4.2.1 Historical Background: From the Linear Model to Innovation Systems

Innovation systems were introduced to address the shortcomings of the classic linear model of innovation theory (Balconi et al. Reference Balconi, Brusoni and Orsenigo2010). The linear model asserts that the successful production and diffusion of an innovation originates with basic research, followed by applied R&D, production and marketing. It is a model remit of feedback loops and only considers commercial actors who are presumed to be fully rational in nature, meaning that they can perfectly weigh the advantages and disadvantages of all options and make a rational choice (Price & Bass Reference Price and Bass1969). Furthermore, the linear model ignores the fundamental role institutions (i.e. ‘the rules of the game’) play in innovation dynamics (Godin, Reference Godin2006), These rules, regulations, customs, norms and values strongly guide decision-making processes, and therefore the directionality that innovation takes. Additionally, the linear model proposes that innovation diffusion occurs through a ‘technology push’ pathway, suggesting that with enough basic science, R&D, production and marketing, a market will begin to form, thus achieving success (Di Stefano et al., Reference Di Stefano, Gambardella and Verona2012).

In the 1970s and 1980s, scholars questioned the restrictive nature of the linear model, in particular, whether a narrow set of commercial actors could account for all relevant activity and if full rationality was realistic (Balconi et al. Reference Balconi, Brusoni and Orsenigo2010; Price & Bass Reference Price and Bass1969). Additionally, innovation processes are circuitous in nature, influenced by a wide range of constantly evolving and shifting factors, leading to many positive and negative feedback loops. It also became evident that rules, regulations and other institutions play a critical role in innovation processes, guiding the directionality that innovators take. Finally, many innovations are reliant on a market pull-strategy, and particularly those with a perceived societal benefit (in the eyes of the decision-makers): sustainable technologies – such as renewable energy technologies – are oft-cited examples, but other technologies that affect society at large can include telecommunications, military procurement, healthcare or civil infrastructure. A market-pull approach preemptively generates demand through regulatory requirements, public procurement, national research agendas, subsidies, tax breaks, government discourse, voluntary private certification systems or societal expectations (Mowery & Rosenberg, Reference Mowery and Rosenberg1979; Nemet, Reference Nemet2009; Deknatel & van der Loos, Reference Deknatel and van der Loos2025). By generating demand, companies will subsequently invest in R&D to produce a viable product.

As a result of this more holistic thinking, the notion of a ‘system’ emerged. ‘This more systemic view of the innovation process explicitly recognizes the potentially complex interdependencies and possibilities for multiple kinds of interactions between the various elements of the innovation process’ (Edquist & Hommen, Reference Edquist and Hommen1999, pp. 64–65).

4.2.2 Early Models: National, Regional and Sectoral Innovation Systems

While being more holistic and comprehensive than the linear model, economic growth remained a central element in IS. IS theory originally centred on the nation-state and was technology neutral, known as ‘NIS’ (Edquist, Reference Edquist and Fagerberg2004; Freeman, Reference Freeman, Dosi, Freeman, Nelson, Silverberg and Soete1998). NIS is framed around individual countries, constantly in competition; the ones with the most successful IS are the most likely to emerge as the most economically successful and industrially developed, and thereby gain competitive advantage (Dosi et al., Reference Dosi, Freeman, Nelson, Silverberg and Soete1988; B.-A. Lundvall, Reference Lundvall1985; B.-Å. Lundvall et al., Reference Lundvall, Johnson and Andersen2002; R. R. Nelson & Rosenberg, Reference Nelson, Rosenberg and Nelson1993). The works by Freeman on the industrial emergence of Japan and Nelson & Rosenberg on the United States laid the groundwork for IS (Freeman, Reference Freeman, Dosi, Freeman, Nelson, Silverberg and Soete1998; R. R. Nelson & Rosenberg, Reference Nelson, Rosenberg and Nelson1993). As countries play a fundamental role in innovation policy setting, knowledge bases and industrial capacity, their ability to generate successful IS became fundamental to their economic growth (Furman et al., Reference Furman, Porter and Stern2002; Fagerberg & Srholec, Reference Fagerberg and Srholec2008).

Two additional strands of IS theory – RIS and SIS – emerged in the mid-1990s, also with an emphasis on economic growth. RIS takes a similar approach as NIS, but bounds its scope to regions rather than nations, with regions being defined as sub-national (rather than supra-national) spaces (Asheim & Gertler, Reference Asheim and Gertler2009; Cooke, Reference Cooke1992). Classic examples of successful regions are Silicon Valley in California, USA, Route 128 in Boston, Massachusetts, USA, or the Dutch Randstad agglomeration of Amsterdam, The Hague, Leiden, Rotterdam and Utrecht. While not discounting the relevance of the nation-state in policy setting, financial investments or national agendas, RIS highlights the unique characteristics within a region that make one more successful than another, regardless of the specific innovation in question (Doloreux & Parto, Reference Doloreux and Parto2005). Here, proximity plays a critical role in knowledge exchange and networking (Asheim & Gertler, Reference Asheim and Gertler2009).

Sectoral IS scholars are particularly interested in the way in which a group of actors can operate (and influence) a particular institutional arrangement to sell, provide and produce a set of goods or services (Malerba, Reference Malerba2002, p. 247). A sector contains a range of products that are designed to serve a specific good, such as fossil fuel energy provision, the automotive sector, telecoms or electricity (Pavitt, Reference Pavitt1984; Yoon-Zi & Lee, Reference Yoon-Zi and Lee2008; Dolata, Reference Dolata2009; Weber & Schaper-Rinkel, Reference Weber and Schaper-Rinkel2017). It essentially addresses the difference in innovation processes across industries. Fundamentally, none of these three strands of IS explicitly address sustainability challenges, a key condition that led to the popularisation of TIS.

4.2.3 The TIS Framework

Innovation systems emerged as a heuristic through which to frame the development of innovation. Technological innovation systems (TIS) home in on the specifics of emerging technologies and the factors that influence their success (Carlsson & Jacobsson, Reference Carlsson and Jacobsson1994). While all other IS had remained technology-neutral, opting to concentrate on the conditions that affect innovation in general, TIS positioned a given technology as the central unit of analysis.

Hereby, TIS is a theoretical lens predicated on the notion that a core structure of interdependent elements – actors, institutions and networks – will have a positive or negative influence on the emergence of innovation and that many feedback loops influence the innovation trajectory (Hekkert et al., Reference Hekkert, Suurs, Negro, Kuhlmann and Smits2007; Bergek, Jacobsson, & Sandén, Reference Bergek, Jacobsson, Carlsson, Lindmark and Rickne2008; Carlsson & Stankiewicz Reference Carlsson and Stankiewicz1991). Important to note is the conceptual ambiguity concerning the distinction between TIS as a heuristic – a theoretical lens through which to understand or assess a given situation – or TIS as something tangible and real – that is, the electric vehicle technological IS in Japan. While TIS has certainly been used and applied extensively to empirical cases, we suggest that the concept of a TIS, at its core, is a heuristic that can be used to study innovation activity within a socio-technical system. TIS is thus an analytical construct used to describe and appraise the totality of factors and actors that, knowingly and unknowingly, contribute to the development and diffusion of innovation (Edquist, Reference Edquist and Fagerberg2004; Edquist & Hommen, Reference Edquist and Hommen1999).

Within TIS, a wide variety of actors strongly influence the innovation process, namely those beyond the private sector. Actors, also commonly referred to as agents, constitute all individuals organisations, businesses and other entities that play a role in the emergence of any given innovation (Nelson & Rosenberg, Reference Nelson, Rosenberg and Nelson1993; Bento & Fontes, Reference Bento and Fontes2019). These actors, which can also form constellations, can include start-ups, large companies, government organisations, consumers, suppliers, financial entities, educational bodies and NGOs (Edquist & Hommen Reference Edquist and Hommen1999; Musiolik et al. Reference Musiolik, Markard, Hekkert and Furrer2020; Wesche et al., Reference Wesche, Negro, Dütschke, Raven and Hekkert2019) (see Chapter 17 on Actors).

Institutions are the rules of the game and are categorised as either formal or informal (Fuenfschilling & Truffer, Reference Fuenfschilling and Truffer2014). Formal institutions are those codified and dictated by a governing authority, such as a government, a company or a standardisation organisation. Laws, regulations and rules fall into this category and often entail consequences for non-compliance, such as fines or penalties. Informal institutions govern behaviour through norms, values and cognitive understandings (Scott Reference Scott1995). Norms and values persist through moral significance, thereby guiding specific behaviour, such as recycling. Cognitive understandings underpin collective mindsets, such as a broad consensus that climate change is a societal challenge that needs to be tackled. These informal institutions affect the way in which individuals behave without resulting in physical repercussions or being legally binding.

Networks can be tangible or intangible; tangible networks include formal networking organisations or associations, which can be either publicly governed or privately led (van Lente et al., Reference van Lente, Hekkert, Smits and van Waveren2003; Musiolik et al., Reference Musiolik, Markard and Hekkert2012; Kivimaa et al., Reference Kivimaa, Boon, Hyysalo and Klerkx2019). For example, a public networking organisation can use government resources to help connect start-ups with incumbent actors. Private networking organisations include industry associations or lobbying groups (Kivimaa, Reference Kivimaa2014; Sovacool et al., Reference Sovacool, Turnheim, Martiskainen, Brown and Kivimaa2020). Alternatively, informal networks include prior collaborations, ‘friends-of-friends’ or the figurative rolodex (van der Loos et al., Reference van der Loos, Negro and Hekkert2020a). Intermediaries are key actors that support or build networks (see Chapter 18 on Intermediaries).

Actors, institutions and networks form the structure of TIS within which the dynamics of innovation occur and thereby influence the emergence of innovation (Jacobsson & Bergek, Reference Jacobsson and Bergek2004; Jacobsson & Johnson, Reference Jacobsson and Johnson2000). Importantly, it does not automatically presume a well-organised or defined structure with clear goals, policies, visions or actor groups. On the contrary, many TIS are very messy from the outset due to a wide range of factors, such as changes in political directionality, policy uncertainty, industry resistance and the entrance and exit of actors (Agterbosch et al., Reference Agterbosch, Vermeulen and Glasbergen2004; Kamp et al., Reference Kamp, Smits and Andriesse2004; van der Loos et al., Reference van der Loos, Negro and Hekkert2020a, Reference van der Loos, Langeveld, Hekkert, Negro and Truffer2022; Verhees et al., Reference Verhees, Raven, Kern and Smith2015; Wieczorek et al., Reference Wieczorek, Hekkert, Coenen and Harmsen2015).

4.2.4 Sustainability Transitions and the Popularisation of TIS

Throughout the 2000s, TIS gained widespread popularity for two key reasons. First, it arose at a time when sustainable technologies struggled to emerge; TIS was an ideal framework to identify the barriers to the generation (production) and diffusion (distribution) of technologies needed to address severe sustainability challenges. Second, TIS offered a new and valuable analytical lens, providing improved mechanisms to evaluate performance and provide policy advice.

TIS’s popularity was fueled by a need to address many of the world’s environmental concerns, including climate change, air pollution, traffic congestion and environmentally destructive agricultural practices. While TIS was not explicitly founded on the grounds of addressing sustainability challenges – indeed, one of the seminal works by Carlsson and Jacobsson (Reference Carlsson and Jacobsson1994) analyzed factory automation in Sweden – the majority of TIS studies to date have concentrated on sustainable technologies, coinciding with the rise of sustainability transitions as a broader epistemic field (see Chapter 1) (Bergek, Reference Bergek, Boons and McMeekin2019; Markard et al., Reference Markard, Hekkert and Jacobsson2015; Weckowska et al., Reference Weckowska, Weiss, Schwäbe and Dreher2025).

Through the lens of TIS, many scholars study energy (cf. Dewald & Truffer, Reference Dewald and Truffer2011; Hanson, Reference Hanson2018; Hillman & Sandén, Reference Hillman and Sandén2008; Negro, Alkemade, et al., Reference Negro, Alkemade and Hekkert2012; Negro, Vasseur, et al., Reference Negro, Vasseur, Van Sark and Hekkert2012; van der Loos et al., Reference van der Loos, Negro and Hekkert2020a, Reference van der Loos, Negro and Hekkert2020b; Wesche et al., Reference Wesche, Negro, Dütschke, Raven and Hekkert2019; Wesseling et al., Reference Wesseling, Kieft, Fuenfschilling and Hekkert2022; Wieczorek et al., Reference Wieczorek, Negro, Harmsen, Heimeriks, Luo and Hekkert2013), mobility (cf. Trencher & Wesseling, Reference Trencher and Wesseling2022; Wesseling, Reference Wesseling2016; Gong & Hansen, Reference Gong and Hansen2023; Weiss et al., Reference Weiss, Asna, Ashari and Blind2024), agriculture/agri-food (cf. Klerkx et al., Reference Klerkx, van Mierlo, Leeuwis, Darnhofer, Gibbon and Dedieu2012; König et al., Reference König, Janker, Reinhardt, Villarroel and Junge2018; Schiller et al., Reference Schiller, Klerkx, Poortvliet and Godek2020; Tziva et al., Reference Tziva, Negro, Kalfagianni and Hekkert2020; Vermunt et al., Reference Vermunt, Negro, Van Laerhoven, Verweij and Hekkert2020, Reference Vermunt, Wojtynia, Hekkert, Van Dijk, Verburg, Verweij, Wassen and Runhaar2022; Pulmer et al., Reference Plummer, Andersson and Lennerfors2024) and water & sanitation systems (Binz et al., Reference Binz, Harris-Lovett, Kiparsky, Sedlak and Truffer2016; Bichai & Murthy, Reference Bichai, Kajenthira, Grindle and Murthy2018; Weile et al., Reference van Welie, Truffer and Yap2019, Reference van Welie, Boon and Truffer2020; Heiberg & Truffer, Reference Heiberg, Truffer and Binz2020). Beyond these themes, scholars have also applied TIS to the fields of digitalisation (Liu et al., Reference Liu, Gao, Chen, Yu and Zhang2018; Gherher et al., Reference Gherhes, Vorley, Vallance and Brooks2022; John et al., Reference John, Wesseling and Frenken2024) and health (Kukk et al., Reference Kukk, Moors and Hekkert2015, Reference Kukk, Moors and Hekkert2016; Zhang et al., Reference Zhang, Li, Hu and Wang2015; Hidefjäl, Reference Hidefjäll2016; Larisch et al., Reference Larisch, Amer-Wåhlin and Hidefjäll2016; Fisher et al., Reference Fischer, Hekkert, Hüsing and Moors2020).

Importantly, TIS was popularised within the global European north and its growing epistemic community of sustainability transitions scholars (Carlsson & Jacobsson, Reference Carlsson, Elg and Jacobsson2010; Markard et al., Reference Markard, Hekkert and Jacobsson2015; Dhiman et al., Reference Dhiman, Singh, Arjune, Yadav, Yadav and Bansala2023); most empirical studies were (and still are) carried out in European countries. More recently, scholars have used TIS to address similar questions in the Global South, notably China, Brazil, India and certain African nations (Edsand, Reference Edsand2019; Quitzow, Reference Quitzow2015; van Welie et al., Reference van Welie, Truffer and Yap2019; Schiller et al., Reference Schiller, Klerkx, Poortvliet and Godek2020; Dhiman et al., Reference Dhiman, Singh, Arjune, Yadav, Yadav and Bansala2023; Fartash & Ghorbani, Reference Fartash and Ghorbani2023). Amongst this geographic diversification, energy, mobility, agri-food and water & sanitation continue to be the most popular topics (cf. Sixt et al. Reference Sixt, Klerkx and Griffin2018).

4.3.1 Functions

As a novel theoretical framework, TIS in the 1990s necessitated refinement. First, the definition of an actor or institution remained vague, and it was not always clear where the boundaries of the system fell. Studies lacked reproducibility and comparability, meaning that it was difficult to consistently determine which conditions ultimately influenced the success of an innovation (Andersson et al., Reference Andersson, Hojcková and Sandén2023). The relation between an innovation and its success hence remained vague, making it challenging to provide policy advice (Klein Woolthuis et al., Reference Klein, Woolthuis, Lankhuizen and Gilsing2005). Initially, IS focused on its core structural elements (the actors, networks and institutions), which largely remain static.

In the early 2000s, the functions of IS emerged. The TIS functions are the key processes – or ‘dynamics’ – carried out by the actors in the IS according to a set of institutions and interact through formal and informal networks (Jacobsson & Bergek, Reference Jacobsson and Bergek2004; Johnson, Reference Johnson1998; Johnson & Jacobsson, Reference Johnson, Jacobsson, Coombs, Green, Walsh and Richards2001). The functions hence shifted the focus to what the structural elements do and how they interact (Jacobsson & Bergek, Reference Jacobsson and Bergek2004; Johnson & Jacobsson, Reference Johnson, Jacobsson, Coombs, Green, Walsh and Richards2001).

In 2007 and 2008, two seminal works emerged to provide greater insight and specificity into these functions and the dynamics that occur within a given system (Bergek, Jacobsson, Carlsson, et al., Reference Bergek, Jacobsson, Carlsson, Lindmark and Rickne2008; Hekkert et al., Reference Hekkert, Suurs, Negro, Kuhlmann and Smits2007). In Table 4.1, two sets of functions (key processes) are presented based on work from Hekkert et al. Reference Hekkert, Suurs, Negro, Kuhlmann and Smits2007 and Bergek et al. Reference Bergek, Jacobsson, Carlsson, Lindmark and Rickne2008. The functions represent the key processes and determinants for successful innovation upon which to carry out rigorous, comparable and substantive studies.

Notably, Bergek et al. (Reference Bergek, Jacobsson, Carlsson, Lindmark and Rickne2008) combine ‘knowledge development and diffusion’ into Function 2 and introduce ‘positive externalities’ as Function 7. Hekkert et al. (Reference Hekkert, Suurs, Negro, Kuhlmann and Smits2007) split ‘knowledge development’ (Function 2) and knowledge diffusion (Function 3) into two functions but do not explicitly incorporate positive externalities. These two articles by Hekkert et al. (Reference Hekkert, Suurs, Negro, Kuhlmann and Smits2007) and Bergek et al. (Reference Bergek, Jacobsson, Carlsson, Lindmark and Rickne2008) have been cited over 1,700 and 1,440 times, respectively.Footnote ¹

Despite the significant progress made with the introduction of system functions, its operationalisation for empirical cases still left room for improvement; in particular, there were no metrics used to guide the analysis of functional performance – for example, how ‘guidance of the search’ is measured – thereby making it challenging to compare and assess research output, provide policy advice or ensure reproducibility (Hekkert & Negro, Reference Hekkert and Negro2009; Negro et al., Reference Negro, Hekkert and Smits2007). Attention shifted to the operationalisation of the suggested functions; for example, a new pilot project could be a positive indicator of entrepreneurial activity, while a government diffusion target indicated positive guidance of the search. Cancelling a subsidy system would be a negative indicator of resource mobilisation (cf. Negro et al. Reference Negro, Hekkert and Smits2007; Hekkert & Negro, Reference Hekkert and Negro2009; Suurs et al., Reference Suurs2009). While non-exhaustive, the new metrics provided a first classification system through which to measure how well a system is performing at any given time. By identifying weak functions, it became possible to identify the specific root causes of poorly performing IS. These root causes, or systemic problems, could then be linked back to weaknesses within the structure of the system, meaning that the cause of a weak function was due to either the absence or incapability of the system’s actors, institutions and/or networks (Wieczorek, Reference Wieczorek2012).

4.3.2 Motors of Innovation

Following the introduction of functions, the next conceptual step explored how functions interlink and whether they follow a typical sequence or pattern. Building on this idea, the ‘motors of innovation’ specify how functions interact with each other and how their interactions vary depending on the technology’s phase of development (Suurs, Reference Suurs2009; Suurs & Hekkert, Reference Suurs, Hekkert, Verbong and Loorbach2012). The motors of innovation lay out the role of feedback loops and how interactions amongst functions influence and explain functional performance. The four motors of innovation – the Science-technology push motor, Entrepreneurial motor, System building motor and the Market motor – follow the diffusion of innovation pathway from experimentation to wide-scale adoption. Certain functions play a stronger or weaker role in each phase of development and the way in which they interact will change over time. Fulfilling the functions in specific ways for specific phases helps the system progress to the next phase of development, making the functional feedback loops evolve as the system develops (Suurs, Reference Suurs2009; Suurs et al., Reference Suurs, Hekkert, Kieboom and Smits2010; Walrave & Raven, Reference Walrave and Raven2016; Wesseling et al., Reference Wesseling, Kieft, Fuenfschilling and Hekkert2022). For example, ‘resource mobilisation’ should target R&D in early phases of development whereas it should support market formation in later phases. Moreover, ‘market formation’ should create protected niche spaces early on whilst establishing stringent market uptake goals later in the innovation’s development (i.e. x% of electric vehicles by year y). The motors of innovation were further expanded upon to address transformational failures by considering systemic dynamic models (Walrave & Raven, Reference Walrave and Raven2016; Raven & Walrave, Reference Raven and Walrave2020).

4.3.3 TIS-in-Context

While making theoretical and analytical progress, TIS was criticised for failing to encapsulate influential contextual factors (Coenen et al., Reference Coenen, Benneworth and Truffer2012; Wirth & Markard, Reference Wirth and Markard2011). Context plays a substantial role in TIS development but is not incorporated into the functions or structure. In contrast, the MLP explicitly focuses on the interaction dynamics between a niche and the regime (Markard & Truffer Reference Markard and Truffer2008; Weber & Rohracher Reference Weber and Rohracher2012).

To address the issue of context, four conditions were proposed: TIS–TIS, TIS–sectoral, TIS–geographic and TIS–political (Bergek et al., Reference Bergek, Hekkert, Jacobsson, Markard, Sandén and Truffer2015; Hojckova et al., Reference Hojckova, Ahlborg, Morrison and Sandén2020; Šćepanović et al., Reference Šćepanović, Warnier and Nurminen2017; van der Loos et al., Reference van der Loos, Normann, Hanson and Hekkert2021). The TIS–TIS context addresses the potential influence and interaction of one TIS on another TIS. These TIS–TIS interactions can be horizontal in nature, when two TIS offer similar products or services, such as solar photovoltaics and wind power generating electricity (Ulmanen & Bergek, Reference Ulmanen and Bergek2021). They can also be vertical in nature, when a TIS along the supply chain, such as a lithium-ion battery TIS, influences the electric vehicle TIS, but would also be relevant for home battery systems or computers. Positive interactions can include collective lobbying and technological complementarity, while negative interactions might relate to competition over limited resources (e.g. subsidies), labour or feedstock. The TIS–sector context suggests that established sectors play a fundamental role in the development of an emerging TIS, either positively or negatively, because the TIS is inherently embedded in a sector. For example, the oil and gas sector plays a substantial role in the offshore wind TIS by leveraging resources and knowledge (Mäkitie et al., Reference Mäkitie, Andersen, Hanson, Normann and Thune2018, Reference Mäkitie, Normann, Thune and Sraml Gonzalez2019; van der Loos Reference van der Loos, Negro and Hekkert2020a, Reference van der Loos, Negro and Hekkertb). The TIS–political context goes beyond the politics of a TIS and rather addresses long-standing, established and overarching political mechanisms, systems and traditions. Lastly, the TIS–geographic context looks at a country’s geographical positioning and its influence on TIS. This can refer to not only its geographic proximity to markets but also the resources the country possesses that may make it well- or ill-suited for an emerging TIS. Studies include the role of TIS-in-context for Brazilian biogas (De Oliveira & Negro, Reference De Oliveira and Negro2019), offshore wind (Mäkitie et al., Reference Mäkitie, Andersen, Hanson, Normann and Thune2018; van der Loos et al., Reference van der Loos, Normann, Hanson and Hekkert2021), shipping (Bach et al., Reference Bach, Mäkitie, Hansen and Steen2021) and food-waste processing (Ulmanen & Bergek, Reference Ulmanen and Bergek2021).

4.3.4 Place and Scale: Geographic Challenges

An additional challenge that TIS faced was how to address questions of geography (Binz et al., Reference Binz, Truffer and Coenen2014). While TIS has no formal geographic bounds (unlike NIS or RIS), it suffered (and still suffers) from ‘implicit methodological nationalism’, meaning that most TIS studies are carried out within the scope of a particular nation-state. This is due to the important role that nations play in rule setting and knowledge development, as well as the practicalities of research feasibility; indeed, this is an issue that arises in transitions studies more broadly (Coenen & Truffer Reference Coenen and Truffer2012; Fuenfschilling & Binz, Reference Fuenfschilling and Binz2018, p. 737; Truffer & Coenen Reference Truffer and Coenen2012; Truffer, Murphy & Raven Reference Truffer, Murphy and Raven2015). Furthermore, most scholars previously studied a subset of highly developed, wealthy northern European countries, thereby neglecting southern European countries (Bento & Fontes, Reference Bento and Fontes2015, Reference Bento and Fontes2019), and developing countries (Edsand, Reference Edsand2019). Nonetheless, even an expansion to the Global South reproduced the notion of nationally delineated TIS and neglected territorial sensitivities. By focusing on the nation-state, many regional subtleties are lost (Coenen et al., Reference Coenen, Benneworth and Truffer2012; Rohe, Reference Rohe2020; Rohe & Chlebna, Reference Rohe and Chlebna2021). See Chapters 21–23 on the Geography of Transitions for a detailed reflection.

While some scholars homed in on the regional sensitivities of TIS, others directed their attention towards the influence of global dynamics on TIS (Gosens & Coenen, Reference Gosens, Lu and Coenen2015). Binz and Truffer (Reference Binz and Truffer2017) introduced the concept of global innovation systems (GIS) for emerging technologies; they proposed a quadrant within which technologies develop at the global level: one axis refers to a technology’s ‘innovation mode’, based either on ‘science-technology-push’ or ‘doing-using-interacting’; the other axis is the technology’s ‘valuation mode’, based on ‘spatially sticky’ versus ‘globally footloose’ (Binz & Truffer, Reference Binz and Truffer2017). Furthermore, four key resources were identified as essential to the successful emergence of technology on a global scale: knowledge, niche markets, financial investment and legitimacy (Binz et al., Reference Binz, Harris-Lovett, Kiparsky, Sedlak and Truffer2016). While still limited in its research, a few studies have emerged using this framework; however, efforts so far have failed to attain a truly global focus. For example, GIS geographic bounds include the European Union (Heiberg & Truffer Reference Heiberg and Truffer2022), regions (Rohe Reference Rohe2020), national innovativeness (Hopp et al., Reference Hopp, Baeza-González and Sjøtun2024; Cho & Park, Reference Cho and Park2022; Yu et al., Reference Yu, Shi, You and Zhu2021; Tsouri et al., Reference Tsouri, Hanson and Normann2021; Hipp and Binz, Reference Hipp and Binz2020) or how specific regions influence the emergence of the global wind energy IS (Rohe, Reference Rohe2020; Rohe & Chlebna, Reference Rohe and Chlebna2021). GIS studies have used a global patent analysis (Yuan & Li, Reference Yuan and Li2021) and reflected on the governance of global and multi-scalar systems (Binz & Truffer, Reference Binz, Coenen, Murphy and Truffer2020). One study focused on the emergence and evolution of the green methanol GIS at a global scale (Snijders & van der Loos, Reference Snijders2025).

4.3.5 Methodological Approaches

A final challenge that TIS faced was empirical operationalisation. The TIS functions provided an excellent roadmap, which introduced new questions about how to measure them and where to collect data. One of the original methods was to conduct an event-history analysis, derived from newspaper articles (Hekkert and Negro, Reference Hekkert and Negro2009). However, event-history analyses struggle to grasp the deeper complexities of TIS dynamics as they only focus on specific events as reported by the media; these studies have therefore generally been complemented by interviews (De Oliveira & Negro, Reference De Oliveira and Negro2019; Reichardt et al., Reference Reichardt, Negro, Rogge and Hekkert2016; Snijders & van der Loos, Reference Snijders2025). Indeed, interviews are the most used data source for IS studies. A diverse array of actors is included to best capture the system in its entirety and display the interacting dynamics between different actors or actor groups, such as business, government officials, networking organisations, non-governmental organisations and citizens. Patent studies, surveys, process tracing methods and modelling have also been used to generate quantitative or comparable analyses (see Part III for a reflection on Studying Sustainability Transitions) (De Oliveira et al., Reference De Oliveira, Subtil, Lacerda and Negro2020; Li et al., Reference Li, Heimeriks and Alkemade2022; Normann & Hanson, Reference Normann and Hanson2018; Stephan et al., Reference Stephan, Schmidt, Bening and Hoffmann2017). Furthermore, text and media-based methods have been used to quantitatively measure the functional dynamics, using similar datasets as event-history analyses (Weiss & Nemeczek, Reference Weiss and Nemeczek2021, Reference Weiss and Nemeczek2022).

4.4.1 Destabilisation, End-of-Life, Lifecycles and Resilience

Innovation systems have been criticised for being too focused on developing specific technologies, without necessarily questioning the value of the innovation itself (Markard et al., Reference Markard, van Lente, Wells and Yap2021; Reference Markard, Wells, Yap and van Lente2023), its life-cycle, resilience or destabilisation (see Chapter 11 on Disruption and Chapter 7 on Deep Transitions) (Elzinga et al., Reference Elzinga, Janssen, Wesseling, Negro and Hekkert2023; Markard, Reference Markard2020; Schot & Steinmueller, Reference Schot and Steinmueller2018; Weckowska et al., Reference Weckowska, Weiss, Schwäbe and Dreher2025). Since TIS is oriented towards the emergence of new technologies, a focus on diffusion and upscaling prevailed, rather than ‘what comes after’ and ‘what does this mean for society’ (Suurs, Reference Suurs2009; Hekkert et al., Reference Hekkert, Negro, Heimeriks and Harmsen2011). The classic phases of technological development (pre-development, development, take-off, acceleration and stabilisation) only describe how a TIS emerges from an idea to a commercially available product but ignore subsequent phases, the impact on other innovations or effect on existing systems. The rise of new innovations undoubtably leads to the decline or transformation of existing innovations and the IS in which they are embedded (Markard et al. Reference Markard2020).

More recent work introduces a life-cycle perspective to conceptualise the later stages of TIS development, including decline (Markard Reference Markard2020). A life-cycle perspective assumes that a TIS has a beginning and an end. In its beginning, an innovation diffuses and matures, the TIS structure grows and formalises, and then becomes rigid and path-dependent. In the decline phase, TIS structures weaken and disband, actors switch to different technologies and the existing structure deteriorates (Weiss & Nemeczek, Reference Weiss and Nemeczek2022). Markard (Reference Markard2020) identifies several factors characterising TIS decline. First, TIS decline may be triggered by major shocks or the emergence of novel competing technologies (e.g. streaming services replacing DVD rentals). Actors seize opportunities to enter a new TIS and develop new business ventures, thereby contributing to the decline of the focal technology. Second, the context in which the IS is situated can change, creating misalignment and conflict, exerting pressure on the TIS. For example, centralised power systems (e.g. coal power plants) may be threatened in times of conflict, driving their decline while decentralised power systems (like rooftop solar PV) may emerge (Harmash Reference Harmash2024). This can lead to destabilisation, where technology-specific institutional structures, such as regulatory support, are weakened. Additionally, resource flows may decrease due to novel technologies capturing market share or firms reallocating R&D resources to new TIS. Complementary technologies or industries may also destabilise alongside the TIS. These factors can create vicious cycles and negative feedback loops, accelerating decline (Markard, Reference Markard2020).

Inspired by the literature on regional resilience, scholars have also explored how and in what ways a TIS can be resilient by contextualising variety according to threatening and non-threatening innovations to balance exploitation versus experimentation (Boschma Reference Boschma2015; van der Loos et al., Reference van der Loos, Frenken, Hekkert and Negro2024).

4.4.2 Innovation Systems for Societal Transitions

Although TIS was not designed to specifically assess sustainability transitions, as discussed above, researchers have been able to suggest intervention strategies to enable the successful diffusion of sustainable technologies (Kieft et al. Reference Kieft, Harmsen and Hekkert2020). However, with its focus on technology-specific systemic change, TIS is less suited to tackle societal challenges across socio-technical regimes (Hekkert et al., Reference Hekkert, Janssen, Wesseling and Negro2020; Schlaile et al., Reference Schlaile, Urmetzer, Blok, Andersen, Timmermans, Mueller, Fagerberg and Pyka2017). Moreover, the breakdown of existing structures is equally important in transitions, which TIS lacks (Bergek et al., Reference Bergek, Hekkert, Jacobsson, Markard, Sandén and Truffer2015; Bergek, Reference Bergek, Boons and McMeekin2019; Markard, Reference Markard2020). To address grand societal challenges, singular technological solutions might prove inadequate and should be complemented with solutions of a non-technological nature. Thus, there remains a need for a new generation of IS frameworks which can address the complex, uncertain and contested nature of societal transitions.

This led to the emergence of the dedicated innovation system for sustainability (DIS) and the problem-oriented innovation system (PIS). The DIS highlights national innovation capabilities while emphasising the connection between economic growth and sustainability (Pyka, Reference Pyka2017). The PIS, on the other hand, argues IS research has long overlooked the social side of innovation and focuses on macro-level societal problems (Ghazinoory et al., Reference Ghazinoory, Nasri, Ameri, Montazer and Shayan2020). What both these approaches share is a sense of direction towards a specific societal problem. Moreover, the notion of social IS, in which the development and diffusion of innovation are tailored to tackle social issues and needs, has been studied by Fulgencio and Le Fever (Reference Fulgencio and Fever2016).

A more recent approach, which also centres around the concept of directionality, is Mission specific innovation systems (MIS) (Hekkert et al., Reference Hekkert, Janssen, Wesseling and Negro2020; Elzinga et al., Reference Elzinga, Janssen, Wesseling, Negro and Hekkert2023). The MIS framework is a heuristic that encapsulates transformative dynamics by looking at path dependencies and sectoral lock-ins along with their associated IS. Therefore, MIS uses a mission as the central unit of analysis to break away from such path dependencies (Elzinga et al., Reference Elzinga, Janssen, Wesseling, Negro and Hekkert2023; Wesseling & Meijerhof Reference Wesseling and Meijerhof2023).

4.4.3 Mission-Specific Innovation Systems

MIS originates from the work on mission-oriented innovation policy, which is seen as a promising policy tool to tackle societal problems; missions can be characterised as having a measurable, ambitious and timebound objective, suitable for engaging diverse stakeholders in mission governance and in the development as well as the diffusion of innovative solutions (Kattel & Mazzucato, Reference Kattel and Mazzucato2018). Missions that address societal problems aim to counter the complex and uncertain nature of societal transitions; grand societal challenges – also known as wicked problems – are hence the central unit of analysis.

An MIS can be defined as a network of agents and set of institutions that influence the development and diffusion of innovative technological and social solutions and the transformation of existing production and consumption systems with the aim to complete a societal mission (Hekkert et al., Reference Hekkert, Janssen, Wesseling and Negro2020). MIS provides a lens through which to study the interactions among actors and institutions relevant to the focal point that defines the IS – in this case, a mission. An MIS is not a pre-existing institutionalised network that starts to emerge when a mission is launched, but rather a lens for studying how existing actors, institutions and networks are geared towards delivering a real or imaginable mission goal. Analyzing innovation development and diffusion through the lens of a MIS should, therefore, offer insights into whether and where momentum for change is gaining traction, who is involved and what the barriers impeding its progress are. The first empirical MIS studies appeared only recently, including Jütting (Reference Jütting2020; Reference Jutting2024) on mission-oriented innovation ecosystems, Cappellano and Kurowska-Pysz (Reference Cappellano and Kurowska-Pysz2020) on a regional MIS, Klerkx and Begemann (Reference Klerkx and Begemann2020) on agriculture, Coenen et al. (Reference Coenen, Visscher and Volker2023) on the circular economy of the infrastructure sector and Reike et al. (Reference Reike, Hekkert and Negro2023) on the circular economy of the textile industry.

Recently, Elzinga et al. (Reference Elzinga, Janssen, Wesseling, Negro and Hekkert2023) introduced the first outlines of an analytical framework to provide more concrete insights into what underpins MIS. The framework is inspired by TIS but introduces transformative components to capture the dynamics of aligning efforts across sectors and domains to collectively accomplish a mission. This approach addresses criticisms of IS frameworks by incorporating the processes of system decline and regime destabilisation. It therefore transcends a strict technological focus and includes organisational and social solutions, such as shifting mobility patterns to walking and bicycling rather than simply introducing electric vehicles to replace combustion engine vehicles. MIS hence comprehensively encapsulates regime change and the complexities of societal transitions (Hekkert et al., Reference Hekkert, Janssen, Wesseling and Negro2020; Elzinga Reference Elzinga, Janssen, Wesseling, Negro and Hekkert2023). To do so, MIS specifically splits the function ‘guidance of the search’ into ‘providing problem directionality’ and ‘providing solution directionality’ and adds the function ‘coordinating the transition’.

To underpin these dynamics, Elzinga et al. (Reference Elzinga, Janssen, Wesseling, Negro and Hekkert2023) propose a three-step analytical approach. The first is the problem-solution diagnosis, which identifies the different causes of a grand societal challenges that are addressed by the mission and the solutions considered potentially viable by any relevant stakeholder to address these problems. Second, the structural analysis involves mapping the number, type and composition of actors involved in coordinating and executing the transition. A MIS structural analysis requires more elaboration than a classic TIS analysis, mapping not only multiple solutions but also unearthing problematic regime practices and supporting structures, a vital step given the critical role of regime transformation in mission success. The final step, the functional analysis, assesses the performance of different solution directions. The functions offer a means to monitor and evaluate transformational processes within the MIS, acknowledging that mission completion hinges on both destabilising and innovative forces. Importantly, the inclusion of multiple, often contested and competing solution directions within the IS gives rise to additional dynamics concerning the interaction amongst these solution directions.

However, before these frameworks can be applied by policymakers and practitioners, further operationalisation is required. A key value of TIS can be attributed, in part, to its well-defined protocols and indicators for analysis. In contrast, the newer generation of IS perspectives currently lacks this level of analytical clarity, hindering policymakers and practitioners from making informed decisions. Moreover, contextual disparities may play a significant role in the context of diverse societal transitions. By conducting a diversity of studies across varying problem domains and geographical settings, MIS has the potential to yield prescriptive recommendations on how to govern, steer and evaluate grand societal challenges.