Acceptance of Artificially Intelligent Digital Humans in Online Shops A modelling approach

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Madeleine Taglinger
Stephanie Jordan
Alexander H. Kracklauer


Digital human, innovation, artificial intelligence, UTAUT2, online shopping


The UTAUT2 model is used to investigate the factors that influence consumer acceptance of artificially intelligent digital humans in online stores. Digital humans can be defined as a digital avatar that can mimic a full range of human behaviors (Ward, Boom, and Majenburg 2022). Six simple linear regression analyses are conducted to identify the determinants of intention to use digital humans. In the final multiple regression model, which includes the influences of six independent latent variables and three control variables (gender, age, and experience) on behavioral intention, statistically significant influences are identified for two variables: performance expectancy and habit. The results show that there is a tendency to accept the use of digital humans in online stores. Performance expectancy emerges as the strongest positive predictor of behavioral intention. In addition, hedonic motivation shows a positive influence on behavioral intention in the simple regression analysis, while the multiple regression results show a
minimal negative correlation. The results may provide important insights into the adoption of innovative digital human technologies.

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[1] AI Forum of New Zealand. 2019. Artificial Intelligence in Action: Digital Humans Case Study.
artificial-intelligence-in-action-digital-humanscase-study, accessed September 2022.
[2] Denner, E. 2021. Are you listening? Using voice as an immersive customer experience., accessed September 2022.
[3] Fishbein, M., and Ajzen, I. 1975. Belief, attitude, intention and behavior: An introduction to theory and research. In: Massachusetts:
[4] Futurside 2022. We will Live In a World of Digital Humans., accessed
August 2022.
[5] Ganesa, D. P., John, S., and Mane, D. A. S. 2020. Behavioural Intention of AI-Chatbots by Telecom Customers – UTAUT2 Perspective
with Trust. Paper presented at the International Conference on Marketing, Technology and Society.
[6] Gursoy, D., Chi, O. H., Lu, L., and Nunkoo, R. 2019. Consumers acceptance of artificially intelligent (AI) device use in service delivery.
International Journal of Information Management, 49, 157–169.
[7] Homburg, C. 2017. Marketingmanagement: Strategie-Instrumente-Umsetzung-Unternehmensführung (Vol. 6): Springer-Verlag.
[8] Kim, S. S., Malhotra, N. K., and Narasimhan, S. 2005. Research note—two competing perspectives on automatic use: A theoretical
and empirical comparison. Information systems research, 16(4), 418–432.
[9] Kroeber-Riel, W., and Weinberg, P. 2003. Konsumentenverhalten (Vol. 8). München.
[10] Lim, W. M. 2021. History, lessons, and ways forward from the COVID-19 pandemic. Paper presented at the International Journal of Quality Innovation.
[11] Limayem, M., Hirt, S., and Cheung, C. 2007. How habit limits the predictive power of intention: The case of information systems continuance. MIS quarterly, 31(4), 705–737.
[12] Lu, L., Cai, R., and Gursoy, D. 2019. Developing and validating a service robot integration willingness scale. International Journal of
Hospitality Management, 80, 36–51.
[13] Mills, A. M., and Liu, L. F. 2020. Trust in Digital Humans: ACIS 2020 Proceedings.
[14] Monard, F., Uebersax, H.-P., Mousser, J., Furchheim, P., Müller, S., and Hannich, F. 2020. Chatbot-Studie. Die digitalen Helfer im Praxistest. PIDAS_ZHAW_Chatbot_Studie_FINAL.pdf, accessed September 2022.
[15] NTT DATA Business Solutions AG. 2022. Wie Conversational AI Mitarbeitende und Kunden glücklich macht., accessed August 2022.
[16] Schwendener, S. 2018. Technologie-Akzeptanz von Chatbots: eine Anwendung des UTAUTModells. School of Management and Law, Winterthur, Zürich.
[17] UneeQ. 2020. AI chatbots are a good start, but now’s the time to stand out from the crowd., accessed May 2022.
[18] Corti, K.; Gillespie, A. (2016): Co-constructing intersubjectivity with artificial conversational agents: People are more likely to initiate repairs of misunderstandings with agents represented as human. In: Computers in Human Behavior, vol. 58, pp. 431–442.
[19] UneeQ. 2021. What are Digital Humans? A giant leap in brand & customer experience.
What%20are%20digital%20humans%20UneeQ%20eBook%20v2.pdf, accessed May 2022.
[20] Venkatesh, V., Morris, M. G., Davis, G. B., and Davis, F. D. 2003. User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.
[21] Venkatesh, V., Thong, J. Y. L., and 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.
[22] Ward, S. 2020. Corona lockdown? Time to embrace a digital human - AI technology elevating the human experience.
to-embrace-a-digital-human.html, accessed May 2022.
[23] Ward, S., Boom, M., and Majenburg, M. 2022. Digital Human - Elevating the Digital Human experience., accessed May 2022.