Epistemology and Methodology of Research in the AI Age maps how artificial intelligence (AI), digital platforms, and big data reshape the foundations of social science and management research. It connects epistemology and research design to the practical choices researchers now face, from defining questions in data-rich environments to handling algorithmic bias and ethical risk.Written for doctoral students, early-career researchers, and methods instructors, this volume supports dissertation work and methods teaching across sociology, psychology, education, economics, and information science. It also anchors the wider series by setting a shared conceptual and ethical baseline for the volumes that follow.

Epistemology and Methodology of Research in the AI Age maps how artificial intelligence (AI), digital platforms, and big data reshape the foundations of social science and management research. It connects epistemology and research design to the practical choices researchers now face, from defining questions in data-rich environments to handling algorithmic bias and ethical risk.Written for doctoral students, early-career researchers, and methods instructors, this volume supports dissertation work and methods teaching across sociology, psychology, education, economics, and information science. It also anchors the wider series by setting a shared conceptual and ethical baseline for the volumes that follow.

Research Ethics, Integrity, and Transparency guides researchers through ethics, integrity, and transparency when using big data, social media, and AI, with practical advice on consent, GDPR, bias, and open science. Bringing research ethics into the realities of digital data, this volume helps readers plan, run, and publish studies that can stand up to scrutiny. It reframes ethics and transparency as enabling conditions for trustworthy work, not a bureaucratic afterthought, and shows how to translate principles into defensible choices across the full research workflow.Written for PhD candidates and early-career researchers across the social sciences and management, this volume also supports supervisors, methods instructors, research ethics committees, and research offices who need an up-to-date reference for integrity and transparency in the digital age.

Research Ethics, Integrity, and Transparency guides researchers through ethics, integrity, and transparency when using big data, social media, and AI, with practical advice on consent, GDPR, bias, and open science. Bringing research ethics into the realities of digital data, this volume helps readers plan, run, and publish studies that can stand up to scrutiny. It reframes ethics and transparency as enabling conditions for trustworthy work, not a bureaucratic afterthought, and shows how to translate principles into defensible choices across the full research workflow.Written for PhD candidates and early-career researchers across the social sciences and management, this volume also supports supervisors, methods instructors, research ethics committees, and research offices who need an up-to-date reference for integrity and transparency in the digital age.

Quantitative, Qualitative, and Mixed Methodologies in Digital Social Sciences brings together quantitative analysis, qualitative inquiry, and mixed methods for the digital era. This volume shows how to design robust social science research using online surveys, platform data, digital traces, and computational tools, while keeping theory, context, and interpretation in view.Designed for doctoral students, early-career researchers, and applied researchers across sociology, psychology, education, public policy, communication, and management, it supports graduate methods teaching and real research projects in data-rich digital settings.

Quantitative, Qualitative, and Mixed Methodologies in Digital Social Sciences brings together quantitative analysis, qualitative inquiry, and mixed methods for the digital era. This volume shows how to design robust social science research using online surveys, platform data, digital traces, and computational tools, while keeping theory, context, and interpretation in view.Designed for doctoral students, early-career researchers, and applied researchers across sociology, psychology, education, public policy, communication, and management, it supports graduate methods teaching and real research projects in data-rich digital settings.

Quantitative Methods of Research in Digital Landscape equips social science and management researchers to design, run, and interpret statistical analyses for today’s data-rich research environment, from core descriptive statistics and hypothesis tests through to regression modeling and factor analysis, with a clear focus on transparency and responsible inference.Written for PhD and Master’s students, advanced undergraduates, early-career researchers, and practitioners who work with social data in fields such as sociology, management, communication, education, public policy, and media studies, it supports methods training and applied research projects in the digital age.

Quantitative Methods of Research in Digital Landscape equips social science and management researchers to design, run, and interpret statistical analyses for today’s data-rich research environment, from core descriptive statistics and hypothesis tests through to regression modeling and factor analysis, with a clear focus on transparency and responsible inference.Written for PhD and Master’s students, advanced undergraduates, early-career researchers, and practitioners who work with social data in fields such as sociology, management, communication, education, public policy, and media studies, it supports methods training and applied research projects in the digital age.

Qualitative Methods in Digital Inquiry shows how to design, conduct, and analyse qualitative research in online and technology-rich settings, from virtual ethnography and netnography to visual and arts-based approaches.Written for PhD students, early-career researchers, and instructors across the social sciences and management, the book also supports applied researchers in UX, market research, and organisational development.

Qualitative Methods in Digital Inquiry shows how to design, conduct, and analyse qualitative research in online and technology-rich settings, from virtual ethnography and netnography to visual and arts-based approaches.Written for PhD students, early-career researchers, and instructors across the social sciences and management, the book also supports applied researchers in UX, market research, and organisational development.

Data Management and Digital Infrastructure in Social Sciences shows how to move from raw digital data to usable evidence, with practical guidance on data literacy, documentation, platforms, and workflows that support transparency and reproducibility.Written for postgraduate students and early-career researchers in the social sciences and management, the volume also supports instructors and research support staff who need a grounded, course-ready guide to modern data practices, including links to evidence-based management and real-world research settings.

Data Management and Digital Infrastructure in Social Sciences shows how to move from raw digital data to usable evidence, with practical guidance on data literacy, documentation, platforms, and workflows that support transparency and reproducibility.Written for postgraduate students and early-career researchers in the social sciences and management, the volume also supports instructors and research support staff who need a grounded, course-ready guide to modern data practices, including links to evidence-based management and real-world research settings.

AI in Social Sciences Research explains how artificial intelligence (AI) is reshaping social inquiry, from data collection and research design through analysis, interpretation, and theory-building. Treating AI as both a tool kit and a methodological shift, the volume offers a clear route into machine learning (ML), natural language processing (NLP), simulation, and generative AI (GAI) for social researchers while keeping ethics, bias, transparency, and interpretability in view.Designed for postgraduate students, early-career researchers, and instructors teaching digital and computational social science (CSS) methods, the book supports readers who want to use AI with methodological care, not just technical speed.

AI in Social Sciences Research explains how artificial intelligence (AI) is reshaping social inquiry, from data collection and research design through analysis, interpretation, and theory-building. Treating AI as both a tool kit and a methodological shift, the volume offers a clear route into machine learning (ML), natural language processing (NLP), simulation, and generative AI (GAI) for social researchers while keeping ethics, bias, transparency, and interpretability in view.Designed for postgraduate students, early-career researchers, and instructors teaching digital and computational social science (CSS) methods, the book supports readers who want to use AI with methodological care, not just technical speed.

Academic Writing, Visualization, Presentation, and Publishing of Research is a comprehensive guide to scholarly communication in a digital research environment. Written for PhD candidates and early-career researchers in the social sciences and management, it shows how to write with clarity, visualize data with accuracy, present findings with confidence, and navigate publishing with integrity. Rather than treating these as separate skills, the book links them as one workflow: how arguments travel from a dissertation chapter to a figure, from a slide deck to a manuscript, and into peer review and open-access (OA) publication. It also addresses the realities of modern research communication, including collaborative writing platforms, virtual conferences, preprints, and responsible use of artificial intelligence (AI) tools.Written for PhD candidates, early-career researchers, and methods instructors in the social sciences and management, this volume also supports research support staff and supervisors who need a course-ready reference on communicating research well across written, visual, and spoken formats.

Epistemology and Methodology of Research in the AI Age maps how artificial intelligence (AI), digital platforms, and big data reshape the foundations of social science and management research. It connects epistemology and research design to the practical choices researchers now face, from defining questions in data-rich environments to handling algorithmic bias and ethical risk.Written for doctoral students, early-career researchers, and methods instructors, this volume supports dissertation work and methods teaching across sociology, psychology, education, economics, and information science. It also anchors the wider series by setting a shared conceptual and ethical baseline for the volumes that follow.

Epistemology and Methodology of Research in the AI Age maps how artificial intelligence (AI), digital platforms, and big data reshape the foundations of social science and management research. It connects epistemology and research design to the practical choices researchers now face, from defining questions in data-rich environments to handling algorithmic bias and ethical risk.Written for doctoral students, early-career researchers, and methods instructors, this volume supports dissertation work and methods teaching across sociology, psychology, education, economics, and information science. It also anchors the wider series by setting a shared conceptual and ethical baseline for the volumes that follow.

Research Ethics, Integrity, and Transparency guides researchers through ethics, integrity, and transparency when using big data, social media, and AI, with practical advice on consent, GDPR, bias, and open science. Bringing research ethics into the realities of digital data, this volume helps readers plan, run, and publish studies that can stand up to scrutiny. It reframes ethics and transparency as enabling conditions for trustworthy work, not a bureaucratic afterthought, and shows how to translate principles into defensible choices across the full research workflow.Written for PhD candidates and early-career researchers across the social sciences and management, this volume also supports supervisors, methods instructors, research ethics committees, and research offices who need an up-to-date reference for integrity and transparency in the digital age.

Research Ethics, Integrity, and Transparency guides researchers through ethics, integrity, and transparency when using big data, social media, and AI, with practical advice on consent, GDPR, bias, and open science. Bringing research ethics into the realities of digital data, this volume helps readers plan, run, and publish studies that can stand up to scrutiny. It reframes ethics and transparency as enabling conditions for trustworthy work, not a bureaucratic afterthought, and shows how to translate principles into defensible choices across the full research workflow.Written for PhD candidates and early-career researchers across the social sciences and management, this volume also supports supervisors, methods instructors, research ethics committees, and research offices who need an up-to-date reference for integrity and transparency in the digital age.

Quantitative, Qualitative, and Mixed Methodologies in Digital Social Sciences brings together quantitative analysis, qualitative inquiry, and mixed methods for the digital era. This volume shows how to design robust social science research using online surveys, platform data, digital traces, and computational tools, while keeping theory, context, and interpretation in view.Designed for doctoral students, early-career researchers, and applied researchers across sociology, psychology, education, public policy, communication, and management, it supports graduate methods teaching and real research projects in data-rich digital settings.

Quantitative, Qualitative, and Mixed Methodologies in Digital Social Sciences brings together quantitative analysis, qualitative inquiry, and mixed methods for the digital era. This volume shows how to design robust social science research using online surveys, platform data, digital traces, and computational tools, while keeping theory, context, and interpretation in view.Designed for doctoral students, early-career researchers, and applied researchers across sociology, psychology, education, public policy, communication, and management, it supports graduate methods teaching and real research projects in data-rich digital settings.

Quantitative Methods of Research in Digital Landscape equips social science and management researchers to design, run, and interpret statistical analyses for today’s data-rich research environment, from core descriptive statistics and hypothesis tests through to regression modeling and factor analysis, with a clear focus on transparency and responsible inference.Written for PhD and Master’s students, advanced undergraduates, early-career researchers, and practitioners who work with social data in fields such as sociology, management, communication, education, public policy, and media studies, it supports methods training and applied research projects in the digital age.

Quantitative Methods of Research in Digital Landscape equips social science and management researchers to design, run, and interpret statistical analyses for today’s data-rich research environment, from core descriptive statistics and hypothesis tests through to regression modeling and factor analysis, with a clear focus on transparency and responsible inference.Written for PhD and Master’s students, advanced undergraduates, early-career researchers, and practitioners who work with social data in fields such as sociology, management, communication, education, public policy, and media studies, it supports methods training and applied research projects in the digital age.

Qualitative Methods in Digital Inquiry shows how to design, conduct, and analyse qualitative research in online and technology-rich settings, from virtual ethnography and netnography to visual and arts-based approaches.Written for PhD students, early-career researchers, and instructors across the social sciences and management, the book also supports applied researchers in UX, market research, and organisational development.

Qualitative Methods in Digital Inquiry shows how to design, conduct, and analyse qualitative research in online and technology-rich settings, from virtual ethnography and netnography to visual and arts-based approaches.Written for PhD students, early-career researchers, and instructors across the social sciences and management, the book also supports applied researchers in UX, market research, and organisational development.

Data Management and Digital Infrastructure in Social Sciences shows how to move from raw digital data to usable evidence, with practical guidance on data literacy, documentation, platforms, and workflows that support transparency and reproducibility.Written for postgraduate students and early-career researchers in the social sciences and management, the volume also supports instructors and research support staff who need a grounded, course-ready guide to modern data practices, including links to evidence-based management and real-world research settings.

Data Management and Digital Infrastructure in Social Sciences shows how to move from raw digital data to usable evidence, with practical guidance on data literacy, documentation, platforms, and workflows that support transparency and reproducibility.Written for postgraduate students and early-career researchers in the social sciences and management, the volume also supports instructors and research support staff who need a grounded, course-ready guide to modern data practices, including links to evidence-based management and real-world research settings.

AI in Social Sciences Research explains how artificial intelligence (AI) is reshaping social inquiry, from data collection and research design through analysis, interpretation, and theory-building. Treating AI as both a tool kit and a methodological shift, the volume offers a clear route into machine learning (ML), natural language processing (NLP), simulation, and generative AI (GAI) for social researchers while keeping ethics, bias, transparency, and interpretability in view.Designed for postgraduate students, early-career researchers, and instructors teaching digital and computational social science (CSS) methods, the book supports readers who want to use AI with methodological care, not just technical speed.

AI in Social Sciences Research explains how artificial intelligence (AI) is reshaping social inquiry, from data collection and research design through analysis, interpretation, and theory-building. Treating AI as both a tool kit and a methodological shift, the volume offers a clear route into machine learning (ML), natural language processing (NLP), simulation, and generative AI (GAI) for social researchers while keeping ethics, bias, transparency, and interpretability in view.Designed for postgraduate students, early-career researchers, and instructors teaching digital and computational social science (CSS) methods, the book supports readers who want to use AI with methodological care, not just technical speed.

Academic Writing, Visualization, Presentation, and Publishing of Research is a comprehensive guide to scholarly communication in a digital research environment. Written for PhD candidates and early-career researchers in the social sciences and management, it shows how to write with clarity, visualize data with accuracy, present findings with confidence, and navigate publishing with integrity. Rather than treating these as separate skills, the book links them as one workflow: how arguments travel from a dissertation chapter to a figure, from a slide deck to a manuscript, and into peer review and open-access (OA) publication. It also addresses the realities of modern research communication, including collaborative writing platforms, virtual conferences, preprints, and responsible use of artificial intelligence (AI) tools.Written for PhD candidates, early-career researchers, and methods instructors in the social sciences and management, this volume also supports research support staff and supervisors who need a course-ready reference on communicating research well across written, visual, and spoken formats.