example of hypothesis about social media

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• Social media and political participation
• Social media and political knowledge
• Subjective knowledge and social media for news
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Rethinking the Virtuous Circle Hypothesis on Social Media: Subjective versus Objective Knowledge and Political Participation

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Sangwon Lee, Trevor Diehl, Sebastián Valenzuela, Rethinking the Virtuous Circle Hypothesis on Social Media: Subjective versus Objective Knowledge and Political Participation, Human Communication Research , Volume 48, Issue 1, January 2022, Pages 57–87, https://doi.org/10.1093/hcr/hqab014

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Despite early promise, scholarship has shown little empirical evidence of learning from the news on social media. At the same time, scholars have documented the problem of information ‘snacking’ and information quality on these platforms. These parallel trends in the literature challenge long-held assumptions about the pro-social effects of news consumption and political participation. We argue that reliance on social media for news does not contribute to people’s real level of political knowledge (objective knowledge), but instead only influences people’s impression of being informed (subjective knowledge). Subjective knowledge is just as important for driving political participation, a potentially troubling trend given the nature of news consumption on social media. We test this expectation with panel survey data from the 2018 U.S. midterm elections. Two path model specifications (fixed effects and autoregressive) support our theoretical model. Implications for the study of the ‘dark side’ of social media and democracy are discussed.

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• A hypothesis-driven approach to social media insight Bronwen Morgan 17 February 2017
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FEATURE 17 February 2017

A hypothesis-driven approach to social media insight

Bronwen Morgan

Data analytics Features Media Social media UK

Speaking at yesterday’s Social Media Research Summit, organised by the Market Research Society (MRS), Pulsar’s co-founder and vice president of product and research, Francesco D'Orazio, demonstrated how to use social media to validate a specific research hypothesis. 

Social media data has transformed the scope of research. The availability of 11 years of Twitter data at the touch of a button is just the tip of the iceberg, said D'Orazio, who went on to extol the benefits of both the granularity of publicly available data (Twitter and Instagram) and the aggregated nature of Facebook and LinkedIn data. 

But the sheer quantity of interactions can make analysis challenging, which is why D'Orazio believes in the value of an emerging research approach: using social data as a tool to validate specific research hypotheses, rather than as an exploratory tool. 

The traditional ‘emergence’ approach, said D'Orazio, relies on "seeing what crops up" and is based on keywords and stories. The ‘hypothesis’ approach involves framing data: looking at it through the lens of a specific question. In short, the hypothesis approach shifts much of the analysis to before data collection, rather than afterwards. 

The key advantages of this approach are as follows: 

• Keeps the data collection focused and reduces ‘noise'
• Makes analysis faster, more structured and standardised
• Makes it easier to replicate results across teams
• Makes it easier to integrate social data with third party sources such as surveys

D'Orazio took the audience through the process of this method: from client brief, to hypothesis, to data query, to insights. 

In order to move from the client brief to a research hypothesis, the researcher must break the brief down into three elements: What is the business objective? What is the target audience? What are we trying to understand? 

This is then further broken down into two elements: Who is the audience you're trying to reach? And what type of behaviour and moments should be investigated? 

The researcher can then create a hypothesis for each of these elements that can then be investigated in the data. The more focused this is, the better, said D'Orazio. It should be considered as a frame for looking at the data, rather than simply a theory to be validated. 

The next step is to transform the hypothesis into a study definition. A hypothesis will contain – and be related to – a number of elements: language; behaviours; attitudes; moments and occasions.

Transforming the hypothesis means defining the ‘signals’ to look out for, such as audience demographics and a list of terms (and sub-terms) to look out for.

For example, if you're investigating fast food consumption among UK/US millennials, a hypothesis could be that the UK/US millennial audience buy into authenticity and not the fast casual proposition. The terms to look out for could then be: ingredients, pairings, sustainability, price, health, occasions, behaviours and quality. Within quality, for example, there are terms to look out for such as: premium, chef, better, best, amazing, etc.

The last stage is to test the study outputs, including comparing the results across demographics for context, looking at the language used and how it compares to the hypothesis. 

This can offer insight into how consumers talk about a category, including how terms are conflated or distinguished, what behaviours and attitudes relate to the category (for different demographics), and what types of conversations people have. 

This can either validate the original hypothesis, disprove it or drive completely new insight, D'Orazio explained. 

Kathy Doering

7 years ago

Wow! What an excellent, insightful article. With social media monitoring softwares changing all the time, we are able to do more and more with social data and weed out the noise. Location based monitoring and digital image searching are both available and are a great additional resources for researchers and marketers.

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• Published: 01 July 2020

The effect of social media on well-being differs from adolescent to adolescent

• Ine Beyens   ORCID: orcid.org/0000-0001-7023-867X 1 ,
• J. Loes Pouwels   ORCID: orcid.org/0000-0002-9586-392X 1 ,
• Irene I. van Driel   ORCID: orcid.org/0000-0002-7810-9677 1 ,
• Loes Keijsers   ORCID: orcid.org/0000-0001-8580-6000 2 &
• Patti M. Valkenburg   ORCID: orcid.org/0000-0003-0477-8429 1  

Scientific Reports volume  10 , Article number:  10763 ( 2020 ) Cite this article

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The question whether social media use benefits or undermines adolescents’ well-being is an important societal concern. Previous empirical studies have mostly established across-the-board effects among (sub)populations of adolescents. As a result, it is still an open question whether the effects are unique for each individual adolescent. We sampled adolescents’ experiences six times per day for one week to quantify differences in their susceptibility to the effects of social media on their momentary affective well-being. Rigorous analyses of 2,155 real-time assessments showed that the association between social media use and affective well-being differs strongly across adolescents: While 44% did not feel better or worse after passive social media use, 46% felt better, and 10% felt worse. Our results imply that person-specific effects can no longer be ignored in research, as well as in prevention and intervention programs.

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Introduction.

Ever since the introduction of social media, such as Facebook and Instagram, researchers have been studying whether the use of such media may affect adolescents’ well-being. These studies have typically reported mixed findings, yielding either small negative, small positive, or no effects of the time spent using social media on different indicators of well-being, such as life satisfaction and depressive symptoms (for recent reviews, see for example 1 , 2 , 3 , 4 , 5 ). Most of these studies have focused on between-person associations, examining whether adolescents who use social media more (or less) often than their peers experience lower (or higher) levels of well-being than these peers. While such between-person studies are valuable in their own right, several scholars 6 , 7 have recently called for studies that investigate within-person associations to understand whether an increase in an adolescent’s social media use is associated with an increase or decrease in that adolescent’s well-being. The current study aims to respond to this call by investigating associations between social media use and well-being within single adolescents across multiple points in time 8 , 9 , 10 .

Person-specific effects

To our knowledge, four recent studies have investigated within-person associations of social media use with different indicators of adolescent well-being (i.e., life satisfaction, depression), again with mixed results 6 , 11 , 12 , 13 . Orben and colleagues 6 found a small negative reciprocal within-person association between the time spent using social media and life satisfaction. Likewise, Boers and colleagues 12 found a small within-person association between social media use and increased depressive symptoms. Finally, Coyne and colleagues 11 and Jensen and colleagues 13 did not find any evidence for within-person associations between social media use and depression.

Earlier studies that investigated within-person associations of social media use with indicators of well-being have all only reported average effect sizes. However, it is possible, or even plausible, that these average within-person effects may have been small and nonsignificant because they result from sizeable heterogeneity in adolescents’ susceptibility to the effects of social media use on well-being (see 14 , 15 ). After all, an average within-person effect size can be considered an aggregate of numerous individual within-person effect sizes that range from highly positive to highly negative.

Some within-person studies have sought to understand adolescents’ differential susceptibility to the effects of social media by investigating differences between subgroups. For instance, they have investigated the moderating role of sex to compare the effects of social media on boys versus girls 6 , 11 . However, such a group-differential approach, in which potential differences in susceptibility are conceptualized by group-level moderators (e.g., gender, age) does not provide insights into more fine-grained differences at the level of the single individual 16 . After all, while girls and boys each represent a homogenous group in terms of sex, they may each differ on a wide array of other factors.

As such, although worthwhile, the average within-person effects of social media on well-being obtained in previous studies may have been small or non-significant because they are diluted across a highly heterogeneous population (or sub-population) of adolescents 14 , 15 . In line with the proposition of media effects theories that each adolescent may have a unique susceptibility to the effects of social media 17 , a viable explanation for the small and inconsistent findings in earlier studies may be that the effect of social media differs from adolescent to adolescent. The aim of the current study is to investigate this hypothesis and to obtain a better understanding of adolescents’ unique susceptibility to the effects of social media on their affective well-being.

Social media and affective well-being

Within-person studies have provided important insights into the associations of social media use with cognitive well-being (e.g., life satisfaction 6 ), which refers to adolescents’ cognitive judgment of how satisfied they are with their life 18 . However, the associations of social media use with adolescents’ affective well-being (i.e., adolescents’ affective evaluations of their moods and emotions 18 ) are still unknown. In addition, while earlier within-person studies have focused on associations with trait-like conceptualizations of well-being 11 , 12 , 13 , that is, adolescents’ average well-being across specific time periods 18 , there is a lack of studies that focus on well-being as a momentary affective state. Therefore, we extend previous research by examining the association between adolescents’ social media use and their momentary affective well-being. Like earlier experience sampling (ESM) studies among adults 19 , 20 , we measured adolescents’ momentary affective well-being with a single item. Adolescents’ momentary affective well-being was defined as their current feelings of happiness, a commonly used question to measure well-being 21 , 22 , which has high convergent validity, as evidenced by the strong correlations with the presence of positive affect and absence of negative affect.

To assess adolescents’ momentary affective well-being (henceforth referred to as well-being), we conducted a week-long ESM study among 63 middle adolescents ages 14 and 15. Six times a day, adolescents were asked to complete a survey using their own mobile phone, covering 42 assessments per adolescent, assessing their affective well-being and social media use. In total, adolescents completed 2,155 assessments (83.2% average compliance).

We focused on middle adolescence, since this is the period in life characterized by most significant fluctuations in well-being 23 , 24 . Also, in comparison to early and late adolescents, middle adolescents are more sensitive to reactions from peers and have a strong tendency to compare themselves with others on social media and beyond. Because middle adolescents typically use different social media platforms, in a complementary way 25 , 26 , 27 , each adolescent reported on his/her use of the three social media platforms that s/he used most frequently out of the five most popular social media platforms among adolescents: WhatsApp, followed by Instagram, Snapchat, YouTube, and, finally, the chat function of games 28 . In addition to investigating the association between overall social media use and well-being (i.e., the summed use of adolescents’ three most frequently used platforms), we examined the unique associations of the two most popular platforms, WhatsApp and Instagram 28 .

Like previous studies on social media use and well-being, we distinguished between active social media use (i.e., “activities that facilitate direct exchanges with others” 29 ) and passive social media use (i.e., “consuming information without direct exchanges” 29 ). Within-person studies among young adults have shown that passive but not active social media use predicts decreases in well-being 29 . Therefore, we examined the unique associations of adolescents’ overall active and passive social media use with their well-being, as well as active and passive use of Instagram and WhatsApp, specifically. We investigated categorical associations, that is, whether adolescents would feel better or worse if they had actively or passively used social media. And we investigated dose–response associations to understand whether adolescents’ well-being would change as a function of the time they had spent actively or passively using social media.

The hypotheses and the design, sampling and analysis plan were preregistered prior to data collection and are available on the Open Science Framework, along with the code used in the analyses ( https://osf.io/nhks2 ). For details about the design of the study and analysis approach, see Methods.

In more than half of all assessments (68.17%), adolescents had used social media (i.e., one or more of their three favorite social media platforms), either in an active or passive way. Instagram (50.90%) and WhatsApp (53.52%) were used in half of all assessments. Passive use of social media (66.21% of all assessments) was more common than active use (50.86%), both on Instagram (48.48% vs. 20.79%) and WhatsApp (51.25% vs. 40.07%).

Strong positive between-person correlations were found between the duration of active and passive social media use (overall: r  = 0.69, p  < 0.001; Instagram: r  = 0.38, p  < 0.01; WhatsApp: r  = 0.85, p  < 0.001): Adolescents who had spent more time actively using social media than their peers, had also spent more time passively using social media than their peers. Likewise, strong positive within-person correlations were found between the duration of active and passive social media use (overall: r  = 0.63, p  < 0.001; Instagram: r  = 0.37, p  < 0.001; WhatsApp: r  = 0.57, p  < 0.001): The more time an adolescent had spent actively using social media at a certain moment, the more time s/he had also spent passively using social media at that moment.

Table 1 displays the average number of minutes that adolescents had spent using social media in the past hour at each assessment, and the zero-order between- and within-person correlations between the duration of social media use and well-being. At the between-person level, the duration of active and passive social media use was not associated with well-being: Adolescents who had spent more time actively or passively using social media than their peers did not report significantly higher or lower levels of well-being than their peers. At the within-person level, significant but weak positive correlations were found between the duration of active and passive overall social media use and well-being. This indicates that adolescents felt somewhat better at moments when they had spent more time actively or passively using social media (overall), compared to moments when they had spent less time actively or passively using social media. When looking at specific platforms, a positive correlation was only found for passive WhatsApp use, but not for active WhatsApp use, and not for active and passive Instagram use.

Average and person-specific effects

The within-person associations of social media use with well-being and differences in these associations were tested in a series of multilevel models. We ran separate models for overall social media use (i.e., active use and passive use of adolescents’ three favorite social media platforms, see Table 2 ), Instagram use (see Table 3 ), and WhatsApp use (see Table 4 ). In a first step we examined the average categorical associations for each of these three social media uses using fixed effects models (Models 1A, 3A, and 5A) to investigate whether, on average, adolescents would feel better or worse at moments when they had used social media compared to moments when they had not (i.e., categorical predictors: active use versus no active use, and passive use versus no passive use). In a second step, we examined heterogeneity in the within-person categorical associations by adding random slopes to the fixed effects models (Models 1B, 3B, and 5B). Next, we examined the average dose–response associations using fixed effects models (Models 2A, 4A, and 6A), to investigate whether, on average, adolescents would feel better or worse when they had spent more time using social media (i.e., continuous predictors: duration of active use and duration of passive use). Finally, we examined heterogeneity in the within-person dose–response associations by adding random slopes to the fixed effects models (Models 2B, 4B, and 6B).

Overall social media use.

The model with the categorical predictors (see Table 2 ; Model 1A) showed that, on average, there was no association between overall use and well-being: Adolescents’ well-being did not increase or decrease at moments when they had used social media, either in a passive or active way. However, evidence was found that the association of passive (but not active) social media use with well-being differed from adolescent to adolescent (Model 1B), with effect sizes ranging from − 0.24 to 0.68. For 44.26% of the adolescents the association was non-existent to small (− 0.10 <  r  < 0.10). However, for 45.90% of the adolescents there was a weak (0.10 <  r  < 0.20; 8.20%), moderate (0.20 <  r  < 0.30; 22.95%) or even strong positive ( r  ≥ 0.30; 14.75%) association between overall passive social media use and well-being, and for almost one in ten (9.84%) adolescents there was a weak (− 0.20 <  r  < − 0.10; 6.56%) or moderate negative (− 0.30 <  r  < − 0.20; 3.28%) association.

The model with continuous predictors (Model 2A) showed that, on average, there was a significant dose–response association for active use. At moments when adolescents had used social media, the time they spent actively (but not passively) using social media was positively associated with well-being: Adolescents felt better at moments when they had spent more time sending messages, posting, or sharing something on social media. The associations of the time spent actively and passively using social media with well-being did not differ across adolescents (Model 2B).

Instagram use

As shown in Model 3A in Table 3 , on average, there was a significant categorical association between passive (but not active) Instagram use and well-being: Adolescents experienced an increase in well-being at moments when they had passively used Instagram (i.e., viewing posts/stories of others). Adolescents did not experience an increase or decrease in well-being when they had actively used Instagram. The associations of passive and active Instagram use with well-being did not differ across adolescents (Model 3B).

On average, no significant dose–response association was found for Instagram use (Model 4A): At moments when adolescents had used Instagram, the time adolescents spent using Instagram (either actively or passively) was not associated with their well-being. However, evidence was found that the association of the time spent passively using Instagram differed from adolescent to adolescent (Model 4B), with effect sizes ranging from − 0.48 to 0.27. For most adolescents (73.91%) the association was non-existent to small (− 0.10 <  r  < 0.10), but for almost one in five adolescents (17.39%) there was a weak (0.10 <  r  < 0.20; 10.87%) or moderate (0.20 <  r  < 0.30; 6.52%) positive association, and for almost one in ten adolescents (8.70%) there was a weak (− 0.20 <  r  < − 0.10; 2.17%), moderate (− 0.30 <  r  < − 0.20; 4.35%), or strong ( r  ≤ − 0.30; 2.17%) negative association. Figure  1 illustrates these differences in the dose–response associations.

The dose–response association between passive Instagram use (in minutes per hour) and affective well-being for each individual adolescent (n = 46). Red lines represent significant negative within-person associations, green lines represent significant positive within-person associations, and gray lines represent non-significant within-person associations. A graph was created for each participant who had completed at least 10 assessments. A total of 13 participants were excluded because they had completed less than 10 assessments of passive Instagram use. In addition, one participant was excluded because no graph could be computed, since this participant's passive Instagram use was constant across assessments.

WhatsApp use

As shown in Model 5A in Table 4 , just as for Instagram, we found that, on average, there was a significant categorical association between passive (but not active) WhatsApp use and well-being: Adolescents reported that they felt better at moments when they had passively used WhatsApp (i.e., read WhatsApp messages). For active WhatsApp use, no significant association was found. Also, in line with the results for Instagram use, no differences were found regarding the associations of active and passive WhatsApp use (Model 5B).

In addition, a significant dose–response association was found for passive (but not active) use (Model 6A). At moments when adolescents had used WhatsApp, we found that, on average, the time adolescents spent passively using WhatsApp was positively associated with well-being: Adolescents felt better at moments when they had spent more time reading WhatsApp messages. The time spent actively using WhatsApp was not associated with well-being. No differences were found in the dose–response associations of active and passive WhatsApp use (Model 6B).

This preregistered study investigated adolescents’ unique susceptibility to the effects of social media. We found that the associations of passive (but not active) social media use with well-being differed substantially from adolescent to adolescent, with effect sizes ranging from moderately negative (− 0.24) to strongly positive (0.68). While 44.26% of adolescents did not feel better or worse if they had passively used social media, 45.90% felt better, and a small group felt worse (9.84%). In addition, for Instagram the majority of adolescents (73.91%) did not feel better or worse when they had spent more time viewing post or stories of others, whereas some felt better (17.39%), and others (8.70%) felt worse.

These findings have important implications for social media effects research, and media effects research more generally. For decades, researchers have argued that people differ in their susceptibility to the effects of media 17 , leading to numerous investigations of such differential susceptibility. These investigations have typically focused on moderators, based on variables such as sex, age, or personality. Yet, over the years, studies have shown that such moderators appear to have little power to explain how individuals differ in their susceptibility to media effects, probably because a group-differential approach does not account for the possibility that media users may differ across a range of factors, that are not captured by only one (or a few) investigated moderator variables.

By providing insights into each individual’s unique susceptibility, the findings of this study provide an explanation as to why, up until now, most media effects research has only found small effects. We found that the majority of adolescents do not experience any short-term changes in well-being related to their social media use. And if they do experience any changes, these are more often positive than negative. Because only small subsets of adolescents experience small to moderate changes in well-being, the true effects of social media reported in previous studies have probably been diluted across heterogeneous samples of individuals that differ in their susceptibility to media effects (also see 30 ). Several scholars have noted that overall effect sizes may mask more subtle individual differences 14 , 15 , which may explain why previous studies have typically reported small or no effects of social media on well-being or indicators of well-being 6 , 11 , 12 , 13 . The current study seems to confirm this assumption, by showing that while the overall effect sizes are small at best, the person-specific effect sizes vary considerably, from tiny and small to moderate and strong.

As called upon by other scholars 5 , 31 , we disentangled the associations of active and passive use of social media. Research among young adults found that passive (but not active) social media use is associated with lower levels of affective well-being 29 . In line with these findings, the current study shows that active and passive use yielded different associations with adolescents’ affective well-being. Interestingly though, in contrast to previous findings among adults, our study showed that, on average, passive use of Instagram and WhatsApp seemed to enhance rather than decrease adolescents’ well-being. This discrepancy in findings may be attributed to the fact that different mechanisms might be involved. Verduyn and colleagues 29 found that passive use of Facebook undermines adults’ well-being by enhancing envy, which may also explain the decreases in well-being found in our study among a small group of adolescents. Yet, adolescents who felt better by passively using Instagram and WhatsApp, might have felt so because they experienced enjoyment. After all, adolescents often seek positive content on social media, such as humorous posts or memes 32 . Also, research has shown that adolescents mainly receive positive feedback on social media 33 . Hence, their passive Instagram and WhatsApp use may involve the reading of positive feedback, which may explain the increases in well-being.

Overall, the time spent passively using WhatsApp improved adolescents’ well-being. This did not differ from adolescent to adolescent. However, the associations of the time spent passively using Instagram with well-being did differ from adolescent to adolescent. This discrepancy suggests that not all social media uses yield person-specific effects on well-being. A possible explanation may be that adolescents’ responses to WhatsApp are more homogenous than those to Instagram. WhatsApp is a more private platform, which is mostly used for one-to-one communication with friends and acquaintances 26 . Instagram, in contrast, is a more public platform, which allows its users to follow a diverse set of people, ranging from best friends to singers, actors, and influencers 28 , and to engage in intimate communication as well as self-presentation and social comparison. Such diverse uses could lead to more varied, or even opposing responses, such as envy versus inspiration.

Limitations and directions for future research

The current study extends our understanding of differential susceptibility to media effects, by revealing that the effect of social media use on well-being differs from adolescent to adolescent. The findings confirm our assumption that among the great majority of adolescents, social media use is unrelated to well-being, but that among a small subset, social media use is either related to decreases or increases in well-being. It must be noted, however, that participants in this study felt relatively happy, overall. Studies with more vulnerable samples, consisting of clinical samples or youth with lower social-emotional well-being may elicit different patterns of effects 27 . Also, the current study focused on affective well-being, operationalized as happiness. It is plausible that social media use relates differently with other types of well-being, such as cognitive well-being. An important next step is to identify which adolescents are particularly susceptible to experience declines in well-being. It is conceivable, for instance, that the few adolescents who feel worse when they use social media are the ones who receive negative feedback on social media 33 .

In addition, future ESM studies into the effects of social media should attempt to include one or more follow-up measures to improve our knowledge of the longer-term influence of social media use on affective well-being. While a week-long ESM is very common and applied in most earlier ESM studies 34 , a week is only a snapshot of adolescent development. Research is needed that investigates whether the associations of social media use with adolescents’ momentary affective well-being may cumulate into long-lasting consequences. Such investigations could help clarify whether adolescents who feel bad in the short term would experience more negative consequences in the long term, and whether adolescents who feel better would be more resistant to developing long-term negative consequences. And while most adolescents do not seem to experience any short-term increases or decreases in well-being, more research is needed to investigate whether these adolescents may experience a longer-term impact of social media.

While the use of different platforms may be differently associated with well-being, different types of use may also yield different effects. Although the current study distinguished between active and passive use of social media, future research should further differentiate between different activities. For instance, because passive use entails many different activities, from reading private messages (e.g., WhatsApp messages, direct messages on Instagram) to browsing a public feed (e.g., scrolling through posts on Instagram), research is needed that explores the unique effects of passive public use and passive private use. Research that seeks to explore the nuances in adolescents’ susceptibility as well as the nuances in their social media use may truly improve our understanding of the effects of social media use.

Participants

Participants were recruited via a secondary school in the south of the Netherlands. Our preregistered sampling plan set a target sample size of 100 adolescents. We invited adolescents from six classrooms to participate in the study. The final sample consisted of 63 adolescents (i.e., 42% consent rate, which is comparable to other ESM studies among adolescents; see, for instance 35 , 36 ). Informed consent was obtained from all participants and their parents. On average, participants were 15 years old ( M  = 15.12 years, SD  = 0.51) and 54% were girls. All participants self-identified as Dutch, and 41.3% were enrolled in the prevocational secondary education track, 25.4% in the intermediate general secondary education track, and 33.3% in the academic preparatory education track.

The study was approved by the Ethics Review Board of the Faculty of Social and Behavioral Sciences at the University of Amsterdam and was performed in accordance with the guidelines formulated by the Ethics Review Board. The study consisted of two phases: A baseline survey and a personalized week-long experience sampling (ESM) study. In phase 1, researchers visited the school during school hours. Researchers informed the participants of the objective and procedure of the study and assured them that their responses would be treated confidentially. Participants were asked to sign the consent form. Next, participants completed a 15-min baseline survey. The baseline survey included questions about demographics and assessed which social media each adolescent used most frequently, allowing to personalize the social media questions presented during the ESM study in phase 2. After completing the baseline survey, participants were provided detailed instructions about phase 2.

In phase 2, which took place two and a half weeks after the baseline survey, a 7-day ESM study was conducted, following the guidelines for ESM studies provided by van Roekel and colleagues 34 . Aiming for at least 30 assessments per participant and based on an average compliance rate of 70 to 80% reported in earlier ESM studies among adolescents 34 , we asked each participant to complete a total of 42 ESM surveys (i.e., six 2-min surveys per day). Participants completed the surveys using their own mobile phone, on which the ESM software application Ethica Data was installed during the instruction session with the researchers (phase 1). Each 2-min survey consisted of 22 questions, which assessed adolescents’ well-being and social media use. Two open-ended questions were added to the final survey of the day, which asked about adolescents’ most pleasant and most unpleasant events of the day.

The ESM sampling scheme was semi-random, to allow for randomization and avoid structural patterns in well-being, while taking into account that adolescents were not allowed to use their phone during school time. The Ethica Data app was programmed to generate six beep notifications per day at random time points within a fixed time interval that was tailored to the school’s schedule: before school time (1 beep), during school breaks (2 beeps), and after school time (3 beeps). During the weekend, the beeps were generated during the morning (1 beep), afternoon (3 beeps), and evening (2 beeps). To maximize compliance, a 30-min time window was provided to complete each survey. This time window was extended to one hour for the first survey (morning) and two hours for the final survey (evening) to account for travel time to school and time spent on evening activities. The average compliance rate was 83.2%. A total of 2,155 ESM assessments were collected: Participants completed an average of 34.83 surveys ( SD  = 4.91) on a total of 42 surveys, which is high compared to previous ESM studies among adolescents 34 .

The questions of the ESM study were personalized based on the responses to the baseline survey. During the ESM study, each participant reported on his/her use of three different social media platforms: WhatsApp and either Instagram, Snapchat, YouTube, and/or the chat function of games (i.e., the most popular social media platforms among adolescents 28 ). Questions about Instagram and WhatsApp use were only included if the participant had indicated in the baseline survey that s/he used these platforms at least once a week. If a participant had indicated that s/he used Instagram or WhatsApp (or both) less than once a week, s/he was asked to report on the use of Snapchat, YouTube, or the chat function of games, depending on what platform s/he used at least once a week. In addition to Instagram and WhatsApp, questions were asked about a third platform, that was selected based on how frequently the participant used Snapchat, YouTube, or the chat function of games (i.e., at least once a week). This resulted in five different combinations of three platforms: Instagram, WhatsApp, and Snapchat (47 participants); Instagram, WhatsApp, and YouTube (11 participants); Instagram, WhatsApp, and chatting via games (2 participants); WhatsApp, Snapchat, and YouTube (1 participant); and WhatsApp, YouTube, and chatting via games (2 participants).

Frequency of social media use

In the baseline survey, participants were asked to indicate how often they used and checked Instagram, WhatsApp, Snapchat, YouTube, and the chat function of games, using response options ranging from 1 ( never ) to 7 ( more than 12 times per day ). These platforms are the five most popular platforms among Dutch 14- and 15-year-olds 28 . Participants’ responses were used to select the three social media platforms that were assessed in the personalized ESM study.

Duration of social media use

In the ESM study, duration of active and passive social media use was measured by asking participants how much time in the past hour they had spent actively and passively using each of the three platforms that were included in the personalized ESM surveys. Response options ranged from 0 to 60 min , with 5-min intervals. To measure active Instagram use, participants indicated how much time in the past hour they had spent (a) “posting on your feed or sharing something in your story on Instagram” and (b) “sending direct messages/chatting on Instagram.” These two items were summed to create the variable duration of active Instagram use. Sum scores exceeding 60 min (only 0.52% of all assessments) were recoded to 60 min. To measure duration of passive Instagram use, participants indicated how much time in the past hour they had spent “viewing posts/stories of others on Instagram.” To measure the use of WhatsApp, Snapchat, YouTube and game-based chatting, we asked participants how much time they had spent “sending WhatsApp messages” (active use) and “reading WhatsApp messages” (passive use); “sending snaps/messages or sharing something in your story on Snapchat” (active use) and “viewing snaps/stories/messages from others on Snapchat” (passive use); “posting YouTube clips” (active use) and “watching YouTube clips” (passive use); “sending messages via the chat function of a game/games” (active use) and “reading messages via the chat function of a game/games” (passive use). Duration of active and passive overall social media use were created by summing the responses across the three social media platforms for active and passive use, respectively. Sum scores exceeding 60 min (2.13% of all assessments for active overall use; 2.90% for passive overall use) were recoded to 60 min. The duration variables were used to investigate whether the time spent actively or passively using social media was associated with well-being (dose–response associations).

Use/no use of social media

Based on the duration variables, we created six dummy variables, one for active and one for passive overall social media use, one for active and one for passive Instagram use, and one for active and one for passive WhatsApp use (0 =  no active use and 1 =  active use , and 0 =  no passive use and 1 =  passive use , respectively). These dummy variables were used to investigate whether the use of social media, irrespective of the duration of use, was associated with well-being (categorical associations).

Consistent with previous ESM studies 19 , 20 , we measured affective well-being using one item, asking “How happy do you feel right now?” at each assessment. Adolescents indicated their response to the question using a 7-point scale ranging from 1 ( not at all ) to 7 ( completely ), with 4 ( a little ) as the midpoint. Convergent validity of this item was established in a separate pilot ESM study among 30 adolescents conducted by the research team of the fourth author: The affective well-being item was strongly correlated with the presence of positive affect and absence of negative affect (assessed by a 10-item positive and negative affect schedule for children; PANAS-C) at both the between-person (positive affect: r  = 0.88, p < 0.001; negative affect: r  = − 0.62, p < 0.001) and within-person level (positive affect: r  = 0.74, p < 0.001; negative affect: r  = − 0.58, p < 0.001).

Statistical analyses

Before conducting the analyses, several validation checks were performed (see 34 ). First, we aimed to only include participants in the analyses who had completed more than 33% of all ESM assessments (i.e., at least 14 assessments). Next, we screened participants’ responses to the open questions for unserious responses (e.g., gross comments, jokes). And finally, we inspected time series plots for patterns in answering tendencies. Since all participants completed more than 33% of all ESM assessments, and no inappropriate responses or low-quality data patterns were detected, all participants were included in the analyses.

Following our preregistered analysis plan, we tested the proposed associations in a series of multilevel models. Before doing so, we tested the homoscedasticity and linearity assumptions for multilevel analyses 37 . Inspection of standardized residual plots indicated that the data met these assumptions (plots are available on OSF at  https://osf.io/nhks2 ). We specified separate models for overall social media use, use of Instagram, and use of WhatsApp. To investigate to what extent adolescents’ well-being would vary depending on whether they had actively or passively used social media/Instagram/WhatsApp or not during the past hour (categorical associations), we tested models including the dummy variables as predictors (active use versus no active use, and passive use versus no passive use; models 1, 3, and 5). To investigate whether, at moments when adolescents had used social media/Instagram/WhatsApp during the past hour, their well-being would vary depending on the duration of social media/Instagram/WhatsApp use (dose–response associations), we tested models including the duration variables as predictors (duration of active use and duration of passive use; models 2, 4, and 6). In order to avoid negative skew in the duration variables, we only included assessments during which adolescents had used social media in the past hour (overall, Instagram, or WhatsApp, respectively), either actively or passively. All models included well-being as outcome variable. Since multilevel analyses allow to include all available data for each individual, no missing data were imputed and no data points were excluded.

We used a model building approach that involved three steps. In the first step, we estimated an intercept-only model to assess the relative amount of between- and within-person variance in affective well-being. We estimated a three-level model in which repeated momentary assessments (level 1) were nested within adolescents (level 2), who, in turn, were nested within classrooms (level 3). However, because the between-classroom variance in affective well-being was small (i.e., 0.4% of the variance was explained by differences between classes), we proceeded with estimating two-level (instead of three-level) models, with repeated momentary assessments (level 1) nested within adolescents (level 2).

In the second step, we assessed the within-person associations of well-being with (a) overall active and passive social media use (i.e., the total of the three platforms), (b) active and passive use of Instagram, and (c) active and passive use of WhatsApp, by adding fixed effects to the model (Models 1A-6A). To facilitate the interpretation of the associations and control for the effects of time, a covariate was added that controlled for the n th assessment of the study week (instead of the n th assessment of the day, as preregistered). This so-called detrending is helpful to interpret within-person associations as correlated fluctuations beyond other changes in social media use and well-being 38 . In order to obtain within-person estimates, we person-mean centered all predictors 38 . Significance of the fixed effects was determined using the Wald test.

In the third and final step, we assessed heterogeneity in the within-person associations by adding random slopes to the models (Models 1B-6B). Significance of the random slopes was determined by comparing the fit of the fixed effects model with the fit of the random effects model, by performing the Satorra-Bentler scaled chi-square test 39 and by comparing the Bayesian information criterion (BIC 40 ) and Akaike information criterion (AIC 41 ) of the models. When the random effects model had a significantly better fit than the fixed effects model (i.e., pointing at significant heterogeneity), variance components were inspected to investigate whether heterogeneity existed in the association of either active or passive use. Next, when evidence was found for significant heterogeneity, we computed person-specific effect sizes, based on the random effect models, to investigate what percentages of adolescents experienced better well-being, worse well-being, and no changes in well-being. In line with Keijsers and colleagues 42 we only included participants who had completed at least 10 assessments. In addition, for the dose–response associations, we constructed graphical representations of the person-specific slopes, based on the person-specific effect sizes, using the xyplot function from the lattice package in R 43 .

Three improvements were made to our original preregistered plan. First, rather than estimating the models with multilevel modelling in R 43 , we ran the preregistered models in Mplus 44 . Mplus provides standardized estimates for the fixed effects models, which offers insight into the effect sizes. This allowed us to compare the relative strength of the associations of passive versus active use with well-being. Second, instead of using the maximum likelihood estimator, we used the maximum likelihood estimator with robust standard errors (MLR), which are robust to non-normality. Sensitivity tests, uploaded on OSF ( https://osf.io/nhks2 ), indicated that the results were almost identical across the two software packages and estimation approaches. Third, to improve the interpretation of the results and make the scales of the duration measures of social media use and well-being more comparable, we transformed the social media duration scores (0 to 60 min) into scales running from 0 to 6, so that an increase of 1 unit reflects 10 min of social media use. The model estimates were unaffected by this transformation.

Reporting summary

Further information on the research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

The dataset generated and analysed during the current study is available in Figshare 45 . The preregistration of the design, sampling and analysis plan, and the analysis scripts used to analyse the data for this paper are available online on the Open Science Framework website ( https://osf.io/nhks2 ).

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Acknowledgements

This study was funded by the NWO Spinoza Prize and the Gravitation grant (NWO Grant 024.001.003; Consortium on Individual Development) awarded to P.M.V. by the Dutch Research Council (NWO). Additional funding was received from the VIDI grant (NWO VIDI Grant 452.17.011) awarded to L.K. by the Dutch Research Council (NWO). The authors would like to thank Savannah Boele (Tilburg University) for providing her pilot ESM results.

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I.B., J.L.P., I.I.v.D., L.K., and P.M.V. designed the study; I.B., J.L.P., and I.I.v.D. collected the data; I.B., J.L.P., and L.K. analyzed the data; and I.B., J.L.P., I.I.v.D., L.K., and P.M.V. contributed to writing and reviewing the manuscript.

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Beyens, I., Pouwels, J.L., van Driel, I.I. et al. The effect of social media on well-being differs from adolescent to adolescent. Sci Rep 10 , 10763 (2020). https://doi.org/10.1038/s41598-020-67727-7

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How to run and measure social media experiments

Written by by Jamia Kenan

Published on  February 22, 2023

Reading time  7 minutes

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Grab your metaphorical safety goggles, lab coat and test tubes because in this article we’re going to walk through the steps for running and measuring successful social media experiments.

7 Steps for running a social media experiment

With these seven steps, you’ll be testing on social media with ease in no time:

• Formulate a hypothesis
• Choose the right type of social media experiment
• Select your metrics and the network you want to test
• Define the duration of the social media experiment
• Select your variables and control
• Conduct the social media experiment
• Analyze and share the results of your experiment

 1. Formulate a hypothesis

Before you begin, you’ll need a basic understanding of the following:

• The overall goals of your business
• Your current social strategy, including overarching goals per platform
• Your audiences by social network
• Your current social performance
• The questions, notions and ideas you wish to test

Prioritize a hypothesis that will result in the biggest impact on your team’s top-level social media goals . Avoid running several tests at once because it can lead to inconclusive results, especially if you’re focused on managing organic social.

If you’re using Sprout, you can learn about your audiences and performance by channel through our cross-network reports (like the Post Performance Report) or competitor reports (like the Instagram Competitors Report).

To dive even deeper into understanding your audience, use Sprout’s Advanced Listening tools. With Listening, you can build queries to track and analyze social conversations, pin down trends and view consumer sentiments. Seeing the data behind what your audience is talking about and the content they engage with will help you formulate a hypothesis.

2. Choose the right type of social media experiment

Now that you have a hypothesis, it’s time to select the type of social media experiment you will conduct to prove your theory.

There are two main types you can choose from: A/B testing and multivariable testing.

Social media experiment ideas for A/B tests

One of the most common types of social media experiments, an A/B test is an experiment where you change only one variable and keep everything else the same. These types of tests are an excellent way to pinpoint improvements that will make a measurable impact. Some common A/B tests on social include:

• Content types: video vs. a link, photo, GIF, etc.
• Captions: long vs. short
• Copy: question vs. statement, emojis or hashtags
• Images: illustrations vs. photography or animation
• Posting time: Monday at 9:00 a.m. vs. Friday at 4:00 p.m.

For example, if you wanted to test which content type is the most engaging on Instagram Stories, your team could test photo content against video content. The content type would change, but you would use the same caption and post at the same time and day of the week, one week apart.

Using Sprout, the Atlanta Hawks ‘ social team tested a casual approach to videos at community events. A player shot a hand-held video that was compared to the performance of more produced social videos. The casual video format proved to be more successful and sharing the performance data was a major win for the social team.

Social media experiment ideas for multivariable testing

As its name implies, multivariable testing alters two or three variables at once. However, since you’re experimenting with more elements, analyzing and interpreting data can be harder. You’ll also need a large audience to avoid skewing the test.

Some multivariable tests include:

• Short-form animated video vs. long-form live action video
• Varying tones of voice paired with or without emojis
• Multiple call-to-action buttons with different featured images
• Different content types with various captions
• Same content type but different days/times and platforms to see which resonates the most, like Instagram vs. TikTok

Sprout’s social team conducted several multivariable tests to help develop our TikTok marketing strategy , as you’re about to read in the next step.

3.  Select your metrics and the network you want to test

Establish the key metric you want to measure successful content against. This can include impressions, traffic to a particular page such as your brand’s website or a gated resource, and engagement metrics (Think: likes, clicks, comments or shares).

The channel you choose to conduct your experiment will depend on what you’re testing and the social media network you use the most to post that kind of content. Use your network-specific data to inform this decision. Read some of Sprout’s Insights resources to learn which content types perform the best on which platforms.

• Smart steps for content development
• 1 video, 47 uses: Maximizing your Instagram content
• Sponsored posts: How to create effective sponsored content

When our social team started testing TikTok, the main goal was to increase awareness among our target audiences. Accordingly, we selected impressions, video views, profile views and audience growth as key performance indicators.

4. Define the duration of the social media experiment

Don’t fall into the common mistake of not defining a time frame for your social media experiment. Remember that social media strategy is a long game–give time for new initiatives to grow and develop.

Your reporting window depends on your budget, audience size and KPIs, but the most important factor is to reach statistical significance.

Statistical significance refers to the likelihood your test results are the outcome of a defined cause and not chance. To reach statistical significance, you’ll need a large sample size and a control. For example, a sample size of 1,000 is stronger than 100, and your control would be the piece of content you do not change.

Set a duration and look for statistical significance. What are the significance changes? After your testing period, consider optimizing content that didn’t work during that timeframe instead of hitting the breaks on posts that aren’t resonating immediately.

While experimenting with TikTok, the social team reported results after four months since there was enough data available to analyze. They also set a weekly update to our internal social dashboard to continue testing and learning, along with iterating strategy, if needed.

During the first four months, we discovered views for every TikTok remained consistent, with an average of 535 views per video. We were also able to confirm our thoughts/assumptions about the For You Page (FYP) and the TikTok algorithm—each consistently pushed out content to our target audience (social media specialists, managers, digital marketers, etc.).

5. Select your variables and control

If you’re using A/B testing, consider all of the elements of your content that could influence your test results to ensure you’re only testing one variable. Also select your control, which is the content that will not change. For example, if you’re testing images, make sure to not change the copy, audience, timing, etc.

In our social team’s multivariable TikTok experiments, they tested several variables including formats, themes and creative considerations like music, sounds and closed captions.

In the example below, 91% of views came from the FYP, 5% came from a personal profile view and 1% came from direct followers–confirming their hypothesis that the FYP and the algorithm were the key drivers pushing out content to our target audience.

@sproutsocial It’s no secret that social teams are on the path to extreme burnout. @J A Y D E shares why it’s time for leaders to take action. #foryou #socialmediamarketing #socialmediamanager #socialmediatips #socialmedia #foryoupage ♬ Cloudy Sky – Tundra Beats

If you use Sprout, you can use tagging to track the performance of your control and the test post.

6. Conduct the social media experiment

Now it’s time to execute! Use Sprout’s Publishing tools to seamlessly plan, create, optimize and post your content for the experiment. For example, you can use Sprout’s ViralPost® technology to post at optimal send times.

Use the Tag Performance Report to organize, run and analyze your social media experiment results, including your paid campaigns.

Read our guide on creative testing for more tips and examples for conducting social media experiments.

7. Analyze and share the results of your experiment

Review the results of your experiment to identify new opportunities or add insights to your records.

If you’re trying to gain executive buy-in, especially for further testing or resources, you’ll need to communicate and create an effective data story to highlight why your company will benefit from your suggested next steps.

Using Sprout, you can easily access automated, presentation-ready reports to help illustrate your data story. Create custom reports, like this Facebook Performance Summary that includes impressions, engagements, post link clicks and publishing behavior for various content types:

Use experiments to optimize engagement and growth

Here’s a quick overview of the seven steps:

Good luck on your journey to embracing curiosity and thinking like a scientist—your social strategy will thank you.

This article is an excellent first step, but there’s so much more to learn about social media experiments. Step into the (virtual) lab yourself and get a hands-on experience, by signing up for a free trial .

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Original research article, social media usage and acceptance in higher education: a structural equation model.

• 1 Faculty of Technology Management and Business, Universiti Tun Hussein Onn Malaysia, Parit Raja, Johor, Malaysia
• 2 Faculty of Social Sciences and Humanities, School of Education, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia
• 3 College of Education, King Faisal University, Al Hofuf, Saudi Arabia
• 4 Office of the Deputy Vice Chancellor (Education), University of Tasmania, Launceston, TAS, Australia

The adoption and use of social media as an educational technology in higher education has been exacerbated during the COVID-19 pandemic. As a result, this study applied the unified theory of usage and acceptance of technology theory and the technology acceptance model as predictors of behavioral intention to use social media and actual social media use. These, as posited by the model, affect the performance impact of social media usage. This study involved a quantitative survey with 312 undergraduate university students in Malaysia. Using structural equation modeling, this study identified that unified theory of usage and acceptance of technology theory and the technology acceptance model influence behavioral intentions to use and actual use of social media, resulting in an improved performance impact. That is, when students see the value in particular technologies, feel their performance (e.g., passing their studies) will be improved by using that technology, offers behavioral nudges toward adoption and use.

Introduction

During the COVID-19 pandemic, global adoption of emerging technologies has accelerated. Government decisions to enter and exit lockdowns has had an immediate effect on higher education. For example, class cancelations, delayed commencement, emergency remote teaching, and paused international enrollments ( Crawford et al., 2020 ). Teachers and instructors have been required to engage learners online ( Kara et al., 2020 ), although this is not inherently new, only exacerbated. Social media usage, as a form of educational technology, is the focus of this study ( Teräs et al., 2020 ). With its characteristics of digitally, interactivity, hyper textually, virtually, networking, and simulation, social media channels can provide connected synchronous and asynchronous learning environments for students ( Chawinga, 2017 ; Al-Rahmi et al., 2021a , b ). Social media has been evidenced as a contributor to academic performance ( Karakose et al., 2021 ; Sayaf et al., 2022 ).

Social media is pervasive across diverse facets of a student’s life, not limited to educational settings ( Chugh et al., 2021 ; Karakose et al., 2022b ). For example, the use of social media applications by students has skyrocketed (from 11% in 2005 to 90% in US adults aged 18–29 ( Mahdiuon et al., 2020 ), and the impact on academic performance has varied ( Kulidtod and Pasagui, 2017 ). In educational environments, students may use social media for a variety of purposes, including knowledge finding, collaboration, and social interaction ( Elsayed, 2016 ; Rasheed et al., 2020 ; López-Carril et al., 2021 ; Karakose et al., 2022a ). Several studies have shown a connection between social media use and undergraduate university students’ academic performance and achievement ( Alnjadat et al., 2019 ; Al-Maatouk et al., 2020a ; Al-Rahmi et al., 2020 ). Social media resources are claimed to improve learning by supporting social and collaborative learning ( Al-Qaysi et al., 2019 ; Vandeyar, 2020 ). As a result, graduate research students are using a variety of social networking platforms (e.g., Facebook, Twitter, LinkedIn, and TikTok) to aid in the facilitation of their research training and education projects, and social media use has the potential to heighten cross-user information sharing ( Ahmed et al., 2019 ; Almaiah and Al Mulhem, 2019 ). Social media can have both positive and negative impacts on learning. One study highlights for key effects of social media in learning: (i) improving their learning motivation; (ii) enhancing students’ with tutor’s relationships; (iii) offering students a personalized learning environment; and (iv) developing student collaborative and teamwork capabilities ( Wheeler et al., 2008 ). This includes student connectivity, a key attribute of concern during the pandemic ( Phua et al., 2017 ; Tice et al., 2021 ). To further examine this, this study proposes the following re-search questions:

Research question 1. To what extent is student academic performance impacted by students’ actual and behavioral intention to use social media.

Research question 2. To what extent is students’ actual and behavioral intention to use social media influenced by their acceptance and adoption of social media.

This study is situated in Malaysia, in contrast with much of the existing research on this topic (typically in the United States, United Kingdom, and Australia) ( Balakrishnan et al., 2017 ; Alalwan et al., 2019 ; Tight, 2022 ). The significance of this study is in seeking to understand how two competing theoretical frameworks (technology acceptance model and unified theory of acceptance and usage of technology) can act in synergy to enable intention and actual use of social media. To do so, this study begins with a theoretical framework and method, before discussing the findings and implications of this re-search.

Theoretical model

The research model examines the technology acceptance model (perceive use, ease of use, enjoyment, behavioral intent to use, and actual social media usage ( Davis, 1989 ; Venkatesh et al., 2003 ) as well as unified theory of usage and acceptance of technology (performance expectancy, effort expectancy, facilitating conditions, perceived use, perceive ease of use, and perceived enjoyment to behavioral intention to use, and actual social media usage as antecedents of behavioral intention to use, and actual use of, social media (see Figure 1 ). The novelty of the posited theoretical model is not in the individual relationships, many of which have been established and tested (primarily in Western contexts), but in how these form together to make a more com-apprehensive understanding of the influencers of a student’s intention to use technology to improve, and actually improve, their academic performance. The following subsections discuss each relationship within the theorized model, and propose a testable hypothesis.

Figure 1. Antecedents of social media performance impact.

Performance expectancy

Performance expectation refers to individual belief that taking an action will increase their performance ( Nurhayati et al., 2019 ). Unified theory of usage and acceptance of technology has promoted performance expectancy as a key antecedent for the behavioral intention to use a technology ( Venkatesh et al., 2012 ). For example, ( El-Masri and Tarhini, 2017 ) conducted research with university students from Qatar and the United States on learning adoption. In both studies, performance expectancy was found to be one of the most significant predictors of behavioral intention to use technology. Similarly, ( Jung and Lee, 2015 ) found that influence student and educator YouTube adoption in higher education. Performance expectancy is a significant contributor to student acceptance of e-commerce and has a positive impact on the visual resources of social media communication during teaching or learning programs ( Bennani and Oumlil, 2014 ). Indeed, when students expect that their performance will increase by using an educational technology (in this case, social media) they will exhibit intentions to use as well as increase their actual use of technology (i.e., social media) Thus:

Hypothesis 1. Performance expectancy predicts behavioral intention to use social media.

Hypothesis 2. Performance expectancy predicts actual use of social media.

Effort expectancy

Web-based Effort expectancy is the degree of belief that investing effort into a system or technology will enable higher individual performance ( Venkatesh et al., 2003 ). Effort expectancy, according to unified theory of usage and acceptance of technology, is a direct contributor to an individual’s behavioral intention to use technology. For example, one study on pre-service teachers identified their intentions to utilize information technology was heightened by their effort and performance expectancy ( Teo and Noyes, 2014 ). Added, ( Sultana, 2020 ) found undergraduate student online learning readiness was fostered by effort and performance expectancy within the unified theory of usage and acceptance of technology. Hanson et al. (2011) concurs highlighting social media use and acceptance was influenced by effort expectancy. Thus:

Hypothesis 3. Effort expectancy will predict behavioral intention to use social media.

Hypothesis 4. Effort expectancy will predict actual use of social media.

Facilitating conditions

Facilitating conditions is the degree to which a person believes that organizational and techno-logical infrastructure exists to enable the use of the system ( Venkatesh et al., 2003 ). In a higher education con-text, this may be student perception of resource and student support access (e.g., supporting guidelines, workshops, and tutorials). Facilitating conditions is a more recently proposed adaptation to the unified theory of usage and acceptance of technology model [e.g., ( De Alwis et al., 2018 )]. For instance, ( Wong, 2016 ) found that facilitating conditions was a strong predictor for educational technology use compared with perceived ease of use. It is also congruent that when students feel that their environment is conducive to their learning, and the tools they use, that they will be more likely to engage with such tools.

Hypothesis 5. Facilitating conditions will predict behavioral intention to use social media.

Hypothesis 6. Facilitating conditions will predict actual use of social media.

Perceived usefulness

Perceived usefulness has been applied in diverse research contexts to date as an influencer of individual decisions to use technology ( Venkatesh et al., 2003 ; Khayati and Zouaoui, 2013 ; Osubor and Chiemeke, 2015 ), and is a key component of the technology acceptance model. Perceived usefulness can be defined as the degree to which a person believes that using an information system can enhance their job performance or make achieving their goals easier ( Arshad and Akram, 2018 ; Al-Rahmi et al., 2022 ). Technologies that support usefulness also support heightened wellbeing in end users ( Evensen and Omfjord, 2019 ; Al-Rahmi et al., 2021 ), a critical contribution in higher education. In higher education, student and teacher behavioral intentions of embracing and using learning is influenced by Elkaseh et al. (2016); Al-Rahmi et al. (2022) .

Hypothesis 7. Perceived usefulness will predict behavioral intention to use social media.

Hypothesis 8. Perceived usefulness will predict actual use of social media.

Perceived ease of use

Perceived ease of use is identified in the literature as a determinant of behavioral intentions to use technology ( Davis , 1989 ; Sin et al., 2012 ; Taherdoost, 2018 ), including social media for educational purposes. Perceived ease of use can be defined as the degree to which an individual believes using social media for educational purposes require no additional effort. The perceived ease of use of social media has been suggested as a key factor in its rapid growth in adoption in educational settings as a communication tool ( Balakrishnan et al., 2017 ). Given that ( Balakrishnan et al., 2017 ) list some 198 social media providers, user familiarity with social media may also be a key contributor to future adoption in higher education. On balance, however, perceived ease of use of social media may influence future academic performance, particularly in online environments. The model proposed theorizes that when social media tools are seen as easier to use, that students will use them with more frequency, and exhibit intentions to apply social media tools within their learning environment. Thus:

Hypothesis 9. Perceived ease of use will predict behavioral intention to use social media.

Hypothesis 10. Perceived ease of use will predict actual use of social media.

Perceived enjoyment

Perceived enjoyment may be conceptually understood through two key lens in the context of social media. First, enjoyment gained through the use of social networking while spending time asynchronously or synchronously with friends. Second, the enjoyment gained through the provision of support for others ( Di Gangi and Wasko, 2016 ). According to Hsu and Lin (2008) , enjoyment can be defined as the extent to which a person uses a technology because usage provides or invokes a pleasure-based response. Venkatesh (2000) adds that uniqueness (or marginal utility) and exclusion of performance-based consequences from use are also key components of sustained perceptions of enjoyment. Perceived enjoyment is a form of intrinsic motivation that focuses on the process of using a device and represents the satisfaction and enjoyment of doing so. Perceived enjoyment has been linked to a positive attitude toward using a particular technology ( Sharma et al., 2016 ), and can impact user behavior ( Elkaseh et al., 2016 ; Sangeeta and Tandon, 2020 ). Thus, this study posits that when individuals have a belief that the social media tools will be enjoyable, that these individuals (or students) will exhibit higher behavioral intentions to use social media, alongside actual use.

Hypothesis 11. Perceived enjoyment will predict behavioral intention to use social media.

Hypothesis 12. Perceived enjoyment will predict actual use of social media.

Behavioral intention to use

Behavioral intention, in the context of this study, can be described as student intentions to use social media in the immediate future to support their learning ( Venkatesh et al., 2012 ). That is, they will exhibit behavioral intentions to use social media for the purposes of learning and communicating throughout learning processes (e.g., group assignments or knowledge sharing). Previous re-search identifies the use of social media enhances learning ( Al-Rahmi et al., 2015 ), and thus, building an understanding regarding how intentions become actual behaviors is critical to supporting such learning enhancements ( Venkatesh et al., 2003 ). Behavioral intentions indicate how students plan to use social media applications for interactive learning in the future ( Labib and Mostafa, 2015 ; Abdullah Moafa et al., 2018 ). According to recent literature, people who engage with online technologies, and continue to build a positive belief regarding this platform, continue to use these in the future ( Al-Rahmi et al., 2021a ). Likewise, when students present an intention to use social media as an educational technology, many of these students will likely continue to actually use social media providing the barriers are low.

Hypothesis 13. Behavioral intention will predict actual use of social media.

Actual social media use and academic performance impact

As highlighted in previous research ( Balakrishnan et al., 2017 ), students tend to be willing to try new technologies when the barriers are low. However, student behavioral intentions to use social media for learning is not a perfect predictor of actual use, although it likely explains some, as proposed in Hypothesis 13 ( Oberheu, 2016 ). Social media is a valuable tool for enhancing students’ education because it can improve established social relationships, assist them to stay connected, keep them up to date on long-standing interactions and events, and support new contact development ( Phua et al., 2017 ; Chung and Zeng, 2020 ). Social engagement, social networking, exchanging useful knowledge with others via social networking sites, and access to sites that allow students to access online resources that would otherwise be limited in traditional interactions are all examples of the positive effects of social media that can support academic performance ( Radovic et al., 2017 ).

A student or learner’s performance impact is often described as how a particular pedagogy, tool, resource, or support has affected the academic performance or success of that student ( Al-Rahmi et al., 2020 ). For example, increasing rates of success (e.g., pass rates), higher retention (e.g., persistence), or satisfaction. According to Junco and Cotten (2012) ; le Roux and Parry (2017) , social media has been observed to have a direct effect on student educational achievement. In one study, forming a Facebook-oriented social group enables student progression to be simpler and smoother ( Alamri et al., 2020b ). Likewise, communication across multiple platforms (e.g., Twitter and Facebook in Alamri et al. (2020b) can support an extension of learning ( Alamri et al., 2020a ; Al-Maatouk et al., 2020b ). However, the relationship between social media use and performance is not universally accepted, with one study indicating no significant relationship between the two variables ( Oradini and Saunders, 2008 ; Papadakis, 2021 ), and another highlight reduced academic performance ( Kirschner and Karpinski, 2010 ; Arora et al., 2019 ). However, when mentors and teachers establish appropriate social presences in online platforms, students tend to be supported to be better socialized ( Cao et al., 2013 ). This study posits that despite some studies suggesting negative relationships, that in developing nations like Malaysia when students are supported to be more connected in synchronous and asynchronous means that they will be better able to generate knowledge together, and organize key learning activities and moments through social media. This, in turn, should support sustained gains in their academic performance. Thus,

Hypothesis 14. Behavioral intention will predict social media performance impact.

Hypothesis 15. Actual use of social media will predict social media performance impact.

Research method

The method comprised an online survey of Malaysian university students. The data was analyzed using IBM SPSS-26 and AMOS -23. All respondents were invited to complete to provide input on social media for contact and collaboration and their opinion on its effect on academic performance. Confirmatory factor analysis was completed on each measure to ensure validity of constructs included. Construct, convergent, and discriminant validity of the measures were analyzed alongside specific structural equation models and associated model fit ( Alzahrani et al., 2012 ; Hair et al., 2012 ).

Almost 330 questionnaires were circulated, of which 312 were returned by respondents, indicating a 94.5% response rate. An additional 18 were incomplete and excluded. The final sample is 312. Thus, in terms of the respondents’ demographic details: 217 participants (69.6%) were males, 95 participants (30.4%) were females. As for the age of the respondents, 7.1 percent were between 18 and 20, 16 percent between 21 and 24, 35.9 percent between 25 and 29, 20.5 percent between 30 and 34, 13.5 percent between 35 and 40, 4.5 percent were between 41 and 45, and 2.6 percent were 46 or above. Most students used social media several times a day (49.4 percent), compared to 42.9 percent being constantly logged on. However, 5.4 percent of the respondents used social network platforms once every few days, and 0.3 percent use social media platforms more than twice a week but less than every few days.

This study comprises nine construct measures: performance expectancy, effort expectancy, facilitating conditions, perceived usefulness, perceived ease of use, perceived enjoyment, behavioral intention to use social media, actual social media use, and performance impact (see Table 1 ).

Table 1. Overall of validity and reliability for students (Male and Female).

Performance expectancy was measured using five items adapted from Escobar-Rodríguez et al. (2014) , Almaiah and Al Mulhem (2019) . The tool was robust with strong reliability (α = 0.91, composite reliability = 0.91) with average variance explained of 0.66. Confirmatory factor analysis loadings (CFA) were between 0.76 and 0.85.

Effort expectancy was measured across five items from Thomas et al. (2013) ; Escobar-Rodríguez et al. (2014) . The tool was robust with strong reliability (α = 0.93, composite reliability = 0.93) with average variance explained of 0.72. CFA loadings were between 0.82 and 0.86.

Facilitating conditions was measured with five items from the sample survey of Thomas et al. (2013) ; Escobar-Rodríguez et al. (2014) , and Almaiah and Al Mulhem (2019) . The tool was robust with strong reliability (α = 0.90, composite reliability = 0.90) with average variance explained of 0.65. CFA loadings were between 0.72 and 0.87.

Perceived usefulness was measured with five adapted items from Kwon and Wen (2010) ; Abdullah Moafa et al. (2018) . The tool was robust with strong reliability (α = 0.88, composite reliability = 0.88) with average variance explained of 0.60. CFA loadings were between 0.56 and 0.89.

Perceived ease of use was measured with five items adapted from Osubor and Chiemeke (2015) ; Al-Maatouk et al. (2020b) . The tool was robust with strong reliability (α = 0.92, composite reliability = 0.91) with average variance explained of 0.68. CFA loadings were between 0.73 and 0.86.

Behavioral intention to use social media was measured using a five item tool adapted from Kingsley Arthur et al. (2013) ; Escobar-Rodríguez et al. (2014) . The tool was robust with strong reliability (α = 0.91, composite reliability = 0.91) with average variance explained of 0.54. CFA loadings were between 0.67 and 0.80.

Actual social media use was measured with six items from Almaiah and Al Mulhem (2019) ; Al-Rahmi et al. (2020) . The tool was robust with strong reliability (α = 0.91, composite reliability = 0.91) with average variance explained of 0.63. CFA loadings were between 0.73 and 0.85.

Performance impact was measured using six items from Goodhue (1995) ; Karaaslan et al. (2021) . The tool was robust with strong reliability (α = 0.88, composite reliability = 0.88) with average variance explained of 0.60. CFA loadings were between 0.70 and 0.82.

Preliminary modeling

To assure of suitability of the two underlying constructs (unified theory of usage and acceptance of technology, and technology acceptance model), these were first assessed independently for their validity and reliability. The unified theory of usage and acceptance of technology suggests that performance expectancy, effort expectancy, and facilitating conditions collectively predicts performance impact ( Yi et al., 2016 ). This was held true in this study (see Figure 2 , p < 0.05). The model as proposed in Figure 2 shows good model fit (χ2/df = 2.855, CFI = 0.931, TLI = 0.920, RMSEA = 0.077, SRMR = 0.041).

Figure 2. Unified theory of usage and acceptance of technology and performance impact.

The technology acceptance model suggests that perceived usefulness, ease of use, and enjoyment will predict performance impact ( Alalwan et al., 2019 ). As seen in Figure 3 , this was also held true ( p < 0.05). The model as proposed in Figure 3 shows good model fit (χ2/df = 2.721., p = ??, CFI = 0.971, TLI = 0.951, RMSEA = 0.061, SRMR = 0.037).

Figure 3. Technology acceptance model and performance impact.

Primary model

The structural equation model in this study. The model as proposed in Figure 1 (and presented in Figure 4 ) shows good model fit (χ2/df = 2.577, CFI = 0.93, TLI = 0.92, RMSEA = 0.042, SRMR = 0.038 RMR = 0.049).

Figure 4. Antecedents of social media performance impact.

Validity and reliability

Confirmatory factor analysis (CFA) was used to investigate suggested hypotheses, and average variance explained, Cronbach’s alpha, and composite reliability values were used to establish discriminant validity. The study examined discriminant validity for the social media usage implementation for teaching and learning during the COVID-19 pandemic in higher education over the three criteria: first, the relationship index among constructs is less than 0.80 ( Hair et al., 2012 ). Second, the average variance extracted (AVE) of every variable is equal to or greater than 0.5. The average variance extracted (AVE) of every variable is greater than the inter-construct correlations connected to that element ( Fornell and Larcker, 1981 ), (iii); the measurements and the confirmatory factor examination outcomes factor loading of 0.5 or higher is satisfactory, CA 0.70, and CR 0.70 ( Davis et al., 1989 ; Hair et al., 2012 ).

Structural model analysis

The effect of performance impact on their intention to use and actual use of social media, as well as the participation of different classes, was investigated using path modeling analysis. During the hypothesis testing discussion, all results are presented based on learning performance during the COVID-19 pandemic, and findings are contrasted. The model highlights factor loadings were greater than 0.50, a critical cut-off for maintaining these loadings ( Fornell and Larcker, 1981 ; Hair et al., 2012 ). Figure 4 illustrates that all the hypotheses between the fifteen major constructs and the fourteen hypotheses were considered and only one of the fifteen hypotheses was rejected (Hypothesis 11).

Unified theory of usage and acceptance of technology

The unified theory of usage and acceptance of technology is discussed in the first three direct assumptions. Figure 4 shows that student performance expectancy has a significant and positive relationship with their behavioral intention (H1, β = 0.176, t = 3.321, p = 0.001). To put it another way, all students in the current sample have high performance expectancy from their peers, contributing to behavioral intention ability to use information communication, conversation, or share information with their peers. The second hypothesis revealed a significant and positive relationship between performance expectancy for learning and actual social media use during the COVID-19 pandemic (β = 0. 143, t = 2.974, p = 0.001), indicating that the second hypothesis revealed a significant and positive relationship. To put it another way, all students in the current sample have effort expectancy from their peers, contributing to actual social media to use for communication, conversation, or sharing information with their peers.

The third hypothesis was also confirmed with a significant and positive relationship between effort expectancy for learning and behavioral intention (β = 0.086, t = 2.379, p = 0.001). To put it another way, all students in the current sample have effort expectancy for learning and sharing information with their peers, contributing to student behavioral intentions. Thus, the fourth hypothesis revealed a significant and positive relationship between effort expectancy for learning and actual social media use during the COVID-19 pandemic (β = 0.143, t = 2.974, p = 0.001). That means, all students in this study indicate that all learners anticipate putting forth effort in studying and sharing their information with peers, which contributes to actual social media use. According to the fifth hypothesis, there is a significant and positive relationship between facilitating conditions and behavioral intention (β = 0.22, t = 5.526, p = 0.001). That means, all students in this study have facilitating requirements for learning and exchanging information with peers, which contributes to behavioral intentions to use. The sixth hypothesis suggested a significant and positive relationship between facilitating conditions for learning and actual social media use during the COVID-19 pandemic (β = 0.114, t = 2.043, p = 0.001). To put it another way, all students in the current sample have facilitated learning to share information with their peers, which contributes to actual social media use. This is consistent with previous research ( Aldahdouh et al., 2020 ; Mittal et al., 2021 ).

Technology acceptance model

The technology acceptance model is discussed in the second three direct assumptions. As illustrated in Figure 4 , students’ perceived usefulness has a significant and positive relationship with their behavioral intention (β = 0.176, t = 3.321, p < 0.001). To put it another way, all students in the current sample have perceived usability from their peers, contributing to behavioral intention through information communication, conversation, or sharing information with their peers. The eighth hypothesis proposed a significant and positive relationship between PU for learning and social media use during the COVID-19 pandemic (β = 0. 287, t = 3.895, p < 0.001). That means, all learners in this study expect to put forth an eighth effort in studying and sharing their information with peers, which contributes to actual social media use. The ninth hypothesis proposed a significant and positive relationship between perceived ease of use and behavioral intention (β = 0. 168, t = 3.099, p < 0.001). That means, all learners in this study expect to put their ninth effort into studying and sharing their information with peers, which contributes to behavioral intention through information communication, conversation, or sharing information with their peers.

The tenth hypothesis suggested a substantial and positive relationship between perceived ease of use for learning and actual social media use during the COVID-19 pandemic (β = −0.208, t = −2.851, p < 0.001). That means, this study shows that all learners expect to put forth a tenth effort in studying and sharing their information with peers, which contributes to actual social media use. Furthermore, the eleventh hypothesis indicated neither a positive nor a significant relationship between perceived enjoyment for learning and behavioral intention during the COVID-19 pandemic (β = 0. 005, t = 0.094, p < 0.001). Hypothesis 12 suggested a substantial and positive relationship between perceived enjoyment for learning and actual social media usage during the COVID-19 pandemic (β = 0. 352, t = −5.107, p < 0.001). Thus, the twelfth hypothesis indicated that there is a substantial and positive relationship. That means, this study shows that all learners expect to put their twelfth effort into studying and sharing their information with peers, which contributes to actual social media use. For example, Hypothesis 13 proposed a significant and positive relationship between behavioral intention and actual social media usage for teaching and learning during the COVID-19 pandemic (β = 0.204, t = 2.720, p < 0.001). That is, according to this study, learners expect to put in the effort in studying and sharing their knowledge with peers, which contributes to actual social media use. Thus, the fourteenth hypothesis indicated a significant and positive relationship between behavioral intention for learning and performance impact during the COVID-19 pandemic (β = 0. 460, t = 7.885, p < 0.001). That is, according to this study, all learners expect to put forth a fourteenth effort in studying and sharing their knowledge with peers, which contributes to performance impact. The fifteenth hypothesis suggested a substantial and positive relationship between actual social media use for learning and performance impact during the COVID-19 pandemic (β = 0. 326, t = 6.100, p < 0.001) thus, the fifteenth hypothesis indicated that is a substantial and positive relationship. That means, in this study shows that all learners expect to put fifteenth effort in studying and sharing their information with peers, which contributes to performance impact (see Table 2 ). Hence collectively, all the technology acceptance model hypotheses reported reliable with the facts and figures of our research, that reinforce the majority of the previous research that found; the perceived usefulness and ease of using social media enhance the behavioral intention for using social media platforms besides the appropriate social media for the purpose teaching-learning during the COVID-19 pandemic, it increases students’ learners for the teaching besides the learning during the COVID-19 pandemic.

Table 2. Structural model for hypothesis testing results.

The findings of this study suggest an insight into students’ academic performance impacts and relationships with their (performance expectancy, effort expectancy for, facilitating conditions for teaching and learning during the COVID-19 pandemic, perceived usefulness, perceived ease of use for teaching and learning) were significantly affected by the acceptance of using social media for learning during the COVID-19 pandemic. Social media usage eases a context that is described through behavioral intention and actual social media that can support students in social media and makes changes to flexible models of learning a necessity. A little previous research is available that combines and integrates ideas for the acceptance of a group of students as social media as a supportive educational tool in higher education. Student interactions are identified as important stakeholders from the learner’s point of view as they can use social media to facilitate information and co-creation of knowledge ( Alnjadat et al., 2019 ; Alyoussef et al., 2019 ). This may explain the quantitative evidence indicating that students are more likely to use social media in their teaching and learning during the COVID-19 pandemic.

As a result of combining the unified theory of usage and acceptance of technology model factors with the technology acceptance model factors found in this analysis, a new integrated structural model was created. This study also provides preliminary insights into the behavioral intention to use and real social media use benefits in higher education. According to quantitative evidence from the student study, using social media as a support tool can result in intensive, higher learning and comprehension. Students have behavioral aim to use social media, and real use is often perceived ease of use, performance expectancy, and perceived use, which improves students’ academic performance by allowing them to access vital resources from their peers ( Hrastinski and Aghaee, 2012 ; Alyoussef et al., 2019 ; Al-Maatouk et al., 2020b ). The relationship between perceived enjoyment, behavioral intention to use social media, and actual social media use has been investigated using the structural model developed for this research. The findings showed a substantial association between performance expectancy to behavioral intention and real social media use for teaching and learning during the COVID-19 pandemic by using the unified theory of usage and acceptance of technology model variables. Thus, it is reasonable to conclude that students associate this perceived enjoyment factor with their willingness to use social media for educational purposes. The original theoretical basis of the unified theory of usage and acceptance of technology model is reflected in this result ( Mittal et al., 2021 ). This is to be anticipated because when students believe that using social media can benefit them, they are more likely to use it to improve their results. The findings also showed that behavioral intention and actual social media usage for learning is significantly influenced by effort expectancy and facilitating conditions.

The technology acceptance model component of perceived ease of use, perceived useability, and perceived enjoyment supports the original hypothesis of the unified theory of usage and acceptance of technology model ( Mittal et al., 2021 ). The findings revealed that perceived ease of use and perceived useability have a substantial positive impact on behavioral intention and the actual use of social media for teaching and learning during the COVID-19 pandemic. This result is in line with the technology acceptance model’s original theoretical basis ( Elkaseh et al., 2016 ; Alalwan et al., 2019 ; Alyoussef et al., 2019 ; Al-Rahmi et al., 2021b ). The findings also showed that perceived enjoyment has a substantial positive impact on real social media learning use ( Alenazy et al., 2019 ; Alyoussef et al., 2019 ). Although there was no substantial impact of perceived enjoyment on behavioral intention for teaching and learning during the COVID-19 pandemic in this analysis, this is due to the poor use of social media for teaching and learning during the COVID-19 pandemic by students and the fact that there is not enough of them to have a meaningful effect on their peers.

The model established in this study identified the key factors for teaching and learning acceptance during the COVID-19 pandemic, which could be useful for higher education in ensuring the effective adoption of social media resources for learning. Furthermore, by combining an expanded version of the unified theory of usage and acceptance of technology with technology acceptance model variables, this study adds to the existing body of knowledge. As a result, the results of this study would be useful to university policymakers and researchers in determining students’ preferences for using social media platforms, thus increasing students’ acceptance of social media use in educational institutions. This research provides two empirical evidences: first empirical evidence of behavioral intention through performance and effort expectancy, perceived usefulness, ease of use and perceived enjoyment for teaching and learning during the COVID-19 pandemic; and second empirical evidence of actual social media use as a means of performance and effort expectancy, perceived usefulness, ease of use and perceived enjoyment for teaching and learning during the COVID-19 pandemic, which can improve learners’ educational achievements in higher education. This is a substantial theoretical contribution to previous studies of technology acceptance model and unified theory of usage and acceptance of technology that did not recognize the effect behavioral intention and actual social media use had on utilizing social media ( Howard et al., 2015 ; Chawinga, 2017 ; Hossain et al., 2019 ; Al-Rahmi et al., 2020 ).

The COVID-19 home confinement (or “lockdowns”), which saw colleges close, and all instruction become virtual, the education system’s long-term viability was put to the test. The higher education institutions must guarantee that education is inclusive, egalitarian, and of high quality to bridge the digital gap and promote sustainable activities ( Alismaiel, 2021 ; Faura-Martínez et al., 2022 ). Furthermore, COVID-19 had negative effects on the well-being of students in four countries: Cambodia, Nigeria, Oman, and Spain, leading us to learn about COVID-19’s cross-cultural effects on higher education students ( Cifuentes-Faura et al., 2021 ). Based on these findings, we infer that COVID-19 imprisonment improved students’ efficiency by changing their learning tactics into a more consistent habit. As a result of these factors, greater grades in students’ evaluations are predicted because of COVID-19 imprisonment, which may be explained by an increase in their learning performance because of their usage of social media use for collaborative learning. Two of the conclusions based on the findings of this research are outlined below:

• It is important to utilize social media for behavioral intention or actual social media use to encourage students to utilize social media for behavioral intention to use or actual social media usage in educational institutions by influencing students’ academic performance. Components of social media, for instance, such as YouTube, Facebook, and blogs, for instance.

• Educational institutions are encouraged to enroll savvy students with peers or lectures to utilize the platforms of social media in behavioral intention and actual social media use courses without compelling them to follow orders. Accordingly, educational institutions may include all the tools necessary to utilize social media for learning during the COVID-19 pandemic.

Higher education students’ increased use of social media necessitates more focus from both students and teachers for teaching and learning during the COVID-19 pandemic. Students are encouraged to engage in learning through participation in social media, which also allows for personal reflection and collaborative learning during the COVID-19 pandemic. This study aimed to examine the impact of social media as a learning tool has had on academic performance in education. The study’s results shed light on the potential advantages of using social media platforms in higher education in terms of behavioral intention and actual social media use. The study also adds to the growing body of literature on social media in higher education by enhancing knowledge of student attitudes and perspectives on social media usage in higher education. Our findings suggest that using social media for teaching and learning may positively impact academic performance. Furthermore, students’ willingness to use social media for learning would increase if they found it a valuable learning tool during the COVID-19 pandemic. Students, on the other hand, would be unable to use social media for educational purposes if they believe it is unsafe. As a result, when incorporating social media into learning and teaching activities, students must understand the possible risks and drawbacks of using social media in the classroom and devise methods to minimize those risks. Other factors that are important to the adoption and use of social media by academic students in higher education, such as collaborative learning, experience, engagement with students in using social media, and students’ perceptions of social media, were not considered in this study. As a result, future research is needed to look at variables like student participation, collaborative learning during the COVID-19 pandemic, and student involvement in class as they relate to their learning during the COVID-19 pandemic via social media tools.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent from the patients/participants was not required to participate in this study in accordance with the national legislation and the institutional requirements.

Author contributions

AA-R, AS, and EW: conceptualization, methodology, resources, and data curation. AA-R, WA-R, and OA: software. AA-R, AS, EW, JC, and OA: validation. AA-R, AS, EW, and WA-R: formal analysis. AA-R: investigation. AA-R and AS: writing—original draft preparation and writing—review and editing. AA-R, AS, EW, WA-R, OA, and JC: visualization. AS and EW: supervision. AA-R, AS, EW, and WA-R: project administration. All authors read and agreed to the published version of the manuscript.

Acknowledgments

We thank supervisor and Universiti Tun Hussein Onn Malaysia (UTHM) for supporting us. Also, we extend our appreciation to the Deanship of Faculty of Technology Management and Business.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords : unified theory of usage and acceptance of technology theory, technology acceptance model, social media, Facebook, Instagram, YouTube, performance expectancy

Citation: Al-Rahmi AM, Shamsuddin A, Wahab E, Al-Rahmi WM, Alismaiel OA and Crawford J (2022) Social media usage and acceptance in higher education: A structural equation model. Front. Educ. 7:964456. doi: 10.3389/feduc.2022.964456

Received: 08 June 2022; Accepted: 26 July 2022; Published: 22 August 2022.

Reviewed by:

Copyright © 2022 Al-Rahmi, Shamsuddin, Wahab, Al-Rahmi, Alismaiel and Crawford. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Ali Mugahed Al-Rahmi, [email protected] ; Waleed Mugahed Al-Rahmi, [email protected]

Social Media

Why do people even use social media, we all have a basic drive to connect with others. social media helps us do that..

Posted July 18, 2023 | Reviewed by Devon Frye

• Self-determination theory explains our fundamental needs to feel connected, authentic, and accomplished.
• Research shows that social media helps people accomplish self-determination goals.
• When people’s social media use is based on their self-determined motivation, they feel happier.

It seems like social media apps are constantly making the news. From the CDC report about teen mental health earlier this year to the meteoric rise of the new Threads app this month, people seem constantly fascinated by the popularity of programs like Instagram and TikTok. Since their invention, these apps remain a fixation point in our discourse. Out of all the things that humans do around the world, social media sure grabs a huge share of our attention !

Maybe the burning question we really want to answer is: Why do people use social media in the first place? What’s the appeal? What do we really get out of it? What draws (literally) billions of people daily to these apps?

As with anything in psychology, there’s no one single easy answer. Some people use social media apps because they want to network professionally. Some want to be popular and get lots of affirmation (likes). Some want to laugh and make others laugh. Some want to promote an important cause, like a religious or political aspiration.

So, what’s the common thread (pun intended) with these goals? They’re all part of our quest to fulfill basic psychological motives.

Everyone Has Basic Psychological Needs

In previous pieces for this column, I argued that self-determination theory gives us a framework to deeply understand people’s fundamental mental needs, which are then translated into specific motivations and goals. On a very basic level, we strive to fulfill (a) competence , or the feeling that we can do things well, (b) autonomy , which involves developing authenticity and personal freedom, and (c) relatedness , which is fulfilled through social connectivity and bonding .

These motives get rendered into specific things like riding bikes, playing musical instruments or video games, learning facts about the world, having drinks with old friends, or getting a promotion at work. Just as we constantly desire physical things to sustain our bodies (food, water, sleep, exercise, and sex ), we also have cravings for psychological things (achievement, excitement, and social alliances).

When we break down this array of psychological needs, social media use now seems much more intuitive. People use apps like Instagram or Threads because they want to accomplish their goals and satisfy their emotional necessities. This is one of the key insights from decades of psychological research and from user experience studies at big tech companies like Meta.

Research Evidence Links Self-Determination With Social Media

For instance, researchers in the U.S. and Australia observed a correlation between satisfying their relatedness needs and social media enjoyment. The more they felt like using social media helped them connect positively with others (“ I really like the people I interact with on Facebook ”), the more highly satisfied they felt with their social media usage (“ I am satisfied with my experience on Facebook ”). The researchers concluded that social media apps make people happy because they help fulfill a need for social connection.

In another study, researchers from Turkey and the U.S. examined daily social media use over two weeks. They asked participants about specific actions (scrolling, sharing, liking, and friending). The researchers observed that simply engaging in these online activities was totally unrelated to well-being, meaning that they didn’t make people feel better or worse.

But when participants were motivated to use social media because they found it beneficial in some way (“ Because I thought that it was fun and interesting ”), then their well-being did improve. That is, when social media use was motivated by authentic values and interests, then people reported feeling more vitality and were more satisfied with their life.

A recent study from researchers in the U.S. and Russia found a similar pattern. When people felt more “free,” and “connected” while using social media, this predicted higher well-being, compared to when they felt more “pressured” and “ lonely .” The authors wrote that when people experience a self-determined motivation for their social media use, it “may deliver a distinct socio-emotional benefit” to their well-being.

They also conducted two experiments where they instructed participants to use a social media app of their choice in the laboratory. Participants completed surveys for self-determined motivation and emotional well-being both before and after using social media. The researchers replicated the same pattern as before—when participants felt that they were using social media to fulfill their psychological needs (particularly for social connection), they felt happier.

This explains why people are drawn to use social media in the first place. Humans have a fundamental need to feel connected, authentic, and capable. This could take many forms, of course. Maybe it’s sharing family vacation photos, posting job ads, or marveling at feats of athletic performance—or adorable kittens. But all of it really boils down to self-determination.

So then why does it seem like some people sometimes suffer because of their online activities?

Self-Determination Also Explains Bad Experiences Online

Dissatisfaction with social media can be explained by the same principles. People feel unhappy with social media (or texting and email, for that matter) when they feel like they’re being controlled. In other words, when people feel compelled to like and share posts, like they have no choice but to text someone back, or as if they must check their email, those are linked with suffering because the behaviors are driven by extrinsic goals.

Thankfully, few of us (if any) are forced to use social media. We do it because we enjoy it. If anyone was forced to log on and scroll through dozens of posts, it would lose all its appeal quite rapidly.

This is why I find the claims about social media causing poor mental health to be so baffling. All of the knowledge we have suggests the opposite is true. Even researchers who argue that sometimes people suffer while using social media will contextualize this within a more nuanced perspective than the oversimplified “social media is bad” narrative. Rather, they claim that social media benefits people’s mental health when it provides opportunities for belonging and meaningful social connections. But social media can hurt people’s mental health when it’s done for the wrong reasons.

Conclusion: Make Social Media Work For You

In conclusion, I would ask you, the reader, to be more mindful of this the next time you log on to your preferred social app. Ask yourself: What are your goals? What is your motivation? Are you trying to connect with friends, or promote your business, or learn cool science facts, or laugh at some clever memes ? Whatever you’re craving, think about how you can accomplish that, and you will be more likely to feel a boost in your happiness and well-being.

Clark, J. L., Algoe, S. B., & Green, M. C. (2018). Social network sites and well-being: The role of social connection. Current Directions in Psychological Science, 27 (1), 32-37.

Manuoğlu, E., & Uysal, A. (2020). Motivation for different Facebook activities and well-being: A daily experience sampling study. Psychology of Popular Media, 9 (4), 456.

Sheldon, K. M., & Titova, L. (2023). Social media use and well-being: testing an integrated self-determination theory model. Media Psychology. DOI: 10.1080/15213269.2023.2185259

Wang, X., & Li, Y. (2016). Users' satisfaction with social network sites: A self-determination perspective. Journal of Computer Information Systems, 56 (1), 48-54.

Dylan Selterman, Ph.D., is an Associate Teaching Professor at Johns Hopkins University in the Department of Psychological and Brain Sciences. He teaches courses and conducts research on personality traits, happiness, relationships, morality/ethics, game theory, political psychology, and more.

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Opinion: Does social media rewire kids’ brains? Here’s what the science really says

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America’s young people face a mental health crisis, and adults constantly debate how much to blame phones and social media. A new round of conversation has been spurred by Jonathan Haidt’s book “The Anxious Generation,” which contends that rising mental health issues in children and adolescents are the result of social media replacing key experiences during formative years of brain development.

The book has been criticized by academics , and rightfully so. Haidt’s argument is based largely on research showing that adolescent mental health has declined since 2010, coinciding roughly with mass adoption of the smartphone. But of course, correlation is not causation. The research we have to date suggests that the effects of phones and social media on adolescent mental health are probably much more nuanced.

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That complex picture is less likely to get attention than Haidt’s claims because it doesn’t play as much into parental fears. After all, seeing kids absorbed in their phones, and hearing that their brains are being “rewired,” calls to mind an alien world-domination plot straight from a sci-fi film.

And that’s part of the problem with the “rewiring the brain” narrative of screen time. It reflects a larger trope in public discussion that wields brain science as a scare tactic without yielding much real insight.

First, let’s consider what the research has shown so far . Meta-analyses of the links between mental health and social media give inconclusive or relatively minor results. The largest U.S. study on childhood brain development to date did not find significant relationships between the development of brain function and digital media use . This month, an American Psychological Assn. health advisory reported that the current state of research shows “ using social media is not inherently beneficial or harmful to young people” and that its effects depend on “pre-existing strengths or vulnerabilities, and the contexts in which they grow up.”

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So why the insistence from Haidt and others that smartphones dangerously rewire the brain? It stems from misunderstandings of research that I have encountered frequently as a neuroscientist studying emotional development, behavioral addictions and people’s reactions to media.

Imaging studies in neuroscience typically compare some feature of the brain between two groups: one that does not do a specific behavior (or does it less frequently) and one that does the behavior more frequently. When we find a relationship, all it means is either that the behavior influences something about the functioning of this brain feature, or something about this feature influences whether we engage in the behavior.

In other words, an association between increased brain activity and using social media could mean that social media activates the identified pathways, or people who already have increased activity in those pathways tend to be drawn to social media, or both.

Fearmongering happens when the mere association between an activity such as social media use and a brain pathway is taken as a sign of something harmful on its own. Functional and structural research on the brain cannot give enough information to objectively identify increases or decreases in neural activity, or in a brain region’s thickness, as “good” or “bad.” There is no default healthy status quo that everybody’s brains are measured against, and doing nearly any activity involves many parts of the brain.

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“The Anxious Generation” neglects these subtleties when, for example, it discusses a brain system known as the default mode network. This system decreases in activity when we engage with spirituality, meditation and related endeavors, and Haidt uses this fact to claim that social media is “not healthy for any of us” because studies suggest that it by contrast increases activity in the same network.

But the default mode network is just a set of brain regions that tend to be involved in internally focused thinking, such as contemplating your past or making a moral judgment, versus externally focused thinking such as playing chess or driving an unfamiliar route. Its increased activity does not automatically mean something unhealthy.

This type of brain-related scare tactic is not new. A common version, which is also deployed for smartphones , involves pathways in the brain linked to drug addiction, including areas that respond to dopamine and opioids. The trope says that any activity associated with such pathways is addictive, like drugs, whether it’s Oreos , cheese , God , credit card purchases , sun tanning or looking at a pretty face . These things do involve neural pathways related to motivated behavior — but that does not mean they damage our brains or should be equated with drugs.

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Adolescence is a time when the brain is particularly plastic, or prone to change. But change doesn’t have to be bad. We should take advantage of plasticity to help teach kids healthy ways to self-manage their own use of, and feelings surrounding, smartphones.

Do I expect future findings on the adolescent brain to immediately quell parents’ fears on this issue? Of course not — and the point is that they shouldn’t. Brain imaging data is a fascinating way to explore interactions between psychology, neuroscience and social factors. It’s just not a tool for declaring behaviors to be pathological. Feel free to question whether social media is good for kids — but don’t misuse neuroscience to do so.

Anthony Vaccaro is a postdoctoral research associate at the University of Southern California’s Psychology department.

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