Emerging Research Directions Research Articles

Looking into Corporate Boardrooms Through the Lens of Gender Diversity: A Bibliometric Review and META Analysis

The present study examines the literature on the corporate boardroom through the lens of gender diversity. The study identified 1413 studies from Scopus database to identify the corporate board gender diversity ranging from a period of 1998 to 2021 (November). The identified studies are reviewed by using VOS viewer software and R-Studio. The study also employed META analysis to further analyse the data and draw meaningful results. The study identifies the current themes in the literature of corporate boardroom diversity, find impediments to the growth in literature, find the most relevant paper over the years, most prolific authors, and most influential journal. The study also suggests the further scope of the study. We conclude by providing the five potential emerging research directions. The study identifies and discusses the main areas and current development in the field of corporate board diversity in terms of gender and suggests future research directions.

A Brief Review of Deep Neural Network Implementations for ARM Cortex-M Processor

Deep neural networks have recently become increasingly used for a wide range of applications, (e.g., image and video processing). The demand for edge inference is growing, especially in the areas of relevance to the Internet-of-Things. Low-cost microcontrollers as edge devices are a promising solution for optimal application systems from several points of view such as: cost, power consumption, latency, or real-time execution. The implementation of these systems has become feasible due to the advanced development of hardware architectures and DSP capabilities, while the cost and power consumption have been maintained at a low level. The aim of the paper is to provide a literature review on the implementation of deep neural networks using ARM Cortex-M core-based low-cost microcontrollers. As an emerging research direction, there are a limited number of publications that address this topic at the moment. Therefore, the research papers that stand out have been analyzed in greater detail, to promote further interest of researchers to bring AI techniques to low power standard ARM Cortex-M microcontrollers. The article addresses a niche research domain. Despite the increasing interest manifested toward both (1) edge AI applications and (2) theoretical contributions in DNN optimization and compression, the number of existing publications dedicated to the current topic is rather limited. Therefore, a comprehensive literature survey using systematic mapping is not possible. The presentation focuses on systems that have shown increased efficiency in resource-constrained applications, as well as the predominant impediments that still hinder their implementation. The reader will take away the following concepts from this paper: (1) an overview of applications, DNN architectures, and results obtained using ARM Cortex-M core-based microcontrollers, (2) an overview of low-cost hardware devices and SW development solutions, and (3) understanding recent trends and opportunities.

Enhanced Multifactorial Evolutionary Algorithm With Meme Helper-Tasks.

Evolutionary multitasking (EMT) is an emerging research direction in the field of evolutionary computation. EMT solves multiple optimization tasks simultaneously using evolutionary algorithms with the aim to improve the solution for each task via intertask knowledge transfer. The effectiveness of intertask knowledge transfer is the key to the success of EMT. The multifactorial evolutionary algorithm (MFEA) represents one of the most widely used implementation paradigms of EMT. However, it tends to suffer from noneffective or even negative knowledge transfer. To address this issue and improve the performance of MFEA, we incorporate a prior-knowledge-based multiobjectivization via decomposition (MVD) into MFEA to construct strongly related meme helper-tasks. In the proposed method, MVD creates a related multiobjective optimization problem for each component task based on the corresponding problem structure or decision variable grouping to enhance positive intertask knowledge transfer. MVD can reduce the number of local optima and increase population diversity. Comparative experiments on the widely used test problems demonstrate that the constructed meme helper-tasks can utilize the prior knowledge of the target problems to improve the performance of MFEA.

The random walk-based gravity model to identify influential nodes in complex networks

The identification of influential nodes in complex networks has been a topic of immense interest. In most cases, the local approach represented by degree centrality performs well but has limitations when dealing with the bridge nodes. In order to solve the problem of being trapped in the locality, researchers have proposed many useful methods. The gravity model is an emerging research direction among them. However, such models have to exhaust the shortest distance between all nodes, which renders them impractical and difficult to run over large graphs. In order to address this issue, we propose a random walk-based gravity model to identify influential spreaders. Our proposed model decreases the time complexity of calculating the shortest distance—a critical step in the conventional gravity models, from O(|V|2) to O(|V|*γ*lr(l-r)), and reduces space complexity of O(|V|2) to O(<K>2|V|), where <K>2≪|V| and γ*lr(l-r)≪|V|. Some random walk properties are also investigated to support our model. In order to demonstrate the feasibility of the proposed gravity centrality, we have verified its spreading ability and convergence speed under different random walk strategies. Experimental results indicate that our method performs far better than most gravity models.

A Review of Advancing Two-Dimensional Material Membranes for Ultrafast and Highly Selective Liquid Separation.

Membrane-based nanotechnology possesses high separation efficiency, low economic and energy consumption, continuous operation modes and environmental benefits, and has been utilized in various separation fields. Two-dimensional nanomaterials (2DNMs) with unique atomic thickness have rapidly emerged as ideal building blocks to develop high-performance separation membranes. By rationally tailoring and precisely controlling the nanochannels and/or nanoporous apertures of 2DNMs, 2DNM-based membranes are capable of exhibiting unprecedentedly high permeation and selectivity properties. In this review, the latest breakthroughs in using 2DNM-based membranes as nanosheets and laminar membranes are summarized, including their fabrication, structure design, transport behavior, separation mechanisms, and applications in liquid separations. Examples of advanced 2D material (graphene family, 2D TMDs, MXenes, metal–organic frameworks, and covalent organic framework nanosheets) membrane designs with remarkably perm-selective properties are highlighted. Additionally, the development of strategies used to functionalize membranes with 2DNMs are discussed. Finally, current technical challenges and emerging research directions of advancing 2DNM membranes for liquid separation are shared.

Emerging Graphene Derivatives and Analogues for Efficient Energy Electrocatalysis

AbstractThe demand for highly efficient conversion and storage of renewable energy sources calls for advanced electrocatalytic technologies. 2D graphene has long been investigated as a versatile material platform with flexible structure and tunable surface properties for the design of efficient electrocatalysts. In recent years, new types of graphene derivatives and analogues keep emerging and showing great potential for highly active and selective electrocatalytic applications. In this review, recent progresses on emerging graphene derivatives and analogues for electrocatalysis are summarized. This review starts from the introduction of the material requirements for efficient electrocatalysis and the advantages of 2D graphene derivatives and analogues. Then new advances on graphene derivatives for electrocatalysis are introduced, including twisted graphene superlattices, molecular‐level single‐atom catalysts, and graphene‐supported dual‐atom catalysts or atomic clusters. The next section deals with the emerging research directions of graphene analogues for electrocatalysis, including 2D metal‐free materials, metallenes, 2D transition metal borides (MBenes), and new design strategies of them. Finally, an overview on the development status of this field is provided, and perspectives on the future development of efficient electrocatalytic technologies based on 2D materials are proposed.

Industry convergence and value innovation: a bibliometric analysis and systematic review

PurposeThe purpose of this paper is twofold: first, to present a bibliometric analysis and systematic literature review of industry convergence and value innovation to understand the current research status; second, to provide a coherent theoretical research framework for future research.Design/methodology/approachThis study adopts a two-step analysis approach by combining bibliometric analysis and systematic literature review to explore the research topic of industry convergence and value innovation. Besides, two bibliometric tools, HistCite and VOSviewer, were applied to this study.FindingsThis study found that Stefanie Bröring and Fredrik Hacklin are the top two most influential authors among all authors in the sample publications. Technological Forecasting and Social Change is one of the top-ranking journal that often publishes this topic of articles. Germany and the University of Munster are the most influential country and institutions, respectively. Besides, five core research themes were identified based on keywords co-occurrence map, theoretical lenses, factors promoting industry convergence, indicators of industry convergence, the impact of industry convergence and emerging research directions. Based on the above analysis, this paper constructed a theoretical research framework of industry convergence and value innovation.Research limitations/implicationsThis paper only draw data from one database – Web of Science – which cannot provide broad coverage of the research topic. Besides, the bibliometric method of this paper is based on high local citation score and high-frequency words, articles in the skirting subjects’ area may not be analyzed.Practical implicationsWith the rapid development of technology, such as nanotechnology, radio - frequency identification (RFID), etc., the iterative upgrading of products also comes. As a result, the boundary between industries is gradually blurred, and the phenomenon of industry convergence appears. Therefore, managerial decision-makers are facing challenges of how to respond to the convergence phenomena. From the firm level, firms are facing the problem of value innovation of the existing product, new product development and core competence improvement. Industries are facing the problem of transformation and upgrading. This paper provides certain theoretical insights for both firms and industries to guide the practice accordingly.Originality/valueThis paper is the first to use a bibliometric method to examine the topic of industry convergence and value innovation. In addition, this paper presents an in-depth analysis of this topic and provides a comprehensive theoretical research framework for future study.

LGR4, a G Protein-Coupled Receptor With a Systemic Role: From Development to Metabolic Regulation

Leucine-rich repeat-containing G protein-coupled receptor 4 (LGR4/GPR48), a member of the GPCR (G protein-coupled receptors) superfamily, subfamily B, is a common intestinal crypt stem cell marker. It binds R-spondins/Norrin as classical ligands and plays a crucial role in Wnt signaling potentiation. Interaction between LGR4 and R-spondins initiates many Wnt-driven developmental processes, e.g., kidney, eye, or reproductive tract formation, as well as intestinal crypt (Paneth) stem cell pool maintenance. Besides the well-described role of LGR4 in development, several novel functions of this receptor have recently been discovered. In this context, LGR4 was indicated to participate in TGFβ and NFκB signaling regulation in hematopoietic precursors and intestinal cells, respectively, and found to be a new, alternative receptor for RANKL (Receptor Activator of NF kappa B Ligand) in bone cells. LGR4 inhibits the process of osteoclast differentiation, by antagonizing the interaction between RANK (Receptor Activator of NF kappa B) and its ligand-RANKL. It is also known to trigger anti-inflammatory responses in different tissues (liver, intestine, cardiac cells, and skin), serve as a sensor of the circadian clock in the liver, regulate adipogenesis and energy expenditure in adipose tissue and skeletal muscles, respectively. The extracellular domain of LGR4 (LGR4-ECD) has emerged as a potential new therapeutic for osteoporosis and cancer. LGR4 integrates different signaling pathways and regulates various cellular processes vital for maintaining whole-body homeostasis. Yet, the role of LGR4 in many cell types (e.g. pancreatic beta cells) and diseases (e.g., diabetes) remains to be elucidated. Considering the broad spectrum of LGR4 actions, this review aims to discuss both canonical and novel roles of LGR4, with emphasis on emerging research directions focused on this receptor.

Intelligent metasurfaces: control, communication and computing

AbstractControlling electromagnetic waves and information simultaneously by information metasurfaces is of central importance in modern society. Intelligent metasurfaces are smart platforms to manipulate the wave–information–matter interactions without manual intervention by synergizing engineered ultrathin structures with active devices and algorithms, which evolve from the passive composite materials for tailoring wave–matter interactions that cannot be achieved in nature. Here, we review the recent progress of intelligent metasurfaces in wave–information–matter controls by providing the historical background and underlying physical mechanisms. Then we explore the application of intelligent metasurfaces in developing novel wireless communication architectures, with particular emphasis on metasurface-modulated backscatter wireless communications. We also explore the wave-based computing by using the intelligent metasurfaces, focusing on the emerging research direction in intelligent sensing. Finally, we comment on the challenges and highlight the potential routes for the further developments of the intelligent metasurfaces for controls, communications and computing.

Critical Cognitive Interpretation of The Time Machine from the Perspective of Proximization Theory

As a product of cognitive linguistics and critical discourse analysis, proximization theory is one of the emerging research directions in the field of linguistics. Most of the current research using this theory focuses on political conflict topics, and there are fewer studies using it for analysis of literary works. The present study examines The Time Machine from the perspective of proximization theory (PT) to unveil the legitimation strategies of science fiction qualitatively and quantitatively. The objectives are to enrich the explanation power of this theory and analyze how it can avail the expression and description of the implicit theme of science fiction. We apply CLAWS to annotate the whole novel, search and calculate the frequency of each lexico-grammatical item through AntConc 3.5.9, and then analyze the novella through proximization theory in spatial, temporal, and axiological dimensions. Findings show that: spatially, the author subsumes the most salient Time Traveler to the camp of IDC, and with the first-person narrator and sense verbs, the whole picture of decay can make the readers horribly feel on the spot in person toward a dim human destiny; temporally, Wells prefers the past-present concept shift than the future-present. The former is to legitimize his critique of over-reliance on science and severe class exploitation, and the latter is to make predictions of a future society that may face decay. The use of contrasting construal makes the novel's plot more mysterious, stimulates the reader's interest, and paves the way for the revelation of the theme; axiologically, considering that England put science and industrial development on the pedestal at that time and that prevailing evolution theory puts people in a period of blind optimism, Wells cautions against the trap of utopia and the swamp of materialism with emphasis on negative ideologies and values of ODC. The analysis of literary works from the perspective of PT is conducive to the visualization of the implicit themes expressed in the novel, full of absurdity and exaggeration with data support, highlighting the author's humanistic concern and broadening the research object of proximization theory.

Construction of Swimmer's Underwater Posture Training Model Based on Multimodal Neural Network Model.

Swimming monitoring based on acceleration sensor is an emerging research direction in the field of human motion recognition. As a public sport, swimming has a wide audience. The swimming monitoring system can facilitate people to monitor and record their own swimming data, so as to formulate a reasonable training plan. Aiming at the defects of single modal information representation ability, high contingency, and easy to be influenced by the outside world, this paper adopts the underwater posture training model of swimmers to perform multimodal information fusion. In this paper, a multimodal information fusion method based on evolutionary neural network is proposed, and an intelligent perception information processing model of the intelligent subject system is constructed. Aiming at the defect that the accuracy and speed of the underwater posture monitoring of swimmers cannot be guaranteed in a complex environment, an evolutionary neural network optimized by a multimodal adaptive genetic algorithm is constructed to perform multimodal information fusion to ensure the effectiveness of the system in the face of complex information. Regarding attitude detection, it mainly uses the three dimensions of the angle of movement, the influence of gravity, and the strength and speed of the movement to measure. The MPU6050 module processor has a wide range of applications and is a mold processing tool with high performance and level. It completes the data processing, data calculation, and data storage of the inspection system in this paper. This paper further studies the working principle, structure, and operation process of this module and adjusts the time error in the detection of carrier motion and attitude so that the processing function of this module can play an optimal state. Four kinds of swimming posture measurement experiments were carried out on the swimmers, and the experimental data were analyzed. The whole system is controlled by the host computer man-machine interaction software remotely and in real time through commands. The experimental results show that the system realizes the detection of the basic posture, meets the basic requirements of the system design, and provides a certain foundation for the follow-up research.

Critical Cognitive Interpretation of The Time Machine from the Perspective of Proximization Theory

As a product of cognitive linguistics and critical discourse analysis, proximization theory is one of the emerging research directions in the field of linguistics. Most of the current research using this theory focuses on political conflict topics, and there are fewer studies using it for analysis of literary works. The present study examines The Time Machine from the perspective of proximization theory (PT) to unveil the legitimation strategies of science fiction qualitatively and quantitatively. The objectives are to enrich the explanation power of this theory and analyze how can it avail the expression and description of the implicit theme of science fisction. We apply CLAWS to annotate the whole novel, search and calculate the frequency of each lexico-grammatical item through AntConc 3.5.9, and then analyze the novella through proximization theory in spatial, temporal and axiological dimensions. Findings show that: spatially, the author subsumes the most salient Time Traveler to the camp of IDC, and with the first-person narrator and sense verbs, the whole picture of decay can make the readers horribly feel on the spot in person toward a dim human destiny; temporally, Wells prefers the past-present concept shift than the future-present. The former is to legitimize his critique of over-reliance on science and severe class exploitation, and the latter is to make predictions of a future society that may face decay. The use of contrasting construal makes the novel’s plot more mysterious, stimulates the reader’s interest, and paves the way for the revelation of the theme; axiologically, considering that England put science and industrial development on the pedestal at that time and that prevailing evolution theory puts people in a period of blind optimism, Wells cautions against the trap of utopia and the swamp of materialism with emphasis on negative ideologies and values of ODC. The analysis of literary works from the perspective of PT is conducive to the visualization of the implicit themes expressed in the novel, full of absurdity and exaggeration with data support, highlighting the author’s humanistic concern and broadening the research object of proximization theory.

The marine nitrogen cycle: new developments and global change.

The ocean is home to a diverse and metabolically versatile microbial community that performs the complex biochemical transformations that drive the nitrogen cycle, including nitrogen fixation, assimilation, nitrification and nitrogen loss processes. In this Review, we discuss the wealth of new ocean nitrogen cycle research in disciplines from metaproteomics to global biogeochemical modelling and in environments from productive estuaries to the abyssal deep sea. Influential recent discoveries include new microbial functional groups, novel metabolic pathways, original conceptual perspectives and ground-breaking analytical capabilities. These emerging research directions are already contributing to urgent efforts to address the primary challenge facing marine microbiologists today: the unprecedented onslaught of anthropogenic environmental change on marine ecosystems. Ocean warming, acidification, nutrient enrichment and seawater stratification have major effects on the microbial nitrogen cycle, but widespread ocean deoxygenation is perhaps the most consequential for the microorganisms involved in both aerobic and anaerobic nitrogen transformation pathways. In turn, these changes feed back to the global cycles of greenhouse gases such as carbon dioxide and nitrous oxide. At a time when our species casts a lengthening shadow across all marine ecosystems, timely new advances offer us unique opportunities to understand and better predict human impacts on nitrogen biogeochemistry in the changing ocean of the Anthropocene.

Structure-Based Varieties of Polymeric Nanocarriers and Influences of Their Physicochemical Properties on Drug Delivery Profiles.

Carriers are equally important as drugs. They can substantially improve bioavailability of cargos and safeguard healthy cells from toxic effects of certain therapeutics. Recently, polymeric nanocarriers (PNCs) have achieved significant success in delivering drugs not only to cells but also to subcellular organelles. Variety of natural sources, availability of different synthetic routes, versatile molecular architectures, exploitable physicochemical properties, biocompatibility, and biodegradability have presented polymers as one of the most desired materials for nanocarrier design. Recent innovative concepts and advances in PNC‐associated nanotechnology are providing unprecedented opportunities to engineer nanocarriers and their functions. The efficiency of therapeutic loading has got considerably increased. Structural design‐based varieties of PNCs are widely employed for the delivery of small therapeutic molecules to genes, and proteins. PNCs have gained ever‐increasing attention and certainly paves the way to develop advanced nanomedicines. This article presents a comprehensive investigation of structural design‐based varieties of PNCs and the influences of their physicochemical properties on drug delivery profiles with perspectives highlighting the inevitability of incorporating both the multi‐stimuli‐responsive and multi‐drug delivery properties in a single carrier to design intelligent PNCs as new and emerging research directions in this rapidly developing area.

Ultrasound-Mediated Release of Gaseous Signaling Molecules for Biomedical Applications.

Although nitric oxide (NO), carbon monoxide (CO), and hydrogen sulfide (H2 S) have been considered as notorious gas pollutants for decades, they are considered as endogenous gaseous signaling molecules (GSMs), which have been widely recognized for their important signaling functions and prominent medical applications in human physiology. To achieve local delivery of GSMs to optimize therapeutic efficacy and reduce systemic side effects, stimuli-responsive nanocarriers have been successfully developed. Among them, ultrasound is considered as an attractive theranostic modality that can be used to track drug carriers, trigger drug release, and improve drug deposition, etc. In this minireview, recent achievements in designing ultrasound-responsive nanocarriers for the controlled delivery of GSMs and their biomedical applications are summarized. This emerging research direction enables the controlled delivery of GSMs to deep tissues, and the combination of ultrasound imaging techniques offers many possibilities for the fabrication of new theranostic platforms.

Bibliometric analysis of digital twin literature: a review of influencing factors and conceptual structure

ABSTRACT This article systematically summarises the digital twin technology in the existing scientific field using the bibliometric method, puts forward the critical areas and research directions at different stages of the life cycle of the digital twin research topic. Using R-Studio ‘Biblioshiny’ to measure scientifically, we visually analyze 514 articles related to digital twin in the Web of Science's core set from 2014 to 2021 (including SCI-Expanded and SSCI database). Firstly, we analyzed the core journals, articles, authors, institutions, relevant countries to determine the vital influence of digital twin-related literature. Secondly, we use theme maps to draw the research topics of digital twin-related literature as icons on the conceptual structure and offer four cluster parts (Motor themes, highly developed and isolated themes, Emerging themes, and Basic themes). Finally, we concluded by summarising the limitations of our study and subsequently highlighted future research directions. The study shows that monitoring, maintenance, and computer vision are research topics with good development momentum. Disturbance observer, reinforcement learning, and friction are relatively small research topics with a good development foundation, and scheduling, process modelling, and deep reinforcement learning are emerging research directions.

Nanoscale Design of Pd‐Based Electrocatalysts for Oxygen Reduction Reaction Enhancement in Alkaline Media

Palladium (Pd)‐based electrocatalysts have recently emerged as one class of the foremost promising candidates for the oxygen reduction reaction (ORR) in alkaline media due to their excellent ORR activity and durability and lower costs compared with platinum. Insightful design of Pd‐based nano‐architectures with optimized active surface sites and maximal intrinsic performance is central to promoting the ORR applications. To further accelerate the sluggish ORR kinetics at the cathode of fuel cells and substantially decrease the overall cost of the electrocatalysts, various strategies, including controlled sizes and shapes with selected crystallographic facets, crystal‐phase engineering, heteroatom doping, tailored surface strains, and surface engineering by de‐alloying, have been extensively developed in the past decade. In this review, a brief introduction to the fundamental ORR mechanisms of Pd‐based electrocatalysts in alkaline media is presented, followed by a thorough discussion on various strategies for delicately designing high‐performance Pd‐based catalysts with corresponding examples. Thereafter, the perspectives and new insights into the challenges are outlined, and some emerging research directions related to the rational design and controlled synthesis of Pd‐based ORR electrocatalysts are also proposed.

Micro/Nanostructures for Light Trapping in Monocrystalline Silicon Solar Cells

Due to the ongoing depletion of fossil energy, alternative energy‐sources and their respective conversion technologies have become very essential. An inexhaustible and clean energy form, which is already widely used, is solar energy. However, despite much progress in recent decades, due to the limitations of materials and manufacturing technology, the ability of solar cells to convert light into electrical energy is still not very high. Enhancing light absorption and reducing electric loss are the keys to improving the overall conversion efficiency of solar cells and reducing raw material costs. The former can be achieved by using micro/nanostructures and other surface features, while the latter can be realized by surface passivation. Researchers have developed different silicon‐surface texturing methods to fabricate random or periodic micro/nanostructures on the surface of silicon wafers. Thanks to the special and efficient light‐trapping effects of silicon micro/nanostructures, both full angle and wideband light absorption can be achieved. Different passivation methods and materials have also been widely studied, which helps to improve the surface recombination of photogenerated carriers caused by light trapping structure and significantly enhance the power conversion efficiency of Si solar cells. In this work, theoretical studies of enhanced light‐trapping in micro/nanostructures are introduced. In addition, several advanced methods for preparing micro/nanostructures on the surface of monocrystalline silicon are discussed. These can be classified as top‐down and bottom‐up approaches. Furthermore, passivation methods for micro/nanostructures on the surface of monocrystalline silicon solar cells are reviewed, including chemical passivation and field‐effect passivation. Finally, advantages and disadvantages of the micro/nanostructure preparation technologies, and light‐trapping effects of the micro/nanostructures, which were fabricated using these manufacturing technologies were summarized. Moreover, the effects of different passivation technologies on the optical properties and electrical properties of these micro/nanostructures are studied. An outlook of expected and emerging research directions for monocrystalline silicon solar cells concludes this study.

Photoresponsive DNA materials and their applications.

Photoresponsive nucleic acids attract growing interest as functional constituents in materials science. Integration of photoisomerizable units into DNA strands provides an ideal handle for the reversible reconfiguration of nucleic acid architectures by light irradiation, triggering changes in the chemical and structural properties of the nanostructures that can be exploited in the development of photoresponsive functional devices such as machines, origami structures and ion channels, as well as environmentally adaptable 'smart' materials including nanoparticle aggregates and hydrogels. Moreover, photoresponsive DNA components allow control over the composition of dynamic supramolecular ensembles that mimic native networks. Beyond this, the modification of nucleic acids with photosensitizer functionality enables these biopolymers to act as scaffolds for spatial organization of electron transfer reactions mimicking natural photosynthesis. This review provides a comprehensive overview of these exciting developments in the design of photoresponsive DNA materials, and showcases a range of applications in catalysis, sensing and drug delivery/release. The key challenges facing the development of the field in the coming years are addressed, and exciting emergent research directions are identified.

Quality management issues in Vietnam: A systematic mapping analysis

Quality management is a research topic that received scholars’ attention over three decades, especially in Vietnam - a developing country that strives to improve sustainable quality productivity within the enterprise in particular and in the economy in general. The purpose of this work is to find out a grouping of aspects that previous studies have made significant contributions towards issues on quality management in Vietnam by a systematic mapping analysis method. Findings are believed to be a proportion addressing of policy issues and identifying research gaps or trends in literature. To achieve this, our work develops a bibliometric analysis of the literature. The study sets out strategies for using Graphext big data analysis tool, using material collected from Scopus database source. As a result, 33 selected articles were published from 2003 to present, most of which were published by authors working in Vietnam. Research directions are diverse, with emerging research directions related to service, production quality management, water environment quality management, and so on. In addition, among the previous research results, only the positive relationship between profitability and quality management capacity can be confirmed. Details will be provided in the study results section.