Zihong Huang

Zihong Huang

Assistant Professor @ Rawls, Texas Tech University

Posts by Collection

portfolio

publications

Precision CrowdSourcing: Closing the Loop to Turn Information Consumers into Information Contributors

Published in Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing (CSCW), 2016

We introduce a theoretical framework called precision crowdsourcing whose goal is to help turn online information consumers into information contributors. The framework looks at the timing and nature of the requests made of users and the feedback provided to users with the goal of increasing long-term contribution and engagement in the site or system. We present the results of a field experiment in which almost 3000 users were asked to tag movies (plus a null control group) as we varied the selection of task (popular/obscure), timing of requests (immediate or varying delays), and relational rhetoric (neutral, system reciprocal, other users reciprocal) of the requests. We found that asking increases tags provided overall, though asking generally decreases the provision of unprompted tags. Users were more likely to comply with our request when we asked them to tag obscure movies and when we used reciprocal request rhetoric.

Recommended citation: Zhao, Qian, Zihong Huang, F. Maxwell F. Harper, Loren G. Terveen, and Joseph A. Konstan. "Precision CrowdSourcing: Closing the Loop to Turn Information Consumers into Information Contributors." In Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing, pp. 1613-1623. ACM, 2016. [Download paper here]

Pure and Hybrid Crowds in Crowdfunding Markets

Published in Financial Innovation, 2016

This study documents and compares two crowd designs for crowdfunding, namely pure crowds, where all crowd members participate as equals, and hybrid crowds, where crowd members are led by an expert investor. The hybrid design is rarely studied in the crowdfunding literature despite its large presence in equity crowdfunding. We examine industry practices from various countries in terms of crowd designs, review relevant literature on this topic, and develop a conceptual framework for choosing between pure and hybrid crowds. We identify several inefficiencies of pure crowds in crowdfunding platforms and discuss the advantages of hybrid crowds. We then develop a conceptual framework that illustrates the factors for choosing between pure and hybrid crowds. Finally, we discuss the issue of how to manage and regulate lead investors in hybrid crowds. Our study contributes to the crowdfunding literature and to crowdfunding practice in multiple ways.

Recommended citation: Chen, Liang, Zihong Huang, and De Liu. "Pure and hybrid crowds in crowdfunding markets." Financial Innovation 2.1 (2016): 19. [Download paper here]

Better to Give than to Receive: Impact of Donation Option on Reward-based Crowdfunding Campaigns

Published in Information Systems Research, 2023

Motivated by the adoption of donation schemes at some leading reward-based crowdfunding platforms, we examine the effect of adding a donation scheme to reward-based crowdfunding and explore its underlying mechanisms. This work also helps to fill the knowledge gap on the role of funding schemes. Leveraging an unannounced site change at a leading crowdfunding platform, we estimated the impact of introducing the donation scheme by developing and applying a novel two-step matching and difference-in-differences technique for cohorted quasi-experimental settings. We find that the introduction of the donation scheme increased the success rate of reward campaigns by 19%. The increased success occurred mainly in reward campaigns with prosocial causes. Further analyses of underlying mechanisms reveal that the increased campaign success came mainly from campaigns that received donations. The added donation channel not only had a primary effect, as evidenced by a third of campaigns attracting donations, but also a secondary “crowd-in” effect on the reward channel, as shown by a positive impact of early donations on subsequent contributions through the reward channel, beyond the known effects of early contributions. Our findings suggest that, for reward campaigns with prosocial causes, the addition of a donation channel not only provides a better fit for some backers of reward campaigns, but also inspires others to be more willing to contribute through the reward channel.

Recommended citation: Jason Chan, Zihong Huang, De Liu, Zhigang Cai (2023) Better to Give Than to Receive: Impact of Adding a Donation Scheme to Reward-Based Crowdfunding Campaigns. Information Systems Research, https://doi.org/10.1287/isre.2023.1224 [Download paper here]

Economics of Social Media Fake Accounts

Published in Management Science, 2024

Amid the rise of the influencer economy, fake social media accounts have become prevalent on many social media platforms. Yet the problem of fake accounts is still poorly understood, and so is the effectiveness of coping strategies. This research models the ecosystem of fake accounts in an influencer economy and obtains insights on fake-account purchasing behaviors, the impact of anti-fake efforts, and the roles of various contextual factors. We show that as the anti-fake effort increases, the equilibrium may transition from a “pooling” equilibrium where a low-quality influencer buys fake accounts to mimic a high-quality one, to a “costly-separating” equilibrium where a high-quality influencer may buy fake accounts to prevent mimicry from a low-quality influencer, and to a “naturally-separating” equilibrium where low- and high-quality influencers are separated without buying fake accounts. We find that increasing anti-fake efforts and increasing social media literacy may sometimes result in more fake accounts. A purely profit-driven platform always prefers a pooling equilibrium with zero anti-fake effort. As a platform puts more weight on consumer welfare, it may exert a positive effort to induce a separating equilibrium, but the platform’s preferred anti-fake effort tends to be lower than that of consumers. We also find that the platform sometimes prefers a lower social media literacy and a lower fake-account base price, whereas consumers prefer the opposite. In contrast, improving the anti-fake technology level can benefit both the platform and consumers. Our main insights are applicable to scenarios with more influencer types and repeated interactions.

Recommended citation: Huang, Z., & Liu, D. (2024). Economics of social media fake accounts. Accepted for publication at Management Science. [Download paper here]

talks

Economics of Social Media Fake Accounts

Amid the rise of the influencer economy, fake social media accounts have become a prevalent problem on many social media platforms. Yet the problem of fake accounts is still poorly understood and so is the effectiveness of coping strategies. This research models the ecosystem of fake accounts in an influencer economy and obtains insights on fake-account purchasing behaviors, the impact of anti-fake efforts, and the roles of social media literacy, anti-fake technology, and costs of fake accounts. We show that not only low-quality influencers may buy fake accounts to mimic high-quality ones in a “pooling” equilibrium, high-quality influencers may also buy to prevent mimicry in a “costly-separating” equilibrium. There is also a “naturally-separating” equilibrium where the two types are separated without buying fake accounts. We find that increasing anti-fake efforts and social media literacy may cause more fake accounts. The platform generally prefers either a zero-effort pooling equilibrium or a high-effort naturally-separating equilibrium. Compared to the level of anti-fake efforts preferred by consumers, the platform may be overly or insufficiently aggressive. Some anti-fake strategies, such as increasing social media literacy and fake-account costs, may benefit consumers but not the platform. One exception is increasing the effectiveness of anti-fake technology, which benefits both the platform and consumers and reduces the number of fake accounts.

AI-empowered Venture Capital (VC): The Impact of AI Adoption on VC Firms’ Success

In the venture capital (VC) industry, a new breed of VC firms has emerged to embrace AI-empowered investment strategy instead of relying on human judgments. Although AI has already demonstrated advantages over humans in some domains, it is unclear whether AI can lead to superior performance in the venture capital industry. This research fills this gap by estimating the causal impact of a VC firm’s adoption of AI-empowered investment strategy on its success using matched portfolios of startups from AI-empowered and non-AI-empowered VC firms. We find that AI adoption tends to increase the success of a VC firm in terms of successful exits (e.g. IPO and acquisition) of startups it invests in. In addition, we also find that AI-empowered investment strategy can reduce racial bias but increase gender and local bias. The increase of gender bias is responsible for the superior performance of AI-empowered investment strategy while the reduction of racial bias and the increase of local bias is not.

teaching

IDSC 4444: Descriptive and Predictive Analytics (Fall 2018,2022 Spring 2020,2021,2022)

Teaching Assistant, Carlson School of Management, University of Minnesota, 2018

In a world of ever growing information sources, any student of business should be equipped with the ability to analyze data to produce actionable insights. Equally important is the capacity to understand such analysis and to present it to key stakeholders. IDSc 4444 offers an introduction to basics of data manipulation, visualization and analysis for business intelligence.

IDSC 6050: Information Technologies and Solutions (Fall 2018)

Teaching Assistant, Carlson School of Management, University of Minnesota, 2018

This course is about current/emerging technologies that are used in modern net-enhanced organizations. Topics covered will include mobile communications, information security, cloud computing, blockchains, and emerging IT trends.

MSBA 6410: Exploratory Data Analytics and Visualization (Fall 2019)

Teaching Assistant, Carlson School of Management, University of Minnesota, 2019

MSBA 6410 is a required course for the M.S. in Business Analytics program. This course is designed to prepare aspiring data scientists for the rapidly changing digital environment faced by companies and their need to discover novel and actionable patterns, enabling data-driven decision making.

IDSC 4210: Interactive Data Visualization for Business Analytics (Spring 2020)

Teaching Assistant, Carlson School of Management, University of Minnesota, 2020

IDSC 4210 is an elective course for the undergraduate Business Analytics minor at the Carlson School of Management. It focuses on the fundamental and widely used exploratory data analysis technique of interactive visualization that is integral to modern business analytics.

MSBA 6441: Causal Inference via Econometrics and Experimentation (Spring 2021)

Teaching Assistant, Carlson School of Management, University of Minnesota, 2021

MSBA 6441 is intended to help students obtain skills in data analysis, with the following specific goals: to familiarize you with the process of assembling and analyzing a dataset to address a business problem; to introduce the notion of an “experiment” and the distinction between correlation and causation, in data; to explore statistical techniques that can be used with observational data to achieve reliable causal inferences, in the absence of experiments; to further increase your level of comfort working with R.

IDSC 4444: Descriptive and Predictive Analytics (Fall 2020, Spring 2021)

Instructor, Carlson School of Management, University of Minnesota, 2021

In a world of ever growing information sources, any student of business should be equipped with the ability to analyze data to produce actionable insights. Equally important is the capacity to understand such analysis and to present it to key stakeholders. IDSc 4444 offers an introduction to basics of data manipulation, visualization and analysis for business intelligence.

ISQS 3345: Analytics and Development with Python (Fall 2023, Fall 2024)

Instructor, Rawls College of Business, Texas Tech University, 2024

The subjective of this course is to provide a pragmatic and hands-on introduction to the Python programming language, with an emphasis on practical application with simple examples. Python is one of the most popular, powerful and versatile programming language not just among professional developers but also for business analysts and users from various fields. While Python may be a simple language relative to other alternatives, its power derives from the vast realms of open-source frameworks available for users from various fields where, for example, machine learning (ML) or process optimization matter, thereby making it a formidable tool. Hence, getting familiar with it would pave the way for the future career for most of students.