Exploring consumers' response to text-based chatbots in e-commerce: the moderating role of task complexity and chatbot disclosure

Abstract

PurposeArtificial intelligence (AI)-based chatbots have brought unprecedented business potential. This study aims to explore consumers' trust and response to a text-based chatbot in e-commerce, involving the moderating effects of task complexity and chatbot identity disclosure.Design/methodology/approachA survey method with 299 useable responses was conducted in this research. This study adopted the ordinary least squares regression to test the hypotheses.FindingsFirst, the consumers' perception of both the empathy and friendliness of the chatbot positively impacts their trust in it. Second, task complexity negatively moderates the relationship between friendliness and consumers' trust. Third, disclosure of the text-based chatbot negatively moderates the relationship between empathy and consumers' trust, while it positively moderates the relationship between friendliness and consumers' trust. Fourth, consumers' trust in the chatbot increases their reliance on the chatbot and decreases their resistance to the chatbot in future interactions.Research limitations/implicationsAdopting the stimulus–organism–response (SOR) framework, this study provides important insights on consumers' perception and response to the text-based chatbot. The findings of this research also make suggestions that can increase consumers' positive responses to text-based chatbots.Originality/valueExtant studies have investigated the effects of automated bots' attributes on consumers' perceptions. However, the boundary conditions of these effects are largely ignored. This research is one of the first attempts to provide a deep understanding of consumers' responses to a chatbot.

What drives continuance intention to use a food-ordering chatbot? An examination of trust and satisfaction

“…Eren (2021) demonstrated that perceived trust in a bank's chatbot significantly affected customer satisfaction from chatbot use. Cheng et al (2021) found that users' trust in textbased chatbots in e-commerce will influence their reliance on the chatbot. In summary, trust will be one of the key factors influencing usage intention and customer attitude in chatbot context.…”

“…Concerning the factors of positive attitude, trust and satisfaction will be the main factors of online behaviors in the chatbot context (Rajaobelina et al, 2021;Cheng et al, 2021;Nguyen et al, 2021). Previous research indicated that trust could be an affective attitude (Jones, 1996).…”

Voice Assistant vs. Chatbot – Examining the Fit Between Conversational Agents’ Interaction Modalities and Information Search Tasks

“…Another promising research area is the comparison of speech-based, text-based, and human interactions, in particular the impact of disclosing the CA's non-human identity. While this topic has already received initial attention for text-based CA interactions (Cheng et al, 2021;Mozafari et al, 2021aMozafari et al, , 2021b, it becomes progressively important given the increasing naturalness of synthetic speech output.…”

A literature review on users' behavioral intention toward chatbots' adoption

“…Particularly, in five studies combined theories have been used (TAM and DOI; TAM and ECM and ISS; TAM & SST; TAM, DOI and TOE) [1,29,35,37,40], whereas in seven (18%) research papers information system (IS) continuance models such as SOR, U&G, TPB, CAT, TRA, SERVQUAL, extended post acceptance model were applied. Cheng et al [52] used SOR theory to explain the behavior of consumers toward chatbots in the context of e-commerce.…”