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THE MODERATING ROLE OF GENERAL ATTITUDE TOWARDS ARTIFICIAL INTELLIGENCE IN THE IMPACT OF DIGITAL TRANSFORMATION ON EMPLOYEE SATISFACTION

Year 2024, , 335 - 364, 06.12.2024
https://doi.org/10.54452/jrb.1406459

Abstract

This study focuses on understanding the effects of digital transformation processes in the business world on employee satisfaction. The purpose of this study is to determine whether the general attitude towards artificial intelligence plays a moderating role in the effect of digital transformation on employee satisfaction. At the same time, the study was also reinforced and elaborated with demographic questions directed to the employees. The population of the study consists of private enterprises operating in the retail sector in Istanbul. The sample is the decision-making white- collar (N= 522) current employees working in these retail sectors. SPSS 24.0 statistical package programe was used to analyze the data. Normality test was performed to determine whether the data set was suitable for parametric tests. Kurtosis and skewness values were used to evaluate normality. Pearson correlation analysis was performed to determine the direction and severity of the relationship between the variables. Moderating analysis was performed to determine how the relationship between an independent variable and a dependent variable is affectedy a third variable. According to the results obtained from the study, there are quite high and significant correlations between digital transformation and other variables in the correlation analysis. According to the moderating effect analysis, it was observed that the general attitude towards artificial intelligence did not moderate the effect of digital transformation on the variables. In demographic variables, significant differences are observed in all variables and sub-dimensions.

References

  • Agustina, R., Yusuf, M., Sutiyan, O. S. J., Ardianto, R., & Norvadewi, N. (2024). Employee performance mediated quality of work life relationship satisfaction on the job and organizational commitment. Jurnal Darma Agung, 30(2), 589-605.
  • Ahmad, S. F., Han, H., Alam, M. M., Rehmat, M., Irshad, M., Arraño-Muñoz, M., & Ariza-Montes, A. (2023). Impact of artificial intelligence on human loss in decision making, laziness and safety in education. Humanities and Social Sciences Communications, 10(1), 1-14.
  • Akter, S., Michael, K., Uddin, M. R., McCarthy, G., & Rahman, M. (2022). Transforming business using digital innovations: the application of ai, blockchain, cloud and data analytics. Annals of Operations Research, 308(1), 7-39.
  • Ali, B. J., & Anwar, G. (2021). An empirical study of employees’ motivation and its influence job satisfaction. International Journal of Engineering, Business and Management, 5(2), 21-30.
  • Alsawafi, A., Lemke, F., & Yang, Y. (2021). The impacts of internal quality management relations on the triple bottom line: a dynamic capability perspective. International Journal of Production Economics, 232, 107927.
  • Bankins, S., & Formosa, P. (2023). The ethical implications of artificial intelligence (ai) for meaningful work. Journal of Business Ethics, 185, 1-16.
  • Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: from big data to big impact. MIS quarterly, 36(4), 1165-1188.
  • Cohen, S. G., & Bailey, D. E. (1997). What makes teams work: group effectiveness research from the shop floor to the executive süite. Journal of management, 23(3), 239-290.
  • Dignum, V. (2018). Ethics in artificial intelligence: introduction to the special issue. Ethics and Information Technology, 20(1), 1-3.
  • Faeq, D. K. (2022). The importance of employee involvement in work activities to overall productivity. International Journal of Humanities and Education Development (IJHED), 4(5), 15-26.
  • Feroz, A. K., Zo, H., & Chiravuri, A. (2021). Digital transformation and environmental sustainability: a review and research agenda. Sustainability, 13(3), 1530.
  • Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & Vayena, E. (2018). AI4People—an ethical framework for a good ai society: opportunities, risks, principles, and recommendations. Minds and machines, 28, 689-707.
  • George, D., & Mallery, P. (2010). SPSS for windows step by step: a simple guide and reference 17.0 update. 10 th Edition, Boston, Pearson Press.
  • Gerlich, M. (2023). Perceptions and acceptance of artificial intelligence: a multi-dimensional study. Social Sciences, 12(9), 502.
  • Gledson, B., Zulu, S. L., Saad, A. M., & Ponton, H. (2024). Digital leadership framework to support firm-level digital transformations for construction 4.0. Construction Innovation, 24(1), 341-364.
  • Gogtay, N. J., & Thatte, U. M. (2017). Principles of correlation analysis. Journal of Association of Physicians of India, 65.
  • Gong, C., & Ribiere, V. (2021). Developing a unified definition of digital transformation. Technovation, 102, 102217.
  • Hackman, J. R., & Oldham, G. R. (1976). Motivation through the design of work: test of a theory. Organizational Behavior and Human Performance, 16(2), 250-279.
  • Hadlington, L., Binder, J., Gardner, S., Karanika-Murray, M., & Knight, S. (2023). The use of artificial intelligence in a military context: development of the attitudes toward ai in defense (aaid) scale. Frontiers in Psychology, 14, 1164810.
  • Haleem, A., Javaid, M., Qadri, M. A., Singh, R. P., & Suman, R. (2022). Artificial intelligence (ai) applications for marketing: a literature-based study. International Journal of Intelligent Networks, 3, 119-132.
  • Hayajneh, N., Suifan, T., Obeidat, B., Abuhashesh, M., Alshurideh, M., & Masa’deh, R. E. (2021). The relationship between organizational changes and job satisfaction through the mediating role of job stress in the jordanian telecommunication sector. Management Science Letters, 11(1), 315-326.
  • Inayat, W., & Jahanzeb Khan, M. (2021). A study of job satisfaction and its effect on the performance of employees working in private sector organizations. Peshawar, Education Research International, 2021, 1-9.
  • Irabor, I. E., & Okolie, U. C. (2019). A review of employees’ job satisfaction and its affect on their retention. annals of spiru haret üniversity. Economic Series, 19(2), 93-114.
  • Kaya, F., Aydin, F., Schepman, A., Rodway, P., Yetişensoy, O., & Demir Kaya, M. (2022). The roles of personality traits, ai anxiety, and demographic factors in attitudes toward artificial intelligence. International Journal of Human-Computer Interaction, 40(2), 497-514. https://doi.org/10.1080/10447.318.2022.2151730
  • Khanom, M. T. (2023). Business strategies in the age of digital transformation. Journal of Business, 8(01), 28-35.
  • Khattak, M. A., Ali, M., Azmi, W., & Rizvi, S. A. R. (2023). Digital transformation, diversification and stability: what do we know about banks?. Economic Analysis and Policy, 78, 122-132.
  • Khogali, H. O., & Mekid, S. (2023). The blended future of automation and ai: Examining some long-term societal and ethical impact features. Technology in Society, 73, 102232.
  • Kraut, A. (1998). Job satisfaction: application, assessment, causes, and consequences. Personnel Psychology, 51(2),513.
  • LaGree, D., Olsen, K., Tefertiller, A., & Vasquez, R. (2024). Combatting the “great discontent”: the impact of employability culture and leadership empowerment on career growth, loyalty and satisfaction. Corporate Communications: An International Journal, 29(3), 291-311.
  • Lee, J., Kao, H. A., & Yang, S. (2014).. Service innovation and smart analytics for industry 4.0 and big data environment. Procedia cirp, 16, 3-8.
  • Li, P., Bastone, A., Mohamad, T. A., & Schiavone, F. (2023). How does artificial intelligence impact human resources performance. evidence from a healthcare institution in the united arab emirates. Journal of Innovation & Knowledge, 8(2), 100340.
  • Meyer, J. P., & Allen, N. J. (1991). A three-component conceptualization of organizational commitment. Human resource management review, 1(1), 61-89.
  • Nadkarni, S., & Prügl, R. (2021). Digital transformation: a review, synthesis and opportunities for future research. Management Review Quarterly, 71, 233-341.
  • Nasifoglu Elidemir, S., Ozturen, A., & Bayighomog, S. W. (2020). Innovative behaviors, employee creativity, and sustainable competitive advantage: a moderated mediation. Sustainability, 12(8), 3295.
  • Nugroho, S. A., Paskarini, I., & Pratiwi, X. I. (2023). Work-life balance and job satisfaction of shipyard industry employees in surabaya. International Journal of Public Health Science (IJPHS), 12(1), 146-154.
  • O’Reilly, C. A., Caldwell, D. F., & Barnett, W. P. (1989). Work group demography, social integration, and turnover. Administrative science quarterly, 21-37.
  • Rader, E., & Gray, R. (2015). Understanding user beliefs about algorithmic curation in the facebook news feed. In Proceedings of the 33rd annual ACM conference on human factors in computing systems (pp. 173-182).
  • Riketta, M. (2005). Organizational identification: a meta-analysis. Journal of vocational behavior, 66(2), 358-384.
  • Saarikko, T., Westergren, U. H., & Blomquist, T. (2020). Digital transformation: five recommendations for the digitally conscious firm. Business Horizons, 63(6), 825-839.
  • Sağlam, M. (2021). İşletmelerde geleceğin vizyonu olarak dijital dönüşümün gerçekleştirilmesi ve dijital dönüşüm ölçeğinin türkçe uyarlaması. İstanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi, 20(40), 395-420. doi:10.46928/iticusbe.764373
  • Sartori, L., & Bocca, G. (2023). Minding the gap (s): public perceptions of ai and socio-technical imaginaries. AI & society, 38(2), 443-458.
  • Schaap, R., Stevels, V. A., de Wolff, M. S., Hazelzet, A., Anema, J. R., & Coenen, P. (2023). “I noticed that when i have a good supervisor, it can make a lot of difference.” a qualitative study on guidance of employees with a work disability to improve sustainable employability. Journal of Occupational Rehabilitation, 33(1), 201-212.
  • Shahzad, K., Khan, S. A., Iqbal, A., & Shabbir, O. (2023). Effects of motivational and behavioral factors on job productivity: an empirical investigation from academic librarians in pakistan. Behavioral Sciences, 13(1), 41.
  • Şimşek, H., Çetinkaya, F. F., & Aytekin, C. (2019). Örgütsel kalite göstergesi olarak çalışan memnuniyeti: bir ölçek geliştirme çalışması. Fırat Üniversitesi Sosyal Bilimler Dergisi, 29(2), 233-245.
  • Tai, M. C. T. (2020). The impact of artificial intelligence on human society and bioethics. Tzu-Chi Medical Journal, 32(4), 339.
  • Tannenbaum, S. I., & Yukl, G. (1992). Training and development in work organizations. Annual review of psychology, 43(1), 399-441.
  • Taye, M. M. (2023). Understanding of machine learning with deep learning: architectures, workflow. Applications and Future Directions, Computers, 12(5), 91.
  • Uzkurt, C., Kumar, R., Semih Kimzan, H., & Eminoğlu, G. (2013). Role of innovation in the relationship between organizational culture and firm performance: a study of the banking sector in turkey. European Journal of innovation management, 16(1), 92-117.
  • Vendraminelli, L., Macchion, L., Nosella, A., & Vinelli, A. (2023). Design thinking: strategy for digital transformation. Journal of Business Strategy, 44(4), 200-210.
  • Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Dong, J. Q., Fabian, N., & Haenlein, M. (2021). Digital transformation: a multidisciplinary reflection and research agenda. Journal of business research, 122, 889-901.
  • Watkins, K. E., & Marsick, V. J. (1993). Sculpting the learning organization: lessons in the art and science of systemic change. Jossey-Bass Inc., 350 Sansome Street, San Francisco, CA 94104-1310.
  • Zaki, M. (2019). Digital transformation: harnessing digital technologies for the next generation of services. Journal of Services Marketing, 33(4), 429-435.

DİJİTAL DÖNÜŞÜMÜN ÇALIŞAN MEMNUNİYETİ ÜZERİNDEKİ ETKİSİNDE YAPAY ZEKAYA YÖNELİK GENEL TUTUMUN DÜZENLEYİCİ ROLÜ

Year 2024, , 335 - 364, 06.12.2024
https://doi.org/10.54452/jrb.1406459

Abstract

Bu çalışma, iş dünyasındaki dijital dönüşüm süreçlerinin çalışan memnuniyeti üzerindeki etkilerinin anlaşılmasına odaklanmaktadır. Bu çalışmanın amacı, yapay zekaya yönelik genel tutumun, dijital dönüşümün çalışan memnuniyeti üzerindeki etkisinde moderatör bir rol oynayıp oynamadığını tespit etmektir. Aynı zamanda çalışanlara yöneltilen demografik sorularla da çalışma pekiştirilmiş ve detaylandırılmıştır. Çalışmanın evreni, İstanbul ilinde perakende sektöründe faaliyet gösteren özel bir işletmelerden oluşmaktadır. Örneklem ise, bu perakende sektörlerinde çalışan karar verici beyaz yakalı (N= 522) mevcut çalışanlardır. Verilerin analizinde SPSS 24.0 istatistik paket programı kullanılmıştır. Veri setinin parametrik testler için uygun olup olmadığını belirlemek adına normallik testi yapılmıştır. Normalliğin değerlendirilmesinde basıklık ve çarpıklık değerleri kullanılmıştır. Değişkenler arasındaki ilişkinin yönü ve şiddetini belirlemek için Pearson korelasyon analizi gerçekleştirilmiştir. Bir bağımsız değişken ile bağımlı değişken arasındaki ilişkinin, üçüncü bir değişken tarafından nasıl etkilendiğini belirlemek için düzenleyicilik analizi gerçekleştirilmiştir. Çalışmadan elde edilen sonuçlara göre, korelasyon analizinde dijital dönüşüm ile diğer değişkenler arasında oldukça yüksek ve anlamlı korelasyonlar bulunmaktadır. Düzenleyici etki analizine göre, yapay zekaya yönelik genel tutumun dijital dönüşümün değişkenler üzerindeki etkisini düzenlemediği görülmüştür. Demografik değişkenlerde ise tüm değişkenler ve alt boyutlarında anlamlı farklılıklar görülmektedir.

References

  • Agustina, R., Yusuf, M., Sutiyan, O. S. J., Ardianto, R., & Norvadewi, N. (2024). Employee performance mediated quality of work life relationship satisfaction on the job and organizational commitment. Jurnal Darma Agung, 30(2), 589-605.
  • Ahmad, S. F., Han, H., Alam, M. M., Rehmat, M., Irshad, M., Arraño-Muñoz, M., & Ariza-Montes, A. (2023). Impact of artificial intelligence on human loss in decision making, laziness and safety in education. Humanities and Social Sciences Communications, 10(1), 1-14.
  • Akter, S., Michael, K., Uddin, M. R., McCarthy, G., & Rahman, M. (2022). Transforming business using digital innovations: the application of ai, blockchain, cloud and data analytics. Annals of Operations Research, 308(1), 7-39.
  • Ali, B. J., & Anwar, G. (2021). An empirical study of employees’ motivation and its influence job satisfaction. International Journal of Engineering, Business and Management, 5(2), 21-30.
  • Alsawafi, A., Lemke, F., & Yang, Y. (2021). The impacts of internal quality management relations on the triple bottom line: a dynamic capability perspective. International Journal of Production Economics, 232, 107927.
  • Bankins, S., & Formosa, P. (2023). The ethical implications of artificial intelligence (ai) for meaningful work. Journal of Business Ethics, 185, 1-16.
  • Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: from big data to big impact. MIS quarterly, 36(4), 1165-1188.
  • Cohen, S. G., & Bailey, D. E. (1997). What makes teams work: group effectiveness research from the shop floor to the executive süite. Journal of management, 23(3), 239-290.
  • Dignum, V. (2018). Ethics in artificial intelligence: introduction to the special issue. Ethics and Information Technology, 20(1), 1-3.
  • Faeq, D. K. (2022). The importance of employee involvement in work activities to overall productivity. International Journal of Humanities and Education Development (IJHED), 4(5), 15-26.
  • Feroz, A. K., Zo, H., & Chiravuri, A. (2021). Digital transformation and environmental sustainability: a review and research agenda. Sustainability, 13(3), 1530.
  • Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & Vayena, E. (2018). AI4People—an ethical framework for a good ai society: opportunities, risks, principles, and recommendations. Minds and machines, 28, 689-707.
  • George, D., & Mallery, P. (2010). SPSS for windows step by step: a simple guide and reference 17.0 update. 10 th Edition, Boston, Pearson Press.
  • Gerlich, M. (2023). Perceptions and acceptance of artificial intelligence: a multi-dimensional study. Social Sciences, 12(9), 502.
  • Gledson, B., Zulu, S. L., Saad, A. M., & Ponton, H. (2024). Digital leadership framework to support firm-level digital transformations for construction 4.0. Construction Innovation, 24(1), 341-364.
  • Gogtay, N. J., & Thatte, U. M. (2017). Principles of correlation analysis. Journal of Association of Physicians of India, 65.
  • Gong, C., & Ribiere, V. (2021). Developing a unified definition of digital transformation. Technovation, 102, 102217.
  • Hackman, J. R., & Oldham, G. R. (1976). Motivation through the design of work: test of a theory. Organizational Behavior and Human Performance, 16(2), 250-279.
  • Hadlington, L., Binder, J., Gardner, S., Karanika-Murray, M., & Knight, S. (2023). The use of artificial intelligence in a military context: development of the attitudes toward ai in defense (aaid) scale. Frontiers in Psychology, 14, 1164810.
  • Haleem, A., Javaid, M., Qadri, M. A., Singh, R. P., & Suman, R. (2022). Artificial intelligence (ai) applications for marketing: a literature-based study. International Journal of Intelligent Networks, 3, 119-132.
  • Hayajneh, N., Suifan, T., Obeidat, B., Abuhashesh, M., Alshurideh, M., & Masa’deh, R. E. (2021). The relationship between organizational changes and job satisfaction through the mediating role of job stress in the jordanian telecommunication sector. Management Science Letters, 11(1), 315-326.
  • Inayat, W., & Jahanzeb Khan, M. (2021). A study of job satisfaction and its effect on the performance of employees working in private sector organizations. Peshawar, Education Research International, 2021, 1-9.
  • Irabor, I. E., & Okolie, U. C. (2019). A review of employees’ job satisfaction and its affect on their retention. annals of spiru haret üniversity. Economic Series, 19(2), 93-114.
  • Kaya, F., Aydin, F., Schepman, A., Rodway, P., Yetişensoy, O., & Demir Kaya, M. (2022). The roles of personality traits, ai anxiety, and demographic factors in attitudes toward artificial intelligence. International Journal of Human-Computer Interaction, 40(2), 497-514. https://doi.org/10.1080/10447.318.2022.2151730
  • Khanom, M. T. (2023). Business strategies in the age of digital transformation. Journal of Business, 8(01), 28-35.
  • Khattak, M. A., Ali, M., Azmi, W., & Rizvi, S. A. R. (2023). Digital transformation, diversification and stability: what do we know about banks?. Economic Analysis and Policy, 78, 122-132.
  • Khogali, H. O., & Mekid, S. (2023). The blended future of automation and ai: Examining some long-term societal and ethical impact features. Technology in Society, 73, 102232.
  • Kraut, A. (1998). Job satisfaction: application, assessment, causes, and consequences. Personnel Psychology, 51(2),513.
  • LaGree, D., Olsen, K., Tefertiller, A., & Vasquez, R. (2024). Combatting the “great discontent”: the impact of employability culture and leadership empowerment on career growth, loyalty and satisfaction. Corporate Communications: An International Journal, 29(3), 291-311.
  • Lee, J., Kao, H. A., & Yang, S. (2014).. Service innovation and smart analytics for industry 4.0 and big data environment. Procedia cirp, 16, 3-8.
  • Li, P., Bastone, A., Mohamad, T. A., & Schiavone, F. (2023). How does artificial intelligence impact human resources performance. evidence from a healthcare institution in the united arab emirates. Journal of Innovation & Knowledge, 8(2), 100340.
  • Meyer, J. P., & Allen, N. J. (1991). A three-component conceptualization of organizational commitment. Human resource management review, 1(1), 61-89.
  • Nadkarni, S., & Prügl, R. (2021). Digital transformation: a review, synthesis and opportunities for future research. Management Review Quarterly, 71, 233-341.
  • Nasifoglu Elidemir, S., Ozturen, A., & Bayighomog, S. W. (2020). Innovative behaviors, employee creativity, and sustainable competitive advantage: a moderated mediation. Sustainability, 12(8), 3295.
  • Nugroho, S. A., Paskarini, I., & Pratiwi, X. I. (2023). Work-life balance and job satisfaction of shipyard industry employees in surabaya. International Journal of Public Health Science (IJPHS), 12(1), 146-154.
  • O’Reilly, C. A., Caldwell, D. F., & Barnett, W. P. (1989). Work group demography, social integration, and turnover. Administrative science quarterly, 21-37.
  • Rader, E., & Gray, R. (2015). Understanding user beliefs about algorithmic curation in the facebook news feed. In Proceedings of the 33rd annual ACM conference on human factors in computing systems (pp. 173-182).
  • Riketta, M. (2005). Organizational identification: a meta-analysis. Journal of vocational behavior, 66(2), 358-384.
  • Saarikko, T., Westergren, U. H., & Blomquist, T. (2020). Digital transformation: five recommendations for the digitally conscious firm. Business Horizons, 63(6), 825-839.
  • Sağlam, M. (2021). İşletmelerde geleceğin vizyonu olarak dijital dönüşümün gerçekleştirilmesi ve dijital dönüşüm ölçeğinin türkçe uyarlaması. İstanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi, 20(40), 395-420. doi:10.46928/iticusbe.764373
  • Sartori, L., & Bocca, G. (2023). Minding the gap (s): public perceptions of ai and socio-technical imaginaries. AI & society, 38(2), 443-458.
  • Schaap, R., Stevels, V. A., de Wolff, M. S., Hazelzet, A., Anema, J. R., & Coenen, P. (2023). “I noticed that when i have a good supervisor, it can make a lot of difference.” a qualitative study on guidance of employees with a work disability to improve sustainable employability. Journal of Occupational Rehabilitation, 33(1), 201-212.
  • Shahzad, K., Khan, S. A., Iqbal, A., & Shabbir, O. (2023). Effects of motivational and behavioral factors on job productivity: an empirical investigation from academic librarians in pakistan. Behavioral Sciences, 13(1), 41.
  • Şimşek, H., Çetinkaya, F. F., & Aytekin, C. (2019). Örgütsel kalite göstergesi olarak çalışan memnuniyeti: bir ölçek geliştirme çalışması. Fırat Üniversitesi Sosyal Bilimler Dergisi, 29(2), 233-245.
  • Tai, M. C. T. (2020). The impact of artificial intelligence on human society and bioethics. Tzu-Chi Medical Journal, 32(4), 339.
  • Tannenbaum, S. I., & Yukl, G. (1992). Training and development in work organizations. Annual review of psychology, 43(1), 399-441.
  • Taye, M. M. (2023). Understanding of machine learning with deep learning: architectures, workflow. Applications and Future Directions, Computers, 12(5), 91.
  • Uzkurt, C., Kumar, R., Semih Kimzan, H., & Eminoğlu, G. (2013). Role of innovation in the relationship between organizational culture and firm performance: a study of the banking sector in turkey. European Journal of innovation management, 16(1), 92-117.
  • Vendraminelli, L., Macchion, L., Nosella, A., & Vinelli, A. (2023). Design thinking: strategy for digital transformation. Journal of Business Strategy, 44(4), 200-210.
  • Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Dong, J. Q., Fabian, N., & Haenlein, M. (2021). Digital transformation: a multidisciplinary reflection and research agenda. Journal of business research, 122, 889-901.
  • Watkins, K. E., & Marsick, V. J. (1993). Sculpting the learning organization: lessons in the art and science of systemic change. Jossey-Bass Inc., 350 Sansome Street, San Francisco, CA 94104-1310.
  • Zaki, M. (2019). Digital transformation: harnessing digital technologies for the next generation of services. Journal of Services Marketing, 33(4), 429-435.
There are 52 citations in total.

Details

Primary Language English
Subjects Business Administration
Journal Section Articles
Authors

Ayşe Meriç Yazıcı 0000-0001-6769-2599

Filiz Sivaslıoğlu 0000-0002-8524-6928

Publication Date December 6, 2024
Submission Date December 18, 2023
Acceptance Date July 14, 2024
Published in Issue Year 2024

Cite

APA Yazıcı, A. M., & Sivaslıoğlu, F. (2024). THE MODERATING ROLE OF GENERAL ATTITUDE TOWARDS ARTIFICIAL INTELLIGENCE IN THE IMPACT OF DIGITAL TRANSFORMATION ON EMPLOYEE SATISFACTION. Journal of Research in Business, 9(2), 335-364. https://doi.org/10.54452/jrb.1406459