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A systematic method to create search strategies for emerging technologies based on the Web of Science: illustrated for ‘Big Data’

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Abstract

Bibliometric and “tech mining” studies depend on a crucial foundation—the search strategy used to retrieve relevant research publication records. Database searches for emerging technologies can be problematic in many respects, for example the rapid evolution of terminology, the use of common phraseology, or the extent of “legacy technology” terminology. Searching on such legacy terms may or may not pick up R&D pertaining to the emerging technology of interest. A challenge is to assess the relevance of legacy terminology in building an effective search model. Common-usage phraseology additionally confounds certain domains in which broader managerial, public interest, or other considerations are prominent. In contrast, searching for highly technical topics is relatively straightforward. In setting forth to analyze “Big Data,” we confront all three challenges—emerging terminology, common usage phrasing, and intersecting legacy technologies. In response, we have devised a systematic methodology to help identify research relating to Big Data. This methodology uses complementary search approaches, starting with a Boolean search model and subsequently employs contingency term sets to further refine the selection. The four search approaches considered are: (1) core lexical query, (2) expanded lexical query, (3) specialized journal search, and (4) cited reference analysis. Of special note here is the use of a “Hit-Ratio” that helps distinguish Big Data elements from less relevant legacy technology terms. We believe that such a systematic search development positions us to do meaningful analyses of Big Data research patterns, connections, and trajectories. Moreover, we suggest that such a systematic search approach can help formulate more replicable searches with high recall and satisfactory precision for other emerging technology studies.

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Notes

  1. WoS Topical Search (in the Advanced Search feature) captures occurrences in titles, abstracts, authors’ keywords, and Keywords Plus fields. A narrower option considered is provided to search just within titles.

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Acknowledgments

We acknowledge support from the US National Science Foundation (Award #1527370—“Forecasting Innovation Pathways of Big Data & Analytics”). Besides, we are grateful for the scholarship provided by the China Scholarship Council (CSC Student ID 201406030005). The findings and observations contained in this paper are those of the authors and do not necessarily reflect the views of the National Science Foundation and China Scholarship Council.

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Authors and Affiliations

  1. School of Management and Economics, Beijing Institute of Technology, Beijing, 100081, China

    Ying Huang

  2. Lab of Knowledge Management and Data Analysis (KMDA), Beijing Institute of Technology, Beijing, 100081, China

    Ying Huang

  3. School of Public Policy, Georgia Institute of Technology, Atlanta, GA, 30313, USA

    Ying Huang & Alan L. Porter

  4. Search Technology, Inc., Atlanta, GA, 30092, USA

    Alan L. Porter

  5. Department of Economics and Management, Karlsruhe Institute of Technology, 76131, Karlsruhe, Germany

    Jannik Schuehle

  6. Enterprise Innovation Institute, Georgia Institute of Technology, Atlanta, GA, 30332, USA

    Jan Youtie

Authors
  1. Ying Huang
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  2. Jannik Schuehle
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  3. Alan L. Porter
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  4. Jan Youtie
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Correspondence to Alan L. Porter.

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Huang, Y., Schuehle, J., Porter, A.L. et al. A systematic method to create search strategies for emerging technologies based on the Web of Science: illustrated for ‘Big Data’. Scientometrics 105, 2005–2022 (2015). https://doi.org/10.1007/s11192-015-1638-y

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  • DOI: https://doi.org/10.1007/s11192-015-1638-y

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