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Social networks in marketing research 2001–2014: a co-word analysis

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Abstract

This article aims to explore the evolution of social network in marketing research by analyzing the co-occurrence index and network structures of keywords. We find that the number of articles which subjective tittle consist of social networks within 19 marketing journals and 9 UTD (Utdallas list of top journals) management journals increase significantly and the number of keywords whose frequency are no less than two also grow dramatically since 2010, the network structures of keywords 2010–2014 become more dispersed shows as most of keywords’ centralities are between 0.32 and 0.63, and more keywords have strong relationships (Higher Cosine Index) with social networks or networks than 2001–2009. We also conclude that social network analysis has been mainly applied to study relationships, diffusion, influence, customer analysis, and enterprise management five subfields. Since mobile internet, intelligent devices, new media and digital technology are developing rapidly, social networks will be a powerful tool to study the related research fields.

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Notes

  1. That means the total number of top 4 and other 15 important marketing journals, so the number of article increase from around 20 to over 30 and never fell below 30.

References

  • Aaker, D. A., Kumar, V., & Day, G. S. (2008). Marketing research. Hoboken: Wiley.

    Google Scholar 

  • Anderson, C. J., Wasserman, S., & Crouch, B. (1999). A p* primer: logit models for social networks. Social Networks, 21(1), 37–66.

    Article  Google Scholar 

  • Aral, S., & Walker, D. (2011). Creating social contagion through viral product design: A randomized trial of peer influence in networks. Management Science, 57(9), 1623–1639.

    Article  Google Scholar 

  • Bagozzi, R. P. (Ed.). (1994). Principles of marketing research (pp. 317–385). Cambridge, Oxford, Mass: Blackwell.

    Google Scholar 

  • Baumgartner, H., & Homburg, C. (1996). Applications of structural equation modeling in marketing and consumer research: A review. International Journal of Research in Marketing, 13(2), 139–161.

    Article  Google Scholar 

  • Bélisle, J. F., & Bodur, H. O. (2010). Avatars as information: Perception of consumers based on their avatars in virtual worlds. Psychology and Marketing, 27(8), 741–765.

    Article  Google Scholar 

  • Bernard, H. R., Johnsen, E. C., Killworth, P. D., McCarty, C., Shelley, G. A., & Robinson, S. (1990). Comparing four different methods for measuring personal social networks. Social Networks, 12(3), 179–215.

    Article  Google Scholar 

  • Bhatia, T., & Wang, L. (2011). Identifying physician peer-to-peer effects using patient movement data. International Journal of Research in Marketing, 28(1), 51–61.

    Article  Google Scholar 

  • Chellappa, R. K., & Saraf, N. (2010). Alliances, rivalry, and firm performance in enterprise systems software markets: A social network approach. Information Systems Research, 21(4), 849–871.

    Article  Google Scholar 

  • Dehdarirad, T., Villarroya, A., & Barrios, M. (2014). Research trends in gender differences in higher education and science: A co-word analysis. Scientometrics, 101(1), 273–290.

    Article  Google Scholar 

  • Dholakia, U. M., Bagozzi, R. P., & Pearo, L. K. (2004). A social influence model of consumer participation in network-and small-group-based virtual communities. International Journal of Research in Marketing, 21(3), 241–263.

    Article  Google Scholar 

  • Freeman, L. C., Roeder, D., & Mulholland, R. R. (1980). Centrality in social networks: II. Experimental results. Social Networks, 2(2), 119–141.

    Article  Google Scholar 

  • Furrer, O., Thomas, H., & Goussevskaia, A. (2008). The structure and evolution of the strategic management field: A content analysis of 26 years of strategic management research. International Journal of Management Reviews, 10(1), 1–23.

    Article  Google Scholar 

  • Garton, L., Haythornthwaite, C., & Wellman, B. (1997). Studying online social networks. Journal of Computer-Mediated Communication, 3(1), 1–32.

    Google Scholar 

  • Godes, D. (2011). Commentary-invited comment on “opinion leadership and social contagion in new product diffusion”. Marketing Science, 30(2), 224–229.

    Article  Google Scholar 

  • Goel, S., & Goldstein, D. G. (2013). Predicting individual behavior with social networks. Marketing Science, 33(1), 82–93.

    Article  Google Scholar 

  • Green, P. E., & Tull, D. S. (1970). Research for marketing decisions. London: Prentice-Hall.

  • Gross, R., & Acquisti, A. (2005). Information revelation and privacy in online social networks. In Proceedings of the 2005 ACM workshop on Privacy in the electronic society (pp. 71–80). ACM

  • Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40(3), 414–433.

    Article  Google Scholar 

  • He, Q. (1999). Knowledge discovery through co-word analysis. Library Trends, 48(1), 133–159.

    Google Scholar 

  • Hu, Y., & Van den Bulte, C. (2014). Nonmonotonic status effects in new product adoption. Marketing Science, 33(4), 509–533.

    Article  Google Scholar 

  • Hunter, D. R. (2007). Curved exponential family models for social networks. Social Networks, 29(2), 216–230.

    Article  Google Scholar 

  • Iacobucci, D., & Churchill, G. (2009). Marketing research: Methodological foundations. Boston: Cengage Learning.

    Google Scholar 

  • Kostoff, R. N. (1993). Co-word analysis. Evaluating R&D impacts: Methods and practice (pp. 63–78). US: Springer.

    Chapter  Google Scholar 

  • Kozinets, R. V., De Valck, K., Wojnicki, A. C., & Wilner, S. J. (2010). Networked narratives: Understanding word-of-mouth marketing in online communities. Journal of Marketing, 74(2), 71–89.

    Article  Google Scholar 

  • Kratzer, J., & Lettl, C. (2009). Distinctive roles of lead users and opinion leaders in the social networks of schoolchildren. Journal of Consumer Research, 36(4), 646–659.

    Article  Google Scholar 

  • Lazzarini, S. G. (2007). The impact of membership in competing alliance constellations: Evidence on the operational performance of global airlines. Strategic Management Journal, 28(4), 345–367.

    Article  Google Scholar 

  • Leydesdorff, L. (2008). On the normalization and visualization of author co-citation data: Salton’s Cosine versus the Jaccard index. Journal of the American Society for Information Science and Technology, 59(1), 77–85.

    Article  Google Scholar 

  • Lin, M., Prabhala, N. R., & Viswanathan, S. (2013). Judging borrowers by the company they keep: Friendship networks and information asymmetry in online peer-to-peer lending. Management Science, 59(1), 17–35.

    Article  Google Scholar 

  • Liu, G. Y., Hu, J. M., & Wang, H. L. (2012). A co-word analysis of digital library field in China. Scientometrics, 91(1), 203–217.

    Article  Google Scholar 

  • Lowrey, T. M., Otnes, C. C., & Ruth, J. A. (2004). Social influences on dyadic giving over time: A taxonomy from the giver’s perspective. Journal of Consumer Research, 30(4), 547–558.

    Article  Google Scholar 

  • Muñoz-Leiva, F., Viedma-del-Jesús, M. I., Sánchez-Fernández, J., & López-Herrera, A. G. (2012). An application of co-word analysis and bibliometric maps for detecting the most highlighting themes in the consumer behaviour research from a longitudinal perspective. Quality & Quantity, 46(4), 1077–1095.

    Article  Google Scholar 

  • Newman, M. E., & Park, J. (2003). Why social networks are different from other types of networks. Physical Review E, 68(3), 036122.

    Article  Google Scholar 

  • Obstfeld, D. (2005). Social networks, the Tertius lungens and orientation involvement in innovation. Administrative Science Quarterly, 50(1), 100–130.

    Google Scholar 

  • Park, S. H., & Luo, Y. D. (2001). Guanxi and organizational dynamics: Organizational networking in Chinese firms. Strategic Management Journal, 22(5), 455–477.

    Article  Google Scholar 

  • Pattison, P., & Robins, G. (2002). Neighborhood-based models for social networks. Sociological Methodology, 32(1), 301–337.

    Article  Google Scholar 

  • Robins, G., Snijders, T., Wang, P., Handcock, M., & Pattison, P. (2007). Recent developments in exponential random graph (p*) models for social networks. Social Networks, 29(2), 192–215.

    Article  Google Scholar 

  • Ronda-Pupo, G. A., & Guerras-Martin, L. Á. (2012). Dynamics of the evolution of the strategy concept 1962–2008: A co-word analysis. Strategic Management Journal, 33(2), 162–188.

    Article  Google Scholar 

  • Salton, G., & McGill, M. J. (1983). Introduction to modern information retrieval. New York: McGraw-Hill.

    MATH  Google Scholar 

  • Wang, X., Cheng, Q., & Lu, W. (2014a). Analyzing evolution of research topics with NEViewer: A new method based on dynamic co-word networks. Scientometrics, 101(2), 1253–1271.

    Article  Google Scholar 

  • Wang, X. D., Liu, J. J., & Sheng, F. S. (2014b). Analysis of hotspots in the field of domestic knowledge discovery based on co-word analysis method. Cybernetics and Information Technologies, 14(5), 145–158.

    Article  Google Scholar 

  • Watts, D. J., & Dodds, P. S. (2007). Influential, networks, and public opinion formation. Journal of Consumer Research, 34(4), 441–458.

    Article  Google Scholar 

  • Wikipedia. (2014). (EB/OL) http://wikipedia.org/wiki/Social_network

  • Wu, C. C., & Leu, H. J. (2014). Examining the trends of technological development in hydrogen energy using patent co-word map analysis. International Journal of Hydrogen Energy, 39(33), 19262–19269.

    Article  Google Scholar 

  • Wuyts, S., Verhoef, P. C., & Prins, R. (2009). Partner selection in B2B information service markets. International Journal of Research in Marketing, 26(1), 41–51.

    Article  Google Scholar 

  • Yang, S., & Allenby, G. M. (2003). Modeling interdependent consumer preferences. Journal of Marketing Research, 40(3), 282–294.

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. University of Chinese Academy of Sciences, Beijing, China

    Zongshui Wang, Hong Zhao & Yan Wang

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  1. Zongshui Wang
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  2. Hong Zhao
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  3. Yan Wang
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Correspondence to Zongshui Wang or Hong Zhao.

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Wang, Z., Zhao, H. & Wang, Y. Social networks in marketing research 2001–2014: a co-word analysis. Scientometrics 105, 65–82 (2015). https://doi.org/10.1007/s11192-015-1672-9

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