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The spot market has been gradually recognized as an important alternative purchasing source. To maintain a flexible replenishment strategy, call, put and bidirectional option contracts, as a risk hedging, are in combined usage with the spot market, respectively. The purpose of this paper is to analyze a finite-horizon replenishment problem with option contracts in the context of a spot market.

Based on stochastic dynamic programming, the firm’s optimal replenishment policy with either call, put or bidirectional option contracts is always shown to be order-up-to type, characterized by an upper threshold and a lower one. The corresponding policy parameters in different cases are calculated through an approximate algorithm. This research highlights the effectiveness of option contracts on the firm’s operational strategies and overall profitability.

This study reveals that the firm is better off with option contracts than without them. When the price parameters are the same for different option contracts, bidirectional option contracts are the best choice among these flexible contracts; otherwise, unilateral option contracts might be either better or worse than bidirectional ones. In addition, if low inventory costs and high spot price volatility are confronted, the firm prefers to call option contracts rather than put ones; otherwise, there exists an opposite conclusion.

In addition to highlight the advantage of option contracts over wholesale price contracts, this paper provides interesting observations with respect to the effect of different option contracts on the firm. Many significant insights derived from this research do not only contribute to the provider’s feasible design of the supply contracts, but also contribute to the user’s rational operational strategies for higher profitability.

The purpose of this study is to compare the impact of multi‐period pricing, as an example of more dynamic pricing and discounting strategy with that of a present less dynamic alternative on customer satisfaction and consumers' willingness to make trade‐offs between price and remaining shelf‐life.

The authors conducted interviews with three food retail managers in South Korea to gather practical information about the management of perishable foods, which informed the design of a survey in which consumers in South Korea were questioned about their perceptions of the two strategies, with respect to nine perishable food products in three categories. The data collected were analysed by one‐way ANOVA and the t‐test.

The findings of this research present an improved understanding of the impact of a multi‐period pricing strategy on consumer satisfaction and customer behaviour for perishable foods. The conclusions have the potential to significantly assist food retailers to understand the consumers' perspective on the benefits of a more dynamic pricing strategy.

The findings suggest that food retailers can enhance customer satisfaction by offering an earlier but lower discount, and increasing it as perishable food items approach their expiry date, rather than a higher discount when the expiry date is imminent.

The findings in this study are significant since they serve as the first step in measuring the value of dynamic pricing approaches that provide better trade‐off options between price and remaining shelf‐life from consumers' perspectives.

The purpose of this paper is to put forward a multi-period dynamic pricing strategy for perishable food considering consumers’ price fairness perception. The impacts of the multi-period retail price, food freshness and inventory shortage risk on consumers’ heterogeneous willingness to pay (WTP) and their strategic purchasing behaviours are studied.

The authors present a price optimization model for perishable food, and conduct a laboratory experiment to justify the theoretical model. The data collected are analysed by correlation analysis and nonparametric test.

The results obtained reveal, first, food freshness and inventory shortage risk have effect on consumers’ heterogeneous WTP. Second, different retail prices lead to consumers’ strategically purchasing behaviours. Finally, consumers’ intertemporal price fairness perception and the food retailer’s long-term utility maximization can be achieved by developing multi-period dynamic pricing strategy.

This study suggests the perishable food retailer to apply a step-by-step price markdown strategy. It aims at eliminating price unfairness perceptions caused by loss of freshness and high shortage risk of the perishable food in the subsequent selling periods within the shelf life. Some valuable managerial insights towards perishable pricing for food retailers are discussed.

This study serves as the first step to utilize a laboratory experiment to dig out consumers’ intertemporal WTP towards perishable food. It also presents a novel way for describing consumers’ intertemporal price fairness perception by equalizing consumers’ average utilities considering consumer surplus, food freshness and shortage risk at different selling periods. The line of research on dynamic pricing concerning consumers’ price fairness perception is quite new in academic research, and has arisen due to its importance for food retailers of maximizing their long-term revenues and also of constructing mutual benefit and lasting connections with the consumers.

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.

The purpose of this paper is to investigate the dynamic acquisition pricing strategy for collecting used products (also known as cores or returns) in a finite planning horizon. In particular, this paper studies a cost-minimization model in which a firm offers acquisition price that impacts the quantity of the returns, and remanufactures the used product to satisfy the customer demand.

This paper uses multi-period stochastic dynamic programming theory to model a remanufacturing system that faces the random demand for remanufactured products. The number of the returns at each period is uncertain and increases linearly with the acquisition price offered.

The study shows that when the uncertainty of demand for remanufactured products increases, the remanufacturer should hold a higher core stock level to minimize the expected total cost and thus a higher acquisition price is needed to attract returns. However, given demand uncertainty, the optimal price decreases in the initial core stock level in each period. It also indicates that the optimal acquisition price increases in the variance of the returns, but decreases in the mean of the returns.

The findings suggest that a remanufacturer could reduce the expected total cost by adjusting the acquisition price according to the number of returns periodically.

Introducing the impact of supply uncertainty on the acquisition price of used products, this paper uses a multi-period dynamic model, instead of single period model in previous studies, to examine the remanufacturer’s dynamic acquisition pricing policy.

Providers of cloud computing storage services (CCSS) charge offers in several unit bundles for a lump sum per bundle. This non-linear pricing approach is known as a bucket-pricing plan (BPP). If a customer exploits the purchased bucket, he/she can opt for the next higher bucket or refrain from further CCSS use. CCSS suppliers are faced with an optimization problem concerning the number of buckets as well as their lower and upper storage volume boundaries. The purpose of this paper is to develop a model, which supports CCSS suppliers in deriving a BPP-structure and which maximizes their profit in varying market constellations.

The authors develop a multi-period model of tariff choice decisions of private customers of CCSS. The model is applied in Monte Carlo simulations to determine profit-maximal tariff structures as a function of different market characteristics such as median demand saturation, demand heterogeneity, average price per storage unit and bucket ceiling allocation (identical size of each bucket within the frame set by the lower and upper overall boundary, varying sizes of the buckets offered, so that the interval between two ceilings consecutively increases for subsequent buckets) and type of a customer’s utility function.

The simulation analysis suggests that demand heterogeneity and average price per unit are the most influential factors for CCSS tariff structure optimization. Price plans with more than two buckets tend to generate higher profits than simple schemes with two buckets only if demand heterogeneity is low and the average price per storage unit is high and/or median saturation level of customers is low.

Despite the popularity of BPP among providers of CCSS for consumers, there is a lack of scholarly modeling work on the profit implications of the number of buckets entailed in a scheme and the size/ceilings of the various buckets on offer. The model suggested in this paper is a first step toward narrowing this research gap.

This article aims to apply a multi‐period model of insurance market equilibrium to solve for the insureds' optimal demand for insurance, as well as insurers' optimal supply.

Most approaches to competitive equilibrium in the insurance market involve the construction of demand and supply curves based on maximizing the insureds' and insurers' expected utility for a single time period. However, it is important to recongnize that, for a given utility function, the demand (supply) decisions of insureds (insurers) in a single‐period model may differ substantially from those under a multi‐period formulation. In this article, first, separate multi‐period models of demand and supply are constructed, and then a dynamic solution for equilibrium price and quantity is provided.

Although a single‐period model generally requires the assumption of an exact loss distribution to compute expected utilities, the multi‐period model requires only the expected loss and its associated stochastic process (in this case, a Brownian motion). One implication of this approach is that it may explain phenomena of market prices failing to achieve Pareto optimality for a single period.

This approach may be used to generate new hypotheses related to the underwriting cycle. Specifically, the insureds' demand and insurers' supply decisions may both be based on expected discounted future cash flows. The non‐trivial multi‐period equilibrium insurance price may provide additional insights into the volatility of insurance market prices.

The purpose of this paper is to present a new method and model for determining the optimal decision-making for the pricing strategy to the Fuji apple in Shaanxi of Chain which is representing fresh agricultural products under the e-commerce environment.

Considering the rapid development of information technology as well as internet that actually motivate the e-commerce, Fuji apple is a distinctive product in China’s Shaanxi; its sales channels have extended to online sales under the wave of e-commerce. Internet trading platforms make it possible to trade online in real time between suppliers and customers who live in different geographical areas. In this paper, the authors study how to price online to maximize the total revenue. The challenge is to optimally price two different qualities of apple. Based on the consumer surplus theory, the authors use the method that builds the function of the relationship between the proportion of consumers purchasing different qualities of products and price.

This paper presents a generalized model to determine the optimal pricing that maximizes the total revenue of a fruit grower over a finite planning horizon. The authors divided discount into two intervals and discussed the optimal discounting and pricing at both intervals. Then they determined the optimal pricing strategy for Fuji apple in Shaanxi of Chain under the e-commerce environment.

This paper makes up for the lack of existing studies of pricing under the e-commerce environment. A new method and approach to the traditional pricing strategy is established and applied to a management decision-making problem with Chinese characteristics in reality.

A total of 12 chapters in this volume represent some current research on important topics in finance and economics. Bajaj et al. demonstrate through a time series analysis that the IPO underwriting spreads seem to be competitive, in contrast to the findings of Chen and Ritter (2000). Sealey argues that it is necessary for the regulator and deposit insurer to be an integral part to mitigate the moral hazard problem in bank regulation. Lee develops a multi-period pricing model to examine the impact of forbearance and potential moral hazard behavior on the cost of deposit insurance. Hao and Roberts show that lead lenders have significant positive influence on loan yield spreads. Daly et al. show that coincident indicators developed to track a state's gross outputs have significant influence on state-level aggregate bank performance.

The purpose of this paper is to show how yield management and dynamic pricing, which originated in the airline industry, are now diffusing in other service industries. The aim is to demonstrate that these techniques can be profitably applied to telecommunications and similar sectors and to examine the particular conditions of their implementation, development and efficiency.

The main concepts of yield management, dynamic pricing and CRM are carefully scrutinized. Also discussed is the concept of natural demand curve that aims at reaching a better compromise between the capacity of a company and the demand in an environment where services cannot be sold in advance. In order to sustain the analysis and demonstrate its managerial implications, five case studies are presented that exemplify some aspects of yield management techniques in the telecommunication sector.

Since the telecommunications are undergoing a process of increased competition and dynamic convergence, yield management techniques can help telecom operators to optimize the benefits they can derive from a subtle management of information networks and partnerships. However, such an approach is more difficult to implement in the telecommunication industry than in the airlines sector because of the difficulty to control (and sometimes refuse) network access to customers.

Capacity and revenue management become critical differentiation factors in improving service quality, loyalty and profitability. Given the increase in competitive pressure, the main objective of operators to sell customer access database to potential partners represents a radical change in the nature of financial and information flows and leads to a “customized management of services supply”.