Work

Beyond the efficiency trap: Regional AI exposure, AI knowledge, and the asymmetric governance of data-driven value propositions

Year: 2026

Type: article

Abstract: Artificial intelligence (AI) is reshaping corporate strategy, yet how regional AI exposure (RAE), the intensity of local industry peers' AI inventive activities, shapes firms' data-driven value propositions (DDVPs) remains unclear. Using panel data on Chinese listed firms from 2013 to 2023, we examine the mechanism linking RAE to DDVPs. We find that higher RAE is associated with greater subsequent AI knowledge generation, which is more strongly related to efficiency-oriented than product-oriented DDVPs, reflecting an efficiency trap. Dynamic capabilities govern this conversion asymmetrically: sensing and seizing capabilities primarily strengthen efficiency-oriented DDVP, whereas reconfiguring capability is particularly important for elevating product-oriented DDVP. These findings extend DDVP research beyond firm internal explanations by incorporating regional AI environments and show how distinct dynamic capability dimensions shape heterogeneous value-creation orientations.

Source: Technovation

DOI: 10.1016/j.technovation.2026.103730

Authors: Minmin Huang, Rui Yang

Institution: Soochow University

Language: English

Topic: Innovation and Knowledge Management

Subfield: Strategy and Management

Field: Business, Management and Accounting

Domain: Social Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $3,890