An Analysis Of Adoption Of Digital Intelligent Assistant In Reference Of Unified Theory Of Acceptance And Use Of Technology
Main Article Content
Abstract
Digital Intelligent Assistants (DIAs) have emerged as ubiquitous technologies reshaping human-computer interaction paradigms. Understanding the factors influencing their adoption is crucial for both researchers and practitioners. This paper investigates the adoption of DIAs through the lenses of the Unified Theory of Acceptance and Use of Technology (UTAUT). UTAUT provides a theoretical framework to explore how performance expectancy, effort expectancy, social influence, and facilitating conditions influence users' behavioral intentions and actual usage. By synthesizing insights from UTAUT, this study aims to provide a comprehensive understanding of the adoption dynamics surrounding DIAs. The findings contribute to theoretical advancements by refining UTAUT in the context of DIAs in shaping user attitudes and behaviors. Additionally, practical implications are discussed, offering recommendations for stakeholders to optimize DIA adoption strategies and enhance user experiences. Overall, this research contributes to advancing knowledge in the field of technology adoption, paving the way for more effective design, implementation, and utilization of DIAs in diverse settings
Article Details
References
• Bowen, J., & Morosan, C. (2018). Beware hospitality industry: The robots are coming. Worldwide Hospitality and Tourism Themes, 10(6), 726–733. doi:10.1108/WHATT-07-2018-0045
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13, 3: 319–340.
• Davis, F. D., Bagozzi, R. P. and Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace1. Journal of Applied Social Psychology, 22, 14: 1111–1132, doi: 10.1111/j.1559-1816.1992.tb00945.x.
• Fornell, C. and Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 1: 39–50.
• Han, H., Belk, R. W., & Lee, J. S. (2019). Price value perceptions of smart speakers: Hedonic vs. utilitarian performance. *Journal of Retailing and Consumer Services*, 49, 145-155.
• Hah, H. Y. (2010). The value creation of arti¯cial intelligence customer service in E-self service. (1489613 M.S.), Purdue University, Ann Arbor.
• Hair, J. F. (Jr.), Hult, G. T. M., Ringle, C. M. and Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). 2nd edn. Sage Publications, California, CA.
• Huang, M. H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21 (2), 155–172. doi:10.1177/1094670517752459
• Katja Wagner, Frederic Nimmermann and Hanna Schramm-Klein. (2019). Is It Human? The Role of Anthropomorphism as a Driver for the Successful Acceptance of Digital Voice Assistants. In: Proceedings of the 52nd Hawaii International Conference on System Sciences, 2019.
• Kim, K. J. and Shin, D.-H. (2015). An acceptance model for smart watches implications for the adoption of future wearable technology. Internet Research, 25, 4: 527–541. doi: 10.1108/IntR-05-2014-0126.
• Koç, T., Turan, A. H. and Okursoy, A. (2016). Acceptance and usage of a mobile information system in higher education: An empirical study with structural equation modeling. International Journal of Management Education, 14, 3: 286–300, doi: 10.1016/j. ijme.2016.06.001.
• Li, X., Wu, H., & Yang, Z. (2021). Social influence and consumer adoption intention of smart speakers: A moderated mediation model. *Journal of Retailing and Consumer Services*, 63, 102681.
• Nass, C., Moon, Y., & Carney, P. (1996). Are people polite to computers? Responses to computer-based interviewing systems. *Journal of Applied Social Psychology*, 26(11), 1063-1090.
• Nass, C., & Moon, Y. (2000). Machines and mindlessness: Social responses to computers. *Journal of Social Issues*, 56(1), 81-103.
• McLean, G., & Osei-Frimpong, K. (2019). Hey Alexa… Examine the variables influencing the use of Artificial Intelligent In-home Voice Assistants. Computers in Human Behavior. 28-37, doi:10.1016/j.chb.2019.05.009
• Shoot, B.: St. Louis University Installs Amazon Echo Dots Campus-Wide. (2018). Retrieved on November 15,2022, from http:// fortune.com/2018/08/15/amazon-alexa-echo-back-to-school-dorm-room/
• Trakulmaykee, N., Wongsirichot, T. and Trakulmaykee, Y. (2018). A comparative study of factor's in°uences a®ecting tourist's intention to use mobile food information: Independent tourists and package tourists. International Journal of Innovation and Technology Management, 15, 1: 1850002, doi: 10.1142/s0219877018500025
• Tom Dieck, M. C., Jung, T. H., Kim, W. G. and Moon, Y. (2017). Understanding the internet of things ecosystem: Multi-level analysis of users, society, and ecology. Digital Policy, Regulation and Governance, 19, 1: 77–100
• Usoro, A., Shoyelu, S. and Kuo¯e, M. (2010). Task-technology ¯t and technology acceptance models applicability to e-tourism. Journal of Economic Development, Management, IT, Finance, and Marketing, 2, 1: 1–32.
• 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., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. *Decision Sciences*, 39(2), 273-315.
• Venkatesh, V., Thong, J., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178.
• Venkatesh, V., & Davis, F. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204.
• Zhou, T., Lu, Y., & Wang, B. (2020). Exploring Users’ Acceptance of Digital Voice Assistants: Integrating Hedonic Motivation into the UTAUT Model. *Information Systems Frontiers*, 22(1), 1-16.