Table 5 Sample of studies using data science as a methodology
From: Data science: developing theoretical contributions in information systems via text analytics
Title of M-type studies | Contribution | Goal |
|---|---|---|
Cerchiello, P., & Giudici, P. (2016). Big data analysis for financial risk management. Journal of Big Data, 3(1), 18 | Theory | Prediction |
Hurtado, J. L., Agarwal, A., & Zhu, X. (2016). Topic discovery and future trend forecasting for texts. Journal of Big Data, 3(1), 7 | Theory | Prediction |
Padmaja, B., Prasad, V. V. R., & Sunitha, K. V. N. (2016). TreeNet analysis of human stress behavior using socio-mobile data. Journal of Big Data, 3(1), 24 | Theory | Explanation and prediction |
van Altena, A. J., Moerland, P. D., Zwinderman, A. H., & Olabarriaga, S. D. (2016). Understanding big data themes from scientific biomedical literature through topic modeling. Journal of Big Data, 3(1), 23 | Theory | Analysis |
Wu, H., Wu, H., Zhu, M., Chen, W., & Chen, W. (2017). A new method of large-scale short-term forecasting of agricultural commodity prices: Illustrated by the case of agricultural markets in Beijing. Journal of Big Data, 4(1), 1 | Theory and artifact | Prediction and design |
Geva, H., Oestreicher-Singer, G., & Saar-Tsechansky, M. (2019). Using Retweets When Shaping Our Online Persona: Topic Modeling Approach. MIS Quarterly, 43(2) | Theory | Explanation |
Gong, J., Abhishek, V., & Li, B. (2018). Examining the Impact of Keyword Ambiguity on Search Advertising Performance: A Topic Model Approach. MIS Quarterly, 42(3), 805–829 | Theory and artifact | Explanation and design |
Yahav, I., Shmueli, G., & Mani, D. (2016). A tree-based approach for addressing self-selection in impact studies with big data. MIS Quarterly, 40(4), 819–848 | Theory and artifact | Design |
Shi, Z., Lee, G. M., & Whinston, A. B. (2016). Toward a Better Measure of Business Proximity: Topic Modeling for Industry Intelligence. MIS quarterly, 40(4), 1035–1056 | Theory and artifact | Design |