Big data, 4IR and electronic banking and banking systems applications in South Africa and Nigeria

The study deals with the adoption of mobile banking services by respondents in UAE and the perception of risk factors by them. A model was developed on the Customer Adoption Process of mobile banking. The model is validated based on the data collected using the questionnaire from a sample of 90 respondents in UAE. Factor analysis is used to evaluate and analyze the responses. Belief in technology and the value it creates are the major driving force for respondents to adopt mobile banking. Respondents perceive that mobile banking helps in proper financial planning due to continuous monitoring the transactions and time saving. Lack of privacy in the mobile banking transactions and not all banks offering mobile banking services in UAE are the major challenges perceived by the respondents for non-adoption of mobile banking. Respondents identify time risk, financial risk and performance risk as the most predominant risk factors compared to other risks in the adoption process.

Information economy, being the newest type, in the course of formation acquires its distinctive features, which include a significant change in the needs of investors, producers, consumers and other economic relation participants. In order to achieve a competitive information economy, state support for high-tech industries is needed. It is crucial to create a clear legal framework, give boost to the formation of intellectual capital based on other countries' experience. Implementation of the strategy for high-tech industries development in Ukraine is a decisive step in creating a platform for information technology dissemination, creation of new competitive products with high added value. The purpose of the article is to investigate the transformation to the information economy, to analyze the industry competitiveness, to define the opportunities for information sphere improvement. The theoretical aspect of the emergence and formation of the information economy category is studied, the approaches to the defining this economic category and its derivatives have been studied and generalized, essential features of the information type of economic relations have been investigated, and their main components have been determined. In the article, the relationship between the level of information economy development and the competitiveness of domestic enterprises' products has been described, the factors influencing high-tech industries development in Ukraine are considered, and suggestions as for increasing the assistance to the development of knowledge-intensive sectors, including information technology, are proposed.

Regardless of the nature, size, or business sector, organizations are now collecting burgeoning various volumes of data in different formats. As much as voluminous data are necessary for organizations to draw good insights needed for making informed decisions, traditional architectures and existing infrastructures are limited in delivering fast analytical processing needed for these Big Data. For success organizations need to apply technologies and methods that could empower them to cost effectively analyze these Big Data. However, many organizations in developing countries are constrained with limited access to technology, finances, infrastructure and skilled manpower. Yet, for productive use of these technologies and methods needed for Big Data analytics, both the organizations and their workforce need to be prepared. The major objective for this study was to investigate developing countries organizations’ readiness for Big Data analytics. Data for the study were collected from a public sector in South Africa and analyzed quantitatively. Results indicated that scalability, ICT infrastructure, top management support, organization size, financial resources, culture, employees’ e-skills, organization’s customers’ and vendors are significant factors for organizations’ readiness for Big Data analytics. Likewise strategies, security and competitive pressure were found not to be significant. This study contributes to the scanty literature of Big Data analytics by providing empirical evidence of the factors that need attention when organizations are preparing for Big Data analytics.