The operations efficiency, service quality and resources productivity, are the core
aspects of the call centres competitive advantage in massive market competition.
Thus, subjective evaluation is the leniency, perception and bias in performance
evaluation which impact the efficiency of the operations and leads to frustrated
customers. The study aims to determine the subjective performance evaluation in call
centres to get a more objective measurement. It can be achieved by identifying
factors affecting resources performance evaluation through the development of a
conceptual model to reduce or eliminate the effect of subjective factors contained in
the performance evaluation.
The research approach is based on quantitative methodology through cross-sectional
self-reports for 224 participants’ work in eight outsource call centres located in Egypt.
The research aims to determine the subjective evaluation factors biases the true
performance. It is followed by a machine learning practical application using neural
networks for auto-detection the subjective context in the recorded calls to be
considered through the evaluation process.
The key findings of the study are nine subjective factors out of fifteen that have a
direct influence on subjective performance evaluation. The actual performance is the
performance evaluation after eliminating the subjective performance. Two different
methods have concluded the actual performance. The first method excludes the
subjective factors from the resulting evaluation to determine the actual performance.
The second method is a prediction model defining subjectivity percent as a call centre
baseline for future performance evaluation. Furthermore, the study highlights the
potential subjective variables and the degree of influence for each variable.
The theoretical contribution is determining the subjective factor and proposing the
model to measure and predict the subjectivity in the call centre. The study
recommended a restatement for the resource-based theory considering the
subjective evaluation effect on performance evaluation. The practical application
contribution is based on automating the detection and prediction of subjectivity using
a machine learning approach through cascaded Convolutional Neural Networks,
which achieved 75% accuracy in classifying the subjectivity for two study constructs:
agents and customer behaviour.
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/19188 |
| Date | January 2020 |
| Creators | Ahmed, Abdelrahman M. |
| Contributors | Sivarajah, Uthayasankar, Mahroof, Kamran, Perrett, Robert A., German, Hayley |
| Publisher | University of Bradford, Faculty of Management, Law and Social Sciences |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Thesis, doctoral, DBA |
| Rights | <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/"><img alt="Creative Commons License" style="border-width:0" src="http://i.creativecommons.org/l/by-nc-nd/3.0/88x31.png" /></a><br />The University of Bradford theses are licenced under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/">Creative Commons Licence</a>. |
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