Keywords

1 Why Study Happiness and the Internet?

Following the dawn of the new millennium, research on happiness increased dramatically, largely spurred on by the fact that people increasingly rate happiness as a major life goal. For example, recent surveys have indicated that the strong majority of people across many countries rate happiness as more important than income [3]. Lyubomirsky [4] sums this research up, “…in almost every culture examined by researchers, people rank the pursuit of happiness as one of their most cherished goals in life” (p. 239).

In addition, there is a large body of evidence that suggests situational factors, in particular wealth, play a surprisingly small role in determining happiness. Some suggest that this may be the result of society moving into a post-materialistic phase, where basic needs have been largely met for many in industrialized countries, so pursuit of self fulfillment becomes more important [4].

Finally, there are number of studies that indicate that happy people, in general, have a positive effect on society. For example, there is evidence that happier people are more successful and socially engaged [5].

2 What Is Happiness?

For the most part, researchers agree that happiness is inherently subjective, In fact, the term is often used interchangeably with “subjective well-being” (SWB) [6]. Myers [7], one of the leading researchers in the area, stated that happiness is “…whatever people mean when describing their lives as happy” (p. 57). Despite the potential for ambiguity with such a definition, there is considerable agreement, at least across Western culture, as to what happiness means [8]. Most people equate happiness with experiences of joy, contentment, and positive well being; as well as a feeling that life is good, meaningful, and worthwhile [9].

As a consequence, self-report measures have served as the primary measure of happiness. Examples include the Satisfaction with Life Scale (SLS), the Subjective Happiness Scale (SHS), and the Steen Happiness Index (SHI). Psychometric studies of these self-report measures indicate that they are, by and large, reliable over time, despite changing circumstances; they correlate strongly with friends and family ratings of happiness; and they are statistically reliable. Lyubomirsky [9] sums this up, “A great deal of research has shown that the majority of these measures have adequate to excellent psychometric properties and that the association between happiness and other variables usually cannot be accounted for by transient mood” (p. 239). These psychometric studies illustrate the general agreement among people as to what constitutes happiness.

One other interesting point, regarding the definition of happiness and its measurement, is that mean happiness is consistently above a mid-line point in most populations sampled [6]. For example, three in ten Americans say they are “very happy”, only 1 in ten report that they are “not too happy”, and 6 in 10 say they are “pretty happy” [7]. Therefore, there appears to be a positive set point, where most people appear to be moderately happy, and this is independent of age and gender [8].

3 Individual Difference and Happiness

Happiness is surprisingly stable over time [9] even with major changes in life circumstances [10], and there appears to be no time in life that is most satisfying [11]. These findings are consistent with research that indicates some individual difference traits are predictive of happiness. Further, happiness may also be strongly tied to genetic predisposition. We now turn to a discussion of this research.

Twin studies indicate that there is a strong genetic component in happiness [12, 13]. For example, Tellegen et al. [13] assessed the well being of twins at ages 20 and 30. They correlated the happiness scores between monozygotic twins at stage 1 with the score for their twin at stage 2 (cross time/cross twin) and found a correlation of .4, while the test-retest correlation where each twin’s score was correlated with himself/herself was only .5. Further the cross twin/cross time correlation for dizygotic twins was only .07. Therefore, heritability appears to account for a large part of the stability in happiness.

As mentioned, some other individual difference measures have been found to consistently correlate with happiness, in particular extroversion. For example, in a cross-cultural study Lucas et al. found that extraversion correlated with positive affect in virtually all 40 nations they examine [15]. Extroversion, as a predictor of happiness, is strongly related to the literature to be discussed, which relates social interaction with happiness, in that there is a clear relationship between the number and quality of social relationships and happiness. One would expect that an extrovert would be more likely to seek out and form these types of relationships.

Religiosity is another variable that has been found to consistently predict happiness [7]. In addition, those who report higher levels of religiosity tend to recover greater happiness after suffering from negative life events [15]. This finding has been found for peoples’ self reports of their degree of religiosity, and for behavioral measures such as Church attendance [7]. As with extroversion, the impact of religiosity may be, at least partly, explained by the importance of social interaction in determining happiness, in that those who attend Church regularly, and interact with others in a positive social environment, are more likely to be happy [17]. Further, people often derive meaning and purpose from religious practices, which is another important correlate of happiness [7].

In addition to behavioral tendencies, with respect to individual differences, the research of Lyubomirsky et al. provides substantial evidence that there are consistent differences between happy and unhappy people in the ways they process (“construe”) information. For example, studies from Lyubomirsky’s laboratory have found that happy people are less sensitive to social comparisons [18], tended to feel more positive about decisions after they were made [19], construed events more positively [19], and are less inclined to self-reflect and dwell on themselves [18]. This difference in information processing dispositions in happy vs. unhappy people is presumably one reason why the effects of circumstantial factors are relatively minimal.

Another individual difference factor, which has been identified as important in predicting happiness, is the autoletic personality, which refers to people who tend to regularly experience “flow” [20]. Flow refers to a kind of experience that is engrossing and enjoyable to such a degree that it becomes “autoletic” – worth doing for its own sake [21]. The autoletic personality and the flow concept are consistent with the views of happiness researchers who have suggested that engagement is a fundamental component of a happy life [22].

4 Happiness and the Internet

Studies that have examined the relationship between the Internet and happiness have been conducted at least since the relatively early days of the World Wide Web. Most of these have focused on communication/collaborative activities and the Internet. As we mentioned, these types of activities have been found in non-internet studies to be strongly related to happiness.

4.1 The Internet Paradox

In 1998 Kraut et al. reported the results of a reasonably extensive study of early World Wide Web users where they followed the activity of mostly first time Internet users over a period of years. Researchers administered periodic questionnaires and server logs indicating participant activity on the web. (Participants were provided with free computers and internet connections) [22].

Over all, the results showed that the Internet had a largely negative impact on social activity, in that those who used the Internet more communicated with family and friends less. They also reported higher levels of loneliness. Interestingly, they also found that email, a communication activity, constituted the participants main use of the Internet. The researchers coined the term “internet paradox” to describe this situation in which a social technology reduced social involvement.

These researchers speculated that this negative social effect was due to a type of displacement, in which their time spent online displaced face-to-face social involvement. Although they note that users spent a great deal of time using email, they suggest that this constitutes a low quality social activity and this is why they did not see positive effects on well being [22]. They find further support for this supposition in a study reported in 2002, where they found that business professionals who used email found it less effective than face-to-face communication or the telephone in sustaining close social relationships [23].

Since the time that this Internet paradox was identified, a number of studies over the next twelve years have found, fairly consistently, results that contradict the Kraut et al. results. More recent studies have indicated the potential positive social effects of the Internet and their relationship to well being. Further, the effect appears to be getting stronger as the Internet and the users mature.

In fact, one of the first challenges to this Internet paradox was provided by Kraut himself when he published follow up results for participants in the original Internet-paradox study, including data for additional participants. In this paper, “Internet Paradox Revisited,” researchers report that the negative social impact on the original sample had dissipated over time and, for those in their new sample, the Internet had positive effects on communication, social involvement, and well being [24]. Therefore, it appears that the results of the original Kraut et al. study were largely due to the participants’ inexperience with the Internet. Within just a few years, American society’s experience with the Internet had increased exponentially. Further, the Kraut studies concentrated on email, whereas there are many other social communication tools available on the modern web.

4.2 Displacement Versus Stimulation Hypothesis

More recently, researchers have examined the relationship between on-line communication and users’ over all social networks, explicitly addressing the question of whether or not on-line communication “displaces” higher quality communication, or “stimulates” it. Presumably, the former would negatively affect well being, while the latter would enhance it [25].

In one large scale study, over 1000 Dutch teenagers were surveyed regarding the nature of their on line communication activities, the number and quality of friendships, and their well being.

They found strong support for the stimulation hypothesis. More specifically, these researchers developed a causal model, which indicated that instant messaging lead to more contact with friends, which lead to more meaningful social relationships, which, in turn, predicted well being. Interestingly, they did not find this same effect for chat in a public chat room. They attributed this finding to the fact that participants reported that they interacted more with strangers in the chat room as compared to their interaction with friends with instant messaging [26].

4.3 The Internet and Social Connectedness

Despite studies, such as the one just mentioned, which have found a relationship between internet use and positive outcomes, there is still a great deal of press suggesting that the internet can effect users negatively, causing social isolation, and shrinking of social networks. This is purported to be especially true for adolescents [27].

Researchers with the Pew Internet and Daily Life Project set out to examine this concern directly in one of the most comprehensive studies of the effect of the Internet on social interaction, reported in 2009 [28]. Contrary to fears, they found that:

  • A variety of Internet activities were associated with larger and more diverse core discussion networks.

  • Those who participated most actively with social media were more likely to interact with those from diverse backgrounds, including race and political view.

  • Internet users are just as likely as others to visit a neighbor in person, and they are more likely to belong to a local voluntary organization.

  • Internet use is often associated with local activity in community spaces such as parks and restaurants, and Internet connections are more and more common in such venues.

Although these outcomes did not explicitly include happiness, they do support the contention that Internet activities can enhance the amount and quality of social relationships, which has been implicated in a number of studies as a strong and consistent predictor of happiness.

5 Research Overview

This study is a replication and extension of two previous studies conducted in 2016 [1] and 2017 [2], which also explored the relationship between Internet activities and happiness. In the 2016 study an internet-use-scale (IUS) was developed and subjected to initial psychometric analyses, and modified accordingly, resulting in a 13 item scale, representing three categories of internet use: Affective Expression, Information Gathering, and Total Time (spent on the internet). Results from both previous studies indicated that happiness measures were negatively related to the Time and Affective Expression categories, and positively related to Information Gathering. The current research extends these studies with a new sample, and some additional analyses, utilizing the larger data pool. I administered the Internet Use Scale, IUS and the same two happiness measures: Flourishing Scale [3] and the Satisfaction with Life [4] scale, as used in the previous studies.

6 Questions

6.1 Internet Use and Happiness

What is the relationship between Internet use and happiness?

6.2 Internet Use and Happiness Over Time

How does the relationship between happiness and Internet use, and the mean happiness and use scores differ between the 2016, 2017, and 2018 samples?

7 Research Method

7.1 Participants

Thirty-three students enrolled in an undergraduate course in digital media at a small Midwestern technological research University in the spring of 2018 served as the participants in this study.

7.2 Measures

The Internet use scale (IUS) [1] was administered to assess internet use. The 13-item scale represents three internet use categories: Affective Expression, Information Gathering, and Time Spent on the Internet. The Flourishing Scale (FS) [3], and the Satisfaction with Life Scale (SWLS) [4] were administered to represent happiness.

7.3 Procedure

Participants completed the survey on-line, which consisted of the measures delineated above.

8 Results

8.1 Relationship Between Internet Usage and Happiness

In order to assess the relationship between happiness and Internet use measures, and to compare the three samples (2016, 2017, and 2018), a series of zero-order correlations were computed among the happiness and internet use measures. These results appear in Table 1.

Table 1. Correlation between internet use and happiness as a function of year
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8.2 Internet Use, and Happiness as a Function of Time

In order to compare the three samples on their internet use and happiness, a series of one-way Analyses of Variance (ANOVA) were computed with the three internet usage factors, and two happiness scores as the dependent measures and time (2016 vs 2017 vs 2018) as the independent variable. Note that these scores were computed as means of the items such that the Internet usage scores could range from −7 to +7 with higher scores representing more affective expression, information gathering, and time spent on the Internet. Scores on both happiness scales ranged from 1–7 with greater scores representing higher levels of happiness. These results are presented in Table 2.

Table 2. Internet usage and happiness as a function of sample year
Full size table

8.3 Combining Years

In order to evaluate the over-all relationship between the use factors and happiness, a series of zero-order correlations were computed among the happiness and Internet use measures, using date from all three years combined. These results are listed in Table 3.

Table 3. Internet usage and happiness (combined data from all years)
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In order the explore the impact of individual scale items on happiness measures, a final series of correlations were computed between each scale item and the two happiness measures. Table 4 lists the correlations for all items, where the relationship was statistically significant at the p < .01 level for at least one happiness measure.

Table 4. Internet use scale items the significantly correlated with happiness (combined data from all years)
Full size table

9 Conclusions

These results were largely consistent with the studies from the previous two years [1, 2]. Taken together, the results paint a picture of the happy versus unhappy Internet user. First, those who spend more time on the Internet are less happy. This was demonstrated in the 2016 sample [1], replicated in the 2017 sample [2], and again in this sample. When all the data is combined, the time-on-the-internet factor is significantly and negatively correlated with both happiness measures.

Second, there is some indication that, when interacting with the Internet, those who report spending more time gathering information and carrying out research score higher on happiness measures. However, this relationship is not as strong or consistent as with the other factors, especially when considering the current research, where the only significant relationship between the information gathering factor and happiness was negative. Further, when using the entire data set, the information gathering factor was significantly related to the flourishing measure, but not satisfaction with life. However, the analyses of individual scale items, did find a significant relationship between the item “I often us the internet for checking facts” and the flourishing scale. Note that this usage factor refers to the degree to which someone is likely to spend time checking facts on the internet, but also the degree to which one is aware of the potential inaccuracy of information. One item scored positively on this sub-scale is “I’m skeptical of the accuracy of information I find on the internet”. This was further supported by a qualitative analysis, carried out in the 2017 study where a number of those in with high-happiness scores reported that they found factual inaccuracy on the Internet as a major negative; while those with low-happiness scores did not mention it [2].

Third, those who use the Internet as a method for negative affective expression are less happy, consistently across the years and the analyses. Although this factor is not significantly related to the SWLS in this study, it is strongly and significantly related to flourishing, especially in the current sample. Further, when all the data was combined the relationship between the affective expression factor and both happiness measures were significantly and negatively related. In the qualitative analyses, carried out in the 2017 study, a common theme emerged with respect to what people liked least about the Internet, which was the aversion people have for negative-affective expression. Across both happiness groups users found it aversive when “people are jerks”, as one participant put it [2].

Finally, we carried out some analyses to compare changes in the participants’ views between the 2016, 2017, and 2018 samples, with respect to their happiness and Internet usage scores. The good news, in terms of what we’ve learned about the Internet and happiness, is that those in the 2017 sample report spending significantly less time online than the 2016 sample; when online are significantly less likely to participate in affective expression; and, perhaps consequently, scored significantly higher on the satisfaction with life scores; and this trend appears to be largely continuing with the 2018 sample.