Abstract Over the past decade, the surge of peer‐to‐peer (P2P) ride‐sharing has significantly cut the market share and profitability for taxis, but taxis remain a major service provider in the personal transportation service industry. This paper analytically examines a market with two segments of consumers based on their travel distances, where a P2P platform and a traditional taxi company have different inconvenience costs and compete for customers through pricing. Our analysis shows that consumers’ inconvenience costs and the relative size or travel–distance heterogeneity of the two consumer segments play an important role in determining the firms’ equilibrium targeting and pricing decisions. We find that the taxi's inconvenience cost can have non‐monotonic effects on firms’ prices. An increase in the taxi's inconvenience cost can reduce both firms’ profits because it can induce both firms to lower their prices. In an extension, we show that distance‐based price discrimination (charging different unit prices based on the consumer's travel distance) can lead to win–win or lose–lose outcomes for both firms. Our results have useful managerial and regulatory implications.
{"title":"Competition between P2P Ridesharing Platforms and Traditional Taxis","authors":"Wen Diao, Baojun Jiang, Lin Tian","doi":"10.1111/poms.14062","DOIUrl":"https://doi.org/10.1111/poms.14062","url":null,"abstract":"Abstract Over the past decade, the surge of peer‐to‐peer (P2P) ride‐sharing has significantly cut the market share and profitability for taxis, but taxis remain a major service provider in the personal transportation service industry. This paper analytically examines a market with two segments of consumers based on their travel distances, where a P2P platform and a traditional taxi company have different inconvenience costs and compete for customers through pricing. Our analysis shows that consumers’ inconvenience costs and the relative size or travel–distance heterogeneity of the two consumer segments play an important role in determining the firms’ equilibrium targeting and pricing decisions. We find that the taxi's inconvenience cost can have non‐monotonic effects on firms’ prices. An increase in the taxi's inconvenience cost can reduce both firms’ profits because it can induce both firms to lower their prices. In an extension, we show that distance‐based price discrimination (charging different unit prices based on the consumer's travel distance) can lead to win–win or lose–lose outcomes for both firms. Our results have useful managerial and regulatory implications.","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136263359","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}
Ying Zhang, Sriram Narayanan, Tharo Soun, Kalyanmoy Deb, Dustin Cole
Abstract The UN Sustainable Development Goals have recently emphasized the importance of diversity inclusion. Motivated by this goal, we examine the impact of employing individuals with disabilities in apparel manufacturing cells on overall team productivity. We formulate a series of deterministic and stochastic productivity maximization assignment problems to study the impact of disability inclusion on a production line. We extend the baseline productivity maximization formulation to study a multi‐objective problem that simultaneously maximizes productivity, disability diversity, and language diversity. Each analysis is performed across two different garments drawn from a real‐world setting. The models are tested using archival time study data collected in partnership with an apparel manufacturing firm where more than 75% of the billed work hours are from individuals with significant disabilities. The following insights emerge from the analyses. First, the productivity of teams which have individuals across multiple, different types of disabilities is higher than the productivity of teams with employees who share a single, specific type of disability. Second, teams that employ both individuals with and without disabilities perform slightly better than teams that consist of only individuals with disabilities. In some instances, contrary to intuition, teams of only individuals with disabilities even have higher productivity than teams of only individuals with no disabilities. Finally, our results from the multi‐objective problem that simultaneously maximizes productivity, disability diversity, and language diversity suggest that productivity is not generally sensitive to increases in disability diversity. However, productivity is sensitive at extreme levels of disability diversity and language diversity. Limitations and possible future extensions of the study are discussed. This article is protected by copyright. All rights reserved
{"title":"Maximizing disability diversity, language diversity and productivity: A study in apparel manufacturing","authors":"Ying Zhang, Sriram Narayanan, Tharo Soun, Kalyanmoy Deb, Dustin Cole","doi":"10.1111/poms.14073","DOIUrl":"https://doi.org/10.1111/poms.14073","url":null,"abstract":"Abstract The UN Sustainable Development Goals have recently emphasized the importance of diversity inclusion. Motivated by this goal, we examine the impact of employing individuals with disabilities in apparel manufacturing cells on overall team productivity. We formulate a series of deterministic and stochastic productivity maximization assignment problems to study the impact of disability inclusion on a production line. We extend the baseline productivity maximization formulation to study a multi‐objective problem that simultaneously maximizes productivity, disability diversity, and language diversity. Each analysis is performed across two different garments drawn from a real‐world setting. The models are tested using archival time study data collected in partnership with an apparel manufacturing firm where more than 75% of the billed work hours are from individuals with significant disabilities. The following insights emerge from the analyses. First, the productivity of teams which have individuals across multiple, different types of disabilities is higher than the productivity of teams with employees who share a single, specific type of disability. Second, teams that employ both individuals with and without disabilities perform slightly better than teams that consist of only individuals with disabilities. In some instances, contrary to intuition, teams of only individuals with disabilities even have higher productivity than teams of only individuals with no disabilities. Finally, our results from the multi‐objective problem that simultaneously maximizes productivity, disability diversity, and language diversity suggest that productivity is not generally sensitive to increases in disability diversity. However, productivity is sensitive at extreme levels of disability diversity and language diversity. Limitations and possible future extensions of the study are discussed. This article is protected by copyright. All rights reserved","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135059334","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}
Abstract Many on‐demand service platforms employ surge pricing policies, charging higher prices and raising provider compensation when customer demand exceeds provider supply. There is increasing evidence that service providers understand these pricing policies and strategically collude to induce artificial supply shortages by reducing the number of providers showing as available online. We study a stylized mathematical model of a setting in which an on‐demand service platform determines its pricing and provider compensation policies, anticipating their impact on customer demand and the participation of strategic providers, who might collectively decide to limit the number of providers showing online as available. We find that collusion can substantially harm the platform and customers, especially when the potential demand is large, and the supply of providers in nearby regions is limited. We explore two pricing policies that a platform could employ in the presence of (potential) provider collusion: a bonus pricing policy that offers additional provider payments on top of the regular compensation and the optimal pricing policy that maximizes the platform's expected profit while taking strategic provider behavior fully into consideration. Both policies feature a compensation structure that ensures that total provider earnings increase in the number of providers available, thereby encouraging all providers to offer their service. We show that both policies can effectively mitigate the impact of potential provider collusion, with the bonus pricing policy often performing near‐optimally. As it might be difficult for a platform to accurately estimate the propensity of providers to collude, we numerically evaluate how platform profits are affected if the pricing policy is designed based on possibly incorrect estimates of the providers' propensity to collude. Our observations suggest that a platform should design its pricing policy under the assumption that all providers are strategic and consider collusion, as the losses associated with implementing such a policy in settings with minor risk of collusion are limited, while the potential losses from failing to consider rampant collusion can be significant.
{"title":"Hiding in plain Sight: Surge pricing and strategic providers","authors":"Jiaru B. Bai, H. Sebastian Heese, Manish Tripathy","doi":"10.1111/poms.14064","DOIUrl":"https://doi.org/10.1111/poms.14064","url":null,"abstract":"Abstract Many on‐demand service platforms employ surge pricing policies, charging higher prices and raising provider compensation when customer demand exceeds provider supply. There is increasing evidence that service providers understand these pricing policies and strategically collude to induce artificial supply shortages by reducing the number of providers showing as available online. We study a stylized mathematical model of a setting in which an on‐demand service platform determines its pricing and provider compensation policies, anticipating their impact on customer demand and the participation of strategic providers, who might collectively decide to limit the number of providers showing online as available. We find that collusion can substantially harm the platform and customers, especially when the potential demand is large, and the supply of providers in nearby regions is limited. We explore two pricing policies that a platform could employ in the presence of (potential) provider collusion: a bonus pricing policy that offers additional provider payments on top of the regular compensation and the optimal pricing policy that maximizes the platform's expected profit while taking strategic provider behavior fully into consideration. Both policies feature a compensation structure that ensures that total provider earnings increase in the number of providers available, thereby encouraging all providers to offer their service. We show that both policies can effectively mitigate the impact of potential provider collusion, with the bonus pricing policy often performing near‐optimally. As it might be difficult for a platform to accurately estimate the propensity of providers to collude, we numerically evaluate how platform profits are affected if the pricing policy is designed based on possibly incorrect estimates of the providers' propensity to collude. Our observations suggest that a platform should design its pricing policy under the assumption that all providers are strategic and consider collusion, as the losses associated with implementing such a policy in settings with minor risk of collusion are limited, while the potential losses from failing to consider rampant collusion can be significant.","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135011263","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}
We study the effect of managerial time‐horizon on two key operations decisions: inventory levels and capacity investment. For identification, we exploit a quasi‐natural experiment provided by the staggered adoption of constituency statutes, which alleviate managerial short‐termism by providing legal protection to executives adopting a long‐term approach in their corporate decisions. Using a staggered difference‐in‐differences design, we find that, after the reforms, firms incorporated in constituency states (treated firms) increased inventory and capacity investment by 5.2% and 15.4%, respectively, relative to firms not incorporated in constituency states (control firms). We also find that these increases are gradual and persist over time, suggesting that they are structural in nature. We further show that the effect of constituency statutes on inventory levels and capacity investment are stronger for firms with ex‐ante higher level of managerial short‐termism, such as firms with low institutional ownership. Performance also increases relatively more for affected firms with higher ex‐ante managerial short‐termism. Our results pass a battery of robustness and validity tests. Interestingly, while constituency statutes are intended to protect executives from short‐term oriented shareholder sanctions, our findings suggest that these statutes ultimately benefited not only executives, but also potentially the long‐term interest of shareholders. Even in the absence of regulation, executives facing short‐term pressure could intensify communication efforts with shareholders on how specific operational decisions and investments would be beneficial to the company. Notably, executives could use various media channels to help shape retail investors’ attitude towards operational investments that may create future value.This article is protected by copyright. All rights reserved
{"title":"Managerial flexibility, capacity investment, and inventory levels","authors":"K. Aral, Erasmo Giambona, L. V. Van Wassenhove","doi":"10.1111/poms.14067","DOIUrl":"https://doi.org/10.1111/poms.14067","url":null,"abstract":"We study the effect of managerial time‐horizon on two key operations decisions: inventory levels and capacity investment. For identification, we exploit a quasi‐natural experiment provided by the staggered adoption of constituency statutes, which alleviate managerial short‐termism by providing legal protection to executives adopting a long‐term approach in their corporate decisions. Using a staggered difference‐in‐differences design, we find that, after the reforms, firms incorporated in constituency states (treated firms) increased inventory and capacity investment by 5.2% and 15.4%, respectively, relative to firms not incorporated in constituency states (control firms). We also find that these increases are gradual and persist over time, suggesting that they are structural in nature. We further show that the effect of constituency statutes on inventory levels and capacity investment are stronger for firms with ex‐ante higher level of managerial short‐termism, such as firms with low institutional ownership. Performance also increases relatively more for affected firms with higher ex‐ante managerial short‐termism. Our results pass a battery of robustness and validity tests. Interestingly, while constituency statutes are intended to protect executives from short‐term oriented shareholder sanctions, our findings suggest that these statutes ultimately benefited not only executives, but also potentially the long‐term interest of shareholders. Even in the absence of regulation, executives facing short‐term pressure could intensify communication efforts with shareholders on how specific operational decisions and investments would be beneficial to the company. Notably, executives could use various media channels to help shape retail investors’ attitude towards operational investments that may create future value.This article is protected by copyright. All rights reserved","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44695899","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 proposes price cannibalization as a growth strategy despite prior findings that suggests avoiding it. We focus on a multi‐class, capacity‐constrained pricing problem in which each of the product classes has a price range. Specifically, we examine the effects of price range overlaps and introduce it as a revenue‐maximizing pricing strategy. Price cannibalization happens when sales in some product classes decrease due to the existence of overlaps between the price ranges. We employ a multi‐method approach. First, we define a Markovian Decision Problem (MDP) to obtain the revenue‐maximizing strategy in a two‐class sales scenario. We show that price range overlaps are part of the optimal strategy. Second, we collect multichannel data from a European storage company to examine how price range overlaps impact a customer's purchase decisions. The results show that the existence of price range overlaps leads to cannibalization, but increases spending and improves conversion. Finally, we use simulations to compare several pricing strategies and demonstrate the long‐term effects of using price range overlaps in pricing algorithms in complex situations. Our findings suggest that using price range overlaps, though leads to cannibalization, actually helps companies avoid spoilage and early sellouts, leading to better capacity utilization and higher revenue.This article is protected by copyright. All rights reserved
{"title":"Should price cannibalization be avoided or embraced? A multi‐method investigation","authors":"Atabak Mehrdar, Ting Li","doi":"10.1111/poms.14063","DOIUrl":"https://doi.org/10.1111/poms.14063","url":null,"abstract":"This paper proposes price cannibalization as a growth strategy despite prior findings that suggests avoiding it. We focus on a multi‐class, capacity‐constrained pricing problem in which each of the product classes has a price range. Specifically, we examine the effects of price range overlaps and introduce it as a revenue‐maximizing pricing strategy. Price cannibalization happens when sales in some product classes decrease due to the existence of overlaps between the price ranges. We employ a multi‐method approach. First, we define a Markovian Decision Problem (MDP) to obtain the revenue‐maximizing strategy in a two‐class sales scenario. We show that price range overlaps are part of the optimal strategy. Second, we collect multichannel data from a European storage company to examine how price range overlaps impact a customer's purchase decisions. The results show that the existence of price range overlaps leads to cannibalization, but increases spending and improves conversion. Finally, we use simulations to compare several pricing strategies and demonstrate the long‐term effects of using price range overlaps in pricing algorithms in complex situations. Our findings suggest that using price range overlaps, though leads to cannibalization, actually helps companies avoid spoilage and early sellouts, leading to better capacity utilization and higher revenue.This article is protected by copyright. All rights reserved","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46023092","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 have been proactively holding data science competitions via online contest platforms to look for innovative solutions from the crowd. When firms are designing such competitions, a key question is: What should be a better contest design to motivate contestants to exert more effort? We model two commonly observed contest structures (one‐stage and two‐stage) and two widely adopted prize structures (high‐spread and low‐spread). We employ economic experiments to examine how contest design affects contestants’ effort level. The results reject the base model with rationality assumption. We find that contestants exert significantly more effort in both the first stage and the second stage of the two‐stage contest. Moreover, it is better to assign most prizes to the winner in the two‐stage contest while it does not matter in one‐stage. To explain the empirical regularities, we develop a behavioral economics model that captures contestants’ psychological aversion to falling behind and continuous exertion of effort. Our findings demonstrate that it is important for contest organizers to account for the non‐pecuniary factors that can influence contestants’ behavior in designing a competition.This article is protected by copyright. All rights reserved
{"title":"Designing contests for data science competitions: Number of stages and prize structures","authors":"Jialu Liu, Keehyung Kim","doi":"10.1111/poms.14061","DOIUrl":"https://doi.org/10.1111/poms.14061","url":null,"abstract":"Firms have been proactively holding data science competitions via online contest platforms to look for innovative solutions from the crowd. When firms are designing such competitions, a key question is: What should be a better contest design to motivate contestants to exert more effort? We model two commonly observed contest structures (one‐stage and two‐stage) and two widely adopted prize structures (high‐spread and low‐spread). We employ economic experiments to examine how contest design affects contestants’ effort level. The results reject the base model with rationality assumption. We find that contestants exert significantly more effort in both the first stage and the second stage of the two‐stage contest. Moreover, it is better to assign most prizes to the winner in the two‐stage contest while it does not matter in one‐stage. To explain the empirical regularities, we develop a behavioral economics model that captures contestants’ psychological aversion to falling behind and continuous exertion of effort. Our findings demonstrate that it is important for contest organizers to account for the non‐pecuniary factors that can influence contestants’ behavior in designing a competition.This article is protected by copyright. All rights reserved","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45674032","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}
We study the robust production and maintenance control for a production system subject to degradation. A periodic maintenance scheme is considered, and the system production rate can be dynamically adjusted before maintenance, serving as a proactive way of degradation management. Optimal control of the degradation rate aims to strike a balance between the risk of failure and the production profit. We first consider the scenario in which the degradation rate increases linearly with the production rate. Different from the existing literature that posits a parametric stochastic degradation process, we suppose that the degradation increment during a period lies in an uncertainty set, and our objective is to minimize the maintenance cost in the worst case. The resulting model is a robust mixed‐integer linear program. We derive its robust counterpart and establish structural properties of the optimal production plan. These properties are then used for real‐time condition‐based control of the production rate through reoptimization. The model is further generalized to the nonlinear production‐degradation relation. Based on a real production‐degradation dataset from an extruder system, we conduct comprehensive numerical experiments to illustrate the application of the model. Numerical results show that our model significantly outperforms existing methods in terms of the mean and variance of cost rate when degradation model misspecification is presented.This article is protected by copyright. All rights reserved
{"title":"Robust condition‐based production and maintenance planning for degradation management","authors":"Qiuzhuang Sun, Piao Chen, Xin Wang, Zhi-Sheng Ye","doi":"10.1111/poms.14071","DOIUrl":"https://doi.org/10.1111/poms.14071","url":null,"abstract":"We study the robust production and maintenance control for a production system subject to degradation. A periodic maintenance scheme is considered, and the system production rate can be dynamically adjusted before maintenance, serving as a proactive way of degradation management. Optimal control of the degradation rate aims to strike a balance between the risk of failure and the production profit. We first consider the scenario in which the degradation rate increases linearly with the production rate. Different from the existing literature that posits a parametric stochastic degradation process, we suppose that the degradation increment during a period lies in an uncertainty set, and our objective is to minimize the maintenance cost in the worst case. The resulting model is a robust mixed‐integer linear program. We derive its robust counterpart and establish structural properties of the optimal production plan. These properties are then used for real‐time condition‐based control of the production rate through reoptimization. The model is further generalized to the nonlinear production‐degradation relation. Based on a real production‐degradation dataset from an extruder system, we conduct comprehensive numerical experiments to illustrate the application of the model. Numerical results show that our model significantly outperforms existing methods in terms of the mean and variance of cost rate when degradation model misspecification is presented.This article is protected by copyright. All rights reserved","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43424957","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}
Jeff Shockley, Jason R. W. Merrick, X. Liu, Jeffery S. Smith
Companies across many industries seek to understand how customer ordering impacts supply chain distribution performance. In the U.S. medical supplies industry, wholesalers are uniquely positioned to use information about downstream customers to study and potentially influence buyer policies and practices due to their industry scale and data visibility. In this study, wholesale medical supplies buyers are first examined based on their ordering practices over a two‐year window using the theoretical lens of data clumpiness – patterns of data non‐conformity to equal spacing ‐ to derive insights into how their ordering practices affect the distribution efficiency of the medical supplies wholesale distributor. The analysis also considers how different buyer and industry characteristics moderate these upstream ordering effects. The results reveal several significant findings for both theory and practice. First, buyers exhibiting less clumpiness in order‐sizing and greater clumpiness in order‐timing practices drive greater distribution efficiencies for the wholesale distributor. These effects are greater when buyers have more category experience and lower when ordering across multiple categories. Industry customers’ use of centralized purchasing also tends to lower wholesale distributor efficiency. Still, these negative effects can be mitigated when customer ordering practices favor replenishment based on customer needs and consistent order sizing. After discussing the implications of our analysis, we offer additional practical and theoretical extensions of our approach that can be applied to study other industry supply chains or that could affect related healthcare purchasing markets.This article is protected by copyright. All rights reserved
{"title":"How much do customer ordering practices drive medical supplies distribution (in)efficiency for primary care markets?","authors":"Jeff Shockley, Jason R. W. Merrick, X. Liu, Jeffery S. Smith","doi":"10.1111/poms.14068","DOIUrl":"https://doi.org/10.1111/poms.14068","url":null,"abstract":"Companies across many industries seek to understand how customer ordering impacts supply chain distribution performance. In the U.S. medical supplies industry, wholesalers are uniquely positioned to use information about downstream customers to study and potentially influence buyer policies and practices due to their industry scale and data visibility. In this study, wholesale medical supplies buyers are first examined based on their ordering practices over a two‐year window using the theoretical lens of data clumpiness – patterns of data non‐conformity to equal spacing ‐ to derive insights into how their ordering practices affect the distribution efficiency of the medical supplies wholesale distributor. The analysis also considers how different buyer and industry characteristics moderate these upstream ordering effects. The results reveal several significant findings for both theory and practice. First, buyers exhibiting less clumpiness in order‐sizing and greater clumpiness in order‐timing practices drive greater distribution efficiencies for the wholesale distributor. These effects are greater when buyers have more category experience and lower when ordering across multiple categories. Industry customers’ use of centralized purchasing also tends to lower wholesale distributor efficiency. Still, these negative effects can be mitigated when customer ordering practices favor replenishment based on customer needs and consistent order sizing. After discussing the implications of our analysis, we offer additional practical and theoretical extensions of our approach that can be applied to study other industry supply chains or that could affect related healthcare purchasing markets.This article is protected by copyright. All rights reserved","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44648222","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}
For many multiplayer games, including massively multiplayer online role‐playing games (or MMORPGs), consumer skill sets with the game play an important role in engagement. Despite their importance, many aspects of consumers’ skill sets are still less well understood. This research considers the formation and evolution of players’ skill sets from two perspectives: (1) learning‐by‐doing, in which a consumer gradually improves his or her skill set with the game from past experiences with other players, and (2) learning about matched players’ skill sets from their observed characteristics (i.e., learning‐about‐others). Using policy simulations, we further demonstrate how inferences of players’ latent skill sets could help game developers design strategies for better engagement, from the perspectives of version upgrades, targeted user visibility, and artificial intelligence (AI)–powered bots.This article is protected by copyright. All rights reserved
{"title":"Learning and Skill set Formation: A Structural Examination of Version Upgrades, User Visibility and AI Strategies","authors":"Jialie Chen","doi":"10.1111/poms.14065","DOIUrl":"https://doi.org/10.1111/poms.14065","url":null,"abstract":"For many multiplayer games, including massively multiplayer online role‐playing games (or MMORPGs), consumer skill sets with the game play an important role in engagement. Despite their importance, many aspects of consumers’ skill sets are still less well understood. This research considers the formation and evolution of players’ skill sets from two perspectives: (1) learning‐by‐doing, in which a consumer gradually improves his or her skill set with the game from past experiences with other players, and (2) learning about matched players’ skill sets from their observed characteristics (i.e., learning‐about‐others). Using policy simulations, we further demonstrate how inferences of players’ latent skill sets could help game developers design strategies for better engagement, from the perspectives of version upgrades, targeted user visibility, and artificial intelligence (AI)–powered bots.This article is protected by copyright. All rights reserved","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47550591","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 this paper, we employ large‐scale sensor data to examine the impact of data‐based intelligence and work‐related experience on the time efficiency of individual taxi drivers, measured by their propensity of choosing the fastest routes. The identification strategy is built on (1) a unique exogenous policy shock‐banning taxi‐hailing app with an embedded GPS system, and (2) a measure of nonrecurring congestion avoidance, enabled by the real‐time sensor data, which serves as a proxy for GPS usage. Our empirical model provides evidence that data‐based intelligence improves taxi drivers’ routing decisions by close to 3% as measured by trip speed. Our results further demonstrate that inexperienced drivers have a higher chance of choosing the fastest route, as they are more likely to rely on the real‐time traffic information from GPS technology than experienced drivers. The general implications of our findings on the adoption and utilization of data‐based performance‐enhancing technology are discussed in closing.
{"title":"The role of data‐based intelligence and experience on time efficiency of taxi drivers: An empirical investigation using large‐scale sensor data","authors":"Yingda Lu, Youwei Wang, Yuxin Chen, Yun Xiong","doi":"10.1111/poms.14056","DOIUrl":"https://doi.org/10.1111/poms.14056","url":null,"abstract":"In this paper, we employ large‐scale sensor data to examine the impact of data‐based intelligence and work‐related experience on the time efficiency of individual taxi drivers, measured by their propensity of choosing the fastest routes. The identification strategy is built on (1) a unique exogenous policy shock‐banning taxi‐hailing app with an embedded GPS system, and (2) a measure of nonrecurring congestion avoidance, enabled by the real‐time sensor data, which serves as a proxy for GPS usage. Our empirical model provides evidence that data‐based intelligence improves taxi drivers’ routing decisions by close to 3% as measured by trip speed. Our results further demonstrate that inexperienced drivers have a higher chance of choosing the fastest route, as they are more likely to rely on the real‐time traffic information from GPS technology than experienced drivers. The general implications of our findings on the adoption and utilization of data‐based performance‐enhancing technology are discussed in closing.","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":" ","pages":"3665 - 3682"},"PeriodicalIF":5.0,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49241800","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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