achieved. Interviews were audio-recorded, transcribed verbatim, and analyzed following the systematic procedure of thematic analysis and thematic network construction. Trustworthiness was ensured through prolonged engagement, member clarification during interviews, researcher triangulation, and inter-coder agreement assessment. The analysis aimed to identify patterns of meaning leading to the extraction of basic, organizing, and global themes forming the conceptual structure of the proposed framework. Findings: The inferential analysis revealed a coherent multidimensional structure of social value consisting of three interrelated global themes: strategic requirements of social value, social value perception, and social value implementation structures. These dimensions collectively demonstrate that social value operates as a strategic organizational capability rather than a peripheral ethical activity. The findings indicate that effective integration of social value depends on alignment between strategic decision-making, stakeholder management mechanisms, performance perception processes, and organizational execution systems. The thematic relationships further suggest that organizations generate sustainable social value when governa…
subject: Articles
  topic: section
subject: Social value
  topic: keywords
subject: strategic management
  topic: keywords
subject: stakeholder theory approach
  topic: keywords
subject: implementation structures.
  topic: keywords
creator: Amirhossein Abedi
creator: Khadijeh Ebrahimi Kahrizsangi
creator: Mahnam Molaei
rights: Copyright (c) 2026 Amirhossein Abedi (Author); Khadijeh Ebrahimi Kahrizsangi (Corresponding Author); Mahnam Molaei (Author)
date: 2026-03-30
publication date: 2026-03-30
starting page: 1
ending page: 14
doi: 10.61838/kman.ijimob.5099

An Analytical Approach to the Function of Internal Auditors’ Job Well-Being in Auditor Professional Maturity
type: item
description: Objective: The objective of this study was to develop and validate a comprehensive model of internal auditors’ job well-being and examine its effect on auditor professional maturity. Methods and Materials: This research adopted a mixed-methods design combining qualitative and quantitative approaches. In the qualitative phase, thematic analysis was conducted through semi-structured interviews with expert internal auditors and academic specialists to identify the underlying dimensions of internal auditors’ job well-being. Extracted themes were categorized into overarching, organizing, and basic themes, forming the conceptual framework of the model. In the quantitative phase, data were collected using a researcher-developed questionnaire measuring job well-being dimensions and a standardized instrument assessing auditor professional maturity. The statistical population consisted of internal auditors, and sampling was conducted using purposive selection in the qualitative phase and survey-based sampling in the quantitative phase. The Delphi technique was employed to confirm content validity and expert consensus regarding identified components. Measurement model reliability and validity were evaluated using confirmatory factor analysis, Cronbach’s alpha, composite reliability, convergent validity (AVE), and discriminant validity (Fornell–Larcker criterion). Structural Equation Modeling based on the Partial Least Squares (PLS-SEM) approach was applied to test the research hypothesis and assess model fit. Findings: The structural model demonstrated satisfactory measurement and structural fit indices. Results indicated that internal auditors’ job well-being has a positive and statistically significant effect on auditor professional maturity (β = 0.564, T = 20.226). The model explained approximately 52.9% of the variance in professional maturity. All dimensions of job well-being—including professional satisfaction, growth and development opportunities, social support and com…
subject: Articles
  topic: section
subject: Occupational well-being of internal auditors
  topic: keywords
subject: auditor's professional maturity
  topic: keywords
subject: structural factors
  topic: keywords
subject: environmental factors
  topic: keywords
creator: Nooshin Dinyarian
creator: Javad Ramezani
creator: Ali Fallah
creator: Mehdi Khalilpour
rights: Copyright (c) 2026 Nooshin Dinyarian (Author); Javad Ramezani (Corresponding Author); Ali Fallah, Mehdi Khalilpour (Author)
date: 2026-03-01
publication date: 2026-03-01
starting page: 1
ending page: 17
doi: 10.61838/kman.ijimob.5101

Identification of the Fundamental Dimensions of Corporate Entrepreneurship and Corporate Governance: A Delphi Study in Entrepreneurial Companies
type: item
description: Objective: The present study aimed to identify and conceptualize the fundamental dimensions and components of corporate entrepreneurship and corporate governance within entrepreneurial companies through an exploratory qualitative approach. Methods and Materials: This research employed a qualitative exploratory design using the classical Delphi method to develop a contextualized conceptual framework. The study population consisted of senior managers and academic experts with extensive experience in entrepreneurial firms, selected through purposive and snowball sampling techniques. Data collection was conducted in two iterative Delphi rounds involving 12 experts. In the first round, an initial list of factors derived from literature review and preliminary qualitative exploration was evaluated and expanded by panel members. In the second round, participants reassessed the refined factors after receiving structured feedback, including median scores and response ranges. Consensus among experts was assessed using Kendall’s coefficient of concordance (W), and statistical analyses were performed using SPSS Version 22. Qualitative content analysis with inductive coding was applied to organize concepts into subcategories, core dimensions, and an integrated conceptual model describing the interaction between corporate entrepreneurship and corporate governance. Findings: The findings indicated that corporate entrepreneurship is structured around five interrelated dimensions: human resource prerequisites, managerial competencies, external opportunities, internationalization infrastructure, and market orientation. Simultaneously, corporate governance was conceptualized through five principal dimensions: corporate accountability, information transparency and reliability, board effectiveness, corporate responsibility, and company member competencies. The statistically significant Kendall’s coefficient confirmed expert consensus regarding the identified dimensions. Results suggest t…
subject: Articles
  topic: section
subject: Corporate Entrepreneurship
  topic: keywords
subject: Corporate Governance
  topic: keywords
subject: Delphi Method
  topic: keywords
subject: Qualitative Study
  topic: keywords
creator: Shahriar Fatehi
creator: Jahangir Yadollahi Farsi
creator: Seyed Mostafa Razavi
rights: Copyright (c) 2025 Shahriar Fatehi, Jahangir Yadollahi Farsi, Seyed Mostafa Razavi (Author)
date: 2026-05-01
publication date: 2026-05-01
starting page: 1
ending page: 13
doi: 10.61838/

Predictive Modeling of Innovation Failure Risk from Organizational Stress, Workload Distribution, and Team Conflict Using Machine Learning Classification
type: item
description: Objective: The objective of this study was to develop and validate a machine learning–based predictive model for estimating innovation failure risk using organizational stress, workload distribution, and team conflict as primary predictors. Methods and Materials: This quantitative cross-sectional study was conducted among 612 full-time employees from innovation-driven organizations in Malaysia. Data were collected using standardized survey instruments measuring organizational stress, workload distribution, team conflict, and perceived innovation failure risk. After psychometric validation, the dataset underwent preprocessing including normalization, outlier detection, and feature engineering. Innovation failure risk was converted into a binary classification outcome. Multiple machine learning classifiers were trained and compared, including logistic regression, support vector machines, random forest, gradient boosting, and extreme gradient boosting. Hyperparameter optimization and nested cross-validation were applied to ensure model stability and generalizability. Findings: The XGBoost classifier achieved the highest predictive performance with an accuracy of 94%, precision of 93%, recall of 92%, F1-score of 92%, and AUC of 0.97, significantly outperforming all baseline models. Feature importance analysis revealed that emotional exhaustion and task overload were the strongest predictors of innovation failure risk, followed by relationship conflict and resource imbalance. The final model demonstrated high sensitivity for detecting high-risk innovation cases, confirming the robustness and reliability of the proposed predictive framework. Conclusion: The findings demonstrate that innovation failure risk is strongly driven by human-centered organizational factors and can be accurately predicted using advanced machine learning models. The proposed framework provides organizations with a powerful early-warning system for preventing innovation breakdowns and strengthening …
subject: Articles
  topic: section
subject: Innovation failure risk
  topic: keywords
subject: organizational stress
  topic: keywords
subject: workload distribution
  topic: keywords
subject: team conflict
  topic: keywords
subject: machine learning
  topic: keywords
subject: predictive analytics
  topic: keywords
subject: organizational behavior
  topic: keywords
subject: innovation management
  topic: keywords
creator: Daniel Thompson
creator: Nur Aisyah Rahman
creator: Priyanka Deshmukh
rights: Copyright (c) 2026
date: 2026-01-01
publication date: 2026-01-01
starting page: 1
ending page: 10
doi: 10.61838/kman.ijimob.5070

Feature Importance Analysis of Organizational Citizenship Behaviors in Driving Product Innovation Performance via XGBoost Models
type: item
description: Objective: The objective of this study was to identify and quantify the relative importance of organizational citizenship behavior dimensions in predicting product innovation performance using an explainable machine learning framework. Methods and Materials: This study employed a quantitative cross-sectional design with data collected from 412 employees working in medium and large organizations. Organizational citizenship behaviors were measured across five dimensions including conscientiousness, civic virtue, courtesy, altruism, and sportsmanship, while product innovation performance was assessed using multidimensional performance indicators. Data were analyzed using the XGBoost algorithm with hyperparameter optimization and five-fold cross-validation. Feature importance was examined using both gain-based metrics and SHAP (SHapley Additive exPlanations) values to ensure transparent and robust interpretation of predictor contributions. Model robustness was evaluated through comparison with Random Forest and Elastic Net models. Findings: The XGBoost model demonstrated strong predictive performance, explaining 68.4% of the variance in product innovation performance. Feature importance analysis revealed that conscientiousness was the most influential predictor, followed by civic virtue, courtesy, altruism, and sportsmanship. Conscientiousness exhibited the highest SHAP contribution, indicating its dominant role in driving innovation outcomes. Comparative analysis showed that XGBoost significantly outperformed Random Forest and Elastic Net across all evaluation metrics, confirming its superior capacity to capture nonlinear behavioral effects on innovation performance. Conclusion: The findings confirm that organizational citizenship behaviors constitute critical behavioral drivers of product innovation performance and that advanced machine learning models provide superior analytical precision for understanding their complex influence. The study offers strong empirical ev…
subject: Articles
  topic: section
subject: Organizational Citizenship Behavior
  topic: keywords
subject: Product Innovation Performance
  topic: keywords
subject: Machine Learning; XGBoost
  topic: keywords
subject: Feature Importance
  topic: keywords
subject: SHAP Analysis
  topic: keywords
creator: Emre Yıldız
rights: Copyright (c) 2026
date: 2026-01-01
publication date: 2026-01-01
starting page: 1
ending page: 10
doi: 10.61838/kman.ijimob.5069

Predicting Open Innovation Success from Trust Networks, Communication Density, and Collaborative Behaviors Using Graph Neural Networks
type: item
description: Objective: The objective of this study was to develop and validate a graph neural network model for predicting open innovation success based on trust networks, communication density, and collaborative behaviors in Nigerian organizations. Methods and Materials: This study employed a quantitative, cross-sectional research design involving 487 professionals from innovation-intensive organizations in Nigeria across the technology, manufacturing, finance, telecommunications, and energy sectors. Data were collected using validated instruments measuring inter-organizational trust, communication density, and collaborative behaviors, combined with objective network data extracted from organizational collaboration platforms. Multilayer networks were constructed in which nodes represented individuals and edges represented trust relations, communication ties, and collaborative interactions. A graph neural network architecture integrating graph convolutional and attention mechanisms was trained to predict open innovation success. Model performance was evaluated using root mean squared error, mean absolute error, and explained variance, and results were compared against baseline machine learning models. Findings: The graph neural network demonstrated strong predictive performance (R² = 0.82, RMSE = 0.31, MAE = 0.24), significantly outperforming gradient boosting (R² = 0.64), random forest (R² = 0.61), and support vector regression (R² = 0.58). Explainability analysis revealed that trust networks exerted the strongest influence on innovation success (importance weight = 0.41), followed by collaborative behaviors (0.30) and communication density (0.29). Multilevel network analysis showed that team-level structures had the largest standardized effect on innovation success (β = 0.44, p < 0.001), followed by individual-level (β = 0.36, p < 0.001) and organizational-level networks (β = 0.28, p < 0.001). Conclusion: The findings demonstrate that open innovation success is primarily driv…
subject: Articles
  topic: section
subject: Open innovation
  topic: keywords
subject: trust networks
  topic: keywords
subject: communication density
  topic: keywords
subject: collaborative behaviors
  topic: keywords
subject: graph neural networks
  topic: keywords
subject: organizational networks
  topic: keywords
creator: Sophie Williams
creator: Chinedu Okafor
creator: Mustafa Al-Hadithi
rights: Copyright (c) 2026
date: 2026-01-01
publication date: 2026-01-01
starting page: 1
ending page: 9
doi: 10.61838/kman.ijimob.5068

Modeling Organizational Ambidexterity through Ensemble Learning: Behavioral and Structural Predictors of Exploratory and Exploitative Innovation
type: item
description: Objective: The objective of this study was to model organizational ambidexterity by applying ensemble machine learning techniques to identify the behavioral and structural predictors of exploratory and exploitative innovation. Methods and Materials: This explanatory study employed a cross-sectional design involving 487 middle- and senior-level managers from medium and large organizations across major industries in Chile. Data were collected using validated instruments measuring leadership cognitive flexibility, learning orientation, psychological safety, risk tolerance, cross-functional integration, decentralization, resource flexibility, knowledge-sharing systems, and dual innovation outcomes. The analytical framework integrated traditional statistical validation with an ensemble learning architecture composed of Random Forest, Gradient Boosting, XGBoost, and Support Vector Regression models. Model training applied stratified sampling, five-fold cross-validation, and hyperparameter optimization, while performance was evaluated using R², RMSE, MAE, and explained variance. Explainable AI techniques based on SHAP were employed to interpret nonlinear relationships and predictor contributions. Findings: The ensemble model demonstrated superior predictive performance for both exploratory innovation (R² = 0.81, RMSE = 0.25) and exploitative innovation (R² = 0.84, RMSE = 0.22), significantly outperforming individual machine learning algorithms. Leadership cognitive flexibility and learning orientation emerged as the strongest predictors of exploratory innovation, whereas cross-functional integration and structural decentralization exerted the greatest influence on exploitative innovation. Psychological safety, risk tolerance, knowledge sharing, and resource flexibility contributed significantly to both innovation dimensions, with SHAP analysis revealing asymmetric and nonlinear interaction effects across predictors. Conclusion: The results confirm that organizational ambid…
subject: Articles
  topic: section
subject: organizational ambidexterity
  topic: keywords
subject: ensemble learning
  topic: keywords
subject: exploratory innovation
  topic: keywords
subject: exploitative innovation
  topic: keywords
subject: behavioral predictors
  topic: keywords
subject: structural enablers
  topic: keywords
subject: machine learning
  topic: keywords
subject: innovation management
  topic: keywords
creator: Valentina Rojas
creator: Mikko Lahtinen
rights: Copyright (c) 2026
date: 2026-01-01
publication date: 2026-01-01
starting page: 1
ending page: 10
doi: 10.61838/kman.ijimob.5067

Machine Learning Identification of Innovation Bottlenecks: A Behavioral Analytics Approach Using Gradient Boosting Models
type: item
description: Objective: The objective of this study was to develop and validate a machine learning–based behavioral analytics framework for identifying organizational innovation bottlenecks through the interaction of leadership, psychological, and behavioral factors. Methods and Materials: This study employed a cross-sectional explanatory design involving 547 employees and middle-level managers from diverse organizations in Georgia. Data were collected using validated behavioral, psychological, and organizational measures capturing resistance to change, psychological safety, leadership support, communication friction, knowledge sharing, learning orientation, and innovation outcomes. Organizational performance indicators were integrated with survey data to enhance behavioral signal extraction. Gradient boosting algorithms (XGBoost, LightGBM, CatBoost) and an optimized ensemble model were implemented using five-fold cross-validation and Bayesian hyperparameter tuning. Feature engineering and explainable artificial intelligence techniques (SHAP values) were applied to uncover the relative importance and interaction effects of predictors. Findings: The ensemble model demonstrated strong predictive performance (R² = 0.846, RMSE = 0.387), explaining nearly 85% of the variance in innovation bottleneck intensity. Resistance to change was the strongest positive predictor, while leadership support for innovation, psychological safety, knowledge sharing quality, and proactive behavior significantly reduced bottleneck severity. Communication friction and excessive process formalization amplified innovation constraints. Behavioral segmentation revealed four distinct innovation profiles, with “Resistant Traditionalists” exhibiting the highest bottleneck levels and “Adaptive Innovators” the lowest. Conclusion: The findings confirm that innovation bottlenecks are systemic behavioral–organizational phenomena emerging from complex non-linear interactions among leadership dynamics, employee psycho…
subject: Articles
  topic: section
subject: Innovation bottlenecks
  topic: keywords
subject: behavioral analytics
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subject: organizational behavior
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subject: leadership
  topic: keywords
subject: psychological safety
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subject: innovation management
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creator: Ji-Hoon Park
creator: Giorgi Beridze
creator: Farah Nadia Zulkifli
rights: Copyright (c) 2026
date: 2025-01-01
publication date: 2025-01-01
starting page: 1
ending page: 10
doi: 10.61838/kman.ijimob.5066

A Deep Learning Framework for Modeling the Impact of Transformational Leadership and Organizational Climate on Continuous Innovation Capability
type: item
description: Objective: The objective of this study was to develop and validate a deep learning model capable of explaining and predicting the combined effects of transformational leadership and organizational climate on continuous innovation capability within organizational settings. Methods and Materials: The study employed a quantitative cross-sectional design using data collected from 684 full-time employees and managers working in medium-to-large organizations in Germany. Transformational leadership was measured using the Multifactor Leadership Questionnaire, organizational climate was assessed through a standardized organizational climate scale, and continuous innovation capability was captured using a multidimensional innovation capability instrument. After establishing construct validity and reliability through confirmatory factor analysis, the dataset was analyzed using a deep neural network implemented with TensorFlow and Keras. Data were split into training, validation, and test sets (70–15–15). Model optimization employed Bayesian hyperparameter tuning, and performance was evaluated using mean squared error, root mean squared error, coefficient of determination, and prediction accuracy. Model interpretability was enhanced using SHAP analysis and permutation feature importance techniques. Findings: Inferential results demonstrated strong predictive performance of the deep learning model (R² = .89 training, .87 validation, .86 test; prediction accuracy = 90.4% on test set). Transformational leadership and organizational climate jointly explained a substantial proportion of variance in continuous innovation capability. Feature importance analysis revealed inspirational motivation and innovation-oriented climate as the most influential predictors, followed by intellectual stimulation and supportive climate. The deep neural network significantly outperformed random forest, gradient boosting, and multiple regression models. Conclusion: The findings confirm that continuous …
subject: Articles
  topic: section
subject: Transformational Leadership
  topic: keywords
subject: Organizational Climate
  topic: keywords
subject: Continuous Innovation Capability
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subject: Deep Learning
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subject: Organizational Innovation
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subject: Artificial Intelligence in Management
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creator: Thabo Mokoena
creator: Johannes Richter
rights: Copyright (c) 2026
date: 2026-01-01
publication date: 2026-01-01
starting page: 1
ending page: 10
doi: 10.61838/kman.ijimob.5065

Investigating the Impact of Organizational Learning and Innovation on Sustainable Human Resource Development in Hospitals of Kermanshah
type: item
description: Objective: The objective of this study was to examine the impact of organizational learning and organizational innovation on sustainable human resource development in hospitals of Kermanshah using a structural equation modeling approach. Methods and Materials: This applied study employed a descriptive–correlational design using structural equation modeling to investigate the relationships among organizational learning, innovation, and sustainable human resource development. The statistical population consisted of all employees working in hospitals affiliated with Kermanshah University of Medical Sciences and the Social Security Organization in 2023 (N = 2,560). Using Cochran’s formula and stratified random sampling with proportional allocation, 328 valid questionnaires were collected and analyzed. Data were gathered using a researcher-developed questionnaire consisting of measures for empowerment and skill development, workforce diversity and equity, social development participation, sustainable quality of work life improvement, sustainable human resource development, environmental commitment and training, sustainable learning and innovation, and green human resource governance and leadership. The questionnaire used a five-point Likert scale and demonstrated acceptable validity and reliability. Data were analyzed using SPSS version 26 and SmartPLS version 4, including confirmatory factor analysis and structural equation modeling to evaluate the measurement and structural models. Findings: The results of structural equation modeling indicated that all components of organizational learning and innovation had significant positive effects on sustainable human resource development (p = 0.001). Sustainable human resource development showed the strongest effect (β = 0.888, t = 8.490), followed by sustainable learning and innovation (β = 0.685, t = 6.854), green human resource governance and leadership (β = 0.655, t = 7.403), and sustainable quality of work life improvement…
subject: Articles
  topic: section
subject: Organizational Learning
  topic: keywords
subject: Organizational Innovation
  topic: keywords
subject: Sustainable Human Resource Development
  topic: keywords
subject: Healthcare Organizations
  topic: keywords
subject: Structural Equation Modeling
  topic: keywords
subject: Sustainability-Oriented Leadership
  topic: keywords
creator: Saeed Khani
creator: Abbas Babaeinejad
creator: Hamid Reza Mollaei
creator: Saeed Sayadi
rights: Copyright (c) 2025 Saeed Khani (Author); Abbas Babaeinejad (Corresponding Author); Hamid Reza Mollaei, Saeed Sayadi (Author)
date: 2026-03-30
publication date: 2026-03-30
starting page: 1
ending page: 14
doi: 10.61838/kman.ijimob.5092