Network centrality and marketing technology (MarTech) adoption in emerging markets: a network configuration perspective

Abstract

Purpose This study examines how different configurations of network characteristics influence marketing technology (MarTech) adoption among small and medium enterprises (SMEs) in emerging markets. Drawing on network theory, institutional theory, and absorptive capacity, the research investigates the complex interplay among network centrality, tie-strength diversity, and institutional contexts in shaping technology adoption outcomes. Through a comparative analysis of two distinct emerging markets, the study develops a deeper understanding of how institutional development levels affect network effectiveness while revealing specific mechanisms through which firms leverage their network positions for successful technology adoption. The research particularly focuses on how different network configurations create unique pathways to adoption success across varying institutional contexts. Design/methodology/approach The study employs a multi-stage mixed-method approach, collecting and analyzing data from SMEs in Ghana and India that have undertaken marketing technology adoption initiatives. The research design combines polynomial regression analysis with fuzzy-set Qualitative Comparative Analysis (fsQCA) to identify complex patterns of network configurations leading to successful adoption. This methodological approach enables the identification of both linear and non-linear effects while revealing multiple equifinal pathways to adoption success. Comprehensive robustness checks, including 2SLS estimation, quantile regression, and detailed endogeneity analyses, ensure the validity of the findings. Findings The results reveal a complex, curvilinear relationship between network centrality and technology adoption, with benefits diminishing beyond specific thresholds that vary systematically with institutional development levels. Network configuration effectiveness is moderated by tie-strength diversity, with optimal configurations differing between high- and low-institutional-void contexts. Five distinct configurational pathways to successful adoption emerge, demonstrating equifinality in network strategies. Absorptive capacity acts as a crucial capability multiplier, particularly pronounced in environments with significant institutional voids. Cross-country analysis reveals stronger network effects in Ghana than in India, with the difference widening at higher levels of adoption sophistication. Research limitations/implications The study advances network theory by introducing the concept of network efficiency thresholds and demonstrating how institutional contexts shape optimal network configurations. For practitioners, it provides specific guidance for network portfolio optimization across different institutional environments, including concrete strategies for relationship management and capability development. The findings inform policymakers about the need for differentiated support strategies based on institutional development levels, suggesting specific mechanisms for enhancing absorptive capacity and facilitating network formation. The research also offers insights into digital ecosystem development in emerging markets, recommending platforms that support both strong and weak tie formation. Originality/value This study makes several contributions to network and institutional theories. It challenges linear assumptions about network benefits by revealing how institutional contexts fundamentally alter network effectiveness. The paper identifies specific mechanisms through which firms adapt their network configurations to compensate for institutional weaknesses while demonstrating how absorptive capacity's value varies systematically with institutional development. The findings advance understanding of technology adoption in emerging markets by revealing multiple successful configurational pathways, providing both theoretical advancement and practical guidance for managing digital transformation in institutionally complex environments.