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Sarlab, Roghayeh, Farid Fathi, Maryam. (1400). Design of Fuzzy Cognitive Mapping Model of Factors Affecting Personalized Advertising in the Sporting Goods Industry. جلوه هنر, 1(2), 213-230. doi: 10.22051/sbj.2022.39717.1025
Roghayeh Sarlab; Maryam Farid Fathi. "Design of Fuzzy Cognitive Mapping Model of Factors Affecting Personalized Advertising in the Sporting Goods Industry". جلوه هنر, 1, 2, 1400, 213-230. doi: 10.22051/sbj.2022.39717.1025
Sarlab, Roghayeh, Farid Fathi, Maryam. (1400). 'Design of Fuzzy Cognitive Mapping Model of Factors Affecting Personalized Advertising in the Sporting Goods Industry', جلوه هنر, 1(2), pp. 213-230. doi: 10.22051/sbj.2022.39717.1025
Sarlab, Roghayeh, Farid Fathi, Maryam. Design of Fuzzy Cognitive Mapping Model of Factors Affecting Personalized Advertising in the Sporting Goods Industry. جلوه هنر, 1400; 1(2): 213-230. doi: 10.22051/sbj.2022.39717.1025

Design of Fuzzy Cognitive Mapping Model of Factors Affecting Personalized Advertising in the Sporting Goods Industry

Sports Business Journal
مقاله 13، دوره 1، شماره 2، مهر 2021، صفحه 213-230    اصل مقاله (867.62 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22051/sbj.2022.39717.1025
نویسندگان
Roghayeh Sarlab* 1؛ Maryam Farid Fathi2
1Assistant Professor, Department of Sport Management, Faculty of Sport Science, Urmia University, Urmia, Iran.
2PhD in Sports Management, Department of Sport Management, Faculty of Physical Education and Sport Science, University of Tabriz, Tabriz, Iran.
چکیده
Purpose: This study was the design of a fuzzy cognitive mapping model of factors affecting personalized advertising in the sporting goods industry.
Methodology: The primary research method of this paper, the descriptive-exploratory study, was semi-structured interviews with the informed and expert individuals who were active in the intended domain of the study. Sampling was done using snowball sampling to select participants from among the Specialists in sports marketing, sports brand, and sports industry and university professors in Sports Management, Business Management, Technology management who had the intended characteristics. After conducting 14 interviews and collecting data, the obtained data were coded. After summarizing and theming the issues raised by the experts, seven main indicators and 33 Sub-indices were obtained in personalized advertisements in the sporting goods industry. Then, based on the knowledge of experts, the relationships between concepts were drawn in the form of fuzzy cognition maps. Excel, FCMapper, and FCM EXPERT software were used to draw fuzzy cognitive maps and calculate the related indicators.
Findings: According to the table, customer relationship management is the most affected by other factors. Also, the research results showed that Customer Knowledge Management has the most significant impact on the factors of the ad personalization model. Customer relationship management, Clustering customers, and Customer Knowledge Management, respectively, have the highest degree of centrality; this means that they have the most impact on the model.
Originality: In this study, we examined the development of a fuzzy cognitive mapping model of factors affecting personalized advertising in the sporting goods industry that has not been studied so far.
کلیدواژه‌ها
Advertising؛ Fuzzy Cognitive Mapping؛ Marketing؛ Personalized؛ Sports Industry
عنوان مقاله [English]
تدوین مدل نگاشتی شناختی فازی عوامل مؤثر بر تبلیغات شخصی سازی شده در صنعت کالاهای ورزشی
نویسندگان [English]
رقیه سرلاب1؛ مریم فرید فتحی2
1استادیار، گروه مدیریت ورزشی، دانشکده علوم ورزشی، دانشگاه ارومیه، ارومیه، ایران.
2دکترای مدیریت ورزشی، گروه مدیریت ورزشی، دانشکده تربیت بدنی و علوم ورزشی، دانشگاه تبریز، تبریز، ایران.
چکیده [English]
هدف: این پژوهش به تدوین مدل نگاشتی شناختی فازی عوامل مؤثر بر تبلیغات شخصی‌سازی شده در صنعت کالاهای ورزشی با استفاده از روش کیفی و نظریه زمینه‌ای بر اساس رویکرد گلیزر و مبتنی بر رویکرد نگاشتی شناختی فازی است.
روش‌: ابزار اصلی این پژوهش توصیفی-اکتشافی، مصاحبه نیمه ساختاریافته با افراد آگاه و خبره فعال در حوزه مورد نظر بود. نمونه‌گیری با استفاده از گلوله برفی برای انتخاب شرکت‌کنندگان از بین متخصصان بازاریابی ورزشی، برندهای ورزشی و صنعت ورزش و اساتید دانشگاه در رشته‌های مدیریت ورزشی، مدیریت بازرگانی، مدیریت فناوری که دارای ویژگی‌های مورد نظر بودند، انجام شد. پس از انجام 14 مصاحبه، داده‌های به دست‌آمده کدگذاری شد. پس از جمع‌بندی موضوعات مطرح شده توسط کارشناسان، 7 شاخص اصلی و 33 زیرشاخص در تبلیغات شخصی سازی شده در صنعت لوازم ورزشی به دست آمد. سپس بر اساس دانش صاحب‌نظران، روابط بین مفاهیم در قالب مدل نگاشتی شناختی فازی ترسیم شد. برای ترسیم مدل شناختی فازی و محاسبه شاخص‌های مربوطه از نرم‌افزارهای Excel، FCMapper  و FCM EXPERT  استفاده شد.
یافته‌ها: بر اساس نتایج، مدیریت ارتباط با مشتری بیشترین تأثیر را از سایر عوامل می‌پذیرد. همچنین، نتایج تحقیق نشان داد که مدیریت دانش مشتری بیشترین تأثیر را بر عوامل مدل شخصی‌سازی تبلیغات دارد. مدیریت ارتباط با مشتری، مشتریان خوشه‌بندی و مدیریت دانش مشتری به ترتیب دارای بالاترین درجه مرکزیت هستند. این بدان معنی است که آن‌ها بیشترین تأثیر را بر روی مدل دارند.
اصالت و ابتکار مقاله: در این مطالعه، به تدوین مدل نگاشتی شناختی فازی عوامل مؤثر بر تبلیغات شخصی‌سازی شده در صنعت کالاهای ورزشی بررسی شد که تاکنون مطالعه‌ای بر روی آن انجام نشده است.
کلیدواژه‌ها [English]
بازاریابی, تبلیغات, شخصی سازی شده, صنعت ورزش, نگاشتی شناختی فازی
مراجع
  1. Baek, T. H., & Morimoto, M. (2012). Stay Away From Me. Journal of Advertising, 41(1), 59-76. https://doi.org/10.2753/JOA0091-3367410105
  2. Ball, A. D., Coelho, P. S., & Vilares, M. J. (2006). Service personalization and loyalty. Journal of Services Marketing, 20(6), 391 - 403. https://doi.org/10.1108/08876040610691284
  3. Banini, G. A., & Bearman, R. A. (1998). Application of fuzzy cognitive maps to factors affecting slurry rheology. International Journal of Mineral Processing, 52(4), 233-244. https:// doi.org/10.1016/S0301-7516(97)00071-9
  4. Bose, R., & Sugumaran, V. (2003). Application of knowledge management technology in customer relationship management. Knowledge and Process Management, 10(1), 3-17. https://doi.org/10.1002/kpm.163
  5. De Keyzer, F., Dens, N., & De Pelsmacker, P. (2015). Is this for me? How Consumers Respond to Personalized Advertising on Social Network Sites. Journal of Interactive Advertising, 15(2), 124-134. https://doi.org/10.1080/15252019.2015.1082450
  6. Eden, C., Ackermann, F., & Cropper, S. (1992). The analysis of cause maps. Journal of Management Studies, 29(3), 309-324. https://doi.org/10.1111/j.1467-6486.1992.tb00667.x
  7. Evans, M. (1989). Consumer behaviour towards fashion. European Journal of Marketing, 23(7), 7-16. https://doi.org/10.1108/EUM0000000000575
  8. Feng, J., Li, H., Huang, M., Liu, S., Ou, W., Wang, Z., & Zhu, X. (2018, April 23-27). Learning to collaborate: Multi-scenario ranking via multi-agent reinforcement learning. Proceedings of the 2018 World Wide Web Conference, Lyon France. https://arxiv.org/ pdf/1809.06260.pdf
  9. Furnham, A. (2019). Advertising: The contribution of applied cognitive psychology. Applied Cognitive Psychology, 33(2), 168-175. https://doi.org/10.1002/acp.3458
  10. Garcia-Murillo, M., & Annabi, H. (2002). Customer knowledge management. Journal of the Operational Research Society, 53(8), 875-884. https://doi.org/10.1057/palgrave.jors. 2601365
  11. Geib, M., Kolbe, L. M., & Brenner, W. (2006). CRM collaboration in financial services networks: a multi‐case analysis. Journal of Enterprise Information Management, 19(6), 591-607. https://doi.org/10.1108/17410390610708481
  12. Hadian, H., Razavi, S. M. H., Boroumand, M. R., & Amirnejad, S. (2020). Strategies for Developing Economy of Iran’s Sports Industry. Annals of Applied Sport Science, 8(4), 0-0. https://doi.org/10.29252/aassjournal.843
  13. Harary, F., & Norman, R. Z. (1965, January 1). Structural Models: An Introduction to the Theory of Directed Graphs (1st ed.). John Wiley & Sons. https://www.amazon.com/Structural-Models-Introduction-Theory-Directed/dp/047135130X
  14. Hosseini, M., & Shabani, M. (2015). New approach to customer segmentation based on changes in customer value. Journal of Marketing Analytics, 3(3), 110-121. https://doi.org/10.10 57/jma.2015.10
  15. Jai, T.-M., Burns, L. D., & King, N. J. (2013). The effect of behavioral tracking practices on consumers’ shopping evaluations and repurchase intention toward trusted online retailers. Computers in Human Behavior, 29(3), 901-909. https://doi.org/10.1016/j.chb.2012.12.021
  16. Kandasamy, W. V., & Smarandache, F. (2003). Fuzzy cognitive maps and neutrosophic cognitive maps. Infinite Study. https://arxiv.org/ftp/math/papers/0311/0311063.pdf
  17. Keimasi, M., & Khoshnevis, M. (2021). Analyzing Customers' Behaviors with Approach of Measuring Visual Attention of Users to Websites Banner Advertising. Consumer Behavior Studies Journal, 8(1), 22-37. https://doi.org/10.34785/J018.2021.489
  18. Khodakarami, F., & Chan, Y. E. (2014). Exploring the role of customer relationship management (CRM) systems in customer knowledge creation. Information & Management, 51(1), 27-42. https://doi.org/10.1016/j.im.2013.09.001
  19. Kim, J. W., Lee, B. H., Shaw, M. J., Chang, H.-L., & Nelson, M. (2001). Application of Decision-Tree Induction Techniques to Personalized Advertisements on Internet Storefronts. International Journal of Electronic Commerce, 5(3), 45-62. https://doi.org/ 10.1080/10864415.2001.11044215
  20. Kokkinos, K., Lakioti, E., Papageorgiou, E., Moustakas, K., & Karayannis, V. (2018). Fuzzy cognitive map-based modeling of social acceptance to overcome uncertainties in establishing waste biorefinery facilities. Frontiers in Energy Research, 6, 112. https:// doi.org/10.3389/fenrg.2018.00112
  21. Kończak, J. (2021). Sports themes in advertising. Journal of Physical Education and Sport, 21, 1179-1184.
  22. Kosko, B. (1986). Fuzzy cognitive maps. International Journal of Man-Machine Studies, 24(1), 65-75. https://doi.org/10.1016/S0020-7373(86)80040-2
  23. Kosko, B. (1993). Adaptive inference in fuzzy knowledge networks. In D. Dubois, H. Prade, & R. R. Yager (Eds.), Readings in Fuzzy Sets for Intelligent Systems (pp. 888-891). Morgan Kaufmann. https://www.sciencedirect.com/science/article/pii/B9781483214504500936
  24. Meng, W., Ding, R., Chung, S. P., Han, S., & Lee, W. (2016, February 16, 21-24). The Price of Free: Privacy Leakage in Personalized Mobile In-Apps Ads. The Network and Distributed System Security (NDSS), San Diego, CA, USA. https://wenke.gtisc.gatech. edu/papers/ndss16_mobile_ad.pdf
  25. Mouratiadou, I., & Moran, D. (2007). Mapping public participation in the Water Framework Directive: A case study of the Pinios River Basin, Greece. Ecological Economics, 62(1), 66-76. https://doi.org/10.1016/j.ecolecon.2007.01.009
  26. O'Donnell, K., & Cramer, H. (2015, May 18-22). People's perceptions of personalized ads. Proceedings of the 24th International Conference on World Wide Web, Florence, Italy. http://www2015.thewebconf.org/documents/proceedings/companion/p1293.pdf
  27. Özesmi, U., & Özesmi, S. L. (2004). Ecological models based on people’s knowledge: a multi-step fuzzy cognitive mapping approach. Ecological Modelling, 176(1), 43-64. https:// doi.org/10.1016/j.ecolmodel.2003.10.027
  28. Papageorgiou, E. I., Markinos, A., & Gemptos, T. (2009). Application of fuzzy cognitive maps for cotton yield management in precision farming. Expert Systems with Applications, 36(10), 12399-12413. https://doi.org/10.1016/j.eswa.2009.04.046
  29. Park, K. S., & Kim, S. H. (1995). Fuzzy cognitive maps considering time relationships. International Journal of Human-Computer Studies, 42(2), 157-168. https://doi.org/10. 1006/ijhc.1995.1007
  30. Pihl, P. (2006). An Analysis of the Sports Equipment Industry and One of Its Leading Companies, Head, NV [Master Thesis, Business Administration, Management, and Operations, Liberty University]. Lynchburg, Virginia, United States. https://digitalcommons. liberty.edu/cgi/viewcontent.cgi?article=1176&context=honors
  31. Rajagopal, S. (2011). Customer data clustering using data mining technique. International Journal of Database Management Systems, 3(4), 1-11. https://doi.org/10.5121/ijdms.2011.3401
  32. Ran, F., & Zhang, X. (2011). Financial competitiveness evaluation on sporting goods listed enterprises: A China study. African Journal of Business Management, 5(17), 7404-7409. https://doi.org/10.5897/AJBM11.137
  33. Rodriguez-Repiso, L., Setchi, R., & Salmeron, J. L. (2007). Modelling IT projects success: Emerging methodologies reviewed. Technovation, 27(10), 582-594. https://doi.org/10. 1016/j.technovation.2006.12.006
  34. Sajid, S. I. (2016). Social media and its role in marketing. Business and Economics Journal, 7(1). https://doi.org/10.4172/2151-6219.1000203
  35. Sarlab, R., & Seyed Ameri, M. (2021). The Effect of Exchange Rate Volatility on Trade Balance Sports Sector in Selected Countries in the MENA. Sport Management Studies, 12(64), 17-32. https://doi.org/10.22089/smrj.2020.8573.2930
  36. Savić, Z., Ranđelović, N., Stojanović, N., Stanković, V., & Šiljak, V. (2018). The sports industry and achieving top sports results. Facta Universitatis, Series: Physical Education and Sport, 15(3), 513-522. https://doi.org/10.22190/FUPES1703513S
  37. Schumann, J. H., von Wangenheim, F., & Groene, N. (2014). Targeted online advertising: Using reciprocity appeals to increase acceptance among users of free web services. Journal of Marketing, 78(1), 59-75. https://doi.org/10.1509/jm.11.0316
  38. Song, H. S., Kyeong Kim, J., & Kim, S. H. (2001). Mining the change of customer behavior in an internet shopping mall. Expert Systems with Applications, 21(3), 157-168. https:// doi.org/10.1016/S0957-4174(01)00037-9
  39. Stryczek, M. (2011). Aksjologia sportu. Katolicki Uniwersytet Lubelski Jana Pawła II. Wydział Teologii. Katedra.
  40. Turban, E., King, D., Lee, J. K., Liang, T. P., & Turban, D. (2010). Electronic Commerce 2010: A Managerial Perspective. Prentice Hall. https://books.google.com/books?id=Qg RuPwAACAAJ
  41. Ur, B., Leon, P. G., Cranor, L. F., Shay, R., & Wang, Y. (2012, July 11-13). Smart, useful, scary, creepy: perceptions of online behavioral advertising. The eighth symposium on usable privacy and security, Washington, United States. https://www.cylab.cmu.edu/_files/ pdfs/tech_reports/CMUCyLab12007.pdf
  42. van Vliet, M., Flörke, M., Varela-Ortega, C., Çakmak, E., Khadra, R., Esteve, P., D’agostino, D., Dudu, H., Bärlund, I., & Kok, K. (2017). FCMs as a common base for linking participatory products and models. In Environmental modeling with stakeholders (pp. 145-169). Springer. https://publications.jrc.ec.europa.eu/repository/handle/JRC95999
  43. Vesanen, J., & Raulas, M. (2006). Building bridges for personalization: A process model for marketing. Journal of Interactive Marketing, 20(1), 5-20. https://doi.org/10.1002/dir.20052
  44. Yan, J., Liu, N., Wang, G., Zhang, W., Jiang, Y., & Chen, Z. (2009, April 20-24). How much can behavioral targeting help online advertising?. Proceedings of the 18th international conference on World wide web, Madrid, Spain. https://citeseerx.ist.psu.edu/viewdoc/ download?doi=10.1.1.215.1473&rep=rep1&type=pdf
  45. Zhao, X., Gu, C., Zhang, H., Yang, X., Liu, X., Liu, H., & Tang, J. (2021, February 2-9). Dear: Deep reinforcement learning for online advertising impression in recommender systems. The Thirty-Fifth AAAI Conference on Artificial Intelligence.  https://www.semantic scholar.org/paper/DEAR%3A-Deep-Reinforcement-Learning-for-Online-in-Zhao- Gu/9 4e91560da6e649c2d5eda9e989526697ee3e885
  46. Zhu, Y.-Q., & Chang, J.-H. (2016). The key role of relevance in personalized advertisement: Examining its impact on perceptions of privacy invasion, self-awareness, and continuous use intentions. Computers in Human Behavior, 65, 442-447. https://doi.org/ 10.1016/j.chb.2016.08.048
  47. Ziyadin, S., Doszhan, R., Borodin, A., Omarova, A., & Ilyas, A. (2019). The role of social media marketing in consumer behaviour. E3S Web of Conferences.  https://www.e3s-conferences. org/articles/e3sconf/pdf/2019/61/e3sconf_itese18_04022.pdf
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