Understanding configurations of relational attractiveness of the customer firm using fuzzy set QCA

By Z. Tóth, Christoph Thiesbrummel, Stephan C. Henneberg, P. Naudé. 2015

160 citations recorded by the source corpus.

This study introduces the concept of relational attractiveness of the customer (RAC) which is defined as the attitude of the supplier towards the customer firm in order to maintain and/or to improve an existing business relationship. Social Exchange Theory explains relational exchanges in business markets and provides a configurational logic for RAC. Based on an empirical investigation of the RAC perception of senior managers from 617 supplier companies, this study explores conditions of relational customer attractiveness, including financial and non-financial benefits, costs, trust and dependency. Using a fuzzy set qualitative comparative analysis (fsQCA), the study uncovers different configurations of these conditions that lead to high or low relational customer attractiveness. The results show that financial benefits are present in all configurations leading to high relational customer attractiveness, but other conditions also need to be present.

Subfields: Sociology and Political Science, Strategy and Management

View Paper: https://www.semanticscholar.org/paper/b18a7d75bef8477a240f82dac560641dc0b31455

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Citations in the corpus

Fuzzy-set qualitative comparative analysis (fsQCA) in entrepreneurship and innovation research – the rise of a method (2017, background): “…Henik (2015), Ho and Lu (2015), Huarng (2015), Iseke et al. (2015), Leischnig and Kasper-Brauer (2015), Ni et al. (2015), Tho and Trang (2015), Tóth et al. (2015), Aguilera-Caracuel et al. 2014, Chang and Cheng (2014), Garcia-Castro and Aguilera (2014), Fan and Zhu (2014), Leischnig et al.…”
Digitalization, business models, and SMEs: How do business model innovation practices improve performance of digitalizing SMEs? (2019, background): “…calibration rules can be used for the explanatory variables (conditions), where the crossover point can be set to the median, and full nonmembership and full membership can be set to the 10th and 90th percentile, respectively (Linton & Kask, 2017; Tóth, Thiesbrummel, Henneberg, & Naudé, 2015).”