Project Abstract

Combining computational methods with Situational Judgement Tests to assess human values in a diverse workforce is explored in the context of a rapidly evolving globalised work environment.The motivation for this this project is to analyse Situational Judgement Tests using advanced computational techniques to more accurately predict and understand employee behaviour in a multicultural context. The research aims to develop a computational model that uses machine learning and generative artificial intelligence to predict ‘operative’ human values in different professional contexts.The methodology combines rule-based systems and AI techniques to deeply dissect and analyse a corpus of situational judgement tests, focusing on extracting and assessing the human values embedded in the responses. This innovative combination of computational and psychometric tools hopes to improve the accuracy and adaptability of HRM practices across cultural boundaries.As the project is a Masters project, expected findings include the identification of key trends in the value-driven decision-making process and the enhancement of the alignment of employee values with organisational goals. Through computational analyses, the project hopes to provide deeper insights into how individuals’ decisions and performance are influenced by their values in the workplace.The intended outcome of this research is a more effective model for predicting job performance and cultural fit through an enhanced understanding of employee values. If the project is successful, this will mean that organisations can better align individuals with roles that fit their values, leading to increased job satisfaction and organisational effectiveness. This contribution will not only advance academic understanding in the fields of psychology and human resources, but will also provide practical tools to improve workforce management in a global context.

Keywords: Computational Human Values Analysis, Advanced Situational Judgement Testing, Integrating Human Values in HRM

 Conference Details

Session: Presentation Stream 32 at Presentation Slot 9

Location: GH037 at Wednesday 8th 13:30 – 17:00

Markers: Thomas Reitmaier, Raziyeh Moghaddas (GTA)

Course: MSc Computer Science, Masters PG

Future Plans: I’m looking for an industry placement