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This paper aims to identify the more accurate method of estimating a firm’s degree of operating leverage (DOL) between two popular DOL estimation techniques: that proposed by Mandelker and Rhee (M&R), and that proposed by O’Brien and Vanderheiden (O&V).

O’Brien and Vanderheiden argue that M&R measure growth in operating earnings relative to the growth in sales rather than DOL. The authors estimate the relative growth estimate, RGE, from the O&V technique (operating earnings growth rate/sales growth rate) and compare this with the DOL estimates from the M&R technique to see if they are similar.

The authors find that the DOL estimates from the M&R method are indistinguishable from the relative growth estimates from the O&V method, providing the first direct evidence that O&V’s critique is correct. The M&R DOL estimates primarily measure the growth in operating earnings relative to the growth in sales, not DOL.

A firm’s DOL is a determinant of its common stock’s systematic risk, which determines a firm’s equity cost of capital. The equity cost of capital is a fundamental part of capital budgeting, capital structure and stock price analysis. Accurately estimating a firm’s DOL is important to researchers and corporate financial managers. Existing diversity in DOL estimation techniques raises questions about the validity of various techniques and limits comparability of existing studies. This paper demonstrates why the O&V technique should be used in place of the M&R method.

Recently, we (Judiesch, Schmidt, & Mount, 1992) concluded that the Schmidt et al. (1979) SDy estimation procedure results in downwardly biased estimates of utility. This conclusion led us to propose a modification of the Schmidt et al. method that involves estimating SDy as the product of estimates of the coefficient of variation (SDy/ Y) and an objective estimate of the average value of employee output (Y). The present article reviews the rationale underlying our conclusion that this modification of the Schmidt et al. method of estimating SDy results in more accurate estimates of SDy, and hence, utility.

This paper is a summary of a successfully defended doctoral dissertation. The purpose of this paper is to place this research in context to emerging areas of project management and service science, management and engineering and to encourage others to embark on further research related to this important topic.

Results reported in this paper were based upon action learning from research in which a project management tool for estimating deployment cost was developed by capturing the knowledge of subject matter experts (SMEs) and subsequently tested against projects from various geographic areas.

There were two primary findings. A development and analysis of the conceptual estimating framework supports the assertion that the use of the framework provides an awareness of the project that may not otherwise be observed or, at best, would be observed later in the life of the project and potentially addressed at a higher cost. A strong association has been found between the conceptual estimate produced by the comprehensive framework and the conceptual estimate produced manually through the use of SMEs.

From academic perspective, the synthesis of technology management, business processes, and the conceptual estimating framework enhances the body of knowledge of project management. For practical applications, the method and framework employed can be utilized to build functioning conceptual estimating tools for deployment, which can lead to cost savings during the estimating process and, as this study surmises, will lead to more effective project management, control, and implementation.

Perceptions of trust have been identified as an important element in the risk communication process. This research is concerned with establishing the degree of trust the general public has in various possible sources of information about the health effects associated with consuming genetically modified food. Participants were asked directly about the degree to which they would trust information about the health effects associated with consuming genetically modified food from a variety of sources, including a fictitious source included as a control. They were also asked about the degree to which they believed each source had a vested interest in misinforming the public about the possible health effects associated with such consumption, and the degree of knowledge they believed each source had about any possible health effects. The results indicate that perceptions of “vested interest” and “degree of knowledge” are important elements in determining levels of trust, although probably not exhaustive. Furthermore, that younger consumers are likely to be the most responsive audience for risk information, but general audience response to risk information is likely to be influenced by preconceptions about the source of the information, preconceptions that can be derived entirely from the name of the information source.

This study compared per selectee utility estimates for the job of medical claims examiner based on applications of the Brogden‐Cronbach‐Gleser (BCG) and Raju‐Burke‐Normand (RBN) utility analysis models. The RBN model's per selectee utility estimate, based on a transformed observed performance rating standard deviation (σR), was closest to the per selectee utility estimate computed with an empirically‐derived σY value. The implications of these results for estimating human resource program utility are discussed.

This study was carried out to analyze the importance of consumer preference data in forecasting demand in apparel retailing.

To collect preference data, 729 hypothetical stock keeping units (SKU) were derived using a full factorial design, from a combination of six attributes and three levels each. From the hypothetical SKU's, 63 practical SKU's were selected for further analysis. Two hundred two responses were collected from a store intercept survey. Respondents' utility scores for all 63 SKUs were calculated using conjoint analysis. In estimating aggregate demand, to allow for consumer substitution and to make the SKU available when a consumer wishes to buy more than one item in the same SKU, top three highly preferred SKU's utility scores of each individual were selected and classified using a decision tree and was aggregated. A choice rule was modeled to include substitution; by applying this choice rule, aggregate demand was estimated.

The respondents' utility scores were calculated. The value of Kendall's tau is 0.88, the value of Pearson's R is 0.98 and internal predictive validity using Kendall's tau is 1.00, and this shows the high quality of data obtained. The proposed model was used to estimate the demand for 63 SKUs. The demand was estimated at 6.04 per cent for the SKU cotton, regular style, half sleeve, medium priced, private label. The proposed model for estimating demand using consumer preference data gave better estimates close to actual sales than expert opinion data. The Spearman's rank correlation between actual sales and consumer preference data is 0.338 and is significant at 5 per cent level. The Spearman's rank correlation between actual sales and expert opinion is −0.059, and there is no significant relation between expert opinion data and actual sales. Thus, consumer preference model proves to be better in estimating demand than expert opinion data.

There has been a considerable amount of work done in choice-based models. There is a lot of scope in working in deterministic models.

The proposed consumer preference-based demand estimation model can be beneficial to the apparel retailers in increasing their profit by reducing stock-out and overstocking situations. Though conjoint analysis is used in demand estimation in other industries, it is not used in apparel for demand estimations and can be greater use in its simplest form.

This research is the first one to model consumer preferences-based data to estimate demand in apparel. This research was practically tested in an apparel retail store. It is original.

This article aims to develop procedures for estimation and prediction in case of Type-I hybrid censored samples drawn from a two-parameter generalized half-logistic distribution (GHLD).

The GHLD is a versatile model which is useful in lifetime modelling. Also, hybrid censoring is a time and cost-effective censoring scheme which is widely used in the literature. The authors derive the maximum likelihood estimates, the maximum product of spacing estimates and Bayes estimates with squared error loss function for the unknown parameters, reliability function and stress-strength reliability. The Bayesian estimation is performed under an informative prior set-up using the “importance sampling technique”. Afterwards, we discuss the Bayesian prediction problem under one and two-sample frameworks and obtain the predictive estimates and intervals with corresponding average interval lengths. Applications of the developed theory are illustrated with the help of two real data sets.

The performances of these estimates and prediction methods are examined under Type-I hybrid censoring scheme with different combinations of sample sizes and time points using Monte Carlo simulation techniques. The simulation results show that the developed estimates are quite satisfactory. Bayes estimates and predictive intervals estimate the reliability characteristics efficiently.

The proposed methodology may be used to estimate future observations when the available data are Type-I hybrid censored. This study would help in estimating and predicting the mission time as well as stress-strength reliability when the data are censored.

Challenges associated with ensuring the accuracy and reliability of cost estimation of highway construction bid items are of significant interest to state highway transportation agencies. Even with the existing research undertaken on the subject, the problem of inaccurate estimation of highway bid items still exists. This paper aims to assess the accuracy of the cost estimation methods employed in the selected studies to provide insights into how well they perform empirically. Additionally, this research seeks to identify, synthesize and assess the impact of the factors affecting highway unit prices because they affect the total cost of highway construction costs.

This paper systematically searched, selected and reviewed 105 papers from Scopus, Google Scholar, American Society of Civil Engineers (ASCE), Transportation Research Board (TRB) and Science Direct (SD) on conceptual cost estimation of highway bid items. This study used content and nonparametric statistical analyses to determine research trends, identify, categorize the factors influencing highway unit prices and assess the combined performance of conceptual cost prediction models.

Findings from the trend analysis showed that between 1983 and 2019 North America, Asia, Europe and the Middle East contributed the most to improving highway cost estimation research. Aggregating the quantitative results and weighting the findings using each study's sample size revealed that the average error between the actual and the estimated project costs of Monte-Carlo simulation models (5.49%) performed better compared to the Bayesian model (5.95%), support vector machines (6.03%), case-based reasoning (11.69%), artificial neural networks (12.62%) and regression models (13.96%). This paper identified 41 factors and was grouped into three categories, namely: (1) factors relating to project characteristics; (2) organizational factors and (3) estimate factors based on the common classification used in the selected papers. The mean ranking analysis showed that most of the selected papers used project-specific factors more when estimating highway construction bid items than the other factors.

This paper contributes to the body of knowledge by analyzing and comparing the performance of highway cost estimation models, identifying and categorizing a comprehensive list of cost drivers to stimulate future studies in improving highway construction cost estimates.

Seeks to present a microeconomic model to analyse theoretically BSC, to develop a simplified model version and to apply it empirically.

The model assumes exponential production and demand functions with constant scale factors and elasticities. It is estimated for Nokia's time‐series 1993‐2002 and partly for 35 Compustat firms.

Direct statistical estimates act properly only as initial values iteratively adjusted for the level of the model. Model parameters show in experiments a significant effect on decision variables such as selling price. Most firms show decreasing returns to scale that are found also in a cross‐sectional analysis.

The model assumes constant elasticities and growth which should be relaxed. Most numerical experiments are limited to Nokia's data. Estimates applied in experiments are not fully justified on statistical grounds. More effort should be made to reach a consistent set of estimates at the level of the model.

In growth strategy, price discounts may lead to declining profitability, while productivity is increasing. This results in peculiar causal relationships in strategic mapping of BSC. If strategy is shifted towards revenue maximization, more focus should be given to customer relationships and development and learning in BSC. Firms should in strategic planning pay special attention to rate of discount and planning horizon, because they affect selling price.

This research paper presents a new model specification. It gives novel empirical evidence on parameter estimation and strategic behaviour in BSC framework.

The purpose of this article is to estimate value at risk (VaR) using quantile regression and provide a risk analysis for defaultable bond portfolios.

The method proposed is based on quantile regression pioneered by Koenker and Bassett. The quantile regression approach allows for a general treatment on the error distribution and is robust to distributions with heavy tails.

This article provides a risk analysis for defaultable bond portfolios using quantile regression method. In the proposed model we use information variables such as short‐term interest rates and term spreads as covariates to improve the estimation accuracy. The study also finds that confidence intervals constructed around the estimated VaRs can be very wide under volatile market conditions, making the estimated VaRs less reliable when their accurate measurement is most needed.

Provides a risk analysis for defaultable bond using quantile regression approach.