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Consumer acceptance of the use of artificial intelligence in online shopping: evidence from Hungary

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  • Szabolcs Nagy
  • Noemi Hajdu

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

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  • Szabolcs Nagy & Noemi Hajdu, 2022. "Consumer acceptance of the use of artificial intelligence in online shopping: evidence from Hungary," Papers 2301.01277, arXiv.org.
    • Handle: RePEc:arx:papers:2301.01277
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    Cited by:

    1. 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.
    2. 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.
    3. Ayoub Asad & Balawi Ayman, 2022. "A New Perspective for Marketing: The Impact of Social Media on Customer Experience," Journal of Intercultural Management, Sciendo, vol. 14(1), pages 87-103, March.
    4. 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).
    5. Md. Emam Hossain & Subarna Biswas, 2024. "Technology acceptance model for understanding consumer’s behavioral intention to use artificial intelligence based online shopping platforms in Bangladesh," SN Business & Economics, Springer, vol. 4(12), pages 1-61, December.
    6. 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.

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    More about this item

    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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