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This study aims to examine the triple relationship between capital regulation, banking lending and economic growth in a dual markets. Specifically, the author seeks to explore how changes in capital regulation can impact banking lending practices and subsequently influence economic growth, while also investigating the reciprocal effects of banking lending on economic growth.

The author follows several previous studies such as Shrieves and Dahl (1992), Beck and Levine (2002), Altunbas et al. (2007), Saeed et al. (2020) and Stewart et al. (2021) to identify a system of three equations, regarding economic growth, capital and banking financing growth, respectively. The author estimates the parameters of all equations simultaneously using the seemingly unrelated regression method (Zellner, 1962) for a sample of 46 Islamic banks and 113 conventional banks during 2002–2022. These banks operate in 13 Muslim countries from Middle East and North Africa and Southeast Asia.

The author’s findings demonstrate that in the case of Islamic banking, an increase in loan growth stimulates economic growth, while an increasing capital ratio positively influences economic growth but is accompanied by a reduction in loan growth. This result corroborates the findings of Stewart et al. (2021), which indicate that regulatory capital reduces unstable credit while improving gross domestic product growth. However, in the case of conventional banks, the response to an increase in loan growth on Gross Domestic Product Per Capita Growth (GDPCG) is ambiguous, while the capital ratio improves GDPCG and promotes LOANG, which, in turn, increases risk.

The Islamic banks can continue to significantly contribute to economic growth by effectively directing their available capital toward viable investment opportunities and supporting sustainable financial practices, even in the presence of potential constraints on loan growth. As for conventional banks, they are invited to increase their capital levels to ensure a strong and resilient financial system that can support lending and facilitate economic growth.

To the best of the author’s knowledge, this paper is the first to explore the triple relationship between capital requirements, Islamic bank lending and economic growth.

Empirical studies, to date, show that financial inclusion (FI) enhances financial stability (FS) by promoting a large deposit base, reducing information asymmetry, and strengthening market power on the one hand, and leads to financial fragility by expanding credit without proper screening, increasing operational costs, and provoking borrowers' moral hazard on the other. Thus, the most important issue is to maintain FS while extending formal financial services to the impoverished and disadvantaged segments of society. Therefore, this paper investigates the efficacy of macroprudential regulations (MPRs) to align these policy divergences.

To accomplish the objective and facilitate policy implications, the authors use aggregated and disaggregated measures of both FI and MPRs, employ advanced econometric models that minimize endogeneity and ensure robustness, and investigate their joint effectiveness in upholding FS using data of 138 countries spanning the 2004–2017 years.

The findings indicate that the effectiveness of MPRs is instrument specific. Some MPRs that obstruct access to formal financial services, in particular, moderate the advantage of FI in achieving FS, while others boost the effect of inclusion in attaining financial sector stability. Therefore, prudence should be emphasized while designing MPRs as a tool for aligning the policy trade-off between FI and FS.

To the best of the authors knowledge, this paper extends previous empirical research by investigating the conditioning impact of MPRs in the FI-FS nexus.

The authors' work aims to identify the employer-specific drivers of the college (or university) wage gap, which has been identified as one of the major determinants of the dynamics of overall wage and income inequality in the past decades. The authors focus on three employer-level features that can be associated with asymmetries in the employment relation orientation adopted for college and non-college-educated employees: (1) size, (2) the share of standard employment and (3) the pervasiveness of incentive pay schemes.

The authors' establishment-level analysis (data from the Basic Survey on Wage Structure (BSWS), 2005–2018) focusses on Japan, an economy characterised by many unique economic and institutional features relevant to the aims of the authors' analysis. The authors use an adjusted measure of firm-specific college wage premium, which is not biased by confounding individual and establishment-level factors and reflects unobservable characteristics of employees that determine the payment of a premium. The authors' empirical methods account for the complexity of the relationships they investigate, and the authors test their baseline outcomes with econometric approaches (propensity score methods) able to address crucial identification issues related to endogeneity and reverse causality.

The authors' findings indicate that larger establishment size, a larger share of regular workers and more pervasive implementation of IPSs for college workers tend to increase the college wage gap once all observable workers, job and establishment characteristics are controlled for. This evidence corroborates the authors' hypotheses that a larger establishment size, a higher share of regular workers and a more developed set-up of performance pay schemes for college workers are associated with a better capacity of employers to attract and keep highly educated employees with unobservable characteristics that justify a wage premium above average market levels. The authors provide empirical evidence on how three relevant establishment-level characteristics shape the heterogeneity of the (adjusted) college wage observed across organisations.

The authors' contribution to the existing knowledge is threefold. First, the authors combine the economics and management/organisation literature to develop new insights that underpin the authors' testable empirical hypotheses. This enables the authors to shed light on employer-level drivers of wage differentials (size, workforce composition, implementation of performance-pay schemes) related to many structural, institutional and strategic dimensions. The second contribution lies in the authors' measure of the “adjusted” college wage gap, which is calculated on the component of individual wages that differs between observationally identical workers in the same establishment. As such, the metric captures unobservable workers' characteristics that can generate a wage premium/penalty. Third, the authors provide empirical evidence on how three relevant establishment-level characteristics shape the heterogeneity of the (adjusted) college wage observed across organisations.

Few empirical studies examined the relationship between life expectancy and income in India. This study aims to examine the impact of life expectancy on economic growth in India by incorporating all the major states of India.

This study is based on secondary data and includes 16 major states of India covering the periods 2000–2014. The author used panel fixed effect model (FEM) to examine the impact of life expectancy on economic growth.

Empirical analysis revealed a positive trend in life expectancy in India. In association with life expectancy, the author found continuous growth in the elderly population. The result of the FEM shows that gains in life expectancy positively affect economic growth in India. The empirical findings do not support any negative impact of life expectancy gains on economic growth.

This study is the outcome of the independent and original research work of the authors and contributes significantly to the literature on the demography–economic relationship. The findings of this study help the author to understand that life expectancy gain is in no way a constraint, rather the skill and experience of the workforce are crucial to determining economic growth.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-06-2022-0422.

This paper attempts to investigate the impact of the Russia–Ukraine war on the returns and volatility of the United States (US) natural gas futures market.

The study uses secondary data of 996 trading day provided by the US Department of Energy and investing.com websites and applies the event study methodology in addition to the generalized autoregressive conditional heteroscedastic (GARCH) family models.

The findings from the exponential EGARCH (1,1) estimate are the best indication of a significant positive effects of the Ukraine–Russia war on the returns and volatility of the US natural gas futures prices. The cumulative abnormal returns (CARs) of the event study show that the natural gas futures prices reacted negatively but not significantly to the Russian–Ukraine war at the event date window [−1,1] and the [−15, −4] event window. CARs for the longer pre and post-event window display significant positive values and coincides with the standard finance theory for the case of the US natural gas futures over the Russia–Ukraine conflict.

This is the first study to examine the impact of the Russia–Ukraine war on natural gas futures prices in the United States. Thus, it provides indications on the behavior of investors in this market and proposes new empirical evidence that help in investment analyses and decisions.

This investigation aims to determine the effect of trade uncertainty on domestic investment (DI) and foreign direct investment (FDI) for the Turkish economy from the first quarter of 2005 to the first quarter of 2020.

The authors adopt the vector autoregression (VAR) model augmented with Fourier terms. Using this methodology, the authors obtain the empirical results of the impulse-response functions and the variance decomposition analysis.

The empirical results demonstrate that a shock to trade uncertainty has a slight negative impact on DI for up to approximately 1.5 years, whereas its impact on FDI is negative but long-lasting. Moreover, the contribution of trade uncertainty to FDI is relatively higher than to DI in the error variance decomposition for the investigated period. These empirical results can be beneficial for shaping the Turkish authorities' trade policies in the following periods.

These findings have implications within the macroeconomic setting. Government authorities can provide tax exemptions for specified sectors and debureaucratize investment processes for both domestic and foreign entrepreneurs. Additionally, institutional quality and property rights should be protected strictly and developed gradually.

This study is the first to examine the impact of world trade uncertainty on Türkiye’s DI and FDI. Because trade uncertainty might act as fixed costs, this creates the option value of waiting and seeing the market, and firms hesitate to incur investment.

The purpose of this paper is to identify the challenges surrounding the implementation of digital technology (DT) agri-food supply chain (AFSC) and explore how these challenges relate to the various sustainability dimensions. Additionally, it aims to assess how these challenges are interconnected in relation to achieving sustainable development goals (SDGs).

The study employs a mixed-method approach utilizing the EFA-ISM-Fuzzy DEMATEL technique. To support and validate the findings, exploratory factor analysis (EFA) categorized 12 critical challenges in sustainable dimensions from 141 participants' responses. Furthermore, interpretive structural modeling (ISM) and decision-making trial and evaluation (DEMATEL) methods were used to obtain the interrelationship and hierarchical structure of the challenges.

The study identified 12 critical challenges while adopting DT in AFSC. These challenges were categorized into four sustainable dimensions: technological, economic, environmental and social. These challenges hinder the achievement of SDGs as well. Lack of regulatory and policy framework with security and privacy issues were the key challenges faced while adopting DT. These observations emphasize the necessity for government and policymakers to prioritize tackling the identified challenges to successfully endorse and execute DT initiatives in AFSC while also fulfilling the SDGs.

The implication underscores the need for collaboration among various stakeholders, such as governments, policymakers, businesses and researchers. By collectively addressing these challenges, DT can be leveraged optimally, fostering sustainable practices and making progress toward achieving the SDGs within the AFSC.

The study uses a combination technique of EFA and ISM-DEMATEL to identify the challenges faced in Indian AFSC while adopting DT and categorizes the interrelation between the challenges along with fulfilling the SDGs.

Artificial Intelligence-based smart farming technologies have brought impressive changes in farming. This paper aims at exploring the farmers’ intention to adopt smart farming technologies (SFT). Also, the authors intend to know how far the belief of farmers on land as God influences their decision to adopt SFT.

The data were gathered from 500 farmers chosen purposively. A well-crafted survey instrument was employed to amass data from farmers for measuring their adoption of SFT. As the authors sought to measure the farmers’ behavioural intention (BI) towards the adoption of SFT, the technology acceptance model developed by Davis (1989) came in handy, including perceived usefulness (PU), perceived ease of use (PEU) and BI. The authors have adopted this model as it was considered a superior model. The items on the attitude of confidence (AC) were adapted from Adrian et al. (2005). Survey instruments of Thompson and Higgins (1991) and Compeau and Higgins (1995) were also referred to finalize the statements relating to attitude towards use. Moreover, the authors developed items relating to the perceived belief of land as God based on frequent interaction with the farmers.

The study results divulged that attitude to use (AU) is directly influenced by the rural farmers’ PU, PEU and AC. Similarly, this investigation has observed behaviour intention directly influenced by the AU of farmers. It is observed that AU was the most influential variable, which ultimately influenced the BI to use SFT.

This study has an important limitation in the form of representing only the culture, belief and value system of farmers in India.

The outcome of this study will facilitate the policymakers to draw suitable policy measures keeping the sensitivities of the farmers in mind in their technology adoption drive. The agricultural officers can encourage farmers to take logical decisions by supplying adequate information in a time-bound manner. Marketers can make suitable adjustments in their sales and promotion activities that focus on farmers.

The belief of farmers on land as God has a small yet unmissable influence on farmers’ AU and BI in their technology adoption decision. Based on the above evidence, the authors recommend that marketers fine-tune their product design, product packaging and promotional activities keeping the belief and sensitivities of farmers at the core of their marketing campaign.

This article provides original insights by demonstrating the positive influence of PU, PEU and AC on technology adoption by farmers. This research is the first of a kind linking the belief of farmers on land as God with smart farming technology adoption in farming.

Construction companies are increasingly adopting sensing technologies like laser scanners, making it necessary to upskill the future workforce in this area. However, limited jobsite access hinders experiential learning of laser scanning, necessitating the need for an alternative learning environment. Previously, the authors explored mixed reality (MR) as an alternative learning environment for laser scanning, but to promote seamless learning, such learning environments must be proactive and intelligent. Toward this, the potentials of classification models for detecting user difficulties and learning stages in the MR environment were investigated in this study.

The study adopted machine learning classifiers on eye-tracking data and think-aloud data for detecting learning stages and interaction difficulties during the usability study of laser scanning in the MR environment.

The classification models demonstrated high performance, with neural network classifier showing superior performance (accuracy of 99.9%) during the detection of learning stages and an ensemble showing the highest accuracy of 84.6% for detecting interaction difficulty during laser scanning.

The findings of this study revealed that eye movement data possess significant information about learning stages and interaction difficulties and provide evidence of the potentials of smart MR environments for improved learning experiences in construction education. The research implication further lies in the potential of an intelligent learning environment for providing personalized learning experiences that often culminate in improved learning outcomes. This study further highlights the potential of such an intelligent learning environment in promoting inclusive learning, whereby students with different cognitive capabilities can experience learning tailored to their specific needs irrespective of their individual differences.

The classification models will help detect learners requiring additional support to acquire the necessary technical skills for deploying laser scanners in the construction industry and inform the specific training needs of users to enhance seamless interaction with the learning environment.