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Effects of Social Media Use on Psychological Well-Being: A Mediated Model

Dragana ostic.

1 School of Finance and Economics, Jiangsu University, Zhenjiang, China

Sikandar Ali Qalati

Belem barbosa.

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

Syed Mir Muhammad Shah

3 Department of Business Administration, Sukkur Institute of Business Administration (IBA) University, Sukkur, Pakistan

Esthela Galvan Vela

4 CETYS Universidad, Tijuana, Mexico

Ahmed Muhammad Herzallah

5 Department of Business Administration, Al-Quds University, Jerusalem, Israel

6 Business School, Shandong University, Weihai, China

Associated Data

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

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 ):

Conceptual model.

• 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.

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.

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.

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 .

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

Discriminant validity and correlation.

Bold values are the square root of the AVE .

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 ).

Summary of path coefficients and hypothesis testing.

Structural model.

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

Goodness of fit → SRMR = 0.063; d_ULS = 1.589; d_G = 0.512; chi-square = 2,910.744 .

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

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.

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.

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

• Abbas R., 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. 10.1080/1369118X.2016.1261168 [ CrossRef ] [ Google Scholar ]
• Adnan M., Anwar K. (2020). Online learning amid the COVID-19 pandemic: students' perspectives . J. Pedagog. Sociol. Psychol. 2 , 45–51. 10.33902/JPSP.2020261309 [ CrossRef ] [ Google Scholar ]
• Ali Qalati S., Li W., Ahmed N., Ali Mirani M., Khan A. (2021). Examining the factors affecting SME performance: the mediating role of social media adoption . Sustainability 13 :75. 10.3390/su13010075 [ CrossRef ] [ Google Scholar ]
• Bagozzi R. P., Yi Y. (1988). On the evaluation of structural equation models . J. Acad. Mark. Sci. 16 , 74–94. 10.1007/BF02723327 [ CrossRef ] [ Google Scholar ]
• Bagozzi R. P., Yi Y., Phillips L. W. (1991). Assessing construct validity in organizational research . Admin. Sci. Q. 36 , 421–458. 10.2307/2393203 [ CrossRef ] [ Google Scholar ]
• Bano S., Cisheng W., Khan A. N., 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. 10.1016/j.childyouth.2019.06.002 [ CrossRef ] [ Google Scholar ]
• Barbosa B., Chkoniya V., Simoes D., Filipe S., 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., 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. 10.1177/1090198119863768 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Brown G., 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. 10.1016/j.techsoc.2019.101176 [ CrossRef ] [ Google Scholar ]
• Campbell D. T., Fiske D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix . Psychol. Bull. 56 , 81–105. 10.1037/h0046016 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Carlson J. R., Zivnuska S., Harris R. B., Harris K. J., Carlson D. S. (2016). Social media use in the workplace: a study of dual effects . J. Org. End User Comput. 28 , 15–31. 10.4018/JOEUC.2016010102 [ CrossRef ] [ Google Scholar ]
• 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. 10.1177/1461444813516836 [ CrossRef ] [ Google Scholar ]
• Chappell N. L., Badger M. (1989). Social isolation and well-being . J. Gerontol. 44 , S169–S176. 10.1093/geronj/44.5.s169 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Chatterjee S. (2020). Antecedents of phubbing: from technological and psychological perspectives . J. Syst. Inf. Technol. 22 , 161–118. 10.1108/JSIT-05-2019-0089 [ CrossRef ] [ Google Scholar ]
• Chen H.-T., 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. 10.1016/j.chb.2017.06.011 [ CrossRef ] [ Google Scholar ]
• Choi D.-H., 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. 10.1080/1369118X.2019.1574860 [ CrossRef ] [ Google Scholar ]
• Chotpitayasunondh V., Douglas K. M. (2016). How phubbing becomes the norm: the antecedents and consequences of snubbing via smartphone . Comput. Hum. Behav. 63 , 9–18. 10.1016/j.chb.2016.05.018 [ CrossRef ] [ Google Scholar ]
• Chotpitayasunondh V., Douglas K. M. (2018). The effects of phubbing on social interaction . J. Appl. Soc. Psychol. 48 , 304–316. 10.1111/jasp.12506 [ CrossRef ] [ Google Scholar ]
• Cohen J. (1998). Statistical Power Analysis for the Behavioural Sciences . Hillsdale, NJ: Lawrence Erlbaum Associates. [ Google Scholar ]
• 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. 10.4103/jfcm.JFCM_71_17 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• David M. E., Roberts J. A., 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. 10.1080/10447318.2017.1349250 [ CrossRef ] [ Google Scholar ]
• Dhir A., Yossatorn Y., Kaur P., 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. 10.1016/j.ijinfomgt.2018.01.012 [ CrossRef ] [ Google Scholar ]
• Dutot V., Bergeron F. (2016). From strategic orientation to social media orientation: improving SMEs' performance on social media . J. Small Bus. Enterp. Dev. 23 , 1165–1190. 10.1108/JSBED-11-2015-0160 [ CrossRef ] [ Google Scholar ]
• Ellison N. B., Steinfield C., 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. 10.1111/j.1083-6101.2007.00367.x [ CrossRef ] [ Google Scholar ]
• Fan M., Huang Y., Qalati S. A., Shah S. M. M., Ostic D., Pu Z. (2021). Effects of information overload, communication overload, and inequality on digital distrust: a cyber-violence behavior mechanism . Front. Psychol. 12 :643981. 10.3389/fpsyg.2021.643981 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Fornell C., Larcker D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error . J. Market. Res. 18 , 39–50. 10.1177/002224378101800104 [ CrossRef ] [ Google Scholar ]
• Gökçearslan S., Uluyol Ç., Sahin S. (2018). Smartphone addiction, cyberloafing, stress and social support among University students: a path analysis . Child. Youth Serv. Rev. 91 , 47–54. 10.1016/j.childyouth.2018.05.036 [ CrossRef ] [ Google Scholar ]
• Gong S., Xu P., Wang S. (2021). Social capital and psychological well-being of Chinese immigrants in Japan . Int. J. Environ. Res. Public Health 18 :547. 10.3390/ijerph18020547 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Guazzini A., Duradoni M., Capelli A., Meringolo P. (2019). An explorative model to assess individuals' phubbing risk . Fut. Internet 11 :21. 10.3390/fi11010021 [ CrossRef ] [ Google Scholar ]
• Hair J. F., Risher J. J., Sarstedt M., Ringle C. M. (2019). When to use and how to report the results of PLS-SEM . Eur. Bus. Rev. 31 , 2–24. 10.1108/EBR-11-2018-0203 [ CrossRef ] [ Google Scholar ]
• Hair J. F., Sarstedt M., Pieper T. M., 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. 10.1016/j.lrp.2012.09.008 [ CrossRef ] [ Google Scholar ]
• Hair J. F., Sarstedt M., Ringle C. M., Gudergan S. P. (2017). Advanced Issues in Partial Least Squares Structural Equation Modeling. Thousand Oaks, CA: Sage. [ Google Scholar ]
• Hajek A., 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. 10.1159/000512793 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Henseler J., Ringle C. M., 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. [ Google Scholar ]
• 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. 10.3389/fpsyg.2021.636520 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jeong S.-H., Kim H., Yum J.-Y., Hwang Y. (2016). What type of content are smartphone users addicted to? SNS vs. games . Comput. Hum. Behav. 54 , 10–17. 10.1016/j.chb.2015.07.035 [ CrossRef ] [ Google Scholar ]
• Jiao Y., Jo M.-S., 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. 10.1080/10919392.2016.1264762 [ CrossRef ] [ Google Scholar ]
• Jordan P. J., Troth A. C. (2019). Common method bias in applied settings: the dilemma of researching in organizations . Austr. J. Manag. 45 , 3–14. 10.1177/0312896219871976 [ CrossRef ] [ Google Scholar ]
• Karikari S., Osei-Frimpong K., Owusu-Frimpong N. (2017). Evaluating individual level antecedents and consequences of social media use in Ghana . Technol. Forecast. Soc. Change 123 , 68–79. 10.1016/j.techfore.2017.06.023 [ CrossRef ] [ Google Scholar ]
• 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., 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. 10.1016/j.chb.2017.03.033 [ CrossRef ] [ Google Scholar ]
• Kim K., Milne G. R., Bahl S. (2018). Smart phone addiction and mindfulness: an intergenerational comparison . Int. J. Pharmaceut. Healthcare Market. 12 , 25–43. 10.1108/IJPHM-08-2016-0044 [ CrossRef ] [ Google Scholar ]
• Kircaburun K., Alhabash S., Tosuntaş S. B., 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. 10.1007/s11469-018-9940-6 [ CrossRef ] [ Google Scholar ]
• Leong L.-Y., Hew T.-S., Ooi K.-B., Lee V.-H., Hew J.-J. (2019). A hybrid SEM-neural network analysis of social media addiction . Expert Syst. Appl. 133 , 296–316. 10.1016/j.eswa.2019.05.024 [ CrossRef ] [ Google Scholar ]
• Li L., Griffiths M. D., Mei S., 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. 10.3389/fpsyt.2020.00877 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Li W., Qalati S. A., Khan M. A. S., Kwabena G. Y., Erusalkina D., Anwar F. (2020b). Value co-creation and growth of social enterprises in developing countries: moderating role of environmental dynamics . Entrep. Res. J. 2020 :20190359. 10.1515/erj-2019-0359 [ CrossRef ] [ Google Scholar ]
• Li X., 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. 10.1016/j.chb.2014.02.012 [ CrossRef ] [ Google Scholar ]
• Matthews L., Hair J. F., Matthews R. (2018). PLS-SEM: the holy grail for advanced analysis . Mark. Manag. J. 28 , 1–13. [ Google Scholar ]
• Meshi D., Cotten S. R., Bender A. R. (2020). Problematic social media use and perceived social isolation in older adults: a cross-sectional study . Gerontology 66 , 160–168. 10.1159/000502577 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mou J., Shin D.-H., 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. 10.1080/0144929X.2016.1203024 [ CrossRef ] [ Google Scholar ]
• Nunnally J. (1978). Psychometric Methods . New York, NY: McGraw-Hill. [ Google Scholar ]
• Oghazi P., Karlsson S., Hellström D., Hjort K. (2018). Online purchase return policy leniency and purchase decision: mediating role of consumer trust . J. Retail. Consumer Serv. 41 , 190–200. [ Google Scholar ]
• 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. 10.1016/j.ijintrel.2018.08.002 [ CrossRef ] [ Google Scholar ]
• Podsakoff P. M., MacKenzie S. B., Lee J.-Y., 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. 10.1037/0021-9010.88.5.879 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Podsakoff P. M., Organ D. W. (1986). Self-reports in organizational research: problems and prospects . J. Manag. 12 , 531–544. 10.1177/014920638601200408 [ CrossRef ] [ Google Scholar ]
• Preacher K. J., Hayes A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models . Behav Res. Methods 40 , 879–891. 10.3758/brm.40.3.879 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• 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. 10.1016/j.amepre.2017.01.010 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Putnam R. D. (1995). Tuning in, tuning out: the strange disappearance of social capital in America . Polit. Sci. Polit. 28 , 664–684. 10.2307/420517 [ CrossRef ] [ Google Scholar ]
• Putnam R. D. (2000). Bowling Alone: The Collapse and Revival of American Community . New York, NY: Simon and Schuster. [ Google Scholar ]
• 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. 10.1177/0272684X211004945 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Reer F., Tang W. Y., 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. 10.1177/1461444818823719 [ CrossRef ] [ Google Scholar ]
• Ringle C., Wende S., Becker J. (2015). SmartPLS 3 [software] . Bönningstedt: SmartPLS. [ Google Scholar ]
• Ringle C. M., Sarstedt M., Straub D. (2012). A critical look at the use of PLS-SEM in MIS Quarterly. MIS Q . 36, iii–xiv. 10.2307/41410402 [ CrossRef ] [ Google Scholar ]
• Roberts J. A., 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. 10.1080/10447318.2019.1646517 [ CrossRef ] [ Google Scholar ]
• Salehan M., Negahban A. (2013). Social networking on smartphones: when mobile phones become addictive . Comput. Hum. Behav. 29 , 2632–2639. 10.1016/j.chb.2013.07.003 [ CrossRef ] [ Google Scholar ]
• Sarstedt M., Cheah J.-H. (2019). Partial least squares structural equation modeling using SmartPLS: a software review . J. Mark. Anal. 7 , 196–202. 10.1057/s41270-019-00058-3 [ CrossRef ] [ Google Scholar ]
• Schinka K. C., VanDulmen M. H., Bossarte R., 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. 10.1080/00223980.2011.584084 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Shi S., Mu R., Lin L., Chen Y., Kou G., Chen X.-J. (2018). The impact of perceived online service quality on swift guanxi . Internet Res. 28 , 432–455. 10.1108/IntR-12-2016-0389 [ CrossRef ] [ Google Scholar ]
• Shoukat S. (2019). Cell phone addiction and psychological and physiological health in adolescents . EXCLI J. 18 , 47–50. 10.17179/excli2018-2006 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Shrestha N. (2021). Factor analysis as a tool for survey analysis . Am. J. Appl. Math. Stat. 9 , 4–11. 10.12691/ajams-9-1-2 [ CrossRef ] [ Google Scholar ]
• Stouthuysen K., Teunis I., Reusen E., 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. 10.1016/j.elerap.2017.11.002 [ CrossRef ] [ Google Scholar ]
• Swar B., 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). [ Google Scholar ]
• 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. 10.1371/journal.pone.0210294 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• 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. 10.3389/fpsyt.2019.00455 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Tefertiller A. C., Maxwell L. C., 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. 10.1080/15205436.2019.1653468 [ CrossRef ] [ Google Scholar ]
• Tehseen S., Qureshi Z. H., Johara F., 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. [ Google Scholar ]
• Tehseen S., Ramayah T., Sajilan S. (2017). Testing and controlling for common method variance: a review of available methods . J. Manag. Sci. 4 , 146–165. 10.20547/jms.2014.1704202 [ CrossRef ] [ Google Scholar ]
• 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. 10.3390/medicina55020037 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Twenge J. M., Campbell W. K. (2019). Media use is linked to lower psychological well-being: evidence from three datasets . Psychiatr. Q. 90 , 311–331. 10.1007/s11126-019-09630-7 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Vallespín M., Molinillo S., 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. 10.1108/IMDS-03-2016-0089 [ CrossRef ] [ Google Scholar ]
• Van Den Eijnden R. J., Lemmens J. S., Valkenburg P. M. (2016). The social media disorder scale . Comput. Hum. Behav. 61 , 478–487. 10.1016/j.chb.2016.03.038 [ CrossRef ] [ Google Scholar ]
• Whaite E. O., Shensa A., Sidani J. E., Colditz J. B., 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. 10.1016/j.paid.2017.10.030 [ CrossRef ] [ Google Scholar ]
• Williams D. (2006). On and off the'net: scales for social capital in an online era . J. Comput. Mediat. Commun. 11 , 593–628. 10.1016/j.1083-6101.2006.00029.x [ CrossRef ] [ Google Scholar ]

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Social media marketing strategy: definition, conceptualization, taxonomy, validation, and future agenda

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• Volume 49 , pages 51–70, ( 2021 )

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• Fangfang Li   ORCID: orcid.org/0000-0002-4883-1730 1 ,
• Jorma Larimo 1 &
• Leonidas C. Leonidou 2  

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Although social media use is gaining increasing importance as a component of firms’ portfolio of strategies, scant research has systematically consolidated and extended knowledge on social media marketing strategies (SMMSs). To fill this research gap, we first define SMMS, using social media and marketing strategy dimensions. This is followed by a conceptualization of the developmental process of SMMSs, which comprises four major components, namely drivers, inputs, throughputs, and outputs. Next, we propose a taxonomy that classifies SMMSs into four types according to their strategic maturity level: social commerce strategy, social content strategy, social monitoring strategy, and social CRM strategy. We subsequently validate this taxonomy of SMMSs using information derived from prior empirical studies, as well with data collected from in-depth interviews and a quantitive survey among social media marketing managers. Finally, we suggest fruitful directions for future research based on input received from scholars specializing in the field.

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Introduction

The past decade has witnessed the development of complex, multifarious, and intensified interactions between firms and their customers through social media usage. On the one hand, firms are taking advantage of social media platforms to expand geographic reach to buyers (Gao et al. 2018 ), bolster brand evaluations (Naylor et al. 2012 ), and build closer connections with customers (Rapp et al. 2013 ). On the other hand, customers are increasingly empowered by social media and taking control of the marketing communication process, and they are becoming creators, collaborators, and commentators of messages (Hamilton et al. 2016 ). As the role of social media has gradually evolved from a single marketing tool to that of a marketing intelligence source (in which firms can observe, analyze, and predict customer behaviors), it has become increasingly imperative for marketers to strategically use and leverage social media to achieve competitive advantage and superior performance (Lamberton and Stephen 2016 ).

Despite widespread understanding among marketers of the need to engage customers on social media platforms, relatively few firms have properly strategized their social media appearance and involvement (Choi and Thoeni 2016 ; Griffiths and Mclean 2015 ). Rather, for most companies, the ongoing challenge is not to initiate social media campaigns, but to combine social media with their marketing strategy to engage customers in order to build valuable and long-term relationships with them (Lamberton and Stephen 2016 ; Schultz and Peltier 2013 ). However, despite the vast opportunities social media offer to companies, there is no clear definition or comprehensive framework to guide the integration of social media with marketing strategies, to gain a rigorous understanding of the nature and role of social media marketing strategies (SMMSs) (Effing and Spil 2016 ).

Although some reviews focusing on the social media phenomenon are available (e.g., Lamberton and Stephen 2016 ; Salo 2017 ), to date, an integrative evaluation effort focusing on the strategic marketing perspective of social media is missing. This is partly because the social media literature largely derives elements from widely disparate fields, such as marketing, management, consumer psychology, and computer science (Aral et al. 2013 ). Moreover, research on SMMSs mainly covers very specific, isolated, and scattered aspects, which creates confusion and limits understanding of the subject (Lamberton and Stephen 2016 ). Furthermore, research deals only tangentially with a conceptualization, operationalization, and categorization of SMMSs, which limits theory advancement and practice development (Tafesse and Wien 2018 ).

To address these problems, and also to respond to repeated pleas from scholars in the field (e.g., Aral et al. 2013 ; Guesalaga 2016 ; Moorman and Day 2016 ; Schultz and Peltier 2013 to identify appropriate strategies to leverage social media in today’s changing marketing landscape, we aim to systematically consolidate and extend the knowledge accumulated from previous research on SMMSs. Specifically, our objectives are fivefold: (1) to clearly define SMMS by blending issues derived from the social media and marketing strategy literature streams; (2) to conceptualize the process of developing SMMSs and provide a theoretical understanding of its constituent parts; (3) to provide a taxonomy of SMMSs according to their level of strategic maturity; (4) to validate the practical value of this taxonomy using information derived from previous empirical studies, as well as from primary data collection among social media marketing managers; and (5) to develop an agenda for promising areas of future research on the subject.

Our study makes three major contributions to the social media marketing literature. First, it offers a definition and a conceptualization of SMMS that help alleviate definitional deficiency and increase conceptual clarity on the subject. By focusing on the role of social connectedness and interactions in resource integration, we stress the importance of transforming social media interactions and networks into marketing resources to help achieve specific strategic goals for the firm. In this regard, we provide theoretical justification of social media from a strategic marketing perspective. Second, using customer engagement as an overarching theory, we develop a model conceptualizing the SMMS developmental process. Through an analysis of each component of this process, we emphasize the role of insights from both firms and customers to better understand the dynamics of SMMS formulation. We also suggest certain theories to specifically explain the particular role played by each of these components in developing sound SMMSs. Third, we propose a taxonomy of SMMSs based on their level of strategic maturity that can serve as the basis for developing specific marketing strategy concepts and measurement scales within a social media context. We also expect this taxonomy to provide social media marketing practitioners with fruitful insights on why to select and how to use a particular SMMS in order to achieve superior marketing results.

Defining SMMS

Although researchers have often used the term “social media marketing strategy” in their studies (e.g., Choi and Thoeni 2016 ; Kumar et al. 2013 ; Zhang et al. 2017 ), they have yet to propose a clear definition. Despite the introduction of several close terms in the past, including “social media strategy” (Aral et al. 2013 ; Effing and Spil 2016 ), “online marketing strategy” (Micu et al. 2017 ), and “strategic social media marketing” (Felix et al. 2017 ), these either fail to take into consideration the different functions/features of social media or neglect key marketing strategy issues. What is therefore required is an all-encompassing definition of SMMS that will capture two fundamental elements—namely, social media and marketing strategy. Table 1 draws a comparison between social media and marketing strategy on five dimensions (i.e., core, orientation, resource, purpose, and premise) and presents the resulting profile of SMMS.

• Social media

In a marketing context, social media are considered platforms on which people build networks and share information and/or sentiments (Kaplan and Haenlein 2010 ). With their distinctive nature of being “dynamic, interconnected, egalitarian, and interactive organisms” (Peters et al. 2013 , p. 281), social media have generated three fundamental shifts in the marketplace. First, social media enable firms and customers to connect in ways that were not possible in the past. Such connectedness is empowered by various platforms, such as social networking sites (e.g., Facebook), microblogging sites (e.g., Twitter), and content communities (e.g., YouTube), that allow social networks to build from shared interests and values (Kaplan and Haenlein 2010 ). In this regard, “social connectedness” has also been termed as “social ties” (e.g., Muller and Peres 2019 ; Quinton and Wilson 2016 ), and the strength and span of these ties determine whether they are strong or weak (Granovetter 1973 ). Prior studies have shown that tie strength is an important determinant of customer referral behaviors (e.g., Verlegh et al. 2013 ).

Second, social media have transformed the way firms and customers interact and influence each other. Social interaction involves “actions,” whether through communications or passive observations, that influence others’ choices and consumption behaviors (Chen et al. 2011 ). Nair et al. ( 2010 ) labeled such social interactions as “word-of-mouth (WOM) effect” or “contagion effects.” Muller and Peres ( 2019 ) argue that social interactions rely strongly on the social network structure and provide firms with measurable value (also referred to as “social equity”). In social media studies, researchers have long recognized the importance of social influence in affecting consumer decisions, and recent studies have shown that people’s connection patterns and the strength of social ties can signify the intensity of social interactions (e.g., Aral and Walker 2014 ; Katona et al. 2011 ).

Third, the proliferation of social media data has made it increasingly possible for companies to better manage customer relationships and enhance decision making in business (Libai et al. 2010 ). Social media data, together with other digital data, are widely characterized by the 3Vs (i.e., volume, variety, and velocity), which refer to the vast quantity of data, various sources of data, and expansive real-time data (Alharthi et al. 2017 ). A huge amount of social media data derived from different venues (e.g., social networks, blogs, forums) and in various formats (e.g., text, video, image) can now be easily extracted and usefully exploited with the aid of modern information technologies (Moe and Schweidel 2017 ). Thus, social media data can serve as an important source of customer analysis, market research, and crowdsourcing of new ideas, while capturing and creating value through social media data represents the development of a new strategic resource that can improve marketing outcomes (Gnizy 2019 ).

• Marketing strategy

According to Varadarajan ( 2010 ), a marketing strategy consists of an integrated set of decisions that helps the firm make critical choices regarding marketing activities in selected markets and segments, with the aim to create, communicate, and deliver value to customers in exchange for accomplishing its specific financial, market, and other objectives. According to the resource-based view of the firm (Barney 1991 ), organizational resources (e.g., financial, human, physical, informational, relational) help firms enhance their marketing strategies, achieve sustainable competitive advantage, and gain better performance. These resources can be either tangible or intangible and can be transformed into higher-order resources (i.e., competencies and capabilities), enabling the delivery of superior value to targeted buyers (Hunt and Morgan 1995 ; Teece and Pisano 1994 ).

Different marketing strategies can be arranged on a continuum, on which transaction marketing strategy and relationship marketing strategy represent its two ends, while in between are various mixed marketing strategies (Grönroos 1991 ). Webster ( 1992 ) notes that long-standing customer relationships should be at the core of marketing strategy, because customer interaction and engagement can be developed into valuable relational resources (Hunt et al. 2006 ). Morgan and Hunt ( 1999 ) also claim that firms capitalizing on long-term and trustworthy customer relationships can help design value-enhancing marketing strategies that will subsequently generate competitive advantages and lead to superior performance.

From a strategic marketing perspective, social media interaction entails a process that allows not only firms, but also customers to exchange resources. For example, Hollebeek et al. ( 2019 ) assert that customers can devote operant (e.g., knowledge) and operand (e.g., equipment) resources while interacting with firms. Importantly, Gummesson and Mele ( 2010 ) argue that interactions occur not simply in dyads, but also between multiple actors within a network, underscoring the critical role of network interaction in resource integration. Notably, customer-to-customer interactions are also essential, especially for the higher level of engagement behaviors (Fehrer et al. 2018 ).

Thus, social media interconnectedness and interactions (i.e., between firm–customer and between customer–customer) can be considered strategic resources, which can be further converted into marketing capabilities (Morgan and Hunt 1999 ). A case in point is social customer relationship management (CRM) capabilities, in which the firm cultivates the competency to use information generated from social media interactions to identify and develop loyal customers (Trainor et al. 2014 ). With the expanding role of social media from a single communication tool to one of gaining customer and market knowledge, marketers can strategically develop distinct resources from social media based on extant organizational resources and capabilities.

Drawing on the previous argumentation, we define SMMS as an organization’s integrated pattern of activities that, based on a careful assessment of customers’ motivations for brand-related social media use and the undertaking of deliberate engagement initiatives, transform social media connectedness (networks) and interactions (influences) into valuable strategic means to achieve desirable marketing outcomes. This definition is parsimonious because it captures the uniqueness of the social media phenomenon, takes into consideration the fundamental premises of marketing strategy, and clearly defines the scope of activities pertaining to SMMS.

Although the underlying roots of traditional marketing strategy and SMMS are similar, the two strategies have three distinctive differences: (1) as opposed to the traditional approach, which pays peripheral attention to the heterogeneity of motivations driving customer engagement, SMMS emphasizes that social media users must be motivated on intellectual, social, cultural, or other grounds to engage with firms (and perhaps more importantly with other customers) (Peters et al. 2013 ; Venkatesan 2017 ); (2) the consequences of SMMS are jointly decided by the firm and its customers (rather than by individual actors’ behaviors), and it is only when the firm and its customers interact and build relationships that social media technological platforms become real resource integrators (Singaraju et al. 2016 ; Stewart and Pavlou 2002 ); and (3) while customer value in traditional marketing strategies is narrowly defined to solely capture purchase behavior through customer lifetime value, in the case of SMMS, this value is expressed through customer engagement, comprising both direct (e.g., customer purchases) and indirect (e.g., product referrals to other customers) contributions to the value of the firm (Kumar and Pansari 2016 ; Venkatesan 2017 ).

Conceptualizing the process of developing SMMSs

The conceptualization of the process of developing SMMSs is anchored on customer engagement theory, which posits that firms need to take deliberate initiatives to motivate and empower customers to maximize their engagement value and yield superior marketing results (Harmeling et al. 2017 ). Kumar et al. ( 2010 ) distinguish between four different dimensions of customer engagement value, namely customer lifetime value, customer referral value, customer influence value, and customer knowledge value. This metric has provided a new approach for customer valuation, which can help marketers to make more effective and efficient strategic decisions that enable long-term value contributions to customers. In a social media context, this customer engagement value enables firms to capitalize on crucial customer resources (i.e., network assets, persuasion capital, knowledge stores, and creativity), of which the leverage can provide firms with a sustainable competitive advantage (Harmeling et al. 2017 ).

Customer engagement theory highlights the importance of understanding customer motivations as a prerequisite for the firm to develop effective SMMSs, because heterogeneous customer motivations resulting from different attitudes and attachments can influence their social media behaviors and inevitably SMMS outcomes (Venkatesan 2017 ). It also stresses the role of inputs from both firm (i.e., social media engagement initiatives) and customers (i.e., social media behaviors), as well as the importance of different degrees of interactivity and interconnectedness in yielding sound marketing outcomes (Harmeling et al. 2017 ). Pansari and Kumar et al. ( 2017 ) argue that firms can benefit from such customer engagement in both tangible (e.g., higher revenues, market share, profits) and intangible (e.g., feedbacks or new ideas that help to product/service development) ways.

Based on consumer engagement theory, we therefore conceive the process of developing an SMMS as consisting of four interlocking parts: (1) drivers , that is, the firm’s social media marketing objectives and the customers’ social media use motivations; (2) inputs , that is, the firm’s social media engagement initiatives and the customers’ social media behaviors; (3) throughputs , that is, the way the firm connects and interacts with customers to exchange resources and satisfy needs; and (4) outputs , that is, the resulting customer engagement outcome. Figure 1 shows this developmental process of SMMS, while Table 2 indicates the specific theoretical underpinnings of each part comprising this process.

A conceptualization of the process of developing social media marketing strategies

Firms’ social media marketing objectives

Though operating in a similar context, SMMSs may differ depending on the firm’s strategic objectives (Varadarajan 2010 ). According to resource dependence theory (Pfeffer and Salancik 1978 ), the firm’s social media marketing objectives can be justified by the need to acquire external resources (which do not exist internally) that will help it accommodate the challenges of environmental contingencies. In a social media context, customers can serve as providers of resources, which can take several forms (Harmeling et al. 2017 ). Felix et al. ( 2017 ) distinguish between proactive and reactive social media marketing objectives, which can differ by the type of market targeted (e.g., B2B vs. B2C) and firm size. While for proactive objectives, firms use social media to increase brand awareness, generate online traffic, and stimulate sales, in the case of reactive objectives, the emphasis is on monitoring and analyzing customer activities.

Customers’ social media use motivations

Social media use motivations refer to various incentives that drive people’s selection and use of specific social media (Muntinga et al. 2011 ). The existence of these motivations is theoretically grounded on uses and gratifications theory (Katz et al. 1973 ), which maintains that consumers are actively and selectively involved in media usage to gratify their psychological and social needs. In a social media context, motivations can range from utilitarian and hedonic purposes (e.g., incentives, entertainment) to relational reasons (e.g., identification, brand connection) (Rohm et al. 2013 ). Muntinga et al. ( 2011 ) also categorize consumer–brand social media interactions as motivated primarily by entertainment, information, remuneration, personal identity, social interaction, and empowerment.

Firms’ social media engagement initiatives

Firms take initiatives to motivate and engage customers so that they can make voluntary contributions in return (Harmeling et al. 2017 ; Pansari and Kumar 2017 ). These firm actions can also be theoretically explained by resource dependence theory (Pfeffer and Salancik 1978 ), which argues that firms need to take initiatives to encourage customers to interact with them, to generate useful autonomous contributions that will alleviate resource shortages. Harmeling et al. ( 2017 ) identify two primary forms of a firm’s marketing initiatives to engage customers using social media: task-based and experiential. While task-based engagement initiatives encourage customer engagement behaviors with structured tasks (e.g., writing a review) and usually take place in the early stages of the firm’s social media marketing efforts, experiential engagement initiatives employ experiential events (e.g., multisensory events) to intrinsically motivate customer engagement and foster emotional attachment. Thus, firm engagement initiatives can be viewed as a continuum, where at one end, the firm uses monetary rewards to engage customers and, at the other end, the firm proactively works to deliver effective experiential incentives to motivate customer engagement.

Customers’ social media behaviors

The use of social media by customers yields different behavioral manifestations, ranging from passive (e.g., observing) to active (e.g., co-creation) (Maslowska et al. 2016 ). These customer social media behaviors can be either positive (e.g., sharing) or negative (e.g., create negative content), depending on customers’ attitudes and information processes during interactions (Dolan et al. 2016 ). Harmeling et al. ( 2017 ) characterize customers with positive behaviors as “pseudo marketers” because they contribute to firms’ marketing functions using their own resources, while those with negative behaviors may turn firm-created “hashtags” into “bashtags.” Drawing on uses and gratifications theory, Muntinga et al. ( 2011 ) also categorize customers’ brand-related behaviors in social media into three groups: consuming (e.g., reading a brand’s posts), contributing (e.g., rating products), and creating (e.g., publishing brand-related content).

Throughputs

Within the context of social media, both social connectedness and social interaction can be explained by social exchange theory, which proposes that social interactions are exchanges through which two parties acquire benefits (Blau 1964 ). Based on this theory, such a social exchange involves a sequence of interactions between firms and customers that are usually interdependent and contingent on others’ actions, with the goal to generate sound relationships (Cropanzano and Mitchell 2005 ). Thus, successful exchanges can advance interpersonal connections (referred to as social exchange relationships) with beneficial effects for the interacting parties (Cropanzano and Mitchell 2005 ).

Social connectedness

Social connectedness indicates the number of ties an individual has on social networks (Goldenberg et al. 2009 ), while Kumar et al. ( 2010 ) define connectedness with additional dimensions, including the number of connections, the strength of the connections, and the location in the network. Social media research suggests that connectedness has a significant impact on social influence. For example, Hinz et al. ( 2011 ) show that the use of “hubs” (highly connected people) in viral marketing campaigns can be eight times more successful than strategies using less connected people. Verlegh et al. ( 2013 ) also examine the impact of tie strength on making referrals in social media and confirm that people tend to interpret ambiguous information received from strong ties positively, but negatively when this information comes from weak ties.

Social interaction

Social interaction within a social media context is quite complex, as it represents multidirectional and interconnected information flows, rather than a pure firm monologue (Hennig-Thurau et al. 2013 ). This is because, on the one hand, social media have empowered customers to be equal actors in firm–customer interactions through sharing, gaming, expressing, and networking, while, on the other hand, customer–customer interactions have emerged as a growing market force, as customers can influence each other with regard to their attitudinal or behavioral changes (Peters et al. 2013 ). Chen et al. ( 2011 ) identify two types of social interactions—namely, opinion- or preference-based interactions (e.g., WOM) and action- or behavior-based interactions (e.g., observational learning)—with each requiring different strategic actions to be taken. Chahine and Malhotra ( 2018 ) also show that two-way (multiway) interaction strategies that allow reciprocity result in higher market reactions and more positive relationships.

• Customer engagement

The outputs are expressed in terms of customer engagement, which reflects the outcome of firm–customer (as well as customer–customer) connectedness and interaction in social media (Harmeling et al. 2017 ). Footnote 1 It is essentially a reflection of “the intensity of an individual’s participation in and connection with an organization’s offerings and/or organizational activities, which either the customer or the firm initiates” (Vivek et al. 2012 , p. 127). The more customers connect and interact with the firm’s activities, the higher is the level of customer engagement created (Kumar and Pansari 2016 ; Malthouse et al. 2013 ) and the higher the customer’s value addition to the firm (Pansari and Kumar 2017 ). Although the theoretical explanation of the notion of customer engagement has attracted a great deal of debate among scholars in the field, research (e.g., Brodie et al. 2011 ; Hollebeek et al. 2019 ; Kumar et al. 2019 ) has also begun adopting the service-dominant (S-D) logic (Vargo and Lusch 2004 ) because of its emphasis on customers’ interactive and value co-creation experiences in market relationships. Following the service-dominant (S-D) logic, Hollebeek et al. ( 2019 ) stress the role of customer resource integration, customer knowledge sharing, and learning as foundational in the customer engagement process, which can subsequently lead to customer individual/interpersonal operant resource development and co-creation.

Despite its pivotal role in social media marketing, extant literature has not yet attained agreement on the specific measurement of customer engagement. For example, Muntinga et al. ( 2011 ) conceptualize customer engagement in social media as comprising three stages: consuming (e.g., following, viewing content), contributing (e.g., rating, commenting), and creating (e.g., user-generated content). Maslowska et al. ( 2016 ) propose three levels of customer engagement behaviors: observing (e.g., reading content), participating (e.g., commenting on a post), and co-creating (e.g., partaking in product development). Moreover, Kumar et al. ( 2010 ) distinguish between transactional (i.e., buying the product) and non-transactional (i.e., sharing, commenting, referring, influencing) behaviors of customer engagement derived from social media connectedness and interactions.

Taxonomy of SMMSs

The distinctive differences among firms engaged in social media marketing with regard to their strategic objectives, organizational resources and capabilities, and focal industries and market structures, imply that there must also be differences in the SMMSs pursued. In this section, we first explain the criteria classifying SMMSs into different groups and then provide an analysis of their content.

Classification criteria of SMMSs

Drawing from the extant literature, we propose three important criteria that can be used to distinguish SMMSs: the nature of the firm’s strategic social media objectives with regard to using social media, the direction of interactions taking place between the firm and the customers, and the level of customer engagement achieved.

Strategic social media objectives refer to the specific organizational goals to be achieved by implementing SMMSs (Choi and Thoeni 2016 ; Felix et al. 2017 ). These can range from transactional to relational-oriented, depending on the strategist’s mental models of business–customer interactions (Rydén et al. 2015 ). Different mental models have a distinctive impact on managers’ social media sense-making, which is responsible for framing the specific role defined by social media in their marketing activities (Rydén et al. 2015 ). Rydén et al. ( 2015 ) identify four types of social media marketing objectives with four different mental models that can guide SMMSs —namely, to promote and sell (i.e., business-to-customers), to connect and collaborate (i.e., business-with-customers), to listen and learn (i.e., business-from-customers), and to empower and engage (i.e., business-for-customers).

The direction of the social media interactions can take three different forms. These include (1) one-way interaction , that is, traditional one-way communication in which the firm disseminates content (e.g., advertising) on social media and customers passively observe and react (Hoffman and Thomas 1996 ); (2) two-way interaction , that is, reciprocal and interactive communication with exchanges on social media, which can be further distinguished into firm-initiated interaction (in which the firm takes the initiative to begin the conversation) and customer participation (by liking, sharing, or commenting on the content) and customer-initiated interaction (in which the customer is the initiator of conversations by inquiring, giving feedback, or even posting negative comments about the firm, while the firm listens and responds to customer voice) (Van Noort and Willemsen 2012 ); and (3) collaborative interaction, that is, the highest level of interaction that builds on frequent and reciprocal activities in which both the firm and the customer have the power to influence each other (Joshi 2009 ).

With regard to the level of customer engagement, as noted previously, this heavily depends on the strength of connections and the intensity of interactions between the firm and the customers in social media, comprising both transactional and non-transactional elements (Kumar et al. 2010 ). Because customer engagement is the result of a dynamic and iterative process, which makes specifying the exact stage from participating to producing rather difficult (Brodie et al. 2011 ), we adopt the approach proposed by various scholars in the field (e.g., Dolan et al. 2016 ; Malthouse et al. 2013 ) to view this as a continuum, ranging from very low levels of engagement (e.g., “liking” a page) to very high levels of engagement (e.g., co-creation).

Types of SMMSs

With these three classificatory criteria, we can identify four distinct SMMSs, representing increasing levels of strategic maturity: social commerce strategy, social content strategy, social monitoring strategy, and social CRM strategy. Footnote 2 Fig.  2 illustrates this taxonomy for SMMSs, Table 3 shows the differences between these four strategies, while Appendix Table 6 provides real company examples using these strategies. In the following, we analyze each of these SMMSs by explaining their nature and characteristics, the particular role played by social media, and the specific organizational capabilities required for their adoption.

Taxonomy of social media marketing strategies

Social commerce strategy

Social commerce strategy refers to the “exchange-related activities that occur in, or are influenced by, an individual’s social network in computer-mediated social environments, whereby the activities correspond to the need recognition, pre-purchase, purchase, and post-purchase stages of a focal exchange” (Yadav et al. 2013 , p. 312). Rydén et al. ( 2015 , p. 6) claim that this way of using social media is not to create conversation and/or engagement; rather, the reasons for “the initial contact and the end purpose are to sell.” Similarly, Malthouse et al. ( 2013 ) argue that social media promotional activities do not actively engage customers because they do not make full use of the interactive role of social media. Thus, social commerce strategy can be considered as the least mature SMMS because it has a mainly transactional nature and is preoccupied with short-term goal-oriented activities (Grönroos 1994 ). It is essentially a one-way communication strategy intended to attract customers in the short run.

In this strategy, social media are claimed to be the new selling tool that has changed the way buyers and sellers interact (Marshall et al. 2012 ). They offer a new opportunity for sellers to obtain customer information and make the initial interaction with the customer more efficient (Rodriguez et al. 2012 ). Meanwhile, firms are also increasingly using social media as promising outlets for promotional/advertising purposes given their global reach (e.g., Dao et al. 2014 ; Zhang and Mao 2016 ), especially to the millennial generation (Confos and Davis 2016 ). However, as firms’ social media activities in this strategy are more transactional-oriented, customers tend to be passive and reactive. Customers contribute transactional value through purchases, but without a higher level of engagement. Therefore, we conclude that, within the context of this strategy, customers exchange their monetary resources (e.g., purchases) with the firm’s promotional offerings.

To better develop this strategy, Guesalaga ( 2016 ) highlights the need to understand the drivers of using social media in the selling process. He further stresses that personal commitment plays a crucial role in using social media as selling tools. Similarly, Järvinen and Taiminen ( 2016 ) urged for an integration of marketing with the sales department in order to gain better insights from social media marketing efforts. The importance of synergistic effects between social media and traditional media (e.g., press mentions, television, in-store promotions) has also been stressed in supporting social commerce activities (e.g., Jayson et al. 2018 ; Kumar et al. 2016 ; Stephen and Galak 2012 ). Thus, selling capabilities are crucial in this strategy, requiring the possession of adequate selling skills and the use of multiple selling channels to synergize social media effects.

Social content strategy

Social content strategy refers to “the creation and distribution of educational and/or compelling content in multiple formats to attract and/or retain customers” (Pulizzi and Barrett 2009 , p. 8). Thus, this type of SMMS aims to create and deliver timely and valuable content based on customer needs, rather than promoting products (Järvinen and Taiminen 2016 ). By attracting audiences with valuable content, the increase in customer engagement may ultimately boost product/service sales (Malthouse et al. 2013 ). Holliman and Rowley ( 2014 , p. 269) also claim that content marketing is a customer-centric strategy and describe the value of content as “being useful, relevant, compelling, and timely.” Therefore, this strategy provides a two-way communication in which firms take the initiative to deliver useful content and customers react positively to this content. The basic premises of this strategy are to create brand awareness and popularity through content virality, stimulate customer interactions, and spread positive WOM (De Vries et al. 2012 ; Swani et al. 2017 ).

Social media in this strategy have been widely used as communication tools for branding and WOM purposes (Holliman and Rowley 2014 ; Libai et al. 2013 ). On the one hand, firms generate content by their own efforts on social media (termed as ‘firm-generated’ or ‘marker-generated’ content) to actively engage consumers. On the other hand, firms encourage customers to generate the content (termed as ‘user-generated’ content) through the power of customer-to-customer interactions, as in the case of exchanging comments and sharing the brand-related content. In this way, firms provide valuable content in exchange for customer-owned resources, such as network assets and persuasion capital, to generate positive WOM and achieve a sustainable trusted brand status.

To pursue a social content strategy, firms build on capabilities focusing on how content is designed and presented (expressed in the form of a social message strategy) and how content is disseminated (expressed in the form of a seeding strategy). Thus, understanding customer engagement motivations and social media interactive characteristics is central to designing valuable content and facilitating customer interactions that would help to stimulate content sharing among customers (Malthouse et al. 2013 ). Designing compelling and valuable content in order to transform passive social media observers into active participants and collaborators is also key capability required by firms adopting this strategy (Holliman and Rowley 2014 ). Empowering customers and letting them speak for the brand is another way to engage customers with brands. Therefore, in this strategy, marketing communication capabilities are important for effective marketing content development and dissemination.

Social monitoring strategy

Social monitoring strategy refers to “a listening and response process through which marketers themselves become engaged” (Barger et al. 2016 , p. 278). In contrast with social content strategy, which is more of a “push” communication approach with content delivered, social monitoring strategy requires the firm’s active involvement in the whole communication process (from content delivery to customer response) (Barger et al. 2016 ). More specifically, social monitoring strategy is not only to observe and analyze the behaviors of customers in social media (Lamberton and Stephen 2016 ), but also to actively search for and respond to customer online needs and complaints (Van Noort and Willemsen 2012 ). A social monitoring strategy is thus characterized by a two-way communication process, in which the initiation comes from customers who comment and behave on social media, while the company takes advantage of customer behavior data to listen, learn, and react to its customers. Thus, the key objective of this strategy is to enhance customer satisfaction and cultivate stronger relationships with customers through ongoing social media listening and responding.

With today’s abundance of attitudinal and behavioral data, firms adopting this strategy use social media platforms as “tools” or “windows” to listen to customer voices and gain important market insights to support their marketing decisions (Moe and Schweidel 2017 ). Moreover, Carlson et al. ( 2018 ) argue that firms can take advantage of social media data to identify innovation opportunities and facilitate the innovation process. Hence, social media monitoring enables firms to assess consumers’ reactions, evaluate the prosperity of social media marketing initiatives, and allocate resources to different types of conversations and customer groups (Homburg et al. 2015 ). In other words, customers in this strategy are expected to be active in social media interactions, providing instantaneous and real-time feedback. This has in a way helped product development and experience improvements with resource inputs from customers’ knowledge stores.

Social monitoring strategy emphasizes the importance of carefully listening and responding to social media activities to have a better understanding of customer needs, gain critical market insights, and build stronger customer relationships (e.g., Timoshenko and Hauser 2019 ). It therefore requires firms to be actively involved in the whole communication process with customers, as customer engagement is not dependent on rewards, but is developed through the ongoing reciprocity between the firm and its customers (Barger et al. 2016 ). Thus, organizational capabilities, such as marketing sensing through effective information acquisition, interpretation and responding, are essential for the successful implementation of this strategy. More specifically, monitoring and text analysis techniques are needed to gather and capture social media data rapidly (Schweidel and Moe 2014 ). Noting the damage caused by electronic negative word of mouth (e-NWOM) on social media, firms adopting this strategy also require special capabilities to appropriately respond to customer online complaints and requests (Kim et al. 2016 ).

Social CRM strategy

Among the four SMMSs identified, social CRM strategy is characterized by the highest degree of strategic maturity, because it reflects “a philosophy and a business strategy supported by a technology platform, business rules, processes, and social characteristics, designed to engage the customer in a collaborative conversation in order to provide mutually beneficial value in a trusted and transparent business environment” (Greenberg 2009 , p. 34). The concept of social CRM is designed to combine the benefits derived from both the social media dimension (e.g., customer engagement) and the CRM dimension (e.g., customer retention) (Malthouse et al. 2013 ). In contrast with the traditional CRM approach, which assumes that customers are passive and only contribute to customer life value, social CRM strategy emphasizes the active role of customers who are empowered by social media and can make a contribution to multiple forms of value (Kumar et al. 2010 ). In brief, a social CRM strategy is a form of collaborative interaction, including firm–customer, inter-organizational, and inter-customer interactions, that are intended to engage and empower customers, so as to build mutually beneficial relationships with the firm and lead to superior performance.

Social media have become powerful enablers of CRM (Choudhury and Harrigan 2014 ). For example, Charoensukmongkol and Sasatanun ( 2017 ) argue that the integration of social media and CRM provides a possibility for firms to segment their customers based on similar characteristics, and can customize marketing offerings to the specific preferences of individual customers. With social CRM strategy, firms can enhance the likelihood of customer engagement through one-to-one social media interactions. Customers at this stage are collaborative and interactive in value creation, such as voluntarily providing innovative ideas and collaborating with brands (Jaakkola and Alexander 2014 ). Hence, besides resource like network assets, persuasion capital, and knowledge stores, engaged customers also contribute their creativity resource for value co-creation.

Social CRM capability is “a firm-level capability and refers to a firm’s competency in generating, integrating, and responding to information obtained from customer interactions that are facilitated by social media technologies” (Trainor et al. 2014 , p. 271). Therefore, firms should be extremely creative to combine social media data with its CRM system, as well as to link the massive social media data on customer activities to other data sources (e.g., customer service records) to generate better customer-learning and innovation opportunities (Choudhury and Harrigan 2014 ; Moe and Schweidel 2017 ). Social CRM strategy also emphasizes the significance of reciprocal information sharing and collaborations that are supported by the firm’s culture and commitment, operational resources, and cross-functional cooperation (Malthouse et al. 2013 ; Schultz and Peltier 2013 ). To sum up, social CRM capabilities, organizational learning capabilities connected with relationship management and innovation are essential prerequisites to building an effective social CRM strategy.

Validation of proposed SMMSs

Using the previously developed classification of SMMSs (i.e., social commerce strategy, social content strategy, social monitoring strategy, and social CRM strategy) as a basis, we reviewed the pertinent literature to collate useful knowledge supporting the content of each of these strategies. Table 4 provides a summary of the key empirical insights derived from the extant studies reviewed, together with resulting managerial lessons.

To validate the practical usefulness of our proposed classificatory framework of SMMSs, we first conducted a series of in-depth interviews with 15 social media marketing practitioners, who had their own firm/brand accounts on social media platforms, at least one year of social media marketing experience, and at least three years’ experience in their current organization (see Web Appendix 1 ). Interviewees represented companies located in China (8 companies), Finland (5 companies), and Sweden (2 companies) and involved in a variety of industries (e.g., digital tech, tourism, food, sport). All interviews were based on a specially designed guide (which was sent to participants in advance to prepare them for the interview) and were audiotaped and subsequently transcribed verbatim (see Web Appendix 2 ).

The main findings of this qualitative study are the following: (1) social media are mainly used as a key marketing channel to achieve business objectives, which, however, differentiates in terms of product-market type, organization size, and managerial mindset; (2) distinct differences exist across organizations in terms of their social media initiatives to deliver content, generate reactions, and develop social CRM; (3) there are marked variations in customer engagement levels across participant firms, resulting from the adoption of different SMMSs; (4) the firm’s propensity to use a specific SMMSs is enhanced by infrastructures, systems, and technologies that help to actively search, access, and integrate data from different sources, as well as facilitate the sharing and coordination of activities with customers; and (5) the adoption of a specific SMMS does not follow a sequential pattern in terms of strategic maturity development, but rather, depends on the firm’s strategic objectives, its willingness to commit the required resources, and the deployment of appropriate organizational capabilities.

To further confirm the existence of differences in profile characteristics among the four types of SMMSs, we conducted an electronic survey among a sample of 52 U.S. social media marketing managers who were randomly selected. For this purpose, we designed a structured questionnaire incorporating the key parameters related to SMMSs, namely firms’ strategic objectives, firms’ engagement initiatives, customers’ social media behaviors, social media resources and capabilities required, direction of interactions, and customer engagement levels (see Web Appendix 3 ).

Specifically, we found that: (1) each of the four SMMSs emphasize different types of strategic objectives, ranging from promoting and selling, in the case of social commerce strategy, to empowering and engaging in social CRM strategy; (2) experiential engagement initiatives geared to customer engagement were more evident at the advanced level, as opposed to the lower level strategies; (3) passive customer social media behaviors were more characteristic of the social commerce strategy, while more active customer behaviors were observed in the case of social CRM strategy; (4) the more advanced the maturity of the SMMS employed, the higher the level customer engagement, as well as the higher requirements in terms of organizational resources and specialized capabilities; and (5) one-way interaction was associated more with social commerce strategy, two-way interaction was more evident in the social content strategy and the social monitoring strategy, and collaborative interaction was a dominant feature in the social CRM strategy (see Web Appendix 4 ).

Future research directions

While the extant research offers insightful information and increased knowledge on SMMSs, there is still plenty of room to expand this field of research with other issues, especially given the rapidly changing developments in social media marketing practice. To gain a more accurate picture about the future of research on the subject, we sought the opinions of academic experts in the field through an electronically conducted survey among authors of academic journal articles written on the subject. We specifically asked them: (1) to suggest the three most important areas that research on SMMSs should focus on in the future; (2) within each of the areas suggested, to indicate three specific topics that need to be addressed more; and (3) within each topic, to illustrate analytical issues that warrant particular attention (see Web Appendix 5 ). Altogether, we received input from 43 social media marketing scholars who suggested 6 broad areas, 13 specific topics, and 82 focal issues for future research, which are presented in Table 5 .

Among the research issues proposed, finding appropriate metrics to measure performance in SMMSs seems to be an area to which top priority should be given. This is because performance is the ultimate outcome of these strategies, for which there is still little understanding due to the idiosyncratic nature of social media as a marketing tool (e.g., Beckers et al. 2017 ; Trainor et al. 2014 ). In particular, it is important to shed light on both short-term and long-term performance, as well as its effectiveness, efficiency, and adaptiveness aspects (e.g., Barger et al. 2016 ). Another key priority area stressed by experts in the field involves integrating to a greater extent various strategic issues regarding each of the marketing-mix elements in a social media context. This would help achieve better coordination between traditional and online marketing tools (e.g., Kolsarici and Vakratsas 2018 ; Kumar et al. 2017 ).

Respondents in our academic survey also stressed the evolutionary nature of knowledge with regard to each of the four SMMSs and proposed multiple issues for each of them. Particular attention should be paid to how inputs from customers and firms are interrelated in each of these strategies, taking into consideration the central role played by customer engagement behaviors and firm initiatives (e.g., Sheng 2019 ). Respondents also pinpointed the need for more emphasis on social CRM strategy (which is relatively under-researched), while there should also be a closer assessment of new developments in both marketing (e.g., concepts and tools) and social media (e.g., technologies and platforms) that can lead to the emergence of new types of SMMSs (e.g., Ahani et al. 2017 ; Choudhury and Harrigan 2014 ).

Respondents also noted that up to now the preparatory phase for designing SMMSs has been overlooked, and that therefore there is a need to shed more light on this because of its decisive role in achieving positive results. For example, issues relating to market/competitor analysis, macro-environmental scanning, and target marketing should be carefully studied in conjunction with formulating sound SMMSs, to better exploit opportunities and neutralize threats in a social media context (e.g., De Vries et al. 2017 ). By contrast, our survey among scholars in the field stressed the crucial nature of issues relating to SMMS implementation and control, which are of equal, or even greater, importance than those of strategy formulation (e.g., Järvinen and Taiminen 2016 ). The academics also indicated that, by their very nature, social media transcend national boundaries, thus leaving plenty of room to investigate the international ramifications of SMMSs, using cross-cultural research (e.g., Johnston et al. 2018 ).

Implications and conclusions

Theoretical implications.

Given the limited research on SMMSs, this study has several important theoretical implications. First, we are taking a step in this new theoretical direction by providing a workable definition and conceptualization of SMMS that combines both social media and marketing strategy dimensions. The study complements and extends previous research (e.g., Harmeling et al. 2017 ; Singaraju et al. 2016 ) that emphasized the value of social media as resource integrator in exchanging customer-owned resources, which can provide researchers with new angles to address the issue of integrating social media with marketing strategy. Such integrative efforts can have a meaningful long-term impact on building a new theory (or theories) of social media marketing. They also point to a deeper theoretical understanding of the roles played by resource identification, utilization, and reconfiguration in a SMMS context.

We have also extended the idea of “social interaction” and “social connectedness” in a social media context, which is critical because the power of a customer enabled by social media connections and interactions is of paramount importance in explaining the significance of SMMSs (Hennig-Thurau et al. 2013 ). More importantly, our study suggests that firms should take the initiative to motivate and engage customers, which will lead to wider and more extensive interactions. In particular, we show that a firm can leverage its social media usage through the use of different engagement initiatives to enforce customer interactivity and interconnectedness. Such enquiries can provide useful theoretical insights into the strategic marketing role played by social media in today’s highly digitalized and globalized world.

We are also furthering the customer engagement literature by proposing an SMMS developmental process. As firm–customer relationships evolve in a social media era, it is critical to identify those factors that have an impact on customer engagement. Although prior studies (e.g., Harmeling et al. 2017 ; Pansari and Kumar 2017 ) have demonstrated the engagement value contributed by customers and the need for engagement initiatives taken by firms, we are extending this idea to provide a more holistic view by highlighting the role of insights from both firms and customers to better understand the dynamics of SMMS formulation. We also suggest certain theories to specifically explain the role played by each of the components of the process in developing sound SMMSs. We capture the unique characteristics of social media by suggesting that these networks and interactions are tightly interrelated with the outcome of SMMS, which is customer engagement. Our proposed SMMS developmental process may therefore provide critical input for new studies focusing on customer engagement research.

 Finally, we build on various criteria to distinguish among four SMMSs, each representing a different level of strategic maturity. We show that a SMMS is not homogeneous, but needs to be understood in a wider, more nuanced way, as having different strategies relying on different goals and deriving insights from firms and their customers, ultimately leading to different customer engagement levels. In this regard, the identification of the key SMMSs stemming from our analysis can serve as the basis for developing specific marketing strategy constructs and scales within a social media context. We also indicate that different SMMSs can be implemented and yield superior competitive advantage only when the firm is in a position to devote to it the right amount and type of resources and capabilities (e.g., Gao et al. 2018 ; Kumar and Pansari 2016 ).

Managerial implications

Our study also has serious implications for managers. First, our analysis revealed that the ever-changing digital landscape on a global scale calls for a reassessment of the ways to strategically manage brands and customers in a social media context. This requires companies to understand the different goals for using social media and to develop their strategies accordingly. As a starting point, firms could explore customer motivations for using social media and effectively deploy the necessary resources to accommodate these motivations. They should also think carefully about how to engage customers when implementing their marketing strategies, because social media become resource integrators only when customers interact with and provide information on them (Singaraju et al. 2016 ).

Managers need to set objectives at the outset to guide the effective development, implementation, and control of SMMSs. Our study suggests four key SMMSs achieving different business goals. For example, the goal of social commerce strategy is to attract customers with transactional interests, that of social content strategy and social monitoring strategy is to deliver valuable content and service to customers, and that of social CRM strategy is to build mutually beneficial customer relationships by integrating social media data with current organizational processes. Unfortunately, many companies, especially smaller ones, tend to create their social media presence for a single purpose only: to disseminate massive commercial information on their social media web pages in the hope of attracting customers, even though these customers may find commercially intensive content annoying.

This study also suggests that social media investments should focus on the integration of social media platforms with internal company systems to build special social media capabilities (i.e., creating, combining, and reacting to information obtained from customer interactions on social media). Such capabilities are vital in developing a sustainable competitive advantage, superior market and financial performance. However, to achieve this, firms must have the right organizational structural and cultural transformation, as well as substantial management commitment and continuous investment.

Lastly, social media have become powerful tools for CRM, helping to transform it from traditional one-way interaction to collaborative interaction. This implies that customer engagement means not only encouraging customer engagement on social media, but also proactively learning from and collaborating with customers. As Pansari and Kumar et al. ( 2017 ) indicate, customer engagement can contribute both directly (e.g., purchase) and indirectly (e.g., customer knowledge value) to the firm. Therefore, interacting with customers via social media provides tremendous opportunities for firms to learn more about their customers and opens up new possibilities for product/service co-creation.

Conclusions

The exploding use of social media in the past decade has underscored the need for guidance on how to build SMMSs that foster relationships with customers, advance customer engagement, and increase marketing performance. However, a comprehensive definition, conceptualization, and framework to guide the analysis and development of SMMSs are lacking. This can be attributed to the recent introduction of social media as a strategic marketing tool, while both academics and practitioners still lack the necessary knowledge on how to convert social media data into actionable strategic marketing tools (Moe and Schweidel 2017 ). This insufficiency also stems from the fact that the adoption of more advanced SMMSs requires the possession of specific organizational capabilities that can be used to leverage social media, with the support of a culture that encourages breaking free from obsolete mindsets, emphasizing employee skills with intelligence in data and customer analytical insights, and operational excellence in organizational structure and business processes (Malthouse et al. 2013 ).

Our study takes the first step toward addressing this issue and provides useful guidelines for leveraging social media use in strategic marketing. In particular, we provide a systematic consolidation and extension of the extant pertinent SMMS literature to offer a robust definition, conceptualization, taxonomy, and validation of SMMSs. Specifically, we have amply demonstrated that the mere use of social media alone does not generate customer value, which instead is attained through the generation of connections and interactions between the firm and its customers, as well as among customers themselves. These generated social networks and influences can subsequently be used strategically for resource transformation and exchanges between the interacting parties. Our conceptualization of the SMMS developmental process also suggests that firms first need to recognize customers’ motivations to engage in brand-related social media activities and encourage their voluntary contributions.

Although the four SMMSs identified in our study (i.e., social commerce strategy, social content strategy, social monitoring strategy, and social CRM strategy) denote progressing levels of strategic maturity, their adoption does not follow a sequential pattern. As our validation procedures revealed, this will be determined by the firm’s strategic objectives, resources, and capabilities. Moreover, the success of the various SMMSs will depend on the firm’s ability to identify and leverage customer-owned resources, as in the case of transforming customers from passive receivers of the firm’s social media offerings to active value contributors. It will also depend on the firm’s willingness to allocate resources in order to foster collaborative conversations, develop appropriate responses, and enhance customer relationships. These will all ultimately help to build a sustainable competitive advantage and enhance business performance.

Although in our conceptualization of the process of developing SMMSs we treat customer engagement as the output of this process, we fully acknowledge that firms’ ultimate objective to engage in social media marketing activities is to improve their market (e.g., customer equity) and financial (e.g., revenues) performance. In fact, extant social media marketing research (e.g., Kumar et al. 2010 ; Kumar and Pansari 2016 ; Harmeling et al. 2017 ) repeatedly stresses the conducive role of customer engagement in ensuring high performance results.

SMMSs are difficult to operationalize by focusing solely on the elements of the marketing mix (i.e., product, price, distribution, and promotion), mainly because many other important parameters are involved in their conceptualization, such as relationship management, market development, and business innovation issues. However, each SMMS seems to have a different marketing mix focus, with social commerce strategy emphasizing advertising and sales, social content strategy emphasizing branding and communication, social monitoring strategy emphasizing service and product development, and social CRM strategy emphasizing customer management and innovation.

Acommerce (2019). How L’Oréal uses e-commerce’s social commerce to simplify transaction and increase customer engagement. Available at https://www.acommerce.asia/case-study-loreal-social-commerce/ .

Ahani, A., Rahim, N. Z. A., & Nilashi, M. (2017). Forecasting social CRM adoption in SMEs: A combined SEM-neural network method. Computers in Human Behavior, 75 , 560–578.

Article   Google Scholar  

Alharthi, A., Krotov, V., & Bowman, M. (2017). Addressing barriers to big data. Business Horizons, 60 (3), 285–292.

Aral, S., & Walker, D. (2014). Tie strength, embeddedness, and social influence: A large-scale networked experiment. Management Science, 60 (6), 1352–1370.

Aral, S., Dellarocas, C., & Godes, D. (2013). Social media and business transformation: A framework for research. Information Systems Research, 24 (1), 3–13.

Barger, V., Peltier, J. W., & Schultz, D. E. (2016). Social media and consumer engagement: A review and research agenda. Journal of Research in Interactive Marketing, 10 (4), 268–287.

Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17 , 99–120.

Beckers, S. F. M., van Doorn, J., & Verhoef, P. C. (2017). Good, better, engaged? The effect of company-initiated customer engagement behavior on shareholder value. Journal of the Academy of Marketing Science, 46 (3), 366–383.

Blau, P. (1964). Exchange and power in social life . New York: Wiley.

Google Scholar  

Brodie, R. J., Hollebeek, L. D., Juric, B., & Ilic, A. (2011). Customer engagement: Conceptual domain, fundamental propositions, and implications for research. Journal of Service Research, 14 (3), 252–271.

Carlson, J., Rahman, M., Voola, R., & De Vries, N. (2018). Customer engagement behaviors in social media: Capturing innovation opportunities. Journal of Services Marketing, 32 (1), 83–94.

Chahine, S., & Malhotra, N. K. (2018). Impact of social media strategies on stock price: The case of twitter. European Journal of Marketing, 52 (7–8), 1526–1549.

Charoensukmongkol, P., & Sasatanun, P. (2017). Social media use for CRM and business performance satisfaction: The moderating roles of social skills and social media sales intensity. Asia Pacific Management Review, 22 (1), 25–34.

Chen, Y., Wang, Q., & Xie, J. (2011). Online social interactions: A natural experiment on word of mouth versus observational learning. Journal of Marketing Research, 48 (2), 238–254.

Choi, Y., & Thoeni, A. (2016). Social media: Is this the new organizational stepchild? European Business Review, 28 (1), 21–38.

Choudhury, M. M., & Harrigan, P. (2014). CRM to social CRM: The integration of new technologies into customer relationship management. Journal of Strategic Marketing, 22 (2), 149–176.

Confos, N., & Davis, T. (2016). Young consumer-brand relationship building potential using digital marketing. European Journal of Marketing, 50 (11), 1993–2017.

Cropanzano, R., & Mitchell, M. S. (2005). Social exchange theory: An interdisciplinary review. Journal of Management, 31 (6), 874–900.

Dao, W. V. T., Le, A. N. H., Cheng, J. M. S., & Chen, D. C. (2014). Social media advertising value: The case of transitional economies in Southeast Asia. International Journal of Advertising, 33 (2), 271–294.

De Vries, L., Gensler, S., & Leeflang, P. S. H. (2012). Popularity of brand posts on brand fan pages: An investigation of the effects of social media marketing. Journal of Interactive Marketing, 26 (2), 83–91.

De Vries, L., Gensler, S., & Leeflang, P. S. H. (2017). Effects of traditional advertising and social messages on brand-building metrics and customer acquisition. Journal of Marketing, 81 (5), 1–15.

Dolan, R., Conduit, J., Fahy, J., & Goodman, S. (2016). Social media engagement behavior: A uses and gratifications perspective. Journal of Strategic Marketing, 24 (3–4), 261–277.

Effing, R., & Spil, T. A. M. (2016). The social strategy cone: Towards a framework for evaluating social media strategies. International Journal of Information Management, 36 (1), 1–8.

Fehrer, J. A., Woratschek, H., Germelmann, C. C., & Brodie, R. J. (2018). Dynamics and drivers of customer engagement: Within the dyad and beyond. Journal of Service Management, 29 (3), 443–467.

Felix, R., Rauschnabel, P. A., & Hinsch, C. (2017). Elements of strategic social media marketing: A holistic framework. Journal of Business Research, 70 , 118–126.

Gao, H., Tate, M., Zhang, H., Chen, S., & Liang, B. (2018). Social media ties strategy in international branding: An application of resource-based theory. Journal of International Marketing, 26 (3), 45–69.

Gnizy, I. (2019). Big data and its strategic path to value in international firms. International Marketing Review, 36 (3), 318–341.

Goldenberg, J., Han, S., Lehmann, D. R., & Hong, J. W. (2009). The role of hubs in the adoption process. Journal of Marketing, 73 (2), 1–13.

Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology, 78 (6), 1360–1380.

Greenberg, P. (2009). CRM at the speed of light: Social CRM strategies, tools, and techniques for engaging consumers . New York: McGraw-Hill Osborne Media.

Griffith, G. (2016). Get to know customers better by monitoring social media sentiment. Available at https://www.raconteur.net/business-innovation/get-to-know-customers-better-by-monitoring-social-media-sentiment .

Griffiths, M., & Mclean, R. (2015). Unleashing corporate communications via social media: A UK study of brand management and conversations with customers. Journal of Customer Behavior, 14 (2), 147–162.

Grönroos, C. (1991). The marketing strategy continuum: Towards a marketing concept for the 1990s. Management Decision, 29 (1), 72–76.

Grönroos, C. (1994). From marketing mix to relationship marketing. Management Decision, 32 (2), 4–20.

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.

Gummesson, E., & Mele, C. (2010). Marketing as value co-creation through network interaction and resource integration. Journal of Business Market Management, 4 (4), 181–198.

Hamilton, M., Kaltcheva, V. D., & Rohm, A. J. (2016). Social media and value creation: The role of interaction satisfaction and interaction immersion. Journal of Interactive Marketing, 36 , 121–133.

Harmeling, C. M., Moffett, J. W., Arnold, M. J., & Carlson, B. D. (2017). Toward a theory of customer engagement marketing. Journal of the Academy of Marketing Science, 45 (3), 312–335.

Hennig-Thurau, T., Hofacker, C. F., & Bloching, B. (2013). Marketing the pinball way: Understanding how social media change the generation of value for consumers and companies. Journal of Interactive Marketing, 27 (4), 237–241.

Hinz, O., Skiera, B., Barrot, C., & Becker, J. U. (2011). Seeding strategies for viral marketing: An empirical comparison. Journal of Marketing, 75 (6), 55–71.

Hoffman, D. L., & Thomas, P. N. (1996). Marketing in hypermedia computer-mediated environments: Conceptual foundations. Journal of Marketing, 60 (3), 50–69.

Hollebeek, L. D., Srivastava, R. K., & Chen, T. (2019). S-D logic-informed customer engagement: Integrative framework, revised fundamental propositions, and application to CRM. Journal of the Academy of Marketing Science, 47 , 161–185.

Holliman, G., & Rowley, J. (2014). Business to business digital content marketing: Marketers’ perceptions of best practice. Journal of Research in Interactive Marketing, 8 (4), 269–293.

Homburg, C., Ehm, L., & Artz, M. (2015). Measuring and managing consumer sentiment in an online community environment. Journal of Marketing Research, 52 (5), 629–641.

Hunt, S. D., & Morgan, R. M. (1995). The comparative advantage theory of competition. Journal of Marketing, 59 (2), 1–15.

Hunt, S. D., Arnett, D. B., & Madhavaram, S. (2006). The explanatory foundations of relationship marketing theory. Journal of Business & Industrial Marketing, 21 (2), 72–87.

Jaakkola, E., & Alexander, M. (2014). The role of customer engagement behavior in value co-creation: A service system perspective. Journal of Service Research, 17 (3), 247–261.

Järvinen, J., & Taiminen, H. (2016). Harnessing marketing automation for B2B content marketing. Industrial Marketing Management, 54 , 164–175.

Jayson, R., Block, M. P., & Chen, Y. (2018). How synergy effects of paid and digital owned media influence brand sales considerations for marketers when balancing media spend. Journal of Advertising Research, 58 (1), 77–89.

Johnston, W. J., Khalil, S., Le, A. N. H., & Cheng, J. M. S. (2018). Behavioral implications of international social media advertising: An investigation of intervening and contingency factors. Journal of International Marketing, 26 (2), 43–61.

Joshi, A. W. (2009). Continuous supplier performance improvement: Effects of collaborative communication and control. Journal of Marketing, 73 (1), 133–150.

Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of social media. Business Horizons, 53 (1), 59–68.

Katona, Z., Zubcsek, P. P., & Sarvary, M. (2011). Network effects and personal influences: The diffusion of an online social network. Journal of Marketing Research, 48 (3), 425–443.

Katz, E., Gurevitch, M., & Haas, H. (1973). On the use of mass media for important things. American Sociological Review, 38 , 164–181.

Kim, S. J., Wang, R. J. H., Maslowska, E., & Malthouse, E. C. (2016). “Understanding a fury in your words”: The effects of posting and viewing electronic negative word-of-mouth on purchase behaviors. Computers in Human Behavior, 54 , 511–521.

Kolsarici, C., & Vakratsas, D. (2018). Synergistic, antagonistic, and asymmetric media interactions. Journal of Advertising, 47 (3), 282–300.

Kumar, V., & Pansari, A. (2016). Competitive advantage through engagement. Journal of Marketing Research, 53 (4), 497–514.

Kumar, V., Aksoy, L., Donkers, B., Venkatesan, R., Wiesel, T., & Tillmans, S. (2010). Undervalued or overvalued customers: Capturing total customer engagement value. Journal of Service Research, 13 (3), 297–310.

Kumar, V., Bhaskaran, V., Mirchandani, R., & Shah, M. (2013). Creating a measurable social media marketing strategy: Increasing the value and ROI of intangibles and tangibles for hokey pokey. Marketing Science, 32 (2), 194–212.

Kumar, A., Bezawada, R., Rishika, R., Janakiraman, R., & Kannan, P. K. (2016). From social to sale: The effects of firm-generated content in social media on customer behavior. Journal of Marketing, 80 (1), 7–25.

Kumar, V., Choi, J. W. B., & Greene, M. (2017). Synergistic effects of social media and traditional marketing on brand sales: Capturing the time-varying effects. Journal of the Academy of Marketing Science, 45 (2), 268–288.

Kumar, V., Rajan, B., Gupta, S., & Dalla Pozza, I. (2019). Customer engagement in service. Journal of the Academy of Marketing Science, 47 (1), 138–160.

Lamberton, C., & Stephen, A. T. (2016). A thematic exploration of digital, social media, and mobile marketing: Research evolution from 2000 to 2015 and an agenda for future inquiry. Journal of Marketing, 80 (6), 146–172.

Libai, B., Bolton, R., Bugel, M. S., de Ruyter, K., Gotz, O., Risselada, H., & Stephen, A. T. (2010). Customer-to-customer interactions: Broadening the scope of word of mouth research. Journal of Service Research, 13 (3), 267–282.

Libai, B., Muller, E., & Peres, R. (2013). Decomposing the value of word-of-mouth seeding programs: Acceleration versus expansion. Journal of Marketing Research, 50 (2), 161–176.

Malthouse, E. C., Haenlein, M., Skiera, B., Wege, E., & Zhang, M. (2013). Managing customer relationships in the social media era: Introducing the social CRM house. Journal of Interactive Marketing, 27 (4), 270–280.

Marshall, G. W., Moncrief, W. C., Rudd, J. M., & Lee, N. (2012). Revolution in sales : The impact of social media and related technology on the selling environment. Journal of Personal Selling & Sales Management, 32 (3), 349–363.

Maslowska, E., Malthouse, E. C., & Collinger, T. (2016). The customer engagement ecosystem. Journal of Marketing Management, 32 (5–6), 469–501.

Micu, A., Micu, A. E., Geru, M., & Lixandroiu, R. C. (2017). Analyzing user sentiment in social media: Implications for online marketing strategy. Psychology & Marketing, 34 (12), 1094–1100.

Moe, W. W., & Schweidel, D. A. (2017). Opportunities for innovation in social media analytics. Journal of Product Innovation Management, 34 (5), 697–702.

Moorman, C., & Day, G. S. (2016). Organizing for marketing excellence. Journal of Marketing, 80 (6), 6–35.

Morgan, R. M., & Hunt, S. (1999). Relationship-based competitive advantage: The role of relationship marketing in marketing strategy. Journal of Business Research, 46 (3), 281–290.

Muller, E., & Peres, R. (2019). The effect of social networks structure on innovation performance: A review and directions for research. International Journal of Research in Marketing, 36 (1), 3–19.

Muntinga, D. G., Moorman, M., & Smit, E. G. (2011). Introducing COBRAs: Exploring motivations for brand-related social media use. International Journal of Advertising, 30 (1), 13–46.

Nair, H. S., Manchanda, P., & Bhatia, T. (2010). Asymmetric social interactions in physician prescription behavior: The role of opinion leaders. Journal of Marketing Research, 47 (5), 883–895.

Naylor, R. W., Lamberton, C. P., & West, P. M. (2012). Beyond the “like” button: The impact of mere virtual presence on brand evaluations and purchase intentions in social media settings. Journal of Marketing, 76 (6), 105–120.

Pansari, A., & Kumar, V. (2017). Customer engagement: The construct, antecedents, and consequences. Journal of the Academy of Marketing Science, 45 (3), 294–311.

Peters, K., Chen, Y., Kaplan, A. M., Ognibeni, B., & Pauwels, K. (2013). Social media metrics - a framework and guidelines for managing social media. Journal of Interactive Marketing, 27 (4), 281–298.

Pfeffer, J., & Salancik, G. (1978). The external control of organizations: A resource dependence perspective . New York: Harper & Row.

Pulizzi, J., & Barrett, N. (2009). Get content, get customers: Turn prospects into buyers with content marketing . New York, NY: McGraw-Hill.

Quinton, S., & Wilson, D. (2016). Tensions and ties in social media networks: Towards a model of understanding business relationship development and business performance enhancement through the use of LinkedIn. Industrial Marketing Management, 54 , 15–24.

Rapp, A., Beitelspacher, L. S., Grewal, D., & Hughes, D. E. (2013). Understanding social media effects across seller, retailer, and consumer interactions. Journal of the Academy of Marketing Science, 41 (5), 547–566.

Ravi, K. (2018). Six ways Nike built a strong brand on social media. Available at https://blog.unmetric.com/nike-social-media .

Robert, Y. (2018). How Asos is making E-commerce all-inclusive. Available at https://www.forbes.com/sites/yolarobert1/2018/10/23/now-trending-how-asos-is-making-e-commerce-all-inclusive/#37e9d67b297d .

Rodriguez, M., Peterson, R. M., & Krishnan, V. (2012). Social media’s influence on business-to-business sales performance. Journal of Personal Selling & Sales Management, 32 , 365–378.

Rohm, A., Kaltcheva, V. D., & Milne, G. R. (2013). A mixed-method approach to examining brand-consumer interactions driven by social media. Journal of Research in Interactive Marketing, 7 (4), 295–311.

Rydén, P., Ringberg, T., & Wilke, R. (2015). How managers’ shared mental models of business-customer interactions create different sensemaking of social media. Journal of Interactive Marketing, 31 , 1–16.

Saldanha, F. P., Cohendet, P., & Pozzebon, M. (2014). Challenging the stage-gate model in crowdsourcing: The case of fiat Mio in Brazil. Technology Innovation Management Review, 4 (9), 28–35.

Salo, J. (2017). Social media research in the industrial marketing field: Review of literature and future research directions. Industrial Marketing Management, 66 , 115–129.

Schultz, D. E., & Peltier, J. (2013). Social media’s slippery slope: Challenges, opportunities and future research directions. Journal of Research in Interactive Marketing, 7 (2), 86–99.

Schweidel, D. A., & Moe, W. W. (2014). Listening in on social media: A joint model of sentiment and venue format choice. Journal of Marketing Research, 51 (4), 387–402.

Sheng, J. (2019). Being active in online communications: Firm responsiveness and customer engagement behavior. Journal of Interactive Marketing, 46 , 40–51.

Singaraju, S. P., Nguyen, Q. A., Niininen, O., & Sullivan-Mort, G. (2016). Social media and value co-creation in multi-stakeholder systems: A resource integration approach. Industrial Marketing Management, 54 , 44–55.

Stampler, L. (2013). How Dove’s “Real Beauty Sketches” became the most viral video ad of all time. Available at https://www.businessinsider.com/how-doves-real-beauty-sketches-became-the-most-viral-ad-video-of-all-time-2013-5?r=US&IR=T .

Stephen, A. T., & Galak, J. (2012). The effects of traditional and social earned media on sales: A study of a microlending marketplace. Journal of Marketing Research, 49 (5), 624–639.

Stewart, D. W., & Pavlou, P. A. (2002). From consumer response to active consumer: Measuring the effectiveness of interactive media. Journal of the Academy of Marketing Science, 30 (4), 376–396.

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.

Tafesse, W., & Wien, A. (2018). Implementing social media marketing strategically: An empirical assessment. Journal of Marketing Management, 34 (9–10), 732–749.

Teece, D., & Pisano, G. (1994). The dynamic capabilities of firms: An introduction. Industrial and Corporate Change, 3 (3), 537–556.

Timoshenko, A., & Hauser, J. R. (2019). Identifying customer needs from user-generated content. Marketing Science, 38 (1), 1–20.

Trainor, K. J., Andzulis, J., Rapp, A., & Agnihotri, R. (2014). Social media technology usage and customer relationship performance: A capabilities-based examination of social CRM. Journal of Business Research, 67 (6), 1201–1208.

Van Noort, G., & Willemsen, L. M. (2012). Online damage control: The effects of proactive versus reactive webcare interventions in consumer-generated and brand-generated platforms. Journal of Interactive Marketing, 26 (3), 131–140.

Varadarajan, R. (2010). Strategic marketing and marketing strategy: Domain, definition, fundamental issues and foundational premises. Journal of the Academy of Marketing Science, 38 (2), 119–140.

Vargo, S., & Lusch, R. (2004). Evolving to a new dominant logic for marketing. Journal of Marketing, 68 (1), 1–17.

Venkatesan, R. (2017). Executing on a customer engagement strategy. Journal of the Academy of Marketing Science, 45 (3), 289–293.

Verlegh, P. W. J., Ryu, G., Tuk, M. A., & Feick, L. (2013). Receiver responses to rewarded referrals: The motive inferences framework. Journal of the Academy of Marketing Science, 41 (6), 669–682.

Vivek, S. D., Beatty, S. E., & Morgan, R. M. (2012). Customer engagement: Exploring customer relationships beyond purchase. Journal of Marketing Theory and Practice, 20 (2), 127–145.

Waldo, L. (2014). Hertz hands the social media keys over from marketing, to customer care. Available at http://futurecare.today/news/hertz-hands-the-social-media-keys-over-from-marketing-to-customer-care/#sthash.0t3MYPZq.M2vFKOV9.dpbs .

Wang, X. F. (2017). Case study: Increasing customers’ loyalty with social CRM. Available at https://go.forrester.com/blogs/17-01-23 - case_study_increasing_customers_loyalty_with_social_crm/ .

Webster, F. E. (1992). The changing role of marketing in the corporation. Journal of Marketing, 56 (4), 1–17.

Yadav, M. S., de Valck, K., Hennig-Thurau, T., Hoffman, D. L., & Spann, M. (2013). Social commerce: A contingency framework for assessing marketing potential. Journal of Interactive Marketing, 27 (4), 311–323.

Zhang, J., & Mao, E. (2016). From online motivations to ad clicks and to behavioral intentions: An empirical study of consumer response to social media advertising. Psychology and Marketing, 33 (3), 155–164.

Zhang, Y., Moe, W. W., & Schweidel, D. A. (2017). Modeling the role of message content and influencers in social media rebroadcasting. International Journal of Research in Marketing, 34 (1), 100–119.

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Li, F., Larimo, J. & Leonidou, L.C. Social media marketing strategy: definition, conceptualization, taxonomy, validation, and future agenda. J. of the Acad. Mark. Sci. 49 , 51–70 (2021). https://doi.org/10.1007/s11747-020-00733-3

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J.D. Vance once called VP Kamala Harris a 'childless cat lady.' What does that mean?

Republican vice presidential candidate  Sen. J.D. Vance called Vice President Kamala Harris a "childless cat lady" in 2021 and it has recently resurfaced online.

You may have heard the term before and if you haven't, you may be wondering, what's so wrong with a lady who likes cats?

What does it mean to call someone a cat lady? Here's what we know.

J.D. Vance's 'cat lady' comment

In an interview with Tucker Carlson on his evening show back in July 2021, Vance took aim at Harris and other democratic politicians for not having children.

"We are effectively run in this country … by a bunch of childless cat ladies who are miserable at their own lives and the choices that they’ve made, and so they wanna make the rest of the country miserable, too,"  Vance told Carlson . "It's just a basic fact. You look at Kamala Harris, Pete Buttigieg, AOC (Alexandria Ocasio-Cortez), the entire future of the Democrats is controlled by people without children."

This statement is not entirely true as Harris has  two stepchildren  with her husband,  Second Gentleman Douglas Emhoff and Pete Buttigieg adopted twins with his husband in August 2021. Buttigieg and his husband were in the middle of the adoption process when Vance made those comments.

What does the term 'cat lady' mean?

Cat lady is typically used as a derogatory term used toward women who have chosen to not have children. While men who do not have children are often lauded and called bachelors.

"Women often are likened to cats in a pejorative way — for example, a common insult is to call a woman 'catty,' said Leora Tanenbaum , author of "I Am Not a Slut: Slut-Shaming in the Age of the Internet." "With a man, on the other hand, you would never use that word and are more likely to describe him as 'spiteful.' And we describe arguments between women as 'catfights,' while an argument between men is just, well, an 'argument.'"

Women  have long been treated differently in society.

"The expectation that all women will and should become mothers was forged by a long history that understood reproduction as American women’s primary civic contribution," says  Peggy Heffington , an assistant senior instructional professor at the University of Chicago who teaches and writes on feminism, women's movements and motherhood in American and European history.

Vance's comments imply that anyone who is not a parent, who he deems to be a "childless cat lady" should not be running this country.

More: Kamala Harris, Taylor Swift, Jennifer Aniston and when we reduce women to 'childless cat ladies'

Swifties respond to the negative connotations for "cat ladies"

The "cat lady" insult specifically carries with it some connotations. "You are physically unattractive, or a workaholic, which makes you a deviant person; no wonder you have a pet cat, because that’s the only one who loves you," Tanenbaum says. Tell that to  all the Swifties  who bought tickets to her groundbreaking  Eras tour  and fired up social media retorts to Vance's comments.

hell hath no fury like a certain childless cat lady who has yet to endorse a presidential candidate https://t.co/YuKVghYNtV pic.twitter.com/xelrRQO3gG — Anna Bower (@AnnaBower) July 23, 2024

Who are other 'childless cat ladies'?

While Swift is one of the most notable 'childless cat ladies' due to the recent success of her " Eras Tour ," many other famous women are considered to be as well.

Jennifer Anniston, Jennifer Coolidge, Helen Mirren, Stevie Nicks, Dolly Parton, Lily Tomlin, Betty White, Oprah Winfrey and many others are all successful women without children.

Aniston called out Vance on her Instagram story after his comments resurfaced: "All I can say is… Mr. Vance, I pray that your daughter is fortunate enough to bear children of her own one day. I hope she will not need to turn to IVF as a second option. Because you are trying to take that away from her, too." 

Others are reading: Internet warns JD Vance after video resurfaces implying Kamala Harris is childless cat lady

Katie Wiseman is a trending news intern at IndyStar. Contact her at [email protected]. Follow her on Twitter @itskatiewiseman .