A big data framework for facilitating product innovation processes. Issue 3 (5th June 2017)

• Record Type: Journal Article
• Authors: Zhan, Yuanzhu; Tan, Kim Hua; Ji, Guojun; Chung, Leanne; Tseng, Minglang
• Is Part Of: Business process management journal. Volume 23:Issue 3(2017)
• Page Start: 518
• Page End: 536
• Publication Date: 2017-06-05
• Subjects: Big data -- Accelerated innovation -- Key success factors -- Product innovation processes -- Rapid innovation; Industrial management -- Periodicals; Reengineering (Management) -- Periodicals; Total quality management -- Periodicals; 658.4063
• DOI: 10.1108/BPMJ-11-2015-0157
• Languages: English
• ISSNs: 1463-7154

Abstract

Abstract : Purpose: The purpose of this paper is to suggest how firms could use big data to facilitate product innovation processes, by shortening the time to market, improving customers' product adoption and reducing costs. Design/methodology/approach: The research is based on a two-step approach. First, this research identifies four potential key success factors for organisations to integrate big data in accelerating their product innovation processes. The proposed factors are further examined and developed by conducting interviews with different organisation experts and academic researchers. Then a framework is developed based on the interview outputs. The framework sets out the key success factors involved in leveraging big data to reduce lead times and costs in product innovation processes. Findings: The three determined key success factors are: accelerated innovation process; customer connection; and an ecosystem of innovation. The authors believe that the developed framework based on big data represents a paradigm shift. It can help firms to make new product development dramatically faster and less costly. Research limitations/implications: The proposed accelerated innovation processes demand a shift in traditional organisational culture and practices. It is, though, meaningful only for products and services with short life cycles. Moreover, the framework has not yet been widely tested. Practical implications: This paper points to the vital role of big data in helping firms to accelerate product innovation processes. First of all, it allows organisations to launch new products to market as quickly as possible. Second, it helps organisations to determine the weaknesses of the product earlier in the development cycle. Third, it allows functionalities to be added to a product that customers are willing to pay a premium for, while eliminating features they do not want. Last, but not least, it identifies and then prioritises customer needs for specific markets. Originality/value: The research shows that firms could harvest external knowledge and import ideas across organisational boundaries. An accelerated innovation process based on big data is characterised by a multidimensional process involving intelligence efforts, relentless data collection and flexible working relationships with team members.