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This study aims to describe the fundamentals of teaching risk management in a classroom setting, with an emphasis on the learning interface between higher education and the workplace environment for business management students.

The study reviews literature that uses spreadsheets to visualize and model risk and uncertainty. Using six distinct case-based activities (CBAs), the study illustrates the practical applications of software like Palisade @RISK in risk management education. It helps to close the gap between theory and practice. The software assists in estimating the likelihood of a risk event and the impact or repercussions it will have if it occurs. This technique of risk analysis makes it possible to identify the risks that need the most active control.

@RISK can be used to create models that produce results to demonstrate every potential scenario outcome. When faced with a choice or analysis that involves uncertainty, @RISK can be utilized to enhance the perspective of what the future might contain.

The insights from this study can be used to develop critical thinking, independent thinking, problem-solving and other important skills in learners. Further, educators can apply Bloom’s taxonomy and the problem-solving taxonomy to help students make informed decisions in risky situations.

The paper aims to address the increasing issue of teacher shortages and the lack of diversity in America’s educators. Highly diverse communities need ways to support community members interested in careers as teachers. This article explores one promising approach to reach and inspire high school students considering the teaching profession. Camp ExCEL (Exploring Careers in Education and Leadership) provided a pathway allowing rising high school seniors an opportunity to explore the teaching profession. This pathway utilized the Grow Your Own framework, recruiting students from a diverse community and providing them resources and information that would further efforts to become an educator within their community.

The current study examined outcomes from an education summer camp, using qualitative thematic analysis to reflexively interpret participants’ (n = 29) feelings and beliefs about effective teaching, culturally responsive teaching (CRT), project-based learning (PBL) and their camp experience. Data were collected using Google documents and surveys. The four connected themes that emerged were obstacles and barriers to teaching, qualities of an effective teacher, the impact of culturally responsive teaching and project-based learning on classrooms, and the importance of mentorships within education.

The paper provides insight about how an education camp can support high school students as they explore a career in education. Results suggest that focus on high-quality pedagogy can support student understanding of the career. Students also suggested their perception of effective teaching that includes acknowledging the needs of the whole student, modeling high-quality teaching practices and displaying positive professional dispositions.

Because of the chosen research approach, the research results may lack generalizability. Therefore, researchers are encouraged to conduct and examine education camps further.

The paper includes implications for the development of other education camps, especially in areas with highly diverse populations.

This paper fulfills an identified need to increase the number of persons pursuing a career in education. The focus on a highly diverse community is also an area of need in education. This article details the description of an education camp and the curriculum used, along with findings from data collected during the first year.

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.

Ready-mix concrete delivery problem (RMCDP), a specific version of the vehicle routing problem (VRP), is a relevant supply-chain engineering task for construction management with various formulations and solving methods. This problem can range from a simple scenario involving one source, one material and one destination to a more challenging and complex case involving multiple sources, multiple materials and multiple destinations. This paper presents an Internet of Things (IoT)-supported active building information modeling (BIM) system for optimized multi-project ready-mix concrete (RMC) delivery.

The presented system is BIM-based, IoT supported, dynamic and automatic input/output exchange to provide an optimal delivery program for multi-project ready-mix-concrete problem. The input parameters are extracted as real-time map-supported IoT data and transferred to the system via an application programming interface (API) into a mixed-integer linear programming (MILP) optimization model developed to perform the optimization. The obtained optimization results are further integrated into BIM by conventional project management tools. To demonstrate the features of the suggested system, an RMCDP example was applied to solve that included four building sites, seven eligible concrete plants and three necessary RMC mixtures.

The system provides the optimum delivery schedule for multiple RMCs to multiple construction sites, as well as the optimum RMC quantities to be delivered, the quantities from each concrete plant that must be supplied, the best delivery routes, the optimum execution times for each construction site, and the total minimal costs, while also assuring the dynamic transfer of the optimized results back into the portfolio of multiple BIM projects. The system can generate as many solutions as needed by updating the real-time input parameters in terms of change of the routes, unit prices and availability of concrete plants.

The suggested system allows dynamic adjustments during the optimization process, andis adaptable to changes in input data also considering the real-time input data. The system is based on spreadsheets, which are widely used and common tool that most stakeholders already utilize daily, while also providing the possibility to apply a more specialized tool. Based on this, the RMCDP can be solved using both conventional and advanced optimization software, enabling the system to handle even large-scale tasks as necessary.

This study aims to examine the scholarly impact of funded and non-funded research published in ten core library and information science (LIS) journals published in 2016.

In total, ten high-impact LIS journals were selected using Google Scholar metrics. The source title of each selected journal was searched in the Scopus database to retrieve the articles published in 2016. The detailed information of all the retrieved articles for every journal was exported in a CSV Excel file, and after collecting all the journal articles’ information, all CSV Excel files were merged into a single MS Excel file for data analysis.

The study analyzed 1,064 publications and found that 14% of them were funded research articles. Funded articles received higher average citation counts (24.56) compared to non-funded articles (20.49). Funded open-access articles had a higher scholarly impact than funded closed-access articles. The research area with the most funded articles was “Bibliometrics,” which also received the highest number of citations (1,676) with an average citation count of 24.64. The National Natural Science Foundation of China funded the most papers (30), while the USA funded the highest number of research publications (36) in the field of LIS.

This study highlights the importance of securing funding, open access publishing, discipline-specific differences, diverse funding sources and aiming for higher citations. Researchers, practitioners and policymakers can use these findings to enhance research impact in LIS.

This study explores the impact of funding on research LIS and provides valuable insights into the intricate relationship between funding and research impact.

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 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.