Recognizing Gigerenzer’s (1991) dictum that scientists’ tools are not neutral (tools-in-use influence theory formulation as well as data interpretation), this chapter reports theory and examines data in ways that transcend the dominant logics for variable-based and case-based analyses. The theory and data analysis tests key propositions in complexity theory: (1) no single antecedent condition is a sufficient or necessary indicator of a high score in an outcome condition; (2) a few of many available complex configurations of antecedent conditions are sufficient indicators of high scores in an outcome condition; (3) contrarian cases occur, that is, low scores in a single antecedent condition associates with both high and low scores for an outcome condition for different cases; (4) causal asymmetry occurs, that is, accurate causal models for high scores for an outcome condition are not the mirror opposites of causal models for low scores for the same outcome condition. The study tests and supports these propositions in the context of customer assessments (n = 436) of service facets and service-outcome evaluations for assisted temporary-transformations of self via beauty salon and spa treatments. The findings contribute to advancing a nuanced theory of how customers’ service evaluations relate to their assessments of overall service quality and intentions to use the service. The findings support the need for service managers to be vigilant in fine-tuning service facets and service enactment to achieve the objective of high customer retention.
The authors gratefully acknowledges permission granted by the publisher, Elsevier, to reuse content in this chapter original appearing in Wu, Yeh, Huan, and Woodside (2014).
Wu, P.-L., Yeh, S.-S., Huan, T.-C.(.C.). and Woodside, A.G. (2016), "Complexity Theory, Configural Analysis, and Deepening the Service Dominant Logic", Woodside, A.G. (Ed.) Bad to Good, Emerald Group Publishing Limited, pp. 181-243. https://doi.org/10.1108/978-1-78635-334-420161010Download as .RIS
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