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Are Indian Consumers willing to share personal data to avail personalized recommendations? - Indian Artificial Intelligence Market Perspective | ||
| Interdisciplinary Journal of Management Studies (Formerly known as Iranian Journal of Management Studies) | ||
| دوره 17، شماره 1، فروردین 2024، صفحه 277-293 اصل مقاله (681.58 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/ijms.2023.346385.675242 | ||
| نویسندگان | ||
| Renu Isidore* ؛ C. Joe Arun | ||
| Department of Business Administration, Loyola Institute, Chennai, India | ||
| چکیده | ||
| The willingness of the user to share personal information is an important factor that drives Artificial Intelligence (AI) applications. This study aims to explore the AI beliefs of the consumers who are willing/not willing to share their data with the AI applications. The study was conducted across India by adopting the questionnaire survey method. An Independent sample t-test was conducted on the final sample of 610 respondents to analyze the difference in the means of each of the AI beliefs when divided based on the willingness to share. The results show that the consumers who are willing to share personal data with AI applications have more trust in AI, have a strong preference for AI’s recommendations, currently use these applications, are very aware of these applications, have a positive outlook on their performance and desire several AI applications in the future. They are less worried about the dangers of AI in the future and have less negative feedback. Businesses that invest in AI applications need to educate their target consumers about their data policy and strengthen their beliefs about AI so that they are willing to share personal data to avail recommendations. AI-run applications can be a success only when consumers freely share their preferences without any privacy concerns or trust issues. | ||
| کلیدواژهها | ||
| Artificial intelligence؛ Indian Consumer market؛ Willingness to share personal data؛ Customer-Machine Interaction | ||
| مراجع | ||
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Aguirre, E., Mahr, D., Grewal, D., De Ruyter, K., & Wetzels, M. (2015). Unraveling the personalization paradox: The effect of information collection and trust-building strategies on online advertisement effectiveness. Journal of Retailing, 91(1), 34-49.
Al-Jabri, I. M., Eid, M. I., & Abed, A. (2019). The willingness to disclose personal information: Trade-off between privacy concerns and benefits. Information & Computer Security, 28 (2), 161-181.
Awad, N. F., & Krishnan, M. S. (2006). The Personalization Privacy Paradox: An Empirical Evaluation of Information Transparency and the Willingness to Be Profiled Online for Personalization. MIS Quarterly, 30(1), 13–28. https://doi.org/10.2307/25148715
Beldad, A., van der Geest, T., de Jong, M., & Steehouder, M. (2012). Shall I tell you where I live and who I am? Factors influencing the behavioral intention to disclose personal data for online government transactions. International journal of human-computer interaction, 28(3), 163-177.
Belle, N., Cantarelli, P., & Battaglio, R. P. (2021). To consent, or not to consent? The publicness effect on citizens’ willingness to grant access to personal data in the face of a health crisis. Journal of European Public Policy, 28(5), 782-800.
Benbasat, I., & Wang, W. (2005). Trust in and adoption of online recommendation agents. Journal of the association for information systems, 6(3), 4.
Benda, T., & Lind, V. (2021). Why won't you let (A) I help you?: A quantitative study that explains the effects of AI perceptions on willingness to disclose personal information to AI. Linnaeus University, Master Thesis.
Bigras, É., Léger, P. M., & Sénécal, S. (2019). Recommendation agent adoption: how recommendation presentation influences employees’ perceptions, behaviors, and decision quality. Applied Sciences, 9(20), 4244.
Britt, P. (2020). Transparency Is Critical to Resolve Privacy-Personalization Paradox: Companies need to be upfront with consumers before collecting personal information. CRM Magazine, 24(2), 12.
Chatterjee, S., & Sreenivasulu, N. S. (2019). Personal data sharing and legal issues of human rights in the era of artificial intelligence: the moderating effect of government regulation. International Journal of Electronic Government Research (IJEGR), 15(3), 21-36.
Chellappa, R. K., & Sin, R. G. (2005). Personalization versus privacy: An empirical examination of the online consumer’s dilemma. Information technology and management, 6(2), 181-202.
Chen, K., & Rea Jr, A. I. (2004). Protecting personal information online: A survey of user privacy concerns and control techniques. Journal of Computer Information Systems, 44(4), 85-92.
Chopra, K. (2019). Indian shopper motivation to use artificial intelligence: Generating Vroom’s expectancy theory of motivation using grounded theory approach. International Journal of Retail & Distribution Management, https://doi.org/10.1108/IJRDM-11-2018-0251
Consumers International (2019). Artificial Intelligence: Consumer experiences in new technology. Consumers International- Coming Together for Change. Retrieved August 12, 2022, Retrieved from https://www.consumersinternational.org/media/261949/ai-consumerexperiencesinnewtech.pdf
Culnan, M. J. (2000). Protecting privacy online: Is self-regulation working? Journal of Public Policy & Marketing, 19(1), 20-26.
Culnan, M. J., & Armstrong, P. K. (1999). Information privacy concerns, procedural fairness, and impersonal trust: An empirical investigation. Organization Science, 10(1), 104-115.
Epro, B. (2019). Behavioral Data & Artificial Intelligence: The Keys to Marketing Personalization. Dealer Magazine, 26(3), 22–23.
Evens, T., & Van Damme, K. (2016). Consumers’ willingness to share personal data: Implications for newspapers’ business models. International journal on media management, 18(1), 25-41.
Gile, K. J., & Handcock, M. S. (2010). 7. Respondent-driven sampling: An assessment of current methodology. Sociological Methodology, 40(1), 285-327.
Goodman, L. A. (1961). Snowball sampling. The annals of mathematical statistics, 148-170.
Gray, K. (2017). AI can be a troublesome teammate. Harvard Business Review, 20. Retrieved September 9, 2020, Retrieved from https://hbr.org/2017/07/ai-can-be-a-troublesome-teammate
Hughes, C., Robert, L., Frady, K., & Arroyos, A. (2019). Artificial intelligence, employee engagement, fairness, and job outcomes. In Managing Technology and Middle-and Low-skilled Employees. Emerald Publishing Limited, 61-68, DOI: https://doi.org/10.1108/978-1-78973-077-720191005
Jung, Y., Choi, H., & Shim, H. (2020). Individual willingness to share personal health information with secondary information users in South Korea. Health Communication, 35(6), 659-666.
Kalyanakrishnan, S., Panicker, R.A., Natarajan, S., & Rao, S. (2018). Opportunities and challenges for artificial intelligence in India. In Proceedings of the 2018 AAAI/ACM conference on AI, Ethics, and Society 164-170.
Karampela, M., Ouhbi, S., & Isomursu, M. (2019, July). Exploring users’ willingness to share their health and personal data under the prism of the new GDPR: implications in healthcare. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 6509-6512.
Karwatzki, S., Dytynko, O., Trenz, M., & Veit, D. (2017). Beyond the personalization–privacy paradox: Privacy valuation, transparency features, and service personalization. Journal of Management Information Systems, 34(2), 369-400.
Kushwaha, B. P., Singh, R. K., & Tyagi, V. (2021). Investigating privacy paradox: Consumer data privacy behavioural intention and disclosure behaviour. Academy of Marketing Studies Journal, 25(1), 1-10.
Kim, J. (2020). The influence of perceived costs and perceived benefits on AI-driven interactive recommendation agent value. Journal of Global Scholars of Marketing Science, 30(3), 319-333.
Komiak, S. Y., & Benbasat, I. (2006). The effects of personalization and familiarity on trust and adoption of recommendation agents. MIS quarterly, 941-960.
Kumar, V., Rajan, B., Venkatesan, R., & Lecinski, J. (2019). Understanding the role of artificial intelligence in personalized engagement marketing. California Management Review, 61(4), 135-155.
Leppäniemi, M., Karjaluoto, H., & Saarijärvi, H. (2017). Customer perceived value, satisfaction, and loyalty: the role of willingness to share information. The International Review of Retail, Distribution and Consumer Research, 27(2), 164-188.
Li, X., Hess, T. J., & Valacich, J. S. (2008). Why do we trust new technology? A study of initial trust formation with organizational information systems. The Journal of Strategic Information Systems, 17(1), 39-71.
Longini, C., & Cian, L. (2020). When Do We Trust AI’s Recommendations More Than People’s? Harvard Business Review, (October), 2–7. Retrieved from https://hbr.org/2020/10/when-do-we-trust-ais-recommendations-more-than-peoples
Luo, X., Tong, S., Fang, Z., & Qu, Z. (2019). Frontiers: machines vs. humans: The impact of artificial intelligence chatbot disclosure on customer purchases. Marketing Science, 38(6), 937–947.
Montaner, M., López, B., & De La Rosa, J. L. (2003). A taxonomy of recommender agents on the internet. Artificial intelligence review, 19(4), 285-330.
Montgomery, A. L., & Smith, M. D. (2009). Prospects for Personalization on the Internet. Journal of Interactive Marketing, 23(2), 130-137.
Murray, K. B., & Häubl, G. (2009). Personalization without interrogation: Towards more effective interactions between consumers and feature-based recommendation agents. Journal of Interactive Marketing, 23(2), 138-146.
Phelps, J., Nowak, G., & Ferrell, E. (2000). Privacy concerns and consumer willingness to provide personal information. Journal of public policy & marketing, 19(1), 27-41.
Perdereaux-Weekes, A. W. (2021). To Investigate the Impact of Data Privacy Regulation on Disclosure Decisions: Examining Consumers’ Willingness to Share or Withhold Personal Identifiable Information in the Wake of GDPR, CCPA, and LGDP (Doctoral dissertation, St. Thomas University).
Postma, O. J., & Brokke, M. (2002). Personalisation in practice: The proven effects of personalisation. Journal of Database Marketing & Customer Strategy Management, 9(2), 137-142.
PwC (2018). Artificial intelligence in India – hype or reality Impact of artificial intelligence across industries and user groups. Retrieved August 12, 2022, Retrieved from https://www.pwc.in/assets/pdfs/consulting/technology/data-and-analytics/artificial-intelligence-in-indiahype-or-reality/artificial-intelligence-in-india-hype-or-reality.pdf
Rhee, C. E., & Choi, J. (2020). Effects of personalization and social role in voice shopping: an experimental study on product recommendation by a conversational voice agent. Computers in Human Behavior, 109, 106359.
Salesforce (2018). Artificial Intelligence in Asia: Trust, Understanding and the Opportunity to Re-Skill. Retrieved from https://www.sellingsimplifiedinsights.asia/asset/IoT-&-Business-Intelligence/Salesforce_Artificial-intelligence-in-Asia_Trust-Understanding-and-the-opportunity-to-reskill.pdf
Schubert, R., Koumoutsakos, P., Arampatzis, G., Wang, Y., Hug, F., & Marinica, I. (2018). Are People Willing to Share Their Personal Data?: Insights from Two Survey Studies. Collegium Helveticum, 1, 1-41.
Schudy, S., & Utikal, V. (2017). ‘You must not know about me’—On the willingness to share personal data. Journal of Economic Behavior & Organization, 141, 1-13.
Senecal, S., & Nantel, J. (2004). The influence of online product recommendations on consumers’ online choices. Journal of retailing, 80(2), 159-169.
Sheng, H., Nah, F. F. H., & Siau, K. (2008). An experimental study on ubiquitous commerce adoption: Impact of personalization and privacy concerns. Journal of the Association for Information Systems, 9(6), 15.
Song, C. S., & Kim, Y. K. (2020, December). Should We Be Afraid of Artificial Intelligence? Consumer Willingness to Share Personal Information with Fashion Sales Robots. In International Textile and Apparel Association Annual Conference Proceedings (Vol. 77, No. 1). Iowa State University Digital Press.
Song, C. S., & Kim, Y. K. (2021). Predictors of consumers’ willingness to share personal information with fashion sales robots. Journal of Retailing and Consumer Services, 63, 102727.
Sundar, S. S., & Kim, J. (2019, May). Machine heuristic: When we trust computers more than humans with our personal information. In Proceedings of the 2019 CHI Conference on human factors in computing systems (1-9).
Wang, W., & Benbasat, I. (2005). Trust in and adoption of online recommendation agents. Journal of the association for information systems, 6(3), 72-101.
Xiao, B. & Benbasat, I. (2007). E-Commerce Product Recommendation Agents: Use, Characteristics, and Impact. MIS Quarterly, 31(1), 137–209. https://doi.org/10.2307/25148784
Xiao, B., & Benbasat, I. (2007). E-commerce product recommendation agents: Use, characteristics, and impact. MIS quarterly, 137-209.
Yeh, C. H., Wang, Y. S., Lin, S. J., Tseng, T. H., Lin, H. H., Shih, Y. W., & Lai, Y. H. (2018). What drives internet users’ willingness to provide personal information?. Online Information Review, 42(6), 923-939.
Zaheer, N., & Trkman, P. (2017). An information sharing theory perspective on willingness to share information in supply chains. The International Journal of Logistics Management, 28(2), 417-443.
Zamora, J. (2017). Rise of the chatbots: Finding a place for artificial intelligence in India and US. In Proceedings of the 22nd international conference on intelligent user interfaces companion, 109-112.
Zhang, Q., Lu, J., & Jin, Y. (2021). Artificial intelligence in recommender systems. Complex & Intelligent Systems, 7(1), 439-457. | ||
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