Digital Trace Data for Scientific Research: Some Observations and Recommendations

Gregory Vial ()
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Gregory Vial: HEC Montréal

Chapter Chapter 2 in Digital Trace Data Research in Information Systems, 2026, pp 23-39 from Springer

Abstract: Abstract In recent years, researchers have increasingly relied on the analysis of digital trace data (DTD) to study previously inaccessible empirical research questions as well as to craft novel theoretical contributions. Seeking to contribute to these ongoing developments, the purpose of this chapter is twofold. First, we draw attention to the specific nature of DTD as a type of data, acknowledging the need to evaluate—and to address—potential misalignments that can arise between three superimposed layers that connect the occurrence of events in the real world to the retrieval of DTD by researchers. Building on this argument, we then explore the specificities of working with DTD as a source of data to perform research, proposing that researchers mobilize practices and platforms used in the domain of software development to facilitate the management of DTD retrieval, curation, transformation, and analysis. Reflecting on the nature and the use of DTD for scientific research, this chapter contributes to answer calls to uphold IS research’s long-standing tradition of transparency, rigor, and knowledge sharing.

Keywords: Digital trace data; Research process; Data quality; Software development; Software platforms (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prochp:978-3-032-05497-5_2

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DOI: 10.1007/978-3-032-05497-5_2

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