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Open innovation and innovation management
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The impact of network structure on innovation efficiency: An agent-based study in the context of innovation networks

This article investigates the impact of network structure on innovation efficiency by establishing a simulation model of innovation process in the context of innovation networks. The results indicate that short path lengths between vertices are conductive to high efficiency of explorative innovations, dense clusters are conductive to high efficiency of exploitative innovations, and high small-worldness is conductive to high efficiency of the hybrid of these two innovations. Moreover, we discussed the reason of the results and give some suggestions to innovators and innovation policy makers.

 

The impact of network structure on innovation efficiency: An agent-based study in the context of innovation networks
Lei Hua and Wenping Wang

Complexity

http://dx.doi.org/10.1002/cplx.21583


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Online collaboration: Scientists and the social network

Giant academic social networks have taken off to a degree that no one expected even a few years ago. A Nature survey explores why.

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7 Innovation Myths That Kill Performance - Forbes

7 Innovation Myths That Kill Performance - Forbes | Open innovation and innovation management | Scoop.it
“ 7 Innovation Myths That Kill Performance Forbes 7 Innovation Myths That Kill Performance. comments, called-out. Comment Now. Follow Comments Following Comments Unfollow Comments. Comment Now. Follow Comments Following Comments Unfollow Comments.”
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How to Use the “Network Density” Formula to Measure the Health of a Community

How to Use the “Network Density” Formula to Measure the Health of a Community | Open innovation and innovation management | Scoop.it

A lot of community managers just go with their gut on this one, or use proxy metrics like signups, posts per day, klout scores, retweets or some other metric that is fairly hollow, but there are better ways.


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luiy's curator insight, March 14, 2013 12:27 PM

How can you determine the health of a community?

A lot of community managers just go with their gut on this one, or use proxy metrics like signups, posts per day, klout scores, retweets or some other metric that is fairly hollow, but there are better ways.

This is very much a work in progress, so I’d love to collaborate. If anyone has any thoughts, please jump in the comments sections and let’s discuss. That being said, most of this isn’t new, it’s just stolen, adapted and generally simplified from concepts like Network Theory, Affinity Groups, Clustering Coefficients, Small World Networks, and other things I will never fully understand or convince people to invest tech into.

Let’s dig in…

What is Network Density?

First off, Network Density (ND for short) isn’t one number, it’s more like blood pressure where they say “80 over 120″. I have no idea what the 80 or the 120 mean, but it works as an analogy. So, with that in mind, ND breaks down roughly as:

Average Distance Between Users : Number of Paths : Frequency of Interactions

or simply put…

AD : NP : F

Lets break each part down…

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Stanford Large Network Dataset Collection

Stanford Large Network Dataset Collection | Open innovation and innovation management | Scoop.it
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Wikipedia and Network Effects

Wikipedia and Network Effects | Open innovation and innovation management | Scoop.it

Encouragement that the Wikipedia model—a model that relies on the collective wisdom of a large number of unpaid volunteers—could be viable was provided by the NASA ClickWorkers experiment, which ran from November 2000 to September 2001. In the experiment by NASA, unpaid volunteers visited NASA’s website to mark and classify craters and “honeycomb” terrain on Mars. (4) The study produced two surprising and interesting results. First, people are willing to engage in an unpaid, novel, and productive experience merely for the fun of it. Second, an amalgamation of data contributed by many unskilled volunteers can be virtually indistinguishable from the output of a trained worker. Thus, large groups of people are capable of producing high-quality work for free.


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Popularity versus similarity in growing networks

The principle that ‘popularity is attractive’ underlies preferential attachment, which is a common explanation for the emergence of scaling in growing networks. If new connections are made preferentially to more popular nodes, then the resulting distribution of the number of connections possessed by nodes follows power laws, as observed in many real networks. Preferential attachment has been directly validated for some real networks (including the Internet), and can be a consequence of different underlying processes based on node fitness, ranking, optimization, random walks or duplication. Here we show that popularity is just one dimension of attractiveness; another dimension is similarity. We develop a framework in which new connections optimize certain trade-offs between popularity and similarity, instead of simply preferring popular nodes.

 

Popularity versus similarity in growing networks

Fragkiskos Papadopoulos, Maksim Kitsak, M. Ángeles Serrano, Marián Boguñá & Dmitri Krioukov

Nature (2012) http://dx.doi.org/10.1038/nature11459


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What Works for Social Sharing-B2B vs. B2C

What Works for Social Sharing-B2B vs. B2C | Open innovation and innovation management | Scoop.it

Figuring out what is best for your company's social sharing accounts can be tough.  Not only do you have to figure out what to share, but you also need to know how and when to say it. 

 

Compendium ran a study with more than 200 companies, checking on how social media marketers can get optimum engagement in business-to-business and business-to-consumer conversations.

 

The study looked at factors such as word count, punctuation, time of posting, and day of the week of posting to determine success factors.

 

It turns out that who you’re marketing to makes a massive difference.

If you’re talking to consumers, Monday and Wednesday are best on Twitter, and Monday for LinkedIn.

However if your company is marketing to other businesses, post to LinkedIn on Sunday, and to Twitter on Wednesday.

And while hashtags are great for consumer-focused posts, they really don’t work at all for B2B marketers.

And the one commonality?

Is it best to share on Tuesday or Thursday? Does it matter if your tweet is 15 words or 25?

And does using a question mark really reduce your chances of viral growth?

Question marks significantly reduce clickthrough on posts aimed at both consumers and business clients. Posts with question marks get between 25 and 52% fewer clicks than other posts.

 

Ultimately, there is no universal rule, at least, that applies to all audiences and all brands.

 

There is no perfect Tweet just like there is no perfect email subject line or “best time” to post a blog.

While data like this is interesting, it’s not absolute.

It can be incorporated into your regular experimenting and can serve as a nice guide, but you won’t find the answer in an Infographic, you’ll find it in your own analytics.

 

Compendium's Social Sharing Guides:

B2B Social Sharing Guide. http://bit.ly/RIM4qq

B2C Social Sharing Guide. http://bit.ly/QFH9oH

 

By Lee Jorgenson. http://bit.ly/RILWXL

Source. http://bit.ly/R2cwM7


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The impact of network structure on innovation efficiency: An agent-based study in the context of innovation networks

This article investigates the impact of network structure on innovation efficiency by establishing a simulation model of innovation process in the context of innovation networks. The results indicate that short path lengths between vertices are conductive to high efficiency of explorative innovations, dense clusters are conductive to high efficiency of exploitative innovations, and high small-worldness is conductive to high efficiency of the hybrid of these two innovations. Moreover, we discussed the reason of the results and give some suggestions to innovators and innovation policy makers.

 

The impact of network structure on innovation efficiency: An agent-based study in the context of innovation networks
Lei Hua and Wenping Wang

Complexity

http://dx.doi.org/10.1002/cplx.21583


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15 Examples of Open Innovation between Big Companies & Startups - Innovation Excellence (blog)

15 Examples of Open Innovation between Big Companies & Startups Innovation Excellence (blog) 15 Examples of Open Innovation between Big Companies & Startups Open innovation is a strategic game for big companies and one of the most important moves...
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The Hidden Biases in Big Data

The Hidden Biases in Big Data | Open innovation and innovation management | Scoop.it
Blindly trusting it can lead you to the wrong conclusions.
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Spread of Academic Success in a High School Social Network

Spread of Academic Success in a High School Social Network | Open innovation and innovation management | Scoop.it

Application of social network analysis to education has revealed how social network positions of K-12 students correlate with their behavior and academic achievements. However, no study has been conducted on how their social network influences their academic progress over time. Here we investigated correlations between high school students’ academic progress over one year and the social environment that surrounds them in their friendship network. We found that students whose friends’ average GPA (Grade Point Average) was greater (or less) than their own had a higher tendency toward increasing (or decreasing) their academic ranking over time, indicating social contagion of academic success taking place in their social network.

 

Blansky D, Kavanaugh C, Boothroyd C, Benson B, Gallagher J, et al. (2013) Spread of Academic Success in a High School Social Network. PLoS ONE 8(2): e55944. http://dx.doi.org/10.1371/journal.pone.0055944

 


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Complexity Digest's curator insight, February 14, 2013 5:13 PM

This research was made mainly by high school students.

Eric L Berlow's curator insight, February 15, 2013 10:35 AM

close network neighborhood of friends, not acquaintances, determines future academic success

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Taylor & Francis Online :: Behaviour & Information Technology - Volume 31, Issue 12

Taylor & Francis Online :: Behaviour & Information Technology - Volume 31, Issue 12 | Open innovation and innovation management | Scoop.it
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The top 20 data visualisation tools

The top 20 data visualisation tools | Open innovation and innovation management | Scoop.it

From simple charts to complex maps and infographics, Brian Suda's round-up of the best – and mostly free – tools has everything you need to bring your data to life...

A common question is how to get started with data visualisations. Beyond following blogs, you need to practice – and to practice, you need to understand the tools available. In this article, get introduced to 20 different tools for creating visualisations...


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Randy Rebman's curator insight, January 28, 2013 12:33 PM

This looks like it might be a good source for integrating infographics into the classroom.

Louise Robinson-Lay's curator insight, March 12, 2013 3:40 AM

A great tool for building infographics.

Caroline Matet's curator insight, April 22, 2013 4:08 PM

Le top 20 des outils pour faire ses propres data visualisations

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Finland is about to start using crowdsourcing to create new laws

Finland is about to start using crowdsourcing to create new laws | Open innovation and innovation management | Scoop.it
The Finnish government has approved the technology behind a new 'Open Ministry' platform, which will act as a hub for citizens who want new laws voted on in the country's parliament. But could that work elsewhere?

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10 Social Media Statistics | Social Media | Arrae Creative

10 Social Media Statistics | Social Media | Arrae Creative | Open innovation and innovation management | Scoop.it
10 Fascinating Social Media Statistics: In this blog we feature some pretty incredible social media statistics via an infographic design.
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