Yorgos Marinakis

Lean startup for materials ventures and other science-based ventures: under what conditions is it useful?

Materials-based ventures face a high degree of technology uncertainty and market uncertainty when engaging in the technology entrepreneurial process. Recently, the lean startup methodology (LSM) has been introduced to practice and education as an integrated approach on how entrepreneurs can resolve these uncertainties when starting up a business. While the literature provides examples of LSMs successful application in a range of application areas, its focus application tends to be on consumer software. The purpose of this article is to discuss the degree to which LSM can be applied to the context of technology entrepreneurship. We find that LSM has strengths in addressing market uncertainty, but is largely silent on addressing technology uncertainty. In situations of a low degree of technology readiness, strongly intertwined process and product innovation processes such as those common in materials translation, and of addressing business markets, LSM may not suffice. We discuss the case of competence leveraging as one where technology uncertainty is lower for materials companies and illustrate the benefits on LSM in this context.

Internet of Things Technology Diffusion Forecasts

Prognosticators and pundits are forecasting an explosion over the next decade in the number of sensors connected to wired and wireless networks, also referred to as the Internet of Things. The challenge is that these sensor forecasts are being made without taking into account the infrastructure required to manufacture and operate the sensors. Financial forecasts of individual infrastructure components have been made, but they give point forecasts rather than diffusion curves. It is also often not clear what models these forecasters are using, as they are often in proprietary reports. The present study provides sensor and sensor infrastructure technology component diffusion forecasts using a sigmoidal model of product diffusion. A plurality of technology diffusion curves was computed, one for each sensor infrastructure component technology. To identify the potential lack of availability of a component or a set of components, the forecast curves were then examined for temporal commonalities and differences. Thus this study provides a method for forecasting an emerging technology.

Here there be dragons: The TSensors systems technology roadmap

Abundance refers to the thesis that four emerging forces, namely exponential technologies, the Do-it-yourself (DIY) innovator, Technophilanthropists, and the Rising Billion, will solve the most significant and intractable world social problems. One such exponentially growing technology is the sensor. It has been estimated that there will be a trillion sensors, or “TSensors,” by 2020. These TSensors will comprise a portion of the so-called proposed Internet of Things. In the present article we construct a technology forecast for that portion of the Internet of Things that is required to support the manufacture and operation of the TSensors. We utilize the technology roadmap framework, in which we highlight the importance of consortia and provide Technology Readiness Levels for component technologies.

A test of a design process scale

Design is a type of innovation that focuses on creating new product and service meanings. Models of the design process are important because they can help firms manage their product and service design processes to obtain competitive advantage. Empirically-based models of the design process are particularly valuable because they help us avoid cognitive biases when constructing the models and because they can lead to new theory development. Yet such empirically-based models are relatively small in number and not utilized outside of their original studies. Using the first two stages of Ravasi and Stiglianis model of the design process, which model was based on a review of 125 articles and 20 books published between 1989 and 2011, we constructed a scale comprising four sets of redundant reflective measures. We then surveyed 131 design firms internationally with those measures. We then fit the scale to the survey results by using Confirmatory Factory Analysis. Using a variety of goodness of fit statistics, we found that a large portion of the scale fit the data.