Characterizing the emergence of a technological field:Expectations, agendas and networks in Lab-on-a-chip technologies

this paper we develop and use mapping tools to investigate emerging technological fields by studying the dynamics of expectations, agenda building and early networks. In our approach, expectations describe shared beliefs with regard to prospective entities and positions. Agendas are sets of priorities present to guide the actors in their work. The structure that arises as a result of the actions and interactions of actors is the emerging network. For emerging technologies these processes are susceptible to change and the technological paths that may arise are still easy to influence. We propose that not only looking at expectation dynamics, but also including agenda setting and networks dynamics is essential in order to successfully capture the complexities of the emergence of technological paths. A major challenge for this work lies in unveiling the socio-technical dynamics leading to path emergence. For this purpose we investigate the phenomena of irreversibilities that emerge during the ongoing interactions of researchers, institutes, policy makers and firms. With these aspects in mind, we will use a broadened view of expectation dynamics in order to arrive at an improved understanding of the building blocks of path emergence. We illustrate our approach with a case study of Lab-on-a-chip technology for medical and pharmaceutical applications

Asymmetric positioning and emerging paths

Emerging technologies give rise to speculation, new strategies and a redistribution of roles. A broad range of actors anticipates to future developments on the basis of commonly available expectations. These anticipations allocate roles to selves, others and imagined future artefacts. The allocations of roles can hamper future developments. In this paper we investigate the dynamics of the allocation of roles and their importance for emerging technological fields. We draw on positioning theory as developed by Harre and Van Langenhove.In this paper we compare speech-acts from relevant actors in the emerging field of Lab-on-a-chip technology for medical application. Data was collected by conducting 50 + interviews throughout the innovation chain. In these interviews we elaborated interactively socio-technical scenarios of which speech-acts were derived. In the analysis we focus on about 300 speech-acts that relate to the story-line of Point-of-Care testing. Analysing the resulting patterns gives insight in the self-restricting effects that result from asymmetric positioning. Asymmetry results from the lack of overlap in positionings of selves, others, and future artefacts. We will show asymmetric positioning in our data and will show how our analysis contributes to an understanding of the state of emerging technological fields and to positioning theory. (C) 2007 Elsevier Ltd. All rights reserved

Asymmetric positioning and emerging paths

Emerging technologies give rise to speculation, new strategies and a redistribution of roles. A broad range of actors anticipates to future developments on the basis of commonly available expectations. These anticipations allocate roles to selves, others and imagined future artefacts. The allocations of roles can hamper future developments. In this paper we investigate the dynamics of the allocation of roles and their importance for emerging technological fields. We draw on positioning theory as developed by Harre and Van Langenhove.In this paper we compare speech-acts from relevant actors in the emerging field of Lab-on-a-chip technology for medical application. Data was collected by conducting 50 + interviews throughout the innovation chain. In these interviews we elaborated interactively socio-technical scenarios of which speech-acts were derived. In the analysis we focus on about 300 speech-acts that relate to the story-line of Point-of-Care testing. Analysing the resulting patterns gives insight in the self-restricting effects that result from asymmetric positioning. Asymmetry results from the lack of overlap in positionings of selves, others, and future artefacts. We will show asymmetric positioning in our data and will show how our analysis contributes to an understanding of the state of emerging technological fields and to positioning theory. (C) 2007 Elsevier Ltd. All rights reserved

Prospective positioning of industrial players : The case of theranostics

How are the perceptions of industrial agents affected by newly emerging technological fields? One way to investigate this is to explore the prospective roles that companies involved attribute to themselves and to others by using positioning theory as the point of departure. An appropriate example of an emerging technology to study is theranostics (dedicated diagnostics linked with therapy) given that this affects multiple, relatively unrelated, industries (old and new) simultaneously. By studying annual reports we gain insight into how industries react strategically towards emerging technological fields. We also make a contribution to positioning theory itself

Industry strategies on theranostics: Need for structural alignment

Theranostics is said to change the way patients will manage their disease. Such a change assumes that diagnostics and therapeutics become increasingly linked based on genetic information. Companies that adhere to this vision have different strategies to address theranostics. The resulting industry dynamics are studied using findings from our own research on theranostics strategies expressed in companies' annual reports and two other major studies on pharmacogenomics. We put forward that, for structurally taking up theranostics, there is limited structural alignment between (bio)pharmaceutical companies, and specialised firms in diagnostics and pharmacogenomics. Also, regulatory authorities should take a more anticipatory stance towards diagnostic and pharmacogenomics companies

Prospective positioning of industrial players: The case of theranostics

How are the perceptions of industrial agents affected by newly emerging technological fields? One way to investigate this is to explore the prospective roles that companies involved attribute to themselves and to others by using positioning theory as the point of departure. An appropriate example of an emerging technology to study is theranostics (dedicated diagnostics linked with therapy) given that this affects multiple, relatively unrelated, industries (old and new) simultaneously. By studying annual reports we gain insight into how industries react strategically towards emerging technological fields. We also make a contribution to positioning theory itself

Industry strategies on theranostics: Need for structural alignment

Theranostics is said to change the way patients will manage their disease. Such a change assumes that diagnostics and therapeutics become increasingly linked based on genetic information. Companies that adhere to this vision have different strategies to address theranostics. The resulting industry dynamics are studied using findings from our own research on theranostics strategies expressed in companies' annual reports and two other major studies on pharmacogenomics. We put forward that, for structurally taking up theranostics, there is limited structural alignment between (bio)pharmaceutical companies, and specialised firms in diagnostics and pharmacogenomics. Also, regulatory authorities should take a more anticipatory stance towards diagnostic and pharmacogenomics companies

Technical Aspects and Validation of a New Biofeedback System for Measuring Lower Limb Loading in the Dynamic Situation

Background: A variety of techniques for measuring lower limb loading exists, each with their own limitations. A new ambulatory biofeedback system was developed to overcome these limitations. In this study, we described the technical aspects and validated the accuracy of this system. Methods: A bench press was used to validate the system in the static situation. Ten healthy volunteers were measured by the new biofeedback system and a dual-belt instrumented treadmill to validate the system in the dynamic situation. Results: Bench press results showed that the sensor accurately measured peak loads up to 1000 N in the static situation. In the healthy volunteers, the load curves measured by the biofeedback system were similar to the treadmill. However, the peak loads and loading rates were lower in the biofeedback system in all participants at all speeds. Conclusions: Advanced sensor technologies used in the new biofeedback system resulted in highly accurate measurements in the static situation. The position of the sensor and the design of the biofeedback system should be optimized to improve results in the dynamic situation