Inter-relational Model for understanding Chatbot acceptance across retail sectors

TL;DR

This paper proposes an inter-relational model to understand factors influencing chatbot acceptance in retail, considering sector-specific differences and integrating multiple theoretical constructs.

Contribution

It introduces a comprehensive framework combining trust, satisfaction, and sector moderation to analyze chatbot acceptance across retail sectors.

Findings

Identifies key antecedents affecting chatbot acceptance.

Highlights sector-specific variations in acceptance factors.

Provides a research framework for future empirical validation.

Abstract

Despite the rising interest in chatbots, deployment has been slow in the retail sector. In the absence of comparative cross sector research on the user acceptance of chatbots in retail, we present a model and a research framework that proposes customer and chatbot antecedents using trust and customer satisfaction as relationship mediators and word of mouth and expectation of continuity as relationship outcomes. In determining our framework, we assimilate constructs from different models and theories overarching user experience with chatbots, technology acceptance and relationship marketing and propose a selection of 11 constructs as antecedents. Furthermore, we suggest retail sectors as one of our 4 moderators. Eventually, we provide insight into our current activities that is expected to identify which factors impact relationship outcomes to which extent across different retail sectors.