Table 8 Sample of studies using data science as a research method and object of study
From: Data science: developing theoretical contributions in information systems via text analytics
Title of RMO-type studies | Contribution | Goal |
|---|---|---|
Baechle, C., Agarwal, A., & Zhu, X. (2017). Big data driven co-occurring evidence discovery in chronic obstructive pulmonary disease patients. Journal of Big Data, 4(1), 9 | Theory and artifact | Explanation, prediction and design |
Etani, N. (2015). Database application model and its service for drug discovery in Model-driven architecture. Journal of Big Data, 2(1), 16 | Theory and artifact | Prediction, design and action |
Hayes, M. A., & Capretz, M. A. (2015). Contextual anomaly detection framework for big sensor data. Journal of Big Data, 2(1), 2 | Artifact | Design |
Kumar, S., & Toshniwal, D. (2016). A novel framework to analyze road accident time series data. Journal of Big Data, 3(1), 8 | Theory and artifact | Prediction and design |
Mavragani, A., & Tsagarakis, K. P. (2019). Predicting referendum results in the Big Data Era. Journal of Big Data, 6(1), 3 | Theory and artifact | Prediction and design |
Subroto, A., & Apriyana, A. (2019). Cyber risk prediction through social media big data analytics and statistical machine learning. Journal of Big Data, 6(1), 50 | Theory and artifact | Explanation, prediction and design |
Yang, J., & Yecies, B. (2016). Mining Chinese social media UGC: A big-data framework for analyzing Douban movie reviews. Journal of Big Data, 3(1), 3 | Artifact | Analysis and design |
Liebman, E., Saar-Tsechansky, M., & Stone, P. (Forthcoming). The Right Music at the Right Time: Adaptive Personalized Playlists Based on Sequence Modeling. MIS Quarterly, 43(3), 765–786 | Theory and artifact | Prediction, design and action |
Son, J., Brennan, P.F., & Zhou, S. (Forthcoming). A Data Analytics Framework for Smart Asthma Management Based on Remote Health Information Systems with Bluetooth-Enabled Personal Inhalers. MIS Quarterly, 44 | Artifact | Design and action |
Mo, J., Sarkar, S., & Menon, S. (2018). Know When to Run: Recommendations in Crowdsourcing Contests. MIS Quarterly, 42(3), 919–944 | Theory and artifact | Prediction and design |
Abbasi, A., Zhou, Y., Deng, S., & Zhang, P. (2018). Text Analytics to Support Sense-Making in Social Media: A Language-Action Perspective. MIS Quarterly, 42(2), 427–464 | Theory and artifact | Design |
Lin, Y. K., Chen, H., Brown, R. A., Li, S. H., & Yang, H. J. (2017). Healthcare Predictive Analytics for Risk Profiling in Chronic Care: A Bayesian Multitask Learning Approach. MIS Quarterly, 41(2), 473–496 | Theory and artifact | Prediction and design |
Zhang, K., Bhattacharyya, S., & Ram, S. (2016). Large-Scale Network Analysis for Online Social Brand Advertising. MIS Quarterly, 40(4), 849–868 | Artifact | Design |