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

Xusen Cheng,Ying Bao,Alex Zarifis,Wankun Gong,Jian Mou

doi:10.1108/intr-08-2020-0460

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.

Highlights

Disembodied conversational agents (DCAs) have become quite common (Araujo, 2018; Luo et al, 2019)
After adding the interaction terms to the model, the results show that task complexity significantly moderates the relationship between friendliness and consumers’ trust toward the chatbot (Model 3: β 5 À0.073, p < 0.05), supporting H3b
We mainly target the impacts of two specific attributes on consumers’ responses in this study

Summary

Introduction

Disembodied conversational agents (DCAs) have become quite common (Araujo, 2018; Luo et al, 2019). Galvanized by artificial intelligence (AI) and machine learning, DCAs in the form of chatbots can simulate human interactions through text chats or voice commands in various fields and have brought unprecedented business potential (Luo et al, 2019). A chatbot is defined as “a machine conversation system which interacts with human users via natural conversational language” Many famous brands and major platforms, such as eBay, Facebook, WeChat, Amazon and Apple’s Siri, have rolled out chatbots to take orders, recommend products or provide other customer services (Thompson, 2018; Go and Sundar, 2019). Gartner predicts that by 2021, almost 15% of the customer services will be completely handled by AI (Mitchell, 2018)

Objectives

Findings

Conclusion