An investigation of decision support knowledge production, transfer and adoption for it outsourcing

Information Technology Outsourcing (ITO) is a widely-adopted strategy for IT governance. ITO decisions are very complicated and challenging for many organisations. During the past three decades of ITO research, numerous decision support artefacts (e.g. frameworks, models, tools) to support organisational ITO decisions have been described in academic publications. However, the scope, rigour, relevance and adoption of this research by industry practitioners had not been assessed. This study investigates the production, transfer and adoption of academic research-generated knowledge for ITO decision support through multiple perspectives of ITO researchers and practitioners (e.g. IT managers, IT consultants) to provide insights into the research problem. A mixed-methods research approach underpinned by the critical realism paradigm is employed in this study. The study comprised three phases. In Phase A, the scope of extant research for supporting ITO decisions is identified through a systematic literature review and critical assessment of the rigour and relevance of this body of research is conducted using a highly regarded research framework. One hundred and thirty three articles on IT outsourcing (including cloud sourcing) were identified as ITO decision support academic literature. These articles suggested a range of Multiple Criteria Decision Making (MCDM), optimisation and simulation methods to support different IT outsourcing decisions. The assessment of these articles raised concerns about the limited use of reference design theories, validation and naturalistic evaluation in ITO decision support academic literature. Recommendations to enhance the rigour and relevance of ITO decision support research are made in this thesis. Phase B involved interviewing and surveying academic researchers who published academic literature on ITO decision support artefacts. This phase reports researchers’ reflections on their ITO research experience and knowledge transfer activities undertaken by them. The findings indicate researchers’ motivations, knowledge transfer mechanisms, and communication/ interaction channels with industry may explain effective knowledge transfer. Impact-minded researchers were significantly more effective than publication-minded researchers in knowledge transfer. In Phase C, interviews and a survey of practitioners engaged in IT outsourcing shed light on use of academic-generated knowledge. Academic research was the least used source of decision-making knowledge among ITO practitioners. Practitioners preferred to seek advice from their peers, IT vendors and consultants to inform their ITO decision making. Two communities of users and non-users of academic research were identified in our sample of ITO practitioners, with non-users forming the majority. Six factors that may influence the use of academic research by practitioners were identified. Non-users of academic research held perceptions that academic research was not timely, required too much time to read, was far from the real world and that it was not a commonly-used knowledge source for practitioners. Also, non-users of academic research read academic research less frequently and did not perceive themselves as an audience for academic research. This study engaged two fields of research: ITO decision support and academic knowledge transfer/utilisation (including research-practice gap). ITO decision support research provide the specific context for a critical assessment of academic knowledge production, transfer and adoption. For ITO DSS, this study identified the scope, rigour and relevance of the field, and improvement opportunities. This study confirms that a research-practice gap exists in the ITO decision support field as previously suggested by some scholars. Also, this study made a significant contribution to the highly complex and contested field of research utilisation and the research-practice gap. The relationship between research and practice in terms of knowledge production, transfer and utilisation is modelled using social system theory. Multiple theories are applied through a retroductive (abductive) analysis to shed light on the root causes of the research-practice gap. This study suggests that the lack of adequate appreciation of research relevance in academic reward schemes and the academic publishing structure are the main root causes of the research-practice gap in the knowledge production side. Moreover, various institutional mechanisms exist in knowledge transfer and adoption domains that influence the knowledge adoption channels of practitioners. As a result, academic research does not become a priority source of ITO decision support knowledge for practitioners. This study suggests that to overcome the barriers to academic research adoption by practitioners, the effective structural coupling mechanism between the system of science (knowledge production domain) and organisation systems (knowledge consumption domain) needs to be identified and activated

A prescriptive analytics approach for energy efficiency in datacentres.

Given the evolution of Cloud Computing in recent years, users and clients adopting Cloud Computing for both personal and business needs have increased at an unprecedented scale. This has naturally led to the increased deployments and implementations of Cloud datacentres across the globe. As a consequence of this increasing adoption of Cloud Computing, Cloud datacentres are witnessed to be massive energy consumers and environmental polluters. Whilst the energy implications of Cloud datacentres are being addressed from various research perspectives, predicting the future trend and behaviours of workloads at the datacentres thereby reducing the active server resources is one particular dimension of green computing gaining the interests of researchers and Cloud providers. However, this includes various practical and analytical challenges imposed by the increased dynamism of Cloud systems. The behavioural characteristics of Cloud workloads and users are still not perfectly clear which restrains the reliability of the prediction accuracy of existing research works in this context. To this end, this thesis presents a comprehensive descriptive analytics of Cloud workload and user behaviours, uncovering the cause and energy related implications of Cloud Computing. Furthermore, the characteristics of Cloud workloads and users including latency levels, job heterogeneity, user dynamicity, straggling task behaviours, energy implications of stragglers, job execution and termination patterns and the inherent periodicity among Cloud workload and user behaviours have been empirically presented. Driven by descriptive analytics, a novel user behaviour forecasting framework has been developed, aimed at a tri-fold forecast of user behaviours including the session duration of users, anticipated number of submissions and the arrival trend of the incoming workloads. Furthermore, a novel resource optimisation framework has been proposed to avail the most optimum level of resources for executing jobs with reduced server energy expenditures and job terminations. This optimisation framework encompasses a resource estimation module to predict the anticipated resource consumption level for the arrived jobs and a classification module to classify tasks based on their resource intensiveness. Both the proposed frameworks have been verified theoretically and tested experimentally based on Google Cloud trace logs. Experimental analysis demonstrates the effectiveness of the proposed framework in terms of the achieved reliability of the forecast results and in reducing the server energy expenditures spent towards executing jobs at the datacentres.N/