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Many models in operations management assume that faced with excess inventory, retailers offer price discounts to increase sales. This discount is assumed to be a certain dollar amount per unit or a certain percent of the regular price. However, many retailers use nonlinear pricing, e.g. "Buy one, get one for 50% off" and "Buy two, get the third free", which we refer to as BOGO. We analyze BOGO promotions within the newsvendor model framework and identify the reasons for its popularity among retailers. We describe the type of products for which BOGO promotions are suitable, identify the optimal BOGO discount, and its effect on the retailer's profit. We compare the BOGO promotions to the straight price reduction promotions and analyze the newsvendor's optimal choice between them. We also examine the two promotions in the presence of strategic consumers. We find that for reasonable values of consumer marginal utility and a given order quantity, BOGO reduces strategic consumers' chances of obtaining a unit at a discount and their incentive to wait. Therefore, the newsvendor orders a larger quantity with BOGO than with a straight price reduction. Thus, BOGO promotions can be used by the newsvendor to counter strategic consumer behavior. Also, we find that in the presence of strategic consumers, there are some cases where it is optimal for the retailer to wait and discount the product at the end of the season. However, with BOGO it is always optimal to offer the BOGO promotion at the start of the season when realized demand is low.

We study the inter-temporally optimal innovation strategies of incumbent manufacturing firms that compete in an established market and can extend their product line through product innovation. Firms invest in production capacity and R&D knowledge stock, where the R&D knowledge stock and the current R&D investment determine the hazard rate of innovation. Our findings show that the firms’ optimal R&D strategies are driven by a subtle interplay between the relative positions of their R&D knowledge stocks and their current relative positions on the established market. First, we find that under symmetric investment costs the knowledge leader should spend more on R&D than the knowledge laggard only if it has a substantially smaller market share on the established market. If the knowledge leader’s market share is sufficiently large, its optimal investment in R&D is so small that its innovation rate is lower than the knowledge laggard’s. Second, optimal investment in R&D knowledge is negatively affected by the opponent’s production capacity on the established market if the competitor has not innovated yet. However, we find that this effect is reversed after the competitor has successfully introduced the new product on the submarket. Third, the manufacturing firm with higher costs of adjusting production capacity for the established product has a higher incentive to engage in product innovation and might even achieve a higher total discounted profit than its more efficient competitor.

Recently, luxury fashion brands can webcast fashion shows and analyze market feedback from social media exposure (SME). See now buy now (SNBN), a new product introduction system that allows consumers to purchase products after fashion shows, has been deemed a revolutionary movement in the fashion industry. Whether SNBN can replace see now buy later (SNBL), the traditional system in which consumers purchase products several months after fashion shows, is unclear. In this article we evaluate the value of demand learning through SME for SNBL and SNBN. We develop a stylish two-period model to characterize SNBL and SNBN with SME. We find that when the feedback accuracy and holding cost are sufficiently low, SNBN performs better than SNBL with SME. Furthermore, for SNBL, when the consumer valuation is relatively low, learning market responses from SME is effective and profit improvement with SME decreases. For SNBN, SME is not always effective. Our results imply that brands, such as Gucci, should sell trendy and fashionable items via SNBL without SME and classic products (products with high consumer preference) via SME. Brands, such as Burberry, should sell low holding cost products (e.g., shirts) through SNBN and high holding cost products (e.g., leather bags) through SNBL.

Problem definition: Fulfillment flexibility, the ability of distribution centers (DCs) to fulfill demand originating from other DCs, can help e-retailers reduce lost sales and improve service quality. Because the cost of full flexibility is prohibitive, we seek to understand the value of partially flexible fulfillment networks under simple and effective fulfillment policies. Academic/practical relevance: We propose a general method for understanding the practical value of (partial) fulfillment flexibility using a data-driven model, theoretical analysis, and numerical simulations. Our method applies to settings with local fulfillment (i.e., order fulfillment from the originating DC) prioritization and possible customer abandonment, two features that are new to the fulfillment literature. We then apply this method for a large e-retailer. We also introduce a new class of spillover limit fulfillment policies with attractive theoretical and practical features. Methodology: Our analysis uses dynamic and stochastic optimization, applied probability, and numerical simulations. Results: We derive optimal fulfillment policies in stylized settings, as well as bounds on the performance under an optimal policy using theoretical analysis, to provide guidelines on which policies to test in numerical simulations. We then use simulations to estimate for our industrial partner that a proposed fulfillment network with additional flexibility equates to a profit improvement on the order of tens of millions of U.S. dollars. Managerial implications: We provide an approach for e-retailers to understand when fulfillment flexibility is most valuable. We find that fulfillment flexibility provides the most benefit for our collaborator when gross profits are high relative to fulfillment costs or centrally held inventory is low. Also, we identify the risks of myopic fulfillment with additional flexibility and demonstrate that an effective spillover limit policy mitigates these risks. History: This paper has been accepted as part of the 2019 Manufacturing & Service Operations Management Practice-Based Research Competition. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0981 .

Zysk jest istotną cechą odnoszącą się do szeroko rozumianej gospodarki. Pomnażanie własnego majątku jest działalnością człowieka, który ze względu na swoje naturalne cechy jest zobowiązany do oceny swojego postępowania moralnego w świetle zasad etycznych i z uwzględnieniem tych ideałów, które niejako składają się na cel jakiegokolwiek działania człowieka. Chrześcijaństwo oferuje pewien system wartości i zasad, które pozwalają na ocenę moralną ekonomicznego aspektu życia człowieka. Analiza zysku oparta na faktach biblijnych musi wskazywać na wartość pracy i ideę własności prywatnej. Natchnieni pisarze podkreślają centralne miejsce istoty ludzkiej w swoim opisie materialnego aspektu stworzonej rzeczywistości. Dlatego każda pożyteczna działalność powinna być podporządkowana dobru człowieka. Te systemy ekonomiczne, które podważają fundamentalną wartość godności człowieka lub absolutyzują materialną stronę rzeczy, należy, zgodnie z Biblią, uznać za niesprawiedliwe, a nawet śmiertelne dla całego rodzaju ludzkiego. Na podstawie objawionej prawdy i biorąc pod uwagę encyklikę społeczną papieża Benedykta XVI Caritas in veritate można wskazać trzy kryteria współczesnej refleksji moralnej na temat zarobkowania, a mianowicie personalizację aspektów działalności zarobkowej, umożliwienie bogactwa, które ma generować korzyści społeczne, które ma przynosić i odnoszące się do dobra wspólnego. Pozytywna ocena zarobkowania w perspektywie tych zasad sprawia, że ​​dochodowa działalność przyczynia się do rzeczywistego wzrostu każdego członka społeczności wraz z całym społeczeństwem.

Desde hace algunos años se viene sosteniendo en Alemania una discusión acerca de la fundamentación teórica de la contabilidad de costes, así como de la necesidad de mantener un sistema de costes independiente. El abanico de planteamientos posibles abarca desde la total integración de la contabilidad de costes en la contabili-dad externa hasta el de una separación lo más completa posible de ambos sistema contables. Esta aportación pre-tende presentar, desde el punto de vista alemán, cómo se ha desarrollado la discusión y cuál es la situación ac-tual. Como conclusión del trabajo se aboga por una separación, sustentada teóricamente, entre ambos sistemas de contabilidad. In Germany, a discussion has been in progress for a number of years on the theoretical substantiation and the necessity of an independent cost accounting system. The spectrum of views involved ranges from a complete integration of cost accounting (internal income statement) into the profit and loss statement (external income statement) to as complete a separation as possible of the two income accounting systems. This contribu-tion will represent, from a German standpoint, how the discussion has developed, and what its present state is. The conclusion of this contribution is a recommendation of a theoretically substantiated separation of the two types of income statements.

With the prosperity of global trade and maritime transportation, dry port is playing a significant role in freight transport industry through relieving seaport congestion and facilitating improved logistics solutions. This article studies the rail shuttle service pricing and scheduling problem in a dry port system with one dry port and multiple shippers. Bilevel models are developed to address the interaction between the dry port and shippers, with the dry port as the leader and individual shippers as followers. The optimal properties of the proposed bilevel model are analyzed, and a solution procedure is proposed to obtain the global optimal solution. Numerical studies are carried out to investigate the sensitivity of optimal decisions, and performance of both parties with respect to various system parameters. This study shows that offering a higher storage price could not always bring higher profit for the dry port. An extremely high storage price will reduce shippers’ total costs, especially for shippers with high production rates. If the seaports’ cutoff time is short, shippers with low production rates would have to bear high expenses. It is also found that low setup costs at shippers will bring not only cost savings for shippers, but also profit improvement for the dry port.

We study an integrated airline schedule design and fleet assignment model for constructing schedules by simultaneously selecting from a pool of optional flights and assigning fleet types to these scheduled flights. This is a crucial tactical decision which greatly influences airline profit. As passenger demand is often substitutable among available fare products between the same origin-destination pair, we study an optimization model that integrates within it a passenger choice model for fare product selections. To tackle the formidable computational challenge of solving this large-scale network design problem, we propose a decomposition approach based on partitioning the flight network into smaller subnetworks by exploiting weak dependency in the network structure. The decomposition relies on a series of approximation analyses and a novel fare split problem to reliably measure the approximation quality introduced by the partition by optimally allocating fares of products shared by flights in different subnetworks. We present several reformulations by representing fleet assignment and schedule decisions using composite variables and formally characterize the relationship of their strengths. This gives rise to a new reformulation that is able to flexibly trade off strength and size. We conduct detailed computational experiments using two realistically sized airline instances to demonstrate the effectiveness of our approach. Under a simulated passenger booking environment with both perfect and imperfect forecasts, we show that the fleeting and scheduling decisions informed by our approach deliver significant and robust profit improvement over benchmark implementations and previous models in the literature.

We consider a stochastic EOQ-type model, with demand operating in a two-state random environment. This environment alternates between exponentially distributed periods of high demand and generally distributed periods of low demand. The inventory level starts at some level q, and decreases according to different compound Poisson processes during the periods of high demand and of low demand. Refilling of the inventory level to level q is required when level 0 is hit or when an expiration date is reached, whichever comes first. If such an event occurs during a high demand period, an order is instantaneously placed; otherwise, ordering is postponed until the beginning of the next high demand period. We determine various performance measures of interest, like the distribution of the inventory level at time t and of the inventory demand up to time t, the distribution of the time until refilling is required, the expected time between two refillings, the expected amount of discarded material and the expected total amount of material held in between two refillings, and the expected values of various kinds of shortages. For a given cost/revenue structure, we can thus determine the long-run average profit.

The “buy online and pick up in store” (BOPS) mode is gaining tremendous popularity among retailers since it is convenient for consumers and brings additional store sales to retailers. However, operating the BOPS channel requires additional investment, which is a challenge for retailers. This article considers the pricing strategies of competing dual-channel retailers, focuses on whether and when they should adopt the BOPS strategy, and explores the impacts of market factors on the equilibrium outcomes. Since retailers’ decisions are usually made sequentially in reality, we use the Stackelberg game model to analyze retailers’ optimal strategies. First, we show that the follower's price is not always lower than the leader's price. Specifically, when the unit additional profit from cross-selling of the follower is low enough, the follower will set a higher price than the leader. Second, we find that retailers prefer the BOPS strategy when the fixed costs for offering BOPS channels are low enough, or when the difference between the additional profits from cross-selling of two retailers is sufficiently large. Third, we present an interesting insight: an increase in product return probability or retailer cost of handling a returned product can be beneficial to retailers.

Recent research shows that microenterprises in developing countries are constrained by their managerial capacity, especially in the areas of marketing, record keeping, financial planning, and stock control. In a stratified randomized controlled trial, experienced businesswomen in Ethiopia were given a formal business training that addressed these constraints. A second-stage mentoring component in which a random selection of female mentees within the social and business network of the trainees from the first-stage business training received customized mentoring from these “trained mentors.†Pooled results using three rounds of post-training surveys carried out over three years show that business training causes profit and sales to improve by 0.21 standard deviation, while business practices improve by 0.13 standard deviation. The overall impact of mentoring is muted—strong impacts are observed on the adoption of business practices among mentees, but there is no statistically significant impact on profits.

Balancing the supply and demand for ride-sourcing companies is a challenging issue, especially with real-time requests and stochastic traffic conditions of large-scale congested road networks. To tackle this challenge, this article proposes a robust and scalable approach that integrates reinforcement learning (RL) and a centralized programming (CP) structure to promote real-time taxi operations. Both real-time order matching decisions and vehicle relocation decisions at the microscopic network scale are integrated within a Markov decision process framework. The RL component learns the decomposed state-value function, which represents the taxi drivers' experience, the off-line historical demand pattern, and the traffic network congestion. The CP component plans nonmyopic decisions for drivers collectively under the prescribed system constraints to explicitly realize cooperation. Furthermore, to circumvent sparse reward and sample imbalance problems over the microscopic road network, this article proposed a temporal-difference learning algorithm with prioritized gradient descent and adaptive exploration techniques. A simulator is built and trained with the Manhattan road network and New York City yellow taxi data to simulate the real-time vehicle dispatching environment. Both centralized and decentralized taxi dispatching policies are examined with the simulator. This case study shows that the proposed approach can further improve taxi drivers' profits while reducing customers' waiting times compared to several existing vehicle dispatching algorithms.

Cloud services request lower cost compared to traditional software of self-purchased infrastructure due to the characteristics of on-demand resource provisioning and pay-as-you-go mode.In the cloud services market, service providers attempt to make more profits from their services,while users hope to choose low-cost services with high-quality.The conflict of interests between users and service providers is an important challenge for the booming cloud service market. This paper characterizes this application problem formally based on a utility game model of service providers and users. In the model, QoS is considered as the basis for determining the utilities of both parties.By analyzing the behaviors of users and service providers,we introduce the concept of reputation cost for the first time in the model and find a QoS solution that balances the utilities of users and service providers in service transactions.In such a balance, any change in either party's strategy will result in a loss of utility. And then a QoS optimization method is designed to obtain a near-optimal QoS solution for a tradeoff between user satisfaction and provider profit. Extensive simulation experiments are conducted to substantiate the effectiveness of our method.The results are applicable to win-win service applications between service providers and users.

With food delivery services, customers can hire delivery workers to pick up food on their behalf. To investigate the long-term impact of food delivery services on the restaurant industry, we model a restaurant serving food to customers as a stylized single-server queue with two streams of customers. One stream consists of tech-savvy customers who have access to a food delivery service platform. The other stream consists of traditional customers who are not able to use a food delivery service and only walk in by themselves. We study a Stackelberg game, in which the restaurant first sets the food price; the food delivery platform then sets the delivery fee; and, last, rational customers decide whether to walk in, balk, or use a food delivery service if they have access to one. If the restaurant has a sufficiently large established base of traditional customers, we show that the food delivery platform does not necessarily increase demand but may just change the composition of customers, as the segment of tech-savvy customers grows. Hence, paying the platform for bringing in customers may hurt the restaurant’s profitability. We demonstrate that either a one-way revenue-sharing contract with a price ceiling or a two-way revenue-sharing contract can coordinate the system and create a win-win situation. Furthermore, under conditions of no coordination between the restaurant and the platform, we show, somewhat surprisingly, that more customers having access to a food delivery service may hurt the platform itself and the society, when the food delivery service is sufficiently convenient, and the delivery-worker pool is large enough. This is because the restaurant can become a delivery-only kitchen and raise its food price by focusing on food-delivery customers only, leaving little surplus for the platform. This implies that limiting the number of delivery workers can provide a simple yet effective means for the platform to improve its own profitability while benefiting social welfare. This paper was accepted by Charles Corbett, operations management.

We examine price competition with homogeneous products in the presence of general common ownership arrangements allowing for different corporate control structures. We show that equilibria with positive profits exist (including the monopoly outcome) when the manager places the same weight on the profi of her firm as on the average profit of all the other firms. This condition supports symmetric and asymmetric stakes and can arise as an equilibrium of a network formation game or a bargaining process.

In this paper, we investigate the problem of whether a retailer should bargain over the wholesale price of a product with a manufacturer or accept the price unilaterally determined by the manufacturer. To investigate the problem, we develop a game-theoretic model describing a supply chain organized by one manufacturer and one retailer. Intuitively, if there is a bargaining opportunity, a retailer is better off bargaining over the wholesale price rather than simply accepting the price determined by the manufacturer. Consistent with this intuition, we first show the benchmark outcome that the retailer achieves a higher profit through bargaining over the price when the manufacturer uses only a single-channel supply chain, in which the manufacturer sells only through the retailer. Conversely, however, we demonstrate that this intuitive result can be completely reversed in a dual-channel supply chain environment, in which the manufacturer is able to sell products not only through the retailer but also directly to end-consumers. Specifically, we find that if a retailer's bargaining power is sufficiently strong and if consumers' substitutability between channels is substantially high, full acceptance of the wholesale price dictated by the manufacturer earns the retailer a higher profit than bargaining. This counterintuitive result is a warning to retailers in dual-channel environments because if a retailer does not accept the wholesale price dictated by a manufacturer, but heedlessly exploits its strong power and bargains with the manufacturer, it may ultimately harm itself and reduce its own profits.

With the recent growing number of cyberattacks and the constant lack of effective defense methods, cyber risks become ubiquitous in enterprise networks, manufacturing plants, and government computer systems. Cyber-insurance provides a valuable approach to transfer the cyber risks to insurance companies and further improve the security status of the insured. The designation of effective cyber-insurance contracts requires the considerations from both the insurance market and the dynamic properties of the cyber risks. To capture the interactions between the users and the insurers, we present a dynamic moral-hazard type of principal-agent model incorporated with Markov decision processes, which are used to capture the dynamics and correlations of the cyber risks as well as the user's decisions on the protections. We study and fully analyze a case with a two-state two-action user under linear coverage insurance, and we further show the risk compensation, Peltzman effect, linear insurance contract principle, and zero-operating profit principle in this case. Numerical experiments are provided to verify our conclusions and further extend to cases of a four-state three-action user under linear coverage insurance and threshold coverage insurance., Comment: We have made several corrections

Depopulated regions face declines in traffic demand for airline service. The airline network in Hokkaido, Japan, plays an important role in the regional economy, healthcare, and sightseeing. Toward maintaining this airline network, the “bundled privatization” of airports has been discussed. Under such a system, several airports are privatized as a package for management by a single company. This study proposes a model for analyzing the behaviors of airport operators, airline companies, and passengers when bundled privatization is introduced to an airline network. Under bundled privatization, an airport management company can determine the landing fees of all airports such as to maximize the profits of all its airports, while an airline company can determine its service frequencies and aircraft sizes such as to maximize its profit. As a result of these two profit-maximization behaviors, an equilibrium state is obtained. The proposed model is applied to the airline network in Hokkaido, and the results of numerical calculations are discussed.

Despite extensive research on the value of collaborative forecasting among supply chain partners, there has been limited attention to the actual mechanisms of forecast sharing. We propose using cost-function-based prediction markets as means of sharing demand forecasts. We consider a decentralized two-stage supply chain with a supplier who sells to a retailer, in which both partners receive demand forecast updates. In the first stage, the supplier and the retailer agree on a collaborative forecasting scheme to form a consensus forecast. We consider two schemes: collaborative forecasting using cost-function-based prediction markets in which partners pay a set-up cost to operate a prediction market, and direct collaborative forecasting in which partners directly share their forecasts with each other and resolve their forecast differences through costly actions. In the second stage, we analyze the game between the supplier and the retailer to determine the retailer’s optimal order quantity as well as the supplier’s optimal wholesale price. We show that demand forecast sharing using cost-function-based prediction markets results in a Pareto improvement of the partners’ profit over the alternative of directly sharing forecasts. Furthermore, we show that this Pareto improvement is monotone with respect to supply chain partners’ forecast accuracy. We conduct an extensive numerical study to provide further insights into the effect of the return on forecast sharing investment, forecast accuracy, and level of demand uncertainty on the benefits gained by implementing cost-function-based prediction markets.

Green product design is recognized as one of the important approaches to improve environmental and social performance and achieve sustainable development. In the context of constraints of carbon emission, this article proposes a bilevel joint optimization (BJO) approach to optimize the green product line design with the consideration of the interactive relationship between firms in a competitive marketplace. A nonlinear, integer bilevel programming model with the consideration of carbon emission is developed based on the leader-follower decision framework. A bilevel nested genetic algorithm is presented to solve this optimization model. A case study of notebook computer involving one leader and one follower is developed to test the proposed optimization model. It is concluded that the presented BJO method can achieve a relatively better optimal solution to increase the profit of the product line while decreasing the amount of carbon emission for both leader and follower.

Nowadays the majority of retail customers use multiple channels. We investigate the assortment, space and inventory problem for an omni-channel retailer operating with interconnected bricks-and-mortar stores and an online shop. For this problem it becomes essential to consider customers’ demand interactions across channels. Current literature mainly focuses on single-channel assortments and ignores cross-channel substitution. We contribute the first integrated omni-channel model that determines assortments for the online and bricks-and-mortar channel with stochastic, space-elastic and out-of-assortment and out-of-stock demand both for in-channel substitution and cross-channel substitution. A specialized heuristic is developed that is based on an iterative solution of a binary problem and demand updates. Our approach achieves near-optimal results for small instances and higher objective values as an alternative heuristic for larger instances. With the full integration of channels, omni-channel retailers can realize a profit increase that mainly depends on the magnitude of substitution rates. We further show numerically that in-channel substitution has a stronger impact on profits than cross-channel substitution when costs are equal across channels.

Due to emergence of Internet of Things (IoT), fog computing is gaining momentum in the IT industry. The fog nodes are owned by the fog service providers (FSPs) and are not intended to provide the services to the users for free. If FSP charges high for its services or does not stick with its promised quality of service, existing users of that FSP may leave early or may churn to some other FSP. In such competitive scenario, to survive and to maximize the profit in the long run, FSPs should accept the requests of the users considering both technical and non-technical parameters. Since both FSP and IoT users are strategic decision makers, game theoretic analysis may help FSPs to maximize their payoffs. With the change in the strategy of the player, equilibrium solution may change and therefore this dynamic scenario is formulated as an extensive game form. A subgame perfect equilibrium, obtained for this game using backward induction, makes admission control policies suitable for different environment which helps the FSPs in maximizing their profit in the long run. A comparative analysis of the proposed work with state of art indicates that the proposed work outperforms and generates better revenue to the FSPs.

The main goal of a cloud provider is to make profits by providing services to users. Existing profit optimization strategies employ homogeneous user models in which user personality is ignored, resulting in fewer profits and particularly notably lower user satisfaction that in turn, leads to fewer users and reduced profits. In this paper, we propose efficient personality-aware request scheduling schemes to maximize the profit of the cloud provider under the constraint of user satisfaction. Specifically, we first model the service requests at the granularity of individual personality and propose a personalized user satisfaction prediction model based on questionnaires. Subsequently, we design a personality-guided integer linear programming (ILP)-based request scheduling algorithm to maximize the profit under the constraint of user satisfaction, which is followed by an approximate but lightweight value assessment and cross entropy (VACE)-based profit improvement scheme. The VACE-based scheme is especially tailored for applications with high scheduling resolution. Extensive simulation results show that our satisfaction prediction model can achieve the accuracy of up to 83%, and our profit optimization schemes can improve the profit by at least 3.96% as compared to the benchmarking methods while still obtaining a speedup of at least 1.68x.

Problem definition: Solar power companies have introduced innovative non-ownership business models—leasing and power purchase agreement (PPA)—in addition to sales. Under these models, the company installs solar panels for a customer, who purchases the electricity generated from the panels. Under leasing, a customer pays a fixed fee, whereas under PPA, a customer pays a per-unit price for the electricity generated. The adoption of solar panels is also promoted by investment and generation subsidies, which are received by customers under sales, but by the solar power company under non-ownership models. In addition, some states currently have restrictions on non-ownership model(s) to protect utility firms’ profits. Motivated by this context, we analyze the economic and environmental implications of such subsidies and restrictions. Academic/practical relevance: The share of non-ownership business models of the residential solar installations is approximately 40%, and we contribute to the operations management literature by being the first to endogenize a solar power company’s business model decisions to inform policymakers regarding the ongoing discussion on subsidies and restrictions. Methodology: We develop a sequential game between a profit-maximizing solar power company and utility-maximizing residential customers to characterize a solar power company’s optimal business model decisions. Results: We find that a higher investment subsidy makes it more attractive for the company to offer sales, whereas a higher generation subsidy makes it more attractive to offer non-ownership models. Contrary to conventional wisdom, we show that restricting the solar power company from offering leasing can actually lead to higher total adoption and generation of renewable energy. Managerial implications: We show that, although a higher generation subsidy leads to higher total adoption and generation of renewable energy, interestingly, a higher investment subsidy can backfire by leading to lower total adoption and generation. Consequently, a higher investment subsidy can actually lead to a higher profit for the utility firm. Finally, we find that restricting leasing can be detrimental for the utility firm and beneficial for the environment.

This paper proposes a new mathematical formulation for the optimal operation scheduling of a remote PV farm and a set of distributed electric vehicle charging stations (D-EVCSs), which are owned by a private entity. The proposed model is formulated to maximize the profit of the private investor through optimal electric vehicles (EVs) charging coordination and pricing mechanism. The proposed pricing mechanism aims to achieve an expected revenue by the private investor, while guaranteeing a low EVs charging price to ensure the EV owners satisfaction. The D-EVCS integrates a battery storage system (BSS) and rooftop PV for the EVs supply application. However, the rooftop PV existence is constrained to the limited footprint of the EVCS. For this reason, the investment in a remote PV farm and its impact on the expected revenue and the EVs charging price is introduced in this paper. The power generated by the remote PV farm can be transacted to the D-EVCSs using a power purchase agreement through the utility grid infrastructure. In such a case, the electricity service and distribution fees are paid to the utility grid for overseeing the PV farm power transaction. Also, the opportunity of the D-EVCSs to participate in the provision of operating reserve and demand response ancillary services is considered in the proposed model. Different case studies are evaluated to validate the effectiveness of the proposed model.

Problem definition: We study a monopolistic robust pricing problem in which the seller does not know the customers’ valuation distribution for a product but knows its mean and variance. Academic/practical relevance: This minimal requirement for information means that the pricing managers only need to be able to answer two questions: How much will your targeted customers pay on average? To measure your confidence in the previous answer, what is the standard deviation of customer valuations? Methodology: We focus on the maximin profit criterion and derive distribution-free upper and lower bounds on the profit function. Results: By maximizing the tight profit lower bound, we obtain the optimal robust price in closed form as well as its distribution-free, worst-case performance bound. We then extend the single-product result to study the robust pure bundle pricing problem where the seller only knows the mean and variance of each product, and we provide easily verifiable, distribution-free, sufficient conditions that guarantee the pure bundle to be more robustly profitable than à la carte (i.e., separate) sales. We further derive a distribution-free, worst-case performance guarantee for a heuristic scheme in which customers choose between buying either a single product or a pure bundle. Moreover, we generalize separate sales and pure bundling to a scheme called clustered bundling that imposes a price for each part (i.e., cluster) of a partition of all products and allows customers to choose one or multiple parts (i.e., clusters), and we provide various algorithms to compute clustered bundling heuristics. In parallel, most of our results hold for the minimax relative regret criterion as well. Managerial implications: The robust price for a single product is in closed form under the maximin profit or minimax relative regret criterion and hence, is easily computable. Its interpretation can be easily explained to pricing managers. We also provide efficient algorithms to compute various mixed bundling heuristics for the multiproduct problem.

The cloud resource provision policy of a content provider in the presence of competitors on globally distributed cloud platforms plays a significant role in maximizing its profit. However, developing an optimal resource provision policy is quite challenging, due to the difficult to capture the competition relationship between two competitive CPs and to obtain the budget of the competitors which is usually kept private. To solve this problem, in this paper, we propose a learning-driven cloud resource provision policy for a CP with competitors. We formulate the competition between the CPs as a lottery Colonel Blotto game in which the payoff of each region is positively related to the resource advantage achieved by the CP, formulate the budget allocation problem as a Markov decision process, and obtain the sub-optimal resource provision policy by reinforcement learning and deep reinforcement learning-based algorithms. We also prove the convergence of the sub-optimal solution. Finally, we validate our proposed method using real-world CPs statistics. The results show that the budget information is critical for a CP to make policy decisions, and it is better for CPs with smaller budget to focus their budget resources in regions with higher values.

The introduction of store brands not only brings greater pressure to the upstream manufacturers, but also induces consumers to pay growing attention to transaction fairness when choosing among different brands. In this work, we study how a national brand manufacturer can adopt direct consumer incentive to defer a retailer’s store brand entry in the presence of consumer fairness concern. Using a Stackelberg game model, we analyze the market entry strategy of the retailer and the incentive strategy of the manufacturer, and investigate how the consumer fairness concern affects the firms’ strategies and performances. Our results show that consumer fairness behavior in transactions has a negative effect on the retailer’s profit, and leads the retailer to lower retail prices of both brands. The incentive provided by the manufacturer can alleviate consumers’ fairness concern for the national brand, and under certain conditions, it can effectively discourage the retailer from building her own brand. We further show that our main results still hold when we take into consideration the incentive frequency in the selling period and when consumers are fairness-concerned about the national brand manufacturer.

During the past decade, the demand for biofuel has rapidly increased because of fossil fuel scarcity and pollution, which has resulted in the accelerating commercialization of the global biofuel markets with a potential 500 exajoules per year by 2050. This paper evaluates the economic benefits of five biomass types and proposes a stochastic programming model for the optimal design of the global biodiesel supply chain under the demand and price uncertainty. The biomass is produced in Southeast Asia and oil is extracted. Then, the vegetable oil is shipped to Europe and North America for biodiesel conversion and fulfilling the local demand. The results show that palm tree is a cost-effective clean energy with high benefits and low-carbon emissions. Based on the economic evaluation of biomass, the optimal capital investment, allocation of cultivated area, and transportation paths are identified to maximize the expected global profit in this supply chain in 2008–2012 by the proposed supply chain optimization model.

The development of Blockchain-based mobile applications are impeded due to the resource limitations of mobile devices. Computation offloading can be a viable solution. In this paper, we consider a two-layer computation offloading paradigm including an edge computing service provider (ESP) and a cloud computing service provider (CSP). We formulate a multi-leader multi-follower Stackelberg game to address the computing resource management problem in such a network, by jointly maximizing the profits of each service provider (SP) and the payoffs of individual miners. We study two practical scenarios: a fixed-miner-number scenario for permissioned blockchains and a dynamic-miner-number scenario for permissionless blockchains. For the fixed-miner-number scenario, we discuss two different edge operation modes, i.e., the ESP is connected (to the CSP) or standalone, which form different miner subgames based on whether each miners strategy set is mutually dependent. The existence and uniqueness of Stackelberg equilibrium (SE) in both modes are analyzed, according to which algorithms are proposed to achieve the corresponding SE(s). For the dynamic-miner-number scenario, we focus on the impact of population uncertainty and find that the uncertainty inflates the aggressiveness in the ESP resource purchasing. Numerical evaluations are presented to verify the proposed models.

The energy consumption disparity is an important factor affecting product pricing. The risk and the cost of new energy products are generally higher than those of traditional energy products, which results in conflicting pricing strategies between manufacturers and retailers. Applying the concepts from dynamic cooperative game theory, such as improved Shapley value, in this paper, we have studied the optimal pricing strategies of manufacturers and retailers in a cooperative and a noncooperative game. Based on policies and market conditions in China, we have used a logistic regression model to determine the impact of product sales, devised pricing strategies affected by the relevant government tax, and subsidy policy. The results show that, first, under the basic condition in which the manufacturer obtains profit, the optimal pricing of the retailer in a noncooperative game is more than that in a cooperative game. Second, the differential coefficient of the energy consumption of the two products in the cooperative game is greater than that in the noncooperative game. Third, the cooperative game not only plays an incentivizing role for retailers in setting reasonable prices of new energy products but also encourages manufacturers, producing a greater difference of energy consumption between traditional energy products and new energy products, which further reduces the consumption of new energy products. Finally, the strategies of sharing the green marketing costs by manufacturers and retailers can improve the total profits of the supply chain in addition to their own.

This study examines the impact of carpool on network traffic in a highly idealized futuristic world, where all travelers are willing to participate in carpool arranged by a Transportation Network Company. We build a parsimonious carpool model that focuses on the trade-off between inconvenience costs and travel cost savings. Underlying the model is a nonlinear bipartite matching problem that seeks to maximize commuters’ welfare. By assuming the congestion effect is negligible, we derive several useful analytical results. When the inconvenience cost is less than the median trip valuation of a rider, the platform could always achieve an almost perfect match while maximizing commuters’ welfare, which corresponds to a 50% reduction in vehicular traffic flow. In the case of perfect match, if there is an even number of travelers, we propose a pricing policy that possesses all desired properties of the Vickrey-Clark-Groves (VCG) policy – a benchmark truthful policy for achieving socially optimal solution – but runs a lower deficit. Otherwise, we show the VCG policy always generates a profit. If the inconvenience cost is too high, the perfect match is no longer socially optimal, but the VCG policy still yields a positive profit. Solutions from numerical experiments generally agree with the analytical results. They also suggest that matching across O-D pairs occurs only when it has a significantly lower inconvenience cost than matching within, an unlikely event in reality. Moreover, when cross O-D matching does become prevalent, it leads to higher vehicle miles travelled, hence worse congestion. Thus, from the point of view of traffic management, cross O-D carpool should not be encouraged.

Varied forms of mobility are rapidly transforming communities across the world. In Africa’s cities and urban peripheries, the results of human movements include ever more diverse sets of new arrivals living alongside longer-term residents as they seek protection, profit, and passage elsewhere. Some move on and others return home, while still others shift within in search of new opportunities or security. In the absence of muscular state institutions or dominant cultural norms, these areas have become estuarial zones in which varied communities of convenience are taking shape. Unlike well-documented urban gateways or ghettos, these communities range from radical forms of exclusion to remarkable modes of accommodation that enable people to extract usufruct rights: to live in but not become fully part of the cities they occupy. Using examples from Maputo, Johannesburg, and Nairobi, this article explores the nature of these estuaries in ways that challenge the conceptual foundations typically informing debates over migrant rights, integration, and the boundaries of belonging. This means eroding clear distinctions between hosts and guests along with a call to reevaluate the relative importance of state institutions and policies. Most fundamentally, it questions new residents’ interests in localized political and social recognition and participation. The article concludes by suggesting the need to reconsider the forms and scale of community through which the newly urbanized claim rights and the nature of the rights they desire.

The objective of this study is to apply business intelligence in identifying potential customers by providing relevant and timely data to business entities in the Retail Industry. The data furnished is based on systematic study and scientific applications in analyzing sales history and purchasing behavior of the consumers. The curated and organized data as an outcome of this scientific study not only enhances business sales and profit, but also equips with intelligent insights in predicting consumer purchasing behavior and related patterns. In order to execute and apply the scientific approach using K-Means algorithm, the real time transactional and retail dataset are analyzed. Spread over a specific duration of business transactions, the dataset values and parameters provide an organized understanding of the customer buying patterns and behavior across various regions. This study is based on the RFM (Recency, Frequency and Monetary) model and deploys dataset segmentation principles using K-Means Algorithm. A variety of dataset clusters are validated based on the calculation of Silhouette Coefficient. The results thus obtained with regard to sales transactions are compared with various parameters like Sales Recency, Sales Frequency and Sales Volume.

In this paper, we propose a pricing scheme, named PRIME, for provisioning mobile sensors-as-a-Service (mSe-aaS) in the Mobile Sensor-Cloud (MSC) architecture, with an aim to optimally distribute the financial profit among different actors of MSC. Unlike traditional sensor-cloud, MSC introduces a new actor as device owner, whose mobile device On the other hand, the device and sensor owners earn certain revenues, based on the usage of the sensor nodes and the mobile devices, for provisioning mSe-aaS to the end-users. MSC is a contemporary architecture, and therefore, no pricing scheme exists for it. In this work, we consider the presence of device owner, sensor owner, Sensor-cloud Service Provider (SCSP), and end-user to determine an optimal pricing strategy. In order to design such a strategy, we use the Lagrangian multiplier method and apply Karush-Kuhn-Tucker (KKT) conditions. On the other hand, an end-user has multiple options to select a SCSP among the available ones. Therefore, based on the reputation of all the available SCSPs, PRIME enables an end-user to select a suitable one. Extensive experimental results report that PRIME increases the profit of sensor and device owners by 25.67% and 29.12%, respectively.

Consumers may need help using an inherently complex or information-intensive product after purchase. This paper studies the incentive for a manufacturer or a retailer to invest in pre-sales service effort that would reduce the consumer's likelihood of seeking after-sales support. Because providing pre-sales and after-sales support would incur costs for the provider, it is not evident whether the manufacturer or the retailer would be more effective in doing so, and whether both services should be provided by the same firm. We consider different models in which the pre-sales service and the after-sales support is provided by the same or different firms, and show that the service configuration in which both pre-sales service and after-sales support is provided by the retailer is desirable to both the retailer and the manufacturer. Interestingly, in this service configuration, although the pre-sales service is provided at the highest level among all configurations, consumers can show up in the largest numbers to seek after-sales support. We also study the possibility of the firms collaborating in providing pre-sales service, by allowing the manufacturer to share a portion of the retailer's cost of providing pre-sales service. As expected, if the manufacturer provides the after-sales support, cost-sharing by the manufacturer may increase both channel members' profits. However, if the retailer provides after-sales support, cost-sharing by the manufacturer helps increase the manufacturer's profit but, surprisingly, reduces the retailer's profit. In this case, the overall channel can still be better off, and as such, the manufacturer may be able to implement cost-sharing by providing an appropriate transfer to the retailer.

Platform-hardware business models (e.g., integrating a software technology platform into a hardware device such as a smartphone) have been widely applied in various industries. This study investigates the response of competing original equipment manufacturers (OEMs) when the developer strategically updates the platform quality. Consumers have two-dimensional valuations of the product: one valuation for the device provided by the OEM and another valuation for the platform provided by the developer. The developer offers two types of agreements: acceptance and rejection of preinstallation of core services that profit the developer. OEMs can preinstall the core services and pay nothing to use the platform, or they can pay a wholesale price for the platform and reject the installation of the core services. We propose a game-theoretic model to examine the strategic agreement choices of dominant and weak OEMs when the developer strategically offers an updated platform quality to enhance a weak OEM's competitiveness. We find that the developer benefits from the OEMs’ acceptance only when the exogenous wholesale price for the dominant OEM is low and the cost difference is moderate. Also, when the research and development (R&D) efficiency is high, an increase in the cost difference induces a conversion from acceptance to rejection for the dominant OEM, and an opposite outcome for the weak OEM. One OEM's installation acceptance may enhance or weaken the advantage of the other OEM's acceptance for the consumer surplus, depending on the value of the R&D efficiency. Several extensions are examined to complement the basic model, and implications behind each strategic choice are revealed.

Foreign direct investment (FDI) can increase productivity and wages. However, it is also often accompanied by primary income deficits as foreign-owned firms repatriate their profits. The welfare effects of FDI are thus ambiguous. A particularly illustrative example of this phenomenon are the Visegrad 4 (V4) countries (Czech Republic, Hungary, Poland, Slovakia). This paper investigates whether FDI can be beneficial in the presence of profit repatriation using a general equilibrium model calibrated to the V4 economies. Counterfactual simulations suggest that the benefits of FDI outweigh the costs for these countries. On average, a 1% increase in the share of foreign firms is associated with a 0.17% increase in welfare. However, incentivising foreign firms to reinvest more of their profits domestically is, ceteris paribus, welfare-improving. A 10-percentage-point increase in the profit repatriation rate is associated with a 1.06% welfare gain on average.

We consider a retailer that sells a perishable product, making joint pricing and inventory ordering decisions over a finite time horizon of T periods with lost sales. Exploring a real-life data set from a leading supermarket chain, we identify several distinctive challenges faced by such a retailer that have not been jointly studied in the literature: the retailer does not have perfect information on (1) the demand-price relationship, (2) the demand noise distribution, (3) the inventory perishability rate, and (4) how the demand-price relationship changes over time. Furthermore, the demand noise distribution is nonparametric for some products but parametric for others. To tackle these challenges, we design two types of data-driven pricing and ordering (DDPO) policies for the cases of nonparametric and parametric noise distributions. Measuring performance by regret, that is, the profit loss caused by not knowing (1)–(4), we prove that the T-period regret of our DDPO policies are in the order of [Formula: see text] and [Formula: see text] in the cases of nonparametric and parametric noise distributions, respectively. These are the best achievable growth rates of regret in these settings (up to logarithmic terms). Implementing our policies in the context of the aforementioned real-life data set, we show that our approach significantly outperforms the historical decisions made by the supermarket chain. Moreover, we characterize parameter regimes that quantify the relative significance of the changing environment and product perishability. Finally, we extend our model to allow for age-dependent perishability and demand censoring and modify our policies to address these issues. This paper was accepted by David Simchi-Levi, Management Science Special Section on Data-Driven Prescriptive Analytics.

Among the drivers of inequality, income distribution between wages and profits at the level of individual industries plays a key role. The factors shaping the parallel evolution of wages and profits are investigated in this article building on Post-Keynesian and evolutionary perspectives. We develop a simultaneous model on wage and profit dynamics identifying as key determinants the capital-labour conflict, the expansion of value added per hour worked, different strategies of technological change and offshoring, and the relevance of trade unions. The empirical investigation includes an industry-level analysis on 38 manufacturing and service sectors for six major European countries from 1994 to 2014. Wage and profit dynamics are shown to be rooted in structural change and capital-labour conflict, with profits driven by product innovation and offshoring, and wages rising faster where new products are relevant and trade unions have a greater role.

The purpose of this article is to present a novel biobjective biform game framework for optimizing strategy selection of nodes (agents) in bilateral link network formation under dual objectives of profit and link security. First, in the first part of the noncooperative game, we construct various possible strategy combinations (situations) of all players (nodes) in the network, which define the competitive environment in the second part of the cooperative game. Second, in the second part, we derive coalitions' payoffs under the two objectives of profit and link security and propose the biobjective Banzhaf values to allocate the corresponding cooperative games in all strategy situations. Then, a biobjective noncooperative game is formed on the basis of the obtained biobjective Banzhaf values. Third, we redefine Pareto-Nash equilibriums (in the sense of pure strategies) and obtain the Pareto-Nash equilibriums of the biobjective noncooperative game in the first part. Subsequently, the choice of the obtained Pareto-Nash equilibriums is regarded as a biobjective group decision-making problem, which is solved by employing the redefined Borda choice function method with objective weights. Hence, we can obtain the optimal strategy choices of all the nodes (players) for the entire bilateral link network formation. Finally, numerical examples and comparative analysis are given to verify the validity and applicability of the proposed models and methods.

what has affected the countrys economic climate today is the implementation of international sanctions against iran. the effect of the whip effect on supply chains performance is one of the main reasons for reducing the performance of supply chains when demand changes occur during the supply chain. Bullwhip effect as one of main issues in reducing of supply chains efficiency, occurs when the change of the demand in supply chain is faced with large fluctuations. Awareness of the causes and effects of supply chain whip helps organizations to adopt policies to reduce the intensity of its occurrence and its damaging effects. knowledge of the causes of the Bullwhip effect helps the organization and supply chains to mitigate the severity of its occurrence and its destructive effects. the main purpose of this paper is to design a suitable model to investigate the effects of sanctions on the supply chain in terms of satisfaction of people, drug prices and producers. moreover, the effect of the effect of whip effect on the effect of effect on the whole chain has been analyzed. These policies were examined, including government support for consumers, reduction of government exchange rates, lower prices of raw materials, and a reduction in laxative effects. The supplier, distributor and overall profit of the drug supply chain. the above research in a distinct position is that, according to previous studies, drug production analysis has been considered less attention with the dynamics approach of the systems. in this study, the supply chain of a particular drug (Deferoxamine), examines the effect of whip and policy to reduce its effect on the whole chain. A dynamic medicine supply chain has been first modeled; After that, causal loop diagram has been plotted and finally system’s flow chart has been drawn. More over, simulation results have been discussed, and at the end of the research four policies for improving current situation have been suggested.

Authorisation is a commonly used strategy for OEMs who do not have remanufacturing capability but still want to extract some benefits from the remanufacturing market. In this paper, we analyse two authorisation strategies: one is dealer authorisation, i.e., the OEM turns the independent remanufacturer (IR) into an authorised dealer selling both new and remanufactured products; the other is remanufacturing authorisation, i.e., the OEM authorises the IR to remanufacture used products by charging an authorisation fee. A stylised model is developed to compare the three scenarios: competition, cooperation with dealer authorisation and cooperation with remanufacturing authorisation. Our results show that cooperation depends on the effect of authorisation on the improvement of customers’ willingness-to-pay for remanufactured products. Although remanufacturing authorisation is preferred by the OEM, only dealer authorisation is achievable under certain conditions. Interestingly, while the OEM’s profit decreases in new product cost in general, it may increase when higher cost causes a strategy switch from competition (dealer authorisation) to remanufacturing authorisation. Furthermore, we analyse the environmental and social impact of cooperation and identify the conditions under which cooperation benefits the environment and consumers. While cooperation can achieve a win-win-win situation on the triple bottom line, the choice of authorisation mode always presents a conflict between environmental and consumer benefit.

We study trust building in credence goods markets in a dynamic setting. An extreme lemon problem arises in the one-shot game and results in no trade. In the repeated game, an expert's honesty is monitored through consumers' rejection of his recommendations. We characterize the optimal equilibrium for any discount factor. The expert's profit in the optimal equilibrium weakly increases in the discount factor but cannot achieve the first best because the optimal equilibrium either involves insufficient treatment for the serious problem or excessive treatment for the minor problem. The monitoring technology and the equilibrium outcome contrast sharply with their counterparts for experience goods markets. Competition enhances efficiency by allowing consumers to search for second opinions after rejecting the first treatment recommendation, but the efficiency gain comes at the cost of less honesty.

Network pricing games provide a framework for modeling real-world settings with two types of strategic agents: operators of a network and users of the network. Operators of the network post a price so as to attract users and maximize profit; users of the network select routes based on these prices and congestion from other users. Motivated by the fact that equilibrium in these games may not exist, may not be unique, and may induce an inefficient network performance, our main result is to observe that a simple regulation on the network owners’ market solves these three issues. Specifically, if an authority could set appropriate caps (upper bounds) on the tolls (prices) operators can charge, then the game among the link operators has a unique and strong Nash equilibrium and the users’ game results in a Wardrop equilibrium that achieves the optimal total delay. We call any price vector with these properties a great set of tolls and investigate the efficiency of great tolls with respect to the users’ surplus. We derive a bicriteria bound that compares the users’ surplus under great tolls with the users’ surplus under optimal tolls. Finally, we consider two different extensions of the model. First, we assume that operators face operating costs that depend on the amount of flow on the link, for which we prove existence of great tolls. Second, we allow operators to own more than one link. In this case, we prove that, when operators own complementary links (i.e., links for which an increase in toll value may only increase the flow on the other owned links), any toll vector that induces the optimal flow and that is upper bounded by the marginal tolls is a great set of tolls, and furthermore, we show that, when all links in the network are complementary, then the aforementioned toll vector is also a strong cap equilibrium.

This study investigates the role of a credit guarantee company and a peer-to-peer (P2P) lending platform in the financing business of a capital-constrained retailer who faces uncertain demand over a single period. The credit guarantee company is divided into two types: conservative and adventurous. The conservative credit guarantee company aims to earn a certain expected return by setting its guarantee service fee rate, whereas the adventurous one sets the guarantee service fee rate to maximize its profit while ignoring the expected return. In a decentralized financing system, the guarantee types will influence all local optimal decision variables in Stackelberg equilibrium, and the optimal order quantity setting under the adventurous credit guarantee type is more flexible than that under the conservative one. We then analyze the centralized financing system and prerequisites for participation to obtain the supply chain financing (SCF) coordinating strategy. Under the adventurous credit guarantee, the P2P lending platform financing system can achieve SCF coordination, but under the conservative credit guarantee, SCF coordination cannot be achieved. When a credit guarantee company is adventurous and the coordination condition on order quantity is satisfied, the information service fee rate linearly decreases in the guarantee service fee rate, and the P2P lending platform’s and the credit guarantee company’s expected profits have different monotonic properties with the information service fee rate (or the guarantee service fee rate) increasing. Finally, we identify the boundary of the retailer’s initial working capital and the suitable ranges of the service fee rates to achieve SCF coordination.

The emissions trading scheme (ETS) is considered an effective policy tool for achieving emission reductions. At present, China’s ETS pilots have completed several compliance periods and provided rich experience for a national ETS. Taking advantage of the variations in the regional ETS pilots across regions and sectors and over time, this study used a difference-in-difference-in-differences (DDD) model to examine the effects of ETS pilots and differential program design on industry risk and the impacts of risk on future investment horizon. First, we found that the ETS pilots significantly increase profit variability (total risk) in China’s industry; that is, ETS induces greater uncertainty in the covered sectors. In terms of industry heterogeneity, non-electricity sectors (specifically ferrous metal) engage in higher levels of risk taking. Second, the influence of such program features as the allowance allocation (total amounts and approaches), China Certification Emission Reduction (CCER) trading volumes, and penalties are heterogeneous. Finally, during the period around the treatment, industry risk can significantly encourage longer rather than shorter investment. In particular, the punishment mechanism and benchmarking allocation play a positive role in regulating the risk‒investment relationship.

The identity of sustainable entrepreneurs progresses and is primed by certain identity dimensions, including society, community, or competition as their frames of references, their social motivation, and their self-evaluation. Because sustainable entrepreneurs aim to achieve the goals of generating profit and create social and/or ecological value at the same time, they experience two types of tensions: one that concerns social and ecological and the other with an extension of economic aspects. To leverage these, we find identity shifts dependent on the tension sustainable entrepreneurs’ experience, activating a certain combination of identity dimension. Building on 29 case studies with European sustainable entrepreneurs, the study investigates the dynamic role of entrepreneurial identity, thereby informing about fluid hybrid identity conflict management strategies. This adds to theory on entrepreneurial identity that has widely found multiple, yet coherent entrepreneurial identities and conflict management strategies.

We compare the profit of the optimal third-degree price discrimination policy against a uniform pricing policy. A uniform pricing policy offers the same price to all segments of the market. Our main result establishes that for a broad class of third-degree price discrimination problems with concave profit functions (in the price space) and common support, a uniform price is guaranteed to achieve one half of the optimal monopoly profits. This profit bound holds for any number of segments and prices that the seller might use under third-degree price discrimination. We establish that these conditions are tight and that weakening either common support or concavity can lead to arbitrarily poor profit comparisons even for regular or monotone hazard rate distributions., Comment: 26 pages, 5 figures

The deep tillage of soil consumes fuel at higher costs, which creates a large amount of carbon emissions. For large-scale farming in particular, the threat to the environment cannot be ignored. Currently, very few studies have considered the impact of the size of the deep tillage area on carbon emissions. Effectively controlling the size of the deep tillage area can reduce the emission of carbon dioxide at a certain level and optimize profits, which is crucial to the sustainable development of agricultural supply chains. In this paper, we design a new contract-farming rice supply chain network based on “Cooperative + Storage Company”, which considers tillage as the start point for the supply chain echelon. A multi-product, multi-period, bi-objective optimization model is proposed to maximize profit and minimize carbon emissions in the network. Moreover, we consider the grain rotation task of the storage company in the modeling. The L p -metrics method is used to solve the bi-objective model. Subsequently, an actual scenario is used to verify the effectiveness of the model. The results indicate that changes in the size of the deep tillage area have a significant impact on carbon emissions, and the size of the deep tillage area is negatively correlated with the total farm size, thereby affecting the carbon dioxide release. The results also indicate that carbon emissions can be minimized by increasing the number of cooperatives or expanding the size of farms. The proposed model can aid decision-makers in selecting an optimal set of solutions based on their preferences or policies.