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This study investigates theoretical relationships among personality characteristics, preferences for different types of rewards and the propensity to choose a job in auditing by management-related higher education students. Specifically, the authors consider motivation, locus of control (internal and external) and self-efficacy (SE) as personality characteristics and financial, extrinsic, support and intrinsic as types of rewards.

Data were collected through a questionnaire targeted at management-related higher education students in Portugal. Partial least squares structural equation modelling was used to analyse the data.

The full sample results show that different types of motivation, locus of control and SE are related to different reward preferences. The authors also find a positive association between a preference for extrinsic rewards and the propensity to choose a job in auditing. Moreover, when the authors consider the role of working experience in the model, the authors find that the reward preferences that drive the choice of an auditing job differ according to that experience.

This study enriches the literature by assessing preferences for different types of rewards, considering multiple personality characteristics and a comprehensive set of rewards. Furthermore, the authors identify the reward preferences that drive the choice of an auditing career. This knowledge empowers auditing firms to devise recruitment strategies that resonate with candidates’ preferences, which boosts the capacity of these companies to attract new auditors.

This paper aims to investigate the corrosion resistance of two types of coatings – one is ceria sol coating and the other is ceria sol coating modified by ZnO nanoparticles on 7075 aluminum alloy in 3.5% NaCl solution.

Aluminum alloys were dipped into ceria sol and ceria sol modified by ZnO nanoparticles separately and removed after 10 min from the solutions and dried at 110°C for 30 min and heated at 500 °C for 30 min to form the coatings. The coatings have been characterized by using field emission scanning electron microscopy (FE-SEM), electrochemical impedance spectroscopy (EIS), X-ray diffraction (XRD) and energy dispersive spectroscopy (EDS). The EIS tests were performed in a corrosive solution of 3.5% NaCl.

The results showed that the coating of ceria sol modified by ZnO nanoparticles has higher corrosion resistance than the ceria sol coating and the bare sample. Also, the best efficiency is related to aluminum sample immersion after 1 h in NaCl corrosive solution for coating modified by ZnO nanoparticles.

In this research, the modification of ceria sol coating by ZnO nanoparticles had an effect on improving the corrosion behavior of aluminum alloy. It is also understood that modification of coatings is an effective parameter on corrosion resistance.

While previous studies have highlighted the importance of incorporating environmental sustainability in building designs, there is a paucity of studies that assess the extent to which design teams in developing countries consider environmental sustainability at the building design stage. Therefore, using Zambia as a case study, this study examined the extent to which infrastructure design teams in a developing country consider environmental sustainability at the design stage.

The study used a qualitative research approach using structured interviews because there are hardly any studies which have explored the extent to which designers incorporate environmental sustainability in infrastructure designs in developing countries. The data is analysed thematically using the ATLAS.ti software.

The results show that environmental sustainability is not an important design consideration because it is secondary to functional, technical and aesthetic considerations. Environmental considerations are also made in an ad hoc manner and when it is cost-effective for the project. Regulatory requirements pertaining to environmental protection are adhered to without any cost considerations. It was, therefore, theorised that building design teams in developing countries make technical, functional and aesthetic consideration during the infrastructure design stage ahead of environmental considerations.

There is a paucity of studies that have investigated whether building infrastructure designers consider issues of environmental sustainability at the design stage in developing countries. The findings have practical implications on how developing countries can foster environmental sustainability at the design stage and avoid generating a building infrastructure stock that will require environmental resilience adaptation in the future.

This paper aims to investigate the ability of traditional biopolymers, such as funori or the nanoscale form of cellulose nanocrystals, to consolidate fragile paper and preserve it for as long as possible.

Degraded papers dating back two centuries were separated into paper samples for consolidation processes. Funori – a marine spleen – was used as a traditional consolidation material and a mixture with ZnO NPs compared with modern materials, such as cellulose nanocrystals. The samples were aged for 25 years, examinations and analyses were performed using scanning electron microscopy and color change was assessed using the CIELAB system, X-ray diffraction and Fourier-transform infrared spectroscopy.

According to the results, using traditional materials to consolidate damage, such as funori, after aging resulted in glossiness on the surface, a color change and increased water content and oxidation. Furthermore, samples treated with a mixture of ZnO NPs and funori revealed that the mixture improved the sample properties and increased the degree of crystallization. Cellulose nanocrystals improved the surface, filled gaps, formed bridges between the fibers and acted as a protector from aging effects.

This paper highlights the ability of nanomaterials to enhance the properties of materials as additives and treat the paper manuscripts from weaknesses.

This paper aims to apply several data mining techniques for predicting the daily precision improvement of Jakarta Islamic Index (JKII) prices based on big data of symmetric volatility in Indonesia’s Islamic stock market.

This research uses big data mining techniques to predict daily precision improvement of JKII prices by applying the AdaBoost, K-nearest neighbor, random forest and artificial neural networks. This research uses big data with symmetric volatility as inputs in the predicting model, whereas the closing prices of JKII were used as the target outputs of daily precision improvement. For choosing the optimal prediction performance according to the criteria of the lowest prediction errors, this research uses four metrics of mean absolute error, mean squared error, root mean squared error and R-squared.

The experimental results determine that the optimal technique for predicting the daily precision improvement of the JKII prices in Indonesia’s Islamic stock market is the AdaBoost technique, which generates the optimal predicting performance with the lowest prediction errors, and provides the optimum knowledge from the big data of symmetric volatility in Indonesia’s Islamic stock market. In addition, the random forest technique is also considered another robust technique in predicting the daily precision improvement of the JKII prices as it delivers closer values to the optimal performance of the AdaBoost technique.

This research is filling the literature gap of the absence of using big data mining techniques in the prediction process of Islamic stock markets by delivering new operational techniques for predicting the daily stock precision improvement. Also, it helps investors to manage the optimal portfolios and to decrease the risk of trading in global Islamic stock markets based on using big data mining of symmetric volatility.

This research is a pioneer in using big data mining of symmetric volatility in the prediction of an Islamic stock market index.

This study aims to investigate the daily performance of the proposed microgrid (MG) that comprises photovoltaic, wind turbines and is connected to the main grid. The load demand is a residential area that includes 20 houses.

The daily operational strategy of the proposed MG allows to vend and procure utterly between the main grid and MG. The smart metre of every consumer provides the supplier with the daily consumption pattern which is amended by demand side management (DSM). The daily operational cost (DOC) CO2 emission and other measures are utilized to evaluate the system performance. A grey wolf optimizer was employed to minimize DOC including the cost of procuring energy from the main grid, the emission cost and the revenue of sold energy to the main grid.

The obtained results of winter and summer days revealed that DSM significantly improved the system performance from the economic and environmental perspectives. With DSM, DOC on winter day was −26.93 ($/kWh) and on summer day, DOC was 10.59 ($/kWh). While without considering DSM, DOC on winter day was −25.42 ($/kWh) and on summer day DOC was 14.95 ($/kWh).

As opposed to previous research that predominantly addressed the long-term operation, the value of the proposed research is to investigate the short-term operation (24-hour) of MG that copes with vital contingencies associated with selling and procuring energy with the main grid considering the environmental cost. Outstandingly, the proposed research engaged the consumers by smart meters to apply demand-sideDSM, while the previous studies largely focused on supply side management.

The increasing globalization and technological advancements have increased the information spillover on stock markets from various variables. However, there is a dearth of a comprehensive review of how stock market volatility is influenced by macro and firm-level factors. Therefore, this study aims to fill this gap by systematically reviewing the major factors impacting stock market volatility.

This study uses a combination of bibliometric and systematic literature review techniques. A data set of 54 articles published in quality journals from the Australian Business Deans Council (ABDC) list is gathered from the Scopus database. This data set is used to determine the leading contributors and contributions. The content analysis of these articles sheds light on the factors influencing market volatility and the potential research directions in this subject area.

The findings show that researchers in this sector are becoming more interested in studying the association of stock markets with “cryptocurrencies” and “bitcoin” during “COVID-19.” The outcomes of this study indicate that most studies found oil prices, policy uncertainty and investor sentiments have a significant impact on market volatility. However, there were mixed results on the impact of institutional flows and algorithmic trading on stock volatility, and a consensus cannot be established. This study also identifies the gaps and paves the way for future research in this subject area.

This paper fills the gap in the existing literature by comprehensively reviewing the articles on major factors impacting stock market volatility highlighting the theoretical relationship and empirical results.

This study examined how economic freedom and its related components, such as open markets, regulatory efficiency, rule of law and the size of government, affect bank risk behavior, focusing on the Japanese context.

The study employs a two-step GMM framework on the annual data of Japanese banks ranging from 2005 to 2020 to empirically test the hypotheses. Furthermore, we also use the ordinary least square method to ensure the robustness of our mainline findings.

The finding suggests that economic freedom increases the banks' risk-taking, thus making them fragile. The results also highlight that out of the four main subcomponents of economic freedom, regulatory efficiency and government size increase bank risk-taking, while the rule of law and open markets decrease banks' risk-taking. Additionally, we examine how the banks' specific characteristics affect the results by creating a subsample based on capitalization and liquidity ratios. Overall, the results are consistent with the baseline findings. Moreover, the results are robust to alternative proxy measures of risk.

The study's findings have several implications for regulators and policymakers. The results suggest that regulators and policymakers should reconsider their strategies for economic freedom to ensure that they promote stability in the banking system and reduce banks' risk-taking inclinations.

Although previous studies have examined the impact of economic freedom on bank stability and risk-taking, this study is the first to do so in the Japanese context, contributing to the literature by providing new insights and empirical evidence.

We conducted a systematic review to explore the potential for the application of blockchain technologies for supply chain resilience in a small-scale agri-food business context.

As part of the research methodology, scientific databases such as Web of Science, Google Scholar and Scopus were used to find relevant articles for this review.

The systematic review of articles (n = 57) found that the use of blockchain technology in the small-scale agri-food business sector can reduce the risk of food fraud by assuring the provenance of food products.

Only a few papers were directly from a small-scale agribusiness context. Key challenges that limit the implementation of blockchain and other distributed ledger technologies include concerns over the disclosure of proprietary information and trade secrets, incomplete or inaccurate information, economic and technical difficulties, low levels of trust in the technology, risk of human error and poor governance of process-related issues.

The application of blockchain technology ensures that the risks and costs associated with non-compliance, product recalls and product loss are reduced. Improved communication and information sharing can increase resilience and better support provenance claims and traceability. Better customer relationships can be built, increasing supply chain efficiency and resilience.

This paper extends the authors’ previous research work investigating resilience for municipal infrastructure from an asset management perspective. Therefore, this paper aims to formulate a pavement resilience index while incorporating asset management and the associated resilience indicators from the authors’ previous research work.

This paper introduces a set of holistic-based key indicators that reflect municipal infrastructure resiliency. Thenceforth, the indicators were integrated using the weighted sum mean method to form the proposed resilience index. Resilience indicators weights were determined using principal components analysis (PCA) via IBM SPSS®. The developed framework for the PCA was built based on an optimization model output to generate the required weights for the desired resilience index. The output optimization data were adjusted using the standardization method before performing PCA.

This paper offers a mathematical approach to generating a resilience index for municipal infrastructure. The statistical tests conducted throughout the study showed a high significance level. Therefore, using PCA was proper for the resilience indicators data. The proposed framework is beneficial for asset management experts, where introducing the proposed index will provide ease of use to decision-makers regarding pavement network maintenance planning.

The resilience indicators used need to be updated beyond what is mentioned in this paper to include asset redundancy and structural asset capacity. Using clustering as a validation tool is an excellent opportunity for other researchers to examine the resilience index for each pavement corridor individually pertaining to the resulting clusters.

This paper provides a unique example of integrating resilience and asset management concepts and serves as a vital step toward a comprehensive integration approach between the two concepts. The used PCA framework offers dynamic resilience indicators weights and, therefore, a dynamic resilience index. Resiliency is a dynamic feature for infrastructure systems. It differs during their life cycle with the change in maintenance and rehabilitation plans, systems retrofit and the occurring disruptive events throughout their life cycle. Therefore, the PCA technique was the preferred method used where it is data-based oriented and eliminates the subjectivity while driving indicators weights.