Organizations worldwide face critical concerns related to cybersecurity threats and information security policy (ISP) compliance. Even though humans are the weakest link in the cybersecurity chain, information security professionals understand the importance of promoting individual information security behaviors because employees are also the first line of defense against ever-increasing cyber threats. Despite a recent trend of working from home, organizations do not make significant differences in their information security interventions for remote workers, relying mainly on VPNs as the only used tool, essentially making employees follow in-office standard information security policies because they are “virtually in-office.” Our study suggests that organizations need to recognize the unique context of remote work and consider personal motivations when shaping information security practices. Furthermore, our study indicates that in order to motivate remote employees to follow secure information security practices, organizations should consider personal characteristics instead of focusing on generic interventions. For instance, our study compares onsite and remote workers, suggesting that personal values are more relevant in remote work settings. Our findings exemplify just one of the many potential personal characteristics to be considered, highlighting how personal values are important motivators for ISP compliance and how they differ for onsite and remote workers in their importance when following information security rules.
{"title":"Promoting Security Behaviors in Remote Work Environments: Personal Values Shaping Information Security Policy Compliance","authors":"Carlos I. Torres, Robert E. Crossler","doi":"10.1287/isre.2021.0563","DOIUrl":"https://doi.org/10.1287/isre.2021.0563","url":null,"abstract":"Organizations worldwide face critical concerns related to cybersecurity threats and information security policy (ISP) compliance. Even though humans are the weakest link in the cybersecurity chain, information security professionals understand the importance of promoting individual information security behaviors because employees are also the first line of defense against ever-increasing cyber threats. Despite a recent trend of working from home, organizations do not make significant differences in their information security interventions for remote workers, relying mainly on VPNs as the only used tool, essentially making employees follow in-office standard information security policies because they are “virtually in-office.” Our study suggests that organizations need to recognize the unique context of remote work and consider personal motivations when shaping information security practices. Furthermore, our study indicates that in order to motivate remote employees to follow secure information security practices, organizations should consider personal characteristics instead of focusing on generic interventions. For instance, our study compares onsite and remote workers, suggesting that personal values are more relevant in remote work settings. Our findings exemplify just one of the many potential personal characteristics to be considered, highlighting how personal values are important motivators for ISP compliance and how they differ for onsite and remote workers in their importance when following information security rules.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141526505","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}
Pub Date : 2024-07-01DOI: 10.1287/isre.2024.intro.v35.n2
Ahmed Abbasi, Robin Dillon, H. Raghav Rao, Olivia R. Liu Sheng
“The Century of Disasters” refers to the increased frequency, complexity, and magnitude of natural and man-made disasters witnessed in the 21st century: the impact of such disasters is exacerbated by infrastructure vulnerabilities, population growth/urbanization, and a challenging policy landscape. Technology-enabled disaster management (TDM) has an important role to play in the Century of Disasters. We highlight four important trends related to TDM, smart technologies and resilience, digital humanitarianism, integrated decision-support and agility, and artificial intelligence–enabled early warning systems, and how the confluence of these trends lead to four research frontiers for information systems researchers. We describe these frontiers, namely the technology-preparedness paradox, socio-technical crisis communication, predicting and prescribing under uncertainty, and fair pipelines, and discuss how the eight articles in the special section are helping us learn about these frontiers.History: Senior editor, Suprateek Sarker.Funding: This study was funded by the National Science Foundation (NSF) [Grants 2240347 and IIS-2039915]. H. R. Rao is also supported in part by the NSF [Grant 2020252]. The usual disclaimer applies.
{"title":"Preparedness and Response in the Century of Disasters: Overview of Information Systems Research Frontiers","authors":"Ahmed Abbasi, Robin Dillon, H. Raghav Rao, Olivia R. Liu Sheng","doi":"10.1287/isre.2024.intro.v35.n2","DOIUrl":"https://doi.org/10.1287/isre.2024.intro.v35.n2","url":null,"abstract":"“The Century of Disasters” refers to the increased frequency, complexity, and magnitude of natural and man-made disasters witnessed in the 21st century: the impact of such disasters is exacerbated by infrastructure vulnerabilities, population growth/urbanization, and a challenging policy landscape. Technology-enabled disaster management (TDM) has an important role to play in the Century of Disasters. We highlight four important trends related to TDM, smart technologies and resilience, digital humanitarianism, integrated decision-support and agility, and artificial intelligence–enabled early warning systems, and how the confluence of these trends lead to four research frontiers for information systems researchers. We describe these frontiers, namely the technology-preparedness paradox, socio-technical crisis communication, predicting and prescribing under uncertainty, and fair pipelines, and discuss how the eight articles in the special section are helping us learn about these frontiers.History: Senior editor, Suprateek Sarker.Funding: This study was funded by the National Science Foundation (NSF) [Grants 2240347 and IIS-2039915]. H. R. Rao is also supported in part by the NSF [Grant 2020252]. The usual disclaimer applies.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141503754","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}
Panpan Wang, Liuyi He, Jifeng Luo, Zhiyan Wu, Han Zhang
Despite the increasing popularity of telehealth, the diffusion of online health communities lags behind because of the limited physician participation. The low adoption levels of telehealth could be attributed to the social environment rather than a baseline reluctance to adopt. By utilizing a panel data set of physicians’ adoption over eight years, we empirically investigate the impacts of geographically and socially close adopters and examined the interaction of proximity influences and competition in adoption. Our results suggest that positive effects of both geographic and social proximity influence on adoption when local competition among physicians on OHCs is low. The positive impact of socially close prior adopters increases with local competition, whereas that of geographically close prior adopters decreases with local competition. Therefore, online health communities could leverage proximity influence by incorporating information cues such as the cumulative adoption rates of close peers to facilitate physician adoption. However, the framing of information cues should consider interactions of competition and proximity influence. Platform managers need to balance the direct crowding-in effect of competition and the adverse moderating effect by which it diminishes the influence of geographic proximity, especially for low-title physicians. For high-title physicians, who are more independent, emphasize the usefulness of online platforms.
{"title":"The Impact of Geographic and Social Proximity on Physicians: Evidence from the Adoption of an Online Health Community","authors":"Panpan Wang, Liuyi He, Jifeng Luo, Zhiyan Wu, Han Zhang","doi":"10.1287/isre.2020.0663","DOIUrl":"https://doi.org/10.1287/isre.2020.0663","url":null,"abstract":"Despite the increasing popularity of telehealth, the diffusion of online health communities lags behind because of the limited physician participation. The low adoption levels of telehealth could be attributed to the social environment rather than a baseline reluctance to adopt. By utilizing a panel data set of physicians’ adoption over eight years, we empirically investigate the impacts of geographically and socially close adopters and examined the interaction of proximity influences and competition in adoption. Our results suggest that positive effects of both geographic and social proximity influence on adoption when local competition among physicians on OHCs is low. The positive impact of socially close prior adopters increases with local competition, whereas that of geographically close prior adopters decreases with local competition. Therefore, online health communities could leverage proximity influence by incorporating information cues such as the cumulative adoption rates of close peers to facilitate physician adoption. However, the framing of information cues should consider interactions of competition and proximity influence. Platform managers need to balance the direct crowding-in effect of competition and the adverse moderating effect by which it diminishes the influence of geographic proximity, especially for low-title physicians. For high-title physicians, who are more independent, emphasize the usefulness of online platforms.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141343951","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 era of digital transformation, understanding personalized privacy preferences is essential for firms and policymakers to build trust and ensure compliance. Traditional methods rely on private data and explicit user input, which can be invasive and impractical. This paper introduces a novel framework that leverages public data, specifically social media posts, to predict individual privacy preferences. By employing deep learning and natural language processing, the framework extracts psychosocial traits such as lifestyle, risk preferences, and emotional states from public data, offering a nonintrusive and scalable approach. Findings reveal that psychosocial traits derived from social media provide greater predictive power than traditional private data. This model aids businesses and policymakers by offering a deeper understanding of user privacy concerns, enabling the development of effective privacy policies and practices. This innovative approach not only enhances consumer privacy control and trust but also optimizes data management for platforms and informs better regulatory decisions, showcasing the practical implications of utilizing public data for privacy preference prediction.
{"title":"Learning Personalized Privacy Preference from Public Data","authors":"Wen Wang, Beibei Li","doi":"10.1287/isre.2023.0318","DOIUrl":"https://doi.org/10.1287/isre.2023.0318","url":null,"abstract":"In the era of digital transformation, understanding personalized privacy preferences is essential for firms and policymakers to build trust and ensure compliance. Traditional methods rely on private data and explicit user input, which can be invasive and impractical. This paper introduces a novel framework that leverages public data, specifically social media posts, to predict individual privacy preferences. By employing deep learning and natural language processing, the framework extracts psychosocial traits such as lifestyle, risk preferences, and emotional states from public data, offering a nonintrusive and scalable approach. Findings reveal that psychosocial traits derived from social media provide greater predictive power than traditional private data. This model aids businesses and policymakers by offering a deeper understanding of user privacy concerns, enabling the development of effective privacy policies and practices. This innovative approach not only enhances consumer privacy control and trust but also optimizes data management for platforms and informs better regulatory decisions, showcasing the practical implications of utilizing public data for privacy preference prediction.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141345057","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}
Online labor platforms increasingly use monitoring systems to manage remote workers. This study assesses whether and how these systems mitigate employer bias in hiring foreign versus domestic workers. Leveraging the exogenous introduction of a monitoring system for time-based projects on a leading online labor platform, we employ a difference-in-differences model to estimate the impact of monitoring systems on mitigating employers’ tendency to bias against hiring foreign workers (home bias). Results indicate a significant reduction in home bias, along with a 15% increase in the hiring of foreign workers following the introduction of the monitoring system. The mitigation effect is notably stronger in high-routine projects or when employers lack prior positive experiences with foreign workers, two scenarios characterized by low external uncertainty and high internal uncertainty, respectively. Moreover, employers no longer exhibit a stronger home bias in scenarios of lower moral hazard risk or coordination costs. These findings lend support to the effectiveness of monitoring systems in mitigating employers’ home bias through facilitating contractual control and coordination. Our study offers important implications for the design of online labor platforms and policymaking.
{"title":"Monitoring and Home Bias in Global Hiring: Evidence from an Online Labor Platform","authors":"Chen Liang, Yili Hong, Bin Gu","doi":"10.1287/isre.2021.0526","DOIUrl":"https://doi.org/10.1287/isre.2021.0526","url":null,"abstract":"Online labor platforms increasingly use monitoring systems to manage remote workers. This study assesses whether and how these systems mitigate employer bias in hiring foreign versus domestic workers. Leveraging the exogenous introduction of a monitoring system for time-based projects on a leading online labor platform, we employ a difference-in-differences model to estimate the impact of monitoring systems on mitigating employers’ tendency to bias against hiring foreign workers (home bias). Results indicate a significant reduction in home bias, along with a 15% increase in the hiring of foreign workers following the introduction of the monitoring system. The mitigation effect is notably stronger in high-routine projects or when employers lack prior positive experiences with foreign workers, two scenarios characterized by low external uncertainty and high internal uncertainty, respectively. Moreover, employers no longer exhibit a stronger home bias in scenarios of lower moral hazard risk or coordination costs. These findings lend support to the effectiveness of monitoring systems in mitigating employers’ home bias through facilitating contractual control and coordination. Our study offers important implications for the design of online labor platforms and policymaking.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141350606","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}
Dongcheng Zhang, Hanchen Jiang, M. Qiang, Kunpeng Zhang, Liangfei Qiu
Practice- and Policy-Oriented Abstract Digital platforms commonly use monetary incentives to motivate users to perform specific tasks. Existing studies have shown the effects of introducing such monetary rewards on task participation and performance on public platforms. However, little is known about the impact of canceling rewards, and particularly less attention is paid to corporate platforms. Our study examines the impact of canceling monetary incentives using quasi-natural experiments on a corporate platform. We find that canceling monetary incentives is not simply the reverse process of their introduction. Specifically, compared with the increase in task participation when rewards were initially introduced, canceling these rewards leads to a sharper decrease in participation. Additionally, although introducing rewards has no significant effect on task performance, canceling rewards causes a significant decline in performance. These results suggest that canceling monetary rewards has a net negative impact on task participation and performance. Furthermore, we examine the heterogeneity of this impact concerning user motivation types and working competency levels. We also discuss the similarities and differences between corporate and public platforms in the impact of monetary incentives. Our results provide important practical implications for enterprise information systems and general information systems regarding their design of incentive strategies.
{"title":"Time to Stop? An Empirical Investigation on the Consequences of Canceling Monetary Incentives on a Digital Platform","authors":"Dongcheng Zhang, Hanchen Jiang, M. Qiang, Kunpeng Zhang, Liangfei Qiu","doi":"10.1287/isre.2022.0017","DOIUrl":"https://doi.org/10.1287/isre.2022.0017","url":null,"abstract":"Practice- and Policy-Oriented Abstract Digital platforms commonly use monetary incentives to motivate users to perform specific tasks. Existing studies have shown the effects of introducing such monetary rewards on task participation and performance on public platforms. However, little is known about the impact of canceling rewards, and particularly less attention is paid to corporate platforms. Our study examines the impact of canceling monetary incentives using quasi-natural experiments on a corporate platform. We find that canceling monetary incentives is not simply the reverse process of their introduction. Specifically, compared with the increase in task participation when rewards were initially introduced, canceling these rewards leads to a sharper decrease in participation. Additionally, although introducing rewards has no significant effect on task performance, canceling rewards causes a significant decline in performance. These results suggest that canceling monetary rewards has a net negative impact on task participation and performance. Furthermore, we examine the heterogeneity of this impact concerning user motivation types and working competency levels. We also discuss the similarities and differences between corporate and public platforms in the impact of monetary incentives. Our results provide important practical implications for enterprise information systems and general information systems regarding their design of incentive strategies.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141356345","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}
Thiemo Wambsganss, Andreas Janson, Matthias Söllner, Ken Koedinger, J. Leimeister
This study explores the potential of dynamic, machine learning (ML)-based modeling to enhance students’ argumentation skills—a crucial component in education and professional success. Traditional educational tools often rely on static modeling, which does not adapt to individual learner needs or provide real-time feedback. In contrast, our research introduces an innovative ML-based system designed to offer dynamic, personalized feedback on argumentation skills. We conducted three empirical studies comparing this system against traditional methods such as scripted and adaptive support modeling. Our results show that dynamic behavioral modeling significantly improves learners’ objective argumentation skills across domains, outperforming all established methods. The results further indicate that, compared with adaptive support, the effect of the dynamic modeling approach holds across complex (large effect) and simple tasks (medium effect) and supports learners with lower and higher expertise alike. This research has important implications for educational policy and practice; incorporating such dynamic systems could transform learning environments by providing scalable, individualized support. This would not only foster essential skills but also cater to diverse learner profiles, potentially reducing educational disparities. Our work suggests a shift toward integrating more adaptive technologies in educational settings to better prepare students for the demands of the modern workforce.
本研究探讨了基于机器学习（ML）的动态建模在提高学生论证技能方面的潜力--论证技能是教育和职业成功的重要组成部分。传统的教育工具通常依赖于静态建模，无法适应学习者的个性化需求或提供实时反馈。相比之下，我们的研究引入了一种基于 ML 的创新系统，旨在为论证技能提供动态、个性化的反馈。我们进行了三项实证研究，将该系统与脚本化和自适应支持建模等传统方法进行了比较。我们的研究结果表明，动态行为建模显著提高了学习者在各个领域的客观论证技能，优于所有既有方法。结果进一步表明，与自适应支持相比，动态建模方法的效果在复杂任务（大效果）和简单任务（中效果）中都能保持，并能为专业技能较低和较高的学习者提供支持。这项研究对教育政策和实践具有重要意义；纳入这种动态系统可以提供可扩展的个性化支持，从而改变学习环境。这不仅能培养基本技能，还能满足不同学习者的需求，从而缩小教育差距。我们的工作表明，教育环境应向整合更多适应性技术的方向转变，使学生更好地适应现代劳动力的需求。
{"title":"Improving Students’ Argumentation Skills Using Dynamic Machine-Learning–Based Modeling","authors":"Thiemo Wambsganss, Andreas Janson, Matthias Söllner, Ken Koedinger, J. Leimeister","doi":"10.1287/isre.2021.0615","DOIUrl":"https://doi.org/10.1287/isre.2021.0615","url":null,"abstract":"This study explores the potential of dynamic, machine learning (ML)-based modeling to enhance students’ argumentation skills—a crucial component in education and professional success. Traditional educational tools often rely on static modeling, which does not adapt to individual learner needs or provide real-time feedback. In contrast, our research introduces an innovative ML-based system designed to offer dynamic, personalized feedback on argumentation skills. We conducted three empirical studies comparing this system against traditional methods such as scripted and adaptive support modeling. Our results show that dynamic behavioral modeling significantly improves learners’ objective argumentation skills across domains, outperforming all established methods. The results further indicate that, compared with adaptive support, the effect of the dynamic modeling approach holds across complex (large effect) and simple tasks (medium effect) and supports learners with lower and higher expertise alike. This research has important implications for educational policy and practice; incorporating such dynamic systems could transform learning environments by providing scalable, individualized support. This would not only foster essential skills but also cater to diverse learner profiles, potentially reducing educational disparities. Our work suggests a shift toward integrating more adaptive technologies in educational settings to better prepare students for the demands of the modern workforce.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141363016","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}
Social media platforms, like Facebook, often display assertive call-to-action (CTA) ads that encourage direct purchases or app installs. These ads can show popularity cues (e.g., number of “likes”) and peer endorsements (e.g., friends who “liked” the ad). Although such signals can positively influence user engagement for informational ads, our research reveals they can backfire for assertive CTA ads. Through field tests on Facebook and incentive-compatible experiments, we find that popularity cues do not improve and that peer endorsements actually harm click performance on assertive CTA ads. The negative effect of peer endorsements is amplified when they come from dissimilar friends. Underlying this effect is users’ persuasion knowledge getting activated; they view these signals as manipulative advertising tactics for the assertive CTAs, resulting in psychological reactance. However, the detrimental impact is mitigated when peer endorsements come from friends with similar preferences. For advertisers, our findings suggest discounting popularity and peer endorsement metrics when evaluating assertive CTA ad performance. Platforms, like Facebook, should also consider making these signals optional for such ads. Overall, exercising discretion with these social proof signals for assertive purchase/install messaging can improve advertising outcomes.
{"title":"The Effect of Popularity Cues and Peer Endorsements on Assertive Social Media Ads","authors":"Ashish Agarwal, Shun-Yang Lee, Andrew B. Whinston","doi":"10.1287/isre.2021.0606","DOIUrl":"https://doi.org/10.1287/isre.2021.0606","url":null,"abstract":"Social media platforms, like Facebook, often display assertive call-to-action (CTA) ads that encourage direct purchases or app installs. These ads can show popularity cues (e.g., number of “likes”) and peer endorsements (e.g., friends who “liked” the ad). Although such signals can positively influence user engagement for informational ads, our research reveals they can backfire for assertive CTA ads. Through field tests on Facebook and incentive-compatible experiments, we find that popularity cues do not improve and that peer endorsements actually harm click performance on assertive CTA ads. The negative effect of peer endorsements is amplified when they come from dissimilar friends. Underlying this effect is users’ persuasion knowledge getting activated; they view these signals as manipulative advertising tactics for the assertive CTAs, resulting in psychological reactance. However, the detrimental impact is mitigated when peer endorsements come from friends with similar preferences. For advertisers, our findings suggest discounting popularity and peer endorsement metrics when evaluating assertive CTA ad performance. Platforms, like Facebook, should also consider making these signals optional for such ads. Overall, exercising discretion with these social proof signals for assertive purchase/install messaging can improve advertising outcomes.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141374862","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}
Ziru Li, Gunwoong Lee, T. S. Raghu, Zhan (Michael) Shi
Although regional data protection and privacy regimes are often cited as major barriers to crossborder digital trade, mitigating consumer privacy concerns through regulations can potentially increase the demand for foreign digital products or services. This study delves into this by assessing the impact of the General Data Protection Regulation (GDPR) on the global mobile app market. Contrary to the belief that such regulations hinder digital trade, our data show a notable post-GDPR increase in top foreign apps in European Union countries, suggesting that the GDPR may alleviate privacy concerns and encourage the adoption of foreign digital products. This finding is crucial for policymakers dealing with data and privacy issues as it indicates the potential of these regulations to balance economic growth with privacy and security protection. The study suggests that data and privacy regulations can address data concerns without significantly harming digital trade. Additionally, it uncovers an opportunity for multinational companies. Although compliance costs are higher, clear privacy regulations could lessen consumer domestic bias, opening doors to international markets. Therefore, evaluating privacy regulations’ impact on global markets means considering both their benefits for demand and their costs for suppliers.
{"title":"Impact of the General Data Protection Regulation on the Global Mobile App Market: Digital Trade Implications of Data Protection and Privacy Regulations","authors":"Ziru Li, Gunwoong Lee, T. S. Raghu, Zhan (Michael) Shi","doi":"10.1287/isre.2022.0421","DOIUrl":"https://doi.org/10.1287/isre.2022.0421","url":null,"abstract":"Although regional data protection and privacy regimes are often cited as major barriers to crossborder digital trade, mitigating consumer privacy concerns through regulations can potentially increase the demand for foreign digital products or services. This study delves into this by assessing the impact of the General Data Protection Regulation (GDPR) on the global mobile app market. Contrary to the belief that such regulations hinder digital trade, our data show a notable post-GDPR increase in top foreign apps in European Union countries, suggesting that the GDPR may alleviate privacy concerns and encourage the adoption of foreign digital products. This finding is crucial for policymakers dealing with data and privacy issues as it indicates the potential of these regulations to balance economic growth with privacy and security protection. The study suggests that data and privacy regulations can address data concerns without significantly harming digital trade. Additionally, it uncovers an opportunity for multinational companies. Although compliance costs are higher, clear privacy regulations could lessen consumer domestic bias, opening doors to international markets. Therefore, evaluating privacy regulations’ impact on global markets means considering both their benefits for demand and their costs for suppliers.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141370518","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}
Suicide is a major cause of death among 15- to 29-year-olds globally, claiming more than 50,000 lives in the United States in 2023 alone. Despite governmental efforts to provide support, many individuals experiencing suicidal thoughts do not seek help but are increasingly turning to social media to express their feelings. This trend offers a critical opportunity for timely detection and intervention of suicidal ideation. We develop an innovative transformer-based model for suicidal ideation detection (SID) that combines domain knowledge with dynamic embedding and lexicon-based enhancements. Our model, which is tested on social media data in two languages from different platforms, outperforms existing state-of-the-art models for SID. We have also explored its applicability to detecting depression and its practical implementation in real-world scenarios. Our research contributes significantly to the field, offering new methods for timely and proactive intervention in suicidal ideation, with potential wide-reaching effects on public health, economics, and society. Methodologically, our approach advances the integration of human expertise into AI models to enhance their effectiveness.
{"title":"KETCH: A Knowledge-Enhanced Transformer-Based Approach to Suicidal Ideation Detection from Social Media Content","authors":"Dongsong Zhang, Lina Zhou, Jie Tao, Tingshao Zhue, Guodong (Gordon) Gao","doi":"10.1287/isre.2021.0619","DOIUrl":"https://doi.org/10.1287/isre.2021.0619","url":null,"abstract":"Suicide is a major cause of death among 15- to 29-year-olds globally, claiming more than 50,000 lives in the United States in 2023 alone. Despite governmental efforts to provide support, many individuals experiencing suicidal thoughts do not seek help but are increasingly turning to social media to express their feelings. This trend offers a critical opportunity for timely detection and intervention of suicidal ideation. We develop an innovative transformer-based model for suicidal ideation detection (SID) that combines domain knowledge with dynamic embedding and lexicon-based enhancements. Our model, which is tested on social media data in two languages from different platforms, outperforms existing state-of-the-art models for SID. We have also explored its applicability to detecting depression and its practical implementation in real-world scenarios. Our research contributes significantly to the field, offering new methods for timely and proactive intervention in suicidal ideation, with potential wide-reaching effects on public health, economics, and society. Methodologically, our approach advances the integration of human expertise into AI models to enhance their effectiveness.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141195684","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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