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The Integration of Artificial Intelligence in Retail: Benefits, Challenges and a Dedicated Conceptual Framework

Author

Listed:
  • Ionut Anica-Popa

    (University of Economic Studies, Bucharest, Romania)

  • Liana Anica-Popa

    (University of Economic Studies, Bucharest, Romania)

  • Cristina Radulescu

    (University of Economic Studies, Bucharest, Romania)

  • Marinela Vrincianu

    (University of Economic Studies, Bucharest, Romania)

Abstract

The aim of this study is to identify the practical benefits and associated risks generated by the implementation of artificial intelligence (AI) in retail and capitalize on the results by developing a conceptual framework for integrating AI technologies in the information systems of retail companies. To this end, a systematic study of recent literature was conducted by carefully examining the topic of AI implementations. The main results of the documentation were used to substantiate the conceptual framework introduced by the paper. The research revealed a variety of advanced solutions, benefits, but also risks that AI generates in retail, in different segments of the value chain, abbreviated CECoR, from improving the customer experience (Customer Experience, CE) with the help of virtual agents (chatbots, virtual assistants, etc.), to cost reductions (Cost, Co) by using smart shelves, and to increasing revenues (Revenue, R) due to product recommendations and personalized offers or discounts. The proposed conceptual framework is focused on customer profiles and includes recommendations on AI implementations in a retail company, from the perspective of CECoR drivers. The results of the research can be capitalized by practitioners and researchers in the field, who are presented with concrete examples of benefits, challenges, and risks generated by AI technologies. The CECoR framework could be a useful tool for both retail and AI specialists, providing common and clear guidelines for initiating and overseeing projects for integrating AI in a company’s information systems. Literature-based CECoR analysis dimensions have allowed the restriction of the research area, which is particularly wide, at the confluence of retail and AI. The originality of the article lies in the CECoR orientation of the research and the conceptual framework focused on customer profiling.

Suggested Citation

  • Ionut Anica-Popa & Liana Anica-Popa & Cristina Radulescu & Marinela Vrincianu, 2021. "The Integration of Artificial Intelligence in Retail: Benefits, Challenges and a Dedicated Conceptual Framework," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 23(56), pages 120-120, February.
    • Handle: RePEc:aes:amfeco:v:23:y:2021:i:56:p:120
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    References listed on IDEAS

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    1. Kumar, V. & Anand, Ankit & Song, Hyunseok, 2017. "Future of Retailer Profitability: An Organizing Framework," Journal of Retailing, Elsevier, vol. 93(1), pages 96-119.
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    5. Rainer Quante & Herbert Meyr & Moritz Fleischmann, 2009. "Revenue management and demand fulfillment: matching applications, models and software," Springer Books, in: Herbert Meyr & Hans-Otto Günther (ed.), Supply Chain Planning, pages 57-88, Springer.
    6. Bradlow, Eric T. & Gangwar, Manish & Kopalle, Praveen & Voleti, Sudhir, 2017. "The Role of Big Data and Predictive Analytics in Retailing," Journal of Retailing, Elsevier, vol. 93(1), pages 79-95.
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    9. Joshua A. Gerlick & Stephan M. Liozu, 2020. "Ethical and legal considerations of artificial intelligence and algorithmic decision-making in personalized pricing," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 19(2), pages 85-98, April.
    10. Lee, In & Shin, Yong Jae, 2020. "Machine learning for enterprises: Applications, algorithm selection, and challenges," Business Horizons, Elsevier, vol. 63(2), pages 157-170.
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    Cited by:

    1. Pelau Corina & Volkmann Christine & Barbul Maria & Bojescu Irina, 2023. "The Role of Attachment in Improving Consumer-AI Interactions," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 17(1), pages 1075-1084, July.
    2. Ologunebi, John, 2023. "An analysis of customer retention strategies in e-commerce fashion business in the UK: A case study of Primark," MPRA Paper 119040, University Library of Munich, Germany.
    3. Georgiana-Ioana Țîrcovnicu & Camelia-Daniela Hațegan, 2023. "Integration of Artificial Intelligence in the Risk Management Process: An Analysis of Opportunities and Challenges," Journal of Financial Studies, Institute of Financial Studies, vol. 15(8), pages 198-214, November.
    4. repec:fst:rfsisf:v:8:y:2023:i:15:p:198-214 is not listed on IDEAS
    5. Andreja Mihailović & Julija Cerović Smolović & Ivan Radević & Neli Rašović & Nikola Martinović, 2021. "COVID-19 and Beyond: Employee Perceptions of the Efficiency of Teleworking and Its Cybersecurity Implications," Sustainability, MDPI, vol. 13(12), pages 1-26, June.

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    More about this item

    Keywords

    artificial intelligence; retail; customer experience; cost reduction; revenue increase; CECoR conceptual framework.;
    All these keywords.

    JEL classification:

    • F43 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Economic Growth of Open Economies
    • M15 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - IT Management
    • N70 - Economic History - - Economic History: Transport, International and Domestic Trade, Energy, and Other Services - - - General, International, or Comparative
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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