3.1. Data
This paper explores the characteristics of Mexican engineering publications that tend to receive the highest number of citations.
The study is based on all articles registered from 2004 to 2017 in the eighteen categories classified as engineering in the WoS Core Collection. We collapsed the eighteen categories into seven broad categories, like LOC [
22], for the purpose of analysis. The classification is shown in
Table 1.
It is important to point out that not all Mexican engineering research products are published in WoS. Other outcomes not considered in this paper include patents, books, proceedings, consulting reports, research projects, prototypes, startups, and articles in journals not included in WoS. Still, one of the main advantages of using WoS publications is that it is an objective measure that covers the whole country in all engineering fields, and other studies have and could use the same source and reproduce the results to compare them with other countries or research areas [
23].
Bibliometric data were collected from the WoS between March and April 2022. A specific query was written to capture publications related to engineering categories for the period 1970 to 2021:
CU=Mexico AND (SU=AEROSPACE OR SU=AGRICULTURAL OR SU=BIOMEDICAL OR SU=CELL & TISSUE OR SU=CHEMICAL OR SU=CIVIL OR SU=COMPUTER SCIENCE SOFTWARE OR SU=ELECTRICAL & ELECTRONIC OR SU=ENVIRONMENTAL OR SU=GEOLOGICAL OR SU=INDUSTRIAL OR SU=MANUFACTURING OR SU=MARINE OR SU=MECHANICAL OR SU=METALLURGY & METALLURGICAL OR SU=MULTIDISCIPLINARY OR SU=OCEAN OR SU=PETROLEUM)
The data contained 46,722 publications indexed in WoS, published by at least one Mexican author from 1970 to 2021. However, we decided to focus our attention only on articles published from 2004 to 2017. This is 13,322 articles and 135,927 citations. The decision was based first on the fact that the articles have received many more citations (mean 19.02 and standard deviation 31.50) than other types of publications. For example, the mean number of citations for proceedings is 0.8085 with a standard deviation of 0.0214; the mean number of citations for books is 0.4766; and the mean number of citations for other types of publications is 0.27. Second, we focus on articles from 2004 to 2017 because the number of articles started to grow significantly since 2004. According to the Science and Technology Indicators produced by the National Science Foundation (
https://ncses.nsf.gov/pubs/nsb20214/data, accessed on 16 August 2023), the average rate of global publication output in engineering from 1997 to 2003 was 0.05%, and in 2004 and 2005 it was 18% and 23%, respectively. Scopus was created in 2004. The growth rate between 1970 and 2003 was much lower (1.9%) than after 2004, which has been more than 3 times larger (6.6%). We restricted the analysis to 2017 because we consider the number of citations in a 5-year window, considering that a larger proportion of citations are received in the first 5 years of publication; this is the year of publication and the next 4 years [
24]. We also exclude all articles with nine or more authors, considering that these kinds of publications have different characteristics of collaboration [
25].
Figure 1 shows the evolution of publications per year and the number of citations per year.
As seen in
Table 2, there are significant differences in the number of publications and citations among different types of engineering. It is important to stress that the purpose of this analysis is to highlight differences, as there are more broad areas of knowledge [
26]. This does not mean that researchers are more or less productive, depending on the field of engineering. Moreover, 58% of the papers are classified in more than one type of engineering; given this overlap, the sum of the papers by type of engineering surpasses the total number of all papers in engineering.
Electronics is the type of engineering that congregates the highest number of publications, and the article with the highest number of citations in the sample is in this area. It is important to highlight that some articles in this area are also included in other fields such as chemistry and biologics, for example, those publications related to the design of instruments or equipment for pharmaceutical purposes. Thus, this area includes multidisciplinary articles.
The field with the highest average number of citations is biologics, which is also the field with the highest average number of coauthors, countries, and proportion of international collaboration, either bilateral or multilateral. On the contrary, civil engineering has the smallest number of publications, the average number of citations, and the least proportion of international collaboration.
3.1.1. Coauthorship
Figure 2 shows that over the period of analysis, there has been a steady growth (24%) in the average number of coauthors in all engineering research areas. This increasing trend in coauthorship is similar to what Thelwall and Maflahi [
27] found. However, as was seen in
Table 2, there are differences in the size of teams among the different types of engineering. The largest teams are in biologics followed by electronics, and the smallest are in mechanics and management. Related to the mean number of countries, the differences are quite small, so measuring this indicator by type of collaboration seems more appropriate.
3.1.2. Type of Collaboration
Figure 3 shows that most of the knowledge that is produced in engineering in Mexico involves collaboration, mainly international (56%), either bilateral or multilateral, and only 3% of the articles are solo-authored papers. Domestic collaboration is the largest form, but it is the one that has grown the least (158%); on the other side, multilateral collaboration used to be the smallest, but it is the one that has grown the most (511%) and already exceeded bilateral collaboration, which over the period of analysis grew 204%.
Many studies have shown that not all types of collaboration produce the same impact [
20,
28].
Table 3 shows that domestic collaboration is the type of collaboration that receives the least number of citations. Surprisingly, on average, solo-authored papers received even more citations than papers written under this type of collaboration. This contrasts with Wuchty et al. [
7], findings that suggest that teams produce exceptionally high-impact research in comparison with solo works.
More than half (56%) of all the knowledge that has been created in engineering in Mexico involves international collaboration, and this is the form of collaboration that, on average, has the highest impact, mainly multilateral collaboration.
Table 4 shows the total number of publications by type of engineering and type of collaboration. As can be seen, electronics is the type of engineering with the highest collaboration; only 2.11% of the knowledge production is solo-authored papers. Biologics is the field with the highest international collaboration, either bilateral or multilateral (71%). As was highlighted before, civil engineering has the smallest proportion of international collaboration.
3.2. Model
To study the impact of a paper, it is assumed that the baseline function is:
Two different proxies of a paper’s impact were considered. As in other papers such as Ruano-Ravina and Álvarez-Dardet [
29] and Guo et al. [
30], the total number of citations a paper has received is the first dependent variable. Considering that older papers have received more citations just because they have been published for more years, the number of citations a paper has received in the first five years of publication was the second dependent variable [
24]. X
i are the independent and control variables (a detailed description of each variable can be found in
Appendix A):
Variables related to the team of coauthors:
- ○
Number of coauthors;
- ○
Number of countries.
Variables related to the type of collaboration:
- ○
Solo authored (Solo);
- ○
Domestic collaboration (Dom);
- ○
Bilateral collaboration (Bi);
- ○
Multilateral collaboration (Multi).
Control variables:
- ○
At least one coauthor from the USA.
Variables related to the most productive institutions in Mexico:
- ○
UNAM (Universidad Nacional Autónoma de Mexico);
- ○
IPN (Instituto Politécnico Nacional);
- ○
UAM (Universidad Autónoma Metropolitana);
- ○
CINVESTAV (Centro de Investigación y de Estudios Avanzados del IPN);
- ○
IMP (Instituto Mexicano del Petróleo);
- ○
UDG (Universidad de Guadalajara);
- ○
UGU (Universidad de Guanajuato);
- ○
Other institution.
Ci is the error term.
The reason for including a coauthor from the USA as a control variable was because 48.9% of the papers with international collaboration have at least one coauthor from the USA. Narvaez-Berthelemot et al. [
31] also find that the main international collaborator of Mexican researchers is the USA. Controlling for the seven most productive universities as institutions of affiliation was necessary because there is a wide dispersion in the size and productivity of Mexican universities [
32].
Considering the nature of the data, a negative binomial (NB) model is used. This model is used when the dependent variable takes integer values and the variance is significantly greater than the mean. Moreover, NB models relate the dependent variable Y to one or more predictor variables, Xi, which can be quantitative or categorical. The procedure fits a weighted least squares model. Likelihood ratio tests were performed to test the significance of the model coefficients.
Table 5 shows the descriptive statistics of the variables used.
Table 6 shows the correlation among the nine variables in our models. As expected, there is a high correlation between multilateral collaboration and the number of countries, as well as between papers written with at least one USA coauthor and domestic and multilateral collaboration. Thus, none of the models include highly correlated variables. Moreover, to prove that our model has no autocorrelation problems, we submitted it to the serial correlation test of Wooldridge [
33]; the results confirm that our models do not have such a problem.
Different specification models were considered to analyze how the effect of one independent variable is moderated when other variables are included. Thus, five different models were run.
Model 1: Number of coauthors, controlling for coauthors from the USA;
Model 2: Number of countries, controlling for coauthors from the USA;
Model 3: Number of coauthors and number of countries, controlling for coauthors from the USA;
Model 4: Variables related to the type of collaboration;
Model 5: Number of coauthors, variables related to the type of collaboration, controlling for the most productive institutions in Mexico.
Alternative models, like models 1 to 4, were also run for the most productive institutions in Mexico. The results are similar to those discussed in the next section.