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Regulators can adjust penalties to compensate for incomplete monitoring of regulated parties that are subject to legal rules, but compensating penalty adjustments often are unavailable when regulated parties are subject to legal standards. Incomplete monitoring consequently invites greater noncompliance under standards than under rules. This chapter develops a model that quantifies some of the specific tradeoffs that regulators face in designing standards regimes under incomplete monitoring. The model also considers the extent to which suboptimal compliance due to incomplete monitoring is likely to result in deadweight loss in different settings.

Various types of warranty programs are offered for consumer products. The two most common are a linear pro-rata warranty or a lump-sum warranty, if product failure occurs prior to the specified warranty time. In this chapter we consider additional types of warranty programs that allow the consumer to purchase a one-time extended warranty in the event of no failure within the initial warranty period. For the extended period, warranty may be linearly pro-rated, starting at an amount that is lower than the initial purchase price. Alternatively, for the extended period, warranty may be a lump-sum amount, that is less than the initial warranty amount. Expressions for the expected costs under each of the programs are derived. Guidelines are provided for determining the parameters of each warranty program under relevant constraints. Sensitivity analysis is also conducted to determine the effect of the problem parameters on the expected warranty costs.

Due to a lack of data, many maintenance optimisation models have to be initialised on the basis of expert judgment. Rather than eliciting the parameters of a continuous lifetime distribution, experts give more reliable answers when assessing a discrete lifetime distribution. If the prior uncertainty in the probabilities of failure per unit time is expressed in terms of a Dirichlet distribution, Bayes estimates can be obtained of three cost‐based criteria to compare maintenance decisions over unbounded time‐horizons: first, the expected average costs per unit time; second, the expected discounted costs over an unbounded horizon; and third, the expected equivalent average costs per unit time. Illustrates the maintenance model by determining optimal age replacement and lifecycle costing policies, which optimally balance both the failure cost against the preventive repair cost, and the initial cost against the future cost.

For certain consumer durables, such as automobiles, warranty policies involve two attributes. These could be the time elapsed since sale of the product and usage of the product at a given point in time. Warranty may be invoked by the consumer if both time and usage are within specified warranty parameters when a product failure occurs. In this chapter, we assume that usage and product age are related through a random variable, the usage rate, which may have a certain probabilistic distribution as influenced by consumer behavior patterns. Additionally, product failure rate is influenced by the usage rate and product age. The integrated model includes expected unit warranty costs, expected unit research and development costs, and expected unit production costs. It is assumed that in production, there is a learning effect with time. A multiobjective model is incorporated with the objectives being market share and proportion of expected warranty costs relative to total manufacturing expenditures per unit. The goals could be conflicting in nature. The problem then is to determine the warranty policy parameters while attaining certain desirable values of the two objectives.

Actual costs frequently deviate from the estimated costs in either favorable or adverse direction in construction projects. Conventional cost evaluation methods do not take the uncertainty and correlation effects into account. In this regard, a simulation-based cost risk analysis model, the Correlated Cost Risk Analysis Model, previously has been proposed to evaluate the uncertainty effect on construction costs in case of correlated costs and correlated risk-factors. The purpose of this paper is to introduce the detailed evaluation of the Cost Risk Analysis Model through scenario and sensitivity analyses.

The evaluation process consists of three scenarios with three sensitivity analyses in each and 28 simulations in total. During applications, the model’s important parameter called the mean proportion coefficient is modified and the user-dependent variables like the risk-factor influence degrees are changed to observe the response of the model to these modifications and to examine the indirect, two-sided and qualitative correlation capturing algorithm of the model. Monte Carlo Simulation is also applied on the same data to compare the results.

The findings have shown that the Correlated Cost Risk Analysis Model is capable of capturing the correlation between the costs and between the risk-factors, and operates in accordance with the theoretical expectancies.

Correlated Cost Risk Analysis Model can be preferred as a reliable and practical method by the professionals of the construction sector thanks to its detailed evaluation introduced in this paper.

The purpose of this paper is to examine empirically how operational performance and contextual factors contribute to differences in overall patient care costs across different hospitals.

Administrative data are employed from a sample of hospitals in New York State to construct measures of contextual factors, operational performance, and cost per patient. Operational performance and cost variables are adjusted to account for case mix differences across hospitals. Hierarchical regression is used to analyze the effects of contextual and operational variables on cost performance.

Increased length of stay, increased patient volume, and educational mission were associated with higher cost per patient. Mortality performance was associated with lower cost per patient. However, it was not found that location, size, or ownership status had a significant relationship with cost performance.

This paper identifies several significant relationships between contextual and operational variables and hospital costs. From a managerial perspective, these findings highlight the fact that some drivers of cost in hospitals are under the control of managers. One of the primary cost drivers in the study is length of stay, which implies that there is significant room for improvement in healthcare performance through a focus on operational excellence.

For researchers, the present study highlights the relative importance of operational versus contextual factors, with respect to cost performance in hospitals. The results of this study also provide direction for additional research into the role operational performance might play in determining the overall organizational performance in a hospital.

The purpose of this article is to compare maintenance policies based on Weibull and q-Weibull models.

This paper uses analytical developments, several figures and tables for graphical and numerical comparison. Previously published hydropower equipment data are used as examples.

Models for optimal maintenance interval determination based on q-Weibull distribution were defined. Closed-form expressions were found, and this allows the application of the method with small computational effort.

The use of the q-Weibull model to guide the definition of maintenance strategy allows decision-making to be more consistent with sample data. The flexibility of the q-Weibull model is able to produce failure rate modeling with five different formats: decreasing, constant, increasing, unimodal and U-shaped. In this way, the maintenance strategies resulting from this model should be more assertive.

Expressions for determining the optimal interval of preventive maintenance were deduced from q-Weibull distribution. Expected costs per maintenance cycle of Brazilian hydropower equipment were calculated with q-Weibull and Weibull distributions. These results were compared in terms of absolute values and trends. Although a large number of works on corrective and preventive maintenance have been proposed, no applications of the q-Weibull distribution were found in literature.

IT-enabled service offshoring has become a vital and widespread practice for firms seeking to realize various advantages. However, many firms suffer from “hidden costs” (the discrepancies between the expected and actual costs of offshoring), and these firms often find a disappointing outcome from their offshoring decisions. The purpose of this paper is to explore whether and how the adoption of an offshoring strategy can reduce such hidden costs and how this effect can be moderated by contextual factors, including the complexity of tasks and the accumulation of experience.

Based on survey data from the Offshoring Research Network, this study uses hierarchical regression analysis to empirically test the hypothesized relationships.

A corporate-wide strategy for guiding offshoring decisions may effectively reduce cost-estimation errors. This effect is amplified by increasing task complexity, but decreases with growing offshoring experience. Regardless of whether a strategy is initially in place, most firms learn to avoid cost-estimation errors only after several years. This finding suggests that firms have a limited ability to mitigate hidden costs in the short term.

The guidelines specified by an overarching strategy can better rationalize cost estimation and goal setting for individual offshoring projects, provide incentives for project participants to achieve preset aspirations, and enhance cost-efficiency in fulfilling offshoring activities and in coping with emerging contingencies. Firms tend to benefit more from establishing a formal strategy to reduce the hidden costs of more complex projects, especially if the firms involved have little offshoring experience.

This study empirically examines the hidden costs in offshoring from a strategic management perspective. This approach extends our understanding of cost estimates in offshoring, and it explores the influence of corporate strategy in the alignment of expected and achieved performances from IT-enabled service offshoring. The study also examines the boundaries of strategy’s ability to affect hidden costs, and it expands our knowledge of the relationship between strategy and experience.

This paper aims to explore benefits customers expect from a long‐term relationship with their bank and the costs associated with such a relationship; it further tests these relational benefits and costs as segmentation variables.

A qualitative study based on three focus groups was designed to provide initial input on different types of expected relational benefits and costs. Then, quantitative data were collected from a survey of 209 real bank customers.

Analysis reveals five types of expected benefits and two types of costs. Four clusters were formed out of these seven expected benefits/costs. These clusters are also different on demographic, behavioral and psychographic variables and present clear and consistent relational profiles.

Scales developed from the focus groups need further validation. Also, findings should be considered as sector and context specific. This work brings additional insight into the nature of expected relational benefits and costs, supports their usefulness for customer segmentation and offers opportunities for studying relational benefits and costs in an integrated way.

Findings provide managers with a better understanding of what customers value in the relationship with their bank and what keeps customers back from having a “close” relationship. Also, relational benefits/costs segmentation is suggested as a powerful tool for targeting and positioning.

The study identifies new types of relational benefits and costs. It is the first time expected relational benefits and costs are studied together and confirmed as meaningful segmentation variables.

In explaining ethno-territorial conflicts, leadership preferences have an odd status. In case studies, leadership preferences are often viewed as highly significant causes but are not usually defined and measured explicitly. In large-sample statistical studies, leadership preferences are only captured by weakly related proxy variables. This paper aims to fill this gap by developing suitable theory, which can be used consistently in both case study and statistical applications.

Formal bargaining models are used to examine the expected impact of variation in leadership preferences. Relevant leadership characteristics are then used to construct measures of variation in leadership preferences, which are applied in case studies.

In bargaining models, variation in leadership preferences is expected to have a significant impact on ethno-territorial conflict outcomes. More extreme nationalist leaders and, more conditionally, strongly power-seeking leaders, should be more likely to be willing to use force to modify the status quo – although more moderate nationalist leaderships are also willing to do so under certain conditions. In five case studies, these formally derived hypotheses receive initial empirical support.

Theoretically and empirically, further refinement of research on variation in leadership preferences promises to add significant value. Formally, it is worth investigating the expected impact of additional preference types. Empirically, it is important to invest in measures of leadership preferences across large samples.