The Algorithm Discount: Explaining Consumers' Valuation of Human- versus Algorithm-Created Digital Products

• 成果类型: Article
• 署名作者: Rix, Jennifer; Berger, Benedikt; Hess, Thomas; Rzepka, Christine
• 署名单位: University of Munich; University of Munster
• 刊物名称: JOURNAL OF MANAGEMENT INFORMATION SYSTEMS
• ISSN/ISSBN: 0742-1222
• DOI: 10.1080/07421222.2025.2487308
• 发表日期: 2025
• 页码: 633-668
• 关键词: mixed-methods research; CONJOINT-ANALYSIS; trust; willingness; preferences; people; perceptions; TECHNOLOGY; prediction; GUIDELINES

摘要

Owing to advances in generative artificial intelligence (AI), machines can now create digital products like software applications or media content, evoking calls to label such products as AI-made. Research on the handmade effect and algorithm aversion suggests that consumers react negatively to digital products that have been created by generative AI systems instead of humans. It is unclear why consumers show this reaction, which we refer to as algorithm discount. To answer this question, we conducted a mixed-methods study in the context of digital news offerings, comprising 41 qualitative interviews and a choice-based conjoint analysis with 421 respondents. The results show that consumers' beliefs about the love and effort imbued in the product, their curiosity about algorithmically generated products, and specific product characteristics, such as the type of news article, determine the algorithm discount. These findings extend our understanding of the emergence of consumers' aversion to algorithm-created products and offer providers of such products insight into potential countermeasures.