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The purpose of this paper is to investigate what factors influence the gap between caller's perception of how long they think they waited and how long they actually waited on hold and to determine what call managers can do to reduce this gap called estimation error.

A field experiment was conducted with a corporation's call center.

The findings were: the higher the estimation error of callers, the less satisfied they are; music increases estimation error, unless callers can choose the music; waiting information reduces estimation error; callers with urgent issues have more estimation error and they overestimate more; and females have higher estimation error and they overestimate more than males.

Limitations are one call center in one context. Implications are identification of antecedents of overestimation.

The paper provides guidelines for call center managers for reducing estimation error and increasing caller satisfaction. It discusses the need for understanding callers and measuring items that are important to them.

The study investigates an under researched variable called estimation error. Study also provides information about some of the causes for why consumers overestimate or underestimate their waiting time. Study provides guidelines from an actual call center and discusses variables that managers can easily use to decrease estimation error and overestimation.

This paper studies the empirical application of an asset pricing model derived from the irrational individual behavior of loss aversion. Previous research using loss aversion asset pricing finds conclusive evidence that estimations match market equity premium and volatility using simulation data. We find that within its empirical application, the estimated errors are comparable to errors estimated from the capital asset pricing model. This study of the correlations between rational and irrational asset pricing model from the empirical results finds validity for both estimated values. Finally, we see the importance of cultures, economic development and financial development on asset pricing through an empirical examination of five pacific-basin countries in the estimation of asset pricing models.

Steer-by-wire (SBW) system mainly relies on sensors, controllers and motors to replace the traditionally mechanical transmission mechanism to realize steering functions. However, the sensors in the SBW system are particularly vulnerable to external influences, which can cause systemic faults, leading to poor steering performance and even system instability. Therefore, this paper aims to adopt a fault-tolerant control method to solve the safety problem of the SBW system caused by sensors failure.

This paper proposes an active fault-tolerant control framework to deal with sensors failure in the SBW system by hierarchically introducing fault observer, fault estimator, fault reconstructor. Firstly, the fault observer is used to obtain the observation output of the SBW system and then obtain the residual between the observation output and the SBW system output. And then judge whether the SBW system fails according to the residual. Secondly, dependent on the residual obtained by the fault observer, a fault estimator is designed using bounded real lemma and regional pole configuration to estimate the amplitude and time-varying characteristics of the faulty sensor. Eventually, a fault reconstructor is designed based on the estimation value of sensors fault obtained by the fault estimator and SBW system output to tolerate the faulty sensor.

The numerical analysis shows that the fault observer can be rapidly activated to detect the fault while the sensors fault occurs. Moreover, the estimation accuracy of the fault estimator can reach to 98%, and the fault reconstructor can make the faulty SBW system to retain the steering characteristics, comparing to those of the fault-free SBW system. In addition, it was verified for the feasibility and effectiveness of the proposed control framework.

As the SBW fault diagnosis and fault-tolerant control in this paper only carry out numerical simulation research on sensors faults in matrix and laboratory/Simulink, the subsequent hardware in the loop test is needed for further verification.

Aiming at the SBW system with parameter perturbation and sensors failure, this paper proposes an active fault-tolerant control framework, which integrates fault observer, fault estimator and fault reconstructor so that the steering performance of SBW system with sensors faults is basically consistent with that of the fault-free SBW system.

The different types of estimators of rational expectations models are surveyed. A key feature is that the model′s solution has to be taken into account when it is estimated. The two ways of doing this, the substitution and errors‐in‐variables methods, give rise to different estimators. In the former case, a generalised least‐squares or maximum‐likelihood type estimator generally gives consistent and efficient estimates. In the latter case, a generalised instrumental variable (GIV) type estimator is needed. Because the substitution method involves more complicated restrictions and because it resolves the solution indeterminacy in a more arbitary fashion, when there are forward‐looking expectations, the errors‐in‐variables solution with the GIV estimator is the recommended combination.

Two different ‘a posteriori’ error estimation techniques are proposed in this paper. The effectiveness of the error estimates in adaptive mesh refinement for 2D and 3D electrostatic problems are also analyzed with numerical test results. The post‐processing method employs an improved solution to estimate the error, whereas the gradient of field method utilizes the gradient of the field solution for estimating the ‘a posterior’ error. The gradient of field method is computationally inexpensive, since it solves a local problem on a patch of elements. The error estimates are tested by solving a set of self‐adjoint boundary value problems in 2D and 3D using a hierarchical minimal tree based mesh refinement algorithm. The numerical test results and the performance evaluation establish the effectiveness of the proposed error estimates for adaptive mesh refinement.

The purpose of this paper is to investigate how management attention and supply chain complexity affect the decision-making process and cost estimation accuracy of supply chain design (SCD) decisions.

The research follows an embedded case study design. Through the lens of the behavioural theory of the firm, the SCD decision process and realised outcomes are investigated through longitudinal data collection across ten embedded cases with varying degrees of supply chain decision-making complexity and management attention.

The findings suggest that as supply chain decision-making complexity increases, cost estimation accuracy decreases. The extent to which supply chain decision-making complexity is readily recognised influences the selection of strategies for information search and analysis and, thus, impacts resulting cost estimation errors. The paper further shows the importance of management attention for cost estimation accuracy, especially management attention based on conflicting goals induce behaviours that improve estimation ability.

A framework proposing a balance between supply chain decision-making complexity and management attention in SCD decisions is proposed. However, as an embedded case study the research would benefit from replication to externally validate results.

The method used in this study can identify how supply chain complexity is related to cost estimation errors and how management attention is associated with behaviours that improve cost estimation accuracy, indicating the importance of management attention in complex supply chain decision-making.

This paper uses two studies to examine taxpayers' knowledge of tax incentives for charitable giving and also explores the consequences of this knowledge on charitable giving decisions. The first study surveys 600 US taxpayers to establish a baseline understanding of how making a charitable contribution affects taxpayers. In the second study, we conduct an experiment with 201 US taxpayers in which we manipulate the knowledge of taxpayers by providing an educational intervention; we also measure, if, how much is donated in a hypothetical scenario under various tax deductibility conditions. The first study indicates fewer than half of participants understand the basic principles of how charitable donations affect tax liability. Our second study reveals that a short educational video is extremely effective at improving taxpayers' understanding and helping them accurately estimate the tax benefit associated with charitable giving. However, through moderated mediation analysis, we also show that participants who received this educational intervention and accurately estimated the tax benefits in turn decreased their charitable giving. We conclude that the majority of US taxpayers do not understand whether they benefit from certain deductions and may be overestimating the benefit they receive from charitable giving, resulting in giving more than they intend.

The purpose of this paper is to develop an online smoothing zero-velocity-update (ZUPT) method that helps achieve smooth estimation of human foot motion for the ZUPT-aided inertial pedestrian navigation system.

The smoothing ZUPT is based on a Rauch–Tung–Striebel (RTS) smoother, using a six-state Kalman filter (KF) as the forward filter. The KF acts as an indirect filter, which allows the sensor measurement error and position error to be excluded from the error state vector, so as to reduce the modeling error and computational cost. A threshold-based strategy is exploited to verify the detected ZUPT periods, with the threshold parameter determined by a clustering algorithm. A quantitative index is proposed to give a smoothness estimate of the position data.

Experimental results show that the proposed method can improve the smoothness, robustness, efficiency and accuracy of pedestrian navigation.

Because of the chosen smoothing algorithm, a delay no longer than one gait cycle is introduced. Therefore, the proposed method is suitable for applications with soft real-time constraints.

The paper includes implications for the smooth estimation of most types of pedal locomotion that are achieved by legged motion, by using a sole foot-mounted commercial-grade inertial sensor.

This paper helps realize smooth transitions between swing and stance phases, helps enable continuous correction of navigation errors during the whole gait cycle, helps achieve robust detection of gait phases and, more importantly, requires lower computational cost.

In order to improve the estimation accuracy of soil organic matter, this paper aims to establish a modified model for hyperspectral estimation of soil organic matter content based on the positive and inverse grey relational degrees.

Based on 82 soil sample data collected in Daiyue District, Tai'an City, Shandong Province, firstly, the spectral data of soil samples are transformed by the first order differential and logarithmic reciprocal first order differential and so on, the correlation coefficients between the transformed spectral data and soil organic matter content are calculated, and the estimation factors are selected according to the principle of maximum correlation. Secondly, the positive and inverse grey relational degree model is used to identify the samples to be identified, and the initial estimated values of the organic matter content are obtained. Finally, based on the difference information between the samples to be identified and their corresponding known patterns, a modified model for the initial estimation of soil organic matter content is established, and the estimation accuracy of the model is evaluated using the mean relative error and the determination coefficient.

The results show that the methods of logarithmic reciprocal first order differential and the first-order differential of the square root for transforming the original spectral data are more effective, which could significantly improve the correlation between soil organic matter content and spectral data. The modified model for hyperspectral estimation of soil organic matter has high estimation accuracy, the average relative error (MRE) of 11 test samples is 4.091%, and the determination coefficient (R²) is 0.936. The estimation precision is higher than that of linear regression model, BP neural network and support vector machine model. The application examples show that the modified model for hyperspectral estimation of soil organic matter content based on positive and inverse grey relational degree proposed in this article is feasible and effective.

The model in this paper has clear mathematical and physics meaning, simple calculation and easy programming. The model not only fully excavates and utilizes the internal information of known pattern samples with “insufficient and incomplete information”, but also effectively overcomes the randomness and grey uncertainty in the spectral estimation of soil organic matter. The research results not only enrich the grey system theory and methods, but also provide a new approach for hyperspectral estimation of soil properties such as soil organic matter content, water content and so on.

The paper succeeds in realizing both a modified model for hyperspectral estimation of soil organic matter based on the positive and inverse grey relational degrees and effectively dealing with the randomness and grey uncertainty in spectral estimation.

Estimates of systematic risk or beta are an important determinant of the cost of capital. The standard technique used to compile beta estimates is an ordinary least squares regression of stock returns on market returns using four to five years of monthly data. This convention assumes that a longer time series of data will not adequately capture risks associated with existing assets. This paper seeks to address this issue.

Each year from 1980 to 2004, equity betas are estimated for 1,717 Australian firms over periods of four to 45 years, and form equal value portfolios of high, medium and low beta stocks. The paper compares expected returns – derived from the capital asset pricing model (CAPM) and subsequent realised market returns – and actual returns over subsequent annual and four‐year periods.

The paper shows that the ability of beta estimates to predict future stock returns systematically increases with the length of the estimation window and when the Vasicek bias correction is applied. However, estimation error is insignificantly different from that associated with a naïve assumption that beta equals one for all stocks.

The implication is that using all available returns data in beta estimation, along with the Vasicek bias correction, reduces the imprecision of expected returns estimates derived from the CAPM. A limitation of the method is the use of conditional realised returns as a proxy for expected returns, given that it is not possible directly to observe expected returns incorporated into share prices.

The paper contributes to the understanding of corporate finance practitioners and academics, who routinely use beta estimates derived from ordinary least squares regression.