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Liangfei Qiu - University of Florida. Gainesville, FL, US

Liangfei Qiu Liangfei Qiu

PricewaterhouseCoopers ISOM Professor | University of Florida

Gainesville, FL, UNITED STATES

Liangfei Qiu is an expert in social technology, including social media and social networks, as well as artificial intelligence.

Biography

Qiu is an expert in social technology, including social media and social networks, as well as artificial intelligence and financial technology (fintech).

Industry Expertise (2)

Financial Services

Social Media

Areas of Expertise (4)

Telecommunications Networks

Healthcare Analytics

Prediction Markets

Gig Economy

Media Appearances (2)

The impact of real-time feedback in employee reviews

EurekAlert!  online

2021-05-20

The study, "Are Traditional Performance Reviews Outdated? An Empirical Analysis on Continuous, Real-time Feedback in the Workplace," was conducted by Michael Rivera and Subodha Kumar from Temple University and Liangfei Qiu from the University of Florida. The authors found that the relationship source (peer, subordinate or supervisor) impacts real-time feedback, which tends to be more critical when it comes from supervisors.

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Why Negotiators Should Be on Social Networks

INSEAD Knowledge  

2019-07-16

Social ties encourage “best behaviour” in negotiations. If there is a high likelihood you will encounter someone another time in the future, you will naturally be less inclined to deceive them. But since no human is perfect, “bad behaviour” may still happen, even if by mistake. Are people more forgiving when the culprit is a network friend? Ravi Bapna (University of Minnesota), Liangfei Qiu (University of Florida) and Sarah Rice (Texas A&M University) examined this question in their paper “Repeated Interactions vs. Social Ties: Quantifying the Economic Value of Trust, Forgiveness, and Reputation Using a Field Experiment”.

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Social

Articles (5)

How learning effects influence knowledge contribution in online Q&A community? A social cognitive perspective

Decision Support Systems

Chencheng Shi, Ping Hu, Weiguo Fan, Liangfei Qiu

2021 Informative contributions are critical for the healthy development of online Q&A communities, which have gained increasing popularity in solving personalized open-ended problems. However, little is known about whether past contribution behaviors and corresponding community feedbacks received affect the characteristics of subsequent contributions. Drawing upon the social cognitive theory, we examine the learning effects on users' knowledge contribution behaviors. Specifically, we focus on two types of learning effects: enactive learning from one's past contribution experience and vicarious learning from observation of others' performances in a question thread. Using a dataset collected from one of the largest online Q&A communities in China, we find that the length feature of past user contributions that garner highly positive feedback, no matter through enactive or vicarious learning, would influence the informativeness of subsequent contributions in the community. These learning effects are more effective for users with higher social status. The enactive learning effect is stronger for contributors with higher social status. For the vicarious learning on higher-status contributors, the influence of high-vote long answers is stronger, but the high-vote short answers show a weaker effect. Our research provides a deeper understanding of knowledge contribution behaviors in online knowledge communities and guides for establishing a healthy knowledge contribution environment.

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On the Spillover Effects of Online Product Reviews on Purchases: Evidence from Clickstream Data

Information Systems Research

Young Kwark, Gene Moo Lee, Paul A Pavlou, Liangfei Qiu

2021 We study the spillover effects of the online reviews of other covisited products on the purchases of a focal product using clickstream data from a large retailer. The proposed spillover effects are moderated by (a) whether the related (covisited) products are complementary or substitutive, (b) the choice of media channel (mobile or personal computer (PC)) used, (c) whether the related products are from the same or a different brand, (d) consumer experience, and (e) the variance of the review ratings. To identify complementary and substitutive products, we develop supervised machine-learning models based on product characteristics, such as product category and brand, and novel text-based similarity measures. We train and validate the machine-learning models using product pair labels from Amazon Mechanical Turk. Our results show that the mean rating of substitutive (complementary) products has a negative (positive) effect on purchasing of the focal product. Interestingly, the magnitude of the spillover effects of the mean ratings of covisited (substitutive and complementary) products is significantly larger than the effects on the focal product, especially for complementary products. The spillover effect of ratings is stronger for consumers who use mobile devices versus PCs. We find the negative effect of the mean ratings of substitutive products across different brands on purchasing of a focal product to be significantly higher than within the same brand. Lastly, the effect of the mean ratings is stronger for less experienced consumers and for ratings with lower variance. We discuss implications on leveraging the spillover effect of the online product reviews of related products to encourage online purchases.

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Multidimensional Observational Learning in Social Networks: Theory and Experimental Evidence

Information Systems Research

Liangfei Qiu, Arunima Chhikara, Asoo Vakharia

2021 The prevalence of consumers sharing their purchases on social media platforms (e.g., Instagram and Pinterest) and the use of this information by potential future consumers have substantial implications for online retailing. In this study, we examine how product characteristics and the type of information provider jointly impact purchase decisions in a social network setting. We first propose an analytical observational learning framework integrating the impact of product differentiation and social ties. Then, we use two experimental studies to validate our analytical results and provide additional insights. Our key findings are that the effect of learning from strangers is stronger for vertically differentiated products than for horizontally differentiated products. However, the effect of learning from friends does not depend on whether the underlying product is horizontally or vertically differentiated. What is more interesting is the nuanced role of social ties: For horizontally differentiated products, the effect of learning increases with the strength of social ties. In addition, contact-based tie strength is more important than structure-based tie strength in accelerating observational learning. These findings provide motivation for online retailers to generate alternative strategies for increasing product sales through social networks. For example, online retailers offering horizontally differentiated products have strong incentives to cooperate with social media platforms (e.g., Instagram and Pinterest) in encouraging customers to share their purchase information.

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Environmental Factors in Operations Management: The Impact of Air Quality on Product Demand

Production and Operations Management

Ying Ding, Yanping Tu, Jingchuan Pu, Liangfei Qiu

2021 The operations management literature has recently begun to analyze how novel data sources help practitioners better understand product demand. We extend this stream of research by analyzing how air quality, a prominent environmental factor that has received little attention in prior studies, can impact product demand. Specifically, we examine how air quality affects the demand for different product color options, and find a greater demand for blue-color product option on air-polluted days (vs. clear days). We attribute this pattern to compensatory consumption induced by need deprivation. Specifically, poor air quality deprives people of the visual experience of seeing a blue sky, leading them to seek compensation by acquiring blue-color options. By analyzing a three-year purchase-related dataset from an online retailer (Study 1) and conducting a field experiment (Study 2) and two laboratory experiments (Studies 3 and 4), we establish the external validity, internal validity, and robustness of this finding. We also provide empirical support for deprived visual experience as the mechanism: The proposed effect is driven by air quality indicators that affect visibility (Study 1) and is mediated by experienced visibility (Study 3). We further identify a theoretically relevant individual difference variable as a moderator: prior experience with air pollution, which strengthens the proposed effect in the laboratory setting because prior experience enables people to “relive” the deprived visual experience more vividly (Study 4). Given the prevalence of air pollution across the globe, our research sheds light on how practitioners can improve their operational decisions by factoring in air quality.

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Informational Efficiency of Cryptocurrency Markets

SSRN

Liangfei Qiu, Mahendrarajah Nimalendran, Praveen Pathak, Mariia Petryk

2021 We study the price discovery process of cryptocurrencies that trade in unregulated and fragmented markets and have a non-traditional business model. ICOs exhibit significant inefficiencies with VRs (Variance Ratios) less than 0.7 for up to 5-years from the issue date. IEOs with a similar business model as ICOs but underwritten by exchanges are consistently more efficient than ICOs over 200 days, while IPOs are efficient within 30 days of issue. The study suggests that due diligence by exchanges and regulators can lead to more efficient prices. In addition, several unique features of ICOs (platform, proof type, and algorithm type) influence their efficiency. Finally, we analyze the influence of social media on the efficiency of cryptocurrencies. Our results show that one standard deviation increase in public attention (Reddit points) will reduce the inefficiency measure by 5%.

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Liangfei Qiu: UF Teaching with Technology

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Languages (1)

  • English