Is Big Data Adoption Shaping Business Landscapes? An Overview of Current Hotspots and Future Trends

Authors

DOI:

https://doi.org/10.56294/dm2025536

Keywords:

Big data, Business Transformation, Big Data Adoption, Bibliometric Analysis

Abstract

Introduction: Most bibliometrics reviews in the prior studies have focused on tracking the evolution, applications, and implications of Big Data in business through different sectors using Web of Science or Scopus databases. Moreover, none of these studies has addressed the differences between developed and developing countries. These gaps indicate that we need a bibliometric review that can identify current trends and unexplored areas. 
Objectives: This study aims to use a bibliometric approach to examine how Big Data is used in businesses using WoS and Scopus databases. 
Methods: A Systematic Literature Review was conducted based on the country's economic status using the SPAR-4-SLR protocol for this research. 
Results: The results show a significant growth in publications since 2013 among developed countries and since 2014 among developing ones such as the United States and the United Kingdom, along with China and India, respectively. Also, Machine Learning Overlaps Artificial Intelligence alongside Analytics, fueling innovative data-driven business processes around Big Data. 
Conclusions: This article explores the transformative power of Big Data across domains, stressing its ability to cause substantial breakthroughs within the digital economy

References

Abdian, S., Shahri, M. H., & Khadivar, A. (2023). A Bibliometric Analysis of Research on Big Data and Its Potential to Value Creation and Capture. Iranian Journal of Management Studies, 16(1), 1–24. https://doi.org/10.22059/IJMS.2021.319211.674442

Aboelmaged, M., & Mouakket, S. (2020). Influencing models and determinants in big data analytics research: A bibliometric analysis. Information Processing and Management, 57(4), 102234. https://doi.org/10.1016/j.ipm.2020.102234

Agarwal, R., & Dhar, V. (2014). Big data, data science, and analytics: The opportunity and challenge for IS research. In Information systems research (Vol. 25, Issue 3, pp. 443–448). INFORMS.

Archak, N., Ghose, A., & Ipeirotis, P. G. (2011). Deriving the pricing power of product features by mining consumer reviews. Management Science, 57(8), 1485–1509.

Ardito, L., Scuotto, V., Del Giudice, M., & Petruzzelli, A. M. (2019). A bibliometric analysis of research on Big Data analytics for business and management. Management Decision, 57(8), 1993–2009. https://doi.org/10.1108/MD-07-2018-0754

Batistič, S., & van der Laken, P. (2019). History, Evolution and Future of Big Data and Analytics: A Bibliometric Analysis of Its Relationship to Performance in Organizations. British Journal of Management, 30(2), 229–251. https://doi.org/10.1111/1467-8551.12340

Bouarar, A. C., Mouloudj, S., & Mouloudj, K. (2022). Digital Transformation. In COVID-19’s Impact on the Cryptocurrency Market and the Digital Economy (pp. 33–52). IGI Global. https://doi.org/10.4018/978-1-7998-9117-8.ch003

Bughin, J., Chui, M., & Manyika, J. (2010). Clouds, big data, and smart assets: Ten tech-enabled business trends to watch. McKinsey Quarterly, 56(1), 75–86.

Cetindamar, D., Kocaoglu, D., Lammers, T., & Merigo, J. M. (2019). A Bibliometric Analysis of Technology Management Research at PICMET for 2009–2018. 2019 Portland International Conference on Management of Engineering and Technology (PICMET), 1–7. https://doi.org/10.23919/PICMET.2019.8893667

Cetindamar, Dilek, & Phaal, R. (2023). Technology Management in the Age of Digital Technologies. IEEE Transactions on Engineering Management, 70(7), 2507–2515. https://doi.org/10.1109/TEM.2021.3101196

Chadegani, A. A., Salehi, H., Yunus, M. M., Farhadi, H., Fooladi, M., Farhadi, M., & Ebrahim, N. A. (2013). A Comparison between Two Main Academic Literature Collections: Web of Science and Scopus Databases. Asian Social Science, 9(5), 18–26. https://doi.org/10.5539/ass.v9n5p18

Chavez, H., Albornoz, M. B., & Martín, F. (2022). ‘Big data’ Research: A Bibliometric Analysis of the Scopus Database, 2009–2019. Journal of Scientometric Research, 11(1), 64–78. https://doi.org/10.5530/jscires.11.1.7

Chen, C., Dubin, R., & Kim, M. C. (2014). Emerging trends and new developments in regenerative medicine: a scientometric update (2000–2014). Expert Opinion on Biological Therapy, 14(9), 1295–1317.

Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 1165–1188.

Delias, P., & Kitsios, F. C. (2023). Operational research and business intelligence as drivers for digital transformation. Operational Research, 23(3), 45. https://doi.org/10.1007/s12351-023-00784-8

Du, Z., Liu, J., & Wang, T. (2022). Augmented Reality Marketing: A Systematic Literature Review and an Agenda for Future Inquiry. Frontiers in Psychology, 13(June), 1–18. https://doi.org/10.3389/fpsyg.2022.925963

El-Alfy, E.-S. M., & Mohammed, S. A. (2020). A review of machine learning for big data analytics: bibliometric approach. Technology Analysis and Strategic Management, 32(8), 984–1005. https://doi.org/10.1080/09537325.2020.1732912

Falagas, M. E., Pitsouni, E. I., Malietzis, G. A., & Pappas, G. (2008). Comparison of PubMed, Scopus, Web of Science, and Google Scholar: strengths and weaknesses. The FASEB Journal, 22(2), 338–342. https://doi.org/10.1096/fj.07-9492LSF

Fauzi, M. A., Kamaruzzaman, Z. A., & Abdul Rahman, H. (2023). Bibliometric review on human resources management and big data analytics. International Journal of Manpower, 44(7), 1307–1327. https://doi.org/10.1108/IJM-05-2022-0247

Fischer, H., Seidenstricker, S., & Poeppelbuss, J. (2023). The triggers and consequences of digital sales: a systematic literature review. Journal of Personal Selling & Sales ManageMent, 43(1), 5–23.

George, Gerar, Haas, M. R., & Pentland, A. (2014). Big data and management. In Academy of management Journal (Vol. 57, Issue 2, pp. 321–326). Academy of Management Briarcliff Manor, NY.

George, Gerard, Osinga, E. C., Lavie, D., & Scott, B. A. (2016). Big data and data science methods for management research. In Academy of Management Journal (Vol. 59, Issue 5, pp. 1493–1507). Academy of Management Briarcliff Manor, NY.

Gonzalez-Brambila, C. N., Reyes-Gonzalez, L., Veloso, F., & Perez-Angón, M. A. (2016). The Scientific Impact of Developing Nations. PLOS ONE, 11(3), e0151328. https://doi.org/10.1371/journal.pone.0151328

Harzing, A.-W., & Alakangas, S. (2016). Google Scholar, Scopus and the Web of Science: a longitudinal and cross-disciplinary comparison. Scientometrics, 106(2), 787–804. https://doi.org/10.1007/s11192-015-1798-9

Huang, Y., Porter, A. L., Cunningham, S. W., Robinson, D. K. R., Liu, J., & Zhu, D. (2018). A technology delivery system for characterizing the supply side of technology emergence: Illustrated for Big Data & Analytics. Technological Forecasting and Social Change, 130(August 2017), 165–176. https://doi.org/10.1016/j.techfore.2017.09.012

Kalantari, A., Kamsin, A., Kamaruddin, H. S., Ale Ebrahim, N., Gani, A., Ebrahimi, A., & Shamshirband, S. (2017). A bibliometric approach to tracking big data research trends. Journal of Big Data, 4(1), 30. https://doi.org/10.1186/s40537-017-0088-1

Karaboğa, T., Şehitoğlu, Y., & Karaboğa, H. A. (2022). The Evolution of Big Data in Knowledge Management: A Bibliometric Analysis. Bilgi Dünyası, 23(1), 49–79. https://doi.org/10.15612/BD.2022.645

Khan, G. F., & Wood, J. (2015). Information technology management domain: emerging themes and keyword analysis. Scientometrics, 105(2), 959–972. https://doi.org/10.1007/s11192-015-1712-5

Khanra, S., Dhir, A., & Mäntymäki, M. (2020). Big data analytics and enterprises: a bibliometric synthesis of the literature. Enterprise Information Systems, 14(6), 737–768. https://doi.org/10.1080/17517575.2020.1734241

Kipper, L. M., Furstenau, L. B., Hoppe, D., Frozza, R., & Iepsen, S. (2020). Scopus scientific mapping production in industry 4.0 (2011–2018): a bibliometric analysis. International Journal of Production Research, 58(6), 1605–1627. https://doi.org/10.1080/00207543.2019.1671625

Kozlowski, S. W. J., Chen, G., & Salas, E. (2017). One hundred years of the Journal of Applied Psychology: Background, evolution, and scientific trends. Journal of Applied Psychology, 102(3), 237.

Kraus, S., Breier, M., Lim, W. M., Dabić, M., Kumar, S., Kanbach, D., Mukherjee, D., Corvello, V., Piñeiro-Chousa, J., Liguori, E., Palacios-Marqués, D., Schiavone, F., Ferraris, A., Fernandes, C., & Ferreira, J. J. (2022). Literature reviews as independent studies: guidelines for academic practice. Review of Managerial Science, 16(8), 2577–2595. https://doi.org/10.1007/s11846-022-00588-8

Kuhn, T. S. (2012). The structure of scientific revolutions. University of Chicago press.

Kulakli, A., & Osmanaj, V. (2020). Global Research on Big Data in Relation with Artificial Intelligence (A Bibliometric Study: 2008-2019). International Journal of Online and Biomedical Engineering (IJOE), 16(02), 31. https://doi.org/10.3991/ijoe.v16i02.12617

Kwilinski, A. (2023). The Relationship between Sustainable Development and Digital Transformation: Bibliometric Analysis. Virtual Economics, 6(3), 56–69. https://doi.org/10.34021/ve.2023.06.03(4)

Lafuente-Lechuga, M., Cifuentes-Faura, J., & Faura-Martínez, U. (2021). Sustainability, Big Data and Mathematical Techniques: A Bibliometric Review. Mathematics, 9(20), 2557. https://doi.org/10.3390/math9202557

Lim, W. M., Rasul, T., Kumar, S., & Ala, M. (2022). Past, present, and future of customer engagement. Journal of Business Research, 140(November 2021), 439–458. https://doi.org/10.1016/j.jbusres.2021.11.014

Liu, X., Sun, R., Wang, S., & Wu, Y. J. (2019). The research landscape of big data: a bibliometric analysis. Library Hi Tech, 38(2), 367–384. https://doi.org/10.1108/LHT-01-2019-0024

Loebbecke, C., & Picot, A. (2015). Reflections on societal and business model transformation arising from digitization and big data analytics: A research agenda. The Journal of Strategic Information Systems, 24(3), 149–157. https://doi.org/10.1016/j.jsis.2015.08.002

Lu, N., Zhang, G., & Lu, J. (2014). Concept drift detection via competence models. Artificial Intelligence, 209(1), 11–28. https://doi.org/10.1016/j.artint.2014.01.001

Lundberg, L. (2023). Bibliometric mining of research directions and trends for big data. Journal of Big Data, 10(1), 112. https://doi.org/10.1186/s40537-023-00793-6

McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D. J., & Barton, D. (2012). Big data: the management revolution. Harvard Business Review, 90(10), 60–68.

Merendino, A., Dibb, S., Meadows, M., Quinn, L., Wilson, D., Simkin, L., & Canhoto, A. (2018). Big data, big decisions: The impact of big data on board level decision-making. Journal of Business Research, 93(September), 67–78. https://doi.org/10.1016/j.jbusres.2018.08.029

Mishra, D., Gunasekaran, A., Papadopoulos, T., & Childe, S. J. (2018). Big Data and supply chain management: a review and bibliometric analysis. Annals of Operations Research, 270(1–2), 313–336. https://doi.org/10.1007/s10479-016-2236-y

Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & PRISMA Group*, the. (2009). Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Annals of Internal Medicine, 151(4), 264–269.

Moher, D., Shamseer, L., Clarke, M., Ghersi, D., Liberati, A., Petticrew, M., Shekelle, P., Stewart, L. A., & Group, P.-P. (2015). Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Systematic Reviews, 4, 1–9.

Nobre, G. C., & Tavares, E. (2017). Scientific literature analysis on big data and internet of things applications on circular economy: a bibliometric study. Scientometrics, 111, 463–492.

Paul, J., Lim, W. M., O’Cass, A., Hao, A. W., & Bresciani, S. (2021). Scientific procedures and rationales for systematic literature reviews (SPAR‐4‐SLR). International Journal of Consumer Studies, 45(4), 1–16. https://doi.org/10.1111/ijcs.12695

Ragazou, K., Passas, I., Garefalakis, A., Galariotis, E., & Zopounidis, C. (2023). Big Data Analytics Applications in Information Management Driving Operational Efficiencies and Decision-Making: Mapping the Field of Knowledge with Bibliometric Analysis Using R. Big Data and Cognitive Computing, 7(1), 13. https://doi.org/10.3390/bdcc7010013

Rassenfosse, G. de, & Seliger, F. (2019). Sources of knowledge flow between developed and developing nations. Science and Public Policy, 47(1), 16–30. https://doi.org/10.1093/scipol/scz042

Reshi, A., Shah, A., Shafi, S., & Qadri, M. H. (2023). Big Data in Healthcare - A Comprehensive Bibliometric Analysis of Current Research Trends. Scalable Computing: Practice and Experience, 24(3), 531–549. https://doi.org/10.12694/scpe.v24i3.2155

Rialti, R., Marzi, G., Ciappei, C., & Busso, D. (2019). Big data and dynamic capabilities: a bibliometric analysis and systematic literature review. Management Decision, 57(8), 2052–2068. https://doi.org/10.1108/MD-07-2018-0821

Shi, Y., Zhu, J., & Charles, V. (2021). Data science and productivity: A bibliometric review of data science applications and approaches in productivity evaluations. Journal of the Operational Research Society, 72(5), 975–988. https://doi.org/10.1080/01605682.2020.1860661

Shukla, A. K., Muhuri, P. K., & Abraham, A. (2020). A bibliometric analysis and cutting-edge overview on fuzzy techniques in Big Data. Engineering Applications of Artificial Intelligence, 92(April), 103625. https://doi.org/10.1016/j.engappai.2020.103625

Sun, W., Huang, P., Song, H., & Feng, D. (2020). Bibliometric analysis of acute pancreatitis in Web of Science database based on CiteSpace software. Medicine, 99(49), e23208. https://doi.org/10.1097/MD.0000000000023208

Tandon, A., Kaur, P., Mäntymäki, M., & Dhir, A. (2021). Blockchain applications in management: A bibliometric analysis and literature review. Technological Forecasting and Social Change, 166(February), 120649. https://doi.org/10.1016/j.techfore.2021.120649

Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a Methodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review. British Journal of Management, 14(3), 207–222. https://doi.org/10.1111/1467-8551.00375

Verganti, R., Vendraminelli, L., & Iansiti, M. (2020). Innovation and Design in the Age of Artificial Intelligence. Journal of Product Innovation Management, 37(3), 212–227. https://doi.org/10.1111/jpim.12523

Yubo, S., Ramayah, T., Hongmei, L., Yifan, Z., & Wenhui, W. (2023). Analysing the current status, hotspots, and future trends of technology management: Using the WoS and scopus database. Heliyon, 9(9), e19922. https://doi.org/10.1016/j.heliyon.2023.e19922

Zhang, J. Z., Srivastava, P. R., Sharma, D., & Eachempati, P. (2021). Big data analytics and machine learning: A retrospective overview and bibliometric analysis. Expert Systems with Applications, 184(May), 115561. https://doi.org/10.1016/j.eswa.2021.115561

Zhang, X., Yu, Y., & Zhang, N. (2021). Sustainable supply chain management under big data: a bibliometric analysis. Journal of Enterprise Information Management, 34(1), 427–445. https://doi.org/10.1108/JEIM-12-2019-0381

Zhang, Yi, Huang, Y., Porter, A. L., Zhang, G., & Lu, J. (2019). Discovering and forecasting interactions in big data research: A learning-enhanced bibliometric study. Technological Forecasting and Social Change, 146(April 2018), 795–807. https://doi.org/10.1016/j.techfore.2018.06.007

Zhang, Yi, Zhang, G., Zhu, D., & Lu, J. (2017). Scientific evolutionary pathways: Identifying and visualizing relationships for scientific topics. Journal of the Association for Information Science and Technology, 68(8), 1925–1939.

Zhang, Yucheng, Zhang, M., Li, J., Liu, G., Yang, M. M., & Liu, S. (2021). A bibliometric review of a decade of research: Big data in business research – Setting a research agenda. Journal of Business Research, 131(April 2019), 374–390. https://doi.org/10.1016/j.jbusres.2020.11.004

Downloads

Published

2025-01-01

Issue

Section

Original

How to Cite

1.
Al-Momani AM, Al-Sharafi MA, AL Jarrah MA, Hijaeen OW. Is Big Data Adoption Shaping Business Landscapes? An Overview of Current Hotspots and Future Trends. Data and Metadata [Internet]. 2025 Jan. 1 [cited 2025 Feb. 5];4:536. Available from: https://dm.ageditor.ar/index.php/dm/article/view/536