A S ocial Capit al Perspect ive of Participant Contribution in Open - PowerPoint PPT Presentation

Presentation for Singapore Management University A “ S ocial Capit al Perspect ive” of Participant Contribution in Open Source Communities –The Case of Linux Myong Rae (Ray) Chang, Ph.D. July 14, 2011

2 Agenda Thread-level LINUX & OSSD Dynamic Process OSS Development Process: Network Capital  Contribution Theoretical Motivation Challenges PLS Analyses Research Method Results Implications Model Four Dimensions of Network Content Network Capital Measures Analysis Limitations & Future Study

3 The Linux Kernel  Desktop/Laptop: , Server/Mainframe: , Supercomputer: 65% 92% 3%  Smartphone (Android): [rough averages from multiple sources, worldwide, 2010 ] 25% Linus Tovalds , Jan. 2011 “ Not j ust Android. What I’ ve f ound t hat has been most f un f or me has been when people are using Linux in ways t hat I don’t use it or in ways t hat I never int ended it t o be used, people using it in embedded areas , and wit h cellphones like Android but also all t he crazy people using it in print ers and TVs.”

4 Linux Open Source Community  The largest OSS development community  B eing evolved dynamically at every second  A “virtual” workplace open to any participants from any place in the world at any time (but only “ hackers” survive)  “ Open” to any contribution on “ voluntary” basis (note: many are now paid workers from Linux-related companies)  All “peer-reviewed” process : from ideas to codes & feedbacks  Driven by crowd wisdom, not by dedicated plans or profits  Highly technical and rigorous discussions: it should work!  Administered by “ Maintainers ” of numerous subsystems

5 Threaded Discussion  A hist ory of int ellect ual knowledge-exchanges on a single specific t opic http:/ / www.gossamer- threads.com/ lists/ linux/ kernel/ 655933

6 A Network Representation A Message-exchange “Network” of a Thread = a knowledge exchange network built on reply-to relations formed by the messages within a thread Node : each part icipant Link or tie : relat ion of in-reply-t o (wit h t he at t ribut e of message mult iplicit y) init iat or A: [RFC] CPU controllers? t ie st rengt h (mult i-link) B: Re: [RFC] CPU controllers? B A C: Re: [RFC] CPU controllers? D: Re: [RFC] CPU controllers? D B: Re: [RFC] CPU controllers? C Note: Although each message is “ broadcast” to all subscribers of the mailing list, only respondents to the message would take special meaning w.r.t . the topic; i.e., the network is a construction based on “ reciprocity ” relationship, a characteristics of online relationship.

7 Patterns of Thread Network Thread# # 6559 55933 Thread# # 376114 14 A “ signat ure ” of how t hey have communicat ed/ collaborat ed on a specific t ask Thread# # 91 913809

8 Evolution of A Thread Network Size rk S work Netwo Thread# # 91 913809 seed eed % M Messa ssages (Tim ime) e)

9 Dynamics of Network Measures 110% Relative Change of Network Measure 100% 90% 80% Density 70% Avg. Geodesic Distance 60% Degree Centralization 50% 40% Betweenness Centralization 30% 20% 10% 0% 10 20 30 40 50 60 70 80 90 100 % Messages

10 Evolution of A Thread Network Size We are int erest ed in rk S work “ early-st age” net work building Netwo t o influence t he t hread performance in t he lat er st ages % M Messa ssages Growi wing Stable le Mature re

11 Research Questions  Will the early-stage accumulation of network capital affect participant contribution in the later stage?  Then, what types of network capital will be associated with participant contribution in terms of quantity & quality?  During the lifecycle of OSSD, how can we help to elicit “ more” and “ better” participant contribution?

12 Theoretical Background  Social Capital Perspectives in Online Communities  S t ruct ural propert ies (posit ion, st ruct ure)  individual’s knowledge sharing behavior [Cross & Cummings 2004; Nerkar & Paruchuri, 2005]  Role of relat ional propert ies  learning and knowledge t ransfer [Hansen 1999; Uzzi & Lancast er 2003]  Various t ypes of social capit al(st ruct ural, cognit ive, relat ional)  knowledge sharing of individuals and groups [Wasko & Faj ar 2005 in elect ronic net works of pract ice; Kuk 2006 in an OS S communit y]  Theoretical Challenges  Fragment ed view vs. a “ comprehensive ” framework: a holist ic model int egrat ing various dimensions of social capit al  Extant isolated element-level approach (actor, dyadic link, ego-centric [Borgatti & Foster 2003]) vs. u nderst anding of act ors’ “ collective behavior ” : a net work (e.g., t hread) level approach  “ S t at ic” snapshot approach vs. “ Dynamic ” aspect of social capit al

13 Four Dimensions of Network Capital High Low Network Structural Capital - Network centralization Relational Capital Link - Network strength Node Governance Capital - Administrator participation Time Dynamic Capital - Network growing speed T=12 hrs T=6 hrs

14 Research Model: PLS Growing-stage positive Network Capital (1/3) negative Network H1a OSS Performance (2/3) Centralization H1b Contribution Code in H2a Quantity Initial Network Message Strength H2b H5 H3a Inhibiting Administrator Contribution Climate Participation H3b Quality H4a Network H4b Growing Speed

15 Hypotheses 1a/1b Contribution Structural Quantity H1a Capital Network Centralization H1b Contribution Quality vs.  Two Contrasting Views on Centralization  Discourages diverse views  Rapid diffusion of innovative knowledge vs.  Negative impact on creativity  Easy access to experts with lower cost  Reduces autonomy of participants  Integration of diverse ideas  In Thread-level Collaboration:  Benefits outweigh the costs from the lack of idea diversity  Hub-structure facilitates member contribution  Continuous review and feedback systems  Multiplicity of views and ideas more easily integrated

16 Hypotheses 2a/2b Contribution Relational Quantity H2a Capital Network Strength H2b Contribution Quality vs.  Tie Strength (link thickness)  Useful conduits for knowledge exchange (in many social network studies)  Essential for substantive contribution  Network-level Strength  A network is stronger if containing more strong ties.  Building a normative environment fostering collaboration and coordination  A sense of “ reciprocity” ensures continuing supportive exchanges and generating in-depth discussions.

17 Hypotheses 3a/3b Contribution Governance Quantity H3a Capital Admin Participation H3b Contribution Quality vs.  In Traditional Organization Settings  Leader’s involvement is effective for affective and continuance contributions.  S trong governance encourage self-concept-based motivation.  In OSS Communities  Admin’s tight control evokes a cathedral type of decision making structure.  Undermining participants’ autonomy and sense of ownership [von Krogh 2003].  Members do not participate when most work is conducted by a leader.

18 Hypotheses 4a/4b Contribution Dynamic Quantity H4a Capital Network Growing Speed H4b Contribution Quality vs. T=6 hrs T=12 hrs  Research on Interpersonal Communications  The quantity and quality of exchanged knowledge are highly associated with the rate at which knowledge is delivered [Carlson & Zmud 1999].  Faster responses allow receivers to act upon in a timely manner.  In Knowledge-intensive Online Communities  Level of detail or extensiveness is often more important than response time.  Rapid responses might decrease the perceived value of knowledge exchanged.  S low responses tend to be more rational and cognitive (faster ones be more emotional).  Developers place more weight on “ accuracy” : slow responses reduce uncertainty and equivocality by providing more complete knowledge [Weiss et al. 2006].

19 Hypotheses 5 and Controls Contribution Code in Initial Quantity Message H5 Inhibiting Contribution Climate Quality  Quantity  Quality  Controls  Increasing # of proposed ideas  Initial message with codes proposes  higher probability of quality ideas more concrete ideas to call for more  [Barki & Pinsonneault 2001] contribution [Roberts et al. 2006]  Inhibiting culture structurally prevent members’ contribution [Bogozzi & Dholakia 2006]

20 Method: Data Mining  RFC (Request For Comments) t hreads collect ed from archive  S uggest ion/ discussion/ collaborat ion on new innovat ive ideas & feat ures  6,852 RFC t hreads during Jan. 2000 ~ Jun. 2008 (over 90% of t hem had less t han 15 messages)  Used t hreads wit h enough(>25) messages  223 RFC threads  For each t hread, a mat ching set of a net work file and a t ext file of messages were creat ed (only original cont ent s used aft er removing quot es, program codes, et c.) LINUX Communit y 223 Net works (growing st age) Net work Comput ing Measures Measures C/ C++ C/ C++ 223 OS S 223 t ext files of RFC Threads LIWC Cont ribut ion whole messages