A Fitting Approach to Construct and Measurement Alignment: The Role of Big Data in Advancing Dynamic Theories

Margaret M. Luciano, John E. Mathieu, Semin Park, Scott I. Tannenbaum

Research output: Contribution to journalArticlepeer-review

58 Scopus citations


Many phenomena of interest to management and psychology scholars are dynamic and change over time. One of the primary impediments to the examination of dynamic phenomena has been challenges associated with collecting data at a sufficient frequency and duration to accurately model such changes. Emerging technologies that produce nearly continuous streams of big data offer great promise to address those challenges; however, they introduce new methodological challenges and construct validity concerns. We seek to integrate the emerging big data technologies into the existing repertoire of measurement techniques and advance an iterative process to enhance their measurement fit. First, we provide an overview of dynamic constructs and temporal frameworks, highlighting their measurement implications. Second, we discuss different data streams and feature emerging technologies that leverage big data as a means to index dynamic constructs. Third, we integrate the previous sections and advance an iterative approach to achieving measurement fit, highlighting factors that make some measurement choices more suitable and viable than others. In so doing, we hope to accelerate the advancement of dynamic theories and methods.

Original languageEnglish (US)
Pages (from-to)592-632
Number of pages41
JournalOrganizational Research Methods
Issue number3
StatePublished - Jul 1 2018

All Science Journal Classification (ASJC) codes

  • General Decision Sciences
  • Strategy and Management
  • Management of Technology and Innovation


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