Data matters in IS theory building

Data matters in IS theory buildingThe Journal of the Association for Information System (JAIS) has been since its inception a theorydriven outlet that aspires to generate plausible, credible, generalizable, and illuminating understanding about information technology (and related socio-technical systems) in human enterprise.Therefore, our focus has been on promoting strong theory and associated inquiries in the information systems field as witnessed by our theory development workshops, recent editorial notes (e.g., http://aisel.aisnet.org/jais/vol9/iss2/5/or http://aisel.aisnet.org/jais/vol9/iss8/21/)and several JAIS best paper awards (e.g., http://aisel.aisnet.org/jais/vol9/iss10/5/).These emphasize primarily new theoretical models and original theory building.All this would suggest that we are neither a datadriven journal nor interested in empirics of our field.Yet, nothing could be further from the truth.JAIS, while emphasizing theory generation as our mission, recognizes the central role of data in this endeavor.This is witnessed in our publication profile, which involves a large number of rigorous empirical studies, our interest in engaging in construct and instrument development, and the care we place in guarding rigorous data collection and analysis during the review process.To underline the criticality of data in theory building within our field, our senior editor team has recently instituted new data policies for JAIS (see http://aisel.aisnet.org/jais/policies.html)that we expect to guide authors on how data matters need to be addressed in prospective submissions to JAIS.We hope that these policies can be seen as a first step in clarifying our expectations and views on data matters.In particular, these policies state guidelines for: 1) access to data for editors and reviewers during the review process, as well as some requirements for included data sets in submissions involving quantitative or qualitative analysis; 2) conditions under which the same data set can be used across a number of papers and submissions; 3) ethical standards associated with data collection, storage, and distribution; and 4) data liability in that JAIS bears no legal responsibility of the correctness of the data and related conclusions.We regard these policies to be instrumental in clarifying and unifying our stance toward the treatment of data and data sets in theory-driven submissions.It also shows that we do not take data matters lightly, and signals that we value data as a journal (JAIS).We also believe that these policies are important for development of the field.We hope that prospective authors will examine and recognize these guidelines before submitting their manuscripts to JAIS.