مطالعات مدیریت کسب و کار هوشمند

مطالعات مدیریت کسب و کار هوشمند

شماره جاری

بر اساس شماره‌های نشریه

بر اساس نویسندگان

بر اساس موضوعات

نمایه نویسندگان

نمایه کلیدواژه ها

درباره نشریه

اهداف و چشم انداز

اصول اخلاقی انتشار مقاله

بانک ها و نمایه نامه ها

پیوندهای مفید

پرسش‌های متداول

فرایند پذیرش مقالات

اطلاعات آماری نشریه

اخبار و اعلانات

تفاهم نامه با انجمن انفورماتیک ایران

راهنمای نگارش

فایل ها

داوران 1399

داوران1400

داوران سال 1401

داوران 1402

راهنمای ثبت نام درPublons

طراحی مدل پیامدهای کاربرد هوش مصنوعی و یادگیری ماشین در تبلیغات و فروش

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشیار مدیریت بازرگانی، دانشکده علوم اجتماعی، دانشگاه محقق اردبیلی، اردبیل، ایران

2 دانشجوی دکتری، دانشکده علوم اجتماعی، دانشگاه محقق اردبیلی، اردبیل، ایران

چکیده

دنیای دیجیتال فرصت‌های متعددی را برای بازاریابان فراهم می‌کند تا به مشتری دسترسی پیدا کنند. بااین‌حال، در دنیای پرسرعت، یافتن راه‌های جدید و نوآورانه برای تبلیغات و فروش محصولات و خدمات بسیار مهم است. با توجه به پیشرفت هوش مصنوعی و توسعه آن در حوزه تبلیغات و فروش، متخصصان در حال حاضر ابزارهایی برای بازتعریف کامل درک فعلی از برندسازی، بازاریابی، تبلیغات و فروش دارند. محبوبیت روزافزون اینترنت و افزایش استفاده از دستگاه‌های تلفن همراه، حجم عظیمی از داده‌ها را برای مصرف‌کنندگان تولید می‌کند که سیستم‌های مبتنی بر هوش مصنوعی را تغذیه می‌کنند. این پژوهش از نوع پژوهش‌های آمیخته با رویکرد کیفی و کمی است که ازنظر هدف، کاربردی و ازلحاظ نحـوه گـردآوری داده، از نوع مطالعات توصیفی پیمایشی است. جامعه آماری پژوهش، مدیران و کارشناسان متخصص در حوزه بازاریابی دیجیتال و IT در حوزه تبلیغات و فروش، بودند که با استفاده از روش نمونه‌گیری گلوله برفی انتخاب شدند. در بخش کیفی ابزار گردآوری اطلاعات، بررسی کتابخانه‌ای و مقالات، مصاحبه و در بخش کمـی پرسشنامه بود. در بخش کیفی روش تحلیل داده‌ها، با استفاده از تحلیل تم که با نرم‌افزار MAXQDA و بـا استفاده از روش کدگذاری تدوین شد و در بخش کمی، روش تحلیل بر مبنای آزمون همبستگی کندال بود. مطابق با نتایج پژوهش، 7 تم اصلی، 22 تم فرعی و 44 کد کشف شدند که شامل پیامدهای کاربرد هوش مصنوعی و یادگیری ماشین در تبلیغات و فروش بودند.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Designing a model of the consequences of the application of artificial intelligence and machine learning in advertising and sales

نویسندگان [English]

  • Hosein Rahimi kolour 1
  • Rahim Mohammad khani 2

1 Associate Professor of Business Administration, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran

2 PhD student, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran

چکیده [English]

The digital world provides many opportunities for marketers to reach customers. However, in the fast-paced world, finding new and innovative ways to advertise and sell products and services is very important. Due to the advancement of artificial intelligence and its development in the field of advertising and sales, professionals now have the tools to completely redefine the current understanding of branding, marketing, advertising and sales. The growing popularity of the Internet and the increased use of mobile devices are generating massive amounts of consumer data that feed artificial intelligence-based systems. This research is a type of mixed research with a qualitative and quantitative approach, which is a survey descriptive study in terms of its purpose, application, and in terms of data collection. The statistical population of the research was managers and experts in the field of digital marketing and IT in the field of advertising and sales, who were selected using the snowball sampling method. In the qualitative part, the tools for collecting information were library and articles review, interviews, and in the quantitative part, questionnaires. In the qualitative part of the data analysis method, using the theme analysis that was compiled with MAXQDA software and using the coding method, and in the quantitative part, the analysis method was based on Kendall's correlation test. According to the results of the research, 7 main themes, 22 sub-themes and 44 codes were discovered, which included the consequences of using artificial intelligence and machine learning in advertising and sales

کلیدواژه‌ها [English]

  • : artificial intelligence
  • machine learning
  • big data
  • advertising and sales
مراجع
  1. لندران اصفهانی؛ سعید، فلاح چم آسمانی؛ فریناز، آقاجانی، مجتبی. (1402). طراحی الگوی بازاریابی محتوای اخلاقی دیجیتال در راستای تقویت برند محصولات دانش‌بنیان حوزه هوش مصنوعی، اخلاق در علم و فناوری، 18(5)، 84-93. http://ethicsjournal.ir/article-1-3014-fa.html
  2. نظرپور؛ محمود، نسل موسوی؛ سیدحسین، حسینی شیروانی، میرسعید. (1399). کاربرد هوش مصنوعی در حسابرسی مالیاتی. دانش حسابرسی، 20(81)، 198-226 https://sid.ir/paper/967020/fa References

References

  1. Anton, E., Behne, A., & Teuteberg, F. (2020) The humans behind artificial intelligence – an operationalization of AI competencies. In: Proceeding of the 28th European conference on information systems (ECIS), Marrakeh, Morocco, 15–17 June. ENCIES" (2020). https://aisel.aisnet.org/ecis2020_rp/141
  2. Amirkolaii, K. N., Baboli, A., Shahzad, M. K., & Tonadre, R. (2017). Demand Forecasting for Irregular Demands in Business Aircraft Spare Parts Supply Chains by using Artificial Intelligence (AI). IFAC-PapersOnLine, 50(1), 15221–15226. https://doi.org/10.1016/‌j.ifacol.2017.08.2371
  3. Belk,W., Belanche, R., & Flavian, C. (2023): Key concepts in artificial intelligence and technologies 4.0 in services. Service Business (2023) 17:1–9
  4. https://doi.org/10.1007/s11628-023-00528-w
  5. Basri,W. (2020); Examining the impact of artificial intelligence (AI)-assisted social media marketing on the performance of small and medium enterprises: toward effective business management in the Saudi Arabian context, Int. J. Comput. Intell. Syst. 13 (1) (2020) 142. https://link.springer.com/article/10.2991/ijcis.d.200127.002
  6. Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F., (2020). Explainable Artificial Intelligence (XAI). Inf. Fusion 58 (2019), 82–115. https://doi.org/10‌.1016/‌j.inffus.2019.12.012.
  7. Benabdelouahed, R., & Dakouan, C. (2020). The use of artificial intelligence in social media: opportunities and perspectives. In: Expert journal of marketing 8 (1), 82 - 87.
  8. https://marketing.expertjournals.com/ark:/16759/EJM_806benabdelouahed82-87.pdf.
  9. Berryhill, J., Heang, K.K., Clogher, R., & McBride, K., (2019). Hello, World: Artificial intelligence and its use in the public sector. OECD. https://oecd-opsi.org/wp-content/uploads/2019/11/AI-Report-Online.pdf
  10. Bandara, K., Shi, P., Bergmeir, C., Hewamalage, H., Tran, Q., & Seaman, B. (2019). Sales Demand Forecast in E-commerce Using a Long Short-Term Memory Neural Network Methodology. Neural Information Processing, 462–474. https://link.springer.com/chapter/10.1007/978-3-030-36718-3_39
  11. Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative research in psychology, 3(2), 77-101. https://www.tandfonline.com/doi/abs/10.1191/1478088706QP063O
  12. Capatina,A., Kachour,M., Lichy,J., Micu,A., Micu,A.E., & Codignola, F. (2020); Matching the future capabilities of an artificial intelligence-based software for social media marketing with potential users’ expectations, Technol. Forecast. Soc. Change 151 (2020), 119794. https://doi.org/10.1016/j.techfore.2019.119794
  13. Choi, J., & Lim, K. (2020). Identifying machine learning techniques for classification of target advertising, comminucation and information science, 6 (2020) 175–180. https://doi.org/10.1016/j.icte.2020.04.012
  14. F, (2019); AI Knowledge Map: How to Classify AI Technologies, Vol. 50. Springer. https://doi.org/10.1007/978-3-030-04468-8_4.
  15. Chen, G., Xie, P., Dong, J., & Wang, T. (2019). Understanding programmatic creative: The role of AI. Journal of Advertising,48, 347–355. https://doi.org/10.1080/00913367.2019.1654421
  16. Creswell, J. W. (2007). Qualitative Inquiry and Research Design: Choosing among five Approaches (pp. 53-84). Thousands Oaks: Sage Publications.
  17. De Bruyn. A, Viswanathan. V, Beh. Y.S, Brock. J.K.U., & Wangenheim.F, (2020); Artificial intelligence and marketing: pitfalls and opportunities. J. Interact. Mark. 51, 91–105. https://doi.org/10.1016/j.intmar.2020.04.007.
  18. Dick, S., (2019). Artificial intelligence. Harv. Data Sci. Rev. 1 (1), 1–8. https://doi.org/10.1162/99608f92.92fe150c
  19. Deng, S., Tan, C., Wang, W., & Pan, Y. (2019). Smart generation system of personalized advertising copy and its application to advertising practice and research. Journal of Advertising,48, 356–365. https://doi.org/10.1080/00913367.2019.1652121
  20. Frank, M.R., Wang, D., Cebrian, M., & Rahwan, I., (2019). The evolution of citation graphs in artificial intelligence research. Nat. Mach. Intell. 1 (2), 79–85. https://doi. org/10.1038/s42256-019-0024-5.
  21. Garnelo, M., & Shanahan, M., (2019). Reconciling deep learning with symbolic artificial intelligence: representing objects and relations. Curr. Opin. Behav. Sci. 29, 17–23. https://doi.org/10.1016/‌j.cobeha.2018.12.010
  22. Helberger, N., J. HAuh, G. Milne, J. Strycharz, & H.Sundaram. (2020). Macro and exogenous factors in computational advertising: Key issues and new research directions. Journal of Advertising 49 (4):377–93. doi:10.1080/00913367.2020.1811179
  23. Huh, J., & Malthouse, E. (2020). Advancing computationaladvertising: Conceptualiztion of the field and future directions. Journal of Advertising 49 (4):367–76.
  24. doi:10.1080/00913367.2020.1795759.
  25. Jani, K., Chaudhuri, M., Patel, H., & Shah M (2020) Machine learning in films: an approach towards automation in film censoring. J Data Inf Manag 2(1):55–64. https://doi.org/10.1007/s42488-019-00016-9
  26. Jordan, M., (2019). Artificial intelligence—the revolution hasn’t happened yet. Harv. Data Sci. Rev. 1 (1) https://doi.org/10.1162/99608f92.f06c6e61
  27. Kietzmann, J., Paschen, J., & Treen E (2018) Artificial intelligence in advertising. J Advert Res 58(3):263–267. DOI: 10.2501/JAR-2018-035
  28. Lai, Z. (2021). Research on Advertising Core Business Reformation Driven by Artificial Intelligence, Journal of Physics: Conference Series, 1757 (2021) 012018 doi:10.1088/1742-6596/1757/1/012018
  29. Luo, X., Shaojun, M., & Qu, Z. (2020). Artificial Intelligence Coaches for Sales Agents: Caveats and Solutions, Journal Marketing, 85 (2). https://doi.org/10.1177/0022242920956676
  30. Lakshmisri, S. (2018); Streamlining cloud application with AI technology, International journal of innovations in engineering research and technologiy (IJIERT). 5 (10), 2018. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3785667
  31. T.P., & Madhavaram, S. (2023). AI-Enabled marketing capabilities and the hierarchy of capabilities: Conceptualization, proposition development, and research avenues. Journal of Business Research. 157 (2023), 11348
  32. https://doi.org/10.1016/j.jbusres.2022.113485.
  33. -Mikalef , P., Islam , N., Parida, V., Singh, H., & Altwaijry, N. (2023). Artificial intelligence (AI) competencies for organizational performance: A B2B marketing capabilities perspective, Journal of Bussiness Reasearch, 164 (2023), 113998, https://doi.org/10.1016/Aj.jbusres.2023.113998..
  34. Otte, R. (2019). Künstliche Intelligenz für Dummies [Artificial Intelligence for Dummies]. WILEY-VCH Verlag GmbH.
  35. O’Leary, D.E. (2008). Decision support system evolution: predicting, facilitating, and managing knowledge evolution. In: Burstein, F., Holsapple, C. (Eds.), Handbook on Decision Support Systems, 2 (2). 345–367. https://doi.org/10.1007/978-3-540-48716-6_17
  36. Paschen, J.,  Wilson, M., & Ferreira, J. (2020). Collaborative intelligence: How human and artificial intelligence create value along the B2B sales funnel,Business Horizon,
  37. 63(3), 403-414.https://doi.org/10.1016/j.bushor.2020.01.003
  38. Ram, J., & Zhang, Z., (2022). Examining the needs to adopt big data analytics in B2B organizations: development of propositions and model of needs. J. Bus. Ind. Mark. 37 (4),
  39. 790–809. https://doi.org/10.1108/JBIM-10-2020-0464.
  40. Rusthollkarhu, S., Hautamaki, P., & Aarikka-Stenroos, L. (2021). Value (co-)creation in B2B sales ecosystems. The Journal of Business and Industrial Marketing, 36(4), 590–598. https://doi.org/10.1108/JBIM-03-2020-0130
  41. Reim, W., Åström, J., & Eriksson, O. (2020). Implementation of Artificial Intelligence (AI): A Roadmap for Business Model Innovation. AI, 1(2), 180-191. https://www.mdpi.com/2673-2688/1/2/11
  42. Ren, S., Chan, H.-L., & Siqin, T. (2020). Demand forecasting in retail operations for fashionable products: methods, practices, and real case study. Annals of Operations Research, 291(1), 761–777. https://link.springer.com/article/10.1007/s10479-019-03148-8
  43. r, V., Oghazi, P., Toorajipour, R., & Nazarpour, A. (2021). Export sales forecasting using artificial intelligence. Technological Forecasting and Social Change, 163, 120480. https://doi.org/10.1016/j.techfore.2020.120480
  44. Schiessl, D., Dias, H.B.A., & Korelo,J.C., (2021); Artificial Intelligence in Marketing: a Network Analysis and Future Agenda, in: Journal of Marketing Analytics, 2021, 1–12. https://link.springer.com/article/10.1057/s41270-021-00143-6
  45. Shelly, R. (2021) Themed Issue Introduction: Promises and Perils of Artificial Intelligence and Advertising, Journal of Advertising, 50(1), 1-10, DOI: 10.1080/00913367.2020.1868233.
  46. Shah, N., Sarth, E., Nandish B., Hirwa, C., & Shah, M. (2020). Research Trends on the Usage of Machine Learning and Artificial Intelligence in Advertising, Augmented Human Research volume 5, Article number: 19 (2020. https://link.springer.com/article/10.1007/s41133-020-00038-8
  47. Tiwari, R., Srivastava,S., & Gera,R., (2020); Investigation of artificial intelligence techniques in finance and marketing, Procedia Comput. Sci. 173 (2020) 149–157. https://doi.org/10.1016/j.procs.2020.06.019
  48. Volkmar, G., Fischer, P., &  Reinecke, S. (2022). Artificial Intelligence and Machine Learning: Exploring drivers, barriers, and future developments in marketing management, Journal of Business Research, 149 (2022), 599-614. https://doi.org/10.1016/j.jbusres.2022.04.007
  49. Verma, S., Sharma, R., Deb, S., & Maitra, D. (2021). Artificial intelligence in marketing: Systematic review and future research direction. International Journal of Information Management Data Insights, 1(1), 100002. https://doi.org/10.1016/j.jjimei.2020.100002
  50. Wang, W., Shasha D., Chee,W., & Yu, P. (2019),“Smart Generation System of Personalized Advertising Copy and Its Application to Advertising Practice and Research,” Journal of Advertising, 48 (4), 356–365. https://doi.org/10.1080/00913367.2019.1652121
  51. Wamba, S.F., Gunasekaran, A., Akter, S., Ren, S.J. fan, Dubey, R., & Childe, S.J. (2017). Big data analytics and firm performance: effects of dynamic capabilities. J. Bus. Res. 70. https://doi.org/10.1016/j.jbusres.2016.08.009
  52. Xuebing, Q., & Zhibin, J. (2019) The Impact of AI on the Advertising Process: The Chinese Experience Journal of Advertising 48(3) p 339. https://doi.org/10.1080/00913367.2019.1652122

References [in Persian]

  1. Nazarpour; Mahmoud, Nesl Mousavi; Seyed Hossein, Hosseini Shirvani, Mir Saeed. (2019). Application of artificial intelligence in tax audit. Audit Knowledge, 20(81), 198-226. https://sid.ir/paper/967020/fa
  2. Landran Isfahani; Saeed, Falah Cham Asmani; Farinaz, Aghajani, Mojtabi. (2023). Designing a digital ethical content marketing model in order to strengthen the brand of knowledge-based products in the field of artificial intelligence, Ethics in Science and Technology, 18(5), 84-93. (In Persian) http://ethicsjournal.ir/article-1-3014-fa.html

‌

 

 

مطالعات مدیریت کسب و کار هوشمند
دوره 12، شماره 48
تیر 1403
صفحه 223-270
فایل ها
سابقه مقاله
  • تاریخ دریافت: 21 آبان 1402
  • تاریخ بازنگری: 20 اسفند 1402
  • تاریخ پذیرش: 14 اسفند 1402
هم رسانی
ارجاع به این مقاله
آمار