Advances in Research Methods

Track Description
Proliferation of unique and interesting digital phenomena and the explosion of data has created numerous opportunities for IS researchers to provide new methodological and theoretical insights. This track reflects advanced in research methods across all IS traditions: behavioral, economics, organizational, design science and data science/computational as well as at their intersections. Any paper that offers new methods that can address IS-related questions is welcome. This includes, but is not restricted to advances in empirical methods, field and lab experiments, netnographical approaches, machine learning, computational methods, grounded theory, statistical models and econometrics, causal inference, data and text mining, predictive/prescriptive analytics, visual analytics, and crowdsourcing.

Contributions in methods can be motivated by establishing shortcomings of extant approaches or could have the ability to address entirely new problems or applications relevant to the emerging digital world, including business and societal challenges.

Track Chairs
Indranil Bardhan, University of Texas at Austin,
Varun Grover, University of Arkansas,
Balaji Padmanabhan, University of South Florida,
Ulrike Schultze, Southern Methodist University,

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Monday, December 14th

Adverse Drug Event Prediction using Noisy Literature-Derived Knowledge Graphs

Abel Lim Jun Hong, National University of Singapore
Ragunathan Mariappan, National University of Singapore
Vaibhav Rajan, National University of Singapore

Algorithmic Intelligence in Research: Prevalent Topic Modeling Practices and Implications for Rigor in IS and Management Research

Wendy Günther, University of Exeter
Mayur P. Joshi, Western University

An Explainable Machine Learning Framework for Fake Financial News Detection

Xiaohui Zhang, Arizona Sate University
Qianzhou Du, Nanjing University
Zhongju Zhang, Arizona State University

Building an Apparatus: Disclosing Affectivity in Sociomaterial Research

Joaquin Santuber, Hasso-Plattner-Institut
Christian Dremel, Chair of Industrial Information Systems
Babajide Alamu Owoyele, Hasso Plattner Institute- HPI Stanford Design Thinking Research Programme
Jonathan A. Edelman, Hasso-Plattner-Institute

Can Human Judgement be Machine-Sourced? An Approach to Measure the Perceptual Dimensions Embedded in Software

Poonacha K. Medappa, Tilburg University, Tilburg School of Economics and Management
Shirish C. Srivastava, HEC Paris

Improving Causal Inference with Text as Data in Empirical IS Research: A Machine Learning Approach

Guopeng Yin, Risk Management Foundation of the Harvard Medical Institution(CRICO)
Jian Chen, The Ohio State University

Measuring Social Proximity via Knowledge Graph Embedding

Ruiyun Xu, City University of Hong Kong
Hailiang Chen, The University of Hong Kong
J Leon Zhao, City University of Hong Kong

Personalized Promotion Recommendation: A Dynamic Adaptation Modeling Approach

Cong Wang, Carnegie Mellon University
Xunhua Guo, Tsinghua University
Guannan Liu, Beihang University
Guoqing Chen, Tsinghua University

Time Construction in Information Systems Research: a Meta-Analytic Examination of Longitudinal Studies

wafa BOUAYNAYA, University of Picardy
Mathieu DUNES, CRIISEA research center

Transfer Learning in Dynamic Data Environments: Trade-offs in Response to Changes

Jiaxu Peng, Central University of Finance and Economics, Beijing China
Jungpil Hahn, National University of Singapore