Data-Driven Computationally Intensive Theory Development

• 成果类型: Article
• 署名作者: Berente, Nicholas; Seidel, Stefan; Safadi, Hani
• 署名单位: University of Notre Dame; University of Liechtenstein; University System of Georgia; University of Georgia
• 刊物名称: INFORMATION SYSTEMS RESEARCH
• ISSN/ISSBN: 1047-7047
• DOI: 10.1287/isre.2018.0774
• 发表日期: 2019
• 页码: 50-64
• 关键词: GROUNDED THEORY; information-systems; Social media; data science; big data; emergence; analytics

摘要

Increasingly abundant trace data provide an opportunity for information systems researchers to generate new theory. In this research commentary, we draw on the largely manual tradition of the grounded theory methodology and the highly automated process of computational theory discovery in the sciences to develop a general approach to computationally intensive theory development from trace data. This approach involves the iterative application of four general processes: sampling, synchronic analysis, lexical framing, and diachronic analysis. We provide examples from recent research in information systems.