Learning from errors involves analysis and identification of error causes, as well as implementation of solutions to prevent similar errors in the future. The present thesis extends this conceptualization: Integrating research on errors from different contexts, from high reliability organizations to training environments, service occupations, and creative settings, this thesis submits that individuals may experience a variety of error learning types that include task, prevention, response, coping, and meta-learning. The thesis also presents a corresponding Learning from Errors (LFE) measurement inventory with five distinct error learning constructs and offers initial evidence of their validity. Furthermore, the thesis investigates the role of growth- and security-related motives in attaining the five error learning types. Specifically, relying on regulatory focus theory (Higgins, 1997, 1998), the thesis presents a model linking growth concerns with error learning types that maximize achievement and security concerns with error learning types that minimize threat. The findings from three samples confirm distinct influences of growth and security concerns on error learning, however the observed distinctions are different from those hypothesized. Contrary to expectations, security concerns exhibited wide-ranging positive associations with all error learning types, with particularly sizeable contributions to prediction of task, prevention, and response learning. Growth concerns, on the other hand, showed relatively modest influence on prevention and response learning, while positively contributing to task, coping, and meta-learning. Overall, this work highlights the multifaceted nature of learning from errors by providing an integrated theoretical typology, empirically validating the proposed error learning types, and highlighting distinctions in their motivational antecedents
