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The low success rate of megaprojects stems from the opportunism triggered by uncertainty. Developing trust between participants is an effective means to reduce uncertainty, but this process is inevitably affected by contracts. The purpose of this paper is to examine the impact of uncertainty on participants’ opportunism in megaprojects and the effect of trust on reducing uncertainty. At the same time, the moderating effects of contractual control are tested.

This research classifies trust into competence-based trust and goodwill-based trust and categorizes uncertainty into environmental uncertainty and behavioral uncertainty. Partial least squares structural equation modeling is used to test the hypotheses based on data collected from 172 respondents.

The results show a positive correlation between the two types of uncertainty and opportunism. For the governance of uncertainty, competence-based trust can reduce environmental uncertainty, but it is ineffective for behavioral uncertainty, and goodwill-based trust has a significant effect on both types of uncertainty. The test of moderating effects shows that contractual control strengthens the effect of competence-based trust but weakens the effect of goodwill-based trust, which means that contractual control complements competence-based trust and substitutes for goodwill-based trust.

This research enriches the theory of megaproject management. First, it validates the role of competence-based trust and goodwill-based trust in reducing the different types of uncertainty in megaprojects. Second, this study clarifies the substitution or complementarity between contractual control and different dimensions of trust in the context of high uncertainty, which provides a comprehensive answer to prior research inconsistencies on contractual control and trust.

For practice, this research provides some implications for megaproject management. First, project managers should recognize that the match between trust and project uncertainty is key to the success of megaproject governance. For example, some megaprojects involve many organizations, and there are many difficulties in behavioral supervision and performance appraisal. Therefore, developing goodwill-based trust between participants through positive interactions is an effective means to reduce the behavioral uncertainty of all participants and to curb opportunistic behaviors.

This research validated the role of competence-based trust and goodwill-based trust in reducing the different types of uncertainty in megaprojects. Furthermore, it clarifies the substitution or complementarity between contractual control and different dimensions of trust in the context of high uncertainty, which provides a comprehensive answer to prior research inconsistencies on contractual control and trust.

Understanding the impact of project complexity on the mega construction project success will help improve the efficiency of project management. However, the influencing mechanism of project complexity on project success has not been clearly depicted. This paper aims to divide project complexity and project success into five dimensions, trying to explore the impact of different complexity combinations on mega construction project success.

This study applies the qualitative comparative analysis (QCA) method to find out the impact of the combinations of different project complexities on the every dimension of mega construction project success. First, 21 cases were interviewed and scored in five categories of project complexities and five aspects of project success. Second, with the QCA program, the combinations of different project complexities influencing every aspect of mega construction project success was obtained.

This research found that high organizational complexity or a combination of high environmental complexity and goal complexity can lead to serious schedule delays of mega construction projects, high technological complexity and goal complexity are important reasons for cost overrun, high technological complexity or a combination of high environmental complexity and low organizational complexity usually lead to low quality of mega construction projects, high goal complexity and cultural complexity are important factors affecting the key stakeholders' satisfaction and high technological complexity and environmental complexity are the reasons for the poor sustainability of mega construction projects.

This study clearly reveals the influencing mechanism of project complexity on mega construction project success, which can help the project managers to understand and assess the complexity of mega construction projects and accurately predict their negative impacts on the mega construction projects.

As project managers are in the central position of sustainable project management (SPM), their competencies become an important factor that affects the outcome of SPM. However, literature lacks a clear description of the project manager competence system required by SPM. The purpose of this study is to explore what competencies are required by sustainable project management and analyze the hierarchy and attributes of these competencies.

Aiming to address the problem, several methods were applied in this study. First, with a literature review, semi-structured interviews and Delphi technology, 23 project manager competencies required by SPM are identified. Second, the fuzzy interpretive structural modeling–matrix impact cross-reference multiplication applied to classification (FISM–MICMAC) method is used to analyze the data from 21 experienced project managers in the construction industry, revealing the hierarchy and attributes of the project manager competency system required by SPM.

The results indicate that the project manager competency system required by SPM includes nine micro levels. According to the nature of the competencies, these nine levels can be summarized into five macro levels. Furthermore, all competencies can be divided into three categories: independent, autonomous and dependent.

This study not only provides project managers and scholars with a further understanding of project manager competencies but also helps contractors make informed and objective judgments in the selection and/or appointments of project managers who have the appropriate competencies for SPM.

The purpose of this paper is to develop a numerical simulation method based on the transient upstream finite element method (FEM) and Schottky emission theory to reveal the distribution characteristics of space charge in oil-paper insulation.

The main insulation medium of the converter transformer in high voltage direct current transmission is oil-paper insulation. However, the influence of space charge is difficult to be fully considered in the insulation design and simulation of converter transformers. To reveal the influence characteristics of the space charge, this paper proposes a numerical simulation method based on Schottky emission theory and the transient upstream FEM. This method considers the influence of factors, such as carrier mobility, carrier recombination coefficient, trap capture coefficient and diffusion coefficient on the basis of multi-physics field coupling calculation of the electric field and fluid field.

A numerical simulation method considering multiple charge states is proposed for the space charge problem in oil-paper insulation. Meanwhile, a space charge measurement platform based on the electrostatic capacitance probe method for oil-paper insulation structure is built, and the effectiveness and accuracy of the numerical simulation method is verified.

A variety of models are calculated and analyzed by the numerical simulation method in this paper, and the distribution characteristics of the space charge and total electric field in oil-paper insulation medium with single-layer, polarity reversal of plate voltage and double-layer are obtained. The research results of this paper have the guiding significance for the engineering application of oil-paper insulation and the optimal design of converter transformer insulation.

The purpose of this study is to establish a modified accident causation model for highway construction accidents (ACM-HC) and describe the establishment process of the ACM-HC.

Based on the 2–4 Model, a framework of the ACM-HC was constructed, and the accident causal factors (CF) were extracted from four aspects of human, material, environment and management. In addition, association rule mining (ARM) was introduced to analyze accident investigation reports to obtain the interrelationships between the factors. Based on the framework, factors and ARM results, the ACM-HC was established. Finally, the ACM-HC was verified with a tunnel collapse accident.

Both the external and internal causes of contractor cause accidents. The flaws of safety management of other stakeholders are external causes. In terms of the internal causes, there are four stages: direct causes, indirect causes, radical causes and root causes. More specifically, the direct causes refer to the unsafe acts and the unsafe conditions; ineffective safety supervision and poor individual factors of frontline workers constitute the indirect causes; the radical causes lie in the flaws of construction procedures and technical schemes; the root causes are related to the poor individual factors of decision makers and managers.

The ACM-HC expresses the causes, sequence and mechanism of highway construction accidents in a visual way. In addition, this study describes a process of using a qualitative–quantitative hybrid approach to establish a modified ACM, which provides a different perspective for the establishment of an ACM.

The purpose of this paper is to verify that improved immune network can be used to design new controller for engineering.

First, the definition of artificial immune controller is given out. Second, the disadvantage of Varela immune network which is not fit for control system is pointed out. Third, based on the analysis, the Varela immune network is modified for the purpose of designing controller with the mechanisms of immune network. And an immune controller based on improved Varela immune network (improved Varela immune network model (IVINM)‐AIC) is designed out. Its theoretic background is described in detail.

Based on the theoretic analysis and experiment of motor speed control, it is found that Varela immune work can be used to design immune controller. The experiments results show that IVINM‐AIC is much more robust, stable and anti‐delay and less overshoot than classical proportion, integration, and differentiation controller. It is good at controlling nonlinear system which is single input single output (SISO) system. The limitation of IVINM‐AIC is that it is used for simple SISO system.

The theoretic analysis of improved Varela immune network controller is original and it is useful for the analysis and design of new and complex immune controller. The experiment design is useful for comparison of new test in future.

The objective of this paper is to propose a consumer-behavior-based intelligence (CBBI) model to identify market structure so as to monitor product competition. Competitive intelligence extracted from Chinese e-business clickstream data is exploited to examine the relevance of consumers' heterogeneous behavioral feedback, namely, click, tag-into-favorite, time-of-browsing, add-into-cart, and remove-from-cart, to visualize the competitive product market structure and to predict product-level sales.

Our proposed CBBI model consists of visualization and prediction, which explore e-business clickstream data. We conduct the visualization and segmentation of market structure in the form of a perceptual map by employing K-means clustering algorithm and multidimensional scaling technique. Concurrently, we developed an updated Bayesian linear regression (BLR) to predict product-level sales by considering consumers' heterogeneous feedback. Our updated BLR specifically integrated the estimated knowledge of the previous periods to verify whether product sales are period-dependent due to the consumer memory effect in e-commerce, improving the conventional BLR of diffuse prior distribution setup in terms of mean absolute error (MAE) and root mean squared error (RMSE).

Considering the performance of consumers' heterogeneous actions, the present research visualized three different segments of the competitive market structure in a perceptual map, and its horizontal axis is shown as a signal of the ascending trend of product sales. The previous five-day period was ascertained to be the best size of a time window for the consumer memory effect on product sales prediction. This hypothesis is supported by the concept that product sales are period-dependent. The results of the proposed updated BLR indicate that consumer tag-into-favorite, add-into-cart, and remove-from-cart feedback have positive impacts on product-level sales while click and time-of-browsing have the opposite effect.

While the identified competitive product market structure elaborates consumer heterogeneous feedback toward alternative product choices, this paper contributes by extending those homogeneous consumer preferences-related marketing studies. The perceptual map's configuration in respect to period-dependent product sales facilitates the effective inclusion of consumer behavior application in product sales prediction research in e-commerce. This paper helps sellers and retailers better comprehend the impacts of heterogeneous feedback and the consumer memory effect on the degree of competition in the form of product sales. The research results also offer a managerial implication about shaping the competitive edge by conducting different product management strategies in e-commerce platforms.

This paper aims to provide a symplectic conservation numerical analysis method for the study of nonlinear LC circuit.

The flux linkage control type nonlinear inductance model is adopted, and the LC circuit can be converted into the Hamiltonian system by introducing the electric charge as the state variable of the flux linkage. The nonlinear Hamiltonian matrix equation can be solved by perturbation method, which can be written as the sum of linear and nonlinear terms. Firstly, the linear part can be solved exactly. On this basis, the nonlinear part is analyzed by the canonical transformation. Then, the coefficient matrix of the obtained equation is still a Hamiltonian matrix, so symplectic conservation is achieved.

Numerical results reveal that the method proposed has strong stability, high precision and efficiency, and it has great advantages in long-term simulations.

This method provides a novel and effective way in studying the nonlinear LC circuit.

This study models cost-efficiency against revenue for hotels in the Pearl River Delta (PRD) – in Guangzhou, Hong Kong and Macau – by considering regional differences and weight restrictions on revenue output.

The authors modified and applied a context-dependent assurance region data envelopment analysis (CAR-DEA) model in assessing the performance of 41 hotels in the PRD. The model considers the relationships among output variables and sets the revenue composition of the hotels as weight restrictions in accounting for the relative importance of different revenue sources.

When assessing the 41 hotels all together, those in Guangzhou outperformed the hotels in other two cities by showing better pure technical efficiency (PTE), while those in Macau had the best scale efficiency (SE). When the assurance region (AR) restriction was imposed, the hotels in Macau outperformed those in the other two cities by showing better SE. When considering regional differences, the Macau hotels ranked first in terms of both the average efficiency score and the overall ranking. All the sample hotels in Guangzhou and half of the sample hotels in Hong Kong and Macau exhibited increasing, constant and decreasing returns to scale.

The research results are limited by data quality and the variables included in the models.

The study helps hotel practitioners in the PRD better assess their cost-efficiency performance by considering regional differences and operational parameters so as to strategically improve their performance.

This study improves upon previous hotel efficiency studies by considering the influence of different operational parameters across different localities. It can be extended to examine the performance of different calibers of hotels, restaurants or tourism entities located in various localities and possessing different operational characteristics.

Comparative opinions widely exist in online reviews as a common way of expressing consumers’ ideas or preferences toward certain products. Such opinion-rich texts are key proxies for detecting product competitiveness. The purpose of this paper is to set up a model for competitiveness analysis by identifying comparative relations from online reviews for restaurants based on both pattern matching and machine learning.

The authors define the sub-category of comparative sentences according to Chinese linguistics. Classification rules are set up for each type of comparative relations through class sequence rule. To improve the accuracy of classification, a comparative entity dictionary is then introduced for further identifying comparative sentences. Finally, the authors collect reviews for restaurants from Dianping.com to conduct experiments for testing the proposed model.

The experiments show that the proposed method outperforms the baseline methods in terms of precision in identifying comparative sentences. On the basis of such comparison-rich sentences, product features and comparative relations are extracted for sentiment analysis, and sentimental score is assigned to each comparative relation to facilitate competitiveness analysis.

Only the explicit comparative relations are discussed, neglecting the implicit ones. Besides that, the study is grounded in the assumption that all features are homogeneous. In some cases, however, the weights to different aspects are not of the same importance to market.

On the basis of comparative relation mining, product features and comparative opinions are extracted for competitiveness analysis, which is of interest to businesses for finding weakness or strength of products, as well as to consumers for making better purchase decisions.

Comparative relation mining could be possibly applied in social media for identifying relations among users or products, and ranking users or products, as well as helping companies target and track competitors to enhance competitiveness.

The authors propose a research framework for restaurant competitiveness analysis by mining comparative relations from online consumer reviews. The results would be able to differentiate one restaurant from another in some aspects of interest to consumers, and reveal the changes in these differences over time.