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The effects of the Covid-19 pandemic on the performance of academic journals: the case of Turkey

Os efeitos da pandemia do Covid-19 no desempenho das revistas acadêmicas: o caso da Turquia

Abstract

Since the beginning of 2020, “Covid-19” has affected the whole world in an unprecedented way in modern times. It is inevitable that this pandemic, which has negatively affected many fields, will also have an impact on academic journals. The aim of this study is to determine the effect of the Covid-19 pandemic on the performance of academic journals. In our study, a “Data Envelopment Analysis” methodology with 3 inputs and 3 outputs was used to determine the relative “performance of the journals”. Within the scope of the study, 109 journals published in “Turkey” and scanned in “Web of Science” indexes were examined. Results show that eleven journals were efficient in 2019, while in 2020 this number decreased to seven. Four fields have been positively affected by the pandemic and journals publishing in these fields have increased their efficiencies. Eighteen fields were adversely affected by the pandemic and the efficiency of journal publishing in these fields decreased. Eleven fields and journals publishing in these fields maintained their efficiency both before and during the pandemic. As the Covid-19 pandemic is not over yet, our data is limited. In the coming years, more detailed and comprehensive studies can be carried out with more extensive data and a further number of journals from different countries.

Keywords
Data envelopment analysis; Multi-criteria decision making; Peer-reviewed journals; Ranking

Resumo

Desde o início de 2020, a Covid-19 afetou o mundo inteiro de uma forma sem precedentes nos tempos modernos. É inevitável que essa pandemia, que afetou negativamente muitos campos, também tenha impacto nos periódicos acadêmicos. O objetivo deste estudo é determinar o efeito da pandemia de Covid-19 no desempenho de revistas acadêmicas. Neste estudo, foi utilizada uma metodologia de “Análise Envoltória de Dados” com 3 entradas e 3 saídas para determinar o “desempenho relativo dos periódicos”. No âmbito do estudo, foram examinados 109 periódicos publicados na Turquia e indexados na Web of Science. Os resultados mostram que onze periódicos foram eficientes em 2019, enquanto em 2020 esse número diminuiu para sete. Quatro áreas foram afetadas positivamente pela pandemia e os periódicos que publicam nessas áreas aumentaram sua eficiência. Dezoito áreas foram afetadas negativamente pela pandemia e a eficiência dos periódicos que publicam nessas áreas diminuiu. Onze áreas e periódicos que publicam nessas respectivas áreas mantiveram sua eficiência antes e durante a pandemia. Como a pandemia do Covid-19 ainda não acabou, nossos dados são limitados. Nos próximos anos, estudos mais detalhados e abrangentes poderão ser realizados com dados mais extensos e maior número de periódicos de diferentes países.

Palavras-chave
Análise envoltória de dados; Tomada de decisão multicritério; Revistas revisadas por pares; Classificação

Introduction

Today, many institutions, including higher education institutions, are giving more and more importance to rankings in order to increase their quality. Universities give importance to academic journal rankings in order to better determine their academic output (Rosenthal; Weiss, 2017Rosenthal, E. C.; Weiss, H. J. A data envelopment analysis approach for ranking journals. Omega, v. 70, p. 135-147, 2017.). This article proposes a ranking procedure for journals in the Emerging Sources Citation Index (ESCI), Social Sciences Citation Index (SSCI), Arts and Humanities Citation Index (A&HCI) and Science Citation Index (SCI) indexes published in Turkey based on Data Envelopment Analysis (DEA), using data from Scimago Journal & Country Rank. One of the advantages of our research is that it offers the opportunity to compare journals published in different disciplines with each other. Another advantage is that it is not based on subjective personal opinions, but on real data from past years.

From the beginning of 2020, the world is facing an unprecedented health crisis affecting almost all segments of society. When faced with global crises such as Covid-19, which concern all humanity, obtaining and disseminating accurate and rapid scientific information is of great importance. The fact that the publication processes of academic journals sometimes take years is an obstacle to the rapid dissemination of this new scientific knowledge (Horbach, 2020Horbach, S. P. Pandemic publishing: medical journals strongly speed up their publication process for Covid-19. Quantitative Science Studies, v. 1, n. 3, p. 1056-1067, 2020.). In this study, the data from 2019 and 2020 were used and analysed. In this way, it will be revealed what effect the Covid-19 pandemic has on this ranking, and which types of journals are affected by this pandemic.

In general, there are two approaches to ranking journals: stated preference and expressed preference. The first of these, expert evaluation, is the academic community’s evaluation of journals according to their own personal views. The second is the method that takes into account the actual publication and citation values of the journals. Many of the current assessments are based on the first method, expert assessments. Since these evaluations are ultimately based on individual opinions, it is difficult to talk about 100% objectivity (Mingers; Harzing, 2007Mingers, J.; Harzing, A. W. Ranking journals in business and management: a statistical analysis of the Harzing data set. European Journal of Information Systems, v. 16, n. 4, p. 303-316, 2007.). In the literature, there is an ongoing debate regarding the superiority or suitability of such methods and new method proposals (Garfield, 1972Garfıeld, E. Citation analysis as a tool in journal evaluation. Science, v. 178, p. 471-479, 1972.; Pinski; Narin, 1976Pinski, G.; Narin F. Citation influence for journal aggregates of scientific publications: theory, with application to the literatüre of physics. Information Processing & Management, v. 12, n. 5, p. 297-312, 1976.; DuBois; Reeb, 2000DuBois, F. L.; Reeb, D. Ranking the international business journals. Journal of International Business Studies, v. 31, n. 4, p. 689-704, 2000.; Katerattanakul; Han; Hong, 2003Katerattanakul, P.; Han, B.; Hong, S. Objective quality ranking of computing journals. Communications of the ACM, v. 46, n. 10, p. 111-114, 2003.; Gursoy; Sandstorm, 2016Gursoy, D.; Sandstrom, J. K. An updated ranking of hospitality and tourism journals. Journal of Hospitality & Tourism Research, v. 40, n. 1, p. 3-18, 2016.; Barrick et al., 2019Barrick, J. A. et al. Ranking accounting journals by topical area and methodology. Journal of Information Systems, v. 33, n. 2, p. 1-22, 2019.). Rather than add to these discussions, this study will use data from Scimago Institutions Rankings and Science Journal as inputs and outputs in our DEA application.

DEA is a non-parametric method widely used for efficiency and/or productivity measurement (Doğan; Ersoy, 2017Doğan, N. Ö.; Ersoy, Y. Etkinlik ölçümü: Tekstil sektöründen bir işletme örneği. Hitit Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, v. 10, n. 1, p. 35-44, 2017.). The advantage of using DEA to rank business journals is that it allows comparing similar outputs (e.g., citations, impact factors, and others) against similar inputs (e.g., articles) across different disciplines (Rosenthal; Weiss, 2017Rosenthal, E. C.; Weiss, H. J. A data envelopment analysis approach for ranking journals. Omega, v. 70, p. 135-147, 2017.). That is, the power of DEA models that rank journals using citation data is thought to come from the use of such rankings as input/output data directly from the Scimago Institutions Rankings and Science Journal database; hence, these rankings are based on the concept of efficiency in comparing the various outputs of the article bases in different journals.

Relevant journal ranking literature is reviewed before proceeding with an immediate analysis. Following this, the different citation measures used in the DEA model are described and discussed before applying the methodology. As explained in this section, there are gaps in the literature that we aim to fill. Although there are some studies in the literature on journal ranking; to best to our knowledge, a study using DEA and focusing on Turkey does not exist in the literature. More importantly, the issue of how the Covid-19 outbreak affected this ranking has not been encountered in the current literature.

There have been numerous scholarly attempts to rank journals in the field of operations management (OM) and/or other business, science, and social sciences. For the most part, these studies either used citation-based data or investigated respondents’ perceptions of journal quality using survey methods.

Vokurka (1996)Vokurka, R. The relative importance of journals used in operations management research: a citation analysis. Journal of Operations Management, v. 14, n. 4, p. 345-55, 1996. conducted a research based on the number of citations to evaluate the performance of journal publishing in the field of production management. He compiled the journals cited in articles published in Decision Sciences, Journal of Operations Management, and Management Science and ranked these journals according to their citation performance.

Wing (1997)Wing, C. K. The ranking of construction management journals. Construction Management & Economics, v. 15, n. 4, p. 387-398, 1997. made an evaluation of the survey method in her study in which twenty-two journals publishing in the field of construction management have been dealt with. The participants were asked to evaluate only the journals that were published in the fields they were knowledgeable about. Despite the small sample, statistically significant results were obtained as a result of the research.

DuBois and Reeb (2000)DuBois, F. L.; Reeb, D. Ranking the international business journals. Journal of International Business Studies, v. 31, n. 4, p. 689-704, 2000. evaluated thirty internationally published business journals by using both the survey method and analysing the citation numbers in their studies.

Pechlaner et al. (2004)Pechlaner, H. et al. A ranking of international tourism and hospitality journals. Journal of Travel Research, v. 42, n. 4, p. 328-332, 2004. tried to rank tourism and hospitality journals according to the readership of the journals, their scientific and practical relevance, their general reputation, and the importance of their publication in the journals for the academic career of the participants.

There are many studies in the literature using DEA. DEA is one of the most widely used methods in efficiency, productivity or performance measurement in its most general concept. However, only one article (Rosenthal; Weiss, 2017Rosenthal, E. C.; Weiss, H. J. A data envelopment analysis approach for ranking journals. Omega, v. 70, p. 135-147, 2017.) was found that aimed to rank journals. This was the biggest reason for us to do the study by focusing on Turkey.

Apart from ranking journals, DEA has been used in many fields for ranking and efficiency measurement. In the literature, there are studies aiming to measure the efficiency of schools and universities using DEA (Bessent; Bessent, 1980Bessent, A. M.; Bessent, E. W. Determining the comparative efficiency of schools through data envelopment analysis. Educational Administration Quarterly, v. 16 n. 2, p. 57-75, 1980.; Athanassopoulos; Shale, 1997Athanassopoulos, A. D.; Shale, E. Assessing the comparative efficiency of higher education institutions in the UK by the means of data envelopment analysis. Education Economics, v. 5 n. 2, p. 117-134, 1997.; Avkiran, 2001Avkiran, N. K. Investigating technical and scale efficiencies of Australian universities through data envelopment analysis. Socio-Economic Planning Sciences, v. 35 n. 1, p. 57-80, 2001.; Johnes, 2006Johnes, J. Data Envelopment Analysis and its application to the measurement of efficiency in higher education. Economics of Education Review, v. 25 n. 3, p. 273-288, 2006.; Kuah; Wong, 2011Kuah, C. T.; Wong, K. Y. Efficiency assessment of universities through data envelopment analysis. Procedia Computer Science, v. 3, p. 499-506, 2011.). In their study, Abbott and Doucouliagos (2003)Abbott, M.; Doucouliagos, C. The efficiency of Australian universities: a data envelopment analysis. Economics of Education Review, v. 22 n. 1, p. 89-97, 2003. used nonparametric techniques to estimate the technical and scale efficiency of each of the Australian universities. Various output and input measures are used. The results showed that, regardless of the output-input mix, Australian universities as a whole recorded a high level of productivity relative to each other. In addition, similar studies were conducted on university preferences (Sarrico et al., 1997Sarrico, C. S. et al. Data envelopment analysis and university selection. Journal of the Operational Research Society, v. 48 n. 12, p. 1163-1177, 1997.).

The method was used in bank efficiency studies (Sherman; Gold, 1985Sherman, H. D.; Gold, F. Bank branch operating efficiency: evaluation with data envelopment analysis. Journal of Banking & Finance, v. 9 n. 2, p. 297-315, 1985.; Sherman; Ladino, 1995Sherman, H. D.; Ladino, G. Managing bank productivity using data envelopment analysis (DEA). Interfaces, v. 25 n. 2, p. 60-73, 1995.; Golany; Storbeck, 1999Golany, B.; Storbeck, J. E. A data envelopment analysis of the operational efficiency of bank branches. Interfaces, v. 29 n. 3, p. 14-26, 1999.; Paradi; Rouatt; Zhu, 2011Paradi, J. C.; Rouatt, S.; Zhu, H. Two-stage evaluation of bank branch efficiency using data envelopment analysis. Omega, v. 39 n. 1, p. 99-109, 2011.; Paradi; Zhu, 2013Paradi, J. C.; Zhu, H. A survey on bank branch efficiency and performance research with data envelopment analysis. Omega, v. 41 n. 1, p. 61-79, 2013.), hospital efficiency and quality assessments (Vassiloglou; Giokas, 1990Vassiloglou, M.; Giokas, D. A study of the relative efficiency of bank branches: an application of data envelopment analysis. Journal of the Operational Research Society, v. 41 n. 7, p. 591-597, 1990.; Thanassoulis, 1999Thanassoulis, E. Data envelopment analysis and its use in banking. Interfaces, v. 29 n. 3, p. 1-13, 1999.; Nayar; Ozcan, 2008Nayar, P.; Ozcan, Y. A. Data envelopment analysis comparison of hospital efficiency and quality. Journal of Medical Systems, v. 32 n. 3, p. 193-199, 2008.) and airport efficiency and performance (Gillen; Lall, 1997Gillen, D.; Lall, A. Developing measures of airport productivity and performance: an application of data envelopment analysis. Transportation Research Part E: Logistics and Transportation Review, v. 33 n. 4, p. 261-273, 1997.; Barros; Dieke, 2007Barros, C. P.; Dieke, P. U. Performance evaluation of Italian airports: a data envelopment analysis. Journal of Air Transport Management, v. 13 n. 4, p. 184-191, 2007.).

According to Baysal; Uygur; Toklu (2004)Baysal, M. E.; Uygur, M.; Toklu, B. Veri Zarflama Analizi İle Tcdd Limanlarinda Bir Etkinlik Ölçümü Çalişmasi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, v. 19 n. 4, 2004.; in international trade, ports have a role that directly affects the economy of the country. Depending on the increasing trade volume, ports should be well planned and developed in order to obtain optimum port capacities. Existing ports and their capacities should be used in the most effective way, and scientific methods and techniques should be used for this. In their study, DEA was applied to determine the efficiency of 7 State Railways of the Republic of Turkey (TCDD) ports serving in Turkey. As a result of the implementation, the efficiency value of each port and the recommended potential improvements for inactive ports are given. There are other studies in this area as well (Roll; Hayuth, 1993Roll, Y.; Hayuth, Y. E. H. U. D. A. Port performance comparison applying data envelopment analysis (DEA). Maritime Policy and Management, v. 20 n. 2, p. 153-161, 1993.; Martinez-Budria et al., 1999Martinez-Budria, E. et al. A study of the efficiency of Spanish port authorities using data envelopment analysis. International Journal of Transport Economics, p. 237-253, 1999.).

Mansourirad (2013)Mansourirad, E. A categorical fuzzy DEA method to evaluate efficiency of hotels based on stars rating. Applied Mathematical Sciences, v. 7, n.73, p. 3625-3628, 2013. proposed a fuzzy DEA method that enables grouping and measurement according to appropriate categories in comparing the efficiency of hotels. The method compares hotels in different star classes in their categories and does not allow the comparison of small-scale hotels with large-scale hotels or internationally branded hotels. The application of the proposed method is shown in 20 hotels with different star classifications from 1 to 5. There are other studies in this area as well (Johns; Howcroft; Drake, 1997Johns, N.; Howcroft, B.; Drake, L. The use of data envelopment analysis to monitor hotel productivity. Progress in Tourism and Hospitality Research, v. 3 n. 2, p. 119-127, 1997.; Hwang; Chang, 2003Hwang, S. N.; Chang, T. Y. Using data envelopment analysis to measure hotel managerial efficiency change in Taiwan. Tourism Management, v. 24 n. 4, p. 357-369, 2003.).

Studies on the effects of the Covid-19 pandemic have become one of the most popular topics more recently. In this context, there are many studies on different subjects, such as its effects on the environment (Eroğlu, 2020Eroğlu, H. Effects of Covid-19 outbreak on environment and renewable energy sector. Environment, Development and Sustainability, p. 1-9, 2020.; Kroll et al., 2020Kroll, J. H. et al. The complex chemical effects of Covid-19 shutdowns on air quality. Nature Chemistry, v. 12, n. 9, p. 777-779, 2020.; Zambrano-Monserrate; Ruano; Sanchez-Alcalde, 2020Zambrano-Monserrate, M. A.; Ruano, M. A.; Sanchez-Alcalde, L. Indirect effects of Covid-19 on the environment. Science of the Total Environment, v. 728, p.138813, 2020.; Liu; Wang; Zheng, 2021Liu, F.; Wang, M.; Zheng, M. Effects of Covid-19 lockdown on global air quality and health. Science of the Total Environment, v. 755, p. 142533, 2021.), businesses (Donthu; Gustafsson, 2020Donthu, N.; Gustafsson, A. Effects of Covid-19 on business and research. Journal of Business Research, v. 117, p. 284, 2020.; Gursoy; Chi, 2020Gursoy, D.; Chi, C. G. Effects of Covid-19 pandemic on hospitality industry: review of the current situations and a research agenda. Journal of Hospitality Marketing & Management, v. 29, n. 5, p. 527-529, 2020.; Jiang; Wen, 2020Jiang, Y.; Wen, J. Effects of Covid-19 on hotel marketing and management: a perspective article, International Journal of Contemporary Hospitality Management, v. 32, n. 8, p. 2563-2573, 2020.), daily life (Chakraborty; Maity, 2020Chakraborty, I.; Maity, P. Covid-19 outbreak: migration, effects on society, global environment and prevention. Science of the Total Environment, v. 728, p. 138882, 2020.; Haleem; Jayaid; Vaishya, 2020Haleem, A.; Javaid, M.; Vaishya, R. Effects of Covid-19 pandemic in daily life. Current Medicine Research and Practice, v. 10, n. 2, p. 78, 2020.), scientists (Korbel; Stegle, 2020Korbel, J. O.; Stegle, O. Effects of the Covid-19 pandemic on life scientists. Genome Biology, v. 21, p. 113, 2020.; Myers et al., 2020Myers, K. R. et al. Unequal effects of the Covid-19 pandemic on scientists. Nature Human Behaviour, v. 4, n. 9, p. 880-883, 2020.). In addition, the economic effects (Ceylan; Ozkan; Mulazimogullari, 2020Ceylan, R. F.; Ozkan, B.; Mulazimogullari, E. Historical evidence for economic effects of Covid-19. The European Journal of Health Economics, v. 21, p. 817-823, 2020.; Jackson et al., 2020Jackson, J. K. et al. Global economic effects of Covid-19. Congressional Research Service, 2020.; Topcu; Gulal, 2020Topcu, M.; Gulal, O. S. The impact of Covid-19 on emerging stock markets. Finance Research Letters, v. 36, p. 101691, 2020.) and psychological effects of Covid-19 (Cullen; Gulati; Kelly, 2020Cullen, W.; Gulati, G.; Kelly, B. D. Mental health in the Covid-19 pandemic. QJM: An International Journal of Medicine, v. 113, n. 5, p. 311-312, 2020.; Durankuş; Aksu, 2020Durankuş, F.; Aksu, E. Effects of the Covid-19 pandemic on anxiety and depressive symptoms in pregnant women: a preliminary study. The Journal of Maternal-Fetal & Neonatal Medicine, p. 1-7, 2020.; Forte et al., 2020Forte, G. et al. The enemy which sealed the world: effects of Covid-19 diffusion on the psychological state of the Italian population. Journal of Clinical Medicine, v. 9, n. 6, p. 1802, 2020.; Gualano et al., 2020Gualano, M. R. et al. Effects of Covid-19 lockdown on mental health and sleep disturbances in Italy. International Journal of Environmental Research and Public Health, v. 17, n. 13, p. 4779, 2020.; Kontoangelos; Economou; Papageorgiou, 2020Kontoangelos, K.; Economou, M.; Papageorgiou, C. Mental health effects of Covid-19 pandemia: a review of clinical and psychological traits. Psychiatry Investigation, v. 17, n. 6, p. 491, 2020.; Orgilés et al., 2020Orgilés, M. et al. Immediate psychological effects of the Covid-19 quarantine in youth from Italy and Spain. Frontiers in Psychology, v. 11, p. 2986, 2020.; Ammar et al., 2021Ammar, A. et al. Effects of home confinement on mental health and lifestyle behaviours during the Covid-19 outbreak: insights from the ECLB-Covid19 multicentre study. Biology of Sport, v. 38, n. 1, p. 9, 2021.) are dealt with. However, there is no study on how this pandemic affects the performance of academic journals. Therefore, this study aims to fill this gap.

Methodological Procedures

In this study, the performance of academic journals operating in Turkey and scanned in Web of Science indexes was revealed by using the DEA method. DEA is a non-parametric and widely used method in the measurement of organizational performance, specifically the dimensions of efficiency and/or productivity.

Data Envelopment Analysis

DEA is a data-driven approach, which started with Farrell’s basic work in 1957 and was brought to the field of operations research by Charnes, Cooper and Rhodes in 1978. It can be considered as a new tool that is designed to evaluate the performance of a set of similar units called Decision Making Unit (DMU) and make it possible to transform a large number of inputs into a large number of outputs (Doğan; Tanç, 2008Dogan, N. Ö.; Tanç, A. Konaklama isletmelerinde veri zarflama analizi yöntemiyle faaliyet denetimi: kapadokya örnegi. Atatürk Üniversitesi Iktisadi ve Idari Bilimler Dergisi, v. 22, n. 1, p. 239-259, 2008.). DEA is originally designed to measure the relative efficiency of public sector activities and non-profit organizations, such as educational institutions and healthcare facilities. However, later on, this method has also been applied to many profit-oriented organizations. Hospitals, universities, military units, local governments, courts, businesses, etc. have been the subject of DEA applications (Basso; Funari, 2001Basso, A.; Funari, S. A data envelopment analysis approach to measure the mutual fund performance. European Journal of Operational Research, v. 135, n. 3, p. 477-492, 2001.; Cooper; Seiford; Zhu, 2004Cooper, W. W.; Seiford L. M.; Zhu J. Handbook on Data Envelopment Analysis. Boston: Springer, 2004.).

DEA comes to the forefront as a method that does not have the weaknesses of other methods in cases where the relationships between multiple inputs and multiple outputs among the DMUs are complex. In addition, DEA is a proven method for the efficiency and/or productivity of the public sector and service sector, since it covers the relevant units regardless of the unit of measurement (Prieto; Zofio, 2001Prıeto, A. M.; Zofio L. J. Evaluating efficientness in public provision of infrastructure and equipment: the case of spanish municipalities. Journal of Productivity Analysis, v. 15, p. 41-58, 2001.; Cooper; Seiford; Zhu, 2004Cooper, W. W.; Seiford L. M.; Zhu J. Handbook on Data Envelopment Analysis. Boston: Springer, 2004.).

The Input-Oriented Classical CCR Model and Its Super Efficiency Version

A model developed by Charnes, Cooper and Rhodes became known as CCR model, which is the abbreviation of the names of these three authors. The CCR model is based on the assumption of constant returns to scale. The return to scale structure, which is fixed in CCR models, has been relaxed by Banker, Charnes and Cooper, and models in which it is possible to deal with variable returns to scale have been developed. These models are known as BCC models. Both the CCR and BCC models are the most basic models in DEA, and each has both input and output oriented versions. In this study, analyses were carried out using the input-oriented CCR model and its super-efficiency extension. Classical CCR models can be easily used in efficiency measurement. However, when there is more than one efficient unit, the order of priority among the efficient ones cannot be determined, and the models assign an efficiency score of “1” corresponding to 100% to each of the efficient DMUs. Therefore, super efficiency models are used to rank the efficient DMUs between each other (Doğan, 2015Doğan, N. Ö. Vza süper etkinlik modelleri ile etkinlik ölçümü: Kapadokya da faaliyet gösteren balon işletmeleri üzerine bir uygulama. Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, v. 29, n. 1, p. 187-204, 2015.).

m i n   ϴ 0 s . t . n Σ λ j x i j θ 0 x i 0 ,   i = 1 , , m j = 1 n Σ λ j y r j y r 0 ,   r = 1 , , s j = 1 λ j 0 ,   j = 1 , , n (1)

The input-oriented CCR model is Model (1) (Xu; Ouenniche, 2012Xu, B.; Ouenniche, J. A data envelopment analysis-based framework for the relative performance evaluation of competing crude oil prices’ volatility forecasting models. Energy Economics, v. 34, p. 576-583, 2012.). In this model, λj represents non-negative scalars where j=1,…,n, and ϴ0 represent DMU0 whose efficiency is evaluated. In this model, which takes into account the assumption of constant returns to scale (CRS), DMU0 should take a value of 1 to be efficient. When this value is less than 1, DMU0 is not efficient.

min θ 0 s.t . Σ n λ j x i j θ 0 x i 0 , i = 1 , , m Σ n λ j y r j y r 0 , r = 1 , , s (2)

As explained in the paragraphs above; Model (1) and Model (2) are “input-oriented CCR” and “input-oriented CCR super-efficiency” models, respectively.

Two important points should be considered while applying the DEA. The first is the selection of comparable units, known as DMUs, with a similar input-output structure. At this point, homogeneity should be taken into account when selecting the DMUs. That is, DMUs must work for the same or similar targets under the same or similar conditions. The second is that the number of DMUs must be large enough to obtain meaningful results. DEA can be problematic when the total number of inputs and outputs in a dataset approaches the total number of DMUs. In the literature, the number of DMUs is taken as at least 2 or 3 times the sum of the number of inputs and outputs (Colbert; Levary; Shaner, 2000Colbert, A.; Levary, R. R.; Shaner, M. C. Determining the relative efficiency of MBA programs using DEA. European Journal of Operational Research, v. 125, n. 3, p. 656-669, 2000.; Haas; Murphy 2003Haas, D. A.; Murphy H. F. Compensating for non-homogeneity İn decision-making units in Data Envelopment Analysis. European Journal of Operational Research, v. 144, p. 530–544, 2003.).

Data and Procedure

In our analysis, 3 inputs and 3 outputs were used. Inputs are; Total Docs. (2019, 2020), Total Docs. (3 years), Total Refs. (2019, 2020); and the outputs are H index, Total Cites (3 years), Cites/Doc. (2 years). The inputs and outputs in Table 1 refer to:

  • Total Docs. (2019): Journal’s published articles in 2019. All types of documents are considered.

  • Total Docs. (3 years): Journal’s published articles in 2018, 2017 and 2016. All types of documents are considered.

  • Total Refs. (2019): Number of references included in the journal’s published articles in 2019.

  • Total Cites (3 years): Citations in 2019 received by journal’s documents published in 2018, 2017, and 2016.

  • Cites/Doc. (2 years): Average citation per document in a 2-year period. This metric is widely used as impact index.

  • H index: Journal’s number of articles (h) that have received at least h citations over the whole period.

Table 1
Journal Statistics for 2019.

The inputs and outputs in Table 2 refer to:

  • Total Docs. (2020): Journal’s published articles in 2020. All types of documents are considered.

  • Total Docs. (3 years): Journal’s published articles in 2019, 2018 and 2017. All types of documents are considered.

  • Total Refs. (2020): Number of references included in the journal’s published articles in 2020.

  • Total Cites (3 years): Citations in 2020 received by journal’s documents published in 2019, 2018, 2017.

  • Cites/Doc. (2 years): Average citation per document in a 2-year period. This metric is widely used as impact index.

  • H index: Journal’s number of articles (h) that have received at least h citations over the whole period.

Table 2
Journal Statistics for 2020.

Journals with zero in their data in 2019 or 2020 are not included in the analysis in order to obtain accurate results. After excluding such journals, the analysis was made in 109 journals. Efficiency measurement calculations of journals were made using the input-oriented CCR model (Model 1) and its supper efficiency version (Model 2) with the help of the Efficiency Measurement System (EMS) package program (Scheel, 2006Scheel, H. Efficiency measurement system (Ems). Holger Scheel’s DEA Page, 2006.). Since it is aimed at minimizing the inputs without any change in the outputs (while the outputs are kept constant), the input-oriented model is used. As mentioned earlier, the CCR model considers “constant returns to scale”.

Results

The EMS output of 2019 is shown in Table 3. The values in this table are obtained by applying Model (2). When Table 3 is examined; it is seen that a total of eleven journals, numbered 13, 17, 37, 39, 43, 57, 67, 80, 83, 85, and 90, are above the efficiency limit and therefore efficient. It is possible to see this in the “Efficiency Scores” column of the table. The efficiency scores of these journals are 195.14%, 108.08%, 219,63%, 101.29%, 103,78%, 179,30%, 171,09%, 117,24%, 139,75%, 102,38% and 100.32%, respectively. When we examine the categories in which these journals publish, it is seen that there is a journal in each of the following categories (The numbers in parentheses indicate which numbered journal is in that category): Agricultural and Biological Sciences (miscellaneous) (80), Cardiology and Cardiovascular Medicine (17), Cell Biology (80), Condensed Matter Physics (37), Critical Care and Intensive Care Medicine (85), Earth and Planetary Sciences (miscellaneous) (83), Economics and Econometrics (13), Emergency Medicine (85), Endocrinology (39), Endocrinology, Diabetes and Metabolism (39), Engineering (miscellaneous) (37), Finance (13), Genetics (80), Hematology (90), Industrial and Manufacturing Engineering (67), Materials Science (miscellaneous) (67), Microbiology (80), Molecular Biology (80), Nuclear Medicine and Imaging (57), Orthopedics and Sports Medicine (43), Pediatrics, Perinatology and Child Health (39), Physical Therapy, Sports Therapy and Rehabilitation (43), Physiology (80), Radiology (57), Radiology, Nuclear Medicine and Imaging (17), Sports Science (43).

Table 3
EMS Outputs for 2019.

The EMS output of 2020, obtained as a result of running Model (2), is shown in Table 4. It is seen from Table 4 that a total of seven journals, numbered 3, 13, 31, 37, 43, 83 and 85, are found as efficient. The efficiency scores of these journals are 591,14%, 162,36%, 103,68%, 188,29%, 108,28%, 109,47% and 210,05%, respectively. When we examine the categories in which these journals publish, it is again seen that there is a journal in each of the following categories: Condensed Matter Physics (37), Critical Care and Intensive Care Medicine (85), Development (3), Earth and Planetary Sciences (miscellaneous) (83), Economics and Econometrics (13), Emergency Medicine (85), Engineering (miscellaneous) (37), Finance (13), Orthopedics and Sports Medicine (43), Physical Therapy, Sports Therapy and Rehabilitation (43), Political Science and International Relations (31), Sociology and Political Science (31), Sports Science (43), Tourism, Leisure and Hospitality Management (3).

Table 4
EMS Outputs for 2020.

Although the data are limited as it is still an ongoing pandemic, some deductions can be drawn from present data. It was observed that the efficiency of the journals in the fields of Development, Political Science and International Relations, Tourism, Leisure and Hospitality Management, Sociology and Political Science increased with the effect of the Covid-19 pandemic. Furthermore, the efficiency of the journals in the fields of Agricultural and Biological Sciences (miscellaneous), Cardiology and Cardiovascular Medicine, Cell Biology, Endocrinology, Endocrinology, Diabetes and Metabolism, Genetics, Hematology, Industrial and Manufacturing Engineering, Materials Science (miscellaneous), Microbiology, Molecular Biology, Nuclear Medicine and Imaging, Pediatrics, Perinatology and Child Health, Physiology, Physical Therapy, Sports Therapy and Rehabilitation, Radiology, Radiology, Nuclear Medicine and Imaging decreased with the same effect. Condensed Matter Physics, Critical Care and Intensive Care Medicine, Earth and Planetary Sciences (miscellaneous), Economics and Econometrics, Emergency Medicine, Engineering (miscellaneous), Finance, Orthopedics and Sports Medicine, Physical Therapy, Sports Therapy and Rehabilitation, Sports Science fields found efficient both before and during the pandemic.

Considering the year 2019, the closest journals to the efficiency frontier are Journal No. 10 with an efficiency score of 99.10%, Journal No. 77 with an efficiency score of 97.99%, and Journal No. 36 with an efficiency score of 95.69%. These journals are inefficient. However, since they are very close to the efficiency frontier, it will be easier for them to make improvements to be efficient in comparison to the journals that are far or too far away from the efficiency frontier.

If we look at the efficiency scores again, it can be seen that journal No. 42 is in last place with an efficiency score of 3.82%. Journal No. 16 with 4.06%, Journal No. 29 with 5.14%, and Journal No. 53 with 7.01% are other journals having very low efficiency scores. Like these journals, other inefficient journals (all journals with efficiency scores less than 100%) should reconsider their performance.

The results also include information on the reference groups of inefficient journals and how often efficient journals are cited as a reference for inefficient journals. It is possible to see this information in the “Benchmarks” column and reach the information about which inefficient decision units are similar to which units in the reference groups and how much improvement should be made here. However, this is not the case for efficient decision units. Because it is against the nature of DEA to write an efficient decision unit as a linear combination of other efficient units, as well as an inefficient unit (Doğan; Tanç, 2008Dogan, N. Ö.; Tanç, A. Konaklama isletmelerinde veri zarflama analizi yöntemiyle faaliyet denetimi: kapadokya örnegi. Atatürk Üniversitesi Iktisadi ve Idari Bilimler Dergisi, v. 22, n. 1, p. 239-259, 2008.).

Looking at the “Benchmarks” column; journal numbered 37 is sixty-seven times referenced by inactive journals for comparison while journal numbered 13 is fifty-four times, journal number 57 is fifteen times, journal number 67 is thirty-four times, journal number 83 is eighteen times, journal number 80 is fourteen times, journal number 17 is forty-five times, journal number 43 is twenty-two times, journal number 85 is three times, journal number 39 is one time, and journal number 90 is eight times. Keeping in mind that journal No. 37, Journal No. 13, Journal No. 83, Journal No. 17, Journal No. 43 and Journal No. 39 are Q2, then it is noteworthy that journal No. 67 is a reference unit for thirty-four journals. While most of the Q2 or even Q1 journals are inefficient, it can be concluded that journal No. 67, which is a Q3 journal, has succeeded in obtaining its outputs by using the inputs/resources optimally. It is possible to make similar comments for other journals.

If we look again at the “Benchmarks” column, focusing on some of the inefficient journals; It is seen that journals 13 and 17 are included in the reference group of the inefficient journal No. 36. The values in parentheses show how the inefficient unit is similar to the units in the reference set and how much it should take them as an example. Journal No. 36; is similar to journal No. 13 by 90%, and journal No. 17 by 220%, and if it makes improvements by taking these journals as an example at these rates, it will be efficient. If we look at journal numbered 88; It is seen that there are journals No. 17 and 80 in their reference group. Journal No. 88 can make improvements by taking journal No. 17 as a sample of 75% and journal No. 80 by 35%. It is possible to make similar comments for other inefficient journals like journals No. 36 and 88. As an example, the calculation of the target value for one of the entries in journal No. 36, “Total Docs. 2019” is given below:

194 , 4 = ( 0 , 90 * 40 ) + ( 2 , 2 * 72 )

The value of “194.4” in the above equation shows the value that Journal No. 36 should target in order to be efficient. “0.90” indicates the weight of journal No. 13, “40” indicates the input value of Journal No. 13; “2.2” indicates the weight of Journal No. 17, and “72” represents the input value of Journal No. 17. The model proposes that Journal No. 36 reduce the number of articles published annually from 203 to 194. This indicates a reduction in the number of articles by 4.42%. Target input values for other inactive journals can be found using a similar approach. In Table 5, real input values and targeted input values for all journals are presented. The same comments and calculations made for 2019 can also be made for 2020.

Table 5
Real Input Values and Target Input Values for Journals (2019-2020).

Discussion

The findings obtained as a result of the DEA method give very realistic results in terms of guiding the journal editors and reviewers. DEA is a very useful tool in evaluating the relative efficiency of decision-making units. In this study, efficiency measurements of academic journals operating in Turkey and scanned in Web of Science indexes for the years 2019 and 2020 were made. It is expected that the results obtained from the study will lead the editors of the journals to be more selective about whether or not to evaluate the articles submitted for the review, and also to the referees to be more rigorous in their evaluations. With carefully selected indicators (input/output variables), DEA identifies the variables that cause inefficiency and makes suggestions about the improvements that inefficient units can do to be efficient.

In the study, 109 journals operating in Turkey and scanned in Web of Science indexes were evaluated within the scope of efficiency measurement, 11 of them for 2019 and 7 for 2020 were found to be efficient. Six of these 11 journals efficient in 2019 are Q2 and seven are Q3 journals. In 2020, one of the 7 journals is Q1, two of them are Q2, and the other four are Q3. Inefficient 98 (2019) and 102 (2020) journals obtained efficiency scores according to their proximity to and distance from the efficiency frontier. However, it is easier for those who are very close to the efficiency frontier to make improvements to be efficient compared to the journals that are far or too far from the efficiency limit. A number of suggestions have been made for inefficient units to be efficient. Due to the use of the input-oriented DEA model in the study, while the output variables are kept constant, it is stated at what rate reductions will be made in the input variables. It is also shown to what extent inefficient journals can reduce the “Total Documents.”, “Total Documents. (3 years)” and “Total References.” entries. It is important to what extent the resources are used optimally to be efficient. In this context, journal editors are shown how to optimize the usage levels of these three entries.

Conclusion

Today’s intensely competitive environment and changing economic conditions require efficient use of resources. The survival of the decision-making units depends on the extent to which they achieve their set goals and use their resources efficiently. Information about efficiency can be obtained by making a comparison between the inputs and outputs of the observed values and the inputs and outputs of optimal values. Nowadays, the concept of “performance” is gaining importance in all sectors. In the most general terms, performance can be defined as the degree of success achieved by a business in a certain period of time. In other words, performance is the quantitative and qualitative expression of where an individual, a group, or an enterprise can reach the intended goal with that job. The aim of this study is to evaluate the performance of journals operating in Turkey and included in the Web of Science indexes by DEA method, and then to compare the data of 2019 and 2020 to determine the effect of the Covid-19 outbreak on the performance of these journals.

In future studies on this subject, more comprehensive DEA analyses can be made by including both the following years due to the ongoing Covid-19 pandemic and previous years to make the study more consistent. In addition, the scope of the study can be further expanded by including journals operating in other countries.

Como citar este artigo/How to cite this article

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Publication Dates

  • Publication in this collection
    02 Dec 2022
  • Date of issue
    2022

History

  • Received
    25 Apr 2022
  • Accepted
    11 May 2022
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