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The shadow economy (SE) has been a serious issue with varied dimensions in all countries that significantly affect economic growth. Therefore, all countries have made an effort to tackle the SE by pursuing several measures. This study aims to investigate the impact of financial markets (stock and bond) in reducing the SE while considering the role of country risk (political, economic and financial) in N-11 countries.

The study employed first-generation methodological techniques, including a unit root test to identify stationarity in the series, a panel cointegration test and panel autoregressive distributive lag (ARDL) to estimate long-run and short-run relationships. Finally, the Granger causality is applied to determine the direction of the causal relationship.

The study explored that country risk factors are crucial in reducing the size of the SE. Moreover, the significant moderating role of country risk factors in the financial market development and SE nexus suggests that by controlling the country's risk, financial market development can negatively affect the SE.

Due to the availability of data, the study used data, ranging from 1995 to 2015, because the tax burden data is available from 1995 while the maximum data for the SE is available till 2015, using Medina and Schneider's (2019) data estimates for the SE.

The previous studies have focused explicitly on the role of financial institutions' development in the SE. To the best of the author's knowledge, no previous study is attempted to investigate the role of financial markets (bonds and stock) in the size of the SE. Furthermore, previous studies have ignored the important role of country risk factors in the size of the SE. This study investigates the impact of country risk on the SE and the moderating role of country risk in the development of financial markets and the SE nexus.

The literature argues that human activities are responsible for environmental pollution and world climate change. Africa is the second-largest continent by population and its socio-economic conditions are adversely affected by climate change due to environmental pollution. Therefore, this study investigates the relationship between human activities, the environment and the economic growth of 38 African countries from 2000 to 2018.

This study employs a simultaneous equations model called Seemingly unrelated regression.

Human capital development (HDI) and environmental health (EH) have a positive and non-linear relationship, while HDI and ecosystem vitality (EV) are negative and non-linear. The relationship between gross domestic product per capita (GDPPC) and EH is negative and non-linear. However, the GDPPC-EV link is insignificant. Trade openness (TO) and EV have a negative and non-linear relationship, but trade significantly improves EH. Urbanisation-EV relationship is positive and non-linear. However, urbanisation significantly reduces EH. Technology has a positive and non-linear relationship with EH and EV. The study also found that EH and EV positively affects the economy.

This is the first study to analyse the most concerned countries in-depth. Thus, it provides appropriate and sound policies that consider the unique characteristics of the nations. Moreover, it uses a robust estimation technique that overcomes the endogeneity problem and offers insight into the relationship between the variables, including the feedback effect of the environment on growth.

In today's global economy, developing supply chain agility (SCA) and lean practices (LP) as resource-based view and dynamic capabilities are essential for firms to sustain their competitive advantage (CA) and enhance their operational performance (OP). The purpose of this paper is to develop and empirically test a framework to investigate how CA is achieved through SCA and LP and how these, in turn, can enhance a firm's OP.

For data collection, the authors adopted the survey method using self-administered questionnaires. Two-source survey data were collected in two rounds (separated by a two-month lag time) from supply chain managers, operational managers and general managers. The purpose of collecting data in two rounds was to reduce common-method bias. Likert scale (1–5) was used in the questionnaire. Smart PLS 3 and SPSS 23 were used for the data analysis purpose.

SCA was found to directly and positively affect OP. LP also positively affected OP. In addition, CA fully mediated the relationship between SCA, LP and OP.

This study encourages the managers of manufacturing firms to adapt LP and their supply chains (SCs) to become agile and leverage the advantages of their implementation to improve their OP and succeed in the market.

This study is one of the first to investigate the effect of SCA and LP on OP. Furthermore, the first study examines CA's mediating impact on the relationship between SCA, LP and OP.

Implementing building information modeling (BIM) in construction projects offers many benefits. However, the use of BIM in project cost management is still limited. This study aims to review the current trends in the application of BIM in project cost management.

This study systematically reviews the literature on the application of BIM in project cost management. A total of 46 related articles were identified and analyzed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses method.

Eighteen approaches to applying BIM in project cost management were identified. The approaches can be grouped into cost control and cost estimation. Also, BIM can be applied independently or integrated with other techniques. The integrated approaches for cost control include integration with genetic algorithms, Monte Carlo simulation, lean construction, integrated project delivery, neural network and value engineering. On the contrary, integrated approaches for cost estimation include integration with cost-plus pricing, discrepancy analysis, construction progress curves, estimation standards, algorithms, declarative mappings, life cycle sustainability assessment, ontology, Web-based frameworks and structured query language.

To the best of the authors’ knowledge, this study is the first to systematically review prior literature on the application of BIM in project cost management. As a result, the study provides a comprehensive understanding of the current state of the art and fills the literature gap. Researchers and industry professionals can use the study findings to increase the benefits of implementing BIM in construction projects.

The purpose of this study is to investigate the impact of Corona Virus Disease 2019 (COVID-19) on the outcome of construction projects and explore the moderating effects of emerging technologies on the relationship between COVID-19 and construction project outcomes.

Data for the study was collected through a Web-based, semistructured questionnaire. The responses of 62 construction practitioners were analyzed using a hierarchical linear regression model. The model consists of 16 independent variables, three control variables (organization size, organization type and project size), one moderator (adoption level of emerging technologies) and three dependent variables (project time, project cost and project quality).

The study confirms the negative significant impact of the COVID-19 pandemic on the performance of construction projects. It also identifies the significant moderating effects of emerging technologies in mitigating the impact of COVID-19 on construction projects. Further, it shows a significant increase in the application of emerging technologies in construction projects during the COVID-19 pandemic. Based on the findings related to the moderating impact of the technology, this study provides a clear set of recommendations for construction firms, public sector and research community in combating the unavoidable situation similar to the COVID-19 pandemic in the future.

To the best of the authors’ knowledge, this is the first study to identify the moderating role of technology on the impact of COVID-19 on the performance of the construction sector in Pakistan. The findings can also be used for the construction sectors of other developing countries.

The purpose of this paper is to look at the asymmetric effect of oil production on environmental degradation in OPEC member countries from 1970–2019.

The authors build a nonlinear panel ARDL–PMG model using the Shin et al. (2014) nonlinear autoregressive distributed lag (ARDL) approach in panel form to assess both the short- and long-run impact of positive and negative oil production movements on CO₂ emissions.

The result demonstrates that the variables are cointegrated. According to the linear long run coefficients, oil production, FDI inflows and economic growth both have a positive and significant relationship with CO₂ emissions, implying that they deteriorate environmental quality in OPEC countries, while renewable energy has a negative relationship with CO₂, implying that increasing renewable energy improves environmental quality. The asymmetric findings prove that positive and negative shocks of oil production exert a positive effect on carbon emissions in short run and long run.

To begin with, the empirical assessments do not include all OPEC member nations; researchers are advised to resolve this constraint by looking at the economies of other OPEC members. Albeit the lack of data for other energy sources may serve as another constraint of this research, future research is expected to broaden the current framework via other energy sources such as nuclear, electricity, biomass, solar as well as wind.

The research adds to the body of knowledge as many of the prevailing studies in the literature failed to look at the asymmetric effect of oil production on the quality of environment. This is another gap in the literature that the current study is set out to fill. This study adds oil production as an explanatory variable and helps to extend the existing literature for OPEC countries, which could propose a solution to deal with ensuing environmental issues.

The unexpected spread of COVID-19 rapidly switched from a health crisis to an economic one. The Architectural, Engineering and Construction (AEC) industry experienced drastic impacts, especially in Africa. Several studies investigated COVID-19 impacts on the AEC industry, but very few were conducted in Africa. This study aims to cover this gap, address detailed overview of negative and positive impacts of COVID-19 on the AEC field, especially in the different African regions, and highlight their causes and the measures taken to overcome them.

The authors combined a Preferred Reporting Items for Systematic Reviews and Meta-Analyses-based Systematic Literature Review (SLR) and a survey involving 87 AEC companies operating in Africa. The SLR initially used four scientific databases; however, considering the limited Africa-related found data, institutional and governmental databases were also included.

Globally, implementing the mandated restrictive measures against COVID-19 caused significant losses for developers, designers and contractors but helped the information and communication technologies operators to thrive. In the five African regions, the AEC industry experienced 22 heavy impacts that can be split into four categories: financial, managerial/strategic, operational and opportunities. This paper thoughtfully explains the causes of COVID-19 impacts and presents the undertaken measures by the African private and public sectors to overcome them. Generally, the African AEC industry lost 51% of the total sales in 2020.

This paper contains all aspects related to health hazard influences on the AEC industry, especially in Africa. Researchers and decision-makers may use it to build new approaches or strategies related to risk management or design technological solutions.

The main purpose of this study is to evaluate the probability of default and examine the relationship between default risk and financial performance, with dynamic panel moderation of firm size.

This study utilizes a total of 1,500 firm-year observations from 2013 to 2018 using dynamic panel data approach of generalized method of moments to test the relationship between default risk and financial performance with the moderation effect of the firm size.

This study establishes the findings that default risk significantly impacts the financial performance. The relationship between distance-to-default (DD) and financial performance is positive, which means the relationship of the independent and dependent variable is inverse. Moreover, this study finds that the firm size is a significant positive moderator between DD and financial performance.

This study provides new and useful insight into the literature on the relationship between default risk and financial performance. The results of this study provide investors and businesses related to nonfinancial firms in the Pakistan Stock Exchange (PSX) with significant default risk's impact on performance. This study finds, on average, the default probability in KSE ALL indexed companies is 6.12%.

The evidence of the default risk and financial performance on samples of nonfinancial firms has been minimal; mainly, it has been limited to the banking sector. Moreover, the existing studies have only catered the direct effect of only. This study fills that gap and evaluates this relationship in nonfinancial firms. This study also helps in the evaluation of Merton model's performance in the nonfinancial firms.

The purpose of this research study is to extract and identify named entities from Hadith literature. Named entity recognition (NER) refers to the identification of the named entities in a computer readable text having an annotation of categorization tags for information extraction. NER is an active research area in information management and information retrieval systems. NER serves as a baseline for machines to understand the context of a given content and helps in knowledge extraction. Although NER is considered as a solved task in major languages such as English, in languages such as Urdu, NER is still a challenging task. Moreover, NER depends on the language and domain of study; thus, it is gaining the attention of researchers in different domains.

This paper proposes a knowledge extraction framework using finite-state transducers (FSTs) – KEFST – to extract the named entities. KEFST consists of five steps: content extraction, tokenization, part of speech tagging, multi-word detection and NER. An extensive empirical analysis using the data corpus of Urdu translation of Sahih Al-Bukhari, a widely known hadith book, reveals that the proposed method effectively recognizes the entities to obtain better results.

The significant performance in terms of f-measure, precision and recall validates that the proposed model outperforms the existing methods for NER in the relevant literature.

This research is novel in this regard that no previous work is proposed in the Urdu language to extract named entities using FSTs and no previous work is proposed for Urdu hadith data NER.