account (Fig. 1a). Our method suggests considering three levels of analysis: (1) the domestic one grouping the country- or area-based innovation actors as done in the literature hitherto, (2) the foreign one grouping the innovation actors from the partner countries, and (3) the global one grouping the two previously defined systems. Hence, the global system may be considered as composed of the ‘domestic’ and foreign sub- systems, each with three innovation actors leading to six actors at the global level (Fig. 1b). The two sub-systems interact and exert on each other a mutual influence that may act on the synergy within each other by the mutual relationships they entertain. The relationships existing between the actors on Fig. 1a (as represented by arrows) also exist within the domestic sub-system on the one hand and the foreign one on the other hand (Fig. 1b). Abstraction is done of these relationships on Fig. 1b, however. Studying such a Sextuple Helix (Leydesdorff 2012) requires the computation of 2 6 = 64 sectors data. 1 A simpler way to proceed consists in considering the global system as if actors were from the same geographical area and studying separately the three domestic, foreign and global Triple Helix systems. Thus, one can compute the mutual informa- tion and transmission power of the domestic, foreign and the global systems using the formulas given above. We suggest using the normalised difference between the global and domestic transmission power as the effect of international collaboration on knowledgeflow within an innovation system.
management system with other key resources and competences in companies is the element that can result in the development and support of a competitive advantage sustained by product and process innovation. Alavi and Leidner (2001) add that the competitive advantage does not depend on the knowledge itself that can be found at a certain moment in a certain system, but on the ability to obtain new knowledge from the available one. Starting from Nonaka’s observation that “in an economy in which the only certainty is that there is no certainty, an important source of competitive advantage is its knowledge and manipulation”, t he authors Holsapple, Jones and Singh (2007, pp. 54-55) suggest a “knowledge value chain” that identifies five main activities and four secondary activities regarding knowledge management, able to guarantee higher achievements in an organisation. The main activities are the generation, procurement, selection, assimilation and production of knowledge, and the secondary activities refer to its measuring, controlling, coordinating and leadership. These activities influence an organisation’s capacity to innovate, and also its productivity, promptness and reputation. We can transfer the model suggested by Holsapple at a national level as well, where one can find the same problems and the same relation between knowledge and achievement. Preiss (1999) declares that, currently, the competitive advantage does not consist in the rapid access to capital, but in the adequate access to knowledge and innovation, and shows that, if, in the past, companies were different according to their access to markets, nowadays, due to the democratization of technologies, the hierarchy of companies is established according to the speed with which they answer to the changes in their environment, which highly depends on the knowledge resource, considered the main resource that maintains an organisation’s competitively. According to him, the necessary knowledge is not obtained, however, from an injection made at a certain moment, but from a necessary knowledgeflow which should always be available, knowledgeflow which the author considers synonym with innovation, emphasizing that it should always be new knowledge. This knowledgeflow has ups and downs and is related to the other two flows present in the enterprise network nowadays – the money flow and the flow of goods and services. The core competence of a company is given by the company’s access to a knowledgeflow or to the strategic know-how, as Gupta and Govindarajan (1991, 2000) call it, by its capacity to assimilate the new knowledgeflow, as well as by the rate with which it can create new and useful knowledge (in other words, its capacity to innovate).
Both findings are in line with our expectation and the theoretical background discussed in section 2. However, when it comes to regional knowledge spillovers, we can observe a statistically significant negative relationship between this independent variable and the probability of innovating. This finding is rather interesting as it is at odds with our expectations. A possible explanation is that a knowledge competition effect prevails over the knowledgeflow effect. In other words, since firms located in the same region draw the supply of skilled labour force from the same pool of workers, then a high level of accumulated knowledge by competing firms might imply that not much knowledge is left for our representative firm. This might have a negative effect on the probability to innovate. This hypothesis is corroborated by the fact that the sign of regional knowledge spillover changes when we consider the interaction between such knowledge spillover and our human capital variable. In the latter case the relationship is positive and significant. This implies that the knowledge present in the region can be better exploited by those firms which have managed to reach a good level of human capital. Note that human capital and the interaction between sectoral spillovers and internal human capital are not statistically significant.
Because knowledge spillover is an unconscious process, its main form is university- industry communication aimed at tacit knowledge which is difficult to code. It includes flow of talent, R & D collaboration and spin-off. Universities’ function of talent training ensure its rich human resources and talents have plenty of tacit knowledge accumulated in the environment of universities. So talents could become medium that let knowledgeflow from universities to companies through talents flow in different space and make connections with the environment around them. R & D collaboration is a kind of part- nership between universities and industry because of knowledge complementarity of them, especially university-industry strategic alliance based on long-term and stable cooperation. During R & D collaboration, researchers from universities and technical persons from firms exchange their knowledge by informal contacts and attending aca- demic conferences so that to realize knowledge spillover of universities. Spin-offs are science and technology enterprises which come into being from universities, are based on scientific research achievements, and are founded by college teachers and research- ers . Entrepreneurs of universities are from colleges and have innumerable links with universities. During the process of startup, they communicate and cooperate con- stantly with organizations outside universities. And other companies in clusters will learn from spin-offs on account of their advantages in knowledge. So Spin-off becomes another way for knowledge spill over.
The ‘Mode 3’ fractal innovation ecosystem is the nexus or hub of the emerging 21st century fractal innovation ecosystem, in which people, culture, and technology (Carayannis and Gonzalez, 2003), forming the essential fractal innovation ecosystem building block or ‘ knowledge nugget ’ (Carayannis 2004), meet and interact to catalyze creativity, trigger invention, and accelerate innovation across scientific and techno- logical disciplines, public and private sectors in a top-down, policy-driven and a bottom-up entrepreneurship-empowered fashion. Mode 3 allows and emphasizes the co-existence and co-evolution of different knowledge and innovation paradigms. In fact, a key hypothesis is as follows: “The competitiveness and superiority of a know- ledge system is greatly determined by its adaptive capacity to combine and integrate different knowledge and innovation modes via co-evolution, co-specialization and co- opetition of knowledge stock and knowledgeflow dynamics (for example, Mode 1, Mode 2, Triple Helix, linear and non-linear innovation) ” (Carayannis 2008b; Carayannis and Campbell 2006, 2009).
Both forms of knowledge sharing, either it is tacit or explicit increase the thinking capability and understanding to encourage knowledge creation and ideas that play a vital role in making critical decisions and influence the organizational performance (Reychav, 2009). The knowledge sharing gives birth to better and new innovative ideas that will enhance the quality of existing products, processes, or services that result in better performance of a firm. The tacit knowledge sharing can determine the performance of the firm as a whole (Anh, 2006). Within an organization, explicit knowledge transfer sharing improves the effectiveness and operational efficiency (Wang, 2012). In any organization, the determination of organizational learning can be the sharing of knowledge brings many advantages to an organization through the achievements of organizational goals (Down, 2001; van Woerkom & Sanders, 2010). Knowledge sharing has different dimensions and play critical roles in different ways to improve performance (Du, Ai, & Ren, 2007). Therefore based on the above study, we propose hypotheses such as.
Abstract: This paper examines the nature of social interfaces that has emerged in the context of social innovations with vulnerable and marginalised tribal communities along the Tansa Reservoir in Maharashtra, India. This paper is part of a larger action research project that strives towards improving the livelihoods of tribal women through collectives such as self-help groups. The analysis presented in this paper per- tains to the experiences of 13 tribal women who have come forward to form a self- help group to supplement their livelihoods. According to the tribal women, the col- lective spaces that the self-help group provide has itself been termed as innovation. In the above-mentioned context, this paper specifically examines the nature of diverse values and beliefs, interests, knowledge and power among different actors involved in promoting livelihood-based women’s collectives. It also explores the nature of re- sponse among tribal women to the intervention of outside experts in the day-to-day activities of their collective. The findings of this paper illustrate the discontinuities associated with the collective and specifically on the nature of frictions, disagreements and conflicts between actors, which are mediated and transformed at critical junc- tures. This signifies an underlying asymmetry between the knowledge systems of tribal women and outside experts respectively. Furthermore, this paper argues that if not properly nurtured, such innovative collective spaces can become sites of domination and agents for the perpetuation of mere socio-technical interest. Instead, the dis- course of social innovation needs to be socially embedded within the issues of rights, recognition, representation and empowerment of those people who are vulnerable and marginalised in the society.
3 Previous research has identified several external sources to complement innovation, such as customers, competitors, suppliers and other market participants , . In contrast to the well- developed research on customer and market information for product innovation, studies on how a firm generates competitor intelligence for innovative output have largely been neglected . In general, competitor intelligence research is often linked with that centered on competitive intelligence  while, over time, research attention has evolved from early environmental scanning to competitive intelligence collection and dissemination for strategic decision optimization , . Existing research points out that competitor analysis is a relatively weak business practice requires further enhancement. For instance, according to Gilad , approximately 55% of companies disappear from the Fortune 500 list each year, partially due to failure to assess the role of competitors in the market. Thus, it is vital to obtain competitor knowledge in order to sustain a business in an increasingly competitive market . Existing literature on open innovation and competitive intelligence reveals some gaps for further exploration. Most studies concern information collection techniques of a descriptive nature, followed by case-based research from large, multi-national organizations in advanced markets . Limited research has provided empirical evidence on a large-scale quantitative basis to support the inflow of external knowledge to improve business performance , , especially from the perspectives of Small and Medium-sized Enterprises (SMEs) in emerging markets , , . In fact, SMEs are increasingly practicing open innovation activities -, and in the face of scarce resources and limited capability, open innovation creates a new learning paradigm for SMEs to innovate .
With the arrival of the era of big data, how to quickly extract key information from massive data and bring value to enterprises and individuals is the focus of attention. At present, many traditional data analysis methods can also be used in large data analysis, such as clustering analysis, factor analysis, correlation analysis and regression analysis. At present, many traditional data analysis methods can also be used in large data analysis, such as clustering analysis, factor analysis, correlation analysis and regression analysis. Information literacy is the essential basic quality of new military talents, the need of lifelong learning and the motive force of innovation. The evaluation of information literacy of cadets in military academies is the research direction of the further development of information literacy theory. By referring to the information literacy standards and indicators formulated by scholars at home and abroad, this paper tries to formulate an “information literacy evaluation index system” for our cadets, and based on this evaluation body. The department evaluates and analyses the current situation of trainees' information literacy.
About these six briefly described models, it can be concluded that in a knowledge society (and knowledge democracy), at the national level, a network-style linkage of knowledge is being processed; each model fulfills a specific contribution for the ‘creation, diffu- sion, and use of knowledge’ (see Carayannis and Campbell 2006, 2010). In reference to sustainable development, under the aspect of global warming, we should add whether in the future a state (nation- state) that is leading in world politics as well as in the world economy is also being determined by the social (societal) potential to balance new knowledge, know-how, and innovation with nature. The basic innovation ‘core model’ of the Triple Helix focuses on the knowledge economy. Quadruple Helix already brings in the perspective of the knowledge society (and of knowledge democracy). From the point-of- view of the Quadruple Helix innovation model, it is evident that there should be a coevolution of the knowledge economy and of knowledge society (see also Dubina et al. 2012). The Quintuple Helix finally stres- ses the socioecological perspective of the natural environ- ments of society. Social ecology focuses on the interaction, codevelopment and coevolution of society, and nature (Carayannis and Campbell 2010, p. 59). The ‘biophysical structures’ or ‘biophysical structures of society’ mark areas of an overlap between culture (the cultural) and nature (the natural). Furthermore, between these biophysical structures and nature, there operates a metabolism (a ‘so- cial metabolism’ , with the potential of a ‘sociometabolic transition’). Here, also specific ‘metabolic profiles’ apply (see Fischer-Kowalski 1998; Fischer-Kowalski and Hüttler 1999; Fischer-Kowalski and Haberl 2007; Haberl et al. 2004, pp. 201–202, 204; see also Hopwood et al. 2005; Kates et al. 2001). ‘Sociometabolic regimes represent dy- namic equilibria of society-nature interactions and are characterized by typical patterns of material and energy flows (metabolic profiles)’ (Krausmann et al. 2008, p. 1). The European Commission (2009) identified the ‘socio- ecological transition’ as one of the major challenges for current and future societies and economies. The Quintu- ple Helix innovation model offers here an answer that is oriented toward problem-solving and sustainable develop- ment, furthermore, indicating how this socioecological transition may be mastered in combination with know- ledge production and innovation (see Figure 2). In fact, this socioecological transition behaves also as a (social) driver for innovation, creating incentives for more know- ledge and better innovation.
Discontinuity of external technical paradigm, process of technology accumulation, in- ternal economies of scope, innovative induction mechanism and innovation transaction costs drive innovation clustering . Organisation for Economic Co-operation and Development (OECD) formally put forward that innovation clustering is the produc- tion network of enterprises which highly interrelated with each other in value-added production chain . Innovation clustering gather more inclines to process then inno- vation cluster, which reflect the technological trajectory of research activities in innova- tion cooperation process . Compared with general innovation network  , the innovation subject in innovation clustering will form a temporary innovation network focusing on temporary specific goal, which is product and process oriented. The typical innovation clustering subject is the national innovation system, which is a network among national departments and institutions . It will facilitate national economic development through interaction and connection between each subject in complex in- novation system  . The innovation clustering based on national innovation system highlights the efficient allocation of innovation resources and diffusion of innovation effect in a national range , which need more cooperation and mechanisms to foster innovation network.
Such a definition is important due to multiple reasons. The first is given by Hermann, Pentek & Otto (2016) who state that “although Industry 4.0 is currently a top priority for many companies, research centers, and universities, a generally accepted understanding of the term does not exist. As a result, discussing the topic on an academic level is difficult” (p.1). But only with a clear definition that is derived from academical research, practitioners can adjust their work-style in the right way. To make it clearer: A well formulated conceptualization is needed before operationalization can take place (Quaglia et al., 2015). However, academic research can deliver much more than conceptualization. It can “fit real-world problems and settings into scientific method” (Bolton & Stolcis, 2003, p. 627), and based on this can give “clear, immediate, and important implications for managers” (p. 628). To enable these processes, a universal definition is needed. A second reason is concerning the pressure increasing globalization puts on companies, forcing them to work closer together than ever (Levitt, 1993). An “increasing global competition on product quality and production costs” (Brettel et al., 2014, p.37) is therefore requiring all companies to combine their research power and technological knowledge. But how should one work together if they do not even share the same definition of the ground principle? A last reason why a common understanding of Industry 4.0 is needed, lays with the uncertainty this industrial revolution brings with it (Schmidt et al., 2015). It is known that the new Industry will come, but nobody knows exactly how or when. To take this uncertainty of managers and employees, this paper will give a definition that allows to better prepare for the upcoming situation. From all the above the first research question of this paper evolves:
Finally, it is worth noting some of the limitations of our analy- sis, and possible areas for future research. First, and perhaps most important, our analysis remains essentially cross-sectional limit- ing our ability to make causal statements. Future analysis might usefully exploit the increasing panel data component within the UKIS both with a view to establishing causality and examining the longer term effects of the externalities identiﬁed here. Second, the range of local characteristics we consider here is relatively narrow. The availability of ﬁnance locally, the characteristics and inﬂuence of local markets and the impacts of population density, for exam- ple, remain as yet unexplored. A more comprehensive treatment of local area inﬂuences might also involve the use of, for example, a multilevel modelling approach which allows for the decomposi- tion of the multiple levels of heterogeneity in ﬁrm-level innovation performance. Third, limitations to the UKIS itself mean that our analysis of the importance of ﬁrms’ own external knowledge search and the resulting externalities takes on a rather special charac- ter. More speciﬁcally, while we are able to identify the intensity of knowledge search – interactive and non-interactive – by ﬁrms located in each area we are unable to say where their partners or contacts are located. Our results therefore provide little insight into the value of local innovation partnerships but relate instead to the engagement of local ﬁrms in innovation partnerships wher- ever their contacts or partners are located. This limits our ability to contribute to debates about the value of local clusters or networks, although in general terms our results do suggest the general value of innovation partnering or openness.
A second factor, which has been explored, was the opportunities for employees to collaborate with colleagues. Opportunity is related to the characteristics and chances organizations offered its employees to work together in a social innovative way (Sterling & Boxall, 2012). Researchers (among others Blumberg & Pringle, 1982) claimed that resources are just required in order to reach a certain performance. Likewise resources are needed for the utilizing knowledge. If organizations want their employe§es to utilize more knowledge, it is important to provide these employees the available time, enough spaces, sharing encouragement en related mechanism to activate knowledge sharing and utilization (Chen, Chang & Tseng, 2012). This can be confirmed with the results of this study. The opportunities were an important predictor for the social innovation application demonstrated within the organization. It can be said that when organizations think that knowledge is not utilized optimally, perhaps it can be attributed to the opportunities offered. Specifically, the results showed that when the organizations allowed employees to work together to ensure that employees are able to invent new techniques, methods and tools, the employees showed to have a very high level of social innovation application.
Knowledge management and organizational Performance have received much attention in recent times, owing to the increased recognition which has been accorded knowledge as a source of organizational success and sustainability. Researchers and practitioners have become increasingly interested in striving to understand how the two notions can be harnessed in order to attain that success The current study examines the nature of the relationship between knowledge management and organizational Performance in manufacturing companies in and around Chennai, with the aim of providing a unified framework for understanding how the above-mentioned knowledge-based concepts relate to each other. A mixed methodology approach was applied to achieve the set objective. Quantitative data were collected using questionnaires from 50 respondents from reputed manufacturing companies. The application of cronbach alpha coefficient has been applied to check the reliability for all the variables of knowledge management and organizational performance practices.
As the term “knowledge creation” suggests, we are confronted with a process of creating something new (not only) at the front-end of every innovation. Hence, it seems wise to adopt principles and techniques from the field of arts, as they are mainly concerned with processes of creating something (new). This applies both for the process of constructing such enabling environments and for working in/with them. In both domains we are confronted with hyper- complex problems that normally cannot be solved by purely analytical tools and strictly scientific methods. There is no single “best” solution for an Enabling Space or a COIN. Neither are there predictable “best solutions” for innovation problems in most cases. Hence, classical systematic, very precise, mechanistic, or purely scientific or analytical tools will not suffice, as, in most cases, these problems are tough and wicked problems (Dorst, 2003, 2006; Gedenryd, 1998; Stokes, 2007)—they are typical design problems. For instance, “Thinking from the future” (e.g., Peschl & Fundneider 2008a; Scharmer 2007) requires a completely different set of tools, competencies, as well as enabling contexts compared to classical extrapolation from the past. Design theory (e.g.,Krippendorff 2006; Krippendorff 2011; Glanville 1998; Glanville 2007; Laurel 2003), theory-U (Scharmer, 2001, 2007a; Senge et al., 2004; Peschl, Fundneider, 2008a; Kaiser, Fordinal, 2010) or design thinking (e.g., Brown 2008; Brown 2009; d.school 2010; Sanders & Stappers 2008) provide tools which are suitable for such problems and for solving them in a more “arty”/designerly way—it is a different way of approaching problems and a different way of thinking which is based in the arts, design, and humanities style of thinking. Of course, this does not exclude analytic tools—by combining these approaches they are offering alternative methodological strategies opening up new solution spaces.
In terms of the elasticity of the coefficients, all of the individual coefficients for Inter- net usage, innovation and technical knowledge in all the countries are inelastic given that they are below 1.0 (< 1.0). This implies that the degree of impact of a change in any of the independent variables will lead to a less than proportionate change in human development. This further implies that for the human development values to be signifi- cantly increased, there would need to be a strong or high level of change in Internet usage, technical knowledge and innovation within ECOWAS. The significant impact of Internet usage, innovation and technical knowledge (interaction of Internet usage and innovation) on human development implies that Internet usage has a significant role to play to influence the outcome of human development at both the individual country level and the ECOWAS community as a whole. Also, this result proves that some coun- tries are more involved in developing Internet usage alone, innovation alone, or techni- cal knowledge alone and some countries, none of the three cases above.
However, does the academic firm represent primarily an ideal-typical concept, or does the academic firm exist (do academic firms exist) also in real terms? The commer- cial firm appears to define the dominant and established norm in the world of contem- porary business. The empirical appropriateness or the proof of fitness for the ideas of the academic firm perhaps still needs to be demonstrated or verified. Academic firms are or would be exposed to an economic environment, where success often means to cope with and to profit from mechanisms and forces of severe competition in a con- tinuously globalizing world. But the concept of “ co-opetition ” (Brandenburger and Nalebuff 1997) suggests also that success in competition means to develop networks with overlapping patterns of cooperation and competition. Between the two (conceptu- ally) extreme poles of the academic firm and the commercial firm, many and several in-between forms of organization or hybrid combinations are possible. The academic firm represents a challenging proposition for current business. The academic firm, however, indicates also routes and paths, for how next-stage changes and future changes and future successes in the world of business and the knowledge economy (in the knowledge economy) can be approached and achieved. The academic firm is inter- ested in bringing together innovation and entrepreneurship for development, more so for sustainable development.
Three of the most prevalent policies for promoting engagement between universities and firms are the establishment of technology transfer offices, establishing science parks and outreach programmes. The main function of technology transfer offices is to assist faculty with the legal processes of disclosing and patenting intellectual property, establishing start up companies and arranging sales of licenses. Technology transfer offices are increasingly involved in promoting spin-offs, which can also extend to university provided venture capital (Steffensen, et al., 1999). The success of these technology transfer offices is linked to a number of organizational, cultural and environmental factors including the professionalism of the agents, style of management and leadership, the compensation of the agents and the existence of a clear strategy for creating spin-out companies (Markman, et al., 2005a; Markman, et al., 2005b Debackere and Veugelers, 2005; Lockett, et al., 2003, Carlsson and Fridh, 2002; Chapple, et al., 2005). One of the key explanations for this UK-US differential in knowledge transfer is experience and accumulated knowledge, since the US has been involved in public sector knowledge transfer activities significantly longer than the UK (Franklin, et al., 2001). Historical and embedded university attitudes towards industry are also important as the most entrepreneurial universities, including MIT, Stanford and Carnegie Mellon, have long histories of working with industry. Newer institutions such as Sunderland University and Oxford Brookes have made major contribution to regional development due to their ability to quickly adapt to new climates (Glasson, 2003).