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شناسایی عوامل مؤثر بر هوش مصنوعی در بازاریابی صنعت بانکداری با رویکرد فراترکیب | ||
| بررسی‌های مدیریت رسانه | ||
| دوره 3، شماره 3، 1403، صفحه 311-341 اصل مقاله (652.95 K) | ||
| نوع مقاله: مقاله مروری | ||
| شناسه دیجیتال (DOI): 10.22059/mmr.2024.384200.1124 | ||
| نویسندگان | ||
| عطاءاله هرندی* 1؛ علیرضا ابراهیمی2 | ||
| 1استادیار، گروه استراتژی و سیاست‌گذاری کسبوکار، دانشکده مدیریت کسبوکار، دانشکدگان مدیریت، دانشگاه تهران، تهران، ایران. | ||
| 2دانشجوی دکتری، گروه مدیریت سیاست‌گذاری بازرگانی، پردیس بین‌المللی کیش، دانشگاه تهران، تهران، ایران. | ||
| 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 | ||
| هوش مصنوعی؛ بازاریابی؛ صنعت بانکداری؛ رسانه‌های اجتماعی | ||
| عنوان مقاله [English] | ||
| Identifying Factors Affecting Artificial Intelligence in the Social Media Marketing with a Metasynthesis Approach | ||
| نویسندگان [English] | ||
| Ata Harandi1؛ Alireza Ebrahimi2 | ||
| 1Assistant Prof., Department of Strategy and Business Policy, Faculty of Business Management, College of Management, University of Tehran, Tehran, Iran. | ||
| 2Ph.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 | ||
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آمار تعداد مشاهده مقاله: 118 تعداد دریافت فایل اصل مقاله: 112 |
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