Value of Technology Enabled Networks
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The document discusses research on the value of social tagging systems. It finds that who a person connects with through a tagging system, rather than just how frequently they use the system, positively impacts their personal innovativeness. Brokering diverse connections between subgroups provides access to non-redundant information and indirectly impacts innovativeness. The research was conducted through a survey of 150 employees at a professional services firm who used a tagging system, and evaluated based on nominations from colleagues.
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Value of Technology Enabled Networks
- 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 2
- 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 3
- 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 4
- 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 5
- 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 Page 6
- 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? Page 7
- 9. Social Tagging Page 9
- 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! 13
- 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 14
- 15. Codification Personalization Knowledge objects Tags Virtualization Technology mediated networks
- 16. MITRE’s bookmarking/tagging system (onomi) allows for collaborative filtering 1 Page 16
- 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 Page 18
- 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. Page 19
- 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” Page 20
- 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, Page 21
- 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) Page 22
- 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) Page 23
- 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 24
- 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) Page 25
- 26. Effective Reach Page 26
- 27. Research Methodology 27
- 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. Page 28
- 29. Results 29
- 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 Page 30 © Salvatore Parise and Bala Iyer, Babson College
- 31. Predicting Personal Innovativeness Page 31
- 32. Implications 32
- 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? Page 33
- 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? Page 34
- 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. 35
- 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). Page 36