Big data in marketing literature
PDF

Keywords

Big Data
Marketing

How to Cite

Pinarbasi, F., & Canbolat, Z. N. (2019). Big data in marketing literature. International Journal of Business Ecosystem & Strategy (2687-2293), 1(2), 15-24. https://doi.org/10.36096/ijbes.v1i2.107

Abstract

The concept of big data is one of the important issues in business decision making in recent years. The expansion of social media platforms, the increase in data production devices and the evaluation and interpretation of the data produced by developing technology become crucial. Previous studies in the big data area have addressed the issue in limited contexts, and there are few studies in the field of marketing with a bibliometric approach. This study, which aims to examine how big data concept is evaluated in marketing literature, examines the publications on big data in indexed marketing journals using bibliometric methodology. This study starts with descriptive statistical information and then includes the top published journals, authors and corresponding author’s countries statistics. This study also includes most influential studies for big data concept in marketing literature, employs spectroscopy for detecting historical roots of studies and finally plots growth progress of keywords for predicting, future themes. This study contributes to current literature by providing a summarizing and instructive content for researchers interested in big data in marketing.

 

https://doi.org/10.36096/ijbes.v1i2.107
PDF

References

Akter, S., & Wamba, S. F. (2016). Big data analytics in E-commerce: a systematic review and agenda for future research. Electronic Markets, 26(2), 173-194. https://doi.org/10.1007/s12525-016-0219-0

Amado, A., Cortez, P., Rita, P., & Moro, S. (2018). Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis. European Research on Management and Business Economics, 24(1), 1-7. https://doi.org/10.1016/j.iedeen.2017.06.002

Arthur, L. (2013). Big data marketing: engage your customers more effectively and drive value. John Wiley & Sons.

Aria, M. & Cuccurullo, C. (2017) bibliometrix: An R-tool for comprehensive science mapping analysis, Journal of Informetrics, 11(4), 959-975. https://doi.org/10.1016/j.joi.2017.08.007

Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS quarterly, 36(4).

Clavirate (2018). KeyWords Plus generation, creation, and changes. Retrieved from https://support.clarivate.com/ScientificandAcademicResearch/s/article/KeyWords-Plus-generation-creation-and-changes?language=en_US

Cuccurullo, C., Aria, M., & Sarto, F. (2016). Foundations and trends in performance management. A twenty-five years bibliometric analysis in business and public administration domains. Scientometrics, 108(2), 595-611. https://doi.org/10.1007/s11192-016-1948-8

De Bakker, F. G., Groenewegen, P., & Den Hond, F. (2005). A bibliometric analysis of 30 years of research and theory on corporate social responsibility and corporate social performance. Business & society, 44(3), 283-317. https://doi.org/10.1177/0007650305278086

De Mauro, A., Greco, M., & Grimaldi, M. (2016). A formal definition of Big Data based on its essential features. Library Review, 65(3), 122-135. https://doi.org/10.1108/LR-06-2015-0061

Dijcks, J. P. (2013). Oracle: Big data for the enterprise. Oracle white paper.

Erevelles, S., Fukawa, N., & Swayne, L. (2016). Big Data consumer analytics and the transformation of marketing. Journal of Business Research, 69(2), 897-904. https://doi.org/10.1016/j.jbusres.2015.07.001

Fan, S., Lau, R. Y., & Zhao, J. L. (2015). Demystifying big data analytics for business intelligence through the lens of marketing mix. Big Data Research, 2(1), 28-32. https://doi.org/10.1016/j.bdr.2015.02.006

Fetscherin, M., & Heinrich, D. (2015). Consumer brand relationships research: A bibliometric citation meta-analysis. Journal of Business Research, 68(2), 380-390. https://doi.org/10.1016/j.jbusres.2014.06.010

Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International journal of information management, 35(2), 137-144. https://doi.org/10.1016/j.ijinfomgt.2014.10.007

Gantz, J., & Reinsel, D. (2012). The digital universe in 2020: Big data, bigger digital shadows, and biggest growth in the far east. IDC iView: IDC Analyze the future, 2007(2012), 1-16.

Gretzel, U., Sigala, M., Xiang, Z., & Koo, C. (2015). Smart tourism: foundations and developments. Electronic Markets, 25(3), 179-188. https://doi.org/10.1007/s12525-015-0196-8

Grewal, D., Roggeveen, A. L., & Nordfält, J. (2017). The future of retailing. Journal of Retailing, 93(1), 1-6. https://doi.org/10.1016/j.jretai.2016.12.008

Gunasekaran, A., Papadopoulos, T., Dubey, R., Wamba, S. F., Childe, S. J., Hazen, B., & Akter, S. (2017). Big data and predictive analytics for supply chain and organizational performance. Journal of Business Research, 70, 308-317. https://doi.org/10.1016/j.jbusres.2016.08.004

J. Manyika, M. Chui, B. Brown, J. Bughin, R. Dobbs, C. Roxburgh, A. Hung Big data: the next frontier for innovation, competition, and productivity Tech. rep. McKinsey Global Institute (2011) available at: http://www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_for_innovation

Kim, J., & McMillan, S. J. (2008). Evaluation of internet advertising research: A bibliometric analysis of citations from key sources. Journal of Advertising, 37(1), 99-112. https://doi.org/10.2753/JOA0091-3367370108

LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2011). Big data, analytics and the path from insights to value. MIT sloan management review, 52(2), 21-32.

Marx, W., Bornmann, L., Barth, A., & Leydesdorff, L. (2014). Detecting the historical roots of research fields by reference publication year spectroscopy (RPYS). Journal of the Association for Information Science and Technology, 65(4), 751-764. https://doi.org/10.1002/asi.23089

McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D. J., & Barton, D. (2012). Big data: the management revolution. Harvard business review, 90(10), 60-68.

R Core Team (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.

RStudio Team (2016). RStudio: Integrated Development for R. RStudio, Inc., Boston, MA URL http://www.rstudio.com/.

Rust, R. T., & Huang, M. H. (2014). The service revolution and the transformation of marketing science. Marketing Science, 33(2), 206-221. https://doi.org/10.1287/mksc.2013.0836

Scimagojr (2019). Retrieved from https://www.scimagojr.com/

Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of Big Data challenges and analytical methods. Journal of Business Research, 70, 263-286. https://doi.org/10.1016/j.jbusres.2016.08.001

Tirunillai, S., & Tellis, G. J. (2014). Mining marketing meaning from online chatter: Strategic brand analysis of big data using latent dirichlet allocation. Journal of Marketing Research, 51(4), 463-479. https://doi.org/10.1509/jmr.12.0106

Wamba, S. F., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How ‘big data’can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234-246. https://doi.org/10.1016/j.ijpe.2014.12.031

Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J. F., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356-365. https://doi.org/10.1016/j.jbusres.2016.08.009

We are Social (2019). Global Digital Report 2019. Retrieved from https://wearesocial.com/global-digital-report-2019

Wedel, M., & Kannan, P. K. (2016). Marketing analytics for data-rich environments. Journal of Marketing, 80(6), 97-121. https://doi.org/10.1509/jm.15.0413

Wickham, H. (2016). ggplot2: elegant graphics for data analysis. Springer.

Wickham, H. (2007). Reshaping data with the reshape package. Journal of statistical software, 21(12), 1-20.

WoS - ISI Web of Knowledge (2019). Retrieved from www.webofknowledge.com/WOS

Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429-472. https://doi.org/10.1177/1094428114562629

© 2020 by the authors. Licensee BSC International Publishing, Istanbul, Turkey. This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).