Intensifying university - industry collaboration through sharing of technology / Ungku Shamir Ungku Hamzah

Faculty of Manufacturing and Mechatronic Engineering Technology

Thesis (Doctor of Philosophy) -- Universiti Malaysia Pahang – 2026

Includes bibliographical references

This is a study on intensifying universities and industry collaboration (UIC) through technology sharing. The automotive manufacturing industry have been chosen. The research consists of 3 objectives; (1) Investigate the current state of awareness, readiness and acceptance towards Industry 4.0. (2) Evaluate the industry’s perception towards the universities as being the best partner for digital transformation. (3) Investigate the critical variables influencing effective university industry collaboration within the framework of innovation. Questionnaires were used to selected respondents knowledgeable and familiar in the fields of study. The results were also verified with secondary data researched in the Literature Review. Statistical analyses were done to determine the mean (frequency), correlations between variables and the reliability of coefficient (Cronbach’s Alpha). In the first Objective, 3 sets of variables were correlated; (1) Awareness and Plan which yielded R2 0.7333, a high positive relationship, (2) Awareness and Affordability which yielded R2 0.1515, a low positive relationship and (3) Awareness and Relevance which yielded R2 -0.2727, a weak negative relationship. In the second Objective, 93% agreed ‘Universities as best partner’, 87% believed ‘university has the latest knowledge in this field (digital transformation)’ and 93% agreed with ‘Industry 4.0 as the best platform to work with the universities’. In the third Objective, the overall Model Summary for Factors Influencing UIC (with ‘Innovation’ as being the independent variable) regression analysis yielded a Multiple R of 0.8230, which indicated a sound model. In correlating the independent variable ‘Innovation’ with other variables; 3 main variables stood out – Perception (0.583), Potential (0.555) and Technology (0.526), categorised as first tier variables. The correlations exercise were continued with other categories of variables identified as second, third, fourth and fifth tier variables forming a flow (pipeline) and thus a framework. In conclusion, issues in adopting Industry 4.0 such as cost, support and legacy system are barriers to be overcome and require support from the government and the OEMs especially. Universities make the best partners for digital transformation however, cultural and priorities differences exist. This barrier can be addressed by utilising effective intermediaries such as TTO. Lastly, achieving key variables based on their priorities (satisfying first tier variables - Perception, Potential and Technology before moving on the subsequent tiers of variables). This will result to a practical and effective framework which is important in order to have a successful collaboration between the university and the industry.