The WebAI paradigm of innovation research: Extracting insight from organizational web data through AI

This paper introduces the WebAI paradigm as a promising approach for innovation studies, business analytics, and informed policymaking. By leveraging artificial intelligence to systematically analyze organizational web data, WebAI techniques can extract insights into organizational behavior, innovation activities, and inter-organizational networks. We identify five key properties of organizational web data (vastness, comprehensiveness, timeliness, liveliness, and relationality) that distinguish it from traditional innovation metrics, yet necessitate careful AI-based processing to extract scientific value. We propose methodological best practices for data collection, AI-driven text analysis, and hyperlink network modeling. Outlining several use cases, we demonstrate how WebAI can be applied in research on innovation at the micro-level, technology diffusion, sustainability transitions, regional development, institutions and innovation systems. By discussing current methodological and conceptual challenges, we offer several propositions to guide future research to better understand i) websites as representations of organizations, ii) the systemic nature of digital relations, and iii) how to integrate WebAI techniques with complementary data sources to capture interactions between technological, economic, societal, and ecological systems.