DEVELOPING INTEGRATED SERVICE ROBOT ACCEPTANCE MODEL (ISRAM) IN HOSPITALITY USING META-ANALYTIC STRUCTURAL EQUATION MODELING (MASEM) APPROACH

Abstract

The widespread adoption of service robots in the hospitality industry has prompted researchers to examine the factors influencing customer intentions to use these technologies. This study aims to address the fragmentation in existing research by employing Meta-Analytic Structural Equation Modeling (MASEM) to investigate the theoretical foundations of customer intentions to use service robots in hotels. First, a comprehensive literature review identifies four prominent theoretical models, namely the Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), Service Robot Acceptance Model (SRAM), and Artificially Intelligent Device Use Acceptance (AIDUA) model. Next, a meta-analysis synthesizes data from 55 studies examining customer intentions, encompassing 63 independent datasets and 27,421 individuals, consolidating 65 commonly examined relationships within the literature. Using MASEM, this study empirically compares the performance of the TAM, UTAUT, and AIDUA models, revealing the superior performance of the AIDUA model in explaining customer intentions. Building on these findings, an integrated theoretical model, Integrated Service Robot Acceptance Model (ISRAM), is proposed. This model leverages the strengths of the individual frameworks to address theoretical gaps and achieve a more comprehensive understanding of service robot acceptance. It also introduces customer experience with service robots as a moderator to enhance the model's practical relevance. The model is empirically validated through MASEM which demonstrates that ISRAM explains 75% of the variance in customer intentions, significantly outperforming the individual models. This study contributes to the theoretical discourse by addressing the fragmentation in understanding service robot acceptance through the development of an overarching framework. It provides important implications to bridge the gaps between theory testing and theory development and is a humble step forward towards theory development. In addition to the theoretical contributions, practical implications are also offered, providing hospitality managers with actionable insights into the effective deployment of service robots.