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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.

Introduction: Artificial intelligence (AI) is being extensively used to solve complex problems in the industry. AI provides several benefits such as providing visibility in the processes, reducing time, improving accuracy, saving time, helping in the decision-making process, etc. Due to the range of benefits of AI technologies, organisations readily adopt this technology. However, there are several challenges that the organisation faces during the implementation of AI. These challenges are in context to human resource (HR) development for successful implementation of AI across different functions and are discussed in this chapter.

Purpose: Although we know that AI technology is widely accepted in human resource management (HRM) due to its various benefits. But the organisations face many challenges during the implementation of AI. The focus of the study is to explore the literature on AI in HRM, identify the challenges of implementing AI and provide potential future research direction based on a systematic literature review.

Methodology: To explore the literature on AI in HRM, the study undertakes a systematic literature review. The study identifies, analyse and classifies the literature to provide a holistic view of HR challenges in implementing AI. The study is built on a review of 47 documents, including the articles, book chapters and conference papers using the Scopus database for the past 10 years (2012–27 January 2022).

Findings: The study provides an overview of the documents published in Scopus in this area through a systematic literature review. The study reveals that a significant amount of growth in the publication has been shown in the past 10 years. The maximum and continuous growth is shown after 2017. The maximum number of papers are published in India, the USA and China. The study identifies major eight challenges of AI implementation in HRM. The study also provides a secondary case to deep dive in this area based on a systematic literature review.

Research Limitation/Implication: The challenges identified in the study are not empirically tested. Each of the identified challenges should be empirically examined. This study has expanded the body of knowledge of AI in HRM. This study will help the academicians and practitioners work on the identified challenges and help the organisations ease in adopting AI.

Originality/Value: This study represents the first work that integrates AI implementation challenges in HRM.

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.

World legends and the scientific community have taken the devastating impact of poverty issue seriously which has been reflected in the growing trend of research in this area. Hence, this paper aims to conduct a bibliometric analysis on poverty alleviation literature, discuss the various dimensions of poverty alleviation and deliver some ideas for future research.

This study deploys a combined quali-quantitative method familiar as meta-literature review on 454 articles collected from the Web of Science (WoS) database with Social Science Citation Index (SSCI) coverage over the period 1971–2020. Using Rstudio, VOSviewer and Excel, the collected data have been analysed from different lenses.

This study considers the most contributing scientific actors like authors, journals, topics, institutions and countries as parameters for analysing articles. Based on the analysis from various perspectives, it determines five main research streams upon which it provides some potential research directions to be considered in future research.

This study solely relies on the articles available in the WoS database with index in SSCI. However, it excludes analysing thousands of articles on the same topic available in other platforms.

This study provides a retrospective on the scientific works and collective efforts of scholars germane to poverty alleviation from the highest ranked journals, which would help better understand the literature development and the intellectual structure of this field.

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 investigates the impact of institutional factors on financial deepening and its implications on bank credit in Africa.

The paper employs different panel econometric models to examine the heterogeneity of 50 African countries from 2000 to 2019. The estimators include panel corrected standard errors, system generalized method of moments, quantile and threshold regressions.

The results show that rule of law, regulatory quality, government effectiveness, voice and accountability, control of corruption and political stability significantly influence financial deepening in Africa. However, government effectiveness has a higher effect on middle- and high-income countries, while other indicators have a high impact on low-income countries. All institutional indicators have stronger effects, almost double, at higher financial depth levels than for countries with lower levels. Government effectiveness and regulatory quality impact financial deepening more for countries with strong institutions than weak ones. Thus, the relationship between institutional qualities and credit provided by banks is non-monotonic.

The findings suggest that strengthening appropriate institutional factors based on country heterogeneity may effectively stimulate debt financing in Africa, the primary source of financing for small and medium-sized enterprises and entrepreneurs.

The novelty of this paper is that previous studies did not sufficiently scrutinize the heterogeneity of the structure of African economies – i.e. differences in institution, credit and income levels.