Data Visualization of Decision Support Systems Research in Sustainability: A Bibliometric Analysis
Ahmad Faiz Ghazali1*, Aishah Suhaimi1, Syahrul Fahmy Wahab2
1 Universiti Teknologi MARA Cawangan Johor Kampus Segamat, Malaysia
2 University College TATI (Terengganu Advanced Technical Institute), Malaysia
*Corresponding Author: [email protected] Accepted: 15 September 2022 | Published: 1 October 2022
DOI:https://doi.org/10.55057/ijarti.2022.4.3.3
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Abstract: The development of scientific research for decision support system in the area of sustainability is growing steadily from 1991 with an increasing trend up to 2022. It is of utmost necessity to identify the potential areas as well as the severity of this research. Thus, the aim of this study is to visualize the scientific research on decision support system on sustainability by conducting bibliometric analysis. The data visualization was performed using the VOSviewer software and data analysis tool available in the Scopus database. A total of 1736 publications in relation to decision support system review related to sustainability, were extracted from Scopus database ranging from 1991 to 2022. Co-citation analysis and co-word analysis were conducted to visualize the evolution of research themes in this field. Four main clusters were identified which mainly related to decision support system applications, decision approach, environmental aspect and water management. The findings of this study help researchers to understand the nature of decision support system research in sustainability from across the world and suggest future research directions.
Keywords: Data visualization, decision support systems (DSS), sustainability, bibliometric analysis, co-word analysis
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1. Introduction
A decision support systems (DSS) is a computer-based information system that support organizations in making decisions (Rashidi et al., 2018). DSS is useful in several levels such as management or operations, in making choices based on decision alternatives. Knowledge- based and risk-based systems can be included in DSS. DSS research has been applied in many areas including but not limited in hybrid learning (Ghazali et al., 2022), adaptive learning (Yahya et al., 2020), food security (Ghazali & Suhaimi, 2020), crime investigations (Ghazali et al., 2016; Noor et al., 2014) and tendering (Mohemad et al., 2010). Sustainability is the area of research that have been on the rise especially when the impacts of climate changes explicitly taken place in many locations around the globe. The scientific research for DSS on sustainability has gained attention which is proven by its steady increase since 1991. The area of sustainability itself has become the world’s target as outlined in 17 Sustainable Development Goals (UN, 2022). Important decision-making process that impact many countries and nations are now becoming the global focus.
The current decision-making processes are comparatively more difficult considering vast amount of input from big data and output for decision alternatives from many levels in different
types of dynamic organizations. The task or responsibility that can be performed, the accountable roles involved, must be explicitly known in a decision transaction system in order to react to events underpin it (French et al., 2007). During an emergency event, the emergency management process at certain point can involve many types of DSS, depending on its stages and wherever the process is integrated in a DSS. (French, Carter & Niculae, 2007; Turoff et al., 2004). There has been a significant increase of research on DSS and sustainability in the current decade. It is most possibly happens due to the increase awareness of the consequences from global warming. The idea of sustainability can be redefined by embracing three different perspectives which are economic, social and environmental (Kuhlman et al., 2010). Data visualization for the insights between researchers and decision-makers in initiating fruitful collaborations can be provided by bibliometric analysis. Bibliometric analysis has been utilized as preliminary study before researchers focusing on specific theme for systematic literature review (SLR) and academic research proposals. Research clusters identified are including but not limited to; 1) decision support system applications, 2) decision approach, 3) environment, and 4) water. Visualizing co-word network is a very good tool to support decision-making processes in determining priority of DSS research on sustainability from its associated keywords by clusters. Visualization has capability to provide immediate insights to researchers and decision makers through its easy-to-interpret presentations using various forms of images, graphs, networks, or color codes. The snapshot of bibliometric analysis conducted in this study is explained in the next section.
2. Snapshot of Bibliometric Analysis
Normally, the purpose of a literature review is to map, consolidate and evaluate the domain area, and identify knowledge gaps to be filled in for future research. There are different types of literature review techniques: systematic literature review, content analysis, meta-analysis, bibliometric analysis, and others. However, the objectives of a study will determine how literature review techniques will be applied. Some researchers have adopted the content analysis method (Seuring & Müller, 2008; Wong, Wong & Boon-Itt, 2015) to analyse the development of research in supply chain domain. This method relies on subjective judgement which may lead to unreliable results due to misinterpretations. Furthermore, it is very difficult and time consuming to distinguish the most related papers from the vast resources available.
Bibliometric analysis is a method that includes statistical analysis of published articles and citations in order to measure their impact. For this study, the bibliometric analysis is considered the appropriate method to be applied due to three reasons: First, bibliometric analysis can handle large amounts of articles easier, faster and more precisely; Second, bibliometric analysis allows the capture of more comprehensive information; Third, the inter-action visualization interface of bibliometric analysis tools make researchers able to understand the development of research domain. Besides that, the bibliometric analysis is well rooted in grounded and well- established theories such as impact theory and structure hole theory (Chen, 2006).
Two aspects of bibliometric mapping that can be distinguished are the construction of bibliometric maps and the graphical representation of such maps. In the bibliometric literature, most attention is paid to the construction of biblio-metric maps. The construction of a map is a process that consists of three steps which are similarity index, map construction and interpretation. Similarity matrix is calculated based on the co-occurrence matrix while a visualization map is constructed by applying the VOS mapping technique to the similarity matrix and the map is then interpreted (Van Eck & Waltman, 2009). Data on co-occurrences of words can be used to con-struct so-called co-word maps, which are maps that provide a visual representation of the structure of a scientific field (Van Eck & Waltman, 2009). The
number of co-occurrences of two keywords is the number of publications in which both keywords occur together in the title, abstract, or keyword list (Van Eck & Waltman, 2014).
Similarity measures for co-occurrence data focusing on the association strength is a probabilistic measure. Van Eck & Waltman (2009) confirmed from both theoretical and empirical analyses that co-occurrence data can best be normalized using a probabilistic measure.
Bibliometric co-citation is relied on the assumption that published articles in scholarly journal build their research on similar articles published before (Van Raan, 2012). This technique can be used to evaluate journal performance. This is because different journals focus on different sub-fields of decision support system and sustainability research. In a co-citation analysis of researchers, the relatedness of researchers is determined based on the degree to which they are cited in the same publication. The more often two researchers are cited in the same publication, the stronger their relatedness is (Perianes-Rodriguez, A., Waltman, L., van Eck, 2016).
3. Methodology
Data Search Strategy
The bibliographic data were obtained from the most comprehensive global abstracts and citation database known as Scopus with the use of the decision support system and sustainability in the titles, abstracts and keyword fields or TITLE-ABS-KEY (‘decision support system’ AND ‘sustainability’). The analysed period of publication was collected in the year from 1991 to March 2022. The preliminary search yielded 2738 articles. Then, the articles were carefully reviewed for the relevancy to decision support system and sustainability research and limited to article type only. As a result, 1736 articles were retained for further analysis.
Elements of data were extracted from each information source such as the abstract, the author’s affiliation and the name of institution, the year of publication, the names of sources and the number of citations. Scopus Analyzer was applied to check on the descriptive analysis. Then, the data were exported to Excel spread sheet for data cleaning and VOSviewer was applied for co-citation analysis and co-word analysis.
Data Analysis
Excel spread sheet and Scopus Analyzer were employed to identify the most productive journals, publication trend, contributing institutions, contributing countries and contributing authors based on the number of published information sources as well as most cited articles.
SCImago Journal and Country rank website was referred to in order to check for journal ranking and H-index. The retrieved CSV text files were exported to VOSviewer to construct and visualize bibliometric network. Visualization of co-citations network and co-word network was conducted to explore research patterns and clusters in the field of study (Van Eck &
Waltman, 2014).
4. Results and Discussion
Figure 1 shows the distribution of documents published per year. It shows an upward trend manifesting the increasing interest in research area of decision support system and sustainability researches. During the first ten years, the publication was low. The interest in decision support system and sustainability research started to grow mainly from 1990 to 2020.
The growing interest in the research rise exponentially after 2017 where more papers were published between the period of 1991 and March 2022.
Figure 1: Number of articles per year
Based on Scopus Analyser, the results also revealed that most of the highly cited articles in Food safety researches area were written by researchers affiliated to Technical University of Denmark (31), Wegeningen University & Research (22) and Hong Kong Polytechnic University (20). Figure 2 illustrates the top ten institutions that published Decision Support System and sustainability research articles.
Figure 2: Top 10 most productive institutions in DSS research on sustainability
The articles in decision support system and sustainability were published by authors from 114 countries across the globe. The most prolific countries which produced decision support system and sustainability articles were from the United States, Italy, United Kingdom and Germany.
These countries produced more than 43.55% of the articles pertaining decision support system and sustainability. Based on the data from Scopus database, 15 journals were identified as the productive journals that published the most articles in decision support system and sustainability researches between 1991 and March 2022.
As shown in Table 1, the most productive journals within the period of 30 years were Sustainability with 116 publications, followed by Journal of Cleaner Production with 91 publications. Both journals originated from the Switzerland and United Kingdom with SCImago Journal Rank of 0.66 and 1.92, respectively. A majority of the top 15 productive journals came from the Netherlands followed by United Kingdom. Of all the 15 productive journals, the highest H-index of 275 belongs to the journal known as Science Of The Total Environment. It is interesting to note that 14 out of 15 productive journals which published articles on decision support system and sustainability research were categorized under Quartile 1 (Q1), such as Sustainability (SJR- 0.66), Journal of Cleaner Production (SJR – 1.92) and Journal of Environmental Management (SJR – 1.48).
Table 1: Top 15 productive journals publishing the most articles in decision support system researches in supply chain during 1995-March 2022
Journal 1991-2022 SJR 2021 H Country of
Origin Rank
Sustainability 116 0.66 109 Switzerland Q1
Journal Of Cleaner Production
91 1.92 232 United
Kingdom Q1
Journal Of Environmental
Management 40 1.48 196 United States Q1
Science Of The Total
Environment 39 1.81 275 Netherlands Q1
Ecological Indicators 27 1.28 145 Netherlands Q1
Computers And Electronics In
Agriculture 24 1.6 133 Netherlands Q1
Environmental Modelling And
Software 20 1.43 146 Netherlands Q1
International Journal Of Life
Cycle Assessment 18 1.12 113 Germany Q1
International Journal Of
Production Research 17 2.78 153 United
Kingdom Q1
Water (Switzerland) 16 0.72 69 Switzerland Q1
Energies 15 0.65 111 Switzerland Q1
Journal 1991-2022 SJR 2021 H Country of
Origin Rank Sustainable Cities And Society 14 2.02 82 Netherlands Q1 Journal of Environmental
Management 13 1.48 196 United States Q1
Resources Conservation And
Recycling 13 2.59 150 Netherlands Q1
Water Science And
Technology 15 0.45 145 United
Kingdom Q2
Based on Scopus Analyzer, the findings generated ten most highly cited articles on food safety as displayed in Table 2. For each paper, the first author, year of publication, journal name and number of total citations are provided. The most influential article was cited 2084 times by many authors in decision support system and sustainability studies and this article was published inProceedings of the National Academy of Sciences of the United States of America.
In all, the ten highly cited articles attracted 6260 citations. In addition, the most cited article was written by Cash et al. (2003) entitled “Knowledge systems for sustainable development”, which received a total of 2087 citations, which is 33.3% from total citation among the ten prominent authors in Scopus up to March 2022.
Table 2: Ten most cited decision support system researches in sustainability articles
Author(s) Title Year Journal Total
Citation Cash D.W., Clark
W.C., Alcock F., Dickson N.M., Eckley N., Guston D.H., Jäger J., Mitchell R.B.
Knowledge systems for sustainable development 2003
Proceedings of the National Academy of Sciences of the United States of America
2084
McNie E.C.
Reconciling the supply of scientific information with user demands: an analysis of the problem and review of the literature
2007 Environmental Science
and Policy 637
Bai C., Sarkis J.
Integrating sustainability into supplier selection with grey system and rough set methodologies
2010 International Journal of Production Economics 579
Andrews S.S., Karlen D.L., Mitchell J.P.
A comparison of soil quality indexing methods for vegetable production systems in Northern California
2002 Agriculture, Ecosystems and Environment 568 Ahvenniemi H.,
Huovila A., Pinto- Seppä I., Airaksinen M.
What are the differences between sustainable and smart cities?
2017 Cities 509
Tedeschi L.O.
Assessment of the adequacy of mathematical models
2006 Agricultural Systems 417
Author(s) Title Year Journal Total Citation Cui Z., Zhang H.,
Chen X., Zhang C., Ma W., Huang C., Zhang W., Mi G., Miao Y., Li X., Gao Q., Yang J., Wang Z., Ye Y., Guo S., Lu J., Huang J., Lv S., Sun Y., Liu Y., Peng X., Ren J., Li S., Deng X., Shi X., Zhangc Q., Yang Z., Tang L., Wei C., Jia L., Zhang J., He M., Tong Y., Tang Q., Zhong X., Liu Z., Cao N., Kou C., Ying H., Yin Y., Jiao X., Zhang Q., Fan M., Jiang R., Zhang F., Dou Z.
Pursuing sustainable productivity with millions of smallholder farmers
2018 Nature 404
Morrissey A.J., Browne J.
Waste management models and their application to sustainable waste management
2004 Waste Management 404
Santoyo-Castelazo E., Azapagic A.
Sustainability assessment of energy systems:
Integrating environmental, economic and social aspects
2014 Journal of Cleaner
Production 332
Ramani K.,
Ramanujan D., Bernstein W.Z., Zhao F., Sutherland J., Handwerker C., Choi J.-K., Kim H., Thurston D.
Integrated sustainable life cycle design: A Review 2010
Journal of Mechanical Design, Transactions of the ASME
326
5. Total Link Strength (TLS) of Prominent authors in DSS and sustainability
Table 3 shows the ten prominent authors in halal supply chain research. These results were obtained from VOSviewer bibliometric software. In co-citation analysis, the unit of analysis for the study is on researchers or authors. According to co-citation analysis, the relatedness of authors is determined based on the degree to which they are cited in the same publication and the more often two authors are cited in the same publication, the stronger their relatedness would be (PerianesRodriguez, A., Waltman, L., van Eck, 2016; Van Eck & Waltman, 2014).
It is suggested that the cut-off point need to be established if the study sample had a large number of citations for each author. By doing so, only the most influential papers with the most prominent authors will be selected. Thus, this study selected the authors with the minimum number of citations which had been cited at least 10 times.
Based on the findings, out of 6218 authors, only 33 authors met the threshold (minimum 5 number of documents) and were selected for co-citation network analysis. For each of the 33
authors, the total strength of the co-citation links with other authors was calculated. However, for this study, only ten authors with the greatest total link strengths are shown as in Table 3.
For this study, Hristozov, D., Malsch, I., Marcomini, A. and Semenzin, E. were identified as an author who has the greatest total link strength (21) and the highest number of citations (65).
Table 3: The ten most prominent authors with highest total link strength
Authors Documents Citations Total Link Strength
Hristozov, D. 5 65 21
Malsch, I. 5 65 21
Marcomini, A. 5 65 21
Semenzin, E. 5 65 21
Zabeo, A. 6 149 18
Rosen, I. 7 197 11
Soderqvist, T. 6 197 11
Linkov, I. 6 111 10
Norrman, J. 6 191 10
Sacile, R. 8 562 10
Ouammi, A. 5 336 8
6. Data Visualization Co-word Network of Decision Support System and Sustainability
Co-word network is applied for the purpose of preparing visualization or mapping of links between keywords or research areas. This analysis was created to show the relationships among the keywords in each field (Leung, Sun, & Bai, 2017). The visualization process was prepared by importing a text file derived from the Scopus database. In the period of 1991-2022, 1736 articles were identified, and the keywords were extracted for the process of generating maps.
As a result of the extraction process, of the 13449 keyword, 136 keywords occurred with a minimum number of occurrences of at least 30 times. By using VOSviewer, it is possible to develop a map of links between keywords and map of clusters of specific research area.
Furthermore, this network visualization tool can assist researchers by providing more information about the incidence of co-occurrence of keywords in any research area.
Figure 4 illustrates the data visualization co-word network concerning decision support system and sustainability research from the year 1991 to 2022. The map shows the links between the keywords which occurred in this particular research area. It is interesting to note that the thickness of the lines indicates the strength of the co-occurrence of keywords. Those elements that are located at the edges of the visualization are characterized by a small number of links between them, whereas a central location means strong relationships connected to numerous groups of other keywords (Lulewicz-Sas, 2017). Data visualization of decision support system research in sustainability is shown in Figure 3. The bigger the sizes of the letter word, the higher occurrences it signify to the users, researchers, and decision-makers.
Figure 3: Data Visualization Co-Word Network
Located at the central part of the map, the finding shows that the strongest keyword is ‘decision support system’ OR decision support systems’ which is linked to more diverse groups of other keywords. In other words, the keyword ‘decision support system’ OR decision support systems’ are commonly used in most of the research. Furthermore, the analytical tool generated four research clusters within the concept of decision support system and the map of the density of keywords citations can be seen. According to the findings using VOSviewer, the oldest cluster is marked in blue, green and yellow as the latest generated cluster by year. This oldest cluster marked in blue was identified related to water management. The other cluster of research were summarized in Table 4 which is related to decision support system application, decision approach, and environmental aspect of sustainability.
Table 4: Cluster of 135 items (4 clusters)
Cluster Theme Keywords
1 decision
support system applicatio ns
Cluster 1 (40 items)
analytic hierarchy process, analytical hierarchy process, artificial intelligence, biomass, costs, decision analysis, decision making, decision support framework, decision support system, decision support systems, decision support tools, decision supports, decision theory, Design, developing countries, economic and social efficiency, energy efficiency, energy utilization, environmental impact, environmental technology, fuzzy logic, greenhouse gases, hierarchical systems, Investments, life cycle, life cycle analysis, life cycle assessment, Manufacture, multi-criteria decision, multiobjective optimization, Optimization, product design, Recycling, sensitivity analysis, supply chain management, supply chain, sustainability assessment, sustainability development.
2 decision
approach
Cluster 2(38 items)
assessment method, Biodiversity, Canada, decision support, decision
Cluster Theme Keywords
support system, Ecology, ecosystem service, Ecosystems, Eurasia, Europe, forest management, Forestry, geographic information, Gis, Indicators, information management, information systems, integrated approach, Italy, land use, Management, management practice, multicriteria analysis, participatory approach, performance assessment, Planning, planning method, policy making, Spain, Stakeholder, strategic approach, strategic planning, Sustainability, sustainability indicators, trade-off, transportation planning, urban area, urban planning.
3 Environm
ent
Cluster 3 (35 items)
Algorithm, Australia, conceptual framework, conservation of natural, controlled study, cost benefit analysis, cost-benefit analysis, decision support system, decision support technique, Economics, Ecosystem, Environment, environmental assessment, environmental economic, environmental impact, environmental management, environmental monitoring, environmental planning, environmental protection, environmental sustainability, feasibility study, Female, Human, Humans, Male, Methodology, models, theoretical, priority journal, Procedures, risk assessment, roisk management, Software, Uncertainty, uncertainty analysis, waste management.
4 Water Cluster 4 (22 items)
Agriculture, China, climate change, computer simulation, Crops, economic analyses, Groundwater, Irrigation, Modeling, numerical moel, Rivers, Simulation, united states, Wastewater, wastewater treatment, water conservation, water management, water quality, water resource, water resources, water supply, Watersheds.
7. Conclusion
The key journals, influential institutions, impactful and trending articles were identified. It can be concluded that the Sustainability and Journal of Cleaner Production were the leading journals, and among the most influential institutions were Technical University of Denmark (31), Wegeningen University & Research (22) and Hong Kong Polytechnic University (20).
Hristozov, D., Malsch, I., Marcomini, A. and Semenzin, E. were identified as an author who has the greatest total link strength (21) and the highest number of citations (65) were the most prominent authors from 1991 to 2022. Finally, the decision support system related to sustainability work or discussion by researchers can be divided into four themes; decision support system application, decision approach, water management and environmental aspect.
Meanwhile, the Implementation issues of decision support system and sustainability need to be studied intensively, where more concept paper need to be highlighted to summarized main finding according to these identified four themes, which mainly focused on the sustainability aspect. However, much research is still needed especially in decision support system related to sustainability by conducting bibliometric analysis. Perhaps a research can be conducted that focuses on using other bibliographic databases such as Web of Science and other content databases, namely ProQuest, Emerald, Ebscohost and others, either from or outside Malaysia.
References
Cash, D., Clark, W., Alcock, F., Dickson, N., Selin, N., Guston, D. & Jäger, J., & Mitchell, R.
(2003). Knowledge Systems for Sustainable Development. Proceedings of the National Academy of Sciences of the United States of America. 100. 8086-91.
10.1073/pnas.1231332100.
Chen, C. (2006). Detecting and Visualising Emerging Trends and Transient Patterns in Scientific Literature. Journal of Association Information Science Technology. 57, 359-377.
French, Simon & Turoff, Murray. (2007). Decision Support Systems. Communications of the ACM. 50. 39-40. 10.1145/1226736.1226762.
French, S., Carter, E., & Niculae, C. Decision support in nuclear and radiological emergency situations: Are we too focused on models and technology? International Journal of Risk Assessment and Management (2007).
Kuhlman, Tom & John, Farrington. (2010). What is Sustainability?. Sustainability. 2.
10.3390/su2113436.
Leung, X.Y., Sun, J., Bai, B. (2017). Bibliometric of Social Media Research: A Co-citation and Co-word Analysis. International Journal of Hospitality Management. 66, 35-45.
Ghazali, A. F., Alias, S., & Amran, Z. A. (2020). Decision Support Systems in a Smart City:
A Review, International Conference on Industry 4.0: A Global Revolution in Business and Productivity, SEGi University Kota Damansara.
Ghazali, A. F., Mokhtar, N. A., Suhaimi, A., & Yusoff, R. M. (2021). Visual Decision Support System Model for Managing Unemployment by Hybrid Education, Emerging Advances in Integrated Technology 2(2), 16-22, Penerbit UTHM.
Ghazali, A. F., Noor, N. M. M., Mohemad, R., & Saman, M. Y. M. (2016). Development of a Decision Support System with Risk: Supporting Police and Government in Crime Prevention, Research Journal of Applied Sciences, 11 (6), 340-344.
Ghazali, A. F., Suhaimi, A., Yusoff, R. M. & Mokhtar, N. A. (2022). Mobile-based Visual Decision Support System for Hybrid Learning in Post-COVID-19 Pandemic, Proceedings of the 2nd International Conference on Design Industries & Creative Culture, Design Decoded 2021, 24th-24th August 2021, European Union Digital Library.
Ghazali, A. F., & Suhaimi, A. (2020). Visual Decision Support System for Food Security, ASEAN Workshop on Information Science and Technology (AWIST 2020).
Mohemad, R., Hamdan, A. R., Othman, Z. A., & Noor, N. M. M. (2010). Decision support systems (DSS) in construction tendering processes, International Journal of Computer Science Issues.
Noor, N. M. M., Ghazali, A. F., Mohd, M. Y. B. & Nor, M. N. M. (2014). Framework of Risk- based Decision Support System in Crime Investigation, International Conference Decision Support System Technology, 447-458.
Perianes-Rodriguez, A., Waltman, L., van Eck, N.J. (2016). Constructing Bibliometric Net- works: A Comparison Between Full and Fractional Counting. Journal of
Informetrics. 10, 1178-1195.
Rashidi, Maria & Ghodrat, Maryam & Samali, Bijan & Mohammadi, Masoud. (2018).
Decision Support Systems. 10.5772/intechopen.79390. Jain, Rajni. (2016). Decision Support Systems: an Overview.
Seuring, S., Müller, M. (2008). From a Literature Review to a Conceptual Framework for Sustainable Supply Chain Management. Journal of Cleaner Production. 16, 1699- 1710.
Turoff, M., Chumer, M., Van de Walle, B., and Yao, X. The design of a dynamic emergency response management information system (DERMIS). Journal of Information Theory,
Technology, and Applications 5, 4 (2004), 1–36.
Van Eck, N.J., Waltman, L. (2009). How to Normalise Co-occurrence Data? An Analysis of Some Well-known Similarity Measures. Journal of the American Society for
Information Science and Technology. 60, 1635-1651.
Van Eck, N.J., Waltman, L. (2014). Visualising Bibliometric Networks. In: Y. Ding, R.
Rousseau, D. Wolfram. (eds.) Measuring scholarly impact: Methods and practice, pp. 285-320. Springer.
Van Raan, A.F. (2012). Properties of Journal Impact concerning Bibliometric Research Group Performance Indicators. Scientometrics. 92, 457-469.
Wong, C.Y., Wong, C.W., Boon-Itt, S. (2015). Integrating Environmental Management into Supply Chains: A Systematic Literature Review and Theoretical Framework.
International Journal of Physical Distribution & Logistics Management. 45, 43-68.
United Nations (2022). Do you know all 17 SDGs? Official website United Nations, Department of Economic and Social Affairs, Sustainable Development. Retrieved from https://sdgs.un.org/goals (2022, September 6).
Yahya, W. F., Mohd, F., Daud, N. I. M., & Zamin, N. (2020). Decision Support System Framework for Personalized Adaptive Learning based on Behavioral Modeling, Penerbit Universiti Malaysia Kelantan.
