MIS Distinguished Speaker Series

Temple University

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Feb 19 – Keong Tae Kim to present “Risk Disclosure in Crowdfunding”

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Risk Disclosure in Crowdfunding

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Keong Tae Kim

Associate Professor
Decision Sciences and Managerial Economics
CUHK Business School
The Chinese University of Hong Kong

Friday, Feb 19

9 – 10 am | Zoom

(send an email to ayabakan@temple.edu to get the Zoom link)

Abstact:

How should crowdfunding platforms alleviate information asymmetry between creators and crowdfunders? In traditional financial markets, public companies are required to disclose potential risks to their investors, and such risk disclosure requirements are enforced by legal and fiduciary regulations. In the crowdfunding context, however, such information asymmetry concerns are often addressed by crowd-based platforms. In this study, we examine whether and how risk disclosure of crowdfunding projects influences crowdfunders’ project perceptions and funding decisions. To examine the impact of risk disclosure holistically, we exploit a natural experiment, run two controlled experiments, and conduct a text-based machine learning analysis. We find that crowdfunders respond negatively to projects’ risk disclosure, while the negative effect of risk disclosure is smaller for projects that are more likely to deliver an expected reward. In addition, crowdfunders are sensitive to the content and presentation of disclosed risk information from projects and respond accordingly, though the association is stronger for more complex projects. We find that when projects are complex and challenging, funders pay more attention to risk information and the likelihood of receiving the promised rewards. These findings have implications for disclosure policies in crowd-based platforms and provide guidance for entrepreneurs seeking funds from crowds.

October 5 – Anand Gopal to Present “A for Effort? Using the Crowd to Identify Moral Hazard in NYC Restaurant Hygiene Inspections”

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Department of Management Information Systems and Data Science Institute

A for Effort? Using the Crowd to Identify Moral Hazard in NYC Restaurant Hygiene Inspections

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Anandasivam Gopal

Dean’s Professor of Information Systems

Robert H. Smith School of Business, University of Maryland

Friday, October 5, 2018

10:30 AM – noon

Fred Fox Boardroom (Alter 378)

Abstract

From an upset stomach to a life-threatening foodborne illness, getting sick is all too common after eating in restaurants. While health inspection programs are designed to protect consumers, such inspections typically occur at wide intervals of time, allowing restaurant hygiene to remain unmonitored in the interim periods. Information provided in online reviews may be effectively used in these interim periods to gauge restaurant hygiene. In this paper, we provide evidence for how information from online reviews of restaurants can be effectively used to identify cases of hygiene violations in restaurants, even after the restaurant has been inspected and certified. We use data from restaurant hygiene inspections in New York City from the launch of an inspection program from 2010 to 2016, and combine this data with online reviews for the same set of restaurants. Using supervised machine learning techniques, we then create a hygiene dictionary specifically crafted to identify hygiene-related concerns, and use it to identify systematic instances of moral hazard, wherein restaurants with positive hygiene inspection scores are seen to regress in their hygiene maintenance within 90 days of receiving the inspection scores. To the extent that social media provides some visibility into the hygiene practices of restaurants, we argue that the effects of information asymmetry that lead to moral hazard may be partially mitigated in this context. Based on our work, we also provide strategies for how cities and policy-makers may design effective restaurant inspection programs, through a combination of traditional inspections and the appropriate use of social media.

November 10 – Yuqing (Ching) Ren to Present “The Significance of Task Significance in Online Marketplaces for Work”

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The Significance of Task Significance in Online Marketplaces for Work

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Yuqing (Ching) Ren

Associate Professor & Lawrence Fellow

Carlson School of Management, University of Minnesota

Friday, November 10, 2017

10:30 AM – 12:00 PM

Speakman Hall Suite 200

Abstract

Online marketplaces for work like Amazon Mechanical Turk serve as new platforms to source mundane yet important tasks such as cleaning data or tagging images. While these platforms provide a fast and cost effective way of getting work done, low payment and the lack of face-to-face contact make it difficult for job requesters to motivate and monitor workers. In this study, we explore task significance as a new approach to motivate workers and improve work quality by informing workers of the purpose of the task and who benefits from it. We conducted a laboratory experiment and a field experiment using Amazon Mechanical Turk in which participants proofread either Wikipedia articles to help the public or digitized books to help underprivileged people access e-books. Task significance improved work quality in both experiments, especially when participants recalled the purpose statement information. A majority of participants who received the purpose statement, however, ignored it. Further analysis showed that delivering the purpose statement in rich media formats did not increase the likelihood of recall but worker attributes such as English ability, income levels, and personality traits influenced the likelihood of recall. Compared to task significance, increasing monetary payment by 50% had no impact on work quality. Intrinsic motivation such as task enjoyment had both a direct positive effect on work quality and an interaction with task significance in the sense that workers performed the highest when both types of motivations were high. Overall, our research highlights the promise of task significance as a way to motivate online crowds and also the unexpected challenge of promoting task significance in an online context.