The Engagement of the Social Networks in the ACB Basketball League

Abstract

We currently live in a technological age, and in continuous growth. Within this age, social networks have emerged as a way of communicating and creating value in branding. Sports clubs have adapted their way of communicating with their fans and have made social networks an indispensable tool for their daily communication. The aim of the present study is to analyze how the position in the ranking affects the engagement of the social networks of 18 basketball clubs that make up the Endesa ACB League during match days 16 to 34. Specific formulas were used in study to analyze social networks of Facebook, Twitter, Instagram, and YouTube. The results show great variability of engagement results as well as a positive correlation between the ranking in the table and engagement on the different social networks, with Instagram having the highest correlation of all the social networks.

1. Introduction

Social media has changed the way people and companies communicate, with websites being created based on social network sites (CSNs), which allow for direct interaction and connection with consumers [1,2,3]. Social networks assist users to solve problems related to a product or service free of charge, significantly reducing the costs of service presentation and increasing levels of perceived value [4,5]. Moreover, they also have great timeliness and influence because of their viral spread of information [6]. Additionally, social media has challenged the one-way model (i.e., websites), providing two-way interactive communication where users can engage with and create content (i.e., social networks such as Facebook and Instagram though comments, shares, etc.) [7]. However, as Sotelo-González [8] reveals, instead of using social networks as a potential tool to interact with the consumers (two-way communication), there are a few entities that use social networks as a one-way communication channel. Additionally, the role of consumers has developed from traditionally passive information “receivers” to information co-creators [9,10]. Thus, the new consumer profile expects more than to just interact and promote information about the entity, creating co-value to the entity.

With the continuous development of CSNs and customers, the literature defines customer engagement as the interactivity of a follower with an account, initiated by the user himself, assuming an emotional involvement and/or commitment of the user towards the published contents [11]. Literature scholars agree that customer engagement can be defined as “the repeated interactions between consumers and brand that strengthen the emotional, psychological or physical investment a customer has in that brand” [12,13,14,15]. Thus, highly engaged customers on the social platforms play an important role in generating contents, co-creating customer experience and value, and referring products, services, and brands to other customers [13,16]. Consequently, they are co-creators of the brand image and excellent referrers due to their active participation through messages and recommendations. Therefore, most CSNs managers agree that highly engaged customers determine the sustainability of their CSNs [17]. In fact, the more engagement a social network has, the more information will be distributed among followers, making it more likely to develop the brand, club, or athlete and producing greater engagement among users [18].

In the field of sports, dynamic social media content and communication strategies are needed to drive traffic and maintain a strong interactive relationship with fans [19]. Researchers have discussed the organizational potential of social networks and whether they could have positive impacts for sports entities in terms of enhanced user engagement and greater revenues. According to He, Zha, and Li [20], companies use social networks to: (i) acquire new customers, (ii) build on customer satisfaction and brand loyalty, (iii) improve sales and revenues, and (iv) increase their reputation centered on brand image. Researchers [21,22,23] investigated the role of social media in relation to marketing, focusing on strategies of fans’ behavior, such as commenting or sharing a post, liking, etc. [24,25]. Related with fans, researchers have investigated the motivations of sport audiences and drivers of online engagement [7,26].

Despite the importance of this topic, there is a gap in the literature regarding which social networks are used in the sport clubs and what their impact is on interactions depending on their position in the league. For this reason, a hypothesis arose about the influence of the ranking position on the position of the ACB League. Likewise, despite the relevant issue, there is only one study that examines this relationship in basketball clubs: Herrera-Torres et al.’s [27] research. In this context, sport managers have a decisive role to play in the development of the field of sport management in general, and in the field of social media management in particular. Therefore, the present study attempts to gain a deeper knowledge about the influence occurring between the ranking position of the clubs that compete in the ACB League and the engagement of their social networks. Thus, the aim of present study is to analyze the engagement of the eighteen basketball clubs that make up the Endesa ACB League during the first quarter of the year 2022 and until the classification of the quarter finals on the social networks Facebook, Twitter, Instagram, and YouTube, and to observe how the position in the ranking affects the engagement of the different social networks of each club.

2. Materials and Methods

2.1. Participants

The sample is composed of a total of eighteen professional basketball clubs (n = 18) participating in the ACB League—known for sponsorship reasons as Liga Endesa—belonging to the Association of Basketball Clubs (ACB) in the 2021–2022 season. The inclusion criteria for the participants were: (i) being part of the ACB League, (ii) having official social networks, and (iii) having published at least one post in the last three months. The authors discussed whether or not to include the Real Madrid and Futbol Club Barcelona clubs due to the difference in the mean compared to the clubs mentioned above. The results showed that the values of Real Madrid and Futbol Club Barcelona were much higher than the mean of the group, being ± twice the standard deviation (μ ± 2σ) with respect to the rest of the clubs (i.e., Real Madrid average Facebook engagement (1.638) vs. Valencia average Facebook engagement (131)). Thus, due to the large difference observed and their possible influence on the results, it was finally decided to eliminate Real Madrid and Futbol Club Barcelona from the sample.

2.2. Instruments

The SM-SPORTS record sheet was used for data collection. It included each of the social networks to be analyzed (Facebook, Twitter, Instagram, and YouTube). Once these were identified, the data relating to them were added to obtain the engagement of each club, in calendar month periods according to each social network. To do this, it was necessary to use the Rival IQ tool [28], which was used to monitor the activity and interactions carried out by the followers and fans of these social networks. We used the formulas proposed by Oviedo-García et al. [27] to determine engagement, to quantify user interaction in the different social networks. For the Facebook social network, the formula adapted by Herrera-Torres et al. [29] was used, since the one suggested by Oviedo-García et al. [27] incorporates the value of other clicks, which is only visible to the administrators of each account (see Figure 1).

2.3. Procedure

An observational analysis of the content of the selected social networks in the different sports clubs of the Endesa ACB League was carried out. The data collection was carried out in the period from 4 January to 14 May 2022 (match days 16 to 34) to deeply examine if the ranking position has an influence on the social media engagement of the above-mentioned basketball clubs during a long period, rather than just one month. The second half of the year was selected as the study period because the social networks show more activity, as teams are playing the last league qualification matches and this creates more interaction on social networks. In addition, it is considered that in the second half of the year, the teams are already more realistically positioned in the ranking according to their level of play due to all the matches already played (unlike in the first half of the year). Firstly, each of the clubs’ websites was visited to identify the social networks they used. Then, the social networks of the basketball clubs were entered into the Rival IQ [28] tool to obtain the necessary data on the interactions of each social network, and to calculate the engagement in the MS-SPORTS log sheet. This data collection was carried out on the last day of each month (see Figure 2).

2.4. Statistical Analysis

Descriptive statistics are shown as mean ± standard deviation (SD). Significance was accepted at p < 0.05 for all analyses performed. Normal distribution and homogeneity were checked by Kolmogorov–Smirnov and Levene’s tests, respectively. Analyses of variance of repeated measures (ANOVAs) were used to analyze the effect of time in different variables (Facebook, Twitter, Instagram, and YouTube). Effect size is indicated with partial eta squared for Fs. Finally, multiple pairwise comparisons were employed for obtaining differences between variables, and the Bonferroni correction was used to compensate the multiple post hoc comparisons. Effect size is indicated with Cohen’s d for pairwise comparisons. A Pearson correlation coefficient, r, was used to examine the relationship between ranking, budget, and engagement (Facebook, Twitter, Instagram, and YouTube). The interpretation of the d, regardless of the sign, followed the scale: very small (0.01), small (0.20), medium (0.50), large (0.80), very large (1.20), and huge (2.0), as initially suggested by Cohen et al. [30] and expanded by Sawilowsky et al. [31]. Data were analyzed using Statistica software (version 13.3; Statsoft, Inc., Tulsa, OK, USA).

3. Results

Descriptive statistics were calculated for each network (Facebook, Twitter, Instagram, and YouTube) during five months (January, February, March, April, and May) (see

Table 1. Social networks’ engagement of the ACB League basketball clubs.

			Engagement
Ranking	Club	Budget	Facebook	Twitter	Instagram	YouTube
3rd	C.B. Canarias Lenovo	7.00	153.93 ± 8.06	32.77 ± 12.78	431.91 ± 134.68	19.96 ± 8.30
4th	Valencia	22.00	131.31 ± 28.92	19.86 ± 9.15	1045.20 ± 111.43	14.48 ± 4.46
5th	Baskonia	16.00	58.98 ± 29.98	29.31 ± 8.99	960.58 ± 186.18	12.75 ± 7.16
6th	Burgos	6.00	183.72 ± 43.00	36.70 ± 7.96	953.84 ± 106.74	14.15 ± 4.93
7th	Joventut	5.00	131.12 ± 65.39	53.94 ± 2.56	1198.93 ± 138.68	13.13 ± 2.85
8th	Gran Canaria	10.00	79.17 ± 19.36	20.26 ± 3.29	652.80 ± 48.79	8.63 ± 0.84
9th	Andorra	5.00	29.99 ± 8.22	11.63 ± 2.89	249.64 ± 62.62	4.31 ± 2.02
10th	Manresa	2.50	137.62 ± 13.59	21.02 ± 11.21	1518.61 ± 293.28	11.68 ± 2.83
11th	Unicaja	8.00	40.69 ± 4.95	15.61 ± 3.61	526.24 ± 38.84	13.49 ± 0.95
12th	Murcia	2.50	60.77 ± 24.83	17.00 ± 1.98	354.19 ± 70.94	12.72 ± 3.79
13th	Zaragoza	3.00	88.26 ± 34.07	16.26 ± 5.61	729.82 ± 103.45	6.92 ± 2.87
14th	Obradoiro	2.50	55.74 ± 32.14	15.58 ± 2.30	384.47 ± 52.98	9.31 ± 3.46
15th	Fuenlabrada	2.00	57.07 ± 1.24	17.70 ± 6.71	161.81 ± 50.67	1.27 ± 0.61
16th	Betis	3.50	137.97 ± 87.05	87.78 ± 35.31	454.86 ± 172.62	7.21 ± 1.39
17th	Bilbao	2.00	201.50 ± 37.60	37.00 ± 8.39	948.08 ± 87.26	20.24 ± 7.07
18th	Breogan	1.20	97.61 ± 28.26	25.87 ± 4.90	315.61 ± 51.28	10.70 ± 1.63
			102.84 ± 10.04	28.64 ± 5.74	680.41 ± 76.23	11.31 ± 2.53

).

Different analyses of variance of repeated measures (ANOVA) were used to analyze the average of the clubs’ engagement for each social network (Facebook, Twitter, Instagram, and YouTube) and time (January, February, March, April, and May). The datasets revealed a significant effect for Twitter, Instagram, and YouTube (F = 5.42, p = 0.02, η² = 0.27, F = 8.84, p = 0.001, η² = 0.08, respectively). However, Facebook was not significant (F= 1.24, p = 0.31, η² = 0.12) (see Figure 3).

At this point, post hoc comparisons with each social network (Facebook, Twitter, Instagram, and YouTube) and time (January, February, March, April, and May) did not reveal significant differences in the case of Facebook in any months. However, in the case of Twitter, there were significant differences between January vs. May, February vs. May, March vs. April, March vs. May, and April vs. May (p = 0.01, p = 0.01, p = 0.02, p = 0.001, p = 0.01, respectively). A post hoc comparison with Instagram revealed significant differences between January vs. April, January vs. May, February vs. April, February vs. March, March vs. April, and March vs. May (p = 0.01, p = 0.001, p = 0.02, p = 0.01, p = 0.01, and p = 0.001, respectively). A new post hoc comparison with YouTube showed significant differences between January vs. March, January vs. April, January vs. May, February vs. April, March vs. April, and March vs. May (p = 0.03, p = 0.01, p = 0.03, p = 0.05, p = 0.01, and p = 0.001, respectively) (see

Table 2. Post-hoc comparison between different social networks.

							Post-Hoc Comparison
Network	J	F	M	A	M	ANOVA	J vs. F	J vs. M	J vs. A	J vs. Ma	F vs. M	F vs. A	F vs. Ma	M vs. A	M vs. Ma	A vs. Ma
Facebook	98.24 ± 55.46	96.61 ± 59.45	93.78 ± 52.50	107.15 ± 68.67	118.43 ± 68.99	p = 0.31 η2 = 0.08	p = 0.79	p = 0.70	p = 0.60	p = 0.13	p = 0.81	p = 0.53	p = 0.08	p = 0.24	p = 0.11	p = 0.38
Twitter	24.54 ± 13.39	24.56 ± 13.52	26.02 ± 22.56	29.99 ± 26.03	38.10 ± 26.21	p = 0.02 * η2 = 0.27	p = 0.96	p = 0.70	p = 0.24	p = 0.01 *	p = 0.70	p = 0.24	p = 0.01 *	p = 0.02 *	p = 0.001 **	p = 0.01 *
Instagram	610.0 ± 405.86	642.77 ± 339.85	631.33 ± 383.85	726.44 ± 385.91	791.51 ± 464.55	p = 0.001 ** η2 = 0.37	p = 0.37	p = 0.59	p = 0.01 *	p = 0.001 **	p = 0.62	p = 0.02 *	p = 0.01 *	p = 0.01 *	p = 0.001 **	p = 0.13
YouTube	10.52 ± 4.25	10.67 ± 5.72	7.83 ± 4.44	14.23 ± 7.12	13.30 ± 7.25	p = 0.001 ** η2 = 0.37	p = 0.89	p = 0.03 *	p = 0.01 *	p = 0.03 *	p = 0.05 *	p = 0.01 *	p = 0.06	p = 0.001 **	p = 0.001 **	p = 0.22

Note: J, January; F, February; M, March; A, April; Ma, May. * Denotes significance at p < 0.05 and ** denotes significance at p < 0.01.

for more information).

Posteriorly, a correlation analysis was performed between the engagement mean of each network (Facebook, Twitter, Instagram, and YouTube) and budget and ranking. Crucially, no correlations were found between any variables. However, a new correlation analysis between ranking and budget revealed large significant effects (r = −0.71 and p = 0.001) (see Figure 4 for more information).

4. Discussion

Nowadays, social media greatly benefits professional sports entities in their efforts to increase their awareness, and to establish a relationship with their fans on a worldwide basis [24]. Therefore, social media has changed the way people and companies communicate and connect with their consumers [1,2,3]. In the field of sports, social media provides a unique environment for sports fans to extend their sport experiences and identities, and to facilitate sports-related expression [32], as well as a strong interactive relationship with fans and teams [19]. Additionally, sport managers have a decisive role in social media management. There are several studies that attempt to analyze the use of social networks as a sports marketing strategy [33], or as a means of advertising and promoting public relations [34]. However, these studies are far from the main objective of the present research, as they address key issues, with the exception of the study of Herrera-Torres et al. [29], or directly address the concept of engagement in the sports sector. For all those reasons, the aim of present study was to analyze the engagement of social networks of the eighteen basketball clubs that make up the Endesa ACB League, and the relation between the ranking position of the club and the engagement of their different social networks.

The results obtained show that all clubs use the social networks Facebook, Twitter, Instagram, and YouTube. These data coincide with those found by IAB Spain [35] and with the opinion of CSNs managers, those that claim highly engaged customers determine the sustainability of their CSNs [17]. In this sense, social networks are an excellent tool for fans to proclaim their passion for the team and keep fans active. Furthermore, social networks are characterized by two-way interactive communication, which entails that fans can create content and engagement with the club in question [36]. Responding to a post increases the visibility of the post, and consequently the audience of the team, to a much larger set of consumers than the actual online followers of the team [37].

The results also show that Facebook, Twitter, and Instagram are the most used networks to create interactions with their followers, highlighting the low use of YouTube despite being a social network where images and videos can be easily included [29]. Furthermore, the results obtained show a general increase in interactions on the social networks of the clubs and in the amount of followers month by month. It is believed that this circumstance may be due to the excitement of approaching the end of the season with the mission of fulfilling the objectives of each club (regular championship, qualifying for the league playoffs, staying away from relegation, etc.). Moreover, this situation would also be related to the commitment of the clubs’ fans, who offer unconditional support to their team [12]. Furthermore, fans see social media as a reliable source of information about their team, updating information and acquiring knowledge about the team in general [24].

Finally, it should be noted that the results of the correlations show that there were no significant correlations between engagement and ranking position, except in the case of Instagram and YouTube, where the results were slightly significant. The results found are in contrast to those found by Herrera-Torres et al. [29], who showed a correlation between engagement and ranking position. These results make sense if we bear in mind the words of Stavros et al. [38], who state that social networks allow users to become more intensely and continuously passionate about the sports organization if there are better results. This reinforces the idea that the higher the number of victories and the better the results, the more followers interact on social networks, which translates into higher engagement values. For all these reasons, it is suggested that further studies could be carried out to corroborate and contrast the results obtained so far with greater consistency. However, the results of the correlations between the engagement variables and the clubs’ budgets show significant differences. These results could be explained by the fact that, if there are better sporting results, there will also be a higher engagement of their fans and, therefore, a possible higher turnover of their products, such as merchandising sales [39].

5. Limitations and Future Research

This study represents an initial effort toward a deeper analysis of fans’ engagement in social media of basketball clubs. Given the global appeal of sport in general and the ACB League in particular, it would be interesting to broaden the research scope by performing a longitudinal study during the whole season. Similarly, a comparison with fan clubs of other well-known basketball leagues worldwide, such as the National Basketball Association, would increase the generality of the findings. In addition, since sport plays an important role as a communication tool, it would be interesting to see the effects of social media interactions on fans who are not members of fan clubs, and the effect it could have on ticket sales. Finally, it would be interesting to undertake some qualitative research with executives and marketing managers of the ACB League to gain insights into the teams’ social media strategy, with a view to identifying any discrepancies between fan expectations and the strategic vision of the team management.

6. Conclusions

Social media has changed the way people and companies communicate and their relationship with fans. Even with the importance of the issue, there is a gap in the literature related to fans’ engagement in social media in the field of sport. The results of the present investigation show that all the basketball clubs have social media profiles. There was no correlation between ranking position and engagement, but it could be observed that it was positive when the correlation was linked with the budget of the club.