طراحی مدل رفتار مشتریان بانکی در رسانه‌های اجتماعی برخط

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشیار گروه مدیریت بازرگانی، دانشگاه گیلان

2 عضو هیات علمی گروه مدیریت بازرگانی، دانشگاه پیام نور، تهران

3 استادیار گروه مدیریت صنعتی، دانشگاه شهید بهشتی

چکیده

این پژوهش با هدف کاوش در رفتار مشتریان بانکی در ایجاد پایگاه کلان‌داده به ویژه در محیط رسانه­های اجتماعی آنلاین با استفاده از نظریة داده بنیاد و داده کاوی انجام پذیرفت. جامعة آماری مشتمل بر 15 نفر از مشتریان و10 نفر از مدیران بانک‌های دولتی و خصوصی شهر تهران که فضای رسانه­های اجتماعی آنلاین را تجربه کرده اند، است، که با آنها مصاحبه نیمه باز انجام شد. در این پژوهش از روش نمونه گیری نظری استفاده شد. برای بررسی روایی پژوهش، بر اساس شاخص­های کرسول مقایسه­ای میان مدل نهایی این پژوهش با مدل­های قبلی انجام پذیرفت. برای تحلیل داده­های حاصل از شبکه­های اجتماعی از نرم افزارNVIVO10 و برای شناسایی روش­ها و نتایج حاصل از داده از نرم افزار IBM SPSS Modeler 140.2 استفاده شد. در این پژوهش، ابتدا رفتار مشتریان در ایجاد پایگاه کلان‌داده بررسی گردید. سپس، ارتباط آمیخته­های بازاریابی بانکی مبتنی بر مشتری محوری در ارتباط با پایگاه کلان‌داده مبتنی بر داده کاوی و سیستم توصیه­گر بررسی شد. در نهایت، مدل رفتار مشتریان بانکی در رسانه‌های اجتماعی آنلاین با رویکردی جامع، ضمن برطرف کردن نقایص مدل­های قبلی، ارائه گردید.

کلیدواژه‌ها


عنوان مقاله [English]

Designing a Customer Behavior Model for Online Social Media Using Big Databases

نویسندگان [English]

  • Esmaeil Malekakhlagh 1
  • Yousef Mohammadkarimi 2
  • Davood Talebi 3
1 Associate Professor, Department of Business Management, University of Guilan
2 Faculty member, Department of Business Management, Payame Noor University, Tehran
3 Assistant Professor, Department of Industrial Management, Shahid Beheshti University
چکیده [English]

Introduction: Customers' use of technology-based intelligent information can put forth some key decision variables as a good starting point for investor decisions in financial and banking markets. On the other hand, the increasing number of commercial banks has raised concerns about customer turnover, especially for old and well-known banks. In this regard, the data about the interactions of banks and customers on online social media and mobile phones have always been an important source of banking marketing research. Analysis in banking research is generally based on questionnaires. Nowadays, however, macro-databases include numerous words, images, videos, and non-numeric outputs in terms of the volume, speed and variety of digital processes and often obscure traditional statistical analyses. Researchers have become interested in macro-databases for marketing decisions and creative marketing campaigns, and some have suggested them for the analysis of customer behavior. The use of macro-databases has, thus, significantly become a successful approach in today's marketing.
In order to conduct this research, first, the behavior of customers in creating a large database, especially in the social media environment, is examined through surveys and by studying customers' attitudes toward a product, service or bank. Then, the relationship between the customer-centric banking marketing mixed with the data-driven macro-database and the referral system is examined. Finally, the paradigm model of research is presented for the behavior of banking customers in online social media.
Methodology: This research seeks to provide a new model for analyzing the behavior of bank customers using the big data method. It is applied in terms of purpose and descriptive in terms of data collection. In order to finalize the analytical model of bank customers' behavior using big data, a qualitative research method was used. Also, the data theory method was used as a basis for tthe construction of the theory. The statistical population of the study consisted of two groups. The first group contained the customers who were significantly active in online social networking cyberspace, and the second group was for the bank managers who were well acquainted with the social media space. Accordingly, the statistical sample of the study included 15 bank customers who had extensive online financial and credit transactions with the bank as well as 10 bank managers who were well acquainted with the social media communication environment. The sampling method in this research is theoretical. For this purpose, the sampling continued until the model reached the saturation. The researcher collected and selected the data in such a way that he could discover the hidden background of various texts and images of online social interactions using the NVIVO10 software to formulate the final theory.
Results and Discussion: In intervening situations, the customer behavior was created using the psychological characteristics which represented five factors of personality. In this case, the internal and external sources of control and certain complex concepts were identified and explored. There were two categories of customer behavior to deal with. It was found that positive advertisement can increase online space-based negotiations using three types of knowledge in this area. Customers seek knowledge about others by looking at their profiles, photos and texts and also try to motivate others by sending them texts and comments. Thus, they make product and theoretical comparisons that result in mutual awareness.
Conclusion: Based on an operational model, this study suggests that the netnography method (blogging) be used to analyze the content of users' comments on virtual networks and discover the hidden layers and underpinnings of comments and texts written by users and members of virtual networks. Also, the culture of bank customers in relation to values, customs, and Iranian cultural meanings should be analyzed so that banks can have the knowledge of common concepts in this area. It is also suggested that a combination of social media data and transaction records be used to study the impact of social media behavior on banking purchasing behavior. Another suggestion is that, before commercializing their products and services, banks should place them in the online space to notify the groups there and use their opinions. They can thus offer better products and services according to the views and behaviors of customers. In addition, it is suggested that banks invest in marketing intelligence to become a competitive source for customers.

کلیدواژه‌ها [English]

  • Customer behavior
  • Social media
  • Bank
  • Big data
Abd Aziz, N. (2018). The influence of coproduction’s factors and corporate image toward attitudinal loyalty: Islamic financial banking services delivery in Malaysia. Journal of Islamic Marketing 9(2): 421–438.
Abbasolu, M. A., Gedik, B., and Ferhatosmanolu, H. (2013). "Aggregate profile clustering for telco analytics,"Proceedings of the VLDB Endowment (6:12), pp 1234-1237.
Abdul Waheed Siyal, Ding Donghong, Waheed Ali Umrani, Saeed Siyal, Shaharbano Bhand, (2019). Predicting Mobile Banking Acceptance and Loyalty in Chinese Bank Customers, SAGE Open, Volume: 9 issue: 2: 1–21.
Ahmed Suhail Ajina, (2017). The Role of Social Media Engagement in Influencing Customer Loyalty in Saudi Banking Industry, International Review of Management and Marketing, 9(3), 87-92.
Akheela Khanum, Sajjad Anees Nagrami, M.C.Trivedi, (2016). Use of Social Media to Drive Business Advantage in Banking, ACEIT Conference Proceeding.
Antonella Angelini, Paola Ferretti, Gabriele Ferrante & Paolo Graziani (2017). Social Media Development Paths in Banks, Journal of Promotion Management, 23:3, 345-358.
Baker. A., Ricciardi.k, (2014). The Psychology of Financial Planning and Investing.wiley.p23
Bayus, B.L. (2013), “Crowdsourcing new product ideas over time: an analysis of the Dell IdeaStorm community”, Management Science, Vol. 59 No. 1, pp. 226-244.
Blackwell, R.D., Miniard, P.W. and Engel, J.F. (2005). Consumer Behavior, 10th ed., South-Western College Publications.
Chan, K.W., Li, S.Y. and Zhu, J.J. (2015). “Fostering customer ideation in crowdsourcing community: the role of peer-to-peer and peer-to-firm interactions”, Journal of Interactive Marketing, Vol. 31, pp. 42-62.
Christos Giannakis-Bompolisa, Christina Boutsouki, (2014). Customer Relationship Management in the Era of Social Web and Social Customer: An Investigation of Customer Engagement in the Greek Retail Banking Sector, Social and Behavioral Sciences, 148, 67 – 78.
De Valck, K. (2007). The war of the eTribes: Online conflicts and communal consumption. In B. Cova, R. Kozinets, & A. Shankar (Eds.), Consumer tribes (pp. 260_274). Oxford: Butterworth-Heinemann.
Erevelles, S., Fukawa, N. and Swayne, L. (2015), “Bid data consumer analytics and the transformation of marketing”, Journal of Services Marketing, 22 (4), pp. 303-315.
Fathyan.Mohammad, Nasirzadeh.Alnaz, (2019), Banking Customer Segmentation Based on Attitudes and Financial Behavior to Improve Bank Interaction with Customers, Defense Management Innovation Management, Volume 2, Number 2, Issue 4, Page 29-56. (In Persian)
Feiz Davood, Shabani.Atefeh, (2018). Investigating the Impact of Marketing Action on Social Media; A Typical Hack Growth Strategy on Customer Behavioral Attitudes and Verbal Advertising, Modern Marketing Research, Volume 8, Number 4, pp. 45-68. (In Persian)
Goulding, C. (2005). Grounded theory, ethnography and phenomonology: A comparative analysis of three qualitative strategies for marketing research. European Journal of Marketing, 39(3), 294_308.
Handelman, J. (1998). Ensouling consumption: A netnographic exploration of the meaning of boycotting behavior. Advances in Consumer Research, 25(1), 475-480.
Hoque, M.E., Kabir Hassan, M., Hashim, N.M.H.N. et al. (2019). Factors affecting Islamic banking behavioral intention: the moderating effects of customer marketing practices and financial considerations. Journal of Financial Service Marketing, 24, 44–58.
Klapdor, S., Anderl, E.A., von Wangenheim, F. and Schumann, J.H. (2015), “Finding the right words: the influence of keyword characteristics on performance of paid search campaigns”, Journal of Interactive Marketing, Vol. 28 No. 4, pp. 285-301.
King, R.A., Racherla, P. Bush, V.D. (2014). “What we know and don’t know about online word-of-mouth: a review and synthesis of the literature”, Journal of Interactive Marketing, Vol. 28 No. 3, pp. 167-183
Knudsen and Kjeldgaard (2014). online reception analysis: big data in qualitative marketing research, Research in Consumer Behavior, Volume 16, 217_242.
Kozinets, R. V. (2009). Netnography: Doing ethnographic research online. Los Angeles, CA:Sage.
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H.(2011). Big data: The next frontier for innovation, competition, and productivity. Sydney: McKinsey Global Institute.
Malthouse, E.C. (2007). “Mining for trigger events with survival analysis”, Data Mining and Knowledge Discovery,Vol. 15 No. 3, pp. 383-402.
Mayer-Scho¨ nberger, V., & Cukier, K. (2013). Big data. Boston, MA: Houghton Mifflin Harcourt.
Mitic, M. and Kapoulas, A. (2012). "Understanding the role of social media in bank marketing", Marketing Intelligence & Planning, Vol. 30 No. 7, pp. 668-686.
Ngai, E. W., Xiu, L., and Chau, D. C. (2009)"Application of data mining techniques in customer relationship management: A literature review and classification," Expert systems with applications (36:2) pp 2592-2602.
Normandeau, K. (2013). “Beyond volume, variety and velocity is the issue of big data veracity”, Inside BigData, available at: http://insidebigdata.com/2013/09/12/beyondvolume- variety-velocity-issue-big-data-veracity/ (accessed 15 April 2015).
Roshandel Arbabani.Taher, Mahmoodzadeh Ahad, (2017). Designing Advertising Model through Social Media to Influence Customer Desire, Business Management, Volume 9, Number 4, pp. 76-76.(In Persian)
Yasin.Mahmoud , Lucia Porcu, Francisco Liébana-Cabanillas, (2019). The Effect of Brand Experience on Customers’ Engagement Behavior within the Context of Online Brand Communities: The Impact on Intention to Forward Online Company-Generated Content, Sustainability,11,4649,1-17.
Zare Pour.Zeinab, Kordavich.Asdollah, Shahabi.allah, (2019). Investigating the Impact of Various Barriers to Customer Relocation on Their Loyalty (Case Study: Mellat Bank Customers in Tehran), Business Management Research, Volume 11, Number 21; Spring and Summer 1398, pp. 115-140. .(In Persian)