Today, the Big Data term has a multidimensional approach where five main characteristics stand out: volume, velocity, veracity, value and variety. It has changed from being an emerging theme to a growing research area. In this respect, this study analyses the literature on Big Data in the Economics, Econometrics and Finance field. To do that, 1.034 publications from 2015 to 2019 were evaluated using SciMAT as a bibliometric and network analysis software. SciMAT offers a complete approach of the field and evaluates the most cited and productive authors, countries and subject areas related to Big Data. Lastly, a science map is performed to understand the intellectual structure and the main research lines (themes)

Cobo Martín, Manuel Jesús

Gamboa-Rosales, Nadia Karina

López-Robles, José Ricardo

Ramirez-Rosales, Selene

Rodríguez-Salvador, Marisela

Procedia Computer Science

English

López-Robles, José-Ricardo

Gamboa-Rosales, Nadia-Karina

Ramírez-Rosales, Selene

Cobo-Martín, Manuel-Jesús

E-LIS repository

THE LAST FIVE YEARS OF BIG DATA RESEARCH IN 
ECONOMICS, ECONOMETRICS AND FINANCE: 
IDENTIFICATION AND CONCEPTUAL ANALYSIS
THE INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND 
QUANTITATIVE MANAGEMENT (ITQM)
November 3-6, 2019 / Granada (Spain)
JOSÉ RICARDO LÓPEZ-ROBLES, MARISELA RODRÍGUEZ-SALVADOR, NADIA KARINA GAMBOA-ROSALES, SELENE RAMIREZ-ROSALES, 
MANUEL JESÚS COBO
ITQM 2019
TABLE OF CONTENTS
1. Introduction
2. Methodology
3. Dataset
4. Conceptual Analysis
5. Conclusions
TABLE OF CONTENTS
1. Introduction
2. Methodology
3. Dataset
4. Conceptual Analysis
5. Conclusions
1. INTRODUCTION
CONTEXT
Big Data has evolved from being an emerging topic to a growing research 
area in business, science and education fields. 
The Big Data concept has a multidimensional approach, and it can be 
defined as a term describing the storage and analysis of large and complex 
data sets using a series of advanced techniques.
The professionals involved in this area of knowledge are seeking to uncover 
the conceptual structure of a research area of interest are worth and 
necessary. 
OBJECTIVE
The main aim of this contribution is to develop a bibliometric analysis to 
evaluate the performance and conceptual evolution of the Big Data Research 
in Economics, Econometrics and Finance from 2015 to 2019.
The analysis is developed using SciMAT.
TABLE OF CONTENTS
1. Introduction
2. Methodology
3. Dataset
4. Conceptual Analysis
5. Conclusions
2. METHODOLOGY
SOFTWARE TOOL
SciMAT was employed to develop a longitudinal conceptual science mapping 
analysis based on co-words bibliographic networks. 
METHODOLOGY STAGES
1. Detection of the research themes. Co-word analysis, followed by a 
clustering of keywords to topics/themes. The similarity between the 
keywords is assessed using the equivalence index.
2. Visualizing research themes and thematic network. Strategic 
diagram and thematic network (centrality and density). Research themes 
mapped in a two-dimensional strategic diagram and classified into four 
groups (Figure 1): i) motor, ii) basic/transversal, iii) highly developed-
isolated, and iv) emerging/declining 
3. Performance analysis. Relative contribution of the research themes to 
the whole research field: number of published documents, number of 
citations, and different types of bibliometric indices (h-index).
2. METHODOLOGY
(b) Thematic network(a) Strategic diagram
TABLE OF CONTENTS
1. Introduction
2. Methodology
3. Dataset
4. Conceptual Analysis
5. Conclusions
3. DATASET
CORPUS AND DATABASE
Big Data research documents published in the Scopus.
QUERY
TITLE-ABS-KEY ("big-data" OR "big data" OR "bigdata") AND (LIMIT-TO (SUBJAREA, "ECON")).
AND (LIMIT-TO (PUBYEAR, 2019) OR LIMIT-TO (PUBYEAR, 2018) OR LIMIT-TO (PUBYEAR,
2017) OR LIMIT-TO (PUBYEAR, 2016) OR LIMIT-TO (PUBYEAR, 2015)).
TIME PERIOD 
The corpus was evaluated in a single five-years period: 2015, 2016, 2017, 2018 and 2019.
CORPUS SIZE
 1,034 documents (articles, proceedings, reviews…), 5,1054 cites and 5,551 keywords.
 Citations count up to 28th July 2019.
 2015: 126 documents, 2,445 cites and 983 keywords.
 2016: 201 documents, 1,467 cites and 893 keywords.
 2017: 336 documents, 759 cites and 1,417 keywords.
 2018: 233 documents, 377 cites and 1,325 keywords.
 2019: 138 documents, 56 cites and 933 keywords.
DOCUMENTS AND CITATIONS BY YEAR
TABLE OF CONTENTS
1. Introduction
2. Methodology
3. Dataset
4. Conceptual Analysis
5. Conclusions
4. CONCEPTUAL ANALYSIS
Strategic diagram 2015-2019
4. CONCEPTUAL ANALYSIS
Theme Quadrant Documents Citations h-index
BIG-DATA-ANALYTICS-(BDA) 4 127 1,225 13
MACHINE-LEARNING-(ML) 3 85 221 8
DATA-SETS 3 71 294 9
SUPPLY-CHAIN-MANAGEMENTS-(SCM) 4 65 1,088 12
DATA-MINING 1 57 255 6
DATA-HANDLING 1 54 110 4
INTERNET-OF-THINGS-(IOT) 1 54 483 9
DECISION-MAKING-PROCESS 4 50 622 8
SECURITY-AND-PRIVACY 2 27 222 7
INFORMATION-AND-COMMUNICATION-TECHNOLOGY-(ICT) 2 17 116 4
DIGITALIZATION 2 15 98 3
CYBERSECURITY-LAW 2 12 19 2
The first period has lower number of publications than the other periods, we could 
identify ten themes related to the Big Data research field. In this regard, we could 
highlight five key themes (motor theme and basic and transversal themes) of the 
knowledge field: DATA-MINING, INTERNET-OF-THINGS-(IOT), DATA-HANDLING, 
SUPPLY-CHAIN-MANAGEMENTS-(SMC), DECISION-MAKING-PROCESS and BIG-
DATA-ANALYTICS-(BDA).
4. CONCEPTUAL ANALYSIS
4. CONCEPTUAL ANALYSIS
4. CONCEPTUAL ANALYSIS
4. CONCEPTUAL ANALYSIS
TABLE OF CONTENTS
1. Introduction
2. Methodology
3. Dataset
4. Conceptual Analysis
5. Conclusions
CONCLUSIONS
SUMMMARY
 An amount of 1,034 documents (articles, proceedings and reviews) were 
retrieved from the Scopus.
 The corpus was evaluated in a single five-years period.
 2015: 126 documents, 2,445 cites and 983 keywords.
 2016: 201 documents, 1,467 cites and 893 keywords.
 2017: 336 documents, 759 cites and 1,417 keywords.
 2018: 233 documents, 377 cites and 1,325 keywords.
 2019: 138 documents, 56 cites and 933 keywords.
 The impact achieved is summarized in the following indicators: 
 Average citations per publication: 3.40
 Sum of Times Cited (without self-citations): 5,104 (4,584) 
 Citing articles (without self-citations): 2,954 (2,609)
 h-index: 30 publications
CONCLUSIONS
MAIN CONCLUSION
 The size of literature related to Big Data research field showed a 
noticeable increase in the past decade (2015-2019). Given the large 
volume of publications and citations received in this field, it is expected 
that the use of these will be seen as part of other knowledge fields.
 The main themes used in the Big Data literature are: BIG-DATA-
ANALYTICS-(BDA), MACHINE-LEARNING-(ML), DATA-SETS, SUPPLY-
CHAIN-MANAGEMENTS-(SCM) and DATA-MINING.
FUTURE WORKS
 Evaluate the evolution and application of the research themes in other 
disciplines.
 Study the relationship to other disciplines as Business Intelligence and
Competitive Intelligence. 
THANK YOU
THE INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND 
QUANTITATIVE MANAGEMENT (ITQM)
Acknowledgments: This work was supported by European Union FEDER funds (TIN2016-75850-R). Additionally, the authors
acknowledge the support by DGRI-Dirección General de Relaciones Exteriores (Mexico) and CONACYT-Consejo Nacional de
Ciencia y Tecnología (Mexico) to carry out this research.
ITQM 2019
REFERENCES
Oussous A, Benjelloun FZ, Lahcen AA, Belfkih S, Big Data technologies: A survey, Journal of King Saud University-Computer and Information Sciences
2018; 30:431-448.
López-Robles JR, Otegi-Olaso JR, Porto Gómez I, Cobo MJ, 30 years of intelligence models in management and business: A bibliometric review, Int J Inf
Manage 2019; 48:22-38.
López-Robles JR, Otegi-Olaso JR, Arcos R, Gamboa-Rosales NK, Gamboa-Rosales H, Mapping the structure and evolution of JISIB: A bibliometric
analysis of articles published in the Journal of Intelligence Studies in Business between 2011 and 2017, J. Intell. Stud. Bus. 2018; 8.
López-Robles JR, Otegi-Olaso JR, Gamboa-Rosales NK, Gamboa-Rosales H, Cobo MJ, 60 Years of Business Intelligence: A Bibliometric Review from
1958 to 2017, New Trends in Intelligent Software Methodologies, Tools and Techniques: Proceedings of the 17th International Conference SoMeT_18,
IOS Press; 2018, p. 395.
McAfee A, Brynjolfsson E, Davenport TH, Patil DJ, Barton DC, Big data: the management revolution, Harv. Bus. Rev. 2012; 90:60-68.
López-Robles JR, Otegi-Olaso JR, Porto Gómez I, Gamboa-Rosales NK, Gamboa-Rosales H, Robles-Berumen H, Bibliometric Network Analysis to
Identify the Intellectual Structure and Evolution of the Big Data Research Field, International Conference on Intelligent Data Engineering and Automated
Learning, Springer; 2018, p. 113-120.
López-Robles JR, Guallar J, Otegi-Olaso JR, Gamboa-Rosales NK, El profesional de la información (EPI): bibliometric and thematic analysis (2006-2017),
El profesional de la información 2019; 28:e280417.
Cobo MJ, SciMat: herramienta software para el análisis de la evolución del conocimiento científico. Propuesta de una metodología de evaluación,
Granada: Universidad de Granada; 2012.
Cobo MJ, López‐Herrera AG, Herrera‐Viedma E, Herrera F, SciMAT: A new science mapping analysis software tool, J. Am. Soc. Inf. Sci. Technol. 2012;
63:1609-1630.
Martínez MA, Herrera M, López-Gijón J, Herrera-Viedma E, H-Classics: Characterizing the concept of citation classics through H-index, Scientometrics
2014; 98:1971-1983.
Gutiérrez-Salcedo M, Martínez MA, Moral-Munoz JA, Herrera-Viedma E, Cobo MJ, Some bibliometric procedures for analyzing and evaluating research
fields, Appl Intell 2018; 48:1275-1287.


The last five years of Big Data Research in Economics, Econometrics and Finance: Identification and conceptual analysis

Cobo, Manuel J.

RODIN - Repositorio de Objetos de Docencia e Investigación de la Universidad de Cádiz

E-LIS

THE LAST FIVE YEARS OF BIG DATA RESEARCH IN ECONOMICS, ECONOMETRICS AND FINANCE: IDENTIFICATION AND CONCEPTUAL ANALYSISTHE INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND QUANTITATIVE MANAGEMENT (ITQM)November 3-6, 2019 / Granada (Spain)JOSÉ RICARDO LÓPEZ-ROBLES, MARISELA RODRÍGUEZ-SALVADOR, NADIA KARINA GAMBOA-ROSALES, SELENE RAMIREZ-ROSALES, MANUEL JESÚS COBOITQM 2019TABLE OF CONTENTS1. Introduction2. Methodology3. Dataset4. Conceptual Analysis5. ConclusionsTABLE OF CONTENTS1. Introduction2. Methodology3. Dataset4. Conceptual Analysis5. Conclusions1. INTRODUCTIONCONTEXTBig Data has evolved from being an emerging topic to a growing research area in business, science and education fields. The Big Data concept has a multidimensional approach, and it can be defined as a term describing the storage and analysis of large and complex data sets using a series of advanced techniques.The professionals involved in this area of knowledge are seeking to uncover the conceptual structure of a research area of interest are worth and necessary. OBJECTIVEThe main aim of this contribution is to develop a bibliometric analysis to evaluate the performance and conceptual evolution of the Big Data Research in Economics, Econometrics and Finance from 2015 to 2019.The analysis is developed using SciMAT.TABLE OF CONTENTS1. Introduction2. Methodology3. Dataset4. Conceptual Analysis5. Conclusions2. METHODOLOGYSOFTWARE TOOLSciMAT was employed to develop a longitudinal conceptual science mapping analysis based on co-words bibliographic networks. METHODOLOGY STAGES1. Detection of the research themes. Co-word analysis, followed by a clustering of keywords to topics/themes. The similarity between the keywords is assessed using the equivalence index.2. Visualizing research themes and thematic network. Strategic diagram and thematic network (centrality and density). Research themes mapped in a two-dimensional strategic diagram and classified into four groups (Figure 1): i) motor, ii) basic/transversal, iii) highly developed-isolated, and iv) emerging/declining 3. Performance analysis. Relative contribution of the research themes to the whole research field: number of published documents, number of citations, and different types of bibliometric indices (h-index).2. METHODOLOGY(b) Thematic network(a) Strategic diagramTABLE OF CONTENTS1. Introduction2. Methodology3. Dataset4. Conceptual Analysis5. Conclusions3. DATASETCORPUS AND DATABASEBig Data research documents published in the Scopus.QUERYTITLE-ABS-KEY ("big-data" OR "big data" OR "bigdata") AND (LIMIT-TO (SUBJAREA, "ECON")).AND (LIMIT-TO (PUBYEAR, 2019) OR LIMIT-TO (PUBYEAR, 2018) OR LIMIT-TO (PUBYEAR,2017) OR LIMIT-TO (PUBYEAR, 2016) OR LIMIT-TO (PUBYEAR, 2015)).TIME PERIOD The corpus was evaluated in a single five-years period: 2015, 2016, 2017, 2018 and 2019.CORPUS SIZE 1,034 documents (articles, proceedings, reviews…), 5,1054 cites and 5,551 keywords. Citations count up to 28th July 2019. 2015: 126 documents, 2,445 cites and 983 keywords. 2016: 201 documents, 1,467 cites and 893 keywords. 2017: 336 documents, 759 cites and 1,417 keywords. 2018: 233 documents, 377 cites and 1,325 keywords. 2019: 138 documents, 56 cites and 933 keywords.DOCUMENTS AND CITATIONS BY YEARTABLE OF CONTENTS1. Introduction2. Methodology3. Dataset4. Conceptual Analysis5. Conclusions4. CONCEPTUAL ANALYSISStrategic diagram 2015-20194. CONCEPTUAL ANALYSISTheme Quadrant Documents Citations h-indexBIG-DATA-ANALYTICS-(BDA) 4 127 1,225 13MACHINE-LEARNING-(ML) 3 85 221 8DATA-SETS 3 71 294 9SUPPLY-CHAIN-MANAGEMENTS-(SCM) 4 65 1,088 12DATA-MINING 1 57 255 6DATA-HANDLING 1 54 110 4INTERNET-OF-THINGS-(IOT) 1 54 483 9DECISION-MAKING-PROCESS 4 50 622 8SECURITY-AND-PRIVACY 2 27 222 7INFORMATION-AND-COMMUNICATION-TECHNOLOGY-(ICT) 2 17 116 4DIGITALIZATION 2 15 98 3CYBERSECURITY-LAW 2 12 19 2The first period has lower number of publications than the other periods, we could identify ten themes related to the Big Data research field. In this regard, we could highlight five key themes (motor theme and basic and transversal themes) of the knowledge field: DATA-MINING, INTERNET-OF-THINGS-(IOT), DATA-HANDLING, SUPPLY-CHAIN-MANAGEMENTS-(SMC), DECISION-MAKING-PROCESS and BIG-DATA-ANALYTICS-(BDA).4. CONCEPTUAL ANALYSIS4. CONCEPTUAL ANALYSIS4. CONCEPTUAL ANALYSIS4. CONCEPTUAL ANALYSISTABLE OF CONTENTS1. Introduction2. Methodology3. Dataset4. Conceptual Analysis5. ConclusionsCONCLUSIONSSUMMMARY An amount of 1,034 documents (articles, proceedings and reviews) were retrieved from the Scopus. The corpus was evaluated in a single five-years period. 2015: 126 documents, 2,445 cites and 983 keywords. 2016: 201 documents, 1,467 cites and 893 keywords. 2017: 336 documents, 759 cites and 1,417 keywords. 2018: 233 documents, 377 cites and 1,325 keywords. 2019: 138 documents, 56 cites and 933 keywords. The impact achieved is summarized in the following indicators:  Average citations per publication: 3.40 Sum of Times Cited (without self-citations): 5,104 (4,584)  Citing articles (without self-citations): 2,954 (2,609) h-index: 30 publicationsCONCLUSIONSMAIN CONCLUSION The size of literature related to Big Data research field showed a noticeable increase in the past decade (2015-2019). Given the large volume of publications and citations received in this field, it is expected that the use of these will be seen as part of other knowledge fields. The main themes used in the Big Data literature are: BIG-DATA-ANALYTICS-(BDA), MACHINE-LEARNING-(ML), DATA-SETS, SUPPLY-CHAIN-MANAGEMENTS-(SCM) and DATA-MINING.FUTURE WORKS Evaluate the evolution and application of the research themes in other disciplines. Study the relationship to other disciplines as Business Intelligence andCompetitive Intelligence. THANK YOUTHE INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND QUANTITATIVE MANAGEMENT (ITQM)Acknowledgments: This work was supported by European Union FEDER funds (TIN2016-75850-R). Additionally, the authorsacknowledge the support by DGRI-Dirección General de Relaciones Exteriores (Mexico) and CONACYT-Consejo Nacional deCiencia y Tecnología (Mexico) to carry out this research.ITQM 2019REFERENCESOussous A, Benjelloun FZ, Lahcen AA, Belfkih S, Big Data technologies: A survey, Journal of King Saud University-Computer and Information Sciences2018; 30:431-448.López-Robles JR, Otegi-Olaso JR, Porto Gómez I, Cobo MJ, 30 years of intelligence models in management and business: A bibliometric review, Int J InfManage 2019; 48:22-38.López-Robles JR, Otegi-Olaso JR, Arcos R, Gamboa-Rosales NK, Gamboa-Rosales H, Mapping the structure and evolution of JISIB: A bibliometricanalysis of articles published in the Journal of Intelligence Studies in Business between 2011 and 2017, J. Intell. Stud. Bus. 2018; 8.López-Robles JR, Otegi-Olaso JR, Gamboa-Rosales NK, Gamboa-Rosales H, Cobo MJ, 60 Years of Business Intelligence: A Bibliometric Review from1958 to 2017, New Trends in Intelligent Software Methodologies, Tools and Techniques: Proceedings of the 17th International Conference SoMeT_18,IOS Press; 2018, p. 395.McAfee A, Brynjolfsson E, Davenport TH, Patil DJ, Barton DC, Big data: the management revolution, Harv. Bus. Rev. 2012; 90:60-68.López-Robles JR, Otegi-Olaso JR, Porto Gómez I, Gamboa-Rosales NK, Gamboa-Rosales H, Robles-Berumen H, Bibliometric Network Analysis toIdentify the Intellectual Structure and Evolution of the Big Data Research Field, International Conference on Intelligent Data Engineering and AutomatedLearning, Springer; 2018, p. 113-120.López-Robles JR, Guallar J, Otegi-Olaso JR, Gamboa-Rosales NK, El profesional de la información (EPI): bibliometric and thematic analysis (2006-2017),El profesional de la información 2019; 28:e280417.Cobo MJ, SciMat: herramienta software para el análisis de la evolución del conocimiento científico. Propuesta de una metodología de evaluación,Granada: Universidad de Granada; 2012.Cobo MJ, López‐Herrera AG, Herrera‐Viedma E, Herrera F, SciMAT: A new science mapping analysis software tool, J. Am. Soc. Inf. Sci. Technol. 2012;63:1609-1630.Martínez MA, Herrera M, López-Gijón J, Herrera-Viedma E, H-Classics: Characterizing the concept of citation classics through H-index, Scientometrics2014; 98:1971-1983.Gutiérrez-Salcedo M, Martínez MA, Moral-Munoz JA, Herrera-Viedma E, Cobo MJ, Some bibliometric procedures for analyzing and evaluating researchfields, Appl Intell 2018; 48:1275-1287.

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The last five years of Big Data Research in Economics, Econometrics and Finance: Identification and conceptual analysis

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