Underlying Factors of Technology Acceptance and User Experience of Machine Learning Functions in Accounting Software: A Qualitative Content Analysis

Abstract

This research examines which factors influence users’ technology acceptance (TA) and user experience (UX) of machine learning (ML) functions in accounting software. Although the two methods are widely acknowledged, they are rarely understood in unity. This study analyses factors underlying UX and TA of ML function in accounting software. It contributes to the ongoing discussion in the Human-Computer Interaction (HCI) community about the relation between UX and TAM, and it does so with a focus on AI functions of software and within a business domain. Six hypotheses were established based on the three concepts of innovativeness, trust, and satisfaction to understand their influence on TA and UX. To evaluate the hypotheses and answer the research question, an accounting software (AS) was chosen as a case. A qualitative content analysis was done of user experts’ perceptions of acceptance and experience with ML functions. The study concludes that innovativeness, trust, and satisfaction influence users’ TA and UX of ML functions in AS confirming the six hypotheses. The results are discussed in relation to the literature on UX, TAM, and accounting. The study questions the measurability of TAM and UX and suggests re-evaluating the use of these methods for products with artificial intelligence.