Innovative resources driven artificial intelligence orientation: The moderating role of environmental and executives’ characteristics

Wucheng Han, Weijie Zhu, Zhaoli Song and Ruoyu Lu

Technology in Society, 2025, vol. 81, issue C

Abstract: In the era of rapid artificial intelligence (AI) technology advancement, enterprises increasingly consider AI as a strategic imperative. However, the determinants of AI adoption and strategy formulation in enterprises remain elusive. Drawing on an integrated Resource-Based View (RBV) and Strategic Choice Theory (SCT) framework, this study presents a theoretical model that scrutinizes the factors and boundary conditions influencing enterprises in crafting AI strategies. Empirical assessments of theoretical hypotheses were conducted utilizing survey data gathered from 717 executives across global firms. We found strong evidence that internal innovation resources (IIR) and collaborative innovation networks (CIN) have a positive impact on firm's AI orientation. The study revealed that environmental uncertainty (EU) can negatively moderate the relationship between internal innovation resources, collaborative innovation networks, and AI orientation. A positive moderating effect was observed between corporate digital culture (DC) and the relationship between internal innovation resources, collaborative innovation networks, and AI orientation. Meanwhile, we found a positive moderating effect of Executives' Not-Invented-Here Syndrome (NIHS) on the relationship between internal innovation resources and AI orientation. This research contributes to the ongoing discourse on AI strategy in management and offers valuable guidance for organizations seeking to harness the potential of AI in a rapidly changing business environment.

Keywords: Artificial intelligence orientation; Internal innovation resources; Collaborative innovation networks; Environmental uncertainty; Not-invented-here syndrome; Resource-based view; Strategic choice theory (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:teinso:v:81:y:2025:i:c:s0160791x25000272

DOI: 10.1016/j.techsoc.2025.102837

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