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This paper aims to focus on the assessment of the ability of computer models with imperfect functional forms and uncertain input parameters to represent reality.

In this assessment, both the agreement between a model’s predictions and available experiments and the robustness of this agreement to uncertainty have been evaluated. The concept of satisfying boundaries to represent input parameter sets that yield model predictions with acceptable fidelity to observed experiments has been introduced.

Satisfying boundaries provide several useful indicators for model assessment, and when calculated for varying fidelity thresholds and input parameter uncertainties, reveal the trade-off between the robustness to uncertainty in model parameters, the threshold for satisfactory fidelity and the probability of satisfying the given fidelity threshold. Using a controlled case-study example, important modeling decisions such as acceptable level of uncertainty, fidelity requirements and resource allocation for additional experiments are shown.

Traditional methods of model assessment are solely based on fidelity to experiments, leading to a single parameter set that is considered fidelity-optimal, which essentially represents the values which yield the optimal compensation between various sources of errors and uncertainties. Rather than maximizing fidelity, this study advocates for basing model assessment on the model’s ability to satisfy a required fidelity (or error tolerance). Evaluating the trade-off between error tolerance, parameter uncertainty and probability of satisfying this predefined error threshold provides us with a powerful tool for model assessment and resource allocation.

Hospitality work setting is error-prone, rendering error handling critical for effective organizational operation and quality of service delivery. An organization’s attitude toward errors can be traced back to one fundamental question: should errors be tolerated/accepted or not? This study aims to examine the relationships between error tolerance and hospitality employees’ three critical work behaviors, namely, learning behavior, error reporting and service recovery performance. Psychological safety and self-efficacy are hypothesized to be the underlying attitudinal mechanisms that link error tolerance with these behavioral outcomes.

This study relied on a survey methodology, collecting data from 304 frontline restaurant employees in Turkey and their direct supervisors. SPSS 25.0 and Amos 25.0 were used for analysis.

The results revealed that error tolerance had direct positive relationships with employees’ psychological safety and self-efficacy, both of which had positive impacts on learning behavior and error reporting. In addition, learning behavior positively influenced employees’ service recovery performance, as rated by the employees’ supervisors.

This study identifies error tolerance as an organizational distal factor that influences employees’ learning behavior, error reporting and service recovery performance; and identifies self-efficacy and psychological safety as mediators of the relationship between error tolerance and behavioral outcomes. The findings help clarify the longstanding debate over the relationship between an organization’s attitude toward errors and its employees’ learning behavior. The findings also shed light on the advantages of tolerating error occurrence for organizations, which is especially important as most hospitality organizations pursue perfection with aversive attitudes toward errors.

On the basis of conservation of resources theory, the purpose of this paper is to propose a framework linking an organizational factor, organizational error tolerance, with employees’ psychological well-being through gains of psychological resources: perceived organizational support (POS) and organization-based self-esteem (OBSE).

Across three-wave data collected from 220 hotel frontline employees, this study tests the proposed model using structural equation modeling through AMOS.

Employees’ perceived organizational error tolerance positively influenced their psychological well-being through significant sequential mediation effects of POS and OBSE.

This study contributes to the existing literature of psychological resources, positive psychology and error management by providing insights into how organizational practice in error situations can be positively related to employees’ psychological well-being.

This paper identifies error-related organizational practices as precursor of individual psychological well-being and explores the non-work-related outcome variable of error management for the first time. The examination of the linkage between organizational error tolerance and employees’ psychological well-being via the underlying mechanism of psychological resources provides the insight into how resources dynamics play important roles in influencing employees’ psychological well-being.

This paper develops the statistical error analysis model for assembling, to derive measures of controlling the geometric variations in assembly with multiple assembly stations, and to provide a statistical tolerance prediction/distribution toolkit integrated with CAD system for responding quickly to market opportunities with reduced manufacturing costs and improved quality. First the homogeneous transformation is used to describe the location and orientation of assembly features, parts and other related surfaces. The desired location and orientation, and the related fixturing configuration (including locator position and orientation) are automatically extracted from CAD models. The location and orientation errors are represented with differential transformations. The statistical error prediction model is formulated and the related algorithms integrated with the CAD system so that the complex geometric information can be directly accessed. In the prediction model, the manufacturing process (joining) error, induced by heat deformation in welding, is taken into account.

Although the innovative behaviour of public employees is critical for the creation of public value and meeting of public interests, the authors are uncertain about the role of the human resource (HR) system in affecting individual behaviour as past studies tended to discuss innovation at the organisational level of analysis. Based on corporate human resource management (HRM) literature, the authors draw from the ability-motivation-opportunity (AMO) model to examine the influence of innovation-based HR practices on work-related risk propensity and innovative behaviour and the moderating role of perceived error tolerance of public sector organisations.

Dyadic data were collected from supervisors and their subordinates in various public sector organisations in the UAE. The authors collected valid responses from 100 managers and 200 employees.

This study's findings demonstrate that the HR system in the public sector shapes employees' behaviour at the individual level of analysis, consistent with the corporate HRM literature. The authors reveal that innovation-based HR practices significantly promote employees' innovative work behaviour because they trigger their inclination and disposition to take risks. Furthermore, the authors provide evidence that such risk-taking propensity at work is heightened under the conditions of a high level of error tolerance by the organisational management.

This study's findings point out the importance of implementing innovation-based HR practices, such as recruitment, reward and training, to drive public sector employees' innovative work behaviour as they could galvanise their risk-taking propensity and, subsequently, innovative behaviour. Public sector managers also need to develop an innovation culture tolerant toward employees' mistakes to further foster employees' work innovativeness. Policy wise, this study's findings could be integrated into the national innovation strategy to drive the national growth in the UAE.

This study sheds light on the drivers behind innovative behaviour among public employees, which is a less researched area, especially in a non-Western context.

This study aims to overcome the involved challenging issues and provide high-precision eigensolutions. General eigenproblems in the system of ordinary differential equations (ODEs) serve as mathematical models for vector Sturm-Liouville (SL) and free vibration problems. High-precision eigenvalue and eigenfunction solutions are crucial bases for the reliable dynamic analysis of structures. However, solutions that meet the error tolerances specified are difficult to obtain for issues such as coefficients of variable matrices, coincident and adjacent approximate eigenvalues, continuous orders of eigenpairs and varying boundary conditions.

This study presents an h-version adaptive finite element method based on the superconvergent patch recovery displacement method for eigenproblems in system of second-order ODEs. The high-order shape function interpolation technique is further introduced to acquire superconvergent solution of eigenfunction, and superconvergent solution of eigenvalue is obtained by computing the Rayleigh quotient. Superconvergent solution of eigenfunction is used to estimate the error of finite element solution in the energy norm. The mesh is then, subdivided to generate an improved mesh, based on the error.

Representative eigenproblems examples, containing typical vector SL and free vibration of beams problems involved the aforementioned challenging issues, are selected to evaluate the accuracy and reliability of the proposed method. Non-uniform refined meshes are established to suit eigenfunctions change, and numerical solutions satisfy the pre-specified error tolerance.

The proposed combination of methodologies described in the paper, leads to a powerful h-version mesh refinement algorithm for eigenproblems in system of second-order ODEs, that can be extended to other classes of applications in damage detection of multiple cracks in structures based on the high-precision eigensolutions.

Learning from errors is important for employees, particularly at early stages of their career. The purpose of this paper is to examine the influence of perceived trainer responses to errors on trainee learning from errors in a workplace setting. In Study 1, the authors test a model that examines the associations between perceived trainer responses to errors and trainee learning from errors, which are mediated by affective-motivational adaptivity. In Study 2, the authors further hypothesize that the link between perceived trainer responses and affective-motivational adaptivity is moderated by perceived error climate.

The authors test the hypotheses using data from 213 Swiss apprentices (Study 1) and 1,012 German apprentices (Study 2) receiving dual vocational training.

Study 1 suggests that negative trainer reaction impedes trainee learning from errors by impairing trainees’ affective-motivational adaptability. Trainer tolerance of errors and trainer support following errors were not related to trainee learning from errors. Study 2 indicates that perceived error climate is an important boundary condition that affects the relationship between trainer responses and trainee learning from errors.

This study contributes to research on learning from errors in three ways. First, it enriches the understanding regarding the role of trainers in enhancing learning from errors in organizations. Second, it extends research on learning from errors by investigating the interaction effects between perceived trainer responses and error climate. Third, it refines knowledge about the role of positive affect in learning from errors. Findings of this study also offer practical insights to trainers and managers regarding what they should do to encourage trainee learning from errors.

The article seeks to highlight an unconventional way out of the “innovation dilemma”, relevant to many business organizations. Innovative management and risk friendliness are necessary, but the way in which failure is handled and the resulting fear of making mistakes block the (innovative) efforts of specialists and managers.

The article draws on theoretical as well as empirical work. First, the seemingly crystal‐clear concept of “error” is elaborated and the rare category of “creative errors” is introduced. Second, illustrative findings from a case study in the automotive sector are reported.

The paper suggests that a culturally exacerbated antipathy towards errors ultimately leads to a situation of pronounced innovation incompetence in which creative behavior is avoided. The article points out that it is not an “absolution of mistakes” that is required, but a tolerance for legitimate errors which should only occur under exceptional circumstances. The authors underlines that fairness in dealing with errors is considerably more important than a misguided attempt to create or maintain harmony. In addition to the philosophy and potential of this unusual incentive system, possible practical implementation problems are considered.

Generalization is limited because the findings are based on only one case study. However, because of the vital importance of the so‐called “innovation dilemma”, the exploratory findings of the study may trigger further empirical research on “creative errors”.

An initiative like the “Creative Error of the Month” may help to bring about cultural change towards a climate of trust and confidence in which innovative commitment is treated fairly even if it does not in fact succeed.

Common approaches to enhance organizational innovativeness reward “success stories” or praise “zero‐based cultures”. This case study suggests that a sophisticated initiative revolving around the “hidden” innovative potential of “creative errors” and “tragic failures” might prove to be a successful offbeat attempt at stimulating creativity and innovative behaviour in enterprises.

This study presents a novel hp-version adaptive finite element method (FEM) to investigate the high-precision eigensolutions of the free vibration of moderately thick circular cylindrical shells, involving the issues of variable geometrical factors, such as the thickness, circumferential wave number, radius and length.

An hp-version adaptive finite element (FE) algorithm is proposed for determining the eigensolutions of the free vibration of moderately thick circular cylindrical shells via error homogenisation and higher-order interpolation. This algorithm first develops the established h-version mesh refinement method for detecting the non-uniform distributed optimised meshes, where the error estimation and element subdivision approaches based on the superconvergent patch recovery displacement method are introduced to obtain high-precision solutions. The errors in the vibration mode solutions in the global space domain are homogenised and approximately the same. Subsequently, on the refined meshes, the algorithm uses higher-order shape functions for the interpolation of trial displacement functions to reduce the errors quickly, until the solution meets a pre-specified error tolerance condition. In this algorithm, the non-uniform mesh generation and higher-order interpolation of shape functions are suitable for addressing the problem of complex frequencies and modes caused by variable structural geometries.

Numerical results are presented for moderately thick circular cylindrical shells with different geometrical factors (circumferential wave number, thickness-to-radius ratio, thickness-to-length ratio) to demonstrate the effectiveness, accuracy and reliability of the proposed method. The hp-version refinement uses fewer optimised meshes than h-version mesh refinement, and only one-step interpolation of the higher-order shape function yields the eigensolutions satisfying the accuracy requirement.

The proposed combination of methodologies provides a complete hp-version adaptive FEM for analysing the free vibration of moderately thick circular cylindrical shells. This algorithm can be extended to general eigenproblems and geometric forms of structures to solve for the frequency and mode quickly and efficiently.

This paper aims to illustrate the tolerance optimization method based on the assembly accuracy constrain, precession constrain and the cost of production of the assembly product.

A tolerance optimization method is an excellent way to perform product assembly performance. The tolerance optimization method is adapted to the process analysis of the hatch and skin of an aircraft. In this paper, the tolerance optimization techniques are applied to the tolerance allocation for step difference analysis (example: step difference between aircraft cabin door and fuselage outer skin). First, a mathematical model is described to understand the relationship between manufacturing cost and tolerance cost. Second, the penalty function method is applied to form a new equation for tolerance optimization. Finally, MATLAB software is used to calculate 170 loops iteration to understand the efficiency of the new equation for tolerance optimization.

The tolerance optimization method is based on the assembly accuracy constrain, machinery constrain and the cost of production of the assembly product. The main finding of this paper is the lowest assembly and lowest production costs that met the product tolerance specification.

This paper illustrated an efficient method of tolerance allocation for products assembly. After 170 loops iterations, it founds that the results very close to the original required tolerance. But it can easily say that the different number of loops iterations may have a different result. But optimization result must be approximate to the original tolerance requirements.

It is evident from Table 4 that the tolerance of the closed loop is 1.3999 after the tolerance distribution is completed, which is less than and very close to the original tolerance of 1.40; the machining precision constraint of the outer skin of the cabin door and the fuselage is satisfied, and the assembly precision constraint of the closed loop is satisfied.

The research may support further research studies to minimize cost tolerance allocation using tolerance cost optimization techniques, which must meet the given constrain accuracy for assembly products.