Control, sustainability, and risk: A structural perspective within financial decision systems

Coaching competency and (exploratory) structural equation modeling: A substantive-methodological synergy

Introduction/Main Objectives: This study examines how government support moderates the effects of financial planning, financial record-keeping systems, and capital accessibility on the financial sustainability of Small and Medium Enterprises (SMEs) in the border regions of Kuningan Regency. These SMEs often face limited infrastructure and institutional access, making their financial resilience a critical issue. Background Problems: The research addresses the problem of limited financial sustainability among border-area SMEs by exploring the influence of internal financial practices and external institutional support. Novelty: While prior studies have examined SME financial performance, this research uniquely applies Institutional Theory to analyze how government support—as an institutional factor—affects the relationship between internal financial management and sustainability in border areas. Research Methods: A quantitative approach involving 114 SMEs from Kuningan’s border districts was used. Data were analyzed using Structural Equation Modeling with Partial Least Squares (SEM-PLS) to assess direct and moderating effects. Finding/Results: Financial planning, record-keeping, and access to capital significantly influence financial sustainability. Government support also positively moderates the link between financial record-keeping and sustainability, but does not consistently moderate other relationships. Conclusion: Financial sustainability in border SMEs is strongly influenced by internal financial practices and selectively supported by government intervention, emphasizing the need for more targeted policies. Research Limitation/Implications: The study is context-specific to Kuningan’s border areas, but it highlights the importance of strengthening financial capabilities and responsive institutional support to improve SME sustainability in similar peripheral regions.

Purpose: The study aimed to examine the role of financial management practices in the financial sustainability of mission hospitals in Kiambu County, Kenya. It seeks to understand how financial planning, funding practices, working capital management, and health sector regulations impact the economic stability and operational viability of these vital healthcare institutions. Methodology: The study utilized a descriptive cross-sectional research design to explore the financial management practices and their impact on the financial sustainability of mission hospitals in Kiambu County, Kenya. Data collected on various financial indicators and management practices were analyzed using the Statistical Package for the Social Sciences (SPSS) version 22. Descriptive statistics such as mean and standard deviation were computed to summarize the financial management practices observed in the hospitals. The findings were presented using tables to display the summary statistics and charts and graphs to illustrate trends and comparisons across different financial indicators. Additionally, inferential analyses, including Pearson correlation and multiple linear regression, were conducted to assess relationships between variables and determine the predictors influencing the financial sustainability of the hospitals. Findings: The study achieved an 89.6% response rate, revealing a predominantly male (53.4%) and mature workforce, with most respondents aged 31–40 and over 51. Significant positive relationships were found between financial sustainability and financial planning and control practices (β1 = 0.241, p = 0.003), financing and funding practices (β2 = 0.231, p = 0.002), and working capital management practices (β3 = 0.332, p = 0.000). Financial management practices explained 39.2% of the variation in financial sustainability. Health sector regulations significantly moderated the relationship between financial planning and control practices and financial sustainability (β4a = -0.215, p = 0.035), but had no significant moderating effect on financing or working capital management practices. Unique Contribution to Theory, Practice and Policy: The study findings align with several financial theories, providing valuable insights into financial management practices in mission hospitals. They support the Pecking Order Theory by highlighting the prioritization of internal financing to maintain autonomy and minimize information asymmetry. Additionally, the findings contribute to the Cash Conversion Cycle Theory, emphasizing the role of efficient working capital management in enhancing liquidity and reducing the need for external financing. Moreover, the research supports contingency theory by underscoring the influence of financial management practices on financial sustainability and advocating for tailored financial strategies in mission hospitals. The study also contributes to the theory of budgeting by stressing the importance of systematic budgeting in financial decision-making for resource allocation and financial stability. These insights offer valuable guidance for both policymakers and practitioners in mission hospitals, informing policy decisions and providing practical recommendations to enhance financial management practices and achieve greater financial sustainability in these healthcare institutions.

Research background: Big data-driven artificial Internet of Things (IoT) fintech algorithms can provide real-time personalized financial service access, strengthen risk management, and manage, monitor, and mitigate transaction operational risks by operational credit risk management, suspicious financial transaction abnormal pattern detection, and synthetic financial data-based fraud simulation. Blockchain technologies, automated financial planning and investment advice services, and risk scoring and fraud detection tools can be leveraged in financial trading forecasting and planning, cryptocurrency transactions, and financial workflow automation and fraud detection. Algorithmic trading and fraud detection tools, distributed ledger and cryptocurrency technologies, and ensemble learning and support vector machine algorithms are pivotal in predictive analytics-based risk mitigation, customer behavior and preference-based financial product and service personalization, and financial transaction and fraud detection automation. Credit scoring and risk management tools can offer financial personalized recommendations based on customer data, behavior, and preferences, in addition to transaction history, by generative adversarial and deep learning recurrent neural networks. Purpose of the article: We show that blockchain and edge computing technologies, generative artificial IoT-based fintech algorithms, and transaction monitoring and credit scoring tools can be harnessed in financial decision-making processes and loan default rate mitigation for transaction, payment, and credit process efficiency. Generative and predictive artificial intelligence (AI) algorithmic trading systems can drive coherent customer service operations, provide tailored financial and investment advice, and influence financial decision processing, while performing real-time risk assessment and financial and trading risk scenario simulation across fluctuating market conditions. Fraud and money laundering prevention tools, blockchain and financial transaction technologies, and federated and decentralized machine learning algorithms can articulate algorithmic profiling-based transaction data patterns and structures, credit assessment, loan repaying likelihood prediction, and interest rate and credit lending risk management by real-time financial pattern and economic forecast-based credit analysis across investment payment and transaction record infrastructures. Methods: Research published between 2023 and 2024 was identified and analyzed across ProQuest, Scopus, and the Web of Science databases by use of screening and quality assessment software systems such as Abstrackr, AMSTAR, AXIS, CADIMA, CASP, Catchii, DistillerSR, Eppi-Reviewer, MMAT, Nested Knowledge, PICO Portal, Rayyan, ROBIS, and SRDR+. Findings & value added: The main value added derived from the systematic literature review is that generative AI-based operational risk management, fraud detection, and transaction monitoring tools can provide personalized financial support and services and clarify financial and credit decisions and operations by financial decision-making process automation in dynamic business environments based on fraud detection capabilities and transaction data analysis and assessment. The benefits for theory and current state of the art are that credit risk and financial forecasting tools, artificial IoT-based fintech and generative AI algorithms, and algorithmic trading and distributed ledger technologies can be deployed in financial decision-making and customer behavior pattern optimization, credit score assessment, and money laundering and fraudulent payment detection. Policy implications reveal that investment management and algorithmic credit scoring tools can streamline financial activity operational efficiency, design financial planning analysis and forecasting, and carry out financial service and transaction data analysis for informed transaction decision-making and fraudulent behavior pattern and incident detection, taking into account credit history and risk evaluation and improving personalized experiences.

Allen and Meyer's(1990) 3-component model of organizational commitment(OC) was investigated using exploratory structural equation modeling(ESEM) with samples of full-time social workers at social welfare organizations in South Korea. Confirmatory factor analysis(CFA) has been at the heart of testing factor structure of the 3-component model in organizational commitment research wherein each scale of the affective, continuance, and normative commitment is reported to measure conceptually and empirically separable construct. The present study applied ESEM, specifically ‘intra-scale’ and ‘inter-scale’ ESEM, to scrutinize factor structure of the 3-component scales. ESEM methodology uses exploratory approach in that all cross-loadings are estimated between each measure and factors, with uniqueness correlated according to the researcher's hypotheses as in CFA. In this respect, ESEM can be viewed as an open approach to item analysis distinguished from the conventional (closed) approach, such as EFA and CFA. This study provided detailed assessment of the 3-component model through comparisons of factor structures estimated by EFA, CFA and ‘intra’ ESEM, followed by the ‘inter’ ESEM conducted on all other variables(assumed to be similar constructs with or antecedents of OC). As a result, the ‘intra-scale’ ESEM showed a substantially better fit and yielded more discriminated factors(less correlated) than did EFA and CFA that are models for planned scale. The ‘inter-scale’ ESEM revealed how seriously method effect can distort an original factor structure in empirical data measured together with multiple scales of other constructs. Using ESEM has advantages of estimating common factor structures, controlling for common method effect that are typically included in measures in applied research. Also, it allows for much more possibilities that each item can measure multiple constructs so as to reveal more realistic factor structures. Taken together, the present results suggest a need to conceptualize and validate a new scale for organizational commitment reflecting Korean culture.

Allen and Meyer's(1990) 3-component model of organizational commitment(OC) was investigated using exploratory structural equation modeling(ESEM) with samples of full-time social workers at social welfare organizations in South Korea. Confirmatory factor analysis(CFA) has been at the heart of testing factor structure of the 3-component model in organizational commitment research wherein each scale of the affective, continuance, and normative commitment is reported to measure conceptually and empirically separable construct. The present study applied ESEM, specifically ‘intra-scale’ and ‘inter-scale’ ESEM, to scrutinize factor structure of the 3-component scales. ESEM methodology uses exploratory approach in that all cross-loadings are estimated between each measure and factors, with uniqueness correlated according to the researcher's hypotheses as in CFA. In this respect, ESEM can be viewed as an open approach to item analysis distinguished from the conventional (closed) approach, such as EFA and CFA. This study provided detailed assessment of the 3-component model through comparisons of factor structures estimated by EFA, CFA and ‘intra’ ESEM, followed by the ‘inter’ ESEM conducted on all other variables(assumed to be similar constructs with or antecedents of OC). As a result, the ‘intra-scale’ ESEM showed a substantially better fit and yielded more discriminated factors(less correlated) than did EFA and CFA that are models for planned scale. The ‘inter-scale’ ESEM revealed how seriously method effect can distort an original factor structure in empirical data measured together with multiple scales of other constructs. Using ESEM has advantages of estimating common factor structures, controlling for common method effect that are typically included in measures in applied research. Also, it allows for much more possibilities that each item can measure multiple constructs so as to reveal more realistic factor structures. Taken together, the present results suggest a need to conceptualize and validate a new scale for organizational commitment reflecting Korean culture.

Purpose As public awareness of the concept of sustainable development has increased, a new investor market has appeared. These investors will only make investments in sustainable financial instruments. Yet, how corporate managers can effectively exploit this new financing concept to make their companies risk resilient remains unaddressed. This study, a conceptual research, aims to examine the impact of sustainable finance (SF) on business risk resilience (BR) and the impact of SF on risk management infrastructure (RI). It also addresses the impact of RI on BR and the mediating effect of the former between SF and BR in the corporate world. Finally, this research explores the moderating effect of managerial capability (MC) and firm technology-focused innovation capability (IC) between SF and RI. Design/methodology/approach This study incorporates both theoretical and empirical works in the sustainability, innovation, risk management and HRM fields. Afterwards, it constructs a conceptual model alongside suppositions that can be tested in further studies. Findings This study proposes that SF will enhance BR and RI. Moreover, RI will promote BR and positively intervene between SF and BR. Furthermore, MC and IC will reinforce the SF–RI impact such that the SF–RI impact will be strengthened for companies whose MCs and ICs are high than low. Research limitations/implications This research affords suggestions for researchers in multidisciplinary fields. It reinforces BR and RI by introducing SF, MC and IC as tactical devices. It also serves as a reference point for forthcoming academics to investigate this conceptual model, empirically, in diverse industries worldwide. Practical implications Practical lessons for finance, investment and risk managers, as well as corporate investors are discussed. Originality/value This study provides a new research model that demonstrates how SF can be exploited to promote BR and build RI. It also shows how RI can bolster BR and how RI can connect SF to BR. This new model also exhibits how MC and IC moderate the impacts of SF and RI. Thus, it attempts to advance existing knowledge and theoretical frameworks.

The Statistics Anxiety Rating Scale (STARS) is a 51-item scale commonly used to measure college students’ anxiety regarding statistics. To date, however, limited empirical research exists that examines statistics anxiety among ethnically diverse or first-generation graduate students. We examined the factor structure and reliability of STARS scores in a diverse sample of students enrolled in graduate courses at a Minority-Serving Institution (n = 194). To provide guidance on assessing dimensionality in small college samples, we compared the performance of best-practice factor analysis techniques: confirmatory factor analysis (CFA), exploratory structural equation modeling (ESEM), and Bayesian structural equation modeling (BSEM). We found modest support for the original six-factor structure using CFA, but ESEM and BSEM analyses suggested that a four-factor model best captures the dimensions of the STARS instrument within the context of graduate-level statistics courses. To enhance scale efficiency and reduce respondent fatigue, we also tested and found support for a reduced 25-item version of the four-factor STARS scale. The four-factor STARS scale produced constructs representing task and process anxiety, social support avoidance, perceived lack of utility, and mathematical self-efficacy. These findings extend the validity and reliability evidence of the STARS inventory to include diverse graduate student populations. Accordingly, our findings contribute to the advancement of data science education and provide recommendations for measuring statistics anxiety at the graduate level and for assessing construct validity of psychometric instruments in small or hard-to-survey populations.

This study explored the financial literacy and planning practices of Overseas Filipino Workers (OFWs) in Bahrain, focusing on how demographic factors shape their financial behavior and decision-making. Using a quantitative descriptive research design, data were processed through SPSS and analyzed with t-tests, ANOVA, and Pearson correlation. The results highlighted significant variations in financial literacy based on education, job type, and number of dependents. Respondents with higher educational attainment and those engaged in white-collar work demonstrated stronger financial outcomes. However, overall financial literacy was generally limited, particularly in the areas of financial awareness, knowledge, behavior, culture, and risk management, challenges that were more evident among those with heavier financial responsibilities. Similarly, financial planning practices covering investment, budgeting, taxation, risk management, retirement, and estate planning were found to be insufficient. Correlation analysis further revealed that financial awareness, knowledge, behavior, culture, risk management, and the use of e-finance were positively linked to better financial planning, especially in investment, budgeting, and risk management. Conversely, estate planning showed no significant association with respondents’ financial attitudes. The findings emphasize the importance of developing targeted financial education initiatives. Programs that strengthen skills in investment, budgeting, and risk management are especially needed to empower OFWs to make informed financial choices, secure their long-term stability, and build resilience for themselves and their families. Keywords: OFW Financial literacy, OFW financial planning, OFW investment planning, OFW risk management insurance.

Cross-loadings are common in multidimensional instruments; however, they cannot be appropriately addressed in conventional structural equation modeling (SEM) owing to the assumption of zero cross-loadings in standard confirmatory factor analysis (CFA). Although it has been proposed that exploratory structural equation modeling (ESEM) and Bayesian structural equation modeling (BSEM) can address this issue more flexibly, their performance in structural parameter estimation has not been adequately compared. This study uses simulated data to evaluate and compare SEM, ESEM, and BSEM in estimating structural models under different manipulation conditions (i.e., sample size, target loading, cross-loading, and path coefficient). The results demonstrated that the performances of these approaches were similar in the case of zero cross-loadings. SEM performed worse as cross-loadings increased, and the performance of BSEM significantly depended on the accuracy of the priors for cross-loadings. ESEM was inferior to BSEM with correctly specified prior means for cross-loadings in most evaluation measures and exhibits unstable performance in conditions with small target loadings. Recommended strategies for selecting an appropriate modeling approach are discussed based on our findings.

Minor cross-loadings on non-targeted factors are often found in psychological or other instruments. Forcing them to zero in confirmatory factor analyses (CFA) leads to biased estimates and distorted structures. Alternatively, exploratory structural equation modeling (ESEM) and Bayesian structural equation modeling (BSEM) have been proposed. In this research, we compared the performance of the traditional independent-clusters-confirmatory-factor-analysis (ICM-CFA), the nonstandard CFA, ESEM with the Geomin- or Target-rotations, and BSEMs with different cross-loading priors (correct; small- or large-variance priors with zero mean) using simulated data with cross-loadings. Four factors were considered: the number of factors, the size of factor correlations, the cross-loading mean, and the loading variance. Results indicated that ICM-CFA performed the worst. ESEMs were generally superior to CFAs but inferior to BSEM with correct priors that provided the precise estimation. BSEM with large- or small-variance priors performed similarly while the prior mean for cross-loadings was more important than the prior variance.

Sustainable finance is playing an unavoidable role in achieving SDGs at world level. It demands the contribution from all the stake holders for the holistic development in any country. The basic need for the same is accessibility of finance. Traditional finance can meet the demand of present to some extend but for long term future and its growth, it is crucial for all the entities to appreciate the importance of sustainable development through sustainable finance. This study is being done with the objective of justifying the role and positive influence of maintainable finance on acceptable development. With the use of well-structured questionnaire data is collected from 1201 respondents including individuals and financial professional in South Gujarat region. Responses are analyzed through various advance statistical techniques like Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM). Using these advanced statistical techniques, it is found that Sustainable finance greatly influence achievement of continuous development. The results of current research enable that sustainable finance can assist in attaining the specific SDGs like no poverty, zero hunger, environmental issues etc. Also, it is found that it is important for all the entities to come together for attainment of sustainable development through the availability and use of sustainable finance. Hence, the study would help financial service providers, companies and policy makers while preparing for the roadmap of sustainable development.

In the face of mounting environmental, social, and governance (ESG) challenges, sustainable finance has emerged as a transformative force, channelling capital towards sustainable practices and fostering a more resilient global economy. This paper delves into the difficult position of sustainable finance in addressing ESG issues, highlighting its multifaceted technique to mitigating environmental degradation, selling social well-being, and improving company governance. Sustainable finance integrates ESG considerations into investment decisions, risk management, and financial reporting, encompassing green finance and social finance. It drives the transition towards a low-carbon economy by directing investments towards renewable energy, energy efficiency, and sustainable infrastructure. Additionally, it promotes social inclusion by financing initiatives that enhance education, healthcare, and access to financial services for underserved communities. Moreover, sustainable finance strengthens corporate governance by encouraging transparent and accountable practices that benefit shareholders, employees, and society. The paper also examines sustainable finance’s impact on financial performance. While some suggest a trade-off between ESG considerations and financial returns, evidence shows sustainable investments can deliver comparable or superior risk-adjusted returns. This positive correlation is attributed to factors such as reduced operational costs, enhanced brand reputation, and improved risk management practices associated with ESG-compliant companies. In conclusion, sustainable finance is a powerful tool for addressing ESG challenges and driving a transition towards a more sustainable and equitable world. By integrating ESG considerations into financial decision-making, sustainable finance catalyses positive change for a resilient and prosperous future.

This study examines the impact of financial control on the financial sustainability of non-governmental organizations (NGOs) in Dar es Salaam, Tanzania. Specifically, it assesses how transparency in financial reporting, compliance with financial regulations, and internal financial oversight mechanisms contribute to NGO financial sustainability. The study is based on Agency Theory it and follows a Positivist research paradigm with a Descriptive research design and quantitative methodology. A total of 243 respondents, including program managers, grant managers, accountants, senior management team members, and finance officers, were selected using purposive sampling. Primary data were collected through structured questionnaires, while secondary data were obtained from financial reports and policy documents. Data analysis involved both descriptive and inferential statistics, with correlation analysis and simple regression modeling were used to determine the strength and significance of the relationships between financial control mechanisms and financial sustainability. The findings reveal a strong positive correlation (R = 0.757) between financial controls and financial sustainability, with 57.3% of the variation in financial sustainability explained by financial controls (R² = 0.573, p = 0.026). This suggests that effective financial controls, significantly enhance the long-term financial stability of NGOs. The study concludes that robust financial control mechanisms such as clear financial reporting, strict regulatory compliance, and strong internal oversight are crucial for NGO financial sustainability. It recommends that NGOs in Dar es Salaam enhance their financial management frameworks to improve accountability and ensure long-term operational stability. Future research should explore specific financial control practices with the highest impact on sustainability and their applicability across different regions and types of NGOs.

Investigators frequently use the Perceived Stress Scale (PSS-10) to evaluate the extent to which external demands exceed perceived capacity to manage pressure. Analysts utilizing confirmatory factor analysis (CFA) assert that a bifactor model best fits PSS-10 data, though support exists for a two-factor conceptualisation. Since theorists contend that CFA has limitations, this paper assessed whether exploratory structural equation modelling (ESEM) provided a superior factorial solution. Accordingly, this research assessed the adequacy of two-factor vs. bifactor models using CFA and ESEM. Additionally, analyses tested convergent validity, invariance, and predictive validity in relation to well-being outcomes (Life Satisfaction and Somatic Complaints). In Study 1, 1556 (802 males, 754 females) UK-based participants completed the PSS-10 at time points six months apart. In Study 2, 1630 (838 males, 784 females, eight non-binary) UK-based participants completed the PSS-10 alongside measures of Life Satisfaction and Somatic Complaints. Study 1, using latent modelling, found that the two-factor ESEM model (containing Distress and Counter-Stress factors) produced superior fit (vs. CFA and bifactor solutions). In Study 2, structural equation modelling revealed acceptable predictive validity for the two-factor solution; Distress predicted Somatic Complaints and Counter-Stress predicted Life Satisfaction. Gender (Study 1 and 2) and time (Study 1) demonstrated measurement invariance. Latent means across studies indicated that females (vs. males) scored higher on Distress. Overall, ESEM estimated the PSS-10 more accurately. Findings supported the utility of Distress and Counter-Stress factors for predicting well-being indicators. Future research is necessary to consider this distinction in relation to allied health outcomes.