Book contents
- The Cambridge Handbook of Behavioural Data Science
- Cambridge Handbooks in Psychology
- Reviews
- The Cambridge Handbook of Behavioural Data Science
- Copyright page
- Dedication
- Contents
- Contributors
- Preface
- Introduction
- Part I Introduction to Behavioural Data Science
- Part II Human Behaviour
- Part III Algorithmic Behaviour
- Part IV Systems and Culture
- Part V Applications
- 29 Applications of Behavioural Data Science
- 30 Pro-Social Nudging
- 31 Social Media Analytics
- 32 Quantifying Luck
- 33 Quantifying the Connection between Scenic Beauty and Reported Health Using Deep Learning and Econometrics
- 34 Money, Methodology and Happiness
- 35 Human–Data Interaction
- 36 Natural Language Processing in Behavioural Data Science
- 37 Understanding Collective Behaviour Using Online Data and Mobile Phones
- 38 Burstier Events
- 39 Behavioural Data Science in Financial Services
- 40 XR, VR and AR Applications in Behavioural Data Science
- 41 On Cryptoasset Traders’ Behaviour
- 42 Behavioural Data Science of Cybersecurity
- 43 Behavioural Data Science Ethics and Governance Pre-AI Act
- 44 Behavioural Data Science Ethics and Governance Post-AI Act
- Part VI Concluding Remarks
- Main Abbreviations and Acronyms
- Glossary
- References
40 - XR, VR and AR Applications in Behavioural Data Science
from Part V - Applications
Published online by Cambridge University Press: 29 May 2026
Book contents
- The Cambridge Handbook of Behavioural Data Science
- Cambridge Handbooks in Psychology
- Reviews
- The Cambridge Handbook of Behavioural Data Science
- Copyright page
- Dedication
- Contents
- Contributors
- Preface
- Introduction
- Part I Introduction to Behavioural Data Science
- Part II Human Behaviour
- Part III Algorithmic Behaviour
- Part IV Systems and Culture
- Part V Applications
- 29 Applications of Behavioural Data Science
- 30 Pro-Social Nudging
- 31 Social Media Analytics
- 32 Quantifying Luck
- 33 Quantifying the Connection between Scenic Beauty and Reported Health Using Deep Learning and Econometrics
- 34 Money, Methodology and Happiness
- 35 Human–Data Interaction
- 36 Natural Language Processing in Behavioural Data Science
- 37 Understanding Collective Behaviour Using Online Data and Mobile Phones
- 38 Burstier Events
- 39 Behavioural Data Science in Financial Services
- 40 XR, VR and AR Applications in Behavioural Data Science
- 41 On Cryptoasset Traders’ Behaviour
- 42 Behavioural Data Science of Cybersecurity
- 43 Behavioural Data Science Ethics and Governance Pre-AI Act
- 44 Behavioural Data Science Ethics and Governance Post-AI Act
- Part VI Concluding Remarks
- Main Abbreviations and Acronyms
- Glossary
- References
Summary
Extended reality (XR), encompassing virtual reality (VR) and augmented reality (AR), has become a crucial tool in Behavioural Data Science. This chapter explores the applications of XR, VR and AR in this field, with a focus on analysing human behaviour and decision-making in immersive environments. The chapter begins with an overview of XR, VR and AR technologies and their potential in Behavioural Data Science. It discusses the advantages of using immersive environments for studying human behaviour, such as the ability to control and manipulate variables, measure behaviour in real time and simulate complex scenarios. It reviews various applications of XR, VR and AR in Behavioural Data Science. The chapter covers how immersive environments aid in studying decision-making, social interaction, learning, training and cognitive processes, with specific examples like using VR for consumer behaviour studies and AR for employee training. The challenges and opportunities of applying XR, VR and AR in Behavioural Data Science are also discussed. This includes the need for advanced data collection and analysis tools, ethical considerations around data privacy and security and potential new applications in fields like healthcare, education and entertainment. The chapter emphasises the significance of XR, VR and AR in understanding human behaviour and decision-making in immersive environments. It calls for ongoing research to further explore the potential applications of these technologies in Behavioural Data Science and to develop new tools and methods for analysing data from immersive environments.
Information
- Type
- Chapter
- Information
- The Cambridge Handbook of Behavioural Data Science , pp. 721 - 738Publisher: Cambridge University PressPrint publication year: 2026
References
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