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This study aims to fill the gap in income-environment literature by adding agricultural contribution to the nexus. The authors investigate the short-run and long-run impact of agricultural contribution, renewable energy consumption, real income, trade liberalisation and urbanisation on carbon emissions for a balanced panel of five South Asian Association for Regional Cooperation (SAARC) countries spanning the period 1990-2013.

Pedroni and Kao cointegration techniques have been used to test the existence of long-run relationship between the variables. The directions of causal relationships have been verified using Granger causality tests. Further, the long-run parameters of the baseline equation have been estimated by using the fully modified ordinary least squares, the technique developed by Pedroni, (2001a) for heterogeneous cointegrated panels.

The result reveals that agricultural contribution and renewable energy consumption improve environmental quality in the long run, while urbanisation and per capita real income degrade it. The study did not find any evidence of “pollution heaven hypothesis” in the selected countries. The Granger causality tests confirm bidirectional causality between carbon emissions and income and between carbon emissions and urbanisation. In addition, there is unidirectional causality running from agricultural contribution to renewable energy consumption.

This is the only study to investigate the role of agriculture sector in carbon mitigation from a panel of South Asian economies. To the best of the authors’ knowledge, it is also the first study to test the applicability of “pollution heaven hypothesis” for SAARC countries.

The purpose of this paper is to investigate the long-run effect of financial sector development, energy use and economic growth on carbon emissions for Turkey, in presence of possible regime shifts over a period of 1960-2013.

Along with the conventional unit root tests, Zivot-Andrews unit root test with structural break has been employed to check the stationarity of variables. The cointegrating relationship between variables is investigated by using the autoregressive distributed lag bounds test and Hatemi-J threshold cointegration test.

The results confirm a cointegrating relationship between the variables. The long-run relationship between the variables has gone through two endogenous structural breaks in 1976 and 1986. Development of financial sector improves environmental quality whereas energy use and economic growth degrade it. The results challenge the validity of environmental Kuznets curve hypothesis in Turkish economy.

The study uses domestic credit to private sector as a proxy for development of financial sector. The model can be improved by constructing an index of financial development instead of using a single determinant as a proxy for financial development.

The study may pave the way for policy makers to capture important environmental pollutants in better way and develop effective and efficient energy and economic policies. This may make significant contribution to curbing CO₂ emissions while sustaining economic growth.

This is the only study to examine long-run impact of financial sector development on carbon emissions, using the threshold cointegration approach. Hence, the study is a gentle request to reduce the possible omitted variable econometric estimation bias and fill the gap in the existing literature.

The Covid-19 prediction process is more indispensable to handle the spread and death occurred rate because of Covid-19. However early and precise prediction of Covid-19 is more difficult because of different sizes and resolutions of input image. Thus these challenges and problems experienced by traditional Covid-19 detection methods are considered as major motivation to develop JHBO-based DNFN.

The major contribution of this research is to design an effectual Covid-19 detection model using devised JHBO-based DNFN. Here, the audio signal is considered as input for detecting Covid-19. The Gaussian filter is applied to input signal for removing the noises and then feature extraction is performed. The substantial features, like spectral roll-off, spectral bandwidth, Mel-frequency cepstral coefficients (MFCC), spectral flatness, zero crossing rate, spectral centroid, mean square energy and spectral contract are extracted for further processing. Finally, DNFN is applied for detecting Covid-19 and the deep leaning model is trained by designed JHBO algorithm. Accordingly, the developed JHBO method is newly designed by incorporating Honey Badger optimization Algorithm (HBA) and Jaya algorithm.

The performance of proposed hybrid optimization-based deep learning algorithm is estimated by means of two performance metrics, namely testing accuracy, sensitivity and specificity of 0.9176, 0.9218 and 0.9219.

The JHBO-based DNFN approach is developed for Covid-19 detection. The developed approach can be extended by including other hybrid optimization algorithms as well as other features can be extracted for further improving the detection performance.

The proposed Covid-19 detection method is useful in various applications, like medical and so on.

Developed JHBO-enabled DNFN for Covid-19 detection: An effective Covid-19 detection technique is introduced based on hybrid optimization–driven deep learning model. The DNFN is used for detecting Covid-19, which classifies the feature vector as Covid-19 or non-Covid-19. Moreover, the DNFN is trained by devised JHBO approach, which is introduced by combining HBA and Jaya algorithm.

The current study aims to map the scientific output of grey literature (GL) through bibliometric approaches.

The source for data extraction is a comprehensive “indexing and abstracting” database, “Web of Science” (WOS). A lexical title search was applied to get the corpus of the study – a total of 4,599 articles were extracted for data analysis and visualisation. Further, the data were analysed by using the data analytical tools, R-studio and VOSViewer.

The findings showed that the “publications” have substantially grown up during the timeline. The most productive phase (2018–2021) resulted in 47% of articles. The prominent sources were PLOS One and NeuroImage. The highest number of papers were contributed by Haddaway and Kumar. The most relevant countries were the USA and UK.

The study is useful for researchers interested in the GL research domain. The study helps to understand the evolution of the GL to provide research support further in this area.

The present study provides a new orientation to the scholarly output of the GL. The study is rigorous and all-inclusive based on analytical operations like the research networks, collaboration and visualisation. To the best of the authors' knowledge, this manuscript is original, and no similar works have been found with the research objectives included here.

Kashmir is widely recognised as a prominent tourist destination within the Himalayan region of India. The Valley is abundant with a diverse range of valuable tourism assets. In order to ensure the sustainable utilization of these tourism resources, the implementation of an appropriate tourism policy is necessary. The primary objective of this study is to analyse government policies pertaining to the expansion and advancement of tourism in the Kashmir region. Additionally, the study also seeks to evaluate the potential for tourism and the influx of tourists in Kashmir. The Government of India has developed a preliminary tourist policy, as indicated by the research findings. It also focuses on enhancing human resources and tourism infrastructure, ensuring the safety and security of tourists and promoting tourism education within the state. Furthermore, the government is diligently endeavouring to foster the growth of ecotourism and lesser known tourist locations through collaborative efforts with many relevant entities. This study utilises secondary data sources to analyse the economic implications of tourism in the region of Jammu and Kashmir. It aims to investigate several indicators of economic progress, including tourist arrivals, job creation, the state's gross domestic product (GDP), infrastructure development and regional advancement. In addition to the agricultural industry, the tourist sector has emerged as a prominent contributor to the economy, serving as a significant source of income and employment opportunities.

Drawing on the Job Demands-Resources (JD-R) theory, the primary goal of this study is to conceptualise and empirically validate a theoretical framework that explains the process by which digital job resources influence the innovative work behaviour of technological professionals. Specifically, this study aims to examine the impact of digital job resources, especially digital training, and digital communication, on employee digital engagement. Furthermore, it investigates the influence of digital engagement on digital leadership and the effect of digital leadership on innovative work behaviour. Lastly, the study examines whether digital engagement and digital leadership serially mediate the relationship between digital job resources and innovative work behaviour.

Data were collected from full-time technological professionals using multiple sampling techniques. A total of 307 samples were utilised for the final data analysis. Partial Least Squares Structural Equation Modelling (PLS-SEM), employing SmartPLS 4.0, was used to test the study hypotheses.

The findings of this study emphasize that digital engagement and digital leadership are pivotal in mediating the impact of digital communication on technological professionals' innovative work behaviour. Specifically, our results show that digital communication significantly shapes the digital engagement of these professionals. Digital engagement, in turn, positively influences digital leadership, which then fosters technological professionals’ innovative work behaviour. Notably, both digital engagement and digital leadership serve as mechanisms that link digital communication and innovative work behaviour. Contrary to our initial expectations, the study finds that digital training neither directly affects digital engagement nor has an indirect effect on innovative work behaviour.

The present study is distinct in offering a theoretical framework outlining the steps through which digital resources influence technological professionals' digital engagement, digital leadership capabilities, and their innovative work behaviour. Prior studies have predominantly focused on antecedents of innovative work behaviour, with an emphasis on individual characteristics and organisational environmental factors. There is limited research exploring how, or even if, digital job resources – such as digital training and digital communication – affect employees’ innovative work behaviour. Additionally, the examination of the interrelationship between digital engagement and digital leadership is notably lacking in existing literature. Much of the research has instead probed the converse relationship: how leadership styles impact employees' engagement. Lastly, this research is among the pioneering efforts to consider the serial mediating role of digital engagement and digital leadership between digital job resources and innovative work behaviour, a topic that remains underrepresented in academic discourse. This study addresses these gaps.

Quality 4.0 refers to a modern quality management approach that uses Industry 4.0 technologies, integration and digitalisation. This research explores the current understandings of Quality 4.0 in various publications. The focus is on Quality 4.0 concepts or explanations, available models, motivation and readiness factors for adoption, enablers and technologies that can be leveraged.

A qualitative approach was deployed to collect the findings. This paper employs bibliometric, scientometric and visual analytic tools to identify and analyse articles from Scopus, Web of Science (WOS), Google Scholar databases and other sources such as ScienceDirect and Taylor and Francis.

The bibliometric results revealed that Quality 4.0 publications began in 2016 and increased dramatically in 2020 and 2021, with India leading the way while scientometric analysis found no clear definition of Quality 4.0 hitherto. However, several authors have defined the concept of Quality 4.0, arguing that it is characterised by digitalisation and integration, Industry 4.0 technologies applications and big data management. Some of the Quality 4.0 models published in the theoretical underpinnings include total quality management (TQM) in the basis of Industry 4.0 model, the European Foundation for quality management model, Quality 4.0 model combining operational technology (OT) and information technology (IT) through digital transformation and the LSN Research eleven axes of Quality 4.0 model. The research highlights key enablers of Quality 4.0 adoption, such as enabling technologies, big data capability, skilled and competent workers, collaboration and leadership support.

The findings can benefit Quality 4.0 researchers and practitioners on the available Quality 4.0 models, motivation and readiness factors for Quality 4.0 adoption, enablers and leveraged technologies in Quality 4.0.

This study attempted to explore the current understandings of Quality 4.0 concepts to sediment these emerging quality management concepts for manufacturing industries.

The purpose of this paper is to determine whether the original and the revised versions of the existing prediction models are the best tools for assessing the going concern assumption of a firm in the creditor-oriented regime.

The analysis begins from estimating the classification accuracy of the original versions of the bankruptcy, going concern and liquidation prediction models. At the second step, the revised versions of the aforesaid existing prediction models are developed. At the third step, the accounting-based going concern prediction model is proposed by using multiple discriminant analysis for the creditor-oriented regime. The sample contains the financial ratios of manufacturing firms for the period 1997–2014.

The finding indicates that the five discriminatory variables, which belong to “income statement” and “statement of financial position,” of the proposed model are not only useful for evaluating the going concern assumption of a firm, but also give aid for evaluating the financial fraud risk of a firm as compared to the original and revised versions of the prediction models that are developed for the debtor-oriented regime.

The external validity of the proposed prediction model can be tested on the large data sets of the countries where the liquidation provisions are a part of their local corporate law.

The proposed accounting prediction model will be helpful for the internal and external auditors in order to determine the going concern assumption at planning, performing and evaluation stages.

The proposed accounting-based going concern prediction model is based on liquidated firms.

The complexity of projects has become a serious issue and obstacle in their successful completion. In order to overcome these complexities, it has become imperative to identify the relevant management competencies of project managers. The purpose of this paper is to address the problem of cost, time and scope in engineering infrastructure projects due to their complexities through management competencies.

In the first phase of the study, 32 experts were interviewed through semi-structured pre-tested questionnaire. In this phase, essential elements of complexities were identified initially. This was followed by finding required dimensions of competencies to counter these complexities and to acquire improved performance. In the final stage, required levels of competencies for specific elements of complexity were identified. In the second phase, 85 “project managers” were also approached to get feedback about their recently completed public sector engineering infrastructure projects in Pakistan.

The study identified additional dimensions, i.e. honesty, enthusiasm and dedication, in the case of competencies and adverse law and order situation, political instability, land issues, energy crisis and weak authorization of project managers in the case of complexities. Leadership, management skill, communication skill, effectiveness and result orientation were identified as top quality traits required. The study concluded that there is a significant impact of management competencies and complexities on project performance.

The study contributes to a better understanding of how to improve performance in complex engineering infrastructure projects through adopting management competencies. It also empirically illustrates the relations among project management competencies, complexities and project performance. Although the research is grounded on public sector infrastructure projects, its findings may also be helpful for practices in project management of other sectors.

This era is the time for upskilling and producing workforce that is capable of effectively dealing with the day-by-day increasing demand of the industry. As the world is changing, its needs are changing in the same way and at the same speed. The world has become more digitized now. Moreover, we have a dramatic shift from Education 1.0 to Education 4.0 these days. The world now is practicing the internet of things, cloud storage, cyber-physical system and artificial intelligence. The purpose of this study is to identify the factors that affect the level of motivation toward the integration and implication of Industrial Revolution (IR) 4.0 in the education system of Pakistan by considering the Government policies.

For analyzing these factors, 150 research articles were sought out, out of which 84 were chosen for reviewing purpose based on the authentication of and relevance to this study by considering the Pakistani context. All the research articles have been selected from reputed indexed journals from databases (Scopus and Web of Science).

The findings of this review suggest that many factors affect the motivation toward integration and implementation of IR 4.0. These factors mainly include human factors, intrinsic values and influencing factors. Moreover, there is no such framework that provides the base to Education 4.0 in Pakistan because the things exist but are not systematic. Educators are motivated, but there are no resources; policies are there, but there is no practice or implementation. Pakistan is lacking in the latest trends related to Education 4.0 and even has no experience because people are used to doing things manually, but technology is the need of this era. Furthermore, the findings of this study will be useful for developing a systematic plan or a framework of the integration and implication of IR 4.0 that ultimately gives rise to Education 4.0 in the education system of Pakistan.

To the authors’ knowledge, no other study has been conducted on this topic in Pakistan. Also, there is a very little work done on this topic anywhere else in the world. The world is still exploring the IR 4.0, and our topic is solemnly related to these resolutions. Thus, there is a very little amount of literature related to this study.