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During the first half of the 20th century, lake lowering was a common practice for agricultural drainage in Sweden. Although this phenomenon led to drastic changes in the Swedish landscape, it has been remarkably understudied. Furthermore, previous research on lake lowering, and on the adjacent subject of hydropower, has mostly taken a social constructivist approach. In contrast, the aim of this study was to generate a posthumanist, symmetric account of a lake lowering project, where the non-human actants involved in the network were seen to act. This in order to better understand the driving forces and power relations emerging in a large technical project. The study took the form of an actor-network analysis of the lowering of Lake Tämnaren in the south-east of Sweden. The analysis consists of a chronological account of the project between the years 1923-1967. During the years predating 1950, investigations into both material and social matters were carried out by two engineers who became spokespersons of the many collectives of actants affected by the lake lowering. In the years between 1950-1953 when the project was carried out, the spokespersons’ accounts of reality were challenged, with varying results. After the works had been completed, the question of maintaining a suitable water level in the lake remained controversial and concerned managing the newly built dam. In brief, the conclusions of the study highlight the role of material agency in transporting and changing power between actants in a network. Forms of material agency were for example: the lake flooding, the hardness of soil, or the floodgates of a dam remaining open.

A mixed-methods study, conducted in 2007-2008, designed to quantify and assess behaviors that either protect or expose data at academic institutions to information security attacks. This study focused on computing practices at two academic institutions: East Tennessee State University and Milligan College. Interviews with six information technology professionals and online surveys were used to assess faculty and staff members' awareness and practice of safe computing behaviors. The constant comparison method was used to analyze qualitative data. Descriptive statistics and univariate and multivariate analysis of variance techniques were used to analyze the quantitative data. Overall, the analyses indicated that the faculty and staff members at these institutions were equally aware of information security issues and practices and tended to practice safe computing behaviors--though apparently at a level that was less than commensurate with their awareness of these behaviors. Raised awareness correlated with safe computing behaviors, as did computer usage: those who had used computers for more than 20 years appeared to be more aware of safe practice than those who had used computers for 20 years or less. Password management emerged as a major challenge for the participants. They were also concerned with phishing emails and they tended not to be aware of FERPA regulations.

Technology is an integral part of both modern culture and day-to-day communication. Older adults' relationships with technology are completely different than younger adults' because of the way they have learned to incorporate it into their lives. Past research has shown that certain personality traits can predict technology use in younger adults. The current research hopes to take that finding and see if it applies to older adults, too. Four hypotheses were generated. Participants took a survey that consisted of five different scales and measures. Participants also were asked to answer demographic questions. Independent-sample t-tests and bivariate correlations were run on the data. Results showed that general technology use was not significantly correlated to a participant's age. There were significant correlations between the two age groups and psychological well-being, feelings of attachment to peers, technology use subscales and sensation seeking subscales. Future studies should examine the possible relationship of technology use subscales amongst the older population and their attitudes towards technology.

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This thesis examines literary texts that represent encounters with model organisms in ways that enact an interspecies ethics that turns the narrative of bodily relationality embedded within the model organism into a source of care, friendship, respect, and mourning. My project understands model organisms as material beings as well as semiotic and narrative entities; I suggest that the very ‘materiality’ of the model organism’s body is symbolic precisely because it is designed to refer to bodies other than its own. The model organism involves a double relationality between the categories of ‘animal’ and ‘human’ because it serves as a mediator between human nature and nature at large. This is not to say that that human biology is not part of ‘nature’ but rather that anthropocentric and human exceptionalist ideologies pervade discourses of human biology and thus the model organism provides a link to our biological and corporeal ‘selves’ in a way that maintains species divisions. The texts I analyze throughout this dissertation offer alternative ways of thinking about the model organism by exposing the multiple meanings and narratives that coexist within them both as representations and as living sentient beings. This project centers around two questions: How do cultural texts represent and negotiate disconnects between how model organisms signify within scientific discourses and their broader cultural identities? How does literature specifically engage with scientific knowledge in ways that both disrupt and affirm the status of the model organism as a scientific object? / Thesis / Doctor of Philosophy (PhD)

In this dissertation research, I try to deepen the understanding of the logic and history behind science of science policy approaches and to substitute for this scientific evidence-based science policy model an evidence-critical and -informed model in which scientific and democratic claims are promoted simultaneously. Science of science policy, or what I call the scientizing science policy (SSP) discourse, is a strategic response of science policy community members to the following two socio-political developments: the government performance management reform movement and a new social contract of science. These two developments have motivated the science policy community to construct new science R&D management strategies that make science R&D investment more effective and economically beneficial than before. Former Presidential Science Advisor John Marburger played an important role in articulating an SSP approach at the federal level that opened up a political space for the larger SSP discourse to emerge and take hold. Other heterogeneous science policy community actors, including science agency managers and academic researchers, have also engaged and played major roles in shaping the premises, strategies, and directions that make up the SSP discourse by articulating their own approaches to SSP. The SSP discourse constitutes a series of strategies such as economizing and quantifying R&D investment decisions. In particular, to implement the ideas of performance reform and a new social contract of science in the field of science policy and management, the SSP community members have prioritized the development of data, models, and evidence related to federal R&D investment by funding studies on new scientific data, tools, and quantitative methods through the National Science Foundation (NSF) Science of Science and Innovation Policy (SciSIP) program. Interagency collaboration organized and supported by the Office of Science and Technology Policy (OSTP) is another key feature promoted by the SSP community. Through this research of the rise and development of the SSP discourse, I emphasize the following aspects that are relevant to both science policy practice and research community members. First, the SSP discourse demonstrates the influence of the performance reform movement on science, technology, and innovation policy and R&D management. Second, the SSP discourse has the strong potential to shift science policy makers' focus from planning and implementing to evaluating federal R&D programs. Third, the SSP discourse not only reflects, but also promotes the tendency of public policy makers, politicians, and the public to rely on scientific claims and evidence when they are engaged in discussions or policy decision making processes related to science and technology. Fourth, the SSP discourse alters the balance of authority and influence among science policy actors, including science agency managers, scientists, and executive branch offices in the decision making process on federal R&D priority and investment. Fifth, even though there are conflicts and disagreements among science policy community members on the visions and future of the NSF SciSIP program, the SSP discourse is valuable as a space in which heterogeneous science policy research and practice community members can interact, learn from each other, and collaborate to develop U.S. science, technology, and innovation policy. I conclude by proposing an evidence-critical and -informed science policy in which the SSP discourse contributes to promoting democratic values in the science policy decision process. In particular, the evidence-critical and -informed model focuses on not only using scientific data and evidence when making federal R&D decisions, but also on promoting the democratic and deliberative process in monitoring R&D activities' performance and social outcomes. In this model, I view the public as a legitimate stakeholder for evaluating federal R&D investment. This evidence-informed model can be implemented under the SSP discourse if the new R&D data, models, and tools developed by the NSF SciSIP-funded research are coupled with a new government performance website in which the public can access information about federal R&D activities as well as provide feedback about R&D investments to science policy makers. / Ph. D.

In this thesis, I discuss how the undergraduate computer scientist is trained, and how they learn what I am calling computational intuition. Computational intuition describes the methodology in which computer scientists approach their problems and solve them through the use of computers. Computational intuition is a series of skills and a way of thinking or approaching problems that students learn throughout their education. The main way that computational intuition is taught to students is through the experience they gain as they work on homework and classwork problems. To develop computational intuition, students learn explicit knowledge and techniques as well as knowledge that is tacit and harder to teach within the lectures of a classroom environment. Computational intuition includes concepts that professors and students discuss which include “computer science intuition,” “computational thinking,” general problem solving skills or heuristics, and trained judgement. This way of learning is often social, and I draw on the pedagogy of cognitive apprenticeship to understand the interactions between the professors, tutors, and other students help learners gain an understanding of the “computer science intuition.” It is this method of thinking that computer scientists at the Claremont Colleges have stated as being one of the most essential items that should be taught and gained throughout their education and signals a wider understanding of computer science as a field.

Although ERP systems have been depicted as a solution in many organizations, there are many negative reports on ERP success, benefits, and effect on user’s performance. Previous research noted that there is a lack of knowledge and awareness of ERP systems and their overall value to ERP organizations. ERP systems have been widely studied during the past decade, yet they often fail to deliver the intended benefits originally expected. One notable reason for their failures is the lack of understanding in users’ requirements. This dissertation study was designed to understand the relative importance of system quality (SQ), IQ (IQ), service quality (SVQ), and their influence on ERP users. The dependent variable individual impact (II) was used to represent the ERP success at the individual level of analysis. The research by Petter, DeLone, and McLean (2008) established the basis for this research. In addition, this study examined the moderating effect of users’ characteristics variables (age, gender, experience, and position) on the II variable. The study further compared the results of this research with Petter et al.’s (2008) research to test whether the overall findings of this research differ from their research. A web-based survey was used to collect data for this study. A number of ERP users from private and public sectors in the Middle East participated in this survey. The survey screening process provided 218 usable responses for further analysis. Using SPSS 23, the researcher determined the validity and reliability of the items. The result of the exploratory factor analysis (EFA) via principal component analysis (PCA) loaded SQ items on four components, IQ on three components, SVQ on one component, and II on one component. Following the EFA results, the researcher investigated the items’ reliability, internal consistency, convergent validity, and discriminant validity. Hypothetical relationships were examined using structural equation modeling (SEM) based on the partial least squares (PLS) technique. The moderating effect was examined using the multigroup analysis (MGA) method. This dissertation study contributed to the body of knowledge by highlighting the importance of SQ, IQ, and SVQ in impacting ERP users’ learnability, awareness/recall, decision effectiveness, and individual productivity in an ERP environment. The results of this research can be used by ERP vendors to deliver an integrated and customized ERP system to organizations based on region. This research bridged the gap in the literature on the need to conduct more ERP research in the Middle East. Understanding the relative importance of information systems (IS) success factors brings the attention of ERP organizations and vendors to focus their efforts on the leading issues perceived by end users. Assessing the level of IS impact from multiple users may help organizations to offer all types of training to develop better attitudes toward ERP systems. Organizations can also build a rigorous approach to assess the impacts of IS on ERP users’ performance and productivity. As a result, this could help in improving productivity, learnability, awareness, and decision effectiveness of the end users.