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Purpose – Are members of socially dominant groups aware of the privileges they enjoy? We address this question by applying the notion of hypocognition to social privilege. Hypocognition is defined as lacking a rich cognitive or linguistic representation (i.e., a schema) of a concept in question. By social privilege, we refer to advantages that members of dominant social groups enjoy because of their group membership. We argue that such group members are hypocognitive of the privilege they enjoy. They have little cognitive representation of it. As a consequence, their social advantage is invisible to them.

Approach – We provide a narrative review of recent empirical work demonstrating and explaining this lack of expertise and knowledge in socially dominant groups (e.g., White People, men) about discrimination and disadvantage encountered by other groups (e.g., Black People, Asian Americans, women), relative what members of those other groups know.

Findings – This lack of expertise or knowledge is revealed by classic cognitive psychological measures. Relative to members of other groups, social dominant group members generate fewer examples of discrimination that other groups confront, remember fewer instances after being presented a list of them, and are slower to respond when classifying whether these examples are discriminatory.

Social Implications – These classic measures of cognitive expertise about social privilege predict social attitude differences between social groups, specifically whether people perceive the existence of social privilege as well as believe discrimination still exists in contemporary society. Hypocognition of social privilege also carries implications for informal interventions (e.g., acting “colorblind”) that are popularly discussed.

The emerging natures of big data – volume, velocity, variety, value and veracity – exert higher stress on employees and demand greater creativity from them, causing extreme difficulties in the talent management of organizations in the big data era. The purpose of this paper is to explore the effect of challenge stressors on creativity and the boundary conditions of the relationship.

Multisource data were collected including 593 followers and their 98 supervisors from organizations that are confronting a big data induced management revolution. Hierarchical regression analysis and bootstrapping analysis were used to test the mediation and moderation mechanism.

The results showed that job burnout mediated the negative relationship between challenge stressors and creativity and that this indirect effect was attenuated by an employee’s core self-evaluation (CSE) and servant leadership. In contrast, whether work engagement mediated the relationship between challenge stressors and creativity was contingent on the level of an employee’s CSE and servant leadership. Specifically, the mediating effect was significant only when an employee’s CSE or servant leadership was high.

The results contribute to our understanding of the relationship between challenge stressor and creativity in the big data era. Specifically, relying on the job demands–resources model, this study empirically opens the “black box” between challenge stressors and creativity by exploring two opposing intermediate mechanisms. In addition, this study reveals boundary conditions by investigating dispositional and contextual factors that can accentuate the positive effect while attenuating the negative effect of challenge stressors on employee creativity.

For the last two decades economics literature and debates have increasingly referred to institutions as the answers to the long-lasting queries regarding how stock market performance rises and what policies can be implement to encourage best outcomes in terms of stock market performances in Malaysia so that the analysis of the institutional basis under which any stock market functions has now converted an essential issue of investigation. This study attempts to capture the relationship between stock market movements and institutional quality (IQ) using autoregressive distributed lag bounds testing approach, over 33 years during the period of 1984–2016. The finding suggests that IQ positively and significantly affects stock market performance. Moreover, it is also showing that there is, in fact, a causal relationship between institutions and stock market performance. The findings are robust to changes in specification and a host of transparency measures.

Presents a unified information systems theory where stochastic information, fuzzy information, rough information, grey information, unascertained information and white and black information are all special cases. A unified concept of information, named blind information, is introduced. Also, relevant mathematical representations of various types of information are presented.

This paper aims to estimate the international competitiveness of China’s biomass energy products during 2007-2016 in the context of the Belt and Road Initiative.

In this paper, the authors used the constant market share model and the revealed comparative advantage index to analyze the evolution trend of China’s biomass products’ international competitiveness during the past decade from 2007 to 2016 based on the market structure of the Belt and Road Initiative.

The results show that: China’s major biomass energy products have no comparative advantages in the world market, nevertheless, their international competitiveness is on the rise; China’s biomass energy products have been agglomerated to the regional markets where the market demand growth is fast in the Belt and Road countries; and the unreasonable structure is an important factor influencing the international competitiveness of China’s biomass exports.

The authors analyzed the international competitiveness of China’s biomass energy products based on the “Belt and Road Initiative” with all the trading items, in an effort to propose policy implications for enhancing the comparative advantages of China’s biomass products in the international market especially in the Belt and Road regions.

The construction project is implemented under uncertainty environment, and the product of construction is very complex. Selecting a project delivery system/approach is a critical task, which determines the project schedule, quality and investment objectives. The purpose of this paper is to propose a decision-making model for the selection of project delivery system which is based on information entropy and unascertained measure model.

A decision-making model based on information entropy and unascertained set is employed to select project delivery approach. In order to overcome the subjective evaluations from the experts, the theory of “entropy weight” is applied to modify the experts’ subjective weight. The multi-attribute unascertained measure decision making is fitted to deal with the uncertainty information for selection of project delivery system.

The proposed methodology is more comprehensive compared with the previous work, especially in the uncertainty environment.

There is some further work that should be considered, such as how to deal with the imprecise and subjective information given by the experts; how to determine the weight of the experts’; finding a set of importance factors influencing the selection of a delivery system is a complex task to further research.

The proposed method can help the construction owner to select a most fitted project delivery system of a construction project.

A new approach to select project delivery approach is proposed based on information entropy and unascertained set.

This paper aims to address not only technical and economic challenges in electrical distribution system but also environmental impact and the depletion of conventional energy resources due to rapidly growing economic development, results rising energy consumption.

Generally, the network reconfiguration (NR) problem is designed for minimizing power loss. Particularly, it is devised for maximizing power loss reduction by simultaneous NR and distributed generation (DG) placement. A loss sensitivity factor procedure is incorporated in the problem formulation that has identified sensitivity nodes for DG optimally. An adaptive weighted improved discrete particle swarm optimization (AWIDPSO) is proposed for ascertaining a feasible solution.

In AWIDPSO, the adaptively varying inertia weight increases the possible solution in the global search space and it has obtained the optimum solution within lesser iteration. Moreover, it has provided a solution for integrating more amount of DG optimally in the existing distribution network (DN).

The AWIDPSO seems to be a promising optimization tool for optimal DG placement in the existing DN, DG placement after NR and simultaneous NR and DG sizing and placement. Thus, a strategic balance is derived among economic development, energy consumption, environmental impact and depletion of conventional energy resources.

In this study, a standard 33-bus distribution system has been analyzed for optimal NR in the presence of DG using the developed framework. The power loss in the DN has reduced considerably by indulging a new and innovative approaches and technologies.