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Consumer Acceptance of the Use of Artificial Intelligence in Online Shopping: Evidence From Hungary

Author

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  • Szabolcs Nagy

    (University of Miskolc, Miskolc, Hungary)

  • Noemi Hajdu

    (University of Miskolc, Miskolc, Hungary)

Abstract

The rapid development of technology has drastically changed the way consumers do their shopping. The volume of global online commerce has significantly been increasing partly due to the recent COVID-19 crisis that has accelerated the expansion of e-commerce. A growing number of webshops integrate Artificial Intelligence (AI), state-of-the-art technology into their stores to improve customer experience, satisfaction and loyalty. However, little research has been done to verify the process of how consumers adopt and use AI-powered webshops. Using the technology acceptance model (TAM) as a theoretical background, this study addresses the question of trust and consumer acceptance of Artificial Intelligence in online retail. An online survey in Hungary was conducted to build a database of 439 respondents for this study. To analyse data, structural equation modelling (SEM) was used. After the respecification of the initial theoretical model, a nested model, which was also based on TAM, was developed and tested. The widely used TAM was found to be a suitable theoretical model for investigating consumer acceptance of the use of Artificial Intelligence in online shopping. Trust was found to be one of the key factors influencing consumer attitudes towards Artificial Intelligence. Perceived usefulness as the other key factor in attitudes and behavioural intention was found to be more important than the perceived ease of use. These findings offer valuable implications for webshop owners to increase customer acceptance.

Suggested Citation

  • Szabolcs Nagy & Noemi Hajdu, 2021. "Consumer Acceptance of the Use of Artificial Intelligence in Online Shopping: Evidence From Hungary," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 23(56), pages 155-155, February.
    • Handle: RePEc:aes:amfeco:v:23:y:2021:i:56:p:155
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    References listed on IDEAS

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    1. Stigler, George J., 2011. "Economics of Information," Ekonomicheskaya Politika / Economic Policy, Russian Presidential Academy of National Economy and Public Administration, vol. 5, pages 35-49.
    2. Iacob Catoiu & Mihai Orzan & Octav-Ionut Macovei & Claudia Iconaru, 2014. "Modelling Users` Trust in Online Social Networks," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 16(35), pages 289-289, February.
    3. John G. Lynch , Jr. & Dan Ariely, 2000. "Wine Online: Search Costs Affect Competition on Price, Quality, and Distribution," Marketing Science, INFORMS, vol. 19(1), pages 83-103, April.
    4. Paschen, Jeannette & Wilson, Matthew & Ferreira, João J., 2020. "Collaborative intelligence: How human and artificial intelligence create value along the B2B sales funnel," Business Horizons, Elsevier, vol. 63(3), pages 403-414.
    5. Viswanath Venkatesh, 2000. "Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model," Information Systems Research, INFORMS, vol. 11(4), pages 342-365, December.
    6. Lee Cronbach, 1951. "Coefficient alpha and the internal structure of tests," Psychometrika, Springer;The Psychometric Society, vol. 16(3), pages 297-334, September.
    7. Pantano, Eleonora & Pizzi, Gabriele, 2020. "Forecasting artificial intelligence on online customer assistance: Evidence from chatbot patents analysis," Journal of Retailing and Consumer Services, Elsevier, vol. 55(C).
    8. Roland T. Rust & Ming-Hui Huang, 2014. "The Service Revolution and the Transformation of Marketing Science," Marketing Science, INFORMS, vol. 33(2), pages 206-221, March.
    9. Yoo, Weon-Sang & Lee, Yunjung & Park, JungKun, 2010. "The role of interactivity in e-tailing: Creating value and increasing satisfaction," Journal of Retailing and Consumer Services, Elsevier, vol. 17(2), pages 89-96.
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    Cited by:

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    3. Faruk Ahmeti & Hykmete Bajrami, 2024. "Exploring the Impact of Technology Acceptance Model Constructs on Consumer Behavior in SMEs: with A focus on E-Marketing Strategies," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 1, pages 66-88.
    4. Sufyan Habib & Nawaf N. Hamadneh, 2021. "Impact of Perceived Risk on Consumers Technology Acceptance in Online Grocery Adoption amid COVID-19 Pandemic," Sustainability, MDPI, vol. 13(18), pages 1-16, September.
    5. Pejman Ebrahimi & Khadija Aya Hamza & Eva Gorgenyi-Hegyes & Hadi Zarea & Maria Fekete-Farkas, 2021. "Consumer Knowledge Sharing Behavior and Consumer Purchase Behavior: Evidence from E-Commerce and Online Retail in Hungary," Sustainability, MDPI, vol. 13(18), pages 1-20, September.
    6. Rahman, Muhammad Sabbir & Bag, Surajit & Hossain, Md Afnan & Abdel Fattah, Fadi Abdel Muniem & Gani, Mohammad Osman & Rana, Nripendra P., 2023. "The new wave of AI-powered luxury brands online shopping experience: The role of digital multisensory cues and customers’ engagement," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).

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    More about this item

    Keywords

    consumer acceptance; artificial intelligence; online shopping; AI-powered webshops; technology acceptance model; trust; perceived usefulness; perceived ease of use; attitudes; behavioural intention; Hungary;
    All these keywords.

    JEL classification:

    • L81 - Industrial Organization - - Industry Studies: Services - - - Retail and Wholesale Trade; e-Commerce
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing
    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General

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