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Micro, small and medium-sized enterprises (MSMEs) are the backbone of economic development for every economy. They contribute to local economic development through household wealth creation, employment generation and poverty reduction. Despite this pivotal role, MSMEs lack access to finance, and scholarship on the enabling role of financial inclusion on micro, small and medium-sized enterprises' performance is scant. The authors contribute to closing the knowledge gap by examining the enabling effect of financial inclusion on MSMEs using the FinScope MSME 2017 survey for the Kingdom of Eswatini. This paper aims to discuss the aforementioned objective.

The study used the re-centered influence function regression framework to estimate unconditional quantile regressions and the generalized ordered logit model to analyze the data.

The findings from the unconditional quantile regression revealed that small changes in access to bank accounts, saving for business, formal saving, stokvel and informal saving at the 50th and 75th percentiles have a positive and statistically significant effect on microenterprises' annual turnover profit. Conversely, small changes in formal insurance have a mixed effect on annual turnover profit. At the 10th and 25th percentiles, a small increment in insurance reduces annual turnover profit but increases microenterprise annual turnover profit at the 75th percentile. Meanwhile, the evidence from the generalized ordered logit model showed that financial inclusion reduces the likelihood of microenterprises being classified as least developed and increased the chances of microenterprises falling into emerging and developed business categories.

This study makes use of a cross-sectional survey dataset, as a result, it does not infer causal relationships over the long term, but rather an association between the independent and dependent variables.

Overall, formal and informal financial inclusion enhances the annual turnover profit for microenterprises, particularly at the 50th and 75th percentiles in the Kingdom of Eswatini. The authors recommend a specialized institution such as a micro, small and medium-sized partial credit guarantee scheme to improve the quality and affordability of credit for microenterprises, and a mix of financial and non-financial supports depending on the development stage to boost a sustainable microenterprises' sector.

The study uses two advanced cross-sectional techniques, the recentered influence function framework and the generalized ordered logit model to analyze the data. The paper is original and contributes to the discussion of the role of financial inclusion in enabling microenterprises' success in Africa, using the FinScope 2017 survey of microenterprises in Eswatini as a case study.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-10-2020-0689.

Frequently the universities' Placement Service is based on the student's hard profile at the expense of soft traits. On the other side, the “person–organization fit” axiom suggests firms are looking for profiles with specific soft skills to face the increasing level of environmental turbulence. This research aims to understand if high-resilience students also have high academic achievements and how the three components of resilience (emotional intelligence, positive thinking, planfulness) can have different impact on individual performances.

The research was conducted on students enrolled on different courses of studies and years in an Economics and Law faculty. A questionnaire was administered during the first exam session (ante-Covid) and the second and third exam sessions (post-Covid). This questionnaire consists of 84 questions related to planfulness, emotional intelligence and positive thinking, whose combination can be considered a measure of resilience. In fact, the Principal Component Analysis (PCA) was carried to identify these three new variables (the components) based on the 84 initial ones. Finally, an ordered logit model was implemented to verify whether, and in what direction, planfulness, emotional intelligence, positive thinking and Covid 19 (the independent variables) affected the students' performance (the dependent one).

While planfulness positively affected academic performance, emotional intelligence affected it negatively. The impact of positive thinking and Covid was not significant, and thus what emerged from the preliminary analysis of the grades is not confirmed.

This is a case study of a university experience that is paying great care in preparing students to satisfy the firms' work demands. To confirm and refine results the sample will be expanded to other faculties and other life/soft skills will be investigated.

This soft trait approach—that studies how various measures of soft skills are related to course grades—has a two-fold significance by crafting universities' placement activities and facilitating firms' onboarding.

This is a case study of a university experience; a university that is paying great attention to preparing students ready to satisfy the firms' work demands but also citizens capable of supporting the growth of their nation and society in general.

The research can be considered a first step towards the inclusion of the formal evaluation of the students' life skills in their academic path, creating a link with their achievements.

Despite large bodies of research related to the impacts of e-commerce on last-mile logistics and sustainability, there has been limited effort to evaluate urban freight using an equity lens. Therefore, this study proposes a modeling framework that enables researchers and planners to estimate the baseline equity performance of a major e-commerce platform and evaluate equity impacts of possible urban freight management strategies. The study also analyzes the sensitivity of various operational decisions to mitigate bias in the analysis.

The model adapts empirical methodologies from activity-based modeling, transport equity evaluation, and residential freight trip generation (RFTG) to estimate person- and household-level delivery demand and cargo van traffic exposure in 41 U.S. Metropolitan Statistical Areas (MSAs).

Evaluating 12 measurements across varying population segments and spatial units, the study finds robust evidence for racial and socio-economic inequities in last-mile delivery for low-income and, especially, populations of color (POC). By the most conservative measurement, POC are exposed to roughly 35% more cargo van traffic than white populations on average, despite ordering less than half as many packages. The study explores the model’s utility by evaluating a simple scenario that finds marginal equity gains for urban freight management strategies that prioritize line-haul efficiency improvements over those improving intra-neighborhood circulations.

Presents a first effort in building a modeling framework for more equitable decision-making in last-mile delivery operations and broader city planning.

Using a panel of over 433,000 college students from over 600 institutions of higher education in the USA, we investigate students’ opinions of leadership skill development during their undergraduate tenure.

The data used in this analysis come from the Higher Education Research Institute, which runs the Cooperative Institutional Research Program (CIRP) housed at the University of California, Los Angeles. Among others, the CIRP administers two surveys known as The Freshman Survey (TFS) and the College Senior Survey (CSS).

The present research supports the extant literature and conventional wisdom of academic and student affairs professionals with regards to engagement in leadership classes or training where students have an opportunity to increase their knowledge bases through course content, and when there are opportunities for them to apply leadership principles, the students are more likely to report an increase in leadership capacity upon completion of their collegiate degree.

If colleges and universities are serious about fulfilling their espoused visions, then it is essential that awareness of leadership courses and applied opportunities be heightened and made a strategic priority to ensure resources are allocated in appropriate places to support these key efforts. It also suggests greater collaboration between academic and student affairs, as well as other departments (e.g. athletics and centers), is needed, as well as prioritizing experiential learning.

Obesity is a significant public health issue. With obesity increasing worldwide, risk factors for obesity need to be better understood and require careful examination. This study aims to examine mental health as a risk factor for obesity using longitudinal data from Australia.

The main identification strategy relies on the recent death of a close friend and a serious injury or illness to a family member as exogenous shocks to mental health.

The authors’ preferred estimates, which account for the endogeneity of mental health, suggest that mental health has a significant negative impact on obesity. This result proves to be robust to a suite of sensitivity checks. Further investigations reveal that poor mental health leads to increased smoking, which also has an effect on obesity.

The study’s findings provide a new perspective on how good mental health helps curb obesity.

This paper aims to examine prospective graduate students' attitudes toward educational loan borrowing in an experimental setting.

Participants were randomly assigned to two treatment groups and one control group. Subjects in experimental group 1 received financial education: a short online course on the economic viability of getting a master's degree and how to finance it with a graduate student loan, while subjects in experimental group 2 received financial education along with information on the availability bias.

Relying on a control group in the assessment of financial literacy education intervention impacts, this research finds positive causal treatment effects on individuals’ attitudes toward debt-financed graduate education. In comparison to the control group, experimental subjects perceived the possibility of going into debt with a graduate loan to complete a master’s degree as less stressful and worrying.

This study has important educational policy implications to prevent students from stopping investing in human capital by perceiving educational loan debt as something stressful or worrying. The results can help potential (and current) grad students develop a feasible financial plan for graduate school by encouraging higher education institutions to implement educational loan information and financial education into university seminar courses for better graduate student loan decision-making.

Student attitudes toward debt have been analyzed in the context of higher education, but only a few researchers internationally have used an experimental design to study personal financial decision-making.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

The paper aims to examine agrochemical traders’ tax morale in three Ghanaian regions.

Primary data were collected from 92 respondents using structured questionnaires. A multistage sampling technique was employed and used in selecting respondents.. Descriptive statistics, factor analysis and quantile regression analysis were used to analyse data obtained via the questionnaires.

The study found tax reporting knowledge, tax calculating knowledge and tax payment knowledge to be the keen factors influencing agrochemical traders’ tax knowledge. It was also revealed that age, religion and marriage positively influence the tax morale of traders. Inversely, gender, high level of education and monthly sales were found to affect tax morale negatively. Moreover, trust (respect, trustworthiness and expertise knowledge) negatively influenced tax morale. Authorities’ tax knowledge and power (sanction and lockdown) were revealed to impact tax morale positively. However, tax morale decreases amongst agrochemical traders with higher tax morale when sanction increases.

Unlike previous studies which focussed on tax morale amongst individuals and firms outside the agribusiness sector, this study examined the tax morale within the informal agrochemical trading sector, which has recently attracted colossal patronage due to the high usage of agrochemicals amongst farmers in Africa and Ghana. This study also assumed tax morale to be at different levels; hence the factors that affect the morale at different levels differ. Therefore, the study examined the factors influencing tax morale amongst agrochemical traders by segregating tax morale into quartiles. Relating to theory, the economic deterrence theory was used to ground the study, which is not usually used in most tax morale studies.

Although the Net Promoter Score (NPS) index is simple, NPS has weaknesses that make NPS's interpretation misleading. The main criticism is that identical index values can correspond to different levels of customer loyalty. This makes difficult to determine whether the company is improving/deteriorating in two different years. The authors describe the application of statistical tools to establish whether identical values may/may not be considered similar under statistical hypotheses.

Equal NPSs with a “similar” component composition should have a two-way table satisfying marginal homogeneity hypothesis. The authors compare the marginals using a cumulative marginal logit model that assumes a proportional odds structure: the model has the same effect for each logit. Marginal homogeneity corresponds to null effect. If the marginal homogeneity hypothesis is rejected, the cumulative odds ratio becomes a tool for measuring the proportionality between the odds.

The authors propose an algorithm that helps managers in their decision-making process. The authors' methodology provides a statistical tool to recognize customer base compositions. The authors suggest a statistical test of the marginal distribution homogeneity of the table representing the index compositions at two times. Through the calculation of cumulative odds ratios, the authors discriminate against the hypothesis of equality of the NPS.

The authors' contribution provides a statistical alternative that can be easily implemented by business operators to fill the known shortcomings of the index in the customer satisfaction's context. This paper confirms that although a single number summarizes and communicates a complex situation very quickly, the number is ambiguous and unreliable if not accompanied by other tools.