![]() Several researchers have pioneered DS research in IS, yet over the past 15 years, little DS research has been done within the discipline. DS is of importance in a discipline oriented to the creation of successful artifacts. The paper motivates, presents, demonstrates in use, and evaluates a methodology for conducting design science (DS) research in information systems (IS). We discuss implications for interface and algorithm design, meta-issues around automating qualitative research, and suggestions for future work. Our top-performing system generates coding that matches human coders on inter-rater reliability measures. Based on our findings, we built prototypes to partially automate coding using simple natural language processing techniques. Researchers also require any assistive tool to be transparent about its recommendations. Further, researchers desire automation after having developed a codebook and coded a subset of data, particularly in extending their coding to unseen data. We found that across disciplines, researchers follow several coding practices well-suited to automation. Could coding be partially automated, and should it be? To answer this question, we interviewed researchers and observed them code interview transcripts. However, much of the process can be tedious and repetitive, becoming prohibitive for large datasets. Qualitative researchers perform an important and painstaking data annotation process known as coding.
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