AI Like ChatGPT, Users Like Us: How ChatGPT Drivers and AI Efficacy Affect Consumer Behaviour
DOI:
https://doi.org/10.34021/ve.2023.06.04(3)Keywords:
ChatGPT; AI; AI services; intention to use; serendipity experience.Abstract
Since OpenAI first unveiled ChatGPT, an artificial intelligence-based chatbot service, to the public, expectations for high utility and various possibilities have attracted researchers, industry, and consumers. The current study identified the influencing factors of consumer acceptance of ChatGPT that approached transformational innovation. For research purposes, 251 innovative consumers who use ChatGPT were recruited online, and the research model was tested by employing PLS (partial least squares) analysis. The study demonstrated the impact of consumers’ perceptions of the two AI features (human-like characteristics and performance characteristics) on their intention to use AI through their efficacy in AI services and service satisfaction. Moreover, the serendipity experience could lead to positive use intention. Considering that few empirical studies investigated actual user behaviour since ChatGPT services are still in the early stages of the market, this study might provide several implications for researchers and practitioners.Downloads
References
Rivas, P. & Zhao, L. (2023). Marketing with ChatGPT: Navigating the Ethical Terrain of GPT-Based Chatbot Technology. AI, 4(2), 375–384. https://doi.org/10.3390/ai4020019
Hu, K. (2023). ChatGPT Sets Record for Fastest-Growing User Base - Analyst Note. Reuters, https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01
Teubner, T., Flath, C.M., Weinhardt, C., van der Aalst, W., & Hinz, O. (2023). Welcome to the Era of ChatGPT et al. The Prospects of Large Language Models. Business & Information Systems Engineering, 65, 95–101. https://doi.org/10.1007/s12599-023-00795-x
Marketing Science Institute (2020). Research priorities: 2020‐2022 report. https://www.msi.org/wp-content/uploads/2021/07/MSI-2020-22-Research-Priorities-final.pdf-WORD.pdf
Malodia, S., Kaur, P. Ractham, P., Zakashita, M., & Dhir, A. (2022). Why Do People Avoid and Postpone the Use of Voice Assistants for Transactional Purposes? A Perspective from Decision Avoidance Theory. Journal of Business Research, 146, 605–618. https://doi.org/10.1016/j.jbusres.2022.03.045
Go, E., & Sundar, S. S. (2019). Humanizing Chatbots: The Effects of Visual, Identity and Conversational Cues on Humanness Perceptions. Computers in Human Behavior, 97, 304–316. https://doi.org/10.1016/j. chb.2019.01.020
Kumar, P, Kumar, S., & Dutot, V. (2023). Artificial Intelligence (AI)-Enabled CRM Capability in Healthcare: The Impact on Service Innovation. International Journal of Information Management, 69, 102598. https://doi.org/10.1016/j.ijinfomgt.2022.102598
Hoy, M.B. (2018). Alexa, Siri, Cortana, and More: An Introduction to Voice Assistants. Medical Reference Services Quarterly, 37(1), 81–88. https://doi.org/10.1080/02763869.2018.1404391
Mann, J. A., MacDonald, B.A., Kuo, I.H., Li, X., & Broadbent, E. (2015). People Respond Better to Robots than Computer Tablets Delivering Healthcare Instructions. Computers in Human Behavior, 43, 112–117.
Gray, K. (2017). AI Can Be a Troublesome Teammate. Harvard Business Review, https://hbr.org/2017/07/ai-can-be-a-troublesome-teammate.
Tussyadiah, I. & Miller, G. (2019). Perceived Impacts of Artificial Intelligence and Responses to Positive Behaviour Change Intervention. In J. Pesonen, J. Neidhardt (Eds), Information and Communication Technologies in Tourism 2019. Springer.
McLean, G., Osei-Frimpong, K., & Barhorst, J. (2021). Alexa, Do Voice Assistants Influence Consumer Brand Engagement? Examining the Role of AI Powered Voice Assistants in Influencing Consumer Brand Engagement. Journal of Business Research, 124, 312–328. https://doi.org/10.1016/j.jbusres.2020.11.045
Bao, Z. & Yang, J. (2022). Why Online Consumers Have the Urge to Buy Impulsively: Roles of Serendipity, Trust and Flow Experience. Management Decision, 60(12), 3350–3365. https://doi.org/10.1108/MD-07-2021-0900
Kim, A., Affonso, F.M., Laran, J. & Durante, K.M. (2021). Serendipity: Chance Encounters in The Marketplace Enhance Consumer Satisfaction. Journal of Marketing, 85(4), 141–157. https://doi.org/10.1177/00222429211000344
Pelau, C., Dabija, D.C., & Ene, I. (2023). What Makes an AI Device Human-Like? The Role of Interaction Quality, Empathy and Perceived Psychological Anthropomorphic Characteristics in the Acceptance of Artificial Intelligence in the Service Industry. Computers in Human Behavior, 122, 106855. https://doi.org/10.1016/j.chb.2021.106855
Huffman, A.H., Jason Whetten, and William H. Huffman (2013). Using Technology in Higher Education: The Influence of Gender Roles on Technology Self-Efficacy. Computers in Human Behavior, 29(4), 1779–1786. https://doi.org/10.1016/j.chb.2013.02.012
Weinmann, M., Schneider, C., & vom Brocke, J. (2016). Digital Nudging. Business & Information Systems Engineering, 58, 433–436. . https://doi.org/10.1007/s12599-016-0453-1
Nass, C. & Moon, Y. (2000). Machines and Mindlessness: Social Responses to Computers. Journal of Social Issues, 56, 81–103. https://doi.org/10.1111/0022-4537.00153
Castelo, N., Schmitt, B. & Sarvary, M. (2018). Human or Robot? The Uncanny Valley in Consumer Robots. In A. Gershoff, R. Kozinets, & T. White (Eds.), Advances in Consumer Research, (pp. 183–187), Association for Consumer Research.
Balakrishnan, J., Abed, S.S., & Jones, P. (2022). The Role of Meta-UTAUT Factors, Perceived Anthropomorphism, Perceived Intelligence, and Social Self-Efficacy in Chatbot-Based Services? Technological Forecasting and Social Change, 180, 121692. https://doi.org/10.1016/j.techfore.2022.121692
Cao, C., Zhao, L., & Hu, Y. (2019). Anthropomorphism of Intelligent Personal Assistants (IPAs): Antecedents and Consequences. PACIS.
Chandra, S., Shirish, A., & Srivastava, S.C. (2022), To Be or Not to Be…. Human? Theorizing the Role of Human-Like Competencies in Conversational Artificial Intelligence Agents. Journal of Management Information Systems, 39(4), 969–1005. https://doi.org/10.1080/07421222.2022.2127441
Deng, G. & Fei, S. (2023). Exploring the Factors Influencing Online Civic Engagement in A Smart City: The Mediating Roles of ICT Self-Efficacy and Commitment to Community. Computers in Human Behavior, 143, 107682. https://doi.org/10.1016/j.chb.2023.107682
Lee, J. & Lee, H. (2014). Developing and Validating a Citizen-Centric Typology for Smart City Services. Government Information Quarterly, 31, S93–S105. https://doi.org/10.1016/j.giq.2014.01.010
Limbu, Y.B., Wolf, M., & Lunsford, N.D. (2012). Perceived Ethics of Online Retailers and Consumer Behavioral Intentions: The Mediating Roles of Trust and Attitude. Journal of Research in Interactive Marketing, 6(2), 133–154. https://doi.org/10.1108/17505931211265435
Kwak, Y., Ahn, J.W. & Seo, Y.H. (2022). Influence of AI Ethics Awareness, Attitude, Anxiety, and Self-Efficacy on Nursing Students’ Behavioral Intentions. BMC nursing, 21(1), 1–8.
Hmoud, B. I. & László, V. (2020). Artificial Intelligence in Human Resources Information Systems: Investigating Its Trust and Adoption Determinants. International Journal of Engineering and Management Sciences, 5(1), 749–765. https://doi.org/10.21791/IJEMS.2020.1.65
Rahman, M., Ming, T.H., Baigh, T.A., & Sarker, M. (2021). Adoption of Artificial Intelligence in Banking Services: An Empirical Analysis. International Journal of Emerging Markets, 18(10), 4270-4300. https://doi.org/10.1108/IJOEM-06-2020-0724
Schuetz, S. & Venkatesh, S. (2020). The Rise of Human Machines: How Cognitive Computing Systems Challenge Assumptions of User-System Interaction. Journal of the Association for Information Systems, 21(2), 460–482. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3680306
White, J. & Ahern, E. (2022). Self‐efficacy, Sympathy, and Attributions: Understanding Helping Intentions towards Disclosers of Mental Health Concerns on Social Media. Journal of Applied Social Psychology, 53(4), 275–286. https://doi.org/10.1111/jasp.12938
Malle, B.F. & Ullman, D. (2021). A Multi-dimensional Conception and Measure of Human-Robot Trust. In C. S. Nam and J. B. Lyons (eds.), Trust in human-robot interaction: research and applications (pp. 3–25). Elsevier.
Cao, D., Sun, Y., Goh, E., Wang, R., & Kuiavska, K. (2022). Adoption of Smart Voice Assistants Technology Among Airbnb Guests: A Revised Self-Efficacy-Based Value Adoption Model (SVAM). International Journal of Hospitality Management, 101, 103124. https://doi.org/10.1016/j.ijhm.2021.103124
e Cunha, M.P. (2006). Serendipity: Why Some Organizations Are Luckier Than Others. Nova SBE Working Paper Series wp472, Universidade Nova de Lisboa, Nova School of Business and Economics.
McCay-Peet, L. & Toms, E. (2010). Measuring the Dimensions of Serendipity in Digital Environments. Information Research: An International Electronic Journal, 16(3), 1–6.
Lutz, C., Hoffmann, C.P., & Meckel, M. (2017). Online Serendipity: A Contextual Differentiation of Aantecedents and Outcomes. Journal of the Association for Information Science and Technology, 68(7), 1698–1710. https://doi.org/10.1002/asi.23771
Kumar, J.A., Bervell, B., Annamalai, N., & Osman, S. (2020). Behavioral Intention to Use Mobile Learning: Evaluating the Role of Self-Efficacy, Subjective Norm, and WhatsApp Use Habit. IEEE Access, 8, 208058–208074. https://ieeexplore.ieee.org/document/9258902
Hong, J.W. (2022). I Was Born to Love AI: The Influence of Social Status on AI Self-Efficacy and Intentions to Use AI. International Journal of Communication, 16, 20. https://ijoc.org/index.php/ijoc/article/view/17728
Cheng, Y. & Jiang, H. (2020). How Do AI-Driven Chatbots Impact User Experience? Examining Gratifications, Perceived Privacy Risk, Satisfaction, Loyalty, and Continued Use. Journal of Broadcasting & Electronic Media, 64(4), 592–614. https://doi.org/10.1080/08838151.2020.1834296
Kim, Y., Myeong, S., & Ahn, M.J. (2023). Living Labs for AI-Enabled Public Services: Functional Determinants, User Satisfaction, and Continued Use. Sustainability, 15(11), 8672. https://doi.org/10.3390/su15118672
Rodríguez-Ardura, I. & Meseguer-Artola, A. (2020). Editorial: How to Prevent, Detect and Control Common Method Variance in Electronic Commerce Research. Journal of Theoretical and Applied Electronic Commerce Research, 15(2), 1–5.
Hsu, C.-L. & Lin, J. C.-C. (2023). Understanding the User Satisfaction and Loyalty of Customer Servicechatbots. Journal of Retailing and Consumer Services, 71, 103211. https://doi.org/10.1016/j.jretconser.2022.103211
Baumgartner, H. & Steenkamp, J.B.E.M. (2001). Response Styles in Marketing Research: A Cross-National Investigation. Journal of Marketing Research, 38 (2), 143–156. https://doi.org/10.1509/jmkr.38.2.143.18840
Zhang, J., Zhu, Y., Wu, J., & Yu-Buck, G.F. (2023). A Natural Apology is Sincere: Understanding Chatbots’ Performance in Symbolic Recovery. International Journal of Hospitality Management, 108, 103387. https://doi.org/10.1016/j.ijhm.2022.103387
Agag, G. M., & El-Masry, A.A. (2017). Why Do Consumers Trust Online Travel Websites? Drivers and Outcomes of Consumer Trust toward Online Travel Websites. Journal of Travel Research, 56(3), 347–369. https://doi.org/10.1177/0047287516643185
Moussawi, S. & Koufari, M. (2019). Perceived Intelligence and Perceived Anthropomorphism of Personal Intelligent Agents: Scale Development and Validation. Proceedings of the 52nd Hawaii International Conference on System Sciences. http://hdl.handle.net/10125/59452
Lee, W. & Shin, S. (2018). Effects of Product Smartness on Satisfaction: Focused on the Perceived Characteristics of Smartphone. Journal of Theoretical and Applied Electronic Commerce Research, 13(2), 1–14. https://doi.org/10.4067/S0718-18762018000200102
Compeau, D.R. & Higgins, C.A. (1995). Application of Social Cognitive Theory to Training for Computer Skills. Information Systems Research, 6(2), 118–143. https://doi.org/10.1287/isre.6.2.118
Lin, H.-H. & Wang (2006). An Examination of the Determinants of Customer Loyalty in Mobile Commerce Context. Information & Management, 43(3), 271–282. https://doi.org/10.1016/j.im.2005.08.001
Kim, M.J., & Hall, C.M. (2019). Can Climate Change Awareness Predict Pro-Environmental Practices in Restaurants? Comparing High and Low Dining Expenditure. Sustainability, 11(23), 6777. https://doi.org/10.3390/su11236777
Lam, L. W. (2012). Impact of Competitiveness on Salespeople’s Commitment and Performance. Journal of Business Research, 65(9), 1328–1334. https://doi.org/10.1016/j.jbusres.2011.10.026
Fornell, C. & Larcker, D.F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 8(1), 39–50. https://doi.org/10.1177/0022243781018001
Hair Jr, J.F., Sarstedt, M., Hopkins, L., & Kuppelwieser, V.G. (2014). Partial Least Squares Structural Equation Modeling (PLS-SEM): An Emerging Tool in Business Research. European Business Review, 26(2), 106–121. https://doi.org/10.1108/EBR-10-2013-0128
Wong, K. K-K. (2016). Technical Note: Mediation Analysis, Categorical Moderation Analysis, and Higher-order Constructs Modeling in Partial Least Squares Structural Equation Modeling (PLS-SEM): A B2B Example Using SmartPLS. Marketing Bulletin, 26, 1–22.
Ashok, M., Madan, R., Joha, A. & Sivarajah, U. (2022). Ethical Framework for Artificial Intelligence and Digital Technologies. International Journal of Information Management, 62, 102433. https://doi.org/10.1016/j.ijinfomgt.2021.102433
Borenstein, J. & Howard, A. (2021). Emerging Challenges in AI and The Need for AI Ethics Education. AI Ethics, 1, 61–65. https://doi.org/10.1007/s43681-020-00002-7
Siau, K. & Wang, W. (2020). Artificial Intelligence (AI) Ethics: Ethics of AI and Ethical AI. Journal of Database Management, 31(2), 74–87. https://doi.org/10.4018/JDM.2020040105
Seals, S. M. and Shalin, V.L. (2023). Long-Form Analogies Generated by ChatGPT Lack Human-Like Psycholinguistic Properties. Computation and Language, https://arxiv.org/abs/2306.04537
OpenAI. (2023). GPT-4 is OpenAI’s Most Advanced System, Producing Safer and More Useful Responses. https://openai.com/research/gpt-4
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