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The purpose of this study is to compare revenue guarantee programs in a build-operation-transfer project (BOT). Two types of revenue guarantee programs are formulated: a payment-based annual revenue guarantee program and a period-extension-based cumulative revenue guarantee program.

Monte Carlo simulation is used to model the real option approach. This method is applied to a toll road project in the Philippines wherein the expected payoffs of the government and the concessionaire are simulated over an evaluation period that includes the concession period. The condition under which the expected government return in one program is equal to that in the other program is shown. These programs are then evaluated by incorporating a project risk factor into the project return.

The results show that the cumulative revenue guarantee program is preferred to the annual revenue guarantee program. However, the optimal solution depends on the government's return-risk preference.

The simulation was implemented with limited cases of discrete input with respect to the cost-sharing parameter, the annual guarantee rate, and the guaranteed cumulative revenue. A simulation with more input values should be performed to achieve results that are more general and sophisticated.

It is indicated that the simple reduction of risk does not necessarily mean that government expected payoff will increase.

It is expected that this method contributes to the proper decisions made by the governments with respect to the choice of a revenue guarantee mechanism in BOT projects.

The purpose of this paper is to quantify the monetary amount of relationship investment in an investment banking context, investigate the drivers behind these relationship investments and look for evidence indicating reciprocity from the clients who receive these relationship investments. Relationship marketing has been one of the dominant mantras in marketing strategy circles, yet there is a lack of empirical evidence to prove significant relationship investment and reciprocity between exchange partners.

Relationship investment as the monetary amount by which the fair value of a loan at issuance is below its par value is measured. Regression analysis is used to study the drivers of relationship investment, including relationship depth, relationship breadth and relationship potential. Finally, reciprocity is studied as the extent to which bank’s expectations are realized through future revenues.

Based on 164 loans issued by a multinational investment bank, it was found that the bank provides significant monetary benefit to its corporate clients. The amount of monetary benefit provided to each client depends on the breadth and potential of the bank-borrower relationship. The author also finds evidence suggesting that the clients reciprocated these relationship investments and the bank anticipated the reciprocity by clients.

This paper is the first to empirically show a significant monetary investment in a relationship-marketing context, with the intention of building stronger relationship with clients and earning future revenues through reciprocity.

The purpose of this paper is to evaluate the risk management benefits provided by the supplemental coverage option (SCO) insurance plan which was created in the 2014 Farm Bill. Specifically, the marginal expected utility benefits are compared with the potential additional subsidy cost introduced by the new program for a stylized example of a corn producer.

The paper uses a stylized simulation model examines the preferred insurance program choice for a typical Midwestern corn farmer. The expected utility of the farmer is calculated under their preferred insurance program choice both with and without the availability of the SCO program, and compared to the case where crop insurance is not available. Scenarios are examined for a range of farmer risk aversion levels, different levels of correlation between farm-level and county-level corn yields, and case with and without insurance premium subsidies.

The SCO program is found to enter into the preferred insurance program choice for risk averse farmers. As risk aversion increases, farmers are estimated to prefer higher coverage levels for individual products along with SCO coverage. While the availability of existing crop insurance programs are shown to substantially increase the expected utility of farmers, the marginal impact of adding SCO to the crop insurance program is relatively small. Furthermore, the additional expected benefits generated by SCO are shown to include both risk management and expected return components. With subsidies removed, the estimated marginal benefits provided by SCO are reduced significantly.

The findings of this paper can help inform the policy debate for future farm bills as agricultural support programs continue to evolve. The results in this paper can also be used to help explain farm-level decision making related to crop insurance program choices.

This paper contributes to the literature by documenting a new, federally supported risk management programs made available to farmers in the 2014 Farm Bill and evaluates the marginal benefits the SCO program offers US crop producers.

Selecting suitable and competent partners is an important prerequisite to improve the performance of collaborative product innovation (CPI). The purpose of this paper is to propose an integrated multi-criteria approach and a decision optimization model of partner selection for CPI from the perspective of knowledge collaboration.

First, the criteria for partner selection are presented, considering comprehensively the knowledge matching degree of the candidates, the knowledge collaborative performance among the candidates, and the overall expected revenue of the CPI alliance. Then, a quantitative method based on the vector space model and the synergetic matrix method is proposed to obtain a comprehensive performance of candidates. Furthermore, a multi-objective optimization model is developed to select desirable partners. Considering the model is a NP-hard problem, a non-dominated sorting genetic algorithm II is developed to solve the multi-objective optimization model of partner selection.

A real case is analyzed to verify the feasibility and validity of the proposed model. The findings show that the proposed model can efficiently select excellent partners with the desired comprehensive attributes for the formation of a CPI alliance.

Theoretically, a novel method and approach to partner selection for CPI alliances from a knowledge collaboration perspective is proposed in this study. In practice, this paper also provides companies with a decision support and reference for partner selection in CPI alliances establishment.

This paper aims to consider spatial effects in the analysis of the relationship of revenue and service quality. When firms’ customers are located in spatially dispersed areas, it can be difficult to manage service quality on a geographically small scale because the relative importance of service quality might vary spatially. Moreover, standard approaches discussed so far in the marketing science literature usually neglect spatial effects, such as spatial dependencies (e.g. spatial autocorrelation) and spatial drift (spatial non-stationarity).

The authors propose a comprehensive but intelligible approach based on spatial econometric methods that cover spatial dependencies and spatial drift simultaneously. In particular, they incorporate the spatial expansion method (spatial drift) into spatial econometric models (e.g. spatial lag model).

Using real company data on seasonal ticket revenue (dependent variable) and service quality (independent variables) of a regional public transport service provider, the authors find that the elasticity for the length of the public transport network is between 0.2 and 0.5, whereas the elasticity for the headway is between −0.2 and 0.6, for example. The authors control for several socio-economic, socio-demographic and land-use variables.

Based on the empirical findings, the authors show that addressing spatial effects of service data can improve management’s ability to implement programs aimed at enhancing seasonal ticket revenue. Therefore, they derive a spatial revenue response function that enables managers to identify small-scale areas that are most efficient in terms of increasing revenue by service improvement.

The paper addresses the need to account for spatial effects in revenue response functions of public transport companies.

State and federal revenues fell well short of projections in 2002. While revenues normally turn down in a recession, those revenue shortfalls were much greater than would have been expected given how mild the 2001 recession turned out to be. This paper examines some of the reasons for the large forecast variances observed in recent years using specific examples from forecasts made for the state of Minnesota. Key factors identified include inaccurate forecast for U.S. economic growth; inadequate, untimely and inaccurate data; imperfect models; and unrecognized changes in the structure of the economy. These factors came together and reinforced each other, ultimately producing a larger reduction in state revenues than could have been anticipated in advance.

Purpose: The purpose of this paper is to describe a decision support tool based on various types of contracts in a two‐level supply chain. A supply chain (SC) consists of disparate but interdependent members, dependent on each other to manage various resources (inventory, money and information). The conflicting objectives between these members may cause uncertainties in supply and demand, which can be managed by adopting coordination with the help of contracts (such as buyback, revenue sharing and quantity flexibility). Design/methodology/approach: A decision support tool for SC coordination using contracts (DSTSCCC) has been developed to explore the applicability of contracts and to compare different types of contracts in various situations. The DSTSCC is comprised of an analytical module, which is an extension of the classical newsboy problem and a simulation module. Findings: DSTSCCC helps in determining decision variables for different scenarios of contracts in the best interest of all SC members as well as whole SC. Practical implications: DSTSCCC is a simple‐to‐use and easy‐to‐implement decision making tool which helps in taking decisions prior to the actual start of SC activities. The prior decisions may help to handle future exceptions. SC members may jointly select the most profitable contract to share risks and rewards. Originality/value: DSTSCCC comprised of analytical module and simulation module presents an Integrative framework which cannot be dealt in isolation. The output of analytical module becomes input for simulation to quantify performance measures. The improvement in performance measures after satisfying the objectives of all SC members helps in realizing coordination in SC.

Returns to a model farm are simulated to assess the impact of marketing and insurance risk management tools as measured by mean net returns and returns at 5% value‐at‐risk (VaR). Results indicate that revenue insurance strategies and strategies involving a combination of price and yield protection provide substantial downside revenue protection, while mean net returns only modestly differ from the benchmark harvest sale strategy when considering all years between 1986 and 2000.

The purpose of this paper is to maximize the expected revenue of the outpatient department considering patient preferences and choices.

Patient preference refers to the preferred physician and time slot that patients hold before asking for appointments. Patient choice is the appointment decision the patient made after receiving a set of options from the scheduler. The relationship between patient choices and preferences is explored. A dynamic programming (DP) model is formulated to optimize appointment scheduling with patient preferences and choices. The DP model is transformed to an equivalent linear programming (LP) model. A decomposition method is proposed to eliminate the number of variables. A column generation algorithm is used to resolve computation problem of the resulting LP model.

Numerical studies show the benefit of multiple options provided, and that the proposed algorithm is efficient and accurate. The effects of the booking horizon and arrival rates are studies. A policy about how to make use of the information of patient preferences is compared to other naive polices. Experiments show that more revenue can be expected if patient preferences and choices are considered.

This paper proposes a framework for appointment scheduling problem in outpatient departments. It is concluded that more revenue can be achieved if more choices are provided for patients to choose from and patient preferences are considered. Additionally, an appointment decision can be made timely after receiving patient preference information. Therefore, the proposed model and policies are convenient tools applicable to an outpatient department.

The purpose of this paper is to analyze the effects of private investor's fair preference on the governmental compensation mechanism based on the uncertainty of income for the public-private-partnership (PPP) project.

Based on the governmental dilemma for the compensation of PPP project, a generalized compensation contract is designed by the combination of compensation before the event and compensation after the event. Then the private investor's claimed concession profit is taken as its fair reference point according to the idea of the BO model, and its fair utility function is established by improving the FS model. Thus the master-slave counter measure game is applied to conduct the behavior modeling for the governmental compensation contract design.

By analyzing the model given in this paper, some conclusions are obtained. First, the governmental optimal compensation contract is fair incentive for the private investor. Second, the private fair preference is not intuitively positive or negative related to the social efficiency of compensation. Only under some given conditions, the correlation will show the consistent effect. Third, the private fair behavior’s impact on the efficiency of compensation will become lower and lower as the social cost of compensation reduces. Fourth, the governmental effective compensation scheme should be carried out based on the different comparison scene of the private claimed portfolio profit and the expected revenue for the project.

This study analyzes the effects of private investor's fair preference on the validity of governmental generalized compensation contract of the PPP project for the first time; and the governmental generalized compensation contract designed in this study is a pioneering and exploratory attempt.