Mariana G. Andrade-Rojas, Terence J. V. Saldanha, Abhishek Kathuria, Jiban Khuntia, Waifong Boh
Innovation is vital for the growth of small and medium-sized enterprises (SMEs). However, SMEs face deficiencies that hinder their innovation output. This study examines how information technology (IT) helps SMEs address two salient deficiencies: technological deficiency (deficiency in internal technical knowledge and skills) and government support deficiency (deficiency in favorable government policies and incentives). Our findings suggest that SME managers can achieve greater innovation output by orienting their IT-enabled innovation efforts in an open or closed manner to address specific deficiencies. They can address technological deficiency by focusing their IT efforts on promoting innovation within the firm, that is, using IT to support closed innovation. In contrast, SMEs that face government support deficiency should give preference to IT Use for Open Innovation Activities to collaborate with external constituents such as customers and suppliers. Furthermore, because of the emergence of digital platforms (e.g., crowdsourcing), managers may be overly biased toward the use of open innovation. Our results suggest that both open and closed IT-enabled innovation have value. We exhort SME managers not to disregard either form of IT-enabled innovation but rather to tailor their approach to suit their organizational context based on specific deficiencies that their firm faces.
{"title":"How Information Technology Overcomes Deficiencies for Innovation in Small and Medium-Sized Enterprises: Closed Innovation vs. Open Innovation","authors":"Mariana G. Andrade-Rojas, Terence J. V. Saldanha, Abhishek Kathuria, Jiban Khuntia, Waifong Boh","doi":"10.1287/isre.2021.0096","DOIUrl":"https://doi.org/10.1287/isre.2021.0096","url":null,"abstract":"Innovation is vital for the growth of small and medium-sized enterprises (SMEs). However, SMEs face deficiencies that hinder their innovation output. This study examines how information technology (IT) helps SMEs address two salient deficiencies: technological deficiency (deficiency in internal technical knowledge and skills) and government support deficiency (deficiency in favorable government policies and incentives). Our findings suggest that SME managers can achieve greater innovation output by orienting their IT-enabled innovation efforts in an open or closed manner to address specific deficiencies. They can address technological deficiency by focusing their IT efforts on promoting innovation within the firm, that is, using IT to support closed innovation. In contrast, SMEs that face government support deficiency should give preference to IT Use for Open Innovation Activities to collaborate with external constituents such as customers and suppliers. Furthermore, because of the emergence of digital platforms (e.g., crowdsourcing), managers may be overly biased toward the use of open innovation. Our results suggest that both open and closed IT-enabled innovation have value. We exhort SME managers not to disregard either form of IT-enabled innovation but rather to tailor their approach to suit their organizational context based on specific deficiencies that their firm faces.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":"57 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140566711","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}
Ram D. Gopal, Xiao Qiao, Moris S. Strub, Zonghao Yang
This paper investigates the suitability of online loans as an investment through the lens of a portfolio optimization framework. We propose general characteristics-based portfolio policy (GCPP), a framework which overcomes unique challenges associated with building a portfolio of online loans. GCPP directly models the portfolio weight of a loan as a flexible function of its characteristics and does not require direct estimation of the distributional properties of loans. Using an extensive data set spanning over one million loans from 2013 to 2020 from LendingClub, we show that GCPP portfolios can achieve an average annualized internal rate of return (IRR) of 8.86% to 13.08%, significantly outperforming an equal-weight portfolio of loans. To assess the attractiveness of online loans, we then compare the performance of the GCPP portfolio to traditional investment vehicles including stocks, bonds, and real estate. The results demonstrate that a portfolio of online loans earns competitive or higher rates of return compared to traditional asset classes with limited comovement. These results indicate that online loans are an attractive novel asset class for investors. Together, we demonstrate that GCPP is an approach that can help platforms better serve both borrowers and lenders en route to growing their business.
{"title":"Gaining a Seat at the Table: Enhancing the Attractiveness of Online Lending for Institutional Investors","authors":"Ram D. Gopal, Xiao Qiao, Moris S. Strub, Zonghao Yang","doi":"10.1287/isre.2022.0638","DOIUrl":"https://doi.org/10.1287/isre.2022.0638","url":null,"abstract":"This paper investigates the suitability of online loans as an investment through the lens of a portfolio optimization framework. We propose general characteristics-based portfolio policy (GCPP), a framework which overcomes unique challenges associated with building a portfolio of online loans. GCPP directly models the portfolio weight of a loan as a flexible function of its characteristics and does not require direct estimation of the distributional properties of loans. Using an extensive data set spanning over one million loans from 2013 to 2020 from LendingClub, we show that GCPP portfolios can achieve an average annualized internal rate of return (IRR) of 8.86% to 13.08%, significantly outperforming an equal-weight portfolio of loans. To assess the attractiveness of online loans, we then compare the performance of the GCPP portfolio to traditional investment vehicles including stocks, bonds, and real estate. The results demonstrate that a portfolio of online loans earns competitive or higher rates of return compared to traditional asset classes with limited comovement. These results indicate that online loans are an attractive novel asset class for investors. Together, we demonstrate that GCPP is an approach that can help platforms better serve both borrowers and lenders en route to growing their business.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":"28 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140567001","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}
Lianlian (Dorothy) Jiang, Jinghui (Jove) Hou, Xiao Ma, Paul A. Pavlou
The healthcare market struggles with information asymmetry, limiting patients’ ability to make informed hospital choices. Aiming to bridge this gap, review platforms like Yelp have begun displaying hospitals’ clinical quality data alongside consumer reviews. However, our research uncovers that Yelp’s introduction of maternity care clinical quality measures unexpectedly resulted in lower subsequent Yelp ratings for high-quality hospitals with insufficient staffing. Employing precise foot traffic data and transfer deep learning, we discovered that high-quality, yet understaffed, hospitals experienced a surge in patient volume, which strained their resources and diminished patient satisfaction, leading to negative reviews. This finding has significant implications, signaling the unintended consequences of revealing clinical quality measures, including potential financial losses for hospitals because of reduced federal funding. This research not only contributes to our understanding the dynamics of patient satisfaction but also, offers actionable insights for high-quality hospitals to mitigate the negative impacts of unexpected visibility on review platforms. Our research underscores the importance for patients to discern between objective clinical quality measures and self-reported subjective ratings in their decision-making process. This research applies machine learning and transfer deep learning techniques to healthcare analytics, offering a deeper understanding of the interplay between information disclosure, online reviews, patient satisfaction, and hospital management.
{"title":"Punished for Success? A Natural Experiment of Displaying Clinical Hospital Quality on Review Platforms","authors":"Lianlian (Dorothy) Jiang, Jinghui (Jove) Hou, Xiao Ma, Paul A. Pavlou","doi":"10.1287/isre.2021.0630","DOIUrl":"https://doi.org/10.1287/isre.2021.0630","url":null,"abstract":"The healthcare market struggles with information asymmetry, limiting patients’ ability to make informed hospital choices. Aiming to bridge this gap, review platforms like Yelp have begun displaying hospitals’ clinical quality data alongside consumer reviews. However, our research uncovers that Yelp’s introduction of maternity care clinical quality measures unexpectedly resulted in lower subsequent Yelp ratings for high-quality hospitals with insufficient staffing. Employing precise foot traffic data and transfer deep learning, we discovered that high-quality, yet understaffed, hospitals experienced a surge in patient volume, which strained their resources and diminished patient satisfaction, leading to negative reviews. This finding has significant implications, signaling the unintended consequences of revealing clinical quality measures, including potential financial losses for hospitals because of reduced federal funding. This research not only contributes to our understanding the dynamics of patient satisfaction but also, offers actionable insights for high-quality hospitals to mitigate the negative impacts of unexpected visibility on review platforms. Our research underscores the importance for patients to discern between objective clinical quality measures and self-reported subjective ratings in their decision-making process. This research applies machine learning and transfer deep learning techniques to healthcare analytics, offering a deeper understanding of the interplay between information disclosure, online reviews, patient satisfaction, and hospital management.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":"86 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140566708","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}
Xin Zhang, Wei Thoo Yue, Ran (Alan) Zhang, Yugang Yu
The explosion of consumer data has spawned a burgeoning data broker industry, pivotal in targeted advertising. A recent media report estimates the worth of the data broker industry by 2030 at approximately $382 billion. Key players such as Oracle and Lotame gather data from diverse sources to create and sell valuable insights. Digital publishers often rely on these brokers to gain (i) individual insights drawn from their own data or (ii) collective insights drawn from both their data and those of their competitors, improving their targeting precision. This study develops an analytical model to explore the competitive implications of data brokers in a targeted advertising market with two competing publishers and a mass of advertisers. It reveals that the data broker might strategically price their insights to exclusively offer collective insights to one publisher, thereby altering market competition. Moreover, the broker’s actions can either foster or hinder competition among publishers, depending on its strategic interests. Despite potentially reducing market competition through exclusive selling, the provision of collective insights can enhance aggregate welfare by enhancing publishers’ targeting capability. This illustrates the nuanced interplay between data brokers, publishers, and advertisers in shaping the landscape of targeted advertising.
{"title":"Blessing or Curse? Implications of Data Brokers for Publisher Competition","authors":"Xin Zhang, Wei Thoo Yue, Ran (Alan) Zhang, Yugang Yu","doi":"10.1287/isre.2022.0227","DOIUrl":"https://doi.org/10.1287/isre.2022.0227","url":null,"abstract":"The explosion of consumer data has spawned a burgeoning data broker industry, pivotal in targeted advertising. A recent media report estimates the worth of the data broker industry by 2030 at approximately $382 billion. Key players such as Oracle and Lotame gather data from diverse sources to create and sell valuable insights. Digital publishers often rely on these brokers to gain (i) individual insights drawn from their own data or (ii) collective insights drawn from both their data and those of their competitors, improving their targeting precision. This study develops an analytical model to explore the competitive implications of data brokers in a targeted advertising market with two competing publishers and a mass of advertisers. It reveals that the data broker might strategically price their insights to exclusively offer collective insights to one publisher, thereby altering market competition. Moreover, the broker’s actions can either foster or hinder competition among publishers, depending on its strategic interests. Despite potentially reducing market competition through exclusive selling, the provision of collective insights can enhance aggregate welfare by enhancing publishers’ targeting capability. This illustrates the nuanced interplay between data brokers, publishers, and advertisers in shaping the landscape of targeted advertising.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":"86 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140566667","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}
Mi Zhou, Vibhanshu Abhishek, Edward H. Kennedy, Kannan Srinivasan, Ritwik Sinha
Businesses have widely used email ads to directly send promotional information to consumers. Whereas email ads serve as a convenient tool that allows firms to target consumers online, there is little evidence of their multichannel impact on consumer spending in both online and brick-and-mortar stores. In this paper, we utilize a unique high-dimensional data set from one of the world’s largest office supplies retailers to link each consumer’s online behaviors to item-level purchase records in physical stores. We employ a doubly robust estimator that incorporates nonparametric machine learning methods for causal estimation of observational data. Our results show that email ads significantly increase the retailer’s sales across different channels. We also investigate the effects of email ads on diverse consumer behaviors along the purchase funnel and find that increased sales result from increased purchase probability and a wider variety of products purchased by consumers. Further, we examine several moderating factors, such as product types and consumer segments, that influence the multichannel effects of email advertising. Our study provides empirical evidence for the economic impact of email ads on consumer behavior across different channels and the underlying mechanisms thereof, offering direct implications for multichannel retailers seeking to improve their digital marketing strategies.
{"title":"Linking Clicks to Bricks: Understanding the Effects of Email Advertising on Multichannel Sales","authors":"Mi Zhou, Vibhanshu Abhishek, Edward H. Kennedy, Kannan Srinivasan, Ritwik Sinha","doi":"10.1287/isre.2020.0557","DOIUrl":"https://doi.org/10.1287/isre.2020.0557","url":null,"abstract":"Businesses have widely used email ads to directly send promotional information to consumers. Whereas email ads serve as a convenient tool that allows firms to target consumers online, there is little evidence of their multichannel impact on consumer spending in both online and brick-and-mortar stores. In this paper, we utilize a unique high-dimensional data set from one of the world’s largest office supplies retailers to link each consumer’s online behaviors to item-level purchase records in physical stores. We employ a doubly robust estimator that incorporates nonparametric machine learning methods for causal estimation of observational data. Our results show that email ads significantly increase the retailer’s sales across different channels. We also investigate the effects of email ads on diverse consumer behaviors along the purchase funnel and find that increased sales result from increased purchase probability and a wider variety of products purchased by consumers. Further, we examine several moderating factors, such as product types and consumer segments, that influence the multichannel effects of email advertising. Our study provides empirical evidence for the economic impact of email ads on consumer behavior across different channels and the underlying mechanisms thereof, offering direct implications for multichannel retailers seeking to improve their digital marketing strategies.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":"23 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140303228","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}
Dominik Molitor, Martin Spann, Anindya Ghose, Philipp Reichhart
Firms have two distinct options when delivering content to consumers’ mobile devices: mobile push and mobile pull. Mobile push delivers firm-initiated (ad) content directly to consumers, while mobile pull requires consumers to initiate requests for (ad) content. This study tests the impact of mobile push and mobile pull on consumers’ coupon redemption behavior in a large-scale randomized field experiment in a geo-conquesting setting, targeting customers located around competitor retail stores with mobile coupons to drive them to stores of the focal retailer. The results show that mobile push increases coupon redemption rates by 6.0%, with substantial heterogeneity based on app-specific use experience and store density: App-specific use experience negatively moderates the effect of mobile push delivery on redemptions, likely because both usage experience and push notifications reduce app-specific search costs, thereby acting as substitutes for one another. In areas with higher store density, the positive effect of mobile push delivery on the redemption likelihood is greater, suggesting that push notifications can highlight the focal coupon among alternative store choices, thereby reducing consumer switching costs. These findings have important implications for retailers and brands in creating competitive mobile targeting campaigns that effectively leverage both mobile push and pull delivery mechanisms.
{"title":"Mobile Push vs. Pull Targeting and Geo-Conquesting","authors":"Dominik Molitor, Martin Spann, Anindya Ghose, Philipp Reichhart","doi":"10.1287/isre.2021.0206","DOIUrl":"https://doi.org/10.1287/isre.2021.0206","url":null,"abstract":"Firms have two distinct options when delivering content to consumers’ mobile devices: mobile push and mobile pull. Mobile push delivers firm-initiated (ad) content directly to consumers, while mobile pull requires consumers to initiate requests for (ad) content. This study tests the impact of mobile push and mobile pull on consumers’ coupon redemption behavior in a large-scale randomized field experiment in a geo-conquesting setting, targeting customers located around competitor retail stores with mobile coupons to drive them to stores of the focal retailer. The results show that mobile push increases coupon redemption rates by 6.0%, with substantial heterogeneity based on app-specific use experience and store density: App-specific use experience negatively moderates the effect of mobile push delivery on redemptions, likely because both usage experience and push notifications reduce app-specific search costs, thereby acting as substitutes for one another. In areas with higher store density, the positive effect of mobile push delivery on the redemption likelihood is greater, suggesting that push notifications can highlight the focal coupon among alternative store choices, thereby reducing consumer switching costs. These findings have important implications for retailers and brands in creating competitive mobile targeting campaigns that effectively leverage both mobile push and pull delivery mechanisms.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":"21 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140149233","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 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":"84 1","pages":""},"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":"98 1","pages":""},"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":"22 1","pages":""},"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":"66 1","pages":""},"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}
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