Information, Social Networks & Individual Success

Information, Social Networks & Individual Success MIT Center for E-Business / Boston University Marshall Van Alstyne With S. Aral, E. Brynjolfsson, N. Bulkley, N. Gandal, C. King, J. Zhang Sponsored by NSF #9876233, Intel Corp & BT marshall@mit.edu © 2006 Van Alstyne, Brynjolfsson & Aral © 2006 All Rights Reserved

IT and Productivity: The Data Speak Computers are associated with greater productivity... Productivity (relative to industry average) IT Stock (relative to industry average) ...But what explains the substantial variation across firms? © 2006 Van Alstyne, Brynjolfsson & Aral

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 © 2006 Van Alstyne, Brynjolfsson & Aral

Create Final Pool / Facilitate Client Placement (~ 6) Initial Search / Create Initial Pool Vet Candidates Conduct Due Diligence Create Interview Pool / Interview Internally Capture Requirements The Setting – Executive Recruiting Executive Search Process • Partner brings in client contract • Partner negotiates internal labor market to compose a team with consultants and researchers (load balancing and regional approval) • A Phased Search (Matching) Process with information inputs / outputs : Firm uses IT in 2 ways: • Communication Vehicle (e.g. Phone, Email) • 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 © 2006 Van Alstyne, Brynjolfsson & Aral

Gaining access to live e-mail To: Marshall Van Alstyne <mvanalst@umich.edu> Subject: Re: YOUR PROPOSAL Date: Sun, 17 Nov 2002 09:54:23 -0500 Cc: averhey@umich.edu, Geoffrey Parker <gparker@tulane.edu> 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. © 2006 Van Alstyne, Brynjolfsson & Aral

This is what we “see” 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: Reconstructing semantics is difficult. We do not read attachments but do record type & size information (e.g. 157kb .doc file) © 2006 Van Alstyne, Brynjolfsson & Aral

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 © 2006 Van Alstyne, Brynjolfsson & Aral

External Internal An E-mail “Fingerprint” Consultant - Sent vs. Received 8000 6000 4000 Sent 2000 0 c2 c6 c7 c9 c10 c12 c14 c16 c18 c21 c23 c27 c29 c30 c71 -2000 -4000 Received -6000 -8000 -10000 -12000 © 2006 Van Alstyne, Brynjolfsson & Aral

Topology Comprehending the Social Networks

Social Network Efficiencies • Connect to hubs • Central nodes who bridge structural holes are significantly more effective. • Send short messages • Consultants have higher billings (.56, p<.01) and are more central (see 1). • Communicate declarative information • Gets better reply rates. • Procedural tips shared laterally not across hierarchy (or better FTF) • Career Ladder • Explore early vs. exploit late © 2006 Van Alstyne, Brynjolfsson & Aral

There are culture differences. One firm shares more. Most disagree that info never enters DB Responses to Information Sharing Questions 1-4 3.00 2.50 2.00 Firm X 1.50 Firm Y Firm Z 1.00 0.50 0.00 -0.50 -1.00 Q1 Colleagues give me credit for info that I share. Q2 Colleagues willingly share their private search info with me. Q3: I volunteer all relevant info to colleagues. Q4: A lot of my personal knowledge never reaches the corp. database. © 2006 Van Alstyne, Brynjolfsson & Aral

Incentive theory works Weighting of Compensation Structure Least Most Med. 100% 90% 80% 70% Whole company performance 60% Project team(s) performance 50% Individual performance 40% 30% 20% 10% 0% Firm X Firm Y Firm Z Narrower incentives mean narrower info sharing. © 2006 Van Alstyne, Brynjolfsson & Aral

Firm X automates more processes Perceptions of IT Applications 1.20 1.00 0.80 0.60 0.40 Firm X 0.20 Firm Y 0.00 Firm Z -0.20 -0.40 -0.60 -0.80 -1.00 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 © 2006 Van Alstyne, Brynjolfsson & Aral

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 processed bear little correlation with productivity… © 2006 Van Alstyne, Brynjolfsson & Aral

A Model of Information Work: Task Completion & Compensation ITvariables Intermediate Output Intermediate Output IT variables Final Output Final Output Individual Compensation Individual Compensation Multitasking Database Skill Completion Rate Revenue Compensation Duration per Task Email Contacts © 2006 Van Alstyne, Brynjolfsson & Aral

Model Specification Qi – Output ($, Completions, Duration …) Hi – Job Level (Partner, Consultant, Rsch …) Xi – Human Capital (Ed., Exp., Labor) Yi – IT Factor (Email, Ties, Behaviors…) © 2006 Van Alstyne, Brynjolfsson & Aral

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 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 © 2006 Van Alstyne, Brynjolfsson & Aral

A Model of Information Work: Tasks & Completion Rate Intermediate Output Intermediate Output Final Output Final Output Individual Compensation Individual Compensation Multitasking Completion Rate Revenue Compensation Duration per Task Do multitasking and duration affect completed projects ? © 2006 Van Alstyne, Brynjolfsson & Aral

MT $ CP D Y MT What Drives Revenue Generation? On average, • 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 © 2006 Van Alstyne, Brynjolfsson & Aral

A Model of Information Work: ITvariables Intermediate Output Intermediate Output IT variables Final Output Final Output Multitasking Database Skill Completion Rate Revenue Duration per Task Email Contacts Do IT skills & social networks affect multitasking and duration? © 2006 Van Alstyne, Brynjolfsson & Aral

Multitasking and Duration depend on DB-Skill and Contact Networks a a Multitasking Coefficients Duration Coefficients Unstandardized Coefficients Unstandardized Coefficients B Std. Error t Sig. B Std. Error t Sig. (Constant) -1.769 6.223 -.284 .779 -26.821 147.052 -.182 .857 Consult Dummy 2.396 1.762 1.360 .186 16.382 36.720 .446 .660 Partner Dummy 2.636 2.056 1.282 .212 20.128 45.193 .445 .660 Total Internal Contacts .126*** .043 2.941 .007 1.906* .987 1.931 .066 in Incoming Emails DB_SKILL .009** .004 2.375 .026 .169* .083 2.027 .054 a. a. Dependent Variable: MULTTSKS Dependent Variable: AVEDUR b. b. Adjusted R2 = .24 with controls forGENDER, YRS_ED, YRS_EXP. Adjusted R2 = .18 with controls for GEN., ED., and EXP. • Contact networks and DB-Skill help workers multitask • But average duration suffers. IT Intermed © 2006 Van Alstyne, Brynjolfsson & Aral

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 © 2006 Van Alstyne, Brynjolfsson & Aral

Multitasking Asynchronous Information Seeking Helps! Synchronous Information Seeking Hurts! © That Girl • Email • DB Access • Phone Initial Synchronize: • Face to Face © 2006 Van Alstyne, Brynjolfsson & Aral

A Model of Information Work: Executive Recruiting Case ITvariables Intermediate Output Intermediate Output IT variables Final Output Final Output Individual Compensation Individual Compensation Multitasking Database Skill Completion Rate Revenue Compensation Duration per Task Email Contacts © 2006 Van Alstyne, Brynjolfsson & Aral

Check: Revenue & Compensation do depend on IT Skills a a Revenue Coefficients Compensation Coefficients Unstandardized Coefficients Unstandardized Coefficients B Std. Error t Sig. B Std. Error t Sig. (Constant) -333896.63 306222.69 -1.090 .286 133654.46 152918.8 .874 .388 Consult Dummy 420625.63*** 86713.60 4.851 .000 148254.60*** 29454.27 5.033 .000 Partner Dummy 354668.03*** 101188.43 3.505 .002 317464.32*** 44561.70 7.124 .000 Total Internal Contacts 11657.50*** 2102.10 5.546 .000 1953.29** 841.10 2.322 .026 in Incoming Emails DB_SKILL 326.32* 194.74 1.676 .106 -204.22* 116.98 -1.746 .089 a. a. Dependent Variable: REV02 Dependent Variable: SALARY b. b. Adjusted R2 = .53 with controls forGENDER, YRS_ED, YRS_EXP. Adjusted R2 = .77 with controls for GEN., ED., and EXP. The more observable contact network helps revenue and compensation. The less observable DB-skill helps revenue but hurts compensation. IT $ Comp © 2006 Van Alstyne, Brynjolfsson & Aral

Network Structure Matters a a New Contract Revenue Coefficients Contract Execution Revenue Coefficients Unstandardized Coefficients Unstandardized Coefficients B Std. Error Adj. R2 Sig. F  B Std. Error Adj. R2 Sig. F  (Base Model) 0.40 0.19 Size Struct. Holes 13770*** 4647 0.52 .006 7890* 4656 0.24 .100 Betweenness 1297* 773 0.47 .040 1696** 697 0.30 .021 a. a. Dependent Variable: Bookings02 Dependent Variable: Billings02 b. b. Base Model: YRS_EXP, PARTDUM, %_CEO_SRCH, SECTOR(dummies), %_SOLO. N=39. *** p<.01, ** p<.05, * p<.1 Bridging diverse communities is significant. Being in the thick of information flows is significant. © 2006 Van Alstyne, Brynjolfsson & Aral

Information Flows Matter a a New Contract Revenue Coefficients Contract Execution Revenue Coefficients Unstandardized Coefficients Unstandardized Coefficients B Std. Error Adj. R2 Sig. F  B Std. Error Adj. R2 Sig. F  (Base Model) 0.40 0.19 Best structural pred. 12604.0*** 4454.0 0.52 .006 1544.0** 639.0 0.30 .021 Ave. E-Mail Size -10.7** 4.9 0.56 .042 -9.3* 4.7 0.34 .095 Colleagues’ Ave. -198947.0 168968.0 0.56 .248 -368924.0** 157789.0 0.42 .026 Response Time a. a. Dependent Variable: Bookings02 Dependent Variable: Billings02 b. b. Base Model: YRS_EXP, PARTDUM, %_CEO_SRCH, SECTOR(dummies), %_SOLO. N=39. *** p<.01, ** p<.05, * p<.1 Sending shorter e-mail helps get contracts and finish them. Faster response from colleagues helps finish them. © 2006 Van Alstyne, Brynjolfsson & Aral

H5: Recruiters with larger personal rolodexes generate no more or less output * p < 0.10, ** p < 0.05, *** p < 0.01, Standard err in paren. Instead, a larger private rolodex is associated with: • 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 © 2006 Van Alstyne, Brynjolfsson & Aral

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 Significant at  10% level

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 Significant at  10% level

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 Significant at  10% level

Takeaways 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. • Economics: incentive design mechanisms do correspond with information sharing. • Social network strategies are (i) bridging info pools (ii) being an info hub and (iii) career ladder => Structure matters! • 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! • Give information back. Data monitoring is not a sin if the principal use is to support those who provide it. © 2006 Van Alstyne, Brynjolfsson & Aral

Takeaways 2 • 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 • Certain white collar knowledge mgmt practices can be routinized. Remove or automate tedium of data capture. Most successful folks will share. • Consider hires for willingness to share and use IT, not just individual performance. Corollary: you may need to reward this. • Use IT and ESS both to support multitasking and increase speed. This helps people accomplish more work. © 2006 Van Alstyne, Brynjolfsson & Aral

Information, Social Networks & Individual Success