AI-Supported Analysis of the Relationship Between Diversity Climate and Innovative Work Behavior: The Role of Psychological Well-Being and Education Level
Yıl 2025,
Cilt: 6 Sayı: 4, 524 - 544, 27.12.2025
Gamze Güner Kibaroğlu
,
Esra Aydın
Öz
This study aims to investigate the impact of diversity climate on innovative work behavior and the moderating effects of psychological well-being and education level by using an AI-supported analytical framework. By integrating AI into the research methodology, this study also aims to advance analytical approaches in organizational behavior literature. Data were collected from 317 participants through convenience sampling. To analyze the data, structural equation modelling (SEM) and AI-supported modelling were utilized. The results indicated that there was a positive and significant relationship between diversity climate and innovative work behavior. In addition, psychological well-being had a significant moderator role, strengthening this relationship. Although education level did not significantly moderate the direct relationship between diversity climate and innovative work behavior, it had an indirect moderating role through its interaction with psychological well-being. AI-based analyses supported these results, offering an alternative perspective to the organizational behavior literature and insights for managing these factors together.
Kaynakça
-
Alexiev, A. S., Jansen, J. J., Van den Bosch, F. A., & Volberda, H. W. (2010). Top management team advice seeking and exploratory innovation: The moderating role of TMT heterogeneity. Journal of Management Studies, 47(7), 1343-1364. https://doi.org/10.1111/j.1467-6486.2010.00919.x
-
Baig, L. D., Azeem, M. F., & Paracha, A. (2022). Cultivating innovative work behavior of nurses through diversity climate: The mediating role of job crafting. SAGE Open Nursing, 8. https://doi.org/10.1177/23779608221095432
-
Bailey, D., Faraj, S., Hinds, P., von Krogh, G., & Leonardi, P. (2019). Special issue of organization science: Emerging technologies and organizing. Organization Science, 30(3), 642-646. https://doi.org/10.1287/orsc.2019.1299
-
Bansal, R., Gupta, S., & Shankar, K. R. (2024). Exploring artificial intelligence PEAS framework for enhanced decision-making. Recent Research Reviews Journal, 3(2), 397-409. https://doi.org/10.36548/rrrj.2024.2.007
-
Bednar, P. M., & Welch, C. (2017). The innovation-diffusion cycle: Time for a sociotechnical agenda [Position paper]. In Proceedings of IFIP WG8.6 Working Conference: Re-Imagining Diffusion of Information Technology and Systems: Opportunities and Risks, University of Minho, School of Engineering, Guimarães, Portugal.
-
Bock, A. J., Opsahl, T., George, G., & Gann, D. M. (2012). The effects of culture and structure on strategic flexibility during business model innovation. Journal of Management Studies, 49(2), 279-305. https://doi.org/10.1111/j.1467-6486.2011.01030.x
-
Canbul Yaroğlu, A. (2024). The effects of artificial intelligence on organizational culture in the perspective of the hermeneutic cycle: The intersection of mental processes. Systems Research and Behavioral Science. https://doi.org/10.1002/sres.3037
-
Chen, F. F. (2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 14(3), 464-504. https://doi.org/10.1080/10705510701301834
-
Cheng, Y., & Wang, Y. (2025). Leveraging artificial intelligence–powered chatbots for nonprofit organizations: Examining the antecedents and outcomes of chatbot trust and social media engagement. Journal of Philanthropy, 30(1), e70013. https://doi.org/10.1002/nvsm.70013
-
Chrobot-Mason, D., & Aramovich, N. P. (2013). The psychological benefits of creating an affirming climate for workplace diversity. Group and Organization Management, 38(6), 659-689. https://doi.org/10.1177/1059601113509835
-
Çimen, İ., & Yücel, C. (2017). Innovative behavior scale (IWB): Adaptation to Turkish culture. Cumhuriyet International Journal of Education, 6(3), 365-381.
-
Clegg, C. W., Robinson, M. A., Davis, M. C., Bolton, L. E., Pieniazek, R. L., & McKay, A. (2017). Applying organizational psychology as a design science: A method for predicting malfunctions in socio-technical systems (PreMiSTS). Design Science, 3, e6. https://doi.org/10.1017/dsj.2017.4
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Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128-152. https://doi.org/10.2307/2393553
-
de Jong, J., & den Hartog, D. (2010). Measuring innovative work behaviour. Creativity and Innovation Management, 19(1), 23-36. https://doi.org/10.1111/j.1467-8691.2010.00547.x
-
Dean, P. J. (2001). Editorial-Lewin's action research (action learning), and Trist's socio-technical systems approach to changing whole systems. Performance Improvement Quarterly, 14(1), 4-10. https://doi.org/10.1111/j.1937-8327.2001.tb00198.x
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Diener, E., Wirtz, D., Tov, W., Kim-Prieto, C., Choi, D. W., Oishi, S., & Biswas-Diener, R. (2010). New well-being measures: Short scales to assess flourishing and positive and negative feelings. Social Indicators Research, 97(2), 143-156. https://doi.org/10.1007/s11205-009-9493-y
-
Dinnar, S. M., Dede, C., Johnson, E., Straub, C., & Korjus, K. (2021). Artificial intelligence and technology in teaching negotiation. Negotiation Journal, 37(1), 65-82. https://doi.org/10.1111/nejo.12351
-
Eisenhardt, K. M. (1989). Building theories from case study research. The Academy of Management Review, 14(4), 532-550. https://doi.org/10.2307/258557
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Emery, F. E., & Trist, E. L. (1960). Socio-technical systems. In C. W. Churchman & M. Verhulst (Eds.), Management science models and techniques (pp. 83-97). Pergamon.
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Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.2307/3151312
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Foster, K., Roche, M., Giandinoto, J. A., & Furness, T. (2020). Workplace stressors, psychological well-being, resilience, and caring behaviours of mental health nurses: A descriptive correlational study. International Journal of Mental Health Nursing, 29(1), 56-68. https://doi.org/10.1111/inm.12610
-
Gonzalez, J. A., & DeNisi, A. S. (2009). Cross-level effects of demography and diversity climate on organizational attachment and firm effectiveness. Journal of Organizational Behavior, 30(1), 21-40. https://doi.org/10.1002/job.498
-
Güner Kibaroğlu, G. (2023). Çeşitlilik yönetimi ve çeşitlilik iklimi. M. Avcı, & E. Kara (Eds.), Birey, örgüt ve toplum içinde (pp. 217-228). Eğitim Yayınevi.
-
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (Sixth edition). Pearson Prentice Hall.
-
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modelling. Journal of the Academy of Marketing Science, 43(1), 115-135. https://doi.org/10.1007/s11747-014-0403-8
-
Hock-Doepgen, M., Heaton, S., Clauss, T., & Block, J. (2025). Identifying microfoundations of dynamic managerial capabilities for business model innovation. Strategic Management Journal, 46(2), 470-501. https://doi.org/10.1002/smj.3663
-
Jauhari, H., & Singh, S. (2013). Perceived diversity climate and employees’ organizational loyalty. Equality, Diversity and Inclusion: An International Journal, 32(3), 262-276. https://doi.org/10.1108/EDI-12-2012-0119
-
Kilinc, T., Sjödin, D., & Parida, V. (2025). Navigating digital servitization for the twin transition: How manufacturers can support customers with digitalization and sustainability. Business Strategy and the Environment, 34(5), 5370-5385. https://doi.org/10.1002/bse.4255
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Koley, S., Sengupta, S., Biswas, B., Datta, K., Jana, M., & Mitra, A. (2024). Applications of artificial intelligence and machine learning-enabled businesses: A SWOT analysis for human society. In S. Dixit, M. Maurya, V. Jain, & G. Subramaniam (Eds.), Artificial intelligence-enabled businesses: How to develop strategies for innovation (pp. 227-261). Wiley. https://doi.org/10.1002/9781394234028.ch13
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Kull, T. J., Ellis, S. C., & Narasimhan, R. (2013). Reducing behavioral constraints to supplier integration: A socio-technical systems perspective. Journal of Supply Chain Management, 49(1), 64-86. https://doi.org/10.1111/jscm.12002
-
Lawrence, T. (1991). Impacts of artificial intelligence on organizational decision making. Journal of Behavioral Decision Making, 4(3), 195-214. https://doi.org/10.1002/bdm.3960040306
-
Leung, K., & Wang, J. (2015). Social processes and team creativity in multicultural teams: A socio-technical framework. Journal of Organizational Behavior, 36(7), 1008-1025. https://doi.org/10.1002/job.2021
-
Luo, X., Qian, W., Liu, M., Yu, X., & Liu, Y. (2024). Towards sustainability: Digital capability, sustainable business model innovation, and corporate environmental responsibility of high-performing enterprises in an emerging market. Business Strategy and the Environment, 33(6), 5606-5623. https://doi.org/10.1002/bse.3766
-
McKay, P. F., Avery, D. R., & Morris, M. A. (2008). Mean racial‐ethnic differences in employee sales performance: The moderating role of diversity climate. Personnel Psychology, 61(2), 349-374. https://doi.org/10.1111/j.1744-6570.2008.00116.x
-
Meng, K., Mahapatra, M. S., & Xiao, J. J. (2025). Artificial intelligence and consumer financial behavior: A systematic literature review and agenda for future research. Journal of Consumer Behaviour, 24(4), 1755-1786. https://doi.org/10.1002/cb.2497
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Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2019). Big data analytics capabilities and innovation: The mediating role of dynamic capabilities and moderating effect of the environment. British Journal of Management, 30(2), 272-298. https://doi.org/10.1111/1467-8551.12343
-
Mumford, E. (2006). The study of socio-technical design: Reflections on its successes, failures and potential. Information Systems Journal, 16(4), 317-342. https://doi.org/10.1111/j.1365-2575.2006.00221.x
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Ng, T. W. H., & Feldman, D. C. (2009). How broadly does education contribute to job performance?. Personnel Psychology, 62(1), 89-134. https://doi.org/10.1111/j.1744-6570.2008.01130.x
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Çeşitlilik İklimi ile Yenilikçi İş Davranışı Arasındaki İlişkinin Yapay Zeka Destekli Analizi: Psikolojik İyi Oluş ve Eğitim Düzeyinin Rolü
Yıl 2025,
Cilt: 6 Sayı: 4, 524 - 544, 27.12.2025
Gamze Güner Kibaroğlu
,
Esra Aydın
Öz
Bu çalışma, çeşitlilik ikliminin yenilikçi iş davranışı üzerindeki etkisini ve psikolojik iyi oluş ile eğitim düzeyinin bu ilişkideki düzenleyici rollerini, yapay zekâ destekli bir analitik çerçeve kullanarak incelemeyi amaçlamaktadır. Araştırma yöntemine yapay zekânın entegre edilmesiyle, örgütsel davranış literatüründe ileri düzey analitik yaklaşımlar alanına katkı sağlanması da hedeflenmiştir. Veriler, kolayda örnekleme yöntemiyle seçilen 317 katılımcıdan toplanmıştır. Verilerin analizinde yapısal eşitlik modellemesi (SEM) ve yapay zekâ destekli modelleme yöntemleri kullanılmıştır. Sonuçlar, çeşitlilik iklimi ile yenilikçi iş davranışı arasında pozitif ve anlamlı bir ilişki olduğunu göstermiştir. Ayrıca, psikolojik iyi oluş bu ilişkiyi güçlendirerek anlamlı bir düzenleyici rol oynamıştır. Eğitim düzeyi ise çeşitlilik iklimi ile yenilikçi iş davranışı arasındaki doğrudan ilişkiyi anlamlı şekilde düzenlemese de, psikolojik iyi oluşla etkileşimi yoluyla dolaylı bir düzenleyici role sahip olduğu görülmüştür. Yapay zekâ temelli analizler elde edilen bulguları destekleyerek örgütsel davranış literatürüne alternatif bir bakış açısı sunmakta ve söz konusu faktörlerin birlikte yönetilmesine ilişkin çıkarımlar yapılmasını sağlamaktadır.
Kaynakça
-
Alexiev, A. S., Jansen, J. J., Van den Bosch, F. A., & Volberda, H. W. (2010). Top management team advice seeking and exploratory innovation: The moderating role of TMT heterogeneity. Journal of Management Studies, 47(7), 1343-1364. https://doi.org/10.1111/j.1467-6486.2010.00919.x
-
Baig, L. D., Azeem, M. F., & Paracha, A. (2022). Cultivating innovative work behavior of nurses through diversity climate: The mediating role of job crafting. SAGE Open Nursing, 8. https://doi.org/10.1177/23779608221095432
-
Bailey, D., Faraj, S., Hinds, P., von Krogh, G., & Leonardi, P. (2019). Special issue of organization science: Emerging technologies and organizing. Organization Science, 30(3), 642-646. https://doi.org/10.1287/orsc.2019.1299
-
Bansal, R., Gupta, S., & Shankar, K. R. (2024). Exploring artificial intelligence PEAS framework for enhanced decision-making. Recent Research Reviews Journal, 3(2), 397-409. https://doi.org/10.36548/rrrj.2024.2.007
-
Bednar, P. M., & Welch, C. (2017). The innovation-diffusion cycle: Time for a sociotechnical agenda [Position paper]. In Proceedings of IFIP WG8.6 Working Conference: Re-Imagining Diffusion of Information Technology and Systems: Opportunities and Risks, University of Minho, School of Engineering, Guimarães, Portugal.
-
Bock, A. J., Opsahl, T., George, G., & Gann, D. M. (2012). The effects of culture and structure on strategic flexibility during business model innovation. Journal of Management Studies, 49(2), 279-305. https://doi.org/10.1111/j.1467-6486.2011.01030.x
-
Canbul Yaroğlu, A. (2024). The effects of artificial intelligence on organizational culture in the perspective of the hermeneutic cycle: The intersection of mental processes. Systems Research and Behavioral Science. https://doi.org/10.1002/sres.3037
-
Chen, F. F. (2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 14(3), 464-504. https://doi.org/10.1080/10705510701301834
-
Cheng, Y., & Wang, Y. (2025). Leveraging artificial intelligence–powered chatbots for nonprofit organizations: Examining the antecedents and outcomes of chatbot trust and social media engagement. Journal of Philanthropy, 30(1), e70013. https://doi.org/10.1002/nvsm.70013
-
Chrobot-Mason, D., & Aramovich, N. P. (2013). The psychological benefits of creating an affirming climate for workplace diversity. Group and Organization Management, 38(6), 659-689. https://doi.org/10.1177/1059601113509835
-
Çimen, İ., & Yücel, C. (2017). Innovative behavior scale (IWB): Adaptation to Turkish culture. Cumhuriyet International Journal of Education, 6(3), 365-381.
-
Clegg, C. W., Robinson, M. A., Davis, M. C., Bolton, L. E., Pieniazek, R. L., & McKay, A. (2017). Applying organizational psychology as a design science: A method for predicting malfunctions in socio-technical systems (PreMiSTS). Design Science, 3, e6. https://doi.org/10.1017/dsj.2017.4
-
Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128-152. https://doi.org/10.2307/2393553
-
de Jong, J., & den Hartog, D. (2010). Measuring innovative work behaviour. Creativity and Innovation Management, 19(1), 23-36. https://doi.org/10.1111/j.1467-8691.2010.00547.x
-
Dean, P. J. (2001). Editorial-Lewin's action research (action learning), and Trist's socio-technical systems approach to changing whole systems. Performance Improvement Quarterly, 14(1), 4-10. https://doi.org/10.1111/j.1937-8327.2001.tb00198.x
-
Diener, E., Wirtz, D., Tov, W., Kim-Prieto, C., Choi, D. W., Oishi, S., & Biswas-Diener, R. (2010). New well-being measures: Short scales to assess flourishing and positive and negative feelings. Social Indicators Research, 97(2), 143-156. https://doi.org/10.1007/s11205-009-9493-y
-
Dinnar, S. M., Dede, C., Johnson, E., Straub, C., & Korjus, K. (2021). Artificial intelligence and technology in teaching negotiation. Negotiation Journal, 37(1), 65-82. https://doi.org/10.1111/nejo.12351
-
Eisenhardt, K. M. (1989). Building theories from case study research. The Academy of Management Review, 14(4), 532-550. https://doi.org/10.2307/258557
-
Emery, F. E., & Trist, E. L. (1960). Socio-technical systems. In C. W. Churchman & M. Verhulst (Eds.), Management science models and techniques (pp. 83-97). Pergamon.
-
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.2307/3151312
-
Foster, K., Roche, M., Giandinoto, J. A., & Furness, T. (2020). Workplace stressors, psychological well-being, resilience, and caring behaviours of mental health nurses: A descriptive correlational study. International Journal of Mental Health Nursing, 29(1), 56-68. https://doi.org/10.1111/inm.12610
-
Gonzalez, J. A., & DeNisi, A. S. (2009). Cross-level effects of demography and diversity climate on organizational attachment and firm effectiveness. Journal of Organizational Behavior, 30(1), 21-40. https://doi.org/10.1002/job.498
-
Güner Kibaroğlu, G. (2023). Çeşitlilik yönetimi ve çeşitlilik iklimi. M. Avcı, & E. Kara (Eds.), Birey, örgüt ve toplum içinde (pp. 217-228). Eğitim Yayınevi.
-
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (Sixth edition). Pearson Prentice Hall.
-
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modelling. Journal of the Academy of Marketing Science, 43(1), 115-135. https://doi.org/10.1007/s11747-014-0403-8
-
Hock-Doepgen, M., Heaton, S., Clauss, T., & Block, J. (2025). Identifying microfoundations of dynamic managerial capabilities for business model innovation. Strategic Management Journal, 46(2), 470-501. https://doi.org/10.1002/smj.3663
-
Jauhari, H., & Singh, S. (2013). Perceived diversity climate and employees’ organizational loyalty. Equality, Diversity and Inclusion: An International Journal, 32(3), 262-276. https://doi.org/10.1108/EDI-12-2012-0119
-
Kilinc, T., Sjödin, D., & Parida, V. (2025). Navigating digital servitization for the twin transition: How manufacturers can support customers with digitalization and sustainability. Business Strategy and the Environment, 34(5), 5370-5385. https://doi.org/10.1002/bse.4255
-
Koley, S., Sengupta, S., Biswas, B., Datta, K., Jana, M., & Mitra, A. (2024). Applications of artificial intelligence and machine learning-enabled businesses: A SWOT analysis for human society. In S. Dixit, M. Maurya, V. Jain, & G. Subramaniam (Eds.), Artificial intelligence-enabled businesses: How to develop strategies for innovation (pp. 227-261). Wiley. https://doi.org/10.1002/9781394234028.ch13
-
Kull, T. J., Ellis, S. C., & Narasimhan, R. (2013). Reducing behavioral constraints to supplier integration: A socio-technical systems perspective. Journal of Supply Chain Management, 49(1), 64-86. https://doi.org/10.1111/jscm.12002
-
Lawrence, T. (1991). Impacts of artificial intelligence on organizational decision making. Journal of Behavioral Decision Making, 4(3), 195-214. https://doi.org/10.1002/bdm.3960040306
-
Leung, K., & Wang, J. (2015). Social processes and team creativity in multicultural teams: A socio-technical framework. Journal of Organizational Behavior, 36(7), 1008-1025. https://doi.org/10.1002/job.2021
-
Luo, X., Qian, W., Liu, M., Yu, X., & Liu, Y. (2024). Towards sustainability: Digital capability, sustainable business model innovation, and corporate environmental responsibility of high-performing enterprises in an emerging market. Business Strategy and the Environment, 33(6), 5606-5623. https://doi.org/10.1002/bse.3766
-
McKay, P. F., Avery, D. R., & Morris, M. A. (2008). Mean racial‐ethnic differences in employee sales performance: The moderating role of diversity climate. Personnel Psychology, 61(2), 349-374. https://doi.org/10.1111/j.1744-6570.2008.00116.x
-
Meng, K., Mahapatra, M. S., & Xiao, J. J. (2025). Artificial intelligence and consumer financial behavior: A systematic literature review and agenda for future research. Journal of Consumer Behaviour, 24(4), 1755-1786. https://doi.org/10.1002/cb.2497
-
Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2019). Big data analytics capabilities and innovation: The mediating role of dynamic capabilities and moderating effect of the environment. British Journal of Management, 30(2), 272-298. https://doi.org/10.1111/1467-8551.12343
-
Mumford, E. (2006). The study of socio-technical design: Reflections on its successes, failures and potential. Information Systems Journal, 16(4), 317-342. https://doi.org/10.1111/j.1365-2575.2006.00221.x
-
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