Abstract
This article reviews 26 years (1994–2020) of research on electronic business published in reputable journals of the field. The basic aim behind this study is to define the growth potential of electronic business and marketing as a theoretical field and provide insights on past, present, and future scientific production in the field. By using bibliometrics and topic modeling techniques, annual scientific production and growth, latent topic structures, and trends by years, information on total citations and networks were examined. While the authors defined the research orientations by uncovering the main topics associated with electronic business, they created an understanding of the possible future research directions of fourteen topics discovered. The results show that, while the transaction-focused publications prevailed in the early years of electronic business and marketing journals, from the mid2000s, the focus has shifted towards marketing-focused publications. Moreover, to understand the main orientations in the field, the authors conducted a citation analysis to define the most influential topics published in the journals. The article also provides information on the most influential researchers in the field.
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Aria, M., & Cuccurullo, C. (2019). bibliometrix: An R tool for comprehensive science mapping analysis. R package version 2.3.2, https://cran.r-project.org/web/packages/bibliometrix/bibliometrix.pdf.
Blei, D. M. (2012). Probabilistic topic models. Communications of the ACM, 55(4), 77–84.
Blei, D., & Lafferty, J. D. (2009). Topic Models. In A. Srivstava & M. Shahami (Eds.), Text mining classification, clustering, and applications (pp. 101–124). Boca Raton: Chapman and Hall/CRC.
Blei, D., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Approach, 3, 993–1022.
Bouma, G. (2009). Normalized (pointwise) mutual information in collacation extraction. In Proceedings of GSCL, pp. 31–40.
Callon, M., Courtial, J. P., & Laville, F. (1991). Co-word analysis as a tool for describing the network of interactions between basic and technological research-the case of polymer chemistry. Scientometrics, 22, 155–205.
Chang, J. (2015). lda: Collapsed Gibbs sampling method for topic models. R package version 1.4.2, https://cran.r-project.org/web/packages/lda/lda.pdf.
Chang, J., Graber, J., Gerrish, S., Wang, C., & Blei, D. M. (2009). Reading tea leaves: how humans interpret topic models. Advances in Neural Information Processing Systems, 22, 288–296.
Cho, Y., Fu, P., & Wu, C. (2017). Popular research topics in marketing journals, 1995–2014. Journal of Interactive Marketing, 40, 52–72.
Choi, K., Lee, J.H., Willis, C. & Downie, J.S. (2015). Topic modeling users’ interpretations of songs to inform subject access in music digital libraries. In Proceedings of the 15th ACM/IEEE-CS Joint Conference on Digital Libraries, 183–186.
Feinerer, I. & Hornik, K. (2019). tm: Text mining package, R package version 0.7–7, https://cran.r-project.org/web/packages/tm/tm.pdf.
Griffiths, T. L., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National academy of Sciences, 101(suppl 1), 5228–5235.
Huber, J., Kamakura, W., & Mela, C. F. (2014). A topical history of JMR. Journal of Marketing Research, 51(1), 84–91.
Kannan, P. K., & Li, H. A. (2017). Digital marketing: A framework, review and research agenda. International Journal of Research in Marketing, 34(1), 22–45.
Lamberton, C., & Stephen, A. T. (2016). A thematic exploration of digital, social media, and mobile marketing: Research evolution from 2000 to 2015 and an agenda for future inquiry. Journal of Marketing, 80, 146–172.
Leone, R. P., Robinsoni, L. M., Bragge, J., & Somervuori, O. (2012). A citation and profiling analysis of pricing research from 1980 to 2010. Journal of Business Research, 65(7), 1010–1024.
Mela, C. F., Ross, J., & Deng, Y. (2013). A keyword history of marketing science. Marketing Science, 32(1), 8–18.
Mimno, D., Wallach, H.M., Talley, E., Landers, M. & McCallum, A. (2011). Optimizing semantic coherence in topic models. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (pp. 262–272), Association of Computal Linguistics, UK (July 27–31).
Newman, D., Lau, J.H., Gieser, K. & Baldwin, T. (2010). Automatic evaluation of topic coherence. In Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 100–108.
Newman, D., Bonilla, E.V. & Buntine, W. (2011). Improving topic coherence with regularized topic models. In Advances in Neural Information Processing Systems, Vol. 24, pp. 496–504.
Polonsky, M. J., & Ringer, A. (2012). Twenty years of the journal of marketing theory and practice. Journal of Marketing Theory and Practice, 20(3), 243–262.
Rapp, J. M., & Hill, R. P. (2015). Lordy, lordy, look who’s 40! the journal of consumer research reaches a milestone. Journal of Consumer Research, 42(1), 19–29.
Sievert, C. & Shirley, K. (2016). LDAvis: interactive visualization of topic models, R package version 0.3.2, https://cran.r-project.org/web/packages/LDAvis/LDAvis.pdf.
Sievert, C. & Shirley, K. (2014). LDAvis: A method for visualizing and interpreting topic models. In Proceedings of the Workshop on Interactive Language Learning, Visualization, and Inferences, pp. 63–70.
Taddy, M.A. (2011). On estimation and selection for topic models. Artificial Intelligence and Statistics, pp. 1184–1193.
Vanhala, M., Lu, C., Peltonen, J., Sundqvist, S., Nummenmaa, J., & Järvelin, K. (2020). The usage of large data sets in online consumer behaviour: a bibliometric and computational text-mining-driven analysis of previous research. Journal of Business Research, 106, 46–59.
Wang, X., Bendle, N. T., Mai, F., & Cotte, J. (2015). The journal of consumer research at 40: A historical analysis. Journal of Consumer Research, 42, 5–18.
West, D. (2007). Directions in marketing communications research an analysis of the journal of advertising. International Journal of Advertising, 26(4), 543–554.
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Ozansoy Çadırcı, T., Sağkaya Güngör, A. 26 years left behind: a historical and predictive analysis of electronic business research. Electron Commer Res 21, 223–243 (2021). https://doi.org/10.1007/s10660-021-09459-y
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DOI: https://doi.org/10.1007/s10660-021-09459-y