The study seeks to explain how sustainable entrepreneurship occurs in a resource constrained setting. This is important as it improves our understanding of how entrepreneurs respond to adversity and develop opportunities that jointly address the social, environmental and economic dimensions of entrepreneurship. Previous research has discussed the antecedents, outcomes and contextual conditions that drive sustainable entrepreneurship. However, what is absent from this growing research body is knowledge of the mechanisms through which individuals engage in this type of entrepreneurship. The study seeks to answer the following research question: “How do individuals faced with resource constraints engage in sustainable entrepreneurship?” Using effectuation as a lens, a multi-method qualitative approach based on multiple case studies was adopted in this research and a mix of inductive and deductive analyses, also referred to as abductive analysis was employed. A sample of 5 sustainable enterprises were purposively selected in Uganda, located in sub-Saharan Africa. The results show that resource constraints compel the entrepreneurs to seek expertise and resources from others with mutual goals while controlling expenses. In the process the entrepreneur learns and adapts to the emergent opportunity. The entrepreneur’s actions are further influenced by passion that sustains the activity in the face of challenges. In this research, sustainable entrepreneurship is further explicated showing that the social, economic and environmental objectives exist in a state of shifting, supportive interaction of one another. The study clarifies our understanding of how entrepreneurs cope with inadequate resources. It explains the mechanisms through which individuals contending with resource constraints employ control as opposed to prediction strategies to exploit entrepreneurship opportunities. In this way the study contributes to the literature by proposing the fusion of cognitive and affective dimensions in realizing sustainable entrepreneurship goals. The study further suggests that the multiple objectives that typify the pursuits of sustainable entrepreneurs serve as supportive mechanisms and this puts into question arguments that these firms face comparatively larger challenges than those that singularly pursue economic objectives. / Thesis (PhD)--University of Pretoria, 2018. / Gordon Institute of Business Science (GIBS) / PhD / Unrestricted

There is much debate on the best way to educate students on how to generate hypotheses to enhance clinical reasoning in nursing education. To increase opportunities for nursing programs to promote the discovery of accurate and broad-level hypotheses, scholars recommend abductive reasoning which offers an alternative approach to hypothetico-deductive reasoning. This study explored the effects of abductive reasoning training on hypothesis generation abilities (accuracy, expertise, breadth) of first and second year baccalaureate nursing students in a problem-based learning curriculum. A quasi-experiment with 64 participants (29 control, 35 experimental) was conducted. Based on their allocation, study participants either took part in abductive reasoning training or informal group discussion. Three different test questionnaires, each with a unique care scenario, were used to assess participants’ hypothesis generation abilities at baseline, immediate post-test and one-week follow-up. Content validity for care scenarios and other study materials was obtained from content academic experts. Compared to control participants, experimental participants showed significant improvements at follow-up on hypothesis accuracy (p=0.05), expertise (p=0.006), and breadth (p=0.003). While control participants’ hypotheses displayed a superficial understanding of care situations, experimental participants’ hypotheses reflected increased accuracy, expertise and breadth. This study shows that abductive reasoning, as a scaffolding teaching and learning strategy, can allow nursing students to discover underlying salient patterns in order to better understand and explain the complex realities of care situations. Educating nursing students in abductive reasoning could enable them to adapt existing competencies when trying to accurately and holistically understand newer complex care situations. This could lead to a more holistic, person-based approach to care which will allow nursing students to see various health-related issues as integrated rather than separate. / Thesis / Doctor of Philosophy (PhD) / This study explored the effects of a training program on hypothesis generation abilities of nursing students. The training program aimed to teach students how to think more broadly about care situations. Student’s hypothesis generation abilities were measured through the use of three care scenarios, each of which was presented before, immediately after and one-week after the training program. Only first and second year nursing students were included in the study. About half of the students were provided with the training while the other half were provided with informal discussion about hypothesis generation. After one-week, it was discovered that students who received the training had improved significantly in their ability to generate broad hypotheses. These students also generated hypotheses that were more accurate than the other group of students who did not receive the training. Due to the training, students’ abilities in discovering the important aspects of the care situation also improved.

The complex and unpredictable nature of cultural behavioral changes has posed multiple challenges for marketing practitioners during recent years. These challenges include, but are not limited to, inaccurate market forecasts, market failures, wasted resources etc.This study attempts to tackle that problem by exploring cultural dimensional interactions and their potential role in consumers’ cultural behavioral change process. Due to both fields of cultural behavioral change and cultural dimensional interactions being under researched, having insufficient theoretical basis, this study adapted an abductive research approach. Additionally, this study conducted its data gathering process abroad and within the restaurant industry, so as to capture the necessary aspects of the studied phenomenon.The research was conducted by identifying two separate research subgroups: “culturally unaffected” Swedes, living in Sweden, and “culturally affected” Swedes, living in Spain. The two subgroups were interviewed on their preferred consumer behaviors in a restaurant setting. Consequently, their answers were matched against each other, in order to locate any differences in consumer behavioral preferences, indicating potential cultural behavioral changes. When provided with the participants’ rationale for the potential cultural behavioral changes, the study analyzed it through the lens of Hofstede model, looking for possible interactions between its cultural dimensions.The findings of this study suggest that cultural dimensional interactions are, in fact, existent and have a role in consumers’ cultural behavioral change process. The assumption is that the cultural dimensional interactions are regulatory in their nature and are used by the consumers to create desirable consumer situations, in order to maintain their preferred consumer behaviors in a culturally unfamiliar environment. Additionally, the findings suggest a much bigger role of consumer´s core values in the process of cultural behavioral change, than previously thought. Core values are speculated to serve as a trigger that starts the cultural dimensional interactions, due to the consumers’ adamant unwillingness to alter their core values and consumer behaviors linked to them. Due to this study´s abductive research approach, as well as insufficient theoretical basis in the fields of cultural dimensional interactions and cultural behavioral changes, these findings don´t provide any definitive conclusions. As of that, numerous prospects for future research are discussed and recommended.

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There has been significant growth in online social science experiments in order to understand behavior at-scale, with finer-grained data collection. Considerable work is required to perform data analytics for custom experiments. In this dissertation, we design and build composable and extensible automated software pipelines for evaluating social phenomena through iterative experiments and modeling. To reason about experiments and models, we design a formal data model. This combined approach of experiments and models has been done in some studies without automation, or purely conceptually. We are motivated by a particular social behavior, namely collective identity (CI). Group or CI is an individual's cognitive, moral, and emotional connection with a broader community, category, practice, or institution. Extensive experimental research shows that CI influences human decision-making. Because of this, there is interest in modeling situations that promote the creation of CI in order to learn more from the process and to predict human behavior in real life situations. One of our goals in this dissertation is to understand whether a cooperative anagram game can produce CI within a group. With all of the experimental work on anagram games, it is surprising that very little work has been done in modeling these games. Also, abduction is an inference approach that uses data and observations to identify plausibly (and preferably, best) explanations for phenomena. Abduction has broad application in robotics, genetics, automated systems, and image understanding, but have largely been devoid of human behavior. We use these pipelines to understand intra-group cooperation and its effect on fostering CI. We devise and execute an iterative abductive analysis process that is driven by the social sciences. In a group anagrams web-based networked game setting, we formalize an abductive loop, implement it computationally, and exercise it; we build and evaluate three agent-based models (ABMs) through a set of composable and extensible pipelines; we also analyze experimental data and develop mechanistic and data-driven models of human reasoning to predict detailed game player action. The agreement between model predictions and experimental data indicate that our models can explain behavior and provide novel experimental insights into CI. / Doctor of Philosophy / To understand individual and collective behavior, there has been significant interest in using online systems to carry out social science experiments. Considerable work is required for analyzing the data and to uncover interesting insights. In this dissertation, we design and build automated software pipelines for evaluating social phenomena through iterative experiments and modeling. To reason about experiments and models, we design a formal data model. This combined approach of experiments and models has been done in some studies without automation, or purely conceptually. We are motivated by a particular social behavior, namely collective identity (CI). Group or CI is an individual’s cognitive, moral, and emotional connection with a broader community, category, practice, or institution. Extensive experimental research shows that CI influences human decision-making, so there is interest in modeling situations that promote the creation of CI to learn more from the process and to predict human behavior in real life situations. One of our goals in this dissertation is to understand whether a cooperative anagram game can produce CI within a group. With all of the experimental work on anagrams games, it is surprising that very little work has been done in modeling these games. In addition, to identify best explanations for phenomena we use abduction. Abduction is an inference approach that uses data and observations. Abduction has broad application in robotics, genetics, automated systems, and image understanding, but have largely been devoid of human behavior. In a group anagrams web-based networked game setting we do the following. We use these pipelines to understand intra-group cooperation and its effect on fostering CI. We devise and execute an iterative abductive analysis process that is driven by the social sciences. We build and evaluate three agent-based models (ABMs). We analyze experimental data and develop models of human reasoning to predict detailed game player action. We claim our models can explain behavior and provide novel experimental insights into CI, because there is agreement between the model predictions and the experimental data.

The aim of this study is to investigate how self-perceptions of expertise among sports coaches may develop, regress, and redevelop over time within the context of coaching, in light of recent reconceptualisations of expertise, expertise development, sports coaching, coach development, and adult learning. The developmental journeys of four expert-like sports coaches are explored using a life history/life course approach. Written life history accounts are gathered, and repeated semi-structured interviews undertaken (six per participant over two years), focussing upon critical incidents related to coach development and perceptions of expertise, to capture interpretations and feelings. Narrative inquiry is employed to investigate and represent participants' lived experiences, and how they create meaning and identity from them. Co-constructed storied accounts of expert-like coaches' developmental journeys are produced featuring local exemplary knowledge. Looking across the stories and their respective interconnections, to speculate on wider theoretical implications is a further aspect of the study. Theoretical standpoints from a new wave of literature across different subject domains, and a Bourdieusian perspective, are used as guiding interpretive frameworks. This study reveals a more nuanced and complex holistic portrayal of perceived expertise development in contrast to oversimplified conceptions that currently dominate in this field of inquiry. This uniquely longitudinal in-depth exploration of the lived developmental journey of expert-like coaches provides illuminating detail on the process, influences, and continuation of expertise development (that may inform the facilitation and flourishing of other practitioners); uncovering a more intricate conceptualisation of expertise development, encompassing the importance of change and adaptation upon ongoing and recursive (re)development.

The complexity of biological systems constitute a significant problem for the development of biological models. This inspired the creation of a few Computational Scientific Discovery systems that attempt to address this problem in the context of metabolomics through the use of computers and automation. These systems have important limitations, however, like limited revision and experiment design abilities and the inability to revise refuted models. The goal of this project was to address some of these limitations. The system developed for this project, "Huginn", was based on the use of Abductive Logic Programming to automate crucial development tasks, like experiment design, testing consistency of models with experimental results and revision of refuted models. The main questions of this project were (1) whether the proposed system can successfully develop Metabolic Network Models and (2) whether it can do it better than its predecessors. To answer these questions we tested Huginn in a simulated environment. Its task was to relearn the structures of disrupted fragments of a state-of-the-art model of yeast metabolism. The results of the simulations show that Huginn can relearn the structure of metabolic models, and that it can do it better than previous systems thanks to the specific features introduced in it. Furthermore, we show how the design of extended crucial experiments can be automated using Answer Set Programming for the first time.

In today&#039 / s world, composite web services are widely used in service oriented computing, web mashups and B2B Applications etc. Most of these services are composed manually. However, the complexity of manually composing web services increase exponentially with the increase in the number of available web services, the need for dynamically created/updated/discovered services and the necessity for higher amount of data bindings and type mappings in longer compositions. Therefore, current highly manual web service composition techniques are far from being the answer to web service composition problem. Automatic web service composition methods are recent research efforts to tackle the issues with manual techniques. Broadly, these methods fall into two groups: (i) workflow based methods and (ii) methods using AI planning. This thesis investigates the application of AI planning techniques to the web service composition problem and in particular, it proposes the use of the abductive event calculus in this domain. Web service compositions are defined as templates using OWL-S (&quot / OWL for Services&quot / ). These generic composition definitions are converted to Prolog language as axioms for the abductive event calculus planner and solutions found by the planner constitute the specific result plans for the generic composition plan. In this thesis it is shown that abductive planning capabilities of the event calculus can be used to generate the web service composition plans that realize the generic procedure.