Human-Robot Interaction Modelling for Recruitment and Retention of Employees

  • Rajiv KhoslaEmail author
  • Mei-Tai Chu
  • Khanh Nguyen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9752)


The well-executed recruitment and retention of employees in organisations in a highly competitive global market has grown significantly in the last decade. The need for managers to be emotionally intelligent for better management and productivity to deal with employees from generation Y and Z is also in great demand. In this paper we presents a framework which embodies human computer interaction techniques like facial emotion recognition, speech recognition and synthesis in socially assistive robot with human-like communication modalities to capture, analyse, profile and benchmark verbal and non-verbal data during a real-time job interview for hiring salespersons. This research fundamentally changes how employers can leverage the data analysis to seek for the best job applicant and how they perceive the use of human computer interaction (HCI) techniques and information technology in human resource management practice. Existing approaches for recruitment primarily rely on selection criteria and/or psychometric techniques followed by face to face interviews by subjective judgements of human beings. For example, the high turnover of salespersons in the industry has shown limited success of these procedures. Additionally, existing approaches lack benchmarking analysis internally by comparing the profile of most cultural fit employees. Thus, this research incorporates behavioural psychology, data mining, image processing, HCI modelling and techniques to provide a more holistic recruitment application using emotionally aware social robot. The implications of this research not only apply into the hiring and benchmarking of employees, but also collecting big data (verbal and non-verbal) for decision-making, personalised profiling and training.


Human-robot interaction Job interview Emotion recognition Profiling and benchmarking Verbal and non-verbal data analytics 


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Management and Marketing, La Trobe Business SchoolLa Trobe UniversityMelbourneAustralia

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