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Gender, culture and determinants of behaviouralintents to adopt mobile commerce among the YGeneration in transition economies: evidence fromKazakhstanKim-Choy Chunga

a Marketing, KIMEP University, 2 Abai Avenue, Almaty, 050010, KazakhstanAccepted author version posted online: 14 May 2013.Published online: 25 Jun 2013.

To cite this article: Kim-Choy Chung (2014) Gender, culture and determinants of behavioural intents to adopt mobilecommerce among the Y Generation in transition economies: evidence from Kazakhstan, Behaviour & Information Technology,33:7, 743-756, DOI: 10.1080/0144929X.2013.805243

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Behaviour & Information Technology, 2014Vol. 33, No. 7, 743–756, http://dx.doi.org/10.1080/0144929X.2013.805243

Gender, culture and determinants of behavioural intents to adopt mobile commerce among theY Generation in transition economies: evidence from Kazakhstan

Kim-Choy Chung∗

Marketing, KIMEP University, 2 Abai Avenue, Almaty 050010, Kazakhstan

(Received 14 February 2012; final version received 1 May 2013)

This study investigates perceived risk and trust in relationship to the Diffusion of Innovation Theory [Rogers, E.M., 1962.Diffusion of innovations. Glencoe, IL: The Free Press; 1983. Diffusion of innovations. 3rd ed. New York: The Free Press] from acultural perspective to understand the determinants of behavioural intent to adopt mobile commerce among the Y Generation inKazakhstan. Surveys from 345 university-level students and subsequent structural equation modelling revealed perceived risk,trustworthiness and Rogers’ five innovation characteristics are important determinants. Perceived risk and trustworthiness areimportant determinants because of the high uncertainty avoidance characteristics of the Kazakh society. This study advancestheory regarding culture- and generation-based characteristics to transition economies by verifying theoretical propositionregarding the behavioural intent towards mobile commerce adoption, resulting in a greater understanding of mobile commerceadoption among the Y Generation in transition economies. Marketing implications are discussed.

Keywords: perceived risk; trust; Diffusion of Innovation Theory; mobile commerce; Y generation; transition economies

1. IntroductionMobile commerce or M-commerce is any transaction,involving the transfer of ownership or rights to use goodsand services, which is initiated and/or completed usingmobile access to computer-mediated networks with the helpof an electronic device, such as a mobile phone, a per-sonal digital assistant or a smartphone (Tiwari and Buse2007). Due to its inherent characteristics such as ubiquity,personalisation and flexibility, mobile devices are attrac-tive media for directly communicating with consumers whohave become busier and more difficult to reach (Leek andChristodoulides 2009). However, there is a marginal useof mobile devices for transaction purposes in some mobiledevice services (Kleijnen et al. 2007). For instance, bar-rier to widespread adoption of mobile device for paymentsremained in the Europe (Sybase 2008), USA and Australia(Wessels and Drennan 2009) and elsewhere except Japan(Fitzpatrick 2009, Sadi and Noordin 2011). The phenome-nal success of the mobile commerce in Japan is attributedto cultural background (Fitzpatrick 2009) and to consumer-group-specific factors such as love for gadgetry (Baldi andThaung 2002).

1.1. Y GenerationThe Y Generation refers to a specific cohort of individu-als born, roughly, between 1980 and 1994 who are nowentering colleges and universities (McCrindle Research2008). Globally, they are the most educated generation

ever. For instance, almost half of all students in Australiago on to University and another quarter study at Tech-nical and Further education after completing Year 12(McCrindle Research 2008). Compared to their previousgeneration (X Generation), there are generally higher usageand familiarity with communications, media and digitaltechnologies in the Y Generation (Junco and Mastrodi-casa 2007). Recent multinational report by Mobiledirect(2010) indicates that Y Generation users of mobile phones(including Kazakhstani) prefer mobile Web to desktopaccess to Internet. This represents an ideal group to studythe moderating affect of culture on mobile commerceadoption.

1.2. Transition economiesA transition economy or transitional economy is an econ-omy which is changing from a centrally planned economyto a free market (Lowitzsch and Pacherova 1998), as foundin Vietnam, China and former Soviet Union Republics,including Kazakhstan. These former communist countrieshad experienced a phase of stark, totalitarian rule; and dis-trust in public institutions and anonymous people remainedstrong, providing one of the possible reasons for the disap-pointing performance of many transitions economy (BaafiAntwi 2010). The transition economy is often charac-terised by the changing and creating of institutions, andchanges in the role of the state, thereby, the creation offundamentally different governmental institutions; and the

∗Email: kimchung@kimep.kz

© 2013 Taylor & Francis

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promotion of private-owned enterprises, markets and inde-pendent financial institutions (Lowitzsch and Pacherova1998). In addition, the former Soviet Republics experiencedsubstantial income disparity within country (Sukiassyan2007). A study by infoDev (2007) indicates that informationand communications technology (ICT) use among firms inthe transition countries of Poland, Russia, Latvia, Lithua-nia and Estonia are primarily geared towards improvedproduction and transaction processes (procurements andmarketing activity), rather than the development of newor improved products. There is a dearth of study in under-standing the adoption of mobile commerce in the transitioneconomies, especially within the central Asian countries(ICT Marketing 2007).

1.3. KazakhstanKazakhstan, a central Asian Muslim-majority nation, hasa population of 16 millions people and an economy thatcentres on the exports of oil, base metals, chemicals andagriculture (CIA – The World Factbook 2009). Its Inter-net penetration rate is 34.3% with 5,300,000 Internet users(Internet World Stats 2010), while its mobile phone penetra-tion rate is 100% with 16 million subscribers (InternationalTelecommunication Union 2009).

1.4. CultureCulture has been shown to affect virtually every aspectof social life; and idea, product or innovation that rein-forces cultural norms or values are likely to have a bettereffect than those that are culturally foreign to a spe-cific group (McCort and Malhotra 1993). There are manyclassifications of culture, but Hofstede’s (1980) culturaldimensions of Individualism–collectivism, masculinity–femininity, power distance, uncertainty avoidance andlong-term orientation are much researched and validated(Søndergaard 1994). According to Hofstede, high mas-culinity suggests male domination in the society, whereashigh power distance suggests the continued propagation ofinequalities of power and wealth within the society. Highcollectivism indicates a closely knit, social framework, thatis built on trust and sharing between in-group members,with strong emphasis on group decisions and conformity.High uncertainty avoidance suggests a high level of uncer-tainty and ambiguity in the society as reflected in a highconcern for rules, regulations and controls. There is a lackof evidence to suggest that Kazakhstan is having Hofst-ede’s (1980) cultural dimensions of a Moslem nation (highmasculinity, high power distance, high collectivism), a gapaddressed by this study.

1.5. Current state of mobile commerce in KazakhstanGiven the high number of mobile subscribers (100%penetration rate), the relative youth of its population

(median age = 29.8) and its geographic vastness, onewould expect Kazakhstan to thrive on mobile commerceactivities. In contrast, there is a low level of mobile pay-ments for goods purchased via the mobile portals in Kaza-khstan (Halyk Bank Press Service 2009). The three mostfrequent mobile services usage in Kazakhstan are outgo-ing calls within the network, outgoing calls to the networkof other mobile operators and outgoing calls to fixed tele-phones within the same city as the caller (The Agency ofStatistics of Kazakhstan 2009). These observations con-tradicted the notion that the tech-savvy Y Generation isreceptive of new technological innovation like mobilecommerce (Freestone and Mitchell 2004).

This study investigates

(1) Hofstede’s (1980) cultural dimensions ofKazakhstan.

(2) The determinants of the behavioural intent to adoptmobile commerce among the Y Generation inKazakhstan.

(3) Whether culture has an influence on the determi-nants of the behavioural intent to adopt mobilecommerce among the Y Generation in Kazakhstan?

(4) Whether incomes and gender differences have animpact on the determinants of the behavioural intentto adopt mobile commerce among the Y Generationin Kazakhstan?

Mobile commerce adoption in this study refers to anytransaction, involving the transfer of ownership or rights touse goods and services initiated via a mobile phone/mobileInternet.

2. The rationales of this studyFirst, there is a gap of study in understanding the adoptionof mobile commerce in the transition economies, especiallywithin the central Asian countries (ICT Marketing 2007)from a cultural perspective. Recent study indicates thatmobile purchase intentions are significantly related to eth-nic identification (Becerra and Korgaonkar 2010) and socialinfluence (Campbell and Russo 2003, Sadi and Noordin2011). As mobile system is socially constructed percep-tions and uses of mobile telephony are negotiated throughsocial interaction (Campbell and Russo 2003) – patterns ofusage/adoption of mobile commerce are collective ratherthan an individual phenomenon (Stewart 2010) and areinfluenced more strongly by culture than they would be ifthey were truly individual-level behaviour. Second, exist-ing theory on technology adoption such as the Diffusion ofInnovation (DOI) Theory (Rogers 1962, 1983) was concep-tualised from the developed world (Song et al. 2008), wherethere is more socio-economic stability than in transitioneconomies; and is not empirically tested in the transitioneconomy. Being a former Soviet Union Republic, Kaza-khstan inherited a pernicious legacy of communism: a high

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level of mistrust in anonymous people and in public/privateinstitutions (Baafi Antwi 2010) that may affect mobile com-merce adoption. Third, results on the influence of genderon technology adoption have been mixed. For instance,Damanpour and Schneider (2006) indicate gender affectsinitiation, adoption decision and implementation phasesof the innovation adoption process. In contrast, Mazmanet al. (2009) indicate that females are more inclined thanmales in the usage of innovation because of their greatervulnerability to social influence.

Fourth, the state-of-the-art empirical methods: utilisingmultivariate analysis of variance and structural equationmodelling (SEM) of random survey data, this study estab-lishes the empirical link (which was lacking in prior studies)between trust, culture and behaviour in terms of technologyacceptance among the Y Generation. By integrating trust-worthiness and perceived risk with Rogers’ DOI (1983) fiveinnovation characteristics, with Hofstede’s (1980) culturaldimensions as the moderating factors, this study improveson the DOI explanatory power. Overall, this study advancestheory regarding culture- and generation-based charac-teristics to transition economies by verifying theoreticalproposition regarding the behavioural intent towards mobilecommerce adoption, resulting in a greater understandingof mobile commerce adoption among the Y Generation intransition economies.

3. DOI theoryAccording to the DOI theory (Rogers 1962, 1983), tech-nological innovation is communicated through particularchannels, among members of a social system over time,working through the following five stages:

(1) Knowledge: Exposure to the existence and under-standing of the functions of the innovation.

(2) Persuasion (the forming of a favourable atti-tude to the innovation): Here, the individual isinterested in the innovation and actively seeksinformation/detail about the innovation.

(3) Decision: In this stage, the individual weighs theadvantages/disadvantages of using the innovationand then decides whether to adopt or reject the inno-vation. Alternatively, innovation decisions may becollective (where a decision is reached by consen-sus among the members of a system), or authority-based (where a decision is imposed by anotherperson or organisation which possesses requisitepower, status or technical expertise).

(4) Implementation: During this stage, the individualdetermines the usefulness of the innovation andmay search for further information about it.

(5) Confirmation: In this stage, the individual finalisestheir decision to continue using the innovation andmay use the innovation to its fullest potential.

The DOI (Rogers 1962, 1983) also articulates thatthere are five perceived characteristics of innovation thatforms a favourable or unfavourable attitude towards theinnovation:

(1) Compatibility: The degree to which an innovationis perceived as consistent with the existing values,past experiences and the needs of potential adopters.An idea that is incompatible with the values andnorms of a social system will not be adopted asrapidly as an innovation that is compatible.

(2) Complexity: The degree to which an innovation isperceived as being relatively difficult to understandand use. New ideas that are simpler to under-stand are adopted more rapidly than innovationsthat require the adopter to develop new skills andunderstandings.

(3) Observability: The degree to which the use andbenefits of the innovation is visible to others, andtherefore act as a further stimulus to uptake byothers.

(4) Trialability: The degree to which an innovation maybe experimented with on a limited basis. An inno-vation that is trialable represents less uncertainty tothe individual who is considering it for adoption,who can learn by doing.

(5) Relative advantage: The greater the perceived rel-ative advantage of an innovation (than the one itsupersedes), the more rapid its rate of adoptionwill be.

4. HypothesesRogers’ DOI (1962, 1983) has been adopted and stud-ied within the context of mobile commerce. However, themajority of studies in this area adopted the perspectiveof studying the trimmed down perceived characteristicsof innovation based on Tornatzky and Klein’s (1982)meta-analysis research findings that recommend relativeadvantage, complexity and complexity as basis of studyin adoption decisions (Teo and Pok 2003, Wu and Wang2005). Adopting Tornatzky and Klein’s (1982) recommen-dation, Wu and Wang (2005) indicate that perceived relativeadvantage and compatibility influence favourable attitudetowards mobile commerce. Similarly, an empirical study byTanakinjal et al. (2010) suggests that relative advantage;compatibility, complexity and trialability of innovationdetermine user’s intention to adopt mobile marketing inMalaysia. The characteristic of observability of innovationwas not investigated in Tanakinjal et al. (2010) because ofthe subjective interpretations of the term in Rogers’ DOI(Tornatzky and Klein 1982). Lin (2010) provides furtherevidence that perceived relative advantage, ease of use andcompatibility influence attitude and behavioural intentionabout adopting mobile device for transaction purposes in

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Taiwan. However, a recent case study by Borg and Pers-son (2010) supported the relevancy of all five perceivedcharacteristics of innovation in Rogers’ DOI (1983) informing favourable attitude towards mobile transaction inSouth Africa. Correspondingly, the following hypothesesare proposed:

H1: Compatibility determines the behavioural intent toadopt mobile commerce among the Y Generation inKazakhstan.H2: Complexity determines the behavioural intent to adoptmobile commerce among the Y Generation in Kazakhstan.H3: Observability determines the behavioural intent toadopt mobile commerce among the Y Generation inKazakhstan.H4: Trialability determines the behavioural intent to adoptmobile commerce among the Y Generation in Kazakhstan.H5: Relative advantage determines the behavioural intentto adopt mobile commerce among the Y Generation inKazakhstan.

However, there are some limitations in the Rogers’ DOI(1962, 1983). In particular, it does not include privacy, secu-rity issues and social factors such as trust that may influenceusers’ attitude towards new innovation. Trust characterisesthe expectation that a party’s word or promise is reliableand that one party will fulfil its obligations in an exchangerelationship (Anderson and Narus 1990) and is importantbecause it helps consumers overcome perceptions of uncer-tainty and helps build appropriate favourable expectationsof performance and other desired benefits. Trust is an out-come of gradual and consistent effort over time, evolvingthrough the process of a growth of knowledge and under-standing of people with whom one must interact, plus theactual experience of interacting with that party (Ludwig2006). Trust is one of the major reason influencing peoples’decisions in giving their personal data via an electronic orvirtual medium (Siau and Shen 2003). This view is iden-tical to Leppäniemi et al. (2006), who suggest the needfor empirical investigations into the factors that affect con-sumers’ willingness to provide personal information andgrant permissions to use it in mobile marketing. Recentstudies indicate that trust influences intention to use mobilecommerce services in South Africa (Joubert and Van Belle2009) and Malaysia (Toh et al. 2009, Sadi and Noordin2011). Taking the perspective that trusts is a willing depen-dency on another’s action and that the outcome of trust is anevaluation of the congruence between expectations of thetrusted party and actions (Hupcey et al. 2001), this studyproposed:

H6: Trustworthiness determines the behavioural intentto adopt mobile commerce among the Y Generation inKazakhstan.

In addition, perceived risk is a necessary antecedent fortrust to be operative and an outcome of trust building is areduction in the perceived risk of the transaction or rela-tionship (Mitchell 1999). Perceived risk is an uncertainty

Perceived risk

Trustworthiness

Relative advantage

Compatibility

Complexity

Trialability

Behaviouralintent to adopt

mobilecommerce

Observability

Hofstede culturaldimension

UncertaintyAvoidance

Collectivism

Power distance

Masculinity

Figure 1. Hypothesised research model of the behavioural intentto adopt mobile commerce among the Y Generation in Kazakhstan.

regarding the possible negative consequences of using aproduct or service and is a combination of uncertaintywith the possibility of serious of outcome (Bauer et al.2005). Perceived risk is affected by individual, situationalor cultural factors (Weber and Hsee 1998). Consumers areconcerned about purchasing a product from virtual environ-ment without physically examining the products or sendingtheir personal information through the electronic medium(Toh et al. 2009). Therefore, it is presumed that peoplemay feel a certain degree of risk or uncertainty when pur-chasing a product through the mobile medium. Toh et al.(2009), Joubert and Van Belle (2009) and Tanakinjal et al.(2010) view perceived risk as influential determinant ofbehavioural intention to adopt mobile commerce. Further,it has been suggested that trust and perceived risk variesacross nationalities and cultures (Jarvenpaa and Tractinsky1999). Consumers coming from an individualistic culturehave a greater tolerance for risk than those from a collec-tivistic culture (Jarvenpaa and Tractinsky 1999) and thatpeople from a high uncertainty avoidance culture would beless risk-taking because they are motivated by a fear of fail-ure or loss (Bontempo et al. 1997). Prior studies (Geissler2006, Srite et al. 2008) indicate the possible link betweenculture and behaviour in terms of technology acceptance.Consequently, the following hypotheses are proposed:

H7: Perceived risk determines the behavioural intentto adopt mobile commerce among the Y Generation inKazakhstan.H8: Culture has an impact on the determinants of thebehavioural intent to adopt mobile commerce among theY Generation in Kazakhstan.

The above eight hypotheses are summarised in a con-ceptual as shown in Figure 1.

5. Research methodology5.1. Questionnaire and samplingsSeven-point Likert scale was used in the questionnaire(self-completed), namely 1 being least agreeable to 7 forbeing most agreeable. The measures used in this study wereadapted from the following scales:

(1) Behavioural intention scale (3 items): Nysveenet al. (2005).

(2) Trustworthiness (4 items): Hupcey et al. (2001).

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(3) Perceived risk (4 items): Bauer et al. (2005).(4) Rogers’ (1983) five perceived characteristics of

innovation (24 items).(5) Hofstede (1980) cultural scales (13 items): Yoo and

Donthu (2002).

In addition, the questionnaire contained the follow-ing demographics: age, gender, cities, monthly stipend,monthly family income, number of years using the Inter-net and mobile phone, including for short message service(SMS). A professional translator translated all the (English)scale items into Russian and a different translator didthe back translation. A group of five bilingual academics(English and Russian) then discussed the inconsistencies inthe translation and revised the translated items to avoid anymisunderstanding. The translated questionnaire was thenpre-tested with a group of 30 students and internal con-sistency was found on all measures (Cronbach alpha >.6).The targeted samples were university-level students (the YGeneration group) to address the critique of in-group differ-ences found in Hofstede’s (1980) cultural dimensions andthe moderating effect of consumer-group-specific factors.A total of 670 questionnaires were randomly distributedin three private institutions of higher learning in Almatyand Astana. Almaty and Astana are the two largest citiesin Kazakhstan in terms of population. The return rate was56.72% (380 questionnaires), but only 345 questionnaireswere valid (complete) for further analysis.

5.2. Statistical analysisAnalysis of the surveys consisted of four phases. Phase 1consisted of a check for data normality and outliers. His-tograms of data distribution showed reasonable normaldistributions (bell-shaped curve) for all measurement vari-ables. The scatter plots revealed univariate normality ofdata, while the box plot revealed some outliers but wereretained in the data-set, after comparing the 5% trimmedmean value and the original mean value, found no signif-icant differences. Out of the 345 questionnaires returned,233 were from Almaty and 112 from Astana. All respon-dents have access to the Internet and more than two years ofexperience using the mobile phones for sending/receivingSMS. The respondents were equally distributed across gen-der. Slightly, half of them reported a monthly stipend ofmore than US$200 and a monthly family income of morethan US$3000. The overall mean age of the respondent is22.3 years.

Receiving calls (mean = 6.51 out of max 7), SMS(mean = 6.13) and callings (mean = 5.99) are the mainmobile phone usage among the respondents (Table 1),reflecting the survey results by The Agency of Statis-tics of Kazakhstan (2009). Playing games (mean = 5.19)and downloading ringing tones via SMS/Internet por-tals (mean = 5.18) are also popular. While respondents

showed positive behavioural intent towards mobile com-merce adoption (overall mean = 5.22), in reality, there islimited usage of the mobile phone for payments/purchases(mean = 3.22), watching movies (mean = 1.86) and bank-ing (mean = 1.10), contrary to the assertion that the YGeneration is easily receptive to new technological inno-vation. This observation affirmed that one’s intent may nottranslate into actual action, possibly because of the lacked ofexplicit motivational content needed to induce an intentionto act (Bagozzi 2006) or they are concerned with certainissues (possibly perceived risk/trust) that holds them backto act on their intention.

Phase 2 analysed on the cultural orientations ofthe respondents; and whether mobile phone usage,income and gender differences have moderating effects onthe behavioural intention and on the determinants of thebehavioural intent to adopt mobile commerce among theY Generation in Kazakhstan. The respondents showed highuncertainty characteristics, power distance and collectivism(Table 2). The multivariate analysis of variance showed nosignificant difference in all innovation characteristic mea-surement variables in terms of gender, monthly stipendand monthly family incomes at the 5% significant level(p >.05, two-tailed test). Even though there is a limitednumber of Gen X in this study (4% of respondents areaged >40), multivariate analysis of variance showed nosignificant difference between those age <40 and >40 interms of the behavioural intent to adopt mobile commerce.However, there are significant differences on the impactof mobile phone usage on the behavioural intent to adoptmobile commerce (Wilk’s λ = .752, F = 1.831, p = .030)at the 5% significant level. Those using mobile phonesfor playing games (mean = 5.78) and downloading ringingtones via SMS/Internet portal (mean = 5.45) showed morefavourable attitude towards mobile commerce adoption.

To validate the conceptual model, a series of exploratoryfactor analyses (EFAs) (Phase 3) and confirmatory factoranalyses (Phase 4) were conducted to test the reliability,and convergent and discriminant validities of the mea-sures (Anderson and Gerbing 1988). All the measurementvariables in the study (except Hofstede’s cultural dimen-sion scales as they are well validated) were subjectedto an EFA using Statistical Package for Social Science(principal component analysis (PCA), varimax rotationtechniques). Varimax rotation was chosen because of itsease of interpretation (Tabachnick and Fidell 1996). ForVarimax, a simple solution means that each factor (com-ponent) has a small number of large loadings and a largenumber of small loadings. Tabachnick and Fidell (1996)recommended a correlation coefficient of 0.3, a Kaiser–Meyer–Olkin (KMO) index of >0.6 and Bartlett’s Testof Sphericity p <.05 as appropriate for the factor analy-sis. The test for performing PCA revealed the presence ofmany coefficients above 0.3 in the correlation matrix andall measurement variables have initial communalities valuegreater than 0.6. The KMO value for sampling adequacy

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Table 1. Descriptive profiles of respondents.

Variables (n = 345) Mean SD

Number of years using the Internet 3.05 0.101Number of years using the mobile phone, including for SMS 3.29 0.788Mobile phone usage

(>2 times perweek)

Receiving calls 6.51 0.51

Callings 5.99 0.33SMS for communication 6.13 0.79Play games 5.19 1.28Download ringing tones via SMS/Internet portal 5.18 0.83Watching movies (including MMS) 1.86 1.34Mobile Internet (read news, email) 4.17 1.32For calculations 4.84 0.89Payment/purchases (including for car parking) 3.22 1.30Mobile banking 1.10 1.15

Behavioural intent towards mobile commerce adoption (overall mean = 5.22)I intend to use my mobile phone to get relevant marketing messages in the near future 5.43 0.98I intend to use my mobile phone for product/service purchases in the near future 5.12 1.32I intend to adopt mobile commerce in the near future 5.11 1.29

%Age (overall mean = 22.3)< 40 years 96> 40 years 4IncomesMonthly stipend< 1500 Tenge (US$100) 8.31500–3000 Tenge (between US$100 and US$200) 46.1> 3000 Tenge (US$200) 54.4Monthly family income< 150, 000 Tenge (US$1000) 5.9150,000–450,000 Tenge (between US$1000 and US$3000) 42.0> 450, 000 Tenge (US$3000) 52.1

Note: Bold values indicate the top five main usage of mobile phone.

was 0.859, and Bartlett’s Test of Sphericity was significant(Sig = 0.0), supporting the factorability of the correlationmatrix. The EFA procedures extracted eight factors, repre-senting 73.18% of the total variance explained. Variablesin five extracted factors correspond to the five perceivedinnovation characteristics as suggested by Rogers’ (1983)DOI. All items (except two items related to complexity con-struct) showed loading higher than 0.7 on their respectiveconstruct, providing evidence of acceptable item conver-gence on the intended constructs, while the average varianceextracted (AVE) for all extracted eight factors was above therecommended threshold of 0.5 for satisfactory convergentvalidity (Hair et al. 2010). Further, all extracted factors hadcomposite reliability coefficient (CR) exceeding the cut-offvalue of 0.6 for internal consistency (Hair et al. 2010). Dis-criminant validity of all extracted factors was demonstratedsince the AVE for each factor was greater than the squareof the construct’s correlations with the other factors in theconceptual model (Anderson and Gerbing 1988). The AVE,reliability test results, factor loadings and communalities ofmeasurement variables are presented in Table 3.

In Phase 4, Anderson and Gerbing’s (1988) two-stepSEM procedures using AMOS 7.0 was used as a further

validity test of the measurement model and structural modeltest for hypothesis testing. All confirmatory factor analy-sis estimation processes for individual measurement modelshowed sufficient evidence of goodness of fit betweenthe measuring model and the sample data (comparative-fitindex (CFI) >.95 and goodness-of-fit index (GFI) >.95).However, initial structural test (full model) did not pro-duce good fit statistics (CFI = .890, GFI = .887). Sub-sequent analysis of the modification indices of the fullmodel resulted in a modified conceptual model as shownin Figure 2. Its normed chi-square (χ2/df or CMIN/DF) of2.30 with 427 degrees of freedom (DF) is within the accept-able range of good fit of <1.0 χ2/df <3.0 at α = 0.05 levelas suggested by Holmes-Smith (2000), while its root meansquare error of approximation of 0.047 is less than Hairet al.’s (2010) recommended values of <0.05 for good fit.Its CFI and GFI are 0.927 and 0.905, respectively, indicatingadequate fit between the sample data and the modified con-ceptual model. The lower expected cross-validation index(ECVI = 1.209) of the modified conceptual model com-pared to the original conceptual model (ECVI = 1.606)signalled the former, representing the best fit to the dataoverall. The improvement in model fit for the modified

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Table 2. Multivariate statistics of Hofstede’s (1980) cultural dimensions in terms of gender among thesampling population in Kazakhstan.

Test of between-subjects effect

Male Female

Cultural scale items (code) Mean SD Levene’s p

Masculinity (overall mean = 4.77, Wilks’ λ = .968,p = .036∗)

.189 .587

Solving difficult problems usually requires an activeforcible approach, which is typical of men (M1)

5.34 1.09

There are some jobs that a man can always do betterthan a woman (M2)

4.75 1.11

Men usually solve problems better than women (M3) 4.22∗ 1.35 5.10∗ 3.35∗Collectivism (overall mean = 5.39, Wilks’ λ = .978,

p = .077)Individuals should sacrifice self-interest for the group

that they belong to (C1)5.75 0.94

Group loyalty should be encouraged even if individualgoals suffer (C2)

5.69 1.22

Group welfare is more important than individualrewards (C3)

5.21 1.29

Individuals should stick with the group even throughdifficulties (C4)

4.92 1.03

Uncertainty avoidance (overall mean = 5.85, Wilks’λ = .999, p = .894)

Rules/regulations are important because they informme of what is expected of me (U1)

5.92 0.89

It is important to have instructions spelled out indetail so that I always know what I’m expected todo (U2)

5.85 0.92

It is important to closely follow instructions andprocedures (U3)

5.79 1.06

Power distance (overall mean = 5.48, Wilks’λ = .999, p = .927)

People in lower positions should not disagree withdecisions made by people in higher positions (P3)

5.61 0.84

People in higher positions should make mostdecisions without consulting people in lowerpositions (P1)

5.41 0.98

People in higher positions should not ask the opinionsof people in lower positions too frequently (P2)

5.42 1.14

∗Significant at p = .05, all mean scores out of maximum 7.

conceptual model as compared to the original conceptualmodel appeared significant (�χ2

(1) = 103.52, p = .026).As such, the modified conceptual model provides the basisfor hypothesis testing in this study. The SEM (standard-ised path coefficients) and reliability test statistics of themodified conceptual model are presented in Figure 2.

6. Findings and discussionAs shown in Figure 2, there was sufficient evidence tosupport all hypotheses in this study. Hypothesis 1 gener-ated a regression weight of .26. Respondents in this studyindicated that information search via mobile devices ascompatible with information search through the Internet(mean = 5.37) and that the usage of mobile devices forproduct and service information fit their information gather-ing style (mean = 5.56), affecting their behavioural intent to

adopt mobile commerce (overall mean = 5.22). This resultis consistent with the technology adoption literature (Wuand Wang 2005, Borg and Persson 2010) that compatibilityof new innovation with user’s lifestyle is one of the majordeterminants of the behavioural intent towards the adoptionof new innovation.

Hypothesis 2 generated the lowest regression weight of.19 among the various determinants, possibly a reflection ofthe tech-savvy characteristic of the Y Generation. The keyindicators in this aspect are ‘Learning to use mobile com-merce would be easy for me’ (mean = 5.41); ‘If I were toadopt mobile commerce, it would be easy for me to adapt’(mean = 5.26); and ‘If I were to adopt mobile commerce, itwould be easy due to my previous experience with mobilephone usage’ (mean = 5.43). With reference to the descrip-tive statistics, this result can be interpreted as respondentsindicating that it would be easy for them to learn and adapt to

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Table 3. Reliability test statistics and factor analysis component scores.

Factor scale AVE CR Item (code) Loading R2

Perceived risk .61 .861 I think mobile commerce will not put my privacy at risk(Risk1)

.848 .748

There is no more privacy risk involved in receivingmarketing messages via mobile phone than thereis when getting marketing messages via email/TVadvertisement (Risk3)

.828 .795

I feel the current regulations on mobile communication inKazakhstan minimize my privacy risks (Risk2)

.801 .712

I think it is safe to do a transaction/purchase via the mobilephone (Risk4)

.729 .651

Trustworthiness .60 .847 Provided that my permission is given, I consider mobilecommerce as a trustworthy source of personalisedmarketing messages (T3)

.843 .785

Mobile commerce is a trustworthy source of information(T2)

.826 .757

Mobile commerce is reliable because mobile marketingmessages are up-to-date (T4)

.774 .715

I consider mobile commerce as a reliable way to receiverelevant information (T1)

.631 .610

Observability .50 .726 People using mobile marketing services performed bettercommunicating to their customers than those doingbusiness the traditional ways (OB3)

.784 .629

There are many mobile services that I can use (OB1) .758 .636Many people have started using mobile commerce (OB2) .752 .626People using mobile commerce are better informed than

those using the TV, newspaper and magazines about theproduct/service they intended to purchase (OB4)

.651 .617

Relative advantage .57 .793 If I were to adopt mobile commerce, the quality of myinformation would improve (RA2)

.849 .701

If I were to adopt mobile commerce, it would enhance myeffectiveness on information gathering (RA3)

.805 .765

If I were to adopt mobile commerce, it would enable me toget product/service information more quickly (RA1)

.781 .656

Compatibility .72 .885 If I were to adopt mobile commerce, it would fit my productand services information gathering style (COM2)

.846 .865

If I were to adopt mobile commerce, it would fit well withthe way I like to seek relevant product and servicesinformation (COM3)

.793 .815

If I were to adopt mobile commerce, it would be compatiblewith my Internet searching methods (COM1)

.783 .782

Complexity .76 .899 Learning to use mobile commerce would be easy for me(CPLX1)

.867 .844

If I were to adopt mobile commerce, it would be easy forme to adapt (CPLX2)

.854 .878

If I were to adopt mobile commerce, it would be easy dueto my previous experience with mobile phone usage(CPLX3)

.784 .786

Trialability .70 .869 Before deciding on whether or not to adopt mobilecommerce, I would be able to use it on a trial basis(TRY1)

.836 .788

Before deciding on whether or not to adopt mobilecommerce, I would be able to test the suitability of theservices (TRY2)

.830 .834

I would be permitted to use mobile commerce on a trialbasis long enough to see what it can do (TRY3)

.829 .780

Behavioural intent .63 .791 I intend to use my mobile phone to get relevant marketingmessages in the near future (B1)

.843 .698

I intend to use my mobile phone for product/servicepurchases in the near future (B2)

.812 .721

I intend to adopt mobile commerce in the near future (B3) .793 .717

Note: R2 = square multiple correlation (communalities).

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Mean Std dev3.33 .9743.19 .905

n there is when getting 3.25 .7825.12 .822

use it on a trial basis. 5.13 1.5295.20 1.450

at it can do. 5.19 1.5813.91 .7513.41 .804

g business the 3.51 .997

3.34 .9704.92 .9244.84 .915

commerce as a trustworthy source of personalised marketing messages 6.28 .937-to-date. 5.12 .844

5.41 1.4935.26 1.3845.43 1.4415.37 1.1065.56 1.0735.29 1.2615.56 1.3904.91 1.380

veness on information gathering. 4.96 1.2965.43 .9805.12 1.3215.11 1.290

x

Figure 2. Descriptive and SEM statistics of the modified conceptual model.

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mobile commerce because of their experience with mobilephone usage (all respondents in this study indicated thatthey have more than two years of mobile phone usage forSMS), thus affecting their favourable intent towards mobilecommerce.

There was sufficient evidence to support Hypothe-sis 3 (regression weight = .27). Hypothesis 4 generated asignificant regression weight of .27, suggesting that thebehavioural intent to adopt mobile commerce among theGeneration Y in Kazakhstan is contingent on their pre-adoption ability to use it on a trial basis (mean = 5.13),to test the suitability of mobile services (mean = 5.20)and the possibility to use mobile commerce on a trialbasis longs enough to see what it can do (mean = 5.19).There was also significant support for Hypothesis 5 (regres-sion weight = .23). Respondents in this study indicatedthat adopting mobile commerce would enable them toget product or service information more quickly (mean =5.56), the quality of information would improve (mean =4.91) and that it would enhance their effectiveness oninformation gathering (mean = 4.96), supporting Wu andWang’s (2005) finding that relative advantage affects thebehavioural intent towards mobile commerce.

Hypothesis 6 also generated a significant regressionweight of .25. Main indicators in this aspect are ‘Mobilecommerce is reliable because mobile marketing messagesare up-to-date’ (mean = 5.12); ‘Mobile commerce is atrustworthy source of information’ (mean = 4.84): ‘I con-sider mobile commerce as a reliable way to receive rele-vant information’ (mean = 4.92); and ‘Provided that mypermission is given, I consider mobile commerce as atrustworthy source of personalised marketing messages’(mean = 6.28). In particular, the later is the highest scoredvariable in this study, indicating that permission for person-alised marketing messages is important to imbue trust andto avoid the risk of personal privacy violation in mobilemarketing services. Alternatively, its high score could beexplained that users’ experiences with Internet service makethem aware of the existence of potential risk in virtualtransaction (all respondents have the Internet access) andthus, they have a better understanding within the mobilecommerce context.

Hypothesis 7 had the highest regression weight of.28 among the various determinants in this study. Whilerespondents in this study agreed that it is safe to do atransaction/purchase via the mobile phone (mean = 5.12),they have the tendencies to disagree that mobile com-merce will not put their privacy at risk (mean = 3.33),that there is no more privacy risk involved in receivingmarketing messages via mobile phone than there is whenobtaining marketing messages via email or TV advertise-ment (mean = 3.25), and that the current regulations onmobile communication in Kazakhstan minimise their pri-vacy risks (mean = 3.19), findings that reflected the highlymonitored, surveillance and over regulated ICT sector inthe country.

As for the cultural dimensions, collectivism (regressionweight = .27), power distance (regression weight = .20)and uncertainty avoidance (regression weight = .25) hadmoderating effects on the behavioural intent to adoptmobile commerce among the respondents. In partic-ular, collectivism had a moderating effect on com-patibility (regression weight = .46) and observability(regression weight = .38), while power distance had amoderating effect on observability of mobile com-merce (regression weight = .20). Uncertainty avoidancewas strongly correlated to both trustworthiness (p = .59)and perceived risk (p = .39). In addition, both trustworthi-ness and perceived risk were correlated (p = .29). Thus,Hypothesis 8 was supported.

As with any process or state, cultural patterns neces-sarily have their underlying causes. The demise of thecommand economy and the rapid introduction of capi-talistic principles following the fall of the Soviet Unionand independence represented a rapid and traumatic shiftin the prevailing economic system with little prepara-tion, creating a jarring experience for Kazakhstan (Brown1998). The uncommonly strong governmental interferencein markets and related activities of participants, the weakprivate-ownership rights, heavy bureaucracy and constantlychanging legislation in this transition economy further com-pounded this uncertainty. Having learned to accept govern-ment edicts to resolve economic matters and century-longsubjugation by the Russian politics during the Soviet Union(Brown 1998) and increasing autocratic behaviour of itsgovernment post Soviet Union, the appetite for uncertaintyavoidance in the Kazakhs society is further propagated. Thisinherent strong uncertainty avoidance characteristic of theKazakhs society, with the highly monitored and surveillanceICT sector in Kazakhstan may transmit into risk aversionbehaviour (trialability compatibility & observability) withregards to new innovation such as mobile commerce.

In addition, studies by Branca (2008) and Mazman et al.(2009) showed a positive relationship of gender to innova-tion adoption. Yet in this study, the effect of gender wasfound to be not significant within the context of mobilecommerce. One possible explanation for this finding couldbe that gender equality, in terms of education (100% liter-acy rate, CIA – The World Factbook 2009) and economicactivities existed in Kazakhstan. While the Kazakhs societyis patriarchal, women played a central role in daily eco-nomic life and had a greater autonomy than their sisters inneighbouring sedentary societies (Barfield 1993).

7. Marketing implicationsThe high penetration rate of mobile telecommunication andthe strong empirical evidence in support of all hypothesesin this study suggested the viability of mobile commerce inKazakhstan. While this study showed moderate support forobservability as the determinant of the behavioural intenttowards mobile commerce adoption (possibly influenced

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by the infancy stage of mobile commerce in Kazakhstan),marketers need to recognise that adoption decisions arefrequently influenced by peers or social circle to seek legit-imacy (McCort and Malhotra 1993). This is especially soin the collectivist society of Kazakhstan (Table 2) as collec-tivist cultures tend to be others-oriented with a high need forconformity (Hofstede 1980). Study by Abrahamson (1996)shows that choice about whether to adopt or not an innova-tion can relate to the existence of fads and fashion amongmember of a social network. With this regards, mobile ser-vice providers need to enlist more retailers and firms tosupport the use of mobile devices for customer transac-tions, to increase the observability of mobile commerce tostimulus uptake by others (Rogers 1983).

Further, perceived risk was the main determinant of thebehavioural intent towards the adoption of mobile com-merce in Kazakhstan (highest regression weight). Marketersneed to be aware that security is always the concern of usersinvolving in monetary transaction and personal data trans-mission in the virtual setting. A physical flagship store ordistribution centre in Kazakhstan may allay customer fearsof transacting virtually and to project observability and toreduce perceived risk. A short trial using mobile device forconsumption transaction may positively affect user’s atti-tude towards the practicality, convenience and perceptionof risk in mobile commerce. Besides, the learning processundergone during the trial would serve as a test of compat-ibility of innovation with user’s personal and work-relatedactivities. The compatibility issue in personal and work-related activities is important because it deals with theirperception on the importance of the innovation to vari-ous tasks to be performed at present and future. If mobilecommerce is compatible with user lifestyle (informationgathering search) and the traditional way of performing var-ious activities in their work environment, higher adoptionrate of mobile commerce would ensure. Evidence in sup-port of this suggestion indicated that the extent to whichconsumers believe that mobile banking can be integratedinto their daily routine positively influences their intentionto use mobile banking (Wessels and Drennan 2009). Mobileservice providers should improve their compatibility withuser requirements, past experience, lifestyle and beliefs tofulfil customer’s expectations.

In addition, the significant of trustworthiness as thedeterminant of the behavioural intent to adopt mobile com-merce in this study suggested that in order for widespreadadoption of mobile commerce to occur, the element of trustmust be addressed by mobile service providers. This isespecially so with regards to permission for personalisedmarketing messages, which had the highest score amongthe measurement variables in this study. Unsolicited SMSmessages and other mobile spam-like mobile advertise-ments raise privacy concerns relating to the utilisation ofthe personal data for personalised mobile marketing mes-sages (Carroll et al. 2007). One solution to address thisconcern is to employ permission-based mobile marketing

so that mobile commerce users are in control of the typesand volume of marketing messages they receive throughtheir mobile phones. It also has the advantage of mobile ser-vice consumers getting relevant information to their needs.Getting consumers’ permission before sending the person-alised marketing messages can also reduce concerns aboutprivacy violation, thus developing trust in the mobile ser-vice providers. Consumers’ belief in their own capability ofgranting permission and the unique personal characteristicsof the mobile phone helps to foster confidence in their abilityto take the initiative in adopting mobile commerce. Per-sonalised marketing messages can be seen as a strategy toreduce clutter and search costs for consumers and improvingmarket-targeting precision for marketers (Krishnamurthy2001). The opportunity to reach customers in a more per-sonal way is even more critical given the increasinglycompetition for customers in the marketplace, diminish-ing consumer loyalties, reduced faith in mass marketingamid incidence of audience and media fragmentation ascommunications channels proliferated (Luxton et al. 2009).However, excessive messages are associated with negativeattitudes towards mobile marketing, and no more than threemarketing messages a day should be considered (Barwiseand Strong 2002).

The significance of perceived risk, trustworthiness andrelative advantage as determinants of the behavioural inten-tion towards the adoption of mobile commerce impliesthat mobile commerce providers in Kazakhstan shouldemphasise security factors in their advertisement to alle-viate the uncertainty or anxiety of potential adoptee, whileconsistently informing consumers about the advantages ofmobile commerce, such as convenience, ubiquity, flexibil-ity and personalisation of messages. Further, the mobiletelecommunications market is socially constructed. Thereare three principal actors involved in the mobile telecom-munications market: providers, institutions (government,public authorities and trade association) and the users oradopters. The adopter characteristics and of those promot-ing the innovation and the communication channel thatthey use affect the likelihood that an innovation will beadopted. As opinion leaders are important in persuadingothers to adopt, and that interpersonal and local channelsare relatively important at the persuasion stage (Rogers1983), marketers should enlist opinion leaders and pub-lic figures for word-of-mouth communication, as who saywhat are more important than what is being said in collec-tivist society (Hall 1976) as in Kazakhstan. This impliesthat marketing strategist in Kazakhstan should seek toproduce both peer and opinion leader impact. Emphasison early adopters and peers who have adopted mobilecommerce would enhance the advertising dollars. This isbecause early adopters are often opinion leaders, serve asrole models for many other members of the social systemand are instrumental in getting an innovation to the pointof critical mass, and hence, in the successful diffusion ofan innovation (Rogers 1983). Critical mass refers to the

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point at which enough individuals have adopted an innova-tion that the innovation’s further rate of adoption becomesself-sustaining.

Potential mobile commerce in Kazakhstan includesmobile ticketing, mobile marketing and mobile banking.Mobile banking can come handy in sparsely populatedcountry like Kazakhstan where often people in rural orremote areas have to travel long distance to the nearest bankin the towns or city. Mobile ticketing and mobile market-ing can be via SMS and MMS (utilising timed slideshowof images, text, audio and video), where smartphones areincreasingly visible among the Y generation in the Kaza-khstan. To increase the potentiality of mobile marketingamong the Y generation, mobile service marketers shouldemphasise more on hedonic needs because a mobile phoneis not only a fashion item for young consumers, but alsoserves as a multi-purpose device, for example for down-loading videos and music, surfing the Internet and takingpictures (Grant 2007).

8. Research limitationsThe limitations of this paper are as follows: First, it has notyet explored other potential factors (technology enabler,network aggregator, content provider and wireless opera-tors) that may influence users’ intention decision towardsthe adoption of mobile commerce. Second, the result ofthis study is limited by the geographic and socio-economicgroup represented by the sample, limiting the generalisationof this study finding to the Y Generation in Kazakhstan. Sur-veys are taken from only two cities in Kazakhstan and fromprivate institutions of higher learning where students havefamily incomes higher than the national average.

9. Contribution of researchGiven the paucity of research conducted in transitionaleconomies, this study represents a novel study that applieselements of Rogers’ DOI conceptualisations to Kazakhconsumers relative to mobile commerce. Further, the rela-tionships between perceived risk, trust and Rogers’ fiveinnovation characteristics have not been empirically estab-lished in the transition economy where there is a hugemistrust in anonymous people and in public/private institu-tions. By integrating trustworthiness and perceived risk withRogers’ DOI (1980) five innovation characteristics, withHofstede’s (1980) cultural dimensions as the moderatingfactors, this study improves on the DOI explanatory power,resulting in a greater understanding on the behaviouralintent to adopt mobile commerce among the Y Generationin transition economies. On the practical side, this studymight be of a genuine value, especially to practitionerswho work in the global telecommunications and marketingareas.

10. ConclusionIn achieving the empirical validation of the research model(using random survey data and subsequent SEM), this studysupported the hypotheses that perceived risk, trustwor-thiness and Rogers’ (1983) five perceived characteristicsof innovations: namely compatibility, complexity, observ-ability, trialability and relative advantage determine thebehavioural intent to adopt mobile commerce among theY Generation in Kazakhstan. High uncertainty avoidance,high power distance, masculinity and high collectivismare prevalent among the respondents in this study. Bothtrustworthiness and perceived risk are strongly correlatedto uncertainty avoidance, suggesting that these two deter-minants are significant because of the high uncertaintyavoidance characteristics of the Kazakh society, a com-monality among transition economies. The validated modelalso revealed that collectivism had a moderating effect oncompatibility and observability, while power distance hada moderating effect on observability of mobile commerce.Multivariate analysis of variance indicated that gender hasno significant affect on adoption intention. Implications formarketers and mobile service providers are discussed. Inparticular, the issue of risk (security in monetary transac-tion), privacy and permission-based marketing are impor-tant factors to consider due to the virtual and personalnature of mobile commerce. The significance of trustwor-thiness and high collectivism (especially conformity), inthis study, also suggested that appropriate marketing frame-work within Y Generation of Kazakhs should focus onrelationship within a group of significant others (in-group),departing from Western marketing theories that centredaround the individual.

ReferencesAbrahamson, E., 1996. Management fashion. Academy of Man-

agement Review, 21 (1), 254–285.Anderson, J.C. and Gerbing, D.W., 1988. Structural equation

modelling practice: a review and recommended two-stepapproach. Psychological Bulletin, 103 (3), 411–423.

Anderson, J.C. and Narus, J.A., 1990. A model of distributorfirm and manufacturer firm working partnerships. Journalof Marketing, 54 (1), 42–58.

Baafi Antwi, J., 2010. What does a comparison between Latvia,Lithuania, Kazakhstan and Kyrgyzstan tell about the cog-nition and institutions as social capital in the extendedmarket order? [online]. Available from: http://mpra.ub.uni-muenchen.de/22835/ [Accessed 27 January 2010].

Bagozzi, R.P., (2006). Consumer action: automaticity, purposive-ness and self-regulation. In: N. Malhotra, ed. Review ofmarketing research, New York, NY: Sharpe, 3–42.

Baldi, S. and Thaung, P.P., 2002. The entertaining way to M-commerce: Japan’s approach to the mobile Internet – a modelfor Europe? Electronic Markets, 12 (1), 6–13.

Barfield, T., 1993. The nomadic alternative. Upper Saddle River,NJ: Prentice Hall.

Barwise, P. and Strong, C., 2002. Permission-based mobileadvertising. Journal of Interactive Marketing, 16 (1), 14–24.

Dow

nloa

ded

by [

Car

negi

e M

ello

n U

nive

rsity

] at

02:

49 0

9 N

ovem

ber

2014

Behaviour & Information Technology 755

Bauer, H.H., et al., 2005. Driving consumer acceptance of mobilemarketing: a theoretical framework and empirical study.Journal of Electronic Commerce Research, 6 (3), 181–192.

Becerra, E.P. and Korgaonkar, P.K., 2010. The influence of ethnicidentification in digital advertising: how Hispanic Ameri-cans’ response to pop-up, e-mail, and banner advertisingaffects online purchase intentions. Journal of AdvertisingResearch, 50 (3), 279–291.

Bontempo, R.N., Bottom, W.P., and Weber, E.U., 1997. Cross-cultural differences in risk perception: a model-basedapproach. Risk Analysis, 17 (4), 479–488.

Borg, F. and Persson, M., 2010. Assessing factors influencing thediffusion of mobile banking in South Africa: a case studyon the company Wizzit. Thesis (Bachelor degree). School ofBusiness, Economic and Law, Goteborg University, SouthAfrica.

Branca, A.S., 2008. Demographic influences on behaviour: anupdate to the adoption of bank delivery channels. Interna-tional Journal of Bank Marketing, 26 (4), 237–259.

Brown, J.R., 1998. Culture, chaos and capitalism: privatization inKazakhstan. Journal of International Economic Law, 19 (4),909–964.

Campbell, S.W. and Russo, T.C., 2003. The social constructionof mobile telephony: an application of the social influ-ence model to perceptions and uses of mobile phoneswithin personal communication networks. CommunicationMonographs, 70 (4), 317–334.

Carroll, A., Barnes, S.J., and Scornavacca, E., 2007. Consumerperceptions and attitudes toward SMS advertising: recentevidence from New Zealand. International Journal of Adver-tising, 26 (1), 79–98.

CIA – The World Factbook, 2009. Kazakhstan [online]. Availablefrom: https://www.cia.gov/library/publications/the-world-factbook/geos/kz.html [Accessed 27 September 2010].

Damanpour, F. and Schneider, M., 2006. Initiation, adoption, andimplementation of innovation: effects of context, organiza-tion, and leaders. British Journal of Management, 17 (3),215–236.

Fitzpatrick, M., 2009. Why mobile Japan leads the world [online].Available from: http://www.guardian.co.uk/technology/2007/sep/27/guardianweeklytechnologysection.mobilephones[Accessed 1 September 2010].

Freestone, O. and Mitchell, V.W., 2004. Generation Y attitudestowards E-ethics and Internet related misbehaviours. Journalof Business Ethics, 54 (2), 21–28.

Geissler, M., 2006. Aligning technology with culture: connect-ing information and communication technology adoptionto cultural dimensions. Dissertation (Doctoral). CapellaUniversity.

Grant, I., 2007. Why young consumers are not open to mobile mar-keting communication. International Journal of Advertising,26 (2), 223–246.

Hair, J.F., et al., 2010. Multivariate data analysis with readings.Englewood Cliffs, NJ: Prentice Hall.

Hall, E.T., 1976. Beyond culture. Garden City, NY: Doubleday.Halyk Bank Press Service, 2009. Halyk Bank is a leader in the

market of remote banking services [online]. Available from:http://www.halykbank.kz/eng/news/view/673 [Accessed 27September 2010].

Hofstede, G., 1980. Culture’s consequences: international differ-ences in work-related values. Beverly Hills, CA: Sage.

Holmes-Smith, P., 2000. Introduction to structural equation mod-elling using AMOS 4.0 & LISREL 8.30. Canberra: SchoolResearch, Evaluation and Measurement Services.

Hupcey, J.E., et al., 2001. An exploration and advancement ofthe concept of trust. Journal of Advanced Nursing, 36 (2),282–293.

ICT Marketing, 2007. Electronic commerce development in Kaza-khstan [online]. Available from: http://www.ictm.kz/default.php?ch_id=104 [Accessed 28 September 2010].

infoDev, 2007. ICT, innovation, and economic growth in transitioneconomies: a multi-country study of Poland, Russia, and theBaltic Countries [online]. Washington, DC: infoDev/WorldBank. Available from: http://www.infodev.org/publications[Accessed 1 October 2010].

International Telecommunication Union, 2009. Mobile cel-lular, subscriptions per 100 people [online]. Availablefrom: http://www.itu.int/en/pages/default.aspx [Accessed 28September 2010].

Internet World Stats, 2010. Kazakshtan Internet usage, broad-band and telecommunications reports [online]. Avail-able from: http://www.internetworldstats.com/asia/kz.htm[Accessed 28 September 2010].

Jarvenpaa, S.L. and Tractinsky, N., 1999. Consumer trust inan Internet store: a cross-cultural validation. Journal ofComputer-Mediated Communication, 5 (2), 287–291.

Joubert, J. and Van Belle, J.-P., 2009. The importance of trust andrisk in M-commerce: a South African perspective. Proceed-ings, Pacific Asia Conference on Information Systems, IndianSchool of Business, Hyderabad, India, 10–12 July 2009.

Junco, R. and Mastrodicasa, J.M., 2007. Connecting to the net.Generation: what higher education professionals need toknow about today’s students. Washington, DC: NASPA.

Kleijnen, M., Ruyter, K., and Wetzels, M., 2007. An assessmentof value creation in mobile service delivery and the moderat-ing role of time consciousness. Journal of Retailing, 83 (1),33–46.

Krishnamurthy, S., 2001. A comprehensive analysis of permissionmarketing. Journal of Computer Mediated Communication,6 (2), 1–19.

Leek, S. and Christodoulides, G., 2009. Next-generation mobilemarketing: how young consumers react to bluetooth-enabled advertising. Journal of Advertising Research, 49 (1),44–53.

Leppäniemi, M., Sinisalo, J., and Karjaluoto, H., 2006. A review ofmobile marketing research. International Journal of MobileMarketing, 1 (1), 2–12.

Lin, H.-F., 2010. An empirical investigation of mobile bankingadoption: the effect of innovation attributes and knowledge-based trust. International Journal of Information Manage-ment, 30 (6), 33–45.

Lowitzsch, J. and Pacherova, P., 1998. Das novellierte polnischeund slowakische Insolvenzrecht. Zeitschrift für Ostrecht undRechtsvergleichung, 211.

Ludwig, B., 2006. Trust formation in collaborative new productdevelopment. The Journal of Product Innovation Manage-ment, 23 (1), 56–72.

Luxton, S., Mahmood, U., and Ferraro, C., 2009. Mobile phonemarketing: challenges and perceived attitudes. Proceed-ings, ANZMAC 2009, Melbourne, 30 November–2 December2009.

Mazman, S.G., Usluel, Y.K., and Çevik, V., 2009. Social influ-ence in the adoption process and usage of innovation: genderdifferences. Proceedings, World Academy of Science, Engi-neering and Technology- International Conference on Com-puter and Instructional Technologies (ICCIT 2009), 28–30January, United Dubai, Arab Emirates, 409–412.

McCort, D.J. and Malhotra, N.K., 1993. Culture and consumerbehaviour: toward an understanding of cross-cultural con-sumer behaviour in international marketing. Journal ofInternational Consumer Marketing, 62 (2), 91–127.

McCrindle Research, 2008. Fast facts covering emerging trends,generational changes and demographic shifts in Aus-tralia [online]. McCrindle Research, Sydney. Available

Dow

nloa

ded

by [

Car

negi

e M

ello

n U

nive

rsity

] at

02:

49 0

9 N

ovem

ber

2014

756 K.-C. Chung

from: www.mccrindle.com.au/fastfacts.htm [Accessed 1September 2010].

Mitchell, V.W., 1999. Consumer perceived risk: conceptualisa-tions and models. European Journal of Marketing, 33 (1/2),163–195.

Mobiledirect, 2010. Gen Y users prefer mobile web to desktopor laptop access to Internet [online]. Available from: http://mobileredirect.com/2010/11/24/gen-y-users-prefer-mobile-web-to-desktop-or-laptop-access-to-internet/ [Accessed 1September 2010].

Nysveen, H., Pedersen, P.E., and Thorbjørnsen, H., 2005. Inten-tions to use mobile services: antecedents and cross-servicecomparisons. Journal of the Academy of Marketing Science,33 (3), 330–346.

Rogers, E.M., 1962. Diffusion of innovations. Glencoe, IL: TheFree Press.

Rogers, E.M., 1983. Diffusion of innovations. 3rd ed. New York:The Free Press.

Sadi, A.H.M.S. and Noordin, M.F., 2011. Factors influencing theadoption of M-commerce: an exploratory analysis. Proceed-ings, 2011 International Conference on Industrial Engineer-ing and Operations Management, Kuala Lumpur, Malaysia,22–24 January 2011.

Schwartz, S.H., 1990. Individualism-collectivism: critique andproposed refinements. Journal of Cross-Cultural Psychol-ogy, 21 (2), 139–157.

Siau, K. and Shen, Z., 2003. Building customer trust in mobilecommerce. Communications of the ACM, 46 (4), 91–94.

Søndergaard, M., 1994. Hofstede’s consequences: a study ofreviews, citations and replications. Organization Studies, 15(3), 447–453.

Song, J., Koo, C., and Kim, Y., 2008. Investigating antecedentsof behavioral intentions in mobile commerce. Journal ofInternet Commerce, 6 (1), 13–34.

Srite, M., Thatcher, J.B., and Galy, E., 2008. Does within-culture variation matter? An empirical study of computerusage. Journal of Global Information Management, 16 (1),1–25.

Stewart, J., 2010. Why social shopping is the next big thingfor mobile? [online]. Available from: http://www.mobilecommercedaily.com/2010/07/27/why-social-shopping-is-the-next-big-thing-for-mobile [Accessed 13 March 2011].

Sukiassyan, G., 2007. Inequality and growth: what does the transi-tion economy data say? Journal of Comparative Economics,35 (1), 35–56.

Sybase, 2008. Research reveals global appetite for mobile bankingservices [online]. Available from: http://www.sybase.com/detail?id=1054292 [Accessed 28 September 2010].

Tabachnick, B.G. and Fidell, L.S., 1996. Using multivariatestatistics. 3rd ed. New York: Harper Collins.

Tanakinjal, G.H., Deans, K.R., and Gray, B.J., 2010. Third screencommunication and the adoption of mobile marketing: aMalaysia perspective. International Journal of MarketingStudies, 2 (1), 36–47.

Teo, T. and Pok, S., 2003. Adoption of WAP-enabled mobilephones among Internet users. Omega, 31 (6), 483–498.

The Agency of Statistics of Kazakhstan, 2009. Telecommunica-tions [online]. Available from: http://www.eng.stat.kz/Pages/default.aspx [Accessed 1 October 2010].

Tiwari, R. and Buse, S., 2007. The mobile commerce prospects:a strategic analysis of opportunities in the banking sector.Hamburg: Hamburg University Press.

Toh, T.W., et al., 2009. What drives Malaysian m-commerce adop-tion? An empirical analysis. Industrial Management & DataSystems, 109 (3), 370–388.

Tornatzky, L.G. and Klein, K.J., 1982. Innovation characteristicsand innovation adoption-implementation: a meta-analysis offindings. IEEE Transactions on Engineering Management,29 (1), 28–45.

Weber, E.U. and Hsee, C., 1998. Cross-cultural differencesin risk perception, but cross-cultural similarities in atti-tudes towards perceived risk. Management Science, 44 (9),1205–1217.

Wessels, L. and Drennan, J., 2009. An investigation of con-sumer acceptance of M-banking in Australia. Proceedings,ANZMAC 2009, Melbourne, 30 November–2 December2009.

Wu, J.H. and Wang, S.C., 2005. What drives mobile com-merce? An empirical evaluation of the revised technologyacceptance model. Information & Management, 42 (5),719–729.

Yoo, B. and Donthu, N., 2002. The effects of marketing educationand individual cultural values on marketing ethics of students.Journal of Marketing Education, 24 (2), 92–103.

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