This research is an in-depth study concerning the practice of Risk and Opportunity (R&O) assessment during the early phases of major project alliances in Australia. The particular focus is on how R&Os are assessed and how the profile of R&Os are set up and transferred during tender phases (from pre-tender, tender development, through to tender negotiation and construction start-up). The motivation for this research was that project risk assessments are reported to be inadequately assessed and ineffectively managed throughout the project life cycle in spite of the existence of well developed theories and procedures. However, little is known factually about how effectively R&Os are actually assessed in practice during the early project phases, the factors that influence the risk assessment performance and the nature of the issues causing the deficiency of the tender risk assessment system. Although much has been written on the theoretical development of risk assessment systems, tools and techniques, very few studies have been conducted to discover empirically how both risks and opportunities are actually assessed during the tender phase in construction practice. These problems suggest that there is a need for in-depth studies of the practice of R&O management, in order to better understand the fundamental nature of the issues that might be causing the reported ineffectiveness of current risk management practice as a basis for improvements. The researcher was immersed in the actual projects at critical stages in order to gain fine-grained access to investigate the issues ‘in situ.’ Four major construction project alliances were selected for in-depth investigation, from amongst twenty large mining and construction projects considered in the course of this research. The research methodology used centred on participant observations and document analysis supported by interviews, discussions and mini-surveys to triangulate the findings. Data were gathered and analysed on how information and rapport associated knowledge of R&Os were captured, converted and transferred during tender phases, from pre-tender, tender development, commercial negotiation up to construction start-up. Risk assessment systems, tools and techniques used during the tender phase in the projects studied were first examined and analysed. This revealed the R&O assessment profile transfer process plus its variatious patterns, the associated practical problems and gaps in both theoretical models as well as practical implementation. The process and analysis were then interpreted and modelled using the four modes of the SECI (Socialisation-Externalisation-Combination-Internalisation) model developed in Organizational Knowledge Creation Theory (Nonaka and Takeuchi, 1995). This provided a better understanding about the fundamental nature of the issues that might be causing the reported inadequacies of current practice. Implications of this knowledge conversion perspective for the adoption of risk assessment tools and techniques were identified. The major findings of this research are: (1) The R&O assessment profile transfer across the tender phases described as an information transfer model, reveals that while considerable effort goes into formal R&O documentation during the tender phase, there are a number of “breakdowns” in the assessment process. The two most critical issues are the disconnections between the quantitative assessment process and qualitative process, and a discontinuous process during hand-over from the tender phase to construction start-up phase; (2) These breakdowns can be explained when the R&O assessment process is modelled as a knowledge conversion and transfer process rather than an information transfer process described in finding 1. The analysis reveals subsequent knowledge losses during the process and knowledge gaps between theory and practice. People, process and technology factors influencing tender risk assessment are also discussed; (3) Viewing R&O assessment as a knowledge conversion process points to the need for the adoption of tools that can enhance the effectiveness of risk assessment through a knowledge elicitation, capture, communication and consolidation process; (4) The study proposes the SECI model as a suitable framework to better understand the nature of reported problems and reveals possible explanations for the deficiencies in the R&O assessment systems observed in practice. These major thesis findings provide an alternative way of looking at risk assessment, shifting risk assessment from an information-based process to a knowledge conversion-based approach for a more sustainable and effective R&O assessment system. Using the SECI model to describe risk and opportunity assessment processes as a knowledge conversion process has major implications for education and training in the practice of R&O assessment. R&O assessment becomes a learning process rather than a mere information passing process. Knowledge is revealed and captured and becomes internalised as an integral part of R&O assessment. This helps to explain why attempts to capture “lessons learned” as a separate of conventional R&O procedures frequently fail. Alliances and similar modes of project delivery that involve the collaboration of many stakeholders are likely to be used increasingly in construction, and it is essential we understand the ways in which information and knowledge of risks and opportunities are managed, especially in the early phases of major projects. Thus the new insights provided by these findings have significant implications for the formation and operation of risk and opportunity procedures in alliances and major projects more generally. Project alliance is recommended as a good procurement strategy that can enhance the effective transfer and use of knowledge about project uncertainties, thus assisting project organizations in achieving an effective risk management performance. Future research is needed to further explore the relationship between the characteristics of project alliances and the effectiveness of risk management over the whole project life cycle, with the support of the analysis framework from knowledge conversion process proposed in this thesis. Building on these findings, future research work is needed to understand how to effectively enhance the conversion of tacit knowledge about uncertainty into explicit knowledge. This will aid the development of a practical risk management framework across the whole project life cycle together with supporting risk assessment tools that utilize a knowledge-centered approach. The findings also point to the need for more research on the risk attitudes and related people factors, in the conduct of risk and opportunity assessments across the project life cycle.

In!recent years, machine!learning has developed to the!extent that it can be utilized and implemented to create business value in organizations  by  either reducing costs or increasing  innovation and growth opportunities. Machine learning can unlock possibilities to create a better! product and experience, and thereby aid in gaining a stronger position in the industry. With millions of users traveling through their e2commerce platform, the case company of this thesis, a subscription based digital service company, has the potential tocreate an improved customer  experience using optimization and machine learning, generating business value and revenue. With limited resources and need for prioritization, understanding in which areas it would be most beneficial and generate most value to implement machine learning is critical. This thesis conducted an empirical study and thematic analysis based on semistructured interviews with machine learning engineers and managers at a subscription based digital service company to investigate how to assess when it is beneficial to utilize machine learning for optimization problems within an e2commerce organization. Impact, confidence, and effort were identified as suitable factors to assess the return on investment (ROI) of machine learning. In addition to this, three factors associated with machine learning were identified as required to have in place!or to consider in order to ensure a successful machine learning implementation. These three factors were data, business metrics (what to optimize), and discovery/research. / Under senare år har maskininlärning utvecklats i den mån att det kan utnyttjas och genomföras för att skapa affärsvärde i organisationer genom att antingen minska kostnaderna eller öka innovations- och tillväxtmöjligheter. Maskininlärning kan låsa upp möjligheter att skapa en bättre produkt och användarupplevelse och därigenom bidra till att få en starkare position i branschen. Med miljontals användare som reser via sin e-handelsplattform har fallstudieföretaget av denna avhandling, ett abonnemangsbaserat digitalt serviceföretag, potential att skapa en förbättrad kundupplevelse med hjälp av optimering och maskininlärning, som  genererar affärsvärde och intäkter för organisationen. Med begränsade resurser och behov av prioritering är det viktigt att förstå inom vilka områden det är mest fördelaktigt och skapar mest värde att implementera maskininlärning. Denna avhandling genomförde en empirisk studie och tematisk analys baserad på halvstrukturerade intervjuer med maskininlärningsingenjörer och managers på ett abonnemangsbaserat digitalt serviceföretag för att undersöka hur man bedömer när det är fördelaktigt att använda maskininlärning för optimeringsproblem inom en e-handelsorganisation. Impact, confidence och effort identifierades som lämpliga faktorer för att bedöma avkastningen på investeringar (ROI) för maskininlärning. Utöver detta identifierades tre faktorer som hör samman med maskininlärning som krävs att ha på plats eller att överväga för att säkerställa en framgångsrik maskininlärningsimplementation. Dessa tre faktorer var data, business metrics (vad man optimerar) och discovery/research.