FRAMEWORK PROPOSAL FOR EVALUATING HOW MACHINE LEARNING IS BEING USED FOR COMPETITIVE ADVANTAGE AMONG CROSS-INDUSTRY ENTERPRISES

Aug 4, 2026

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With the advent of the 4th industrial revolution, industries face major disruption from the inflow and integration of new technologies. Some enterprises succeed through digitalization and business-model innovation, while many fail to deliver effective digital transformations. Big data reshaped business models, pushing leaders to take risks. Machine learning (ML) gained prominence by automating and augmenting tasks, improving performance, saving time and cost, and supporting competitive advantage. This dissertation proposes a conceptual evaluation framework, grounded in an extensive literature review, to assess cross-industry ML use in core processes. Framework application reveals deployment patterns and how ML intertwines with business models for sustained competitive advantage.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages118

Reading Time4 h 57 min

AccessFree to download

Abstract

Background: digital transformation and the role of ML

Literature review: ML and business models

Conceptual framework proposal

Framework application and findings

Contributions and cross-industry insights

DEPARTAMENT OF MECHANICAL AND INDUSTRIAL ENGINEERING JOSÉ MIGUEL MENDES LOPES SANTOS CORREIA BSc in Industrial Engineering and Management FRAMEWORK PROPOSAL FOR EVALUATING HOW MACHINE LEARNING IS BEING USED FOR COMPETITIVE ADVANTAGE AMONG CROSS-INDUSTRY ENTERPRISES MASTER IN INDUSTRIAL ENGINEERING AND MANAGEMENT NOVA University Lisbon march, 2023 DEPARTAMENT OF MECHANICAL AND INDUSTRIAL ENGINEERING FRAMEWORK PROPOSAL FOR EVALUATING HOW MACHINE LEARNING IS BEING USED FOR COMPETITIVE ADVANTAGE AMONG CROSS-INDUSTRY ENTERPRISES JOSÉ MIGUEL MENDES LOPES SANTOS CORREIA BSc in Industrial Engineering and Management Adviser: Aneesh Zutshi Assistant Professor, NOVA University Lisbon Examination Committee: Chair: Isabel Nunes Associate Professor, NOVA University Lisbon Rapporteurs: Radu Godina Assistant Professor, NOVA University Lisbon Adviser: Aneesh Zutshi Assistant Professor, NOVA University Lisbon MASTER IN INDUSTRIAL ENGINEERING AND MANAGEMENT NOVA University Lisbon march, 2023 FRAMEWORK PROPOSAL FOR EVALUATING HOW MACHINE LEARNING IS BEING USED FOR COMPETITIVE ADVANTAGE AMONG CROSS-INDUSTRY ENTERPRISES Copyright © José Correia, NOVA School of Science and Technology, NOVA University Lisbon. The NOVA School of Science and Technology and the NOVA University Lisbon have the right, perpetual and without geographical boundaries, to file and publish this dissertation through printed copies reproduced on paper or on digital form, or by any other means known or that may be invented, and to disseminate through scientific repositories and admit its copying and distribution for non-commercial, educational or research purposes, as long as credit is given to the author and editor. To my ever supporting family, girlfriend, friends, and cat. ABSTRACT With the advent of the 4th industrial revolution, every industry has been severely impacted by the inflow and integration of new technologies. Some enterprises have been successful in this new age, are the industry leaders or top contenders, and lead the way in terms of digitalization. However, in reality, most companies usually fail in their digital transformations. With big data, Business Models (BM) underwent severe transformations and business leaders had to take risks. One of the technologies that gained most relevance is Machine Learning (ML) and has proved to grant competitive advantage over market competitors. ML can automate and augment traditionally human tasks. Both automation and augmentation save time and money for enterprises and lead to an overall better performance. However, thereis also much overexcitement regarding this technology, to enhance a company’s core competences and their overall BM, which has led to deployment problems. It is in this context that the present dissertation will develop. Understanding about how enterprises from different industries are using ML in their main processes is the core driver for this work. For such, an extensive literature review was performed, which encompassed the most relevant aspects of ML and BMs. What follows is the proposal of a conceptual framework to evaluate how companies are using ML technology in their own context and across industries. The results from the framework application give a deeper insight tohow ML applications are being deployed, in the context of the enterprise, for competitive advantage. Cross-industry tendencies, patterns, and trends were also identified. The contribution of this study is concerned with providing a better view to how ML intertwines with the BM. Keywords: Machine Learning, Business Model, Competitive Advantage, Digital Transformation

FAQs

Why does the dissertation focus on machine learning for competitive advantage?

Machine learning automates and augments traditionally human tasks, improving performance and reducing time and cost. It also supports enterprises in building competitive advantage, which is a central theme of the study.

What approach does the dissertation use to evaluate how companies use ML?

It develops a conceptual framework to evaluate how enterprises deploy ML in their own context and across industries. The framework is built after an extensive literature review covering ML and business models.

What insights are produced by applying the proposed framework?

The application provides deeper insight into how ML is deployed within enterprises for competitive advantage. It also identifies cross-industry tendencies, patterns, and trends, including how ML intertwines with business models.

Aug 4, 2026

Upload by Eliana