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A Systematic Literature Review of Data Analytics Methods Used in Global Corporate Investment Strategies

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Dissertation(1.11 MB)

Date

2026

Authors

Abrol, Ilankshi

Supervisor

Vaidya, Ranjan

Item type

Dissertation

Degree name

Master of Business

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Publisher

Auckland University of Technology

Abstract

Corporate investment strategies are increasingly enhanced by data analytics methods, including artificial intelligence (AI), machine learning (ML), and big data analytics. These technologies reshape financial decision-making by enabling faster forecasting, more accurate risk assessment, and optimized capital allocation. Across industries and regions, analytics tools also support strategic agility and alignment between data capabilities and business goals. Based on secondary data, this dissertation investigates how data analytics methods enhance global corporate investment strategies, addressing the lack of integrated, cross-sectoral understanding of how analytics capabilities, governance, and ESG (environmental, social, and governance) considerations jointly shape investment decision-making. A systematic literature review (SLR) was conducted following PRISMA 2020 guidelines and Braun and Clarke’s (2019) six-phase reflexive thematic analysis framework. A total of 25 peer-reviewed articles published between 2015 and 2025 were analysed to synthesise interdisciplinary insights across sectors. The review identifies AI/ML, ESG integration, and data governance as critical enablers of corporate investment strategy, highlighting how these technologies shape analytics-driven decision-making. Organisational factors – including leadership commitment, digital infrastructure, cultural readiness, and regulatory alignment – emerged as pivotal success factors, in translating analytics investments into improved risk intelligence, strategic agility, and long-term value creation. Ethical concerns such as algorithmic bias and transparency remain persistent barriers, along with data quality and global regulatory fragmentation. Based on the literature review, this study proposes an integrated conceptual framework grounded in Dynamic Capabilities Theory (Teece, 2018) and the Strategic Alignment Model (Coltman et al., 2015). The framework is designed for financial leaders, policymakers, and corporate strategists seeking to enhance decision-making through analytics integration. It serves as a practical guide for applying data analytics in complex investment environments by linking technological enablers (AI, ESG, data governance, and FinTech) with strategic outcomes. The framework’s uniqueness lies in its holistic, cross-sectoral synthesis of technological, organisational, and ethical dimensions, offering both theoretical and practical contributions to the evolving field of data-driven corporate finance. This research provides theoretical insights into the strategic value of data analytics and practical guidance for implementing analytics-enabled investment strategies across global corporate contexts.

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http://hdl.handle.net/10292/21020

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Masters Dissertations