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Knowledge Spillovers in the Open Source CommunityEvidence in Github
Tong Wang
1Business SchoolUniversity of Edinbugh
Toulouse Digital Seminar, 2017
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 1 / 21
MotivationKnowledge Transfer
Knowledge transfer is prevalence in academia and programmingcommunity
Product development in community-based organisations is becomingincreasingly important, e.g. quite a few commercial softwares havetheir open-source versions.
There are basically two ways of learning:
Interaction with individuals (e.g. email exchange, brain storm, etc.)Read and study other good projects (e.g. read academic papers, etc.)
Learning (knowledge spillover) could also be direct or indirect.
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 2 / 21
MotivationKnowledge Transfer
Knowledge transfer is prevalence in academia and programmingcommunity
Product development in community-based organisations is becomingincreasingly important, e.g. quite a few commercial softwares havetheir open-source versions.
There are basically two ways of learning:
Interaction with individuals (e.g. email exchange, brain storm, etc.)Read and study other good projects (e.g. read academic papers, etc.)
Learning (knowledge spillover) could also be direct or indirect.
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 2 / 21
MotivationKnowledge Transfer
Knowledge transfer is prevalence in academia and programmingcommunity
Product development in community-based organisations is becomingincreasingly important, e.g. quite a few commercial softwares havetheir open-source versions.
There are basically two ways of learning:
Interaction with individuals (e.g. email exchange, brain storm, etc.)Read and study other good projects (e.g. read academic papers, etc.)
Learning (knowledge spillover) could also be direct or indirect.
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 2 / 21
MotivationKnowledge Transfer
Knowledge transfer is prevalence in academia and programmingcommunity
Product development in community-based organisations is becomingincreasingly important, e.g. quite a few commercial softwares havetheir open-source versions.
There are basically two ways of learning:
Interaction with individuals (e.g. email exchange, brain storm, etc.)
Read and study other good projects (e.g. read academic papers, etc.)
Learning (knowledge spillover) could also be direct or indirect.
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 2 / 21
MotivationKnowledge Transfer
Knowledge transfer is prevalence in academia and programmingcommunity
Product development in community-based organisations is becomingincreasingly important, e.g. quite a few commercial softwares havetheir open-source versions.
There are basically two ways of learning:
Interaction with individuals (e.g. email exchange, brain storm, etc.)Read and study other good projects (e.g. read academic papers, etc.)
Learning (knowledge spillover) could also be direct or indirect.
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 2 / 21
MotivationKnowledge Transfer
Knowledge transfer is prevalence in academia and programmingcommunity
Product development in community-based organisations is becomingincreasingly important, e.g. quite a few commercial softwares havetheir open-source versions.
There are basically two ways of learning:
Interaction with individuals (e.g. email exchange, brain storm, etc.)Read and study other good projects (e.g. read academic papers, etc.)
Learning (knowledge spillover) could also be direct or indirect.
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 2 / 21
Motivation(Cont’d)
Factors that affect the effectiveness and efficiency of learningprocedure remain largely unexplored.
In a UGC(User generating Content)-like open-source community, dothe ways of learning change?
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 3 / 21
Motivation(Cont’d)
Factors that affect the effectiveness and efficiency of learningprocedure remain largely unexplored.
In a UGC(User generating Content)-like open-source community, dothe ways of learning change?
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 3 / 21
Literature Review
Grewal, Lilien, and Mallapragada (2006) investigates how the networkembeddedness of projects and project managers influences the successof projects;
Goyal, van der Leij, and Moraga-Gonzalez (2006) studies the networkproperty of coauthorship network in economics;
Manski (2000), Sacerdote (2001), and Angrist and Lang (2004) studyneighborhood effects and spillovers in many other aspects such aslabor and education.
Fershtman and Gandal (2011) studies knowledge spillover in anenvironment where the knowledge producers and knowledgeconsumers are different.
Neil Gandal and Uriel Stettner (2016) evaluates the importance ofprogram modification and fuction additional to project success.
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 4 / 21
Literature Review
Grewal, Lilien, and Mallapragada (2006) investigates how the networkembeddedness of projects and project managers influences the successof projects;
Goyal, van der Leij, and Moraga-Gonzalez (2006) studies the networkproperty of coauthorship network in economics;
Manski (2000), Sacerdote (2001), and Angrist and Lang (2004) studyneighborhood effects and spillovers in many other aspects such aslabor and education.
Fershtman and Gandal (2011) studies knowledge spillover in anenvironment where the knowledge producers and knowledgeconsumers are different.
Neil Gandal and Uriel Stettner (2016) evaluates the importance ofprogram modification and fuction additional to project success.
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 4 / 21
Literature Review
Grewal, Lilien, and Mallapragada (2006) investigates how the networkembeddedness of projects and project managers influences the successof projects;
Goyal, van der Leij, and Moraga-Gonzalez (2006) studies the networkproperty of coauthorship network in economics;
Manski (2000), Sacerdote (2001), and Angrist and Lang (2004) studyneighborhood effects and spillovers in many other aspects such aslabor and education.
Fershtman and Gandal (2011) studies knowledge spillover in anenvironment where the knowledge producers and knowledgeconsumers are different.
Neil Gandal and Uriel Stettner (2016) evaluates the importance ofprogram modification and fuction additional to project success.
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 4 / 21
Literature Review
Grewal, Lilien, and Mallapragada (2006) investigates how the networkembeddedness of projects and project managers influences the successof projects;
Goyal, van der Leij, and Moraga-Gonzalez (2006) studies the networkproperty of coauthorship network in economics;
Manski (2000), Sacerdote (2001), and Angrist and Lang (2004) studyneighborhood effects and spillovers in many other aspects such aslabor and education.
Fershtman and Gandal (2011) studies knowledge spillover in anenvironment where the knowledge producers and knowledgeconsumers are different.
Neil Gandal and Uriel Stettner (2016) evaluates the importance ofprogram modification and fuction additional to project success.
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 4 / 21
Literature Review
Grewal, Lilien, and Mallapragada (2006) investigates how the networkembeddedness of projects and project managers influences the successof projects;
Goyal, van der Leij, and Moraga-Gonzalez (2006) studies the networkproperty of coauthorship network in economics;
Manski (2000), Sacerdote (2001), and Angrist and Lang (2004) studyneighborhood effects and spillovers in many other aspects such aslabor and education.
Fershtman and Gandal (2011) studies knowledge spillover in anenvironment where the knowledge producers and knowledgeconsumers are different.
Neil Gandal and Uriel Stettner (2016) evaluates the importance ofprogram modification and fuction additional to project success.
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 4 / 21
Contribution
Empirically examine the association between projectsuccess/popularity and network measures in a situation whereknowledge producers and consumers are the same group
Identify the importance of Activity and Effort in the learning process
Throw some light on social learning process.
One of the First attempts to use big data approach to analyzeknowledge spillover.
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 5 / 21
Contribution
Empirically examine the association between projectsuccess/popularity and network measures in a situation whereknowledge producers and consumers are the same group
Identify the importance of Activity and Effort in the learning process
Throw some light on social learning process.
One of the First attempts to use big data approach to analyzeknowledge spillover.
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 5 / 21
Contribution
Empirically examine the association between projectsuccess/popularity and network measures in a situation whereknowledge producers and consumers are the same group
Identify the importance of Activity and Effort in the learning process
Throw some light on social learning process.
One of the First attempts to use big data approach to analyzeknowledge spillover.
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 5 / 21
Contribution
Empirically examine the association between projectsuccess/popularity and network measures in a situation whereknowledge producers and consumers are the same group
Identify the importance of Activity and Effort in the learning process
Throw some light on social learning process.
One of the First attempts to use big data approach to analyzeknowledge spillover.
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 5 / 21
Data Source
Github offers a unique opportunity to examine our researchquestions
Github is a social coding platform
Github is the world largest code hosting and open-source development
platform
Every action on Github is recorded and thus is obtainable.
“fork” is a good measure for popularity.
More importantly, the network structure of Github is quite clear
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 6 / 21
Data Source
Github offers a unique opportunity to examine our researchquestions
Github is a social coding platform
Github is the world largest code hosting and open-source development
platform
Every action on Github is recorded and thus is obtainable.
“fork” is a good measure for popularity.
More importantly, the network structure of Github is quite clear
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 6 / 21
Data Source
Github offers a unique opportunity to examine our researchquestions
Github is a social coding platform
Github is the world largest code hosting and open-source development
platform
Every action on Github is recorded and thus is obtainable.
“fork” is a good measure for popularity.
More importantly, the network structure of Github is quite clear
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 6 / 21
Data Source
Github offers a unique opportunity to examine our researchquestions
Github is a social coding platform
Github is the world largest code hosting and open-source development
platform
Every action on Github is recorded and thus is obtainable.
“fork” is a good measure for popularity.
More importantly, the network structure of Github is quite clear
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 6 / 21
Data Source
Github offers a unique opportunity to examine our researchquestions
Github is a social coding platform
Github is the world largest code hosting and open-source development
platform
Every action on Github is recorded and thus is obtainable.
“fork” is a good measure for popularity.
More importantly, the network structure of Github is quite clear
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 6 / 21
A sample page of a project on Github
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 7 / 21
Data Sources
We obtained our data by SQL Querying Github Archive and networkspidering.
All projects and contributor information before Jun. 2016 wasretrieved and stored in the spider server, the raw data is 300G +.
The contributors and projects are identified by unique IDs.
Project is linked to at least one contributor. Contributors areconnected by their joint projects, so finally we get a two-modenetwork.
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 8 / 21
Data Sources
We obtained our data by SQL Querying Github Archive and networkspidering.
All projects and contributor information before Jun. 2016 wasretrieved and stored in the spider server, the raw data is 300G +.
The contributors and projects are identified by unique IDs.
Project is linked to at least one contributor. Contributors areconnected by their joint projects, so finally we get a two-modenetwork.
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 8 / 21
Data Sources
We obtained our data by SQL Querying Github Archive and networkspidering.
All projects and contributor information before Jun. 2016 wasretrieved and stored in the spider server, the raw data is 300G +.
The contributors and projects are identified by unique IDs.
Project is linked to at least one contributor. Contributors areconnected by their joint projects, so finally we get a two-modenetwork.
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 8 / 21
Data Sources
We obtained our data by SQL Querying Github Archive and networkspidering.
All projects and contributor information before Jun. 2016 wasretrieved and stored in the spider server, the raw data is 300G +.
The contributors and projects are identified by unique IDs.
Project is linked to at least one contributor. Contributors areconnected by their joint projects, so finally we get a two-modenetwork.
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 8 / 21
Network Illustration
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 9 / 21
Descriptive Statistics: Observations
Most of the Github Projects only have 1 contributor, most of thecontributors only have 0 or 1 project.
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 10 /
21
Descriptive Statistics: Observations(Cont’d)
Most of the projects raise little attention. Being popular is generally verydifficult in the Open-Source Community.
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 11 /
21
The Project Network
The nodes of this network areprojects.
There is a link between twodifferent project nodes if thereare contributors who participatein both projects.
Each link may have a valuewhich reflects the number ofcontributors who participate inboth projects.
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 12 /
21
The Project Network
The nodes of this network areprojects.
There is a link between twodifferent project nodes if thereare contributors who participatein both projects.
Each link may have a valuewhich reflects the number ofcontributors who participate inboth projects.
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 12 /
21
The Project Network
The nodes of this network areprojects.
There is a link between twodifferent project nodes if thereare contributors who participatein both projects.
Each link may have a valuewhich reflects the number ofcontributors who participate inboth projects.
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 12 /
21
Varibles
Popularity/Success Measurement: the number of forks
Activity: No commit during the last 18 months.
Node degree: measurement of the direct effect, the degree is literallythe number of projects with which has a direct link
Closeness Centrality as the measure of indirect effect, define closenesscentrality as C (i) = N−1
Σj∈Nd(i ,j)
Existing Period: the number of years that have elapsed since theproject first appeared
Number of Contributors: the number of contributors who participatedin the project
Popular Language: The most popular 5 languages used in TIOBEIndex
Number of Comments: A proxy variable for the effort of contributors
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 13 /
21
Varibles
Popularity/Success Measurement: the number of forks
Activity: No commit during the last 18 months.
Node degree: measurement of the direct effect, the degree is literallythe number of projects with which has a direct link
Closeness Centrality as the measure of indirect effect, define closenesscentrality as C (i) = N−1
Σj∈Nd(i ,j)
Existing Period: the number of years that have elapsed since theproject first appeared
Number of Contributors: the number of contributors who participatedin the project
Popular Language: The most popular 5 languages used in TIOBEIndex
Number of Comments: A proxy variable for the effort of contributors
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 13 /
21
Varibles
Popularity/Success Measurement: the number of forks
Activity: No commit during the last 18 months.
Node degree: measurement of the direct effect, the degree is literallythe number of projects with which has a direct link
Closeness Centrality as the measure of indirect effect, define closenesscentrality as C (i) = N−1
Σj∈Nd(i ,j)
Existing Period: the number of years that have elapsed since theproject first appeared
Number of Contributors: the number of contributors who participatedin the project
Popular Language: The most popular 5 languages used in TIOBEIndex
Number of Comments: A proxy variable for the effort of contributors
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 13 /
21
Varibles
Popularity/Success Measurement: the number of forks
Activity: No commit during the last 18 months.
Node degree: measurement of the direct effect, the degree is literallythe number of projects with which has a direct link
Closeness Centrality as the measure of indirect effect, define closenesscentrality as C (i) = N−1
Σj∈Nd(i ,j)
Existing Period: the number of years that have elapsed since theproject first appeared
Number of Contributors: the number of contributors who participatedin the project
Popular Language: The most popular 5 languages used in TIOBEIndex
Number of Comments: A proxy variable for the effort of contributors
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 13 /
21
Varibles
Popularity/Success Measurement: the number of forks
Activity: No commit during the last 18 months.
Node degree: measurement of the direct effect, the degree is literallythe number of projects with which has a direct link
Closeness Centrality as the measure of indirect effect, define closenesscentrality as C (i) = N−1
Σj∈Nd(i ,j)
Existing Period: the number of years that have elapsed since theproject first appeared
Number of Contributors: the number of contributors who participatedin the project
Popular Language: The most popular 5 languages used in TIOBEIndex
Number of Comments: A proxy variable for the effort of contributors
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 13 /
21
Varibles
Popularity/Success Measurement: the number of forks
Activity: No commit during the last 18 months.
Node degree: measurement of the direct effect, the degree is literallythe number of projects with which has a direct link
Closeness Centrality as the measure of indirect effect, define closenesscentrality as C (i) = N−1
Σj∈Nd(i ,j)
Existing Period: the number of years that have elapsed since theproject first appeared
Number of Contributors: the number of contributors who participatedin the project
Popular Language: The most popular 5 languages used in TIOBEIndex
Number of Comments: A proxy variable for the effort of contributors
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 13 /
21
Varibles
Popularity/Success Measurement: the number of forks
Activity: No commit during the last 18 months.
Node degree: measurement of the direct effect, the degree is literallythe number of projects with which has a direct link
Closeness Centrality as the measure of indirect effect, define closenesscentrality as C (i) = N−1
Σj∈Nd(i ,j)
Existing Period: the number of years that have elapsed since theproject first appeared
Number of Contributors: the number of contributors who participatedin the project
Popular Language: The most popular 5 languages used in TIOBEIndex
Number of Comments: A proxy variable for the effort of contributors
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 13 /
21
Varibles
Popularity/Success Measurement: the number of forks
Activity: No commit during the last 18 months.
Node degree: measurement of the direct effect, the degree is literallythe number of projects with which has a direct link
Closeness Centrality as the measure of indirect effect, define closenesscentrality as C (i) = N−1
Σj∈Nd(i ,j)
Existing Period: the number of years that have elapsed since theproject first appeared
Number of Contributors: the number of contributors who participatedin the project
Popular Language: The most popular 5 languages used in TIOBEIndex
Number of Comments: A proxy variable for the effort of contributors
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 13 /
21
Illustration of Closeness Centrality
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 14 /
21
Preliminary Exploration: Neural Networks
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 15 /
21
Basic Model Specification
Assume the initial success level is α and the final success level is Si .
If direct spillover effect makes sense, then a project with a higherdegree is more likely to be successful. That is, in the regressionfunction Si = α + βDi ; βshould be significant.
If indirect spillover effect makes sense, then the final success levelshould be:
Si = α +∑j
γ
d(i , j)+ βDi
since it is natural to assume that longer distance implies lowerimpact. But since we know C (i) = N−1
Σj∈Nd(i ,j) , so the equation above
can be transformed to :
Si = α +γ
N − 1C (i) + βDi
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 16 /
21
Basic Model Specification
Assume the initial success level is α and the final success level is Si .
If direct spillover effect makes sense, then a project with a higherdegree is more likely to be successful. That is, in the regressionfunction Si = α + βDi ; βshould be significant.
If indirect spillover effect makes sense, then the final success levelshould be:
Si = α +∑j
γ
d(i , j)+ βDi
since it is natural to assume that longer distance implies lowerimpact. But since we know C (i) = N−1
Σj∈Nd(i ,j) , so the equation above
can be transformed to :
Si = α +γ
N − 1C (i) + βDi
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 16 /
21
Basic Model Specification
Assume the initial success level is α and the final success level is Si .
If direct spillover effect makes sense, then a project with a higherdegree is more likely to be successful. That is, in the regressionfunction Si = α + βDi ; βshould be significant.
If indirect spillover effect makes sense, then the final success levelshould be:
Si = α +∑j
γ
d(i , j)+ βDi
since it is natural to assume that longer distance implies lowerimpact. But since we know C (i) = N−1
Σj∈Nd(i ,j) , so the equation above
can be transformed to :
Si = α +γ
N − 1C (i) + βDi
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 16 /
21
Main results: Basic Model
Table: Effect of Activity
Dependent variable:
lfork
(1) (2)
lCPP 0.208∗∗∗ (0.002) 0.203∗∗∗ (0.002)lyear −0.929∗∗∗ (0.005) −0.920∗∗∗ (0.005)activity 0.339∗∗∗ (0.003) 0.146∗∗∗ (0.006)ldegree 0.082∗∗∗ (0.001) 0.036∗∗∗ (0.001)lcloseness −0.106∗∗∗ (0.020) −0.057∗ (0.031)lNO Comments 0.312∗∗∗ (0.002) 0.303∗∗∗ (0.002)popular 0.019∗∗∗ (0.002) 0.019∗∗∗ (0.002)activity:ldegree 0.074∗∗∗ (0.002)activity:lcloseness −0.013 (0.041)Constant 2.687∗∗∗ (0.009) 2.783∗∗∗ (0.010)
Observations 691,582 691,582R2 0.251 0.254Adjusted R2 0.251 0.254Residual Std. Error 0.914 (df = 691574) 0.912 (df = 691572)F Statistic 33,077.240∗∗∗ (df = 7; 691574) 26,125.280∗∗∗ (df = 9; 691572)
Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 17 /
21
Main results(Cont’d)
Table: Effects of Effort
Dependent variable:
lfork
(1) (2)
lCPP 0.208∗∗∗ (0.002) 0.197∗∗∗ (0.002)lyear −0.929∗∗∗ (0.005) −0.917∗∗∗ (0.005)activity 0.339∗∗∗ (0.003) 0.342∗∗∗ (0.003)ldegree 0.082∗∗∗ (0.001) 0.066∗∗∗ (0.001)lcloseness −0.106∗∗∗ (0.020) −0.056∗∗∗ (0.021)lNO Comments 0.312∗∗∗ (0.002) −0.048∗∗∗ (0.008)popular 0.019∗∗∗ (0.002) 0.019∗∗∗ (0.002)ldegree:lNO Comments 0.047∗∗∗ (0.001)lNO Comments:lcloseness 0.782∗∗∗ (0.047)Constant 2.687∗∗∗ (0.009) 2.709∗∗∗ (0.009)
Observations 691,582 691,582R2 0.251 0.256Adjusted R2 0.251 0.256Residual Std. Error 0.914 (df = 691574) 0.910 (df = 691572)F Statistic 33,077.240∗∗∗ (df = 7; 691574) 26,508.230∗∗∗ (df = 9; 691572)
Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 18 /
21
Discussion
A longer exposure helps.
More contributors, more forks.
Direct project spillover effect is significantly positive.
Knowledge spillover from inactive projects to active projects.
Manager’s effort is crucial to activate indirect spillovers.
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 19 /
21
Discussion
A longer exposure helps.
More contributors, more forks.
Direct project spillover effect is significantly positive.
Knowledge spillover from inactive projects to active projects.
Manager’s effort is crucial to activate indirect spillovers.
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 19 /
21
Discussion
A longer exposure helps.
More contributors, more forks.
Direct project spillover effect is significantly positive.
Knowledge spillover from inactive projects to active projects.
Manager’s effort is crucial to activate indirect spillovers.
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 19 /
21
Discussion
A longer exposure helps.
More contributors, more forks.
Direct project spillover effect is significantly positive.
Knowledge spillover from inactive projects to active projects.
Manager’s effort is crucial to activate indirect spillovers.
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 19 /
21
Discussion
A longer exposure helps.
More contributors, more forks.
Direct project spillover effect is significantly positive.
Knowledge spillover from inactive projects to active projects.
Manager’s effort is crucial to activate indirect spillovers.
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 19 /
21
Concluding Remarks
the knowledge distribution pattern on Social Coding platform is quitedifferent with that on traditional open source community.
indirect spillovers are generally weaker.
repo manager’s effort can make a lot of difference.
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 20 /
21
Concluding Remarks
the knowledge distribution pattern on Social Coding platform is quitedifferent with that on traditional open source community.
indirect spillovers are generally weaker.
repo manager’s effort can make a lot of difference.
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 20 /
21
Concluding Remarks
the knowledge distribution pattern on Social Coding platform is quitedifferent with that on traditional open source community.
indirect spillovers are generally weaker.
repo manager’s effort can make a lot of difference.
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 20 /
21
Future works
compare spillover effects of strong connections and weak connections
Explore the effect of bot-contributor
adding more control variables, such as project size and projectlanguage
Try other measurement of success
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 21 /
21
Future works
compare spillover effects of strong connections and weak connections
Explore the effect of bot-contributor
adding more control variables, such as project size and projectlanguage
Try other measurement of success
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 21 /
21
Future works
compare spillover effects of strong connections and weak connections
Explore the effect of bot-contributor
adding more control variables, such as project size and projectlanguage
Try other measurement of success
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 21 /
21
Future works
compare spillover effects of strong connections and weak connections
Explore the effect of bot-contributor
adding more control variables, such as project size and projectlanguage
Try other measurement of success
Tong Wang (Universities of Edinburgh) Knowledge Spillovers in the Open Source CommunityToulouse Digital Seminar, 2017 21 /
21
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