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Introduction to the special issue on innovative data sources for empirically building and validating theories in Operations Management
Author(s) -
Gattiker Thomas F.,
Parente Diane H.
Publication year - 2007
Publication title -
journal of operations management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.649
H-Index - 191
eISSN - 1873-1317
pISSN - 0272-6963
DOI - 10.1016/j.jom.2006.10.001
Subject(s) - computer science , strengths and weaknesses , data science , data quality , field (mathematics) , quality (philosophy) , empirical research , process (computing) , data management , data collection , management science , knowledge management , data mining , marketing , business , engineering , psychology , sociology , metric (unit) , philosophy , mathematics , epistemology , pure mathematics , operating system , social psychology , social science
All research methods have strengths and weaknesses. The two dominant empirical methodologies in operations management are the survey and the case study. Reliance on a limited number of methodologies can influence the body of knowledge that that a field generates – and even the problems that the field collectively chooses to investigate or not investigate. The special issue attempts to “push the envelope” in terms of the data sources used in operations management. In particular, the following data sources are used: laboratory study, customer comment data, third‐party web site quality ratings, process characteristics collected from e‐commerce web sites themselves, environmental performance reports, and a publicly available research database. Researchers contemplating their data gathering strategy must consider the strengths and weaknesses of each approach. The introduction to special issue discusses positives and negatives of both conventional and innovative data sources.