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  • 1. Learning Links: Social Networks and Organizational Learning Presentation to the PhD Tribunal IESE/Universidad de Navarra Jordi Comas, Candidate, 2007
  • 2. <ul><li>Zeus and Athena… in the interest of being provocative…We need to finally overturn the subtle yet powerful egocentrism of the individual. Individuals are the nexus of flows, not withstanding our self-awareness. </li></ul><ul><li>This is the relational, the connectionist era. </li></ul>
  • 3. Let’s Study Org Learning <ul><li>How do organizations “go from ideas to action, from the spark of the possible to the application of the practical?” </li></ul><ul><li>Organizations are all about learning in response to the environment. </li></ul><ul><ul><li>Learning includes processes in which the members of an organization 1) acquire information about the organization and its environment 2) turn that information into various kinds of knowledge and 3) perform based on the knowledge at the individual and collective level (synthesized from Leavitt and March, Schulz, Argote) </li></ul></ul><ul><li>Organizations are made up of people and knowledge. </li></ul><ul><li>People and knowledge come from a social context=they have a social component. </li></ul>
  • 4. Networks… <ul><li>Networks are the concrete differences in social context. They embody the past and shape the future. They are the structures of information and influence which enable and constrain actors. </li></ul><ul><ul><li>Hence, time is critical to unlocking enabling and constraining. </li></ul></ul><ul><li>Networks exist at many levels: dyad, group, whole network (organization), and society. </li></ul><ul><li>Network research focuses on dynamics of networks (endogenous effects) and dynamics on networks (exogenous effects). </li></ul>
  • 5. Black Box 1- Org Learning Adaptation (Routines, Artefacts, Strategy) Stimulus (Dissatisfaction, Puzzle, Curiosity) 1) Org Learning  Learning
  • 6. Black Box 2- Network Effects 2) Networks ↓ Networks of information and influence Outcomes (Adaptation and Changed Networks) StructuredAction
  • 7. Inside the two 2 Black Boxes 1) “Learning” 2) “Action” Stimulus (Dissatisfaction, Puzzle, Curiosity) Adaptation (Routines, Artefacts, Strategy) Networks of information and influence Outcomes (Adaptation and Changed Networks) 1) Org Learning  2) Structure ↓
  • 8. Figure 1.2: Inside the Black Box <ul><li>Organizational Learning is the creation, retention, and transfer of knowledge about the problems and solutions an organization faces. </li></ul><ul><ul><li>Knowledge is multidimensional (tacit-explicit and group-individual). </li></ul></ul>Networks are the multilevel architecture (dyad-group-network) of flows of information and influence.
  • 9. The Learning-Network Nexus (the inside of the two overlapping black boxes). Networks Knowledge Creation Networks Knowledge Retention Networks Knowledge Transfer
  • 10. Network-Theoretical Perspectives A fruitful analysis of any human action-- including economic action, my subject here—requires us to avoid the atomization implicit in the theoretical extremes of under- and over-socialized views. Actors do not behave or decide as atoms outside a social context, nor do they adhere slavishly to a script written for them by the particular intersection of socio-cultural categories they happen to occupy. Their attempts at purposive action are instead embedded in concrete, ongoing systems of social relations (Granovetter, 1992, 32). People do learn, but a person and her knowledge are not bounded by the skin and skull; both are dispersed across network ties. The actor is a nexus of relationships, and knowledge is stored and used through activating ties. This perspective is a relational perspective, and it is an approach that threads the needle between over and under-socialized views of people and actions (Comas 2007). The shift is away from mechanistic, steady-state concepts of organizations and towards concepts that incorporate change, flux, and real time distributed action and decision-making. … An action perspective grasps organizations as complex systems where many different things are always happening at once, where the global behavior of the organization as a whole is grasped as ‘emergent’ out of local and individual action rather than from any top-down plan or design (Nohria and Berkley, 1994, 73).
  • 11. Convergence with Recent Trends in Organizational Learning Social relationships matter for knowledge creation, retention, and transfer. When properties of units, properties of relationships and properties of knowledge fit or are congruent with each other, knowledge retention, and transfer increase. Knowledge creation, by contrast, may be stimulated by a lack of congruence or parts that do not fit together. Experience can be structured to promote learning outcomes in firms. Where boundaries are drawn matters for knowledge creation, retention, and transfer…And embedding knowledge in transactive memory systems, short-hand languages, routines, technologies, and other knowledge repositories can promote knowledge retention and transfer in firms (Argote, McEvily, & Reagans, 2003a). “… sophisticated forms of intelligence emerge from the interactions among loosely linked organizational components …This also implies that the most critical aspect of knowledge management is not the management of knowledge content per se . Rather, it has to do with creating an environment rich with knowledge cues and managing the social processes by which organizational units interact ” (Fiol 2002, 120).
  • 12. The two need each other <ul><li>More reviews of Organizational Learning than empirical studies. “Learning is a notoriously difficult process to study empirically.” </li></ul><ul><li>Network studies tend to bias structure over agency. </li></ul>
  • 13. Middle of the middle <ul><li>Nod to Merton </li></ul><ul><li>Tendencies towards Black Boxes… (would be nice to have some examples?) </li></ul>Network analysis all too often denies in practice the crucial notion that social structure, culture, and human agency all presuppose each other; it either neglects or inadequately conceptualizes the crucial dimension of subjective meaning and motivation and thereby fails to show exactly how it is that intentional, creative human action serves in part to constitute those very social networks that in turn so powerfully constrain actors (Emirbayer & Goodwin, 1994a, 1413).
  • 14. My personal motivation <ul><li>I came to this project with several concerns: </li></ul><ul><li>- How to empirically study learning in process </li></ul><ul><li>- How to integrate methods to study duality of structure and action </li></ul><ul><li>- Whether networks matter in this large group/small organization setting </li></ul><ul><li>- How to study networks longitidunally </li></ul><ul><li>-Opportunistic data collection </li></ul>
  • 15. Three empirical chapters <ul><li>From individual  organization </li></ul><ul><li>From knowledge creation to knowledge retention and transfer. </li></ul>
  • 16. Research site <ul><li>4 Companies of Management 101 [Mg 101] students in Fall 2003. </li></ul><ul><ul><li>MG 101 companies start as undifferentiated groups assigned according to individual schedule preferences (pseudo-experiment). </li></ul></ul><ul><ul><li>Double-bottom line companies (financial and social performance). </li></ul></ul><ul><ul><li>Intense experience for participants </li></ul></ul><ul><li>Overall, constrained environment </li></ul><ul><li>Overall, valuable for longitudinal data on networks and learning </li></ul><ul><li>Applicability is more to the process than the type of organization. </li></ul>
  • 17. Methods Used <ul><li>Mixed methodology of quantitative (network surveys) and qualitative data (census, interviews, papers, archives, observations). </li></ul><ul><li>Data collected and analyzed simultaneously. </li></ul>
  • 18. Network Surveys <ul><li>Full roster </li></ul><ul><li>Membership is the assumed network boundary </li></ul><ul><li>Three questions using a 5 point Likert scale </li></ul><ul><li>Three points in time, early, middle, and late. </li></ul>
  • 19.  
  • 20. Qualitative Sources <ul><li>Student papers (4/student) </li></ul><ul><li>Company reports and archives </li></ul><ul><li>Interviews with students, professors, and teaching assistants </li></ul><ul><li>Direct observations </li></ul><ul><li>Knowledge census </li></ul><ul><li>Qualitative data used to: </li></ul><ul><ul><li>Ground network analysis </li></ul></ul><ul><ul><li>Triangulate ideas and idea sources </li></ul></ul><ul><ul><li>Generate variables for binary logistic regression </li></ul></ul>
  • 21. Knowledge Census
  • 22. Breaking Down the Learning-Network Nexus <ul><li>I looked at particular moments in the learning-network nexus. </li></ul><ul><li>One moment is individuals and creating knowledge (Ch 2). </li></ul><ul><li>A second is groups and retaining and transferring knowledge (Ch 3). </li></ul>
  • 23. Overview of a Mg 101 Company
  • 24.  
  • 25.  
  • 26.  
  • 27. Brokerage and/or Closure <ul><li>How does the individual’s network position effect learning in terms of knowledge creation and retention? </li></ul><ul><li>Knowledge creation and retention observed as ideas: </li></ul><ul><ul><li>Idea formation </li></ul></ul><ul><ul><li>Idea quality (radical or incremental) </li></ul></ul><ul><ul><li>Idea adoption </li></ul></ul><ul><li>Network position=social capital. </li></ul>
  • 28. Social Capital: Brokerage-Closure Debate <ul><li>Social capital is about advantage through preferential access to resources. </li></ul><ul><li>One possibility is closure: ego embedded in locally dense cluster. </li></ul><ul><li>Other possibility is brokerage: ego bridges (fills structural holes) between groups </li></ul>
  • 29. <ul><li>Closure </li></ul><ul><li>Ego is central and embedded </li></ul><ul><li>Greater trust, control, cultural consistency </li></ul><ul><li>Effective access to others’ knowledge </li></ul><ul><li>Brokerage </li></ul><ul><li>Ego is central and his alters’ are not linked. </li></ul><ul><li>Greater opportunity to leverage alters </li></ul><ul><li>Greater access to diversity of ideas. </li></ul>
  • 30. Research Questions Brokerage Closure Idea Generation ? ? Research Question 1: Which Form of Social Capital Will Make an Actor More Likely to Have an Idea?
  • 31. Research Question 2: Does Brokerage or Closure Affect Radical Ideas? Research Question 3: Does Brokerage or Closure Affect Adoption? Can Either Overcome the Liability of Radical Ideas? Brokerage Closure Radical Ideas Adopted ▬ ? ? ? ?
  • 32. Research Question 4: How do network path (initial configuration), idea actions, and network structure affect each actor’s final social capital? <ul><li>Initial Network Structure </li></ul><ul><ul><li>Brokerage </li></ul></ul><ul><li>Closure </li></ul><ul><li>Idea Actions </li></ul><ul><ul><li>Having Ideas </li></ul></ul><ul><li>Radical Ideas </li></ul><ul><li>Idea Adoption </li></ul><ul><li>Current Network Position </li></ul><ul><ul><li>Brokerage </li></ul></ul><ul><li>Closure </li></ul>RQ 1-3 RQ 4 RQ 4
  • 33. Descriptive Statistics
  • 34. Descriptive Statistics
  • 35. Summing up… <ul><li>Having ideas, in this limited case, has little to do with one’s social capital of advice-seeking relationships. </li></ul><ul><li>Brokerage does matter for having radical ideas </li></ul><ul><li>There are two kinds of brokerage… </li></ul><ul><ul><li>Brokerage as flow matters for having ideas adopted even while radical ideas are less likely to be adopted. </li></ul></ul><ul><li>Idea involvement (agency) boosts closure for an actor over time. </li></ul><ul><li>Such agency has little effect on brokerage as constraint. Brokerage, important for radical ideas and idea adoption, comes from endogenous network effects. </li></ul><ul><ul><li>Initial closure, centrality and later popularity lead to later LOW constraint. </li></ul></ul><ul><ul><li>Initial flow leads to later HIGH constraint. </li></ul></ul>
  • 36. Results (using binary logistic regression) Idea Generation RQ1: What effects idea generation? Results : The model explains almost no variance. Individual action matters more than social capital for generating ideas Agency, Creativity
  • 37. Results (using binary logistic regression) RQ 2: What effects idea quality? Results: Brokerage as constraint does. A more likely effect is 1.5 times (SD of constraint x coefficient). RQ 3: What effects idea adoption? Results: Closure has a slight negative effect when it is simply more connections (In Degree). Brokerage as flow has a strong effect and is greater than the liability of radical ideas. Brokerage as Constraint Closure as InDegree Radical Ideas Adopted 1/3 4 - 5% 2 Brokerage as Flow
  • 38. Results (using OLS regression) Closure (measured as centrality) <ul><li>Idea Actions </li></ul><ul><ul><li>Having Ideas </li></ul></ul><ul><li>Network Structure </li></ul><ul><ul><li>Flow </li></ul></ul>0.65 <ul><li>Initial Network Structure </li></ul><ul><ul><li>Closure </li></ul></ul>RQ4: What happens to closure after idea actions? Results: Closure affected by current network, idea actions, and network path. Values are standardized OLS regression coefficients. 0.2 0.12
  • 39. Results (using OLS regression) <ul><li>Network Structure </li></ul><ul><ul><li>InDegree </li></ul></ul>0.32 RQ4 What happens to brokerage after idea actions? Results 2: Brokerage affected mostly by network path, a little by current network structure, and NOT by idea actions. Values are standardized OLS regression coefficients. -0.2 -0.65 0.52 Brokerage (Measured as constraint) <ul><li>Initial Network Structure </li></ul><ul><ul><li>Constraint </li></ul></ul><ul><ul><li>Centrality </li></ul></ul><ul><ul><li>Flow </li></ul></ul>
  • 40. Implications <ul><li>These mixed results suggest that it is not a dichotomous choice, but a choice of brokerage and/or closure. Other researchers are moving towards an integrated approach. </li></ul><ul><ul><li>Burt writes: “…while brokerage across structural holes is the source of added value, closure can be critical to realizing the value buried in structural holes” (Burt 2001, 25). </li></ul></ul><ul><li>Part of the answer to which kind of social capital is better will depend on </li></ul><ul><ul><li>The ends actors are seeking and </li></ul></ul><ul><ul><li>Whether we think of benefits to the actor or to the organization. </li></ul></ul><ul><li>Trade-offs between closure and brokerage have many implications, especially depending on whether we are examining a zero-sum gain resource, such as promotions, versus a growth resource, such as new ideas. </li></ul><ul><li>The individual actor faces several tendencies which limit brokerage opportunities: </li></ul><ul><ul><li>Becoming more connected and hence central; </li></ul></ul><ul><ul><li>The decaying of others’ ties after they band-wagon to her due to her structural hole; </li></ul></ul><ul><ul><li>The effect of local pressures to reciprocate and balance triads. </li></ul></ul>
  • 41. Suggested research strands…apparent paradoxes <ul><li>The paradox of radical ideas: </li></ul><ul><ul><li>The social capital that helps an actor have a radical idea is not the same social capital that can helps get an idea adopted. What’s worse, radical ideas by themselves endure a liability of being radical. Organizations in need of more radical ideas to feed into strategy making, organizational learning, or innovation will want to unlock this paradox since the people best able to generate radical ideas may not have the social capital to overcome the inertia of incremental conservatism. </li></ul></ul><ul><li>The paradox of brokering: </li></ul><ul><ul><li>Actors who fill structural holes, those who in this context were more likely to generate radical ideas, may lose their advantageous position as they enact their brokerage opportunity. Ideas bring people together; as we saw here having ideas tended to add to one’s centrality. The impulse to connect one’s alters, to enact the latent value of structural holes may have the effect of winnowing future brokering opportunities. </li></ul></ul>
  • 42. Juggling Exploration/Exploitation <ul><li>Tackling other elements of learning-network nexus: knowledge retention and transfer at level of subgroup (clique) and whole network. </li></ul><ul><li>This has been discussed as the trade off between exploration and exploitation. </li></ul><ul><ul><li>“ The essence of exploration is experimentation with new alternatives. Its returns are uncertain, distant, and often negative.” </li></ul></ul><ul><ul><li>The essence of exploitation is “the refinement and extension of existing competences, technologies, and paradigms. Its returns are positive, proximate, and predictable” (March 1991, 85). </li></ul></ul>
  • 43. The stakes to balancing (or juggling) <ul><li>Levinthal and March (1993) argued that “The basic problem confronting an organization is to engage in sufficient exploitation to ensure its current viability, and, at the same time, to devote enough energy to exploration to ensure future viability” (105). </li></ul>
  • 44. Exploration/exploitation <ul><li>Due to limited resources, exploration/exploitation are orthogonally related at any one moment in time. </li></ul><ul><ul><li>Resources include: </li></ul></ul><ul><ul><ul><li>Capital </li></ul></ul></ul><ul><ul><ul><li>Attention </li></ul></ul></ul><ul><ul><ul><li>Cognition </li></ul></ul></ul><ul><ul><li>I argue that network structure is a fourth constraint leading to the trade-off between exploration and exploitation. </li></ul></ul>
  • 45. Figure 4.1 Exploration-Exploitation Balance as Network Problem Dynamic View of Exploration/Exploitation
  • 46. My approach <ul><li>Look for a relationship between exploration/exploitation and network structure. </li></ul><ul><li>Examine how changes in exploration/exploitation and networks evolve over time. </li></ul><ul><li>A holistic approach to observing exploration/exploitation </li></ul><ul><li>Quantitative, descriptive, approach to networks </li></ul>
  • 47. Why would networks matter? <ul><li>Network structures will enhance or suppress the underlying variation of knowledge. </li></ul><ul><li>Network structures are the conduits of transfer of information and influence. </li></ul><ul><li>Reagans and McEvily (2003) discuss the benefits of network cohesion (clustering) and range (number of clusters accessible to a given cluster). </li></ul><ul><li>I build on the two properties: cohesion and range. </li></ul><ul><li>I add multiple types of relationships: advice and communication. </li></ul>
  • 48. “Connected Clustering” <ul><li>To ascertain clustering (cohesion): </li></ul><ul><ul><li>Number of components </li></ul></ul><ul><ul><li>Number of overlapping cliques </li></ul></ul><ul><li>To ascertain connection </li></ul><ul><ul><li>Advice/communication embedding </li></ul></ul><ul><ul><li>Strong advice/weak communication embedding </li></ul></ul><ul><ul><li>Overall cohesion (strength of weakest path) </li></ul></ul>
  • 49. Ascertaining exploration/exploitation
  • 50. Ascertaining exploration/exploitation II
  • 51.  
  • 52. Preliminary conclusions <ul><li>Some evidence of congruence: </li></ul><ul><ul><li>More exploratory company had more distinctive subgroups and high cohesion. </li></ul></ul><ul><ul><li>More exploitative companies had fewer components and more clique overlap; they also had more communication/advice embedding in strong ties. </li></ul></ul><ul><ul><li>Clustering evidence goes against expectations (should be higher for exploratroy). </li></ul></ul>
  • 53. Research Hypothesis <ul><li>☼ Hypothesis One: Dynamic networks and shifts in exploration-exploitation (ambidexterity) are associated with each other </li></ul><ul><li>☼ Hypothesis Two: The learning-network association leads to better adaptation. </li></ul>
  • 54.  
  • 55. Conclusions <ul><li>OnTrack benefited the most, with the highest evaluations of their performance and the highest adaptation score. </li></ul><ul><li>Inertia was persistently exploitative. It suffered less for going against the grain from exploration to exploitation, than Backtracker or Explorer did (they ended in more exploratory learning and networks.) </li></ul><ul><li>Suggestively reinforces echoes March’s (1991) finding that short-term pressures to be myopic towards favoring (seeing) exploitation opportunities perpetuate exploitative learning. </li></ul><ul><li>Inertia did well in this setting, but if it needed to exist for longer or adapt to more dynamic circumstances, its lock in to exploitative learning may have left it disadvantaged, relative to a company like Backtracker that demonstrated more dynamic network formations. </li></ul>
  • 56. Overall Conclusions <ul><li>Why “Learning Links”? The two fields need each other: </li></ul><ul><ul><li>The actors who do the learning in organizations are always embedded in networks, “on-going systems of concrete relations.” </li></ul></ul><ul><ul><li>Knowledge, the material of learning processes, is socially mediated. </li></ul></ul><ul><ul><li>Actors, in the course of learning, create and recreate their networks. Often in ways that are surprising or counter-intuitive. </li></ul></ul>
  • 57. From here? <ul><li>Examine the apparent paradox of brokering with more rigorous research design. Examine individuals in multiple networks and track network and action over longer period of time. </li></ul><ul><li>Develop more precise theory of how knowledge types are effected by network ties. </li></ul><ul><li>Examine networks in new, synthetic worlds. </li></ul><ul><li>Explore network forms of organization as a development of the Information Age (Castells). One salient example is Al-Qaeda from the mid 1990s-2001 and 2001-2007. </li></ul>
  • 58. Response Rates to Network Surveys
  • 59. Regression Models from Chapter 2
  • 60. Table 4.3- Overview of Network Measures Valued for both Embedding Advice and Friendship Valued for both Embedding Advice and Communication Binary (Strong Advice) x i ↔x j =(1,0) Weighted Clustering Coefficient Valued, Digraph (Advice) x i  x j =(1-5), x j  x i =(1-5) Balance - Weak Transitivity Valued, Symmetrical (Advice) x i ↔x j =(1-5) Centralization to most central (eigenvector) Binary (Strong Advice) x i ↔x j =(1,0) Centralization- Valued Digraph (Advice) x i  x j =(1-5), x j  x i =(1-5) Cohesion II- Valued Digraph (Advice) x i  x j =(1-5), x j  x i =(1-5) Cohesion I- Binary (Strong Advice) x i ↔x j =(1,0) Density- Relationship Network Property
  • 61.  
  • 62. R  T Even more dramatic example of above. 0.68 0.233 Embedding Advice and Friendship T  T More embedding will help norm reinforcement. 0.619 0.46 Embedding Advice and Communication R  T Same as above. 28.9 0 Weighted Clustering Coefficient R  T Much more closure. 73.8 38.2 Balance Transitivity- R  T This is highest centralization to central actors. 16.05% 13.07% Centralization to most central (eigenvector) R  T. Central actors, in this case the officers, have many more links to them. 49.90% 10.57% Centralization- R  T Getting less cohesive while more dense suggest centralization. 0.5 0.563 Cohesion II- R  R Least cohesive of four, and increasing. 2.3 2.08 Cohesion I- R  T. Most change of the four companies and becomes densest company. Biggest SD of density which is consistent with more centralization. .13 (.39) .03 (.18) Density (Standard Deviation)- Trajectory R=Exploratory; T=Exploitative Time 2 Time 1 Category Company A- OnTrack
  • 63. Company B: Inertia: Inertial Density Leads to Exploitation T  T Communication and friendship are embedded with advice, a trend that simply continues. 0.59 0.47 Embedding Advice and Friendship T  T Communication and advice are embedded with advice, a trend that simply continues. 0.62 0.5 Embedding Advice and Communication R  T They ended with the lowest clustering after staring with the second highest. 23.5 23.3 Weighted Clustering Coefficient T  T Triples that exist close off. 66.5 48.1 Balance Transitivity- T  T Each actor closer to central. Most centralized of the four. 16.96% 11.81% Centralization to most central (eigenvector) T  T Closer to star. 45.87% 19.89% Centralization- T  T Closer to each being one degree. 0.52 0.43 Cohesion II- T  T Getting more cohesive. Shorter paths among members. Only company to move in this direction. 2.33 2.84 Cohesion I- T  T Starts as the most dense, ends as second most. .10(.30) .081 (.273) Density- Trajectory R=Exploratory; T=Exploitative Time 2 Time 1 Category Company B- Inertia
  • 64. R  T Towards embedding and exploitation. 0.41 0.19 Embedding Advice and Friendship T  T Flat and towards exploitation. 0.62 0.56 Embedding Advice and Communication R  R Clustering not increasing, and is fairly high in beginning and end. 39.3 37.5 Weighted Clustering Coefficient T  T More closure of triples. Reflects move to exploitation. 76.8 50.8 Balance Transitivity- T  R Moving to less centralized in terms of connection to central actors on average. Means that slight increases in density and balance (below) are not contributing to centralization. 9.67% 13.13% Centralization to most central (eigenvector) T  R More centralization, but by end much less than A and B. 23.65% 11.82% Centralization- T  R Longer path length, less cohesive. 0.43 0.48 Cohesion II- T  R Less cohesive, increasing possibility for exploration. 3.0 2.5 Cohesion I- T  R Flat-- Still low density and with substantial standard density. Not very dense at the end in line with more exploration. .07 (.26) .06 (.24) Density- Trajectory R=Exploratory; T=Exploitative Time 2 Time 1 Category Company C- Backtracker
  • 65. Company D: Explorer: Flattening Network Leads to Perpetual Exploring T  R Starts high, but little increase in embedding. 0.59 0.49 Embedding Advice and Friendship T  R Starts high, but little increase in embedding. 0.46 0.48 Embedding Advice and Communication T  T Low clustering early and late. 24.2 10.7 Weighted Clustering Coefficient R  R Given the increase in density but lack of increase in centralization, the extra connections have to go somewhere. In clustering and transitivity, D is increasing. 74.6 56.7 Balance Transitivity- R  R Also flat. 13% 13% Centralization to most central (eigenvector) R  R Very uncentralized. No star actor. 18.85% 13.45% Centralization- R  R Cohesiveness declining slightly from initially moderate level 0.48 0.5 Cohesion II- R  R Cohesion does not budge. Longer paths between actors means more social distance. 2.74 2.75 Cohesion I- T  R Density stays low. .09(.286) .041 (0.198) Density- Trajectory Time 2 Time 1 Category Company D

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