This study examines three rental models—long-term, on-demand, and hybrid—in the sharing economy and evaluates their impacts on social welfare and consumer surplus. Our analysis reveals the significance of consumers’ setup and transaction costs in determining the optimal rental-model for platforms. We find that the relative setup cost and the relative transaction cost between owners and renters play a crucial role in shaping the equilibrium market price and optimality of the three rental models, whereas the total costs determine the equilibrium transaction volume and sustainability of the three models, with high costs posing barriers to the viability of on-demand and hybrid models. In practice, platforms can maximize consumer surplus by selecting an optimal rental model that narrows the gap in setup costs between renters and owners. Policymakers should implement favorable policies or subsidies to balance consumers’ participation incentives on both sides of the market, leading to mutually beneficial social outcomes.
{"title":"On-Demand, Long-Term, or Hybrid? An Economic Analysis of Optimal Rental Models on Sharing Platforms","authors":"Jianqing Chen, Nan Feng, Zhiling Guo, Wenyi Zhang","doi":"10.1287/isre.2022.0441","DOIUrl":"https://doi.org/10.1287/isre.2022.0441","url":null,"abstract":"This study examines three rental models—long-term, on-demand, and hybrid—in the sharing economy and evaluates their impacts on social welfare and consumer surplus. Our analysis reveals the significance of consumers’ setup and transaction costs in determining the optimal rental-model for platforms. We find that the relative setup cost and the relative transaction cost between owners and renters play a crucial role in shaping the equilibrium market price and optimality of the three rental models, whereas the total costs determine the equilibrium transaction volume and sustainability of the three models, with high costs posing barriers to the viability of on-demand and hybrid models. In practice, platforms can maximize consumer surplus by selecting an optimal rental model that narrows the gap in setup costs between renters and owners. Policymakers should implement favorable policies or subsidies to balance consumers’ participation incentives on both sides of the market, leading to mutually beneficial social outcomes.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140656374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Our research reveals the significant impact of evolving policy uncertainty on hospital strategies to mitigate cost-based performance deficits in clinical care processes through differentiation in search for health information technology (HIT). Key for hospital administrators and managers, our findings reveal the benefit of leveraging external benchmarks for performance feedback, enabling strategic, innovative approaches to HIT investments tailored to enhance clinical care efficiency and cost-effectiveness. Moreover, our findings have important implications for healthcare policymakers, highlighting the necessity to consider the varied responses of hospitals to policy fluctuations. Recognizing that hospitals proactively adapt their HIT portfolios in anticipation of new laws, and that these adaptations are most diverse under moderate policy uncertainty, is crucial. This nuanced understanding can guide policymakers in fostering an environment that encourages both the exploration of novel technologies and the widespread adoption of effective solutions across the healthcare spectrum. By bridging the gap between policy development and hospital administration, our work offers a road map for aligning strategic technological differentiation with policy objectives, ultimately enhancing healthcare delivery and outcomes.
我们的研究揭示了不断变化的政策不确定性对医院战略的重大影响，这些战略旨在通过对医疗信息技术（HIT）的差异化搜索来缓解临床护理流程中基于成本的绩效赤字。对于医院管理者和经理来说，我们的研究结果揭示了利用外部基准进行绩效反馈的益处，从而为提高临床护理效率和成本效益而量身定制战略性、创新性的 HIT 投资方法。此外，我们的研究结果对医疗政策制定者也有重要意义，它强调了考虑医院对政策波动的不同反应的必要性。认识到医院会主动调整其 HIT 投资组合以应对新的法律，而且这些调整在适度的政策不确定性下最为多样化，这一点至关重要。这种细致入微的理解可以指导政策制定者营造一种环境，既鼓励探索新技术，又鼓励在医疗保健领域广泛采用有效的解决方案。通过弥合政策制定与医院管理之间的差距，我们的工作提供了一个路线图，使战略性技术差异与政策目标保持一致，最终改善医疗服务的提供和结果。
{"title":"How Hospitals Differentiate Health Information Technology Portfolios for Clinical Care Efficiency: Insights from the HITEACH Act","authors":"J. Pye, Arun Rai, John Qi Dong","doi":"10.1287/isre.2021.0260","DOIUrl":"https://doi.org/10.1287/isre.2021.0260","url":null,"abstract":"Our research reveals the significant impact of evolving policy uncertainty on hospital strategies to mitigate cost-based performance deficits in clinical care processes through differentiation in search for health information technology (HIT). Key for hospital administrators and managers, our findings reveal the benefit of leveraging external benchmarks for performance feedback, enabling strategic, innovative approaches to HIT investments tailored to enhance clinical care efficiency and cost-effectiveness. Moreover, our findings have important implications for healthcare policymakers, highlighting the necessity to consider the varied responses of hospitals to policy fluctuations. Recognizing that hospitals proactively adapt their HIT portfolios in anticipation of new laws, and that these adaptations are most diverse under moderate policy uncertainty, is crucial. This nuanced understanding can guide policymakers in fostering an environment that encourages both the exploration of novel technologies and the widespread adoption of effective solutions across the healthcare spectrum. By bridging the gap between policy development and hospital administration, our work offers a road map for aligning strategic technological differentiation with policy objectives, ultimately enhancing healthcare delivery and outcomes.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140672296","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}
Mariana G. Andrade-Rojas, Terence J. V. Saldanha, Abhishek Kathuria, Jiban Khuntia, Waifong Boh
Innovation is vital for the growth of small and medium-sized enterprises (SMEs). However, SMEs face deficiencies that hinder their innovation output. This study examines how information technology (IT) helps SMEs address two salient deficiencies: technological deficiency (deficiency in internal technical knowledge and skills) and government support deficiency (deficiency in favorable government policies and incentives). Our findings suggest that SME managers can achieve greater innovation output by orienting their IT-enabled innovation efforts in an open or closed manner to address specific deficiencies. They can address technological deficiency by focusing their IT efforts on promoting innovation within the firm, that is, using IT to support closed innovation. In contrast, SMEs that face government support deficiency should give preference to IT Use for Open Innovation Activities to collaborate with external constituents such as customers and suppliers. Furthermore, because of the emergence of digital platforms (e.g., crowdsourcing), managers may be overly biased toward the use of open innovation. Our results suggest that both open and closed IT-enabled innovation have value. We exhort SME managers not to disregard either form of IT-enabled innovation but rather to tailor their approach to suit their organizational context based on specific deficiencies that their firm faces.
{"title":"How Information Technology Overcomes Deficiencies for Innovation in Small and Medium-Sized Enterprises: Closed Innovation vs. Open Innovation","authors":"Mariana G. Andrade-Rojas, Terence J. V. Saldanha, Abhishek Kathuria, Jiban Khuntia, Waifong Boh","doi":"10.1287/isre.2021.0096","DOIUrl":"https://doi.org/10.1287/isre.2021.0096","url":null,"abstract":"Innovation is vital for the growth of small and medium-sized enterprises (SMEs). However, SMEs face deficiencies that hinder their innovation output. This study examines how information technology (IT) helps SMEs address two salient deficiencies: technological deficiency (deficiency in internal technical knowledge and skills) and government support deficiency (deficiency in favorable government policies and incentives). Our findings suggest that SME managers can achieve greater innovation output by orienting their IT-enabled innovation efforts in an open or closed manner to address specific deficiencies. They can address technological deficiency by focusing their IT efforts on promoting innovation within the firm, that is, using IT to support closed innovation. In contrast, SMEs that face government support deficiency should give preference to IT Use for Open Innovation Activities to collaborate with external constituents such as customers and suppliers. Furthermore, because of the emergence of digital platforms (e.g., crowdsourcing), managers may be overly biased toward the use of open innovation. Our results suggest that both open and closed IT-enabled innovation have value. We exhort SME managers not to disregard either form of IT-enabled innovation but rather to tailor their approach to suit their organizational context based on specific deficiencies that their firm faces.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140566711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ram D. Gopal, Xiao Qiao, Moris S. Strub, Zonghao Yang
This paper investigates the suitability of online loans as an investment through the lens of a portfolio optimization framework. We propose general characteristics-based portfolio policy (GCPP), a framework which overcomes unique challenges associated with building a portfolio of online loans. GCPP directly models the portfolio weight of a loan as a flexible function of its characteristics and does not require direct estimation of the distributional properties of loans. Using an extensive data set spanning over one million loans from 2013 to 2020 from LendingClub, we show that GCPP portfolios can achieve an average annualized internal rate of return (IRR) of 8.86% to 13.08%, significantly outperforming an equal-weight portfolio of loans. To assess the attractiveness of online loans, we then compare the performance of the GCPP portfolio to traditional investment vehicles including stocks, bonds, and real estate. The results demonstrate that a portfolio of online loans earns competitive or higher rates of return compared to traditional asset classes with limited comovement. These results indicate that online loans are an attractive novel asset class for investors. Together, we demonstrate that GCPP is an approach that can help platforms better serve both borrowers and lenders en route to growing their business.
{"title":"Gaining a Seat at the Table: Enhancing the Attractiveness of Online Lending for Institutional Investors","authors":"Ram D. Gopal, Xiao Qiao, Moris S. Strub, Zonghao Yang","doi":"10.1287/isre.2022.0638","DOIUrl":"https://doi.org/10.1287/isre.2022.0638","url":null,"abstract":"This paper investigates the suitability of online loans as an investment through the lens of a portfolio optimization framework. We propose general characteristics-based portfolio policy (GCPP), a framework which overcomes unique challenges associated with building a portfolio of online loans. GCPP directly models the portfolio weight of a loan as a flexible function of its characteristics and does not require direct estimation of the distributional properties of loans. Using an extensive data set spanning over one million loans from 2013 to 2020 from LendingClub, we show that GCPP portfolios can achieve an average annualized internal rate of return (IRR) of 8.86% to 13.08%, significantly outperforming an equal-weight portfolio of loans. To assess the attractiveness of online loans, we then compare the performance of the GCPP portfolio to traditional investment vehicles including stocks, bonds, and real estate. The results demonstrate that a portfolio of online loans earns competitive or higher rates of return compared to traditional asset classes with limited comovement. These results indicate that online loans are an attractive novel asset class for investors. Together, we demonstrate that GCPP is an approach that can help platforms better serve both borrowers and lenders en route to growing their business.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140567001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lianlian (Dorothy) Jiang, Jinghui (Jove) Hou, Xiao Ma, Paul A. Pavlou
The healthcare market struggles with information asymmetry, limiting patients’ ability to make informed hospital choices. Aiming to bridge this gap, review platforms like Yelp have begun displaying hospitals’ clinical quality data alongside consumer reviews. However, our research uncovers that Yelp’s introduction of maternity care clinical quality measures unexpectedly resulted in lower subsequent Yelp ratings for high-quality hospitals with insufficient staffing. Employing precise foot traffic data and transfer deep learning, we discovered that high-quality, yet understaffed, hospitals experienced a surge in patient volume, which strained their resources and diminished patient satisfaction, leading to negative reviews. This finding has significant implications, signaling the unintended consequences of revealing clinical quality measures, including potential financial losses for hospitals because of reduced federal funding. This research not only contributes to our understanding the dynamics of patient satisfaction but also, offers actionable insights for high-quality hospitals to mitigate the negative impacts of unexpected visibility on review platforms. Our research underscores the importance for patients to discern between objective clinical quality measures and self-reported subjective ratings in their decision-making process. This research applies machine learning and transfer deep learning techniques to healthcare analytics, offering a deeper understanding of the interplay between information disclosure, online reviews, patient satisfaction, and hospital management.
{"title":"Punished for Success? A Natural Experiment of Displaying Clinical Hospital Quality on Review Platforms","authors":"Lianlian (Dorothy) Jiang, Jinghui (Jove) Hou, Xiao Ma, Paul A. Pavlou","doi":"10.1287/isre.2021.0630","DOIUrl":"https://doi.org/10.1287/isre.2021.0630","url":null,"abstract":"The healthcare market struggles with information asymmetry, limiting patients’ ability to make informed hospital choices. Aiming to bridge this gap, review platforms like Yelp have begun displaying hospitals’ clinical quality data alongside consumer reviews. However, our research uncovers that Yelp’s introduction of maternity care clinical quality measures unexpectedly resulted in lower subsequent Yelp ratings for high-quality hospitals with insufficient staffing. Employing precise foot traffic data and transfer deep learning, we discovered that high-quality, yet understaffed, hospitals experienced a surge in patient volume, which strained their resources and diminished patient satisfaction, leading to negative reviews. This finding has significant implications, signaling the unintended consequences of revealing clinical quality measures, including potential financial losses for hospitals because of reduced federal funding. This research not only contributes to our understanding the dynamics of patient satisfaction but also, offers actionable insights for high-quality hospitals to mitigate the negative impacts of unexpected visibility on review platforms. Our research underscores the importance for patients to discern between objective clinical quality measures and self-reported subjective ratings in their decision-making process. This research applies machine learning and transfer deep learning techniques to healthcare analytics, offering a deeper understanding of the interplay between information disclosure, online reviews, patient satisfaction, and hospital management.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140566708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xin Zhang, Wei Thoo Yue, Ran (Alan) Zhang, Yugang Yu
The explosion of consumer data has spawned a burgeoning data broker industry, pivotal in targeted advertising. A recent media report estimates the worth of the data broker industry by 2030 at approximately $382 billion. Key players such as Oracle and Lotame gather data from diverse sources to create and sell valuable insights. Digital publishers often rely on these brokers to gain (i) individual insights drawn from their own data or (ii) collective insights drawn from both their data and those of their competitors, improving their targeting precision. This study develops an analytical model to explore the competitive implications of data brokers in a targeted advertising market with two competing publishers and a mass of advertisers. It reveals that the data broker might strategically price their insights to exclusively offer collective insights to one publisher, thereby altering market competition. Moreover, the broker’s actions can either foster or hinder competition among publishers, depending on its strategic interests. Despite potentially reducing market competition through exclusive selling, the provision of collective insights can enhance aggregate welfare by enhancing publishers’ targeting capability. This illustrates the nuanced interplay between data brokers, publishers, and advertisers in shaping the landscape of targeted advertising.
{"title":"Blessing or Curse? Implications of Data Brokers for Publisher Competition","authors":"Xin Zhang, Wei Thoo Yue, Ran (Alan) Zhang, Yugang Yu","doi":"10.1287/isre.2022.0227","DOIUrl":"https://doi.org/10.1287/isre.2022.0227","url":null,"abstract":"The explosion of consumer data has spawned a burgeoning data broker industry, pivotal in targeted advertising. A recent media report estimates the worth of the data broker industry by 2030 at approximately $382 billion. Key players such as Oracle and Lotame gather data from diverse sources to create and sell valuable insights. Digital publishers often rely on these brokers to gain (i) individual insights drawn from their own data or (ii) collective insights drawn from both their data and those of their competitors, improving their targeting precision. This study develops an analytical model to explore the competitive implications of data brokers in a targeted advertising market with two competing publishers and a mass of advertisers. It reveals that the data broker might strategically price their insights to exclusively offer collective insights to one publisher, thereby altering market competition. Moreover, the broker’s actions can either foster or hinder competition among publishers, depending on its strategic interests. Despite potentially reducing market competition through exclusive selling, the provision of collective insights can enhance aggregate welfare by enhancing publishers’ targeting capability. This illustrates the nuanced interplay between data brokers, publishers, and advertisers in shaping the landscape of targeted advertising.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140566667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Online Health Consultation Communities (OHCCs) have emerged as vital platforms connecting patients with physicians for online consultations. However, finding the right match between patients and physicians can be tricky due to physicians’ changing capacity to consult on the OHCC. Our study delves into how signals provided by OHCCs on physicians’ professional status and behaviors can help make these matches successful, especially when considering their capacity fluctuations. We differentiate between two types of signals pertaining to physicians–owned, pertaining to their professional status, and earned, pertaining to their OHCC activity and patient reviews of the physician. Employing a Hidden Markov Model to analyze data from a large OHCC on physicians’ voluntary online consultations with patients, we find the role of the signals in efficient matching to be contingent on the capacity state of the physician. Physicians’ professional status is particularly important when they have less time available, and showing active participation in the community can make the status even more impactful. Conversely, when physicians have more availability, patient feedback becomes crucial, even diminish the importance of professional status. These insights suggest that OHCCs should tailor how information on physicians’ professional status and patient feedback are presented depending on physicians’ availability as this can help patients to make better choices. By being active in the OHCC and earning favorable patient feedback, physicians with more availability can improve their attractiveness to patients, even offsetting concerns that can stem from the lack of seniority of the physician. The findings underscore the need for OHCCs to develop signaling and matching mechanisms that consider the capacity of physicians, thereby fostering efficient and satisfactory patient-physician consultations.
{"title":"Signaling Effects Under Dynamic Capacity in Online Matching Platforms: Evidence from Online Health Consultation Communities","authors":"Liwei Chen, Arun Rai, Wei Chen, Xitong Guo","doi":"10.1287/isre.2021.0150","DOIUrl":"https://doi.org/10.1287/isre.2021.0150","url":null,"abstract":"Online Health Consultation Communities (OHCCs) have emerged as vital platforms connecting patients with physicians for online consultations. However, finding the right match between patients and physicians can be tricky due to physicians’ changing capacity to consult on the OHCC. Our study delves into how signals provided by OHCCs on physicians’ professional status and behaviors can help make these matches successful, especially when considering their capacity fluctuations. We differentiate between two types of signals pertaining to physicians–owned, pertaining to their professional status, and earned, pertaining to their OHCC activity and patient reviews of the physician. Employing a Hidden Markov Model to analyze data from a large OHCC on physicians’ voluntary online consultations with patients, we find the role of the signals in efficient matching to be contingent on the capacity state of the physician. Physicians’ professional status is particularly important when they have less time available, and showing active participation in the community can make the status even more impactful. Conversely, when physicians have more availability, patient feedback becomes crucial, even diminish the importance of professional status. These insights suggest that OHCCs should tailor how information on physicians’ professional status and patient feedback are presented depending on physicians’ availability as this can help patients to make better choices. By being active in the OHCC and earning favorable patient feedback, physicians with more availability can improve their attractiveness to patients, even offsetting concerns that can stem from the lack of seniority of the physician. The findings underscore the need for OHCCs to develop signaling and matching mechanisms that consider the capacity of physicians, thereby fostering efficient and satisfactory patient-physician consultations.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140372332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mi Zhou, Vibhanshu Abhishek, Edward H. Kennedy, Kannan Srinivasan, Ritwik Sinha
Businesses have widely used email ads to directly send promotional information to consumers. Whereas email ads serve as a convenient tool that allows firms to target consumers online, there is little evidence of their multichannel impact on consumer spending in both online and brick-and-mortar stores. In this paper, we utilize a unique high-dimensional data set from one of the world’s largest office supplies retailers to link each consumer’s online behaviors to item-level purchase records in physical stores. We employ a doubly robust estimator that incorporates nonparametric machine learning methods for causal estimation of observational data. Our results show that email ads significantly increase the retailer’s sales across different channels. We also investigate the effects of email ads on diverse consumer behaviors along the purchase funnel and find that increased sales result from increased purchase probability and a wider variety of products purchased by consumers. Further, we examine several moderating factors, such as product types and consumer segments, that influence the multichannel effects of email advertising. Our study provides empirical evidence for the economic impact of email ads on consumer behavior across different channels and the underlying mechanisms thereof, offering direct implications for multichannel retailers seeking to improve their digital marketing strategies.
{"title":"Linking Clicks to Bricks: Understanding the Effects of Email Advertising on Multichannel Sales","authors":"Mi Zhou, Vibhanshu Abhishek, Edward H. Kennedy, Kannan Srinivasan, Ritwik Sinha","doi":"10.1287/isre.2020.0557","DOIUrl":"https://doi.org/10.1287/isre.2020.0557","url":null,"abstract":"Businesses have widely used email ads to directly send promotional information to consumers. Whereas email ads serve as a convenient tool that allows firms to target consumers online, there is little evidence of their multichannel impact on consumer spending in both online and brick-and-mortar stores. In this paper, we utilize a unique high-dimensional data set from one of the world’s largest office supplies retailers to link each consumer’s online behaviors to item-level purchase records in physical stores. We employ a doubly robust estimator that incorporates nonparametric machine learning methods for causal estimation of observational data. Our results show that email ads significantly increase the retailer’s sales across different channels. We also investigate the effects of email ads on diverse consumer behaviors along the purchase funnel and find that increased sales result from increased purchase probability and a wider variety of products purchased by consumers. Further, we examine several moderating factors, such as product types and consumer segments, that influence the multichannel effects of email advertising. Our study provides empirical evidence for the economic impact of email ads on consumer behavior across different channels and the underlying mechanisms thereof, offering direct implications for multichannel retailers seeking to improve their digital marketing strategies.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140303228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Neil C. Thompson, Xue Luo, Brian McKenzie, Edana Richardson, Brian Flanagan
User-generated content, for example, on Wikipedia, is easily accessed but has uncertain reliability. This makes it attractive to use but also creates risk, so there should be limits to who uses Wikipedia and for what purposes. In this paper, we use a randomized control trial to show that Wikipedia’s influence extends to judicial decision making, a field that is highly professional and supposed to follow strict procedures. This causal evidence further emphasizes the widespread influence of Wikipedia and other frequently accessed user-generated content on important social outcomes. Our findings also reveal boundaries to user-generated content’s influence. Although Wikipedia’s influence does extend to courts of “first instance” (where the case is first decided), it does not extend to higher courts (Court of Appeals, Supreme Court). These results suggest that normative prohibitions do seem to be sufficient to keep Wikipedia from influencing the most-important, well-resourced parts of law but that these prohibitions are insufficient in areas where time and resource pressures are greater. By showing that Wikipedia is influencing such an important and formal domain, our paper reinforces the importance of improving the accuracy and reliability of user-generated content, especially in domains with far-reaching societal consequences. Because there is no obvious way to prevent individuals from taking advantage of user-generated content professionally or nonprofessionally, our findings also contribute to the ongoing discussion of how to build public repositories of knowledge into more reliable storehouses.
{"title":"User-Generated Content Shapes Judicial Reasoning: Evidence from a Randomized Control Trial on Wikipedia","authors":"Neil C. Thompson, Xue Luo, Brian McKenzie, Edana Richardson, Brian Flanagan","doi":"10.1287/isre.2023.0034","DOIUrl":"https://doi.org/10.1287/isre.2023.0034","url":null,"abstract":"User-generated content, for example, on Wikipedia, is easily accessed but has uncertain reliability. This makes it attractive to use but also creates risk, so there should be limits to who uses Wikipedia and for what purposes. In this paper, we use a randomized control trial to show that Wikipedia’s influence extends to judicial decision making, a field that is highly professional and supposed to follow strict procedures. This causal evidence further emphasizes the widespread influence of Wikipedia and other frequently accessed user-generated content on important social outcomes. Our findings also reveal boundaries to user-generated content’s influence. Although Wikipedia’s influence does extend to courts of “first instance” (where the case is first decided), it does not extend to higher courts (Court of Appeals, Supreme Court). These results suggest that normative prohibitions do seem to be sufficient to keep Wikipedia from influencing the most-important, well-resourced parts of law but that these prohibitions are insufficient in areas where time and resource pressures are greater. By showing that Wikipedia is influencing such an important and formal domain, our paper reinforces the importance of improving the accuracy and reliability of user-generated content, especially in domains with far-reaching societal consequences. Because there is no obvious way to prevent individuals from taking advantage of user-generated content professionally or nonprofessionally, our findings also contribute to the ongoing discussion of how to build public repositories of knowledge into more reliable storehouses.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140229279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dominik Molitor, Martin Spann, Anindya Ghose, Philipp Reichhart
Firms have two distinct options when delivering content to consumers’ mobile devices: mobile push and mobile pull. Mobile push delivers firm-initiated (ad) content directly to consumers, while mobile pull requires consumers to initiate requests for (ad) content. This study tests the impact of mobile push and mobile pull on consumers’ coupon redemption behavior in a large-scale randomized field experiment in a geo-conquesting setting, targeting customers located around competitor retail stores with mobile coupons to drive them to stores of the focal retailer. The results show that mobile push increases coupon redemption rates by 6.0%, with substantial heterogeneity based on app-specific use experience and store density: App-specific use experience negatively moderates the effect of mobile push delivery on redemptions, likely because both usage experience and push notifications reduce app-specific search costs, thereby acting as substitutes for one another. In areas with higher store density, the positive effect of mobile push delivery on the redemption likelihood is greater, suggesting that push notifications can highlight the focal coupon among alternative store choices, thereby reducing consumer switching costs. These findings have important implications for retailers and brands in creating competitive mobile targeting campaigns that effectively leverage both mobile push and pull delivery mechanisms.
{"title":"Mobile Push vs. Pull Targeting and Geo-Conquesting","authors":"Dominik Molitor, Martin Spann, Anindya Ghose, Philipp Reichhart","doi":"10.1287/isre.2021.0206","DOIUrl":"https://doi.org/10.1287/isre.2021.0206","url":null,"abstract":"Firms have two distinct options when delivering content to consumers’ mobile devices: mobile push and mobile pull. Mobile push delivers firm-initiated (ad) content directly to consumers, while mobile pull requires consumers to initiate requests for (ad) content. This study tests the impact of mobile push and mobile pull on consumers’ coupon redemption behavior in a large-scale randomized field experiment in a geo-conquesting setting, targeting customers located around competitor retail stores with mobile coupons to drive them to stores of the focal retailer. The results show that mobile push increases coupon redemption rates by 6.0%, with substantial heterogeneity based on app-specific use experience and store density: App-specific use experience negatively moderates the effect of mobile push delivery on redemptions, likely because both usage experience and push notifications reduce app-specific search costs, thereby acting as substitutes for one another. In areas with higher store density, the positive effect of mobile push delivery on the redemption likelihood is greater, suggesting that push notifications can highlight the focal coupon among alternative store choices, thereby reducing consumer switching costs. These findings have important implications for retailers and brands in creating competitive mobile targeting campaigns that effectively leverage both mobile push and pull delivery mechanisms.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140149233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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