This article addresses the topic of transition and system change, and proposes endogenous regime transformation as alternative transition path to technological substitution. Using sociological and institutional insights, the dynamics of regime transformation are conceptualised. Two characteristics are pressure from outsider groups and adjustments in cognitions, norms and formal rules, enacted by regime insiders. The transformation perspective is illustrated with a historical case study, the hygienic transition from cesspools to integrated sewer systems in The Netherlands. The case study is used to make additions to the transformation perspective, particularly with regard to tactical and strategic games.

Technological innovation is manifested in the development of new products, processes and techniques such that emerging technologies often substitute for more mature technologies. The interaction between technologies is typically referred to as competition , implying a confrontational interaction. The setting of technology strategy is thus often concerned with issues relating to the competition between emerging technologies and the response of mature technologies to the offense from emerging technologies-strategies for attack and defense. In this paper it is argued that the interaction between technologies should be viewed in a broader sense than mere competition, and it is suggested that a multi-mode framework provides a much richer setting for assessing the interaction of two or more technologies. This concept has been successfully applied in biological and organizational ecology, and it is shown that it can be equally useful when applied to the dynamics of technological interaction. It is proposed that the effect that one technology has on another's growth rate be taken as a classification criterion. Examples are given to illustrate that three major modes of interaction exist, namely pure competition, symbiosis and predator-prey. In addition, the notion that the interaction between technologies can in general shift temporally from one mode to another is motivated. It is suggested that, since the characteristics of the three modes differ from one another, it is appropriate to develop managerial strategies that apply specifically to each of the three modes, instead of just applying generic ‘competition’ strategies.

Pasteur’s Quadrant model, published by Stokes in 1997, presents a two-dimensional abstract conceptual framework that proved immensely helpful to study and discuss institutional and policy arrangements in science. However, during the last 10 years the PQ model was also applied in a series of large-scale, survey-based studies worldwide to classify individual modern-day researchers according to their research orientation and performance. This paper argues that such applications are inadequate to capture key characteristics of individual researchers, especially those within the heterogeneous ‘Pasteur type’ group who engage in ‘use-inspired’ basic scientific research. Addressing this shortcoming, Pasteur’s Cube (PC) model introduces a new heuristic tool. Departing from a three-dimensional conceptual framework of research-related activities, the model enables a range of typologies to describe and study the large variety of academics at today’s research-intensive universities. The PC model’s analytical robustness was tested empirically in two interrelated ‘proof of concept’ studies: an exploratory survey among 150 European universities and a follow-up case study of Leiden University in the Netherlands. Both studies, collecting data for the years 2010–2015, applied a metrics-based taxonomy to classify individual academic researchers according to four performance categories: scientific publication output, research collaboration with the business sector, patents filings, and being engaging in entrepreneurial activities. The collective results of both studies provide more clarity on relevant subgroups of use-inspired researchers. The PC model can be used to guide empirical, metrics-based investigations of research activities and productivities, applies this approach to two case studies, and demonstrates the utility of the method while also reinforcing and enriching the growing body of literature showing that cross-sectoral and cross-functional research activities are more scientifically productive than research carried out in isolation of the context of use. Introducing the ‘Crossover Collaborator’ subtype helps to explain why Pasteur type researchers tend to outperform other types of researchers in terms of publication output and citation impact.

This paper aims to make two conceptual contributions to the growing research strand on policy feedbacks and socio-technical systems, which highlights that policies can generate effects in socio-technical systems that subsequently shape policymaking and strengthen policy trajectories. While policy feedbacks are relevant for understanding accelerated low-carbon transitions, the first contribution is to suggest that a fuller understanding should also include feedbacks within socio-technical systems. We therefore propose an integrated conceptual framework that opens up the black box of socio-technical systems and complements policy feedbacks with a more differentiated conceptualisation of socio-technical feedbacks between firms, users, wider publics, and technology. These socio-technical feedbacks can generate cost reductions, performance improvements, increasing confidence and investments, favourable public debates, and enhanced user adoption. We suggest and show that positive policy feedbacks as well as positive socio-technical feedbacks are needed to accelerate low-carbon transitions. The second contribution is to elaborate the moderating role of technological characteristics such as design complexity and customisation needs in positively or negatively shaping feedback loops. We apply our conceptual framework to case studies of energy efficiency lighting and smart meters in the UK, showing empirically how positive policy feedbacks and socio-technical feedbacks in the former accelerated the LED transition and led to stronger policies over time, whereas negative feedbacks in the latter case resulted in slower than anticipated diffusion and delayed deployment targets.