Extending UTAUT with perceived intelligence for the adoption of artificial intelligence in MSMEs and startups in Indonesia: A multi-group analysis

Abstract

The adoption of artificial intelligence (AI) is becoming increasingly important for micro-, small, and medium-sized enterprises (MSMEs) and startups in Indonesia as it enhances their competitiveness, efficiency, and innovation. However, factors driving AI adoption may differ between these two business types. This study investigates these differential factors by extending the unified theory of acceptance and use of technology model and adding the perceived intelligence construct. The explanatory quantitative research design employs purposive sampling, collecting data from 322 respondents (247 MSMEs and 75 start-ups). This extended model is analyzed using variance-based structural equation modeling (PLS-SEM) and multi-group analysis (MGA). The PLS-SEM results show that for MSMEs, performance expectancy, facilitating conditions, and perceived intelligence significantly influence behavioral intention, which, along with facilitating conditions, drives actual use. Conversely, for startups, performance expectancy and effort expectancy mainly drive behavioral intention, which in turn leads to actual use. Crucially, the MGA confirms significant differences between the two business types in terms of the effect of effort expectancy, facilitating conditions, and perceived intelligence on behavioral intention, as well as the effect of facilitating conditions on actual use. The study’s results contribute to the literature by validating the need for specific technology adoption models. Moreover, the study offers practical implications by emphasizing the importance of tailoring AI adoption strategies to the unique characteristics and needs of MSMEs and startups.