Club Convergence in R&D Expenditure across European Regions

The increasing disparities between European regions constitute a great challenge for sustainable development and require identification of the factors responsible for this process. Given the substantive role of R&D in shaping innovativeness and economic development, understanding its dynamics and spatial patterns can provide new insights into regional growth prospects. Although prior studies have investigated the patterns of innovation convergence, apparently none has attempted to test the convergence club hypothesis in R&D expenditure in the European regional scope. Therefore, the present study aims to fill this gap. The paper aims at examining the convergence path of R&D expenditure across European regions and at identifying the factors conditioning club membership. Data were retrieved from Eurostat’s regional database and Regional Innovation Scoreboard datasets over 2008–2018. Employing a nonlinear time-varying factor model, we reveal that R&D expenditure in the examined regions follows the pattern of club convergence. The results of our research allow to identify five convergence clubs characterised by distinct disparities in the R&D expenditures. We also demonstrate that the emergence of the identified convergence clubs might be attributable to the initial differences in human capital, external knowledge embedded in patents and technological structures across regions as measured by employment in medium-high and high-tech manufacturing and knowledge-intensive services. These results provide policy implications in terms of the formulation and implementation of more tailored innovation policies, based on smart development and specialisation strategies. The presence of business R&D convergence clubs requires shifting EU policy actions towards a more sustainable model promoting both the advantages of the strongest regions and the development opportunities in less-developed ones.

Formation of the composition of programs for the knowledge–intensive industry development using bacterial optimization algorithms and fuzzy logic methods

Today the knowledge–intensive industry development is carried out by the programs that combine a set of innovation and investment projects aimed at achieving a single goal and implemented in general constraints. The presence of a larger number of project characteristics (in particular, terms, resources, performers, etc.), which must be taken into account when forming the composition of the program, leads to the formulation of the problem of multicriteria optimization. As its solution, it is proposed to use an algorithm of bacterial optimization, supplemented by a procedure for forming initial positions using fuzzy logic methods.

Impact of Transit Quality on Anchor-Mediated Knowledge Economies Across Large U.S. Cities

Universities, medical centers, and headquarters are the driving forces behind cities’ innovation productivity and anchor-based urban revitalization efforts, such as innovation districts. As a result, there is increasing competition for corporate anchors, as well as emerging partnerships and conflicts. In each case, transit has a major role to play. Yet we know little about precisely how transit fits into the dynamics of anchor-based revitalization. To address this empirical gap, this study employed structural equation modeling to grasp both the direct and indirect impacts of transit on the knowledge-intensive firm location in 500 large U.S. cities. We conceptualized the indirect impact of transit as mediated by anchors with a composite value that we developed to quantitatively represent the presence and size of major innovation anchors in each city. According to our findings, the positive impact of transit on a city’s overall knowledge economy occurred through its role in supporting anchor institutions, a role that, in turn, significantly increased the likelihood of knowledge-based firms locating in the city. In short, transit quality was positively associated with larger and more established anchors, as well as the availability of more employees, which expands the city’s talent pool, a critical driver of knowledge-intensive employers’ location decisions. These findings call for greater attention to be paid to transit in cities’ anchor-based urban revitalization plans and to partnerships between cities, metropolitan organizations, and anchors in planning future transit systems.

Approaches to Efficiency Assessing of Regional Knowledge-Intensive Services Sector Development Using Data Envelopment Analysis

The sector of knowledge-intensive services is one of the fastest-growing sectors in the present-day economy of knowledge, which explains the scientific interest in developing methods of its quantitative assessment. The object of the research is the development of new approaches to the mathematical modeling of the efficiency of the regional knowledge-intensive services sector, based on a distance function approach to assess productivity changes. An approach was proposed to analyze the efficiency of this sector using data envelopment analysis and Malmquist productivity index and its components. The article presents the results of the assessment of indicators characterizing the development of knowledge-intensive services in education, innovation, and ICT obtained from 80 Russian regions for the period 2010–2020. To perform the analysis, the following input variables were used: volume of investments in fixed assets in ICT; share of personnel employed in the ICT; share of internal expenditures on R&D in GRP; the number of personnel engaged in R&D; share of innovative-active organizations and registered patents; funding for higher education institutions; and the number of higher education institutions graduated. Output variables were number of used advanced production technologies in the region; share of innovative goods, works, and services in GRP, and use of the intellectual property. As a result of applying the data envelopment analysis, Malmquist productivity index and its components, data were obtained on the positive dynamics of the development of the knowledge-intensive services sector in Russian regions and conclusions were drawn about the sector’s growth sources due to economies of scale.

Mathematical Modeling of Knowledge-Intensive Products Export Energy Costs

This article presents the mathematical modeling of export energy costs. The knowledge-intensive products export energy costs are a set of financial, material, labor costs and resources consumption. Export costs are a multilevel system of indicators. The author sets out the order of mathematical modeling. At the first stage, the main trends in reducing export energy costs are identified. A set of integrated logistics support measures is modeled. The modeling of knowledge-intensive products export energy costs is a system of technical and economic indices and a cost value dependance: Maintenance and Repair, Material and Technical Maintenance, Business Model of After-Sales Service, Formation of the Cost of Insurance and Investment Management Strategy. Formulas, practical calculation examples and graphs are presented. The Ishikawa systematic analysis method is used to visualize data and dependence relations. The author's mathematical modeling of knowledge-intensive products export costs includes the calculation of integrated logistic support costs. Exporting countries require a high level of technical products efficiency. It has been established that "the more complex a knowledge-intensive products is, the higher the reliability of systems is". Reliability indices are basic. The author's scientific study confirms a hypothesis of the knowledge-intensive products export feasibility only with high reliability and efficiency factors. The author developed a set of integrated logistical support measures for knowledge-intensive products.

Transforming adaptive management in knowledge-intensive enterprises

The article considers the aspects of transforming the adaptive management of knowledge-intensive enterprises under highly volatile institutional conditions in the current technological order. The initial scientific hypothesis was that the quality and efficiency of enterprises in the medium and long term is a consequence of management system transformation, as a result of using a wide range of digital technologies in the process of implementating preventive management measures aimed at maintaining a certain level of correspondence between the digital environment and the internal capacity of the management system.The structural aspects of the concept of adaptive transformation of the management system of knowledge-intensive enterprises in a highly variable digital environment have been analysed. An algorithm that represents a complete independent system has been proposed in which the order and levels of interconnection between individual subsystems and elements have been determined, with the adaptive transformation process itself as the central link, which is supported by a complex of different subsystems. A methodological approach for implementating a multicomponent process of adaptive management system development has been presented, detailing the various methodological recommendations and developments. The methodological techniques used for the indicative assessment of the level of efficiency of the management system of knowledge-intensive enterprises have been outlined.An independent and unified digital management environment for knowledge-intensive enterprises enables real-time analysis of business processes in order to make timely, high-quality and effective decisions in the following key areas: investment and financial activities; enterprise knowledge and personnel management; production and technical management; logistics, operations and disposal. The article offers methodological recommendations on the reengineering of business processes in knowledge-intensive enterprises in a new technological mode.

Social Impacts of Blockchain Innovations in the Malaysian Socio-Economic Transformation 2030

This chapter highlights the challenges, opportunities, and social impacts of blockchain technology in the creation of a vibrant STIE ecosystem for Malaysia. As a systemic approach, the 10-10 Malaysian Science, Technology, Innovation, and Economy (10-10 MySTIE) Framework outlines the applications of blockchain vis-à-vis nine other technologies in 10 socio-economic sectors or Malaysian Grand Challenges (MGC). As Malaysia embarks on socio-economic transformation into a knowledge-intensive economy by 2030, five challenges must be addressed before leveraging on the opportunities presented by blockchain innovations in the MGCs. The economic and social impacts of blockchain as a catch-up or leap-frogging technology can be further explored to raise the return on value and competitiveness in the corresponding 30 national niche areas. In conclusion, readers may benefit from experiential learning using the process flow for ecosystem development and templates for 8i ecosystem and collaborative platform for carbon footprint using blockchain innovations.

Knowledge Management in University-Software Industry Collaboration

The university-software industry collaboration relationship has been represented a key resource, to the extent that together they can more easily promote technological development that underpins innovation solutions. Through a literature review, this chapter aims to explore the concepts and the facilitator or inhibitor factors associated with the collaboration relationships between university and software industry, taking knowledge management into account. This chapter is organized as follows. In the first section, the authors briefly introduce university, software industry, and knowledge management. The following section, based on the literature reviewed, provides a critical discussion of the university-software industry collaboration relationship, knowledge management in knowledge intensive organizations or community, and knowledge management in collaboration relationship between these two types of industries. Finally, in the rest of the sections, the authors point to future research directions and conclude.

Knowledge Management Initiatives in Agile Software Development

Agile software development (ASD) is a knowledge-intensive and collaborative activity and thus Knowledge Management (KM) principals should be applied to improve the productivity of the whole ASD process from the beginning to the end of the phase. The goal is to map the evidence available on existing researches on KM initiatives in ASD in order to identify the state of the art in the area as well as the future research. Therefore, investigation of various aspects such as purposes, types of knowledge, technologies and research type are essential. The authors conducted a systematic review of literature published between 2010 and December 2017 and identified 12 studies that discuss agile requirements engineering. They formulated and applied specific inclusion and exclusion criteria in two distinct rounds to determine the most relevant studies for their research goal. Reuse of knowledge of the team is the perspective that has received more attention.