Does ICT Drive Fintech firm Performance? Evidence from BRICS ‎Countries ‎

Neifar, Malika (2024): Does ICT Drive Fintech firm Performance? Evidence from BRICS ‎Countries ‎.

Abstract

Purpose: The scope of this paper is to see if the aggregate information and communications technology index (ICT) drives firm performance (profitability and efficiency) for BRICS countries from a des-aggregate panel data of the firm-yearly level (by country) during 2014-2022, from an aggregate monthly time series data and a panel data of country-monthly level during 2014-01-2014-12, all covering the Covid outbreak event. Design/methodology/approach: Through static and dynamic long-run (LR) panel models, the Bayesian VAR-X short-run (SR) approach, and the time series and the panel (LR and SR) ARDL models, we investigate the stability of the linkage between firm performance and the aggregate ICT vis à vis the Covid outbreak. Findings: Using an international sample of 316 FinTech firms from BRICS countries, we find that ICT mechanisms on their own are in general negatively associated with firm performance (profitability and efficiency) with some exceptions. We also find that the ICT and the firm-performance relationship is more significant among countries with respect to the considered pre ou post Covid 19 outbreak period. Originality: The novelty of this research is based on the idea of studying the effect of the aggregate ICT on firm performance by using several dynamic approaches so that we can estimate the SR adjustments that arise from the impact of ICT to the LR relationship.

• Item Type: MPRA Paper
• Language: English
• Keywords: FinTech Firm performance and ICT; BRICS area; Dynamic Panel Regressions and GMM for ‎firm level panel data; Bayesian VAR-X and ARDL models for TS data; PARDL for macro ‎panel data; Covid 19 outbreak‎
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C11 - Bayesian Analysis: General; C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes; C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C23 - Panel Data Models ; Spatio-temporal Models; O - Economic Development, Innovation, Technological Change, and Growth > O3 - Innovation ; Research and Development ; Technological Change ; Intellectual Property Rights > O33 - Technological Change: Choices and Consequences ; Diffusion Processes

References

Aduba, J. J., Asgari, B., & Izawa, H. (2023). Does FinTech ‎penetration drive financial ‎development? Evidence from panel analysis of emerging and developing economies. ‎Borsa ‎‎_Istanbul Review, 23(5), 1078–1097. Retrieved from ‎http://www.elsevier.com/journals/borsa-istanbul-review/2214-8450‎

Alam, K., Adeyinka, A., & Wiesner, R. (2019). Smaller businesses and e-innovation: A winning ‎combination in Australia. J. Bus.Strategy, 41, 39–48.‎

Antonakakisa, N., Cunado, J., Filis, G., & Gracia, F. P. (2017). Oil dependence, quality of ‎political institutions and economic growth: A panel VAR approach. Resources ‎Policy, ‎‎53, 147–163. doi:doi:10.1016/j. resourpol.2017.06.005‎

Berger, A., Boubakri, N., Guedhami, O., & Li, X. (2019). Liquidity creation performance and ‎financial stability consequences of Islamic banking: Evidence from a multinational ‎study. J. Financ. Stab, 44, 100692.‎

Breusch, T., & Pagan, A. (1980). The LM test and its applications to model specification in ‎econometrics. Review of Economic Studies , 47, 239–254.‎

Carr, N. G. (2003). IT doesn’t matter. . Harvard Business Review, 41-49.‎

Coffie, C. P., & al. (2020). Determinants of FinTech payment services diffusion by SMEs in ‎Sub-Saharan Africa: evidence from Ghana. Information Technology for Development, 1-‎‎22.‎

D’Mello, R., & Toscano, F. (2020). Economic policy uncertainty and short-term financing: The ‎case of trade credit. J. Corp. Financ, 64, 101686.‎

Da Rocha Lima Filho, R. I. (2022). Estimating the production function for the Brazilian ‎industrial sector: A ‎Bayesian panel VAR approach. Cogent Business & Management, ‎‎9(1), 2025752. doi:DOI: ‎‎10.1080/23311975.2022.2025752‎

Davydov, D., Fungáčová, Z., & Weill, L. (2018). Cyclicality of bank liquidity creation. J. Int. ‎Financ. Mark. Inst. Money, 55, 81–93.‎

Díaz, V., & Huang, Y. (2017). The role of governance on bank liquidity creation. J. Bank. ‎Financ., 77, 137–156.‎

Dong, X., Zhang, Y., & Worthington, A. C. (2018). Determinants of the Use of Fintech Finance ‎among Chinese Small and Medium-Sized Enterprises. IEEE: IEEE International ‎Symposium on Innovation and Entrepreneurship (TEMS-ISIE).‎

Frees, E. (1995). Assessing cross-sectional correlations in panel data. Journal of Econometrics, ‎‎64, 393-414.‎

Huang, H.-C., Wang, X., & Xiong, X. (2022). When macro time series meets micro panel data: ‎A clear and present danger. Energy Econ, 114, 106289.‎

LaBerge, L., O’Toole, C., Schneider, J., & Smaje, K. (2020). How COVID-19 Has Pushed ‎Companies over the Technology ‎Tipping Point and Transformed Business Forever. ‎McKinsey & Company. Retrieved from https://www.mckinsey.com/business-‎functions/strategy

LaBerge, L., O’Toole, C., Schneider, J., & Smaje, K. (2020). How COVID-19 Has Pushed ‎Companies over the Technology ‎Tipping Point and Transformed Business Forever. ‎McKinsey & Company. Retrieved from https://www.mckinsey.com/business-‎functions/strategy‎-and-corporate-finance/our-insights/how-covid-19-has-pushed-‎companies-over-the-technology-tipping-point-and-‎transformed-business-forever ‎

Lepetit, L., Nys, E., Rous, P., & Tarazi, A. ( 2008). Bank income structure and risk: An ‎empirical analysis of European banks. J. Bank. Financ, 32(8), 1452–1467.‎

Mahdi, I., & Abbes, M. (2018). Relationship between capital, risk and liquidity: a comparative ‎study between Islamic and conventional banks in MENA region. Res. Int. Bus. Finance , ‎‎45, 588–596.‎

Neifar, M. (2022c). Econometric Tools for Panel Data; Step by Step for writing a Scientific ‎Paper with STATA and Eviews. United Kingdom: LAP LAMBERT Academic ‎Publisheing- ISBN: 978-620-5-63005-1.‎

Neifar, M., & Gharbi, L. (2023b). Stability and insolvency sensitivity to Tunisian bank specific ‎and macroeconomic effects. Journal of Islamic Accounting and Business Research, ‎‎14(2), 339-359. doi:https://doi.org/10.1108/JIABR-08-2021-0236‎

Nguyen, N., & Phan, H. (2017). Policy uncertainty and mergers and acquisitions. J. Financ. ‎Quant. Anal, 52(2), 613–644.‎

Pantea, S., Sabadash, A., & Biagi, F. (2017). Are ICT displacing workers in the short run? ‎Evidence ‎from seven European countries. . Information Economics and Policy, 39, 36-‎‎44. doi:doi.org/10.1016/j.infoecopol.2017.03.002‎

Pesaran, M. (2004). General diagnostic tests for cross section dependence in panels . CESifo, ‎Working Paper 1229; IZA Discussion Paper 1240.‎

Pesaran, M. H., Shin, Y., & Smith, R. J. (1999). Pooled mean group estimation of dynamic ‎heterogeneous panels. Journal of the ‎American Statistical Association, 94, 621–634. ‎Retrieved from http://www.jstor.org/stable/2670182‎

Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of ‎level relationships. Journal of Applied Econometrics, 16, 289-326.‎

Pesaran, M., Shin, Y., & Smith, R. (2001). Bounds testing approaches to the analysis of level ‎relationships. Journal of Applied Econometrics, 16, 289-326.‎

Roach, S. S. (1991). Services under Siege: the restructuring imperative. Harvard Business ‎Review, 39(2), ‎82-92.‎

Roeder, J., & al. (2018). Make or break. Business model determinants of FinTech venture ‎success. Lüneburg, Germany: Proceedings of the Multikonferenz Wirtschaftsinformatik.‎

Serge, K. .., Rugemintwari, C., & Sauviat, A. (2019). Is fintech good for bank performance? The ‎case of mobile money in the East African Community. doi:doi: 10.2139/ssrn.3401930‎

Shim, J. (2019). Loan portfolio diversification, market structure and bank stability. J. Bank. ‎Financ, 104, 103–115.‎

Sissy, A., Amidu, M., & Abor, J. (2017). The effects of revenue diversification and cross border ‎banking on risk and return of banks in Africa. Res. Int. Bus.‎

Steenbergen, T. M. (2017). The IPO Performance of FinTech Companies . Erasmus University ‎Rotterdam: Diss. Master thesis. MSc Economics & Business.‎

Sukhinina, A., & Koroleva, E. (2020). Determinants of FinTech performance: case of Russia. ‎Saint - Petersburg, Russia: SPBPU DTMIS’20, November 18-19, .‎

Tang, M., Hu, Y., Corbet, S., Hou, Y., & Oxley, L. (2024). Fintech, bank diversification and ‎liquidity: Evidence from China. ‎International Business and Finance, 67, 102082. ‎doi:https://doi.org/10.1016/j.ribaf.2023.102082‎

Tran, V., Lin, C.-T., & Nguyen, H. (2016). Liquidity creation, regulatory capital, and bank ‎profitability. Int. Rev. Financ. Anal, 48, 98–109.‎

Ulmanis, J., & Deniņš, A. (2012). A management model of ICT adoption in Latvia. Procedia – ‎Social and ‎Behavioral Sciences, 41, 251-264. doi:doi.org/10.1016/j.sbspro.2012.04.029‎ Vazquez, F., & Federico, P. (2015). Bank funding structures and risk: Evidence from the global ‎financial crisis. J. Bank. Financ, 61, 1–14.‎

Wahyu, A., & KISWARA, E. (2017). Analyzing Operational and Financial Performance on The ‎Financial Technology (Fintech) Firm (Case Study on Samsung Pay). Diss: Fakultas ‎Ekonomika dan Bisnis.‎

Wen-Lung, S., & al. (2020). Understanding fintech continuance: perspectives from self-efficacy ‎and ECT-IS theories. Industrial Management & Data Systems .‎

Zhou, Q., Gao, P., & Chimhowu, A. (2019). ICTs in the transformation of rural enterprises in ‎China: A multi-layer perspective. Technol. Forecast. Soc. Change, 145, 12–23.‎