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Online classified advertising: a review and bibliometric analysis

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Abstract

The study of online classified advertising has been evolving recently, with rapid growth in the quantity of publications. Many studies have focused on certain aspects of online classified advertising, such as its societal influence. However, an additional analysis of those studies using rigorous bibliometric tools, which are supposed to offer further research guidance, has not yet been performed. This paper therefore begins by identifying 105 published articles, of which 60 works of proven influence are selected. With the help of rigorous bibliometric and network techniques, established and potential research clusters are identified, together with the collaborative relationships among contributing authors and organizations. A systematic review of this field is helpful in graphically depicting the literature over time and identifying current research focuses as well as emerging trends for future research.

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Notes

  1. Please visit www.aimgroup.com/services/classified-intelligence-report for more specific information.

  2. The category of each paper included in our review is presented in Table 1.

  3. Retrieved from www.craigslist.org/about/factsheet on July 7, 2016.

  4. Administered by Elsevier publishing group, Scopus (www.elsevier.com/solutions/scopus) is claimed to be the largest abstract and citation database, covering over 20,000 peer-reviewed journals, including those published by Elsevier, Emerald, Informs and Springer, etc. In addition, the Scopus database also contains the most reputable international journals, some of which may be relatively new, but influential. One primary limitation of Scopus is the restricted access to pre-1996 peer-reviewed studies, which will not be a problem for our research, however, because the first paper concerning online classified ads was not published until the early 21st century.

  5. http://interest.science.thomsonreuters.com/forms/HistCite/.

  6. www.harzing.com/pop.htm.

  7. http://www8.umu.se/inforsk/Bibexcel.

  8. http://cluster.cis.drexel.edu/~cchen/citespace/.

  9. http://vlado.fmf.uni-lj.si/pub/networks/pajek/.

  10. http://www.vosviewer.com.

  11. The cosine coefficient is a measurement of co-citation similarity between items. Suppose A is the set of papers that cite i and B is the set of papers that cite j, then \(w_{ij} = \frac{{\left| {A \cap B} \right|}}{{\sqrt {\left| A \right| \times \left| B \right|} }}\), where \(\left| A \right|\) and \(\left| B \right|\) are the citation counts of i and j respectively, and \(\left| {A \cap B} \right|\) is the co-citation count, i.e., the number of times they are cited together.

  12. The size of a cluster corresponds to the number of all index terms within it.

  13. The silhouette value of a cluster is used to estimate the uncertainty involved in identifying its nature. It ranges from −1 to 1, and the value of 1 indicates a perfect separation from other clusters. Readers interested in the technical details of silhouette value can refer to Rousseeuw (1987) for more information.

  14. According to Chen et al. (2010), in CiteSpace, the candidates of cluster labels are selected from noun phrases or keywords of papers in each cluster. The keywords can be ranked through three different algorithms: \(tf*idf\) (Salton et al. 1975), log-likelihood ratio tests (Dunning 1993) and mutual information. As discussed, labels selected by \(tf*idf\) tend to reflect the most salient aspect of a cluster, while those chosen by log-likelihood ratio tests and mutual information tend to represent the unique aspect of a cluster. In our study, we use the \(tf*idf\) algorithm to label clusters.

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Acknowledgements

The authors thank editors and the anonymous reviewers for providing insightful suggestions. The authors are also grateful to the financial support of Fundamental Research Funds for the Central Universities in China (Project Number: 1200219312).

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Correspondence to Wei Qiu.

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Fang, C., Zhang, J. & Qiu, W. Online classified advertising: a review and bibliometric analysis. Scientometrics 113, 1481–1511 (2017). https://doi.org/10.1007/s11192-017-2524-6

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