Fr-OR-S85-2 - Individual And Contextual Factors Influencing Learning From Errors In Insurance Companies

Human error and accidents
Oral Presentation
Part of:
Friday May 19   11:45 AM to 12:00 PM (15 minutes)
Occupational and organizational safety
Human error and accidents
Individual and contextual factors influencing learning from errors in insurance companies
V. Leicher*, R. H. Mulder 1
1Institute of Educational Science, University of Regensburg, Regensburg, Germany
Content: Purpose
The goal of this study is to identify relevant individual and contextual factors influencing learning from errors at work.
We conducted a cross sectional survey in the insurance industry (N= 171). Insurance agents´ engagement in social learning activities was measured by scales on their general / specific cause analysis and development of new strategies. We also measured relevance for learning, emotional strain, motivational tendency to cover up errors and perceived safe team climate. We carried out descriptive statistics and structural equitation modeling.
Our model shows an acceptable fit to the data (χ2/df = 1.59; SRMR =.08; CFI=.90; RMSEA=.05, 90% CI .05-.08). Our results show that error strain influences safe team climate significant negatively (β =-.28). Safe team climate significantly influences the tendency to cover up error in a negative way (β =-.49). The engagement in social learning activities is influenced positively by the estimation of an error as chance for learning (β =.14) and negatively by the tendency to cover up errors (β =-.30).
Limitations of our study are that we had a relatively small sample size and participants are working in different insurance companies and by this are facing different organizational circumstances.
Research/ Practical implications
Implications of our results concern the importance of error friendly climate in organizations that allows employees to openly address errors and to discuss critical situations.
Originality / Value
As a replication study this paper contributes to the generalization of results by transferring a model of learning from errors in the domain of insurance industry.

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