Chapter 2: Literature review
2.4. Internet literacy, network competence, and Internet self efficacy
As mentioned previously, the dynamics of Internet parenting may be affected by parents’ relative familiarity in using the Internet or the technology. This knowledge is widely known under the term “Internet literacy”, defined by Livingstone, Bober, and Helsper (2005) in relations to the three following functions:
1. Access – Internet literacy is required to access online contents and to regulate the conditions of access.
2. Understanding – Internet literacy is required to effectively and critically evaluate online information and opportunities.
3. Create – Internet literacy allow users to produce as well as receive online contents, interacting as well as participating online. Internet literacy is part of larger concept of information literacy that is often understood in terms of effective use of information (Bawden, 2001)
In 2010, Lou et al. examined further the relationship between Parents’ Internet literacy, that is, a general understanding and tendencies to use Internet and their parenting styles at home, and their attitudes towards Child’s Internet risk and Internet mediation.
Survey was conducted among 822 parents of sixth grade students in Kaohsiung, Taiwan.
Their relatively high Internet literacy means they are nowadays more likely to trust their children with Internet usage and adopt a laisser – faire parenting style (2010). Familiarity with technology will also mean they are far more aware on the benefits technology bring to
their children’s learning and information gaining, although they would raise concerns for setting up rules and concerns with regards to children’s Internet addiction (2010).
Savolainen (2002) explores the concept of Internet literacy further under the term network competence. In general sense, competence deals with an individual’s ability to adapt effectively to the surrounding environment over time to achieve goals. He argued that the use of the term “competence” means that there are adaptation period to this new
technology and there are functional elements into this and that mastering the technology could help users achieving some goals (e.g., online shopping, tele-voting, searching for health related information) (2002).
The question of whether or not parents utilize network competence to monitor and regulate children’s online activities is more difficult to answer at this point, as most generation y parents have mastered the Internet before they even become parents.
However, this definition can explain whether parent’s confidence in their own network competence affect how they supervise children’s online activities. One important
determinant of network competence is one’s self efficacy. Self efficacy theory was coined and mentioned by Bandura (1977) as one of the constructs in Social cognitive theory. Self efficacy is described as one’s judgment about their own capabilities to succeed or perform certain tasks. One’s self efficacy gives him or her ideas on what he or she can accomplish in certain situations. The higher one self efficacy is to certain task, the more efforts and persistence will be put in by individuals, and therefore, the more likely it is for individuals to succeed in that task (1977).
Savolainen (2002) speaks of Internet self efficacy as a form of self evaluation of his or her own network competence that affects confidence in manipulating Internet
technology, influence one’s decision making , as well as ensuring persistence in the interest
of online activities. Eastin and Larose (2000) specify that Internet self – efficacy does not directly correlated with the actual skills in performing Internet – related task, but instead, has more to do with judgment of ability or beliefs of what he or she can accomplish online now or in the future.
Lee (2013) explained that unlike traditional media, parental mediation of children’s Internet use requires Internet – related skills. Turow and Kavanaugh (2003) supports this notion when mentioning of parents’ restrictive strategies: “the biggest drawback of
monitors and safe haven sites – and a big drawbacks of filters as well – is that parents with only basic computer and web skills may not feel comfortable of using them. Revisiting the above mentioned parental mediation strategies by Livingstone and Helsper, while active &
co-use and interaction restrictions are not associated with the need to master computer / Internet use, utilizing technical restrictions and monitoring on the other hand, are very much depending on parents’ Internet – related skills. And as it is believed that one’s self – efficacy could substitute or a strong predictor of one’s actual ability (Munro et al., 1997;
Ajzen, 1991), it is predicted:
H3: Parents’ Internet self efficacy is positively correlated with use of technical restrictions and monitoring strategies.
RQ3: How do parents’ Internet self efficacy correlate two other mediation strategies, namely interaction restrictions and active / co-use?
Figure 1. Concept map of current study’s variables and hypotheses.
Parental mediation
Risk perception Internet self efficacy
Parent’s news exposure
H3, RQ3
H2, RQ2 H1, RQ1
Chapter 3 Methodology
The present study will propose a quantitative study using a survey methodology, which will allow the analysis of the interaction of multiple variables present in this study.
3.1. Survey samples
The participants of the study were parents who currently have children studying in elementary schools in Taipei (grade 1 to grade 6). As Taipei is listed as the city with highest broadband Internet penetration rate in Taiwan (73.68%), it provides appropriate opportunity to investigate the issue of Internet parenting from the East Asian context in a high tech environment (CNA, 2006). The study recruited a total of 207 participants. One or two schools were selected from each administrative district in Taipei, resulting in a total number of 18 schools from 12 administrative districts. From each school, ten to fifteen parents from that school were recruited to participate in the survey. The 12 administrative districts in Taipei City, included Songshan, Xinyi, Daan, Zhongshan, Zhongzheng, Datong, Wanhua,Wenshan, Nangang, Neihu, Shilin, and Beitou. The list of elementary schools was acquired from website www.tp.edu.tw. Schools in each district were assigned a number and be then selected randomly using a random number program www.randomizer.org to make sure each school in each district has an equal chance to be selected. Once the school was selected, participants were recruited using a convenient sampling during the time when they pick up their children from school. They were asked to spare their time to fill out the
survey, which was said to take around five minutes. While the gender and the age of the parents will be taken into consideration in the analysis, they were not the basis for
sampling. During the research, due to some problems acquiring data from some schools, a few number of schools were displaced from the sample, and replacement schools were chosen randomly. The final list of schools visited is attached in the Appendix C.
3.2. Measurements
The major variables in the current study included Internet self efficacy, parental Internet mediation, risk perception, and media usage. Factor analysis was first conducted for each scale of the aforementioned variables. It was confirmed that all the items
generated only one dimension prior to reliability tests.
Internet self efficacy
The measurement for Internet self efficacy is adapted and carefully chosen from the study by Eastin & Larose (2000) and Hsu & Chiu (2003). The measurement uses a 5-point Likert scale, ranging from strongly disagree to strongly agree. It generally contains statements regarding their confidence on completing Internet related tasks as well as their familiarity with the Internet (e.g., I feel confident installing an application or software, I feel confident understanding terms or words related to Internet). The reliability test indicates Cronbach’s α = .880 (N=8)
Parental Internet mediation
The measurement for Internet mediation is adapted from the measurement utilized by Livingstone and Helsper (2008; 2010). The measurement uses a 5-point likert scale, ranging from strongly agree to strongly disagree. The statements generally contains efforts
of the parents to mediate children’s Internet activities, that include statements indicating restrictive mediation (e.g., I set up filters / monitoring software on websites), statements indicating active mediation (e.g., I talk to child about Internet use), and statements
indicating co – viewing (I stay nearby when child is online). For items indicating active/co use and interaction restrictions, Cronbach’s α = .749 (N =9). For items indicating
monitoring and technical restrictions, Cronbach’s α =.868 (N=5) Factor analysis was conducted on all thirteen items of parental mediation, and it can be concluded that it yields four components, though for the purposes of this paper, interaction and active – co use strategies will be studied together, and Monitoring and technical restrictions will be studied together.
Table 2
Factor loadings for the parental mediation items
Item Interaction Active/ co-use Monitoring Technical 1. I stay nearby when children is online. .582
2. I set rules for my child about time .542 spent online.
3. I put computer separate to my
child’s bedroom .515
4. I talk to child about Internet use. .495 5. I set up filters/ monitoring software
on websites. .537
6. I check websites that child visited. .755
7. I check child’s SNS. .774
8. I check child’s email messages. .778
9. I check who my child has been .741
chatting with online.
10. I forbid child to use SNS. .427 11. I forbid child to download
things on the Internet. .565 12. I forbid child to give out personal
info online. .577
13. I forbid child to buy things online. .698
Risk perception
The measurement for risk perception is a self – designed measurement that refers to the classifications of Internet risk by Staksrud and Livingstone (2009). Specifically, it asks parents to assess the likelihood and severity of each type of Internet risk (contact, content, conduct) in relation to their children. The measurement uses 5-point likert scales, ranging from strongly agree to strongly disagree. Cronbach’s α = .925 (N=6).
Media usage
The measurement for media usage is a self – designed measurement that intends to investigate their acquisition of knowledge regarding Internet risk. As such, the
measurement asks participants how many days do they watch / see the news from each of the commonly used media (television, newspaper, Internet).
Media exposure to Internet issue
The measurement then asks how often do they see the news regarding Internet risk on the medias mentioned above using a 5-point likert scale, ranging from common to never).
In the last part, the measurement asks the approximate age of the participants, the age of the children who are in the elementary schools, and the household monthly income for reference. The initially English - written questionnaire had been translated into Chinese language by Chinese instructor in National Chengchi University. Both of the questionnaires are attached in this proposal for reference. A pre – test on the measurement was conducted before the execution of the study with a total of ten parents, which brought to light some formatting error on the questionnaire.
Chapter 4 Results
4.1. Demographics of sample
Out of the total 207 participants, females made up for the majority with 146 respondents (70.5%), while 59 respondents (28.5%) were males. In terms of the parents’
age group, the age between 41 – 45 years old made up the highest percentage with 41.7%
(N= 85), followed by 36-40 years old age group with 23.5% (N= 48), and Over 45 years old with 17.6% (N= 36). As for the children’s average age, the peak can be found in 8 years
old with 22.4% (N= 46), followed closely by 10 years old with 2.0 % (N= 41). The median of Children’s age was 9. The data on average household income indicated there are no significant differences between top three variables: NTD 60,001 – 80,000 with 23% (N=
47), NTD 80,001 – 110,000 with 20.6% (n=42), and NTD 110,001 – 140,000 with 19.6%
(N=40). Median score fell under NTD 80,001 – 110,000.
Table 3
Gender and age of the respondents
Item Variable N %
Gender Male 59 28.8%
Female 146 71.2%
Age Group Under 25 years old 1 0.5%
25-30 years old 8 3.9%
31-35 years old 26 12.7%
36-40 years old 48 23.5%
41-45 years old 85 41.7%
Over 45 years old 36 17.6%
4.2 General findings
The following is a report of descriptive results of the major variables in the current study.
Internet self efficacy
The survey on Internet self efficacy items indicated that the large majority of participants were able to find information online with 87.5 % answered or agree or strongly agree. When it comes to understanding terms related to Internet and explaining why a task will not run on the Internet, participants were less confident with 54.6 % and 50.7%
responded agree or strongly agree respectively. The highest self efficacy was found on “I feel confident using an e-mail” item, with 96.6% answered agree or strongly agree. The results also showed that the majority of participants are able to navigate social media (82.1% answered agree or strongly agree). Other items being asked included “I feel confident chatting with other people online“ (82.1% agree or strongly agree), “I feel confident installing an application or software” (74.4% agree or strongly agree), and “I feel confident filtering or blocking websites with inappropriate contents (59.4%).”
Parental mediation
In regards to interaction restrictions, 59.4% of parents would stay nearby when the child is online, with 25% were neutral, and 15.5% disagreed. 84.5% of parents would not put computer in child’s bedroom, while 87.4% would set rules about time child spent online. 76.2% of parents also found it helpful to talk to their child about Internet use. When it comes to technical restrictions and monitoring strategies, 58 % of parents would set up filters on the website, while more than half the parents would monitor child’s visited websites (58.3%), check child’s SNS (52.2%), and check child’s online chat partners (55.1%), however only 43% agree to check child’s email messages. Another thing worth of note is that about 44% of parents disagree on forbidding child to use SNS, with 37.2% were neutral about it and only 18.3% agreed to do so. Other results include most parents
agreeing to forbid child’s online shopping (70%), and agreeing to forbid child to give out personal info online (67.2%).
Risk perception
The majority of the parents agreed or strongly agreed that their child may be exposed to inappropriate contents (61.9%), while 60.1% of parents agree that these
contents are likely to severely affect their child. Slightly less parents agreed that their child is likely to experience risky online interaction (54.6%), but slightly more parents agreed that their child would be severely affected by risky online interaction (61.9%). Last but not least, when asked whether their child is likely to initiate or produce risky contents or interactions online, more parents disagreeing (40.1%) than agreeing (33.3%) with 26.6%
were neutral. In regards to the severity of the conduct risk, about 40.1% agree that it will severely affect their child.
Media usage
In terms of which media outlet do parents hear more about Internet risk news, television (54.2% answered often or common) and Internet (56.3%) are the two major sources. Newspapers were rated lower as the source of Internet risk news, with only 26.1%
of parents answered common or often. In terms of the media that parents normally use to read or watch news, Internet had a slight edge with 74% of parents answered 5 to 7 days, followed by television (66.8% answered 5 to 7 days), and newspapers (55.4% answered 5 to 7 days).
4.3. Tests of hypotheses
In this section, the previously stated hypothesis will be tested in relation with the data acquired. The Statistical Package for the Social Sciences (SPSS) program was utilized to validate, compute, and describe the relations between variables that shape the
hypothesis.
H1: Parents’ exposure to Internet risk news would increase their risk perception of Internet use for their children.
In order to test the first hypothesis, Pearson’s correlation was used to test the relationship between Parents’ exposure to Internet risk news and parents’ risk perception towards Internet. Partial correlation was used for the analysis, taking into account the participants’ age and gender. The result showed a positive correlation (r = .20) with a significance of p<.01 level therefore supporting the hypothesis one.
RQ1: How do different media (i.e. television, newspapers, and Internet) influence parents’ perceptions and attitude towards Internet risk?
A more comprehensive look on Pearson’s correlations among parents’ exposure to risk news and parents’ risk perception showed there was a moderate positive correlation between exposure to Internet risk news via Internet and parents’ risk perceptions towards Internet (r = .303) with a significance of p<.001 level. No significant results were found between exposure to Internet risk news via television, via newspapers, and parents’ risk perception. The number of days parents see news online also correlated (r=.320, p<.001) with how often they see the Internet risk news online. No such results were found with television and with newspapers.
H2: Parents who perceive Internet as a high risk to their children’s safety would be more likely to mediate children’s Internet use.
For testing the second hypothesis (and third), regression analysis was used to examine different variables that might explain or predict parental mediation behavior. As shown in Table 3, the hierarchical regression model that included self efficacy, risk perception, media usage and exposure overall explained for about 43% of variance in predicting parental mediation behavior (R square=.427). In particular, when risk perception was entered at the
second level after demographic variables were controlled, the variance explained increased drastically (β= .429, p< .001, R square change = .236). Such finding indicated risk
perception as an important positive predictor for parental mediation behavior. The variable resulted in it can be concluded that parents’ risk perception towards Internet has a moderate to strong positive correlation with Parental Mediation, therefore supporting H2.
Table 4
Hierarchical multiple regression analysis of parental mediation
Level Variables β t p-value
1 Sex .068 .945 .346
Parent’s age -.140 .-1.637 .103
Children’s age
.003 .038 .970
Household Income .192 2.549 .012*
R square =.043
2 Risk Perception .498 7.788 .000***
R square change =.235
3 Self Efficacy .322 4.227 .000***
R square change = .095
4 Days Watching TV News -.098 -1.570 .118
Days Reading Newspapers -.001 -.019 .985
Days Seeing Internet News -.007 -.096 .924 R square change = .009
5 Issue Exposure on TV 227 2.997 .003**
Issue Exposure on Newspapers 060 .872 .385 Issue Exposure on Internet -.080 -.960 .338 R square change = .045
R square total = .427 Note: ***=p <.001, **=p<.01, *=p<.05
RQ2: How do different types of risk perceptions (i.e. content, contact, or conduct) correlated with different use of parental mediation strategies?
To answer RQ2, Pearson correlations were conducted between different types of risk perception (Content, Contact, Conduct) and parental mediation. Partial correlation method was used to control participants’ sex and age variables. Results showed that all three types of risk perception have a moderate to strong positive correlations with parental mediation, with Content reported as the highest (r = .478), followed by Contact (r=.410) and Conduct (r
=.393). The results were all significant at p <.001.
H3: Parents’ Internet self efficacy is positively correlated with use of technical restrictions and monitoring strategies.
As the regression table suggested, self efficacy had some effect on parental mediation (R square change =.094), ( R square =.115) and further regression analysis was conducted on interaction restrictions, active co-use strategies of parental mediation and the one with technical restrictions, monitoring strategies of parental mediation. The results indicated that internet self efficacy had a slightly bigger impact on technical restrictions and monitoring strategies (R square =.115) than on interaction restrictions and active co-use strategies (R square =.053). Partial Pearson’s correlations were also conducted to test the H3 while
controlling the participants’ sex and age variables. The results showed that parents’ Internet self efficacy correlated positively with technical restrictions and monitoring strategies of parental mediation (r = .348, p<.001), therefore both the correlations and the regression analysis supported H3. In regards to two other types of parental mediation strategies, results also showed a positive correlation between interaction restrictions, active & co-use strategies and parental mediation (r=.248, p<.001) albeit slightly weaker than the other two strategies.
Chapter 5 Discussion
5.1. Major findings and implications
As expected, the participants from Taipei sample showed less proof of digital divide when it comes to household income and Internet access, based on their general competence in operating Internet technology. A large majority of sample knew how to find information online, chat with others online, and navigate social media. Results from the media usage section also revealed that people read more Internet news and read fewer newspapers, with television news ranked in between. According to the survey, participants read news related to Internet risk the least amount in the newspapers. While television and Internet fare pretty similar with each other in this regards, television was found to be one medium that significantly affects parental mediation, while Internet was found to significantly affect parents’ risk perception. This confirmed that parents’ fear and behavior on Internet mediation had been influenced or aggravated by media portrayals of Internet risk. Clark’s (2009)
argument that such apprehension is to be documented more so among parents from lower
argument that such apprehension is to be documented more so among parents from lower