Students’ demand forsmartphones

Structural relationships of product features,brand name, product price and social influence

Norazah Mohd SukiLabuan School of International Business & Finance and Universiti MalaysiaSabah, Labuan International Campus, Federal Territory of Labuan, Malaysia

Abstract

Purpose – The study aims to examine structural relationships of product features, brand name,product price and social influence with demand for Smartphones among Malaysian students’.Design/methodology/approach – Data collected from 320 valid pre-screened university studentsstudying at the pubic higher learning institution in Federal Territory of Labuan, Malaysia who haveexperience in using and owning Smartphones, using a structured questionnaire, with closed-endedquestions, employing a convenient sampling technique, were tested against the research model usingthe structural equation modeling (SEM) approach via AMOS 20.0 computer program.Findings – Empirical results via SEM divulged that brand name and social influence have an effecton the increasing demand for Smartphones among Malaysian students. The first is confirmed as themost influential factor, followed by the latter. This is according to standardised path coefficients andtheir statistical signicance.Research limitations/implications – This study contributes significantly to a more thoroughunderstanding of the determinants of demand for Smartphones among Malaysian students’ byemphasizing the dimensions of product features, brand name, product price and social influence.Students’ demand for Smartphones is highly influenced by aspects of the brand name of theSmartphone itself and social influence from friends and family members.Practical Implications – Smartphone providers, manufacturers, application developers andprogrammers should improve the current ability and performance of Smartphones by upgradinghardware and software driven services for better user interaction and engagement in order to bemarketable and sustainable in meeting consumer unlimited needs and wants. They should alsoemphasize their brand name in advertising to be well positioned in consumers’ minds when makingthe decision to purchase and own a Smartphone. Positive viral marketing spread via social networkingsites could help Smartphone providers to strengthen the competitive advantage of their productbrands. The attractiveness of the message content of the advertisement on the social networking sitescould improve marketing strategies of the Smartphone brand.Originality/value – This study provides valuable insight into consumer behavior regardingSmartphones demand by examining the factors that influence students’ demand for using and owningSmartphones, which is not much covered in the literature in Malaysia context. The lessons can also bereplicated in other countries for marketing Smartphones.

Keywords Smartphones, Product features, Brand names, Product price, Social influence,Structural equation modeling, Malaysia

Paper type Research paper

1. IntroductionSmartphones are a combination of personal device assistants and mobile phones thatuse advanced operating systems and permit users to install new applications, beconstantly connected to the internet, and provide multifarious functionalities of both.Smartphones are increasingly entwined in people’s everyday activities as it makestheir lives easier access the internet 24 hours a day, seven days a week, and 365 days a

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year anywhere. The top smartphones that consumers own include the iPhone 5, GalaxyS III, and HTC One X, because they are able to transfer more mobile data than the iPador Galaxy Tab 2 10.1.

Mobile phones have become an inseparable part of everyday life and a majority ofpeople carry them all the time (Smura et al., 2009). Heavy mobile phone users possess ahigher level of knowledge, have more social participation, maintain extensiveinterpersonal networks, and have contact with people not only within the social systembut also outside it (Suki and Suki, 2007). Consumers do not view smartphones just asdevices for calling and texting, instead as multi-use devices for gaming, socializing,and downloading applications which results in a radical shift in behaviour patterns(Shin, 2012), lifestyle and status.

Forest Interactive (2013) stated that IE Market Research reported Malaysia willhave 41.9 million mobile subscribers in 2013 with 17.3 million (41.4 per cent)subscribing to Maxis, 13.8 million (33.0 per cent) to Celcom, and 10.7 million (25.6 percent) to DiGi. Malaysia is the third most mobile payment-ready nation in SoutheastAsia after Singapore and Philippines, as reported by Mastercard (2011). The increasein use of smartphones among consumers including students has made it unlimitedconsumer demands. Hence, this study aims to examine structural relationships ofproduct features, brand name, product price, and social influence with demand forsmartphones among Malaysian students’.

The paper is organized as follows: the next section reviews prior studies on therelationships between product features, brand name, product price, and socialinfluence with demand for smartphones among users. The next section, methodology,describes the data, variables, and analysis approach. In the results section, modelresults are presented. The last section recapitulates the key ndings and provides anumber of practical implications and future directions.

2. Literature reviewFactors affecting users’ demand for smartphones are complex and cannot bedetermined using a single factor or a simple reason (Chow et al., 2012) and it variesamong individuals. This section emphasizes product features, brand name, productprice, and social influences.

2.1 Product featuresProduct features are related to the attributes of a product that help to meet thesatisfaction level of consumers’ needs and wants through owning the product, use, andutilization of the product (Kotler and Armstrong, 2007). smartphones like iPhone andBlackBerry have larger and higher resolution screens and offer consumers a widearray of features, including mobile web browsing, thousands of applications, e-mail,instant messaging, picture messaging, video, and audio playback, global positioningsystem (GPS), games, a video camera, picture, and video editing (Persaud and Azhar,2012). The most popular operating systems for mobile phones are Symbian, RIMBlackberry, Apple iPhone, Windows mobile, Google Android, and Linux. Google’sAndroid operating system has propelled Samsung to the top of global smartphonesales in 2011 in Southeast Asia (GfK Retail and Technology, 2011). Users largely valuethe smartphone features such as full-screen viewing for images and video and largertext and buttons with stylish design according to their preferences. Design was foundto be the most important determinant of consumer response and new product salessuccess (Bloch, 1995; Crilly et al., 2004; Mohd Azam et al., 2012). Usability is also

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affecting consumer choice of the mobile phone (Mack and Sharples, 2009). Hence, thestudy proposed the following hypothesis:

H1. Product features significantly influence students’ demand for smartphones.

2.2 Brand nameBrand is an exclusive name which indicates product to the market. The legal term forbrand is trademark. More and more companies realize that one of their most pricelessassets is the brand name related to their products or services (Cornelis, 2010). Usersprefer a widely accepted brand of smartphone as it reflects a unique status symbol thatdisplays information quickly with minimal aggravation and clean graphical interfacefor touch screen interactions. For instance, brand of smartphone available in themarket includes Samsung, BlackBerry, Apple, HTC, Nokia, and LG. Interestingly,Apple, a brand of smartphone, has very high brand loyalty because it discriminatesdifferent types of consumers by selling different versions with different amounts ofstorage space, and different colours, at different price points (ChangeWave Research,2010). Khasawneh and Hasouneh (2010) found that the product’s brand nameinfluenced consumers’ evaluation and subsequently affected their buying decision.Thus, the following hypothesis is posited:

H2. Brand name significantly influences students’ demand for smartphones.

2.3 Product priceProduct price is the amount of money expected, required, or given in payment for aproduct (Swani and Yoo, 2010). There are two types of product price. High-pricedproducts are market brands where image is seen as the key factor. Low-priced brandstend to be purchased for utilitarian value, with the consumer relying on the perceivedvalue for price. Consumers typically look for low prices of brands or substitutes to getthe best value (Swani and Yoo, 2010). Smartphone owners use channels such as mobilecoupons, apps, QR codes, text messages, links to information videos, and mobiledisplay ads to compare prices among retailers and research products on theirsmartphones (Moosylvania, 2013). Furthermore, they are not afraid of spending largesums on their devices. Product price influences consumers’ buying decisions and thesales margin (Bloch, 1995; Crilly et al., 2004; Mohd Azam et al., 2012). Based on theabove, the following hypothesis is developed:

H3. Product price significantly influences students’ demand for smartphones.

2.4 Social influenceSocial influence related to an individual may cause another person to change his or herfeelings, attitudes, and behaviour, intentionally or unintentionally (Rashotte, 2007).Friends and family members are social influences that are. Consumers are dependenton their smartphones when they have high continuous use and are reluctant to be apartfrom them (Tian et al., 2009). Their expectations for future purchase behaviour will beaffected by their past experience as they are heavily dependent on smartphonesbecause of the underlying motives (Kuhlmeier and Knight, 2005). Users are concernedwhether their friends like the brand of smartphone they are currently using and would

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buy a smartphone if it helped them to fit in with their social group. Therefore, thefollowing hypothesis is proposed:

H4. Social influence significantly affects students’ demand for smartphones.

Based on the above said reviews of literature, a theoretical framework is proposed (seeFigure 1).

3. MethodologyData were collected from pre-screened university students studying at the pubic higherlearning institution in Federal Territory of Labuan, Malaysia who have experience inusing and owning smartphones via the self-administered questionnaire, a method ofquantitative research employing convenience sampling techniques, from 1 to 30September 2012. Initially, 400 questionnaires were administered, however a total of320 unique and usable responses were successfully collected for a response rate of80 per cent. The structured close-ended questionnaire was designed in line with thisstudy’s research objective of examining structural relationships of product features,brand name, product price and social influence with demand for smartphones amongMalaysian students’. The questionnaire comprised three sections. Section A consistedof demographic profile of respondents, Section B requested the respondents to provideresponses on their personal experiences using smartphones and Section C examinedthe factors affecting users’ demand for smartphones. In total, 17 items (described inAppendix A), were used to measure the four independent variables: product features,brand name, product price, and social influence, and five questions were used tomeasure the dependent variable: demand for smartphones on a five-point Likert scaleranging from 1 strongly disagree to 5 strongly agree.

Survey instruments were adapted from the following sources: product features(Market Analysis and Consumer Research Organization, 2004), brand name (Rio et al.,2001), product price (Cheong and Park, 2005), social influences (Tian et al., 2009), anddemand for smartphones (Park and Chen, 2007; Venkatesh et al., 2003). Completedquestionnaires were keyed in the computer program Statistical Package for SocialSciencesversion 20.0 to process and analyse the data in the form of descriptives such asfrequencies, mean, standard deviation, skew, and kurtosis. Next, a graphical modeldevelopment known as path diagram was drawn using the structural equationmodeling (SEM) technique via AMOS 20.0 computer program which has the ability toensure the consistency of the model with the data and to estimate effects amongconstructs.

ProductFeatures

Demand forSmartphones

Brand Name

Price

SocialInfluence

H1

H2

H3

H4Figure 1.

Theoretical framework

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4. Data analysisTable I displays the demographic profile of the respondents. A total of 320 participantswere included in the final sample, out of which more than half of the respondents werefemale (58.8 per cent), the remainder (41.2 per cent) were male. They were mostly 18-20years old (90.3 per cent), and nearly half held STPM certification (49.7 per cent).

4.1 Experience using smartphonesThe users’ experience using smartphones is presented in Table II which infers that thepreferred type of operating system currently used is Android (53.1per cent), followedby iPhoneRIM (30.6 per cent). OS Windows is less used (2 per cent). More than three-quarters can afford to buy smartphones for under RM2,000. However, about 5 per centof the users bought smartphones costing more than RM2,501. Almost half of therespondents used smartphones for social network purposes (49.4 per cent), followed bytext messaging (SMS) (24.7 per cent). They also used smartphones to play games (15.3per cent) and listen to music (5 per cent). The activity least often performed wassearching maps and using GPS (1 per cent). The most preferred news on smartphonesis from sources like Twitter and Facebook (52.2 per cent), followed by Google News,Yahoo News (34.7 per cent). About one-quarter of the users spent less than two hourson smartphone each day and 30 per cent have spent more than six hours.

4.2 SEMA two-step SEM approach, measurement model and structural model, was employed toconfirm the reliability and validity of the measures before examining the structuralrelationship between constructs. This study used maximum likelihood for theestimation method as it provides a consistent approach to parameter estimationproblems that can be developed for a large variety of estimation situations.

4.3 Measurement modelThe measurement model was assessed via the evaluations of the reliability, convergentvalidity, and discriminant validity of the construct measures.

4.4 ReliabilityThe reading of Cronbach’s a and composite reliability for all the variables, assummarized in Table III, is 40.50, except for PR1: “I am willing to buy a Smartphone

Frequency %

GenderMale 132 41.2Female 188 58.8Age (years old)18-20 289 90.321-23 29 9.1424 2 0.6Education levelSTPM 159 49.7Matriculation 122 38.1Diploma 39 12.2

Table I.Demographic profileof respondents

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even if the price is higher”, on their expected factor, indicating a relatively high level ofconstruct reliability (Hair et al., 2010).

4.5 Convergent validityThe standardized loading items shown in Table III were considered significant as theysurpassed the cut-off value of 0.50 (Hair et al., 2010). The average variances extracted(AVE) of latent constructs, range from 0.765 to 0.809, and exceed the recommendedthreshold value of 0.50 (Hair et al., 2010), which meant that more than one-half of thevariances observed in the items were accounted for by their hypothesized constructs.Hence, the current data have good convergent validity.

4.6 Discriminant validityDiscriminant validity was checked by comparing the shared variances between factorswith the square root of AVE for each construct. Table IV shows that all sharedvariances of one construct with other constructs were lower than the square root ofAVE of the individual factors, confirming adequate discriminant validity. Hence, eachconstruct was statistically different from the others.

The results described in Table IV reveal that there is a signicant positive correlationbetween all variables at the 0.01 level. Product features has the strongest correlation

Frequency %

Preferred type of operating system currently usingiPhoneRIM 98 30.6BlackBerry 45 14.1Android 170 53.1OS Window 7 2.2Price of smartphoneoRM1,500 197 61.6RM1,501-RM2,000 82 25.6RM2,001-RM2,500 26 8.1RM2,501-RM3,000 8 2.54RM3,001 7 2.2Activities performed using smartphoneSocial network 158 49.4Text messaging (SMS) 79 24.7Map, GPS 4 1.3Playing games 49 15.3Official and documentation 14 4.4Listening to music 16 4.9Preferred news views on a smartphoneGoogle news, Yahoo news 111 34.7Twitter and Facebook 167 52.2Receive news alert on Smartphone 20 6.3Listen to audio news (podcast or streaming audio) 12 3.8Watch news video 10 3.0Time spent on smartphone within 24 hours1-2 75 23.43-4 93 29.15-6 57 17.846 95 29.7

Table II.Experience using

smartphone

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with demand for smartphones (r¼ 0.639, po0.01), followed by brand name (r¼ 0.633,po0.01). Hence, there is no multicolinearity problem in this research. All the itemranges are negatively skewed from �0.094 to �0.590, however, still beneath 72.0.The kurtosis values range from �0.583 to 0.202, well below the cut-off value of 710.Both the skewness and kurtosis are less than the threshold value, thus ensuringthat the data used in the study is normally distributed. Means for all constructsrange from 3.235 to 3.459 on a scale of 1¼ strongly disagree to 5¼ strongly agree.These means demonstrated that most of the respondents had a positive demandfor smartphones.

Constructs ItemsStandardized

loadingsCronbach’s

aCompositereliability

Average varianceextracted

Product features PF1 0.834 0.939 0.826 0. 821PF2 0.891PF3 0.877PF4 0.874PF5 0.835

Brand name BD1 0.753 0.891 0.834 0.747BD2 0.843BD3 0.824BD4 0.784

Product price PR2 0.748 0.748 0.853 0.810PR3 0.573PR4 0.784

Social influence SI1 0.696 0.783 0.874 0.807SI2 0.720SI3 0.780SI4 0.662

Demand forSmartphone

DD1 0.593 0.855 0.847 0.811DD2 0.771DD3 0.717DD4 0.817DD5 0.740

Table III.Reliability andvalidity analysis

1 2 3 4 5

1 Product features 0.9062 Brand name 0.811** 0.8643 Product price 0.639** 0.642** 0.9004 Social influence 0.440** 0.395** 0.351** 0.8985 Demand for smartphone 0.639** 0.633** 0.500** 0.542** 0.900Mean 3.53 3.30 3.29 3.12 3.27Standard deviation 1.10 1.02 0.848 0.796 0.764Skewness �0.590 �0.466 �0.323 �0.094 �0.141Kurtosis �0.583 �0.458 0.202 �0.059 �0.141

Notes: **Correlation is significant at the 0.01 level (two-tailed). Diagonal elements show the averagevariance extracted; off-diagonal elements show the shared variance

Table IV.Inter-constructcorrelations

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4.7 Structural modelThe structural model in the SEM was evaluated by examining fit indices and variance-explained estimates. A variety of indices were used to assess the model’s overall t(see Table V). The results indicated that the w2 of the model was 396.151 with170 of freedom (w2/df¼ 2.330) and root mean square error of approximation (RMSEA)of 0.065.

The indices value for comparative fit index (CFI), goodness of fit index (GFI), andnormed fit index (NFI) were above 0.90 and RMSEA below 0.08 (Bentler, 1990; Byrne,2001), indicating a satisfactory fit. Therefore, the hypothesized model was a good fitand acceptable. The results in Figure 2 exhibit that all independent variablesaccounted for 65 per cent of the total variance in demand for smartphones (R2¼ 0.65).As a consequence, the results are a sign of adequate model fit between the proposedresearch model and the empirical data.

Table VI and Figure 2 disclose the standardized path coefficients of the structuralmodel under investigation which indicate the strength of the direct relationshipbetween constructs. The strongest significant determinant of the demand forsmartphones among Malaysian users was brand name (b2¼ 0.573, po0.05),supporting H2. Next, social influence (b4¼ 0.317, po0.05) as posited in H4 alsosignificantly influenced the demand for smartphones users. Unexpectedly, the demandfor smartphones among Malaysian users is not affected by two factors: productfeatures (b1¼ 0.054, p40.05) and product price (b3¼�0.006, p40.05), thus H1 and H3were not sustained.

5. DiscussionEmpirical analysis across a sample of 320 respondents via SEM technique wasexecuted in examining structural relationships of product features, brand name,product price and social influence with demand for smartphones among Malaysianstudents’. Results divulged that brand name and social influence affected the positivedemand for smartphones among Malaysian students’, thus supporting H2 and H4.The first is confirmed as the most influential factor, followed by the latter. This isaccording to standardized path coefficients and their statistical signicance as shown inTable VI and Figure 2.

As expected, brand name was found to have a significant effect on the demand forsmartphones among Malaysian students’. This portends that users who are particularabout brand names are more likely to have positive demand towards buying and usingsmartphones. The ndings are consistent with preceding discovery by Khasawneh andHasouneh (2010). Most of the users are likely to select smartphones that areinternationally recognized, branded, and trustworthy. Besides that, they will only buytheir favourite brands of smartphone. For example, Samsung and iPhone are the mostpreferred brands of smartphones.

Further investigation of the study uncovered that the increasing demand forsmartphones among Malaysian students’ is also affected by social influence. This in

w2 df w2/df CFI GFI NFI RMSEA PNFI PCFI

Recommended values n/a n/a o3.0 40.9 40.9 40.9 o0.08 40.5 40.5Model values 396.151 170 2.330 0.950 0.901 0.916 0.065 0.741 0.769

Table V.Goodness-of-fit indices

for structural model

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turn implies that students’ who have strong social influence from friends and familymembers tend to have positive demand for smartphones, signifying their decisionmaking and use rates are mostly influenced by people around them. This is inaccordance with findings by Goldman (2010), Park and Chen (2007) and Rashotte(2007). For example, when users want to purchase a smartphone, they will seekrecommendations from their friends and family members who have a lot of experienceusing and owning smartphones in order to make a more informed choice ofsmartphone and benefit from reduced perceived risk. They also execute viral

BrandNameBD3e8

1

BD2e71

BD1e6

1

1

SocialInfluenceSI3e16

SI2e15

SI1e14

1

1 1

1

BD4e91

SI4e171

ProductFeatures

PF2e2

PF1e1

PF3e3

PF4e4

PF5e5

1 1

1

1

1

PricePR3e12

PR2e11

PR4e13

1

11

1

1

Demand ofSmartphone

DD4 e21

DD5 e22

DD3 e20

DD2 e19

DD1 e18

11

1

1

1

1

z1

1R 2=0.65

�4=0.317

�3=–0.006

�2=0.573

�1=0.054

Figure 2.The results ofstructural model

Path Estimate SE CR p

H1 Product features-demand for Smartphone 0.054 0.111 0.367 0.714H2 Brand name-demand for Smartphone 0.573* 0.147 3.476 0.000H3 Product price-demand for Smartphone �0.006 0.095 �0.063 0.950H4 Social influence-demand for Smartphone 0.317* 0.061 5.165 0.000

Note: *po0.05

Table VI.Relationship with demandfor smartphone

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marketing regarding smartphone latest product information, features, and promotionsand sharing it with friends by posting it to Facebook page. Smartphones enhancesocial influences as they enable owners to view visual information, full-screen productimagery, dynamic zoom, 360 spin, and enjoy product tours.

The design of smartphone which is slimmer and lighter offers users someconvenience in accessing applications such as FaceTime, Passbook, Quickoffice, DropBox, Instagram, and Maps without any fuss. Moreover, the FaceTime feature allowsusers of smartphone to talk to each other with video. Though users worldwide choosesmartphones based on the attractive design and applications, better product quality,speedier internet accessibility, and outstanding operating system (i.e. Apple iPhone,RIM Blackberry, Google Android or others), the more advanced features of cuttingedge smartphones still had no impact on the current respondents in affecting theirdemand for smartphones, thereby not supporting H1 and contrary to support byforegoing studies (Bloch, 1995; Crilly et al., 2004; Mack and Sharples, 2009; Mohd Azamet al., 2012).

Likewise, the effect of price on demand for smartphones did not appear, meaningthat H3 is not sustained. This is divergent with discoveries of prior research work(Bloch, 1995; Crilly et al., 2004; Mohd Azam et al., 2012; Swani and Yoo, 2010).Consumers will only buy smartphones during price reduction periods as they treatbuying smartphones like buying high-risk products. However, they are willing to buysmartphones even though the price is higher as they think that owning and using asmartphone could enhance their image.

6. Conclusion and recommendationsThis study contributes to a more thorough understanding of the determinants ofdemand for smartphones among Malaysian students’ by emphasizing the dimensionsof product features, brand name, product price, and social influence. Students’ demandfor smartphones is highly influenced by aspects of the brand name of the smartphoneitself and social influence from friends and family members. Smartphone providers,manufacturers, application developers, and programmers should improve the currentability and performance of smartphones by upgrading hardware and software drivenservices for better user interaction and engagement in order to be marketable andsustainable in meeting consumer unlimited needs and wants. They should alsoemphasize their brand name in advertising to be well positioned in consumers’ mindswhen making the decision to purchase and own a smartphone. Positive viral marketingspread via social networking sites could help smartphone providers to strengthen thecompetitive advantage of their product brands. The attractiveness of the messagecontent of the advertisement on the social networking sites could improve marketingstrategies of the smartphone brand.

There is opportunity for future research to expand the sample size and investigatein different geographical areas in order to enhance the understanding of demand forsmartphones. The R2 for this study was 0.65, implying there are additional factors thataffect demand for smartphones to be explored further, other than product features,brand name, product price, and social influence. Further research could enhance theproposed model by inputting mediating variables, such as lifestyle, culture, as well asdemographics such as gender or age, etc. Furthermore, it would be relevant toinvestigate differences in product demand among smartphone users and basicmobile phone users. However, this study advanced understanding by the smartphoneproviders, manufacturers, application developers, and programmers to obtain

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empirical and actionable insights about the determinants of demand for smartphonesand how to improve smartphone user experience and plan directions in the smartphonemarket.

References

Bentler, P.M. (1990), “Comparative fit indexes in structural models”, Psychological Bulletin.,Vol. 107 No. 2, pp. 238-246.

Bloch, P.H. (1995), “Seeking the ideal form: product design and consumer response”, Journal ofMarketing, Vol. 59 No. 3, pp. 16-29.

Byrne, B.M. (2001), Structural Equation Modeling with AMOS: Basic Concepts, Applications, andProgramming, Lawrence Erlbaum Associates Inc, Mahwah, NJ.

ChangeWave Research (2010), “Another major leap for Google android OS among consumers”,available at: http://investorplace.com/2010/09/another-major-leap-for-google-android-os-among-consumers/ (accessed 17 October 2012).

Cheong, J.H. and Park, M.C. (2005), “Mobile Internet acceptance in Korea”, Internet Research,Vol. 15 No. 2, pp. 125-140.

Chow, M.M., Chen, L.H., Yeow, J.A. and Wong, P.W. (2012), “Conceptual paper: factor affecting thedemand of Smartphone among young adult”, International Journal on Social ScienceEconomics and Art, Vol. 2 No. 2, pp. 44-49.

Cornelis, P.C.M. (2010), “Effect of co-branding in the theme park industry: a preliminary study”,International Journal and Contemporary Hospitality Management, Vol. 22 No. 6,pp. 775-796.

Crilly, N., Moultrie, J. and Clarkson, P.J. (2004), “Seeing things: consumer response to the visualdomain in product design”, Design Studies, Vol. 25 No. 6, pp. 547-577.

Forest Interactive (2013), “Malaysia statistics”, available at: www.forest-interactive.com/malaysia-2/ (accessed 19 November 2012).

GfK Retail and Technology (2011), “Boom times continue as Southeast Asia’s smartphone marketvalue expands”, available at: http://bit.ly/LQX14p) (accessed 21 November 2012).

Goldman, S.M. (2010), “Transformers”, Journal of Consumer Marketing, Vol. 27 No. 5, pp. 469-473.

Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E. and Tatham, R.L. (2010), Multivariate DataAnalysis, Pearson Prentice Hall, Upper Saddle River, NJ.

Khasawneh, K. and Hasouneh, A.B.I. (2010), “The effect of familiar brand names on consumerbehaviour: a Jordanian perspective”, International Research Journal of Finance Economics,Vol. 43 No. 1, pp. 34-57.

Kotler, P. and Armstrong, G. (2007), Principle of Marketing, 12th ed., Pearson Education,Boston, MA.

Kuhlmeier, D. and Knight, J. (2005), “Antecedents to Internet based purchasing: a multinationalstudy”, International Marketing Review, Vol. 22 No. 4, pp. 460-473.

Mack, Z. and Sharples, S. (2009), “The importance of usability in product choice: a mobile phonecase study”, Ergonomics, Vol. 52 No. 12, pp. 1514-1528.

Market Analysis and Consumer Research Organization (2004), “Study of mobile phone amongthe teenagers and youth in Mumbai”, available at: www.itu.int/osg/spu/ni/futuremobile/socialaspects/IndiaMacro MobileYouthStudy04.pdf (accessed 25 October 2012).

Mastercard (2011), “Mobile payments readiness index”, available at: http://mobilereadiness.mastercard.com/the-index (accessed 15 October 2012).

Mohd Azam, O., Abdullah Zawawi, T., Zainal Abidin, S., Tan, S.Y. and Abdullah Sani, A. (2012),“A study of the trend of smartphone and its usage behaviour in Malaysia”, InternationalJournal on New Computer Architectures and their Applications, Vol. 2 No. 1, pp. 274-285.

246

CWIS30,4

Moosylvania (2013), “The shopping experience in a smartphone world”, available at:www.internetretailer.com/2012/12/31/smartphone-owners-want-more-mobile-information-stores (accessed 23 November 2012).

Park, Y. and Chen, J.V. (2007), “Acceptance and adoption of the innovative use of Smartphone”,Industrial Management and Data Systems, Vol. 107 No. 9, pp. 1349-1365.

Persaud, A. and Azhar, I. (2012), “Innovative mobile marketing via Smartphones: are consumersready”, Marketing Intelligence and Planning, Vol. 30 No. 4, pp. 418-443.

Rashotte, L. (2007), “Social influences”, available at: www.blackwellpublishing.com443/sociologt/docs/ (accessed 5 October 2012).

Rio, A.B., Vazquez, R. and Iglesias, V. (2001), “The role of the brand name in obtaining differentialadvantages”, Journal of Product and Brand Management, Vol. 10 No. 7, pp. 452-465.

Shin, D.H. (2012), “Cross analysis of usability and aesthetic in smart device: what influencesusers’ preferences”, Cross Cultural Management: An International Journal, Vol. 19 No. 4,pp. 563-587.

Smura, T., Kivi, A. and Toyli, J. (2009), “A framework for analysing the usage of mobile services”,Journal of Policy, Regulation and Strategy for Telecommunications, Information andMedia, Vol. 11 No. 4, pp. 53-67.

Suki, N.M. and Suki, N.M. (2007), “Mobile phone usage for m-learning: comparing heavy andlight mobile phone users”, Campus Wide Information System, Vol. 24 No. 5, pp. 355-365.

Swani, K. and Yoo, B.H. (2010), “Interactions between price and price deal”, Journal of Productand Brand Management, Vol. 19 No. 2, pp. 143-152.

Tian, L., Shi, J. and Yang, Z. (2009), “Why does half the world’s population have a mobile phone?An examination of consumers’ attitudes toward mobile phones”, CyberPshychology andBehaviour, Vol. 12 No. 5, pp. 513-516.

Venkatesh, V., Morris, M.G., Davis, G.B. and Davis, F.D. (2003), “User acceptance of informationtechnology: toward a unified view”, MIS Quarterly, Vol. 27 No. 3, pp. 425-478.

Further reading

Auter, P.J. (2007), “Portable social groups: willingness to communicate, interpersonalcommunication gratifications and cell phone use among young adults”, InternationalJournal of Mobile Communication, Vol. 5 No. 2, pp. 139-156.

Bouwman, H., Reuver, M.D. and Visser, A. (2010), “Understanding trends in mobile servicebundles”, available at: www.tbm.tudelft.nl/fileadmin/FaculteitTBM/Over_de_Faculteit/Afdelingen/Afdeling_Infrastructure_Systems_and_Services/Sectie_Informatie_en_Communicatie_Technologie/medewerkers/mark_de_reuver/Publications/doc/Bouwman_et_al__Understanding_trends_in_mobile_service_bundles.pdf (accessed 17 October2012).

( The Appendix follows overleaf. )

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Appendix

About the author

Norazah Mohd Suki is an Associate Professor at the Labuan School of International Business &Finance, Universiti Malaysia Sabah, Labuan International Campus. She has successfullysupervised several postgraduate students at MBA and PhD level. Her research interests includeElectronic Marketing, E-Commerce, M-Commerce, Consumer Behaviour, Mobile Learning andareas related to Marketing. She actively publishes articles in international journals. She is theEditor-in-Chief to Labuan e-Journal of Muamalat & Society, a member in advisory board forseveral outstanding journals. She has sound experiences as a speaker to public and privateuniversities, government bodies on courses related to Structural Equation Modelling (SEM),Statistical Package for Social Sciences (SPSS), Research Methodology. Norazah Mohd Suki canbe contacted at: azahsuki@yahoo.com

Product featuresPF1 I like smartphone’s designPF2 Smartphone has more applications than basic mobile phonePF3 Smartphone provides high quality of gamesPF4 Smartphone’s internet accessibility is speedier than basic mobile phonePF5 I use smartphone due to its operation system (Apple iPhone, RIM Blackberry, Google Android

or others)Brand nameBD1 I prefer to buy an internationally recognized smartphoneBD2 I prefer to buy a trustworthy brand of smartphoneBD3 I will only buy my favourite brand of smartphoneBD4 Brand name is a major factor that influences my decision towards buying a smartphoneProduct pricePR1 I am willing to buy a smartphone even though the price is higherPR2 I will only buy a smartphone during a price reduction periodPR3 I think that using smartphone is expensive overallPR4 Price is my main consideration when deciding whether to buy a smartphoneSocial influenceSI1 Almost all of my friends and family members use smartphonesSI2 My friends and family members think that we should all use smartphonesSI3 My friends and family members influenced me to buy smartphoneSI4 People around me have encouraged me to use smartphoneDemand for smartphoneDD1 I intend to continue using smartphone although there is new mobile phone in the marketDD2 I intend to increase my use of the smartphone in the futureDD3 I intend to use smartphone for e-commerceDD4 I will find more details about smartphonesDD5 I intend to recommend others to use smartphone

Table AI.Measurement instruments

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