Thesis (Ph.D.)--University of the Witwatersrand, Faculty of Commerce, Law and Management, School of Economic & Business Sciences, November 2016 / Community Health Workers or “CHWs” are often the only link to healthcare for millions of people in the developing world. They are the first point of contact with the formal care system, and represent the most immediate and cost effective way to save lives and improve healthcare outcomes in low-resource contexts. Mobile-health or ‘mHealth’ technologies may have potential to support CHWs at the point-of-care and enhance their performance. Yet, there is a gap in substantive empirical evidence on whether the use of mHealth tools enhances CHW performance, and how their use contributes to enhanced healthcare service delivery, especially in low-resource communities. This is a problem because a lack of such evidence would pose an obstacle to the effective large-scale implementation of mHealth-enabled CHW projects in low-resource settings. This thesis was motivated to address this problem in the Kenyan community health worker context. First, it compared the performance of CHWs using mHealth tools to those using traditional paper-based systems. Second, it developed and tested a replicable Technology-to-Performance Chain (TPC) model linking a set of CHW task and mHealth tool characteristics, to use and user performance outcomes, through four perspectives of Task-Technology Fit (TTF), namely Matching, Moderation, Mediation, and Covariation. A quasi-experimental post-test only research design was adopted to compare performance of CHWs using an mHealth tool to those using traditional paper-based systems. A primary structured questionnaire survey instrument was used to collect data from CHWs operating in the counties of Siaya, Nandi, and Kilifi, who were using an mHealth tool to perform their tasks (n = 257), and from CHWs operating in the counties of Nairobi and Nakuru using traditional paper-based systems to perform their tasks (n = 353). Results showed that CHWs using mHealth tools outperform their counterparts using paper-based systems, as they were observed to spend much less time completing their monitoring, prevention, and referral reports weekly, and report higher percentages of both timeous and complete monthly cases. In addition, mHealth tool users were found to have more positive perceptions of the effects of the technology on their performance, compared to those using traditional paper-based systems. An explanatory, predictive, research design was adopted to empirically assess the effects of a ‘fit’ between the CHW task and mHealth technology (TTF) on use of the mHealth technology and on CHW user performance. TTF was tested from the Matching, Moderation, Mediation, and Covariation ‘fit’ perspectives using the cross-sectional survey data collected from the mHealth tool users (n = 257). Results revealed that there are various unique ways in which a ‘fit’ between the task and technology can have significant impacts on use and user performance. Specifically, results showed that the paired-match of time criticality task and technology characteristics impacts use, while that of time criticality and information dependency task and technology characteristics impacts user performance. Results also showed that the cross-product interaction of mobility task and interdependence technology characteristics impacts use, and that of mobility task and interdependence and information dependency technology characteristics, impacts user performance. Similarly, the cross-product interaction of information dependency task and time criticality technology characteristics impacts user performance. Moreover, results showed that a perceived ‘fit’ between CHW task and mHealth technology characteristics partially and fully mediates the effects of user needs and tool functions on use and user performance, whereas ‘fit’ as an observed pattern of holistic configuration among these task and technology characteristics impacts use and user performance. It was also found that the perfect ‘fit’ between CHW task and mHealth tool technology characteristics leads to the highest levels of use and user performance, while a misfit leads to a decline in use and user performance. Notably, an over-fit of mHealth technology support to the CHW task leads to declining use levels, while an under-fit leads to diminishing user performance. Of the four ‘fit’ perspectives tested, the matching and cross-product interaction of task and technology characteristics offer the most dynamic insights into use and user performance impacts, whereas user-perception and holistic configuration, were also shown to be significant, thus further reinforcing these effects. Tests of a full TPC model revealed that greater mHealth tool use had a positive effect on the effectiveness, efficiency, and quality of CHW performance in the delivery of patient care. Moreover, it was found that ‘facilitating conditions’ and ‘affect toward use’ had positive effects on mHealth tool use. Furthermore, a perceptual TTF was found to have positive effects on mHealth tool use and CHW performance. Of note, this perceived TTF construct was found to be simultaneously a stronger predictor of mHealth tool use than ‘facilitating conditions’ and ‘affect toward use’, and a stronger predictor of CHW performance than mHealth tool use. Consequently, TTF was confirmed as the central construct of the TPC. The findings constitute significant empirical insights into the use of mHealth tools amongst CHWs in low resource settings and the extent to which mHealth contributes to the enhancement of their overall performance in the capture, storage, transmission, and retrieval, of health data as part of their typical workflows. This study has provided much needed evidence of the importance of a ‘fit’ between CHW task and mHealth technology characteristics for enabling mHealth impacts on CHW performance. The study also shows how these inter-linkages could improve the use of mHealth tools and the performance of CHWs in their delivery of healthcare services in low-resource settings, within the Kenyan context. Findings can inform the design of mHealth tools to render more adequate support functions for the most critical CHW user task needs in a developing world context. This study has contributed to the empowerment of CHWs at the point-of-care using mHealth technology-enabled service delivery in low-resource settings, and contributes to the proper and successful ‘scaling-up’ of implemented mHealth projects in the developing world. / MT 2018