Researcher Seeks to Understand the Motivations of 3D Model Designers
Article/interview from On3Dprinting about my research.
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Some publicity for my research on 3D printing industry
Inspiration for new digital economy research directions
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The digital economy, in short, is a very broad subject. If you were to create a research project in the DE the possibilities are endless. So how do you decide what research direction to take?
Firstly, multidisciplinary is a key ingredient – throw in terms like HCI, privacy, GPS, trajectories, heterogeneous, impact and my favourite, serendipity, and you’re nearly there. The last element that often differentiates DE research from other disciplines is the abstraction from the norm’. Research that includes large scale text based games like Day of the Figurines, or controlling the London Eye lights with your arm movements is slightly abstract from our every day experiences. So coming up with a research project that are novel, interesting and most importantly have ‘impact’, could require some inspiration. Here are a few ideas to get the “creative juices flowing” – as my lecturer once said…
1. Walk while you text app. Despite needing to convince the ethics committee that anyone should be using this app, you could track the trajectories of individuals using this app in different environments – such as walking round the arc du triumph or a dense forest.
2. While you are in Paris you could set up a blow up bridge – endless possibilities…
3. Heres one for human factors: Change in workload while you eat McDonalds and drive.
4. Team work anyone…? I think I need to understand how this would actually work in the real world before deciding what to do with it.
5. Pepper spray phone case. Ubiquitous personal defence systems?
6. Procrastination enabling clothes. This is obviously for those who have Google glass and have ascended the need for hand-operated technology.
7. Proximity and interactions in personal tanks? I don’t know why this is even for sale…
8. Lastly, one for the big kids. Romo. A programable and interactive robot, powered by your phone. I think we should just buy one anyway…
What to buy (and what not to buy) for Digital Economy Researchers
The internet – as we can probably guess – is made up of lots of strange and ‘interesting’ things to see, do and buy. However strange or different, all products are targeted at someone. I feel I may have found a collection of items that could have been targeted at that ‘someone’ within the digital economy. There are a few well-known products in the mix, alongside other you’d be slightly embarrassed by if your colleagues caught sight of your order confirmation email.
1. Nimbus
This Smart Dashboard can be used to keep track of emails, tweets and weather, along with the usual time and date. I quite like the idea, but I normally have my phone next to me all day which does the same thing. On the plus side, this product was socially designed at Quirky, so part of the revenue goes back into the community.
2. The social shower curtain
I don’t want this…
3. G-PAWS pet GPS tracker
All I need now is a cat! I would actually like to track my cat, if I had one, just out of curiosity.
4. Makey Makey
This allows you to turn anything into a keyboard, or even musical instruments. I can imagine this to be fun for those who know how to operate such gadgets, but less technical folk are unlikley to have as much fun…
5. Twitter loo roll
No explanation needed. Apparently its all sold out!
6. The Smiirl
A physical Facebook ‘like’ counter ideally for shop windows or bars, not for your desk…
7. Unlock doormat
“Each to their own” as the saying goes…
8. Bluetooth Gloves
If I have to hold my phone, or hand to my ear, I might as well talk on my phone – plus what happens if the gloves get wet, like on a snowy day , when you typically wear gloves…
9. A Facebook hanger
Again “each to their own”.
10. The Ikettle
Basically for the impatient person inside you who needs coffee the minuet they walk through the door!
Bridging the gap between offline and online identification: are we moving too fast?
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].
Digital Revolutions
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A few weeks ago, our team attended a summer school on Digital Revolutions in Oxford, England, that brought together researchers from several doctoral training centres across the country. The event also had a open mic feature where people could present any idea that had crossed their mind during the conference followed by breakaway groups to discuss these ideas. I presented our vision for new online services for researcher and again we used this opportunity to run an in-promptu focus group.
Open User Innovation 2013
Presented July 2013 at the Open User Innovation Workshop in Brighton, UK.
A unified model of consumer characteristics in service innovation
Alongside the growing interest in lead user innovation, research on other types of users-innovators has grown and become a common practice amongst scholars to compare traits, abilities, behavior and motives with that of lead users. In this paper we explore common characteristics across several user types by conducting a thematic analysis, to identify various overlaps between individuals and their indicators in the context of service innovation. In a preliminary analysis, we identify similarities among lead users, user-innovators, co-creators, user-entrepreneurs and links to opinion leaders. We compare the environment and methodologies chosen for each research study to help explain common characteristics used to classify particular user types. Our preliminary findings show that environment structures both physical and virtual can limit the information gained by the researcher. We observe that some methodological approaches do not provide researcher and users with the opportunity to provide or acquire specific information, leading to ambiguity, causing some of the observed overlap between user types. In addition to these observations we use a model to represent the aggregation of multiple innovative-consumers. The model also represents relationships between groups of consumers and their indicators. Finally we use the model to show how a staged identification process could address the current issues of online lead-user identification in large communities. With the number to studies on innovation within online communities continues to grow – the intended outcome of our research is to provide a foundation of understanding of the characteristic of different users types, and to highlight potential issues when searching for innovative individuals in environments where access to information can be limited and ambiguous. We aim to develop an understanding of how researcher can effectively use characteristics as indictors for potential lead users amongst other online community members. Authors: Matthew Terrell, George Kuk and Alexa Spence
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