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The impact of cultural differences on technology adoption

Sang-Gun Leea, Silvana Trimib,* , Changsoo Kimc

a Department of Business Administration, Sogang Business School, Sogang University, Seoul, South Korea

b Department of Management, College of Business Administration, University of Nebraska, Lincoln, NE 68588-0491, USA

c

Abiz Management Research Institute, College of Business Administration, Ajou University, Suwon, South Korea

1 Introduction

Duringthelasttwodecades,InformationandCommunication

Technology(ICT)hasseendramaticadvancesanddiffusion.Many

ICTproductsorserviceshavebecomenecessitiesofeverydaylife

The effectivenessand efficiencyofICT deployment anduseare

influenced by national (Al-Ghatani, 2003; Erumban & de Jong,

2006; Straub, 1994; Taras, Steel, & Kirkman, 2011, 2012) and

organizationalcultures(Cao&Everard,2008;Lim,Yeow,&Yuen,

2010;Schiller&Cui,2010).Nationalculturesevenplaysignificant

rolesin thedevelopment of nationalinformation infrastructure

(Apfelthaler, Muller, & Rehder, 2002; Dimitratos, Petrou,

Pla-koyiannaki,&Johnson,2011;Garfield&Watson,1998; Ralston,

Hallinger,Egri,&Naothinsuhk,2005).Culturesatthenationallevel

exertasubtle,yetpowerful,influenceonpeopleandorganizations

(Leidner & Kayworth, 2006) Systems quality and culture

significantlyaffecttrustintheICTartifactandthereforeintheir

adoption(Vance,Elie-dit-cosaque,&Straub,2008)

PriorresearchintheeffectofcultureonICTdiffusion,asshown

inTables1and2,mostlyusedsurveyresearch,casestudiesorfield

investigation involving limited numbers of subjects To truly

capturetheimpactofnationalcultureontechnologyadoption,a

studyshouldinclude theentirepopulation ofa country.In this

study, we examined theimpact of national culture onmobile

phone adoption by including the entire population of mobile

phonesubscribersofeachcountrystudied.Wealsouseandadopt themostappropriateresearchmodelsinthisstudy:theBassmodel fortheproductdiffusion/adoption,andtheHofstede’smodelfor thenationalculturaldimensions

1.1 TheBassmodel TheBassmodel(1969,2004)hasbeenemployedbynumerous studiestoanalyzesalesandthediffusionprocessofaproduct.In thisstudy,theBassmodel isusedfortheverifiedofficial time-seriesdata(from1985to2008)ofthenumberofallmobilephone adopters (seeTable 3).The Bass modelincludes theinnovation effect,which comesfrom theadopter’sself-perceptionand the product’s utility;andtheimitation effect,whichresultsinfrom interactionsbetween earlyadoptersand potentialadoptersof a product.Inthecumulativecurveofadoption,theinnovationeffect showsa convexshape,whiletheimitationeffecthasa concave curve,asshowninFig.1

1.2 TheHofstedemodel Hofstede’s(1991)culturaldimensionsmodelclassifiesnational culturesintofourtypes.Griffith,Hu,andRyans(2000),basedon theHofstede’sdimensions,suggestedtwoextremeculturaltypes forstudy:TypeI(individualistic,weakuncertaintyavoidance,and low long-term orientation) and Type II (collectivistic, strong uncertainty avoidance,and highlong-termorientation) Forour research,thesetwoextremecontrastingculturetypesarechosen

toinvestigatetheeffectofnationalculturesontheadoption of mobilephones.Weselectedtwocountries,oneforeachofthetwo

Journal of World Business 48 (2013) 20–29

A R T I C L E I N F O

Keywords:

Cross-cultural research

Cultural dimensions

Diffusion models

National culture

Technology adoption

A B S T R A C T

ThisstudyexaminestheimpactofTypeIandTypeIIculturaldifferencesonmobilephoneadoption patterns.WeuseHofstede’sculturaldimensionstoexamineculturaldifferencesoftwocountries(TypeI: theU.S.;TypeII:S.Korea)andemploytheBassdiffusionmodeltodelineateinnovationandimitation effectsonmobilephone adoption.TheresultsshowthatinTypeI cultureinnovationfactorhasa significantlyhigherlevelofeffectonadoptionthanitdoesinTypeIIculture;andinTypeIIculture imitationfactorhasahigherdegreeofeffectonadoptionthanitdoesinTypeIculture.Thesefindings implythatinindividualisticcultures,peopletendtoseekinformationontheirownfromdirectand formalsources, whereas incollectivisticcultures,peoplerelymoreon subjectiveevaluation ofan innovation,conveyedfromother-like-mindedindividualswhoalreadyhaveadoptedtheinnovation

ß2012ElsevierInc.Allrightsreserved

* Corresponding author Tel.: +82 2 705 7987; fax: +82 2 705 8519.

E-mail addresses: slee1028@sogang.ac.kr (S.-G Lee), strimi@unlnotes.unl.edu

(S Trimi), changsookim321@gmail.com (C Kim).

ContentslistsavailableatSciVerseScienceDirect

j ourn a l hom e pa g e : ww w e l se v i e r c om / l oca t e / j w b

1090-9516/$ – see front matter ß 2012 Elsevier Inc All rights reserved.

Table 1

Hofstede’s cultural values.

Cultural value Definition

Individualism/collectivism (IC) Degree to which the individual emphasizes his/her own needs as opposed to the group needs and prefer to act as an individual

rather than as a member of a group.

Power distance (PD) Degree to which large differentials of power and inequality are accepted as normal by the individual Power distance will

condition the extent to which the employee accepts that his/her superiors have more power.

Uncertainty avoidance (UA) Uncertainty avoidance is the level of risk accepted by the individual, which can be gleaned by his/her emphasis on rule

obedience, ritual behavior and labor mobility This dimension examines the extent to which one feels threatened by ambiguous situations.

Gender role orientation (MF) The degree to which gender inequalities are espoused by an individual Individuals who espouse masculine values emphasize

work goals such as earnings, advancement, competitiveness, performance and assertiveness On the other hand, individuals who espouse feminine values tend to emphasize personal goals such as a friendly atmosphere, comfortable work environment, quality of life and warm personal relationships.

Long-term orientation (LTO) The degree to which society does or does not embrace long-term devotion to traditional values.

Table 2

Culture, ICT adoption and diffusion at the national level.

Researcher Independent variables Dependant variables Methodology and measure of national culture Published journal

Straub (1994) Perceived usefulness, ease

of use

Media use (and fax), national culture (UA)

Multi-method study (field interviews, survey, policy capturing) comparing U.S and Japanese respondents, Hofstede’s cultural indices

Information Systems Research

Straub, Keil, and

Brenner (1997)

Perceived usefulness, ease

of use

Information systems use, national culture (IC, UA, PD, MF)

Survey of airline employees from U.S., Japan and Switzerland, Hofstede’s culture indices

Information and Management

Galliers et al (1998) National culture Rate of technology adoption Single site case study, culture not explicitly

measured

Information Technology for Development

Garfield and

Watson (1998)

National culture (UA, PD) Structure of national

information Infrastructure

Descriptive case study of government archives across 7 countries, Hofstede’s cultural indices

Journal of Strategic Information Systems

Griffith (1998) National culture (PD) Satisfaction with Group

Support Systems (GSS)

Laboratory experiment comparing U.S and Bulgarian student GSS teams, Hofstede’s culture indices

Interacting with Computers

Jarvanpaa and

Leidner (1998)

Resource-based competencies

Information services industry diffusion national culture (IC, UA)

Single site case study (semi-structured interviews) of Mexican firm, Hofstede’s culture indices

Information Systems Research

Hasan and

Ditsa (1999)

National culture (UA, PD,

IC, MF)

Technology transfer outcome Interpretive field study of 10 organizations in

Middle East, Africa and Australia, Hofstede’s culture indices

Journal of Global Information Management

Al-Ghatani (2003) Perceived attributes of

technology

Rate of technology adoption, national culture

Survey of 1200 Saudi managers and government officials, culture not explicitly measured

Information Technology for Development

Thatcher

et al (2003)

National culture (UA, IC,

PD, MF), qualitative and quantitative work overload

Personal innovativeness with information technology

Survey of U.S college students, cultural indices by Hofstede

Journal of Computer Information Systems

Chui and

Kwok (2008)

National culture dimensions

Life insurance consumption Survey research with data from 1976 to 2001

across 41 countries, cultural indices by Hofstede

Journal of International Business Studies

Linghui and

Koveos (2008)

GDP, national culture (UA, MF)

National culture (IC, LTO, PD) Survey research, cultural indices by Hofstede Journal of International

Business Studies

Fischer and

Mansell (2009)

National culture (IC, PD), economic variables

Types of organizational commitment

Survey research, cultural indices by Hofstede Journal of International

Business Studies Adapted from Leidner and Kayworth (2006)

Table 3

The Numbers of mobile phone subscribers in the U.S and South Korea.

1985 340,213 4685 1997 55,312,293 6,828,169

1986 681,825 7093 1998 69,209,321 13,982,919

1987 1,230,855 10,265 1999 86,047,003 23,442,724

1988 2,069,441 20,353 2000 109,478,031 26,816,398

1989 3,508,944 39,718 2001 128,500,000 29,045,596

1990 5,283,055 80,005 2002 141,800,000 32,342,493

1991 7,557,148 166,198 2003 160,637,000 33,591,758

1992 11,032,753 271,868 2004 184,819,000 36,586,052

1993 16,009,461 471,784 2005 213,000,000 38,342,323

1994 24,134,421 960,258 2006 248,180,000 40,197,115

1995 33,758,661 1,641,293 2007 263,000,000 43,497,541

1996 44,042,992 3,180,989 2008 270,500,000 45,606,984

SouthKoreaforTypeIIculture.Inthisresearch,wehypothesize

andtest whetherinTypeIculture(i.e.,theU.S.)theinnovation

effectishigherandimitationeffectloweronadoptingICTthanin

TypeIIculture.TherationaleisthattheTypeIcharacteristicsof

strongindividualism,weakuncertaintyavoidance,andlow

long-term orientation will reinforce personal and product

innova-tiveness and will moderate the imitation effect in technology

adoption.Conversely,inTypeIIculture(i.e.,SouthKorea)imitation

willhaveahigherdegreeofeffectandinnovationalowereffectin

ICTadoptionthancountriesofTypeIculturebecausecollectivism,

strong uncertainty avoidance, and high long-term orientation

intensify the imitation behavior of adopters and moderate

personalinnovativeness

Insum,ourresearchquestionsareasfollows:

Q1.Is thereasignificantpatterninICTadoptionthat canbe

explainedbythediffusionmodel?

Q2 If so, do the culturaldifferences have anyeffect in the

diffusionpattern?

Thepaperisorganizedasfollows:In thesecondsection,we

summarize Hofstede’s cultural dimensions and relevant prior

researchontheimpactofculturalvaluesonthediffusionofIT;The

third section presents development of hypotheses based on

relevant prior studies on cultural dimensions and technology

adoption; The fourth section presents the study’s research

methodologyanddatacollectionprocedure;Inthefifthsection,

the results of our analysis will be provided; The sixth section

discusses the finding of the study; and finally the conclusion

sectionincludesimplications,contributions,andlimitationsofthe

study

2 Literaturereview

2.1 Cultureandculturaldimensions

Cultureisconceptualizedassharedsymbols,normsandvalues

inasocialcollectivitysuchasacountry.Themostpopularcultural

theorythathasbeenadoptedininformationsystems(IS)research

isHofstede’smodel

Hofstede(1980)definedcultureas‘‘thecollective

program-mingofthemindwhichdistinguishesthemembersofonehuman

groupfromanother.’’Hofstedealsodevelopedanindexmodeland

proposed four widely cited dimensions of national culture:

individualism/collectivism, power distance, uncertainty

avoid-anceandgenderroleorientation(Hofstede&Hofstede,2005).The long-term orientation was later added as a fifth dimension (Hofstede&Bond,1988).Table1summarizesHofstede’scultural valuesandtheirdefinitions

Hofstede’sculturaldimensionshavebeenemployedbymany

IS studies These studies suggest that national culture has significantrelationshipswiththestructureofnational informa-tioninfrastructure,therateoftechnologyadoption,technology transfer,andpersonalinnovativeness(Galliers,Madon,&Rashid, 1998;Garfield&Watson,1998;Hasan&Ditsa,1999;Thatcher, Srite,Stepina,&Liu,2003).Alargenumberofsubjectsarerequired

to measure a nation’s culturalcharacteristics for an unbiased study.AsshowninTable2,mostofthesestudiesusedspecific typesofsubjects,suchascollegestudents,airlineemployeesand governmentofficials

2.2 Culturetypesbasedonculturaldimensions Griffith et al (2000) studied Type I and Type II cultures, according to three of the five aforementioned dimensions: individualism,powerdistance,and uncertaintyavoidance.Type

Iculture(e.g.,theUnitedStatesandCanada)has ‘‘individualistic-small powerdistance-weak uncertainty avoidance’’ characteris-tics.TypeIIculture(e.g.,ChileandMexico)involves‘‘collectivistic– large power distance–strong uncertainty avoidance’’ character-istics

Thisclassificationofculturaltypeshasbeenexaminedandused

by manystudies Huffand Kelly(2003)investigated whether a firm’snationalculturehasanimpactonitsinternalandexternal trustpropensities.ConsistentwithGriffithetal.’s(2000)study,the resultsshowedthatmanagersinTypeIculture,theUnitedStates, managersdemonstratedahigherlevelofexternaltrustthandid managersinTypeIIculture(Asia–China,Korea,Taiwan,etc.).Kim (2008) used the concept of cultural types to identify self-perception-based versus transference-based trust determinants

incomputer-mediatedtransactions.Inhisstudy,themeanvalues

oftheself-perception-basedtrustdeterminantsoftheU.S.sample (i.e.,TypeI)werehigherthanthoseoftheKoreansample(i.e.,Type II),whereasall themeanvaluesofthetransference-basedtrust determinantsoftheTypeIIculturewerehigherthanthoseofTypeI culture.Thisresultshowsthatself-perception-baseddeterminants aremorelikelyrelatedtoTypeIculturethantoTypeIIcultureand thattransference-basedtrustdeterminantsarelesslikelyrelated

toTypeIculturethanTypeIIculture.Consequently,TypeIculture

isasocietythatgivesgreatconsiderationtoindividualperception whileTypeIIcultureaccountsforasocietythatgivesmuchweight

tosocialperception

2.3 Thediffusionofinnovationandculturetypes Theadoptionanddiffusionofnewideasornewproductsbya socialsystemwerethoroughlydiscussedbyRogers(1983,1995, 2003).TheDiffusionofInnovationTheory(DIT)suggeststhatthe patternsofITacceptance(termedadoptioninthiscontext)withina networkofusersareshapedviaaprocessofcommunicationand social influence, where later adopters are informed of the availabilityandutilityofnewITinnovationsbyearlieradopters (Rogers,1995)

Thepatternofthecumulativeadoptionfrequencyofinnovation over time forms an S-shaped curve This curve explains the behavior of adoptersand is referred toas thediffusion model Althoughthereare manyvarianttypesof diffusionmodels,the Bassmodelisperhapsthemostpopular.Basedonthetimingof adoption,Bassclassifiedtheadoptersasfollows:(1)Innovators; (2)Earlyadopters;(3)Earlymajority;(4)Latemajority;and(5) Laggards(Fig.2).Bassdefinedinnovatorsasindividualswhoearly

Fig 1 Innovation and imitation effects on technology adoption.

Adapted from Mahajan, Muller, and Wind (2000)

S.-G Lee et al / Journal of World Business 48 (2013) 20–29 22

others in a social system While imitators are defined as the

adopterswhosetimingofadoptionisinfluencedbythepressureof

theirsocialsystems;thepressureincreasesforlateradoptersas

thenumberofpreviousadoptersincreases

Bass(1969)developedagrowthmodeltodeterminethetiming

oftheinitialpurchasesofnewproducts.Theeffectsofinnovation

andimitationaresuggestedinhismodel.TheBassmodelcanbe

statedas:

dNðtÞ

whereN(t)isthecumulativenumberofadoptersattimet,misthe

numberofpotentialadoptersoftheinnovation,pisanon-negative

constant (usually as the coefficient of external influence or

innovation), q is a non-negative constant (the coefficient of

imitation),anddN(t)/dtisthefirstderivativeofN(t)representing

therateofdiffusionattimet.TheS-shapedcumulativeadoption patternaswellasthenon-cumulativeadoptionpatternisshownin Fig.3

Eq.(1)isthesumofexternal(innovation)Eq.(2)andinternal (imitation)Eq.(3).TheexternalorColeman’s(1966)model,Eq.(2), assumes that the diffusion rate at time t is based on such innovationfactorsasusefulnessandeasy-of-useofthe

technolo-gy and therefore depends only on the number of potential adoptersinthesocialsystemattimet(Fig.4).Thisassumption means that only limited communication exists between early adoptersandpotentialadoptersandthatearlyadoptersdonot significantlyaffectthedecisionsofpotentialadopters(Coleman,

1966)

dNðtÞ

Theinternalmodel,Eq.(3),alsoreferredtoasthepureimitation diffusion model, posits that diffusion occurs through social contacts.Theprimaryfunctionofcommunicationamong individ-ualsisthroughtheinteractionbetweenearlyadoptersandfuture adoptersviasuchimitationfactorsassubjectivenormandwordof mouth.Thismodelishighlyusefulwheninvestigatingtheimpact

of early adopters’ experiences to determine late adopters’ behavior

dNðtÞ

The innovation effect p (Eq (2)) comes from individual perception; while, imitation effect q (Eq (3)) comes from the social effects, influenced by cultural factors As we mentioned earlier,TypeIculturedescribesasocietythatgivesmuchweightto individualperception;inanindividualisticculturaltype, individ-ualslookaftertheirself-interests(Hofstede,1980;Griffithetal.,

2000) Type II culture describes a society that gives much consideration to social perception Members of a collectivist culturaltypetendtosharesimilaropinionsandbeliefs,working toward afeelingofharmoniousinterdependence (Griffithetal.,

2000).Hence,highindividualism(ID)reflectspvalueoftheexternal modelthat assumesnocommunication;while,highcollectivism

Fig 2 Adopter categories and the diffusion curve.

Adapted from Rogers (1995)

Fig 3 The Bass/Mansfield model ( 1969 , 1961 ).

(CO) reflects q value of the internal model that assumes that

diffusion is based on the enhanced interaction between early

adoptersandfutureadopters

Additionally, uncertainty avoidance (UA) is also related to

diffusionfactors.CulturesofweakUAtypeaccepthigherlevelsof

risk and do not attempt tocontrol uncertainty Individualsare

socializedtoacceptit(Hofstede,1980;Inkeles&Levinson,1969)

Alternatively, culturesof strong UA type attempt to formulate

waysofcontrollingfutureevents,thusreducinguncertaintyand

risk (Hofstede, 1980; Inkeles & Levinson, 1969) Until risk

acceptance has disappeared, members of a strong UA cultural

societyhesitatetoacceptanewtechnology.Thisissimilartothe

S-shaped diffusion process According to theory of diffusion/

adoption, adopters hesitate toadopt technology for theperiod

from thestart point to thecritical mass point (16%adoption)

However,aftersuperiorperformanceofthetechnologyhasbeen

confirmedbyearlyadopters,theadoptionrateincreasesrapidly

Thus, we can firmly statethat the imitation effect in a TypeII

cultureisgreaterthaninaTypeIculture

Finally,long-termorientation(LTO)alsoenhancestheimitation

effect.AsocietythathasahighLTOscoreemphasizesvaluessuch

aspersistence,buildingrelationships,thrift,loyaltyand

trustwor-thiness.Meanwhile,asocietythathaslowLTOemphasizesvalues

suchaspersonalsteadinessandstability.China,JapanandSouth

KorearepresentcountriesthathavehighLTOscores.Inhigh-LTO

cultures, traditions and commitments become impediments to

change;however,onceachangeissociallyaccepted,thespeedof

change is extremely fast Consequently, a high LTO culture is

related toa lowinnovation effectbuta highimitation effectof

diffusion We omitted power distance (PD) dimension because

mobilephoneisnotaproductthatispurchasedbasedontop-down

decisions

We summarize therelations of culturaldimensions, type of

cultures, and factors of diffusion in Fig 5: in Type I culture

innovationhasagreatereffectonadoptionthanitdoesinaTypeII

culture;inaTypeIIculturehowever,imitationhasagreatereffect

onadoptionthanitdoesinaTypeIculture

3 Developmentofhypotheses

In this section, we present the research concept used to

analyzetheadoptionofmobilephonesintwocountries,theU.S

andKorea.Wechosethesetwocountriesasourstudysamples

because they have similarities in the maturity of their ICT

adoption ande-commerce,buteachrepresentsa verydistinct

culturetype

3.1 TypesIculture(theU.S.)versusTypeIIculture(SouthKorea) The U.S and South Korea are with contrasting cultural dimensions: Korea’s Hofstede(1980)scoresarenearly opposite

tothoseoftheUnitedStatesacrossallfiveculturaldimensions.The UnitedStateshashigherindividualism(ID:score=91)andlower power distance (PD: score=40), UA (score=46) and LTO (score=29).By contrast, SouthKorea haslower ID(score=18) thanthoseoftheU.S.andtheworldaverage,whileithashigherPD (score=60),UA(score=85)andLTO(score=75)

Until1997,thepercentageofmobilephoneadoptionintheU.S washigherthanthatinSouthKorea(Fig.6).However,therehas beenanexplosioninmobilephoneadoptioninSouthKoreasince

1996leadingtoamobilephoneadoptionratemuchgreaterthan thatof theU.S since1998.We believethisrapidadoptionrate demonstrates a strongimitation effect reflecting cultural char-acteristicsofSouthKorea

Tovalidateourbelief,wefirstexaminedwhetherornotthere

is a significant pattern of mobile phone adoption in both countries To do this, our fitted model should reject the White-noise model The null hypothesis of this test is the White-noise model If the White-noise null hypothesis is rejected, we canmakethe assertion thatthereare innovation andimitationeffects.Ifthefittedmodel(theBassmodel)rejects the White-noise model, then we could also suggest that our proposed Bass model can explain the cultural aspects of technologyadoptioninvariousnations.TheBassmodelconsists

of theinnovation(external) effectandthe imitation(internal)

Fig 5 Cultural dimensions, culture types, and diffusion factors.

S.-G Lee et al / Journal of World Business 48 (2013) 20–29 24

effect It would be difficult to explain the different adoption

patternsin thetwocountriesviatheeffectsof innovationand

imitation unless the alternative model (the Bass nonlinear

estimationmodel) isaccepted

Hence,ourfirsthypothesisisasfollows

H1 Theadoptionrateofmobilephoneissignificantlyrelatedto

typesofcultures

Tofurtherinvestigatetheeffectofculture,wedevelopedtwo

other hypotheses.We suggest two typesof diffusionof mobile

phoneadoptionbasedonthetypesofculture.InaTypeIculture

(theU.S.),culturalvaluessuchasindividualism,weakuncertainty

avoidance,andshort-termorientationcouldenhancethe

innova-tion effect and moderate the imitation effect in technology

adoption(Fig.7).InaTypeIIculture(SouthKorea)culturalvalues

suchascollectivism,stronguncertaintyavoidance,andlong-term

orientationcouldenhancetheimitationeffectandmoderatethe

innovationeffectofadoption(Fig.8).Thenumbersinparentheses

are Hofstede’s scores Hence, the following hypotheses are

developed

H2a Theinnovationeffectonthemobilephoneadoptioninthe U.S.isgreaterthaninS.Korea

H2b Theimitationeffectonthemobilephoneadoptionisgreater

inS.KoreathanintheU.S

4 Researchmethodanddatacollection 4.1 Non-linearBassdiffusionmodel TheBassmodelisoneofseveraltypesofdiffusionmodels.The linearapproachoftheBassdiffusionmodelhascertain economet-ric limitations, such as multicollinearity and nonavailability of standard errors for the crucial parameters – p (coefficient of external influence), q (coefficient of internal influence), and m (numberofeventualadopters)(Mahajan,Muller,&Bass,1990).To overcomesuchlimitations,weadoptedthenonlinearleastsquares (NLS)approach(Venkatraman,Loh,&Koh,1994)

Toassessinnovationandimitationinfluencesover time,this studyutilizeda time-seriesanalysisusingSASsoftware.Weset constant m as the approximate real populations (potential

Type I: Individualism – Weak Uncertainty Avoidance – Short Term Orientation

Imitation Factors

Innovation Factors

Usefulness Ease of Use

Subjective Norm

Word of Mouth

Individualism (91)

Weak Uncertainty Avoidance (46)

Adoption

Short-term Orientation (29)

Fig 7 Adoption process of Type I (the U.S.) culture.

Type II: Collectivism – Strong Uncertainty Avoidance – Long Term Orientation

Imitation Factors

Innovation Factors

Usefulness Ease of Use

Subjective Norm

Word of Mouth

Collectivism (low individualism) (18)

Strong Uncertainty Avoidance (85)

Adoption

Long-term Orientation (75)

2004) The gradientdescent parameterestimation methodwas

used

In thehypothesis test,thenullhypothesis assumesthatthe

adoptionpatterncanbemodeledasaWhite-noiseorasarandom

walkprocess,i.e.,thedifferencesinthenoncumulativeadoption

time-seriesappeartoberandom.Thatis,

wherex(t)isthenumberofadoptersattimet,andtheresidualse(t)

haveazeromean

Ontheotherhand,ouralternativehypothesisistheBassmodel

asshowninEq.(1).UsingthederivationofMahajan,Sharma,and

Bettis(1988),theregressionanalogueisstatedas:

xðtÞ¼b1xðt1Þþb2Nðt1ÞþeðtÞ (5)

where b1¼1þqp, b1>1, b2¼q=m, b2<0, and

Nðt1Þ¼N2ðt1ÞN2ðt2Þ

This linear model has some weaknesses as stated above

Venkatramanetal.(1994)andSrinivasanandMason(1986)found

that the nonlinear least squares (NLS) estimation procedure

actually performs better and generates a more significant

estimated value than its ordinary least squares (OLS) and

maximum likelihood estimation (MLE) counterparts Therefore,

inourstudywechosetheNLSestimationprocedure

4.2 NonlinearestimationoftheBassdiffusionmodel

We applied the nonlinear estimate procedure used by

Venkatramanetal.(1994).Weemployedthefollowingfunctional

formwhichisalsoouralternativehypothesisforthefirsttest:

xðtÞ ¼ m 1expððpþqÞtÞ

1þðq=pÞexpððpþqÞtÞ

1expððpþqÞðt1ÞÞ

1þðq=pÞexpððpþqÞðt1ÞÞ





(6)

4.3 White-noisetest

TheWhite-noisetestwasusedtodeterminewhether ornot

there are innovation or imitation effects in the technology

adoptionprocess.Ifthenullhypothesisisrejected,thenwecan

saythatthereareinnovationorimitationeffects.Sinceweusea

nonlinear model as the alternative hypothesis, which is

non-nested, the use of the F-test is econometrically inappropriate

(Venkatraman et al., 1994) Therefore,we employ the J-test of

Davidson and MacKinnon (1981) We performed the following

regression:

xðtÞ¼ð1aÞfðtÞþagðtÞˆ þeðtÞ (7)

wheref(t)=x(t1)+e(t)isthenullWhite-noisemodel,g(t)isthe

predictedvalueunderanappropriatealternativemodelbasedon

themaximumlikelihoodestimation,aisaconstant,ande(t)isa

randomerrorthatisnormallyandindependentlydistributed,with

themeanvalueofzeroandaconstantvariance.Theeconometric

propertiesofestimationandinferenceusingtheaboveequation

enable us to test the alternative hypothesis by applying the

conventionalasymptotict-testforthenullhypothesis,andwith

a=0

4.4 Datacollection

TheverifiedofficialdataoftheITUWorldTelecommunication,

and theKoreanCommunications Commissionwereusedinthe

timeseriesstudy.Table3showstheverifiedofficialdataofthetwo

countries This data was adopted in the nonlinear estimation modelforHypotheses1and2

5 Results 5.1 Statisticalresults Thisfirsttestconductedwastoascertainwhetherornotthe out-fittedmodelsarerandomwalkprocesses.Table4summarizes theresultsofthehypothesestests.Allmodels(U.S.andS.Korea) rejectedtheWhite-noisemodel(p<0.05),indicatingthat there were innovation and imitation effects The F-values for model fitnesswerealsohighlysignificant(p<0.001).Thefittedmodels had highR2 values ranging from 0.7621 to 0.8999 All results supportedourfirsthypothesis

Figs.9and10describethefitnessofoursuggestedBassmodels TheBassfittedmodelfortheU.S.hasamoresimilargraphpattern (Fig.9 withagreaterR2value(0.8999)thanthatofSouthKorea (0.7621) However,theestimatedgraph forSouth Koreabythe BassmodelhasanS-shape(Fig.10).Thismeansthatthemobile phone adoption pattern of South Korea could also be well explained by DIT although the R2 (0.7621) of that model was smallerthanthatoftheU.S.model

ForHypothesis2,theinnovationeffectvaluep(0.000631)ofthe U.S.mobilephonemarketwasgreaterthanthatofSouthKorea (0.00119).Moreover,theimitationeffectvalueq(0.5328)ofSouth Korea wasgreater thanthat ofthe U.S (0.3337) Theseresults supportourprediction.Insum,sincetheBassmodelrejectedthe White-noisemodel,wecouldconcludethatcoefficientspandqof eachmodelcouldrepresentthecharacteristicsoftwodistinctive cultures

Table 4 NLS specifications for comparing the U.S with S Korea.

Country U.S mobile phone

subscribers (1985–2008)

S Korea mobile phone subscribers (1985–2008) Model Bass/Mansfield Bass/Mansfield Parameter estimation

p (innovation effect) 0.00119 0.000631

q (imitation effect) 0.3337 0.5328 Model fitness

F-value 56.95 *** 20.29 ***

R 2 0.7621 0.8999 White-noise testing

Null value a= 0 a= 0 Test statistic t = 2.65 **

t = 2.86 **

**

p < 0.05.

*** p < 0.01.

Fig 9 Comparison between the actual mobile phone adoption and the Bass fitted S.-G Lee et al / Journal of World Business 48 (2013) 20–29

26

6 Discussion

Thisstudyadoptedalongitudinalperspectivetothestudyof

mobilephoneadoptionintwocoutrieswithcontrastingcultures,

the U.S and South Korea Through the hypotheses testing, we

found that innovation and imitation effects are important

determinantsforeachcountry’smobilephonediffusion

Specifi-cally,theinnovationvaluepofaTypeIculture(i.e.,theU.S.)is

greater than that of a Type II culture (South Korea) and the

imitationvalueqofaTypeIIcultureisgreaterthanthatofaTypeI

culture

6.1 HigherinnovationeffectintheU.S

Theexternalmodelassumesnocommunicationbetweenearly

adopters and late adopters (Bass, 1969) The innovation effect

value p reflects technology acceptance that is formed by

self-perceptionandself-interestthroughindividuals’directexperience

aboutusefulnessandeaseofuse.Ifanindividualistrulyimpressed

bythesuperiorperformanceofanewinnovativetechnology,then

thisindividualwouldadoptthattechnology.Peoplein

individual-isticcultures(i.e.,TypeI)aremorelikelytoseekinformationon

theirownfromdirectandformalsources;theyviewthemselvesas

independentdecisionmakersandaresomewhatseparatedfrom

thesocialcontext(Kim,2008).Theseindividualisticcharacteristics

may increase the innovation effect on ICT adoption in Type I

culture

AsshowninFigs.6,9and10,thepercentageofmobilephone

adoptionintheinitialstage(from1985to1996)wasmuchgreater

intheU.S.thaninSouthKorea;however,during1997–2000,the

mobilephoneadoptionincreaseintheU.S.wasmuchslowerthan

inSouthKorea.ThesenumbersshowthatinTypeIculture,with

lowuncertaintyavoidanceandshort-termorientation,the

innova-tioneffectintechnologyadoptionishigher

6.2 HigherimitationeffectinSouthKorea

Theinternalmodelassumesthatthetechnologyadoptionrate

isdeterminedbytheinteractionbetweenearlyadoptersandfuture

adopters.Theimitationeffectvalueqreflectssocialinfluencessuch

as subjective norm (SN) and word ofmouth Specifically, for the

inexperienced users in Type II cultures, the effect of SN on

perceptions and behavior is likely to be greater (Karahanna &

Straub, 1999; Venkatesh & Davis, 2000) During the diffusion

process,mostpeopledonotevaluateaninnovationonthebasisof

self-assessment but rather ona subjectiveevaluation conveyed

fromother-like-mindedindividualswhohavealreadyadoptedthe

innovation(Rogers,2003).Peopleinthecollectivisticcultures(i.e.,

Type II) place a great importance on ‘we’ rather than on the

individual‘I’.InEastAsia,individualidentityisbasedonthesocial networktowhichonebelongs(Hofstede,1980).Koreansregard thesenseofbelongingnessasoneoftheircentralculturalvalues becauseoftheirConfucianroots(Lee,2003).InSouthKorea,the initialadoptionrateofmobilephonewasverylow.However,after

1997,whenthesocialperceptionofmobilephoneswasdeveloped rapidly,theadoptionratehasincreaseddramatically(Figs.6and

10).This adoption pattern shows the collectivistic behavior of SouthKoreans

TheEastAsianConfucianvaluesarealsocloselyrelatedtothe higherimitationeffectofmobilephoneadoption.Oneofthemain differencesbetweenshort-termandlong-termorientationsocieties

isthewillingnesstosubordinateoneselffora commonpurpose (Hofstede,1980).TheSouth Koreangovernmentencouragedits people to subscribe to mobile phone services by providing subsidies (now, mobile service providers KT, SKT and LGU are givingsubsidies).Keepingpacewithgovernmentalpolicy,people

inSouthKoreaareincreasinglyeagertosubscribetomobilephone services.Thus,notsimplycheapcosts,buttheConfucianvalueof commonpurposegoingbackintohistorymighthavecontributed

tothehigherrateofmobilephoneadoptionandITdevelopmentin SouthKorea

7 Conclusion 7.1 Researchcontributions Thisresearchprovidesseveraltheoreticalcontributions.These contributions should create a new fertile ground for future researchabouttheculturalimpactonICTadoption

(1)Thesample:untilnow,ITadoptionstudiesrelatedtoculture havemainlybeendonebasedonsmallsamplesofpopulation

Toanalyzetheeffectsofnationalcultureonthemobilephone adoption patterns, thisstudy used the entire population of mobilephonesubscribersof tworepresentativecountriesof TypeIandTypeIIculture,thusprovidingamoreobjectiveand accuratemeasurementofculturaleffectsonICTadoption (2)Methodology:ThisstudyisoneofthefewISstudies(according

toourknowledge)thatemployamathematicalmodelforits research

(3)Timeseriesdata:Thisresearchprovidesacompleteviewofthe entiremobilephoneadoptionprocess,allowingustoobserve thevariancesintheadoptionfactorsthroughouttheadoption phases (early, development, maturity) We delineated the adoptioncyclebasedonculturaltypesasshowninFig.11.In theinitialperiod,theadoptionrateinanindividualisticculture (TypeI)isgreaterthanthatofacollectivisticculturesociety (Type II); duringthe development period, a Type IIculture societyhasagreaterrateofadoption;andinthematuritystage, theslopeoftheadoptioncurveforTypeIIculturedecreases, whilethatofTypeIcultureismaintainedorslightlydeclined 7.2 Managerialrelevance

Today,organizationsoperateand compete inthenetworked globalmarket.Evenasmallturbulenceinonecorneroftheworld maycausehugeandunpredictablechangesinotherplaces.ICThas been widely adopted to manage dynamic global forces while consideringthediversityinculturesandmarkets.Thisstudyhas somepracticalinsightsthatarerelevanttomanagementstrategies forICTadoption,basedonnationalcultureandadoptionstages.For example, to penetrate in a Type I culture country (the U.S., Australia, Canada, Netherlands, etc.) with a new technology, organizationsshouldfocusonspecificinnovativefeaturesofthe technology orproduct, suchas perceived usefulness, perceived

Fig 10 Comparison between the actual mobile phone adoption and the Bass fitted

model for S Korea.

hand,inTypeIIculturalcountries(Korea,Brazil,Japan,Thailand,

etc.),introductionofanewtechnologyorproductrequiresstrategies

thatcouldinfluencethesocialdeterminantsbecausepeopleina

Type II culture depend on other like-mind peers’ evaluation

Therefore,inthesecollectivisticcountries,developing,participating,

andcloselyfollowingword-of-mouthtoolssuchasbulletinboards,

blogs,orsocialnetworksareoftheutmostimportancebecausethey

buildsocialandemotionalcuesforadoptions

Inthepast,product-focusedinnovationandstrategiesworked

to expand the customer base Today’s sophisticated and

well-informedcustomers want thetotalecosystemof anICT (e.g.,a

smart phone) Organizations must devise value-added service

innovations tomeet customers’needs.Such serviceinnovations

surrounding a technological productrepresent tacitknowledge

that would bedifficult to imitate by competitors (Chesbrough,

2011).Managementstrategiesthatcombine serviceinnovations

withculturaluniquenessofcustomersareessentialforeffective

technologyadoption

7.3 Limitationsandfutureresearchneeds

Thelimitationsofthisstudyareasfollows;first,weuseddata

fromonlytwocountries,onerepresentingTypeIandtheotherfor

TypeIIculture.Togeneralizethefindingsofourstudy,time-series

dataof manyothercountries wouldbeneeded.Second,mobile

phoneadoptionmaynotbethebestrepresentativeofICTs.The

findingsofthestudywouldbemorerobustandprovideaclearer

picture(perhaps,adifferentone)ifweuseotherICTdevicesand

thencomparetheresults.Third,ourstudyusedHofstede’sscores,

reported inhis1980work,asindices ofculturalvaluesof each

country,ratherthandirectlymeasuringeachconsumer’scultural

values today This could overlook the possibility of individual

differences,aswellasthefastchangingculturalvaluesofnations

due to globalization and technological advances Hofstede’s

culturalparadigm hasalsobeencriticized bysome researchers

(e.g.,McSweeney,2002).Lastly,otherfactorsforadoption,suchas

a country’s specific situation, government policies, market

competition,and economic development trend couldaffectthe

processofmobilephoneadoption.Thisstudydidnotincludesuch

externalfactors

Acknowledgment

Thisworkwassupported bytheSogangUniversityResearch

Grantof2012(201210007.01)

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