ORIGINAL RESEARCH article

Effects of social media use on psychological well-being: a mediated model.

\nDragana Ostic&#x;

  • 1 School of Finance and Economics, Jiangsu University, Zhenjiang, China
  • 2 Research Unit of Governance, Competitiveness, and Public Policies (GOVCOPP), Center for Economics and Finance (cef.up), School of Economics and Management, University of Porto, Porto, Portugal
  • 3 Department of Business Administration, Sukkur Institute of Business Administration (IBA) University, Sukkur, Pakistan
  • 4 CETYS Universidad, Tijuana, Mexico
  • 5 Department of Business Administration, Al-Quds University, Jerusalem, Israel
  • 6 Business School, Shandong University, Weihai, China

The growth in social media use has given rise to concerns about the impacts it may have on users' psychological well-being. This paper's main objective is to shed light on the effect of social media use on psychological well-being. Building on contributions from various fields in the literature, it provides a more comprehensive study of the phenomenon by considering a set of mediators, including social capital types (i.e., bonding social capital and bridging social capital), social isolation, and smartphone addiction. The paper includes a quantitative study of 940 social media users from Mexico, using structural equation modeling (SEM) to test the proposed hypotheses. The findings point to an overall positive indirect impact of social media usage on psychological well-being, mainly due to the positive effect of bonding and bridging social capital. The empirical model's explanatory power is 45.1%. This paper provides empirical evidence and robust statistical analysis that demonstrates both positive and negative effects coexist, helping to reconcile the inconsistencies found so far in the literature.

Introduction

The use of social media has grown substantially in recent years ( Leong et al., 2019 ; Kemp, 2020 ). Social media refers to “the websites and online tools that facilitate interactions between users by providing them opportunities to share information, opinions, and interest” ( Swar and Hameed, 2017 , p. 141). Individuals use social media for many reasons, including entertainment, communication, and searching for information. Notably, adolescents and young adults are spending an increasing amount of time on online networking sites, e-games, texting, and other social media ( Twenge and Campbell, 2019 ). In fact, some authors (e.g., Dhir et al., 2018 ; Tateno et al., 2019 ) have suggested that social media has altered the forms of group interaction and its users' individual and collective behavior around the world.

Consequently, there are increased concerns regarding the possible negative impacts associated with social media usage addiction ( Swar and Hameed, 2017 ; Kircaburun et al., 2020 ), particularly on psychological well-being ( Chotpitayasunondh and Douglas, 2016 ; Jiao et al., 2017 ; Choi and Noh, 2019 ; Chatterjee, 2020 ). Smartphones sometimes distract their users from relationships and social interaction ( Chotpitayasunondh and Douglas, 2016 ; Li et al., 2020a ), and several authors have stressed that the excessive use of social media may lead to smartphone addiction ( Swar and Hameed, 2017 ; Leong et al., 2019 ), primarily because of the fear of missing out ( Reer et al., 2019 ; Roberts and David, 2020 ). Social media usage has been associated with anxiety, loneliness, and depression ( Dhir et al., 2018 ; Reer et al., 2019 ), social isolation ( Van Den Eijnden et al., 2016 ; Whaite et al., 2018 ), and “phubbing,” which refers to the extent to which an individual uses, or is distracted by, their smartphone during face-to-face communication with others ( Chotpitayasunondh and Douglas, 2016 ; Jiao et al., 2017 ; Choi and Noh, 2019 ; Chatterjee, 2020 ).

However, social media use also contributes to building a sense of connectedness with relevant others ( Twenge and Campbell, 2019 ), which may reduce social isolation. Indeed, social media provides several ways to interact both with close ties, such as family, friends, and relatives, and weak ties, including coworkers, acquaintances, and strangers ( Chen and Li, 2017 ), and plays a key role among people of all ages as they exploit their sense of belonging in different communities ( Roberts and David, 2020 ). Consequently, despite the fears regarding the possible negative impacts of social media usage on well-being, there is also an increasing number of studies highlighting social media as a new communication channel ( Twenge and Campbell, 2019 ; Barbosa et al., 2020 ), stressing that it can play a crucial role in developing one's presence, identity, and reputation, thus facilitating social interaction, forming and maintaining relationships, and sharing ideas ( Carlson et al., 2016 ), which consequently may be significantly correlated to social support ( Chen and Li, 2017 ; Holliman et al., 2021 ). Interestingly, recent studies (e.g., David et al., 2018 ; Bano et al., 2019 ; Barbosa et al., 2020 ) have suggested that the impact of smartphone usage on psychological well-being depends on the time spent on each type of application and the activities that users engage in.

Hence, the literature provides contradictory cues regarding the impacts of social media on users' well-being, highlighting both the possible negative impacts and the social enhancement it can potentially provide. In line with views on the need to further investigate social media usage ( Karikari et al., 2017 ), particularly regarding its societal implications ( Jiao et al., 2017 ), this paper argues that there is an urgent need to further understand the impact of the time spent on social media on users' psychological well-being, namely by considering other variables that mediate and further explain this effect.

One of the relevant perspectives worth considering is that provided by social capital theory, which is adopted in this paper. Social capital theory has previously been used to study how social media usage affects psychological well-being (e.g., Bano et al., 2019 ). However, extant literature has so far presented only partial models of associations that, although statistically acceptable and contributing to the understanding of the scope of social networks, do not provide as comprehensive a vision of the phenomenon as that proposed within this paper. Furthermore, the contradictory views, suggesting both negative (e.g., Chotpitayasunondh and Douglas, 2016 ; Van Den Eijnden et al., 2016 ; Jiao et al., 2017 ; Whaite et al., 2018 ; Choi and Noh, 2019 ; Chatterjee, 2020 ) and positive impacts ( Carlson et al., 2016 ; Chen and Li, 2017 ; Twenge and Campbell, 2019 ) of social media on psychological well-being, have not been adequately explored.

Given this research gap, this paper's main objective is to shed light on the effect of social media use on psychological well-being. As explained in detail in the next section, this paper explores the mediating effect of bonding and bridging social capital. To provide a broad view of the phenomenon, it also considers several variables highlighted in the literature as affecting the relationship between social media usage and psychological well-being, namely smartphone addiction, social isolation, and phubbing. The paper utilizes a quantitative study conducted in Mexico, comprising 940 social media users, and uses structural equation modeling (SEM) to test a set of research hypotheses.

This article provides several contributions. First, it adds to existing literature regarding the effect of social media use on psychological well-being and explores the contradictory indications provided by different approaches. Second, it proposes a conceptual model that integrates complementary perspectives on the direct and indirect effects of social media use. Third, it offers empirical evidence and robust statistical analysis that demonstrates that both positive and negative effects coexist, helping resolve the inconsistencies found so far in the literature. Finally, this paper provides insights on how to help reduce the potential negative effects of social media use, as it demonstrates that, through bridging and bonding social capital, social media usage positively impacts psychological well-being. Overall, the article offers valuable insights for academics, practitioners, and society in general.

The remainder of this paper is organized as follows. Section Literature Review presents a literature review focusing on the factors that explain the impact of social media usage on psychological well-being. Based on the literature review, a set of hypotheses are defined, resulting in the proposed conceptual model, which includes both the direct and indirect effects of social media usage on psychological well-being. Section Research Methodology explains the methodological procedures of the research, followed by the presentation and discussion of the study's results in section Results. Section Discussion is dedicated to the conclusions and includes implications, limitations, and suggestions for future research.

Literature Review

Putnam (1995 , p. 664–665) defined social capital as “features of social life – networks, norms, and trust – that enable participants to act together more effectively to pursue shared objectives.” Li and Chen (2014 , p. 117) further explained that social capital encompasses “resources embedded in one's social network, which can be assessed and used for instrumental or expressive returns such as mutual support, reciprocity, and cooperation.”

Putnam (1995 , 2000) conceptualized social capital as comprising two dimensions, bridging and bonding, considering the different norms and networks in which they occur. Bridging social capital refers to the inclusive nature of social interaction and occurs when individuals from different origins establish connections through social networks. Hence, bridging social capital is typically provided by heterogeneous weak ties ( Li and Chen, 2014 ). This dimension widens individual social horizons and perspectives and provides extended access to resources and information. Bonding social capital refers to the social and emotional support each individual receives from his or her social networks, particularly from close ties (e.g., family and friends).

Overall, social capital is expected to be positively associated with psychological well-being ( Bano et al., 2019 ). Indeed, Williams (2006) stressed that interaction generates affective connections, resulting in positive impacts, such as emotional support. The following sub-sections use the lens of social capital theory to explore further the relationship between the use of social media and psychological well-being.

Social Media Use, Social Capital, and Psychological Well-Being

The effects of social media usage on social capital have gained increasing scholarly attention, and recent studies have highlighted a positive relationship between social media use and social capital ( Brown and Michinov, 2019 ; Tefertiller et al., 2020 ). Li and Chen (2014) hypothesized that the intensity of Facebook use by Chinese international students in the United States was positively related to social capital forms. A longitudinal survey based on the quota sampling approach illustrated the positive effects of social media use on the two social capital dimensions ( Chen and Li, 2017 ). Abbas and Mesch (2018) argued that, as Facebook usage increases, it will also increase users' social capital. Karikari et al. (2017) also found positive effects of social media use on social capital. Similarly, Pang (2018) studied Chinese students residing in Germany and found positive effects of social networking sites' use on social capital, which, in turn, was positively associated with psychological well-being. Bano et al. (2019) analyzed the 266 students' data and found positive effects of WhatsApp use on social capital forms and the positive effect of social capital on psychological well-being, emphasizing the role of social integration in mediating this positive effect.

Kim and Kim (2017) stressed the importance of having a heterogeneous network of contacts, which ultimately enhances the potential social capital. Overall, the manifest and social relations between people from close social circles (bonding social capital) and from distant social circles (bridging social capital) are strengthened when they promote communication, social support, and the sharing of interests, knowledge, and skills, which are shared with other members. This is linked to positive effects on interactions, such as acceptance, trust, and reciprocity, which are related to the individuals' health and psychological well-being ( Bekalu et al., 2019 ), including when social media helps to maintain social capital between social circles that exist outside of virtual communities ( Ellison et al., 2007 ).

Grounded on the above literature, this study proposes the following hypotheses:

H1a: Social media use is positively associated with bonding social capital.

H1b: Bonding social capital is positively associated with psychological well-being.

H2a: Social media use is positively associated with bridging social capital.

H2b: Bridging social capital is positively associated with psychological well-being.

Social Media Use, Social Isolation, and Psychological Well-Being

Social isolation is defined as “a deficit of personal relationships or being excluded from social networks” ( Choi and Noh, 2019 , p. 4). The state that occurs when an individual lacks true engagement with others, a sense of social belonging, and a satisfying relationship is related to increased mortality and morbidity ( Primack et al., 2017 ). Those who experience social isolation are deprived of social relationships and lack contact with others or involvement in social activities ( Schinka et al., 2012 ). Social media usage has been associated with anxiety, loneliness, and depression ( Dhir et al., 2018 ; Reer et al., 2019 ), and social isolation ( Van Den Eijnden et al., 2016 ; Whaite et al., 2018 ). However, some recent studies have argued that social media use decreases social isolation ( Primack et al., 2017 ; Meshi et al., 2020 ). Indeed, the increased use of social media platforms such as Facebook, WhatsApp, Instagram, and Twitter, among others, may provide opportunities for decreasing social isolation. For instance, the improved interpersonal connectivity achieved via videos and images on social media helps users evidence intimacy, attenuating social isolation ( Whaite et al., 2018 ).

Chappell and Badger (1989) stated that social isolation leads to decreased psychological well-being, while Choi and Noh (2019) concluded that greater social isolation is linked to increased suicide risk. Schinka et al. (2012) further argued that, when individuals experience social isolation from siblings, friends, family, or society, their psychological well-being tends to decrease. Thus, based on the literature cited above, this study proposes the following hypotheses:

H3a: Social media use is significantly associated with social isolation.

H3b: Social isolation is negatively associated with psychological well-being.

Social Media Use, Smartphone Addiction, Phubbing, and Psychological Well-Being

Smartphone addiction refers to “an individuals' excessive use of a smartphone and its negative effects on his/her life as a result of his/her inability to control his behavior” ( Gökçearslan et al., 2018 , p. 48). Regardless of its form, smartphone addiction results in social, medical, and psychological harm to people by limiting their ability to make their own choices ( Chotpitayasunondh and Douglas, 2016 ). The rapid advancement of information and communication technologies has led to the concept of social media, e-games, and also to smartphone addiction ( Chatterjee, 2020 ). The excessive use of smartphones for social media use, entertainment (watching videos, listening to music), and playing e-games is more common amongst people addicted to smartphones ( Jeong et al., 2016 ). In fact, previous studies have evidenced the relationship between social use and smartphone addiction ( Salehan and Negahban, 2013 ; Jeong et al., 2016 ; Swar and Hameed, 2017 ). In line with this, the following hypotheses are proposed:

H4a: Social media use is positively associated with smartphone addiction.

H4b: Smartphone addiction is negatively associated with psychological well-being.

While smartphones are bringing individuals closer, they are also, to some extent, pulling people apart ( Tonacci et al., 2019 ). For instance, they can lead to individuals ignoring others with whom they have close ties or physical interactions; this situation normally occurs due to extreme smartphone use (i.e., at the dinner table, in meetings, at get-togethers and parties, and in other daily activities). This act of ignoring others is called phubbing and is considered a common phenomenon in communication activities ( Guazzini et al., 2019 ; Chatterjee, 2020 ). Phubbing is also referred to as an act of snubbing others ( Chatterjee, 2020 ). This term was initially used in May 2012 by an Australian advertising agency to describe the “growing phenomenon of individuals ignoring their families and friends who were called phubbee (a person who is a recipients of phubbing behavior) victim of phubber (a person who start phubbing her or his companion)” ( Chotpitayasunondh and Douglas, 2018 ). Smartphone addiction has been found to be a determinant of phubbing ( Kim et al., 2018 ). Other recent studies have also evidenced the association between smartphones and phubbing ( Chotpitayasunondh and Douglas, 2016 ; Guazzini et al., 2019 ; Tonacci et al., 2019 ; Chatterjee, 2020 ). Vallespín et al. (2017 ) argued that phubbing behavior has a negative influence on psychological well-being and satisfaction. Furthermore, smartphone addiction is considered responsible for the development of new technologies. It may also negatively influence individual's psychological proximity ( Chatterjee, 2020 ). Therefore, based on the above discussion and calls for the association between phubbing and psychological well-being to be further explored, this study proposes the following hypotheses:

H5: Smartphone addiction is positively associated with phubbing.

H6: Phubbing is negatively associated with psychological well-being.

Indirect Relationship Between Social Media Use and Psychological Well-Being

Beyond the direct hypotheses proposed above, this study investigates the indirect effects of social media use on psychological well-being mediated by social capital forms, social isolation, and phubbing. As described above, most prior studies have focused on the direct influence of social media use on social capital forms, social isolation, smartphone addiction, and phubbing, as well as the direct impact of social capital forms, social isolation, smartphone addiction, and phubbing on psychological well-being. Very few studies, however, have focused on and evidenced the mediating role of social capital forms, social isolation, smartphone addiction, and phubbing derived from social media use in improving psychological well-being ( Chen and Li, 2017 ; Pang, 2018 ; Bano et al., 2019 ; Choi and Noh, 2019 ). Moreover, little is known about smartphone addiction's mediating role between social media use and psychological well-being. Therefore, this study aims to fill this gap in the existing literature by investigating the mediation of social capital forms, social isolation, and smartphone addiction. Further, examining the mediating influence will contribute to a more comprehensive understanding of social media use on psychological well-being via the mediating associations of smartphone addiction and psychological factors. Therefore, based on the above, we propose the following hypotheses (the conceptual model is presented in Figure 1 ):

H7: (a) Bonding social capital; (b) bridging social capital; (c) social isolation; and (d) smartphone addiction mediate the relationship between social media use and psychological well-being.

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Figure 1 . Conceptual model.

Research Methodology

Sample procedure and online survey.

This study randomly selected students from universities in Mexico. We chose University students for the following reasons. First, students are considered the most appropriate sample for e-commerce studies, particularly in the social media context ( Oghazi et al., 2018 ; Shi et al., 2018 ). Second, University students are considered to be frequent users and addicted to smartphones ( Mou et al., 2017 ; Stouthuysen et al., 2018 ). Third, this study ensured that respondents were experienced, well-educated, and possessed sufficient knowledge of the drawbacks of social media and the extreme use of smartphones. A total sample size of 940 University students was ultimately achieved from the 1,500 students contacted, using a convenience random sampling approach, due both to the COVID-19 pandemic and budget and time constraints. Additionally, in order to test the model, a quantitative empirical study was conducted, using an online survey method to collect data. This study used a web-based survey distributed via social media platforms for two reasons: the COVID-19 pandemic; and to reach a large number of respondents ( Qalati et al., 2021 ). Furthermore, online surveys are considered a powerful and authenticated tool for new research ( Fan et al., 2021 ), while also representing a fast, simple, and less costly approach to collecting data ( Dutot and Bergeron, 2016 ).

Data Collection Procedures and Respondent's Information

Data were collected by disseminating a link to the survey by e-mail and social network sites. Before presenting the closed-ended questionnaire, respondents were assured that their participation would remain voluntary, confidential, and anonymous. Data collection occurred from July 2020 to December 2020 (during the pandemic). It should be noted that, because data were collected during the pandemic, this may have had an influence on the results of the study. The reason for choosing a six-month lag time was to mitigate common method bias (CMB) ( Li et al., 2020b ). In the present study, 1,500 students were contacted via University e-mail and social applications (Facebook, WhatsApp, and Instagram). We sent a reminder every month for 6 months (a total of six reminders), resulting in 940 valid responses. Thus, 940 (62.6% response rate) responses were used for hypotheses testing.

Table 1 reveals that, of the 940 participants, three-quarters were female (76.4%, n = 719) and nearly one-quarter (23.6%, n = 221) were male. Nearly half of the participants (48.8%, n = 459) were aged between 26 and 35 years, followed by 36 to 35 years (21.9%, n = 206), <26 (20.3%, n = 191), and over 45 (8.9%, n = 84). Approximately two-thirds (65%, n = 611) had a bachelor's degree or above, while one-third had up to 12 years of education. Regarding the daily frequency of using the Internet, nearly half (48.6%, n = 457) of the respondents reported between 5 and 8 h a day, and over one-quarter (27.2%) 9–12 h a day. Regarding the social media platforms used, over 38.5 and 39.6% reported Facebook and WhatsApp, respectively. Of the 940 respondents, only 22.1% reported Instagram (12.8%) and Twitter (9.2%). It should be noted, however, that the sample is predominantly female and well-educated.

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Table 1 . Respondents' characteristics.

Measurement Items

The study used five-point Likert scales (1 = “strongly disagree;” 5 = “strongly agree”) to record responses.

Social Media Use

Social media use was assessed using four items adapted from Karikari et al. (2017) . Sample items include “Social media is part of my everyday activity,” “Social media has become part of my daily life,” “I would be sorry if social media shut down,” and “I feel out of touch, when I have not logged onto social media for a while.” The adapted items had robust reliability and validity (CA = 783, CR = 0.857, AVE = 0.600).

Social Capital

Social capital was measured using a total of eight items, representing bonding social capital (four items) and bridging social capital (four items) adapted from Chan (2015) . Sample construct items include: bonging social capital (“I am willing to spend time to support general community activities,” “I interact with people who are quite different from me”) and bridging social capital (“My social media community is a good place to be,” “Interacting with people on social media makes me want to try new things”). The adapted items had robust reliability and validity [bonding social capital (CA = 0.785, CR = 0.861, AVE = 0.608) and bridging social capital (CA = 0.834, CR = 0.883, AVE = 0.601)].

Social Isolation

Social isolation was assessed using three items from Choi and Noh (2019) . Sample items include “I do not have anyone to play with,” “I feel alone from people,” and “I have no one I can trust.” This adapted scale had substantial reliability and validity (CA = 0.890, CR = 0.928, AVE = 0.811).

Smartphone Addiction

Smartphone addiction was assessed using five items taken from Salehan and Negahban (2013) . Sample items include “I am always preoccupied with my mobile,” “Using my mobile phone keeps me relaxed,” and “I am not able to control myself from frequent use of mobile phones.” Again, these adapted items showed substantial reliability and validity (CA = 903, CR = 0.928, AVE = 0.809).

Phubbing was assessed using four items from Chotpitayasunondh and Douglas (2018) . Sample items include: “I have conflicts with others because I am using my phone” and “I would rather pay attention to my phone than talk to others.” This construct also demonstrated significant reliability and validity (CA = 770, CR = 0.894, AVE = 0.809).

Psychological Well-Being

Psychological well-being was assessed using five items from Jiao et al. (2017) . Sample items include “I lead a purposeful and meaningful life with the help of others,” “My social relationships are supportive and rewarding in social media,” and “I am engaged and interested in my daily on social media.” This study evidenced that this adapted scale had substantial reliability and validity (CA = 0.886, CR = 0.917, AVE = 0.688).

Data Analysis

Based on the complexity of the association between the proposed construct and the widespread use and acceptance of SmartPLS 3.0 in several fields ( Hair et al., 2019 ), we utilized SEM, using SmartPLS 3.0, to examine the relationships between constructs. Structural equation modeling is a multivariate statistical analysis technique that is used to investigate relationships. Further, it is a combination of factor and multivariate regression analysis, and is employed to explore the relationship between observed and latent constructs.

SmartPLS 3.0 “is a more comprehensive software program with an intuitive graphical user interface to run partial least square SEM analysis, certainly has had a massive impact” ( Sarstedt and Cheah, 2019 ). According to Ringle et al. (2015) , this commercial software offers a wide range of algorithmic and modeling options, improved usability, and user-friendly and professional support. Furthermore, Sarstedt and Cheah (2019) suggested that structural equation models enable the specification of complex interrelationships between observed and latent constructs. Hair et al. (2019) argued that, in recent years, the number of articles published using partial least squares SEM has increased significantly in contrast to covariance-based SEM. In addition, partial least squares SEM using SmartPLS is more appealing for several scholars as it enables them to predict more complex models with several variables, indicator constructs, and structural paths, instead of imposing distributional assumptions on the data ( Hair et al., 2019 ). Therefore, this study utilized the partial least squares SEM approach using SmartPLS 3.0.

Common Method Bias (CMB) Test

This study used the Kaiser–Meyer–Olkin (KMO) test to measure the sampling adequacy and ensure data suitability. The KMO test result was 0.874, which is greater than an acceptable threshold of 0.50 ( Ali Qalati et al., 2021 ; Shrestha, 2021 ), and hence considered suitable for explanatory factor analysis. Moreover, Bartlett's test results demonstrated a significance level of 0.001, which is considered good as it is below the accepted threshold of 0.05.

The term CMB is associated with Campbell and Fiske (1959) , who highlighted the importance of CMB and identified that a portion of variance in the research may be due to the methods employed. It occurs when all scales of the study are measured at the same time using a single questionnaire survey ( Podsakoff and Organ, 1986 ); subsequently, estimates of the relationship among the variables might be distorted by the impacts of CMB. It is considered a serious issue that has a potential to “jeopardize” the validity of the study findings ( Tehseen et al., 2017 ). There are several reasons for CMB: (1) it mainly occurs due to response “tendencies that raters can apply uniformity across the measures;” and (2) it also occurs due to similarities in the wording and structure of the survey items that produce similar results ( Jordan and Troth, 2019 ). Harman's single factor test and a full collinearity approach were employed to ensure that the data was free from CMB ( Tehseen et al., 2017 ; Jordan and Troth, 2019 ; Ali Qalati et al., 2021 ). Harman's single factor test showed a single factor explained only 22.8% of the total variance, which is far below the 50.0% acceptable threshold ( Podsakoff et al., 2003 ).

Additionally, the variance inflation factor (VIF) was used, which is a measure of the amount of multicollinearity in a set of multiple regression constructs and also considered a way of detecting CMB ( Hair et al., 2019 ). Hair et al. (2019) suggested that the acceptable threshold for the VIF is 3.0; as the computed VIFs for the present study ranged from 1.189 to 1.626, CMB is not a key concern (see Table 2 ). Bagozzi et al. (1991) suggested a correlation-matrix procedure to detect CMB. Common method bias is evident if correlation among the principle constructs is >0.9 ( Tehseen et al., 2020 ); however, no values >0.9 were found in this study (see section Assessment of Measurement Model). This study used a two-step approach to evaluate the measurement model and the structural model.

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Table 2 . Common method bias (full collinearity VIF).

Assessment of Measurement Model

Before conducting the SEM analysis, the measurement model was assessed to examine individual item reliability, internal consistency, and convergent and discriminant validity. Table 3 exhibits the values of outer loading used to measure an individual item's reliability ( Hair et al., 2012 ). Hair et al. (2017) proposed that the value for each outer loading should be ≥0.7; following this principle, two items of phubbing (PHUB3—I get irritated if others ask me to get off my phone and talk to them; PHUB4—I use my phone even though I know it irritated others) were removed from the analysis Hair et al. (2019) . According to Nunnally (1978) , Cronbach's alpha values should exceed 0.7. The threshold values of constructs in this study ranged from 0.77 to 0.903. Regarding internal consistency, Bagozzi and Yi (1988) suggested that composite reliability (CR) should be ≥0.7. The coefficient value for CR in this study was between 0.857 and 0.928. Regarding convergent validity, Fornell and Larcker (1981) suggested that the average variance extracted (AVE) should be ≥0.5. Average variance extracted values in this study were between 0.60 and 0.811. Finally, regarding discriminant validity, according to Fornell and Larcker (1981) , the square root of the AVE for each construct should exceed the inter-correlations of the construct with other model constructs. That was the case in this study, as shown in Table 4 .

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Table 3 . Study measures, factor loading, and the constructs' reliability and convergent validity.

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Table 4 . Discriminant validity and correlation.

Hence, by analyzing the results of the measurement model, it can be concluded that the data are adequate for structural equation estimation.

Assessment of the Structural Model

This study used the PLS algorithm and a bootstrapping technique with 5,000 bootstraps as proposed by Hair et al. (2019) to generate the path coefficient values and their level of significance. The coefficient of determination ( R 2 ) is an important measure to assess the structural model and its explanatory power ( Henseler et al., 2009 ; Hair et al., 2019 ). Table 5 and Figure 2 reveal that the R 2 value in the present study was 0.451 for psychological well-being, which means that 45.1% of changes in psychological well-being occurred due to social media use, social capital forms (i.e., bonding and bridging), social isolation, smartphone addiction, and phubbing. Cohen (1998) proposed that R 2 values of 0.60, 0.33, and 0.19 are considered substantial, moderate, and weak. Following Cohen's (1998) threshold values, this research demonstrates a moderate predicting power for psychological well-being among Mexican respondents ( Table 6 ).

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Table 5 . Summary of path coefficients and hypothesis testing.

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Figure 2 . Structural model.

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Table 6 . Strength of the model (Predictive relevance, coefficient of determination, and model fit indices).

Apart from the R 2 measure, the present study also used cross-validated redundancy measures, or effect sizes ( q 2 ), to assess the proposed model and validate the results ( Ringle et al., 2012 ). Hair et al. (2019) suggested that a model exhibiting an effect size q 2 > 0 has predictive relevance ( Table 6 ). This study's results evidenced that it has a 0.15 <0.29 <0.35 (medium) predictive relevance, as 0.02, 0.15, and 0.35 are considered small, medium, and large, respectively ( Cohen, 1998 ). Regarding the goodness-of-fit indices, Hair et al. (2019) suggested the standardized root mean square residual (SRMR) to evaluate the goodness of fit. Standardized root mean square is an absolute measure of fit: a value of zero indicates perfect fit and a value <0.08 is considered good fit ( Hair et al., 2019 ). This study exhibits an adequate model fitness level with an SRMR value of 0.063 ( Table 6 ).

Table 5 reveals that all hypotheses of the study were accepted base on the criterion ( p -value < 0.05). H1a (β = 0.332, t = 10.283, p = 0.001) was confirmed, with the second most robust positive and significant relationship (between social media use and bonding social capital). In addition, this study evidenced a positive and significant relationship between bonding social capital and psychological well-being (β = 0.127, t = 4.077, p = 0.001); therefore, H1b was accepted. Regarding social media use and bridging social capital, the present study found the most robust positive and significant impact (β = 0.439, t = 15.543, p = 0.001); therefore, H2a was accepted. The study also evidenced a positive and significant association between bridging social capital and psychological well-being (β = 0.561, t = 20.953, p = 0.001); thus, H2b was accepted. The present study evidenced a significant effect of social media use on social isolation (β = 0.145, t = 4.985, p = 0.001); thus, H3a was accepted. In addition, this study accepted H3b (β = −0.051, t = 2.01, p = 0.044). Furthermore, this study evidenced a positive and significant effect of social media use on smartphone addiction (β = 0.223, t = 6.241, p = 0.001); therefore, H4a was accepted. Furthermore, the present study found that smartphone addiction has a negative significant influence on psychological well-being (β = −0.068, t = 2.387, p = 0.017); therefore, H4b was accepted. Regarding the relationship between smartphone addiction and phubbing, this study found a positive and significant effect of smartphone addiction on phubbing (β = 0.244, t = 7.555, p = 0.001); therefore, H5 was accepted. Furthermore, the present research evidenced a positive and significant influence of phubbing on psychological well-being (β = 0.137, t = 4.938, p = 0.001); therefore, H6 was accepted. Finally, the study provides interesting findings on the indirect effect of social media use on psychological well-being ( t -value > 1.96 and p -value < 0.05); therefore, H7a–d were accepted.

Furthermore, to test the mediating analysis, Preacher and Hayes's (2008) approach was used. The key characteristic of an indirect relationship is that it involves a third construct, which plays a mediating role in the relationship between the independent and dependent constructs. Logically, the effect of A (independent construct) on C (the dependent construct) is mediated by B (a third variable). Preacher and Hayes (2008) suggested the following: B is a construct acting as a mediator if A significantly influences B, A significantly accounts for variability in C, B significantly influences C when controlling for A, and the influence of A on C decreases significantly when B is added simultaneously with A as a predictor of C. According to Matthews et al. (2018) , if the indirect effect is significant while the direct insignificant, full mediation has occurred, while if both direct and indirect effects are substantial, partial mediation has occurred. This study evidenced that there is partial mediation in the proposed construct ( Table 5 ). Following Preacher and Hayes (2008) this study evidenced that there is partial mediation in the proposed construct, because the relationship between independent variable (social media use) and dependent variable (psychological well-being) is significant ( p -value < 0.05) and indirect effect among them after introducing mediator (bonding social capital, bridging social capital, social isolation, and smartphone addiction) is also significant ( p -value < 0.05), therefore it is evidenced that when there is a significant effect both direct and indirect it's called partial mediation.

The present study reveals that the social and psychological impacts of social media use among University students is becoming more complex as there is continuing advancement in technology, offering a range of affordable interaction opportunities. Based on the 940 valid responses collected, all the hypotheses were accepted ( p < 0.05).

H1a finding suggests that social media use is a significant influencing factor of bonding social capital. This implies that, during a pandemic, social media use enables students to continue their close relationships with family members, friends, and those with whom they have close ties. This finding is in line with prior work of Chan (2015) and Ellison et al. (2007) , who evidenced that social bonding capital is predicted by Facebook use and having a mobile phone. H1b findings suggest that, when individuals believe that social communication can help overcome obstacles to interaction and encourage more virtual self-disclosure, social media use can improve trust and promote the establishment of social associations, thereby enhancing well-being. These findings are in line with those of Gong et al. (2021) , who also witnessed the significant effect of bonding social capital on immigrants' psychological well-being, subsequently calling for the further evidence to confirm the proposed relationship.

The findings of the present study related to H2a suggest that students are more likely to use social media platforms to receive more emotional support, increase their ability to mobilize others, and to build social networks, which leads to social belongingness. Furthermore, the findings suggest that social media platforms enable students to accumulate and maintain bridging social capital; further, online classes can benefit students who feel shy when participating in offline classes. This study supports the previous findings of Chan (2015) and Karikari et al. (2017) . Notably, the present study is not limited to a single social networking platform, taking instead a holistic view of social media. The H2b findings are consistent with those of Bano et al. (2019) , who also confirmed the link between bonding social capital and psychological well-being among University students using WhatsApp as social media platform, as well as those of Chen and Li (2017) .

The H3a findings suggest that, during the COVID-19 pandemic when most people around the world have had limited offline or face-to-face interaction and have used social media to connect with families, friends, and social communities, they have often been unable to connect with them. This is due to many individuals avoiding using social media because of fake news, financial constraints, and a lack of trust in social media; thus, the lack both of offline and online interaction, coupled with negative experiences on social media use, enhances the level of social isolation ( Hajek and König, 2021 ). These findings are consistent with those of Adnan and Anwar (2020) . The H3b suggests that higher levels of social isolation have a negative impact on psychological well-being. These result indicating that, consistent with Choi and Noh (2019) , social isolation is negatively and significantly related to psychological well-being.

The H4a results suggests that substantial use of social media use leads to an increase in smartphone addiction. These findings are in line with those of Jeong et al. (2016) , who stated that the excessive use of smartphones for social media, entertainment (watching videos, listening to music), and playing e-games was more likely to lead to smartphone addiction. These findings also confirm the previous work of Jeong et al. (2016) , Salehan and Negahban (2013) , and Swar and Hameed (2017) . The H4b results revealed that a single unit increase in smartphone addiction results in a 6.8% decrease in psychological well-being. These findings are in line with those of Tangmunkongvorakul et al. (2019) , who showed that students with higher levels of smartphone addiction had lower psychological well-being scores. These findings also support those of Shoukat (2019) , who showed that smartphone addiction inversely influences individuals' mental health.

This suggests that the greater the smartphone addiction, the greater the phubbing. The H5 findings are in line with those of Chatterjee (2020) , Chotpitayasunondh and Douglas (2016) , Guazzini et al. (2019) , and Tonacci et al. (2019) , who also evidenced a significant impact of smartphone addiction and phubbing. Similarly, Chotpitayasunondh and Douglas (2018) corroborated that smartphone addiction is the main predictor of phubbing behavior. However, these findings are inconsistent with those of Vallespín et al. (2017 ), who found a negative influence of phubbing.

The H6 results suggests that phubbing is one of the significant predictors of psychological well-being. Furthermore, these findings suggest that, when phubbers use a cellphone during interaction with someone, especially during the current pandemic, and they are connected with many family members, friends, and relatives; therefore, this kind of action gives them more satisfaction, which simultaneously results in increased relaxation and decreased depression ( Chotpitayasunondh and Douglas, 2018 ). These findings support those of Davey et al. (2018) , who evidenced that phubbing has a significant influence on adolescents and social health students in India.

The findings showed a significant and positive effect of social media use on psychological well-being both through bridging and bonding social capital. However, a significant and negative effect of social media use on psychological well-being through smartphone addiction and through social isolation was also found. Hence, this study provides evidence that could shed light on the contradictory contributions in the literature suggesting both positive (e.g., Chen and Li, 2017 ; Twenge and Campbell, 2019 ; Roberts and David, 2020 ) and negative (e.g., Chotpitayasunondh and Douglas, 2016 ; Jiao et al., 2017 ; Choi and Noh, 2019 ; Chatterjee, 2020 ) effects of social media use on psychological well-being. This study concludes that the overall impact is positive, despite some degree of negative indirect impact.

Theoretical Contributions

This study's findings contribute to the current literature, both by providing empirical evidence for the relationships suggested by extant literature and by demonstrating the relevance of adopting a more complex approach that considers, in particular, the indirect effect of social media on psychological well-being. As such, this study constitutes a basis for future research ( Van Den Eijnden et al., 2016 ; Whaite et al., 2018 ) aiming to understand the impacts of social media use and to find ways to reduce its possible negative impacts.

In line with Kim and Kim (2017) , who stressed the importance of heterogeneous social networks in improving social capital, this paper suggests that, to positively impact psychological well-being, social media usage should be associated both with strong and weak ties, as both are important in building social capital, and hence associated with its bonding and bridging facets. Interestingly, though, bridging capital was shown as having the greatest impact on psychological well-being. Thus, the importance of wider social horizons, the inclusion in different groups, and establishing new connections ( Putnam, 1995 , 2000 ) with heterogeneous weak ties ( Li and Chen, 2014 ) are highlighted in this paper.

Practical Contributions

These findings are significant for practitioners, particularly those interested in dealing with the possible negative impacts of social media use on psychological well-being. Although social media use is associated with factors that negatively impact psychological well-being, particularly smartphone addiction and social isolation, these negative impacts can be lessened if the connections with both strong and weak ties are facilitated and featured by social media. Indeed, social media platforms offer several features, from facilitating communication with family, friends, and acquaintances, to identifying and offering access to other people with shared interests. However, it is important to access heterogeneous weak ties ( Li and Chen, 2014 ) so that social media offers access to wider sources of information and new resources, hence enhancing bridging social capital.

Limitations and Directions for Future Studies

This study is not without limitations. For example, this study used a convenience sampling approach to reach to a large number of respondents. Further, this study was conducted in Mexico only, limiting the generalizability of the results; future research should therefore use a cross-cultural approach to investigate the impacts of social media use on psychological well-being and the mediating role of proposed constructs (e.g., bonding and bridging social capital, social isolation, and smartphone addiction). The sample distribution may also be regarded as a limitation of the study because respondents were mainly well-educated and female. Moreover, although Internet channels represent a particularly suitable way to approach social media users, the fact that this study adopted an online survey does not guarantee a representative sample of the population. Hence, extrapolating the results requires caution, and study replication is recommended, particularly with social media users from other countries and cultures. The present study was conducted in the context of mainly University students, primarily well-educated females, via an online survey on in Mexico; therefore, the findings represent a snapshot at a particular time. Notably, however, the effect of social media use is increasing due to COVID-19 around the globe and is volatile over time.

Two of the proposed hypotheses of this study, namely the expected negative impacts of social media use on social isolation and of phubbing on psychological well-being, should be further explored. One possible approach is to consider the type of connections (i.e., weak and strong ties) to explain further the impact of social media usage on social isolation. Apparently, the prevalence of weak ties, although facilitating bridging social capital, may have an adverse impact in terms of social isolation. Regarding phubbing, the fact that the findings point to a possible positive impact on psychological well-being should be carefully addressed, specifically by psychology theorists and scholars, in order to identify factors that may help further understand this phenomenon. Other suggestions for future research include using mixed-method approaches, as qualitative studies could help further validate the results and provide complementary perspectives on the relationships between the considered variables.

Data Availability Statement

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

Ethics Statement

The studies involving human participants were reviewed and approved by Jiangsu University. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

This study is supported by the National Statistics Research Project of China (2016LY96).

Conflict of Interest

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

Abbas, R., and Mesch, G. (2018). Do rich teens get richer? Facebook use and the link between offline and online social capital among Palestinian youth in Israel. Inf. Commun. Soc. 21, 63–79. doi: 10.1080/1369118X.2016.1261168

CrossRef Full Text | Google Scholar

Adnan, M., and Anwar, K. (2020). Online learning amid the COVID-19 pandemic: students' perspectives. J. Pedagog. Sociol. Psychol. 2, 45–51. doi: 10.33902/JPSP.2020261309

PubMed Abstract | CrossRef Full Text | Google Scholar

Ali Qalati, S., Li, W., Ahmed, N., Ali Mirani, M., and Khan, A. (2021). Examining the factors affecting SME performance: the mediating role of social media adoption. Sustainability 13:75. doi: 10.3390/su13010075

Bagozzi, R. P., and Yi, Y. (1988). On the evaluation of structural equation models. J. Acad. Mark. Sci. 16, 74–94. doi: 10.1007/BF02723327

Bagozzi, R. P., Yi, Y., and Phillips, L. W. (1991). Assessing construct validity in organizational research. Admin. Sci. Q. 36, 421–458. doi: 10.2307/2393203

Bano, S., Cisheng, W., Khan, A. N., and Khan, N. A. (2019). WhatsApp use and student's psychological well-being: role of social capital and social integration. Child. Youth Serv. Rev. 103, 200–208. doi: 10.1016/j.childyouth.2019.06.002

Barbosa, B., Chkoniya, V., Simoes, D., Filipe, S., and Santos, C. A. (2020). Always connected: generation Y smartphone use and social capital. Rev. Ibérica Sist. Tecnol. Inf. E 35, 152–166.

Google Scholar

Bekalu, M. A., McCloud, R. F., and Viswanath, K. (2019). Association of social media use with social well-being, positive mental health, and self-rated health: disentangling routine use from emotional connection to use. Health Educ. Behav. 46(2 Suppl), 69S−80S. doi: 10.1177/1090198119863768

Brown, G., and Michinov, N. (2019). Measuring latent ties on Facebook: a novel approach to studying their prevalence and relationship with bridging social capital. Technol. Soc. 59:101176. doi: 10.1016/j.techsoc.2019.101176

Campbell, D. T., and Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychol. Bull. 56, 81–105. doi: 10.1037/h0046016

Carlson, J. R., Zivnuska, S., Harris, R. B., Harris, K. J., and Carlson, D. S. (2016). Social media use in the workplace: a study of dual effects. J. Org. End User Comput. 28, 15–31. doi: 10.4018/JOEUC.2016010102

Chan, M. (2015). Mobile phones and the good life: examining the relationships among mobile use, social capital and subjective well-being. New Media Soc. 17, 96–113. doi: 10.1177/1461444813516836

Chappell, N. L., and Badger, M. (1989). Social isolation and well-being. J. Gerontol. 44, S169–S176. doi: 10.1093/geronj/44.5.s169

Chatterjee, S. (2020). Antecedents of phubbing: from technological and psychological perspectives. J. Syst. Inf. Technol. 22, 161–118. doi: 10.1108/JSIT-05-2019-0089

Chen, H.-T., and Li, X. (2017). The contribution of mobile social media to social capital and psychological well-being: examining the role of communicative use, friending and self-disclosure. Comput. Hum. Behav. 75, 958–965. doi: 10.1016/j.chb.2017.06.011

Choi, D.-H., and Noh, G.-Y. (2019). The influence of social media use on attitude toward suicide through psychological well-being, social isolation, and social support. Inf. Commun. Soc. 23, 1–17. doi: 10.1080/1369118X.2019.1574860

Chotpitayasunondh, V., and Douglas, K. M. (2016). How “phubbing” becomes the norm: the antecedents and consequences of snubbing via smartphone. Comput. Hum. Behav. 63, 9–18. doi: 10.1016/j.chb.2016.05.018

Chotpitayasunondh, V., and Douglas, K. M. (2018). The effects of “phubbing” on social interaction. J. Appl. Soc. Psychol. 48, 304–316. doi: 10.1111/jasp.12506

Cohen, J. (1998). Statistical Power Analysis for the Behavioural Sciences . Hillsdale, NJ: Lawrence Erlbaum Associates.

Davey, S., Davey, A., Raghav, S. K., Singh, J. V., Singh, N., Blachnio, A., et al. (2018). Predictors and consequences of “phubbing” among adolescents and youth in India: an impact evaluation study. J. Fam. Community Med. 25, 35–42. doi: 10.4103/jfcm.JFCM_71_17

David, M. E., Roberts, J. A., and Christenson, B. (2018). Too much of a good thing: investigating the association between actual smartphone use and individual well-being. Int. J. Hum. Comput. Interact. 34, 265–275. doi: 10.1080/10447318.2017.1349250

Dhir, A., Yossatorn, Y., Kaur, P., and Chen, S. (2018). Online social media fatigue and psychological wellbeing—a study of compulsive use, fear of missing out, fatigue, anxiety and depression. Int. J. Inf. Manag. 40, 141–152. doi: 10.1016/j.ijinfomgt.2018.01.012

Dutot, V., and Bergeron, F. (2016). From strategic orientation to social media orientation: improving SMEs' performance on social media. J. Small Bus. Enterp. Dev. 23, 1165–1190. doi: 10.1108/JSBED-11-2015-0160

Ellison, N. B., Steinfield, C., and Lampe, C. (2007). The benefits of Facebook “friends:” Social capital and college students' use of online social network sites. J. Comput. Mediat. Commun. 12, 1143–1168. doi: 10.1111/j.1083-6101.2007.00367.x

Fan, M., Huang, Y., Qalati, S. A., Shah, S. M. M., Ostic, D., and Pu, Z. (2021). Effects of information overload, communication overload, and inequality on digital distrust: a cyber-violence behavior mechanism. Front. Psychol. 12:643981. doi: 10.3389/fpsyg.2021.643981

Fornell, C., and Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. J. Market. Res. 18, 39–50. doi: 10.1177/002224378101800104

Gökçearslan, S., Uluyol, Ç., and Sahin, S. (2018). Smartphone addiction, cyberloafing, stress and social support among University students: a path analysis. Child. Youth Serv. Rev. 91, 47–54. doi: 10.1016/j.childyouth.2018.05.036

Gong, S., Xu, P., and Wang, S. (2021). Social capital and psychological well-being of Chinese immigrants in Japan. Int. J. Environ. Res. Public Health 18:547. doi: 10.3390/ijerph18020547

Guazzini, A., Duradoni, M., Capelli, A., and Meringolo, P. (2019). An explorative model to assess individuals' phubbing risk. Fut. Internet 11:21. doi: 10.3390/fi11010021

Hair, J. F., Risher, J. J., Sarstedt, M., and Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. Eur. Bus. Rev. 31, 2–24. doi: 10.1108/EBR-11-2018-0203

Hair, J. F., Sarstedt, M., Pieper, T. M., and Ringle, C. M. (2012). The use of partial least squares structural equation modeling in strategic management research: a review of past practices and recommendations for future applications. Long Range Plann. 45, 320–340. doi: 10.1016/j.lrp.2012.09.008

Hair, J. F., Sarstedt, M., Ringle, C. M., and Gudergan, S. P. (2017). Advanced Issues in Partial Least Squares Structural Equation Modeling. Thousand Oaks, CA: Sage.

Hajek, A., and König, H.-H. (2021). Social isolation and loneliness of older adults in times of the CoViD-19 pandemic: can use of online social media sites and video chats assist in mitigating social isolation and loneliness? Gerontology 67, 121–123. doi: 10.1159/000512793

Henseler, J., Ringle, C. M., and Sinkovics, R. R. (2009). “The use of partial least squares path modeling in international marketing,” in New Challenges to International Marketing , Vol. 20, eds R.R. Sinkovics and P.N. Ghauri (Bigley: Emerald), 277–319.

Holliman, A. J., Waldeck, D., Jay, B., Murphy, S., Atkinson, E., Collie, R. J., et al. (2021). Adaptability and social support: examining links with psychological wellbeing among UK students and non-students. Fron. Psychol. 12:636520. doi: 10.3389/fpsyg.2021.636520

Jeong, S.-H., Kim, H., Yum, J.-Y., and Hwang, Y. (2016). What type of content are smartphone users addicted to? SNS vs. games. Comput. Hum. Behav. 54, 10–17. doi: 10.1016/j.chb.2015.07.035

Jiao, Y., Jo, M.-S., and Sarigöllü, E. (2017). Social value and content value in social media: two paths to psychological well-being. J. Org. Comput. Electr. Commer. 27, 3–24. doi: 10.1080/10919392.2016.1264762

Jordan, P. J., and Troth, A. C. (2019). Common method bias in applied settings: the dilemma of researching in organizations. Austr. J. Manag. 45, 3–14. doi: 10.1177/0312896219871976

Karikari, S., Osei-Frimpong, K., and Owusu-Frimpong, N. (2017). Evaluating individual level antecedents and consequences of social media use in Ghana. Technol. Forecast. Soc. Change 123, 68–79. doi: 10.1016/j.techfore.2017.06.023

Kemp, S. (January 30, 2020). Digital 2020: 3.8 billion people use social media. We Are Social . Available online at: https://wearesocial.com/blog/2020/01/digital-2020-3-8-billion-people-use-social-media .

Kim, B., and Kim, Y. (2017). College students' social media use and communication network heterogeneity: implications for social capital and subjective well-being. Comput. Hum. Behav. 73, 620–628. doi: 10.1016/j.chb.2017.03.033

Kim, K., Milne, G. R., and Bahl, S. (2018). Smart phone addiction and mindfulness: an intergenerational comparison. Int. J. Pharmaceut. Healthcare Market. 12, 25–43. doi: 10.1108/IJPHM-08-2016-0044

Kircaburun, K., Alhabash, S., Tosuntaş, S. B., and Griffiths, M. D. (2020). Uses and gratifications of problematic social media use among University students: a simultaneous examination of the big five of personality traits, social media platforms, and social media use motives. Int. J. Mental Health Addict. 18, 525–547. doi: 10.1007/s11469-018-9940-6

Leong, L.-Y., Hew, T.-S., Ooi, K.-B., Lee, V.-H., and Hew, J.-J. (2019). A hybrid SEM-neural network analysis of social media addiction. Expert Syst. Appl. 133, 296–316. doi: 10.1016/j.eswa.2019.05.024

Li, L., Griffiths, M. D., Mei, S., and Niu, Z. (2020a). Fear of missing out and smartphone addiction mediates the relationship between positive and negative affect and sleep quality among Chinese University students. Front. Psychiatr. 11:877. doi: 10.3389/fpsyt.2020.00877

Li, W., Qalati, S. A., Khan, M. A. S., Kwabena, G. Y., Erusalkina, D., and Anwar, F. (2020b). Value co-creation and growth of social enterprises in developing countries: moderating role of environmental dynamics. Entrep. Res. J. 2020:20190359. doi: 10.1515/erj-2019-0359

Li, X., and Chen, W. (2014). Facebook or Renren? A comparative study of social networking site use and social capital among Chinese international students in the United States. Comput. Hum. Behav . 35, 116–123. doi: 10.1016/j.chb.2014.02.012

Matthews, L., Hair, J. F., and Matthews, R. (2018). PLS-SEM: the holy grail for advanced analysis. Mark. Manag. J. 28, 1–13.

Meshi, D., Cotten, S. R., and Bender, A. R. (2020). Problematic social media use and perceived social isolation in older adults: a cross-sectional study. Gerontology 66, 160–168. doi: 10.1159/000502577

Mou, J., Shin, D.-H., and Cohen, J. (2017). Understanding trust and perceived usefulness in the consumer acceptance of an e-service: a longitudinal investigation. Behav. Inf. Technol. 36, 125–139. doi: 10.1080/0144929X.2016.1203024

Nunnally, J. (1978). Psychometric Methods . New York, NY: McGraw-Hill.

Oghazi, P., Karlsson, S., Hellström, D., and Hjort, K. (2018). Online purchase return policy leniency and purchase decision: mediating role of consumer trust. J. Retail. Consumer Serv. 41, 190–200.

Pang, H. (2018). Exploring the beneficial effects of social networking site use on Chinese students' perceptions of social capital and psychological well-being in Germany. Int. J. Intercult. Relat. 67, 1–11. doi: 10.1016/j.ijintrel.2018.08.002

Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., and Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. J. Appl. Psychol. 88, 879–903. doi: 10.1037/0021-9010.88.5.879

Podsakoff, P. M., and Organ, D. W. (1986). Self-reports in organizational research: problems and prospects. J. Manag. 12, 531–544. doi: 10.1177/014920638601200408

Preacher, K. J., and Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behav Res. Methods 40, 879–891. doi: 10.3758/brm.40.3.879

Primack, B. A., Shensa, A., Sidani, J. E., Whaite, E. O., yi Lin, L., Rosen, D., et al. (2017). Social media use and perceived social isolation among young adults in the US. Am. J. Prev. Med. 53, 1–8. doi: 10.1016/j.amepre.2017.01.010

Putnam, R. D. (1995). Tuning in, tuning out: the strange disappearance of social capital in America. Polit. Sci. Polit. 28, 664–684. doi: 10.2307/420517

Putnam, R. D. (2000). Bowling Alone: The Collapse and Revival of American Community . New York, NY: Simon and Schuster.

Qalati, S. A., Ostic, D., Fan, M., Dakhan, S. A., Vela, E. G., Zufar, Z., et al. (2021). The general public knowledge, attitude, and practices regarding COVID-19 during the lockdown in Asian developing countries. Int. Q. Commun. Health Educ. 2021:272684X211004945. doi: 10.1177/0272684X211004945

Reer, F., Tang, W. Y., and Quandt, T. (2019). Psychosocial well-being and social media engagement: the mediating roles of social comparison orientation and fear of missing out. New Media Soc. 21, 1486–1505. doi: 10.1177/1461444818823719

Ringle, C., Wende, S., and Becker, J. (2015). SmartPLS 3 [software] . Bönningstedt: SmartPLS.

Ringle, C. M., Sarstedt, M., and Straub, D. (2012). A critical look at the use of PLS-SEM in “MIS Quarterly.” MIS Q . 36, iii–xiv. doi: 10.2307/41410402

Roberts, J. A., and David, M. E. (2020). The social media party: fear of missing out (FoMO), social media intensity, connection, and well-being. Int. J. Hum. Comput. Interact. 36, 386–392. doi: 10.1080/10447318.2019.1646517

Salehan, M., and Negahban, A. (2013). Social networking on smartphones: when mobile phones become addictive. Comput. Hum. Behav. 29, 2632–2639. doi: 10.1016/j.chb.2013.07.003

Sarstedt, M., and Cheah, J.-H. (2019). Partial least squares structural equation modeling using SmartPLS: a software review. J. Mark. Anal. 7, 196–202. doi: 10.1057/s41270-019-00058-3

Schinka, K. C., VanDulmen, M. H., Bossarte, R., and Swahn, M. (2012). Association between loneliness and suicidality during middle childhood and adolescence: longitudinal effects and the role of demographic characteristics. J. Psychol. Interdiscipl. Appl. 146, 105–118. doi: 10.1080/00223980.2011.584084

Shi, S., Mu, R., Lin, L., Chen, Y., Kou, G., and Chen, X.-J. (2018). The impact of perceived online service quality on swift guanxi. Internet Res. 28, 432–455. doi: 10.1108/IntR-12-2016-0389

Shoukat, S. (2019). Cell phone addiction and psychological and physiological health in adolescents. EXCLI J. 18, 47–50. doi: 10.17179/excli2018-2006

Shrestha, N. (2021). Factor analysis as a tool for survey analysis. Am. J. Appl. Math. Stat. 9, 4–11. doi: 10.12691/ajams-9-1-2

Stouthuysen, K., Teunis, I., Reusen, E., and Slabbinck, H. (2018). Initial trust and intentions to buy: The effect of vendor-specific guarantees, customer reviews and the role of online shopping experience. Electr. Commer. Res. Appl. 27, 23–38. doi: 10.1016/j.elerap.2017.11.002

Swar, B., and Hameed, T. (2017). “Fear of missing out, social media engagement, smartphone addiction and distraction: moderating role of self-help mobile apps-based interventions in the youth ,” Paper presented at the 10th International Conference on Health Informatics (Porto).

Tangmunkongvorakul, A., Musumari, P. M., Thongpibul, K., Srithanaviboonchai, K., Techasrivichien, T., Suguimoto, S. P., et al. (2019). Association of excessive smartphone use with psychological well-being among University students in Chiang Mai, Thailand. PLoS ONE 14:e0210294. doi: 10.1371/journal.pone.0210294

Tateno, M., Teo, A. R., Ukai, W., Kanazawa, J., Katsuki, R., Kubo, H., et al. (2019). Internet addiction, smartphone addiction, and hikikomori trait in Japanese young adult: social isolation and social network. Front. Psychiatry 10:455. doi: 10.3389/fpsyt.2019.00455

Tefertiller, A. C., Maxwell, L. C., and Morris, D. L. (2020). Social media goes to the movies: fear of missing out, social capital, and social motivations of cinema attendance. Mass Commun. Soc. 23, 378–399. doi: 10.1080/15205436.2019.1653468

Tehseen, S., Qureshi, Z. H., Johara, F., and Ramayah, T. (2020). Assessing dimensions of entrepreneurial competencies: a type II (reflective-formative) measurement approach using PLS-SEM. J. Sustain. Sci. Manage. 15, 108–145.

Tehseen, S., Ramayah, T., and Sajilan, S. (2017). Testing and controlling for common method variance: a review of available methods. J. Manag. Sci. 4, 146–165. doi: 10.20547/jms.2014.1704202

Tonacci, A., Billeci, L., Sansone, F., Masci, A., Pala, A. P., Domenici, C., et al. (2019). An innovative, unobtrusive approach to investigate smartphone interaction in nonaddicted subjects based on wearable sensors: a pilot study. Medicina (Kaunas) 55:37. doi: 10.3390/medicina55020037

Twenge, J. M., and Campbell, W. K. (2019). Media use is linked to lower psychological well-being: evidence from three datasets. Psychiatr. Q. 90, 311–331. doi: 10.1007/s11126-019-09630-7

Vallespín, M., Molinillo, S., and Muñoz-Leiva, F. (2017). Segmentation and explanation of smartphone use for travel planning based on socio-demographic and behavioral variables. Ind. Manag. Data Syst. 117, 605–619. doi: 10.1108/IMDS-03-2016-0089

Van Den Eijnden, R. J., Lemmens, J. S., and Valkenburg, P. M. (2016). The social media disorder scale. Comput. Hum. Behav. 61, 478–487. doi: 10.1016/j.chb.2016.03.038

Whaite, E. O., Shensa, A., Sidani, J. E., Colditz, J. B., and Primack, B. A. (2018). Social media use, personality characteristics, and social isolation among young adults in the United States. Pers. Indiv. Differ. 124, 45–50. doi: 10.1016/j.paid.2017.10.030

Williams, D. (2006). On and off the'net: scales for social capital in an online era. J. Comput. Mediat. Commun. 11, 593–628. doi: 10.1016/j.1083-6101.2006.00029.x

Keywords: smartphone addiction, social isolation, bonding social capital, bridging social capital, phubbing, social media use

Citation: Ostic D, Qalati SA, Barbosa B, Shah SMM, Galvan Vela E, Herzallah AM and Liu F (2021) Effects of Social Media Use on Psychological Well-Being: A Mediated Model. Front. Psychol. 12:678766. doi: 10.3389/fpsyg.2021.678766

Received: 10 March 2021; Accepted: 25 May 2021; Published: 21 June 2021.

Reviewed by:

Copyright © 2021 Ostic, Qalati, Barbosa, Shah, Galvan Vela, Herzallah and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Sikandar Ali Qalati, sidqalati@gmail.com ; 5103180243@stmail.ujs.edu.cn ; Esthela Galvan Vela, esthela.galvan@cetys.mx

† ORCID: Dragana Ostic orcid.org/0000-0002-0469-1342 Sikandar Ali Qalati orcid.org/0000-0001-7235-6098 Belem Barbosa orcid.org/0000-0002-4057-360X Esthela Galvan Vela orcid.org/0000-0002-8778-3989 Feng Liu orcid.org/0000-0001-9367-049X

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Pros & cons: impacts of social media on mental health

  • Ágnes Zsila 1 , 2 &
  • Marc Eric S. Reyes   ORCID: orcid.org/0000-0002-5280-1315 3  

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The use of social media significantly impacts mental health. It can enhance connection, increase self-esteem, and improve a sense of belonging. But it can also lead to tremendous stress, pressure to compare oneself to others, and increased sadness and isolation. Mindful use is essential to social media consumption.

Social media has become integral to our daily routines: we interact with family members and friends, accept invitations to public events, and join online communities to meet people who share similar preferences using these platforms. Social media has opened a new avenue for social experiences since the early 2000s, extending the possibilities for communication. According to recent research [ 1 ], people spend 2.3 h daily on social media. YouTube, TikTok, Instagram, and Snapchat have become increasingly popular among youth in 2022, and one-third think they spend too much time on these platforms [ 2 ]. The considerable time people spend on social media worldwide has directed researchers’ attention toward the potential benefits and risks. Research shows excessive use is mainly associated with lower psychological well-being [ 3 ]. However, findings also suggest that the quality rather than the quantity of social media use can determine whether the experience will enhance or deteriorate the user’s mental health [ 4 ]. In this collection, we will explore the impact of social media use on mental health by providing comprehensive research perspectives on positive and negative effects.

Social media can provide opportunities to enhance the mental health of users by facilitating social connections and peer support [ 5 ]. Indeed, online communities can provide a space for discussions regarding health conditions, adverse life events, or everyday challenges, which may decrease the sense of stigmatization and increase belongingness and perceived emotional support. Mutual friendships, rewarding social interactions, and humor on social media also reduced stress during the COVID-19 pandemic [ 4 ].

On the other hand, several studies have pointed out the potentially detrimental effects of social media use on mental health. Concerns have been raised that social media may lead to body image dissatisfaction [ 6 ], increase the risk of addiction and cyberbullying involvement [ 5 ], contribute to phubbing behaviors [ 7 ], and negatively affects mood [ 8 ]. Excessive use has increased loneliness, fear of missing out, and decreased subjective well-being and life satisfaction [ 8 ]. Users at risk of social media addiction often report depressive symptoms and lower self-esteem [ 9 ].

Overall, findings regarding the impact of social media on mental health pointed out some essential resources for psychological well-being through rewarding online social interactions. However, there is a need to raise awareness about the possible risks associated with excessive use, which can negatively affect mental health and everyday functioning [ 9 ]. There is neither a negative nor positive consensus regarding the effects of social media on people. However, by teaching people social media literacy, we can maximize their chances of having balanced, safe, and meaningful experiences on these platforms [ 10 ].

We encourage researchers to submit their research articles and contribute to a more differentiated overview of the impact of social media on mental health. BMC Psychology welcomes submissions to its new collection, which promises to present the latest findings in the emerging field of social media research. We seek research papers using qualitative and quantitative methods, focusing on social media users’ positive and negative aspects. We believe this collection will provide a more comprehensive picture of social media’s positive and negative effects on users’ mental health.

Data Availability

Not applicable.

Statista. (2022). Time spent on social media [Chart]. Accessed June 14, 2023, from https://www.statista.com/chart/18983/time-spent-on-social-media/ .

Pew Research Center. (2023). Teens and social media: Key findings from Pew Research Center surveys. Retrieved June 14, 2023, from https://www.pewresearch.org/short-reads/2023/04/24/teens-and-social-media-key-findings-from-pew-research-center-surveys/ .

Boer, M., Van Den Eijnden, R. J., Boniel-Nissim, M., Wong, S. L., Inchley, J. C.,Badura, P.,… Stevens, G. W. (2020). Adolescents’ intense and problematic social media use and their well-being in 29 countries. Journal of Adolescent Health , 66(6), S89-S99. https://doi.org/10.1016/j.jadohealth.2020.02.011.

Marciano L, Ostroumova M, Schulz PJ, Camerini AL. Digital media use and adolescents’ mental health during the COVID-19 pandemic: a systematic review and meta-analysis. Front Public Health. 2022;9:2208. https://doi.org/10.3389/fpubh.2021.641831 .

Article   Google Scholar  

Naslund JA, Bondre A, Torous J, Aschbrenner KA. Social media and mental health: benefits, risks, and opportunities for research and practice. J Technol Behav Sci. 2020;5:245–57. https://doi.org/10.1007/s41347-020-00094-8 .

Article   PubMed   PubMed Central   Google Scholar  

Harriger JA, Thompson JK, Tiggemann M. TikTok, TikTok, the time is now: future directions in social media and body image. Body Image. 2023;44:222–6. https://doi.org/10.1016/j.bodyim.2021.12.005 .

Article   PubMed   Google Scholar  

Chi LC, Tang TC, Tang E. The phubbing phenomenon: a cross-sectional study on the relationships among social media addiction, fear of missing out, personality traits, and phubbing behavior. Curr Psychol. 2022;41(2):1112–23. https://doi.org/10.1007/s12144-022-0135-4 .

Valkenburg PM. Social media use and well-being: what we know and what we need to know. Curr Opin Psychol. 2022;45:101294. https://doi.org/10.1016/j.copsyc.2020.101294 .

Bányai F, Zsila Á, Király O, Maraz A, Elekes Z, Griffiths MD, Urbán R, Farkas J, Rigó P Jr, Demetrovics Z. Problematic social media use: results from a large-scale nationally representative adolescent sample. PLoS ONE. 2017;12(1):e0169839. https://doi.org/10.1371/journal.pone.0169839 .

American Psychological Association. (2023). APA panel issues recommendations for adolescent social media use. Retrieved from https://apa-panel-issues-recommendations-for-adolescent-social-media-use-774560.html .

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Acknowledgements

Ágnes Zsila was supported by the ÚNKP-22-4 New National Excellence Program of the Ministry for Culture and Innovation from the source of the National Research, Development and Innovation Fund.

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Social-media influence on teen mental health goes beyond just cause and effect

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In her review of Jonathan Haidt’s book The Anxious Generation ( Nature 628 , 29–30; 2024 ), Candice Odgers focuses on whether there is a causal relationship between social-media use and poor social outcomes among young people. However, this gives an incomplete view of Haidt’s argument, which puts social-media use in a wider context.

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An Evidence-based Perspective on Social Media Use Among Young People

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Social media is fully ingrained in young people’s everyday lives, shaping how they connect, communicate, learn, and interact with the world. As social media has proliferated in young people’s lives, so have concerns about its role in the decline of youth mental health. Although concerns about the adverse effects of social media on young people are widespread, it is crucial to acknowledge the benefits it can offer as well.

The third installment of Hopelab’s National Survey, “ A Double-Edged Sword: How Diverse Communities of Young People Think About the Multifaceted Relationship Between Social Media and Mental Health, ” released in partnership with Common Sense Media, explores the nuances of the relationship between social media and youth mental health. Importantly, this survey was co-created with young people, who provided direction and input regarding survey content and worked with the study team to prioritize and interpret results. Teens and young adults — especially BIPOC and LGBTQ+ young people — bring their own unique lived experiences to social media spaces and, as a result, have different experiences with the content and communities they find there.

Many conversations surrounding social media and young people focus solely on harm and characterize them as passive users. This research shows that young people’s relationship with social media is much more complex. Social media is an important source of connection, support, and affirmation for young people, but it also brings challenges that young people navigate to minimize harmful impacts. There is an opportunity to use and understand this data to create better solutions grounded in the realities of young people’s experiences and needs.

Group of multiracial teenage students using mobile phones on school - Young friends watching social media content on smartphones

Young friends watching social media content on smartphones

Evidence of ways social media may differentially support youth well-being

Research indicates that social media has a dual nature for young people, with both positive and negative implications. There is also great diversity in the ways social media impacts young people. As stated in the American Psychological Association’s Health Advisory on Social Media Use in Adolescence, “Social media is not inherently beneficial or harmful to young people…the effects of social media likely depend on what teens can do and see online, teens’ preexisting strengths or vulnerabilities, and the contexts in which they grow up.” Efforts to mitigate the harmful effects of social media must take into account the wide range of benefits it can offer young people and consider differential impacts across identities.

  • Social Connection: Young people who are Black, LGBTQ+, or suffer from depressive symptoms are among those who ascribe even greater importance to the role of social media in helping connect with family and feel less alone. For example, relative to white and Latinx peers, Black young people more often point to the importance of social media for connecting with family (72% Black vs. 62% Latinx and 61% white). Notably, when compared with their white (53%) and Latinx (60%) peers, Black (77%) young people find social media more important for connecting with people who share similar concerns about their mental health or well-being. Further, LGBTQ+ young people (74% LGBTQ+ vs. 52% straight and cisgender) and those with moderate to severe depression symptoms (66% moderate to severe vs. 48% none) are significantly more likely than their peers to say social media is important for feeling less alone.
  • Information Access: Young people who are Black (64%), Latinx (59%), or have moderate to severe depression symptoms (57%) are more likely to endorse the importance of social media for finding information or resources about mental health or well-being than their white peers (44%) and those without depressive symptoms (47%). Black and Latinx young people are also more likely to say that social media platforms are essential for learning about professional or academic opportunities compared to their white peers (80% Black vs. 63% Latinx vs. 53% white). 
  • Identity Affirmation: Almost 9 in 10 (89%) LGBTQ+ young people at least sometimes come across comments celebrating LGBTQ+ identities on social media. Further, a little over half (52%) of LGBTQ+ young people indicate a preference for communicating over social media rather than in person, compared to less than 4 in 10 straight and cisgender young people (38%). In subsequent interviews and focus groups, LGBTQ+ young people said that in the current climate of increased restrictions and hate toward those who are trans and queer, online communication often felt safer and more supportive.

Evidence of ways social media may exacerbate harm among diverse subgroups of young people

Although social media is often an essential source of social connection, information, and affirmation for young people, particularly those who are LGBTQ+, BIPOC, or experiencing symptoms of depression, it also comes with potential harm to these same groups. The lack of transparency from technology companies has prevented a complete understanding of the magnitude of social media’s impact on young people’s mental health and well-being. In May 2023 , U.S. Surgeon General Vivek Murthy advised that companies needed to take “immediate action to protect kids now” because the effects of social media on kids and teens were so largely unknown.

  • Social Comparison: Sixty-four percent of young people with moderate to severe depressive symptoms indicate that when they use social media, they feel as if others’ lives are better than theirs, compared to 38% of those with no symptoms, and 60% see or hear things on social media that make them feel bad about the way they look, compared to 25% of those with no symptoms. Similar findings emerged for LGBTQ+ social media users compared to straight and cisgender peers related to feeling like other people’s lives are better than their own (60% vs. 47%, respectively) and exposure to content that makes them feel bad about their body or appearance (55% vs. 37%, respectively).
  • Hateful and hurtful content : Latinx youth (56%) report more frequently encountering racist comments, as compared to their Black peers (47%). White (60%) and Latinx (59%) youth are also more likely than Black youth (53%) to encounter body-shaming comments. Three-fourths of LGBTQ+ youth, however, encounter both transphobic (75%) and homophobic comments (76%) on social media, versus only about half of non-LGBTQ+ youth (55% and 49%, respectively).
  • Negative News: Compared to their white peers (41%), Black youth (53%) and Latinx youth (48%) more often indicate that they feel the emotional toll of negative news consumption. Looking at LGBTQ+ respondents, fully 6 in 10 LGBTQ+ youth report experiencing the emotional impact of negative news, compared to 43% of straight and cisgender young people. Those with moderate to severe depressive symptoms (62%) are also more likely than those with no symptoms (32%) to report that they see so much bad news that they feel stressed and anxious.

Multiracial group of teenagers using their cell phones at high school.

Evidence of ways young people take actions to balance risks and benefits

The social media debate isn’t a good vs. bad issue. It’s a complex system where both harm and benefit exist. While aspects of social media can exacerbate mental health issues for young people, our survey data suggests that they are aware of the harms and are not passive users. They possess agency and take actions to maximize benefits while minimizing harm by curating their feeds, managing time spent online, and avoiding harmful content. Understanding the nuances of how young people balance the risks with the benefits of social media is the key to creating a safer, more empowering digital environment for and with young people.

  • Avoiding Negative Content: Nearly 9 in 10 (89%) LGBTQ+ youth who use social media say that, over the past year, they have tried to avoid content they do not like on these platforms, compared with just under three-fourths (74%) of their non-LGBTQ+ peers who use social media. Further, fully 9 in 10 young people with moderate to severe depressive symptoms have tried to see less of what they do not like on social media compared to 67% of those with no symptoms.
  • Curating Social Media Feeds: LGBTQ+ youth are also significantly more likely than straight and cisgender youth to have tried curating their feed (78% vs. 65%). In addition, 8 in 10 young people with moderate to severe (81%) depressive symptoms have taken actions to try to curate their social media feed, compared to just over half (55%) of those with no symptoms.
  • Managing Time Concerns:   About three-fourths of Black (74%) and Latinx (73%) young people who use social media have taken a temporary break from an account due to concerns about spending too much time on it, compared to slightly over half of white youth (56%). Further, the majority of Black (56%) and Latinx (54%) youth chose to take a permanent break from an account in the past year for this reason, versus only about one in three white youth (32%). Young people with moderate to severe depressive symptoms were also more likely to take a temporary (76%) or permanent (56%) break from a social media account due to time-related concerns compared to those with no symptoms (51% and 32%, respectively). 
  • Managing Harassment and Negative Experiences : Black (42%) and Latinx (40%) young people are about twice as likely as white youth (21%) to have taken a permanent break from a social media account—and more than one and a half times more likely to have taken a temporary break (48%, 47%, and 30%, respectively)—due to harassment or other negative experiences online. Additionally, young people with moderate to severe depressive symptoms are more than twice as likely to have taken a temporary (58%) and permanent break (44%) due to online harassment and other negative experiences than those with no symptoms (25% and 20%, respectively).

The actions young people are taking to balance the positives and negatives of social media speak volumes about their agency and decision-making abilities. However, it also underscores the need for platforms to address online safety and well-being issues. Our work creating opportunities for young people to thrive is far from over. We must use this research to guide our next steps, remembering that the experiences of young people – with diverse perspectives – inform the data. Progress will only be successful if we continue to center young people and partner with them to build solutions that meet their needs.

Read the complete 2024 National Survey here.

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Adolescence and Social Media: the Slippery Sands of Research on a Global Phenomenon

2024 marks 20 years since the birth of social media. Since then, it has become a major communication force in the lives of teenagers - a 2024 Pew survey claims that 93% of American youth use it, for example. Unsurprisingly, research on its impact has followed suit. But just how reliable are the conclusions in this new field of studies? In April 2024, HEC Professors Tina Lowrey and L.J. Shrum co-authored a research paper with their former doctoral student Elena Fumagalli (H18), showing conflicting findings on the negative and positive effects of social media on youth. They warn against major policies and lawsuits founded on inconclusive studies and contradictory scientific research. Professors Lowrey and Shrum share with Breakthroughs their study to try to make sense of a subject matter inflaming public debate. Extracts.

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HEC Paris Marketing Professors L.J. Shrum and Tina M. Lowrey

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In this publication, your stated objective was to make sense of a plethora of research on social media and its impact on teenagers. You also hoped to create a template for consumer researchers working on this topic. Indeed, you claim that academics are not unanimous on this impact. Could you elaborate? 

L.J. Shrum: Sure. What people need to remember is that everything's in constant flux. Researchers in this field of new technology have a good term for it: the moving target problem. So, for example, what Facebook was when it started is not what Facebook is now, right? Even its name has changed. So, how do you make sense of research on Facebook 10 years ago and 15 years ago in comparison to now? Even the name has changed. And the same thing is true for almost every single social media platform. It's very difficult to define. In our approach,  we focused primarily on the last five years, which is when some huge breakthroughs have happened in terms of acquiring good data and doing the proper analyses. 

This is a selective review of research on 13- to 21-year-olds. Why choose this age category, Tina? 

Tina Lowrey: We wanted to look at adolescence, and thought it was important to include older adolescents who are starting to become young adults just because the research that was out there was showing certain patterns of impact based on age. For females, the largest vulnerable age group is 11 to 13, and for males, it's more like 14 to 15. And so, we just felt like that was a good age range to capture.

Despite this dearth of quality research and its very unequal quality, politicians and journalists appear to be very categorical in the decisions they make or the articles they publish. We've seen, for example, the Kids Off Social Media Act. That’s a bipartisan attempt to ban social media for children under the age of 13 in the United States. L.J., you're not just a consumer psychologist; you also have a PhD in communications. How would you explain the fact that people are making major decisions based on research that's divided? 

L.J. Shrum: Politicians and journalists often make some blanket statements, and, for the most part, they’re not far off target, to be fair. But the devil is in the details. And, so, what we're finding in the research that's coming out is that it's not just how old you are, it's not just what gender you are, it's also how you use it and the type of social media that you use. Those are nuances that are very difficult to make a sound bite out of, and also to legislate on. 

Politicians and journalists often make some blanket statements. They’re not far off target, but the devil is in the details: it's not just how old and what gender you are, it's also how you use social media and the type of social media that you use.

Tina Lowrey, there is nevertheless a certain convergence of the research that you looked at. Let's start with one: it seems that social media is negative for young girls. Could you elaborate? 

Tina Lowrey:  Yes. So, I think that part of the reason is the onset of puberty. There are a lot of things going on, it’s a rough time, it was this way even before social media existed.

But social media can exacerbate some of the body image issues that young women have. Again, social media didn't create this problem. But social media ratchets up the impact with considerable negative impact on young teenage girls. And I think that the other issue L.J. mentioned, is that it depends on which social media we're talking about and how you're using it. And previous research does not really account for causality. Someone who has lower self-esteem might be using social media differently to someone with higher self-esteem. Which raises the question: which came first, the social media usage or the self-esteem level? So, it's very complicated, but newer research that's more longitudinal in nature partially addresses that concern of causality. 

L.J. Shrum: Coming back to the first point. We can see the same phenomenon with boys. Research seems to concur that the negative impact just happens to map onto puberty. We’re not sure whether it is causal, but that's pretty elegant. As for later on, what research seems to suggest strongly is also an increase in negative effects at around age 19 for both boys and girls. Well, that just happens to correlate or coincide with big sociological changes: we're moving out of the nest, we're going to college, we're going to work. And those these times are very anxious. And so, I think that's when you see the exacerbation that Tina refers to. That age group is about transitioning from adolescence into young adulthood. Negative well-being in that period has been around for decades. But I think again, social media just exacerbates these problems. 

Tina Lowrey:  And it's also very targeted. We know from some whistleblowers that the social media platforms are intentionally making it addictive. I do think that television and radio pale in comparison to how targeted social media can be. Finally, I think it’s there all the time; it’s ubiquitous - you can be completely on it 23 hours a day if you don't need any sleep. (Laugh)

There’s also a very pragmatic side to your research paper. For example, you call for more research on the underlying factors of social media usage on well-being, and you also are calling for more long-term studies on the impact on people. Could you explain how you feel this can be implemented? 

L.J. Shrum:  Sure. It became obvious during our analysis that more recent research points to what we call moderators. These are factors which cause greater or lesser negative impact. Now, we have this definition of social media that's very broad and scholars can't even agree on how to define it. Research shows there are different types of social media: Instagram is different to Twitter. One is very image based; the other is word based. There are ones that have different purposes. And so, what's starting to come out is that certain types of social media may have negative effects at a young age. If you take Instagram, it may have a bigger negative effect, let's say, on adolescent body image, whereas other types of social media may have none of the effects you would predict. So, understanding which social media are causing a problem, what kinds of problems they're causing, and also how you use it is essential. Yet, there’s just not enough research accurately defining social media. We also suggest further research on what we refer to as active or passive uses. Are you posting, and are you engaging, or are you just lurking and looking? These usages seem to have different effects on adolescents. Active is associated with a more positive impact on self-esteem than the more passive use of social media where you're not actively engaged, so you're being negatively impacted by what you see and hear.

research paper impact of social media

Tina Lowrey’s research interests include children's understanding of brand symbolism, gift giving and ritual, and the application of psycholinguistic...

LJ Shrum

L. J. Shrum is a consumer psychologist whose research applies social cognition concepts to understand the determinants of consumer judgments. He has...

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The effect of social media on the development of students’ affective variables

1 Science and Technology Department, Nanjing University of Posts and Telecommunications, Nanjing, China

2 School of Marxism, Hohai University, Nanjing, Jiangsu, China

3 Government Enterprise Customer Center, China Mobile Group Jiangsu Co., Ltd., Nanjing, China

The use of social media is incomparably on the rise among students, influenced by the globalized forms of communication and the post-pandemic rush to use multiple social media platforms for education in different fields of study. Though social media has created tremendous chances for sharing ideas and emotions, the kind of social support it provides might fail to meet students’ emotional needs, or the alleged positive effects might be short-lasting. In recent years, several studies have been conducted to explore the potential effects of social media on students’ affective traits, such as stress, anxiety, depression, and so on. The present paper reviews the findings of the exemplary published works of research to shed light on the positive and negative potential effects of the massive use of social media on students’ emotional well-being. This review can be insightful for teachers who tend to take the potential psychological effects of social media for granted. They may want to know more about the actual effects of the over-reliance on and the excessive (and actually obsessive) use of social media on students’ developing certain images of self and certain emotions which are not necessarily positive. There will be implications for pre- and in-service teacher training and professional development programs and all those involved in student affairs.

Introduction

Social media has turned into an essential element of individuals’ lives including students in today’s world of communication. Its use is growing significantly more than ever before especially in the post-pandemic era, marked by a great revolution happening to the educational systems. Recent investigations of using social media show that approximately 3 billion individuals worldwide are now communicating via social media ( Iwamoto and Chun, 2020 ). This growing population of social media users is spending more and more time on social network groupings, as facts and figures show that individuals spend 2 h a day, on average, on a variety of social media applications, exchanging pictures and messages, updating status, tweeting, favoring, and commenting on many updated socially shared information ( Abbott, 2017 ).

Researchers have begun to investigate the psychological effects of using social media on students’ lives. Chukwuere and Chukwuere (2017) maintained that social media platforms can be considered the most important source of changing individuals’ mood, because when someone is passively using a social media platform seemingly with no special purpose, s/he can finally feel that his/her mood has changed as a function of the nature of content overviewed. Therefore, positive and negative moods can easily be transferred among the population using social media networks ( Chukwuere and Chukwuere, 2017 ). This may become increasingly important as students are seen to be using social media platforms more than before and social networking is becoming an integral aspect of their lives. As described by Iwamoto and Chun (2020) , when students are affected by social media posts, especially due to the increasing reliance on social media use in life, they may be encouraged to begin comparing themselves to others or develop great unrealistic expectations of themselves or others, which can have several affective consequences.

Considering the increasing influence of social media on education, the present paper aims to focus on the affective variables such as depression, stress, and anxiety, and how social media can possibly increase or decrease these emotions in student life. The exemplary works of research on this topic in recent years will be reviewed here, hoping to shed light on the positive and negative effects of these ever-growing influential platforms on the psychology of students.

Significance of the study

Though social media, as the name suggests, is expected to keep people connected, probably this social connection is only superficial, and not adequately deep and meaningful to help individuals feel emotionally attached to others. The psychological effects of social media on student life need to be studied in more depth to see whether social media really acts as a social support for students and whether students can use social media to cope with negative emotions and develop positive feelings or not. In other words, knowledge of the potential effects of the growing use of social media on students’ emotional well-being can bridge the gap between the alleged promises of social media and what it actually has to offer to students in terms of self-concept, self-respect, social role, and coping strategies (for stress, anxiety, etc.).

Exemplary general literature on psychological effects of social media

Before getting down to the effects of social media on students’ emotional well-being, some exemplary works of research in recent years on the topic among general populations are reviewed. For one, Aalbers et al. (2018) reported that individuals who spent more time passively working with social media suffered from more intense levels of hopelessness, loneliness, depression, and perceived inferiority. For another, Tang et al. (2013) observed that the procedures of sharing information, commenting, showing likes and dislikes, posting messages, and doing other common activities on social media are correlated with higher stress. Similarly, Ley et al. (2014) described that people who spend 2 h, on average, on social media applications will face many tragic news, posts, and stories which can raise the total intensity of their stress. This stress-provoking effect of social media has been also pinpointed by Weng and Menczer (2015) , who contended that social media becomes a main source of stress because people often share all kinds of posts, comments, and stories ranging from politics and economics, to personal and social affairs. According to Iwamoto and Chun (2020) , anxiety and depression are the negative emotions that an individual may develop when some source of stress is present. In other words, when social media sources become stress-inducing, there are high chances that anxiety and depression also develop.

Charoensukmongkol (2018) reckoned that the mental health and well-being of the global population can be at a great risk through the uncontrolled massive use of social media. These researchers also showed that social media sources can exert negative affective impacts on teenagers, as they can induce more envy and social comparison. According to Fleck and Johnson-Migalski (2015) , though social media, at first, plays the role of a stress-coping strategy, when individuals continue to see stressful conditions (probably experienced and shared by others in media), they begin to develop stress through the passage of time. Chukwuere and Chukwuere (2017) maintained that social media platforms continue to be the major source of changing mood among general populations. For example, someone might be passively using a social media sphere, and s/he may finally find him/herself with a changed mood depending on the nature of the content faced. Then, this good or bad mood is easily shared with others in a flash through the social media. Finally, as Alahmar (2016) described, social media exposes people especially the young generation to new exciting activities and events that may attract them and keep them engaged in different media contexts for hours just passing their time. It usually leads to reduced productivity, reduced academic achievement, and addiction to constant media use ( Alahmar, 2016 ).

The number of studies on the potential psychological effects of social media on people in general is higher than those selectively addressed here. For further insights into this issue, some other suggested works of research include Chang (2012) , Sriwilai and Charoensukmongkol (2016) , and Zareen et al. (2016) . Now, we move to the studies that more specifically explored the effects of social media on students’ affective states.

Review of the affective influences of social media on students

Vygotsky’s mediational theory (see Fernyhough, 2008 ) can be regarded as a main theoretical background for the support of social media on learners’ affective states. Based on this theory, social media can play the role of a mediational means between learners and the real environment. Learners’ understanding of this environment can be mediated by the image shaped via social media. This image can be either close to or different from the reality. In the case of the former, learners can develop their self-image and self-esteem. In the case of the latter, learners might develop unrealistic expectations of themselves by comparing themselves to others. As it will be reviewed below among the affective variables increased or decreased in students under the influence of the massive use of social media are anxiety, stress, depression, distress, rumination, and self-esteem. These effects have been explored more among school students in the age range of 13–18 than university students (above 18), but some studies were investigated among college students as well. Exemplary works of research on these affective variables are reviewed here.

In a cross-sectional study, O’Dea and Campbell (2011) explored the impact of online interactions of social networks on the psychological distress of adolescent students. These researchers found a negative correlation between the time spent on social networking and mental distress. Dumitrache et al. (2012) explored the relations between depression and the identity associated with the use of the popular social media, the Facebook. This study showed significant associations between depression and the number of identity-related information pieces shared on this social network. Neira and Barber (2014) explored the relationship between students’ social media use and depressed mood at teenage. No significant correlation was found between these two variables. In the same year, Tsitsika et al. (2014) explored the associations between excessive use of social media and internalizing emotions. These researchers found a positive correlation between more than 2-h a day use of social media and anxiety and depression.

Hanprathet et al. (2015) reported a statistically significant positive correlation between addiction to Facebook and depression among about a thousand high school students in wealthy populations of Thailand and warned against this psychological threat. Sampasa-Kanyinga and Lewis (2015) examined the relationship between social media use and psychological distress. These researchers found that the use of social media for more than 2 h a day was correlated with a higher intensity of psychological distress. Banjanin et al. (2015) tested the relationship between too much use of social networking and depression, yet found no statistically significant correlation between these two variables. Frison and Eggermont (2016) examined the relationships between different forms of Facebook use, perceived social support of social media, and male and female students’ depressed mood. These researchers found a positive association between the passive use of the Facebook and depression and also between the active use of the social media and depression. Furthermore, the perceived social support of the social media was found to mediate this association. Besides, gender was found as the other factor to mediate this relationship.

Vernon et al. (2017) explored change in negative investment in social networking in relation to change in depression and externalizing behavior. These researchers found that increased investment in social media predicted higher depression in adolescent students, which was a function of the effect of higher levels of disrupted sleep. Barry et al. (2017) explored the associations between the use of social media by adolescents and their psychosocial adjustment. Social media activity showed to be positively and moderately associated with depression and anxiety. Another investigation was focused on secondary school students in China conducted by Li et al. (2017) . The findings showed a mediating role of insomnia on the significant correlation between depression and addiction to social media. In the same year, Yan et al. (2017) aimed to explore the time spent on social networks and its correlation with anxiety among middle school students. They found a significant positive correlation between more than 2-h use of social networks and the intensity of anxiety.

Also in China, Wang et al. (2018) showed that addiction to social networking sites was correlated positively with depression, and this correlation was mediated by rumination. These researchers also found that this mediating effect was moderated by self-esteem. It means that the effect of addiction on depression was compounded by low self-esteem through rumination. In another work of research, Drouin et al. (2018) showed that though social media is expected to act as a form of social support for the majority of university students, it can adversely affect students’ mental well-being, especially for those who already have high levels of anxiety and depression. In their research, the social media resources were found to be stress-inducing for half of the participants, all university students. The higher education population was also studied by Iwamoto and Chun (2020) . These researchers investigated the emotional effects of social media in higher education and found that the socially supportive role of social media was overshadowed in the long run in university students’ lives and, instead, fed into their perceived depression, anxiety, and stress.

Keles et al. (2020) provided a systematic review of the effect of social media on young and teenage students’ depression, psychological distress, and anxiety. They found that depression acted as the most frequent affective variable measured. The most salient risk factors of psychological distress, anxiety, and depression based on the systematic review were activities such as repeated checking for messages, personal investment, the time spent on social media, and problematic or addictive use. Similarly, Mathewson (2020) investigated the effect of using social media on college students’ mental health. The participants stated the experience of anxiety, depression, and suicidality (thoughts of suicide or attempts to suicide). The findings showed that the types and frequency of using social media and the students’ perceived mental health were significantly correlated with each other.

The body of research on the effect of social media on students’ affective and emotional states has led to mixed results. The existing literature shows that there are some positive and some negative affective impacts. Yet, it seems that the latter is pre-dominant. Mathewson (2020) attributed these divergent positive and negative effects to the different theoretical frameworks adopted in different studies and also the different contexts (different countries with whole different educational systems). According to Fredrickson’s broaden-and-build theory of positive emotions ( Fredrickson, 2001 ), the mental repertoires of learners can be built and broadened by how they feel. For instance, some external stimuli might provoke negative emotions such as anxiety and depression in learners. Having experienced these negative emotions, students might repeatedly check their messages on social media or get addicted to them. As a result, their cognitive repertoire and mental capacity might become limited and they might lose their concentration during their learning process. On the other hand, it should be noted that by feeling positive, learners might take full advantage of the affordances of the social media and; thus, be able to follow their learning goals strategically. This point should be highlighted that the link between the use of social media and affective states is bi-directional. Therefore, strategic use of social media or its addictive use by students can direct them toward either positive experiences like enjoyment or negative ones such as anxiety and depression. Also, these mixed positive and negative effects are similar to the findings of several other relevant studies on general populations’ psychological and emotional health. A number of studies (with general research populations not necessarily students) showed that social networks have facilitated the way of staying in touch with family and friends living far away as well as an increased social support ( Zhang, 2017 ). Given the positive and negative emotional effects of social media, social media can either scaffold the emotional repertoire of students, which can develop positive emotions in learners, or induce negative provokers in them, based on which learners might feel negative emotions such as anxiety and depression. However, admittedly, social media has also generated a domain that encourages the act of comparing lives, and striving for approval; therefore, it establishes and internalizes unrealistic perceptions ( Virden et al., 2014 ; Radovic et al., 2017 ).

It should be mentioned that the susceptibility of affective variables to social media should be interpreted from a dynamic lens. This means that the ecology of the social media can make changes in the emotional experiences of learners. More specifically, students’ affective variables might self-organize into different states under the influence of social media. As for the positive correlation found in many studies between the use of social media and such negative effects as anxiety, depression, and stress, it can be hypothesized that this correlation is induced by the continuous comparison the individual makes and the perception that others are doing better than him/her influenced by the posts that appear on social media. Using social media can play a major role in university students’ psychological well-being than expected. Though most of these studies were correlational, and correlation is not the same as causation, as the studies show that the number of participants experiencing these negative emotions under the influence of social media is significantly high, more extensive research is highly suggested to explore causal effects ( Mathewson, 2020 ).

As the review of exemplary studies showed, some believed that social media increased comparisons that students made between themselves and others. This finding ratifies the relevance of the Interpretation Comparison Model ( Stapel and Koomen, 2000 ; Stapel, 2007 ) and Festinger’s (1954) Social Comparison Theory. Concerning the negative effects of social media on students’ psychology, it can be argued that individuals may fail to understand that the content presented in social media is usually changed to only represent the attractive aspects of people’s lives, showing an unrealistic image of things. We can add that this argument also supports the relevance of the Social Comparison Theory and the Interpretation Comparison Model ( Stapel and Koomen, 2000 ; Stapel, 2007 ), because social media sets standards that students think they should compare themselves with. A constant observation of how other students or peers are showing their instances of achievement leads to higher self-evaluation ( Stapel and Koomen, 2000 ). It is conjectured that the ubiquitous role of social media in student life establishes unrealistic expectations and promotes continuous comparison as also pinpointed in the Interpretation Comparison Model ( Stapel and Koomen, 2000 ; Stapel, 2007 ).

Implications of the study

The use of social media is ever increasing among students, both at school and university, which is partly because of the promises of technological advances in communication services and partly because of the increased use of social networks for educational purposes in recent years after the pandemic. This consistent use of social media is not expected to leave students’ psychological, affective and emotional states untouched. Thus, it is necessary to know how the growing usage of social networks is associated with students’ affective health on different aspects. Therefore, we found it useful to summarize the research findings in recent years in this respect. If those somehow in charge of student affairs in educational settings are aware of the potential positive or negative effects of social media usage on students, they can better understand the complexities of students’ needs and are better capable of meeting them.

Psychological counseling programs can be initiated at schools or universities to check upon the latest state of students’ mental and emotional health influenced by the pervasive use of social media. The counselors can be made aware of the potential adverse effects of social networking and can adapt the content of their inquiries accordingly. Knowledge of the potential reasons for student anxiety, depression, and stress can help school or university counselors to find individualized coping strategies when they diagnose any symptom of distress in students influenced by an excessive use of social networking.

Admittedly, it is neither possible to discard the use of social media in today’s academic life, nor to keep students’ use of social networks fully controlled. Certainly, the educational space in today’s world cannot do without the social media, which has turned into an integral part of everybody’s life. Yet, probably students need to be instructed on how to take advantage of the media and to be the least affected negatively by its occasional superficial and unrepresentative content. Compensatory programs might be needed at schools or universities to encourage students to avoid making unrealistic and impartial comparisons of themselves and the flamboyant images of others displayed on social media. Students can be taught to develop self-appreciation and self-care while continuing to use the media to their benefit.

The teachers’ role as well as the curriculum developers’ role are becoming more important than ever, as they can significantly help to moderate the adverse effects of the pervasive social media use on students’ mental and emotional health. The kind of groupings formed for instructional purposes, for example, in social media can be done with greater care by teachers to make sure that the members of the groups are homogeneous and the tasks and activities shared in the groups are quite relevant and realistic. The teachers cannot always be in a full control of students’ use of social media, and the other fact is that students do not always and only use social media for educational purposes. They spend more time on social media for communicating with friends or strangers or possibly they just passively receive the content produced out of any educational scope just for entertainment. This uncontrolled and unrealistic content may give them a false image of life events and can threaten their mental and emotional health. Thus, teachers can try to make students aware of the potential hazards of investing too much of their time on following pages or people that publish false and misleading information about their personal or social identities. As students, logically expected, spend more time with their teachers than counselors, they may be better and more receptive to the advice given by the former than the latter.

Teachers may not be in full control of their students’ use of social media, but they have always played an active role in motivating or demotivating students to take particular measures in their academic lives. If teachers are informed of the recent research findings about the potential effects of massively using social media on students, they may find ways to reduce students’ distraction or confusion in class due to the excessive or over-reliant use of these networks. Educators may more often be mesmerized by the promises of technology-, computer- and mobile-assisted learning. They may tend to encourage the use of social media hoping to benefit students’ social and interpersonal skills, self-confidence, stress-managing and the like. Yet, they may be unaware of the potential adverse effects on students’ emotional well-being and, thus, may find the review of the recent relevant research findings insightful. Also, teachers can mediate between learners and social media to manipulate the time learners spend on social media. Research has mainly indicated that students’ emotional experiences are mainly dependent on teachers’ pedagogical approach. They should refrain learners from excessive use of, or overreliance on, social media. Raising learners’ awareness of this fact that individuals should develop their own path of development for learning, and not build their development based on unrealistic comparison of their competences with those of others, can help them consider positive values for their activities on social media and, thus, experience positive emotions.

At higher education, students’ needs are more life-like. For example, their employment-seeking spirits might lead them to create accounts in many social networks, hoping for a better future. However, membership in many of these networks may end in the mere waste of the time that could otherwise be spent on actual on-campus cooperative projects. Universities can provide more on-campus resources both for research and work experience purposes from which the students can benefit more than the cyberspace that can be tricky on many occasions. Two main theories underlying some negative emotions like boredom and anxiety are over-stimulation and under-stimulation. Thus, what learners feel out of their involvement in social media might be directed toward negative emotions due to the stimulating environment of social media. This stimulating environment makes learners rely too much, and spend too much time, on social media or use them obsessively. As a result, they might feel anxious or depressed. Given the ubiquity of social media, these negative emotions can be replaced with positive emotions if learners become aware of the psychological effects of social media. Regarding the affordances of social media for learners, they can take advantage of the potential affordances of these media such as improving their literacy, broadening their communication skills, or enhancing their distance learning opportunities.

A review of the research findings on the relationship between social media and students’ affective traits revealed both positive and negative findings. Yet, the instances of the latter were more salient and the negative psychological symptoms such as depression, anxiety, and stress have been far from negligible. These findings were discussed in relation to some more relevant theories such as the social comparison theory, which predicted that most of the potential issues with the young generation’s excessive use of social media were induced by the unfair comparisons they made between their own lives and the unrealistic portrayal of others’ on social media. Teachers, education policymakers, curriculum developers, and all those in charge of the student affairs at schools and universities should be made aware of the psychological effects of the pervasive use of social media on students, and the potential threats.

It should be reminded that the alleged socially supportive and communicative promises of the prevalent use of social networking in student life might not be fully realized in practice. Students may lose self-appreciation and gratitude when they compare their current state of life with the snapshots of others’ or peers’. A depressed or stressed-out mood can follow. Students at schools or universities need to learn self-worth to resist the adverse effects of the superficial support they receive from social media. Along this way, they should be assisted by the family and those in charge at schools or universities, most importantly the teachers. As already suggested, counseling programs might help with raising students’ awareness of the potential psychological threats of social media to their health. Considering the ubiquity of social media in everybody’ life including student life worldwide, it seems that more coping and compensatory strategies should be contrived to moderate the adverse psychological effects of the pervasive use of social media on students. Also, the affective influences of social media should not be generalized but they need to be interpreted from an ecological or contextual perspective. This means that learners might have different emotions at different times or different contexts while being involved in social media. More specifically, given the stative approach to learners’ emotions, what learners emotionally experience in their application of social media can be bound to their intra-personal and interpersonal experiences. This means that the same learner at different time points might go through different emotions Also, learners’ emotional states as a result of their engagement in social media cannot be necessarily generalized to all learners in a class.

As the majority of studies on the psychological effects of social media on student life have been conducted on school students than in higher education, it seems it is too soon to make any conclusive remark on this population exclusively. Probably, in future, further studies of the psychological complexities of students at higher education and a better knowledge of their needs can pave the way for making more insightful conclusions about the effects of social media on their affective states.

Suggestions for further research

The majority of studies on the potential effects of social media usage on students’ psychological well-being are either quantitative or qualitative in type, each with many limitations. Presumably, mixed approaches in near future can better provide a comprehensive assessment of these potential associations. Moreover, most studies on this topic have been cross-sectional in type. There is a significant dearth of longitudinal investigation on the effect of social media on developing positive or negative emotions in students. This seems to be essential as different affective factors such as anxiety, stress, self-esteem, and the like have a developmental nature. Traditional research methods with single-shot designs for data collection fail to capture the nuances of changes in these affective variables. It can be expected that more longitudinal studies in future can show how the continuous use of social media can affect the fluctuations of any of these affective variables during the different academic courses students pass at school or university.

As already raised in some works of research reviewed, the different patterns of impacts of social media on student life depend largely on the educational context. Thus, the same research designs with the same academic grade students and even the same age groups can lead to different findings concerning the effects of social media on student psychology in different countries. In other words, the potential positive and negative effects of popular social media like Facebook, Snapchat, Twitter, etc., on students’ affective conditions can differ across different educational settings in different host countries. Thus, significantly more research is needed in different contexts and cultures to compare the results.

There is also a need for further research on the higher education students and how their affective conditions are positively and negatively affected by the prevalent use of social media. University students’ psychological needs might be different from other academic grades and, thus, the patterns of changes that the overall use of social networking can create in their emotions can be also different. Their main reasons for using social media might be different from school students as well, which need to be investigated more thoroughly. The sorts of interventions needed to moderate the potential negative effects of social networking on them can be different too, all requiring a new line of research in education domain.

Finally, there are hopes that considering the ever-increasing popularity of social networking in education, the potential psychological effects of social media on teachers be explored as well. Though teacher psychology has only recently been considered for research, the literature has provided profound insights into teachers developing stress, motivation, self-esteem, and many other emotions. In today’s world driven by global communications in the cyberspace, teachers like everyone else are affecting and being affected by social networking. The comparison theory can hold true for teachers too. Thus, similar threats (of social media) to self-esteem and self-worth can be there for teachers too besides students, which are worth investigating qualitatively and quantitatively.

Probably a new line of research can be initiated to explore the co-development of teacher and learner psychological traits under the influence of social media use in longitudinal studies. These will certainly entail sophisticated research methods to be capable of unraveling the nuances of variation in these traits and their mutual effects, for example, stress, motivation, and self-esteem. If these are incorporated within mixed-approach works of research, more comprehensive and better insightful findings can be expected to emerge. Correlational studies need to be followed by causal studies in educational settings. As many conditions of the educational settings do not allow for having control groups or randomization, probably, experimental studies do not help with this. Innovative research methods, case studies or else, can be used to further explore the causal relations among the different features of social media use and the development of different affective variables in teachers or learners. Examples of such innovative research methods can be process tracing, qualitative comparative analysis, and longitudinal latent factor modeling (for a more comprehensive view, see Hiver and Al-Hoorie, 2019 ).

Author contributions

Both authors listed have made a substantial, direct, and intellectual contribution to the work, and approved it for publication.

This study was sponsored by Wuxi Philosophy and Social Sciences bidding project—“Special Project for Safeguarding the Rights and Interests of Workers in the New Form of Employment” (Grant No. WXSK22-GH-13). This study was sponsored by the Key Project of Party Building and Ideological and Political Education Research of Nanjing University of Posts and Telecommunications—“Research on the Guidance and Countermeasures of Network Public Opinion in Colleges and Universities in the Modern Times” (Grant No. XC 2021002).

Conflict of interest

Author XX was employed by China Mobile Group Jiangsu Co., Ltd. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

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  • Aalbers G., McNally R. J., Heeren A., de Wit S., Fried E. I. (2018). Social media and depression symptoms: A network perspective. J. Exp. Psychol. Gen. 148 1454–1462. 10.1037/xge0000528 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Abbott J. (2017). Introduction: Assessing the social and political impact of the internet and new social media in Asia. J. Contemp. Asia 43 579–590. 10.1080/00472336.2013.785698 [ CrossRef ] [ Google Scholar ]
  • Alahmar A. T. (2016). The impact of social media on the academic performance of second year medical students at College of Medicine, University of Babylon, Iraq. J. Med. Allied Sci. 6 77–83. 10.5455/jmas.236927 [ CrossRef ] [ Google Scholar ]
  • Banjanin N., Banjanin N., Dimitrijevic I., Pantic I. (2015). Relationship between internet use and depression: Focus on physiological mood oscillations, social networking and online addictive behavior. Comp. Hum. Behav. 43 308–312. 10.1016/j.chb.2014.11.013 [ CrossRef ] [ Google Scholar ]
  • Barry C. T., Sidoti C. L., Briggs S. M., Reiter S. R., Lindsey R. A. (2017). Adolescent social media use and mental health from adolescent and parent perspectives. J. Adolesc. 61 1–11. 10.1016/j.adolescence.2017.08.005 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Chang Y. (2012). The relationship between maladaptive perfectionism with burnout: Testing mediating effect of emotion-focused coping. Pers. Individ. Differ. 53 635–639. 10.1016/j.paid.2012.05.002 [ CrossRef ] [ Google Scholar ]
  • Charoensukmongkol P. (2018). The impact of social media on social comparison and envy in teenagers: The moderating role of the parent comparing children and in-group competition among friends. J. Child Fam. Stud. 27 69–79. 10.1007/s10826-017-0872-8 [ CrossRef ] [ Google Scholar ]
  • Chukwuere J. E., Chukwuere P. C. (2017). The impact of social media on social lifestyle: A case study of university female students. Gender Behav. 15 9966–9981. [ Google Scholar ]
  • Drouin M., Reining L., Flanagan M., Carpenter M., Toscos T. (2018). College students in distress: Can social media be a source of social support? Coll. Stud. J. 52 494–504. [ Google Scholar ]
  • Dumitrache S. D., Mitrofan L., Petrov Z. (2012). Self-image and depressive tendencies among adolescent Facebook users. Rev. Psihol. 58 285–295. [ Google Scholar ]
  • Fernyhough C. (2008). Getting Vygotskian about theory of mind: Mediation, dialogue, and the development of social understanding. Dev. Rev. 28 225–262. 10.1016/j.dr.2007.03.001 [ CrossRef ] [ Google Scholar ]
  • Festinger L. (1954). A Theory of social comparison processes. Hum. Relat. 7 117–140. 10.1177/001872675400700202 [ CrossRef ] [ Google Scholar ]
  • Fleck J., Johnson-Migalski L. (2015). The impact of social media on personal and professional lives: An Adlerian perspective. J. Individ. Psychol. 71 135–142. 10.1353/jip.2015.0013 [ CrossRef ] [ Google Scholar ]
  • Fredrickson B. L. (2001). The role of positive emotions in positive psychology: The broaden-and-build theory of positive emotions. Am. Psychol. 56 218–226. 10.1037/0003-066X.56.3.218 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Frison E., Eggermont S. (2016). Exploring the relationships between different types of Facebook use, perceived online social support, and adolescents’ depressed mood. Soc. Sci. Compu. Rev. 34 153–171. 10.1177/0894439314567449 [ CrossRef ] [ Google Scholar ]
  • Hanprathet N., Manwong M., Khumsri J., Yingyeun R., Phanasathit M. (2015). Facebook addiction and its relationship with mental health among Thai high school students. J. Med. Assoc. Thailand 98 S81–S90. [ PubMed ] [ Google Scholar ]
  • Hiver P., Al-Hoorie A. H. (2019). Research Methods for Complexity Theory in Applied Linguistics. Bristol: Multilingual Matters. 10.21832/HIVER5747 [ CrossRef ] [ Google Scholar ]
  • Iwamoto D., Chun H. (2020). The emotional impact of social media in higher education. Int. J. High. Educ. 9 239–247. 10.5430/ijhe.v9n2p239 [ CrossRef ] [ Google Scholar ]
  • Keles B., McCrae N., Grealish A. (2020). A systematic review: The influence of social media on depression, anxiety and psychological distress in adolescents. Int. J. Adolesc. Youth 25 79–93. 10.1080/02673843.2019.1590851 [ CrossRef ] [ Google Scholar ]
  • Ley B., Ogonowski C., Hess J., Reichling T., Wan L., Wulf V. (2014). Impacts of new technologies on media usage and social behavior in domestic environments. Behav. Inform. Technol. 33 815–828. 10.1080/0144929X.2013.832383 [ CrossRef ] [ Google Scholar ]
  • Li J.-B., Lau J. T. F., Mo P. K. H., Su X.-F., Tang J., Qin Z.-G., et al. (2017). Insomnia partially mediated the association between problematic Internet use and depression among secondary school students in China. J. Behav. Addict. 6 554–563. 10.1556/2006.6.2017.085 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Mathewson M. (2020). The impact of social media usage on students’ mental health. J. Stud. Affairs 29 146–160. [ Google Scholar ]
  • Neira B. C. J., Barber B. L. (2014). Social networking site use: Linked to adolescents’ social self-concept, self-esteem, and depressed mood. Aus. J. Psychol. 66 56–64. 10.1111/ajpy.12034 [ CrossRef ] [ Google Scholar ]
  • O’Dea B., Campbell A. (2011). Online social networking amongst teens: Friend or foe? Ann. Rev. CyberTher. Telemed. 9 108–112. [ PubMed ] [ Google Scholar ]
  • Radovic A., Gmelin T., Stein B. D., Miller E. (2017). Depressed adolescents positive and negative use of social media. J. Adolesc. 55 5–15. 10.1016/j.adolescence.2016.12.002 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Sampasa-Kanyinga H., Lewis R. F. (2015). Frequent use of social networking sites is associated with poor psychological functioning among children and adolescents. Cyberpsychol. Behav. Soc. Network. 18 380–385. 10.1089/cyber.2015.0055 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Sriwilai K., Charoensukmongkol P. (2016). Face it, don’t Facebook it: Impacts of social media addiction on mindfulness, coping strategies and the consequence on emotional exhaustion. Stress Health 32 427–434. 10.1002/smi.2637 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Stapel D. A. (2007). “ In the mind of the beholder: The interpretation comparison model of accessibility effects ,” in Assimilation and Contrast in Social Psychology , eds Stapel D. A., Suls J. (London: Psychology Press; ), 143–164. [ Google Scholar ]
  • Stapel D. A., Koomen W. (2000). Distinctiveness of others, mutability of selves: Their impact on self-evaluations. J. Pers. Soc. Psychol. 79 1068–1087. 10.1037//0022-3514.79.6.1068 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Tang F., Wang X., Norman C. S. (2013). An investigation of the impact of media capabilities and extraversion on social presence and user satisfaction. Behav. Inform. Technol. 32 1060–1073. 10.1080/0144929X.2013.830335 [ CrossRef ] [ Google Scholar ]
  • Tsitsika A. K., Tzavela E. C., Janikian M., Ólafsson K., Iordache A., Schoenmakers T. M., et al. (2014). Online social networking in adolescence: Patterns of use in six European countries and links with psychosocial functioning. J. Adolesc. Health 55 141–147. 10.1016/j.jadohealth.2013.11.010 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Vernon L., Modecki K. L., Barber B. L. (2017). Tracking effects of problematic social networking on adolescent psychopathology: The mediating role of sleep disruptions. J. Clin. Child Adolesc. Psychol. 46 269–283. 10.1080/15374416.2016.1188702 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Virden A., Trujillo A., Predeger E. (2014). Young adult females’ perceptions of high-risk social media behaviors: A focus-group approach. J. Commun. Health Nurs. 31 133–144. 10.1080/07370016.2014.926677 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Wang P., Wang X., Wu Y., Xie X., Wang X., Zhao F., et al. (2018). Social networking sites addiction and adolescent depression: A moderated mediation model of rumination and self-esteem. Pers. Individ. Differ. 127 162–167. 10.1016/j.paid.2018.02.008 [ CrossRef ] [ Google Scholar ]
  • Weng L., Menczer F. (2015). Topicality and impact in social media: Diverse messages, focused messengers. PLoS One 10 : e0118410 . 10.1371/journal.pone.0118410 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Yan H., Zhang R., Oniffrey T. M., Chen G., Wang Y., Wu Y., et al. (2017). Associations among screen time and unhealthy behaviors, academic performance, and well-being in Chinese adolescents. Int. J. Environ. Res. Public Health 14 : 596 . 10.3390/ijerph14060596 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Zareen N., Karim N., Khan U. A. (2016). Psycho-emotional impact of social media emojis. ISRA Med. J. 8 257–262. [ Google Scholar ]
  • Zhang R. (2017). The stress-buffering effect of self-disclosure on Facebook: An examination of stressful life events, social support, and mental health among college students. Comp. Hum. Behav. 75 527–537. 10.1016/j.chb.2017.05.043 [ CrossRef ] [ Google Scholar ]

Social Media Adoption, Usage And Impact In Business-To-Business (B2B) Context: A State-Of-The-Art Literature Review

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  • Published: 02 February 2021
  • Volume 25 , pages 971–993, ( 2023 )

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research paper impact of social media

  • Yogesh K. Dwivedi 1 ,
  • Elvira Ismagilova 2 ,
  • Nripendra P. Rana 2 &
  • Ramakrishnan Raman 3  

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Social media plays an important part in the digital transformation of businesses. This research provides a comprehensive analysis of the use of social media by business-to-business (B2B) companies. The current study focuses on the number of aspects of social media such as the effect of social media, social media tools, social media use, adoption of social media use and its barriers, social media strategies, and measuring the effectiveness of use of social media. This research provides a valuable synthesis of the relevant literature on social media in B2B context by analysing, performing weight analysis and discussing the key findings from existing research on social media. The findings of this study can be used as an informative framework on social media for both, academic and practitioners.

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1 Introduction

The Internet has changed social communications and social behaviour, which lead to the development of new forms of communication channels and platforms (Ismagilova et al. 2017 ). Social media plays an important part in the digital transformation of businesses (Kunsman 2018 ). Digital transformation refers to the globally accelerated process of technical adaptation by companies and communities as a result of digitalisation (Sivarajah et al. 2019 ; Westerman et al. 2014 ). Web is developed from a tool used to provide passive information into the collaborative web, which allows and encourages active user engagement and contribution. If before social networks were used to provide the information about a company or brand, nowadays businesses use social media in their marketing aims and strategies to improve consumers’ involvement, relationship with customers and get useful consumers’ insights (Alalwan et al. 2017 ). Business-to-consumer (B2C) companies widely use social media as part of their digital transformation and enjoy its benefits such as an increase in sales, brand awareness, and customer engagement to name a few (Barreda et al. 2015 ; Chatterjee and Kar 2020 ; Harrigan et al. 2020 ; Kamboj et al. 2018 ; Kapoor et al. 2018 ).

From a marketing and sales research perspective, social media is defined as “the technological component of the communication, transaction and relationship building functions of a business which leverages the network of customers and prospects to promote value co-creation” (Andzulis et al. 2012 p.308). Industrial buyers use social media for their purchase as they compare products, research the market and build relationships with salesperson (Itani et al. 2017 ). Social media changed the way how buyers and sellers interact (Agnihotri et al. 2016 ) by enabling open and broad communications and cooperation between them (Rossmann and Stei 2015 ). Social media is an important facilitator of relationships between a company and customers (Agnihotri et al. 2012 ; Tedeschi 2006 ). Customers are more connected to companies, which make them more knowledgable about product selection and more powerful in buyer-seller relationships (Agnihotri et al. 2016 ). Social media also helps companies to increase business exposure, traffic and providing marketplace insight (Agnihotri et al. 2016 ; Stelzner 2011 ). As a result, the use of social media supports business decision processes and helps to improve companies’ performance (Rossmann and Stei 2015 ).

Due to digitalisation customers are becoming more informed and rely less on traditional selling initiatives (Ancillai et al. 2019 ). Buyers are relying more on digital resources and their buying process more often involves the use of social media. For example, in the research B2B buyer survey, 82% of buyers stated that social media content has a significant impact on the purchase decision (Ancillai et al. 2019 ; Minsky and Quesenberry 2016 ). As a result, these changes in consumer behaviour place high pressure on B2B salespeople and traditional sales companies (Ancillai et al. 2019 ). By using evidence from major B2B companies and consultancy report some studies claim that social media can be applied in sales to establish effective dialogues with buyers (Ancillai et al. 2019 ; Kovac 2016 ; McKinsey and Company 2015 ).

Now, business-to-business (B2B) companies started using social media as part of their digital transformation. 83% of B2B companies use social media, which makes it the most common marketing tactic (Pulizzi and Handley 2017 ; Sobal 2017 ). More than 70% of B2B companies use at least one of the “big 4” social media sites such as LinkedIn, Twitter, Facebook and YouTube. Additionally, 50% of the companies stated that social media has improved their marketing optimization and customer experience, while 25% stated that their revenue went up (Gregorio 2017 ; Sobal 2017 ). Even though B2B companies are benefitting from social media used by marketers, it is argued that research on that area is still in the embryonic stage and future research is needed (Salo 2017 ; Siamagka et al. 2015 ; Juntunen et al. 2020 ; Iannacci et al. 2020 ). There is a limited understanding of how B2B companies need to change to embrace recent technological innovations and how it can lead to business and societal transformation (Chen et al. 2012 ; Loebbecke and Picot 2015 ; Pappas et al. 2018 ).

The topic of social media in the context of B2B companies has started attracting attention from both academics and practitioners. This is evidenced by the growing number of research output within academic journals and conference proceedings. Some studies provided a comprehensive literature review on social media use by B2B companies (Pascucci et al. 2018 ; Salo 2017 ), but focused only on adoption of social media by B2B or social media influence, without providing the whole picture of the use of social media by B2B companies. Thus, this study aims to close this gap in the literature by conducting a comprehensive analysis of the use of social media by B2B companies and discuss its role in the digital transformation of B2B companies. The findings of this study can provide an informative framework for research on social media in the context of B2B companies for academics and practitioners.

The remaining sections of the study are organised as follows. Section 2 offers a brief overview of the methods used to identify relevant studies to be included in this review. Section 3 synthesises the studies identified in the previous section and provides a detailed overview. Section 4 presents weight analysis and its findings. Next section discusses the key aspects of the research, highlights any limitations within existing studies and explores the potential directions for future research. Finally, the paper is concluded in Section 6 .

2 Literature Search Method

The approach utilised in this study aligns with the recommendations in Webster and Watson ( 2002 ). This study used a keyword search-based approach for identifying relevant articles (Dwivedi et al. 2019b ; Ismagilova et al. 2020a ; Ismagilova et al. 2019 ; Jeyaraj and Dwivedi 2020 ; Williams et al. 2015 ). Keywords such as “Advertising” OR “Marketing” OR “Sales” AND TITLE (“Social Media” OR “Web 2.0” OR “Facebook” OR “LinkedIn” OR “Instagram” OR “Twitter” OR “Snapchat” OR “Pinterest” OR “WhatsApp” OR “Social Networking Sites”) AND TITLE-ABS-KEY (“B2B” OR “B to B” OR “Business to Business” OR “Business 2 Business”) were searched via the Scopus database. Scopus database was chosen to ensure the inclusion of only high quality studies. Use of online databases for conducting a systematic literature review became an emerging culture used by a number of information systems research studies (Dwivedi et al. 2019a ; Gupta et al. 2019 ; Ismagilova et al. 2020b ; Muhammad et al. 2018 ; Rana et al. 2019 ). The search resulted in 80 articles. All studies were processed by the authors in order to ensure relevance and that the research offered a contribution to the social media in the context B2B discussion. The search and review resulted in 70 articles and conference papers that formed the literature review for this study. The selected studies appeared in 33 separate journals and conference proceedings, including journals such as Industrial Marketing Management, Journal of Business and Industrial Marketing and Journal of Business Research.

3 Literature Synthesis

The studies on social media research in the context of B2B companies were divided into the following themes: effect of social media, adoption of social media, social media strategies, social media use, measuring the effectiveness of use of social media, and social media tools (see Table 1 ). The following subsections provide an overview of each theme.

3.1 Effect of Social Media

Some studies focus on the effect of social media for B2B companies, which include customer satisfaction, value creation, intention to buy and sales, building relationships with customers, brand awareness, knowledge creation, perceived corporate credibility, acquiring of new customers, salesperson performance, employee brand engagement, and sustainability (Table 2 ).

3.1.1 Customer Satisfaction

Some studies investigated how the use of social media affected customer satisfaction (Agnihotri et al. 2016 ; Ancillai et al. 2019 ; Rossmann and Stei 2015 ). For example, Agnihotri et al. ( 2016 ) investigated how the implementation of social media by B2B salesperson affects consumer satisfaction. Salesperson’s social media use is defined as a “salesperson’s utilization and integration of social media technology to perform his or her job” (Agnihotri et al. 2016 , p.2). The study used data from 111 sales professionals involved in B2B industrial selling to test the proposed hypotheses. It was found that a salesperson’s use of social media will have a positive effect on information communication, which will, in turn, lead to improved customer satisfaction with the salesperson. Also, it was investigated that information communication will be positively related to responsiveness, which impacts customer satisfaction.

Another study by Rossmann and Stei ( 2015 ) looked at the antecedents of social media use, social media use by B2B companies and their effect on customers. By using data from 362 chief information officers of B2B companies the study found the following. Social media usage of sales representative has a positive impact on customer satisfaction. Age has a negative effect on content generation. It seems that older salespeople use social media in passive ways or interacting with the customer rather than creating their own content. It was found that the quality of corporate social media strategy has a positive impact on social media usage in terms of the consumption of information, content generation, and active interaction with customers. Also, the expertise of a salesperson in the area of social media has a positive impact on social media usage.

3.1.2 Value Creation

Research in B2B found that social media can create value for customers and salesperson (Agnihotri et al. 2012 ; Agnihotri et al. 2017 ). Agnihotri et al. ( 2012 ) proposed a theoretical framework to explain the mechanisms through which salespeople’s use of social media operates to create value and propose a strategic approach to social media use to achieve competitive goals. The study draws on the existing literature on relationship marketing, task–technology fit theory, and sales service behavior to sketch a social media strategy for business-to-business sales organizations with relational selling objectives. The proposed framework describes how social media tools can help salespeople perform service behaviors (information sharing, customer service, and trust-building) leading to value creation.

Some researchers investigated the role of the salesperson in the value creation process after closing the sale. By employing salesperson-customer data within a business-to-business context, Agnihotri et al. ( 2017 ) analysed the direct effects of sales-based CRM technology on the post-sale service behaviors: diligence, information communication, inducements, empathy, and sportsmanship. Additionally, the study examines the interactive effects of sales-based CRM technology and social media on these behaviors. The results indicate that sales-based CRM technology has a positive influence on salesperson service behaviors and that salespeople using CRM technology in conjunction with social media are more likely to exhibit higher levels of SSBs than their counterparts with low social media technology use. Data were collected from 162 salespeople from India. SmartPLS was used to analyse the data.

3.1.3 Intention to Buy and Sales

Another group of studies investigated the effect of social media on the level of sales and consumer purchase intention (Ancillai et al. 2019 ; Itani et al. 2017 ; Salo 2017 ; Hsiao et al. 2020 ; Mahrous 2013 ). For example, Itani et al. ( 2017 ) used the theory of reasoned actions to develop a model that tests the factors affecting the use of social media by salesperson and its impact. By collecting data from 120 salespersons from different industries and using SmartPLS to analyse the data, it was found that attitude towards social media usefulness did not affect the use of social media. It was found that social media use positively affects competitive intelligence collection, adaptive selling behaviour, which in turn influenced sales performance. Another study by Ancillai et al. ( 2019 ) used in-depth interviews with social selling professionals. The findings suggest that the use of social media improves not only the level of sales but also affects relationship and customer performance (trust, customer satisfaction, customer referrals); and organisational performance (organisational selling performance and brand performance).

It was investigated that social media has a positive effect on the intention to purchase (Hsiao et al. 2020 ; Mahrous 2013 ). For instance, Mahrous ( 2013 ) by reviewing the literature on B2B and B2C companies concluded that social media has a significant influence on consumer buying behaviour.

3.1.4 Customer Relationships

Another group of studies focused on the effect of social media on customer relationships (Bhattacharjya and Ellison 2015 ; Gáti et al. 2018 ; Gruner and Power 2018 ; Hollebeek 2019 ; Iankova et al. 2018 ; Jussila et al. 2011 ; Kho 2008 ; Niedermeier et al. 2016 ; Ogilvie et al. 2018 ). For example, Bhattacharjya and Ellison ( 2015 ) investigated the way companies build relationships with customers by using responsive customer relationship management. The study analysed customer relationship management activities from Twitter account of a Canadian company Shopify (B2B service provider). The company uses Twitter to engage with small business customers, develops and consumers. Jussila et al. ( 2011 ), by reviewing the literature, found that social media leads to increased customer focus and understanding, increased level of customer service and decreased time-to-market.

Gáti et al. ( 2018 ) focused their research efforts on social media use in customer relationship performance, particularly in customer relations. The study investigated the adoption and impact of social media by salespeople of B2B companies. By using data of 112 salespeople from several industries the study found that the intensity of technology use positively affects attitude towards social media, which positively affects social media use. Intensive technology use in turn positively affects customer relationship performance (customer retention). PLS-SEM was applied for analysis.

Another study by Gruner and Power ( 2018 ) investigated the effectiveness of the use of multiple social media platforms in communications with customers. By using data from 208 large Australian organisations, the paper explores how companies’ investment in one form of social media impacts activity on another form of social media. A regression analysis was performed to analyse the data. It was found that widespread activities on LinkedIn, Twitter and YouTube have a negative effect on a company’s marketing activity on Facebook. Thus, having it is more effective for the company to focus on a specific social media platform in forming successful inter-organisational relationships with customers.

Hollebeek ( 2019 ) proposed an integrative S-D logic/resource-based view (RBV) model of customer engagement. The proposed model considers business customer actors and resources in driving business customer resource integration, business customer resource integration effectiveness and business customer resource integration efficiency, which are antecedents of business customer engagement. Business customer engagement, in turn, results in business customer co-creation and relationship productivity.

Niedermeier et al. ( 2016 ) investigated the use of social media among salespeople in the pharmaceutical industry in China. Also, the study investigated the impact of social media on building culturally specific Guanxi relationships-it involves the exchange of factors to build trust and connection for business purpose. By using in-depth interviews with 3 sales managers and a survey of 42 pharmaceutical sales representatives that study found that WeChat is the most common social media platform used by businesses. Also, it was found to be an important tool in building Guanxi. Future studies should focus on other industries and other types of cultural features in doing business.

Ogilvie et al. ( 2018 ) investigated the effect of social media technologies on customer relationship performance and objective sales performance by using two empirical studies conducted in the United States. The first study used 375 salespeople from 1200 B2B companies. The second study used 181 respondents from the energy solution company. It was found that social media significantly affects salesperson product information communication, diligence, product knowledge and adaptability, which in turn affect customer relationship performance. It was also found that the use of social media technologies without training on technology will not lead to good results. Thus, the results propose that companies should allocate the resources required for the proper implementation of social media strategies. Future research should examine how the personality traits of a salesperson can moderate the implementation of social media technologies.

While most of the studies focused on a single country, Iankova et al. ( 2018 ) investigated the perceived effectiveness of social media by different types of businesses in two countries. By using 449 respondents from the US and the UK businesses, it was found that social media is potentially less important, at the present time, for managing ongoing relationships in B2B organizations than for B2C, Mixed or B2B2C organizations. All types of businesses ascribe similar importance to social media for acquisition-related activities. Also it was found that B2B organizations see social media as a less effective communication channel, and to have less potential as a channel for the business.

3.1.5 Brand Awareness

Some researchers argued that social media can influence brand awareness (Ancillai et al. 2019 ; Hsiao et al. 2020 ). For instance, Hsiao et al. ( 2020 ) investigated the effect of social media in the fashion industry. By collecting 1395 posts from lookbook.nu and employing regression analysis it was found that the inclusion of national brand and private fashion brands in the post increased the level of popularity which leads to purchasing interest and brand awareness.

3.1.6 Knowledge Creation

Multiple types of collaborative web tools can help and significantly increase the collaboration and the use of the distributed knowledge inside and outside of the company (McAfee 2006 ). Kärkkäinen et al. ( 2011 ) by analysing previous literature on social media proposed that social media use has a positive effect on sharing and creation of customer information and knowledge in the case of B2B companies.

3.1.7 Corporate Credibility

Another study by Kho ( 2008 ) states the advantages of using social media by B2B companies, which include faster and more personalised communications between customer and vendor, which can improve corporate credibility and strengthen the relationships. Thanks to social media companies can provide more detailed information about their products and services. Kho ( 2008 ) also mentions that customer forums and blog comments in the B2B environment should be carefully monitored in order to make sure that inappropriate discussions are taken offline and negative eWOM communications should be addressed in a timely manner.

3.1.8 Acquiring New Customers

Meire et al. ( 2017 ) investigated the impact of social media on acquiring B2B customers. By using commercially purchased prospecting data, website data and Facebook data from beverage companies the study conducted an experiment and found that social media us an effective tool in acquiring B2B customers. Future work might assess the added value of social media pages for profitability prediction instead of prospect conversion. When a longer timeframe becomes available (e.g., after one year), the profitability of the converted prospects can be assessed.

3.1.9 Salesperson Performance

Moncrief et al. ( 2015 ) investigated the impact of social media technologies on the role of salesperson position. It was found that social media affects sales management functions (supervision, selection, training, compensation, and deployment) and salesperson performance (role, skill, and motivation). Another study by Rodriguez et al. ( 2012 ) examines the effect of social media on B2B sales performance by using social capital theory and collecting data from 1699 B2B salespeople from over 25 different industries. By employing SEM AMOS, the study found that social media usage has a positive significant relationship with selling companies’ ability to create opportunities and manage relationships. The study also found that social media usage has a positive and significant relationship with sales performance (based on relational measurers of sales that focus on behaviours that strengthen the relationship between buyers and sellers), but not with outcome-based sales performance (reflected by quota achievement, growth in average billing size, and overall revenue gain).

3.1.10 Employee Brand Management

The study by Pitt et al. ( 2018 ) focuses on employee engagement with B2B companies on social media. By using results from Glassdoor (2315 five-star and 1983 one-star reviews for the highest-ranked firms, and 1013 five star and 1025 one-star reviews for lowest ranked firms) on employee brand engagement on social media, two key drivers of employee brand engagement by using the content analysis tool DICTION were identified-optimism and commonality. Individuals working in top-ranked companies expressed a higher level of optimism and commonality in comparison with individuals working in low-ranked companies. As a result, a 2 × 2 matrix was constructed which can help managers to choose strategies in order to increase and improve employee brand engagement. Another study by Pitt et al. ( 2017 ) focused on employee engagement of B2B companies on social media. By using a conceptual framework based on a theory of word choice and verbal tone and 6300 reviews collected from Glassdoor and analysed using DICTION. The study found that employees of highly ranked B2B companies are more positive about their employer brand and talk more optimistically about these brands. For low ranked B2B companies it was found that employees express a greater level of activity, certainty, and realism. Also, it was found that they used more aggressive language.

3.1.11 Sustainability

Sustainability refers to the strategy that helps a business “to meet its current requirements without compromising its ability to meet future needs” (World Commission Report on Environment and Development 1987 , p 41). Two studies out of 70 focused on the role of social media for B2B sustainability (Sivarajah et al. 2019 ; Kasper et al. 2015 ). For example, Sivarajah et al. ( 2019 ) argued that big data and social media within a participatory web environment to enable B2B organisations to become profitable and remain sustainable through strategic operations and marketing related business activities.

Another study by Kasper et al. ( 2015 ) proposed the Social Media Matrix which helps companies to decide which social media activities to execute based on their corporate and communication goals. The matrix includes three parts. The first part is focusing on social media goals and task areas, which were identified and matched. The second part consists of five types of social media activities (content, interaction/dialog, listening and analysing, application and networking). The third part provides a structure to assess the suitability of each activity type on each social media platform for each goal. The matrix was successfully tested by assessing the German B2B sector by using expert interviews with practitioners.

Based on the reviewed studies, it can be seen that if used appropriately social media have positive effect on B2B companies before and after sales, such as customer satisfaction, value creation, intention to buy and sales, customer relationships, brand awareness, knowledge creation, corporate credibility, acquiring new customers, salesperson performance, employee brand management, and sustainability. However, limited research is done on the negative effect of social media on b2b companies.

3.2 Adoption of Social Media

Some scholars investigated factors affecting the adoption of social media by B2B companies (Buratti et al. 2018 ; Gáti et al. 2018 ; Gazal et al. 2016 ; Itani et al. 2017 ; Kumar and Möller 2018 ; Lacka and Chong 2016 ). For instance, Lacka and Chong ( 2016 ) investigated factors affecting the adoption of social media by B2B companies from different industries in China. The study collected the data from 181 respondents and used the technology acceptance model with Nielsen’s model of attributes of system acceptability as a theoretical framework. By using SEM AMOS for analysis the study found that perceived usability, perceived usefulness, and perceived utility positively affect adoption and use of social media by B2B marketing professionals. The usefulness is subject to the assessment of whether social media sites are suitable means through which marketing activities can be conducted. The ability to use social media sites for B2B marketing purposes, in turn, is due to those sites learnability and memorability attributes.

Another study by Müller et al. ( 2018 ) investigated factors affecting the usage of social media. By using survey data from 100 Polish and 39 German sensor suppliers, it was found that buying frequency, the function of a buyer, the industry sector and the country does not affect the usage of social media in the context of sensor technology from Poland and Germany. The study used correlation analysis and ANOVA.

Lashgari et al. ( 2018 ) studied the adoption and use of social media by using face-to-face interviews with key managers of four multinational corporations and observations from companies’ websites and social media platforms. It was found that that the elements essential in forming the B2B firm’s social media adoption strategies are content (depth and diversity), corresponding social media platform, the structure of social media channels, the role of moderators, information accessibility approaches (public vs. gated-content), and online communities. These elements are customized to the goals and target group the firm sets to pursue. Similarly, integration of social media into other promotional channels can fall under an ad-hoc or continuous approach depending on the scope and the breadth of the communication plan, derived from the goal.

Similar to Lashgari et al. ( 2018 ), Shaltoni ( 2017 ) used data from managers. The study applied technology organisational environmental framework and diffusion of innovations to investigate factors affecting the adoption of social media by B2B companies. By using data from marketing managers or business owners of 480 SMEs, the study found that perceived relative advance, perceive compatibility, organizational innovativeness, competitor pressure, and customer pressure influence the adoption of social media by B2B companies. The findings also suggest that many decision-makers in B2B companies think that Internet marketing is not beneficial, as it is not compatible with the nature of B2B markets.

Buratti et al. ( 2018 ) investigated the adoption of social media by tanker shipping companies and ocean carriers. By using data from 60 companies the following was found. LinkedIn is the most used tool, with a 93.3% adoption rate. Firm size emerges as a predictor of Twitter’s adoption: big companies unveil a higher attitude to use it. Finally, the country of origin is not a strong influential factor in the adoption rate. Nonetheless, Asian firms clearly show a lower attitude to join SM tools such as Facebook (70%) and LinkedIn (86.7%), probably also due to governmental web restrictions imposed in China. External dimensions such as the core business, the firm size, the geographic area of origin, etc., seem to affect network wideness. Firm size, also, discriminates the capacity of firms to build relational networks. Bigger firms create networks larger than small firms do. Looking at geographical dimensions, Asian firms confirm to be far less active on SM respect to European and North American firms. Finally, the study analyzed the format of the contents disclosed by sample firms, observing quite limited use of photos and videos: in the sample industries, informational contents seem more appropriate for activating a dialogue with stakeholders and communication still appears formulated in a very traditional manner. Preliminary findings suggest that companies operating in conservative B2B services pursue different strategic approaches toward SMM and develop ad hoc communication tactics. Nonetheless, to be successful in managing SM tools, a high degree of commitment and a clear vision concerning the role of SM within communication and marketing strategy is necessary.

Gazal et al. ( 2016 ) investigated the adoption and measuring of the effectiveness of social media in the context of the US forest industry by using organisational-level adoption framework and TAM. By using data from 166 companies and performing regression analysis, the following results were received. Years in business, new sales revenue, product type, amount of available information on a company website, perceived importance of e-commerce and perceived ease of use of social media significantly affected social media use. Also, it was found that companies’ strategies and internal resources and capabilities and influence a company’s decision to adopt social media. Also, it was found that 94 of respondents do not measure the ROI from social media use. The reason is that the use of social media in marketing is relatively new and companies do not possess the knowledge of measuring ROI from the use of social media. Companies mostly use quantitative metrics (number of site visits, number of social network friends, number of comments and profile views) and qualitative metrics (growth of relationships with the key audience, audience participation, moving from monologue to dialogue with consumers. Facebook was found to be the most effective social media platform reported by the US forest industry.

The study by Kumar and Möller ( 2018 ) investigated the role of social media for B2B companies in their recruitment practices. By using data from international B2B company with headquarter in Helsinki, Finland comprised of 139 respondents it was found that brand familiarity encourages them to adopt social media platforms for a job search; however, the effect of the persuasiveness of recruitment messages on users’ adoption of social media platforms for their job search behavior is negative. The study used correlation analysis and descriptive analysis to analyse the data.

Nunan et al. ( 2018 ) identified areas for future research such as patterns of social media adoption, the role of social media platforms within the sales process, B2B consumer engagement and social media, modeling the ROI of social media, and the risks of social media within B2B sales relationships.

The study by Pascucci et al. ( 2018 ) conducted a systematic literature review on antecedents affecting the adoption and use of social media by B2B companies. By reviewing 29 studies published in academic journal and conferences from 2001 to 2017, the study identified external (pressure from customers, competitors, availability of external information about social media) and internal factors (personal characteristics -managers age, individual commitment, perceptions of social media-perceived ease of use, perceived usefulness, perceived utility), which can affect adoption of social media.

The study by Siamagka et al. ( 2015 ) aims to investigate factors affecting the adoption of social media by B2B organisations. The conceptual model was based on the technology acceptance model and the resource-based theory. AMOS software and Structural equation modelling were employed to test the proposed hypotheses. By using a sample of 105 UK companies, the study found that perceived usefulness of social media is influenced by image, perceived ease of use and perceived barriers. Also, it was found that social media adoption is significantly determined by organisational innovativeness and perceived usefulness. Additionally, the study tested the moderating role of organisational innovativeness and found that it does not affect the adoption of social media by B2B organisations. The study also identified that perceived barriers to SNS (uncertainty about how to use SNS to achieve objectives, employee’s lack of knowledge about SNS, high cost of investment needed to adopt the technology) have a negative impact on perceived usefulness of social media by B2B organisations. The study also used nine in-depth interviews with B2B senior managers and social media specialists about adoption of social media by B2B. It was found that perceived pressure from stakeholders influences B2B organisations’ adoption intention of social media. Future research should test it by using quantitative methods.

While most of the studies focused on the antecedents of social media adoption by B2B companies, Michaelidou et al. ( 2011 ) investigated the usage, perceived barriers and measuring the effectiveness of social media. By using data from 92 SMEs the study found that over a quarter of B2B SMEs in the UK are currently using SNS to achieve brand objectives, the most popular of which is to attract new customers. The barriers that prevent SMEs from using social media to support their brands were lack of staff familiarity and technical skills. Innovativeness of a company determined the adoption of social media. It was found that most of the companies do not evaluate the effectiveness of their SNS in supporting their brand. The most popular measures were the number of users joining the groups/discussion and the number of comments made. The findings showed that the size of the company does not influence the usage of social media for small and medium-sized companies. Future research should investigate the usage of social media in large companies and determine if the size can have and influence on the use. The benefits of using social media include increasing awareness and communicating the brand online. B2B companies can employ social media to create customer value in the form of interacting with customers, as well as building and fostering customer relationships. Future research should investigate the reasons why most of the users do not assess the effectiveness of their SNS. Future research should also investigate how the attitude towards technology can influence the adoption of social media.

Based on the reviewed studies it can be seen that the main factors affecting the adoption of social media by B2B companies are perceived usability, technical skills of employees, pressure from stakeholders, perceived usefulness and innovativeness.

3.3 Social Media Strategies

Another group of studies investigated types of strategies B2B companies apply (Cawsey and Rowley 2016 ; Huotari et al. 2015 ; Kasper et al. 2015 ; McShane et al. 2019 ; Mudambi et al. 2019 ; Swani et al. 2013 ; Swani et al. 2014 ; Swani et al. 2017 ; Watt 2010 ). For example, Cawsey and Rowley ( 2016 ) focused on the social media strategies of B2B companies. By conducting semi-structured interviews with marketing professionals from France, Ireland, the UK and the USA it was found that enhancing brand image, extending brand awareness and facilitating customer engagement were considered the most common social media objective. The study proposed the B2B social media strategy framework, which includes six components of a social media strategy: 1) monitoring and listening 2) empowering and engaging employees 3) creating compelling content 4) stimulating eWOM 5) evaluating and selecting channels 6) enhancing brand presence through integrating social media.

Chirumalla et al. ( 2018 ) focused on the social media engagement strategies of manufacturing companies. By using semi-structured interviews (36), observations (4), focus group meetings (6), and documentation, the study developed the process of social media adoption through a three-phase engagement strategy which includes coordination, cooperation, and co-production.

McShane et al. ( 2019 ) proposed social media strategies to influence online users’ engagement with B2B companies. Taking into consideration fluency lens the study analysed Twitter feeds of top 50 social B2B brands to examine the influence of hashtags, text difficulty embedded media and message timing on user engagement, which was evaluated in terms of likes and retweets. It was found that hashtags and text difficulty are connected to lower levels of engagement while embedded media such as images and videos improve the level of engagement.

Swani et al. ( 2014 ) investigate the use of Twitter by B2B and B2C companies and predict factors that influence message strategies. The study conducted a longitudinal content analysis by collecting 7000 tweets from Fortune 500 companies. It was found that B2B and B2C companies used different message appeals, cues, links and hashtags. B2B companies tend to use more emotional than functional appeals. It was found that B2B and B2C companies do not use hard-sell message strategies.

Another study by Swani et al. ( 2013 ) aimed to investigate message strategies that can help in promoting eWOM activity for B2B companies. By applying content analysis and hierarchical linear modeling the study analysed 1143 wall post messages from 193 fortune 500 Facebook accounts. The study found that B2B account posts will be more effective if they include corporate brand names and avoid hard sell or explicitly commercial statement. Also, companies should use emotional sentiment in Facebook posts.

Huotari et al. ( 2015 ) aimed to investigate how B2B marketers can influence content creation in social media. By conducting four face-to-face interviews with B2B marketers, it was found that a B2B company can influence content creation in social media directly by adding new content, participating in a discussion and removing content through corporate user accounts and controlling employees social media behaviour. Also, it can influence it indirectly by training employees to create desired content and perfuming marketing activities that influence other users to create content that is favorable for the company.

Most of the studies investigated the strategies and content of social media communications of B2B companies. However, the limited number of studies investigated the importance of CEO engagement on social media in the company’s strategies. Mudambi et al. ( 2019 ) emphasise the importance of the CEO of B2B companies to be present and active on social media. The study discusses the advantages of social media presence for the CEO and how it will benefit the company. For example, one of the benefits for the CEO can be perceived as being more trustworthy and effective than non-social CEOs, which will benefit the company in increased customer trust. Mudambi et al. ( 2019 ) also discussed the platforms the CEO should use and posting frequencies depending on the content of the post.

From the above review of the studies, it can be seen that B2B companies social media strategies include enhancing brand image, extending brand awareness and facilitating customer engagement. Companies use various message strategies, such as using emotional appeal, use of brand names, and use of hashtags. Majority of the companies avoid hard sell or explicitly commercial statement.

3.4 Social Media Use

Studies investigated the way how companies used social media and factors affecting the use of social media by B2B (Andersson et al. 2013 ; Bernard 2016 ; Bolat et al. 2016 ; Denktaş-Şakar and Sürücü 2018 ; Dyck 2010 ; Guesalaga 2016 ; Habibi et al. 2015 ). For example, Vasudevan and Kumar ( 2018 ) investigated how B2B companies use social media by analysing 325 brand posts of Canon India, Epson India, and HP India on Linkedin, Facebook, and Twitter. By employing content analysis the study found that most of the posts had a combination of text and message. More than 50% of the posts were about product or brand-centric. The study argued that likes proved to be an unreliable measure of engagement, while shares were considered a more reliable metric. The reason was that likes had high spikes when brand posts were boosted during promotional activities.

Andersson and Wikström ( 2017 ) used case studies of three B2B companies to investigate reasons for using social media. It was found that companies use social media to enhance customer relationships, support sales and build their brands. Also, social media is used as a recruiting tool, a seeking tool, and a product information and service tool.

Bell and Shirzad ( 2013 ) aimed to conduct social media use analysis in the context of pharmaceutical companies. The study analysed 54,365 tweets from the top five pharmaceutical companies. The study analysed the popular time slots, the average number of positive and negative tweets and its content by using Nvivo9.

Bernard ( 2016 ) aims to examine how chief marketing officers use social media. By using case studies from IBM experience with social media it was found that B2B CMO’s are not ready to make use of social media. It was proposed that social media can be used for after-sales service, getting sales leads, engaging with key influencers, building the company’s reputation and enhancing the industry status of key individuals. B2B firms need to exploit the capabilities of processing massive amounts of data to get the most from social media.

Bolat et al. ( 2016 ) explore how companies apply mobile social media. By employing a grounded theory approach to analyse interviews from 26 B2B company representatives from UK advertising and marketing sector companies. It was found that companies use social media for branding, sensing market, managing relationships, and developing content.

Denktaş-Şakar and Sürücü ( 2018 ) investigated how social media usage influence stakeholder engagement focusing on the corporate Facebook page of 30 3PLs companies. In total 1532 Facebook posts were analysed. It was found that the number of followers, post sharing frequency, negatively affect stakeholder engagement. It was found that content including photos facilitates more stakeholder engagement (likes, comment, share) in comparison with other forms. Vivid posts and special day celebration posts strengthen relationships with stakeholders.

Dyck ( 2010 ) discussed the advantages of using social media for the device industry. Social media can be used for product innovation and development, to build a team and collaborate globally. Also, there is an opportunity to connect with all of the stakeholders needed in order to deliver the device to the market. Additionally, it provides to receive feedback from customers (doctors, hospitals) in real-time.

The study by Guesalaga ( 2016 ) draws on interactional psychology theory to propose and test a model of usage of social media in sales, analysing individual, organizational, and customer-related factors. It was found that organizational competence and commitment to social media are key determinants of social media usage in sales, as well as individual commitment. Customer engagement with social media also predicts social media usage in sales, both directly and (mostly) through the individual and organizational factors analysed, especially organizational competence and commitment. Finally, the study found evidence of synergistic effects between individual competence and commitment, which is not found at the organizational level. The data obtained by surveying 220 sales executives in the United States were analysed using regression analysis.

Habibi et al. ( 2015 ) proposed a conceptual model for the implementation of social media by B2B companies. Based on existing B2B marketing, social media and organisational orientational literature the study proposed that four components of electronic market orientation (philosophical, initiation, implementation and adoption) address different implementation issues faced in implementing social media.

Katona and Sarvary ( 2014 ) presented a case of using social media by Maersk-the largest container shipping company in the world. The case provided details on the program launch and the integration strategy which focused on integrating the largest independent social media operation into the company’s broader marketing efforts.

Moore et al. ( 2013 ) provided insights into the understanding of the use of social media by salespersons. 395 salespeople in B2B and B2C markets, utilization of relationship-oriented social media applications are presented and examined. Overall, findings show that B2B practitioners tend to use media targeted at professionals whereas their B2C counterparts tend to utilize more sites targeted to the general public for engaging in one-on-one dialogue with their customers. Moreover, B2B professionals tend to use relationship-oriented social media technologies more than B2C professionals for the purpose of prospecting, handling objections, and after-sale follow-up.

Moore et al. ( 2015 ) investigated the use of social media between B2B and B2C salespeople. By using survey data from 395 sales professionals from different industries they found that B2B sales managers use social selling tools significantly more frequently than B2C managers and B2C sales representatives while conducting sales presentations. Also, it was found that B2B managers used social selling tools significantly more frequently than all sales representatives while closing sales.

Müller et al. ( 2013 ) investigated social media use in the German automotive market. By using online analysis of 10 most popular car manufacturers online social networks and surveys of six manufacturers, 42 car dealers, 199 buyers the study found that social media communication relations are widely established between manufacturers and (prospective) buyers and only partially established between car dealers and prospective buyers. In contrast to that, on the B2B side, social media communication is rarely used. Social Online Networks (SONs) are the most popular social media channels employed by businesses. Manufacturers and car dealers focus their social media engagement, especially on Facebook. From the perspective of prospective buyers, however, forums are the most important source of information.

Sułkowski and Kaczorowska-Spychalska ( 2016 ) investigated the adoption of social media by companies in the Polish textile-clothing industry. By interviewing seven companies representatives of small and medium-sized enterprises the study found that companies started implementing social media activities in their marketing activities.

Vukanovic ( 2013 ) by reviewing previous literature on social media outlined advantages of using social media for B2B companies, which include: increase customer loyalty and trust, building and improving corporate reputation, facilitating open communications, improvement in customer engagement to name a few.

Keinänen and Kuivalainen ( 2015 ) investigated factors affecting the use of social media by B2B customers by conducting an online survey among 82 key customer accounts of an information technology service company. Partial least squares path modelling was used to analysed the proposed hypotheses. It was found that social media private use, colleague support for using SM, age, job position affected the use of social media by B2B customers. The study also found that corporate culture, gender, easiness to use, and perception of usability did not affect the use of social media by B2B customers.

By using interviews and survey social media research found that mostly B2B companies use social media to enhance customer relationships, support sales, build their brands, sense market, manage relationships, and develop content. Additionally, some companies use it social media as a recruitment tool. The main difference between B2B and B2C was that B2B sales managers use social selling tools significantly more frequently than B2C managers.

3.5 Measuring the Effectiveness of Social Media

It is important for a business to be able to measure the effectiveness of social media by calculating return on investment (ROI). ROI is the relationship between profit and the investment that generate that profit. Some studies focused on the ways B2B companies can measure ROI and the challenges they face (Gazal et al. 2016 ; Michaelidou et al. 2011 ; Vasudevan and Kumar 2018 ). For example, Gazal et al. ( 2016 ) investigated the adoption and measuring of the effectiveness of social media in the context of the US forest industry by using organisational-level adoption framework and TAM. By using data from 166 companies it was found that 94% of respondents do not measure the ROI from social media use. The reason is that the use of social media in marketing is relatively new and companies do not possess the knowledge of measuring ROI from the use of social media. Companies mostly use quantitative metrics (number of site visits, number of social network friends, number of comments and profile views) and qualitative metrics (growth of relationships with the key audience, audience participation, moving from monologue to dialogue with consumers).

Another study by Michaelidou et al. ( 2011 ) found that most of the companies do not evaluate the effectiveness of their SNS in supporting their brand. The most popular measures were the number of users joining the groups/discussion and the number of comments made.

Vasudevan and Kumar ( 2018 ) investigated how B2B companies use social media and measure ROI from social media by analysing 325 brand posts of Canon India, Epson India, and HP India on Linkedin, Facebook, and Twitter. By employing content analysis the study found that most of the post has a combination of text and message. More than 50% of the posts were about product or brand-centric. The study argued that likes proved to be an unreliable measure of engagement, while shares were considered a more reliable metric. The reason was that likes had high spikes when brand posts were boosted during promotional activities. Future research should conduct longitudinal studies.

By reviewing the above studies, it can be concluded that companies still struggle to find ways of measuring ROI and applying correct metrics. By gaining knowledge in how to measure ROI from social media activities, B2B companies will be able to produce valuable insights leading to better marketing strategies (Lal et al. 2020 ).

3.6 Social Media Tools

Some studies proposed tools that could be employed by companies to advance their use of social media. For example, Mehmet and Clarke ( 2016 ) proposed a social semiotic multimodal (SSMM) framework that improved the analysis of social media communications. This framework employs multimodal extensions to systemic functional linguistics enabling it to be applying to analysing non-language as well as language constituents of social media messages. Furthermore, the framework also utilises expansion theory to identify, categorise and analyse various marketing communication resources associated with marketing messages and also to reveal how conversations are chained together to form extended online marketing conversations. This semantic approach is exemplified using a Fairtrade Australia B2B case study demonstrating how marketing conversations can be mapped and analysed. The framework emphasises the importance of acknowledging the impact of all stakeholders, particularly messages that may distract or confuse the original purpose of the conversation.

Yang et al. ( 2012 ) proposed the temporal analysis technique to identify user relationships on social media platforms. The experiment was conducted by using data from Digg.com . The results showed that the proposed techniques achieved substantially higher recall but not very good at precision. This technique will help companies to identify their future consumers based on their user relationships.

Based on the literature review, it can be seen that B2B companies can benefit by using the discussed tools. However, it is important to consider that employee should have some technical skills and knowledge to use these tools successfully. As a result, companies will need to invest some resources in staff training.

4 Weight Analysis

Weight analysis enables scrutiny of the predictive power of independent variables in studied relationships and the degree of effectiveness of the relationships (Jeyaraj et al. 2006 ; Rana et al. 2015 ; Ismagilova et al. 2020a ). The results of weight analysis are depicted in Table 3 providing information about an independent variable, dependent variable, number of significant relationships, number of non-significant relationships, the total number of relationships and weight. To perform weight analysis, the number of significant relationships was divided by the total number of analysed relationships between the independent variable and the dependent variable (Jeyaraj et al. 2006 ; Rana et al. 2015 ). For example, the weight for the relationship between attitude towards social media and social media is calculated by dividing ‘1’ (the number of significant relationships) by ‘2’ (the total number of relationships) which equals 0.5.

A predictor is defined as well-utilised if it was examined five or more times, otherwise, it is defined as experimental. It can be seen from Table 3 that all relationships were examined less than five times. Thus all studied predictors are experimental. The predictor is defined as promising when it has been examined less than five times by existing studies but has a weight equal to ‘1’ (Jeyaraj et al. 2006 ). From the predictors affecting the adoption of social media, it can be seen that two are promising, technical skills of employees and pressure from stakeholders. Social media usage is a promising predictor for acquiring new customers, sales, stakeholder engagement and customer satisfaction. Perceived ease of use and age of salesperson are promising predictors of social media usage. Even though this relationship was found to be significant every time it was examined, it is suggested that this variable, which can also be referred to as experimental, will need to be further tested in order to qualify as the best predictor. Another predictor, average rating of product/service, was examined less than five times with a weight equal to 0.75, thus it is considered as an experimental predictor.

Figure 1 shows the diagrammatic representation of the factors affecting different relationships in B2B social media with their corresponding weights, based on the results of weight analysis. The findings suggest that promising predictors should be included in further empirical studies to determine their overall performance.

figure 1

Diagrammatic representation of results of weight analysis. Note: experimental predictors

It can be seen from Fig. 1 that social media usage is affected by internal (e.g. attitude towards social media, technical skills of employees) and external factors (e.g. pressure from stakeholders) of the company. Also, the figure depicts the effect of social media on the business (e.g. sales) and society (e.g. customer satisfaction).

5 Discussion

In reviewing the publications gathered for this paper, the following themes were identified. Some studies investigated the effect of social media use by B2B companies. By using mostly survey to collect the data from salespeople and managers, the studies found that social media has a positive effect on number of outcomes important for the business such as customer satisfaction, value creation, intention to buy and sales, customer relationships, brand awareness, knowledge creation, corporate credibility, acquiring new customers, salespersons performance, employee brand management, and sustainability. Most of the outcomes are similar to the research on social media in the context of B2C companies. However, some of the outcomes are unique for B2B context (e.g. employee brand management, company credibility). Just recently, studies started investigating the impact of the use of social media on sustainability.

Another group of studies looked at the adoption of social media by B2B companies (Buratti et al. 2018 ; Gáti et al. 2018 ; Gazal et al. 2016 ; Itani et al. 2017 ; Kumar and Möller 2018 ). The studies investigated it mostly from the perspectives of salespersons and identify some of the key factors which affect the adoption, such as innovativeness, technical skills of employees, pressure from stakeholders, perceived usefulness, and perceived usability. As these factors are derived mostly from surveys conducted with salespersons findings can be different for other individuals working in the organisation. This it is important to conduct studies that will examine factors affecting the adoption of social media across the entire organisation, in different departments. Using social media as part of the digital transformation is much bigger than sales and marketing, it encompasses the entire company. Additionally, most of the studies were cross-sectional, which limits the understanding of the adoption of social media by B2B over time depending on the outcomes and environment (e.g. competitors using social media).

Some studies looked at social media strategies of B2B companies (Cawsey and Rowley 2016 ; Huotari et al. 2015 ; Kasper et al. 2015 ; McShane et al. 2019 ; Mudambi et al. 2019 ). By employing interviews with companies’ managers and analysing its social media platforms (e.g. Twitter) it was found that most of the companies follow the following strategies: 1) monitoring and listening 2) empowering and engaging employees 3) creating compelling content 4) stimulating eWOM 5) evaluating and selecting channels 6) enhancing brand presence through integrating social media (Cawsey and Rowley 2016 ). Some studies investigated the difference between social media strategies of B2B and B2C companies. For example, a study by Swani et al. ( 2017 ) focused on effective social media strategies. By applying psychological motivation theory the study examined the key differences in B2B and B2C social media message strategies in terms of branding, message appeals, selling, and information search. The study used Facebook posts on brand pages of 280 Fortune companies. In total, 1467 posts were analysed. By using Bayesian models, the results showed that the inclusion of corporate brand names, functional and emotional appeals and information search cues increases the popularity of B2B messages in comparison with B2C messages. Also, it was found that readers of B2B content show a higher message liking rate and lower message commenting rate in comparison with readers of B2C messages.

The next group of studies looked at social media use by B2B companies (Andersson et al. 2013 ; Bernard 2016 ; Bolat et al. 2016 ; Denktaş-Şakar and Sürücü 2018 ; Dyck 2010 ; Guesalaga 2016 ; Habibi et al. 2015 ). B2B companies use social media for enhancing and managing customer relationships (Andersson and Wikström 2017 ; Bolat et al. ( 2016 ); branding (Andersson and Wikström 2017 ; Bolat et al. 2016 ), sensing market (Bolat et al. 2016 ) and co-production (Chirumalla et al. 2018 ). Additionally, it was mentioned that some of the B2B companies use social media as a recruiting tool, and tool which helps to collaborate globally (Andersson and Wikström 2017 ; Dyck 2010 ).

It is important for companies to not only use social media to achieve positive business outcomes but also it is important to measure their achievements. As a result, some of the studies focused on the measuring effectiveness of social media (Gazal et al. 2016 ; Michaelidou et al. 2011 ; Vasudevan and Kumar 2018 ). Surprisingly, it was found that not so many companies measure ROI from social media (Gazal et al. 2016 ; Michaelidou et al. 2011 ). The ones who do it mostly use quantitative metrics (number of site visits, number of social network friends, number of comments and profile views) and qualitative metrics (growth of relationships with key audience, audience participation, moving from monologue to dialogue with consumers) (Gazal et al. 2016 ). Some future studies should investigate how ROI influences the strategy of B2B companies over period of time.

The last group of studies focused on social media tools used by B2B companies (Keinänen and Kuivalainen 2015 ; Mehmet and Clarke 2016 ; Yang et al. 2012 ). By using number of social media tools (Social Semiotic Multimodal) companies are able to improve their analysis of social media communications and identify their future consumers based on their user relationships. Studies investigating barriers and factors adoption of various social media tools by B2B companies are needed.

After reviewing studies on b2B social media, weight analysis was performed. Based on the results of weight analysis the conceptual model for future studies was proposed (Fig.  2 ). It is important to note that a limited number of studies focused and empirically tested factors affecting the adoption, use, and effect of social media. As a result, identified factors were considered as experimental (examined less than five times). It is too early to label these experimental predictors as worst or best, thus their further investigation is encouraged.

figure 2

Social media impact on digital transformation and sustainable societies

Additionally, our review of the literature on B2B social media identified dominant research methods used by scholars. Qualitative and quantitative techniques were used by most of these studies. Closer analysis of 70 publications reviewed in this study revealed the multiple techniques applied for gathering data. Quantitative methods used in the studies mostly used surveys (see Table 4 ).

The data was mostly gathered from salespersons, managers and data from social media platforms (e.g. Twitter, Facebook). Just a limited number of studies employed consumer reported data (see Table 5 ).

On the other hand, publications using qualitative methods mainly used interviews and web scraping for the collection of the required data. To analyse the data studies used a variety of techniques including SEM, regression analysis and content analysis being one of the most used (see Table 6 ).

5.1 Digital Transformation and Sustainability Model

Based on the conducted literature review and adapting the model by Pappas et al. ( 2018 ) Fig. 2 presents the digital transformation and sustainability model in the context of B2B companies, which conceptualise the social media ecosystems, and the factors that need to collaborate to enable the use of social media towards the achievement of digital transformation and the creation of sustainable societies. The model comprises of social media stakeholders, the use of social media by B2B companies, and effect of social media on business and society.

5.1.1 Social Media Stakeholders

Building on the discussion and model provided by Pappas et al. ( 2018 ), this paper posits that the social media ecosystem comprises of the data stakeholders (company, society), who engage on social media (posting, reading, using information from social media). The use of social media by different stakeholders will lead to different effects affecting companies, customers and society. This is an iterative process based on which the stakeholders use their experience to constantly improve and evolve their use of social media, which has impacts on both, business and society. The successful implementation of this process is key to digital transformation and the creation of sustainable societies. Most of the current studies (Andersson et al. 2013 ; Bernard 2016 ; Bolat et al. 2016 ; Denktaş-Şakar and Sürücü 2018 ; Dyck 2010 ; Guesalaga 2016 ) focus mostly on the company as a stakeholder. However, more research is needed on other types of stakeholders (e.g. society).

5.1.2 Use of Social Media by B2B Companies

Social media affects not only ways how companies connect with their clients, but it is also changing their business models, the way how the value is delivered and profit is made. To successfully implement and use social media, B2B companies need to consider various social media tools, antecedents/barriers of its adoption, identify suitable social media strategies which are in line with the company’s overall strategy, and measure effectiveness of the use of social media. There are various factors that affect the use of social media by B2B companies. The study found that social media usage is influenced by perceived ease of use, adoption of social media, attitude towards social media and age of salesperson.

The majority of the studies focus on the management of the marketing department. However, digital transformation is much bigger than just marketing as it encompasses the entire organisation. As a result, future studies should look like the entire organisation and investigate barriers and factors affecting the use of social media.

It is crucial for companies to design content which will be noticed on social media by their potential, actual and former customers. Social media content should be interesting and offer some beneficial information, rather than just focus on services the company provides. Companies could use fresh views on relevant industry news, provide information how they are contributing to society and environment, include humour in their posts, share information about the team, make it more personal. It is also useful to use images, infographics, and video content.

It is also important for companies to measure digital marketing actions. More studies are needed on how to isolate the impact of specific media marketing actions to demonstrate their impact on the desired business outcomes (Salo 2017 ). Thus, future studies can consider how particular social media channels (e.g. Facebook, LinkedIn) in a campaign of a new product/ service influence brand awareness and sales level. Also, a limited number of studies discussed the way B2B companies can measure ROI. Future research should investigate how companies can measure intangible ROI, such as eWOM, brand awareness, and customer engagement (Kumar and Mirchandani 2012 ). Also, future research should investigate the reasons why most of the users do not assess the effectiveness of their SNS. Furthermore, most of the studies focused on likes, shares, and comments to evaluate social media engagement. Future research should focus on other types of measures. More research needs considering the impact of legislation on the use of social media by companies. Recent B2B studies did not consider recent legislation (General Data Protection Regulation 2018 ) in the context B2B (Sivarajah et al. 2019 ).

5.1.3 Effect of Social Media on Business and Society

Social media plays an important part in the company’s decision-making process. Social media can bring positive changes into company, which will result in improving customer satisfaction, value creation, increase in sales, building relationships with customers, knowledge creation, improve the perception of corporate credibility, acquisition of new customers, and improve employment brand engagement. Using information collected from social media can help companies to have a set of reliable attributes that comprise social, economic and environmental aspects in their decision-making process (Tseng 2017 ). Additionally, by using social media B2B companies can provide information to other stakeholders on their sustainability activities. By using data from social media companies will be able to provide products and services which are demanded by society. It will improve the quality of life and result in less waste. Additionally, social media can be considered as a tool that helps managers to integrate business practices with sustainability (Sivarajah et al. 2019 ). As a result, social media use by B2B companies can lead to business and societal changes.

A limited number of studies investigated the effect of social media on word of mouth communications in the B2B context. Future research should investigate the differences and similarities between B2C and B2B eWOM communications. Also, studies should investigate how these types of communications can be improved and ways to deal with negative eWOM. It is important for companies to respond to comments on social media. Additionally, future research should investigate its perceived helpfulness by customers.

Majority of studies (Agnihotri et al. 2016 ; Ancillai et al. 2019 ; Rossmann and Stei 2015 ; Agnihotri et al. 2012 ; Agnihotri et al. 2017 ; Itani et al. 2017 ; Salo 2017 ; Bhattacharjya and Ellison 2015 ; Gáti et al. 2018 ; Gruner and Power 2018 ; Hollebeek 2019 ) investigated positive effect of social media such consumer satisfaction, consumer engagement, and brand awareness. However, it will be interesting to consider the dark side of social media use such as an excessive number of requests on social media to salespeople (Agnihotri et al. 2016 ), which can result in the reduction of the responsiveness; spread of misinformation which can damage the reputation of the company.

Studies were performed in China (Lacka and Chong 2016 ; Niedermeier et al. 2016 ), the USA (Guesalaga 2016 ; Iankova et al. 2018 ; Ogilvie et al. 2018 ), India (Agnihotri et al. 2017 ; Vasudevan and Kumar 2018 ), the UK (Bolat et al. 2016 ; Iankova et al. 2018 ; Michaelidou et al. 2011 ). It is strongly advised that future studies conduct research in other countries as findings can be different due to the culture and social media adoption rates. Future studies should pay particular attention to other emerging markets (such as Russia, Brazil, and South Africa) as they suffer from the slow adoption rate of social media marketing. Some companies in these countries still rely more on traditional media for advertising of their products and services, as they are more trusted in comparison with social media channels (Olotewo 2016 ). The majority of studies investigate the effect of social media in B2B or B2C context. Future studies should pay attention to other contexts (e.g. B2B2B, B2B2C). Another limitation of the current research on B2B companies is that most of the studies on social media in the context of B2B focus on the effect of social media use only on business outcomes. It is important for future research to focus on societal outcomes.

Lastly, most of the studies on social media in the context of B2B companies use a cross-sectional approach to collect the data. Future research can use the longitudinal approach in order to advance understanding of social media use and its impact over time.

5.2 Research Propositions

Based on the social media research in the context of B2B companies and the discussion above the following is proposed, which could serve as a foundation for future empirical work.

Social media is a powerful tool to deliver information to customers. However, social media can be used to get consumer and market insights (Kazienko et al. 2013 ). A number of studies highlighted how information obtained from a number of social media platforms could be used for various marketing purposes, such as understanding the needs and preferences of consumers, marketing potential for new products/services, and current market trends (Agnihotri et al. 2016 ; Constantinides et al. 2008 ). It is advised that future research employs a longitudinal approach to study the impact of social media use on understanding customers. Therefore, the following proposition can be formulated:

Proposition 1

Social media usage of B2B companies has a positive influence on understanding its customers.

By using social media companies can examiner valuable information on competitors. It can help to understand competitors’ habits and strategies, which can lead to the competitive advantage and help strategic planning (Dey et al. 2011 ; Eid et al. 2019 ; Teo and Choo 2001 ). It is advised that future research employs a longitudinal approach to study the impact of social media use on understanding its competitors. As a result, using social media to understand customers and competitors can create business value (Mikalef et al. 2020a ) for key stakeholders and lead to positive changes in the business and societies. The above discussion leads to the following proposition:

Proposition 2

Social media usage of B2B companies has a positive influence on understanding its competitors.

Proposition 3

Culture influences the adoption and use of social media by B2B companies.

Usage of social media can result in some positive marketing outcomes such as building new customer relationships, increasing brand awareness, and level of sales to name a few (Agnihotri et al. 2016 ; Ancillai et al. 2019 ; Dwivedi et al. 2020 ; Rossmann and Stei 2015 ). However, when social media is not used appropriately it can lead to negative consequences. If a company does not have enough resources to implement social media tools the burden usually comes on a salesperson. A high number of customer inquiries, the pressure to engage with customers on social media, and monitor communications happening on various social media platforms can result in the increased workload of a salesperson putting extra pressure (Agnihotri et al. 2016 ). As a result, a salesperson might not have enough time to engage with all the customers online promptly or engage in reactive and proactive web care. As a result, customer satisfaction can be affected as well as company reputation. To investigate the negative impact of social media research could apply novel methods for data collection and analysis such as fsQCA (Pappas et al. 2020 ), or implying eye-tracking (Mikalef et al. 2020b ). This leads to the following proposition:

Proposition 4

Inappropriate use of social media by B2B companies has a negative effect on a) customer satisfaction and b) company reputation.

According to Technology-Organisation-Environment (TOE) framework environmental context significantly affects a company’s use of innovations (Abed 2020 ; Oliveira and Martins 2011 ). Environment refers to the factors which affect companies from outside, including competitors and customers. Adopting innovation can help companies to change the rules of the competition and reach a competitive advantage (Porter and Millar 1985 ). In a competitive environment, companies have a tendency to adopt an innovation. AlSharji et al. ( 2018 ) argued that the adoption of innovation can be extended to social media use by companies. A study by AlSharji et al. ( 2018 ) by using data from 1700 SMEs operating in the United Arab Emirates found that competitive pressure significantly affects the use of social media by SMEs. It can be explained by the fact that companies could feel pressure when other companies in the industry start adopting a particular technology and as a result adopt it to remain competitive (Kuan and Chau 2001 ). Based on the above discussion, the following proposition can be formulated:

Proposition 5

Competitive pressure positively affects the adoption of social media by B2B companies.

Companies might feel that they are forced to adopt and use IT innovations because their customers would expect them to do so. Meeting customers’ expectations could result in adoption of new technologies by B2B companies. Some research studies investigated the impact of customer pressure on companies (AlSharji et al. 2018 ; Maduku et al. 2016 ). For example, a study by Maduku et al. ( 2016 ) found that customer pressure has a positive effect on SMEs adoption of mobile marketing in the context of South Africa. Future research could implement longitudinal approach to investigate how environment affects adoption of social media by B2B companies. This leads to the formulation of the following proposition:

Proposition 6

Customer pressure positively affects the adoption of social media by B2B companies.

6 Conclusion

The aim of this research was to provide a comprehensive systematic review of the literature on social media in the context of B2B companies and propose the framework outlining the role of social media in the digital transformation of B2B companies. It was found that B2B companies use social media, but not all companies consider it as part of their marketing strategies. The studies on social media in the B2B context focused on the effect of social media, antecedents, and barriers of adoption of social media, social media strategies, social media use, and measuring the effectiveness of social media. Academics and practitioners can employ the current study as an informative framework for research on the use of social media by B2B companies. The summary of the key observations provided from this literature review is the following: [i] Facebook, Twitter, and LinkedIn are the most famous social media platforms used by B2B companies, [ii] Social media has a positive effect on customer satisfaction, acquisition of new customers, sales, stakeholder engagement, and customer relationships, [iii] In systematically reviewing 70 publications on social media in the context of B2B companies it was observed that most of the studies use online surveys and online content analysis, [iv] Companies still look for ways to evaluate the effectiveness of social media, [v] Innovativeness, pressure from stakeholders, perceived usefulness, and perceived usability have a significant positive effect on companies’ adoption to use social media, [vi] Lack of staff familiarity and technical skills are the main barriers that affect the adoption of social media by B2B, [vii] Social media has an impact not only on business but also on society, [viii] There is a dark side of social media: fake online reviews, an excessive number of requests on social media to salespeople, distribution of misinformation, negative eWOM, [ix] Use of social media by companies has a positive effect on sustainability, and [x] For successful digital transformation social media should change not only the way how companies integrate it into their marketing strategies but the way how companies deliver values to their customers and conduct their business. This research has a number of limitations. First, only publications from the Scopus database were included in literature analysis and synthesis. Second, this research did not use meta-analysis. To provide a broader picture of the research on social media in the B2B context and reconcile conflicting findings of the existing studies future research should conduct a meta-analysis (Ismagilova et al. 2020c ). It will advance knowledge of the social media domain.

Abed, S. S. (2020). Social commerce adoption using TOE framework: An empirical investigation of Saudi Arabian SMEs. International Journal of Information Management, 53 , 102118.

Article   Google Scholar  

Agnihotri, R., Kothandaraman, P., Kashyap, R., & Singh, R. (2012). Bringing “social” into sales: The impact of salespeople’s social media use on service behaviors and value creation. Journal of Personal Selling and Sales Management, 32 (3), 333–348. https://doi.org/10.2753/PSS0885-3134320304 .

Agnihotri, R., Dingus, R., Hu, M. Y., & Krush, M. T. (2016). Social media: Influencing customer satisfaction in B2B sales. Industrial Marketing Management, 53 , 172–180. https://doi.org/10.1016/j.indmarman.2015.09.003 .

Agnihotri, R., Trainor, K. J., Itani, O. S., & Rodriguez, M. (2017). Examining the role of sales-based CRM technology and social media use on post-sale service behaviors in India. Journal of Business Research, 81 , 144–154. https://doi.org/10.1016/j.jbusres.2017.08.021 .

Alalwan, A. A., Rana, N. P., Dwivedi, Y. K., & Algharabat, R. (2017). Social media in marketing: A review and analysis of the existing literature. Telematics and Informatics, 34 (7), 1177–1190.

AlSharji, A., Ahmad, S. Z., & Bakar, A. R. A. (2018). Understanding social media adoption in SMEs. Journal of Entrepreneurship in Emerging Economies, 10 (2), 302–328.

Ancillai, C., Terho, H., Cardinali, S., & Pascucci, F. (2019). Advancing social media driven sales research: Establishing conceptual foundations for B-to-B social selling. Industrial Marketing Management, 82 , 293–308. https://doi.org/10.1016/j.indmarman.2019.01.002 .

Andersson, S., & Wikström, N. (2017). Why and how are social media used in a B2B context, and which stakeholders are involved? Journal of Business and Industrial Marketing, 32 (8), 1098–1108. https://doi.org/10.1108/JBIM-07-2016-0148 .

Andersson, S., Evers, N., & Griot, C. (2013). Local and international networks in small firm internationalization: Cases from the Rhône-Alpes medical technology regional cluster. Entrepreneurship & Regional Development, 25 (9–10), 867–888.

Andzulis, J. M., Panagopoulos, N. G., & Rapp, A. (2012). A review of social media and implications for the sales process. Journal of Personal Selling & Sales Management, 32 (3), 305–316.

Barreda, A. A., Bilgihan, A., Nusair, K., & Okumus, F. (2015). Generating brand awareness in online social networks. Computers in Human Behavior, 50 , 600–609.

Bell, D., & Shirzad, S, R. (2013). Social media domain analysis (SoMeDoA): A pharmaceutical study. 9th international conference on web information systems and technologies, 561–570.  https://doi.org/10.5220/0004499105610570 .

Bernard, M. (2016). The impact of social media on the B2B CMO. Journal of Business and Industrial Marketing, 31 (8), 955–960. https://doi.org/10.1108/JBIM-10-2016-268 .

Bhattacharjya, J., & Ellison, A, B. (2015). Building business resilience with social media in B2B environments: The emergence of responsive customer relationship management processes on twitter. Working Conference on Virtual Enterprises doi: https://doi.org/10.1007/978-3-319-24141-8_15 .

Bolat, E., Kooli, K., & Wright, L. T. (2016). Businesses and mobile social media capability. Journal of Business and Industrial Marketing, 31 (8), 971–981. https://doi.org/10.1108/JBIM-10-2016-270 .

Buratti, N., Parola, F., & Satta, G. (2018). Insights on the adoption of social media marketing in B2B services. TQM Journal, 30 (5), 490–529. https://doi.org/10.1108/TQM-11-2017-0136 .

Cawsey, T., & Rowley, J. (2016). Social media brand building strategies in B2B companies. Marketing Intelligence and Planning, 34 (6), 754–776. https://doi.org/10.1108/MIP-04-2015-0079 .

Chatterjee, S., & Kar, A. K. (2020). Why do small and medium enterprises use social media marketing and what is the impact: Empirical insights from India. International Journal of Information Management, 53 , 102103.

Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36 , 1165–1188.

Chirumalla, K., Oghazi, P., & Parida, V. (2018). Social media engagement strategy: Investigation of marketing and R&D interfaces in manufacturing industry. Industrial Marketing Management, 74 , 138–149. https://doi.org/10.1016/j.indmarman.2017.10.001 .

Constantinides, E., Romero, C. L., & Boria, M. A. G. (2008). Social media: A new frontier for retailers?. In European retail research (pp. 1–28) . Wiesbaden: Gabler Verlag.

Google Scholar  

Denktaş-Şakar, G., & Sürücü, E. (2018). Stakeholder engagement via social media: An analysis of third-party logistics companies. Service Industries Journal, 40 , 866–889. https://doi.org/10.1080/02642069.2018.1561874 .

Dey, L., Haque, S, M., Khurdiya, A., & Shroff, G. (2011). Acquiring competitive intelligence from social media. In proceedings of the 2011 joint workshop on multilingual OCR and analytics for noisy unstructured text data (pp. 1-9) . https://doi.org/10.1145/2034617.2034621 .

Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019a). Re-examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical model. Information Systems Frontiers, 21 (3), 719–734.

Dwivedi, Y, K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., ... & Galanos, V. (2019b). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management , 101994, doi: https://doi.org/10.1016/j.ijinfomgt.2019.08.002 .

Dwivedi, Y, K., Ismagilova, E., Hughes, D. L., Carlson, J., Filieri, R., Jacobson, J., ... & Kumar, V. (2020). Setting the future of digital and social media marketing research: Perspectives and research propositions. International Journal of Information Management , 102168, doi: https://doi.org/10.1016/j.ijinfomgt.2020.102168 .

Dyck, P. V. (2010). As social media evolves, the device industry must also. Medical Device and Diagnostic Industry, 32 (8).

Eid, R., Abdelmoety, Z., & Agag, G. (2019). Antecedents and consequences of social media marketing use: An empirical study of the UK exporting B2B SMEs. Journal of Business & Industrial Marketing., 35 , 284–305.

Gáti, M., Mitev, A., & Bauer, A. (2018). Investigating the impact of salespersons’ use of technology and social media on their customer relationship performance in B2B settings. Trziste, 30 (2), 165–176. https://doi.org/10.22598/mt/2018.30.2.165 .

Gazal, K., Montague, I., Poudel, R., & Wiedenbeck, J. (2016). Forest products industry in a digital age: Factors affecting social media adoption. Forest Products Journal, 66 (5-6), 343–353. https://doi.org/10.13073/FPJ-D-15-00007 .

General Data Protection Regulation (2018). Guide to the General Data Protection Regulation. Available at https://www.gov.uk/government/publications/guide-to-the-general-data-protection-regulation . Accessed 28 Jan 2021.

Gregorio, J. (2017). 10 reasons to diversify your digital marketing efforts. Digital marketing Philippines. Available at https://digitalmarketingphilippines.com/10-reasons-to-diversify-your-digital-marketing-efforts// Accessed on April , 27 , 2019.

Gruner, R. L., & Power, D. (2018). To integrate or not to integrate? Understanding B2B social media communications. Online Information Review, 42 (1), 73–92. https://doi.org/10.1108/OIR-04-2016-0116 .

Guesalaga, R. (2016). The use of social media in sales: Individual and organizational antecedents, and the role of customer engagement in social media. Industrial Marketing Management, 54 , 71–79. https://doi.org/10.1016/j.indmarman.2015.12.002 .

Gupta, P., Chauhan, S., & Jaiswal, M. P. (2019). Classification of smart city research-a descriptive literature review and future research agenda. Information Systems Frontiers, 21 (3), 661–685.

Habibi, F., Hamilton, C. A., Valos, M. J., & Callaghan, M. (2015). E-marketing orientation and social media implementation in B2B marketing. European Business Review, 27 (6), 638–655. https://doi.org/10.1108/EBR-03-2015-0026 .

Harrigan, P., Miles, M. P., Fang, Y., & Roy, S. K. (2020). The role of social media in the engagement and information processes of social CRM. International Journal of Information Management, 54 , 102151.

Hollebeek, L. D. (2019). Developing business customer engagement through social media engagement-platforms: An integrative SD logic/RBV-informed model. Industrial Marketing Management, 81 , 89–98.

Hsiao, S. H., Wang, Y. Y., Wang, T., & Kao, T. W. (2020). How social media shapes the fashion industry: The spillover effects between private labels and national brands. Industrial Marketing Management, 86 , 40–51.

Huotari, L., Ulkuniemi, P., Saraniemi, S., & Mäläskä, M. (2015). Analysis of content creation in social media by B2B companies. Journal of Business and Industrial Marketing, 30 (6), 761–770. https://doi.org/10.1108/JBIM-05-2013-0118 .

Iankova, S., Davies, I., Archer-Brown, C., Marder, B., & Yau, A. (2018). A comparison of social media marketing between B2B, B2C and mixed business models. Industrial Marketing Management, 81 , 169–179. https://doi.org/10.1016/j.indmarman.2018.01.001 .

Iannacci, F., Fearon, C., & Pole, K. (2020). From acceptance to adaptive acceptance of social media policy change: A set-theoretic analysis of b2b SMEs. Information Systems Frontiers , 1-18. https://doi.org/10.1007/s10796-020-09988-1 .

Ismagilova, E., Dwivedi, Y. K., Slade, E., & Williams, M. D. (2017). Electronic word of mouth (eWOM) in the marketing context: A state of the art analysis and future directions . Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-52459-7 .

Ismagilova, E., Hughes, L., Dwivedi, Y. K., & Raman, K. R. (2019). Smart cities: Advances in research—An information systems perspective. International Journal of Information Management, 47 , 88–100.

Ismagilova, E., Slade, E. L., Rana, N. P., & Dwivedi, Y. K. (2020a). The effect of electronic word of mouth communications on intention to buy: A meta-analysis. Information Systems Frontiers, 22 , 1203–1226. https://doi.org/10.1007/s10796-019-09924-y .

Ismagilova, E., Slade, E., Rana, N. P., & Dwivedi, Y. K. (2020b). The effect of characteristics of source credibility on consumer behaviour: A meta-analysis. Journal of Retailing and Consumer Services, 53 , 101736.

Ismagilova, E., Rana, N, P., Slade, E., & Dwivedi, Y, K. (2020c). A meta-analysis of the factors affecting eWOM providing behaviour. European Journal of Marketing. doi: https://doi.org/10.1108/EJM-07-2018-0472 , ahead-of-print.

Itani, O. S., Agnihotri, R., & Dingus, R. (2017). Social media use in B2b sales and its impact on competitive intelligence collection and adaptive selling: Examining the role of learning orientation as an enabler. Industrial Marketing Management, 66 , 64–79. https://doi.org/10.1016/j.indmarman.2017.06.012 .

Jeyaraj, A., & Dwivedi, Y. K. (2020). Meta-analysis in information systems research: Review and recommendations. International Journal of Information Management, 55 , 102226.

Jeyaraj, A., Rottman, J. W., & Lacity, M. C. (2006). A review of the predictors, linkages, and biases in IT innovation adoption research. Journal of Information Technology, 21 (1), 1–23.

Juntunen, M., Ismagilova, E., & Oikarinen, E. L. (2020). B2B brands on twitter: Engaging users with a varying combination of social media content objectives, strategies, and tactics. Industrial Marketing Management, 89 , 630–641.

Jussila, J. J., Kärkkäinen, H., & Leino, M. (2011). Benefits of social media in business-to-business customer interface in innovation. 15th International Academic MindTrek Conference: Envisioning Future Media Environments . MindTrek, 2011 , 167–174. https://doi.org/10.1145/2181037.2181065 .

Kamboj, S., Sarmah, B., Gupta, S., & Dwivedi, Y. (2018). Examining branding co-creation in brand communities on social media: Applying the paradigm of stimulus-organism-response. International Journal of Information Management, 39 , 169–185.

Kapoor, K. K., Tamilmani, K., Rana, N. P., Patil, P., Dwivedi, Y. K., & Nerur, S. (2018). Advances in social media research: Past, present and future. Information Systems Frontiers, 20 (3), 531–558.

Kärkkäinen, H., Jussila, J., & Janhonen, J. (2011). Managing customer information and knowledge with social media in business-to-business companies. ACM International Conference Proceeding Series, doi: https://doi.org/10.1145/2024288.2024309 .

Kasper, H., Koleva, I., & Kett, H. (2015). Social media matrix matching corporate goals with external social media activities doi: https://doi.org/10.1007/978-3-662-46641-4_17 .

Katona, Z., & Sarvary, M. (2014). Maersk line: B2B social media-“it’s communication, not marketing. California Management Review , 56(3), 142–156. doi: https://doi.org/10.1525/cmr.2014.56.3.142 .

Kazienko, P., Szozda, N., Filipowski, T., & Blysz, W. (2013). New business client acquisition using social networking sites. Electronic Markets, 23 (2), 93–103. https://doi.org/10.1007/s12525-013-0123-9 .

Keinänen, H., & Kuivalainen, O. (2015). Antecedents of social media B2B use in industrial marketing context: Customers’ view. Journal of Business and Industrial Marketing, 30 (6), 711–722. https://doi.org/10.1108/JBIM-04-2013-0095 .

Kho, N. D. (2008). B2B gets social media. EContent, 31 (3), 26–30.

Kovac, M. (2016). Social media works for B2B sales, too. Harvard Business Review. Retrieved from https://hbr.org/2016/01/social-media-worksfor-b2b-sales-too . Accessed on April , 27 , 2020 .

Kuan, K. K., & Chau, P. Y. (2001). A perception-based model for EDI adoption in small businesses using a technology–organization–environment framework. Information & Management, 38 (8), 507–521.

Kumar, V., & Mirchandani, R. (2012). Increasing the ROI of social media marketing. MIT Sloan Management Review, 54 (1), 55–61.

Kumar, A., & Möller, K. (2018). Extending the boundaries of corporate branding: An exploratory study of the influence of brand familiarity in recruitment practices through social media by B2B firms. Corporate Reputation Review, 21 (3), 101–114. https://doi.org/10.1057/s41299-018-0046-7 .

Kunsman, T. (2018). Internal marketing: Why your company should prioritize it. https://everyonesocial.com/blog/internal-marketing/ . Accessed on September, 25, 2019.

Lacka, E., & Chong, A. (2016). Usability perspective on social media sites' adoption in the B2B context. Industrial Marketing Management, 54 , 80–91. https://doi.org/10.1016/j.indmarman.2016.01.001 .

Lal, B., Ismagilova, E., Dwivedi, Y.,. K., & Kwayu, S. (2020). Return on Investment in Social Media Marketing: Literature review and suggestions for future research. In Digital and Social Media Marketing (pp. 3–17) . Cham: Springer.

Lashgari, M., Sutton-Brady, C., Solberg Søilen, K., & Ulfvengren, P. (2018). Adoption strategies of social media in B2B firms: A multiple case study approach. Journal of Business and Industrial Marketing, 33 (5), 730–743. https://doi.org/10.1108/JBIM-10-2016-0242 .

Loebbecke, C., & Picot, A. (2015). Reflections on societal and business model transformation arising from digitization and big data analytics: A research agenda. The Journal of Strategic Information Systems, 24 (3), 149–157.

Maduku, D. K., Mpinganjira, M., & Duh, H. (2016). Understanding mobile marketing adoption intention by south African SMEs: A multi-perspective framework. International Journal of Information Management, 36 (5), 711–723.

Mahrous, A, A. (2013). Social media marketing: Prospects for marketing theory and practice on the social web. E-marketing in developed and developing countries: Emerging practices (pp. 56-68) doi: https://doi.org/10.4018/978-1-4666-3954-6.ch004 Retrieved from www.scopus.com .

McAfee, A. P. (2006). Enterprise 2.0: The dawn of emergent collaboration. Enterprise, 2 , 15–26.

McKinsey & Company (2015). Transforming the business through social tools. McKinsey.com , ( http://www.mckinsey.com/industries/high-tech/our-insights/transformingthe-business-through-social-tools ).

McShane, L., Pancer, E., & Poole, M. (2019). The influence of B to B social media message features on brand engagement: A fluency perspective. Journal of Business-to-Business Marketing, 26 (1), 1–18. https://doi.org/10.1080/1051712X.2019.1565132 .

Mehmet, M. I., & Clarke, R. J. (2016). B2B social media semantics: Analysing multimodal online meanings in marketing conversations. Industrial Marketing Management, 54 , 92–106. https://doi.org/10.1016/j.indmarman.2015.12.006 .

Meire, M., Ballings, M., & Van den Poel, D. (2017). The added value of social media data in B2B customer acquisition systems: A real-life experiment. Decision Support Systems, 104 , 26–37. https://doi.org/10.1016/j.dss.2017.09.010 .

Michaelidou, N., Siamagka, N. T., & Christodoulides, G. (2011). Usage, barriers and measurement of social media marketing: An exploratory investigation of small and medium B2B brands. Industrial Marketing Management, 40 (7), 1153–1159. https://doi.org/10.1016/j.indmarman.2011.09.009 .

Mikalef, P., Pappas, I. O., Krogstie, J., & Pavlou, P. A. (2020a). Big data and business analytics: A research agenda for realizing business value. Information & Management, 57 (1), 103237.

Mikalef, P., Sharma, K., Pappas, I, O., & Giannakos, M. (2020b). Seeking information on social commerce: An examination of the impact of user-and marketer-generated content through an eye-tracking study. Information Systems Frontiers , 1-14, doi: https://doi.org/10.1007/s10796-020-10034-3 .

Minsky, L., & Quesenberry, K, A. (2016). How B2B sales can benefit from social selling . Harvard Business Review. Available at https://hbr.org/2016/11/84-of-b2b-sales-start-with-a-referral-not-a-salesperson . Accessed 28 Jan 2021.

Moncrief, W. C., Marshall, G. W., & Rudd, J. M. (2015). Social media and related technology: Drivers of change in managing the contemporary sales force. Business Horizons, 58 (1), 45–55. https://doi.org/10.1016/j.bushor.2014.09.009 .

Moore, J. N., Hopkins, C. D., & Raymond, M. A. (2013). Utilization of relationship-oriented social media in the selling process: A comparison of consumer (B2C) and industrial (B2B) salespeople. Journal of Internet Commerce, 12 (1), 48–75. https://doi.org/10.1080/15332861.2013.763694 .

Moore, J. N., Raymond, M. A., & Hopkins, C. D. (2015). Social selling: A comparison of social media usage across process stage, markets, and sales job functions. Journal of Marketing Theory and Practice, 23 (1), 1–20. https://doi.org/10.1080/10696679.2015.980163 .

Mudambi, S. M., Sinha, J. I., & Taylor, D. S. (2019). Why B-to-B CEOs should be more social on social media. Journal of Business-to-Business Marketing, 26 (1), 103–105. https://doi.org/10.1080/1051712X.2019.1565144 .

Muhammad, S. S., Dey, B. L., & Weerakkody, V. (2018). Analysis of factors that influence customers’ willingness to leave big data digital footprints on social media: A systematic review of literature. Information Systems Frontiers, 20 (3), 559–576.

Müller, L., Griesbaum, J., & Mandl, T. (2013). Social media relations in the german automotive market. Paper presented at the proceedings of the IADIS international conference ICT, society and human beings 2013, Proceedings of the IADIS International Conference e-Commerce 2013, 19–26.

Müller, J. M., Pommeranz, B., Weisser, J., & Voigt, K. I. (2018). Digital, social media, and Mobile marketing in industrial buying: Still in need of customer segmentation? Empirical evidence from Poland and Germany. Industrial Marketing Management, 73 , 70–83. https://doi.org/10.1016/j.indmarman.2018.01.033 .

Niedermeier, K. E., Wang, E., & Zhang, X. (2016). The use of social media among business-to-business sales professionals in China: How social media helps create and solidify guanxi relationships between sales professionals and customers. Journal of Research in Interactive Marketing, 10 (1), 33–49. https://doi.org/10.1108/JRIM-08-2015-0054 .

Nunan, D., Sibai, O., Schivinski, B., & Christodoulides, G. (2018). Reflections on “social media: Influencing customer satisfaction in B2B sales” and a research agenda. Industrial Marketing Management, 75 , 31–36. https://doi.org/10.1016/j.indmarman.2018.03.009 .

Ogilvie, J., Agnihotri, R., Rapp, A., & Trainor, K. (2018). Social media technology use and salesperson performance: A two study examination of the role of salesperson behaviors, characteristics, and training. Industrial Marketing Management, 75 , 55–65. https://doi.org/10.1016/j.indmarman.2018.03.007 .

Oliveira, T., & Martins, M. F. (2011). Literature review of information technology adoption models at firm level. Electronic Journal of Information Systems Evaluation, 14 (1), 110.

Olotewo, J. (2016). Social media marketing in emerging markets. International Journal of Online Marketing Research, 2 (2), 10–18.

Pappas, I. O., Mikalef, P., Giannakos, M. N., Krogstie, J., & Lekakos, G. (2018). Big data and business analytics ecosystems: Paving the way towards digital transformation and sustainable societies. Information Systems and e-Business Management, 16 , 479–491.

Pappas, I. O., Papavlasopoulou, S., Mikalef, P., & Giannakos, M. N. (2020). Identifying the combinations of motivations and emotions for creating satisfied users in SNSs: An fsQCA approach. International Journal of Information Management, 53 , 102128.

Pascucci, F., Ancillai, C., & Cardinali, S. (2018). Exploring antecedents of social media usage in B2B: A systematic review. Management Research Review, 41 (6), 629–656. https://doi.org/10.1108/MRR-07-2017-0212 .

Pitt, C. S., Plangger, K. A., Botha, E., Kietzmann, J., & Pitt, L. (2017). How employees engage with B2B brands on social media: Word choice and verbal tone. Industrial Marketing Management, 81 , 130–137. https://doi.org/10.1016/j.indmarman.2017.09.012 .

Pitt, C. S., Botha, E., Ferreira, J. J., & Kietzmann, J. (2018). Employee brand engagement on social media: Managing optimism and commonality. Business Horizons, 61 (4), 635–642. https://doi.org/10.1016/j.bushor.2018.04.001 .

Porter, M. E., & Millar, V. E. (1985). How information gives you competitive advantage. Harvard Business Review, 63 (4), 149–160.

Pulizzi, J., & Handley, A. (2017). B2C content marketing-2018 benchmarks, budgets, and trends—North America. Available at https://contentmarketinginstitute.com/wp-content/uploads/2016/09/2017_B2B_Research_FINAL.pdf Accessed on April , 27 , 2020.

Rana, N. P., Dwivedi, Y. K., & Williams, M. D. (2015). A meta-analysis of existing research on citizen adoption of e-government. Information Systems Frontiers, 17 (3), 547–563.

Rana, N. P., Luthra, S., Mangla, S. K., Islam, R., Roderick, S., & Dwivedi, Y. K. (2019). Barriers to the development of smart cities in Indian context. Information Systems Frontiers, 21 (3), 503–525.

Rodriguez, M., Peterson, R. M., & Krishnan, V. (2012). Social media's influence on business-to-business sales performance. Journal of Personal Selling and Sales Management, 32 (3), 365–378. https://doi.org/10.2753/PSS0885-3134320306 .

Rossmann, A., & Stei, G. (2015). Sales 2.0 in business-to-business (B2B) networks: Conceptualization and impact of social media in B2B sales relationships . Paper presented at the Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft Fur Informatik (GI), 244 67–78. Available at https://subs.emis.de/LNI/Proceedings/Proceedings244/67.pdf . Accessed 28 Jan 2021.

Salo, J. (2017). Social media research in the industrial marketing field: Review of literature and future research directions. Industrial Marketing Management, 66 , 115–129. https://doi.org/10.1016/j.indmarman.2017.07.013 .

Shaltoni, A. M. (2017). From websites to social media: Exploring the adoption of internet marketing in emerging industrial markets. Journal of Business and Industrial Marketing, 32 (7), 1009–1019. https://doi.org/10.1108/JBIM-06-2016-0122 .

Siamagka, N. T., Christodoulides, G., Michaelidou, N., & Valvi, A. (2015). Determinants of social media adoption by B2B organizations. Industrial Marketing Management, 51 , 89–99. https://doi.org/10.1016/j.indmarman.2015.05.005 .

Sivarajah, U., Irani, Z., Gupta, S., & Mahroof, K. (2019). Role of big data and social media analytics for business to business sustainability: A participatory web context. Industrial Marketing Management . https://doi.org/10.1016/j.indmarman.2019.04.005 .

Sobal, A. (2017). 30 statistics about B2B social media usage. Available at https://www.weidert.com/blog/statistics-about-b2b-social-media-usage Accessed on April , 27 , 2020.

Stelzner, M. (2011). 2012 social media marketing industry report. Social media examiner . Available at https://www.socialmediaexaminer.com/socialmedia-marketing-industry-report-2012/ . Accessed 28 Jan 2021.

Sułkowski, Ł., & Kaczorowska-Spychalska, D. (2016). Social media in the process of marketing evolution in polish textile-clothing industry. Fibres and Textiles in Eastern Europe, 24 (5), 15–20. https://doi.org/10.5604/12303666.1215521 .

Swani, K., Milne, G., & Brown, B. P. (2013). Spreading the word through likes on facebook: Evaluating the message strategy effectiveness of fortune 500 companies. Journal of Research in Interactive Marketing, 7 (4), 269–294. https://doi.org/10.1108/JRIM-05-2013-0026 .

Swani, K., Brown, B. P., & Milne, G. R. (2014). Should tweets differ for B2B and B2C? An analysis of fortune 500 companies' twitter communications. Industrial Marketing Management, 43 (5), 873–881. https://doi.org/10.1016/j.indmarman.2014.04.012 .

Swani, K., Milne, G. R., Brown, B. P., Assaf, A. G., & Donthu, N. (2017). What messages to post? Evaluating the popularity of social media communications in business versus consumer markets. Industrial Marketing Management, 62 , 77–87. https://doi.org/10.1016/j.indmarman.2016.07.006 .

Tedeschi, B. (2006). Like shopping? Social networking? Try social shopping. New York Times, 11 , 09.

Teo, T. S., & Choo, W. Y. (2001). Assessing the impact of using the internet for competitive intelligence. Information & Management, 39 (1), 67–83.

Tseng, M. L. (2017). Using social media and qualitative and quantitative information scales to benchmark corporate sustainability. Journal of Cleaner Production, 142 , 727–738.

Vasudevan, S., & Kumar, F. J. P. (2018). Social media and B2B brands: An Indian perspective. International Journal of Mechanical Engineering and Technology, 9 (9), 767–775.

Vukanovic, Z. (2013). Managing social media value networks: From publisher (broadcast) to user-centric (broadband-narrowcast) business models. Handbook of social media management: Value chain and business models in changing media markets (pp. 269-288) doi: https://doi.org/10.1007/978-3-642-28897-5_16 .

Wang, Y., Rod, M., Ji, S., & Deng, Q. (2017). Social media capability in B2B marketing: Toward a definition and a research model. Journal of Business and Industrial Marketing, 32 (8), 1125–1135. https://doi.org/10.1108/JBIM-10-2016-0250 .

Watt, I. (2010). Changing visions of parliamentary libraries: From the enlightenment to Facebook. IFLA Journal, 36 (1), 47–60. https://doi.org/10.1177/0340035209359574 .

Webster, J., & Watson, R.,. T. (2002). Analyzing the past to prepare for the future: Writing a literature review. MIS quarterly, 26 (2), xiii–xxiii.

Westerman, G., Bonnet, D., & McAfee, A. (2014). Leading digital: Turning technology into business transformation . Boston, MA: Harvard Business Press.

Williams, M. D., Rana, N. P., & Dwivedi, Y. K. (2015). The unified theory of acceptance and use of technology (UTAUT): A literature review. Journal of Enterprise Information Management, 28 (3), 443–488.

World Commission Report on Environment and Development. (1987). Our Common Future . Oxford: Oxford University Press.

Yang, C. C., Yang, H., Tang, X., & Jiang, L. (2012). Identifying implicit relationships between social media users to support social commerce. In ACM international conference proceeding series (pp. 41–47). https://doi.org/10.1145/2346536.2346544 .

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Dwivedi, Y.K., Ismagilova, E., Rana, N.P. et al. Social Media Adoption, Usage And Impact In Business-To-Business (B2B) Context: A State-Of-The-Art Literature Review. Inf Syst Front 25 , 971–993 (2023). https://doi.org/10.1007/s10796-021-10106-y

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