Systematic Review Approach Research Articles

A Review of Research on Collaborative Assessments in the Open Distance and e-Learning Environment

This paper reviews 38 studies conducted between 2015 and 2022 on collaborative assessments in open-distance and e-learning (ODeL) contexts, focusing on the benefits, types, challenges, and strategies to improve collaborative assessments. This qualitative review aims to investigate collaborative assessments within the ODeL comprehensively. The objectives encompass thoroughly exploring theoretical foundations and empirical evidence to illuminate the pedagogical implications and effectiveness of collaborative assessment methodologies. Employing a systematic literature review approach, various scholarly articles, research papers, and educational studies were scrutinised to synthesise the current landscape. The review shows that effective communication, feedback, and appropriate technology are critical factors in promoting successful collaborative assessments, which can result in improved engagement, motivation, and better learning outcomes. However, challenges such as scheduling difficulties, technology challenges, group dynamics, and assessment quality may arise. Using social constructivism, this paper addresses criticisms of collaborative assessment in the ODeL context, identifies types of collaborative assessments, and presents strategies for implementation and addressing challenges. Based on these findings, recommendations are presented to educators, urging the integration of collaborative assessment methods into open distance and e-learning frameworks, accompanied by pedagogical support and training to optimise their efficacy and promote enriched learning experiences. The paper concludes by suggesting future research areas and recommendations for educators and instructional designers seeking to implement collaborative assessments in ODeL environments. Collaborative assessments can contribute to collaborative learning, providing students with a sense of community, engagement, and responsibility.

Unlocking the Potential of Industry 4.0 for Supply Chain Flexibility and Agility: A Systematic Literature Review

This paper reports on a comprehensive literature review using the Systematic Literature Review approach to improve agility and flexibility at the Sustainable Supply Chain Management (SSCM) level. The review identified 51 papers that satisfied the research criteria. The literature on SCM-level I4.0, flexibility, and agility was classified into four major research lines, namely interrelationships between SCM strategies, I4.0 technologies’ role in enabling Supply Chain Flexibility and Agility, barriers to achieving supply chain-level flexibility, and drivers to improve supply chain-level flexibility. Content analysis of the papers from each group was also provided, along with gaps detected and avenues for future research. The findings can help organizations understand how to leverage I4.0 practices and technologies to improve their flexibility and agility capabilities in the SSCM context, as well as identify potential drivers and barriers to their achievement.

Vedic mathematics for sustainable knowledge: a systematic literature review

PurposeThis research lies in the domain of Vedic mathematics, and it explores the application of the related Vedic sutras in different branches of mathematics, science, education, and engineering across Asia and Europe.Design/methodology/approachThe study embraced a qualitative research design followed by a systematic literature review (SLR) approach which describes the significance of Vedic mathematics. The study made use of purposive sampling through which the data were collected from 102 articles by using inclusion and exclusion criteria. It includes publication years, the types of research methods, and uses of Vedic mathematics sutras in different branches of knowledge stated by the researchers. Its goal is to offer a more thorough explanation and an evaluation of how the inquiry affected the conclusions. The articles examined in this review included all the journal articles and doctoral theses from the databases of Google Scholar, Science Direct, Scopus, and Sodhganga which were published during the period 2010–2022.FindingsThe research found that the application of the sutras of Vedic mathematics has been increasing immensely in India. The researchers in this area are fond of qualitative research methods. This research has shown that sutras of Vedic mathematics especially “Urdhvatiryakbhyam” and “Nikhilam Navatascaramana Dasastah” have been frequently used in mathematics and engineering in technical higher education. The impact of other sutras has been quite useful, which augments that in many disciplines where the applications of Vedic mathematics are prevalent, it can be functional. The study concludes by reprising the result, its limitations, and the use of Vedic mathematics as a sustainable source of knowledge.Research limitations/implicationsVedic mathematics is an area where a lot of potential applications are created in science, mathematics, engineering, and education. Even with the latest technological advancements like learning analytics, artificial intelligence has its connection with this branch of learning, which is the greatest treasure of the Indian knowledge system. The research in this area is not reported in any databases or any standard format so researchers find it difficult to locate and study this broad conceptual domain.Practical implicationsIt will help the reader and other academic stakeholders to widen their view on the new and innovative techniques of Vedic mathematics. It is advised that additional studies would look at and evaluate papers published after this time so that readers may get a wider view of the concept of Vedic mathematics.Social implicationsIt will help society to know the essence of Vedic mathematics that how useful it is. Vedic mathematics helps learners to learn in a very factual and accurate manner especially while dealing with mathematical calculations. It will enhance the problem-solving skills among learners. It will be beneficial for all types of learners which will help them to become better individuals for a nation.Originality/valueThe paper enriches understanding of the potential applications of different sutras from Vedic literature in different fields of knowledge. The outcome of the research encourages educationists and policymakers to include Vedic mathematics in the curriculum to foster quantitative reasoning and problem-solving at varied levels of learning.

A framework for assessing the blue equity of social-ocean systems in marine governance transformation

The equitable utilization of marine resources and the maintenance of healthy and sustainable marine ecosystems are essential to advancing the United Nations Sustainable Development Goals (SDGs) and promoting the well-being of all humanity. In this paper, we propose a Blue Equity Assessment Framework, which is based on a systematic literature review approach and takes into account the characteristics of Social-Ocean Systems (SOSs). The framework consists of Distributional Equity, Procedural Equity, Recognitional Equity, and Contextual Equity. The aim of this framework is to conduct equity analyses of policies or behaviors within SOSs in order to assess whether blue equity is explicitly implemented and practiced in these societal norms and policy mechanisms. The assessment framework reveals that blue equity has a positive synergy for the majority of the SDGs. This synergy is analyzed in terms of its influence on the assessment dimensions of distributional justice, procedural justice, recognitional justice, and contextual justice. The findings aim to enhance societal understanding of blue equity issues, thereby guiding policymakers in the prioritization of blue equity in the development and utilization of marine resources and in the transformation of global ocean governance.

Exploring the Frontiers of Data Analysis: A Comprehensive Review

The process of assessing, cleansing, transforming, and interpreting data to find trends, patterns, or insights that might guide choices and help manage problems is known as data analysis. Data analysis is a leading light on the cutting edge of contemporary research, revealing the path of knowledge across many areas. It includes the methodical examination of data to find trends, patterns, and insights that are helpful for the analytical and creative processes. This review also examines how data analysis is developing, emphasizing new approaches, paradigms, viewpoints, and graphical data displays. In addition to the significant improvements brought about by artificial intelligence, deep learning, and machine learning, it emphasizes statistical inference, exploratory data analysis, and data pretreatment. One of the main ideas behind this review was to use a systematic literature review approach along with meta-analysis techniques to look for new developments and trends in how data is analyzed in a lot of different areas. The article also addresses how data visualization could improve comprehension and dissemination of the results. In promoting responsible data use and legal rules, it also looks at how analytics affects society, the law, and ethical issues. The evaluation underscores the diverse disciplines that employ data analysis, underscoring the need for interdisciplinary coherence and comprehensible algorithms. Finally, this thorough research offers recommendations for analyzing and determining the boundaries of data analysis, in addition to offering insightful viewpoints and opinions that are helpful for academics, professionals, and decision-makers. If we just stay up to speed with the latest advancements and strive to be more, we can utilize data analysis to its fullest potential for complicated problems and positively impact society. Keywords: Data mining, big data analytics, machine learning, algorithms, and data analysis and statistics

Theoretical perspectives in reverse logistics research

PurposeTheory is an essential prerequisite in the development and maturation of any scholarly discipline. This study offers insight into theory development in reverse logistics (RL) studies, provides a synopsis of the theories employed in RL studies, and presents a comprehensive framework for choosing and applying theories in RL studies.Design/methodology/approachUsing the systematic literature review approach, 265 various RL articles were analysed to discover the trend in using theories in RL studies and classify the individual theories employed. The analysis of the theoretical classification is presented to explain the type and frequency of the usage of theories.FindingsThe analysis discovered 52 specific theories from the sample. These theories were categorised under various categories: competitive, inventory, economic, decision, etc. The institutional, stakeholder, transaction cost economies, resource-based view, natural resource-based view, dynamic capability, agency and theory of planned behaviour were some of the key theories discovered. Finally, a comprehensive framework is provided to aid researchers in choosing and utilising theories.Research limitations/implicationsThis study gives authors, reviewers and editors perspectives on utilising theories in RL studies. It will give them the impetus to develop theories in RL and limit the borrowing or extension of theories from other disciplines to RL studies.Originality/valueTo the best of the researcher's knowledge, this is the first attempt to comprehensively provide an anatomical perspective into theory usage in RL studies. Besides, this study's proposed framework for selecting and using theories is a novelty in the domain of RL.

Garden of forking paths in ERP research - Effects of varying pre-processing and analysis steps in an N400 experiment.

This study tackles the Garden of Forking Paths, as a challenge for replicability and reproducibility of ERP studies. Here, we applied a multiverse analysis to a sample ERP N400 dataset, donated by an independent research team. We analyzed this dataset using 14 pipelines selected to showcase the full range of methodological variability found in the N400 literature using systematic review approach. The selected pipelines were compared in depth by looking into statistical test outcomes, descriptive statistics, effect size, data quality, and statistical power. In this way we provide a worked example of how analytic flexibility can impact results in research fields with high dimensionality such as ERP, when analyzed using standard null-hypothesis significance testing. Out of the methodological decisions that were varied, high-pass filter cut-off, artifact removal method, baseline duration, reference, measurement latency and locations, and amplitude measure (peak vs. mean) were all shown to affect at least some of the study outcome measures. Low-pass filtering was the only step which did not notably influence any of these measures. This study shows that even some of the seemingly minor procedural deviations can influence the conclusions of an ERP study. We demonstrate the power of multiverse analysis in both identifying the most reliable effects in a given study, and for providing insights into consequences of methodological decisions.

Artificial Intelligence-Based Landslide Studies in Indonesia: A Systematic Review in Recent Years

Abstract Landslide is still a hot topic in geological hazard discussion, including Indonesia. Various methods, including Artificial Intelligence (AI), are used to do research development on landslide topics. Therefore, this paper aims to present a comprehensive review of AI-based landslide studies that focus on specific application area, feature engineering method (FEM), and Digital Elevation Model (DEM) sources used in the studies. This research used a qualitative method with a systematic review approach toward recent landslide studies (2012-2022) that investigated systematically in a synthesis. The exploration resulted in 26 papers from national and international indexed journals or proceedings, which filtered into 13 articles that discuss or mention the specific application area, FEM, and DEM sources. The analysis shows that AI applications in landslide studies are dominated for landslide susceptibility mapping and still a few for other applications. It also shows that almost all AI-based landslide studies chose SRTM as the source of DEM. Regarding FEM, only five articles discussed important landslide factor selection. There are four FEMs that were used in those studies, i.e., variable deduction, certainty factor model, C.45 algorithm, and variable importance ranking. From the deep analysis of those 13 articles, it can be concluded that AI-based landslide studies in Indonesia still need to be developed instead of focusing on landslide susceptibility mapping only. Studies to find effective landslide factors and compatible DEM resources using AI also can be new opportunities for landslide experts.

A Critical Overview of Islamic State of Iraq and Syria (ISIS)’s Origin, Goals, and Its Fall through the US and Allied Forces

The origins of ISIS were traced to the Arab Spring that created instability in the Middle East. This instability was exacerbated by the American-led coalition's invasion of Iraq in 2003, prompted by the 9/11 attacks. The paper aims to have an overview of the origin and goals of the Islamic State of Iraq and Syria (ISIS) and the strategies employed by the U.S. and Allied Forces to make ISIS fall. A systematic literature review approach was adopted to review the selected literature. ISIS was formed out of various militant groups in the country whose goal is to establish an Islamic caliphate governed by strict Sharia law. The goal of ISIS was to establish an Islamic caliphate in Iraq, Syria, and beyond with a caliph as the leader of the territories, thereby eradicating state borders and governments they deemed to be illegitimate. This caliphate is to be governed by strict Sharia law. This analysis delves into the strategies used by the US-led coalition to reclaim territories from ISIS. These strategies include military strategies, economic strategies, political strategies, and social strategies. It was, therefore, recommended that to combat ISIS's ideology effectively, the United States may need to collaborate more with Muslim clerics all over the world to promote interpretations of the Quran that counter radicalization in Islam.

Soil legislation and policies: Bibliometric analysis, systematic review and quantitative approaches with an emphasis on the specific cases of the European Union and Portugal

The literature shows that there are dimensions related to soil legislation and policy in the European Union contexts that can be better explored through bibliometric analysis, systematic reviews and quantitative approaches. Therefore, this research aims to analyse documents on soil legislation and policies, highlighting the specific cases of Portugal and the European Union (EU). The aim is to identify suggestions to improve the Portuguese and European Union soil policy instruments and measures. To achieve these objectives, a bibliometric analysis (considering text and bibliographic data) and systematic review were carried out, as well as a survey of the available soil legislation (considering qualitative data and quantitative analysis). The results show that soil legislation and policy have become more relevant in recent years and that concerns are about soil health, protection and safety, as well as risk mitigation, biodiversity preservation and the maintenance of ecosystem services. However, some topics could be further explored in future research, namely those related to multidisciplinarity, smart methodologies, soil salinisation, innovation and quantitative approaches to assessing policy impacts. This study presents suggestions that can be considered by the Portuguese and European Union policymakers to improve the respective soil legislation and policies. Defining a regulatory system for soils in the European Union has not been easy over time, although there have been attempts, given the specificities of the contexts related to soils and the reluctance of some member states to take certain measures. The approaches and analysis topics considered are innovative (there aren't many scientific documents on the topics that address bibliometric analysis and quantitative assessments with qualitative data) and bring novelty to the literature.

Gene polymorphism impact on opioid analgesic usage.

Acute pain, moderate-to-severe cancer pain, and persistent malignant pain are all frequently treated with opioids. It is regarded as one of the main tenets of analgesic treatment. The relationship between human opioid sensitivity and genetic polymorphism differences has received little attention up to this point in research. Nonetheless, there is mounting proof that pharmacogenomic diversity could affect how each person reacts to opioids. Finding out how gene polymorphism affects analgesic use is the aim of this investigation, particularly opioids. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses standards were followed in the preparation of the systematic review approach used in this work. Oxycodone, fentanyl, raclopride, tramadol, ketorolac, morphine, ropivacaine, levobupivacaine, subfentanyl, remifentanil, and nortriptyline were the opioid medications used in the study, which was based on 13 publications. From those articles, we reviewed the impact of gene polymorphism on pain management and drug pharmacokinetics. Based on this systematic review, we concluded that gene polymorphism of gene affects analgesic, specifically opioid mechanisms.

The best machine learning model for fraud detection on e-platforms: a systematic literature review

The internet has been instrumental in the development and facilitation of online payment systems. However, its associated fraudulent activities on eplatforms cannot be overlooked. As a result, there has been a growing interest in the application of machine learning (ML) algorithms for fraud detection on financial e-platforms. The goal of this research is to identify common types of fraud on financial e-platform, highlight different machine learning algorithms employed in fraud detection, and derive the best machine learning algorithms for fraud detection on e-platforms. To achieve this goal, the research followed a nine steps systematic review approach to retrieve Journals and conference publications from science direct, Google Scholar and IEEE Xplore between 2018 and 2023. Out of 2,071 articles identified and screened, 44 publications (23 articles and 21 conference proceedings) satisfied the inclusion criteria for further analysis. The random forest algorithm turned out to be the best ML algorithm because it ranked first in the frequency of usage analysis and ranked first in the performance analysis with an average accuracy of 96.67%. Overall, this review has identified the kinds of fraud on financial e-platforms, and proclaimed the best and least ML algorithm for fraud detection on financial e-platform. This can help guide future research and inform the development of more effective fraud detection systems.

Consumer adoption of digital health services: A systematic literature review and research agenda

AbstractDigital health services (DHSs) have revolutionized the healthcare sector to provide quality care facilities that are affordable, efficient, and universally accessible. By making patients the focus of the healthcare delivery process, these services can offer more personalized and effective health solutions and thus facilitate a more robust and resilient health ecosystem. The adoption of these services increased manifold during the COVID‐19 pandemic and gained immense attention from researchers across the globe. Despite this, there is limited knowledge about the facets of DHS adoption from the consumers' perspective. Thus, it is imperative to synthesize the extant literature to provide a holistic view of DHS adoption. This article offers an organized, retrospective view of the DHS literature, using a framework‐based systematic review approach to identify factors contributing to DHS adoption from the consumers' perspective. To do this, 63 articles from the Web of Science database over the last 23 years were systematically reviewed using two well‐known organising frameworks. The Antecedents‐Decisions‐Outcomes framework uncovers the known associations of constructs in the DHS literature. Followed by the theories‐contexts‐methods framework that offers information on the theoretical, contextual, and methodological approaches applied in the DHS research. Additionally, the article identifies the major gaps in the existing body of literature and offers a few recommendations for future investigation for researchers and practitioners in the fields of consumer behaviour, technology, and health management. The global perspective of this review has multiple ramifications that can foster a more sustainable healthcare system by giving insight into consumers' perspectives.

The best machine learning model for fraud detection on e-platforms: a systematic literature review

The internet has been instrumental in the development and facilitation of online payment systems. However, its associated fraudulent activities on e-platforms cannot be overlooked. As a result, there has been a growing interest in the application of machine learning (ML) algorithms for fraud detection on financial e-platforms. The goal of this research is to identify common types of fraud on financial e-platform, highlight different machine learning algorithms employed in fraud detection, and derive the best machine learning algorithms for fraud detection on e-platforms. To achieve this goal, the research followed a nine steps systematic review approach to retrieve Journals and conference publications from science direct, Google Scholar and IEEE Xplore between 2018 and 2023. Out of 2,071 articles identified and screened, 44 publications (23 articles and 21 conference proceedings) satisfied the inclusion criteria for further analysis. The random forest algorithm turned out to be the best ML algorithm because it ranked first in the frequency of usage analysis and ranked first in the performance analysis with an average accuracy of 96.67%. Overall, this review has identified the kinds of fraud on financial e-platforms, and proclaimed the best and least ML algorithm for fraud detection on financial e-platform. This can help guide future research and inform the development of more effective fraud detection systems.

Reflections on government‐led social assistance programmes under Zimbabwe's National Social Protection Policy Framework: A social contract lens

AbstractSocial protection has gained global recognition for its role in addressing poverty, yet delivering social protection remains an intractable challenge for governments in the Global South. In this article, we assess the performance of government‐run social assistance in Zimbabwe. Our assessment begins in 2016 when the National Social Protection Policy Framework (NSPPF) was promulgated to maximize returns on social investments. Utilizing a systematic review approach, we collated literature published between 2016–2023. We find that despite adopting the NSPPF, social protection programming remains a challenge in Zimbabwe. We find that the disbursement of transfers to beneficiaries are delayed, benefits are misaligned with market forces due to inflation, most programmes are run without monitoring and evaluation structures, funding in the sector remains inadequate to reduce poverty and vulnerability, and no grievance and redress mechanisms exist. Our synthesis underscores the need for the refreshment of the social contract in Zimbabwe, a renewal of political will to finance social assistance programmes, engagement to transform policy into action, the expansion of the civic space to ensure citizens effectively participate in calling for social assistance, as well as concerted efforts towards harmonizing existing social assistance programmes. We highlight the need to embed social protection within a human rights‐based framework, and the need for robust monitoring frameworks along with predictable and dedicated financing.

Why Institutions Matter: Analysis of Their Significance in Shaping Economic, Environmental, And Policy Transformation

The role of institutions in shaping societal systems and responding to complex global challenges has gained increased attention in recent decades. Institutions have functioned to shape the development and sustainability of society. Nevertheless, numerous institutionally-led challenges that restrict and alter sustainability and development across various aspects of society have also emerged. While institutional importance is widespread, their relationship with development is debated and contested. Through a systematic review approach, this paper explores the concept of institutions to understand whether they matter for development and examine their relevance within economic, policy, and environmental structures. The study indicates that indeed institutions play a significant role in accelerating economic, policy, and environmental growth across space and time. It demonstrates how it drives policy cycles, fosters economic progress, and shapes environmental transition. The study reveals that institutions create order, facilitate innovation, drive market transactions, and environmental security by mitigating conflicts, enforcing regulations, enhancing cooperation, ensuring the distribution of resources, and promoting sustainable ecological guidelines. However, complexities in their functions, structures, and adaptation exist, which can stimulate instabilities, stagnation, and decline in societal progression. Achieving enhanced institutional capacity toward societal development and progress thus requires unwavering political commitment, innovative actions, and coordinated decision-making.

Systematic Literature Review: Using Mind Mapping to Improve Students' Creative Thinking Abilities

This research aims to explore the influence of mind mapping on improving students' creative thinking abilities. Through a Systematic Literature Review (SLR) approach, this article collects and analyzes relevant research results to provide a comprehensive picture of this topic. The results of the study show that mind mapping helps students organize information visually, which makes it easier for them to see relationships between concepts more clearly and holistically. The use of mind mapping has also been proven to stimulate students' creativity by encouraging them to think divergently and produce various new ideas. Additionally, mind mapping facilitates collaboration among students, allowing them to share ideas and work together on group projects, which is important for the development of creative thinking skills. By helping students see the relationships between concepts more clearly and holistically, mind mapping has proven to be effective in improving creative thinking abilities. Therefore, it is recommended that the integration of mind mapping in the learning process be implemented more widely in the educational curriculum. Teacher training regarding mind mapping techniques is also an important factor in ensuring effective and optimal implementation and improving students' creative thinking abilities. Thus, mind mapping not only functions as a learning tool but also as an effective method for developing students' overall creative potential.

AGRICULTURAL SECTORS VULNERABILITY TO CLIMATE CHANGE AND VARIABILITY IN MACHAKOS COUNTY: A SYSTEMATIC REVIEW OF LITERATURE

Identifying the extent of exposure, agricultural systems sensitivity, and adaptive capacity is essential in addressing agricultural vulnerability in an area and can lead to timely, appropriate, and effective adaptation strategies.Using the systematic literature review approach, this paper documented the vulnerability to climate change and variability of the agriculture sector in Machakos County, Kenya. The results revealed high exposure to climate change and variability, high sensitivity, and low adaptive capacity. With regard to exposure, a warming trend was observed in the County, while climate model outputs predicted enhanced warming and drying towards the end of the 21st century. Farming systemswerefound to be highly sensitive to impacts of climate change and variability, characterized by high incidences of drought, high rural population density, high percentage of smallholder farmers, and extremelyhigh susceptibility to land degradation. Adaptive capacity is generally low but depicted by substantial social capital and highly diversified agricultural production.

Root Cause Analysis Implementation to Improve Tissue Mill Automation System Availability in a Mill of the Biggest Indonesia Pulp Paper Industry: A Systematic Literature Review Approach

Increasing rivalry enforces Indonesia pulp paper industry to focus on competitive products in the market and the choice is to develop a Tissue Mill which is equipped with an automation system that has less manpower but require high availability that ensures production runs continuously. The minimum target for the availability of automation systems is 99.95%, which since commissioning in 2019 has not been reached on average per year until April 2024. Analysis of system failures that causing downtime was conducted. Analysis by comparing similar cases in various literatures and references as well for corrective actions and summarize suggested improvements to reduce downtime possibility in order to obtain 99.95% availability or even higher.

Fraud Prevention System with Whistleblowing System in Health Services: A Systematic Review

The objective of this study is to ascertain the strategies employed in mitigating fraudulent activities within the realm of health services through the utilization of a whistleblowing mechanism. Methods: The present study employed a systematic review approach to examine relevant literature by utilizing specific keywords such as "whistleblowing system" and "fraud Healthcare." These keywords were then used to search for publications in the Scopus database, focusing on journals that mostly relied on secondary data. The search was conducted using the publish or perish tool. 8. Subsequently, a curation of scholarly publications and articles was conducted, employing a criterion centered on the thematic relevance of their titles. The selected publications have the objective of mitigating fraudulent activities within the healthcare industry and other interconnected industries that contribute to research endeavors. The findings of this study suggest that the implementation of a fraud reporting system is vital inside an organization, particularly within the context of health services. To enhance the caliber of healthcare services, internal healthcare staff and participants may report well, it is imperative to establish a system that ensures the preservation of reporter confidentiality, hence facilitating an increase in the volume of reports.