A big data framework for facilitating product innovation processes

Abstract

Purpose-This paper suggests how firms could use big data to facilitate the product innovation processes, by shortening the time to market, improving customers' product adoption and reducing costs. Design/methodology/approach-Our research is based on a two-step approach. First, this research summarised and identified four potential key success factors for organisations to integrate big data in accelerating their product innovation processes. The proposed factors were further examined and developed by conducting interviews with different organisation experts and academic researchers. Then, a framework was 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: a) Accelerated innovation process; b) Customer connection; and c) Ecosystem of innovation. The developed framework based on big data we believe 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 demands a shift in traditional organisational culture and practices. It is, though, meaningful only for products and services with short product 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. Secondly, it helps organisations to determine the weaknesses of the product earlier in the development cycle. Thirdly, it allows functionalities to be added to a product that customers are willing to pay a premium for, while eliminating features they don't 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 processes based on Page 1 of 28 Business Process Management Journal 2 big data is characterised by a multidimensional process involving intelligence efforts, relentless data collection and flexible working relationships with team members.

Research streams on digital transformation from a holistic business perspective: a systematic literature review and citation network analysis

“…For instance, Barton and Court (2012), also the most cited article in this cluster (indegree: 26), present a practical perspective on how to improve companies' performance with advanced analytics. Zhan et al (2017) suggest how firms could use BD to facilitate product innovation processes. Moreover, Tan and Zhan (2017) present three principles related to BD which support new product development.…”

Unlocking the power of big data in new product development

“…Previous literature has demonstrated how firms are able to benefit from teaming up with customers or from acquiring customers' feedback and input (Brown et al 2002;Narver et al 2004;Franke et al 2009;Bharadwaj et al 2012;Noble et al 2012;Cooper 2014). Such methods and techniques come from both marketing research and R&D. They centre on three key phases in the NPD process: prospect identification; development; testing and product launch (Van Kleef et al 2005;Zhan et al 2016). Many studies have looked principally at the theoretical and early NPD phases (Schaarschmidt and Kilian 2014;Van Kleef et al 2005).…”

Data: A collaborative ?

“…Big data has raised expectations of being particularly beneficial for the firms' innovation: the relation between a firm's use of big data and the likelihood of the firm innovating is contingent on it investment in IT-specific knowledge and skills (Niebel et al, 2019;Fosso Wamba and Mishra, 2017). Product innovation is now being fuelled by knowledge gained from the firm's ability to exploit Big Data (Chen et al, 2015;Wang et al, 2017;Zhan et al, 2017). Established by Gartner (Laney, 2001), the founding and consensus model of the 3Vs (data volume, variety and velocity) disrupts conventional statistical methods and transforms how organizations, large or small, approach and use information (McAfee et al, 2012).…”

“…This approach is the source of new products that are less expensive, better distributed and more accessible, such as those produced by 23andMe, an SME that is shaking up the pharmaceutical industry by utilizing the data of its customers, offering them genetic testing kits online that can be used by them at home. The technological dimension of the product and the customers' perception of its relevance attest to the impact of data on product innovation (Zhan et al, 2017).…”

“…Big Data increases the firm's ability to meet the needs of its customers by capturing transactional or behavioural data that were previously inaccessible (Vajjhala and Ramollari, 2016;Xie et al, 2016). The transformation of Big Data into information and then organizational knowledge has the potential to stimulate both incremental and radical product innovation (Erevelles et al, 2016;Sen et al, 2016;Zhan et al, 2017). As a result, many SMEs are investing in this technology to gain competitive advantage based on innovation (Donnelly et al, 2015;Goes, 2014).…”