Economics, Business and Management: Recent Advances Vol. 3

Technology Transfer Offices’ (TTOs) Strategic Entrepreneurship Model for Strengthening Research Universities Innovation Ecosystem: A Review

2026-09-01T10:09:10+00:00

Technology transfer offices (TTOs) play an increasingly important role in universities by supporting researchers in translating scientific knowledge into commercially and socially valuable outcomes. Strategic entrepreneurship, on the other hand, is the fusion of entrepreneurial elements and strategic management, including opportunity-seeking and advantage-seeking behaviour in developing effective new ventures or established firms. The integration of strategic entrepreneurship elements within TTOs could enhance the effectiveness of their roles and responsibilities in supporting the technology transfer process in RUs. The aim of this study is to nurture strategic entrepreneurship within TTOs to enhance their entrepreneurial role in enabling successful technology transfer for research universities (RUs). This study synthesises selected literature using a systematic method to analyse the roles and responsibilities of TTOs in RUs, critical factors for nurturing strategic entrepreneurship within TTOs, and a recommended TTO Strategic Entrepreneurship Model for strengthening the RUs' innovation ecosystem. Entrepreneurship has been identified as a crucial element of the technology transfer process. The results show that TTOs adopting strategic entrepreneurship approaches could create pathways for a sustainable entrepreneurial culture, support entrepreneur-researcher characteristics, and promote best practices in technology transfer to strengthen the innovation ecosystem in RUs. These findings are expected to improve the successful commercialisation rate of RUs' innovations in line with regional economic growth in Malaysia. In this study, a conceptual framework is proposed to show how a TTO Strategic Entrepreneurship Model could strengthen the RUs' innovation ecosystem. This is among the few studies on the commercialisation of innovation, a topic that remains underexplored. Future research is expected to include the development of a framework for nurturing strategic entrepreneurship within TTOs to enhance the commercialisation of innovation through RUs' technology transfer.

Exploring Artificial Intelligence in Economics: Methods, Applications and Limitations

2026-09-01T10:14:48+00:00

The rapid expansion of data in the digital economy has necessitated the adoption of advanced analytical tools in economic research. This chapter examines the growing integration of Artificial Intelligence (AI) in economic analysis, emphasising its methodological foundations, diverse applications, and inherent limitations. It explores key AI techniques including machine learning, deep learning, and natural language processing, and their effectiveness in enhancing predictive accuracy, pattern recognition, and decision-making across economic domains.

The study highlights the application of AI in both macroeconomic and microeconomic contexts, such as economic forecasting, demand estimation, dynamic pricing, labour market analysis, financial risk management, and policy formulation. By leveraging large and complex datasets, AI-driven models offer significant improvements over traditional econometric approaches in terms of efficiency and scalability.

However, the chapter also critically evaluates the challenges associated with AI adoption, including issues of data privacy, algorithmic bias, and lack of model transparency. These concerns underscore the need for ethical frameworks and explainable AI systems in economic practice. The findings suggest that while AI has transformative potential in reshaping economic analysis and policymaking, a balanced approach that integrates technological innovation with ethical considerations is essential for sustainable and inclusive economic development.

Socio-economic and Institutional Dimensions of Women’s Roles in Fishing Livelihoods in Chilika Lagoon, Odisha, India

2026-09-01T10:17:52+00:00

Chilika Lagoon, the largest brackish-water lagoon in India, supports the livelihoods of thousands of fishing households. Although men are commonly involved in fishing, women play an essential role in post-harvest activities, including fish processing, drying, selling and household management. Recognising these contributions is important for promoting gender-inclusive development and sustainable resource management. This study examines the socio-economic and livelihood roles of women in fishing communities surrounding Chilika Lagoon, identifies the challenges they encounter and suggests measures to improve their participation and well-being. Primary data were collected through structured surveys with 300 women from three major fishing communities. Regression analysis and a Tornado diagram were used for data analysis. The findings indicate that women make a substantial contribution to household income through post-harvest activities, including drying, processing and marketing. They also participate in community conservation and collective efforts to address unsustainable fishing practices. However, limited access to socio-economic resources, environmental security, health support and institutional services has affected their livelihoods and constrained their empowerment. Women therefore play a crucial role in sustaining fishing livelihoods in Chilika Lagoon, although their contributions often remain under-recognised. Strengthening skill development, resource access and participation in decision-making is essential for improving livelihood resilience and supporting sustainable fisheries management in the region.

Mapping the Evolution and Future of Forensic Accounting

2026-09-01T10:21:11+00:00

Forensic accounting has developed from a specialised form of investigative accounting into a heterogeneous field spanning fraud examination, dispute advisory, expert evidence, digital investigation and technology-enabled risk analysis. This expansion has increased its practical relevance but has also produced persistent ambiguity about professional boundaries, theoretical foundations and standards of proof. This critical narrative review maps the field’s evolution and evaluates the evidence supporting its principal methods, competencies and technological directions. Literature published from 1 January 1990 to 2 June 2026 was sought through accessible scholarly indexes, repositories and targeted citation searching, with selected earlier sources retained for historical and theoretical context. The synthesis shows that forensic accounting is best understood as an engagement-oriented practice defined by investigative purpose and evidential accountability rather than by any single technique. Its strongest contribution lies in integrating accounting reconstruction, corroborative inquiry and legally intelligible communication. Fraud theories remain useful for structuring hypotheses but are weak as stand-alone predictive models, particularly where collusion, organisational incentives and adaptive concealment dominate. Statistical indicators, text analysis and machine-learning models can improve triage and pattern recognition, yet most studies rely on retrospective, jurisdiction-specific and imperfectly labelled datasets. Such tools therefore support investigation; they do not independently establish intent, attribution or admissible proof. Blockchain and artificial intelligence extend traceability and analytical scale but also intensify risks involving off-chain data quality, opacity, privacy, model drift, adversarial manipulation and automation bias. Education and professional governance have not consistently kept pace with these changes. The future of the field depends less on adopting increasingly complex tools than on validating them prospectively, documenting provenance, integrating human judgement, and aligning analytical outputs with legal and ethical standards. A more mature forensic accounting discipline will require common competency frameworks, stronger comparative research, transparent evaluation benchmarks and explicit separation between risk signals and evidential conclusions.