This study investigates the effects of product-specific first-meet coupons, a targeted promotional strategy designed at the product level to attract new customers with various coupon face values across products, on key market outcomes on the online retail platform. Leveraging a unique dataset from a leading Asian online retail platform, spanning three months of transactions across 14,672 products, 760 brands, and over 28 million customers, we employ propensity score matching and difference-in-differences methods within a quasi-experimental framework. Our results demonstrate that first-meet coupons significantly boost new customer acquisition and sales volume without spiking product returns. Heterogeneity analyses reveal that the impact is particularly pronounced for well-known brands, products with moderate discounts or high word-of-mouth volume, and middle-aged female consumers. Building on these findings, we utilize a causal forest model to optimize product-level discount strategies, enhancing customer acquisition efficiency. Our study provides actionable insights to help retail platforms design more effective promotional policies.
{"title":"The value of product-specific first-meet coupons: Enhancing customer acquisitions and sales through causal forest","authors":"Cheng Fang , Yong-Wu Zhou , Xiaojing Feng , Kedi Wang","doi":"10.1016/j.omega.2026.103528","DOIUrl":"10.1016/j.omega.2026.103528","url":null,"abstract":"<div><div>This study investigates the effects of product-specific first-meet coupons, a targeted promotional strategy designed at the product level to attract new customers with various coupon face values across products, on key market outcomes on the online retail platform. Leveraging a unique dataset from a leading Asian online retail platform, spanning three months of transactions across 14,672 products, 760 brands, and over 28 million customers, we employ propensity score matching and difference-in-differences methods within a quasi-experimental framework. Our results demonstrate that first-meet coupons significantly boost new customer acquisition and sales volume without spiking product returns. Heterogeneity analyses reveal that the impact is particularly pronounced for well-known brands, products with moderate discounts or high word-of-mouth volume, and middle-aged female consumers. Building on these findings, we utilize a causal forest model to optimize product-level discount strategies, enhancing customer acquisition efficiency. Our study provides actionable insights to help retail platforms design more effective promotional policies.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"142 ","pages":"Article 103528"},"PeriodicalIF":7.2,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Auditing is widely used to motivate suppliers to exert more corporate social responsibility (CSR) efforts. However, trade journals reported that intensified auditing backfired, reducing suppliers’ CSR efforts and prompting them to hide violations to pass audits. We conduct an experiment to examine suppliers’ behavioral biases and their impact on this “backfiring effect”. The experiment provides evidence for two key behavioral biases: loss aversion and probability weighting. Interestingly, the two biases have opposite influences: loss aversion mitigates, but probability weighting aggravates the “backfiring effect”. Despite their conflicting influences, our analysis reveals that loss aversion dominates, resulting in an overall alleviation of the “backfiring effect” by behavioral biases. Our findings imply that, in practice, managers can improve CSR by making good use of behavioral biases’ positive impact.
{"title":"How do behavioral biases affect backfiring of intensified auditing on corporate social responsibility?","authors":"Luyao Li , Xiaobo Zhao , Wanshan Zhu , Jinxing Xie","doi":"10.1016/j.omega.2026.103527","DOIUrl":"10.1016/j.omega.2026.103527","url":null,"abstract":"<div><div>Auditing is widely used to motivate suppliers to exert more corporate social responsibility (CSR) efforts. However, trade journals reported that intensified auditing backfired, reducing suppliers’ CSR efforts and prompting them to hide violations to pass audits. We conduct an experiment to examine suppliers’ behavioral biases and their impact on this “backfiring effect”. The experiment provides evidence for two key behavioral biases: loss aversion and probability weighting. Interestingly, the two biases have opposite influences: loss aversion mitigates, but probability weighting aggravates the “backfiring effect”. Despite their conflicting influences, our analysis reveals that loss aversion dominates, resulting in an overall alleviation of the “backfiring effect” by behavioral biases. Our findings imply that, in practice, managers can improve CSR by making good use of behavioral biases’ positive impact.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"142 ","pages":"Article 103527"},"PeriodicalIF":7.2,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-20DOI: 10.1016/j.omega.2026.103524
Wenju Wang , Xuedong Liang , Xiangrui Chao , Zhongbin Wang
In recent years, social media platforms (SMPs) such as Douyin have amassed large user bases and substantial traffic by offering engaging content services. To capitalize on this traffic, many SMPs have integrated in-app shopping features, seeking to monetize user engagement through self-operated or agency-based e-commerce models. However, this strategic shift inevitably introduces competition with well-established traditional e-commerce platforms (TEPs). Given the complementary strengths of SMPs in traffic volume and TEPs in traffic conversion, fostering cooperation is considered an effective strategy for mitigating this emerging competition. Unfortunately, the dynamics of such co-opetition have not been systematically analyzed. To address this research gap, this paper pioneers a systematic exploration of co-opetition strategies between the SMP and the TEP from the perspective of traffic and its distribution strategy. Our results are as follows. First, while cooperation can enhance the traffic conversion rate (TCR) for the SMP’s self-operated product, it can also intensify market competition. As a result, cooperation may reduce both the SMP’s profits and the e-commerce market’s overall average TCR compared to non-cooperation. Second, although the TEP can gain additional traffic and revenue through cooperation, it can be detrimental when the market size is small. Finally, there is a significant misalignment in the cooperation preferences between the two platforms. Mutual benefits are primarily achievable in moderate-sized markets. Even worse, when the SMP’s relative traffic conversion efficiency is high, cooperation may paradoxically lead to a lose-lose situation. We further extend the model in several directions and demonstrate the robustness of our main findings.
{"title":"E-commerce for social media platforms: Co-opetition and traffic distribution","authors":"Wenju Wang , Xuedong Liang , Xiangrui Chao , Zhongbin Wang","doi":"10.1016/j.omega.2026.103524","DOIUrl":"10.1016/j.omega.2026.103524","url":null,"abstract":"<div><div>In recent years, social media platforms (SMPs) such as Douyin have amassed large user bases and substantial traffic by offering engaging content services. To capitalize on this traffic, many SMPs have integrated in-app shopping features, seeking to monetize user engagement through self-operated or agency-based e-commerce models. However, this strategic shift inevitably introduces competition with well-established traditional e-commerce platforms (TEPs). Given the complementary strengths of SMPs in traffic volume and TEPs in traffic conversion, fostering cooperation is considered an effective strategy for mitigating this emerging competition. Unfortunately, the dynamics of such co-opetition have not been systematically analyzed. To address this research gap, this paper pioneers a systematic exploration of co-opetition strategies between the SMP and the TEP from the perspective of traffic and its distribution strategy. Our results are as follows. First, while cooperation can enhance the traffic conversion rate (TCR) for the SMP’s self-operated product, it can also intensify market competition. As a result, cooperation may reduce both the SMP’s profits and the e-commerce market’s overall average TCR compared to non-cooperation. Second, although the TEP can gain additional traffic and revenue through cooperation, it can be detrimental when the market size is small. Finally, there is a significant misalignment in the cooperation preferences between the two platforms. Mutual benefits are primarily achievable in moderate-sized markets. Even worse, when the SMP’s relative traffic conversion efficiency is high, cooperation may paradoxically lead to a lose-lose situation. We further extend the model in several directions and demonstrate the robustness of our main findings.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"142 ","pages":"Article 103524"},"PeriodicalIF":7.2,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-16DOI: 10.1016/j.omega.2026.103515
Li Jiang , Zhongyuan Hao
We investigate how a provider’s private quality information influences the adoption of pricing strategies, with implications for the occurrence of unethical practices and social welfare. Specifically, we consider a market where a provider caters to consumers with various needs. Consumers are uncertain about the intensity of their needs or the provider’s quality. The provider employs either uniform pricing or non-uniform pricing for various service types. After discerning a consumer’s need, the provider may refuse to treat the consumer (termed as consumer dumping) or recommend a service, in which case the provider may recommend a serious service to a consumer with a minor need but perform a minor service (termed as overcharging). The consumer accepts service only when perceiving its value to exceed the price. We demonstrate that the provider signals (conceals) quality with a quality-dependent (quality-invariant) menu when the provider’s likelihood of offering high-quality service is low (high). Depending on cost structure and consumer composition, the provider may dump consumers under uniform pricing or overcharge consumers under non-uniform pricing. Quality revelation eliminates unethical behavior, while it may benefit the provider but has no consequential effects on consumers as a whole. It is noteworthy that even imperfect quality revelation, which is subject to biases in quality disclosure, can improve the profit of the provider and enhance social welfare. Moreover, we alert regulators to market conditions when imposing price caps, as imprudent regulations may drive the market into disorder and incubate unethical behavior.
{"title":"Holding private quality information: implications for unethical practices and social welfare in credence goods markets","authors":"Li Jiang , Zhongyuan Hao","doi":"10.1016/j.omega.2026.103515","DOIUrl":"10.1016/j.omega.2026.103515","url":null,"abstract":"<div><div>We investigate how a provider’s private quality information influences the adoption of pricing strategies, with implications for the occurrence of unethical practices and social welfare. Specifically, we consider a market where a provider caters to consumers with various needs. Consumers are uncertain about the intensity of their needs or the provider’s quality. The provider employs either uniform pricing or non-uniform pricing for various service types. After discerning a consumer’s need, the provider may refuse to treat the consumer (termed as consumer dumping) or recommend a service, in which case the provider may recommend a serious service to a consumer with a minor need but perform a minor service (termed as overcharging). The consumer accepts service only when perceiving its value to exceed the price. We demonstrate that the provider signals (conceals) quality with a quality-dependent (quality-invariant) menu when the provider’s likelihood of offering high-quality service is low (high). Depending on cost structure and consumer composition, the provider may dump consumers under uniform pricing or overcharge consumers under non-uniform pricing. Quality revelation eliminates unethical behavior, while it may benefit the provider but has no consequential effects on consumers as a whole. It is noteworthy that even imperfect quality revelation, which is subject to biases in quality disclosure, can improve the profit of the provider and enhance social welfare. Moreover, we alert regulators to market conditions when imposing price caps, as imprudent regulations may drive the market into disorder and incubate unethical behavior.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"142 ","pages":"Article 103515"},"PeriodicalIF":7.2,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-16DOI: 10.1016/j.omega.2025.103509
Zihao Jiao , Xiaoxin Xie , Mengyi Sha , Wei Qi
In recent years, the surge in advanced internet computing workloads in data centers, has intensified the challenge of ensuring energy efficiency while maintaining stable and resilient computational services. In practice, widely deployed integrated centralized work-scheduling and energy-management systems are designed to optimize power distribution, renewable-energy utilization, and computational efficiency. However, it faces significant sociotechnical challenges that hinder their effective implementation. To address these issues, we integrate reserve regulation and task allocation to further optimize the operations of multiple data centers in a computational resource-sharing platform. We model the problem as a two-stage stochastic integer program. In the first stage, we optimize backup battery capacity for each data center, and in the second, we introduce a non-preemptive M/M/1 queue with task priorities. We then analyze stationary processing times and apply second-order cone programming for improved computational tractability. We develop an outer-approximation algorithm to improve computational efficiency for large-scale problems, while our integrated strategy balances cost efficiency, environmental sustainability, and resilience at minimal costs. A case study demonstrates that the integrated strategy for multiple data centers reduces total costs by 10.39% and 10.88% compared to task allocation and backup-battery reserve regulation strategies, respectively. Task priorities save 3%–4% in operational costs, while the strategy ensures stable operations and stronger resilience during power interruptions. The outer-approximation algorithm outperforms commercial solvers by 30%, and task replication improves renewable-energy utilization and energy efficiency in smaller data centers. These findings highlight the potential of our strategy to enhance data center efficiency, sustainability, and resilience.
{"title":"Toward resilient green cloud computing: Joint operations of energy storage and spatial task allocation","authors":"Zihao Jiao , Xiaoxin Xie , Mengyi Sha , Wei Qi","doi":"10.1016/j.omega.2025.103509","DOIUrl":"10.1016/j.omega.2025.103509","url":null,"abstract":"<div><div>In recent years, the surge in advanced internet computing workloads in data centers, has intensified the challenge of ensuring energy efficiency while maintaining stable and resilient computational services. In practice, widely deployed integrated centralized work-scheduling and energy-management systems are designed to optimize power distribution, renewable-energy utilization, and computational efficiency. However, it faces significant sociotechnical challenges that hinder their effective implementation. To address these issues, we integrate reserve regulation and task allocation to further optimize the operations of multiple data centers in a computational resource-sharing platform. We model the problem as a two-stage stochastic integer program. In the first stage, we optimize backup battery capacity for each data center, and in the second, we introduce a non-preemptive M/M/1 queue with task priorities. We then analyze stationary processing times and apply second-order cone programming for improved computational tractability. We develop an outer-approximation algorithm to improve computational efficiency for large-scale problems, while our integrated strategy balances cost efficiency, environmental sustainability, and resilience at minimal costs. A case study demonstrates that the integrated strategy for multiple data centers reduces total costs by 10.39% and 10.88% compared to task allocation and backup-battery reserve regulation strategies, respectively. Task priorities save 3%–4% in operational costs, while the strategy ensures stable operations and stronger resilience during power interruptions. The outer-approximation algorithm outperforms commercial solvers by 30%, and task replication improves renewable-energy utilization and energy efficiency in smaller data centers. These findings highlight the potential of our strategy to enhance data center efficiency, sustainability, and resilience.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"142 ","pages":"Article 103509"},"PeriodicalIF":7.2,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-13DOI: 10.1016/j.omega.2026.103512
Bei Lin , Xiaoyu Ji , Yingtong Wang , Yingfu He
Carbon Capture, Utilization, and Storage (CCUS) is pivotal for achieving carbon neutrality, while its large-scale, cost-effective deployment faces challenges regarding infrastructure integration, emission uncertainty, and policy design. This study proposes a novel hierarchical adaptive robust optimization framework for the robust infrastructure design for CCUS under carbon emission uncertainty. Methodologically, the framework integrates strategic hub location with robust infrastructure and flow planning through a bilevel decomposition: an upper-level K-means++ clustering-based heuristic endogenously identifies hub configurations, while a lower-level adaptive robust model optimizes pipeline establishment, storage selection, and transport flows. To ensure computational tractability, we develop an Enhanced Column-and-Constraint Generation algorithm incorporating a modified outer approximation method. We validate the framework using realistic case studies, yielding several insights. First, emission uncertainty plays only a subordinate role in strategic hub selection, as both hub configurations and major pipelines remain stable across uncertainty budgets. This finding suggests that planners can make investment decisions with confidence. Second, storage choices are highly sensitive to the interplay between oil prices and sink-specific subsidies, underscoring the need for flexible and diversified storage portfolios. Third, dynamic subsidies that adjust based on oil market conditions can effectively shift storage toward saline aquifers at modest fiscal costs. This proposed framework thus provides a decision-support tool for CCUS planning, offers quantitative evidence for policy design, and enables CCUS planning decisions to align with societal carbon neutrality goals.
{"title":"Robust infrastructure design for Carbon Capture Utilization and Storage considering carbon emission uncertainty","authors":"Bei Lin , Xiaoyu Ji , Yingtong Wang , Yingfu He","doi":"10.1016/j.omega.2026.103512","DOIUrl":"10.1016/j.omega.2026.103512","url":null,"abstract":"<div><div>Carbon Capture, Utilization, and Storage (CCUS) is pivotal for achieving carbon neutrality, while its large-scale, cost-effective deployment faces challenges regarding infrastructure integration, emission uncertainty, and policy design. This study proposes a novel hierarchical adaptive robust optimization framework for the robust infrastructure design for CCUS under carbon emission uncertainty. Methodologically, the framework integrates strategic hub location with robust infrastructure and flow planning through a bilevel decomposition: an upper-level K-means++ clustering-based heuristic endogenously identifies hub configurations, while a lower-level adaptive robust model optimizes pipeline establishment, storage selection, and transport flows. To ensure computational tractability, we develop an Enhanced Column-and-Constraint Generation algorithm incorporating a modified outer approximation method. We validate the framework using realistic case studies, yielding several insights. First, emission uncertainty plays only a subordinate role in strategic hub selection, as both hub configurations and major pipelines remain stable across uncertainty budgets. This finding suggests that planners can make investment decisions with confidence. Second, storage choices are highly sensitive to the interplay between oil prices and sink-specific subsidies, underscoring the need for flexible and diversified storage portfolios. Third, dynamic subsidies that adjust based on oil market conditions can effectively shift storage toward saline aquifers at modest fiscal costs. This proposed framework thus provides a decision-support tool for CCUS planning, offers quantitative evidence for policy design, and enables CCUS planning decisions to align with societal carbon neutrality goals.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"141 ","pages":"Article 103512"},"PeriodicalIF":7.2,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We address the class of two-stage Stochastic Programs embedding, in their second stage, a set of Discrete Choice Problems (tsSP-DCPs), one independent from the other, but all linked by the first-stage decisions This decisional structure can be found within many managerial and organizational contexts in relation to several applications such as location–allocation, routing, scheduling, and sequencing. Generally, solving a two-stage stochastic program requires the analytical derivation of the second-stage problem’s expected optimum, which in turn implies calculating a multidimensional integral. Therefore, a common practice is approximating the random variables involved through a finite set of scenarios and solving a huge scenario-dependent program, which affects the scalability of making optimal decisions under uncertainty. However, under some assumptions commonly adopted in the discrete choice context, we can prove that a closed-form analytical expression of the expected second-stage optimum of a tsSP-DCP can be derived, and an exact scenario-independent equivalent deterministic program can be obtained. Through a numerical showcase, we validate our approach in terms of efficiency and effectiveness. Our equivalent deterministic form, which only requires estimating a few parameters in practice, is far less computationally demanding than any scenario-based deterministic equivalent forms, thereby simplifying the decision-making process. Finally, we show that our methodology can be generalized to address a larger class of two-stage stochastic programs, i.e., those in which the second-stage expected optimum is decomposable into a finite number of expectations of Extreme Values and in which second-stage utilities may also depend on first-stage decisions.
{"title":"An exact scenario-independent deterministic equivalent form of stochastic programs embedding Multivariate Extreme Value discrete choice problems","authors":"Michel Bierlaire , Edoardo Fadda , Lohic Fotio Tiotsop , Daniele Manerba","doi":"10.1016/j.omega.2026.103514","DOIUrl":"10.1016/j.omega.2026.103514","url":null,"abstract":"<div><div>We address the class of two-stage Stochastic Programs embedding, in their second stage, a set of Discrete Choice Problems (tsSP-DCPs), one independent from the other, but all linked by the first-stage decisions This decisional structure can be found within many managerial and organizational contexts in relation to several applications such as location–allocation, routing, scheduling, and sequencing. Generally, solving a two-stage stochastic program requires the analytical derivation of the second-stage problem’s expected optimum, which in turn implies calculating a multidimensional integral. Therefore, a common practice is approximating the random variables involved through a finite set of scenarios and solving a huge scenario-dependent program, which affects the scalability of making optimal decisions under uncertainty. However, under some assumptions commonly adopted in the discrete choice context, we can prove that a closed-form analytical expression of the expected second-stage optimum of a tsSP-DCP can be derived, and an exact scenario-independent equivalent deterministic program can be obtained. Through a numerical showcase, we validate our approach in terms of efficiency and effectiveness. Our equivalent deterministic form, which only requires estimating a few parameters in practice, is far less computationally demanding than any scenario-based deterministic equivalent forms, thereby simplifying the decision-making process. Finally, we show that our methodology can be generalized to address a larger class of two-stage stochastic programs, i.e., those in which the second-stage expected optimum is decomposable into a finite number of expectations of Extreme Values and in which second-stage utilities may also depend on first-stage decisions.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"142 ","pages":"Article 103514"},"PeriodicalIF":7.2,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146001714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-31DOI: 10.1016/j.omega.2025.103511
Yusheng Wang , Yongjian Li , Fangchao Xu
Firms are now promoting interactions among users by constructing user ecology, thereby fostering a intra-user network effect to enhance the value proposition of their products. However, compatibility, a pivotal attribute of such ecology, may potentially transform this potent network effect into a double-edged sword, particularly from the upgrade perspective. This study develops a stylized model to explore the interplay between upgrade strategy and user ecology construction. Initially, we analyze the optimal upgrade strategies for firms, distinguishing between those with no/partial/comprehensive user ecology. Subsequently, we carry out the analysis of the optimal design for the user ecology. Moreover, we explore the effectiveness of strategically disposing of partial ecology. The primary findings illustrate the importance of upgrade costs in scenarios without user ecology, where the line-extension strategy dominants the replacement strategy. In the presence of user ecology, we elucidate the demand aggregation effect that may hinder users from buying a new-generation product. Consequently, the replacement strategy emerges as optimal when product differentiation is low. Intriguingly, the existence of user ecology may impede firms from introducing new-generation products. The construction of user ecology provides advantages for firms in emerging markets but may be detrimental in mature markets. Furthermore, our results highlight that comprehensive user ecology may compromise firm’s profit. Disposing of partial ecology strategically can enhance performance, especially when both network effect and product differentiation are low. Lastly, we further investigate the impact of repeat purchases, proportion of new users, and compatibility of the ecology on the main results.
{"title":"User ecology: The optimal ecology construction and product upgrade strategies","authors":"Yusheng Wang , Yongjian Li , Fangchao Xu","doi":"10.1016/j.omega.2025.103511","DOIUrl":"10.1016/j.omega.2025.103511","url":null,"abstract":"<div><div>Firms are now promoting interactions among users by constructing user ecology, thereby fostering a intra-user network effect to enhance the value proposition of their products. However, compatibility, a pivotal attribute of such ecology, may potentially transform this potent network effect into a double-edged sword, particularly from the upgrade perspective. This study develops a stylized model to explore the interplay between upgrade strategy and user ecology construction. Initially, we analyze the optimal upgrade strategies for firms, distinguishing between those with no/partial/comprehensive user ecology. Subsequently, we carry out the analysis of the optimal design for the user ecology. Moreover, we explore the effectiveness of strategically disposing of partial ecology. The primary findings illustrate the importance of upgrade costs in scenarios without user ecology, where the line-extension strategy dominants the replacement strategy. In the presence of user ecology, we elucidate the demand aggregation effect that may hinder users from buying a new-generation product. Consequently, the replacement strategy emerges as optimal when product differentiation is low. Intriguingly, the existence of user ecology may impede firms from introducing new-generation products. The construction of user ecology provides advantages for firms in emerging markets but may be detrimental in mature markets. Furthermore, our results highlight that comprehensive user ecology may compromise firm’s profit. Disposing of partial ecology strategically can enhance performance, especially when both network effect and product differentiation are low. Lastly, we further investigate the impact of repeat purchases, proportion of new users, and compatibility of the ecology on the main results.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"141 ","pages":"Article 103511"},"PeriodicalIF":7.2,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The ongoing electrification of the transport sector, driven by the numerous advantages of electric vehicles (EVs), introduces new challenges related to charging logistics, particularly due to long charging durations and uncertain conditions, posing significant negative impacts on grid stability and user satisfaction. While existing literature on EV charging scheduling often assumes deterministic charging durations, real-world conditions introduce randomness due to uncontrollable factors such as battery state-of-charge (SoC), fluctuating grid demand, and ambient temperature. In this paper, we address the Electric Vehicle Charging Scheduling Problem (EVCSP) under uncertain charging durations. First, we introduce a novel, flexible multi-objective scheduling model operating on a continuous time horizon, considering stochastic charging durations and incorporating controlled preemptions during charging, where the non-preemptive mode is a particular case. Then, we prove that finding a feasible assignment of EVs to chargers is strongly NP-hard under this uncertainty, even assuming identical chargers. Our model accounts for realistic constraints, including heterogeneous charger power levels and vehicle-charger compatibility, aiming to minimize the conditional expected values of grid overload and total tardiness, while also minimizing the undelivered energy to users. Given the problem’s computational complexity, we adapt four evolutionary algorithms (EAs), namely, extensions of the Non-Dominated Sorting Genetic Algorithm (NSGA), namely NSGA-II and NSGA-III, alongside other state-of-the-art multi-objective metaheuristics, including the Multi-Objective Cuckoo Search (MOCS) algorithm, and the Multi-Objective Grey Wolf Optimizer (MOGWO) by defining problem-specific operators to explore the search space and efficiently approximate the optimal Pareto front. Assuming lognormally distributed charging durations, we conducted a comparative experimental analysis on real-world data to evaluate the four methods and revealed that MOCS algorithm outperforms the other competitors.
{"title":"Multi-objective electric vehicle charging scheduling under stochastic duration uncertainty","authors":"Aimen Khiar , Mohamed el Amine Brahmia , Ammar Oulamara , Lhassane Idoumghar","doi":"10.1016/j.omega.2025.103506","DOIUrl":"10.1016/j.omega.2025.103506","url":null,"abstract":"<div><div>The ongoing electrification of the transport sector, driven by the numerous advantages of electric vehicles (EVs), introduces new challenges related to charging logistics, particularly due to long charging durations and uncertain conditions, posing significant negative impacts on grid stability and user satisfaction. While existing literature on EV charging scheduling often assumes deterministic charging durations, real-world conditions introduce randomness due to uncontrollable factors such as battery state-of-charge (SoC), fluctuating grid demand, and ambient temperature. In this paper, we address the <em>Electric Vehicle Charging Scheduling Problem</em> (EVCSP) under uncertain charging durations. First, we introduce a novel, flexible multi-objective scheduling model operating on a continuous time horizon, considering stochastic charging durations and incorporating controlled preemptions during charging, where the non-preemptive mode is a particular case. Then, we prove that finding a feasible assignment of EVs to chargers is strongly NP-hard under this uncertainty, even assuming identical chargers. Our model accounts for realistic constraints, including heterogeneous charger power levels and vehicle-charger compatibility, aiming to minimize the conditional expected values of grid overload and total tardiness, while also minimizing the undelivered energy to users. Given the problem’s computational complexity, we adapt four evolutionary algorithms (EAs), namely, extensions of the Non-Dominated Sorting Genetic Algorithm (NSGA), namely NSGA-II and NSGA-III, alongside other state-of-the-art multi-objective metaheuristics, including the Multi-Objective Cuckoo Search (MOCS) algorithm, and the Multi-Objective Grey Wolf Optimizer (MOGWO) by defining problem-specific operators to explore the search space and efficiently approximate the optimal Pareto front. Assuming lognormally distributed charging durations, we conducted a comparative experimental analysis on real-world data to evaluate the four methods and revealed that MOCS algorithm outperforms the other competitors.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"141 ","pages":"Article 103506"},"PeriodicalIF":7.2,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nowadays, e-commerce is associated with many returns due to emotional consumption, information asymmetry, factory defects, or, more generally, customer dissatisfaction. However, little attention has been paid to reverse logistics in the e-commerce industry, although it has been proven crucial to improving the perceived quality of service and profit revenue. Depending on the nature of the goods, one successful option is to design combined forward-and-reverse logistics systems, where the collection of returns is ensured along with the traditional distribution of products, together with hub-and-spoke networks in which both distribution and collection demand from many spokes are aggregated into a few hubs. In this context, we study a variant of the vehicle routing problem with divisible deliveries and pickups, in which each hub may be associated with a mandatory delivery demand and a mandatory return pickup demand, and it may be visited more than once within the same or different routes. To address realistic scenarios, and given the large fluctuation of demand within the aggregating hubs, we also assume that an uncertain optional pickup quantity may arise and formulate the problem through two-stage Stochastic Programming, proposing and modeling ad-hoc recourse actions. Moreover, an integer L-shaped method enhanced with ad-hoc valid inequalities is developed for solving the resulting problem. Managerial insights on the underlying tactical and operational policies are inferred from extensive computational experiments on a case study and on realistic artificial instances.
{"title":"Incorporating stochastic optional pickup demand in routing operations with divisible services for hub-and-spoke e-commerce returns management systems","authors":"Alessandro Gobbi , Daniele Manerba , Francesca Vocaturo","doi":"10.1016/j.omega.2025.103510","DOIUrl":"10.1016/j.omega.2025.103510","url":null,"abstract":"<div><div>Nowadays, e-commerce is associated with many returns due to emotional consumption, information asymmetry, factory defects, or, more generally, customer dissatisfaction. However, little attention has been paid to reverse logistics in the e-commerce industry, although it has been proven crucial to improving the perceived quality of service and profit revenue. Depending on the nature of the goods, one successful option is to design combined <em>forward-and-reverse</em> logistics systems, where the collection of returns is ensured along with the traditional distribution of products, together with <em>hub-and-spoke</em> networks in which both distribution and collection demand from many spokes are aggregated into a few hubs. In this context, we study a variant of the vehicle routing problem with divisible deliveries and pickups, in which each hub may be associated with a mandatory delivery demand and a mandatory return pickup demand, and it may be visited more than once within the same or different routes. To address realistic scenarios, and given the large fluctuation of demand within the aggregating hubs, we also assume that an uncertain optional pickup quantity may arise and formulate the problem through two-stage Stochastic Programming, proposing and modeling ad-hoc recourse actions. Moreover, an integer L-shaped method enhanced with ad-hoc valid inequalities is developed for solving the resulting problem. Managerial insights on the underlying tactical and operational policies are inferred from extensive computational experiments on a case study and on realistic artificial instances.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"141 ","pages":"Article 103510"},"PeriodicalIF":7.2,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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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