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      Mapping the evolution of entrepreneurship as a field of research (1990–2013): A scientometric analysis

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      Journal: PLoS ONE
      Publisher: Public Library of Science

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          This article applies scientometric techniques to study the evolution of the field of entrepreneurship between 1990 and 2013. Using a combination of topic mapping, author and journal co-citation analyses, and overlay visualization of new and hot topics in the field, this article makes important contribution to the entrepreneurship research by identifying 46 topics in the 24-year history of entrepreneurship research and demonstrates how they appear, disappear, reappear and stabilize over time. It also identifies five topics that are persistent across the 24-year study period––institutions and institutional entrepreneurship, innovation and technology management, policy and development, entrepreneurial process and opportunity, and new ventures ––which I labeled as The Pentagon of Entrepreneurship. Overall, the analyses revealed patterns of convergence and divergence and the diversity of topics, specialization, and interdisciplinary engagement in entrepreneurship research, thus offering the latest insights on the state of the art of the field.

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          Most cited references61

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          Finding and evaluating community structure in networks.

          M. Newman, M Girvan (2004)
          We propose and study a set of algorithms for discovering community structure in networks-natural divisions of network nodes into densely connected subgroups. Our algorithms all share two definitive features: first, they involve iterative removal of edges from the network to split it into communities, the edges removed being identified using any one of a number of possible "betweenness" measures, and second, these measures are, crucially, recalculated after each removal. We also propose a measure for the strength of the community structure found by our algorithms, which gives us an objective metric for choosing the number of communities into which a network should be divided. We demonstrate that our algorithms are highly effective at discovering community structure in both computer-generated and real-world network data, and show how they can be used to shed light on the sometimes dauntingly complex structure of networked systems.
          Article 0 comments Cited 493 times – based on 0 reviews     Review now
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            Finding community structure in very large networks

            Cristopher Moore, Aaron Clauset, M. Newman (2004)
            The discovery and analysis of community structure in networks is a topic of considerable recent interest within the physics community, but most methods proposed so far are unsuitable for very large networks because of their computational cost. Here we present a hierarchical agglomeration algorithm for detecting community structure which is faster than many competing algorithms: its running time on a network with n vertices and m edges is O(m d log n) where d is the depth of the dendrogram describing the community structure. Many real-world networks are sparse and hierarchical, with m ~ n and d ~ log n, in which case our algorithm runs in essentially linear time, O(n log^2 n). As an example of the application of this algorithm we use it to analyze a network of items for sale on the web-site of a large online retailer, items in the network being linked if they are frequently purchased by the same buyer. The network has more than 400,000 vertices and 2 million edges. We show that our algorithm can extract meaningful communities from this network, revealing large-scale patterns present in the purchasing habits of customers.
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              Business Model Design and the Performance of Entrepreneurial Firms

              Raphael Amit, Christoph Zott (2007)
              Article 0 comments Cited 255 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Yanto Chandra:
                ORCID: http://orcid.org/0000-0003-1083-5813
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Sergi Lozano: Role: Editor
                Journal
                Journal ID (nlm-ta): PLoS One
                Journal ID (iso-abbrev): PLoS ONE
                Journal ID (publisher-id): plos
                Journal ID (pmc): plosone
                Title: PLoS ONE
                Publisher: Public Library of Science (San Francisco, CA USA )
                ISSN (Electronic): 1932-6203
                Publication date (Electronic): 4 January 2018
                Publication date Collection: 2018
                Volume: 13
                Issue: 1
                Electronic Location Identifier: e0190228
                Affiliations
                [001]Department of Public Policy, City University of Hong Kong, Hong Kong SAR, China
                Institut Català de Paleoecologia Humana i Evolució Social (IPHES), SPAIN
                Author notes

                Competing Interests: The author has declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0003-1083-5813
                Article
                Publisher ID: PONE-D-17-16247
                DOI: 10.1371/journal.pone.0190228
                PMC ID: 5754054
                PubMed ID: 29300735
                SO-VID: d6f93b34-95d1-4854-9255-f1da9ece8438
                Copyright © © 2018 Yanto Chandra
                License:

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                Date received : 10 May 2017
                Date accepted : 11 December 2017
                Page count
                Figures: 7, Tables: 2, Pages: 24
                Funding
                Funded by: City University of Hong Kong Faculty StartUp Grant
                Award ID: Project Number: 7200449
                Award Recipient :
                ORCID: http://orcid.org/0000-0003-1083-5813
                This article was supported by City University of Hong Kong's Faculty StartUp Grant, Project Number: 7200449.
                Categories
                Subject: Research Article
                Subject: Research and Analysis Methods
                Subject: Research Assessment
                Subject: Scientometrics
                Subject: Research and Analysis Methods
                Subject: Research Assessment
                Subject: Citation Analysis
                Subject: Research and Analysis Methods
                Subject: Research Assessment
                Subject: Bibliometrics
                Subject: Science Policy
                Subject: Science Policy and Economics
                Subject: Social Sciences
                Subject: Sociology
                Subject: Social Research
                Subject: Social Sciences
                Subject: Sociology
                Subject: Communications
                Subject: Marketing
                Subject: Biology and Life Sciences
                Subject: Neuroscience
                Subject: Cognitive Science
                Subject: Cognitive Psychology
                Subject: Decision Making
                Subject: Biology and Life Sciences
                Subject: Psychology
                Subject: Cognitive Psychology
                Subject: Decision Making
                Subject: Social Sciences
                Subject: Psychology
                Subject: Cognitive Psychology
                Subject: Decision Making
                Subject: Biology and Life Sciences
                Subject: Neuroscience
                Subject: Cognitive Science
                Subject: Cognition
                Subject: Decision Making
                Subject: Social Sciences
                Subject: Economics
                Subject: Labor Economics
                Subject: Employment
                Custom metadata
                Data Availability The work described in this article relies on publicly available bibliographic materials collected from Web of Science, as is conventionally used in scientometrics study. The data can be accessed in the following Dataverse: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/CGDOHW. The DOI number for the dataset is: doi: 10.7910/DVN/CGDOHW. Any questions regarding the data can be directed to the author.

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                ScienceOpen disciplines: Uncategorized

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