شناسایی پیشایندها‌ و پسایندهای‌ کاربرد هوش مصنوعی در تدوین برنامه بازاریابی با رویکرد ترکیبی: تحلیل کتاب سنجی و فراترکیب

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، گروه مدیریت بازرگانی، دانشکده اقتصاد و علوم اداری، دانشگاه مازندران، بابلسر، ایران. (نویستده مسئول)

2 دانشیار، گروه مدیریت بازرگانی، دانشکده اقتصاد، مدیریت و حسابداری، دانشگاه یزد، یزد، ایران

چکیده

استفاده روز افزون از سیستم‌ها و برنامه‌های هوش مصنوعی در صنایع و بخش‌های مختلف شرکت­ها، منجر به رقابت تنگاتنگ بین واحدها و فراهم­سازی فرصت‌های متنوعی برای فرآیند بازاریابی شده است. با این حال، تحقیقات در زمینه کاربرد هوش مصنوعی در تدوین برنامه بازاریابی پراکنده بوده است و نیاز به یک پژوهش جامع در زمینه روند تحقیقات گذشته و پیش‌روی این موضوع وجود دارد. مطالعه­ی حاضر با هدف تدوین برنامه بازاریابی مبتنی بر هوش مصنوعی با رویکرد ترکیبی و با استفاده از تحلیل شبکه­ی کتاب سنجی پیشینه­ی تحقیقاتی موجود منتشر شده بین سال­های 2010 تا 2022 و استراتژی فراترکیب انجام  شده است. مرور جامع 115 مقاله به شناسایی عملکرد کنشگران علمی مانند مناسب­ترین نویسندگان و مناسب­ترین منابع کمک کرده است. علاوه براین، تحلیل هم­نویسندگی و هم­رخدادی با استفاده از نرم افزار VOSviewer، شبکه­ی مفهومی و عقلانی را پیشنهاد نموده است. با به‌کارگیری روش فراترکیب برای بررسی ابعاد برنامه بازریابی مبتنی بر هوش مصنوعی تعداد 59 مقاله مورد بررسی قرار گرفت که در بین مقالات مورد بررسی، بیشترین درصد مطالعات انجام شده مربوط به عامل محصول/ مصرف­کننده (38%) و کمترین درصد مطالعات انجام شده مربوط به عامل قیمت/ هزینه (14%) می­باشد. بر اساس نتایج مطالعه فراترکیب انجام گرفته جهت تدوین برنامه بازاریابی، می­توان از هوش مصنوعی مکانیکی برای استانداردسازی، از هوش مصنوعی فکری برای شخصی‌سازی و از هوش مصنوعی احساسی برای رابطه‌سازی استفاده کرد. برای بررسی پیشایندها و پسایندهای استفاده از هوش مصنوعی در  تدوین برنامه بازریابی تعداد 34 مقاله مورد بررسی قرار گرفت که پیشایندها شامل عوامل تکنولوژیکی، سازمانی، محیطی، رفتاری و فردی  و پسایندها شامل تجربه مشتری، مدیریت سفر مشتری، سودآوری، مزیت رقابتی، رضایت مشتری، وفاداری مشتری، مدیریت ارتباط با مشتری، درگیری مشتری می­باشند.

کلیدواژه‌ها

عنوان مقاله [English]

Identifying the Antecedents and Consequences of the Use of Artificial Intelligence in Developing a Marketing Plan With a Mixed Approach: Bibliometric Analysis and Meta Synthesis

نویسندگان [English]

  • Zahra Kazemi Saraskanrood 1
  • amirreza konjkav monfared 2
1 Faculty of Economics and Administrative Sciences, Mazandaran University, Babolsar, Iran
2 Business Management Department, Faculty of Economics, Management and Accounting, Yazd University, Yazd, Iran
چکیده [English]

Aim and Introduction: Today, we are witnessing the widespread use of artificial intelligence in various marketing fields. For example, Prime Air Amazon.com uses drones to automate transportation. Domino's Pizza is experimenting with self-driving cars and delivery robots to deliver pizzas to customers' doorsteps. Lexus uses IBM Watson to script its "Intuition Drive" TV commercials. Based on emotional analysis, affective technology detects the emotions of consumers while watching advertisements. Replica is a chatbot based on machine learning that offers emotional support to customers by mimicking their communication styles. It has even been claimed that artificial intelligence will fundamentally change the future of marketing. However, academic marketing research to date has not provided sufficient guidance on how to best leverage the benefits of AI for marketing impact. Artificial intelligence systems and programs have become widespread in various industries and sectors of companies and organizations. This widespread adoption has intensified competition among them and has also created numerous opportunities for marketing strategies and processes. However, research on the application of artificial intelligence in marketing planning has been scattered, and there is a need for comprehensive research on the past and future trends of this issue. The current study aims to identify the antecedents and consequences of applying artificial intelligence in developing a marketing plan.
Methodology: This study was conducted using a mixed approach. The quantita-tive section involved bibliometric network analysis of existing research published between 2010 and 2022, while the qualitative section utilized the meta-synthesis strategy. A comprehensive review of 115 articles indexed in the Scopus database helped identify the performance of scientific actors, such as the most suitable authors and sources. In addition, co-authorship and co-occurrence analysis using VOSviewer software suggested a conceptual and rational network. By applying the meta-synthesis method to investigate the dimensions of marketing plans based on artificial intelligence, 59 out of 115 indexed articles were   analyzed.
Finding: According to the results, only one paper was published in 1985 in the field of "Artificial Intelligence and Marketing." Until 2010, when this issue was gradually noticed by researchers, the trend of publishing articles from 2010 to 2022 showed an upward trajectory. In 2021, 50 articles were published, and in 2022, 21 articles have been published so far. Most of the articles related to artifi-cial intelligence and marketing have been published in the Australian Journal of Marketing. To determine the most effective source, five of the most suitable sources were compared based on their H-index and SJR index. The Journal of Business Research has the highest H-index, while the Journal of Marketing Sci-ence Academy ranks highest in terms of SJR index. These two sources are con-sidered the most reputable and suitable for research purposes. "Liu Wai" achieved the highest rank among all researchers by publishing 3 articles in the field of artificial intelligence and marketing. "Liu Wei" has an H-index of 8 and 16 published articles, while "Huang" has an H-index of 75 and 328 published ar-ticles, indicating that "Huang" has more citation records than the other authors. Among the analyzed articles, the highest percentage of studies focused on the product/consumer factor (38%), while the lowest percentage of studies examined the price/cost factor (14%). Based on the results of the meta-synthesis study, it is possible to use mechanical artificial intelligence for standardization, intellectual artificial intelligence for personalization, and emotional artificial intelligence for relationalization. Based on the results, the antecedents of using artificial intelligence in developing a marketing plan include technological, organizational, environmental, behavioral, and individual factors. The consequences include customer experience, customer journey management, profitability, competitive advantage, customer satisfaction, customer loyalty, customer relationship management, and customer engagement.
Discussion and Conclusion: The research findings of the review of all scientific sources conducted from 2010 to 2022 show that, so far, no comprehensive re-search has been done on the integration of the Rabazi program stage. Therefore, the most important finding of this research is the review, analysis, and classifica-tion of the planning phase of artificial intelligence based on metacombination. In this research, 59 articles that directly examined the issue of marketing planning based on artificial intelligence were selected for analysis. The selected studies were coded, and ultimately, 152 distinct primary codes were identified. In the next step, the codes were categorized into 35 concepts or sub-categories. Finally, based on the results of the analysis, concepts were grouped into 4 main categories as dimensions of the evaluation program based on artificial intelligence, 5 main categories as antecedents of the evaluation program based on artificial intelligence, and 7 main categories as suffixes of the marketing program based on artificial intelligence. The quality test also confirmed their validity. Finally, based on the analysis, a model for incorporating artificial intelligence into the development of a marketing plan is proposed. The model consists of three dimensions of artificial intelligence: mechanical, intellectual, and emotional. The benefits of this model include standardization, personalization, and relationship building. It utilizes marketing mix dimensions.

کلیدواژه‌ها [English]

  • Artificial Intelligence
  • Marketing Plan
  • Bibliometric Analysis
  • Network Analysis
  • Meta Synthesis

مراجع

  1. Huang, M. H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49, 1–21.
  2. 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, Conference: MWAIS 2020 Proceedings.
  3. Ameen, N., Tarhini, A., Reppel, A., & Anand, A. (2021). Customer experiences in the age of artificial intelligence. Computers in Human Behavior, 114, 106548.
  4. American Marketing Association (2017). What is marketing? https://www. ama.org/the-definition-of-marketing-what-is-marketing/
  5. 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.
  6. Armstrong, G., & Kotler, P. (2015). Marketing Principle. Amookhteh Publication. [In Persian]
  7. Asgharinajib, M., Feiz, D., Maleki MinBashRazgah, M., zarei, A. (2022). Identifying the discourse framework of solutions to deal with climate change based on artificial intelligence: An Exploratory Investigation of B2B Firms. Commercial Strategies, 18(18), 183-203. [In Persian]
  8. Avery, J., & Steenburgh, T. (2018). HubSpot and motion AI: Chatbotenabled CRM. Harvard Business School case 518-067, February.
  9. Banker, S., & Khetani, S. (2019). Algorithm overdependence: How the use of algorithmic recommendation systems can increase risks to consumer well-being. Journal of Public Policy & Marketing, 38(4), 500–515.
  10. Berry, L. L., Seiders, K., & Grewal, D. (2002). Understanding service convenience. Journal of Marketing, 1-17.
  11. Campbell, C., Sands, S., Ferraro, C., Tsao, H.-Y., & Mavrommatis, A. (2020). From data to action: How marketers can leverage AI. Business Horizons, 63(2), 227–243.
  12. 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.
  13. Catalano, A. (2013). Patterns of graduate students' information seeking behavior: a meta‐synthesis of the literature. Journal of documentation.
  14. Chatterjee, S., Nguyen, B., Ghosh, S. K., Bhattacharjee, K. K., & Chaudhuri, S. (2020). Adoption of artificial intelligence integrated CRM system: an empirical study of Indian organizations. The Bottom Line.
  15. Chen, C., Ibekwe-SanJuan, F., & How, 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 silva.
  16. 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.
  17. Chung, T. S., Wedel, M., & Rust, R. T. (2016). Adaptive personalization using social networks. Journal of the Academy of Marketing Science, 44(1), 66–87.
  18. Cohen, B. (2019, July 24). How AI Will Impact Distribution – Leveraging the Power of AI #1. Retrieved from https://www.naw.org/how-ai-will-impact-distribution-leveraging-thepower-of-ai-1/.
  19. Colgate, E., Wannasuphoprasit, W., & Peshkin, M. (1996). Cobots: Robots for collaboration with human operators. In Proceedings of the ASME Dynamic Systems and Control Division, New York, 58, 433-439.
  20. 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.
  21. Dabbous, A., Barakat, K. A., & Sayegh, M. M. (2021). Enabling organizational use of artificial intelligence: an employee perspective. Journal of Asia Business Studies, 16 (2), 245-266.
  22. Daqar, M. A. A., & Smoudy, A. K. (2019). The role of artificial intelligence on enhancing customer experience. International Review of Management and Marketing, 9(4), 22.
  23. Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(2), 24–42.
  24. Dekimpe, M. (2020). Retailing and retailing research in the age of big data analytics. International Journal of Research in Marketing, 37, 3–14.
  25. Dorfer, S. (2018, May 31). Shoppable Content: AI App Browzzin. Retrieved from https://www.stylus.com/kppfqb
  26. Dzyabura, D., & Hauser, J. R. (2011). Active machine learning for consideration heuristics. Marketing Science, 30(5), 757–944.
  27. Elsayed Fayed, A. (2021). Artificial Intelligence for marketing plan: the case for e-marketing companies. 684080133.
  28. Fahimnia, B., Sarkis, J., & Davarzani, H. (2015). Green supply chain management: A review and bibliometric analysis. International Journal of Production Economics, 162, 101–114.
  29. 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.
  30. Fountaine, T., McCarthy, B., & Saleh, T. (2019). Building the AI-powered organization. Harvard Business Review, July–August, 63–73.
  31. Ghasemnia Arabi, N., & Safaie Ghadikolaie, A. (2019). Comparison Of The Performances Of Classical Models And Artificial Intelligence In Predicting Bank Customers' Credit Status. Journal of Business Administration, 10(20), 51-69. [In Persian]
  32. Ghobakhloo, M., & Ching, N. T. (2019). Adoption of digital technologies of smart manufacturing in SMEs. Journal of Industrial Information Integration, 16, 100107.
  33. Ghosh, S., Chatterjee, S., Chaudhuri, R., & Nguyen, B. (2019). Are CRM systems ready for AI integration?. The Bottom Line, 32(2), 144-157.
  34. Gopinath, D. (2019). Human + machine: How content analytics delivers unsurpassed value to advertisers. MSI Lunch Lecture, (Sept 25).
  35. Grewal, D., Hulland, J., Kopalle, P. K., & Karahanna, E. (2020). The future of echnology and marketing: A multidisciplinary perspective. Journal of the Academy of Marketing Science, 48(1),1–8.
  36. 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.
  37. Hartmann, J., Huppertz, J., Schamp, C. P., & Heitmann, M. (2019). Comparing automated text classification methods. International Journal of Research in Marketing, 36(1), 20–38.
  38. Hilken, T., de Ruyter, K., Chylinski, M., Mahr, D., & Keeling, D. I. (2017). Augmenting the eye of the beholder: exploring the strategic potential of augmented reality to enhance online service experiences. Journal of the Academy of Marketing Science, 45(6), 884-905.
  39. Hoffman, D. L., & Novak, T. P. (2018). Consumer and object experience in the internet of things: An assemblage theory approach. Journal of Consumer Research, 44(6), 1178–1204.
  40. Hoon, C. (2013). Meta-synthesis of qualitative case studies: An approach to theory building. Organizational Research Methods, 16(4), 522-556.
  41. Hoyer, W. D., Kroschke, M., Schmitt, B., Kraume, K., & Shankar, V. (2020). Transforming the customer experience through new technologies. Journal of Interactive Marketing, 51, 57–71.
  42. Huang, M. H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155–172.
  43. Huang, M. H., Rust, R. T., & Maksimovic, V. (2019). The feeling economy: Managing in the next generation of artificial intelligence (AI). California Management Review, 61(4), 43–65.
  44. Iwuanyanwu, C. C. (2021). Determinants and Impact of Artificial Intelligence on Organizational Competitiveness: A Study of Listed American Companies. Journal of Service Science and Management, 14(5), 502-529.
  45. Jarek, K., & Mazurek, G. (2019). Marketing and artificial intelligence. Central European Business Review, 8(2), 46.
  46. Jesus, A. d. (2019, February 16). AI for Pricing – Comparing 5 Current Applications. Retrieved from https://emerj.com/ai-sector-overviews/ai-for-pricing-comparing-5- current-applications/
  47. Jöhnk, J., Ollig, P., Oesterle, S., & Riedel, L. N. (2020). The Complexity of Digital Transformation-Conceptualizing Multiple Concurrent Initiatives. In Wirtschaftsinformatik (Zentrale Tracks) (pp. 1051-1066).
  48. Kaput, M. (2016). The Marketer's Guide to Artificial Intelligence Terminology. Retrieved from https://www.marketingaiinstitute.com/blog/the-marketers-guide-to-artificialintelligence-terminology.
  49. Kaput, M. (2016). The Marketer's Guide to Artificial Intelligence Terminology. Retrieved from https://www.marketingaiinstitute.com/blog/the-marketers-guide-to-artificialintelligence-terminology.
  50. Keiningham, Timothy, Joan Ball, Sabine Benoit, Helen L. Bruce, Alexander Buoye, Julija Dzenkovska, Linda Nasr, Yi-Chun Ou, and Mohamed Zaki (2017). "The interplay of customer experience and commitment." Journal of Services Marketing 31 (2), 148-160.
  51. 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.
  52. Kumar, V., Rajan, B., Venkatesan, R., & Lecinski, J. (2019). Understanding the role of artificial intelligence in personalized engagement marketing. California Management Review, 61(4), 135–155.
  53. Kushwaha, A. K., Kumar, P., & Kar, A. K. (2021). What impacts customer experience for B2B enterprises on using AI-enabled chatbots? Insights from Big data analytics. Industrial Marketing Management, 98, 207-221.
  54. Ladhari, R., Souiden, N., & Dufour, B. (2017). The role of emotions in utilitarian service settings: The effects of emotional satisfaction on product perception and behavioral intentions. Journal of Retailing and Consumer Services, 34, 10-18.
  55. Lauterborn, B. (1990). New marketing litany; Four Ps Passé; C-words take over. Advertising Age, 61(41), 26.
  56. 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.
  57. Libai, B., Bart, Y., Gensler, S., Hofacker, C. F., Kaplan, A., Kötterheinrich, K., & Kroll, E. B. (2020). Brave new world? On AI and the management of customer relationships. Journal of Interactive Marketing, 51(1), 44-56.
  58. 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), 765–786.
  59. Liu, X., Singh, P. V., & Srinivasan, K. (2016). A structured analysis of unstructured big data by leveraging cloud computing. Marketing Science, 35(3), 363–388.
  60. 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.
  61. 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.
  62. McDuff, D., & Czerwinski, M. (2018). Designing emotionally sentient agents. Communications of the ACM, 61(12), 74–83.
  63. 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.
  64. Mikalef, P., Conboy, K., & Krogstie, J. (2021). Artificial intelligence as an enabler of B2B marketing: A dynamic capabilities micro-foundations approach. Industrial Marketing Management, 98, 80-92.
  65. Misra, K., Schwartz, E. M., & Abernethy, J. (2019). Dynamic online pricing with incomplete information using multiarmed bandit experiments. Marketing Science, 38(2), 226–252.
  66. Mogaji, E., & Nguyen, N. P. (2021). Managers' understanding of artificial intelligence in relation to marketing financial services: insights from a cross-country study. International Journal of Bank Marketing.
  67. Mohammed, M. A., Moles, R. J., & Chen, T. F. (2016). Meta-synthesis of qualitative research: the challenges and opportunities. International journal of clinical pharmacy, 38(3), 695-704.
  68. Nair, K., & Gupta, R. (2021). Application of AI technology in modern digital marketing environment. World Journal of Entrepreneurship, Management and Sustainable Development, 17(3), 318-328.
  69. Nanayakkara, N. W. O. K. D. S. P. (2020, November). Application of Artificial Intelligence in Marketing Mix: A Conceptual Review. In Proceedings of the International Conference on Business & Information (ICBI).
  70. 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.
  1. Olawumi, T. O. & Chan, D. W. M. (2019). Critical success factors for implementing building information modeling and sustainability practices in construction projects: A Delphi survey, Sustainable Development, Jan. 2019, DOI: 10.1002/sd.1925.
  2. Omisakin, O. M., Bandara, C., & Kularatne, I. (2020). Designing a Customer Feedback Service Channel Through AI to Improve Customer Satisfaction in the Supermarket Industry. Journal of Information & Knowledge Management, 19(03), 2050015.
  3. 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.
  4. Paschen, J., Kietzmann, J., & Kietzmann, T. C. (2019). Artificial intelligence (AI) and its implications for market knowledge in B2B marketing. Journal of Business & Industrial Marketing.
  5. Power, B. (2017). How Harley-Davidson used artificial intelligence to increase New York sales leads by 2,930%. Harvard Business Review digital article, (may 30), https://hbr.org/2017/05/howharley-davidson-used-predictive-analytics-to-increase-new-yorksales-leads-by-2930.
  6. Puntoni, S., Walker Reczek, R., Giesler, M., & Botti, S. (2021). Consumers and artificial intelligence: An experiential perspective. Journal of Marketing, 85(1), 131–151.
  7. Rajaobelina, L. (2018). The impact of customer experience on relationship quality with travel agencies in a multichannel environment. Journal of Travel Research, 57(2), 206-217.
  8. Ramos-Rodríguez, A. N. & Ruiz-Navarro, J. (2004). Changes in The Intellectual Structure of Strategic Management Research, Strategic Management Journal 25(10):981-1004
  9. Rasty, A., Abbaspour, R. (2022). The impact of AI on the improvement of modern banking. National conference of management and electronic business. [In Persian]
  10. Ring, N., Jepson, R., & Ritchie, K. (2011). Methods of synthesizing qualitative research studies for health technology assessment. International journal of technology assessment in health care, 27(4), 384-390.
  11. Rosário, A. (2021). The Background of Artificial Intelligence Applied to Marketing. Academy of strategic management journal, 20, 1-19.
  12. Rust, R. T. (2020). The future of marketing. International Journal of Research in Marketing, 37(1), 15–26.
  13. Sandelowski M, Barroso J. (2007). Handbook for synthesizing qualitative research. New York: Springer.
  14. Saura, J. R. (2021). Using data sciences in digital marketing: framework, methods, and performance metrics. Journal of Innovation and Knowledge, 6(2), 92–102. https://doi. org/10.1016/j.jik.2020.08.001. April-June 2021.
  15. Seranmadevi, R., & Kumar, A. (2019). Experiencing the AI emergence in Indian retail–Early adopters approach. Management Science Letters, 9(1), 33–42.
  16. Shankar, V. (2018). How artificial intelligence (AI) is reshaping retailing. Journal of retailing, 94(4), vi-xi.
  17. Sidaoui, K., Jaakkola, M., & Burton, J. (2020). AI feel you: customer experience assessment via chatbot interviews. Journal of Service Management.
  18. Silva, M. (2015). A systematic review of Foresight in Project Management literature. Procedia Computer Science, 64, 792-799.
  19. Singh, J., Nambisan, S., Bridge, R. G., & Brock, J. (2020). One-voice strategy for customer engagement. Journal of Service Research, 1– 24.
  20. Soleiman Mafi, A., khodadad Hosseini, H., Kordnaij, A., Hajipour, .B. (2022).Conceptualization of the corporate strategy assessment model with Meta-Synthesis approach, Commercial Strategies, 18(18), 1-24. [In Persian]
  1. Sutton, D. (2018). How AI helped one retailer reach new customers. Harvard Business Review, (may 28), https://hbr.org/2018/05/how-ai-helped-one-retailer-reach-new-customers.
  2. Taddeo, M., & Floridi, L. (2018). How AI can be a force for good. Science, 361(6404), 751-752.
  3. 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, 2(1), 1-14.
  4. Tjebane, M. M., Musonda, I., & Okoro, C. (2022). Organisational Factors of Artificial Intelligence Adoption in the South African Construction Industry. Front. Built Environ, 8, 823998.
  5. 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.
  6. Varki, S., Cooil, B., & Rust, R. T. (2000). Modeling fuzzy data in qualitative marketing research. Journal of Marketing Research, 37(4), 480-489.
  7. 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.
  8. Wirth, N. (2018). Hello marketing, what can artificial intelligence help you with?. International Journal of Market Research, 60(5), 435-438.
  9. Yong-Hak, J. (2013). Web of science. Thomson Reuters.
  10. Yoo, B., Donthu, N., & Lee, S. (2000). An examination of selected marketing mix elements and brand equity. Journal of the academy of marketing science, 28(2), 195-211.
  11. 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 Morroco: Attempt of a theoretical study. European Scientific Journal, ESJ, 17(29), 106.
  12. Yun, J. H., Lee, E.-J., & Kim, D. H. (2021). Behavioral and neural evidence on consumer responses to human doctors and medical artiicial intelligence. Psychology & Marketing, 38(4), 610–625.
نشریه علمی راهبردهای بازرگانی
دوره 19، شماره 20
بهمن 1401
صفحه 73-99

فایل ها

هم رسانی

ارجاع به این مقاله

آمار