Entrepreneurial orientation, competitive advantage and strategic knowledge management capability in Malaysian family firms

Purpose

The purpose of this study is to test the thesis that the family firm’s success hinges on effective strategic knowledge management (SKM) capability coupled with an entrepreneurial orientation (EO). Contingency theory holds that entrepreneurial success is contingent on strategic capabilities and resource orchestration theory explains how well family firms nurture capabilities to structure, bundle and leverage resources that define competitive advantage (CA). This study combines these two theoretical viewpoints to propose the effects of EO and SKM capability on CA to achieve successful performance in family firms.

Design/methodology/approach

This study uses a hybrid approach applying structural equation modelling (SEM) and deep-learning artificial intelligence (DL-AI) analysis to survey data on 268 Malaysian family firms.

Findings

SEM results confirm that CA mediates the relationship between innovativeness, proactiveness and risk-taking dimensions of EO and firm performance. Autonomy and competitive aggressiveness have no bearing, however. The relationships among innovativeness, proactiveness and risk-taking with CA and performance are positively moderated by SKM capability, becoming more potent at higher levels. Moreover, four additional DL-AI models reveal the necessity of specific EO dimensions and the interacting effects of EO–SKM capability to influence CA and to attain performance success subsequently.

Originality/value

This study theorizes and presents two new boundary conditions to a knowledge-based theory of the family firm and its firm performance. First, CA mediates the relationship between EO and performance; and second, SKM capability moderates the relationships between EO and CA and between EO and family firm performance. Methodologically, this study uses DL-AI to embrace non-linearity and prioritize predictor variables based on normalized importance to produce greater accuracy over regression analysis. Hence, DL-AI adds methodological novelty to the knowledge management and family firm literature.