Big data in marketing literature

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.
  

Keywords: {'name': 'Big Data'}, {'name': 'Marketing'}

Authors & affiliations

Fatih Pinarbasi
Affiliation not supplied

Zehra Nur Canbolat
Affiliation not supplied

Deposited references

Supplied references, not a list of publications citing this article.

DOI: 10.1007/s12525-016-0219-0

DOI: 10.1016/j.iedeen.2017.06.002

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

DOI: 10.1016/j.joi.2017.08.007

DOI: 10.2307/41703503

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

DOI: 10.1007/s11192-016-1948-8

DOI: 10.1177/0007650305278086

DOI: 10.1108/LR-06-2015-0061

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

DOI: 10.1016/j.jbusres.2015.07.001

DOI: 10.1016/j.bdr.2015.02.006

DOI: 10.1016/j.jbusres.2014.06.010

DOI: 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.

DOI: 10.1007/s12525-015-0196-8

DOI: 10.1016/j.jretai.2016.12.008

DOI: 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

DOI: 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.

DOI: 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/.

DOI: 10.1287/mksc.2013.0836

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

DOI: 10.1016/j.jbusres.2016.08.001

DOI: 10.1509/jmr.12.0106

DOI: 10.1016/j.ijpe.2014.12.031

DOI: 10.1016/j.jbusres.2016.08.009

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

DOI: 10.1509/jm.15.0413

DOI: 10.1007/978-3-319-24277-4

DOI: 10.18637/jss.v021.i12

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

DOI: 10.1177/1094428114562629