Innovation, Social Networks, Research and Development

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Ad Hoc Innovation by Users of Social Networks: The Case of Twitter

Ad Hoc Innovation by Users of Social Networks: The Case of Twitter

Such research and development, however, is increasingly threatened by a very heavy- handed push towards the commercialisation of access to Twitter data. Where earlier, Twitter was generally prepared to provide research access to comparatively vast datasets (enabling easy access to the combined public feeds of several hundred thousand users, or even to the overall ‘firehose’ of all tweets, on request), more recent changes have meant that any access to the streams of more than 5,000 users at one time must take place through Twitter’s commercialisation partner, Gnip, at considerable cost (cf. MELANSON 2011). While perhaps commercially justified, such changes cannot but have a chilling effect on research and development in the Twitter ecosystem – especially where such research is conducted by publicly funded institutions. If, as a result, evidence of Twitter’s value in public communication becomes more scarce, leaving behind only the basic market research conducted by commercial research companies, Twitter will achieved little more than to silence some of its strongest advocates; alternatively, however, these new restrictions may also lead to a new burst of unforeseen – and from the company’s perspective, unwanted – user innovation, as researchers explore semi-legitimate approaches to gathering data by using server farms or similar distributed mechanisms, for example.
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Stimulating innovation on social product development: an analysis of social behaviors in online innovation communities

Stimulating innovation on social product development: an analysis of social behaviors in online innovation communities

Schaefer by stating the necessity of developing deep understanding on it. A number of studies indicated that whether an idea can be popular or not is affected by voter’s social behaviors in OICs [18, 19]. Since OICs are operated as social networks for both voters and ideators, a similar influence from ideators’ social behaviors on the popularity of their ideas might exist as well. Investigating how the social behaviors of ideators are associated with their idea popularity can help us develop a deep understanding on idea popularity. However, few studies have been focusing on testing this assumption. In order to address this research gap, an empirical study is conducted which collects a large-scale, quantitative data set from the Microsoft online innovation community, including 5468 users, 11985 ideas as well as associated social interaction data between September 2014 (when the platform was launched) and September 2018. We focus on understanding what social behaviors of ideators lead to the high popularity of their ideas. With the analysis of users’ online idea posting and commenting behaviors, our results reveal that the overall popularity of a user’s ideas is positively related to the contribution of new ideas and the diversity of his/her comments on others’ ideas, while negatively related to the number of comments the user had posted. Moreover, user’s innovation capability always poses a positive effect on both overall and average popu- larity of his/her ideas. These findings can help firms better understand idea popularity, and apply it for idea evaluation in order to improve the effectiveness and efficiency of social product development.
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Social Networks and Software Development

Social Networks and Software Development

Andriole (2010) describes research into how companies are using Web 2.0 technologies and how well those technologies were being used. Andriole interviewed “[a]pproximately 15 senior managers”[sic] (Andriole, 2010) from five different companies, including a big pharmaceutical company, a global chemicals company, a national real estate and mortgage company, a global IT company, and a large financial services company. On simple questions like “Which Web 2.0 technologies have you piloted?” answers differed little. All companies, for example, had piloted blogs and Wikis. For more complex questions like “How would you quantify the impact in knowledge management, rapid application development, customer relationship management, collaboration, communication, innovation and training?”, answers were longer and had more variation. One of the more important points learned through the interviews was “that Web 2.0 technologies, in spite of the hype, are entering the enterprise slowly but deliberately” (Andriole, 2010).
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Social capital and knowledge in interorganizational networks: Their joint effect on innovation

Social capital and knowledge in interorganizational networks: Their joint effect on innovation

In industries that face rapid technological change, a single company rarely commands the full range of expertise that is needed to create timely and cost-effective new product innovation. Strategies to reduce development costs, lessen the inherent risks of product introduction, and access technology/know-how that is otherwise unavailable internally have led firms to establish alliances and cooperative agreements. In this context, organizations can exchange resources for mutual benefit, counteracting technological specialization and a scarcity of resources and decreasing the risk associated with major research projects (Kotabe and Swan, 1995; De Man and Duysters, 2005). Firms with complementary knowledge can combine their specific strengths and develop new technologies or products that any single partner would not have been able to create on its own (Gerpott, 1995). Furthermore, alliances may increase innovativeness because they act as searchlights that allow firms to scan their environment for promising new technologies at low cost (Duysters and De Man, 2003).
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Research on the Construction of Inclusive Innovation Networks

Research on the Construction of Inclusive Innovation Networks

Second, to promote the market connection refers to the enterprises and other members of the network operations in the BOP market will gradually get rid of the poor rejection in the formal market outside of the various constraint condi- tions, promote the BOP market exchanges and interaction with other markets, thereby the BOP community and population into a wider market and social network. On the one hand, by connecting with other markets, BOP local prod- ucts and services can achieve higher value in a larger market. Such as Shandong Shouguang vegetable group’s products are exported to Europe, Japan, South Korea, Hong Kong, connection and interaction of the different levels the market has led the local industrial upgrading, benefit more people BOP. On the other hand, market connections also enlarge the opportunity space of BOP market, reduce the market access restriction and enhance the market power of BOP producers. For a long time, on the edge of the BOP producers as small and mar- ket participants, both the lack of close to the end market channels directly, also cannot timely access to market information, always at a disadvantage position in the negotiation and trading. But connection degree of deepening, as the market more transparent and rapid information flows, BOP producers more market awareness and accumulated new bridge of social capital, the original region closed form of asymmetric information and power status will be eased signifi- cantly. Especially the BOP network members of the respective resource capacity are different, to give full play to their comparative advantages, enterprises tend to assign part of the network to motivate BOP power producers, improve the overall quality and efficiency. For example, in the case of duck, the organization and management of farmers are mainly borne by the farmers’ self-established aquaculture association in order to concentrate on developing products and de- veloping markets. As the company’s market range from Linwu county extension to even state in Guangdong, Chenzhou, Hunan, appropriate chapter, Guiyang and Ningyuan, aquaculture association has been expanding and growing and mutual connection, can price, capital, technology and policy aspects of consulta- tion with companies and government departments, the protection of the rights of the BOP producers.
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Towards modelling language innovation acceptance in online social networks

Towards modelling language innovation acceptance in online social networks

As mentioned it is not only the individual that changes language, but the interactions and roles within a commu- nity that influence the change. Social roles of users within OSNs have been studied in earnest (though not looking at language). Through assessing and automatically classifying interaction patterns within Reddit [6], models were able to predict ‘answer’ roles within Reddit; and showed that user roles transcended multiple communities within the network, meaning that users maintain the same interaction patterns within different communities and potentially different net- works. However, this was limited to highly specialist com- munities that had highly dynamic interactions on specific topics. Again, through the use of topic-specific networks, opinion leaders were identified and assessed for their reach within the network [35] and ignored the dynamics of user roles over time.
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Promoting innovation via social networks and open company culture

Promoting innovation via social networks and open company culture

 Evaluation platforms contribute to an entreprise‘s success/failure – powerful recommendations-entertainment  reputation as a digital currency – voluntary identification with a brand  advertising: pull-, instead of push-strategy  Increasing Relevance of Social Media for…

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Social networks and innovation: concepts, tools and recent empirical contributions

Social networks and innovation: concepts, tools and recent empirical contributions

The above suggests that the most prominent problem encountered in research involving network data lies in defining the boundaries of the population being studied. Large populations are difficult to survey, so most studies, including those reviewed in this work, focus on small groups of individuals/firms. The criteria adopted to specify the boundaries of a community are based primarily on the object/purpose of the analysis. For example, most studies on knowledge flows in industrial clusters set geographic boundaries (e.g. firms and their technicians located in the cluster being investigated). Similarly, studies on collaborations among scientists set the boundaries in terms of knowledge/discipline domains or academic affiliations. Setting the boundaries to a population is particularly important in network studies since analyses focus explicitly on the interactions between the actors in the population studied. Omission or arbitrary specification of these boundaries leads to biases and misleading results (Marsden, 1990). 17 In terms of data collection, different methodologies can be used including questionnaires, interviews, observations, archival records, experiments, or a mix of all or some of these methods. Questionnaire based surveys are the most widely used method of data collection, often complemented by in-depth interviews with key informants or archival records. Questionnaires ask respondents, for example to indicate from a list, those individuals/organisations with whom she/he has a direct contact. A roster (i.e. list) is a common format in studies of delimited populations; in this case the researcher needs to know all the members in the set prior to data gathering. Free recall is used where the boundaries are not known, i.e. the researcher has to ask respondents to ‘name the people with
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Behavioral foundations for open innovation: Knowledge gifts and social networks

Behavioral foundations for open innovation: Knowledge gifts and social networks

11 (2005) has dubbed the action problem for social network theory. The action problem may even be more pertinent in an OI setting where formal means to ensure knowledge exchange are absent to even indirectly have an effect (cf. Aalbers et al. 2014). Coordination through social relations is inevitable, but cannot be formally enforced, given what is exchanged in the context of OI relations. Referring to Figure 1, either a specific Alter (1A) or others one is more indirectly related to (Alter- II; 1B) may but need not act in the expected manner since individual motivations or interpretations may prevent them from doing so (Uehera 1990). Interaction of the generalized type, (1C), may be least conducive to joint activity if interests do not align since not even an indirect structural connection exists between Ego and Y who reciprocates. In such a situation Ego may nonetheless initiate exchange in hopes of reciprocation and inclusion into a community (Ferrary 2003; Van der Eijk et al. 2009). In each of the three cases, reciprocation is not inevitable, however, even when a direct relation exists (Ekeh 1974; Ferrary 2003). As a result of the foregoing discussion, in case of Figure 1A, market, hierarchy as well as social relations type of coordination can be expected to work. In a situation that Figure 1B represents, however market coordination is not (less) likely to be effective. At the very least, market coordination of the classical kind using bilateral contracts no longer work unequivocally – other mechanism must be relied on (more). Other governance mechanisms can be those that are available in a hierarchy, or those that can be employed in social exchange situations. The situation portrayed in Figure 1C will require a variety of social interaction governance mechanisms to work.
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Maastricht Economic and social Research Institute on Innovation and Technology,

Maastricht Economic and social Research Institute on Innovation and Technology,

As the results for all of these separate questions will depend not only on the groups of goods but also the samples of countries, goods and years under consideration, we look at the country terms of trade in this paper, for developed and developing countries. We are therefore not mainly interested in primary commodities (the traditional approach) or in manufactures or their cointegration in this paper. 5 Bleaney and Greenaway (1993) have shown that commodity price changes of 1 per cent induce a change in net barter term of trade of 0.3 per cent. Powell (1991) and Lutz (1999a) find a value about 0.5 per cent. But even this aspect of the terms of trade debate is not uncontroversial. Aggregate commodity indices and country-level terms of trade are found to be unrelated by Cashin and Pattillo (2006) for Sub-Saharan Africa. These papers do not provide results for trends in country terms of trade though. Bidarkota and Crucini (2000) report trends in country terms of trade, which are negative throughout but insignificantly so. They group countries according to volatility in terms of trade, not income or poverty. Ram (2004) looked at net barter terms of trade at the country level and found that 16 of 26 countries investigated had significantly negative trends (5 others had insignificantly negative trends). We will look at a larger set of countries classified according to their per capita income. Whether the changes come in the form of trends shifting up and down, in a few steps, swings, cycles or other forms, what matters for long-run development is the long-run average trend. Supply (factor accumulation and technical progress) and demand forces (and the implied income and price elasticities of export demand) are assumed to determine these developments. 6 Many of these developments (including speculation and buffer stocks) behind
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Entrepreneurship, innovation and networks: Lessons for regional development policy

Entrepreneurship, innovation and networks: Lessons for regional development policy

the heroes of Greek mythology, who travelled afar and endured great dangers in search of the golden fleece (Saxenian, 2006). In the case of Silicon Valley, foreign born, US educated engineers who have learned the Valley model, have had great successes and then collaborated with their home-country counterparts to develop the context for entrepreneurial development. They are ideally positioned (as both insiders and outsiders at home and abroad) to search beyond prevailing routines to identify opportunities for complementary “peripheral” participation in the global economy and to work with public officials on the corresponding adaptation and redesign of relevant institutions and firms in their native countries. They are, in other words, exemplary protagonists of the process of self-discovery and open industrial policy; although surely there are different institutional arrangements in other contexts that are as effective as well, (Hausmann and Rodrik, 2002). Diasporas are not new, nor is the interest of policymakers and scholars in their developmental potential (Saxenian, 2006). What is new, or relatively so, is the focus on the highly educated migrants who have long been viewed as a serious loss to poor economies (the brain drain). Low transportation and communications costs now allow those who go abroad for further training or in search of work to interact and collaborate with their home-country counterparts far more extensively than was feasible in earlier eras of emigration. A small but growing number of migrants have even become fully “transnational”, with dual citizenship and residences in both their home and their adopted countries. Early research on diaspora contributions investigated remittances or direct investments, which can provide a stable source of finance and alleviate poverty, but typically have a limited long- term impact. The recent literature, by contrast, suggests that skilled migrants can alter the developmental trajectory of a poor country through the diffusion of knowledge and/or technology transfers, in a shift from a brain drain of talent away from the home country to “brain circulation” between the home country and the core economies (Saxenian, Motoyama and Quan 2002, Saxenian, 2006). Much of the newer literature (and the public policies which it has stimulated) treats the diaspora as an asset, valuable insofar as it adds to the home country’s stock of capital not through remittances, but in intellectual property or reputational capital or related forms of wealth.
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Evaluating the impact of social networks in rural innovation systems: An overview

Evaluating the impact of social networks in rural innovation systems: An overview

Finally, data collection for census and ego networks may suffer from missing links (Santos and Barrett 2007). Missing data may arise if contacts generated by individuals live outside the geographical area in which the survey takes place. There may also be persons who are very hard to reach for the analyst or who are simply unwilling to participate in a survey. All of the studies discussed in Section 3 suffer from this problem, and missing links may make up to one-third of the names generated by individuals (Krishnan and Sciubba 2007). Missing links may lead to a systematic bias in the regression results, which is hard to correct for. To avoid this problem, Conley and Udry (2001), as well as Santos and Barrett (2007), applied a random matching technique, in which individuals were confronted with a random set of persons within their village and were asked whether they would be willing to form a relationship with these individuals. When using random matching, full information on egos and their alters becomes available. Moreover, data collection is simplified because the random matches may be selected within the same geographical boundary. The possibility that weak ties are neglected is also reduced when applying random matching (Santos and Barrett 2007).
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Roles in social networks: methodologies and research issues

Roles in social networks: methodologies and research issues

Evaluating approaches. In the case when the roles are explicit and undoubtedly clear (e.g. the role of a mother, a manager etc.), or they are based on well- defined criteria (e.g. maximum number of inlinks in a network), evaluating a role identification technique is quite straightforward. Nevertheless, when the criteria of a role include subjectivity, such as in the case of ranking experts or influencers within a social network, then organizing experiments and evaluating results is not evident [2]. In this context, evaluation of role iden- tification methodologies presents a research challenge. The evaluation of influence identification can be done by seeing whether people that are supposed to be influenced are indeed influenced. Throughout the liter- ature, there are some evaluation propositions. For in- stance, web sites that host liked user posts (such as the digg.com) are used [3], assuming that posts of influ- encers are often liked and, as a result, they may appear in such sites.
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Research Networks and Inventors’ Mobility as Drivers of Innovation: Evidence from Europe

Research Networks and Inventors’ Mobility as Drivers of Innovation: Evidence from Europe

Thus, in Table 4 we include the variable OUT_COLL, which compares the number of co-inventors outside the region with the total number of inventors within a region. We also include the variable Inward Migration Rate (IMR), which is calculated counting the number of inventors moving into the region compared to the total number of inventors identified in that region. We expect, other things being equal, a positive and significant effect for both variables, for the same reason that a positive effect was expected for the scale of networks and labour mobility within the region. This cross- regional knowledge should be critical for innovation because of the fact that the imported knowledge is even more relevant than that already held locally. Here, we only show the estimations with the CONNECTIVITY variable, though the other models do not change our conclusions greatly.
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The challenge of social innovation : approaches and key mechanisms of development

The challenge of social innovation : approaches and key mechanisms of development

(EU Programme for Employment and Social Innovation (EaSI), The Theoretical, Empirical and Policy Foundations for Social Innovation in Europe (TEPSIE), Community Investment Package, PROGRESS (financial instrument supporting the development and coordination of EU policy in the employment, social inclusion and social protection, working conditions, anti-discrimination and gender equality), European platform against poverty and social exclusion, Building a European Network of Incubators for Social Innovation (BENISI), Social Business Initiative (SBI), Social Innovation Europe Initiative, European Innovation Partnership on Active and Healthy Ageing (ECEIPAHA), URBACT (Social innovation in cities), Regio Stars, Digital Social Innovation, Innovation Union and Digital Agenda for Europe, Collective Awareness Platforms for Sustainability and Social Innovation (CAPS), European Social Fund, European Regional Development Fund and others); - Websites of key organizations (EU Commission, OECD, BRICS and others). The first stage of research was to collect information about social innovation practices in OECD and BRICS countries, in sectors such as education, culture, childcare, healthcare, job-seeking assistance and rehabilitation, among others. The second stage involved the classification and description of the main three directions of support for social innovation: the creation of an enabling environment, the organization of innovation processes, and the promotion of systemic innovation. In the third stage, the key mechanisms of social innovation were identified and systematized. In particular, mechanisms such as public funding of socially-oriented NGOs, collaboration and changing roles, the integration of private capital with public and charitable support were investigated, along with other factors.
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Programme on Innovation, Higher Education and Research for Development IHERD

Programme on Innovation, Higher Education and Research for Development IHERD

The decision on the location of the Pan-African Institute for Governance, Humanities and Social Science was based on bidding amongst universities in Central Africa with the decision made by an expert panel. Data on the composition of this panel and the criterion they used are missing. The PAU Institute in Cameroon has the same strategic orientation as the PAU initiative at large; however, the African Union Commission has recognised that the institutional conditions of the selected PAU Institutes are not perfect for achieving the PAU objectives. Thus, specific measures will be put in place so that the Institutes can meet the level required in five years’ time.
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D2.4 Collective Intelligence Software for Social Innovation Networks: Testbed Deployments

D2.4 Collective Intelligence Software for Social Innovation Networks: Testbed Deployments

groups  and  social  networks  facilitators  by  discussing  on  existing  and  new  methods  for  cooperation.  Anim-­‐fr  has  already  tested  (by   handcraft)  the  harvesting  and  synthesis  process  tested  here,  produced  a  collective  text  on  ”Cooperation  explained  to  my  redneck   brother  in  law”  (arguments  on  cooperation  for  those  who  don’t  believe  in  it).  The  group  will  work  on  a  new  discussion  focused  on   community   management.   While   the   group   is   an   international   French   speaking   community,   some   of   them   will   meet   in   Brest   (France)  on  July  2  to  4,  during  the  “cooperative  usage  forum”  (6th  edition)  where  a  Catalyst  workshop  is  organized  to  discuss  the   results  of  the  first  iteration  of  the  testbed.  
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Monitoring social media and networks; identifying keys to unlock collaboration and innovation

Monitoring social media and networks; identifying keys to unlock collaboration and innovation

Examining a network in fine detail can highlight the nature or strength of connection between individuals. Taking an overview can demonstrate the wider shape of the network, whether an individual is at the core or periphery, and whether there are individuals creating bridges between different clusters in the network. At this wider level it becomes clear that humans “huddle” around particular points, ideas, spaces or places. Often good ideas are in the hands of those who have connections which span structural holes in organisational networks. This creates the imperative to identify areas where connectivity is high but also where it is lacking so that steps can be taken at all levels of an organisation to engage a change process designed to drive innovation and collaboration.
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Research on the Coupling and Coordinated Development of Financial Innovation and Economic Development

Research on the Coupling and Coordinated Development of Financial Innovation and Economic Development

The speed of economic development is affected by many factors. In order to objectively evaluate the effect of financial innovation to economic development, the comprehensive evaluation index of economic development (CEED) as the dependent variable, the paper take the index system of financial innovation right major indicators as independent variables, and select the added value financial industry(AVFS), fixed assets investment financial industry(FAIFS), deposits of financial institutions (DAFI), the amount of loans of financial institutions (LAFI), securities investment fund scale (SIF), total futures turnover (TFT), the amount of equity financing (EF) as control variables for regression analysis, the regression model is as follows:
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Research, development and innovation in the Czech food industry – perspectives

Research, development and innovation in the Czech food industry – perspectives

The object of the analysis is the r&D (research and development) base of the czech food sector. generally, the leading institution for the research and develop- ment is the Academy of Science cr. its Technology centre (Tc AS cr) is executing the function of the national information centre for European research. The Technology centre is working out analytic and perspective studies of r&D and innovation and it is involved in the transnational technology transfer. The centre worked out the green Paper of research, Development and innovation in the czech republic. This study is identifying significant barriers of r&D and innovation. They are economic (high innovation cost, economics risks, lack of information sources), market (small interests of costumers) and knowl- edge (lack of professional employees) (Klusáček et al. 2008).
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