Enhancing Social and Intellectual Collaboration inInnovation Networks: A Study of Entrepreneurial Networks
in an Urban Technological University
Jacqueline P Verrilli, Christine Z Miller and Nik Rokop
The purpose of this paper is to document efforts to build collaborative networks of faculty, staff,students, alumni, corporations, government, and entrepreneurial advocates to help create a
new generation of high impact entrepreneurs at Illinois Tech, an urban technical university
located in Chicago. The focus of our study is the Entrepreneurship Academy Council (EA), a
university-wide, academically-focused organization with an overall goal of fostering acommunity of entrepreneurship on campus that transcends departments and units. How might
we use the tools of dynamic social network analysis to study, map, and influence the existing
networks within the EA? The EA organization includes the EA Council comprised of faculty,staff, students and administrators from across the university. Specifically, the EA Council
charter states that:
The goal of the Entrepreneurship Academy Council is to help create a newgeneration of high impact entrepreneurs graduating from Illinois Tech. Ourstrategy includes (1) creating a strong entrepreneurial culture at Illinois Tech, (2)developing an infrastructure with the resources needed to support this culture, (3)developing processes through which students can achieve the dream of becoming
entrepreneurs, and (4) establishing a collaborative network with alumni,
corporations, government and entrepreneurial advocates that fosters a universitybased entrepreneurial ecosystem (italics added).
From the Entrepreneurship Academy Council 2015/16 Annual Report
Collaborative innovation networks, or COINs, are groups of self-motivated individuals fromvarious parts of an organization or from multiple organizations who, driven by a common vision,
work together on a new idea (Gloor, et al., 2003). COINs are recognized as an essential element
in fostering innovation. Powell, et al. (1996,116) referred to these “networks of learning” as thelocus of innovation:
When the knowledge base of an industry is both complex and expanding and thesources of expertise are widely dispersed, the locus of innovation will be found in
networks of learning, rather than in individual firms.
In a University environment, individual colleges are analogous to individual firms that may haveparties interested in collaborating on a single innovation. The colleges also have thecharacteristics of dispersed expertise and complex and expanding knowledge bases. An
additional element is the diverse student population: at a technical University, many students haveself-selected into tracks of gaining employment in, or engaging in some area of innovation. The
EA was created with the intention of fostering COIN development that would facilitate connections
among and between the students, faculty, alumni, administration, firms and resources for1entrepreneurs such as funding and legal assistance. Measures of EA success could include anincrease in student participation in New Business Venture competitions, an increase in student
start-ups, an increase in faculty/student collaborations in innovations, and an increase inutilization of the Knapp Center for Entrepreneurship. Methods of capturing these metrics will need
to be coordinated and, potentially, developed.
In this preliminary study we understand RESILIENCE as the capacity to recover quickly fromdifficulties and adapt to discontinuous change. In the context of our study this includes:● Engaging diverse stakeholders, including faculty, students, staff, administrators,alumni, industry, and funding entities● Adapting to a constantly changing student body○ That has different expectations than previous generations○ That engages in various new technologies for the pursuit of social andprofessional goals○ That learns in non-traditional ways that are less text-based and moreexperiential● Adapting to rapidly changing technology● Adapting to changes in political and policy regimes that affect enrollment andfunding
We seek to discover existing collaborative innovation networks among EA Council members,
mapping their internal university connections and their associations with alumni and external
private and public entities. Using a mixed methods approach that includes dynamic social network
analysis (SNA) tools and interviews with EA Council members we are mapping and analyzing
existing networks. This will allow us to identify opportunities to enhance the social and intellectual
collaborations that foster entrepreneurship and innovation within the university and among its
stakeholders.

Methodology:
This research employs the use of the dynamic social network analysis (SNA) software Condor.
Using Condor, we collected the meta email data (IP addresses of senders and receivers, datesof communication and responses) from the Executive Director of the EA Council, the Chairmanof the EA Council, the Director of the Knapp Center of Entrepreneurship, a business school facultyof innovation, and two entrepreneurial students. The Condor analysis provided a snapshot of thetotal communication for “All Mail” in the email accounts associated with the university IPaddresses for each subject. We then merged the datasets from the individuals in order to assess
the status of the EA as a COIN. The snapshots are color-coded by domain which indicates thenode type (a university constituent such as student, faculty, or staff of the university). We further
identify the individual with special color coding and identify the other study subject in the set ofcommunication. We conducted the three main Centrality calculations in Condor to map thecurrent state of the network using the Executive Director and Chair as the primary researchsubjects.
2Analysis:
A visual analysis shows that a substantial amount of communication by the Executive Directorand Chairman of the EA is outward communication denoted by the shape and direction of thelinks in Condor (Appendix A). This is similar to the star-like structure, which indicates low
closeness centrality and degree centrality. In other words, a network in which there are noconnections between nodes, and a network structure that shows a low level of existing
collaborative networks or COINs. The internal Condor calculations of betweeness confirm thevisual analysis. Appendix B and C depict the visual maps of the email networks of the Director ofthe Knapp Center for Entrepreneurship, the faculty of Design and Innovation, and an
Entrepreneurial Student. The elaborate interconnectedness and higher centrality calculations
show the robustness of their networks.
Conclusions and Next Steps:
Our preliminary analysis suggests that there is significant opportunity to create a more matureand robust network within and around the EA Council. For example, we see individual subject
networks that show tightly connected node clusters with high degree centrality in the network of
the Director of the Knapp Center of Entrepreneurship, the entrepreneurial students, and thebusiness school faculty member.
Based on this preliminary work our next steps include one-on-one interviews with individual
subjects to discuss and interpret their network visualizations and continuing to conduct additional
queries using Condor. We intend to present the network visualizations at the EA Council meetingin fall 2017 to engage members in discussions to consider ways in which they can implement theirstrategy by building more robust collaborative and innovative networks as indicated by moregalaxy-like structures in the Condor network maps.

3References:
Carlson, Carl R. and Nik Rokop. 2016. Entrepreneurship Academy Council 2015/16 Annual
Report, Illinois Institute of Technology, Chicago, IL. https://web.iit.edu/entrepreneurshipacademy. Accessed May 25, 2017.

Gloor, Peter A., Rob Laubacher, Scott B.C. Dynes, Yan Zhao. 2003. Visualization ofCommunication Patterns in Collaborative Innovation Networks: Analysis of some W3C working
groups. 12th International Conference on Information and Knowledge Management (CKIM), New
Orleans, LA. November 03-08, 2003.

Powell, Walter W., Kenneth W. Koput and Laurel Smith-Doerr. 1996. Interorganizational
Collaboration and the Locus of Innovation: Networks of Learning in Biotechnology.
Administrative Science Quarterly, Vol. 41, No. 1 (Mar., 1996), pp. 116-145

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Appendix A:

Static view of Executive Director’semail to members of theEntrepreneurship Academy Council
during 2016-17 academic year.
Degree Centrality values range from
3.0 to 22.0. Executive Director’snode is depicted in white andDegree Centrality value is 22.0.
Map is color-coded by domain.

Chair of EA Council’s emails to EA
members. Chair’s node is depicted
in white at the center of the map.
Map is color-coded by domain.
Note star formation of emails
indicating a predominance ofoutward communication. A small
galaxy, noted by more edges(interconnections), is depicted to theleft of the Chairs’ node.

Enlargement of EA Chair’s email
map. Degree Centrality calculations
range from 1.0 to 350.0 (Chair’sfigure – Chair’s node in white).
Note galaxy to slight upper left ofChair’s node indicating a functioning
network.

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Appendix B:

Director of Knapp Center email
map. Degree Centrality
calculations range from 1.0 to 398.0(Director’s figure – Director’s nodein white).
Note “halo” of ancillary nodes
surrounding main communication
network.

Enlargement of Director of KnappCenter’s email network.

Extreme enlargement of Director of
Knapp Center’s email network toshow higher order networked
nodes.

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Appendix C:

Faculty of innovation email map.
Note “halo” of ancillary nodes
surrounding main communication
network.

Extreme enlargement of faculty’s
email network to show higher order
networked nodes. DegreeCentrality ranges from 0.0 to
1,399.0 (faculty’s figure).

Entrepreneurial Student’s email
map. Degree Centrality figures
range from 1.0 to 995.0.
Map indicates a galaxy network.

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