The car market is a high-involvement, high-information market, in which consumers are expected to go through extensive searches. Cars are highly symbolic artefacts. The marque and model say a lot about the owner, and evidently, a car is far beyond a purely rational, functionally based purchase. However, car manufacturers face a serious problem as worldwide marque loyalty levels, from purchase to purchase, average below 50%, and tend to decline over time. Evidently, the analysis of factors affecting car marque loyalty is a research topic of significant managerial importance. This study attempts to empirically address the structure of marque loyalty in the car market and has a dual objective: First, to relate marque loyalty to a set of consumer characteristics under a theoretical framework, and second, to examine the impact of current car’s attribute-level performance on loyalty. In this direction, this study illustrates the value of Adaptive Network-based Fuzzy Inference System (ANFIS), as a bridge between qualitative and quantitative approaches, in an attempt to identify alternative complex antecedent conditions that give rise to marque loyalty in the car market. The proposed approach offers to conventional correlational quantitative approaches three benefits: (1) asymmetry (i.e., relationships between independent and dependent variables are treated as non-linear/asymmetric), (2) equifinality (i.e., multiple pathways may lead to the same outcome), and (3) causal complexity (i.e., combinations of antecedent conditions lead to the outcome, and hence, the focus is not on net-effects, but on combinatorial-synergistic effects). To demonstrate these merits, ANFIS is compared to a conventional econometric forecasting technique, namely logistic regression.

• George Baltas(Athens University of Economics & Business, Greece)
• Stelios Tsafarakis(Technical University of Crete, Greece)
• Charalampos Saridakis(University of Leeds, UK)