A model of success
In conclusion, I now have a version 1.0 model for the success of AcademicTech startups consisting of two parts. Part 1 is visualised in Figure 1 below, and part 2 is pictured in Figure 2.
Figure 1: Model for the success of AcademicTech startups (part 1): base part.
The model starts with the concept that success is a relative notion, but that it clearly has something to do with meeting (or exceeding) certain ambitions, expectations, or goals. For AcademicTech for-profit startups, there are at least two distinct dimensions to how founders see their success: a business and a research/knowledge impact component. Next, there are external factors that generally impact success (whichever way success is defined!), as enablers, accelerators, or even preconditions. In addition, there is a wide range of drivers of success (gathered above in five categories). Tracking the right metrics is crucial to measuring and managing the drivers of success.
This model essentially describes the base framework. It does not take into account that drivers of success, metrics, and even the notion of success may change when a startup develops through the four maturity stages. It also does not consider that being able to move to a next stage is a success in itself. The crucial progression in time is incorporated in the stage- and time-phased model for the success of AcademicTech startups, visualised in Figure 2.
Figure 2: Model for the success of AcademicTech startups (part 2): stage- and time-phased part.
I now have a much better picture of what success looks like, as my hypothetical model has developed into a two-part version 1.0 model for the success of AcademicTech startups. My model also helps understand where an individual AcademicTech startup is located on their roadmap to success.
Don’t be fooled by a model that seems to tell you that the path to success is all laid out and logical, and that it is just hard work. Embedded in the model is how moderators of success, metrics, and even the notion of success itself may change when a startup develops and matures. It is an iterative route, not a linear one. While the startup team has to stay focused and work systematically, their agility, which enables them to maximise chances, is crucial. In order to be successful, they have to be able to manage this dual mindset.
Do I now have a model that enables me to actually evaluate the success of the AcademicTech startups in my sample? I don’t think so. Having arrived at my model, with at the top the notion that success is relative, made me realise that there is no objective approach to collecting and analysing quantitative data on the success of AcademicTech startups in my sample, as I had intended to do in the second phase of my 2019 market research. Moreover, my sample is very diverse in terms of the maturity of the startups and also their scope of activities and value creation.
I won’t be able to, nor do I want to, generally score, analyse, or conclude which AcademicTech startups are most successful. First and foremost, success depends on how founders define their ambitions, expectations, and goals in (at least) two distinct dimensions, typical for AcademicTech startups: business and research/knowledge impact.
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Yvonne Campfens is a Consultant, Market Researcher, and AcademicTech Entrepreneur, Yvonne has researched and worked alongside many starts-ups in the scholarly communications ecosystem.
This is an edited version of an article previously shared by Yvonne on LinkedIn. The original post can be found here.