eThesis - School of Business electronic theses

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The objective of this thesis is to create and subsequently empirically test a service quality measurement model suitable for situations where the relationships between a manufacturing and a distribution organization. Service quality is commonly identified as a key success factor but the majority of existing quality models are developed for b2c settings and are not suitable for measuring the service quality in a b2b relationship.

Based on previous research on the field of service quality measurement, a quality model was developed. This model includes hypothesized service quality dimensions, which represent different aspects of service quality. The model suggests that input dimensions positively affect the process dimensions, which subsequently have a positive effect on output quality. These dimensions were Tangibles & visuals, Information, Employee assurance, Accessibility, Service delivery, Employee response, Service outcome quality and Customer value. Concurrently with the quality constructs, a set of indicators was developed in order to measure these latent quality constructs.

Quantitative empirical research was carried out in order to test the hypothesized model. Data was collected from the case company's distributors via a survey that comprised of the abovementioned indicators and a total of 55 usable datasets were received. The data was analyzed using Partial Least Squares (PLS) method. The developed model predicted 63% - 76% of the process and outcome dimensions of service quality, depending on the dimension. Reliability and validity of the model was confirmed and all the above-mentioned hypothesized positive relationships were supported.

Study findings support the widespread idea that service quality has both a process and an outcome structure that contribute to the overall perceived service quality. The findings also show that providing sufficient accessibility and assurance from employees contribute most strongly to process dimension. Furthermore, Employee response is the strongest predictor of output quality. Suggestions for future research include a refined model of the one introduced in this study that would utilize the Gap-approach.