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This state-of-the-art Handbook provides an overview of the role of big data analytics in various areas of business and commerce, including accounting, finance, marketing, human resources, operations management, fashion retailing, information systems, and social media. It provides innovative ways of overcoming the challenges of big data research and proposes new directions for further research using descriptive, diagnostic, predictive, and prescriptive analytics.With contributions from leading academics and practitioners, the Handbook analyses how big data analytics can be used in different sectors, including detecting credit fraud in the financial sector, identifying potential diseases in health care, and increasing customer loyalty in the telecommunication sector. Chapters explore the use of artificial intelligence in accounting, the construction of successful data science ecosystems using the public cloud, and transformational models of personal data protection in the digital era. The Handbook also discusses the difficulties of adopting a data science platform and how the public cloud can aid companies in overcoming these challenges.Exploring how industries rely on predictive analytics to improve their decision-making, this Handbook will be essential reading for students and scholars in business analytics, economics, information systems, innovation and technology, and research methods. It will also benefit data analysts, economists, human resource managers, marketers, neuroscientists, and social science researchers.
Edited by Shahriar Akter, Faculty of Business and Law, University of Wollongong, Australia and Samuel Fosso Wamba, Department of Information, Operations and Management Sciences, TBS Business School, France
Contents:1 Introduction to the Handbook of Big Data Research Methods 1Shahriar Akter, Samuel Fosso Wamba, Shahriar Sajib and Sahadat Hossain2 Big data research methods in financial prediction 11Md Lutfur Rahman and Shah Miah3 Big data, data analytics and artificial intelligence in accounting: an overview 32Sudipta Bose, Sajal Kumar Dey and Swadip Bhattacharjee4 The benefits of marketing analytics and challenges 52Madiha Farooqui5 How big data analytics will transform the future of fashion retailing 72Niloofar Ahmadzadeh Kandi6 Descriptive analytics and data visualization in e-commerce 86P.S. Varsha and Anjan Karan7 Application of big data Bayesian interrupted time-series modeling forintervention analysis 105Neha Chaudhuri and Kevin Carillo8 How predictive analytics can empower your decision making 117Nadia Nazir Awan9 Gaussian process classification for psychophysical detection tasks inmultiple populations (wide big data) using transfer learning 128Hossana Twinomurinzi and Hermanus C. Myburgh10 Predictive analytics for machine learning and deep learning 148Tahajjat Begum11 Building a successful data science ecosystem using public cloud 165Mohammad Mahmudul Haque12 How HR analytics can leverage big data to minimise employees’exploitation and promote their welfare for sustainable competitive advantage 179Kumar Biswas, Sneh Bhardwaj and Sawlat Zaman13 Embracing Data-Driven Analytics (DDA) in human resourcemanagement to measure the organization performance 195P.S. Varsha and S. Nithya Shree14 A process framework for big data research: social network analysisusing design science 214Denis Dennehy, Samrat Gupta and John Oredo15 Notre-Dame de Paris cathedral is burning: let’s turn to Twitter 233Serge Nyawa, Dieudonné Tchuente and Samuel Fosso Wamba16 Does personal data protection matter in data protection law?A transformational model to fit in the digital era 266Gowri Harinath17 The future of AI-based CRM 278Khadija Alnofeli, Shahriar Akter and Venkata Yanamandram18 Descriptive analytics methods in big data: a systematic literature review 294Nilupulee Liyanagamage and Mario FernandoIndex
‘Big data research methods have gained dramatic momentum in the world. Researchers and practitioners extend this line of research constantly by producing journals, posts, news articles and podcasts. However, there is a paucity of a book that covers descriptive, diagnostic, predictive and prescriptive method-based research papers under one umbrella. This is one of those books which will immerse a reader in the past, present and future of big data analytics methods. It is an exceptional book that is grounded in evidence and meaningful to practice.’
David I. Suster, Mari Mino-Kenudson, Saul Suster, USA) Suster, David, MD (Assistant Professor, Department of Pathology, Rutgers University, New Jersey Medical School, Newark, New Jersey, USA) Mino-Kenudson, Mari, MD (Director, Pulmonary Pathology Service, Department of Pathology, Massachusetts General Hospital, Professor of Pathology, Harvard Medical School, Boston, Massachusetts, USA) Suster, Saul (Professor and Chairman Emeritus, Department of Pathology, Medical College of Wisconsin, Milwaukee, Wisconsin, David I Suster