Harvesting reflective knowledge exchange for inbound open innovation in complex collaborative networks: an empirical verification in Europe
This study aims to investigate the effects of open innovation (OI) and big data analytics (BDA) on reflective knowledge exchange (RKE) within the context of complex collaborative networks. Specifically, it considers the relationships between sourcing knowledge from an external environment, transferring knowledge to an external environment and adopting solutions that are useful to appropriate returns from innovation.
This study analyzes the connection between the number of patent applications and the amount of OI, as well as the association between the number of patent applications and the use of BDA. Data from firms in the 27 European Union countries were retrieved from the Eurostat database for the period 2014–2019 and were investigated using an ordinary least squares regression analysis.
Because of its twofold lens based on both knowledge management and OI, this study sheds light on OI collaboration modes and highlights the crucial role they could play in innovation. In particular, the results suggest that OI collaboration modes have a strong effect on innovation performance, stimulating the search for RKE.
This study furthers a deeper understanding of RKE, which is shown to be an important mechanism that incentivizes firms to increase their efforts in the innovation process. Further, RKE supports firms in taking full advantage of the innovative knowledge they generate within their inter-organizational network.
With an increasing number of companies applying smart manufacturing (Industry 4.0) technologies, and therefore gathering records from multiple enterprise data sources, a potential for big data analytics (BDA) is seemingly limitless. Still not every firm that implemented smart manufacturing reports gathering or making use of big data emerging from those processes, let alone extracting value from them. This study investigates business value creation mechanisms from BDA in smart manufacturing. Relying on several use cases and project stories described in publicly available sources, we analyze key drivers, applications, barriers, success factors, and business benefits of BDA in smart manufacturing. We summarize our findings in a comprehensive framework capturing first- and second- order effects of BDA implementation on Industry 4.0 processes. Our work aims at contributing to the body of knowledge on BDA and smart manufacturing, and at guiding practitioners in identifying and assessing various application scenarios for those technologies.
Companies, seeking to be a leader in the market, invest a huge resources and develop the internal infrastructure of research and development, as well as try to attract the best talent and outside organizations in order to use their potential in the creation and implementation of innovative products and services. At the same time, researchers who want to realize their ideas and knowledge, small companies with developments do not always have the necessary resources and capacity to implement and commercialize its own decisions independently. The paper presents the critical factors that need to overcome for building quality relationships between the companies that use the style of "open" innovation and discusses innovative platforms that act as a connecting link between the organizations that require solutions of complex research problems, and researchers - solvers such tasks.
Predictive maintenance is a powerful maintenance strategy that makes it possible to significantly reduce operation and maintenance costs of public, commercial and industrial environments. It is a complex data-driven process, which tries to forecast future states of company assets. On one hand it prerequisites condition monitoring of components on machine level. On the other hand it demands the integration of the collected data with other management information systems. Digitization and especially the advent of big data science bring along promising opportunities to create effective smart monitoring and predictive maintenance applications. The aim of this research is to examine the possibilities of a predictive maintenance framework based on the design principles of Industry 4.0 and recent developments in distributed computing, Big Data and Machine Learning. It introduces numerous enabling technologies such as industrial Internet of things, standardized communication protocols, as well as edge and cloud computing. Moreover, it takes a deeper look at data analytical techniques and tools, and analyses performance of well-known machine learning algorithms. Paper proposes architecture of a predictive maintenance framework based on existing software and hardware solutions. As a proof of concept, a real-life smart heating, ventilation, and air conditioning (HVAC) application system is created and tested to demonstrate the possibilities of the proposed PdM framework.
This book aims to identify promising future developmental opportunities and applications for Tech Mining. Specifically, the enclosed contributions will pursue three converging themes:The increasing availability of electronic text data resources relating to Science, Technology & Innovation (ST&I) The multiple methods that are able to treat this data effectively and incorporate means to tap into human expertise and interests Translating those analyses to provide useful intelligence on likely future developments of particular emerging S&T targets.
Tech Mining can be defined as text analyses of ST&I information resources to generate Competitive Technical Intelligence (CTI). It combines bibliometrics and advanced text analytic, drawing on specialized knowledge pertaining to ST&I. Tech Mining may also be viewed as a special form of “Big Data” analytics because it searches on a target emerging technology (or key organization) of interest in global databases. One then downloads, typically, thousands of field-structured text records (usually abstracts), and analyses those for useful CTI. Forecasting Innovation Pathways (FIP) is a methodology drawing on Tech Mining plus additional steps to elicit stakeholder and expert knowledge to link recent ST&I activity to likely future development. A decade ago, we demeaned Management of Technology (MOT) as somewhat selfsatisfied and ignorant. Most technology managers relied overwhelmingly on casual human judgment, largely oblivious of the potential of empirical analyses to inform R&D management and science policy. CTI, Tech Mining, and FIP are changing that.
International Conference on Information Systems (ICIS) is the major annual meeting of the Association for Information Systems (AIS) , which has over 4,000 members representing universities in over 95 countries worldwide. It is the most prestigious gathering of academics and practitioners in the IS discipline, and provides a forum for networking and sharing of latest ideas and highest calibre scientific work amongst the IS profession. Each year, over 1,000 IS academic professionals from around the world participate in the conference program, which includes about 60 sessions and 180 presentations, in addition to keynotes and panels. The theme of ICIS 2017 is Transformation Society with Digital innovation.
The objective is to provide an opportunity for Big Data researchers and practitioners to build a dynamic community for open and constructive discussions and exchange of academia and industrial experience.
ICIS 2017 SIG on BDA Proceedings include the topics of Business process modelling, Enterprise Architecture, Data Processing and on e-Commerce to find out the new industrial impacts of applied Big Data analytics, e.g. in marketing, risk-assurance, logistics and quality management.
International Conference on Information Systems (ICIS) is the major annual meeting of the Association for Information Systems (AIS) , which has over 4,000 members representing universities in over 95 countries worldwide. It is the most prestigious gathering of academics and practitioners in the IS discipline, and provides a forum for networking and sharing of latest ideas and highest calibre scientific work amongst the IS profession. Each year, over 1,000 IS academic professionals from around the world participate in the conference program, which includes about 60 sessions and 180 presentations, in addition to keynotes and panels.
This book constitutes the refereed proceedings of the 28th International Conference on Data Analytics and Management in Data Intensive Domains, DAMDID/RCDL 2016, held in Ershovo, Moscow, Russia, in October 2016.
The 16 revised full papers presented together with one invited talk and two keynote papers were carefully reviewed and selected from 57 submissions. The papers are organized in topical sections on semantic modeling in data intensive domains; knowledge and learning management; text mining; data infrastructures in astrophysics; data analysis; research infrastructures; position paper.
Contrary to more advanced countries, Russia’s district heating hardly embraces radical innovations. Moving forward with breakthrough solutions, even if they have proven their effectiveness at leading European companies and are supported by federal and regional authorities, encounters significant obstacles. These obstacles include inflexible corporate management, including when interacting with customers, and inexperience in creating internal corporate startups and managing risks in the early stages of R&D. The authors review the innovation activity of heating companies, analyze the difficulties in adopting innovations, and compare the strategies and performance indicators of Russian and Finnish energy companies. Special emphasis is given to the Moscow district heating system. Analysis shows that its’ strategic development in the past decade has focused primarily on reframing the organizational set-up, not innovation. As a result, business processes and cash flows were largely streamlined but European level of productivity was not achieved. The creation of a single vertically integrated entity in Moscow’s energy industry has limited the ability to develop alternative district heating and cooling systems. Energy infrastructure innovation centres are sparse and feature limited specialization and competition. Large companies tend to follow the ‘closed innovation’ model where R&D activities are concentrated within an organization, and focus on incremental innovations while lagging in radical innovations in cogeneration and trigeneration. Under these conditions, short-term planning dominates, while mid- and long-term planning are virtually non-existent. The paper concludes with recommended measures to support the innovative development of Russian heating companies that can be split into institutional and corporate recommendations. The first group concerns stimulating competition in the heat supply market and creating a stable legal and investment environment. The second group calls for technological modernization, development of long-term corporate strategies that include investment programmes, systematic analysis of the best international practices for innovative development, and the formation of partner networks involving foreign innovative, consulting, and research centres.
The most important task of management of innovative enterprise is the creation of distributed innovation process, which adapts to the specific objectives of the organization and the relevant internal and external environment and facilitates exchange and creation of knowledge as a result of collaboration within innovation community. The article describes proposed approach to the innovation process and the rationale of the solution.
The paper examines the structure, governance, and balance sheets of state-controlled banks in Russia, which accounted for over 55 percent of the total assets in the country's banking system in early 2012. The author offers a credible estimate of the size of the country's state banking sector by including banks that are indirectly owned by public organizations. Contrary to some predictions based on the theoretical literature on economic transition, he explains the relatively high profitability and efficiency of Russian state-controlled banks by pointing to their competitive position in such functions as acquisition and disposal of assets on behalf of the government. Also suggested in the paper is a different way of looking at market concentration in Russia (by consolidating the market shares of core state-controlled banks), which produces a picture of a more concentrated market than officially reported. Lastly, one of the author's interesting conclusions is that China provides a better benchmark than the formerly centrally planned economies of Central and Eastern Europe by which to assess the viability of state ownership of banks in Russia and to evaluate the country's banking sector.
The paper examines the principles for the supervision of financial conglomerates proposed by BCBS in the consultative document published in December 2011. Moreover, the article proposes a number of suggestions worked out by the authors within the HSE research team.