IDEAS home Printed from https://ideas.repec.org/a/spr/elmark/v33y2023i1d10.1007_s12525-023-00629-4.html
   My bibliography  Save this article

Chatbot commerce—How contextual factors affect Chatbot effectiveness

Author

Listed:
  • Pei-Fang Hsu

    (National Tsing Hua University)

  • Tuan (Kellan) Nguyen

    (Middlesex University)

  • Chen-Ya Wang

    (National Tsing Hua University)

  • Pei-Ju Huang

    (National Tsing Hua University)

Abstract

The emergence of Chatbots has attracted many firms to sell their merchandise via chats and bots. Although Chatbots have received tremendous interest, little is understood about how different usage contexts affect Chatbots’ effectiveness in mobile commerce. Due to differences in their nature, not all shopping contexts are suitable for Chatbots. To address this research gap, this study examines how contextual factors (i.e., intrinsic task complexity that embraces shopping task attributes and group shopping environment, and extrinsic task complexity that entails information intensity) affect user perceptions and adoption intentions of Chatbots as recommendation agents in mobile commerce. Applying the lenses of cognitive load theory (CLT) and common ground theory (CGT), we perform an experiment and apply quantitative analytical approaches. The results show that Chatbots are more suitable in the context of one-attribute, information-light, and group-buying tasks, whereas traditional Apps are suitable for multi-attribute, information-intensive, and single-buying scenarios. These findings make important theoretical contributions to the IT adoption literature as well as to CLT and CGT theory by contextualizing the evolving state of Chatbot commerce and providing guidelines for designing better Chatbot user experiences, thereby enhancing user perceptions and adoption intentions.

Suggested Citation

  • Pei-Fang Hsu & Tuan (Kellan) Nguyen & Chen-Ya Wang & Pei-Ju Huang, 2023. "Chatbot commerce—How contextual factors affect Chatbot effectiveness," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-22, December.
    • Handle: RePEc:spr:elmark:v:33:y:2023:i:1:d:10.1007_s12525-023-00629-4
    DOI: 10.1007/s12525-023-00629-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12525-023-00629-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s12525-023-00629-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Lin Li & Kyung Young Lee & Emmanuel Emokpae & Sung-Byung Yang, 2021. "What makes you continuously use chatbot services? Evidence from chinese online travel agencies," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 575-599, September.
    2. Young Eun Lee & Izak Benbasat, 2011. "Research Note ---The Influence of Trade-off Difficulty Caused by Preference Elicitation Methods on User Acceptance of Recommendation Agents Across Loss and Gain Conditions," Information Systems Research, INFORMS, vol. 22(4), pages 867-884, December.
    3. Wood, Robert E., 1986. "Task complexity: Definition of the construct," Organizational Behavior and Human Decision Processes, Elsevier, vol. 37(1), pages 60-82, February.
    4. V. Kumar & Ashutosh Dixit & Rajshekar (Raj) G. Javalgi & Mayukh Dass, 2016. "Research framework, strategies, and applications of intelligent agent technologies (IATs) in marketing," Journal of the Academy of Marketing Science, Springer, vol. 44(1), pages 24-45, January.
    5. Kasilingam, Dharun Lingam, 2020. "Understanding the attitude and intention to use smartphone chatbots for shopping," Technology in Society, Elsevier, vol. 62(C).
    6. Sylvie Borau & Tobias Otterbring & Sandra Laporte & Samuel Fosso Wamba, 2021. "The most human bot: Female gendering increases humanness perceptions of bots and acceptance of AI," Post-Print hal-03648092, HAL.
    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. Andreas Engelmann & Gerhard Schwabe, 2024. "Certified data chats for future used car markets," Electronic Markets, Springer;IIM University of St. Gallen, vol. 34(1), pages 1-22, December.

    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. Mark Anthony Camilleri & Ciro Troise, 2023. "Live support by chatbots with artificial intelligence: A future research agenda," Service Business, Springer;Pan-Pacific Business Association, vol. 17(1), pages 61-80, March.
    2. Jan, Ihsan Ullah & Ji, Seonggoo & Kim, Changju, 2023. "What (de) motivates customers to use AI-powered conversational agents for shopping? The extended behavioral reasoning perspective," Journal of Retailing and Consumer Services, Elsevier, vol. 75(C).
    3. Tao Zhang & Chao Feng & Hui Chen & Junjie Xian, 2022. "Calming the customers by AI: Investigating the role of chatbot acting-cute strategies in soothing negative customer emotions," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2277-2292, December.
    4. 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).
    5. Massilva Dekkal & Manon Arcand & Sandrine Prom Tep & Lova Rajaobelina & Line Ricard, 2024. "Factors affecting user trust and intention in adopting chatbots: the moderating role of technology anxiety in insurtech," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 29(3), pages 699-728, September.
    6. Pham, Hong Chuong & Duong, Cong Doanh & Nguyen, Giang Khanh Huyen, 2024. "What drives tourists’ continuance intention to use ChatGPT for travel services? A stimulus-organism-response perspective," Journal of Retailing and Consumer Services, Elsevier, vol. 78(C).
    7. Aron Lindberg & Nicholas Berente & James Gaskin & Kalle Lyytinen, 2016. "Coordinating Interdependencies in Online Communities: A Study of an Open Source Software Project," Information Systems Research, INFORMS, vol. 27(4), pages 751-772, December.
    8. Ramon Rico & Cristina Gibson & Miriam Sanchez-Manzanares & Mark A. Clark, 2020. "Team adaptation and the changing nature of work: Lessons from practice, evidence from research, and challenges for the road ahead," Australian Journal of Management, Australian School of Business, vol. 45(3), pages 507-526, August.
    9. Ho, Manh-Tung & Mantello, Peter & Ghotbi, Nader & Nguyen, Minh-Hoang & Nguyen, Hong-Kong T. & Vuong, Quan-Hoang, 2022. "Rethinking technological acceptance in the age of emotional AI: Surveying Gen Z (Zoomer) attitudes toward non-conscious data collection," Technology in Society, Elsevier, vol. 70(C).
    10. Hu, Hai-hua & Ma, Fang, 2023. "Human-like bots are not humans: The weakness of sensory language for virtual streamers in livestream commerce," Journal of Retailing and Consumer Services, Elsevier, vol. 75(C).
    11. Mu Shengdong & Zheng Zeng & Wei Jintong & Wang Yuanyuan & Xiong Ying, 2022. "RETRACTED ARTICLE: Research on heterogeneous customer hotel supply chain channel selection model based on game theory," Operations Management Research, Springer, vol. 15(3), pages 998-1009, December.
    12. Matthijs J. Verhulst & Anne-Françoise Rutkowski, 2018. "Decision-Making in the Police Work Force: Affordances Explained in Practice," Group Decision and Negotiation, Springer, vol. 27(5), pages 827-852, October.
    13. Koo, Chulmo & Wati, Yulia & Jung, Jason J., 2011. "Examination of how social aspects moderate the relationship between task characteristics and usage of social communication technologies (SCTs) in organizations," International Journal of Information Management, Elsevier, vol. 31(5), pages 445-459.
    14. Shahla Ghobadi & John Campbell & Stewart Clegg, 2017. "Pair programming teams and high-quality knowledge sharing: A comparative study of coopetitive reward structures," Information Systems Frontiers, Springer, vol. 19(2), pages 397-409, April.
    15. Odette M. Pinto, 2015. "Effects of Advice on Effectiveness and Efficiency of Tax Planning Tasks," Accounting Perspectives, John Wiley & Sons, vol. 14(4), pages 307-329, December.
    16. Caglio, Ariela & Ditillo, Angelo, 2008. "A review and discussion of management control in inter-firm relationships: Achievements and future directions," Accounting, Organizations and Society, Elsevier, vol. 33(7-8), pages 865-898.
    17. Christian Julmi, 2019. "When rational decision-making becomes irrational: a critical assessment and re-conceptualization of intuition effectiveness," Business Research, Springer;German Academic Association for Business Research, vol. 12(1), pages 291-314, April.
    18. Thomas P. Novak & Donna L. Hoffman, 2019. "Relationship journeys in the internet of things: a new framework for understanding interactions between consumers and smart objects," Journal of the Academy of Marketing Science, Springer, vol. 47(2), pages 216-237, March.
    19. Svetlana Zemlyak & Olga Gusarova & Svetlana Sivakova, 2022. "Assessing the Influence of Collaborative Technology Adoption—Mediating Role of Sociotechnical, Organizational, and Economic Factors," Sustainability, MDPI, vol. 14(21), pages 1-14, November.
    20. Siwalak Jierasup & Adisorn Leelasantitham, 2024. "A Change from Negative to Positive of Later Adoption Using the Innovation Decision Process to Imply Sustainability for HR Chatbots of Private Companies in Thailand," Sustainability, MDPI, vol. 16(13), pages 1-29, July.

    More about this item

    Keywords

    Recommendation agent; Innovation adoption; Chatbot; Cognitive load theory; Common ground theory;
    All these keywords.

    JEL classification:

    • M15 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - IT Management

    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:spr:elmark:v:33:y:2023:i:1:d:10.1007_s12525-023-00629-4. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.