The last 10 years have seen a significant increase in the provision of consumer services through technology. Computers, mobile phones, the Internet and self-service kiosks are examples of technology platforms that have enabled services to be offered to consumers in new ways. In South Africa, technology-enabled financial services have the potential to expand financial inclusion, especially at the bottom of the pyramid. There is a need to understand how consumers adopt technology-enabled services. Using grounded theory, an enhancement to the Technology Acceptance Model is proposed and developed to explain adoption of technologyenabled financial services. Confirmatory factor analysis is used to validate the model against data obtained from a survey. The proposed model fits the data well. Implications of the model are discussed.

Financial exclusion has been shown to have negative socioeconomic effects on citizens, especially at the bottom of the economic pyramid. South Africa suffers from high levels of financial exclusion, disproportionately at the bottom of the pyramid. This study investigates nine factors identified from the literature as being positively associated with financial exclusion using a logistic regression model. The findings show that the most significant factors associated with being financially excluded at the bottom of the pyramid in South Africa were educational level, primary source of income, age, home language and number of dependents. The study further found that gender, relationship status and home ownership were not associated with being financially excluded. An interesting finding was that living in a rural area as opposed to an urban area was not significantly associated with being excluded. The findings and their implications for expanding financial inclusion at the bottom of the pyramid are discussed.

A method is presented for reducing the required sample sizes for reporting energy savings with predetermined statistical accuracy in lighting retrofit measurement and verification projects, where the population of retrofitted luminaires is to be tracked over time. The method uses a Dynamic Generalised Linear Model with Bayesian forecasting to account for past survey sample sizes and survey results and forecast future population decay, while quantifying estimation uncertainty. A genetic algorithm is used to optimise multiyear sampling plans, and distributions are convolved using a new method of moments technique using the Mellin transform instead of a Monte Carlo simulation. Two cases studies are investigated: single population designs, and stratified population designs, where different kinds of lights are replaced in the same retrofit study. Results show significant cost reductions and increased statistical efficiency when using the proposed Bayesian framework.

Since the early 1970s tremendous growth has been seen in the research of software reliability growth modeling. In general, software reliability growth models (SRGMs) are applicable to the late stages of testing in software development and they can provide useful information about how to improve the reliability of software products. A number of SRGMs have been proposed in the literature to represent time-dependent fault identification / removal phenomenon; still new models are being proposed that could fit a greater number of reliability growth curves. Often, it is assumed that detected faults are immediately corrected when mathematical models are developed. This assumption may not be realistic in practice because the time to remove a detected fault depends on the complexity of the fault, the skill and experience of the personnel, the size of the debugging team, the technique, and so on. Thus, the detected fault need not be immediately removed, and it may lag the fault detection process by a delay effect factor. In this paper, we first review how different software reliability growth models have been developed, where fault detection process is dependent not only on the number of residual fault content but also on the testing time, and see how these models can be reinterpreted as the delayed fault detection model by using a delay effect factor. Based on the power function of the testing time concept, we propose four new SRGMs that assume the presence of two types of faults in the software: leading and dependent faults. Leading faults are those that can be removed upon a failure being observed. However, dependent faults are masked by leading faults and can only be removed after the corresponding leading fault has been removed with a debugging time lag. These models have been tested on real software error data to show its goodness of fit, predictive validity and applicability.

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