This paper aims to provide a detailed case study of a corporate foresight for innovation (CFI) project done by the Higher School of Economics’ (HSE) (Moscow, Russia) corporate foresight (CF) unit for a large state-owned Russian service company. It demonstrates how CFI methods lead to recommendations and how these recommendations result in decisions.
Drawing from being part of the project team, review of the project documents and interviews, the case describes a multi-phased CFI project which incorporated several CF methods. Techniques used for the project itself included grand challenges and trend analysis, analysis of best practices through use of benchmarking and horizon scanning, interviews, expert panels, wild card and weak signals analysis, cross impact analysis, SWOT and backcasting. The project used a broad-base of secondary information, expert panels consisting of company experts and HSE CF team personnel, interviews with senior management and an extensive literature review using HSE’s propriety iFORA system.
In all 17 CFI recommendation and over 100 implementation recommendations were made; 94 per cent of the CFI recommendations were accepted with most implemented at the time this case was written. The case also identifies five enabling factors that collectively both helped the CFI project and led to a high rate of recommendation acceptance and one factor that hindered CFI project success.
The case study provides detailed information and insight that can help others in conducting CF for innovation projects and establishes a link between CF methods and innovation-based recommendations and subsequent decisions.
In-depth case studies that show academe and practitioners how CFI leads to recommendations and is linked to subsequent decisions have been identified as a gap in the literature. This paper therefore seeks to address this need by presenting a detailed CF case for a corporate innovation project.
This paper investigates the association between internal barriers to innovation and the propensity of technology-based SMEs to cooperate with universities and research institutes (URIs). We examine empirically two types of internal company barriers – financial and knowledge obstacles to innovation. The data source is the latest edition of the Brazilian Innovation Survey (PINTEC). We analyse the full sample of technology-based SMEs as well as the subsamples of high-tech manufacturing companies and knowledge-intensive business services (KIBS). Financial obstacles are shown to be strongly related to the propensity of KIBS to collaborate with URIs. Knowledge obstacles are moderately related to the propensity of high-tech manufacturing SMEs to collaborate with URIs. We conclude that while URIs have other important roles in the techno-economic system, their perceived contribution to alleviating internal innovation barriers for technology-based SMEs may be less prominent than policy decision-makers in emerging economies may expect.
University-industry innovation networks (UIINs) are important agents of innovation, as they bring together the unique profiles of higher education and industry partners. Knowledge growth in these networks does not happen automatically. We analyze the impact of network density and heterogeneity on knowledge growth in UIINs. Knowledge grows through knowledge transfer, spillover, and knowledge innovation. Knowledge growth is a function of each agent's initial knowledge level, network density, and agent heterogeneity. To analyze these correlates of knowledge growth, we use a knowledge growth model based on multiple agents and simulate knowledge growth in a UIIN. Our results show that network density positively influences knowledge growth. Initially, this positive impact increases and then disappears with a further increase in network density. We also find that heterogeneity moderates the relationship between density and knowledge growth. Through the positive moderating effect of its impact on knowledge innovation, it promotes new knowledge generation in the entire innovation network, thus providing a basis for subsequent knowledge transfer. Our study supports and enriches the contingency view of knowledge growth in innovation networks.
Many startups use Lean Startup (LS). But is it effective? While there are emerging qualitative findings, quantitative evidence does not yet exist. To address this gap, we developed an operationalization of the degree to which startups use LS (Lean Startup Capability, LSC). We then analyzed the LSC-performance relationship. We found a strong and robust relationship. A discussion contextualizes our findings. The LSC operationalization is relevant for research as future efforts can build on and extend it. The contribution to entrepreneurial practice is that we carved out the element of LSC, and showed that LS is indeed linked to performance.
This paper discusses the challenges of technological entrepreneurship education in the current education system and the questions that need to be answered to improve the efficacy and efficiency of technological entrepreneurship education. The nature of technological entrepreneurship requires a diversified set of skills for success; however, the traditional education system focuses on single discipline. Consequently, it is difficult for either engineers and scientists who are lacking managerial skills or management students who are lacking of engineer or science oriented knowledge to be successful. A further concern is that different communities have entirely different perceptions of how entrepreneurship is defined often causing both confusion and disagreement in communications between researchers and educators with each other. The paper considers the existing literature and develops a series of comprehensive questions that still need to be addressed. By answering these questions, the traditional education methods can be transformed to be more appropriate and useful for technological entrepreneurship education.
Доклад подготовлен Национальным исследовательским университетом «Высшая школа экономики» (НИУ ВШЭ) в сотрудничестве с Научно-исследовательским институтом аэрокосмического мониторинга «АЭРОКОСМОС» (НИИ «АЭРОКОСМОС») по результатам реализации научно-исследовательской работы «Исследование и прогнозирование потребностей экономики в пространственных данных, данных дистанционного зондирования Земли и геоинформационных технологиях, а также услугах, сервисах и продуктах, созданных на их основе» (шифр «ГеоДата»), выполненной по заказу Федеральной службы государственной регистрации, кадастра и картографии (Росреестра). В издании представлены основные итоги комплексного изучения сферы создания и использования пространственных данных. Исследование проведено на основе официальных данных Росреестра, собственных разработок Института статистических исследований и экономики знаний (ИСИЭЗ) НИУ ВШЭ и НИИ «АЭРОКОСМОС». Доклад рассчитан на широкий круг читателей, интересующихся цифровой трансформацией экономики и общества.
В сборнике представлены актуальные статистические данные, отражающие уровень и динамику развития цифровой экономики России. По ряду индикаторов приведены международные сопоставления.
В публикации использованы материалы Минкомсвязи России, Росстата, Банка России, ОЭСР, Евростата, Международного союза электросвязи (МСЭ), Конференции ООН по торговле и развитию (ЮНКТАД), Всемирной организации интеллектуальной собственности (ВОИС), а также разработки Института статистических исследований и экономики знаний Национального исследовательского университета "Высшая школа экономики".
Researchers focus on understanding the nature of ecosystems and societies as well as explaining how paradigms change. These efforts are presented and disseminated through scholarly work in scientific literature. The pool of knowledge generated through databases allows one to track how our understanding changes and how paradigms shift through time. The present study is concerned with the domain of innovation policy, which is affected directly by societal and technological change and is a good archetype for demonstrating the scientific change perspective. In recent years, scientometrics has been frequently used to measure and analyze progress in science, technology and innovation. This study makes use of a combination of scientometric analysis and evolutionary framework analysis to demonstrate the evolution of innovation policy domain. Kuhn’s seminal approach is applied for classifying and interpreting the phases across the evolution of the domain within a 30-year timeframe. The analysis demonstrates that the innovation policy domain is at the “crisis stage” as a result of ongoing with transformations in the society, technology, economy and policy. These transformations affect both supply and demand sides of innovation and call for an evolution in the innovation policy domain. Although this by no means represents that the innovation policy domain is in a “deadlock”, the present study asserts that there is a new quest in innovation policy by adapting, re-framing or re-constructing the scope of the domain. The anticipated paradigm shift is expected to lead to a more de-centralized and distributed understanding of the world for innovation policy making.
Fuel cell electric vehicles (FCEVs) have been considered as the future vision for the automotive industry. An increasing number of concepts and prototypes have been introduced in the last decade. In parallel with the technological development, recent discussions about global warming and climate change bring public support for emission free vehicles. Despite of the advancements and support, the speed of introduction of FCEVs is still not at the desirable levels. From a transition management perspective, the present paper seeks to answer the underlying factors behind the implementation of the FCEVs. The discussion goes beyond a technical one to cover broad factors and interests of stakeholders with an ‘eagle-eye view’. Following a discussion the key drivers of change for the FCEV sector and wild cards with disruptive effects, the paper proposes a strategic roadmap template to set an agenda for a successful transition towards FCEVs.
By considering India's 52 large urban agglomerations, this paper finds the relationship between higher level of education and poverty and inequality in urban India. Besides using city level education data from University Grants Commission (UGC), the study uses two rounds of National Sample Survey (NSS) unit-level data on 'consumption expenditure', and 'employment and unemployment' for the year 2011-2012. An empirical analysis using OLS regression method has shown that city level education, proxied by city-wise total number of PhD students enrolled in the universities, has a negative impact on city level poverty rate as seen by poverty head-count ratio, poverty gap ratio, and squared poverty gap ratio. On the other hand, city level education has a positive impact on city level inequality. City-wise work force participation rate has a negative effect on city poverty rate. The article suggests that we need appropriate city level policy to promote higher level education for reduction in city level inequality and poverty rate for sustainable urban development in India.
This book provides an impressive overview of emerging technologies, especially nanotechnologies and biotechnologies, and their prospective applications. It identifies and describes existing and potential markets for emerging technologiy-based applications, and projects scenarios for macroeconomic development based on these technologies. Integrated roadmaps for the development of a nano- and bioindustry are shown and policy measures and corporate strategies developed to advance these technologies. These measures are illustrated using roadmaps and policy case studies.The book combines a practical, comprehensive overview of the technical side of emerging technologies and their applications in various fields with an analysis of market developments and characteristics.