literature review about the impact of social media

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Social Media and Mental Health: Benefits, Risks, and Opportunities for Research and Practice

• Published: 20 April 2020
• Volume 5 , pages 245–257, ( 2020 )

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• John A. Naslund 1 ,
• Ameya Bondre 2 ,
• John Torous 3 &
• Kelly A. Aschbrenner 4  

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Introduction

Social media has become a prominent fixture in the lives of many individuals facing the challenges of mental illness. Social media refers broadly to web and mobile platforms that allow individuals to connect with others within a virtual network (such as Facebook, Twitter, Instagram, Snapchat, or LinkedIn), where they can share, co-create, or exchange various forms of digital content, including information, messages, photos, or videos (Ahmed et al. 2019 ). Studies have reported that individuals living with a range of mental disorders, including depression, psychotic disorders, or other severe mental illnesses, use social media platforms at comparable rates as the general population, with use ranging from about 70% among middle-age and older individuals to upwards of 97% among younger individuals (Aschbrenner et al. 2018b ; Birnbaum et al. 2017b ; Brunette et al. 2019 ; Naslund et al. 2016 ). Other exploratory studies have found that many of these individuals with mental illness appear to turn to social media to share their personal experiences, seek information about their mental health and treatment options, and give and receive support from others facing similar mental health challenges (Bucci et al. 2019 ; Naslund et al. 2016b ).

Across the USA and globally, very few people living with mental illness have access to adequate mental health services (Patel et al. 2018 ). The wide reach and near ubiquitous use of social media platforms may afford novel opportunities to address these shortfalls in existing mental health care, by enhancing the quality, availability, and reach of services. Recent studies have explored patterns of social media use, impact of social media use on mental health and wellbeing, and the potential to leverage the popularity and interactive features of social media to enhance the delivery of interventions. However, there remains uncertainty regarding the risks and potential harms of social media for mental health (Orben and Przybylski 2019 ) and how best to weigh these concerns against potential benefits.

In this commentary, we summarized current research on the use of social media among individuals with mental illness, with consideration of the impact of social media on mental wellbeing, as well as early efforts using social media for delivery of evidence-based programs for addressing mental health problems. We searched for recent peer reviewed publications in Medline and Google Scholar using the search terms “mental health” or “mental illness” and “social media,” and searched the reference lists of recent reviews and other relevant studies. We reviewed the risks, potential harms, and necessary safety precautions with using social media for mental health. Overall, our goal was to consider the role of social media as a potentially viable intervention platform for offering support to persons with mental disorders, promoting engagement and retention in care, and enhancing existing mental health services, while balancing the need for safety. Given this broad objective, we did not perform a systematic search of the literature and we did not apply specific inclusion criteria based on study design or type of mental disorder.

Social Media Use and Mental Health

In 2020, there are an estimated 3.8 billion social media users worldwide, representing half the global population (We Are Social 2020 ). Recent studies have shown that individuals with mental disorders are increasingly gaining access to and using mobile devices, such as smartphones (Firth et al. 2015 ; Glick et al. 2016 ; Torous et al. 2014a , b ). Similarly, there is mounting evidence showing high rates of social media use among individuals with mental disorders, including studies looking at engagement with these popular platforms across diverse settings and disorder types. Initial studies from 2015 found that nearly half of a sample of psychiatric patients were social media users, with greater use among younger individuals (Trefflich et al. 2015 ), while 47% of inpatients and outpatients with schizophrenia reported using social media, of which 79% reported at least once-a-week usage of social media websites (Miller et al. 2015 ). Rates of social media use among psychiatric populations have increased in recent years, as reflected in a study with data from 2017 showing comparable rates of social media use (approximately 70%) among individuals with serious mental illness in treatment as compared with low-income groups from the general population (Brunette et al. 2019 ).

Similarly, among individuals with serious mental illness receiving community-based mental health services, a recent study found equivalent rates of social media use as the general population, even exceeding 70% of participants (Naslund et al. 2016 ). Comparable findings were demonstrated among middle-age and older individuals with mental illness accessing services at peer support agencies, where 72% of respondents reported using social media (Aschbrenner et al. 2018b ). Similar results, with 68% of those with first episode psychosis using social media daily were reported in another study (Abdel-Baki et al. 2017 ).

Individuals who self-identified as having a schizophrenia spectrum disorder responded to a survey shared through the National Alliance of Mental Illness (NAMI) and reported that visiting social media sites was one of their most common activities when using digital devices, taking up roughly 2 h each day (Gay et al. 2016 ). For adolescents and young adults ages 12 to 21 with psychotic disorders and mood disorders, over 97% reported using social media, with average use exceeding 2.5 h per day (Birnbaum et al. 2017b ). Similarly, in a sample of adolescents ages 13–18 recruited from community mental health centers, 98% reported using social media, with YouTube as the most popular platform, followed by Instagram and Snapchat (Aschbrenner et al. 2019 ).

Research has also explored the motivations for using social media as well as the perceived benefits of interacting on these platforms among individuals with mental illness. In the sections that follow (see Table 1 for a summary), we consider three potentially unique features of interacting and connecting with others on social media that may offer benefits for individuals living with mental illness. These include: (1) Facilitate social interaction; (2) Access to a peer support network; and (3) Promote engagement and retention in services.

Facilitate Social Interaction

Social media platforms offer near continuous opportunities to connect and interact with others, regardless of time of day or geographic location. This on demand ease of communication may be especially important for facilitating social interaction among individuals with mental disorders experiencing difficulties interacting in face-to-face settings. For example, impaired social functioning is a common deficit in schizophrenia spectrum disorders, and social media may facilitate communication and interacting with others for these individuals (Torous and Keshavan 2016 ). This was suggested in one study where participants with schizophrenia indicated that social media helped them to interact and socialize more easily (Miller et al. 2015 ). Like other online communication, the ability to connect with others anonymously may be an important feature of social media, especially for individuals living with highly stigmatizing health conditions (Berger et al. 2005 ), such as serious mental disorders (Highton-Williamson et al. 2015 ).

Studies have found that individuals with serious mental disorders (Spinzy et al. 2012 ) as well as young adults with mental illness (Gowen et al. 2012 ) appear to form online relationships and connect with others on social media as often as social media users from the general population. This is an important observation because individuals living with serious mental disorders typically have few social contacts in the offline world and also experience high rates of loneliness (Badcock et al. 2015 ; Giacco et al. 2016 ). Among individuals receiving publicly funded mental health services who use social media, nearly half (47%) reported using these platforms at least weekly to feel less alone (Brusilovskiy et al. 2016 ). In another study of young adults with serious mental illness, most indicated that they used social media to help feel less isolated (Gowen et al. 2012 ). Interestingly, more frequent use of social media among a sample of individuals with serious mental illness was associated with greater community participation, measured as participation in shopping, work, religious activities, or visiting friends and family, as well as greater civic engagement, reflected as voting in local elections (Brusilovskiy et al. 2016 ).

Emerging research also shows that young people with moderate to severe depressive symptoms appear to prefer communicating on social media rather than in-person (Rideout and Fox 2018 ), while other studies have found that some individuals may prefer to seek help for mental health concerns online rather than through in-person encounters (Batterham and Calear 2017 ). In a qualitative study, participants with schizophrenia described greater anonymity, the ability to discover that other people have experienced similar health challenges and reducing fears through greater access to information as important motivations for using the Internet to seek mental health information (Schrank et al. 2010 ). Because social media does not require the immediate responses necessary in face-to-face communication, it may overcome deficits with social interaction due to psychotic symptoms that typically adversely affect face-to-face conversations (Docherty et al. 1996 ). Online social interactions may not require the use of non-verbal cues, particularly in the initial stages of interaction (Kiesler et al. 1984 ), with interactions being more fluid and within the control of users, thereby overcoming possible social anxieties linked to in-person interaction (Indian and Grieve 2014 ). Furthermore, many individuals with serious mental disorders can experience symptoms including passive social withdrawal, blunted affect, and attentional impairment, as well as active social avoidance due to hallucinations or other concerns (Hansen et al. 2009 ), thus potentially reinforcing the relative advantage, as perceived by users, of using social media over in person conversations.

Access to a Peer Support Network

There is growing recognition about the role that social media channels could play in enabling peer support (Bucci et al. 2019 ; Naslund et al. 2016b ), referred to as a system of mutual giving and receiving where individuals who have endured the difficulties of mental illness can offer hope, friendship, and support to others facing similar challenges (Davidson et al. 2006 ; Mead et al. 2001 ). Initial studies exploring use of online self-help forums among individuals with serious mental illnesses have found that individuals with schizophrenia appeared to use these forums for self-disclosure and sharing personal experiences, in addition to providing or requesting information, describing symptoms, or discussing medication (Haker et al. 2005 ), while users with bipolar disorder reported using these forums to ask for help from others about their illness (Vayreda and Antaki 2009 ). More recently, in a review of online social networking in people with psychosis, Highton-Williamson et al. ( 2015 ) highlight that an important purpose of such online connections was to establish new friendships, pursue romantic relationships, maintain existing relationships or reconnect with people, and seek online peer support from others with lived experience (Highton-Williamson et al. 2015 ).

Online peer support among individuals with mental illness has been further elaborated in various studies. In a content analysis of comments posted to YouTube by individuals who self-identified as having a serious mental illness, there appeared to be opportunities to feel less alone, provide hope, find support and learn through mutual reciprocity, and share coping strategies for day-to-day challenges of living with a mental illness (Naslund et al. 2014 ). In another study, Chang ( 2009 ) delineated various communication patterns in an online psychosis peer-support group (Chang 2009 ). Specifically, different forms of support emerged, including “informational support” about medication use or contacting mental health providers, “esteem support” involving positive comments for encouragement, “network support” for sharing similar experiences, and “emotional support” to express understanding of a peer’s situation and offer hope or confidence (Chang 2009 ). Bauer et al. ( 2013 ) reported that the main interest in online self-help forums for patients with bipolar disorder was to share emotions with others, allow exchange of information, and benefit by being part of an online social group (Bauer et al. 2013 ).

For individuals who openly discuss mental health problems on Twitter, a study by Berry et al. ( 2017 ) found that this served as an important opportunity to seek support and to hear about the experiences of others (Berry et al. 2017 ). In a survey of social media users with mental illness, respondents reported that sharing personal experiences about living with mental illness and opportunities to learn about strategies for coping with mental illness from others were important reasons for using social media (Naslund et al. 2017 ). A computational study of mental health awareness campaigns on Twitter provides further support with inspirational posts and tips being the most shared (Saha et al. 2019 ). Taken together, these studies offer insights about the potential for social media to facilitate access to an informal peer support network, though more research is necessary to examine how these online interactions may impact intentions to seek care, illness self-management, and clinically meaningful outcomes in offline contexts.

Promote Engagement and Retention in Services

Many individuals living with mental disorders have expressed interest in using social media platforms for seeking mental health information (Lal et al. 2018 ), connecting with mental health providers (Birnbaum et al. 2017b ), and accessing evidence-based mental health services delivered over social media specifically for coping with mental health symptoms or for promoting overall health and wellbeing (Naslund et al. 2017 ). With the widespread use of social media among individuals living with mental illness combined with the potential to facilitate social interaction and connect with supportive peers, as summarized above, it may be possible to leverage the popular features of social media to enhance existing mental health programs and services. A recent review by Biagianti et al. ( 2018 ) found that peer-to-peer support appeared to offer feasible and acceptable ways to augment digital mental health interventions for individuals with psychotic disorders by specifically improving engagement, compliance, and adherence to the interventions and may also improve perceived social support (Biagianti et al. 2018 ).

Among digital programs that have incorporated peer-to-peer social networking consistent with popular features on social media platforms, a pilot study of the HORYZONS online psychosocial intervention demonstrated significant reductions in depression among patients with first episode psychosis (Alvarez-Jimenez et al. 2013 ). Importantly, the majority of participants (95%) in this study engaged with the peer-to-peer networking feature of the program, with many reporting increases in perceived social connectedness and empowerment in their recovery process (Alvarez-Jimenez et al. 2013 ). This moderated online social therapy program is now being evaluated as part of a large randomized controlled trial for maintaining treatment effects from first episode psychosis services (Alvarez-Jimenez et al. 2019 ).

Other early efforts have demonstrated that use of digital environments with the interactive peer-to-peer features of social media can enhance social functioning and wellbeing in young people at high risk of psychosis (Alvarez-Jimenez et al. 2018 ). There has also been a recent emergence of several mobile apps to support symptom monitoring and relapse prevention in psychotic disorders. Among these apps, the development of PRIME (Personalized Real-time Intervention for Motivational Enhancement) has involved working closely with young people with schizophrenia to ensure that the design of the app has the look and feel of mainstream social media platforms, as opposed to existing clinical tools (Schlosser et al. 2016 ). This unique approach to the design of the app is aimed at promoting engagement and ensuring that the app can effectively improve motivation and functioning through goal setting and promoting better quality of life of users with schizophrenia (Schlosser et al. 2018 ).

Social media platforms could also be used to promote engagement and participation in in-person services delivered through community mental health settings. For example, the peer-based lifestyle intervention called PeerFIT targets weight loss and improved fitness among individuals living with serious mental illness through a combination of in-person lifestyle classes, exercise groups, and use of digital technologies (Aschbrenner et al. 2016b , c ). The intervention holds tremendous promise as lack of support is one of the largest barriers towards exercise in patients with serious mental illness (Firth et al. 2016 ), and it is now possible to use social media to counter such. Specifically, in PeerFIT, a private Facebook group is closely integrated into the program to offer a closed platform where participants can connect with the lifestyle coaches, access intervention content, and support or encourage each other as they work towards their lifestyle goals (Aschbrenner et al. 2016a ; Naslund et al. 2016a ). To date, this program has demonstrated preliminary effectiveness for meaningfully reducing cardiovascular risk factors that contribute to early mortality in this patient group (Aschbrenner, Naslund, Shevenell, Kinney, et al., 2016), while the Facebook component appears to have increased engagement in the program, while allowing participants who were unable to attend in-person sessions due to other health concerns or competing demands to remain connected with the program (Naslund et al. 2018 ). This lifestyle intervention is currently being evaluated in a randomized controlled trial enrolling young adults with serious mental illness from real world community mental health services settings (Aschbrenner et al. 2018a ).

These examples highlight the promise of incorporating the features of popular social media into existing programs, which may offer opportunities to safely promote engagement and program retention, while achieving improved clinical outcomes. This is an emerging area of research, as evidenced by several important effectiveness trials underway (Alvarez-Jimenez et al. 2019 ; Aschbrenner et al. 2018a ), including efforts to leverage online social networking to support family caregivers of individuals receiving first episode psychosis services (Gleeson et al. 2017 ).

Challenges with Social Media for Mental Health

The science on the role of social media for engaging persons with mental disorders needs a cautionary note on the effects of social media usage on mental health and wellbeing, particularly in adolescents and young adults. While the risks and harms of social media are frequently covered in the popular press and mainstream news reports, careful consideration of the research in this area is necessary. In a review of 43 studies in young people, many benefits of social media were cited, including increased self-esteem and opportunities for self-disclosure (Best et al. 2014 ). Yet, reported negative effects were an increased exposure to harm, social isolation, depressive symptoms, and bullying (Best et al. 2014 ). In the sections that follow (see Table 1 for a summary), we consider three major categories of risk related to use of social media and mental health. These include: (1) Impact on symptoms; (2) Facing hostile interactions; and (3) Consequences for daily life.

Impact on Symptoms

Studies consistently highlight that use of social media, especially heavy use and prolonged time spent on social media platforms, appears to contribute to increased risk for a variety of mental health symptoms and poor wellbeing, especially among young people (Andreassen et al. 2016 ; Kross et al. 2013 ; Woods and Scott 2016 ). This may partly be driven by the detrimental effects of screen time on mental health, including increased severity of anxiety and depressive symptoms, which have been well documented (Stiglic and Viner 2019 ). Recent studies have reported negative effects of social media use on mental health of young people, including social comparison pressure with others and greater feeling of social isolation after being rejected by others on social media (Rideout and Fox 2018 ). In a study of young adults, it was found that negative comparisons with others on Facebook contributed to risk of rumination and subsequent increases in depression symptoms (Feinstein et al. 2013 ). Still, the cross-sectional nature of many screen time and mental health studies makes it challenging to reach causal inferences (Orben and Przybylski 2019 ).

Quantity of social media use is also an important factor, as highlighted in a survey of young adults ages 19 to 32, where more frequent visits to social media platforms each week were correlated with greater depressive symptoms (Lin et al. 2016 ). More time spent using social media is also associated with greater symptoms of anxiety (Vannucci et al. 2017 ). The actual number of platforms accessed also appears to contribute to risk as reflected in another national survey of young adults where use of a large number of social media platforms was associated with negative impact on mental health (Primack et al. 2017 ). Among survey respondents using between 7 and 11 different social media platforms compared with respondents using only 2 or fewer platforms, there were 3 times greater odds of having high levels of depressive symptoms and a 3.2 times greater odds of having high levels of anxiety symptoms (Primack et al. 2017 ).

Many researchers have postulated that worsening mental health attributed to social media use may be because social media replaces face-to-face interactions for young people (Twenge and Campbell 2018 ) and may contribute to greater loneliness (Bucci et al. 2019 ) and negative effects on other aspects of health and wellbeing (Woods and Scott 2016 ). One nationally representative survey of US adolescents found that among respondents who reported more time accessing media such as social media platforms or smartphone devices, there were significantly greater depressive symptoms and increased risk of suicide when compared with adolescents who reported spending more time on non-screen activities, such as in-person social interaction or sports and recreation activities (Twenge et al. 2018 ). For individuals living with more severe mental illnesses, the effects of social media on psychiatric symptoms have received less attention. One study found that participation in chat rooms may contribute to worsening symptoms in young people with psychotic disorders (Mittal et al. 2007 ), while another study of patients with psychosis found that social media use appeared to predict low mood (Berry et al. 2018 ). These studies highlight a clear relationship between social media use and mental health that may not be present in general population studies (Orben and Przybylski 2019 ) and emphasize the need to explore how social media may contribute to symptom severity and whether protective factors may be identified to mitigate these risks.

Facing Hostile Interactions

Popular social media platforms can create potential situations where individuals may be victimized by negative comments or posts. Cyberbullying represents a form of online aggression directed towards specific individuals, such as peers or acquaintances, which is perceived to be most harmful when compared with random hostile comments posted online (Hamm et al. 2015 ). Importantly, cyberbullying on social media consistently shows harmful impact on mental health in the form of increased depressive symptoms as well as worsening of anxiety symptoms, as evidenced in a review of 36 studies among children and young people (Hamm et al. 2015 ). Furthermore, cyberbullying disproportionately impacts females as reflected in a national survey of adolescents in the USA, where females were twice as likely to be victims of cyberbullying compared with males (Alhajji et al. 2019 ). Most studies report cross-sectional associations between cyberbullying and symptoms of depression or anxiety (Hamm et al. 2015 ), though one longitudinal study in Switzerland found that cyberbullying contributed to significantly greater depression over time (Machmutow et al. 2012 ).

For youth ages 10 to 17 who reported major depressive symptomatology, there were over 3 times greater odds of facing online harassment in the last year compared with youth who reported mild or no depressive symptoms (Ybarra 2004 ). Similarly, in a 2018 national survey of young people, respondents ages 14 to 22 with moderate to severe depressive symptoms were more likely to have had negative experiences when using social media and, in particular, were more likely to report having faced hostile comments or being “trolled” from others when compared with respondents without depressive symptoms (31% vs. 14%) (Rideout and Fox 2018 ). As these studies depict risks for victimization on social media and the correlation with poor mental health, it is possible that individuals living with mental illness may also experience greater hostility online compared to individuals without mental illness. This would be consistent with research showing greater risk of hostility, including increased violence and discrimination, directed towards individuals living with mental illness in in-person contexts, especially targeted at those with severe mental illnesses (Goodman et al. 1999 ).

A computational study of mental health awareness campaigns on Twitter reported that while stigmatizing content was rare, it was actually the most spread (re-tweeted) demonstrating that harmful content can travel quickly on social media (Saha et al. 2019 ). Another study was able to map the spread of social media posts about the Blue Whale Challenge, an alleged game promoting suicide, over Twitter, YouTube, Reddit, Tumblr, and other forums across 127 countries (Sumner et al. 2019 ). These findings show that it is critical to monitor the actual content of social media posts, such as determining whether content is hostile or promotes harm to self or others. This is pertinent because existing research looking at duration of exposure cannot account for the impact of specific types of content on mental health and is insufficient to fully understand the effects of using these platforms on mental health.

Consequences for Daily Life

The ways in which individuals use social media can also impact their offline relationships and everyday activities. To date, reports have described risks of social media use pertaining to privacy, confidentiality, and unintended consequences of disclosing personal health information online (Torous and Keshavan 2016 ). Additionally, concerns have been raised about poor quality or misleading health information shared on social media and that social media users may not be aware of misleading information or conflicts of interest especially when the platforms promote popular content regardless of whether it is from a trustworthy source (Moorhead et al. 2013 ; Ventola 2014 ). For persons living with mental illness, there may be additional risks from using social media. A recent study that specifically explored the perspectives of social media users with serious mental illnesses, including participants with schizophrenia spectrum disorders, bipolar disorder, or major depression, found that over one third of participants expressed concerns about privacy when using social media (Naslund and Aschbrenner 2019 ). The reported risks of social media use were directly related to many aspects of everyday life, including concerns about threats to employment, fear of stigma and being judged, impact on personal relationships, and facing hostility or being hurt (Naslund and Aschbrenner 2019 ). While few studies have specifically explored the dangers of social media use from the perspectives of individuals living with mental illness, it is important to recognize that use of these platforms may contribute to risks that extend beyond worsening symptoms and that can affect different aspects of daily life.

In this commentary, we considered ways in which social media may yield benefits for individuals living with mental illness, while contrasting these with the possible harms. Studies reporting on the threats of social media for individuals with mental illness are mostly cross-sectional, making it difficult to draw conclusions about direction of causation. However, the risks are potentially serious. These risks should be carefully considered in discussions pertaining to use of social media and the broader use of digital mental health technologies, as avenues for mental health promotion or for supporting access to evidence-based programs or mental health services. At this point, it would be premature to view the benefits of social media as outweighing the possible harms, when it is clear from the studies summarized here that social media use can have negative effects on mental health symptoms, can potentially expose individuals to hurtful content and hostile interactions, and can result in serious consequences for daily life, including threats to employment and personal relationships. Despite these risks, it is also necessary to recognize that individuals with mental illness will continue to use social media given the ease of accessing these platforms and the immense popularity of online social networking. With this in mind, it may be ideal to raise awareness about these possible risks so that individuals can implement necessary safeguards, while highlighting that there could also be benefits. Being aware of the risks is an essential first step, before then recognizing that use of these popular platforms could contribute to some benefits like finding meaningful interactions with others, engaging with peer support networks, and accessing information and services.

To capitalize on the widespread use of social media and to achieve the promise that these platforms may hold for supporting the delivery of targeted mental health interventions, there is need for continued research to better understand how individuals living with mental illness use social media. Such efforts could inform safety measures and also encourage use of social media in ways that maximize potential benefits while minimizing risk of harm. It will be important to recognize how gender and race contribute to differences in use of social media for seeking mental health information or accessing interventions, as well as differences in how social media might impact mental wellbeing. For example, a national survey of 14- to 22-year olds in the USA found that female respondents were more likely to search online for information about depression or anxiety and to try to connect with other people online who share similar mental health concerns when compared with male respondents (Rideout and Fox 2018 ). In the same survey, there did not appear to be any differences between racial or ethnic groups in social media use for seeking mental health information (Rideout and Fox 2018 ). Social media use also appears to have a differential impact on mental health and emotional wellbeing between females and males (Booker et al. 2018 ), highlighting the need to explore unique experiences between gender groups to inform tailored programs and services. Research shows that lesbian, gay, bisexual, or transgender individuals frequently use social media for searching for health information and may be more likely compared with heterosexual individuals to share their own personal health experiences with others online (Rideout and Fox 2018 ). Less is known about use of social media for seeking support for mental health concerns among gender minorities, though this is an important area for further investigation as these individuals are more likely to experience mental health problems and online victimization when compared with heterosexual individuals (Mereish et al. 2019 ).

Similarly, efforts are needed to explore the relationship between social media use and mental health among ethnic and racial minorities. A recent study found that exposure to traumatic online content on social media showing violence or hateful posts directed at racial minorities contributed to increases in psychological distress, PTSD symptoms, and depression among African American and Latinx adolescents in the USA (Tynes et al. 2019 ). These concerns are contrasted by growing interest in the potential for new technologies including social media to expand the reach of services to underrepresented minority groups (Schueller et al. 2019 ). Therefore, greater attention is needed to understanding the perspectives of ethnic and racial minorities to inform effective and safe use of social media for mental health promotion efforts.

Research has found that individuals living with mental illness have expressed interest in accessing mental health services through social media platforms. A survey of social media users with mental illness found that most respondents were interested in accessing programs for mental health on social media targeting symptom management, health promotion, and support for communicating with health care providers and interacting with the health system (Naslund et al. 2017 ). Importantly, individuals with serious mental illness have also emphasized that any mental health intervention on social media would need to be moderated by someone with adequate training and credentials, would need to have ground rules and ways to promote safety and minimize risks, and importantly, would need to be free and easy to access.

An important strength with this commentary is that it combines a range of studies broadly covering the topic of social media and mental health. We have provided a summary of recent evidence in a rapidly advancing field with the goal of presenting unique ways that social media could offer benefits for individuals with mental illness, while also acknowledging the potentially serious risks and the need for further investigation. There are also several limitations with this commentary that warrant consideration. Importantly, as we aimed to address this broad objective, we did not conduct a systematic review of the literature. Therefore, the studies reported here are not exhaustive, and there may be additional relevant studies that were not included. Additionally, we only summarized published studies, and as a result, any reports from the private sector or websites from different organizations using social media or other apps containing social media–like features would have been omitted. Although, it is difficult to rigorously summarize work from the private sector, sometimes referred to as “gray literature,” because many of these projects are unpublished and are likely selective in their reporting of findings given the target audience may be shareholders or consumers.

Another notable limitation is that we did not assess risk of bias in the studies summarized in this commentary. We found many studies that highlighted risks associated with social media use for individuals living with mental illness; however, few studies of programs or interventions reported negative findings, suggesting the possibility that negative findings may go unpublished. This concern highlights the need for a future more rigorous review of the literature with careful consideration of bias and an accompanying quality assessment. Most of the studies that we described were from the USA, as well as from other higher income settings such as Australia or the UK. Despite the global reach of social media platforms, there is a dearth of research on the impact of these platforms on the mental health of individuals in diverse settings, as well as the ways in which social media could support mental health services in lower income countries where there is virtually no access to mental health providers. Future research is necessary to explore the opportunities and risks for social media to support mental health promotion in low-income and middle-income countries, especially as these countries face a disproportionate share of the global burden of mental disorders, yet account for the majority of social media users worldwide (Naslund et al. 2019 ).

Future Directions for Social Media and Mental Health

As we consider future research directions, the near ubiquitous social media use also yields new opportunities to study the onset and manifestation of mental health symptoms and illness severity earlier than traditional clinical assessments. There is an emerging field of research referred to as “digital phenotyping” aimed at capturing how individuals interact with their digital devices, including social media platforms, in order to study patterns of illness and identify optimal time points for intervention (Jain et al. 2015 ; Onnela and Rauch 2016 ). Given that most people access social media via mobile devices, digital phenotyping and social media are closely related (Torous et al. 2019 ). To date, the emergence of machine learning, a powerful computational method involving statistical and mathematical algorithms (Shatte et al. 2019 ), has made it possible to study large quantities of data captured from popular social media platforms such as Twitter or Instagram to illuminate various features of mental health (Manikonda and De Choudhury 2017 ; Reece et al. 2017 ). Specifically, conversations on Twitter have been analyzed to characterize the onset of depression (De Choudhury et al. 2013 ) as well as detecting users’ mood and affective states (De Choudhury et al. 2012 ), while photos posted to Instagram can yield insights for predicting depression (Reece and Danforth 2017 ). The intersection of social media and digital phenotyping will likely add new levels of context to social media use in the near future.

Several studies have also demonstrated that when compared with a control group, Twitter users with a self-disclosed diagnosis of schizophrenia show unique online communication patterns (Birnbaum et al. 2017a ), including more frequent discussion of tobacco use (Hswen et al. 2017 ), symptoms of depression and anxiety (Hswen et al. 2018b ), and suicide (Hswen et al. 2018a ). Another study found that online disclosures about mental illness appeared beneficial as reflected by fewer posts about symptoms following self-disclosure (Ernala et al. 2017 ). Each of these examples offers early insights into the potential to leverage widely available online data for better understanding the onset and course of mental illness. It is possible that social media data could be used to supplement additional digital data, such as continuous monitoring using smartphone apps or smart watches, to generate a more comprehensive “digital phenotype” to predict relapse and identify high-risk health behaviors among individuals living with mental illness (Torous et al. 2019 ).

With research increasingly showing the valuable insights that social media data can yield about mental health states, greater attention to the ethical concerns with using individual data in this way is necessary (Chancellor et al. 2019 ). For instance, data is typically captured from social media platforms without the consent or awareness of users (Bidargaddi et al. 2017 ), which is especially crucial when the data relates to a socially stigmatizing health condition such as mental illness (Guntuku et al. 2017 ). Precautions are needed to ensure that data is not made identifiable in ways that were not originally intended by the user who posted the content as this could place an individual at risk of harm or divulge sensitive health information (Webb et al. 2017 ; Williams et al. 2017 ). Promising approaches for minimizing these risks include supporting the participation of individuals with expertise in privacy, clinicians, and the target individuals with mental illness throughout the collection of data, development of predictive algorithms, and interpretation of findings (Chancellor et al. 2019 ).

In recognizing that many individuals living with mental illness use social media to search for information about their mental health, it is possible that they may also want to ask their clinicians about what they find online to check if the information is reliable and trustworthy. Alternatively, many individuals may feel embarrassed or reluctant to talk to their clinicians about using social media to find mental health information out of concerns of being judged or dismissed. Therefore, mental health clinicians may be ideally positioned to talk with their patients about using social media and offer recommendations to promote safe use of these sites while also respecting their patients’ autonomy and personal motivations for using these popular platforms. Given the gap in clinical knowledge about the impact of social media on mental health, clinicians should be aware of the many potential risks so that they can inform their patients while remaining open to the possibility that their patients may also experience benefits through use of these platforms. As awareness of these risks grows, it may be possible that new protections will be put in place by industry or through new policies that will make the social media environment safer. It is hard to estimate a number needed to treat or harm today given the nascent state of research, which means the patient and clinician need to weigh the choice on a personal level. Thus, offering education and information is an important first step in that process. As patients increasingly show interest in accessing mental health information or services through social media, it will be necessary for health systems to recognize social media as a potential avenue for reaching or offering support to patients. This aligns with growing emphasis on the need for greater integration of digital psychiatry, including apps, smartphones, or wearable devices, into patient care and clinical services through institution-wide initiatives and training clinical providers (Hilty et al. 2019 ). Within a learning healthcare environment where research and care are tightly intertwined and feedback between both is rapid, the integration of digital technologies into services may create new opportunities for advancing use of social media for mental health.

As highlighted in this commentary, social media has become an important part of the lives of many individuals living with mental disorders. Many of these individuals use social media to share their lived experiences with mental illness, to seek support from others, and to search for information about treatment recommendations, accessing mental health services and coping with symptoms (Bucci et al. 2019 ; Highton-Williamson et al. 2015 ; Naslund et al. 2016b ). As the field of digital mental health advances, the wide reach, ease of access, and popularity of social media platforms could be used to allow individuals in need of mental health services or facing challenges of mental illness to access evidence-based treatment and support. To achieve this end and to explore whether social media platforms can advance efforts to close the gap in available mental health services in the USA and globally, it will be essential for researchers to work closely with clinicians and with those affected by mental illness to ensure that possible benefits of using social media are carefully weighed against anticipated risks.

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Dr. Naslund is supported by a grant from the National Institute of Mental Health (U19MH113211). Dr. Aschbrenner is supported by a grant from the National Institute of Mental Health (1R01MH110965-01).

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Naslund, J.A., Bondre, A., Torous, J. et al. Social Media and Mental Health: Benefits, Risks, and Opportunities for Research and Practice. J. technol. behav. sci. 5 , 245–257 (2020). https://doi.org/10.1007/s41347-020-00134-x

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Research Article

Social impact in social media: A new method to evaluate the social impact of research

Roles Investigation, Writing – original draft

* E-mail: [email protected]

Affiliation Department of Journalism and Communication Studies, Universitat Autonoma de Barcelona, Barcelona, Spain

Affiliation Department of Psychology and Sociology, Universidad de Zaragoza, Zaragoza, Spain

Roles Conceptualization, Investigation, Methodology, Supervision, Writing – review & editing

Affiliation Department of Sociology, Universitat Autonoma de Barcelona, Barcelona, Spain

Affiliation Department of Sociology, Universitat de Barcelona (UB), Barcelona, Spain

• Cristina M. Pulido, 
• Gisela Redondo-Sama, 
• Teresa Sordé-Martí, 
• Ramon Flecha

• Published: August 29, 2018
• https://doi.org/10.1371/journal.pone.0203117
• Reader Comments

The social impact of research has usually been analysed through the scientific outcomes produced under the auspices of the research. The growth of scholarly content in social media and the use of altmetrics by researchers to track their work facilitate the advancement in evaluating the impact of research. However, there is a gap in the identification of evidence of the social impact in terms of what citizens are sharing on their social media platforms. This article applies a social impact in social media methodology (SISM) to identify quantitative and qualitative evidence of the potential or real social impact of research shared on social media, specifically on Twitter and Facebook. We define the social impact coverage ratio (SICOR) to identify the percentage of tweets and Facebook posts providing information about potential or actual social impact in relation to the total amount of social media data found related to specific research projects. We selected 10 projects in different fields of knowledge to calculate the SICOR, and the results indicate that 0.43% of the tweets and Facebook posts collected provide linkages with information about social impact. However, our analysis indicates that some projects have a high percentage (4.98%) and others have no evidence of social impact shared in social media. Examples of quantitative and qualitative evidence of social impact are provided to illustrate these results. A general finding is that novel evidences of social impact of research can be found in social media, becoming relevant platforms for scientists to spread quantitative and qualitative evidence of social impact in social media to capture the interest of citizens. Thus, social media users are showed to be intermediaries making visible and assessing evidence of social impact.

Citation: Pulido CM, Redondo-Sama G, Sordé-Martí T, Flecha R (2018) Social impact in social media: A new method to evaluate the social impact of research. PLoS ONE 13(8): e0203117. https://doi.org/10.1371/journal.pone.0203117

Editor: Sergi Lozano, Institut Català de Paleoecologia Humana i Evolució Social (IPHES), SPAIN

Received: November 8, 2017; Accepted: August 15, 2018; Published: August 29, 2018

Copyright: © 2018 Pulido et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: The research leading to these results has received funding from the 7th Framework Programme of the European Commission under the Grant Agreement n° 613202 P.I. Ramon Flecha, https://ec.europa.eu/research/fp7/index_en.cfm . The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The social impact of research is at the core of some of the debates influencing how scientists develop their studies and how useful results for citizens and societies may be obtained. Concrete strategies to achieve social impact in particular research projects are related to a broader understanding of the role of science in contemporary society. There is a need to explore dialogues between science and society not only to communicate and disseminate science but also to achieve social improvements generated by science. Thus, the social impact of research emerges as an increasing concern within the scientific community [ 1 ]. As Bornmann [ 2 ] said, the assessment of this type of impact is badly needed and is more difficult than the measurement of scientific impact; for this reason, it is urgent to advance in the methodologies and approaches to measuring the social impact of research.

Several authors have approached the conceptualization of social impact, observing a lack of generally accepted conceptual and instrumental frameworks [ 3 ]. It is common to find a wide range of topics included in the contributions about social impact. In their analysis of the policies affecting land use, Hemling et al. [ 4 ] considered various domains in social impact, for instance, agricultural employment or health risk. Moving to the field of flora and fauna, Wilder and Walpole [ 5 ] studied the social impact of conservation projects, focusing on qualitative stories that provided information about changes in attitudes, behaviour, wellbeing and livelihoods. In an extensive study by Godin and Dore [ 6 ], the authors provided an overview and framework for the assessment of the contribution of science to society. They identified indicators of the impact of science, mentioning some of the most relevant weaknesses and developing a typology of impact that includes eleven dimensions, with one of them being the impact on society. The subdimensions of the impact of science on society focus on individuals (wellbeing and quality of life, social implication and practices) and organizations (speeches, interventions and actions). For the authors, social impact “refers to the impact knowledge has on welfare, and on the behaviours, practices and activities of people and groups” (p. 7).

In addition, the terms “social impact” and “societal impact” are sometimes used interchangeably. For instance, Bornmann [ 2 ] said that due to the difficulty of distinguishing social benefits from the superior term of societal benefits, “in much literature the term ‘social impact’ is used instead of ‘societal impact’”(p. 218). However, in other cases, the distinction is made [ 3 ], as in the present research. Similar to the definition used by the European Commission [ 7 ], social impact is used to refer to economic impact, societal impact, environmental impact and, additionally, human rights impact. Therefore, we use the term social impact as the broader concept that includes social improvements in all the above mentioned areas obtained from the transference of research results and representing positive steps towards the fulfilment of those officially defined social goals, including the UN Sustainable Development Goals, the EU 2020 Agenda, or similar official targets. For instance, the Europe 2020 strategy defines five priority targets with concrete indicators (employment, research and development, climate change and energy, education and poverty and social exclusion) [ 8 ], and we consider the targets addressed by objectives defined in the specific call that funds the research project.

This understanding of the social impact of research is connected to the creation of the Social Impact Open Repository (SIOR), which constitutes the first open repository worldwide that displays, cites and stores the social impact of research results [ 9 ]. The SIOR has linked to ORCID and Wikipedia to allow the synergies of spreading information about the social impact of research through diverse channels and audiences. It is relevant to mention that currently, SIOR includes evidence of real social impact, which implies that the research results have led to actual improvements in society. However, it is common to find evidence of potential social impact in research projects. The potential social impact implies that in the development of the research, there has been some evidence of the effectiveness of the research results in terms of social impact, but the results have not yet been transferred.

Additionally, a common confusion is found among the uses of dissemination, transference (policy impact) and social impact. While dissemination means to disseminate the knowledge created by research to citizens, companies and institutions, transference refers to the use of this knowledge by these different actors (or others), and finally, as already mentioned, social impact refers to the actual improvements resulting from the use of this knowledge in relation to the goals motivating the research project (such as the United Nations Sustainable Development Goals). In the present research [ 3 ], it is argued that “social impact can be understood as the culmination of the prior three stages of the research” (p.3). Therefore, this study builds on previous contributions measuring the dissemination and transference of research and goes beyond to propose a novel methodological approach to track social impact evidences.

In fact, the contribution that we develop in this article is based on the creation of a new method to evaluate the evidence of social impact shared in social media. The evaluation proposed is to measure the social impact coverage ratio (SICOR), focusing on the presence of evidence of social impact shared in social media. Then, the article first presents some of the contributions from the literature review focused on the research on social media as a source for obtaining key data for monitoring or evaluating different research purposes. Second, the SISM (social impact through social media) methodology[ 10 ] developed is introduced in detail. This methodology identifies quantitative and qualitative evidence of the social impact of the research shared on social media, specifically on Twitter and Facebook, and defines the SICOR, the social impact coverage ratio. Next, the results are discussed, and lastly, the main conclusions and further steps are presented.

Literature review

Social media research includes the analysis of citizens’ voices on a wide range of topics [ 11 ]. According to quantitative data from April 2017 published by Statista [ 12 ], Twitter and Facebook are included in the top ten leading social networks worldwide, as ranked by the number of active users. Facebook is at the top of the list, with 1,968 million active users, and Twitter ranks 10 th , with 319 million active users. Between them are the following social networks: WhatsApp, YouTube, Facebook Messenger, WeChat, QQ, Instagram,Qzone and Tumblr. If we look at altmetrics, the tracking of social networks for mentions of research outputs includes Facebook, Twitter, Google+,LinkedIn, Sina Weibo and Pinterest. The social networks common to both sources are Facebook and Twitter. These are also popular platforms that have a relevant coverage of scientific content and easy access to data, and therefore, the research projects selected here for application of the SISM methodology were chosen on these platforms.

Chew and Eysenbach [ 13 ] studied the presence of selected keywords in Twitter related to public health issues, particularly during the 2009 H1N1 pandemic, identifying the potential for health authorities to use social media to respond to the concerns and needs of society. Crooks et al.[ 14 ] investigated Twitter activity in the context of a 5.8 magnitude earthquake in 2011 on the East Coast of the United States, concluding that social media content can be useful for event monitoring and can complement other sources of data to improve the understanding of people’s responses to such events. Conversations among young Canadians posted on Facebook and analysed by Martinello and Donelle [ 15 ] revealed housing and transportation as main environmental concerns, and the project FoodRisc examined the role of social media to illustrate consumers’ quick responses during food crisis situations [ 16 ]. These types of contributions illustrate that social media research implies the understanding of citizens’ concerns in different fields, including in relation to science.

Research on the synergies between science and citizens has increased over the years, according to Fresco [ 17 ], and there is a growing interest among researchers and funding agencies in how to facilitate communication channels to spread scientific results. For instance, in 1998, Lubchenco [ 18 ] advocated for a social contract that “represents a commitment on the part of all scientists to devote their energies and talents to the most pressing problems of the day, in proportion to their importance, in exchange for public funding”(p.491).

In this framework, the recent debates on how to increase the impact of research have acquired relevance in all fields of knowledge, and major developments address the methods for measuring it. As highlighted by Feng Xia et al. [ 19 ], social media constitute an emerging approach to evaluating the impact of scholarly publications, and it is relevant to consider the influence of the journal, discipline, publication year and user type. The authors revealed that people’s concerns differ by discipline and observed more interest in papers related to everyday life, biology, and earth and environmental sciences. In the field of biomedical sciences, Haustein et al. [ 20 ] analysed the dissemination of journal articles on Twitter to explore the correlations between tweets and citations and proposed a framework to evaluate social media-based metrics. In fact, different studies address the relationship between the presence of articles on social networks and citations [ 21 ]. Bornmann [ 22 ] conducted a case study using a sample of 1,082 PLOS journal articles recommended in F1000 to explore the usefulness of altmetrics for measuring the broader impact of research. The author presents evidence about Facebook and Twitter as social networks that may indicate which papers in the biomedical sciences can be of interest to broader audiences, not just to specialists in the area. One aspect of particular interest resulting from this contribution is the potential to use altmetrics to measure the broader impacts of research, including the societal impact. However, most of the studies investigating social or societal impact lack a conceptualization underlying its measurement.

To the best of our knowledge, the assessment of social impact in social media (SISM) has developed according to this gap. At the core of this study, we present and discuss the results obtained through the application of the SICOR (social impact coverage ratio) with examples of evidence of social impact shared in social media, particularly on Twitter and Facebook, and the implications for further research.

Following these previous contributions, our research questions were as follows: Is there evidence of social impact of research shared by citizens in social media? If so, is there quantitative or qualitative evidence? How can social media contribute to identifying the social impact of research?

Methods and data presentation

A group of new methodologies related to the analysis of online data has recently emerged. One of these emerging methodologies is social media analytics [ 23 ], which was initially used most in the marketing research field but also came to be used in other domains due to the multiple possibilities opened up by the availability and richness of the data for different research purposes. Likewise, the concern of how to evaluate the social impact of research as well as the development of methodologies for addressing this concern has occupied central attention. The development of SISM (Social Impact in Social Media) and the application of the SICOR (Social Impact Coverage Ratio) is a contribution to advancement in the evaluation of the social impact of research through the analysis of the social media selected (in this case, Twitter and Facebook). Thus, SISM is novel in both social media analytics and among the methodologies used to evaluate the social impact of research. This development has been made under IMPACT-EV, a research project funded under the Framework Program FP7 of the Directorate-General for Research and Innovation of the European Commission. The main difference from other methodologies for measuring the social impact of research is the disentanglement between dissemination and social impact. While altmetrics is aimed at measuring research results disseminated beyond academic and specialized spheres, SISM contribute to advancing this measurement by shedding light on to what extent evidence of the social impact of research is found in social media data. This involves the need to differentiate between tweets or Facebook posts (Fb/posts) used to disseminate research findings from those used to share the social impact of research. We focus on the latter, investigating whether there is evidence of social impact, including both potential and real social impact. In fact, the question is whether research contributes and/or has the potential to contribute to improve the society or living conditions considering one of these goals defined. What is the evidence? Next, we detail the application of the methodology.

Data collection

To develop this study, the first step was to select research projects with social media data to be analysed. The selection of research projects for application of the SISM methodology was performed according to three criteria.

Criteria 1. Selection of success projects in FP7. The projects were success stories of the 7 th Framework Programme (FP7) highlighted by the European Commission [ 24 ] in the fields of knowledge of medicine, public health, biology and genomics. The FP7 published calls for project proposals from 2007 to 2013. This implies that most of the projects funded in the last period of the FP7 (2012 and 2013) are finalized or in the last phase of implementation.

Criteria 2. Period of implementation. We selected projects in the 2012–2013 period because they combine recent research results with higher possibilities of having Twitter and Facebook accounts compared with projects of previous years, as the presence of social accounts in research increased over this period.

Criteria 3. Twitter and Facebook accounts. It was crucial that the selected projects had active Twitter and Facebook accounts.

Table 1 summarizes the criteria and the final number of projects identified. As shown, 10 projects met the defined criteria. Projects in medical research and public health had higher presence.

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https://doi.org/10.1371/journal.pone.0203117.t001

After the selection of projects, we defined the timeframe of social media data extraction on Twitter and Facebook from the starting date of the project until the day of the search, as presented in Table 2 .

https://doi.org/10.1371/journal.pone.0203117.t002

The second step was to define the search strategies for extracting social media data related to the research projects selected. In this line, we defined three search strategies.

Strategy 1. To extract messages published on the Twitter account and the Facebook page of the selected projects. We listed the Twitter accounts and Facebook pages related to each project in order to look at the available information. In this case, it is important to clarify that the tweets published under the corresponding Twitter project account are original tweets or retweets made from this account. It is relevant to mention that in one case, the Twitter account and Facebook page were linked to the website of the research group leading the project. In this case, we selected tweets and Facebook posts related to the project. For instance, in the case of the Twitter account, the research group created a specific hashtag to publish messages related to the project; therefore, we selected only the tweets published under this hashtag. In the analysis, we prioritized the analysis of the tweets and Facebook posts that received some type of interaction (likes, retweets or shares) because such interaction is a proxy for citizens’ interest. In doing so, we used the R program and NVivoto extract the data and proceed with the analysis. Once we obtained the data from Twitter and Facebook, we were able to have an overview of the information to be further analysed, as shown in Table 3 .

https://doi.org/10.1371/journal.pone.0203117.t003

We focused the second and third strategies on Twitter data. In both strategies, we extracted Twitter data directly from the Twitter Advanced Search tool, as the API connected to NVivo and the R program covers only a specific period of time limited to 7/9 days. Therefore, the use of the Twitter Advanced Search tool made it possible to obtain historic data without a period limitation. We downloaded the results in PDF and then uploaded them to NVivo.

Strategy 2. To use the project acronym combined with other keywords, such as FP7 or EU. This strategy made it possible to obtain tweets mentioning the project. Table 4 presents the number of tweets obtained with this strategy.

https://doi.org/10.1371/journal.pone.0203117.t004

Strategy 3. To use searchable research results of projects to obtain Twitter data. We defined a list of research results, one for each project, and converted them into keywords. We selected one searchable keyword for each project from its website or other relevant sources, for instance, the brief presentations prepared by the European Commission and published in CORDIS. Once we had the searchable research results, we used the Twitter Advanced Search tool to obtain tweets, as presented in Table 5 .

https://doi.org/10.1371/journal.pone.0203117.t005

The sum of the data obtained from these three strategies allowed us to obtain a total of 3,425 tweets and 1,925 posts on public Facebook pages. Table 6 presents a summary of the results.

https://doi.org/10.1371/journal.pone.0203117.t006

We imported the data obtained from the three search strategies into NVivo to analyse. Next, we select tweets and Facebook posts providing linkages with quantitative or qualitative evidence of social impact, and we complied with the terms of service for the social media from which the data were collected. By quantitative and qualitative evidence, we mean data or information that shows how the implementation of research results has led to improvements towards the fulfilment of the objectives defined in the EU2020 strategy of the European Commission or other official targets. For instance, in the case of quantitative evidence, we searched tweets and Facebook posts providing linkages with quantitative information about improvements obtained through the implementation of the research results of the project. In relation to qualitative evidence, for example, we searched for testimonies that show a positive evaluation of the improvement due to the implementation of research results. In relation to this step, it is important to highlight that social media users are intermediaries making visible evidence of social impact. Users often share evidence, sometimes sharing a link to an external resource (e.g., a video, an official report, a scientific article, news published on media). We identified evidence of social impact in these sources.

Data analysis

γ i is the total number of messages obtained about project i with evidence of social impact on social media platforms (Twitter, Facebook, Instagram, etc.);

T i is the total number of messages from project i on social media platforms (Twitter, Facebook, Instagram, etc.); and

n is the number of projects selected.

Analytical categories and codebook

The researchers who carried out the analysis of the social media data collected are specialists in the social impact of research and research on social media. Before conducting the full analysis, two aspects were guaranteed. First, how to identify evidence of social impact relating to the targets defined by the EU2020 strategy or to specific goals defined by the call addressed was clarified. Second, we held a pilot to test the methodology with one research project that we know has led to considerable social impact, which allowed us to clarify whether or not it was possible to detect evidence of social impact shared in social media. Once the pilot showed positive results, the next step was to extend the analysis to another set of projects and finally to the whole sample. The construction of the analytical categories was defined a priori, revised accordingly and lastly applied to the full sample.

Different observations should be made. First, in this previous analysis, we found that the tweets and Facebook users play a key role as “intermediaries,” serving as bridges between the larger public and the evidence of social impact. Social media users usually share a quote or paragraph introducing evidence of social impact and/or link to an external resource, for instance, a video, official report, scientific article, news story published on media, etc., where evidence of the social impact is available. This fact has implications for our study, as our unit of analysis is all the information included in the tweets or Facebook posts. This means that our analysis reaches the external resources linked to find evidence of social impact, and for this reason, we defined tweets or Facebook posts providing linkages with information about social impact.

Second, the other important aspect is the analysis of the users’ profile descriptions, which requires much more development in future research given the existing limitations. For instance, some profiles are users’ restricted due to privacy reasons, so the information is not available; other accounts have only the name of the user with no description of their profile available. Therefore, we gave priority to the identification of evidence of social impact including whether a post obtained interaction (retweets, likes or shares) or was published on accounts other than that of the research project itself. In the case of the profile analysis, we added only an exploratory preliminary result because this requires further development. Considering all these previous details, the codebook (see Table 7 ) that we present as follows is a result of this previous research.

https://doi.org/10.1371/journal.pone.0203117.t007

How to analyse Twitter and Facebook data

To illustrate how we analysed data from Twitter and Facebook, we provide one example of each type of evidence of social impact defined, considering both real and potential social impact, with the type of interaction obtained and the profiles of those who have interacted.

QUANESISM. Tweet by ZeroHunger Challenge @ZeroHunger published on 3 May 2016. Text: How re-using food waste for animal feed cuts carbon emissions.-NOSHAN project hubs.ly/H02SmrP0. 7 retweets and 5 likes.

The unit of analysis is all the content of the tweet, including the external link. If we limited our analysis to the tweet itself, it would not be evidence. Examining the external link is necessary to find whether there is evidence of social impact. The aim of this project was to investigate the process and technologies needed to use food waste for feed production at low cost, with low energy consumption and with a maximal evaluation of the starting wastes. This tweet provides a link to news published in the PHYS.org portal [ 25 ], which specializes in science news. The news story includes an interview with the main researcher that provides the following quotation with quantitative evidence:

'Our results demonstrated that with a NOSHAN 10 percent mix diet, for every kilogram of broiler chicken feed, carbon dioxide emissions were reduced by 0.3 kg compared to a non-food waste diet,' explains Montse Jorba, NOSHAN project coordinator. 'If 1 percent of total chicken broiler feed in Europe was switched to the 10 percent NOSHAN mix diet, the total amount of CO2 emissions avoided would be 0.62 million tons each year.'[ 25 ]

This quantitative evidence “a NOSHAN 10 percent mix diet, for every kilogram of broiler chicken feed, carbon dioxide emissions carbon dioxide emissions were reduced by 0.3 kg to a non-food waste diet” is linked directly with the Europe 2020 target of Climate Change & Energy, specifically with the target of reducing greenhouse gas emissions by 20% compared to the levels in 1990 [ 8 ]. The illustrative extrapolation the coordinator mentioned in the news is also an example of quantitative evidence, although is an extrapolation based on the specific research result.

This tweet was captured by the Acronym search strategy. It is a message tweeted by an account that is not related to the research project. The twitter account is that of the Zero Hunger Challenge movement, which supports the goals of the UN. The interaction obtained is 7 retweets and 5 likes. Regarding the profiles of those who retweeted and clicked “like”, there were activists, a journalist, an eco-friendly citizen, a global news service, restricted profiles (no information is available on those who have retweeted) and one account with no information in its profile.

The following example illustrates the analysis of QUALESISM: Tweet by @eurofitFP7 published on4 October 2016. Text: See our great new EuroFIT video on youtube! https://t.co/TocQwMiW3c 9 retweets and 5 likes.

The aim of this project is to improve health through the implementation of two novel technologies to achieve a healthier lifestyle. The tweet provides a link to a video on YouTube on the project’s results. In this video, we found qualitative evidence from people who tested the EuroFit programme; there are quotes from men who said that they have experienced improved health results using this method and that they are more aware of how to manage their health:

One end-user said: I have really amazing results from the start, because I managed to change a lot of things in my life. And other one: I was more conscious of what I ate, I was more conscious of taking more steps throughout the day and also standing up a little more. [ 26 ]

The research applies the well researched scientific evidence to the management of health issues in daily life. The video presents the research but also includes a section where end-users talk about the health improvements they experienced. The quotes extracted are some examples of the testimonies collected. All agree that they have improved their health and learned healthy habits for their daily lives. These are examples of qualitative evidence linked with the target of the call HEALTH.2013.3.3–1—Social innovation for health promotion [ 27 ] that has the objectives of reducing sedentary habits in the population and promoting healthy habits. This research contributes to this target, as we see in the video testimonies. Regarding the interaction obtained, this tweet achieved 9 retweets and 5 likes. In this case, the profiles of the interacting citizens show involvement in sport issues, including sport trainers, sport enthusiasts and some researchers.

To summarize the analysis, in Table 8 below, we provide a summary with examples illustrating the evidence found.

https://doi.org/10.1371/journal.pone.0203117.t008

Quantitative evidence of social impact in social media

There is a greater presence of tweets/Fb posts with quantitative evidence (14) than with qualitative evidence (9) in the total number of tweets/Fb posts identified with evidence of social impact. Most of the tweets/Fb posts with quantitative evidence of social impact are from scientific articles published in peer-reviewed international journals and show potential social impact. In Table 8 , we introduce 3 examples of this type of tweets/Fb posts with quantitative evidence:

The first tweet with quantitative social impact selected is from project 7. The aim of this project was to provide high-quality scientific evidence for preventing vitamin D deficiency in European citizens. The tweet highlighted the main contribution of the published study, that is, “Weekly consumption of 7 vitamin D-enhanced eggs has an important impact on winter vitamin D status in adults” [ 28 ]. The quantitative evidence shared in social media was extracted from a news publication in a blog on health news. This blog collects scientific articles of research results. In this case, the blog disseminated the research result focused on how vitamin D-enhanced eggs improve vitamin D deficiency in wintertime, with the published results obtained by the research team of the project selected. The quantitative evidence illustrates that the group of adults who consumed vitamin D-enhanced eggs did not suffer from vitamin D deficiency, as opposed to the control group, which showed a significant decrease in vitamin D over the winter. The specific evidence is the following extracted from the article [ 28 ]:

With the use of a within-group analysis, it was shown that, although serum 25(OH) D in the control group significantly decreased over winter (mean ± SD: -6.4 ± 6.7 nmol/L; P = 0.001), there was no change in the 2 groups who consumed vitamin D-enhanced eggs (P>0.1 for both. (p. 629)

This evidence contributes to achievement of the target defined in the call addressed that is KBBE.2013.2.2–03—Food-based solutions for the eradication of vitamin D deficiency and health promotion throughout the life cycle [ 29 ]. The quantitative evidence shows how the consumption of vitamin D-enhanced eggs reduces vitamin D deficiency.

The second example of this table corresponds to the example of quantitative evidence of social impact provided in the previous section.

The third example is a Facebook post from project 3 that is also tweeted. Therefore, this evidence was published in both social media sources analysed. The aim of this project was to measure a range of chemical and physical environmental hazards in food, consumer products, water, air, noise, and the built environment in the pre- and postnatal early-life periods. This Facebook post and tweet links directly to a scientific article [ 30 ] that shows the precision of the spectroscopic platform:

Using 1H NMR spectroscopy we characterized short-term variability in urinary metabolites measured from 20 children aged 8–9 years old. Daily spot morning, night-time and pooled (50:50 morning and night-time) urine samples across six days (18 samples per child) were analysed, and 44 metabolites quantified. Intraclass correlation coefficients (ICC) and mixed effect models were applied to assess the reproducibility and biological variance of metabolic phenotypes. Excellent analytical reproducibility and precision was demonstrated for the 1H NMR spectroscopic platform (median CV 7.2%) . (p.1)

This evidence is linked to the target defined in the call “ENV.2012.6.4–3—Integrating environmental and health data to advance knowledge of the role of environment in human health and well-being in support of a European exposome initiative” [ 31 ]. The evidence provided shows how the project’s results have contributed to building technology for improving the data collection to advance in the knowledge of the role of the environment in human health, especially in early life. The interaction obtained is one retweet from a citizen from Nigeria interested in health issues, according to the information available in his profile.

Qualitative evidence of social impact in social media

We found qualitative evidence of the social impact of different projects, as shown in Table 9 . Similarly to the quantitative evidence, the qualitative cases also demonstrate potential social impact. The three examples provided have in common that they are tweets or Facebook posts that link to videos where the end users of the research project explain their improvements once they have implemented the research results.

https://doi.org/10.1371/journal.pone.0203117.t009

The first tweet with qualitative evidence selected is from project 4. The aim of this project is to produce a system that helps in the prevention of obesity and eating disorders, targeting young people and adults [ 32 ]. The twitter account that published this tweet is that of the Future and Emerging Technologies Programme of the European Commission, and a link to a Euronews video is provided. This video shows how the patients using the technology developed in the research achieved control of their eating disorders, through the testimonies of patients commenting on the positive results they have obtained. These testimonies are included in the news article that complements the video. An example of these testimonies is as follows:

Pierre Vial has lost 43 kilos over the past nine and a half months. He and other patients at the eating disorder clinic explain the effects obesity and anorexia have had on their lives. Another patient, Karin Borell, still has some months to go at the clinic but, after decades of battling anorexia, is beginning to be able to visualise life without the illness: “On a good day I see myself living a normal life without an eating disorder, without problems with food. That’s really all I wish right now”.[ 32 ]

This qualitative evidence shows how the research results contribute to the achievement of the target goals of the call addressed:“ICT-2013.5.1—Personalised health, active ageing, and independent living”. [ 33 ] In this case, the results are robust, particularly for people suffering chronic diseases and desiring to improve their health; people who have applied the research findings are improving their eating disorders and better managing their health. The value of this evidence is the inclusion of the patients’ voices stating the impact of the research results on their health.

The second example is a Facebook post from project 9, which provides a link to a Euronews video. The aim of this project is to bring some tools from the lab to the farm in order to guarantee a better management of the farm and animal welfare. In this video [ 34 ], there are quotes from farmers using the new system developed through the research results of the project. These quotes show how use of the new system is improving the management of the farm and the health of the animals; some examples are provided:

Cameras and microphones help me detect in real time when the animals are stressed for whatever reason,” explained farmer Twan Colberts. “So I can find solutions faster and in more efficient ways, without me being constantly here, checking each animal.”

This evidence shows how the research results contribute to addressing the objectives specified in the call “KBBE.2012.1.1–02—Animal and farm-centric approach to precision livestock farming in Europe” [ 29 ], particularly, to improve the precision of livestock farming in Europe. The interaction obtained is composed of6 likes and 1 share. The profiles are diverse, but some of them do not disclose personal information; others have not added a profile description, and only their name and photo are available.

Interrater reliability (kappa)

The analysis of tweets and Facebook posts providing linkages with information about social impact was conducted following a content analysis method in which reliability was based on a peer review process. This sample is composed of 3,425 tweets and 1,925 Fb/posts. Each tweet and Facebook post was analysed to identify whether or not it contains evidence of social impact. Each researcher has the codebook a priori. We used interrater reliability in examining the agreement between the two raters on the assignment of the categories defined through Cohen’s kappa. We used SPSS to calculate this coefficient. We exported an excel sheet with the sample coded by the two researchers being 1 (is evidence of social impact, either potential or real) and 0 (is not evidence of social impact) to SPSS. The cases where agreement was not achieved were not considered as containing evidence of social impact. The result obtained is 0.979; considering the interpretation of this number according to Landis & Koch [ 35 ], our level of agreement is almost perfect, and thus, our analysis is reliable. To sum up the data analysis, the description of the steps followed is explained:

Step 1. Data analysis I. We included all data collected in an excel sheet to proceed with the analysis. Prior to the analysis, researchers read the codebook to keep in mind the information that should be identified.

Step 2. Each researcher involved reviewed case by case the tweets and Facebook posts to identify whether they provide links with evidence of social impact or not. If the researcher considers there to be evidence of social impact, he or she introduces the value of 1into the column, and if not, the value of 0.

Step 3. Once all the researchers have finished this step, the next step is to export the excel sheet to SPSS to extract the kappa coefficient.

Step 4. Data Analysis II. The following step was to analyse case by case the tweets and Facebook posts identified as providing linkages with information of social impact and classify them as quantitative or qualitative evidence of social impact.

Step 5. The interaction received was analysed because this determines to which extent this evidence of social impact has captured the attention of citizens (in the form of how many likes, shares, or retweets the post has).

Step 6. Finally, if available, the profile descriptions of the citizens interacting through retweeting or sharing the Facebook post were considered.

Step 7. SICOR was calculated. It could be applied to the complete sample (all data projects) or to each project, as we will see in the next section.

The total number of tweets and Fb/posts collected from the 10 projects is 5,350. After the content analysis, we identified 23 tweets and Facebook posts providing linkages to information about social impact. To respond to the research question, which considered whether there is evidence of social impact shared by citizens in social media, the answer was affirmative, although the coverage ratio is low. Both Twitter and Facebook users retweeted or shared evidence of social impact, and therefore, these two social media networks are valid sources for expanding knowledge on the assessment of social impact. Table 10 shows the social impact coverage ratio in relation to the total number of messages analysed.

https://doi.org/10.1371/journal.pone.0203117.t010

The analysis of each of the projects selected revealed some results to consider. Of the 10 projects, 7 had evidence, but those projects did not necessarily have more Tweets and Facebook posts. In fact, some projects with fewer than 70 tweets and 50 Facebook posts have more evidence of social impact than other projects with more than 400 tweets and 400 Facebook posts. This result indicates that the number of tweets and Facebook posts does not determine the existence of evidence of social impact in social media. For example, project 2 has 403 tweets and 423 Facebooks posts, but it has no evidence of social impact on social media. In contrast, project 9 has 62 tweets, 43 Facebook posts, and 2 pieces of evidence of social impact in social media, as shown in Table 11 .

https://doi.org/10.1371/journal.pone.0203117.t011

The ratio of tweets/Fb posts to evidence is 0.43%, and it differs depending on the project, as shown below in Table 12 . There is one project (P7) with a ratio of 4.98%, which is a social impact coverage ratio higher than that of the other projects. Next, a group of projects (P3, P9, P10) has a social impact coverage ratio between 1.41% and 2,99%.The next slot has three projects (P1, P4, P5), with a ratio between 0.13% and 0.46%. Finally, there are three projects (P2, P6, P8) without any tweets/Fb posts evidence of social impact.

https://doi.org/10.1371/journal.pone.0203117.t012

Considering the three strategies for obtaining data, each is related differently to the evidence of social impact. In terms of the social impact coverage ratio, as shown in Table 13 , the most successful strategy is number 3 (searchable research results), as it has a relation of 17.86%, which is much higher than the ratios for the other 2 strategies. The second strategy (acronym search) is more effective than the first (profile accounts),with 1.77% for the former as opposed to 0.27% for the latter.

https://doi.org/10.1371/journal.pone.0203117.t013

Once tweets and Facebook posts providing linkages with information about social impact(ESISM)were identified, we classified them in terms of quantitative (QUANESISM) or qualitative evidence (QUALESISM)to determine which type of evidence was shared in social media. Table 14 indicates the amount of quantitative and qualitative evidence identified for each search strategy.

https://doi.org/10.1371/journal.pone.0203117.t014

First, the results obtained indicated that the SISM methodology aids in calculating the social impact coverage ratio of the research projects selected and evaluating whether the social impact of the corresponding research is shared by citizens in social media. The social impact coverage ratio applied to the sample selected is low, but when we analyse the SICOR of each project separately, we can observe that some projects have a higher social impact coverage ratio than others. Complementary to altmetrics measuring the extent to which research results reach out society, the SICOR considers the question whether this process includes evidence of potential or real social impact. In this sense, the overall methodology of SISM contributes to advancement in the evaluation of the social impact of research by providing a more precise approach to what we are evaluating.

This contribution complements current evaluation methodologies of social impact that consider which improvements are shared by citizens in social media. Exploring the results in more depth, it is relevant to highlight that of the ten projects selected, there is one research project with a social impact coverage ratio higher than those of the others, which include projects without any tweets or Facebook posts with evidence of social impact. This project has a higher ratio of evidence than the others because evidence of its social impact is shared more than is that of other projects. This also means that the researchers produced evidence of social impact and shared it during the project. Another relevant result is that the quantity of tweets and Fb/posts collected did not determine the number of tweets and Fb/posts found with evidence of social impact. Moreover, the analysis of the research projects selected showed that there are projects with less social media interaction but with more tweets and Fb/posts containing evidence of social media impact. Thus, the number of tweets and Fb/posts with evidence of social impact is not determined by the number of publication messages collected; it is determined by the type of messages published and shared, that is, whether they contain evidence of social impact or not.

The second main finding is related to the effectiveness of the search strategies defined. Related to the strategies carried out under this methodology, one of the results found is that the most effective search strategy is the searchable research results, which reveals a higher percentage of evidence of social impact than the own account and acronym search strategies. However, the use of these three search strategies is highly recommended because the combination of all of them makes it possible to identify more tweets and Facebook posts with evidence of social impact.

Another result is related to the type of evidence of social impact found. There is both quantitative and qualitative evidence. Both types are useful for understanding the type of social impact achieved by the corresponding research project. In this sense, quantitative evidence allows us to understand the improvements obtained by the implementation of the research results and capture their impact. In contrast, qualitative evidence allows us to deeply understand how the resultant improvements obtained from the implementation of the research results are evaluated by the end users by capturing their corresponding direct quotes. The social impact includes the identification of both real and potential social impact.

Conclusions

After discussing the main results obtained, we conclude with the following points. Our study indicates that there is incipient evidence of social impact, both potential and real, in social media. This demonstrates that researchers from different fields, in the present case involved in medical research, public health, animal welfare and genomics, are sharing the improvements generated by their research and opening up new venues for citizens to interact with their work. This would imply that scientists are promoting not only the dissemination of their research results but also the evidence on how their results may lead to the improvement of societies. Considering the increasing relevance and presence of the dissemination of research, the results indicate that scientists still need to include in their dissemination and communication strategies the aim of sharing the social impact of their results. This implies the publication of concrete qualitative or quantitative evidence of the social impact obtained. Because of the inclusion of this strategy, citizens will pay more attention to the content published in social media because they are interested in knowing how science can contribute to improving their living conditions and in accessing crucial information. Sharing social impact in social media facilitates access to citizens of different ages, genders, cultural backgrounds and education levels. However, what is most relevant for our argument here is how citizens should also be able to participate in the evaluation of the social impact of research, with social media a great source to reinforce this democratization process. This contributes not only to greatly improving the social impact assessment, as in addition to experts, policy makers and scientific publications, citizens through social media contribute to making this assessment much more accurate. Thus, citizens’ contribution to the dissemination of evidence of the social impact of research yields access to more diverse sectors of society and information that might be unknown by the research or political community. Two future steps are opened here. On the one hand, it is necessary to further examine the profiles of users who interact with this evidence of social impact considering the limitations of the privacy and availability of profile information. A second future task is to advance in the articulation of the role played by citizens’ participation in social impact assessment, as citizens can contribute to current worldwide efforts by shedding new light on this process of social impact assessment and contributing to making science more relevant and useful for the most urgent and poignant social needs.

Supporting information

S1 file. interrater reliability (kappa) result..

This file contains the SPSS file with the result of the calculation of Cohen’s Kappa regards the interrater reliability. The word document exported with the obtained result is also included.

https://doi.org/10.1371/journal.pone.0203117.s001

S2 File. Data collected and SICOR calculation.

This excel contains four sheets, the first one titled “data collected” contains the number of tweets and Facebook posts collected through the three defined search strategies; the second sheet titled “sample” contains the sample classified by project indicating the ID of the message or code assigned, the type of message (tweet or Facebook post) and the codification done by researchers being 1 (is evidence of social impact, either potential or real) and 0 (is not evidence of social impact); the third sheet titled “evidence found” contains the number of type of evidences of social impact founded by project (ESISM-QUANESIM or ESISM-QUALESIM), search strategy and type of message (tweet or Facebook posts); and the last sheet titled “SICOR” contains the Social Impact Coverage Ratio calculation by projects in one table and type of search strategy done in another one.

https://doi.org/10.1371/journal.pone.0203117.s002

Acknowledgments

The research leading to these results received funding from the 7 th Framework Programme of the European Commission under Grant Agreement n° 613202. The extraction of available data using the list of searchable keywords on Twitter and Facebook followed the ethical guidelines for social media research supported by the Economic and Social Research Council (UK) [ 36 ] and the University of Aberdeen [ 37 ]. Furthermore, the research results have already been published and made public, and hence, there are no ethical issues.

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Asian Journal of Education and Social Studies

Published: 2024-03-16

DOI: 10.9734/ajess/2024/v50i41334

Page: 320-328

Issue: 2024 - Volume 50 [Issue 4]

Review Article

Article Metrics

Impact of social media on society: a literature review.

Zahid Ahmad Wani

Shantha College of Allied Health Science, Affiliated to RGUHS, Bangalore, India.

Ayesha Bhat

Shantha College of Physiotherapy, Affiliated to RGUHS, Bangalore, India.

Vartika vishnoi

Vivek University Bijnor, UP, India.

Hina Praveen

Gautham College, Affiliated to RGUHS, Bangalore, India.

Naveen H Simon *

Shantha College of Nursing, Affiliated to RGUHS, Bangalore, India.

Diana Hephzibah

*Author to whom correspondence should be addressed.

This manuscript explores the profound impact of social media on society, with a focus on social behavior, politics, and cultural norms. Employing a systematic literature review, including diverse sources, case studies, and real-world examples, the study provides a nuanced and comprehensive analysis. The brevity enforced by character limits on platforms like Twitter has shaped concise and direct communication, with emojis, memes, and hashtags becoming integral to online discourse. The rapid spread of information on social media has, however, led to the dissemination of misinformation, influencing public opinion and potentially affecting political outcomes. Collaboration among individuals, policymakers, and technology developers is crucial to shape the future of social media responsibly. This necessitates cultivating a culture of responsible use, promoting digital literacy, and advocating for policies that safeguard user rights and well-being. Striking a balance between innovation and ethical considerations is paramount to ensure the continued positive contribution of social media to society.

Keywords: Cultural landscapes, interpersonal relationships, paradigm shift, social media

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The Impact of Digital Marketing on Local Businesses

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Influence of green practices on user loyalty in sport clubs

• Guillermo Morán-Gámez 1 ,
• Antonio Fernández-Martínez   ORCID: orcid.org/0000-0001-6750-8640 2 ,
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Humanities and Social Sciences Communications volume  11 , Article number:  1246 ( 2024 ) Cite this article

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The sports industry contributes significantly to environmental degradation through its economic practices. As a result, there has been interest in reducing the impact through various initiatives in sectors such as sporting events, facility management, etc. However, these initiatives have not been studied in the sport sector, eluding the beneficial effects it could have on market positioning and user retention. Therefore, this study aims to assess the effect of green practices implemented by sports clubs on their users’ perceptions and loyalty. The GPSport and EPOD2 questionnaires were used on a total of 1732 users of 27 Spanish sports clubs, of whom 70% were men. In data processing, standard deviation, skewness, and kurtosis were used for psychometric properties; Cronbach’s alpha, AVE, and CR were used for correlations between factors, and CMIN, DF, and CFI were used to test the invariance of the factor structure across user groups. Perceptions of the service were predominantly positive. The construct with the highest value was satisfaction (4.36 ± 0.73), followed by loyalty (4.19 ± 0.79), with green practices scoring the lowest (3.01 ± 0.94). Perceived quality was a significant and direct antecedent of satisfaction, while satisfaction was a significant and direct antecedent of loyalty, with the former relationship being the strongest. Green practices were directly and significantly related to perceived quality, predominantly indirectly related to satisfaction, and mostly directly related to loyalty. For female users, the relevance of green practices as an antecedent of perceived quality and satisfaction was significantly higher. The results obtained suggest that green practices are an added value that improves users’ perceptions, especially among females, justifying the use of marketing strategies based on the adoption of green practices.

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Despite receiving less attention, sports facilities are no exception to these polluting processes, as they use equipment made of rubber, metals and plastics, as well as large quantities of carbon and petroleum derivatives (Smith & Westerbeek, 2004 ; Stinnett & Gibson, 2016 ). Moreover, these services are developed in sports infrastructures that produce a negative environmental impact throughout their life cycle, i.e. during their construction, as they alter the soil, the landscape, and the ecosystems of sensitive areas, and during their operation and maintenance, as they use large quantities of water, energy, and chemical products (Elnour et al., 2022 ; Wall-Tweedie & Nguyen, 2018 ). The transfer of users to the facilities, waste management or the lack of efficiency in the use of resources were other polluting aspects typical of sports facilities (Rahmani et al., 2020 ; Shahron et al., 2020 ).

This environmental footprint, together with a renewed ecological awareness, has resulted in a new consumer profile with new requirements related to sustainability, which can effectively influence their decisions (Martínez-Valverde, 2017 ; Torres-Moraga & Vidal-Buitano, 2022 ). This new state of affairs has led the sports sector to take a greater interest in the environmental impact of their economic activities by progressively increasing the presence of sustainability (Silveira et al., 2022 ; Smith, Westerbeek ( 2007 ); Trail & McCullough, 2020a ). Sustainability’s increasing visibility and ability to influence the decisions of sports consumers (Guevara et al., 2023 ) has led to the prioritisation of the development of green marketing strategies, sustainable initiatives, and the implementation of green practices in the sector (Eslaminejad et al., 2022 ; Kim, 2017 ; Wilby et al., 2023 ).

These green initiatives are manifold and varied, and can be witnessed in the construction, management and maintenance of sports facilities, such as inefficient and sustainable sports equipment (Elnour et al., 2022 ; McCullough et al., 2020 ; Rahmani et al., 2020 ) or in the adoption of an ecocentric perspective in the management of sports organisations (Stinnett & Gibson, 2016 ). In addition, several international sporting events—such as the 2014 FIFA World Cup in Brazil, the 2018 FIFA World Cup in Russia, the London 2012 Olympic Games, the Rio 2016 Olympic Games (Trendafilova & McCullough, 2018 )—have made sustainability part and parcel of their organisation and have implemented a number of green initiatives (Wilby et al., 2023 ). These initiatives took the form of implementing zero-emission, zero-waste policies, neutralising carbon and energy surpluses, and encouraging spectator travel using sustainable transport (McCullough, 2023 ).

The implementation of green practices, designed to meet new consumer demands and reduce environmental impact, shapes sports fans’ green behaviour and mentality, as they are influenced by the actions of the clubs they follow (Inoue & Kent, 2012 ; Trail, 2015 , 2016 ; Trail & McCullough, 2020a ), feeding back into it. Sustainability improves consumer perceptions of the sports service on offer and of the green strategies to which they relate (Cayolla & Escadas, 2022 ; Trail & McCullough, 2020b ). This strategy improves their connection with sports entities, however, despite this influence, not all sectors of the sports industry are in the same stage of sustainable development (McCullough et al., 2019 ). Most studies concern sporting events (e.g. Trendafilova & McCullough, 2018 ) or large sports clubs (e.g. Inoue & Kent, 2012 ), while sports practice services have not garnered enough attention to look into the effects of implementing green practices. Increased competition stemming from the growing interest in physical activity has led the latter to begin improving and expanding their service offering (Nuviala et al., 2021 ). This pursuit, coupled with the high environmental impact of their services, provides an opportunity for the implementation of green practices that help them to become more competitive (Yadav & Pathak, 2016 ).

The main objective of this study will be, therefore, to assess the effects of green practices on the perceptions and loyalty of sports club users. Due to the lack of research demonstrating the existence of these relationships in the sports sector, several hypotheses were formulated based on economic sectors whose purposes match those of sports services: spare time, leisure, recreation, and health.

Literature review and hypotheses

Green practices.

Faced with the current delicate ecological situation, the environmental movement, which considers the economic activity of organisations as one of the main causes of the environmental problem, is losing traction with respect to the green movement, which opts for a change of stance by pointing to organisations as a necessary part of the solution (Monteiro et al., 2015 ; Peattie & Charter, 2003 ; Song & Yu, 2018 ). Organisations now have the opportunity to take on a leading ecological role by embracing sustainability through the adoption of sustainable development, which is defined by the World Commission on Environment and Development in 1998 as ‘development that meets the needs of the present without compromising the ability of future generations to meet their own needs’ (WHC, 2015 ).

In order to achieve sustainability, organisations have chosen to implement sustainable strategies and actions (Han et al., 2009 ; Martínez-García de Leaniz et al. ( 2018 ); Moise et al., 2021 ), proposing that their progress is closely linked to the creation of added social and ecological value (Moore & Manring, 2009 ; Radulescu et al., 2018 ). Consequently, different organisations have a proactive attitude towards the production of services and products with a focus on ecological responsibility (Fernando et al., 2019 ; Vargas et al., 2018 ), seeking to counteract the negative impact on their consumers and the organisation itself through the development of green practices (Acuña-Moraga et al., 2022 ; Yu et al., 2017 ).

A sustainable strategy based on green practices, in addition to providing those who implement them with better competitive positioning in the market (Yadav & Pathak, 2016 ), enhances the supply of services (Kim et al., 2017 ; Merli et al., 2019 ) by attracting more consumers and satisfying users in a more efficient way (Yusof et al., 2017 ). Green practices are understood as actions carried out by organisations that participate in the maintenance, respect, and care of the environment (Dang‐Van et al., ( 2022 ); Kim et al., 2017 ; Rahman & Reynolds, 2016 ), their purpose is to reduce the negative impact of such organisations through conservation and sustainability (Moise et al., 2021 ; Yadav & Pathak, 2016 , 2017 ). These practices, once implemented, result in efficient energy use, improved water conservation, pollution prevention, green purchasing, and sustainable food, among others (Chou et al., 2012 ).

Perceived quality and green practices

The quality of service should be considered from the consumer’s point of view and can be defined as a post-consumption individual evaluation (Duclos-Bastías et al., 2024 ) in which the consumer makes a judgement of the excellence or superiority of service by comparing their expectations and the perceived result (Bitner, 1992 ; Bitner, Hubbert ( 1994 ); Bolton & Drew, 1991 ; Zeithaml, 1988 ). Perceived quality refers to the set of characteristics that meet consumer requirements and to the absence of deficiencies (Borré & Vega, 2014 ; Crosby, 1988 ; Mundina & Calabuig, 1999 ), with there being a consensus on its multidimensional nature (Díaz-Chao et al., 2015 ).

With the increasing visibility of the green movement, Shapoval et al. ( 2018 ) stated that consumers consider sustainable elements as an important part of their perception of quality, thus in order to improve customers’ perceived quality, organisations must adopt green practices (Chen & Chang, 2013 ). These practices cannot be detrimental to the service but must be focused on improving the quality of the experience (Lee & Cheng, 2018 ). Therefore, given the lack of studies on the impact of green practices on users’ perceived quality of sports services, and keeping in mind the findings from different leisure- and recreation-related sectors—such as the green hotel sector (Chang, Cheng ( 2022 ); Lee & Cheng, 2018 ; Lemy, 2016 ) and the organic restaurant sector (Liou & Namkung, 2012 ; Namkung & Jang, 2013 ), in which green practices were found to be positively associated with different aspects or types of quality—we can formulate the initial hypothesis of our study:

H1: Green practices implemented by sports clubs have a direct positive impact on the quality perceived by consumers .

It is well documented in sports services that users’ perceived quality is an antecedent of user satisfaction (Aznar-Ballesta & Vernetta, 2023 ; Gálvez-Ruiz et al., 2023 ; García-Fernández et al., 2018 ; Sevilmiş et al., 2024 ). Sports organisations keen to provide a quality service consider it a decisive objective to determine the proper functioning of the components of their service to ensure that users’ needs are met in an efficient way (Elasri-Ejjaberi et al., 2016 ), thus achieving the ultimate goal – loyalty (Alonso-Dos Santos et al., ( 2024 ); Nicolás-López & Escaravajal-Rodríguez, 2020 ). This relationship between perceived quality and satisfaction aligns with the resulting relationships observed in other sectors offering sustainable leisure services, such as green hotels (Assaker et al., 2020 ; Merli et al., 2019 ) and green catering (Chaturvedi et al., 2022 ; Namkung & Jang, 2013 ; Riva et al., 2022 ). Consequently, our second hypothesis is as follows:

H2: The perceived quality by the users of the sports clubs is directly and positively associated with their satisfaction levels .

Satisfaction and green practices

Tse et al. ( 1990 ) proposed the satisfaction construct as a process, as a subjective, dynamic, and multidimensional evaluation following the act of consumption. This evaluation is carried out based on affective, cognitive, and behavioural dimensions (Oliver, 1993 ). However, the most widely used perspective is the cognitive-affective one, developed by authors such as Kozak et al. ( 2004 ) and Wirtz and Bateson ( 1999 ). These authors approach satisfaction by seeking to overcome partial points of view, highlighting the importance of integrating the basic characteristics of both the cognitive and affective perspectives. Consequently, this means that a prior rational, cognitive analysis does not preclude the subsequent generation of effect, nor does it fail to consider its subsequent behavioural implications. Broadly speaking, satisfaction is an overall evaluation of the experience of consumption and purchase of a service (Anderson et al., 1994 ; Christopher et al., 1994 ).

With consumers’ increasing environmental awareness, user satisfaction is influenced by the ability of organisations to meet consumers’ sustainability requirements (Yusof et al., 2017 ). The influence of green practices on user satisfaction has been analysed and demonstrated in different spare time, leisure, and recreation sectors, such as the hotel sector (Assaker et al., 2020 ; Hu et al., 2010 ; Merli et al., 2019 ; Moise et al., 2020 ; Thai & Nguyen, 2022 ; Yusof et al., 2017 ) and the catering sector (Bekar et al., 2020 ; Chaturvedi et al., 2022 ; Shapoval et al., 2018 ; Wu et al., 2021 ). Therefore, our third hypothesis is the following:

H3: Green practices implemented by sports clubs are both directly and indirectly related to consumer satisfaction levels .

The significance of consumer satisfaction research lies in the fact that satisfied consumers put the competition to one side and will return to buy a product or service again, while also telling those closest to them thereof (Lañe & Kotler, 2006 ). Specifically in the sports sector, research suggests that satisfaction influences users’ future behaviour (Gálvez-Ruiz et al., 2023 ; García-Pascual et al., 2019 ), which is crucial in the development of customer retention and loyalty strategies (Cronin et al., 2000 ). In other words, the greater users’ perceived satisfaction with a sports service, the greater their likelihood of reusing the service (Alguacil et al., 2019 ; Dueñas-Dorado et al., 2021 ; Fernández-Martínez et al., 2020 ; Köse et al. ( 2021 )). Similar relationships have been established in studies on services implementing green practices, such as green hotel services (Merli et al., 2019 ; Yarimoglu & Gunay, 2020 ; Yusof et al., 2017 ) and sustainable catering services (Chaturvedi et al., 2022 ; Shapoval et al., 2018 ). Thus, our fourth hypothesis is as follows:

H4: Sports club users’ satisfaction is directly and positively correlated with their loyalty levels .

Loyalty and green practices

Customer loyalty is defined as the repeated purchasing behaviour of a product or service due to favourable attitudes or decision-making based on an evaluative process (Jacoby & Kyner, 1973 ). Loyalty can be approached from two perspectives, behavioural and attitudinal. Behavioural loyalty is expressed as the level of exchanges that take place between the company and consumers, such as repeat business and frequency of purchases by consumers (Li & Petrick, 2008 ), as well as the likelihood that consumers will highly recommend the service and their intention to do so (Bettencourt, 1997 ; Dick & Basu, 1994 ). In turn, attitudinal loyalty is understood as a consumer preference, their partiality to a brand, product, or service (Dick & Basu, 1994 ).

In order to improve retention and repeat consumption rates, as well as productivity and, ultimately, higher profits (García-Fernández et al., 2014 ), the sports sector has opted for the development of quality, as it is the most relevant element to achieving competitive superiority (García et al., 2013 ; Lloréns & Fuentes, 2000 ), as well as being a clear antecedent to loyalty (Alonso-Dos Santos et al., ( 2024 ); Baker & Crompton, 2000 ). As an antecedent, customer-perceived quality has a predominantly indirect effect on loyalty (Cronin et al., 2000 ; Marakanon & Panjakajornsak, 2016 ). However, the most widely adopted position within the context of sports services concerns either both the direct and the indirect relationship between quality and satisfaction, or a position concerning at least one of the two (Serrano & Segado, 2015 ). This dual relationship of quality has been analysed in low-cost fitness services (García et al., 2013 ; García-Fernández et al., 2014 ), in sporting events (Cronin et al., 2000 ), in sports services (Alguacil et al., 2019 ; García et al., 2013 ; Nuviala et al., 2012 ), and in non-mixed sports services (Nuviala et al., 2021 ) (Fig. 1 ).

Structural model predicting the influence of green practices on user loyalty.

Service quality is a highly relevant factor regarding the future intentions of green service users (Ahn & Kwon, 2020 ; Chang, Cheng, 2022 ), meaning that the use of green practices needs to be tied to both tangible and intangible aspects of quality (Lemy, 2016 ). This is a widely studied relationship that is consistent with the economic sectors on which the hypotheses of this study are based, such as green hotels (Assaker et al., 2020 ; Merli et al., 2019 ; Pianroj, 2012 ) and green restaurants (Namkung & Jang, 2013 ; Riva et al., 2022 ). Consequently, the fifth hypothesis is:

H5: The perceived quality by the users of the sports club is both directly and indirectly related to user loyalty .

In the new green market, the ability of sustainable initiatives to generate a predisposition towards a leisure company, as well as their direct and indirect influence on user loyalty, has been demonstrated. The green hotel sector (Assaker et al., 2020 ; Merli et al., 2019 ; Moise et al., 2020 ; Thai & Nguyen, 2022 ; Yusof et al., 2017 ;) and the green catering sector (Dan-Van et al., 2022; Jang et al., 2015 ; Li et al., 2019 ) have been identified as sustainable leisure sectors. Therefore, our sixth hypothesis is the following:

H6: Green practices implemented by sports clubs have a direct and indirect positive impact on users loyalty levels .

Participants

A total of 1732 users of 27 sports clubs voluntarily participated in this study. Their mean age was 22.63 ± 8.28 years, ranging from 18 to 74. Thirty percent of respondents were women, 57.6% had secondary education, 54% reported exercising two to three times per week, and 67.8% reported spending between one and two hours in each training session (Table 1 ).

A multi-item questionnaire containing four variables was used (Table 2 ). The items used to assess the quality and satisfaction constructs were taken from the Sports Organisations Perception Scale, version 2 (EPOD2), an instrument devised by Nuviala et al. ( 2013 ). Quality was measured using seven items, while satisfaction was measured using four. Loyalty to the sports organisation was measured using five items, as proposed by Nuviala et al. ( 2014 ). Finally, green practices were measured through the six-item Green Practices Scale for Sport Organizations and Events (GPSport), a scale developed by Morán-Gámez et al. ( 2024 ). To measure the level of agreement or disagreement with the different latent variables, a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree) was used. Several sociodemographic items were added to the original scales, including age, sex, education, frequency of physical activity, and time spent per session.

First, we contacted sports club managers and explained the purpose of the study to them. Subsequently, the users of these clubs were invited to participate in the study by answering all the questions in an anonymous questionnaire once they had been individually briefed about the purpose of the study. After giving their consent, previously trained and instructed research collaborators addressed their questions and administered the questionnaire, which lasted no longer than five minutes. The study was approved by the Bioethics Committee for one of the collaborating institutions.

Data analysis

Firstly, the psychometric properties of the items were checked by measuring means, standard deviations, skewness, and kurtosis. This enabled us to confirm univariate and multivariate normality. Univariate normality was tested using the criterion proposed by Kline ( 2011 ), according to which levels below three for skewness and values below ten for kurtosis are considered normal. Multivariate normality was then checked using Mardia’s test (Mardia, 1970 ), which, according to Bollen ( 1989 ), should be less than p ( p  + 2), where p is the number of observed variables.

Secondly, correlations between the study factors, internal consistency (Cronbach’s alpha), average variance extracted (AVE), composite reliability (CR), and Harman’s one-factor test were calculated. Acceptable Cronbach’s alpha values are around 0.70, while correct Cronbach’s alpha values range from 0.80 to 0.90 (Streiner, 2003 ). Adequate CR values should be higher than 0.6 (Bagozzi & Yi, 1988 ), while adequate AVE values should be higher than 0.5 (Hair et al., 2009 ). Podsakoff et al. ( 2003 ) indicate that Harman’s one-factor test is performed by examining the results of an exploratory factor analysis (EFA) and checking whether the first factor extracted explains more than 50% of the variance.

Thirdly, a multi-group analysis was carried out to test the invariance of the factor structure across user groups. The aim of this analysis was to establish whether the model relating quality, green practices, satisfaction, and loyalty was the same for male and female users. The model must first be tested on the total sample of users (model 0), and then tested separately on the user groups by sex (male users=model 0a; female users=model 0b). Secondly, different models with restrictions on the relationships are assessed. This assessment was carried out following the maximum likelihood (ML) method (Thompson, 2004 ). Typically, the following indices are used to measure the overall fit of structural equation models: the chi-squared value (CMIN), the degrees of freedom (DF), the CMIN/DF ratio, the comparative fit index (CFI), and the root mean square error of approximation (RMSEA).

CFI values ≥ 0.95 are considered acceptable (Hu & Bentler, 1999 ). RMSEA values < 0.08 indicate an acceptable fit (Schermelleh-Engel et al., 2003 ) and RMSEA values ≤ 0.06 indicate a good fit (Hu & Bentler, 1999 ). Regarding the CMIN/DF ratio, a perfect model would yield a value of 1.00, and ratios below 2.00 would be considered indicators of a very good model fit, while values below 5.00 would be regarded as acceptable (Hu & Bentler, 1999 ; MacCallum et al., 2001 ; Yuan, 2005 ). Measurement invariance between groups was assessed using the Δ χ 2 test and the recommendations made by Chen ( 2007 ), according to which ΔCFI cut-off value was ≤0.01 and ΔRMSEA cut-off value was ≤0.015. Finally, standardised regression coefficients were calculated to identify relationships in the model. Regression weights and critical coefficients were compared to estimate group differences using AMOS (v. 22.0) software from IBM (IBM Corp., Armonk, NY, USA).

Table 3 shows the skewness and kurtosis values of the items used in our research. As can be seen, they are below the expected limits, and therefore it is safe to say that there is univariate normality. Mardia’s coefficient value was 212.782, showing that the multivariate normality assumption is fulfilled, since it is lower than that suggested by Bollen ( 1989 ), which is based on the formula p ( p  + 2), where p is the number of variables observed. In our study, there are 22 observed variables, and Mardia’s coefficient provided by the AMOS programme is lower than the product of 22*(22 + 2) = 528.

The EFA explained 33.08% of the total variance, which is below the critical 50% level, suggesting that the common method bias is not likely to significantly affect the study results. The correlation between constructs was then checked, as shown in Table 2 . Reliability (CR and Cronbach’s alpha) and validity (AVE) were also tested. Cronbach’s alpha values were above 0.837, AVE values were above 0.507, and CR values were above 0.878.

Once the preliminary analyses were performed and univariate and multivariate normality were determined, the model was tested using the ML method. As shown in Table 4 , the fit indices of the model exhibit correct values for all users (model 0), as well as for both male and female groups (model 0a and model 0b), thus confirming the validity of the factorial structure of the model relating green practices, quality, satisfaction, and loyalty.

The invariance of the model as a function of sex was checked to be able to establish differences between the groups at a later stage. When considering the difference in χ 2 between the various models tested against each other, no differences were identified between models 1 and 2, or models 4 and 5, meaning that the invariance of the model with respect to sex could not be established. However, the CFI and RMSEA indices of the models yielded very similar values, with differences between them of less than 0.01 and 0.015 respectively, suggesting factorial invariance of the model depending on the sex of the users (Table 4 ).

Once the invariance of the model was verified, the standardised coefficients of the relationships between the latent variables were calculated for all users and then for the two groups of users by sex. The data show that green practices are an antecedent of quality (Table 5 ), with differences depending on the sex of the users. Likewise, it can be observed that satisfaction has green practices as an antecedent in the total number of users and among female users, with differences according to sex. Quality, on the other hand, is an antecedent of satisfaction in both sexes. Loyalty has green practices, quality, and satisfaction as direct antecedents. There are gender differences in the relationship between quality and loyalty, with the direct effect being greater among female users, while the indirect effect is greater among male users.

This study aims to expand the knowledge base on the influence of green practices implemented by sports clubs on the perceptions and future intentions of their users. More specifically, this research was intended to ascertain the influence of green practices on the perceived quality, satisfaction, and loyalty of users of sports services offered by sports clubs. To this end, the results obtained were described and found to support the significance of sustainable strategies developed by sports clubs through green practices when it comes to users’ perceptions of the service and their willingness to consume it once again.

Our research found that green practices and perceived quality have a positive, significant relationship within the context of sports services, which is in line with other results reported in studies on leisure services that engage in green practices, such as organic restaurants and green hotels (Lee et al., 2018 ). These results show that users perceive green practices as quality factors that can determine the superiority of a service in comparison to others, thus conferring excellence. Our data also indicate that green practices that focus on the rationalisation of the service, its functionality, its efficiency, and the reduction of its environmental impact improve the perception of the components of quality, such as communications and the sports facility itself.

A significant and predominantly indirect relationship was found between green practices and satisfaction by means of the perceived quality by users of sports services. This suggests that green practices related to different aspects of the service alone are not sufficient to meet user needs. In other words, green practices need to be combined with a high-quality service in order to be appreciated and valued by users, acting as an added value. These results are similar to research in other leisure-related service sectors in which green initiatives have been introduced, such as green hotels (Assaker et al., 2020 ; Moise et al., 2020 ) and sustainable catering services (Gao & Mattila, 2014 ). However, these results are at odds with those reported by Yusof et al. ( 2017 ) in green hotels in Malaysia, where green initiatives predominantly influenced satisfaction. Their research did not consider the perceived quality of the service, thus eluding one of the most important determinants of user perception. It could be assumed that the mere presence of green practices, irrespective of the functional improvement they may bring to the service, would enhance the overall satisfaction with the service by endowing it with an identity or, as the case may be, an image associated with sustainability that would encourage the generation of affective responses towards the service being consumed (Hwang & Lee, 2019 ).

Green practices were found to be linked to loyalty, directly and indirectly, through perceived quality and satisfaction. Similar findings were identified in studies on hotels implementing green initiatives (Assaker, 2020 ; Han et al., 2019 ; Moise et al., 2018 ; Sun et al., 2022 ) and on green restaurants (Shapoval et al., 2018 ). The significant direct relationship between loyalty and green practices reinforces the theory suggesting that an organisation’s involvement in a social cause that matters to consumers will generate more empathy, support, and consumption intentions (Preziosi et al., 2019 ). Moise et al. ( 2021 ), in their study on green hotels, found that green practices are indirectly related to loyalty, predominantly through quality. This is in consonance with our previous analysis, in which the functional contribution of green practices mainly constitutes a supplementary improvement of user perception and is capable of making users repurchase the service or at least generate in them the intention to do so.

Our results also suggest that green practices generate more loyalty than satisfaction, with loyalty being understood as a long-term investment (Islam et al., 2021 ) and satisfaction as an overall rational and emotional post-consumption judgement (Kozak et al., 2004 ; Wirtz & Bateson, 1999 ). This leads us to believe that green practices, more influenced by perceived quality than satisfaction, have a long-term rather than a short-term effect. Users need to be confident that these practices will be implemented and developed over a long period of time, trusting that they will not become a one-off or cease to be carried out in the near future (Kang & Hur, 2012 ).

Furthermore, green practices were the construct with the lowest value of those considered in the study. Although our data indicate that green practices are a significant antecedent to the perceptions and intentions of sports club users, the relationships between green practices and satisfaction, perceived quality, and loyalty, were the constructs with the lowest values in the research. This could be explained by the fact that users are aware of the capabilities of large sports entities (Orr, 2021 ), as they are assumed to have a duty of care towards the environment (McCullough et al., 2019 ), as well as a responsibility to undertake environmental initiatives to mitigate their environmental impact (Chaves-Castro et al., 2024 ; Pelcher et al., 2023 ). These roles could be extended to sports service provider organisations such as sports clubs; however, they are less inclined to implement such practices due to their limited size, capacity, and/or perceived utility (Misener & Doherty, 2009 ), a situation that favours green practices to be regarded as a given, i.e., user expectations cannot be fully met by implementing these practices alone (Yu et al., 2017 ; Zareh et al., 2023 ), or to be viewed as greenwashing, which is perceived as misleading and has a negative influence on the service (de Freitas-Netto et al., 2020 ).

The results showed that the relationship between perceived quality and green practices is moderated by sex, similar to those reported by DiPietro et al. ( 2013 ) on sustainable restaurant services. These authors also note that green practices did not have the same impact on user perceptions in relation to sex, with women attributing greater relevance to green practices in their role as an antecedent to perceived quality and satisfaction than their male counterparts. Sex also moderates the relationship between green practices and satisfaction, as shown by the results obtained from the green hotel sector by Moise et al. ( 2021 ) and Wang et al. ( 2018 ), for instance. The sustainable retail sector also identifies women as more susceptible, as they consider satisfaction in relation to green practices in a more positive light (Marín-García et al., 2022 ).

Our results also suggest that perceived quality is directly and positively related to user loyalty. Significant differences were found between men and women, with women obtaining higher values regarding this relationship. These results are similar to those found in research carried out in sports facilities in southern Spain (Huang & Chiu, 2024 ; Quirante-Mañas et al., 2023 ).

Our findings are similar to most of those reported in the literature referred to above, in which sex is positioned as a significant moderator, with women showing greater attachment and susceptibility towards sustainable attitudes and purchases, which is also in consonance with the results obtained in the tourism sector (Ibnou-Laaroussi et al., 2020 ; Taufique, 2022 ) and in the green catering sector (Moon, 2021 ). Finally, our study reinforces the perspective that green practices are a value-added strategy related to environmental sustainability and benefit the operations carried out by sports organisations by effectively attracting and retaining users. These practices do not represent a change in the production process of the service or in its raison d’être, but rather an addition of elements that are capable of reducing its costs and its impact on the environment.

Conclusions

This study aims to examine the relationships between user perceptions, loyalty, and strategic actions aimed at reducing the environmental impact of the service offered by sports clubs, namely green practices. The theoretical implications of our results explain the effect of green practices as a strategy to improve the service offered in sports clubs, including a series of practical consequences that will improve the efficiency and management of sports entities.

From a theoretical standpoint, the originality of this research lies in the study of green practices that influence the functionality of the sports service and their effects on consumers’ overall decisions and judgements. Green practices were directly and positively related to all the study constructs and relationships, indicating that they would help attain good managerial outcomes. One relevant finding from the constructs and data obtained is the relationship between a sustainable strategy and user loyalty, an indirect relationship that was mediated by the perceived quality of the service, signalling that green practices improve the service and should not be viewed in a vacuum, as their relationship with satisfaction was significantly weaker than their relationship with perceived quality.

On a practical level, the results reveal that simply focusing on green practices would not suffice to add value or increase satisfaction among consumers of sports services. Green practices need to be part and parcel of a set of business strategies that helps the sports organisation to improve the efficiency and quality of the organisation, thus increasing satisfaction and loyalty among its users while influencing their future decisions. Examples to be applied in sports clubs may include green practices related to the implementation of sustainable management systems that will help to optimise the different resources of the organisation and reinvest them in innovation or service extensions, such as the use of LED lights and/or solar panels and the optimisation of water use and reuse in sports facilities. Other green practices include the availability of recycling bins, which could help users engage in recycling during their leisure time, and the thermal cladding of areas where sports activities take place, which would improve the user experience and consequently their perceptions of the service. Taking the number of female users into consideration would be of interest, as their perceptions are more influenced by the implementation of green practices, which could improve their consideration of the different aspects of the service and their level of satisfaction with their purchase. Finally, the development of green practices and knowledge of their outcomes can serve as a precedent and provide valuable information on the potential effects of introducing a sustainable marketing strategy in a sports club.

Limitations and future research

Limitations of this study include the fact that most of the data used were collected in the Autonomous Community of Andalusia (Spain), which may hinder the extrapolation of our conclusions to other geographical areas. In addition, this study did not consider the types of sports facilities in which the sports activities took place, which could have provided us with useful information for the interpretation of the effect of green practices in each sports club.

In addition to measuring user perceptions, future lines of research could also consider user familiarity with green practices in order to offer a more detailed picture of these perceptions. This would provide an opportunity to establish whether green practices are recognised as sustainable aspects or merely as components of efficient management. In line with the above, sustainable initiatives and their identification are linked to the identities of companies and customers, the relationship between the two (Yadav & Pathak, 2016 ), and consumer relations, as their measurement would considerably supplement the contribution of this study. Finally, insight into users’ motivation to consume in a sustainable manner, including their ability and opportunities to do so, would provide additional information to sports organisations that are not implementing green practices, which would enable them to devise new strategies in this regard.

Data availability

The datasets generated during and/or analysed during the current study are available from an open-access repository: https://hdl.handle.net/10433/21634 .

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Analysing the Impact of Social Media on Students’ Academic Performance: A Comparative Study of Extraversion and Introversion Personality

Sourabh sharma.

International Management Institute (IMI), Bhubaneswar, India

Ramesh Behl

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The advent of technology in education has seen a revolutionary change in the teaching–learning process. Social media is one such invention which has a major impact on students’ academic performance. This research analyzed the impact of social media on the academic performance of extraversion and introversion personality students. Further, the comparative study between these two personalities will be analysed on education level (postgraduate and undergraduate) and gender (male and female). The research was initiated by identifying the factors of social media impacting students’ academic performance. Thereafter, the scale was developed, validated and tested for reliability in the Indian context. Data were collected from 408 students segregated into 202 males and 206 females. Two hundred and thirty-four students are enrolled in postgraduation courses, whereas 174 are registered in the undergraduate programme. One-way ANOVA has been employed to compare the extraversion and introversion students of different education levels and gender. A significant difference is identified between extraversion and introversion students for the impact of social media on their academic performance.

Introduction

Social Networking Sites (SNS) gained instant popularity just after the invention and expansion of the Internet. Today, these sites are used the most to communicate and spread the message. The population on these social networking sites (SNS) has increased exponentially. Social networking sites (SNS) in general are called social media (Boyd & Ellison, 2008 ). Social media (SM) is used extensively to share content, initiate discussion, promote businesses and gain advantages over traditional media. Technology plays a vital role to make SM more robust by reducing security threats and increasing reliability (Stergiou et al., 2018 ).

As of January 2022, more than 4.95 billion people are using the Internet worldwide, and around 4.62 billion are active SM users (Johnson, 2022 ). In India, the number of Internet users was 680 million by January 2022, and there were 487 million active social media users (Basuray, 2022 ). According to Statista Research Department ( 2022 ), in India, SM is dominated by two social media sites, i.e. YouTube and Facebook. YouTube has 467 million users followed by Facebook with 329 million users.

Although almost all age groups are using SM platforms to interact and communicate with their known community (Whiting & Williams, 2013 ), it has been found that social media sites are more popular among youngsters and specifically among students. They use SM for personal as well as academic activities extensively (Laura et al., 2017 ). Other than SM, from the last two years, several online platforms such as Microsoft Teams, Zoom and Google Meet are preferred to organize any kind of virtual meetings, webinars and online classes. These platforms were used worldwide to share and disseminate knowledge across the defined user community during the pandemic. Social media sites such as Facebook, YouTube, Instagram, WhatsApp and blogs are comparatively more open and used to communicate with public and/or private groups. Earlier these social media platforms were used only to connect with friends and family, but gradually these platforms became one of the essential learning tools for students (Park et al., 2009 ). To enhance the teaching–learning process, these social media sites are explored by all types of learning communities (Dzogbenuku et al., 2019 ). SM when used in academics has both advantages and disadvantages. Social media helps to improve academic performance, but it may also distract the students from studies and indulge them in other non-academic activities (Alshuaibi et al., 2018 ).

Here, it is important to understand that the personality traits of students, their education level and gender are critical constructs to determine academic performance. There are different personality traits of an individual such as openness, conscientiousness, extraversion and introversion, agreeableness and neuroticism (McCrae & Costa, 1987 ). This cross-functional research is an attempt to study the impact of social media on the academic performance of students while using extraversion and introversion personality traits, education levels and gender as moderating variables.

Literature Review

There has been a drastic change in the internet world due to the invention of social media sites in the last ten years. People of all age groups now share their stories, feelings, videos, pictures and all kinds of public stuff on social media platforms exponentially (Asur & Huberman, 2010 ). Youth, particularly from the age group of 16–24, embraced social media sites to connect with their friends and family, exchange information and showcase their social status (Boyd & Ellison, 2008 ). Social media sites have many advantages when used in academics. The fun element of social media sites always helps students to be connected with peers and teachers to gain knowledge (Amin et al., 2016 ). Social media also enhances the communication between teachers and students as this are no ambiguity and miscommunication from social media which eventually improves the academic performance of the students (Oueder & Abousaber, 2018 ).

When social media is used for educational purposes, it may improve academic performance, but some associated challenges also come along with it (Rithika & Selvaraj, 2013 ). If social media is incorporated into academics, students try to also use it for non-academic discussions (Arnold & Paulus, 2010 ). The primary reason for such distraction is its design as it is designed to be a social networking tool (Qiu et al., 2013 ). According to Englander et al. ( 2010 ), the usage of social media in academics has more disadvantages than advantages. Social media severely impacts the academic performance of a student. The addiction to social media is found more among the students of higher studies which ruins the academic excellence of an individual (Nalwa & Anand, 2003 ). Among the social media users, Facebook users’ academic performance was worse than the nonusers or users of any other social media network. Facebook was found to be the major distraction among students (Kirschner & Karpinski, 2010 ). However, other studies report contrary findings and argued that students benefited from chatting (Jain et al., 2012 ), as it improves their vocabulary and writing skills (Yunus & Salehi, 2012 ). Social media can be used either to excel in academics or to devastate academics. It all depends on the way it is used by the students. The good or bad use of social media in academics is the users’ decision because both the options are open to the students (Landry, 2014 ).

Kaplan and Haenlein ( 2010 ) defined social media as user-generated content shared on web 2.0. They have also classified social media into six categories:

• Social Networking Sites: Facebook, Twitter, LinkedIn and Instagram are the social networking sites where a user may create their profile and invite their friends to join. Users may communicate with each other by sharing common content.
• Blogging Sites: Blogging sites are individual web pages where users may communicate and share their knowledge with the audience.
• Content Communities and Groups: YouTube and Slideshare are examples of content communities where people may share media files such as pictures, audio and video and PPT presentations.
• Gaming Sites: Users may virtually participate and enjoy the virtual games.
• Virtual Worlds: During COVID-19, this type of social media was used the most. In the virtual world, users meet with each other at some decided virtual place and can do the pre-decided things together. For example, the teacher may decide on a virtual place of meeting, and students may connect there and continue their learning.
• Collaborative Content Sites: Wikipedia is an example of a collaborative content site. It permits many users to work on the same project. Users have all rights to edit and add the new content to the published project.

Massive open online courses (MOOCs) are in trend since 2020 due to the COVID-19 pandemic (Raja & Kallarakal, 2020 ). MOOCs courses are generally free, and anyone may enrol for them online. Many renowned institutions have their online courses on MOOCs platform which provides a flexible learning opportunity to the students. Students find them useful to enhance their knowledge base and also in career development. Many standalone universities have collaborated with the MOOCs platform and included these courses in their curriculum (Chen, 2013 ).

Security and privacy are the two major concerns associated with social media. Teachers are quite apprehensive in using social media for knowledge sharing due to the same concerns (Fedock et al., 2019 ). It was found that around 72% teachers were reluctant to use social media platforms due to integrity issues and around 63% teachers confirmed that security needs to be tightened before using social media in the classroom (Surface et al., 2014 ). Proper training on security and privacy, to use social media platforms in academics, is needed for  students and teachers (Bhatnagar & Pry, 2020 ).

The personality traits of a student also play a significant role in deciding the impact of social media on students’ academic performance. Personality is a dynamic organization which simplifies the way a person behaves in a situation (Phares, 1991 ). Human behaviour has further been described by many renowned researchers. According to Lubinski ( 2000 ), human behaviour may be divided into five factors, i.e. cognitive abilities, personality, social attitudes, psychological interests and psychopathology. These personality traits are very important characteristics of a human being and play a substantial role in work commitment (Macey & Schneider, 2008 ). Goldberg ( 1993 ) elaborated on five dimensions of personality which are commonly known as the Big Five personality traits. The traits are “openness vs. cautious”; “extraversion vs. introversion”; “agreeableness vs. rational”; “conscientiousness vs. careless”; and “neuroticism vs. resilient”.

It has been found that among all personality traits, the “extraversion vs. introversion” personality trait has a greater impact on students’ academic performance (Costa & McCrae, 1999 ). Extrovert students are outgoing, talkative and assertive (Chamorro et al., 2003 ). They are positive thinkers and comfortable working in a crowd. Introvert students are reserved and quiet. They prefer to be isolated and work in silos (Bidjerano & Dai, 2007 ). So, in the present study, we have considered only the “extraversion vs. introversion” personality trait. This study is going to analyse the impact of social media platforms on students’ academic performance by taking the personality trait of extraversion and introversion as moderating variables along with their education level and gender.

Research Gap

Past research by Choney ( 2010 ), Karpinski and Duberstein ( 2009 ), Khan ( 2009 ) and Kubey et al. ( 2001 ) was done mostly in developed countries to analyse the impact of social media on the students’ academic performance, effect of social media on adolescence, and addictiveness of social media in students. There are no published research studies where the impact of social media was studied on students’ academic performance by taking their personality traits, education level and gender all three together into consideration. So, in the present study, the impact of social media will be evaluated on students’ academic performance by taking their personality traits (extraversion and introversion), education level (undergraduate and postgraduate) and gender (male and female) as moderating variables.

Objectives of the Study

Based on the literature review and research gap, the following research objectives have been defined:

• To identify the elements of social media impacting student's academic performance and to develop a suitable scale
• To test the  validity and reliability of the scale
• To analyse the impact of social media on students’ academic performance using extraversion and introversion personality trait, education level and gender as moderating variables

Research Methodology

Sampling technique.

Convenience sampling was used for data collection. An online google form was floated to collect the responses from 408 male and female university students of undergraduation and postgraduation streams.

Objective 1 To identify the elements of social media impacting student's academic performance and to develop a suitable scale.

A structured questionnaire was employed to collect the responses from 408 students of undergraduate and postgraduate streams. The questionnaire was segregated into three sections. In section one, demographic details such as gender, age and education stream were defined. Section two contained the author’s self-developed 16-item scale related to the impact of social media on the academic performance of students. The third section had a standardized scale developed by John and Srivastava ( 1999 ) of the Big Five personality model.

Demographics

There were 408 respondents (students) of different education levels consisting of 202 males (49.5%) and 206 females (50.5%). Most of the respondents (87%) were from the age group of 17–25 years. 234 respondents (57.4) were enrolled on postgraduation courses, whereas 174 respondents (42.6) were registered in the undergraduate programme. The result further elaborates that WhatsApp with 88.6% and YouTube with 82.9% are the top two commonly used platforms followed by Instagram with 76.7% and Facebook with 62.3% of students. 65% of students stated that Google doc is a quite useful and important application in academics for document creation and information dissemination.

Validity and Reliability of Scale

Objective 2 Scale validity and reliability.

Exploratory factor analysis (EFA) and Cronbach’s alpha test were used to investigate construct validity and reliability, respectively.

The author’s self-designed scale of ‘social media impacting students’ academic performance’ consisting of 16 items was validated using exploratory factor analysis. The principle component method with varimax rotation was applied to decrease the multicollinearity within the items. The initial eigenvalue was set to be greater than 1.0 (Field, 2005 ). Kaiser–Meyer–Olkin (KMO) with 0.795 and Bartlett’s test of sphericity having significant values of 0.000 demonstrated the appropriateness of using exploratory factor analysis.

The result of exploratory factor analysis and Cronbach’s alpha is shown in Table ​ Table1. 1 . According to Sharma and Behl ( 2020 ), “High loading on the same factor and no substantial cross-loading confirms convergent and discriminant validity respectively”.

Exploratory factor analysis and Cronbach’s alpha for the self-developed scale of “Social media impact on academic performance”

The self-developed scale was segregated into four factors, namely “Accelerating Impact”, “Deteriorating Impact”, “Social Media Prospects” and “Social Media Challenges”.

The first factor, i.e. “Accelerating Impact”, contains items related to positive impact of social media on students’ academic performance. Items in this construct determine the social media contribution in the grade improvement, communication and knowledge sharing. The second factor “Deteriorating Impact” describes the items which have a negative influence of social media on students’ academic performance. Items such as addiction to social media and distraction from studies are an integral part of this factor. “Social Media Prospects” talk about the opportunities created by social media for students’ communities. The last factor “Social Media Challenges” deals with security and privacy issues created by social media sites and the threat of cyberbullying which is rampant in academics.

The personality trait of an individual always influences the social media usage pattern. Therefore, the impact of social media on the academic performance of students may also change with their personality traits. To measure the personality traits, the Big Five personality model was used. This model consists of five personality traits, i.e. “openness vs. cautious”; “extraversion vs. introversion”; “agreeableness vs. rational”; “conscientiousness vs. careless”; and “neuroticism vs. resilient”. To remain focussed on the scope of the study, only a single personality trait, i.e. “extraversion vs. introversion” with 6 items was considered for analysis. A reliability test of this existing scale using Cronbach’s alpha was conducted. Prior to the reliability test, reverse scoring applicable to the associated items was also calculated. Table ​ Table2 2 shows the reliability score, i.e. 0.829.

Cronbach’s alpha test for the scale of extraversion vs. introversion personality traits

Objective 3 To analyse the impact of social media on students’ academic performance using extraversion and introversion personality traits, education level and gender as moderating variables.

The research model shown in Fig.  1 helps in addressing the above objective.

Social media factors impacting academic performances of extraversion and introversion personality traits of students at different education levels and gender

As mentioned in Fig.  1 , four dependent factors (Accelerating Impact, Deteriorating Impact, Social Media Prospects and Social Media Challenges) were derived from EFA and used for analysing the impact of social media on the academic performance of students having extraversion and introversion personality traits at different education levels and gender.

Students having a greater average score (more than three on a scale of five) for all personality items mentioned in Table ​ Table2 2 are considered to be having extraversion personality or else introversion personality. From the valid dataset of 408 students, 226 students (55.4%) had extraversion personality trait and 182 (44.6%) had introversion personality trait. The one-way ANOVA analysis was employed to determine the impact of social media on academic performance for all three moderators, i.e. personality traits (Extraversion vs. Introversion), education levels (Undergraduate and Postgraduate) and gender (Male and Female). If the sig. value for the result is >  = 0.05, we may accept the null hypothesis, i.e. there is no significant difference between extraversion and introversion personality students for the moderators; otherwise, null hypothesis is rejected which means there is a significant difference for the moderators.

Table ​ Table3 3 shows the comparison of the accelerating impact of social media on the academic performance of all students having extraversion and introversion personality traits. It also shows a comparative analysis on education level and gender for these two personality traits of students. In the first comparison of extraversion and introversion students, the sig. value is 0.001, which indicates that there is a significant difference among extraversion and introversion students for the “Accelerating Impact” of social media on academic performance. Here, 3.781 is the mean value for introversion students which is higher than the mean value 3.495 of extraversion students. It clearly specifies that the accelerating impact of social media is more prominent in the students having introversion personality traits. Introversion students experienced social media as the best tool to express thoughts and improve academic grades. The result is also consistent with the previous studies where introvert students are perceived to use social media to improve their academic performance (Amichai-Hamburger et al., 2002 ; Voorn & Kommers, 2013 ). Further at the education level, there was a significant difference in postgraduate as well as undergraduate students for the accelerating impact of social media on the academic performance among students with extraversion and introversion, and introverts seem to get better use of social media. The gender-wise significant difference was also analysed between extraversion and introversion personalities. Female introversion students were found to gain more of an accelerating impact of social media on their academic performance.

One-way ANOVA: determining “Accelerating Impact” among extraversion and introversion personality traits students at different education levels and genders

Significant at the 0.05 level

Like Table ​ Table3, 3 , the first section of Table ​ Table4 4 compares the deteriorating impact of social media on the academic performance of all students having extraversion and introversion personality traits. Here, the sig. value 0.383 indicates no significant difference among extraversion and introversion students for the “Deteriorating Impact” of social media on academic performance. The mean values show the moderating deteriorating impact of social media on the academic performance of extraversion and introversion personality students. Unlimited use of social media due to the addiction is causing a distraction in academic performance, but the overall impact is not on the higher side. Further, at the education level, the sig. values 0.423 and 0.682 of postgraduate and undergraduate students, respectively, show no significant difference between extraversion and introversion students with respect to “Deteriorating Impact of Social Media Sites”. The mean values again represent the moderate impact. Gender-wise, male students have no difference between the two personality traits, but at the same time, female students have a significant difference in the deteriorating impact, and it is more on extroverted female students.

One-way ANOVA: Examining “Deteriorating Impact” among extraversion and introversion personality traits students at different education levels and genders

The significant value, i.e. 0.82, in Table ​ Table5 5 represents no significant difference between extraversion and introversion personality students for the social media prospects. The higher mean value of both personality students indicates that they are utilizing the opportunities of social media in the most appropriate manner. It seems that all the students are using social media for possible employment prospects, gaining knowledge by attending MOOCs courses and transferring knowledge among other classmates. At the education level, postgraduation students have no significant difference between extraversion and introversion for the social media prospects, but at the undergraduate level, there is a significant difference among both the personalities, and by looking at mean values, extroverted students gain more from the social media prospects. Gender-wise comparison of extraversion and introversion personality students found no significant difference in the social media prospects for male as well as female students.

One-way ANOVA: Examining “Social Media Prospects” among extraversion and introversion personality traits students at different education levels and genders

Table ​ Table6 6 shows the comparison of the social media challenges of all students having extraversion and introversion personality traits. It is also doing a comparative analysis on education level and gender for these two personality traits of students. All sig. values in Table ​ Table6 6 represent no significant difference between extraversion and introversion personality students for social media challenges. Even at the education level and gender-wise comparison of the two personalities, no significant difference is derived. The higher mean values indicate that the threat of cyberbullying, security and privacy is the main concern areas for extraversion and introversion personality students. Cyberbullying is seen to be more particularly among female students (Snell & Englander, 2010 ).

One-way ANOVA: Examining “Social Media Challenges” among extraversion and introversion personality traits students at different education levels and genders

The use of social media sites in academics is becoming popular among students and teachers. The improvement or deterioration in academic performance is influenced by the personality traits of an individual. This study has tried to analyse the impact of social media on the academic performance of extraversion and introversion personality students. This study has identified four factors of social media which have an impact on academic performance. These factors are: accelerating impact of social media; deteriorating impact of social media; social media prospects; and social media challenges.

Each of these factors has been used for comparative analysis of students having extraversion and introversion personality traits. Their education level and gender have also been used to understand the detailed impact between these two personality types. In the overall comparison, it has been discovered that both personalities (extraversion and introversion) have a significant difference for only one factor, i.e. “Accelerating Impact of Social Media Sites” where students with introversion benefited the most. At the education level, i.e. postgraduate and undergraduate, there was a significant difference between extraversion and introversion personalities for the first factor which is the accelerating impact of social media. Here, the introversion students were found to benefit in postgraduate as well as undergraduate courses. For the factors of deteriorating impact and social media challenges, there was no significant difference between extraversion and introversion personality type at the different education levels.

Surprisingly, for the first factor, i.e. the accelerating impact of social media, in gender-wise comparison, no significant difference was found between extraversion and introversion male students. Whereas a significant difference was found in female students. The same was the result for the second factor, i.e. deteriorating impact of social media of male and female students. For social media prospects and social media challenges, no significant difference was identified between extraversion and introversion students of any gender.

Findings and Implications

The personality trait of a student plays a vital role in analysing the impact of social media on their academic performance. The present study was designed to find the difference between extraversion and introversion personality types in students for four identified factors of social media and their impact on students’ academic performance. The education level and gender were also added to make it more comprehensive. The implications of this study are useful for institutions, students, teachers and policymakers.

This study will help the institutions to identify the right mix of social media based on the personality, education level and gender of the students. For example, technological challenges are faced by all students. It is important for the institutions to identify the challenges such as cyberbullying, security and privacy issues and accordingly frame the training sessions for all undergraduate and postgraduate students. These training sessions will help students with extraversion and introversion to come out from possible technological hassles and will create a healthy ecosystem (Okereke & Oghenetega, 2014 ).

Students will also benefit from this study as they will be conscious of the possible pros and cons that exist because of social media usage and its association with students’ academic performance. This learning may help students to enhance their academic performance with the right use of social media sites. The in-depth knowledge of all social media platforms and their association with academics should be elucidated to the students so that they may explore the social media opportunities in an optimum manner. Social media challenges also need to be made known to the students to improve upon and overcome with time (Boateng & Amankwaa, 2016 ).

Teachers are required to design the curriculum by understanding the learning style of students with extraversion and introversion personality type. Innovation and customization in teaching style are important for the holistic development of students and to satisfy the urge for academic requirements. Teachers should also guide the students about the adverse impacts of each social media platform, so that these can be minimized. Students should also be guided to reduce the time limit of using social media (Owusu-Acheaw & Larson, 2015 ).

Policymakers are also required to understand the challenges faced by the students while using social media in academics. All possible threats can be managed by defining and implementing transparent and proactive policies. As social media sites are open in nature, security and privacy are the two major concerns. The Government of India should take a strong stand to control all big social media companies so that they may fulfil the necessary compliances related to students’ security and privacy (Kumar & Pradhan, 2018 ).

The overall result of these comparisons gives a better insight and deep understanding of the significant differences between students with extraversion and introversion personality type towards different social media factors and their impact on students’ academic performance. Students’ behaviour according to their education level and gender for extraversion and introversion personalities has also been explored.

Limitation and Future Scope of Research

Due to COVID restrictions, a convenient sampling technique was used for data collection which may create some response biases where the students of introversion personality traits may have intentionally described themselves as extroversion personalities and vice versa. This study also creates scope for future research. In the Big Five personality model, there are four other personality traits which are not considered in the present study. There is an opportunity to also use cross-personality comparisons for the different social media parameters. The other demographic variables such as age and place may also be explored in future research.

Author contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Dr. SS and Prof. RB. The first draft of the manuscript was written by Dr. SS, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

No funds, grants, or other support was received.

Availability of data and material

Declarations.

The authors declare that they have no conflict of interest.

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Verbal informed consent was obtained from the participants.

Verbal consent is obtained for publication

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Sourabh Sharma, Email: ni.ude.hbimi@hbaruos .

Ramesh Behl, Email: ude.imi@lhebr .

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