© 2004 m. van alstyne, all rights reserved. information, e-mail & output mit center for...

© 2004 M. Van Alstyne, All rights reserved.

Information, E-Mail & Output

MIT Center for E-BusinessMarshall Van Alstyne

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

© 2004 All Rights Reserved


How do IT and information flowsaffect productivity?


Agenda

• Study overview & technology

• Visualizing organizational information and social networks.

• Participant perceptions (surveys)

• Statistical models of behavior and output

0%10%20%30%40%50%60%70%80%90%

100%

Firm X Firm Y Firm Z

Least Most Med.

Behaviors

Social Networks


The Current Study

• More than 70 people in exec. search– 27 Partners, 29 Consultants, 13 Rsch, 2 IT staff

• 3 Firms originally, 2 subsequent• Three Data Sets per firm

– (i) Surveys, (ii) E-Mail, (iii) Accounting

• 613 projects and 45,000+ email messages• Of all info gathering modes, avg 20% time on email• Measurable Outputs:

– (i) $, (ii) # complete contracts, (iii) duration, (iv) # simultaneous


Tools & Technology

Organizations under an E-Mail Microscope


The Survey

• 52 Questions on information sources, perceptions, time/value, background, etc.

• All java based, sliding answers & associated calculator

86% at 3 firms


Levels of Feedback


Email habits show work patterns


Work patterns differ by job type


Topology

Comprehending the Social Networks


Real communications cluster by category and by geography

Sector 1

COO & Pres.

Sector 2Sector 3

ResearchStaff


Schematic Shows “Structural Holes”

The central node “bridges” diverse communities

Partners, Consultants & Rsch

Sector 1

COO & Pres.

Sector 2Sector 3

ResearchStaff

Hi value folks are connected consumers

Sector 1

COO & Pres.

Sector 2Sector 3

ResearchStaff


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


Perceptions of Information Overload

• Bear little correlation with e-mail received.

• Fall with increasing IT proficiency.

• Rise with delayed colleague response times.


Statistical Models

Information practices that matter…


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

Baseline Revenue Model 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+5

------------------------------------------------------------------------------ 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------------------------------------------------------------------------------

Gender, Age, Education, Experience not significant.Capital K is constant and in .


Hypotheses

H1: IT use is correlated with increased revenues at individual level.

H2: Increased revenues are correlated with increased compensation.

H3: Intermediate measures of performance increase with increased use of IT.

H4: Performance improves with better network position and with information flow.

H5: The amount of information, measured by private rolodex size, increases performance.


H1: Database Skill and Contact Networks are correlated with Revenue

Coefficients

-333896.627 306222.694 -1.090 .286

420625.625*** 86713.603 4.851 .000

354668.025*** 101188.432 3.505 .002

11657.500*** 2102.097 5.546 .000

326.320* 194.735 1.676 .106

(Constant)

Consult Dummy

Partner Dummy

Total Internal Contactsin Incoming Emails

DB_SKILL

Model B Std. Error

Unstandardized Coefficients

t Sig.

Dependent Variable: REV02a.

Adjusted R2 = .53 controlling for GENDER, YRS_ED, YRS_EXP.b.

Both measures of IT are strongly correlated withincreased output, controlling for gender, education, experience, and job category.


A Model of Information Work: Executive Recruiting Case

Revenue CompensationCompletion

Rate Principal-Agent

Multitasking

Duration perTask

DatabaseSkill

EmailContacts

Black Box Production Fn

IT variables Intermediate Output Final Output

IndividualCompensation








Rate

Multitasking

Duration perTask

DatabaseSkill

EmailContacts




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


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


IT variables Intermediate Output IndividualCompensation


Rate

Multitasking

Duration perTask

DatabaseSkill

EmailContacts

IT variables Intermediate Output IndividualCompensation


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 Bridging Structural Gaps


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 more significant for landing new contracts.

Being in the thick of information flows is more significant for contract execution.


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 is positively related to both new contracts and contract execution.

Faster response from colleagues is positively related to contract execution revenues.


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

Null rejected only for bookings, not for any other output measure. Instead, a larger private rolodex is associated with:

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


Social Networks have different effects depending on job role

•Larger structural holes helps generate business but can hurt job execution. •Sending more email helps job execution but has no measurable effect on generating business. IT

Coefficientsa

-227802 185001 -1.23 .223

12795** 5705 2.243 .032

148887* 74581 1.996 .054

-3316 9132 -.363 .719

565088 735771 .768 .448

(Constant)

Size of Structural Holes

Partner Dummy

Num External E-MailSent (per day)

Concentration Internal Sent

B Std. Error


t Sig.

Dependent Variable: BOOKINGSa.

Coefficientsa

523237*** 121745 4.30 .000

-6988* 3988 -1.75 .089

-87118* 51235 -1.70 .098

17137*** 5856 2.93 .006

-455568 475974 -.95 .345

B Std. Error


t Sig.

Dependent Variable: BILLINGSa.

Bookings Billings

Adjusted R2 = .45 with controls for SECTOR, %_CEO, YRS_EXP.b.

Adjusted R2 = .51 with controls for SECTOR,CEO, and EXP.b.

$

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------------------------------------------------------------------------------

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

IT Factors


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

that matters.


Takeaways 1

1. We have strong evidence associating different IT practices with measures of white collar output.

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

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

4. Perceived (a) control over and (b) ability to use IT correlate with output. Give folks control, better GUI, skills & confidence.

5. Perceived information overload corresponds very little to actual communication flows but rather to

Lower comfort with IT

Longer response times from colleagues


Takeaways 2

6. Structure matters. Being more “central” in the information flows tracks your productivity … and your salary.

7. Flow matters. Send shorter communications and encourage timely response from colleagues (and be prompt yourself!).

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

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

10. Use IT to support multitasking. This helps people accomplish more work.


Questions?

© 2004 m. van alstyne, all rights reserved. information, e-mail & output mit center for...

Documents

info sys

routine info

relevant info

narrower info sharing

information sources

information sharing

private search info

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