Big Data and Its Impact on the Iranian Banking Industry Achieving Competitive Advantage

Subject Areas : Industrial Management

foad Kouhzadi ¹ , hossin gharebiglou ² , hossin boudaghi khaje nouber ³ , yaghoub alavi matin ⁴

1 - PhD Student, Management, Ajab Shir Branch, Islamic Azad University, Ajab Shir, Iran
2 - Assistant Professor of Management, Ajab Shir Branch, Islamic Azad University, Ajab Shir, Iran
3 - Assistant Professor, Department of Management, Tabriz Branch, Islamic Azad University, Tabriz, Iran
4 - Assistant Professor, Department of Management, Tabriz Branch, Islamic Azad University, Tabriz, Iran

Received: 2021-08-03 Accepted : 2022-05-01 Published : 2022-04-21

Keywords: Big data, Customer privacy issues, Banking, ethical, Achieving competitive advantage,

Abstract :

AbstractThe increasing volume and complexity of digital data and its role as a tool to support the competitive advantage for banks, has increased the need to use new tools and techniques in data acquisition, analysis and processing, and in this context, access to Data flow in new financial transactions has become more difficult, and this has doubled the need for bank managers to use the big data strategy to solve financial challenges and take advantage of opportunities to achieve a sustainable competitive advantage. The purpose of this study is to identify the effects of big data strategy on achieving a competitive advantage in the Iranian banking industry. Present the banking industry. The hypotheses of the model were evaluated using the views of the research sample, including CEOs, managers and employees of information and communication technology, managers and marketing staff of 20 banks through a questionnaire, and the results show that banks using macro strategy Data gains a competitive advantage. In addition, ethical issues and customer privacy affect the relationship between big data strategy and gaining a competitive advantage.

References:

1. Ansari, M., Rahmani, H. Rahmani, K., Pasbani, M., Askari, M.A (2013). Presenting a conceptual model of the effect of knowledge management implementation success on gaining competitive advantage in small and medium banks. Journal of Business Management, 5(1), 21-40.


2. Arora, S., Singha, K., and Sahney, S. (2017). Understanding consumer’s showrooming behaviour: Extending the theory of planned behaviour. Asia Pacific Journal of Marketing and Logistics, 29(2), 409-431.‏


3. Bradlow, E. T., Gangwar, M., Kopalle, P., and 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


4. Cukier, K. and Mayer-Schoenberger, V. (2013). The rise of big data: How it's changing the way we think about the world. Foreign Aff, 92, 28.‏


5. Dhar, Subhankar, and Upkar, Varshney. (2011), “Challenges and Business Models for Mobile Location-based Services and Advertising,” Communications of the ACM, 54 (5), 121–8.


6. Dittrich, David, and Erin, Kenneally. (2012), the Menlo Report: Ethical Principles Guiding Information and Communication Technology Research, US Department of Homeland Security.


7. Eggers, F. (2010). Grow with the flow: entrepreneurial marketing and thriving young firms. International Journal of Entrepreneurial Venturing, 1(3), 227. https://doi.org/10.1504/IJEV.2010.031024


8. Erevelles, S., Fukawa, N., and Swayne, L. (2016). Big Data consumer analytics and the transformation of marketing. Journal of Business Research, 69(2), 897–904.


9. Fong, Nathan M., Zheng Fang and Xueming Luo. (2015). Geo-Conquesting: Competitive Locational Targeting of Mobile Promotions. Journal of Marketing Research, 52 (October (5)), 726–35.


10. Grewal, D., Roggeveen, A. L., and Nordfält, J. (2017). The Future of Retailing. Journal of Retailing, 93(1), 1–6.


11. Hofacker, C. F. Malthouse, E. C. and Sultan, F. (2016). Big Data and consumer behavior: imminent opportunities. Journal of Consumer Marketing, 33(2), 89–97.


12. Hui, Sam K. Eric T. Bradlow and Peter S. Fader (2009), “Testing Behavioral Hypotheses Using an Integrated Model of Grocery Store Shopping Path and Purchase Behavior,” Journal of Consumer Research, 36 (3), 478–93.


13. Kilcourse, B., and Rosenblum, P. (2014). Retail Analytics Moves to the Frontline. USA: Retail Systems Research.


14. Kopalle, Praveen K. P.K., Kannan, Lin Bao Boldt and Neeraj Arora (2012), “The Impact of Household Level Heterogeneity in Reference Price Effectson Optimal Retailer Pricing Policies,” Journal of Retailing, 88 (1), 102–14.


15. Kumar, V., Anand, A., and Song, H. (2017). Future of Retailer Profitability: An Organizing Framework. Journal of Retailing, 93(1), 96–119.


16. Li, H., and Kannan, P. K. (2014). Attributing conversions in a multichannel online marketing environment: An empirical model and a field experiment. Journal of Marketing Research, 51(1), 40-56.


17. Manyika, J., Chui, M. Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., and Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity.‏


18. Mckinsey, M. G. I. (2011). Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute.


19. Motamarri, S., Akter, S., and Yanamandram, V. (2017). Does big data analytics influence frontline employees in services marketing? Business Process Management Journal, 23(3), 623–644.


20. Newvantage Partners. (2012). Big Data Executive Survey: Creating a Big Data Environment to Accelerate Business Value [Online]. NewVantage Partners LLC. Available:http://newvantage.com/wp-content/uploads/2012/12/NVP-Big-Data-Survey-Accelerate-Business-Value.pdf


21. Newvantage Partners. (2014). Big Data Executive Survey 2014: An Update on the Progress of Big Data in the Large Corporate World [Online]. Boston: NewVantage Partners LLC. Available: http://newvantage.com/wp-content/uploads/2014/12/Big-Data-Survey-2014-Summary-Report-110314.pdf


22. Ostrom, A. L., Parasuraman, A., Bowen, D. E. Patricio, L., and Voss, C. A. (2015). Service research priorities in a rapidly changing context. Journal of Service Research, 18(2), 127-159.‏


23. Rapp, Adam, Thomas L. Baker, Daniel G. Bachrach, Jessica Ogilvie and Lauren Skinner Beitelspacher. (2015). Perceived Customer Showrooming Behaviorand the Effect on Retail Salesperson Self-efficacy and Performance. Journal of Retailing, 91 (2), 358–69.


24. Rust, R. T., and Huang, M. H. (2014). The service revolution and the transformation of marketing science. Marketing Science Journal, 33(2), 206-221.‏


25. Shapiro, Carl and Hal R., Varian, (2013). Information Rules: A Strategic Guide to the Network Economy, Harvard Business Press.


26. Shibata, T. and Kurachi, Y. (2015). Big data analysis solutions for driving innovation in on-site decision making. Fujitsu Scientific and Technical Journal, 51, 33-41.


27. Verhoef, Peter C., Scott A., Neslin and Björn Vroomen, (2007). Mul-tichannel Customer Management: Understanding the Research-shopperPhenomenon. International Journal of Research in Marketing, 24 (2), 129–48.


28. Wilder, K. M., Collier, J. E., and Barnes, D. C. (2014). Tailoring to customers’ needs: Understanding how to promote an adaptive service experience with frontline employees. Journal of Service Research, 17(4), 446-459.‏


29. Yang, M., and Gabrielsson, P. (2017). Entrepreneurial marketing of international high-tech business-to-business new ventures: A decision-making process perspective. Industrial Marketing Management, 64, 147–160.