Learning from errors in insurance companies

Learning from errors in insurance companies PurposeThe 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/approachThe 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.FindingsFindings 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/implicationsLimitation 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/valueReplication 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Management Development Emerald Publishing

Learning from errors in insurance companies

Loading next page...
 
/lp/emerald/learning-from-errors-in-insurance-companies-YNldzHRpmF
Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
0262-1711
D.O.I.
10.1108/JMD-06-2017-0211
Publisher site
See Article on Publisher Site

Abstract

PurposeThe 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/approachThe 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.FindingsFindings 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/implicationsLimitation 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/valueReplication 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.

Journal

Journal of Management DevelopmentEmerald Publishing

Published: Mar 5, 2018

There are no references for this article.

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off