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The objective of this study is to examine the long-run relationship between workers' remittances and economic growth in Sri Lanka using time series data spanning 1975–2021.

This study employed both exploratory data analysis (EDA) and inferential data analysis (IDA) tools. EDA includes the scatter plots, confidence ellipse with Kernel fit, whereas IDA covers unit root test, the autoregressive distributed lag (ARDL) bounds technique, the Granger's causality test, and impulse response function (IRF) analysis.

EDA confirms that workers' remittances have a positive relationship with per-capita gross domestic product (GDP). All variables used in this study are I(1). This study is exhibited that workers' remittances have a positive long-run relationship with per-capita GDP. The estimated coefficient of the error correction term shows that the dependent variable moves towards the long-run equilibrium path. Workers' remittances have a short-run and long-run causal relationship with per-capita GDP. The IRF analysis indicates that a one standard deviation shock to workers' remittances has initially an immediate significant positive impact on economic growth.

This study provides insights into workers' remittances in economic growth in Sri Lanka. Further, the findings of this study also provide evidence that workers' remittances increase economic growth.

Using ARDL bounds test, Granger's Causality test and IRF analysis for examining the relationship between workers' remittances and economic growth are the originality of this study.

This study aims to define, classify and interconnect the wide range of known entrepreneurial abilities with terms such as skills, capabilities and competencies, which have been used inconsistently within the entrepreneurial field.

This investigation is based on a systematic literature review and strengthened by a meta-analysis equipped with a bibliometric study to assist the generation of outcomes with a quantitative investigation.

This study proposes an evolving entrepreneurial ability model which interconnects genetic and acquired skill types, capabilities and competencies and is equipped with an Entrepreneurial Skills Map essential to operate in the 21st century.

The proposed model is specific to the entrepreneurial field.

This study supports universities and government agencies for the development of educational programs to prepare current and future entrepreneurs to match the changes in the new environment that has emerged with the COVID-19 pandemic.

This research contributes to the entrepreneurship research domain by shedding light on the inconsistent use of non-standardised terminologies and providing an entrepreneurial model and updated skills map to guide scholars to frame research in the post-COVID era with more clarity.

This study aims to explore the synergistic effect of oxide nanoparticles (ZnO, Fe₂O₃, SiO₂) and cerium nitrate inhibitor on anti-corrosion performance of epoxy coating. First, cerium nitrate inhibitors are absorbed on the surface of various oxide nanoparticles. Thereafter, epoxy nanocomposite coatings have been fabricated on carbon steel substrate using these oxide@Ce nanoparticles as both nano-fillers and nano-inhibitors.

To evaluate the impact of oxides@Ce nanoparticles on mechanical properties of epoxy coating, the abrasion resistance and impact resistance of epoxy coatings have been examined. To study the impact of oxides@Ce nanoparticles on anti-corrosion performance of epoxy coating for steel, the electrochemical impedance spectroscopy has been carried out in 3% NaCl solution.

ZnO@Ce3+ and SiO2@Ce3+ nanoparticles provide more enhancement in the epoxy pore network than modification of the epoxy/steel interface. Whereas, Fe2O3@Ce3+ nanoparticles have more to do with modification of the epoxy/steel interface than to change the epoxy pore network.

Incorporation of both oxide nanoparticles and inorganic inhibitor into the epoxy resin is a promising approach for enhancing the anti-corrosion performance of carbon steel.

This study aims at introducing a method based on the failure mode, effects and criticality analysis (FMECA) to aid in selecting the most suitable formwork system with the minimum overall cost.

The research includes a review of the literature around formwork selection and analysis of data collected from the building construction industry to understand material failure modes. An FMECA-based model that estimates the total cost of a formwork system is developed by conducting a two-phased semi-structured interview and regression and statistical analyses. The model comprises material, manpower and failure mode costs. A case study of fifteen buildings is analysed using data collected from construction projects in the UAE to validate the model.

Results obtained indicate an average accuracy of 89% in predicting the total formwork cost using the proposed method. Moreover, results show that the costs incurred by failure modes account for 11% of the total cost on average.

The analysis is limited to direct costs and costs associated with risks; other costs and risk factors are excluded. The proposed framework serves as a guide to construction project managers to enhance decision-making by addressing the indirect cost of failure modes.

The research proposes a novel formwork system selection method that improves upon the subjective conventional selection process by incorporating the risks and uncertainties associated with the failure modes of formwork systems into the decision-making process.

The purpose of this study was to determine the factors associated with functionality, a clinical criterion that could predict frailty in the elderly people in a rural context.

This project is a cross-sectional descriptive analysis of 342 adults of age >60 years who are residents of Putumayo province in Colombia. Information regarding demographic characteristics, medical history, health perception and current illness was collected. The Mini-Mental State Examination (MMSE) protocol was used to perform cognitive evaluation; the Yesavage Geriatric Depression Scale was used to establish depressive symptoms; and the Hamilton Rating Scale was used to assess anxiety level. Questionnaire was used to evaluate performance on instrumental activities of daily living that lead to functional independence [daily life questionnaire (DLQ)]. The medical outcomes study scale was used to assess social parameters.

A moderate and negative correlation was found between the DLQ score and age (r = −0.49; 95% CI: −0.57 to −0.47), whereas a positive correlation was found with education (r = 0.17; 95% CI: 0.07–0.27). Older adults with economic independence achieved a higher score in functional performance than those with economic dependence (standardized mean difference = 0.55; 95% CI: 0.33–0.77). This study observed a moderate correlation a moderate correlation between the MMSE cognitive performance (r = 0.56; 95% CI: 0.48−0.63) and the depressive symptomatology of Yesavage Scale (r = −0.36, 95% CI: −0.44 to −0.26). Finally, the structural model determined that age (r = −0.37), economic dependence (r = −0.383) and cognitive state (r = 0.309) determine the functional component.

This study provides empirical support about older adults living in rural contexts, around the functionality variable from a multidimensional approach, highlighting the sociodemographic and cognitive variables. Consequently, the policy of social support in older adults must be oriented toward the development of a range of divergent intervention strategies.

The study deals with the assessment of functionality in the elderly people from an interdisciplinary approach in the rural setting which presents a greater risk of physical and socioeconomic vulnerability. Therefore, the community, the health professionals and the government entities should help implement active aging programs for this population.

Modern human society has continuous advancements that have a negative impact on the quality of the air. Daily transportation, industrial and residential operations churn up dangerous contaminants in our surroundings. Addressing air pollution issues is critical for human health and ecosystems, particularly in developing countries such as Egypt. Excessive levels of pollutants have been linked to a variety of circulatory, respiratory and nervous illnesses. To this end, the purpose of this research paper is to forecast air pollution concentrations in Egypt based on time series analysis.

Deep learning models are leveraged to analyze air quality time series in the 6th of October City, Egypt. In this regard, convolutional neural network (CNN), long short-term memory network and multilayer perceptron neural network models are used to forecast the overall concentrations of sulfur dioxide (SO₂) and particulate matter 10 µm in diameter (PM₁₀). The models are trained and validated by using monthly data available from the Egyptian Environmental Affairs Agency between December 2014 and July 2020. The performance measures such as determination coefficient, root mean square error and mean absolute error are used to evaluate the outcomes of models.

The CNN model exhibits the best performance in terms of forecasting pollutant concentrations 3, 6, 9 and 12 months ahead. Finally, using data from December 2014 to July 2021, the CNN model is used to anticipate the pollutant concentrations 12 months ahead. In July 2022, the overall concentrations of SO₂ and PM₁₀ are expected to reach 10 and 127 µg/m³, respectively. The developed model could aid decision-makers, practitioners and local authorities in planning and implementing various interventions to mitigate their negative influences on the population and environment.

This research introduces the development of an efficient time-series model that can project the future concentrations of particulate and gaseous air pollutants in Egypt. This research study offers the first time application of deep learning models to forecast the air quality in Egypt. This research study examines the performance of machine learning approaches and deep learning techniques to forecast sulfur dioxide and particular matter concentrations using standard performance metrics.

The incompatibility of natural fibers with polymer matrices is one of the key obstacles restricting their use in polymer composites. The interfacial connection between the fibers and the matrix was weak resulting in a lack of mechanical properties in the composites. Chemical treatments are often used to change the surface features of plant fibers, yet these treatments have significant drawbacks such as using substantial amounts of liquid and chemicals. Plasma modification has recently become very popular as a viable option as it is easy, dry, ecologically friendly, time-saving and reduces energy consumption. This paper aims to explore plasma treatment for improving the surface adhesion characteristics of sisal fibers (SFs) without compromising the mechanical attributes of the fiber.

A cold glow discharge plasma (CGDP) modification using N₂ gas at varied power densities of 80 W and 120 W for 0.5 h was conducted to improve the surface morphology and interfacial compatibility of SF. The mechanical characteristics of unmodified and CGDP-modified SF-reinforced epoxy composite (SFREC) were examined as per the American Society for Testing and Materials standards.

The cold glow discharge nitrogen plasma treatment of SF at 120 W (30 min) enhanced the SFREC by nearly 122.75% superior interlaminar shear strength, 71.09% greater flexural strength, 84.22% higher tensile strength and 109.74% higher elongation. The combination of improved surface roughness and more effective lignocellulosic exposure has been responsible for the increase in the mechanical characteristics of treated composites. The development of hydrophobicity in the SF had been induced by CGDP N₂ modification and enhanced the size of crystals and crystalline structure by removing some unwanted constituents of the SF and etching the smooth lignin-rich surface layer of the SF particularly revealed via FTIR and XRD.

Chemical and physical treatments have been identified as the most efficient ways of treating the fiber surface. However, the huge amounts of liquids and chemicals needed in chemical methods and their exorbitant performance in terms of energy expenditure have limited their applicability in the past decades. The use of appropriate cohesion in addition to stimulating the biopolymer texture without changing its bulk polymer properties leads to the formation and establishment of plasma surface treatments that offer a unified, repeatable, cost-effective and environmentally benign replacement.

The authors are sure that this technology will be adopted by the polymer industry, aerospace, automotive and related sectors in the future.

This research aims to identify, examine and evaluate barriers to the adoption of digital servitization in manufacturing firms in the case of the Sri Lankan manufacturing sector and analyze the inter-relationships among digital servitization barriers.

Based on the comprehensive literature review, 13 barriers were identified. The grey decision-making trial and evaluation laboratory (grey-DEMATEL) approach was used to uncover and analyze the relationships among barriers in terms of their overall influence and dependencies.

A prominent barrier to the success of adopting digital servitization in the Sri Lankan manufacturing sector is the lack of digital strategy in developing activities related to the design of digital service packages, organizational structures and processes. Supply chain integration is the most influential factor, which plays an important role in developing a competitive advantage by encouraging innovation process capabilities in servitized companies.

Industry practitioners can develop guidelines for adopting digital servitization practices based on the importance and interdependencies of different barriers and thereby prioritize projects within a program of digital servitization adoption in their organizations.

Research studies on barriers to digital servitization are limited to exploratory nature and have adopted mainly the qualitative approach, such as in-depth interviews. No empirical study has investigated the inter-relationships among digital servitization adoption barriers in the manufacturing sector. This study provides a holistic view of different barriers to the adoption of digital servitization in the manufacturing sector as a basis for developing comprehensive digital servitization strategies to manage and leverage complexity in digital transformation.

The present study assesses the commercial bank profit efficiency and its relationship to banking sector competition in a rapidly growing emerging economy, India from 2009 to 2019 using stochastic frontier analysis (SFA).

Lerner indices, conventional and efficiency-adjusted, quantify competition. Two SFA models are employed to calculate alternative profit efficiency (inefficiency) scores: the two-step time-decay approach proposed by Battese and Coelli (1992) and the recently developed single-step pairwise difference estimator (PDE) by Belotti and Ilardi (2018). In the first step of the BC92 framework, profit inefficiency is calculated, and in the second step, Tobit and Fractional Regression Model (FRM) are utilized to evaluate profit inefficiency correlates. PDE concurrently solves the frontier and inefficiency equations using the maximum likelihood process.

The results suggest that foreign banks are less profit efficient than domestic equivalents, supporting the “home-field advantage” hypothesis in India. Further, increasing competition drives bank managers to make riskier lending and investment choices, decreasing bank profit efficiency. However, this effect varies depending on bank ownership and size.

Literature on the competition bank efficiency link is conspicuously scant, with a focus on technical and cost efficiency. Less is known regarding the influence of competition on bank profit efficiency. The article is one of the first to examine commercial bank profit efficiency and its relationship to banking sector competition. Additionally, the study work represents one of the first applications of the FRM presented by Papke and Wooldridge (1996) and the PDE provided by Belotti and Ilardi (2018).

The purpose of this study is to investigate the influence of IFRS convergence on annual report readability in an emerging market context, with an emphasis on the contents of management discussion and analysis (MD&A), notes to the accounts (Notes) and the whole annual report.

The study performs firm-fixed effect regression on a sample of 143 Indian listed companies over a period spanning from 2012 to 2021 to examine the influence of IFRS convergence on readability. This assessment primarily focuses on broader spectrums of readability dimensions, namely annual report length and complexity, wherein complexity is measured using the Gunning Fog, Flesch Reading ease and Flesch-Kincaid grade index.

As Indian firms shift to IFRS reporting, the findings suggest that annual reports have become significantly lengthier and more complex, causing deterioration in readability. The Notes section, in particular, exhibits the most significant increase in length and complexity, followed by the entire annual report and MD&A section. Furthermore, the findings also indicate that the complexity of the Notes section is instrumental in the observed complexity growth of the whole annual report in the post-IFRS period.

The current study employs readability indices rather than directly taking into consideration the opinions of actual users of annual reports to determine readability. As a result, the study does not provide direct evidence on how information in annual reports affects users' readability.

The findings provide insightful information to managers and policymakers about the difficulties stakeholders may encounter while reading IFRS-based annual reports, which ultimately impact their investment decisions. Thus, there is an important managerial implication from this, depending upon the severity of complexity corporations participate in while complying with IFRS in the post-IFRS period.

Analyzing the influence of exogenous information shock, such as IFRS convergence, on readability is critical, particularly for emerging markets like India, where a lack of financial literacy and weaker enforcement already have detrimental effects on the capital market. In light of this, the current study provides a comprehensive examination of the impact of IFRS convergence on annual report readability and contributes to the growing IFRS literature in the less explored emerging market context.