IDEAS home Printed from https://ideas.repec.org/a/aes/amfeco/v23y2021i56p46.html
   My bibliography  Save this article

Artificial Intelligence in Retail: Benefits and Risks Associated With Mobile Shopping Applications

Author

Listed:
  • Victoria Stanciu

    (Bucharest University of Economic Studies, Romania)

  • Sinziana-Maria Rindasu

    (Bucharest University of Economic Studies, Romania)

Abstract

The objective of the study is to examine the practical implications of using artificial intelligence (AI) based solutions in the case of retail mobile applications, to enhance the online shopping experience and improve the engagement by also having in mind the privacy of the users. We examined 117 shopping applications available in the Google Play market and investigated the permissions required for each application and the categories of personal data collected from the users. Based on the information gathered, we provided practical methods to integrate artificial intelligence-based solutions to offer a new set of services, partially unavailable in physical stores. Some of the permissions identified, if exploited by malicious users, can affect individuals’ privacy. The fact that artificial intelligence is a fast-developing technology constitutes the main challenge in the effort of creating proper regulations. This research provides practical directions regarding the benefits of integrating artificial intelligence solutions in retail mobile applications in an ethical manner, protecting the users’ privacy.

Suggested Citation

  • Victoria Stanciu & Sinziana-Maria Rindasu, 2021. "Artificial Intelligence in Retail: Benefits and Risks Associated With Mobile Shopping Applications," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 23(56), pages 1-46, February.
    • Handle: RePEc:aes:amfeco:v:23:y:2021:i:56:p:46
    as

    Download full text from publisher

    File URL: http://www.amfiteatrueconomic.ro/temp/Article_2978.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Grzegorz Mazurek & Karolina Małagocka, 2019. "Perception of privacy and data protection in the context of the development of artificial intelligence," Journal of Management Analytics, Taylor & Francis Journals, vol. 6(4), pages 344-364, October.
    2. Babin, Barry J & Darden, William R & Griffin, Mitch, 1994. "Work and/or Fun: Measuring Hedonic and Utilitarian Shopping Value," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 20(4), pages 644-656, March.
    3. Ho, Mia Hsiao-Wen & Chung, Henry F.L., 2020. "Customer engagement, customer equity and repurchase intention in mobile apps," Journal of Business Research, Elsevier, vol. 121(C), pages 13-21.
    4. Ha, Sejin & Stoel, Leslie, 2009. "Consumer e-shopping acceptance: Antecedents in a technology acceptance model," Journal of Business Research, Elsevier, vol. 62(5), pages 565-571, May.
    5. Rese, Alexandra & Ganster, Lena & Baier, Daniel, 2020. "Chatbots in retailers’ customer communication: How to measure their acceptance?," Journal of Retailing and Consumer Services, Elsevier, vol. 56(C).
    6. Anja Lambrecht & Catherine Tucker, 2019. "Algorithmic Bias? An Empirical Study of Apparent Gender-Based Discrimination in the Display of STEM Career Ads," Management Science, INFORMS, vol. 65(7), pages 2966-2981, July.
    7. de Bellis, Emanuel & Venkataramani Johar, Gita, 2020. "Autonomous Shopping Systems: Identifying and Overcoming Barriers to Consumer Adoption," Journal of Retailing, Elsevier, vol. 96(1), pages 74-87.
    8. Kolbe, Richard H & Burnett, Melissa S, 1991. "Content-Analysis Research: An Examination of Applications with Directives for Improving Research Reliability and Objectivity," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 18(2), pages 243-250, September.
    9. Axel Gautier & Ashwin Ittoo & Pieter Cleynenbreugel, 2020. "AI algorithms, price discrimination and collusion: a technological, economic and legal perspective," European Journal of Law and Economics, Springer, vol. 50(3), pages 405-435, December.
    10. Natarajan, Thamaraiselvan & Balasubramanian, Senthil Arasu & Kasilingam, Dharun Lingam, 2017. "Understanding the intention to use mobile shopping applications and its influence on price sensitivity," Journal of Retailing and Consumer Services, Elsevier, vol. 37(C), pages 8-22.
    11. Hirschman, Elizabeth C, 1992. "The Consciousness of Addiction: Toward a General Theory of Compulsive Consumption," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 19(2), pages 155-179, September.
    12. Emilio Calvano & Giacomo Calzolari & Vincenzo Denicolò & Sergio Pastorello, 2019. "Algorithmic Pricing What Implications for Competition Policy?," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 55(1), pages 155-171, August.
    13. Li, Xiong & Zhao, Xiaodong & Xu, Wangtu (Ato) & Pu, Wei, 2020. "Measuring ease of use of mobile applications in e-commerce retailing from the perspective of consumer online shopping behaviour patterns," Journal of Retailing and Consumer Services, Elsevier, vol. 55(C).
    14. Poushneh, Atieh & Vasquez-Parraga, Arturo Z., 2017. "Discernible impact of augmented reality on retail customer's experience, satisfaction and willingness to buy," Journal of Retailing and Consumer Services, Elsevier, vol. 34(C), pages 229-234.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Acquila-Natale, Emiliano & Iglesias-Pradas, Santiago, 2021. "A matter of value? Predicting channel preference and multichannel behaviors in retail," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    2. Rayburn, Steven W. & Anderson, Sidney T. & Zank, Gail M. & McDonald, Imani, 2022. "M-atmospherics: From the physical to the digital," Journal of Retailing and Consumer Services, Elsevier, vol. 64(C).
    3. Laura Abrardi & Carlo Cambini & Laura Rondi, 2022. "Artificial intelligence, firms and consumer behavior: A survey," Journal of Economic Surveys, Wiley Blackwell, vol. 36(4), pages 969-991, September.
    4. Rodríguez-López, María Eugenia & Higueras-Castillo, Elena & Rojas-Lamorena, Álvaro J. & Alcántara-Pilar, Juan Miguel, 2024. "The future of TV-shopping: predicting user purchase intention through an extended technology acceptance model," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    5. Simon Martin & Alexander Rasch, 2022. "Collusion by Algorithm: The Role of Unobserved Actions," CESifo Working Paper Series 9629, CESifo.
    6. Hsu, Sheila Hsuan-Yu & Tsou, Hung-Tai & Chen, Ja-Shen, 2021. "“Yes, we do. Why not use augmented reality?†customer responses to experiential presentations of AR-based applications," Journal of Retailing and Consumer Services, Elsevier, vol. 62(C).
    7. Stefan Hoffmann & Tom Joerß & Robert Mai & Payam Akbar, 2022. "Augmented reality-delivered product information at the point of sale: when information controllability backfires," Journal of the Academy of Marketing Science, Springer, vol. 50(4), pages 743-776, July.
    8. Iranmanesh, Mohammad & Min, Connie Low & Senali, Madugoda Gunaratnege & Nikbin, Davoud & Foroughi, Behzad, 2022. "Determinants of switching intention from web-based stores to retail apps: Habit as a moderator," Journal of Retailing and Consumer Services, Elsevier, vol. 66(C).
    9. Wu, Jih-Hwa & Wu, Chih-Wen & Lee, Chin-Tarn & Lee, Hsiao-Jung, 2015. "Green purchase intentions: An exploratory study of the Taiwanese electric motorcycle market," Journal of Business Research, Elsevier, vol. 68(4), pages 829-833.
    10. Holdack, Eric & Lurie-Stoyanov, Katja & Fromme, Harro Fabian, 2022. "The role of perceived enjoyment and perceived informativeness in assessing the acceptance of AR wearables," Journal of Retailing and Consumer Services, Elsevier, vol. 65(C).
    11. Garaus, Marion & Wolfsteiner, Elisabeth & Wagner, Udo, 2016. "Shoppers' acceptance and perceptions of electronic shelf labels," Journal of Business Research, Elsevier, vol. 69(9), pages 3687-3692.
    12. Kamoonpuri, Sana Zehra & Sengar, Anita, 2023. "Hi, May AI help you? An analysis of the barriers impeding the implementation and use of artificial intelligence-enabled virtual assistants in retail," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    13. Brusch, Ines & Rappel, Nina, 2020. "Exploring the acceptance of instant shopping – An empirical analysis of the determinants of user intention," Journal of Retailing and Consumer Services, Elsevier, vol. 54(C).
    14. Bailey, Ainsworth Anthony & Bonifield, Carolyn M. & Arias, Alejandro, 2018. "Social media use by young Latin American consumers: An exploration," Journal of Retailing and Consumer Services, Elsevier, vol. 43(C), pages 10-19.
    15. Pedro Cuesta-Valiño & Pablo Gutiérrez Rodríguez & Estela Núñez-Barriopedro, 2020. "Perception of Advertisements for Healthy Food on Social Media: Effect of Attitude on Consumers’ Response," IJERPH, MDPI, vol. 17(18), pages 1-20, September.
    16. Close, Angeline G. & Kukar-Kinney, Monika, 2010. "Beyond buying: Motivations behind consumers' online shopping cart use," Journal of Business Research, Elsevier, vol. 63(9-10), pages 986-992, September.
    17. Vieira, Valter Afonso & Rafael, Diego Nogueira & Agnihotri, Raj, 2022. "Augmented reality generalizations: A meta-analytical review on consumer-related outcomes and the mediating role of hedonic and utilitarian values," Journal of Business Research, Elsevier, vol. 151(C), pages 170-184.
    18. Rob Kim Marjerison & Youran Zhang & Hanyi Zheng, 2022. "AI in E-Commerce: Application of the Use and Gratification Model to The Acceptance of Chatbots," Sustainability, MDPI, vol. 14(21), pages 1-16, November.
    19. Martin, Simon & Rasch, Alexander, 2022. "Collusion by algorithm: The role of unobserved actions," DICE Discussion Papers 382, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    20. Sirgy, M. Joseph & Lee, Dong-Jin & Yu, Grace B. & Gurel-Atay, Eda & Tidwell, John & Ekici, Ahmet, 2016. "Self-expressiveness in shopping," Journal of Retailing and Consumer Services, Elsevier, vol. 30(C), pages 292-299.

    More about this item

    Keywords

    artificial intelligence; machine learning algorithms; retail; ethics; privacy; mobile shopping applications;
    All these keywords.

    JEL classification:

    • L81 - Industrial Organization - - Industry Studies: Services - - - Retail and Wholesale Trade; e-Commerce
    • K40 - Law and Economics - - Legal Procedure, the Legal System, and Illegal Behavior - - - General
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aes:amfeco:v:23:y:2021:i:56:p:46. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Valentin Dumitru (email available below). General contact details of provider: https://edirc.repec.org/data/aseeero.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.