Research Article
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Year 2021, Volume: 8 Issue: 4, 218 - 234, 31.12.2021
https://doi.org/10.17261/Pressacademia.2021.1529

Abstract

References

  • Pranata N., Faraday A., (2019). Big data-based peer-to-peer lending fintech: surveillance system through the utilization a of google play review, ADBI Working Paper Series.
  • Gomber P., et.al., (2017). Digital Finance and FinTech: current research and future research directions, Journal of Business Economics, 87, 537– 580.
  • Ahmed H., Daim T., Basoglu N., (2009). Information Technology Diffusion in Higher Education, Technology in Society, Elsevier.
  • Martins M., Oliveira T, (2008). Determinants of Information Technology Diffusion: a Study at the Firm Level for Portugal, Academic Conferences Ltd., Electronic Journal Information Systems Evaluation,11(1), 27-34.
  • Micheni E., (2015). Diffusion of big data and analytics in developing countries. The International Journal of Engineering and Science (IJES), Volume 4(8), 44-50.
  • Agrawal K., (2013), The Assimilation of Big Data Analytics (BDA) by Indian Firms: a Technology Diffusion Perspective, Indian Institute of Management, Ahmedabad, http://vslir.iima.ac.in:8080/jspui/bitstream/11718/11489/1/TIK-PP-246-The_Assimilation_of_Big_Data_Analytics- 313-Agrawal_b.pdf
  • Sahin I., (2006). Detailed review of Rogers’ diffusion of innovations theory and educational technology-related studies based on Rogers’ theory. The Turkish Online Journal of Educational Technology, 5(2), 14-23.
  • Chen T., Ni Y., (2019). Research on BIM technology diffusion barrier - based on innovation diffusion theory, IOP Conf. Ser.: Earth Environ. Sci. 218 012031.
  • Ghezzi A., Belocco R., (2013). Technology diffusion theory revisited: a Regulation, Environment, Strategy, Technology model for technology activation analysis of Mobile ICT, https://www.researchgate.net/publication/236626987
  • Bagby J., Reitter D., (2016). Anticipatory FinTech Regulation: On Deploying Big Data Analytics to Predict the Direction, Impact and Control of Financial Technology, Journal of Innovation Management, 32–54. https://hesso.tind.io/record/1996/files/Schueffel_Tamingthebeast_2016.pdf
  • Al-Qirim N., et.al., (2017). Determinants of Big Data Adoption and Success, ICACS '17, Jeju Island, Republic of Korea, © 2017 Association for Comput ing Machinery.
  • Gandomi A., Haider M., (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management 35, 137–144.
  • Jarunee W., (2019). FinTech banking industry: a systemic approach, Foresight, Emerald Publishing, 19(6), 189-199.
  • Kim Y., Park Y, (2015). An empirical study on the adoption of “fintech” service: focutilised on mobile payment services. Advanced Science and Technology Letters, 114, 136-140.
  • Liu Y., (2015). Big data and predictive business analytics. The Journal of Business Forecasting, 33(4), 40-42.
  • Bogusz C., (2015), Digital Traces, Ethics, and Insight: Data-driven services in Fintech, Book: The Rise and Development of FinTech, Taylor & Francis.
  • Fichman R., (1992). Information Technology Diffusion: A Review of Empirical Research, MIT Sloan School of Management.
  • Dhar V., Stein M., (2017). FinTech Platforms and Strategy. Economic and business dimensions, Communications of the ACM, 60(10), 32-35.
  • Abi-Lahoud E., et. al., (2017). Linking Data to Understand Th-te Fintech Ecosystem, http://ceur-ws.org/Vol-2044/paper18/paper18.pdf
  • Jagtiani J., Lemieux C., (2019). The roles of alternative data and machine learning in fintech lending: Evidence from the Lending Club consumer platform, Financial Management Association International, Wiley publications, https://onlinelibrary.wiley.com/doi/abs/10.1111/fima.12295
  • Nann S., et. al., (2013). Predictive Analytics On Public Data - The Case of Stock Markets. Association for Information Systems, https://aisel.aisnet.org/ecis2013_cr/102
  • BimBim K., (2019), Fintech Loan Billing is very Troubling - Penagihan Pinjaman Fintech Sangat Meresahkan, petition at https://www.change.org/p/ojk-penagihan-pinjaman-fintech-sangat-meresahkan (accessed 28 January 2019).
  • Jumena E., (2018). Fintech Lending Jangan Jadi Digital Rentenir, Kompas, https://ekonomi.kompas.com/read/2018/03/04/223700926/fintechlending-jangan-jadi-digital-rentenir (accessed 6 September 2018).
  • Rossiana G., (2018). OJK Calls Fintech Moneylenders, This is Association Defense, CNBC Indonesia, https://www.cnbcindonesia.com/fintech/20180306123942-37-6370/ ojk-sebut-fintech-rentenir-ini-pembelaan-asosiasi (accessed 6 September 2018)

BIG DATA ANALYTICS: DIRECTION AND IMPACT ON FINANCIAL TECHNOLOGY

Year 2021, Volume: 8 Issue: 4, 218 - 234, 31.12.2021
https://doi.org/10.17261/Pressacademia.2021.1529

Abstract

Purpose- Digital infrastructure and technology advancements are steering the innovations in financial sector globally. The technology and data
driven aspect has fueled the Fintech sector, evolving at the tangent of mighty finance sector and revolutionary technology domain, especially the
digital technologies. The purpose of this paper is to show that most FinTech innovations, are significantly driven by big data analytics and its
efficient implementation.
Methodology- The use of latest ICT technologies lightens up the finance operations and services to exponential levels. Big data analytics is new
and requires comprehensive studies as a research field specially in the finance domain. The intent here is to study an adoption model specially IT
diffusion mode to Big data analytics that could detect key success predictors. The study tests the model for adoption of big data as novel
technology and the related issues. The paper also presents a review of academic journals, literature, to study the diffusion and adoption of big
data in to the finance domain.
Findings - The research reflects a significant interest and utility about Big data analytics value that epitomizes the rise of Fintech phenomenon.
Big data analytics may provide some competencies to the organizations that may consider its several dimensions along with its framework in the
pre-adoption phase or adoption phase or implementation or diffusion phase. The research also attempts to describe the several dimensions of
Big data analytics as a new technology. This shall be of good interest to the researchers, professionals, academicians and policy-makers.
Conclusion- The paper first defines big data to consolidate the different discourse and literature on big data. We also reflect the point that
predictive-analytics (with structured data) overshadows other forms: descriptive and prescriptive analytics (with unstructured data) which
constitutes more than 90% of big data. We also reflected on analytics techniques for unstructured data: audio, video, and social media data, as
well as predictive analytics. In the analysis and testing part we also performed the testing of the IT diffusion model which concludes that there
are significant relationships among IT-planning, IT-implementation and IT-diffusion.

References

  • Pranata N., Faraday A., (2019). Big data-based peer-to-peer lending fintech: surveillance system through the utilization a of google play review, ADBI Working Paper Series.
  • Gomber P., et.al., (2017). Digital Finance and FinTech: current research and future research directions, Journal of Business Economics, 87, 537– 580.
  • Ahmed H., Daim T., Basoglu N., (2009). Information Technology Diffusion in Higher Education, Technology in Society, Elsevier.
  • Martins M., Oliveira T, (2008). Determinants of Information Technology Diffusion: a Study at the Firm Level for Portugal, Academic Conferences Ltd., Electronic Journal Information Systems Evaluation,11(1), 27-34.
  • Micheni E., (2015). Diffusion of big data and analytics in developing countries. The International Journal of Engineering and Science (IJES), Volume 4(8), 44-50.
  • Agrawal K., (2013), The Assimilation of Big Data Analytics (BDA) by Indian Firms: a Technology Diffusion Perspective, Indian Institute of Management, Ahmedabad, http://vslir.iima.ac.in:8080/jspui/bitstream/11718/11489/1/TIK-PP-246-The_Assimilation_of_Big_Data_Analytics- 313-Agrawal_b.pdf
  • Sahin I., (2006). Detailed review of Rogers’ diffusion of innovations theory and educational technology-related studies based on Rogers’ theory. The Turkish Online Journal of Educational Technology, 5(2), 14-23.
  • Chen T., Ni Y., (2019). Research on BIM technology diffusion barrier - based on innovation diffusion theory, IOP Conf. Ser.: Earth Environ. Sci. 218 012031.
  • Ghezzi A., Belocco R., (2013). Technology diffusion theory revisited: a Regulation, Environment, Strategy, Technology model for technology activation analysis of Mobile ICT, https://www.researchgate.net/publication/236626987
  • Bagby J., Reitter D., (2016). Anticipatory FinTech Regulation: On Deploying Big Data Analytics to Predict the Direction, Impact and Control of Financial Technology, Journal of Innovation Management, 32–54. https://hesso.tind.io/record/1996/files/Schueffel_Tamingthebeast_2016.pdf
  • Al-Qirim N., et.al., (2017). Determinants of Big Data Adoption and Success, ICACS '17, Jeju Island, Republic of Korea, © 2017 Association for Comput ing Machinery.
  • Gandomi A., Haider M., (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management 35, 137–144.
  • Jarunee W., (2019). FinTech banking industry: a systemic approach, Foresight, Emerald Publishing, 19(6), 189-199.
  • Kim Y., Park Y, (2015). An empirical study on the adoption of “fintech” service: focutilised on mobile payment services. Advanced Science and Technology Letters, 114, 136-140.
  • Liu Y., (2015). Big data and predictive business analytics. The Journal of Business Forecasting, 33(4), 40-42.
  • Bogusz C., (2015), Digital Traces, Ethics, and Insight: Data-driven services in Fintech, Book: The Rise and Development of FinTech, Taylor & Francis.
  • Fichman R., (1992). Information Technology Diffusion: A Review of Empirical Research, MIT Sloan School of Management.
  • Dhar V., Stein M., (2017). FinTech Platforms and Strategy. Economic and business dimensions, Communications of the ACM, 60(10), 32-35.
  • Abi-Lahoud E., et. al., (2017). Linking Data to Understand Th-te Fintech Ecosystem, http://ceur-ws.org/Vol-2044/paper18/paper18.pdf
  • Jagtiani J., Lemieux C., (2019). The roles of alternative data and machine learning in fintech lending: Evidence from the Lending Club consumer platform, Financial Management Association International, Wiley publications, https://onlinelibrary.wiley.com/doi/abs/10.1111/fima.12295
  • Nann S., et. al., (2013). Predictive Analytics On Public Data - The Case of Stock Markets. Association for Information Systems, https://aisel.aisnet.org/ecis2013_cr/102
  • BimBim K., (2019), Fintech Loan Billing is very Troubling - Penagihan Pinjaman Fintech Sangat Meresahkan, petition at https://www.change.org/p/ojk-penagihan-pinjaman-fintech-sangat-meresahkan (accessed 28 January 2019).
  • Jumena E., (2018). Fintech Lending Jangan Jadi Digital Rentenir, Kompas, https://ekonomi.kompas.com/read/2018/03/04/223700926/fintechlending-jangan-jadi-digital-rentenir (accessed 6 September 2018).
  • Rossiana G., (2018). OJK Calls Fintech Moneylenders, This is Association Defense, CNBC Indonesia, https://www.cnbcindonesia.com/fintech/20180306123942-37-6370/ ojk-sebut-fintech-rentenir-ini-pembelaan-asosiasi (accessed 6 September 2018)
There are 24 citations in total.

Details

Primary Language English
Subjects Business Administration
Journal Section Articles
Authors

Arun Khatrı This is me 0000-0002-5895-7951

Np Sıngh This is me 0000-0002-3006-9522

Nakul Gupta This is me 0000-0002-8781-3287

Publication Date December 31, 2021
Published in Issue Year 2021 Volume: 8 Issue: 4

Cite

APA Khatrı, A., Sıngh, N., & Gupta, N. (2021). BIG DATA ANALYTICS: DIRECTION AND IMPACT ON FINANCIAL TECHNOLOGY. Journal of Management Marketing and Logistics, 8(4), 218-234. https://doi.org/10.17261/Pressacademia.2021.1529

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