Necessary Condition Analysis (NCA) in Tourism and Hospitality Research: A Methodological Alternative for Causal Inference and Theoretical Insights

This study introduces Necessary Condition Analysis (NCA) as an alternative to traditional additive models such as regression analysis and structural equation modeling (SEM), which are commonly used in tourism and hospitality research yet have theoretical and practical limitations. While conventional methods focus on average effects and sufficient conditions, NCA identifies essential conditions that must be met for a desired outcome to occur. This approach offers a more nuanced understanding of causal relationships, especially in complex behavioral contexts. As an illustrative case, this study examines whether cognitive and affective destination images act as necessary conditions for tourists’ behavioral intentions. By conducting NCA using SmartPLS 4.0, the study finds that both cognitive and affective images are indeed necessary for the formation of behavioral intention. Notably, affective image requires a higher threshold to generate strong behavioral intention, underscoring the critical role of emotional perceptions in tourist decision-making. These findings demonstrate the theoretical value of NCA in identifying asymmetrical causality that traditional additive models may fail to capture. Practically, the results suggest that tourism managers should ensure minimum levels of emotional and cognitive appeal to promote visitor behaviors effectively. The study advocates for broader application of NCA in tourism research to better understand bottleneck conditions and guide strategic planning in destination marketing and experience design.