An Intelligent Method for Lead User Identification in Customer Collaborative Product Innovation

For customer collaborative product innovation (CCPI), lead users are powerful enablers of product innovation. Identifying lead users is vital to successfully carrying out CCPI. In this paper, in order to overcome the shortcomings of traditional evaluation methods, a novel intelligent method is proposed to identify lead users efficiently based on the cost-sensitive learning and support vector machine theory. To this end, the characteristics of lead users in CCPI are first analyzed and concluded in-depth. On its basis, considering the sample misidentification cost and identification accuracy rate, an improved cost-sensitive learning support vector machine (ICS-SVM) method for lead user identification in CCPI is further proposed. A real case is provided to illustrate the effectiveness and advantages of the ICS-SVM method on lead user identification in CCPI. The case results show that the ICS-SVM method can effectively identify lead users in CCPI. This work contributes to user innovation literature by proposing a new way of identifying highly valuable lead users and offers a decision support for the efficient user management in CCPI.

Digital Public Services in Smart Cities – an Empirical Analysis of Lead User Preferences

Smart cities (SC) attract constant criticism due their technocratic conceptualization. To solve this issue, scholars call for a citizen-centric approach aligning the SC agenda to the citizens’ needs and preferences. However, examining SC user preferences is still in its infancy and only few studies provide empirical insights. By surveying 105 lead users, this article is able to predict the preferred type of SC services, the preferred channels for accessing smart services and the factors fostering SC service usage. In doing so, the study states a preliminary assessment of SC user preferences setting the scientific groundwork for citizen-centric SC concepts.