Systematic Literature Review and Meta-Analysis Journal <p>The Systematic Literature Review and Meta-Analysis Journal is a multidisciplinary journal focused on the research articles, reviews and empirical research that has used Systematic Literature Review and Meta-Analysis (SLR-M) methods in their research. The journal aimed to facilitate the research in all fields of life until the SLR-M methods have been applied. </p> en-US (Dr. Muhammad Imran Qureshi) (Nohman Khan) Tue, 19 Oct 2021 00:07:13 -0400 OJS 60 Empowering Heutagogy for 21st Century Learning <p><em>Heutagogy a term which describes self-determined learning with&nbsp;principles deeply engrained in andragogy has recently gained popularity as a learning technique due to limited attention for a period of time. The core principle of heutagogical teaching and learning technique is to encourage learners to be self-determined and is grounded upon the development of learner’s individual capability with the primary goal of grooming learners to gracefully survive the complexities of the rapidly globalizing economies. The renewed attention towards heutagogy is partly due to the advancement of technology. With the learner-centric design, technological advancement greatly supports the heutagogical approach by sustaining the expansion of learner generated subject. Therefore, this article collates crucial commentaries and opinions from various scholars in the field. This article is built grounded on various relevant literatures and thoroughly defines the intriguing concept of heutagogy and andragogy to a certain extent. This article offers a foundation for further discussions and impactful research into heutagogy as an emerging learning technique and a theory which would revolutionize the education system while nurturing life-long and mature learners who are academically and professionally prepared for the rapidly globalizing economy. </em></p> <h5><strong>&nbsp;</strong></h5> Gowrie Vinayan, Davindran Harikirishanan Copyright (c) 2021 Systematic Literature Review and Meta-Analysis Journal Tue, 19 Oct 2021 00:00:00 -0400 Latency-aware Straggler Mitigation Strategy in Hadoop MapReduce Framework: A Review <p>Processing huge and complex data to obtain useful information is challenging, even though several big data processing frameworks have been proposed and further enhanced. One of the prominent big data processing frameworks is MapReduce. The main concept of MapReduce framework relies on distributed and parallel processing. However, MapReduce framework is facing serious performance degradations due to the slow execution of certain tasks type called stragglers. Failing to handle stragglers causes delay and affects the overall job execution time. Meanwhile, several straggler reduction techniques have been proposed to improve the MapReduce performance. This study provides a comprehensive and qualitative review of the different existing straggler mitigation solutions. In addition, a taxonomy of the available straggler mitigation solutions is presented. Critical research issues and future research directions are identified and discussed to guide researchers and scholars</p> Ajibade Lukuman Saheed, Abu Bakar Kamalrulnizam, Ahmed Aliyu, Tasneem Darwish Copyright (c) 2021 Systematic Literature Review and Meta-Analysis Journal Tue, 19 Oct 2021 00:00:00 -0400 Weed detection using machine learning: A systematic literature review <p>Recently, many researchers and practitioners used Machine Learning (ML) algorithms in digital agriculture to help farmers in decision making. This study aims to identify, assess and synthesize research papers that applied ML algorithms in weed detection using the Systematic Literature Review (SLR) Protocol. Based on our defined search string, we retrieved a total of 439 research papers from three electronic databases, of which 20 papers were selected based on the selection criteria and thus, were synthesized and analyzed in detail. The most applied ML algorithm is Neural Networks in these models. Thirteen evaluation parameters were identified, of which accuracy is the most used parameter. 75% of the selected papers used cross-validation as the evaluation approaches, while the rest used holdout. The challenges most encountered were insufficient data and manual labeling of the pixel during image segmentation. Based on the ML algorithms identified, we concluded that supervised learning techniques are the most used techniques in weed detection.</p> Bashir Salisu Abubakar Copyright (c) 2021 Systematic Literature Review and Meta-Analysis Journal Tue, 19 Oct 2021 00:00:00 -0400 Understand China’s cross-border e-commerce industry: a market entry mode <p>Cross-border e-commerce is emerging explosively globally while China as the world’s largest trading country is gradually shifted its traditional trading market to the online marketplace to fit new retail norms of buying online across borders. The supply-demand dynamism and infrastructure readiness are two key drivers governing the exponential growth; however, multiples factors including cultural differences in consumer behaviour, unstructured laws and regulation, the inefficiency of transformed value chain management, limitation on currency payment, and distancing logistics remain unsolved challenges. Hence, this paper aims to deliver some key takeaways for the foreign company to take advantage of cross-border e-commerce as the channel to entering China’s consumer market. Using a combination of various keywords as the searching formula, a total of 17 relevant papers exacted from the Scopus database is in inclusion for the reviewing works. Only the journal paper published in the year 2021 and the content is highly relevant are included. From there, four predominant themes identify are the national-based regulatory environment, market-driven value creation, service-based ecosystem reformation, and digitally drive business transformation. Viewing the huge market potential, cross-border e-commerce is, therefore, a good start points for the foreign company to step into China market.</p> Choy Soon Tan Copyright (c) 2021 Systematic Literature Review and Meta-Analysis Journal Tue, 19 Oct 2021 00:00:00 -0400