The Influence of Customer Involvement in Data Analytics on Innovation: An Abstract

Abstract

This research suggests that customer engagement goes beyond the task of providing data for new product development (NPD), and that customers may play a more active role in data analytics. We view customer involvement in data analytics as consisting of two aspects: Customer as Data Provider (CDP), defined as customers (passively) providing data through different channels; and Customer as Data Analyst (CDA), defined as customers (actively) participating in data analytics, such as acquisition and implementation, to co-develop innovative outputs. While CDP is consistent with the mainstream research on data analytics, CDA introduces a new aspect of customer involvement in data analytics practice.

Big data is often used to understand customer needs. Thus, we further explore how customer needs characteristics moderate the proposed effects of customer involvement in data analytics on new product performance. We examine two characteristics, tacitness and diversity, and suggest that they both moderate the main effects, but their moderating mechanisms are opposite.

We collected survey data of Business-to-Business (B2B) innovation projects. All 148 respondents held management-related positions at the time of data collection. Data quality was satisfactory based on reliability, confirmatory factor analysis, discriminant validity, and common method bias tests. Results showed that both CDP and CDA were positively related to new product performance. In addition, it was found that while customer need tacitness negatively moderated the effect of CDP on new product performance, it positively moderated that of CDA. Meanwhile, customer need diversity positively moderated the effect of CDP.

To our knowledge, we are the first to empirically test CDA. Extant literature is dominated by a conventional view of CDP. However, as big data prevails, we challenge the assumption that customers always serve as a data source; instead, they can participate more in data analytics for NPD. As a result, this research contributes a new insight into the literature of data analytics.