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The purpose of this paper is to collect data from humans as they generate insights from the visualised results of computational fluid dynamics (CFD) scientific simulation. The authors hypothesise the behaviour of their insight errors (IEs) and proceed to quantify the IEs provided by the crowd participants. They then use the insight framework to model the behaviours of the errors. Using the crowd responses and models from the framework, they test the hypotheses and use the results to validate the framework for the speedup of CFD applications.

The authors use a randomised between-subjects experiment with blocking. CFD grid resolution is the independent variable while IE is the dependent variable. The experiment has one treatment factor with five levels. In case varying timestamps has an effect on insight variance levels, the authors block the responses by timestep. In total, 150 participants are randomly assigned to one of five groups and also randomly assigned to one of five blocks within a treatment. Participants are asked to complete a benchmark and open-ended task.

The authors find that the variances of insight and perception errors have a U-shaped relationship with grid resolution, that similar to the previously studied visualisation applications, the IE framework is valid for insights generated from CFD results and grid resolution can be used to predict the variance of IE resulting from observing CFD post-processing results.

To the best of the authors’ knowledge, no other work has measured IE variance to present it to simulation users so that they can use it as a feedback metric for selecting the ideal grid resolution when using grid resolution to speedup CFD simulation.

The purpose of this paper is to reduce the calculation burden and speed up the estimation process of Allan variance method while ensuring the exactness of the analysis results.

A series of six‐hour static tests have been implemented at room temperature, and the static measurements have been collected from MEMS IMU. In order to characterize the various types of random noise terms for the IMU, the basic definition and main procedure of the Allan variance method are investigated. Unlike the normal Allan variance method, which has the shortcomings of processing large data sets and requiring long computation time, a modified Allan variance method is proposed based on the features of data distribution in the log‐log plot of the Allan standard deviation versus the averaging time.

Experiment results demonstrate that the modified Allan variance method can effectively estimate the noise coefficients for MEMS IMU, with controllable computation time and acceptable estimation accuracy.

This paper proposes a time‐controllable Allan variance method which can quickly and accurately identify different noise terms imposed by the stochastic fluctuations.

This study examines the relationship between demand forecasting error and retail inventory management in an uncertain supplier yield context. Replenishment is segmented into off-season and peak-season, with the former characterized by longer lead times and higher supply uncertainty. In contrast, the latter incurs higher acquisition costs but ensures certain supply, with the retailer's purchase volume aligning with the acquired volume. Retailers can replenish in both phases, receiving goods before the sales season. This paper focuses on the impact of the retailer's demand forecasting bias on their sales period profits for both phases.

This study adopts a data-driven research approach by drawing inspiration from real data provided by a cooperating enterprise to address research problems. Mathematical modeling is employed to solve the problems, and the resulting optimal strategies are tested and validated in real-world scenarios. Furthermore, the applicability of the optimal strategies is enhanced by incorporating numerical simulations under other general distributions.

The study's findings reveal that a greater disparity between predicted and actual demand distributions can significantly reduce the profits that a retailer-supplier system can earn, with the optimal purchase volume also being affected. Moreover, the paper shows that the mean of the forecasting error has a more substantial impact on system revenue than the variance of the forecasting error. Specifically, the larger the absolute difference between the predicted and actual means, the lower the system revenue. As a result, managers should focus on improving the quality of demand forecasting, especially the accuracy of mean forecasting, when making replenishment decisions.

This study established a two-stage inventory optimization model that simultaneously considers random yield and demand forecast quality, and provides explicit expressions for optimal strategies under two specific demand distributions. Furthermore, the authors focused on how forecast error affects the optimal inventory strategy and obtained interesting properties of the optimal solution. In particular, the property that the optimal procurement quantity no longer changes with increasing forecast error under certain conditions is noteworthy, and has not been previously noted by scholars. Therefore, the study fills a gap in the literature.

This study established a two-stage inventory optimization model that simultaneously considers random yield and demand forecast quality, and provides explicit expressions for optimal strategies under two specific demand distributions. Furthermore, the authors focused on how forecast error affects the optimal inventory strategy and obtained interesting properties of the optimal solution. In particular, the property that the optimal procurement quantity no longer changes with increasing forecast error under certain conditions is noteworthy, and has not been previously noted by scholars. Therefore, the study fills a gap in the literature.

Several research studies on Lean Six Sigma (LSS) have been done using the survey methodology. However, the use of surveys often relies on the measurement of variables, which cannot be directly observed, with attendant measurement errors. The purpose of this study is to develop a methodological framework consisting of a combination of four tools for identifying and assessing measurement error during survey research.

This paper evaluated the viability of the framework through an experimental study on the assessment of project management success in a developing country environment. The research design combined a control group, pretest and post-test measurements with structural equation modeling that enabled the assessment of differences between honest and fake survey responses. This paper tested for common method variance (CMV) using the chi-square test for the difference between unconstrained and fully constrained models.

The CMV results confirmed that there was significant shared variance among the different measures allowing us to distinguish between trait and faking responses and ascertain how much of the observed process measurement is because of measurement system variation as opposed to variation arising from the study’s constructs.

The study was conducted in one country, and hence, the results may not be generalizable.

Measurement error during survey research, if not properly addressed, can lead to incorrect conclusions that can harm theory development. It can also lead to inappropriate recommendations for practicing managers. This study provides findings from a framework developed and assessed in a LSS project environment for identifying faking responses. This paper provides a robust framework consisting of four tools that provide guidelines on distinguishing between fake and trait responses. This tool should be of great value to researchers.

Since equity markets have a dynamic nature, the purpose of this paper is to investigate the performance of a revision procedure for domestic and international portfolios, and provides an empirical selection strategy for optimal diversification from an American investor's point of view. This paper considers the impact of estimation errors on the optimization processes in financial portfolios.

This paper introduces the concept of portfolio resampling using Monte Carlo method. Statistical inferences methodology is applied to construct the sample acceptance regions and confidence regions for the resampled portfolios needing revision. Tracking error variance minimization (TEVM) problem is used to define the tracking error efficient frontiers (TEEF) referring to Roll (1992). This paper employs a computation method of the periodical after revision return performance level of the dynamic diversification strategies considering the transaction cost.

The main finding is that the global portfolio diversification benefits exist for the domestic investors, in both the mean-variance and tracking error analysis. Through TEEF, the dynamic analysis indicates that domestic dynamic diversification outperforms international major and emerging diversification strategies. Portfolio revision appears to be of no systematic benefit. Depending on the revision of the weights of the assets in the portfolio and the transaction costs, the revision policy can negatively affect the performance of an investment strategy. Considering the transaction costs of portfolios revision, the results of the return performance computation suggest the dominance of the global and the international emerging markets diversification over all other strategies. Finally, an assessment between the return and the cost of the portfolios revision strategy is necessary.

The innovation of this paper is to introduce a new concept of the dynamic portfolio management by considering the transaction costs. This paper investigates the performance of a revision procedure for domestic and international portfolios and provides an empirical selection strategy for optimal diversification. The originality of the idea consists on the application of a new statistical inferences methodology to define portfolios needing revision and the use of the TEVM algorithm to define the tracking error dynamic efficient frontiers.

Peer evaluations of performance increasingly are being used to make organizational decisions and to provide individuals with performance related feedback. Using Kenny’s social relations model (SRM), data from 14 teams of undergraduate students who completed performance ratings of themselves and other team members were analyzed. Results indicated a significant target variance effect for the majority of performance dimensions and a significant perceiver variance effect for all performance dimensions. Results further indicated that, in general, how individuals see themselves is not congruent with how others see them, how individuals see themselves is congruent with how they see others, how individuals are seen on a particular dimension is related to how they are seen on other performance dimensions, and, how a person is seen by others does not relate to how that individual sees others. Implications, limitations, and suggestions for future research using the SRM are discussed.

The purpose of this paper is to investigate the linkage between self‐managing team (SMT) member perceptions of collective efficacy and customer‐perceived service quality, and the most cost‐efficient way to reliably assess collective efficacy and customer‐perceived service quality, using generalizability theory (G‐theory).

Longitudinal design; employee and customer survey data from 52 teams of a major financial services institution were collected at two points in time.

First of all, results of OLS regression analysis show a positive effect of collective efficacy on customer‐perceived service quality. In addition, taking a G‐theory approach, the results indicate that collective efficacy possesses a higher psychometric quality than customer‐perceived service quality and that the costs of reliably comparing SMTs on collective efficacy are considerably lower compared to customer‐perceived service quality. Finally, for both constructs, the results reveal subtle but relevant differences in psychometric quality and costs of data collection across different types of service (routine versus non‐routine) settings.

To begin with, as a linkage construct, collective efficacy provides managers a mechanism for team intervention by means of task‐focused team building, role‐play exercises, and using feedback to increase service employee confidence. Secondly, when deciding to use survey data as one means to compare performance of organizational units, managers should first determine to what extent the distinct measurement design facets (e.g. items, persons, and occasions) account for variance in measures and sample correspondingly to save money on data collection. In doing so, they should explicitly take into account the type of service context and type of respondent.

This study identifies collective efficacy and customer‐perceived service quality as a set of service SMT performance measures that meaningfully connects employee and customer perceptions at the group level. Secondly, a G‐theory approach was used to assess the psychometric quality of these two measures and how data collection costs can be minimized to achieve a desired level of generalizability.

Research on international marketing usually involves comparing different groups of respondents. When using structural equation modeling (SEM), group comparisons can be misleading unless researchers establish the invariance of their measures. While methods have been proposed to analyze measurement invariance in common factor models, research lacks an approach in respect of composite models. The purpose of this paper is to present a novel three-step procedure to analyze the measurement invariance of composite models (MICOM) when using variance-based SEM, such as partial least squares (PLS) path modeling.

A simulation study allows us to assess the suitability of the MICOM procedure to analyze the measurement invariance in PLS applications.

The MICOM procedure appropriately identifies no, partial, and full measurement invariance.

The statistical power of the proposed tests requires further research, and researchers using the MICOM procedure should take potential type-II errors into account.

The research presents a novel procedure to assess the measurement invariance in the context of composite models. Researchers in international marketing and other disciplines need to conduct this kind of assessment before undertaking multigroup analyses. They can use MICOM procedure as a standard means to assess the measurement invariance.

The purpose of this paper is to investigate the influence of organizational factors on individual decision-making under conditions of uncertainty and time pressure. A method to assess the impact of individual and organizational factors on individual decisions is proposed and experimented in the context of triage decision-making process.

The adopted methodology is based on the bias-variance decomposition formula. The method, usually applied to assess the predictive accuracy of heuristics, has been adjusted to discriminate between the impact of organizational and individual factors affecting heuristic processes. To test the methodology, 25 clinical scenarios have been designed and submitted, through simulations, to the triage nurses of two Spanish hospitals.

Nurses’ decisions are affected by organizational factors in certain task conditions, such as situations characterized by complete and coherent information. When relevant information is lacking and available information is not coherent, decision-makers base their assessments on their personal experience and gut feeling.

Discriminating between the influence of organizational factors and individual ones is the starting point for a more in-depth understanding of how organization can guide the decision process. Using simulations of clinical scenarios in field research does not allow for capturing the influence of some contextual factors, such as the nurses’ stress levels, on individual decisions. This issue will be addressed in further research.

Bias and variance are useful measurements for detecting process improvement actions. A bias prevalence requires a re-design of organizational settings, whereas training would be preferred when variance prevails.

The main contribution of this work concerns the novel interpretation of bias and variance concepts to assess organizational factors’ influence on heuristic decision-making processes, taking into account the level of complexity of decision-related tasks.

Uncertainty can arise for a manipulator because its motion can deviate unpredictably from the assumed dynamical model and because sensors might provide information regarding the system state that is imperfect because of noise and imprecise measurement. This paper aims to propose a method to estimate the probable error ranges of the entire trajectory for a manipulator with motion and sensor uncertainties. The aims are to evaluate whether a manipulator can safely avoid all obstacles under uncertain conditions and to determine the probability that the end effector arrives at its goal area.

An effective, analytical method is presented to evaluate the trajectory error correctly, and a motion plan was executed using Gaussian models by considering sensor and motion uncertainties. The method used an integrated algorithm that combined a Gaussian error model with an extended Kalman filter and a linear–quadratic regulator. Iterative linearization of the nonlinear dynamics was used around every section of the trajectory to derive all of the prior probability distributions before execution.

Simulation and experimental results indicate that the proposed trajectory planning method based on the motion and sensor uncertainties is indeed highly convenient and efficient.

The proposed approach is applicable to manipulators with motion and sensor uncertainties. It helps determine the error distribution of the predefined trajectory. Based on the evaluation results, the most appropriate trajectory can be selected among many predefined trajectories according to the error ranges and the probability of arriving at the goal area. The method has been successfully applied to a manipulator operating on the Chinese Space Station.