The Determinant factors of cloud computing adoption for electoral data management in Nigeria
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Abstract
Nigeria is one of the democratic nations that has embraced technology in its voting processes, such as voter registration and validation. This embrace has advanced over the years from using the Open ballot system to the Continuous Accreditation and Voting System in the Nigerian elections. The technological approach used in Nigeria for electoral processes, which is meant to make the processes more accessible and ensure the validation of voters, has been reported to be inappropriately implemented. There is evidence in the literature that the Nigeria electoral database system faces several challenges, including security vulnerabilities, reliability issues, limited scalability, and inefficiencies in data handling. These shortcomings necessitate exploring an alternative solution, such as cloud computing adoption. This is due to some benefits, such as accessibility, enhanced information security, collaboration, reliability, increased transparency, scalability, and data management capabilities, that cloud computing provides. There is insufficient literature on the factors that can affect cloud computing adoption in electoral data management in a specific country like Nigeria. To address this gap in the literature, the research would identify the factors that can affect the integration of cloud computing in an electoral system, and this would be done by leveraging the Technological, Organisational, and Environmental (TOE) theoretical framework. A combination of cross-sectional and inferential design is utilised to investigate the factors influencing cloud computing adoption. Data is collected from three distinct population groups: officials from the Independent National Electoral Commission (INEC), Civil Society Organisations (CSOs), and the electorate. A 5-point Likert scale questionnaire is employed to collect data from a sample size of 600 respondents, including 300 INEC officials, 150 CSOs, and 150 electorate members. Descriptive and correlational statistics, SEM (Structural Equation Modelling), AMOS, and SPSS version 20 are employed to analyse the collected data. The descriptive analysis results reveal the TOE framework's robustness in assessing the factors influencing the adoption of cloud computing for data management in INEC. Findings indicate that all stakeholders, including INEC officials, CSOs, and the Nigerian electorate, display technological readiness regarding infrastructure and human resources for cloud computing adoption. However, stakeholder commitment and regulatory support towards adopting cloud computing for data management in the Nigerian electoral system still need to be improved. Correlational statistics reveal significant relationships between Technological, Organisational, and Environmental factors and the decision to adopt different levels of cloud computing in Nigeria. These findings underscore the importance of these factors in the specific adoption levels. Based on the results, it is concluded that improving technological factors such as security, privacy, reliability, and desirability of cloud computing services, enhancing organisational factors like INEC's size, top management support, and technological readiness, and increasing environmental factors such as electorates' pressure, stakeholders' pressure, and regulatory support would significantly increase the likelihood of cloud computing adoption in the Nigerian electoral system. To facilitate the demand and adoption of cloud computing by government agencies like INEC, it is recommended that cloud computing service providers prioritise the maintenance and improvement of security, reliability, and desirability in their services. Additionally, INEC should enhance staff capacity and training in cloud-related technology to enhance their readiness. The findings of this research provide evidence for electorates, stakeholders, and regulatory agencies to adopt cloud computing technology, as it offers reliability in addressing electoral malpractices due to poor electoral data management.