Extant prediction studies mostly using pure-data driven machine learning methods to predict, but their black-box prediction processes and results are usually difficult to explain to human. Our study proposes an explainable theory-driven prediction method, providing step by step guideline, from goal definition, data preparation, variable selection, to prediction accuracy. We demonstrate our proposed theory-driven prediction using a MOOC (massive open online course) example. MOOC vendors use freemium business model to deliver courses online, in which some learners use free version to learn basic content, while others pay for premium content. Our study predicts those MOOC paying customers, and can serve as a useful template to scholars who aim to conduct explainable theory-based prediction.
Hsu, Pei-fang; He, Wen-Yang; and Chen, Shih-Chu, "Who is willing to pay? Explainable theory-based prediction vs. pure data-driven prediction" (2022). PACIS 2022 Proceedings. 295.
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