Academic Big Data
One of the Contemporary Challenges of the Marketing Research
Keywords:
Big Data, Academic researchAbstract
The Big Data phenomenon has attracted the attention of marketing researchers in recent years, who seek to understand how to extract relevant information from this exponential increase in data availability. The research priorities for the Institute of Marketing Sciences demonstrate this concern to the request of development of new tools of collection and analysis of information. However, to date, there has been no debate about the academic implications of this phenomenon. In this way, the present article aims at how Big Data can affect academic research in the field of marketing. For this, based on studies in several areas of knowledge, it presents a discussion of the possible concepts and characteristics of volume, velocity, variety, veracity, value and others V’s. Then, several possibilities of studies are listed as tools that facilitate analysis and interpretation of the information. Finally, the challenges that the research imposes are published, especially with the publications pressure and lack of time, ethical issues about de use of the data and the accessibility of information.
References
Ann Keller, S., Koonin, S. E., & Shipp, S. (2012). Big data and city living - what can it do for us? Significance, 9(4), 4–7. https://doi.org/10.1111/j.1740-9713.2012.00583.x
Borne, K. (2014). Top 10 Big Data Challenges – A Serious Look at 10 Big Data V’s. Retrieved August 30, 2017, from https://mapr.com/blog/top-10-big-data-challenges-serious-look-10-big-data-vs/
Boyd, D., & Crawford, K. (2012). Critical questions for Big Data. Information, Communication & Society, 15(5), 662–679. https://doi.org/10.1080/1369118X.2012.678878
Bradlow, E. T., Gangwar, M., Kopalle, P., & Voleti, S. (2017). The Role of Big Data and Predictive Analytics in Retailing. Journal of Retailing, 93(1), 79–95. https://doi.org/10.1016/j.jretai.2016.12.004
Chintagunta, P., Hanssens, D. M., & Hauser, J. R. (2016). Editorial—Marketing Science and Big Data. Marketing Science, 35(3), 341–342. https://doi.org/10.1287/mksc.2016.0996
Dessureault, S. (2016). Understanding big data. CIM Magazine, 11(1).
Elragal, A. (2014). ERP and Big Data: The Inept Couple. Procedia Technology, 16(December 2014), 242–249. https://doi.org/10.1016/j.protcy.2014.10.089
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
Fisher, D., DeLine, R., Czerwinski, M., & Drucker, S. (2012). Interactions with Big Data Analytics. Interactions, 50–59. Retrieved from https://pdfs.semanticscholar.org/de29/c51d169cdb5066f6832e9a8878900c43f100.pdf
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. Retrieved from https://www.emc.com/collateral/analyst-reports/idc-the-digital-universe-in-2020.pdf
Gil, A. C. (2008). Métodos e técnicas de pesquisa social (6th ed.). São Paulo: Atlas.
Grover, P., & Kar, A. K. (2017). Big Data Analytics: A Review on Theoretical Contributions and Tools Used in Literature. Global Journal of Flexible Systems Management, 18(3), 203–229. https://doi.org/10.1007/s40171-017-0159-3
Guerrero-Bote, V. P., & Moya-Anegón, F. (2012). A further step forward in measuring journals’ scientific prestige: The SJR2 indicator. Journal of Informetrics, 6(4), 674–688. https://doi.org/10.1016/j.joi.2012.07.001
Herschel, R., & Miori, V. M. (2017). Ethics & Big Data. Technology in Society, 49, 31–36. https://doi.org/10.1016/j.techsoc.2017.03.003
Jacobs, A. (2009). The pathologies of big data. Communications of the ACM, 52(8), 36. https://doi.org/10.1145/1536616.1536632
Lam, S. K., Sleep, S., Hennig-Thurau, T., Sridhar, S., & Saboo, A. R. (2017). Leveraging Frontline Employees’ Small Data and Firm-Level Big Data in Frontline Management. Journal of Service Research, 20(1), 12–28. https://doi.org/10.1177/1094670516679271
Liu, X., Singh, P. V., & Srinivasan, K. (2016). A Structured Analysis of Unstructured Big Data by Leveraging Cloud Computing. Marketing Science, 35(3), 363–388. https://doi.org/10.1287/mksc.2015.0972
Lukoianova, T., & Rubin, V. L. (2013). Veracity Roadmap: Is Big Data Objective, Truthful and Credible? Advances in Classification Research. Retrieved from https://s3.amazonaws.com/academia.edu.documents/32744189/Lukioanova_Rubin_ADVANCES_In_CLASS_Workshop_Draft_120113_VR_FINAL.pdf?AWSAccessKeyId=AKIAIWOWYYGZ2Y53UL3A&Expires=1504086360&Signature=0dlkLpsAOWP4%2BnXv0tC72JPhji8%3D&response-content-disposition=inline%3B filename%3DVeracity_Roadmap_Is_Big_Data_Objective_T.pdf
Marketing Science Institute. (2016). Research Priorities 2016-2018. Cambridge: Marketing Science Institute. Retrieved from www.msi.org
Markus, M. L., & Topi, H. (2015). Big data, big decisions for science, society, and business. New York. Retrieved from https://www.bentley.edu/files/2015/10/08/BigDataWorkshopFinalReport.pdf
McAfee, A., & Brynjolfsson, E. (2012). Big Data: The management revolution. Harvard Business Review, 90(10), 60–68.
Miah, S. J., Vu, H. Q., Gammack, J., & McGrath, M. (2017). A Big Data Analytics Method for Tourist Behaviour Analysis. Information & Management, 54(6), 771–785. https://doi.org/10.1016/j.im.2016.11.011
Richard, J., Plimmer, G., Fam, K.-S., & Campbell, C. (2015). Publishing success of marketing academics: antecedents and outcomes. European Journal of Marketing, 49(1/2), 123–145. https://doi.org/10.1108/EJM-06-2013-0311
Rouse, M. (2016). Big Data. Retrieved August 28, 2017, from http://searchcloudcomputing.techtarget.com/definition/big-data-Big-Data
SCImago. (2007). SJR — SCImago Journal & Country Rank. Retrieved August 24, 2017, from http://www.scimagojr.com
Sheng, J., Amankwah-Amoah, J., & Wang, X. (2017). A multidisciplinary perspective of big data in management research. International Journal of Production Economics, 191, 97–112. https://doi.org/10.1016/j.ijpe.2017.06.006
Singh Jain, A. D., Mehta, I., Mitra, J., & Agrawal, S. (2017). Application of Big Data in Supply Chain Management. Materials Today: Proceedings, 4(2), 1106–1115. https://doi.org/10.1016/j.matpr.2017.01.126
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
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
Wilkie, W. L., & Moore, E. S. (2003). Scholarly research in marketing: Exploring the “4 Eras” of thought development. Journal of Public Policy & Marketing, 22(2), 116–146. https://doi.org/10.1509/jppm.22.2.116.17639
Xu, Z., Frankwick, G. L., & Ramirez, E. (2016). Effects of big data analytics and traditional marketing analytics on new product success: A knowledge fusion perspective. Journal of Business Research, 69(5), 1562–1566. https://doi.org/10.1016/j.jbusres.2015.10.017
