Skip to content

Author

Bang Hai Truong

2 papers indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Open access Jul 2026

The Influence of Business Analytics on Management Decision Making: A Case of Big Data

The proposed study explores the impact of Big Data-driven business analytics on management accounting information systems (MAIS) and managerial decision-making in Vietnamese firms. Despite research worldwide pointing to the revolutionary nature of analytics in forecasting, performance measurement, and risk management, empirical studies remain scarce in emerging economies. A quantitative method was used to collect data (n=81 Vietnamese firms) in a structured Likert-scale questionnaire that was structured. Cronbach's Alpha, Exploratory Factor Analysis (EFA), and One-Sample t-tests were used to test the reliability, validity, and hypothesis testing. The finding suggests that sophisticated analytical tools, definite decision-making needs, and quality data assurance are the key to the enhanced effectiveness of MAIS. Additionally, Big Data analytics has a positive influence on the decision accuracy, efficiency, IT integration, risk management, and data security. Both the null hypotheses (H01 and H02) were rejected which proved that there is a strong relation between the adoption of Big Data and the enhancement of managerial decisions. Furthermore, the study shows that integrating digital infrastructures, such as cloud data systems, automated accounting tools, and AI-driven analytics, significantly enhances MAIS performance. Although these results are obtained, the researchers also mention such limitations as a small sample, the use of self-reports, and the lack of causal process analysis. The study has some practical implications for SMEs and highlights the necessity of increasing analytical competencies. The findings also emphasize the importance of strengthening IT governance, cybersecurity protocols, and data management standards to support analytics-driven MAIS. The future research must investigate certain ways in which analytics enhance the performance of organizations in different markets.

Thuc Duy Tran, Bang Hai Truong, M. Bańka et al. · 0 citations
Open access Aug 2026

A Collective Intelligence Framework for Fake News Detection on Social Networks

The rapid diffusion of user-generated content on social networks has amplified the reach of fake news, creating an urgent need for detection methods that combine scalability with epistemic robustness. This paper proposes a Collective Intelligence (CI) framework for fake news detection that formalizes the crowd assessing a news item as an intelligent collective characterized by diversity, independence, decentralization, and aggregation. A directed weighted graph is used to represent the collective, where vertices denote users, edge weights encode reputation-derived influence, and each user contributes a veracity judgment together with a set of stance-bearing content and context features. We instantiate collective independence through a reputation-based influence measure adapted from prior work and integrate the resulting independence-aware weights into a two-stage aggregation pipeline: (i) a supervised classifier that produces machine-generated veracity scores from news content, and (ii) a consensus operator that fuses machine scores with independence-weighted crowd signals. The framework is evaluated on the GossipCop split of the FakeNewsNet corpus, treating the tweet propagation graph associated with each news item as the collective. Experimental results show that the CI-based model outperforms content-only and unweighted crowd baselines in accuracy, precision, recall, and F1-score, and that independence-aware aggregation contributes the largest incremental gain among the four CI principles. These findings support the view that treating social-media crowds as structured collectives, rather than as bags of independent votes, yields measurable robustness against coordinated misinformation.

Trung Văn Nguyễn, Bang Hai Truong · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.