The Effects of Demographics,Technology Accessibility,and Unwillingness to Communicate in Predicting Internet Gratifications and Heavy Internet Use Among Adolescents

Abstract

Despite the fact that research examining Internet gratifications is over two decades old, it has still failed to provide conceptual links depicting the relationships among Internet gratification, Internet users’ characteristics, and heavy Internet use. To address these gaps, a survey-based study was conducted with a total of 1,914 adolescent Internet users (aged 12–18 years). The study results indicate that heavy Internet use is likely to be displayed by older male adolescents with more Internet use experience. In comparison, adolescents exhibiting higher reward seeking and improved academic performance are not likely to also exhibit heavy Internet use. Furthermore, adolescents seeking higher connecting gratification are likely to exhibit heavy Internet use, but an increase in their information-seeking gratification is associated with a reduction in their Internet use. In terms of predicting Internet gratifications, it was found that older female adolescents with higher academic performance, higher reward seeking, and lower daily Internet use tend to seek higher content gratification. Similarly, adolescents exhibiting higher approach avoidance and reward seeking tend to seek higher social and content gratifications. The study concludes with the theoretical and practical implications for various stakeholders, including Internet researchers and practitioners, educational researchers, and technology designers and developers.

Keywords

adolescents demographics heavy Internet use Internet gratifications technology accessibility uses and gratifications research unwillingness to communicate scale

Introduction

Nowadays, Internet use has become almost unavoidable. According to the most recent Internet World statistics, there are 2.4 billion Internet users worldwide (34.3% of the entire world population), 1.076 billion of whom (almost half) live in Asia (Asia Internet Usage, 2013; World Internet Usage, 2013). This widespread popularity has motivated media researchers to examine the potential gratifications of Internet use. The Uses and Gratifications (U&G) theory is a well-known and practiced theoretical framework in the field of media research, which is used to understand users’ potential gratifications from any given medium. The U&G theory presents a psychological communication perspective, according to which (1) users have different individual uses and choices, due to which different users utilize a given media platform for different reasons (Severin & Taknard, 1997) and (2) users try to satisfy their own social and psychological needs with media use, such as seeking information, consuming entertainment, connecting with others, reinforcing their personal identity, and escaping (Dimmick, Sikand, & Patterson, 1994; Lin, 1999; Rubin, 1983). According to Maslow (1970), the user’s psychological needs are often cognitive and emotional in nature, but gratifications sought from any media use are utility driven and goal oriented (Palmgreen & Rayburn, 1979). Therefore, a utility-driven notion of media use can be used to explain users’ motivations in relation to the U&G of any media use (Leung, 2014).

In the last decade, Internet use has witnessed a huge 566.4% increase in its adoption (Asia Internet Usage, 2013). India is a fast progressing, developing economy, with a consumer base of over 400 million people (Ranchhod & Gurau, 2014), and a huge market for consumer goods and services second only to China. India hosts over 155 million Internet users, behind only the United States and China in world Internet user rankings (India Internet Usage, 2013). Some of the reasons underlying the rising adoption and use of the Internet by the Indian population are the continuous investment in Internet-related infrastructure which has resulted in lower Internet access tariffs; the availability of the Internet, even on cheap mobile phones, and the rising popularity of Internet use among the young Indian population due to the introduction of popular social networks (e.g., Facebook); and Internet-based messaging applications, such as WhatsApp, Skype, and Viber.

Despite this rise in Internet adoption, penetration, and popularity in India, the Indian population has been largely understudied from the perspective of Internet gratifications. The majority of previous research examining Internet gratifications is in the context of American, European, and Chinese populations (Roy, 2009). The present study therefore examines the relationships among Internet gratifications and the background characteristics of Indian Internet users. Additionally, differences in Internet gratifications sought by heavy and light Internet users are investigated.

Background Literature on Internet U&Gs

Internet U&Gs and the Target User Group

A review of the prior Internet U&G work revealed that the majority of studies have been conducted with college students and adults, whereas very young adolescents (aged 12–16 years) have been ignored (see Table 1). The majority of the existing work focuses on a wide age range of participants, for example, 16–54 or 16–75-year-old Internet users (Johnson & Kaye, 2003; Kaye, 1998; Kaye & Johnson, 2004; Leung, 2001, 2009; Roy, 2009; Stafford, Stafford, & Schkade, 2004). Only a few prior studies have focused on specific age-groups, for example, college students (Diddi & LaRose, 2006; Kim & Haridakis, 2009; Leung 2003). To the best of our knowledge, Leung (2014) carried out the first study that investigated Internet gratifications among adolescent Internet users (aged 9–19 years). However, the study objectives were different from those of the present study in a variety of ways. Leung (2014) examined the role of Internet gratifications in predicting Internet addiction symptoms and Internet risks. In comparison, the present study investigates how adolescents’ demographic profile, technology accessibility status, and unwillingness to communicate (personality attributes) predict various Internet gratifications among adolescent Internet users. Furthermore, Leung’s (2014) study recruited Hong Kong-based Chinese Internet users, so the study results are culture dependent, for example, Hong Kong witnessed very high Internet penetration in the early 2000s, while Indian society is now still evolving in terms of Internet growth and usage. It is likely that adolescent Internet users from these two countries seek different Internet gratifications.

Table 1.

Review of Earlier Internet U&G Literature.

Author and Year	Obtained Internet Gratifications	Sample Size, Age	Instrument Development
Kaye, 1998	Six gratifications, namely, entertainment, social interaction, pass time, escape, information, and website preference	16–54 years	Television viewing motivation scale
Korgaonkar and Wolin, 1999	Seven gratifications, namely, social escapism, transactional security and privacy, information, interactive control, socialization, non-transactional privacy, and economic motivation	No specific age	41-item instrument based on previous U&G studies
Chou and Hsiao, 2000	Five gratifications, namely, escape, interpersonal relationship, use behavior, intertext, and anonymity	910 college students aged 20–25 years	Prior U&G studies and interviews
Ko, 2000	Four Internet usage gratifications were social escapism, pass time, interactive control, and information	18–37 years	Prior U&G studies
Papacharissi and Rubin, 2000	Five factors, namely, interpersonal utility, pass time, information seeking, convenience, and entertainment	86% < 30 years	27-item instrument based on previous U&G studies
Leung, 2001	Six gratifications, namely, entertainment, affection, escape, surveillance, arousal, and social interaction	16–75 years	37-item instrument based on previous U&G studies and focus discussions
Kaye and Johnson, 2002	Four gratifications, namely, guidance, information seeking/surveillance, entertainment, and social utility	Mean age 31.2	18-item instrument based on previous U&G studies
Johnson and Kaye, 2003	Five Internet gratifications were shopping/finance, information seeking, recreational social connecting, and technical uses, and media entertainment	14–83 years	22-item instrument derived from previous U&G studies
Cho, Gil de Zúñiga, Rojas, and Shah, 2003	Six gratifications, namely, social connecting, learning, and personal acquisition, surveillance, consumption use, and social interaction use	27–47 years	Previous U&G studies
Leung, 2003	Six Internet gratifications were surveillance, escape, affection, entertainment, relaxation, social bonding, and social identity	16–24 years	32-item instrument based on previous media U&G studies and focus discussions
Song, Larose, Eastin, and Lin, 2004	Seven main Internet gratifications, namely, virtual community, information seeking, monetary compensation, diversion, personal status, and relationship maintenance	699 students (aged 16–24 years)	Prior Internet U&G studies, national Internet surveys, and qualitative intervention
Kaye and Johnson, 2004	Gratifications for using Internet (web) were guidance, entertainment/social utility, convenience and information seeking. Gratifications for using chat rooms/instant messaging for political information were guidance information seeking, entertainment social/utility, and convenience	14–83 years	22-item instrument derived from previous U&G studies
Stafford, Stafford, and Schkade, 2004	Five content gratifications, namely, education, information seeking, knowledge, learning, and research; six process gratifications, namely, resources, search engine, searching, surfing, technology, and websites; four social gratifications, namely, chatting, friends, interactions, and people	18–55 years	45 items from an open-ended questionnaire
Diddi and LaRose, 2006	Four Internet gratifications, namely, surveillance, escapism, pass time, and entertainment	Median age = 19 years	Previous U&G studies
Grace-Farfaglia, Dekkers, Sundararajan, Peters, and Park, 2006	Seven factors, namely, social companionship, economic gain, self-improvement, entertainment, escape, fame and aesthetic, and respect	Mean age = 32.9 (SD = 11.77) aged 15–80 years	Previous validated instruments based on U&G were utilized
Yang and Tung, 2007	Two gratifications, namely, social entertainment and searching for information	1,708 adolescents (grade 10th–12th)	Prior Internet motives instruments
Roy, 2009	Six factor solution for Internet gratifications, namely, self-development, wide exposure, user friendly, relaxation, career opportunities, and global exchange	525 Internet users (25–50 years)	26-item instrument based on the focus discussion
Kim and Haridakis, 2009	Six factor Internet gratification solution, namely, habitual entertainment, caring for others, information seeking, excitement, control, and escape	Mean age 21.5 years	45 items from the Internet motives scale and 4 items of the television motives scale
Leung, 2009	Three main factors, namely, self-efficacy, perceived competence, and desire for control	14–70 years	23-item instrument developed from previous U&G studies and focus discussion
Zhou, 2010	Four main gratifications were personal relaxation, maintaining interpersonal relationship, achieving high level in the game, and earn virtual money	342 users (18–22 years)	Qualitative intervention
Lee, Lee & Jang, 2011	Four factor gratification scale, namely, entertainment, information seeking, social interaction, and homeland orientation	Not available	Previous media U&G studies
Chen and Kim, 2013	Six factor Internet gratification solution, namely, virtual content, diversion, self-presentation, relationship maintenance, relationship building, and information seeking.	1,044 university students	Previous media U&G studies
Leung, 2014	Six gratifications were entertainment, status gaining, experiencing opinions, identity experimentation, information seeking, and passing time	718 adolescents aged 9–19 years	31-item instrument was developed based on the previous U&G studies and a focus discussion

Note. U&G = uses and gratification.

The Internet U&G Instrument

We examined the prior Internet U&G literature from the perspective of “development or utilization” of gratification constructs (see Table 1). It was found that most examinations did not try to utilize a holistic Internet gratification instrument that addresses possible gratifications among the target population. A similar view was expressed by Song, Larose, Eastin, and Lin (2004) who strongly criticized earlier Internet U&G studies due to their overreliance on a single instrument (e.g., the television viewing scale or the Internet motives scale) developed over two decades ago to determine potential present day Internet gratifications. Other related limitations include failure to determine new gratifications specific to new media (Song, Larose, Eastin, & Lin, 2004) and preparing typologies of gratifications based on a priori theoretical frameworks instead of post hoc exploratory factor analysis (EFA; Song et al., 2004). Furthermore, Song et al. (2004) suggested that future Internet U&G research must depart from the existing operational and conceptual approaches. In order to address this limitation, the present study utilized an extensive, 78-item Internet gratification instrument prepared based on an extensive qualitative enquiry with the target users and on the prior U&G literature.

Users’ Background Characteristics and Internet U&G

Prior Internet U&G research has suggested that future studies must examine the relationships among Internet U&Gs and Internet users’ background characteristics (Kim & Haridakis, 2009). A review of the earlier literature revealed that most of the existing Internet U&G work that has focused on examining the aforementioned relationship was limited to variables such as age, gender, socioeconomic status, and daily time spent on Internet usage. Therefore, the prior literature does not provide understanding of what other background characteristics (apart from age, gender, socioeconomic status, and Internet use time) differ among Internet users and the gratifications they seek from their Internet use.

The prior Internet U&G literature on Internet users’ demographic profiles suggests that young, high socioeconomic status users tend to use the Internet to satisfy their internal needs, to engage in computer-mediated interaction, surveillance, and consumption uses, and to achieve connecting, learning, and acquisition goals (Cho et al., 2003). In comparison, young and low socioeconomic status users are likely to use the Internet to attain gratification from connecting (Cho et al., 2003). Similarly, it was found that demographic characteristics, cultural values, and “Internet connecting type” were the main factors that explained why the same technology is adopted differently in different countries and cultures (Grace-Farfaglia, Dekkers, Sundararajan, Peters, & Park, 2006). Other important findings were gratifications of web, bulletin boards, and chat forums did not correlate with the gender or income of Internet users (Kaye & Johnson, 2004); age was a significant negative predictor of entertainment (Leung, 2003); and demographic variables, namely, gender, age, education, and income did not play any significant role in predicting Internet gratifications (Leung, 2003).

Prior Internet U&G findings on Internet users’ technology accessibility suggest that Internet use experience and Internet usage were positively correlated with Internet gratifications (LaRose & Eastin, 2004). In contrast, Kaye and Johnson (2004) found that Internet experience did not share any correlation with the gratifications of web and chat forums. A possible reason for this difference in the findings could be that the latter study examined the gratifications of Internet-specific activities, for example, web browsing and chat forums.

The Unwillingness to Communicate Scale (UCS) measures the “chronic tendency of any human being to avoid and/or devalue oral communication” (Burgoon, 1976, p. 60). The UCS construct has been associated with alienation, self-esteem, and apprehension to communicate (Burgoon, 1976). The UCS has two subconstructs, namely, approach-avoidance (UCS-AA) and reward-seeking (UCS-R). The UCS-AA refers to anxiety, introversion, and a tendency to avoid participation in general communication, while the UCS-R refers to distrust and an internal tendency to remain isolated. Prior literature suggests that Internet users with high UCS-AA and UCS-R actually find virtual mediums convenient and comfortable (Papacharissi & Mendelson, 2000). Furthermore, Internet users with high UCS-AA and UCS-R scores tend to exhibit significant correlations with interpersonal gratification, social activity, and life satisfaction (Papacharissi & Mendelson, 2000). Similarly, Sheldon (2008) found that students exhibiting high UCS are less likely to meet new people and have fewer friends on social media.

Heavy–Light Internet Users and Internet U&G

Earlier Internet U&G research stressed the need to examine the differences between heavy (those who tend to use the Internet for longer durations) and light (those who spend only a few hours on Internet use) Internet users. Wallace (1999) suggested that the Internet offers an attractive and absorbing psychological space, due to which Internet users tend toward heavy use. Stafford (2003) conceptualized heavy Internet users as “Internet innovators,” since they spend a great deal of time on the Internet and participate in online offerings from the various Internet-based companies. Furthermore, heavy Internet users are treated as loyal customers and are often targeted by Internet-based companies for online offerings (Stafford, 2003). In comparison, light Internet users were referred to as “non-innovators.” Leung (2003) found that various Internet gratifications including showing encouragement, socialization, affection, connecting, and escapism from real-life problems motivated heavy usage of the Internet. Stafford and Gonier (2004) found that light and heavy Internet users were significantly different in the Internet U&Gs they seek and Ko (2000) found that heavy users tend to have higher motivation and a more positive attitude toward the Internet compared to light users. Furthermore, heavy users are more involved in the content of websites and are more likely to visit informational websites. Similarly, Roy (2009) found that heavy and light Internet users differed in the user friendliness and career opportunity U&Gs, while Kargaonkar and Wolin (1999) also confirmed the presence of differences between heavy and light users in terms of the gratifications sought from Internet use. However, earlier Internet U&G research failed to determine the relationships among heavy and light Internet users and their background characteristics and Internet gratifications.

Predicting Internet U&Gs

Prior Internet U&G research has not established which variables successfully predict Internet gratifications or how Internet users differ in the gratifications they seek from the Internet. Leung (2003) found that gender, age, education, and monthly income and Kaye and Johnson (2004) found that gender and income did not play any role in predicting Internet gratifications.

Study Methodology

Research Questions

Research Question 1: What are the relationships among Internet gratifications, adolescents’ demographic profiles, technology accessibility status, and UCS?

Research Question 2: How do adolescents’ demographic profiles, technology accessibility status, and UCS discriminate between light and heavy Internet users?

Research Question 3: How do Internet gratifications discriminate between light and heavy Internet users?

Research Question 4: How do demographic profile, technology accessibility status, and UCS among adolescent Internet users predict Internet gratifications?

Study Sample and Sampling Procedure

A randomly drawn sample of 1,914 adolescent Internet users, representing 10 private schools from four different Indian cities, participated in this study. First, a list of 25 schools was prepared by randomly drawing their names from an online directory. These selected schools were typical “English-speaking” private junior and senior high schools in India that accept students from middle income groups. The pool represented schools from over six different cities of North–Western India. Second, all 25 schools were first contacted via an e-mail, which was later followed up by a phone call. At this stage, the schools were clearly informed about the research objectives of this study, the research process, the anticipated benefits, and the related practical implications. Of these 25 schools, only 14 responded positively to our invitation and invited us for further discussions. Third, our researcher met representatives from each of the participating schools, for example, the principal or the school management. During this meeting, participating schools were briefed on the intended survey-based study, its objectives, research questions, and anticipated benefits. A copy of the survey questionnaire was also submitted to the participating schools in order to obtain the necessary approvals. Fourth, 10 of the 14 schools agreed to participate in this study. Subsequently, this study was advertised among the target age-group adolescents via teachers, notice boards, morning announcements, and 5 min in-class announcements by the researcher in those 10 schools. Fifth, since English was the medium of communication and instruction in the participating schools, the questionnaire was also presented in English. Participation in this study was voluntary and anonymous, and all students were granted an equal chance of participation. Various time slots to complete the survey were specially advertised by the schools. The researcher, in the presence of the schoolteachers, administered all survey-answering sessions. Before the actual study, a short pilot study with 25 students (12 female and 13 male) aged 12–18 years was performed. This pilot study enabled the researcher to determine and correct any confusing, missing, or nonapplicable survey items. The participants later completed the updated survey.

Study Measures

Internet gratifications

A 78-item scale for examining Internet U&G was developed based on the prior Internet U&G studies, media U&G research, and our own qualitative intervention with the target Internet users (Dhir, Chen, & Nieminen, 2016). The pool of items was examined using EFA. The process involved the Maximum likelihood method with “Promax rotation.” Those items with a factor loading below 0.50 were deleted and the process was repeated until a stable set of items and factorial structure were obtained. The EFA resulted in a six-factor solution, representing six Internet gratifications, namely, information seeking (α = .86), exposure (α = .87), connecting (α = .87), coordination (α = .87), social influence (α = .83), and entertainment (α = .88). This six-factor model had 27 items and explained 65.25% of the variance in the Internet gratifications. Next, the resulting six-factor solution was examined using confirmatory factor analysis (χ²/df = 3.74, confirmatory factor index = 0.93, Tucker–Lewis index = 0.93, root mean square error of approximation = 0.05). The obtained model fit indices are considered acceptable for an exploratory study such as the present one (Hu & Bentler, 1999; Kline, 2011). This confirmed that the six-factor solution had a good model fit. In addition, the 27-item Internet U&G instrument possesses sufficient content, discriminant and convergent validities, and construct reliabilities (Dhir et al., 2016).

Demographics

A total of 6 items assessing the demographic profile of the study participants were included in the survey. These items were age, gender, monthly family income, academic performance, parental attitude toward Internet use, and change in school performance (CSP) after starting to use the Internet. The age of the participants ranged from 12 to 18 years (Mean = 14.88 years, SD = 1.44; see Table 2).

Table 2.

Demographic Profile of the Study Participants.

Demographic Variables	Category	Percentage (Frequency)
Age	12–13 years	18.2 (346)
	14 years	21.8 (414)
	15 years	25.7 (489)
	16 years	20.7 (394)
	17 years	10.6 (201)
	18 years	3.0 (56)
Gender	Male	61.3 (1,173)
Gender	Female	38.7 (739)
Family monthly income	Below 20,000 INR	10.2 (188)
	20,001–40,000 INR	33.8 (624)
	40,001–60,000 INR	35.2 (650)
	Above 60,001 INR	20.8 (385)
Academic performance	Below 40%	1.0 (19)
	Between 41–60%	15.2 (286)
	Between 61–80%	48.5 (912)
	Above 80%	35.3 (664)
Change in school performance after starting Internet use	Improved	46.0 (880)
	Unchanged	38.9 (745)
	Become worse	5.3 (101)
Parental attitude toward Internet use	Always supportive	8.0 (148)
	Support if in limit	60.0 (1,108)
	Offended if I use too much	17.5 (324)
	Always get offended	3.7 (69)

Note. INR refers to Indian Rupee (1$ = 62 INR).

Technology accessibility

Four items addressing technology accessibility-related issues among adolescents were added to the survey. These items were computer ownership, home Internet ownership, Internet use experience, and total daily time spent on the Internet. The mean daily time spent on the Internet was 1.78 hr (SD = 1.24), while the mean number of years using the Internet was 2.79 years (SD = 1.73).

The Unwillingness to Communicate scale

A 20-item Unwillingness to Communicate scale (UCS; Burgoon, 1976) composed of 10 items of UCS-AA (α = .75) and 10 items of UCS-R (α = .70) was utilized.

Results

Internet Gratifications and Users’ Background Characteristics

The age of the adolescents had a very weak positive correlation with information seeking (r = .05, p < .01), exposure (r = .06, p < .01), connecting (r = .08, p < .01), coordination (r = .08, p < .01), social influence (r = .05, p < .01), and entertainment (r = .05, p < .01). Gender differences were examined using independent sample t-test results (see Table 3). It was found that male adolescents prefer connecting, coordination, and social influence when compared to female adolescents. However, female adolescents seek more information and exposure gratifications when compared to male adolescents. A t-test did not return a significant difference between the male and the female adolescents for entertainment gratification.

Table 3.

Gender Differences in the Internet Gratifications.

Internet Gratifications	Gender	Mean	SD	t Value	Sig	Cohen’s d	Effect Size
Information seeking (IS)	Male	3.79	0.87	−7.82	.01	−.36	−0.18
Information seeking (IS)	Female	4.07	0.67	−7.82	.01	−.36	−0.18
Exposure (EX)	Male	3.66	1.06	−8.50	.01	−.39	−0.19
Exposure (EX)	Female	4.03	0.83	−8.50	.01	−.39	−0.19
Connecting (CT)	Male	3.49	0.91	6.07	.01	.29	0.14
Connecting (CT)	Female	3.22	0.98	6.07	.01	.29	0.14
Coordination (CE)	Male	3.49	0.91	7.82	.01	.37	0.18
Coordination (CE)	Female	2.87	0.95	7.82	.01	.37	0.18
Social influence (SI)	Male	3.06	0.93	7.96	.01	.38	0.18
Social influence (SI)	Female	2.71	0.93	7.96	.01	.38	0.18
Entertainment (ET)	Male	3.64	1.00	1.14	.26	.06	0.03
Entertainment (ET)	Female	3.58	0.99	1.14	.26	.06	0.03

The correlation analysis results show that academic performance shared a weak positive correlation with information seeking (r = .15, p < .01) and exposure (r = .16, p < .01). However, academic performance has a very weak negative correlation with social influence (r = −.12, p < .01) and connecting (r = −.08, p < .01). No significant relationship was found among academic performance, entertainment, or coordination gratification. CSP after starting Internet use was found to have a very weak negative correlation with connecting (r = −.07, p < .01), coordination (r = −.08, p < .01), social influence (r = −.10, p < .01), and entertainment (r = −.05, p < .05). No significant correlation relationships were found among CSP, information seeking, and exposure.

Parental attitudes toward Internet use (parental control) at home have a very weak positive correlation with connecting (r = .07, p < .01), coordination (r = .06, p < .05), and social influence gratifications (r = .09, p < .01). However, it is very weakly negatively correlated with information seeking (r = −.07, p < .01) and exposure (r = −.06, p < .01). No significant correlation was found with entertainment gratification.

The independent sample t-test results suggest that Internet users with a personal computer tend to seek higher connecting, t = 3.03, p = .01, Cohen’s d = .30, effect size (r) = .15, M (SD) = 3.40 (.95) versus 3.12 (.94); coordination, t = 2.65, p = .01, d = .26, r = .13, M (SD) = 3.10 (.96) versus 2.86 (.90); and entertainment, t = 2.67, p = .01, d = .28, r = .14, M (SD) = 3.63 (.99) versus 3.34 (1.09) gratifications compared to Internet users without a personal computer at home.

Similarly, the independent t-test results suggest that Internet users with a personal Internet home connection tend to seek higher information seeking, exposure, social influence, entertainment, connecting, and coordination gratifications compared to Internet users without personal Internet connectivity (see Table 4).

Table 4.

Differences in the Internet Gratifications of Internet Users With and Without Home Internet.

Internet Gratifications	Home Internet	Mean	SD	t Value	Sig	Cohen’s d	Effect Size
Information seeking (IS)	Yes	3.92	0.80	2.67	.01	.17	0.08
Information seeking (IS)	No	3.78	0.85	2.67	.01	.17	0.08
Exposure (EX)	Yes	3.82	0.99	2.09	.04	.12	0.06
Exposure (EX)	No	3.70	1.02	2.09	.04	.12	0.06
Connecting (CT)	Yes	3.45	0.94	6.11	.01	.36	0.18
Connecting (CT)	No	3.11	0.93	6.11	.01	.36	0.18
Coordination (CE)	Yes	3.13	0.96	7.82	.01	.24	0.12
Coordination (CE)	No	2.90	0.97	7.82	.01	.24	0.12
Social influence (SI)	Yes	2.97	0.94	3.71	.01	.23	0.11
Social influence (SI)	No	2.75	0.97	3.71	.01	.23	0.11
Entertainment (ET)	Yes	3.67	0.98	5.04	.01	.29	0.14
Entertainment (ET)	No	3.38	1.04	5.04	.01	.29	0.14

Daily Internet use had a weak positive correlation with connecting (r = .15, p < .01) and social influence (r = .14, p < .01). Furthermore, daily Internet use had a very weak positive correlation with coordination (r = .10, p < .01) and entertainment (r = .07, p < .01). In contrast, daily Internet use had a very weak negative correlation with information seeking (r = −.10, p < .01) and exposure (r = −.08, p < .01).

The correlation analysis results show that UCS-AA has a very weak positive correlation with information seeking (r = .08, p < .01) and exposure (r = .09, p < .01) gratifications. However, UCS-AA had a medium positive correlation with connecting (r = .32, p < .01), coordination (r = .38, p < .01), social influence (r = .32, p < .01), and entertainment (r = .23, p < .01) gratifications. Similar results were obtained for the relationships among UCS-R and Internet gratifications, that is, weak correlation with information seeking (r = .16, p < .01) and exposure (r = .17, p < .01) and medium correlation with connecting (r = .30, p < .01), coordination (r = .32, p < .01), social influence (r = .33, p < .01), and entertainment (r = .24, p < .01) gratifications. Finally, monthly income and Internet use experience did not share any correlation with any of the six Internet gratifications (see Table 5).

Table 5.

Relationship Between Internet Gratifications and Users’ Background Characteristics.

Study Variables	Information Seeking (IS)	Exposure (EX)	Connecting (CT)	Coordination (CE)	Social Influence (SI)	Entertainment (ET)
Age	+	+	+	+	+	+
Academic performance	+	+	−	NA	−	NA
Change in school performance	NA	NA	−	−	−	−
Parental control	−	−	+	+	+	NA
Monthly income	NA	NA	NA	NA	NA	NA
Internet ownership at home	−	−	−	−	−	−
Computer ownership	NA	NA	−	−	NA	−
Daily Internet use	−	−	+	+	+	+
Internet use experience	NA	NA	NA	NA	NA	NA
UCS-AA	+	+	++	++	++	++
UCS-R	+	+	++	++	++	++

Note. UCS-AA = Unwillingness to Communicate Scale–Approach-Avoidance; UCS-R = Unwillingness to Communicate Scale–Reward-Seeking; NA = not applicable. The symbol (+) refers to weak positive, (−) weak negative, and (++) strong positive correlation between study variables. The symbol NA refers to insignificant correlation between two variables.

Discriminating Heavy and Light Internet Users

A logistic regression analysis ascertained the effect of the demographic variables, technology accessibility status, UCS-AA, and UCS-R scores on the likelihood that a given adolescent is a heavy user. The logistic regression model is statistically significant, χ² (11) = 108.34, p < .01. The model explains 12.0% (Nagelkerke R²) of the variance in the heavy Internet use of the adolescents and correctly classifies 63.5% of the cases. Of 11 predictor variables, only 8 are statistically significant. The positive predictors are age (p = .00, Wald = 15.72, Exp [B] = 1.20), gender (p = .00, Wald = 14.55, Exp [B] = 1.67), Internet at home (p = .00, Wald = 13.98, Exp [B] = 2.40), Internet experience (p = .01, Wald = 9.92, Exp [B] = 1.13), and UCS-AA (p = .01, Wald = 1.36, Exp [B] =6.81), while the negative predictors are academic performance (p = .01, Wald = 9.70, Exp [B] = .75), CSP (p = .01, Wald = 10.06, Exp [B] = .72), and UCS-R (p = .03, Wald = 4.82, Exp [B] = .75).

A similar logistic regression was performed using the six Internet gratifications as predicting variables in order to examine the likelihood that a given adolescent is a heavy user. The logistic regression model is statistically significant, χ² (6) = 65.41, p < .01. The model explained 4.5% (Nagelkerke R²) of the variance in heavy Internet use among adolescents and correctly classified 58.8% of cases. Of the six predictor variables, only two Internet gratifications are statistically significant, namely, connecting (p = .01, Wald = 19.164, Exp [B] =1.368) as a positive and information seeking (p = .01, Wald = 14.27, Exp [B] = .76) as a negative predictor.

Predicting Internet Gratifications

Hierarchical multiple regression was performed by utilizing the adolescents’ background characteristics to predict each of the six Internet gratifications (dependent variables; see Tables 6–8). The results suggest that hierarchical multiple regression is able to explain 7.5% and 8.3% of the variance in content gratifications (information seeking and exposure; see Table 6). Similarly, regression models were able to explain 14.5% and 16.1% of variance in the connecting and coordination gratifications (social gratifications; see Table 7). Finally, the adolescents’ background characteristics are successful in explaining 15.1% and 6% of the variance in process gratifications (social influence and entertainment gratifications; see Table 8).

Table 6.

Predicting Information Seeking and Exposure Gratifications Using Demographics, Technology Accessibility, and Unwillingness to Communicate.

Independent Variables	Information Seeking (IS)			Exposure (EX)
Independent Variables	β	t Value	Sig	β	t Value	Sig
Model A: Demographics
Age	.11	3.62	.01	.11	3.57	.01
Gender	.12	4.01	.01	.11	3.56	.01
Monthly income	.01	0.24	.81	.02	0.58	.57
Academic performance	.13	4.31	.01	.13	4.41	.01
Parental control	.01	0.32	.75	−.03	−0.87	.39
CSP	−.04	−1.46	.15	−.02	−0.80	.43
Adjusted R²	3.9%			3.9%
Model B: Technology accessibility
Computer ownership	−.05	−1.43	.15	−.03	−0.77	.44
Internet ownership	−.04	−1.21	.23	−.08	−2.55	.01
Daily Internet use	−.12	−4.05	.01	−.07	−2.45	.01
Internet use experience	−.03	−1.03	.30	−.02	−0.56	.58
Adjusted R²	5.5%			4.8%
Model C: UCS
UCS-AA	−.02	−0.51	.61	−.020	−0.58	.57
UCS-R	.16	4.53	.01	.20	5.85	.01
Adjusted R²	7.5%			8.3%

Note. CSP = change in school performance; UCS-AA = Unwillingness to Communicate Scale–Approach-Avoidance; UCS-R = Unwillingness to Communicate Scale–Reward-Seeking; Sig = significance.

Table 7.

Predicting Connecting and Coordination Gratifications Using Demographics, Technology Accessibility, and Unwillingness to Communicate.

Independent variables	Connecting (CT)			Coordination (CN)
Independent variables	β	t Value	Sig	β	t Value	Sig
Model A: Demographics
Age	.09	3.12	.01	.08	2.70	.01
Gender	−.12	−4.03	.01	−.15	−5.03	.01
Monthly income	.00	0.06	.95	.02	0.75	.46
Academic performance	−.04	−1.36	.17	−.01	−0.17	.87
Parental control	.06	1.92	.06	.04	1.37	.17
CSP	−.06	−2.05	.04	−.08	−2.67	.01
Adjusted R² value	3.4%			3.6%
Model B: Technology accessibility
Computer ownership	−.02	−0.55	.58	.01	0.04	.97
Internet ownership	−.06	−1.69	.09	−.06	−1.78	.08
Daily Internet use	.08	2.64	.01	.04	1.20	.23
Internet use experience	.01	0.15	.88	−.01	−.17	.86
Adjusted R² value	4.2%			3.8%
Model C: UCS
UCS-AA	.22	6.65	.01	.25	7.4	.01
UCS-R	.15	4.44	.01	.16	4.8	.01
Adjusted R² value	14.5%			16.1%

Note. CSP = change in school performance; UCS-AA = Unwillingness to Communicate Scale–Approach-Avoidance; UCS-R = Unwillingness to Communicate Scale–Reward-Seeking; sig = significance.

Table 8.

Predicting Social Influence and Entertainment Gratifications Using Demographics, Technology Accessibility, and Unwillingness to Communicate.

Independent Variables	Social Influence (SI)			Entertainment (ET)
Independent Variables	β	t Value	Sig	β	t Value	Sig
Model A: Demographics
Age	.06	1.94	.05	.05	1.71	.09
Gender	−.16	−5.32	.01	−.04	−1.36	.18
Monthly income	−.02	−.54	.59	−.00	−0.09	.93
Academic performance	−.07	−2.27	.02	.03	0.82	.41
Parental control	.07	2.25	.03	.05	1.60	.11
CAP	−.08	−2.69	.01	−.03	−0.91	.36
Adjusted R² value	5.1%			.2%
Model B: Technology accessibility
Computer ownership	.00	0.11	.91	.01	0.34	.73
Internet ownership	−.04	−1.28	.20	−.06	−1.84	.07
Daily Internet use	.09	2.88	.01	.02	0.78	.43
Internet use experience	.00	0.13	.90	.02	0.74	.46
Adjusted R² value	5.7%			.4%
Model C: UCS
UCS-AA	.16	4.72	.01	.14	3.96	.01
UCS-R	.20	5.94	.01	.14	3.89	.01
Adjusted R² value	15.1%			6%

Note. UCS-AA = Unwillingness to Communicate Scale–Approach-Avoidance; UCS-R = Unwillingness to Communicate Scale–Reward-Seeking; Sig = significance.

Discussion

Gratifications and Demographics

The results of this study reveal that the correlations between age and Internet U&G were very small. Considering the large sample size of the present study (N = 1,914), the weak correlation values suggest that a relationship does not in fact exist. Kaye and Johnson (2004) and Leung (2003) also found that age of Internet users and Internet U&Gs do not share any relationship. There are two possible reasons underlying these results: (1) Due to the integration of Internet use in the school curriculum, adolescent students from grades 8–12 (aged 12–19 years) are required to utilize the Internet more and more, both inside and outside school, for their assignments. Due to this, students from different age-groups are equally exposed to the Internet. This could be one possible reason why adolescents from different age-groups seek similar gratifications from Internet use. (2) Due to the ongoing development in Internet infrastructure in India, the availability of cheap computing devices, and the low cost of the Internet, adolescents from different age-groups have equal access to the Internet. Furthermore, the majority of this study participants come from low to middle income families.

Possible reasons behind the gender differences in the sought Internet gratifications could be: (1) We learnt from our observation exercises in Indian schools that male adolescents experience greater freedom and autonomy of Internet use than female adolescents due to a common gender bias in Indian society. Therefore, male adolescents utilize the Internet more openly than females to connect and coordinate with friends, peers, and family and consider using the Internet as a social influence. In comparison, female adolescents might face societal and family pressure, which cause them to be less open about seeking higher levels of coordination, connecting, and social influence gratifications from the Internet and (2) female adolescents’ academic achievement has consistently outperformed that of male adolescents in India in the past several years (based on the schools’ student databases). Seeking exposure and information is associated with academic performance and educational success. Due to this, female adolescents are drawn to these gratifications. These findings are not consistent with the results of Kaye and Johnson (2004). However, it should be noted that the present study context is general Internet usage and related gratifications, while Kaye and Johnson (2004) examined the gratifications of web, bulletin boards, and chat forums.

The results of this study show very weak correlations among academic performance, connecting, and social influence gratifications. Furthermore, both high- and low-scoring adolescents do not tend to differ in their entertainment and coordination gratifications. This shows that all adolescents (low and high scorers) equally value entertainment, coordination, social influence, and connecting gratifications and consider them important. Other than this, the results of this study suggest that academic performance and content gratifications (information seeking and exposure) share a weak positive correlation. This suggests that academically strong adolescents tend to utilize the Internet for content gratification. Two possible reasons could be: (1) During our field studies with Indian schools, it was observed that high-scoring students tend to utilize the Internet for seeking new information related to their studies and (2) Indian schools have created a popular belief among Indian students that the Internet must only be utilized for educational purposes, for example, doing assignments, finding new content related to courses, and so on. Furthermore, it was observed that high-scoring students were more receptive of these kinds of popular beliefs, since their school teachers promote them.

Adolescents with differing monthly family incomes do not differ in the Internet gratifications they seek, which is consistent with the findings of Kaye and Johnson (2004) and Leung (2004). Possible reasons are the availability of cheap computing devices (e.g., smartphones and personal computers), reductions in the price of Internet data plans, and the inclusion of Internet-based teaching and education in the Indian school curricula.

Regarding the relationship between parental control and Internet gratifications, the obtained correlations were very weak. Due to the large sample size of the present study, it would be correct to assume that there is actually no relationship between parental control and Internet U&Gs. The possible reasons could be that parental control was assessed by a single-item variable. Hence, the present study obtained a very weak relationship. Therefore, future studies should utilize a multi-item construct to assess parental control and the relationship must then be reassessed.

Finally, CSP shared a very weak correlation relationship with Internet U&Gs. This study results showed that 46% (n = 880) of the adolescents tend to believe that their academic performance has improved and 38.9% (n = 745) considered it remained unchanged after starting Internet use (see Table 2). This might be due to the fact that a popular stereotype is prevalent among Indian schools (observed during our field studies) that Internet use always has a positive impact on educational learning and academic success. This stereotype may lead adolescents to believe that their academic performance has either improved or at least did not change, even if they are using the Internet for longer hours. This could be one possible reason explaining why we obtained a very weak or almost no relationship between CAP after Internet use and U&Gs.

Gratification and Technology Accessibility

Adolescents with a personal home Internet connection tended to score only a little higher in all six Internet gratifications compared to adolescents without Internet connectivity. However, a bare minimum difference exists between Internet users with and without a home Internet connection on the content gratifications (information seeking and exposure) based on the results of Cohen’s d and effect size (see Table 4). These findings are consistent with the results of Grace-Farfaglia, Dekkers, Sundararajan, Peters, and Park (2006) and suggest that (1) adolescents with home Internet connectivity tend to utilize the Internet more because they have access all of the time, and hence seek more Internet gratification and (2) home Internet connectivity is not a prerequisite for seeking higher content gratifications. Most schools now provide Internet use in their library, so interested students can utilize the Internet for educational purposes during their free time. Furthermore, students without home Internet can visit Internet cafes for their content gratification.

Adolescents who own a computer tend to seek more social (connecting and coordination) and entertainment gratification than those who do not own one. However, the differences were weak. Furthermore, adolescents with and without computer ownership do not differ in the exposure, information seeking, or social influence gratifications. Probable reasons could be (1) adolescents with a personal computer have an additional communication and entertainment channel compared to those without a personal computer and (2) content and social influence gratifications are dependent on Internet connectivity but are independent of computer ownership.

No significant correlations between Internet U&Gs and Internet use experience were found, which is consistent with the findings of Kaye and Johnson (2004). One reason could be that the gratifications adolescent Internet users receive from the Internet do not change with the passage of time. Hence, adolescents with more or less Internet use experience seek the same level of the six Internet gratifications.

Finally, adolescents spending more time each day on the Internet tend to seek little higher connecting and social influence gratifications, consistent with the findings of LaRose and Eastin (2004). However, Internet users with high and low daily Internet time spent did not significantly differ in their content (information seeking and exposure), coordination, and entertainment gratifications due to very weak correlations. Possible reasons include (1) connecting with new friends or maintaining connections with existing friends and family on the Internet requires time. Similarly, seeking social influence from Internet usage also requires spending more time and (2) content gratification (exposure and information seeking) is mostly centered on finding a specific piece of information. Therefore, content gratification does not result in an increase in daily Internet use.

Gratification and the UCS

We found that adolescents with high UCS-AA and UCS-R scores tend to seek higher process (entertainment and social influence) and social (connecting and coordination) gratifications. However, their UCS-AA and UCS-R scores shared very weak correlations with their Internet content gratification. These findings are consistent with the findings of Papacharissi and Mendelson (2000) according to which Internet users with higher UCS-AA and UCS-R scores tend to experience higher social activity and interpersonal gratifications via the Internet. Similarly, Sheldon (2008) also revealed that Internet users with high UCS-AA and UCS-R scores are less likely to meet new people face-to-face but feel comfortable online.

Heavy Versus Light Internet Users

The time spent on Internet use each day was utilized for computing heavy (spending more than 1 hr per day) and light (spending less than 1 hr per day) Internet users. The results of the logistic regression analysis revealed that adolescents with a home Internet connection are 2.40 times more likely than adolescents without home Internet to exhibit heavy Internet use, male adolescents are 1.67 times more likely than female adolescents, older adolescents are 1.20 times more likely than younger adolescents, more experienced Internet users are 1.13 times more likely than less experienced Internet users, and adolescents with higher UCS-AA scores are 1.36 times more likely than those with lower scores. The results confirm the findings of previous Internet addiction and Internet dependency research (e.g., Ko, 2000; Leung, 2003; Roy, 2009; Wallace, 1999).

In addition, the logistic regression results show that self-perception of improved academic performance, lower academic performance, and increases in UCS-R are associated with a reduction in the likelihood of exhibiting heavy Internet use. Possible reasons include (1) when adolescents devote more time to improving their academic performance, they ultimately utilize the Internet less, (2) when adolescents suffer from degradation of academic performance, for example, receiving a worse grade or percentage in exams, they tend to utilize the Internet less in order to improve their performance, and (3) Increases in UCS-R scores mean that adolescents suffer from a high tendency to remain isolated. This tendency could lead to a reduction in heavy Internet use.

Only two Internet gratifications, connecting and information seeking, are statistically significant in discriminating heavy and light Internet users. This is consistent with the findings of Leung (2003) that heavy users have a higher tendency to establish social bonds and connect with others through the Internet. However, heavy and light Internet users do not differ in entertainment, exposure, coordination, or social influence gratification. We found that adolescents seeking gratification from connecting are 1.37 times more likely to exhibit heavy Internet use, while the increase in information-seeking gratification is associated with a reduction in the likelihood of exhibiting heavy Internet use. These findings suggest that adolescents are likely to become heavy Internet users when they start seeking gratification from connecting (i.e., connecting with peers, friends, and family) because maintaining social ties and connecting with family and friends on the Internet requires a considerable amount of time. However, when adolescents utilize the Internet mainly to seek information, then they are less likely to become heavy Internet users. This is due to the fact that seeking information on the Internet is related to finding specific information, which, as a whole, requires considerably less time compared to connecting gratification. These findings are synchronous with the present study findings that daily Internet use time is positively correlated with the social and process gratifications and negatively correlated with content gratification.

Predicting Internet Gratifications

In the case of content gratification, we found that older adolescents, females, and adolescents who experience higher academic performance spend less time each day on the Internet and who have higher UCS-R scores tend to seek information-related gratification. In comparison, older female adolescents with better academic performance and those with higher UCS-R scores tend to experience exposure gratification. The variables of Internet ownership and time spent using the Internet each day of this study seem to possess very low predictive powers (see Table 6).

With respect to social gratification, we found that older male adolescents who are experiencing improved academic performance, spending more time each day on the Internet, and those with high UCS-AA and UCS-R scores tend to experience connecting gratification. Similar results were found in the case of coordination gratification where older male adolescents with improved academic performance and those with high UCS-AA and UCS-R scores tend to experience coordination gratification. However, it should be noted that age and daily Internet use possess very low predictive powers (see Table 7).

Finally, in the case of process gratification, we found that male adolescents with high UCS-AA and UCS-R scores tend to experience social influence gratification. In comparison, adolescents with higher UCS-AA and UCS-R scores tend to experience entertainment gratification. It should be noted that academic performance, parental control, change in academic performance, and daily time spent on the Internet possess low predicting powers in the context of social influence gratification (see Table 8).

These results are consistent with the findings related to the relationship between social gratification and an adolescent’s background profile. Furthermore, our study results are consistent with the findings of Leung (2003) and Kaye and Johnson (2004). The very weak correlations among age, gender, income, and gratifications in the current study actually support Leung’s (2003) and Kaye and Johnson’s (2004) claims that age, gender, and income are not related to Internet gratifications.

Study Implications

The present study has examined Internet U&Gs among adolescents aged 12–18 years, utilizing a 27-item Internet U&G scale that addresses the limitations of previous Internet U&G studies. Our empirical study has added new knowledge to the existing Internet U&G research by providing conceptual linkages shared among Internet gratifications, adolescents’ background characteristics, and heavy Internet use, as suggested by Kim and Haridakis (2009). The results of this study are of great relevance to Internet and media researchers and practitioners.

Internet-based service companies can utilize our study results to target adolescent Internet users and transform their existing and forthcoming service offerings in order to increase their reach and profits. Internet-based service companies are interested in heavy Internet users, since they are the loyal users of any Internet-based offerings. The importance of heavy Internet users has increased exponentially due to the fact that most organizations are now moving toward establishing their brand communities on social networking sites, and in groups, and are offering their services and products via the Internet. In addition, the role of consumers has recently been stressed in user-centric service innovation (e.g., idea generation, cocreation, verification, and feedback), and the Internet acts as a medium to practice service innovation. The present study findings reveal that when adolescents start seeking too much connecting gratification, then it leads to heavy Internet use. Therefore, service companies should aim to fulfill “connecting” gratification through their Internet offerings, since it will likely lead to heavy use among its users. This study results on heavy Internet use clearly reveal that male and older adolescents, with anytime access to Internet use, and adolescents with high UCS-AA scores are likely to resort to heavy Internet use.

The relationship between heavy Internet use and adolescents’ background characteristics also suggests that heavy Internet use may lead adolescents to Internet addiction. Furthermore, the internal urge for connecting gratification among adolescents can lead to Internet dependency. This knowledge is of special relevance to psychologists, educational researchers, social workers, teachers, and parents who are concerned about heavy Internet use among adolescents. Therefore, we recommend that parents and educational psychologists keep a close eye on daily time spent on the Internet by young adolescents. The content and process Internet gratifications do not contribute to heavy Internet use but connecting gratification does.

The results of this study for predicting Internet U&Gs shed light on differences in content, social, and process gratifications and reveal the role of various variables in predicting Internet U&Gs among adolescents. We found that female adolescents with higher UCS-R scores tend to seek content gratification, male adolescents with higher UCS-AA and UCS-R scores tend to seek social gratification, and male adolescents with higher UCS-AA and UCS-R scores tend to seek social influence gratification. These results are relevant to user-interface designers and managers who can customize their services or products in order to target various consumer segments. In addition, these findings are useful for U&G researchers and practitioners because they shed light on the three types of gratification and their relationship with Internet users’ background characteristics. Overall, the present study has theoretical and practical implications for a wide audience (including managers, researchers, practitioners, and teachers).

Study Limitations and Future Work

The present study has several limitations that also open up new avenues for future research. First, the present study only focuses on the relationships among Internet U&Gs and three categories of adolescents’ background characteristics, namely, attributes related to demographic variables, technology accessibility status, and unwillingness to communicate. Therefore, we recommend that other researchers and practitioners investigate the relationships among Internet U&Gs and other user background characteristics, for example, self-efficacy, shyness, alienation, loneliness, risky online behavior, socioeconomic status, addiction, and dependency.

Second, the dichotomization of heavy and light Internet users was purely based on the amount of time spent on the Internet each day (e.g., heavy Internet users spend more than 1 hr per day). This classification is not scientific; rather, it is based on the previous work of Roy (2009) and Kargaonkar and Wolin (1999). Therefore, we recommend that other researchers come up with a solid scientific basis (e.g., change in behavior and impact on health and well-being) for classifying heavy and light Internet users. Furthermore, it is possible that another scientific basis for dichotomizing light and heavy users might lead to study results that differ from the present ones.

Third, some of this study variables, namely, academic performance, CAP, parental control, and economic status, were measured by a single item. Therefore, these variables possess a high degree of measurement error, which might have added bias to this study results. Therefore, we advise other researchers to instead utilize multi-item constructs representing the aforementioned variables. It might be possible that this transformation will lead to different results from our own.

Fourth, the percentage of variance explained in the heavy Internet use as well the variance explained in the entertainment, exposure, and information-seeking gratifications is particularly low. Therefore, we recommend that fellow researchers determine suitable predictor variables for increasing the percentage of variance in the heavy use and other aforementioned gratifications.

Fifth, two of this study variables, namely, “academic performance” and “CAP,” have revealed interesting findings on heavy Internet use among adolescents. However, these relationships require further examination. We recommend that researchers utilize a well-grounded, valid, and reliable empirical construct to evaluate academic performance and CAP and their relationship with heavy Internet use.

Sixth, the present research questions have been validated with adolescent Internet users from a single country and culture. Therefore, further research is required to examine if the present study findings are applicable in a wider or generalized context. Furthermore, researchers and practitioners should exercise caution while interpreting these results, due to the fact that this study participants were adolescents whose behavior and practices might not be equivalent to those of older Internet users and adults. We also recommend that researchers carry out studies with similar constructs and other age-groups of Internet users. In addition to this, panel, longitudinal, and cross-cultural studies with similar research questions and constructs are needed to ascertain the validity of the present study findings over time and with a wider range of cultures.

Footnotes

Authors’ Note

We would like to acknowledge the support received from the Ministry of Science and Technology, Taiwan, under Grant Number NSC 102-2628-S-011-001-MY4.

Declaration of Conflicting Interests

The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: We acknowledge the support received from the Academy of Finland, Mind the Gap (Project Number 1265528), Researcher’s mobility grant to Taiwan (Decision No. 265969) and South Africa (Decision No. 277571), Teknillisen korkeakoulun tukisäätiö, TEKES funded research projects Data to Intelligence (D2I; Project No 21143201), and Mobile Financial Services (MoFS; Project No 211440).

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