Analytics-based decision-making for service systems: A qualitative study and agenda for future research |
| |
| Institution: | 1. Sydney Business School, University of Wollongong, NSW 2522, Australia;2. School of Management, Operations & Marketing, Faculty of Business, University of Wollongong, NSW 2522, Australia;3. Toulouse Business School, Toulouse University, 20 Boulevard Lascrosses, 31068 Toulouse, France;4. Montpellier Business School, 2300 Avenue des Moulins, 34080 Montpellier, France;5. Kent Business School, University of Kent, Kent, CT2 7NZ, England, United Kingdom;1. Department of Information Systems and Business Analytics, Hofstra University, 11501, USA;2. H. John Heinz III College of Information Systems and Public Policy, Carnegie Mellon University, 15213, USA;1. Petróleo Brasileiro S.A.- Petrobras University, Rua Ulysses Guimarães, 565 – Cidade Nova, Rio de Janeiro, RJ, 20.211-225, Brazil;2. Fluminense Federal University, Faculty of Engineering, Department of Industrial Engineering, Industrial Engineering Graduate Program, Rua Passo da Pátria, 156, bloco D, Sala 306, São Domingos, Niterói, RJ, 24.210-240, Brazil;1. IMT Atlantique, LEMNA, 4 rue Alfred Kastler, BP 20722, 44307 Nantes Cedex 3, France;2. University of Nantes, LEMNA, SKEMA Business School, Chemin de la Censive du Tertre, BP 52231, 44322 Nantes Cedex 3, France;3. University of South Florida, Muma College of Business, 4202 E. Fowler Avenue, BSN 3403, Tampa, FL 33620, USA;1. AG Corporate Semantic Web, Institute of Computer Science, Free University of Berlin, Germany;2. Opus College of Business, University of St. Thomas, Minneapolis Campus, 1000 LaSalle Ave, Minneapolis, MN 55403, USA;3. Montpellier Business School, Montpellier Research in Management, 2300 Avenue des Moulins, 34185 Montpellier, France |
| |
| Abstract: | While the use of big data tends to add value for business throughout the entire value chain, the integration of big data analytics (BDA) to the decision-making process remains a challenge. This study, based on a systematic literature review, thematic analysis and qualitative interview findings, proposes a set of six-steps to establish both rigor and relevance in the process of analytics-driven decision-making. Our findings illuminate the key steps in this decision process including problem definition, review of past findings, model development, data collection, data analysis as well as actions on insights in the context of service systems. Although findings have been discussed in a sequence of steps, the study identifies them as interdependent and iterative. The proposed six-step analytics-driven decision-making process, practical evidence from service systems, and future research agenda, provide altogether the foundation for future scholarly research and can serve as a step-wise guide for industry practitioners. |
| |
| Keywords: | Big data analytics Decision-making Service systems |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
