Qiang Wei, Yao Mu, Xunhua Guo, Weijie Jiang, Guoqing Chen
Recommender systems are widely used by platforms/merchants to find the products that are likely to interest consumers. However, existing dynamic methods still face challenges with regard to diverse behaviors, variability in interest shifts, and the identification of psychological dynamics. Premised on the marketing funnel perspective to analyze consumer shopping journeys, this study proposes a novel and effective machine learning approach for product recommendation, namely, multi-stage dynamic Bayesian network (MS-DBN), which models the generative processes of consumers’ interactive behaviors with products in light of their stage transitions and interest shifts. In this way, consumers’ stage-interest-behavior dynamics can be learnt, especially the variability in interest shifts. This provides managerial implications for practice. MS-DBN demonstrates significant performance advantage with general applicability by extracting the generalizable regularity during shopping journeys, which compensates the diversity and sparsity frequently observed in consumer behaviors. In addition, aided by the identification strategies integrated into the learning process, the latent variables in the model can be detected such that consumers’ invisible psychological stages and interests in products can be identified from their observed behaviors, shedding light on the targeted marketing of platforms/merchants and thus enriching the practical value of the approach.
{"title":"Dynamic Bayesian Network–Based Product Recommendation Considering Consumers’ Multistage Shopping Journeys: A Marketing Funnel Perspective","authors":"Qiang Wei, Yao Mu, Xunhua Guo, Weijie Jiang, Guoqing Chen","doi":"10.1287/isre.2020.0277","DOIUrl":"https://doi.org/10.1287/isre.2020.0277","url":null,"abstract":"Recommender systems are widely used by platforms/merchants to find the products that are likely to interest consumers. However, existing dynamic methods still face challenges with regard to diverse behaviors, variability in interest shifts, and the identification of psychological dynamics. Premised on the marketing funnel perspective to analyze consumer shopping journeys, this study proposes a novel and effective machine learning approach for product recommendation, namely, multi-stage dynamic Bayesian network (MS-DBN), which models the generative processes of consumers’ interactive behaviors with products in light of their stage transitions and interest shifts. In this way, consumers’ stage-interest-behavior dynamics can be learnt, especially the variability in interest shifts. This provides managerial implications for practice. MS-DBN demonstrates significant performance advantage with general applicability by extracting the generalizable regularity during shopping journeys, which compensates the diversity and sparsity frequently observed in consumer behaviors. In addition, aided by the identification strategies integrated into the learning process, the latent variables in the model can be detected such that consumers’ invisible psychological stages and interests in products can be identified from their observed behaviors, shedding light on the targeted marketing of platforms/merchants and thus enriching the practical value of the approach.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135696117","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}
Coopetition has been a common practice, especially among emerging markets. The coopetition relationship between a ride-sharing platform and a car-rental firm is distinct in that they operate under two different business models. Although the platform controls both its demand and supply by setting rider prices and driver wages, the car-rental firm operates under the traditional model with a fixed supply and cost structure. Both the platform and car-rental firm compete for riders seeking transportation. If the two cooperate, a driver is allowed to rent from the rental firm and drive for the platform; otherwise, only those owning personal vehicles are allowed to drive for the platform. We show that such supply-side cooperation intensifies demand-side price competition and decreases total revenue. Therefore, coopetition is mutually beneficial only when it leads to a significant decrease in supply costs. We find that the two firms are likely to form a coopetition relationship when the total rider market size is not high, the degree of rider substitutability between the two firms is low, and the platform has a significant market-size advantage over the rental firm. Coopetition between the platform and the rental firm benefits riders and hurts drivers, but it benefits society overall.
{"title":"When Sharing Economy Meets Traditional Business: Coopetition Between Ride-Sharing Platforms and Car-Rental Firms","authors":"Chenglong Zhang, Jianqing Chen, Srinivasan Raghunathan","doi":"10.1287/isre.2022.0011","DOIUrl":"https://doi.org/10.1287/isre.2022.0011","url":null,"abstract":"Coopetition has been a common practice, especially among emerging markets. The coopetition relationship between a ride-sharing platform and a car-rental firm is distinct in that they operate under two different business models. Although the platform controls both its demand and supply by setting rider prices and driver wages, the car-rental firm operates under the traditional model with a fixed supply and cost structure. Both the platform and car-rental firm compete for riders seeking transportation. If the two cooperate, a driver is allowed to rent from the rental firm and drive for the platform; otherwise, only those owning personal vehicles are allowed to drive for the platform. We show that such supply-side cooperation intensifies demand-side price competition and decreases total revenue. Therefore, coopetition is mutually beneficial only when it leads to a significant decrease in supply costs. We find that the two firms are likely to form a coopetition relationship when the total rider market size is not high, the degree of rider substitutability between the two firms is low, and the platform has a significant market-size advantage over the rental firm. Coopetition between the platform and the rental firm benefits riders and hurts drivers, but it benefits society overall.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135696514","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}
Swift and unexpected shifts of financial regulations can have profound implications for the general population. This is evidenced by China’s abrupt transition in its stance on P2P lending in 2018. Initially embracing these platforms, the abrupt regulatory pivot to widespread shutdowns. Our empirical research, drawing upon credit application data, demonstrates how this indiscriminate approach hindered economic development opportunities for a significant portion of borrowers, particularly the underprivileged. As a remedy, we advocate for the implementation of AI-driven regulatory frameworks. Rather than a monolithic approach to all borrowers, AI helps distinguish between real financial risks and those that can be managed. This nuanced strategy safeguards individuals’ economic progression, while efficiently mitigating financial hazards. For policymakers and industry stakeholders, our findings underscore the importance of contemplating the broader ramifications of regulatory decisions and harnessing innovative methodologies, such as AI, to strike an optimal balance.
{"title":"Consequences of China’s 2018 Online Lending Regulation and the Promise of PolicyTech","authors":"Yidi Liu, Xin Li, Zhiqiang (Eric) Zheng","doi":"10.1287/isre.2021.0580","DOIUrl":"https://doi.org/10.1287/isre.2021.0580","url":null,"abstract":"Swift and unexpected shifts of financial regulations can have profound implications for the general population. This is evidenced by China’s abrupt transition in its stance on P2P lending in 2018. Initially embracing these platforms, the abrupt regulatory pivot to widespread shutdowns. Our empirical research, drawing upon credit application data, demonstrates how this indiscriminate approach hindered economic development opportunities for a significant portion of borrowers, particularly the underprivileged. As a remedy, we advocate for the implementation of AI-driven regulatory frameworks. Rather than a monolithic approach to all borrowers, AI helps distinguish between real financial risks and those that can be managed. This nuanced strategy safeguards individuals’ economic progression, while efficiently mitigating financial hazards. For policymakers and industry stakeholders, our findings underscore the importance of contemplating the broader ramifications of regulatory decisions and harnessing innovative methodologies, such as AI, to strike an optimal balance.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135244447","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}
Firms increasingly harness data that are created as by-products of information systems usage to evaluate and manage employees. However, such “trace data” can be a double-edged sword. The data can provide a whole new visibility into work practices but also, make work less transparent if the employees start to change their behavior to shape the data. We study this dilemma in the context of knowledge work that has traditionally eluded behavioral measurement. We show that when knowledge workers become aware of data collection and have an interest in how their work may be represented by the data, they start to actively perform the data. We identify different patterns by which employees shape work practices to produce trace data. The changes affect not only the actions and data of the focal employee but also, the actions and data of their colleagues and subordinates. Therefore, to fully realize the potential of trace data, managers may need to get involved in designing the data and to set a trace data policy that states how the data will be used in the organization.
{"title":"The Performative Production of Trace Data in Knowledge Work","authors":"Aleksi Aaltonen, Marta Stelmaszak","doi":"10.1287/isre.2019.0357","DOIUrl":"https://doi.org/10.1287/isre.2019.0357","url":null,"abstract":"Firms increasingly harness data that are created as by-products of information systems usage to evaluate and manage employees. However, such “trace data” can be a double-edged sword. The data can provide a whole new visibility into work practices but also, make work less transparent if the employees start to change their behavior to shape the data. We study this dilemma in the context of knowledge work that has traditionally eluded behavioral measurement. We show that when knowledge workers become aware of data collection and have an interest in how their work may be represented by the data, they start to actively perform the data. We identify different patterns by which employees shape work practices to produce trace data. The changes affect not only the actions and data of the focal employee but also, the actions and data of their colleagues and subordinates. Therefore, to fully realize the potential of trace data, managers may need to get involved in designing the data and to set a trace data policy that states how the data will be used in the organization.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136308390","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}
Review-in-review (RIR) is a feature that allows viewers to generate positive or negative evaluations for primary quality evaluations of a product (e.g., ratings and reviews). This study reveals that it can cause social desirability bias in primary ratings: Reviewers who desire social recognition are driven to adjust their ratings (about 7.4% likelihood) to elicit more helpful responses and avoid unhelpful ones. This bias can be shown as distorted conformity to the prior rating distribution or extremity, depending on the RIR types. The model identifies how bias magnitude correlates with users’ social characteristics, thereby identifying vulnerable individuals. Platforms can incentivize less vulnerable users and remind susceptible ones to decrease the bias and can supplement rating conditional on the identified vulnerability extent (e.g., the distribution by the “independent” raters) to mitigate the bias’s impact on rating viewers. The simulation analysis compares the bias under different counterfactual RIR system designs, finding a composite RIR system (e.g., helpful and unhelpful RIRs) partially neutralizes the bias, obviating the need to remove all RIR features. The model further adapts to evaluate underexplored RIRs forms and can provide a “de-biased” metric while preserving individual ratings.
{"title":"Does Help Help? An Empirical Analysis of Social Desirability Bias in Ratings","authors":"Jinyang Zheng, Yong Tan, Guopeng Yin, Jianing Ding","doi":"10.1287/isre.2020.0406","DOIUrl":"https://doi.org/10.1287/isre.2020.0406","url":null,"abstract":"Review-in-review (RIR) is a feature that allows viewers to generate positive or negative evaluations for primary quality evaluations of a product (e.g., ratings and reviews). This study reveals that it can cause social desirability bias in primary ratings: Reviewers who desire social recognition are driven to adjust their ratings (about 7.4% likelihood) to elicit more helpful responses and avoid unhelpful ones. This bias can be shown as distorted conformity to the prior rating distribution or extremity, depending on the RIR types. The model identifies how bias magnitude correlates with users’ social characteristics, thereby identifying vulnerable individuals. Platforms can incentivize less vulnerable users and remind susceptible ones to decrease the bias and can supplement rating conditional on the identified vulnerability extent (e.g., the distribution by the “independent” raters) to mitigate the bias’s impact on rating viewers. The simulation analysis compares the bias under different counterfactual RIR system designs, finding a composite RIR system (e.g., helpful and unhelpful RIRs) partially neutralizes the bias, obviating the need to remove all RIR features. The model further adapts to evaluate underexplored RIRs forms and can provide a “de-biased” metric while preserving individual ratings.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136263991","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}
To reduce the availability of hacking tools for use in cybersecurity offenses, many countries have enacted computer misuse acts (CMA) that criminalize the production, distribution, and possession of such tools with criminal intent. Nevertheless, our research illuminates an unintended consequence: the chilling effect of CMA enforcement on legitimate cybersecurity discussions, some of which may be desirable for cybersecurity research, within online hack forums. More importantly, this study uniquely examines the chilling effect stemming from users’ fear of legal harm. Drawing on decision-making theories related to choice under uncertainty, we derive new insights into how legal enforcement can suppress lawful acts and reveal the dynamics of social categorization online. Our research offers valuable insights for policymakers and forum administrators. Policymakers can use our findings to mitigate unnecessary uncertainty in legal enforcement such as CMA. This includes developing legal cases to prevent false prosecutions, implementing tailored communication strategies for inexperienced individuals, and considering supplementary measures like licensing and community recognition. A transparent mechanism involving a neutral panel can also be established to ensure legal interpretations align with community norms. Forum administrators, on the other hand, can provide additional information and guidelines, foster responsible online environments, and align resources with professional standards to navigate the uncertain legal landscape and mitigate the chilling effect on knowledge-sharing.
{"title":"Chilling Effect of the Enforcement of Computer Misuse Act: Evidence from Publicly Accessible Hack Forums","authors":"Qiu-Hong Wang, Ruibin Geng, Seung Hyun Kim","doi":"10.1287/isre.2019.0346","DOIUrl":"https://doi.org/10.1287/isre.2019.0346","url":null,"abstract":"To reduce the availability of hacking tools for use in cybersecurity offenses, many countries have enacted computer misuse acts (CMA) that criminalize the production, distribution, and possession of such tools with criminal intent. Nevertheless, our research illuminates an unintended consequence: the chilling effect of CMA enforcement on legitimate cybersecurity discussions, some of which may be desirable for cybersecurity research, within online hack forums. More importantly, this study uniquely examines the chilling effect stemming from users’ fear of legal harm. Drawing on decision-making theories related to choice under uncertainty, we derive new insights into how legal enforcement can suppress lawful acts and reveal the dynamics of social categorization online. Our research offers valuable insights for policymakers and forum administrators. Policymakers can use our findings to mitigate unnecessary uncertainty in legal enforcement such as CMA. This includes developing legal cases to prevent false prosecutions, implementing tailored communication strategies for inexperienced individuals, and considering supplementary measures like licensing and community recognition. A transparent mechanism involving a neutral panel can also be established to ensure legal interpretations align with community norms. Forum administrators, on the other hand, can provide additional information and guidelines, foster responsible online environments, and align resources with professional standards to navigate the uncertain legal landscape and mitigate the chilling effect on knowledge-sharing.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135013991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper investigates the ramifications of information feed integration on user engagements and contributions in online content-sharing platforms by exploiting a natural experiment occurred in a leading knowledge-sharing platform that integrated informal social posts with professional knowledge content in one feed. Our results show that the juxtaposition of incongruous types of content increased mindset switching and cognitive strain, thus hurting user engagements. We also reveal a novel crowding-out effect, viz., the integration heightened concerns that posting informal social posts would dilute the contributor’s professional image, thus inhibiting user contributions. Our findings hold important practical implications for all platforms that host (or are considering hosting) diverse types of user-generated content (UGC). Additional content curation tools can potentially enhance user engagement and retention, but their effectiveness hinges on a foundational and crucial element—the presentation format of heterogeneous content types. Essentially, the value of curating informal social posts in a knowledge-sharing platform would diminish when those content intrudes upon and conflict with the professional domain. This insight underscores that any UGC platforms, when adopting a diversity-oriented strategy, should pay close attention to heterogeneity between different content types for the purpose of optimizing user experiences and promoting user contributions.
{"title":"Consequences of Information Feed Integration on User Engagement and Contribution: A Natural Experiment in an Online Knowledge-Sharing Community","authors":"Zike Cao, Yingpeng Zhu, Gen Li, Liangfei Qiu","doi":"10.1287/isre.2022.0043","DOIUrl":"https://doi.org/10.1287/isre.2022.0043","url":null,"abstract":"This paper investigates the ramifications of information feed integration on user engagements and contributions in online content-sharing platforms by exploiting a natural experiment occurred in a leading knowledge-sharing platform that integrated informal social posts with professional knowledge content in one feed. Our results show that the juxtaposition of incongruous types of content increased mindset switching and cognitive strain, thus hurting user engagements. We also reveal a novel crowding-out effect, viz., the integration heightened concerns that posting informal social posts would dilute the contributor’s professional image, thus inhibiting user contributions. Our findings hold important practical implications for all platforms that host (or are considering hosting) diverse types of user-generated content (UGC). Additional content curation tools can potentially enhance user engagement and retention, but their effectiveness hinges on a foundational and crucial element—the presentation format of heterogeneous content types. Essentially, the value of curating informal social posts in a knowledge-sharing platform would diminish when those content intrudes upon and conflict with the professional domain. This insight underscores that any UGC platforms, when adopting a diversity-oriented strategy, should pay close attention to heterogeneity between different content types for the purpose of optimizing user experiences and promoting user contributions.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134912289","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}
Clocking-in cash-back (CIC), an emerging gamified business model in online learning, has recently garnered significant attention. CIC allows users to secure a full refund of the course fee through consecutive completion of specific tasks within a required time window. These tasks, known as clocking in, encompass activities such as daily assignments and sharing progress updates on social media. By employing this gamification system, the firm effectively monitors user efforts, categorizing them as winners or quitters based on clocking-in completion. In this paper, we examine how a firm should set the optimal time window for its course and how the time window is affected by context-specific factors. We identify two opposing effects associated with extending the time window on users’ quitting time: the psychological disutility increasing effect (negative) and the effort cost decreasing effect (positive). Our results indicate that, as quitters’ positive word-of-mouth effects increase, there are cases in which the firm should opt for shortening the time window. Additionally, we find that, as the marginal content creation cost rises, the firm may find it more advantageous to raise the difficulty level by shortening the time window. Our findings provide valuable insights that online learning firms can utilize to enhance their design of the CIC mechanism.
{"title":"Clocking in or Not? Optimal Design of a Novel Gamified Business Model in Online Learning","authors":"Yi Gao, Subodha Kumar, Dengpan Liu","doi":"10.1287/isre.2021.0138","DOIUrl":"https://doi.org/10.1287/isre.2021.0138","url":null,"abstract":"Clocking-in cash-back (CIC), an emerging gamified business model in online learning, has recently garnered significant attention. CIC allows users to secure a full refund of the course fee through consecutive completion of specific tasks within a required time window. These tasks, known as clocking in, encompass activities such as daily assignments and sharing progress updates on social media. By employing this gamification system, the firm effectively monitors user efforts, categorizing them as winners or quitters based on clocking-in completion. In this paper, we examine how a firm should set the optimal time window for its course and how the time window is affected by context-specific factors. We identify two opposing effects associated with extending the time window on users’ quitting time: the psychological disutility increasing effect (negative) and the effort cost decreasing effect (positive). Our results indicate that, as quitters’ positive word-of-mouth effects increase, there are cases in which the firm should opt for shortening the time window. Additionally, we find that, as the marginal content creation cost rises, the firm may find it more advantageous to raise the difficulty level by shortening the time window. Our findings provide valuable insights that online learning firms can utilize to enhance their design of the CIC mechanism.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135938451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the growing popularity of e-commerce, nearly every prominent retailer is aiming to turn omni-channel. One crucial decision in this pursuit is the identification of the joint assortment. In this study, we contribute by examining joint assortment and product prices for a retailer that sells products through both brick-and-mortar and online channels. Our analysis indicates that the optimal assortment should be thought of as a portfolio of two types of products: customized and omni-channel. Customized products are priced in such a way that they are targeted toward customers who prefer to shop from the channel the products are sold through. In contrast, omni-channel products are priced attractively so that all customers consider buying them. The relative mix of these products depends on how flexible customers are in shopping from the channel they do not prefer and the number of customers who prefer each channel. Additionally, we investigate whether the conventional wisdom of selling niche products through the online channel is always optimal. We find that this suggestion may be sub-optimal when the online channel has greater cost of including a product in the assortment and fewer preferring customers compared with the brick-and-mortar channel.
{"title":"Optimal Joint Assortment for an Omni-Channel Retailer","authors":"A. Sapra, Subodha Kumar","doi":"10.1287/isre.2021.0596","DOIUrl":"https://doi.org/10.1287/isre.2021.0596","url":null,"abstract":"With the growing popularity of e-commerce, nearly every prominent retailer is aiming to turn omni-channel. One crucial decision in this pursuit is the identification of the joint assortment. In this study, we contribute by examining joint assortment and product prices for a retailer that sells products through both brick-and-mortar and online channels. Our analysis indicates that the optimal assortment should be thought of as a portfolio of two types of products: customized and omni-channel. Customized products are priced in such a way that they are targeted toward customers who prefer to shop from the channel the products are sold through. In contrast, omni-channel products are priced attractively so that all customers consider buying them. The relative mix of these products depends on how flexible customers are in shopping from the channel they do not prefer and the number of customers who prefer each channel. Additionally, we investigate whether the conventional wisdom of selling niche products through the online channel is always optimal. We find that this suggestion may be sub-optimal when the online channel has greater cost of including a product in the assortment and fewer preferring customers compared with the brick-and-mortar channel.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44583058","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 realm of disaster response operations, effective resource management is crucial. This research introduces an innovative approach that proactively determines the optimal quantities of resources that should be requested by local agencies. This determination is based on both current and anticipated demands, thereby ensuring a more efficient and effective response to disasters. The approach first utilizes a method that combines deep learning and temporal point process for predicting irregularly spaced future demands, and then, it formulates the resource allocation problem faced with randomly arrived demands as a stochastic optimization model. The superiority of this approach over existing resource allocation methods is demonstrated using both real-world data and simulated scenarios. The findings highlight the need for a shift from reactive to proactive strategies. Moreover, the research emphasizes the potential of advanced techniques, such as deep learning and stochastic optimization, in disaster management. These techniques can provide valuable tools for policy makers and practitioners in the field, enabling them to make more informed and effective decisions. Policies that encourage the adoption of such optimized resource allocation strategies could lead to more effective disaster response operations.
{"title":"Proactive Resource Request for Disaster Response: A Deep Learning-Based Optimization Model","authors":"Hongzhe Zhang, Xiaohang Zhao, Xiao Fang, Bintong Chen","doi":"10.1287/isre.2022.0125","DOIUrl":"https://doi.org/10.1287/isre.2022.0125","url":null,"abstract":"In the realm of disaster response operations, effective resource management is crucial. This research introduces an innovative approach that proactively determines the optimal quantities of resources that should be requested by local agencies. This determination is based on both current and anticipated demands, thereby ensuring a more efficient and effective response to disasters. The approach first utilizes a method that combines deep learning and temporal point process for predicting irregularly spaced future demands, and then, it formulates the resource allocation problem faced with randomly arrived demands as a stochastic optimization model. The superiority of this approach over existing resource allocation methods is demonstrated using both real-world data and simulated scenarios. The findings highlight the need for a shift from reactive to proactive strategies. Moreover, the research emphasizes the potential of advanced techniques, such as deep learning and stochastic optimization, in disaster management. These techniques can provide valuable tools for policy makers and practitioners in the field, enabling them to make more informed and effective decisions. Policies that encourage the adoption of such optimized resource allocation strategies could lead to more effective disaster response operations.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135150294","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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