Unlocking Hidden Insights with fsQCA Analysis and Two-factor Perspective

Abstract

In addition to exploring symmetric effects, researchers have sought to categorize service attributes based on asymmetric effects. Symmetric impacts imply that customer (dis)satisfaction directly correlates with an attribute's presence or absence. Conversely, certain attributes, known as "delighting" attributes, cause satisfaction when present, but their absence does not result in a corresponding level of dissatisfaction. On the other hand, "must-be" attributes lead to dissatisfaction when absent, but their presence does not cause a significant level of satisfaction. Service providers can leverage this understanding to allocate resources more effectively. However, customers often evaluate quality based on multiple attributes simultaneously, rendering individual categorization insufficient. Therefore, it is essential to understand the asymmetric effects of attribute configurations. We propose employing fuzzy set Qualitative Comparative Analysis (fsQCA) to identify configurations as delighting, performance-related, must-be, or even reversed. This involves identifying configurations associated with high and low outcomes and determining their types. This study shifts the focus from individual attributes to configurations, thereby enriching the literature on asymmetric effects.