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Big data has received considerable attention from the information systems (IS) discipline over the past few years, with several recent commentaries, editorials, and special issue introductions on the topic appearing in leading IS outlets. These papers present varying perspectives on promising big data research topics and highlight some of the challenges that big data poses. In this editorial, we synthesize and contribute further to this discourse. We offer a first step toward an inclusive big data research agenda for IS by focusing on the interplay between big data's characteristics, the information value chain encompassing people-process-technology, and the three dominant IS research traditions (behavioral, design, and economics of IS). We view big data as a disruption to the value chain that has widespread impacts, which include but are not limited to changing the way academics conduct scholarly work. Importantly, we critically discuss the opportunities and challenges for behavioral, design science, and economics of IS research and the emerging implications for theory and methodology arising due to big data's disruptive effects.

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We conduct a systematic review and coding of all the empirical research published in the two premier journals of our discipline, namely MIS Quarterly (MISQ) and Information Systems Research (ISR), from 2000 to 2016. We follow a comparable coding procedure to the ones employed by similar published studies (e.g., Grover and Lyytinen 2015), which consists of journal selection, sampling of articles, development of coding schemes, coding, and validation.