Browsing by Author "Meyer, Tanya"

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    Exploring chronotype, conscientiousness, workplace flexibility and work overload within the job demands–resources model
    (Stellenbosch : Stellenbosch University, 2021-03) Meyer, Tanya; Boonzaier, Michele; Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Industrial Psychology.
    ENGLISH SUMMARY : Modern organisations to survive and thrive in the economy, they need to obtain a competitive advantage. This can be achieved through various streams, such as better delivery of products and services that are also quick and efficient, together with better pricing and more flexible options. In order to achieve these goals, employees will have to be managed differently, as the manner in which they are managed will have a direct effect on their efficiency, productivity and general wellbeing, which in turn will influence whether these goals are being met. Two important well-being measures among employees are burnout and work engagement, which have a direct effect on the achievement of a competitive advantage by an organisation. Employee engagement is a known component of the attainment of a competitive advantage by organisations, while employees are the only component in this attainment that cannot be replicated or duplicated, therefore making people and their engagement the centre of the achievement of a competitive advantage. While employee engagement aids the achievement of a competitive advantage, a burned-out workforce leads to several negative consequences on an individual, organisational and social level that hamper the organisation’s ability to achieve such an advantage. While burnout was originally popularised as a condition that only affects employees in the helping professions, it has now become widely known that individuals from all occupational groups can be affected by burnout. Employees who hold tremendous value to the attainment of a competitive advantage and are known to work autonomously, namely the knowledge workers, are also experiencing burnout. Knowledge workers experience high levels of emotional and mental stress due to constant demands for creativity, innovation and superior problem-solving. The present study therefore aimed to answer the following research-initiating question: Why does variance exist in the work engagement and burnout levels of knowledge workers? To answer this question, a thorough analysis of the literature was done to determine the factors that could account for this variance in the work engagement and burnout levels of knowledge workers. Following the literature review, a conceptual model is proposed based on the job-demands resources theory, with work overload as a job demand, workplace flexibility as a job resource, conscientiousness as a personal resource and chronotype as a special variable. The model was tested using an ex post facto correlational research design. The snowball and convenience sampling methods were used to collect data through online questionnaires. The Morningness-Eveningness Questionnaire (MEQ) was used to assess chronotype, work engagement was assessed using the Utrecht Work Engagement Scale (UWES-17), the Oldenburg Burnout Inventory (OLBI) was used to test the burnout construct, conscientiousness was assessed using the Big Five Inventory (BFI), work overload was assessed using the Job Demands-Resources Scale (JDRS) and, finally, workplace flexibility was assessed with only two items from the recent literature. The final sample comprised 218 responses and statistical analysis were done to provide the findings for the current study. Various statistical analyses were conducted, the first to determine whether the construct was reliable and valid. Item analysis indicated good internal consistency, followed by a confirmatory factor analysis (CFA), which indicated that further investigation needed to be done. An exploratory factor analysis (EFA) was therefore conducted to determine the factor structure that best represents the data. A decision was made to retain the two-factor structure for burnout and the three-factor structure of work engagement. The univariate factor structure of workplace flexibility was supported, while conscientiousness and work overload, two univariate structures that originally were found to be two-factor structures, were maintained for the analysis. Work overload was split based on items indicating mental load and emotional load, while conscientiousness seems to be split based on positive and negative items. An additional CFA was done after the new factor structures of work overload and conscientiousness were determined, and the model displayed improved fit. The final analysis done was PLS-SEM to determine the path coefficients. The majority of the main path coefficients were found to be statistically significant. Three of the eight main hypotheses were found to be statistically insignificant. Of the three hypothesised moderating effects, two were found to be insignificant, while the moderating effect of work overload on the relationship between workplace flexibility and work engagement was found to tend towards significance. The study contributes to the body of literature on knowledge workers in South Africa by broadening knowledge regarding these workers. Furthermore, this study has several practical implications for recruiting knowledge workers and burnout interventions and provides insights and recommendations for future research.
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    Optimality assessment with optimality recovery for multi-modal process operations
    (Stellenbosch : Stellenbosch University, 2023-03) Meyer, Tanya; Louw, Tobias Muller; Bradshaw, Steven Martin; Stellenbosch University. Faculty of Engineering. Dept. of Process Engineering.
    ENGLISH ABSTRACT: The field of optimality assessment (OA) is a recent development within data-driven process monitoring. OA is a plant-wide approach to real-time optimisation that aims to minimise nonoptimal online operation caused by (1) disturbances that cannot be rejected by the regulatory control system, or (2) inevitable controlled variable setpoint drift. The distinguishing design factor of OA, as opposed to fault- and quality-related process monitoring, is the incorporation of the comprehensive economic index to quantify overall plant optimality or performance. Since optimality is only available in retrospect, the estimation of optimality during real-time operation allows for prompt intervention when nonoptimal conditions arise, so as to prevent prolonged conditions of deteriorated Performance. This work proposes an alternative to the conventional latent variable model-based OA workflows, which employ monitoring charts founded on Shewhart- or similarity-based statistics. The proposed OA workflow is designed to account for continuous and multimodal industrial process data without transition states. The workflow is developed under the framework of a novel optimality landscape which captures various stable modes in the historical process dataset as well as their associated optimality grade. In addition, the proposed optimality landscape captures the cause for the historical operating point shifting from one mode to another, which is termed a modal shift. Two types of modal shifts are captured, namely those that are caused by disturbances, or by SP change(s) that are implemented by the control system or operational team. The offline phase of the proposed OA workflow constructs a holistic reference tool called the optimality graph. The nodes of the optimality graph are discovered by 𝑘-means clustering in the latent variable space, whereas the edges are discovered by the proposed TASLA (time-based alignment of modal shifts and plant log algorithm) technique. Furthermore, metrics are developed for selecting hyperparameters that result in an optimality graph which best reflects the optimality landscape. The online phase of the proposed workflow essentially projects online conditions onto the historical optimality graph using latent variable techniques, such that the closest reference mode is identified. Consequently, real-time conditions are assigned the optimality grade of the identified reference mode. The online optimality graph reveals which actions can be implemented to shift the online operating point toward modes of differing optimality. A key outcome of this work is the utility of the online optimality graph as an optimality advisory that provides the operational team with historically substantiated decision support for implementing optimality recovery. The performance of the proposed OA workflow is tested using a simulated Tennessee-Eastman Process dataset. The proposed workflow captures the historical optimality landscape of the pseudo-industrial dataset and estimates real-time optimality well upon comparison to the ground truth. The performance of each latent variable extraction technique – namely PCA, PLS, ICA – is evaluated by considering the spread of optimality within each mode of the related optimality graph. PLS is deemed the most suited to extracting features that are reflective of the optimality landscape. The holistic nature of the proposed PLS-based OA workflow offers a good alternative to existing latent variable model-based OA workflows.