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Faculty of Management Sciences

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    Roots and causes of occupational stress amongst female academics in Universities of Technology in South Africa
    (Sciedu Press, 2022) Mathews, Mercillene; Khumalo, Njabulo; Dlamini, Bongani Innocent
    tress and stress-related problems have negative human resource and financial implications for Universities of Technology (UoT) in terms of absenteeism, productivity, organizational effectiveness, employee morale and medical aid subsidies. For tertiary institutions, the impact of stressed academics on core business activities relating to students and examinations are far-reaching. The paper assessed the roots and causes of occupational stress amongst female academics in a UoT in South Africa. The paper adopted a qualitative research approach with a focus group of selected female academics in the UoT. The paper revealed that workload and performance management, as well as family life and personal life; teaching vs research and administration; Covid-19 and online teaching and learning; holidays and leave and lack of leave; meetings and support deficiency; resources and lack of care and empathy, as well as poor HR, bullying and imposition and a lack of professionalism; nepotism and favouritism; retrenchments and instability, along with poor recognition and appreciation, were the roots that contribute to occupational stress in the UoT in SA. The paper recommends that effective interventions be implemented by the UoT in order to manage the stress of these female academics, thereby reducing the negative impact thereof on themselves and the institution. University policy-makers should devise a variety of solutions in a well-balanced package that places responsibility on both the university and staff to manage occupational stress.
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    Exploring perceptions of occupational stress amongst female academics in the Faculty of Management Sciences at the Durban University of Technology
    (2022) Mathews, Mercillene Perrene; Dlamini, Bongani Innocent; Khumalo, Njabulo
    Stress and stress-related problems have severe human resource and financial implications for organizations in terms of absenteeism, productivity, organizational effectiveness, employee morale and medical aid subsidies. For a tertiary institution like the Durban University of Technology (DUT), the impact of stressed academics on core business activities relating to students and examinations are far-reaching. The study established that female academics at the institution perceive themselves to be experiencing occupational stress and consequently defined the types of stressors that these academics are facing. Furthermore, the researcher attempted to establish the role of DUT, if any, in the management of stress amongst its female academics. A qualitative research methodology was adopted with a focus group of purposely selected female academics from the Faculty of Management Sciences. The findings of this study demonstrated that female academics in the Faculty of Management Sciences at the Durban University of Technology are indeed experiencing high stress levels as defined in the background to the study. Recommendations on possible effective interventions were also made in order to manage the stress that these female academics are experiencing, thereby reducing the negative impact thereof on themselves and DUT.
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    A review of physiological signal processing via Machine Learning (ML) for personal stress detection
    (IEEE, 2022-04-28) Lourens, Melanie Elizabeth; Beram, Shehab Mohamed; Borah, Bidyut Bikash; Dube, Anand Prakash; Deka, Aniruddha; Tripathi, Vikas
    Personal stress is maintained and measured by Machine learning. The device which is wearable has been used for the monitoring of personal self stress and data collection. In this research, it has been talked about the factors by which the physiological signal of the stress has been assessed. On the other hand, different type of technology has been used for the detection of the personal stress such as Electrocardiography (ECG) and many other devices. The observation and difficulties has been seen in this research by using this device and the technology. Stress disorder or ailment is one of the most common ailments in all individuals around the world. Stress and anxiety can greatly influence the life, emotion, behavioural pattern and thinking attributes of individuals. It is important to address this issue sooner or later. Psychological signal processing through machine learning effectively assists to detect the stress disorder at an early stage. The general system often considers some variables to detect stress. They are electrocardiogram, galvanic response, heart rate, respiration and many other elements. The ML tend to use algorithms to compare and contrast data to fetch effective e results. The paper has also carried out a statistical analysis based on three variables to fetch a proper result that provided the study group to comprehend a better understanding of the scenario. The researchers have taken the 'percentage of stress rate' cases' are considered independent variables whereas 'usage of a machine learning system' is considered a dependant variable. The study group has fetched and collected numerous data related to these three variables to get a better understanding.