A method-engineering approach to obtain long-term knowledge in service productivity management
Rauer Hans Peter
For benchmarking services productivity a multitude of factors has to be considered. Thereby constructing and selecting suitable and meaningful sets of factors to measure productivity in service benchmarking helps storing domain knowledge. To allow for multiple factors in the productivity calculation, the Data Envelopment Analysis (DEA) is conjointly applied with the Analytical Hierarchy Process (AHP) that does a preliminary analysis on the subjective importance of the factors. The raw data for the construction of productivity models is derived from scientific publications on service productivity. This way, the paper helps utilizing theoretical knowledge in a practical context. The utilization is demonstrated with an illustrative example.
Benchmarking; Service Science; Service productivity