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Performance-Based Franchisee Segmentation
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AI
DOI：10.1080/1051712X.2025.2612376.png)
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Abstract
En 中文
PurposeThe aim of the study is to extend the service profit chain as a framework for segmenting franchisees based on their service performance. The extended service profit chain employed considers both objective and subjective inputs, including service quality, transactional value, customer satisfaction, and customer retention.MethodologyPartial Least Squares (PLS) is used to analyze longitudinal data from automotive dealerships and a PLS-Finite Mixture model is employed to identify latent (performance) segments. Importance-Performance Analysis (IPA) is then used to further examine the differences between segments.FindingsOur findings deepen current understandings of the relationships between service performance inputs and outputs, and guide managers on how and where to allocate resources to improve profitability.Originality/valueSegmentation is particularly important in B2B contexts and provides the foundation for both strategy formulation and resource allocation. This study draws on four consecutive years longitudinal data for every dealership (n = 180) of a major global brand.Practical implicationsExisting franchisor frameworks for classifying franchisees in the automotive sector consist of two binaries: metro-rural, and large-small. This resultant two-by-two is simplistic and dated. It lacks nuance, does not account for heterogeneity, and undermines efforts to meaningfully benchmark within and across segments. It also fails to consider underlying latent factors which may influence dealer performance or help signpost strategies which may be effective in helping manage dealership performance.
Keywords:
Market segmentation
franchisees
service performance
FIMIX-PLS
automotive
Journal
J
IF:
2.5
Papers:
24
Citations:
563
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