Jamilah Mahmood

Big Big Data Governance (BDG) has become essential for ensuring the secure, efficient, and strategic use of data in modern organizations. This study investigates the current state of BDG practices within Public Higher Learning Institutions (PHLIs) in Malaysia and Nigeria using a mixed-methods approach that integrates a review of existing literature, in-depth qualitative interviews, and a structured quantitative survey. The literature review identified core components of effective BDG roles and responsibilities, activities and processes, which informed the development of the study instruments. Qualitative data were gathered through interviews with ICT and MIS directors and analyzed using reflexive thematic analysis, while quantitative data were collected from 119 operational staff and analyzed using descriptive statistics. Findings from both data sources revealed fragmented BDG practices, low levels of tool deployment and integration, and inconsistencies in role definition and assignment, policy implementation, and institutional coordination. While institutions in Malaysia demonstrated relatively more mature and established BDG programs; responses from Nigerian institutions reflected early-stage development or complete absence of data governance structures. Notably, a large proportion of institutions lacks awareness or access to technical platforms necessary for effective BDG practices. The study highlights the need for tools and technologies, and the integration of BDG responsibilities across all institutional levels. This research contributes new empirical evidence on the challenges and disparities in BDG implementation. It offers actionable recommendations and proposes future research directions focused on BDG maturity models, multi-regional assessments, and the role of emerging technologies in strengthening big data governance frameworks in higher education.

The exponential growth of intelligent systems technologies, including Artificial Intelligence (AI), Internet of Things (IoT), Machine Learning (ML), and Smart Connected Products, has intensified the difficulties of data governance. Organizations adopting these technologies face challenges in managing the volume, variety, and velocity of data while ensuring quality, security, compliance, and ethical integrity. This study investigates the state of big data governance (BDG) programs in organizations leveraging intelligent systems technologies, using a systematic literature review guided by PRISMA. A synthesis of insights from 74 peer-reviewed articles, conferences, and industry reports reveals both the significant benefits of BDG such as enhanced decision-making, operational efficiency, and regulatory compliance and persistent challenges, including fragmented governance frameworks, limited scalability, data quality issues, and ethical concerns. To address these gaps, this study proposes the big data governance maturity assessment model (BDG MAM), a novel framework developed to assess and enhance the maturity of BDG programs. The BDG MAM evaluates governance maturity across four key dimensions: people, process, data, and technology. It provides a structured roadmap for organizations to benchmark governance practices, prioritize improvements, and implement effective strategies. The model was validated through a pilot study conducted with a public higher learning institution, demonstrating its practical applicability and effectiveness in real-world scenarios. By bridging theoretical insights with practical implementation, this study advances academic discourse and provides practitioners with a robust approach to navigate the challenges of modern data governance practices, ensuring sustainable and effective management of heterogeneous data environment.

While sparking a big debate among academics, generative artificial intelligence (GAI) tools are becoming integral to academic research, holding the potential to transform traditional research and peer review methods. This systematic literature review investigates the emergent role of GAI tools in academic research workflow and scholarly publications by analyzing 44 articles. The process of identifying the most relevant publications was done following the preferred reporting items for systematic reviews and meta-analyses method. The findings provide a thorough understanding of how GAI is currently being utilized in the various aspects of academic research workflow and peer review process, including concerns, limitations, and proactive measures to better employ these tools effectively. Our review suggests the need for more research to develop appropriate policies and guidelines, enhance researchers’ artificial intelligence literacy through targeted training, and ensure ethical use of these tools to boost research productivity and quality.

The advent of big data has brought about significant opportunities and valuable insights to modern organizations. However, the proliferation of governance challenges is increasing dramatically, which necessitates searching for innovative approaches for managing, securing, and complying with government and organizational policies. This study conducted a systematic literature review and explored the profound impact of big data governance on modern organizations and highlighted the existing prevalent challenges and evaluated the mitigation approaches. We obtained the results of our defined research questions from peer-reviewed articles, industry reports, and academic theses published between (2019-2024). The results of our findings are categorized into four fundamental components of big data governance (i.e., people, process, data, technology), through illustrative tables and figures. Furthermore, this study has identified the need for effective and adaptive frameworks and models that can respond to emerging technologies and big data governance practices. After identifying the existing approaches for harnessing and mitigating the existing big data governance challenges, this study has proposed a new hybrid approach that integrates the viable system model and big data governance process model. The proposed approaches not only mitigate the existing challenges, but also serves as a diagnostic tool for identifying the maturity level of data governance and pinpoint the areas for improvement. However, the practical implementation of the proposed approaches remains a limitation of this study, urging avenues for future studies to explore its applicability and impact on modern organizations.

Persuasive communication is a form of communication that encourages or changes consumer behaviour towards a certain product or service. Several researches have been conducted to understand the efficiency and effectiveness of persuasive communication. Among these researches is the development of the persuasive system design model that guides the development of e-commerce websites with persuasive features. However, the persuasive system design model does not take into consideration the effect of social media as it was developed when the social media world is coming into being. Considerations were not given to the social media element in the model development. Hence the need to incorporate the social media features that will extend the persuasive system design model to become all-encompassing and comprehensive. The aim of this study is to extend the persuasive system design model in business to consumer (B2C) e-Commerce. We employed a systematic literature review to identify the factors that will extend the persuasive system design model in a comprehensive manner. This study develops measurement items that will lead to the examination of the proposed model. These items were investigated for their reliability and validity using 30 respondents in Malaysia. The results showed that the items are a good representation of the constructs and hence can be used to assess the proposed model. This study will help in changing business trends that will lead to a successful implementation of persuasive communication in B2C e-commerce in Malaysia.

Review on Knowledge Sharing Behavior Studies: Theories and Research Approaches