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My Carnegie Mellon University Master\'s Thesis
My Carnegie Mellon University Master\'s Thesis
My Carnegie Mellon University Master\'s Thesis
My Carnegie Mellon University Master\'s Thesis
My Carnegie Mellon University Master\'s Thesis
My Carnegie Mellon University Master\'s Thesis
My Carnegie Mellon University Master\'s Thesis
My Carnegie Mellon University Master\'s Thesis
My Carnegie Mellon University Master\'s Thesis
My Carnegie Mellon University Master\'s Thesis
My Carnegie Mellon University Master\'s Thesis
My Carnegie Mellon University Master\'s Thesis
My Carnegie Mellon University Master\'s Thesis
My Carnegie Mellon University Master\'s Thesis
My Carnegie Mellon University Master\'s Thesis
My Carnegie Mellon University Master\'s Thesis
My Carnegie Mellon University Master\'s Thesis
My Carnegie Mellon University Master\'s Thesis
My Carnegie Mellon University Master\'s Thesis
My Carnegie Mellon University Master\'s Thesis
My Carnegie Mellon University Master\'s Thesis
My Carnegie Mellon University Master\'s Thesis
My Carnegie Mellon University Master\'s Thesis
My Carnegie Mellon University Master\'s Thesis
My Carnegie Mellon University Master\'s Thesis
My Carnegie Mellon University Master\'s Thesis
My Carnegie Mellon University Master\'s Thesis
My Carnegie Mellon University Master\'s Thesis
My Carnegie Mellon University Master\'s Thesis
My Carnegie Mellon University Master\'s Thesis
My Carnegie Mellon University Master\'s Thesis
My Carnegie Mellon University Master\'s Thesis
My Carnegie Mellon University Master\'s Thesis
My Carnegie Mellon University Master\'s Thesis
My Carnegie Mellon University Master\'s Thesis
My Carnegie Mellon University Master\'s Thesis
My Carnegie Mellon University Master\'s Thesis
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My Carnegie Mellon University Master\'s Thesis

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Exploring the role of Social Networks in Intra-Corporate Crowdsourcing initiatives such as Stock Market for Innovations.

Exploring the role of Social Networks in Intra-Corporate Crowdsourcing initiatives such as Stock Market for Innovations.

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  • 1. Exploring the role of Social Networks in Intra-Corporate Crowdsourcing initiatives such as Stock Market for Innovations. Jonas Rolo jrolo@andrew.cmu.edu October 7, 2011 Advisory Committee: David Krackhardt Andrei Villarroel P age |1
  • 2. AbstractSeveral Companies have been started using crowdsourcing initiatives internallywhere only their employees participate. One of the more recent of these initiativesare Stock Markets for Innovation (SMI) where companies can tap into the creativitypower 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”, howeverintra-corporate crowdsourcing (ICC), being enclosed in the company realm, might beunder the influence of the Social Networks. We study a similar setup to a SMI insidea Master’s class and the results from this study show that indeed the Social Networkof study is correlated with the behaviour of the SMI participants mainly on theevaluation procedures of ideas and performances of the other participants. Themost important network characteristic playing a role in this evaluation procedure ispower, where the most powerful participants get higher evaluation on their ideas orperformance. This result seems to show that the intra-corporate ICC initiatives mightjust be another tool for the most powerful actors to reinforce their social power. Themanagement implication is that knowledge diffusion in the SMI initiatives might notbe that different than what existed previously in the social network and themanagement team should have this in mind when considering applying an ICCinitiative similar to a SMI. P age |2
  • 3. Contents1. INTRODUCTION ............................................................................................. 42. LITERATURE REVIEW...................................................................................... 52.1. Crowdsourcing .............................................................................................. 52.1.1. Intra Corporate Crowdsourcing (ICC) ............................................................. 62.1.2. Stock Market for Innovations (SMI) ............................................................... 62.2. Social Networks ............................................................................................. 72.2.1. Structural Holes ............................................................................................. 82.2.2. Knowledge Brokering .................................................................................. 102.2.1. Bonacich Power and Centrality .................................................................... 103. THEORY AND HYPOTHESIS ........................................................................... 124. DATA, METHODOLOGY, NETWORKS AND MEASURES .................................. 164.1. Data ............................................................................................................ 164.2. Methodology............................................................................................... 184.3. Network Construct ...................................................................................... 194.3.1. Study Network ............................................................................................ 194.3.1. Comments Network .................................................................................... 224.4. Measures .................................................................................................... 235. RESULTS ...................................................................................................... 246. LIMITATIONS AND CONCLUSIONS ................................................................ 326.1. Limitations .................................................................................................. 326.2. Conclusions ................................................................................................. 33BIBLIOGRAPHY ........................................................................................................ 36 P age |3
  • 4. 1. INTRODUCTIONThe literature on crowdsourcing has focused mainly on the firm externalcrowdsourcing initiatives and only recent research has started to tackle closedcrowdsourcing initiative made only with employees of a company, a Intra-CorporateCrowdsourcing (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 onIdea Markets or SMI show that this type of ICC initiatives offers promisingadvantages for new product innovation: “the platform and the formal processmotivates employees to communicate their ideas to management”, “by filtering theideas 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 endof 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’sinternal crowds, the social networks of the crowds were formed long before anynew ICC initiative, and thus it is to expect that the social networks might influencethe behaviour of the employees in the ICC initiative. In a ICC Stock Market forInnovation (SMI), Villarroel & Reis, (2011) show that “speculative activity is positivelyassociated with better innovation performance”. An example of this speculativeactivity could be seen in pulling or collusion strategies to invest heavily on one ideafor this to be one of the most invested ideas (approved by the market) and thesubmitter and all investors win with this. The pre-existing social networks might justbe the tool these participants are using to perform the speculative activity.Our research question is to understand if there is any correlation between the socialnetwork of the participants and their behaviour in the SMI, and if this correlationexists, to understand what individual network measures drive innovative activity andperformance in the SMI. Inside the firm the social networks might play an importantrole in the ICC initiatives and might be a good way of predicting some outcomes of P age |4
  • 5. these initiatives. Knowing the social networks characteristics and nodes’characteristics might help to understand and predict part of the outcome of the ICCinitiative.2. LITERATURE REVIEW 2.1. Crowdsourcing“Crowdsourcing is an online, distributed problem solving and production model thathas emerged in recent years” (Brabham 2008: pp. 75. The term was first used in2006 by Howe and Robinson and it represents the act of a company or institutiontaking 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 tooutsource can be from a wide variety and can be aggregated in three different typesof 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 crowdcreates page contents that build up to Wikipedia’s communitarian knowledge.Another example is Innocentive, a company that supplies innovative technicalsolutions for tough R&D problems using a worldwide crowd of scientists.Innocentive’s clients are companies with high R&D expenditures that want to getsolutions to their unsolved R&D problems. With a mix of crowd creation and crowdvoting there is Threadless.com, a web-based t-shirt selling company thatcrowdsources the design for their shirts and the voting for the best T-shirts from aglobal crowd through an online competition. iStockphoto.com is another example, aweb-based company that sells photography, animations, and video clips for clientsto use on websites, in brochures, in business presentations and so on. A crowd ofphotographers and film makers submit their photographs and video clips and voteon the best photos and videos to rank the website stock. On the crowd fundingthere is the micro credit example of Kiva, a project that receives proposals of several P age |5
  • 6. projects to be funded in small amounts of money and that a crowd can lend moneyto.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). Studiessuggest that crowdsourcing is a “very good source of social capital for corporations”(Villarroel & Reis, 2010) and its openness gives the ability for organizations toincrease 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 thefirm. Nonetheless, there are firms that have implemented crowdsourcing withintheir 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 entirepool of employees. Companies with a sufficiently large1 (Howe 2008) internalcommunity of contributors, use crowdsourcing as a way to get ideas or solutions totheir problems, without running the risk of exposing their best solutions tocompetitors. Firms can ask their employees to design the new company logo, toname the mascot or a new product, to make advertising and to give new ideas onseveral 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 is1 Sufficiently large means that the number of active participants is above 1,000. P age |6
  • 7. an online application that replicates a stock market where employees submit,comment and invest on innovation ideas. The most invested ideas are the ones thefirm will study the viability of implementation and its submitter receives a monetaryprize if the idea is implemented. The Stock Market for Innovation has the followingstages: 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 thatthe crowd is not completely undefined as Howe describes for the externalcrowdsourcing initiatives. Even if the crowd is big in absolute number, there areemployees that know and have been interacting with one another for years beyondthe ICC initiative. 2.2. Social NetworksA social structure of individuals (organizations, countries, etc) and their relations ofinterdependency can be represented as a social network, where nodes representthe individual actors and ties (edges, links) represent the relationship between theseactors. The resulting graph-based structures of social networks are often verycomplex because there can be many kinds of ties between the nodes (friendship, P age |7
  • 8. kinship, common interest, financial exchange, dislike, sexual relationships, orrelationships of beliefs, knowledge or prestige). Social network analysis (SNA) is thearea that studies social networks using network theory which is the study of graphstructures using network measures. “Social network research has been applied inseveral academic fields and has shown that social networks can be found andoperate on many levels, from families up to the level of nations, and play a criticalrole in determining the way problems are solved, organizations are run, and thedegree to which individuals succeed in achieving their goals” (Wikipedia – SocialNetwork). 2.2.1. Structural HolesStructural 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 byone path of connection. In other words there is a structural hole between twocomponents of a network if there is only one path that connects those twocomponents.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 bothnodes “B” and “C”, thus between “B” and “C” there is a structural hole. “A” can P age |8
  • 9. exploit the fact that B and C do not have a connection and trade knowledge,information or resources possessed by B but not by C and vice versa.The network measure of structural holes can be measured by effective size, whichmeasures the number of non redundant ties in a Ego network (how many actors isEgo connected with that are not connected to each other).Burt (1992) defines the effective size of a persons ego network as:whereandand Z is the data -- the matrix of network ties.Structural holes are important because actors of the social networks with nonredundant connections (structural holes) are in a position of being gatekeepers ofinformation and resources from one component of the network to the other. Beingin 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 andaccess to unique information and resources an actor can act as a broker ofknowledge and do knowledge brokering. P age |9
  • 10. 2.2.2. Knowledge BrokeringAccording to Hargadon (1998) “knowledge brokers” span multiple markets andtechnology domains and innovate by brokering knowledge from where it is knownto where it is not”. A innovation made by a knowledge broker is typically a knowsolution in one technology field that the knowledge broker can transform and adaptto apply as a new solution to an unsolved problem in a different technology field. Inthe organizational social networks, knowledge brokers have several structural holes(non redundant connections) that give them the possibility to work asintermediaries in the transfer of information, knowledge or resources and do abrokerage activity over these structural holes. The best way to identify possibleknowledge brokers is the network measure of structural holes. 2.2.1. Bonacich Power and CentralityIn his paper of 1987 - Power and Centrality: A Family of Measures – Bonacich arguesthat “being connected to well connected others makes an actor central, but notpowerful. On the contrary, being connected to others that are not well connectedmakes one powerful although not central”. His argument sets upon thedependability of the other actors to whom Ego is connected. If the actors that areconnected to Ego are, themselves, well connected, they are not highly dependent onhim. These actors, have many contacts, just as Ego does and they don´t have to gothrough him to get what they want. On the other hand, if the actors that areconnected to Ego are, themselves, not well connected, then they are dependent onhim because they have to go through him to get what they want. Bonacich created ameasure (Bonacich Power Centrality) that captures this dichotomy of Power andCentrality, and shows that the more connections the actors in your neighbourhoodhave, the more central you are; the fewer the connections the actors in yourneighbourhood have, the more powerful you are. P a g e | 10
  • 11. Figure 2 – Illustration Bonacich Power and Centrality (designed in PowerPoint)In Figure 2 we can see an illustration of the differences in position of the actors andthe dichotomy of power and centrality. Node “A” has the higher centrality becausehe his connected with nodes that are well connected (B and C). On the other handnodes “B” and “C” are not as central as “A” but are very powerful because they areconnected with several nodes that are not well connected. The Bonacich PowerCentrality (Bonacich 1987) network measure is given by: C ( ,  )   ( I  R) 1 R1α is a scaling vector, which is set to normalize the score; β reflects the extent towhich 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 allones.The magnitude of β reflects the radius of power. Small values of β weight localstructure, larger values weight global structure. If β is positive, then ego has highercentrality when tied to people who are central. If β is negative, then ego has highercentrality when tied to people who are not central. As β approaches zero, you getdegree centrality. P a g e | 11
  • 12. This network measure is interesting to use in the field of knowledge diffusionbecause with just one measure we can test if innovative activity or performance inan SMI is correlated with network power or centrality attributes of the participants3. THEORY AND HYPOTHESISFor both firms and individuals it is recognized that boundary-spanning ties haveadvantages in access to sources of external Knowledge and information (Allen &Cohen, 1969; Allen, Tushman & Lee, 1979). The literature has also shown therelevant role of accessing knowledge and information across boundaries whenperforming innovation activities inside organizations (Hagardon, 1998; Hansen,1999; Ancona & Caldwell, 1992 ;Burt, 2004). Hagardon (1998) explain that firmswhich position themselves as knowledge brokers have an advantage over traditionalmanufacturing firms in innovating activities. Hansen (1999) shows that researchunits that have weak ties with other subunits of the firm have an advantage insearching for useful knowledge on other subunits. Ancona & Caldwell (1992) showthat “teams carrying out complex tasks in uncertain environments (such R&D) needhigh levels of external interaction to be high performing”. Burt (2004) explains thatorganization elements that are positioned close to structural holes (brokers) haveaccess to less redundant and more unique information and that are better preparedto have good ideas than other elements. His results show that “brokers that spanover structural holes between groups in the organization are more likely to expresstheir ideas, less likely to have their ideas dismissed and more likely to have theirideas evaluated as valuable”.In the crowdsourcing literature there is evidence of openness and technicalmarginality as important factors in innovation activities. For example, Jeppesen andLakhani (2010) show that in a crowdsourcing initiative involving scientific problemsand lump sum prizes, winning solutions are positively related to increasing distancebetween the solver’s field of expertise and the focal field of the problem. The resultsfrom this research somehow make us think that the wining solutions are correlatedwith knowledge brokering, since the solver´s field of expertise is distant from the P a g e | 12
  • 13. focal field of the problem. These solvers are probably using solutions from their fieldof expertise and adapting them as new solutions for problems in a different field ofexpertise. Our first question is if the knowledge brokering position in theorganizational social network is positively correlated with innovative activity. ICCinitiatives like the SMI can help us to answer this question because we have acrowdsourcing initiative with innovation activity and is made inside a closed crowdof employees from which we can know the social network.Thus, in internal corporate crowdsourcing, it is expected that more creativeparticipants will have a knowledge brokering position in the organizational socialnetwork.Hypothesis 1 – Innovation activity in intra-corporate crowdsourcinginitiatives, as the Stock Market for Innovation, is positively correlated withbrokering position in the organizational social network.As mentioned above, the effect of the social network on the behaviour of individualson the corporate crowdsourcing initiatives is expected to happen even if only at asubtle level. This possibility of effect is very important to analyse mainly on thevaluation of the ideas in the internal corporate crowdsourcing initiatives such asMarkets for Innovation. In these Markets for Innovation the evaluation ofperformance of the participants is regularly made by the number of comments andinvestments each participant get. A similar evaluation is made for the submittedideas, where the ideas that receive more comments and investments are the mostvalued and more suitable for implementation.When participating on an ICC initiative such as SMI, an individual is constrained byhis time limitations and level of effort necessary to do it. The individual accordingwith his preferences and abilities will dedicate some time and effort to participate in P a g e | 13
  • 14. this event. To make a decision of what ideas to analyse, comment or invest in theSMI, a participant will not try to get the information on all the ideas, previouscomments and investments. When someone is buying a second hand car it does notseek information on all the second hand cars available in the market. Typically tooptimize his choice the person will ask to their friends, or to friends of friends, ifthey know someone trustworthy that have a car to sell. Then it is reasonable toassume 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 analysecomments and invest in their friend’s ideas, or friends of friend’s ideas. With thisassumption I think that a participant will have more interest in analysing andcomment ideas from people he is connected with. Thus, the organizational socialnetwork will be correlated with the participation on ICC initiativesHypothesis 2 – The Organizational Social Network is positively correlatedwith the behaviour (analysis, comments and investments) of participants ona ICC initiative as the SMI.Following Hypothesis 2, it is important to go beyond in the analysis of thecorrelation between the Social Network and the Behaviour (analysis, comments andinvestments) in the SMI. Hypothesis 2 analyses this correlation at the network leveland it is also interesting to make an analysis at the individual level. Why do someideas and participants receive more comments (higher valued) than others in theSMI? Is there any social network individual characteristic that drives the participantsto receive more comments and investments? To answer this question it is importantto analyse the social network structure and test if there is any correlation betweenthe individual network characteristics of the participants and the number ofcomments they receive.There are two main concepts that give theoretical support for a correlation betweenthe individual network characteristic and the number of comments or investments P a g e | 14
  • 15. received. The first concept is again the Structural Holes and the KnowledgeBrokering. As already mentioned in Hypothesis 1, Knowledge brokers are expectedto be the most creative participants in the ICC initiatives as the SMI due to theirpossibility of spanning over structural holes. The supposedly creativity of theseparticipants will allow them to submit more creative ideas and to make morecreative and more valuable comments to the other’s ideas. This creativity of theideas and comments made by the Knowledge Broker will allow him to receive morecomments and investments from other participants than what we would expectfrom any other participant. Additionally the position of a Knowledge Broker bridgingover 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 networkthat other participants do not access, possibly granting him an exclusive audiencethat might make comments or investments on his ideas or previous comments.Hypothesis 3a – Comments and investments received ICC initiatives as SMIis 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 actorcentral, but not powerful. On the contrary, being connected to others that are notwell connected makes one powerful although not central. His argument sets uponthe dependability of the other actors to whom the individual is connected. If theactors that the individual are connected to are, themselves, well connected, they arenot highly dependent on him. These actors, have many contacts, just as you do andthey don´t have to go through you to get what they want. On the other hand, if theactors 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 whatthey want. Bonacich created a measure (Bonacich Power Centrality) that capturesthis dichotomy of Power and Centrality, and shows that the more connections theactors in your neighbourhood have, the more central you are; the fewer theconnections the actors in your neighbourhood, the more powerful you are. Since P a g e | 15
  • 16. making a comment to an idea or previous comment requires some time and effortwe believe power will be more important and effective than centrality in collectingcomments made by other participants.Participant A that is connected to a peripheral participant B (with just oneconnection) will exert his social power and if B makes a comment it will do it on theidea or previous comment from A. On the contrary, participant C that is connectedto a central participant D will probably receive fewer comments because D willdivide his effort to comment for several ideas or comments from the participants towhom he is connected to. Assuming that the effort to comment on ideas orprevious comments is randomly distributed over the network and on average aparticipant makes 3 comments (the real average value in our data is 3.2), then aparticipant connected to a peripheral participant will receive in average threecomments from this participant. Contrarily a participant connected to a participantwith three connections will receive on average one comment from this participant.Hypothesis 3b – Comments and investments received on a ICC initiative as aSMI is positively correlated with social network power of the SMIparticipants.4. DATA, METHODOLOGY, NETWORKS AND MEASURES 4.1. DataThe data2 as basis of analysis is from an Innovation Management course with 86Master’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 and2 Data supplied by Prof. Andrei Villarroel from the Católica University of Portugal P a g e | 16
  • 17. study and its frequency in a scale from 1 to 5 ( 1 – once; 2 – rarely; 3 – sometimes; 4 – quite often and 5 – always). This survey was done in the beginning of the course before any individual or group assignment and before the crowdsourcing initiative was initiated. Data from a crowdsoursing initiative for Innovation performed on the Innovation Management course taught by Professor Andrei Villarroel in Portugal in the spring semester of 2010. As a part of the coursework and grade, the students participated in the online Innovation crowdsourcing (IC) initiative by submitting innovation ideas for new products or services and by visiting and commenting on other’s ideas. In this IC initiative all the 86 students participated, 22 innovation ideas were submitted, 331 comments were made to ideas or previous comments and 1815 visits (1519 to ideas and 296 to student profiles) were made. More specifically the data from the IC initiative has information of which students submitted ideas, which students visited those ideas and profiles of other students and which students made online comments on ideas and previous comments. Information on class performance for the 86 students: crowdsourcing grade, group grade, individual grade and final grade.Table 1 has the descriptive statistics of all the individual level variables used: Variable N Mean Stdev Min Max Ideas 86 0.26 0.44 0.00 1.00 Final Grade 86 67.54 10.90 35.18 91.72 Comments received 86 3.85 5.88 0.00 25.00 Symmetric Strong Study Network Todal degree Centrality 86 3.77 2.20 0.00 10.00 Structural Holes 81 0.17 0.29 -0.25 0.64 Bonacich Power Centrality 81 0.86 0.52 0.35 2.56 Underlying Graph Study Network Todal degree Centrality 86 9.07 3.17 2.00 16.00 Structural Holes 86 0.57 0.21 -0.22 0.83 Bonacich Power Centrality 86 0.93 0.36 0.15 1.86 Table 1 – Descriptive Statistics from all the used individual level variables P a g e | 17
  • 18. This IC initiative was a lighter version of a SMI where investments were not included.The grade the students got for participating in this IC initiative is calculated by aformula that accounts the number of ideas posted, comments made, commentsreceived, visits made and visits received by each student. We don’t have the specificvaluation for each of these actions in the IC initiative 4.2. MethodologyTo prepare the several datasets and to create the necessary social networks to ouranalysis we propose the steps described in Table 2:Step Procedure1 Construct the study social network from the survey results and calculate the correspondent network measures.2 Construct the comments and visits social networks from the activity (comments and visits) in the crowdsourcing for innovation. These inferred networks are directed graphs to be an image of the students’ behavior on the internal crowdsourcing initiative.3 Calculate the network measures at the individual level that will be used as explanatory variables in the Hypotheses analysis.4 Test our Hypotheses by analyzing the correlation between social network metrics of each student and their behavior in the internal crowdsourcing initiative (ideas, comments, visits) using as control measures the performance on class (individual grade).Table 2 – Steps for the preparation of the datasets P a g e | 18
  • 19. To test our Hypotheses we used two types of econometric analysis:  A more traditional econometrics approach of Probit regression model and a Poisson regression model. These models assume the independence between all observations which might not be true with all the network measures. For example if A and B share a common group of friends then their friend’s social networks will be correlated. Being aware of this fact I will use these methods to test Hypothesis 1, Hypothesis 3a and Hypothesis 3b since there is no other way of getting correlations between network measures and attributes of the same network.  A quadratic assignment procedure for inference on multiple-regression coefficients (MRQAP), which is a method equivalent to the general linear regression model but is specific to be used with network data. As mentioned in the previous point, Network data might violate the assumption of independence between all observations and also there is the risk of equal observations across several individuals which mean the errors terms could be correlated if a linear regression was used. The Multiple Regression Quadratic Assignment Procedure (MRQAP) is used exactly to comply with the assumption of independence between all observations. This method estimates the standard errors using several permutations of the dependent variable data set, resulting in multiple random datasets with the dependent variable. Hypothesis 2 is tested with the MRQAP method. 4.3. Network Construct 4.3.1. Study NetworkThe first network needed to construct is the Study Network (SN) that is a reflex ofthe results from the survey data indicating for each student the students that hestudies and works with. The study relation is a physical relation (if A studies with Bthen B studies with A) since the study relation is reciprocal, we can only constructreciprocal networks. It does not make sense to use a Directed Graph since thatwould be to assume that A studies with B but B does not study with A which is P a g e | 19
  • 20. physically impossible. Thus we constructed two variations of the Study Network, oneconsidering reciprocal identification where a tie exists when A identifies B and viceversa (Symmetric Strong Study Network) and the other considering that a tie exists ifeither A indentifies B or vice versa (Underlying Graph Study Network).Figure 3 shows a picture of the Symmetric Strong Study Network analyzed in ourstudy: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 ourstudy: P a g e | 20
  • 21. Figure 4 – Illustration of the Underlying Graph Study Network (designed in R studio)In this case the Underlying Graph is acceptable to use because there are some issueswith the open questions of surveys that might generate subjectivity. In the answers(Bertrand, M. & Mullainathan S.,2001). The survey had the restriction of five personsto nominate, limiting the choice of study partners, the scale of intensity of study(always, often, sometimes, etc) is subjective and because people do not alwaysremember everyone with whom they studied or worked. We believe that due to allof 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 weconsider that the identification just from one student is enough to consider theexistence of a reciprocal tie. P a g e | 21
  • 22. 4.3.1. Comments NetworkThe Comments Network is a reflex of the behaviour of all participants in theInnovation Crowdsourcing initiative in terms of the comments made or received. Toconstruct the Comments Network we used two variations, the Directed Graph andthe Strong Network. The variation of Underlying Graph does not make sense toconstruct because that would be to assume that if A commented on B, then Bcommented on A which might not be true. Additionally, the Strong Network hadclose to 10 edges and all other nodes are isolated. Thus, the Directed Graphvariation is the only network considered meaningful on the Comments Network.Figure 5 shows a picture of the Direct Graph Comments Network analyzed in ourstudy:Figure 5 – Illustration of the Direct Graph Comments Network (designed in R studio) P a g e | 22
  • 23. 4.4. MeasuresFor Hypothesis 1 the needed dependent variable was a measure of the creativeactivity and we used a binary variable that describes whether a participant of thecrowdsourcing initiative did or did not submit an innovation idea. We use a binaryvariable because no participant has submitted more than one innovation idea.Has explanatory variables for Hypothesis 1, we used several network measurescalculated from the two variations of the Study Networks (Strong Network andUnderlying Graph). The network measures used were Total Degree Centrality,Structural Holes and Bonacich Power Centrality. We also could have used otherpopular network measures such as Betweeness Centrality and EigenvectorCentrality, however we opted not to use them because these measures have similarcalculation methods with the other measures we were already using and in theregressions we would be capturing the same effects. As control variable we used theIndividual Course Grade because it is a good measure of individual performance aswe could expect that students with better individual grades will also want to have agood grade in the internal crowdsourcing initiative.Using the regressions methods of QAP or MRQAP we can use networks asdependent and explanatory variables, thus for Hypothesis 2 we used as dependentvariable the Comments Network (Direct Graph) and as explanatory variables weused the two Study Networks (Strong Ties and Underlying Graph).For Hypothesis 3 the dependent variable used was the number of commentsreceived by each participant in the internal corwdsourcing initiative, as explanatoryvariables we used the same network measures from the Study Network, alreadyexplained for Hypothesis 1 and as control variable we also used the IndividualCourse 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 VariablesH1 creative activity Brokering position variables Individual performanceMeasures Binary var. ( submitted Structural hole Degree and Bonacichfor H1 idea in the SMI) measure Power Centrality Individual course grade Participants Behaviour Organizational SocialH2 in the SMI NetworkMeasuresfor H2 Comments Network Study Network Visits to Ideas Network Comments and investments received Social NetworkH3 in the SMI Power creative activity Individual performanceMeasures comments received in Bonacich Power Binary var. ( submittedfor 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 7Total Degree -0.0033 -0.2214 0.5239 -0.1451Centrality 0.962 0.103 0.225 0.809Bonachic Power -0.1517 -0.7647 -2.2070 -0.2722Centrality 0.631 0.113 0.207 0.897 0.6513 1.5526 1.8347 1.7476Structural Holes 0.255 0.056 * 0.048 ** 0.123 0.0344 0.0252 0.0332 0.0335 0.0321 0.0369 0.0327Individual grade 0.126 0.250 0.132 0.144 0.153 0.112 0.160Adjusted R2 -0.023 -0.021 -0.021 -0.031 -0.031 -0.034 -0.045Observations 81 81 81 81 81 81 81Dependent Variable - Binary variable (1 if student posted idea and 0 if not)top value - Coefficient lower value - SignificanceSignif. codes: *** < 0.01; ** < 0.05; * < 0.1Table 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 7Total Degree -0.0492 -0.0552 -0.2355 -0.2520Centrality 0.320 0.529 0.291 0.297Bonachic Power -0.3390 -0.1905 1.6706 1.7056Centrality 0.436 0.792 0.391 0.385 -0.5533 -0.3046 0.1046 0.2168Structural Holes 0.441 0.800 0.935 0.866 0.0358 0.0337 0.0369 0.0350 0.0372 0.0374 0.0381Individual 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 -0.045Observations 86 86 86 86 86 86 86Dependent Variable - Binary variable (1 if student posted idea and 0 if not)top value - Coefficient lower value - SignificanceSignif. codes: *** < 0.01; ** < 0.05; * < 0.1Table 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 correlatedwith the binary variable Ideas. For both Study Networks (Strong Ties and UnderlyingGraph) all the Probit regressions have negative Adjusted R-squared which meansthat the model does not explain at all the creative activity of the participants in theinternal crowdsourcing initiative. According to this result it is clear that being in abridging position to broker knowledge across structural holes is not related withcreativity activity in the Innovation Crowdsourcing initiative of the class and thus ourHypothesis 1 is not supported. Additionally, as already mentioned none of the othernetwork measures is correlated with the creative activity of submitting an idea tothe Innovation Crowdsourcing initiative of the class. This means that weather astudent submits or not an idea to the Innovation Crowdsourcing initiative of theclass is independent of which other students they study with. Probably the networkinformation that we have for this analysis is not enough to have more enlighteningresults. Not having any other information (demographic, schooling, workingexperience, area of expertise, etc.) regarding the students makes us assume in ouranalysis 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 controlvariables, but also to incorporate it into the Study Network creating a Meta-Network. For instance, incorporating the students’ individual information oftechnical knowledge and working experience would allow us to construct two otherattribute Networks, the Network of the Students’ Technical Knowledge and theNetwork of the Students’ Working Experience. These two networks would be muchricher for the analysis because we could see the students that have access toresources that others don’t and clearly identify the students close to the structuralholes of the Technical Knowledge Network. These students, through theirpositioning of knowledge brokers over technical knowledge structural holes, shouldbe the more creative participants in the Innovation Crowdsourcing initiative of theclass. P a g e | 26
  • 27. To analyse Hypothesis 2, first we used QAP correlation tests to see the correlationsbetween the Comments Network and the two Study Networks (Strong Tie andUnderlying Graph). To understand the goodness of fit of the Study Networks inexplaining the Comments Network we used a MRQAP where the dependent variableis the Comments Network and the explanatory variables are the two StudyNetworks. Since the Strong Tie Study Network has a correlation of 1 with theUnderlying Graph Study Network we cannot combine both these networks in thesame MRQAP. To see the effects of these networks we ran 2 MRQAPs and used as asecond explanatory variable in each MRQAP a Network created from the visits of theparticipants to the ideas submitted in the internal crowdsourcing initiative.MRQAP 1: Comments Network = β0 + β1 x Symmetric Strong StudyNetwork + β2 x Visits to Ideas Network + µMRQAP 2: Comments Network = β0 + β1 x Underlying. Graph StudyNetwork + β2 x Visits to Ideas Network + µAnd to confirm that this Network of Visits to Ideas is not highly correlated with anyof the Study Networks we also ran a QAP between the Visits to Ideas Network andthe 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 0Table 6 – Results from the QAP regressions between the Symmetric Strong and UnderlyingGraph Study Networks with the Comments Network (Directed Graph) and Visits to IdeasNetwork (Directed Graph)The top part of Table 6 shows that both Study Networks are significantly correlatedwith the Comments Network and that the Underlying Graph Network is morecorrelated (13.3%) than the Symmetric Strong Study Network (7.9%). Thesecorrelation numbers might not seem to be high but assuming that in a purecrowdsourcing initiative this correlation should be spurious and close to 0%, thenseeing correlation numbers of 7.9% and 13.3%, we have to admit that the SocialNetworks might be having some effects on the behaviour of the participants ofinnovation crowdsourcing initiative of the class. Additionally we can see that theVisits to Ideas Network is significantly but not highly correlated with both StudyNetworks which allow us to use it as a explanatory variable in the MRQAPS that willallow us to test Hypothesis 2. Table 7 shows the results from the above describedMRQAP 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-PermTable 7 – Results from the MRQAP regressions 1 and 2Table 6 shows that indeed both Study Networks are significant in explaining thecomments networks, although the Symmetric Strong Study Network is onlysignificant up to a 10% level and its coefficient is not very high in magnitude. TheUnderlying Study Network is significant at a 1% level and its coefficient is alreadyhigh in magnitude. To have an idea of the impact of these results, the interpretationof the coefficient from the Underlying Graph means that in average for every twostudents that studied together there is 11.26% probability that one student willmake a comment to the other when participating on the innovation crowdsourcinginitiative of the class. This is even more surprising considering the non significance ofthe Visits to Ideas Network in this model. This seems to be evidence that thecomments the students make in the innovation crowdsourcing initiative of the classare influenced by their social study network but not by the ideas they visited. Theresult above gives support to our Hypothesis 2. The adjusted R-squared of themodels is a relatively acceptable value for the procedure of the MRQAP and themost important is that this value is not negative and is not very close to zero. Theadjusted 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 datasetswhich lowers the adjusted R-squared.The results from Hypothesis 2 show that the Underlying Graph is the Study Networkthat best explains the Comments Network, thus to test our Hypothesis 3 we decidedonly to use the Underlying Graph Study Network in this analysis. For Hypothesis 3we ran a Poisson Model having as a dependent variable the number of commentsreceived in the crowdsourcing initiative by each student, as explanatory variablesseveral network measures (Bonacich Power Centrality and Structural Holes) and ascontrol variables the Individual Course Grade and the binary variable Ideas (1 ifstudent submitted an idea or 0 if not). We used the Poisson model because thedistribution of the dependent variable of the model (comments received) is prettysimilar to a Poisson distribution.Log (Comments received) = β0 + β1 x Bonacich Power Centrality + β2 x StructuralHoles + β3 x Individual Course Grade + β4 x IdeasWe measured the Bonacich Power Centrality with a positive beta, which means thata positive coefficient from this measure will indicate that receiving comments on theinnovation crowdsourcing initiative of the class has a positive correlation withindividuals that are more central, but less powerful. If the coefficient is negative, thiswill indicate that the less central, but more powerful, individuals are the onesreceiving more comments. The results from the Poisson regression are shown in theTable 8: P a g e | 30
  • 31. Table 7 – Results from the Poisson regression on the Underlying Graph Study NetworkThe results from Table 7 show that the most important driver to receive commentsin the innovation crowdsourcing initiative of the class is to submit an idea. Thisresult strongly supports the Hypothesis 3a in showing that receiving comments onthe innovation crowdsourcing initiative of the class is positively correlated with thecreative activity. However, the Structural Holes variable is not significant in thisanalysis which does not come to a surprise since the results from Hypothesis 1 showthat the Structural Holes measure is not correlated with the creative activity in theinnovation crowdsourcing initiative of the class. Once again, if we had access to theadditional information from the individuals, it would allow us to analyse the StudyNetwork data as a Meta-Network and possibly shed more light into the StructuralHoles story.Regarding the Bonacich Power Centrality variable we can see that in all models thecoefficient 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 participantsconnected 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 tohave better evaluation of ideas or performance. Contrarily, participants that areconnected to highly connected participants will receive fewer comments becausethe highly connected participants will divide their effort to comment for severalideas or comments from the competing participants to whom they are connected to.Thus, this supports our Hypothesis 3b - Comments on corporate crowsourcinginitiatives, as Stock market for Innovations is positively correlated with socialnetwork power of the SMI participants. As we hypothesized, the individual networkcharacteristic that is important in the evaluation procedure of ideas andperformance of the participants is the power and not centrality, contrarily to whatintuitively one could think.6. LIMITATIONS AND CONCLUSIONS 6.1. LimitationsIn this study we wanted to analyse the role of social networks in the ICC initiativeslike the SMI. The biggest limitation from our research is that the analysis is done ona setup (students from a Master’s class) somehow different from a corporation. Thislimitation can be divided into two different aspects:First, the analysed SMI was not a real SMI because it did not have investments. Thislimitation takes some reality and probably dynamics from the SMI made on the classsince there were no investments or prizes involved. If the analysed SMI hadinvestments and prizes involved we believe that the students would take this ICCmuch more seriously and we would expect the student’s behaviour to be even morepronounced. We would expect to see higher coefficients and lower significancelevels. In the analysis if the SMI involved investments and prizes.Second, the social network of students is significantly different from a social networkof 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 organizationalhierarchic network that can also have influence of its own (promotions, careerprogression, layoff, etc) despite the influence of the social network. We believe thatthe analysed students social network again attenuates the dynamics of interactionsbetween actors since students don’t have that much at stake with each other thatmotivates them to be extremely active in their social network. If the SMI was doneon a corporation with the presence of employee social network and theorganizational hierarchic network, we believe that the behaviour of the social actorswould be much more active in their social roles and we would again probably seehigher coefficients and lower significance levels. 6.2. ConclusionsSeveral Companies have been using crowdsourcing initiatives to perform diversetasks. One of the more recent of these initiatives are internal Stock Markets forInnovation where companies can tap into the creativity power of their employeesthrough an online stock market where employees create, comment and invest onnew ideas for products, processes and services, that might be implemented by thecompany. However these initiatives that should be based in the “wisdom of thecrowds” might be under the influence of the Social Networks that exist in thecompany way before the crowdsourcing initiatives.From the theoretical idea that internal corporate crowdsourcing initiatives might beunder the influence of social networks we studied a similar crowdsourcing initiativemade 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 constructedsocial networks (Study and Comments) and made several analyses that supportsome of our theoretical hypothesis.The results from our analysis show that Hypothesis 2 – The Organizational SocialNetwork 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 datawe can say that the Social Network is correlated with the behaviour of theparticipants on the innovation crowdsourcing initiative of the class. However, thisinfluence of the Social Networks on the innovation crowdsourcing initiative of theclass cannot be seen on the creative activity (submission of ideas) and ourHypothesis 1 – Innovation activity in intra-corporate crowdsourcing initiatives, as theStock Market for Innovation, is positively correlated with brokering position in theorganizational social network. – is not supported. To have a better and morecomplete analysis of Hypothesis 1 it would require more individual information ofthe participants in the innovation crowdsourcing initiative of the class.As no surprise, the creative activity (submission of ideas) is the most importantdriver to receive comments in the corporate crowdsourcing initiative and the resultson this analysis support Hypothesis 3a – Comments received on corporatecrowdsourcing initiatives, as Stock market for Innovations is positively correlatedwith creative activity.The area where our findings show influence of the Social Networks on the corporatecrowdsourcing initiative is the evaluation of ideas and performances of theparticipants. Perhaps the most interesting result in this paper is that the mostimportant characteristic playing a role in this evaluation procedure is power, wherethe most powerful participants get higher evaluation on their ideas or performance.This idea is comes from the results that support our Hypothesis 3b – Commentsand investments received on a ICC initiative as a SMI is positively correlatedwith social network power of the SMI participants, which means that isnegatively correlated with the Bonacich Power Centrality.This last result seems to be a similar situation to the one found in the results ofVillarroel & Reis, (2011) - “speculative activity is positively associated with betterinnovation 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 socialpower in their own benefit. One implication of our results is that knowledgediffusion in the SMI initiatives might not be that different than what existedpreviously and might be even more easily manipulated (Legitimization) by thepowerful actors. If so, the advantage of “increased efficiency at the fuzzy front endof the new product development process” (Soukhoroukova et al 2010) might be inrisk of being offset by the possible manipulation of the powerful actors. Anymanagement team should have this in mind when considering applying an ICCinitiative similar to a SMI. Further studies are needed in this area to analyze moredeeply the effect of Social Network and Organizational Hierarchic Network in theoutcomes of ICC such SMI. P a g e | 35
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