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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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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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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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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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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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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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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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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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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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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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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:

1)(),( 1RRIC

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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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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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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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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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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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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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:

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

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

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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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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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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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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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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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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 | 24

Dependent Variable Explanatory Variable Other Variables Control Variables

H1 creative activity Brokering position

Other network

variables Individual performance

Measures

for H1

Binary var. ( submitted

idea in the SMI)

Structural hole

measure

Degree and Bonacich

Power Centrality Individual course grade

H2

Participants Behaviour

in the SMI

Organizational Social

NetworkMeasures

for H2 Comments Network Study Network Visits to Ideas Network

H3

Comments and

investments received

in the SMI

Social Network

Power creative activity Individual performance

Measures

for H3

comments received in

the SMI

Bonacich Power

Centrality

Binary var. ( submitted

idea in the SMI) Individual course grade

In Table 3 is a resume of the measures for each variable of the three hypotheses:

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.

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

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

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

-0.0033 -0.2214 0.5239 -0.1451

0.962 0.103 0.225 0.809

-0.1517 -0.7647 -2.2070 -0.2722

0.631 0.113 0.207 0.897

0.6513 1.5526 1.8347 1.7476

0.255 0.056 * 0.048 ** 0.123

0.0344 0.0252 0.0332 0.0335 0.0321 0.0369 0.0327

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

Individual grade

Bonachic Power

Centrality

Total Degree

Centrality

Strong Network

Structural Holes

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

-0.0492 -0.0552 -0.2355 -0.2520

0.320 0.529 0.291 0.297

-0.3390 -0.1905 1.6706 1.7056

0.436 0.792 0.391 0.385

-0.5533 -0.3046 0.1046 0.2168

0.441 0.800 0.935 0.866

0.0358 0.0337 0.0369 0.0350 0.0372 0.0374 0.0381

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

Total Degree

Centrality

Bonachic Power

Centrality

Structural Holes

Individual grade

Underlying Graph

P a g e | 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 | 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 | 28

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

QAP - 500 permutations Correlation Significance

Symmetric Strong Study Netwok 0.079 0.015

Underlying Graph Study Network 0.133 0

QAP - 500 permutations Correlation Significance

Symmetric Strong Study Netwok 0.029 0.01

Underlying Graph Study Network 0.034 0

QAP correlation test with Comments Network

QAP correlation with Visits to Ideas Network

P a g e | 29

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

MRQAP - 500 permutations MRQAP 1 MRQAP 2

0.0074 0.0065

0.1050 0.1150

0.0420

0.052*

0.1126

0.005***

Adjusted R-Squared: 0.02710 0.03280

top value - Coeficient

Signif. codes: *** < 0.01; ** < 0.05; * < 0.1

lower value - Sig.Y-Perm

Dependent Variable - Comments Network

Visits Ideas Network

Symmetric Strong Study Netwok

Underlying Graph Study Network

P a g e | 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 | 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 | 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 | 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 | 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 | 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 | 36

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