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on Marketing |
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Issue of 2025–09–08
two papers chosen by Marco Novarese, Università degli Studi del Piemonte Orientale |
| By: | Mrinalini Choudhary (Shenandoah University, Virginia, USA) |
| Abstract: | Artificial intelligence has substantially transformed the digital marketing landscape. Marketers continue to rely on advanced algorithms and data-driven insights to create effective marketing campaigns. Future trends indicate that marketers will rely increasingly on Generative AI and AI integration to enhance customer experience through targeted marketing and highly personalized content. As AI progresses, it becomes increasingly important to consider the ethical concerns of using it in marketing. Researchers have highlighted significant ethical concerns about consumer manipulation, discrimination, and data privacy. This research investigates the ethical consequences, particularly analyzing how AI-driven techniques such as predictive modeling and digital nudging might influence customer decisions. It explores the way in which AI algorithms can exploit consumer vulnerabilities and potentially override rational decision-making processes. Based on the findings from current research and industry trends, the paper proposes an ethical framework for AI in digital marketing, emphasizing transparency, consumer autonomy, and the preservation of human agency. Furthermore, this research emphasizes the need to ensure that AI technologies are as a constructive force in marketing that not only protects consumer rights but also retains societal value. |
| Keywords: | Artificial Intelligence, Algorithm, Ethics, Consumer Autonomy, Digital Marketing |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:smo:raiswp:0495 |
| By: | Ashvin Gandhi; Brett Hollenbeck; Zhijian Li |
| Abstract: | Fake product reviews—and the manipulation of reputation systems by sellers more broadly—are a widespread issue for two-sided platforms. We study two primary channels through which such manipulation can affect market outcomes: (i) creating misinformation about the reviewed product, and (ii) breeding mistrust in ratings system overall. To examine these in the Amazon.com marketplace, we measure misinformation by observing products purchasing fake reviews and measure mistrust by eliciting shoppers’ beliefs about the prevalence of fake reviews on Amazon through an incentivized survey experiment. We incorporate these into a structural model of demand in which consumers form beliefs about product quality based on observed reviews and perceptions about their trustworthiness. Counterfactual policy simulations indicate that fake reviews reduce consumer welfare, shift sales from honest to dishonest sellers, and ultimately harm the platform. Welfare losses arise primarily from misinformation that leads to worse purchases. While mistrust also leads to purchasing mistakes, the consumer harms of mistrust are largely offset by increased price competition under a weakened ratings system. Finally, we identify key limitations in platforms’ incentives to police manipulation and evaluate enforcement alternatives. |
| JEL: | L00 L1 L10 L11 L15 M3 M31 M38 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34161 |