Problems and Prospects of Cloud Computing to the Automobile Industry in Nigeria

Abstract

This study was carried out to identify the problems and prospects of cloud computing to the automobile Industry in Nigeria Two research questions were answered and two null hypotheses were tested at 0.05 level of significance A population of 130 respondents was selected Simple random sampling was used in the selection of the sample size of the respondents giving the total of 117 respondents using the Taro Yamane formula. This consists of SO experts in Automobile Technology and 37 ICT (Computer/ Cyber Security Science) respectively. The descriptive survey design was employed and the target population was made up of experts in Automobile Technology and ICT (Computer/ Cyber Security Science). The instrument used for data collection was a structured questionnaire titled: "Questionnaire for Identifying the Problems and Prospects of Cloud Computing to the Automobile Industry in Nigeria (QPPCCAIN')". QPPCCAIN was subjected to face and content validation by five experts; 2 in Automobile Technology and 3 in Computer/ Cyber Security. Cronbach Alpha was used to determine the reliability coefficient of the questionnaire and it was found to be 0.84. The data collected from the respondents here analyzed using mean. Standard Deviation (SD) and t-test statistics. The findings on the problems of cloud computing to the automobile industry in Nigeria include among others security, availability and stability of internet network service The Findings related to the prospects of cloud computing to the automobile industry in Nigeria include among others, cloud computing connects and adds security to the vehicles; it also turns a chicle into a hub of infotainment Based on the findings, it was recommended among others that there should be a political will to the genuine growth of ICT in the automobile industry. Efforts should be made by government and private individuals and organization by introducing energy-efficient and intelligent vehicle to enhance of real-time traffic alert.