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This study aims to examine the impact of digital health technology adoption on female physicians’ work–life balance (WLB) in Bahrain. This study also examines the impact of two moderating variables: career stage, based on the kaleidoscope career model (KCM) and the presence of domestic workers, based on Becker’s theory of the allocation of time.

An electronic quantitative survey was administered to female physicians working in Bahrain. The survey gathered data on various aspects of digital health technology, WLB and demographic characteristics such as age and the presence of domestic workers. From a target population of approximately 1,000 female physicians in Bahrain, 102 participated in the survey.

The authors found that the effect of digital health technologies on WLB is positive in general and specifically for the early-career stage; however, it harms WLB in the middle and late-career stages. This is consistent with KCM predictions. In addition, there is no moderating effect of having domestic helpers.

This paper contributes to the theoretical understanding of the effect of technology on WLB by expanding the traditional model (KCM) to include an economic model of how female physicians allocate their time between work and home responsibilities, including the effect that a domestic worker can have on this allocation. Beyond these theoretical contributions, this paper is also the first to study technology and WLB in the health sector in Bahrain following the COVID-19 pandemic.

Financial anxiety has become a global concern and a growing research area with significant potential to contribute to the behavioral and personal finance literature. Despite this, the literature is fragmented and inconsistent. Prior studies vary greatly in the breadth of definitions and measures of financial anxiety. There has been no systematic evaluation of literature on financial anxiety antecedents, consequences, and coping strategies. This systematic review fills this gap.

We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched Scopus and Web of Science and identified 55 eligible studies published between 2009 and 2024.

Financial anxiety is defined and measured differently in different research domains. We identified several antecedents, including socio-demographic factors (e.g. gender, age, ethnicity, income, employment, racial background, and language proficiency), personality traits, compulsive and impulsive buying behavior, depression or other mental issues, family health issues, and the COVID-19 pandemic and consequences of financial anxiety, including psychological and psychic health, societal and personal relations, financial behavior and well-being, and job-related outcomes. In addition, the literature presents six financial anxiety coping strategies (self-imposed coping mechanisms, spiritual and theological resources, increased financial capability, social and family support, seeking professional help, and language proficiency training). Several future research directions are presented.

This review represents the first systematic compilation and evaluation of the research findings on financial anxiety.

Despite various consequences for different stakeholders in the mustard ecosystem, India prohibited blending in mustard oil to achieve self-reliance in edible oils and promote consumer health. This paper uncovers the implications of this policy on mustard production, consumption and prices.

This paper deploys system dynamics (SD) to model the mustard ecosystem. SD uses simulation modeling to comprehend the nonlinear behavior of complex systems over time utilizing causal-loop and stock-flow diagrams.

While the mustard price does not vary in the short run, it diverges toward a higher side in the long run due to the changed policy mandate. Surprisingly, due to the predominance of market prices, the policy administered minimum support price (MSP) was found to have a limited influence on mustard prices. Hence, the focus should be on supply augmentation through non-price-based measures like disseminating information to enhance the yield rate of seed production and promoting the adoption of efficient technologies with higher oil conversion efficiency.

The paper allows policymakers to quantitatively evaluate the effectiveness of policy interventions to mitigate the adverse impacts of policy mandate. It presents a reliable roadmap for policymakers to roll out effective policies.

The paper uncovers the system-level impact of policy on stakeholders and examines the effectiveness of MSP.

This study aims to investigate the effects of environmental, social and governance (ESG) performance on financial risk disclosure of European companies. It analyzed the relationships between ESG factors and financial risk disclosure between 2010 and 2020.

To test their hypotheses in this study, the authors used the multivariate regression analysis on panel data using the Thomson Reuters ASSET4 database and the annual reports of 154 European companies listed in the ESG index between 2010 and 2020.

Empirical evidence shows a positive association between European companies' environmental and governance performance with financial risk disclosure, whereas social performance does not influence financial risk disclosure. Concerning the control variables, the findings demonstrate that firm size and profitability are significant factors in changing the financial risk disclosure. Nevertheless, firms’ leverage is insignificantly correlated with financial risk disclosure.

This study extends the stream of accounting literature by focusing on the financial risk disclosure, a topic that has received little attention in previous research. Furthermore, to the best of the authors’ knowledge, this study is one of the first that provides ESG companies with evidence of the effect of ESG factors on financial risk disclosure in a developed market like Europe.

Deposits, a liability component of banks’ balance sheet, are an important source of funding for commercial and retail banks. In this study, the authors consider deposits as dependent variable and examine factors (bank-specific, macrofinance and structural) that could plausibly explain deposits. Subsequently, the findings are expected to support analysts, bank managers and regulators, especially in small economies such as Fiji, for asset–liability management.

This study uses a balanced panel of six commercial banks and two credit institutions over the period 2000–2022. To control for bank heterogeneity, a fixed-effect regression method is used.

Bank-specific variables such as bank size, profitability, loan-to-deposit ratio and bank stability are positively associated with bank deposits, whereas the capital adequacy ratio is negatively associated with bank deposits. Macroeconomic variables such as remittances and gross domestic product per capita are positively associated with bank deposits. Moreover, institutional variables such as control of corruption, political stability and regulatory quality are positively associated with bank deposits. However, tail events such as the global financial crisis of 2007–09 and the COVID-19 pandemic negatively influence bank deposits. Structural breaks for 2007 and 2011 of two banks (Bank of the Baroda and Bank of the South Pacific, respectively) are positively associated with bank deposits.

Previous studies have considered profitability, competition, nonperforming loans and stability of banks in Fiji. To the best of the authors’ knowledge, this study is the first to consider the determinants of bank deposits, an important source of funds for banks in many small countries including Fiji. In addition, this study examines the impact of structural breaks, tail events such as the recent pandemic (COVID-19) and institutional variables.

This paper examines and compares the role of social protection to promote inclusive growth in two countries (once related), Pakistan and Bangladesh, from 1984 to 2020, using annual time series data.

The study used principal component analysis to develop the index of social protection, inclusive growth and macroeconomic stability. It also employed co-integration with impulse response function and fully modified ordinary least squares test for long-run cointegration.

The key results highlighted that social protection positively promotes inclusive growth in both countries. However, Bangladesh attains a high position in achieving inclusive growth through the mechanism of welfare programs. Findings show that institutional quality, macroeconomic stability and globalization are the positive and significant drivers of inclusive growth in both countries. It also confirms that macroeconomic stability and globalization are contributing more to achieving inclusive growth in Bangladesh as compared to Pakistan.

Institutions and macroeconomic stability in both countries are critical toward providing a transparent system of welfare schemes to achieve inclusive growth. Shocks to social protection schemes in Pakistan are inconsistent for achieving inclusive growth as compared to Bangladesh.

The study extends the empirical measurement of social protection and inclusive growth while using protracted dimensions and indicators. It further examines and compares the dynamics of social protection programs for inclusive growth in two countries once related. For further originality and reliability, this study checks the robustness of long-run estimates by disaggregating the institutional quality and globalization into their key dimensions.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-07-2023-0548

The purpose of this study is to examine the relationship between pollutant emissions, financial development and IFRS in developed and developing countries between 1998 and 2022.

Data were obtained from World Development Indicators and World Governance Indicators of the World Bank.

Using FGLS and GMM estimators, the results provide evidence that financial development has a significant positive impact on a variety of pollutant emissions. However, this positive impact is moderated by IFRS for the overall sample and country income groups.

Governments and regulatory organizations should support companies’ investments in clean energy and technologies to slow down environmental degradation. Tax credits and subsidies may be helpful to achieve this goal. Also, governments may encourage companies to cooperate with universities and research institutions to develop environment-friendly production and distribution methods to reduce pollution. Although stakeholders may obtain information about environmental issues in financial statements that are prepared in accordance with IFRS, there is a need for standardization of their contents.

Greenhouse gases are major contributors to climate change and global warming. In addition to private costs borne by producers, the production and consumption of products have social costs arising from pollution that affects air, water, and soil. Pollution adversely affects people's physiological and psychological health, which decreases labor productivity, thereby leading to a decrease in economic growth.

According to the author’s knowledge, this is the first study that examines the impact of IFRS on the relationship between financial development and pollutant emissions.

This study addresses the ever-increasing cyber risks confronting the global banking sector, particularly in India, amid rapid technological advancements. The purpose of this study is to de velop an innovative cyber fraud (CF) response system that effectively controls cyber threats, prioritizes fraud, detects early warning signs (EWS) and suggests mitigation measures.

The methodology involves a detailed literature review on fraud identification, assessment methods, prevention techniques and a theoretical model for fraud prevention. Machine learning-based data analysis, using self-organizing maps, is used to assess the severity of CF dynamically and in real-time.

Findings reveal the multifaceted nature of CF, emphasizing the need for tailored control measures and a shift from reactive to proactive mitigation. The study introduces a paradigm shift by viewing each CF as a unique “fraud event,” incorporating EWS as a proactive intervention. This innovative approach distinguishes the study, allowing for the efficient prioritization of CFs.

The practical implications of such a study lie in its potential to enhance the banking sector’s resilience to cyber threats, safeguarding stability, reputation and overall risk management.

The originality stems from proposing a comprehensive framework that combines machine learning, EWS and a proactive mitigation model, addressing critical gaps in existing cyber security systems.

This study aims to investigate the nuances of celebrity spokesperson effectiveness in business-to-business (B2B) advertising. Specifically, the study addresses the question of endorser effectiveness in the presence of product complexity (high vs low) and how this effect is moderated by endorser gender. In addition, the study also explores whether the way an endorser is placed in the advertisement (product-facing vs audience-facing) would have differential effects on the buyer.

The present study uses experimental design to fulfil the study objectives. Two experiments are conducted (total n = 201) on employees in a purchasing role in organisations, with the dependent variable being dwell time (captured using an eye-tracking device).

Major findings indicate that celebrity gender has a moderating effect depending on the product complexity. Results also indicate a significant effect of how the celebrity is placed in the advertisement on the buyer.

The findings emphasise the role of the spokesperson in B2B advertising using neuro-behavioural data. It also contributes to the theoretical nuances of spokesperson gender in B2B advertising and the role of kinesics in advertising using spokespersons.

The study provides guidelines on the choice of the spokesperson and their physical posture in the advertisement for B2B advertisers that may lead to communication effectiveness.

The present study is in a domain that is scarcely researched in B2B and adds novelty as it uses physiological data instead of self-reported measures.

Identifying and prioritizing supply chain risk is significant from any product’s quality and reliability perspective. Under an input-process-output workflow, conventional risk prioritization uses a risk priority number (RPN) aligned to the risk analysis. Imprecise information coupled with a lack of dealing with hesitancy margins enlarges the scope, leading to improper assessment of risks. This significantly affects monitoring quality and performance. Against the backdrop, a methodology that identifies and prioritizes the operational supply chain risk factors signifies better risk assessment.

The study proposes a multi-criteria model for risk prioritization involving multiple decision-makers (DMs). The methodology offers a robust, hybrid system based on the Intuitionistic Fuzzy (IF) Set merged with the “Technique for Order Performance by Similarity to Ideal Solution.” The nature of the model is robust. The same is shown by applying fuzzy concepts under multi-criteria decision-making (MCDM) to prioritize the identified business risks for better assessment.

The proposed IF Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) for risk prioritization model can improve the decisions within organizations that make up the chains, thus guaranteeing a “better quality in risk management.” Establishing an efficient representation of uncertain information related to traditional failure mode and effects analysis (FMEA) treatment involving multiple DMs means identifying potential risks in advance and providing better supply chain control.

In a company’s supply chain, blockchain allows data storage and transparent transmission of flows with traceability, privacy, security and transparency (Roy et al., 2022). They asserted that blockchain technology has great potential for traceability. Since risk assessment in supply chain operations can be treated as a traceability problem, further research is needed to use blockchain technologies. Lastly, issues like risk will be better assessed if predicted well; further research demands the suitability of applying predictive analysis on risk.

The study proposes a hybrid framework based on the generic risk assessment and MCDM methodologies under a fuzzy environment system. By this, the authors try to address the supply chain risk assessment and mitigation framework better than the conventional one. To the best of their knowledge, no study is found in existing literature attempting to explore the efficacy of the proposed hybrid approach over the traditional RPN system in prime sectors like steel (with production planning data). The validation experiment indicates the effectiveness of the results obtained from the proposed IF TOPSIS Approach to Risk Prioritization methodology is more practical and resembles the actual scenario compared to those obtained using the traditional RPN system (Kim et al., 2018; Kumar et al., 2018).

This study provides mathematical models to simulate the supply chain risk assessment, thus helping the manufacturer rank the risk level. In the end, the authors apply this model in a big-sized organization to validate its accuracy. The authors validate the proposed approach to an integrated steel plant impacting the production planning process. The model’s outcome substantially adds value to the current risk assessment and prioritization, significantly affecting better risk management quality.