Information Social Networks Individual Success MIT Center

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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 All Rights Reserved © 2006 Van Alstyne, Brynjolfsson & Aral

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

The Setting – Executive Recruiting Executive Search Process 1. 2. 3. 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 : Capture Requirements Initial Search / Create Initial Pool Vet Candidates Conduct Due Diligence Create Interview Pool / Interview Internally Create Final Pool / Facilitate Client Placement (~ 6) Firm uses IT in 2 ways: 1. 2. 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 Subject: Re: YOUR PROPOSAL Date: Sun, 17 Nov 2002 09: 54: 23 -0500 Cc: averhey@umich. edu, Geoffrey Parker 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” Ann. Message-ID: 0000 C 74 E 9 F 197619354 B 912 FA 038789 E 97 DD 070095 FBFC 9 E 5 C 710 C 45 AD 83 BE 1 BA 97654 F 300000025 D 7 D 70000 95 FBFC 9 E 5 C 710 C 45 AD 83 BE 1 BA 97654 F 30000015 D 02090000 Date: 11/17/2002 09: 54: 23 PM From: Chi. User. WWW 2 To: Chi. User. WWW 34 CC: Chi. User. WWW 2 , Chi. User. EEE 137 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. 157 kb. 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

An E-mail “Fingerprint” Consultant - Sent vs. Received 8000 6000 4000 Sent 2000 External Internal c 9 1 c 7 c 6 0 c 3 9 c 2 7 c 2 3 c 2 1 c 2 8 c 1 6 c 1 4 c 1 2 c 1 0 0 -2000 -4000 Received -6000 -8000 -10000 -12000 © 2006 Van Alstyne, Brynjolfsson & Aral

Topology Comprehending the Social Networks

Social Network Efficiencies 1. Connect to hubs • 2. Central nodes who bridge structural holes are significantly more effective. Send short messages • 3. Consultants have higher billings (. 56, p<. 01) and are more central (see 1). Communicate declarative information • • 4. 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 Firm Y Firm Z 1. 50 1. 00 0. 50 0. 00 -0. 50 -1. 00 Q 1 Colleagues give me credit for info that I share. Q 2 Colleagues willingly share their private search info with me. Q 3: I volunteer all relevant info to colleagues. © 2006 Van Alstyne, Brynjolfsson & Aral Q 4: A lot of my personal knowledge never reaches the corp. database.

Incentive theory works Weighting of Compensation Structure 100% Least Most Med. 90% 80% 70% Whole company performance Project team(s) performance Individual performance 60% 50% 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 0. 20 0. 00 -0. 20 -0. 40 -0. 60 -0. 80 -1. 00 Firm X Firm Y Firm Z Q 7 We use info sys to coord sched & project handoffs Q 14 My data requirements are routine Q 15 For routine info, the process of getting it is automated © 2006 Van Alstyne, Brynjolfsson & Aral Q 41 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 processed bear little correlation with Brynjolfsson & Aral productivity… © 2006 Van Alstyne,

A Model of Information Work: Task Completion & Compensation IT variables Database Skill Intermediate Output Individual Compensation Revenue Compensation Multitasking Completion Rate Email Contacts Final Output Duration per Task © 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 -------+---------------Model | 1. 9341 e+11 6 3. 2236 e+10 Residual | 8. 2136 e+11 34 2. 4158 e+10 -------+---------------Total | 1. 0148 e+12 40 2. 5369 e+10 HR Factors = = = 41 1. 33 0. 2691 0. 1906 0. 0478 1. 6 e+05 ---------------------------------------rev 02 | 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 --------------------------------------- Source | SS df MS -------+---------------Model | 4. 6776 e+11 6 7. 7959 e+10 Residual | 1. 6051 e+11 26 6. 1735 e+09 -------+---------------Total | 6. 2827 e+11 32 1. 9633 e+10 IT Factors Number of obs F( 6, 34) Prob > F R-squared Adj R-squared Root MSE Number of obs F( 6, 26) Prob > F R-squared Adj R-squared Root MSE = = = 33 12. 63 0. 0000 0. 7445 0. 6856 78572 ---------------------------------------rev 02 | 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 --------------------------------------- © 2006 Van Alstyne, Brynjolfsson & Aral

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

What Drives Revenue Generation? MT CP $ D 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. § MT 2 is negative, implying Y an inverted-U shaped relationship MT © 2006 Van Alstyne, Brynjolfsson & Aral

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

Multitasking and Duration depend on DB-Skill and Contact Networks Coefficientsa Multitasking Duration Unstandardized Coefficients B Std. Error t Coefficientsa Unstandardized Coefficients Sig. B Std. Error t Sig. (Constant) Consult Dummy -1. 769 2. 396 6. 223 1. 762 -. 284 1. 360 . 779. 186 -26. 821 16. 382 147. 052 36. 720 -. 182. 446 . 857. 660 Partner Dummy 2. 636 2. 056 1. 282 . 212 20. 128 45. 193 . 445 . 660 Total Internal Contacts in Incoming Emails . 126*** . 043 2. 941 . 007 1. 906* . 987 1. 931 . 066 DB_SKILL . 009** . 004 2. 375 . 026 . 169* . 083 2. 027 . 054 a. b. a. Dependent Variable: MULTTSKS Adjusted R 2 =. 24 with controls for GENDER, YRS_ED, YRS_EXP. b. Dependent Variable: AVEDUR Adjusted R 2 =. 18 with controls for GEN. , ED. , and EXP. • Contact networks and DB-Skill help workers multitask • But average duration suffers. IT © 2006 Van Alstyne, Brynjolfsson & Aral Intermed

Relation Between IT & Multitasking § F 2 F – small magnitude positive with MT. § Interviews indicate that a certain number of F 2 F 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, antipoaching § 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! © That Girl Synchronous Information Seeking Hurts! • Email • DB Access • Phone Initial Synchronize: • Face to Face © 2006 Van Alstyne, Brynjolfsson & Aral

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

Check: Revenue & Compensation do depend on IT Skills Coefficientsa Revenue Unstandardized Coefficients B (Constant) Std. Error Unstandardized Coefficients t Sig. B Std. Error t Sig. 306222. 69 -1. 090 . 286 133654. 46 152918. 8 . 874 . 388 86713. 60 4. 851 . 000 148254. 60*** 29454. 27 5. 033 . 000 Partner Dummy 354668. 03*** 101188. 43 Total Internal Contacts 11657. 50*** 2102. 10 in Incoming Emails 3. 505 . 002 317464. 32*** 44561. 70 7. 124 . 000 5. 546 . 000 1953. 29** 841. 10 2. 322 . 026 DB_SKILL 1. 676 . 106 -204. 22* 116. 98 -1. 746 . 089 Consult Dummy a. b. -333896. 63 Coefficientsa Compensation 420625. 63*** 326. 32* 194. 74 Dependent Variable: REV 02 Adjusted R 2 =. 53 with controls for GENDER, YRS_ED, YRS_EXP. a. Dependent Variable: SALARY b. Adjusted R 2 =. 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. © 2006 Van Alstyne, Brynjolfsson & Aral IT $ Comp

Recall Network Position… Betweenness Constrained vs. Unconstrained

Network Structure Matters Coefficientsa New Contract Revenue Unstandardized Coefficients B Std. Error (Base Model) Size Struct. Holes Betweenness a. b. Adj. R 2 Contract Execution Revenue Coefficientsa Unstandardized Coefficients Sig. F B Std. Error 0. 40 Adj. R 2 Sig. F 0. 19 13770*** 4647 0. 52 . 006 7890* 1297* 773 0. 47 . 040 1696** Dependent Variable: Bookings 02 Base Model: YRS_EXP, PARTDUM, %_CEO_SRCH, SECTOR(dummies), %_SOLO. 4656 0. 24 . 100 697 0. 30 . 021 a. Dependent Variable: Billings 02 b. 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 Coefficientsa New Contract Revenue Unstandardized Coefficients B Std. Error Best structural pred. Ave. E-Mail Size Colleagues’ Ave. Response Time a. b. Unstandardized Coefficients Adj. R 2 (Base Model) Contract Execution Revenue Coefficientsa Sig. F B Std. Error 0. 40 12604. 0*** -10. 7** -198947. 0 Adj. R 2 Sig. F 0. 19 4454. 0 0. 52 . 006 1544. 0** 639. 0 0. 30 . 021 4. 9 0. 56 . 042 -9. 3* 4. 7 0. 34 . 095 168968. 0 0. 56 . 248 157789. 0 0. 42 . 026 Dependent Variable: Bookings 02 Base Model: YRS_EXP, PARTDUM, %_CEO_SRCH, SECTOR(dummies), %_SOLO. -368924. 0** a. Dependent Variable: Billings 02 b. 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

H 5: 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 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! Give information back. Data monitoring is not a sin if the principal use is to support those who provide it. 5. © 2006 Van Alstyne, Brynjolfsson & Aral

Takeaways 2 6. Perceived information overload corresponds very little to actual communication flows but rather to w w w 7. 8. 9. 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