Friday, 7 October 2022

Effective Strategies for Increasing Citation Frequency

Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2344585

Effective Strategies for Increasing Citation Frequency

International Education Studies, Vol. 6, No. 11, pp. 93-99, 2013

7 Pages Posted: 25 Oct 2013

Nader Ale Ebrahim

University of Malaya (UM) - Department of Engineering Design and Manufacture; Centre for Research Services, Institute of Management and Research Services (IPPP), University of Malaya (UM)

Hadi Salehi

Islamic Azad University, Najafabad Branch

Mohamed Amin Embi

Universiti Kebangsaan Malaysia - Faculty of Education

Hossein Gholizadeh

University of Ottawa, Ottawa Hospital Research Institute

Seyed Mohammad Motahar

Universiti Kebangsaan Malaysia - Faculty of Information Science and Technology

Ali Ordi

Universiti Teknologi Malaysia - Advance Informatics School (AIS)

Date Written: October 23, 2013

Abstract

Due to the effect of citation impact on The Higher Education (THE) world university ranking system, most of the researchers are looking for some helpful techniques to increase their citation record. This paper by reviewing the relevant articles extracts 33 different ways for increasing the citations possibilities. The results show that the article visibility has tended to receive more download and citations. This is probably the first study to collect over 30 different ways to improve the citation record. Further study is needed to explore and expand these techniques in specific fields of study in order to make the results more precisely.

Keywords: University ranking, Improve citation, Citation frequency, Research impact, Open access, h-index

JEL Classification: L11, L1, L2, M11, M12, M1, M54, Q1, O1, O3, P42, P24, P29, Q31, Q32, L17

Suggested Citation:

Ale Ebrahim, Nader and Ale Ebrahim, Nader and Salehi, Hadi and Embi, Mohamed Amin and Habibi, Farid and Gholizadeh, Hossein and Motahar, Seyed Mohammad and Ordi, Ali, Effective Strategies for Increasing Citation Frequency (October 23, 2013). International Education Studies, Vol. 6, No. 11, pp. 93-99, 2013, Available at SSRN: https://ssrn.com/abstract=2344585

From Open Access to Open Data

Source: https://blogs.sun.ac.za/libraryresearchnews/2022/09/27/from-open-access-to-open-data/

From Open Access to Open Data

September 27, 2022 / Marié Roux / 0 Comments

Image source: JulieBeck

In the past decade, the management of research data has come to take on a more prominent role at tertiary education institutions around the world. This has been primarily due to the increasingly data-centric route that academic research has taken. At the same time, academic institutions have come to be influenced by the Open Access movement which advocates for the unrestricted access and use of published research outputs. A convergence of these two developments has culminated in a call for unrestricted access and use of research data. The term commonly reserved for data that can be freely used, re-used and redistributed by anyone is: ‘Open Data’.

The move to make research data more openly accessible is being pushed through by research funding agencies as well as academic journal publishers. In recent years, some of Stellenbosch University’s research funders have instituted mandates that require the implementation of adequate research data management practices in order to enable the open accessibility of research data. Such practices should comply with certain best practices, such as the following requirements:

research data should be managed through the creation and submission of data management plans; and
research data should comply with the FAIR Data Principles by being findable, accessible, interoperable and reusable.

As a leading research institution, Stellenbosch University sought to address this issue by setting up an institutional research data repository known as SUNScholarData. SUNScholarData is a multidisciplinary institutional research data repository that was launched in August 2019. The repository is used for the registration, archival storage, sharing and dissemination of research data produced or collected in relation to research conducted under the auspices of Stellenbosch University. SUNScholarData creates a medium through which Stellenbosch University’s research data can be made findable and accessible. It also facilitates the interoperability and re-usability of the university’s research data.

SUNScholarData was not set up in isolation. Instead, it was set up as part of a broader implementation which also included the formulation of a governance framework and the development of supporting research data services that are provided by dedicated staff members at the University’s Library and Information Services. Such services include data curation, client consultations and related training sessions. The use of SUNScholarData is supported by the SUNScholarData Library Guide. This is an online resource that provides a wealth of information about the repository in an easily digestible manner.

For more information about how you can make your research data openly accessible please contact the Research Data Services division at: rdm@sun.ac.za.

Research Impact & Researcher Identity

Source:

Article Impact

Article Level Metrics (ALMs) quantify how individual articles are being cited, used, shared and discussed.

Traditional metrics to measure scholarly visibility and
Altmetrics to measure social visibility

Traditional metrics measure the number of times an article has been cited.

Altmetrics measure how an article is being discussed in the public or social realm. Types of measurements include:

Views
Downloads
Mentions

However, ALMs have their own share of limitations. Like journal-based metrics, they may be prone to manipulation and are unable to distinguish between positive and negative attention.

More information on common ALMs and their limitations is available in the SPARC Primer and Metrics Toolkit.

Information and definitions of metrics in this section adapted from Metrics Toolkit. It is licensed under CC-BY 4.0.

Traditional Article Level Metrics

Citation Count

What is it?

The simplest and most common ALM, Citation Count indicates the number of times that an article has been cited by other research outputs since it was published

Limitations

Citation count may be different depending on the coverage of the database or tool you use to calculate it.
This metric is influenced by many factors that don't necessarily reflect the impact of an article, including publication date and differences in publishing practices across disciplines.
Citation count may be inflated (either intentionally or unintentionally) by the common practice of self-citation in scholarly research.
- Some tools like Scopus allow you to exclude citations when calculating citation count.

Tools Available for Citation Count

Web of Science allows you to track and analyze citation data in the sciences, social sciences, and humanities. It is best used to measure the impact of articles in the sciences. This is a Licensed product offered by the University of Toronto Libraries.
- Watch this video tutorial to on How to do a Cited Reference Search.
Scopus provides citation tracking, visualizations, and analysis tools for articles. This is a Licensed product offered by the University of Toronto Libraries.
Google Scholar provides citation data for a wide range of research outputs, including scholarly articles, conference proceedings and more. Create a profile on Google Scholar Citations to track the number of citations articles that you've published have received.
Publisher Websites: Many publishers are now providing citation counts for articles in their journals, such as:

Field Normalized Citation Impact

What is it?

Field Normalized Citation Impact (FNCI) shows how well an article is cited compared to similar documents in the same field of research. It is calculated as the ratio between the citations received by an article and the average number of citations received by all other similar publications within a particular database. A value greater than 1.0 means the article has been cited more than average compared to similar publications, and a value less than 1.0 means the article has been cited less than average.

Limitations

This metric is calculated based on citation count, and is therefore subject to many of the same limitations.
Different tools use different methods for calculating FNCI and therefore articles can only be compared based on values provided by the same tool.
Classifications of subject area or discipline are not always accurate, and this can therefore affect the accuracy of the ranking or percentile.

Tools Available

Scopus calculates a Field Weighted Citation Impact (FWCI) for each of its articles, which is "the ratio of the document's citations to the average number of citations received by all similar documents over a three-year window." Similar documents are those with the same year of publication, document type, and disciplines. This is a Licensed product offered by the University of Toronto Libraries.
NIH iCite Database is a research impact tool from the U.S. National Institutes of Health that calculates an article's Relative Citation Ratio (RCR), “a field-normalized metric that shows the scientific influence of one or more articles relative to the average NIH-funded articles.”

Citation Percentiles and Rankings

What is it?

The position of an article compared to other articles in the same discipline, country and/or time period based on the number of citations they have received. Often expressed as a percentile or a “Highly Cited’ label based on percentile rankings.

Limitations

This metric is calculated based on citation count, and is therefore subject to many of the same limitations.
Different tools use different methods for calculating citation percentile or rankings. Articles can only be compared based on values provided by the same tool.
Like with Field Normalized Citation Impact, classifications of subject area or discipline are not always accurate and this can affect the accuracy of the ranking or percentile.

Tools Available

Scopus includes citation percentile as part of their Article Metrics.
- Under Citation Benchmarking, view the article's citation percentile within each of the fields of research that it has been classified under.
SciVal is a benchmarking tool that allows you to measure the research impact of a pre-defined publication set, such as articles published by a particular author, research group, department or institution. Outputs in Top Percentiles demonstrates the number of articles from the publication set that are the among the most-cited publications in Scopus.
InCites Essential Science Indicators allows you to view articles in the top citation percentiles of a research field. Papers may receive two designations, which also appear as icons next to the article in Web of Science search results:
- Hot Papers are in the top 1% compared to other papers in the same field and publication year
- Highly Cited Papers are articles published in the last 2 years that have been cited the most in the last two months (top 0.1% of articles in compared to articles in the same field and publication year).

Altmetrics

Many scholars believe traditional metrics do not give the whole picture of research impact, especially in fields outside the sciences. Altmetrics use a range of measurements to show research impact. They measure both impact on a field or discipline and impact on society. They "expand our view of what impact looks like, but also of what’s making the impact. This matters because expressions of scholarship are becoming more diverse" (from http://altmetrics.org/manifesto/).

Altmetrics can be useful for early-career researchers or new publications that need time to gain citation counts. They also account for other types of publications, such as datasets, code, or blogging.

This list is not exhaustive, and many more Altmetrics resources can be found on the Metrics Toolkit website. Other Altmetrics include

Altmetric Attention Score and Donut

What is it?

Altmetric tracks mentions of articles and other research outputs such as datasets on social media, news outlets, and bookmarking services. These metrics are visualized using an Altmetric Attention Score and colourful 'donut'.

Altmetric Attention Score is a weighted score of the attention a research output has received. It is based on three main factors: Volume, Sources and Authors.
The donut visualizes where online attention is coming from. Each colour in the donut represents a different type of online output.

Limitations

The Altmetric Attention score does not measure the quality of the research, but identifies the level of online activity.
Online attention can be both positive and negative.
Altmetrics can be gamed, and bots and web crawlers may impact online activity data.

Tools available

This metrics can be found in all products offered by Altmetric, including the free researcher bookmarklet and on many journal publisher websites and repositories (such as Figshare).

PlumX Metrics

What is it?

PlumX Metrics from Plum Analytics "provide insights into the ways people interact with individual pieces of research output... in the online environment." These metrics are divided into five categories:

Citations
Usage
Captures
Mentions
Social Media

Limitations

Usage and Captures don't always reflect the actual number of readers and users of an article. For example, someone may download, save or favourite a research output but never actually read or meaningfully engage with it.
Altmetrics can be gamed, and bots and web crawlers may impact Usage and Social Media data.
As with other ALMs, PlumX Metrics are unable to make distinctions of quality, and online attention can be both positive and negative.

Tools Available

PlumX Metrics and the Plum Print visualization are available on a wide range of platforms and publisher's websites, including Engineering Village, Mendeley, Scopus and Science Direct.

Measuring Research Quality and Impact - Research Guide

Source: https://libguides.murdoch.edu.au/measure_research/researcher

Measuring Research Quality and Impact - Research Guide

Researcher Impact

Demonstrating Impact

Researchers may be asked to demonstrate the quantity, quality and impact of their research publications for a variety of purposes, including:

to enhance researcher profiles and promote their research to potential collaborators and investors
to benchmark their productivity for performance reviews
to support promotion or tenure applications
to support grant or other funding applications and progress reports

The output of an individual researcher can be measured using citation metrics, other alternative metrics, measures of esteem, and indices such as an h-index or field weighted citation impact (FWCI).

Calculating Your Field Weighted Citation Impact or h-index

FWCI

The field-weighted citation impact (FWCI) is an author metric which compares the total citations actually received by a researcher's publications to the average number of citations received by all other similar publications from the same research field. The global mean of the FWCI is 1.0, so an FWCI of 1.50 means 50% more cited than the world average; whereas, an FWCI of .75 means 25% less cited than the world average.

The following tool can be used to calculate your FWCI:

SciVal

SciVal allows you to compare the research performance of individual researchers and research groups to Australian and global benchmarks, and to identify and evaluate collaboration partners. Benchmarking data for individual researchers includes FWCI and h-index.

h-index

The h-index is an author metric, first proposed by J. E. Hirsch in 2005, that attempts to measure both the productivity and citation impact of the publications of an author.

The h-index is a measure of an individual's impact on the research community based upon the number of papers published and the number of citations these papers have received.

The method of calculating a h-index is the number of articles in a database that have received the same number or more citations over time. As an example, a researcher with an h-index of 20 has (of their total number of publications) 20 papers which have been cited at least 20 times each:

Source: Wikipedia

An h-index is not skewed by a single highly cited paper nor by a large number of poorly cited papers.

As well as SciVal, the following tools can also be used to calculate your h-index:

InCites

InCites is a customised, citation-based research evaluation tool on the Web that enables you to analyse the institutional productivity and benchmark your output against peers worldwide. Incites also provides the Category Normalized Citation Impact (CNCI) of a document which is calculated by dividing the actual citation count by an expected rate for documents with the same document type, year of publication, and subject area.

Scopus - Scopus is an online citation database that indexes resources in the health and life sciences, chemistry, physics, mathematics, engineering, earth and environmental sciences, social sciences, psychology, and economics, business and management. Scopus allows you to measure your h-index using the Scopus citation data and to create a h-graph.

Web of Science Core Collection - Web of Science Core Collection is an online citation database that covers the sciences, social sciences, arts and humanities. It allows you to measure your h-index using the Web of Science data and to create a h-graph.

Note: As these databases index different sets of journals over different time periods, your h-index may vary significantly between the two tools. You should check all tools and carefully select the measurement that best represents your output. When a h-index is included in documents, you should state which tool you used and the date the data was obtained. A more accurate h-index may be obtained by collating data from both of these tools, and other citations databases, using an ORCID; however if you wish to create a true h-index based on all unique citations to your publications from all sources, you will need to calculate it manually. The fewer papers you have the more significant each citation becomes in terms of calculating your h-index.

Google Scholar - The My Citations feature requires a Google login and password. The Publish or Perish software analyses academic citations based on information from a variety of data sources, including Google Scholar and Microsoft Academic Search, to obtain the raw citations, and presents a range of metrics including the h-index.

Note: Google Scholar citations have been criticised for duplications and misidentifications. Careful analysis and verification of results is recommended.

Other Metrics and Measures of Esteem

Alternative metrics and measures of esteems may also indicate the quality and impact of a researcher's publications.

Measures of esteem are additional factors which may provide evidence of research quality. These measures may include:

awards and prizes
rankings in prestigious lists
membership of professional or academic organisations
positions on national and international committees or strategic advisory bodies
influence on government or public policy
industrial advisory roles
research fellowships
patents or other commercial output
international collaborations
successfully completed research grants or projects
editorial or reviewer role for a significant journal or reference work
keynote and plenary addresses at conferences, conference organisation or presentations at significant international conferences

Researcher Profiles and Identifiers

Research profiles allow you to:

Manage your publications list
Avoid misidentification
Track citation counts
Attribute your research output to Murdoch University
Promote your research locally, nationally and globally
Be identified by potential collaborators
Enhance your Murdoch staff profile page by including a link to other profiles

There are a number of researcher profile services available and you may find that more than one profile service is required to include all of your research. Each service has different advantages.

Free researcher profiles are available from the following services:

ORCiD (Open Researcher and Contributor iD)
ORCiD is a persistent digital identifier which distinguishes you from other researchers. It provides two basic services:

a registry to obtain a unique identifier and manage a record of activities, and
interfaces that support system-to-system communication and authentication.

Registering for an ORCiD profile streamlines manuscript submission and grant applications. It enables you to attach your identity to research objects such as articles, citations, datasets, patents, experiments and equipment. Some journals now require you to submit your ORCiD as part of the process when submitting articles for publication.

Find out more information about the role of ORCiD in the Murdoch University research process.

Author Identifier - Scopus (Elsevier)
The Scopus Author Identifier, by Elsevier, assigns a unique number to each author in the Scopus database and groups together all their publications. It accounts for variant versions of names by matching affiliations, addresses, subject areas, co-authors and dates of publication. It automatically calculates citation counts and the h-index.

ResearcherID - Web of Science (Clarivate Analytics)
ResearcherID, by Clarivate Analytics, assigns a specific ID number to each registered researcher in order to reduce identity issues. Researchers can add the details of their publications to their individual profile and these are made available to anyone searching the database. Citation metrics such as number of times cited and h-index are automatically calculated using details from Web of Science.

Citations - Google Scholar
Create a profile in Google Scholar to ensure correct attribution of your publications and citations. Profiles can be private or public. All citations should be checked for accuracy.

Resources

MyRI (Measuring your Research Impact) online tutorial - Module 2 : Track your Research Impact - a cross institutional project from University College Dublin, Dublin City University, Dublin Institute of Technology and Maynooth University.

Research Metrics Guidebook (SciVal) - Elsevier

Scopus Quick Reference Guide - Elsevier

Web of Science Quick Reference Guide - Clarivate Analytics

Weighted Aggregated Sum Product Assessment (WASPAS)

Source: https://doi.org/10.18280/mmep.090403

Weighted Aggregated Sum Product Assessment

Shahryar Sorooshian^*| Noor Azlinna Azizan | Nader Ale Ebrahim

Corresponding Author Email:

sorooshian@gmail.com

Received: 25 May 2022 | Accepted: 2 August 2022 | Published: 31 August 2022 | Citation

OPEN ACCESS

Abstract:

Multiple Criteria decision-making (MCDM) approaches are required for complex issue resolution. However, it is debatable whether MCDMs applicability will be able to stay within the analytical possibilities for dealing with divorce issues. As a result, one of the newly introduced MCDMs Weighted aggregated sum product assessment (WASPAS), is chosen for evaluation. Therefore, this study examined 388 documents from archive of the Scopus database. To evaluate the diversity of the technique's application, the data analysis looked at a few bibliometrics measures. So, the publishing hosts and their influence patterns are displayed, the vast majority of the documents were in the fields of engineering and computer science. According to the findings, the WASPAS can deal with variety of problem-solving scenarios. Hence, this work is summed up with the aim of lowering uncertainty among decision-makers and scholars.

Keywords:

multiple criteria decision-making (MCDM), weighted aggregated sum product assessment (WASPAS), literature analysis

1. Introduction

Multiple Criteria Decision-Making (MCDM) techniques are widely employed in industry and academia. The Weighted Product Model (WPM) and the Weighted Sum Model (WSM) are two of the most common techniques among all MCDMs [1-6]. However, when WASPAS (weighted aggregated sum product assessment) decision-making technique first was suggested in 2012, it was considered to be among the most compelling alternative MCDM techniques because it combines the Weighted Product Model (WPM) and the Weighted Sum Model (WSM) into a single procedure [5, 7].

The WASPAS technique was offered by Zavadskas et al. [6]; and it in successful subset of the recent generation of MCDMs [5, 6, 8]. It is argued that WASPAS' algorithm is simple, and it is capable of producing more accurate decision outcome than classic WSM and WPM techniques [5, 7, 9]. WASPAS has received substantial attention from decision makers from all sorts of backgrounds due to the simplicity of the computing process and the accuracy of the outputs, and it is now widely referenced as an excellent decision support tool [5, 7].

Based on previous practices [10-13], WASPAS's utilization necessitates four following steps for analyses of the data:

(1) Creation of a decision matrix, X=[x(i,j)]_mn, where n is the number of criteria, m is the number of alternatives, and x_ij is the performance of the ith alternative in relation to the jth criterion.

(2) Normalization of all entries in the decision matrix using the following two equations to make the metrics non-dimensional. The formula, where the normalized value of x(i,j) is x^´(i,j), for beneficial criteria is x^´(i,j)=x(i,j)/maximum_i x(i,j) and for non-beneficial criteria it is x^´(i,j)=minimum_i x(i,j) /x(i,j).

(3) The first relative significance of the ith option, in one hand, is analogous to the WSM technique. The Q⁽¹⁾i= $\sum_{j = 1}^{n} X (i, j) \cdot W (j)$

formula is used to compute the overall relative significance of the ith alternative where w(j) is the relative significance (weight) of the jth criteria. The second relative significance of the same ith alternative, in the other hand, is calculated using the WPM approach using the Q⁽²⁾_i

\prod_{j = 1}^{n} X (i, j)^{\land} W (j)

formula.

(4) Q_i=λQ⁽¹⁾_i+(1 - λ)Q⁽²⁾_i is a generalized equation established in WASPAS for calculating the total relative significance of ith alternative, where λ is the combination parameter in the range of 0 to 1. WASPAS method is transformed into WPM when the value of λ is 0, and WSM method when it is 1. λ is been used to remedy MCDM issues in ranking accuracy.

A case is borrowed to practice the WASPAS MCDM, referencing an application of WASPAS from a reference [14] and simplified here. In a decision, there are 3 criteria (C1, C2 and C3), the first two of which is beneficial but the second one is cost-related (not beneficial). C1 and C2 are equal important, 25%, but C3 is more important, 50%. Based on MCDM WASPAS, the decision maker has 5 alternatives from which to choose. Their decision matrix in shown via Table 1, as step 1 of WASPAS. Normalization of all entries in the decision matrix in shown in Table 2, as WASPAS step 2.

Multiplying the relative importance of each criterion leads us to the next step of attribute optimization. For one, the first cell followed by WSM approach gives A1 as Q⁽¹⁾_i: = $\sum_{j = 1}^{n} X (i, j) \cdot W (j)$

=(5/5×0.25)+(3/5×0.25)+(8000/8500×0. 50). However WPM approach for A1 gives Q⁽²⁾_i =

\prod_{j = 1}^{n} X (i, j)^{\land} W (j)

= (5/5

\land

0.25) × (3/5

\land

0.25) × (8000/8500

\land

0. 50). Finally, the last step in WASPAS, after getting Q⁽¹⁾_i and Q⁽²⁾_ifor all alternatives, is finding Q_i=λQ⁽¹⁾_i+(1- λ)Q⁽²⁾_ias ,with decided λ =1/2 , it became (Q⁽¹⁾_i/2)+(Q⁽²⁾_i /2) for all alternatives. WASPAS results in choosing the best alternatives as shown in Table 3.

Table 1. Decision matrix

	C1 (Qualitative weight (1-5))	C2 (Qualitative weight (1-5))	C3 (Cost $)
A1	5	3	8500
A2	5	3	8000
A3	5	5	8500
A4	3	4	8000
A5	5	5	8500

Table 2. Normalized matrix

	C1	C2	C3
A1	5/5	3/5	8000/8500
A2	5/5	3/5	8000/8000
A3	5/5	5/5	8000/8500
A4	3/5	4/5	8000/8000
A5	5/5	5/5	8000/8500

Table 3. Ranking of alternatives

WSM	WPM	WASPAS	Ranking
0.870588	0.853834	0.862211
0.9	0.880112	0.890056
0.970588	0.970143	0.970365	Best
0.85	0.832358	0.841179	Worst
0.970588	0.970143	0.970365	Best

WASPAS method has been used in various research projects, however there is no synopsis of its usage yet. This study attempts to fill the literature gap by conducting a WASPAS literature survey.

2. Method

There are two major databases for collecting scholarly publications data, namely Web of Science (WoS) and Scopus [15]. To select the best database for data collection The search formulation of (TITLE-ABS-KEY ("Weighted aggregated sum product assessment") OR TITLE-ABS-KEY ("WASPAS")) was used on 12/4/2022 to search for related terms via titles, abstracts, and keywords. The number of documents retrieved from WoS and SCOPUS databases was 369 and 388 respectively. Therefore, the data used in this study came from the archive of Scopus databases. The data for the last year, 2022, was not complete, because of the search time. Nonetheless, 388 documents, including 326 articles, 48 conference Paper, 8 book chapters, 4 review papers, and 2 conference reviews, were among the results of the data search. Among them, the status of 27 documents was ‘Article in Press’.

There were no limitations in the time frame and search fields. However, the first article in the research area was published in the year 2012. All data were exported to a comma-separated value (CSV) file for further analysis. One document was written in Croatian, and non-English. Thus, it together with two conference reviews were taken out, limiting the analysis to 385 documents. Documents were analyzed based on their type, distribution of subject category, output, authorship, country of publication, publication patterns, and distribution of documents with the Scopus [16] analysis platform. Besides, the latest version of VOSviower [17] software (Version 1.6.18 released in January 2022) was used for detailed bibliometric analysis. VOSviewer was selected as it was solely designed for scientific research, bibliometric maps, and graphical data representation [18]. The VOSviewer software is able to read CSV file which was exported from the Scopus database and analysis them according to some defined analysis like co-authorships networks, and keywords’ co-occurrence. Besides, ScienceScape [19] was used as a supplementary online data visualization tool.

3. Results

As illustrated in Figure 1, the use of the WASPAS method in scientific documents is steadily expanding year after year, according to Scopus statistics. Among the WASPAS users, Edmundas Kazimieras Zavadskas from Vilnius Gediminas Technical University, who developed the method, was strongly top author listed based on the archived document number, with 11.4 percent of the documents. His university colleague, Jurgita Antucheviciene, was second listed with 3.9 percent of the documents, and another university colleague, Zenonas Turskis, was next with 3.3 percent of the documents.

1.png

Figure 1. Documents by year

Using the data of Figure 1, results Figure 2 with a non-linear regression equation based on the Power Model with N publications and A years of method’s age as A=0.03N^3.53; The response variable in a power mode non-linear regression is related to the factor increased to a power [20]. That outcome predicts, for instance, about 400 published documents are likelihood to be archived via Scopus in 2025. Plotting visualization of Figure 2 provides the fitting validation.

2.png

Figure 2. Publications prediction

Moreover, as shown in Figure 3, although Zavadskas leads the strongest earliest known network of co-authorship based on the collected documents, recent 2-3 years observed limited but new networks of newcomers. The overlay visualization of a co-authorship network was constructed based on 385 documents. In this visualization, the size of the circle represents the number of documents published. Each colony represents a group of researchers who works and published together.

3.png

Figure 3. Overlay visualization of co-authorships

Table 4. Documents by study field

Study Field	Documents
Engineering	171
Computer Science	136
Mathematics	76
Business, Management and Accounting	68
Environmental Science	59
Energy	55
Social Sciences	48
Decision Sciences	41
Materials Science	37
Economics, Econometrics and Finance	26
Physics and Astronomy	26
Chemistry	15
Chemical Engineering	13
Earth and Planetary Sciences	9
Multidisciplinary	5
Agricultural and Biological Sciences	4
Medicine	4
Biochemistry, Genetics and Molecular Biology	3
Arts and Humanities	2
Health Professions	1
Neuroscience	1
Psychology	1

Figure 4 depicts an inference that demonstrates the capability of the WASPAS method to be used in conjunction with other MCDM methods such as AHP, TOPSIS, Entropy, SWARA, MOORA, COPRAS, as well as fuzzy sets and sensitivity analysis. It also shows the method’s applicability in various study areas such as risk management, sustainability research, location decisions, and wind power-related works. Furthermore, as illustrated in Figure 5, the variety of applications of the WASPAS decision-making method in diverse fields has grown in recent years.

In Figure 5, The density of utilized keywords over time is represented by color themes ranging from bright to dark, similar to a standard heat-maps. More to be discussed, even though the WASPAS method was created for decision-makers and based on applied mathematics, engineering and computer science are two of the method's most prominent users, accounting for 80 percent of the documents. Table 4 shows the distribution of the documents by study field. In addition, the statistics in this table demonstrates the method's broad applicability across more than 20 different fields.

Figure 6 looks at the top authors, keywords, and publishing journals and how they are linked. Figure 7, however, depicts the top ten countries with the most archived documents, and as can be seen, India dominates with nearly one-fourth of the count, followed by Iran. However, according to the data, some important scholarly actors, such as Japan, Austria, Finland, Norway, Vietnam, Ukraine, and a few others, are not yet WASPAS users.

4.png

Figure 4. Network visualization of Keywords

5.png

Figure 5. Keywords Evolution over time

6.png

Figure 6. Authors, keywords, and journals

7.png

Figure 7. Documents by country

4. Conclusions

The purpose of this study was to give insight into the deployment of WASPAS. The research conducted an examination of the Scopus database, as well as the requisite bibliometric analyses. While this review is coming to an end, it has the potential to increase the conceptual productivity of the WASPAS by carefully assessing the elements, evaluating current bibliometric information, and comprehending the different motivations for the method's diverse usage. WASPAS is capable to solve MCDMs in variety of fields, especially in engineering and computer science context. The readers acquire a plausible view after gaining a thorough grasp of solicitation and the execution of the WASPAS.

Acknowledgment

The publication fee of this work is supported by Prince Sultan University.

References

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