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The purpose of the study is to describe the implications of strategic lead times (SLTs) for return on investment (ROI).

This study was part of an interactive research project and is based on the logic of theory application leading to theory building. It uses a multiple case study with five holistic single cases. Empirical data (ED) have mainly been collected from interviews and focus groups.

The length of and uncertainty in SLTs have implications for companies' financial performance. These implications vary in strength and can be either direct or indirect. These findings are incorporated into a framework on SLTs' implications for ROI.

The presented array of SLTs' implications for ROI could be further investigated, focussing on their strength. Additionally, it would be interesting to substantiate the findings in the context of environmental and social sustainability (i.e. the triple bottom line).

The findings offer practitioners a rich description and understanding of SLTs' actual implications for financial performance in terms of ROI. This knowledge can support practitioners in analysing supply chain designs based on financial performance.

Using a combination of a relative financial performance measure (ROI) and a set of SLTs (systems perspective), this study focuses on SLTs' actual implications for ROI. The findings provide evidence that different sections of a supply chain can have different implications for revenue, cost and investment (i.e. the three absolute measures related to ROI).

This paper aims to analyze the financial implications associated with Ugandan universities employing e-learning techniques and to suggest sound policy solutions to the problems the investigation found.

To illustrate how e-learning is used in Uganda, the study used a descriptive research design. The study used a quantitative methodology to highlight the various experiences associated with utilizing e-learning platforms from various universities. A questionnaire survey was distributed to university students, and the data were analyzed using descriptive statistics in order to understand the usage of e-learning and Persons correlation to understand the degree of the relationship between the study variables.

The study findings reveal that e-learning approaches in universities are favorably correlated with their financial implications (r = 0.598; p-value 0.05). It was excruciatingly difficult to access online learning resources and platforms, and there was a severe lack of power. Some academic staff members lacked adequate e-learning platform training, and students noted that e-learning eased communication between students and lecturers, whereas Internet subscription and acquisition of mobile devices were expensive.

The research was limited to three districts in western Uganda, and thus, it may not be possible to extrapolate the results to the entire nation.

Universities are open to these changes since the digital world is moving more quickly, overall, and this trend has crept into education as well. Policymakers and other universities may utilize the study's findings to inform their decisions.

The study offers useful insights into how e-learning systems have altered university teaching methods.

This paper aims to assess whether digital financial inclusion (DFI) supports Egypt's CO₂ reduction efforts. More specifically, this paper examines the dynamics between digital finance, traditional financial inclusion (TFI) and renewable energy on carbon emission in Egypt.

The study employed the autoregressive distributive lag (ARDL) model for Egypt over the period 1990–2020 to estimate an extended STIRPAT model for long-run linkages of DFI, traditional bank-based financial inclusion and renewable energy on carbon emissions, along with other control variables.

The results showed that using digital financial services limits carbon emissions in the long run but not in the short run, indicating that Egypt is still in its early stage of digitalization (DFI < 0.5). Moreover, renewable energy proved to have a significant negative impact on carbon emissions in the long run, implying that more investments in renewable energy projects will improve environmental quality.

The findings from this study help policymakers incorporate DFI policies into climate change adaptation strategies and execute better green growth policies that integrate DFI with energy-efficient technologies investments for a better environment.

Foster economic growth and sustinabaility.

This study contributes to the literature by quantifying the DFI in Egypt using a two-stage principal component analysis and then examines its impact on carbon emission reduction efforts. In addition, this paper extends the research on the environment from the perspective of digital finance, making it possible to excavate more deeply into the relationship between financial inclusion and carbon emission and draw more explicit policy implications for sustainable economic growth.

People are increasingly responsible for making sound financial decisions to foster their financial satisfaction and well-being, which magnifies the importance of financial literacy, and this concept and measurement is still not yet crystallized in the literature, specifically capturing different behavior perceptions. Moreover, there is not a distinction based on different classifications of behavior, such as over or underconfidence, to understand the relation between literacy and decision process. To fill this gap, this paper aims to investigate whether the financial literacy conceptual model proposed applies similarly to every group independently of their previous self-confidence perception. For this purpose and quality control, OECD (2016) data were used with a final sample of 1,487 Brazilian citizens. Quantitative analysis technique using partial least squares structural equations path modeling and differences between groups using multi-group analysis was applied. In line with general studies, when analyzing the financial literacy usual model for the group as a whole, financial knowledge construct positively influences self-confidence, and both together positively affect financial behavior. However, for individuals with low financial knowledge and low self-confidence, as well as for those with too much or too little confidence, the model did not hold. Therefore, self-confidence perception influences the way financial knowledge is used for financial decisions and should be addressed in financial education and training to be more effective.

To operationalize the variables and test the paper’s hypotheses, the authors used the methodology developed in OECD (2016), based on the research instrument’s Brazilian application adapted from the questionnaire developed in OECD (2015), with data initially used and made available by Garber and Koyama (2016). Based on the recommendations of Hair Jr et al. (2017a, 2017b), the authors used partial least squares modeling PLS-PM (SmartPLS 3.2.6) to estimate the structural models.

Concerning structural relationships, the final model showed knowledge with a positive influence on self-confidence, self-confidence with a positive effect on behavior and knowledge with a positive influence on behavior, both directly and, through its relationship with self-confidence, indirectly. This underscores that, for the total sample, the greater people’s knowledge and self-confidence, the better their behavior. The unexpected absence of attitude in the final model, even allowing for potential measurement problems, brings up an important reflection on the mediating effect that the self-control variable may exert between attitude and behavior. A person may believe that saving for the future is important (attitude) but whether they actually save (behavior) may depend on self-control, which is needed to prevent immediate gains from being prioritized in practice.

The findings reported so far concern the study’s total sample. However, as expected from the literature review that provides the basis for the sixth and the most important hypothesis, respondents were found to be heterogeneous in terms of knowledge and self-confidence levels. These differences were evaluated by means of multi-group analyses that indicated that the model does not apply to respondents with low knowledge and low self-confidence and to those who are over- and underconfident. This implies inferring that financial education programs may be of little use if they only address technical knowledge development and fail to consider behavioral aspects such as those related to self-confidence, as this paper points out, and others. This signals the importance of diagnosing people’s profiles to enable developing solutions capable of minimizing the presence of behavioral biases. This need to be studied further.

The results imply inferring that financial education programs may be of little use if they only address technical knowledge development and fail to consider behavioral aspects such as those related to self-confidence, as this paper points out, and others. Models must be reviewed in light of natural diferences of cognition and lead to customized financial education.

This signals the importance of diagnosing people’s profiles to enable developing solutions capable of minimizing the presence of behavioral biases. Therefore, not only training topics in personal finance but also a deeper education program since the kindergarden must be considered.

Its practical contribution is to suggest the development of financial education programs that also take account of the potential presence of behavioral biases, which may prevent the misallocation of (scarce) public- and private-sector funds stemming from a limited focus on developing the population’s actual financial knowledge.

The purpose of this paper is to examine the 2009 to 2016 financial performance of the US Hockey Inc., using financial effectiveness indicators and financial efficiency ratios.

With the assistance of financial trend analysis, archival data were used to examine the financial performance (evaluated by net income), financial effectiveness (indicated by total assets and total revenues) and financial efficiency (examined by programme services ratios and return on assets) of US Hockey Inc.

On average, the financial performance of the organization was positive ($30,895 net income per year). Financial effectiveness was steady with increases in assets and revenues. Financial efficiency was poor with 79% of revenues spent on programme services and 1.45% average return on asset.

The results can be generalized to similar national non-profit sports federations but not corporate sports entities with dissimilar financial goals.

The results revealed that national non-profit sports federations can boost their financial performance by maintaining a double strategically focus on both financial effectiveness and financial efficiency.

The study used both financial effectiveness and financial efficiency measures to evaluate the financial performances of a national non-profit sports federation – a neglected approach similar studies.

Financial health of a corporation is a great concern for every investor level and decision-makers. For many years, financial solvency prediction is a significant issue throughout academia, precisely in finance. This requirement leads this study to check whether machine learning can be implemented in financial solvency prediction.

This study analyzed 244 Dhaka stock exchange public-listed companies over the 2015–2019 period, and two subsets of data are also developed as training and testing datasets. For machine learning model building, samples are classified as secure, healthy and insolvent by the Altman Z-score. R statistical software is used to make predictive models of five classifiers and all model performances are measured with different performance metrics such as logarithmic loss (logLoss), area under the curve (AUC), precision recall AUC (prAUC), accuracy, kappa, sensitivity and specificity.

This study found that the artificial neural network classifier has 88% accuracy and sensitivity rate; also, AUC for this model is 96%. However, the ensemble classifier outperforms all other models by considering logLoss and other metrics.

The major result of this study can be implicated to the financial institution for credit scoring, credit rating and loan classification, etc. And other companies can implement machine learning models to their enterprise resource planning software to trace their financial solvency.

Finally, a predictive application is developed through training a model with 1,200 observations and making it available for all rational and novice investors (Abdullah, 2020).

This study found that, with the best of author expertise, the author did not find any studies regarding machine learning research of financial solvency that examines a comparable number of a dataset, with all these models in Bangladesh.

This paper aims to conceptualize and empirically illustrate the challenges that financial market regulation presents to politicians and the organization tasked with specifying regulations and supervising their implementation in the interest of users and consumers of financial instruments. It analyses the problem from the viewpoint of the governor's dilemma and the control/competence conflict, the linked problem of the rent-seeking of agents/intermediators and consumers of financial instruments. Political accountability problems are enhanced by the materiality of the technologies used, i.e. algo trading.

The paper theoretically conceptualizes and empirically illustrates the argument.

The paper finds that regulators of digitalized financial markets are faced with considerable problems and depend on private agents when regulating financial transactions. However, the new technological instruments also offer new possibilities for securing compliance.

Further research should focus more in-depth on the cooperation between public and private actors in the specification and implementation of regulatory details. It should further investigate the conditions which allow regulators to use RegTech in the surveillance of financial firms.

Since financial market transactions are opaque for most users, the creation of more transparency is crucial to hold regulators accountable in their activity of surveillance of financial firms. New algorithm-based technologies may lend important support in doing so.

By linking the different analytical perspectives, i.e. the governor's dilemma vis-à-vis the intermediator or agent and the possible rent-seeking of intermediators, under the condition of a highly developed technology of financial transactions as well as the market structure, the paper offers new insights into the limits as well as new opportunities of regulating financial markets allowing for political accountability of regulators and financial firms.

This paper aims to identify, examine, and present an empirical research design of behavioral finance of potential investors during Covid-19.

A well-structured questionnaire was designed; a survey was conducted among potential investors using convenience sampling, and 200 valid responses were collected. The research work uses multiple regression and discriminant function analysis to evaluate the influence of cognitive factors on the financial decision-making of investors.

Recency and familiarity bias are proven to have the highest significant impact on the financial decisions of investors followed by confirmation bias. Overconfidence bias had a negligible effect on the decision-making process of the respondents and found insignificant.

Covid-19 is a temporary phase that may lead to changes in financial behavior and investors’ decisions in the near future.

The paper will help academicians, scholars, analysts, practitioners, policymakers and firms dealing with capital markets to execute their job responsibilities with respect to the cognitive bias in terms of taking financial decisions.

The present investigation attempts to fill the gap in the literature on the intended topic because it is evident from literature on the chosen subject that no study has been undertaken to evaluate the impact of cognitive biases on financial behavior of investors during Covid-19.

To investigate the consequence of COVID-19 pandemic on the financial reporting and disclosure (FRD) practices, the study has been conducted. Moreover, this paper highlights the significance of FRD practices in any emergency period and its relevance with legitimacy theory in Bangladesh Perspective.

The COVID-19 pandemic has adverse impact on business. Hence, all the business activities have been categorized into five major aspects which are financial factors, business operations, business contracts, business value and stakeholders. These five major activities have been considered as independent variable. By analyzing various policy recommendations and guidelines of global and local accounting bodies, a structured questionnaire was developed in association with related IAS and IFRSs. Then, it was distributed among the accounting professionals of Bangladesh who are currently engaged in financial statement preparation and auditing services. Finally, data was analyzed through structural equation modeling (SEM) to test the hypothetical relationship between dependent variable and independent variable.

This study finds that financial factors, business contracts and stakeholders have significant relationship with the financial reporting and disclosure practices during the COVID-19 pandemic period. However, business operation and business value have no significant relationship with financial reporting and disclosure practices.

This study tries to analyze why and how firms should disclose essential information (both financial and non-financial) to the financial statement users during the COVID-19 pandemic. This study can be used as benchmark to issue a separate policy or standard for reporting any kind of adverse event in the financial reporting and disclosure practices.

To our best knowledge, we believe that this is first kind of study undertaken to investigate the consequence of COVID-19 pandemic on the FRD practices in the context of Bangladesh. This study is kind of exploratory in nature. Hence, future studies can explore industry-based financial reporting and disclosure practice in any pandemic period.

This paper aims to assess the impact of digital financial innovation on financial system development in Common Market for eastern and Southern Africa (COMESA). This paper evaluates the dynamic relationship between digital financial innovation measures and financial system development using time series data from COMESA countries for the period 1997–2019.

A dynamic autoregressive distributed lag model (ARDL) was adopted and the mean group (MG), pooled mean group (PMG) and dynamic fixed effect (DFE) of the model were estimated to evaluate the short- and long-run impact. In addition, the dynamic generalized method of moments (DGMM) was adopted for a robustness check. The Hausman test results show PMG to be the most consistent and efficient estimator, while the coefficient of lagged dependent variable of different GMM is less than the fixed effect coefficient, and, as such, suggests system GMM is the most suitable estimator. Data for the study were sourced from World Bank Development Indicator (WDI, 2020), World Governance Indicator (WGI, 2020) and World Bank Global Financial Development Database (GFD, 2020).

The result shows that digital financial innovation significantly impacts financial system development in the long run. As such, the evidence revealed that automated teller machines (ATMs), point of sale (POS), mobile payments (MP) and mobile banking are significant and contribute positively to financial system development in the long run, while mobile money (MM) and Internet banking (INB) are insignificant but exhibit positive and inverse relationship with financial development respectively. Further investigation revealed that institutional quality and a stable macroeconomic environment including their interactive term are significantly imperative in predicting financial system development in the COMESA region.

Researchers recommend a cohesive and conscious policy that would checkmate the divergence in the short run and suggest a common regional innovative financial strategy that could be pursued to incentivize technology transfer needed to promote financial system development in the long run. More so, plausible product and process innovations may be adapted to complement innovative institutions in the different components of the COMESA financial system.

Digital financial innovation services if well managed increase the inherent benefits in financial system development.

To the best of the authors’ knowledge, this paper presents new background information on digital financial innovation that may stimulate the development of the financial system, particularly in the COMESA region. It also exposes the relevance of digital financial innovation, institutional quality and stable macroeconomic environment as well as their interactive effect on COMESA financial system development.