Tabulación de encuestas

Chapter 2 showed several OR/MS approahes for supporting strategy proesses. How-

ever, thisdotoralthesisexplores theuseofSD modellingto supporttheinternationalisa-

tion proess throughstrategy rehearsal. Although there areother approahes to rehearse

strategy,suhasagent-basedmodels,protimpatofmarketstrategy,andbalanedsore-

ard(Dysonetal.,2007),IdeidedtouseSDmodellingbeausethisapproahhelpspeople

understand how strategies will perform over time, what might be wrong, and what kind

of intervention an be done (Kun and Moreroft, 2009). Also SD an helpompanies to

develop orretive ations from real-world feedbak with virtual performane to improve

strategi foresight (Kun andMoreroft, 2007a).

In traditional SD, the sequene of modelling ativities isas follows: theoneptualisa-

tion of the problemati situation; the development of model equations; the development

of a simulation to understand dynami behaviour; the evaluation of alternative poliies;

learning from the model and hoie of appropriate poliy; and nally, implementation

(Forrester, 1994). However, when thereis no onsensusamong deision-makersabout the

systemunderlying deisionsand thehypotheses(theory)generated forexplaininghowthe

system is not reahable beause deision-makers have dierent ideas (mental models) of

theproblem(reality)(KunandMoreroft,2009). Rouwette(2011)arguesthatfailitated

modellingisusefulinsupportingtheresolutionofstrategiissueswhenitisexpetedtoim-

prove ommuniation between deision makers, foster onsensus and reate ommitment.

Otherwise, models developed and interpreted by outside experts rarely hange the way

managers think about strategi issues (Senge and Sterman, 1992). Hene, the failitated

modelling proess an improve CEOs' abilities to view newsituations systematially and

dynamially(Moreroft,2007). ThisviewofSDmodellingonsidersthatCEOsthemselves

beomethe modellersof theirstrategy proess. Although thereisahighriskof modelling

a mental model that is aeted by mispereption of feedbaks (Sterman, 1989a), this is

thereal driver thatleads toimplement thestrategy.

Deision-makers argue that information about their rm's performane feeds bak to

modifyor reinforetheinitial deisions forimplementingastrategy (Kunand Moreroft,

2010). This feedbak proesses and its irular ausality has been a entral point in the

study of the dynamis of systems insoial sienes (Rahmandad et al.,2009). In system

dynamismodelling, aausalrelationshipisdenotedusinganarrowonnetinga

X

fator

thatis ausallyinuening

Y

(e.g.,

X→Y

). Thisrelationship an be eitherpositive(

+

)

ornegative(

−

). Whentherelationshipispositive(/negative)means thataninreasein

X

leadsto aninrease(/derease) ofthelevelof

Y

. Whenthereareseveralsigniant ausal

fators

X1, X2, . . . , Xn

inuening

Y

,thisrelationship is formallydenoted:

However, when

X

and

Y

are aeted mutually (

X

⇄

Y

), these relationships reate a

feedbak loop. Feedbak loopsare dened asinterations among omponents ofa system

linkingations,events,orpieesofinformation (Sterman,1989a). Eventhoughomplexity

isarelativeterm,most omplexbehaviourusuallydependsonthenumberandthenature

of interations among the omponents of a system instead of on the omplexity of the

omponents themselves (Gharajedaghi, 2006). All dynamis arise from the interation

of two types of feedbak loops, positive and negative. Positive loops are self-reinforing

thereby generating exponential growth or deline. For example, if a rm inreases its

marketing ativities to gain market share in a loal market, its ustomers may respond

by buying more produts on sale. On the other hand, negative loops are self-orreting

therebyounteratinghange. Followingthelastexample,thehigherprodutdemandan

aet the ustomer servie quality when market dynamis are rapid relative to apaity

adjustment. In fat,foreasting errorsmayleadto a lakof apaity thatnallyan lead

to aderease inthenumberofustomers(See Figure3.2 ).

Figure 3.2: Example ofreinforing and balaningfeedbak loops

Within the SD eld, the onept of feedbak information plays a entral role in ex-

plainingthelinkbetween systemstrutureandbehaviour(Sterman,2000). Thefeedbaks

enhane the understanding of how individuals or groups make one or more deisions in

eah time period, based on the information available (Gary etal., 2008). However, dur-

ing the deision-making proess, managers might be aeted by insuient, unlear, or

oniting information about the results obtained from their deisions (Kun and More-

roft, 2007a). Aording to Moreroft (2007:5), `people's abilityto managetheir omplex

world an be improved by visualizing and simulating it'. In this sense, stok-and-ow

diagrams help to build mental models and represent thebasi knowledge strutures that

managersuseto make strategideisions (Kun andMoreroft, 2009). Inthisresearh,it

has onsidered a stok-and-ow diagram a set of strategi resoures onneted by arrows

denotingtheausalinuenesamongthevariablesextratedfromtheCEO'smentalmodel

of the analysed proess. Strategi resoures an be onsidering as asset stoks (Dierikx

andCool, 1989). Thesestoksaregraphiallyrepresentedasaretanglethataumulates

theirinowsandoutows. Aninowisdrawnasavalveonneted withanarrowthatbe-

ginsfromasoure andpoint atthe stok. Outowsarerepresentedwiththesame symbol

butthe owisoriginated inthestokandendsupinasink(soureandsinkaredrawnas

a loud)(Kun and Moreroft,2007a).

equations are employed to simulate the logial onsequenes, whih help rms to under-

stand the dynamis underlying the system. In this sense, Equation 3.11 formalises a

strategiresoure

i

(stok)(

Rt

)astheinitialvalueoftheresoure(

Ri(0)

)plustheintegral of investment inthis resoureovertime (

ri(t)

):

Ri(t) =Ri(0) +

Z

t

0

ri(t)dt

(3.11)

Theurrent rateofaumulation

ri(t)

ofresoure

i

atthetime

t

isafuntionoftheur- rentlevelofallexistingresouresaeting it(

R1(t), R2(t), . . . , Rn(t)

,inludingexogenous fatorsdenotedgenerially as

E(t)

.

ri(t) =

f

(R1(t), R2(t), . . . , Rn(t), E(t))

(3.12)

Asset stokaumulations lie at the ore of all SD representations, whetheror not the

modellers oftheanalysishoosetomake itexpliit(Warren, 2004). Warren(2004)argues

that Stoks provide the onlymehanism by whih information andmaterial an be passed

forward through time. Unfortunately, the ognitive ability of managers visualising the

outomeof assetstokandlosedloopsystemsislimited, espeiallywhen aertaination

hasavaryingeetonanoutomedependingonitsintensitylevel(Sterman,1989b). These

non-lineardynamis arenot anunusualphenomenon. Indeed,manymanagerial situations

are aeted by onditions that hange depending on unertain fators. For example, in

Figure3.3therateof ustomerlossan impattheompanydierently depending onthe

levelof rise orfall oftheprodut demand andthe servie apaity.

As people's pereptions about a partiular system dier beause of the way in whih

a onsensusis reahed among individuals. Forrester (1994) postulates thatthelevel-rate-

feedbak struture of SD is indeed the fundamental struture of the real soial/physial

systems. However, withinorganisations, this ideaannot bevalidated againstthepart of

thereal world being modelled beausestakeholders areonstantly hangingthestruture

of the system. Regarding this view, models only show the pereption of the real world

at a speipoint of time. Thisaets managers' modesof theorising their ownstrategi

deisions (Colbert, 2004).

In this researh, simulation of SD models are based on the CEOs' pereption of their

own organisation's set of resoures. Hene, the simulations allowed the analysis of the

robustness of a CEO's assumptions about the pereived resoure system. This is not the

traditional SD approah of simulation. Nonetheless, onethere is agreement with CEOs

insupporting oneviewofstrategy,SD modellinganhelptoexplorethesolutionspaeby

rehearsing thepreferredstrategy through simulation. Inthis ase, SD models areusedas

transitional objets to failitatedialogue andtheexplorationoffutureinternationalisation

strategies. Then `simulations an help managers disover hidden pitfalls in strategy by

allowing them to rehearse resoure building(a task whih isdynamially omplex due to

interdependenies, time delays andnon-linearities' (Kunand Moreroft, 2009:198).

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