Scientific and Technological Research Article
Patterns of scientific communication on E-commerce: a bibliometric study in the Scopus database
Patrones de comunicación científica sobre E-commerce: un estudio bibliométrico en la base de datos Scopus
Fernando Ledesma1 
*, Beltrán Enrique Malave
González2 *
ABSTRACT
Research requires policies that help define the lines of work and that favor the production of knowledge in areas that interest the states. E-commerce is carried out through the Internet as any transaction or exchange of commercial information based on data transmission. This manuscript aims to describe the global, regional and national trends and patterns of scientific production in E-commerce in Scopus. We applied a descriptive bibliometric method (cross-sectional descriptive observation) of the scientific production on E-commerce indexed in the Scopus database from 2011 to 2020. The universe consisted of 20667 documents. A sustained increase in scientific production was evidenced after 2015. The conference and original articles stood out regarding the number of documents. The most cited documents were review articles, books, and original articles. Statistically significant correlations were found between the analyzed productivity and impact indicators. After 2018, more works on e-commerce were published; but more citations of works published before this year were generated. That is, there is an expansion of E-commerce and, in turn, a decrease in the pattern of literature consumption. Most of the articles on e-commerce were contributed by Chinese authors, followed by those from the United States and India.
Keywords: Bibliometrics, E-commerce, research trends.
JEL classification: L81; R39.
RESUMEN
La investigación requiere de políticas que ayuden a definir las líneas de trabajo y que se favorezca la producción de conocimiento en las áreas que sean de interés para los estados. El e-commerce que se lleva a cabo a través de internet como cualquier forma de transacción o intercambio de información comercial basada en la transmisión de datos a través de esta red de comunicación. Este manuscrito tiene como propósito describir las tendencias y patrones mundiales, regionales y nacionales de la producción científica en E-commerce en Scopus. En dicho propósito se utilizó un método bibliométrico descriptivo (observacional descriptivo de corte transversal) de la producción científica en E-commerce contenida en la base de datos Scopus en el periodo de 2011-2020. El universo estuvo constituido por 20667 documentos. Se evidenció un aumento de la producción científica sostenido luego del 2015; sobresalen en cuanto al número de documentos los artículos originales de conferencia y los artículos originales, los documentos más citados fueron los artículos de revisión seguidos de los libros y los artículos originales. Se encontraron correlaciones estadísticamente significativas entre los indicadores de productividad e impacto analizados. Después de 2018 se publicaron más trabajos sobre e-commerce, pero se generaron más citas de trabajos publicados previamente a este año, es decir hay una expansión del conocimiento en el área objeto de estudio y a su vez un decrecimiento en el patrón de consumo de la literatura. La mayoría de los artículos sobre comercio electrónico fueron aportados por autores chinos, seguidos por los de Estados Unidos e India.
Palabras clave: Bibliometría, E-commerce, tendencias en investigación.
Clasificación JEL: L81; R39.
Received: 10-02-2022 Revised: 12-04-2022 Accepted: 19-07-2022 Published: 27-07-2022
Editor: Carlos Alberto Gómez Cano, https://orcid.org/0000-0003-0425-7201
1Unilever Argentina, Buenos Aires, Argentina.
2Universidad Abierta Interamericana, Buenos Aires, Argentina.
Cite as: Ledesma, F. y Malave, B. (2022). Patterns of scientific communication on E-commerce: a bibliometric study in the Scopus database. Región Científica, 1(1), 202213. https://doi.org/10.58763/rc202214
INTRODUCTION
The market dynamics have led organizations to focus on customer satisfaction, where information and communication technologies have become fundamental tools for selling goods and services online, known as e-commerce (Sanabria et al., 2016).
There are significant differences between traditional and online shopping; the most crucial difference is the technological interactive degree (Sánchez and Arroyo, 2016). This component has prompted academic analysis to use technology adoption models to study the behavior of virtual consumers in countries or regions with different focuses or study areas, such as in tourism (Escobar-Rodríguez & Carvajal-Trujillo, 2014; Mohamed Fadel Bukhari et al., 2013); online banking (Vásquez, 2021) or general shopping (Tan et al., 2013).
Although the application context of these studies is typically presented in countries with advanced development in new information technologies, the Internet, and e-commerce (Europe, North America, and Asian countries like China and South Korea), few studies describe this dynamic in developing countries. This lack has hindered advanced theoretical research (Mesías et al., 2011; Sánchez and Arroyo, 2016).
While it is true that the found studies confirm the moderating effect of culture or other social factors, few studies consider the differences between developed countries and developing or third-world countries (Tan et al., 2013; Yoon, 2009).
Research requires policies that help define work lines and favor knowledge production in areas of interest to states (Zahra et al., 2021). Achieving this is crucially dependent on scientific collaboration, involving researchers' interaction to exchange skills, competencies, or resources, which leads to results surpassing those achieved by individual efforts (Katz and Martin, 2016). This collaboration becomes especially important in countries without the resources to produce research addressing their research needs (Ynalvez & Shrum, 2014), particularly in the e-commerce area, where multidisciplinary work is required.
The first step to enhancing global research capacity is better understanding trends in research publications. To date, few analyses objectively document global e-commerce research trends and patterns. The degree of e-commerce adoption development varies worldwide depending on the geographical area (Sánchez and Arroyo, 2016). In this scenario, considering the importance of bibliometrics and its subset, scientometrics, in decision-making, an analysis of scientific publication behavior becomes vitally important (López, 2017; Pal, 2020).
There needs to be more knowledge of scientific production in e-commerce from the perspective of its quantity, evolution, quality, specialization, performance, and scientific collaboration. Likewise, developing research that provides useful bibliometric information could become a tool for decision-making based on the best scientific evidence.
The research aimed to describe global, regional, and national trends and patterns of scientific production in E-commerce contained in Scopus from a scientometric perspective. For this purpose, a descriptive bibliometric design (Descriptive Observational cross-sectional) was used for the scientific production in E-commerce from 2011-2020. The universe consisted of 20,667 documents from the Scopus database.
Data Source
Scopus (http://www.scopus.com/home.url), created in 2004 by Elsevier B. V., is the largest abstract and citation database of peer-reviewed literature and high-quality web sources, covering about 25,100 serial publication titles from more than 5,000 publishers; 23,452 of which are peer-reviewed journals. Scopus offers the most comprehensive overview of the world's research output in science, technology, medicine, social sciences, arts, and humanities (Elsevier, 2021a).
Search Strategy
To select the scientific production on E-commerce, the following search strategy was used: TITLE-ABS-KEY(E-commerce) OR TITLE-ABS-KEY(e-commerce) AND (LIMIT-TO (PUBYEAR,2020) OR LIMIT-TO (PUBYEAR,2019) OR LIMIT-TO (PUBYEAR,2018) OR LIMIT-TO (PUBYEAR,2017) OR LIMIT-TO (PUBYEAR,2016) OR LIMIT-TO (PUBYEAR,2015) OR LIMIT-TO (PUBYEAR,2014) OR LIMIT-TO (PUBYEAR,2013) OR LIMIT-TO (PUBYEAR,2012) OR LIMIT-TO (PUBYEAR,2011)).
|
Table 1. Main bibliometric indicators |
|
|
Variable |
Concepto |
|
Number of documents (Ndoc) |
Total number of documents |
|
Type of documents |
Ranking according to document classification by the Scopus database total number of documents |
|
Percentage of citable documents |
Percentage of documents belonging to the typology of Originals, Revisions, and Conference Proceedings, concerning the total number of documents. |
|
Number of Citations (Ncit) |
Number of citations received for any document |
|
Citations per document (Cpd) |
Number of citations divided by document number |
|
International collaboration (Ncolab) |
A percentage of the production published in collaboration is considered an article with international collaboration when authors from different countries participate. |
|
H-index |
The H-index is the number of articles (h) that have received at least h citations. It is used to quantify the productivity and scientific impact of aggregates. |
|
Thematic Specialization |
It is the relative effort that the country dedicates to a specific discipline.
The Subject Matter Specialization Rate (TTR) is calculated as follows:
The Thematic Specialization Index (TSI) is calculated as follows: TSI = (TSI – 1) / (TSI + 1)
TSI values (between -1 and 1): The 0 represented the position of the world. Values above 0 indicate greater specialization of scientific production concerning the world. Concerning the world. Values below 0 indicate lower specialization concerning the world. |
|
Number of documents per million inhabitants |
Number of documents between the inhabitants for the aggregates (countries) per million. |
|
Number of documents per Gross Domestic Product per capita of the country |
Number of documents between Gross Domestic Product per capita for the aggregates (countries). |
|
Number of documents per country's Ease of Doing Business Index |
Number of documents among the Ease of Doing Business Index provided by the World Bank (Banco Mundial, 2021) for aggregates (countries). |
Source: Own elaboration.
Documents were downloaded into a database from Scopus in RIS (Research Information Systems) format. They were then processed using various programs (Bibexel, Excel, and Access) to obtain bibliometric indicators according to aggregates.
Network Visualization: The co-occurrence matrices for analyzing social networks among countries and terms were developed with the Bibexel and VOSviewer 1.6.17 programs (https://www.vosviewer.com/) to visualize the relationships within these networks. Field normalization for countries and keywords was carried out, specifically focusing on the latter, selecting terms with an appearance frequency of 700 or more.
The co-occurrence of categories was determined based on the categories to which the resources (journals, conference proceedings) belonged and where the documents were published, following the categories provided by Scopus for journals termed All Science Journal Classification Codes (ASJC) (Elsevier, 2021b). Terms with an appearance frequency of 300 or more were selected.
Statistical Analysis: Descriptive statistical techniques were used (absolute numbers and percentages) as well as inferential statistics. Differences will be considered significant with p-values less than 0.05. Results for the variables will be processed using Microsoft Excel and the SPSS statistical package.
RESULTS
The distribution of the 20,667 documents on e-commerce from 2011 to 2020 is shown in Figure 1. The production of literature exhibits a rising trend, which becomes more pronounced after 2015. As for international collaboration, it has been increasing steadily and gradually each year. On the other hand, citations display a seasonality, with a noticeable decline after 2018.
Figure 1.
Worldwide distribution of scientific production, number of citations, and international e-commerce collaboration by year.
Source: Own elaboration.
The year-on-year growth rate of global scientific production in e-commerce by year, was positive in most years, except in 2013, 2013, and 2015. Table 2 shows the distribution according to the type of documents, where original conference papers, original articles, and book chapters stand out; the most cited types of documents were review articles, followed by books and original articles.
|
Table 2. Distribution of worldwide scientific production in e-commerce according to the type of documents. |
|||
|
Tipo de documento |
Ndoc |
Ncit |
Cpd |
|
Original conference article |
9962 |
37288 |
3,74 |
|
Original article |
8723 |
96742 |
11,09 |
|
Book Chapter |
838 |
1865 |
2,23 |
|
Conference review article |
482 |
1 |
0,00 |
|
Review |
337 |
6261 |
18,58 |
|
Book |
122 |
2006 |
16,44 |
|
Editorial |
50 |
186 |
3,72 |
|
Short communication |
37 |
533 |
14,41 |
|
Others |
116 |
301 |
2,59 |
|
Total |
20667 |
145183 |
7,02 |
Source: Own elaboration.
Table 3 shows the distribution of countries from 2011 to 2020; China is the most productive country with a share in 30.72% of the documents, followed by the United States (14.55%), India (11.78%), United Kingdom (4.16%).
|
Table 3. Indicators of productivity, collaboration, leadership, and scientific impact of scientific production in e-commerce by country. |
||||||
|
Quartile |
Country |
%Ndoc |
%Ncolab |
%Lider |
Cpd |
H-Index |
|
Q1 |
China |
30,72% |
19,7% |
94,5% |
5 |
67 |
|
United States |
14,55% |
42,3% |
71,7% |
14 |
84 |
|
|
India |
11,78% |
9,7% |
95,6% |
5 |
40 |
|
|
United Kingdom |
4,16% |
49,7% |
65,9% |
14 |
50 |
|
|
Indonesia |
3,49% |
9,6% |
97,9% |
2 |
16 |
|
|
Germany |
3,29% |
34,8% |
81,0% |
10 |
42 |
|
|
Taiwan |
3,05% |
23,6% |
90,0% |
12 |
39 |
|
|
Malaysia |
2,73% |
31,4% |
84,4% |
6 |
26 |
|
|
Australia |
2,70% |
56,0% |
62,1% |
13 |
42 |
|
|
Italy |
2,28% |
37,9% |
79,7% |
10 |
32 |
|
|
Canada |
2,11% |
51,7% |
64,5% |
15 |
38 |
|
|
Spain |
2,00% |
38,7% |
77,0% |
14 |
38 |
|
|
South Korea |
1,98% |
37,8% |
75,9% |
14 |
36 |
|
|
Hong Kong |
1,82% |
73,5% |
49,3% |
18 |
39 |
|
|
France |
1,67% |
51,9% |
69,6% |
13 |
34 |
|
|
Japan |
1,63% |
30,0% |
79,2% |
4 |
19 |
|
|
Iran |
1,38% |
21,3% |
88,8% |
7 |
24 |
|
|
Brazil |
1,37% |
24,3% |
91,2% |
5 |
19 |
|
|
Singapore |
1,20% |
63,7% |
62,1% |
13 |
32 |
|
|
Netherlands |
1,10% |
57,9% |
64,5% |
13 |
27 |
|
|
Turkey |
1,00% |
27,1% |
84,5% |
7 |
19 |
|
|
Poland |
1,00% |
22,2% |
87,4% |
6 |
18 |
|
|
Saudi Arabia |
0,91% |
52,9% |
66,1% |
10 |
23 |
|
|
Russian Federation |
0,88% |
15,5% |
90,1% |
2 |
11 |
|
|
Greece |
0,75% |
28,8% |
84,6% |
8 |
20 |
|
|
Thailand |
0,75% |
22,1% |
87,7% |
5 |
14 |
|
|
Portugal |
0,70% |
37,5% |
82,6% |
8 |
18 |
|
|
South Africa |
0,63% |
25,4% |
83,1% |
5 |
15 |
|
|
Pakistan |
0,60% |
50,8% |
76,6% |
7 |
15 |
|
|
Belgium |
0,58% |
49,2% |
70,0% |
12 |
19 |
|
|
Viet Nam |
0,58% |
50,4% |
68,9% |
8 |
16 |
|
|
Sweden |
0,56% |
46,6% |
67,2% |
13 |
21 |
|
|
Switzerland |
0,56% |
54,8% |
67,8% |
14 |
20 |
|
|
Q2 |
Czech Republic |
0,53% |
21,1% |
84,4% |
3 |
9 |
|
Austria |
0,52% |
60,7% |
62,6% |
11 |
17 |
|
|
Romania |
0,50% |
16,3% |
92,3% |
3 |
10 |
|
|
Jordan |
0,48% |
34,3% |
83,8% |
7 |
13 |
|
|
Israel |
0,47% |
42,3% |
81,4% |
13 |
17 |
|
|
Finland |
0,46% |
48,4% |
63,2% |
10 |
14 |
|
|
Morocco |
0,45% |
10,8% |
94,6% |
2 |
7 |
|
|
United Arab Emirates |
0,44% |
47,3% |
73,6% |
9 |
15 |
|
|
Bangladesh |
0,39% |
22,2% |
92,6% |
4 |
9 |
|
|
Egypt |
0,39% |
46,3% |
70,0% |
6 |
10 |
|
|
Nigeria |
0,37% |
54,5% |
75,3% |
7 |
12 |
|
|
Ukraine |
0,35% |
29,2% |
87,5% |
8 |
14 |
|
|
New Zealand |
0,33% |
57,4% |
66,2% |
7 |
12 |
|
|
Ireland |
0,30% |
41,0% |
70,5% |
6 |
11 |
|
|
Norway |
0,28% |
68,4% |
57,9% |
16 |
18 |
|
|
Mexico |
0,25% |
44,2% |
73,1% |
6 |
9 |
|
|
Slovakia |
0,24% |
44,0% |
82,0% |
6 |
11 |
|
|
Colombia |
0,23% |
66,7% |
77,1% |
4 |
8 |
|
|
Macau |
0,21% |
59,1% |
56,8% |
12 |
10 |
|
|
Denmark |
0,20% |
54,8% |
64,3% |
12 |
13 |
|
|
Iraq |
0,20% |
48,8% |
70,7% |
5 |
9 |
|
|
Philippines |
0,19% |
32,5% |
82,5% |
3 |
5 |
|
|
Chile |
0,18% |
64,9% |
83,8% |
15 |
10 |
|
|
Tunisia |
0,18% |
35,1% |
94,6% |
8 |
9 |
|
|
Lithuania |
0,17% |
31,4% |
74,3% |
9 |
9 |
|
|
Serbia |
0,16% |
50,0% |
79,4% |
5 |
5 |
|
|
Oman |
0,16% |
63,6% |
57,6% |
8 |
9 |
|
|
Sri Lanka |
0,15% |
12,9% |
93,5% |
2 |
4 |
|
|
Qatar |
0,15% |
77,4% |
48,4% |
6 |
7 |
|
|
Peru |
0,14% |
24,1% |
82,8% |
2 |
5 |
|
|
Hungary |
0,14% |
58,6% |
79,3% |
17 |
10 |
|
|
Slovenia |
0,13% |
48,1% |
59,3% |
6 |
8 |
|
|
Lebanon |
0,13% |
59,3% |
70,4% |
6 |
8 |
|
|
Croatia |
0,13% |
23,1% |
92,3% |
2 |
4 |
|
|
Q3 |
Cyprus |
0,12% |
60,0% |
84,0% |
11 |
6 |
|
Kuwait |
0,12% |
40,0% |
72,0% |
12 |
8 |
|
|
Kazakhstan |
0,12% |
33,3% |
95,8% |
1 |
3 |
|
|
Algeria |
0,11% |
50,0% |
86,4% |
8 |
5 |
|
|
Ghana |
0,10% |
38,1% |
81,0% |
7 |
7 |
|
|
Ecuador |
0,10% |
45,0% |
80,0% |
4 |
3 |
|
|
Bulgaria |
0,09% |
15,8% |
84,2% |
2 |
3 |
|
|
Latvia |
0,09% |
16,7% |
88,9% |
2 |
3 |
|
|
Argentina |
0,08% |
70,6% |
58,8% |
7 |
5 |
|
|
Bahrain |
0,08% |
31,3% |
87,5% |
5 |
5 |
|
|
Luxembourg |
0,06% |
84,6% |
53,8% |
8 |
5 |
|
|
Uzbekistan |
0,05% |
36,4% |
72,7% |
8 |
3 |
|
|
Estonia |
0,05% |
60,0% |
90,0% |
1 |
3 |
|
|
North Macedonia |
0,04% |
33,3% |
77,8% |
9 |
2 |
|
|
Zimbabwe |
0,04% |
33,3% |
66,7% |
1 |
2 |
|
|
Palestine |
0,04% |
75,0% |
75,0% |
6 |
4 |
|
|
Costa Rica |
0,04% |
12,5% |
100,0% |
6 |
3 |
|
|
Myanmar |
0,04% |
25,0% |
87,5% |
8 |
3 |
|
|
Georgia |
0,03% |
57,1% |
42,9% |
12 |
3 |
|
|
Cuba |
0,03% |
85,7% |
71,4% |
7 |
2 |
|
|
Bosnia and Herzegovina |
0,03% |
57,1% |
71,4% |
3 |
2 |
|
|
Sudan |
0,03% |
50,0% |
83,3% |
0 |
1 |
|
|
Trinidad and Tobago |
0,03% |
66,7% |
33,3% |
11 |
4 |
|
|
Ethiopia |
0,03% |
50,0% |
66,7% |
1 |
1 |
|
|
Kenya |
0,02% |
100,0% |
20,0% |
35 |
4 |
|
|
Botswana |
0,02% |
60,0% |
60,0% |
4 |
2 |
|
|
Albania |
0,02% |
40,0% |
80,0% |
1 |
2 |
|
|
Jamaica |
0,02% |
50,0% |
75,0% |
1 |
2 |
|
|
Fiji |
0,02% |
25,0% |
100,0% |
5 |
2 |
|
|
Tanzania |
0,02% |
50,0% |
100,0% |
4 |
3 |
|
|
Belarus |
0,02% |
100,0% |
25,0% |
3 |
2 |
|
|
Nepal |
0,01% |
33,3% |
100,0% |
19 |
2 |
|
|
Azerbaijan |
0,01% |
0,0% |
100,0% |
0 |
1 |
|
|
Cameroon |
0,01% |
100,0% |
100,0% |
1 |
1 |
|
|
Q4 |
Yemen |
0,01% |
100,0% |
33,3% |
30 |
2 |
|
Brunei Darussalam |
0,01% |
66,7% |
66,7% |
0 |
0 |
|
|
Libya |
0,01% |
33,3% |
66,7% |
1 |
1 |
|
|
Iceland |
0,01% |
100,0% |
66,7% |
18 |
2 |
|
|
Guatemala |
0,01% |
100,0% |
33,3% |
21 |
3 |
|
|
Kyrgyzstan |
0,01% |
33,3% |
66,7% |
0 |
1 |
|
|
Papua New Guinea |
0,01% |
100,0% |
66,7% |
22 |
3 |
|
|
Mongolia |
0,01% |
0,0% |
100,0% |
1 |
1 |
|
|
Uganda |
0,01% |
50,0% |
50,0% |
2 |
1 |
|
|
Namibia |
0,01% |
0,0% |
100,0% |
0 |
0 |
|
|
Uruguay |
0,01% |
50,0% |
50,0% |
2 |
1 |
|
|
Swaziland |
0,01% |
50,0% |
50,0% |
7 |
2 |
|
|
Malta |
0,01% |
50,0% |
50,0% |
5 |
1 |
|
|
Zambia |
0,01% |
100,0% |
50,0% |
11 |
2 |
|
|
Mauritius |
0,01% |
50,0% |
100,0% |
18 |
1 |
|
|
Montenegro |
0,01% |
50,0% |
100,0% |
1 |
1 |
|
|
Monaco |
0,01% |
50,0% |
50,0% |
7 |
2 |
|
|
Liechtenstein |
0,01% |
100,0% |
0,0% |
77 |
2 |
|
|
Bhutan |
0,00% |
0,0% |
100,0% |
1 |
1 |
|
|
Venezuela |
0,00% |
100,0% |
0,0% |
19 |
1 |
|
|
Armenia |
0,00% |
100,0% |
0,0% |
3 |
1 |
|
|
Dominican Republic |
0,00% |
100,0% |
0,0% |
1 |
1 |
|
|
Moldova |
0,00% |
100,0% |
0,0% |
0 |
0 |
|
|
Rwanda |
0,00% |
100,0% |
0,0% |
12 |
1 |
|
|
Guam |
0,00% |
100,0% |
100,0% |
0 |
0 |
|
|
Maldives |
0,00% |
0,0% |
100,0% |
2 |
1 |
|
|
Senegal |
0,00% |
0,0% |
100,0% |
3 |
1 |
|
|
Cambodia |
0,00% |
100,0% |
100,0% |
1 |
1 |
|
|
Togo |
0,00% |
100,0% |
100,0% |
1 |
1 |
|
|
Mozambique |
0,00% |
100,0% |
0,0% |
2 |
1 |
|
|
Bolivia |
0,00% |
100,0% |
0,0% |
9 |
1 |
|
|
Syrian Arab Republic |
0,00% |
100,0% |
100,0% |
0 |
0 |
|
|
Guinea |
0,00% |
100,0% |
0,0% |
2 |
1 |
|
|
Puerto Rico |
0,00% |
100,0% |
100,0% |
10 |
1 |
|
Source: Own elaboration.
Figure 2 shows the thematic specialization and the H Index, where the size of the kernel is the number of documents in the country. Countries further up the axis have a greater impact, and those to the right have a greater subject specialization. In contrast, those further to the left of the axis have subject specialization below the world average.
Figure 2.
Thematic specialization and H-index of scientific production in e-commerce by country.
Source: Own elaboration.
Table 4 shows the correlation between productivity indicators (Ndoc, Ncolab, Lider) and impact (Ncit, H Index), where a statistically significant correlation was found in all cases with a p-value of less than 0.0001.
|
Table 4. Pearson correlation coefficient between indicators of productivity, collaboration, leadership, and scientific impact of scientific production in e-commerce by country. |
|||||
|
Variables |
Ndoc |
Ncolab |
Ncit |
Lider |
Índice_H |
|
Ndoc |
- |
||||
|
Ncolab |
0,9020 P<0,0001 |
- |
|||
|
Ncit |
0,8795 P<0,0001 |
0,9862 P<0,0001 |
- |
||
|
Lider |
0,9943 P<0,0001 |
0,8525 P<0,0001 |
0,8252 P<0,0001 |
- |
|
|
Índice_H |
0,7090 P<0,0001 |
0,8425 P<0,0001 |
0,8394 P<0,0001 |
0,6531 P<0,0001 |
- |
Source: Own elaboration.
When analyzing the matrix of co-occurrence of terms (Figure 3) and thematic categories (Figure 4) using the bibliometric maps, those with co-occurrence for terms 100 or more times and for categories 2 or more times were considered.
Figure 3.
Network of co-occurrence of terms of scientific production in e-commerce.
Source: Own elaboration.
Figure 4.
Category network of scientific production in e-commerce by country.
Source: Own elaboration.
There is a need for agile, cross-sector partnerships to research, test, and implement analyses in a "perpetual beta testing" model and have an honest conversation with the public about how to use their data best to meet these needs (Curtis, 2016). By focusing on the sustained growth of scientific production since 2016, and noting that only 3 of the prior periods experienced a negative growth rate, it can be inferred that, from an academic perspective, research and knowledge transfer over the past decade indicate that globalization and information technologies are advancing rapidly. E-commerce has become an innovative means to enhance the scientific performance of organizations (Cui et al., 2017; Shiau & Dwivedi, 2013).
Although journals published more articles in the earlier years, those specific to e-commerce began publishing more after 2007. This may be because information science journals were more consolidated and were better positioned to capture the trend. The citation count, however, shows a different trend. This trend could have been more perceptible in the initial years. However, the number of works published in information science journals surpassed those in e-commerce after 2001, even as both experienced significant growth spurts. However, around 2007, the number of articles published in e-commerce journals eclipsed those in information science journals (Hsu & Chiang, 2017).
Study Limitations
This research has some limitations. Firstly, the analysis was based on publications retrieved from the Scopus database using "e-commerce" as the main keyword. Therefore, the analysis should have considered more articles related to e-commerce that did not include this keyword. This is a significant limitation as an article about internet marketing that uses the e-business model is generally seen as highly related to e-commerce but might have yet to be retrieved using our search criteria. On the other hand, the results might differ if resources from other bibliographic databases were included in the study. Nevertheless, the findings presented in this study provide valuable insights for those interested in the general profile of high-quality e-commerce research.
CONCLUSIONS
This study employed bibliometric analysis to explore the research profile on e-commerce in resources indexed in Scopus. Likewise, the documents were analyzed, observing a mostly positive growth rate, more pronounced in the last 5 years of study.
Post-2018, more e-commerce papers were published, but more citations were generated from works published before this year. This implies expanding knowledge in the study area and, concurrently, decreasing the literature consumption pattern. However, the fact that the documents are more recent means they have had fewer opportunities to be cited.
The majority of the articles on e-commerce were contributed by Chinese authors, followed by those from the United States and India. This may reflect the popularity of e-commerce practices in the United States and China.
The analysis showed that e-commerce research is significantly developing globally, with large research hubs concentrating on almost all scientific production. This could be because these countries have the highest implementation of this commercial channel. Current studies are being carried out focused on emerging economies, as it is in these countries where SMEs constitute a large proportion of the business sector.
The marked polarization in productivity, collaboration, and impact indicators suggests a need to delve deeper into the interpretations of network structures and associated relational indicators. Moreover, it's crucial to advance the analysis of the value and implications that the results of this study might have for the management of scientific and technological activities of the countries analyzed on the subject, with a more interdisciplinary and comprehensive perspective.
From a qualitative perspective, it was observed that the main research fields on e-commerce were electronic markets, consumer behavior, social theory, and economic analysis. These focus on macro variables in the context of e-commerce adoption. The researchers' interest in these aspects of e-commerce adoption study may be explained by the fact that research has been centered and specialized in particular factors determining its use. It is oriented towards the business sector and consumers' perception and satisfaction with the variables involved in its adoption. These variables are perceived trust, attitude towards use, perceived ease of use, perceived utility, security, and innovative nature.
REFERENCES
Banco Mundial. (2021). Índice de facilidad para hacer negocios. Banco Mundial. https://datos.bancomundial.org/indicator/IC.BUS.EASE.XQ?end=2019&start=2019&view=bar
Cui, Y., Mou, J., y Liu, Y. (2017). Bibliometric and visualized analysis of research on e-commerce journals. Proceedings of the International Conference on Electronic Commerce, 1-7. https://doi.org/10.1145/3154943.3180441
Curtis, H. (2016). The evolving relationship between business and science. EuroScientist Journal. https://www.euroscientist.com/evolving-relationship-business-science/
Elsevier. (2021a). Content—How Scopus Works—Scopus. Elsevier. https://www.elsevier.com/solutions/scopus/how-scopus-works/content
Elsevier. (2021b). What is the complete list of Scopus Subject Areas and All Science Journal Classification Codes (ASJC)? Scopus: Access and use Support Center. https://service.elsevier.com/app/answers/detail/a_id/15181/supporthub/scopus/
Escobar-Rodríguez, T., y Carvajal-Trujillo, E. (2014). Online purchasing tickets for low cost carriers: An application of the unified theory of acceptance and use of technology (UTAUT) model. Tourism Management, 43, 70-88. https://doi.org/10.1016/j.tourman.2014.01.017
Hsu, C. y Chiang, C. (2017). Electronic Commerce Research Profiles: Comparing E-Commerce and Information Systems Journals. Pacific Asia Journal of the Association for Information Systems, 9(3), artículo 4. https://doi.org/10.17705/1pais.09303
Katz, J. y Martin, B. (2016). What is research collaboration? Res Policy, 26(1), 1-18. https://n9.cl/gfi6s
López, W. (2017). ¿Puede la cienciometría contribuir a la evaluación de la producción intelectual? Univ. Psychol, 16(4), 1-3. https://n9.cl/2afb1
Mesías, J., Giraldo, J. y Díaz, B. (2011). Aceptación del e-commerce en Colombia: Un estudio para la ciudad de Medellín. Revista Facultad de Ciencias Económicas: Investigación y Reflexión, 19(2), 9-23. https://doi.org/10.18359/rfce.2245
Mohamed Fadel Bukhari, S., Ghoneim, A., Dennis, C., y Jamjoom, B. (2013). The antecedents of travellers’ e-satisfaction and intention to buy airline tickets online: A conceptual model. Journal of Enterprise Information Management, 26(6), 624-641. https://doi.org/10.1108/JEIM-07-2013-0040
Pal, J. (2020). Understanding research productivity in the realm of evaluative scientometrics. Annals of Library and Information Studies (ALIS), 67(1), Art. 1. https://n9.cl/caf7n
Sanabria, V., Torres, L. y López, L. (2016). Comercio electrónico y nivel de ventas en las MiPyMEs del sector comercio, industria y servicios de Ibagué. Revista EAN, 80, 132-154. https://n9.cl/gcq9q
Sánchez, J. y Arroyo, F. (2016). Diferencias de la adopción del comercio electrónico entre países. Suma de Negocios, 7(16), 141-150. https://doi.org/10.1016/j.sumneg.2016.02.008
Shiau, W. y Dwivedi, Y. (2013). Citation and co-citation analysis to identify core and emerging knowledge in electronic commerce research. Scientometrics, 94(3), 1317-1337. https://doi.org/10.1007/s11192-012-0807-5
Sibiya, P. (2011). The role of research and development in industry and commerce. International Journal of Scientific and Engineering Research, 2(8), 1-10. https://n9.cl/rfsw3
Tan, K., Chong, S., y Lin, B. (2013). Intention to use internet marketing: A comparative study between Malaysians and South Koreans. Kybernetes, 42(6), 888-905. https://doi.org/10.1108/K-12-2012-0122
Vásquez, L. (2021). Propuesta de implementación de un switch transaccional financiero para administrar los medios de pagos de las entidades bancarias conectadas a NetPay S.A. [Tesis de Maestría, Universidad Católica de Santiago de Guayaquil]. http://repositorio.ucsg.edu.ec/handle/3317/15913
Ynalvez, M. y Shrum, W. (2014). Professional networks, scientific collaboration, and publication productivity in resource constrained research institutions in a developing country. Res Policy, 40(2), 204-216. https://n9.cl/qeaa4
Yoon, C. (2009). The effects of national culture values on consumer acceptance of e-commerce: Online shoppers in China. Information & Management, 46(5), 294-301. https://doi.org/10.1016/j.im.2009.06.001
Zahra, A., Nurmandi, A., Tenario, C., Rahayu, R., Benectitos, S., Mina, F. y Haictin, K. (2021). Bibliometric Analysis of Trends in Theory-related Policy Publications. Emerging Science Journal, 5(1), Art. 1. https://doi.org/10.28991/esj-2021-01261
FINANCING
No external financing.
CONFLICT OF INTEREST STATEMENT
The authors declare that they have no conflict of interest.
AUTHORSHIP CONTRIBUTION:
Conceptualization: Fernando Ledesma and Beltrán Enrique Malave González.
Research: Fernando Ledesma and Beltrán Enrique Malave González.
Methodology: Fernando Ledesma and Beltrán Enrique Malave González.
Writing - original draft: Fernando Ledesma and Beltrán Enrique Malave González.
Writing - revision and editing: Fernando Ledesma and Beltrán Enrique Malave González.