Search

PurposeThis paper aims to examine the optimal promotion strategy of an e-retailer, who may advertise, or launch rebates initiative to encourage consumers' disseminating electronic word-of-mouth (eWOM) messages, with an aim to boost product sales.Design/methodology/approachThis paper analyzes the decisions of the e-retailer in a two-period model, using utility function approach and backward induction method, and obtains the optimal solutions in four promotion strategies.FindingsThe study finds that rebate scheme greatly impacts the timing of advertising, and neither lower nor higher consumers' eWOM effort invariably benefits the retailer, rather, a medium level is the best choice for the retailer. When eWOM impact power is at a relatively high level, it can supplement advertising effect to attract more consumers' purchase. Otherwise, eWOM may counteract the role of advertising.Originality/valueDifferent from the extant literature focusing on advertising or eWOM without rebates, the paper studies the issue of advertising and eWOM with rebates in two- period model which seldom addresses before the authors examine the optimal timing of advertising and eWOM with/without rebates in four promotion strategies i.e. the A-NE model the NE-A model the A-ER model and the ER-A model.

Background: Battery charging mode is a prevalent method of transshipping power to new energy vehicles. Unfortunately, due to the limited capacity of batteries, typical new energy vehicles can only travel for approximate 350 miles on a single charge and require hours to be recharged. Battery swapping mode, as a novel alternative, can offer ideal solutions by depleted batteries being exchanged for recharged ones at swapping stations in the middle of long trips, thus will inevitably influence potential consumers’ purchase behaviors accordingly.Methods: To examine the impact of swapping mode and charging mode on consumers’ purchase intention, this paper examines a duopolistic market consisting of two new energy vehicle makers (i.e., new energy vehicle maker with swapping mode and one with charging mode), who adopting swapping mode and charging mode to service consumers, respectively. Considering swapping mode characterized with low initial investment and ease of use for consumers, the new energy vehicle makers with charging mode, capitalize on battery extended warranty service in response to rivals’ utilization of swapping mode, thereby non-cooperative game models are formulated, in which government subsidies are taken into account, both the optimal production decision for the new energy vehicle maker with swapping mode maker and one with charging mode are analyzed under three scenarios: without extended warranty service, with extended warranty service, and with extended warranty service and subsidy.Results: The results show that two makers’ market dominance relies on the ratio of the swapping station’s convenience to the extended warranty service and the valuation incremental rate. Additionally, we also find that the government subsidy can dramatically improve the performance of the new energy vehicle maker with swapping mode at the initial stage, but if the subsidy is insufficient size at the sequent stage, this will lead to policy failure and inefficiency in propelling the diffusion of swapping mode.Conclusions: The emerging swapping mode technology is a promising innovative one in boosting new energy vehicle diffusion, which will supplement to charging mode, thus reduce climate change dramatically

Due to rejecting order in a single supply chain for lack of adequate capacity, a multi-chain system is introduced to avoid this potential operational risk. Based on four categories of order: direct order, reserve order, chain-to-chain order and rejected order, the framework of order selection in multi-chain system (MCS) is presented, and the model of order selection and planning under chain-to-chain collaboration is formulated. Then, the Lagrange algorithm is used to solve this problem through Lagrange relaxation and decomposition. Finally, numerical study show that opportunity cost of rejecting reserve order and production cost of chain-to-chain order have significant impacts on order selection, and there exists a critical threshold value of the combination of two factors. Through the combination, the multi-chain system can obtain the optimal status, meanwhile manager can utilize this to realize different strategies in MCS.

New energy vehicles have been recognized as a sustainable alternative to lower gasoline consumption and emissions in the transportation sector. To alleviate environmental pressure, a spectrum of government policies has been introduced to inspire the production and penetration of new energy vehicles (NEVs). Meanwhile, some of the incentive policies are facing renewals and modifications to meet consumers&rsquo, demand of purchase and the present growth of the NEV industry. This means that the understanding of what the current and upcoming policies are, how to formulate policy portfolios, and consumers&rsquo, purchasing NEV behavior in a response to these policies and its adjustment are of practical and academic importance for the NEV sector. Different from prior research which analyzed the role of government policy as a whole, we here separately examined the impact of policy portfolios (i.e., production policy, purchase/usage policy and recycle policy) on NEV adoption from the product life cycle perspective. The hypotheses were empirically tested by analyzing data collected from 299 respondents in China. The results showed that production policy has a significantly positive impact on financial benefits, esteem needs and infrastructure, whereas it insignificantly influences NEV performance, similarly, purchase/usage policy positively affects esteem needs and infrastructure, yet its effect on financial benefits is found to be insignificant, meanwhile, recycle policy has a significantly positive effect on financial benefits, and esteem needs as well as NEV performance. Furthermore, financial benefits, esteem needs, NEV performance and infrastructure are found to significantly and positively impact on consumers&rsquo, purchase intention. Parallel to this, we observed perceived usefulness and perceived ease of use play partially mediating relations between policy portfolios and consumers&rsquo, adoption intent. Hints for decision-makers and avenues for future study are discussed in this research.

Due to scarcity of designers in fast fashion industry and proliferation of the Internet, small- and medium-sized garment makers have gradually turned to external designers to enhance their innovation efficiency via crowdsourcing initiative. However, few have investigated the issue of fast fashion customized-design matching decision in the crowdsourcing context. Different from previous works, we split crowdsourcing matching decision process into three hierarchical submodels in terms of three key factors, namely, surplus, due date, and goodwill. From a dynamic perspective, we first develop a two-sided matching model where garment makers and online designers select one another by maximizing their total surpluses with an aim to reach robust final pairs and derive the corresponding conditions under which the optimal pairs can be obtained. Then, the extensions of the matching model are conducted by incorporating the critical factors of due date and garment makers’ goodwill, respectively. Followed by that, an improved Gale–Shapley algorithm is devised to solve the crowdsourcing matching problems. The results illustrate when garment makers exceed online designers in number, crowdsourcing design tasks without due-date constraint are more attractive for designers’ participation than those with due-date constraint, and garment makers intend to share the incremental surpluses with designers to maximize the total surpluses. By contrast, when online designers surpass garment makers in number, designers prefer due-date design tasks to those without it. In addition, regardless of whether under the irregular or regular case, the model with goodwill concern always outperforms the two others. Moreover, celebrated garment makers are invited to post design tasks, thus enabling to entice more designers’ engagement in crowdsourcing activities, which in turn facilitating to transfer myopic designers to strategic ones. Finally, sensitivity analysis further verifies the models are stable and robust.

New energy vehicles (NEVs) are becoming more and more prevalent for economic and environmental reasons. This paper investigates the issue of the impacts of subsidy policy and dual credit policy on NEVs and conventional vehicles (CVs) production decision from an across-chain perspective, in a co-opetitive context, where exists a CV supply chain and a NEV supply chain with two important schemes involved, i.e., government subsidies and dual credit. While previous literature has discussed government subsidies excessively, they seldom study the role of dual credit policy in promoting NEVs. To examine the differences between two schemes, a mixed integer linear programing (MILP) is utilized to develop a stylized production model for a CV supply chain and NEV supply chain system that incorporating subsidies and dual credit trading simultaneously. Using a Lagrange heuristic algorithm to provide an optimal solution regarding NEV and CV production decision as well as dual-credit trading. Simulations are performed on realistic profiles that show, (i) implementing the dual credit policy increases the profit of NEV supply chain, whereas the profit of CV supply chain and of whole supply chain system decline simultaneously, and the schedule of CVs/NEVs without across-chain cooperation is arranged more evenly than that with across-chain cooperation during the transit period to NEVs. Meanwhile, (ii) under dual credit policy, gradually-decreasing subsidies can partially offset the negative impacts of dual credit policy on the NEV supply chain, the subsidies can only serve as a temporary supplement to profits. In addition, (iii) there exists an optimal NEV credit price p ∗ to maximize the overall profit of the whole system, and a corresponding threshold value of p ∗ for two categories of cars, when above the threshold, the per-CV profit outperforms the per-NEV one and vice versa.

Due to inadequate designers in fast fashion industry and the development of the Internet, small-and-medium-sized garment makers have gradually turned to external talents to enhance their new product design efficiency via crowdsourcing initiative. This paper presents a new framework of crowdsourcing supply chain for fast fashion industry. First, a basic multiperiod order model is established in a crowdsourcing supply chain system, where a garment maker chooses the best one among available solutions submitted by online designers (i.e., crowdsources) in each period, then transforms it into finished product, and sells to consumers through a retailer. Second, we extend this model by integrating the factors of capital turnover, the retailer’s risk-aversion, and the garment maker’s minimum production quantity. Moreover, utilizing wholesale price, buyback, and profit-sharing scheme designs a mixed contract for coordinating crowdsourcees, retailer, and garment maker of crowdsourcing supply chain for achieving Pareto optimality. The models help the garment maker determining the optimal production quantities of crowdsourcing designed products and enable the retailer placing the optimum orders and setting the reasonable risk level. In addition, we also find that the incentive policy for crowdsourcing designers can be fulfilled by using a profit-sharing scheme with a piecewise function of order quantity instead of a linear function.

New energy vehicles have been recognized as the future direction of development in automobile industry. This paper investigates the issue of the impacts of subsidy policy and dual credit policy on new energy vehicle and fuel vehicle production decision considering battery recycling, in a competitive environment, where the market demand is determined by heterogeneous consumers' utility. Due to that the market size of new energy vehicles is swiftly expanding, used battery is ushering in a period of renewal and replacement, but the performance efficiency of two policies is unclear in the presence of battery recycling. Considering the market of three primary players, the new energy vehicle manufacturer, the fuel vehicle manufacturer and government, non-cooperative game models are formulated in which the battery recycling rate and consumers' environmental awareness are taken into accounts. Both the optimal policy decision for the government and the manufacturer is analyzed under three scenarios: subsidy policy, dual credit policy with single credit, and that with two credits, respectively. The results show battery recycling rate is the most crucial element influencing the new energy vehicle manufacturer's competitive status under both policies. Meanwhile, no matter how changes in values of consumers' environmental awareness, recycling rate, and subsidies for consumers, the new energy vehicle manufacturer stays a vulnerable position unless the government offers a new financial subsidy for the new energy vehicle manufacturer, rather than for consumers. Additionally, only in case of dual credit policy with two credits, increasing battery rate yields a demand spillover effect with a maximum percentage of 20.59%, which further verifies the efficiency of dual credit policy over subsidy one.

Mass Customization (MC) is a flexible manufacturing system with features of Mass Production (MP) and Customization Production (CP). However, there is few researches about competition aamp; cooperation between the upstream MP firm (module manufacturer) and downstream CP firm (assembler) under MC supply chain scenario. From supply chain perspective, this paper first develops the base models considering the influences of return policy, modularity level, production lead time and pricing factors. Furthermore, according to the different decision-making situations, three kinds of MC supply chain models in competitive or cooperative environment (i.e. simultaneous-move game, sequential-move game and the cooperative game) have been built, then, the optimal solution of each model have been analyzed and compared, and coordination mechanism is design to cooperate in MC supply chain via profit-sharing with Nash bargaining power. Finally, through the numerical analysis, we find the highest profit is from the cooperative setting, then followed by in simultaneous-move and sequential-move one, the reason is that the lowest product price and the largest market demand easily occurs in the cooperative game compared with the others, the upstream module manufacturer takes advantage of MP to increase the modularity level and decrease manufacturing cost for the whole supply chain, the downstream assembler task is to shorten the lead time according to customerarsquo;s needs, while the wholesale price in cooperative game higher than simultaneous-move game and sequential-move game can ensure each firmarsquo;s benefits, effectively prevent from the effect of double marginalization and obtain Pareto optimality.

This paper investigates ultimate boundedness and a weak attractor for stochastic Hopfield neural networks (HNN) with time-varying delays. By employing the Lyapunov method and the matrix technique, some novel results and criteria on ultimate boundedness and an attractor for stochastic HNN with time-varying delays are derived. Finally, a numerical example is given to illustrate the correctness and effectiveness of our theoretical results.

The huge market and perfect production system in China are attracting more multi-national companies’ interest to invest in China in the form of Foreign Direct Investment (FDI). Therefore, multi-national companies are willing to integrate and optimize global supply chain networks of their own, which enable them to reduce cost and improve market response. As a result, multi-national companies usually embed into local industrial clusters through financial and technological comparative merits to sharpen their competitive edge. This paper considers the across-chain network equilibrium problem involving process of competition and melting between this new global chain and an already existing local chain. The authors model the optimizing behavior of these two chains, derive the equilibrium conditions, and establish the variational inequality formulation, and solve it by using the modified algorithm. Finally, the authors illustrate the model through numerical example and discuss relationships among the price, quantity, technological progress, and satisfaction among two dynamic phases.

In this paper, by analyzing the characteristics of data exchange process of SaaS-based collaboration supporting platform for industrial chain, a dynamic and secure business data exchange model for the platform is established. On this basis, methods for business data automatic obtain and format conversion, client authentication based on encryption lock and SOAP extension, business data encryption based on public key of the platform, and instant key created by client is discussed. The implementation of these methods is studied based on a .NET environment. Furthermore, the dynamic and secure business data exchange model is used in the SaaS platform of the automotive industry chain and SaaS platform of the injection industry chain, as it meets the multi-source and heterogeneous information exchange requirements.

This paper investigates dynamical behaviors of stochastic Cohen-Grossberg neural network with delays and reaction diffusion. By employing Lyapunov method, Poincaré inequality and matrix technique, some sufficient criteria on ultimate boundedness, weak attractor, and asymptotic stability are obtained. Finally, a numerical example is given to illustrate the correctness and effectiveness of our theoretical results.

Owing to its inherent modeling flexibility, simulation is often regarded as the proper means for supporting decision making on supply chain management. The ultimate success of CSC (cluster supply chain), however, is determined by a combination of the analyst's skills, the chain members' involvement, and the modeling capabilities of the simulation tool. In this paper, a distributed simulation framework is presented to facilitate collaborative cluster supply chains simulation. This integrated framework is meant to construct a cross-chain simulation while hiding model details within the enterprises. This can be realized by building the simulation components as web service agents on top of the Runtime Infrastructure of the HLA (High level Architecture) and integrating simulation application on top of SOA (Web Service-Oriented Architecture).

In the view of cluster supply chain, the relationship between flexibility and logistics capacity was studied by empirical analysis. Information sharing was introduced as a mediator variable. By using statistical methods of factor and regression analysis combined with the SPSS13.0 statistical software, we found the results as follows: the three dimensions of cluster supply chain flexibility (adaptation flexibility, innovation flexibility, communication flexibility) are significantly associated with the two dimensions of logistics capacity (delivery competence and responsiveness). Information sharing fully mediates the relationship between communication ability and delivery competence and partially mediates the relationship among other dimensions.

Increased global competition and demand for greater product variety result in the emergence of dynamic supply chain, which need to be co-operated without sacrificing their flexibility. Traditional application systems such as ERP, lack the autonomy and flexibility required by dynamic network. EDI is costly and EDI interfaces miss the decision values and logistic control required for supply chain management. Given the limitations of EDI application and traditional application system, this paper is aimed at exploring a new adaptable software architecture to realize enterprise application integration across organizational network. The functional structure and service platform of a software component architecture in the supply chain management are presented. Due to the simple and low-cost method provided for information communication and sharing by Web Services, we select Web Services as our implementation technology of the software component architecture. The integral management in the architecture is executed by supply chain engines (SCEs). The SCEs manage integrated business processes intelligently and flexibly as required for dynamic supply chain. A web-service-based system architecture is proposed for the communication of the run-time data of tasks among processes in heterogeneous environments, and a prototype of the system implemented is presented.

This paper addresses the distribution network design problem under a supply chain system that involves selecting the optimum numbers, locations and capacities of plants and warehouses to open so that all customer demand is satisfied at minimum total costs of the distribution network. We develop a mixed integer programming allowing for multiple levels of capacities available to the warehouses and plants. The paper also provide an efficient heuristic solution procedure for this supply chain system problem, and presents a computational study to investigate the value of coordinating production and distribution planning.