Crowd-based Platforms

Permanent URI for this collectionhttps://hdl.handle.net/10125/107506

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Comparing Social Media Communities using Functional Data Analysis
(2024-01-03) Champon, Xiaoxia; Jayalath, Chathura; Rand, William; Jasser, Jasser; Garibay , Ivan
Various social media communities can lead conversations in entirely divergent directions, shaping the nature of information shared on these platforms. Deliberate disinformation and manipulated messages, disseminated both within and beyond these communities, hold the potential to reshape public opinion on a broader scale. A constructive analysis that delves into the disparities between these opposing groups could prove invaluable in discerning the pathways through which information flows. Our research examines the temporal dynamics of social media groups, assessing their behavior through metrics such as time dependent post and retweets. Using functional data analysis, we investigate Tweets related to incidents like the Skripal/Novichok case and the Bucha Crimes. Our goal is to quantify the disparities between these communities and uncover the strategies employed by each group to promote specific campaigns. Our preliminary findings shed new light on the mechanics of information dissemination, offering insights that may inform decisions about optimal response times.
Optimal Content-Sharing Strategy for Online Streaming Platforms
(2024-01-03) Yoo, Eunsol; Kim, Kihoon
Many subscription-based video-on-demand (SVOD) platforms are currently focusing on creating exclusive content to attract more subscribers. However, it is not clear that an exclusive-content strategy will eventually increase the platforms' profits and/or the number of the platforms' subscribers. This research models two competing platforms' choices between leasing and not leasing exclusive content investigating the impact of their choices on the SVOD market size and the competition results. We show the conditions under which the two platforms can increase their profits by sharing a certain amount of exclusive content.
Two-sided Long Tails on Blockchain-Based Crowdsourcing Platforms
(2024-01-03) Wu, Bingyi; Liu, Charles Zhechao; Choo, Kim-Kwang Raymond; Zhu, Hongyi; Chang, Shen
Motivated by the recent adoption of a blockchain-based token-weighted incentive mechanism in online crowdsourcing markets, which has sparked concerns over power and popularity concentration, this study investigates the interactions between the distributions on the two sides (voters and contributors) of a blockchain-based crowdsourcing platform (BCP). Built upon the theories of long tails and two-sided markets, we propose a two-sided long-tail framework to uncover the distributional dynamics on BCPs. Our weekly panel data analyses show that the concentration in token holdings distribution among voters negatively affects content growth, which in turn leads to a higher content popularity concentration. Such an adverse effect is more pronounced in niche content growth compared to popular content growth. The results also show that the distribution of voters' token holdings positively affects content diversity, which in turn results in a more evenly distributed content popularity. The theoretical contributions and practical implications of our findings are discussed.
What Doesn’t Kill You Makes You Stronger? Evidence from Vampire Attack on Decentralized Exchange
(2024-01-03) Zhao, Xi; Li, Jian; Wan, Xiang (Shawn); Zang, Xinyu; Cheng, Hsing
Our study examines the impact of vampire attack, a unique platform entry strategy in the blockchain ecosystem. The cryptocurrency market has grown dramatically over the past decades since the emergence of Bitcoin protocol in 2008. Decentralized exchanges (DEXs), allowing blockchain users to directly buy and sell cryptos on the chain, started to blossom recently with trading volume reaching 80% on-chain market share at its peak. As the DEX becomes immensely popular, so does the vampire attack, where the entrant attacker completely clones the incumbent’s platform and “sucks” its liquidity by providing tokenized rewards. We implement a quasi-experimental design to uncover the deposit-side and exchange-side impact of the first and most famous vampire attack launched by Sushiswap (attacker) against Uniswap (incumbent). Surprisingly, the vampire attack leads to no significant downturn on the deposit-side, and even promotes the exchange-side performance. We plan to further uncover the underlying reasons that contribute to these intriguing results.
How Should We Deal with Malicious Customers’ Threats in Online Review? Perspectives of Retailers, Customers, and Platforms
(2024-01-03) Bhargava, Hemant; Lee, Junghee; Qu, Xinxue; Sun, Daewon
We show that existence of malicious customers will distort the retailer's overall rating and under certain conditions, complying to malicious customers' request can actually increase its profit. In addition, we examine the importance of information provided by the platform, including the actually product quality and customer preference. Counter-intuitively, we show that retailer may not always use the perfect information as compliance with malicious customers can obtain positive ratings from them. This work also generates important implications for both retailers and platforms when dealing with malicious reviews.
The Power of Collective Insight: Harnessing the Wisdom of the Crowd for Digital Auction Success
(2024-01-03) Ivanov, Anton; Tacheva, Zhasmina; Mukherjee, Ujjal
During the COVID-19 pandemic, online auction markets gained significant importance for their efficient and transparent handling of the shift in economic activity. Renowned digital car auction platform Bring a Trailer (BaT) experienced remarkable growth, with its revenue reaching $828 million in 2020. This study investigates the factors contributing to BaT's success, focusing on the role of its unique community comment feature, which leverages the collective intelligence of its users, known as wisdom of the crowd (WOC) in IS literature. Analyzing a dataset of 13,451 car listings, the study explores how WOC elements such as information quality and user identity impact auction performance. The analysis reveals that helpful WOC content positively influences auction outcomes, reducing information asymmetry and increasing market efficiency. The impact of WOC contributor reputations, however, showed less consistent results. These findings are relevant for stakeholders in the automotive market, providing insights to inform decision-making in online auction markets.
Bridge or Barrier? Digital Distance and Its Effects on Peer-to-Peer Lending
(2024-01-03) Sabzehzar, Amin; Zhou, Yingxin; Hou, Jingbo
Although existing literature has demonstrated that biases and preferences arising from offline group proximity manifest in online peer-to-peer lending markets and hinder lending activities, the role of biases and preferences originating from online group proximity is still unclear. Using data from Facebook and a popular peer-to-peer prosocial lending platform named Kiva, we examine how digital distance (online social connectedness barriers) affects online prosocial peer-to-peer lending behavior across states in the United States. We find that digital distance barriers between states inhibit prosocial lending between them, in addition to other offline distance-related barriers studied in the literature, such as geographical distance. Also, we find that advanced IT infrastructure can, to an extent, compensate for the negative effect of digital distance on prosocial lending. These findings suggest that online social disconnectedness creates barriers to prosocial lending, but advanced IT infrastructure can help mitigate these barriers.
Tech Giants and New Entry Threats
(2024-01-03) Pan, Yang; Song, Weiling
Tech giants like Amazon, Apple, Facebook, Google, and Microsoft have drawn antitrust concerns due to their perceived power, potentially stifling new startups, especially through the so-called kill zone within their product domains. This study demonstrates that tech-giant entrants face a significantly lower likelihood of obtaining follow-on financing and achieving long-term survival in recent years. However, such phenomena are transitory and concentrated among tech-giant entrants lacking patents and operating within segments characterized by high network effects. There is no evidence that the M&As conducted by tech giants deter entries. Furthermore, tech giants experience more entries compared to the average tech incumbent.
Do Masks Protect Children? Evidence from Florida’s Mask Mandate Ban Using Large-Scale School Transmission Data
(2024-01-03) Wang, Qili; Rao, Xinzhi; Li, Alice; Qiu, Liangfei
Our study examines the causal impact of mask mandates on COVID-19 transmission in elementary and middle schools using a natural experiment in Florida. While randomized controlled trials (RCTs) have been the gold standard for causal investigation, they face challenges such as lower compliance rates and typically focus only on the direct impact on mask wearers, overlooking the potential benefits of transmission reduction. Our natural experiment overcomes these issues, providing a broader view of mask mandates’ effects. The results show a 20.6% increase in COVID-19 cases when mask mandates are banned. We also explore the moderating effects of school size, search volume for “mask,” and racial and poverty groups on the impact of the mask ban. Our study underscores the critical role of mask mandates and showcases the potential of utilizing publicly accessible data to generate insights on significant societal issues – a principle at the core of crowd-based platforms.
Social Movements against Racial Discrimination and Minority Entrepreneurship in Crowdfunding
(2024-01-03) Chen, Shiqin; Hu, Lin; Yin, Xueyan; Li, Kun; Fang, Yulin
Racial discrimination on crowdfunding platforms makes it harder for minority entrepreneurs to access early-stage capital. Social movements like BLM mitigate racial bias and may promote minority entrepreneurship. However, it is unclear what the specific impacts of BLM on minority start-ups, especially the spillover effects on crowdfunding activities. We conduct a large-scale computational study leveraging a complete dataset from Kickstarter and employ multiple methods. We identified the founders' race and whether the project’s nature is connected to minorities. We find evidence from both the demand and supply sides, indicating that the BLM movement did promote minority entrepreneurship on crowdfunding platforms. We also find some interesting changes in backers' behavior. Significant contributions are made by linking the social movement and racial discrimination literature to a body of IS research on entrepreneurship in crowdfunding.
Unleashing the Power of Visual Storytelling: Leveraging Cover Photo Narrativity to Attract Backers in Online Crowdfunding Platforms
(2024-01-03) Liu, Xiaochen; Jin, Yu; Li, Ziru; Fang, Yulin; Ye, Qiang
Although extensive research exists on factors that affect crowdfunding campaign performance, little is known about how to attract potential backers and encourage them to visit a campaign's homepage. To address this gap, this study utilizes narrative transportation theory to investigate how the narrativity of a campaign's cover photo influences backers' participation and fundraising performance. Furthermore, the study examines whether the effectiveness of the narrative strategy varies depending on the type of project. Results from analyzing 2,721 reward-based crowdfunding campaigns on kickstarter.com suggest that incorporating narrative elements is particularly useful for founders of intangible rewards. This study contributes to the crowdfunding literature by providing insights into how backers become aware of specific campaigns and by guiding campaign design for founders.
Crowdfunding Herding During the Post-goal Stage
(2024-01-03) Lu, Shuya; Tseng, Shih-Lun "Allen"; Wu, Jianan; Wu, Yinglu; Zhu, Fengxia
Rewards-based crowdfunding has garnered significant attention in IS research, with a focus on backer behaviors leading up to a project's funding goal. However, little attention has been given to backers' motivations for overfunding a project. This study aims to address this gap by examining potential backers’ herd behavior after a project reaches its funding goal. By analyzing data from 5,445 projects on Kickstarter, we discover that herd behavior occurs not only before the goal is achieved but also after. Additionally, we explore the dynamics of herding and find that, in the post-goal stage, the combination of funding magnitude and time elapsed influences changes in herding. Specifically, funding magnitude significantly enhances herding as a project timeline progresses from the midpoint to the deadline. This interaction effect adds to the extensive body of research on the goal-gradient effect and the deadline effect, shedding light on their interplay in the post-goal stage.
Generative AI or Real Users? Investigating the Relative Impact of Generative AI vs. Humans on Online Review Quality
(2024-01-03) Shan, Guohou; Jia, Shizhen
Online reviews matter for customers, firms, and platforms increasingly. The recent advancement of generative Artificial Intelligence (AI) techniques makes it possible to generate online reviews automatically. However, the relative impact of generative AI vs. humans on online review generation is unknown. On the one hand, generative AI can generate high quality reviews because they are trained on diverse and high-quality data. On the other hand, generative AI hallucinates and may generate fabricated content, threatening the quality of the generated reviews. Using data from one of the biggest online review platforms, Yelp.com, we apply fixed effect models to understand the relative impact of generative AI vs. humans on the quality of generated reviews. We find that reviews from generative AI averagely have bigger ratings, a higher level of inconsistency between rating and sentiment, shorter, harder to read, and more positive and subjective content. Our study has both theoretical and practical implications.
The Market of Programming Skills: Market Tightness and Pay Equity
(2024-01-03) Wu, Zhenhua; Hu, Lin; Li, Kun; Gu, Bin
The demand for programming skills has grown dramatically in recent years, driven by the growth of the technology sector and the increasing importance of software in the global economy. As a result, understanding the market for programming skills is critical for policymakers, employers, and workers alike. This paper aims to contribute to this understanding by examining the dynamics of the programming skills market from both the demand and supply sides. Specifically, we use job posting data from Burning Glass Institute and search intensity data from Google Trends to construct a measure of market tightness, which captures the gap between the supply and demand of various programming languages. We then investigate how this measure of market tightness is related to the compensation of programming professionals, and how this relationship varies by gender, age, and other demographic characteristics. Our results shed light on the complex dynamics of the programming skills market and offer insights for policymakers and practitioners seeking to promote economic growth and workforce development.
Introduction to the Minitrack on Crowd-based Platforms
(2024-01-03) Chen, Pei-Yu; Huang, Nina; Gu, Bin

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