Preparing minds for an automated future : how competence feedback shapes AI self-efficacy and acceptance
Leonard Samuel Keck · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · 2026
This thesis explores how competence-oriented feedback can shape employees’ readiness to adopt generative AI by strengthening AI self-efficacy, addressing the research question: how does feedback influence AI self-efficacy, and how does AI self-efficacy in turn affect workplace AI acceptance? Using a sample of 208 participants divided into experimental and control groups, the study employed a survey-based online experimental design to assess whether task-related, competence-oriented feedback after an AI knowledge quiz increases AI self-efficacy and AI acceptance, using validated scales and bootstrapped mediation analysis (PROCESS Model 4). The results show that competence-oriented feedback significantly increases AI self-efficacy, that AI self-efficacy strongly predicts AI acceptance, and that AI self-efficacy significantly mediates the effect of feedback on AI acceptance, with effects robust to demographic and usage covariates. These findings suggest that competence beliefs are a causally relevant and adaptable driver of AI acceptance and can be strengthened through brief, scalable feedback interventions, even when objective task performance is not directly linked to acceptance-related attitudes. This study contributes causal evidence to the emerging literature on AI self-efficacy and AI acceptance and extends technology acceptance research by highlighting competence beliefs as an important psychological factor in AI adoption. Future research should examine the durability of feedback effects over time, compare human- versus AI-generated feedback sources and credibility perceptions, and incorporate behavioral adoption measures to test whether increases in self efficacy translate into sustained workplace AI use.