Despite the amount of literature on the antecedents and outcomes of IT outsourcing, the vast majority of this research has focused on factors at the firm level. Environmental factors such as industry and country characteristics have received little attention. Environmental factors should be taken into account in IT outsourcing research because firms are open systems and their behaviors are significantly influenced by material-resource and institutional environments. Moreover, previous research at the firm level has not evaluated the effectiveness of IT outsourcing and insourcing in terms of how they create value for firms. To address the paucity of macro-level research in IT outsourcing as well as to augment our knowledge at the firm level, this thesis investigates IT outsourcing issues at three levels, namely, at the firm, industry, and country level. More specifically, I expand firm-level research by jointly examining the impacts of IT outsourcing and insourcing on IT-enabled organizational capabilities and firm performance (Essay #1). I also investigate the roles of industry-level factors such as munificence, dynamism, concentration, and capital intensity (Essay #2) and country-level variables such as the maturity of the IT-related legal system, generalized trust, uncertainty avoidance, Internet penetration, and the maturity of the IT outsourcing market of a country (Essay #3) in the diffusion of IT outsourcing practice.

Ph.D. (Computer Science) / Please refer to full text to view abstract

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In recent years, the impact of global forces such as the increasing pace of technological innovation and the growing affluence of emerging economies has changed the role of Information Technology (IT). New sourcing models and increasing competitive pressure have had a significant effect on the way technologies are delivered and subsequently the role of the Chief Information Officer (CIO) has gradually been migrating from one of a support role to that of playing a crucial part in the execution of corporate strategies. Whilst previous academic studies appear to be focused on the different competencies of the CIO, there have been few studies concerning how CIOs perceive their role and their future. Drawing on the various concepts from role theory, this PhD thesis constitutes the first known study aimed at presenting the role demands, constraints and the choices as perceived by the CIOs. Using a qualitative approach, 25 semi-structured interviews were conducted with both CIOs and senior IT leaders. Empirical evidence highlights the critical importance of role choices in determining what type of CIO an individual will be. It has also enabled the development of two new CIO role models; the Abeyant CIO and the Transmuted CIO. The Abeyant CIO model has been established to help understand the role enactment of CIOs who have not yet made the transition from a manager to a leader. In this scenario, it is asserted that individuals are the recipient of a role that was formulated by the role set, and that this ultimately determines the level of demands and constraints within the individual’s environment. In contrast, the transmuted CIO scenario emphasises that individuals have been through a process of self-reflection and they have made conscious choices throughout their careers that have resulted in approaching the CIO role differently. It is theorised in the transmuted CIO scenario that individuals are not the mere recipient of role set expectations and that they are actively involved in role making.

Thesis (M.Sc.)--University of the Witwatersrand, Faculty of Science, School of Computer Science and Applied Mathematics, 2017. / Partially Observable Markov Decision Processes (POMDPs) provide a principled mathematical framework allowing a robot to reason about the consequences of actions and observations with respect to the agent's limited perception of its environment. They allow an agent to plan and act optimally in uncertain environments. Although they have been successfully applied to various robotic tasks, they are infamous for their high computational cost. This thesis demonstrates the use of knowledge transfer, learned from previous experiences, to accelerate the learning of POMDP tasks. We propose that in order for an agent to learn to solve these tasks quicker, it must be able to generalise from past behaviours and transfer knowledge, learned from solving multiple tasks, between di erent circumstances. We present a method for accelerating this learning process by learning the statistics of action choices over the lifetime of an agent, known as action priors. Action priors specify the usefulness of actions in situations and allow us to bias exploration, which in turn improves the performance of the learning process. Using navigation domains, we study the degree to which transferring knowledge between tasks in this way results in a considerable speed up in solution times. This thesis therefore makes the following contributions. We provide an algorithm for learning action priors from a set of approximately optimal value functions and two approaches with which a prior knowledge over actions can be used in a POMDP context. As such, we show that considerable gains in speed can be achieved in learning subsequent tasks using prior knowledge rather than learning from scratch. Learning with action priors can particularly be useful in reducing the cost of exploration in the early stages of the learning process as the priors can act as mechanism that allows the agent to select more useful actions given particular circumstances. Thus, we demonstrate how the initial losses associated with unguided exploration can be alleviated through the use of action priors which allow for safer exploration. Additionally, we illustrate that action priors can also improve the computation speeds of learning feasible policies in a shorter period of time. / MT2018

Thesis (Ph. D. in Technology, Management and Policy)--Massachusetts Institute of Technology, Engineering Systems Division, Technology, Management, and Policy Program, 2011. / Cataloged from PDF version of thesis. This version of the catalog record was uploaded July 2013. / Includes bibliographical references. / Remotely piloted aircraft and the people that control them are changing how the US military operates aircraft and those who fly, yet few know what "drone" operators actually do, why they do what they do, or how they shape and reflect remote air warfare and human-machine relationships. What do the remote operators and intelligence personnel know during missions to "protect and avenge" coalition forces in Iraq and Afghanistan and how do they go about knowing what they know? In an ethnographic and historical analysis of the Air Force's preeminent weapon system for the counterinsurgencies in the two countries, this study describes how social, technical, and cognitive factors mutually constitute remote air operations in war. Armed with perspectives and methods developed in the fields of the history of technology, sociology of technology, and cognitive anthropology, the author, an Air Force fighter pilot, describes how distributed crews represent, transform, and propagate information to find and kill targets and traces the observed human and machine interactions to policy assumptions, professional identities, employment concepts, and technical tools. In doing so, he shows how the people, practices, and machines associated with remotely piloted aircraft have been oriented to and conditioned by trust in automation, experience, skill, and social interactions and how they have influenced and reflected the evolving operational environment, encompassing organizations, and communities of practice. / by Timothy M. Cullen. / Ph.D.in Technology, Management and Policy

Thesis: S.M. in Technology and Policy, Massachusetts Institute of Technology, Engineering Systems Division, 2014. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 119-122). / In many mining markets, one of the central business planning decisions faced by firms is where, when, and by how much to expand their production capacity. Appropriate investment planning methodology is important to both the mining industry and the wider economy. Currently, new mine investment decisions are most often based on the classic project evaluation methodology of discounted cash flow analysis (DCF) applied to the individual potential projects, which is flawed in its inadequate consideration of risk and flexibility, of impact on the profit of the firm, and of competitive dynamics in oligopoly markets. More sophisticated methods that account for these complexities have been proposed in academic literature; however, their value in realistic market settings has been little demonstrated in past literature and they are rarely adopted in practice. This thesis compares four investment decision paradigms of increasing scope and complexity in a three-firm mineral commodity market, based primarily on the firm-level cash flow NPV outcome in Monte Carlo simulations of the market. The analysis is conducted for various market scenarios of different demand growth patterns, volatility, demand elasticity, and supply structure. Simulation results show that in almost all scenarios, the game theoretic and option-based best-firm-NPV policies outperform the positive-mine-NPV policy substantially for all firms, regardless their market and cost position. However, the difference between the best-firm-NPV policy and the positive-firm-NPV policy is often small, depending on the scenario. Overall, the evaluation conducted in this thesis contributes to our understanding of how useful having more sophisticated investment decision methods might be to the firms and under what market conditions. / by Yuanjian Carla Li. / S.M. in Technology and Policy

Thesis: S.M. in Technology and Policy, Massachusetts Institute of Technology, Engineering Systems Division, 2014. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 123-128). / In the coming years and decades, shifts in weather, population, land use, and other human factors are expected to have an impact on the occurrence and severity of landslides. A landslide inventory database from Switzerland is used to perform two types of analysis. The first presents a proof of concept for an analogue method of detecting the frequency in landslide activity with future climate change conditions. Instead of relying on modeled precipitation, it uses composites of atmospheric variables to identity the conditions that are associated with days on which a landslide occurred. The analogues are compared to relevant meteorological variables in MERRA reanalysis data to achieve a success rate of over 50% in matching observed landslide days within 7 days. The second analysis explores the effectiveness of machine learning as a technique to evaluate the likelihood of a slide to create high damage. The algorithm is tuned to accommodate unbalanced data, extraneous variables, and variance in voting to achieve the best predictive success. This method provides an efficient way of calculating vulnerability and identifying the spatial and temporal factors which influence it. The results are able to identify high damage landslides with a success of upwards of 70%. A machine-learning based model has the potential for use as a policy tool to identify areas of high risk. / by Erin Leidy. / S.M. in Technology and Policy

The scope of requirements engineering must include high-level business objectives and strategies to achieve traceability between IT and business needs in order to ensure alignment. B-SCP handles this issue effectively. However, we must also deal with the evolution of business strategy operationlised in business processes and supporting it. MAP, goal/strategy modelling technique handles such evolution effectively. Integration of MAP with B-SCP can help us to address deficiency of addressing requirements evolution in the B-SCP framework. This thesis presents the first steps in a research project that integrates the requirements engineering methodologies, B-SCP and MAP, in order to manage evolution of strategic IT. Semantic similarities between B-SCP and MAP motivate us to combine both techniques. The integration theory results in three alternative mechanisms through which we can connect MAP with B-SCP. The usefulness of the three mechanisms is tested on two exemplars -- small one and detailed one. These mechanisms have advantages and disadvantages depending upon the complexity of the business system. MAP has a Gap Analysis process inbuilt so this saves on the overhead of inventing a new approach. In addition, MAP also extends B-SCP's capability by the addition of non-deterministic process modelling which allows B-SCP framework to offer multiple views of requirements. During the integration process, we encountered a number of issues. One important was that B-SCP identifies goals as a snapshot in time which are aligned hierarchically from higher level model to lower level model, however MAP identifies processes that strive to achieve goals and do not provide alignment between different levels of MAPs. This was a challenging issue to deal with during the integration.