The automation and augmentation of work with artificial intelligence (AI) and intelligent systems are transforming not just organizations and industries, but potentially entire labor markets - with humans being replaced by, or working together with, ever smarter algorithms and robots. There is a concern among workers that whole classes of job roles and occupations are at risk of extinction, while demand for other job roles in other occupations grow at an increasing rate. Overall, AI and intelligent systems are causing disruptions in a variety of contexts including but not limited to e-commerce, m-commerce, labor, organizations, human interactions with smart technologies, and human robot interactions. Our researchers under this theme are examining the impact of AI and intelligent systems on business strategy development, the effect of AI on the market, economy and society, and the business value and unanticipated consequences of AI. We are also proposing novel AI/machine learning constructs, models, methods, or instantiations with implications for intelligent automation and/or process automation in organizations and society.
|WEI Kwok-Kee||TEO Hock Hai||Atreyi KANKANHALLI||JIANG Zhenhui||GOH Khim Yong||HUANG Ke-Wei||TAN Chuan Hoo||Danny POO||Rudy SETIONO|
|Bernard Tan||PHAN Tuan Quang||Keith Barrett CARTER||Vaibhav RAJAN||Stanley KOK||CHEN Nan||LEK Hsiang Hui||LU Weiquan||Shalinda ADIKARI|
- CHEN Nan, “Governance, Ideology, and the Chinese Economy: Applications of Big Data and Geospatial Analysis”, NUS Start Up Grant
- GOH Khim Yong, “Quantifying the Economic Value of Live Streaming Applications A Machine Learning and Econometric Approach”, Institute of Data Science Grant
- HAHN Jungpil, “Enhancing General Practices (GPs) and Clinics Operations and Fraud Detection with AI”, AI Singapore and MHC Medical Network Grants
- HUANG Ke-Wei, “Computer Vision for Automating Regression Diagnostic Tests”, Ministry of Education Tier 1 Grant
- HUANG Ke-Wei, “The Singapore Smart Virtual Checkpoint for Global Talent”, Institute of Data Science Grant
- RAJAN, Vaibhav, “Deep Multi-View Learning Methods for Discovering Targeted Cancer Therapeutics”, Ministry of Education Tier 1 Grant
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
Liu, Y., Jiang, Z., & Chan, H.C. (2019). Touching products virtually: Facilitating consumer mental imagery with gesture control and visual presentation. Journal of Management Information Systems, 36(3), 823-854. https://doi.org/10.1080/07421222.2019.1628901
Ghanvatkar, S., Rajan, V., & Kankanhalli, A. (2019). User models for personalized physical activity interventions: A scoping review. JMIR mHealth and uHealth, 7(1):e11098. https://doi.org/10.2196/11098.
Li, M.X., Tan, C.H., Wei, K.K., & Wang, K.L. (2017). Sequentiality of product review information provision: An information foraging perspective. MIS Quarterly, 41(3), 867-892. https://doi.org/10.25300/MISQ/2017/41.3.09
Yi, C., Jiang, Z., & Benbasat, I. (2017). Designing for diagnosticity and serendipity: An investigation of social product-search mechanisms. Information Systems Research, 28(2), 413-429. https://doi.org/10.1287/isre.2017.0695
Ghanvatkar, S., & Rajan, V. (forthcoming). Deep recurrent neural networks for mortality prediction in intensive care using clinical time series at multiple resolutions. Proceedings of ICIS 2019, Munich.
Loebbecke, C., El Sawy, O., Kankanhalli, A., Markus, M.L., Te'eeni, D., and Wrobel, S. (forthcoming). Artificial intelligence meets IS researchers: Can it replace us? Proceedings of ICIS 2019, Munich.
Qiu, L., Rajan, V., & Tan, B.C.Y. (forthcoming). Battling Alzheimer’s disease through early detection: A deep multimodal learning approach. Proceedings of ICIS 2019, Munich.
Wang, Q., & Huang, K.W. (forthcoming). Displaced or augmented? How does artificial intelligence affect our jobs: Evidence from LinkedIn. 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/
Nguyen, H.D., Eiring, O., & Poo, D. (2018). “In-Situ simulation in design science research: Evaluation of complex design artifacts, Proceedings of ICIS 2018, San Francisco. https://aisel.aisnet.org/icis2018/design/Presentations/2/
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/
Zheng, C., Chen, P., & Tan, B.C.Y. (2018). Helping adult learners to keep learning: Towards personalized interventions, Proceedings of ICIS 2018, San Francisco. https://aisel.aisnet.org/icis2018/education/Presentations/16/
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/