A human capital predictive model for agent performance in contact centres

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ID: 19510
2011
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Abstract
Orientation: Currently no integrative model exists that can explain the phenomena contributing to agent performance in the South African contact centre industry. Research purpose: The primary focus of this article was to develop a theoretically derived human capital predictive model for agent performance in contact centres and Business Process Outsourcing (BPO) based on a review of current empirical research literature. Motivation for the study: The study was motivated by the need for a human capital predictive model that can predict agent and overall business performance. Research design: A nonempirical (theoretical) research paradigm was adopted for this study and more specifically a theory or model-building approach was followed. A systematic review of published empirical research articles (for the period 2000–2009) in scholarly search portals was performed. Main findings: Eight building blocks of the human capital predictive model for agent performance in contact centres were identified. Forty-two of the human capital contact centre related articles are detailed in this study. Key empirical findings suggest that person– environment fit, job demands-resources, human resources management practices, engagement, agent well-being, agent competence; turnover intention; and agent performance are related to contact centre performance. Practical/managerial implications: The human capital predictive model serves as an operational management model that has performance implications for agents and ultimately influences the contact centre’s overall business performance. Contribution/value-add: This research can contribute to the fields of human resource management (HRM), human capital and performance management within the contact centre and BPO environment.
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jacobs2011asa Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Jacobs, Chris;Roodt, Gert;
Journal sa journal of industrial psychology
Year 2011
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