Employee Motivation To Learn: An Innovative Hybrid Approach By Combining Traditional And Machine Learning Methods

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Sin, Audrey (2022) Employee Motivation To Learn: An Innovative Hybrid Approach By Combining Traditional And Machine Learning Methods. Other Degree thesis, Victoria University.

Abstract

Employee learning is vital for professional and business success. The technological disruptions are prompting organisations towards radical transformation, but organisations are struggling to motivate employees to learn. The turning point to organisational crises caused by agency dilemmas and environmental factors are an employee learning management strategy and a robust decision-making framework. It is necessary and critical to promote employee motivation to learn for sustainable employee job satisfaction, performance, and well-being. Existing literature on employee learning has been controversial; coming to a different conclusion as to how attitudinal and environmental motivation influences employee learning. Many empirical studies are done using traditional statistical methods but still missing a hybrid approach that uses both traditional and machine learning methods to automatically identify the indicators and predict future occurrences of employees' learning motivation. Various learning theories have advanced through the years but still lack a comprehensive approach to solving the issue of unmotivated employees in learning. This thesis aims to design a comprehensive theoretical framework for employee learning motivation. Using a hybrid approach that includes traditional quantitative and machine learning methods to assess the relationships between perceived Employer Of Choice (EOC) utility, SelfDeterMination (SDM) and AI support and validate the autonomous and cost-effective predictive model. Data analysis is further enhanced with some machine learning techniques for model optimisation. A customised activation function of ReLu and Sigmoid called BSigReLu was formulated and introduced to create a robust optimisation model to enable efficient predictive analytics. Measurement indices are used to provide an unbiased model comparison and validation. The results of regression analysis show that EOC utility perception, SDM and employee learning are positively related. The extent to which AI support plays a role in motivating employees to learn has been hypothesised and statistically validated. The stronger the AI support, the higher employees’ sense of attitudinal and environmental motivation to learn. The hybrid model was selected based on model good-fit indices. An unbiased estimate using the 2-fold cross-validation operationalised with 5 iterations paired t-test indicate the use of the hybrid method can achieve better generalisation performance than the state-of-the-art conventional method. Results show that the introduction of predictive analytics could proactively capture the issue earlier so that intervention can be introduced earlier to avoid unmotivated employee learning crises vii occurrence. The findings from this research are new because of the hybrid approach adopted that improves computational efficiency. This thesis highlights some important considerations at a practical level for practitioners, researchers, academics, and policymakers to improve employee learning outcomes using award citation, AI support and an innovative machine learning approach. It provides insights on the considerations needed to promote greater utility on the employer of choice award citation, practices, and policies. This thesis also expanded on the influence of AI support and established its capability to reengineer employees’ motivation by strengthening human-machine collaboration in the context of employee learning. This thesis makes several contributions to the theory and practices in employee learning motivation. An integrative framework is developed using Self-Determination Theory (SDT) and agency theory. Given the dynamics and complexity involved in the employee learning context, combining different approaches provides a more comprehensive approach to addressing the given problem. New variables called AI support and perceived EOC utility are introduced, and their motivational relationships have been validated. The hybrid approach provides an analytical robust approach that has not been attempted before. The use of machine learning is contemporary and capable of automatically and effectively classifying employee learning motivation signals for early intervention. The models presented are the first step in designing the multi-agent strategies toward achieving self-motivated employee learning outcomes based on the role of EOC utility, SDM and AI support that contributes to employer competitiveness and most importantly employee job satisfaction, performance, and well-being. Keywords: motivation, employer of choice, employee learning, self-determination theory, multi-agent system, AI, Python, machine learning, deep learning, extreme learning machines, ensemble learning, kernel, activation function, hybrid.

Additional Information

Doctor of Business Administration

Item type Thesis (Other Degree thesis)
URI https://vuir.vu.edu.au/id/eprint/44742
Subjects Current > FOR (2020) Classification > 3507 Strategy, management and organisational behaviour
Current > FOR (2020) Classification > 4611 Machine learning
Current > FOR (2020) Classification > 5204 Cognitive and computational psychology
Current > Division/Research > Institute for Sustainable Industries and Liveable Cities
Keywords motivation, employer of choice, employee learning, self-determination theory, multi-agent system, AI, Python, machine learning, deep learning, extreme learning machines, ensemble learning, kernel, activation function, hybrid
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