Researching the identification of rare subjects, such as lead-users, is often heavily reliant on self-reported subjective measures – often validated by experts on observations of lead-user behaviour. With the growth of the internet and its accessibility, both our professional and social networks have expanded to the point where we can now operate in multiple online spaces at once. Each environment we join can be occupied by a dedicated group of individuals who share information on very specific subjects and topics, to much more open environments were maintaining the connection to other individuals is often more important than the discussion points.
The expansion of online environments and activities have open doors for academia and industry researchers alike to access specific groups of consumers and end product users, who operate in what are often referred to as ‘user communities’ or ‘brand communities’ (for a full review see: Franke, N., Hippel, E. Von & Schreier, M., 2006). This presents each with an opportunity to search for creative and innovative consumers who are almost guaranteed to prove resourceful when integrated into the development of new products. The extensive collection of research that we have access to, since the first investigation into consumers driven innovation in 1978 by Eric Von Hippel, provides us with more than a foundation of understanding of consumer integration and the rationale for investing in the collaborative structures that facilitate the exchange of knowledge.
User-communities, as opposed to social networks, contain thousands, sometimes millions of users. At this scale the popular self-reported measures – often used as part of the identification methodology – can only sample a very small percentage of the community, limiting the potential to find rare subjects such as lead-users, and further our understand of their behaviour in online environments. As most platforms are not designed to collected or showcase the characteristics that are frequently used as indicators for innovative behaviour, nor are they structured in such a way that allows us to measure and collect the data suitable to identify innovative consumers with ease, we have to consider alternative ‘proxy’ measure in replacement of self-reported measures (Bilgram et al. 2008).
Some scholars have opted to use passive measures to collect weblog data, without the need to engage with the community they are analysing. The data collected is often retrospective data from a fixed period in time, where known collaborative activities have happened (Marchi et al. 2011). By analysing the data in various ways, known characteristic of lead-users are assumed to be associated with a handful of community members. However, it is known the individuals operate differently in different environments, no where more so than between the online and offline world. We disclose different amounts of information and act according to the outcomes we desire. Therefore measuring someone in the offline world requires a different, more tailored, approached to the online world. This means in order to measure known characteristics of lead-users in online environments, a comparison between online and offline measures needs to be assessed to ensure that we can identify lead-users, and not inform an ‘under qualified’ individual to be integrated into the collaborative process, as this will most certainly affect the outcome of the innovation and its attractiveness.
From research on consumer differences, such as Magnusson’s (2009) investigation into “ordinary users”, we know that different types of consumers are suited to different stages in the product development process. Therefore to ensure product innovation management obtain a return on the investment into consumer integration and structures for knowledge exchange, they need to be able to understand three key differences. Firstly the differences between types of consumers. Secondly, what consumer is best suited to each stage in the product development, and finally how to differentiate these innovative-consumers in the large online environments.
We currently assume self-reported measures and indicators are transferable from offline to online environments. However we know that information disclosure varies between different types of environment, and that people act differently online. Therefore we need to ensure when we invest in using a particular identification methodology, in a large community, we understand what types of consumers we want to obtain in return.
My current research study is looking at the transition of measures in different environments, to examine whether we are moving between environments too quickly, and how this can affect the way we identify innovative consumers. The research continues to look at how we can further understand online behaviour and information disclosure of innovative consumers by comparing multiple data collection methods.
Magnusson, P.R., 2009. Exploring the contributions of involving ordinary users in ideation of technology-based services. Journal of Product Innovation Management, 26, pp.578–593.
Hippel, E. Von, 1978. Successful Industrial Products from Customer Ideas. Journal of Marketing.
Franke, N., Hippel, E. Von & Schreier, M., 2006. Finding commercially attractive user innovations : A test of lead user theory. Journal of Product Innovation Management, 23, pp.301–315.
Bilgram, V., Brem, A. & Voigt, K.-I., 2008. User-Centric Innovations in New Product Development — Systematic Identification of Lead Users Harnessing Interactive and Collaborative Online-Tools. International Journal of Innovation Management, 12(03), pp.419–458. Available at: http://www.worldscientific.com/doi/abs/10.1142/S1363919608002096.
Marchi, G., Giachetti, C. & de Gennaro, P., 2011. Extending lead-user theory to online brand communities: The case of the community Ducati. Technovation, 31(8), pp.350–361. Available at: http://linkinghub.elsevier.com/retrieve/pii/S0166497211000599 [Accessed March 19, 2012].
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