The Impact of Subjective Factors on Performance Evaluation: The Applied Case of Outsourced Call Centres in Egypt Based on Neural Networks Approach

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

Spoken content retrieval beyond pipeline integration of automatic speech recognition and information retrieval

The dramatic increase in the creation of multimedia content is leading to the development of large archives in which a substantial amount of the information is in spoken form. Efficient access to this information requires effective spoken content retrieval (SCR) methods. Traditionally, SCR systems have focused on a pipeline integration of two fundamental technologies: transcription using automatic speech recognition (ASR) and search supported using text-based information retrieval (IR). Existing SCR approaches estimate the relevance of a spoken retrieval item based on the lexical overlap between a user’s query and the textual transcriptions of the items. However, the speech signal contains other potentially valuable non-lexical information that remains largely unexploited by SCR approaches. Particularly, acoustic correlates of speech prosody, that have been shown useful to identify salient words and determine topic changes, have not been exploited by existing SCR approaches. In addition, the temporal nature of multimedia content means that accessing content is a user intensive, time consuming process. In order to minimise user effort in locating relevant content, SCR systems could suggest playback points in retrieved content indicating the locations where the system believes relevant information may be found. This typically requires adopting a segmentation mechanism for splitting documents into smaller “elements” to be ranked and from which suitable playback points could be selected. Existing segmentation approaches do not generalise well to every possible information need or provide robustness to ASR errors. This thesis extends SCR beyond the standard ASR and IR pipeline approach by: (i) exploring the utilisation of prosodic information as complementary evidence of topical relevance to enhance current SCR approaches; (ii) determining elements of content that, when retrieved, minimise user search effort and provide increased robustness to ASR errors; and (iii) developing enhanced evaluation measures that could better capture the factors that affect user satisfaction in SCR