Over the course of three to four decades, most well-established companies lose their dominating position in the market or fail entirely. Their failure occurs even though they have resources for sensing shifting market trends, skills and assets to develop next-generation technologies, and the financial means to fill skill gaps and afford risky investments. Nevertheless, incumbents obviously find it very difficult to invest in innovation that takes attention and resources away from a highly successful core business. A solution to this "innovator's dilemma" is the concept of "organizational ambidexterity," which has garnered considerable attention among researchers in organization and innovation. According to empirical findings and emergent theory, companies can improve their financial performance and ensure their long-term survival by balancing their innovation activities, so that they are equally focused on exploratory (discontinuous) and exploitative (incremental, continuous) innovations. But how can such a balance be achieved? The literature on the organizational theory and related fields (product innovation, knowledge management, creativity, etc.) identifies more than 300 contributing factors to innovation and ambidexterity: many are interdependent so that their impacts compound or cancel each other. Moreover, for many factors, there is limited empirical data and the size of impacts is unknown. To understand which managerial actions lead to ambidexterity, this dissertation develops a novel approach to the study and analysis of complex casual systems with high uncertainty: exploratory fuzzy cognitive mapping. Fuzzy Cognitive Mapping (FCM) is a semi-quantitative system modeling and simulation technique. It is used to represent qualitative information about complex systems as networks of casual relationships that can be studied computationally. Exploratory modeling and analysis (EMA) is a new approach to modeling and simulation of complex systems when there is high uncertainty about the structural properties of the system. This work is the first to combine both approaches. The work makes several contributions: First, it shows that only a small fraction of management interventions will actually lead to ambidexterity while most will, at best, improve one type of innovation at the expense of the other. Second, it provides a simulation tool to management researchers and practitioners that allows them to test ideas for improving ambidexterity against a model that reflects our current collective knowledge about innovation. And third, it develops a range of techniques (and software code) for exploratory FCM modeling, such as methods for transforming qualitative data to FCM, for exploratory simulation of large and complex FCM models, and for data visualization. They can be utilized to study other similarly complex and uncertain systems.

No description available.

Electric Vehicle (EV) has become an increasingly important topic in recent years due to energy and environmental concerns. Governments started to focus on remedies to the upcoming climate change threat and seek solutions through policies and regulations. The negative impact of carbon emissions along with pressure from governmental and social organizations force automotive manufacturers to shift to alternative energy sources. However, EV transition is a complex problem because its stakeholders are very diverse including governments, policy makers, EV manufacturers, and Non-Government Organizations (NGOs). Consequently, the barriers to EV adoption are not only consumer oriented, rather exist under many categories. The literature has yet to offer a comprehensive, quantified list of barriers to EV adoption. Although the enacted policies are known, the effectiveness of these policies in mitigating EV adoption is not known. The objective of this research is to assess policy alternatives for mitigation of EV adoption barriers by developing a comprehensive evaluation model. Barriers are grouped under Social, Technical, Environmental, Economic and Political (STEEP) perspectives that are perceived by decision makers as important for adoption process. The decision model of research links the perspectives to barriers, and policy alternatives. The research implements the hierarchical decision model (HDM) to construct a generalized policy assessment framework. Data for EV adoption barriers were collected from the abovementioned stakeholders. Experts' qualitative judgments were collected and quantified using the pair-wise comparison method. The final rankings and effectiveness of policy alternatives were calculated. This research's results showed that the most important perspective is Economic. The top three most important barriers to EV adoption were identified as Initial Cost, Battery Cost, and Entrenched Technology Resistance, respectively. The most effective policy in mitigating EV adoption barriers is R&D Incentives. The research also extended the policy effectiveness research with Policy Effectiveness Curves by reaching out to additional experts. These curves helped determine the effectiveness of each of the 6 policies at different implementation levels. Based on these results, 25 scenarios were applied by combinations of policies at different implementation levels to investigate how the effectiveness of policies can change compared to today's conditions.

No description available.

Global deployment in information technology (IT) requires understandings of the cultural constraints in technology acceptance and usage behavior. Prior research indicates that the salient technology acceptance models may not be applicable to all cultures since empirical support was mainly obtained from North America. Cultural impact on user acceptance is still at the early stage of research. There has been little research done on technology acceptance and usage behavior in the context of China, which exhibits distinctive cultural differences from countries in North America. The purpose of this thesis is to test the cross-cultural applicability of Technology Acceptance Model in the People’s Republic of China (PRC), and to investigate the influence of cultural values on user acceptance of IT. Based on a synthesis of technology acceptance and cultural theories, this study incorporates work related cultural values into Technology Acceptance Model (TAM). The four cultural values of individualism/collectivism, power distance, uncertainty avoidance, and masculinity/femininity identified by Hofstede are posited to either directly influence or to moderate user acceptance to email in the context of the PRC. Data were collected from the banking industry in the PRC. Cultural values were measured at the individual level to avoid over generalizations of cultural typology and to remedy the inadequacy of post hoc explanation in conventional IS cultural studies. Instead of using the scores of national culture proposed by Hofstede’s cultural study in 1980, this study measures cultural values in terms of personal traits to reflect the changes in and the complexity of cultural values in face of a two-decades of societal change. Structural equation models (SEMs) and moderated structural equation models (MSEMs) are used in the study to explore the direct impact and the moderating effect of cultural values. Confirmatory factor analysis and structural path analysis using LISREL were performed to analyze data collected. The findings show that cultural values have both direct impact and moderation effect on user acceptance to email. The TAM general model was found to be applicable in the Chinese context. Collectivism was found to be an antecedent of Subjective Norms, while Masculinity has no significant influence on Perceived Usefulness. In addition, Power Distance was found to moderate the relationship between Subjective Norms and Intention to Use email; while Uncertainty Avoidance moderates the relationship between Perceived Ease of Use and Perceived Usefulness of email. The findings are expected to provide insights that can help international businesses to enhance technology acceptance across national boundaries. An understanding of the relationship between cultural values and technology acceptance should help organizations understand the influence of core societal values on email acceptance and so to better utilize social and cultural practices in organizational technology diffusion. This study suggests a few guidelines for better utilizing computer mediated communication technology in regard to the cultural challenges.

The South African titanium metal industry is underdeveloped and has a fragmented value chain. The aim of this study was to investigate fragmentation within the local titanium metal value chain by using industry technology roadmapping as a tool to comment on the completeness of each value chain stage. Roadmapping for the period 2021 to 2030 was applied to a novel value chain produced for the local titanium metal industry. Within the fragmented value chain, it was indicated that from the eight identified stages only four were established locally. The identified stages of the titanium metal value chain were: stage 1 - mineral reserves; stage 2 - slag; stage 3 – TiCl4; stage 4 – sponge; stage 5 – melted products; stage 6 – mill products; stage 7 – metal powder production and stage 8 – metal powder products. Stages 1, 2, 6 and 8 are already established in South Africa. The roadmapping type selected to address the fragmented South African titanium metal value chain was an industry technology roadmap. This type of roadmap focuses on forecasting the development, commercialisation and deployment of new technologies. The overall industry roadmap was designed using individual roadmaps for the value chain stages. These individual roadmaps were used as a guideline on what to include and what to exclude from the overall industry roadmap. The technology roadmap layout consisted of five layers namely market, product, technology, R&D, and resources. These roadmapping layers were applied to each stage of the titanium metal value chain resulting in the production of individual roadmaps for each stage. The roadmap model was based on the three fundamental questions in roadmapping: “Where are we now?” addressing the current state of each value chain stage, “Where do we want to go?” addressing the vision elements for each value chain stage and “How will we get there?” addressing how the vision elements would be achieved for each value chain stage. The methodology used in this study relied on data collection from two main sources. The first was primary data collected through conducting interviews and a survey. The main aim of the interviews (conducted with industry and R&D experts) was to establish a vision element for each of the titanium metal value chain stages which was validated though the survey. Secondary data was then combined with the collected expert driven data in order to follow the selected roadmapping approach and complete a roadmap for each of the value chain stages following the specified layers (top-down approach). The vision elements were then combined to obtain an overall South African titanium metal value chain vision. The consolidated vision, based on what should be included in the South African titanium metal value chain and what not, was used as driver for compiling the overall South African titanium metal industry roadmap. This was done by considering and combining the required actions needed from the value chain stage roadmaps (top-down visioning approach) to achieve the identified overall roadmap (bottom-up visioning approach). The newly developed vision for the South African titanium metal industry is: South Africa should continue to mine and upgrade titanium mineral concentrates in a sustainable and efficient manner. The country should commit to the establishment of two additional stages within the titanium metal value chain, which is TiCl4 production and titanium metal powder production. Capacity and expertise within the two already developed downstream stages (mill product and powder product production) should be expanded for both the local and the export markets. Within the mill product market, the focus should be on producing products for the medical, chemical and aerospace industries while the powder product markets should focus on medical, aerospace, leisure and automotive industries. In addition to the vision and roadmap establishment, other outcomes of this study indicated that the South African titanium metal industry is fragmented and should remain fragmented over the next decade. This thesis presents novel research on the production of a roadmap for a fragmented industry. To the best knowledge of the author, no other industry roadmap approach has first considered the production of individual roadmaps in a fragmented value chain and then combine them into a single overall industry technology roadmap. South Africa can apply the newly obtained titanium industry vision and roadmap, since there was no prior vision that addressed the development of the industry’s fragmented value chain. The newly produced roadmap can be used to advance and develop the South African titanium metal industry in order to improve local value addition to the already existing resources. / Thesis (PhD)--University of Pretoria, 2020. / The main sponsor wat the CSIR, Additive Manufacturing Initiative (AMI) UP Postgraduate Bursary for Masters and Doctoral students / Graduate School of Technology Management (GSTM) / PhD / Unrestricted

The aim of this thesis is to develop an innovative theoretical understanding of technological innovation as a social phenomenon and to demonstrate the results of its application to the sector of UK's marine energy technology. Via a creative analysis and critique of various theoretical approaches to technology, I identify several key elements of a theory capable of understanding technological change, which I then develop based on the critical juxtaposition of the approaches of Pierre Bourdieu and Cornelius Castoriadis. Technological innovation is understood as the ultimate outcome of the relations of cooperation and competition formed by radically creative agents, capable of ex-nihilo creation, who participate in private and public institutions of a quasi-regulated technological field. After arguing in favour of applying a primarily subjectivist epistemology with objectivist elements, I present a research methodology based on semi-structured interviews. The results of the data analysis highlight several key features of technological change as it takes place within the technological field of UK's marine energy technology. Firstly, I present the ways the technological field influences the agents therein and helps them develop their craft. Secondly, I explore how the agents of the field use their craft as they create ex-nihilo. Thirdly I show the interactions between the technological field and other social institutions/spaces such as the economic sector and the general public. Subsequently, I analyse the internal organization of the technological field and its impact upon the trajectories that technological innovation follows therein. Finally, I make the first tentative steps towards developing policy advice for the sector. I conclude that, as long as policy makers manage to develop a precise understanding of the technological field of marine energy technology, then they actually can design policy capable of positioning the technological innovations therein within a preferred path.

I noticed that maintenance workers had the same itinerary when emptying trashcans, meanwhile some trashcans needed to be emptied urgently. Traditionally, ETSU maintenance operate on daily routes to pick trash on designated time, regardless the level of the containers. The time, resources and labor invested in collecting the trash could be saved. Therefore, I decided to use the Internet of Things (IoT) to create a device that will optimize trash collection, to reduce costs and pollution.

There is a dearth of decision-support models or frameworks to aid managers in the Information and Communications Technology (ICT) industry in uniformly assessing the key factors in the decision to standardize innovative technologies. Making the proper decision is consequential and potentially fraught with risks for the firm such as competitive exposure, high expenditures with inadequate returns, restrictive inbound or outbound patent licensing obligations, and related complications. This study presents a framework to guide managers in the ICT industry in assessing the factors that inform the decision to participate in the development of technology standards. Using multi-criteria decision analysis and judgment data from panels of experts, a robust model is developed that comprehends the essential criteria and outcomes within the context of computer interconnect technologies. The resultant, generalizable model is validated against the case of the extant Universal Serial Bus (USB) interconnect standard and found to be congruent with the assessment of the experts. Scholarship on technology standards development is rich and multifaceted--spanning numerous streams of inquiry. This research contextualizes technology standardization within the economic, strategic, organizational, and legal perspectives. The resultant model demonstrates that strategic planning is regarded by the experts as the principal driver in the decision to participate in a technology standardization effort. Furthermore, the primacy of commitment and leadership within the standards-setting organization is unambiguously established through rigorous quantitative analysis. The proposed model verifies that the firm's desire to align its product roadmap to the emerging standard is the chief criterion in the decision to contribute to the standards development effort. Other criteria of high interest include the leveraging of network externalities to glean disruptive trends within the ecosystem, the exploration of opportunities to expand the total available market for the firm, and the availability and terms of IP licenses. Sensitivity analysis affirms the overall predictive strength and robustness of the model and its widespread applicability. Future research on model expansion and application to other technologies, as well as the development of uniform patent valuation methods will further enrich the knowledge base.

The primary goal of this research is to help any organization, which is planning to transform to the big data analytics era, by providing a systematic and comprehensive model that this organization can use to better understand what factors influence big data projects. Also, the organization's current status against those factors. Finally, what enhancements are needed in the organization's current capabilities for optimal management of factors influencing an upcoming big data project. However, big data applications are vast and cover many sectors, and while most of the factors influencing big data projects are common across sectors, there are some factors that are related to the specific circumstances of each sector. Therefore, this research will focus on one sector only, which is the smart city sector, and its generalizability to other sectors is discussed at the end of the research. In this research, literature review and experts feedback were used to identify the most critical factors influencing big data projects, with focus on smart city. Then, the HDM methodology was used to elicit experts' judgment to identify the relative importance of those factors. In addition, experts' feedback was used to identify possible statuses an organization might have regarding each factor. Finally, a case study of four projects related to the City of Portland, Oregon, was conducted to demonstrate the practicality and value of the research model. The research findings indicated that there are complicated internal and external, sometimes competing, factors affecting big data projects. The research identified 18 factors as being among the most important factors affecting smart-city-related big data projects. Those factors are grouped into four perspectives: people, technology, legal, and organization. Furthermore, the case study demonstrated how the model could pinpoint shortcomings in a city's capabilities before the project start, and how to address those shortcomings to increase chances of a successful big data project.