Ping Qiu's Resume
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Under Review
Ongoing Research
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Honors & Awards
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Research Output
Disentangling the impact of bidding price on advertising performance in E-commerce search advertising: The moderating role of product competitiveness
📝 Abstract
Though E-commerce search advertising has become an increasingly prevalent approach for online retailers to promote their products, it is nontrivial for online retailers to use search advertising effectively, particularly without a comprehensive understanding of the effect of bidding price. This research investigates the effect of bidding price on product click-through rate and conversion rate, with a focus on the moderating role of product competitiveness. We collected data consisting of 10,734 search advertising records of 421 advertised stock keeping units and 206 keywords from a retailer operating on a leading e-commerce platform. This dataset was matched with data of 39,066 rival products to construct the indexes of product competitiveness in terms of mouth and product competitiveness in terms of sale price. Contradictory to the wisdom that there is a monotone relationship between bidding price and click-through/conversion rate, our result reveals an inverted U-shaped relationship between bidding price and click-through/conversion rate. Moreover, product competitiveness in terms of word-of-mouth weakens the inverted U-shaped relationship between bidding price and click-through/conversion rate, while product competitiveness in terms of sale price only weakens the inverted U-shaped relationship between bidding price and click-through rate, but not conversion rate. This research enriches the literature on search advertising by untangling the impact of bidding price and the moderating role of product competitiveness.
Designing tax plans in international environmental agreements with heterogeneous benefits
📝 Abstract
We present an analysis of carbon abatement regulation through a self-enforcing international environmental agreement (IEA) model featuring two types of countries with dissimilar abatement benefits. The IEA involves a tax plan function that allocates an emission tax rate to each type of country under every coalition of voluntary signatories. An efficient tax plan is one that maximizes social welfare under a stable coalition, while an optimal tax plan maximizes the average payoff of a stable coalition. We demonstrate that an efficient or optimal tax plan always exists, and that the corresponding value of social welfare or average coalition payoff is greater than that under certain traditional tax systems. If the benefit heterogeneity between the two types of countries is sufficiently small, full cooperation and social optimum can be achieved or approximated through an efficient or optimal plan. Conversely, a high degree of heterogeneity will result in a relatively small coalition and an inefficient outcome, regardless of the tax plan employed.
How to Kickstart New Product Variant in E-Commerce: Roles of Variant Similarity and Word-of-Mouth
📝 Abstract
In e-commerce platforms, a product category typically encompasses multiple variants in different colors and styles. Effectively managing these product variants, particularly the strategy of introducing new variants, is crucial for online retailers' success. However, there is limited theoretical understanding on the effectiveness of adding new variants especially in Ecommerce retailing. This study examines how retailers should introduce new product variants by investigating the impact of variant similarity on sales performance, as well as the moderating role of word-of-mouth (WoM) in the online retail context. Analyzing data from an online retailer operating in a leading e-commerce platform, we find that variant similarity between new and existing variants has inverted U-shaped relationships with both the new variant's sales and the existing variant's sales. Furthermore, WoM moderates both curvilinear relationships by strengthening these inverted U-shaped curves. Our findings provide valuable insights for online retailers' variant introduction strategies and contribute to the literature on e-commerce product category management, new product variant launch, and WoM.
Keyword Advertising for Multiple Products: A Joint Optimization Strategy for Bidding and Budget Allocation Based on Hierarchical Architecture
📝 Abstract
This research proposes a hierarchical optimization framework to address the complex decision making challenges faced by ecommerce retailers in multiproduct keyword advertising. In search advertising, retailers must simultaneously manage two interdependent key decisions: bid setting and budget allocation. The optimal bidding strategy depends on the available budget level, while effective budget allocation depends on advertising performance under different bids. Existing research is often limited to single dimension or single-campaign scenarios. This study decomposes the decision process into two levels: the upper strategic level manages budget allocation through a Markov Decision Process for dynamic allocation across multiple periods, and applies differentiated KKT optimality conditions for efficient allocation among different types of campaigns (mature products, promotional products, new products) within a single period; the lower execution level integrates operations research-based bidding models to determine the optimal bid for each campaign under given budget constraints. This dual layer architecture effectively mitigates the curse of dimensionality while enabling precise modeling and quantitative analysis of market mechanisms, dynamically adapting to market environment changes and seasonal fluctuations. Extensive simulation experiments demonstrate that the proposed framework achieves superior performance compared to benchmark strategies.
Disentangling the Impact of AI Anthropomorphism on Planning Satisfaction: An Experimental Investigation
📝 Abstract
As artificial intelligence systems become increasingly integrated into planning tasks, the degree of anthropomorphism—attributing human-like characteristics to AI—has emerged as a critical design consideration. This study investigates whether excessive anthropomorphism reduces user satisfaction with AI-assisted planning compared to normal anthropomorphism levels. We conducted a between-subjects experiment with participants randomly assigned to normal or excessive anthropomorphism conditions. Planning satisfaction was measured using a validated multi-item scale with high reliability. Analysis of covariance (ANCOVA) controlled for three theoretically relevant covariates: perceived threat from AI, technology familiarity, and AI literacy. All scales demonstrated excellent internal consistency. Importantly, all covariates significantly correlated with the dependent variable but showed no significant correlation with the experimental condition, confirming proper covariate selection and successful experimental control—a critical requirement for valid ANCOVA inference. Results revealed a significant main effect of anthropomorphism on planning satisfaction. Participants in the normal anthropomorphism condition reported significantly higher satisfaction than those in the excessive anthropomorphism condition. This effect remained robust after controlling for all covariates, indicating that the impact of anthropomorphism level on planning satisfaction is consistent across diverse user characteristics and independent of individual differences in AI perceptions and technological proficiency. These findings provide experimental evidence that excessive anthropomorphism significantly reduces planning satisfaction. Designers of AI planning systems should carefully calibrate anthropomorphic features to avoid crossing the threshold from engagement-enhancing to satisfaction-reducing. The results have important implications for human-AI interaction design, particularly in contexts where user satisfaction and trust are paramount.
Unveiling the nonlinear effect of ESG greenwashing on trade credit provision: A signaling theory perspective
📝 Abstract
The escalating environmental, social, and governance (ESG) pressure from stakeholders has precipitated a surge in corporate greenwashing practices. While trade credit represents a critical financing resource, the relationship between ESG greenwashing and trade credit provision remains inadequately explored. To narrow this gap, this study draws upon the signaling theory to explore the relationship between ESG greenwashing and trade credit provision. Furthermore, recognizing that signal effectiveness is contingent upon signal cost and signal observability, this study examines the moderating effects of female representation and industry competition. Analyzing panel data from 1,394 Chinese listed companies spanning 2009-2023, we reveal an inverted U-shaped relationship between ESG greenwashing and trade credit provision. Notably, both female representation and industry competition intensify this curvilinear association, resulting in a steeper inverted U-shaped curve. These findings offer valuable insights for deterring corporate ESG greenwashing and promoting authentic sustainable initiatives in the business landscape.
Journal Introduction
Information & Management is a top journal in the field of Information Systems, JCR SSCI Q1 Top journal with an impact factor of 8.2. The journal focuses on publishing high-quality research results in information technology management, digital economy, e-commerce, digital transformation and other fields.
Journal Introduction
Energy Economics is a top journal in the field of Economics, JCR SSCI Q1 Top journal with an impact factor of 14.2. The journal is the highest impact factor professional journal in economics.
Conference Introduction
AOM Annual Meeting is the annual academic conference of the Academy of Management, one of the most important academic events in the global management field. This research has been included in AOM Annual Meeting Proceedings 2025 and is currently under review by the Journal of Business Research.
Honors & Awards
🎓 Academic Awards
🏫 University Honors
🏅 Competition Awards
Programming Skills
Python
Amazon Data Scraping Program
This program uses the Selenium automation framework to batch scrape product information from Amazon platform, including prices, number of reviews, ratings and other key data, supporting keyword search and multi-page data collection. The program has comprehensive error handling mechanisms and data cleaning functions.
View CodeBootstrap Mediation Effect Testing Program
This program implements statistical testing of complex mediation effects based on the Bootstrap resampling method, specifically designed to handle advanced mediation models containing nonlinear relationships (inverted U-shaped effects) and multiple moderating variables. The program systematically decomposes and quantifies direct effects, indirect effects and total effects by constructing three regression equation systems, and generates robust standard errors and 95% confidence intervals through 1000 Bootstrap resamples, providing a complete and rigorous analytical framework for statistical inference of complex causal mechanisms.
View CodeMathematica
Stata
LaTeX
HTML
Supervisor Introduction
Dr. Zhao Cai is a Professor of Information Systems at Nottingham University Business School China (NUBS China). Before joining NUBS China in September 2018, he was a postdoctoral researcher at the University of Science and Technology of China (USTC) and a visiting scholar at Copenhagen Business School (CBS).
Professor Cai's research interests focus on the intersection of digital operations management, platform competition, and digital health. His research has been published in several top journals, including MIS Quarterly (MISQ), Journal of Operations Management (JOM), Journal of the Academy of Marketing Science (JAMS), and Journal of the Association for Information Systems (JAIS).
Professor Cai is also the director of National Natural Science Foundation of China (NSFC) General and Youth Fund projects. In terms of academic service, he serves as Managing Editor of Industrial Management & Data Systems (IMDS) and Associate Editor of Internet Research (IR).
🎓 Main Academic Positions
Journal Editorial Positions:Industrial Management & Data Systems (Managing Editor), Internet Research (Associate Editor)
Conference Positions:Pacific Asia Conference on Information Systems (PACIS) Track Chair, Americas' Conference on Information Systems (AMCIS) Mini-track Chair
Conference Editor:International Conference on Information Systems (ICIS), European Conference of Information Systems (ECIS) Associate Editor
Grant Projects:Principal Investigator of National Natural Science Foundation of China (NSFC) Youth Fund Project
Professor Hing Kai Chan has published over 250 academic articles and 9 monographs. His research has been published in many top journals, including Production and Operations Management, European Journal of Operational Research, International Journal of Operations and Production Management, International Journal of Production Economics, Decision Support Systems, and IEEE Transactions.
Professor Chan currently serves as Co-Editor of Industrial Management & Data Systems and Associate Editor of Transportation Research Part E: Logistics and Transportation Review. In addition, he is also an editorial board member of several renowned journals, including International Journal of Operations and Production Management, International Journal of Production Economics, etc.
Over the years, Professor Chan has (co-)edited more than 20 special issues for highly respected international journals. Before joining academia, Professor Chan accumulated rich practical experience in the electronics manufacturing industry, which provided a solid industrial background for his academic research.
🎓 Main Academic Positions
Journal Editorial Positions:Industrial Management & Data Systems (Co-Editor), Transportation Research Part E (Associate Editor)
Editorial Board Members:International Journal of Operations and Production Management, International Journal of Production Economics, etc.
Special Issue Editor:Edited special issues for more than 20 internationally renowned journals
Xiangtianrui Kong is currently a tenured Associate Professor in the Department of Supply Chain Management, School of Economics, Shenzhen University, Distinguished Researcher, PhD Supervisor, and Assistant Dean of the School of Economics, Shenzhen University. His research areas include intelligent auctions, Physical Internet, and decision optimization. His research results have been published in more than 40 domestic and international renowned journals, including "Chinese Journal of Management Science", Transportation Research Part B, Transportation Research Part E, International Journal of Production Economics, IEEE Transaction on Automation Science and Engineering, etc. He has published one monograph "Digital Fresh Agricultural Product Auction Mechanism and Operations Optimization".
He currently hosts multiple longitudinal and state-owned enterprise horizontal projects, including National Natural Science Foundation of China general and youth projects, Ministry of Education Humanities and Social Sciences youth projects, Guangdong Natural Science Foundation general projects, Guangdong Philosophy and Social Sciences youth projects, Hong Kong Research Grants Council major project sub-projects, Shenzhen Science and Innovation Commission major science and technology research sub-projects, etc. He provides consulting services for many well-known leading enterprises in manufacturing and logistics industries, including Kunming International Flower Auction Center, Hong Kong Aircraft Engineering Co., Ltd., Lenovo Group, China Tobacco, Nanshan Group, Midea Group, Sinopharm Group, etc., and has been authorized multiple invention patents and utility models in the field of intelligent auctions.
🎓 Main Academic Positions
Journal Editorial Positions:Industrial Management & Data Systems Editorial Board Member
Academic Organizations:Director of Management and Decision Science Professional Committee of China Association for Modernization of Management, Director of Higher Education Management Branch of China Society for Optimization, Overall Planning and Economic Mathematics, Director of Guangdong Economics Society
Expert Committees:Member of Shenzhen Logistics and Supply Chain Expert Committee
Talent Programs:Shenzhen "Peacock Plan" Overseas High-level Talent, Shenzhen Nanshan District Navigator Talent, Shenzhen University Liyuan Outstanding Young Scholar
AOM Conference Video
How to kickstart new product variant in E-commerce: Roles of variant similarity and word-of-mouth
Conference Name:AOM Annual Meeting
Conference Location:Copenhagen, Denmark
Conference Time:July 2025
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