Pub Date : 2024-06-10DOI: 10.1177/10591478241264022
Salar Ghamat, Mojtaba Araghi, Lauren E. Cipriano, Michael Silverman
Antibiotic resistance is an ongoing public health crisis fueled by overuse and misuse of antibiotics. The goal of this paper is to examine the impact of action-based incentive payments on reducing inappropriate antibiotic prescriptions in primary care, where thirty to fifty percent of antibiotic prescriptions are inappropriate. Various financial incentive programs to reduce the rate of inappropriate antibiotic prescriptions have been implemented and studied empirically. However, there have not been analytical studies to evaluate payment model contract design features and the potential for payment models to impact diagnosis decision making. We develop a stylized physician compensation model to study the interaction between a payer and a provider. The payer offers a payment contract, with a bonus tied to the prescription, to maximize social welfare, considering total costs of providing care and social harm from antibiotic resistance. Given the contract offered and their own opportunity cost associated with factors such as fear of misdiagnosis and time spent explaining to patients why antibiotics are not indicated, the provider chooses whether or not to prescribe antibiotics to patients for whom antibiotics are not clinically indicated. We consider four cases: when diagnostic accuracy relies on symptom presentation vs. additional diagnostic testing and when the opportunity cost of not prescribing antibiotics is public vs. private information of the provider. When there is no information asymmetry, an action-based incentive payment can coordinate care and achieve the first-best policy, decreasing the rate of inappropriate prescribing, even when incentive payments can affect the diagnosis behavior. However, when the diagnosis depends on additional testing, the first-best policy results in fewer inappropriate antibiotic prescriptions, when the test has high specificity. Therefore, when an accurate technical diagnostic is available, a simple to implement action-based incentive payment can be effective in reducing inappropriate antibiotic prescribing. In the realistic setting where the provider’s opportunity cost is private information, an action-based incentive payment cannot eliminate inappropriate antibiotic prescribing. In these settings, the introduction of point of care diagnostics to aid in objective diagnostic criteria will reduce the unintended consequences of the contract.
{"title":"EXPRESS: Influencing Primary Care Antibiotic Prescription Behavior Using Financial Incentives","authors":"Salar Ghamat, Mojtaba Araghi, Lauren E. Cipriano, Michael Silverman","doi":"10.1177/10591478241264022","DOIUrl":"https://doi.org/10.1177/10591478241264022","url":null,"abstract":"Antibiotic resistance is an ongoing public health crisis fueled by overuse and misuse of antibiotics. The goal of this paper is to examine the impact of action-based incentive payments on reducing inappropriate antibiotic prescriptions in primary care, where thirty to fifty percent of antibiotic prescriptions are inappropriate. Various financial incentive programs to reduce the rate of inappropriate antibiotic prescriptions have been implemented and studied empirically. However, there have not been analytical studies to evaluate payment model contract design features and the potential for payment models to impact diagnosis decision making. We develop a stylized physician compensation model to study the interaction between a payer and a provider. The payer offers a payment contract, with a bonus tied to the prescription, to maximize social welfare, considering total costs of providing care and social harm from antibiotic resistance. Given the contract offered and their own opportunity cost associated with factors such as fear of misdiagnosis and time spent explaining to patients why antibiotics are not indicated, the provider chooses whether or not to prescribe antibiotics to patients for whom antibiotics are not clinically indicated. We consider four cases: when diagnostic accuracy relies on symptom presentation vs. additional diagnostic testing and when the opportunity cost of not prescribing antibiotics is public vs. private information of the provider. When there is no information asymmetry, an action-based incentive payment can coordinate care and achieve the first-best policy, decreasing the rate of inappropriate prescribing, even when incentive payments can affect the diagnosis behavior. However, when the diagnosis depends on additional testing, the first-best policy results in fewer inappropriate antibiotic prescriptions, when the test has high specificity. Therefore, when an accurate technical diagnostic is available, a simple to implement action-based incentive payment can be effective in reducing inappropriate antibiotic prescribing. In the realistic setting where the provider’s opportunity cost is private information, an action-based incentive payment cannot eliminate inappropriate antibiotic prescribing. In these settings, the introduction of point of care diagnostics to aid in objective diagnostic criteria will reduce the unintended consequences of the contract.","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141363815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-10DOI: 10.1177/10591478241263857
Cuong Le, Tien Mai
We study the assortment optimization problem under general linear constraints, where the customer choice behavior is captured by the Cross-Nested Logit model. In this problem, there is a set of products organized into multiple subsets (or nests), where each product can belong to more than one nest. The aim is to find an assortment to offer to customers so that the expected revenue is maximized. We show that, under the Cross-Nested Logit model, the unconstrained assortment problem is NP-hard even when there are only two nests, and the problem is generally NP-hard to approximate to any constant factors. To tackle this challenging problem, we develop a new discretization mechanism to approximate the problem by a linear fractional program with a performance guarantee of [Formula: see text], for any accuracy level ε > 0. We then show that optimal solutions to the approximate problem can be obtained by solving mixed-integer linear programs. We further show that our discretization approach can also be applied to solve a joint assortment optimization and pricing problem, as well as an assortment problem under a mixture of Cross-Nested Logit models to account for multiple classes of customers. Our empirical results on a large number of randomly generated test instances demonstrate that, under a performance guarantee of 90% (i.e., expected revenues are guaranteed to be at least 90% of the optimal revenue), the percentage gaps between the objective values obtained from our approximation methods and the optimal expected revenues are no larger than 1.2%.
{"title":"EXPRESS: Constrained Assortment Optimization under the Cross-Nested Logit Model","authors":"Cuong Le, Tien Mai","doi":"10.1177/10591478241263857","DOIUrl":"https://doi.org/10.1177/10591478241263857","url":null,"abstract":"We study the assortment optimization problem under general linear constraints, where the customer choice behavior is captured by the Cross-Nested Logit model. In this problem, there is a set of products organized into multiple subsets (or nests), where each product can belong to more than one nest. The aim is to find an assortment to offer to customers so that the expected revenue is maximized. We show that, under the Cross-Nested Logit model, the unconstrained assortment problem is NP-hard even when there are only two nests, and the problem is generally NP-hard to approximate to any constant factors. To tackle this challenging problem, we develop a new discretization mechanism to approximate the problem by a linear fractional program with a performance guarantee of [Formula: see text], for any accuracy level ε > 0. We then show that optimal solutions to the approximate problem can be obtained by solving mixed-integer linear programs. We further show that our discretization approach can also be applied to solve a joint assortment optimization and pricing problem, as well as an assortment problem under a mixture of Cross-Nested Logit models to account for multiple classes of customers. Our empirical results on a large number of randomly generated test instances demonstrate that, under a performance guarantee of 90% (i.e., expected revenues are guaranteed to be at least 90% of the optimal revenue), the percentage gaps between the objective values obtained from our approximation methods and the optimal expected revenues are no larger than 1.2%.","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141361605","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 article focuses on the social choice problem in which decisions are based on the utility of multiple stakeholder types. The sum of these utilities – Utilitarian welfare - is one of the most important objectives in solving the social choice problem. While it is the most efficient solution, maximizing Utilitarian welfare may lead to unfair outcomes. However, Encouraging a Utilitarian decision-maker to adopt a fair decision is challenging due to the associated efficiency loss. This article takes a novel perspective by motivating a Utilitarian decision-maker to make fair decisions from an uncertainty-averse standpoint. We study the problem where the proportions of stakeholder types are uncertain and propose a distributionally robust optimization (DRO) model that maximizes the worst-case Utilitarian welfare over an ϕ-divergence-based uncertainty set. We provide three aspects of the relationship between fairness and the uncertain-averse Utilitarian welfare maximization. First, we establish that the worst-case Utilitarian welfare adheres to all five axioms of unfairness-averse cardinal welfare functions with two stakeholder types and satisfies four of these when this number exceeds two. Second, we demonstrate that with the maximal extent of uncertainty aversion, the DRO model identifies the Egalitarian welfare maximizer, which prioritizes fairness. Further, given serveral conventional assumptions, the proposed model selects the Nash welfare maximizer, an objective trade-off between efficiency and fairness, with moderate levels of uncertainty aversion. Lastly, we present numerical studies of two specific instances of the social choice problem – resource allocation and facility location – to show that, as uncertainty aversion increases, our model increasingly emphasizes fairness.
{"title":"EXPRESS: Fairness as a Robust Utilitarianism","authors":"Maoqi Liu, Qingchun Meng, Guodong Yu, Zhi-Hai Zhang","doi":"10.1177/10591478241262285","DOIUrl":"https://doi.org/10.1177/10591478241262285","url":null,"abstract":"This article focuses on the social choice problem in which decisions are based on the utility of multiple stakeholder types. The sum of these utilities – Utilitarian welfare - is one of the most important objectives in solving the social choice problem. While it is the most efficient solution, maximizing Utilitarian welfare may lead to unfair outcomes. However, Encouraging a Utilitarian decision-maker to adopt a fair decision is challenging due to the associated efficiency loss. This article takes a novel perspective by motivating a Utilitarian decision-maker to make fair decisions from an uncertainty-averse standpoint. We study the problem where the proportions of stakeholder types are uncertain and propose a distributionally robust optimization (DRO) model that maximizes the worst-case Utilitarian welfare over an ϕ-divergence-based uncertainty set. We provide three aspects of the relationship between fairness and the uncertain-averse Utilitarian welfare maximization. First, we establish that the worst-case Utilitarian welfare adheres to all five axioms of unfairness-averse cardinal welfare functions with two stakeholder types and satisfies four of these when this number exceeds two. Second, we demonstrate that with the maximal extent of uncertainty aversion, the DRO model identifies the Egalitarian welfare maximizer, which prioritizes fairness. Further, given serveral conventional assumptions, the proposed model selects the Nash welfare maximizer, an objective trade-off between efficiency and fairness, with moderate levels of uncertainty aversion. Lastly, we present numerical studies of two specific instances of the social choice problem – resource allocation and facility location – to show that, as uncertainty aversion increases, our model increasingly emphasizes fairness.","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141382338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-05DOI: 10.1177/10591478241262279
Xiaoyan Qian, Quan Zhou, T. Olsen
Equity investment in agricultural cooperatives (co-ops) is typically limited to farmer-members; yet farmers are usually cash-constrained. In addition to the common stock that is held by farmer-members, many co-ops are changing their financial structure by raising equity from external investors. This helps co-ops to collect capital, but also brings to the fore the conflicting benefits of farmers and external investors. In this paper, we develop a two-stage game-theoretic model to examine a start-up co-op’s farm-gate pricing and financing strategies, considering two types of external fund: preferred stock that bears a fixed return rate and outside stock that shares the net profit (in proportion to equity) with common stock. We characterize the co-op’s strategies in different scenarios and generate the following insights. First, while both types of external equity outperform the case with common stock only, preferred stock generally outperforms outside stock due to its lower financial cost, higher tolerance for fund size limits, and flexibility in setting farm-gate prices. However, outside stock can outperform preferred stock if it allows a higher fund size limit. Second, the co-op’s financial strategy exhibits a similar structure in equilibrium regardless whether it is preferred stock or outside stock, despite their distinct financial terms. Finally, farm-gate pricing has a unique role in co-ops affecting the returns to farmers and external investors, which also highlights the conflicting roles of farmers as both patrons and investors when external equity is used.
{"title":"EXPRESS: Financing and Farm-gate Pricing Strategies for Agricultural Cooperatives with Cash-constrained Farmers","authors":"Xiaoyan Qian, Quan Zhou, T. Olsen","doi":"10.1177/10591478241262279","DOIUrl":"https://doi.org/10.1177/10591478241262279","url":null,"abstract":"Equity investment in agricultural cooperatives (co-ops) is typically limited to farmer-members; yet farmers are usually cash-constrained. In addition to the common stock that is held by farmer-members, many co-ops are changing their financial structure by raising equity from external investors. This helps co-ops to collect capital, but also brings to the fore the conflicting benefits of farmers and external investors. In this paper, we develop a two-stage game-theoretic model to examine a start-up co-op’s farm-gate pricing and financing strategies, considering two types of external fund: preferred stock that bears a fixed return rate and outside stock that shares the net profit (in proportion to equity) with common stock. We characterize the co-op’s strategies in different scenarios and generate the following insights. First, while both types of external equity outperform the case with common stock only, preferred stock generally outperforms outside stock due to its lower financial cost, higher tolerance for fund size limits, and flexibility in setting farm-gate prices. However, outside stock can outperform preferred stock if it allows a higher fund size limit. Second, the co-op’s financial strategy exhibits a similar structure in equilibrium regardless whether it is preferred stock or outside stock, despite their distinct financial terms. Finally, farm-gate pricing has a unique role in co-ops affecting the returns to farmers and external investors, which also highlights the conflicting roles of farmers as both patrons and investors when external equity is used.","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141385359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-21DOI: 10.1177/10591478241259422
Panagiotis Adamopoulos, Vilma Todri
Consumers often rely on their social connections or social technologies, such as (automated) system-generated recommender systems, to navigate the proliferation of diverse products and services offered in online and offline markets and cope with the corresponding choice overload. In this study, we investigate the relationship between the consumers’ social connectedness and the economic impact of recommender systems. Specifically, we examine whether the social connectedness levels of consumers moderate the effectiveness of online recommendations toward increasing product demand levels. We study this novel research question using a combination of datasets and a demand-estimation model. Interestingly, the empirical results show a positive moderating effect of social connectedness on the demand effect of online-to-offline recommendations. Further delving into the findings, we also provide empirical evidence that social identification might explain why denser social connectedness with local users accentuates the effects of collaborative filtering online-to-offline recommendations. Our study enhances the understanding of community factors affecting the efficacy of social technologies in multi-channel operations while also extending the social identity theory in operations in the digital realm. The results also have intriguing operational implications for operations managers and practitioners, while suggesting several interesting avenues for future research on social technologies and operations management.
{"title":"EXPRESS: Consumer Social Connectedness and Persuasiveness of Collaborative-Filtering Recommender Systems: Evidence from an Online-to-Offline Recommendation App","authors":"Panagiotis Adamopoulos, Vilma Todri","doi":"10.1177/10591478241259422","DOIUrl":"https://doi.org/10.1177/10591478241259422","url":null,"abstract":"Consumers often rely on their social connections or social technologies, such as (automated) system-generated recommender systems, to navigate the proliferation of diverse products and services offered in online and offline markets and cope with the corresponding choice overload. In this study, we investigate the relationship between the consumers’ social connectedness and the economic impact of recommender systems. Specifically, we examine whether the social connectedness levels of consumers moderate the effectiveness of online recommendations toward increasing product demand levels. We study this novel research question using a combination of datasets and a demand-estimation model. Interestingly, the empirical results show a positive moderating effect of social connectedness on the demand effect of online-to-offline recommendations. Further delving into the findings, we also provide empirical evidence that social identification might explain why denser social connectedness with local users accentuates the effects of collaborative filtering online-to-offline recommendations. Our study enhances the understanding of community factors affecting the efficacy of social technologies in multi-channel operations while also extending the social identity theory in operations in the digital realm. The results also have intriguing operational implications for operations managers and practitioners, while suggesting several interesting avenues for future research on social technologies and operations management.","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141116480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-21DOI: 10.1177/10591478241259408
Tian Li, Huajiang Luo, Weixin Shang
We study two competing firms’ incentives for demand information sharing and their production timing strategies. One firm adopts routine timing, where her production time is fixed according to her previous product models’ manufacturing time. The other firm uses strategic timing, where his production time can be strategically chosen to occur before, concurrently with, or after that of the routine-timing firm. The firms decide whether to disclose their private demand information and make quantity decisions based on the available demand information, either simultaneously or sequentially. We analyze the optimal production timing decisions for the strategic firm under different information sharing scenarios and find that a preemptive move is generally not optimal. We demonstrate that endogenous production timing can create incentives for information sharing and characterize the conditions under which both firms share information, one firm shares information, or neither firm shares information. Additionally, we uncover several interesting implications of information sharing under endogenous production timing: firms are more likely to share information in intensified competition, a firm may benefit from its rival’s superior information capability, and the option of information sharing enhances social welfare, which may also benefit from more intense competition.
{"title":"EXPRESS: Information Sharing between Competitors with Endogenous Production Timing","authors":"Tian Li, Huajiang Luo, Weixin Shang","doi":"10.1177/10591478241259408","DOIUrl":"https://doi.org/10.1177/10591478241259408","url":null,"abstract":"We study two competing firms’ incentives for demand information sharing and their production timing strategies. One firm adopts routine timing, where her production time is fixed according to her previous product models’ manufacturing time. The other firm uses strategic timing, where his production time can be strategically chosen to occur before, concurrently with, or after that of the routine-timing firm. The firms decide whether to disclose their private demand information and make quantity decisions based on the available demand information, either simultaneously or sequentially. We analyze the optimal production timing decisions for the strategic firm under different information sharing scenarios and find that a preemptive move is generally not optimal. We demonstrate that endogenous production timing can create incentives for information sharing and characterize the conditions under which both firms share information, one firm shares information, or neither firm shares information. Additionally, we uncover several interesting implications of information sharing under endogenous production timing: firms are more likely to share information in intensified competition, a firm may benefit from its rival’s superior information capability, and the option of information sharing enhances social welfare, which may also benefit from more intense competition.","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141118055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-19DOI: 10.1177/10591478241258197
Sanghoon Cho, Mark Ferguson, Jongho Im, Pelin Pekgün
We develop a novel statistical method to estimate customer choice among a firm’s portfolio of offerings when the firm cannot directly observe customers who choose not to purchase any product. This censored demand problem is prevalent in many industries such as hotels, airlines, and retail. Although several methods have been proposed to address this problem, they require some level of data aggregation across arrivals and/or choice sets, which results in information loss and potentially biased estimates. Therefore, they have limited applicability in an environment where the prices of a firm’s portfolio of offerings vary over time and sometimes even across different customers. Our proposed method combines several desirable properties, which makes it a better fit for realistic datasets where the available choice sets or attributes of the products in the choice sets change over time. We consider two additional types of information for identification of our model parameters: 1) additional mild assumptions on the customers’ utility function, and 2) external information about a firm’s market share. We then develop a robust estimation procedure that accounts for inaccuracies in either information type and let the data determine the best approach. Through Monte-Carlo simulations, we show that our approach provides promising predictions of customer choice behavior when compared with other generally used methods and clearly outperforms those methods in scenarios where the product prices change frequently over time. Utilizing a real hotel transaction dataset provided by Oracle Labs, we further illustrate the improved estimation accuracy of our method compared to benchmark methods. Relative to existing approaches for estimating customer choice-based models, our proposed methodology better suits environments employing dynamic pricing and personalized offering practices, such as hospitality or online retailing.
{"title":"EXPRESS: Robust Demand Estimation with Customer Choice-Based Models for Sales Transaction Data","authors":"Sanghoon Cho, Mark Ferguson, Jongho Im, Pelin Pekgün","doi":"10.1177/10591478241258197","DOIUrl":"https://doi.org/10.1177/10591478241258197","url":null,"abstract":"We develop a novel statistical method to estimate customer choice among a firm’s portfolio of offerings when the firm cannot directly observe customers who choose not to purchase any product. This censored demand problem is prevalent in many industries such as hotels, airlines, and retail. Although several methods have been proposed to address this problem, they require some level of data aggregation across arrivals and/or choice sets, which results in information loss and potentially biased estimates. Therefore, they have limited applicability in an environment where the prices of a firm’s portfolio of offerings vary over time and sometimes even across different customers. Our proposed method combines several desirable properties, which makes it a better fit for realistic datasets where the available choice sets or attributes of the products in the choice sets change over time. We consider two additional types of information for identification of our model parameters: 1) additional mild assumptions on the customers’ utility function, and 2) external information about a firm’s market share. We then develop a robust estimation procedure that accounts for inaccuracies in either information type and let the data determine the best approach. Through Monte-Carlo simulations, we show that our approach provides promising predictions of customer choice behavior when compared with other generally used methods and clearly outperforms those methods in scenarios where the product prices change frequently over time. Utilizing a real hotel transaction dataset provided by Oracle Labs, we further illustrate the improved estimation accuracy of our method compared to benchmark methods. Relative to existing approaches for estimating customer choice-based models, our proposed methodology better suits environments employing dynamic pricing and personalized offering practices, such as hospitality or online retailing.","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141124615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-14DOI: 10.1177/10591478241257660
Tarun Jain, J. Hazra, Ram Gopal
3D printing technology has opened up possibilities of product design collaborations between device providers and customers. To enable an environment of co-creation, device providers are now renting 3D printers via the 3D-as-a-Service (3DaaS) model. Although prior research has examined pricing and quality issues in the traditional manufacturing setup, these studies have not analyzed such decisions in the 3D printing supply chain setting, where end users possess the ability to customize product designs. Therefore, several important questions remain unanswered from the perspective of the 3D printing device provider. For example, what is the appropriate pricing model for providing 3DaaS? How do factors like the extent of design customization and the complexity influence the pricing strategy of the 3DaaS firm? Our analysis shows that if the customers’ impact on the product quality is relatively high or low, the pay-per-build pricing model generates a higher profit than the fixed-fee pricing model. Interestingly, we also find that if customers frequently print highly intricate product designs, the firm might choose the pay-per-build pricing model, only if the likelihood of design failure for these complex structures is low. Otherwise, the firm might opt for a fixed-fee pricing model.
三维打印技术为设备提供商和客户之间的产品设计合作提供了可能性。为了营造共同创造的环境，设备提供商目前正通过三维即服务（3DaaS）模式租用三维打印机。虽然之前的研究已经考察了传统制造设置中的定价和质量问题，但这些研究还没有分析过 3D 打印供应链环境中的此类决策，因为最终用户拥有定制产品设计的能力。因此，从 3D 打印设备提供商的角度来看，有几个重要问题仍未得到解答。例如，提供 3DaaS 的合适定价模式是什么？设计定制化程度和复杂性等因素如何影响 3DaaS 公司的定价策略？我们的分析表明，如果客户对产品质量的影响相对较高或较低，按制造付费定价模式比固定收费定价模式产生的利润更高。有趣的是，我们还发现，如果客户经常打印高度复杂的产品设计，公司可能会选择按构建付费的定价模式，但前提是这些复杂结构设计失败的可能性较低。否则，企业可能会选择固定收费定价模式。
{"title":"EXPRESS: 3D Printing-as-a-Service: an Economic Analysis of Pricing and Co-creation","authors":"Tarun Jain, J. Hazra, Ram Gopal","doi":"10.1177/10591478241257660","DOIUrl":"https://doi.org/10.1177/10591478241257660","url":null,"abstract":"3D printing technology has opened up possibilities of product design collaborations between device providers and customers. To enable an environment of co-creation, device providers are now renting 3D printers via the 3D-as-a-Service (3DaaS) model. Although prior research has examined pricing and quality issues in the traditional manufacturing setup, these studies have not analyzed such decisions in the 3D printing supply chain setting, where end users possess the ability to customize product designs. Therefore, several important questions remain unanswered from the perspective of the 3D printing device provider. For example, what is the appropriate pricing model for providing 3DaaS? How do factors like the extent of design customization and the complexity influence the pricing strategy of the 3DaaS firm? Our analysis shows that if the customers’ impact on the product quality is relatively high or low, the pay-per-build pricing model generates a higher profit than the fixed-fee pricing model. Interestingly, we also find that if customers frequently print highly intricate product designs, the firm might choose the pay-per-build pricing model, only if the likelihood of design failure for these complex structures is low. Otherwise, the firm might opt for a fixed-fee pricing model.","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140981585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-09DOI: 10.1177/10591478241256381
Motivated by a desire to understand who benefits from public-sector supplier diversity, and why, we conducted a comparative case study across six U.S. state-level supplier diversity programs. We collected/analyzed qualitative data via semi-structured interviews with high-level state leaders and collected spending data via 2017–2021 semiannual/annual reports. We found that businesses owned by White women benefit more from supplier diversity programs than those owned by ethnic minorities (Blacks, Hispanics, Indigenous, and Asians). Adopting an intersectional invisibility perspective, we conduct within- and cross-case analyses of the collected data, we highlight key themes and make several recommendations for how public-sector supplier diversity programs can systemically address disparities and improve their performance. We also offer recommendations for future research into this ecosystem.
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Pub Date : 2024-05-09DOI: 10.1177/10591478241256662
Buqing Ma, Guang Li, Guangwen Kong
Consumer information sharing is considered an effective strategy to attract consumers, yet certain high-end retailers, such as Bergdorf Goodman and Farfetch, tend to hinder consumers from sharing information through online reviews. We study a retailer’s strategy for consumer information sharing in a supply chain. We find that a retailer’s information sharing strategy can prevent manufacturers from extracting excessive profit when consumers are heterogeneous in their valuations of the selling product. Specifically, a retailer can achieve a higher profit margin by targeting all consumer segments. By strategically choosing the information sharing strategy to influence consumer beliefs, the retailer can induce the manufacturer to conform to the retailer’s preferred targeting segment through a low wholesale price. Thus, a high-end retailer, whose consumers have a high ex-ante quality belief, favors hindering information sharing among consumers because it enables the retailer to target all consumer segments. Interestingly, deterring consumers from learning about the product quality may generate more consumer surplus. Our main results are robust under extensions such as consumer search behavior, consumer waiting, and multiple product selling. When selling multiple products, a retailer with a large quality variation is better off hindering consumers from sharing information. Our work shows that strategically choosing a consumer information sharing strategy enables retailers to enhance profit margins in their interactions with upstream manufacturers.
{"title":"EXPRESS: To Hinder or to Facilitate: Retailers’ Strategy of Consumer Information Sharing","authors":"Buqing Ma, Guang Li, Guangwen Kong","doi":"10.1177/10591478241256662","DOIUrl":"https://doi.org/10.1177/10591478241256662","url":null,"abstract":"Consumer information sharing is considered an effective strategy to attract consumers, yet certain high-end retailers, such as Bergdorf Goodman and Farfetch, tend to hinder consumers from sharing information through online reviews. We study a retailer’s strategy for consumer information sharing in a supply chain. We find that a retailer’s information sharing strategy can prevent manufacturers from extracting excessive profit when consumers are heterogeneous in their valuations of the selling product. Specifically, a retailer can achieve a higher profit margin by targeting all consumer segments. By strategically choosing the information sharing strategy to influence consumer beliefs, the retailer can induce the manufacturer to conform to the retailer’s preferred targeting segment through a low wholesale price. Thus, a high-end retailer, whose consumers have a high ex-ante quality belief, favors hindering information sharing among consumers because it enables the retailer to target all consumer segments. Interestingly, deterring consumers from learning about the product quality may generate more consumer surplus. Our main results are robust under extensions such as consumer search behavior, consumer waiting, and multiple product selling. When selling multiple products, a retailer with a large quality variation is better off hindering consumers from sharing information. Our work shows that strategically choosing a consumer information sharing strategy enables retailers to enhance profit margins in their interactions with upstream manufacturers.","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140995670","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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