Machine Learning for International Business with R

Modelling

Author

Affiliation

HEC Montréal

Published

July 15, 2025

Introduction

Machine Learning for International Business with R opens at the intersection of two critical trends in modern research and industry: the growing complexity of global business questions and the rapid advancement of data-driven methods to answer them. International business (IB) scholars and practitioners today face an explosion of available data – from financial markets and supply chains to social media and remote sensors – and with it, a need for more sophisticated analytical tools. Machine learning (ML) offers a rich toolbox to uncover patterns and make predictions in these complex datasets, but applying ML in a meaningful way for IB requires more than just algorithms. It demands a rigorous methodological framework that ensures results are credible, reproducible, and relevant to the cross-border decision-making context. This textbook provides that framework, marrying state-of-the-art machine learning techniques with the practical realities of international business research, all grounded in the open-source R programming ecosystem.

A foundational theme of this text is reproducible research, which has become a cornerstone of scientific integrity in the data era. Across the sciences, a “reproducibility crisis” has been recognized – a 2016 survey in Nature found that over 70% of researchers had failed to reproduce another scientist’s results, and even about half could not reproduce their own prior findings. Social sciences and business research are not immune to this crisis. In fact, issues like undisclosed data mining and p-hacking (tuning analyses until results look significant) have led to published findings in IB that later prove unreliable. The stakes are especially high in international business, where empirical results might inform corporate strategy or economic policy across countries. If decisions are based on analyses that cannot be replicated or trusted, the credibility of both research and practice suffers. Ensuring reproducibility is thus not a mere technical nicety but a critical imperative for cumulative knowledge in IB – it allows findings to be validated and built upon confidently, rather than eroding trust in data-driven insights.

To address these challenges, this book emphasizes tools and practices for transparency and rigor at every step. We begin by introducing modern platforms for literate programming and version control, specifically leveraging R’s capabilities. Literate programming – implemented via R Markdown and its successor, Quarto – allows us to weave narrative and analysis together, embedding the actual code and data outputs within the explanations. Every figure, table, or statistical result in this textbook is generated from code printed alongside it, ensuring that readers can inspect how a result was obtained and reproduce it themselves. Likewise, we use Git and GitHub for version control, illustrating how to maintain a complete history of changes to data and code. This approach guards against the common pitfalls of ad-hoc analysis: no more “spreadsheet sorcery” or mysterious code files – instead, we have an auditable trail of each step. By using these tools, we document the entire workflow as we proceed, from data import and cleaning to model training and evaluation. Reproducibility is not treated as an afterthought; it is integrated into the learning process so that readers build good habits from the start.

Another central theme is the end-to-end nature of data science for international business. Rather than viewing machine learning as a magic black box, the text approaches it as a process: beginning with how we formulate a question and collect data, through processing and exploring that data, and only then applying sophisticated algorithms, and finally interpreting and communicating results. This holistic pipeline perspective is crucial for IB applications. For example, an analyst might ask how demographic shifts affect a country’s savings rates or how cultural differences manifest in consumer sentiment. Before applying a predictive model to such questions, one must ensure the question is well-defined in context, the relevant cross-country data is gathered (perhaps from sources like the World Bank or the UN), and the data is cleaned and understood. Part I of the book walks through this data-science pipeline, demonstrating with real-world IB scenarios how careful preparation leads to more robust modeling. We will see that seemingly mundane tasks like handling missing data or normalizing variables can significantly impact the validity of an international comparison. By iterating between exploratory data analysis and problem refinement, one can avoid biases or errors that might otherwise propagate into the modeling stage. This iterative refinement is illustrated in the text with examples such as examining the relationship between countries’ age profiles and savings rates – a simple case that shows how initial insights can lead to revised questions or models. Embracing this research cycle is especially important in international settings, where data often come from heterogeneous sources and measurements (e.g. different countries’ definitions and collection methods) and thus require extra care before meaningful modeling can occur.

With a solid foundation in place, the book then delves into machine learning techniques tailored for international business problems. We cover a broad spectrum of ML approaches, from classical statistical models to cutting-edge algorithms, always with an eye toward their application in the IB domain. Part II begins with regression techniques, showing how modern machine-learning approaches both extend and challenge traditional econometric models. Concepts like the bias–variance trade-off are explained in practical terms, as they help us understand the balance between model complexity and generalizability in, say, predicting foreign investment flows or market demand. We discuss regularization methods (ridge, lasso, etc.) that are vital when dealing with high-dimensional data common in international studies (for instance, when a model includes dozens of country-level indicators). Part III moves into classification methods – from logistic regression and decision trees to ensemble methods and support vector machines – which are applicable to problems like segmenting global consumers or forecasting whether a company will enter a new market (yes/no outcomes). Throughout, the interpretability of models is emphasized alongside accuracy; in a business context, a model’s predictions are far more useful if we can explain why a certain country or firm was classified in a particular way, linking back to theoretical understanding.

Part IV of the textbook tackles the frontier of learning from unstructured data, reflecting the reality that much of the information in international business is embedded in text, audio, or images rather than neat spreadsheets. Here we explore how to transform and analyze such data so that they can inform business decisions. For example, we demonstrate text mining on international survey responses and news articles, extracting sentiment and topics that help explain market trends. We delve into techniques for handling audio data (like interview recordings or spoken presentations), and visual data such as photographs or satellite images – the latter being increasingly used to proxy economic activity or infrastructure development across countries. Special attention is given to the vectorization of unstructured inputs (turning text or images into numerical features) and the scalability challenges that arise with big data. These chapters show how tools in R (and occasionally Python, integrated via Quarto) can be harnessed to create structured knowledge from raw information. Importantly, we maintain our focus on reproducible practice: whether it’s a natural language processing workflow or a geospatial analysis, readers learn to implement it in a way that others can retrace and adapt. By the end of Part IV, the reader is not only adept with advanced machine learning methods, but also capable of handling real-world data complexity – merging data sources, coping with data quality issues, and deploying analyses that are both reproducible and scalable.

Lastly, the book does not shy away from broader considerations, including ethical and societal implications of machine learning in international business. As we adopt ML techniques, we must also ask questions about fairness, accountability, and transparency. Bias in algorithms or data can have global repercussions – for instance, if an AI system systematically disadvantages firms from certain countries or demographic groups, it can reinforce inequities. Throughout the chapters and in dedicated sections, we discuss guidelines for responsible AI, such as ensuring privacy when using personal data and being vigilant about bias when algorithms are applied across different cultural contexts. One chapter (Chapter 13) focuses explicitly on AI and Ethics, reinforcing that technical proficiency should be coupled with ethical vigilance. By incorporating these discussions, the introduction and the text as a whole aim to produce not only skilled analysts but also responsible practitioners who can navigate the international arena with an ethical compass.

This introduction outlines a journey that the reader will undertake in this textbook: from solid foundations in reproducible research practices and data preprocessing, through a comprehensive arsenal of machine learning methods, to applications that span the diverse landscape of international business. The unifying thread is one of integration – integrating substantive business context with analytical rigor, and integrating powerful computational tools with principled research design. Readers will find that by the conclusion of this book, they are capable of developing end-to-end machine learning solutions to international business problems and, equally important, communicating and validating those solutions in a transparent, reproducible manner. This reflects a modern approach to international business education: one that balances quantitative savvy with contextual understanding and ethical responsibility, preparing readers to contribute insights in a world where data and globalization are ever more intertwined.

Citing this book

The full reference is:

BibTeX:

@book{gsdsqr,
  author = {Thierry Warin},
  year = 2025,
  title = {Machine Learning for International Business with R},
  publisher = {Forthcoming},
  address = {Forthcoming},
  URL = {https://warin.ca/mlibr},
  doi = {Your DOI (if available)}
}

Acknowledgements

A special thanks goes to my MSc students at HEC Montreal, whose insights, enthusiasm, and questions during our sessions have greatly enriched this book. Your contributions, whether through discussion, feedback, or collaboration, have been invaluable, and I am deeply grateful for your support.

# Introduction {.unnumbered} *Machine Learning for International Business with R* opens at the intersection of two critical trends in modern research and industry: the growing complexity of global business questions and the rapid advancement of data-driven methods to answer them. International business (IB) scholars and practitioners today face an explosion of available data – from financial markets and supply chains to social media and remote sensors – and with it, a need for more sophisticated analytical tools. Machine learning (ML) offers a rich toolbox to uncover patterns and make predictions in these complex datasets, but applying ML in a meaningful way for IB requires more than just algorithms. It demands a rigorous **methodological framework** that ensures results are credible, reproducible, and relevant to the cross-border decision-making context. This textbook provides that framework, marrying state-of-the-art machine learning techniques with the practical realities of international business research, all grounded in the open-source R programming ecosystem. A foundational theme of this text is **reproducible research**, which has become a cornerstone of scientific integrity in the data era. Across the sciences, a “reproducibility crisis” has been recognized – a 2016 survey in *Nature* found that over 70% of researchers had failed to reproduce another scientist’s results, and even about half could not reproduce their own prior findings. Social sciences and business research are not immune to this crisis. In fact, issues like undisclosed data mining and *p-hacking* (tuning analyses until results look significant) have led to published findings in IB that later prove unreliable. The stakes are especially high in international business, where empirical results might inform corporate strategy or economic policy across countries. If decisions are based on analyses that cannot be replicated or trusted, the credibility of both research and practice suffers. **Ensuring reproducibility is thus not a mere technical nicety but a critical imperative** for cumulative knowledge in IB – it allows findings to be validated and built upon confidently, rather than eroding trust in data-driven insights. To address these challenges, this book emphasizes **tools and practices for transparency and rigor** at every step. We begin by introducing modern platforms for literate programming and version control, specifically leveraging R’s capabilities. Literate programming – implemented via R Markdown and its successor, Quarto – allows us to weave narrative and analysis together, embedding the actual code and data outputs within the explanations. Every figure, table, or statistical result in this textbook is generated from code printed alongside it, ensuring that readers can inspect *how* a result was obtained and reproduce it themselves. Likewise, we use Git and GitHub for version control, illustrating how to maintain a complete history of changes to data and code. This approach guards against the common pitfalls of ad-hoc analysis: no more “spreadsheet sorcery” or mysterious code files – instead, we have an auditable trail of each step. By using these tools, we **document the entire workflow** as we proceed, from data import and cleaning to model training and evaluation. Reproducibility is not treated as an afterthought; it is integrated into the learning process so that readers build good habits from the start. Another central theme is the **end-to-end nature of data science for international business**. Rather than viewing machine learning as a magic black box, the text approaches it as a **process**: beginning with how we formulate a question and collect data, through processing and exploring that data, and only then applying sophisticated algorithms, and finally interpreting and communicating results. This holistic pipeline perspective is crucial for IB applications. For example, an analyst might ask how demographic shifts affect a country’s savings rates or how cultural differences manifest in consumer sentiment. Before applying a predictive model to such questions, one must ensure the question is well-defined in context, the relevant cross-country data is gathered (perhaps from sources like the World Bank or the UN), and the data is cleaned and understood. Part I of the book walks through this *data-science pipeline*, demonstrating with real-world IB scenarios how careful preparation leads to more robust modeling. We will see that seemingly mundane tasks like handling missing data or normalizing variables can significantly impact the validity of an international comparison. By iterating between **exploratory data analysis** and problem refinement, one can avoid biases or errors that might otherwise propagate into the modeling stage. This iterative refinement is illustrated in the text with examples such as examining the relationship between countries’ age profiles and savings rates – a simple case that shows how initial insights can lead to revised questions or models. Embracing this *research cycle* is especially important in international settings, where data often come from heterogeneous sources and measurements (e.g. different countries’ definitions and collection methods) and thus require extra care before meaningful modeling can occur. With a solid foundation in place, the book then delves into **machine learning techniques tailored for international business problems**. We cover a broad spectrum of ML approaches, from classical statistical models to cutting-edge algorithms, always with an eye toward their application in the IB domain. Part II begins with regression techniques, showing how modern machine-learning approaches both extend and challenge traditional econometric models. Concepts like the bias–variance trade-off are explained in practical terms, as they help us understand the balance between model complexity and generalizability in, say, predicting foreign investment flows or market demand. We discuss regularization methods (ridge, lasso, etc.) that are vital when dealing with high-dimensional data common in international studies (for instance, when a model includes dozens of country-level indicators). Part III moves into classification methods – from logistic regression and decision trees to ensemble methods and support vector machines – which are applicable to problems like segmenting global consumers or forecasting whether a company will enter a new market (yes/no outcomes). Throughout, the **interpretability** of models is emphasized alongside accuracy; in a business context, a model’s predictions are far more useful if we can explain *why* a certain country or firm was classified in a particular way, linking back to theoretical understanding. Part IV of the textbook tackles the frontier of **learning from unstructured data**, reflecting the reality that much of the information in international business is embedded in text, audio, or images rather than neat spreadsheets. Here we explore how to transform and analyze such data so that they can inform business decisions. For example, we demonstrate text mining on international survey responses and news articles, extracting sentiment and topics that help explain market trends. We delve into techniques for handling audio data (like interview recordings or spoken presentations), and visual data such as photographs or satellite images – the latter being increasingly used to proxy economic activity or infrastructure development across countries. Special attention is given to the **vectorization** of unstructured inputs (turning text or images into numerical features) and the scalability challenges that arise with big data. These chapters show how tools in R (and occasionally Python, integrated via Quarto) can be harnessed to create structured knowledge from raw information. Importantly, we maintain our focus on reproducible practice: whether it’s a natural language processing workflow or a geospatial analysis, readers learn to implement it in a way that others can retrace and adapt. By the end of Part IV, the reader is not only adept with advanced machine learning methods, but also capable of **handling real-world data complexity** – merging data sources, coping with data quality issues, and deploying analyses that are both reproducible and scalable. Lastly, the book does not shy away from broader considerations, including **ethical and societal implications** of machine learning in international business. As we adopt ML techniques, we must also ask questions about fairness, accountability, and transparency. Bias in algorithms or data can have global repercussions – for instance, if an AI system systematically disadvantages firms from certain countries or demographic groups, it can reinforce inequities. Throughout the chapters and in dedicated sections, we discuss guidelines for responsible AI, such as ensuring privacy when using personal data and being vigilant about bias when algorithms are applied across different cultural contexts. One chapter (Chapter 13) focuses explicitly on *AI and Ethics*, reinforcing that technical proficiency should be coupled with ethical vigilance. By incorporating these discussions, the introduction and the text as a whole aim to produce not only skilled analysts but also **responsible practitioners** who can navigate the international arena with an ethical compass. This introduction outlines a journey that the reader will undertake in this textbook: **from solid foundations in reproducible research practices and data preprocessing, through a comprehensive arsenal of machine learning methods, to applications that span the diverse landscape of international business**. The unifying thread is one of integration – integrating substantive business context with analytical rigor, and integrating powerful computational tools with principled research design. Readers will find that by the conclusion of this book, they are capable of developing end-to-end machine learning solutions to international business problems and, equally important, **communicating and validating those solutions in a transparent, reproducible manner**. This reflects a modern approach to international business education: one that balances quantitative savvy with contextual understanding and ethical responsibility, preparing readers to contribute insights in a world where data and globalization are ever more intertwined. ::: {.content-visible when-format="html"} ## Citing this book The full reference is: BibTeX: ``` @book{gsdsqr, author = {Thierry Warin}, year = 2025, title = {Machine Learning for International Business with R}, publisher = {Forthcoming}, address = {Forthcoming}, URL = {https://warin.ca/mlibr}, doi = {Your DOI (if available)} } ``` ::: ## Acknowledgements {-} A special thanks goes to my MSc students at HEC Montreal, whose insights, enthusiasm, and questions during our sessions have greatly enriched this book. Your contributions, whether through discussion, feedback, or collaboration, have been invaluable, and I am deeply grateful for your support.