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The Linear Model of Innovation
Abstract
One of the first (conceptual) frameworks developed for understanding the relation of science and technology to the economy has been the linear model of innovation. The model postulated that innovation starts with basic research, is followed by applied research and development, and ends with production and diffusion. The precise source of the model remains nebulous, having never been documented. Several authors who have used, improved, or criticized the model in the past fifty years rarely acknowledged or cited any original source. The model usually was taken for granted. According to others, however, it comes directly from V. Bush’s Science: The Endless Frontier ([1945] 1995). This article traces the history of the linear model, suggesting that it developed in three steps corresponding to three scientific communities looking at science analytically. The article argues that statistics is a main reason the model is still alive despite criticisms, alternatives, and having been proclaimed dead.
... Farmers are supposed to be passive in this process. Researchers are in charge of solving farmers' problems to find the best solution for them (Godin 2006). This approach was followed in the 'Training and Visit System'. ...
... This is the first theory of technological innovation. It is termed as the linear model of innovation [Godin, 2006]. This linear model had some limitations. ...
Innovation plays a prominent role in the contemporary world. It brings novel products and services to the market while exploring new ways of production, distribution, marketing and impacts all other aspects of organizations including human behaviour through hard as well as soft methods. Open innovation has been introduced as a new paradigm in innovation management. This study examines the impact of open innovation on innovation performance of the listed companies in Sri Lanka with the moderating effect of inter-organizational networks. Open innovation practices have been recognized in this study in two folds-as outside-in open innovation and inside-out open innovation Data were collected through a survey among 165 top-level managers of listed companies in Sri Lanka. The sample was selected using purposive and snowballing sampling methods. After testing the validity and the reliability of the instrument and collected data, simple regression analysis was conducted to Issue 3/2021 30 test the hypotheses. The study findings indicate that both outside-in open innovation and inside-out open innovation positively and significantly impact innovation performance. Further, results show that inter-organization networks positively moderate the effect of both inside-out open innovation and outside-in open innovation on innovation performance.
... Modelling Innovation and technological change has been a topic of economic research already for very long time. In the 1940s the linear model of innovation was introduced to model the relationship between science, technology and the economy (Godin, 2006). Still today, the analysis of technological change in bioeconomy models is mostly based on mainstream neoclassical economics methods (Köhler et al., 2018;Arthur, 2021) where the emphasis is on understanding the impact of technological change rather than understanding its drivers such as research and development. ...
With its update of the Bioeconomy Strategy and the Green Deal, the European Commission committed itself to a transformation towards a sustainable and climate-neutral European Union. This process is characterised with an enormous complexity, which policymaking needs to acknowledge for designing transition pathways. Modelling can support policymaking in dealing with uncertainty and complexity. This article reviews emerging and new developments and approaches to model the development of the bioeconomy. We focused our review on how bioeconomy modelling addresses key enabling factors related to (i) climate change, (ii) biodiversity, (iii) circular use of biomass, (iv) consumer behaviour related to biomass and bioproducts use, and (v) innovation and technological change. We find that existing modelling frameworks offer large possibilities for extensions and considerations for analysing short-run impacts related to climate change and circularity, and to lesser degree for biodiversity, and we identify possibilities for developing further the existing bioeconomy models. However, addressing key processes related to societal and technological changes is more challenging with existing/conventional modelling approaches, as they specifically relate to how innovations transform economic structures and how consumers learn and change their preferences and what kind of dynamics are to be expected. We indicate how emerging modelling techniques such as Agent-Based Modelling could improve and complement existing bioeconomy modelling efforts by allowing for the consideration of structural change and, more generally, transformation of the economic metabolism. This modelling approach eclecticism asks for a better description of modelling targets, a sound reflection on the meaning of time horizons and a closer cooperation between the different research communities. Furthermore, it will benefit from the developments in big data and artificial intelligence from which we expect valuable guideposts for designing future modelling strategies.
... While digital innovation is a strategic necessity, many companies struggle with its operational realization and efficient implementation (Svahn and Henfridsson 2012). Specifically, established companies are still used to developing physical products along with well-established product life cycles of several years (Cooper 2016;Godin 2006). However, digital technologies do not only change the products; they also change the process of innovating itself, making it more iterative and customer-centric (Ciriello et al. 2018;Yoo et al. 2010). ...
Digital innovation provides manifold opportunities and challenges. Companies need to master digital innovation cycles faster and leverage the increasing amount of available data. In this regard, artificial intelligence (AI) is of great interest, providing applications and tools to support humans and to enable more data-driven, customer-centric innovation. To date, however, the potential of AI for digital innovation processes remains poorly understood and needs to be further investigated. Thus, we developed a comprehensive framework for investigating the potential of AI along the steps of the digital innovation process. Drawing from the concept of cognitive skills and relating them to digital innovation activities as a theoretical lens, we used an initial survey with innovation experts and a focus group discussion with AI experts to assess the potential of AI along the steps of the digital innovation process. Our work contributes to the descriptive knowledge at the interface between digital innovation and AI
... If the definition of innovation is not solely based on product development but also on knowledge production, what types of knowledge processes are considered innovative? The traditional linear view of innovation, (i.e., the transition from basic research, to applied research, to development, to production) implies that tech progresses only in a linear fashion (Godin, 2006;Kline, 1985;Massey, Quintas, & Wield, 1992). Therefore, the final outcome, as measured by patents for example, is the only part of the chain that is valued. ...
Across the globe, economic developers and policymakers are building “innovation districts” –master planned developments with the aim of concentrating the actors, entities, inputs, and physical infrastructure considered essential to process and product innovation. Promoters have repeatedly hailed Barcelona’s “22@bcn” (est. 2000) and Boston’s “Seaport Innovation District” (est. 2010) for their success in attracting talent, increasing jobs, scaling startups, and transitioning regions into a high-tech economy. Built within the city and the urban-periphery alike, innovation districts point to a new spatial layout for capitalist production. This dissertation is an in-depth comparative case study of five innovation districts: Boston, Detroit, Park Center (North Carolina), St. Louis, and Dublin (Ireland). I engage a qualitative approach that includes on-site observations and semi-structured interviews with over 100 key supporters of innovation districts–from residents and workers to the university affiliates, developers, incubator owners, venture capitalists, non-profit managers, private executives, elected officials, and consultants driving growth decisions. In developing a more robust definition of innovation districts than the strategy mobilized by growth coalitions, I situate the emergence of innovation districts and their extractive logics along a historic trajectory of capitalist production from manufacturing material goods to new forms of immaterial production. Relying on content analysis of primary documents, maps, legal statues, and architectural renditions, I document how the planning process for each innovation district encloses public space and lived experience within that space, relinquishing it for private profit. Through detailed case studies I argue that economic developers and policymakers opportunistically used innovation district strategy to trigger real estate development after the 2008/2009 global financial crisis. The allure of the innovation district concept –that of an entrepreneurial haven for science and design breakthroughs and the acceleration of discoveries to the market—succeeded in selling the innovation district strategy for financial, political, and popular backing during a time period of complete construction standstill. However, in places with robust entrepreneurial ecosystems, supporters lost sight of the benefits of the innovation district as a support for startups and entrepreneurs in favor of more established companies seeking proximity to talent. Using census data, I trace the changing demographic makeup of each innovation district from its date of inception to its current state to demonstrate how innovation district strategy contributes to the splintering of resources. Lastly, I conclude the dissertation with a theoretical discussion gesturing how innovation districts might exacerbate issues of precarity for the entrepreneur who sits at the center of this experimentation and is increasingly interpellated by a state-led ideology that eagerly encourages self-provisioning.
... Accordingly, innovation often results from a cooperation between actors with a heterogenous, yet somewhat related, background (Corsaro et al. 2012). Thus, innovation is not a linear process going through set stages from basic research, product development, production, and marketing (Godin 2006). ...
This cumulative dissertation (one framing paper and four individual research papers) analyzes the regional facets of the multi-scalar Technological Innovation System (TIS) around onshore wind. It provides a comprehensive literature review on the geography of the wind energy TIS. Previous research focused on early system dynamics in turbine manufacturing and pioneering regions. It usually characterizes the TIS as spatially sticky. Yet, multiple gaps remain in our understanding of the regional level in this TIS. They concern the role, relevance, and spatiality of innovation processes in various regional contexts, across the value chain of the industry, and over the technology’s life cycle as the TIS becomes more established and mature.
These research gaps are empirically investigated through depth-case studies about the wind sector in five German regions. Results are presented in four individual research papers.
Overall, the dissertation provides a more nuanced view on how the importance of spatial proximity varies across a technology’s value chain and for different innovation system resources. Furthermore, it highlights and systematizes variances between innovation processes in regions and how they are impacted by developments at the national or global scale, or by shifts in broader socio-technical systems. Elements of established TIS do not automatically stabilize, as is shown using the examples of ‘networks’ and ‘technological legitimacy’. These insights offer advice to practitioners on where companies could concentrate their efforts to develop and utilize wind turbines most efficiently. Understanding the spatial dimension of this TIS might also improve place-specific policies aiming to build up and support regional wind industries or roll out turbines more rapidly.
++ This dissertation was submitted on the 21st of July 2021. The defense took place on the 3rd of December 2021 ++
https://doi.org/10.13140/RG.2.2.17381.32489/1
... This work focuses on the first two types and particularly on innovation-driven hardware technologies, in terms of introducing products onto the market and improving the entrepreneurs' performance. Profit, the ultimate purpose of entrepreneurial activities and critical factor of business success and continuous growth, primarily occurs through the return of investments, which are achieved by selling products (Godin 2006). The process of transforming ideas into viable products and launching them onto the market is known as commercialization. ...
The complexities and unique requirements of entrepreneurship and commercialization require novel prototyping approaches that go beyond their traditional application in engineering design. Prototyping, as a key part of Design Thinking, can address these requirements by focusing on creative problem-solving and by supporting entrepreneurs’ reasoning. The broader requirements of entrepreneurship are not explicitly addressed in existing prototyping tools and instances of inefficient prototyping are still observed in entrepreneurial settings. This paper presents insights from expert interviews concerning prototyping and entrepreneurship, along with the formulation of the foundations of a novel prototyping support tool, tailored for entrepreneurs. Prototyping’s approaches and challenges, user-prototype interactions, and prototypes’ role in funding attraction are among the discussed themes.
The knowledge economy is a complex and dynamical system, where knowledge and skills are discovered through research, diffused via education, and deployed by industry. Dynamically aligning the supply of new knowledge with the demand for practical skills through education is critical for developing national innovation systems that maximize human flourishing. In this paper, we evaluate the complex alignment of skills across the knowledge economy by creating an integrated semantic model that neurally encodes invented, instructed, and instituted skills across three major datasets: research abstracts from the Web of Science, teaching syllabi from the Open Syllabus Project, and job advertisements from Burning Glass. Analyzing the high dimensional knowledge and skills space inscribed by these data, we draw critical insight about systemic misalignment between the diversity of skills supplied and demanded in the knowledge economy. Consistent with insights from economic geography, demand for skills from industry exhibits high entropy (diversity) at local, regional, and national levels, demonstrating dense complementarities between them at all levels of the economy. Consistent with the economics and sociology of innovation, we find low entropy in the invention of new knowledge and skills through research, as specialist researchers cluster within universities. We provide new evidence, however, for the low entropy of skills taught at local, regional, and national levels, illustrating a massive mismatch between diversity in skills supplied versus demanded. This misalignment is sustained by the spatial and institutional mismatch in the organization of education by researchers at the site of skill invention over use. Our findings suggestively trace the societal costs of tethering education to researchers with narrow knowledge rather than students with broad skill needs.
El presente trabajo analiza las políticas de vinculación y transferencia tecnológica (VyTT) del Centro Atómico Bariloche (CAB) de la Comisión Nacional de Energía Atómica (CNEA) desde 2006, año de reactivación del Plan Nuclear. A partir del contexto y surgimiento histórico del área de VyTT de la CNEA y de la constitución de una oficina de transferencia tecnológica en el CAB, se presentan las características de las actividades allí impulsadas, su interdependencia jerárquica y funcional, el marco normativo interno y las características de la principal unidad de vinculación tecnológica, administradora de los recursos. El análisis de la información relevada permite deducir que las actividades de VyTT del CAB respondieron a un modelo de tipo horizontal, con la característica ofertista-vinculacionista observada en las universidades y otros organismos científico-tecnológicos de Argentina durante los años noventa. Algunas características diferenciales observadas permiten inferir que este modelo horizontal coexistió con otro contenido en una planificación tecnológica nacional sectorial, que se desarrolló en el marco del mencionado Plan Nuclear.
This article focuses on the Brazilian Company for Industrial Research and Innovation, (EMBRAPII), a social organisation created as an intermediary agent to encourage partnerships between research centres and companies. The main objective was to overcome the shortcomings of Brazil’s conventional STI public policies, the technological and innovation results of which were considered unsatisfactory. EMBRAPII innovates in governance and funding, regarded as an instrument capable of boosting companies’ engagement in collaborative technology and innovation projects. This article concludes by acknowledging EMBRAPII’s evident qualities with respect to the conventional STI policies, even though its achievements seems lower than expected in terms of the number of companies involved. The research findings endorse Ergas’ assumption that policies cannot be assessed in isolation. ‘The first and most fundamental is the dependence of technology policy outcomes on their economic and institutional environment.…’.
Today’s industrial firms are seeking to adopt strategies in which new technologies play key role in the sustainability of the industry and growth. The record on growth, as the most important measure for the long-run success in economy, requires continued innovation in the wide range of products, services as well as advanced methods on the production process and delivery. Furthermore, the importance of the technologic advancement has been confirmed by many studies, especially by emphasizing intellectual human capital and university science on the commercialization of scientific work and discoveries. The study gives an insight on the R&D investments: types, phases, and outcomes from the investments, as well as analysis on the literature for the R&D aspects, innovation, and technological improvement. Besides, evidence is given from the 30 world countries on R&D investments as share of GDP. Comparison among countries are made, as well as among three types of the group composed of 10 countries: (a) countries with the highest GDP in the world, (b) countries with the highest GDP per capita, and (c) Balkan countries. The estimation is done based on the data for year 2017. Additionally, there are analyzed and elaborated policies for investments in R&D at the European level, and evidence is provided also for the world top R&D investments distribution.
This review seeks to understand the implications of using “adoption” to evaluate agricultural soil management outreach in the twenty-first century. The act of changing from one practice to another practice is referred to as “adoption”. The concept of adoption is closely associated with the design and evaluation of agricultural extension programs. Although focusing on adoption is deeply entrenched in agricultural extension, some scholars question the usefulness of the concept in light of the complexity and uncertainty that characterises farming in the twenty-first century. We present a purposeful review of literature that considers adoption in relation to three general approaches to agricultural extension; top-down, bottom-up and co-constructionist, with an emphasis on land management in Australia. The conceptual fit of adoption as a measure of success for each extension approach is explored. We conclude that the usefulness of adoption of individual practices or tools as a measure of success needs to be considered in context. Failing to reflect on what adoption means in any particular program or activity risks ignoring or misunderstanding real change and impacts and /or shaping activities to fit a simple, linear adoption expectation. We suggest that adoption remains a useful concept, but could be best considered as a gateway to increased reflection and reflexivity when projects and activities are being developed.
This article presents the results of the interlaboratory comparison (ILC) study of the
following four characteristics of ceramic tile adhesives (CTAs): initial tensile adhesion strength,tensile adhesion strength after heat ageing, tensile adhesion strength after immersion in water, and tensile adhesion strength after freeze–thaw cycles. The results showed that the objective of the ILC was achieved—the z-score analysis carried out following ISO 13528 allowed for classifying all results obtained by 23 laboratories out of 27 as satisfactory. The results of the remaining four laboratories were rated worse. Despite the achieved goal, the ILC notes high heterogeneity of the results in terms of failure patterns, as well as significant differences between the lowest and the highest values of tensile adhesion strength for various measurement conditions. The results of the ILC were discussed in terms of the possibility of including them in the risk analysis conducted by the manufacturer. The results of the ILC are also valuable information for market surveillance authorities, who, in the authors’ opinion, should be more cautious about results on samples taken from the market. The ILC results for CTAs are also a valuable recommendation for a possible revision of EN 12004.
Transport systems are not purely about technology. In terms of the social functions of transport, new technologies in transport brought society to a different level of spatial interaction and socio-economic development.
In Colombia, the country’s biodiversity has been put at the heart of its bioeconomy policies. STS scholars have analyzed bioeconomy as the generation, commodification, and sale of ownership and biological material. Nonetheless, little attention has been given to bioprospecting initiatives in developing countries, let alone the incorporation of bioprospecting in bioeconomy policy. Further, the role of narratives about the value of the biological in supporting nation-building, or the relationship between nature, state, and its citizens, remain understudied. Based on interviews and fieldwork in policymaking committees, I argue that assumptions about biodiversity’s value and its valorization are supported by the use of genomic technologies; this allows further processes of value creation to remain uninterrogated. The need for Colombia to aspire to better futures accounts for the stability of assumptions about biodiversity’s value in recent decades. The right political climate has generated momentum for biological expeditions of Colombia’s territory and the rethinking of the social compact, in a country seeking to heal the wounds of an internal conflict with armed guerrillas – to become a more diverse and cohesive society. Ultimately, knowledge of biodiversity embeds assumptions of what nature is for, supporting a sociotechnical imaginary of how the country should be.
Despite the importance and benefits of measuring innovation in businesses, standard methods for innovation measurement, such as the Oslo Manual, largely exclude the agricultural sector. As a result, innovation scholars in developing countries struggle to develop knowledge to understand innovation in agricultural businesses.
This paper proposes a conceptual framework to guide research on the measurement of innovation in the agricultural sector in developing countries, focusing initially on commercial agricultural businesses. The proposed framework provides an analytical reference point with detailed methodological guidelines for scholars and practitioners of innovation studies who wish to understand the complexities and dynamics of innovation in commercial agricultural businesses (including forestry and fishery businesses). A practical example, based on an empirical study that used this framework to measure innovation in South Africa’s agricultural sector, illustrates how it can be put into practice.
This chapter presents a conceptual framework to investigate social innovation dynamics in the production of spaces for food growing in cities. The framework combines social innovation research from process theories and discussion on urban spaces to provide a basis for interpreting how processes of change have occurred. The chapter begins by analysing and breaking down existing process models of social innovation into their core components and proposes integration of the main dynamics: the progress of the innovation, changes in social relations, and collaborative agency. It then constructs a conceptual framework comprising of four key processes: (1) Identification of human needs or societal challenges to sustainable development; (2) Development of social relations in systems or structures; (3) Provision of opportunity for social empowerment; (4) Reflection of socio-spatial development practice. The proposed framework draws attention to the importance of partnership working and intermediation activities in order to improve access to public spaces, whilst contributing to social justice and healthy orientated urban environments.
Health innovation studies and the health disciplines highlight the importance of using knowledge to improve human welfare. However, these disciplines rarely yield discussion about this issue. The objective of this paper is to establish a dialogue between health innovation studies and the health disciplines, and to reveal the complementarities between these approaches. We present a revision of selected models of health knowledge use. From health innovation studies, we consider two models focused on the nature of health innovation, and two others that orient health innovation studies towards addressing inclusive development issues. From the health disciplines, we analyse translational research and knowledge translation models. Using a systemic perspective, we structure our analysis of complementarities on four analytical dimensions:
(i) The actors, proposing the recognition of the public sector, the productive sector, the scientific community, and health services providers. We also define two dynamic actors: knowledge users and knowledge beneficiaries.
(ii) The interactions, considering them as asymmetrical to facilitate knowledge flows.
(iii) The process, based on specific models of healthcare activities and a broad set of validation mechanisms (not only market-related).
(iv) The institutional framework, proposing consideration of formal institutions (e.g. regulations) and informal institutions (e.g. socio-cultural background).
In Finland, the discussion surrounding the impact of research on society has continued for two decades without a consensus on how impact should be part of research evaluation. In this paper, I examine the conceptions of social impact in the field of social sciences from the perspectives of policy authors and academics by reviewing policy documents and conducting semi-structured interviews with 14 policy advisors, evaluation experts, and faculty professors. The policy data sets are from 2003 to 2018, the period in which the Finnish discussion on research impact has been the most active. Interpretivist notions on the social impact of the social sciences argue that representations of social impact derive from juxtapositional conceptions of how social science knowledge is part of society. Grounded theory techniques were used to analyze policy artifacts and stakeholder interviews. I identified five interpretative frames. These are impact governance, operationalization of impact, politicization of research utilization, guiding arrangements, and social impact of the social sciences. A key finding is that policy advisors and academic experts tend to integrate divergent vocabularies of research utilization and its evaluation, resulting in uncertainty about the conceptualization and operationalization of impact. Integrity of research utilization in research policy and research evaluation could increase the social capacity of the social sciences by helping to understand the conceptual contribution of these fields to public policies and public discussion.
This chapter uses bibliometrics and topic modeling to explore the accessible tourism research domain structure and builds on this result to adjust its scope and applications in management fields. As far as bibliometrics is concerned, the co-authorship analysis highlights the field’s invisible college by identifying the most impactful scholars in the research area and the degree of their collaboration. The assessment of the occurrence of the keywords used in the sample manuscripts helps to reveal methodologies and research directions in the field. The co-citation analysis reveals the main domain’s intellectual roots in the groupings formed by accumulated co-citation trails in scientific literature. Additionally, topic modeling—a machine learning technique that automatically identifies “topics” in a given corpus—is used to explore the constructs’ key themes and applications. We find evidence of commonalities and polarizing differences among understandings across this research domain, suggesting opportunities and challenges for future research.
In recent years, the research field of bioeconomy has experienced significant global growth based on an increasing number of annual publications in the last 10 years. Bioeconomy received a strong political push by European policymakers after the instalment of a “knowledge-based bioeconomy” 15 years ago. While playing an essential role in recent European Union (EU) policies, bioeconomy still lacks a coherent understanding across multiple layers and especially regarding innovation activities. Innovations undoubtedly form one of the basic building blocks of the success of the knowledge-based bioeconomy and its increasing reach, but it must nevertheless be noted that frequently their innovation is not well-understood, and misconceptions prevail. Therefore, this chapter attempts to characterize innovation in bioeconomy. Based on a theoretical discussion of different concepts and aspects of innovation and a literature review at the intersection of bioeconomy and innovation, a catalogue of criteria about what can influence innovation in bioeconomy is proposed. Thus, seven criteria categories are deduced, as well as multiple keywords assigned to each of them. The proclaimed categories are then discussed and ultimately help to identify innovation triggers for bioeconomy. Thus, the article attempts to propose a realistic foundation and theoretical assessment of innovation in bioeconomy to reinforce future discourse on the matter.KeywordsConcept of bioeconomy and innovationTheoretical assessmentCatalogue of criteria
This article makes two contributions to the COVID-related green recovery literature, which is dominated by optimistic and speculative opportunity narratives. Mobilising insights from political science theories, our conceptual contribution is to suggest that green recovery analyses should focus not only on the opportunities (‘permissive conditions’) created by the COVID-crisis, but also on the actions and strategic considerations (‘productive conditions’) and pre-existing contexts (‘critical antecedents’) that shape the exploitation of these opportunities. Our empirical contribution is to analyse actual green recovery spending plans of three leading countries (France, Germany, and the UK), focusing on the size, sectoral allocation, and implementation choices, which differ markedly. France spends the most, both in absolute and GDP-relative terms, followed by Germany. UK spending is 43% less than France. The UK and France mostly support existing sectors (buildings, railways), while Germany focuses 57.8% of its funding on new technologies (electric vehicles, hydrogen). We explain country differences by analysing different mixes of strategic considerations, including climate mitigation, jobs, GDP growth, productivity, exports, global competitiveness, regional support, social fairness, party politics, and electoral ambitions. We relate these different motivations to pre-existing context conditions such as varying socio-economic effects of the COVID-crisis, pre-existing concerns (e.g., high unemployment, social and regional inequalities), the economic importance of particular sectors, and pre-existing climate policy plans. We conclude that the green recovery plans are powerfully shaped by pre-existing contexts, plans and developments, which constrain the use of the crisis-induced opportunities.
Introduction. The purpose of the study is to analyse the level of Russia's science and technology system and to determine the country's placein the international rankings by comparing the performance metrics achieved so far with the showings of the world's leading S&T powers, followed by the appropriate managerial decision-making in the areas requiring the government's immediate attention. Methods. The research tools employed are statistical data grouping, comparative analysis and the formal logical methods. Results and Discussion. Since 2014, various factors (namely foreign policy and economic factors, geopolitical situation, oil market volatility and instability of the national currency) have acted as catalysts of crisis in the national economy, leading to a decline in Russia's international rankings. Following a degree of adaptation to the external changes and a partial economic recovery by 2018-2020, Russia's position has levelled off, but the pre-crisis levels have yet to be achieved. The results of the survey show an overall positive dynamic, with the most significant progress observable in the rankings that characterise competitiveness of the national economy. Conclusion. The findings of the study have practical relevance and can be used to adjust the national policy on scientific and technological development.
The term ‘systemic innovation’ is increasing in use, but there is no consensus on its meaning: five understandings of the term can be identified, each based on a different view of what the word ‘systemic’ should refer to. The first understanding focuses on technologies, where the innovation in focus is synergistically integrated with other complementary innovations, going beyond the boundaries of a single organization. Therefore, ‘systemic’ refers to technological innovations interacting in a larger product system. A second use of the term refers to the development of policies and governance at a local, regional or national scale to create an enabling environment for innovation systems. Here, ‘systemic’ means recognition that innovation systems can be enabled and/or constrained by a meta‐level policy system. The third use of the term says that an innovation is ‘systemic’ when its purpose is to change societal laws and norms to place new enablers and constraints on innovation in the interests of ecological sustainability. What makes this systemic is acknowledgement of the existence of nested systems: innovation systems are parts of economic systems, which are parts of societal systems, and all societies exist on a single planetary ecosystem. The fourth use focuses on collaboration in innovation networks with multiple actors. This has evolved from the first understanding of systemic innovation, but the critical difference is the primary focus on people and processes rather than technological products. The word ‘systemic’ refers to the interdependency of actors in a business or community context, leading to a need to cocreate value and innovate in concert or through coevolutionary dynamics. The fifth use of the term ‘systemic innovation’ concerns how people engage in a process to support systemic thinking and action, and it is primarily this process, and the thinking and action it gives rise to, that is seen as systemic, rather than the innovation system that they exist within or are trying to create. It is this fifth understanding that accords with most of the literature on systems thinking published over the last 50 years. The current paper offers a contemporary perspective on what systems thinkers mean by ‘systemic’, and this not only enables us to provide a redefinition of ‘systemic innovation’, but it also helps to show how all four previous forms of innovation that have been described as systemic can be enhanced by the practice of systems thinking.
Internationally, fire and land management agencies are increasingly using forms of predictive services to inform wildfire planning and operational response. This trend is particularly pronounced in Australia where, over the past two decades, there has been an alignment between increases in investments in fire behaviour analysis tools, the training and development of fire behaviour analysts (FBANs), and official inquiries recommending the expanded use of these tools and analysts. However, while there is a relative lack of scholarship on the utilisation of predictive services, existing research suggests that institutional investment and availability are poor indicators of use in contexts with established social dynamics of trust and authority. To better understand the utilisation of predictive services in Australia, we undertook a survey of key predictive services users (e.g. incident controllers, planning officers) in order to test several hypotheses developed from existing studies and ethnographic fieldwork. Our results provide directions for further research and indicate that, rather than simply invest in tools and systems, there is a need for fire management agencies to foster personal connection between predictive services practitioners, their tools and their users.
Researchers have long recognized multiple ways of innovating. However, the expositions fail to connect the microeconomics of production sets to the real-world institutional variety required to build technological capabilities and innovate. This paper argues for explicit attention to institutional variety in the heuristics used in innovation policy and practice, and analyses three such heuristics. While some types of social challenges can be addressed through formal science and industrial R&D, the most common proxies for innovation, most industrializing contexts will require changes in institutions and organizations to frame and solve local development problems. The analysis thus bridges the traditional microeconomics of production sets with innovation and development priorities.
This study uses mixed methods—classical citation analysis, altmetric analysis, a survey with researchers as respondents, and text analysis of the abstracts of scientific articles—to investigate gender differences in the aims and impacts of research. We find that male researchers more often value and engage in research mainly aimed at scientific progress, which is more cited. Female researchers more often value and engage in research mainly aimed at contributing to societal progress, which has more abstract views (usage). The gender differences are observed among researchers who work in the same field of research and have the same age and academic position. Our findings have implications for evaluation and funding policies and practices. A critical discussion of how societal engagement versus citation impact is valued, and how funding criteria reflect gender differences, is warranted.
Die Forschung zur Innovationspolitik ist vielfältig. Sie reflektiert den Verlauf abstrakter Diskurse der Innovationsforschung und der politischen Ökonomie. Angelsächsische und institutionalistische Ansätze prägen den Diskurs. Dabei weichen lineare Modelle von Innovationsprozessen zunehmend komplexeren Ansätzen wie denen zu Innovationssystemen oder „triple-helices“. Auch die Bedeutung von Ideen und Diskursen für den policy-Prozess erhält zunehmend Aufmerksamkeit.
When designers of activities concerned with the dissemination of research findings pursue their practice all too often, the linear model is implicitly or explicitly the default position. This paper uses the lens of praxis (theory-informed practical action) to offer a shift in possibility away from reliance on the linear model of innovation to a second-order modality of research practice more suited to sustainable development goal (SDG) implementation. Whilst second-order modalities of knowledge production, necessary to enact sustainability transitions and transformative praxis, are emerging many lack purposefully designed modes of praxis and are limited in their considerations regarding enactment and performativity. We report on the testing and evaluation of a purpose-driven design of a ‘learning system’ within the Australian urban-water sector to enact a ‘systemic inquiry’ capable of generating social learning. Changes in understanding and/or practices by those who participated were achieved, thus satisfying key criteria for initiating but not necessarily sustaining social learning. The praxis innovation described is suited to situations best framed as uncertain, complex and contested. The positive outcomes of the enacted ‘learning system’ support the case for more investment in action-oriented research to support innovation in the field of second-order systems praxis.
The world of working is changing and the technological transformations are playing a relevant role in this change. In particular, new technologies are making the physical boundaries of traditional offices increasingly permeable, allowing the diffusion of New Ways of Working (Demerouti et al., 2014; Koops and Helms, 2014), such as smart working. This paper, based on a qualitative research and discursive interviews, intends to reflect on the introduction and top-down management of smart working within a banking institution. At the same time, it aims to grasp the role attributed to and played by technology in its implementation. Starting from the two reconstructed stories, I shall show if and how the innovations introduced whereby technologies enable us to work remotely, are changing existing power relations and what control dynamics emerge from the field.
In US national security policy the protection of technological innovation is of signal importance. US policy stresses, on the one hand, the need to protect technological innovation to ensure its global economic and military predominance. This reflects the classic view of innovation articulated in security studies: technological innovation is the foundation of economic and military power. Yet this account captures only one aspect of technological innovation in US national security thinking. Drawing on a combination of socio-technical imaginaries frameworks and critical theories of technology, this article argues that technological innovation is not merely a means to the end of American national security. Rather, a series of sociological and normative ideas disclosed by US policy frame market-led innovation as necessary, just, and central to the reproduction of American national identity. A specific way of creating technological systems and artefacts is the object of security in American national security policy. The security of technological innovation is central to securing the American way of life.
This paper advances a Marxist approach to the critical study of innovation. Such an approach offers alternative analytical tools for understanding the social and political aspects of innovation that are increasingly coming into focus within academic and practitioner fields. After outlining the emerging field of critical innovation studies and its key concerns, I turn to the question of how a Marxist critique differs from other forms of critical scholarship. I then introduce Marx’s application of the concept of subsumption to account for the relation between innovation and capital and to demonstrate the strength of a Marxist approach to the critical study of innovation.
In recent years, it is not only an innovator (institution), who is important for technology innovation, but increasingly important have become the micro and macro environment, since technological innovation requires cooperation, supportive legal, political, managerial, economic, social or environmental factors. As analogies in Japan, the USA, India or China, also the European Community, has to respond adequately to this trend through supportive legislation, management, infrastructure and cooperation. This article has the key purpose to outline selected issues and challenges of technological innovation and management in the world, as input into the comprehensive analysis of technology patenting in the EU countries since 1980. As a basic research methodology we chose patent research and database analyses of the EPO and WIPO databases. We examined the granted patents in comparison with several additional indicators, such as, a number of inhabitants, national GDP, VC investment, or patent publications by technology. In this limited patent analysis, we tried to distinguish between developing and developed countries of the EU. The result of this research shows that there are significant differences between developed and developing countries of the EU, not only in terms of technological innovation and patent intensity, or commercialisation of innovation, but also related to the focus and management of the technology innovation.
Recently the gaming industry became one of the fastest-growing industries, the size of the video gaming industry for profit purpose is growing rapidly and the engagement factor of these games is getting higher, especially with youth group. On the other hand, the social development community and especially the innovations teams in this field started to get involved in this market, seeking for social achievements. The area of social development in Egypt started to transform its strategies toward innovative solutions to achieve its goals.
Whilst basic science rapidly produces new insights into the biological determinants of human health and disease, clinical innovation is often said to lag behind, as it fails to rapidly turn such knowledge into new tools for innovative patient care. This view of biomedical innovation constitutes a ‘translational lag narrative’, which is widely present in current research policy. This paper presents a qualitative content analysis of a corpus of documents (n = 28) issued by key policy actors in the domain of clinical translation between 2000 and 2018 in the United States and the European Union. The aim is to reconstruct how policy discourse articulates the translational lag narrative, and to analyze how the latter relates to specific sociotechnical imaginaries of progress and of the role of policymaking in their realization. The article identifies key impediments to clinical translation and highlights salient differences in the sociotechnical imaginaries of translation in the US and the EU. In the US, policy discourse around translation is mostly driven by the perceived need to re-instate linearity in the transition from knowledge-production to clinical innovation. In the European context, instead, the driving imaginary of the policy discourse around clinical translation has to do with constructing a distinctly European model of economic growth centered around the idea of a knowledge-based economy, thereby connecting policy stimuli for translation with broader political imaginations. This analysis elucidates how publicly staged narratives about science and technology in the biomedical field simultaneously contain state-specific visions of progress and statecraft.
InnovationInnovation is one of the main drivers of economic growthEconomic growth. It is therefore no surprise that governments are increasingly trying to promote innovationInnovation through legal instrumentsLegal instruments. For this reason, the contribution analyses the interplay of law and economicsLaw and economics in the regulationRegulation of innovationInnovation. The author examines the economic rationale behind such regulationRegulation. He assesses in which cases governments have legitimate reasons to interfere in the market economy in order to promote innovationInnovation. Furthermore, the legal possibilities and limits of influencing innovationInnovation are examined. In addition, the discussion includes the problems and economic consequences of legislators’ attempts to promote innovationInnovation. Finally, the contribution attempts to identify “best practicesBest practices” for the furtherance of innovationInnovation through regulatory means.
With the pace of development and competitiveness, innovation plays an important role to capture the market share. Various countries have effective strategies to enhance Research and Development (R&D) and exchange value added products in international market. So, based on this the aim of this research is to examine the role of R&D, industrial design and charges for intellectual property in innovative exports in South Korean economy. Time series data for the period 1998 to 2017, Ordinary Least Square (OLS) and Generalized Method of Moments (GMM) models are used to determine the dynamic interrelationship among the study variables. In summary, the overall results show that there is co-integration rank of in both trace test and value test at 1% significance level. Moreover, OLS and GMM findings depict that there is significant and positive coefficient for ID & RD which represent that they have positive impact on HT. Whereas, the IP displays a negative and significant relationship with high technology exports accordingly. Lastly, the diagnostic tests show that model is stable for the study time period and result is reliable. The current study also suggests some policy implications which can enhance innovative export products of South Korea while enhancing R&D.
What does it mean for AI to be innovative, and does new always mean better, particularly in terms of the ethical and societal implications of AI? This interview-driven study of 26 stakeholders of AI in the fields of technology research, law, and policy elucidates key tensions in producing innovative AI, as they are understood across sectors. As these stakeholders articulate a discourse on innovation, there is revealed a complex relationship between how innovative AI is conceived, both in terms of what is considered innovation, and how that innovation is seen to be restricted or supported through policy and regulatory action. Ultimately, this discourse operates on similar terms across these stakeholder groups, and presents a knotted, interlinked view of regulatory, design, and policy concerns across the ecology of AI, from its data to its use.
All social entrepreneurship experts state that the issue of definition is the main problem affecting research in this field. Although there is no single definition, this is clearly a new domain in the field of research on entrepreneurship. The main objective of this study is to further knowledge in this area by means of a systematic review of scientific literature to determine the conceptual development of social entrepreneurship and to identify the most interesting research trends. This study uses a research method, known as latent semantic analysis (LSA), which has been applied to a database of keywords collected from a rigorous selection of academic articles. The results show that this phenomenon has emerged from two parallel currents within the organization management field; on the one hand, the non-governmental organization (NGO) and voluntary tradition and, on the other, the world of business ethics and corporate social responsibility (CSR). The main lines of future research highlighted in the analysis include the measurement of social impact, venture philanthropy, and hybrid organizations.
Although science has been a formidably successful force of social and technological development in the modern era, and a main reason for the wealth and well-being of current societies compared to previous times, a fundamental distrust characterizes its current status in society. According to prevalent discourse, science is insufficiently productive and in need of stricter governance and bureaucratic management, with performance evaluation by the means of quantitative metrics as a key tool to increase efficiency. The basis of this notion appears to be a belief that the key or only purpose of science is to drive economic growth, or sustainable development in combination with economic growth. In this article, these beliefs are analyzed and deconstructed with the help of a theoretical toolbox from the classic sociology of science and recent conceptualizations of economization, democratization, and commodification of scientific knowledge and the institution of science, connecting these beliefs to broader themes of market fundamentalism and to the metric fixation of current society. With the help of a historical-sociological analysis, this article shows that the current ubiquity of performance evaluation in science for the most part is pointless and counterproductive, and that this state of science policy is in dire need of reevaluation in order to secure science’s continued productivity and contribution to social and technological innovation.
In this paper I discuss four different paradigms through which science and
technology have been governed, situating each in historical context. Starting
with the ubiquitous ‘linear model of innovation’ I locate its origins and
provenance, how it came to be replaced, at least in part, through a ‘grand
challenges’ paradigm of science policy and funding, how this paradigm in
turn has been subjected to rigorous analytical critique by a co-production
model of science and society, and how this model, in part, is being put into
practice through a framework of responsible research and innovation.
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The recognised complexity of the innovation process not only makes it difficult to manage, explain and study, but also presents problems for the development of a generalised model.Cooper's recent article in ‘R&D Management’ suggests that the construction of a generalised model is inappropriate because there appear to be a number of different types of innovation processes. However, many of the studies of innovation postulate or assume a general model which represents the stages involved in the firm.This paper reviews the models of the innovation process which have been proposed and the author classifies them according to his taxonomy of different types:(a) Departmental-stage models(b) Activity-stage models(c) Decision-stage models(d) Conversion process models(e) Response modelsThe advantages and disadvantages of each approach are discussed and the conclusion reached is that still more research needs to be concentrated on the nature of the innovation process within the firm itself. The absence of a definitive model compounds the problems of semantics and definitions when discussing innovation.
Location of new products, 191. — The maturing product, 196. — The standardized product, 202.
Introduction, 97. — I. The propensity to develop pure science, 98. — II. The propensity to invent, 102. — III. The propensity
to innovate, 105. — IV. The propensity to finance innovation, 108. — V. The propensity to accept innovations, 110.
I. Introduction, 417. — II. Industrial research and development: invention and innovation, 418. — III. Schumpeter's theory
of innovation, 421. — IV. Conclusion: innovation a normal business procedure, 427.
Introduction, 596. — Innovation and technological change, 597. — Invention and innovation, 599. — Summary and conclusions,
605.
An important factor in the poor performance of the British economy in the twentieth century is the low level of investment by British firms in research and development. In this article, Professor Mowery compares the development of industrial research in Great Britain and the United States between 1900 and 1950, focusing on the reasons for the much lower levels of industrial research investment in Britain. He concludes that the substantial British lag can be attributed to differences in corporate structure, educational systems, and government policy.
Industrial research is acclaimed as the motor of contemporary American economic growth. This paper discusses several recent histories of corporate laboratories and industrial research by explicating common themes and assumptions. Alfred D. Chandler's work on business organization in late nineteenth-century America is the interpretative framework used in two recent and important histories - Leonard Reich's The Making of American Industrial Research: Science and Business at GE and Bell, 1876-1926, and George Wise's Willis R. Whitney, General Electric, and the Origins of US Industrial Research. Both works provide rich accounts concerning the reasons why firms invested in research laboratories. Both also reveal the problems inherent in using Robert Merton's sociology of scientists in discussions of researchers working outside of the discipline-oriented university. The remainder of the paper replaces the normative sociological framework found in these recent histories with an analysis that locates the first corporate laboratories within the history of late-nineteenth- and early twentieth-century American science.
Today the far-seeing industrialist no longer relies solely on the accumulated knowledge of the workers in his own factory, or even those in his own industry and on the chance possibilities of their producing new knowledge at a rate that will meet the needs of industrial expansion, but he sets up organizations for scientific research ... (Sir Arthur Fleming, 1928).1
Various communities of scholars have very different ideas about the sources of industrial innovation, but each of these ideas seems insufficient to explain the complete phenomena. An oversimplified model of the innovation process has led economists to argue whether research or 'market pull' is central to industrial innovation. However, an improved model shows the reality - including the role of research - to be much more complex. Elements of the 'chain-linked model' for the relationships among research, invention, innovation, and production are discussed.
Basic research is a central concept of science and science policy. This article examines the role statistics played in helping to create the concept and shows how it was in part constructed by statistics to serve social and political agendas. Most of this statistical work was conducted in the United States in the 1940s and 1950s, then standardized by the OECD in the 1960s.
Getting an innovation adopted is difficult; a common problem is increasing the rate of its diffusion. Diffusion is the communication of an innovation through certain channels over time among members of a social system. It is a communication whose messages are concerned with new ideas; it is a process where participants create and share information to achieve a mutual understanding. Initial chapters of the book discuss the history of diffusion research, some major criticisms of diffusion research, and the meta-research procedures used in the book. This text is the third edition of this well-respected work. The first edition was published in 1962, and the fifth edition in 2003. The book's theoretical framework relies on the concepts of information and uncertainty. Uncertainty is the degree to which alternatives are perceived with respect to an event and the relative probabilities of these alternatives; uncertainty implies a lack of predictability and motivates an individual to seek information. A technological innovation embodies information, thus reducing uncertainty. Information affects uncertainty in a situation where a choice exists among alternatives; information about a technological innovation can be software information or innovation-evaluation information. An innovation is an idea, practice, or object that is perceived as new by an individual or an other unit of adoption; innovation presents an individual or organization with a new alternative(s) or new means of solving problems. Whether new alternatives are superior is not precisely known by problem solvers. Thus people seek new information. Information about new ideas is exchanged through a process of convergence involving interpersonal networks. Thus, diffusion of innovations is a social process that communicates perceived information about a new idea; it produces an alteration in the structure and function of a social system, producing social consequences. Diffusion has four elements: (1) an innovation that is perceived as new, (2) communication channels, (3) time, and (4) a social system (members jointly solving to accomplish a common goal). Diffusion systems can be centralized or decentralized. The innovation-development process has five steps passing from recognition of a need, through R&D, commercialization, diffusions and adoption, to consequences. Time enters the diffusion process in three ways: (1) innovation-decision process, (2) innovativeness, and (3) rate of the innovation's adoption. The innovation-decision process is an information-seeking and information-processing activity that motivates an individual to reduce uncertainty about the (dis)advantages of the innovation. There are five steps in the process: (1) knowledge for an adoption/rejection/implementation decision; (2) persuasion to form an attitude, (3) decision, (4) implementation, and (5) confirmation (reinforcement or rejection). Innovations can also be re-invented (changed or modified) by the user. The innovation-decision period is the time required to pass through the innovation-decision process. Rates of adoption of an innovation depend on (and can be predicted by) how its characteristics are perceived in terms of relative advantage, compatibility, complexity, trialability, and observability. The diffusion effect is the increasing, cumulative pressure from interpersonal networks to adopt (or reject) an innovation. Overadoption is an innovation's adoption when experts suggest its rejection. Diffusion networks convey innovation-evaluation information to decrease uncertainty about an idea's use. The heart of the diffusion process is the modeling and imitation by potential adopters of their network partners who have adopted already. Change agents influence innovation decisions in a direction deemed desirable. Opinion leadership is the degree individuals influence others' attitudes
This article reviews a number of models of the process of technological innovation and identtities the important elements involved. Although no universal model is applicable to all processes of technological innovation, given the importance of technology in the long-tern strategy of research-intensive trns , a knowledge of the elements involved in the process is essential to today's managers. Models of the process of innovation must be developed contingent upon thejirn's needs. The critical factors, which must be conszdered when developing such a model, are identified.
This paper presents some research conclusions as a number of “propositions” about technological innovation which are intended mainly for managers of business and government institutions. The author claims that western nations are foregoing great opportunities by taking a short term view. In particular, he believes that screening is done at too early stage, administrators and managers fail to realise the long time span of the innovative process, and there is a widespread assumption that government agencies must only support “sure-winners”.
Not only is technology changing rapidly, but the process of the commercialisation of technological change—the industrial innovation process—is changing also. The paper traces developments in the dominant perceived model of industrial innovation from the simple linear ‘technology push’ and ‘need pull’ models of the 1960s and early 1970s, through the ‘coupling model’ of the late 1970s to early 1980s, to the ‘integrated’ model of today. The latter (the 4th Generation innovation process) marked a shift from perceptions of innovation as a strictly sequential process to innovation perceived as a largely parallel process. This shift owed much to observations of innovation processes in leading Japanese corporations. Recent developments indicate the possibilities attainable in the proposed ‘strategic integration and networking’ model, elements of which are already in place. According to this 5th generation model, innovation is becoming faster; it increasingly involves inter-company networking; and it employs a new electronic toolkit (expert systems and simulation modelling).
Schumpeter first reviews the basic economic concepts that describe the recurring economic processes of a commercially organized state in which private property, division of labor, and free competition prevail. These constitute what Schumpeter calls "the circular flow of economic life," such as consumption, factors and means of production, labor, value, prices, cost, exchange, money as a circulating medium, and exchange value of money. The principal focus of the book is advancing the idea that change (economic development) is the key to explaining the features of a modern economy. Schumpeter emphasizes that his work deals with economic dynamics or economic development, not with theories of equilibrium or "circular flow" of a static economy, which have formed the basis of traditional economics. Interest, profit, productive interest, and business fluctuations, capital, credit, and entrepreneurs can better be explained by reference to processes of development. A static economy would know no productive interest, which has its source in the profits that arise from the process of development (successful execution of new combinations). The principal changes in a dynamic economy are due to technical innovations in the production process. Schumpeter elaborates on the role of credit in economic development; credit expansion affects the distribution of income and capital formation. Bank credit detaches productive resources from their place in circular flow to new productive combinations and innovations. Capitalism inherently depends upon economic progress, development, innovation, and expansive activity, which would be suppressed by inflexible monetary policy. The essence of development consists in the introduction of innovations into the system of production. This period of incorporation or adsorption is a period of readjustment, which is the essence of depression. Both profits of booms and losses from depression are part of the process of development. There is a distinction between the processes of creating a new productive apparatus and the process of merely operating it once it is created. Development is effected by the entrepreneur, who guides the diversion of the factors of production into new combinations for better use; by recasting the productive process, including the introduction of new machinery, and producing products at less expense, the entrepreneur creates a surplus, which he claims as profit. The entrepreneur requires capital, which is found in the money market, and for which the entrepreneur pays interest. The entrepreneur creates a model for others to follow, and the appearance of numerous new entrepreneurs causes depressions as the system struggles to achieve a new equilibrium. The entrepreneurial profit then vanishes in the vortex of competition; the stage is set for new combinations. Risk is not part of the entrepreneurial function; risk falls on the provider of capital. (TNM)