Arpaci-2015-British Journal of Educational Technology

A comparative study of the effects of cultural differences on theadoption of mobile learning

Ibrahim Arpaci

Ibrahim Arpaci is an assistant professor in the Department of Computer Education and Instructional Technology atGaziosmanpasa University. His current research interests are mobile learning, e-learning systems and e-governmentsystems. Address for correspondence: Dr Ibrahim Arpaci, Assistant Professor, Faculty of Education, Department ofComputer Education and Instructional Technology, Gaziosmanpasa University, Tokat 60150, Turkey. Email:[email protected]

AbstractThe objective of this paper is to understand the impact of cultural differences on mobilelearning adoption through identifying key adoption characteristics in Canada andTurkey, which have markedly different cultural backgrounds. A multi-group analysiswas employed to test the hypothesised relationships based on the data collected by meansof survey questionnaires from 190 and 163 undergraduate students in Turkey andCanada respectively. The results indicated that there is a strong relationship betweenculture and adoption behaviour, and there are major differences in patterns between theadoption behaviours of the two countries. Implications of these findings are discussed.

IntroductionA higher adoption rate of mobile devices is desirable as mobile learning can provide severaladvantages. Possible advantages such as anytime/anywhere access to media-rich content,enhanced interaction between peers, differentiation of learning needs, bespoke learning, reducedcultural barriers and help facilitate collaboration through synchronous and asynchronous com-munication (Corbeil & Valdes-Corbeil, 2007, p 54). Given the arrival of smart mobile devices, thelimitations of traditional mobile devices such as small screen size, poor screen resolution, lack ofdata input capability, low storage, low bandwidth, limited processor speed and short batterylife (Kukulska-Hulme, 2007; Maniar, Bennett, Hand & Allan, 2008) have been largely resolved.However, some issues still need to be addressed, such as compatibility and interoperability ofdifferent systems, security and trust for wireless networks, accessibility and usability of mobileapplications, and content-related issues.

The percentage of mobile telephone subscribers has reached 96% of the population worldwide asof 2014, with mobile broadband as the most dynamic information and communication technol-ogy service (ITU, 2014). Turkey and Canada are representatives of different national cultures,while being two prime examples where the mobile telecommunications industry has experiencedrapid growth. Turkey, with a population of 76.7 million, possesses one of the largest youthpopulations in Europe (TURKSTAT, 2014) of which 91% are mobile telephone subscribers, with aproportion of smart phones of all mobile phones being 33% overall (ICTA, 2014). The number of3G subscribers exceeded 49.3 million (64% of the population) as of December 2013 (ICTA, 2014).

In Canada, the number of mobile phone subscribers surpassed the 27 million (80%) in 2013,with smart phones proportion of totalling 62% (CRTC, 2014). 3G technology, capable of stream-ing video, games and the Internet, is already available to 99% of Canadians (CRTC, 2014).

British Journal of Educational Technology Vol 46 No 4 2015 699–712doi:10.1111/bjet.12160

© 2014 British Educational Research Association

Furthermore, Canada has also started to offer services at even higher broadband speeds movingto 4G Long Term Evolution.An investigation into the differences in patterns of adoption behaviours between the two coun-tries was studied aiming to identify the impact of cultural differences on adoption. The resultsof this study may lead to successful understanding for the adoption of mobile learning underdifferent cultures.The remainder of this paper is organised as follows. First, it reviews the literature on comparativestudies of technology adoption and usage, followed by the research methods and the results of

Practitioner NotesWhat is already known about this topic

• Canada (as an individualistic, low uncertainty avoidance, and low power distanceculture) and Turkey (as a collectivist, high uncertainty avoidance, and high powerdistance culture) have markedly different cultural backgrounds.

• There is a strong relationship between national culture and adoption behaviour.• Unified Theory of Acceptance and Use of Technology (UTAUT) has been widely used to

explain the adoption of information technologies. UTAUT constructs were also used inthis study while being enhanced with individual characteristics, including experienceand personal innovativeness.

What this paper adds

• There is a gap in research investigating the effects of culture in mobile learningadoption.

• In this study, we aim to fill that gap by examining the effects of culture on adoptionbehaviour of students, identifying first the differences between Canada and Turkey interms of their adoption behaviour and second the cultural differences that may affectthose adoption decisions, hereby gaining insight into the effects of cultural differenceson the adoption decision and their factors.

• Results suggest that national culture has a significant effect on the adoption mobilelearning by undergraduate students, and there are major differences in their adoptionbehaviour.

Implications for practice and/or policy

• The findings of this study have different implications for the students in the twocountries.

• Instructors, content and application developers, service providers, and device manu-facturers should be sensitive to the cultural differences for a successful adoption.

• An effective strategy for mobile service providers and device manufacturers wouldbe to take into account the cultural background of students while developing andmarketing mobile services and devices in a mobile learning context.

• Content and application developers should consider cultural differences in developingmobile learning content and mobile applications.

• The findings of this study can be used to guide the development of effective blendedlearning strategies.

• For a successful adoption, universities and academics need to be equipped with theacquired literacy and skills regarding the new educational technology.

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data analysis. Finally, a discussion of the research findings and their implications is providedalong with the limitations of the study.

Literature reviewTriandis (1994, p 22) defines culture as “a set of human-made objective and subjective ele-ments.” Triandis identifies four cultural dimensions that apply to all cultures: cultural complexity,cultural tightness, individualism and collectivism. Trompenaars (1993, p 6) however viewsculture as “a way in which a group of people solve problems.” Trompenaars (1996) describesnational culture across several dimensions such as affective versus neutral relationships, univer-salism versus particularism, specificity versus diffuseness, internal versus external control andachievement versus ascription.

Hofstede, Hofstede and Minkov (2010, p 3) define culture as “the collective programming of themind that distinguishing the members of a group or category of people from others.” Hofstede’s(1980, 2001) cultural taxonomy describes national culture along the dimensions of individual-ism versus collectivism, masculinity versus femininity and power distance, and uncertaintyavoidance has been the most popular conceptualisation of national culture.

Recently, Shin (2012) used Hofstede’s cultural dimensions to examine the relations betweenusability and aesthetic values focusing on the cultural differences in Korea and the USA. Theseresults showed that aesthetic, quality, usability and enjoyment are significant predictors of asmart phone user intentions coupled with Hofstede’s cultural dimensions differentially moderat-ing hypothesised paths. Another study of Saadé, Nebebe and Mak (2009) explored the motiva-tional differences among Canadian and Chinese online students by analysing the effects ofintrinsic motivation on technology acceptance model (TAM); showing the influence of perceivedusefulness on intention to use online learning context in Chinese culture by TAM is limited,contrary to the Canadians.

Teo, Su Luan and Sing (2008) investigated the intention to use personal computer using the TAMbased on data collected by an email survey of 245 Malaysian and 250 Singaporean preserviceteachers. Teo’s results showed that perceived usefulness, computer attitude and perceived ease ofuse have a significant influence on behavioural intention in both countries. In another study,Hunter and Beck (2000) investigated the effect of national culture on perceptions of excellentsystems analysts interviewing 70 Canadian and 17 Singaporean excellent analysts. They foundthat excellent analysts in Canada, which is an individualistic and uncertainty-accepting society,follow a more participative approach, whereas analysts in Singapore, which is a collectivist anduncertainty-avoiding society, follow a more dominant and technocratic approach against clients.

Keil et al (2000) explored decision maker’s willingness to continue a troubled information tech-nology (IT) project comparing lab experiments in Finland, Singapore and Netherlands. Theyfound that the countries with a low uncertainly avoidance culture (such as Singapore) exhibitgreater tendency to continue a troubled IT project as their perceived risk is lower than thecountries with a high uncertainty avoidance culture. In another study, Steensma, Marino,Weaver and Dickson (2000) investigated the tendency for SMEs to form technology alliancesbetween firms with a survey of five countries. They found that technology alliances with otherfirms are greater in the countries, which have a high uncertainty avoidance and femininityculture (such as Mexico). In addition, the firms in a collectivistic country (such as Indonesia andMexico) are more likely to form technology alliances than the firms in an individualistic country(such as Australia).

Griffith (1998) explored satisfaction of the students in the USA and Bulgaria with group supportsystems (GSSs). Results showed that students in the USA, which has a high power distance, aremore likely to report being satisfied with the GSS outcome than the students in Bulgaria, which

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has a low power distance. In another study, Hasan and Ditsa (1999) investigated IT adoption by10 organisations in Australia, Africa and Middle East. They found that IT adoption is higher inuncertainty-accepting cultures, successful adoption is more likely to occur in low power distancecultures, collectivist cultures are more favourably disposed to adopt a GSS and, finally, adoptionpatterns vary according to level of femininity that focuses on people and masculinity that focuseson technology.Png, Tan and Wee (2001) explored IT infrastructure adoption with a survey of 153 firms in 23countries. They found that the firms in the countries with a low uncertainty avoidance are morelikely to adopt frame relay. In another study, Srite (2000) investigated innovativeness and trust intechnology based on data collected from 33 countries. Results show that students in a low powerdistance country are found to be more innovative and more trusting of technology.Straub (1994) explored email use of employees based on a survey and follow-up interviews inthe USA and Japan. Results show that employees in the USA, which has a higher uncertaintyavoidance, are more likely to adopt and use email, whereas Japanese employees prefer moreinformation-rich and socially present forms of media. Similarly, Downing, Gallaugher and Segars(2003) explored the effect of national culture on use of media for employee empowerment in theUS and Japanese organisations. They found that organisations in Japan, which is a collectivistand high uncertainty avoidance country, are more willing to use information-rich and sociallypresent forms of media such as face to face and phone to facilitate empowerment, whereasorganisations in the US, which is a individualistic and low uncertainty avoidance society, aremore willing to use lean forms of media such as email, intranets and groupware.Husted (2000) performed a data analysis based on business software alliance records. Hustedshowed that software piracy is more prevalent in collectivist cultures than in individualisticcultures. In another study, Milberg, Burke, Smith and Kallman (1995) investigated the effectof national culture on regulatory approaches to privacy based on a survey of 900 informationsystem audits across 30 countries. They found that the countries with a low level of uncertaintyavoidance and power distance culture exhibit lower levels of government involvement in privacyregulation, whereas the countries with a collectivistic culture exhibit higher levels of governmentinvolvement.Shore et al (2001) explored the effect of national culture on attitudes towards intellectual prop-erty rights based on a survey of students from the USA, Hong Kong, New Zealand and Pakistan.They found that students in the countries with a low power distance, individualistic, a lowuncertainty avoidance and a high masculinity perceived more of an ethical problem with softlifting. In another study, Chow, Deng and Ho (2000) investigated the effect of national cultureon employee’s propensity to share knowledge with co-workers based on a survey and follow-upinterviews with US and Chinese managers. They found that managers in China, which is acollectivist country, are more likely to share knowledge with group members, and they are muchless willing to share knowledge with out-group members.Leidner, Carlsson, Elam and Corrales (1999) investigated the effect of national culture on execu-tive information system (EIS) use based on a survey of Swedish and Mexican senior managers.Results showed that Mexican managers perceived faster decision-making speed with EIS use thanSwedish managers. This suggests that EIS is best suited in countries with a low power distance anduncertainty avoidance. In another study, Mejias, Shepherd, Vogel and Lazaneo (1996/97) inves-tigated the effect of national culture on GSS use by the US and Mexican students. They found thatthe students in Mexico, which is a high power distance and collectivist society, experience higherlevels of participation equity, satisfaction and group consensus than the students in the USA.Overall, the studies reviewed here provide consistent evidence that there is a strong relationshipbetween national culture and adoption behaviour as national culture shapes the environment in



which students learn, with a potential to impact their adoption behaviour. However, there is a gapin research investigating the effects of culture in mobile learning adoption. In this study, we aimto fill that gap by examining the effects of culture on adoption behaviour of students, identifyingfirst the differences between Turkey and Canada in terms of their adoption behaviour and secondthe cultural differences that may affect those adoption decisions, hereby gaining insight into theeffects of cultural differences on the adoption decision and their factors.

Theoretical frameworkRogers defines adoption as “a decision to make full use of an innovation as the best course ofaction, whereas rejection is a decision not to adopt an available innovation” (Rogers, 1983, p 21).This study focuses on adoption of mobile learning through smart phones, which offer enhancedfunctionality and features such as voice communication, email, mobile TV and Internet. In thisstudy, adoption is therefore defined as the decision of a student to use smart phones to conductlearning activities.The cultural theory on which this study is based has been developed by Hofstede (1980) which hasbeen used successfully in cross-cultural studies and validated by many other researchers (Barczak,Hultink & Sultan, 2008; Shin, 2012; Zhao, 2011). This theory has five distinctly different dimen-sions: “uncertainty avoidance, individualism-collectivism, masculinity-femininity, power dis-tance, and long term-short term orientation” (Hofstede, 2001).Individualism is defined as “the degree to which a society emphasizes the role of the individual”(Hofstede, 1984). Canada has an “individualistic” culture, whereas Turkey has a “collectivistic”culture (Hofstede, 2001). Likewise, uncertainty avoidance refers to people’s tolerance of ambi-guity. Canadian culture is a “low uncertainty avoidance” culture, whereas Turkish culture isa “high uncertainty avoidance” culture (Hofstede, 2001). Thus, in this study, we mainly focuson these two dimensions to investigate whether these cultural differences have an impact onadoption.The second theory on which this theoretical framework is based is the Unified Theory of Accept-ance and Use of Technology (UTAUT). This theory, which was developed by Venkatesh, Morris,Davis and Davis (2003), has been widely used to explain the adoption of information technologies.The UTAUT integrates and unifies the characteristics and elements of eight TAMs and proposesfour core constructs: “performance expectancy, effort expectancy, social influence and facilitatingconditions.” These four constructs are used in this study while being enhanced with individualcharacteristics of experience and personal innovativeness.

Constructs and associated hypothesesPerformance and effort expectancyPerformance expectancy defined as “the degree to which an individual believes that using thesystem will help him or her to attain gains in job performance” (Venkatesh et al, 2003, p 447).This construct is similar to the notion of “relative advantage” in the Diffusion of Innovationtheory. The relative advantage of mobile learning over traditional learning methods comes fromunique features of smart phones, including ubiquity, flexibility, accessibility and “always-on”connectivity. Due to the these unique features of smart phones, mobile learning can extendeducational opportunities to all socio-economics levels, encourage a sense of responsibility andafford learners with greater individualism, mobility and ubiquity compared with traditionallearning methods (Attewell, 2005; Franklin, 2011; Klopfer & Squire, 2008; Kukulska-Hulme,2007; Kukulska-Hulme & Traxler, 2005; Murphy, 2006; Naismith, Peter, Giasemi & Sharples,2004; Pei-Luen, Gao & Li-Mei, 2006; Savill-Smith & Kent, 2003; Sharma & Kitchens, 2004).In this study, the key educational activities such as access to online learning materials, down-loading and listening to streaming videos, podcasts, audio books and e-books are used. Their



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effort expectancy defined as “the degree of ease associated with the use of the system” (Venkateshet al, 2003, p 450) of mobile learning using smart phones largely depends on how easy it is toperform these activities. The easier it is to perform these activities, the lower the level of effortexpectancy and the quicker and easier adoption of mobile learning by students.Hofstede’s cultural theory indicates that Canada and Turkey have a short-term oriented culture.This suggests that both cultures measure performance and benefits on a short-term basis. Thisalso prompts students to seek immediate rather than distant results. We therefore predict that theperformance expectancy and effort expectancy will have a strongly significant effect on theadoption of mobile learning in both countries. In line with this discussion, the following hypoth-eses are formulated:

H1: Performance expectancy will have a positive impact on the adoption of mobile learning.

H2: Effort expectancy will have a positive impact on the adoption of mobile learning.

Social influencePeople in individualistic cultures are encouraged to make a decision on their own, whereas peoplein collectivistic cultures are encouraged to decide as a family or community rather than them-selves (Hofstede, 2001). We therefore theorise that social influence defined as “the degree towhich an individual perceives that important others believe he or she should use the new system”(Venkatesh et al, 2003, p 451) will have a stronger effect on the adoption of mobile learning bystudents in collectivistic cultures. Thus, the following hypothesis is formulated:

H3: The relationship between social influence and adoption of mobile learning will be stronger in Turkeythan those in Canada.

Facilitating conditionsThe existence of organisational and technical infrastructure to support students’ use of mobilelearning may have a positive effect on their adoption decision. The available statistics indicate thatCanada, as a developed country, has a more powerful and advance mobile telecommunicationinfrastructures, and therefore, the availability of organisational and technical infrastructure tosupport mobile learning will also be greater in this country. Thus, we predict that facilitatingconditions defined as “the degree to which an individual believes that an organisational andtechnical infrastructure exists to support use of the system” (Venkatesh et al, 2003, p 453) willhave a stronger effect on the adoption of mobile learning in Canada. On the basis of the argu-ments just mentioned, the following hypothesis is formulated:

H4: Facilitating conditions will have a stronger effect on the adoption of mobile learning in Canada thanthose in Turkey.

ExperienceExperience can be defined as the technical knowledge and skills of the individuals that havebeen gained through previous interactions with smart phones. If students believe that they canuse the key features of smart phones, they would be less concerned about the learning curveeffects. We therefore suggest that the existence of the prior experience on mobile devices maypositively affect adoption of mobile learning through smart phones in both countries. Hence, thefollowing hypothesis is formulated:

H5: Experience will have a positive impact on the adoption of mobile learning.

Personal innovativenessHofstede’s cultural theory suggests that uncertainty-accepting cultures are more prone to beaccepting of new ideas and innovations, and more open to try new or different products(Hofstede, 2001). Similarly, Yeniyurt and Townsend (2003) found that having a high score ofpower distance and uncertainty avoidance prevents the acceptance of new products. In another



study, Singh (2006) found that the societies that have a low score of power distance, uncertaintyavoidance and masculinity are more innovative. We therefore predict that the personalinnovativeness defined as “the degree to which an individual is relatively earlier in adopting newideas than other members of a system” (Rogers, 2003, p 22) may have a stronger effect on theadoption of mobile learning in low uncertainty avoidance cultures. Deriving from the abovetheoretical and empirical support, the following hypothesis is formulated:

H6: The relationship between personal innovativeness and adoption of mobile learning will be stronger inCanada than in Turkey.

MethodologyData collectionSignificant cultural differences between Canada and Turkey may have a significant impact ontechnology adoption and usage behaviour. As they are substantially different, particularly inthe dimensions of “individualism versus collectivism” and “uncertainty avoidance,” Canada(high individualism, low uncertainty avoidance) and Turkey (high collectivism, high uncertaintyavoidance) were selected to conduct this study. In total, 353 participants, including 190 Turkish(87 male, 103 female) and 163 Canadian (82 male, 81 female) randomly selected undergraduatestudents whose ages ranged from 18 to 25 years, were recruited for the study. The participantswere asked to indicate their level of agreement using a 5-point scale ranging from “stronglyagree” to “strongly disagree.”The questionnaire items were carefully developed in an attempt to obtain content validity andface validity. In preparing them, we utilised questionnaire items that had been successfully used inprior studies: performance expectancy, effort expectancy, social influence, financial resources(Venkatesh et al, 2003), personal innovativeness (Wang & Qualls, 2007) and experience (Lin & Lin,2008). Accordingly, the questionnaire items were tailored to the adoption of mobile learning. Themeasurement items and their properties, including means, standard deviations (SD), factor load-ings (load) and the p-values of regression coefficients, are shown in Table A1 in the Appendix.

Data analysisA multi-group analysis using amos (v.20; IBM Corp. Released 2011. IBM SPSS AMOS, Armonk,NY, USA) was employed to identify relationships among the constructs and test the hypothesisedrelationships. Kline (2005) recommended a sample size of 100–150 to obtain reliable results instructural equation modelling. The sample size for both cases of the study is more than the upperbound, and thus meets recommended guidelines. The data met the assumptions of parametricstatistics (ie, normal distribution and equal variance). Therefore, as a further analysis, anindependent-samples t-test was carried out to identify significant differences between the groups.

Reliability analysisThe reliability analysis results show that the Cronbach’s alpha value of the questionnaire itemswas .88 for Turkey. For Canada, the reliability of the questionnaire items was .87. These suggestthat the instrument has strong internal consistency with the Cronbach’s alpha values above anacceptable level of .70.

Convergent and discriminant validityThe validity of each construct was assessed by investigating discriminant validity and convergentvalidity. The average variance extracted (AVE) values for each construct exceeded 0.50, demon-strating that the convergent validity for all constructs is adequate as convergent validity is judgedto be adequate when AVE equals or exceeds 0.50 (Hair, Black, Babin & Anderson, 2010). Inaddition, the composite reliability values exceeded the threshold value of .70 recommended byNunnally (1978).



As seen in Table 1, the diagonal elements, which are the square root of the shared variancebetween the constructs and their measures, in the correlation matrix were greater than theoff-diagonal elements, which are correlations between constructs (Fornell & Larcker, 1981). Thissuggests that the each construct shared more variance with its items than it does with otherconstructs, thereby ensuring that no multicollinearity exists among the constructs.

Hypotheses testingThe multi-group analysis was employed to test hypothesised relationships. The results for Canadashow that performance expectancy, effort expectancy and experience have a significant effecton adoption at the .001 level, whereas social influence, facilitating conditions and personalinnovativeness have a significant effect at the .01 level. However, the results for Turkey indicatethat performance expectancy, effort expectancy, social influence and experience have a significanteffect on adoption at the .001 level, whereas personal innovativeness has a significant effect at the.05 level. On the other hand, facilitating conditions have no statistically significant effect on theadoption in Turkey at the .05 level. These results provide support for H1, H2, H3 and H5.

To test H4 and H6, along with their significance levels, path coefficients, which indicate therelative importance of each variable in the combination of predictors, were used. First, distribu-tion of data was analysed by the Kolmogorov–Smirnov test. Normally distributed data werecompared using an independent-samples t-test to determine whether the responses acrossthe variables differ based on the country of the respondents. The independent-samples t-testresults show that there is a significant difference between the countries in all factors except effortexpectancy.

The results indicate that social influence has a much stronger and more significant effect onadoption in Turkey than in Canada; therefore, H4 is supported. Likewise, personal innovativenesshas a much stronger and more significant effect on adoption in Canada than in Turkey; therefore,H6 is supported. Note that the percentage of total variance explained by these factors is 60%for Turkey, whereas it is 65.4% for Canada. These suggest that the tested variables have ahigh explanatory power. Table 2 provides the results of hypotheses testing, including the path

Table 1: Correlation matrix and validity assessment results

AVE CR PE EE SI FC PI Ex

TurkeyPE 0.64 0.84 0.80EE 0.69 0.87 0.66 0.83SI 0.59 0.81 0.45 0.53 0.77FC 0.51 0.75 0.33 0.34 0.46 0.71PI 0.54 0.76 0.32 0.32 0.33 0.27 0.73Ex 0.77 0.91 0.35 0.46 0.52 0.23 0.56 0.88

CanadaPE 0.58 0.71 0.76EE 0.67 0.85 0.39 0.82SI 0.58 0.71 0.27 0.16 0.76FC 0.67 0.86 0.40 0.28 0.42 0.82PI 0.53 0.76 0.16 0.42 0.06 0.19 0.73Ex 0.76 0.90 0.10 0.47 0.04 0.16 0.54 0.87

Note: Diagonal elements are shown in bold. AVE, average variance extracted; CR, composite reliability; EE,effort expectancy; Ex, experience; FC, facilitating conditions; PE, performance expectancy; PI, personalinnovativeness; SI, social influence.



coefficients (unstandardised estimates) with significance levels, the estimate divided by the stand-ard error (abbreviated C.R. for critical ratio) and t-values along with their significance levels.The Pearson’s chi-squared test was conducted to determine whether there was a significantdifference in adoption decision between two groups of undergraduate students. The results showthat cultural differences have a strongly significant effect on adoption behaviour of students at the.001 level (χ2 = 10.81). This finding suggests that there is a higher adoption rate in Canada,which is an individualistic and low uncertainty avoidance culture than in Turkey, which is acollectivistic and high uncertainty avoidance culture.Furthermore, an independent samples t-test was conducted to examine whether there weresignificant differences between adopters and non-adopters in relation to their attitudes and opin-ions on each factor. The test revealed a statistically significant difference between adopters andnon-adopters in all factors at the .0001 level.

Implications and conclusionKey findingsWe hypothesised that performance expectancy, effort expectancy and experience have a positivelysignificant effect on the adoption of mobile learning through smart phones in both countries. Ourfindings, along with previous studies, support these hypotheses. Previously, Zhou, Lu and Wang(2010) combined the UTAUT with task technology fit theory to identify drivers of mobile bankingadoption. Their findings indicate that the performance expectancy, task technology fit, socialinfluence and facilitating conditions have a significant effect on adoption. In another study,Wang, Wu and Wang (2009) applied the UTAUT to predict the acceptance of mobile learningbased on 330 completed questionnaires from five organisations. They found that the self-management of learning, social influence, perceived playfulness, effort expectancy and perfor-mance expectancy are all significant antecedents of mobile learning adoption by organisations.Experience has been also identified as a significant adoption factor as in (Cheong & Park, 2005;Liao & Lu, 2008).We predict that the availability of organisational and technical infrastructure to support mobilelearning will be greater in Canada as a developed country. We therefore hypothesised that facili-tating conditions will have a stronger effect on the adoption of mobile learning in Canada. Ourfindings suggest that facilitating conditions factor is a significant predictor of adoption inCanada, while it has no significant effect on the adoption in Turkey, and thereby provide supportfor this hypothesis. We also hypothesised that social influence will have a stronger effect onadoption in Turkey as a collectivistic country. The findings show that social influence has amuch stronger and more significant effect on the adoption in Turkey, and thus provide supportfor this hypothesis.

Table 2: Hypothesis-testing results

Construct

Turkey Canada

Pathcoefficient C.R.

Pathcoefficient C.R. t-value Results

Performance expectancy 0.333*** 4.13 0.540*** 4.96 4.06*** H1: SupportedEffort expectancy 0.353*** 5.51 0.309*** 4.03 0.433 (NS) H2: SupportedFacilitating conditions 0.059 (NS) 1.95 0.195** 3.21 4.89*** H3: SupportedSocial influence 0.376*** 3.52 0.226** 3.11 5.19*** H4: SupportedExperience 0.999*** 7.78 0.431*** 5.88 13.96*** H5: SupportedPersonal innovativeness 0.056* 1.99 0.137** 3.15 5.67** H6: Supported

*p < .05; **p < .01; ***p < .001. C.R., critical ratio; NS, not significant.



The findings provide support for the last hypothesis, which predicts that personal innovativenesswill have a stronger effect on the adoption of mobile learning in Canada. The findings show thatpersonal innovativeness has a much stronger and more significant effect on the adoption inCanada, and thus provide support for this hypothesis.Hofstede’s cultural theory (1984) imply that Canadian culture is more accepting towardsuncertainty. This requires that Canadians should be more prone to accept new ideas and morewilling to try innovative products. According to results, along with this theory, the significanceand explanatory power of personal innovativeness is much higher in Canada than in Turkey.However, based on our observations, it appears that Canadians use the products that they havepurchased for a longer period of time and do not explore new products frequently. Thus, althoughthey have a high level of innovativeness, Canadians tend to keep their purchased products for alonger time and therefore do not feel the necessity to purchase new items as frequently as theconsumers in Turkey. In addition, it may be beneficial to state that Turkey has the sixth largestyouth mobile subscriber base in the world (TURKSTAT, 2014). Young consumers may have ahigher tendency to be brand obsessed and therefore feel more compelled to purchase new items onthe market even though they may not necessarily have the need or the means for the purchase.Previously, Dittmar (2005) examined the role of gender, age and endorsement of materialisticvalues in buying behaviour. The results show that age has a strongly significant impact on buyingbehaviour with younger customers more prone to compulsive buying.

Implications and directions for future researchThis paper aims to understand the effect of cultural differences on the adoption behaviour ofstudents in two different countries. In Turkey, experience has been identified as the strongestpredictor of adoption behaviour. Therefore, it seems that the existence of previous experience inusing mobile technologies has a positive and significant impact on mobile learning adoptionthrough smart phones. However, facilitating conditions are not a significant predictor of adop-tion. This can be interpreted as there are problems arising from the lack of organisational andtechnical infrastructure to support mobile learning.In Canada, traditional UTAUT constructs were found more important than individual character-istics. Specifically, performance expectancy appeared to be the strongest determinant of adoptionbehaviour. This implies that the advantages of smart phones such as mobility, ubiquity, flexibilityand “always-on” connectivity are key drivers of adoption. Furthermore, personal innovativenessis also a significant predictor of adoption behaviour in Canada. This finding suggests that stu-dents’ adoption decisions are mainly derived from their character traits of being open to new ideasand tending to accept innovative products. In this regard, a high level of personal innovativenesspositively affects adoption.The findings suggest that there are significant differences in adoption behaviour of studentsbetween the two countries. There are also key differences between them in pedagogical contextand perceptions of teaching and learning. Therefore, the implications of the findings for thestudents in these countries will also be different. For example, students in collectivist culturessuch as Turkey are more introvert and depend on group effort. The adoption of mobile learningfacilitates communication among group members and provides easy and flexible access to socialnetworks. For the faculty in collectivist cultures, an effective strategy would be to assign smallgroup projects and use social networks effectively as they are the primary source of informationin collectivist societies. Thus, students may appreciate mobile learning, which allow them tointeract and learn in a more democratic and participatory environment than traditional learningenvironments.On the other hand, students in individualistic cultures such as Canada are more extrovert,self-reliant and more concerned about privacy. The successful adoption of mobile learning may



enable students in individualistic cultures to communicate with their faculty and each other in amore private and personal environment. Moreover, such an adoption may also help social con-struction of knowledge fostering cooperative and collaborative learning activities through syn-chronous and asynchronous communication.Taken together, the present study demonstrated that cultural differences have a significant effecton the adoption mobile learning. Therefore, instructors, content and application developers,service providers, and device manufacturers should be sensitive to the cultural differences for asuccessful adoption. An effective strategy for mobile service providers and device manufacturerswould be to take into account the cultural background of students while developing and market-ing mobile services and devices in a mobile learning context. Likewise, content and applicationdevelopers should also consider these differences in developing mobile learning content andmobile applications.The findings of this research can be used to guide the development of effective blended learningstrategies. For example, in individualistic cultures such as Canada, mobile learning may influencethe effectiveness of blended learning allowing teachers work closely and directly with individualstudents as smart phones provide richer and deeper interactions between them. On the otherhand, in collectivistic countries such as Turkey, mobile learning may enhance group collabora-tion while supporting user-centred learning, thereby addresses individual differences in speed oflearning, allowing students to control content and more importantly fully master their skills.Several limitations of the study should be addressed by future research. First, the proposed modelwas tested in Canada and Turkey; therefore, additional studies in different countries would berequired to enhance the generalisability of findings. Second, future studies of a qualitative naturewould be required to confirm and triangulate the findings. Third, this study focused on smartphones; therefore, the results should be applied to other technologies with caution.The study examined the effect of cultural differences on the adoption of mobile learning focusingon students. Focusing only on the students and neglecting the academics’ adoption mindset is alimitation as academics’ readiness is a vital point in mobile learning. Future research shouldtherefore focus on a comparison between the two countries to see how academics think aboutsuch movement, given that in many countries academics still prohibit students from using mobilephones in the classroom. It would also be interesting to compare how academics think aboutthe real process of integrating mobile learning into the educational system, as mobile learningimplementation needs to be addressed with regard to various educational aspects, such as cur-riculum and pedagogy, institutional readiness, teacher competencies, and long-term financing.To handle such drastic changes in education, not only students are expected to be supportive ofnew learning methodologies; universities and academics also need to be equipped with theacquired literacy and skills regarding the new educational technology.

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Appendix

Table A1: Measurement items and their properties

Construct and items

Turkey Canada

Mean SD Load p Mean SD Load p

Performance expectancy (Venkatesh et al, 2003)Using mobile learning would increase the

efficiency of my studies and work.2.01 0.83 0.76 ** 2.39 0.98 0.70 **

Using smart phones would enhance my accessto information related to my study anytimeand anywhere.

1.86 0.79 0.71 ** 1.96 0.97 0.70 **

Mobile learning enables me to accomplish tasksmore quickly.

2.12 0.94 0.60 *** 2.28 1.00 0.74 ***

Effort Expectancy (Venkatesh et al, 2003)Learning to use smart phones for mobile

learning would be easy.2.12 0.83 0.76 ** 2.13 0.90 0.74 **

It would be easy for me to become skilful atusing mobile learning devices.

2.09 0.82 0.68 ** 1.94 0.88 0.81 **

Access to information related to my study usingsmart phones would be simple.

1.97 0.83 0.74 *** 2.11 0.90 0.62 ***

Facilitating Conditions (Venkatesh et al, 2003)I have the mobile devices necessary for mobile

learning.2.69 1.00 0.88 ** 2.03 1.06 0.77 **

Mobile learning is compatible with othersystems I use.

2.62 1.02 0.85 ** 2.14 0.88 0.90 **

A specific person (or group) is available forassistance with mobile learning difficulties.

1.93 0.79 0.30 *** 2.71 0.87 0.50 ***

Social Influence (Venkatesh et al, 2003)People who influence my behaviour think that I

should use mobile learning devices.2.76 1.43 0.68 ** 2.85 0.94 0.68 **

People who are important to me think that Ishould use mobile learning devices.

2.67 0.97 0.75 ** 2.85 1.00 0.62 **

My university has supported the use of mobilelearning.

2.56 1.09 0.54 *** 2.50 0.88 0.55 ***

Experience (Lin & Lin, 2008)I am aware of the functions of smart phones. 2.46 1.09 0.74 ** 1.68 0.78 0.72 **I am knowledgeable enough to use smart

phones for mobile learning.2.67 1.09 0.92 *** 1.79 0.81 0.81 ***

I am knowledgeable enough to use smartphones for my studies.

2.69 1.11 0.81 ** 1.77 0.86 0.87 **

Personal Innovativeness (Wang & Qualls, 2007)I welcome new ideas. 1.50 0.66 0.33 *** 1.59 0.67 0.55 ***I frequently explore new products. 2.34 1.07 0.80 ** 1.97 0.88 0.71 **I often buy new products first. 3.81 1.11 0.69 ** 2.80 1.15 0.57 **

**p < .001; ***p < .0001. SD, standard deviation.

