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Today, Big Data plays an imperative role in the creation, maintenance and loss of cyber assets of organisations. Research in connection to Big Data and cyber asset management is embryonic. Using evidence, the purpose of this paper is to argue that asset management in the context of Big Data is punctuated by a variety of vulnerabilities that can only be estimated when characteristics of such assets like being intangible are adequately accounted for.

Evidence for the study has been drawn from interviews of leaders of digital transformation projects in three organisations that are within the insurance industry, natural gas and oil, and manufacturing industries.

By examining the extant literature, the authors traced the type of influence that Big Data has over asset management within organisations. In a context defined by variability and volume of data, it is unlikely that the authors will be going back to restricting data flows. The focus now for asset managing organisations would be to improve semantic processors to deal with the vast array of data in variable formats.

Data used as evidence for the study are based on interviews, as well as desk research. The use of real-time data along with the use of quantitative analysis could lead to insights that have hitherto eluded the research community.

There is a serious dearth of the research in the context of innovative leadership in dealing with a threatened asset management space. Interpreting creative initiatives to deal with a variety of risks to data assets has clear value for a variety of audiences.

The purpose of this paper is to provide an integrated analysis base to facilitate computational neuroscience experiments, following a user-led approach to provide access to the integrated neuroscience data and to enable the analyses demanded by the biomedical research community.

The design and development of the N4U analysis base and related information services addresses the existing research and practical challenges by offering an integrated medical data analysis environment with the necessary building blocks for neuroscientists to optimally exploit neuroscience workflows, large image data sets and algorithms to conduct analyses.

The provision of an integrated e-science environment of computational neuroimaging can enhance the prospects, speed and utility of the data analysis process for neurodegenerative diseases.

The N4U analysis base enables conducting biomedical data analyses by indexing and interlinking the neuroimaging and clinical study data sets stored on the grid infrastructure, algorithms and scientific workflow definitions along with their associated provenance information.

This study aims to identify critical capabilities to address unforeseen and novel disruptions, such as those instigated by COVID-19, and explore their role as essential enablers of supply chain resilience and responsiveness, leading to improved performance.

The structural equation modeling technique was employed for analyzing the proposed associations using survey data from 206 manufacturers operating during the COVID-19 pandemic in a developing country, Pakistan.

Key findings show how improvisation and anticipation act distinctly yet jointly to facilitate supply chain resilience and responsiveness during the COVID-19 pandemic. Also, data analytics capability positively affects anticipation and improvisation, which mediate the effect of data analytics on supply chain resilience and responsiveness.

The findings contribute to the theoretical and empirical understanding of the existing literature, suggesting that a combination of improvisation, anticipation and data analytics capabilities is highly imperative for enhancing supply chain resilience and responsiveness in novel and unexpected disruptions.

This is the first study to examine the impact of data analytics on improvisation and anticipation and the latter as complementary capabilities to enhance supply chain resilience and responsiveness. The empirical investigation explores the interplay among data analytics, improvisation, and anticipation capabilities for enhancing supply chain resilience, responsiveness, and performance during the unforeseen and novel disruptions, such as brought to bear by the COVID-19 pandemic.

The purpose of this paper is to examine the effect of related party transactions (RPTs) and types of RPTs (complex, simple and loan) on earnings quality in four East Asian countries: Hong Kong, Malaysia, Singapore and Thailand.

RPTs and types of RPTs are measured using two approaches, magnitude and abnormal (magnitude change). Earnings quality is measured using proxies for accrual earnings management and identified as discretionary accruals (DAC) and performance matched discretional accruals (PMDAC).

The results suggest that firms in these countries experience poor earnings quality when they are engaged in RPT. The effect of RPT-simple on earnings quality is more severe than RPT-complex. However, the presence of higher investor protection and stricter enforcement of regulations in countries like Singapore and Hong Kong reduce the negative impact of RPTs on earnings quality.

The results support the argument that the presence of controlling shareholders in East Asia is likely to lead to engagement with RPTs, which will increase the likelihood of firms’ earnings manipulation via DAC. This study has two limitations. It only focuses on Hong Kong, Malaysia, Singapore and Thailand, and the results may not be generalizable to other countries. Second, this study only measures the magnitude and abnormal RPTs based on the disclosures available in annual reports.

This paper contributes to the literature by examining the effect of RPTs and types of RPTs on earnings quality in four selected East Asian countries. 

In this article, we consider the magnetohydrodynamic (MHD) nanofluid flow over a rotating stretchable disk through porous medium. For porous medium, Darcy’s relation is used. It also encompassed the impact of nanoparticles shape on MHD nanofluid flow and heat transfer. The effect of thermal radiation and Joule heating is also being considered.

Three categories of nanoparticles are taken into deliberation, i.e. copper, silver and titanium oxide. The nanofluid is made of pure water and various types of sphere- and lamina-shaped nanoparticles. By using appropriate similarity transformation, the governing partial differential equations are transformed to ordinary one. The coupled ordinary differential equations system is tackled numerically by bvp4c method.

The impact of various pertinent parameters, i.e. solid volume fraction, Hartman number, thermal radiations parameter, Reynolds number, Eckert number, porosity parameter and ratio parameter, on flow and Nusselt number with a fixed value of Prandtl number at 6.2 is discussed in details. The obtained results are presented in the concluding section. The lamina shape of nanoparticles in silver-water performed an excellent role on temperature distribution. The heat transfer rate of lamina shape in copper-water was found to be greater in the system of flow regime.

The authors have discussed the shape effect of nanoparticles on MHD nanofluid flow over a rotating stretchable disk through porous medium using three categories of nanoparticles, such as copper, silver and titanium oxide. To the best of the authors’ knowledge, this is the first study on mass and heat transfer nanofluid flow and no such study is yet published in literature. A detailed mathematical analysis has also to be carried out to prove the regularity of model. The authors believe that the numerical results are original and have not been copied from any other sources.

The food industry is directly related to the health of humans and society and also that little attention has been paid to the assessment of sustainable supply chain risk management in this area, this will be qualified as an important research area. This study aims to develop a framework for assessing the sustainable supply chain risk management in the realm of the food industry (confectionery and chocolate) with a case study of three generic companies denotes as A1–A3. The proposed risk management was evaluated in three aforementioned manufacturing companies, and these three companies were ranked by the Fuzzy-Weighted Aggregated Sum Product Assessment (F-WASPAS) method in EXCEL.

The evaluation was carried out using integrated multi-criteria decision-making methods Best-Worst method (BWM)-WASPAS. Via an extensive literature review in the area of sustainable supply chain, sustainable food supply chain and risks in this, 9 risk criteria and 59 sub-criteria of risk were identified. Using expert opinion in the food industry, 8 risk criteria and 39 risk sub-criteria were identified for final evaluation. The final weight of the main and sub-criteria was obtained using the F-BWM method via LINGO software. Risk management in the sustainable supply chain has the role of identifying, analyzing and providing solutions to control risks.

The following criteria in each group gained more weight: loss of credibility and brand, dangerous and unhealthy working environment, unproductive use of energy, human error, supplier quality, quality risk, product perishability and security. Among the criteria, the economic risks have the highest weight and among the alternatives, A3 has obtained first ranking.

Modeling of risk for the food supply chain is the unique contribution of this work.

The present research study aims to explore the impact of renewable energy (RE) on investors willing to invest. This current study also investigates the mediation role of perceived benefit (PB) and living creature’s development (LCD) among RE and investors willing to invest.

Pakistani per capita income level is low; usually, the population lives hand to mouth. Only 10% to 15% of the population has been saving and is willing to invest in different sectors. To meet the aim of this study, data were collected from 300 individuals with a 40% response rate investors, equity fund managers and Pakistani stock exchanges using a nonprobability convenient sampling approach. The partial least square structural equation modeling technique and Smart partial least squares 3.0 were used to determine the primary and medicating effects of the variables.

The analysis shows that RE and investor willing to invest strongly linked each other directly and indirectly. PB and LCD significantly partial mediate the connection among RE and investor willing to invest. Hence, the results suggest that RE has more sustainable development goals with using and accessing affordable green and reliable energy.

The present study narrows the research gap by examining the effect of RE on investor willing to invest via PB and LCD. Also, it provides essential information for effective energy policies contributed to the sustainable development goals and gives valuable suggestions for policymaker and government.

This study investigates the impact of the COVID-19 pandemic on investors' sentiments, behavioral biases and investment decisions in the Pakistan Stock Exchange (PSX).

The authors have assessed investors' behaviors and sentiments and the stock market overreaction during COVID-19 using a questionnaire and collected data from 401 investors trading in the PSX.

Results of structural equation modeling revealed that the COVID-19 pandemic affected investors' behaviors, investment decisions and trade volume. It created feelings of fear and uncertainty among market participants. Evidence suggests that behavioral heuristics and biases, including representative heuristic, anchoring heuristic, overconfidence bias and disposition effect, negatively influenced investors' decisions at the PSX.

This study will contribute to behavioral finance literature in the context of developing countries as it has revealed the impact of COVID-19 on the emerging stock market, and its results are generalizable to other emerging stock markets.

The findings of this study will help academicians, researchers and policymakers of developing countries. Academicians can formulate new behavioral models that can depict the solutions of dealing with an uncertain situation like COVID-19. Policymakers like the Securities Exchange Commission and the PSX can formulate crisis management strategies based on behavioral finance concepts to cope with situations like COVID-19 in the future and help lessen investors' losses in the stock markets. The role of the Securities Exchange Commission is crucial as it regulates the financial markets. It can arrange workshops to educate investors to manage their decisions during crisis time and focus on the best use of irrational and rational decision-making at the same time using Lo (2004) adaptive market hypothesis.

The novelty of the paper is that the authors have introduced overconfidence and disposition effect as mediators that create a connection between representative and anchoring heuristics and investment decisions using primary data collected from investors (institutional and retail) to demonstrate the presence of psychological biases during COVID-19, and it has been done for the first time according to authors' knowledge. It is a contribution and addition to the behavioral finance literature in the context of developing countries' stock markets and their efficiency.