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									Introduction
 Explaining Innovation Involvement:
Social Networks, Personal Knowledge
 and the Tertius Iungens Orientation

          David Obstfeld
  Graduate School of Management
   University of California, Irvine

         ICOS Presentation
         December 5, 2003
         Agenda and Overview

1. Return to UM (M GO BLUE!)
2. Today’s study in the context of my
   research program on agency and
   innovation
3. Research context and setting
4. Research motivation, model, hypotheses
5. Methods and measures
6. Results, discussion, and next steps
             Research Motivation
• Successful innovation is crucial to the growth and
  competitiveness of many organizations
  (Schumpeter, 1942; Tushman & Moore, 1988).
• Despite extensive innovation literature, there are
  important opportunities to import and integrate
  perspectives that further illuminate the process
• The mechanisms associated with two critical
  innovation factors, social networks and
  knowledge, are underspecified.
     Agency, Knowledge, and Entrepreneurial
     Action: Issues in the Study of Innovation
1.    Innovation involvement (today’s presentation)
2.    Knowledge articulation
      - Social process of making knowledge more explicit or usable
      - Much more than tacit to explicit (specific articulation behaviors,
        characteristics, persuasion)
      - Continual articulation is central for product development in
        complex technology and design processes
3.    Innovation and knowledge creation as social movement
      - Innovation as collective action and knowledge as contested
4.    Projects and routines
      – Twyla Tharp (The Creative Habit), bracketing, and the
        phenomenological heritage of projects
      - Routines and projects as a means for explaining innovation and
        organizing
  Turning To The Paper at Hand
• The literature on innovation is broad and
  varied
  – Individual, group, organizational, industry
    levels
  – Diffusion, social networks, product champions,
    creativity, power and politics, situated
    knowledge processes
• Today, I will focus on what this literature
  says about involvement in innovation
         The Research Questions
• In the production of innovations, what are the
  social processes of knowledge creation and
  innovation? (fishing license)
• What factors predict the generation of
  innovation in a product development
  environment? (hunting license)
       Research Site Background
• Automotive design effort within an engineering
  division of a “Big Three” automotive manufacturer
• Approximately 450 employees distributed across
  seven main functions
• Key professional backgrounds: engineering and
  design
• Continual interaction with Marketing,
  Manufacturing, Operations, Styling, as well as
  external suppliers
• Field observation period roughly half way through
  5-year design effort
            Case Illustration:
         Design of Manual Shifter
• Observation of 5-month design effort
  including 25 team meetings and more than
  50 interviews
• Interaction of 29 individuals representing 7
  functional interests, other departments, and
  three suppliers
• Why case was selected:
  – intersection of multiple interests
  – introduction of a new innovation
  – new CAD-assisted simulation technology
    identifies design conflicts in multiple scenarios
        Manual Shifter Chronology
• Design “team” struggles to resolve major design
  conflict.
• Interior engineer introduces a new part design that
  is NOT accepted
• NVH engineer introduces new part innovation that
  IS adopted
• Designers master new design simulation and
  resolve design conflicts
Entrepreneurial engineer and designers employ
  skillful tertius iungens (mobilization) and
  knowledge articulation to drive the knowledge
  creation and innovation process.
     Background Theory & Literatures
• Diffusion, product champions, creativity, power and
  politics, situated knowledge processes
• How do social networks influence involvement in
  innovation?
   – Allen, 1977; Tushman, 1978; Burt, 1992, 1997; Podolny & Baron,
     1997; Coleman, 1988; Putnam, 1994; Ahuja, 2000; Ibarra, 1993
• What is the nature of organizational knowledge and how
  does it bear on innovation involvement?
   – Polanyi, 1958; 1960; Nelson & Winter, 1982; Nonaka & Takeuchi,
     1995; Kogut & Zander, 1992, 1995
• How do we describe innovation in terms of collective
  action at the microsocial level?
   – Simmel’s theory of triads; social movement theory (Snow, Zald,
     McAdams, Tilly); sociology of science (Latour, 1987); articulation
     work (Strauss et al., 1987); institutional entrepreneurship
     (Fligstein, 2001)
   Social Network Structure and
     Innovation Involvement
• Alternate theories of social capital (Adler,
  2002; Ahuja, 2000; Baker & Obstfeld,
  1999; Burt, 2002; Putnam, 2000)
• Sparse networks facilitated access to novel
  information
• Dense patterns of interaction facilitated
  extended knowledge sharing
• “Entrepreneurs” displayed both dense and
  sparse ties
            +
  SNP            Innovation Involvement
Hypothesis 1a: The greater the number of structural
holes (i.e., generally the lower the density) in an
individual’s social network, the greater the
individual’s innovation involvement.
Hypothesis 1b: The smaller the number of structural
holes (i.e., the higher the density) in an individual’s
social network, the greater the individual’s
innovation involvement.
     Personal Knowledge and
     Innovation Involvement
• Individual stocks of technical knowledge are
  a basis for technical contribution, absorptive
  capacity, and influence (Suzlanski 1996,
  Cohen & Levinthal, 1990)
• Social knowledge informs timing,
  positioning, and translations. (Dutton,
  Ashford, O’Neill, & Lawrence)
• Multiple indicators: social, technical,
  education, tenure
               +
Knowledge          Innovation Involvement

Hypothesis 2: The greater an individual’s
knowledge, the greater an individual’s
innovation involvement.
  The Other Tertius: Tertius Iungens
• Burt’s invocation of Simmel’s (1950) tertius gaudens (“the
  third who divides”) as logic for structural holes advantage
• Simmel also identifies the third party who acts as a
  mediator, or “non-partisan,” to create or preserve group
  unity
   – “The non-partisan either produces the concord of two colliding
     parties, whereby he withdraws after making the effort of creating
     direct contact between the unconnected or quarreling elements; or
     he functions as an arbiter who balances, as it were, their
     contradictory claims against one another and eliminates what is
     incompatible in them”        (Simmel 1950:146-147).
• Tertius Iungens orientation: behavior creating or
  facilitating ties among people in an actor’s social network.
 The Tertius Iungens Orientation
• From the latin “iungo” (YUNG-oh) meaning “to
  join,” “unite,” or “connect”
   – Early Latin: “to yoke, harness, or mate” or
     metaphorically to unite or to form as in a friendship.
   – Cicero: “Iungere amicitiam cum aliquo”: to form a
     friendship or alliance with another
• FKA as the “union orientation” (Baker &
  Obstfeld, 1999)
• Linking pins – individuals who belong to more
  than one group and link groups back to the rest of
  the organization (Likert)
     Three Types of Brokerage
• Actively maintaining separation between parties
  (TG)
• Introducing or facilitating ties between parties
  where a continuing coordinative role is
  unnecessary, diminishes in importance, or simply
  not offered (TI)
• Introducing or facilitating interaction between
  parties while maintaining an essential coordinative
  role (TI)
               Tertius Iungens In Action
“I created relationships with my direct reports and functional
areas. Frank with George Brown. Sally and Ted Welch. I
create links between my reports and … managers [from other …
areas] to [work together.] I work on both sides of that. Get my
people comfortable with that and get managers comfortable.
When a ball comes off the court, [the line managers are]
comfortable going to my program management person.”
                                           The G5 Program Manager
• Introductions of disconnected alters
• Introductions of previously acquainted alters with respect to a
  project
   – Project – a de novo unit of activity initiated and orchestrated by
     individuals or clusters of individuals to introduce new forms into a
     social context
         +
TI             Innovation Involvement

Hypothesis 3: The greater an individual’s
tertius iungens orientation, the greater the
individual’s innovation involvement.
    Summary: From the Literature,
       From the Field Study
From reading and UM training:
• Social structure/network matters but how tied to innovation
  and by what mechanism?
• Individual knowledge matters, but what kind and by what
  mechanism (e.g., translation, articulation, etc.)?
• Curiosity about facilitating/joining orientation
   – The Ron Burt at ICOS and the birth of TI from TG
• Curiosity about knowledge creation though unsatisfied with
  anecdotal depictions
• WB: “Go to the field.”
         Knowledge Creation/Innovation
                 Framework

    Social
Network Position

 Teritus Iungens
   Orientation                 Innovation
                              Involvement
  Individual
  Knowledge
         Working Model for Innovation
                Involvement

    Social
Network Position

 Tertius Iungens
   Orientation                 Innovation
                              Involvement
  Individual
  Knowledge

  Articulation
      Skill
            Research Method,
            Data, and Measures
• Two stages of data collection
  – Study 1: 12 months of intensive field work
    (and 8 additional months of follow-up observation)
    observing an automotive design process
  – Study 2: Survey-based study to test hypotheses
    generated from theory and ethnography (73 innovations
    identified)
• Multi-method
  – Ethnography of product and process innovation
  – Interviews (over 100 interviews taped and transcribed)
  – Survey research involving individual difference and
    social network variables
             What Is An Innovation?

• Initial list of innovations generated by 26 managers from
  across NewCar as well as patent application list
• Product and process innovations
   – Product innovation: Double boot on manual shifter to enhance
     NVH characteristics
   – Process innovation: Creation of a prototype parts management
     group and process
• Initial list of 81 innovations winnowed to 73 innovations
  (52 product-related, 21 process-related)
• Criterion: “New or major modification, implemented”
   – Reviewed by senior managers
   – Corroborated by field notes and observation
  What Is Innovation Involvement?
• Self-reports of involvement in up to 73 innovations
  reported by respondents
   – Via web-based tool
   – Options to selectively indicate involvement
   – Survey response: 1= initiator, 2= major role, 3 = minor
     role, 4 = know about the innovation, 5 = don’t know about
     the innovation (reverse coded)
• Respondent’s innovation involvement reflects the
  highest level of involvement reported across all
  innovations
• Expert report data collected to validate self reports
• 24% initiators, 31% major role, 28% minor role, 16
  % know about an innovation, 1% did not recognize
  any innovations
Independent Variable Measures
    Social Network/Structural Holes
           Measures: Density
• Density – (a/b where a equals the actual
  relationships among alters in the network
  and b is the maximum number of alter-alter
  ties in the network)
  – The total number of alters in the network is
    given by the formula: b = n(n-1)/2 where n =
    persons in the egocentric network excluding
    ego.
  Social Network/Structural Holes
Measures: Effective Size and Constraint
• Effective size – the number of alters weighted by strength of
  tie, that an ego is directly connected to, minus a “redundancy”
  factor.
   – The more different regions of the network an actor has ties with, the
     greater the potential information and control benefits.
                                         
   – Effective size =
                        j 
                           1   Piq m jq 
                         q                  q  ij
                                          

• Constraint – the extent to which all of ego’s relational
  investments directly or indirectly involve a single alter
   – The more constrained the actor, the fewer opportunities for action.

   – Constraint = cij  ( pij  q p p qj ) 2
                                    iq



• Density and effective size significantly (and negatively)
  correlated; density and constraint significantly and positively
  correlated.
      Individual-level Measures of
              Knowledge
1.   Formal education
2.   Years in the firm
3.   Social knowledge
4.   Technical knowledge
     Social Knowledge Measure
• “In general, how easy would it be for you to get
  candid, ‘behind-the-scenes’ input regarding G5
  issues concerning the following areas?”
• Scale consisting of 10 areas (e.g., body, chassis,
  electrical, interior, power train, vehicle
  development, program management)
• Cronbach alpha (scale reliability): .88
  Technical Knowledge Measure
• “In general, how comfortable are you addressing
  the more advanced technical issues associated
  with the following areas?”
• Familiarity and comfort are strongly associated
  with the experience of tacitly held knowledge
  (Kaplan, 1993)
• Scale consisting of 10 areas (e.g., body, chassis,
  electrical, interior, power train, vehicle
  development, program management)
• Cronbach alpha (scale reliability): .82
    Tertius Iungens Orientation
• Addresses both the introduction of disconnected
  others and the forging of stronger ties between
  those who may already have ties with one another.
• A scale with 6 behaviorally-oriented items.
• “I introduce people to each other who might have
  a common strategic work interest.”
• “I point out the common ground shared by people
  who have different perspectives on an issue.”
• Pretested; n = 55; Cronbach alpha: .85; final
  study: .88
          Control Variables
• Organizational Rank
• Dummy variable for less than one year in
  the firm
                 Table 1: Descriptive Statistics
                             (n=146)

      Measures                Mean                 SD
Innovation
   Involvement                 2.55                 1.06

Density                       56.72                20.88

Number alters                 13.28                 3.68

Effective size                 5.23                 2.61

Constraint                      .34                  .11

Union orientation              4.73                  .92

Social                         4.36                 1.25
knowledge
Technical                      3.99                 1.11
knowledge
Years in firm                  7.39                 7.66

Years in firm                   .89                  .31
(Dummy variable)
Education                       .25                  .43

Organizational rank            2.16                  .84
      Table 2: Ordered Logit Coefficients Predicting Innovation Involvement

                             Model 1                 Model 2               Model 3
                           (with Density)      (with Effective Size)   (with Constraint)

                              (n=146)                (n=146)               (n=146)
Social network
   Density                   0.16*
                             (.008)
   Number alters             -.025
                             (.045)
   Effective size                                     -.124†
                                                      (.064)
   Constraint                                                             2.349
                                                                         (1.515)
Union orientation             .597**                   .584**               .583**
                             (.212)                   (.211)               (.211)
Knowledge
   Social                     .602***                  .584***              .569***
   knowledge                 (.158)                   (.155)               (.155)
   Technical                 -.360*                   -.351*               -.338†
   knowledge                 (.176)                   (.175)               (.174)
   Years in firm              .059*                    .058*                .062*
                             (.027)                   (.027)               (.027)
   Years in firm             1.228*                  1.256*               1.244*
   (Dummy variable)          (.553)                  (.552)               (.551)
   Education                  .914*                    .908*                .906*
                             (.382)                   (.383)               (.383)
Organizational rank           .509*                    .537*                .564*
                             (.224)                   (.223)               (.222)
Chi-square                 73.319***                74.587***            71.103***
                            (df=9)                   (df=8)               (df=9)

Nagelkerke Pseudo R2          .421                    .418                  .411

 † p < .10 * p < .05 ** p < .01 *** p < .001
               Key Findings:
     (Significance with Ordered Logit)
• Social network position: High density and low structural
  holes lead to innovation involvement
• “Tertius iungens” orientation – significant positive
  predictor of innovation involvement
• “Social knowledge” – significant predictor of innovation
  involvement
• Other knowledge measures – years in firm and education –
  as well as rank significantly predict innovation
  involvement
• Next steps: Disaggregate innovations types; separate out
  divisional memberships; consider possible interaction
  effects
             Insights/Contributions
• TI orientation as a measure of mobilization around a dense
  network and a possible precursor of density
   – TI orientation at the root of collective action?
• Density leads to innovation involvement
   – Sparse networks may not support the the continuous articulation
     and creation of knowledge (Ahuja, 2000; Uzzi, 1997)
   – The network most conducive to individual mobility may not be the
     network most conductive to task performance (Hansen, Podolny,
     & Pfeffer, 2000)
   – Possible contingency model for social networks effectiveness?
     Dense networks lead to innovation only in stable organizations or
     institutional fields?
• Social knowledge vs. technical knowledge
                Insights/Contributions
• By isolating the social network mechanism, the TI
  construct explains why the same social structure might
  produce different outcomes
   – The critical move – to specify a mechanism along with a social
     network measure (need to specify a tertius gaudens mechanism?)
   – Social skill – the ability to induce cooperation in others (Fligstein,
     2001)*
   – Social skill as a fixed aspect of the entrepreneur that may employ
     dense or sparse network depending on the social and technical
     context
• Next step: Test in multiple organizations to better identify
  the relationship between social network structure and
  tertius iungens and innovation outcomes
         Working Model for Innovation
                Involvement

             Social
         Network Position

          Tertius Iungens
            Orientation              Innovation
                                    Involvement
Social     Individual
Skill      Knowledge

           Articulation
              Skill
The Second Innovation Mechanism:
          Articulation
Articulation and Its Link To TI Mechanics
• Defined as the social process by which knowledge
  is made more explicit or usable.
• Tacit to explicit; private to public; complex to
  simple; random to ordered; past to present;
  translation; persuasive
• Articulation behaviors: stories, analogies and
  metaphors, perspective taking, formal and informal
  visual representations, humor, physical objects
• Stories and analogies are significantly correlated
  with TI orientation!
• The talk-structure link?
        Research Contribution
• Research contribution: Uses rigorous
  ethnographic and social network analysis to
  gain new insights about how innovation
  involvement unfolds in organizations
• Personal knowledge, social networks, and a
  “tertius iungens” lead to innovation
  involvement
• About innovation but really about concrete
  mechanisms that explain and predict agency
  and change in a variety of contexts
Thank You

								
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