Developing Theory Through Integrating Human and Machine Pattern Recognition

Abstract

New forms of digital trace data are becoming ubiquitous. Traditional methods of qualitative research that aim at developing theory, however, are often overwhelmed by the sheer volume of such data. To remedy this situation, qualitative researchers can engage not only with digital traces, but also with computational tools that are increasingly able to model digital trace data in ways that support the process of developing theory. To facilitate such research, this paper crafts a research design framework based on the philosophical tradition of pragmatism, which provides intellectual tools for dealing with multifaceted digital trace data, and offers an abductive analysis approach suitable for leveraging both human and machine pattern recognition. This framework provides opportunities for researchers to engage with digital traces and computational tools in a way that is sensitive to qualitative researchers’ concerns about theory development. The paper concludes by showing how this framework puts human imaginative capacities at the center of the push for qualitative researchers to engage with computational tools and digital traces

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 64 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.