Consumer complaint behavior: Beyond Hirschman's exit-voice-loyalty framework

Abstract

Studies on consumer complaining behavior mainly focus on complaining behavior predictors (e.g., culture, demographic characteristics, personality characteristics).  Academics and practitioners have accorded little attention to the concept of a consumer complaint behavior and the complaint responses structure. In fact, since Hirschman's "Exit-Voice-Loyalty model," few researchers have attempted to improve and offer new frameworks (e.g., Singh's taxonomy; Day and Landon's dichotomy).

The purpose of this paper is to examine the classical taxonomies of consumers complaining behavior. The first result shows that the conceptual and methodological studies bring about significant variation and differentiation in terms of terminology, variable definitions, number of variables used, and model structure. These different interpretations could naturally generate contradicting classifications and misleading results. The second result refers to the inadequacy of the classical taxonomies to withstand the test of time. Today's consumers have multiple complaining opportunities or platforms that need to be considered in the research model.

Companies are facing challenges when it comes to choosing which communication platform must be prioritized. For instance, social media platforms add another layer of complexity to the study of complaining behavior. In light of these changes, academics should design integrated models to guide research and help practitioners acquire more profound knowledge and design efficient complaints handling systems.

From a managerial perspective, companies hold incomplete and inaccurate customer satisfaction data. While consumer complaint behavior is studied separately, academics have pointed out that complaint actions are part of the post-purchase outcomes. Therefore, including complaints in the satisfaction, dissatisfaction process will only give companies a complete picture. Managers could improve satisfaction data and analysis through the use of sound recovery systems.