Search results

Using a questionnaire, assesses the hygienic practices of a group of 50 street food vendors in Mauritius. The findings showed that generally, food vendors were quite aware of hygienic conditions, which have to be respected while handling and preparing foods. However, it was found that the majority of them were not implementing their knowledge into practice and still perceived that their products were of relatively low risk to the consumers. It was also found that for more than half of the vending households, street food vending was the main income provider. Despite the role of health inspectors in promoting awareness of the risks which poor hygiene practices may lead to, findings from the present study highlight the need for further health education of food vendors.

This paper aims to evaluate the profile of food truck consumers at gourmet events, identifying their food preferences and opinions about the provision of safe food by this segment.

A total of 750 food truck customers in the city of Curitiba, Brazil, participated in the study. The survey investigated the socio-economic profile of the consumers, the frequency of their food consumption, the criteria for their choice of food trucks, their mean spending value, the method of payment used and the opinion of consumers about food cost and food truck hygiene conditions.

Most consumers were female (62.7 per cent), students (31.3 per cent) and with an average age of 29 ± 10 years. Many customers preferred the consumption of salty foods (84.0 per cent), mainly burgers and kabobs. The consumers reported spending between $6.32 and $9.03 during the events, and men spent more money than women (p = 0.000). Both thought that good conditions of hygiene (81.9 per cent), food presentation (46.9 per cent) and service (48.0 per cent) were more important than the product price (19.3 per cent). Consumers thought that the food trucks had an adequate structure (73.5 per cent) and the food handlers (74.4 per cent) had good conditions of hygiene, ensuring the safety of the food sold.

On account of the convenience sampling in an urban environment, the data cannot be generalized to the entire population of the municipality and to other regions.

There are few studies with food trucks consumers in Brazil, to this writing the largest sample ever used for this type of research in this country. The results were designed to be used by professionals working in the area.

This article examines why Universal Primary Education (UPE) has shifted from being a developing country challenge more broadly in the last half of the 20th century to become largely a sub-Saharan Africa (SSA) challenge today. It discusses a number of national and education sector system-wide challenges that have constrained the implementation of UPE in SSA more so than in other developing regions.

This article reviews the literature and policy documents on a wide range of developing country issues, and discusses why most SSA countries have faced unparalleled challenges in achieving UPE.

SSA governments should take the opportunity offered by their post-pandemic “build back better” efforts to fundamentally reset education policies to address the key causes of this major development failure. The overarching objective must be to develop education systems that are more inclusive, equitable and responsive to national development needs by better serving the large population groups, parts of society and economic sectors that currently derive little benefit from public education spending. This article highlights the urgency and challenges associated with achieving this objective.

Although the main responsibility lies with SSA governments, this paper stresses that the global community will be affected in many ways by how effectively this crisis is addressed. Therefore, this effort merits sustained global support including through more catalytic use of aid.

Enterprise resource planning (ERP) systems are increasingly being adopted by organisations in developing countries. As in industrialised countries, this adoption seems beset by significant rates of failure, leading to a large waste of investment and other resources. This paper seeks to understand why such ERP failure occurs.

The paper moves beyond factor lists to make use of an overall “design‐reality gap” model. The model is applied to a case study of ERP failure in a Jordanian manufacturing firm, analysing the situation both before and during ERP implementation through a mix of interviews, observation and document analysis.

The research finds sizeable gaps between the assumptions and requirements built into ERP system design, and the actual realities of the client organisation. It is these gaps – and the failure to close them during implementation – that underlie ERP project failure.

This study shows the relevance and applicability of the design‐reality gap model to understanding ERP failure. Further research can be undertaken applying the model to other ERP cases, including case studies of success.

The paper draws conclusions about good practice in ERP implementation relating to both risk identification and risk mitigation, which must be based on closing design‐reality gaps. It offers examples of both specific and generic actions that help to achieve this. But it also notes limitations existing in some developing country contexts that may continue to constrain the effective use of enterprise resource planning systems.

The paper provides a new model for understanding ERP project success and failure, and for practical risk mitigation. The design‐reality gap model aims to be comprehensive but also contingent; sensitive to the specific conditions of any individual client organisation.

The rise in business activities coupled with free trade liberalisation across countries has entailed an increase in securities transaction as well as insider trading (IT). In fact, IT is characterised by the influence and usage of some prior knowledge concerning sensitive information of a corporate body which results in a financial benefit to the insider trader. The practice of IT is not only unethical but also illegal and this statement is witnessed by the mushrooming of laws across the globe categorising IT as an offence. However, the type of punishment varies in different countries depending on various factors. Consequently, the purpose of this paper is to assess the adequacy and efficiency of IT laws in the context of a developing country being Mauritius.

To achieve the research objective, the Mauritian laws on IT were compared with the corresponding laws of some developed countries like the USA and the UK. As such, a qualitative research method was adopted. In particular, the black letter approach was used to examine the relevant laws of Mauritius, UK and USA on IT. A comparative analysis was conducted concerning IT laws for each country with the view of suggesting recommendations for Mauritian stakeholders to adopt to enhance the existing legal and regulatory framework on IT.

It was found that Mauritian IT laws are largely inspired from both the US and UK corresponding legislation. However, Mauritian laws need to be strengthened by imposing some more severe penalties in terms of fines and terms of imprisonment like the USA has established. The Mauritian Financial Services Commission as the regulator also needs to play a more active role in disseminating particularities of IT laws, offences and penalties to the civil society at large.

At present, this study will be among the first academic writings on the efficiency of IT laws in Mauritius and also, because existing literature is quite scarce on assessing the adequacy of IT legislation in developing countries, this research aims at filling in the gap in literature. The study is carried out with the aim of combining a large amount of empirical, theoretical and factual information that can be of use to various stakeholders and not only to academics.

The purpose of this paper is geared towards the capture and analysis of network traffic using an array ofmachine learning (ML) and deep learning (DL) techniques to classify network traffic into different classes and predict network traffic parameters.

The classifier models include k-nearest neighbour (KNN), multilayer perceptron (MLP) and support vector machine (SVM), while the regression models studied are multiple linear regression (MLR) as well as MLP. The analytics were performed on both a local server and a servlet hosted on the international business machines cloud. Moreover, the local server could aggregate data from multiple devices on the network and perform collaborative ML to predict network parameters. With optimised hyperparameters, analytical models were incorporated in the cloud hosted Java servlets that operate on a client–server basis where the back-end communicates with Cloudant databases.

Regarding classification, it was found that KNN performs significantly better than MLP and SVM with a comparative precision gain of approximately 7%, when classifying both Wi-Fi and long term evolution (LTE) traffic.

Collaborative regression models using traffic collected from two devices were experimented and resulted in an increased average accuracy of 0.50% for all variables, with a multivariate MLP model.