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Purpose – The purpose of this paper is to verify whether an evolutionary model outperforms logistic regression in determining the institutional placement decisions made by a London social service department panel. Design/methodology/approach – Genetic chromodynamics models an algorithm within the Michigan evolutionary classifier. Hence multiple classification rules evolve simultaneously. The dataset as described by Xie et al. is used. Two‐thirds of randomly selected cases are for training and one third for testing. Indicator weights are set between 0 and 1. Findings – Of 275 placements, 40 per cent represent residential homes, 48 per cent nursing homes, 12 per cent nursing long‐stay and two hospital long‐stay. In ten runs, 89.18 per cent were correctly placed (range 81.6 to 97.7 per cent); 5.07 per cent wrongly placed (range 1.2 to 8.0 per cent) and 5.75 per cent unplaced (range 0.0 to 11.5 per cent). Changing the 0.99 weights to 0.90 and 0.80 placed 87.6 and 87.9 per cent correctly. Research limitations/implications – Data came from written records. Errors in transcription and placement could not be checked. Other facts, or the weights, may be influencing placement decisions. Practical implications – Xie et al. matched 78 per cent of 195 placements. The evolutionary model outperformed logistic regression both in placements evaluated (275/195) and accuracy (89/78 per cent). Therefore, it could be used as a first line management information tool, revealing whether guidelines are followed. Originality/value – The authors have developed and tested a computational model, which could be used to evaluate institutional placement decisions in the UK “market”. Further development and exploitation would facilitate greater understanding of the needs old people and the resources necessary for their appropriate management.

The purpose of this study is to present the academic pattern of project-based studies on the Lean Six Sigma (LSS) approach in a comparative manner in the context of Turkey.

Focusing on the context of Turkey, a detailed search was conducted at the National Thesis Center of Turkey, and on Google Scholar between November 28 and December 15, 2021, using the keywords “Lean Six Sigma” and “Lean 6 Sigma” in both Turkish and English. As a result of this search, no conference proceedings on the subject were found. However, 25 postgraduate theses and 19 articles were accessed. It was determined that ten of the 19 articles were produced from these theses. It was observed that nine of the 25 postgraduate theses contained conceptual analyses. At this point, it was concluded that the project-based theses conducted in the context of Turkey provided the necessary representation on the subject. Therefore, the 16 postgraduate theses that had been accessed constituted the sample of the study. These 16 theses were subjected to a comprehensive evaluation with the systematic analysis design, with descriptive content analysis being used as the data analysis tool of the research. In this context, the theses were examined using parameters such as type, number, year, university, institute, department, discipline, original language and title of advisor. Other parameters used were purpose, industry, duration, team, results, tools and techniques of the LSS projects mentioned in the theses.

It was determined that the number of postgraduate theses on LSS was low in number, with only one study having been performed at the doctoral level, and most studies having been produced in 2019. In addition, it was observed that the subject had been studied by different universities, institutes, disciplines and departments with the LSS spreading to different academic disciplines. Most of the relevant theses were written in Turkish, and it has been determined that these theses were mostly carried out under the supervision of academicians with the title of assistant professor. It was also found that the purposes of the theses, the tools used throughout the process and the results obtained were per the LSS spirit. On the other hand, it was determined that the duration of the project and the composition of the team members would vary due to the diversity of features that make the internal dynamics of the projects clear.

The research revealed the academic pattern of the project-based studies on the LSS in the Turkish context. The use of systematic review design and descriptive content analysis as data analysis tools in the research is an important initiative. While the uniqueness of the studies carried out on such a practical subject cannot be discussed, the holistic approach of these studies increases the contribution value of the research. At this point, this study is considered to be especially valuable for an understanding of LSS research in country-specific conditions. In this aspect, it is an aim of the aforementioned research to become a resource for researchers and practitioners interested in LSS, with this research providing a basis for making contextual comparisons in the future.

The purpose of this study is to examine the influence of urbanization on energy consumption, including economic growth, globalization and “foreign direct investment (FDI)” inflow as control variables.

This study uses yearly panel data from 19071 to 2018 on five selected South Asian economies. It applies the “pooled mean group (PMG)” estimator and the “Dumitrescu-Hurlin (D-H)” panel causality test.

The PMG estimators reveal that urbanization causes energy consumption negatively in the long run because of an unusual and messy urbanization process. At the same time, it has no impact on the latter in the short run. Per capita income has both long- and short-run positive influences on energy use. Globalization causes energy consumption positively in the long run but does not affect it in the short run. FDI inflow has a strong positive impact on energy use in the long run and adverse effects in the short run. The Dumitrescu–Hurlin causality test reveals feedback relationships between “urbanization and energy consumption,” “globalization and energy consumption” and one-way causation from “per capita income to energy consumption.” It validates the findings of the PMG estimators.

The results of this study indicate that South Asia may focus on enhancing the availability of energy in the region and producing more renewable energy to add to its energy portfolio to meet growing energy demand, particularly among urban dwellers. Moreover, they should raise their real per capita incomes and augment the standard of living of low-income city dwellers to make urbanization more serviceable and comfortable.

This study is original. As far as the author is aware, this is a maiden attempt to investigate urbanization's effects on energy usage in South Asia in the preview of globalization and FDI.

The purpose of this examine the impact of income inequality and shadow economy on environmental degradation given the growing income inequality, shadow economy and ecological degradation in developing countries. Thus, this study is motivated to offer empirical insight into how income inequality and shadow economy influence the environment in African countries.

Data from 29 countries in Africa between 2000 and 2017 were used, while the novel method of moments quantile regression of Machado and Silva (2019) and Dumitrescu and Hurlin (D-H) (2012) granger causality is used as the estimation techniques.

The results established the presence of cross-sectional dependence and slope heterogeneity in the panel, while Westerlund panel cointegration confirmed the long-run cointegration among the variables. The results from the quantile regression suggest that income inequality increases environmental degradation from the 5th to the 30th quantiles, while from the 70th quantiles, income inequality reduces ecological degradation. The shadow economy negatively influences environmental degradation across the quantiles, strengthening environmental quality. Per capita income (economic growth) and financial development positively impact environmental degradation throughout the quantiles. However, urbanization reduces environmental degradation from 60th to 95th quantiles. The D-H causality established a two-way relationship between income inequality and environmental degradation, while one-way from shadow economy, per capita income and urbanization to environmental degradation were established.

This study provides fresh insights into the nexus between shadow economy and environmental quality in the presence of higher levels of income inequality for the case of African region. The study applies quantile analysis via moment proposed by Machado and Silva (2019). This technique shows that the impact of income inequality and shadow economy on environmental degradation is heterogeneous across the quantiles of ecological footprints in Africa.

While sustainable growth extends the use of resources, it is crucial to explore green growth (GG) that ensures growth sustainability through the adoption of renewable energy. Thus, this study is motivated to investigate the influence of renewable energy on GG in 19 emerging countries spanning a decade and a half (2000–2020). This study aims to provide a quantitative examination of how renewable energy contributes to sustainable economic growth.

This study uses advanced dynamic common correlated effect techniques to assess the long-term effectiveness of renewable energy on GG. Additionally, it uses Dumitrescu and Hurlin causality tests to identify synchronicity between the respective variables.

The findings of this study reveal that the adoption and utilisation of renewable energy effectively promote GG in emerging economies. However, in contrast, the significantly greater negative influence of trade openness on GG compared to renewable energy highlights the inadequacy and limited impact of cleaner energy alone.

To the best of the authors’ knowledge, existing literature predominantly focuses on investigating the relationship between renewable energy and economic growth, with only a limited number of studies exploring the impact on GG. To the best of the authors’ knowledge, this study would be the first to analyse this relationship in these emerging countries. Furthermore, previous estimation frameworks used in prior studies often overlook the crucial factor of cross-sectional dependence (CSD) among countries. Therefore, this study addresses this issue using a contemporary econometric approach that deals not only with CSD but other biases, like endogeneity, autocorrelation, small sample bias, etc.

This study examines whether international tourism demand in the Visegrád countries is influenced by countries' risk rating on environmental, social and governance (ESG) factors, as non-economic factors relating to ESG risks have been ignored by previous researches on determinants of international tourism demand.

The study investigates panel data for the Visegrád countries comprising the Czech Republic, Hungary, Poland and Slovakia over the period 1995–2019. Recently developed techniques of augmented mean group (AMG) and common correlated effects mean group (CCEMG) estimators are employed so as to take care of cross-sectional dependence, nonstationary residuals and possible heterogeneous slope coefficients.

The regression estimates suggest that besides economic factors, the perception of international tourists regarding ESG risk is another important determinant of international tourism demand in the Visegrád countries. The study also established that income levels in the tourists' originating countries are the most critical determinant of international tourism demand to the Visegrád countries.

The research outcomes of the study include the need for the Visegrád countries to direct policies towards further mitigating their ESG risks in order to improve future international tourism demand in the area. They also need to ensure exchange rate stability to prevent volatility and sudden spikes in the relative price of tourism in their countries.

SMEs that manage more sustainability focused initiatives into their core business strategy can possibly benefit from lower expenses, reduced threats and new business opportunities, but in Malaysia most of the SMEs are still far off in terms of adapting to a sustainable business growth outline from a holistic point of view. Hence, this study aims to serve deeper understanding about a strategic innovation focused sustainable growth model on basis of multidisciplinary QBL-QHIM theoretical perspectives where strategic innovation practices intervene in between desired growth and government support (regulations).

This conceptual investigation embraces a newly emerged concern of sustainable business growth in SMEs, considering ranges of literature reviews from the disciplines of management and entrepreneurship. The study also systematically explores the concepts of regulations, strategic innovation practices on basis of QBL and QHIM theories to adjust the sustainable business growth model from a holistic angle.

The outcomes show that multidisciplinary QBL-QHIM conjunction within the interrelationships of the selected constructs holds the potential for innovation focused business growth in a more sustainable, comprehensive and holistic manner. The study also detects that exploitative-explorative innovation practices can possibly mediate in between suitable regulations and sustainable business growth of SMEs, considering supportive external environment.

Researches are encouraged to test the proposed model.

The study indicates a conceptual configuration for policy makers as well as entrepreneurs to ensure sustainable business growth for SMEs. The outcomes of the study also provide useful direction on decision-making process of owner or manager considering social, economic, environmental and spiritual aspects of daily operations.

The conceptual model may possibly able to generate more social values, considering a holistic angle into business activities.

The conceptualization is a unique attempt, considering developing regions to extend the current understanding of strategic innovation focused sustainable growth process of SMEs from a holistic angle.

This chapter investigates the presence of a difference in the systemic risk level between Islamic and conventional banks in Bangladesh. The authors compare systemic resilience of three types of banks: fully fledged Islamic banks, purely conventional banks (CB), and CB with Islamic windows. The authors use the market-based systemic risk measures of marginal expected shortfall and systemic risk to identify which type is more vulnerable to a systemic event. The authors also use ΔCoVaR to identify which type contributes more to a systemic event. Using a sample of observations on 27 publicly traded banks operating over the 2005–2014 period, the authors find that CB is the least resilient sector to a systemic event, and is the one that has the highest contribution to systemic risk during crisis times.

The purpose of this paper is to develop new constitutive models to predict the soil deformation moduli using multi expression programming (MEP). The soil deformation parameters formulated are secant (Es) and reloading (Er) moduli.

MEP is a new branch of classical genetic programming. The models obtained using this method are developed upon a series of plate load tests conducted on different soil types. The best models are selected after developing and controlling several models with different combinations of the influencing parameters. The validation of the models is verified using several statistical criteria. For more verification, sensitivity and parametric analyses are carried out.

The results indicate that the proposed models give precise estimations of the soil deformation moduli. The Es prediction model provides considerably better results than the model developed for Er. The Es formulation outperforms several empirical models found in the literature. The validation phases confirm the efficiency of the models for their general application to the soil moduli estimation. In general, the derived models are suitable for fine‐grained soils.

These equations may be used by designers to check the general validity of the laboratory and field test results or to control the solutions developed by more in‐depth deterministic analyses.

The purpose of this paper is to measure the systemic importance of industry in the world economic system under the system-wide event – the crisis of 2008-2009, by viewing this system as a weighted directed network of interconnected industries.

First, the authors investigate this crisis at three different levels based on network-related indicators: the “macro” global level, the “meso” country level, and the “micro” industry level. This investigation not only provides evidence for the systemic influence, that is, systemic risk, of the crisis, but also reveals the contagion mechanism of the crisis, which supports the stress testing. Second, the authors use a network-related business intelligence algorithm, the combined hyperlink-induced topic search (HITS) algorithm, to measure the contribution of a given individual industry to the overall risk of the economic system or, in other words, the systemic importance of the individual industry.

The HITS algorithm considers both the market information and the interconnectedness of the industries. Based on the stress testing, the performance of the combined HITS is compared with the purely market-based systemic risk measurement. The results show that the combined HITS outperforms the baseline in finding the top N systemically important industries.

The combined HITS algorithm provides a novel network-based perspective of systemic risk measurement.

Measuring the systemic importance based on the combined HITS algorithm can help managers and regulators design effective risk management policies. In this respect, the work initiates a research direction of studying the systemic risk in a business system based on a network-related business intelligence algorithm because the business system can be viewed as an interconnected network.