Journal of Socio-Informatics
Vol.10, No.1

Mar. 2018[Refereed Original Papers］
The Effects of Personality Traits on Smartphone Dependency and
Loneliness: A Study of University Students in Japan

Shaoyu YE,
Atsushi TOSHIMORI,
Tatsuya HORITA
Information networks of Open Data promotion in Local Governmentsof Japan

Akio YOSHIDA,
Tetsuo NODA,
Masami HONDAThe Society of Socio-Informatics

Journal of Socio-Informatics
Vol.10, No.1

Mar. 2018◆CONTENTS◆[Refereed Original Papers］
The Effects of Personality Traits on Smartphone Dependency and Loneliness:
A Study of University Students in Japan

Shaoyu YE,
Atsushi TOSHIMORI,
Tatsuya HORITA…   1Information networks of Open Data promotion in Local Governmentsof Japan
Akio YOSHIDA,
Tetsuo NODA,…  14
Masami HONDAThe Effects of Personality Traits on Smartphone
Dependency and Loneliness: A Study of University
Students in Japan
Keywords:
Smartphone dependency, Social networks, Loneliness, Personality traits, Gender differences

Shaoyu YE, University of Tsukuba
Atsushi TOSHIMORI, University of Tsukuba
Tatsuya HORITA, Tohoku University

Abstract
This study sought to determine the effects of personality traits on smartphone dependency andloneliness, controlling for the effects of students’ self-perception towards instant messaging (IM)and email dependency, and compared them among social networks formed through face-to-face(FTF), IM and email. A total of 216 university students in Japan completed a questionnairemeasuring their degree of personality traits (shyness and sociability), use of smartphones andcomputers, self-perceptions of IM/email dependency, and degree of smartphone dependency andloneliness. The results of factor analysis showed that students’ self-perception of IM/email
dependency was composed of the three factors of self-perception of text messages that Igarashi etal. (2008) indicated (namely, perception of excessive use, emotional reaction and relationshipmaintenance). The results of structural equation modeling showed that students’ shyness andsociability influenced their self-perception of emotional reaction, which had the largest effects on
smartphone dependency. Additionally, students’ sociability led them to overuse IM/email, which
helped reduce their loneliness but also led them to depend on smartphones more. Furthermore,
female students’ social networks via FTF and IM had positive effects on decreasing loneliness,while no similar effects for email were detected. The importance of distinguishing between the
shy-sociable dependency (through the process of emotional reaction) and sociable dependency(through the process of excessive use) is discussed.

1

The Effects of Personality Traits on Smartphone Dependency and Loneliness: A Study of University Students in Japan

YE et aland factors that influence them, to identify both

1. Introduction

consistencies and inconsistencies compared toOver the last two decades, various media usage

reported outcomes.

has influenced people’s communication behaviors

2. Literature review

dramatically. According to a survey conducted by
the Institute for Information and Communications

2.1. Relationship between Internet addiction,

Policy, which targeted 1500 people between themobile/smartphoneages of 13 and 69 years, 68.5％ of teenagers anddependency

andloneliness

94.1％ of people in their twenties have and use
smartphones (IICP, 2015). Compared to other

There are two main theories regarding thegenerations, young persons spend more timeeffects of Internet use on loneliness. The first isaccessing the Internet through smartphones.

the social augmentation hypothesis, which suggests

According to another survey targeting highthat social communication on the Internet

school students, those with a higher level ofincreases the total social resources available todependency on smartphones tend to exhibit poor

individuals by providing additional possibilities for

etiquette in their Internet usage and moreinteraction with others (Bessiere et al., 2004). This

inappropriate communication behaviors (IICP,

has been supported by Shaw and Gant (2002) and2014). The problems related to inappropriate

Sar et al. (2012) among college students andsmartphone usage among young people haveelderly people. Additionally, Davis (2001) proposed

attracted a substantial amount of attention.

a cognitive-behavioral model of PathologicalInternet dependency, or Internet addiction, isInternet Use (PIU), with psychosocial issues suchdefined as the loss of control over one’s Internet

as loneliness and depression serving as precursors

use and is considered an impulse control disorderto PIU. The model demonstrates that individuals(Young, 1999). Previous studies have shown thatwith poor face-to-face (FTF) interactions andInternet

mobile/smartphone

communication skills may prefer computer-dependency relate to problems such as academic

mediated communication (CMC) to interact with

failure (e.g., Lepp et al., 2014); depression, anxiety

others. Therefore, CMC provides a less risky andand stress (e.g., Akin & Iskender, 2011);

more anonymous arena in which to communicate

sleeplessness (e.g., Cao & Su, 2007); lonelinesswith more confidence than FTF interactions,(e.g., Casale & Fioravanti, 2011); and so on.

which may be especially enticing for people withAmong these, loneliness seems to be one of thehigher levels of loneliness or depression (e.g.,most serious symptoms. As Worland (2015)

Morahan-Martin & Schumaker, 2000).

addiction

andargued, loneliness is a potential pandemic “on par

On the other hand, the displacement hypothesis

with obesity and substance abuse,” and manystates that time spent using a particular medium

researchers have established that loneliness is

replaces time spent using other media, such as

related to serious health risks from children to

FTF interaction (Huang, 2010). As a result, using

adolescents and adults (Pittman & Reich, 2016).

the Internet replaces FTF communication inThus, this study examines the relationshipindividuals’ lives. The Internet operates as a

between smartphone dependency and lonelinessmore attractive but ultimately leaner and less2

Journal of Socio-Informatics Vol. 10 No. 1 Mar. 2018

satisfying medium for human interaction, thus

cognitive bias model can only be supported inleading users to feel an increased sense ofterms of CMC. Additionally, Ye (2016) suggested

loneliness. This has been supported by many

that shyness only had direct effects on lonelinessstudies (e.g., Esen et al., 2013; Kose et al., 2016).

but had no mediating effects on their social

Compared to conventional CMC, people today

networks, regardless of FTF, IM or email.

form more intimate relationships by sending text

Currently, people transmit information to a large

messages and making phone calls via various

number of people via social media, while also

instant messaging (IM) applications, and/or by

sending text messages and/or making voice/sending emails based on FTF interactions, than

video

by relying only on FTF interactions (Cleemput,“messengers.” These allow us to suppose that if

2010; Kim et al., 2007). This suggests that theindividuals’ social networks via IM are large

boundary between FTF and online has become

enough as a result of FTF interactions, this will

smaller. Therefore, the relationship between

help them decrease loneliness. On the othersmartphone use and loneliness might be more

hand, as younger people seldom send emails tocomplicated due to the different relationships

connect with intimate friends (Lenhart et al.,between FTF and online interactions.

2005), social networks via email might have

calls

to

a

specific

personusing

weaker effects than those developed via IM.

2.2. 
Relationships between social networks
and loneliness, and the effects of

2.3. Relationship between perception of text

personality traits on these relationships

message dependency and personality
traitsLevin and Stokes (1986) have suggested that
there are two processes that can explain why

In Japan, before smartphones became popular,

loneliness occurs: the social network mediation

mobile phones had been used since 1999 not only

model and the cognitive bias model. The former

as traditional telephones but also to access thesuggests that certain personality traits make it

Internet, to send and receive text messages, anddifficult to form and maintain social relationships

to use the short message service (SMS). Becausewith others; consequently, social networks areof the immediacy and the few temporal andsparse, leading to loneliness. The latter suggests

physical restrictions of text messages, they werethat negative perceptions toward the self andconsidered equivalent to conventional CMC andothers lead people to underestimate their social

were therefore widely used, especially among the

networks;

youngerconsequently,

they

feel

lonelygenerations.

Thus

far,

extensive

regardless of the reality of the social networks.

research has indicated that young generations

Stokes (1985) and Levin and Stokes (1986) haveprefer text messages via mobile phones to othershown that extroversion, depression and attitudes

communication media (Matsushita, 2012). Basedtoward others relate to both of the models. Basedon the implications of Internet dependency/

on these, Igarashi (2002) indicated that in terms

addiction via computers, Igarashi et al. (2008)

of FTF, both the social network mediation model

developed a scale for self-perception of text

and the cognitive bias model are supported whenmessage dependency and conducted a survey on

considering the effects of social skills, while theJapanese high school students, examining the3The Effects of Personality Traits on Smartphone Dependency and Loneliness: A Study of University Students in Japan

through

onlineYE et alrelationship between personality traits andnetworks

dependency on text messages, and how these

without interacting in person. Thus, these shy-communications

affected their daily lives. As a result, it was foundsociable individuals would depend more onthat text message dependency was composed ofsmartphones.

three factors—emotional reaction, perception of

2.4. Gender differences

excessive use and relationship maintenance—
and this text message dependency may occur

Gender differences have been frequently

through two different processes. The first isreported in patterns of Internet/smartphone use

extroverted dependency, which is concernedand addictive behaviors. As IICP indicated (2014,

with the process by which extroversion increases2015), female students spend twice as much time

the perception of excessive use of text messages

as male students on accessing the Internet

and the consequent occurrence of psychological/

through smartphones. Additionally, compared tobehavioral symptoms. The other is triggered

male students, female students have higher

by neuroticism, which intensifies students’levels of social anxiety, which leads them to talkrelationship maintenance and thus increases

with others online more (especially via IM) andpsychological/behavioral symptoms. However,to feel more comfortable using only text (Piece,

as smartphone dependency refers to behavioral

2009). Liang et al. (2016) indicated that, in thesymptoms while loneliness refers to morecase of children in grades 6‑8 in China, male

psychological symptoms, it is necessary to

students’ depression leads them to Internet

measure them separately. Furthermore, as

addiction,

Igarashi et al. (2008) indicated, the self-

addiction makes them have higher levels ofperception of relationship maintenance also

depression. Furthermore, Igarashi et al. (2005)

includes interpersonal relationships throughindicated that in Japan, female students tend to

text messages. Therefore, we believed thatbe more active than male students in forming

individuals’

relationshiplarger and more intimate social networks bymaintenance influences their social networks via

sending more text messages via mobile phones.

IM/email based on their FTF interactions.

Based on these, we believe that female students

self-perceptionofRegarding the relationship between shyness

while

femalestudents’

Internet

will depend on smartphones more than males.

and sociability, as Cheek and Buss (1981)

In sum, the present study aims to examine theindicated, shy people are not necessarily

process illustrated by Figure 1. Here, self-

unsociable; there are also nonshy-unsociable,

perception of IM/email dependency includes thenonshy-sociable and shy-unsociable people. In

three factors of emotional reaction, excessive

general, when communicating in person, shysociable people have a strong motivation to bewith others, while also realizing that they are
fearful and inhibited about mixing socially. This
type of individual tries to avoid interactions inperson. However, this type of individual can form
and maintain a large number of intimate social

Figure 1. The analyzed model in this study4

Journal of Socio-Informatics Vol. 10 No. 1 Mar. 2018use and relationship maintenance, and wewere not typical for university students. As a

examined their effects on behavioral symptoms

result, 10 items were used in the present study(smartphone dependency) and psychologicaland the students were asked to answer by rating

symptoms (loneliness).

on a 5-point scale (5. Very true; 3. Neither/either;
1. Definitely not the case).

3. Method

Part C gathered information about thestudents’ FTF, IM, and email social networks. As

A questionnaire survey was conducted at ain the study of Ye (2016), we asked students tonational university in the Kanto Region, Japan. A

list UP TO 10 people they had met FTF or sent

total of 228 university students submitted theirIMs and emails to in the previous three months

responses. We excluded 12 respondents who didfor important conversations. Here, students were

not use smartphones and/or had incomplete

asked to provide information, for each social

responses (Table 1).

network, about their social contacts’ genders,

The questionnaire had four parts―Part A,

ages, relationships, and frequency of contact

Part B, Part C, and Part D―with the content in(from 4. Almost every day to 1. Less than once

each part as follows.

per month), and the main content of thePart A gathered demographic data, such ascommunication and importance of the person ingender, age, living situation, and so on. The 12

their lives (from 5. Very important to 1. Not

items for shyness were chosen from Sakurai andimportant at all). Additionally, the students were

Sakurai (1991) based on Factor loadings from theasked to answer whether the person(s) listed inthree factors. The 5 items for sociability were

the social network via IM and email were alsochosen from Cheek and Buss (1981). Respondents

listed via FTF, and so on.

were asked to answer using a 5-point scale (5.

Part

D

measured

students’degreeofStrongly agree; 3. Neither/either; 1. Strongly

loneliness. The 20 items were chosen from Moroidisagree) for each of the items.

(1992) and used a 5-point scale (from 5. StronglyPart B measured the students’ media usage
including

computers

andagree to 1. Strongly disagree).

smartphones.

4. Results

Regarding the self-perception of IM/email
dependency, we revised some expressions based

4.1. 
Relevanton the scale for self-perception of text message

informationfor

analyzed

students

dependency from Igarashi et al. (2008) (Table 2).
They were asked to rate the 15 items by using a

Relevant information for the 216 students is

5-point scale (5. Strongly agree; 3. Neither/shown in Table 1. According to the results, 94％

either; 1. Strongly disagree).

personally owned a computer (either desktop or

In addition, we used a short version of thelaptop), and 71.3％ accessed the Internet via

smartphone dependency scale developed forsmartphones for over 2 hours per day. Inhigh school students in Japan (IICP, 2014). Thereparticular, they accessed the Internet via

were 20 items in the original version, but wesmartphones for longer than they did via

excluded those with low scores and those thatcomputers. We conducted t-test (independent
5

The Effects of Personality Traits on Smartphone Dependency and Loneliness: A Study of University Students in Japan

YE et alexample) to examine gender differences in time

significant differences among these scales werespent on accessing the Internet via computers

found except for the smartphone dependencyand(t(214)=2.12, p<.05, male vs female: 28.21 vssmartphones.

The

results

indicatedsignificant differences for both: Male students

30.69).

accessed the Internet via computers longer than

4.3. Factor analysis of the self-perception of

females (t(213)=2.14, p<.05, male vs female: 3.35

IM/email dependency scale

h vs 2.83 h), while female students accessed theInternet via smartphones longer than male

We conducted an exploratory factor analysis

students (t(214)=2.19, p<.05, male vs female: 3.29

on the self-perceptions of IM/email dependency

h vs 3.79 h).

to test the factor structure. Table 2 shows theRegarding the types of IM platforms used,

results of a maximum-likelihood exploratory

96.3％ used LINE, 81.9％ used Twitter, and so

factor analysis with oblique rotation, includingon. However, there were no differences due tothe mean score and standard deviation (SD) for

gender among these preferences.

each item. The results show the same structure
as Igarashi et al. (2008) with regard to the self-

4.2. Internal reliability for each scale

perception of text message dependency (namely,

We confirmed each scale’s internal reliability

emotional reaction, relationship maintenanceby calculating its Cronbach’s α coefficients. As aand self-perception of excessive use). The

result, α for the shyness scale was .89, α for the

Cronbach’s α coefficients were .86, .80, and .76

sociability scale was .90, α for the smartphone

for emotional reaction, relationship maintenancedependency was .89, and α for the loneliness wasand excessive use, respectively, and were almost.90. These all showed high internal reliability.

the same as the results of Igarashi et al. (2008).

Therefore, we used the total score for each scale

4.4. Configurations of social networks

in the following analysis. We conducted a t-test(independent sample) to confirm whether there

Prior to examining the relationship between

was any difference due to gender. As a result, no

the students’ personalities, self-perception of IM/
email dependency, smartphone dependency andloneliness,

Table 1. Information for analyzed students

weneededtoinvestigate

theconfigurations of each social network. Table 3
shows the average number of people in eachsocial network and their relationships. As
mentioned earlier, we asked the respondents tolist up to 10 persons for each social network, so
the maximum number for each social network is
10. As Table 3 indicates, the largest social
network was still via FTF, while that via email was
the smallest. In addition, we also analyzed how
students’ social networks related to each other.
For instance, “IM & FTF” means that those6

Journal of Socio-Informatics Vol. 10 No. 1 Mar. 2018

people who were listed under IM were also listed

be adopted regardless of the social networks via

under FTF. From Table 3, it was found that for

FTF, IM or email. Therefore, the following results

both males and females, approximately 60％ towere about the entire group and females. All the

70％ of the contact persons listed under IM wereindexes and the parameter estimates are shown

also

in Figures 2‑7.

people

with

whom

the

students

communicated in person (male: 3.42/4.94;

We found that they share the following

female: 3.18/5.61). In contrast, most of thecommonalities: (a) Smartphone dependency had

contact persons listed under email had not been

no relationship with social networks or loneliness.

met in person or communicated with via IM.

(b) Self-perception of excessive use and emotional

Furthermore, it was found that female students’reaction had strong effects on smartphonesocial networks with males via FTF, IM and email

dependency, while only excessive use had effects

were all larger than male students’ social

on decreasing loneliness. (c) Shy-sociablenetworks with females.

students

showedthestrongestemotional

We also analyzed with whom the students

reaction, and also relationship maintenance forcommunicated in each social network, including

IM/email, while their self-perception of excessive

the effects of gender differences. As Table 4

use was influenced by sociability only. (d) Shy

shows, for both males and females, over half ofpeople felt lonelier.

the contact persons in their social networks via

Meanwhile, we found that, overall, their self-

FTF and IM were “friends,” and over 30％ wereperception of relationship maintenance hadat the same university. Furthermore, although

effects on increasing loneliness regardless of thethose friends who were “at other universities”

social networks of FTF, IM or email, while thewere not included in social networks via FTF,

females’ social networks via FTF and IM hadthey were included in social networks via IM. On

positive effects on decreasing loneliness.

the other hand, emails were used to communicate

5. Discussions

with academic advisors and family members/
relatives, rather than with friends, for both males

5.1. Key findings

and females.

There were three main purposes of this study.

4.5. 
Personality

of

self-

First, we completed an analysis of the predictive

perception of IM/email dependency,

relationships between self-perception of IM/

smartphone dependency and lonelinessemail dependency, smartphone dependency andWe conducted SEM analysis to test the effects

loneliness among the three social networks (FTF,

personality

traits’traitsoneffectsonself-perceptionofIM and email). Second, we conducted an analysis

dependency towards IM/email, and the effects ofof personality traits’ effects on their self-

both on smartphone dependency and lonelinessperception of IM/email dependency. Finally, weamong the three social networks (FTF, IM andexamined

email), as shown in Figure 1. Additionally, we

associations.

gender’seffects

on

the

above

also conducted multi-group SEM to detect gender

Igarashi et al. (2008) showed that self-

differences; however, no model for males could

perception of excessive use had the strongest
7The Effects of Personality Traits on Smartphone Dependency and Loneliness: A Study of University Students in Japan

YE et alTable 2. Exploratory factor analysis on self-perception of IM/email dependency

Table 3. Configurations of each social network and their relationships

Note: * for significant differences between males and females (p<.05).

Table 4. The contact persons in each pair of social network

Note: a: Lover; b: Friend; c: Family member/Relative; d. Advisor; e. Student at the same university;
f: Student at other university; g. Other faculty member/staff; h. People at part-time job; i. Others.

8Journal of Socio-Informatics Vol. 10 No. 1 Mar. 2018

Figure 2. Results for FTF (Entire)

Figure 5. Results for IM (Female)

Figure 3. Results for FTF (Female)

Figure 6. Results for Email (Entire)

Figure 4. Results for IM (Entire)

Figure 7. Results for Email (Female)effects on psychological/behavioral symptoms,

excessive use had effects on both smartphone

followed by relationship maintenance, while

dependency and loneliness. This might also help

emotional reaction had the weakest influence.

to explain why self-perception of excessive use

However, the current research indicated that

had the largest effects in Igarashi et al. (2008)’s

emotional reaction had the strongest effects on

findings,

smartphone dependency, whereas relationshipsymptoms and behavioral symptoms together.

as

they

measured

psychologicalmaintenance increased loneliness when including

Second, the factor of emotional reaction

both genders. There are some possible reasonsinvolves a strong urge to receive immediate

for these differences.

replies from the recipient of a message. WhenFirst,

this

symptoms

studymeasured(smartphone

behavioral
andusers (especially younger generations) tended to

psychological symptoms (loneliness) separately.

reply immediately, even though text messages

Our results suggest that emotional reaction

are asynchronous. This means that text messages

might have the largest effects on behavioral

had become synchronous tools for users,

symptoms, while self-perception of excessive

especially younger generations. There are now

use has effects on psychological symptoms.

many applications with functions to show whether

Interestingly,

the recipient has already read a text message. We

students’dependency)

using mobile phone text messages previously,

self-perceptionof9The Effects of Personality Traits on Smartphone Dependency and Loneliness: A Study of University Students in Japan

YE et albelieve that these functions have influenced

positive effects on decreasing loneliness (Figures

students to depend on smartphones more. In

3 and 5). As Table 4 shows, students sent IM tocontrast, the factor of relationship maintenancecommunicate with friends, especially those at the

did not lead to smartphone dependency but

same university. Compared to male students,

rather to loneliness when including both males

female students communicated with more cross–and females. This factor is concerned withgender people, who helped them decrease

motivation for perpetual contact via IM/email

loneliness as Argyle (2001) suggests. On theand therefore has the potential for interpersonal

other hand, female students also sent email todependency. As Igarashi et al. (2008) suggested,advisors and family members (Table 4), who areinterpersonal dependency implies a central needless intimate and, therefore, had no effects on

to stay close to others and to maintain nurturing

decreasing their loneliness.

and supportive relationships, and people whoWith regard to personality factors and self-

receive less social support tend to feel lonelier

perception, shyness affected emotional reaction

and use more social media to compensate for theand relationship maintenance, whereas sociability

lack of FTF interactions (Song et al., 2014).

contributed to emotional reaction, excessive use

Third, the effects of the self-perception ofand relationship maintenance. On the basis ofexcessive use seemed to be more complicated.

these, smartphone dependency may occur

Although using IM/email helped students

through two different processes. The first is

decrease loneliness, it also led them to depend onsocial dependency, concerned with the process

smartphones. This factor contains the items

by which sociability increases the perception ofregarding the perception of socially inappropriate

excessive use of IM/email and the consequent

behavior in Japanese culture, such as typing

occurrence of smartphone dependency. This ismessages while having FTF communications

similar to extroverted dependency, as Igarashi etwith others. However, targets in our researchal. (2008) indicated. We found that social students,

are members of the so-called “digital native

especially females, also tended to overuse IM/

generations,”

different

email to decrease their loneliness, so these twoperceptions of inappropriate behaviors because

who

might

have

processes would seem to continue in the future.

they started using mobile devices in elementary

The other type of smartphone dependency isschool or middle school and therefore might

triggeredbyshynessandsociability.

As

consider certain behaviors to be natural ormentioned above, shy-sociable people have a

acceptable. This would be one of the mainstrong motivation to be with others while also

reasons why they had higher levels of self-

avoiding interactions in person because they areperception of excessive use while feeling lessafraid of using appropriate non-verbal cues andlonely. Still, we should note that these individuals

how they will be interpreted (Check & Buss,

need to send messages frequently and actively in1981). However, they are able to form andorder to form and maintain these intimate social

maintain social networks using IM/email without

relationships.

communicating with others in person. Among

Out of our hypotheses, it was found that only

the three factors of self-perception in IM/email

females’ social networks via FTF and IM had

dependency, emotional reaction had the strongest
10Journal of Socio-Informatics Vol. 10 No. 1 Mar. 2018

effects on smartphone dependency and was

survey.

strongly influenced by shyness and sociability.
These suggest that shy-sociable dependency

References(through the process of emotional reaction) may

Akin, A., & Iskender, M. (2011) Internet addiction

occur more easily than the social dependency

and depression, anxiety and stress, International(through the process of excessive use).

Online Journal of Educational Sciences, 3(1), pp.
138-148.

5.2. Future work

Argyle, M. (2001) The Psychology of Happiness (2ndThis study’s results suggest that smartphoneversion). Routledge.

dependency does not necessarily lead to

Bessiere, K., Kiesler, S., Kraut, R., & Boneva, B.

loneliness, which is inconsistent with previous(2008) Effects of internet use and social resources

studies. As mentioned above, smartphoneondependency

Communication and Society 11, pp. 47-70.

in

this

study

includedmorechanges

indepression,

Information

behavioral symptoms, while loneliness refers toCao, F., & Su, L. (2007) Internet addiction among

more psychological symptoms. As previous

Chinese adolescents: prevalence and psychologicalstudiesfeatures, Child: Care, Health and Development

have

reported,peoplewhouse

smartphones for SNS and games are likely to33(3), pp. 275-281.

depend on smartphones more, while those who

Casale, S., & Fioravanti, G (2011) Psychosocial

use smartphones for academic purposes are notcorrelates of internet use among Italian students,(e.g., Salehan & Negahban, 2013). This suggests

International Journal of Psychology, 46(4), pp. 288-

that future research should investigate the298.

different purposes of smartphone usage when

Cheek, J.M., & Buss, A.H (1981) Shyness andexamining the relationships between smartphonesociability. Journal of Personality and Social

dependency and loneliness.

Psychology, 41(2), pp. 330-339.

Additionally, our research results show that

Cleemput, K.V. (2010) “I’ll see you on IM, text, or callfemale students spent more time accessing theyou”: A social network approach of adolescents’

Internet via smartphones than did male students,

use of communication media, Bulletin of Science,

while male students used computers longer,

Technology & Society 30(2), pp. 75-85.

which is consistent with previous studies. This

Davis, R.A. (2001) A cognitive-behavioral model ofsuggests that although smartphone dependency

pathological Internet use, Computers in Humanis obvious for female students, computer Internet

Behavior 17, pp. 187-195.

dependency, like traditional addiction, might still

Esen, B.K., Aktas, E., & Tuncer, I. (2013) An analysis

be a serious problem for male students. Detailedof university students’ internet use in relation toresearch might be necessary in the future.

loneliness and social self- efficacy, Procedia-Social
and Behavioral Sciences 84, pp. 1504-1508.

Acknowledgement

Huang, C. (2010) Internet use and psychological well-This study was supported by JSPS KAKENHI

being: A meta-analysis, Cyberpsychology, Behavior,

Grant Number 15H02923. The authors would likeand Social Networking 13, 241-249.

to thank all the students who helped answer this

Igarashi, T., Motoyoshi, T., Takai, J., & Yoshida, T.

11

The Effects of Personality Traits on Smartphone Dependency and Loneliness: A Study of University Students in Japan

YE et al(2008) No mobile, no life: Self-perception and text-

relationship between cell phone use, academic

message dependency among Japanese highperformance, anxiety, and satisfaction with life inschool students, Computers in Human Behavior

college students, Computers in Human Behavior

24, pp. 2311-2324.

31, pp. 343-350.

Igarashi, T., Takai, J., & Yoshida, T. (2005) Gender

Levin, I. & Sokes, J.P. (1986) An examination of thedifferences in social network development via

relation of individual difference variables tomobile phone text messages: A longitudinal study.

loneliness, Journal of Personality 54, pp. 717-733.

Journal of Social and Personal Relationships 22, pp.

Liang, L, Zhou, D., Yuan, C., Shao, A., & Bian, Y.

691-713.

(2016) Gender differences in the relationship

Igarashi, T. (2002) The effects of social skills on

between internet addiction and depression: A

loneliness through mediation of CMC social

cross-lagged study in Chinese adolescents,

networks, The Japanese Society of Social Psychology

Computers in Human Behavior 63, pp. 463-470.

17(2), pp. 97-108. (in Japanese)

Matsushita, K. (2012) Wakamono to keitaimeiru

Institute for Information and Communications Policy

bunka (Young generations and culture of cell(2015) A survey on the usage time of information

phone text messages), Okada, T. & Matsuda,

communication media and their informationM.

behaviors in 2014, <http://www.soumu.go.jp/

Communication and Society. Yuhikaku Press pp.

main_content/000357570.pdf>

Accessed

2017,

Understanding

KeitaiSociety:

Mobile61-80. (in Japanese)

January 19.

Morahan-Martin, J., & Schumacher, P. (2000)

Institute for Information and Communications Policy

Incidence and correlates of pathological internet(2014) A survey on high school students’ use ofuse among college students, Computers in Humansmartphone applications and Internet dependency

Behavior 16, pp. 13-29.

trend, <http://www.soumu.go.jp/main_content/

Moroi, K. (1989) Loneliness and coping strategies in

000302914.pdf > Accessed 2017, January 19.

university students, The Japanese Journal ofKim, H., Kim, G.J., Park, H.W., & Rice. R.E. (2007)

Educational & Social Psychology 29(2), pp. 141-

Configurations of relationships in different media:

151. (in Japanese)

FTF, email, instant messenger, mobile phone,
and

SMS,

Journalof

Pierce, T. (2009) Social anxiety and technology: Faceto-faceComputer-MediatedCommunication 12, pp. 1183-1207.

communication

versustechnologicalcommunication among teens, Computers

Kose, S., Noyan, C.O., Nurmedov, S. Yilmaz, O., &in

Human Behavior 25, pp. 1367-1372.

Dilbaz, N. (2016) Smartphone addiction and its

Pittman, M., & Reich, B. (2016) Social media andrelationship with social anxiety and loneliness.

loneliness: Why an Instagram picture may be

Behavior & Information Technology 35(7), pp. 520-

worth more than a thousand Twitter words,

525.

Computers in Human Behavior 62, pp. 155-167.

Lenhart, A., Hitlin, P. & Madden, M. (2005) Teens

Sar, A.H., Gokturk, G.Y., Tura, G., & Kazaz, N. (2012)

and Technology. <http://www.pewinternet.org/

Is the internet use an effective method to cope

2005/07/27/teens-and-technology/> Accessed 2017,with elderly loneliness and decrease loneliness

January 19.

symptom? Procedia-Social and Behavioral Sciences

Lepp, A., Barkley, J.E., & Karpinski, A.C. (2014) The

55, pp. 1053-1059.

12

Journal of Socio-Informatics Vol. 10 No. 1 Mar. 2018
Salehan, M., & Negahban, A. (2013) Social

Stokes, J.P. (1985) The relation of social network andnetworking on smartphones: When mobile

individual difference variables to loneliness,

phones become addictive, Computers in Human

Journal of Personality and Social Psychology 48, pp.

Behavior 29, pp. 2632-2639.

981-990.

Sakurai, S., & Sakurai, T. (1991) Construction ofWorland, J. (2015) Why loneliness may be the next

Japanese version of shyness scale for college

big public-health issue. Time Magazine, <http://

students, Bulletin of Nara University of Education

time.com/3747784/loneliness-mor tality/>

Cultural and Social Science 40(1), pp. 235-243. (inAccessed 2017, January 19.

Japanese)

Ye, S.Y. (2016) The effect of shyness on Japanese

Shaw, L.H., & Gant, L.M. (2002) In defense of thecollege students’ loneliness through mediation ofinternet: The relationship between internet

important social networks: Comparing face-to-communication and depression, loneliness, self-

face, instant messaging and e-mail, International

esteem, and perceived social support Cyber

Journal of Psychology 51, pp. 839-840.

psychology & Behavior 5(2), pp. 157-171.

Young, K.S. (1999) The evaluation and treatment ofSong, H., Zmyslinski-Seelig, A., Kim, J., Drent, A.,internet addiction. In. Innovations in clinical

Victor, A., & Omori, K. (2014) Does Facebook

practice: A source book, pp. 19-31.

make you lonely?: A meta analysis, Computers in
Human Behavior 36, pp. 446-452.

13

Information networks of Open Data promotion in Local
Governments of Japan
Keywords:
Local Governments, Open Data, Information Network, Scale-free network, Regional bondAkio YOSHIDA, Jawaharlal Nehru University
Tetsuo NODA, Shimane University
Masami HONDA, Shimane University

Abstract:
Public data collected or possessed by administrative agencies and subsequently released as
Open Data is expected to bring about positive economic effects. The purpose of this study is toobtain findings contributing to the Open Data promotion in Japanese local governments. We
conducted WEB questionnaire survey and obtained information networks of Open Data promotion
in local governments. The network in the open data personnel had different characteristics fromthose of the WEB reference relationship network in the degree distribution. It shows those two
networks have different structures. This result can be indicative that the regional binding plays
an important role in the information network.

14

Journal of Socio-Informatics Vol. 10 No. 1 Mar. 2018

With respect to economic effects by using

Introduction

Open Data, some estimates have been carriedOpen Data, publishing public data whichout by the governments including the EU

governmental agencies collect and hold in theCommission and private research institutions,form easy-to-secondary-use, are expected to havethere are also the estimates made in Japana significant economic effect. On 22th Dec 2016,(Jitsuzumi et al, 2013). Noda (2015) carried out athe council of industrial structure in the Ministry

study of the relevant prior research. Subsequently,of Economy, Trade and Industry of Japan (whichon the basis of the study results, a questionnaire

is discussing the strategy of growth) has decided

survey for local governments of Japan was

to consider making new mechanisms for

conducted (Yoshida et al, 2016). A comparison ofcompanies to utilize big data that the central

the major previous studies and this study aregovernment and local governments obtain in theshown in Table 1. First, this survey aims totraffic and medical fields etc (NHK, 2016). On

estimate the economic effect in line with the20th Jan 2017, Saitama prefecture has created acurrent situation of Open Data in Japan. Second,common format of Open Data in cooperation with

the investigation object is the local government

58 municipalities in the prefecture (Nikkei

which is a provider of data. Third, the estimation

Shimbun,isprocess of the economic effects becomes clear byimportant for both promotion and utility of Openusing a uniform question of WEB questionnaire.

Data (Noda et al, 2017). Japan is on the way of

The purpose of this study in the project is to2017).

The

commonformatOpen Data promotion.

obtain the knowledge how Open Data spread in

In the view of network effect (Katz et al, 1985),

municipalities of Japan by considering throughthe more local governments provide these public

the network analysis of the answers in thedata, the values and utilities of each data sets willquestionnaire how the local governments referincrease. Open Data initiatives in advanced

information of each other. Honda et al (2016)

municipalities bring about certain economic

have already pointed out the role of advanced

effects, and Open Data can spread to various

cities in Open Data promotion. Especially Sabae

places throughout the network. In this process,city in Fukui Prefecture was referred by otherthe following local governments get information

municipalities. In this study, total structures offrom advanced ones. Both the estimation of

information networks are analyzed. Municipalities

economic effects and findings in the way of

can learn the ways of Open Data on websites by

spread can help to promote Open Data.

others. They can also get information about Open

Table 1 Comparison with previous researches(Noda et al, 2017)15

Information networks of Open Data promotion in Local Governments of Japan

YOSHIDA et al

Data from personnel in other municipalities.

Data in the inter-local government were asked. In

Therefore, this study is going to clarify thethe question of the information network, the 182

difference. It will help us to know how to promotelocal governments made up of all subjects

Open Data efficiently.

presented to the respondent by pull-down menu,
to select the appropriate other local government

Methodin the order. Specific questions are as follows. If
we asked them to answer all informants, it would

WEB questionnaire were requested to the

be too much a load for respondents. It might

persons in charge of Open Data at local

decreasegovernments of Japan which have alreadymunicipalities were selected in this survey, based

response

rate.

Therefore

five

implemented Open Data. WEB site for answer

on the name generator method in 1985 GGS

were constructed and e-mails of the request for(Smith et al, 2015).

answer were delivered Using the SPIRAL of◦“Do

Piped Bits Co., Ltd. Period was up to 22nd
February

from

9th

February,

2016.

you

accessthefollowing

other

The

municipalities’ WEB sites, in the reference ofinvestigation objects correspond with Open Datathe state of Open Data? Please select five in themunicipality list of Fukuno (2013) at the time oforder you refer the most.” ...(A)survey added Atsugi city, 182 local governmentsof Japan (Table 2). Response rate is at 57.1％, up◦“Do you get information about Open Data fromto 63.5％ excluding the administrative districts ofpersonnel of public information at the following

major cities.

other municipalities? Please select five in the

The main questions in this survey were,order you get the most, including the case you

percentages and progress degrees of Open Data,attended a seminar.” ...(B)the cost at the time of implementation of Open
Data, the reduction cost of the past business, andInformation networks obtained from the two

the feeling of personnel. In addition, the use ofquestions1 of the respondents were compared by

the data, the possibility of private use, and thedegree distributions.

network of information propagation about Open

Table 2 Investigation object




＊

16

Included major city in city, Number of City is 1741
(Yoshida et al, 2016)

Journal of Socio-Informatics Vol. 10 No. 1 Mar. 2018

Figure 1 Degree distributions for two information networks
y=1.898x －1.0794 R 2=0.6727. The coef ficient ofResults

determination is approximately 0.3 lower thanthat of WEB network. This also can be seen inInformation networks in the WEB site (A), andFigure 1. The number of local governments within the persons in charge of public information (B)the 5 designees is only 1. The cut-off of normal

are shown as piled degree distributions (Figure

distribution seems to occur. It is also reflected in1). The horizontal axis is the number ofthe point that the long tail becomes shorter,

nomination (designee) from the personnel at

designee number Sabae city in Fukui Prefectureother local government which is incoming degree

remains 19 in spite goes up to 48 in the WEB

in the sense that it was chosen by others as the

network.

option of the answer, but also is outgoing degree

Discussion

in the sense that it originated the information.
Here, it is referred to as the designee number.
The vertical axis is the number of local

Degree distribution of the WEB site of thegovernments. Both of the horizontal axis and theinformation network, which fit very well in power

vertical axis are displayed in common logarithm

approximation

to distinguish the features of distributions.

determination 0.9748, can be said to have the

with

the

coefficientof

In the information network of the WEB site,characteristics of the so-called scale-free network

the degree distribution to power approximation

by Barabási et al. (1999). However, theis y=1.5129x

information network of the persons in charge of－0.9128

R =0.9748 (the local governments
2with no designee2 included in the calculation of

publicthe approximate curve as designee number 0.1).

determination 0.6727 is approximately 0.3 lower,

On the other hand, the information network ofis presumed to have different properties from thethe persons in charge of Open Data is calculated

scale-free network.

1	
It can also be considered to use WEB reference
network in their sites. But they may not link up others
even if they referred to them. These ways of asking
realize the comparison of the two networks.

2	Those municipalities also constitute networks bynominating others. Nomination has significancewhen compared with non-nomination.

17

information,thecoefficientofInformation networks of Open Data promotion in Local Governments of Japan

YOSHIDA et al

What are in this back ground? As one of them,that regional bonds work well. But aiming tothere exists a regional binding in the Open Datafurther promotion of Open Data, we should alsoof the municipalities (Honda et al, 2016). Sabae

remember that the regional peer pressure plays

city in Fukui Prefecture which won the large

one of the important roles, and not limit ourselves

number of nomination in this study is a pioneering

by following the success stories of somemunicipality in Open Data and has also grabbed

pioneering local governments.

nationwide

attention.

When

other

local

In the formation of the social network,

governments are going to implement the open

considering human constraints of time anddata, it is a very natural flow to check the WEB

money, it is also necessary to focus on familiarsite of Sabae city trying to refer to the theirand homogeneous relationship. Findings of thisefforts. There also would be many local

study may not stay only in the range of Opengovernments that participated in lectures andData promotion. Future task is to generate themtrying to listen to the person in charge directly.

considering peculiarity of Open Data.

Such reference relationship like scale-free

Reference

network is considered to exist.
On the other hand, 17 other municipalities in◦ACIL Tasman (2008) “The value of spatial

Fukui Prefecture has conducted Open Data at

information: The impact of modern spatial

the time of this survey. Municipalities which has

information

already implemented Open Data are at most 182,

economy,” report prepared for the CRC for Spatialof the nationwide 1963 local governments as seen

Information and ANZLIC, Australia, the Spatialin Table 2. Municipalities with Open Data inInformation Council. http://www.crcsi.com.au/

Fukui Prefecture are 18. This number is the first

assets/Resources/7d60411d-0ab9-45be-8d48-

in Japan except the administrative district ofef8dab5abd4a.pdf (Accessed 2016-06-23)

technologies

on

the

Australian

Osaka City. On the flip side, it does not mean that

◦Barabási, A.-L., and Albert, R. (1999) “Emergence

the impact of Sabae city uniformly deliveredof scaling in random networks”, Science 286, pp.

across the country. It can be said the regional509-512◦Honda, M., Noda, T. and Yoshida, A. (2016),

influence has appeared heavily.

“Positioning of the Precedent Local Government inConclusion

the Influence of Open Data Promotion”, Information
Processing Society of Japan 137th Information

Analysis of the details of the regional bond

System and Social Environment research workshop

yield to another draft, but in this study, compared◦Jitsuzumi, T., Hatta, M. Noda, T. Watanabe, T.

the information network of WEB site with the low(2013) “Innovation Nippon Study Group reportcost of reference, that of the persons in charge of

Economic effect estimation of open data” http://public information had a less tendency of over-innovation-nippon.jp/reports/2013StudyReport_

concentration.

OpenData.pdf (Accessed 2016-06-23)

This study only showed the difference of◦Katz, M.L, and Shapiro, C. (1985) “Network

distributions in information networks between

Externalities, Competition, and Compatibility”,

online and offline, and pointed out the possibility

The American Economic Review 75(3), pp. 424-

18

Journal of Socio-Informatics Vol. 10 No. 1 Mar. 2018440

printing◦NHK (2016) “To consider making mechanisms◦Fukuno, T. (2013) “Japan of open data city map”

that companies can use public big data” http://

http://fukuno.jig.jp/2013/opendatamap

www3.nhk.or.jp/news/html/20161222/k10010(Accessed 2016-02-05)◦Smith, Tom W, Peter Marsden, Michael Hout, and817051000.html (Accessed 2016-12-23)◦Nikkei Shimbun (2017) “Saitama prefecture makes

Jibum Kim. (2015) “General Social Surveys, 1972-

it easy to use open data in common form with

2014: Cumulative Codebook”, ― Chicago: NORC

municipalities”. http://www.nikkei.com/article/

at the University of Chicago. ― (National Data

DGXLASFB20HBQ_Q7A120C1L72000/ (Accessed

Program for the Social Sciences Series, No. 22).

2017-01-25)

http://gss.norc.org/get-documentation/◦Noda, T. (2015), “A consideration about the method

questionnaires (Accessed 2016-06-23)

of economic effect estimation by using Open

◦Vickery, G. (2011) “Review of recent studies onData”, Shimane University Faculty of Law and

PSI re-use and related market developments.”

Letters bulletin Journal of Economics 41, pp. 33-52.

http://ec.europa.eu/newsroom/dae/document.

◦Noda, T., Honda, M. and Yoshida, A. (2017),

cfm?doc_id=1093 (Accessed 2016-06-23)◦Yoshida, A., Noda, T., and Honda M. (2016) “A“Economic Effect by Open Data in Local
Government

inJapan”,

LectureNotes

in

Research of Economic Effects Created by Using

Information Systems and Organisation, Springer,

Open Data in Local Governments”, Proceedings

ICTO 2017: Information and Communicationof the Conference on Society of Socio-Informatics

Technologies in Organizations and Society *in

2016

19

Editor’s Notes
We are very pleased to announce the publication of Volume 10 of Journal of Socio-Informatics.
Through the peer review process, two of the four submitted papers has been accepted as original
paper.
We want to provide a kind of fruitful public space open to every researcher who has interest in socioinformatics from all over the world.
We are inviting you, our readers, to submit papers on socio-informatics or other related fields.
The Call For Papers of next volume of JSI is available on http://www.ssi.or.jp/eng/index03.html
Cordial greetings,

The Editor

Editorial Board of The Society of Socio-Informatics
Editor in Chief
Takafumi HAYASHI, Niigata University
Editor in Subchief
Nobuaki KAWAI, Jissen Women’s University
Yorio KITAMURA, Ritsumeikan University
Seiichiro SAKURAI, Meiji Gakuin University

Editorial Board Members
Kunikazu AMAGASA, Showa Women’s University
Kenichi ITO, Gunma University
Yasufumi IGARASHI, Fukuoka University
Tomoko KANAYAMA, Institute of Advanced Media Arts and Sciences
Shigeo KAWASHIMA, Aoyama Gakuin Women’s Junior College
Tadamasa KIMURA, Rikkyo University
Atsushi KOTERA, Toyo Eiwa University
Sayaka MATSUMOTO, Otsuma Women’s University
Tetsuo NODA, Shimane University
Atsuhiko OHKUNI, Sapporo Gakuin University
Morihiro OGASAHARA, Kansai University
Syungo SAKAKI, Tokyo University of Technology
Masahito SHIMAZAKI, Akita Prefectural University
Akashi SUGIYAMA, Kyushu University
Toru TAKAHASHI, Chuo University
Yasuhiro TSUKAHARA, Meiji University
Yuko TSUCHIYA, Hiroshima University of Economics
Hitoshi YAMAMOTO, Rissho University
Shaoyu YE, University of Tsukuba

Journal of Socio-Informatics Vol.10, No.1
Mar. 2018
Published
The Society of Socio-Informatics (SSI)
Akuahakusan Bld.5F, 1-13-7, Hakusan, Bunkyo-ku, Tokyo
113-0001 Japan
Tel. 03-3812-5223 Fax. 03-3816 -1561
Print/Binding
Shobi Printing Co., Ltd.