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
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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.
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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
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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
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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
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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.
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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,
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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
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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.
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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.
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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.
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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
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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
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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
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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
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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
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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
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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
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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
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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:
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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.
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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)
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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.
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