شناسایی عوامل مؤثر بر هوش مصنوعی در بازاریابی صنعت بانکداری با رویکرد فراترکیب

نوع مقاله : مقاله مروری

نویسندگان

1 استادیار، گروه استراتژی و سیاست‏گذاری کسب‏وکار، دانشکده مدیریت کسب‏و‏کار، دانشکدگان مدیریت، دانشگاه تهران، تهران، ایران.

2 دانشجوی دکتری، گروه مدیریت سیاست‌گذاری بازرگانی، پردیس بین‌المللی کیش، دانشگاه تهران، تهران، ایران.

10.22059/mmr.2024.384200.1124

چکیده

هدف: مطالعه حاضر با هدف شناسایی عوامل مؤثر بر هوش مصنوعی، در بازاریابی رسانه‌های اجتماعی صنعت بانکداری با رویکرد فراترکیب انجام‌ شده است.
روش: مرور جامع ۱۱۵ مقاله به شناسایی عملکرد کنشگران علمی، مانند مناسب‌ترین نویسندگان و مناسب‌ترین منابع کمک کرده است. علاوه‌براین تحلیل هم‌نویسندگی و هم‌رخدادی با استفاده از نرم‌افزار وس ویور، شبکۀ مفهومی و عقلانی را پیشنهاد کرده است. با به‌کارگیری روش فراترکیب برای بررسی ابعاد برنامۀ بازاریابی رسانه‌های اجتماعی مبتنی بر هوش مصنوعی، تعداد ۵۹ مقاله بررسی شد که در بین مقاله‌های بررسی شده، بیشترین درصد مطالعات انجام‌شده مربوط به عامل محصول / مصرف‌کننده (۳۸) و کمترین درصد مطالعات انجام شده مربوط به عامل قیمت هزینه (۱۴) است.
یافته‌ها: برای بررسی پیشایندها و پسایندهای استفاده از هوش مصنوعی در تدوین برنامۀ بازاریابی رسانه‌های اجتماعی، ٣٤ مقاله بررسی شد که پیشایندها شامل عوامل تکنولوژیکی سازمانی محیطی، رفتاری و فردی بود و پسایندها عبارت بودند از: تجربۀ مشتری، مدیریت سفر مشتری، سودآوری، مزیت رقابتی، رضایت مشتری وفاداری مشتری مدیریت ارتباط با مشتری درگیری مشتری.
نتیجه‌گیری: بر اساس نتایج مطالعه فراترکیب انجام‌گرفته برای تدوین برنامۀ بازاریابی رسانه‌های اجتماعی، می‌توان از هوش مصنوعی مکانیکی برای استانداردسازی، از هوش مصنوعی فکری برای شخصی‌سازی و از هوش مصنوعی احساسی برای رابطه‌سازی استفاده کرد.
 

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Identifying Factors Affecting Artificial Intelligence in the Social Media Marketing with a Metasynthesis Approach

نویسندگان [English]

  • Ata Harandi 1
  • Alireza Ebrahimi 2
1 Assistant Prof., Department of Strategy and Business Policy, Faculty of Business Management, College of Management, University of Tehran, Tehran, Iran.
2 Ph.D. Candidate, Department of Business Policy Management, Kish International Campus, University of Tehran, Tehran, Iran.
چکیده [English]

Objective
In today’s world, the rapid development of information technology has led to the creation of vast amounts of data. Approximately 2.5 quintillion bytes of data are generated daily, and this figure is expected to rise with the advent of the Internet of Things (IoT) (Seranmadevi & Kumar, 2019). The availability of reliable data is crucial for producing and utilizing information that enables quick and accurate business decisions. The rapid technological advancements and their widespread distribution globally have created opportunities to gain a competitive advantage through new methods based on data and artificial intelligence (AI) in marketing management. This study aims to enhance the marketing process by providing a systematic and precise approach to identifying research gaps, thereby connecting practical and research actions in the field of marketing and AI. Consequently, the primary objective of this research is to develop a marketing plan based on AI using a hybrid approach and strategy.
Artificial intelligence finds applications in various business contexts. Experts and academics consider AI to be the future of our society. With technological advancements, the world has become a network of interconnected systems (Jeric et al., 2019). The implementation of technology has led to investments in AI to analyze big data and create market intelligence (Stone et al., 2020).
A marketing plan primarily consists of the marketing mix, which is a combination of marketing elements. The marketing mix comprises controllable marketing tools that a company uses to implement its marketing strategy, designed to satisfy consumers (Jerk & Mazurk, 2019). It is divided into four general groups known as the 4Ps. Subsequently, the 4Cs plan was introduced to emphasize the importance of the customer’s position and the formation of effective marketing communications (Tangyananakit & Samasat, 2022).
Shirmohammadi and Mohammadi (2023) conducted a study titled “Using Artificial Intelligence and Internet of Things Based on Big Data to Create Knowledge and Rational Decision Making in B2B Marketing to Improve the Performance of Smart Travel Service Offices.” Their research demonstrated that AI and IoT positively and significantly impact the creation of customer and user knowledge. Additionally, the creation of customer knowledge and user knowledge of foreign markets positively and significantly affects rational marketing decision-making.
Shah Nazari and Sharifi (2023) conducted a study titled “Artificial Intelligence-Based Systems for Consumption in Structural Marketing: Historical Reminder, Current and Future Views.” Their research highlighted the development of conscious systems and special power points to support decision-making situations for companies, especially those with a strategic identity, where good strategic data is essential.
Shaik (2023) conducted a study titled “The Effect of Artificial Intelligence on Marketing,” which demonstrated that artificial intelligence significantly impacts marketing. Nalbant and Aydin (2023) conducted research titled “Development and Transformation in Digital Marketing and Artificial Intelligence Branding: The Dynamics of Digital Technologies in the Metaverse World,” showing that artificial intelligence is effective in digital marketing. Torres et al. (2022) conducted a study titled “Artificial Intelligence and Its Impact on Brand Loyalty: Relationships and Combinations with Satisfaction and Brand Name.” They used Fuzzy Qualitative Comparative Analysis (FS/QCA) along with Structural Equation Modeling (SEM). These methods reveal subtle differences that help understand the effects of different value dimensions. While SEM results emphasize the mediating role of brand love, FS/QCA results show that brand love is a major condition for brand loyalty. Different paths can be sufficient to produce beneficial results. These findings enhance our understanding of AI performance and can guide practitioners in using game experiences to influence consumer behavior.
Research Methodology
The primary objective of this research is to identify the antecedents, dimensions, and consequences of an AI-based marketing program. It is developmental in nature and, since the data were collected without direction and manipulation, it is classified as non-experimental (descriptive) research. This study was conducted within the pragmatism paradigm using a mixed-method approach, incorporating both quantitative and qualitative methods. The bibliometric analysis method and the four-step approach proposed by Costa et al. (2017) were used to review the systematic research background. In the qualitative approach, metacomposition and the seven-step method of Sandlowski and Barroso (2007) were employed.
Bibliometric Analysis
As stated, this study utilized a four-step method proposed by Costa et al. (2017) for bibliometric analysis, which includes:

Selection of bibliometric databases.
Defining keywords (search strategy).
Refining the initial results (entry and exit criteria).
Data analysis plan.

 
Findings
Data were collected from the Scopus database and saved in CSV format for further analysis. A total of 115 documents related to this particular issue have been published up to the mentioned date. The authors have used 789 keywords for the mentioned period. The chart illustrates the number of articles published over different years. As shown, only one article was published in 1985 in the field of artificial intelligence and marketing. From 2010 onwards, this issue gradually gained attention from researchers, and the trend of publishing articles has been on the rise from 2010 to 2022. In 2021, 50 articles were published, and in 2022, 21 articles have been published to date.
Discussion & Conclusion
Existing studies, such as those by Avari (2018), Decamp (2020), Antons and Breidbach (2018), Chang et al. (2009), Chang et al. (2016), Liebman et al. (2019), and Gu et al. (2019), indicate that marketers can use mechanical artificial intelligence for standardization to prepare marketing plans. Intellectual artificial intelligence has a high potential for creating advertising content and personalization. For instance, authors of advertising content, websites, and social networks can facilitate the production of advertisements or post content using artificial intelligence. AI technologies enable the personalization and optimization of customer profiles in different locations and times. Content analysis can assist advertisers in creating more effective content.
Based on the obtained results, the following practical suggestions can be made:

Using AI for customer interaction (e.g., conversational bots to gather data related to customers’ moods and emotions).
Employing AI to create music and write short stories to make advertisements more creative and memorable for customers.
Utilizing mechanical AI to automate adjustments and price changes.
Implementing automation by mechanical AI in the advertising media planning department.
Using AI writers to produce content.

Future research should consider conducting studies using experimental and longitudinal methods, as well as mixed methods. It is also suggested that future researchers address the limitation of excluding non-English language studies

کلیدواژه‌ها [English]

  • Artificial Intelligence
  • Marketing
  • Banking Industry
  • Social Media

مراجع

کاظمی سراسکانرود، زهرا؛ صفری، محمد (1402). طراحی الگوی فرایند بازاریابی مبتنی بر هوش مصنوعی: کاربست راهبرد مرور نظام‌مند. بررسی‌های بازرگانی، 24(123)، 109-126.
کفاش‌پور، آذر؛ هرندی، عطاءاله؛ فاطمی، سیده زهرا (1393). نقش ارزش برند مبتنی بر مشتری در تاثیر تبلیغات بر پاسخ مصرف‌کننده. کاوش‌های مدیریت بازرگانی، 6(12)، 137-148.
هرندی، عطاءاله؛ میرزائیان خمسه، پیوند (1402). مروری نظام‌مند برحاکمیت شرکتی در کسب و کارهای خانوادگی. فصلنامه انجمن علوم مدیریت ایران، 18(69)، 105-134.
یزدان‌پرست، سید مرتضی، جامی‌پور، مونا و جعفری، سیدمحمدباقر (1401). شناسایی و اولویت‌بندی کاربردهای هوش مصنوعی در بازاریابی برخط. کاوش‌های مدیریت بازرگانی، 14(28)، 103-137.
References
Adwan, A. (2024). Can companies in digital marketing benefit from artificial intelligence in content creation? International Journal of Data and Network Science, 8(2), 797-808.
Ambati, L. S., Narukonda, K., Bojja, G. R. & Bishop, D. (2020). Factors influencing the adoption of artificial intelligence in organizations–from an employee’s perspective. MWAIS 2020 Proceedings. 20. https://aisel.aisnet.org/mwais2020/20
Antons, D. & Breidbach, C. F. (2018). Big data, big insights? Advancing service innovation and design with machine learning. Journal of Service Research, 21(1), 17-39.
Arce, C.G.M., Valderrama, D.A.C., Barragán, G.A.V. & Santillán, J. K. A. (2024). Optimizing business performance: Marketing strategies for small and medium businesses using artificial intelligence tools. Migration Letters, 21(S1), 193-201.
Arumugam, T., Arun, R., Natarajan, S., Thoti, K. K., Shanthi, P. & Kommuri, U. K. (2024). Unlocking the power of artificial intelligence and machine learning in transforming marketing as we know it. In Data-Driven Intelligent Business Sustainability (pp. 60-74). IGI Global.
Autor, D. H. & Dorn, D. (2013). The growth of low-skill service jobs and the polarization of the US labor market. American economic review, 103(5), 1553-1597.
Avery, J. (2018). HubSpot and motion AI: Chatbot-enabled CRM. Harvard Business Review Press (China Case Studies).  
Campbell, C., Sands, S., Ferraro, C., Tsao, H. Y. J. & Mavrommatis, A. (2020). From data to action: How marketers can leverage AI. Business horizons, 63(2), 227-243.
Campion, A., Gasco-Hernandez, M., Jankin Mikhaylov, S. & Esteve, M. (2022). Overcoming the challenges of collaboratively adopting artificial intelligence in the public sector. Social Science Computer Review, 40(2), 462-477.
Chen, C., Ibekwe‐SanJuan, F. & Hou, J. (2010). The structure and dynamics of cocitation clusters: A multiple‐perspective cocitation analysis. Journal of the American Society for information Science and Technology, 61(7), 1386-1409.
Chung, T. S., Rust, R. T. & Wedel, M. (2009). My mobile music: An adaptive personalization system for digital audio players. Marketing Science, 28(1), 52-68.
Costa, P. B., Neto, G. M. & Bertolde, A. I. (2017). Urban mobility indexes: A brief review of the literature. Transportation research procedia, 25, 3645-3655.
Dabos, L., Patino-Navarrete, R., Nastro, M., Famiglietti, A., Glaser, P., Rodriguez, C. H., & Naas, T. (2019). SME-4-producing Serratia marcescens from Argentina belonging to clade 2 of the S. marcescens phylogeny. Journal of Antimicrobial Chemotherapy, 74(7), 1836-1841.
De Bruyn, A., Viswanathan, V., Beh, Y. S., Brock, J. K. U. & Von Wangenheim, F. (2020). Artificial intelligence and marketing: Pitfalls and opportunities. Journal of Interactive Marketing, 51(1), 91-105.
Dekimpe, M. G. (2020). Retailing and retailing research in the age of big data analytics. International Journal of Research in Marketing, 37(1), 3-14.
Devang, V., Chintan, S., Gunjan, T. & Krupa, R. (2019). Applications of artificial intelligence in marketing. Annals of Dunarea de Jos University of Galati. Fascicle I. Economics and Applied Informatics, 25(1), 28-36.
Dimitrieska, S., Stankovska, A. & Efremova, T. (2018). Artificial intelligence and marketing. Entrepreneurship, 6(2), 298-304.
Durai, S., Manoharan, G., Priya, T. S., Jayanthi, R., Razak, A. & Ashtikar, S. P. (2024). Quantifying the Impacts of Artificial Intelligence Implementations in Marketing. In Smart and Sustainable Interactive Marketing (pp. 120-144). IGI Global.
Dzyabura, D. & Hauser, J. R. (2019). Recommending products when consumers learn their preference weights. Marketing Science, 38(3), 417-441.
Eriksson, T., Bigi, A. & Bonera, M. (2020). Think with me, or think for me? On the future role of artificial intelligence in marketing strategy formulation. The TQM Journal, 32(4), 795-814.
Fahimnia, B., Sarkis, J. & Davarzani, H. (2015). Green supply chain management: A review and bibliometric analysis. International Journal of Production Economics, 162, 101-114.
Fei, W., Zhang, Z. & Deng, Q. (2021, October). Universal pictures’ SWOT analysis and 4Ps & 4Cs marketing strategies in the post-COVID-19 era. In 2021 International Conference on Public Relations and Social Sciences (ICPRSS 2021) (pp. 494-500). Atlantis Press.
Feng, C. M., Park, A., Pitt, L., Kietzmann, J. & Northey, G. (2021). Artificial intelligence in marketing: A bibliographic perspective. Australasian Marketing Journal, 29(3), 252-263.
Gopinath, D. (2019). Human+ machine: How content analytics delivers unsurpassed value to advertisers. MSI Lunch Lecture, (Sept 25).
Guo, J., Zhang, W., Fan, W. & Li, W. (2018). Combining geographical and social influences with deep learning for personalized point-of-interest recommendation. Journal of Management Information Systems, 35(4), 1121-1153.
Han, R., Lam, H. K., Zhan, Y., Wang, Y., Dwivedi, Y. K. & Tan, K. H. (2021). Artificial intelligence in business-to-business marketing: a bibliometric analysis of current research status, development and future directions. Industrial Management & Data Systems, 121(12), 2467-2497.
Harandi, A., & Mirzaeian Khamseh, P. (2023). A systematic review of corporate governance in family businesses. Iranian journal of management sciences, 18(69), 105-134. (In Persian).
Hartmann, J., Huppertz, J., Schamp, C. & Heitmann, M. (2019). Comparing automated text classification methods. International Journal of Research in Marketing, 36(1), 20-38.
Hermann, E. (2022). Leveraging artificial intelligence in marketing for social good—An ethical perspective. Journal of Business Ethics, 179(1), 43-61.
Hoon, C. (2013). Meta-synthesis of qualitative case studies: An approach to theory building. Organizational research methods, 16(4), 522-556.
Huang, M. H. & Rust, R. T. (2018). Artificial intelligence in service. Journal of service research, 21(2), 155-172.
Huang, M. H. & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49, 30-50.
Jarek, K. & Mazurek, G. (2019). Marketing and Artificial Intelligence. Central European Business Review, 8(2).
Jones, R., & Rowley, J. (2011). Entrepreneurial marketing in small businesses: A conceptual exploration. International small business journal, 29(1), 25-36.
KaffashPoor, A., Harandi, A. O., & Fatemi, S. (2014). The role of customer-based brand equity in the effect of advertising on consumer response. Journal of Business Administration Researches, 6(12), 137-148. (In Persian).
Kazemi Saraskanrood, Z. & Safari, M. (2024). Designing a marketing process model based on artificial intelligence: Application of systematic review strategy. Commercial Surveys, 21(123), 109-126. doi: 10.22034/bs.2023.1999484.2765 (in Persian)
Kopalle, P. K., Gangwar, M., Kaplan, A., Ramachandran, D., Reinartz, W. & Rindfleisch, A. (2022). Examining Artificial Intelligence (AI) technologies in marketing via a global lens: Current trends and future research opportunities. International Journal of Research in Marketing, 39(2), 522-540.
Lee, D., Hosanagar, K. & Nair, H. S. (2018). Advertising content and consumer engagement on social media: Evidence from Facebook. Management science, 64(11), 5105-5131.
Liebman, E., Saar-Tsechansky, M. & Stone, P. (2019). The right music at the right time: Adaptive personalized playlists based on sequence modeling. MIS quarterly, 43(3).
Liu, M., Liu, H. F. & Lee, C. C. (2024). An empirical study on the response of the energy market to the shock from the artificial intelligence industry. Energy, 288, 129655.
Ma, L. & Sun, B. (2020). Machine learning and AI in marketing–Connecting computing power to human insights. International Journal of Research in Marketing, 37(3), 481-504.
Mabad, T., Ali, O., Ally, M., Wamba, S. F. & Chan, K. C. (2021). Making investment decisions on RFID technology: an evaluation of key adoption factors in construction firms. IEEE Access, 9, 36937-36954.
Mariani, M. M., Perez‐Vega, R. & Wirtz, J. (2022). AI in marketing, consumer research and psychology: A systematic literature review and research agenda. Psychology & Marketing, 39(4), 755-776.
Mende, M., Scott, M. L., Van Doorn, J., Grewal, D., & Shanks, I. (2019). Service robots rising: How humanoid robots influence service experiences and elicit compensatory consumer responses. Journal of Marketing Research, 56(4), 535-556.
Nalbant, K. G. & Aydin, S. (2023). Development and transformation in digital marketing and branding with artificial intelligence and digital technologies dynamics in the Metaverse universe. Journal of Metaverse, 3(1), 9-18.
Nalbant, K. G. & Aydın, S. (2023). Development and transformation in digital marketing and branding with artificial intelligence and digital technologies dynamics in the Metaverse universe. Journal of Metaverse, 3(1), 9-18.
Niazi, N., Rashid, M. & Shamugia, Z. (2021). Role of marketing mix (4ps) in building brand equity: Case study of Shell Petrol, UK. International Journal of Applied Business and Management Studies, 6(1), 2021.
Olawumi, T. O. & Chan, D. W. (2019). Critical success factors for implementing building information modeling and sustainability practices in construction projects: A Delphi survey. Sustainable Development, 27(4), 587-602.
Olson, C. & Levy, J. (2018). Transforming marketing with artificial intelligence. Applied Marketing Analytics, 3(4), 291-297.
Olstad, D. L. & Boyland, E. (2023). Towards effective restriction of unhealthy food marketing to children: unlocking the potential of artificial intelligence. International Journal of Behavioral Nutrition and Physical Activity, 20(1), 61.
Pan, M. & Pan, W. (2020). Understanding the determinants of construction robot adoption: Perspective of building contractors. Journal of Construction Engineering and Management, 146(5), 04020040.
Paschen, J., Kietzmann, J., Pitt, L. F. & Pala, E. (2023). Artificial intelligence, marketing, and the history of technology: Kranzberg’s laws as a conceptual lens. Australasian Marketing Journal, 31(1), 81-89.
Peltier, J. W., Dahl, A. J. & Schibrowsky, J. A. (2024). Artificial intelligence in interactive marketing: A conceptual framework and research agenda. Journal of Research in Interactive Marketing, 18(1), 54-90.
Sadeq, N., Nassreddine, G. & Younis, J. (2023). Impact of Artificial Intelligence on E-marketing. International Journal of Trend in Scientific Research and Development (IJTSRD), 7(1), 1318-1331.
Sandelowski, M. & Barroso, J. (2007). Handbook for synthesizing qualitative research. springer publishing company.
Seranmadevi, R. & Kumar, A. (2019). Experiencing the effect of demonetization on service sectors in India. Management Science Letters, 9(3), 389-398.
Shahzad, U., Ghaemi Asl, M. G., Panait, M., Sarker, T. & Apostu, S. A. (2023). Emerging interaction of artificial intelligence with basic materials and oil & gas companies: A comparative look at the Islamic vs. conventional markets. Resources Policy, 80, 103197.
Shaik, M. (2023). Impact of artificial intelligence on marketing. East Asian Journal of Multidisciplinary Research, 2(3), 993-1004.
Soleiman Mafi, A., Khodadad Hosseini, H., Kordnaeij, A. & Hajipour, B. (2022). Conceptualization of the corporate strategy assessment model with Meta-Synthesis approach. Commercial Strategies, 18(18), 1-24.
Stone, M., Aravopoulou, E., Ekinci, Y., Evans, G., Hobbs, M., Labib, A., ... & Machtynger, L. (2020). Artificial intelligence (AI) in strategic marketing decision-making: a research agenda. The Bottom Line, 33(2), 183-200.
Sullivan, Y. & Wamba, S. F. (2024). Artificial intelligence and adaptive response to market changes: A strategy to enhance firm performance and innovation. Journal of Business Research, 174, 114500.
Sutton, R. S. (2018). Reinforcement learning: An introduction. A Bradford Book. MIT press.
Tangpattanakit, J. & Sammasut, T. (2022). Factors on marketing mix from the point of view of consumers (4c’s) that affect the decision to order food by delivery application during Covid-19 of the Generation X consumers in Chonburi. UBRU International Journal Ubon Ratchathani Rajabhat University, 2(1), 1-14.
Tjebane, M. M., Musonda, I. & Okoro, C. (2022). Organisational factors of artificial intelligence adoption in the South African construction industry. Frontiers in Built Environment, 8, 823998.
Turner, C. J., Oyekan, J., Stergioulas, L. & Griffin, D. (2020). Utilizing industry 4.0 on the construction site: Challenges and opportunities. IEEE Transactions on Industrial Informatics, 17(2), 746-756.
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.
Vlačić, B., Corbo, L., e Silva, S. C. & Dabić, M. (2021). The evolving role of artificial intelligence in marketing: A review and research agenda. Journal of Business Research, 128, 187-203.
Wang, H. (2022, March). Understanding the marketing strategies: 4 Ps Marketing mix or other strategies used by Tencent Games in the video game market. In 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022) (pp. 99-104). Atlantis Press.
Yang, C., Yang, P. & Feng, Y. (2021). Effect of achievement-related gamification on brand attachment. Industrial Management & Data Systems, 122(1), 251- 271.
Yazdanparast, S. M., Jami Pour, M. & Jafari, S. M. (2022). Identifying and prioritizing artificial intelligence (AI) applications in online marketing. Journal of Business Administration Researches, 14(28), 103-137. doi: 10.22034/jbar.2022.15783.3850 (in Persian)
Yong-Hak, J. (2013). Web of science. Thomson Reuters.
Yousra, M. & Khalid, C. (2021). Analysis of the variables of intention of the adoption and acceptance of artificial intelligence and big data tools among leaders of organizations in morocco: attempt of a theoretical study. European Scientific Journal, 17(29), 106.
بررسی‌های مدیریت رسانه
دوره 3، شماره 3
1403
صفحه 311-341

فایل ها

هم رسانی

ارجاع به این مقاله

آمار