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Bootstrapping is a practitioner-based term adopted in entrepreneurship to describe the techniques employed in micro, small and medium-sized enterprises (MSMEs) to minimise the need for external funding by securing resources at little or no cost and applying strategies to effectively use resources. Working capital management (WCM) is a term used in financial management to define a set of practices used to manage business resources, including cash management. This paper explores the overlap and divergence between these two disciplinary distinct concepts.

A dual methodology is employed. First, the usage of the two terms in prior literature is analysed and synthesised. Second, the study uses factor analysis to explore how bootstrapping practices described by owners of 167 established MSMEs relate to the components of WCM in financial management.

The factor analysis identifies two main bootstrapping practices employed by MSMEs: (1) delaying payments and owner-related bootstrapping and (2) customer-related bootstrapping. Delaying payments is an integral practice in trade payables management and customer-related bootstrapping includes practices that are integral to trade receivables management. Therefore, links between bootstrapping practices and WCM practices are firmly established.

The study is not without limitations. Based on cross-sectional evidence for established firms in Ireland only, future studies could explore cross-country longitudinal panel data to fully examine life cycle and sectoral effects, as well as other external shocks (for example, COVID-19) on bootstrapping and WCM practices. This study does not explain why some factors (for example, joint utilisation and inventory management) are present in some bootstrapping studies and not in others; further case study research might help explain this. Finally, changes in the business environment facing start-ups and established enterprise, including increased digitalisation, online trading, self-employment, remote hub working and sustainability, offer new avenues for bootstrapping research.

This is the first study to comprehensively explore the conceptual and empirical links between bootstrapping and WCM. This study will enable researchers and practitioners in these two distinct disciplines to learn from each other. Accounting researchers and practitioners can broaden their understanding of how WCM “works” in MSME settings. Similarly, entrepreneurship researchers and practitioners can deepen their understanding of how bootstrapping can be adopted by businesses to manage resources effectively.

The Chinese fund market has witnessed significant developments in recent years. However, although there has been a range of studies assessing fund performance in developed industries, the rapidly developing fund industry in China has received very little attention. This study aims to examine the performance of open-end securities investment funds investing in Chinese domestic equity during the period May 2003 to September 2020. Specifically, applying a non-parametric bootstrap methodology from the literature on fund performance, the authors investigate the role of skill versus luck in this rapidly evolving investment funds industry.

This study evaluates the performance of Chinese equity securities investment funds from 2003–2020 using a bootstrap methodology to distinguish skill from luck in performance. The authors consider unconditional and conditional performance models.

The bootstrap methodology incorporates non-normality in the idiosyncratic risk of fund returns, which is a major drawback in “conventional” performance statistics. The evidence does not support the existence of “genuine” skilled fund managers. In addition, it indicates that poor performance is mainly attributable to bad stock picking skills.

The authors find that the top-ranked funds with positive abnormal performance are attributed to “good luck” not “good skill” while the negative abnormal performance of bottom funds is mainly due to “bad skill.” Therefore, sensible advice for most Chinese equity investors would be against trying to “pick winners funds” among Chinese securities investment funds but it would be recommended to avoid holding “losers.” At the present time, investors should consider other types of funds, such as index/tracker funds with lower transactions. In addition, less risk-averse investors may consider Chinese hedge funds [Zhao (2012)] or exchange-traded fund [Han (2012)].

The paper makes several contributions to the literature. First, the authors examine a wide range (over 50) of risk-adjusted performance models, which account for both unconditional and conditional risk factors. The authors also control for the profitability and investment risks in Fama and French (2015). Second, the authors select the “best-fit” model across all risk-adjusted models examined and a single “best-fit” model from each of the three classes. Therefore, the bootstrap analysis, which is mainly based on the selected best-fit models, is more precise and robust. Third, the authors reduce the possibility that findings may be sample-period specific or may be a survivor (upward) biased. Fourth, the authors consider further analysis based on sub-periods and compare fund performance in different market conditions to provide more implications to investors and practitioners. Fifth, the authors carry out extensive robustness checks and show that the findings are robust in relation to different minimum fund histories and serial correlation and heteroscedasticity adjustments. Sixth, the authors use higher frequency weekly data to improve statistical estimation.

This study investigates the existence of the environmental Kuznets curve (EKC) for deforestation for Ghana over the 1962–2018 the time period.

The study employs a time-varying approach, the bootstrap rolling window Granger causality test to achieve its set objectives.

The results from our study reveals an inverted “N” shape EKC for deforestation, implying that deforestation will initially decrease with increases in economic growth up to a certain income threshold and increases with further increases in economic growth beyond this income threshold up to a higher income threshold and then decrease with further increases in economic beyond the higher income threshold.

The results from the study project show that over time economic growth can serve as a natural panacea to cure and mitigate the ills of deforestation that have plagued Ghana's forests over the years.

The results further highlight the important role of strong institutions in fighting the deforestation menace.

The originality of this study lies in its methodology which allows for feedback from deforestation to the economy. This is in contrast to earlier studies on the EKC for deforestation which allowed causality only from deforestation to the economy.

This paper examines the inflation hedging performance separated into expected and unexpected inflation in Korean equity funds. In particular, using the bootstrap approach, we identify whether the inflation hedging performance is based on skill or luck. We use the equity funds of the average net asset value (NAV) over 5 billion Korean won and over the 80% stock position. The sample data cover the period from January 2002 to March 2015. The main findings are as follows. First, most equity funds demonstrate a hedging performance against the unexpected inflation shock and this hedging performance seems to come from the fund manager’s skill. Second, our findings are robust across the sieve bootstrap results for the serial dependence and heteroscedasticity. Third, the equity funds have slightly different inflation hedging performances depending on their investment style. Among the investment styles, small-cap, growth, or small and growth style funds demonstrate more hedging performance against unexpected inflation shock. This hedging performance seems to come from the fund manager’s skill. Finally, in the case of the funds separated by winner and loser, the winner funds have more hedging performance for unexpected inflation shock than the loser funds.

This study aims to examine the role of an overall model fit assessment in the context of partial least squares path modeling (PLS-PM). In doing so, it will explain when it is important to assess the overall model fit and provides ways of assessing the fit of composite models. Moreover, it will resolve major concerns about model fit assessment that have been raised in the literature on PLS-PM.

This paper explains when and how to assess the fit of PLS path models. Furthermore, it discusses the concerns raised in the PLS-PM literature about the overall model fit assessment and provides concise guidelines on assessing the overall fit of composite models.

This study explains that the model fit assessment is as important for composite models as it is for common factor models. To assess the overall fit of composite models, researchers can use a statistical test and several fit indices known through structural equation modeling (SEM) with latent variables.

Researchers who use PLS-PM to assess composite models that aim to understand the mechanism of an underlying population and draw statistical inferences should take the concept of the overall model fit seriously.

To facilitate the overall fit assessment of composite models, this study presents a two-step procedure adopted from the literature on SEM with latent variables.

This paper clarifies that the necessity to assess model fit is not a question of which estimator will be used (PLS-PM, maximum likelihood, etc). but of the purpose of statistical modeling. Whereas, the model fit assessment is paramount in explanatory modeling, it is not imperative in predictive modeling.

Coronavirus (COVID-19) pandemic forced nationwide lockdown in India. During the period of lockdown usage of eWallet increased by 44%. With the increased usage of digital transactions, cyber-crime attacks also increased as much as by 86%. The socio-economic environment and the peoples’ mindset in the country yet not ready for this kind of rise in digital transactions. The purpose of this study is to capture “security concern” and “comfortability” in regard to using eWallet during the COVID-19 pandemic situation. The study further investigated the influence of demographics such as gender and income on “security concern” and “comfortability” in using eWallet.

This was an empirical study. The respondents were selected using a purposive sampling method. Only those people who had been using eWallet were included in the survey. The questionnaire was circulated to 100 respondents who agreed to participate in the survey. After scrutiny total of 43 questionnaires were found to be completely filled in all aspects, and thus used for analysis. This study used an innovative multi-method approach for analysis. The hypotheses were tested using two methods: the conventional p-value method and the robust BCa bootstrap method. The effect size was also reported.

The findings suggest that female users are more concerned about eWallet security than male users. This study showed that people from the middle-income group are more concerned about the security of digital payments than the people from the lower-income group.

This study covered the influence of two demographic variables “gender” and “income” on security and comfort in using eWallets. Other demographic variables such as age, education, occupation and area of residence (rural or urban) need to be investigated with the inclusion of rural populations. From the findings of this study, this paper argues that the middle-income group in India is more risk intolerant than the lower-income group while higher and lower-income groups are indifferent. A separate detailed study is recommended for additional support. This study used an innovative multi-method approach of analysis and use of bootstrapping. This may encourage other researchers to adopt such approaches.

This study showed that irrespective of the forceful adoption; security concerns are prevailing and on the rise. This is an alarm to developers and service providers that, although the use of eWallets increased exponentially during this COVID-19 pandemic, it is a forceful adoption and not willful. They should not get deceived by rise in eWallet users and must endeavor to improve the security of eWallets otherwise, there may be a sharp decline in eWallet users once the COVID-19 pandemic is over.

This study attempted to capture the comfortability and security concerns of eWallet users during the COVID-19 pandemic. This study used an innovative multi-method approach of analysis and used bootstrapping in addition to the conventional p-value method to test the significance. This study showed that irrespective of the forceful adoption of eWallets owing to the COVID-19 pandemic, security concerns are prevailing and on the rise. The study confirms that gender has an influence on eWallet security. The findings of this study are in partial conformity with the findings of previous researchers.

Under the background of the overall increase of China's economic policy uncertainty and the urgent need for the transformation and upgrading of the substantial economy, this paper studies the time-varying causality between China's economic policy uncertainty and the growth of the substantial economy through bootstrap rolling window causality test, further refines economic policies and studies the causal differences between different types of economic policies and substantial economic growth, refining the conclusions of previous studies.

This paper first studies the causal relationship between China's economic policy uncertainty and substantial economic growth in the full sample period through bootstrap Granger causality test. Then, the paper tests the short-term and long-term stability of the parameters of the VAR model, and it is found that the model parameters are unstable in both the short and long term, so the results of the Granger causality test of the full sample are not credible. Finally, we conduct a dynamic test of the causal relationship between China's economic policy uncertainty and substantial economic growth by means of rolling window, so as to comprehensively analyze the dynamic characteristics and sudden changes of the relationship between them.

The research shows that economic policy uncertainty in China has a significant inhibiting effect on the growth of substantial economy. Growth in the substantial economy will drive up economic policy uncertainty before 2016 and restrain it after that. In addition, this paper further subdivides economic policy uncertainty to explore the causal differences between different types of economic policy uncertainty and substantial economic growth. The test results show that the relationship between them has obvious policy heterogeneity. The fiscal policy uncertainty and the monetary policy uncertainty, as the main policy means in China, has a significant impact on the growth rate of substantial economy in multiple ranges, but the effect time is short. Although trade policy uncertainty has a significant impact on the growth rate of substantial economy only during the financial crisis, the effect lasts for a long time. The impact of exchange rate and capital account policy uncertainty on the growth rate of substantial economy is mainly reflected after 2020.

The values of this paper are as follows: First, the economic policy uncertainty is combined with the growth of substantial economy, which makes up the gap of previous studies. Second, the economic policy uncertainty is further subdivided. The paper explores the causal differences between different types of economic policy uncertainties and the growth of substantial economy, so as to make the research more detailed. Finally, different from the previous static analysis, this paper uses dynamic model to examine the relationship between China's economic policy uncertainty and the growth of substantial economy from a dynamic perspective, with richer research conclusions.

Confidence intervals play a crucial role in economics and finance, providing a credible range of values for an unknown parameter along with a corresponding level of certainty. Their applications encompass economic forecasting, market research, financial forecasting, econometric analysis, policy analysis, financial reporting, investment decision-making, credit risk assessment and consumer confidence surveys. Signal-to-noise ratio (SNR) finds applications in economics and finance across various domains such as economic forecasting, financial modeling, market analysis and risk assessment. A high SNR indicates a robust and dependable signal, simplifying the process of making well-informed decisions. On the other hand, a low SNR indicates a weak signal that could be obscured by noise, so decision-making procedures need to take this into serious consideration. This research focuses on the development of confidence intervals for functions derived from the SNR and explores their application in the fields of economics and finance.

The construction of the confidence intervals involved the application of various methodologies. For the SNR, confidence intervals were formed using the generalized confidence interval (GCI), large sample and Bayesian approaches. The difference between SNRs was estimated through the GCI, large sample, method of variance estimates recovery (MOVER), parametric bootstrap and Bayesian approaches. Additionally, confidence intervals for the common SNR were constructed using the GCI, adjusted MOVER, computational and Bayesian approaches. The performance of these confidence intervals was assessed using coverage probability and average length, evaluated through Monte Carlo simulation.

The GCI approach demonstrated superior performance over other approaches in terms of both coverage probability and average length for the SNR and the difference between SNRs. Hence, employing the GCI approach is advised for constructing confidence intervals for these parameters. As for the common SNR, the Bayesian approach exhibited the shortest average length. Consequently, the Bayesian approach is recommended for constructing confidence intervals for the common SNR.

This research presents confidence intervals for functions of the SNR to assess SNR estimation in the fields of economics and finance.

The purpose of this paper is to measure the technical inefficiency of dairy farms and subsequently investigate the factors affecting technical inefficiency in the Malaysian dairy industry.

This study uses multi-directional efficiency analysis to measure the technical inefficiency scores on a sample of 200 farm observations and single-bootstrap truncated regression model to define factors affecting technical inefficiency.

Managerial and program inefficiency scores are presented for intensive and semi-intensive production systems. The results reveal marked differences in the inefficiency scores across inputs and between production systems.

Intensive systems generally have lowest managerial and program inefficiency scores in the Malaysian dairy farming sector. Policy makers could use this information to advise dairy farmers to convert their farming system to the intensive system.

The results suggest that the Malaysian Government should redefine its policy for providing farm finance and should target young farmers when designing training and extension programs in order to improve the performance of the dairy sector.

The existing literature on Southeast Asian dairy farming has neither focused on investigating input-specific efficiency nor on comparing managerial and program efficiency. This paper aims to fill this gap.

This paper investigates the development of efficiency and the progress of banking integration in the European Union by checking for convergence among banks of European and Eurozone countries as well as contrasting the results with those of United States banks.

Initially, we employ the two-stage semi-parametric double bootstrap DEA method, which absorbs the effects of possible integration barriers in the measurement of efficiency. Afterwards, we apply a panel data model, in order to investigate the process of banking integration by testing for convergence and for convergent clusters in banking efficiency.

Our main findings show that the bank efficiency of the US is considerably higher than that of the Eurozone and the European Union. Although there is no evidence of convergence across the banking groups, our results indicate the presence of club convergence. We also conclude that the US banking system is closer to convergence than the Eurozone and the European Union banks. Nevertheless, this outcome is subject to change in the future due to the fact that Eurozone and European Union banks' speed of convergence is higher than that of US banks.

Our survey is unique in trying to check for convergence while controlling for country-specific and bank-specific factors that affect the efficiency of European and Eurozone banks. Moreover, recent literature does not compare the convergence of efficiency of Eurozone, European and US banking. Finally, in our paper special consideration was given to the comparison of commercial, cooperative and savings banks, as subsets of our banking groups.