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Implementing effective marketing strategies can reduce consumers' perceived risks and promote the development of the remanufacturing market. The paper aims to explore the impact of two marketing strategies on the remanufacturing decisions of an original equipment manufacturer (OEM) and investigates the design of the optimal warranty service (WS) and consumer education (CE) strategies. Moreover, the authors also examine the optimal marketing strategy choices using the three criteria of OEM: profitability, consumer surplus and environmental impact (EI).

The authors develop a stylized model by game theory, which characterizes how an OEM that produces and sells both new and remanufactured products (RPs) should design the optimal remanufacturing marketing activity, and how to choose between different remanufacturing marketing strategies. Moreover, consumer's utility theory is used to describe consumers' perception of different remanufacturing marketing strategies to derive product demand.

The results reveal that increasing the warranty and education levels will not always improve the firm's profitability; the result depends heavily on the size of the functionality-oriented consumer (FOC) segment. Remanufacturing marketing strategies might harm the OEM, consumers and environment under certain conditions. Moreover, the optimal marketing strategy selections are jointly influenced by the FOC segment and the new production cost. There exist triple-win regions in which the OEM should not hesitate to perform WS and CE.

Few studies focus on the design and choice of remanufacturing marketing strategies, especially considering the role of consumer perceived behavior. This research contributes the behavioral remanufacturing marketing management and provides managerial implications for the implementation of OEM remanufacturing marketing strategy.

Adopting efficient marketing strategies is a challenging task in a competitive market place involving complex marketing planning, techniques and mechanisms to identify the best course of action under these circumstances and finding optimal solutions or stable outcomes. Decisions and strategies of competitors in the market influence the selection of the appropriate marketing strategy. The main purpose of this paper is to develop a mathematical methodology based on the game theory approach for planning optimal marketing-mix strategies in dynamic competitive markets, taking into account strategic foresight and interaction effects.

The game theory approach, as a decision-making tool in conflict situations, is suggested for planning and adopting optimal marketing strategy. The main intellectual attraction of the game theory is essentially a question of how to act in gaming situations against highly rational opponents A kind of static, finite and non-cooperative game analytics approach has been developed for this issue, and the proposed model has been implemented to design optimal marketing strategies for two top brands of the automotive parts market in Iran.

The findings of this study show that the optimal marketing-mix strategy for brand A is pricing and for brand B is the product strategy.

Game theory and the Nash equilibrium model can provide a practical approach to find and adopt the right strategy, know competitors' movements and strategies and get more profit.

The integration of the game theory approach into the marketing mix framework has been adopted as a generalized model for marketing strategy planning and analysis as well as to resolve some shortcomings of the marketing mix framework. The Nash equilibrium model has been used to analyze the results. The incorporation of game theory into marketing models has the potential to enrich the scope of marketing modeling.

Under the big data background, there are many influencing factors for investors of new energy vehicles (NEV), and government subsidies promote the sustainable development of the new energy vehicle industry. Therefore, the purpose of the study is to provide solutions for the sustainable development of NEV.

The sustainable marketing strategy of NEV in China is put forward. This paper first analyzes the subsidy policy effect of NEV under the background of big data. It then establishes the online optimal leasing strategy under multiple strategy choices and the online leasing strategy of multiple vehicles under the inflation market.

With the fixed cost of NEV in each lease period, the optimal competition ratio of online decision-makers will continue to decrease with the increase of the difference between prepaid funds and government subsidies. In the decision-making of renting and purchasing multiple vehicles, the general strategy competition ratio is 2.922, while the optimal competition ratio of the online renting and purchasing strategy proposed by the research is 2.723.

The research is limited by the limited data and information collected, so the optimal decision-making model has some limitations. The authors need to find more representative data to optimize the model.

As an emerging industry, NEV have developed rapidly in recent years. Based on the online algorithm and competitive ratio theory, this paper solves the decision-making problem of operators and gives the optimal strategy to promote the green development of the new energy vehicle industry.

This paper proposes the optimal strategy for online investors of new energy vehicle operators by combining online algorithm and competitive ratio theory. The numerical analysis results of the optimal online model under multi strategy selection show that with the same difference between prepaid funds and government subsidies, the time point will be delayed and the time point will be advanced as the cost of leasing NEV in each period increases.

This study aims to analyze the tradeoff between two potential marketing strategies for service providers, namely, market segmentation on the basis of reservation lead time and cooperation with third parties.

This paper proposes an optimization model to describe market segmentation strategy after cooperating with third parties by taking hotels for example.

The results show that the profitability of adopting two strategies simultaneously is lower than that with market segmentation alone under some cases, which is relevant with attributes of travel agencies, such as switch rate and market share.

This study indicates that cooperation with third parties has a negative impact on profitability of hotels using market segmentation in some cases. However, randomness of demand, customer loyalty and existence of cancellation should be considered in further research.

In an e-commerce era, hotels with market segmentation based on reservation lead time, are not required to cooperate with third parties under a number of situations (e.g. high switch rates and small market sizes of travel agencies). In addition, hotels should revise the segmentation strategy based on the change rate of potential demand of individual customers. Furthermore, hotels should enhance customer loyalty, strengthen cooperation with travel agencies that possess large market shares or small switch rates.

The study preliminarily formulates the optimal market segmentation strategy on the basis of reservation lead time after cooperating with third parties, which contributes to the revenue management.

The purpose of this paper is to provide a framework to help the manufacturers to find the optimal decisions regarding the choice of channel member for information sharing.

A game‐theoretical model plus Bayesian forecasting is developed to determine the optimal decisions for the manufacturer.

The results show that the optimal strategy for the manufacturer is to engage in information sharing with one small retailer exclusively, such that the manufacturer can gain the most benefit from information sharing arrangement in a marketing channel with a dominant retailer.

The present study is analyzed by a theoretical model. Future research can explore the same study by collecting data to engage in an empirical test.

This paper provides a useful model framework and pricing strategy for upstream manufacturers who are engaging or planning to engage in information sharing with their retailers.

This paper provides practical and solid advice and examples demonstrating the optimal decisions regarding the choice of channel member for information sharing to best benefit of the manufacturer.

The purpose of this paper is to provide a framework to help business marketers with online and traditional retail channels (multi‐channel retailers) to find the optimal branding strategy and market structure in order to maximize their profits.

A game‐theoretic model is developed to determine the optimal branding strategy and market structure for the dual‐channel stores of a multi‐channel retailer.

The paper demonstrates that an optimal branding strategy and market structure exists for the dual‐channel stores of a multi‐channel retailer. When a retailer uses multiple channels in parallel to sell its product, the optimal branding strategy is to employ as large as possible brand differentiation between the dual‐channel stores, particularly when the price is less sensitive for consumers and the market base size is larger. Furthermore, it is also found that the optimal market structure is the Stackelberg mode, especially when the product brands between the two channels are less differentiated.

The present study assumed that all information is known to both the online and traditional channels of a multi‐channel retailer. However, information could be incomplete. It is recommended that future research explore the value of product brand differentiation under incomplete information settings.

The paper provides a very useful model framework, branding strategy, and market structure for business managers who are using or planning to use multiple channels to sell their products.

This paper fills a conceptual and practical gap for a structured analysis of the current state of knowledge about multi‐channel branding strategies. The paper provides practical and solid advice and examples demonstrating the application of product brand differentiation strategies for business managers.

The purpose of this paper is to analyze manufacturers' production decisions and governments' low-carbon policies in the context of influencer spillover effects.

This paper investigates the impact of the social influencer spillover effect on manufacturers' production decisions when they collaborate with intermediary platforms to sell products through marketplace or reseller modes. Game theory and static numerical comparison are used to analyze our models.

Firstly, under low-carbon policies, the spillover effect does not always benefit manufacturer profits and changes non-monotonically with an increasing spillover effect. Secondly, in cases where there are both a carbon emission constraint and a spillover effect present, if either the manufacturer or intermediary platform holds a strong position, then marketplace mode benefits manufacturer profits. Thirdly, regardless of business mode used when environmental damage coefficient is high for products; government should implement cap-and-trade regulation to optimize social welfare while reducing manufacturers’ carbon emissions.

This study offers theoretical and practical research support to assist manufacturers in optimizing production decisions for compliance with carbon emission limits, enhancing profits through the development of effective influencer marketing strategies, and providing strategies to mitigate carbon emissions and enhance social welfare while sustaining manufacturing activities.

This paper addresses the limitations of prior research by examining how the social influencer spillover effect influences manufacturers' business mode choices under government low-carbon policies and analyzing the social welfare of different carbon emission restrictions when such spillovers occur. Our findings provide valuable insights for manufacturers in selecting optimal marketing strategies and business modes and decision-makers in implementing effective regulations.

The purpose of this paper is to provide a framework to help competitive firms find optimal pricing and brand management strategies in order to maximize their respective profits.

A game‐theoretic model is developed to determine optimal pricing and brand management strategies for competitive firms.

The study demonstrates that optimal pricing and brand management strategies exist for firms in a competitive market. The optimum marketing strategy for competitive firms to employ is the cooperative alliance pricing and brand management strategy, particularly when market competition is very intense.

The present study assumes that all information is known to all parties. However, information could be incomplete. It is recommended that future research explore pricing and brand management strategies under incomplete and asymmetric information settings.

The paper provides a very useful model framework and pricing and brand management strategy for business managers when they are doing business in a competitive market.

The paper fills a conceptual and practical gap for a structured analysis of the current state of knowledge about alliance brand. It provides practical and solid advice and examples demonstrating the application of cooperative alliance pricing and brand management strategies for business managers.

As one of the omnichannel sales models, “buy online and pick up in store” (BOPS) not only is used in the commercial field but also has gradually attracted many scholars’ interests. However, although there are numerous research ideas, most of the current work is still limited to theoretical and empirical research, and few scholars study BOPS through models. This paper aims to discuss the best market conditions and opportunities for the implementation of BOPS against the backdrop of omnichannel by means of mathematical models and data simulations and discuss the optimal price–service strategies under different sales models.

First, from the perspective of different consumer shopping types, this paper separately divides consumers into different groups in traditional “dual channel” and BOPS models. Then, the authors analyze the impact of company market size, consumer service sensitivity and the scale of BOPS on companies’ strategies and the profit of the supply chain. Subsequently, they conduct an empirical analysis through specific values. Finally, the authors further expand the model on the basis of the original research, and discuss the retailer’s fairness concerns and unit compensation strategy to ensure that the research content is more rigorous.

It is observed that whether companies adopt BOPS depends on consumers’ service sensitivity degree and the scale of BOPS consumers and online retailers: when the sensitivity and the proportion of online consumers are high or the number of BOPS consumers is large, it is more advantageous for companies to implement BOPS. Moreover, companies should not only consider the market scale and production cost but also have a precise orientation of consumers’ experience sensitivity and willingness to engage in extra consumption when making price and service strategies. At the same time, the compensation strategy of companies and the peer-regarding fairness concern behavior of offline retailers will affect the optimal price and service strategy in the BOPS model.

These results provide managerial insights for companies preparing to implement BOPS and promote the development of relevant theories in the channel field.

At present, most of the research on BOPS is based on empirical reviews. However, this paper analyzes the applicability and feasibility of implementing BOPS by using specific models, and it will provide some reference for companies preparing to implement BOPS. In addition, this paper also discusses the unit compensation strategy and peer-regarding fairness concern behavior in the BOPS model, which have not been studied by relevant scholars.

The purpose of this study is to provide an optimal joint strategy of multi-period pricing and sales effort for a retailer with a logit choice demand in an integrated channel.

Customer demand is characterized by a logit choice model, it varies over time and is influenced by price and sales effort. The multi-period decision model for the retailer is constructed using a discrete-time dynamic programming method to determine the optimal price and sales effort in each period.

When the inventory level does not exceed a certain threshold, decreasing price and increasing sales effort over time or as inventory level increases are the optimal strategies. However, once the inventory level exceeds the threshold, the optimal strategy is to maintain both price and sales effort constant as the inventory level changes or to increase price and decrease sales effort over time. Additionally, the greater the influence of sales effort on demand or the higher the arrival rate of customers, the higher the optimal price and the greater the optimal sales effort level.

This study contributes to the existing research on dynamic pricing and sales effort in integrated channels by incorporating a logit choice model. Furthermore, it provides valuable management insights for retailers operating in an integrated channel to make pricing and sales effort decisions based on inventory level and time period.