Consumer Adoption of Internet of Things

Abstract

The Internet of Things (IoT), a pivotal technology in enhancing user connectivity, faces a paradox: its widespread potential yet limited consumer adoption. This study addresses this dichotomy by synthesizing a large-scale meta-analytic structural equation modeling (MASEM) and hierarchical linear meta-analysis (HiLMA) of 2736 effect sizes from 52,629 respondents across 138 studies. We propose an integrated and heterogeneous model, underpinned by appraisal theory and the technology acceptance model (TAM), to explain the dynamics of IoT adoption among consumers across heterogeneous settings. Our findings reveal that consumers' primary appraisals of IoT, influenced significantly by hedonic motivation and social influence, are instrumental in forming perceptions of IoT's ease of use and usefulness. These perceptions subsequently guide consumers in navigating the complexities of IoT, such as privacy, risk, and trust issues. These layered appraisals inform consumers' attitudes and intentions toward IoT adoption, wherein its efficacy is heightened in contexts involving critical services versus non-critical ones, in Eastern versus Western regions, within healthcare rather than fashion sectors, and among more experienced and younger demographics, particularly students over the general public. The study also underscores the distinct impact of branded IoT devices over unbranded ones, delineating a heterogeneous understanding of IoT adoption. Therefore, the interpretation of heterogeneities in the combination of effect sizes from extant studies is a seminal attempt to address the IoT paradox by demonstrating which factors explain the variations in the relationship between attitude and intention toward IoT from the consumer's perspective.