IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i21p14270-d960146.html
   My bibliography  Save this article

AI in E-Commerce: Application of the Use and Gratification Model to The Acceptance of Chatbots

Author

Listed:
  • Rob Kim Marjerison

    (Global Business, College of Business and Public Management, Wenzhou-Kean University, Wenzhou 325015, China)

  • Youran Zhang

    (Accounting, College of Business and Public Management, Wenzhou-Kean University, Wenzhou 325015, China)

  • Hanyi Zheng

    (Accounting, College of Business and Public Management, Wenzhou-Kean University, Wenzhou 325015, China)

Abstract

This study applies and builds on the Use and Gratification (U&G) theory to explore consumer acceptance of applied artificial intelligence (AI) in the form of Chatbots in online shopping in China. Data were gathered via an anonymous online survey from 540 respondents who self-identified as frequent online shoppers and are familiar with Chatbots. The results of the data analysis provide empirical evidence indicating that utilitarian factors such as the “authenticity of conversation” and “convenience”, as well as hedonic factors such as “perceived enjoyment”, result in users having a positive attitude towards Chatbots. However, privacy issues and the immaturity of technology have had a negative impact on acceptance. This paper provides both theoretical and practical insights into Chinese attitudes toward Chatbots and may be of interest to e-commerce researchers, practitioners, and U&G theorists.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:21:p:14270-:d:960146
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/21/14270/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/21/14270/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mónika-Anetta Alt & Vizeli Ibolya, 2021. "Identifying Relevant Segments of Potential Banking Chatbot Users Based on Technology Adoption Behavior," Tržište/Market, Faculty of Economics and Business, University of Zagreb, vol. 33(2), pages 165-183.
    2. Eliza Nichifor & Adrian Trifan & Elena Mihaela Nechifor, 2021. "Artificial Intelligence in Electronic Commerce: Basic Chatbots and Consumer Journey," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 23(56), pages 1-87, February.
    3. 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.
    4. 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).
    5. Lo, Alex Y., 2014. "Negative income effect on perception of long-term environmental risk," Ecological Economics, Elsevier, vol. 107(C), pages 51-58.
    6. Viswanath Venkatesh & Fred D. Davis, 2000. "A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies," Management Science, INFORMS, vol. 46(2), pages 186-204, February.
    7. Markus Blut & Cheng Wang & Nancy V. Wünderlich & Christian Brock, 2021. "Understanding anthropomorphism in service provision: a meta-analysis of physical robots, chatbots, and other AI," Journal of the Academy of Marketing Science, Springer, vol. 49(4), pages 632-658, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Scott, Carol F. & Bay-Cheng, Laina Y. & Nochajski, Thomas H. & Lorraine Collins, R., 2024. "Emerging adults’ social media engagement & alcohol misuse: A multidimensional, person-centered analysis of risk," Children and Youth Services Review, Elsevier, vol. 159(C).

    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. Darima Fotheringham & Michael A. Wiles, 2023. "The effect of implementing chatbot customer service on stock returns: an event study analysis," Journal of the Academy of Marketing Science, Springer, vol. 51(4), pages 802-822, July.
    2. Saeideh Sharifi fard & Ezhar Tamam & Md Salleh Hj Hassan & Moniza Waheed & Zeinab Zaremohzzabieh, 2016. "Factors affecting Malaysian university students’ purchase intention in social networking sites," Cogent Business & Management, Taylor & Francis Journals, vol. 3(1), pages 1182612-118, December.
    3. Yu Wang & Shanyong Wang & Jing Wang & Jiuchang Wei & Chenglin Wang, 2020. "An empirical study of consumers’ intention to use ride-sharing services: using an extended technology acceptance model," Transportation, Springer, vol. 47(1), pages 397-415, February.
    4. Sharma, Mahak & Antony, Rose & Sehrawat, Rajat & Cruz, Angel Contreras & Daim, Tugrul U., 2022. "Exploring post-adoption behaviors of e-service users: Evidence from the hospitality sector /online travel services," Technology in Society, Elsevier, vol. 68(C).
    5. Scott, Stephanie & Hughes, Paul & Hodgkinson, Ian & Kraus, Sascha, 2019. "Technology adoption factors in the digitization of popular culture: Analyzing the online gambling market," Technological Forecasting and Social Change, Elsevier, vol. 148(C).
    6. Morgan-Thomas, Anna & Veloutsou, Cleopatra, 2013. "Beyond technology acceptance: Brand relationships and online brand experience," Journal of Business Research, Elsevier, vol. 66(1), pages 21-27.
    7. Mohammadbashir Sedighi & Hamideh Parsaeiyan & Yashar Araghi, 2021. "An Empirical Study of Intention to Continue Using of Digital Ride-hailing Platforms," The Review of Socionetwork Strategies, Springer, vol. 15(2), pages 489-515, November.
    8. Gansser, Oliver Alexander & Reich, Christina Stefanie, 2021. "A new acceptance model for artificial intelligence with extensions to UTAUT2: An empirical study in three segments of application," Technology in Society, Elsevier, vol. 65(C).
    9. Al-Qeisi, Kholoud & Dennis, Charles & Alamanos, Eleftherios & Jayawardhena, Chanaka, 2014. "Website design quality and usage behavior: Unified Theory of Acceptance and Use of Technology," Journal of Business Research, Elsevier, vol. 67(11), pages 2282-2290.
    10. 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.
    11. 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).
    12. 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).
    13. 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.
    14. 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.
    15. Driediger, Fabian & Bhatiasevi, Veera, 2019. "Online grocery shopping in Thailand: Consumer acceptance and usage behavior," Journal of Retailing and Consumer Services, Elsevier, vol. 48(C), pages 224-237.
    16. Junic Kim, 2018. "Platform Adoption Factors in the Internet Industry," Sustainability, MDPI, vol. 10(9), pages 1-12, September.
    17. Mariani, Marcello M. & Hashemi, Novin & Wirtz, Jochen, 2023. "Artificial intelligence empowered conversational agents: A systematic literature review and research agenda," Journal of Business Research, Elsevier, vol. 161(C).
    18. Mari, Alex & Mandelli, Andreina & Algesheimer, René, 2024. "Empathic voice assistants: Enhancing consumer responses in voice commerce," Journal of Business Research, Elsevier, vol. 175(C).
    19. Yoon, Jeewhan & Vonortas, Nicholas S. & Han, SungWon, 2020. "Do-It-Yourself laboratories and attitude toward use: The effects of self-efficacy and the perception of security and privacy," Technological Forecasting and Social Change, Elsevier, vol. 159(C).
    20. Fosso Wamba, Samuel & Bhattacharya, Mithu & Trinchera, Laura & Ngai, Eric W.T., 2017. "Role of intrinsic and extrinsic factors in user social media acceptance within workspace: Assessing unobserved heterogeneity," International Journal of Information Management, Elsevier, vol. 37(2), pages 1-13.

    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:gam:jsusta:v:14:y:2022:i:21:p:14270-:d:960146. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.