Do fit opinions matter? The impact of fit context on online product returns
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This approach offers improved pretreatment control and more credible parallel trends relative to traditional difference-in-differences (DID) and matching methods and has been shown to exhibit lower bias.
“This approach offers improved pretreatment control and more credible parallel trends relative to traditional difference-in-differences (DID) and matching methods and has been shown to exhibit lower bias (Xu 2017, Wang et al. 2021).”
From The Impact of Generative AI on Collaborative Open-Source Software Development: Evidence from GitHub Copilot  (2024) · cites this paper as Wang et al. 2021
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- Authors
- Wang, Y., Ramachandran, V., & Liu Sheng, O. R.
- Affiliations
- University of Wisconsin–Milwaukee (US)
- University of Utah (US)
- Department of Operations Management and Information Systems, David Eccles School of Business, University of Utah, Salt Lake City, Utah 84112
- Venue
- Information Systems Research
- Published
- 2021-03-01
- Volume
- 32
- Issue
- 1
- Pages
- 268-289
- ISSN
- 1047-7047 (ISSN-L)
- 1526-5536
- OpenAlex
- W3123226844
- Cited by
- 72
- References
- 36
- Citations by year
- 2026: 19 · 2025: 13 · 2024: 15 · 2023: 15 · 2022: 7 · 2021: 1 · 2020: 1 · 2019: 1
- FWCI
- 8.73
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Information Systems Research (publishedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Marketing › Consumer Market Behavior and Pricing
- Social Sciences › Business, Management and Accounting › Management Information Systems › Supply Chain and Inventory Management
- Social Sciences › Sociology and Political Science › Digital Marketing and Social Media
- Keywords
- consumer behavior, e-commerce, product fit
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