Data Science & Business Analytics

Research in the “Data Science & Business Analytics” theme seeks to exploit the myriad promises conferred by big data. In the past, traditional descriptive analytics could only equip organizations with fundamental operational observations, signaling deviations and raising alarms when necessary. Moving forward, organizations favor predictive analytics that can help forecast and predict potential relationships between variables and outcomes of concern, so that they can better chart their future and achieve their goals. In this way, analytics has proven useful in helping organizations know what they know and what they don’t. But the untamed potential of big data is even greater – as big data analytics promises to enlighten organizations regarding knowledge discovery, or help them understand what they do not even know they don’t know. However, although big data analytics holds much promise, it is no silver bullet. Our researchers are working on overcoming the challenges of big data in manifold ways. These include efforts on providing tangible and novel insights towards specific business problems e.g., in retail, social networks, and healthcare, as well as fundamental big data challenges e.g., entity resolution, missing value imputation, high-dimensional clustering, and models for transfer learning.

 People

 Grants

• CHEN Nan, “Governance, Ideology, and the Chinese Economy: Applications of Big Data and Geospatial Analysis”, NUS Start Up Grant
• GOH Khim Yong, “Omni-Channel Retailing and Marketing Analytics, Ministry of Education Tier 1 Grant
• HUANG Ke-Wei, “The Singapore Smart Virtual Checkpoint for Global Talent”, Institute of Data Science Grant
• HUANG Ke-Wei, “Analyzing IT Entrepreneurs Spawning by Web Data, Ministry of Education Tier 1 Grant
• KANKANHALLI, Atreyi, “Analytics for Assessing Gamification Effectiveness in a Rehabilitation Platform, Ministry of Education Tier 1 Grant
• OH Hyelim, “Impacts of News Sentiment on Interplays between Content Consumption and Social Media Sharing”, Ministry of Education Tier 1 Grant
• Vaibhav RAJAN, “Models and Algorithms for Clinical Data Analysis”, NUS Start Up Grant

 Selected Publications

Phan, T,Q. (forthcoming), A Multi-Relational Approach for Seeding Decisions in Social Networks. Journal of Marketing Research.

Ge, C., Huang K.W., & Kankanhalli, A. (forthcoming). Platform skills and the value of new hires in the software industry. Research Policy. https://doi.org/10.1016/j.respol.2019.103864

Kasa, S.R., Bhattacharya, S. & Rajan, V. (forthcoming). Gaussian Mixture Copulas for High- Dimensional Clustering and Dependency-based Subtyping. Bioinformatics. https://doi.org/10.1093/bioinformatics/btz599

Yi, C., Jiang, Z., Li, X., & Lu, X. (2019). Leveraging User-Generated Content for Product Promotion: The Effects of Firm-Highlighted Reviews. Information Systems Research. https://doi.org/10.1287/isre.2018.0807

Bhattacharya, P., Phan, T.Q., Bai, X., & Airoldi, E.M. (2019). A coevolution model of network structure and user behavior: The case of content generation in online social networks. Information Systems Research, 30(1), 117-132. https://doi.org/10.1287/isre.2018.0790

Mariappan. R., & Rajan, V. (2019). Deep Collective Matrix Factorization for Augmented Multi-view Learning. Machine Learning 108(8-9) 1395-1420. https://doi.org/10.1007/s10994-019-05801-6

Phan, T.Q., & Godes, D. (2018). The evolution of influence through endogenous link formation. Marketing Science, 37(2), 259-278. https://doi.org/10.1287/mksc.2017.1077

Chen, X., Van der Lans, R., & Phan, T.Q. (2017). Uncovering the importance of relationship characteristics in social networks: Implications for seeding strategies. Journal of Marketing Research, 54(2), 187-201. https://doi.org/10.1509/jmr.12.0511

Ge, C., Huang, K.W., & Png, I.P. (2016). Engineer/scientist careers: Patents, online profiles, and misclassification bias. Strategic Management Journal, 37(1), 232-253. https://doi.org/10.1002/smj.2460

Oh, H., Animesh, A., & Pinsonneault, A. (2016). Free vs. for a fee: The impact of paywall on the pattern and effectiveness of word-of-mouth via social media. MIS Quarterly, 40(1): 31-56. https://doi.org/10.25300/MISQ/2016/40.1.02

Ahangama, S., & Poo, D.C.C. (forthcoming). Application of deep user activity transfer models for cross domain user matching. Proceedings of ICIS 2019, Munich.

Zhao, Y., Hou, L., & Goh, K.Y. (forthcoming). Online advertising in online-to-offline retailing environments: The moderating effects of salesforce and product lines. Proceedings of ICIS 2019, Munich.

Ghanvatkar, S., Rajan, V., & Kankanhalli, A. (2018). Detecting Temporal Pattern Profiles from Smartphones for User Activity Analysis. Proceedings of ICIS 2018, San Francisco. https://aisel.aisnet.org/icis2018/healthcare/Presentations/4/

Zhang, J., Tan, C.H., & Ke, W. (2018). Affect Elicitation Method: A Proposition and Investigation. Proceedings of ICIS 2018, San Francisco. https://aisel.aisnet.org/icis2018/hcri/Presentations/6/

Ahangama, S., & Poo, D.C.C. (2018). Cross Domain Approximate Matching of User Shared Content for User Entity Resolution. Proceedings of ICIS 2018, San Francisco. https://aisel.aisnet.org/icis2018/datascience/Presentations/13/

Qiao, M., & Huang, K.W. (2018). Hierarchical Accounting Variables Forecasting by Deep Learning Methods. Proceedings of ICIS 2018, San Francisco. https://aisel.aisnet.org/icis2018/crypto/Presentations/7/

Gaskin, J., Hahn, J., Park, Y., Pentland, B., & Susarla, A. (2017). Time to Reconsider Time in the Digital Age, Proceedings of ICIS 2017, Seoul. https://aisel.aisnet.org/icis2017/Panels/Presentations/6/

Zhou, Y., Kankanhalli, A., & Huang, K.W. (2017). Predicting exercise behavior in fitness applications: A multi-group study, Proceedings of ICIS 2017, Seoul. https://aisel.aisnet.org/icis2017/IT-and-Healthcare/Presentations/5/