Date of Award

4-20-2026

Thesis Type

PhD

Document Type

Thesis

Degree Name

Doctor of Philosophy (PhD)

Divisions

Institute for Advanced Studies

Institution

Universiti Malaya

Abstract

This dissertation examines the evolutionary dynamics of technological innovation in the new energy vehicle (NEV) industry, focusing on hybrid electric vehicles (HEVs), battery electric vehicles (BEVs), and fuel cell electric vehicles (FCEVs) in comparison with the internal combustion engine (ICE) trajectory. Anchored in the framework of technological paradigms and trajectories, the study addresses three core questions: (1) which trajectory shows the strongest medium-term prospects, (2) which convergence-driven technologies underpin interdependencies across trajectories, and (3) where underexplored technological opportunities (“vacua”) exist within the most viable trajectory. To address these questions, the research applies a patent-based, multi-method strategy using over 120,000 patent families (2003–2022) from the Derwent Innovation Index. First, Gompertz forecasting combined with entropy-weighted indices evaluates trajectory maturity, diffusion, and technological impact. Second, convergence analysis uses co-classification and co-citation networks to identify bridging technologies via dual metrics of diversity and centrality. Third, opportunity vacua are mapped through Latent Dirichlet Allocation (LDA) and Generative Topographic Mapping (GTM), which detect sparsely populated but strategically significant zones. The results indicate that although BEVs lead in adoption, HEVs demonstrate the most balanced growth potential. The composite score ranks HEVs highest (0.196), followed closely by BEVs (0.192), with ICEVs (0.130) third and FCEVs (0.089) fourth. Convergence mapping identifies 68 high-diversity domains, of which 24 also display high centrality, highlighting technologies such as silicon-based inverters, thermal management, and integrated control systems as pivotal cross-trajectory technologies. GTM-based opportunity analysis identifies 24 technological vacuums clustered around energy storage, control systems, transmission mechanisms, and thermal management, while PCA-based robustness testing identifies 13 corresponding vacuums and confirms the overall stability of the opportunity landscape. The study advances theory by extending Dosi’s paradigm-trajectory model to multi-trajectory co-evolution, formalizing a dual-metric framework (diversity × centrality) for convergence analysis, and integrating opportunity discovery with trajectory forecasting. Practically, it offers guidance for firms in R&D portfolio allocation and for policymakers weighing technology-neutral versus selective strategies. By highlighting where momentum, convergence leverage, and opportunity spaces align, the findings provide a structured evidence base to manage NEV transitions and support sustainable mobility futures.

Initial

khm

Additional Information

Thesis (PhD) – Institute for Advanced Studies, Universiti Malaya, 2026.

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