Replicability represents the cornerstone of reliable development in science. In this paper, we develop a framework for enhancing current data-collection practices’ replicability in survey research in information systems. To develop the framework, we built on literature, benchmarks of various scientific associations, and a review of policies and best practices in leading business journals. The framework identifies best practices for transparently collecting data, sharing data and methods, and developing high-quality evidence. We analyzed 82 recently published survey research in nine IS journals as a sample that represents high-quality IS research to identify their replicability and found that not one papers provided enough details for replication. We conclude by discussing our framework’s implications for researchers, journals, and scientific institutions and the role that these entities can play in enhancing IS research’s replicability.
Daneshvar Kakhki, M., Mousavi, R., & Palvia, P. (2021). Evidence Quality, Transparency, and Translucency for Replication in Information Systems Survey Research. Communications of the Association for Information Systems, 49, pp-pp. https://doi.org/10.17705/1CAIS.04903
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