人工智能营销研究综述及展望
Review and Prospect Research on Artificial Intelligence Marketing
DOI: 10.12677/ecl.2024.1341258, PDF,  被引量   
作者: 丁正阳:上海工程技术大学管理学院,上海
关键词: 人工智能营销文献计量Artificial Intelligence Marketing Bibliometrics
摘要: 物联网、大数据分析、区块链和人工智能等颠覆性技术正在改变企业的运营方式。在所有的颠覆性技术中,人工智能(AI)是最新的技术颠覆者,具有巨大的营销转型潜力,其逐渐成为优化市场营销决策、提升消费者体验、构建并保持客户关系的关键因素。然而,人工智能虽然为营销带来了许多益处,但弊端也在不断显现。例如,消费者对AI的认知以及公司对AI应用的限制性,这些都是学者们广泛研究的话题。尽管学者在人工智能营销领域的研究已经取得了一些进展,但由于其应用历史相对较短,仍然存在着研究成果缺乏系统性的整合、缺乏关键的文献、相关概念的混淆、研究焦点和发展路径的不清晰等问题。由此,一个系统的文献综述可以强调人工智能(AI)在市场营销中的重要性,并描绘出未来的研究方向。本研究旨在利用Citespace对2010年至2023年出版的国内有关人工智能营销现存文献进行全面综述,另外,我们将国外的部分文献分成四个方面进行综述,旨在探究两个问题:1) AI技术在营销领域的国内外发展现状。2) 对未来研究方向进行展望。
Abstract: Disruptive technologies such as the Internet of Things, big data analytics, blockchain, and artificial intelligence (AI) are transforming the way businesses operate. Among these disruptive technologies, AI stands as the newest game-changer, boasting immense potential for marketing transformation, gradually emerging as a pivotal factor in optimizing marketing decisions, enhancing consumer experiences, and fostering lasting customer relationships. However, while AI brings numerous benefits to marketing, its drawbacks are also becoming increasingly apparent, exemplified by consumers’ perceptions of AI and companies’ limitations in applying it, both of which are subjects of extensive scholarly inquiry. Despite advancements in AI marketing research, the relatively short history of its application has led to issues like a lack of systematic integration of research findings, scarcity of key literature, confusion over related concepts, and unclear research foci and developmental pathways. Consequently, a systematic literature review can underscore the significance of AI in marketing and chart the course for future research directions. This study aims to conduct a comprehensive review of domestic literature on AI marketing published from 2010 to 2023 using Citespace. Additionally, we will summarize selected international literature under four aspects, addressing two core questions: 1) the current state of AI development in marketing both domestically and internationally; 2) a prognosis of future research directions.
文章引用:丁正阳. 人工智能营销研究综述及展望[J]. 电子商务评论, 2024, 13(4): 1163-1170. https://doi.org/10.12677/ecl.2024.1341258

参考文献

[1] Rekha, A.G., Abdulla, M.S. and Asharaf, S. (2016) Artificial Intelligence Marketing: An Application of a Novel Lightly Trained Support Vector Data Description. Journal of Information and Optimization Sciences, 37, 681-691. [Google Scholar] [CrossRef
[2] Overgoor, G., Chica, M., Rand, W. and Weishampel, A. (2019) Letting the Computers Take over: Using AI to Solve Marketing Problems. California Management Review, 61, 156-185. [Google Scholar] [CrossRef
[3] 张雁冰, 吕巍, 张佳宇. AI营销研究的挑战和展望[J]. 管理科学, 2019, 32(5): 75-86.
[4] 刘玉然. 谈谈人工智能在企业管理中的应用[J]. 价值工程, 2003(4): 95-96.
[5] 司絮. 谈人工智能在客户信息获取与分析的应用[J]. 商场现代化, 2015(12): 88-89.
[6] 韩思齐. 人工智能时代下营销活动的智能化[J]. 现代经济信息, 2016(7): 106.
[7] De Bruyn, A., Viswanathan, V., Beh, Y.S., Brock, J.K. and Von Wangenheim, F. (2020) Artificial Intelligence and Marketing: Pitfalls and Opportunities. Journal of Interactive Marketing, 51, 91-105. [Google Scholar] [CrossRef
[8] Grewal, D., Hulland, J., Kopalle, P.K. and Karahanna, E. (2019) The Future of Technology and Marketing: A Multidisciplinary Perspective. Journal of the Academy of Marketing Science, 48, 1-8. [Google Scholar] [CrossRef
[9] Davenport, T., Guha, A., Grewal, D. and Bressgott, T. (2019) How Artificial Intelligence Will Change the Future of Marketing. Journal of the Academy of Marketing Science, 48, 24-42. [Google Scholar] [CrossRef
[10] Luo, X., Tong, S., Fang, Z. and Qu, Z. (2019) Frontiers: Machines vs. Humans: The Impact of Artificial Intelligence Chatbot Disclosure on Customer Purchases. Marketing Science, 38, 913-1084. [Google Scholar] [CrossRef
[11] Mende, M., Scott, M.L., van Doorn, J., Grewal, D. and Shanks, I. (2019) Service Robots Rising: How Humanoid Robots Influence Service Experiences and Elicit Compensatory Consumer Responses. Journal of Marketing Research, 56, 535-556. [Google Scholar] [CrossRef
[12] Tam, K.Y. and Ho, S.Y. (2005) Web Personalization as a Persuasion Strategy: An Elaboration Likelihood Model Perspective. Information Systems Research, 16, 271-291. [Google Scholar] [CrossRef
[13] Senecal, S. and Nantel, J. (2004) The Influence of Online Product Recommendations on Consumers’ Online Choices. Journal of Retailing, 80, 159-169. [Google Scholar] [CrossRef
[14] Huang, M. and Rust, R.T. (2018) Artificial Intelligence in Service. Journal of Service Research, 21, 155-172. [Google Scholar] [CrossRef
[15] Maedche, A., Legner, C., Benlian, A., Berger, B., Gimpel, H., Hess, T., et al. (2019) AI-Based Digital Assistants: Opportunities, Threats, and Research Perspectives. Business & Information Systems Engineering, 61, 535-544. [Google Scholar] [CrossRef
[16] Manseau, J. (2020) AI in the Workplace: A Qualitative Analysis of Intelligent Employee Assistants.
[17] Longin, L., Bahrami, B. and Deroy, O. (2023) Intelligence Brings Responsibility—Even Smart AI Assistants Are Held Responsible. iScience, 26, Article ID: 107494. [Google Scholar] [CrossRef] [PubMed]
[18] Han, R., Lam, H.K.S., Zhan, Y., Wang, Y., Dwivedi, Y.K. and 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, 2467-2497. [Google Scholar] [CrossRef
[19] Stone, M., Aravopoulou, E., Ekinci, Y., Evans, G., Hobbs, M., Labib, A., et al. (2020) Artificial Intelligence (AI) in Strategic Marketing Decision-Making: A Research Agenda. The Bottom Line, 33, 183-200. [Google Scholar] [CrossRef
[20] Mishra, S., Ewing, M.T. and Cooper, H.B. (2022) Artificial Intelligence Focus and Firm Performance. Journal of the Academy of Marketing Science, 50, 1176-1197. [Google Scholar] [CrossRef
[21] Xu, L. and Mehta, R. (2022) Technology Devalues Luxury? Exploring Consumer Responses to AI-Designed Luxury Products. Journal of the Academy of Marketing Science, 50, 1135-1152. [Google Scholar] [CrossRef
[22] Castelo, N., Bos, M.W. and Lehmann, D.R. (2019) Task-Dependent Algorithm Aversion. Journal of Marketing Research, 56, 809-825. [Google Scholar] [CrossRef
[23] 吴继飞, 于洪彦, 朱翊敏, 等. 人工智能推荐对消费者采纳意愿的影响[J]. 管理科学, 2020, 33(5): 29-43.