Extracting Best-Practice Using Mixed-Methods
| dc.contributor.author | Poppe, Erik | |
| dc.contributor.author | Pika, Anastasiia | |
| dc.contributor.author | Wynn, Moe Thandar | |
| dc.contributor.author | Eden, Rebekah | |
| dc.contributor.author | Andrews, Robert | |
| dc.contributor.author | Hofstede, Arthur H. M. | |
| dc.date.accessioned | 2022-01-17T12:15:13Z | |
| dc.date.available | 2022-01-17T12:15:13Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | Problem Definition: Queensland’s Compulsory Third-Party (CTP) Insurance Scheme provides a mechanism for persons injured as a result of a motor vehicle accident to receive compensation. Managing CTP claims involves multiple stakeholders with potentially conflicting interests. It is therefore pertinent to investigate whether ‘best practice’ for claims processing can be identified and measured so all claimants receive fair and equitable treatment. The project set out to test the applicability of a mixed-method approach to identify ‘best-practice’ using qualitative, process mining, and data mining techniques in an insurance claims processing domain. Relevance: Existing approaches typically identify ‘best practice’ from literature or surveys of practitioners. The study provides insights into an alternative, mixed-method approach to deriving best practice from historical data and domain knowledge. Methodology: The study is a reflective analysis of insights gained from a practical application of a mixed-method approach to determine ‘best practice’. Results: The mixed-method approach has a number of benefits over traditional approaches in uncovering best practice process behavior from historical data in the real-world context (i.e., can identify process behavior differences between high and low performing cases). The study also highlights a number of challenges with regards to the quality and detail of data that needs to be available to perform the analysis. Managerial Implications: The ‘lessons learned’ from this study will directly benefit others seeking to implement a data-driven approach to understand a ‘best-practice’ process in their own organization. | de |
| dc.identifier.doi | 10.1007/s12599-021-00698-9 | |
| dc.identifier.pissn | 1867-0202 | |
| dc.identifier.uri | http://dx.doi.org/10.1007/s12599-021-00698-9 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/37938 | |
| dc.publisher | Springer | |
| dc.relation.ispartof | Business & Information Systems Engineering: Vol. 63, No. 6 | |
| dc.relation.ispartofseries | Business & Information Systems Engineering | |
| dc.subject | Best-practice | |
| dc.subject | Case study | |
| dc.subject | Insurance claim processing | |
| dc.subject | Mixed-method | |
| dc.subject | Process mining | |
| dc.title | Extracting Best-Practice Using Mixed-Methods | de |
| dc.type | Text/Journal Article | |
| gi.citation.endPage | 651 | |
| gi.citation.startPage | 637 |