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Based on a continuous version of the Lanchester advertising model for a duopoly, a mathematical model is developed to determine the optimal advertising policy of a firm responding to the advertising pulsation policy of its competitor. A Dynamic Programming (DP) approach has been employed to arrive at the optimal solution.

It has been mainly demonstrated that under a concave or linear advertising response function, the focal firm's DP policy is superior to its Uniform Advertising Policy (UAP) counterpart (constant advertising spending over time), irrespective of the advertising pulsation policy employed by its rival. Under a convex advertising response function, on the other hand, the focal firm's DP policy is superior to its Advertising/Maintenance Pulsing Policy (APMP) and Advertising Pulsing Policy (APP) counterparts (alternating advertising spending at two levels), irrespective of the advertising pulsation policy used by the competitor.

Although research attention has been given to the modelling process for simultaneous demand‐output management in manufacturing systems, little interest has been demonstrated in service organisations despite the fact that such organisations face unique conditions that further complicate the demand‐output management issue. In response to this lack of emphasis, we review the relevant research from both marketing and operations management and present a cost‐effectiveness model for balancing demand and service output.

As the quality of the environment decreases, enterprises and consumers' awareness of environmental protection is constantly improving. More and more enterprises begin to increase their investment in carbon emission reduction and attract environmentally friendly consumers to buy low-carbon products through advertising. The purpose of this paper is to utilize a realistic differential game model to provide dynamic carbon emission reduction strategies, advertising strategies and cooperation methods for complex supply chain members from a long-term perspective.

This paper uses the extend Vidale-Wolfe model (V-W model) to discuss the dynamic joint emission reduction strategy in the supply chain.

(1) When consumers' awareness of environmental protection increases, on the whole, carbon emission reduction and profit of products show an upward trend. (2) From a long-term perspective, the manufacturer's advertising subsidy to one of the retailers is the best choice. If the strength of the two retailers is unbalanced, the manufacturer will choose to cooperate with the dominant retailer. (3) Advertising, as a marketing means for retailers to promote low-carbon products, can alleviate the adverse effects of prisoner's dilemma in a semi-cooperative state, but it cannot achieve the Pareto optimization result.

This paper focuses on the analysis of the situation that when the manufacturer is the leader and thinks that consumers are active advocates of low-carbon products.

The results of this paper can provide theoretical basis for the joint emission strategy of supply chain members in low-carbon environment.

This monograph progresses from a consideration of definitional issues to the development of a conceptual model for marketing‐logistics interaction and finally to a discussion of the issues of implementation of the model within the context of marketing strategy. Thus, following an introduction, Part II begins with definition of the field and examines the position of physical distribution in relation to marketing. Part III discusses the relationship of physical distribution and macro‐marketing, and is thus concerned about the social, aggregative goals of logistics systems, including the costs of distribution. Part IV continues this argument, examining specifically the influence of physical distribution on channel structure. Part V then focuses on the assumptions underlying the customer service function, asking how physical distribution can influence final demand in the market place. Part VI presents a conceptual model of marketing‐logistics demand stimulation. The operational issues concerned with its implementation are shown in Part VII; and a summary of the relevant points is presented in Part VIII. The concern has been not with presenting either new computational models nor empirical data but with presenting a new perspective on the marketing‐logistics interface. There is a need to reduce the barriers between these fields and to present more useful ways for co‐operation.

The adoption of a model‐building approach to marketing is today inevitable, due to improvements in hardware and software and the increased professionalisation of marketing and its techniques. Aggregate response models are focused upon, particularly the issues of which responses are realistic and should be modelled, how the response can be expressed and how a choice can be made between options available. The traditional model‐building process is described, and the inclusion of correct variables found to be critical, the primary means of doing this being statistical analysis. Simple expressions perform as effectively as more complex ones, and should be used if able to give operationally meaningful results. Cross‐correlation analysis and biased estimation techniques provide good guides to usable variables and their effects.