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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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}
Jian Li, Xiang (Shawn) Wan, Hsing Kenneth Cheng, Xi Zhao
Practice AbstractInitial Coin Offerings (ICOs) have become a new and popular fundraising approach for blockchain start-ups. To motivate blockchain individuals to invest in the subsequent ICO, a growing number of blockchain-based project founders employ the airdrop campaign, through which they distribute a specific amount of free official tokens or promotional tokens to potential investors on the blockchain with or without their permission. Of paramount concern to the blockchain founders contemplating whether to launch an airdrop campaign are whether the airdrop campaign has a positive effect on the potential investors’ investment behaviors in their ICOs and how the efficacy of the airdrop may vary with investors. We find that the promotional airdrop significantly increases the potential investors’ ICO investment. We further find that the airdrop is more effective in increasing the investment for individuals with transacted projects dissimilar to the focal project than those with similar ones. By incorporating the insights from our study into their airdrop campaign strategy, blockchain start-ups can effectively target the right segment of potential investors to enhance the success of their ICOs.
{"title":"Operation Dumbo Drop: To Airdrop or Not to Airdrop for Initial Coin Offering Success?","authors":"Jian Li, Xiang (Shawn) Wan, Hsing Kenneth Cheng, Xi Zhao","doi":"10.1287/isre.2021.0450","DOIUrl":"https://doi.org/10.1287/isre.2021.0450","url":null,"abstract":"Practice AbstractInitial Coin Offerings (ICOs) have become a new and popular fundraising approach for blockchain start-ups. To motivate blockchain individuals to invest in the subsequent ICO, a growing number of blockchain-based project founders employ the airdrop campaign, through which they distribute a specific amount of free official tokens or promotional tokens to potential investors on the blockchain with or without their permission. Of paramount concern to the blockchain founders contemplating whether to launch an airdrop campaign are whether the airdrop campaign has a positive effect on the potential investors’ investment behaviors in their ICOs and how the efficacy of the airdrop may vary with investors. We find that the promotional airdrop significantly increases the potential investors’ ICO investment. We further find that the airdrop is more effective in increasing the investment for individuals with transacted projects dissimilar to the focal project than those with similar ones. By incorporating the insights from our study into their airdrop campaign strategy, blockchain start-ups can effectively target the right segment of potential investors to enhance the success of their ICOs.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139760910","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}
Jiawei Chen, Luo He, Hongyan Liu, Yinghui (Catherine) Yang, Xuan Bi
On short video sharing platforms, users often choose background music for their videos. In this paper, we study the problem of background music recommendation for short videos on short video sharing platforms. In our recommendation setting, the item (music) is not recommended directly to the user, but to the video created by the user. When making music recommendations for videos, we consider three important players: users, videos, and music. We define a unique background music recommendation problem and design a novel background music recommendation model to address the problem. We propose a model based on the deep learning framework to effectively address the distinctive three-way relationships among users, videos, and music. Our model considers not only of the conventional user–music alignment, but also the alignment between videos and music. To evaluate our model, we conduct comprehensive experiments on real-world data collected from one of the most popular short video sharing platforms. Our proposed model significantly outperforms other existing models in recommendation performance. The superiority of our proposed model remains consistent across various scenarios, including cold-start recommendations, data sets with varying density levels, and data sets spanning diverse video categories.
{"title":"Background Music Recommendation on Short Video Sharing Platforms","authors":"Jiawei Chen, Luo He, Hongyan Liu, Yinghui (Catherine) Yang, Xuan Bi","doi":"10.1287/isre.2022.0093","DOIUrl":"https://doi.org/10.1287/isre.2022.0093","url":null,"abstract":"On short video sharing platforms, users often choose background music for their videos. In this paper, we study the problem of background music recommendation for short videos on short video sharing platforms. In our recommendation setting, the item (music) is not recommended directly to the user, but to the video created by the user. When making music recommendations for videos, we consider three important players: users, videos, and music. We define a unique background music recommendation problem and design a novel background music recommendation model to address the problem. We propose a model based on the deep learning framework to effectively address the distinctive three-way relationships among users, videos, and music. Our model considers not only of the conventional user–music alignment, but also the alignment between videos and music. To evaluate our model, we conduct comprehensive experiments on real-world data collected from one of the most popular short video sharing platforms. Our proposed model significantly outperforms other existing models in recommendation performance. The superiority of our proposed model remains consistent across various scenarios, including cold-start recommendations, data sets with varying density levels, and data sets spanning diverse video categories.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139656909","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}
Air pollution contributes to global warming and climate change, leading to extreme weather events and rising sea levels. Promoting sustainable practices has become the focus of policy programs and awareness campaigns. In this study, we propose an effective and powerful way to promote eco-driving behaviors by drawing on data storytelling. Our study shows that animated narrative and narrative sequence can trigger varying emphases on the feasibility and desirability of eco-driving practices, affecting actual driving behaviors and attitudes toward efficient driving. Specifically, in two experiments, we find that a chronological narrative sequence with animation improves subsequent driving efficiency and efficient driving attitudes. Visualization designers may consider employing narrative sequence and animation to facilitate individuals’ information comprehension and behavioral changes. Policymakers can also encourage ecological practices through effective designs of data storytelling.
{"title":"Encouraging Eco-driving with Post-trip Visualized Storytelling: An Experiment Combining Eye-Tracking and a Driving Simulator","authors":"Zhiyin Li, Ben C. F. Choi","doi":"10.1287/isre.2022.0332","DOIUrl":"https://doi.org/10.1287/isre.2022.0332","url":null,"abstract":"Air pollution contributes to global warming and climate change, leading to extreme weather events and rising sea levels. Promoting sustainable practices has become the focus of policy programs and awareness campaigns. In this study, we propose an effective and powerful way to promote eco-driving behaviors by drawing on data storytelling. Our study shows that animated narrative and narrative sequence can trigger varying emphases on the feasibility and desirability of eco-driving practices, affecting actual driving behaviors and attitudes toward efficient driving. Specifically, in two experiments, we find that a chronological narrative sequence with animation improves subsequent driving efficiency and efficient driving attitudes. Visualization designers may consider employing narrative sequence and animation to facilitate individuals’ information comprehension and behavioral changes. Policymakers can also encourage ecological practices through effective designs of data storytelling.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139656912","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}
Policy/Practice-Focused AbstractDespite considerable and continued resource investments, effective solutions to broad-scope problems of social interest or societal grand challenges (GCs) have proven to be elusive in many domains. In multiactor situations that characterize GCs, divergent goals, needs, priorities, and capabilities of global and local actors create organizing design tensions that need to be considered before solutions can be enacted. Emergent digital technologies can play an important and transformative role in addressing the organizing design tensions that pervade such collective action problems. In this article, we draw on Elinor Ostrom’s principles of public value creation and identify a set of eight organizing design tensions that arise from employing global and local perspectives in addressing GCs. We consider novel digital approaches—that involve alternative arrangements of digital and socio-political elements in GC settings—to resolving each of these design tensions. Our discussion foreshadows the considerable opportunity for information systems research to contribute to the broader dialog on GCs; inform GC-related policy and practice at global and local levels; and, more broadly, speed the identification and enactment of effective solutions to grand challenges.
{"title":"Digital Approaches to Societal Grand Challenges: Toward a Broader Research Agenda on Managing Global-Local Design Tensions","authors":"Satish Nambisan, Gerard George","doi":"10.1287/isre.2023.0152","DOIUrl":"https://doi.org/10.1287/isre.2023.0152","url":null,"abstract":"Policy/Practice-Focused AbstractDespite considerable and continued resource investments, effective solutions to broad-scope problems of social interest or societal grand challenges (GCs) have proven to be elusive in many domains. In multiactor situations that characterize GCs, divergent goals, needs, priorities, and capabilities of global and local actors create organizing design tensions that need to be considered before solutions can be enacted. Emergent digital technologies can play an important and transformative role in addressing the organizing design tensions that pervade such collective action problems. In this article, we draw on Elinor Ostrom’s principles of public value creation and identify a set of eight organizing design tensions that arise from employing global and local perspectives in addressing GCs. We consider novel digital approaches—that involve alternative arrangements of digital and socio-political elements in GC settings—to resolving each of these design tensions. Our discussion foreshadows the considerable opportunity for information systems research to contribute to the broader dialog on GCs; inform GC-related policy and practice at global and local levels; and, more broadly, speed the identification and enactment of effective solutions to grand challenges.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139584188","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}
Machine learning is commonly used to estimate the heterogeneous treatment effects (HTEs) in randomized experiments. Using large-scale randomized experiments on Facebook and Criteo platforms, we observe substantial discrepancies between machine learning-based treatment effect estimates and difference-in-means estimates directly from the randomized experiment. This paper provides a two-step framework for practitioners and researchers to diagnose and rectify this discrepancy. We first introduce a diagnostic tool to assess whether bias exists in the model-based estimates from machine learning. If bias exists, we then offer a model-agnostic method to calibrate any HTE estimates to known, unbiased, subgroup difference-in-means estimates, ensuring that the sign and magnitude of the subgroup estimates approximate the model-free benchmarks. This calibration method requires no additional data and can be scaled for large data sets. To highlight potential sources of bias, we theoretically show that this bias can result from regularization, and further use synthetic simulation to show biases result from misspecification and high-dimensional features. We demonstrate the efficacy of our calibration method using extensive synthetic simulations and two real-world randomized experiments. We further demonstrate the practical value of this calibration in three typical policy-making settings: a prescriptive, budget-constrained optimization framework; a setting seeking to maximize multiple performance indicators; and a multitreatment uplift modeling setting.
{"title":"Calibration of Heterogeneous Treatment Effects in Randomized Experiments","authors":"Yan Leng, Drew Dimmery","doi":"10.1287/isre.2021.0343","DOIUrl":"https://doi.org/10.1287/isre.2021.0343","url":null,"abstract":"Machine learning is commonly used to estimate the heterogeneous treatment effects (HTEs) in randomized experiments. Using large-scale randomized experiments on Facebook and Criteo platforms, we observe substantial discrepancies between machine learning-based treatment effect estimates and difference-in-means estimates directly from the randomized experiment. This paper provides a two-step framework for practitioners and researchers to diagnose and rectify this discrepancy. We first introduce a diagnostic tool to assess whether bias exists in the model-based estimates from machine learning. If bias exists, we then offer a model-agnostic method to calibrate any HTE estimates to known, unbiased, subgroup difference-in-means estimates, ensuring that the sign and magnitude of the subgroup estimates approximate the model-free benchmarks. This calibration method requires no additional data and can be scaled for large data sets. To highlight potential sources of bias, we theoretically show that this bias can result from regularization, and further use synthetic simulation to show biases result from misspecification and high-dimensional features. We demonstrate the efficacy of our calibration method using extensive synthetic simulations and two real-world randomized experiments. We further demonstrate the practical value of this calibration in three typical policy-making settings: a prescriptive, budget-constrained optimization framework; a setting seeking to maximize multiple performance indicators; and a multitreatment uplift modeling setting.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139464120","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：