Technology Acceptance Model: Which factors drive the acceptance of AI among employees?

In recent years, artificial intelligence (AI) has rapidly moved from an ideal concept to a technology that can be deployed. Due to its capabilities of mimicking human intelligence, many firms in various industries have started integrating AI. However, AI cannot improve an organization’s performance if it is not used. Unfortunately, employees’ resistance to innovative technologies is a widespread problem. To minimize the adverse effects and costs of employees’ resistance, it is valuable to predict and better understand which factors drive the acceptance of AI among employees. This thesis addresses the ability to predict employees’ acceptance of AI. For this purpose, the traditional Technology Acceptance Model (TAM), which exists of behavioral intention to use (BI), perceived usefulness (PU), and perceived ease of use (PEOU), is extended with trust and social influence related factors such as compliance, image, technological trust, and behavioral uncertainty. In addition, the moderating effect of prior experience with AI was investigated. A survey was designed with 199 participants (N=199) to measure the magnitude and directionality of the effects of the driving factors of acceptance. Within the survey, participants were presented with a taxor audit-related case. The results demonstrate that the extended TAM model is a valid model to predict employees’ acceptance of AI. PU exhibited the strongest significant influence on BI. In contrast, no significant direct effect of PEOU on BI was found. The findings further demonstrated that acceptance behavior differs for experienced compared to inexperienced employees. Another significant result is that employees’ sentiment regarding their prior experience significantly affects the magnitude of the driving factors. These findings advance theory and contribute to future research focused on improving understanding of employees’ acceptance of AI.