Introduction

Today, the absorption of external knowledge has become crucial for firms' product innovation. In the era of "open innovation", scholars, specialists and researchers plead for much more active participation of customers in new product development (NPD) than is seen with traditional market research. To uphold the pace of innovation - as is necessary in a world of swiftly varying technologies and customer needs - West et al., Robert and Candi, and Füller et al., among others, have proposed integrating customers into value creation and utilising customers' acquaintance to reinforce a firm's key competences as well as to comprehend their needs. As a consequence, novel approaches are required to ensure the active integration of customers into NPD. It is, after all, only the customers themselves who are able to evaluate whether they like a new product and whether it fills a previously unmet and quite possibly unrecognised need.

This situation is reinforced by the increasing amounts of data available to business and the associated data-driven efforts at innovation, by new information and communication technologies, as well as by new business models and organisational forms. According to IBM (2013), 90% of the data that exists in the world today was generated in the last 2 years and it is expected the global total of data will reach 35 zettabytes (ZB) by 2020. This is therefore the era of "big data". A key competitive advantage in today's rapidly changing business environment is the ability to extract big data to gain helpful business insights. Being able to use big data allows firms to achieve outstanding performance against their competitors (Oh et al. 2012). For example, retailers can increase their operating margins by 60% through tapping into 'hidden values' in big data.

Although large amounts of both capital and time may be required to build a big data platform and to install the necessary technologies, the long-term benefits provided by big data are vast. Studies show that big data plays a critical role in customer involvement. Many researchers point out that firm can better understand customers' preferences and needs by leveraging the data available through loyalty cards and social media. Big data can be defined as an interactive and large-scale information source resulting from low-cost mass communication. It allows customers to better understand new products and provides simplified methods of multi-media rich interaction between customers and managers. A number of big data analytics and techniques can be found in the literature that relate to customer interaction. However, to the best of our knowledge, there is a lack of empirical studies that shed light on how to enhance customer involvement by using big data in practice. The current study mainly argues that there are huge potential values of big data that remain uncovered in new product development. In order to fulfil the gap, it leads to the following research questions concerning NPD:

  1. How can customer involvement be improved via big data?
  2. How can a firm interact with its customers and involve them in the NPD process?

To answer these questions, this study is organised as follows. First, this study provides an overview of the challenges associated with the recent evolution of approaches to customer involvement. Secondly, we point out that by means of big data, customer involvement can be summarised in a three-phase NPD approach which enables customers share their desires and previously unknown needs through trial-and-error loops. Thirdly, we conduct a longitudinal case study to illustrate how big data can be used to involve customers in NPD in practice. Finally, this paper discusses the implications of using big data to support customer involvement in NPD, discusses the limitations of this investigation, and offers some suggestions for future research.