The Rise of Deepfakes: A Conceptual Framework and Research Agenda for Marketing

IF 4 Q2 BUSINESS
L. Whittaker, Kate Letheren, R. Mulcahy
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引用次数: 18

Abstract

Deepfakes, digital content created via machine learning, a form of artificial intelligence technology, are generating interest among marketers and the general population alike and are often portrayed as a “phantom menace” in the media. Despite relevance to marketing theory and practice, deepfakes—and the opportunities for benefit or deviance they provide—are little understood or discussed. This article introduces deepfakes to the marketing literature and proposes a typology, conceptual framework, and associated research agenda, underpinned by theorizing based on balanced centricity, to guide the future investigation of deepfakes in marketing scholarship. The article makes an argument for balance (i.e., situations where all stakeholders benefit), and it is hoped that this article may provide a foundation for future research and application of deepfakes as “a new hope” for marketing.
深度造假的兴起:营销的概念框架和研究议程
Deepfakes是通过机器学习(一种人工智能技术)创造的数字内容,它引起了营销人员和普通大众的兴趣,媒体经常将其描绘成一种“幽灵般的威胁”。尽管与市场营销理论和实践相关,但深度造假——以及它们提供的利益或偏差的机会——很少被理解或讨论。本文将深度造假引入市场营销文献,并提出了一种类型、概念框架和相关的研究议程,以平衡中心性理论为基础,指导未来市场营销学术中深度造假的研究。本文对平衡(即所有利益相关者都受益的情况)进行了论证,希望本文可以为未来的研究和应用deepfakes作为营销的“新希望”提供基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
14.90
自引率
16.70%
发文量
25
期刊介绍: The Australasian Marketing Journal (AMJ) is the official journal of the Australian and New Zealand Marketing Academy (ANZMAC). It is an academic journal for the dissemination of leading studies in marketing, for researchers, students, educators, scholars, and practitioners. The objective of the AMJ is to publish articles that enrich and contribute to the advancement of the discipline and the practice of marketing. Therefore, manuscripts accepted for publication will be theoretically sound, offer significant research findings and insights, and suggest meaningful implications and recommendations. Articles reporting original empirical research should include defensible methodology and findings consistent with rigorous academic standards.
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