Interfirm partnership resource–lean capability association: exploring the moderating role of learning orientation and performance implications

ABSTRACT

Applying the dynamic capabilities theory and the relational perspective, we examine key cross-firm supply chain resources as determinants of lean capability, the latter’s impact on operational and financial performance, and additionally, the interactive influence of learning orientation. Analyses of 152 manufacturing firms headquartered in the U.S.A. relate lean capability to operational and financial performance outcomes. Resource complementarity and resource specificity significantly associate with supply chain lean capability. A negative interaction effect of learning orientation context is indicated for the resource complementarity–lean association. However, a universality of interactive impact of learning orientation is suggested for the resource specificity-lean capability relationship. Contributions extend dynamic capability theory to supply chain contexts: key supply chain partnership resources configured into lean capability deliver superior performance, with learning orientation present in a boundary condition role. Managerial implications relate to the importance of supply chains in providing key cross-firm resources that configure into lean capabilities in a learning environment for superior performance outcomes.

KEYWORDS:

• Supply chain resources
• dynamic capabilities theory
• lean
• learning orientation
• performance

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. PLS-SEM analysis has become common in marketing and supply chain strategy research. It is flexible in handling non-normal data and has the ability to handle complex models with interaction terms by maximizing the variance explained in the dependent variable. PLS-SEM does not face identification constraints that CB-SEM faces even when analyzing complex models and generates robust parameter estimates. It also overcomes the dichotomy between explanation and prediction needed for academic research with managerial implications (Hair et al., 2019). Although we acknowledge that PLS analysis has some issues (Cadogan & Lee, 2022), we have used multiple methods to verify our results (Henseler & Schuberth, 2022) and believe that our PLS analysis results are consistent with other methods. Given these reasons, we believe that PLS-SEM is appropriate for our study.