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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.

This study aims to explore existing study trends in the halal supply chain (HSC) field as an extension of supply chain studies. Upon examining multiple journal ranks and citation profiles, these trends cover research themes, methodologies, settings (country and data analysis level) and their interactions.

The study followed a systematic, mixed-method review to pinpoint the HSC research themes. The Publish or Perish software, with specific criteria, was used to retrieve and filter 87 HSC articles from 2009 to 2019 from the Google Scholar database. Then, an input–process–output framework was used to classify and discuss potential future research.

This study concludes that HSC research is still in early development. Five themes consisting of 24 different topics were found: the engagement process, quality control assurance, critical success factors, the production and distribution process and HSC operations support. Most of the HSC studies followed conceptual and qualitative interview methods, with special reference to Muslim-majority countries and organization-level analysis. Within one decade, the number of HSC publications grew significantly, though their presentation is mainly in unindexed journals and their citation rate is low. This study thus proposes three main future HSC research points: HSC consequences, processes and antecedents.

Possible practical implications can be expected from the authors’ proposed empirical studies as guidelines to formulate and promote HSC implementation.

No comprehensive HSC research review exists in the literature. This study intends to fill this void by charting cumulative knowledge and proposing a roadmap for future research endeavors.