Chatbot commerce - How contextual factors affect Chatbot effectiveness


Hsu, P., Nguyen, T., Wang, C. and Huang, P. 2023. Chatbot commerce - How contextual factors affect Chatbot effectiveness. Electronic Markets. 33.
TitleChatbot commerce - How contextual factors affect Chatbot effectiveness
AuthorsHsu, P., Nguyen, T., Wang, C. and Huang, P.

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.

KeywordsRecommendation agent; Innovation adoption; Chatbot; Cognitive load theory; Common ground theory
Sustainable Development Goals9 Industry, innovation and infrastructure
Middlesex University ThemeCreativity, Culture & Enterprise
JournalElectronic Markets
Publication dates
Online03 May 2023
PrintDec 2023
Publication process dates
Submitted18 Jun 2022
Accepted17 Jan 2023
Deposited05 May 2023
Output statusPublished
Digital Object Identifier (DOI)
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