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.
Similar content being viewed by others
Notes
Please visit www.aimgroup.com/services/classified-intelligence-report for more specific information.
The category of each paper included in our review is presented in Table 1.
Retrieved from www.craigslist.org/about/factsheet on July 7, 2016.
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.
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.
The size of a cluster corresponds to the number of all index terms within it.
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.
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.
References
Alberta, H. B., Berry, R. M., & Levine, A. D. (2013). Compliance with donor age recommendations in oocyte donor recruitment advertisements in the USA. Reproductive BioMedicine Online, 26, 400–405.
Bartram, P. (2013). The value of data: Big data is now one of businesses’ most important assets. Available at: https://www.questia.com/article/1G1-323661399/. Accessed 25 Feb 2016.
Batagelj, V., & Mrvar, A. (2011). Pajek: Program for analysis and visualization of large network—Reference Manual. Ljubljana: University of Ljubljana.
Bennett, V. M., Seamans, R., & Zhu, F. (2015). Cannibalization and option value effects of secondary markets: Evidence from the US concert industry. Strategic Management Journal, 36(11), 1599–1614.
Bevan, J. L., Galvan, J., Villasenor, J., & Henkin, J. (2016). ’You’ve been on my mind ever since’: A content analysis of expressions of interpersonal attraction in Craigslist’s missed connections posts. Computers in Human Behavior, 54, 18–24.
Brandes, U. (2001). A faster algorithm for betweenness centrality. Journal of Mathematical Sociology, 25(2), 163–177.
Brough, A. R., & Isaac, M. S. (2012). Finding a home for products we love: How buyer usage intent affects the pricing of used goods. Journal of Marketing, 76(4), 78–91.
Chan, J., & Ghose, A. (2012). Internet’s dirty secret: Assessing the impact of online intermediaries on HIV transmission. MIS Quarterly, 38(4), 955–977.
Chan, T. Y., & Park, Y. H. (2015). Consumer search activities and the value of ad positions in sponsored search advertising. Marketing Science, 34(4), 606–623.
Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359–377.
Chen, C., Chen, Y., Horowitz, M., Hou, H., Liu, Z., & Pellegrino, D. (2009). Towards an explanatory and computational theory of scientific discovery. Journal of Infometrics, 3(3), 191–209.
Chen, C., Ibekwe-Sanjuan, F., & Hou, J. (2010). The structure and dynamics of co-citation clusters: A multiple-perspective co-citation analysis. Journal of the American Society for Information Science and Technology, 61(7), 1386–1409.
Cherif, E., & Grant, D. (2014). Analysis of e-business models in real estate. Electronic Commerce Research, 14, 25–50.
Courtial, J. P. (1989). Qualitative models, quantitative tools and network analysis. Scientometrics, 15(5/6), 527–534.
Das, S (2013). Online classified ads’ advantage over newspaper classifieds. Available at: http://blog.adhoards.com/online-classified-ads-advantage-over-newspaper-classifieds/. Accessed 25 Feb 2016.
Dawson, B. L., & McIntosh, W. D. (2006). Sexual strategies theory and Internet personal advertisements. Cyber Psychology and Behavior, 9, 614–617.
Dunning, T. (1993). Accurate methods for the statistics of surprise and coincidence. Computational Linguistics, 19(1), 61–74.
Fagan, P., Pohkrel, P., Herzog, T., Pagano, I., Vallone, D., Trinidad, D. R., et al. (2015). Comparisons of three nicotine dependence scales in a multiethnic sample of young adult menthol and non-menthol smokers. Drug and Alcohol Dependence, 149, 203–211.
Fournier, V. (2002). Classified ads: Print takes advantage of web assets. Newspaper Techniques, 12, 22–23.
Frazier, R. M. (2014). A cannon for cooperation: A review of the interagency cooperation literature. Journal of Public Administration and Governance, 4(1), 1.
Frederick, B. J., & Perrone, D. (2014). ’Party N Play’ on the internet: Subcultural formation, Craigslist, and escaping from stigma. Deviant Behavior, 35(11), 859–884.
Gan, C., & Wang, W. (2015). Research characteristics and status on social media in China: A bibliometric and co-word analysis. Scientometrics, 105(2), 1167–1182.
Grov, C., & Crow, T. (2012). Attitudes about and HIV risk related to the ‘most common place’ MSM meet their sex partners: Comparing men from bathhouses, bars/clubs, and Craigslist.org. AIDS Education and Prevention, 24(2), 102–116.
Grov, C., Rendina, H. J., & Parsons, J. T. (2014). Comparing three cohorts of MSM sampled via sex parties, bars/clubs, and craigslist.org: Implications for researchers and providers. AIDS Education and Prevention, 26(4), 362–382.
Grov, C., Ventuneac, A., Rendina, H. J., Jimenez, R. H., & Parsons, J. T. (2013). Recruiting men who have sex with men on craigslist.org for face-to-face assessment: Implications for research. AIDS and Behavior, 17(2), 773–778.
Hughes, J. R., Fingar, J. R., Budney, A. J., Naud, S., Helzer, J. E., & Callas, P. W. (2014). Marijuana use and intoxication among daily users: An intensive longitudinal study. Addictive Behaviors, 39, 1464–1470.
Khan, G. F., & Wood, J. (2015). Information technology management domain: Emerging themes and keyword analysis. Scientometrics, 105, 959–972.
Kleinberg, J. (1999). Authoritative sources in a hyperlinked environment. Journal of the ACM, 46, 604–632.
Leonidou, C. N., & Leonidou, L. C. (2011). Research into environmental marketing management: A bibliographic analysis. European Journal of Marketing, 45(1/2), 68–103.
Lever, J., Grov, C., Royce, T., & Gillespie, B. J. (2008). Searching for love in all the ‘write’ places: Exploring Internet personals use by sexual orientation, gender, and age. International Journal of Sexual Health, 20(4), 233–246.
Madani, F. (2015). Technology mining bibliometrics analysis: Applying network analysis and cluster analysis. Scientometrics, 105(1), 323–335.
McCormick, A., & Eberle, W. (2013). Discovering fraud in online classified ads. In 26th International Florida artificial intelligence research society conference, Florida (pp 450–455).
Moskowitz, D. A., & Seal, D. W. (2010). GWM looking for sex-serious only: The interplay of sexual ad placement frequency and success on the sexual health of ‘men seeking men’ on Craigslist. Journal of Gay and Lesbian Social Services, 22(4), 399–412.
Munoz-Leiva, F., Porcu, L., & del Barrio-Garcia, S. (2015). Discovering prominent themes in integrated marketing communication research from 1991 to 2012: A co-word analytic approach. International Journal of Advertising, 34(4), 678–701.
Ng, A. Y., Jordan, M. I., & Weiss, Y. (2002). On spectral clustering: Analysis and an algorithm. Advanced in Neural Information Processing Systems, 14(2), 849–856.
Otte, E., & Rousseau, R. (2002). Social network analysis: A powerful strategy, also for the information sciences. Journal of Information Science, 28(6), 441–453.
Palme, E., Dellarocas, C., Calin, M., & Sutanto, J. (2012). Attention allocation in information-rich environment: The case of new aggregators. In 14th Annual international conference on electronic commerce, Singapore (pp. 25–26).
Parthalan, O. R. (2012). Co-occurrence networks: Co-occurrence, text mining, text corpus. New York: VadPress.
Pera, M. S., Qumsiyeh, R., & Ng, Y. K. (2013). Web-based closed-domain data extraction on online advertisements. Information Systems, 38(2), 183–197.
Persson, O., Danell, R., & Schneider, J. W. (2009). How to use BibExcel for various types of bibliometric analysis. In F. Astrom, R. Danell, B. Larsen, & J. W. Schneider (Eds.), Celebrating scholarly communication studies: A Festschrift for Olle Persson at his 60th Birthday (pp. 9–24). Leuven, Belgium: International Society for Scientometrics and Informetrics.
Rosenbaum, M. S., Daunt, K. L., & Jiang, A. (2013). Craigslist exposed: The Internet-mediated hookup. Journal of Homosexuality, 60(4), 505–531.
Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53–65.
Rowley, J., & Slack, F. (2004). Conducting a literature review. Management Research News, 27, 31–39.
Salton, G., Yang, C. S., & Wong, A. (1975). A vector space model for information retrieval. Communications of the ACM, 18(11), 613–620.
Saunders, M., Lewis, P., & Thornhill, A. (2009). Research methods for business students. Harlow: Pearson.
Seamans, R., & Zhu, F. (2013). Responses to entry in multi-sided markets: The impact of Craigslist on local newspapers. Management Science, 60(2), 476–493.
Shi, J., & Malik, J. (2000). Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8), 888–905.
Sideman, A. (2005). Launching classifieds into the Internet age. Presstime, 6, 56–57.
Small, H. (1973). Co-citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for Information Science, 24(4), 265–269.
Sterling, J. (2008). A plan for a US newspaper industry counterattack against disruptive innovators. Strategy and Leadership, 36(1), 20–26.
Subbaraman, M. S., Laudet, A. B., Ritter, L. A., Stunz, A., & Kaskutas, L. A. (2015). Multisource recruitment strategies for advancing addiction recovery research beyond treated samples. Journal of Community Psychology, 43(5), 560–575.
Tallulah, R. (2010). What is the average cost of a classified ad in a newspaper. Available at: http://www.ehow.com/facts_7471851_average-cost-classified-ad-newspaper.html. Accessed 25 Feb 2016.
Tang, J., Zhang, P., & Wu, P. F. (2015). Categorizing consumer behavioral responses and artifact design features: The case of online advertising. Information Systems Frontiers, 17(3), 513–532.
Tran, H., Hornbeck, T., Ha-Thuc, V., Cremer, J., & Srinivasan, P. (2011). Spam detection in online classified advertisements. In The 2011 Joint WICOW/AIR web workshop on web quality, Hyderabad, India (pp. 35–41).
Van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538.
Worthen, M. G. F. (2014). An invitation to use craigslist ads to recruit respondents from stigmatized groups for qualitative interviews. Qualitative Research, 14(3), 371–383.
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).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
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
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11192-017-2524-6