The Impact of Performance Expectations and Perceived Behavioral Control on Employees’ AI Adoption
Downloads
As AI technologies rapidly permeate industries, the key challenge for enterprises is no longer whether to adopt AI, but how to ensure employees can strategically and efficiently leverage AI tools to improve work performance meaningfully. This issue spans multiple dimensions, from employees’ performance expectancy regarding AI’s tangible value to their mastery of operations and application contexts, and their perceived behavioral control. It also involves whether organizations provide sufficient resources, training, and institutional support, and whether team culture and social influence foster learning and knowledge sharing. This study integrates Social Cognitive Theory and Expectation-Confirmation Theory to elucidate the critical roles of performance expectancy and perceived behavioral control in the AI adoption process and to examine how organizational support and social influence affect AI usage performance through these psychological mechanisms. In addition, we assess the moderating effect of creative self-efficacy on AI adoption. Using survey data from 392 technology-sector employees, we conduct an empirical analysis using structural equation modeling. The results indicate that social influence has a greater impact than organizational support. Performance expectancy is the key mediating variable through which AI use enhances work performance. Moreover, creative self-efficacy amplifies the positive effects of managerial support and social influence on performance expectancy and perceived behavioral control. These findings deepen the theoretical foundation of AI adoption and provide practical guidance for enterprises seeking to improve organizational performance and employee productivity through AI technology.
Downloads
[1] Earley, S. (2019). The AI Advantage: How to Put the Artificial Intelligence Revolution to Work by Tom Davenport. Applied Marketing Analytics: The Peer-Reviewed Journal, 4(3), 264-267. doi:10.69554/vsws6836.
[2] Huang, M. H., & Rust, R. T. (2018). Artificial Intelligence in Service. Journal of Service Research, 21(2), 155–172. doi:10.1177/1094670517752459.
[3] Fechete, F., & Nedelcu, A. (2019). Performance Management Assessment Model for Sustainable Development. Sustainability (Switzerland), 11(10), 2779. doi:10.3390/su11102779.
[4] Bandura, A. (1988). Organisational Applications of Social Cognitive Theory. Australian Journal of Management, 13(2), 275–302. doi:10.1177/031289628801300210.
[5] Santoso, S. (2021). Relationship between Social Media, Organizational Support, Subjective Norms and Perceived Behavioral Control to Form Entrepreneurial Intention. Expert Journal of Business and Management, 9(1), 1–10.
[6] Chu, T. H., & Chen, Y. Y. (2016). With Good We Become Good: Understanding e-learning adoption by theory of planned behavior and group influences. Computers and Education, 92–93, 37–52. doi:10.1016/j.compedu.2015.09.013.
[7] Fu, J. R., Farn, C. K., & Chao, W. P. (2006). Acceptance of electronic tax filing: A study of taxpayer intentions. Information and Management, 43(1), 109–126. doi:10.1016/j.im.2005.04.001.
[8] Lin, H., & Fan, W. (2011). Leveraging organizational knowledge through electronic knowledge repositories in public accounting firms: An empirical investigation. Behavioral Research in Accounting, 23(2), 147–167. doi:10.2308/bria-10062.
[9] Liao, J., Chen, J., & Mou, J. (2021). Examining the antecedents of idea contribution in online innovation communities: A perspective of creative self-efficacy. Technology in Society, 66, 101644. doi:10.1016/j.techsoc.2021.101644.
[10] Tierney, P., & Farmer, S. M. (2011). Creative Self-Efficacy Development and Creative Performance over Time. Journal of Applied Psychology, 96(2), 277–293. doi:10.1037/a0020952.
[11] Newman, A., Tse, H. H. M., Schwarz, G., & Nielsen, I. (2018). The effects of employees’ creative self-efficacy on innovative behavior: The role of entrepreneurial leadership. Journal of Business Research, 89, 1–9. doi:10.1016/j.jbusres.2018.04.001.
[12] Aravindan, K. L., Thurasamy, R., Raman, M., Ilhavenil, N., Annamalah, S., & Rathidevi, A. S. (2022). Modeling Awareness as the Crux in Solar Energy Adoption Intention through Unified Theory of Acceptance and Use of Technology. Mathematics, 10(12), 2045. doi:10.3390/math10122045.
[13] Hossain, M. A., & Quaddus, M. (2012). Expectation–Confirmation Theory in Information System Research: A Review and Analysis. Information Systems Theory: Explaining and Predicting Our Digital Society, 1, 441–469. doi:10.1007/978-1-4419-6108-2_21.
[14] Lin, H. Y., & Hsu, M. H. (2015). Using Social Cognitive Theory to Investigate Green Consumer Behavior. Business Strategy and the Environment, 24(5), 326–343. doi:10.1002/bse.1820.
[15] Zhou, T. (2011). An empirical examination of users’ post-adoption behaviour of mobile services. Behaviour and Information Technology, 30(2), 241–250. doi:10.1080/0144929X.2010.543702.
[16] Alnasrallah, W., & Saleem, F. (2022). Determinants of the Digitalization of Accounting in an Emerging Market: The Roles of Organizational Support and Job Relevance. Sustainability (Switzerland), 14(11), 6483. doi:10.3390/su14116483.
[17] Chouchane, R., Fernet, C., Austin, S., & Karoui Zouaoui, S. (2023). Organizational support and intrapreneurial behavior: On the role of employees’ intrapreneurial intention and self-efficacy. Journal of Management and Organization, 29(2), 366–382. doi:10.1017/jmo.2021.14.
[18] Yuan, F., & Woodman, R. W. (2010). Innovative behavior in the workplace: The role of performance and image outcome expectations. Academy of Management Journal, 53(2), 323–342. doi:10.5465/amj.2010.49388995.
[19] Nikou, S. A., & Economides, A. A. (2017). Mobile-based assessment: Investigating the factors that influence behavioral intention to use. Computers and Education, 109, 56–73. doi:10.1016/j.compedu.2017.02.005.
[20] Oh, J. C., & Yoon, S. J. (2014). Predicting the use of online information services based on a modified UTAUT model. Behaviour and Information Technology, 33(7), 716–729. doi:10.1080/0144929X.2013.872187.
[21] Wang, X., & Zhou, R. (2023). Impacts of User Expectation and Disconfirmation on Satisfaction and Behavior Intention: The Moderating Effect of Expectation Levels. International Journal of Human-Computer Interaction, 39(15), 3127–3140. doi:10.1080/10447318.2022.2095479.
[22] Van den Broeck, L., Vandelannote, I., Demanet, J., & Van Houtte, M. (2023). High school never ends. Normative and comparative peer group effects on higher education outcomes through the school-level students’ expectation culture. Educational Review, 75(2), 217–242. doi:10.1080/00131911.2021.1923459.
[23] Yang, T., & Jiang, X. (2023). When colleague got recognized: Third party’s reaction to witnessing employee recognition. Frontiers in Psychology, 14, 968782. doi:10.3389/fpsyg.2023.968782.
[24] Bux, S. (2016). The effect of entrepreneurship education programmes on the mind-set of South African youth. University of Pretoria, Pretoria, South Africa.
[25] Zhao, F., Ahmed, F., Iqbal, M. K., Mughal, M. F., Qin, Y. J., Faraz, N. A., & Hunt, V. J. (2020). Shaping Behaviors through Institutional Support in British Higher Educational Institutions: Focusing on Employees for Sustainable Technological Change. Frontiers in Psychology, 11, 584857. doi:10.3389/fpsyg.2020.584857.
[26] Nguyen, M., Rundle-Thiele, S., Malik, A., & Budhwar, P. (2023). Impact of technology-based knowledge sharing on employee outcomes: moderation effects of training, support and leadership. Journal of Knowledge Management, 27(8), 2283–2301. doi:10.1108/JKM-07-2022-0552.
[27] Bhattacherjee, A., Davis, C. J., Hikmet, N., & Kayhan, V. (2008). User reactions to information technology: Evidence from the healthcare sector. ICIS 2008 Proceedings, 211.
[28] Tisu, L., Lupșa, D., Vîrgă, D., & Rusu, A. (2020). Personality characteristics, job performance and mental health the mediating role of work engagement. Personality and Individual Differences, 153, 109644. doi:10.1016/j.paid.2019.109644.
[29] Hamzah, M. I., Othman, A. K., & Hassan, F. (2020). Mediating effects of individual market orientation on the link between learning orientation and job performance. Journal of Business and Industrial Marketing, 35(4), 655–668. doi:10.1108/JBIM-08-2018-0239.
[30] Lent, R. W., Brown, S. D., & Hackett, G. (2002). Social cognitive career theory. Career Choice and Development, 4(1), 255-311.
[31] Wang, R. T., & Lin, C. P. (2012). Understanding innovation performance and its antecedents: A socio-cognitive model. Journal of Engineering and Technology Management - JET-M, 29(2), 210–225. doi:10.1016/j.jengtecman.2012.01.001.
[32] McAlister, L., Srinivasan, R., & Kim, M. C. (2007). Advertising, research and development, and systematic risk of the firm. Journal of Marketing, 71(1), 35–48. doi:10.1509/jmkg.71.1.35.
[33] Yazdanpanah, M., Feyzabad, F. R., Forouzani, M., Mohammadzadeh, S., & Burton, R. J. F. (2015). Predicting farmers’ water conservation goals and behavior in Iran: A test of social cognitive theory. Land Use Policy, 47, 401–407. doi:10.1016/j.landusepol.2015.04.022.
[34] Bandura, A. (2001). Social cognitive theory: An agentic perspective. Annual Review of Psychology, 52, 1–26. doi:10.1146/annurev.psych.52.1.1.
[35] Ratten, V., & Ratten, H. (2007). Social cognitive theory in technological innovations. European Journal of Innovation Management, 10(1), 90–108. doi:10.1108/14601060710720564.
[36] Hau, Y. S., & Kim, Y. G. (2011). Why would online gamers share their innovation-conducive knowledge in the online game user community? Integrating individual motivations and social capital perspectives. Computers in Human Behavior, 27(2), 956–970. doi:10.1016/j.chb.2010.11.022.
[37] Spaulding, A., Tudor, K., & Mahatanankoon, P. (2015). The Effects of Outcome Expectations on Individual’s Anxiety and Continued Usage of Mobile Devices: A Post-Adoption Study. International Food and Agribusiness Management Review, 18(4), 173–188.
[38] Cai, L., Xiao, Z., & Ji, X. (2023). Impact of supervisor developmental feedback on employee innovative behavior: roles of psychological safety and face orientation. Journal of Managerial Psychology, 38(1), 73–87. doi:10.1108/JMP-12-2021-0670.
[39] Jaiswal, N. K., & Dhar, R. L. (2015). Transformational leadership, innovation climate, creative self-efficacy and employee creativity: A multilevel study. International Journal of Hospitality Management, 51, 30-41. doi:10.1016/j.ijhm.2015.07.002.
[40] Kim, H. J., Hur, W. M., Moon, T. W., & Jun, J. K. (2017). Is all support equal? The moderating effects of supervisor, coworker, and organizational support on the link between emotional labor and job performance. BRQ Business Research Quarterly, 20(2), 124–136. doi:10.1016/j.brq.2016.11.002.
[41] Zhu, J., Xu, S., Ouyang, K., Herst, D., & Farndale, E. (2018). Ethical leadership and employee pro-social rule-breaking behavior in China. Asian Business & Management, 17(1), 59-81. doi:10.1057/s41291-018-0031-0.
[42] Schaarschmidt, M. (2016). Frontline employees’ participation in service innovation implementation: The role of perceived external reputation. European Management Journal, 34(5), 540–549. doi:10.1016/j.emj.2016.02.005.
[43] Pagnotta, M., Laland, K. N., & Coco, M. I. (2020). Attentional coordination in demonstrator-observer dyads facilitates learning and predicts performance in a novel manual task. Cognition, 201, 104314. doi:10.1016/j.cognition.2020.104314.
[44] Wu, B., & Chen, X. (2017). Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in Human Behavior, 67, 221–232. doi:10.1016/j.chb.2016.10.028.
[45] Coetzee, M. (2019). Organisational Climate Conditions of Psychological Safety as Thriving Mechanism in Digital Workspaces. Thriving in Digital Workspaces: Emerging Issues for Research and Practice, 311–327. doi:10.1007/978-3-030-24463-7_16.
[46] Bandura, A. (2005). The social foundations of thought and action: A social cognitive theory. Prentice-Hall, New Jersey, United States.
[47] Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly: Management Information Systems, 27(3), 425–478. doi:10.2307/30036540.
[48] Gong, Y. (2010). Employee learning orientation, transformational leadership, and employee creativity: The mediating role of employee creative self-efficacy. Development and Learning in Organizations: An International Journal, 24(2), 366–372. doi:10.1108/dlo.2010.08124bad.003.
[49] Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly: Management Information Systems, 36(1), 157–178. doi:10.2307/41410412.
[50] Zhao, X., Lynch, J. G., & Chen, Q. (2010). Reconsidering Baron and Kenny: Myths and truths about mediation analysis. Journal of Consumer Research, 37(2), 197–206. doi:10.1086/651257.
[51] Brislin, R. W. (1980). Cross-cultural research methods: Strategies, problems, applications. Environment and Culture, 47-82. Springer, Boston, United States.
[52] Lee, H. Y., Lee, Y. K., & Kwon, D. (2005). The intention to use computerized reservation systems: The moderating effects of organizational support and supplier incentive. Journal of Business Research, 58(11), 1552-1561. doi:10.1016/j.jbusres.2004.07.008.
[53] Lu, J., Yao, J. E., & Yu, C. S. (2005). Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology. Journal of Strategic Information Systems, 14(3), 245–268. doi:10.1016/j.jsis.2005.07.003.
[54] Lin, T. C., & Huang, C. C. (2008). Understanding knowledge management system usage antecedents: An integration of social cognitive theory and task technology fit. Information and Management, 45(6), 410–417. doi:10.1016/j.im.2008.06.004.
[55] Lin, C. P., Chen, K. J., Liu, C. M., & Liao, C. H. (2019). Assessing decision quality and team performance: perspectives of knowledge internalization and resource adequacy. Review of Managerial Science, 13(2), 377–396. doi:10.1007/s11846-017-0253-0.
[56] Suherman, S. (2024). Role of creative self-efficacy and perceived creativity as predictors of mathematical creative thinking: Mediating role of computational thinking. Thinking Skills and Creativity, 53, 101591. doi:10.1016/j.tsc.2024.101591.
[57] Hayes, A. F. (2009). Beyond Baron and Kenny: Statistical mediation analysis in the new millennium. Communication Monographs, 76(4), 408–420. doi:10.1080/03637750903310360.
[58] Ping, R. A. (1995). A Parsimonious Estimating Technique for Interaction and Quadratic Latent Variables. Journal of Marketing Research, 32(3), 336. doi:10.2307/3151985.
[59] Tabri, N., & Elliott, C. M. (2012). Principles and Practice of Structural Equation Modeling. Canadian Graduate Journal of Sociology and Criminology, 1(1), 3787. New York, NY, USA. doi:10.15353/cgjsc.v1i1.3787.
[60] Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39–50. doi:10.1177/002224378101800104.
[61] Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94. doi:10.1007/BF02723327.
[62] Choon Hee, O. (2015). Validity and Reliability of the Customer-Oriented Behaviour Scale in the Health Tourism Hospitals in Malaysia. International Journal of Caring Sciences, 7(3), 771.
[63] Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2019). Multivariate data analysis. Cengage Learning EMEA, Boston, Massachusetts.
[64] Williams, J., & MacKinnon, D. P. (2008). Resampling and distribution of the product methods for testing indirect effects in complex models. Structural Equation Modeling, 15(1), 23–51. doi:10.1080/10705510701758166.
[65] Mitchell, J. I., Gagné, M., Beaudry, A., & Dyer, L. (2012). The role of perceived organizational support, distributive justice and motivation in reactions to new information technology. Computers in Human Behavior, 28(2), 729–738. doi:10.1016/j.chb.2011.11.021.
[66] Khan, A. N., Soomro, M. A., & Pitafi, A. H. (2025). AI in the Workplace: Driving Employee Performance Through Enhanced Knowledge Sharing and Work Engagement. International Journal of Human-Computer Interaction, 41(17), 10699–10712. doi:10.1080/10447318.2024.2436611.
[67] Min, M. (2023). School culture, self-efficacy, outcome expectation, and teacher agency toward reform with curricular autonomy in South Korea: a social cognitive approach. Asia Pacific Journal of Education, 43(4), 951–967. doi:10.1080/02188791.2019.1626218.
[68] Jemini Gashi, L., Bërxulli, D., Konjufca, J., & Cakolli, L. (2023). Effectiveness of career guidance workshops on the career self-efficacy, outcome expectations, and career goals of adolescents: an intervention study. International Journal of Adolescence and Youth, 28(1), 2281421. doi:10.1080/02673843.2023.2281421.
[69] Eisenberger, R., Fasolo, P., & Davis-LaMastro, V. (1990). Perceived Organizational Support and Employee Diligence, Commitment, and Innovation. Journal of Applied Psychology, 75(1), 51–59. doi:10.1037/0021-9010.75.1.51.
[70] Oh, J. H., Johnston, W. J., & Curasi, C. F. (2022). Too much of a good thing?: The impact of ethical controls and perceived controllability on salesforce job performance. Journal of Business and Industrial Marketing, 37(6), 1241–1254. doi:10.1108/JBIM-01-2021-0021.
[71] Fawehinmi, O., Yusliza, M. Y., Tanveer, M. I., & Abdullahi, M. S. (2024). Influence of green human resource management on employee green behavior: The sequential mediating effect of perceived behavioral control and attitude toward corporate environmental policy. Corporate Social Responsibility and Environmental Management, 31(3), 2514–2536. doi:10.1002/csr.2707.
- This work (including HTML and PDF Files) is licensed under a Creative Commons Attribution 4.0 International License.
