Thesis (M.M. (Entrepreneurship and New Venture Creation))--University of the Witwatersrand, Faculty of Commerce, Law and Management, Graduate School of Business Administration, 2013. / This research study analyses a conceptual model investigating the effect of firms’ choice of effectuation or causation processes in strategy formation and firm performance, the effect of firms’ technology orientation in firm performance, and the relationship between effectuation and technology orientation. The study employed a quantitative approach, surveying data from 73 firms in the renewable energy sector of South Africa, using measurement instruments extracted from prior research. By means of multiple regression analysis, the study found that of the effectuation processes, the use of pre-commitments is significantly and positively associated with firm performance. Furthermore, it was found that a pioneering technology orientation is significantly and positively associated with firm performance. The study also found evidence to support the hypothesis that effectuation is closely linked with pioneering. The study contributes to the field of effectuation research by continuing to move the field towards an intermediate phase, by providing valuable insight into the practicalities of the quantitative analysis of effectuation and the problems that arise therein, in particular, issues surrounding measurement aspects. Moreover, by examining performance differentials, this study seeks to increase the relevance of effectuation theory and expand it from a theory of mere description of entrepreneurial behaviour to a theory that identifies performance-enhancing measures. For practitioners and policy makers, this research provides valuable insight into the drivers of entrepreneurial success and the fostering of entrepreneurial activity both in start-ups and existing corporations to spur innovation, productivity, and growth in the economy.

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Students' use of technology for the purpose of academic and leisure pursuits is ever increasing. Technology access, and its subsequent use for the many varied forms of digital reading, is particularly timely and relevant for high school aged students that will likely interact with digital reading for years to come. The relationship between academic technology use, non-academic technology use, and students' scores on the 2009 Program for International Student Assessment (PISA) supplemental Digital Reading Assessment (DRA) as they related to gross domestic product (GDP) were explored in this study. Research questions were answered using extant data collected from the DRA and Information Communication Technology (ICT) survey portions of the 2009 PISA. Results indicated that academic and non-academic technology use ICT survey items were moderately correlated, however the academic and non-academic survey items were only weakly correlated to the DRA score. Moreover, the non-academic mean score was significantly higher than the academic mean score survey items. Finally, a regression analysis showed that GDP accounted for 3.28% of the variance; the non-academic survey explained 0.27% of the variance, while the academic technology use survey items only accounted for .05% of variance in the DRA. The relationship between academic and non-academic technology use as well as countries' overall DRA and GDP is further explored in the discussion.

Illegible written prescriptions and “Doctor’s handwriting” may have been synonymous, but this stereotype has begun to change with the gradual uptake of e-prescriptions. These eprescriptions are electronically captured and delivered prescriptions, and are touted as the solution to the many medical risks caused by written prescriptions. Whilst there is published support for the benefits of e-prescriptions, the uptake of e-prescribing has been too gradual for all patients to enjoy these benefits. The inadequate research into physicians’ adoption of e-prescribing systems presents a need for further study in this area, in an effort to improve the general use of these systems. Based on a review of literature, this study proposes six factors which may explain physicians’ intentions to use e-prescribing systems. These factors are based upon the Unified Theory of Acceptance and Use of Technology (UTAUT). This model is extended in this study by Social Dominance Theory, Commitment-Trust Theory and the Product Evaluation Model. Quantitative data was collected to test the proposed hypotheses. This data was gathered from physicians who have had some exposure to an e-prescription system. 72 usable responses were obtained for this study. The results of the study suggest that Performance Expectancy and Price Value have the highest influence on Behavioural Intention. Effort Expectancy and Social Influence had no direct influence on Behavioural Intention when in the presence of other variables, but they, along with Trust, had an indirect effect on Behavioural Intention through Performance Expectancy. Surprisingly, Social Dominance Orientation was not found to have an influence on Behavioural Intention. Implications, contributions and further research are discussed.

The topic was inspired by my obsession for service excellence. Having served the retail industry as a photographic salesman and ultimately as director of a chain of retail stores, I was startled by some of the blasphemous remarks made to the insurance industry. Furthermore, I was personally subjected to poor customer service by some of the large insurance companies. In my current tenure as a life assurance consultant, I have made it my mission to harness good quality, good values and provide excellent service to the countless patrons of the insurance industry. The purpose of this study was to analyse service quality at Sage Life Insurance Company. Particular attention was paid to the five dimensions of service quality and consumer behaviour. To provide a background to the evaluation, a brief history of the life assurance industry and companies was explored. The evolution of new legislations and the governing bodies was put into perspective. The findings of the study showed that clients' perception of service quality at Sage favoured some dimensions more strongly. Recommendations were made to improve the gaps in customer-relations and a model for Sage Life's customer relationship management was suggested. / Thesis (MBA)-University of Natal, Durban, 2003.

With the knowledge age evolving, colleges and universities should be ever vigilant to assure that the pedagogies practiced are adequately preparing future workers with skills required to keep pace (Scardamalia & Bereiter, 2006). Business managers have identified self-direction and technology use as increasingly important in the 21st century (Partnership for 21st Century Skills, 2006), yet a gap in research of pedagogies that advance self-directedness and promote technology use has been found. To help identify new pedagogies, the purpose of this study was to identify the relationship between self-directed learning (SDL) and technology use of people entering the workplace. A sample of 572 recent university graduates represented the new workforce entrants. Based on the Personal Responsibility Orientation (PRO)-Model of SDL (Brockett & Hiemstra, 1991), factors of self-direction were identified and measured by the Personal Responsibility Orientation -Self Directed Learning Scale (PRO-SDLS) (Stockdale, 2003). Attitudinal factors of technology use were measured by the Computer Technology Use Scale (CTUS) (Conrad & Munro, 2008). Results of this study indicated that while significant relationships between SDL and technology use were found, the effect size of the model tested is low (less than .03). Hierarchical regression indicated the factors of SDL as predictors of computer self-efficacy, attitudes toward technology use and computer anxiety are significant in some cases but account for less than 7% of the variance for any one factor. Additionally, both instruments used in this study are relatively new. While reliability for the PRO-SDLS was found to be consistent with previous research, this study indicates that caution should be taken in using the CTUS. Based on these results, this study includes implications for practice as well as recommendations for future research.

The purpose of this study was to examine factors related to information technology (IT) implementation in the curriculum. The focus was on Malaysian Ministry of Education Polytechnic (MoEP) faculty members' attitudes toward IT, as well as IT availability and IT use in teaching. The response rate from the 332 surveys sent to the MoEP was 75.9%. Faculty members as a whole appeared to have readiness for adoption of changes related to IT use in teaching despite the lack of IT use in general. The use of selected IT items was skewed greatly in the direction of non-use. Faculty attitudes toward the use of IT in their teaching were very positive. The overall professional development experiences in IT that respondents had were greatly skewed toward non-participation. Results for items associated with supports services showed that they were available for faculty use. Sixty-nine percent of the respondents reported to face barriers to the use IT in their teaching. The extent of IT use in general for male respondents and female respondents showed a significant difference among gender. ANOVA revealed no difference between MoEP membership and IT use in general. Analysis of department membership and IT use in general revealed no difference between the two. Highest level of education had a low significant correlation with extent of IT use in general. A low negative correlation was shown between highest level of education and other demographic variables. Age had a moderate positive correlation with years served for the MoEP and a high correlation with years served for the MoE. Years served for the MoEP also has a moderate correlation with years served for the MoE. There were no significant correlations among variables except for online discussion and teaching load. Highest level of education showed a low correlation with email, WWW, and scanner. Multiple regression analysis was conducted to determine what variables were the best predictors of IT use. Results revealed an R2 of 0.04. Highest level of education contributed significantly to the variance. Adoption proneness proved to be a predictor for IT use in teaching, while other selected demographic variables were not significant predictors. / Ph. D.

Thesis (M.M. (ICT Policy and Regulation))--University of the Witwatersrand, Faculty of Commerce, Law and Management, Graduate School of Public and Development Management, 2013. / This study examined different elements within the classroom, school and environment in order to establish their influence on technology implementation in schools. A review of the literature suggests that the integration of computers in schools is influenced by a number of separate but inter-related factors. If technology implementations in schools are to achieve the desired objectives as outlined in the e- Education White Paper, it is important that such efforts are cognizant of the unique characteristics of each school setting. Eight schools in Polokwane (Limpopo, South Africa) were chosen for this study, which was intended to evaluate current technology integration efforts against existing policy; and to see what policy lessons may be drawn from this.

The availability of and access to technology has been steadily increasing in recent years. Especially in light of the COVID-19 pandemic, technology use in some form is almost a daily occurrence in the United States (Vargo et al., 2021). A growing body of work has been examining familial technoference, which include interruptions to family interactions due to technology use, and a sub-focus of this research has specifically focused on parent-child relationships and technological interruptions. Using a comprehensive theoretical approach including an update to the process model of parenting (Belsky, 1984; Taraban and Shaw, 2018) and support from both attachment theory (Ainsworth et al., 1978; Bowlby, 1969) and ecological theory (Bronfenbrenner, 1977; Bronfenbrenner and Ceci, 1994), the current research examined the role of technology in parent-child interactions with parents of two-year-old children. In Study 1, constructs of parental technoference were explored in parents of children between 24-26 months of age to evaluate latent factors of parent technology use from 60 indicators and to identify parent and family characteristics that might predict the factors of technology use. A nationally recruited online sample of 323 parents of two-year-old children completed a set of questionnaires online to examine constructs of parental technology use and predictors of those constructs for Study 1. A CFA was conducted to evaluate the model fit of multiple indicators of parent technology use loading onto four predicted latent factors: Problematic Technology Use, Technoference with Child and Family, Social Support through Technology, and Technology Use as Regulation. The hypothesized model had poor fit, and an Exploratory Factor Analysis was conducted. In the final model, only 35 indicators emerged as significant factors to be included in the final model to map onto five latent constructs: Missing Out due to Technology, Problematic Technology Behaviors, Preoccupation with Technology, Positive Parenting through Technology, and Social Support through Technology. The final latent constructs parsed apart the predicted Problematic Technology Use into distinct constructs of thought (Preoccupation with Technology), behavior (Problematic Technology Behaviors), and consequence (Missing Out due to Technology), while items from the predicted Technoference with Child and Family mapped onto the more general Missing Out due to Technology (in various contexts, not just that within the family). Items from the predicted Technology Use as Regulation and Social Support through Technology mapped closely onto the Positive Parenting through Technology and Social Support through Technology constructs, respectively, albeit with fewer significant factor loadings than predicted. Next, predictors of the latent constructs (perceived stress, social support, parenting satisfaction, parenting self-efficacy, and both parent and child effortful control) were examined. SEM was conducted to determine predictors of these constructs of technology use. Perceived stress was a significant predictor of all five latent constructs. Parenting self-efficacy was a significant predictor of Problematic Technology Behaviors, Positive Parenting through Technology, and Social Support through Technology. Parenting satisfaction was a significant predictor of Problematic Technology Behaviors, Preoccupation with Technology, Positive Parenting through Technology, and Social Support through Technology. Social support was not a significant predictor of any latent constructs. Parent self-regulation was a significant predictor of Missing Out due to Technology and Positive Parenting through Technology. Child self-regulation was a significant predictor of Preoccupation with Technology, Positive Parenting through Technology, and Social Support through Technology. These findings demonstrate that there are distinct patterns of parental technology use that are differentially related to parent and family characteristics. This insight into characteristics that are associated with distinct types of technology use can be helpful in the development of targeted intervention for parents seeking to change their technology use behaviors. In Study 2, the impacts of parent technology use on parent behavior during parent-child interactions were examined through a repeated measures analysis of variance (RMANOVA) and Hierarchical Linear Modeling (HLM). In a randomized experimental design, 57 primary caregivers of 30–36-month-old children participated in three 5-minute free play sessions with their child in these conditions: control (no technology), television, and smartphone. Parent engagement with technology was scored in each condition, as well as parental sensitivity and involvement. First, RMANOVAs were conducted to explore differences in proportions of parent involvement with child play by condition and mean differences in parental sensitivity. There were significant differences in proportions of levels of parent involvement by condition; however, there were no differences in mean levels of parent sensitivity by condition. Due to a significant interaction between proportions of levels of involvement and order of condition, an HLM was conducted to control for change over time and isolate influences of condition on parent behavior. When time was controlled, there was significant negative effect of TV and a significant negative effect of smartphones on parental involvement. Overall, the findings from Study 2 demonstrated that caregivers are less involved with child play when technology is present, and especially so when smartphones are involved. Though there was not an overall effect of technology on caregiver sensitivity, further analysis did reveal that caregivers who attended to technology did have lower sensitivity scores than caregivers who did not attend to technology. The findings from this study replicate prior experimental work examining the role of background TV on caregiver-child interactions and extend findings to include the negative effect of smartphones on caregiver-child interactions. Together, the two studies provide further insight into parental technology use, understanding both antecedents and consequences of parent technology use in contribution to the overall knowledge of the mechanisms through which parent technology use relates to parenting and parent-child interactions. The findings from these studies combined can be used to develop targeted interventions for caregivers who are interested in making decisions about technology use within their families that are aligned with healthy developmental outcomes. / Doctor of Philosophy / Technology, especially personal devices like smartphones, is widely available today in ways that it was not for previous generations of parents. To help families make decisions about how to use technology in their lives and with their families, researchers need to understand the ways caregivers are using technology and how it is related to their parenting. Across two studies, this research investigated technology use among primary caregivers of two-year-old children. The first study looked at how parents are using technology, by asking 323 parents to answer 60 questions from five different surveys about their technology use behaviors through an online questionnaire. Responses to those individual questions were analyzed to see if constructs, or similar behaviors grouped together, emerged. Responses from 36 questions were grouped into five constructs: Missing Out due to Technology, Problematic Technology Behaviors, Preoccupation with Technology, Positive Parenting through Technology, and Social Support through Technology. Parents also filled out questionnaires about their stress, social support, parenting self-efficacy, parenting satisfaction, and their own and their toddler's regulation. These characteristics were analyzed to see if they would predict the different constructs of parent technology use. Ultimately, parent self-regulation predicted four of the constructs. Parents who had more self-regulation rated Missed Out due to Technology lower, rated Problematic Technology Behaviors lower, rated Preoccupation with Technology lower, but also rated Positive Parenting through Technology lower. It is possible that parents who had more self-regulation used less technology overall, regardless of whether the behaviors are helpful or harmful to their parenting. Parents who expressed more satisfaction in their parenting also rated Positive Parenting through Technology lower. One idea is that parents who felt more satisfied with their parenting were more confident and did not use technology to seek out resources or compare themselves to other parents. Parents who reported having more social support used more Social Support through Parenting, possibly because they had more social networks to maintain through digital connection. The second study looked at the impact of technology on caregiver behavior. Fifty-seven caregivers brought their 30-36 month-old children to a research laboratory, where they played together for 15 minutes. For five minutes, there was no technology on, for five minutes, there was a TV on, and for five minutes, the caregiver's cell phone was in the room. Caregivers were less involved in child play when their smartphones were in the room and when the TV was on than when there was no technology on. Specifically, caregivers spent the most amount of time not involved in child play when their smartphones were in the room. When taking into account caregivers' demographic information like age, race, and gender, caregivers who looked at their smartphones were less responsive and warm to their children than caregivers who did not look at their smartphones. These findings suggest that when technology is present, caregivers might be distracted by the technology and be less engaged with their children. In particular, smartphones seem to have more of a negative effect on caregiver behavior than background TV. Together, these two studies demonstrate that caregiver characteristics may play a role in the ways they use technology, and that technology use can affect parenting behaviors. This research is building a foundation to provide specific advice and interventions for parents who are trying to make decisions about how to use technology in their families. Findings like these have the potential to allow parents to make informed decisions for their lifestyles and to support healthy child development.

A number of studies have explored the impact of multitasking on specific cognitive skills, primarily with regard to non-media multitasking activities. While briefly addressing technology, its use in the modern era regarding media multitasking and its associated cognitive declines has not been extensively examined. Forty-nine participants were required to complete a series of cognitive tasks including the Stroop Color and Word Test and the Trail Making Test. Data were also collected for how often participants media multitask, the amount of technology they use, as well as other demographic variables. The goal of this study was to empirically examine the role of technology use and media multitasking on cognitive processes such as selective attention and task switching. It was hypothesized that those grouped as high media-multitaskers would predict a faster reaction time on the Stroop task, in line with previous literature by Cain & Mitroff (2011). Results showed a significant relationship exhibiting a negative correlation between the two factors, thereby accepting the hypothesis. Findings conclude with considerations for both the use and design of technological interfaces and devices as they apply to a variety of operational settings and high-tech environments.