SlideShare uma empresa Scribd logo
1 de 37
Baixar para ler offline
Exploring the role of Social Networks in Intra-Corporate
  Crowdsourcing initiatives such as Stock Market for
                     Innovations.




                         Jonas Rolo

                   jrolo@andrew.cmu.edu

                      October 7, 2011




                     Advisory Committee:


                      David Krackhardt

                       Andrei Villarroel




                                                           P age |1
Abstract


Several Companies have been started using crowdsourcing initiatives internally
where only their employees participate. One of the more recent of these initiatives
are Stock Markets for Innovation (SMI) where companies can tap into the creativity
power of their employees through an online stock market where employees create,
comment and invest on new ideas for products, processes and services.
Crowdsourcing initiatives should be based in the “wisdom of the crowds”, however
intra-corporate crowdsourcing (ICC), being enclosed in the company realm, might be
under the influence of the Social Networks. We study a similar setup to a SMI inside
a Master’s class and the results from this study show that indeed the Social Network
of study is correlated with the behaviour of the SMI participants mainly on the
evaluation procedures of ideas and performances of the other participants. The
most important network characteristic playing a role in this evaluation procedure is
power, where the most powerful participants get higher evaluation on their ideas or
performance. This result seems to show that the intra-corporate ICC initiatives might
just be another tool for the most powerful actors to reinforce their social power. The
management implication is that knowledge diffusion in the SMI initiatives might not
be that different than what existed previously in the social network and the
management team should have this in mind when considering applying an ICC
initiative similar to a SMI.




                                                                                         P age |2
Contents

1.        INTRODUCTION ............................................................................................. 4
2.        LITERATURE REVIEW...................................................................................... 5
2.1.      Crowdsourcing .............................................................................................. 5
2.1.1. Intra Corporate Crowdsourcing (ICC) ............................................................. 6
2.1.2. Stock Market for Innovations (SMI) ............................................................... 6
2.2.      Social Networks ............................................................................................. 7
2.2.1. Structural Holes ............................................................................................. 8
2.2.2. Knowledge Brokering .................................................................................. 10
2.2.1. Bonacich Power and Centrality .................................................................... 10
3.        THEORY AND HYPOTHESIS ........................................................................... 12
4.        DATA, METHODOLOGY, NETWORKS AND MEASURES .................................. 16
4.1.      Data ............................................................................................................ 16
4.2.      Methodology............................................................................................... 18
4.3.      Network Construct ...................................................................................... 19
4.3.1. Study Network ............................................................................................ 19
4.3.1. Comments Network .................................................................................... 22
4.4.      Measures .................................................................................................... 23
5.        RESULTS ...................................................................................................... 24
6.        LIMITATIONS AND CONCLUSIONS ................................................................ 32
6.1.      Limitations .................................................................................................. 32
6.2.      Conclusions ................................................................................................. 33
BIBLIOGRAPHY ........................................................................................................ 36




                                                                                                                                 P age |3
1. INTRODUCTION

The literature on crowdsourcing has focused mainly on the firm external
crowdsourcing initiatives and only recent research has started to tackle closed
crowdsourcing initiative made only with employees of a company, a Intra-Corporate
Crowdsourcing (Villarroel & Reis, 2010 and 2011). One of the most recent Intra-
Corporate Crowdsourcing (ICC) initiatives is Stock Markets for innovation (SMI)
(Villarroel & Reis, 2010 and 2011). Soukhoroukova et al (2010), in a recent study on
Idea Markets or SMI show that this type of ICC initiatives offers promising
advantages for new product innovation: “the platform and the formal process
motivates employees to communicate their ideas to management”, “by filtering the
ideas generated internally the number of ideas brought to management is reduced”
and “the ability to source many ideas can increase efficiency at the fuzzy front end
of the new product development process.”




One area that has not yet been fully studied in crowdsourcing is the role of pre-
existing social networks in ICC initiatives. When considering firm or institution’s
internal crowds, the social networks of the crowds were formed long before any
new ICC initiative, and thus it is to expect that the social networks might influence
the behaviour of the employees in the ICC initiative. In a ICC Stock Market for
Innovation (SMI), Villarroel & Reis, (2011) show that “speculative activity is positively
associated with better innovation performance”. An example of this speculative
activity could be seen in pulling or collusion strategies to invest heavily on one idea
for this to be one of the most invested ideas (approved by the market) and the
submitter and all investors win with this. The pre-existing social networks might just
be the tool these participants are using to perform the speculative activity.




Our research question is to understand if there is any correlation between the social
network of the participants and their behaviour in the SMI, and if this correlation
exists, to understand what individual network measures drive innovative activity and
performance in the SMI. Inside the firm the social networks might play an important
role in the ICC initiatives and might be a good way of predicting some outcomes of
                                                                                            P age |4
these initiatives.    Knowing the social networks characteristics and nodes’
characteristics might help to understand and predict part of the outcome of the ICC
initiative.




2. LITERATURE REVIEW

    2.1.        Crowdsourcing

“Crowdsourcing is an online, distributed problem solving and production model that
has emerged in recent years” (Brabham 2008: pp. 75. The term was first used in
2006 by Howe and Robinson and it represents the act of a company or institution
taking a function once performed by employees and outsourcing it to an undefined
(and generally large) network of people in the form of an open call (Howe 2008: ).
Having in common the dependency on the crowd participation, the functions to
outsource can be from a wide variety and can be aggregated in three different types
of crowdsourcing: Crowd Creation, Crowd Voting and Crowd Funding (Howe 2008),
much like variation, selection, retention (Anderson and Tushman 1990).




Wikipedia is one of the first examples of crowd creation where an immense crowd
creates page contents that build up to Wikipedia’s communitarian knowledge.
Another example is Innocentive, a company that supplies innovative technical
solutions for tough R&D problems using a worldwide crowd of scientists.
Innocentive’s clients are companies with high R&D expenditures that want to get
solutions to their unsolved R&D problems. With a mix of crowd creation and crowd
voting there is Threadless.com, a web-based t-shirt selling company that
crowdsources the design for their shirts and the voting for the best T-shirts from a
global crowd through an online competition. iStockphoto.com is another example, a
web-based company that sells photography, animations, and video clips for clients
to use on websites, in brochures, in business presentations and so on. A crowd of
photographers and film makers submit their photographs and video clips and vote
on the best photos and videos to rank the website stock. On the crowd funding
there is the micro credit example of Kiva, a project that receives proposals of several
                                                                                          P age |5
projects to be funded in small amounts of money and that a crowd can lend money
to.




Crowdsourcing initiatives such as Wikipedia, Innocentive, Threadless, iStockphoto,
Facebook translation, Goldcorp Challenge, have begun to be studied (Howe 2008;
Lakhani and Panetta 2007; Braham 2008; Jeppesen and Lakhani 2010). Studies
suggest that crowdsourcing is a “very good source of social capital for corporations”
(Villarroel & Reis, 2010) and its openness gives the ability for organizations to
increase the resolution rate for problems that had previously remained unsolved
(Lahkani et all 2007).




           2.1.1. Intra Corporate Crowdsourcing (ICC)

The afore mentioned crowdsourcing initiatives rely on contributors external to the
firm. Nonetheless, there are firms that have implemented crowdsourcing within
their boundaries and this can be called Intra-Corporate Crowdsourcing (Villarroel &
Reis, 2010) .In so doing, these firms search internally for solutions to problems
(advertising, innovation, social responsibility, sustainability) tapping into the entire
pool of employees. Companies with a sufficiently large1 (Howe 2008) internal
community of contributors, use crowdsourcing as a way to get ideas or solutions to
their problems, without running the risk of exposing their best solutions to
competitors. Firms can ask their employees to design the new company logo, to
name the mascot or a new product, to make advertising and to give new ideas on
several areas.




           2.1.2. Stock Market for Innovations (SMI)

The Stock Market for Innovation (Villarroel & Reis, 2010 and 2011) is a recent intra-
corporate crowdsourcing initiative that works on a continuum timeframe. A SMI is

1
    Sufficiently large means that the number of active participants is above 1,000.

                                                                                           P age |6
an online application that replicates a stock market where employees submit,
comment and invest on innovation ideas. The most invested ideas are the ones the
firm will study the viability of implementation and its submitter receives a monetary
prize if the idea is implemented. The Stock Market for Innovation has the following
stages:

   As response to a open call challenge, employees create, and submit ideas to the
    online stock market for innovation;

   On the online SMI, employees can comment and invest in ideas to increase their
    own money (specific currency) that can be used to buy prizes or products and
    services offered by the company;

   The ideas can be traded on the online stock market for a certain period of time
    and are valued by the quantity of comments and amount of investments they
    get;

   For each challenge, the ten most valued ideas get approved for implementation
    analysis by the innovation committee. The submitters of the ideas that are
    implemented receive a monetary prize.




The major difference between the SMI and other firm external crowdsourcing is that
the crowd is not completely undefined as Howe describes for the external
crowdsourcing initiatives. Even if the crowd is big in absolute number, there are
employees that know and have been interacting with one another for years beyond
the ICC initiative.




    2.2.         Social Networks

A social structure of individuals (organizations, countries, etc) and their relations of
interdependency can be represented as a social network, where nodes represent
the individual actors and ties (edges, links) represent the relationship between these
actors. The resulting graph-based structures of social networks are often very
complex because there can be many kinds of ties between the nodes (friendship,
                                                                                           P age |7
kinship, common interest, financial exchange, dislike, sexual relationships, or
relationships of beliefs, knowledge or prestige). Social network analysis (SNA) is the
area that studies social networks using network theory which is the study of graph
structures using network measures. “Social network research has been applied in
several academic fields and has shown that social networks can be found and
operate on many levels, from families up to the level of nations, and play a critical
role in determining the way problems are solved, organizations are run, and the
degree to which individuals succeed in achieving their goals” (Wikipedia – Social
Network).




        2.2.1. Structural Holes

Structural Hole is one of many network measures, is a term coined by Burt (1992)
and this term defines the “separation between non redundant contacts” (Burt,
1992). “A Structural Hole is a relationship of non redundancy between two contacts”
(Burt 1992). A non redundant connection is a connection that you can reach only by
one path of connection. In other words there is a structural hole between two
components of a network if there is only one path that connects those two
components.




Figure 1 – Illustration of a structural hole (designed in PowerPoint)




In Figure 1 we can see that the node “A” has a non redundant connection with both
nodes “B” and “C”, thus between “B” and “C” there is a structural hole. “A” can


                                                                                         P age |8
exploit the fact that B and C do not have a connection and trade knowledge,
information or resources possessed by B but not by C and vice versa.




The network measure of structural holes can be measured by effective size, which
measures the number of non redundant ties in a Ego network (how many actors is
Ego connected with that are not connected to each other).


Burt (1992) defines the effective size of a person's ego network as:




where




and




and Z is the data -- the matrix of network ties.




Structural holes are important because actors of the social networks with non
redundant connections (structural holes) are in a position of being gatekeepers of
information and resources from one component of the network to the other. Being
in such a position grants unique access to non redundant and unique information/
resources that no one else in their component can have. With this position and
access to unique information and resources an actor can act as a broker of
knowledge and do knowledge brokering.




                                                                                     P age |9
2.2.2. Knowledge Brokering

According to Hargadon (1998) “knowledge brokers” span multiple markets and
technology domains and innovate by brokering knowledge from where it is known
to where it is not”. A innovation made by a knowledge broker is typically a know
solution in one technology field that the knowledge broker can transform and adapt
to apply as a new solution to an unsolved problem in a different technology field. In
the organizational social networks, knowledge brokers have several structural holes
(non redundant connections) that give them the possibility to work as
intermediaries in the transfer of information, knowledge or resources and do a
brokerage activity over these structural holes. The best way to identify possible
knowledge brokers is the network measure of structural holes.




        2.2.1. Bonacich Power and Centrality

In his paper of 1987 - Power and Centrality: A Family of Measures – Bonacich argues
that “being connected to well connected others makes an actor central, but not
powerful. On the contrary, being connected to others that are not well connected
makes one powerful although not central”. His argument sets upon the
dependability of the other actors to whom Ego is connected. If the actors that are
connected to Ego are, themselves, well connected, they are not highly dependent on
him. These actors, have many contacts, just as Ego does and they don´t have to go
through him to get what they want. On the other hand, if the actors that are
connected to Ego are, themselves, not well connected, then they are dependent on
him because they have to go through him to get what they want. Bonacich created a
measure (Bonacich Power Centrality) that captures this dichotomy of Power and
Centrality, and shows that the more connections the actors in your neighbourhood
have, the more central you are; the fewer the connections the actors in your
neighbourhood have, the more powerful you are.




                                                                                        P a g e | 10
Figure 2 – Illustration Bonacich Power and Centrality (designed in PowerPoint)




In Figure 2 we can see an illustration of the differences in position of the actors and
the dichotomy of power and centrality. Node “A” has the higher centrality because
he his connected with nodes that are well connected (B and C). On the other hand
nodes “B” and “C” are not as central as “A” but are very powerful because they are
connected with several nodes that are not well connected. The Bonacich Power
Centrality (Bonacich 1987) network measure is given by:


                     C ( ,  )   ( I  R) 1 R1
α is a scaling vector, which is set to normalize the score; β reflects the extent to
which you weight the centrality of people ego is tied to; R is the adjacency matrix
(can be valued); I is the identity matrix (1s down the diagonal) and 1 is a matrix of all
ones.




The magnitude of β reflects the radius of power. Small values of β weight local
structure, larger values weight global structure. If β is positive, then ego has higher
centrality when tied to people who are central. If β is negative, then ego has higher
centrality when tied to people who are not central. As β approaches zero, you get
degree centrality.



                                                                                            P a g e | 11
This network measure is interesting to use in the field of knowledge diffusion
because with just one measure we can test if innovative activity or performance in
an SMI is correlated with network power or centrality attributes of the participants




3. THEORY AND HYPOTHESIS

For both firms and individuals it is recognized that boundary-spanning ties have
advantages in access to sources of external Knowledge and information (Allen &
Cohen, 1969; Allen, Tushman & Lee, 1979). The literature has also shown the
relevant role of accessing knowledge and information across boundaries when
performing innovation activities inside organizations (Hagardon, 1998; Hansen,
1999; Ancona & Caldwell, 1992 ;Burt, 2004). Hagardon (1998) explain that firms
which position themselves as knowledge brokers have an advantage over traditional
manufacturing firms in innovating activities. Hansen (1999) shows that research
units that have weak ties with other subunits of the firm have an advantage in
searching for useful knowledge on other subunits. Ancona & Caldwell (1992) show
that “teams carrying out complex tasks in uncertain environments (such R&D) need
high levels of external interaction to be high performing”. Burt (2004) explains that
organization elements that are positioned close to structural holes (brokers) have
access to less redundant and more unique information and that are better prepared
to have good ideas than other elements. His results show that “brokers that span
over structural holes between groups in the organization are more likely to express
their ideas, less likely to have their ideas dismissed and more likely to have their
ideas evaluated as valuable”.




In the crowdsourcing literature there is evidence of openness and technical
marginality as important factors in innovation activities. For example, Jeppesen and
Lakhani (2010) show that in a crowdsourcing initiative involving scientific problems
and lump sum prizes, winning solutions are positively related to increasing distance
between the solver’s field of expertise and the focal field of the problem. The results
from this research somehow make us think that the wining solutions are correlated
with knowledge brokering, since the solver´s field of expertise is distant from the
                                                                                          P a g e | 12
focal field of the problem. These solvers are probably using solutions from their field
of expertise and adapting them as new solutions for problems in a different field of
expertise. Our first question is if the knowledge brokering position in the
organizational social network is positively correlated with innovative activity. ICC
initiatives like the SMI can help us to answer this question because we have a
crowdsourcing initiative with innovation activity and is made inside a closed crowd
of employees from which we can know the social network.




Thus, in internal corporate crowdsourcing, it is expected that more creative
participants will have a knowledge brokering position in the organizational social
network.




Hypothesis 1 – Innovation activity in intra-corporate crowdsourcing
initiatives, as the Stock Market for Innovation, is positively correlated with
brokering position in the organizational social network.




As mentioned above, the effect of the social network on the behaviour of individuals
on the corporate crowdsourcing initiatives is expected to happen even if only at a
subtle level. This possibility of effect is very important to analyse mainly on the
valuation of the ideas in the internal corporate crowdsourcing initiatives such as
Markets for Innovation. In these Markets for Innovation the evaluation of
performance of the participants is regularly made by the number of comments and
investments each participant get. A similar evaluation is made for the submitted
ideas, where the ideas that receive more comments and investments are the most
valued and more suitable for implementation.




When participating on an ICC initiative such as SMI, an individual is constrained by
his time limitations and level of effort necessary to do it. The individual according
with his preferences and abilities will dedicate some time and effort to participate in

                                                                                          P a g e | 13
this event. To make a decision of what ideas to analyse, comment or invest in the
SMI, a participant will not try to get the information on all the ideas, previous
comments and investments. When someone is buying a second hand car it does not
seek information on all the second hand cars available in the market. Typically to
optimize his choice the person will ask to their friends, or to friends of friends, if
they know someone trustworthy that have a car to sell. Then it is reasonable to
assume that participants will optimize their participation in the SMI and will analyse,
comment and invest in a limited number of ideas and will most likely analyse
comments and invest in their friend’s ideas, or friends of friend’s ideas. With this
assumption I think that a participant will have more interest in analysing and
comment ideas from people he is connected with. Thus, the organizational social
network will be correlated with the participation on ICC initiatives




Hypothesis 2 – The Organizational Social Network is positively correlated
with the behaviour (analysis, comments and investments) of participants on
a ICC initiative as the SMI.




Following Hypothesis 2, it is important to go beyond in the analysis of the
correlation between the Social Network and the Behaviour (analysis, comments and
investments) in the SMI. Hypothesis 2 analyses this correlation at the network level
and it is also interesting to make an analysis at the individual level. Why do some
ideas and participants receive more comments (higher valued) than others in the
SMI? Is there any social network individual characteristic that drives the participants
to receive more comments and investments? To answer this question it is important
to analyse the social network structure and test if there is any correlation between
the individual network characteristics of the participants and the number of
comments they receive.




There are two main concepts that give theoretical support for a correlation between
the individual network characteristic and the number of comments or investments

                                                                                          P a g e | 14
received. The first concept is again the Structural Holes and the Knowledge
Brokering. As already mentioned in Hypothesis 1, Knowledge brokers are expected
to be the most creative participants in the ICC initiatives as the SMI due to their
possibility of spanning over structural holes. The supposedly creativity of these
participants will allow them to submit more creative ideas and to make more
creative and more valuable comments to the other’s ideas. This creativity of the
ideas and comments made by the Knowledge Broker will allow him to receive more
comments and investments from other participants than what we would expect
from any other participant. Additionally the position of a Knowledge Broker bridging
over a Structural Hole has high advantages in processes of diffusion of information.
The Knowledge Broker with its bridging ties can reach parts of the social network
that other participants do not access, possibly granting him an exclusive audience
that might make comments or investments on his ideas or previous comments.




Hypothesis 3a – Comments and investments received ICC initiatives as SMI
is positively correlated with creative activity.




The second concept is the Bonacich Power and Centrality concept (Bonacich 1987).
Bonacich argues that being connected to well connected others makes an actor
central, but not powerful. On the contrary, being connected to others that are not
well connected makes one powerful although not central. His argument sets upon
the dependability of the other actors to whom the individual is connected. If the
actors that the individual are connected to are, themselves, well connected, they are
not highly dependent on him. These actors, have many contacts, just as you do and
they don´t have to go through you to get what they want. On the other hand, if the
actors to whom the individual is connected are not, themselves, well connected,
then they are dependent on him because they have to go through him to get what
they want. Bonacich created a measure (Bonacich Power Centrality) that captures
this dichotomy of Power and Centrality, and shows that the more connections the
actors in your neighbourhood have, the more central you are; the fewer the
connections the actors in your neighbourhood, the more powerful you are. Since

                                                                                        P a g e | 15
making a comment to an idea or previous comment requires some time and effort
we believe power will be more important and effective than centrality in collecting
comments made by other participants.




Participant A that is connected to a peripheral participant B (with just one
connection) will exert his social power and if B makes a comment it will do it on the
idea or previous comment from A. On the contrary, participant C that is connected
to a central participant D will probably receive fewer comments because D will
divide his effort to comment for several ideas or comments from the participants to
whom he is connected to. Assuming that the effort to comment on ideas or
previous comments is randomly distributed over the network and on average a
participant makes 3 comments (the real average value in our data is 3.2), then a
participant connected to a peripheral participant will receive in average three
comments from this participant. Contrarily a participant connected to a participant
with three connections will receive on average one comment from this participant.




Hypothesis 3b – Comments and investments received on a ICC initiative as a
SMI is positively correlated with social network power of the SMI
participants.




4. DATA, METHODOLOGY, NETWORKS AND MEASURES

       4.1.         Data

The data2 as basis of analysis is from an Innovation Management course with 86
Master’s students and is constituted by the following three datasets:

      Data from a survey results on the 86 students (100% response) where each
       student indicated the top 5 other students with which they use to work and


2
    Data supplied by Prof. Andrei Villarroel from the Católica University of Portugal

                                                                                        P a g e | 16
study and its frequency in a scale from 1 to 5 ( 1 – once; 2 – rarely; 3 –
    sometimes; 4 – quite often and 5 – always). This survey was done in the
    beginning of the course before any individual or group assignment and before
    the crowdsourcing initiative was initiated.

   Data from a crowdsoursing initiative for Innovation performed on the Innovation
    Management course taught by Professor Andrei Villarroel in Portugal in the
    spring semester of 2010. As a part of the coursework and grade, the students
    participated in the online Innovation crowdsourcing (IC) initiative by submitting
    innovation ideas for new products or services and by visiting and commenting
    on other’s ideas. In this IC initiative all the 86 students participated, 22
    innovation ideas were submitted, 331 comments were made to ideas or
    previous comments and 1815 visits (1519 to ideas and 296 to student profiles)
    were made. More specifically the data from the IC initiative has information of
    which students submitted ideas, which students visited those ideas and profiles
    of other students and which students made online comments on ideas and
    previous comments.

   Information on class performance for the 86 students: crowdsourcing grade,
    group grade, individual grade and final grade.

Table 1 has the descriptive statistics of all the individual level variables used:




                  Variable                     N       Mean      Stdev     Min      Max
    Ideas                                      86       0.26      0.44     0.00      1.00
    Final Grade                                86      67.54     10.90    35.18     91.72
    Comments received                          86       3.85      5.88     0.00     25.00
    Symmetric Strong Study Network
    Todal degree Centrality                    86       3.77     2.20       0.00    10.00
    Structural Holes                           81       0.17     0.29      -0.25     0.64
    Bonacich Power Centrality                  81       0.86     0.52       0.35     2.56
    Underlying Graph Study Network
    Todal degree Centrality                    86       9.07     3.17       2.00    16.00
    Structural Holes                           86       0.57     0.21      -0.22     0.83
    Bonacich Power Centrality                  86       0.93     0.36       0.15     1.86

         Table 1 – Descriptive Statistics from all the used individual level variables

                                                                                            P a g e | 17
This IC initiative was a lighter version of a SMI where investments were not included.
The grade the students got for participating in this IC initiative is calculated by a
formula that accounts the number of ideas posted, comments made, comments
received, visits made and visits received by each student. We don’t have the specific
valuation for each of these actions in the IC initiative




    4.2.         Methodology

To prepare the several datasets and to create the necessary social networks to our
analysis we propose the steps described in Table 2:

Step     Procedure

1        Construct the study social network from the survey results and calculate the
         correspondent network measures.

2        Construct the comments and visits social networks from the activity
         (comments and visits) in the crowdsourcing for innovation. These inferred
         networks are directed graphs to be an image of the students’ behavior on
         the internal crowdsourcing initiative.

3        Calculate the network measures at the individual level that will be used as
         explanatory variables in the Hypotheses analysis.

4        Test our Hypotheses by analyzing the correlation between social network
         metrics of each student and their behavior in the internal crowdsourcing
         initiative (ideas, comments, visits) using as control measures the
         performance on class (individual grade).

Table 2 – Steps for the preparation of the datasets




                                                                                         P a g e | 18
To test our Hypotheses we used two types of econometric analysis:

       A more traditional econometrics approach of Probit regression model and a
        Poisson regression model. These models assume the independence between
        all observations which might not be true with all the network measures. For
        example if A and B share a common group of friends then their friend’s
        social networks will be correlated. Being aware of this fact I will use these
        methods to test Hypothesis 1, Hypothesis 3a and Hypothesis 3b since there
        is no other way of getting correlations between network measures and
        attributes of the same network.

       A quadratic assignment procedure for inference on multiple-regression
        coefficients (MRQAP), which is a method equivalent to the general linear
        regression model but is specific to be used with network data. As mentioned
        in the previous point, Network data might violate the assumption of
        independence between all observations and also there is the risk of equal
        observations across several individuals which mean the errors terms could
        be correlated if a linear regression was used. The Multiple Regression
        Quadratic Assignment Procedure (MRQAP) is used exactly to comply with
        the assumption of independence between all observations. This method
        estimates the standard errors using several permutations of the dependent
        variable data set, resulting in multiple random datasets with the dependent
        variable. Hypothesis 2 is tested with the MRQAP method.



    4.3.        Network Construct

        4.3.1. Study Network

The first network needed to construct is the Study Network (SN) that is a reflex of
the results from the survey data indicating for each student the students that he
studies and works with. The study relation is a physical relation (if A studies with B
then B studies with A) since the study relation is reciprocal, we can only construct
reciprocal networks. It does not make sense to use a Directed Graph since that
would be to assume that A studies with B but B does not study with A which is

                                                                                         P a g e | 19
physically impossible. Thus we constructed two variations of the Study Network, one
considering reciprocal identification where a tie exists when A identifies B and vice
versa (Symmetric Strong Study Network) and the other considering that a tie exists if
either A indentifies B or vice versa (Underlying Graph Study Network).




Figure 3 shows a picture of the Symmetric Strong Study Network analyzed in our
study:




Figure 3 – Illustration of the Symmetric Strong Study Network (designed in R studio)




Figure 4 shows a picture of the Underlying Graph Study Network analyzed in our
study:




                                                                                        P a g e | 20
Figure 4 – Illustration of the Underlying Graph Study Network (designed in R studio)




In this case the Underlying Graph is acceptable to use because there are some issues
with the open questions of surveys that might generate subjectivity. In the answers
(Bertrand, M. & Mullainathan S.,2001). The survey had the restriction of five persons
to nominate, limiting the choice of study partners, the scale of intensity of study
(always, often, sometimes, etc) is subjective and because people do not always
remember everyone with whom they studied or worked. We believe that due to all
of these issues there could have been situations where A and B studied together,
but just one of them has indicated that. Thus, using the Underlying Graph we
consider that the identification just from one student is enough to consider the
existence of a reciprocal tie.




                                                                                        P a g e | 21
4.3.1. Comments Network

The Comments Network is a reflex of the behaviour of all participants in the
Innovation Crowdsourcing initiative in terms of the comments made or received. To
construct the Comments Network we used two variations, the Directed Graph and
the Strong Network. The variation of Underlying Graph does not make sense to
construct because that would be to assume that if A commented on B, then B
commented on A which might not be true. Additionally, the Strong Network had
close to 10 edges and all other nodes are isolated. Thus, the Directed Graph
variation is the only network considered meaningful on the Comments Network.




Figure 5 shows a picture of the Direct Graph Comments Network analyzed in our
study:




Figure 5 – Illustration of the Direct Graph Comments Network (designed in R studio)


                                                                                      P a g e | 22
4.4.        Measures

For Hypothesis 1 the needed dependent variable was a measure of the creative
activity and we used a binary variable that describes whether a participant of the
crowdsourcing initiative did or did not submit an innovation idea. We use a binary
variable because no participant has submitted more than one innovation idea.

Has explanatory variables for Hypothesis 1, we used several network measures
calculated from the two variations of the Study Networks (Strong Network and
Underlying Graph). The network measures used were Total Degree Centrality,
Structural Holes and Bonacich Power Centrality. We also could have used other
popular network measures such as Betweeness Centrality and Eigenvector
Centrality, however we opted not to use them because these measures have similar
calculation methods with the other measures we were already using and in the
regressions we would be capturing the same effects. As control variable we used the
Individual Course Grade because it is a good measure of individual performance as
we could expect that students with better individual grades will also want to have a
good grade in the internal crowdsourcing initiative.




Using the regressions methods of QAP or MRQAP we can use networks as
dependent and explanatory variables, thus for Hypothesis 2 we used as dependent
variable the Comments Network (Direct Graph) and as explanatory variables we
used the two Study Networks (Strong Ties and Underlying Graph).




For Hypothesis 3 the dependent variable used was the number of comments
received by each participant in the internal corwdsourcing initiative, as explanatory
variables we used the same network measures from the Study Network, already
explained for Hypothesis 1 and as control variable we also used the Individual
Course Grade.




                                                                                        P a g e | 23
In Table 3 is a resume of the measures for each variable of the three hypotheses:




            Dependent Variable        Explanatory Variable    Other network
                                                                    Variables           Control Variables
H1          creative activity         Brokering position      variables                 Individual performance
Measures    Binary var. ( submitted   Structural hole         Degree and Bonacich
for H1      idea in the SMI)          measure                 Power Centrality          Individual course grade
            Participants Behaviour    Organizational Social
H2          in the SMI                Network
Measures
for H2      Comments Network          Study Network           Visits to Ideas Network
            Comments and
            investments received      Social Network
H3          in the SMI                Power                   creative activity       Individual performance
Measures    comments received in      Bonacich Power          Binary var. ( submitted
for H3      the SMI                   Centrality              idea in the SMI)        Individual course grade



   Table 3 – Steps for the preparation of the datasets




   5. RESULTS

   To test Hypothesis 1 we ran several Probit regressions for each of the two Study
   Networks (Strong and Underlying Graph), having as a dependent variable a binary
   variable Ideas (1 if posted an idea and 0 if not), as explanatory variables the network
   measures of Total Degree Centrality, Structural Holes and Bonacich Power Centrality
   and as control variable the Individual Course Grade:




   Ideas = β0 + β1 x Total Degree Centrality + β2 x Bonacich Power Centrality +
   β3 x Structural Holes + β4 x Individual Course Grade+ µ




   We ran this model with the network measures of both variations of the Study
   Network (Symmetric Strong Study Network and Underlying Graph Study Network)
   and we ran several Probit models for each network to analyse all possible variable
   interactions. Table 4 and Table 5 show the results from the Probit regression on the
   Symmetric Strong Study Network and on the Underlying Graph Study Network.
                                                                                                           P a g e | 24
Strong Network
                    Probit 1 Probit 2 Probit 3 Probit 4 Probit 5 Probit 6           Probit 7
Total Degree                           -0.0033             -0.2214 0.5239            -0.1451
Centrality                               0.962               0.103  0.225              0.809

Bonachic Power       -0.1517                        -0.7647              -2.2070    -0.2722
Centrality             0.631                          0.113                0.207      0.897

                                0.6513              1.5526      1.8347               1.7476
Structural Holes
                                 0.255              0.056 *   0.048 **                0.123

                     0.0344     0.0252    0.0332    0.0335      0.0321    0.0369     0.0327
Individual grade
                      0.126      0.250     0.132     0.144       0.153     0.112      0.160

Adjusted R2           -0.023    -0.021    -0.021     -0.031     -0.031     -0.034     -0.045
Observations              81        81        81         81         81         81         81

Dependent Variable - Binary variable (1 if student posted idea and 0 if not)
top value - Coefficient                                     lower value - Significance
Signif. codes: *** < 0.01; ** < 0.05; * < 0.1

Table 4 – Results from the Probit regression on the Symmetric Strong Study Network




                                           Underlying Graph
                    Probit 1 Probit 2 Probit 3 Probit 4 Probit 5 Probit 6           Probit 7

Total Degree                             -0.0492               -0.0552   -0.2355    -0.2520
Centrality                                 0.320                 0.529     0.291      0.297

Bonachic Power      -0.3390                        -0.1905                1.6706     1.7056
Centrality            0.436                          0.792                 0.391      0.385

                               -0.5533             -0.3046      0.1046               0.2168
Structural Holes
                                 0.441               0.800       0.935                0.866

                     0.0358     0.0337    0.0369    0.0350      0.0372    0.0374    0.0381
Individual grade
                      0.110      0.125     0.102     0.124       0.105    0.096 *   0.096 *

Adjusted R2           -0.021    -0.021    -0.020     -0.033     -0.033    -0.032     -0.045
Observations              86        86        86         86         86        86         86

Dependent Variable - Binary variable (1 if student posted idea and 0 if not)
top value - Coefficient                                     lower value - Significance
Signif. codes: *** < 0.01; ** < 0.05; * < 0.1

Table 5 – Results from the Probit regression on the Underlying Graph Study Network

                                                                                               P a g e | 25
From the tables above we can see that none of the network measures is correlated
with the binary variable Ideas. For both Study Networks (Strong Ties and Underlying
Graph) all the Probit regressions have negative Adjusted R-squared which means
that the model does not explain at all the creative activity of the participants in the
internal crowdsourcing initiative. According to this result it is clear that being in a
bridging position to broker knowledge across structural holes is not related with
creativity activity in the Innovation Crowdsourcing initiative of the class and thus our
Hypothesis 1 is not supported. Additionally, as already mentioned none of the other
network measures is correlated with the creative activity of submitting an idea to
the Innovation Crowdsourcing initiative of the class. This means that weather a
student submits or not an idea to the Innovation Crowdsourcing initiative of the
class is independent of which other students they study with. Probably the network
information that we have for this analysis is not enough to have more enlightening
results. Not having any other information (demographic, schooling, working
experience, area of expertise, etc.) regarding the students makes us assume in our
analysis that every student is equal and does not have differences in their age,
gender, technical knowledge, and working experience.




Having this information would be very important not only to use as control
variables, but also to incorporate it into the Study Network creating a Meta-
Network. For instance, incorporating the students’ individual information of
technical knowledge and working experience would allow us to construct two other
attribute Networks, the Network of the Students’ Technical Knowledge and the
Network of the Students’ Working Experience. These two networks would be much
richer for the analysis because we could see the students that have access to
resources that others don’t and clearly identify the students close to the structural
holes of the Technical Knowledge Network. These students, through their
positioning of knowledge brokers over technical knowledge structural holes, should
be the more creative participants in the Innovation Crowdsourcing initiative of the
class.


                                                                                           P a g e | 26
To analyse Hypothesis 2, first we used QAP correlation tests to see the correlations
between the Comments Network and the two Study Networks (Strong Tie and
Underlying Graph). To understand the goodness of fit of the Study Networks in
explaining the Comments Network we used a MRQAP where the dependent variable
is the Comments Network and the explanatory variables are the two Study
Networks. Since the Strong Tie Study Network has a correlation of 1 with the
Underlying Graph Study Network we cannot combine both these networks in the
same MRQAP. To see the effects of these networks we ran 2 MRQAPs and used as a
second explanatory variable in each MRQAP a Network created from the visits of the
participants to the ideas submitted in the internal crowdsourcing initiative.




MRQAP 1:        Comments Network = β0 + β1 x Symmetric Strong Study
Network + β2 x Visits to Ideas Network + µ

MRQAP 2:        Comments Network = β0 + β1 x Underlying. Graph Study
Network + β2 x Visits to Ideas Network + µ




And to confirm that this Network of Visits to Ideas is not highly correlated with any
of the Study Networks we also ran a QAP between the Visits to Ideas Network and
the two Study Networks.

Table 6 shows the results from the above described QAP regressions:




                                                                                        P a g e | 27
QAP correlation test with Comments Network
           QAP - 500 permutations        Correlation Significance
       Symmetric Strong Study Netwok        0.079       0.015
       Underlying Graph Study Network       0.133          0



              QAP correlation with Visits to Ideas Network
           QAP - 500 permutations         Correlation Significance
       Symmetric Strong Study Netwok          0.029       0.01
       Underlying Graph Study Network         0.034         0

Table 6 – Results from the QAP regressions between the Symmetric Strong and Underlying
Graph Study Networks with the Comments Network (Directed Graph) and Visits to Ideas
Network (Directed Graph)




The top part of Table 6 shows that both Study Networks are significantly correlated
with the Comments Network and that the Underlying Graph Network is more
correlated (13.3%) than the Symmetric Strong Study Network (7.9%). These
correlation numbers might not seem to be high but assuming that in a pure
crowdsourcing initiative this correlation should be spurious and close to 0%, then
seeing correlation numbers of 7.9% and 13.3%, we have to admit that the Social
Networks might be having some effects on the behaviour of the participants of
innovation crowdsourcing initiative of the class. Additionally we can see that the
Visits to Ideas Network is significantly but not highly correlated with both Study
Networks which allow us to use it as a explanatory variable in the MRQAPS that will
allow us to test Hypothesis 2. Table 7 shows the results from the above described
MRQAP regressions




                                                                                         P a g e | 28
MRQAP - 500 permutations                  MRQAP 1          MRQAP 2
                                                     0.0074           0.0065
              Visits Ideas Network
                                                     0.1050           0.1150

                                                       0.0420
       Symmetric Strong Study Netwok
                                                       0.052*

                                                                      0.1126
      Underlying Graph Study Network
                                                                     0.005***

              Adjusted R-Squared:                      0.02710        0.03280

     Dependent Variable - Comments Network
     Signif. codes: *** < 0.01; ** < 0.05; * < 0.1
     top value - Coeficient                   lower value - Sig.Y-Perm

Table 7 – Results from the MRQAP regressions 1 and 2




Table 6 shows that indeed both Study Networks are significant in explaining the
comments networks, although the Symmetric Strong Study Network is only
significant up to a 10% level and its coefficient is not very high in magnitude. The
Underlying Study Network is significant at a 1% level and its coefficient is already
high in magnitude. To have an idea of the impact of these results, the interpretation
of the coefficient from the Underlying Graph means that in average for every two
students that studied together there is 11.26% probability that one student will
make a comment to the other when participating on the innovation crowdsourcing
initiative of the class. This is even more surprising considering the non significance of
the Visits to Ideas Network in this model. This seems to be evidence that the
comments the students make in the innovation crowdsourcing initiative of the class
are influenced by their social study network but not by the ideas they visited. The
result above gives support to our Hypothesis 2. The adjusted R-squared of the
models is a relatively acceptable value for the procedure of the MRQAP and the
most important is that this value is not negative and is not very close to zero. The
adjusted R-squared from a MRQAP is smaller than what is normal to see in a linear


                                                                                            P a g e | 29
regression because the procedure of permutations creates several random datasets
which lowers the adjusted R-squared.




The results from Hypothesis 2 show that the Underlying Graph is the Study Network
that best explains the Comments Network, thus to test our Hypothesis 3 we decided
only to use the Underlying Graph Study Network in this analysis. For Hypothesis 3
we ran a Poisson Model having as a dependent variable the number of comments
received in the crowdsourcing initiative by each student, as explanatory variables
several network measures (Bonacich Power Centrality and Structural Holes) and as
control variables the Individual Course Grade and the binary variable Ideas (1 if
student submitted an idea or 0 if not). We used the Poisson model because the
distribution of the dependent variable of the model (comments received) is pretty
similar to a Poisson distribution.




Log (Comments received) = β0 + β1 x Bonacich Power Centrality + β2 x Structural
Holes + β3 x Individual Course Grade + β4 x Ideas




We measured the Bonacich Power Centrality with a positive beta, which means that
a positive coefficient from this measure will indicate that receiving comments on the
innovation crowdsourcing initiative of the class has a positive correlation with
individuals that are more central, but less powerful. If the coefficient is negative, this
will indicate that the less central, but more powerful, individuals are the ones
receiving more comments. The results from the Poisson regression are shown in the
Table 8:




                                                                                             P a g e | 30
Table 7 – Results from the Poisson regression on the Underlying Graph Study Network




The results from Table 7 show that the most important driver to receive comments
in the innovation crowdsourcing initiative of the class is to submit an idea. This
result strongly supports the Hypothesis 3a in showing that receiving comments on
the innovation crowdsourcing initiative of the class is positively correlated with the
creative activity. However, the Structural Holes variable is not significant in this
analysis which does not come to a surprise since the results from Hypothesis 1 show
that the Structural Holes measure is not correlated with the creative activity in the
innovation crowdsourcing initiative of the class. Once again, if we had access to the
additional information from the individuals, it would allow us to analyse the Study
Network data as a Meta-Network and possibly shed more light into the Structural
Holes story.




Regarding the Bonacich Power Centrality variable we can see that in all models the
coefficient is negative and significant (at 1% level not controlling for ideas and at 5%
level controlling for ideas). This result give support to the story that participants
connected to less connected participants can exert power over these last and
                                                                                           P a g e | 31
receive more comments on their ideas or previous comments, allowing them to
have better evaluation of ideas or performance. Contrarily, participants that are
connected to highly connected participants will receive fewer comments because
the highly connected participants will divide their effort to comment for several
ideas or comments from the competing participants to whom they are connected to.
Thus, this supports our Hypothesis 3b - Comments on corporate crowsourcing
initiatives, as Stock market for Innovations is positively correlated with social
network power of the SMI participants. As we hypothesized, the individual network
characteristic that is important in the evaluation procedure of ideas and
performance of the participants is the power and not centrality, contrarily to what
intuitively one could think.




6. LIMITATIONS AND CONCLUSIONS

    6.1.        Limitations

In this study we wanted to analyse the role of social networks in the ICC initiatives
like the SMI. The biggest limitation from our research is that the analysis is done on
a setup (students from a Master’s class) somehow different from a corporation. This
limitation can be divided into two different aspects:




First, the analysed SMI was not a real SMI because it did not have investments. This
limitation takes some reality and probably dynamics from the SMI made on the class
since there were no investments or prizes involved. If the analysed SMI had
investments and prizes involved we believe that the students would take this ICC
much more seriously and we would expect the student’s behaviour to be even more
pronounced. We would expect to see higher coefficients and lower significance
levels. In the analysis if the SMI involved investments and prizes.




Second, the social network of students is significantly different from a social network
of corporate employees. Additionally the corporate employee social network is not
                                                                                          P a g e | 32
the only network operating in the corporation; there is also the organizational
hierarchic network that can also have influence of its own (promotions, career
progression, layoff, etc) despite the influence of the social network. We believe that
the analysed students social network again attenuates the dynamics of interactions
between actors since students don’t have that much at stake with each other that
motivates them to be extremely active in their social network. If the SMI was done
on a corporation with the presence of employee social network and the
organizational hierarchic network, we believe that the behaviour of the social actors
would be much more active in their social roles and we would again probably see
higher coefficients and lower significance levels.




    6.2.        Conclusions

Several Companies have been using crowdsourcing initiatives to perform diverse
tasks. One of the more recent of these initiatives are internal Stock Markets for
Innovation where companies can tap into the creativity power of their employees
through an online stock market where employees create, comment and invest on
new ideas for products, processes and services, that might be implemented by the
company. However these initiatives that should be based in the “wisdom of the
crowds” might be under the influence of the Social Networks that exist in the
company way before the crowdsourcing initiatives.




From the theoretical idea that internal corporate crowdsourcing initiatives might be
under the influence of social networks we studied a similar crowdsourcing initiative
made on a Master’s class. Having access to a survey from the study partners’
student have and to the data from the crowdsourcing platform, we constructed
social networks (Study and Comments) and made several analyses that support
some of our theoretical hypothesis.




The results from our analysis show that Hypothesis 2 – The Organizational Social
Network is positively correlated with the behaviour (analysis and comments)
                                                                                         P a g e | 33
of participants on a ICC initiative as the SMI. – is supported and that for our data
we can say that the Social Network is correlated with the behaviour of the
participants on the innovation crowdsourcing initiative of the class. However, this
influence of the Social Networks on the innovation crowdsourcing initiative of the
class cannot be seen on the creative activity (submission of ideas) and our
Hypothesis 1 – Innovation activity in intra-corporate crowdsourcing initiatives, as the
Stock Market for Innovation, is positively correlated with brokering position in the
organizational social network. – is not supported. To have a better and more
complete analysis of Hypothesis 1 it would require more individual information of
the participants in the innovation crowdsourcing initiative of the class.




As no surprise, the creative activity (submission of ideas) is the most important
driver to receive comments in the corporate crowdsourcing initiative and the results
on this analysis support Hypothesis 3a – Comments received on corporate
crowdsourcing initiatives, as Stock market for Innovations is positively correlated
with creative activity.




The area where our findings show influence of the Social Networks on the corporate
crowdsourcing initiative is the evaluation of ideas and performances of the
participants. Perhaps the most interesting result in this paper is that the most
important characteristic playing a role in this evaluation procedure is power, where
the most powerful participants get higher evaluation on their ideas or performance.
This idea is comes from the results that support our Hypothesis 3b – Comments
and investments received on a ICC initiative as a SMI is positively correlated
with social network power of the SMI participants, which means that is
negatively correlated with the Bonacich Power Centrality.




This last result seems to be a similar situation to the one found in the results of
Villarroel & Reis, (2011) - “speculative activity is positively associated with better
innovation performance”. One can ask if ICC initiatives such as SMI might just be

                                                                                          P a g e | 34
another tool for the most powerful actors to use, reinforce and legitimize their social
power in their own benefit. One implication of our results is that knowledge
diffusion in the SMI initiatives might not be that different than what existed
previously and might be even more easily manipulated (Legitimization) by the
powerful actors. If so, the advantage of “increased efficiency at the fuzzy front end
of the new product development process” (Soukhoroukova et al 2010) might be in
risk of being offset by the possible manipulation of the powerful actors. Any
management team should have this in mind when considering applying an ICC
initiative similar to a SMI. Further studies are needed in this area to analyze more
deeply the effect of Social Network and Organizational Hierarchic Network in the
outcomes of ICC such SMI.




                                                                                          P a g e | 35
BIBLIOGRAPHY

Allen, T. J., & Cohen, S. I. (1969), Information flow in research and development
laboratories. Administrative Science Quarterly, 14: 12–19.

Allen, T. J., Tushman, M. L., & Lee, M. S. (1979), Technology transfer as function of
position in the spectrum from research through development to technical services.
Academy of Management Journal, 22: 684–708.

Ancona, D., & Caldwell, D. F. (1992), Bridging the boundary: External activity and
performance in organizational teams. Administrative Science Quarterly, 37: 634–
665.

Bertrand, M. & Mullainathan S.,(2001). Do People Mean What They Say?
Implications for Subjective Survey Data. American Economic Review 91(2): 67-72.

Bonacich, P. (1987) Power and Centrality: A Family of Measures. American Journal of
Sociology, Vol.92, Nº.5, pp. 1170–1182.

Brabham, D. C. (2008), Crowdsourcing as a Model for Problem Solving. An
introduction and cases, in The International Journal of Research into New Media
Technologies, Vol. 14, No. 1, pp. 75-90.

Burt, R. S. (1992), Structural Holes: The Social Structure of Competition. Harvard
University press.

Burt, R. S. (2004) Structural holes and good ideas. American Journal of Sociology,
110: 349–399.

Cohen, W. M. and Levinthal, D. A. (1990), Absorptive capacity: a new perspective on
learning and innovation. Administrative Science Quarterly, Vol. 35, No. 1 pp. 128-
152

Dekker, D., Krackhardt, D., & Snijders, T. A. B. (2007), Sensitivity of MRQAP tests to
collinearity and autocorrelation conditions. Psychometrika, 72(4), 563–581.

Hansen, M. (1999), The search-transfer problem: The role of weak ties in sharing
knowledge across organization subunits. Administrative Science Quarterly, 44: 82–
111.

Hargadon Andrew B. (1998), Firms as Knowledge Brokers: Lessons in Pursuing
Continuous Innovation. California Management Review Vol. 40 No. 3.

Howe, J. (2008), Crowdsourcing: Why the Power of the Crowd Is Driving the Future
of Business. Crown Business, New York.

Jeppesen, L. B. and Lakhani, K. R. (2010), Marginality and Problem Solving
Effectiveness in Broadcast Search. Organization Science Volume 21 Issue 5.

                                                                                         P a g e | 36
Krackhardt, D. (1988), Predicting with networks: Nonparametric multiple regression
analysis of dyadic data. Social Networks, 10(4), 359–381.

Krackhardt, D. (1998), Simmelian tie: Super strong and sticky. In R. M. Kramer & M.
A. Neale (Eds.), Power and influence in organizations: 21–38. Thousand Oaks, CA:
Sage.

Krackhardt, D. (1999) The ties that torture: Simmelian tie analysis in organizations. In
S. B. Andrews & D. Knocke (Eds.), Research in the sociology of organizations, vol. 16:
183–210. Greenwich, CT: JAI Press.

Lakhani, K. R. and Panetta, J. (2007), The principles of Distributed Innovation.
Innovations: Technology, Governance, Globalization 2, Nº 3 (summer 2007).

Lakhani, K. R.; Jeppesen, L. B.; Lohseet, P. A. and Panneta, J. (2007), Openness in
Scientific Problem Solving. Boston: Harvard – Citeseer.

McEvily, B., & Zaheer, A. (1999), Bridging ties: A source of firm heterogeneity in
competitive capabilities. Strategic Management Journal, 20: 1133–1156.

McPherson, J. M.; Smith-Lovin, L. and Cook, J. M. (2001). Birds of a feather:
Homophily in social networks. Annual Review of Sociology, 27: 415–444.

Soukhoroukova A., Spann M., Skiera B. (2010) Sourcing, Filtering, and Evaluating
New Product Ideas: An Empirical Exploration of the Performance of Idea Markets.
Journal of Product Innovation Management. Forthcoming 2010.

Villarroel J.A. & Reis F. (2010). Intra-Corporate crowdsourcing (ICC): Leveraging Upon
Rank and Site Marginality For Innovation. CrowdConf 2010; October 4, 2010

Villarroel J.A. & Reis F. (2011). A Stock Market Approach to Online Distributed
Innovation: The Trade-off Between Speculation and Innovation Performance. ACM
EC Social Computing Workshop 2011.

Wasserman S. & Faust K.(1994). Social Network Analysis: Methods and Applications.
Cambridge University Press




                                                                                           P a g e | 37

Mais conteúdo relacionado

Mais procurados

Marquard lakhani e lml 2014e journal-060714
Marquard lakhani e lml 2014e journal-060714Marquard lakhani e lml 2014e journal-060714
Marquard lakhani e lml 2014e journal-060714Murtuza Ali Lakhani
 
What Is Enterprise 2.0 Public
What Is Enterprise 2.0   PublicWhat Is Enterprise 2.0   Public
What Is Enterprise 2.0 PublicTanya Ney
 
Perspective on virtual collaboration benchmark.ppt
Perspective on virtual collaboration benchmark.pptPerspective on virtual collaboration benchmark.ppt
Perspective on virtual collaboration benchmark.pptLucy Garrick
 
Ten tech-enabled business trands to watch - August 10
Ten tech-enabled business trands to watch - August 10Ten tech-enabled business trands to watch - August 10
Ten tech-enabled business trands to watch - August 10Carl Terrantroy
 
Thinking psychoanalytically about desire in organizations - why we need a 3rd...
Thinking psychoanalytically about desire in organizations - why we need a 3rd...Thinking psychoanalytically about desire in organizations - why we need a 3rd...
Thinking psychoanalytically about desire in organizations - why we need a 3rd...Boxer Research Ltd
 
Marquard lakhani e lml 2014e journal
Marquard lakhani e lml 2014e journalMarquard lakhani e lml 2014e journal
Marquard lakhani e lml 2014e journalMurtuza Ali Lakhani
 
Model-driven Development of Social Network-enabled Applications
Model-driven Development of Social Network-enabled ApplicationsModel-driven Development of Social Network-enabled Applications
Model-driven Development of Social Network-enabled ApplicationsMarco Brambilla
 
Wanted an Active, Viable, Collaborative On-line Community
Wanted an Active, Viable, Collaborative On-line CommunityWanted an Active, Viable, Collaborative On-line Community
Wanted an Active, Viable, Collaborative On-line CommunityLouis-Pierre Guillaume
 
Running Research Communities in Asian markets
Running Research Communities in Asian marketsRunning Research Communities in Asian markets
Running Research Communities in Asian marketsInSites Consulting
 
Twintangibles - IP & IA in the Social Media Age
Twintangibles - IP & IA in the Social Media AgeTwintangibles - IP & IA in the Social Media Age
Twintangibles - IP & IA in the Social Media Agetwintangibles
 
Good practice exchange from a Web 2.0 point of view
Good practice exchange from a Web 2.0 point of viewGood practice exchange from a Web 2.0 point of view
Good practice exchange from a Web 2.0 point of viewePractice.eu
 
Reputation based model for decision making in the digital age
Reputation based model for decision making in the digital ageReputation based model for decision making in the digital age
Reputation based model for decision making in the digital ageTogar Simatupang
 
Social Software in Knowledge Management of Organizations
Social Software in Knowledge Management of OrganizationsSocial Software in Knowledge Management of Organizations
Social Software in Knowledge Management of OrganizationsRalf Klamma
 
The social capital of online influencers: evidence from the food industries
The social capital of online influencers: evidence from the food industriesThe social capital of online influencers: evidence from the food industries
The social capital of online influencers: evidence from the food industriesIvana Pais
 
Communities of Practice
Communities of PracticeCommunities of Practice
Communities of PracticeNoel Hatch
 
Driving impact-through-networks
Driving impact-through-networksDriving impact-through-networks
Driving impact-through-networksNicolas Ponset
 
Why Entrepreneurship Matters
Why Entrepreneurship MattersWhy Entrepreneurship Matters
Why Entrepreneurship MattersKristin Wolff
 

Mais procurados (19)

Marquard lakhani e lml 2014e journal-060714
Marquard lakhani e lml 2014e journal-060714Marquard lakhani e lml 2014e journal-060714
Marquard lakhani e lml 2014e journal-060714
 
What Is Enterprise 2.0 Public
What Is Enterprise 2.0   PublicWhat Is Enterprise 2.0   Public
What Is Enterprise 2.0 Public
 
Perspective on virtual collaboration benchmark.ppt
Perspective on virtual collaboration benchmark.pptPerspective on virtual collaboration benchmark.ppt
Perspective on virtual collaboration benchmark.ppt
 
Ten tech-enabled business trands to watch - August 10
Ten tech-enabled business trands to watch - August 10Ten tech-enabled business trands to watch - August 10
Ten tech-enabled business trands to watch - August 10
 
Thinking psychoanalytically about desire in organizations - why we need a 3rd...
Thinking psychoanalytically about desire in organizations - why we need a 3rd...Thinking psychoanalytically about desire in organizations - why we need a 3rd...
Thinking psychoanalytically about desire in organizations - why we need a 3rd...
 
Event Highlights - Collaboration Retreat 2011
Event Highlights - Collaboration Retreat 2011Event Highlights - Collaboration Retreat 2011
Event Highlights - Collaboration Retreat 2011
 
Marquard lakhani e lml 2014e journal
Marquard lakhani e lml 2014e journalMarquard lakhani e lml 2014e journal
Marquard lakhani e lml 2014e journal
 
Model-driven Development of Social Network-enabled Applications
Model-driven Development of Social Network-enabled ApplicationsModel-driven Development of Social Network-enabled Applications
Model-driven Development of Social Network-enabled Applications
 
Wanted an Active, Viable, Collaborative On-line Community
Wanted an Active, Viable, Collaborative On-line CommunityWanted an Active, Viable, Collaborative On-line Community
Wanted an Active, Viable, Collaborative On-line Community
 
Running Research Communities in Asian markets
Running Research Communities in Asian marketsRunning Research Communities in Asian markets
Running Research Communities in Asian markets
 
Twintangibles - IP & IA in the Social Media Age
Twintangibles - IP & IA in the Social Media AgeTwintangibles - IP & IA in the Social Media Age
Twintangibles - IP & IA in the Social Media Age
 
Ideavibes Presentation to RSA London
Ideavibes Presentation to RSA LondonIdeavibes Presentation to RSA London
Ideavibes Presentation to RSA London
 
Good practice exchange from a Web 2.0 point of view
Good practice exchange from a Web 2.0 point of viewGood practice exchange from a Web 2.0 point of view
Good practice exchange from a Web 2.0 point of view
 
Reputation based model for decision making in the digital age
Reputation based model for decision making in the digital ageReputation based model for decision making in the digital age
Reputation based model for decision making in the digital age
 
Social Software in Knowledge Management of Organizations
Social Software in Knowledge Management of OrganizationsSocial Software in Knowledge Management of Organizations
Social Software in Knowledge Management of Organizations
 
The social capital of online influencers: evidence from the food industries
The social capital of online influencers: evidence from the food industriesThe social capital of online influencers: evidence from the food industries
The social capital of online influencers: evidence from the food industries
 
Communities of Practice
Communities of PracticeCommunities of Practice
Communities of Practice
 
Driving impact-through-networks
Driving impact-through-networksDriving impact-through-networks
Driving impact-through-networks
 
Why Entrepreneurship Matters
Why Entrepreneurship MattersWhy Entrepreneurship Matters
Why Entrepreneurship Matters
 

Destaque

Le apparizioni di Maria a Lourdes
Le apparizioni di Maria a LourdesLe apparizioni di Maria a Lourdes
Le apparizioni di Maria a LourdesMonica Prandi
 
Social Media For Small Businesses
Social Media For Small BusinessesSocial Media For Small Businesses
Social Media For Small BusinessesJenni Lloyd
 
In Car Speech User Interfaces - SwANH education event presentation, January 2009
In Car Speech User Interfaces - SwANH education event presentation, January 2009In Car Speech User Interfaces - SwANH education event presentation, January 2009
In Car Speech User Interfaces - SwANH education event presentation, January 2009Andrew Kun
 
2007 Mobile Concept Development
2007 Mobile Concept Development2007 Mobile Concept Development
2007 Mobile Concept DevelopmentJin Lee
 
What’s New With Web 2.0?
What’s New With  Web 2.0?What’s New With  Web 2.0?
What’s New With Web 2.0?JoAnn MIller
 
Product Managers and Startups
Product Managers and StartupsProduct Managers and Startups
Product Managers and StartupsSoftwareMaven
 
Technologies that will transform small business
Technologies that will transform small businessTechnologies that will transform small business
Technologies that will transform small businessSuhag Mistry
 
0861599 Seconds Left - Basketball in Canada
0861599 Seconds Left - Basketball in Canada0861599 Seconds Left - Basketball in Canada
0861599 Seconds Left - Basketball in Canadaguest19b25
 
Project54 Research Areas
Project54 Research AreasProject54 Research Areas
Project54 Research AreasAndrew Kun
 
2008 Design Strategy & Innovation
2008 Design Strategy & Innovation 2008 Design Strategy & Innovation
2008 Design Strategy & Innovation Jin Lee
 
In-Car Speech User Interfaces and their Effects on Driver Cognitive Load
In-Car Speech User Interfaces and their Effects on Driver Cognitive LoadIn-Car Speech User Interfaces and their Effects on Driver Cognitive Load
In-Car Speech User Interfaces and their Effects on Driver Cognitive LoadAndrew Kun
 
關鍵報告
關鍵報告關鍵報告
關鍵報告bliaou
 
Umk Eng 2 4 Nov.Ppt8
Umk Eng 2 4 Nov.Ppt8Umk Eng 2 4 Nov.Ppt8
Umk Eng 2 4 Nov.Ppt8ssjaspb
 

Destaque (20)

Le apparizioni di Maria a Lourdes
Le apparizioni di Maria a LourdesLe apparizioni di Maria a Lourdes
Le apparizioni di Maria a Lourdes
 
Primerafase
PrimerafasePrimerafase
Primerafase
 
Social Media For Small Businesses
Social Media For Small BusinessesSocial Media For Small Businesses
Social Media For Small Businesses
 
In Car Speech User Interfaces - SwANH education event presentation, January 2009
In Car Speech User Interfaces - SwANH education event presentation, January 2009In Car Speech User Interfaces - SwANH education event presentation, January 2009
In Car Speech User Interfaces - SwANH education event presentation, January 2009
 
2007 Mobile Concept Development
2007 Mobile Concept Development2007 Mobile Concept Development
2007 Mobile Concept Development
 
1029
10291029
1029
 
What’s New With Web 2.0?
What’s New With  Web 2.0?What’s New With  Web 2.0?
What’s New With Web 2.0?
 
Product Managers and Startups
Product Managers and StartupsProduct Managers and Startups
Product Managers and Startups
 
Technologies that will transform small business
Technologies that will transform small businessTechnologies that will transform small business
Technologies that will transform small business
 
0861599 Seconds Left - Basketball in Canada
0861599 Seconds Left - Basketball in Canada0861599 Seconds Left - Basketball in Canada
0861599 Seconds Left - Basketball in Canada
 
1021
10211021
1021
 
Project54 Research Areas
Project54 Research AreasProject54 Research Areas
Project54 Research Areas
 
1118
11181118
1118
 
4. tomos b pe70
4. tomos b pe704. tomos b pe70
4. tomos b pe70
 
2008 Design Strategy & Innovation
2008 Design Strategy & Innovation 2008 Design Strategy & Innovation
2008 Design Strategy & Innovation
 
In-Car Speech User Interfaces and their Effects on Driver Cognitive Load
In-Car Speech User Interfaces and their Effects on Driver Cognitive LoadIn-Car Speech User Interfaces and their Effects on Driver Cognitive Load
In-Car Speech User Interfaces and their Effects on Driver Cognitive Load
 
關鍵報告
關鍵報告關鍵報告
關鍵報告
 
Umk Eng 2 4 Nov.Ppt8
Umk Eng 2 4 Nov.Ppt8Umk Eng 2 4 Nov.Ppt8
Umk Eng 2 4 Nov.Ppt8
 
Retiree sue
Retiree sueRetiree sue
Retiree sue
 
0427
04270427
0427
 

Semelhante a My Carnegie Mellon University Master\'s Thesis

Innovation And Change Of Jesuit School System
Innovation And Change Of Jesuit School SystemInnovation And Change Of Jesuit School System
Innovation And Change Of Jesuit School SystemLanate Drummond
 
Crowdsourcing: A Survey
Crowdsourcing: A SurveyCrowdsourcing: A Survey
Crowdsourcing: A SurveyIJERA Editor
 
Crowdsourcing as a problem solving strategy
Crowdsourcing as a problem solving strategyCrowdsourcing as a problem solving strategy
Crowdsourcing as a problem solving strategyMiia Kosonen
 
Involving Citizens in Policy Making with Participatory Design Research Method...
Involving Citizens in Policy Making with Participatory Design Research Method...Involving Citizens in Policy Making with Participatory Design Research Method...
Involving Citizens in Policy Making with Participatory Design Research Method...Sandra Cecet
 
IS CROWDFUNDING DOOMED IN SWEDEN? WHEN INSTITUTIONAL LOGICS AND AFFORDANCES C...
IS CROWDFUNDING DOOMED IN SWEDEN? WHEN INSTITUTIONAL LOGICS AND AFFORDANCES C...IS CROWDFUNDING DOOMED IN SWEDEN? WHEN INSTITUTIONAL LOGICS AND AFFORDANCES C...
IS CROWDFUNDING DOOMED IN SWEDEN? WHEN INSTITUTIONAL LOGICS AND AFFORDANCES C...Robin Teigland
 
"Antenna for Social Innovation: The Quest for Precision"
"Antenna for Social Innovation: The Quest for Precision""Antenna for Social Innovation: The Quest for Precision"
"Antenna for Social Innovation: The Quest for Precision"Ginés Haro Pastor
 
Antenna For Social Innovation: The Quest for Precision
Antenna For Social Innovation: The Quest for PrecisionAntenna For Social Innovation: The Quest for Precision
Antenna For Social Innovation: The Quest for PrecisionESADE
 
Silverman Research: Collective Intelligence In Organisations Report
Silverman Research: Collective Intelligence In Organisations ReportSilverman Research: Collective Intelligence In Organisations Report
Silverman Research: Collective Intelligence In Organisations ReportSilverman_Research
 
Individual paper_ Internet marketing_Pokrywka
Individual paper_ Internet marketing_PokrywkaIndividual paper_ Internet marketing_Pokrywka
Individual paper_ Internet marketing_PokrywkaVeronika Tarnovskaya
 
Solving the Puzzle of Crowdfunding in Sweden_Teigland et al
Solving the Puzzle of Crowdfunding in Sweden_Teigland et alSolving the Puzzle of Crowdfunding in Sweden_Teigland et al
Solving the Puzzle of Crowdfunding in Sweden_Teigland et alRobin Teigland
 
Doing More with More (Venturespring White Paper)
Doing More with More (Venturespring White Paper)Doing More with More (Venturespring White Paper)
Doing More with More (Venturespring White Paper)Venturespring
 
Case Study Of Hyundai, S Korea Motor Company
Case Study Of Hyundai, S Korea Motor CompanyCase Study Of Hyundai, S Korea Motor Company
Case Study Of Hyundai, S Korea Motor CompanyAngela Washington
 
Dimitrova Review Of Cayey
Dimitrova Review Of CayeyDimitrova Review Of Cayey
Dimitrova Review Of CayeyDima Dimitrova
 
Marketing Strategies For Samsung Electronics
Marketing Strategies For Samsung ElectronicsMarketing Strategies For Samsung Electronics
Marketing Strategies For Samsung ElectronicsAngilina Jones
 
Pick the Odd-ones Out! Conferring Legitimacy of Initial Coin Offerings by Dis...
Pick the Odd-ones Out! Conferring Legitimacy of Initial Coin Offerings by Dis...Pick the Odd-ones Out! Conferring Legitimacy of Initial Coin Offerings by Dis...
Pick the Odd-ones Out! Conferring Legitimacy of Initial Coin Offerings by Dis...Nauman Shahid
 
Innovation 3.0: Embedding into community knowledge - The relevance of trust a...
Innovation 3.0: Embedding into community knowledge - The relevance of trust a...Innovation 3.0: Embedding into community knowledge - The relevance of trust a...
Innovation 3.0: Embedding into community knowledge - The relevance of trust a...innowise research & consulting GmbH
 
X Conference on Intellectual Capital
X Conference on Intellectual CapitalX Conference on Intellectual Capital
X Conference on Intellectual CapitalMarcos CAVALCANTI
 

Semelhante a My Carnegie Mellon University Master\'s Thesis (20)

Innovation And Change Of Jesuit School System
Innovation And Change Of Jesuit School SystemInnovation And Change Of Jesuit School System
Innovation And Change Of Jesuit School System
 
Crowdsourcing: A Survey
Crowdsourcing: A SurveyCrowdsourcing: A Survey
Crowdsourcing: A Survey
 
Thesis proposal presentation
Thesis proposal presentationThesis proposal presentation
Thesis proposal presentation
 
Crowdsourcing as a problem solving strategy
Crowdsourcing as a problem solving strategyCrowdsourcing as a problem solving strategy
Crowdsourcing as a problem solving strategy
 
Open Innovation
Open InnovationOpen Innovation
Open Innovation
 
Involving Citizens in Policy Making with Participatory Design Research Method...
Involving Citizens in Policy Making with Participatory Design Research Method...Involving Citizens in Policy Making with Participatory Design Research Method...
Involving Citizens in Policy Making with Participatory Design Research Method...
 
IS CROWDFUNDING DOOMED IN SWEDEN? WHEN INSTITUTIONAL LOGICS AND AFFORDANCES C...
IS CROWDFUNDING DOOMED IN SWEDEN? WHEN INSTITUTIONAL LOGICS AND AFFORDANCES C...IS CROWDFUNDING DOOMED IN SWEDEN? WHEN INSTITUTIONAL LOGICS AND AFFORDANCES C...
IS CROWDFUNDING DOOMED IN SWEDEN? WHEN INSTITUTIONAL LOGICS AND AFFORDANCES C...
 
"Antenna for Social Innovation: The Quest for Precision"
"Antenna for Social Innovation: The Quest for Precision""Antenna for Social Innovation: The Quest for Precision"
"Antenna for Social Innovation: The Quest for Precision"
 
Antenna For Social Innovation: The Quest for Precision
Antenna For Social Innovation: The Quest for PrecisionAntenna For Social Innovation: The Quest for Precision
Antenna For Social Innovation: The Quest for Precision
 
Open Innovation Processes And Roles In Sm Es Verteramo De Carolis Greco
Open Innovation Processes And Roles In Sm Es   Verteramo De Carolis GrecoOpen Innovation Processes And Roles In Sm Es   Verteramo De Carolis Greco
Open Innovation Processes And Roles In Sm Es Verteramo De Carolis Greco
 
Silverman Research: Collective Intelligence In Organisations Report
Silverman Research: Collective Intelligence In Organisations ReportSilverman Research: Collective Intelligence In Organisations Report
Silverman Research: Collective Intelligence In Organisations Report
 
Individual paper_ Internet marketing_Pokrywka
Individual paper_ Internet marketing_PokrywkaIndividual paper_ Internet marketing_Pokrywka
Individual paper_ Internet marketing_Pokrywka
 
Solving the Puzzle of Crowdfunding in Sweden_Teigland et al
Solving the Puzzle of Crowdfunding in Sweden_Teigland et alSolving the Puzzle of Crowdfunding in Sweden_Teigland et al
Solving the Puzzle of Crowdfunding in Sweden_Teigland et al
 
Doing More with More (Venturespring White Paper)
Doing More with More (Venturespring White Paper)Doing More with More (Venturespring White Paper)
Doing More with More (Venturespring White Paper)
 
Case Study Of Hyundai, S Korea Motor Company
Case Study Of Hyundai, S Korea Motor CompanyCase Study Of Hyundai, S Korea Motor Company
Case Study Of Hyundai, S Korea Motor Company
 
Dimitrova Review Of Cayey
Dimitrova Review Of CayeyDimitrova Review Of Cayey
Dimitrova Review Of Cayey
 
Marketing Strategies For Samsung Electronics
Marketing Strategies For Samsung ElectronicsMarketing Strategies For Samsung Electronics
Marketing Strategies For Samsung Electronics
 
Pick the Odd-ones Out! Conferring Legitimacy of Initial Coin Offerings by Dis...
Pick the Odd-ones Out! Conferring Legitimacy of Initial Coin Offerings by Dis...Pick the Odd-ones Out! Conferring Legitimacy of Initial Coin Offerings by Dis...
Pick the Odd-ones Out! Conferring Legitimacy of Initial Coin Offerings by Dis...
 
Innovation 3.0: Embedding into community knowledge - The relevance of trust a...
Innovation 3.0: Embedding into community knowledge - The relevance of trust a...Innovation 3.0: Embedding into community knowledge - The relevance of trust a...
Innovation 3.0: Embedding into community knowledge - The relevance of trust a...
 
X Conference on Intellectual Capital
X Conference on Intellectual CapitalX Conference on Intellectual Capital
X Conference on Intellectual Capital
 

My Carnegie Mellon University Master\'s Thesis

  • 1. Exploring the role of Social Networks in Intra-Corporate Crowdsourcing initiatives such as Stock Market for Innovations. Jonas Rolo jrolo@andrew.cmu.edu October 7, 2011 Advisory Committee: David Krackhardt Andrei Villarroel P age |1
  • 2. Abstract Several Companies have been started using crowdsourcing initiatives internally where only their employees participate. One of the more recent of these initiatives are Stock Markets for Innovation (SMI) where companies can tap into the creativity power of their employees through an online stock market where employees create, comment and invest on new ideas for products, processes and services. Crowdsourcing initiatives should be based in the “wisdom of the crowds”, however intra-corporate crowdsourcing (ICC), being enclosed in the company realm, might be under the influence of the Social Networks. We study a similar setup to a SMI inside a Master’s class and the results from this study show that indeed the Social Network of study is correlated with the behaviour of the SMI participants mainly on the evaluation procedures of ideas and performances of the other participants. The most important network characteristic playing a role in this evaluation procedure is power, where the most powerful participants get higher evaluation on their ideas or performance. This result seems to show that the intra-corporate ICC initiatives might just be another tool for the most powerful actors to reinforce their social power. The management implication is that knowledge diffusion in the SMI initiatives might not be that different than what existed previously in the social network and the management team should have this in mind when considering applying an ICC initiative similar to a SMI. P age |2
  • 3. Contents 1. INTRODUCTION ............................................................................................. 4 2. LITERATURE REVIEW...................................................................................... 5 2.1. Crowdsourcing .............................................................................................. 5 2.1.1. Intra Corporate Crowdsourcing (ICC) ............................................................. 6 2.1.2. Stock Market for Innovations (SMI) ............................................................... 6 2.2. Social Networks ............................................................................................. 7 2.2.1. Structural Holes ............................................................................................. 8 2.2.2. Knowledge Brokering .................................................................................. 10 2.2.1. Bonacich Power and Centrality .................................................................... 10 3. THEORY AND HYPOTHESIS ........................................................................... 12 4. DATA, METHODOLOGY, NETWORKS AND MEASURES .................................. 16 4.1. Data ............................................................................................................ 16 4.2. Methodology............................................................................................... 18 4.3. Network Construct ...................................................................................... 19 4.3.1. Study Network ............................................................................................ 19 4.3.1. Comments Network .................................................................................... 22 4.4. Measures .................................................................................................... 23 5. RESULTS ...................................................................................................... 24 6. LIMITATIONS AND CONCLUSIONS ................................................................ 32 6.1. Limitations .................................................................................................. 32 6.2. Conclusions ................................................................................................. 33 BIBLIOGRAPHY ........................................................................................................ 36 P age |3
  • 4. 1. INTRODUCTION The literature on crowdsourcing has focused mainly on the firm external crowdsourcing initiatives and only recent research has started to tackle closed crowdsourcing initiative made only with employees of a company, a Intra-Corporate Crowdsourcing (Villarroel & Reis, 2010 and 2011). One of the most recent Intra- Corporate Crowdsourcing (ICC) initiatives is Stock Markets for innovation (SMI) (Villarroel & Reis, 2010 and 2011). Soukhoroukova et al (2010), in a recent study on Idea Markets or SMI show that this type of ICC initiatives offers promising advantages for new product innovation: “the platform and the formal process motivates employees to communicate their ideas to management”, “by filtering the ideas generated internally the number of ideas brought to management is reduced” and “the ability to source many ideas can increase efficiency at the fuzzy front end of the new product development process.” One area that has not yet been fully studied in crowdsourcing is the role of pre- existing social networks in ICC initiatives. When considering firm or institution’s internal crowds, the social networks of the crowds were formed long before any new ICC initiative, and thus it is to expect that the social networks might influence the behaviour of the employees in the ICC initiative. In a ICC Stock Market for Innovation (SMI), Villarroel & Reis, (2011) show that “speculative activity is positively associated with better innovation performance”. An example of this speculative activity could be seen in pulling or collusion strategies to invest heavily on one idea for this to be one of the most invested ideas (approved by the market) and the submitter and all investors win with this. The pre-existing social networks might just be the tool these participants are using to perform the speculative activity. Our research question is to understand if there is any correlation between the social network of the participants and their behaviour in the SMI, and if this correlation exists, to understand what individual network measures drive innovative activity and performance in the SMI. Inside the firm the social networks might play an important role in the ICC initiatives and might be a good way of predicting some outcomes of P age |4
  • 5. these initiatives. Knowing the social networks characteristics and nodes’ characteristics might help to understand and predict part of the outcome of the ICC initiative. 2. LITERATURE REVIEW 2.1. Crowdsourcing “Crowdsourcing is an online, distributed problem solving and production model that has emerged in recent years” (Brabham 2008: pp. 75. The term was first used in 2006 by Howe and Robinson and it represents the act of a company or institution taking a function once performed by employees and outsourcing it to an undefined (and generally large) network of people in the form of an open call (Howe 2008: ). Having in common the dependency on the crowd participation, the functions to outsource can be from a wide variety and can be aggregated in three different types of crowdsourcing: Crowd Creation, Crowd Voting and Crowd Funding (Howe 2008), much like variation, selection, retention (Anderson and Tushman 1990). Wikipedia is one of the first examples of crowd creation where an immense crowd creates page contents that build up to Wikipedia’s communitarian knowledge. Another example is Innocentive, a company that supplies innovative technical solutions for tough R&D problems using a worldwide crowd of scientists. Innocentive’s clients are companies with high R&D expenditures that want to get solutions to their unsolved R&D problems. With a mix of crowd creation and crowd voting there is Threadless.com, a web-based t-shirt selling company that crowdsources the design for their shirts and the voting for the best T-shirts from a global crowd through an online competition. iStockphoto.com is another example, a web-based company that sells photography, animations, and video clips for clients to use on websites, in brochures, in business presentations and so on. A crowd of photographers and film makers submit their photographs and video clips and vote on the best photos and videos to rank the website stock. On the crowd funding there is the micro credit example of Kiva, a project that receives proposals of several P age |5
  • 6. projects to be funded in small amounts of money and that a crowd can lend money to. Crowdsourcing initiatives such as Wikipedia, Innocentive, Threadless, iStockphoto, Facebook translation, Goldcorp Challenge, have begun to be studied (Howe 2008; Lakhani and Panetta 2007; Braham 2008; Jeppesen and Lakhani 2010). Studies suggest that crowdsourcing is a “very good source of social capital for corporations” (Villarroel & Reis, 2010) and its openness gives the ability for organizations to increase the resolution rate for problems that had previously remained unsolved (Lahkani et all 2007). 2.1.1. Intra Corporate Crowdsourcing (ICC) The afore mentioned crowdsourcing initiatives rely on contributors external to the firm. Nonetheless, there are firms that have implemented crowdsourcing within their boundaries and this can be called Intra-Corporate Crowdsourcing (Villarroel & Reis, 2010) .In so doing, these firms search internally for solutions to problems (advertising, innovation, social responsibility, sustainability) tapping into the entire pool of employees. Companies with a sufficiently large1 (Howe 2008) internal community of contributors, use crowdsourcing as a way to get ideas or solutions to their problems, without running the risk of exposing their best solutions to competitors. Firms can ask their employees to design the new company logo, to name the mascot or a new product, to make advertising and to give new ideas on several areas. 2.1.2. Stock Market for Innovations (SMI) The Stock Market for Innovation (Villarroel & Reis, 2010 and 2011) is a recent intra- corporate crowdsourcing initiative that works on a continuum timeframe. A SMI is 1 Sufficiently large means that the number of active participants is above 1,000. P age |6
  • 7. an online application that replicates a stock market where employees submit, comment and invest on innovation ideas. The most invested ideas are the ones the firm will study the viability of implementation and its submitter receives a monetary prize if the idea is implemented. The Stock Market for Innovation has the following stages:  As response to a open call challenge, employees create, and submit ideas to the online stock market for innovation;  On the online SMI, employees can comment and invest in ideas to increase their own money (specific currency) that can be used to buy prizes or products and services offered by the company;  The ideas can be traded on the online stock market for a certain period of time and are valued by the quantity of comments and amount of investments they get;  For each challenge, the ten most valued ideas get approved for implementation analysis by the innovation committee. The submitters of the ideas that are implemented receive a monetary prize. The major difference between the SMI and other firm external crowdsourcing is that the crowd is not completely undefined as Howe describes for the external crowdsourcing initiatives. Even if the crowd is big in absolute number, there are employees that know and have been interacting with one another for years beyond the ICC initiative. 2.2. Social Networks A social structure of individuals (organizations, countries, etc) and their relations of interdependency can be represented as a social network, where nodes represent the individual actors and ties (edges, links) represent the relationship between these actors. The resulting graph-based structures of social networks are often very complex because there can be many kinds of ties between the nodes (friendship, P age |7
  • 8. kinship, common interest, financial exchange, dislike, sexual relationships, or relationships of beliefs, knowledge or prestige). Social network analysis (SNA) is the area that studies social networks using network theory which is the study of graph structures using network measures. “Social network research has been applied in several academic fields and has shown that social networks can be found and operate on many levels, from families up to the level of nations, and play a critical role in determining the way problems are solved, organizations are run, and the degree to which individuals succeed in achieving their goals” (Wikipedia – Social Network). 2.2.1. Structural Holes Structural Hole is one of many network measures, is a term coined by Burt (1992) and this term defines the “separation between non redundant contacts” (Burt, 1992). “A Structural Hole is a relationship of non redundancy between two contacts” (Burt 1992). A non redundant connection is a connection that you can reach only by one path of connection. In other words there is a structural hole between two components of a network if there is only one path that connects those two components. Figure 1 – Illustration of a structural hole (designed in PowerPoint) In Figure 1 we can see that the node “A” has a non redundant connection with both nodes “B” and “C”, thus between “B” and “C” there is a structural hole. “A” can P age |8
  • 9. exploit the fact that B and C do not have a connection and trade knowledge, information or resources possessed by B but not by C and vice versa. The network measure of structural holes can be measured by effective size, which measures the number of non redundant ties in a Ego network (how many actors is Ego connected with that are not connected to each other). Burt (1992) defines the effective size of a person's ego network as: where and and Z is the data -- the matrix of network ties. Structural holes are important because actors of the social networks with non redundant connections (structural holes) are in a position of being gatekeepers of information and resources from one component of the network to the other. Being in such a position grants unique access to non redundant and unique information/ resources that no one else in their component can have. With this position and access to unique information and resources an actor can act as a broker of knowledge and do knowledge brokering. P age |9
  • 10. 2.2.2. Knowledge Brokering According to Hargadon (1998) “knowledge brokers” span multiple markets and technology domains and innovate by brokering knowledge from where it is known to where it is not”. A innovation made by a knowledge broker is typically a know solution in one technology field that the knowledge broker can transform and adapt to apply as a new solution to an unsolved problem in a different technology field. In the organizational social networks, knowledge brokers have several structural holes (non redundant connections) that give them the possibility to work as intermediaries in the transfer of information, knowledge or resources and do a brokerage activity over these structural holes. The best way to identify possible knowledge brokers is the network measure of structural holes. 2.2.1. Bonacich Power and Centrality In his paper of 1987 - Power and Centrality: A Family of Measures – Bonacich argues that “being connected to well connected others makes an actor central, but not powerful. On the contrary, being connected to others that are not well connected makes one powerful although not central”. His argument sets upon the dependability of the other actors to whom Ego is connected. If the actors that are connected to Ego are, themselves, well connected, they are not highly dependent on him. These actors, have many contacts, just as Ego does and they don´t have to go through him to get what they want. On the other hand, if the actors that are connected to Ego are, themselves, not well connected, then they are dependent on him because they have to go through him to get what they want. Bonacich created a measure (Bonacich Power Centrality) that captures this dichotomy of Power and Centrality, and shows that the more connections the actors in your neighbourhood have, the more central you are; the fewer the connections the actors in your neighbourhood have, the more powerful you are. P a g e | 10
  • 11. Figure 2 – Illustration Bonacich Power and Centrality (designed in PowerPoint) In Figure 2 we can see an illustration of the differences in position of the actors and the dichotomy of power and centrality. Node “A” has the higher centrality because he his connected with nodes that are well connected (B and C). On the other hand nodes “B” and “C” are not as central as “A” but are very powerful because they are connected with several nodes that are not well connected. The Bonacich Power Centrality (Bonacich 1987) network measure is given by: C ( ,  )   ( I  R) 1 R1 α is a scaling vector, which is set to normalize the score; β reflects the extent to which you weight the centrality of people ego is tied to; R is the adjacency matrix (can be valued); I is the identity matrix (1s down the diagonal) and 1 is a matrix of all ones. The magnitude of β reflects the radius of power. Small values of β weight local structure, larger values weight global structure. If β is positive, then ego has higher centrality when tied to people who are central. If β is negative, then ego has higher centrality when tied to people who are not central. As β approaches zero, you get degree centrality. P a g e | 11
  • 12. This network measure is interesting to use in the field of knowledge diffusion because with just one measure we can test if innovative activity or performance in an SMI is correlated with network power or centrality attributes of the participants 3. THEORY AND HYPOTHESIS For both firms and individuals it is recognized that boundary-spanning ties have advantages in access to sources of external Knowledge and information (Allen & Cohen, 1969; Allen, Tushman & Lee, 1979). The literature has also shown the relevant role of accessing knowledge and information across boundaries when performing innovation activities inside organizations (Hagardon, 1998; Hansen, 1999; Ancona & Caldwell, 1992 ;Burt, 2004). Hagardon (1998) explain that firms which position themselves as knowledge brokers have an advantage over traditional manufacturing firms in innovating activities. Hansen (1999) shows that research units that have weak ties with other subunits of the firm have an advantage in searching for useful knowledge on other subunits. Ancona & Caldwell (1992) show that “teams carrying out complex tasks in uncertain environments (such R&D) need high levels of external interaction to be high performing”. Burt (2004) explains that organization elements that are positioned close to structural holes (brokers) have access to less redundant and more unique information and that are better prepared to have good ideas than other elements. His results show that “brokers that span over structural holes between groups in the organization are more likely to express their ideas, less likely to have their ideas dismissed and more likely to have their ideas evaluated as valuable”. In the crowdsourcing literature there is evidence of openness and technical marginality as important factors in innovation activities. For example, Jeppesen and Lakhani (2010) show that in a crowdsourcing initiative involving scientific problems and lump sum prizes, winning solutions are positively related to increasing distance between the solver’s field of expertise and the focal field of the problem. The results from this research somehow make us think that the wining solutions are correlated with knowledge brokering, since the solver´s field of expertise is distant from the P a g e | 12
  • 13. focal field of the problem. These solvers are probably using solutions from their field of expertise and adapting them as new solutions for problems in a different field of expertise. Our first question is if the knowledge brokering position in the organizational social network is positively correlated with innovative activity. ICC initiatives like the SMI can help us to answer this question because we have a crowdsourcing initiative with innovation activity and is made inside a closed crowd of employees from which we can know the social network. Thus, in internal corporate crowdsourcing, it is expected that more creative participants will have a knowledge brokering position in the organizational social network. Hypothesis 1 – Innovation activity in intra-corporate crowdsourcing initiatives, as the Stock Market for Innovation, is positively correlated with brokering position in the organizational social network. As mentioned above, the effect of the social network on the behaviour of individuals on the corporate crowdsourcing initiatives is expected to happen even if only at a subtle level. This possibility of effect is very important to analyse mainly on the valuation of the ideas in the internal corporate crowdsourcing initiatives such as Markets for Innovation. In these Markets for Innovation the evaluation of performance of the participants is regularly made by the number of comments and investments each participant get. A similar evaluation is made for the submitted ideas, where the ideas that receive more comments and investments are the most valued and more suitable for implementation. When participating on an ICC initiative such as SMI, an individual is constrained by his time limitations and level of effort necessary to do it. The individual according with his preferences and abilities will dedicate some time and effort to participate in P a g e | 13
  • 14. this event. To make a decision of what ideas to analyse, comment or invest in the SMI, a participant will not try to get the information on all the ideas, previous comments and investments. When someone is buying a second hand car it does not seek information on all the second hand cars available in the market. Typically to optimize his choice the person will ask to their friends, or to friends of friends, if they know someone trustworthy that have a car to sell. Then it is reasonable to assume that participants will optimize their participation in the SMI and will analyse, comment and invest in a limited number of ideas and will most likely analyse comments and invest in their friend’s ideas, or friends of friend’s ideas. With this assumption I think that a participant will have more interest in analysing and comment ideas from people he is connected with. Thus, the organizational social network will be correlated with the participation on ICC initiatives Hypothesis 2 – The Organizational Social Network is positively correlated with the behaviour (analysis, comments and investments) of participants on a ICC initiative as the SMI. Following Hypothesis 2, it is important to go beyond in the analysis of the correlation between the Social Network and the Behaviour (analysis, comments and investments) in the SMI. Hypothesis 2 analyses this correlation at the network level and it is also interesting to make an analysis at the individual level. Why do some ideas and participants receive more comments (higher valued) than others in the SMI? Is there any social network individual characteristic that drives the participants to receive more comments and investments? To answer this question it is important to analyse the social network structure and test if there is any correlation between the individual network characteristics of the participants and the number of comments they receive. There are two main concepts that give theoretical support for a correlation between the individual network characteristic and the number of comments or investments P a g e | 14
  • 15. received. The first concept is again the Structural Holes and the Knowledge Brokering. As already mentioned in Hypothesis 1, Knowledge brokers are expected to be the most creative participants in the ICC initiatives as the SMI due to their possibility of spanning over structural holes. The supposedly creativity of these participants will allow them to submit more creative ideas and to make more creative and more valuable comments to the other’s ideas. This creativity of the ideas and comments made by the Knowledge Broker will allow him to receive more comments and investments from other participants than what we would expect from any other participant. Additionally the position of a Knowledge Broker bridging over a Structural Hole has high advantages in processes of diffusion of information. The Knowledge Broker with its bridging ties can reach parts of the social network that other participants do not access, possibly granting him an exclusive audience that might make comments or investments on his ideas or previous comments. Hypothesis 3a – Comments and investments received ICC initiatives as SMI is positively correlated with creative activity. The second concept is the Bonacich Power and Centrality concept (Bonacich 1987). Bonacich argues that being connected to well connected others makes an actor central, but not powerful. On the contrary, being connected to others that are not well connected makes one powerful although not central. His argument sets upon the dependability of the other actors to whom the individual is connected. If the actors that the individual are connected to are, themselves, well connected, they are not highly dependent on him. These actors, have many contacts, just as you do and they don´t have to go through you to get what they want. On the other hand, if the actors to whom the individual is connected are not, themselves, well connected, then they are dependent on him because they have to go through him to get what they want. Bonacich created a measure (Bonacich Power Centrality) that captures this dichotomy of Power and Centrality, and shows that the more connections the actors in your neighbourhood have, the more central you are; the fewer the connections the actors in your neighbourhood, the more powerful you are. Since P a g e | 15
  • 16. making a comment to an idea or previous comment requires some time and effort we believe power will be more important and effective than centrality in collecting comments made by other participants. Participant A that is connected to a peripheral participant B (with just one connection) will exert his social power and if B makes a comment it will do it on the idea or previous comment from A. On the contrary, participant C that is connected to a central participant D will probably receive fewer comments because D will divide his effort to comment for several ideas or comments from the participants to whom he is connected to. Assuming that the effort to comment on ideas or previous comments is randomly distributed over the network and on average a participant makes 3 comments (the real average value in our data is 3.2), then a participant connected to a peripheral participant will receive in average three comments from this participant. Contrarily a participant connected to a participant with three connections will receive on average one comment from this participant. Hypothesis 3b – Comments and investments received on a ICC initiative as a SMI is positively correlated with social network power of the SMI participants. 4. DATA, METHODOLOGY, NETWORKS AND MEASURES 4.1. Data The data2 as basis of analysis is from an Innovation Management course with 86 Master’s students and is constituted by the following three datasets:  Data from a survey results on the 86 students (100% response) where each student indicated the top 5 other students with which they use to work and 2 Data supplied by Prof. Andrei Villarroel from the Católica University of Portugal P a g e | 16
  • 17. study and its frequency in a scale from 1 to 5 ( 1 – once; 2 – rarely; 3 – sometimes; 4 – quite often and 5 – always). This survey was done in the beginning of the course before any individual or group assignment and before the crowdsourcing initiative was initiated.  Data from a crowdsoursing initiative for Innovation performed on the Innovation Management course taught by Professor Andrei Villarroel in Portugal in the spring semester of 2010. As a part of the coursework and grade, the students participated in the online Innovation crowdsourcing (IC) initiative by submitting innovation ideas for new products or services and by visiting and commenting on other’s ideas. In this IC initiative all the 86 students participated, 22 innovation ideas were submitted, 331 comments were made to ideas or previous comments and 1815 visits (1519 to ideas and 296 to student profiles) were made. More specifically the data from the IC initiative has information of which students submitted ideas, which students visited those ideas and profiles of other students and which students made online comments on ideas and previous comments.  Information on class performance for the 86 students: crowdsourcing grade, group grade, individual grade and final grade. Table 1 has the descriptive statistics of all the individual level variables used: Variable N Mean Stdev Min Max Ideas 86 0.26 0.44 0.00 1.00 Final Grade 86 67.54 10.90 35.18 91.72 Comments received 86 3.85 5.88 0.00 25.00 Symmetric Strong Study Network Todal degree Centrality 86 3.77 2.20 0.00 10.00 Structural Holes 81 0.17 0.29 -0.25 0.64 Bonacich Power Centrality 81 0.86 0.52 0.35 2.56 Underlying Graph Study Network Todal degree Centrality 86 9.07 3.17 2.00 16.00 Structural Holes 86 0.57 0.21 -0.22 0.83 Bonacich Power Centrality 86 0.93 0.36 0.15 1.86 Table 1 – Descriptive Statistics from all the used individual level variables P a g e | 17
  • 18. This IC initiative was a lighter version of a SMI where investments were not included. The grade the students got for participating in this IC initiative is calculated by a formula that accounts the number of ideas posted, comments made, comments received, visits made and visits received by each student. We don’t have the specific valuation for each of these actions in the IC initiative 4.2. Methodology To prepare the several datasets and to create the necessary social networks to our analysis we propose the steps described in Table 2: Step Procedure 1 Construct the study social network from the survey results and calculate the correspondent network measures. 2 Construct the comments and visits social networks from the activity (comments and visits) in the crowdsourcing for innovation. These inferred networks are directed graphs to be an image of the students’ behavior on the internal crowdsourcing initiative. 3 Calculate the network measures at the individual level that will be used as explanatory variables in the Hypotheses analysis. 4 Test our Hypotheses by analyzing the correlation between social network metrics of each student and their behavior in the internal crowdsourcing initiative (ideas, comments, visits) using as control measures the performance on class (individual grade). Table 2 – Steps for the preparation of the datasets P a g e | 18
  • 19. To test our Hypotheses we used two types of econometric analysis:  A more traditional econometrics approach of Probit regression model and a Poisson regression model. These models assume the independence between all observations which might not be true with all the network measures. For example if A and B share a common group of friends then their friend’s social networks will be correlated. Being aware of this fact I will use these methods to test Hypothesis 1, Hypothesis 3a and Hypothesis 3b since there is no other way of getting correlations between network measures and attributes of the same network.  A quadratic assignment procedure for inference on multiple-regression coefficients (MRQAP), which is a method equivalent to the general linear regression model but is specific to be used with network data. As mentioned in the previous point, Network data might violate the assumption of independence between all observations and also there is the risk of equal observations across several individuals which mean the errors terms could be correlated if a linear regression was used. The Multiple Regression Quadratic Assignment Procedure (MRQAP) is used exactly to comply with the assumption of independence between all observations. This method estimates the standard errors using several permutations of the dependent variable data set, resulting in multiple random datasets with the dependent variable. Hypothesis 2 is tested with the MRQAP method. 4.3. Network Construct 4.3.1. Study Network The first network needed to construct is the Study Network (SN) that is a reflex of the results from the survey data indicating for each student the students that he studies and works with. The study relation is a physical relation (if A studies with B then B studies with A) since the study relation is reciprocal, we can only construct reciprocal networks. It does not make sense to use a Directed Graph since that would be to assume that A studies with B but B does not study with A which is P a g e | 19
  • 20. physically impossible. Thus we constructed two variations of the Study Network, one considering reciprocal identification where a tie exists when A identifies B and vice versa (Symmetric Strong Study Network) and the other considering that a tie exists if either A indentifies B or vice versa (Underlying Graph Study Network). Figure 3 shows a picture of the Symmetric Strong Study Network analyzed in our study: Figure 3 – Illustration of the Symmetric Strong Study Network (designed in R studio) Figure 4 shows a picture of the Underlying Graph Study Network analyzed in our study: P a g e | 20
  • 21. Figure 4 – Illustration of the Underlying Graph Study Network (designed in R studio) In this case the Underlying Graph is acceptable to use because there are some issues with the open questions of surveys that might generate subjectivity. In the answers (Bertrand, M. & Mullainathan S.,2001). The survey had the restriction of five persons to nominate, limiting the choice of study partners, the scale of intensity of study (always, often, sometimes, etc) is subjective and because people do not always remember everyone with whom they studied or worked. We believe that due to all of these issues there could have been situations where A and B studied together, but just one of them has indicated that. Thus, using the Underlying Graph we consider that the identification just from one student is enough to consider the existence of a reciprocal tie. P a g e | 21
  • 22. 4.3.1. Comments Network The Comments Network is a reflex of the behaviour of all participants in the Innovation Crowdsourcing initiative in terms of the comments made or received. To construct the Comments Network we used two variations, the Directed Graph and the Strong Network. The variation of Underlying Graph does not make sense to construct because that would be to assume that if A commented on B, then B commented on A which might not be true. Additionally, the Strong Network had close to 10 edges and all other nodes are isolated. Thus, the Directed Graph variation is the only network considered meaningful on the Comments Network. Figure 5 shows a picture of the Direct Graph Comments Network analyzed in our study: Figure 5 – Illustration of the Direct Graph Comments Network (designed in R studio) P a g e | 22
  • 23. 4.4. Measures For Hypothesis 1 the needed dependent variable was a measure of the creative activity and we used a binary variable that describes whether a participant of the crowdsourcing initiative did or did not submit an innovation idea. We use a binary variable because no participant has submitted more than one innovation idea. Has explanatory variables for Hypothesis 1, we used several network measures calculated from the two variations of the Study Networks (Strong Network and Underlying Graph). The network measures used were Total Degree Centrality, Structural Holes and Bonacich Power Centrality. We also could have used other popular network measures such as Betweeness Centrality and Eigenvector Centrality, however we opted not to use them because these measures have similar calculation methods with the other measures we were already using and in the regressions we would be capturing the same effects. As control variable we used the Individual Course Grade because it is a good measure of individual performance as we could expect that students with better individual grades will also want to have a good grade in the internal crowdsourcing initiative. Using the regressions methods of QAP or MRQAP we can use networks as dependent and explanatory variables, thus for Hypothesis 2 we used as dependent variable the Comments Network (Direct Graph) and as explanatory variables we used the two Study Networks (Strong Ties and Underlying Graph). For Hypothesis 3 the dependent variable used was the number of comments received by each participant in the internal corwdsourcing initiative, as explanatory variables we used the same network measures from the Study Network, already explained for Hypothesis 1 and as control variable we also used the Individual Course Grade. P a g e | 23
  • 24. In Table 3 is a resume of the measures for each variable of the three hypotheses: Dependent Variable Explanatory Variable Other network Variables Control Variables H1 creative activity Brokering position variables Individual performance Measures Binary var. ( submitted Structural hole Degree and Bonacich for H1 idea in the SMI) measure Power Centrality Individual course grade Participants Behaviour Organizational Social H2 in the SMI Network Measures for H2 Comments Network Study Network Visits to Ideas Network Comments and investments received Social Network H3 in the SMI Power creative activity Individual performance Measures comments received in Bonacich Power Binary var. ( submitted for H3 the SMI Centrality idea in the SMI) Individual course grade Table 3 – Steps for the preparation of the datasets 5. RESULTS To test Hypothesis 1 we ran several Probit regressions for each of the two Study Networks (Strong and Underlying Graph), having as a dependent variable a binary variable Ideas (1 if posted an idea and 0 if not), as explanatory variables the network measures of Total Degree Centrality, Structural Holes and Bonacich Power Centrality and as control variable the Individual Course Grade: Ideas = β0 + β1 x Total Degree Centrality + β2 x Bonacich Power Centrality + β3 x Structural Holes + β4 x Individual Course Grade+ µ We ran this model with the network measures of both variations of the Study Network (Symmetric Strong Study Network and Underlying Graph Study Network) and we ran several Probit models for each network to analyse all possible variable interactions. Table 4 and Table 5 show the results from the Probit regression on the Symmetric Strong Study Network and on the Underlying Graph Study Network. P a g e | 24
  • 25. Strong Network Probit 1 Probit 2 Probit 3 Probit 4 Probit 5 Probit 6 Probit 7 Total Degree -0.0033 -0.2214 0.5239 -0.1451 Centrality 0.962 0.103 0.225 0.809 Bonachic Power -0.1517 -0.7647 -2.2070 -0.2722 Centrality 0.631 0.113 0.207 0.897 0.6513 1.5526 1.8347 1.7476 Structural Holes 0.255 0.056 * 0.048 ** 0.123 0.0344 0.0252 0.0332 0.0335 0.0321 0.0369 0.0327 Individual grade 0.126 0.250 0.132 0.144 0.153 0.112 0.160 Adjusted R2 -0.023 -0.021 -0.021 -0.031 -0.031 -0.034 -0.045 Observations 81 81 81 81 81 81 81 Dependent Variable - Binary variable (1 if student posted idea and 0 if not) top value - Coefficient lower value - Significance Signif. codes: *** < 0.01; ** < 0.05; * < 0.1 Table 4 – Results from the Probit regression on the Symmetric Strong Study Network Underlying Graph Probit 1 Probit 2 Probit 3 Probit 4 Probit 5 Probit 6 Probit 7 Total Degree -0.0492 -0.0552 -0.2355 -0.2520 Centrality 0.320 0.529 0.291 0.297 Bonachic Power -0.3390 -0.1905 1.6706 1.7056 Centrality 0.436 0.792 0.391 0.385 -0.5533 -0.3046 0.1046 0.2168 Structural Holes 0.441 0.800 0.935 0.866 0.0358 0.0337 0.0369 0.0350 0.0372 0.0374 0.0381 Individual grade 0.110 0.125 0.102 0.124 0.105 0.096 * 0.096 * Adjusted R2 -0.021 -0.021 -0.020 -0.033 -0.033 -0.032 -0.045 Observations 86 86 86 86 86 86 86 Dependent Variable - Binary variable (1 if student posted idea and 0 if not) top value - Coefficient lower value - Significance Signif. codes: *** < 0.01; ** < 0.05; * < 0.1 Table 5 – Results from the Probit regression on the Underlying Graph Study Network P a g e | 25
  • 26. From the tables above we can see that none of the network measures is correlated with the binary variable Ideas. For both Study Networks (Strong Ties and Underlying Graph) all the Probit regressions have negative Adjusted R-squared which means that the model does not explain at all the creative activity of the participants in the internal crowdsourcing initiative. According to this result it is clear that being in a bridging position to broker knowledge across structural holes is not related with creativity activity in the Innovation Crowdsourcing initiative of the class and thus our Hypothesis 1 is not supported. Additionally, as already mentioned none of the other network measures is correlated with the creative activity of submitting an idea to the Innovation Crowdsourcing initiative of the class. This means that weather a student submits or not an idea to the Innovation Crowdsourcing initiative of the class is independent of which other students they study with. Probably the network information that we have for this analysis is not enough to have more enlightening results. Not having any other information (demographic, schooling, working experience, area of expertise, etc.) regarding the students makes us assume in our analysis that every student is equal and does not have differences in their age, gender, technical knowledge, and working experience. Having this information would be very important not only to use as control variables, but also to incorporate it into the Study Network creating a Meta- Network. For instance, incorporating the students’ individual information of technical knowledge and working experience would allow us to construct two other attribute Networks, the Network of the Students’ Technical Knowledge and the Network of the Students’ Working Experience. These two networks would be much richer for the analysis because we could see the students that have access to resources that others don’t and clearly identify the students close to the structural holes of the Technical Knowledge Network. These students, through their positioning of knowledge brokers over technical knowledge structural holes, should be the more creative participants in the Innovation Crowdsourcing initiative of the class. P a g e | 26
  • 27. To analyse Hypothesis 2, first we used QAP correlation tests to see the correlations between the Comments Network and the two Study Networks (Strong Tie and Underlying Graph). To understand the goodness of fit of the Study Networks in explaining the Comments Network we used a MRQAP where the dependent variable is the Comments Network and the explanatory variables are the two Study Networks. Since the Strong Tie Study Network has a correlation of 1 with the Underlying Graph Study Network we cannot combine both these networks in the same MRQAP. To see the effects of these networks we ran 2 MRQAPs and used as a second explanatory variable in each MRQAP a Network created from the visits of the participants to the ideas submitted in the internal crowdsourcing initiative. MRQAP 1: Comments Network = β0 + β1 x Symmetric Strong Study Network + β2 x Visits to Ideas Network + µ MRQAP 2: Comments Network = β0 + β1 x Underlying. Graph Study Network + β2 x Visits to Ideas Network + µ And to confirm that this Network of Visits to Ideas is not highly correlated with any of the Study Networks we also ran a QAP between the Visits to Ideas Network and the two Study Networks. Table 6 shows the results from the above described QAP regressions: P a g e | 27
  • 28. QAP correlation test with Comments Network QAP - 500 permutations Correlation Significance Symmetric Strong Study Netwok 0.079 0.015 Underlying Graph Study Network 0.133 0 QAP correlation with Visits to Ideas Network QAP - 500 permutations Correlation Significance Symmetric Strong Study Netwok 0.029 0.01 Underlying Graph Study Network 0.034 0 Table 6 – Results from the QAP regressions between the Symmetric Strong and Underlying Graph Study Networks with the Comments Network (Directed Graph) and Visits to Ideas Network (Directed Graph) The top part of Table 6 shows that both Study Networks are significantly correlated with the Comments Network and that the Underlying Graph Network is more correlated (13.3%) than the Symmetric Strong Study Network (7.9%). These correlation numbers might not seem to be high but assuming that in a pure crowdsourcing initiative this correlation should be spurious and close to 0%, then seeing correlation numbers of 7.9% and 13.3%, we have to admit that the Social Networks might be having some effects on the behaviour of the participants of innovation crowdsourcing initiative of the class. Additionally we can see that the Visits to Ideas Network is significantly but not highly correlated with both Study Networks which allow us to use it as a explanatory variable in the MRQAPS that will allow us to test Hypothesis 2. Table 7 shows the results from the above described MRQAP regressions P a g e | 28
  • 29. MRQAP - 500 permutations MRQAP 1 MRQAP 2 0.0074 0.0065 Visits Ideas Network 0.1050 0.1150 0.0420 Symmetric Strong Study Netwok 0.052* 0.1126 Underlying Graph Study Network 0.005*** Adjusted R-Squared: 0.02710 0.03280 Dependent Variable - Comments Network Signif. codes: *** < 0.01; ** < 0.05; * < 0.1 top value - Coeficient lower value - Sig.Y-Perm Table 7 – Results from the MRQAP regressions 1 and 2 Table 6 shows that indeed both Study Networks are significant in explaining the comments networks, although the Symmetric Strong Study Network is only significant up to a 10% level and its coefficient is not very high in magnitude. The Underlying Study Network is significant at a 1% level and its coefficient is already high in magnitude. To have an idea of the impact of these results, the interpretation of the coefficient from the Underlying Graph means that in average for every two students that studied together there is 11.26% probability that one student will make a comment to the other when participating on the innovation crowdsourcing initiative of the class. This is even more surprising considering the non significance of the Visits to Ideas Network in this model. This seems to be evidence that the comments the students make in the innovation crowdsourcing initiative of the class are influenced by their social study network but not by the ideas they visited. The result above gives support to our Hypothesis 2. The adjusted R-squared of the models is a relatively acceptable value for the procedure of the MRQAP and the most important is that this value is not negative and is not very close to zero. The adjusted R-squared from a MRQAP is smaller than what is normal to see in a linear P a g e | 29
  • 30. regression because the procedure of permutations creates several random datasets which lowers the adjusted R-squared. The results from Hypothesis 2 show that the Underlying Graph is the Study Network that best explains the Comments Network, thus to test our Hypothesis 3 we decided only to use the Underlying Graph Study Network in this analysis. For Hypothesis 3 we ran a Poisson Model having as a dependent variable the number of comments received in the crowdsourcing initiative by each student, as explanatory variables several network measures (Bonacich Power Centrality and Structural Holes) and as control variables the Individual Course Grade and the binary variable Ideas (1 if student submitted an idea or 0 if not). We used the Poisson model because the distribution of the dependent variable of the model (comments received) is pretty similar to a Poisson distribution. Log (Comments received) = β0 + β1 x Bonacich Power Centrality + β2 x Structural Holes + β3 x Individual Course Grade + β4 x Ideas We measured the Bonacich Power Centrality with a positive beta, which means that a positive coefficient from this measure will indicate that receiving comments on the innovation crowdsourcing initiative of the class has a positive correlation with individuals that are more central, but less powerful. If the coefficient is negative, this will indicate that the less central, but more powerful, individuals are the ones receiving more comments. The results from the Poisson regression are shown in the Table 8: P a g e | 30
  • 31. Table 7 – Results from the Poisson regression on the Underlying Graph Study Network The results from Table 7 show that the most important driver to receive comments in the innovation crowdsourcing initiative of the class is to submit an idea. This result strongly supports the Hypothesis 3a in showing that receiving comments on the innovation crowdsourcing initiative of the class is positively correlated with the creative activity. However, the Structural Holes variable is not significant in this analysis which does not come to a surprise since the results from Hypothesis 1 show that the Structural Holes measure is not correlated with the creative activity in the innovation crowdsourcing initiative of the class. Once again, if we had access to the additional information from the individuals, it would allow us to analyse the Study Network data as a Meta-Network and possibly shed more light into the Structural Holes story. Regarding the Bonacich Power Centrality variable we can see that in all models the coefficient is negative and significant (at 1% level not controlling for ideas and at 5% level controlling for ideas). This result give support to the story that participants connected to less connected participants can exert power over these last and P a g e | 31
  • 32. receive more comments on their ideas or previous comments, allowing them to have better evaluation of ideas or performance. Contrarily, participants that are connected to highly connected participants will receive fewer comments because the highly connected participants will divide their effort to comment for several ideas or comments from the competing participants to whom they are connected to. Thus, this supports our Hypothesis 3b - Comments on corporate crowsourcing initiatives, as Stock market for Innovations is positively correlated with social network power of the SMI participants. As we hypothesized, the individual network characteristic that is important in the evaluation procedure of ideas and performance of the participants is the power and not centrality, contrarily to what intuitively one could think. 6. LIMITATIONS AND CONCLUSIONS 6.1. Limitations In this study we wanted to analyse the role of social networks in the ICC initiatives like the SMI. The biggest limitation from our research is that the analysis is done on a setup (students from a Master’s class) somehow different from a corporation. This limitation can be divided into two different aspects: First, the analysed SMI was not a real SMI because it did not have investments. This limitation takes some reality and probably dynamics from the SMI made on the class since there were no investments or prizes involved. If the analysed SMI had investments and prizes involved we believe that the students would take this ICC much more seriously and we would expect the student’s behaviour to be even more pronounced. We would expect to see higher coefficients and lower significance levels. In the analysis if the SMI involved investments and prizes. Second, the social network of students is significantly different from a social network of corporate employees. Additionally the corporate employee social network is not P a g e | 32
  • 33. the only network operating in the corporation; there is also the organizational hierarchic network that can also have influence of its own (promotions, career progression, layoff, etc) despite the influence of the social network. We believe that the analysed students social network again attenuates the dynamics of interactions between actors since students don’t have that much at stake with each other that motivates them to be extremely active in their social network. If the SMI was done on a corporation with the presence of employee social network and the organizational hierarchic network, we believe that the behaviour of the social actors would be much more active in their social roles and we would again probably see higher coefficients and lower significance levels. 6.2. Conclusions Several Companies have been using crowdsourcing initiatives to perform diverse tasks. One of the more recent of these initiatives are internal Stock Markets for Innovation where companies can tap into the creativity power of their employees through an online stock market where employees create, comment and invest on new ideas for products, processes and services, that might be implemented by the company. However these initiatives that should be based in the “wisdom of the crowds” might be under the influence of the Social Networks that exist in the company way before the crowdsourcing initiatives. From the theoretical idea that internal corporate crowdsourcing initiatives might be under the influence of social networks we studied a similar crowdsourcing initiative made on a Master’s class. Having access to a survey from the study partners’ student have and to the data from the crowdsourcing platform, we constructed social networks (Study and Comments) and made several analyses that support some of our theoretical hypothesis. The results from our analysis show that Hypothesis 2 – The Organizational Social Network is positively correlated with the behaviour (analysis and comments) P a g e | 33
  • 34. of participants on a ICC initiative as the SMI. – is supported and that for our data we can say that the Social Network is correlated with the behaviour of the participants on the innovation crowdsourcing initiative of the class. However, this influence of the Social Networks on the innovation crowdsourcing initiative of the class cannot be seen on the creative activity (submission of ideas) and our Hypothesis 1 – Innovation activity in intra-corporate crowdsourcing initiatives, as the Stock Market for Innovation, is positively correlated with brokering position in the organizational social network. – is not supported. To have a better and more complete analysis of Hypothesis 1 it would require more individual information of the participants in the innovation crowdsourcing initiative of the class. As no surprise, the creative activity (submission of ideas) is the most important driver to receive comments in the corporate crowdsourcing initiative and the results on this analysis support Hypothesis 3a – Comments received on corporate crowdsourcing initiatives, as Stock market for Innovations is positively correlated with creative activity. The area where our findings show influence of the Social Networks on the corporate crowdsourcing initiative is the evaluation of ideas and performances of the participants. Perhaps the most interesting result in this paper is that the most important characteristic playing a role in this evaluation procedure is power, where the most powerful participants get higher evaluation on their ideas or performance. This idea is comes from the results that support our Hypothesis 3b – Comments and investments received on a ICC initiative as a SMI is positively correlated with social network power of the SMI participants, which means that is negatively correlated with the Bonacich Power Centrality. This last result seems to be a similar situation to the one found in the results of Villarroel & Reis, (2011) - “speculative activity is positively associated with better innovation performance”. One can ask if ICC initiatives such as SMI might just be P a g e | 34
  • 35. another tool for the most powerful actors to use, reinforce and legitimize their social power in their own benefit. One implication of our results is that knowledge diffusion in the SMI initiatives might not be that different than what existed previously and might be even more easily manipulated (Legitimization) by the powerful actors. If so, the advantage of “increased efficiency at the fuzzy front end of the new product development process” (Soukhoroukova et al 2010) might be in risk of being offset by the possible manipulation of the powerful actors. Any management team should have this in mind when considering applying an ICC initiative similar to a SMI. Further studies are needed in this area to analyze more deeply the effect of Social Network and Organizational Hierarchic Network in the outcomes of ICC such SMI. P a g e | 35
  • 36. BIBLIOGRAPHY Allen, T. J., & Cohen, S. I. (1969), Information flow in research and development laboratories. Administrative Science Quarterly, 14: 12–19. Allen, T. J., Tushman, M. L., & Lee, M. S. (1979), Technology transfer as function of position in the spectrum from research through development to technical services. Academy of Management Journal, 22: 684–708. Ancona, D., & Caldwell, D. F. (1992), Bridging the boundary: External activity and performance in organizational teams. Administrative Science Quarterly, 37: 634– 665. Bertrand, M. & Mullainathan S.,(2001). Do People Mean What They Say? Implications for Subjective Survey Data. American Economic Review 91(2): 67-72. Bonacich, P. (1987) Power and Centrality: A Family of Measures. American Journal of Sociology, Vol.92, Nº.5, pp. 1170–1182. Brabham, D. C. (2008), Crowdsourcing as a Model for Problem Solving. An introduction and cases, in The International Journal of Research into New Media Technologies, Vol. 14, No. 1, pp. 75-90. Burt, R. S. (1992), Structural Holes: The Social Structure of Competition. Harvard University press. Burt, R. S. (2004) Structural holes and good ideas. American Journal of Sociology, 110: 349–399. Cohen, W. M. and Levinthal, D. A. (1990), Absorptive capacity: a new perspective on learning and innovation. Administrative Science Quarterly, Vol. 35, No. 1 pp. 128- 152 Dekker, D., Krackhardt, D., & Snijders, T. A. B. (2007), Sensitivity of MRQAP tests to collinearity and autocorrelation conditions. Psychometrika, 72(4), 563–581. Hansen, M. (1999), The search-transfer problem: The role of weak ties in sharing knowledge across organization subunits. Administrative Science Quarterly, 44: 82– 111. Hargadon Andrew B. (1998), Firms as Knowledge Brokers: Lessons in Pursuing Continuous Innovation. California Management Review Vol. 40 No. 3. Howe, J. (2008), Crowdsourcing: Why the Power of the Crowd Is Driving the Future of Business. Crown Business, New York. Jeppesen, L. B. and Lakhani, K. R. (2010), Marginality and Problem Solving Effectiveness in Broadcast Search. Organization Science Volume 21 Issue 5. P a g e | 36
  • 37. Krackhardt, D. (1988), Predicting with networks: Nonparametric multiple regression analysis of dyadic data. Social Networks, 10(4), 359–381. Krackhardt, D. (1998), Simmelian tie: Super strong and sticky. In R. M. Kramer & M. A. Neale (Eds.), Power and influence in organizations: 21–38. Thousand Oaks, CA: Sage. Krackhardt, D. (1999) The ties that torture: Simmelian tie analysis in organizations. In S. B. Andrews & D. Knocke (Eds.), Research in the sociology of organizations, vol. 16: 183–210. Greenwich, CT: JAI Press. Lakhani, K. R. and Panetta, J. (2007), The principles of Distributed Innovation. Innovations: Technology, Governance, Globalization 2, Nº 3 (summer 2007). Lakhani, K. R.; Jeppesen, L. B.; Lohseet, P. A. and Panneta, J. (2007), Openness in Scientific Problem Solving. Boston: Harvard – Citeseer. McEvily, B., & Zaheer, A. (1999), Bridging ties: A source of firm heterogeneity in competitive capabilities. Strategic Management Journal, 20: 1133–1156. McPherson, J. M.; Smith-Lovin, L. and Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27: 415–444. Soukhoroukova A., Spann M., Skiera B. (2010) Sourcing, Filtering, and Evaluating New Product Ideas: An Empirical Exploration of the Performance of Idea Markets. Journal of Product Innovation Management. Forthcoming 2010. Villarroel J.A. & Reis F. (2010). Intra-Corporate crowdsourcing (ICC): Leveraging Upon Rank and Site Marginality For Innovation. CrowdConf 2010; October 4, 2010 Villarroel J.A. & Reis F. (2011). A Stock Market Approach to Online Distributed Innovation: The Trade-off Between Speculation and Innovation Performance. ACM EC Social Computing Workshop 2011. Wasserman S. & Faust K.(1994). Social Network Analysis: Methods and Applications. Cambridge University Press P a g e | 37