Exploring employee retention and intention to leave within a call centre

Dhanpat, Nelesh; Madou, Faith D.; Lugisani, Phokela; Mabojane, Rethabile; Phiri, Mbail

doi:10.4102/sajhrm.v16i0.905

Cited by 51 publications

(66 citation statements)

References 37 publications

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“…Productivity evaluation of an agent in call centers is a challenging task. Human subjective evaluations are dominating approaches to the current systems [ 1 , 2 ]. The subjective factors drastically bias and deviate the evaluations from the real or actual performance when they depend on an agent’s tone, communications skills, oral proficiency, and listening skills [ 3 , 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

Agent Productivity Modeling in a Call Center Domain Using Attentive Convolutional Neural Networks

Ahmed

Toral

Shaalan

et al. 2020

Sensors

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Measuring the productivity of an agent in a call center domain is a challenging task. Subjective measures are commonly used for evaluation in the current systems. In this paper, we propose an objective framework for modeling agent productivity for real estate call centers based on speech signal processing. The problem is formulated as a binary classification task using deep learning methods. We explore several designs for the classifier based on convolutional neural networks (CNNs), long-short-term memory networks (LSTMs), and an attention layer. The corpus consists of seven hours collected and annotated from three different call centers. The result shows that the speech-based approach can lead to significant improvements (1.57% absolute improvements) over a robust text baseline system.

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Section: Introductionmentioning

confidence: 99%

Agent Productivity Modeling in a Call Center Domain Using Attentive Convolutional Neural Networks

Ahmed

Toral

Shaalan

et al. 2020

Sensors

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“…Evaluating the performance of call-center agents involves several issues. The first is that the evaluation is performed manually by listening to recorded calls and evaluating the content, which most likely will be a subjective evaluation [ 1 , 2 ]. Proficiency in oral communications is an essential skill in call centers, and it is very important for fulfilling the customers’ needs.…”

Section: Introductionmentioning

confidence: 99%

A Multimodal Approach to Improve Performance Evaluation of Call Center Agent

Ahmed

Shaalan

Toral

et al. 2021

Sensors

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The paper proposes three modeling techniques to improve the performance evaluation of the call center agent. The first technique is speech processing supported by an attention layer for the agent’s recorded calls. The speech comprises 65 features for the ultimate determination of the context of the call using the Open-Smile toolkit. The second technique uses the Max Weights Similarity (MWS) approach instead of the Softmax function in the attention layer to improve the classification accuracy. MWS function replaces the Softmax function for fine-tuning the output of the attention layer for processing text. It is formed by determining the similarity in the distance of input weights of the attention layer to the weights of the max vectors. The third technique combines the agent’s recorded call speech with the corresponding transcribed text for binary classification. The speech modeling and text modeling are based on combinations of the Convolutional Neural Networks (CNNs) and Bi-directional Long-Short Term Memory (BiLSTMs). In this paper, the classification results for each model (text versus speech) are proposed and compared with the multimodal approach’s results. The multimodal classification provided an improvement of (0.22%) compared with acoustic model and (1.7%) compared with text model.

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“…Call centers have become the norm for organizations wishing to remain in close contact with their customers. Interestingly, while call centers ought to offer competitive intelligence to firms, they have to contend with high levels of absenteeism and staff attrition, as is often the case with call centers in South Africa (Poon and Dyantyi 2004;Dhanpat et al 2018;Zurnamer 2019). Research has distilled the dual consequences of high employee turnover for organizations: unrecouped financial investments (Bilau et al 2015;Cohen and Veled-Hecht 2010;Ibrahim 2015) and indirect financial resources such as the loss of valuable knowledge (Mwilu 2016).…”

Section: Introductionmentioning

confidence: 99%

A Structural Equation Modelling Evaluation of Antecedents and Interconnections of Call Centre Agents’ Intention to Quit

Iwu

Opute²,

Aliyu

et al. 2021

JRFM

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Call centers play a significant role in the operational dynamics of different types of businesses. This is especially the case because a call center agent’s demeanor can impair or engender customer satisfaction, which has ramifications for business patronage. Unfortunately, the pressures associated with the role of the call center agent have made staff attrition a norm in the industry. While this does not augur well for the call center or the organizations that they serve, the role of possible antecedents in the equation of staff attrition in South African call centers remains largely unexplored. Using a structural equation modeling approach, this study examined the interconnections between customer orientation, knowledge management, job satisfaction, and employees’ intention to quit. Additionally, the mediating influence of job satisfaction on the association between customer orientation and knowledge management of the intention to quit is examined. This study found significant relationships between knowledge management, customer orientation, and job satisfaction and the dependent variable (intention to quit). In addition, this study establishes that the extent to which job satisfaction may mediate the influence on the intention to quit hinges on the organizational element considered. Two factors limit the extent to which the findings from this study can be generalized. First, this study focused on the call center setting in South Africa. Second, convenience sampling was used in this study. This study points to critical operational practices that call center managers can embrace toward enhancing job satisfaction and reducing intention to quit propensity. Using structural equation analysis, we contend that call centers in the South African setting would effectively address staff attrition if appropriate organizational practices are endorsed toward ensuring employee job satisfaction.

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Exploring employee retention and intention to leave within a call centre

Cited by 51 publications

References 37 publications

Agent Productivity Modeling in a Call Center Domain Using Attentive Convolutional Neural Networks

Agent Productivity Modeling in a Call Center Domain Using Attentive Convolutional Neural Networks

A Multimodal Approach to Improve Performance Evaluation of Call Center Agent

A Structural Equation Modelling Evaluation of Antecedents and Interconnections of Call Centre Agents’ Intention to Quit

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