ICT adoption, innovation and financial development in a digital world: empirical analysis from Africa

Abstract

As a response to a digital era that is seen as a core element of global digitalisation, financial development in many countries including those in the African continent experienced varying patterns. These have made some countries to be categorised as relatively high and others relatively low in terms of their scale on digital economy. Thus, this study empirically investigates the interaction of information and communication technology (ICT) adoption and innovation, and the role of this digitalisation interaction on financial development in Africa, and across the sub-regions. It utilises the Bayesian Vector Auto-Regressive (BVAR) modelling to simulate the impulse response function and variance decomposition across Africa. The study finds that ICTinnovation interaction shock positively drives financial development across all of 6 datasets. This implies that for multinational corporations (MNCs) and other economic agents, ICTinnovation interaction should be strongly applied across all sectors to drive financial development since all sectors require finances to improve performance. Thus, contributes to the empirical testing of the theoretical reflections of digitalisation and digital economy interaction in African countries.

Digital transformation: a conceptual framing for attaining Sustainable Development Goals 4 and 9 in Nigeria

“…This goal underscores the need to ensure that everyone can enjoy the benefits from SDGs and is incorporated into the 2030 programme especially, the SDG 9 (Digital technology for sustainable development goals), which is essential for African countries such as Nigeria (Ejemeyovwi, Osabuohien, & Bowale, 2021; Karakara & Osabuohien, 2019, 2020). This is further emphasised by the World Bank's report on Nigeria which calls for the availability of affordable information and communication technology (ICT) access as a precondition to partake in and benefit from emerging digital transformational efforts, that can lend support to the achievement of these SDGs in Nigeria (Koehler, 2016; UNCTAD, 2019; World Bank Group, 2019).…”

The Interplay between Digitalization, Education and Financial Development: A European Case Study

“…This simple specification of the model was the core of various improvements in VAR models, such as Structural VARs (Amisano and Giannini 2012), Vector Autoregressive Moving Average Models (Lütkepohl and Poskitt 1996;Athanasopoulos and Vahid 2008), and Dynamic Stochastic General Equilibrium (DSGE) Models (Giacomini 2013;Giacomini and Rossi 2016). Due to its informed use of the elements included in the model, such as a superior ability to consider the statistical properties of the data (Woźniak 2016;Ejemeyovwi et al 2021), Bayesian VAR models became popular after the publication of the seminal papers of Doan et al (1984) and Litterman (1986) and, more recently, Sims and Uhlig (1991), Uhlig (1994), and Sims and Zha (1998). These papers have shown how the Bayesian theorem can be applied to traditional VAR models to solve the limitations of unrestricted models to explore dynamic phenomena through the now established method of parameter shrinkage (restrictions imposed on parameters to reduce the parameter set), which improves the models' forecasting accuracy.…”

“…The primary benefit of these indices is their multi-dimensional approach to financial development, which is defined by a combination of financial institutions and market depth, access, and efficiency. Since the IMF calculated and published them, various authors who studied financial development have resorted to them and used them in their studies-see, for example, Laktionova et al (2021), Islam et al (2020), Mignamissi (2021), Ejemeyovwi et al (2021), Su (2021), andBaloch et al (2021). Of these indexes, we have alternatively used in the model the broader Financial Development Index (FDI), but also its two main components-Financial Institutions Index (FII) and Financial Markets Index (FMI)-each capturing financial institutions and markets particularities.…”

“…The last three variables in Table 1, also endogenous to the model, are included to control for differences among countries in terms of nominal GDP growth-a measure of economic advancement, countries' trade openness-as a measure of the intensity of participation in the globalization process, and unemployment rate-as a measure of how education and, lately, digitalization, impact labor markets. Other studies have also employed these variables in their approaches to the links between financial development, education, and digitalization (Çiftçio glu and Bein 2017; Tsaurai 2018; Olowu et al 2019;Owusu-Agyei et al 2020;Habibi and Zabardast 2020;Ejemeyovwi et al 2021;Mignamissi 2021). Besides these three control variables, others might have been considered in our research, based on previous empirical studies, such as inflation, public investment, and foreign aid (Asongu 2014;Asongu et al 2020), government expenditure (Hassan et al 2011), institutional quality (Ejemeyovwi et al 2021), gross domestic capital formation (Habibi and Zabardast (2020), or foreign direct investments, current account balance, external debt, and savings (Owusu-Agyei et al 2020).…”

ICT Adoption and Stock Market Development: Empirical Evidence Using a Panel of African Countries

“…In essence, it was found that countries with a technological lead have relatively developed financial systems. Ejemeyovwi et al (2021) empirically investigated the interaction of ICT adoption and innovation, and the role of this digitalisation interaction on financial development in Africa and across the subregions. The results of their study documented that ICT-innovation interaction shock positively drives financial development.…”