Information Systems Research最新文献

{"title":"Retargeted Versus Generic Product Recommendations: When is it Valuable to Present Retargeted Recommendations?","authors":"Xiang (Shawn) Wan, Anuj Kumar, Xitong Li","doi":"10.1287/isre.2020.0560","DOIUrl":"https://doi.org/10.1287/isre.2020.0560","url":null,"abstract":"Practitioner’s Abstract Online platforms/retailers widely use collaborative filtering (CF)-based generic product recommendations to improve sales. These systems recommend products to a consumer based on the product co-views and co-purchases by other consumers on the website but do not leverage the consumer’s browsing data. Based on a field study on a U.S. fashion apparel and home goods retailer’s website, we show that informing generic CF recommendations to individual consumers’ browsing history can generate substantial additional sales. Specifically, we show that it is optimal to offer generic CF recommendations to a consumer if the consumer has not carted a product and recommend products he or she has seen in the previous sessions (retargeted recommendations) if he or she has carted a product. Our simulation results show that such recommendations could result in a 3% increase in total sales compared with conventional generic CF recommendations. Online platforms/retailers with detailed consumer browsing data can implement such recommendations to achieve higher sales.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136113178","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}

{"title":"Development Trajectory of Blockchain Platforms: The Role of Multirole","authors":"Tianyi Li, Xiaoquan (Michael) Zhang","doi":"10.1287/isre.2022.0243","DOIUrl":"https://doi.org/10.1287/isre.2022.0243","url":null,"abstract":"Understanding the development trajectory of digital platforms is central to digital platform management. We develop a parametric model that investigates the development trajectories of blockchain platforms, accounting for the feedback between blockchains’ utility change and people’s adoption and abandonment behavior. We consider a typical blockchain participant to simultaneously play three roles on the platform, user, investor, and laborer, each contributing to blockchains’ multi-faceted utility: providing service for transaction/interaction, providing a medium for digital investment, and providing workspace for online labor. The model describes a three-phase development trajectory for blockchain platforms: a chaotic initial stage, a rapid growth stage, and a mature stage of stable market cycles. The model was used to match 112 token price series, demonstrating robust performance across different fitting setups and outperforming existing models. The study identifies two temporal parameters, the time delay in quitting the platform and the holding time of the platform’s token, that significantly differentiate blockchains’ development trajectories. We extend the model to study forking events; results suggest that fork launch time is more important than forking amplitude in influencing the main chain’s subsequent development and that forking can increase the exposure of the forked platform.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135858451","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}

{"title":"Leveraging the Digital Tracing Alert in Virus Fight: The Impact of COVID-19 Cell Broadcast on Population Movement","authors":"Anindya Ghose, Heeseung Andrew Lee, Wonseok Oh, Yoonseock Son","doi":"10.1287/isre.2022.0117","DOIUrl":"https://doi.org/10.1287/isre.2022.0117","url":null,"abstract":"Digital tracing alerts (DTAs) have emerged as effective means to share information with agility in responding to disaster outbreaks. Governments are able to instantaneously coordinate the available information to provide information related to the disaster and promote preventive actions. However, despite the opportunities granted by these innovative technologies in managing disasters, privacy concerns can arise in regard to how much of individuals’ private information should be collected and disclosed. With these considerations, we examine the extent to which instant digital tracing alerts and the information included in the alerts affect people’s actions toward disaster management in the context of South Korea. Our results show that collecting and disclosing detailed private information is unnecessary and may instead diminish the effects of DTAs. The effect of digital alerts being more pronounced among young and male individuals and in business-centric areas. Furthermore, because the effectiveness of DTAs decreases with the cumulative number of DTAs received, governments should send alerts that include more urgent information that is directly related to the risk posed by a disaster. Our results provide policymakers and law enforcement with novel insights into whether and how the usage of information technology can facilitate disaster management and to what extent they should collect and expose private information to effectively safeguards public health and safety during a crisis. The fast and comprehensive implementation of DTAs in South Korea in response to the global outbreak offers other countries learning opportunities with respect to successful collaboration among parties involved in the development and design of DTA-related infrastructure and education. We emphasize that collaboration among central policymakers, local/municipal districts, telecommunications companies, and healthcare centers is essential to establishing an innovative IT-driven disaster management infrastructure and mechanisms that help inform citizens in taking desired actions in an emergency or disastrous events.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135968033","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}

{"title":"Dinner at Your Doorstep: Service Innovation via the Gig Economy on Food Delivery Platforms","authors":"Geng Sun, Yeongin Kim, Yinliang (Ricky) Tan, Geoffrey G. Parker","doi":"10.1287/isre.2022.0119","DOIUrl":"https://doi.org/10.1287/isre.2022.0119","url":null,"abstract":"Despite the rapid growth of online food delivery (OFD) market, the impact of its three-sided nature—encompassing consumers, restaurants, and gig drivers—on incentives and payoffs remains unclear compared to the traditional two-sided model. This study examines how OFD platforms make optimal choices in a competitive environment involving pricing and service quality. The analysis reveals that insights from two-sided platforms don’t seamlessly translate to OFD markets. The triad nature of OFD can either dampen or heighten price competition in the buyer-seller market, altering subsidization dynamics for platforms. While conventional platforms suffer from negative network effects due to participation pressure, OFD platforms can adapt service strategies to mitigate this. However, introducing gig labor might not always benefit OFD platforms as it could trigger a prisoner’s dilemma situation by empowering competing platforms. The study underscores the dependence of platform strategies on network effects’ strength. As the gig economy rises, the employment status of gig workers garners controversy. The study demonstrates that implementing minimum wage regulations, while benefiting gig drivers, might diminish societal welfare. These findings offer guidance to policymakers aiming to balance gig workers’ interests with overall societal concerns.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136013440","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}

{"title":"The Impacts of Internet Monitoring on Employees’ Cyberloafing and Organizational Citizenship Behavior: A Longitudinal Field Quasi-Experiment","authors":"Hemin Jiang, Mikko Siponen, Zhenhui (Jack) Jiang, Aggeliki Tsohou","doi":"10.1287/isre.2020.0216","DOIUrl":"https://doi.org/10.1287/isre.2020.0216","url":null,"abstract":"Many organizations have implemented internet monitoring to curb employees’ non-work-related internet activities during work hours, commonly referred to as “cyberloafing.” For managers, two primary considerations emerge: (1) the actual effectiveness of internet monitoring in diminishing cyberloafing and (2) any unintended side effects this monitoring might have on overall employee behavior. From a longitudinal field quasi experiment, we observed that although internet monitoring notably reduced cyberloafing because of amplified employee concerns about potential sanctions and privacy breaches, it unintentionally suppressed their organizational citizenship behavior (OCB). Moreover, a follow-up observation four months after introducing internet monitoring revealed that its capability to mitigate cyberloafing had weakened, yet the dampening effect on OCB continued. We conclude this paper by underlining the value of using internet monitoring as a feedback mechanism on employees’ online behavior, rather than solely as a deterrence measure.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136208784","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}

{"title":"Ontology-Based Intelligent Interface Personalization for Protection Against Phishing Attacks","authors":"Fatemeh Mariam Zahedi, Yan Chen, Huimin Zhao","doi":"10.1287/isre.2021.0065","DOIUrl":"https://doi.org/10.1287/isre.2021.0065","url":null,"abstract":"Millions of users on the Internet have fallen into phishing website traps. Detection tools are designed to warn users against such attacks, but often fail to achieve this purpose. One crucial reason behind this is that users rarely have a chance to interact and build a relationship with a detection tool that stealthily runs at the backend. A warning message on a rarely seen interface from such a tool hardly inspires users’ trust in its authenticity and accuracy. In this study, we propose an ontology-based intelligent interface personalization (OBIIP) design for the warning interfaces of phishing website detection tools. We first constructed an ontology of warning interface elements (OWIE), which is a comprehensive knowledgebase for warning interface design. We then used OWIE in the design and creation of an OBIIP prototype and assessed it in a laboratory experiment and an online experiment. The results show the significant value of OBIIP in improving users’ performance in terms of self-protection against website phishing attacks and building a stronger relationship with the detection tool in terms of trust in and use of the tool.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136209200","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}

{"title":"Racial Discrimination and Anti-discrimination: The COVID-19 Pandemic’s Impact on Chinese Restaurants in North America","authors":"Chuang Tang, Shaobo (Kevin) Li, Yi Ding, Ram D. Gopal, Guanglei Zhang","doi":"10.1287/isre.2021.0568","DOIUrl":"https://doi.org/10.1287/isre.2021.0568","url":null,"abstract":"The coronavirus disease 2019 (COVID-19) pandemic has seen a rise in racial discrimination against Asian communities, notably the Chinese population. Despite growing research on various aspects of the pandemic, there is a notable gap in understanding its behavioral impact regarding racial discrimination. This study delves into the manifestations of COVID-19-related racial discrimination and antidiscrimination efforts on online platforms using large-scale data sets from Yelp.com and SafeGraph. We specifically examined how the pandemic affected Chinese restaurants compared with non-Chinese ones at different pandemic phases. Our findings are significant; the pandemic triggered an immediate surge in racial discrimination, resulting in a substantial decrease in customers visiting Chinese restaurants. Importantly, we applied advanced text mining and machine learning techniques to analyze user behavior, consistently revealing that increased discrimination prompted users to take antidiscrimination actions on online platforms. This research highlights a tangible form of racial discrimination through reduced patronage of Chinese restaurants and underscores the capacity of consumers to combat discrimination on online platforms. It calls for targeted policy interventions to address and prevent racial discrimination, particularly in the context of public health crises.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136210855","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}

{"title":"Are Neighbors Alike? A Semi-supervised Probabilistic Collaborative Learning Model for Online Review Spammers Detection","authors":"Zhiang Wu, Guannan Liu, Junjie Wu, Yong Tan","doi":"10.1287/isre.2022.0047","DOIUrl":"https://doi.org/10.1287/isre.2022.0047","url":null,"abstract":"Review spammers can harm the trustworthy environment of online platforms by purposefully posting unauthentic ratings and comments for products or online merchants, with the aim of gaining improper benefits. Though a vast majority of methods have been proposed to resolve the spammer detection problem, several challenges such as collusion recognition, label scarcity and biased distributions, etc., are still persistent and call for further investigation. Building on the prevalent collusive spamming behaviors and the network homophily theory, we introduce a reviewer network to account for the explicit co-review relations, and then propose a semi-supervised probabilistic collaborative learning model to capture both reviewers' individual behavioral features and the reviewer network. Our model features in integrating partial labels propagation with a pseudo-labeling strategy and the feature-based learning for reviewer network modelling, which is proved theoretically to be a weighted logistic regression on a network-related synthetic data set. The rich parameters that characterize the importance of network information, the strength of network homophily, and the value of unlabeled data, make our model more transparent. The empirical evaluations on two distinctive real-life data sets have demonstrated the effectiveness of our model and the value of unlabeled data learning, in which the reviewer network after proper trimming shows strong homophily effect and plays a vital role. In particular, the proposed model shows robustness against label scarcity and biased label distribution.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136295236","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}

{"title":"An Onto-Epistemological Analysis of Information Privacy Research","authors":"Heng Xu, Nan Zhang","doi":"10.1287/isre.2021.0633","DOIUrl":"https://doi.org/10.1287/isre.2021.0633","url":null,"abstract":"Privacy is one of the most pressing concerns in the continuously evolving landscape of information technology. Despite decades of vigorous and multifaceted exploration in the interdisciplinary field of information privacy, a consensual or unifying theory remains elusive. Moreover, the complexities of issues surrounding privacy are frequently labeled as “too big to understand” in the public press. At this critical juncture, it is beneficial to delve deeper into the foundational assumptions that privacy scholars have about privacy phenomena. In this commentary, we offer a fresh perspective by drawing on Dreyfus’ influential exegesis of the Heideggerian onto-epistemological framework to reflect on these assumptions. The perspective we offer yields three integrative recommendations for future privacy research to open to new research directions. We illustrate how these new directions could not only grow future privacy research but also facilitate the design of more effective privacy-protection measures in practice.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134975863","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}

{"title":"When Variety Seeking Meets Unexpectedness: Incorporating Variety-Seeking Behaviors into Design of Unexpected Recommender Systems","authors":"Pan Li, Alexander Tuzhilin","doi":"10.1287/isre.2021.0053","DOIUrl":"https://doi.org/10.1287/isre.2021.0053","url":null,"abstract":"In this paper, we study the consumers’ variety-seeking behavior in recommender system applications and propose a comprehensive framework to measure such behavior based on past consumption records. The effectiveness of the proposed framework is validated through user questionnaire studies conducted at Alibaba, where our constructed variety-seeking measures match well with consumers’ self-reported levels of their variety-seeking behaviors. We subsequently present a recommendation framework that combines the identified variety-seeking levels with unexpected recommender systems in the data mining literature to address consumers’ heterogenous desire for product variety, in which we provide more unexpected product recommendations to variety-seeking consumers and vice versa. Through off-line experiments on three different recommendation scenarios and a large-scale online controlled experiment at a major video-streaming platform, we demonstrate that those models following our recommendation framework significantly increase various business performance metrics and generate tangible economic impact for the company. Our findings lead to important managerial implications to better understand consumers’ variety-seeking behaviors and design recommender systems. As a result, the best performing model in our proposed frameworks is deployed by the company to serve all consumers on the video-streaming platform.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135591478","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}