Determinants of Self-Service Analytics Adoption Intention

Abstract

The increasing popularity of self-service analytics (SSA) is empowering business users to analyze data and generate actionable insights autonomously. While there are many benefits to SSA tools, there is a scarcity of research on the factors influencing their adoption in business organizations. This article presents an extended technology acceptance model (TAM) that incorporates the task-technology fit (TTF), compatibility, and user empowerment as critical antecedents of users' intention to adopt SSA tools for reporting and analytics tasks. To test the proposed model, data were collected through a questionnaire survey of 211 business users working in different industries in Jordan. The collected data were analysed using structural equation modeling (SEM). The results of this study demonstrate that the task-technology fit, compatibility, and user empowerment are significant predictors of users' perceptions of usefulness and ease of use of SSA tools. Both of perceived usefulness and perceived ease of use have a positive effect on users' intention to adopt SSA tools. Collectively, all these factors account for 51.6 percent of the variance in the behavioral intention. The findings of this study provide several key implications for research and practice, and thus should contribute to the design and adoption of more user-accepted SSA tools and applications.