Weighted Aggregated Sum Product Assessment

 Source: https://figshare.com/articles/journal_contribution/Weighted_Aggregated_Sum_Product_Assessment/21202967/1

 

Weighted Aggregated Sum Product Assessment

 Source: https://www.iieta.org/journals/mmep/paper/10.18280/mmep.090403

Weighted Aggregated Sum Product Assessment

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

Department of Business Administration, University of Gothenburg, Gothenburg 41124, Sweden

Prime School of Logistics, Saito University College, Petaling Jaya 46200, Malaysia

Department of Management, Apadana Institute of Higher Education, Shiraz 7187985443, Iran

Corresponding Author Email: 

sorooshian@gmail.com

Page: 

873-878

|

DOI: 

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

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= ∑nj=1X(i,j)⋅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 ∏nj=1X(i,j)∧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: = ∑nj=1X(i,j)⋅W(j)

=(5/5×0.25)+(3/5×0.25)+(8000/8500×0. 50). However WPM approach for A1 gives Q⁽²⁾_i =∏nj=1X(i,j)∧W(j)= (5/5∧0.25) × (3/5∧0.25) × (8000/8500∧

0. 50). Finally, the last step in WASPAS, after getting Q⁽¹⁾_i and Q⁽²⁾_i for all alternatives, is finding Q_i=λQ⁽¹⁾_i+(1- λ)Q⁽²⁾_i as ,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

[1] Budiharjo, A.P.W., Muhammad, A. (2017). Comparison of weighted sum model and multi attribute decision making weighted product methods in selecting the best elementary school in Indonesia. International Journal of Software Engineering and Its Applications, 11(4): 69-90. https://doi.org/10.14257/IJSEIA.2017.11.4.06

[2] Maliene, V., Dixon-Gough, R., Malys, N. (2018). Dispersion of relative importance values contributes to the ranking uncertainty: Sensitivity analysis of multiple criteria decision-making methods. Applied Soft Computing, 67: 286-298. https://doi.org/10.1016/j.asoc.2018.03.003

[3] Chourabi, Z., Khedher, F., Babay, A., Cheikhrouhou, M. (2019). Multi-criteria decision making in workforce choice using AHP, WSM and WPM. The Journal of The Textile Institute, 110(7): 1092-1101. https://doi.org/10.1080/00405000.2018.1541434

[4] Zavadskas, E.K., Antucheviciene, J., Hajiagha, S.H.R., Hashemi, S.S. (2014). Extension of weighted aggregated sum product assessment with interval-valued intuitionistic fuzzy numbers (WASPAS-IVIF). Applied Soft Computing, 24: 1013-1021. https://doi.org/10.1016/j.asoc.2014.08.031

[5] Alam, K.A., Ahmed, R., Butt, F.S., Kim, S.G., Ko, K.M. (2018). An uncertainty-aware integrated fuzzy AHP-WASPAS model to evaluate public cloud computing services. Procedia Computer Science, 130: 504-509. https://doi.org/10.1016/j.procs.2018.04.068

[6] Zavadskas, E.K., Turskis, Z., Antucheviciene, J., Zakarevicius, A. (2012). Optimization of weighted aggregated sum product assessment. Elektronika ir elektrotechnika, 122(6): 3-6.

[7] Mardani, A., Nilashi, M., Zakuan, N., Loganathan, N., Soheilirad, S., Saman, M.Z.M., Ibrahim, O. (2017). A systematic review and meta-Analysis of SWARA and WASPAS methods: Theory and applications with recent fuzzy developments. Applied Soft Computing, 57: 265-292. https://doi.org/10.1016/j.asoc.2017.03.045

[8] Chakraborty, S., Zavadskas, E.K. (2014). Applications of WASPAS method in manufacturing decision making. Informatica, 25(1): 1-20.

[9] Senapati, T., Chen, G. (2022). Picture fuzzy WASPAS technique and its application in multi-criteria decision-making. Soft Computing, 26(9): 4413-4421. https://doi.org/10.1007/s00500-022-06835-0

[10] Baykasoğlu, A., Gölcük, İ. (2019). Revisiting ranking accuracy within WASPAS method. Kybernetes, 49(3): 885-895. https://doi.org/10.1108/K-01-2019-0052

[11] Chakraborty, S., Zavadskas, E.K., Antucheviciene, J. (2015). Applications of WASPAS method as a multi-criteria decision-making tool. Economic Computation and Economic Cybernetics Studies and Research, 49(1): 5-22.

[12] Saraswat, S.K., Digalwar, A.K., Yadav, S.S. (2021). Sustainability assessment of renewable and conventional energy sources in India using fuzzy integrated AHP-WASPAS approach. Journal of Multiple-Valued Logic & Soft Computing, 37(3): 335-362.

[13] Klumbytė, E., Bliūdžius, R., Medineckienė, M., Fokaides, P.A. (2021). An MCDM model for sustainable decision-making in municipal residential buildings facilities management. Sustainability, 13(5): 2820. https://doi.org/10.3390/su13052820

[14] Tundo, T., Kurniawan, D. (2019). Implementation of the weighted aggregated sum product assessment method in determining the best rice for serabi cake making. International Journal on Informatics for Development, 8(1): 40-46. https://doi.org/10.14421/ijid.2019.08107

[15] Chadegani, A.A., Salehi, H., Yunus, M.M., Farhadi, H., Fooladi, M., Farhadi, M., Ebrahim, N.A. (2013). A comparison between two main academic literature collections: Web of Science and Scopus databases. arXiv preprint arXiv:1305.0377. https://arxiv.org/abs/1305.0377.

[16] https://www.scopus.com/, accessed on Apr. 12, 2022.

[17] https://www.vosviewer.com/, accessed on Apr. 12, 2022.

[18] Van Eck, N., Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2): 523-538. https://doi.org/10.1007/s11192-009-0146-3

[19] https://medialab.github.io/sciencescape/, accessed on Apr. 12, 2022.

[20] Benestad, C., High, J., Worcester, M. Regression Analysis. https://users.wpi.edu/~goulet/MME523/chris_b.htm.

The Impact of Article Length on the Number of Future Citations: A Bibliometric Analysis of General Medicine Journals

 Source: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0049476

• 
  

PLOS ONE

Open Access

Peer-reviewed

Research Article

The Impact of Article Length on the Number of Future Citations: A Bibliometric Analysis of General Medicine Journals

• Matthew E. Falagas ,
• Angeliki Zarkali,
• Drosos E. Karageorgopoulos,
• Vangelis Bardakas,
• Michael N. Mavros

The Impact of Article Length on the Number of Future Citations: A Bibliometric Analysis of General Medicine Journals

• Matthew E. Falagas, 
• Angeliki Zarkali, 
• Drosos E. Karageorgopoulos, 
• Vangelis Bardakas, 
• Michael N. Mavros

x

• Published: February 6, 2013
• https://doi.org/10.1371/journal.pone.0049476
• 
  

• Article
• Authors
• Metrics
• Comments
• Media Coverage

Abstract

Background

The number of citations received is considered an index of study quality and impact. We aimed to examine the factors associated with the number of citations of published articles, focusing on the article length.

Methods

Original human studies published in the first trimester of 2006 in 5 major General Medicine journals were analyzed with regard to the number of authors and of author-affiliated institutions, title and abstract word count, article length (number of print pages), number of bibliographic references, study design, and 2006 journal impact factor (JIF). A multiple linear regression model was employed to identify the variables independently associated with the number of article citations received through January 2012.

Results

On univariate analysis the JIF, number of authors, article length, study design (interventional/observational and prospective/retrospective), title and abstract word count, number of author-affiliated institutions, and number of references were all associated with the number of citations received. On multivariate analysis with the logarithm of citations as the dependent variable, only article length [regression coefficient: 14.64 (95% confidence intervals: (5.76–23.50)] and JIF [3.37 (1.80–4.948)] independently predicted the number of citations. The variance of citations explained by these parameters was 51.2%.

Conclusion

In a sample of articles published in major General Medicine journals, in addition to journal impact factors, article length and number of authors independently predicted the number of citations. This may reflect a higher complexity level and quality of longer and multi-authored studies.

Figures

  

Citation: Falagas ME, Zarkali A, Karageorgopoulos DE, Bardakas V, Mavros MN (2013) The Impact of Article Length on the Number of Future Citations: A Bibliometric Analysis of General Medicine Journals. PLoS ONE 8(2): e49476. https://doi.org/10.1371/journal.pone.0049476

Editor: Santo Fortunato, Aalto University, Finland

Received: May 31, 2012; Accepted: October 11, 2012; Published: February 6, 2013

Copyright: © 2013 Falagas et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: These authors have no support or funding to report.

Competing interests: The authors have declared that no competing interests exist.

Introduction

An article's citations are considered a measure of the scientific recognition the study has received, and thus an indicator of its value and impact on the scientific field [1]. The citations are also the main factor determining the scientific impact of a journal, as expressed by the journal impact factor [2]. This indicator represents the mean number of citations received in an index calendar year, by all the citable articles published in a journal during the previous two years [3], [4]. Researchers commonly aim to publish articles that will attract citations and will thus be regarded to have a high scientific impact, as this may be associated with their career advancement.

Several studies have been conducted to explore the factors associated with the citation count of scientific articles. While the effect of journal impact factor [5]–[10] and study design [11]–[16] on citations received has been established by different studies, the published evidence on other potentially relevant variables, such as open access to the full text of the article, [17]–[20] or article length, [13] seems conflicting.

In this context, we aimed to examine the factors associated with the number of citations received by published articles, focusing on the article's length.

Methods

Data sources

Original human research articles published in the first trimester of 2006 in the 5 highest impact factor journals in the field of general and internal medicine were analyzed (the New England Journal of Medicine, the Lancet, the Journal of the American Medical Association, the Annals of Internal Medicine, and the British Medical Journal). Experimental studies, review articles, and meta-analyses were excluded. The 2006 journal impact factors were retrieved from the Thomson Reuters Journal Citation Reports. The number of citations to each article was last assessed in January 2012, according to the Thomson Reuters Web of Knowledge.

Data extraction

The abstract and/or full-text manuscript of each article was accessed to collect information regarding article length and characteristics that were reported to affect the number of citations in previous studies. Specifically, we documented variables comprised the number of authors and affiliated institutions, title and abstract word count, article length (as the number of pages), number of bibliographic references, study design (human or experimental studies; prospective or retrospective; interventional or observational), access to the article (open access or requiring subscription), and 2006 journal impact factor (JIF).

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Figure 1. Correlation between the article length (number of print pages) and future article citations.

Results from the multivariate regression analysis. The dots represent the individual pairs for the X–Y variables; the straight line is the linear regression line; the dotted lines represent the 95% confidence interval for the regression line. The different journals with their individual IF are shown in different colours.

https://doi.org/10.1371/journal.pone.0049476.g001

Data analysis and statistical methods

Statistical analyses were performed using SPSS Version 20.0. Initially, the association of each independent variable with the dependent variable (citation count) was assessed with univariate analyses (Mann-Whitney for categorical and Spearman's correlation for continuous variables); we used non-parametric methods, because citations of articles published in General Medicine journals are known to have a non-parametric distribution [21]. Variables significantly associated with the citation count in univariate analysis (p<0.10) were then entered in a backward multiple linear regression model to identify independent predictors of higher number of citations. The multiple linear regression model was also run with logarithmic transformation of the dependent variable (number of citations) to assess for a logarithmic, rather than linear relationship between the dependent and independent variables. Since the logarithmic transformed model performed better, only the results of this model were presented. To exclude the possibility of a false positive association between the article length and the number of authors and the number of citations, we repeated the multiple regression analysis separately for each of the journals, as the journal impact factor has been well established to be a major factor affecting citations.

All assumptions of linear regression were met by this model, including lack of error term correlation (Durbin-Watson = 2.013). Graphical examination of residuals did not suggest a violation of the linearity and normality assumption. Multicollinearity was deemed not important (VIF <5) for every independent variable. Homoscedasticity was checked by examination of the scatterplot of residuals and predicted values, and was met when outliers were excluded from the model. We also tested for outliers using added value and residual plots. Three outliers were identified with citations 1314, 1185 and 793, and were excluded. A variable was considered statistically significant if it had a p-value <0.05 in the final multivariable model.

Results

A total of 196 articles were analyzed. Experimental studies were excluded, leading to a total of 192 articles. The citation count varied from 5 to 1314 with a median of 96.5 (mean = 166). The majority of studies were prospective (67.2%), open-access (90.2%) and multi-center (67.2%). The most common type of study in our sample was that of a trial (39.6%, both randomized control trials and non-randomized trials). The study characteristics are presented in Table 1.

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Table 1. Characteristics of the analyzed studies.

https://doi.org/10.1371/journal.pone.0049476.t001

On univariate analysis, all tested independent variables except access (free versus restricted) and multicenter or single-center study, were found to have a statistically significant correlation to citations (Table 2). Therefore, the following variables were entered in the multivariate model: JIF, number of authors, article length, prospective or retrospective design, type of study (interventional or observational), abstract and title word count, number of affiliated institutions, and number of references, with the logarithm of the number of citations as the dependent variable.

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Table 2. Results of statistical analysis.

https://doi.org/10.1371/journal.pone.0049476.t002

A backward linear regression analysis was performed, removing insignificant independent variables one by one. Two variables were found to independently predict the number of citations: article length (number of pages) [regression coefficient (95% confidence interval): 0.079 (0.055–0.102), p<0.001; Figure 1and JIF [0.008 (0.004–0.013), p<0.001; Figure 2]. The variance of citations explained by these factors is 51.2% (adjusted R² = 50.7%), p<0.001. The findings of the univariate and multivariate analyses are presented in table 2.

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Figure 2. Correlation between the impact factor of the journal of publication and future article citations.

Results from the multivariate regression analysis. The dots represent the individual pairs for the X–Y variables; the straight line is the linear regression line; the dotted lines represent the 95% confidence interval for the regression line.

https://doi.org/10.1371/journal.pone.0049476.g002

Subgroup analyses

For the subgroup of articles published in two of the five included journals, article length was found to be the only factor independently associated with citations, with a parameter estimate of 0.080 [(0.032–0.127), p = 0.002] and 0.058 [(0.013–0.104), p = 0.013], respectively. For articles published in the third journal, statistically significant factors included the number of institutions [0.050 (0.001–0.098), p = 0.04] and the number of references [0.014 (0.003–0.024), p = 0.01], while in the fourth journal significant were the number of authors [0.029 (0.006–0.051), p = 0.015] and the number of references [0.025 (0.011–0.040), p = 0.001)]. In the remaining journal, no variable was found to be significantly associated with citations, although that may reflect the smaller sample size (n = 23). Last, article length was significantly associated with the number of citations in the singe-center studies subgroup [0.109 (0.075–0.143), p<0.001].

Discussion

The main finding of this study is that the article length and journal impact factor are independently associated with the number of citations received by each article. Although several previous studies have reported that the journal impact factor is associated with the article citations, this is the first study, to the best of our knowledge, to report a positive association between the article length and the article citations after adjustment for several potentially confounding variables, such as the study design, prospective or retrospective nature of the study, abstract and title word count, number of author–affiliated institutions and number of bibliographic references. Specifically, we found an increase by an average of 0.079 in the logarithm of citations per article for each additional page, 0.008 for every unit of increase in the journal impact factor. The greater article length could reflect increased greater scientific complexity and higher methodological quality of a study; in addition, lengthier articles are expected to contain more information, thus increasing the possibilities that part of it will be appropriate to be cited by other researchers. Furthermore, in lengthier compared with shorter articles, the study methodology and findings could be more clearly and elaborately presented and discussed, and can therefore have a greater impact. It should be highlighted that our findings probably do not apply to long articles where the results have been improperly “inflated”; after all, some of the greatest discoveries in science have been described only briefly [22].

A few studies have assessed, albeit not comprehensively, the impact of the article length on future citations. In the field of Astronomy and Astrophysics, lengthier articles were cited more often in some journals [23]. In the fields of Infectious Diseases, Clinical Microbiology and Antimicrobial Agents, brief reports were cited less often than full articles, even after adjustment for the journal impact factor [24]. This was not the case in another study assessing 504 articles and adjusting for several confounding factors [13]. In contrast to our study, in which we assessed only original study articles, the authors included in their analysis numerous Cochrane reviews and reports from the Technology Assessment database (n = 108), that are typically lengthy; in addition, they excluded articles not meeting specific methodological and clinical relevance criteria. That study reported a slightly negative correlation between the article length and the number of citations received [−0.11 (−0.02 to −0.01)]; however, when Cochrane reviews and reports from the Technology Assessment database were excluded, no association between the article length and citations was identified. Although the difference between these findings and those of our study is probably attributed to the difference in the type of articles assessed (inclusion/exclusion of review articles), it remains to be proven whether our findings can be generalized to a larger part of the biomedical literature than just the 5 highest impact factor journals in General & Internal Medicine.

In addition to the number of print pages, we found that the impact factor of the journal and the number of authors were associated with the citation count. Although we limited our analysis only to articles from high impact factor journals, the articles published in the highest impact factor journals were cited significantly more often. It should be noted that we used the 2006 journal impact factor (that refers to articles published in 2004 and 2005) for our analysis (that referred to articles published in 2006) to avoid a potential bias. In this regard, our findings are in concordance with previous studies that found the journal impact factor to be a major predictor of the article citation count [5]–[10].

Several other variables assessed in previous studies were incorporated in our analysis, but failed to show a statistically significant association with the number of citations. The characteristics and findings of all relevant studies are briefly presented in Table 3. Some authors have described an association between the type of the study and the future citations, with more citations received by meta-analyses and randomized control trials and less citations received by observational studies [11], [12], [14], [16]; their findings are have been limited by selection bias (articles of a specific specialty) [11], [12], [14], [16] and inappropriate adjustment of confounding factors [16]. Such findings were not verified in our analysis, as we found no citation advantage neither for interventional over observational studies, nor for any specific type of study (trial, cohort, cross-sectional or case-control); however, this could also be attributed to the relatively small sample size of each subset of articles of different study type. It has been debated whether open access distribution of articles leads to more citations [18]–[20], [25], [26] or that scientific collaboration positively influence citation count [13], [15], [27]; we did not confirm such an association. Last, we did not observe a significant impact of the title length (word count) on the future citations, in contrast to what other researchers have found [25], [26]. This may be attributed to the lack of adjustment for confounding factors by those studies.

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Table 3. Published studies examining factors that affect citations.

https://doi.org/10.1371/journal.pone.0049476.t003

Our study is subject to certain limitations. First, it is characterized by selection bias, as the articles published in high impact factor journals in General Medicine may not be representative of all published articles; for example, they are more likely to be multi-center RCT than a single-center case-control study. Second, although our results are statistically significant, it is possible that the association does not represent a causal relationship. Third, we did not assess the analyzed articles regarding topic [11], [15], [16], paper quality [9], [10], funding [15], [18] or country of origin of the authors [7], [18], which are factors that have been found to affect citations by other authors. Last, in our assessment of article length, we only analyzed page count (not word count) and inter-journal variance in the number of words per page cannot be excluded.

In conclusion, for original research articles published in the major General Medicine journals, in addition to journal impact factor, the article length independently predicts the number of future citations. This probably reflects a higher complexity level and quality of longer studies and does not apply to inappropriately inflated articles. Additional studies are warranted to verify the generalizability of our findings to a largest part of the biomedical literature.

Author Contributions

Conceived and designed the experiments: MEF DEK. Performed the experiments: AZ VB. Analyzed the data: VB MNM. Contributed reagents/materials/analysis tools: MEF AZ DEK MNM. Wrote the paper: MEF AZ DEK VB MNM.

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Bibliometrics  

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Medicine and health sciences