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Call number: LD2668 .T4 1978 O86 / Master of Science

Telenursing is a highly specialized area of nursing practice that has evolved in response to the advent of new technologies within the delivery of health care. Telenursing has been defined as “the use of communications and information technology [ICT’s] to deliver health and health care services and information over large and small distances (CRNBC, 2016). Telenurses use health information systems (HIS) in the form of a Clinical Decision Support System (CDSS) to assist callers with their health related concerns on a 24/7 basis. As decision making is an integral part of telenurse practice, particularly because they are using a CDSS while assessing the caller over the phone, it was important to understand the factors that influence the decision making process so as to better support telenurse practice in terms of education as well as other supports. This thesis identified those factors and used Rasmussen’s Decision Ladder as a model in order to provide a framework for telenursing. It was found that there were several factors identified that influenced how telenurses made decisions while using a CDSS. Additionally, the decision ladder was validated as a framework to describe telenurse practice. / Graduate

Interest in understanding Human Beings’ behaviour can be traced back to the early days of mankind. However, interest in consumer behaviour is relatively recent. In fact, it is only since the end of World War II and following economic prosperity of some nations (e.g., U.S.A.) that the world witnessed the rise of a new discipline in the early 1950s; namely, Marketing Research. By the end of the 1950s, academic papers on modelling and analysis of consumer behaviour started to appear (Ehrenberg, 1959; Frank, 1962). The purpose of this research is to propose an integrated decision framework for modelling consumer behaviour with respect to store incidence, category incidence, brand incidence, and size incidence. To the best of our knowledge, no published contribution integrates these decisions within the same modelling framework. In addition, the thesis proposes a new estimation method as well as a new segmentation method. These contributions aim at improving our understanding of consumer behaviour before and during consumers’ visits to the retail points of a distribution network, improving consumer behaviour prediction accuracy, and assisting with inventory management across distribution networks. The proposed modelling framework is hybrid in nature in that it uses both non-explanatory and explanatory models. To be more specific, it uses stochastic models; namely, probability distributions, to capture the intrinsic nature of consumers (i.e., inner or built-in behavioural features) as well as any unexplained similarities or differences (i.e., unobserved heterogeneity) in their intrinsic behaviour. In addition, the parameters of these probability distribution models could be estimated using explanatory models; namely, multiple regression models, such as logistic regression. Furthermore, the thesis proposes a piece-wise estimation procedure for estimating the parameters of the developed stochastic models. Also proposed is a three-step segmentation method based on the information provided by the quality of fit of stochastic models to consumer data so as to identify which model better predicts which market segments. In the empirical investigation, the proposed framework was used to study consumer behaviour with respect to individual alternatives of each decision, individual decisions, and all decisions. In addition, the proposed segmentation method was used to segment the panellists into infrequent users, light to medium users, and heavy users, on one hand, and split loyals, loyals, and hardcore loyals, on the other hand. Furthermore, the empirical evidence suggests that the proposed piece-wise estimation procedure outperforms the standard approach for all models and decision levels. Also, the empirical results revealed that the homogeneous MNL outperforms both the heterogeneous NMNL and DMNL when each one of these distributions is applied to all decisions, which suggests the relative homogeneity in consumer decision making at the aggregate or integrated decision level. Last, but not least, through the use of the proposed framework, the thesis sheds light on the importance of consumer choice sequence on the quality of predictions, which affects the quality of segmentation. The reader is referred to chapter 3 for details on these contributions.

Modern advances in multimedia and storage technologies have led to huge archives of human conversations in widely ranging areas. These archives offer a wealth of information in the organization contexts. However, retrieving and managing information in these archives is a time-consuming and labor-intensive task. Previous research applied keyword and computer vision-based methods to do this. However, spontaneous conversations, complex in the use of multimodal cues and intricate in the interactions between multiple speakers, have posed new challenges to these methods. We need new techniques that can leverage the information hidden in multiple communication modalities – including not just “what” the speakers say but also “how” they express themselves and interact with others. In responding to this need, the thesis inquires into the multimodal nature of meeting dialogues and computational means to retrieve and manage the recorded meeting information. In particular, this thesis develops the Meeting Decision Detector (MDD) to detect and track decisions, one of the most important outcomes of the meetings. The MDD involves not only the generation of extractive summaries pertaining to the decisions (“decision detection”), but also the organization of a continuous stream of meeting speech into locally coherent segments (“discourse segmentation”). This inquiry starts with a corpus analysis which constitutes a comprehensive empirical study of the decision-indicative and segment-signalling cues in the meeting corpora. These cues are uncovered from a variety of communication modalities, including the words spoken, gesture and head movements, pitch and energy level, rate of speech, pauses, and use of subjective terms. While some of the cues match the previous findings of speech segmentation, some others have not been studied before. The analysis also provides empirical grounding for computing features and integrating them into a computational model. To handle the high-dimensional multimodal feature space in the meeting domain, this thesis compares empirically feature discriminability and feature pattern finding criteria. As the different knowledge sources are expected to capture different types of features, the thesis also experiments with methods that can harness synergy between the multiple knowledge sources. The problem formalization and the modeling algorithm so far correspond to an optimal setting: an off-line, post-meeting analysis scenario. However, ultimately the MDD is expected to be operated online – right after a meeting, or when a meeting is still in progress. Thus this thesis also explores techniques that help relax the optimal setting, especially those using only features that can be generated with a higher degree of automation. Empirically motivated experiments are designed to handle the corresponding performance degradation. Finally, with the users in mind, this thesis evaluates the use of query-focused summaries in a decision debriefing task, which is common in the organization context. The decision-focused extracts (which represent compressions of 1%) is compared against the general-purpose extractive summaries (which represent compressions of 10-40%). To examine the effect of model automation on the debriefing task, this evaluation experiments with three versions of decision-focused extracts, each relaxing one manual annotation constraint. Task performance is measured in actual task effectiveness, usergenerated report quality, and user-perceived success. The users’ clicking behaviors are also recorded and analyzed to understand how the users leverage the different versions of extractive summaries to produce abstractive summaries. The analysis framework and computational means developed in this work is expected to be useful for the creation of other dialogue understanding applications, especially those that require to uncover the implicit semantics of meeting dialogues.

Memory is a complex cognitive process which has been widely researched within the field of neuropsychology. In clinical studies of adults, the Wechsler Memory Scale (WMS) is widely used. At this time there is no comparable clinical tool within the child literature pertaining to memory. There are studies which have extended the age limits of the WMS, but the youngest sample ranged from 10 to 14 years of age. The present research was conducted in two studies. The first study concerns the development of a memory scale for use with children aged six to twelve. This scale, Wilson's Adapted Memory Scale for Children (WAMS-C), was constructed utilizing the basic structure and subtests of the WMS. The scale was administered to 33 normal children, ranging in age from 6 to 12 years. It was hypothesized that the scale would reflect the developmental nature of memory as well as the relationship between memory and intelligence. The second study compared the memory skills of a learning disabled (LD) sample of children to those of a sample of normal learning (NL) children. A specific profile of academic achievement was used to define the LD children who participated in this study. (Reading and Spelling impaired, and relatively better Arithmetic skills). Research conducted by Rourke and his associates identified this subtype of LD children and provided predictions pertaining to their differential performance on verbal and visual tasks. The WAMS-C contains both verbal and visual memory tasks. It was predicted that these children would (1) do less well than NL children on the memory scale and (2) that these LD children would be impaired on the verbal memory portion of the WAMS-C, compared to NL children, but would exhibit equivalent performance on the visual memory tasks. The results of the studies showed the WAMS-C to reflect the developmental nature of memory and the relationship with intelligence. Also, LD children had significantly lower scores on the WAMS-C. However, neither the verbal or visual subtests differentiated between groups. Rather, subtests which may reflect short-term memory deficits and/or attentional problems appeared responsible for the differences found.

The purpose of this study was to examine agreement of husbands and of wives as groups and to explore differences in responses within individual couples in the house purchasing decision participation relative to items comprising investment value of housing. The sample consisted of 144 married couples between the ages of 30 and 60 who had purchased their house jointly and resided in the Tucson SMSA. Non-parametric tests were used to analyze the data. The Kendall's Coefficient of Concordance measured agreement of husbands and wives as groups. The McNemar Test was used to examine significant differences in responses within individual couples. Results of this study found that for items tested in this study that husbands and wives do agree when making house purchasing decisions. Wives tended to respond with a higher perceived participation score both for self and spousal responses than did their respective husband. Twelve significant differences were found.

When primates make decisions about sensory signals, their choices are biased by the costs and benefits associated with different possible outcomes. However, much remains unknown about the neuronal mechanisms by which reward information is integrated into the perceptual decision-making process. I used electrophysiological, psychophysical and imaging techniques to explore the effect of reward on sensory representations, whilst humans and monkeys made perceptual judgements about structure-from-motion (SFM) stimuli. Electrical microstimulation of visual area V5/MT in the macaque monkey was used to bias per- ceptual judgements, under different available reward sizes for correct choices. The behavioural effect of microstimulation interacted with available reward, and, in the context of a drift diffu- sion model of decision-making, the results demonstrated that reward must influence sensory processing before visual signals and micro stimulation signals are accumulated in sensorimotor areas. In a parallel human psychophysics experiment, viewers made decisions about SFM stim- uti whilst their choices were biased towards one outcome or the other by unequal pay-offs. A full drift -diffusion model was fitted to human choice and reaction time data. There was a signif- icant effect of reward on model drift rate, a parameter known to be dependent upon evidence represented in sensory brain regions. A second set of experiments used magneto encephalography (MEG) to explore activity in visual , areas in human cortex and the effects of reward. Brain responses to retinotopic visual stimuli were localized with three different MEG source analysis methods, and localization accuracy was evaluated by comparison with fMRI maps obtained in the same individuals. The results demon- strated that the beamformer and minimum norm estimate (MNE) methods were most suitable for investigating early visual activity with MEG. Human brain activity was then recorded with MEG whilst viewers made perceptual judgements about SFM stimuli, under unequal pay-offs. The results revealed an effect of reward size on early MEG responses in the region of the occipi- tal cortex and visual precuneus. Taken together, the experiments presented in this thesis provide consistent evidence that in- formation about reward can influence the processing of sensory information during perceptual decisions.

Thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy (PhD) at the Wits business school University of the Witwatersrand June 2016 / Despite the increased work on emotions in organizations, there is a lack of research on the impact of feeling bored in managerial decision-making contexts. Feeling bored was defined, and an expansion to the Hybrid Process Decision-Making Model was proposed. Using this revised definition of feeling bored and the Expanded Decision-Making Process Model, an empirical study with retail middle managers was conducted to examine the relationships between feeling bored and decision-making competence and the role of personality. Results found that feeling bored has a significant negative association with middle managers’ confidence levels, risk perception and decision rules. Results confirmed that personality plays a moderating role in the relationship between feeling bored and decision-making competence. Most notably, the personality trait learning neutralizes the negative effects of feeling bored on decision-making competence, whereas the personality trait sociability has a varied effect depending on which end of the valence/arousal continuum feeling bored is experienced. Limitations to the study, and practical implications for retail organizations, middle managers and for future research, are outlined / MB2016

by Chan Kok-Wing, Chu Ming-Cheung. / Thesis (M.B.A.)--Chinese University of Hong Kong, 1985. / Bibliography: leaves 100-102.