I am working on building a KN for my company and am building a list of
metrics for tracking. Here is what I have so far. Can you offer any
additional that are critical and innovative from your experience? I
really really appreciate your replies.

1. Number & length of visits by topic, geography and user demographics.
(measures level of overall use)
2. Number of article posts. (measures level of sharing in culture)
3. Number of article post replies. (measures engagement)
4. Value of time & money saved via quicker idea & expert finds

Thanks for replies.

Craig, I'd agree with Vanessa that you are looking for
signs of life that reflect the engagement of some critical
mass of people.  I'd look at the list of posts which have
been tagged by a small set of people on delicious here:

http://delicious.com/tag/community_indicators

as some added reading coming originally from Nancy White.

thanks

Ed

On Thu, Sep 11, 2008 at 10:29 AM, Vanessa DiMauro
<vdimauro@...> wrote:
> Hi Craig,
>
>
>
> One Key metric that I look for when building and measuring professional
> communities/knowledge networks is Engagement Level.  I strive to find about
> a 20% level of engagement – meaning some show of activity (contributions,
> quickpoll taking, feedback, messages etc) – essential signs of life – from
> the total membership population. This is the my key health-o-meter metric
> before any granular metrics become focused on. Hope that helps!
>
>
>
> Best
>
> Vanessa
>
>
>
>
>
> Vanessa DiMauro
>
> President, Leader Networks
>
> Creating professional communities
>
> O- 617.484.0778  /  M- 617.417.3893
>
>
>
> E- vdimauro@...
>
> http://www.leadernetworks.com
>
>
>
>
>
>
>
> From: ona-prac@yahoogroups.com [mailto:ona-prac@yahoogroups.com] On Behalf
> Of Craig A. DeLarge
> Sent: Thursday, September 11, 2008 10:18 AM
> To: ona-prac@yahoogroups.com
> Subject: [ona-prac] Assistance Needed: Ideas & Experts Knowledge Network
> Metrics?
>
>
>
> I am working on building a KN for my company and am building a list of
> metrics for tracking. Here is what I have so far. Can you offer any
> additional that are critical and innovative from your experience? I
> really really appreciate your replies.
>
> 1. Number & length of visits by topic, geography and user demographics.
> (measures level of overall use)
> 2. Number of article posts. (measures level of sharing in culture)
> 3. Number of article post replies. (measures engagement)
> 4. Value of time & money saved via quicker idea & expert finds
>
> Thanks for replies.
>
>



--
Edward Vielmetti
Ann Arbor, MI
+1 734 330 2465

Please remove evielmetti@... from your address book.

Hello everyone,

For our company we have done a SNA of several groups. We have asked
several SNA questions which measured some of the dimensions which
could have influence on the knowledge sharing of the group (e.g.,
performance).
Instead of analyzing the relations of individual nodes we want to
analyze the group (and thus network) as a whole and particularly how
to increase the performance of the group(s).

For example, we have measured who knows who. In this question there
was the possibility to answers on a 5-point scale (1- hardly know
each other 5= know each other very well). If we increase this value,
the network (relations between nodes) changes significantly.
Moreover, if two (central) nodes are removed from the network only 3
cliques will exist and not a network as a whole. Therefore, we want
to answer to following question: Which relation between two nodes
would be most effective for the network (e.g., decreases the average
shortest path). Which program do you use to calculate the average
shortest path?
	 The most central nodes in the network do not have to be the
best option. Because they could have the shortest path to a number of
nodes, but the nodes at the outside of the network do not have a
short path to these central nodes. Therefore a `new' relation between
some nodes at the outside of the network could be more efficient. But
how can this be calculated?

Furthermore, `know each other' does not have to be the only
independent variable of how to measure performance (knowledge
sharing). Also `trusting the other person', the `availability of the
other person' and `the value of information getting from the other
person' could well be dimensions of knowledge sharing (we argue this
based on scientific literature). At this moment we are also
developing a formula which takes the values of these several SNA
dimension into account. We would appreciate it very much that in case
you have any advice in this matter you would be able to share it with
us.


At this time we use the programs UCINET, NETDRAW and SPSS.

Hi all - (Apologies for the length of this - as I got to describing
what I'm trying to do, I ended up writing more than I thought I
would....)

I am currently working on an effort to implement a type of "expertise
location" function based on generating a profile of someone from
his/her team/project associations and his/her work products (that's a
pretty simple description for what I'm doing but it gets the point
across).

Part of this results in a set of keywords associated with each person
in the population - the "expertise location" can then be thought of as
providing a standard keyword search engine / interface interface on
top of the set of keywords associated with the people (there's also
some navigation in the application based on the keywords but I'm going
to ignore that for the moment).

For any one person in the system, the defining characteristics are a
set of activities / team memberships and, for each of those, there are
a set of keywords.  Collapsing together all of the keywords into a
"pool" of keywords for a person, you can think of the profile of a
person as a set of weighted keywords (where the weight for a keyword
is the number of occurrences of for that keyword within the set of
activities / team memberships).

There are no specific restrictions about what is a valid keyword
except that anything you might think of as a common "stop word" in a
search engine is excluded (things like "the", "of", etc.).  So my
profile might (in part) look like: ("search", 100), ("knowledge", 40),
("management", 80), ("engineering", 20), etc.

Also, because of how the keywords are generated and weighted, there is
no upper limit on the weight of a keyword for any one person.  To
follow on from my example, I might have an additional 80 keywords with
a total weight of, say, 5000, while someone else might have a total of
40 keywords and a total weight of 4500.

All of this is pretty straightforward and, even though the basic idea
seems pretty simple, the keyword search function across these profiles
provides a surprisingly high correlation to finding someone with a
particular skill or expertise.  (This makes me happy, as I wasn't sure
if it would really "work" as expected like this!)

What I've been considering now is to take these people profiles and
try to do two additional things which are related:  provide a measure
of "similarity" between two people and, from that measure of
similarity, try to identify "invisible" communities of interest (by
identifying pockets of people who have high similarity among
themselves).

The idea of a "similarity" metric is intriguing because by itself it
means that the presentation of a particular person's profile can
include a means to identify people similar to the one you're looking
at.  Though it's kind of crude to liken this to an ecommerce site, I
do think of it as similar to the function you see on many sites where
when you're looking at a product, you are presented with a list of
similar products.  ("People who have found this person interesting
might find these other people interesting!" :-) )

My question:  Has anyone done something similar to this before?  If
so, what approach have you taken to defining the similarity
measurement?

Here's the quandary I've run into, which has prompted my question:

* To measure similarity between two people (X and Y), I first match
the keywords between X and Y.

* For each keyword (KW) the two people have in common, I credit each
person with the minimum of the weight of KW for X and the weight of KW
for Y.   So if two people have the keyword "engineering" and one has a
weight of 20 and another has a weight of 60, their similarity for this
keyword is 20.

* I then sum up these keyword weights to get a total "similarity
weight" between the two people.  Let's say it's 800 across all of the
common keywords for two people.

* Lastly, in order to reflect how much of that commonality describes
each of the people, I calculate the percentage of someone's profile
that is "covered" by the similarity weight to get the overall
"similarity measure".  So if X has a total profile weight of 5000 and
Y has a total profile weight of 4000, that means that the person X is
16% (800/5000) similar to Y, while Y is 20% (800/4000) similar to Y.
This asymmetry makes some sense to me because we are comparing
"different size" profiles (so person Y can seem more like person X
then person X might be similar to person Y).

Now, getting back to my question - I can link people based on either
of these computations - the "similarity weight" or the "similarity
measure" - does either make more sense?

If I use the "similarity weight" then I seem to have an issue where if
two people both have "heavy" profile weights, they can seem highly
similar based on their similarity weight even though their similarity
weight might be a relatively small percentage of their total profile
weight (say an overlap of 1000 when the weights of the two profiles
are 8000 and 10000).  Similarly, two people who have a small profile
weight will seem dissimilar even if they had 100% overlap in their
profiles!

On the other hand, if I use the "similarity measure", it seems likely
that anyone with a "heavy" profile weight will seem to have weak links
because people are likely to have low percentage overlap with them,
while people with small profiles can seem very similar (and so tightly
linked) based on just a couple of keywords.


Any thoughts from ONA practitioners on what might be the best way to
link people in this situation?

Sorry for the length - I'm planning to write about this experiment on
my blog but thought I'd see whether you might have a suggestion for
how to measure this and how to break the logjam in my head :-)

Regards
Lee Romero

PS - Yes, I am aware of the perils of ONA via data mining - I do not
ascribe much to the analysis here other than possibly finding these
"communities of interest" and not so much about how significant
someone's position in the network might be or anything like that.

Lee,

Who picks the words?  Who assigns the words to whom?  Who weights each
word for each person?

Do people also nominate each other for who they actually go to for
expertise on A, B or C?

"X may be a high word & high weight person that no one goes to because
X is a jerk."

You can find similarity by attributes [your approach] or links or both.

See my analysis of political books on Amazon [people that bought this
also bought that...]

http://www.orgnet.com/divided.html

Valdis



On Oct 13, 2008, at 1:44 PM, Lee Romero wrote:

> Hi all - (Apologies for the length of this - as I got to describing
> what I'm trying to do, I ended up writing more than I thought I
> would....)
>
> I am currently working on an effort to implement a type of "expertise
> location" function based on generating a profile of someone from
> his/her team/project associations and his/her work products (that's a
> pretty simple description for what I'm doing but it gets the point
> across).
>
> Part of this results in a set of keywords associated with each person
> in the population - the "expertise location" can then be thought of as
> providing a standard keyword search engine / interface interface on
> top of the set of keywords associated with the people (there's also
> some navigation in the application based on the keywords but I'm going
> to ignore that for the moment).
>
> For any one person in the system, the defining characteristics are a
> set of activities / team memberships and, for each of those, there are
> a set of keywords.  Collapsing together all of the keywords into a
> "pool" of keywords for a person, you can think of the profile of a
> person as a set of weighted keywords (where the weight for a keyword
> is the number of occurrences of for that keyword within the set of
> activities / team memberships).
>
> There are no specific restrictions about what is a valid keyword
> except that anything you might think of as a common "stop word" in a
> search engine is excluded (things like "the", "of", etc.).  So my
> profile might (in part) look like: ("search", 100), ("knowledge", 40),
> ("management", 80), ("engineering", 20), etc.
>
> Also, because of how the keywords are generated and weighted, there is
> no upper limit on the weight of a keyword for any one person.  To
> follow on from my example, I might have an additional 80 keywords with
> a total weight of, say, 5000, while someone else might have a total of
> 40 keywords and a total weight of 4500.
>
> All of this is pretty straightforward and, even though the basic idea
> seems pretty simple, the keyword search function across these profiles
> provides a surprisingly high correlation to finding someone with a
> particular skill or expertise.  (This makes me happy, as I wasn't sure
> if it would really "work" as expected like this!)
>
> What I've been considering now is to take these people profiles and
> try to do two additional things which are related:  provide a measure
> of "similarity" between two people and, from that measure of
> similarity, try to identify "invisible" communities of interest (by
> identifying pockets of people who have high similarity among
> themselves).
>
> The idea of a "similarity" metric is intriguing because by itself it
> means that the presentation of a particular person's profile can
> include a means to identify people similar to the one you're looking
> at.  Though it's kind of crude to liken this to an ecommerce site, I
> do think of it as similar to the function you see on many sites where
> when you're looking at a product, you are presented with a list of
> similar products.  ("People who have found this person interesting
> might find these other people interesting!" :-) )
>
> My question:  Has anyone done something similar to this before?  If
> so, what approach have you taken to defining the similarity
> measurement?
>
> Here's the quandary I've run into, which has prompted my question:
>
> * To measure similarity between two people (X and Y), I first match
> the keywords between X and Y.
>
> * For each keyword (KW) the two people have in common, I credit each
> person with the minimum of the weight of KW for X and the weight of KW
> for Y.   So if two people have the keyword "engineering" and one has a
> weight of 20 and another has a weight of 60, their similarity for this
> keyword is 20.
>
> * I then sum up these keyword weights to get a total "similarity
> weight" between the two people.  Let's say it's 800 across all of the
> common keywords for two people.
>
> * Lastly, in order to reflect how much of that commonality describes
> each of the people, I calculate the percentage of someone's profile
> that is "covered" by the similarity weight to get the overall
> "similarity measure".  So if X has a total profile weight of 5000 and
> Y has a total profile weight of 4000, that means that the person X is
> 16% (800/5000) similar to Y, while Y is 20% (800/4000) similar to Y.
> This asymmetry makes some sense to me because we are comparing
> "different size" profiles (so person Y can seem more like person X
> then person X might be similar to person Y).
>
> Now, getting back to my question - I can link people based on either
> of these computations - the "similarity weight" or the "similarity
> measure" - does either make more sense?
>
> If I use the "similarity weight" then I seem to have an issue where if
> two people both have "heavy" profile weights, they can seem highly
> similar based on their similarity weight even though their similarity
> weight might be a relatively small percentage of their total profile
> weight (say an overlap of 1000 when the weights of the two profiles
> are 8000 and 10000).  Similarly, two people who have a small profile
> weight will seem dissimilar even if they had 100% overlap in their
> profiles!
>
> On the other hand, if I use the "similarity measure", it seems likely
> that anyone with a "heavy" profile weight will seem to have weak links
> because people are likely to have low percentage overlap with them,
> while people with small profiles can seem very similar (and so tightly
> linked) based on just a couple of keywords.
>
>
> Any thoughts from ONA practitioners on what might be the best way to
> link people in this situation?
>
> Sorry for the length - I'm planning to write about this experiment on
> my blog but thought I'd see whether you might have a suggestion for
> how to measure this and how to break the logjam in my head :-)
>
> Regards
> Lee Romero
>
> PS - Yes, I am aware of the perils of ONA via data mining - I do not
> ascribe much to the analysis here other than possibly finding these
> "communities of interest" and not so much about how significant
> someone's position in the network might be or anything like that.
>
> ------------------------------------
>
> Yahoo! Groups Links
>
>
>

Hi Valdis - for the most part, the words are identified through the
person's activities (so, largely, by the person him/herself).

Some examples:

I am a member of a mailing list named "software-development-community"
and I have posted 20 times to that mailing list in the last year on
various topics (different subject lines).

I also may have posted to my blog a number of items with various
titles and slotted into a set of categories (which I have assigned as
authors).

Lastly, I have edited, say, 2 dozen different pages in our wiki site
with various titles and assigned to various keywords.

Based on the above, my profile would include keywords generated from:

1. The names of the mailing lists of which I'm a member with a weight
equal to the # of posts I've made (so each of "software",
"development" and "community" are weighted 20 based on 20 posts).

2. The words from the subject lines of those 20 emails I've posted to
the software-development-community mailing list, with a weight of 1
for each occurrence of any given word.  So, say, I have written 5 of
those posts about Eclipse (and the word "Eclipse" occurs in the
subject line of all five of those) in that span of time, "Eclipse"
would get the weight of 5 from my mailing list posts.

3. The titles and categories for my blog posts are also broken into
keywords and assigned a weight of 1 for each occurrence of any given
word.

4. The titles and categories of each wiki page are broken down into
keywords and assigned a weight equal to the number of times I've
edited any given page (so if I edit a particular page 10 times, each
keyword from the title or category is assigned a weight of 10 for that
page).

And so on.  I say that the keywords are self identified "for the most
part" because I actually probably don't have a lot of control over the
actual names of the projects or teams on which I work - my hope as an
implementor of this idea is that the names will generally be
rationally chosen to reflect words someone might think of to find
someone else using.

I've currently included about a dozen sources of activities or project
/ team memberships in this work - which is not yet sufficient for all
people who might be included but I think it's sufficient to at least
validate this approach to doing the "expertise location" that
originally drove the work.

So if you write a blog post or send an email or edit a wiki page with
the word "jerk" in it, that keyword ends up associated with you.
Because the sources for this are internal (corporate), I generally
don't think that will be that much of a problem.

The idea is to try to identify keywords that are relevant to someone
using a means that directly looks at what you work on or write about
or are assigned to, etc.  Hopefully, it can reduce the need to
maintain some definition of what my own skills are in some system that
I (or my manager or perhaps my co-workers) have to or need to update.
Basically, I'm "tagging" myself indirectly through my work.  (I think
that if you had a system that directly stored skills of workers -
i.e., a "skills inventory database" - that could be treated as nothing
more than an additional data source for this - probably one with a
higher weighting than other sources, obviously).

Does that answer the question?

Thanks for the pointers, too, Valdis.

Regards
Lee

On Mon, Oct 13, 2008 at 2:15 PM, Valdis Krebs <valdis@...> wrote:
> Lee,
>
> Who picks the words?  Who assigns the words to whom?  Who weights each
> word for each person?
>
> Do people also nominate each other for who they actually go to for
> expertise on A, B or C?
>
> "X may be a high word & high weight person that no one goes to because
> X is a jerk."
>
> You can find similarity by attributes [your approach] or links or both.
>
> See my analysis of political books on Amazon [people that bought this
> also bought that...]
>
> http://www.orgnet.com/divided.html
>
> Valdis
>
>
>

As I twittered/tweeted when I first saw this...

IMHO, this is the best Twitter rank/grade/metric out there currently.

He uses my Reach metric.

Valdis
Twitter: valdiskrebs

On Oct 14, 2008, at 8:36 AM, Patti Anklam wrote:

> Do you twitter? Interested in how SNA metrics can be applied to look
> at the reach, social capital etc of twitterers?
>
> Twinfluence: http://twinfluence.com/index.php
>
> Here’s the page on how the statistics are calculated:
>
> http://twinfluence.com/about.php
>
> any comments?
>
> Enjoy,
>
> /patt
>

Thanks, Charles!  That brings a smile to my face.

I figured my own research musings (which is what this is so far) could
not have been all that original (though I'd like to think I have some
original ideas in the mix here).  It sounds like your sonar technology
may validate my own hypothesis, though.

I haven't checked your site yet - is there information available about
this tool (product?) that I could read through?

If I have any other specific questions, I'll take them offlist as well.

Regards
Lee Romero



On Wed, Oct 15, 2008 at 3:32 PM, Charles Armstrong
<charles@...> wrote:
> hallo lee
> what you describe is very close to what trampoline's sonar technology does.
> sonar server gobbles up emails, ldap, documents and so forth ("work
> products" as you term it), deduces each person's expertise and
> knowledge through statistical language modeling, then calculates the
> network characteristics for each person using ona techniques. armed with
> this intelligence sonar can start to identify documents and contacts which
> are likely to be relevant to a particular user.
> the specific use case you mention of identifying emergent communities of
> interest is something we've encountered a fair amount of demand for with our
> flightdeck product. basically it involves identifying people who share a
> strong interest in a particular field (though their interests may diverge in
> other areas) but have no identifiable communication with each other. adding
> a time element to this, up-weighting areas of interest that have only
> started being identified recently, helps highlight fast-growing trends.
> i can't answer your specific question about similarity matching as i'm just
> a humble ethnographer. if you're interested i'd be happy to link you up with
> someone in the team who knows more about the statistical aspects.
> yours : charles
>
> chief executive // trampoline systems ltd
> the trampery, 8-15 dereham place, london EC2A 3HJ
> uk cell +44 7792 456807
> usa cell +1 415 728 8656
> http://trampolinesystems.com
>

If you're located outside Australia, the link below will not work.  It appears
to be a link to the download page.  Fortunately, you can still view the video as
I'm doing from Washington, DC at the following link.  Thanks for this!

jb

http://www.abc.net.au/tv/documentaries/interactive/futuremakers/ep4/



-----Original Message-----
From: ona-prac@yahoogroups.com on behalf of Cai Kjaer
Sent: Sun 11/9/2008 4:16 PM
To: ona-prac@yahoogroups.com
Subject: [ona-prac] Interesting program on Australian TV: How Kevin Bacon Cured
Cancer

Hi all,



A couple of weeks ago our national broadcaster  - ABC - had a great program on
social networks called "How Kevin Bacon Cured Cancer". Certainly worth
watching.they did the famous exercise around six degrees of separation and there
are some great examples of SNA in action. The last couple of minutes should be a
real energizer for us all!



You can see it online at www.abc.net.au/iview <http://www.abc.net.au/iview>



Click on the ABC Docs Category and there it is.be aware it will only be there
for another 18 days.



Rgs





Cai

_____________________________

Cai Kjaer

Partner

Optimice Pty Ltd.

Mobile: +61 411 569 694

Email: cai.kjaer@... <mailto:cai.kjaer@...>

Web: www.optimice.com.au <http://www.optimice.com.au/>  / www.onasurveys.com
<http://www.onasurveys.com/>

Download my business card - http://cai.kjaer.optimice.ecard.ec
<http://cai.kjaer.optimice.ecard.ec/>

At last! Someone is putting the real "R" into CRM systems
(Relationships, rather than Recording).

LLL

--- In ona-prac@yahoogroups.com, "Patti Anklam" <patti@...> wrote:
>
> It's always interesting to see how network fundamentals are
realized in
> business:
>
>
>
> Telephone companies have been using network analysis techniques
for years
> (to track down delinquent customers, I heard), but this new
product from
> Aperio CI, SNAP (Social Network Analysis and Propensities) goes a
level
> further in using SNA metrics for marketing purposes:
>
>
>
> http://www.aperioci.com/CurrentNews.aspx
>
>
>
> /patti
>

Check it out at: http://www.facebook.com/group.php?
gid=32791289299&ref=mf

at trampoline we're currently getting a lot of requests to perform
fast organisational network diagnostics using our sonar email analysis
technology. in most cases this is linked with restructuring or merger
events. we want to form a partnership with a uk-based ona consultant
we can introduce to customers to help them interpret the data sonar
generates and advise on possible interventions. we're looking for
someone business focused rather than academic, and probably a
freelancer or part of a small team rather than a large professional
services outfit.

i've met a lot of talented ona people in north america but nobody here
in britain. can anyone recommend a good ona consultant based in the uk?

please reply to me direct (charles@...) rather than
the list. any suggestions appreciated.

: charles

chief executive // trampoline systems ltd
the trampery, 8-15 dereham place, london EC2A 3HJ
uk cell +44 7792 456807
usa cell +1 415 728 8656
http://trampolinesystems.com

Hello everyone,

I just wanted to introduce myself here. My name is Gustaf Pretorius
and I am qualified as clinical psychologost. I consult in the private
sector and have been tasked with an ONA project for the company I
consult for which is very small (about 50 people in all).

The idea is to use this as a test project to see how we can use ONA to
add to our consulting product offers.

I am an amateur at ONA and I am studying everything I can find to
learn about ONA/SNA and its use in OD and other business applications.

I have found some of the threads very helpful so far. Thanks for
sharing the information. Don't know when I will be able to contribute
but maybe I can ask useful questions.

Kind regards,

Gustaf Pretorius
Pretoria
South-Africa

Hi --

Here is a robust, comprehensive and low-cost solution for soc/org/val
network analysis.

http://valuenetworks.com/public/item/222265

Cordially,

John

----- Original Message -----

From: Laurence Lock Lee

To: ona-prac@yahoogroups.com

Sent: Thursday, January 15, 2009 2:01 AM

Subject: RE: [ona-prac] Requesting your expert inputs on the relevance of specific types of social networks to different organizational forms

Hi Karthik,

I have had a quick look at the spreadsheet. I have run a number of AHP workshops over the years and one of my learnings is that the assessors have to develop some sort of mental model of each factor when they are doing the pairwise comparisons. Otherwise its hard to juggle the formal definitions in your head while doing the assessments and therefore becomes a very arduous task.

I usually provided a number of examples for each factor to help them build up their own mental model of what a particular factor meant to them e.g. …a good example of an emergent  organization is xxxx, …. An IT help desk is an example of an xxxx response etc.. I think if you were able to provide some informal examples for each of the social network types and organizational forms it might help us complete the assessments.

rgds

Laurence Lock Lee PhD

Partner, Optimice Pty Ltd

Ph: +61 (0)407001628

www.optimice.com.au

Blog: http://governanceandnetworks.blogspot.com/

Learn to network, then network to learn

---

From: ona-prac@yahoogroups.com [mailto:ona-prac@yahoogroups.com] On Behalf Of Karthikeyan Iyer
Sent: Thursday, 15 January 2009 2:18 AM
To: ona-prac@yahoogroups.com
Subject: [ona-prac] Requesting your expert inputs on the relevance of specific types of social networks to different organizational forms

Hi,

As part of our (Crafitti Consulting’s) interest in value networks we are attempting to look at different types of enterprise social networks and their relevance to and impact on organizational forms. We used a survey instrument based on the Analytic Hierarchy Process to solicit opinions of experts in the area to get a preliminary understanding of potential mapping and linkages. The results of this preliminary survey indicate that there indeed is, in the minds of experts, some sort of mapping between the types of social networks found in organizations and the nature or form of the organization. We shared our preliminary research in the form of a paper at the International Social Network Analysis Conference held recently at Tata Institute of Social Sciences, Mumbai.

We are eager to pursue this line of thought further by getting a broader spectrum of experts to opine on the same. In this context, I would like to request your expert inputs on the question "What types

of social networks (we have classified into five types) are most likely to be found in what forms of organizations (classified into five types)? Filling inputs in the survey instrument (an excel sheet

that implements the Analytic Hierarchy Process) would take about an hour of your time. If you are interested, I would be happy to send the survey instrument over.

I am not sharing the preliminary results right away in order not to bias your opinion. Once I receive your inputs I will share the paper updated with results from your inputs.

The abstract of our paper is as follows:

Abstract: Recent developments in society and business have triggered the emergence of new forms of organizations, beyond the traditional hierarchical form.  A study of contemporary literature and industry practices reveals the following distinct forms: hierarchical, ambidextrous, collaborative, learning and emergent.

Simultaneously, embedded within organizations are different types of social networks. Our research indicates classifications of social networks along three key dimensions - the type of response generated by these networks (customized response, modular response and routine response), the centrality of the networks (ego-centric, socio-centric and open networks) and the network architecture (centralized networks, request-based networks, hub-swarms and swarms).

This paper examines the relevance of social network types to organizational forms. We have designed a survey instrument based on the Analytic Hierarchy Process to solicit opinions of experts in the above areas.  Preliminary results of this initial survey clearly indicate that specific combinations of social network types are found in particular organization forms. Potential applications of this study towards organizational design and transformation are also explored in the paper.

Thanks,

Karthik.

Karthikeyan Iyer

Founder Director, Co-Crafter

Crafitti Consulting Private Limited "crafting innovation together"

www.crafitti.com, kartzpot.blogspot.com, +91-97405-33255

Karthik, this is really interesting.  I'd also be happy to get
a preprint of the paper.  The "hub-swarm" model seems to
be a useful one to think more about in my own personal
case, since it matches my own experience in organizing
periodic, structured, open meetings.  thanks Ed

On Wed, Jan 14, 2009 at 10:17 AM, Karthikeyan Iyer <iamkart@...> wrote:
> Hi,
>
>
>
> As part of our (Crafitti Consulting's) interest in value networks we are
> attempting to look at different types of enterprise social networks and
> their relevance to and impact on organizational forms. We used a survey
> instrument based on the Analytic Hierarchy Process to solicit opinions of
> experts in the area to get a preliminary understanding of potential mapping
> and linkages. The results of this preliminary survey indicate that there
> indeed is, in the minds of experts, some sort of mapping between the types
> of social networks found in organizations and the nature or form of the
> organization. We shared our preliminary research in the form of a paper at
> the International Social Network Analysis Conference held recently at Tata
> Institute of Social Sciences, Mumbai.
>
>
>
> We are eager to pursue this line of thought further by getting a broader
> spectrum of experts to opine on the same. In this context, I would like to
> request your expert inputs on the question "What types
>
> of social networks (we have classified into five types) are most likely to
> be found in what forms of organizations (classified into five types)?
> Filling inputs in the survey instrument (an excel sheet
>
> that implements the Analytic Hierarchy Process) would take about an hour of
> your time. If you are interested, I would be happy to send the survey
> instrument over.
>
>
>
> I am not sharing the preliminary results right away in order not to bias
> your opinion. Once I receive your inputs I will share the paper updated with
> results from your inputs.
>
>
>
> The abstract of our paper is as follows:
>
>
>
> Abstract: Recent developments in society and business have triggered the
> emergence of new forms of organizations, beyond the traditional hierarchical
> form.  A study of contemporary literature and industry practices reveals the
> following distinct forms: hierarchical, ambidextrous, collaborative,
> learning and emergent.
>
>
>
> Simultaneously, embedded within organizations are different types of social
> networks. Our research indicates classifications of social networks along
> three key dimensions - the type of response generated by these networks
> (customized response, modular response and routine response), the centrality
> of the networks (ego-centric, socio-centric and open networks) and the
> network architecture (centralized networks, request-based networks,
> hub-swarms and swarms).
>
>
>
> This paper examines the relevance of social network types to organizational
> forms. We have designed a survey instrument based on the Analytic Hierarchy
> Process to solicit opinions of experts in the above areas.  Preliminary
> results of this initial survey clearly indicate that specific combinations
> of social network types are found in particular organization forms.
> Potential applications of this study towards organizational design and
> transformation are also explored in the paper.
>
>
>
> Thanks,
>
> Karthik.
>
>
>
>
>
>
>
> Karthikeyan Iyer
>
> Founder Director, Co-Crafter
>
> Crafitti Consulting Private Limited "crafting innovation together"
>
> www.crafitti.com, kartzpot.blogspot.com, +91-97405-33255
>
>
>
>



--
Edward Vielmetti
Ann Arbor, MI

+1 734 330 2465

Hi!
May I also get a copy of a paper? It seems to be interesting and
probably useful to me.

--- In ona-prac@yahoogroups.com, "Karthikeyan Iyer" <iamkart@...> wrote:
>
> Hi,
>
>
>
> As part of our (Crafitti Consulting's) interest in value networks we are
> attempting to look at different types of enterprise social networks and
> their relevance to and impact on organizational forms. We used a survey
> instrument based on the Analytic Hierarchy Process to solicit
opinions of
> experts in the area to get a preliminary understanding of potential
mapping
> and linkages. The results of this preliminary survey indicate that there
> indeed is, in the minds of experts, some sort of mapping between the
types
> of social networks found in organizations and the nature or form of the
> organization. We shared our preliminary research in the form of a
paper at
> the International Social Network Analysis Conference held recently
at Tata
> Institute of Social Sciences, Mumbai.
>
>
>
> We are eager to pursue this line of thought further by getting a broader
> spectrum of experts to opine on the same. In this context, I would
like to
> request your expert inputs on the question "What types
>
> of social networks (we have classified into five types) are most
likely to
> be found in what forms of organizations (classified into five types)?
> Filling inputs in the survey instrument (an excel sheet
>
> that implements the Analytic Hierarchy Process) would take about an
hour of
> your time. If you are interested, I would be happy to send the survey
> instrument over.
>
>
>
> I am not sharing the preliminary results right away in order not to bias
> your opinion. Once I receive your inputs I will share the paper
updated with
> results from your inputs.
>
>
>
> The abstract of our paper is as follows:
>
>
>
> Abstract: Recent developments in society and business have triggered the
> emergence of new forms of organizations, beyond the traditional
hierarchical
> form.  A study of contemporary literature and industry practices
reveals the
> following distinct forms: hierarchical, ambidextrous, collaborative,
> learning and emergent.
>
>
>
> Simultaneously, embedded within organizations are different types of
social
> networks. Our research indicates classifications of social networks
along
> three key dimensions - the type of response generated by these networks
> (customized response, modular response and routine response), the
centrality
> of the networks (ego-centric, socio-centric and open networks) and the
> network architecture (centralized networks, request-based networks,
> hub-swarms and swarms).
>
>
>
> This paper examines the relevance of social network types to
organizational
> forms. We have designed a survey instrument based on the Analytic
Hierarchy
> Process to solicit opinions of experts in the above areas.  Preliminary
> results of this initial survey clearly indicate that specific
combinations
> of social network types are found in particular organization forms.
> Potential applications of this study towards organizational design and
> transformation are also explored in the paper.
>
>
>
> Thanks,
>
> Karthik.
>
>
>
>
>
>
>
> Karthikeyan Iyer
>
> Founder Director, Co-Crafter
>
> Crafitti Consulting Private Limited "crafting innovation together"
>
> www.crafitti.com, kartzpot.blogspot.com, +91-97405-33255
>