These co-dynamics eventually lead to the build-up of shared structural elements or ‘couplings’ between powertrain technologies, such as overlap actors, knowledge overlap, institutional overlap and resource overlap. Co-dynamics are illustrated to carry a mix of positive, negative and neutral influences between powertrain technologies that shape the various biological relationship modes between them, such as competition symbiosis, commensalism, parasitism and amensalism. Some of the proposed co-dynamics are entrepreneurial spawning, policy transfer, knowledge recombination and resource redeployment. This thesis illustrates that the internal dynamics of one powertrain technology become coupled with the internal dynamics of another powertrain technology through what is referred to as ‘co-dynamics’ in the dynapstic framework. Feeding all the L-V quantifications and estimations from the second stage, all the individual socio-technical dimensions of powertrain technologies are integrated via an extensive system dynamics (SD) modelling for the time horizon of 1985 to 2050. In the final stage, this thesis conducts a simulative explanatory–exploratory study to comprehensively simulate all the individual dimensions of the dynapstic framework. The biological Lotka-Volterra (L-V) equations are applied to quantify the individual socio-technical dimensions of powertrain technologies for the period 1985 to 2016. In the second stage, it conducts a quantitative explanatory–exploratory study to quantify the individual dimensions of the dynapstic framework. The various dimensions of the dynapstic framework are initially demonstrated by narrating some case studies, especially in the transportation and energy sectors. Adopting the technological innovation system (TIS) framework and the biological relationship modes, a dynamic approach to socio-technical interactions between technologies is proposed, called ‘dynapstic’. In the first stage, it conducts a qualitative explanatory–exploratory study to construct the conceptual framework. This thesis conducts a qualitative and quantitative explanatory–exploratory study through a three-staged research design of narrative (conceptualisation), quantification and simulation. Inter-powertrain relationships are studied in the early technological lifecycle (TLC) stage, known as an ‘era of ferment’, as it is characterised by an increase in technological variations, intense competition, high market uncertainty, and the frequent exits and entries of firms. The biological inter-population relationships analogy is applied to the three powertrain technologies of internal combustion engine, hybrid and battery electric vehicles (ICEV, HEV, and BEV) in the United States (US) automotive industry.

This thesis takes a biological perspective and investigates whether technologies in an industry interact with one another in the same way that populations do in an ecology. This is particularly imperative in sectors such as energy and transportation that struggle with both economic concerns (e.g., resource scarcity) and ‘grand challenges’, such as unsustainable consumption and production and greenhouse gas (GHG) emissions.

It is critical to investigate the development, diffusion and utilisation of emerging technologies in relation with each other, as well as with incumbent technologies. Based on the identified examples, possible strategies to overcome the challenges are proposed. In success evaluation, the challenge is to objectively measure the effects of method application on the design outcome. Challenges arising in conducting experiments and field studies are caused by a lack of common metrics and established study designs. The overview of 54 identified studies shows a preponderance of non-comparative studies conducted in laboratory environment. The following state-of-the-art review focuses on challenges in validation and on how researchers address them. Systematic mapping structures the identified literature in an overview. A mixed-methods literature study including the years 2010–2020 is conducted. This contribution investigates how validation of design methods is currently conducted in order to clarify this issue and identify approaches that might be suitable as best practices. Validation of design methods is a challenge in design research, as there is a lack of common methodology.

Data set transistor final