Handbook of Big Data Research Methods
Edward Elgar Publishing Ltd (Verlag)
978-1-80088-854-8 (ISBN)
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 1
Shahriar Akter, Samuel Fosso Wamba, Shahriar Sajib and Sahadat Hossain
2 Big data research methods in financial prediction 11
Md Lutfur Rahman and Shah Miah
3 Big data, data analytics and artificial intelligence in accounting: an overview 32
Sudipta Bose, Sajal Kumar Dey and Swadip Bhattacharjee
4 The benefits of marketing analytics and challenges 52
Madiha Farooqui
5 How big data analytics will transform the future of fashion retailing 72
Niloofar Ahmadzadeh Kandi
6 Descriptive analytics and data visualization in e-commerce 86
P.S. Varsha and Anjan Karan
7 Application of big data Bayesian interrupted time-series modeling for
intervention analysis 105
Neha Chaudhuri and Kevin Carillo
8 How predictive analytics can empower your decision making 117
Nadia Nazir Awan
9 Gaussian process classification for psychophysical detection tasks in
multiple populations (wide big data) using transfer learning 128
Hossana Twinomurinzi and Hermanus C. Myburgh
10 Predictive analytics for machine learning and deep learning 148
Tahajjat Begum
11 Building a successful data science ecosystem using public cloud 165
Mohammad Mahmudul Haque
12 How HR analytics can leverage big data to minimise employees’
exploitation and promote their welfare for sustainable competitive advantage 179
Kumar Biswas, Sneh Bhardwaj and Sawlat Zaman
13 Embracing Data-Driven Analytics (DDA) in human resource
management to measure the organization performance 195
P.S. Varsha and S. Nithya Shree
14 A process framework for big data research: social network analysis
using design science 214
Denis Dennehy, Samrat Gupta and John Oredo
15 Notre-Dame de Paris cathedral is burning: let’s turn to Twitter 233
Serge Nyawa, Dieudonné Tchuente and Samuel Fosso Wamba
16 Does personal data protection matter in data protection law?
A transformational model to fit in the digital era 266
Gowri Harinath
17 The future of AI-based CRM 278
Khadija Alnofeli, Shahriar Akter and Venkata Yanamandram
18 Descriptive analytics methods in big data: a systematic literature review 294
Nilupulee Liyanagamage and Mario Fernando
Index
| Erscheinungsdatum | 10.07.2023 |
|---|---|
| Reihe/Serie | Research Handbooks in Information Systems |
| Verlagsort | Cheltenham |
| Sprache | englisch |
| Maße | 169 x 244 mm |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| Wirtschaft ► Betriebswirtschaft / Management ► Unternehmensführung / Management | |
| ISBN-10 | 1-80088-854-6 / 1800888546 |
| ISBN-13 | 978-1-80088-854-8 / 9781800888548 |
| Zustand | Neuware |
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