Negative Effects of Revealing AI Involvement in Products: Mediation by Authenticity and Risk, Moderation by Trust in AI and Familiarity with AI
Keywords:
Artificial intelligence, consumer perceptions, product authorship, authenticity, purchase decisions.Abstract
Background: With artificial intelligence (AI) technologies developing at a rapid pace, there is increasing interest in learning how disclosed AI involvement affects customer perceptions and purchasing choices of AI generated products. This study aims to explore the impact of AI involvement in product creation on consumer perceptions across different product categories. Materials and Methods: A series of experimental studies were conducted using three product types: books, e-books and print canvas. Participants were exposed to products with varying levels of AI involvement, and their perceptions of authenticity, quality, financial risk, and willingness to pay were assessed using self-reported measures. Results: The results revealed significant differences in consumer perceptions based on AI involvement. Products with disclosed AI involvement were associated with lower authenticity, quality, and willingness to pay compared to human-authored products. Additionally, the study identified performance risk and perceived authenticity as significant mediators in the relationship between AI involvement and consumer perceptions. Conclusion: The findings suggest that consumers generally prefer products perceived to be human-authored over those involving AI, particularly in terms of authenticity and perceived quality. These perceptions significantly influence consumer willingness to pay and purchase intent. Grasping these dynamics is essential for companies seeking to incorporate AI technologies into their product development and marketing efforts, suggesting that revealing AI involvement could be risky.
Downloads
References
Abràmoff, M. D., Tarver, M. E., Loyo-Berrios, N., Trujillo, S., Char, D., Obermeyer, Z., Eydelman, M. B., Foundational Principles of Ophthalmic Imaging and Algorithmic Interpretation Working Group of the Collaborative Community for Ophthalmic Imaging Foundation, Washington, D.C., & Maisel, W. H. (2023). Considerations for addressing bias in artificial intelligence for health equity. Npj Digital Medicine, 6(1), 170. https://doi.org/
1038/s41746-023-00913-9
Agarwal, S., & Teas, R. K. (2001). Perceived Value: Mediating Role of Perceived Risk. Journal of Marketing Theory and Practice, 9(4), 1–14. https://doi.org/10.1080/10696679.2001.11501899
Aldousari, A. A., Yasmin, F., Yajid, M. S. A., & Ahmed, Z. U. (2017). Consumer evaluations of store brands: Effects of product-perceived risks. Journal of Transnational Management, 22(2), 71–90. https://doi.org/10.1080/15475778.2017.1302782
Atasoy, O., & Morewedge, C. K. (2018). Digital goods are valued less than physical goods. Journal of Consumer Research, 44(6), 1343–1357.
Bauer, R. A. (1960). Consumer behavior as risk taking. Proceedings of the 43rd National Conference of the American Marketing Association.
Bellaiche, L., Shahi, R., Turpin, M. H., Ragnhildstveit, A., Sprockett, S., Barr, N., Christensen, A., & Seli, P. (2023). Humans versus AI: Whether and why we prefer human-created compared to AI-created artwork. Cognitive Research: Principles and Implications, 8(1), 42. https://doi.org/10.1186/s41235-023-00499-6
Betzing, J. H., Tietz, M., Vom Brocke, J., & Becker, J. (2020). The impact of transparency on mobile privacy decision making. Electronic Markets, 30(3), 607–625. https://doi.org/10.1007/s125
-019-00332-3
Biswas, D., & Biswas, A. (2004). The diagnostic role of signals in the context of perceived risks in online shopping: Do signals matter more on the Web? Journal of Interactive Marketing, 18(3), 30–45. https://doi.org/10.1002/dir.20010
Brendel, A. B., Mirbabaie, M., Lembcke, T.-B., & Hofeditz, L. (2021). Ethical management of artificial intelligence. Sustainability, 13(4), 1974.
Cetinic, E., & She, J. (2021). Understanding and Creating Art with AI: Review and Outlook (arXiv:2102.09109). arXiv. http://arxiv.
org/abs/2102.09109
Chen, Y., Prentice, C., Weaven, S., & Hisao, A. (2022). The influence of customer trust and artificial intelligence on customer engagement and loyalty – The case of the home-sharing industry. Frontiers in Psychology, 13, 912339. https://doi.org/10.3389/
fpsyg.2022.912339
Choung, H., David, P., & Ross, A. (2023). Trust in AI and Its Role in the Acceptance of AI Technologies. International Journal of Human–Computer Interaction, 39(9), 1727–1739. https://doi.org/
1080/10447318.2022.2050543
Craig, M. J. A., & Choi, M. (2024). The role of affective and cognitive involvement in the mitigating effects of AI source cues on hostile media bias. Telematics and Informatics, 88, 102097. https://doi.org/10.1016/j.tele.2024.102097
De Bruyn, A., Viswanathan, V., Beh, Y. S., Brock, J. K.-U., & Von Wangenheim, F. (2020). Artificial intelligence and marketing: Pitfalls and opportunities. Journal of Interactive Marketing, 51(1), 91–105.
de Kerviler, G., Heuvinck, N., & Gentina, E. (2021). “Make an Effort and Show Me the Love!” Effects of Indexical and Iconic Authenticity on Perceived Brand Ethicality. Journal of Business Ethics. https://doi.org/10.1007/s10551-021-04779-3
Deryl, M. Ds., Verma, S., & Srivastava, V. (2023). How does AI drive branding? Towards an integrated theoretical framework for AI-driven branding. International Journal of Information Management Data Insights, 3(2), 100205. https://doi.org/10.1016/j.
jjimei.2023.100205
Dhagarra, D., Goswami, M., & Kumar, G. (2020). Impact of Trust and Privacy Concerns on Technology Acceptance in Healthcare: An Indian Perspective. International Journal of Medical Informatics, 141, 104164. https://doi.org/10.1016/j.ijmedinf.2020.
Frank, D.-A., Jacobsen, L. F., Søndergaard, H. A., & Otterbring, T. (2023). In companies we trust: Consumer adoption of artificial intelligence services and the role of trust in companies and AI autonomy. Information Technology & People, 36(8), 155–173. https://doi.org/10.
/ITP-09-2022-0721
Gupta, S., Kushwaha, Pooja. S., Badhera, U., Chatterjee, P., & Gonzalez, E. D. R. S. (2023). Identification of benefits, challenges, and pathways in E-commerce industries: An integrated two-phase decision-making model. Sustainable Operations and Computers, 4, 200–218. https://doi.org/10.1016/j.susoc.2023.08.005
Ha, H.-Y. (2006). The Effects of Consumer Risk Perception on Pre-purchase Information in Online Auctions: Brand, Word-of-Mouth, and Customized Information. Journal of Computer-Mediated Communication, 8(1), 0–0. https://doi.org/10.1111/j.10
-6101.2002.tb00160.x
Haleem, A., Javaid, M., Asim Qadri, M., Pratap Singh, R., & Suman, R. (2022). Artificial intelligence (AI) applications for marketing: A literature-based study. International Journal of Intelligent Networks, 3, 119–132. https://doi.org/10.1016/j.ijin.
08.005
Hirunyawipada, T., & Paswan, A. K. (2006). Consumer innovativeness and perceived risk: Implications for high technology product adoption. Journal of Consumer Marketing, 23(4), 182–198. https://doi.org/10.1108/07363760610674310
Hong, J.-W. (2018). Bias in perception of art produced by artificial intelligence. 290–303.
Horowitz, M. C., Kahn, L., Macdonald, J., & Schneider, J. (2023). Adopting AI: How familiarity breeds both trust and contempt. AI & SOCIETY. https://doi.org/10.1007/s00146-023-01666-5
Horton Jr, C. B., White, M. W., & Iyengar, S. S. (2023). Bias against AI art can enhance perceptions of human creativity. Scientific Reports, 13(1), 19001. https://doi.org/10.1038/s41598-023-45202-3
Ismatullaev, U. V. U., & Kim, S.-H. (2024). Review of the factors affecting acceptance of AI-infused systems. Human Factors, 66(1), 126–144.
Kalogiannidis, S., Kalfas, D., Papaevangelou, O., Giannarakis, G., & Chatzitheodoridis, F. (2024). The Role of Artificial Intelligence Technology in Predictive Risk Assessment for Business Continuity: A Case Study of Greece. Risks, 12(2), 19. https://doi.org/10.3390/risks12020019
Kang, J., & Kim, S.-H. (2013). What Are Consumers Afraid of? Understanding Perceived Risk toward the Consumption of Environmentally Sustainable Apparel. Family and Consumer Sciences Research Journal, 41(3), 267–283. https://doi.org/10.
/fcsr.12013
Kelly, S., Kaye, S.-A., & Oviedo-Trespalacios, O. (2023). What factors contribute to the acceptance of artificial intelligence? A systematic review. Telematics and Informatics, 77, 101925. https://doi.org/10.1016/j.tele.2022.101925
Kim, N., & Jin, B. E. (2018). Why Buy New When One Can Share? Uncovering Dimensions of Collaborative Consumption of Consumer Goods. International Textile and Apparel Association Annual Conference Proceedings, 75(1), Article 1. https://www.
iastatedigitalpress.com/itaa/article/id/1451/
Lefkeli, D., Karataş, M., & Gürhan-Canli, Z. (2024). Sharing information with AI (versus a human) impairs brand trust: The role of audience size inferences and sense of exploitation. International Journal of Research in Marketing, 41(1), 138–155. https://doi.org/
1016/j.ijresmar.2023.08.011
Lim, J. S., & Zhang, J. (2022). Adoption of AI-driven personalization in digital news platforms: An integrative model of technology acceptance and perceived contingency. Technology in Society, 69, 101965.
Luce, L. (2018). Artificial intelligence for fashion: How AI is revolutionizing the fashion industry. Apress.
Lukyanenko, R., Maass, W., & Storey, V. C. (2022). Trust in artificial intelligence: From a Foundational Trust Framework to emerging research opportunities. Electronic Markets, 32(4), 1993–2020. https://doi.org/10.1007/s12525-022-00605-4
Mariani, M. M., Perez‐Vega, R., & Wirtz, J. (2022). AI in marketing, consumer research and psychology: A systematic literature review and research agenda. Psychology & Marketing, 39(4), 755–776.
Marti, C. L., Liu, H., Kour, G., Bilgihan, A., & Xu, Y. (2024). Leveraging artificial intelligence in firm-generated online customer communities: A framework and future research agenda. Journal of Service Management.
Naz, H., & Kashif, M. (2024). Artificial intelligence and predictive marketing: An ethical framework from managers’ perspective. Spanish Journal of Marketing - ESIC. https://doi.org/10.
/SJME-06-2023-0154
Newman, G. E., & Bloom, P. (2012). Art and authenticity: The importance of originals in judgments of value. Journal of Experimental Psychology: General, 141(3), 558.
Neyazi, T. A., Ng, S. W. T., Hobbs, M., & Yue, A. (2023). Understanding user interactions and perceptions of AI risk in Singapore. Big Data & Society, 10(2), 20539517231213823. https://doi.org/10.1177/20539517231213823
Nguyen, A., Ngo, H. N., Hong, Y., Dang, B., & Nguyen, B.-P. T. (2023). Ethical principles for artificial intelligence in education. Education and Information Technologies, 28(4), 4221–4241. https://doi.org/10.1007/s10639-022-11316-w
Nunes, J. C., Ordanini, A., & Giambastiani, G. (2021). The Concept of Authenticity: What It Means to Consumers. Journal of Marketing, 85(4), 1–20. https://doi.org/10.1177/0022242921997
Rane, N. (2023). Enhancing customer loyalty through Artificial Intelligence (AI), Internet of Things (IoT), and Big Data technologies: Improving customer satisfaction, engagement, relationship, and experience. Internet of Things (IoT), and Big Data Technologies: Improving Customer Satisfaction, Engagement, Relationship, and Experience (October 13, 2023).
Reck, R., Castagna, A. C., Shuqair, S., & Costa Pinto, D. (2022). The transparency paradox: When transparency cues helps or backfires for brands? Journal of Cleaner Production, 372, 133381. https://doi.org/10.1016/j.jclepro.2022.133381
Richey, R. G., Chowdhury, S., Davis‐Sramek, B., Giannakis, M., & Dwivedi, Y. K. (2023). Artificial intelligence in logistics and supply chain management: A primer and roadmap for research. Journal of Business Logistics, 44(4), 532–549. https://doi.org/10.
/jbl.12364
Sääksjärvi, M., & Lampinen, M. (2005). Consumer perceived risk in successive product generations. European Journal of Innovation Management, 8(2), 145–156. https://doi.org/10.1108/14601060
Safeer, A. A., He, Y., Lin, Y., Abrar, M., & Nawaz, Z. (2023). Impact of perceived brand authenticity on consumer behavior: An evidence from generation Y in Asian perspective. International Journal of Emerging Markets, 18(3), 685–704. https://doi.org/10.
/IJOEM-09-2020-1128
Sangtani, V., & Murshed, F. (2017). Product knowledge and salesperson performance: Rethinking the role of optimism. Marketing Intelligence & Planning, 35(6), 724–739. https://doi.
org/10.1108/MIP-11-2016-0199
Sohn, K., & Kwon, O. (2020). Technology acceptance theories and factors influencing artificial Intelligence-based intelligent products. Telematics and Informatics, 47, 101324.
Spais, G., Phau, I., & Jain, V. (2023). AI marketing and AI ‐based promotions impact on consumer behavior and the avoidance of consumer autonomy threat. Journal of Consumer Behaviour, cb.2248. https://doi.org/10.1002/cb.2248
Stampfl, R. W. (1978). Perceived Risk and Consumer Decision Making. Journal of Consumer Studies & Home Economics, 2(3), 231–245. https://doi.org/10.1111/j.1470-6431.1978.tb00324.x
Straub, E. T. (2009). Understanding technology adoption: Theory and future directions for informal learning. Review of Educational Research, 79(2), 625–649.
Sullivan, G. M., & Artino, A. R. (2013). Analyzing and Interpreting Data From Likert-Type Scales. Journal of Graduate Medical Education, 5(4), 541–542. https://doi.org/10.4300/JGME-5-4-18
Sun, X., Pelet, J.-É., Dai, S., & Ma, Y. (2023). The Effects of Trust, Perceived Risk, Innovativeness, and Deal Proneness on Consumers’ Purchasing Behavior in the Livestreaming Social Commerce Context. Sustainability, 15(23), 16320. https://doi.
org/10.3390/su152316320
Tsai, C.-C., Lin, C.-L., & Chen, Y.-H. (2024). Impact of Authenticity Perception on Experiential Value and Customer Satisfaction under Contactless Services. Systems, 12(1), 19. https://doi.org/10.3390/systems12010019
Verhoef, P. C., Stephen, A. T., Kannan, P., Luo, X., Abhishek, V., Andrews, M., Bart, Y., Datta, H., Fong, N., & Hoffman, D. L. (2017). Consumer connectivity in a complex, technology-enabled, and mobile-oriented world with smart products. Journal of Interactive Marketing, 40(1), 1–8.
Wamba-Taguimdje, S.-L., Fosso Wamba, S., Kala Kamdjoug, J. R., & Tchatchouang Wanko, C. E. (2020). Influence of artificial intelligence (AI) on firm performance: The business value of AI-based transformation projects. Business Process Management Journal, 26(7), 1893–1924.
Wanner, J., Herm, L.-V., Heinrich, K., & Janiesch, C. (2022). The effect of transparency and trust on intelligent system acceptance: Evidence from a user-based study. Electronic Markets, 32(4), 2079–2102. https://doi.org/10.1007/s12525-022-00593-5
Wu, L., Dodoo, N. A., & Wen, T. J. (2024). Disclosing AI’s Involvement in Advertising to Consumers: A Task-Dependent Perspective. Journal of Advertising, 1–19. https://doi.org/10.1080/
2024.2309929
Yang, H., Yu, J., Zo, H., & Choi, M. (2016). User acceptance of wearable devices: An extended perspective of perceived value. Telematics and Informatics, 33(2), 256–269.
Zerilli, J., Bhatt, U., & Weller, A. (2022). How transparency modulates trust in artificial intelligence. Patterns, 3(4), 100455. https://doi.org/10.1016/j.patter.2022.100455
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
