A non-linear approach to modelling motivation in the workplace using artificial neural networks

 

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dc.contributor.advisor Baets, Walter en_ZA
dc.contributor.author Jaquet, Jean-Michel en_ZA
dc.date.accessioned 2015-09-15T10:20:15Z
dc.date.available 2015-09-15T10:20:15Z
dc.date.issued 2012 en_ZA
dc.identifier.citation Jaquet, J. 2012. A non-linear approach to modelling motivation in the workplace using artificial neural networks. University of Cape Town. en_ZA
dc.identifier.uri http://hdl.handle.net/11427/13956
dc.description Includes bibliographical references. en_ZA
dc.description.abstract The standard business conception of the employee is as a blank slate machine motivated through a behaviourist system of reward and punishment. In contrast to this conception, studies of human evolution, neurology and cognition suggest that motivation emerges from the interaction of a complex and non-linear system of variables. This two-part study uses a conceptual model of work motivation based on systems and complexity theory to identify and interpret the significance of outlying variables in the motivations of groups of working professionals with different career orientations. In the first part of the fieldwork, fifty respondents from each of four career orientations (business managers, professional creative artists, entrepreneurs and students studying in creative fields) completed a self-assessment tool in which they indicated their strength of agreement or disagreement with the presence of fifteen motivation variables in their pursuit of a work goal. The responses of each career group were clustered using artificial neural network analysis and outlying motivation variables within clusters that differed significantly from the mean were identified. In the second part of the fieldwork, the meanings of outlying variables were interpreted by focus groups representing each of the four different career orientations. While on average, respondents agreed that all motivational variables were fulfilled in their pursuit of a work goal, unsupervised artificial neural network clustering identified between two and four clusters of respondents within each career group that showed responses differing significantly from the mean. These were mainly in the form of disagreement with fulfilment of one or more variables of motivation. Focus groups were able to identify with and provide context to these outlying responses. en_ZA
dc.language.iso eng en_ZA
dc.subject.other Information Systems en_ZA
dc.title A non-linear approach to modelling motivation in the workplace using artificial neural networks en_ZA
dc.type Doctoral Thesis
uct.type.publication Research en_ZA
uct.type.resource Thesis en_ZA
dc.publisher.institution University of Cape Town
dc.publisher.faculty Faculty of Commerce en_ZA
dc.publisher.department Department of Information Systems en_ZA
dc.type.qualificationlevel Doctoral
dc.type.qualificationname PhD en_ZA
uct.type.filetype Text
uct.type.filetype Image
dc.identifier.apacitation Jaquet, J. (2012). <i>A non-linear approach to modelling motivation in the workplace using artificial neural networks</i>. (Thesis). University of Cape Town ,Faculty of Commerce ,Department of Information Systems. Retrieved from http://hdl.handle.net/11427/13956 en_ZA
dc.identifier.chicagocitation Jaquet, Jean-Michel. <i>"A non-linear approach to modelling motivation in the workplace using artificial neural networks."</i> Thesis., University of Cape Town ,Faculty of Commerce ,Department of Information Systems, 2012. http://hdl.handle.net/11427/13956 en_ZA
dc.identifier.vancouvercitation Jaquet J. A non-linear approach to modelling motivation in the workplace using artificial neural networks. [Thesis]. University of Cape Town ,Faculty of Commerce ,Department of Information Systems, 2012 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/13956 en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Jaquet, Jean-Michel AB - The standard business conception of the employee is as a blank slate machine motivated through a behaviourist system of reward and punishment. In contrast to this conception, studies of human evolution, neurology and cognition suggest that motivation emerges from the interaction of a complex and non-linear system of variables. This two-part study uses a conceptual model of work motivation based on systems and complexity theory to identify and interpret the significance of outlying variables in the motivations of groups of working professionals with different career orientations. In the first part of the fieldwork, fifty respondents from each of four career orientations (business managers, professional creative artists, entrepreneurs and students studying in creative fields) completed a self-assessment tool in which they indicated their strength of agreement or disagreement with the presence of fifteen motivation variables in their pursuit of a work goal. The responses of each career group were clustered using artificial neural network analysis and outlying motivation variables within clusters that differed significantly from the mean were identified. In the second part of the fieldwork, the meanings of outlying variables were interpreted by focus groups representing each of the four different career orientations. While on average, respondents agreed that all motivational variables were fulfilled in their pursuit of a work goal, unsupervised artificial neural network clustering identified between two and four clusters of respondents within each career group that showed responses differing significantly from the mean. These were mainly in the form of disagreement with fulfilment of one or more variables of motivation. Focus groups were able to identify with and provide context to these outlying responses. DA - 2012 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2012 T1 - A non-linear approach to modelling motivation in the workplace using artificial neural networks TI - A non-linear approach to modelling motivation in the workplace using artificial neural networks UR - http://hdl.handle.net/11427/13956 ER - en_ZA


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