Unlocking potential: An integrated approach using PLS-SEM, NCA, and fsQCA for informed decision making

This study demonstrates the value of integrating different analytical perspectives to identify significant factors and characterize their importance. Specifically, we combine three analytical methods – partial least squares structural equation modeling (PLS-SEM), necessary condition analysis (NCA), and fuzzy set qualitative comparative analysis (fsQCA) – to create an expanded analytical process that enables informed decision-making. PLS-SEM identifies significant correlations between the predictor and outcome variables, NCA identifies critical bottlenecks required for a specific outcome, and fsQCA identifies configurations of conditions sufficient for producing a specific outcome. By applying this expanded analytical process to subjectively reported data on service quality and perceived accessibility, collected from five Nordic cities, we gain new insights into attracting an aging population to public transport. This study contributes to a better understanding of the nuances in the data, which is valuable for both research and practice.