Exploring artificial intelligence and big data scholarship in information systems: A citation, bibliographic coupling, and co-word analysis

Abstract

This research explores extant research on artificial intelligence (AI) and big data published in the premier Information Systems (IS) journals over a period of 26 years (1997–2022), and it uses the techniques of citation analysis, bibliographic coupling, and co-word analysis. Citation analysis results reveal IS as the most cited reference discipline, followed by general business, organization science, and marketing. Two major topical clusters have been identified — problem domain-specific AI (e.g., predictive analytics, machine learning algorithms, and text mining) and organizational-specific AI (e.g., big data capabilities, firm performance, agility, and strategy). Co-word analysis revealed a gradual shift of scholarly interest from problem-domain-specific AI toward organizational-specific AI. Using the citation data, the most influential (cited) authors, (cited) articles, journals, institutions, and countries are identified. Gaps in extant research and future research paths are discussed.

Keywords

• Information systems
• Artificial intelligence
• Big data research
• Intellectual structure
• Identifying reference disciplines
• Citation analysis