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The purpose of this study is to examine the impact of paradoxical leader behavior (PLB) on leader effectiveness (LE). The authors propose an underlying mechanism of this relationship and examine if the stated relationship is moderated by job-related and structural uncertainty in the organization.

The authors draw on survey data from 127 employees working in public and privately owned organizations and analyze data using AMOS and SPSS.

Results offer partial support to the authors’ hypotheses indicating that paradoxical leader behavior is positively associated to LE. As expected, the relationship is negatively moderated by structural uncertainty indicating that LE is weakened where there is structural uncertainty in the organization. However, in contrast to our other assumption, the authors find a positive (rather than negative) moderating role of job-related uncertainty in the PLB–LE relationship.

Paradoxes are everywhere and inherently embedded in complex and dynamic organizational systems. To deal with paradoxes, organizational leaders often seek to act paradoxically. However, not every leader has a paradoxical mindset, and where a leader has such, it remains unclear if they will prove to be effective leaders. What can further influence the effectiveness of such leaders is the uncertainty that organizations face in the today’s dynamic environments. In this study, the authors contribute to paradox theory and the paradoxical leadership literature by unfolding the boundary conditions which can influence paradoxical leader’s effectiveness.

Considering that uncertain factors widely exist in engineering practice, an adaptive collocation method (ACM) is developed for the structural fuzzy uncertainty analysis.

ACM arranges points in the axis of the membership adaptively. Through the adaptive collocation procedure, ACM can arrange more points in the axis of the membership where the membership function changes sharply and fewer points in the axis of the membership where the membership function changes slowly. At each point arranged in the axis of the membership, the level-cut strategy is used to obtain the cut-level interval of the uncertain variables; besides, the vertex method and the Chebyshev interval uncertainty analysis method are used to conduct the cut-level interval uncertainty analysis.

The proposed ACM has a high accuracy without too much additional computational efforts.

A novel ACM is developed for the structural fuzzy uncertainty analysis.

Most of the previous work on reliability analysis was based on the traditional reliability theory. The calculated results can only reflect the reliability of components at a specific time, which neglects the uncertainty of load and resistance over time. The purpose of this paper is to develop a time-dependent reliability analysis approach based on stochastic process to deal with the problem and apply it to the structural design of railway vehicle components.

First, the parametric model of motor hanger for electric multiple unit (EMU) is established by ANSYS parametric design language, and its structural stress is analyzed according to relevant standards. The Latin hypercube method is used to analyze the sensitivity of the structure, and the uncertainty parameters (sizes and loads) which have great influence on the structural strength are determined. The D-optimal experimental design is carried out to establish the polynomial response surface function, which characterizes the relationship between uncertainty parameters and structural stress. Second, the Poisson stochastic process is adopted to describe the number of loads acting, and the Monte Carlo method is used to obtain the load acting history according to its probability distribution characteristics. The load history is introduced into the response surface function and the uncertainty of other parameters is considered at the same time, and the stress history of the motor hanger is obtained. Finally, the degradation process of structural resistance is described by a Gamma stochastic process, and the time-dependent reliability of the motor hanger is calculated based on the reliability theory.

Time and the uncertainties of parameters have great impact on reliability. The results of reliability decrease with time fluctuation are more reasonable, stable and credible than traditional methods.

In this paper, the proposed method is applied to the structural design of the motor hanger for EMU, which has a good guiding significance for accurately evaluating whether if the design meets the reliability requirements.

The value of this paper is that the method takes both the randomness of load over time and the uncertainty of structural parameters in the design and manufactures process into consideration, and describes the monotonous degradation characteristics of structural resistance. At the same time, the time-dependent reliability of mechanical components is calculated by a response surface method. It not only improves the accuracy of reliability analysis, but also improves the analysis efficiency and solves the problem that the traditional reliability analysis method can only reflect the static reliability of components.

The purpose of this paper is to establish a link between inflation uncertainty and interest rates for five inflation‐targeting countries.

The approach takes the form of a time‐varying parameter model with a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) specification, used to derive impulse uncertainty and structural uncertainty.

This study attempts to establish a link between inflation uncertainty and interest rates for five inflation‐targeting countries, i.e. Canada, Finland, Spain, Sweden, and the UK. Decomposing inflation uncertainty into two components – impulse and structural, a positive association was found between the expected inflation and interest rates. Structural uncertainty has a positive and significant effect on interest rates for some countries. It has also been found that the long‐run effects of inflation on interest rates are less than unity for the post‐inflation targeting period, which implies that in some respect the Central Bank has been successful in targeting inflation. This has allowed the Central Bank to employ a less restrictive monetary policy in an environment of a credible inflation‐targeting strategy.

Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) can be used instead of GARCH modelling.

This is the first study that has tried to establish the link between different types of inflation uncertainty and interest rates for the inflation‐targeting countries to see the effect of inflation targeting.

The uncertainty of the interval variable is represented by interval factor, and the interval variable is described as its mean value multiplied by its interval factor. Based on interval arithmetic rules, an analytical method of interval finite element for uncertain structures but not probabilistic structure or fuzzy structure is presented by combining the interval analysis with finite element method. The static analysis of truss with interval parameters under interval load is studied and the expressions of structural interval displacement response and stress response are deduced. The influences of uncertainty of one of structural parameters or load on the displacement and stress of the structure are examined through examples and some significant conclusions are obtained.

This paper aims to describe current trends in probabilistic structural fire engineering and provides a comprehensive summary of the state-of-the-art of performance-based structural fire engineering (PSFE).

PSFE has been introduced to overcome the limitations of current conventional design approaches used for the design of fire-exposed structures, which investigate assumed worst-case fire scenarios and include multiple thermal and structural analyses. PSFE permits buildings to be designed in relation to a level of life safety or economic loss that may occur in future fire events with the help of a probabilistic approach.

This paper brings together existing research on various sources of uncertainty in probabilistic structural fire engineering, such as elements affecting post-flashover fire development, material properties, fire models, fire severity, analysis methods and structural reliability.

Prediction of economic loss would depend on the extent of damage, which is further dependent on the structural response. The representative prediction of structural behaviour would depend on the precise quantification of the fire hazard. The incorporation of major uncertainty sources in probabilistic structural fire engineering is explained, and the detailed description of a pioneering analysis method called incremental fire analysis is presented.

In the reliability assessment of composite laminate structures with multiple components, the uncertainty space defined around design solutions easily becomes over-dimensioned, and not all of the random variables are relevant. The purpose of this study is to implement the importance analysis theory of Sobol’ to reduce the dimension of the uncertainty space, improving the efficiency toward global convergence of evolutionary-based reliability assessment.

Sobol’ indices are formulated analytically for implicit structural response functions, following the theory of propagation of moments and without violating the fundamental principles presented by Sobol’. An evolutionary algorithm capable of global convergence in reliability assessment is instrumented with the Sobol’ indices. A threshold parameter is introduced to identify the important variables. A set of optimal designs of a multi-laminate composite structure is evaluated.

Importance analysis shows that uncertainty is concentrated in the laminate where the critical stress state is found. Still, it may also be reasonable in other points of the structure. An accurate and controlled reduction of the uncertainty space significantly improves the convergence rate, while maintaining the quality of the reliability assessment.

The theoretical developments assume independent random variables.

Applying Sobol’ indices as an analytical dimension reduction technique is a novelty. The proposed formulation only requires one adjoint system of equilibrium equations to be solved once. Although a local estimate of a global measure, this analytical formulation still holds because, in structural design, uncertainty is concentrated around the mean-values.

The purpose of this paper is to contribute to operations management (OM) practice contingency research by describing the complexity of projects. Complexity is recognised as a key independent (contingent) variable that impacts on many subsequent decisions in the practice of managing projects.

This paper presents a systematic review of relevant literature and synthesises an integrated framework for assessing the complexities of managing projects.

This framework comprises five dimensions of complexity – structural, uncertainty, dynamics, pace and socio‐political complexity. These five dimensions present individuals and organisations with choices about how they respond to each type of complexity, in terms of business case, strategic choice, process choice, managerial capacity and competencies.

The contribution of this paper is to provide a clarification to the epistemology of complexity, to demonstrate complexity as a lived experience for project managers, and offer a common language for both practitioners and future empirical studies considering the individual or organisational response to project complexities. The work also demonstrates an application of systematic review in OM research.

The purpose of this paper is to find how those uncertainty factors influence transaction costs generated and to identify ways to minimize the transaction costs borne by the construction owner.

The literature indicates that there is no consensus on a standard definition of transaction costs in the construction industry. A detailed literature review of research work on transaction costs in construction is conducted in order to identify the determinants of transaction costs in construction projects. A structural equation model is tested on data collected by means of a survey administered to construction owners.

The findings indicate that the transaction costs borne by the owner can be minimized if the owner minimizes the uncertainties inherent in the construction project by making sure the engineering design is as complete as possible before bids are sought from contractors; harmonious relationships between project participants; fair risk allocation; have experience in similar type projects; and contractor selection practices that routinely detect irregular behavior.

The data used in this research are primarily based on the experiences of public owners and the markets in which they operate; a larger representation of private owners could make the conclusions more general. Another limitation of the study is that it relies on a survey of opinions rather than actual records of costs and other hard data.

No empirical study has ever been conducted of transaction-related issues in the construction industry because of the lack of a common understanding of transaction cost. This paper provides the groundwork for such a study.

This paper attempts to reconcile the many determinants of transaction costs in construction projects under uncertainty considered by different researchers in a multitude of research studies.

For fast calculation of complex structure in engineering, correlations among input variables are often ignored in uncertainty propagation, even though the effect of ignoring these correlations on the output uncertainty is unclear. This paper aims to quantify the inputs uncertainty and estimate the correlations among them acorrding to the collected observed data instead of questionable assumptions. Moreover, the small size of the experimental data should also be considered, as it is such a common engineering problem.

In this paper, a novel method of combining p-box with copula function for both uncertainty quantification and correlation estimation is explored. Copula function is utilized to estimate correlations among uncertain inputs based upon the observed data. The p-box method is employed to quantify the input uncertainty as well as the epistemic uncertainty associated with the limited amount of the observed data. Nested Monte Carlo sampling technique is adopted herein to ensure that the propagation is always feasible. In addition, a Kriging model is built up to reduce the computational cost of uncertainty propagation.

To illustrate the application of this method, an engineering example of structural reliability assessment is performed. The results indicate that it may significantly affect output uncertainty whether to quantify the correlation among input variables. Furthermore, an additional advantage for risk management is obtained in this approach due to the separation of aleatory and epistemic uncertainties.

The proposed method takes advantage of p-box and copula function to deal with the correlations and limited amount of the observed data, which are two important issues of uncertainty quantification in engineering. Thus, it is practical and has the ability to predict accurate response uncertainty or system state.