Journal of Management Information Systems
Strategic information technology management
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Information Systems Research
Designing Web Sites for Customer Loyalty Across Business Domains: A Multilevel Analysis
Journal of Management Information Systems
Investment in Enterprise Resource Planning: Business Impact and Productivity Measures
Journal of Management Information Systems
Journal of Management Information Systems
The nature of theory in information systems
MIS Quarterly
Why different motives matter in sustaining online contributions
Electronic Commerce Research and Applications
Proceedings of the 2013 annual conference on Computers and people research
Using information systems to improve energy efficiency: Do smart meters make a difference?
Information Systems Frontiers
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Despite the importance of causal analysis in building a valid knowledge base and in answering managerial questions, the issue of causality rarely receives the attention it deserves in information systems (IS) and management research that uses observational data. In this paper, we discuss a potential outcomes framework for estimating causal effects and illustrate the application of the framework in the context of a phenomenon that is also of substantive interest to IS researchers. We use a matching technique based on propensity scores to estimate the causal effect of an MBA on information technology (IT) professionals' salary in the United States. We demonstrate the utility of this counterfactual or potential outcomes--based framework in providing an estimate of the sensitivity of the estimated causal effects because of selection on unobservables. We also discuss issues related to the heterogeneity of treatment effects that typically do not receive as much attention in alternative methods of estimation, and show how the potential outcomes approach can provide several new insights into who benefits the most from the interventions and treatments that are likely to be of interest to IS researchers. We discuss the usefulness of the matching technique in IS and management research and provide directions to move from establishing association to assessing causation.