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Overcoming transformational failures through policy mixes in the dynamics of technological innovation systems
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The need for challenge-led innovation policies to address grand societal challenges is increasingly recognised at various policy levels. This raises questions how to overcome a variety of ‘failures’ prohibiting innovations to flourish. A key-line of thought in theory and policy emerged since the late 1990s on the role of system failures, next to more conventional market-failure thinking. More recently, scholarly work introduced the notion of ‘transformational failures’, which implies an even broader perspective on innovation failures as resting in challenges related to transforming entire systems of production and consumption. This paper combines the literature on Technological Innovation Systems (TIS) with literature on multi-level approaches to sustainability transitions to make a contribution to this debate. In particular, this paper argues that the current literature, so far, has failed to explore how different kinds of policies, or policy mixes, can overcome transformational failures. The paper uses a simulation model (i.e. a system dynamics model) and illustrative examples on electric vehicles to explore relations between transformational failures and (mixes of) policy interventions. A key conclusion is that, in particular in the case where an emerging TIS is in a competitive relation with an incumbent system, overcoming transformational failures can be realised either by directly addressing the incumbent system, for instance by taking away its resources (which may be political challenging). Alternatively, the model results show that a clever mix of policy interventions elsewhere in the system may lead to sufficient performance improvements of the emerging TIS so that it can challenge the incumbent system on its own – albeit with a need for substantial additional resources.
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... The articles of the second thematic cluster, that we label "Sustainable systems-centric perspective on innovation failure" (in red in Fig. 3), are related to market failure and innovation policies. These topics emerged already in the acceleration phase ("system-centric perspective on innovation failure," see Fig. 2), but in this new cluster there is a special emphasis on green growth and sustainability (e.g., Mazzucato, 2016;Capasso et al., 2019), and related transformational failure (Hekkert et al., 2020;Raven and Walrave, 2020). Conversely, the relative share of articles related more broadly to (non-mission oriented) innovation systems and system failure has decreased. ...
... • Understanding how different contingency factors impact learning from innovation failure (Rhaiem and Amara, 2021) • Examining different types of failure beyond the organizational context, such as personal and professional failures (Amankwah-Amoah, 2024) • Further elucidating the mechanisms through which organizations can learn from others' failures and how this form of learning can incentivize and motivate firms to engage in innovation activities (Amankwah-Amoah, 2024) • Deepening comprehension of factors such as leadership, management skills, learning behavior, resilience, and trust (Oláh et al., 2021;Santos and Ponchio, 2021;Zahoor and Adomako, 2023) able to foster a learning culture that goes beyond organizational boundaries • Understanding the human cost of project setbacks and failure (Todt et al., 2019) • Understanding the conditions under which firms consciously prioritize learning over other activities and how this decision impacts firm performance (Klingebiel et al., 2022) • Investigating learning from innovation failure as a moderator variable in explaining firms' innovativeness that might affect • Adopting a multilevel perspective to explore how learning from innovation failure occurs across different levels (Tao et al., 2023;Freisinger and McCarthy, 2024 • Developing frameworks which support the design of policy interventions that effectively respond to transformational challenges and mitigate transformational failures (Hekkert et al., 2020) • Understanding how to detect transformational failures (Raven and Walrave, 2020) • Investigating what kind of policy interventions are required to reduce resistance to learning from failures (Raven and Walrave, 2020). • Developing new models to describe rebound in the context of circular economy, going beyond economics to include psychology, sociology, industrial ecology, physics, and broader transdisciplinary lenses (Siderius and Poldner, 2021) • Understanding failures on different innovation policies outcomes, as well as their interconnection and evolution over time (Goyal, 2021). ...
... • Understanding how different contingency factors impact learning from innovation failure (Rhaiem and Amara, 2021) • Examining different types of failure beyond the organizational context, such as personal and professional failures (Amankwah-Amoah, 2024) • Further elucidating the mechanisms through which organizations can learn from others' failures and how this form of learning can incentivize and motivate firms to engage in innovation activities (Amankwah-Amoah, 2024) • Deepening comprehension of factors such as leadership, management skills, learning behavior, resilience, and trust (Oláh et al., 2021;Santos and Ponchio, 2021;Zahoor and Adomako, 2023) able to foster a learning culture that goes beyond organizational boundaries • Understanding the human cost of project setbacks and failure (Todt et al., 2019) • Understanding the conditions under which firms consciously prioritize learning over other activities and how this decision impacts firm performance (Klingebiel et al., 2022) • Investigating learning from innovation failure as a moderator variable in explaining firms' innovativeness that might affect • Adopting a multilevel perspective to explore how learning from innovation failure occurs across different levels (Tao et al., 2023;Freisinger and McCarthy, 2024 • Developing frameworks which support the design of policy interventions that effectively respond to transformational challenges and mitigate transformational failures (Hekkert et al., 2020) • Understanding how to detect transformational failures (Raven and Walrave, 2020) • Investigating what kind of policy interventions are required to reduce resistance to learning from failures (Raven and Walrave, 2020). • Developing new models to describe rebound in the context of circular economy, going beyond economics to include psychology, sociology, industrial ecology, physics, and broader transdisciplinary lenses (Siderius and Poldner, 2021) • Understanding failures on different innovation policies outcomes, as well as their interconnection and evolution over time (Goyal, 2021). ...
... The design and operational implementation of a regional innovation policy requires conceptual work to think about what to do, how to do it, and for whom. Among the analytical tools available to practitioners, the Regional Innovation System (RIS) framework fulfils this role in the implementation of more place-based innovation policies (Barca et al., 2012;Hassink, 2020;Porto Gómez et al., 2016;Suorsa, 2014) including the most recent evidence-based notions in regional sciences: the challenge-oriented and mission-oriented approaches (Isaksen et al., 2022;Raven & Walrave, 2020;Tödtling et al., 2022;Wanzenböck & Frenken, 2020). Indeed, the RIS framework was created by researchers whose primary goal was to create useful and deployable tools in the field. ...
... In this article, we proposed to improve the ability of regional institutions to fully understand and effectively describe their respective territories. This can be achieved by using multi-dimensional and multi-actor RIS approaches (e.g., Raven & Walrave, 2020;Tödtling et al., 2022). We hope that this article will pave the way for other work that will feed, amend or criticize the proposed framework, but also reflect on instruments to make regional policymakers aware of certain blind spots from which they suffer concerning the informational foundations of their innovation policies. ...
In order to implement a regional innovation policy, regional decision-makers need an efficient information system that enables them to characterize their territory in detail and identify relevant development opportunities. In this article, we propose a methodological framework for developing such an informational system, emphasising two dimensions whose complementarity is often neglected: on the one hand, the type of information required, and on the other hand, the characteristics of the data to be collected. We explain that the first dimension can be characterised using the Regional Innovation Systems (RIS) approach. The results of this work highlight that four key components need to be analysed in order to describe and understand how the regional innovation system operates: Knowledge production and accumulation; Transfer and commercialisation of innovations; Supporting innovation policies; Regional system cooperation/collaboration. For the second dimension, we draw on the key principles of informational decision support approaches to identify the desirable characteristics of the data. We emphasise that a useful and effective informational system must pay close attention to the characteristics of the data used and consider the selection of databases according to five criteria (Geolocatable; Granularity; Nominative; Simple to use; Accessibility) in order to be able to truly inform the four components of the RIS in a concrete and operational way. We use the example of research laboratories to show the heuristic potential of the proposed framework. We conclude by explaining how this tool can be mobilised to help improve regional innovation policies. In particular, we highlight the role it can play in defining regional policies that are co-designed with regional actors.
... Actually, due to the obvious tendency of government subsidies, tax incentives and their combination implemented by the government to SMEs [56], for example, high-tech enterprises or enterprises that can create a large number of employment opportunities are more likely to get government support [57]. Therefore, whether SMEs can enjoy innovation policy support does not meet the requirement of random distribution. ...
The question of whether and how innovation policy can effectively influence innovation in small and medium-sized enterprises (SMEs) has received limited attention in academic research. This study takes a first step towards filling this gap by examining how innovation policy and policy mixes influence innovation in SMEs. This paper takes China’s National Equities Exchange and Quotations (NEEQ) listed enterprises from 2011 to 2020 as the research sample, and uses the Multi-Level Treatment Effect (MLTE) model to investigate the actual impact of different innovation policies on Small and Medium-sized Enterprise innovation and the heterogeneity of policy effects from the perspective of substantive and strategic innovation. It is found that innovation policies can obviously improve the innovation of SMEs, in particular the substantive innovation, and the effect of policy-mix in stimulating SME innovation is stronger than that of single innovation policy. SMEs that show “strong motivation” and “high ability” in innovation are more likely to be favored by relevant government agencies, and have a greater probability of becoming the implementation targets of innovation policies. As far as single innovation policies are concerned, government subsidy is better than tax incentive for high-tech SMEs, while tax incentive has a stronger role in promoting innovation than government subsidy for non-high-tech SMEs. By illuminating these differentiated impacts and the conditions under which innovation policies are most effective, this work not only advances our fundamental understanding of policy-driven innovation ecosystems but also offers actionable guidance to policymakers seeking to optimize the allocation of support to foster transformative innovation in the SME sector.
... As a matter of fact, in other fields of research using forecasting methods to predict technological transition, e.g. the energy (or innovation) policy research, or research on forecasting of climate change, policy as a factor is central to most of the models (e.g. Jiang and Xu, 2023;Liang et al., 2022;Nicolini and Tavoni, 2017;Raven and Walrave, 2020;Wu et al., 2023), positively influencing adoption. These studies highlight how policy interventions, such as financial incentives (subsidies), can reduce adopters' perceived risks while enhancing the relative advantage of the innovation. ...
Technological forecasting has significantly expanded over the last decades, leading to widespread use of forecasting models for explaining technology adoption and diffusion of innovation. While these models are broadly used, they have faced criticism for narrowing the explanatory components of adoption, focusing on adopters, innovation characteristics, or environmental factors, but seldom combine these to address complex problems holistically. This paper aims to combine actor-and system perspectives on innovation diffusion with the intention to broaden the explanatory power of traditional forecasting models. The study focuses on the case of solar photovoltaic (PV) diffusion in Sweden, surveying 46,507 residential PV adopters that applied for the capital subsidy program between 2009 and 2021 about their adoption satisfaction. Findings suggest that traditional models primarily account for direct effects on adoption satisfaction, whereas incorporating system-level factors captures indirect effects, providing a more comprehensive understanding of technology adoption. This highlights the interplay between actor-and system-level factors and acknowledging the holistic nature of innovation diffusion, which can inform future forecasting practices.
... System dynamics modeling is particularly well-suited for capturing and analyzing feedback effects resulting from energy policy changes [4,[31][32][33][34][35]. These models provide a valuable tool for policymakers to understand the long-term implications of their decisions and to assess policy alternatives for supporting socio-technical low-carbon transitions [36][37][38][39]. Simulation is necessary to study the nature, intensity, and timing of interactions during a transition and to identify the signs of change [40]. ...
Brazil’s wind power industry (WPI) has thrived since the early 2000s, driven by a successful auction-based expansion plan. However, the recent rise of cost-competitive solar power and policy shifts favoring other energy sources, such as natural gas, have created uncertainty about the future of wind energy in Brazil and reduced the wind sector’s legitimacy. Additionally, the cancellation of wind power auctions and support for other energy sources (evidenced by the new regulation for natural gas) has sent mixed signals to the market. These actions have sparked concerns regarding the future trajectory of the WPI. This paper focuses on the long-term effects of this energy policy decision on the so-called legitimacy function of the technological innovation systems (TIS) for the case of WPI in Brazil. The study aims to identify challenges arising from the growing appeal of solar power that may hinder wind energy adoption and to offer policy recommendations to strengthen the wind sector’s legitimacy. A system dynamics model is proposed to quantify such impacts in the long run, showing the interactions between the wind power capacity, wind generation costs, and the legitimacy function of the TIS. Results show the importance of policy consistency and institutional support in fostering a stable environment for renewable energy technologies like wind power to flourish.
... 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, 2016;Raven & Walrave, 2020). ...
Innovation systems take a holistic perspective to frame the dynamics that influence the emergence of innovation. Such systems consider a wide range of actors, institutions and networks to be the key structural building blocks. These structural elements interact, generating positive or negative feedback loops. Innovation systems initially emphasized the competitiveness of nations and were technology neutral. The introduction of technological innovation systems (TIS), the central focus of this book chapter, narrowed the lens to the emergence of specific technologies. In doing so, TIS became largely oriented towards sustainable technologies, which are often hard to bring to market. It hence became a foundational framework in the sustainability transitions research field. The introduction of the TIS ‘functions’, i.e., key dynamics, demonstrated a milestone in the development of the field. TIS has further grown, evolved and matured over the years and remains a popular theoretical lens to grasp the dynamics of innovation, engaging with subjects such as context and geography. Scholars continue to push the bounds of the many different innovation system frameworks, generating new empirical observations and furthering theory. Topics include system interaction, missions, life-cycles and destabilization, giving space to both technological and social innovation for a more sustainable future.
Precision medicine (PM) is transforming healthcare by offering tailored interventions that address individual variability, transforming patient care and outcomes. PM is based on providing health-oriented services according to genetic characteristics, individual and family medical history, lifestyle, place of residence, and other personalized characteristics. This study aims to establish an appropriate framework for implementing PM in Iran. First, the global transition framework to PM was drawn by a systematic review, and then a framework for transition to PM in Iran was drawn by a case study through semi-structured interviews, an expert panel, and an analytic hierarchy process (AHP) questionnaire. The statistical sample of the study comprised PM specialists, researchers, and patients whose PM plays a significant role in their diagnosis and treatment. The sampling method was non-random with a combination of purposive and snowball techniques. The results from the systematic review show that for the transition to PM, we must first move from common medicine to stratified medicine and then PM. Moving toward PM requires strong economic, social, political, institutional, industrial, and, most importantly, technological infrastructures. These infrastructures will vary from country to country. In general, coexistence between the health system and PM technologies did not exist in the beginning, but it will emerge with its development. The resistance of the health system to accepting PM will gradually decrease. Furthermore, the government plays a key role in the early phases, while market and PM demand become more prominent during the development. New health actors will also develop PM, and out-of-date actors will be deleted or replaced. But moving toward PM is slightly different in Iran, particularly in the middle phases of transition.
This paper details a semi-automated method that can calculate intervention thresholds—that is, the minimum required intervention sizes, over a given time frame, that result in a desired change in a system's output behavior pattern. The method exploits key differences in atomic behavior profiles that exist between classifiable pre-and post-intervention behavior patterns. An automated process of systematic adjustment of the intervention variable, while monitoring the key difference, identifies the intervention thresholds. The results in turn can be studied and presented in intervention thresholds graphs in combination with final runtime graphs. Overall, this method allows modelers to move beyond ad hoc experimentation and develop a better understanding of intervention dynamics. This article presents an application of the method to the well-known World 3 model, which helps demonstrate both the procedure and its benefits.
One key approach for studying emerging technologies in the field of sustainability transitions is that of technological innovation systems (TIS). While most TIS studies aim at deriving policy recommendations - typically by identifying system barriers - the actual role of these proposed policies in the TIS is rarely looked at. In addition, often single policy instruments instead of more encompassing policy mixes are considered. We address these shortcomings by applying a more comprehensive policy mix concept within the TIS approach. In doing so we analyze interdependencies between the policy mix and the TIS by shedding light on the role of the policy mix for TIS functioning and performance as well as how TIS developments influence the evolution of the policy mix. We explore these interdependencies for the case of offshore wind in Germany, using data from event history analysis and expert interviews. We find highly dynamic interdependencies with reoccurring patterns of systemic problems and adjustments of the policy mix, which are fuelled by high policy mix credibility and supportive actors. Our study constitutes a first step incorporating the policy mix concept into the TIS approach, thereby enabling a better understanding of real dynamics occurring in TIS.
Reaching a better understanding of the policies and politics of transitions presents a main agenda item in the emerging field of sustainability transitions. One important requirement for these transitions, such as the move towards a decarbonized energy system, is the redirection and acceleration of technological change, for which policies play a key role. In this regard, several studies have argued for the need to combine different policy instruments in so-called policy mixes. However, existing policy mix studies often fall short of reflecting the complexity and dynamics of actual policy mixes, the underlying politics and the evaluation of their impacts. In this paper we take a first step towards an extended, interdisciplinary policy mix concept based on a review of the bodies of literature on innovation studies, environmental economics and policy analysis. The concept introduces a clear terminology and consists of the three building blocks elements, policy processes and characteristics, which can be delineated by several dimensions. Based on this, we discuss its application as analytical framework for empirical studies analyzing the impact of the policy mix on technological change. Throughout the paper we illustrate the proposed concept by using the example of the policy mix for fostering the transition of the German energy system to renewable power generation technologies. Finally, we derive policy implications and suggest avenues for future research.
This paper addresses interactions between technological innovation systems (TIS) and wider “context structures”. While TIS studies have always considered various kinds of contextual influences, we suggest that the TIS framework can be further strengthened by a more elaborated conceptualization of TIS context structures and TIS–context interactions. For that purpose, we identify and discuss four especially important types of context structures: technological, sectoral, geographical and political. For each of these, we provide examples of different ways in which context structures can interact with a focal TIS and how our understanding of TIS dynamics is enhanced by considering them explicitly. Lessons for analysts are given and a research agenda is outlined.
Currently there is no formal model describing the dynamics of technological innovation systems. This paper develops a system dynamics model that integrates the concept of ‘motors of innovation’, following the literature on emerging technological innovation systems, with the notion of ‘transition pathways’ that was developed as part of the multi-level-framework thinking. As such, the main contribution of this paper is a cross-over of two key-frameworks into a system dynamics model that can serve as underpinning for future research. The model’s behaviour is illustrated by means of analyses of TIS dynamics in the context of different transition pathways, under different resourcing conditions. The paper also provides a future research agenda, pursuable by means of experimentation and/or further development of the presented model.
Group model-building here refers to a system dynamics model-building process in which a client group is deeply involved in the process of model construction. The problem that is modelled can be reasonably well defined, but it can also take the form of an ill-defined or messy problem, i.e., a situation in which opinions in a management team differ considerably These messy managerial situations are difficult to handle, primarily because thus far little theoretical work has been conducted to shed more light on the question why these messy situations exist and why it may be difficult for a management team to reach agreement. This article fills this theoretical gap by drawing on literature from sociology, (social) psychology and small-group research. Insights from this literature are discussed and translated into guidelines for conducting Group Model-Building projects for messy problems. The article ends with the conclusion that system dynamicists should include Group Model-Building and facilitation training in their teaching programs. Copyright (C) 1999 John Wiley & Sons, Ltd.
One key approach for studying emerging technologies in the field of sustainability transitions is that of technological innovation systems (TIS). While most TIS studies aim at deriving policy recommendations – typically by identifying system barriers – the actual role of these proposed policies in the TIS is rarely looked at. In addition, often single policy instruments instead of more encompassing policy mixes are considered. We address these shortcomings by applying a more comprehensive policy mix concept within the TIS approach. In doing so we analyze interdependencies between the policy mix and the TIS by shedding light on the role of the policy mix for TIS functioning and performance as well as how TIS developments influence the evolution of the policy mix. We explore these interdependencies for the case of offshore wind in Germany, using data from event history analysis and expert interviews. We find highly dynamic interdependencies with reoccurring patterns of systemic problems and adjustments of the policy mix, which are fuelled by high policy mix credibility and supportive actors. Our study constitutes a first step incorporating the policy mix concept into the TIS approach, thereby enabling a better understanding of real dynamics occurring in TIS.
Grand challenges such as climate change, ageing societies and food security feature prominently on the agenda of policymakers at all scales, from the EU down to local and regional authorities. These are challenges that require the input and collaboration of a diverse set of societal stakeholders to combine different sources of knowledge in new and useful ways – a process that has occupied the minds of economic geographers looking at innovation in recent decades. Work in economic geography has in particular examined infrastructural, capability, network and institutional challenges that may be found in different types of regions. How can these insights improve researchers' and policymakers' understanding of the potential for innovation policies to address grand challenges? In this paper, we review these insights and then identify areas that push economic geographers to go beyond their previous focus and interests, notably by considering innovation policy in light of transformational rather than mere structural failures.