Anass Bayaga

The Effect of Performance Expectancy on Learners’ Intention

This research questions how much the determinants explain the usage of Cloud computing. Responses from a sample taken from public school learners, 48 males and 68 females, were assessed using a 4-point Likert scale instrument based on four main variables of behavioural intention. The gathered data were analysed using the multiple regression analysis, and the standardised beta coefficients acquired for the following three variables did not show any significant influence towards behavioural intention. From the Durbin Watson test the R2 value of 0.066 was obtained for social influence, which means that social influence accounted for only 7% of the variance in behavioural intention scores. Learner demographics accounted for 17% of the variance, and experience accounted for 35% of the variance. These results show that secondary school learners are keen on trying out Internet devices for learning regardless of their social factors, demographics, and experience in using Internet technology. There was a relative influence noted in terms of experience (resulting from persistent use) as a moderating factor towards the adoption of Internet devices. This acceptance of new technologies is driven by the benefits offered by mobile Internet devices and the ease associated with using Internet technologies.

Error Analysis: Case of Pre-service Teachers

Abstract The main aim of the study was (1) to identify errors committed by pre-service teachers in Mathematical literacy and (2) to understand why pre-service teachers continue to make such errors. Having used Newman’s Error Analysis as the theoretical framework for the study, a four-point Likert scale and a content-based structureinterview questionnaire was developed to address the aforestated research objectives. The study was conducted by means of a case study guided by the positivists’ paradigm with research sample of 105 pre-service university teachers as respondents. In order to test the reliability and consistency of the questionnaires for this study, Cronbach’s Alpha was tested for the reliability of standardised items with α = 0.705, suggesting an above average reliable instrument. There was moderately weak correlation of r = +.40 between reviewing homework before submission and correct answers (p<.01). Even though there was a moderate correlation pre-service teachers could not unanimously agree on the confidence when submitting their work. The majority (56%) were indecisive. There was no distinction between those who always took time to answer test questions (42.9%) and those who sometimes (44.8%) did.

Understanding the Value of Evaluating ICT Models to Improve Medium and Small Enterprises

This research investigated evaluation models for understanding the value of Information and Communication Technology (ICT) Operational Risk Management (ORM) in Medium and Small Enterprises (MSEs). Multiple regression, Repeated-Measures Analysis of Variance (RM-ANOVA) and Repeated-Measures Multivariate Analysis of Variance (RM-MANOVA) were performed. The findings of the distribution revealed that only one variable made a significant percentage contribution to the level of ICT operation in MSEs, the Payback method (β = 0.410, p < .000). It may thus be inferred that the Payback method is the prominent variable, explaining the variation in level of evaluation models affecting ICT adoption within MSEs. Consequently, looking at (1) degree of variability explained and (2) predictors, the results revealed that the variable contributed approximately 88.4 % of the variations in evaluation models affecting ICT adoption within MSEs. The analysis of variance also revealed that the regression coefficients were real and did not occur by chance.

Indicators of Institutional Objectives and Operational Risk Identification

Abstract The objective of this study was to investigate the relationship between the determinants of institutional objectives and institutional-wide risk identification. In this study, guided by both qualitative and quantitative methods, the researcher used content analysis and Speraman’s ranked correlation analysis to investigate relationship between determinants of institutional objectives and institutional-wide risk identification. The researcher also controlled for other determinants using partial correlation analysis. The results were in two-folds. Firstly -the University under investigation did not identify and prioritise its risks to a sufficient standard- that the University’s key risks were not linked to its strategic objectives- that the University did not ensure that the awareness of the institution’s key risks is cascaded downward, upward or horizontally through out the university employees. Secondly, the findings indicated that - there was strong, positive correlation between F1 (clientele base-institutional objectives) and risk identification activity- in contrast, there was no relationship between F2 (opportunity for learning) and risk identification- meanwhile, there was evidence to suggest good agreement between F3 (sources of risk) and risk identification- finally, there was a strong, negative correlation between F4 (alumina related institutional objective) and risk identification.

Best Predictor of Cloud Computing Usage among Various Constructs

Abstract This study aimed to analyse the determinants of Cloud computing adoption and use among high school learners. The research question that followed was; which is the best predictor of Cloud computing usage given a set of determinants? The study involved 116 respondents using simple random sampling. The results revealed that performance expectancy was the strongest predictor of the intention to adopt Cloud computing when compared to other constructs. There was a strong positive correlation of r = +0.989 between ‘the use of Internet for learning purposes in enhancing the effectiveness of learning’ and ‘being taught in their studies through Internet devices’. These findings suggest that Cloud computing adoption and application may be enhanced through educating teachers and learners on the potential benefits of this mode of communication in improving the accessibility and dissemination of scholarly content.

Performance Expectancy and Usage of Information Systems and Technology: Cloud Computing (PEUISTCC)

Abstract The objective of the paper was to determine the effect of performance expectancy on the learners’ intention to adopt and use Cloud computing. The Unified Theory of Acceptance and Use of Technology (UTAUT) model is used as the basis of this paper, and the data was collected using questionnaires. The questionnaires were analysed for correlation, significance and variance, to study Cloud computing adoption and use based on performance expectancy. The results of the paper revealed that, out of the ten sub-variables, ‘Mobile devices are also mediums for learning’ made the largest contribution, with 62.1 percent agreeing to the statement. There was a relatively strong positive correlation between the feeling of increased interest in studying using internet devices, and Cloud computing adoption and use. All the sub variables for performance expectancy contributed positively, and they reached the statistical significance of p<0.01 in terms of contribution.