Artificial intelligence (AI) is transforming how consumers in credence-based markets search for, interpret, and trust product information. In the wine sector, where authenticity and quality depend on symbolic and experiential cues, AI-driven recommendation systems increasingly act as new intermediaries. This paper develops a conceptual framework explaining how consumers perceive algorithmic expertise and form trust in AI-generated wine recommendations. Integrating theories of information asymmetry, signalling, source credibility, and trust in automation, the framework identifies AI transparency and source framing as key drivers of perceived expertise and trustworthiness. These perceptions, moderated by literacy, cultural orientation, and risk, influence purchase intention and reliance on AI advice. The study highlights AI as both a signalling and screening institution that can reduce but also redistribute information asymmetries in agri-food markets. The paper concludes with methodological and policy directions for ensuring transparent and consumer-centred AI adoption in the food and wine industries.
References
Akerlof, G. A. (1970). The market for “lemons”: Quality uncertainty and the market mechanism. Quarterly Journal of Economics, 84(3), 488-500. Doi: 10.2307/1879431.
Alter, A. L., & Oppenheimer, D. M. (2009). Uniting the tribes of fluency to form a metacognitive nation. Personality and Social Psychology Review, 13(3), 219-235. Doi: 10.1177/1088868309341564.
Angelov, P. P., Soares, E. A., Jiang, R., Arnold, N. I., & Atkinson, P. M. (2021). Explainable artificial intelligence: an analytical review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 11(5), e1424. Doi: 10.1002/widm.1424.
Arakawa, R., Maeda, K., & Yakura, H. (2024). Supporting experts with a multimodal machine-learning-based tool for human behavior analysis of conversational videos. arXiv preprint arXiv, 2402.11145. Doi: 10.48550/arXiv.2402.11145.
Ashton, R. H. (2013). Is there consensus among wine quality ratings of prominent critics? An empirical analysis of red Bordeaux, 2004-2010. Journal of Wine Economics, 8(2), 225-234. Doi: 10.1017/jwe.2013.18.
Ashton, R. H. (2014). Wine as an experience good: Price versus enjoyment in blind tastings of expensive and inexpensive wines. Journal of Wine Economics, 9(2), 171-182. Doi: 10.1017/jwe.2014.7.
Awad, E., Dsouza, S., Kim, R. et al. (2018). The Moral Machine experiment. Nature, 563, 59-64. Doi: 10.1038/s41586-018-0637-6.
Benbasat, I., & Wang, W. (2005). Trust in and adoption of online recommendation agents. Journal of the Association for Information Systems, 6(3), 4. Doi: 10.17705/1jais.00065.
Beverland, M. (2006). The ‘real thing’: Branding authenticity in the luxury wine trade. Journal of Business Research, 59(2), 251-258. Doi: 10.1016/j.jbusres.2005.04.007.
Camacho, N., De Jong, M., & Stremersch, S. (2014). The effect of customer empowerment on adherence to expert advice. International Journal of Research in Marketing, 31(3), 293-308. Doi: 10.1016/j.ijresmar.2014.03.004.
Caputo, V., & Reardon, T. (2025). Hiding and revealing: A perspective on the paradox of information transparency in different agri-food value chain contexts. Q Open, qoaf017. Doi: 10.1093/qopen/qoaf017.
Cardebat, J. M., & Livat, F. (2016). Wine experts’ rating: a matter of taste?. International Journal of Wine Business Research, 28(1), 43-58. Doi: 10.1108/IJWBR-04-2015-0011.
Cardebat, J. M., Figuet, J. M., & Paroissien, E. (2014). Expert opinion and Bordeaux wine prices: An attempt to correct biases in subjective judgments. Journal of Wine Economics, 9(3), 282-303. Doi: 10.1017/jwe.2014.23.
Castelo, N., Bos, M. W., & Lehmann, D. R. (2019). Task-dependent algorithm aversion. Journal of Marketing Research, 56(5), 809-825. Doi: 10.1177/0022243719851788.
Caswell, J. A., & Mojduszka, E. M. (1996). Using informational labeling to influence the market for quality in food products. American Journal of Agricultural Economics, 78(5), 1248-1253. Doi: 10.2307/1243501.
Charters, S. (2006). Wine and society. Routledge. Doi: 10.4324/9780080458038.
Connelly, B. L., Certo, S. T., Ireland, R. D., & Reutzel, C. R. (2011). Signaling theory: A review and assessment. Journal of Management, 37(1), 39-67. Doi: 10.1177/0149206310388419.
Cuddy, A. J. C., Fiske, S. T., & Glick, P. (2008). Warmth and competence as universal dimensions of social perception. Advances in Experimental Social Psychology, 40, 61-149. Doi: 10.1016/S0065-2601(07)00002-0.
Danner, L., Ristic, R., Johnson, T. E., Meiselman, H. L., Hoek, A. C., & Bastian, S. E. P. (2016). Context and wine quality effects on consumers’ mood, emotions, liking and willingness to pay for Australian Shiraz wines. Food Research International, 89, 254-265. Doi: 10.1016/j.foodres.2016.08.006.
Darby, M. R., & Karni, E. (1973). Free competition and the optimal amount of fraud. Journal of Law and Economics, 16(1), 67-88. Doi: 10.1086/466756.
De Toni, D., Pompermayer, R., Lazzari, F., & Milan, G.S. (2022). The symbolic value of wine, moderating and mediating factors and their relationship to consumer purchase intention. International Journal of Wine Business Research, 34(2), 190-211. Doi: 10.1108/IJWBR-01-2021-0006.
de Visser, E. J., Pak, R., & Shaw, T. H. (2018). From ‘automation’ to ‘autonomy’: The importance of trust repair in human-machine teams. Ergonomics, 63(10), 1253-1271. Doi: 10.1080/00140139.2018.1457725.
Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015). Algorithm aversion: People erroneously avoid algorithms after seeing them err. Journal of Experimental Psychology: General, 144(1), 114-126. Doi: 10.1037/xge0000033.
Dubois, M., Cardebat, J. M., & Georgantzis, N. (2025). External Evaluations under Quality Uncertainty: the Market for Wine Ratings. Wine Economics and Policy. Doi: 10.36253/wep-16620.
Eiband, M., Schneider, H., Bilandzic, M., Fazekas-Connelly, K., & Hussmann, H. (2018). Bringing transparency design into practice. Proceedings of the 23rd International Conference on Intelligent User Interfaces, 211-223. Doi: 10.1145/3172944.3172961.
Festa, G., D’Amato, A., Palladino, R., Papa, A., Cuomo, M.T. (2025). Digital transformation in wine business – from Marketing 5.0 to Industry 5.0 in the world of wine adopting artificial intelligence. European Journal of Innovation Management, Vol. ahead-of-print No. ahead-of-print. Doi: 10.1108/EJIM-04-2024-0465.
Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51-90. Doi: 10.2307/30036519.
Glikson, E., & Woolley, A. W. (2020). Human trust in artificial intelligence: Review of empirical research. Academy of Management Annals, 14(2), 627-660. Doi: 10.5465/annals.2018.0057.
Hancock, P. A., Billings, D. R., Schaefer, K. E., Chen, J. Y. C., Visser, E. J., & Parasuraman, R. (2011). A meta-analysis of factors affecting trust in human-robot interaction. Human Factors, 53(5), 517-527. Doi: 10.1177/0018720811417254.
Hanf, C. H. (2000). Zur Bedeutung von Vertrauenseigenschaften für den Wettbewerb auf Lebensmittelmärkten. Schriften der Gesellschaft für Wirtschaftsund Sozialwissenschaften des Landbaues e.V., 36, 265-271. Doi: 10.22004/ag.econ.210066.
Hoff, K. A., & Bashir, M. (2015). Trust in automation: Integrating empirical evidence on factors that influence trust. Human Factors, 57(3), 407-434. Doi: 10.1177/0018720814547570.
Hovland, C. I., & Weiss, W. (1951). The influence of source credibility on communication effectiveness. Public Opinion Quarterly, 15(4), 635-650. Doi: 10.1086/266350.
Jerez-Villota, E., Jurado, F., & Moreno-Llorena, J. (2025). Understanding Information Propagation in Online Social Networks: A Systematic Mapping Study. IEEE Access, 13, 69194-69235. Doi: 10.1109/ACCESS.2025.3558768.
Kaimann, D., Bru, C. L. M. S., & Frick, B. (2023). Ratings meet prices: The dynamic relationship of quality signals. Journal of Wine Economics, 18(3), 226-244. Doi: 10.1017/jwe.2023.14.
Kim, Y., & Sundar, S. S. (2012). Anthropomorphism of computers: Is it mindful or mindless? Computers in Human Behavior, 28(1), 241-250. Doi: 10.1016/j.chb.2011.09.006.
Kopsacheilis, O., Analytis, P. P., Kaushik, K., Herzog, S. M., Bahrami, B., & Deroy, O. (2024). Crowdsourcing the assessment of wine quality: Vivino ratings, professional critics, and the weather. Journal of Wine Economics, 19(3), 285-304. Doi: 10.1017/jwe.2024.20.
Kramer, M. P., Heussner, J., & Hanf, J. H. (2024). Exploring the use of blockchainbased tokens in the wine industry. International Food and Agribusiness Management Review, 27(2), 271-290. Doi: 10.22434/ifamr2023.0080.
Kunkel, J., Donkers, T., & Ricci, F. (2019). Let Me Explain: Impact of Personal and Impersonal Explanations on Trust in Recommender Systems. Conference on Human Factors in Computing Systems Proceedings, 379-390. Doi: 10.1145/3301275.3302312.
Langer, M., König, C. J., Back, C., & Hemsing, V. (2023). Trust in artificial intelligence: Comparing trust processes between human and automated trustees in light of unfair bias. Journal of Business and Psychology, 38(3), 493-508. Doi: 10.1007/s10869-022-09829-9.
Lee, J. D., & See, K. A. (2004). Trust in automation: Designing for appropriate reliance. Human Factors, 46(1), 50-80. Doi: 10.1518/hfes.46.1.50_3039.
Lewinski, P., den Uyl, T. M., & Butler, C. (2014). Automated facial coding: Validation of basic emotions and FACS AUs in FaceReader. Journal of Neuroscience, Psychology, and Economics, 7(4), 227-236. Doi: 10.1037/npe0000028.
Lindgren, S., & Holmström, J. (2020). A social science perspective on artificial intelligence: Building blocks for a research agenda. Journal of digital social research, 2(3), 1-15. Doi: 10.33621/jdsr.v2i3.65.
Livat, F., Alston, J. M., & Cardebat, J. M. (2019). Do denominations of origin provide useful quality signals? The case of Bordeaux wines. Economic Modelling, 81, 518-532. Doi: 10.1016/j.econmod.2018.06.003.
Lockshin, L., Jarvis, W., d’Hauteville, F., & Perrouty, J. P. (2006). Using simulations from discrete choice experiments to measure consumer sensitivity to brand, region, price, and awards in wine choice. Food Quality and Preference, 17(3-4), 166-178. Doi: 10.1016/j.foodqual.2005.03.009.
Logg, J. M., Minson, J. A., & Moore, D. A. (2019). Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes, 151, 90-103. Doi: 10.1016/j.obhdp.2018.12.005.
Longoni, C., & Cian, L. (2022). Artificial intelligence in utilitarian vs. hedonic contexts: The “word-of-machine” effect. Journal of Marketing, 86(1), 91-108. Doi: 10.1177/0022242920957347.
Macready, A. L., Hieke, S., Klimczuk-Kochańska, M., Szumiał, S., Vranken, L., & Grunert, K. G. (2020). Consumer trust in the food value chain and its impact on consumer confidence: A model for assessing consumer trust and evidence from a 5-country study in Europe. Food Policy, 92, 101880. Doi: 10.1016/j.foodpol.2020.101880.
Madhavan, P., Wiegmann, D. A., & Lacson, F. C. (2006). Automation failures on tasks easily performed by operators undermine trust in automated aids. Human Factors, 48(2), 241-256. Doi: 10.1518/001872006777724408.
Mariani, M. M., Hashemi, N., & Wirtz, J. (2023). Artificial intelligence empowered conversational agents: A systematic literature review and research agenda. Journal of Business Research, 161, 113838. Doi: 10.1016/j.jbusres.2023.113838.
McCluskey, J. J., & Loureiro, M. L. (2003). Consumer preferences and willingness to pay for food labeling: A discussion of empirical studies. Journal of Food Distribution Research, 34(3), 95-102. Doi: 10.22004/ag.econ.27051.
Mcknight, D. H., Carter, M., Thatcher, J. B., & Clay, P. F. (2011). Trust in a specific technology: An investigation of its components and measures. ACM Transactions on management information systems (TMIS), 2(2), 1-25. Doi: 10.1145/1985347.1985353.
Miller, T. (2019). Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence, 267, 1-38. Doi: 10.1016/j.artint.2018.07.007.
Moeskops, B. (2021). Using explainable recommendations in the wine industry (Master’s thesis). Tilburg University. -- https://arno.uvt.nl/show.cgi?fid=161313.
Mueller Loose, S., & Lockshin, L. (2013). Testing the robustness of best-worst scaling for cross-national segmentation with different numbers of choice sets. Food Quality and Preference, 27(2), 230-242. Doi: 10.1016/j.foodqual.2012.02.002.
Munnukka, J., Talvitie-Lamberg, K., & Maity, D. (2022). Anthropomorphism and social presence in Human-Virtual service assistant interactions: The role of dialog length and attitudes. Computers in Human Behavior, 135, 107343. Doi: 10.1016/j.chb.2022.107343.
Ohanian, R. (1990). Construction and validation of a scale to measure celebrity endorsers’ perceived expertise, trustworthiness, and attractiveness. Journal of Advertising, 19(3), 39-52. Doi: 10.1080/00913367.1990.10673191.
Orth, U. R., Lockshin, L., d’Hauteville, F. (2007). The global wine business as a research field. International Journal of Wine Business Research, 19(1), 5-13. Doi: 10.1108/17511060710740316.
Paschen, U., Pitt, C., & Kietzmann, J. (2020). Artificial intelligence: Building blocks and an innovation typology. Business Horizons, 63(2), 147-155. Doi: 10.1016/j.bushor.2019.10.004.
Pieters, R. (2008). A review of eye-tracking research in marketing. Review of Marketing Research, 12, 123-147. Doi: 10.1108/S1548-6435(2008)0000004009.
Pizzi, G., Scarpi, D., & Pantano, E. (2021). Artificial intelligence and the new forms of interaction: Who has the control when interacting with a chatbot?. Journal of Business Research, 129, 878-890. Doi. 10.1016/j.jbusres.2020.11.006.
Plassmann, H., Ramsøy, T. Z., & Milosavljevic, M. (2012). Branding the brain: A critical review and outlook. Journal of consumer psychology, 22(1), 18-36. Doi: 10.1016/j.jcps.2011.11.010.
Ramsøy, T. Z. (2019). Introduction to neuromarketing and consumer neuroscience. Neurons Inc.
Ransbotham, S., Candelon, F., Kiron, D., LaFountain, B., & Khodabandeh, S. (2021). The cultural benefits of artificial intelligence in the Enterprise. MIT Sloan Management Review and Boston Consulting Group, 1.
Reitano, M., Pappalardo, G., Selvaggi, R., Zarbà, C., & Chinnici, G. (2024). Factors influencing consumer perceptions of food tracked with blockchain technology. A systematic literature review. Applied Food Research, 4(2), 100455. Doi: 10.1016/j.afres.2024.100455.
Reitano, M., Segovia, M. S., & Nayga Jr, R. M. (2025). A systematic review on the impact of Artificial Intelligence in the agri-food supply chain. Food Policy, 137, 102983. Doi: 10.1016/j.foodpol.2025.102983.
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why should i trust you?” Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1135-1144). Doi: 10.1145/2939672.2939778.
Schnackenberg, A. K., & Tomlinson, E. C. (2016). Organizational transparency: A new perspective on managing trust in organization-stakeholder relationships. Journal of Management, 42(7), 1784-1810. Doi: 10.1177/0149206314525202.
Shin, D. (2021). The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI. International Journal of Human-Computer Studies, 146, 102551. Doi: 10.1016/j.ijhcs.2020.102551.
Shin, D. (2022). How do people judge the credibility of algorithmic sources? Ai & Society, 37(1), 81-96. Doi: 10.1007/s00146-021-01158-4.
Spence, M. (1973). Job market signaling. Quarterly Journal of Economics, 87(3), 355-374. Doi: 10.2307/1882010.
Tassiello, V., Amatulli, C., Tillotson, J. S., & Laker, B. (2024). aiWOM: Artificial Intelligence Word-of-Mouth. Conceptualizing Consumer-to-AI Communication. International Journal of Human-Computer Interaction, 41(7), 4248-4260. Doi: 10.1080/10447318.2024.2349362.
Teece, D. J. (2018). Business models and dynamic capabilities. Long Range Planning, 51(1), 40-49. Doi: 10.1016/j.lrp.2017.06.007.
Velasco, C., Vargas, J., & Petit, O. (2024). Multisensory experiences and technology in the context of wine experiences. Journal of Wine Research, 35(2), 85-100. Doi: 10.1080/09571264.2024.2310304.
Verma, S., Sharma, R., Deb, S., & Maitra, D. (2021). Artificial intelligence in marketing: Systematic review and future research direction. International Journal of Information Management Data Insights, 1(1), 100002. Doi: 10.1016/j.jjimei.2020.100002.
Von Eschenbach, W.J. (2021). Transparency and the Black Box Problem: Why We Do Not Trust AI. Philos. Technol., 34, 1607-1622. Doi: 10.1007/s13347-021-00477-0.
Wien, A. H., & Peluso, A. M. (2021). Influence of human versus AI recommenders: The roles of product type and cognitive processes. Journal of Business Research, 137, 13-27. Doi: 10.1016/j.jbusres.2021.08.016.