http://hdl.handle.net/10453/148703 2026-04-26T21:31:04Z http://hdl.handle.net/10453/194777 Title: Discover Ethnic Minority Groups' Digital Entrepreneurship Motivation Based on Emotional and Instrumental Effects Authors: Li, L; Kang, K Abstract: Building upon the demonstrated advantages of digital entrepreneurship within the live streaming economy, ethnic minority groups (EMGs) are increasingly leveraging their cultural resources to create marketing content and facilitate commercial activity. To investigate the drivers of digital entrepreneurship among EMGs, this study employs social support theory, categorising influencing factors into dimensions of emotional and instrumental support. Acknowledging the distinct socio-cultural contexts of EMGs relative to the majority population, this research examines variables shaped by these unique backgrounds. An analysis of 517 EMG entrepreneurs indicates that factors, including peer trust and familial approval, positively influence entrepreneurial motivation, thereby shaping subsequent behavioural responses. Furthermore, Importance-Performance Map Analysis (IPMA) reveals that access to advice and financial support constitutes a factor of both higher importance and performance in motivating EMGs' digital entrepreneurship compared to other variables. These findings provide scholars and policymakers with insights to develop targeted strategies that acknowledge the distinctive characteristics of EMG entrepreneurs and foster their participation in the digital economy. 2025-09-15T00:00:00Z http://hdl.handle.net/10453/193750 Title: Analysing AI utilisation in education through learner question types: A constructivist approach Authors: Lee, H; Atif, A; Kang, K Abstract: <jats:p>This study investigated the evolving role of artificial intelligence (AI) in higher education by analysing learner-generated questions through a constructivist framework. Drawing on Piaget and Vygotsky’s theories, student inquiries were categorised into three roles: knowledge transmitter, facilitator and co-learner. Data from 11 students across 12 information technology courses yielded 434 authentic questions, expert labelled and augmented to balance class distributions. Several natural language processing models including bidirectional encoder representations from transformers (BERT; baseline and fine-tuned), disentangled attention BERT approach (DeBERTa) and robustly optimised BERT approach (RoBERTa) were evaluated for their ability to classify these questions. Results indicate that while models excel at processing factual (knowledge transmitter) queries, they face challenges distinguishing higher-order facilitator and co-learner questions. Notably, DeBERTa achieved the highest overall accuracy (86.36%) yet struggled with capturing contextual nuances inherent in complex queries. These findings underscore the potential of AI to support personalised learning and adaptive feedback in educational settings while highlighting the indispensable role of human oversight. Implications for integrating such models into learning management systems and avenues for future research including model refinement, cross-disciplinary validation and ethical AI implementation are discussed.   Implications for practice or policy: Instructors could enhance learner engagement by integrating AI-based question analysis tools to provide tailored feedback based on inquiry depth. Course designers may need to incorporate AI-driven scaffolding strategies to support students' higher-order thinking skills. Learning management systems could benefit from embedding automated question categorisation functions to identify students' learning needs more efficiently. Educational institutions should consider developing ethical guidelines for the use of AI in formative assessment processes.</jats:p> http://hdl.handle.net/10453/193682 Title: An Evaluation of Strategic Digital Transformation Models in Supply Chain Management Authors: Dasanayake, N; Wu, RMX; Alvandi, S; Cheng, E Abstract: Digital Transformation has become an inevitable factor catering to the resilience and agility in supply chains. Even though researchers have explored the adoption of different technologies in multiple functionalities within supply chains, current literature lacks a holistic view of the digital transformation process of supply chains, which entails the strategic adoption of technologies in supply chain operations. This study evaluates the status of digital transformation literature in supply chain management, thereby contributing to the body of knowledge by providing a synthesized evaluation of the digital transformation models and frameworks developed by researchers. Findings show that the existing digital transformation models lack the ability to guide the process with a holistic view, and that scholarly work is dispersed across different stages of the strategy management process. 2025-12-10T00:00:00Z http://hdl.handle.net/10453/193531 Title: From Fragmentation to Framework: Advancing IoT enabled Asset Management in the Rail Sector Authors: Alvandi, S; Karningsih, PD Abstract: The Australian rail sector is a critical enabler of national economic productivity, especially in mining and resource logistics. Ensuring the availability, reliability, and performance of rolling stock assets such as locomotives and wagons is fundamental to maintaining competitiveness in global commodity markets. However, asset management practices across the sector are marked by fragmentation, data silos, and inconsistent adoption of digital technologies, particularly those leveraging the Internet of Things (IoT). This research first analyses the current state of IoT-enabled asset management in the Australian rail sector, using a combined lens of the ISO 55000 Asset Management Standard and IoT technologies to identify strategic gaps and opportunities. The analysis reveals a critical need for a unified, standards-aligned approach that effectively integrates technological innovation with strategic asset management practices. In response, the study proposes an integrated conceptual framework to enhance strategic alignment through IoT enabled asset management. Overall, this research contributes valuable insights into the digital transformation of asset management in the rail industry, addressing the systemic challenges of integration, standardization, and long-term strategic planning. 2025-12-10T00:00:00Z