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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.

The planning of massive vaccination campaigns often falls to nongovernmental organizations that have to face the critical challenge of vaccinating the largest number of people in the shortest time. This study aims to provide an easy tool for minimizing the duration of mass vaccination campaigns in rural and remote areas of developing countries.

This paper presents a linear mathematical model that combines location, scheduling and routing decisions that allows determining where to locate the vaccination centers, as well as the schedule/route that each medical team must follow to meet the target demand in the shortest time possible. In addition, the paper proposes an heuristic approach that can be integrated in a spreadsheet.

As the numerical experiments show, the proposed heuristic provides good solutions in a short time. Due to its simplicity and flexibility, the proposed approach allows decision-makers to analyze and evaluate several possible scenarios for decision-making by simply playing with input parameters.

The integration of the heuristic approach in a spreadsheet provides a simple and efficient tool to help decision-makers while avoiding the need for large investments in information systems infrastructure by user organizations.

Motivated by a real-life problem and different from previous studies, the objective of the planning is to reduce the length of the vaccination campaigns with the available resources and ensure a target coverage instead of planning for minimizing costs or maximizing coverage. Furthermore, for helping implementation to practitioners, the heuristic can be solved in a spreadsheet.

Data analytics (DA) is an emerging topic in management science at large organizations; however, accountants in small and medium-sized enterprises (SMEs) are believed to be lagging far behind in the usage of DA. This study aims to provide a deep understanding of the actual DA activities undertaken by management accountants (MA) and to draw on SMEs’ characteristics to take advantage of DA applications.

This study used a qualitative approach by conducting in-depth interviews with 31 accounting and finance practitioners at senior levels of SMEs, following the use of the MAXQDA 2022 application for data analysis.

Findings in this study suggest variance and trend analysis as the two most popular tasks of DA, and advanced tasks such as contingency analysis, financial modeling, sentiment analysis and regression analysis are unfully performed in SMEs. The outcomes revealed that DA is not anticipated to affect the responsibilities but to expand the role and scope of management accounting.

This study simultaneously builds a theoretical framework about the antecedents, in terms of the external and internal drives and the characteristics of SMEs’ owner-managers, that are most common in all types of SMEs that encourage MA to use a specific technology, data analysis in a more advanced way instead of searching for determinants that affect the adoption of technology in general, as previous studies have conducted.

Although there are studies on DA usage, little has approached the mutual interconnections between how DA is applied by MA in SMEs and what changes in management accounting responsibilities by DA affect. Therefore, the point of this study was to look into how SMEs use DA and what activities MA actually do with DA to discover what traits SMEs need to use DA applications effectively as DA applications become more advanced.

The purpose of this study was to investigate amount of food waste and the number of food packages used in Finnish households with university students. The aim of the paper is to answer the following research questions: How much avoidable food waste is generated in the participating various sized households? How much unavoidable food waste is generated in the participating various sized households? How many food packages are classified as deposit, municipal waste or recycled in the participating various sized households?

The data was collected among the students in Seinäjoki University of Applied Sciences. A total of results from 432 households with 890 persons are presented. The participating households weighed their unavoidable and avoidable food waste and calculated the food packages during one week. The results were analysed in Excel and the statistical significance assessed using a t-test.

The average avoidable and unavoidable food wastages were 498 g/week/person, i.e. 25.9 kg/year/person and 543 g/week/person, i.e. 28.3 kg/year/person, respectively. Single-person households generate more avoidable and unavoidable food waste as well as packages per person than other sized households. The results show that there is no correlation between the amount of avoidable food waste/person, unavoidable food waste/person or packages/person.

This kind of research has sparsely been reported. The food and package wastage definitions vary, and thus it is difficult to compare these results with other reported results.

The purpose of our research on blockchain technology is to unveil its immense potential, understand its applications and implications and identify opportunities to revolutionize existing systems and processes. This research aims to inspire the creation of new innovative solutions for industries. By harnessing blockchain technology, organizations can pinpoint key areas that could significantly benefit from its use, such as streamlining operations, providing secure and transparent digital solutions and fortifying data security.

This study presents a robust multi-criteria decision-making framework for assessing blockchain drivers in selected Indian industries. We initiated with an extensive literature review to identify potential drivers. We then sought the opinions of experts in the field to validate and refine our list. This meticulous process led us to identify 26 drivers, which we categorized into five main categories. Finally, we employed the Best-Worst Method to determine the relative importance of each criterion, ensuring a comprehensive and reliable assessment.

The authors have ranked the blockchain drivers based on their degree of importance using the Best-Worst Method. This study reveals the priority of BC implementation, with the retail industry identified as the most in need, followed by the Banking and Healthcare industries. Various critical factors are identified where blockchain technology could help reduce costs, increase efficiency and enable new innovative business models.

While this study acknowledges potential bias in driver assessment relying on literature and expert opinions, its findings carry significant practical implications. We have identified key areas where blockchain technology could be transformative by focusing on select industries. Future research should encompass other industries and real-world case studies for practical insights that could delve into the adoption challenges and benefits of blockchain technology in many other industries, thereby amplifying the relevance of our findings.

Blockchain is a groundbreaking, innovative technology with immense potential to revolutionize industries. Past research has explored the benefits and challenges of blockchain implementation in specific industries or sectors. This creates a gap in research regarding systematically classifying and ranking the importance of blockchain across different Indian industries. Our research seeks to address this gap by using advanced multi-criteria decision-making techniques. We aim to provide a comprehensive understanding of the significance of blockchain technology in critical Indian industries, offering valuable insights that can inform strategic decision-making and drive innovation in the country’s business landscape.

This study aims to explore the interrelationships among quality and safety metrics within the Global Food Security Index (GFSI). Its primary objective is to identify key indicators and their respective influences on food security outcomes, thereby enriching comprehension of the intricate dynamics within global food security.

The analysis encompasses data from 113 countries for the year 2022, utilizing Bayesian Belief Network (BBN) models to identify significant drivers of both the GFSI and quality and safety dimensions. This methodological approach enables the examination of probabilistic connections among different indicators, providing a structured framework for investigating the complex dynamics of food security.

The study highlights the critical role of regulatory frameworks, access to clean drinking water, and food safety mechanisms in fostering food security. Key findings reveal that “nutrition monitoring and surveillance” has the highest probability (75%) of achieving a high-performance state, whereas “national dietary guidelines” have the highest probability (41%) of achieving a low-performance state. High GFSI performance is associated with excelling in indicators such as “access to drinking water” and “food safety mechanisms”, while low performance is linked to underperformance in “national dietary guidelines” and “nutrition labeling”. “Protein quality” and “dietary diversity” are identified as the most critical indicators affecting both the GFSI and quality and safety dimensions.

This research operationalizes a probabilistic technique to analyze the interdependencies among quality and safety indicators within the GFSI. By uncovering the probabilistic connections between these indicators, the study enhances understanding of the underlying dynamics that influence food security outcomes. The findings highlight the critical roles of regulatory frameworks, access to clean drinking water, and food safety mechanisms, offering actionable insights that empower policymakers to make evidence-based decisions and allocate resources effectively. Ultimately, this research significantly contributes to the advancement of food security interventions and the achievement of sustainable development goals related to food quality and safety.

In response to the challenges posed by the social environment, numerous individuals with autism use coping strategies, engaging in a phenomenon known as “camouflaging” to mask their distinctive traits. Despite the growing attention directed toward this area of study, to date, there are no bibliometric studies delineating the evolution of this research theme. This study aims to analyze scientific research on camouflage and the autistic spectrum published in Web of Science (WoS).

Articles on camouflage and autism were retrieved from the WoS. The advanced search interface used was “Title, Abstract, Keywords (autistic OR asperger syndrome OR Autism OR ASD AND camouflage OR camouflaging AND Masking).” The analysis and visualization of the selected records and their data were carried out using MS Excel (v16.0) and Biblioshiny (v2.0, R package).

Following the selection process, a total of 70 articles were included. Notably, the UK emerged as the leading contributor in terms of the number of published articles. Prominent authors in this domain included William Mandy, Laura Hull and Meng-Chuan Lai, who exhibited the highest number of published articles and citations. The Journal of Autism and Developmental Disorders stood out as the most prolific journal. Predominant research themes encompassed camouflage (and its synonyms, masking or compensation), gender differences and mental health.

This study contributes to existing knowledge on this topic, as well as identifying new areas of research related to autism and camouflage. Moreover, it serves as a valuable resource for health-care professionals, including physicians and clinical psychologists, who may seek to augment their understanding and proficiency in the field.

Industries throughout the world have gone through four phases of the industrial revolution. Today, the authors are encountering the Fourth Industrial Revolution (Industry 4.0 or 4IR), which is characterized by the application of information and communication technologies in the industry. This study aims to evaluate the growth of literature in the context of libraries and the Fourth Industrial Revolution and then identify the active research actors, countries, affiliations and sources and the status of collaboration across various geographical locations.

A systematic literature review was conducted. The data were extracted from the Scopus database incorporating scientometric and altmetric indicators. The search terms were TITLE-ABS-KEY “Industry 4.0” OR “4IR” OR “Fourth Industrial Revolution” AND “Library 4.0” OR “librarian 4.0” OR “libraries” OR “library” OR “library service” without any limitation. The results identified 218 documents out of which 67 core documents are related to libraries. Analysis was performed using MS Excel and VOSviewer.

South Africa, Nigeria and South Korea were the three most productive countries in the field. Library Hi Tech News is the most productive journal. Collaboration among African countries like South Africa, Nigeria and Zimbabwe seems to be good and they constituted the largest share of documents. The research on the Fourth Industrial Revolution in the context of libraries still lacks social media attention.

This study is the first systematic review in this area using a combination of scientometrics and altmetrics. The results will serve as a valuable reference for future researchers and policymakers.

The objective of this review article is to meticulously examine and critically evaluate the present body of literature on sensory marketing. The study aims to explore this domain’s latest trends and prospects using diverse metrics such as annual publication trends, authorship patterns, most cited publications, sources analysis, co-citation patterns, keyword analysis and thematic analysis.

In the study, bibliometric analysis is used to assess the different advancements in the field. To perform performance analysis, science mapping and network analysis, VOSviewer, Bibloshiny and MS Excel are used. Additionally, the Scopus database is used to extract literature related to the subject area.

The most cited author of the domain is Biswas D, while the most prolific source of the domain is Plos One, additionally “An integrative review of sensory marketing: Engaging the senses to affect perception, judgment, and behavior” is stated as the most cited publication in the domain. Moreover, experiential marketing is reported as the most important keyword of the domain. Additionally, other findings reported are stated in the main body of the research study.

Practical implications of the study extended to marketing practitioners, future researchers and various stakeholders interested in the subject area.

What stands this research paper apart from other studies is the comprehensive approach to the evaluation of literature using VOSviewer, Bibloshiny and MS Excel and in-depth evaluation of the domain using performance analysis, science mapping, network analysis and thematic analysis.