sensing –seizing -reconfiguring · sensing –seizing -reconfiguring the critical role of sensing...

Sensing– Seizing- Reconfiguring

Thecriticalroleofsensingindynamiccapabilitiesanditsinteractionwithorganizationstructure

ZurShapiraCCCFacultyDay2016Bocconi University

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FollowingTeece’s (2007)Vision:

Sense Seize Reconfigure

TheSourceoftheProcess MostoftheResearch

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A“Biased”SnapshotofPastResearch:

• Teece,Pisano&Shuen (1997).• Eisenhardt &Martin(2000)• Bingham,Eisenhadt &Furr (2007)• Winter(2003).• Teece (2007).• Helfat &Winter(2011).• Helfat &Peteraf (2015).

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Sensing– Perceiving– Interpreting:• Examples:Pictureanoldoryoungwoman.• Necker’sCube.• CEOcognitionandmentalactivities.• Connie’swritingoncognition;Kathy’swritingon

simplerules.• Heuristicsvs.DualProcessTheory.• FastEnvironmentalChanges.• CEOsasexperts.• BillSimon:Buying–>Sellingbonds.

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Aoccdrnig toarscheearch atCmabrigdeUinervtisy,itdeosn't mttaer inwhatoredr theltteers inawrod are,theolny iprmoetnt tihng istaht thefrist andlsat ltteer beattherghit pclae.Therset canbeatotalmses andyoucansitllraed itwouthit aporbelm.Tihs isbcuseae thehuamn mnid deos notraed ervey lteter byistlef,

butthewrod asawlohe.

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Purple

TheStyleofExpertise:

• Simon’sfocusonPatternRecognition• Theroleofmemory• Degroot’s StudyofChess• AQualitativeFormulaofIntuition• SensingandOrganizationalStructure

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ExpertiseinChessTask: 25 pieces on board

Shown for 10 secondsExperts and laypersons try to reconstruct the board

Condition 1: Pieces spread randomly on boardFinding: No difference between Novices and Grand

Masters. Both reconstruct about 6 pieces correctly.

Condition 2: Pieces on the board reflect a meaningfulsituation.

Finding: Novices reconstruct 6 pieces, Grand Masters 23-24 pieces.

Intuition=Intelligence+alotofexperience

• Dynamiccapabilities:sense->perceive->seize->configure• Howshouldorganizationsbestructuredforaccurateand

timelysensingofshiftsintheenvironment?

HierarchicalSensingModelElad Green&ZurShapira

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CEO

Level2

Level1

Env

• Structureishierarchical(nooverlapinspanofcontrol)

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Level1

Env

• AshiftintheenvironmentismodeledasashockofextentS:S%oftheenvironmentalattributeschange.Attributesthatchangearerandomlyselected.

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0 5 10 15 20 25 30 350.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

• EachDM(DecisionMaker)intheorganization:Ø Perceives correctlyachangeineachenvironmentalattribute

withaprobability(Pi)thatdecreaseswiththeno.ofattributessheisresponsiblefor(Span):

SpanPP i

i

max

=

iP

(insimulations:Pimax =0.8)

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• EachDM(DecisionMaker)intheorganization:Ø Updatesherbeliefsaccordingtoherperceptionofthe

environmentalattributes‒ Ifanattributehasbeenperceivedtochange,the

correspondingbeliefisupdatedtoreflectthechange‒ Beliefsarerepeatedlyupdatedduringthesimulationastime

advances

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• EachDM(DecisionMaker):Ø Interpretsherupdatedbeliefsand decides whethertoalert

hersuperior:DecisionD istoalertifthemagnitudeofinterpretedchangeexceedsathresholdT%

D

>=

otherwisechangedinterpreteofmagnitudeif %Talert

D

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0 10 20 30 40 50 60 700.2

0.25

0.3

0.35

0.4

0.45

0.5

0.55

0.6

0.65

0.7

No. of DMs Attempting Communication

P(R

ecei

ve A

ttent

ion)

• EachDM(DecisionMaker):Ø Ifthedecisionistoalert,theDMgetsthesuperior’sattention

withaprobability(Qa)thatdecreaseswiththeno.ofotherDMs(d)thataremakingconcurrentattempts:

aQ

)Q(Qk)(dQQ aaaa

minmaxmax 1=

d =no.ofDMsattemptingtoalertthesuperior

k =aconstantthatdependsontheno.ofDMsinthatlevel(k>d)

(insimulations:Qa

min =0.2,Qamax =0.7)CCC2016Bocconi

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• TheprocessrepeatsforeachDMintheorganization,frombottomtotop:Ø Perception ofsignalsfromsubordinates

andupdateofDMsbeliefsØ Interpretationanddecision whetherto

alertthesuperiorØ Ifdecisionistoalert- attempttoget

superior’sattention

D

aP

iP

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• TheprocessrepeatsforeachDMintheorganization,frombottomtotop:Ø Perception ofsignalsfromsubordinatesØ Decision whethertoalertthesuperiorØ Ifdecisionistoalert- attempttoget

superior’sattention• TheprocessendswiththeCEO,who

makesthefinalevaluationwhetheranenvironmentalshockhasoccurred.

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HierarchicalSensingModel• Weinduceenvironmentalshocksinarangearoundthe

thresholdT% thattheorganizationistunedtosense.

• Modelparameters:– Variousorganizationaldesigns

E.g.:[1641]denotesanorg.designof16 DMsatlevel1(bottomlevel),4 DMsatlevel2,andthe1 CEO

– Foreachorg.design:multiplelevelsofenv.shockaroundthethresholdT=40%– Foreachenv.shock:500 simulations(resultsareaveragedacrosssimulations)– Eachsimulationlasts:100 rounds(eachroundDMsperceive,updatebeliefs,

interpret,andalert;roundsreflecttime)– #env.attributes:512– DMsthresholdtoidentifyenv.change:40%– Probabilityparameters:

2.0

7.0

8.0

min

max

max

=

=

=

a

a

i

QQP

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0 20 40 60 80 1000

0.2

0.4

0.6

0.8

1Structure: 32 8 2 1

RoundsP(

Sens

ing)

0.300.330.360.390.420.450.48

0 20 40 60 80 1000

0.2

0.4

0.6

0.8

1Structure: 32 4 2 1

Rounds

P(Se

nsin

g)

0.300.330.360.390.420.450.48

0 20 40 60 80 1000

0.2

0.4

0.6

0.8

1Structure: 32 4 1

Rounds

P(Se

nsin

g)

0.300.330.360.390.420.450.48

0 20 40 60 80 1000

0.2

0.4

0.6

0.8

1Structure: 32 2 1

Rounds

P(Se

nsin

g)

0.300.330.360.390.420.450.48

0 20 40 60 80 1000

0.2

0.4

0.6

0.8

1Structure: 32 1

Rounds

P(Se

nsin

g)

0.300.330.360.390.420.450.48

• P(sensing)overtime

• Plotperstructure• Curveperlevelof

env.change

• Probabilityofsensingincreaseswithlevelofenv.change,andsensingdelaydecreases

Notation:Structure[1641]denotesanorg.designof16 DMsatlevel1(bottomlevel),4 DMsatlevel2,andtheCEO

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• Whatistheprobability ofsensingachangeintheenvironment,fordifferentlevelsofenvironmentalshocks?

• Theidealisastepfunction:nosensingbelowthethreshold(zerofalse-alarms),andperfectsensingabovethethreshold(100%truedetections)

P(Sensing)

0.3 0.35 0.4 0.45 0.50

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Env. Change [%]

P(Se

nsin

g)

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P(Sensing)• Plotsbyorganizationalsize(totalno.ofDMs)• Curveperorganizationalstructure• P(Sensing)iscomputedatRound=100and

averagedacrosssimulations

Notation:[1641]denotesanorg.designof16 DMsatlevel1(bottomlevel),4 DMsatlevel2,andtheCEO

0.3 0.35 0.4 0.45 0.50

0.1

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0.8

0.9

1

Env. Change [%]

P(Se

nsin

g)

64 16 4 164 8 2 164 8 164 4 164 2 164 1

0.3 0.35 0.4 0.45 0.50

0.1

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1

Env. Change [%]

P(Se

nsin

g)

32 8 2 132 4 2 132 4 132 2 132 1

0.3 0.35 0.4 0.45 0.50

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Env. Change [%]

P(Se

nsin

g)

16 4 2 116 4 116 2 116 1

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Env. Change [%]

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nsin

g)

64 16 4 164 8 2 164 8 164 4 164 2 164 1

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Env. Change [%]

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nsin

g)

32 8 2 132 4 2 132 4 132 2 132 1

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nsin

g)

64 16 4 164 8 2 164 8 164 4 164 2 164 1

0.3 0.35 0.4 0.45 0.50

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Env. Change [%]

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nsin

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nsin

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P(Sensing)• Plotsbyorganizationalsize(totalno.ofDMs)• Curveperorganizationalstructure• P(Sensing)iscomputedatRound=100and

averagedacrosssimulations

Ø Widerspansofcontrol(flatterorgs.)increaseprobabilityofsensing,butalsoincreasefalse-alarms

0.3 0.35 0.4 0.45 0.50

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Env. Change [%]

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nsin

g)

64 16 4 164 8 2 164 8 164 4 164 2 164 1

0.3 0.35 0.4 0.45 0.50

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32 8 2 132 4 2 132 4 132 2 132 1

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EnhancingSensing• TheCEOmayreceiveanindicationthat“somethingisgoingon”,yet

evidenceofanenvironmentalshockisinconclusive.• Inthiscase,theCEOmaybypasshisimmediatesubordinates(Level

2or3),anddirectly inquirewithDMsatLevel1.

• Configuration:– Org.istunedfora40%env.change– CEOinquireswhen20%ofhis

immediatesubordinatesindicatechange– CEOinquires20%ofDMsatL1

(onlyunderVPswhodidnotindicatechange)– CEO’sperceptionoftheinquiredDMs’

evaluationsiserrorless

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Summary

• Increased competition necessitates targeting top quality projects

• An important determinant of DC is early detection of opportunities and environmental changes hence the need to attract top management’s attention

• CEO’s SDM is an associative rather than a linear process. We need a better understanding of this process.

• Dynamic capabilities application processSense->Perceive->Infer->Seize->Reconfigure

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ALooktotheFuture:

• Themostimportantprogressin(human)scienceoverthelasttwodecades.

• Studyofthebrain• AI

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ThankyouDavidforintroducingustodynamiccapabilities.

Andthankstoallofyouforlistening!

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