Supplementary Data for From Organizational Capabilities to Firm Performance through Corporate Entrepreneurship
Description
This study examines how complexity absorption (CA), transformational leadership (TL), and technological capability (TC) relate to perceived firm performance through corporate entrepreneurship (CE), and how these relationships inform managerial diagnosis and data-derived priorities. Survey data from 310 decision-involved employees and executives of Indonesian laboratory-equipment distributors were analyzed with partial least squares structural equation modeling (PLS-SEM). Importance-performance map analysis (IPMA) assessed modeled total effects and rescaled performance with firm performance as the target. A data-driven fuzzy analytic hierarchy process (FAHP), using Chang's extent analysis, transformed the four construct-level total effects into relative fuzzy priorities under two documented comparison scales. All seven direct relationships and three specific indirect effects were positive and statistically significant, consistent with complementary partial mediation. CA showed the largest direct relationship with CE (β = .385); CE had the largest direct relationship with perceived performance (β = .332). The model explained 60.6% of CE variance and 50.5% of firm-performance variance. IPMA identified TC as the lowest-scoring capability (64.673), while CE had the largest total effect (.332). The revised 1–3 FAHP conversion yielded weights of .569 for CE, .215 each for TL and CA, and zero for TC; the alternative 1–2 conversion gave TC a positive weight (.095). These weights are transformations of the same PLS-SEM results, not independent expert preferences or validated intervention effects. The study therefore highlights CE's intermediate role and demonstrates why structural relevance, current performance, and scale-dependent decision weights warrant separate interpretation. Managerial recommendations remain conditional on resources, feasibility, and further validation. The conclusions apply to this sector-specific, respondent-rated research setting.