Big Data, Accounting and International Development: Trends and challenges
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Published
11-02-2022
Sonia Arroyo Esteban
Elena Urquía-Grande
Alberto Martínez de Silva
Raquel Pérez-Estébanez
https://orcid.org/0000-0002-8734-8303
Elena Urquía-Grande
Alberto Martínez de Silva
Raquel Pérez-Estébanez
Abstract
This article aims to show how the Big Data techniques application in accounting to monitor international cooperation projects are a green-field in the academic world. To obtain an exhaustive vision of the state of the art in academic research in this field, a bibliometric analysis has been carried out, based on multiple Web of Science searches, with focus on international development, Big Data and accounting, adding the holistic vision of the 17 SDGs or “Sustainable Development Goals” of the UN Agenda 2030. Research on Big Data, international development and accounting is a new field that has started in 2015 although academic literature is still scarce. Publications related to SDGs also begin on that date, but with much more prolific academic literature, without explicit references to the use of Big Data in accounting. The article finds deficiencies in existing academic research compared to other enterprise fields in which Big Data techniques are much more developed, and international organization reports lead this line of research, as opposed to the scholarly world. The main practical implication derived from the paper is the need to deepen in real cases of use outside the academic sphere as a starting point to develop this line of research. The development of this research area will help NPOs and governments to have a better accounting to evaluate the impact of their initiatives and cooperation projects. In addition to the bibliometric techniques used for the analysis of main publications, authors and relevant topics focused on this area of study, the authors consider a challenge and an opportunity to take the plunge into this field from academic world, which will undoubtedly improve decision-making in international development, emphasizing the need to gain momentum given the current state of greenfield.
How to Cite
Arroyo Esteban, S., Urquía-Grande, E., Martínez de Silva, A., & Pérez-Estébanez, R. (2022). Big Data, Accounting and International Development: Trends and challenges. Cuadernos De Gestión, 22(1), 193–213. https://doi.org/10.5295/cdg.211513sa
##plugins.themes.bootstrap3.article.details##
Keywords
Big data, International development, Accounting, Monitoring, Sustainable development goals, Bibliometrics
References
Adriaanse, L. S., & Rensleigh, C. (2011). Comparing Web of Science, Scopus and Google Scholar from an Environmental Sciences perspective. South African Journal of Libraries and Information Science, 77(2), 169-178. https://doi.org/10.7553/77-2-58
Adriaanse, L. S., & Rensleigh, C. (2013). Web of science, scopus and google scholar a content comprehensiveness comparison. Electronic Library, 31(6), 727-744. https://doi.org/10.1108/EL-12-2011-0174
Alcaide, G. G., & Ferri, J. G. (2014). La colaboración científica: Principales líneas de investigación y retos de futuro. Revista Espanola de Documentacion Cientifica, 37(4), e062. https://doi.org/10.3989/ redc.2014.4.1186
Amankwah-Amoah. (2016). Emerging economies, emerging challenges: Mobilising and capturing value from big data. Technological Forecasting and Social Change, 110, 167-174. https://doi.org/10.1016/j. techfore.2015.10.022
Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975. https://doi.org/10.1016/j.joi.2017.08.007
Arnaboldi, M., Busco, C., & Cuganesan, S. (2017). Accounting, accountability, social media and big data: revolution or hype? Accounting, Auditing and Accountability Journal, 30(4), 762-776. https://doi. org/10.1108/AAAJ-03-2017-2880
Baldwin, C. Y., Tribendis, J. J., & Clark, J. P. (1984). The Evolution of Market Risk in the U.S. Steel Industry and Implications for Required Rates of Return. The Journal of Industrial Economics, 33(1), 73. https://doi.org/10.2307/2098425
Bergmann, T., Dale, R., Sattari, N., Heit, E., & Bhat, H. S. (2017). The Interdisciplinarity of Collaborations in Cognitive Science. Cognitive Science, 41(5), 1412-1418. https://doi.org/10.1111/cogs.12352
Bertot, J. C., Gorham, U., Jaeger, P. T., Sarin, L. C., & Choi, H. (2014). Big data, open government and e-government: Issues, policies and recommendations. Information Polity, 19(1–2), 5-16. https://doi. org/10.3233/IP-140328
Bhimani, A. (2020). Digital data and management accounting: why we need to rethink research methods. Journal of Management Control, 31(1-2), 9-23. https://doi.org/10.1007/s00187-020-00295-z
Bichteler, J., & Eaton, E. A. (1980). The combined use of bibliographic coupling and cocitation for document retrieval. Journal of the American Society for Information Science, 31(4), 278-282. https://doi. org/10.1002/asi.4630310408
Boeker, M., Vach, W., & Motschall, E. (2013). Google Scholar as replacement for systematic literature searches: Good relative recall and precision are not enough. BMC Medical Research Methodology, 13(1). https://doi.org/10.1186/1471-2288-13-131
Boyack, K. W. (2009). Using detailed maps of science to identify potential collaborations. Scientometrics, 79(1), 27-44. https://doi. org/10.1007/s11192-009-0402-6
Boyack, K. W., & Klavans, R. (2010). Co-citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately? Journal of the American Society for Information Science and Technology, 61(12), 2389-2404. https:// doi.org/10.1002/asi.21419
Boyack, K. W., Small, H., & Klavans, R. (2013). Improving the accuracy of co-citation clustering using full text. Journal of the American Society for Information Science and Technology, 64(9), 1759-1767. https://doi.org/10.1002/asi.22896
Bufrem, L., & Prates, Y. (2005). O saber científico registrado e as práticas de mensuração da informação. Ciência Da Informação, 34(2), 9-25. https://doi.org/10.1590/s0100-19652005000200002
Calero Medina, C. M., & Van Leeuwen, T. N. (2012). Seed journal citation network maps: A method based on network theory. Journal of the American Society for Information Science and Technology, 63(6), 1226-1234. https://doi.org/10.1002/asi.22631
Callon, M., Courtial, J. P., & Laville, F. (1991). Co-word analysis as a tool for describing the network of interactions between basic and technological research: The case of polymer chemsitry. Scientometrics, 22(1), 155-205. https://doi.org/10.1007/BF02019280
Callon, M., Courtial, J. P., Turner, W. A., & Bauin, S. (1983). From translations to problematic networks: An introduction to co-word analysis. Social Science Information, 22(2), 191-235. https://doi. org/10.1177/053901883022002003
Cobo, M. J., Chiclana, F., Collop, A., De Ona, J., & Herrera-Viedma, E. (2014). A bibliometric analysis of the intelligent transportation systems research based on science mapping. IEEE Transactions on Intelligent Transportation Systems, 15(2), 901-908. https://doi. org/10.1109/TITS.2013.2284756
Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field. Journal of Informetrics, 5(1), 146-166. https://doi. org/10.1016/j.joi.2010.10.002
Dagilienė, L., & Klovienė, L. (2019). Motivation to use big data and big data analytics in external auditing. Managerial Auditing Journal, 34(7), 750-782. https://doi.org/10.1108/MAJ-01-2018-1773
Davies, T., Walker, S. B., Rubinstein, M., & Perini, F. (2019). Estado de los datos abiertos: historias y horizontes | Universo Abierto. https://universoabierto.org/2019/11/14/estado-de-los-datos-abiertos-historias-y-horizontes/
De Solla Price, D. J. (1965). Networks of scientific papers. Science, 149(3683), 510. https://doi.org/10.1126/science.149.3683.510
Dekker, R., Engbersen, G., Klaver, J., & Vonk, H. (2018). Smart Refugees: How Syrian Asylum Migrants Use Social Media Information in Migration Decision-Making. Social Media and Society, 4(1). https://doi.org/10.1177/2056305118764439
Franceschini, F., Maisano, D., & Mastrogiacomo, L. (2016). The museum of errors/horrors in Scopus. Journal of Informetrics, 10(1), 174-182. https://doi.org/10.1016/j.joi.2015.11.006
Garfield, E. (1979). Is citation analysis a legitimate evaluation tool? Scientometrics, 1(4), 359-375. https://doi.org/10.1007/BF02019306
Garfield, E. (2004). Historiographic mapping of knowledge domains literature. In Journal of Information Science, 30(2), 119-145. https:// doi.org/10.1177/0165551504042802
Gillespie, M., Ampofo, L., Cheesman, M., Faith, B., Iliadou, E., Issa, A., Osseiran, S., & Skleparis, D. (2016). Something About Refugee Media. In The Open University/France Medias Monde (Issue May). https://www.open.ac.uk/ccig/sites/www.open.ac.uk.ccig/files/Mapping Refugee Media Journeys 16 May FIN MG_0.pdf
Glänzel, W., & Schubert, A. (2004). Analyzing Scientific Collaboration through Co-Authorship. In Handbook of quantitative science and technology research. The use of publication and patent statistics in studies on S&T systems (pp. 257-276).
Hay, S. I., George, D. B., Moyes, C. L., & Brownstein, J. S. (2013). Big Data Opportunities for Global Infectious Disease Surveillance. PLoS Medicine, 10(4). https://doi.org/10.1371/journal. pmed.1001413
Helft, M. (2008). Google Uses Searches to Track Flu’s Spread. The New York Times, 1-5. http://www.nytimes.com/2008/11/12/technology/internet/12flu.html?_r=1&th&emc=th&oref=slogin
Hilbert, M. (2016). Big Data for Development: A Review of Promises and Challenges. Development Policy Review, 34(1), 135-174. https:// doi.org/10.1111/dpr.12142
Janvrin, D. J., & Weidenmier Watson, M. (2017). “Big Data”: A new twist to accounting. Journal of Accounting Education, 38, 3-8. https://doi. org/10.1016/j.jaccedu.2016.12.009
Jarneving, B. (2007). Complete graphs and bibliographic coupling: A test of the applicability of bibliographic coupling for the identification of cognitive cores on the field level. Journal of Informetrics, 1(4), 338-356. https://doi.org/10.1016/j.joi.2007.08.001
Kassebaum, N. J., Arora, M., Barber, R. M., Brown, J., Carter, A., Casey, D. C., Charlson, F. J., Coates, M. M., Coggeshall, M., Cornaby, L., Dandona, L., Dicker, D. J., Erskine, H. E., Ferrari, A. J., Fitzmaurice, C., Foreman, K., Forouzanfar, M. H., Fullman, N., Goldberg, E. M.,… Zuhlke, L. J. (2016). Global, regional, and national disability-adjusted life-years (DALYs) for 315 diseases and injuries and healthy life expectancy (HALE), 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015. The Lancet, 388(10053), 1603-1658. https://doi.org/10.1016/S0140-6736(16)31460-X
Kessler, M. M. (1963). Bibliographic coupling between scientific papers. American Documentation, 14(1), 10-25. https://doi.org/10.1002/asi.5090140103
Kim, G. H., Trimi, S., & Chung, J. H. (2014). Big-data applications in the government sector. Communications of the ACM, 57(3), 78-85. https://doi.org/10.1145/2500873
Kousha, K., & Thelwall, M. (2008). Sources of Google Scholar citations outside the Science Citation Index: A comparison between four science disciplines. Scientometrics, 74(2), 273-294. https://doi. org/10.1007/s11192-008-0217-x
Kuusi, O., & Meyer, M. (2007). Anticipating technological breakthroughs: Using bibliographic coupling to explore the nanotubes paradigm. Scientometrics, 70(3), 759-777. https://doi.org/10.1007/ s11192-007-0311-5
Landefeld, S. (2014). Uses of Big Data for Official Statistics: Privacy, Incentives, Statistical Challenges, and Other Issues Discussion Paper United Nations Global Working Group on Big Data for Official Statistics. 1-20. https://unstats.un.org/unsd/trade/events/2014/beijing/ Steve Landefeld Uses of Big Data for official statistics.pdf
Leydesdorff, L., De Moya-Anegón, F., & Guerrero-Bote, V. P. (2010). Journal maps on the basis of scopus data: A comparison with the journal citation reports of the ISI. Journal of the American Society for Information Science and Technology, 61(2), 352-369. https://doi. org/10.1002/asi.21250
Lim, S. S., Allen, K., Dandona, L., Forouzanfar, M. H., Fullman, N., Goldberg, E. M., Hay, S. I., Holmberg, M., Kutz, M. J., Larson, H. J., Lopez, A. D., McNellan, C. R., Mokdad, A. H., Mooney, M. D., Naghavi, M., Olsen, H. E., Pigott, D. M., Vos, T., Wang, H.,… Zonies, D. (2016). Measuring the health-related Sustainable Development Goals in 188 countries: a baseline analysis from the Global Burden of Disease Study 2015. The Lancet, 388(10053), 1813-1850. https:// doi.org/10.1016/S0140-6736(16)31467-2
Linders, D. (2013). Towards open development: Leveraging open data to improve the planning and coordination of international aid. Government Information Quarterly, 30(4), 426-434. https://doi.org/10.1016/j.giq.2013.04.001
López-Cózar, E. D., Robinson-García, N., & Torres-Salinas, D. (2014). The google scholar experiment: How to index false papers and manipulate bibliometric indicators. Journal of the Association for Information Science and Technology, 65(3), 446-454. https://doi. org/10.1002/asi.23056
López-Herrera, A. G., Cobo, M. J., Herrera-Viedma, E., & Herrera, F. (2016). A bibliometric study about the research based on hybridating the fuzzy logic field and the other computational intelligent techniques: A visual approach. International Journal of Hybrid Intelligent Systems, 7(1), 17-32. https://doi.org/10.3233/his-2010-0102
López-Herrera, A. G., Cobo, M. J., Herrera-Viedma, E., Herrera, F., Bailón-Moreno, R., & Jiménez-Contreras, E. (2009). Visualization and evolution of the scientific structure of fuzzy sets research in Spain. Information Research, 14(4).
Marshakova, I. V. (1981). Citation networks in information science. Scientometrics, 3(1), 13-25. https://doi.org/10.1007/BF02021861
McKinney, E., Yoos, C. J., & Snead, K. (2017). The need for ‘skeptical’ accountants in the era of Big Data. Journal of Accounting Education, 38, 63-80. https://doi.org/10.1016/j.jaccedu.2016.12.007
Melin, G., & Persson, O. (1996). Studying research collaboration using co-authorships. Scientometrics, 36(3), 363-377. https://doi. org/10.1007/BF02129600
Meyer, M., Grant, K., Morlacchi, P., & Weckowska, D. (2014). Triple Helix indicators as an emergent area of enquiry: A bibliometric perspective. Scientometrics, 99(1), 151-174. https://doi.org/10.1007/ s11192-013-1103-8
Moral-Munoz, J. A., Arroyo-Morales, M., Herrera-Viedma, E., & Cobo, M. J. (2018). An Overview of Thematic Evolution of Physical Therapy Research Area From 1951 to 2013. Frontiers in Research Metrics and Analytics, 3. https://doi.org/10.3389/frma.2018.00013
Muñoz-Leiva, F., Sánchez-Fernández, J., Liébana-Cabanillas, F. J., & López-Herrera, A. G. (2012). Applying an automatic approach for showing up the hidden themes in financial marketing research (1961-2010). Expert Systems with Applications, 39(12), 11055-11065. https://doi.org/10.1016/j.eswa.2012.03.017
Muñoz-Leiva, F., Viedma-del-Jesús, M. I., Sánchez-Fernández, J., & López-Herrera, A. G. (2012). An application of co-word analysis and bibliometric maps for detecting the most highlighting themes in the consumer behaviour research from a longitudinal perspective. Quality and Quantity, 46(4), 1077-1095. https://doi.org/10.1007/ s11135-011-9565-3
Nalimov, V. V., & Mul´chenko, Z. M. (1971). Measurement of science: Study of the development of science as an information process. Washington , DC: Foreign Technology Division. In Naukometriya, Izucheniye Razvitiya Nauki kak Informatsionnogo Protsessa (p. 196). Oliver, N., Matic, A., & Frias-Martinez, E. (2015). Mobile Network Data for Public Health: Opportunities and Challenges. Frontiers in Public Health, 3. https://doi.org/10.3389/fpubh.2015.00189
Óskarsdóttir, M., Sarraute, C., Bravo, C., Baesens, B., & Vanthienen, J. (2018). Credit scoring for good: Enhancing financial inclusion with smartphone-based microlending. International Conference on Information Systems 2018, ICIS 2018.
Price, D. J., & Beaver, D. D. (1966). Collaboration in an invisible college. The American Psychologist, 21(11), 1011-1018. https://doi. org/10.1037/h0024051
PRITCHARD, A. (1969). Statistical bibliography or bibliometrics. Journal of Documentation, 25, 348.
Protopop, I. (2016). Big Data and Smallholder Farmers: Big Data Applications in the Agri-Food Supply Chain in Developing Countries. International Food and Agribusiness Management Review, 19, 173-190. https://doi.org/10.22004/ag.econ.240705
Roy, M., Moreau, N., Rousseau, C., Mercier, A., Wilson, A., & Atlani-Duault, L. (2020). Ebola and Localized Blame on Social Media: Analysis of Twitter and Facebook Conversations During the 2014-2015 Ebola Epidemic. Culture, Medicine and Psychiatry, 44(1), 56-79. https://doi.org/10.1007/s11013-019-09635-8
Small, H. (1973). Co‐citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for Information Science, 24(4), 265-269. https://doi. org/10.1002/asi.4630240406
Small, H. (1999). Visualizing science by citation mapping. Journal of the American Society for Information Science, 50(9), 799-813. https://doi.org/10.1002/(SICI)1097-4571(1999)50:9<799::AID-ASI9>3.0.CO;2-G
Thara, D. K., Premasudha, B. G., Ram, V. R., & Suma, R. (2016). Impact of big data in healthcare: A survey. Proceedings of the 2016 2nd International Conference on Contemporary Computing and Informatics, IC3I 2016, 729-735. https://doi.org/10.1109/IC3I.2016.7918057
The State of Mobile Data for Social Good Report (Issue June) (2017). https://www.gsma.com/mobilefordevelopment/wp-content/uploads/2017/06/Mobile-Data-for-Social-Good-Report_29June.pdf
UN DESA. (2015). Inequality and the 2030 Agenda for Sustainable Development. In Development Issues (Vol. 4). https://www. un.org/development/desa/dpad/publication/no-4-inequality-and-the-2030-agenda-for-sustainable-development/
UN Global Pulse, & UNHCR. (2017). Rescue Patterns in the Mediterranean Partners : https://doi.org/Project Series, no. 29, 2017
Williams, B. C., & Plouffe, C. R. (2007). Assessing the evolution of sales knowledge: A 20-year content analysis. Industrial Marketing Management, 36(4), 408-419. https://doi.org/10.1016/j.indmarman.2005.11.003
Adriaanse, L. S., & Rensleigh, C. (2013). Web of science, scopus and google scholar a content comprehensiveness comparison. Electronic Library, 31(6), 727-744. https://doi.org/10.1108/EL-12-2011-0174
Alcaide, G. G., & Ferri, J. G. (2014). La colaboración científica: Principales líneas de investigación y retos de futuro. Revista Espanola de Documentacion Cientifica, 37(4), e062. https://doi.org/10.3989/ redc.2014.4.1186
Amankwah-Amoah. (2016). Emerging economies, emerging challenges: Mobilising and capturing value from big data. Technological Forecasting and Social Change, 110, 167-174. https://doi.org/10.1016/j. techfore.2015.10.022
Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975. https://doi.org/10.1016/j.joi.2017.08.007
Arnaboldi, M., Busco, C., & Cuganesan, S. (2017). Accounting, accountability, social media and big data: revolution or hype? Accounting, Auditing and Accountability Journal, 30(4), 762-776. https://doi. org/10.1108/AAAJ-03-2017-2880
Baldwin, C. Y., Tribendis, J. J., & Clark, J. P. (1984). The Evolution of Market Risk in the U.S. Steel Industry and Implications for Required Rates of Return. The Journal of Industrial Economics, 33(1), 73. https://doi.org/10.2307/2098425
Bergmann, T., Dale, R., Sattari, N., Heit, E., & Bhat, H. S. (2017). The Interdisciplinarity of Collaborations in Cognitive Science. Cognitive Science, 41(5), 1412-1418. https://doi.org/10.1111/cogs.12352
Bertot, J. C., Gorham, U., Jaeger, P. T., Sarin, L. C., & Choi, H. (2014). Big data, open government and e-government: Issues, policies and recommendations. Information Polity, 19(1–2), 5-16. https://doi. org/10.3233/IP-140328
Bhimani, A. (2020). Digital data and management accounting: why we need to rethink research methods. Journal of Management Control, 31(1-2), 9-23. https://doi.org/10.1007/s00187-020-00295-z
Bichteler, J., & Eaton, E. A. (1980). The combined use of bibliographic coupling and cocitation for document retrieval. Journal of the American Society for Information Science, 31(4), 278-282. https://doi. org/10.1002/asi.4630310408
Boeker, M., Vach, W., & Motschall, E. (2013). Google Scholar as replacement for systematic literature searches: Good relative recall and precision are not enough. BMC Medical Research Methodology, 13(1). https://doi.org/10.1186/1471-2288-13-131
Boyack, K. W. (2009). Using detailed maps of science to identify potential collaborations. Scientometrics, 79(1), 27-44. https://doi. org/10.1007/s11192-009-0402-6
Boyack, K. W., & Klavans, R. (2010). Co-citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately? Journal of the American Society for Information Science and Technology, 61(12), 2389-2404. https:// doi.org/10.1002/asi.21419
Boyack, K. W., Small, H., & Klavans, R. (2013). Improving the accuracy of co-citation clustering using full text. Journal of the American Society for Information Science and Technology, 64(9), 1759-1767. https://doi.org/10.1002/asi.22896
Bufrem, L., & Prates, Y. (2005). O saber científico registrado e as práticas de mensuração da informação. Ciência Da Informação, 34(2), 9-25. https://doi.org/10.1590/s0100-19652005000200002
Calero Medina, C. M., & Van Leeuwen, T. N. (2012). Seed journal citation network maps: A method based on network theory. Journal of the American Society for Information Science and Technology, 63(6), 1226-1234. https://doi.org/10.1002/asi.22631
Callon, M., Courtial, J. P., & Laville, F. (1991). Co-word analysis as a tool for describing the network of interactions between basic and technological research: The case of polymer chemsitry. Scientometrics, 22(1), 155-205. https://doi.org/10.1007/BF02019280
Callon, M., Courtial, J. P., Turner, W. A., & Bauin, S. (1983). From translations to problematic networks: An introduction to co-word analysis. Social Science Information, 22(2), 191-235. https://doi. org/10.1177/053901883022002003
Cobo, M. J., Chiclana, F., Collop, A., De Ona, J., & Herrera-Viedma, E. (2014). A bibliometric analysis of the intelligent transportation systems research based on science mapping. IEEE Transactions on Intelligent Transportation Systems, 15(2), 901-908. https://doi. org/10.1109/TITS.2013.2284756
Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field. Journal of Informetrics, 5(1), 146-166. https://doi. org/10.1016/j.joi.2010.10.002
Dagilienė, L., & Klovienė, L. (2019). Motivation to use big data and big data analytics in external auditing. Managerial Auditing Journal, 34(7), 750-782. https://doi.org/10.1108/MAJ-01-2018-1773
Davies, T., Walker, S. B., Rubinstein, M., & Perini, F. (2019). Estado de los datos abiertos: historias y horizontes | Universo Abierto. https://universoabierto.org/2019/11/14/estado-de-los-datos-abiertos-historias-y-horizontes/
De Solla Price, D. J. (1965). Networks of scientific papers. Science, 149(3683), 510. https://doi.org/10.1126/science.149.3683.510
Dekker, R., Engbersen, G., Klaver, J., & Vonk, H. (2018). Smart Refugees: How Syrian Asylum Migrants Use Social Media Information in Migration Decision-Making. Social Media and Society, 4(1). https://doi.org/10.1177/2056305118764439
Franceschini, F., Maisano, D., & Mastrogiacomo, L. (2016). The museum of errors/horrors in Scopus. Journal of Informetrics, 10(1), 174-182. https://doi.org/10.1016/j.joi.2015.11.006
Garfield, E. (1979). Is citation analysis a legitimate evaluation tool? Scientometrics, 1(4), 359-375. https://doi.org/10.1007/BF02019306
Garfield, E. (2004). Historiographic mapping of knowledge domains literature. In Journal of Information Science, 30(2), 119-145. https:// doi.org/10.1177/0165551504042802
Gillespie, M., Ampofo, L., Cheesman, M., Faith, B., Iliadou, E., Issa, A., Osseiran, S., & Skleparis, D. (2016). Something About Refugee Media. In The Open University/France Medias Monde (Issue May). https://www.open.ac.uk/ccig/sites/www.open.ac.uk.ccig/files/Mapping Refugee Media Journeys 16 May FIN MG_0.pdf
Glänzel, W., & Schubert, A. (2004). Analyzing Scientific Collaboration through Co-Authorship. In Handbook of quantitative science and technology research. The use of publication and patent statistics in studies on S&T systems (pp. 257-276).
Hay, S. I., George, D. B., Moyes, C. L., & Brownstein, J. S. (2013). Big Data Opportunities for Global Infectious Disease Surveillance. PLoS Medicine, 10(4). https://doi.org/10.1371/journal. pmed.1001413
Helft, M. (2008). Google Uses Searches to Track Flu’s Spread. The New York Times, 1-5. http://www.nytimes.com/2008/11/12/technology/internet/12flu.html?_r=1&th&emc=th&oref=slogin
Hilbert, M. (2016). Big Data for Development: A Review of Promises and Challenges. Development Policy Review, 34(1), 135-174. https:// doi.org/10.1111/dpr.12142
Janvrin, D. J., & Weidenmier Watson, M. (2017). “Big Data”: A new twist to accounting. Journal of Accounting Education, 38, 3-8. https://doi. org/10.1016/j.jaccedu.2016.12.009
Jarneving, B. (2007). Complete graphs and bibliographic coupling: A test of the applicability of bibliographic coupling for the identification of cognitive cores on the field level. Journal of Informetrics, 1(4), 338-356. https://doi.org/10.1016/j.joi.2007.08.001
Kassebaum, N. J., Arora, M., Barber, R. M., Brown, J., Carter, A., Casey, D. C., Charlson, F. J., Coates, M. M., Coggeshall, M., Cornaby, L., Dandona, L., Dicker, D. J., Erskine, H. E., Ferrari, A. J., Fitzmaurice, C., Foreman, K., Forouzanfar, M. H., Fullman, N., Goldberg, E. M.,… Zuhlke, L. J. (2016). Global, regional, and national disability-adjusted life-years (DALYs) for 315 diseases and injuries and healthy life expectancy (HALE), 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015. The Lancet, 388(10053), 1603-1658. https://doi.org/10.1016/S0140-6736(16)31460-X
Kessler, M. M. (1963). Bibliographic coupling between scientific papers. American Documentation, 14(1), 10-25. https://doi.org/10.1002/asi.5090140103
Kim, G. H., Trimi, S., & Chung, J. H. (2014). Big-data applications in the government sector. Communications of the ACM, 57(3), 78-85. https://doi.org/10.1145/2500873
Kousha, K., & Thelwall, M. (2008). Sources of Google Scholar citations outside the Science Citation Index: A comparison between four science disciplines. Scientometrics, 74(2), 273-294. https://doi. org/10.1007/s11192-008-0217-x
Kuusi, O., & Meyer, M. (2007). Anticipating technological breakthroughs: Using bibliographic coupling to explore the nanotubes paradigm. Scientometrics, 70(3), 759-777. https://doi.org/10.1007/ s11192-007-0311-5
Landefeld, S. (2014). Uses of Big Data for Official Statistics: Privacy, Incentives, Statistical Challenges, and Other Issues Discussion Paper United Nations Global Working Group on Big Data for Official Statistics. 1-20. https://unstats.un.org/unsd/trade/events/2014/beijing/ Steve Landefeld Uses of Big Data for official statistics.pdf
Leydesdorff, L., De Moya-Anegón, F., & Guerrero-Bote, V. P. (2010). Journal maps on the basis of scopus data: A comparison with the journal citation reports of the ISI. Journal of the American Society for Information Science and Technology, 61(2), 352-369. https://doi. org/10.1002/asi.21250
Lim, S. S., Allen, K., Dandona, L., Forouzanfar, M. H., Fullman, N., Goldberg, E. M., Hay, S. I., Holmberg, M., Kutz, M. J., Larson, H. J., Lopez, A. D., McNellan, C. R., Mokdad, A. H., Mooney, M. D., Naghavi, M., Olsen, H. E., Pigott, D. M., Vos, T., Wang, H.,… Zonies, D. (2016). Measuring the health-related Sustainable Development Goals in 188 countries: a baseline analysis from the Global Burden of Disease Study 2015. The Lancet, 388(10053), 1813-1850. https:// doi.org/10.1016/S0140-6736(16)31467-2
Linders, D. (2013). Towards open development: Leveraging open data to improve the planning and coordination of international aid. Government Information Quarterly, 30(4), 426-434. https://doi.org/10.1016/j.giq.2013.04.001
López-Cózar, E. D., Robinson-García, N., & Torres-Salinas, D. (2014). The google scholar experiment: How to index false papers and manipulate bibliometric indicators. Journal of the Association for Information Science and Technology, 65(3), 446-454. https://doi. org/10.1002/asi.23056
López-Herrera, A. G., Cobo, M. J., Herrera-Viedma, E., & Herrera, F. (2016). A bibliometric study about the research based on hybridating the fuzzy logic field and the other computational intelligent techniques: A visual approach. International Journal of Hybrid Intelligent Systems, 7(1), 17-32. https://doi.org/10.3233/his-2010-0102
López-Herrera, A. G., Cobo, M. J., Herrera-Viedma, E., Herrera, F., Bailón-Moreno, R., & Jiménez-Contreras, E. (2009). Visualization and evolution of the scientific structure of fuzzy sets research in Spain. Information Research, 14(4).
Marshakova, I. V. (1981). Citation networks in information science. Scientometrics, 3(1), 13-25. https://doi.org/10.1007/BF02021861
McKinney, E., Yoos, C. J., & Snead, K. (2017). The need for ‘skeptical’ accountants in the era of Big Data. Journal of Accounting Education, 38, 63-80. https://doi.org/10.1016/j.jaccedu.2016.12.007
Melin, G., & Persson, O. (1996). Studying research collaboration using co-authorships. Scientometrics, 36(3), 363-377. https://doi. org/10.1007/BF02129600
Meyer, M., Grant, K., Morlacchi, P., & Weckowska, D. (2014). Triple Helix indicators as an emergent area of enquiry: A bibliometric perspective. Scientometrics, 99(1), 151-174. https://doi.org/10.1007/ s11192-013-1103-8
Moral-Munoz, J. A., Arroyo-Morales, M., Herrera-Viedma, E., & Cobo, M. J. (2018). An Overview of Thematic Evolution of Physical Therapy Research Area From 1951 to 2013. Frontiers in Research Metrics and Analytics, 3. https://doi.org/10.3389/frma.2018.00013
Muñoz-Leiva, F., Sánchez-Fernández, J., Liébana-Cabanillas, F. J., & López-Herrera, A. G. (2012). Applying an automatic approach for showing up the hidden themes in financial marketing research (1961-2010). Expert Systems with Applications, 39(12), 11055-11065. https://doi.org/10.1016/j.eswa.2012.03.017
Muñoz-Leiva, F., Viedma-del-Jesús, M. I., Sánchez-Fernández, J., & López-Herrera, A. G. (2012). An application of co-word analysis and bibliometric maps for detecting the most highlighting themes in the consumer behaviour research from a longitudinal perspective. Quality and Quantity, 46(4), 1077-1095. https://doi.org/10.1007/ s11135-011-9565-3
Nalimov, V. V., & Mul´chenko, Z. M. (1971). Measurement of science: Study of the development of science as an information process. Washington , DC: Foreign Technology Division. In Naukometriya, Izucheniye Razvitiya Nauki kak Informatsionnogo Protsessa (p. 196). Oliver, N., Matic, A., & Frias-Martinez, E. (2015). Mobile Network Data for Public Health: Opportunities and Challenges. Frontiers in Public Health, 3. https://doi.org/10.3389/fpubh.2015.00189
Óskarsdóttir, M., Sarraute, C., Bravo, C., Baesens, B., & Vanthienen, J. (2018). Credit scoring for good: Enhancing financial inclusion with smartphone-based microlending. International Conference on Information Systems 2018, ICIS 2018.
Price, D. J., & Beaver, D. D. (1966). Collaboration in an invisible college. The American Psychologist, 21(11), 1011-1018. https://doi. org/10.1037/h0024051
PRITCHARD, A. (1969). Statistical bibliography or bibliometrics. Journal of Documentation, 25, 348.
Protopop, I. (2016). Big Data and Smallholder Farmers: Big Data Applications in the Agri-Food Supply Chain in Developing Countries. International Food and Agribusiness Management Review, 19, 173-190. https://doi.org/10.22004/ag.econ.240705
Roy, M., Moreau, N., Rousseau, C., Mercier, A., Wilson, A., & Atlani-Duault, L. (2020). Ebola and Localized Blame on Social Media: Analysis of Twitter and Facebook Conversations During the 2014-2015 Ebola Epidemic. Culture, Medicine and Psychiatry, 44(1), 56-79. https://doi.org/10.1007/s11013-019-09635-8
Small, H. (1973). Co‐citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for Information Science, 24(4), 265-269. https://doi. org/10.1002/asi.4630240406
Small, H. (1999). Visualizing science by citation mapping. Journal of the American Society for Information Science, 50(9), 799-813. https://doi.org/10.1002/(SICI)1097-4571(1999)50:9<799::AID-ASI9>3.0.CO;2-G
Thara, D. K., Premasudha, B. G., Ram, V. R., & Suma, R. (2016). Impact of big data in healthcare: A survey. Proceedings of the 2016 2nd International Conference on Contemporary Computing and Informatics, IC3I 2016, 729-735. https://doi.org/10.1109/IC3I.2016.7918057
The State of Mobile Data for Social Good Report (Issue June) (2017). https://www.gsma.com/mobilefordevelopment/wp-content/uploads/2017/06/Mobile-Data-for-Social-Good-Report_29June.pdf
UN DESA. (2015). Inequality and the 2030 Agenda for Sustainable Development. In Development Issues (Vol. 4). https://www. un.org/development/desa/dpad/publication/no-4-inequality-and-the-2030-agenda-for-sustainable-development/
UN Global Pulse, & UNHCR. (2017). Rescue Patterns in the Mediterranean Partners : https://doi.org/Project Series, no. 29, 2017
Williams, B. C., & Plouffe, C. R. (2007). Assessing the evolution of sales knowledge: A 20-year content analysis. Industrial Marketing Management, 36(4), 408-419. https://doi.org/10.1016/j.indmarman.2005.11.003
Issue
Section
Articles
Authors retain copyright of its works. Management Letters/Cuadernos de Gestión publications are licensed under Creative Commons license CC-BY-NC-ND, granting open access rights to society.
Specifically, CC-BY-NC-ND license permits any kind of use, distribution, publicize and copy the article, as long as the original author and source are properly recognized and for Non Commercial purposes.
The author can use the article freely always indicating that it has been published in Management Letters/Cuadernos de Gestión. Any re-edition of the article must be approved by the journal editorial team.