educação x smartphone

Journalism & Mass Communication Quarterly90(4) 715 735

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Theorizing Technology Use

From Access to Utilization: Factors Affecting Smartphone Application Use and Its Impacts on Social and Human Capital Acquisition in South Korea

Jaemin Jung1, Sylvia Chan-Olmsted2, and Youngju Kim3

AbstractThis study examines the mobile divide from the perspective of perception of information and knowledge inequity due to smartphone usage, exploring factors that may influence the use of smartphone applications and assessing discrepancies in social and human capital due to usage differences. A survey of smartphone users revealed that gender, age, personal innovativeness, and consumption skills were significant predictors of the frequent use for applications. Simply having more smartphone applications does not contribute to increases social or human capital; it is usage of these apps that makes a difference.

Keywordssmartphone applications, consumption skills, mobile divide, social capital, human capital

The digital divide, which refers to the gap between people with effective access to computers and the Internet and those with limited or no access, has been a subject of discussion in many countries.1 Although the term initially was used most frequently in

1Korea Advanced Institute of Science and Technology, Seoul, Korea2University of Florida, Gainesville, FL, USA3Korea Press Foundation, Seoul, Korea

Corresponding Author:Youngju Kim, Korea Press Foundation, Seoul, Korea.Email: [email protected]

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716 Journalism & Mass Communication Quarterly 90(4)

the context of physical access to computers, it now refers not only to the inequity in computing/networking hardware ownership, but also to inequity between groups of people in terms of skills and resources necessary to fully utilize information and com-munication technology (ICT). As newer and more sophisticated ICT devices are intro-duced and adopted, it is essential to continuously monitor not only the unequal access to ICT technologies that are becoming mainstream in society, but also possible dis-crepancies in the capability to use such technologies between segments of people. In other words, the concern for digital inequity must extend beyond the haves and have-nots to the gap between those who have rudimentary services and those with advanced services and those who can and cannot.2

One of the fastest-spreading ICT devices in the last decade has been the mobile phone. Beyond regular phone calls and short message service (SMS) exchanges, this ICT device, in the form of the so-called smartphone, is now also a personal multimedia device.3 A recent national survey by Pew revealed that cell phones have become a portal for a growing list of activities.4 For instance, more than 80% of the owners use their phones to send/receive text messages, more than 50% to access the Internet or send/receive email, more than 40% to record video or download apps, and 30% to look for medical information or do some forms of online banking.

According to ComScore,5 a leading digital measurement company, the penetration rate of smartphones in the United States passed 50% in late 2012. Comparatively, more than 60% of the population in South Korea already owned a smartphone in 2012 and the penetration rate would reach 90% by the end of 2013.6 In fact, the Korea Communication Commission predicts that Korea would have the worlds largest smartphone ownership because of the countrys well-built wireless network system, abundant handset choices, and a cultural propensity toward ICT products.7 The diffu-sion of smartphones in South Korea, therefore, provides an excellent context for examining the patterns of smartphone use and their drivers, and for deriving insights into the potential trajectory and social consequences in other growing mobile societies.

As mobile access becomes essential in a society, the lack of access to mobile broad-band and other relevant applications could lead to social drawbacks similar to those of the digital divide rooted in unbalanced access to wired broadband Internet. In addition, because smartphones are different from conventional mobile phones, with an array of mobile content and services available via applications (including news, weather, games, music, messaging, social networking sites [SNS], etc.), the skills, knowledge, and ability to use such applications is also important when discussing the potential of a mobile divide in an increasingly mobile society.

It was predicted that by the end of 2014, mobile users would have downloaded 185 billion applications, with revenues totaling $58 billion.8 With a growing array of appli-cations and increasingly complex functionalities, it is plausible that the use of smart-phone applications may differ based on individual characteristics, such as socioeconomic status, personal innovativeness, and consumption skills. Furthermore, because smartphones are increasingly serving as a conduit to useful communication and information, disparities in smartphone application use in terms of quantity of



Jung et al. 717

application ownership (i.e., breadth) and usage frequency (i.e., depth) may contribute to a divide among groups of people.

This study examines the issue of mobile divide from the perspective of information and relational inequity due to smartphone usage, assuming that smartphones are becoming an essential means of accessing information and communications for most members of a society. Specifically, the study explores the factors that may play a role in the differential usage of smartphone applications. It then assesses discrepancies in social and human capital due to differences in mobile application usage. Such an investigation is important to identifying potential sources of the mobile divide and possible means of reducing the gap.

Literature Review

Individual Characteristics Affecting Smartphone Application Use

Demographics. Most studies on the digital divide have focused on factors that affect the state of the divide between groups or countries in the use of computers or the Inter-net. Access to and use of the Internet differ by socioeconomic status factors, such as gender, income, race, education, and location.9 Bikson and Panis10 identified a signifi-cant disparity in the use of network services by age, income, and education. Many studies have also found a negative relationship between Internet usage and level of education.11

Demographic variables have frequently been linked to innovation adoption. Earlier adopters tend to have more years of formal education and higher income than later adopters.12 In the case of media technology adoption, age was negatively related to adoption.13 Gender is also often considered a critical factor because male and female users tend to consider innovation use as achieving different ends.14 In adoption studies pertaining to mobile technologies, women often had less access to mobile phones in developing countries than their male counterparts.15 Furthermore, Kwon and Chon16 found that gender is a significant determinant in mobile TV adoption. Other studies have shown some dissimilarities between genders with respect to online platform pref-erences and motives.17 Prior studies on computer technology adoption found that males were more likely to adopt computers in early stages.18 Because smartphone applications are in the early stage of diffusion, it is hypothesized that differences in the use of applications are influenced by ones demographic status. Thus:

H1a: Younger, more educated, higher-income, and male respondents will down-load more smartphone applications.

H1b: Younger, more educated, higher-income, and male respondents will use smartphone applications more frequently.

Personal innovativeness. Rogers19 defined innovativeness as the degree to which an individual is relatively early, compared with others in the social system, to adopt an innovation. Hirschman20 conceptualized innovativeness as the desire and willingness




to try new things and different experiences, and to take risks. Agarwal and Prasad expanded the personal innovativeness construct into the domain of information tech-nology, as the willingness of an individual to try out any new information technol-ogy.21 Furthermore, Dabholkar and Bagozzi22 measured the trait of innovativeness from the perspective of novelty seeking, or the degree to which an individual is recep-tive to new ideas and makes innovation decisions independent of the communicated experiences of others. Empirical studies on media technology adoption have largely confirmed the predictive power of innovativeness vis--vis novelty seeking.23

The current study adopts the construct of personal innovativeness, as assessed by the personality traits of novelty seekers and risk takers. In the context of smartphone applications, users who are more innovative are expected to explore more new apps at the apps stores, be willing to download or even pay for more apps, and use diverse types of applications more frequently. Accordingly, the following hypotheses are proposed:

H2a: Personal innovativeness will relate positively to the ownership of more smart-phone applications.

H2b: Personal innovativeness will relate positively to the frequent use of smart-phone applications.

Consumption skills. Scitovsky24 introduced the concept of skilled consumption, asserting that enjoyment of novelty requires learning. Just like production skills enable an individual to become more productive, consumption skills enable individuals to become more productive in their use of products and services. In fact, consumption skills are prerequisites to the full enjoyment of many activities. Scholars have dis-cussed the concept of consumption skills for newer communications technology.25 The more a product or service changes, the more users have to alter their consumption patterns. For example, transition from watching over-the-air TV to a basic cable sub-scription is continuous consumption with a slightly altered consumption pattern. Meanwhile, using interactive services such as gaming or home shopping on TV requires more dramatic changes on the part of the audience.26 Newly introduced prod-ucts typically attract a large number of users very quickly because of their novelty value. However, there may be diminishing interest when one lacks adequate consump-tion skills.27

As communications technology grows in complexity, adequate skills for consump-tion become even more critical. A smartphone will not be smart if its user does not have the skills to utilize its smart features. Van Dijk28 emphasized the importance of digital skills, especially in the use of the Internet.29 In the context of smartphone appli-cation usage, users with a higher level of consumption skills are likely to enjoy the benefits of diverse applications and are more inclined to try different applications. Accordingly, the following hypotheses are proposed:

H3a: Smartphone consumption skills will relate positively to the ownership of more smartphone applications.



Jung et al. 719

H3b: Smartphone consumption skills will relate positively to the frequent use of smartphone applications.

Smartphone use and social capital. The eventual aim of this study is to assess whether the usage of smartphone applications is associated with certain inequalities.30 Most literature on the digital divide addresses inequity in terms of technological opportuni-ties, such as physical access to computers, networks, and other technologies. The notion of social inequality is rarely examined.31 Social capital, broadly referred to as the resources accumulated through interpersonal or group relationships,32 is the core motive and outcome of social activities. At a macro level, Putnam33 elaborated on social capital as a collectively produced and owned entity, often taken to be repre-sented by norms, trust, and social cohesion (network), which accelerate coordination and cooperation for the mutual interest of a community. Other scholars34 focused on social capital as an additional pool of resources for the individual that enable him/her to attain his/her goals. In other words, at the individual level, social capital is the ben-efit, such as the information or support that one receives from ones relationship with others.35 Furthermore, Bourdieu and Wacquant defined social capital as the actual or virtual resources gained by possessing a durable network of mutual acquaintance and recognition.36

Social media such as Facebook and Twitter require bidirectional confirmation for continuous contact with online friends. Researchers have emphasized the importance of Internet-based linkages for the formation of ties, which serve as the foundation of bridg-ing social capital. Because online relationships may be supported by technologies like distribution lists, photo directories, and search capabilities, it is possible that new forms of social capital and relationship building will occur in online social media sites.37 As more social media migrate into the mobile platform, the social capital accumulated in the online space can be realized in the mobile context. In a sense, smartphone applications have the potential to enable a user to be connected anytime and anywhere, thus increas-ing his/her social capital. In fact, recent surveys found that above 40% of cell phone owners use a social networking site on their phone and 28% do so every day.38

Empirical studies have highlighted the possible positive effect of SNSs on social capital.39 Steinfeld, DiMicco, Ellison, and Lampe40 suggested that the intensity of SNS use is closely linked to contacting new friends and bridging social relationships. Williams41 also argued that convenient interfaces on SNSs may open new possibilities for enhancing social capital. Given that empirical evidence suggests that intensive SNS usage significantly affects social capital,42 it is likely that smartphone usage, which is closely associated with SNSs, may affect social capital. A recent study dem-onstrated that smartphone usage has a direct effect on social capital.43 However, as there are many kinds of mobile applications, such as gaming and productivity apps, it is unclear if the ownership or frequent use of smartphone apps is indeed linked to social capital acquisition. Therefore, the following questions are posited.

RQ1: After controlling for other variables, does the ownership of more smartphone applications predict the acquisition of more social capital?




RQ2: After controlling for other variables, does more frequent use of smartphone applications predict the acquisition of more social capital?

Smartphone use and human capital. Human capital theory suggests that knowledge provides individuals with opportunities to increase their cognitive abilities, leading to more productive and efficient activities.44 Previous knowledge assists in the integra-tion and accumulation of new knowledge, as well as incorporating and adapting to new situations.45 Thus, individuals may increase their knowledge as a result of educa-tion, training, and other informal learning opportunities from experiences and infor-mation exchange with others. Theoretically, a broad array of formal and informal learning opportunities can all contribute to an increase in human capital.

As discussed earlier, many social and economic benefits may accrue from a citi-zens greater access to and usage of ICT. For example, the Internet can significantly enhance ones human capital by increasing his or her access to education and train-ing.46 The acquisition of human capital in the context of the smartphone can be seen from two perspectives: the access to informal information with no space/time limita-tions; and the additional mobile access point for formal learning opportunities. Both may lead to an increase in human capital, according to human capital theory.47 Though earlier management studies measured human capital in terms of explicit knowledge, such as total years of formal education,48 recent scholars suggested that human capital is also the result of practical learning that takes place on the job, as well as nonformal education.49 It is plausible that the opportunities to acquire instant information and new ideas with smartphone apps may broaden ones human capital. Pew reported that as many as 64% of U.S. smartphone owners get their news and 31% visited govern-ment websites for information over the phone.50 As online education is picking up steam, most formal learning materials for distant education are now configured to be accessible via smartphones. In fact, researchers found that smartphones have been used as pedagogical tools to develop a ubiquitous learning environment.51 Nevertheless, as in the case of social capital, there are many kinds of mobile apps, and the use of Angry Birds would contribute little to ones knowledge. Thus, the following questions are examined.

RQ3: After controlling for other variables, does the ownership of more smartphone applications predict the acquisition of more human capital?

RQ4: After controlling for other variables, does more frequent use of smartphone applications predict the acquisition of more human capital?

Method

Data and Sample

A professional survey company was employed to conduct an online survey in Korea during May 2011, and a small online shopping coupon (approximately $5) was given as an incentive. Participants were recruited from national online consumer panels



Jung et al. 721

maintained by the leading market research company in Korea. The national consumer panels contained about 640,000 panelists, proportionally representing the Korean pop-ulation by gender and age. A total of 1,572 panelists were contacted initially. After excluding 870 nonsmartphone owners from the pool, 702 respondents completed the online survey. To ensure that the sample characteristics did not skew toward the typi-cal early adopter profiles (e.g., younger males), a quota sampling of subgroups by gender (male and female) and age (21-30, 31-40, 41-50, and above 50) was used to distribute the samples equally into the subgroups. The quota process continued until a sample of 360 subjects for final analysis was reached.52

Measurement

Demographics. Participants age, gender, and level of education were assessed. Their monthly household income was also measured, using a scale from less than one mil-lion Korean won (approximately US$1,000) to more than 7 million Korean won.

Personal innovativeness. The personal innovativeness construct was measured using a 5-point Likert-type scale anchored by 1 (strongly disagree) and 5 (strongly agree) with the following statements: (1) I like to try to new products, (2) I like to learn about new ideas, and (3) I am willing to take risks to try new things.53

Smartphone consumption skills. Smartphone consumption skills were assessed based on the concept of digital skills introduced by van Deursen and van Dijk.54 Following their approach, we identified seven items that may measure the skills for smartphone use. The seven items were pretested to confirm the appropriateness of the skills. Specifi-cally, the respondents were asked whether they had the ability to use each of the seven typical functions of a smartphone: (1) I can send and receive email via smartphone, (2) I can use SNS such as Facebook or Twitter via smartphone, (3) I can engage in interac-tive gaming via smartphone, (4) I can use an SMS application such as Whats app via smartphone, (5) I can edit video clips via smartphone, (6) I can send email with a voice recording attachment via smartphone, and (7) I can purchase and install paid applica-tions via smartphone. Consumption skills were coded 1 for yes and 0 for no and each individuals summed score was between 0 and 7.

Smartphone use (ownership and frequent use). This study measured degree of owner-ship and level of apps usage. To assess degree of ownership, the total number of appli-cations downloaded on each respondents smartphone was used. Because smartphone users may download a number of applications, but use some applications infrequently, we also examined how much the participants used specific types of applications. As there are above 700,000 apps for each major mobile platform (i.e., Android and Apple), it is impractical to measure frequency of usage for individual apps. Therefore, a category system was adopted to simplify the measurement of usage level and to dif-ferentiate the different types of mobile apps.




The Mobile Marketing Association (MMA) grouped apps into five categories: communication (including news), games, multimedia, utilities, and travel.55 The Korea Communications Commission (2012) classified all apps into fourteen categories, including utilities, maps/navigation, music, games/entertainment, and communica-tion.56 The Google Play store has twenty-six categories of applications, while Apples App Store has twenty-three categories. For the purpose of this study, the MMA clas-sification system was compared with the Korean Google Play and Apple categories and revised to include: news/information; entertainment (e.g., games, photos, music, video, etc.); communication (e.g., SNS, messaging, etc.); location-based-services (LBS) (e.g., navigation, Foursquare, etc.); utilities (e.g., productivity-enhancing ser-vices such as scheduling, bar code scanning, address books, etc.); and commerce (e.g., ticketing, shopping, banking, etc.). The usage on the six categories of applications was then measured on a 5-point Likert-type scale from 1 (use rarely) to 5 (use very often).

Social capital. According to Putnam,57 bridging social capital occurs when individu-als from different backgrounds make connections between social networks. These individuals often have only tentative relationships, but what they lack in depth, they make up for in breadth. As a result, bridging may broaden social horizons or open up opportunities for information or new resources. By contrast, bonding social capital occurs when strongly tied individuals, such as family and close friends, provide emo-tional or substantive support for one another. The individuals with bonding social capital have little diversity in their backgrounds, but have stronger personal connec-tions. Chang and Zhu reported that perceived bridging social capital has remarkable influence on users satisfaction and continuance intention of SNS, but perceived bond-ing social capital has none.58

Because the degree of network building was the focus in measuring the construct of social capital here, Williams bridging online social capital scale was adopted.59 Specifically, the following three statements were measured using a 5-point Likert-type scale: (1) I have increased communication with others using smartphone applications, (2) I have increased intimacy with others by using smartphone applications, (3) I have expanded personal relationships with others by using smartphone applications.

Human capital. Various studies have attempted to examine the concept of human capital as the degree of formal education.60 However, no specific scales have been used to measure human capital in a nonformal educational setting. Because the con-struct of human capital here focused on the acquisition of information, knowledge, and other learning opportunities regardless of formality, this study proposed the fol-lowing three items to assess the participants human capital in a smartphone usage context using a 5-point Likert-type scale: (1) I have acquired more information and/or knowledge ever since I started using smartphone applications, (2) I have learned many more new things ever since I started using smartphone applications, and (3) I have had more education/learning/training opportunities ever since I started using smartphone applications.



Jung et al. 723

Results

Descriptive Statistics

The average age of the sample was 38.6. In terms of gender, 50.7% were male. The age division distributions were as follows: 20s = 24.7%; 30s = 25.3%; 40s = 25.0%; and above 50 = 25.0% (as specified by the quota sampling process). In terms of educa-tion, 17.7% of the respondents were high-school graduates, 8.7% had post-graduate degrees, and the remaining 73.7% were college students or had a bachelors degree. Monthly household incomes ranged from less than 2 million Korean won (approxi-mately US$2,000) to above 7 million won.

Participants have been using smartphones for an average of 8.1 months, with smart-phone ownership history ranging between 1 and 36 months. The participants have downloaded approximately 44 applications, with a range from 5 to 192. The average application usage for the six application categories were news/information (3.72), entertainment (3.92), communication (4.14), LBS (3.54), utility (3.93), and commerce (2.89). The summated consumption skill score for the seven specific functions of smartphones was 5.08, with a range of 0 to 7. For multiple-item variables, all the vari-ables were reliable: innovativeness (Cronbachs = .863), social capital ( = .899), and human capital ( = .802). Following are the mean and standard deviation of the items measuring social and human capital. Social capital: item 1 (M = 3.58, SD = 0.84); item 2 (M = 3.43, SD = 0.84); item 3 (M = 3.58, SD = 0.86). Human capital: item 1 (M = 3.81, SD = 0.77); item 2 (M = 3.50, SD = 0.83); item 3 (M = 3.39, SD = 0.85).

Validation of Scales

Confirmatory factor analysis was conducted to test the validity of the scales. The con-vergent and discriminant validity of each variable was examined using the procedure suggested by Fornell and Larcker (i.e., measuring the reliability of each measure/con-struct),61 and the average variance was extracted (AVE) for each construct. According to Hair et al.,62 a measurement item loads highly if its loading coefficient is above 0.5. This analysis showed that most items had factor loadings higher than 0.8, which Fornell and Larcker63 consider to be very significant. Each item loaded significantly on its underlying construct (p < .001 in all cases). The composite reliabilities (CR) are well above the desirable level, .70,64 and AVE of each construct are all higher than .70, indicating good reliability.65 (See Table 1)

To examine discriminant validity, this study compared the shared variance among constructs with the AVE from the individual constructs. The shared variance between constructs was lower than the AVE from the individual constructs, confirming dis-criminant validity. The overall fit of the measurement model is satisfactory, with all relevant goodness of fit indices greater than .90 (GFI = .957, AGFI = .920, NFI = .960, TLI = .962, CFI = .975). Similarly, there is no evidence of misfit as the RMSEA showed a moderate level of .073, complying with the cutoff point suggested by previ-ous studies.66 The standardized RMR was also very good, at .042, well below the




threshold for a good overall fit. In short, the model demonstrated adequate reliability, convergent validity, and discriminant validity.

Influence of Individual Characteristics on Smartphone Application Use

Multiple regression analyses were performed to test the proposed hypotheses. In terms of H1a, the result shows that age ( = .244) was negatively related to the total num-ber of applications downloaded (see Table 1). The younger user segment generally owns more smartphone applications. On the other hand, gender, education, and income were insignificant. In addition, personal innovativeness (H2a) ( = .154) and con-sumption skills (H3a) ( = .142) were significant in their effects on the number of applications downloaded. The regression model showed a significant model fit (F = 5.84, p .001) and explained approximately 10.7% of the total variance.

Regarding the degree of smartphone apps usage, models for each specific applica-tion type showed significant fit and explained more than 10% of the total variance (see Table 2). Among the demographic variables, only gender ( = .137) was significant

Table 1. Validation of Scales (Convergent Validity Test).

Unstandardized SE CR p Standardized AVE CR

HC1 1.000 .000 .593 .889 .876HC2 1.621 .154 10.491 .000 .874HC3 1.522 .146 10.421 .000 .845SC1 1.000 .000 .829 .924 .926SC2 1.084 .060 18.004 .000 .878SC3 1.071 .059 18.184 .000 .887Inno1 1.000 .000 .796 .890 .887Inno2 1.053 .073 14.487 .000 .816Inno3 1.156 .077 14.983 .000 .858

CR = composite reliabilities; AVE = average variance was extracted; HC = human capital; SC = social capital; Inno = innovativeness.

Table 2. Predicting the Total Number of Applications Downloaded.

Predictor variable t-value

Gender .018 0.310Age .244 4.018***Education .077 1.288Income .059 0.927Innovativeness .154 2.617**Consumption skill .142 2.250*R2 .107

*p < .05. **p < .01. ***p < .001.



725

Tab

le 3

. Pr

edic

ting

the

Freq

uent

Use

of A

pplic

atio

ns.

New

s/in

foEn

tert

ainm

ent

Com

mun

icat

ion

LBS

Util

ityC

omm

erce

t-va

lue

t-

valu

e

t-va

lue

t-

valu

e

t-va

lue

t-

valu

e

Gen

der

.137

2.33

4*

.079

1.

398

.1

44

2.71

2***

.0

03

0.04

6

.172

2.

942*

*

.124

2.

241*

Age

.090

1.46

8

.319

5.37

4***

.3

15

5.70

1***

.0

05

0.09

0

.054

0.

890

.019

0.32

7Ed

ucat

ion

.097

1.62

1

.036

0.

622

.0

51

0.94

1.0

450.

775

.052

0.87

1

.072

1.

269

Inco

me

.049

0.77

8.0

240.

934

.064

1.11

0.0

290.

481

.018

0.28

4

.031

0.

518

Inno

vativ

enes

s.1

572.

483*

.0

21

0.33

6.2

213.

858*

**.3

365.

526*

**.2

153.

419*

*.2

944.

943*

**C

onsu

mpt

ion

skill

.049

0.78

1.1

812.

940*

*.2

434.

238*

**.1

081.

766+

.145

2.29

2*.2

634.

409*

**R2

.099

.150

.261

.163

.104

.199

Not

e. L

BS =

loca

tion-

base

d se

rvic

es.

+p

< .1

0. *

p

educação x smartphone

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