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The overall aim of this research is to propose a research incentive framework for academic staff members at the South African universities of technology (UoTs).

An exploratory case study methodology was applied, while a questionnaire was used to (1) identify the factors that stimulated staff members' research activities; (2) assess what was considered an appropriate way of measuring research productivity and (3) identify appropriate research awards, recognition and rewards. Working from the self-determination theory (SDT), the results of the data analysis were used to develop a framework for ensuring crowding-in of research incentives into intrinsic motivation. This framework is anchored on the three main components (competence, autonomy and social relatedness) of the cognitive evaluation theory (CET) that provides guidelines for the design of a research incentive system.

Intrinsically motivated researchers tend to conduct research for their inherent satisfaction because it meets their basic individual psychological need for competence. Existing research incentives and productivity systems fail to provide intrinsic motivation for researchers. Recommendation for a framework for designing research incentive systems is centred on the researchers themselves. This approach contributes to a research environment that provides space for autonomy, creativity, flexibility and innovation and consequently a successful research output that is hinged on the ability to keep researchers intrinsically motivated.

A conceptual framework is proposed specifically for technically focused UoT suggesting that crowding-in the motivation of researcher incentives results in improved intrinsic-based motivation. The autonomy of researchers in particular is regarded as the most important driver of such motivation, with the availability of resources, collegiality and research skills and development ranking as the most important aspects specifically driving intrinsic motivation. The framework not only provides a tool for institutions of higher education focused on developing the technical skills, but also offers management at any type of university challenged with low research outputs and a poor research ethos with an alternative method to improve both the quantity and quality of research outputs.

This paper aims to report on the bibliometric research trends on the application of machine learning/intelligent systems in the prediction of food contamination and the surveillance of foodborne diseases.

In this study, Web of Science (WoS) core collection database was used to retrieve publications from the year 1996–2021. Document types were classified according to country of origin, journals, citation and key research areas. The bibliometric parameters were analyzed using VOSviewer version 1.6.15 to visualize the international collaboration networks, citation density and link strength.

A total of 516 articles across 6 document types were extracted with an average h-index of 51 from 10,570 citations. The leading journal in publications was Science of the Total Environment (3.6%) by Elsevier and the International Journal of Food Microbiology (2.5%). The United States of America (USA) (24%) followed by the People's Republic of China (17.2%) were the most influential countries in terms of publications. The top-cited articles in this study focused on themes such as contamination from packaging materials and on the strategies for preventing chemical contaminants in the food chain.

This report is significant because the public health field requires innovative strategies in forecasting foodborne disease outbreaks to advance effective interventions. Therefore, more collaboration need to be fostered, especially in developing nations regarding food safety research.