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":"10 1","pages":""},"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":"51-52 1","pages":""},"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":"45 1","pages":""},"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":"108 1","pages":""},"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}
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":"254 1","pages":""},"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}
With the rise of digital health platforms, consumers increasingly rely on online reviews when choosing healthcare services. Understanding how these reviews shape consumer decisions is crucial for both platforms and healthcare providers. To explore this, we analyzed a comprehensive data set from a leading online cosmetic surgery platform to understand how process-oriented (focusing on the recovery experience) and outcome-oriented (focusing on the end results) reviews influence the demand for healthcare services. Our findings reveal a striking disparity in the effectiveness of these two types of reviews. Generally, outcome-oriented reviews exhibit greater efficacy in boosting sales. However, the influence of each review type varies with the complexity and popularity of the services. Process-oriented reviews are more compelling for complex healthcare services, while outcome-oriented reviews prove more impactful for simpler, popular services. These insights underscore the need for tailored strategies in incentivizing and managing consumer reviews, vital for healthcare providers and digital health platforms. Furthermore, for policy makers, the study highlights the importance of regulating and guiding online review designs to ensure they accurately reflect the service process and outcome, aiding consumers in making informed decisions.
{"title":"The Impact of Process- vs. Outcome-Oriented Reviews on the Sales of Healthcare Services","authors":"Hongfei Li, Jing Peng, Gang Wang, Xue Bai","doi":"10.1287/isre.2019.0168","DOIUrl":"https://doi.org/10.1287/isre.2019.0168","url":null,"abstract":"With the rise of digital health platforms, consumers increasingly rely on online reviews when choosing healthcare services. Understanding how these reviews shape consumer decisions is crucial for both platforms and healthcare providers. To explore this, we analyzed a comprehensive data set from a leading online cosmetic surgery platform to understand how process-oriented (focusing on the recovery experience) and outcome-oriented (focusing on the end results) reviews influence the demand for healthcare services. Our findings reveal a striking disparity in the effectiveness of these two types of reviews. Generally, outcome-oriented reviews exhibit greater efficacy in boosting sales. However, the influence of each review type varies with the complexity and popularity of the services. Process-oriented reviews are more compelling for complex healthcare services, while outcome-oriented reviews prove more impactful for simpler, popular services. These insights underscore the need for tailored strategies in incentivizing and managing consumer reviews, vital for healthcare providers and digital health platforms. Furthermore, for policy makers, the study highlights the importance of regulating and guiding online review designs to ensure they accurately reflect the service process and outcome, aiding consumers in making informed decisions.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":"2015 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139949575","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}
Strategic Content Generation and Monetization in Financial Social MediaAbstractFinancial social media, which relies on social media analysts (SMAs) to contribute content to investors, is a crucial channel for investors to gain financial information and for SMAs to monetize their content. The interactive nature of financial social media has given SMAs the opportunity to gain access to the investor preferences of their own audience base for financial content. Our study documents that SMAs would exploit this opportunity to strategically generate and monetize content by catering to investor preferences. Specifically, SMAs would increase the (negative) sentiment of the content if paid subscribers’ preferences for (negative) sentiment grow. Additionally, an SMA is more likely to produce paid content when the expected free readership increases and is less likely to do so when the expected paid subscriptions increase. Our findings suggest that the sentiment of financial social media content is not a mere reflection or prediction of stock market movements but also a result of SMAs’ reaction to investor preferences. We thus illustrate an approach to identify the SMAs who may amplify the investors’ confirmation biases because of such catering behaviors so that platform managers and regulators alike can utilize this method to improve the content quality of financial social media.
金融社交媒体中的战略性内容生成和货币化摘要金融社交媒体依靠社交媒体分析师（SMA）为投资者提供内容，是投资者获取金融信息和 SMA 实现内容货币化的重要渠道。金融社交媒体的互动性使 SMA 有机会了解其受众群体中投资者对金融内容的偏好。我们的研究表明，SMA 将利用这一机会，通过迎合投资者的偏好，战略性地生成内容并实现内容货币化。具体来说，如果付费用户对（负面）情绪的偏好增加，SMA 就会增加内容的（负面）情绪。此外，当预期免费读者人数增加时，SMA 更有可能制作付费内容，而当预期付费订阅人数增加时，SMA 则不太可能制作付费内容。我们的研究结果表明，金融社交媒体内容的情绪不仅仅是对股市走势的反映或预测，也是 SMA 对投资者偏好做出反应的结果。因此，我们说明了一种方法，可以识别出因这种迎合行为而可能放大投资者确认偏差的 SMA，从而使平台管理者和监管者都能利用这种方法来提高金融社交媒体的内容质量。
{"title":"Strategic Content Generation and Monetization in Financial Social Media","authors":"Ding Li, Khim-Yong Goh, Cheng-Suang Heng","doi":"10.1287/isre.2022.0482","DOIUrl":"https://doi.org/10.1287/isre.2022.0482","url":null,"abstract":"Strategic Content Generation and Monetization in Financial Social MediaAbstractFinancial social media, which relies on social media analysts (SMAs) to contribute content to investors, is a crucial channel for investors to gain financial information and for SMAs to monetize their content. The interactive nature of financial social media has given SMAs the opportunity to gain access to the investor preferences of their own audience base for financial content. Our study documents that SMAs would exploit this opportunity to strategically generate and monetize content by catering to investor preferences. Specifically, SMAs would increase the (negative) sentiment of the content if paid subscribers’ preferences for (negative) sentiment grow. Additionally, an SMA is more likely to produce paid content when the expected free readership increases and is less likely to do so when the expected paid subscriptions increase. Our findings suggest that the sentiment of financial social media content is not a mere reflection or prediction of stock market movements but also a result of SMAs’ reaction to investor preferences. We thus illustrate an approach to identify the SMAs who may amplify the investors’ confirmation biases because of such catering behaviors so that platform managers and regulators alike can utilize this method to improve the content quality of financial social media.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":"48 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139949480","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}
This study relaxes the efficient market hypothesis by introducing a model that accounts for initial mispricing and explores the effects of algorithmic trading. The research finds that algorithmic strategies can cause significant market volatility and affect financial stability, particularly when they amplify overpricing, leading to bubbles and crashes. Key insights include: Initial mispricing is crucial for algorithmic trading to impact market prices. Market reactions vary with the direction of the trading strategy relative to the asset’s true value. Informed traders can benefit from mispricing, whereas noise traders typically incur losses.Policy implications suggest that algorithmic trading is not universally harmful; its effects depend on the alignment of trading strategies with accurate pricing. The study advises regulators to differentiate between stabilizing and destabilizing trading practices. For traders, the research highlights the importance of adaptive strategies that help correct mispricing to ensure long-term profitability and market health. This research advances our understanding of algorithmic trading’s dual potential and informs the development of more nuanced financial regulations and trading strategies.
{"title":"Mispricing and Algorithm Trading","authors":"Lihong Zhang, Xiaoquan (Michael) Zhang","doi":"10.1287/isre.2021.0570","DOIUrl":"https://doi.org/10.1287/isre.2021.0570","url":null,"abstract":"This study relaxes the efficient market hypothesis by introducing a model that accounts for initial mispricing and explores the effects of algorithmic trading. The research finds that algorithmic strategies can cause significant market volatility and affect financial stability, particularly when they amplify overpricing, leading to bubbles and crashes. Key insights include: Initial mispricing is crucial for algorithmic trading to impact market prices. Market reactions vary with the direction of the trading strategy relative to the asset’s true value. Informed traders can benefit from mispricing, whereas noise traders typically incur losses.Policy implications suggest that algorithmic trading is not universally harmful; its effects depend on the alignment of trading strategies with accurate pricing. The study advises regulators to differentiate between stabilizing and destabilizing trading practices. For traders, the research highlights the importance of adaptive strategies that help correct mispricing to ensure long-term profitability and market health. This research advances our understanding of algorithmic trading’s dual potential and informs the development of more nuanced financial regulations and trading strategies.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":"7 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139949477","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}
Haoyuan Liu, Wen Wen, Anitesh Barua, Andrew B. Whinston
In modern enterprise computing environments, multiple information technology (IT) services from first and third parties are often integrated to form coherent solutions for enterprise customers. In this study, we seek to understand how uncertainties introduced by third-party services shape enterprise customers’ use of various IT services in these multivendor service settings. Specifically, we analyze a case of disruption caused by a third party that affects the multivendor service but does not directly affect the first-party services. We find a temporary increase in the use of first-party services that serve as similar-goal substitutes during the disruption; however, there is a net decline in the total use of services in the long run. To assess what actions the first party can take during such disruptions to turn the challenge into an opportunity, we analyze the first party’s technical support log using deep learning techniques. We find that if the first party offers high-quality technical support that addresses product-related issues, it may be able to make lemonade out of lemons. Such technical support effectively boosts customers’ use of first-party services in the long run. Curiously, however, similar efforts by the first party in the predisruption period are ineffective in achieving the same effect.
在现代企业计算环境中，来自第一方和第三方的多种信息技术（IT）服务经常被整合在一起，为企业客户提供一致的解决方案。在本研究中，我们试图了解第三方服务带来的不确定性如何影响企业客户在这些多供应商服务环境中对各种 IT 服务的使用。具体来说，我们分析了一个由第三方造成的中断案例，该中断影响了多供应商服务，但并不直接影响第一方服务。我们发现，在中断期间，作为类似目标替代品的第一方服务的使用量会暂时增加；但从长远来看，服务的总使用量会出现净下降。为了评估第一方在这种中断期间可以采取哪些行动将挑战转化为机遇，我们使用深度学习技术分析了第一方的技术支持日志。我们发现，如果第一方能提供高质量的技术支持，解决与产品相关的问题，那么它就有可能把柠檬做成柠檬汁。从长远来看，这种技术支持能有效提高客户对第一方服务的使用率。但奇怪的是，第一方在中断前的类似努力并不能达到同样的效果。
{"title":"Making Lemonade from Lemons: A Transaction Cost Economics Perspective on Third-Party Disruptions in a Multivendor Information Technology Service","authors":"Haoyuan Liu, Wen Wen, Anitesh Barua, Andrew B. Whinston","doi":"10.1287/isre.2022.0033","DOIUrl":"https://doi.org/10.1287/isre.2022.0033","url":null,"abstract":"In modern enterprise computing environments, multiple information technology (IT) services from first and third parties are often integrated to form coherent solutions for enterprise customers. In this study, we seek to understand how uncertainties introduced by third-party services shape enterprise customers’ use of various IT services in these multivendor service settings. Specifically, we analyze a case of disruption caused by a third party that affects the multivendor service but does not directly affect the first-party services. We find a temporary increase in the use of first-party services that serve as similar-goal substitutes during the disruption; however, there is a net decline in the total use of services in the long run. To assess what actions the first party can take during such disruptions to turn the challenge into an opportunity, we analyze the first party’s technical support log using deep learning techniques. We find that if the first party offers high-quality technical support that addresses product-related issues, it may be able to make lemonade out of lemons. Such technical support effectively boosts customers’ use of first-party services in the long run. Curiously, however, similar efforts by the first party in the predisruption period are ineffective in achieving the same effect.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":"16 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139771108","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}
This paper proposes a smart ad display system to provide personalized delivery of video ads. The proposed system records consumers’ facial expression and eye gaze stream data as they watch an ad and analyzes data at the frame level. The recognized facial expression and detected eye gaze are matched to the corresponding frame of the video ad, thereby linking facial expressions to specific visual objects appearing in the ad. By tracking a consumer’s facial expressions in response to various visual objects in real time, the system learns the consumer’s individual preferences toward different ads, searches the ad pool, and selects and subsequently displays a new ad that is most likely to elicit positive attitudinal and behavioral responses. We demonstrate the feasibility and effectiveness of the proposed system with two empirical studies. The results show that by tracking a consumer’s facial responses to only one ad or even part of an ad, our proposed system is able to make reasonably accurate inferences about a consumer’s ad preferences, with or without using information about other consumers. These inferences are used to make personalized recommendations that help enhance consumers’ ad viewing experiences and elicit favorable responses.
{"title":"A Smart Ad Display System","authors":"Li Xiao, D. J. Wu, Min Ding","doi":"10.1287/isre.2020.0128","DOIUrl":"https://doi.org/10.1287/isre.2020.0128","url":null,"abstract":"This paper proposes a smart ad display system to provide personalized delivery of video ads. The proposed system records consumers’ facial expression and eye gaze stream data as they watch an ad and analyzes data at the frame level. The recognized facial expression and detected eye gaze are matched to the corresponding frame of the video ad, thereby linking facial expressions to specific visual objects appearing in the ad. By tracking a consumer’s facial expressions in response to various visual objects in real time, the system learns the consumer’s individual preferences toward different ads, searches the ad pool, and selects and subsequently displays a new ad that is most likely to elicit positive attitudinal and behavioral responses. We demonstrate the feasibility and effectiveness of the proposed system with two empirical studies. The results show that by tracking a consumer’s facial responses to only one ad or even part of an ad, our proposed system is able to make reasonably accurate inferences about a consumer’s ad preferences, with or without using information about other consumers. These inferences are used to make personalized recommendations that help enhance consumers’ ad viewing experiences and elicit favorable responses.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":"13 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139760909","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：