2503.20711

This paper is primarily a research paper exploring a novel method for demand estimation by incorporating unstructured data like product images and text (titles, descriptions, reviews). The authors propose using deep learning models to extract relevant features from this data and integrate them into a random coefficients logit model, allowing for the inference of consumer substitution patterns. The paper validates this approach using a choice experiment where they demonstrate its superior ability to predict second choices compared to traditional attribute-based models and a simple logit model. Furthermore, the methodology is applied to a wide range of product categories on Amazon.com, consistently showing that text and image data offer valuable information for identifying product substitutes.

keepSave to notecopy_alldocsAdd noteaudio_magic_eraserAudio OverviewmapMind Map

Conner's 1991 article provides a historical comparison of resource-based theory with five established schools of thought within industrial organization (IO) economics. The paper aims to determine if resource-based theory offers a genuinely new perspective on the firm. It analyzes the similarities and differences between resource-based theory and the neoclassical, Bain-type IO, Schumpeterian, Chicago, and Coase/Williamson transaction cost economics approaches. The analysis explores how each theory views the firm as a source of competitive advantage and the implications for firm strategy. Ultimately, the work seeks to clarify the distinctiveness and potential contributions of the resource-based view to the broader understanding of the firm within economic theory.

"Beyond Conjoint Analysis: Advances in Preference Measurement" reviews the evolution of preference measurement beyond traditional conjoint analysis. The authors propose a framework centered on the problem, task design, and model specification, highlighting recent research and future directions for each component. The paper discusses the expanding applications of preference measurement to various stakeholders and problems, novel data collection methods focusing on engagement and incentives, and advancements in modeling that incorporate complexities like social interactions and behavioral effects. Furthermore, it examines new estimation techniques and the crucial integration of preference measurement with actionable outcomes and stakeholder objectives. The authors advocate for a more comprehensive and context-aware approach to understanding and utilizing consumer preferences.