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The current movement toward digitisation has promoted the adoption of smart building technology globally. Despite its advantages, its usage in developing countries such as Nigeria is still very low. Therefore, the purpose of this paper is to investigate construction professionals' awareness of smart building concepts (SBCs) in the Nigerian construction industry and identify the parameters by which SBCs can be measured.

A quantitative survey was carried out using a questionnaire to gather relevant data in the study area. This paper was conducted on 363 registered construction professionals in the Nigerian construction industry. The collected data were analysed using descriptive statistics and Kruskal–Wallis H test analysis.

This paper indicated that the majority of Nigerian construction professionals are aware of SBCs. Furthermore, the Kruskal–Wallis H test shows no significant difference between the awareness level of the various construction professionals. This paper further revealed energy management systems, IT network connectivity, safety and security management systems and building automation systems as the most significant parameters in which SBCs can be measured.

This paper identified significant parameters influencing SBCs awareness in the Nigerian construction industry. These parameters can be integrated into the building during the design stage and can be incorporated into the policymaking process of construction firms to promote the awareness of SBCs and encourage practices related to construction sustainability.

This paper provides empirical evidence on the awareness of SBCs among construction professionals and significant parameters influencing awareness in the Nigerian construction industry.

The purpose of this study is to establish a synthetic group decision framework based on the Pythagorean fuzzy (PF) set to select the optimal medicine cold chain logistics provider (MCCLP). Fierce market competition makes enterprises must constantly improve every link in the process of enterprise sustainable development. The evaluation of MCCLP in pharmaceutical enterprises is an important link to enhance the comprehensive competitiveness. Because of the fuzziness of expert cognition and the complexity of the decision procedure, PF set can effectively handle the uncertainty and ambiguity in the process of multi-criteria group decision decision-making (MCGDM).

This paper develops an integrated group decision framework through combining the Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique and combined compromise solution (CoCoSo) approach to select a satisfactory MCCLP within PF circumstances. First, the PF set is used to process the ambiguity and uncertainty of the cognition ability of experts. Second, a novel PF knowledge measure is propounded to measure the vagueness of the PF set. Third, a comprehensive criterion weight determination technique is developed through aggregating subjective weights attained utilizing the PF DEMATEL approach and objective weight deduced by knowledge measure method. Furthermore, an integrated MCGDM approach based on synthetic weight and CoCoSo method is constructed.

The outcomes of sensibility analysis and comparison investigation show that the suggested decision framework can help decision experts to choose a satisfactory MCCLP scientifically and reasonably. Accordingly, the propounded comprehensive decision framework can be recommended to enterprises and organizations to assess the MCCLP for their improvement of core competitiveness.

MCCLP selection is not only momentous for pharmaceutical enterprises to improve transportation quality and ensure medicine safety but also provides a strong guarantee for enterprises to improve their core competitiveness. Nevertheless, enterprises face certain challenges due to the uncertainty of the assessment environment as well as human cognition in the process of choosing a satisfactory MCCLP. PF set possesses a formidable capability to address the uncertainty and imprecision information in the process of MCGDM. Therefore, pharmaceutical enterprises can implement the proposed method to evaluate the suppliers to further improve the comprehensive profit of enterprises.

The objective of this paper is to examine crop yield predictions and their implications on MPCI in Ghana. Farmers in developing countries struggle with their ability to deal with agricultural risks. Providing aid for farmers and their households remains instrumental in combatting poverty in Africa. Several studies have shown that correctly understanding and implementing risk management strategies will help in the poverty alleviation agenda.

This study examines the importance of crop yield distributions in Ghana and its implication on multiperil crop insurance (MPCI) rating using the Lasso regression model. A Bonferroni test was employed to test the independence of crop yields across the regions while the Kruskal-Wallis H test was conducted to examine statistical differences in mean yields of crops across the ten regions. The Bayesian information criteria and k-fold cross-validation methods are used to select an appropriate Lasso regression model for the prediction of crop yields. The study focuses on the variability of the threshold yields across regions based on the chosen model.

It is revealed that threshold yields differ significantly across the regions in the country. This implies that the payment of claims will not be evenly distributed across the regions, and hence regional disparities need to be considered when pricing MPCI products. In other words, policymakers may choose to assign respective weights across regions based on their threshold yields.

The primary limitation is the unavailability of regional climate data which could have helped in a better explanation of the variation across the regions.

This is the first study to examine the implications of regional crop yield variations on multiperil crop insurance rating in Ghana.

Whether environmental, social and governance (ESG) factors are a curse or a blessing in the run for performance is still a burning issue. This is all the more true for banks, as their call for action in ESG dimensions clashes with evidence of scandals. As a more aligned to reality view, we propose to regard the mistreatment of ESG issues, both theoretically and empirically, as an undesirable output of banks' everyday activity. Empirically, we question whether 128 leading banks worldwide neglected the minimisation of ESG controversies (ESGC) in pursuing traditional productive efficiency, over the timespan 2011–2021.

To our end, we use oriented distance functions according to the nonparametric efficiency approach of data envelopment analysis (DEA). This framework accounts for undesirable outputs.

Being inefficient in the ESGC domain is not a necessary evil to achieve productive efficiency. Instead, incurring in higher ESGC negatively affects productive efficiency, by causing future decrease of reputation and performance.

We propose a new paradigm of banks’ activity and related efficiency evaluation. In so doing, we favour a real dimension of banks’ engagement in ESG concerns.

Academic scholars have tested students’ entrepreneurial intention (SEI) through the Theory of Planned Behavior (TPB) and Social Cognitive Theory (SCT). Still, the link between entrepreneurial intention and career is missing in previous studies. An extensive literature review developed the rationale that existing theories in the entrepreneurial discipline have limitations in linking entrepreneurial intention with career. This research is conducted to develop a comprehensive model for the relationship between entrepreneurial university support, student entrepreneurial intention, behavior and career. Stimulus-Organism-Behavior-Consequence (SOBC) paradigm from organizational behavior research is borrowed to entrepreneurship literature.

The cross-sectional data was collected from Pakistani university students enrolled in business incubators. A sample of 100 responses was tested with a partial least square–structural equation modelling approach.

The study established that by the underpinning of SOBC, entrepreneurial university support influences students’ entrepreneurial intention. It is also found that the students’ entrepreneurial intention strongly influences their entrepreneurial behavior, leading them to entrepreneurship careers.

The policies influencing students’ entrepreneurial intention and behavior can be developed using the SOBC paradigm. Higher education institutions can improve students’ entrepreneurial intentions and behavior to lead them to entrepreneurship careers.

This research introduced the SOBC paradigm in entrepreneurial intention and behavior literature. SOBC underpinning explored a new dimension of entrepreneurial intention and behavior literature.

This study aims to develop a novel design method to make personalized masks for the effective prevention of pandemic respiratory infectious disease.

The changes in facial shape during speaking were analyzed using a three-dimensional (3D) scanning technique. In total, 13 anthropometric items were measured, and mask patterns were generated using a parametric pattern design method. Three sizing methods were proposed to reflect not only static but also dynamic body dimensions on the mask patterns.

A significant increase or decrease was observed in 10 out of 13 measurement items. Based on this, four items were selected to be used in the mask pattern design. The nose and cheek areas of a mask were fixed to protect the respiratory tract against viruses. The lower jaw area was deformed to improve the fit.

This study is expected to provide fundamental data to understand the changes in facial shape during movement. In addition, it is expected that the development of individualized personal protective equipment with movement adaptability will facilitate an effective response to various pandemic respiratory diseases.

In order to develop a personal protective equipment (PPE) that has a good fit and can protect against pandemic respiratory infectious diseases, morphological analysis was attempted using 3D facial data. It would be possible to design various products and equipment to be worn on the face by using the method proposed in this study.

Economic evaluations often utilise individual-patient data (IPD) to calculate probabilities of events based on observed proportions. However, this approach is limited when interest is in the likelihood of extreme biomarker values that vary by observable characteristics such as blood glucose in gestational diabetes mellitus (GDM). Here, instead of directly calculating probabilities using the IPD, we utilised flexible parametric models that estimate the full conditional distribution, capturing the non-normal characteristics of biomarkers and enabling the derivation of tail probabilities for specific populations. In the case study, we used data from the Born in Bradford study (N = 10,353) to model two non-normally distributed GDM biomarkers (2-hours post-load and fasting glucose). First, we applied fully parametric maximum likelihood to estimate alternative flexible models and information criteria for model selection. We then integrated the chosen distributions in a probabilistic decision model that estimates the cost-effective diagnostic thresholds and the expected costs and quality-adjusted life years (QALYs) of the alternative strategies (‘Testing and Treating’, ‘Treat all’, ‘Do Nothing’). The model adopts the ‘payer’ perspective and expresses results in net monetary benefits (NMB). The log-logistic and Singh-Maddala distributions offered the optimal fit for the 2-hours post-load and fasting glucose biomarkers, respectively. At £13,000 per QALY, maximum NMB with ‘Test and Treat’ (−£330) was achieved for a diagnostic threshold of fasting glucose >6.6 mmol/L, 2-hours post-load glucose >9 mmol/L, identifying 2.9% of women as GDM positive. The case study demonstrated that fully parametric approaches can be implemented in healthcare modelling when interest lies in extreme biomarker values.

The purpose of this paper is to investigate and review the impact of the use of statistical quality control (SQC) development and analytical and numerical methods on average run length for econometric applications.

This study used several academic databases to survey and analyze the literature on SQC tools, their characteristics and applications. The surveys covered both parametric and nonparametric SQC.

This survey paper reviews the literature both control charts and methodology to evaluate an average run length (ARL) which the SQC charts can be applied to any data. Because of the nonparametric control chart is an alternative effective to standard control charts. The mixed nonparametric control chart can overcome the assumption of normality and independence. In addition, there are several analytical and numerical methods for determining the ARL, those of methods; Markov Chain, Martingales, Numerical Integral Equation and Explicit formulas which use less time consuming but accuracy. New ideas of mixed parametric and nonparametric control charts are effective alternatives for econometric applications.

In terms of mixed nonparametric control charts, this can be applied to all data which no limitation in using of the proposed control chart. In particular, the data consist of volatility and fluctuation usually occurred in econometric solutions. Furthermore, to find the ARL as a performance measure, an explicit formula for the ARL of time series data can be derived using the integral equation and its accuracy can be verified using the numerical integral equation.

The aim of the paper is to examine the status and determinants of financial inclusion in India by using the recent micro-level survey data.

The authors construct a multidimensional financial inclusion index to measure the status of financial inclusion in the selected 17 states of India. Subsequently, the authors use the probit model estimation to examine the determinants of all financial inclusion indicators.

The authors find that southern and north-eastern states perform better in the overall financial inclusion index. In contrast, states like Odisha, Madhya Pradesh, Chhattisgarh, Rajasthan and Uttar Pradesh lag behind. The estimated result shows that the probability of being financially included is higher among urban, richer, educated and salaried individuals. Further, the findings indicate the lower penetration of bank branches and ATMs in the rural parts of the country.

While numerous studies have explored financial inclusion from a macro-level perspective, there exists a notable gap in the literature at the micro-level. This paper aims to address this gap and contributes to the existing literature in two ways. Firstly, it uses the recent micro-level survey data to construct a multidimensional financial inclusion index for the selected Indian states. Secondly, it examines individual-level attributes as the determining factors of financial inclusion, which has been overlooked in India.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-03-2023-0162

The study investigates the influence of inclusive growth on tax revenue. It validates the fiscal exchange and resource bargaining theories, which suggest that tax compliance improves when citizens perceive that their tax contributions lead to enhanced welfare and that the government negotiates with people to provide public goods and services in exchange for taxes received.

The paper employs inclusive growth measures, including an integrated GDP and equity growth measure and alternative proxies based on GDP per person employed and Asian Development Bank (ADB) inclusive growth indicators. Using 39 sub-Saharan African countries as a sample, our analysis captures spatial interactions across these contiguous countries using the Fixed-Effect model with the Driscoll and Kraay non-parametric consistent covariance matrix and the spatial Durbin Arellano–Bond linear dynamic panel generalized method of moment (Spatial GMM) approach with an interaction weight matrix to capture interactions between countries in the region.

The paper shows that inclusive growth positively influences tax revenue in the region. This validates the fiscal exchange and resource bargaining hypotheses, demonstrating that tax compliance is positively influenced by public goods provision and the government’s ability to emphasize the necessity of taxes for service provision. It indicates that citizens are more willing to pay taxes when the government effectively promotes welfare. We find a significant positive spatial spillover effect, suggesting that inclusive growth not only boosts tax revenue within a specific country but also extends its benefits to neighboring countries, aligning with the spillover theory.

The study posits that the government implements policies that guarantee effectiveness and accountability in public welfare delivery as well as sufficient tax bases and tax revenue. An inclusive growth policy that engenders GDP growth, employment and equity growth should be implemented since the rate of tax compliance of the citizens improves for every welfare provided by the government.

This study tests the validity of the fiscal exchange and resource bargaining theories in Sub-Saharan Africa. Accommodating spatial dependence and cross-border effects, the study sheds light on how inclusive growth impacts tax revenue across contiguous countries in the region. As such, the region should prioritize regional integration, fostering economic ties and harmonizing policies through knowledge sharing and cross-border investment.