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Enhancing Generative AI Usage for Employees: Key Drivers and Barriers | ||
| Journal of Information Technology Management | ||
| دوره 17، Special Issue on Strategic, Organizational, and Social Issues of Digital Transformation in Organizations، 2025، صفحه 24-54 اصل مقاله (1.61 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/jitm.2025.100696 | ||
| نویسندگان | ||
| Ishraf Zaoui* 1؛ Yousra Hallem2؛ Imed Ben Nasr3؛ Sonia Bougatfa4 | ||
| 1Head of the Research Chair in Digital Innovation & AI, Associate Professor in Marketing, Inseec BBA Lyon, France. | ||
| 2Associate Professor in Marketing, Inseec BBA Lyon, France. | ||
| 3Associate Professor, Excelia Business School, CERIIM, France. | ||
| 4Assistant Professor in Marketing, Inseec BBA Bordeaux, France. | ||
| چکیده | ||
| This study examines the use of AI tools within work environments, particularly Generative AI (Gen-AI). Its objective is to comprehend the factors affecting employees' adoption and utilization of such tools. The research applies the Technology, Organization, and Environment (TOE) framework to pinpoint potential factors and formulate hypotheses regarding their influence on employees' Gen-AI usage frequency. A quantitative research approach was conducted among a sample of 316 American employees. Results suggest that employees' perceived Gen-AI intelligence and warmth positively impact their usage through the mediation of performance expectancy. Effort expectancy only mediates the relationship between perceived Gen-AI intelligence and Gen-AI employee usage. Findings also show that the perceived severity of Gen-AI has a negative influence on employees’ usage and that an organization's absorptive capacity of Gen-AI does not influence employees’ usage. Critical drivers for Gen-AI utilization encompass technological proficiency, peer influence, and regulatory backing. These outcomes underscore the significance of nurturing a corporate culture that encourages innovation and adherence to regulations to successfully integrate Gen-AI in workplaces. | ||
| کلیدواژهها | ||
| Generative Artificial Intelligence (Gen-AI)؛ AI use؛ Technology acceptance؛ Organiza-tional adoption؛ TOE Framework | ||
| مراجع | ||
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Agrawal, A., Gans, J., & Goldfarb, A. (2022). ChatGPT and How AI Disrupts Industries. Harvard Business Review, 1-6.
Akter, S., Hossain, M. A., Sajib, S., Sultana, S., Rahman, M., Vrontis, D., & McCarthy, G. (2023). A framework for AI-powered service innovation capability: Review and agenda for future research. Technovation, 125, 102768
Albelaihi, A., & Khan, N. (2020). Top Benefits and hindrances to cloud computing adoption in Saudi Arabia: A brief study. Journal of Information Technology Management, 12(2), 107-122.
Alduaji, M. (2019). Employing the technology acceptance model to explore the trends of social media adoption and its effect on perceived usefulness and perceived ease of use. Journal of Information Technology Management, 11(2), 129-143.
Ameye, N., Bughin, J., & van Zeebroeck, N. (2023). How uncertainty shapes herding in the corporate use of artificial intelligence technology. Technovation, 127, 102846.
Belanche, D., Casaló, L. V., Schepers, J., & Flavián, C. (2021). Examining the effects of robots' physical appearance, warmth, and competence in frontline services: The Humanness‐Value‐Loyalty model. Psychology & Marketing, 38(12), 2357-2376.
Ben Arfi, W. B., Ben Nasr, I., Kondrateva, G., & Hikkerova, L. (2021). The role of trust in intention to use the IoT in eHealth: Application of the modified UTAUT in a consumer context. Technological Forecasting and Social Change, 167, 120688. https://doi.org/10.1016/j.techfore.2021.120688
Ben Arfi, W., Ben Nasr, I., Khvatova, T., & Ben Zaied, Y. (2021). Understanding acceptance of eHealthcare by IoT natives and IoT immigrants: An integrated model of UTAUT, perceived risk, and financial cost. Technological Forecasting and Social Change, 163, 120437. https://doi.org/10.1016/j.techfore.2020.120437
Bloom, D. E., Prettner, K., Saadaui, J., & Veruete, M. (2023). Artificial intelligence and the skill premium. Working paper, Beta Economics
Buiten, M. C. (2019). Towards intelligent regulation of artificial intelligence. European Journal of Risk Regulation, 10(1), 41-59.
Burton-Jones, A., & Gallivan, M. J. (2007). Toward a deeper understanding of system usage in organizations: a multilevel perspective. MIS Quarterly, 657-679.
Cao, G., Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2021). Understanding employees’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making. Technovation, 106, 102312.
Chatterjee, S., Rana, N. P., Dwivedi, Y. K., & Baabdullah, A. M. (2021). Understanding AI adoption in manufacturing and production firms using an integrated TAM-TOE model. Technological Forecasting and Social Change, 170, 120880.
Chau, P. Y., & Tam , K. Y. (1997). Factors affecting the adoption of open systems: an exploratory study. MIS Quarterly, 1-24.
Chen, Y., & Zahedi, F. M. (2016). Individuals’ Internet security perceptions and behaviors. MIS Quarterly, 40(1), 205-222.
Chin, W. W., Thatcher, J. B., & Wright, R. T. (2012). Assessing common method bias: Problems with the ulmc technique. MIS Quarterly, 36 (3), 1003–1019.
Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128-152.
Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13, 19-339.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003.
DiMaggio, P. J., & Powell, W. W. (1983). The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American Sociological Review, 147-160.
Dwivedi, Y. K., Rana, N. P., Janssen, M., Lal, B., Williams, M. D., & Clement, M. (2017). An empirical validation of a unified model of electronic government adoption (UMEGA). Government Information Quarterly, 34(2), 211-230.
Ellison, N. B., Steinfield, C., & Lampe, C. (2007). The benefits of Facebook “friends:” Social capital and college students’ use of online social network sites. Journal of Computer‐Mediated Communication, 12(4), 1143-1168.
Fiske, S. T., Cuddy, A. J., & Glick, P. (2007). Universal dimensions of social cognition: Warmth and competence. Trends in Cognitive Sciences, 11(2), 77-83.
Fosso Wamba, S., Queiroz, M. M., & Braganza, A. (2022). Preface: artificial intelligence in operations management. Annals of Operations Research, 1-6.
Gilad, Z., Amir, O., & Levontin, L. (2021, May). The effects of warmth and competence perceptions on users' choice of an AI system. In Proceedings of the 2021 CHI conference on human factors in computing systems,1-13.
Gonzalez-Tamayo, L. A., Maheshwari, G., Bonomo-Odizzio, A., & Krauss-Delorme, C. (2024). Successful business behaviour: An approach from the unified theory of acceptance and use of technology (UTAUT). The International Journal of Management Education, 22(2), 100979.
Gregory, R. W., Henfridsson, O., Kaganer, E., & Kyriakou, H. (2021). The role of artificial intelligence and data network effects for creating user value. Academy of Management Review, 46(3), 534-551.
Gursoy, D., Chi, O. H., Lu, L., & Nunkoo, R. (2019). Consumers acceptance of artificially intelligent (AI) device use in service delivery. International Journal of Information Management, 49, 157-169.
Güntürkün, P., Haumann, T., & Mikolon, S. (2020). Disentangling the differential roles of warmth and competence judgments in customer-service provider relationships. Journal of Service Research, 23(4), 476-503.
Ha, T., Sah, Y. J., Park, Y., & Lee, S. (2022). Examining the effects of power status of an explainable artificial intelligence system on users’ perceptions. Behaviour & Information Technology, 41(5), 946-958.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Thousand Oaks, CA: Sage.
Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial least squares structural equation modeling (PLS-SEM) using R (pp. 75–90). Cham: Springer.
Han, T. A., Lenaerts, T., Santos, F. C., & Pereira, L. M. (2022). Voluntary safety commitments provide an escape from over-regulation in AI development. Technology in Society, 68, 101843.
Hashemian, M., Taghi Isaai, M., Mikaeili, F., & Tabatabaie, M. (2012). Effective factors in adoption of electronic banking by customers (Saman Bank). Journal of Information Technology Management, 4(11), 155-174.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135.
Hollinger, R. C., & Clark, J. P. (1983). Deterrence in the workplace: Perceived certainty, perceived severity, and employee theft. Social forces, 62(2), 398-418.
Hsiao, K. L., & Chen, C. C. (2016). What drives in-app purchase intention for mobile games? An examination of perceived values and loyalty. Electronic Commerce Research and Applications, 16, 18-29.
Hu, Z., Ding, S., Li, S., Chen, L., & Yang, S. (2019). Adoption intention of fintech services for bank users: An empirical examination with an extended technology acceptance model. Symmetry, 11(3). https://doi.org/10.3390/sym11030340
Hu, P., Lu, Y., & Gong, Y. (2021). Dual humanness and trust in conversational AI: A person‐centered approach. Computers in Human Behavior, 119, 106727.
Issa, H., Jabbouri, R., & Palmer, M. (2022). An artificial intelligence (AI)-readiness and adoption framework for AgriTech firms. Technological Forecasting and Social Change, 182, 121874.
Jo, H., & Bang, Y. (2023). Understanding continuance intention of enterprise resource planning (ERP): TOE, TAM, and IS success model. Heliyon, 9(10).
John-Mathews, J. M., Cardon, D., & Balagué, C. (2022). From reality to world. A critical perspective on AI fairness. Journal of Business Ethics, 178(4), 945-959.
Judd, C. M., James‐Hawkins, L., Yzerbyt, V., & Kashima, Y. (2005). Fundamental dimensions of social judgment: understanding the relations between judgments of competence and warmth. Journal of Personality and Social Psychology, 89(6), 899–913.
Kelley, S. (2022). Employee perceptions of the effective adoption of AI principles. Journal of Business Ethics, 178(4), 871-893.
Kinkel, S., Baumgartner, M., & Cherubini, E. (2022). Prerequisites for the adoption of AI technologies in manufacturing–Evidence from a worldwide sample of manufacturing companies. Technovation, 110, 102375.
Kim, B., Park, J., & Suh, J. (2020). Transparency and accountability in AI decision support: Explaining and visualizing convolutional neural networks for text information. Decision Support Systems, 134, 113302.
Kim, M. J., & Hall, C. M. (2020). What drives visitor economy crowdfunding? The effect of digital storytelling on unified theory of acceptance and use of technology. Tourism Management Perspectives, 34, 100638.
Koenig-Lewis, N., Palmer, A., & Moll, A. (2010). Predicting young consumers’ take up of mobile banking services. International Journal of Bank Marketing, 28(5), 410-432.
Korsgaard, M. A., & Roberson, L. (1995). Procedural justice in performance evaluation: The role of instrumental and non-instrumental voice in performance appraisal discussions. Journal of Management, 21(4), 657–669.
Kumari, N., & Biswas, A. (2023). Does M-payment service quality and perceived value co-creation participation magnify M-payment continuance usage intention? Moderation of usefulness and severity. International Journal of Bank Marketing, 41(6), 1330-1359.
Li, B., Yao, R., & Nan, Y. (2023). How do friendship artificial intelligence chatbots (FAIC) benefit the continuance using intention and customer engagement?. Journal of Consumer Behaviour, 22(6), 1376-1398.
Lin, H. C., Tu, Y. F., Hwang, G. J., & Huang, H. (2021). From precision education to precision medicine. Educational Technology & Society, 24(1), 123-137.
Lin, C. P., & Bhattacherjee, A. (2008). Elucidating individual intention to use interactive information technologies: The role of network externalities. International Journal of Electronic Commerce, 13(1), 85-108.
Lahlouh, K., Oumessaoud, A., Huaman-Ramirez, R., & Ouhannour, H. (2023). COVID-19 safety leadership, perceived severity, and emotional exhaustion: does safety culture matter? Journal of Safety Research, 87, 496-507.
Lee, Y. S., Kim, T., Choi, S., & Kim, W. (2022). When does AI pay off? AI-adoption intensity, complementary investments, and R&D strategy. Technovation, 118, 102590.
Lei, S., & Chu, L. (2015). Brand equity, consumer satisfaction and brand loyalty: An empirical study of luxury fashion brands consumption in China. The International Journal of Business & Management, 2(11), 22-27.
Liang, H., & Xue, Y. (2009). Avoidance of information technology threats: A theoretical perspective. MIS Quarterly, 71-90.
Liu, X., Wang, Q., Wu, G., & Zhang, C. (2022). Determinants of individuals’ intentions to use central bank digital currency: evidence from China. Technology Analysis & Strategic Management, 1-15.
Lv, X., Liu, Y., Luo, J., Liu, Y., & Li, C. (2021). Does a cute artificial intelligence assistant soften the blow? The impact of cuteness on customer tolerance of assistant service failure. Annals of Tourism Research, 87, 103114.
Marr, B. (2023). The Best Examples Of What You Can Do With ChatGPT. Forbes. https://www.forbes.com/sites/bernardmarr/2023/03/01/the-best-examples-of-what-you-can-do-with-chatgpt/?sh=3cee49e3df11
Moussawi, S., Koufaris, M., & Benbunan-Fich, R. (2021). How perceptions of intelligence and anthropomorphism affect adoption of personal intelligent agents. Electronic Markets, 31, 343-364.
Nov, O., & Ye, C. (2009). Resistance to change and the adoption of digital libraries: An integrative model. Journal of the American Society for Information Science and Technology, 60(8), 1702-1708.
Omar, N. A., Abdullah, N. L., Zainol, Z., & Nazri, M. A. (2021). Consumers’ responsiveness towards contaminated canned sardine in Malaysia: Does perceived severity matter?. Food Control, 123, 107780.
Prasad Agrawal, K. (2024). Towards adoption of generative AI in organizational settings. Journal of Computer Information Systems, 64(5), 636-651.
Queiroz, M. M., & Wamba, S. F. (2019). Blockchain adoption challenges in supply chain: An empirical investigation of the main drivers in India and the USA. International Journal of Information Management, 46, 70-82.
Ramezanian, M. R., & Bossaghzadeh, N. (2011). The effect of absorptive capacity and corporate culture on is implementation success in production companies of automobile segments in the guilan province. Journal of Information Technology Management, 3(9), 41-68.
Ransbotham, S., Gerbert, P., Reeves, M., Kiron, D., Spira, M., 2018. Artificial Intelligence in Business Gets Real. MIT Sloan Management Review.
Ringle, C. M., Sarstedt, M., Mitchell, R., & Gudergan, S. P. (2020). Partial least squares structural equation modeling in HRM research. International Journal of Human Resource Management, 31(12), 1617–1643.
Rogers, E. M. (1962). Diffusion of innovations. Glencoe. Free Press. (1976), New ProductAdoption and Diffusion. Journal of Consumer Research, 2, 290-304.
Salancik, G. R., & Pfeffer, J. (1978). A social information processing approach to job attitudes and task design. Administrative Science Quarterly, 224-253.
Sánchez‐Prieto, J. C., Huang, F., Olmos‐Migueláñez, S., García‐Peñalvo, F. J., & Teo, T. (2019). Exploring the unknown: The effect of resistance to change and attachment on mobile adoption among secondary pre‐service teachers. British Journal of Educational Technology, 50(5), 2433-2449.
Sari, N. P., Prasetya, B. P., Artha, B., & Nurteta, Y. T. (2024). Halal-friendly attributes and tourist satisfaction: exploring the role of perceived value as mediator. Journal Review Pendidikan dan Pengajaran (JRPP), 7(1), 1700-1710.
Schwesig, R., Brich, I., Buder, J., Huff, M., & Said, N. (2023). Using artificial intelligence (AI)? Risk and opportunity perception of AI predict people’s willingness to use AI. Journal of Risk Research, 26(10), 1053-1084.
Sharma, S., & Crossler, R. E. (2014). Disclosing too much? Situational factors affecting information disclosure in social commerce environment. Electronic Commerce Research and Applications, 13(5), 305-319.
Smuha, N. A. (2021). From a ‘race to AI’to a ‘race to AI regulation: regulatory competition for artificial intelligence. Law, Innovation and Technology, 13(1), 57-84.
Sohn, K., & Kwon, O. (2020). Technology acceptance theories and factors influencing artificial Intelligence-based intelligent products. Telematics and Informatics, 47, 101324.
Teo, T. (2008). Pre-service teachers' attitudes towards computer use: A Singapore survey, Australasian Journal of Educational Technology, 24(4).
Teo, T.S.H., Ranganathan, C. & Dhaliwal, J. (2006). Key dimensions of inhibitors for the deployment of web-based business-to-business electronic commerce. IEEE Transactions on Engineering Management, 53(3), 395-411.
Tornatzky, Louis G., Fleischer, Mitchell, Chakrabati, A.K. (1990). The Processes of Technological Innovation. Lexington Books, Lexington, Mass (Issues in Organization and Management Series).
Turan, A., Tunç, A. Ö., & Zehir, C. (2015). A theoretical model proposal: Personal innovativeness and user involvement as antecedents of unified theory of acceptance and use of technology. Procedia-Social and Behavioral Sciences, 210, 43-51.
Tussyadiah, I. P., & Park, S. (2018). Consumer evaluation of hotel service robots. In (Eds.) Stangl, B. & Pesonen, J. Information and Communication Technologies in Tourism, 308–320. Springer.
Van Esch, P., & Black, J. S. (2019). Factors that influence new generation candidates to engage with and complete digital, AI-enabled recruiting. Business Horizons, 62(6), 729-739.
Venkatesh, V. (2022). Adoption and use of AI tools: a research agenda grounded in UTAUT. Annals of Operations Research, 1-12.
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273-315.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 425-478.
Venkatesh, V., Thong, J. Y., & Xu, X. (2016). Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the Association for Information Systems, 17(5), 328-376.
Wach, K., Duong, C. D., Ejdys, J., Kazlauskaitė, R., Korzynski, P., Mazurek, G., ... & Ziemba, E. (2023). The dark side of Gen- artificial intelligence: A critical analysis of controversies and risks of ChatGPT. Entrepreneurial Business and Economics Review, 11(2), 7-30.
White, J. M., & Lidskog, R. (2022). Ignorance and the regulation of artificial intelligence. Journal of Risk Research, 25(4), 488-500.
Zargar, S. M., & Shahriari, Z. (2018). A model for the acceptance of cloud computing technology using dematel technique and system dynamics approach. Journal of Information Technology Management, 10(1), 93-116.
Zhao, Y., Chu, X., & Rong, K. (2023). Cyberbullying experience and bystander behavior in cyberbullying incidents: The serial mediating roles of perceived incident severity and empathy. Computers in Human Behavior, 138, 107484.
Zhu, K., Kraemer, K.L. & Xu, S. (2006). The process of innovation assimilation by firms in different countries: A technology diffusion perspective on e-business. Management Science, 52(10), 1557- 1576.
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