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This chapter presents the indirect preferences for all full rank Gorman and Lewbel demand systems. Each member in this class of demand models is a generalized quadratic expenditure system (GQES). This representation allows applied researchers to choose a small number of price indices and a function of income to specify any exactly aggregable demand system, without the need to revisit the questions of integrability of the demand equations or the implied form and structure of indirect preferences. This characterization also allows for the calculation of exact welfare measures for consumers, either in the aggregate or for specific classes of individuals, and other valuations of interest to applied researchers.

The study develops a mathematical model of the firm to derive theoretical foundations for the balanced scorecard concept (BSC). The model is based on several parts which are integrated into a company model. This model includes the demand function, the production function and the objective function of the firm which are depicted by traditional microeconomic concepts. Demand is presented as a function of price and customer relationship management (CRM) costs. Production is assumed to depend on labor, capital, and development and learning (D&L) costs. Simple dynamics is included both in the demand and production function. The strategy of the firm is depicted by the objective function based on profit and net sales. The output variables of the model are classified as the four perspectives of BSC. The effects of the objectives (strategies) on the importance (shadow prices) of the constraints are analysed. It is shown that a change in the objectives may alter the order of their importance. Thus, a change in the strategy should be accompanied with a change in the focus of BSC. Furthermore, non‐financial and financial performance ratios may change in opposite directions, when the strategy is shifted towards revenue maximization. Thus, inconsistencies with the interpretation of cause and effects may emerge, when the strategy is shifted. Numerical examples are presented to demonstrate the results.

The purpose of this study is to investigate how cross-functional integration supports the execution of the demand-side processes and its effects on both the demand and supply-side processes.

A case study was conducted including a Brazilian multinational manufacturer in the automobile industry and some of its suppliers and dealers. 17 interviews were conducted. A theoretical framework is proposed containing five basic elements, they are: characteristics of the demand/supply processes; involved functions; integration factors; context influencers and impacts of integration on demand and supply processes.

The findings present three demand-side processes (Product Launch, Marketing and Sales and Demand Planning) that demonstrated a greater need for cross-functional integration in the studied case, mainly through informal integration factors.

The empirical results of this study have methodological limitations due to the use of the case study method. Future research should analyze the effects of other context influencers (e.g. natural catastrophes, civil wars and low level of unemployment) on cross-functional integration.

The results highlight that joint planning, willingness to work together, team spirit, adequate communication and cross-functional meetings helped the studied organizations to achieve competitive advantages and improve their performance.

This study provides a theoretical framework that helped to improve the understanding of the interrelationships between demand management constructs and cross-functional integration factors. There are indications that a political–economic crisis has stimulated the existence of a willingness to work together and group spirit among employees who remain in the organization after mass dismissals. This climate of cooperation helped to increase the agility and resilience of the studied supply chain, which is currently affected by a changing market.

A substantial part of airports’ revenues relates to charges covering the costs of services supplied by the airport. Charges are imposed on carriers, which in turn pass them or a percentage of them, on to passengers. In the present chapter, special attention is given to regional airports characterized by low traffic volumes, enabling only one or a few carriers to serve each destination. A classic economic model is presented to analyze how the pass-on rate depends on supply and demand characteristics and market structure. Some illustrative examples assuming combinations of common specifications for market characteristics are also presented, showing pass-on rates ranging from 50% to more than 100%. Consequently, market structure and characteristics of carriers and passengers are decisive for how passengers experience changes in airport charges. The differences between the optimal charge from the perspectives of the airport and the welfare of society are specifically addressed. It is demonstrated that knowledge of the pass-on rate in the monopoly cases may be sufficient to infer how the mark-up will be affected by a change in marginal costs. Consequently, the understanding of the pass-on rate is relevant for airport owners and for decision-makers when considering the welfare of passengers and other politically stated goals.

The purpose of this paper is to use demand behavior estimation to find the sources of price variability among resort hotels at different Spanish destinations.

This paper estimates online demand functions during high season for seven four-star resort hotels located at different Spanish destinations. Different price elasticity values are compared, and different factors affecting price elasticity are analyzed.

The main findings indicate that most of the high season periods display elastic demands, but factors such as a central location at a resort, recent refurbishments, the availability of additional facilities/services and a hotel targeted at the couples and/or half-board segments make the demand more inelastic; the Tenerife hotels had the most price-elastic demand; during the closest booking periods to the date of stay, the demand is usually more elastic; and a higher number of local competitors pushes down hotel prices.

This paper highlights the managerial implications of focusing on more profitable demand segments for hoteliers. This is especially useful for the development of revenue management software aimed at improving forecasts.

该论文评估了位于西班牙不同目的地的七家四星级度假酒店在旺季期间的在线需求函数。比较不同的价格弹性值, 分析影响价格弹性的不同因素。

本文的目的是使用需求行为估计来找出西班牙不同目的地度假酒店价格变化的来源。

主要调查结果表明：（1）大多数旺季期间的需求弹性, 但诸如度假村的中心位置、最近的翻新、额外设施/服务的可用性以及针对夫妻和/的酒店等因素或半食宿使需求更加缺乏弹性; (2) 特内里费酒店的价格弹性需求最大; (3) 在离入住日期最近的预订期间, 需求通常更具弹性; (4) 更多的本地竞争者压低了酒店价格。

本论文强调了酒店经营者应关注更有利可图的需求细分市场的管理意义。研究发现对于开发旨在改进预测的收益管理软件特别有价值。

In the previous issue of this Journal, the authors described, analysed and enlarged a micro‐economic model by Billstroem and Thore. In this part they present their own simulation model. The model is a three loop system of price‐, advertising‐, production‐, and stock‐policy. Main differences compared with the Billstroem/Thore‐approach are: The model includes interactions between the different business policies mentioned. Both control‐variables and exogenous disturbance‐variables have been included. Each of three subsystems is controlled by a regulator and feed‐forward control unit. Therefore the model of Billstroem/Thore can be called a feedback approach, while the model to be introduced is a feedback‐feed‐forward system with disturbance.

Homeownership has been the main focus of housing policies in most countries. Typical means that households use to achieve homeownership is to take out a loan and supplement this with accumulated wealth for a downpayment. This paper aims to analyze the mortgage demand behavior of households in the UK, Australia and Japan.

Using three panel data sets, HILDA for Australia, KHPS for Japan and USS for the UK, the paper estimates three equations using ordinary least squares: mortgage demand function, housing demand function and initial loan to value ratio function.

Though homeownership is a preferred tenure and the mortgages are “recourse” loans, housing markets in these three countries operate in different mortgage market institutional structures. Results indicate that income elasticity of mortgage demand differ despite income elasticity of housing demand being similar. Different mortgage institutions in countries that pose constraints for borrowers also determine mortgage demand. Other factors such as demography and economic conditions have also played an important role in determining mortgage and housing demand.

The paper is first, to the authors’ knowledge, that explores the role of institutions in mortgage demand in a comparative framework for the UK, Japan and Australia.

The objectives of this study are to investigate the relationship between hotel room demand and room rates, and to find a viable solution for the optimal room rate that maximizes the total profit.

There are various studies in the literature on how room rates affect profitability, and how the optimal room rate that maximizes the total revenue can be determined. Most of these studies assume an algebraic relationship between room rates and room demand, and obtain the optimal solution by applying calculus to the revenue or profit function. This study adopts the alternative approach of using a model with a demand function that has been shown to be a superior causal forecasting model in some markets, and develops a new method to optimize the total profit.

The traditional method of applying calculus to the profit function based on a causal forecasting model leads to unrealistic solutions. This gives rise to the paradox that, on the one hand, there is a superior causal forecasting model based on room rates, but on the other hand, the traditional method does not yield a realistic solution for room rate optimization. This study analyzes the underlying causes of this paradox and proposes a method to resolve it.

The findings can be used by hotels to fine‐tune the room rates determined by conventional methods to arrive at a realistic and definitive value for the optimal room rate.

This study highlights the problems that arise with the traditional method of applying calculus to revenue and profit optimization and proposes a new method to resolve it.

The purpose of this paper is to introduce a neural network‐based market agent, which develops optimal bidding strategies for a power generating company (Genco) in a day‐ahead electricity market.

The problem of finding optimal bidding strategy for a Genco is formulated as a two‐level optimization problem. At the top level, the Genco aims at maximizing its total daily profit, and at the bottom level, the independent system operator obtains the power dispatch quantity for each market participant with the objective of maximizing the social welfare. The neural network is trained using a particle swarm optimization (PSO) algorithm with the objective of maximizing daily profit for the Genco.

The effectiveness of the proposed approach is established through several case studies on the benchmark IEEE 30‐bus test system for the day‐ahead market, with an hourly clearing mechanism and dynamically changing demand profile. Both block bidding and linear supply function bidding are considered for the Gencos and the variation of optimal bidding strategy with the change in demand is investigated. The performance is also evaluated in the context of the Brazilian electricity market with real market data and compared with the other methods reported in the literature.

Strategic bidding is a peculiar phenomenon observed in an oligopolistic electricity market and has several implications on policy making and mechanism design. In this work, the transmission line constraints and demand side bidding are taken into account for a more realistic simulation.

To the best of the authors' knowledge, this paper has introduced, for the first time, a neural network‐based market agent to develop optimal bidding strategies of a Genco in an electricity market. Simulation results obtained from the IEEE 30‐bus test system and the Brazilian electricity market demonstrate the superiority of the proposed approach, as compared to the conventional PSO‐based method and the genetic fuzzy rule‐based system approach, respectively.