The rise of two-sided matching platforms such as Uber, Airbnb, Upwork, and Tinder has changed the way we commute, travel, work, and even date. The success of these platforms depends on the role of information: What information and how much information should be provided? In this study, we focus on a defining characteristic of two-sided matching markets—that is, a match depends on the possibly different preferences of the two sides—and argue that the optimal amount of information released depends on the extent to which the preferences of the two sides are mismatched. Specifically, in an empirical context of online dating, we find that when there exists preference mismatch between the two sides, having less match-relevant information about the other side leads to a better matching outcome. Our study provides insights into how the amount of information available to each side affects matching outcomes on two-sided platforms and offers guidance on information design strategies. Additionally, our findings are not confined to dating websites and can be extended to other matching platforms, such as Airbnb and Upwork, where misaligned preferences can exist between the two sides.
{"title":"Mr. Right or Mr. Best: The Role of Information Under Preference Mismatch in Online Dating","authors":"Hongchuan Shen, Chu (Ivy) Dang, Xiaoquan (Michael) Zhang","doi":"10.1287/isre.2022.0233","DOIUrl":"https://doi.org/10.1287/isre.2022.0233","url":null,"abstract":"The rise of two-sided matching platforms such as Uber, Airbnb, Upwork, and Tinder has changed the way we commute, travel, work, and even date. The success of these platforms depends on the role of information: What information and how much information should be provided? In this study, we focus on a defining characteristic of two-sided matching markets—that is, a match depends on the possibly different preferences of the two sides—and argue that the optimal amount of information released depends on the extent to which the preferences of the two sides are mismatched. Specifically, in an empirical context of online dating, we find that when there exists preference mismatch between the two sides, having less match-relevant information about the other side leads to a better matching outcome. Our study provides insights into how the amount of information available to each side affects matching outcomes on two-sided platforms and offers guidance on information design strategies. Additionally, our findings are not confined to dating websites and can be extended to other matching platforms, such as Airbnb and Upwork, where misaligned preferences can exist between the two sides.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140149021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Daniel Suarez, Camilo Gomez, Andrés L. Medaglia, Raha Akhavan-Tabatabaei, Sthefania Grajales
A central challenge in disaster risk management (DRM) is that there are key dependencies and uncertainty between the decisions made at the mitigation, preparedness, response, and recovery stages. Decision support systems for disaster management require information systems that allow timely and reliable integration of data sources from different domains, including information on hazards and vulnerabilities for risk analysis, as well as organizational and logistical information for decision analysis. We propose an analytics-centered framework that integrates predictive and prescriptive models responding to unique characteristics of DRM. The framework relies on probabilistic risk assessment and uses optimization-based simulation of the response phase as a means to inform decisions at the preparedness stage. This paper presents a case study regarding the analysis of preparedness and response decisions for wildfire control in Uruguay. Numerical results illustrate insights from the risk-informed analyses. For instance, slight reductions in the preparedness budget can lead to disproportionate losses during the response stage, whereas slight increases have little effect unless explicitly directed to control high-consequence scenarios. Motivated by a real-world problem, this case study emphasizes the challenges for integrated information systems that enable the potential of analytical decision support frameworks for DRM.
{"title":"Integrated Decision Support for Disaster Risk Management: Aiding Preparedness and Response Decisions in Wildfire Management","authors":"Daniel Suarez, Camilo Gomez, Andrés L. Medaglia, Raha Akhavan-Tabatabaei, Sthefania Grajales","doi":"10.1287/isre.2022.0118","DOIUrl":"https://doi.org/10.1287/isre.2022.0118","url":null,"abstract":"A central challenge in disaster risk management (DRM) is that there are key dependencies and uncertainty between the decisions made at the mitigation, preparedness, response, and recovery stages. Decision support systems for disaster management require information systems that allow timely and reliable integration of data sources from different domains, including information on hazards and vulnerabilities for risk analysis, as well as organizational and logistical information for decision analysis. We propose an analytics-centered framework that integrates predictive and prescriptive models responding to unique characteristics of DRM. The framework relies on probabilistic risk assessment and uses optimization-based simulation of the response phase as a means to inform decisions at the preparedness stage. This paper presents a case study regarding the analysis of preparedness and response decisions for wildfire control in Uruguay. Numerical results illustrate insights from the risk-informed analyses. For instance, slight reductions in the preparedness budget can lead to disproportionate losses during the response stage, whereas slight increases have little effect unless explicitly directed to control high-consequence scenarios. Motivated by a real-world problem, this case study emphasizes the challenges for integrated information systems that enable the potential of analytical decision support frameworks for DRM.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140149207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yumei He, Xingchen Xu, Ni Huang, Yili Hong, De Liu
In the dynamic world of online dating, a key challenge faced by platforms is the cold-start problem, where newly matched users are hesitant to engage due to privacy concerns. Our solution, ephemeral sharing, addresses this by balancing privacy with the need for personal information sharing. This feature allows personal photos to disappear and become untraceable soon after being viewed, reassuring users about their privacy. We conducted a large-scale randomized experiment with more than 70,000 users to evaluate the impact of ephemeral sharing. The results are compelling: users who could share ephemeral photos were more likely to send personal images alongside with their matching request, especially those with human faces, leading to more matches and higher engagement. Significantly, this effect was more pronounced among users who are more sensitive to their privacy. Furthermore, ephemeral sharing was found to reduce users’ concerns related to data collection, dissemination, and identity misuse, thereby increasing the willingness to share personal information. This approach not only enhances user privacy but also stimulates more active engagement on the platform. For dating platforms and similar platforms, adopting ephemeral sharing can revolutionize user experience. It provides a strategic advantage by boosting user personal information sharing and enhancing privacy, crucial for maintaining meaningful communication in online dating. This feature represents a significant step forward in designing user-centric, privacy-conscious platforms.
{"title":"Enhancing User Privacy Through Ephemeral Sharing Design: Experimental Evidence from Online Dating","authors":"Yumei He, Xingchen Xu, Ni Huang, Yili Hong, De Liu","doi":"10.1287/isre.2021.0379","DOIUrl":"https://doi.org/10.1287/isre.2021.0379","url":null,"abstract":"In the dynamic world of online dating, a key challenge faced by platforms is the cold-start problem, where newly matched users are hesitant to engage due to privacy concerns. Our solution, ephemeral sharing, addresses this by balancing privacy with the need for personal information sharing. This feature allows personal photos to disappear and become untraceable soon after being viewed, reassuring users about their privacy. We conducted a large-scale randomized experiment with more than 70,000 users to evaluate the impact of ephemeral sharing. The results are compelling: users who could share ephemeral photos were more likely to send personal images alongside with their matching request, especially those with human faces, leading to more matches and higher engagement. Significantly, this effect was more pronounced among users who are more sensitive to their privacy. Furthermore, ephemeral sharing was found to reduce users’ concerns related to data collection, dissemination, and identity misuse, thereby increasing the willingness to share personal information. This approach not only enhances user privacy but also stimulates more active engagement on the platform. For dating platforms and similar platforms, adopting ephemeral sharing can revolutionize user experience. It provides a strategic advantage by boosting user personal information sharing and enhancing privacy, crucial for maintaining meaningful communication in online dating. This feature represents a significant step forward in designing user-centric, privacy-conscious platforms.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140107080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the realm of online labor platforms, addressing moral hazard is crucial. Reputation systems have been the conventional solution, yet they pose a cold-start problem for newcomers. Alternatively, monitoring systems provide real-time oversight to employers, directly tackling moral hazard. This study combines theory and empirical analysis using data from a leading online labor platform. We find that monitoring systems effectively reduce the cold-start problem, leading to a 27.8% increase in bids on projects, primarily from inexperienced workers. We further find that following the introduction of the monitoring system, employers’ preference for experienced workers diminishes, accompanied by an average reduction of 19.5% in labor costs, whereas we observe no significant decrease in project completion and review rating. Our results collectively suggest that monitoring systems alleviate the cold-start problem in online platforms and contribute to fostering a more inclusive online labor market.
{"title":"Monitoring and the Cold Start Problem in Digital Platforms: Theory and Evidence from Online Labor Markets","authors":"Chen Liang, Yili Hong, Bin Gu","doi":"10.1287/isre.2021.0146","DOIUrl":"https://doi.org/10.1287/isre.2021.0146","url":null,"abstract":"In the realm of online labor platforms, addressing moral hazard is crucial. Reputation systems have been the conventional solution, yet they pose a cold-start problem for newcomers. Alternatively, monitoring systems provide real-time oversight to employers, directly tackling moral hazard. This study combines theory and empirical analysis using data from a leading online labor platform. We find that monitoring systems effectively reduce the cold-start problem, leading to a 27.8% increase in bids on projects, primarily from inexperienced workers. We further find that following the introduction of the monitoring system, employers’ preference for experienced workers diminishes, accompanied by an average reduction of 19.5% in labor costs, whereas we observe no significant decrease in project completion and review rating. Our results collectively suggest that monitoring systems alleviate the cold-start problem in online platforms and contribute to fostering a more inclusive online labor market.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140055543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The findings of this study have important implications for digital platform designers, managers, and regulators. First, the large-scale field experiment provides valuable insights into the relationship between product recommendation and consumer search under different scenarios. It highlights the importance of understanding consumer demand states and previous interests. Platforms can use these findings to customize product recommendations at an individual level and foster channel complementarity between recommendation and search. Second, the study emphasizes the need to consider channel spillovers. Optimizing recommender systems without considering the impact of channel interactions with search engines may lead to suboptimal results. Platforms should aim for a more coordinated integration of recommendation and search channels, as our conceptual framework illustrates how customers in different demand states can be influenced and served by both systems. Third, the findings offer insights into the potential impact of data regulations on e-commerce platforms. The study demonstrates that data regulations have a greater impact on the recommendation channel compared with the search channel. Platforms should find a balance between recommendation and search when facing stringent data regulations. They may strategically focus on the search channel to gather revealed customer interests, leading to a deeper integration of both channels.
{"title":"How Recommendation Affects Customer Search: A Field Experiment","authors":"Zhe Yuan, AJ Yuan Chen, Yitong Wang, Tianshu Sun","doi":"10.1287/isre.2022.0294","DOIUrl":"https://doi.org/10.1287/isre.2022.0294","url":null,"abstract":"The findings of this study have important implications for digital platform designers, managers, and regulators. First, the large-scale field experiment provides valuable insights into the relationship between product recommendation and consumer search under different scenarios. It highlights the importance of understanding consumer demand states and previous interests. Platforms can use these findings to customize product recommendations at an individual level and foster channel complementarity between recommendation and search. Second, the study emphasizes the need to consider channel spillovers. Optimizing recommender systems without considering the impact of channel interactions with search engines may lead to suboptimal results. Platforms should aim for a more coordinated integration of recommendation and search channels, as our conceptual framework illustrates how customers in different demand states can be influenced and served by both systems. Third, the findings offer insights into the potential impact of data regulations on e-commerce platforms. The study demonstrates that data regulations have a greater impact on the recommendation channel compared with the search channel. Platforms should find a balance between recommendation and search when facing stringent data regulations. They may strategically focus on the search channel to gather revealed customer interests, leading to a deeper integration of both channels.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140035826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Our study delves into the understudied realm of volunteer crowdsourcing activities. Analyzing 827,260 volunteers’ participation in 183,445 projects initiated by 74,556 nonprofit organizations over nine years, the study unlocks insights into volunteers’ collaboration relationships and their behaviors, vital for increasing nonpaid labor supply and enhancing platform performance. We introduce a multiplex perspective to reveal how multilayer network dynamics offer enabling and constraining effects on volunteers’ continued participation, engagement, and interorganization movement. Practically, our findings equip crowdsourcing platforms with strategies to refine decision making and bolster volunteer engagement. By integrating novel network features such as tie multiplexity and relational pluralism, platforms can predict user actions more precisely. This fosters recommendation systems that not only elevate volunteer commitment but also facilitate productive interorganization transitions. At the macro level, tie multiplexity may lead to the “rich-get-richer” effect, enlarging the development inequality between large and small/new nonprofit organizations, whereas promoting relational pluralism is a potential remedy. For policymakers, our study offers a blueprint for nurturing volunteer networks and collaboration across organizations. Using a multiplex approach, they can adeptly manage the unpaid labor sector and invigorate nonprofit organizations. Our insights go beyond crowdsourcing as they could be applied to any digital context with multilayer networks, promising more tailored strategies to engage and mobilize users.
{"title":"Understanding Volunteer Crowdsourcing from a Multiplex Perspective","authors":"Yifan Yu, Xue (Jane) Tan, Yong Tan","doi":"10.1287/isre.2022.0290","DOIUrl":"https://doi.org/10.1287/isre.2022.0290","url":null,"abstract":"Our study delves into the understudied realm of volunteer crowdsourcing activities. Analyzing 827,260 volunteers’ participation in 183,445 projects initiated by 74,556 nonprofit organizations over nine years, the study unlocks insights into volunteers’ collaboration relationships and their behaviors, vital for increasing nonpaid labor supply and enhancing platform performance. We introduce a multiplex perspective to reveal how multilayer network dynamics offer enabling and constraining effects on volunteers’ continued participation, engagement, and interorganization movement. Practically, our findings equip crowdsourcing platforms with strategies to refine decision making and bolster volunteer engagement. By integrating novel network features such as tie multiplexity and relational pluralism, platforms can predict user actions more precisely. This fosters recommendation systems that not only elevate volunteer commitment but also facilitate productive interorganization transitions. At the macro level, tie multiplexity may lead to the “rich-get-richer” effect, enlarging the development inequality between large and small/new nonprofit organizations, whereas promoting relational pluralism is a potential remedy. For policymakers, our study offers a blueprint for nurturing volunteer networks and collaboration across organizations. Using a multiplex approach, they can adeptly manage the unpaid labor sector and invigorate nonprofit organizations. Our insights go beyond crowdsourcing as they could be applied to any digital context with multilayer networks, promising more tailored strategies to engage and mobilize users.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140035519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As people increasingly rely on social media to obtain healthcare information, misinformation, such as myths, rumors, and false information on healthcare, is posing a grave threat to public health. This paper investigates a potential remedy for such infodemic by examining a unique countermeasure that Twitter implemented. Instead of resorting to outright censorship, Twitter has taken a more nuanced approach: The platform has been nudging its users toward reputable sources whenever they seek out topics susceptible to misinformation. By analyzing the propagation of news articles that contain misinformation about health topics, we find that misinformation is less likely to initiate a diffusion process on Twitter since the inception of the policy. Moreover, tweets that include a link to misinformation articles are less likely to receive retweets, quotes, or replies. Furthermore, we find that the observed reduction is primarily driven by a decline in diffusion activities by human-like accounts rather than bot-like accounts. Our findings suggest that a misinformation policy that nudges platform users to a credible information source can help effectively curb misinformation diffusion. This approach may serve as a model for other platforms grappling with the challenge of misinformation in the digital age.
{"title":"A Nudge to Credible Information as a Countermeasure to Misinformation: Evidence from Twitter","authors":"Elina H. Hwang, Stephanie Lee","doi":"10.1287/isre.2021.0491","DOIUrl":"https://doi.org/10.1287/isre.2021.0491","url":null,"abstract":"As people increasingly rely on social media to obtain healthcare information, misinformation, such as myths, rumors, and false information on healthcare, is posing a grave threat to public health. This paper investigates a potential remedy for such infodemic by examining a unique countermeasure that Twitter implemented. Instead of resorting to outright censorship, Twitter has taken a more nuanced approach: The platform has been nudging its users toward reputable sources whenever they seek out topics susceptible to misinformation. By analyzing the propagation of news articles that contain misinformation about health topics, we find that misinformation is less likely to initiate a diffusion process on Twitter since the inception of the policy. Moreover, tweets that include a link to misinformation articles are less likely to receive retweets, quotes, or replies. Furthermore, we find that the observed reduction is primarily driven by a decline in diffusion activities by human-like accounts rather than bot-like accounts. Our findings suggest that a misinformation policy that nudges platform users to a credible information source can help effectively curb misinformation diffusion. This approach may serve as a model for other platforms grappling with the challenge of misinformation in the digital age.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140019301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Digital platforms have become increasingly dominant in many industries, bringing the concerns of adverse economic and societal effects (e.g., monopolies and social inequality). Regulators are actively seeking diverse strategies to regulate these powerful platforms. However, the lack of empirical studies hinders the progress toward evidence-based policymaking. This research investigates the regulatory landscape in the context of on-demand delivery, where high commission fees charged by the platforms significantly impact small businesses. Recent regulatory scrutiny has started to cap the commission fees for independent restaurants. We empirically evaluate the effectiveness of platform fee regulation by utilizing regulations across 14 cities and states in the United States. Our analyses unveil an unintended consequence: independent restaurants, the intended beneficiaries of the regulation, experience a decline in orders and revenue, whereas chain restaurants gain an advantage. We show that the platforms’ discriminative responses to the regulation, such as prioritizing chain restaurants in customer recommendations and increasing delivery fees for consumers, may explain the negative effects on independent restaurants. These dynamics underscore the complexity of regulating powerful platforms and the urgency of devising nuanced policies that effectively support small businesses without triggering unintended detrimental effects.
{"title":"Regulating Powerful Platforms: Evidence from Commission Fee Caps","authors":"Zhuoxin Li, Gang Wang","doi":"10.1287/isre.2022.0191","DOIUrl":"https://doi.org/10.1287/isre.2022.0191","url":null,"abstract":"Digital platforms have become increasingly dominant in many industries, bringing the concerns of adverse economic and societal effects (e.g., monopolies and social inequality). Regulators are actively seeking diverse strategies to regulate these powerful platforms. However, the lack of empirical studies hinders the progress toward evidence-based policymaking. This research investigates the regulatory landscape in the context of on-demand delivery, where high commission fees charged by the platforms significantly impact small businesses. Recent regulatory scrutiny has started to cap the commission fees for independent restaurants. We empirically evaluate the effectiveness of platform fee regulation by utilizing regulations across 14 cities and states in the United States. Our analyses unveil an unintended consequence: independent restaurants, the intended beneficiaries of the regulation, experience a decline in orders and revenue, whereas chain restaurants gain an advantage. We show that the platforms’ discriminative responses to the regulation, such as prioritizing chain restaurants in customer recommendations and increasing delivery fees for consumers, may explain the negative effects on independent restaurants. These dynamics underscore the complexity of regulating powerful platforms and the urgency of devising nuanced policies that effectively support small businesses without triggering unintended detrimental effects.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140019280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yi Wu, Weiling Ke, Yuelei Li, Zhijie Lin, Yong Tan
This study explores the decision-making process in online peer-to-peer (P2P) lending, a rapidly growing source of fixed income for investors. We examine how lenders’ bidding amounts are influenced by interest rates and psychological distance, which is determined by the borrower’s demographic attributes. Our findings, based on data from a popular Chinese P2P lending platform, reveal that geographic distance decreases bidding amounts, indicating a home bias effect. Conversely, social distance increases bidding amounts, suggesting a social distance effect. Interestingly, both types of psychological distance amplify the positive impact of interest rates on bidding amounts. Four controlled experiments further validate these relationships. This research not only contributes to the theoretical understanding of P2P lending but also offers practical insights for policymaking in the high-risk financial context.
{"title":"Understanding Lenders’ Investment Behavior in Online Peer-to-Peer Lending: A Construal Level Theory Perspective","authors":"Yi Wu, Weiling Ke, Yuelei Li, Zhijie Lin, Yong Tan","doi":"10.1287/isre.2020.0428","DOIUrl":"https://doi.org/10.1287/isre.2020.0428","url":null,"abstract":"This study explores the decision-making process in online peer-to-peer (P2P) lending, a rapidly growing source of fixed income for investors. We examine how lenders’ bidding amounts are influenced by interest rates and psychological distance, which is determined by the borrower’s demographic attributes. Our findings, based on data from a popular Chinese P2P lending platform, reveal that geographic distance decreases bidding amounts, indicating a home bias effect. Conversely, social distance increases bidding amounts, suggesting a social distance effect. Interestingly, both types of psychological distance amplify the positive impact of interest rates on bidding amounts. Four controlled experiments further validate these relationships. This research not only contributes to the theoretical understanding of P2P lending but also offers practical insights for policymaking in the high-risk financial context.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140425361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Si Xie, Siddhartha Sharma, Amit Mehra, Arslan Aziz
Delivery speed is an essential component of the service provided by online delivery platforms. Because improving actual delivery speed is expensive, platforms can instead create a perception of faster delivery by showing a conservative estimate of the delivery duration when a customer places an order. We use detailed transaction-level data from a major food delivery marketplace to examine the effects of setting conservative delivery speed expectations on customers’ likelihood of future purchases and restaurant choices. When delivery is faster than expected, we find that customers are more likely to purchase again from the platform and the same (focal) restaurant they ordered from. However, we find no significant effect on future purchases from other (nonfocal) restaurants. This is possibly because of a spillover effect, as customers may switch to other restaurants. Our findings thus highlight the effect of setting conservative expected delivery times in a platform setting. Finally, we investigate the trade-off between current and future demand because of setting of a conservative estimated delivery time and show that the gain in future demand is greater than the loss in current demand, establishing the efficacy of our suggested strategy.
{"title":"Strategic Expectation Setting of Delivery Time on Marketplaces","authors":"Si Xie, Siddhartha Sharma, Amit Mehra, Arslan Aziz","doi":"10.1287/isre.2021.0497","DOIUrl":"https://doi.org/10.1287/isre.2021.0497","url":null,"abstract":"Delivery speed is an essential component of the service provided by online delivery platforms. Because improving actual delivery speed is expensive, platforms can instead create a perception of faster delivery by showing a conservative estimate of the delivery duration when a customer places an order. We use detailed transaction-level data from a major food delivery marketplace to examine the effects of setting conservative delivery speed expectations on customers’ likelihood of future purchases and restaurant choices. When delivery is faster than expected, we find that customers are more likely to purchase again from the platform and the same (focal) restaurant they ordered from. However, we find no significant effect on future purchases from other (nonfocal) restaurants. This is possibly because of a spillover effect, as customers may switch to other restaurants. Our findings thus highlight the effect of setting conservative expected delivery times in a platform setting. Finally, we investigate the trade-off between current and future demand because of setting of a conservative estimated delivery time and show that the gain in future demand is greater than the loss in current demand, establishing the efficacy of our suggested strategy.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139949547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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