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Paper Type
ERF
Abstract
In this emerging research forum paper, we propose a novel framework for mixed-design research by integrating machine learning (ML) with grounded theory (GT). Contrary to existing belief that ML can only be used for prediction and not for explaining a phenomenon, in this paper, we illustrate that ML and GT complement each other’s strengths and weaknesses and can be integrated through mixed design research for theory building. We also propose a framework and guidelines to integrate ML in GT, with an example from an ongoing research project. This paper not only attempts to addresses the call for methodologies to employ ML techniques in social sciences research but also provides clear guidelines for executing such empirical research
Recommended Citation
Singh, Jang Bahadur; Vimal Kumar, M; Chandwani, Rajesh; and Varkkey, Biju, "Machine Learning and Grounded Theory: New Opportunities for Mixed-Design Research" (2020). AMCIS 2020 Proceedings. 25.
https://aisel.aisnet.org/amcis2020/data_science_analytics_for_decision_support/data_science_analytics_for_decision_support/25
Machine Learning and Grounded Theory: New Opportunities for Mixed-Design Research
In this emerging research forum paper, we propose a novel framework for mixed-design research by integrating machine learning (ML) with grounded theory (GT). Contrary to existing belief that ML can only be used for prediction and not for explaining a phenomenon, in this paper, we illustrate that ML and GT complement each other’s strengths and weaknesses and can be integrated through mixed design research for theory building. We also propose a framework and guidelines to integrate ML in GT, with an example from an ongoing research project. This paper not only attempts to addresses the call for methodologies to employ ML techniques in social sciences research but also provides clear guidelines for executing such empirical research
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