Learning from errors in insurance companies

• Published in: The Journal of management development 2018-03, Vol.37 (2), p.138-148
• Main Authors: Anselmann, Veronika, Mulder, Regina H
• Format: Article
• Language: English
• Subjects: Cybersecurity; Elder care; Experiential learning; Informal Education; Insurance companies; Insurance industry; Knowledge; Learning activities; Learning Processes; Nursing; Organizational learning; Retail banking; Studies
• ISSN: 0262-1711; 1758-7492
• DOI: 10.1108/JMD-06-2017-0211

Purpose The study pursues two goals: first, as a replication study, the purpose of this paper is to test a model of learning from errors in the domain of insurance industry. Second, to increase insights in learning from errors, the authors focussed on different types of errors. Design/methodology/approach The authors conducted a cross-sectional survey in the insurance industry (N=206). The authors used structural equation modelling and path modelling to analyse the data. To be able to analyse different types of errors, the authors used Critical Incident Technique and asked participants to describe error situations. Findings Findings from the study are that the model of learning from errors could partly be replicated. The results indicate that a non-punitive orientation towards errors is an important factor to reduce the tendency of insurance agents to cover up errors when knowledge and rule-based errors happen. In situations of slips and lapses error strain has a negative influence on trust and non-punitive orientation which in turn both reduce the tendency to cover up errors. Research limitations/implications Limitation is the small sample size. By using Critical Incidents Technique, the authors were able to analyse authentic error situations. Implications of the results concern the importance of error-friendly climate in organisations. Originality/value Replication studies are important to generalise results to different domains. To increase the insight in learning from errors, the authors analysed influencing factors with regard to different types of errors.