The Preference-Driven Lead User Method

Innovation matters! Today, multiple methods exist to develop products and services within the open innovation paradigm. However, the successful application of these methods depends on various aspects like trend forecasting and... [ view full abstract ]

Innovation matters! Today, multiple methods exist to develop products and services within the open innovation paradigm. However, the successful application of these methods depends on various aspects like trend forecasting and the identification of commercially promising contributions – leading to a selection problem.
This problem becomes relevant in the application of the traditional lead user method. If lead users and non-lead users possess a similar evaluative structure, then the concept is commercially attractive (Urban and von Hippel, 1988). In the case of different evaluation structures, lead users are either too far away from non-lead user’s needs or non-lead users just did not realise their need for the novel concept, yet. Today, we know that ordinary customers are able to provide basic evaluations of new product concepts (cf. Heiskanen et al. 2007) and raw conceptual ideas (Kornish and Ulrich 2014). The is linked to the research field of preference measurement and frames the research question “Can the lead user method and preference measurement be combined to result in an integrated method for new product development?”. In addition, von Hippel and von Krogh (2013) identified need-solution pairs and noticed that the discovery of a need is correlated with the discovery of a solution and happens at the same time. Thus, a need becomes real and cognisant when solutions for a prior undiscovered problem are presented.
The Preference-Driven Lead User Method is an answer to this question and benefits from need-solution pairs. The method makes use of the lead user method to stimulate ideation and links this to preference measurement while using a user-based recommendation algorithm to generate reliable data on promising external contributions. The method employs an adaptive online survey to discover the market and to interview market participants on their needs and solutions. Collected ideas are brought into preference measurement to determine the acceptance probability by the remaining sample and to forecast a commercial adoption within the market. It shifts the phase of “projecting lead user data onto the general market of interest” (von Hippel 1986, pp. 802-803) in advance of the lead user workshop. The application of the new method is done in two scenarios. The first one covers industrial IT-security and aims to develop an intrusion prevention system for industrial networks with 246 respondents. The application showed promising results with an increased market potential and a decreased concept novelty. The second example covers the field of mountain biking with 104 respondents and indicates heterogeneity in ratings of novelty and market potential. A further survey covered 311 respondents in the business fields of mechanical engineering industry, the automotive industry, and the field of market intelligence and asked for the methodological applicability of the new method.
The presented method can serve as a basic vehicle to generate evaluation data for lead user and non-lead user contributions before the lead user workshop is hosted and the concept is derived. The practical implications of this research are grounded in the development of key performance indicators based on preference data for future product attributes.