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

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dc.contributor.advisorBaets, Walteren_ZA
dc.contributor.authorJaquet, Jean-Michelen_ZA
dc.date.accessioned2015-09-15T10:20:15Z
dc.date.available2015-09-15T10:20:15Z
dc.date.issued2012en_ZA
dc.descriptionIncludes bibliographical references.en_ZA
dc.description.abstractThe 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.identifier.apacitationJaquet, 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/11427/13956en_ZA
dc.identifier.chicagocitationJaquet, 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/11427/13956en_ZA
dc.identifier.citationJaquet, J. 2012. A non-linear approach to modelling motivation in the workplace using artificial neural networks. University of Cape Town.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
dc.identifier.urihttp://hdl.handle.net/11427/13956
dc.identifier.vancouvercitationJaquet 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/11427/13956en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Information Systemsen_ZA
dc.publisher.facultyFaculty of Commerceen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherInformation Systemsen_ZA
dc.titleA non-linear approach to modelling motivation in the workplace using artificial neural networksen_ZA
dc.typeDoctoral Thesis
dc.type.qualificationlevelDoctoral
dc.type.qualificationnamePhDen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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