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© 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
© 2004 M. Van Alstyne, All rights reserved.
© 2004 M. Van Alstyne, All rights reserved.
How do IT and information flowsaffect productivity?
© 2004 M. Van Alstyne, All rights reserved.
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
© 2004 M. Van Alstyne, All rights reserved.
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
© 2004 M. Van Alstyne, All rights reserved.
Tools & Technology
Organizations under an E-Mail Microscope
© 2004 M. Van Alstyne, All rights reserved.
The Survey
• 52 Questions on information sources, perceptions, time/value, background, etc.
• All java based, sliding answers & associated calculator
86% at 3 firms
© 2004 M. Van Alstyne, All rights reserved.
Levels of Feedback
© 2004 M. Van Alstyne, All rights reserved.
Email habits show work patterns
© 2004 M. Van Alstyne, All rights reserved.
Work patterns differ by job type
© 2004 M. Van Alstyne, All rights reserved.
Topology
Comprehending the Social Networks
© 2004 M. Van Alstyne, All rights reserved.
Real communications cluster by category and by geography
Sector 1
COO & Pres.
Sector 2Sector 3
ResearchStaff
© 2004 M. Van Alstyne, All rights reserved.
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
© 2004 M. Van Alstyne, All rights reserved.
Survey Summaries
Incentives & Behaviors
© 2004 M. Van Alstyne, All rights reserved.
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.
© 2004 M. Van Alstyne, All rights reserved.
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.
© 2004 M. Van Alstyne, All rights reserved.
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
© 2004 M. Van Alstyne, All rights reserved.
Perceptions of Information Overload
• Bear little correlation with e-mail received.
• Fall with increasing IT proficiency.
• Rise with delayed colleague response times.
© 2004 M. Van Alstyne, All rights reserved.
Statistical Models
Information practices that matter…
© 2004 M. Van Alstyne, All rights reserved.
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 .
© 2004 M. Van Alstyne, All rights reserved.
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.
© 2004 M. Van Alstyne, All rights reserved.
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.
© 2004 M. Van Alstyne, All rights reserved.
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
IT variables Intermediate Output Final Output
IndividualCompensation
© 2004 M. Van Alstyne, All rights reserved.
IT variables Intermediate Output Final Output
IndividualCompensation
A Model of Information Work: Executive Recruiting Case
Revenue CompensationCompletion
Rate
Multitasking
Duration perTask
DatabaseSkill
EmailContacts
IT variables Intermediate Output Final Output
IndividualCompensation
© 2004 M. Van Alstyne, All rights reserved.
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
Unstandardized Coefficients
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.
© 2004 M. Van Alstyne, All rights reserved.
Multitasking, Duration and Completion Rate
Time
B
A
CompletedProjects
3
5
© 2004 M. Van Alstyne, All rights reserved.
IT variables Intermediate Output IndividualCompensation
Revenue CompensationCompletion
Rate
Multitasking
Duration perTask
DatabaseSkill
EmailContacts
IT variables Intermediate Output IndividualCompensation
A Model of Information Work: Executive Recruiting Case
Final OutputFinal
Output
© 2004 M. Van Alstyne, All rights reserved.
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
Unstandardized Coefficients
t Sig.
Dependent Variable: REV02a.
Coefficientsa
B Std. Error
Unstandardized Coefficients
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
© 2004 M. Van Alstyne, All rights reserved.
Recall Network Position…
Betweenness Bridging Structural Gaps
© 2004 M. Van Alstyne, All rights reserved.
Network Structure Matters
Coefficientsa
(Base Model)
Size Struct. Holes
Betweenness
B Std. Error
Unstandardized Coefficients
Adj. R2 Sig. F
Dependent Variable: Bookings02a.
Coefficientsa
B Std. Error
Unstandardized Coefficients
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.
© 2004 M. Van Alstyne, All rights reserved.
Information Flows Matter
Coefficientsa
(Base Model)
Best structural pred.
Ave. E-Mail Size
Colleagues’ Ave.Response Time
B Std. Error
Unstandardized Coefficients
Adj. R2 Sig. F
Dependent Variable: Bookings02a.
Coefficientsa
B Std. Error
Unstandardized Coefficients
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.
© 2004 M. Van Alstyne, All rights reserved.
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.
© 2004 M. Van Alstyne, All rights reserved.
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
Unstandardized Coefficients
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
Unstandardized Coefficients
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
© 2004 M. Van Alstyne, All rights reserved.
Having IT is not enough.It’s how you use and manage information
that matters.
© 2004 M. Van Alstyne, All rights reserved.
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
© 2004 M. Van Alstyne, All rights reserved.
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.
© 2004 M. Van Alstyne, All rights reserved.
Questions?