Jialie Chen
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Also affiliated: Lund University (2017); Cornell University (2015–2016)
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Biography and Research Information
OverviewAI-generated summary
Jialie Chen's research investigates consumer behavior and economic decision-making, with a particular focus on the dynamics of online commerce and product strategies. Chen has published work examining the impact of pricing strategies, such as the "ending-9" tactic, within the online shopping funnel, and has explored methods for promoting retail mobile channels using hidden Markov models. Additional research areas include understanding the terms of consumer-firm data exchange and analyzing the influence of advertising content on revenue, specifically in the context of movie releases.
Further extending into behavioral economics and technology adoption, Chen's work also explores learning dynamics and skill set formation in relation to technology upgrades and artificial intelligence strategies. This includes structural examinations of how users interact with and adapt to new technological advancements. Chen also studies purchase and return behaviors, investigating strategies for return-based targeting. The researcher's work has been supported by the National Science Foundation (NSF), including a grant for "Multiscale Differential Geometry Approaches to Protein Interaction Mechanisms" totaling $232,771 and a $24,000 grant for "The 10th SIAM Central States Section Annual Meeting at the University of Arkansas." Chen leads a research group and has an h-index of 6 with 12 publications and 168 citations.
Metrics
- h-index: 6
- Publications: 12
- Citations: 171
Selected Publications
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Editorial Expression of Concern: A structural model of purchases, returns, and return-based targeting strategies (2026)
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Learning and skill set formation: A structural examination of version upgrades, user visibility, and AI strategies (2023)
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Evaluating the ending‐9 pricing strategy along the online shopping funnel (2023)
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Measuring the Effects of Marketing Solicitations (2021)
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A structural model of purchases, returns, and return-based targeting strategies (2021)
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Insight is power: Understanding the terms of the consumer-firm data exchange (2020)
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A Dynamic Model of Rational Addiction with Stockpiling and Learning: An Empirical Examination of E-cigarettes (2020)
Federal Grants 2 $256,771 total
Multiscale Differential Geometry Approaches to Protein Interaction Mechanisms
Collaboration Network
Top Collaborators
- A Dynamic Model of Rational Addiction with Stockpiling and Learning: An Empirical Examination of E-cigarettes
- Measuring the Effects of Marketing Solicitations
- Insight is power: Understanding the terms of the consumer-firm data exchange
- Insight is power: Understanding the terms of the consumer-firm data exchange
- Insight is power: Understanding the terms of the consumer-firm data exchange
- Insight is power: Understanding the terms of the consumer-firm data exchange
- Insight is power: Understanding the terms of the consumer-firm data exchange
- Insight is power: Understanding the terms of the consumer-firm data exchange
- Insight is power: Understanding the terms of the consumer-firm data exchange
- Insight is power: Understanding the terms of the consumer-firm data exchange
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