The emergence of the 2019 novel coronavirus (COVID-19) which was declared a pandemic has spread to 210 countries worldwide. It has had a significant impact on health systems and economic, educational and social facets of contemporary society. As the rate of transmission increases, various collaborative approaches among stakeholders to develop innovative means of screening, detecting and diagnosing COVID-19’s cases among human beings at a commensurate rate have evolved. Further, the utility of computing models associated with the fourth industrial revolution technologies in achieving the desired feat has been highlighted. However, there is a gap in terms of the accuracy of detection and prediction of COVID-19 cases and tracing contacts of infected persons. This paper presents a review of computing models that can be adopted to enhance the performance of detecting and predicting the COVID-19 pandemic cases. We focus on big data, artificial intelligence (AI) and nature-inspired computing (NIC) models that can be adopted in the current pandemic. The review suggested that artificial intelligence models have been used for the case detection of COVID-19. Similarly, big data platforms have also been applied for tracing contacts. However, the nature-inspired computing (NIC) models that have demonstrated good performance in feature selection of medical issues are yet to be explored for case detection and tracing of contacts in the current COVID-19 pandemic. This study holds salient implications for practitioners and researchers alike as it elucidates the potentials of NIC in the accurate detection of pandemic cases and optimized contact tracing.

The actualization of affordable housing remains a challenge. This challenge is exacerbated by the increasing societal demand for the incorporation of sustainability principles into such housing types to improve levels of occupant health and well-being whilst avouching the desired levels of affordability. Innovative technologies and practices have been described as beneficial to the effectuation of sustainable affordable housing. However, knowledge concerning the deployment of innovative technologies and practices in sustainable affordable housing (sustainable, innovative, affordable housing—SIAH) delivery remains nascent. Consequently, there is a lack of a common ontology among stakeholders concerning how to realize SIAH. This study aims to contribute toward the development of this body of knowledge through the establishment of the critical success factors (CSFs) for effective SIAH implementation. To achieve this objective, a systematic review and bibliometric analysis focusing on a juxtaposition of sustainable, innovative and affordable housing concepts was carried out based on the relevant literature. This led to the identification and clustering of CSFs for these housing concepts at individual levels and as a collective (SIAH). The findings of the study consisted of the establishment of four distinct yet interrelated facets through which SIAH can be achieved holistically, namely, housing design, house element, housing production method and housing technology. A total of 127 CSFs were found to be aligned to these facets, subsequently clustered, and conclusively used for the development of a SIAH CSF framework. The most frequently occurring CSFs with predominant interconnections were the utilization of energy-efficient systems/fittings, tenure security, a comfortable and healthy indoor environment, affordable housing price in relation to income and using water-efficient systems/fittings CSFs, and establishing the emergent SIAH CSF framework. The framework in this study is useful in the documentation of SIAH features for construction projects and further studies into SIAH CSFs.

Higher Education Institutions (HEIs) have been incorporating sustainability into education and curricula, where recent research has focussed on sustainability competences, pedagogical approaches, and how to connect them, generally on a single HEI. The process of integrating sustainability into education based on curricula assessment has been explained using adoption of innovations; and has the potential to explain the process of developing competences through pedagogical approaches. The aim of this paper is to investigate this process at academic discipline level. An online survey was developed to investigate teaching sustainability competences in 13 HEIs, from which 678 responses from educators were obtained. The competences and pedagogical approaches from the responses were ranked, and then the connections between the competences and pedagogical approaches per discipline were analysed using a correlations‐based framework, from which three disciplines groups were created. The groups were categorised using diffusion of innovations theory, which indicated that some disciplines are more innovative than others in adopting sustainability competence‐based teaching. The results are used to propose two frameworks to better understand the adoption of sustainability competence‐based teaching: (a) the D‐RAPID framework; and (b) the Disciplinary Multi‐dimensional Sustainability Influence Change for Academia (D‐MuSICA) memework. The adoption of sustainability competence‐base education must expand from a single HEI perspective to a disciplinary collaborative one spanning many HEIs, where academic disciplines should learn from each other's insights and mistakes and provide students with more transdisciplinary skillsets to make societies more sustainable.

Purpose: Qualitative researchers are often confronted with a dilemma of selecting an appropriate approach within which to situate their research. This has led to successive attempts by qualitative researchers in the built environment (BE) to combine two dominant approaches – deductive and inductive; in the conduct of their inquiry. Such attempts can be traced to the poor comprehension of the abductive approach. The purpose of this paper is to elucidate the principles of the abductive approach and illustrate its applicability within the context of BE qualitative research. Design/methodology/approach: In this study, an illustrative case study is used to depict the usefulness of the abductive approach in BE research. The case relied upon is a recently completed study of an infrastructure delivery system and an assessment of the system’s ability to deliver on socio-economic sustainability objectives. Findings: It was established that extant theories, particularly those with a history of provenance, could be used as a basis for the development of testable propositions for assessing certain phenomena, qualitatively. However, the manner in which these propositions are utilised under an abductive approach is pivotal to the generation of credible findings. Research limitations/implications: It is expected that the findings of this paper would create awareness among researchers on the relevance of an abductive approach to qualitative research. Originality/value: This study makes an authentic contribution towards resolving the challenge confronting qualitative researchers within the BE discipline as it pertains to selecting between deductive and inductive approaches. In this case, an abductive approach is suggested and its modalities shown through a comprehensive study.

An identification of strengths, weakness, opportunities, and threats (SWOT) factors remains imperative for enabling a successful Smart Campus transition. The absence of a structured approach for analyzing the relationships between these SWOT factors and the influence thereof on Smart Campus transitions negate effective implementation. This study leverages a systems thinking approach to bridge this gap. Data were collected through a stakeholder workshop within a University of Technology case study and analyzed using qualitative content analysis (QCA). This resulted in the establishment of SWOT factors affecting Smart Campus transitions. Systems thinking was utilized to analyze the relationships between these SWOT factors resulting in a causal loop diagram (CLD) highlighting extant interrelationships. A panel of experts drawn from the United Kingdom, New Zealand, and South Africa validated the relationships between the SWOT factors as elucidated in the CLD. Subsequently, a Smart Campus transition framework predicated on the CLD archetypes was developed. The framework provided a holistic approach to understanding the interrelationships between various SWOT factors influencing Smart Campus transitions. This framework remains a valuable tool for facilitating optimal strategic planning and management approaches by policy makers, academics, and implementers within the global Higher Education Institution (HEI) landscape for managing successful Smart Campus transition at the South African University of Technology (SAUoT) and beyond.