© 2006 Van Alstyne, Brynjolfsson & Aral Information, Social Networks & Individual Success MIT Center for E-Business / Boston University Marshall Van Alstyne.

© 2006 Van Alstyne, Brynjolfsson & Aral

Information, Social Networks& Individual Success

MIT Center for E-Business / Boston UniversityMarshall Van Alstyne

With S. Aral, E. Brynjolfsson, N. Bulkley, N. Gandal, C. King, J. ZhangSponsored by NSF #9876233, Intel Corp & BT

[email protected]

© 2006 All Rights Reserved



IT and Productivity: The Data Speak

IT Stock (relative to industry average)

Productivity(relative to industry average)

Computers are associated with greater productivity...

...But what explains the substantial variation across firms?

Agenda

• Study overview & technology• Visualizing organizational information and

social networks.• Participant perceptions (surveys)• Statistical models of behavior and output• Notable correlations


The Current Study• Three firms initially• Unusually measurable inputs and outputs

– 1300 projects over 5 yrs and – 125,000 email messages over 10 months (avg 20% of time!)– Metrics

(i) Revenues per person and per project, (ii) number of completed projects, (iii) duration of projects, (iv) number of simultaneous projects, (v) compensation per person

• Main firm 71 people in executive search (+2 firms partial data)– 27 Partners, 29 Consultants, 13 Research, 2 IT staff

• Four Data Sets per firm – 52 Question Survey (86% response rate)– E-Mail– Accounting– 15 Semi-structured interviews


The Setting – Executive Recruiting

Executive Search Process1. Partner brings in client contract2. Partner negotiates internal labor market to compose a team with

consultants and researchers (load balancing and regional approval)3. A Phased Search (Matching) Process with information inputs / outputs :

CaptureRequirements

Initial Search /Create Initial

Pool

Vet CandidatesConduct Due

Diligence

Create InterviewPool / Interview

Internally

Create FinalPool / Facilitate

Client Placement(~ 6)

Firm uses IT in 2 ways:1. Communication Vehicle (e.g. Phone, Email)2. Executive Search System (ESS) – a proprietary KMS

Internal Task Coordination (e.g. Assign Tasks & Labor ) External Contract Coordination (e.g. anti-poaching provisions) Knowledge Search (e.g. Candidates, Clients) including external DBs


Tools & Technology

Organizations under an E-Mail Microscope


Gaining access to live e-mail

To: Marshall Van Alstyne <[email protected]> Subject: Re: YOUR PROPOSAL Date: Sun, 17 Nov 2002 09:54:23 -0500 Cc: [email protected], Geoffrey Parker <[email protected]> X-Originating-IP: 68.41.189.43

Ok, i will look for all the pieces today then and try to get everything in Fastlane tonight.

Meeting is up to you. I have to go to DRDA first thing in the morning to hand them all the PAFs so they can process all the proposals. The meeting is to give you one last chance to view the entire proposal package before DRDA pushes the "Send" button. We could also try to do this virtually so neither of us has to travel to the other site.

As far as footers go, let's not worry about it as long as you are page numbering each section individually. I usually add more information to the footer but I don't have time to worry about this detail.

Ann

Stop words are dropped; then the raw text is root-stemmed (e.g. “are”->“is”, “pieces”->“piece”), counted, and hashed.


AnnMessage-ID: 00000000C74E9F197619354B912FA038789E97DD070095FBFC9E5C710C45AD83BE1BA97654F300000025D7D7000095FBFC9E5C710C45AD83BE1BA97654F30000015D02090000 Date: 11/17/2002 09:54:23 PM From: ChiUserWWW2 To: ChiUserWWW34 CC: ChiUserWWW2 , ChiUserEEE137 Subject: 2234380046220310381 -4543232654336644202 3187911263930032313 - 8725299062034745550 6646063218832296471 Content: -7488330257252326972<8>; 3461049762598860849<5>; -4469441121190040841<4>; 4122472038465781083<4>; - 2485003116886841409<3>; 8003219831352894262<3>; 1698764591947117759<2>; 5894537654329429962<2>; - 9076192449175488644<2>; 7750988586697557362<2>; 8871153132300476476<2>; - 7527789141644698404<2>; 8763687632651980147<1>; 3129683954660429336<1>; -6916544271211441138<1>; 6293576012604293570<1>; - 320692498224125839<1>; 8934872354483414290<1>; -6836405471713717833<1>; - 5975878511407257679<1>; -3014223241434893634<1>; - 8934856908841293615<1>; -857818984403519253<1>; 1344343662225282497<1>; 965941123633882107<1>; -3147930629716878416<1>; 7137519577624117188<1>; 7523708256417630601<1>; -6946268052250097500<1>; Attachment Number: 0 Attachment list:

This is what we “see”

Reconstructing semantics is difficult. We do not read attachments but do record type & size information (e.g. 157kb .doc file)


The Survey

• 52 Questions– personal characteristics– time-use– value of tasks– technology skills – technology use– information sources– work habits– information sharing– perceptions

86% response rate


Email habits show work patterns


An E-mail “Fingerprint”

Consultant - Sent vs. Received

-12000

-10000

-8000

-6000

-4000

-2000

0

2000

4000

6000

8000

c10

c12

c14

c16

c18 c2 c2

1c2

3c2

7c2

9c3

0 c6 c7 c71 c9

External

Internal

Sent

Received

Topology

Comprehending the Social Networks


Clustering example from our data

Theoretically, Information Should Matter: Both Levels and Structure

Constrained

Unconstrained


Social Network Efficiencies1. Connect to hubs

• Central nodes who bridge structural holes are significantly more effective.

2. Send short messages• Consultants have higher

billings (.56, p<.01) and are more central (see 1).

3. Communicate declarative information

• Gets better reply rates.• Procedural tips shared

laterally not across hierarchy (or better FTF)

4. Career Ladder• Explore early vs. exploit late


Survey Summaries

Incentives & Behaviors


There are culture differences. One firm shares more. Most disagree that info never enters DB

Responses to Information Sharing Questions 1-4

-1.00

-0.50

0.00

0.50

1.00

1.50

2.00

2.50

3.00

Firm X

Firm Y

Firm Z

Q1 Colleagues give me credit for info that I share.

Q3: I volunteer all relevant info to colleagues.

Q2 Colleagues willingly share their private search info with me.

Q4: A lot of my personal knowledge never reaches the corp. database.


Incentive theory works

Weighting of Compensation Structure

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Firm X Firm Y Firm Z

Whole company performance

Project team(s) performance

Individual performance

Least Most Med.

Narrower incentives mean narrower info sharing.


Firm X automates more processesPerceptions of IT Applications

-1.00-0.80-0.60-0.40-0.200.000.200.400.600.801.001.20

Firm X

Firm Y

Firm Z

Q7 We use info sys to coord sched & project handoffs

Q14 My data requirements are routine

Q15 For routine info, the process of getting it is automated

Q41 We mine our data for correlations and new ideas

Perceived Information Overload

• Bears little correlation with e-mail received.

• Falls with increasing IT proficiency.

• Rises with colleague response delays.

• Falls with increased support staff contact.


Emails “pose threat to IQ”Lack of discipline responding to email reduced productivity by the equivalent of 1 night’s sleep.

“…average IQ loss was measured at 10 points, more than double the four point mean fall found in studies of cannabis users.”

Similarly, in our study, time spent and volume processedbear little correlation with productivity…


Statistical Models

Information practices that matter…


IT variables Intermediate Output Final Output

IndividualCompensation

A Model of Information Work: Task Completion & Compensation

Revenue CompensationCompletion

Rate

Multitasking

Duration perTask

DatabaseSkill

EmailContacts




Model Specification

Qi – Output ($, Completions, Duration …)

Hi – Job Level (Partner, Consultant, Rsch …)

Xi – Human Capital (Ed., Exp., Labor)

Yi – IT Factor (Email, Ties, Behaviors…)

' 'i i i i iQ Y e H X


Source | SS df MS Number of obs = 41-------------+------------------------------ F( 6, 34) = 1.33 Model | 1.9341e+11 6 3.2236e+10 Prob > F = 0.2691 Residual | 8.2136e+11 34 2.4158e+10 R-squared = 0.1906-------------+------------------------------ Adj R-squared = 0.0478 Total | 1.0148e+12 40 2.5369e+10 Root MSE = 1.6e+05

------------------------------------------------------------------------------ rev02 | Coef. Std. Err. t P>|t| [95% Conf. Interval]-------------+---------------------------------------------------------------- partner | 239727.5 141685.8 1.69 0.100 -48212.66 527667.6 consultant | 272197.7 112464.6 2.42 0.021 43642.14 500753.2 gender | -65767.58 55093.9 -1.19 0.241 -177731.8 46196.69 age | 5852.73 4143.612 1.41 0.167 -2568.103 14273.56 yrs_educ | -1842.269 23137.51 -0.08 0.937 -48863.34 45178.81 experience | 681.794 3977.229 0.17 0.865 -7400.908 8764.496 _cons | -69840.65 530698 -0.13 0.896 -1148349 1008667------------------------------------------------------------------------------

HR Factors

Source | SS df MS Number of obs = 33-------------+------------------------------ F( 6, 26) = 12.63 Model | 4.6776e+11 6 7.7959e+10 Prob > F = 0.0000 Residual | 1.6051e+11 26 6.1735e+09 R-squared = 0.7445-------------+------------------------------ Adj R-squared = 0.6856 Total | 6.2827e+11 32 1.9633e+10 Root MSE = 78572

------------------------------------------------------------------------------ rev02 | Coef. Std. Err. t P>|t| [95% Conf. Interval]-------------+---------------------------------------------------------------- icontacts | 6553.851 1804.091 3.63 0.001 2845.488 10262.21 searchtools | 204.9083 159.1239 1.29 0.209 -122.1756 531.9923 betweenness | 107.8983 43.14879 2.50 0.019 19.20467 196.5919 partner | 175545 64618.17 2.72 0.012 42720.41 308369.5 consultant | 298923.3 65735.69 4.55 0.000 163801.7 434045 multtsks | 25275.27 7197.28 3.51 0.002 10481.05 40069.49 _cons | -467132.8 165420.2 -2.82 0.009 -807158.8 -127106.7------------------------------------------------------------------------------

IT Factors


A Model of Information Work: Tasks & Completion Rate

Intermediate Output Final Output



Rate

Multitasking

Duration perTask

Intermediate Output Final Output


Do multitasking and duration affect completed projects ?


Table 3: Panel Data Estimates of Project Completion, Revenues Multitasking, and Duration (n=NormVar) Variables Model 1 Model 2 Model 3 Model 4 Model 5 Dependent Variable:

Revenues nRevenues nRevenues nComp. Projects

nComp. Projects

Specification FGLS FGLS Fixed Effects FGLS Fixed Effects Daily Daily Daily Daily Daily

Controls

Education, Gender, Partner,

Consultant


Consultant


Consultant

Ind. Variables Completed Projects

2149.19*** (43.41)

nMultitasking

1.067*** (.005)

.707*** (.004)

.692*** (.003)

.603*** (.003)

nMultitasking2

-.108***

(.002) -.109***

(.002) -.066***

(.002) -.112***

(.001) nDuration

-.094***

(.002) -.143***

(.003) -.173***

(.003) -.149***

(.002)

Time Controls Month, Year

Month, Year

Month, Year

Month, Year

Month, Year

Log Likelihood -370966.8 194415.3 - 159337.5 - X2(d.f) / F(d.f) 8976.9***

(20) 73509.3***

(22) 5566.41***

(18) 59109.4***

(22) 3848.13***

(18) Observations 78201 78201 100816 81824 100816 ***p<.001; **p<.05; *p<.10

A worker generates $2149.19 per project, per day for the firm.

Multitasking associated with increases in completed projects & revenues.

Longer duration associated with decreases in both completed projects & revenues.

MT2 is negative, implying an inverted-U shaped relationship

What Drives Revenue Generation?

Y

MT

On average,

$CP

MT

D



A Model of Information Work:

RevenueCompletion

Rate

Multitasking

Duration perTask

DatabaseSkill

EmailContacts


Do IT skills & social networks affect multitasking and duration?


Multitasking and Duration depend on DB-Skill and Contact Networks

•Contact networks and DB-Skill help workers multitask •But average duration suffers.

IT Intermed

Coefficientsa

-1.769 6.223 -.284 .779

2.396 1.762 1.360 .186

2.636 2.056 1.282 .212

.126*** .043 2.941 .007

.009** .004 2.375 .026

(Constant)

Consult Dummy

Partner Dummy

Total Internal Contactsin Incoming Emails

DB_SKILL

B Std. Error

Unstandardized Coefficients

t Sig.

Dependent Variable: MULTTSKSa.

Coefficientsa

-26.821 147.052 -.182 .857

16.382 36.720 .446 .660

20.128 45.193 .445 .660

1.906* .987 1.931 .066

.169* .083 2.027 .054

B Std. Error


t Sig.

Dependent Variable: AVEDURa.

Multitasking Duration

Adjusted R2 = .24 with controls for GENDER, YRS_ED, YRS_EXP.b.

Adjusted R2 = .18 with controls for GEN., ED., and EXP.b.


Multitasking, Duration and Completion Rate

Time

B

A

CompletedProjects

3

5


Relation Between IT & Multitasking

F2F – small magnitude positive with MT. Interviews indicate that a certain number of

F2F meetings are necessary for each additional project.

Heavy Multitaskers rely more on asynchronous email and less on synchronous phone communication.

ESS Use positively correlated with multitasking.

Project Coordination – labor, anti-poaching Cross Project Info Seeking Need more information relevant to more

searches.

Interaction Term: Information Seeking and Information Communication are Complements in regards to MT Behavior


MultitaskingAsynchronousInformation

Seeking Helps!

SynchronousInformation

Seeking Hurts!

• Email• DB Access

• Phone

© That Girl

Initial Synchronize: • Face to Face


IT variables Intermediate Output IndividualCompensation


Rate

Multitasking

Duration perTask

DatabaseSkill

EmailContacts

IT variables Intermediate Output IndividualCompensation

A Model of Information Work: Executive Recruiting Case

Final OutputFinal

Output


Check: Revenue & Compensation do depend on IT Skills

The more observable contact network helps revenue and compensation.

The less observable DB-skill helps revenue but hurts compensation.

IT

Coefficientsa

(Constant)

Consult Dummy

Partner DummyTotal Internal Contactsin Incoming Emails

DB_SKILL

B Std. Error


t Sig.

Dependent Variable: REV02a.

Coefficientsa

B Std. Error


t Sig.

Dependent Variable: SALARYa.

Revenue Compensation

-333896.63 306222.69 -1.090 .286

420625.63*** 86713.60 4.851 .000

354668.03*** 101188.43 3.505 .002

11657.50*** 2102.10 5.546 .000

326.32* 194.74 1.676 .106

133654.46 152918.8 .874 .388

148254.60*** 29454.27 5.033 .000

317464.32*** 44561.70 7.124 .000

1953.29** 841.10 2.322 .026

-204.22* 116.98 -1.746 .089

Adjusted R2 = .53 with controls for GENDER, YRS_ED, YRS_EXP.b.

Adjusted R2 = .77 with controls for GEN., ED., and EXP.b.

$ Comp


Recall Network Position…

Betweenness Constrained vs. Unconstrained


Network Structure Matters

Coefficientsa

(Base Model)

Size Struct. Holes

Betweenness

B Std. Error


Adj. R2 Sig. F

Dependent Variable: Bookings02a.

Coefficientsa

B Std. Error


Adj. R2 Sig. F

Dependent Variable: Billings02a.

New Contract Revenue Contract Execution Revenue

0.40

13770*** 4647 0.52 .006

1297* 773 0.47 .040

0.19

7890* 4656 0.24 .100

1696** 697 0.30 .021

Base Model: YRS_EXP, PARTDUM, %_CEO_SRCH, SECTOR(dummies), %_SOLO.b.

N=39. *** p<.01, ** p<.05, * p<.1b.

Bridging diverse communities is significant.

Being in the thick of information flows is significant.


Information Flows Matter

Coefficientsa

(Base Model)

Best structural pred.

Ave. E-Mail Size

Colleagues’ Ave.Response Time

B Std. Error


Adj. R2 Sig. F

Dependent Variable: Bookings02a.

Coefficientsa

B Std. Error


Adj. R2 Sig. F

Dependent Variable: Billings02a.

New Contract Revenue Contract Execution Revenue

0.40

12604.0*** 4454.0 0.52 .006

-10.7** 4.9 0.56 .042

-198947.0 168968.0 0.56 .248

0.19

1544.0** 639.0 0.30 .021

-9.3* 4.7 0.34 .095

-368924.0** 157789.0 0.42 .026

Base Model: YRS_EXP, PARTDUM, %_CEO_SRCH, SECTOR(dummies), %_SOLO.b.

N=39. *** p<.01, ** p<.05, * p<.1b.

Sending shorter e-mail helps get contracts and finish them.

Faster response from colleagues helps finish them.


Do larger personal rolodexes make you more productive?


H5: Recruiters with larger personal rolodexes generate no more or less output

Revenue $ $ for completed searches

Completed searches

Multitasking Duration Duration controlling

for multitasking

Size of rolodex (Q50)

-10.2 (60.3)

-22.9 (32.6)

0.000 (0.001)

0.000 (0.001)

-0.013 (0.021)

-0.013 (0.016)

• Less information sharing• Less DB proficiency• Lower % of e-mail read• Less learning from others• Less perceived credit for ideas given to colleagues• More dissembling on the phone

Instead, a larger private rolodex is associated with:

* p < 0.10, ** p < 0.05, *** p < 0.01, Standard err in paren.


Interesting & Notable Correlations

Within Survey Correlations

Across all 3 job types• Volunteering info Giving credit• Sharing Happiness• Indiv performance Objective metrics

- Supervisor input• Gathering internal/external info Happiness• Yrs Experience - public access web pages• Age Experience, Rolodex• Accurate DB Happier• Overlapping social network Effective use of phone

Significant at 10% level

Correlations w/ Completed Job Searches

For consultants perceived accuracy of corporate DB professed ability to use internal IT support tools having control over the data accessed & used more people contacted per day• - relative time spent processing info on computer screen• - personal knowledge never entered in DB

For partners with info pull (request data not wait for it)• - procedural communication instead of descriptive info• - reporting severe costs to not having info when need it


Correlations w/ Multitasking

For consultants perceived accuracy of corporate DB finds more relative value in internal DB having routine data requirements happy in current job• - relative time spent on public access web pages

For partners if private info not entered in DB, main reason is too tedious


Correlations w/ Revenue

For Consultants number of people contacted via e-mail percent time spent on e-mail (1 firm < 0!) more relative time spent with external DB more value from internal DB• - reporting problem of info overload

For Partners individual (not team) based compensation most relative time spent with external people• - personal knowledge never entered in DB• - there are multiple sources for key info



Having IT is not enough.It’s how you use and manage information

and contacts that matters.


Takeaways 1

1. We have strong evidence associating different IT practices and social networks with measures of white collar output. Social Network links are worth > $6,000 in this context.

2. Economics: incentive design mechanisms do correspond with information sharing.

3. Social network strategies are (i) bridging info pools (ii) being an info hub and (iii) career ladder => Structure matters!

4. Realize efficiencies by (i) connecting to hubs (ii) short msgs (iii) declarative information (iv) encouraging timely response from colleagues (and being prompt yourself!) => Flow matters!

5. Give information back. Data monitoring is not a sin if the principal use is to support those who provide it.


Takeaways 2

6. Perceived information overload corresponds very little to actual communication flows but rather to Lower comfort with IT Longer response times from colleagues With whom you communicate

7. Certain white collar knowledge mgmt practices can be routinized. Remove or automate tedium of data capture. Most successful folks will share.

8. Consider hires for willingness to share and use IT, not just individual performance. Corollary: you may need to reward this.

9. Use IT and ESS both to support multitasking and increase speed. This helps people accomplish more work.


Questions?

[email protected]

[email protected]

mailto:[email protected]

mailto:[email protected]

© 2006 Van Alstyne, Brynjolfsson & Aral Information, Social Networks & Individual Success MIT Center for E-Business / Boston University Marshall Van Alstyne.

Documents

outputs1300 projects

duration of projects

information inputs outputs

number of completed

personmain firm

proposal date

firms partial data27

industry averagecomputers