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Road traffic emissions are generally believed to contribute immensely to air pollution, but the effect of road traffic data sets on air quality (AQ) predictions has not been fully investigated. This paper aims to investigate the effects traffic data set have on the performance of machine learning (ML) predictive models in AQ prediction.

To achieve this, the authors have set up an experiment with the control data set having only the AQ data set and meteorological (Met) data set, while the experimental data set is made up of the AQ data set, Met data set and traffic data set. Several ML models (such as extra trees regressor, eXtreme gradient boosting regressor, random forest regressor, K-neighbors regressor and two others) were trained, tested and compared on these individual combinations of data sets to predict the volume of PM_2.5, PM₁₀, NO₂ and O₃ in the atmosphere at various times of the day.

The result obtained showed that various ML algorithms react differently to the traffic data set despite generally contributing to the performance improvement of all the ML algorithms considered in this study by at least 20% and an error reduction of at least 18.97%.

This research is limited in terms of the study area, and the result cannot be generalized outside of the UK as some of the inherent conditions may not be similar elsewhere. Additionally, only the ML algorithms commonly used in literature are considered in this research, therefore, leaving out a few other ML algorithms.

This study reinforces the belief that the traffic data set has a significant effect on improving the performance of air pollution ML prediction models. Hence, there is an indication that ML algorithms behave differently when trained with a form of traffic data set in the development of an AQ prediction model. This implies that developers and researchers in AQ prediction need to identify the ML algorithms that behave in their best interest before implementation.

The result of this study will enable researchers to focus more on algorithms of benefit when using traffic data sets in AQ prediction.

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.

The aim of this paper is to analyze the status of independence of the judiciary in Bangladesh. It is recognized worldwide that an independent judiciary is the sin qua non of democracy and good governance. However, without separation of the judiciary from other organs of the state absolute independence of judiciary is not possible. An attempt has been made in this paper to sketch the brief historical background of judicial system in Bangladesh through analyzing the meaning and basic principles of judicial independence and to what extent these principles exists in Bangladesh. How did the judiciary finally separate from the executive? After separation of the judiciary, what is the status of executive interference over judiciary in Bangladesh has also been evaluated in this paper.

The study is qualitative in nature and based on secondary sources of materials like books, journal articles, government rules, newspaper reports, etc. Relevant literature has also been collected through Internet browsing.

In this study, it has been found that from time immemorial the judicial system of Bangladesh was not completely independent from the interference of the executive branch of the government. It has also been found that from the beginning of the British colonial rule, the question of separation of the judiciary from the executive had been a continuing debate. Presently, even after separation of the judiciary, the interference of the executive over the judiciary is still continuing.

This paper opens a new window for the policy makers and concerned authorities to take necessary steps for overcoming the existing limitations of the status of judicial dependence in Bangladesh.

The paper will be of interest to legal practitioners, policy makers, members of civil society, and those in the field of judicial system in Bangladesh and some other British colonial common law countries.

In this study, the authors explore the effects of politically controlled boards on bank loan performance in both state-owned commercial banks (SCBs) and private sector commercial banks (PCBs) in Bangladesh.

The data consist of 409 bank-year observations from 46 sample SCBs and PCBs of Bangladesh for the period 2008–17. The authors apply ordinary least squares pooled regression with year fixed effect for baseline econometric analyses and generalized method of moments regression for robustness tests after addressing the endogeneity issue.

The regression results reveal that the presence of bank “boards controlled by politically affiliated directors” (PA) have significant positive effects on non-performing loans (NPLs). Similarly, the presence of “boards controlled by politically affiliated directors without substantial ownership interests” (PAWOI) show positive association with NPLs. In contrast, the presence of “boards controlled by politically affiliated directors with substantial ownership interests” (PAOI) exhibit an inverse relationship with NPLs. These findings support ‘agency conflict’ arguments and document that both PA and PAWOI are detrimental to bank loan performance in Bangladesh, while PAOI do not have significant effect on increasing NPLs.

This study contributes to the existing bank governance literature by providing evidence from an emerging economy perspective, where politically affiliated directors (PADs) exploit their positions for personal and/or political gain at the cost of other stakeholders by taking advantage of relaxed regulatory oversights and investor protections.

The aim of this chapter is to examine the role of real exchange rates in the relationship between tourist arrival and economic growth in Malaysia over the period of 2000–2018. We disaggregate Malaysian tourists into six geographical regions, namely Asia, Singapore, Europe, Pacific region, Americas, and Africa. Using a non-linear autoregressive distributed lag model, we find that the appreciation of real exchange rates with positive growth of economy plays a prominent role in influencing international tourist arrivals from Singapore, other Asian countries, Pacific region, Europe, and Americas. Our study suggests that real appreciation is important in providing some insights into the effectiveness of growth-led-tourism policies. In line with this, some implications are provided at the end of this chapter.

The purpose of this study is to provide a comprehensive analysis on the economic relationship between trade openness and economic growth and to identify current developments, potential research area and future directions. The emphasis is on the identification of annual growth of publications, country-wise distribution, publication pattern, intellectual structure and cluster analysis of scientific production in this field.

This study used evaluative techniques, text mining approach and performance analysis to identify possible patterns and correlation and to measure the impact of authors/citations/scientific production. Further, this study used the bibliometric mapping to represent the structural features of scientific production. This study emphasized on identification of the research hotspots based on occurrence of indexed keywords, productive researchers and journals during 2000–2022. Further, cluster analysis is performed using VOS viewer to analyze the current dynamics and future direction of the association between trade openness and economic growth (Eck and Waltman, 2011). Also, co-citation analysis is used in this study to identify the relations among authors or journals or documents using citation data, whereas the bibliographic coupling/mapping is intended to analyze the citing documents. Similarly, co-word analysis is used to study the article keywords that are mainly used to assess the conceptual structure of a concerning subject.

Economic growth is a function of trade openness, and it is important to analyze the relationship between trade openness and economic growth. Trade openness tends to become more liberalized over time, to contribute more to economic growth. Empirical evidence suggested that there exists a strong association between trade openness and economic growth. Further, keyword timeline analysis illustrated that the linkage between trade openness and economic growth is current area of interest among researchers. As per bibliometric analysis, China, Pakistan and Malaysia are the three most prolific countries in the terms of published articles on this theme. However, the most influential publications based on h-index and citation on trade openness–economic growth relationship is produced by Turkey. Based on cluster analysis, this study suggests that researchers are currently working on trade openness–economic growth relationship with other variables such as FDI, financial development, labor force, environment degradation and carbon emission, while in future, researchers could work on variables such as technology and sustainable development.

There are some limitations of this study. The first limitation is the authors have used Scopus database, leaving the possibility for future research to use Web of Science, Google Scholar or other similar sources. The second limitation is that the authors have used search terms “trade openness “and “economic growth,” although research could be performed using synonyms or even relevant terms in other languages.

Cluster analysis suggested that researchers are currently working on trade openness–economic growth relationship with other variables such as FDI, financial development, labor force, environment degradation and carbon emission, while in future, researchers could work on variables such as technology and sustainable development. Therefore, this study identified the potential research area in this research domain.

To confirm the originality of this study, to the best of the authors’ knowledge, this is the first study to combine bibliometric analysis and cluster analysis on trade openness–economic growth relationship. This study makes a comparison with phenomena/processes/events in contemporary economic and social reality in the field of trade openness and economic growth relationship.

The purpose of this study is to explore the factors influencing the happiness of customers of two global coffee food chains through qualitative research in the post pandemic era. Unlike existing studies, this study will compare and examine the differential points between two global coffee food chains so that others can improve their strategies to improve their competitiveness.

It is qualitative research employing sentiment analysis through “Sprinkler Software” to assess the sentiment of customers of Starbucks and Barista followed by focus interviews through the same customers who have visited both Starbucks and Barista.

The results showed that most important factors which motivate customers and make them happy to go for “Starbucks” or “Barista” are ambience, store location, quality of product offerings and service quality. Value for money, quality of products and service quality are the top three variables affecting the customers and have rated Starbucks better than Barista on these parameters. The happiness level of the same customers who have visited both the coffee chains is more with Starbucks compared to Barista.

This research contributes to better understanding the effects of different marketing strategies adopted by coffee chain stores and can provide direction to Barista and other coffee chains. The stimulus-organism-response (SOR) model in coffee chain store application is an additional contribution to existing knowledge.

• Most important factors which motivate customers and make them happy to go for “Starbucks” or “Barista” are ambience, store location, quality of product offerings and service quality.

• Value for money, quality of products and service quality are the top three variables affecting the customers and have rated Starbucks better than Barista on these parameters.

• The happiness level of the same customers who have visited both the coffee chains are more with Starbuck compared to that of Barista.

• This is significant and can give direction to Barista and other coffee chains through learning from this research.

• Using the extended SOR model, we explain the variation in response in the happiness level of customers of two coffee chains.

• In order to give an insight into the strategies adopted by Starbucks and Barista in emerging markets, a comparison of the happiness levels of clients of both coffee chains is presented.

• This original research can help coffee chains improve their return on investment.

• The SOR model in coffee chain store application is an additional contribution to existing knowledge.

Most important factors which motivate customers and make them happy to go for “Starbucks” or “Barista” are ambience, store location, quality of product offerings and service quality.

Value for money, quality of products and service quality are the top three variables affecting the customers and have rated Starbucks better than Barista on these parameters.

The happiness level of the same customers who have visited both the coffee chains are more with Starbuck compared to that of Barista.

This is significant and can give direction to Barista and other coffee chains through learning from this research.

Using the extended SOR model, we explain the variation in response in the happiness level of customers of two coffee chains.

In order to give an insight into the strategies adopted by Starbucks and Barista in emerging markets, a comparison of the happiness levels of clients of both coffee chains is presented.

This original research can help coffee chains improve their return on investment.

The SOR model in coffee chain store application is an additional contribution to existing knowledge.

With a decrease in consumer spending during the coronavirus disease 2019 (COVID-19) pandemic, many retailers are offering price reductions to stimulate demand. However, little is known about how consumers perceive such price reductions executed during turbulent times. The authors examine whether the timing of price reductions and individual differences impact consumers' evaluations of the retailers offering such reductions.

Using a longitudinal design, the authors inquire into four retailers' motives that consumers may infer from a price decrease at two different times during the COVID-19 crisis.

The authors find that the timing of price reductions plays a key role in shaping consumers' inference of retailers' motives. The authors also uncover individual characteristics that affect consumers' inferences.

This research advances the literature by demonstrating the critical role of timing and individual characteristics in consumers' perceptions of price reductions during times of crisis. The authors findings also provide retailers with actionable insights for their pricing strategies. The findings may be generalizable to other types of crises that may arise in the future.

Pakistan’s natural endowment of labour and land is suitable for labour-intensive agriculture and manufacturing sector. This study aims to investigate international trade competitiveness of Pakistan in 14 major industries of agriculture and manufacturing sector, accounting more than 85 per cent of total export receipts.

The competitiveness of Pakistan in selected industries of agriculture and manufacturing sectors from 2003 to 2014 is investigated using the revealed comparative advantage (RCA) index, introduced by Balassa (1965) on HS data collected from the United Nations Commodity Trade database. The obtained indices in this study are then subjected to panel regression analysis to investigate the effect of domestic productivity growth and real exchange rate on international trade competitiveness of major industries.

The results show that the agriculture sector of Pakistan has higher comparative advantage in raw cotton, cereals, raw leather and fruits. The raw cotton shows the highest competitiveness of 54.46 which is followed by cereals (17.13), leather (9.83) and fruits (1.97). The RCA of the manufacturing sector shows that textile (54.85), carpets (10.72), sports goods (2.18) and beverages (1.47) have higher competitiveness. The RCA, in relatively capital-intensive industries, shows a high disadvantage. The trend analysis shows distorted competitiveness in labour-intensive, textile, carpet and footwear industries. The results of panel regression analysis show that the domestic productivity growth and real exchange rate depreciation have a significant positive impact on the international competitiveness of selected industries. The study urges Pakistan to make its macroeconomic environment investment-friendly and encourage investment in deteriorating labour-intensive industries.

Globalisation has significantly increased international competition, and Pakistan is losing its competitiveness in labour-intensive industries owing to lack of domestic value addition and development efforts. The major problem with the productivity of these industries is the lack of proper infrastructure, acute energy crisis, lack of domestic and foreign investment and overvaluation of real exchange rate. The domestic investors are shifting their capital either to other domestic sectors and/or other investment-friendly countries. Policymakers in Pakistan should address the problems of these important labour-intensive industries. The government needs to understand macroeconomic uncertainties and make investment-friendly policies to encourage domestic and foreign investment. The future studies should perform in-depth research to identify both microeconomic and macroeconomic variables responsible for deterioration in competitiveness of major labour-intensive industries in the agriculture and manufacturing sectors of Pakistan.

This study is a comprehensive examination into the nature and pattern of international competitiveness of Pakistan in 14 important industries of the agriculture and manufacturing sector which has seldom been investigated empirically. The obtained indices in this study are also subjected to panel regression analysis to explore the effect of domestic productivity growth and real exchange rate depreciation on the international competitiveness of Pakistan.