Widening economic inequality has been identified as a moral challenge that constitutes a global impediment to socioeconomic well-being. While incongruities exist within any dynamic system, a sustained unequal value distribution can lead to social and economic obstructions for individuals and communities. Entrepreneurship has been identified as a force for good and subsequently funded as an institutional methodology to disburse well-being by democratizing economic empowerment. Current popular approaches are institutionalized in wealthier Western contexts, encapsulated in linear narratives, and aggressively exported to new, foreign environments. Due to the often-unrecognized philosophical assumptions underlying these narratives, current approaches tend to limit the benefits of entrepreneurship to specific audiences and position the promoting institutions as entrepreneurial imperialists, creating an economic hegemony as they reinforce current power dynamics and save the most valuable entrepreneurial exchanges for those with access and resources, often benefiting the institutions economically. While much has been written on removing the impediments to current entrepreneurial approaches, this dissertation prioritizes practical utility by proposing the need for a refreshed philosophical approach, a new entrepreneurial narrative, and dynamic institutional networks that prioritize autonomy towards more effectively engaging a favorite of current entrepreneurial narratives: the rising generation.

The focus of this study is on enhancing cultural competency and increasing an ethnorelative worldview perspective among instructional designers through an innovative approach that integrates global professionals and reciprocal learning. The study is grounded in the context of Arizona State University’s mission to create inclusive learning experiences, particularly in online education, confronting the challenge of effectively providing instructional design that supports a global learner. The dissertation builds upon the existing literature on instructional design, highlighting the need for cultural competency in a globalized educational context. It underscores the growing necessity for instructional designers to adapt their skills and approaches to meet the diverse needs of global learners. The research aims to achieve professional development experiences through a reciprocal learning framework involving international instructional professionals. The research questions explore the role of reciprocal learning in fostering ethnorelative worldviews and the perceived value of this learning for the professional development of instructional designers. The study addresses critical skills such as cultural empathy, active listening, self-awareness of biases, and a commitment to continual learning. The research highlights the gaps in current instructional design training, particularly in the context of global education and cultural competency, contributing to the field of instructional design by proposing a model that integrates global perspectives into the professional development of instructional designers.

Despite increased attention and funding from companies and governments worldwide over the past several years, cybersecurity incidents (such as data breaches or exploited vulnerabilities) remain frequent, widespread, and severe. Policymakers in the United States have generally addressed these problems discretely, treating them as individual events rather than identifying commonalities between them and forming a more effective broad-scale solution. In other words: the standard approaches to cybersecurity issues at the U.S. federal level do not provide sufficient insight into fundamental system behavior to meaningfully solve these problems. To that end, this dissertation develops a sociotechnical analogy of a classical mechanics technique, a framework named the Socio-Technical Lagrangian (STL). First, existing socio/technical/political cybersecurity systems in the United States are analyzed, and a new taxonomy is created which can be used to identify impacts of cybersecurity events at different scales. This taxonomy was created by analyzing a vetted corpus of key cybersecurity incidents, each of which was noted for its importance by multiple respected sources, with federal-level policy implications in the U.S.. The new taxonomy is leveraged to create STL, an abstraction-level framework. The original Lagrangian process, from the physical sciences, generates a new coordinate system that is customized for a specific complex mechanical system. This method replaces a conventional reference frame –one that is ill-suited for the desired analysis –with one that provides clearer insights into fundamental system behaviors. Similarly, STL replaces conventional cybersecurity analysis with a more salient lens, providing insight into the incentive structures within cybersecurity systems, revealing often hidden conflicts and their effects. The result is not a single solution, but a new framework that allows several questions to be asked and answered more effectively. Synthesizing the findings from the taxonomy and STL framework, the third contribution involves formulating reasonable and effective recommendations for enhancing the cybersecurity system's state for multiple stakeholder groups. Leveraging the contextually appropriate taxonomy and unique STL framework, these suggestions address the reform of U.S. federal cybersecurity policy, drawing insights from various governmental sources, case law, and discussions with policy experts, culminating in analysis and recommendations around the 2023 White House Cybersecurity Strategy.

Artificial Intelligence (AI) is a rapidly advancing field with the potential to impact every aspect of society, including the inventive practices of science and technology. The creation of new ideas, devices, or methods, commonly known as inventions, is typically viewed as a process of combining existing knowledge. To understand how AI can transform scientific and technological inventions, it is essential to comprehend how such combinatorial inventions have emerged in the development of AI.This dissertation aims to investigate three aspects of combinatorial inventions in AI using data-driven and network analysis methods. Firstly, how knowledge is combined to generate new scientific publications in AI; secondly, how technical com- ponents are combined to create new AI patents; and thirdly, how organizations cre- ate new AI inventions by integrating knowledge within organizational and industrial boundaries. Using an AI publication dataset of nearly 300,000 AI publications and an AI patent dataset of almost 260,000 AI patents granted by the United States Patent and Trademark Office (USPTO), this study found that scientific research related to AI is predominantly driven by combining existing knowledge in highly conventional ways, which also results in the most impactful publications. Similarly, incremental improvements and refinements that rely on existing knowledge rather than radically new ideas are the primary driver of AI patenting. Nonetheless, AI patents combin- ing new components tend to disrupt citation networks and hence future inventive practices more than those that involve only existing components. To examine AI organizations’ inventive activities, an analytical framework called the Combinatorial Exploitation and Exploration (CEE) framework was developed to measure how much an organization accesses and discovers knowledge while working within organizational and industrial boundaries. With a dataset of nearly 500 AI organizations that have continuously contributed to AI technologies, the research shows that AI organizations favor exploitative over exploratory inventions. However, local exploitation tends to peak within the first five years and remain stable, while exploratory inventions grow gradually over time. Overall, this dissertation offers empirical evidence regarding how inventions in AI have emerged and provides insights into how combinatorial characteristics relate to AI inventions’ quality. Additionally, the study offers tools to assess inventive outcomes and competence.

ABSTRACT Despite a recognized need for corporations to take greater social responsibility, such responsibility is often lacking in the decisions of corporate America. This lack of attention to social responsibility has numerous implications, not least for the US workforce. Additionally, the workforce itself has a potential role to play in implementing social responsibility. Workers are partly responsible for actions causing negative effects; however, organizations tend to avoid addressing the negative effects as a form of organized irresponsibility. This dissertation examines decisions and actions related to the worker, their work roles, and within their organization. It aims to understand to what extent workers can function as change agents in aligning their organizations with social responsibility as it relates to organizational missions. The methodological approach used to gather data for this dissertation is Socio-Technical Integration Research (STIR), and the framework used to analyze the data is Midstream Modulation. The dissertation advances the STIR methodology in several respects as a result of studying technology startups with a focus towards organizational effects. These advances include measuring how modulations within individual workers’ decisions have outcomes at the organizational level or across multiple departments. Examples of such “organizational modulations” can be seen in two of the three studies at the core of this dissertation. Additionally, I demonstrate that multiple reflexive modulations can be involved in modulation sequences and that modulation sequences can be nested in relation to one another. Furthermore, I present the Collaborative Change Agent Model, which may possibly be utilized to further discuss decisions and embed concepts such as social responsibility and Responsible Innovation in an individual worker’s decision-making process.

Crises at Teton Dam in 1976, Roosevelt Dam in 1980, Tempe Town Lake Dam in 2010, Oroville Dam in 2017, and the Edenville and Sanford Dams in 2020 prove the substantial and continuing threats to communities posed by major dams. Sociotechnical systems of dams encompass both social or governance characteristics as well as the technical or architectural characteristics. To reduce or overcome chances of failure, experts traditionally focus on making the architectural characteristics of dams safe from potential modes of failure. However, governance characteristics such as laws, building codes, and emergency actions plans also affect the ability of systems of dams that include downstream communities to sustainably adapt to crises. Increasingly, emerging threats such as climate change, earthquakes, terrorism, cyberattacks, or wildfires worsen known modes of failure such as overtopping.Considering these emerging threats, my research assesses whether the architectural and governance characteristics of the aging population of systems of dams in the United States can sustainably adapt to challenges posed by emerging threats. First, by analyzing architectural characteristics of dams, my research provides a useful definition of infrastructures of dams. Next, to assess the governance characteristics of dams, I review institutional documents to heuristically outline seven sociotechnical imaginaries and assess whether an eighth based on resilience is appearing. Further, by analyzing interview transcripts and professional conference presentations, and by conducting case studies, my research reveals ways that experts and stakeholders assess the safety and resilience of systems of dams. The combined findings of these studies suggest that experts and stakeholders are not sufficiently informed about or focused upon important aspects of the resilience of dams. Therefore, they may not be able to sustainably adapt to crises caused or worsened by emerging threats such as climate change, earthquakes, terrorism, cyberattacks, or wildfires. I offer explanations of why this is so and formulate recommendations.

Some say that science fiction becomes science. If science fiction eventually becomes science and technology, then US-American science and technology surrounding robots are rooted in white supremacy. Scholarship has previously highlighted the way that films and stories about robots are exclusionary towards Black people and persons of color. These texts, while aptly making the connection between race, Blackness, and technology, do not sufficiently address the embedded design of anti-Blackness in cultural artifacts in the early twentieth century and the anti-Black logics that, to this day, continue to inform how stories about robots are told. Further, these analyses do not consider the connection between cultural artifacts and the material development of emerging technologies; how these embedded racist narratives drive and shape how the technologies are then constructed. In this dissertation, I aim to link how anti-Black scientific popular culture has informed academic scholarship and engineering related to robots in the United States. Stories are an inherently spatial project. Stories about robots are a spatial project intended to create “Cartographies of Subordination.” I contend from 1922 to 1942, US-American robots were mapped into and onto the world; in just twenty short years, I argue a Cartography of Subordination was established. I apply a spatial lens to critique the impact of embedding stories about robots with anti-Blackness. These stories would develop into narratives with material consequences and maintain lasting ties and allegiance to a world invested in white supremacy. I outline how popular culture and stories are transfigured into narratives that have a direct impact on how futures are built. I expose the loop between popular culture and scholarship to unmask how research and development in robotics are based on white-informed futures. My dissertation makes an original geographical contribution to the fields of Human and Cultural Geography by asserting that narrative and popular culture about robots serves to remake Cartographies of Subordination in both science fiction and science and technology broadly. If science fiction has the potential to become real scientific outcomes, I connect culture, geography, and legacies of power in an otherwise overlooked space.

Existing models of military innovation assume general resistance to change within militaries that necessitates an outside influence to induce military innovation. Within these approaches, the complex relationship between technology and innovation is normally addressed by either minimizing the importance of technology or separating it from the social process of innovation. Yet these approaches struggle to reflect emerging dynamics between technology and military innovation, and as a result, potentially contribute to wasted national resources and unnecessarily bloody wars. Reframing the relationship between technology and military innovation can provide novel insights into the apparent inability of militaries to align technology with strategic goals and inform more effective future alignment. This dissertation leverages the insights of constructivist science and technology studies concepts to develop a novel model of military innovation: referred to here as the technology triad. The technology triad describes military sociotechnical systems in a way that highlights change and innovation within militaries. The model describes how doctrine, materiel, and “martial knowledge,” a new concept that relates to socially constructed truths about the conduct of war, interact to produce change and innovation within militaries. After constructing the model and exploring an in-depth application to the development of armored warfare in the United States Army prior to World War II, the case from which the model was developed, the dissertation explores the logical extension of the technology triad to establish a deductive framework against which to test the generalizability of the model. Nuclear weapons innovation in the United States military through the end of the Vietnam War provides a test of the model at the strategic level, and the development and employment of armed drones in the United States, Russia, Israel, and Azerbaijan provide a test of a contemporary innovation for the technology triad. Together, these three cases demonstrate that framing the relationship between technology and military innovation in terms of the technology triad can inform concrete actions that military leaders can take related to the types of technologies that are most likely to be useful in future conflicts and ways to manage military innovations to increase opportunities to achieve strategic objectives.

Threatcasting is a foresight methodology that examines the worst of potential future changes by imagining and crafting a fictional (but very plausible) story of a person, in a detailed setting, experiencing a threat. In this dissertation, I investigate the processes and techniques of threatcasting, focused primarily on the post-analysis phase, and demonstrate it as an open methodology that can embrace varied ways to analyze raw data and seek conclusions. I incorporate best practices of narrative and thematic analysis, qualitative analysis, grounded theory, and hypothesis-driven theories of inquiry. I use interviews from futurists trained on threatcasting ways of thinking and compare two case studies - one using a grounded theory approach on the future of weapons of mass destruction and cyberspace and the other using a hypothesis-driven approach on the future of extremism - to investigate the efficacy of different theoretical approaches to analysis. I introduce definitions of novelty and ways to assess how a novel finding may have more impact on the future than it appears at first glance. Often, this impact comes more from what is not present in threat scenarios than what is included. Finally, I illustrate how threatcasting, as a practice, is a valuable contribution to those in a position to be responsible architects of a better future.

Within the last decade, there has been a lot of hype surrounding the potential medical applications of artificial intelligence (AI) and machine learning (ML) technologies. During the same timespan, big tech companies such as Microsoft, Apple, Amazon, and Google have entered the healthcare market as developers of health-based AI and ML technologies. This project aims to create a comprehensive map of the existing health-AI market landscape for the standard biotech reader and to provide a critical commentary on the existing market structure.