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This bibliometric study is steered to compute the impact of literature published on cloud computing within the fields of information science and library science. The research has been conducted on concentrating the term “Cloud Computing” to search the literature published in both fields, i.e. information science and library science from the time span 2007 to August 2023. This study aims to investigate the top productive country, organizations and highly cited publications.

The period of the exploration was from 2007 to August 2023 for bibliometric analysis and data was collected from the ISI Web of Science. Total 401 documents were retrieved and analyzed to highlight the year-wise distribution of documents type, year-wise most cited articles, prominent journals of the subjects, productivity of organizations, impact of countries and cooccurrences of keywords. The results are grounded on the basis of documents types (articles, early access articles, proceeding papers, book review, editorial material, news items and reviews).

The findings reveal that the most productive year of publication on cloud computing services was 2013. The top productive source is “International Journal of Information Management.” The articles entitled “Assessing the determinants of cloud computing adoption: An analysis of the manufacturing and services sectors” found as the most cited article and the significant increase in citations is also noteworthy. The most productive organizations on the topic include “Islamic Azad University of Iran,” “University Cologne of Germany” and “University Nova Lisboa of Portugal.” The results confirmed that the USA dominates in the production of research on “Cloud Computing Services” and the most repeated keyword in the literature is cloud computing. The research articles are the most cited sources of research.

This bibliometric research is an original piece of work that has been conducted to measure the research production in the field of information science and library science during 2007−2023. This piece of work is valuable for those who want to study the literature on cloud computing in the area of information science and library science.

Many studies have examined pre-diploma students' backgrounds and academic performance with results showing that some did not achieve the expected level of competence. This study aims to examine the relationship between students’ demographic characteristics and their academic achievement at the pre-diploma level using machine learning.

Secondary data analysis was used in this study, which involved collecting information about 1,052 pre-diploma students enrolled at Universiti Teknologi MARA (UiTM) Pahang Branch between 2017 and 2021. The research procedure was divided into two parts: data collecting and pre-processing, and building the machine learning algorithm, pre-training and testing.

Gender, family income, region and achievement in the national secondary school examination (Sijil Pelajaran Malaysia [SPM]) predict academic performance. Female students were 1.2 times more likely to succeed academically. Central region students performed better with a value of 1.26. M40-income students were more likely to excel with an odds ratio of 2.809. Students who excelled in SPM English and Mathematics had a better likelihood of succeeding in higher education.

This research was limited to pre-diploma students from UiTM Pahang Branch. For better generalizability of the results, future research should include pre-diploma students from other UiTM branches that offer this programme.

This study is expected to offer insights for policymakers, particularly, the Ministry of Higher Education, in developing a comprehensive policy to improve the tertiary education system by focusing on the fourth Sustainable Development Goal.

These pre-diploma students were found to originate mainly from low- or middle-income families; hence, the programme may help them acquire better jobs and improve their standard of living. Most students enrolling on the pre-diploma performed below excellent at the secondary school level and were therefore given the opportunity to continue studying at a higher level.

This predictive model contributes to guidelines on the minimum requirements for pre-diploma students to gain admission into higher education institutions by ensuring the efficient distribution of resources and equal access to higher education among all communities.

The Technical Education and Vocational Training Authority (TEVTA) plays a crucial role in the socioeconomic development of a country. Still, it is often stigmatized as a secondary choice in the Global South. This study explored the interrelationships and impacts of factors such as family, school, and society on the perception and reputation of TEVTA.

By employing quantitative methods, the analysis focused on how family, society, and school support influence these perceptions and reputations within TEVTA programs. Social Cognitive Theory is the theoretical underpinning of this study, in which 350 students from 13 TEVTA institutes participated by filling out questionnaires. The data was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) and IBM SPSS 28.

This study indicates that family and societal influences significantly shape students' perceptions, confirming their pivotal role in enhancing the reputation of these programs. School support also emerged as a critical factor, significantly impacting students' perceptions but not directly influencing the programs' reputation. The analysis underscores the importance of understanding the sociocultural context to develop effective strategies for the TEVTA sector in Pakistan. This clear understanding is essential for developing effective strategies to improve the reputation of TEVTA programs in this setting. Moreover, this research offers policy suggestions to make vocational education more attractive and accessible to diverse students, ultimately contributing to the country's socioeconomic development.

This study applied Social Cognitive Theory (SCT) to explore how individual thoughts, environmental influences (such as family, school, and society), and behaviors interact within the context of TEVTA programs. This approach fills gaps in current research and offers a clearer understanding of what affects TEVTA's perception and reputation.