1. The Value of
Technology Enabled Networks
Bala Iyer
October 2011
Peter Gray, Sal Parise and Bala Iyer. Innovation Impacts of Social Tagging Systems,
Vol 35, No. 3, pp 629-643. MIS Quarterly.2011

2. Agenda
 Research question: What is the value of social
tagging
 Introduction to Technology-mediated Networks
 Research Methodology
 Results
 Creating a personal social media strategy
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3. What do we know about social IT (Web 2.0)?
 Awareness and adoption of social IT by organizations
continue to grow, but impacts are mixed/uncertain
 Features and impacts of social IT (blogs, wikis, tagging)
differ from those of traditional IT
 Direct, measurable impacts difficult to assess since focus
is on employee connectivity to unstructured knowledge
 Traditional IT focused on representation
 Social IT focuses on interpretation
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4. What was our research project trying to answer?
 What is the value proposition of social collaboration IT
tools, from the perspective of knowledge workers in
organizations?
 Do social tools improve personal innovativeness?
 We focused on:
 Bookmarking/tagging application
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5. What we found….social technology matters!
 Its not just frequency of use that is important with these
tools, but rather, who you are connecting to
 Brokerage, in technology-mediated networks, has a
positive impact on personal innovativeness
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6. Repositories vs. social IT
 Codification  Personalization
 Technologies can be  Information flows are
used to centralize fundamentally social
information  Different people see
 Search tools to help the world differently
discover best  Who you talk to
practices/solutions matters
 Quality of information
matters
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7. Big Gamble?
 No processes, no control, no validation
 No real inkling what people will use these for
 Throw the switch & stand back?
 How would we know if it’s working?
 How can we justify the investment?
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8. Introduction to
Technology-mediated Networks
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9. Social Tagging
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10. Page 10

11. Page 11

12. Page 12

13. Unstructured knowledge is becoming increasingly
important in today’s workplace
What is “unstructured” knowledge?
Relevant topics that are dynamic and real-time
Content in many forms: videos, podcasts, blogs,
documents, etc.
Often needed on virtual, global project teams
Need access to people, not just content
Difficult to put in formally structured databases
Hard to find!
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14. Technology-mediated networks: the people-to-people connections
enabled by social IT
Yes, there are “productivity” gains: virtual file cabinet,
improve version control of documents, reduce e-mail
But, these tools have social benefits:
– build awareness (of skills, role, projects, department)
– build affiliation (community, common interests)
– build community in online spaces
– Provide richer, dynamic meta-data (profiles, pictures,
ratings, comments)
Ultimately, they build connections among people with
shared interests or help find subject-matter experts
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15. Codification
Personalization
Knowledge
objects
Tags
Virtualization
Technology mediated networks

16. MITRE’s bookmarking/tagging system (onomi) allows for
collaborative filtering 1
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17. Employees sharing common tags
Size: tenure
Center # Color
1 Lavender
2 Blue
5 Red
19 LightMagenta
20 Sepia
48 Melon17
© Salvatore Parise and Bala Iyer, Babson College

18. Social Tagging and Web 2.0
 "I look to see who the other people are on
del.icio.us who tag the same things that I
think are important. Then, I can look and see
what else they've tagged ... and that
multiplies your ability to find things that are
interesting and useful, and other people feed
off of you."
 Howard Rheingold
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19. Many organizational innovations can be explained by the
movement of ideas and information from one social
context to another, “from where they are known to where
they are not” (Hargadon 2002)
The history of innovation is littered with discoveries that
arise from fortuitous interactions between individuals
who were unaware that their separate efforts had
mutual relevance (Hargadon 2002).
. Together, these are “social navigation” (Dieberger
1997), where one individual’s choice of which online
resources to visit is influenced by the prior actions of
others.
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20. Dependent variable
 Personal innovativeness: the extent to which
an individual actively generates, discovers,
and promotes creative ideas
 “This person generates creative work-related
ideas,” and “This person promotes and
champions work-related ideas to others”
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21. Quantity and diversity of Information
 H1: The number of times an individual
accesses social bookmarks will positively
predict his/her level of personal
innovativeness.
 Access larger numbers of bookmarks,
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22. Quantity and diversity of Information
 H2: The number of people an individual
connects to by accessing their social
bookmarks will positively predict his/her level
of personal innovativeness
 Access the bookmarks of larger numbers of
people (ranging from 1 to 100 alters, with a
mean of 24.30)
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23. Tag Following Network Shape
 H3: The extent to which the people an individual
connects to by accessing their social bookmarks
are not themselves connected through the
bookmarking system will positively predict that
individual’s level of personal innovativeness.
 Access the bookmarks of people who are less
likely to provide redundant information (Effective
sizes ranged from 1 to 60.1, with a mean of
11.39)
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24. Brokers – people in networks that have access to
diverse information and connect subgroups
A Broker position in a network has access to & connects different subgroups
(e.g., locations, levels, tenure). Because brokers have access to unique
information, they tend to be innovative and high performers.
Broker score = Total connections minus redundant connections
A1 is in a much better brokerage position than B1 Effective Size
B2 B1
A2 A1
B8
A8
B3 B4
A3 A4
B7
A7
B5 B6
A5 A6
Broker score for A1= 7. No redundancy. Broker score for B1 << 7. Lots of redundancy.
Each node represents a different employee
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25. Tag Following Network Shape
 H4: The extent to which the network of people
an individual connects to by accessing their
social bookmarks provides indirect access to
many non-redundant bookmarks will positively
predict his/her level of personal innovativeness.
 Access the bookmarks of people who
themselves are exposed to more nonredundant
information via their own use of a bookmarking
system (Effective reach ranged from 1 to 90,
with a mean of 22.09)
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26. Effective Reach
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27. Research Methodology
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28. Empirical Approach
 Global professional services firm with strong research focus
 Well developed tagging technology (50k tags in 2 years)
 Stratified sampling of 150 users across high/medium/low usage
 Collected tag-following data for two years; split into year 1 vs. 2
 Subjects nominated 1 to 2 evaluators who provided responses on
 personal innovativeness using Scott and Bruce (1994) scale”
Innovative Performance (stakeholder assessment) 1 to 6
scale used:
1). This person generates creative work-related ideas.
2). This person promotes and champions work-related ideas to others.
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29. Results
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30. Results
 H1: Not supported
 H2: Not supported
 H3: (ß = 0.48, p < .01) – Effective size
 H4: (H4, ß= 0.46, p < .01) – Effective reach
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© Salvatore Parise and Bala Iyer, Babson College

31. Predicting Personal Innovativeness
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32. Implications
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33. Implications
 Network theories offer better explanation than system-
use theories
 Who one obtains info from matters more than how much obtained
 Key theoretical pivot required in the form of new
theoretical processes not contemplated in the original
Structural Holes theory
 Implications for source theory: selectivity in info seeking
may explain some of what has been assumed to be
selectivity in info sharing
 Implications for other E2.0 technologies like blogs wikis?
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34. Managerial Implications
 A low-cost way of directing one’s attention in
useful ways
 First empirical evidence in a non-relational context (we
think)
 Maximize early wins by rolling out to units where
creativity is key?
 Educate employees about brokerage, exploring
novel connections?
 Use network analysis tools to help employees
see how their tag following networks differ from
others?
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35. References
 Peter Gray, Sal Parise and Bala Iyer. Innovation Impacts of
Social Tagging Systems, Vol 35, No. 3, pp 629-643. MIS
Quarterly.2011.
 Salvatore Parise, Bala Iyer, Donna Cuomo, Bill Donaldson.
MITRE Corporation: Using social technologies to get connected.
Ivey Business Journal, January/February 2011.
 Salvatore Parise, Patricia J. Guinan, and Bala Iyer Harnessing
Unstructured Knowledge: The Business Value of Social
Bookmarking at MITRE, IEEE Engineering Management Review,
Volume 38 Number 3, 2010.
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36. Dependent variable
 Of the full set of 150 subjects, 120 (80
percent) provided evaluator names.
 Six-point Likert scales anchored on “strongly
disagree” (1) and “strongly agree” (6)
Reliability across the two measurement items
was 0.95. strong inter-rater reliability (r =
0.92) between pairs of evaluators. (scores
ranged from 1 to 6, mean 4.26, standard
deviation 1.74).
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