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on Marketing |
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Issue of 2026–03–02
two papers chosen by Marco Novarese, Università degli Studi del Piemonte Orientale |
| By: | Stephan Ludwig; Peter J. Danaher; Xiaohao Yang; Yu-Ting Lin; Ehsan Abedin; Dhruv Grewal; Lan Du |
| Abstract: | Accurately measuring consumer emotions and evaluations from unstructured text remains a core challenge for marketing research and practice. This study introduces the Linguistic eXtractor (LX), a fine-tuned, large language model trained on consumer-authored text that also has been labeled with consumers' self-reported ratings of 16 consumption-related emotions and four evaluation constructs: trust, commitment, recommendation, and sentiment. LX consistently outperforms leading models, including GPT-4 Turbo, RoBERTa, and DeepSeek, achieving 81% macro-F1 accuracy on open-ended survey responses and greater than 95% accuracy on third-party-annotated Amazon and Yelp reviews. An application of LX to online retail data, using seemingly unrelated regression, affirms that review-expressed emotions predict product ratings, which in turn predict purchase behavior. Most emotional effects are mediated by product ratings, though some emotions, such as discontent and peacefulness, influence purchase directly, indicating that emotional tone provides meaningful signals beyond star ratings. To support its use, a no-code, cost-free, LX web application is available, enabling scalable analyses of consumer-authored text. In establishing a new methodological foundation for consumer perception measurement, this research demonstrates new methods for leveraging large language models to advance marketing research and practice, thereby achieving validated detection of marketing constructs from consumer data. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.15312 |
| By: | Heiko Karle; Marcel Preuss; Markus Reisinger |
| Abstract: | Platforms that provide product recommendations to consumers, such as marketplaces like Amazon or online travel agencies like Expedia, govern a substantial part of transactions in many markets. In addition to selling via platforms, most firms, however, also operate a direct channel. This paper investigates how the interaction between the platform channel and the firms’ direct channel affects platform design and firms' pricing incentives. We provide a rich game-theoretic model in which platforms give recommendations to consumers about products with high match value and facilitate consumer search, but charge sellers commission rates, whereas sellers compete in prices to balance demand across both channels. We show that the interaction between the channels gives rise to novel mechanisms that have counterintuitive effects. First, higher platform fees induce sellers to prioritize their direct channel—where consumers have lower expected match values and are thus more price-sensitive—leading to lower equilibrium prices. Second, improvements in recommendation quality can paradoxically reduce seller prices by intensifying the competitive pressure on the direct channel. Third, we show that for the platform, the quality of recommendations and the commission rate are strategic substitutes, that is, providing better recommendations should optimally be coupled with lower commission rates. This occurs because both instruments have potentially negative effects on seller prices. Finally, we evaluate recent policy interventions within our framework. We find that fee caps and measures that facilitate transactions on the direct channel can have unintended consequences and reduce consumer surplus by distorting the pricing incentives inherent in the dual-channel structure. |
| Keywords: | platform pricing, recommendation quality, consumer search |
| JEL: | D83 L15 L86 M31 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12465 |