A Bitter Pill to Swallow? The Consequences of Patient Evaluation in Online Health Question-and-Answer Platforms

2023 | Information Systems Research | Citations: 0

Authors: Chen, Chen; Walker, Dylan

Abstract: Online health question-and-answer (Q&A) platforms (OHQPs), where patients post h ... Expand

Abstract: Online health question-and-answer (Q&A) platforms (OHQPs), where patients post health-related questions, evaluate advice from multiple doctors, and direct a bounty (monetary reward) to their most preferred answer, have become a prominent channel for patients to receive medical advice in China. To explore the quality of medical advice on these platforms, we analyzed data on patients’ evaluation of ∼497,000 answers to ∼114,000 questions on one of the most popular OHQPs, 120ask.com, over a three-month period. We assembled a panel of independent physicians and instructed them to evaluate the quality of ∼13,000 answers. We found that the quality of medical advice offered on the platform was high on average, and that low-quality answers were rare (6%). However, our results also indicate that patients lacked the ability to discriminate advice quality. They were as likely to choose the best answer as the worst. The medical accuracy of patient evaluation was worse in critical categories (cancer, internal medicine) and for vulnerable subpopulations (pediatrics). Given that millions of patients seek medical advice from OHQPs in China annually, the social and economic implications of this finding are troubling. To understand how patients evaluate advice, we trained deep neural networks to think like patients, allowing us to identify patients’ positive and negative responses to different heurist cues. Although our results indicate that OHQPs perform well, we identified several concerns that should be addressed through platform design and policy changes. Because the Q&A process lacks peer review mechanisms, signals of advice quality are not conveyed to patients, forcing them to rely on heuristic cues, which cannot effectively guide them toward the best advice. We also found that the platform reputation metric was not correlated with the quality of the advice giver’s advice, may effectively encourage patients to select lesser quality medical advice, and increased the risk of moral hazard for malicious players to intentionally provide less accurate but more agreeable advice for personal gain. Our analysis revealed bad actors on the platform, including drug promoters and spammers. Finally, we found that OHQPs exacerbated care avoidance. We discuss several potential policy changes to address these shortcomings. History: Param Singh, Senior Editor; Idris Adjerid, Associate Editor. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1158. Collapse

Topics: decision making local interpretable model agnostic explanation digital platform health information system pandemic

Methods: artificial neural network psychometrics survey deep learning natural language processing

Theories: lemon market theory

The Impact of Social Reputation Features in Innovation Tournaments: Evidence from a Natural Experiment

2023 | Information Systems Research | Citations: 0

Authors: Deodhar, Swanand J.; Gupta, Samrat

Abstract: This study examines how a change in an online reputation system, specifically th ... Expand

Abstract: This study examines how a change in an online reputation system, specifically the addition of a social reputation feature, affects contestant performance in innovation tournaments. Drawing from the literature on peer recognition and social evaluation anxiety, we project competing effects whereby the feature could either enhance or diminish contestant performance. Furthermore, we hypothesize a series of contingent effects involving the soft reserve, a competitive dynamic that unfolds in tournaments, and a determinant of performance in its own right. Specifically, we hypothesize that the direct influence of the social reputation feature on contestant performance would be predicated on the level of two types of soft reserves in an innovation tournament: that created by the focal contestant and that created by competitors. We test these hypotheses leveraging a natural experiment, where an innovation tournament platform (Kaggle.com) introduced a social reputation feature, allowing contestants to follow other contestants unilaterally. Estimates obtained using a panel data set bracketed within a narrow time window (15 days) around the feature launch reveal that the feature significantly improves the performance. We further report that the two types of soft reserves significantly moderate the positive effect of the social reputation feature on contestant performance, whereby the higher the soft reserve, the weaker the effect of the social reputation feature on contestant performance. These findings have several theoretical and practical implications for managing innovation tournaments.History: Manju Ahuja, Senior Editor; Maha Shaikh, Associate Editor.Funding: The authors acknowledge Research & Publications office of IIM Ahmedabad for providing funding for language editing services.Supplemental Material: The online appendices are available at https://doi.org/10.1287/isre.2022.1118. Collapse

Topics: innovation management online reputation system word of mouth accounting blog

Methods: natural experiment robustness check statistical hypothesis test descriptive statistic computational algorithm

DESIGNING BUSINESS REPUTATION ECOSYSTEMS - A METHOD FOR ISSUING AND TRADING MONETARY RATINGS ON A BLOCKCHAIN

2023 | European Conference On Information Systems | Citations: 0

Authors: Hemmrich, Simon; Bobolz, Jan; Beverungen, Daniel; Blömer, Johannes

Abstract: Many market transactions are subject to information asymmetry about the deliver ... Expand

Abstract: Many market transactions are subject to information asymmetry about the delivered value proposition, causing transaction costs and adverse effects among buyers and sellers. Information systems (IS) research has investigated how review systems can reduce information asymmetry in business-to-consumer markets. However, these systems use textual data or star ratings that cannot be readily applied to business-tobusiness markets, are vulnerable to manipulation, and suffer from other conceptual shortcomings. Building on design science research, we conceptualize a new class of reputation systems based on using payments as monetary ratings for each transaction stored on a blockchain. We show that our system assures content confidentiality so buyers can share, sell, and aggregate their ratings selectively, establishing a reputation ecosystem. Our prescriptive insights advance the design of reputation systems and offer new paths to understanding how payments can be used as signals that reduce information asymmetry in B2B transactions. Collapse

Topics: blockchain cryptocurrency business-to-business online reputation system privacy

Methods: design science design process design artifact

Theories: lemon market theory

BUSINESS REPUTATION SYSTEMS BASED ON BLOCKCHAIN TECHNOLOGY—A RISKY ADVANCE

2023 | European Conference On Information Systems | Citations: 0

Authors: Hemmrich, Simon

Abstract: Reputation is indispensable for online business since it supports customers in ... Expand

Abstract: Reputation is indispensable for online business since it supports customers in their buying decisions and allows sellers to justify premium prices. While IS research has investigated reputation systems mainly as review systems on online platforms for business-to-consumer (B2C) transactions, no proper solutions have been developed for business-to-business (B2B) transactions yet. We use blockchain technology to propose a new class of reputation systems that apply ratings as voluntary bonus payments: Before a transaction is performed, customers commit to pay a bonus that is granted if a service provider has performed a service properly. As opposed to rival reputation systems that build on cumulated ratings or reviews, our system enables monetized reputation mechanisms that are inextricably linked with online transactions. We expect this system class to provide more trustworthy ratings, which might reduce agency costs and serve quality providers to establish a reputation towards new customers, building on second-order trust. Collapse

Topics: online reputation system blockchain word of mouth business-to-business service quality

Methods: design principle design artifact design science

Theories: lemon market theory

MAY I HAVE YOUR ATTENTION, PLEASE? – THE INTERACTION BETWEEN ATTENTION SCREENING AND REPUTATION ON CROWDWORKING PLATFORMS

2023 | European Conference On Information Systems | Citations: 0

Authors: Laux, Florian; Poniatowski, Martin; Kundisch, Dennis

Abstract: ... Expand

Abstract: Collapse

Topics: crowdsourcing online reputation system platform work site web API word of mouth

Methods: experiment experimental design survey experimental task theory development

Optional Verification and Signaling in Online Matching Markets: Evidence from a Randomized Field Experiment

2023 | Information Systems Research | Citations: 0

Authors: Shi, Lanfei; Viswanathan, Siva

Abstract: Online matching platforms lack common informational mechanisms such as ratings a ... Expand

Abstract: Online matching platforms lack common informational mechanisms such as ratings and reviews that serve to reduce information asymmetry in transactional platforms. The lack of verified information about participants further exacerbates issues of information asymmetry in such markets. This study focuses on a novel role of verification in such matching markets—its ability to serve as a credible signal for a user when such verification is made optional and visible to other users. In collaboration with a leading online dating platform with no reputation mechanisms and where most of the information is self-disclosed, we design and conduct a randomized field experiment to examine not only who chooses to verify but also, the effectiveness of such optional verification for different types of users. Interestingly, we find that users on the two sides use the same signal very differently. Males act consistent with the conventional prediction of signaling, with high-type males being more likely to opt in to verification. As for females, we find that medium-type females are the most likely to opt in to verification as compared with high-type females. We also find that such differential opt-in decisions are related to the differences in the credibility of the existing key attribute of each side (viz., income for males and beauty for females). In examining the outcomes of verification, we find that verified users receive more contacts from higher-type users, with the high-type males and medium-type females benefitting the most. More interestingly, we find that verified users become more proactive and reach out to more and better potential partners. Further, the introduction of this voluntary verification facilitates desirable matching outcomes and benefits the platform as a whole. These findings have useful implications for research as well as practice. History: Ravi Bapna, senior editor. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1194. Collapse

Topics: online dating LinkedIn anonymity data breach privacy

Methods: experimental group deep learning survey experiment robustness check

Theories: signaling theory lemon market theory economic theory

Platform Refund Insurance or Being Cast Out: Quantifying the Signaling Effect of Refund Options in the Online Service Marketplace

2023 | Information Systems Research | Citations: 0

Authors: Zheng, Jinyang; Wang, Youwei; Tan, Yong

Abstract: This study examines whether and how an online service marketplace can leverage r ... Expand

Abstract: This study examines whether and how an online service marketplace can leverage refund options endorsed by different parties (i.e., the platform or sellers) to address the “lemons” problem that is caused by the intangibility, variability, and unreturnable nature of the services sought. Through developing a signaling mechanism and a corresponding demand estimation model, we show that both platform refund insurance and a seller-guaranteed refund increase service demand, with platform refund insurance as the more effective option and hence having a more effective signaling mechanism. A reduced-form analysis suggests that sellers with a better reputation or less popularity might benefit less from refund options. An investigation on further use of the more effective refund option, a “having platform refund insurance or being cast out” policy (i.e., retaining platform refund-insured sellers but expelling uninsured ones), using counterfactual simulations and supply-side single interrupted time series designs, reveals the effectiveness of this policy in filtering out low-quality sellers, shown as an improved quality of sellers on the platform due to the change in sellers (i.e., new sellers’ replacing those who were expelled) both immediately after the policy and in the (near) equilibrium, yet a cost (i.e., a loss in demand and consumer welfare) for the platform in the (near) equilibrium due to the changes in characteristics (e.g., price) of sellers. This cost, however, is lower than the benefit from the improved quality of the sellers, so that the platform’s overall performance improves. The study also quantifies the consumer welfare of the online service marketplace and provides practical insight for consumers, sellers, and online service marketplace operators. History: Juan Feng, Senior Editor; Beibei Li, Associate Editor. Funding: This work was supported by the National Natural Science Foundation of China [Grants 71972047, 71831005, and 91746302]. Supplemental Material: The online supplement is available at https://doi.org/10.1287/isre.2022.1162. Collapse

Topics: consumer surplus IS economics electronic market service quality smart watch

Methods: simulation longitudinal research robustness check descriptive statistic time series analysis

Theories: lemon market theory signaling theory

Tackling Challenges of Robustness Measures for Autonomous Agent Collaboration in Open Multi-Agent Systems

2022 | HICSS | Citations: 0

Authors: Jin, David; Kannengießer, Niclas; Sturm, Benjamin; Sunyaev, Ali

Abstract: Open multi-agent systems (OMASs) allow autonomous agents (AAs) to collaborate in ... Expand

Abstract: Open multi-agent systems (OMASs) allow autonomous agents (AAs) to collaborate in coalitions to accomplish complex tasks (e.g., swarm robots exploring new terrain). In OMASs, AAs can arbitrarily join and leave the network. Thus, AAs must often collaborate with unknown AAs that may corrupt coalitions, leading to less robust systems. However, measures to improve robustness of OMASs are subject to challenges, decreasing their effectiveness. To understand how to improve coalition robustness in OMASs and address challenges of existing robustness measures, we carried out a literature review and revealed three types of robustness measures (i.e., collaboration coordination, normative control, and reliability prediction). Moreover, we found 21 challenges for the identified robustness measures and 24 corresponding solutions. By carrying out this literature review, we forge new connections between existing measures and identify challenges and measures that apply to multiple existing measures. Hereby, our work supports more robust collaborations between AAs in open systems. Collapse

Topics: knowledge base distributed ledger technology online reputation system identity management data integrity

Methods: literature study machine learning qualitative content analysis

Product Reviews: A Benefit, a Burden, or a Trifle? How Seller Reputation Affects the Role of Product Reviews

2022 | Management Information Systems Quarterly | Citations: 4

Authors: Wang, Hongpeng; Du, Rong; Shen, Wenqi; Qiu, Liangfei; Fan, Weiguo

Abstract: The sales effect of product reviews has been a contentious issue with competing ... Expand

Abstract: The sales effect of product reviews has been a contentious issue with competing perspectives about when product reviews serve as a benefit, a burden, or a trifle. Unlike previous research that separately investigates the impact of each eWOM system, our study empirically examines the interaction effects of dual eWOM systems, i.e., product reviews and seller reputation. Drawing on reference point theory, we find that seller reputation systems play a reference-point role and determine the efficacy of product reviews. Specifically, negative reviews cause a significant loss in sales for high-reputation sellers but are less detrimental for low-reputation sellers. In contrast, positive reviews can boost sales for low-reputation sellers but are less helpful for high-reputation sellers. These results highlight that seller reputation is a double-edged sword. While a high seller reputation can reduce seller uncertainty and attract more consumers, it may also raise consumers’ expectations and lead to potential negative expectancy violations. Moreover, we explore what strategies may help mitigate the potentially detrimental effect of reference points for high-reputation sellers. Through the lens of restructuring reference points, the reputation reference effect can be adjusted in a more dynamic reputation system (e.g., a reputation badge). Compared to sellers that have never lost their top-rated badge, sellers that have lost their top-rated badge may face an attenuated detrimental impact on sales from the negative expectancy violation due to negative reviews and enjoy a positive impact from positive reviews. We discuss the implications of our findings for both theory and practice. Collapse

Topics: online review word of mouth online word of mouth online reputation system logistics management

Methods: product review instrumental variables estimation Wald test propensity score method robustness check

Vulnerability disclosure mechanisms: A synthesis and framework for market-based and non-market-based disclosures

2021 | Decision Support Systems | Citations: 0

Authors: Ahmed, Ali; Deokar, Amit; Lee, Ho Cheung Brian

Abstract: Vulnerability disclosure has been a controversial topic among scholars and pract ... Expand

Abstract: Vulnerability disclosure has been a controversial topic among scholars and practitioners. Most scholars agree on adopting the responsible disclosure practices for vulnerability disclosures, which give firms a protected period to address the vulnerability before public disclosure is made. However, the firms may not fully utilize the protected period resulting in financial and reputational losses. The recent popularity in market-based disclosure methods such as bug bounty programs has provided new methods to control ethical hackers and effectively manage the disclosure timelines. Through a systematic literature review, we investigate and identify various vulnerability disclosure mechanisms and elaborate the disclosure process of each mechanism. We synthesize and compare the antecedents and consequences of the vulnerability disclosure under market- and non-market-based disclosure mechanisms by proposing two research frameworks. Our analysis suggests that incentivizing hackers in market mechanisms change hackers’ motivations, leading to behavioral changes and eventually giving firms more control over the disclosure process. Additionally, our research frameworks provide a basis for further theorizing in this area. We also identify several open research questions addressing issues and challenges in the marketbased disclosures. The research has important implications for firms, hackers, policymakers, and researchers in this area. Collapse

Topics: data security IT security search engine productivity online reputation system

Methods: structured literature research synthesis theory development literature study meta analysis