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Open Access
Book part
Publication date: 18 July 2022

Christian Versloot, Maria Iacob and Klaas Sikkel

Utility strikes have spawned companies specializing in providing a priori analyses of the underground. Geophysical techniques such as Ground Penetrating Radar (GPR) are harnessed…

Abstract

Utility strikes have spawned companies specializing in providing a priori analyses of the underground. Geophysical techniques such as Ground Penetrating Radar (GPR) are harnessed for this purpose. However, analyzing GPR data is labour-intensive and repetitive. It may therefore be worthwhile to amplify this process by means of Machine Learning (ML). In this work, harnessing the ADR design science methodology, an Intelligence Amplification (IA) system is designed that uses ML for decision-making with respect to utility material type. It is driven by three novel classes of Convolutional Neural Networks (CNNs) trained for this purpose, which yield accuracies of 81.5% with outliers of 86%. The tool is grounded in the available literature on IA, ML and GPR and is embedded into a generic analysis process. Early validation activities confirm its business value.

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Machine Learning and Artificial Intelligence in Marketing and Sales
Type: Book
ISBN: 978-1-80043-881-1

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Essays in Honor of Cheng Hsiao
Type: Book
ISBN: 978-1-78973-958-9

Book part
Publication date: 13 March 2023

Xiao Liu

The expansion of marketing data is encouraging the growing use of deep learning (DL) in marketing. I summarize the intuition behind deep learning and explain the mechanisms of six…

Abstract

The expansion of marketing data is encouraging the growing use of deep learning (DL) in marketing. I summarize the intuition behind deep learning and explain the mechanisms of six popular algorithms: three discriminative (convolutional neural network (CNN), recurrent neural network (RNN), and Transformer), two generative (variational autoencoder (VAE) and generative adversarial networks (GAN)), and one RL (DQN). I discuss what marketing problems DL is useful for and what fueled its growth in recent years. I emphasize the power and flexibility of DL for modeling unstructured data when formal theories and knowledge are absent. I also describe future research directions.

Book part
Publication date: 1 September 2021

Matthew Steeves, Son Nguyen, John Quinn and Alan Olinsky

The purpose of this study is to determine which quantitative metrics are most representative of investor sentiment in the US equity markets. Sentiment is the aggregation of…

Abstract

The purpose of this study is to determine which quantitative metrics are most representative of investor sentiment in the US equity markets. Sentiment is the aggregation of consumers', investors', and producers' thoughts and opinions about the future of the financial markets. By analyzing the change in popular economic indicators, financial market statistics, and sentiment reports, we can gain information on investor reactions. Furthermore, we will use machine learning techniques to develop predictive models that will attempt to forecast whether the stock market will go up or down based on the percent change in these indicators.

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Advances in Business and Management Forecasting
Type: Book
ISBN: 978-1-83982-091-5

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Book part
Publication date: 18 January 2023

Steven J. Hyde, Eric Bachura and Joseph S. Harrison

Machine learning (ML) has recently gained momentum as a method for measurement in strategy research. Yet, little guidance exists regarding how to appropriately apply the method…

Abstract

Machine learning (ML) has recently gained momentum as a method for measurement in strategy research. Yet, little guidance exists regarding how to appropriately apply the method for this purpose in our discipline. We address this by offering a guide to the application of ML in strategy research, with a particular emphasis on data handling practices that should improve our ability to accurately measure our constructs of interest using ML techniques. We offer a brief overview of ML methodologies that can be used for measurement before describing key challenges that exist when applying those methods for this purpose in strategy research (i.e., sample sizes, data noise, and construct complexity). We then outline a theory-driven approach to help scholars overcome these challenges and improve data handling and the subsequent application of ML techniques in strategy research. We demonstrate the efficacy of our approach by applying it to create a linguistic measure of CEOs' motivational needs in a sample of S&P 500 firms. We conclude by describing steps scholars can take after creating ML-based measures to continue to improve the application of ML in strategy research.

Book part
Publication date: 15 March 2021

Jochen Hartmann

Across disciplines, researchers and practitioners employ decision tree ensembles such as random forests and XGBoost with great success. What explains their popularity? This…

Abstract

Across disciplines, researchers and practitioners employ decision tree ensembles such as random forests and XGBoost with great success. What explains their popularity? This chapter showcases how marketing scholars and decision-makers can harness the power of decision tree ensembles for academic and practical applications. The author discusses the origin of decision tree ensembles, explains their theoretical underpinnings, and illustrates them empirically using a real-world telemarketing case, with the objective of predicting customer conversions. Readers unfamiliar with decision tree ensembles will learn to appreciate them for their versatility, competitive accuracy, ease of application, and computational efficiency and will gain a comprehensive understanding why decision tree ensembles contribute to every data scientist's methodological toolbox.

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The Machine Age of Customer Insight
Type: Book
ISBN: 978-1-83909-697-6

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Book part
Publication date: 18 July 2022

Abstract

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Smart Industry – Better Management
Type: Book
ISBN: 978-1-80117-715-3

Book part
Publication date: 28 September 2023

Ram Krishan

Machine learning is an algorithmic-based auto-learning mechanism that improves from its experiences. It makes use of a statistical learning method that trains and develops on its…

Abstract

Machine learning is an algorithmic-based auto-learning mechanism that improves from its experiences. It makes use of a statistical learning method that trains and develops on its own without the assistance of a person. Data, characteristics deduced from the data, and the model make up the three primary parts of a machine learning solution. Machine learning generates an algorithm from subsets of data that can utilise combinations of features and weights different from those obtained from basic principles. In this paper, an analysis of customer behaviour is predicted using different machine learning algorithms. The results of the algorithms are validated using python programming.

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Digital Transformation, Strategic Resilience, Cyber Security and Risk Management
Type: Book
ISBN: 978-1-80455-262-9

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Book part
Publication date: 11 September 2020

D. K. Malhotra, Kunal Malhotra and Rashmi Malhotra

Traditionally, loan officers use different credit scoring models to complement judgmental methods to classify consumer loan applications. This study explores the use of decision…

Abstract

Traditionally, loan officers use different credit scoring models to complement judgmental methods to classify consumer loan applications. This study explores the use of decision trees, AdaBoost, and support vector machines (SVMs) to identify potential bad loans. Our results show that AdaBoost does provide an improvement over simple decision trees as well as SVM models in predicting good credit clients and bad credit clients. To cross-validate our results, we use k-fold classification methodology.

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