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Subject: search.filters.subject.460209 Planning and decision making

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    A framework for enhanced decision making in construction organisations based on quality of pipeline information : a thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy, School of Built Environment, Massey University, New Zealand
    (Massey University, 2025-08-25) Moshood, Taofeeq Durojaye
    The New Zealand government actively pursues the development of a sustainable construction sector that ensures high performance, productivity, innovation, and community well-being through diverse project initiatives spanning residential, non-residential, and infrastructure development. Despite these aspirations, construction projects in New Zealand are frequently delayed and not effectively completed, thus leading to underperformance. Two critical factors contributing to project delays and suboptimal outcomes are inadequate quality of information and ineffective strategic decision-making processes. Formulating effective strategic decisions is a fundamental challenge for construction organisations, significantly impacting their overall strategic goals and operational success. While information management and decision execution are widely recognised as crucial elements in organisational strategy, there remains a notable gap in understanding the intricate relationship between the quality of information and strategic decision-making, particularly within the context of construction business performance. This research addresses this critical knowledge gap by identifying the determinants for successful construction business performance. It investigates the mediating role of quality of information in the relationship between strategic decision-making and the performance of construction businesses in New Zealand. The study employs a comprehensive methodological approach combining systematic literature review, quantitative analysis, and stakeholder validation to develop a robust understanding of these complex relationships. The systematic literature review focused specifically on identifying critical determinants for successful construction business performance in New Zealand, utilising the ATLAS.ti 9 tool for analysis. This comprehensive review highlighted the fundamental role of the quality of information in strategic decision-making processes and its subsequent impact on organisational performance. This systematic analysis led to the development of a conceptual framework and associated hypotheses establishing the relationships between these factors and their impact on strategic decision-making, information quality, and construction business performance. A quantitative survey was conducted with 102 respondents, and the sample size was determined using G*Power analysis to ensure statistical validity. The study examined how strategic decision-making and information quality influence construction business performance in New Zealand. The research method that was employed integrated established theoretical models and employed Partial Least Squares Structural Equation Modelling (PLS-SEM) using SmartPLS4 software. This analytical approach allowed for robust testing of both direct relationships between variables and the mediating effect of information quality on the relationship between strategic management practices and business performance outcomes. The findings revealed a strong positive correlation between strategic decision-making management and construction business performance, with quality of information serving as a crucial mediating factor. A key research component involved validating the proposed strategic decision-making process framework for its sufficiency, clarity, coherence, relevance, and applicability in New Zealand construction organisations. This validation process included in-depth interviews with six industry experts representing various organisational approaches to strategic decision-making. Their responses revealed a spectrum of techniques ranging from highly structured to more informal methodologies, providing valuable insights into the practical application of strategic decision-making frameworks in different organisational contexts. The research outcomes significantly contribute to theoretical understanding and practical application in the construction industry. Establishing clear criteria for successful quality of information and identifying critical factors affecting project implementation success, this study offers valuable guidance for improving practices within the construction sector. The findings emphasise the crucial importance of prioritising both the quality of information and strategic decision-making to effectively lessen project delays and optimise overall performance in the construction sector. Furthermore, the research contributes to developing more effective strategic decision-making processes by highlighting the interconnected nature of the quality of information and strategic planning. The validated framework provides construction organisations with a practical tool for enhancing their strategic decision-making capabilities while considering the critical role of quality of information in achieving successful outcomes. This study's findings have significant implications for construction industry stakeholders, such as project managers, organisational leaders, and policymakers. The research demonstrates that improving the quality of information and strategic decision-making processes can enhance project outcomes and overall organisational performance. The validated framework offers a structured approach for organisations to assess and improve their current practices while considering their unique operational contexts and requirements.
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    Estimate industry cost of equity from machine learning : Master of Business Studies (Finance), Massey University
    (Massey University, 2025) Li, Jingcheng
    This thesis aims to contribute to the existing literature in several specific ways. To the best of our research, there has been relatively limited research applying advanced machine learning (ML) techniques specifically to the estimation of industry-level cost of equity (ICoE). While existing studies have generally focused on firm-level return forecasting or relied heavily on traditional asset pricing models such as CAPM, FF3, or FF5, our study extends this scope by systematically exploring how ML methods perform in the broader context of industry-level equity cost estimation. Through this investigation, we provide empirical insights that help clarify the robustness and applicability of ML techniques at the industry rather than the firm level. We propose a hybrid modelling framework designed to integrate factors selected by machine learning methods into the traditional FF5 asset pricing framework. Specifically, we utilize the extensive JKP factor library, which comprises 153 factors categorized into 13 economic themes, to investigate whether ML-selected factors can provide incremental explanatory power relative to the original, fixed FF5 factors. By adopting this approach, we seek not only to enhance theoretical understandings of factor models but also to offer practical improvements that investors and financial analysts could potentially adopt in their valuation processes. We conduct a detailed comparative analysis between traditional models (especially FF5) and selected ML methods, such as LASSO, Gradient Boosting Machines (GBM), and Light Gradient Boosting Machines (Light GBM). We evaluate the predictive accuracy and stability of these methodologies using a range of commonly accepted error metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Adjusted R². This structured comparison may illustrate the conditions and industry features. Under these conditions and features, ML methods might outperform traditional asset pricing models. To this extent, it may provide useful practical insights for both academic researchers and financial practitioners. Finally, we attempt to bridge existing gaps between traditional asset pricing theories and modern machine learning methodologies with our empirical framework. We validated the potentiality of integrating ML-selected factors into traditional models, and provide a basic approach for future researchers and practitioners interested in improving industry-level equity cost estimation. Overall, our research seeks to provide a novel perspective about factor selection in asset pricing models. Also we seek to suggest methodological improvements that could enhance investment decision-making under complex and volatile market conditions.
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    Prioritising indicators of success in 'Build Back Better' post-disaster frameworks : a thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Construction at Massey University, Albany, New Zealand
    (Massey University, 2025-07-30) Hubbard, Francis
    This study explores the challenges and significance of indicator selection for key decision-makers in post-disaster response, recovery, and reconstruction efforts. When a community is overwhelmed in the aftermath of a disaster - various entities, including aid organisations, local authorities, and national agencies, are mobilised to provide emergency response and support in the subsequent response and recovery phases. These decision-makers rely on choosing appropriate indicators to evaluate the effectiveness of their interventions, track progress, and decide on appropriate actions and activities. Guided by the principle of "Build Back Better," which advocates for a comprehensive and holistic approach to resilience, practitioners need to comprehend the intricate relationships and dependencies among indicators to make informed decisions regarding their selection. This aspect has been identified as a significant weakness in the implementation process for all stakeholders. Employing a novel methodology, this thesis utilises the Hierarchical Decomposition Algorithm to analyse the priority of and the relationship between indicators proposed by the 2016 ‘Build Back Better Framework’, a synthesised framework reflecting a unified approach in disaster management. Empirical evidence from forty case studies examining key decision makers experiences of implementing disaster response efforts validates these findings. The study concludes with a rational process and workflow for determining indicator selection which considers the diverse nature of response and recovery in the pursuit to effectively build back better.
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    Applying AI-based techniques for DDoS anomaly detection and classification using large-scale datasets : a thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy (Ph.D.) in Computer Science, Massey University
    (Massey University, 2024-01-11) Wei, Yuanyuan
    A Distributed Denial-of-Service (DDoS) attack is a type of malicious attempt to disrupt the normal traffic of a targeted server, service, or network by sending a flood of traffic to overwhelm the target or its surrounding infrastructure. DDoS attacks expose significant security vulnerabilities in network devices, allowing for malicious propagation. This presents serious security risks, including potential data loss and financial consequences. To identify and mitigate the impact of DDoS attacks, Artificial Intelligence (AI)-based techniques (e.g. machine learning or deep learning) can be deployed with the aim of improving decision-making in networked infrastructures to enhance reliability, interoperability, trust, security, and stability. Many of the studies that have deployed detection frameworks for DDoS attacks have suffered from the limitations of low detection rates, high false alarm rates, and a lack of scalability. In this context, it is important to apply AI-based techniques for classification and anomaly detection that can detect, prevent, and mitigate DDoS attacks. This research focuses on studying the detection of DDoS attacks. Traditional shallow machine learning-based techniques for DDoS attack classification tend to be ineffective when the volume and features of network traffic, potentially carrying malicious DDoS payloads, increase exponentially as they cannot extract high-importance features automatically. To overcome the limitations in extracting high-importance features, we first investigate the classification of different DDoS attacks based on a hybrid deep learning technique that combines Autoencoder (AE) and Multi-Layer Perceptron (MLP). We propose a hybrid deep learning-based approach to extract the most important features and feed them into the classifier to obtain a multi-class classification of different DDoS attacks. Then, we provide a hybrid deep learning anomaly detection technique called Long Short-Term Memory and Autoencoder (LSTM-AE) based on multivariate time series sequences that can effectively detect potential DDoS attacks. We evaluate the effectiveness of DDoS attack classification and anomaly detection. To evaluate whether the proposed hybrid deep learning-based anomaly detection is more promising, we apply the aforementioned hybrid deep learning-based LSTM-AE anomaly detection technique based on time series sequence analysis to the real-world IoT sensor data (the IoT sensor dataset of Indoor Air Quality (IAQ) from SKOol MOnitoring BOx (SKOMOBO) units deployed on a large scale across the classrooms of primary schools in New Zealand). We demonstrate the proposed hybrid deep learning-based techniques that can effectively detect anomalies in the large-scale IoT dataset. Finally, the outcomes of machine learning or deep learning performance lack transparency, posing challenges in both explaining the results to users and instilling trust in them. To address this issue, we propose a framework that can efficiently classify legitimate traffic and malicious traffic and explain the decision-making of machine learning/deep learning models by deploying Explainable Artificial Intelligence (XAI) techniques.