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The study aims to develop a multilayer high-effective ensemble of ensembles predictive model (stacking ensemble) using several hyperparameter optimized ensemble machine learning (ML) methods (bagging and boosting ensembles) trained with high-volume data points retrieved from Internet of Things (IoT) emission sensors, time-corresponding meteorology and traffic data.

For a start, the study experimented big data hypothesis theory by developing sample ensemble predictive models on different data sample sizes and compared their results. Second, it developed a standalone model and several bagging and boosting ensemble models and compared their results. Finally, it used the best performing bagging and boosting predictive models as input estimators to develop a novel multilayer high-effective stacking ensemble predictive model.

Results proved data size to be one of the main determinants to ensemble ML predictive power. Second, it proved that, as compared to using a single algorithm, the cumulative result from ensemble ML algorithms is usually always better in terms of predicted accuracy. Finally, it proved stacking ensemble to be a better model for predicting PM_2.5 concentration level than bagging and boosting ensemble models.

A limitation of this study is the trade-off between performance of this novel model and the computational time required to train it. Whether this gap can be closed remains an open research question. As a result, future research should attempt to close this gap. Also, future studies can integrate this novel model to a personal air quality messaging system to inform public of pollution levels and improve public access to air quality forecast.

The outcome of this study will aid the public to proactively identify highly polluted areas thus potentially reducing pollution-associated/ triggered COVID-19 (and other lung diseases) deaths/ complications/ transmission by encouraging avoidance behavior and support informed decision to lock down by government bodies when integrated into an air pollution monitoring system

This study fills a gap in literature by providing a justification for selecting appropriate ensemble ML algorithms for PM_2.5 concentration level predictive modeling. Second, it contributes to the big data hypothesis theory, which suggests that data size is one of the most important factors of ML predictive capability. Third, it supports the premise that when using ensemble ML algorithms, the cumulative output is usually always better in terms of predicted accuracy than using a single algorithm. Finally developing a novel multilayer high-performant hyperparameter optimized ensemble of ensembles predictive model that can accurately predict PM_2.5 concentration levels with improved model interpretability and enhanced generalizability, as well as the provision of a novel databank of historic pollution data from IoT emission sensors that can be purchased for research, consultancy and policymaking.

This paper seeks to assess the performance levels of BA-GS-LSSVM compared to popular standalone algorithms used to build NO₂ prediction models. The purpose of this paper is to pre-process a relatively large data of NO₂ from Internet of Thing (IoT) sensors with time-corresponding weather and traffic data and to use the data to develop NO₂ prediction models using BA-GS-LSSVM and popular standalone algorithms to allow for a fair comparison.

This research installed and used data from 14 IoT emission sensors to develop machine learning predictive models for NO₂ pollution concentration. The authors used big data analytics infrastructure to retrieve the large volume of data collected in tens of seconds for over 5 months. Weather data from the UK meteorology department and traffic data from the department for transport were collected and merged for the corresponding time and location where the pollution sensors exist.

The results show that the hybrid BA-GS-LSSVM outperforms all other standalone machine learning predictive Model for NO₂ pollution.

This paper's hybrid model provides a basis for giving an informed decision on the NO₂ pollutant avoidance system.

This research installed and used data from 14 IoT emission sensors to develop machine learning predictive models for NO₂ pollution concentration.

The study aims to analyse the role of digitisation in accounting in enhancing the overall effectiveness of accounting functions. To achieve this, the study provides empirical evidence from the stakeholder’s perspective of digitisation of accounting, auditing, reporting and regulatory compliance procedures.

The study has applied a quantitative approach to identify the thoughts of auditors, accountants and academicians on the impact of digitalised accounting applications on accounting functions. The data was collected by administering an empirical study and a sample of 482 professionals from the accounting, auditing and academic sectors. To analyse and interpret data descriptive statistics, structured equation modelling and mediation analysis has been used.

The finding of the study signifies the relevance of digitalised accounting applications in accounting functions and reveals that there is a significant impact of digitalisation on accounting, auditing, reporting and regulatory compliance aspects of accounting functions. The outcome of the study explores that a digitalised accounting system reduces possible errors and improves the accuracy and transparency of the system.

The study highlighted the importance of developing new methods and techniques that can be used in practice. This indirectly advocates the inclusion of such concepts in accounting curricula to emphasise the need to understand the challenges and opportunities created by digitisation. Furthermore, the study will become a motivation to scholars who intend to explore different areas through which new technologies can be adopted to transform traditional accounting systems.

The contributions of the current study have implications that the adoption of digitised accounting enhances economic efficiency through a reduction in accounting costs, and enhanced accuracy that leads to the elimination of penalties and litigations for non-compliance with regulatory authorities. This indirectly impacts positively on the financial health of the business organisations and economies at large. This implication becomes greater evidential support to the organisations which are yet to plan the adoption and implementation of digital tools in their organisation for accounting functions.

Digitalisation is a relevant part of the accounting function to improve efficiency and accuracy. Since accounting and auditing practitioners struggle to control the accuracy and efficiency of transactions. Furthermore, the outcome of the study assists organisations in gaining real-time access to financial data, transforms workflows and empowers management to make timely informed sound decisions, optimise resource allocation, efficient regulatory compliance and so on.

This study examines the need for an accounting curriculum to evolve in response to the changing business landscape. Specifically, this study aims to explore the relationship between various market forces, the shifting dynamics of business, and the importance of adapting accounting education to these changes.

This study is a combination of both quantitative and qualitative research. A structured questionnaire was administered to 320 professionals to gather data, and the study employed descriptive analysis, one-sample t-tests, and structured equation modeling to analyze the relationship between the variables.

The findings reveal a strong correlation between business transformation and the imperative to adapt accounting education. This study emphasizes the significance of modifying accounting curricula to align with the current market trends. Furthermore, this study addresses the pressing concern of sustainability and the triple bottom line (TBL), advocating specialized education in sustainability accounting programs.

Despite its contributions, this study acknowledges a limitation in its focus solely on the perceptions of professionals and academicians regarding the impact of business transformation on accounting education, without directly examining the prevailing accounting education system. Future research should address this limitation by undertaking a qualitative exploration of the actual accounting education landscape and market requirements.

The implications of the study span the theoretical, regulatory, environmental, and social domains, stressing the need for educational institutions, regulatory bodies, and industry to collaborate in shaping competent and future-ready accounting professionals. A systematic approach would validate and extend the findings of this study, providing deeper insights into the transformative processes necessary to enhance accounting education in response to evolving business landscapes and environmental dynamics.

The outcome of the study assists educational institutions and regulatory bodies in framing policies to adapt accounting education to the evolving business landscape by updating the accounting curriculum as per the changes in the market forces to make graduates relevant and competent.

This paper aims to explore the value that care from a primary mental health care nurse (PMHCN) can bring to people with mental health (MH) problems in health-care delivery.

This study used a focus group of 20 PMHCNs working in primary care (PC) health settings in Saudi Arabia before using thematic content analysis to describe and explore their views and experiences of the integration of MH care into PC units. The researchers used a semi-structured interview format, which began with open-ended probes and proceeded to use of theoretical sampling based on emerging data related to their experiences and perceptions in the integration process.

Four main themes were derived from the thematic analysis of interviews: collaboration, knowledge and experience, awareness and orientation care and influential role.

This study’s use of qualitative research methods has certain limitations, including the small sample size of 20 participants, which means that it may not be representative of all primary MH nurses in primary health-care centers in Saudi Arabia. To make the results applicable to a broader audience, the researcher sought to moderate this limitation by including participants with extensive experience in multiple settings and nurses of different ages.

The cost implications of the PMHCN service are not yet comprehensively understood, but it is apparent that this model is not only regarded extremely positively by MH practitioners but may also have significant benefits in terms of patient outcomes. The configuration of local services and relevant patient demographics will affect the extent to which this study’s findings are transferable. Meanwhile, further research in this area could seek to provide further evidence about the aspects of the PMHCN service model, such as secondary care referrals and waiting times, accident and emergency attendances and patient recovery rates and the impact of providing such a service on health-care practitioners as well as its cost-effectiveness.

This study’s findings suggest that PMHCNs believe that their care improves the quality of PC for patients in PC settings. Elements of the PC placement that professionals particularly valued include their ability to assist patients in their own community and the inclusion of volunteer stakeholders who act as preceptors. The participants expressed a need for improvement of policy related to referrals within the clinics.

This research aims to explore the significance of cooperative efforts in promoting financial participation to enhance the socio-economic empowerment of the rural Muslims.

The primary study with a structured questionnaire has been conducted taking a sample of 398 rural Muslim respondents from various rural regions of south India through proportionate stratified sampling techniques. Regression analysis, paired sample t-test and structural equation modelling (SEM) through statistical package for social sciences (SPSS) 26 & SPSS analysis of moment structures (AMOS) 23 software have been implemented to test the relationship.

The research outcome demonstrated a remarkable difference in the rural Muslim’s socio-economic conditions before and after availing the loans from cooperatives. Consequently, an extension of cooperative efforts widens the scope of financial participation which again has positively enhanced rural Muslim’s socio-economic empowerment.

This study will help various policymakers, academicians and communities to take necessary action for the upliftment of a particular community. The research further adds on to the existing research on the need and importance of cooperative efforts as an alternative finance for marginalised community in developing and emerging countries.

The result of this study is only confined to south India, posing a limitation for the study. Apart from the geographical restriction, the study solemnly covers the rural Muslim community extracting other sections of the society. Hence, for more generalisable pictures of the current results, further research is recommended from other stakeholders’ perspectives.

This paper aims to focus on the relationship between female leadership and the environmental, social and governance (ESG) performance of firms. Specifically, the study examines if firms with women as chief executive officers (CEOs) and/or board chairpersons have higher environmental and social scores.

The study uses data on publicly listed Nordic firms in a panel regression approach to establish the relationship between female leadership and the environmental and social performance of firms.

The result of this study shows that women have a leadership characteristic that increases the weighted average of environmental (E) and social (S) performance of a firm. In particular, pillar score results indicate a positive relationship between female CEOs and the social scores of a firm but no relationship between a female board chairperson and the environmental or social scores of a firm. This implies that gender-based differences affect the CEO’s success, especially in a firm’s social performance. Further analyses show a more significant impact on the E and S performance when a woman replaces a man as CEO of a firm.

While prior research has explored various aspects of gender diversity in corporate leadership and its potential impact, the focus on the Nordic context in this study provides a unique perspective, given the region’s distinct business environment and societal factors. In addition, by examining the collective influence of female leaders and both female CEOs and board chairpersons separately, this study provides a nuanced understanding of how different leadership roles may impact a firm’s ESG performance.

This study aims to address the reliability and construct validity of the Individual Work Performance Questionnaire (IWPQ) in a Vietnamese context. Using the IWPQ as a measurement tool, this research also examined whether demographic features (such as gender, education level, work experience and position in a company) influenced employees’ work performance.

The Vietnamese IWPQ was validated via a two-step process of factor analysis, including an exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). The data collected were randomly divided into two subsamples to independently serve the EFA (n = 341) and CFA (n = 342). ANOVAs and t-tests were also used to examine the differences in individual work performance (IWP) among different demographic categories.

The results of the current study confirmed the applicability of the three-dimensional IWPQ in a Vietnamese context. In addition, they also indicated several demographic features that impacted employees’ patterns of responses to IWPQ dimensions.

This study focused on the construct validity of the IWPQ, without taking the content, face or criterion validity into consideration. Thus, future research should be conducted to yield a more comprehensive validation of the instrument, to measure the relationship between human resource development (HRD) practices and employee performance and to examine the relationship between strategic HRD and IWP and firm outcomes. In addition, the validated Vietnamese version of the IWPQ may inspire comparative studies on individual performance within and between units in an organization and among organizations and industries.

HRD practitioners can now use the validated IWPQ in the Vietnamese language to assess fluctuations in and analyze current staff performance, thereby facilitating human resource management and development. This study also offers recommendations for business leaders and HRD practitioners striving to implement strategic HRD aimed at reducing disparities in gender and between educational qualifications and job assignments at workplace, with the overarching goals of enhancing staff performance. These recommendations prove instrumental in improving staff performance, strengthening organizational efficiency and ultimately tackling the issue of low productivity in Vietnam and neighboring countries.

This study findings underscore the significance of embracing strategic HRD while taking into account individual, organizational and contextual factors that influence IWP. This approach serves to bridge current gaps related to IWP, including Vietnam’s comparatively lower productivity compared to neighboring nations, educational qualifications and role allocations within the workplace, as well as the prevailing work standards and strategic objectives.

To the best of the authors’ knowledge, this rigorously validated Vietnamese version of the IWPQ is the first of its kind in Vietnam, making a significant contribution to inclusivity initiatives and offering substantial evidence to affirm the IWPQ’s relevance across diverse contexts.

Drawing insights from the “Substitutes for Leadership” Theory, this study intends to examine the moderating role of resilience between servant leadership (SL) and employee engagement (EE). Particularly, relying on the issue that there are certain barriers, which hamper the effective execution of SL in the workplace, this study proposes to check that whether or not, resilience can intensify the effect of SL on EE.

A cross-sectional study was conducted on 241 employees working in the Indian Banking and Insurance (B&I) sector using the SPSS (v20), and the AMOS software (v21).

The study’s results revealed that SL is significantly related to EE. Also, this relationship is stronger for the employees who experience a high level of resilience, thus denoting that resilience works as a booster for SL.

This study has certain limitations like being cross-sectional in nature and covering only B&I sector employees.

Since resilience helps in intensifying the effect of SL on EE, the organizational HR managers should devote efforts to make their workforce resilient.

This paper is the first of its kind which empirically investigated the intensifying role of resilience for SL style.

Using a combination of the geographical information system (GIS) and the Canadian water quality index (WQI), the current study sought to provide a long-term general assessment of the water quality of the Shatt Al-Arab River (SAAR), focusing on its suitability for living organisms. Likewise, SPSS statistics was used to develop a nonlinear WQI regression model for the study area.

The study required four decades of data collection on some environmental characteristics of river water. After that, calculate the WQI and conduct the spatial analysis. Eight variables in total, including water temperature, dissolved oxygen, potential hydrogen ions, electrical conductivity (EC), biological oxygen demand, turbidity, nitrate and phosphate, were chosen to calculate the WQI.

Throughout the study periods, the WQI values varied from 55.2 to 79.83, falling into the categories of four (marginal) and three (fair), with the sixth period (2007–2008) showing the most decline. The present research demonstrated that the high concentration of phosphates, the high EC values, and minor changes in the other environmental factors are the major causes of the decline in water quality. The variations in ecological variables' overlap are a senior contributor to changes in water quality in general. Notably, using GIS in conjunction with the WQI has shown to be very effective in reducing the time and effort spent on investigating water quality while obtaining precise findings and information at the lowest possible expense. Calibration and validation of the developed model showed that this model had a perfect estimate of the WQI value. Due to its flexibility and impartiality, this study recommends using the proposed model to estimate and predict the WQI in the study area.

Even though the water quality of the SAAR has been the subject of numerous studies, this is the only long-term investigation that has been done to evaluate and predict its water quality.