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

Document Type : Original Article

Authors

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

Abstract

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.

Keywords

References

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