I “Like” It: The Effects of Social Media Platform and Message on Consumer Engagement Actions

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Rebecca M. Achen University of the Pacific, Stockton, CA, USA

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Ashley Stadler-Blank Xavier University, Cincinnati, OH, USA

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John J. Sailors University of St. Thomas, Minneapolis, MN, USA

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The academic literature reports mixed evidence on how social media platform and message impact consumer engagement. We investigated the effects of three platforms (Facebook, Instagram, and Twitter) and three message themes (sales, informational, and relationship building) on six consumer engagement actions (comment, like, search, share, talk about, and purchase) in a lab experiment. College students responded to social media posts featuring their National Collegiate Athletic Association Division I women’s basketball team. Results for platform show that participants were more likely to comment on Facebook and Twitter (vs. Instagram) and more likely to purchase on Twitter (vs. Instagram). Results for message theme show that participants were more likely to comment, like, and share informational and relationship building posts and more likely to purchase after sales posts. Results for message theme vary by gender for search and talk about (with others). These results can help sport marketers develop social media content that drives specific engagement actions.

Today, the use of social media to improve marketing outcomes is pervasive. Past research documents increases in consumer acquisition, consideration, preference, number of store visits, word of mouth, purchases, relationships, and loyalty due to social media marketing efforts (de Vries et al., 2017; Liu & Lopez, 2016; Rishika et al., 2013). Moreover, conversations related to brands and products can increase word of mouth on social media and increase subsequent consumer preference and purchase behavior (Liu & Lopez, 2016). Investment in social media marketing currently accounts for approximately 15% of marketing budgets—a number that industry experts expect to grow to 16%–22% over the next 5 years (Deloitte, 2022), indicating the increasing importance of social media marketing for strengthening businesses.

Sport is an ideal context to study how social media help build relationships with consumers, given that fans already want to communicate and interact with sport organizations (Wallace et al., 2011). Indeed, millions of fans follow their favorite athletes and teams on social media. Still, after studying Australian A-League soccer fans, Yun et al. (2020) suggest sport organizations may struggle to build and maintain a loyal fan base on social media. In addition, past research argues that the success of social media marketing depends on consumer engagement (Heinonen, 2011; Hennig-Thurau et al., 2010; Santos et al., 2019) and emphasizes that a high number of followers on social media does not always translate into high consumer engagement rates (i.e., the number of actions consumers take per post divided by the total number of followers) for sport organizations. For example, research by Rival IQ (2022) reports consumer engagement rates (i.e., the total number of measurable actions on the platform—including likes, comments, favorites, retweets, shares, and reactions—divided by the total number of followers) in the sport industry at 0.27% for Facebook, 1.84% for Instagram, and 0.08% for Twitter. So, although researchers consistently recommend using social media as a marketing channel to build relationships with customers (e.g., Abeza et al., 2013; Achen, 2017), we still know very little about how sport marketers should manage social media to do so.

To enhance consumer engagement, sport organizations need to better understand how and why consumers engage with social media content (Malhotra et al., 2013) and focus on developing specific content that consumers want to engage with (Annamalai et al., 2021; Heinonen, 2011). Therefore, the purpose of this research is to understand which social media content drives specific consumer engagement actions. Specifically, we tested the impact of three social media platforms (Facebook, Instagram, and Twitter) and three social media message themes (sales, informational, and relationship building) on six consumer engagement actions (comment, like, search, share, talk about, and purchase) in a lab experiment. We use relationship marketing as a theoretical framework for this work and summarize research that investigates the impact of social media platforms and messages on consumer engagement actions.

Theoretical Framework

Relationship Marketing

Because relationship building is an important goal of social media marketing (Schultz & Peltier, 2013) and social media provide opportunities for communicating and interacting with consumers, we approach this research through the lens of relationship marketing. Taking the perspective of the Nordic school of thought, Grönroos (2004) considers relationship marketing a customer-focused strategy that includes two-way communication, interaction, and added value, which creates deeper relationships between firms and consumers. Organizations that implement relationship marketing create opportunities to engage in two-way communication, interact with, and create added value for customers. The relationship itself leads to value creation and satisfies consumer needs (Grönroos, 2004). Importantly, the value of the relationship is based on consumers’ perception of it, which is holistic and cumulative.

Because communications and interactions can take place online via social media, these touch points impact the relationship between firms and consumers. Because relationships are a central part of sport management, and social media can help sport marketers build, maintain, and enhance relationships with consumers, sport marketers should include social media in their communication strategies (Abeza et al., 2013; Abeza, O’Reilly, & Braunstein-Minkove, 2020; Williams & Chinn, 2010). Williams and Chinn (2010) explain that sport fans are now part of the communication process and sport organizations can communicate with them in more informal ways (like social media) to build relationships. Additional opportunities for relationship marketing via social media include humanizing the brand, measuring the consumer pulse, engaging in dialogue, connecting with other fans, delivering content, and providing customer service (Abeza et al, 2019). Previous research confirms that sport organizations can use social media as tools for relationship marketing (Achen, 2014) and social media can help sport marketers connect and build relationships with fans (Pronschinske et al., 2012; Wang & Zhou, 2015). Furthermore, Abeza et al. (2017) recommend that sport organizations use social media for relationship marketing through the communication, interaction, and value process outlined by Grönroos (2004). Follow-up research by Abeza, O’Reilly, Finch, et al. (2020) also provides a detailed application of the Grönroos (2004) framework to build and enhance customer relationships via social media in sport and a number of studies use Grönroos (2004) as a theoretical lens for studying social media in sport (e.g., Abeza et al., 2017; Achen, 2017, 2019; Williams & Chinn, 2010).

Consumer Engagement

While previous research suggests that social media help firms reach consumers and establish, maintain, and enhance relationships (Hennig-Thurau et al., 2010; Schultz & Peltier, 2013), the benefits of using social media to build relationships depend on the level of consumer engagement (Rishika et al., 2013). Consistent with Barger et al. (2016, p. 270), we define consumer engagement on social media as “a set of measurable actions that consumers take on social media in response to brand-related content.” Researchers and practitioners alike agree that consumer engagement is the ultimate goal of social media and that the most effective content engages consumers (Heinonen, 2011; Hennig-Thurau et al., 2010). Because past research suggests that the specific content in social media posts affects consumer engagement (Barger et al., 2016), marketers should focus on developing social media content that adds value for consumers (Lee et al., 2018; Syrdal & Briggs, 2018) to encourage consumer engagement (Malhotra et al., 2013; Schultz & Peltier, 2013). This is particularly pertinent in the sport industry, where Abeza et al. (2019) identify using social media platforms to engage and create value for fans as a major challenge for sport marketers. Thus, sport marketers must gain a better understanding of what promotes consumer engagement on social media.

Understanding how content impacts a variety of consumer engagement actions is important, as some research suggests there is a hierarchy of engagement actions that reflect their relative importance. For example, Kim and Yang (2017) argue that shares represent the highest level of engagement, followed by comments and then likes. The consumers’ online brand-related activities model (Schivinski et al., 2016) identifies three levels of engagement: consumption, contribution, and creation. Consumption is the lowest level, where consumers simply view or read content. Contribution includes actions consumers take, such as liking, commenting, or sharing content. Finally, creation is the highest level, where customers upload or publish content related to the brand. While the majority of past research considers consumer engagement actions that take place on social media platforms (e.g., likes, comments, shares), Trunfio and Rossi (2021) encourage researchers to investigate what leads to consumer engagement actions that go beyond these “vanity metrics.” Thus, in addition to three traditional consumer engagement actions (comment, like, and share), we test three nontraditional consumer engagement actions that are not readily observable by looking at analytics on social media platforms (search for more information, talk about, and purchase) in our empirical work. Thus, in this research, we examine the impact of social media platforms and messages on six specific consumer engagement actions: comment, like, search, share, talk about, and purchase.

Platform

Research in the broader social media literature finds that consumers use different social media platforms for different reasons and in different ways. For example, consumers follow brands on Facebook to make social connections (Kwon et al., 2014) and engage in conversation with brands (Smith et al., 2012), and consumers follow brands on Twitter to find information (Logan, 2014; Kwon et al., 2014) and engage in conversations with brands (Smith et al., 2012).

In terms of motivation for using social media platforms, Buzeta et al. (2020) find that motives differ across platforms and impact the likelihood consumers engage in consumption, contribution, and creation. For example, empowerment has a greater impact on contribution for platforms that are more profile-based (Facebook and Instagram) than content-based (Reddit and YouTube). Remuneration has a greater impact on contribution for platforms that are profile-based than content-based. Finally, remuneration also has a greater impact on consumption for platforms where messages are customized (Facebook and Reddit) versus broadcast (Instagram and YouTube). Similarly, Voorveld et al. (2018) found that motivation to engage with ads on social media is highly dependent on platform (Facebook, Google+, Instagram, LinkedIn, Pinterest, Snapchat, Twitter, and YouTube). The authors argue that social media engagement is very context specific, and researchers should investigate consumer engagement within these specific contexts. Finally, Malhotra et al. (2013) and Coelho et al. (2016) find that interaction differs between Twitter and Facebook, and Facebook and Instagram, and, that the content of the message impacts these differences.

Research in the sport management literature extends these findings to sport teams. For example, consumers tend to use Facebook to find promotions (Haugh & Watkins, 2016). In contrast, consumers use Twitter to find information about sport teams (Gibbs et al., 2014; Hambrick et al., 2010; Haugh & Watkins, 2016), engage in conversations with sport teams and fans (Gibbs et al., 2014; Hambrick et al., 2010), and as a source of entertainment (Hambrick et al., 2010; Haugh & Watkins, 2016). In addition past research finds that consumers are more likely to interact with Facebook than Twitter across a variety of social media messages and professional sport leagues, and that content type has a greater differential effect on Twitter than on Facebook (Achen et al., 2020). This difference tends to be most pronounced for player and personnel promotional messages in the NBA (National Basketball Association). Given the differences across platforms, we compared Facebook, Instagram, and Twitter in our empirical work.

Message

The impact of social media message on consumer engagement is also somewhat unclear. While several studies have found no impact of social media message on consumer engagement actions (Aichner, 2019), other studies document positive and/or negative effects. We summarize these inconsistent effects of social media message on consumer engagement actions in Table 1. Some studies explore the impact of message type on engagement generally across all consumer engagement actions, while other studies find differential effects for specific consumer engagement actions. Consequently, we include the consumer engagement metric in parentheses after each paper listed in Table 1.

Table 1

Summary of the Effects of Social Media Message on Consumer Engagement

Social media messagePositive effect on/increase inNo effectNegative effect on/decrease in
Contests and giveawaysAnanda et al. (2019) (engagement)

de Vries et al. (2012) (likes)

Dolan et al. (2019) (likes, shares)

Schultz (2017) (likes, comments, shares)

Vargo (2016) (retweets, likes)
de Vries et al. (2012) (comments)

Dolan et al. (2019) (consumption, comments)
Malhotra et al. (2013) (likes)
Direct sales (promotions, coupons, or discounts) and product promotionAnanda et al. (2019) (engagement)

Coelho et al. (2016) (comments, likes)

Cvijikj and Michahelles (2013) (comments)

Dolan et al. (2019) (likes, shares)

Luarn et al. (2015) (likes, shares)

Malhotra et al. (2013) (likes, shares)

Schultz (2017) (shares)
Cvijikj and Michahelles (2013) (shares)

Dolan et al. (2019) (consumption, comments)
Cvijikj and Michahelles (2013) (likes)

Malhotra et al. (2013) (likes)

Rietveld et al. (2020) (likes)

Schultz (2017) (likes)

Vargo (2016) (retweets, likes)
EntertainmentCvijikj and Michahelles (2013) (likes, comments, shares)

Dolan et al. (2019) (likes, consumption)

Luarn et al. (2015) (shares)

Malhotra et al. (2013) (likes, shares)

Tafesse (2015) (likes)
de Vries et al. (2012) (comments)

Dolan et al. (2019) (comments, shares)
de Vries et al. (2012) (likes)
EventsCoelho et al. (2016) (likes, comments)

Dolan et al. (2019) (likes, shares, consumption)
Dolan et al. (2019) (comments)Malhotra et al. (2013) (likes)

Schultz (2017) (likes)
HolidayDolan et al. (2019) (likes, consumption)

Schultz (2017) (shares)

Vargo (2016) (retweets, likes)
Dolan et al. (2019) (shares, comments)
InformationCvijikj and Michahelles (2013) (likes, comments)

Dolan et al. (2019) (likes, shares, consumption)

Kim and Yang (2017) (comments)

Luarn et al. (2015) (shares)

Malhotra et al. (2013) (likes, shares)

Rietveld et al. (2020) (comments)

Vargo (2016) (retweets)
Coelho et al. (2016) (engagement)

Cvijikj and Michahelles (2013) (shares)

de Vries et al. (2012) (likes, comments)

Dolan et al. (2019) (comments)
Kim and Yang (2017) (shares)

Rietveld et al. (2020) (likes)

Schultz (2017) (likes, comments, shares)

Vargo (2016) (likes)
Interactivede Vries et al. (2012) (comments)

Dolan et al. (2019) (likes, consumption)

Gutiérrez-Cillán et al. (2017) (engagement)

Kim and Yang (2017) (comments)

Luarn et al. (2015) (likes, comments, shares)

Malhotra et al. (2013) (likes, shares)

Schultz (2017) (likes, comments, shares)

Vargo (2016) (retweets)
de Vries et al. (2012) (likes)

Dolan et al. (2019) (shares, comments)

Kim and Yang (2017) (likes, shares)

Vargo (2016) (likes)
de Vries et al. (2012) (likes, comments)

Tafesse (2015) (likes, shares)
Popular cultureVargo (2016) (retweets, likes)
Social (social elements, social relevancy image, and social causes)Ananda et al. (2019) (engagement)

Dolan et al. (2019) (likes, consumption)

Luarn et al. (2015) (comments)

Vargo (2016) (retweets, likes)
Dolan et al. (2019) (comments, shares)

Gutierrez-Cillán et al. (2017) (engagement)
Malhotra et al. (2013) (likes)

Schultz (2017) (likes, comments)
Trending newsVargo (2016) (retweets, likes)

Research on the impact of message can be difficult to condense because of the wide variety of ways content is categorized and described. In general, past research suggests that firms should not use social media to sell products, given that it does not generate likes (Swani et al., 2013). Informational posts tend to be more effective as personality increases (Lee et al., 2018) and less effective when they feature products (Rietveld et al., 2020). Specific to sport, consumers are more likely to interact with posts that include information about coaches, players, or team success (Maderer et al., 2018), and behind the scenes and player promotion posts tend to increase consumer engagement (Achen, 2015; Achen et al., 2018; Thompson et al., 2014, 2017). Also, consumers are more likely to engage with content that encourages interaction (Thompson et al., 2014). More generally, sport consumers are more likely to like informative or social content compared with remuneration or entertainment content; comment on social content compared with informative, remuneration, and entertainment content; and share information and social content compared with remuneration or entertainment content (Annamalai et al., 2021).

Importantly, researchers observe differences in consumer engagement actions across industries and caution that results may be context dependent (Schultz, 2017). Moreover, we note that most past research features content analyses, where researchers describe the message content that firms post. While useful, these studies have some limitations, especially given that social media algorithms affect the content consumers view. In particular, algorithms tend to demote promotional content and promote organic content. In other words, consumers do not always see all message types, limiting their ability to engage with these posts. In addition, content analyses only allow researchers to assess consumer engagement actions that are readily observable by looking at analytics on the social media site, even though consumers may engage in other (less observable) consumer engagement actions. For example, while researchers and practitioners often study comments, likes, and shares, consumer engagement actions that are not as readily observable on social media (e.g., additional search, word of mouth, and purchase) are commonly left out of studies and so the impact of message on them remains largely unexplored.

Given the inconsistent findings in the existing literature, we attempt to answer calls for more research that goes beyond descriptive data, employing more sophisticated methods (Abeza et al., 2015) to understand how and why consumers engage with social media content (Schultz & Peltier, 2013). Specifically, we employ an experimental design to explore what social media content drives specific consumer engagement actions by manipulating the social media platform and message. In our experiment, we investigate the impact of three platforms (Facebook, Instagram, and Twitter) and three message themes (sales, informational, and relationship building) (cf. Achen et al., 2018; Meng et al., 2015) on six consumer engagement actions (comment, like, search, share, talk about, and purchase) (cf. Barger et al., 2016; Dessart et al., 2015).

While past research focuses on readily observable metrics such as likes, comments, and shares, Trunfio and Rossi (2021) urge researchers and practitioners to consider other metrics, such as sharing outside of social networks and talking about content seen on social media. From a relationship marketing perspective, word of mouth is an important outcome measure as studies demonstrate that deeper relationships lead to more positive word of mouth and that marketing on social media leads to an increase of positive word of mouth (Hutter et al., 2013; Ramsaran-Fowdar & Fowdar, 2013). Given that previous research in sport also finds that fans who engage on social media have stronger relationships and higher purchase intentions (Achen, 2019), we also explored which messages have the potential to lead to purchases. Finally, if an important part of relationship marketing is interactions—content designed to spur additional interaction with the organization—then searching for more information would help build relationships. As such, we also examined search for more information. Because recent research suggests that consumer engagement is context-dependent (Schreiner et al., 2019), we focus on a single college sport team to understand how social media platforms and messages differentially affect consumer engagement actions in sport. Finally, because past research argues that men and women use social media differently (Abdourazakou et al., 2020; Babac & Podobnik, 2016; Haugh & Watkins, 2016), we explore the impact of gender on consumer engagement.

Given this past research on the effects of social media platform and message on consumer engagement actions, we propose the following research questions:

RQ 1: Do consumer engagement actions on social media differ across social media platforms?

RQ 2: Do consumer engagement actions on social media differ based on the social media message?

RQ 3: Do consumer engagement actions on social media differ by gender?

Method

Design

We systematically manipulated social media platform and message, and we measured six consumer engagement actions in a lab experiment. Specifically, we implemented a 3 (social media platform: Facebook vs. Instagram vs. Twitter) × 3 (social media message theme: sales, informational, relationship building) mixed design, where platform was between-subjects (i.e., we randomly assigned participants to view one social media platform) and message theme was within-subjects (i.e., participants viewed all social media message themes).

Participants

We recruited undergraduate students from an online subject pool in the Department of Marketing at Xavier University. Students received an invitation to participate with a Qualtrics link via an announcement in their Canvas course page. Due to sample size constraints in the online subject pool, we collected data over two separate semesters. A total of 180 students (Mage = 20; 44.4% female, 51.7% male, 3.9% other/preferred not to respond) in the first round of data collection and 257 students (Mage = 20; 49.8% female, 48.6% male, 1.6% other/preferred not to respond) in the second round of data collection completed the study online at the time and place of their choosing within a 2-week data collection window.

Materials and Procedure

We created mock social media posts to reflect the three social media message themes (sales, informational, and relationship building) for each platform (Facebook, Instagram, and Twitter) using the platform’s branding and layout. We operationalized each message theme using multiple messages. We included 13 social media messages (see Appendix) based on past research on social media in sport (cf. Achen et al., 2018; Meng et al., 2015). Direct sales, product promotion, and sponsor messages correspond to the sales group; diversion, facility, organization promotion, and team promotion messages correspond to the informational group; and behind the scenes, community outreach, fan, giveaway, interactivity, and player, and personnel promotion messages correspond to the relationship building group. Each social media message featured the students’ National Collegiate Athletic Association Division I women’s basketball team (which competes in the Big East Conference). We selected the women’s basketball team because basketball was the most popular sport on campus and the women’s team had more room for growth in terms of consumer engagement than the men’s team (which was more popular), making it a good context to assess the effects of social media on consumer engagement actions (i.e., ceiling effects were less likely to emerge). Because photos and videos generate more consumer engagement (Kim & Yang, 2017; Schultz, 2017) and professional photos increase consumer engagement (Li & Xie, 2020), each social media post included a professional photo from the university’s website or social media accounts. After viewing each social media message, participants rated how likely they were to perform the following six consumer engagement actions after viewing it: comment, like, search for additional information, share, talk about it with another person, and make a purchase (from 1 = extremely unlikely to 7 = extremely likely). Finally, participants indicated their age and gender.

Results

Mixed Analysis of Variance

First, we verified that the data did not differ across the two time periods via t tests.1 Second, we note that participants primarily identified as female or male, with only 2.5% selecting the other gender categories or “prefer not to respond.” Because this number was so small, we could not include these responses in our analysis as distinct levels of gender and omitted these participants from the analysis.

Next, using factor analysis, we confirmed that each message theme was unidimensional (i.e., messages loaded onto a single factor for each message theme). We then conducted a reliability analysis and confirmed that the consumer engagement action measures within each message theme formed a sufficiently reliable scale (sales: α = .88; informational: α = .89; relationship building: α = .88). Having confirmed that our three social media message themes were each unidimensional and reliable, we averaged the consumer engagement action measures by theme.

Consequently, we report results for the following model: a 3 (social media platform) × 2 (gender) × 3 (social media message theme) × 6 (consumer engagement action) group mixed design, where platform and gender are between-subjects factors and message theme and consumer engagement action are within-subjects factors.

We report between-subjects analysis of variance results in Table 2 and within-subjects analysis of variance results (using the Greenhouse–Geisser correction where appropriate) in Table 3. There were no significant main effects for the between-subjects factors. However, the Consumer engagement action × Platform interaction was significant, F(6.26, 1314.88) = 3.44, p < .001, ηp2=.02; post hoc analyses showed that posts on Instagram were less likely to result in comments than posts on either Facebook or Twitter, and that posts on Instagram were less likely to prompt purchases than posts on Twitter.

Table 2

Between-Subjects ANOVA Results

SourcedfMSFpηp2
Intercept163,189.262,531.40<.001.86
Gender243.151.73.18.01
Social media platform13.270.13.72.00
Social media platform × Gender213.340.53.59.00
Error42024.96

Note. ANOVA = analysis of variance.

Table 3

Within-Subject ANOVA Results

SourcedfMSFpηp2
Social media message theme1.067.194.61.03.01
Social Media Message Theme × Social Media Platform2.132.901.86.15.01
Social Media Message Theme × Gender1.0610.016.42.01.02
Social Media Message Theme × Gender × Social Media Platform2.131.290.82.45.00
Error (message)446.611.56
Consumer engagement action3.131,835.10492.03<.001.54
Consumer Engagement Action × Social Media Platform6.2612.833.44.00.02
Consumer Engagement Action × Gender3.1351.3513.77<.001.03
Consumer Engagement Action × Social Media Platform × Gender6.261.490.40.89.00
Error (action)1,314.883.73
Social Media Message Theme × Consumer Engagement Action4.56106.09208.22<.001.33
Social Media Message Theme × Consumer Engagement Action × Social Media Platform9.120.731.43.17.01
Social Media Message Theme × Consumer Engagement Action × Gender4.565.8911.56<.001.03
Social Media Message Theme × Consumer Engagement Action × Social Media Platform × Gender9.120.210.41.93.00
Error (Social Media Message Theme × Consumer Engagement Action)1,916.160.51

Note. Mauchly’s test of sphericity was significant for social media message theme, consumer engagement action, and their two-way interaction, so results reflect the Greenhouse–Geisser correction. ANOVA = analysis of variance.

The main effect for social media message theme was significant, F(1.06, 446.61) = 4.61, p < .05, ηp2=.01, but this main effect was qualified by significant interactions, including the Social media message theme × Gender interaction, F(1.06, 446.61) = 6.42, p < .05, ηp2=.02. The two-way consumer engagement action and gender interaction were also significant, F(3.13, 1314.88) = 13.77, p < .001, ηp2=.03). These significant two-way interactions were qualified by a significant Social media message theme × Consumer engagement action × Gender interaction, F(4.56, 1916.16) = 11.56, p < .001, ηp2=.03, so we report the Social media message theme × Consumer engagement action results for females (Figure 1) versus males (Figure 2) below. Descriptive statistics can be found in Table 4.

Figure 1
Figure 1

—Mean likelihood for each consumer engagement action (females). Note. The same letter indicates that means are not significantly different for that action.

Citation: International Journal of Sport Communication 17, 1; 10.1123/ijsc.2023-0125

Figure 2
Figure 2

—Mean likelihood for each consumer engagement action (males). Note. The same letter indicates that means are not significantly different for that action.

Citation: International Journal of Sport Communication 17, 1; 10.1123/ijsc.2023-0125

Table 4

Descriptive Statistics

Social media message themeConsumer engagement actionFemalesMales
MSE95% CI (LL)95% CI (UL)MSE95% CI (LL)95% CI (UL)
SalesComment1.600.081.441.771.930.081.782.09
Like4.240.133.984.503.790.133.544.05
Search3.400.113.183.623.030.112.823.24
Share2.290.102.092.482.370.102.182.56
Talk about3.060.112.843.272.810.112.603.01
Purchase2.840.102.653.042.610.102.422.80
InformationalComment1.830.091.652.022.230.092.052.41
Like5.220.124.985.464.600.124.374.83
Search2.750.102.562.952.850.102.663.04
Share2.550.112.342.762.620.102.422.83
Talk about3.210.103.003.413.020.102.823.22
Purchase1.780.091.611.942.070.081.912.24
Relationship buildingComment1.830.091.642.012.220.092.042.40
Like5.200.124.955.444.580.124.354.82
Search2.810.112.603.012.890.102.693.09
Share2.540.112.322.752.610.112.412.82
Talk about3.210.113.003.423.060.102.853.26
Purchase1.790.091.621.962.080.091.922.25

Note. CI = confidence interval.

 

Comment

Post hoc tests revealed that females and males were less likely to comment on a social media post with a sales message than an informational (MFemalediff = −0.23, SE = 0.04, p < .01; MMalediff = −0.30, SE = 0.05, p < .001) or relationship building (MFemalediff = −0.23, SE = 0.05, p < .001; MMalediff = −0.29, SE = 0.05, p < .001) message. The difference between an informational and relationship building message was not significant (MFemalediff = −0.00, SE = 0.03, p > .10; MMalediff = −0.01, SE = 0.01, p > .10). Therefore, sport organizations should avoid posting sales messages if they want consumers to comment on posts.

Like

Post hoc tests revealed a similar pattern to comment as both females and males were less likely to like a social media post with a sales message than an informational (MFemalediff = −0.98, SE = 0.08, p < .001; MMalediff = −0.81, SE = 0.08, p < .001) or relationship building (MFemalediff = −0.96, SE = 0.08, p < .001; MMalediff = −0.79, SE = 0.08, p < .001) message. The difference between an informational and relationship building message was not significant (MFemalediff = 0.03, SE = 0.01, p > .10; MMalediff = 0.02, SE = 0.01, p > .10). Thus, sport organizations should avoid posting sales messages if they want consumers to like posts.

Search

Post hoc tests revealed that females were more likely to search for additional information after viewing a social media post with a sales message than an informational (MFemalediff = 0.65, SE = 0.08, p < .001) or relationship building (MFemalediff = 0.59, SE = 0.08, p < .001) message and more likely to search for additional information following a post with a relationship building versus informational message (MFemalediff = 0.06, SE = 0.02, p < .01). For males, a social media post with an informational (MMalediff = −0.18, SE =0.08, p > .10) or relationship building (MMalediff = −0.04, SE = 0.02, p > .10) message was more likely to prompt a search for additional information than a sales message. Consequently, sport organizations should post sales messages followed by relationship building messages if they want female consumers to search for additional information and avoid posting informational messages if they want male consumers to search for additional information.

Share

Post hoc tests revealed that females were less likely to share a social media post with a sales message than an informational (MFemalediff = −0.27, SE = 0.06, p < .001) or relationship building (MFemalediff = −0.25, SE = 0.06, p < .001) message. There was no difference between informational and relationship building messages on females’ sharing behavior (MFemalediff = 0.02, SE = 0.01, p > .10). The pattern was similar for males, who were less likely to share a social media post with a sales message than an informational (MMalediff = −0.25, SE = 0.07, p < .001) or relationship building (MMalediff = −0.24, SE = 0.07, p < .001) message; the difference between an informational and relationship building message was not significant (MMalediff = −0.01, SE = 0.01, p = 1.00). Therefore, sport organizations should avoid posting sales messages if they want consumers to share posts.

Talk About

Post hoc tests revealed a similar pattern to share for males; males were less likely to talk about a social media post with a sales message than an informational (MMalediff = −0.22, SE = 0.07, p < .01) or relationship building (MMalediff = −0.25, SE = 0.07, p < .001) message; the difference between an informational and relationship building message was not significant (MMalediff = −0.04, SE = 0.02, p = 1.00). For females, there were no significant differences between the three social media message themes (ps > .10). Thus, sport organizations should avoid posting sales messages if they want male consumers to talk about posts (there were no differences across message types for females).

Purchase

Finally, post hoc tests revealed that females and males were more likely to make a purchase after viewing a social media post with a sales message than an informational (MFemalediff = 1.07, SE = 0.07, p < .001; MMalediff = 0.54, SE = 0.07, p < .001) or relationship building (MFemalediff = 1.06, SE = 0.07, p < .001; MMalediff = 0.53, SE = 0.07, p < .001) message. The difference between an informational and relationship building message was not significant (MFemalediff = −0.01, SE = 0.01, p > .10; MMalediff = −0.01, SE = 0.01, p > .10). Consequently, sport organizations should post sales messages if they want consumers to make a purchase.

Discussion

Past social media research in sport indicates a lack of consensus on how social media platform and message interact to drive consumer engagement. Therefore, we investigated the effects of three platforms (Facebook, Instagram, and Twitter) and three message themes (sales, informational, and relationship building) on six consumer engagement actions (comment, like, search, share, talk about, and purchase). We found that for platform, participants were more likely to comment on Facebook and Twitter posts than Instagram posts and more likely to purchase after viewing a Twitter post than Instagram post. However, we note that consumer engagement did not differ by platform for different message themes (i.e., there was no interaction between platform and message theme). Next, we found that message theme and gender differentially affected consumer engagement actions. Both females and males were more likely to comment, like, and share an informational or relationship building post than a sales post and more likely to purchase after viewing a sales post than an informational or relationship building post. In addition, females were more likely to search after viewing a sales post than an informational or relationship building post and more likely to search after viewing a relationship building post than an informational post, whereas males were more likely to search after viewing a sales or relationship building post than an informational post. Finally, males were more likely to talk about an informational or relationship building post than a sales post.

Our research is unique in that we used experimental methods (vs. content analysis) to explore the impact of different social media platforms and messages on consumer engagement actions. Using an experimental approach allowed us to directly compare the performance of different platforms and message themes to one another. Furthermore, in addition to three forms of consumer engagement that are readily observable on social media (e.g., comment, like, share), we included three forms of consumer engagement that are not readily observable on social media (e.g., search, talk about, purchase)—which expands our understanding of how consumer engagement on social media affects consumer behavior beyond these platforms. We note that although some of our findings are consistent with past research (or unsurprising in some cases), employing an experiment with random assignment to manipulated independent variable conditions and including engagement actions that are not readily observable on social media analytics platforms make this study unique and contributes to the existing body work. Continuing to expand our understanding of social media marketing in sport through new methods, consumer engagement actions, and contexts provides researchers and practitioners with a better foundation for making data-driven decisions.

In contrast to past sport management research that shows consumer engagement with social media messages differs by platform (Achen et al., 2020; Haugh & Watkins, 2016) and expert recommendations to post different content on different platforms, we did not find an interaction between social media platform and message theme. This may be because prior work was based on observed behaviors rather than behavioral intentions. Although participants may think about engaging in some behaviors, they may not always do so in a field setting. The disconnect between consumer intentions and behavior represents a limitation of scenario-based experiments in marketing research (although the two tend to be highly correlated). We also manipulated (vs. observed) social media platform. Although random assignment should mitigate concerns that certain consumers spend less or more time on these platforms in reality, future research could control for the amount of time consumers spend on each platform.

In terms of social media message, past research emphasizes the importance of using social media to build relationships (Abeza et al., 2013; Williams & Chinn, 2010). However, there was only one significant difference between relationship building and informational posts (i.e., search for females) across the six consumer engagement actions we measured. In general, informational and relationship building messages performed best. Specifically, these posts performed better than sales posts for comment, like, and share for females and males and talk about for males. There were no significant differences between informational, relationship building, and sales posts for talk about for females. Finally, informational and relationship building posts performed worse for search for females and purchase for males and females. These findings help clarify some of the conflicting results in the existing academic literature. For example, past research identifies both positive (Ananda et al., 2019; Coelho et al., 2016; Cvijikj & Michahelles, 2013; Malhotra et al., 2013; Schultz, 2017) and negative (Schultz, 2017; Swani et al., 2013) effects of sales posts on consumer engagement. We found that sales posts are more effective for search for females and purchase for females and males, but less effective for comment, like, and share for females and males and talk about for males. Therefore, when sport marketers design sales posts for social media, they may want to consider using search-related calls to action for female fans or purchase-related calls to action for female and male fans. Furthermore, observable social media metrics (such as comment, like, and share) may be less relevant for sales messages, in which case, sport organizations may want to focus on more relevant metrics like online traffic or purchases.

From a theoretical standpoint, we expected relationship-building posts to result in higher consumer engagement than informational and sales posts, especially in terms of comments and shares, as previous research finds that relationship building content (e.g., interaction and player promotion) is more likely to result in comments and engagement (Achen, 2015; Achen et al., 2018; de Vries et al., 2012; Gutiérrez-Cillán et al., 2017; Kim & Yang, 2017; Luarn et al., 2015; Malhotra et al., 2013; Schultz, 2017; Thompson et al., 2014, 2017; Vargo, 2016). We found partial support for our theorizing in that relationship building posts performed better than sales posts. However, relationship building posts did not perform better than informational posts. This may be because informational content meets the immediate needs of consumers, making it more likely to be engaged within the moment, while the effects of relational content take time to develop and their effects are not necessarily related to consumer engagement actions, and, instead serve to build feelings of trust, commitment, or loyalty, which we did not measure in this study. In addition, while previous literature suggests that social media should be used primarily for two-way communication and interaction, our findings suggest that doing so may not be any more important than using it as an information channel. Thus, it is possible that relationship development on social media may be less related to the content posted and more related to meeting the needs of followers. It could be that added value (i.e., the third element of relationship marketing according to Grönroos [2004]) is what really drives consumer engagement on social media. Therefore, we encourage future research to explore how sport organizations can add value to firm-consumer relationships through social media. Finally, although Schultz and Peltier (2013) warn against sales posts and much of the literature on social media in sport suggests avoiding selling on social media networks, it appears that these posts are effective in stimulating search and purchases. Consequently, these types of posts seem to meet their intended goals, making them an effective marketing tool for increasing online traffic and purchases. Consumer willingness to engage in these nonobservable actions after viewing a sales post indicates that this type of content may still be appropriate when sport organizations are looking to increase these specific consumer engagement actions.

Our results also help inform social media strategy for sport managers. Interestingly, participants were more likely to make a purchase after viewing a Twitter post than an Instagram post, which has added tools to help businesses encourage purchases on the platform. Participants were also more likely to comment on a Twitter or Facebook post than an Instagram post in our study. Both findings are somewhat surprising in that the college student demographic coincides with Instagram users better than Twitter users. We speculate that these findings may be due to the informative nature of Twitter and the high performance of informational content in our experiment. This finding conflicts with past research, which shows that Facebook posts garner more consumer engagement than Twitter posts (Achen et al., 2020). Differences in study context (i.e., college women’s basketball in our study vs. professional men’s and women’s sports in Achen et al., 2020) or methodology (i.e., experiment vs. content analysis) may explain these differences. If so, the context-dependent nature of consumer engagement with social media may not only vary across industries but within industries and across methodologies as well. This means that sport marketers should take context into account when designing their social media strategy. Identifying peer organizations is essential when researching effective content types and evaluating consumer engagement metrics. In addition, given our findings, researchers should conduct comparative research in the sport industry to determine where differences exist (e.g., between fans, sports, leagues, teams, etc.).

Marketers can also use our results to optimize social media content to drive specific engagement actions. In practice, sport marketers should set goals for each social media platform and then create content to meet those goals. For example, if the goal is to increase comments or likes, sport organizations should post informational (diversion, facility, organization promotion, and team promotion) or relationship building (behind the scenes, community outreach, fan, giveaway, interactivity, and player and personnel promotion) messages versus sales messages (direct sales, product promotion, and sponsor), given that informational and relationship building messages increased comments and likes compared with a sales message in our experiment. If the goal is to increase online traffic among women, sport marketers should post a sales message. If the goal is to increase word of mouth through shares and offline discussions among men, sport organizations should post informational or relationship building messages. Finally, if the goal is to increase purchases, sport marketers should (perhaps unsurprisingly) post a sales message. We also encourage sport organizations to consider the hierarchy of consumer engagement as they design their social media content. Specifically, which consumer engagement actions are most important to them (e.g., consumption, contribution, or creation)? Our results can provide some guidance here. For example, if contribution is most important, firms should post informational or relationship building messages to increase comments.

Limitations and Future Research

We acknowledge several limitations that present opportunities for future research. Although past research argues that “students are generally suitable to serve as participants and, in some cases, are even superior to other groups” due to their homogeneity (which helps establish causal relationships in experiments) and familiarity with social media in college sport (Koschate-Fischer & Schandelmeier, 2014, p. 807), we recognize that a broader sample of sport consumers would yield more generalizable results.

In addition, we examined consumer engagement with social media using women’s college basketball stimuli. Since past research argues (and our results support) that context matters, future research could explore other college and professional sports to see if results replicate. As we noted, the context-dependent nature of consumer engagement with social media may make it difficult to generalize our results across fans, sports, leagues, teams, and so forth.

Furthermore, although we provided a stronger test of the impact of social media platform and message on consumer engagement (vs. content analysis) thanks to the controlled environment and the ability to test engagement actions that are not readily observable, future research could partner with teams to employ field experiments to observe actual consumer behavior using the teams’ social media accounts. Related to additional work on platforms, we encourage future research to assess newer platforms (e.g., TikTok) as well as the different media on these platforms (e.g., videos). As the social media landscape is constantly evolving, there will always be a need to revisit and reassess the impact of message, platform, and gender on consumer engagement actions on the platforms we included as well as the platforms to come.

Next, it may be helpful for future research to measure the amount of value consumers attach to each social media message theme to understand these findings. Moreover, because informational and relationship building messages performed better than sales messages for four of the six consumer engagement actions we measured, we question whether a post that emphasizes informational or relationship building messages and deemphasizes, but still includes, sales messages would be more successful than straight sales messages. More generally, it could be beneficial to identify which combinations of the 13 social media messages or three message themes perform best.

While we used past research on social media in sport as a guide to operationalize three message themes via 13 social media messages, future research could take a more granular approach and compare and contrast social media messages within and across message themes. Finally, we observed differences in consumer engagement between females and males (for search and talk about). Additional research on why this is the case could help marketers with their targeting efforts. Ultimately, we hope this research leads to more work that helps marketers design and execute social media content that enhances consumer engagement.

Note

1.

For robustness, we also reran our analysis defining the data collection period as another between-subjects factor; neither the main effect of data collection period nor its interaction with other factors in the model was significant.

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  • Tafesse, W. (2015). Content strategies and audience response on Facebook brand pages. Marketing Intelligence & Planning, 33(6), 927943.

  • Thompson, A.J., Martin, A.J., Gee, S., & Eagleman, A.N. (2014). Examining the development of a social media strategy for a national sport organisation a case study of tennis New Zealand. Journal of Applied Sport Management, 6(2), 4263. https://trace.tennessee.edu/jasm/vol6/iss2/15

    • Search Google Scholar
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  • Thompson, A.-J., Martin, A.J., Gee, S., & Geurin, A.N. (2017). Fans’ perceptions of professional tennis events’ social media presence: Interaction, insight, and brand anthropomorphism. Communication & Sport, 5(5), 579603.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trunfio, M., & Rossi, S. (2021). Conceptualising and measuring social media engagement: A systematic literature review. Italian Journal of Marketing, 2021(3), 267292.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vargo, C.J. (2016). Toward a tweet typology: Contributory consumer engagement with brand messages by content type. Journal of Interactive Advertising, 16(2), 157168.

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    • Search Google Scholar
    • Export Citation
  • Voorveld, H.A., Van Noort, G., Muntinga, D.G., & Bronner, F. (2018). Engagement with social media and social media advertising: The differentiating role of platform type. Journal of Advertising, 47(1), 3854.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wallace, L., Wilson, J., & Miloch, K. (2011). Sporting Facebook: A content analysis of NCAA organizational sport pages and Big 12 conference athletic department pages. International Journal of Sport Communication, 4(4), 422444.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, Y., & Zhou, S. (2015). How do sports organizations use social media to build relationships? A content analysis of NBA clubs’ Twitter use. International Journal of Sport Communication, 8(2), 133148.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Williams, J., & Chinn, S.J. (2010). Meeting relationship-marketing goals through social media: A conceptual model for sport marketers. International Journal of Sport Communication, 3(4), 422437.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yun, J.H., Rosenberger, P.J., & Sweeney, K. (2020). Drivers of soccer fan loyalty: Australian evidence on the influence of team brand image, fan engagement, satisfaction and enduring involvement. Asia Pacific Journal of Marketing and Logistics, 33(3), 755782.

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    • Search Google Scholar
    • Export Citation

Appendix

  1. Behind the scenes: “Take a behind-the-scenes look at Xavier Women’s Basketball practice!”
  2. Community outreach: “Xavier Women’s Basketball players and coaches give out Halloween candy to kids in the local community at trunk or treat!”
  3. Direct sales: “Xavier Women’s Basketball tickets are available now!”
  4. Diversion: “Xavier Women’s Basketball welcomes the traveling dog show to the arena!”
  5. Facility: “Cintas Center—a beautiful place for a Xavier Women’s Basketball game!”
  6. Fan: “Xavier Women’s Basketball fans are the best fans!”
  7. Giveaway: “Xavier Women’s Basketball Giveaway: Tag your friends for a chance to win tickets and merchandise!”
  8. Interactivity: “Xavier Women’s Basketball wants to know, what was your favorite moment this season? Voting is now open!”
  9. Organization promotion: “GAME DAY: Xavier Women’s Basketball takes on Providence. Catch all the action in-person, on TV and online!”
  10. Player and personnel promotion: “Xavier Women’s Basketball player Morgan Sharps is honored as Big East Player of the Week!”
  11. Product promotion: “Check out the new Xavier Women’s Basketball merchandise that hit stores today!”
  12. Sponsor: “Check out these great deals from Servatti, official sponsor of Xavier Women’s Basketball!”
  13. Team promotion: “GAME RECAP: Xavier Women’s Basketball wins in the home opener tonight!”

(Text and corresponding photos were identical across the three social media platforms).

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  • Figure 1

    —Mean likelihood for each consumer engagement action (females). Note. The same letter indicates that means are not significantly different for that action.

  • Figure 2

    —Mean likelihood for each consumer engagement action (males). Note. The same letter indicates that means are not significantly different for that action.

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    • Search Google Scholar
    • Export Citation
  • Thompson, A.-J., Martin, A.J., Gee, S., & Geurin, A.N. (2017). Fans’ perceptions of professional tennis events’ social media presence: Interaction, insight, and brand anthropomorphism. Communication & Sport, 5(5), 579603.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trunfio, M., & Rossi, S. (2021). Conceptualising and measuring social media engagement: A systematic literature review. Italian Journal of Marketing, 2021(3), 267292.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vargo, C.J. (2016). Toward a tweet typology: Contributory consumer engagement with brand messages by content type. Journal of Interactive Advertising, 16(2), 157168.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Voorveld, H.A., Van Noort, G., Muntinga, D.G., & Bronner, F. (2018). Engagement with social media and social media advertising: The differentiating role of platform type. Journal of Advertising, 47(1), 3854.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wallace, L., Wilson, J., & Miloch, K. (2011). Sporting Facebook: A content analysis of NCAA organizational sport pages and Big 12 conference athletic department pages. International Journal of Sport Communication, 4(4), 422444.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, Y., & Zhou, S. (2015). How do sports organizations use social media to build relationships? A content analysis of NBA clubs’ Twitter use. International Journal of Sport Communication, 8(2), 133148.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Williams, J., & Chinn, S.J. (2010). Meeting relationship-marketing goals through social media: A conceptual model for sport marketers. International Journal of Sport Communication, 3(4), 422437.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yun, J.H., Rosenberger, P.J., & Sweeney, K. (2020). Drivers of soccer fan loyalty: Australian evidence on the influence of team brand image, fan engagement, satisfaction and enduring involvement. Asia Pacific Journal of Marketing and Logistics, 33(3), 755782.

    • Crossref
    • Search Google Scholar
    • Export Citation
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