Using Google’s Natural Language Model to Measure Growth of Knowledge in Information Systems Research

The goal of this paper is to propose a new artificial-intelligence (AI) driven method to evaluate how well the information systems (IS) field engages with other disciplines in the process of building IS knowledge. The proposed method combines the veracity and objectivity of quantitative scientometric methods with the semantic depth and interpretive validity of qualitative content analysis methods, both building on theories of citations and disciplinarity. The results find that the IS field relies mostly on reviewed and perfunctory citation functions that do not truly engage with previous research. This evaluation presents a wake-up call to the field to better leverage and engage with theories from previous research. It also showcases the scientometric bases for enhancing the originality of IS research and help the field become intellectually and socially influential. Keywords: Disciplinary theory, citation theory, artificial intelligence (AI) and natural language processing, information systems (IS) knowledge.