Dr Rick Albers, Radboud University, the Netherlands, presented this seminar entitled Organizing Intra-Organizational Networks for Innovation: introducing the basic concepts as part of the Network Data Analytics workshop hosted by the Social Sciences Compuing Hub at the Whitaker Insitute, NUI Galway on 26th February 2014
Learning Networks and Connective Knowledgeedtechtalk
The document discusses connective knowledge and learning networks. It makes three main points:
1. Knowledge is distributed, emerging from connections in networks rather than represented in individual minds. Concepts exist across interconnected units rather than in single locations.
2. Learning involves connecting specialized information sources. The ability to see connections between different fields and ideas is important. Nurturing connections facilitates continual learning.
3. Effective networks are decentralized, distributed, and disintermediated. Content and services are disaggregated and dynamic. The network encourages participation and emergence of patterns rather than top-down control.
The document discusses networks and network theory. It defines what a network is and provides examples of networks in nature, society, and technology. It also discusses key network concepts like nodes, edges, average path length, clustering coefficients, and different types of networks including random, lattice, and small-world networks. Power laws and scale-free networks are also covered.
Aceds 2015 wie nycpa final oct panel slidesJoe Bartolo
eDiscovery Nightmares - Panel Discussion - October 27th - New York City - Nixon Peabody - ACEDS - Women in eDiscovery - New York City Paralegal Association
This document discusses social network theory and analysis. It defines a social network as including actors (nodes) and the relationships (ties) between them. Social network analysis examines the relationships and structure of relationships between social entities like individuals or organizations. The document outlines some key concepts in social network theory including centrality, which looks at the importance of individual actors, and multiplexity, which refers to relationships serving multiple functions. It also discusses how network characteristics like size, subgroups, and centralization vs decentralization impact information sharing and decision making in organizations.
The document discusses two faces of integrative learning: the networked self and the symphonic self. The networked self focuses on creating intentional connections through play, emergence and flexibility using web 2.0 tools. The symphonic self focuses on achieving integrity and coherence through analysis, selection and synthesis using ePortfolio systems. While the networked self aligns with recent discourse on integrative learning, the symphonic self aligns more with portfolio pedagogy in the US. Both faces are needed for optimal learning, not as an either/or choice.
Activity theory and actor-network theory provide different perspectives for theorizing networks. Activity theory views development as preceding political interests, focusing on activity structures and contradictions over time. Actor-network theory sees political interests as preceding development, emphasizing negotiation and translations in network compositions. Both see networks as heterogeneous, multi-linked, and transformative, but differ in concepts like mediation and whether change is developmental or political. Each can be applied to analyze social media networks.
Modelling Learning & Performance: A Social Networks PerspectiveWalter Paredes
This document proposes a theoretical model to understand how knowledge professionals engage in learning and performance through a social networks perspective. It discusses key theories around social learning, communities of practice, and connectivism. The model hypothesizes that factors like an individual's network density, efficiency, engagement, and weak ties are associated with their learning. It describes collecting data on online student discussions to analyze social network properties and propose a novel "content richness" metric to measure social learning.
This document proposes a theoretical model to understand how social networks impact learning and performance. It analyzes data from an online project management course involving 36 professionals. The model is based on social learning and social network theories. It finds that social learning, rather than direct performance, is influenced by social network properties like structure, relations and position. A measure of "Content Richness" is introduced to measure engagement through message content analysis. Results indicate social network properties are correlated with social learning measures, which indirectly impact performance. The model could help educators enhance learning analytics and outcomes.
Learning Networks and Connective Knowledgeedtechtalk
The document discusses connective knowledge and learning networks. It makes three main points:
1. Knowledge is distributed, emerging from connections in networks rather than represented in individual minds. Concepts exist across interconnected units rather than in single locations.
2. Learning involves connecting specialized information sources. The ability to see connections between different fields and ideas is important. Nurturing connections facilitates continual learning.
3. Effective networks are decentralized, distributed, and disintermediated. Content and services are disaggregated and dynamic. The network encourages participation and emergence of patterns rather than top-down control.
The document discusses networks and network theory. It defines what a network is and provides examples of networks in nature, society, and technology. It also discusses key network concepts like nodes, edges, average path length, clustering coefficients, and different types of networks including random, lattice, and small-world networks. Power laws and scale-free networks are also covered.
Aceds 2015 wie nycpa final oct panel slidesJoe Bartolo
eDiscovery Nightmares - Panel Discussion - October 27th - New York City - Nixon Peabody - ACEDS - Women in eDiscovery - New York City Paralegal Association
This document discusses social network theory and analysis. It defines a social network as including actors (nodes) and the relationships (ties) between them. Social network analysis examines the relationships and structure of relationships between social entities like individuals or organizations. The document outlines some key concepts in social network theory including centrality, which looks at the importance of individual actors, and multiplexity, which refers to relationships serving multiple functions. It also discusses how network characteristics like size, subgroups, and centralization vs decentralization impact information sharing and decision making in organizations.
The document discusses two faces of integrative learning: the networked self and the symphonic self. The networked self focuses on creating intentional connections through play, emergence and flexibility using web 2.0 tools. The symphonic self focuses on achieving integrity and coherence through analysis, selection and synthesis using ePortfolio systems. While the networked self aligns with recent discourse on integrative learning, the symphonic self aligns more with portfolio pedagogy in the US. Both faces are needed for optimal learning, not as an either/or choice.
Activity theory and actor-network theory provide different perspectives for theorizing networks. Activity theory views development as preceding political interests, focusing on activity structures and contradictions over time. Actor-network theory sees political interests as preceding development, emphasizing negotiation and translations in network compositions. Both see networks as heterogeneous, multi-linked, and transformative, but differ in concepts like mediation and whether change is developmental or political. Each can be applied to analyze social media networks.
Modelling Learning & Performance: A Social Networks PerspectiveWalter Paredes
This document proposes a theoretical model to understand how knowledge professionals engage in learning and performance through a social networks perspective. It discusses key theories around social learning, communities of practice, and connectivism. The model hypothesizes that factors like an individual's network density, efficiency, engagement, and weak ties are associated with their learning. It describes collecting data on online student discussions to analyze social network properties and propose a novel "content richness" metric to measure social learning.
This document proposes a theoretical model to understand how social networks impact learning and performance. It analyzes data from an online project management course involving 36 professionals. The model is based on social learning and social network theories. It finds that social learning, rather than direct performance, is influenced by social network properties like structure, relations and position. A measure of "Content Richness" is introduced to measure engagement through message content analysis. Results indicate social network properties are correlated with social learning measures, which indirectly impact performance. The model could help educators enhance learning analytics and outcomes.
A Perspective on Graph Theory and Network ScienceMarko Rodriguez
The graph/network domain has been driven by the creativity of numerous individuals from disparate areas of the academic and the commercial sector. Examples of contributing academic disciplines include mathematics, physics, sociology, and computer science. Given the interdisciplinary nature of the domain, it is difficult for any single individual to objectively realize and speak about the space as a whole. Any presentation of the ideas is ultimately biased by the formal training and expertise of the individual. For this reason, I will simply present on the domain from my perspective---from my personal experiences. More specifically, from my perspective biased by cognitive and computer science.
This is an autobiographical lecture on my life (so far) with graphs/networks.
Dartmouth discussion: What's wrong with "What's wrong with CHAT?"?Clay Spinuzzi
My slide deck from the workshop portion of the 2016 Dartmouth Institute. During this portion, respondents and I discussed my paper "What's Wrong with CHAT?" This deck encapsulates the argument, but also briefly introduces activity theory and discusses its development.
This document discusses a knowledge network system approach to knowledge management. It proposes establishing knowledge systems engineering as a new discipline to organize and manage knowledge systems by integrating knowledge as both an object and process. The knowledge network system is composed of four layers and aims to integrate knowledge resources at different levels through linking knowledge, people, and organizations in a network. Key processes in the system include searching, capturing, integrating, and creating new knowledge through interactions within the network.
Is network theory the best hope for regulating systemic risk?Kimmo Soramaki
The presentation is organised around three policy questions:
1. How can we measure the systemic importance of a bank?
2. Can regulators promote a safer financial system by affecting its topology?
3. Is it possible to devise early-warning indicators from real-time data?
This document provides an introduction to social network analysis, including:
1. An overview of different types of networks, both social and non-social. Examples of networks in different domains are shown.
2. A discussion of fundamental concepts in social network analysis, including how networks can be analyzed at different levels from ego networks to global networks.
3. An outline of common research areas in social network analysis, such as research on network structure, actors, ties, and network dynamics.
4. An introduction to basic network measures that can be used to analyze whole networks, including density, average degree, average distance, and number of components. More advanced metrics are mentioned to be covered in the next session.
This document discusses the potential for integrating network science concepts and tools into undergraduate curriculum. It describes how network analysis can be applied to various academic subjects from biology to history. Sample modules are presented that were implemented in sociology and theater classes at Suffolk University, where students mapped networks in texts to better understand characters and plot lines. The document outlines different delivery methods for network analysis education, from workshops to full courses. It also provides references to network analysis software, tutorials, course materials and introductory books that make the field accessible.
1. The document discusses social network theory (SNT) and how it relates to Google. SNT analyzes relationships between individuals or objects and sees networks as having emergent behaviors. It began in psychology and sociology and focuses on describing social structures.
2. Google's search engine, PageRank, is based on SNT principles by analyzing the web as a social network of linked pages. PageRank assigns importance values to pages based on the importance of the pages linking to them. This personalized search using a user's social network.
3. The document predicts that Google could apply SNT more in its driverless cars and maps. Cars could form dynamic carpools based on users' social networks. Maps could
This document discusses the history and challenges of network visualization. It outlines James Moody's presentation on the topic, which traces the evolution of network visualization from Euler's early work to modern approaches. Key challenges discussed include determining which social space to represent, how to handle multidimensional data, and dealing with issues of scale and density in large networks. The document argues that visualization allows researchers to gain insights that metrics alone cannot provide, by making the invisible visible and communicating complex features effectively.
A Western View of China's Internal and External Innovation Ecosystem - ICT Se...Martha Russell
A network analysis of flows of information and investments a relationship perspective on the internal and external innovation ecosystems of China's ICT sectors. Crowd-sourced English language press release-type information provides a Western view in a systems framework.
In this study, we are looking for insights that can be used to:
•Communicate complexity to co-create vision
•Identify and empower influential individuals for critical actions
•Connect components to catalyze the evolution of the ecosystem
•Develop and implement programs(meetings, funding, initiatives)to foster co-creator networks
•Measure and transform an innovation ecosystem
Sine Celik Jo Van Engelen, Han Brezet, Peter Joore, Linda Wauben
Managing Creativity: Oxymoron or Necessity?
An analysis of social networks for enhancing regional creative output
Lecture 5 Identity Networks, Community 2018 James Stewart
This document discusses identity, community, and communication in the context of the internet and networked society. It covers several key topics:
- The rise of social network approaches and how they analyze relationships between nodes. This includes concepts like weak ties, bridging networks, and centrality.
- Theories of online identity, including the idea of fluid and multiple identities online. It also discusses how social media profiles represent and standardize identity.
- The values and mechanisms of online communities, and how different technologies like forums, blogs, and social networks enable different types of community.
- The history of computer-mediated communication and how early theories analyzed its impact on relationships and behavior compared to face-to-face
The document summarizes a presentation on collaboration in organizations. It defines collaboration and discusses theories of collaboration. It also outlines different types of collaboration, principles of collaboration, tools that enable collaboration, and challenges in implementing collaboration within organizations. The document stresses that collaboration can help organizations be more agile, integrate information and processes more effectively, and optimize resource use.
This document provides an overview of social network analysis and the Sylva software. It begins with key concepts in social network analysis including social structure, social networks, nodes, linkages, and additional terminology. It then discusses what makes social network analysis unique and provides examples of ego-centered and community-centered network analysis. Finally, it describes the features and capabilities of the Sylva software for collecting, storing, visualizing, and analyzing social network data.
This document summarizes a presentation about three potential futures for legal education research: Prometheus, Sisyphus, and Themis.
Prometheus represents a creative but counter-cultural approach that suffers consequences. Sisyphus is characterized by endless, futile effort. Themis proposes a collaborative online research project to systematically study and provide evidence for legal education and professional development. It would include original research, literature reviews, and resources to inform policymaking.
The document discusses how social networks can enable or inhibit organizational work. It outlines different types of social network analysis and theories like actor-network theory. Social networks can improve information flow, identify bottlenecks, drive innovation, and influence knowledge creation and dissemination. Mapping networks generates a typology of nodes like hubs and gatekeepers. Co-opetition involves both collaboration and competition within and between organizations. Actor-network theory assumes symmetry between human and non-human actors and that networks are stabilized through negotiation and translation processes. Problems with the online group discussion included lack of a coordinator and different interests among group members.
V Międzynarodowa Konferencja Naukowa Nauka o informacji (informacja naukowa) w okresie zmian Innowacyjne usługi informacyjne. Wydział Dziennikarstwa, Informacji i Bibliologii Katedra Informatologii, Uniwersytet Warszawski, Warszawa, 15 – 16 maja 2017
10 More than a Pretty Picture: Visual Thinking in Network Studiesdnac
Visualization has been important in network science since its beginnings to make invisible structures visible. While metrics can describe networks, visualizations allow researchers to see relationships and patterns across multiple dimensions that numbers alone cannot reveal. Effective network visualizations communicate insights that would be difficult to understand otherwise, by depicting global patterns and local details simultaneously in a way that builds intuition about the network's structure and generating processes. However, challenges include lack of consistent display frameworks, integrating too much multidimensional information, and issues of scale for large and dynamic networks.
The document discusses using social media data and network analysis to provide insights into innovation ecosystems. It presents data on technology companies worldwide and in various regions. Network maps show relationships between executives in different industry sectors. The analysis can help identify influential actors, foster collaborations, and measure ecosystem transformations. Case studies examine investment networks between China and foreign countries, finding emerging business clusters linked through firm relationships.
2013.11.14 Big Data Workshop Michael BrowneNUI Galway
Michael Browne from the Irish Centre for High End Computing presented this overview of Big Data and Computer Architecture during the Big Data Workshop hosted by the Social Sciences Computing Hub at the Whitaker Institute on the 14th November 2013
2013.11.14 Big Data Workshop Adam Ralph - 1st set of slidesNUI Galway
Adam Ralph from the Irish Centre for High End Computing presented this Introduction to Basic R during the Big Data Workshop hosted by the Social Sciences Computing Hub at the Whitaker Institute on the 14th November 2013
A Perspective on Graph Theory and Network ScienceMarko Rodriguez
The graph/network domain has been driven by the creativity of numerous individuals from disparate areas of the academic and the commercial sector. Examples of contributing academic disciplines include mathematics, physics, sociology, and computer science. Given the interdisciplinary nature of the domain, it is difficult for any single individual to objectively realize and speak about the space as a whole. Any presentation of the ideas is ultimately biased by the formal training and expertise of the individual. For this reason, I will simply present on the domain from my perspective---from my personal experiences. More specifically, from my perspective biased by cognitive and computer science.
This is an autobiographical lecture on my life (so far) with graphs/networks.
Dartmouth discussion: What's wrong with "What's wrong with CHAT?"?Clay Spinuzzi
My slide deck from the workshop portion of the 2016 Dartmouth Institute. During this portion, respondents and I discussed my paper "What's Wrong with CHAT?" This deck encapsulates the argument, but also briefly introduces activity theory and discusses its development.
This document discusses a knowledge network system approach to knowledge management. It proposes establishing knowledge systems engineering as a new discipline to organize and manage knowledge systems by integrating knowledge as both an object and process. The knowledge network system is composed of four layers and aims to integrate knowledge resources at different levels through linking knowledge, people, and organizations in a network. Key processes in the system include searching, capturing, integrating, and creating new knowledge through interactions within the network.
Is network theory the best hope for regulating systemic risk?Kimmo Soramaki
The presentation is organised around three policy questions:
1. How can we measure the systemic importance of a bank?
2. Can regulators promote a safer financial system by affecting its topology?
3. Is it possible to devise early-warning indicators from real-time data?
This document provides an introduction to social network analysis, including:
1. An overview of different types of networks, both social and non-social. Examples of networks in different domains are shown.
2. A discussion of fundamental concepts in social network analysis, including how networks can be analyzed at different levels from ego networks to global networks.
3. An outline of common research areas in social network analysis, such as research on network structure, actors, ties, and network dynamics.
4. An introduction to basic network measures that can be used to analyze whole networks, including density, average degree, average distance, and number of components. More advanced metrics are mentioned to be covered in the next session.
This document discusses the potential for integrating network science concepts and tools into undergraduate curriculum. It describes how network analysis can be applied to various academic subjects from biology to history. Sample modules are presented that were implemented in sociology and theater classes at Suffolk University, where students mapped networks in texts to better understand characters and plot lines. The document outlines different delivery methods for network analysis education, from workshops to full courses. It also provides references to network analysis software, tutorials, course materials and introductory books that make the field accessible.
1. The document discusses social network theory (SNT) and how it relates to Google. SNT analyzes relationships between individuals or objects and sees networks as having emergent behaviors. It began in psychology and sociology and focuses on describing social structures.
2. Google's search engine, PageRank, is based on SNT principles by analyzing the web as a social network of linked pages. PageRank assigns importance values to pages based on the importance of the pages linking to them. This personalized search using a user's social network.
3. The document predicts that Google could apply SNT more in its driverless cars and maps. Cars could form dynamic carpools based on users' social networks. Maps could
This document discusses the history and challenges of network visualization. It outlines James Moody's presentation on the topic, which traces the evolution of network visualization from Euler's early work to modern approaches. Key challenges discussed include determining which social space to represent, how to handle multidimensional data, and dealing with issues of scale and density in large networks. The document argues that visualization allows researchers to gain insights that metrics alone cannot provide, by making the invisible visible and communicating complex features effectively.
A Western View of China's Internal and External Innovation Ecosystem - ICT Se...Martha Russell
A network analysis of flows of information and investments a relationship perspective on the internal and external innovation ecosystems of China's ICT sectors. Crowd-sourced English language press release-type information provides a Western view in a systems framework.
In this study, we are looking for insights that can be used to:
•Communicate complexity to co-create vision
•Identify and empower influential individuals for critical actions
•Connect components to catalyze the evolution of the ecosystem
•Develop and implement programs(meetings, funding, initiatives)to foster co-creator networks
•Measure and transform an innovation ecosystem
Sine Celik Jo Van Engelen, Han Brezet, Peter Joore, Linda Wauben
Managing Creativity: Oxymoron or Necessity?
An analysis of social networks for enhancing regional creative output
Lecture 5 Identity Networks, Community 2018 James Stewart
This document discusses identity, community, and communication in the context of the internet and networked society. It covers several key topics:
- The rise of social network approaches and how they analyze relationships between nodes. This includes concepts like weak ties, bridging networks, and centrality.
- Theories of online identity, including the idea of fluid and multiple identities online. It also discusses how social media profiles represent and standardize identity.
- The values and mechanisms of online communities, and how different technologies like forums, blogs, and social networks enable different types of community.
- The history of computer-mediated communication and how early theories analyzed its impact on relationships and behavior compared to face-to-face
The document summarizes a presentation on collaboration in organizations. It defines collaboration and discusses theories of collaboration. It also outlines different types of collaboration, principles of collaboration, tools that enable collaboration, and challenges in implementing collaboration within organizations. The document stresses that collaboration can help organizations be more agile, integrate information and processes more effectively, and optimize resource use.
This document provides an overview of social network analysis and the Sylva software. It begins with key concepts in social network analysis including social structure, social networks, nodes, linkages, and additional terminology. It then discusses what makes social network analysis unique and provides examples of ego-centered and community-centered network analysis. Finally, it describes the features and capabilities of the Sylva software for collecting, storing, visualizing, and analyzing social network data.
This document summarizes a presentation about three potential futures for legal education research: Prometheus, Sisyphus, and Themis.
Prometheus represents a creative but counter-cultural approach that suffers consequences. Sisyphus is characterized by endless, futile effort. Themis proposes a collaborative online research project to systematically study and provide evidence for legal education and professional development. It would include original research, literature reviews, and resources to inform policymaking.
The document discusses how social networks can enable or inhibit organizational work. It outlines different types of social network analysis and theories like actor-network theory. Social networks can improve information flow, identify bottlenecks, drive innovation, and influence knowledge creation and dissemination. Mapping networks generates a typology of nodes like hubs and gatekeepers. Co-opetition involves both collaboration and competition within and between organizations. Actor-network theory assumes symmetry between human and non-human actors and that networks are stabilized through negotiation and translation processes. Problems with the online group discussion included lack of a coordinator and different interests among group members.
V Międzynarodowa Konferencja Naukowa Nauka o informacji (informacja naukowa) w okresie zmian Innowacyjne usługi informacyjne. Wydział Dziennikarstwa, Informacji i Bibliologii Katedra Informatologii, Uniwersytet Warszawski, Warszawa, 15 – 16 maja 2017
10 More than a Pretty Picture: Visual Thinking in Network Studiesdnac
Visualization has been important in network science since its beginnings to make invisible structures visible. While metrics can describe networks, visualizations allow researchers to see relationships and patterns across multiple dimensions that numbers alone cannot reveal. Effective network visualizations communicate insights that would be difficult to understand otherwise, by depicting global patterns and local details simultaneously in a way that builds intuition about the network's structure and generating processes. However, challenges include lack of consistent display frameworks, integrating too much multidimensional information, and issues of scale for large and dynamic networks.
The document discusses using social media data and network analysis to provide insights into innovation ecosystems. It presents data on technology companies worldwide and in various regions. Network maps show relationships between executives in different industry sectors. The analysis can help identify influential actors, foster collaborations, and measure ecosystem transformations. Case studies examine investment networks between China and foreign countries, finding emerging business clusters linked through firm relationships.
2013.11.14 Big Data Workshop Michael BrowneNUI Galway
Michael Browne from the Irish Centre for High End Computing presented this overview of Big Data and Computer Architecture during the Big Data Workshop hosted by the Social Sciences Computing Hub at the Whitaker Institute on the 14th November 2013
2013.11.14 Big Data Workshop Adam Ralph - 1st set of slidesNUI Galway
Adam Ralph from the Irish Centre for High End Computing presented this Introduction to Basic R during the Big Data Workshop hosted by the Social Sciences Computing Hub at the Whitaker Institute on the 14th November 2013
2013.11.14 Big Data Workshop Adam Ralph - 2nd set of slidesNUI Galway
Adam Ralph from the Irish Centre for High End Computing presented this Introduction to Basic R during the Big Data Workshop hosted by the Social Sciences Computing Hub at the Whitaker Institute on the 14th November 2013
Social network analysis (SNA) - Big data and social data - Telecommunications...Wael Elrifai
Social Network Analysis: Practical Uses and Implementation is a presentation that discusses social network analysis and its practical applications. It introduces key concepts such as social networks, social network analysis, roles in social networks, and graph theory. It also covers metrics and implementations of social network analysis, including calculating metrics from social networks and recommended approaches involving data preparation, metric calculation, model creation and scoring, and measurement. The presentation provides an overview of how social network analysis can be a useful tool for understanding relationships and influence.
2013.11.14 Big Data Workshop Bruno Voisin NUI Galway
Bruno Voisin from the Irish Centre for High End Computing presented this Introduction to Data Analytics Techniques and their Implementation in R during the Big Data Workshop hosted by the Social Sciences Computing Hub at the Whitaker Institute on the 14th November 2013
This document discusses networks and innovation. It provides three perspectives on networks: as an analytical tool to examine relationships; as explanatory concepts where relationships influence behaviors; and as organizational forms. Networks are defined as sets of nodes linked by relationships. The network perspective is that economic actions are embedded in networks that provide opportunities and constraints. Networks can pool resources and develop new ideas, providing access to diverse information and capabilities to drive innovation. Empirical evidence on how network structure impacts innovation is mixed, finding benefits from both brokerage and closure positions in networks. The document then focuses on strategic alliance networks and how network position can generate innovation, with examples from the automotive industry.
This degree is designed to develop agile leaders in new cultures of digital formal and informal learning, with flexible program options in knowledge networking, global information flow, advanced search techniques, learning analytics, social media, game-based learning, digital literature, learning spaces design and more. Ideal for educators, school leaders, ICT integrators, teacher librarians, instructional designers, learning support specialists and teacher educators, who are seeking to develop expertise in global and community networked knowledge environments.
Presentation 'Use of social networks for innovation in health' done by Vicente Traver (SABIEN-ITACA previously TSB-ITACA) during the IBEC 2014 conference held in Gwangju from 20 to 22th November, 2014. Presentation is focused about how social media can be used as driver for innovation in health
Open / Collaborative Innovation Networksinnovation-3
The document discusses the rise of innovation networks as a key success factor for companies. It notes that innovation is increasingly a collaborative cross-company process. Successful companies now manage external innovation networks that include partners like suppliers and research institutions. The presentation provides examples of both physical and virtual innovation networks and discusses how companies can develop open and collaborative innovation capabilities.
Senja Svahn: Innovation networks @ TEDx AaltoUniversityOnTracksAalto on Tracks
This document discusses innovation networks and their characteristics. It outlines the drivers and types of innovation, as well as the main characteristics of innovation networks. Innovation networks emerge through phases that involve exploring ideas, selecting and focusing on certain ideas, mobilizing actors, and competing to produce and distribute innovations. The environment of these networks is characterized by uncertainty and changes in the field that create opportunities. Actors' positions, knowledge, and abilities to learn and integrate knowledge influence how they can shape the emergence of innovation networks.
USING STRUCTURAL HOLES METRICS FROM COMMUNICATION NETWORKS TO PREDICT CHANGE ...Igor Wiese
This document examines using structural hole metrics (SHM) from communication networks to predict change dependencies between software artifacts. It finds that SHM can predict change dependencies with an area under the curve over 0.7. Constraint and hierarchy SHM were most important for one project, while commits and updates were most important when including process metrics. The study provides initial evidence that SHM obtained from communication networks can predict change dependencies as suggested by Conway's Law. Future work could explore additional projects, metrics, and comparisons to other software aspects.
Organizations are increasingly using virtual teams, with 46% reporting their use. The top reasons for using virtual teams are to include talent across geographic locations and boost collaboration between locations. Nearly three-quarters of organizations found brainstorming solutions to be the most successful virtual team behavior. Building team relations and time differences were the top challenges reported. The survey defined and compared virtual teams to other types of teams and provided demographic information about the respondents' organizations.
The document discusses strategies for improving the effectiveness of global virtual teams. It recommends developing collective competence through collaboration on joint tasks. Key challenges for virtual teams include reduced visibility and complexity due to geographical dispersion and cultural diversity. Strategies suggested include clearly defining team roles and processes, choosing communication technologies suited to task complexity, and facilitating trust and open communication.
Innovation Connectomics - Mapping Knowledge Flow in Innovation NetworksSeb Sigloch
The document discusses mapping knowledge flow in innovation networks. It introduces nodes as human or technological actors and ties as relationships between nodes. It then outlines different levels of complexity and granularity for boundaries, from business units and value networks to regional, national, and global innovation systems. The presentation focuses on determining the appropriate level of analysis for understanding knowledge flow, whether more complex systems with lower granularity or simpler systems with higher granularity.
This document summarizes research on social networks and relationships in virtual worlds. It finds that virtual world communities form and operate similarly to real-world communities, with people connecting based on shared interests, experiences, and reputation. Trust is important online as in offline communities and develops through factors like frequency of contact, similarity, and self-disclosure. The anonymity of online interactions gives way to pseudonymity, and reputation systems allow trust to be established. Social network analysis is used to map these relationships and identify influential individuals. Ethical considerations are important to maintain participant trust in research.
AICPA CFO Conference - Managing Decentralized Work Forces & Building Virtual ...Dan Griffiths
In a world where the pace of change continues to accelerate, finance and accounting teams are becoming increasingly fluid. People across the globe are brought together for a particular project and then rapidly redeployed. The command and control systems of yesteryear have been replaced by decentralized, informal networks.
This session will help you develop an understanding of the latest brain science and what it means for working effectively with remote team members. You'll learn how to leverage the growing trend towards a freelance workforce and get practical tips on utilizing the latest collaboration and communication technologies to improve the performance of decentralized teams.
Understanding knowledge network, learning and connectivismAlaa Al Dahdouh
Behaviorism, Cognitivism, Constructivism and other growing theories such as Actor-Network and Connectivism are circulating in the educational field. For each, there are allies who stand behind research evidence and consistency of observation. Meantime, those existing theories dominate the field until the background is changed or new concrete evidence proves their insufficiencies. Connectivists claim that the background or the general climate has recently changed: a new generation of researchers, connectivists propose a new way of conceiving knowledge. According to them, knowledge is a network and learning is a process of exploring this network. Other researchers find this notion either not clear or not new and probably, with no effect in the education field. This paper addresses a foggy understanding of knowledge defined as a network and the lack of resources talking about this topic. Therefore, it tries to clarify what it means to define knowledge as a network and in what way it can affect teaching and learning.
Three sections explain how your company can use social networking to change the economics of innovation and competitiveness: a) how Web 2.0 changes the context of corporate innovation, b) social networks in innovation, c) practice, how to use social networks for innovation
Jugaad (a word taken from Hindi which captures the meaning of finding a low-cost solution to any problem in an intelligent way) is a new way to think constructively and differently about innovation and strategy. Jugaad innovation has a long-lasting tradition in India but is also widespread in the rest of the so-called Bric countries (Brazil, Russia, India and China) and numerous other emerging economies. Jugaad is about extending our developed world understanding of entrepreneurial spirit in the traditional Schumpeterian style (Joseph Schumpeter was the Austrian economist known as the prophet of innovation).
Jugaad means thinking in a frugal way and being flexible, which, in turn, requires the innovator or entrepreneur to adapt quickly to often unforeseen situations and uncertain circumstances in an intelligent way.
Intelligence in this context "isn’t about seeking sophistication or perfection by over-engineering products, but rather about developing a ‘good-enough’ solution that gets the job done".
This document discusses network analysis and its use in mapping and analyzing innovation networks. It provides examples of using network analysis to study the international trade network from 1938 to 2003 and a collaborative R&D network. Key network metrics discussed include degree distribution, density, clustering, and average path length. The analysis of the trade network found its overall structure has remained stable over time but countries' interconnectivity has increased. Analysis of an R&D network found those with a small world structure were more innovative.
This document summarizes a presentation about using NodeXL to visualize social network data from Twitter, MapBox, and other sources. It introduces NodeXL as a free and easy-to-use tool for social network analysis built on Excel. The presentation discusses key concepts in social network theory like nodes, edges, centrality, and clustering. It then shows examples of using NodeXL to analyze innovation networks and visualize data from CrunchBase on capital flows between startups, accelerators, and investors. The purpose is to demonstrate how network analysis and visualization with NodeXL can provide insights into clusters and connections in social and innovation ecosystems.
Social Network Analysis & an Introduction to ToolsPatti Anklam
This document provides an introduction to social network analysis. It discusses how networks can be mapped and analyzed using tools to understand their structure and flow of information. Key aspects of network analysis are introduced, including nodes, ties, centrality metrics, and structural patterns. A variety of tools are presented, ranging from free social media applications to specialized software, that can be used to map and analyze networks. The value of network analysis is in identifying influential individuals, improving collaboration and knowledge sharing, and intervening to change network structures and behaviors.
We've written before about how you can view your community as a network. Here we use the 'network lense' to show how communities typically evolve and what specific actions you might want to take to get to the next level.
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2014.02.26 Network Data Analytics ..Organizing intra-organizational networks for innovation
1. NUI Galway 2014 Workshop on network analytics
Part 1: Organizing Intra-Organizational Networks for
Innovation: introducing the basic concepts
Hendrik Leendert (Rick) Aalbers* PhD
(*) Assistant Professor Strategy & Innovation Radboud University - Institute for Management
Research // Centre for Organization Restructuring
r.aalbers@fm.ru.nl
2. Objective of today
• Introduction to social network analysis,
including:
• Relevance
• Core concepts
• Core methodology
• Main tools and visualization (Ucinet)
• Large online networks
• Reflection on future research possibilities
• Wrap up
2
4. Introducing a network view of the world
4
• People are represented as
nodes.
• Relationships are represented
as edges (or ties)
• (Relationships may be
acquaintanceship, friendship,
co-authorship, etc.)
• Allows analysis using tools of
mathematical graph theory
5. 5
Timeline / history of networks
(based on Freeman, 2000)
• 1736: Euler's paper on “Seven Bridges of Königsberg” ?
• 1937: J.L. Moreno pioneered sociometry
• Sociogram
• 1948: A. Bavelas established the group networks laboratory at MIT
• Centrality
• 1949: A. Rapaport developed a probability based model of information flow
• 50s and 60s: Social Networks studied by researchers in graph theory
• Cohesion, power, cooperation, triads (a.o. Harary et al. 1950s).
• 70s: Field of social network analysis emerged.
• New features in graph theory – more general structural models
• Better computer power – analysis of complex relational data sets
6. What is an industry or interfirm network?
• repeated, enduring exchange
relations with one another and, at the same time, lack a legitimate
organizational authority to arbitrate and resolve disputes that may
arise during the exchange.
Podolny and Page (1998: 59)
7. What is a business or intrafirm network?
A collection of individuals, teams or business units
repeated, enduring exchange relations with one another.
Knowledge exchanged trough a shared social context. Intra
organizational networks facilitate the creation of new
knowledge within organizations (e.g., Kogut &
Zander, 1992; Tsai, 2000; Tsai, 2001)
11. An example of a modern network:
9-11 Hijackers Network
SOURCE: Valdis Krebs
http://www.orgnet.com/
12. 12
Building blocks of an inter/ intra firm network
• Abstract level:
- Nodes
- Ties
• Interorganizational network (between firms)
- Firm level
- Examples: alliances, long-term buyer-supplier relationships
- Relationship is a connection between two firms that can be used
to transfer both tangible and intangible resources such as assets,
knowledge, money, and information.
• Intraorganizational network (within a firm)
- Employees
- Formal, informal
- Advice relationships, innovation, gossip, daily routines/ tasks
- Mandated, unmandated
13. Transaction cost economics (Williamson)
Adopts an undersocialized view of human being
• Human being as an atomistic entity
• Human beings are bounded rational
• Risk of moral hazard
• Risk of opportunistic behavior
Sociology
Adopts an oversocialized view of human being
• Environment determines human behavior
• No room for individual discretion
Economic Sociology (Granovetter, Uzzi)
Adopts an embeddedness perspective
• Economic relationships are embedded in social relationships
• Environment constrains humans but there is room for agency
Networks as alternative lens to the firm
14. Comparing markets, hierarchies and
networks (Powell,1990)
Governance forms
Key features Market Hierarchy Network
Normative basis Contracts / property
rights
Employment
relationship /
authority
Complementary
strengths
Means of
communication
Prices Routines Relational
Degree of
flexibility
High Low Medium
Commitment Low Medium to high Medium to high
Actor choices Independent Dependent Interdependent
15. 15
A network perspective to the firm
• Roots in graph theory
• Network is stored in a
matrix
A B C D E F G H I J
A 1 0 0 1 1 1 1 0 0 1
B 0 0 1 1 1 1 0 1 0 1
C 1 1 0 0 1 1 1 0 0 1
D 0 1 0 0 1 1 1 1 1 0
E 0 0 0 0 0 0 1 0 0 1
F 1 0 0 1 0 1 1 0 0 0
G 1 0 1 0 1 1 0 0 0 0
H 0 1 0 1 1 0 0 1 0 0
I 0 0 0 0 1 1 1 0 0 0
J 0 1 1 1 0 0 1 0 0 0
16. In general, a relation can be: (1) Binary or Valued (2) Directed or Undirected
a
b
c e
d
Undirected, binary Directed, binary
a
b
c e
d
a
b
c e
d
Undirected, Valued Directed, Valued
a
b
c e
d
1 3
4
21
Alright, so where to start?
The value (and direction) of a tie
17. 17
Why does it matter?
Different perspectives to study a network
• Structural embeddedness
- Looks at the quantity and configuration of interfirm relationships
-
(Structure – Conduct – Performance)
- Ignores firm/ individual characteristics
• Relational embeddedness
- Looks at the quality and contents of interfirm relationships
- Interfirm relationships are viewed as source of competitive
advantage/ intra firm relationships as source of innovation
- Invisible
- Causal ambiguous
- Inimitable
19. Structural embeddedness terminology
• Network structure: the collection of actors and their relationships at any
given point in time.
• Network position: the pattern of relations to and from an actor within a
network structure.
Burt (1980: 893)
20. Degree: most likely to influence and be influenced directly
Closeness: most likely to find out first
Betweenness: most likely to broker and synthesize diverse info
Bonachich power: When your centrality depends on your neighbors’
centrality
20
indegree
In each of the following networks, X has higher centrality than Y according to
a particular measure:
outdegree betweenness closeness
Centrality measures
22. 22
When degree is not everything
In what ways does degree fail to capture centrality in the
following graphs?
• ability to broker between groups
• likelihood that information originating anywhere in the network reaches you
23. 23
Betweenness
• Intuition: how many pairs of individuals would have to go through you in order
to reach one another in the minimum number of steps?
• who has higher betweenness, X or Y?
XY
24. 24
• degree
- number of connections
- denoted by size
• closeness
- length of shortest path to all
others
- denoted by color
How closely do degree and betweenness
correspond to closeness?
25. Extreme diversity
channel of broad and
diverse information
Combination
diverse ties provide the
perspective at which
knowledge held in
specialized parts
can be interpreted
Extreme similarity
repository of high-
quality, specialized
information
Relational embeddedness
Diversity vs. similarity (ter Wal 2013)
26. 26
Brokerage
• Centrality only captures part of knowledge brokering
• Centrality does not take division membership of the nodes into account
• Different brokerage roles exist . . .
Gould + Fernandez (1989):
(1) coördinator (2) gatekeeper (3) representative (4) itinerant broker (5) Liaison
Same centrality, different roles
27. Conducting a social network analysis in
the context of the firm
• Identify a Strategically Important Community
– Channeling creative ideas towards market ready innovations
– Integrating networks that cross core processes
– Facilitating post-merger integration and large-scale
organizational change
– Supporting communities of practice
– Identifying change agents for a reorganization to come
– Forming strategic partnerships and alliances
– Improving learning and decision making in top leadership
networks
– Crowd sourcing
– Building political cloud
... Each benefits from a particular form of network configuration
28. Assess meaningful relationships and network
constructs that connect and define these communities
• Relationships that reveal collaboration in a network
e.g., Communication, Information, Problem solving, Innovation
• Relationships that reveal information sharing potential
e.g., access, blockades
• Relationships that reveal rigidity in a network
e.g., decision making, influence, interdependencies
• Relationships that reveal well-being and supportiveness in a network
e.g., liking, friendship, trust
29. How to get to this kind of data
Network survey procedure
• Roster vs snowball method
• Snowball sampling method:
- Useful when boundaries of the network cannot be determined a priori /
particularly relevant for knowledge sharing
- Initial round of 8-10 ‘seeds’ (with different backgrounds); network
measures collected via interviews
- All contacts mentioned by first-round respondents become ‘targets’ for
the second round
- electronic network survey asking them about their network contacts
- Second-round targets: same thing until boundary is reached
- Y-round targets already included or only peripherally involved in the
theme)
29
30. Possible name generator questions (individual level)
30
Source: Aalbers, H.L., Dolfsma. W. Koppius, O. (2013). Rich Ties and Innovative Knowledge Transfer within a Firm. British Journal of Management, DOI:10.1111/1467-8551.12040
Business unit level example: Which units provide your unit with new knowledge or expertise
when your unit is seeking technical advice inside your organization?" (Tsai 2001)
31. The innovation network
Source: Aalbers, Dolfsma & Koppius 2014
So we got the network(s) and the key concepts...
Now what?
32. Combining network methodology into
relevant propositions
Possible angles
Hierarchy
Diversity
Multiplexity
• Actor attributes
• Brokerage
• Longitudinality
• Interventions
• Multi level networks
32
33. Horizontal cross unit ties
• The ties that team members have directly with other organization members
across unit boundaries.
Advantages
• Access to alternative ideas and insights relevant for a firm’s existing strategy, goals,
interests, time horizon, core values and emotional tone (Sethia 1995; Floyd and
Lane 2000).
• Creativity (Burt 2004).
• Complementary functional expertise (Aalbers et al. 2013; Haas 2010; March 1991).
• Team anticipation and prevention of potential weaknesses in technical and
marketing solutions (Leenders et al. 2003).
• Project performance (Cohen and Levinthal 1990; Obstfeld 2005; Tortoriello and
Krackhardt 2010).
33
34. Vertical cross hierarchical ties
• The ties that the team maintains with organization members at higher
hierarchical levels (Jaworski and Kohli 1993; Sheremata 2000).
- Received limited attention – with focus on the project team leader specifically
(Shim and Lee 2001)
Advantages
• Access to higher status positions brings:
- Desirable resources (e.g. funding, prestige, power) (Pfeffer & Salancik, 1978)
- Positive publicity
- Managerial attention & championing (Markham 1998)
- Legitimacy (Brass, 1984; Cross, Rice & Parker 2001; Feldman & March,
1981).
- Blocking off competing projects (Kijkuit & Van den Ende 2007).
- Perspective of how the team output fits in the overall firms objectives and goals
- Stocktaking of what is relevant within the rest of the organization (Hansen et al.
2001; Subramaniam and Youndt 2005; Mom et al. 2009).
34
38. Informal ties matter for knowledge sharing
39
• Informal networks: “interpersonal relationships in the
organization that affect decisions within it, but are either
omitted from the formal scheme or are not consistent with
that scheme”(Simon, 1976, p.148)
- Informal ties are discretionary and emergent (Monge & Contractor,
2001)
- Affective component stronger than instrumental component
(Ibarra, 1993)
- Primary basis for formation of interpersonal trust, which is
necessary for knowledge transfer (Szulanski et al., 2004)
Source: BJoM - Aalbers, Dolfsma & Koppius 2013
39. 40
Formal ties matter for knowledge sharing
• Formal networks: “the planned structure for an
organization”(Simon, 1976, p.147)
- Formal ties are designed or mandated by corporate management
(Monge & Contractor, 2001)
- Not just the org chart, also includes ‘quasi-structures’ such as
committees, task forces, teams and other workflow relations
mandated by the firm (Schoonhoven & Jellinek, 1990)
- Instrumental component stronger than affective component (Ibarra,
1993)
- Builds shared understanding (Gabarro, 1990; Tiwari, Koppius & van Heck, 2011)
and relative absorptive capacity (Lane & Lubatkin, 1998) as basis for more
complex knowledge transfer
Source: BJoM - Aalbers, Dolfsma & Koppius 2013
40. 41
Multiplex ties matter for knowledge sharing
• Multiplexity: Combination of multiple relational contents in a
single tie (Burt 1983; Ibarra, 1993; Rank et al., 2010)
- Ties in an organization are not either formal or informal, many
are a combination of the two, i.e. multiplex ties. (Gulati & Puranam,
2009)
- Multiplex ties are qualitatively different: more intimate (Minor,
1983), more stable (Ibarra, 1995), reduce uncertainty (Albrecht & Ropp,
1984), more supportive (McAllister, 1995) and improve performance
(Roberts & O’Reilly, 1989)
- Multiplex ties create transfer synergy between willingness and
ability: shared understanding from formal ties (ability) and trust
from informal ties (willingness) (Hansen, 2001)
Source: BJoM - Aalbers, Dolfsma & Koppius 2013
41. When studying networks in knowledge sharing, we
need to be aware about what is really driving the
results...
• Formal networks matter at least as much as informal
networks
• Multiplex ties matter much more than just formal or
informal ties
• Most results ascribed to informal networks should
probably be ascribed to multiplex networks instead
42
M
Pure
F
Pure
I
Source: BJoM - Aalbers, Dolfsma & Koppius 2013
42. Measurement 1 – Summer time
Innovation Innovation
Measurement 2 – Winter time
Source: Aalbers 2012
An example of network intervention – network
expansion at a financial services firm
43. Wrap up part 1 – core concepts and relevance
» Social network analysis is a different way of looking at
organization structures
» Networks exist on different levels, which intertwine – thereby
creating different layers to analyse and influence an
organisations performance
» Network analysis can help in multiple contexts, including
R&D/ innovation, process redesign and reorganisations
» Network modeling helps in simplifying complex
relations
» Different modes of analysis can be identified;
including roles, behavior, clustering, and affiliation
» Measuring the behavior of a network requires both statistic as
well as organisational process knowledge
» A common methodology is needed to secure an objective analysis
» Networks can be altered – governing is an option
» SNA is fun!
45. Objective of today – Part 2
• Introduction to social network analysis,
including:
• Relevance
• Core concepts
• Core methodology
• Main tools and visualization (Ucinet)
• Large online networks
• Reflection on future research possibilities
• Wrap up
46
46. Propositions for discussion
47
Certain network positions offer an advantageous opportunity
structure, but whether this opportunity is seized, depends on the
motivation of the actor (Burt, 2010)