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Deepfakes: Deceptions, mitigations, and opportunities

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  • Mustak, Mekhail
  • Salminen, Joni
  • Mäntymäki, Matti
  • Rahman, Arafat
  • Dwivedi, Yogesh K.

Abstract

Deepfakes—artificial but hyper-realistic video, audio, and images created by algorithms—are one of the latest technological developments in artificial intelligence. Amplified by the speed and scope of social media, they can quickly reach millions of people and result in a wide range of marketplace deceptions. However, extant understandings of deepfakes’ implications in the marketplace are limited and fragmented. Against this background, we develop insights into the significance of deepfakes for firms and consumers—the threats they pose, how to mitigate those threats, and the opportunities they present. Our findings indicate that the main risks to firms include damage to image, reputation, and trustworthiness and the rapid obsolescence of existing technologies. However, consumers may also suffer blackmail, bullying, defamation, harassment, identity theft, intimidation, and revenge porn. We then accumulate and present knowledge on the strategies and mechanisms to safeguard against deepfake-based marketplace deception. Furthermore, we uncover and report the various legitimate opportunities offered by this new technology. Finally, we present an agenda for future research in this emergent and highly critical area.

Suggested Citation

  • Mustak, Mekhail & Salminen, Joni & Mäntymäki, Matti & Rahman, Arafat & Dwivedi, Yogesh K., 2023. "Deepfakes: Deceptions, mitigations, and opportunities," Journal of Business Research, Elsevier, vol. 154(C).
  • Handle: RePEc:eee:jbrese:v:154:y:2023:i:c:s0148296322008335
    DOI: 10.1016/j.jbusres.2022.113368
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    References listed on IDEAS

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    1. Ángel Vizoso & Martín Vaz-Álvarez & Xosé López-García, 2021. "Fighting Deepfakes: Media and Internet Giants’ Converging and Diverging Strategies Against Hi-Tech Misinformation," Media and Communication, Cogitatio Press, vol. 9(1), pages 291-300.
    2. Dwivedi, Yogesh K. & Hughes, Laurie & Ismagilova, Elvira & Aarts, Gert & Coombs, Crispin & Crick, Tom & Duan, Yanqing & Dwivedi, Rohita & Edwards, John & Eirug, Aled & Galanos, Vassilis & Ilavarasan, , 2021. "Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy," International Journal of Information Management, Elsevier, vol. 57(C).
    3. Sivarajah, Uthayasankar & Kamal, Muhammad Mustafa & Irani, Zahir & Weerakkody, Vishanth, 2017. "Critical analysis of Big Data challenges and analytical methods," Journal of Business Research, Elsevier, vol. 70(C), pages 263-286.
    4. Salminen, Joni & Kandpal, Chandrashekhar & Kamel, Ahmed Mohamed & Jung, Soon-gyo & Jansen, Bernard J., 2022. "Creating and detecting fake reviews of online products," Journal of Retailing and Consumer Services, Elsevier, vol. 64(C).
    5. Yi Zhao & Sha Yang & Vishal Narayan & Ying Zhao, 2013. "Modeling Consumer Learning from Online Product Reviews," Marketing Science, INFORMS, vol. 32(1), pages 153-169, May.
    6. Chonko, Lawrence B. & Hunt, Shelby D., 1985. "Ethics and marketing management: An empirical examination," Journal of Business Research, Elsevier, vol. 13(4), pages 339-359, August.
    7. Wang, Yuxi & McKee, Martin & Torbica, Aleksandra & Stuckler, David, 2019. "Systematic Literature Review on the Spread of Health-related Misinformation on Social Media," Social Science & Medicine, Elsevier, vol. 240(C).
    8. Justin Malbon, 2013. "Taking Fake Online Consumer Reviews Seriously," Journal of Consumer Policy, Springer, vol. 36(2), pages 139-157, June.
    9. Atuahene-Gima, Kwaku, 1996. "Market orientation and innovation," Journal of Business Research, Elsevier, vol. 35(2), pages 93-103, February.
    10. Michael Luca & Georgios Zervas, 2016. "Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud," Management Science, INFORMS, vol. 62(12), pages 3412-3427, December.
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    4. George Saridakis & Zaheer Khan & Gary Knight & Bochra Idris & Jay Mitra & Huda Khan, 2024. "A Look into the Future: The Impact of Metaverse on Traditional Theories and Thinking in International Business," Management International Review, Springer, vol. 64(4), pages 597-632, August.

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