Received: 5 January 2023 | Revised: 18 April 2023 | Accepted: 7 May 2023 DOI: 10.1111/hequ.12439 RESEARCH ARTICLE Does universities' posting strategy influence their social media engagement? An analysis of the top-­ranked higher education institutions in different countries Paul Capriotti1 | Rodolfo Martínez-­Gras2 | Ileana Zeler3 1 Department of Communication Studies, Universitat Rovira i Virgili, Tarragona, Spain Abstract 2 Department of Sociology II, Universitat de To ensure the widespread dissemination of information and Alicante, Alicante, Spain 3 to foster interaction and dialogue with users, higher educa- Department of Advertising, Public Relations and Audiovisual Communication, tion institutions need to develop an active profile on the social Universitat Autònoma de Barcelona, networks. This paper analyses the influence of universities' Barcelona, Spain posting strategy on their followers' engagement (reaction, vi- Correspondence rality and conversation) by measuring the level of activity and Ileana Zeler, Department of Advertising, Public Relations and Audiovisual type of presence on their social networks. A content analysis Communication, Universitat Autònoma was conducted to analyse 90,000 posts by 70 universities de Barcelona, Carrer de la Vinya, 08193 Bellaterra (Cerdanyola del Vallès), from Europe, the United States and Latin America on their Barcelona, Spain. institutional profiles on Twitter, Facebook and LinkedIn. The Email: ileana.zeler@uab.cat universities' posting activity on their social media is moder- Funding information ate (with an overall mean of 7.04 posts per day), but the inter- Ministerio de Ciencia e Innovación, Grant/ Award Number: PID2019-­106053GB-­I 00 action rate is very low (0.237), far below the recommended levels of engagement. Notably, increased activity by universi- ties on social networks does not lead to greater engagement but points to an inverse relationship between the two. Our findings also indicate that university-­created content (UCC) achieves a higher level of engagement (x = 169.41) than university-­shared content (USC) (x = 126.18). This study ex- plores the effect of universities' posting strategy dimensions on their follower's interaction. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. © 2023 The Authors. Higher Education Quarterly published by John Wiley & Sons Ltd. Higher Educ Q. 2023;00:1–21.  wileyonlinelibrary.com/journal/hequ | 1 14682273, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/hequ.12439 by Readcube (Labtiva Inc.), Wiley Online Library on [29/09/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 2 | CAPRIOTTI et al. 1 | I NTRO D U C TI O N Social media are digital platforms and technologies that facilitate information exchange, content sharing and community building between online users (Kaplan & Haenlein, 2010). These online platforms, known as social networking sites, microblogging, photo-­sharing platforms, video-­sharing platforms, online forums and messag- ing apps, play a predominant role in the way in which organisations communicate with their publics, as they are optimal spaces for connection between digital users (van Wissen, 2017). Their ability to establish instant and borderless communication, generate visibility and develop effective relationships favours their adoption in organ- isational communications strategies. Social networks are also encouraged to communicate with greater proximity, ease and fluency (Capriotti, Zeler, & Camilleri, 2021). To do so, an active profile must be developed to ensure the widespread dissemination of information and to foster interaction and dialogue with the public (Capriotti & Zeler, 2020). Previous research finds a direct relationship between the bases of communication management on so- cial networks and the principles of dialogic communication (Kim et al., 2014; van Wissen, 2017; Wang & Yang, 2020). Through dialogue and interaction, organisations can find out users' opinions and feelings, pro- mote their activities and increase their reputation (Fuertes-­C allén et al., 2014). Engagement is a strong link between the academic community and the audiences on social networks (Sharma et al., 2022). As engagement on social networks significantly affects relationships between organisations and their stakeholders, Brubaker and Wilson (2018) conclude that organisations' posts need to be sufficiently visually appealing as to generate likes from users, promote the sharing of their content or invite them to comment or to strike up conversations. Likes, shares and comments are drivers of social media engagement (Eger et al., 2019; Lappas et al., 2022; Swani & Labrecque, 2020). Social media have led universities to enlist new tools to help them manage their institutional communication (Aguilera Moyano et al., 2010; Hemsley-­Brown & Oplatka, 2006). Social networks promote dialogue and inter- action with students, professors, staff and community (Gori et al., 2020), fostering strong relationships between them (Kimmons et al., 2017). Various studies show that online tools are achieving greater weight in universities' institutional communication (Bonilla Quijada et al., 2022; Brech et al., 2017; Guzmán Duque & Del Moral, 2013; Peruta & Shields, 2016; Tomyuk et al., 2022), although they still lag behind the more traditional channels (Rutter et al., 2017). Nevertheless, the recent literature indicates that the Covid-­19 pandemic has led to a shift in univer- sities' institutional communication with their publics, significantly increasing the use of social media as a commu- nication tool (Rafiq et al., 2021; Sharma et al., 2022). Authors such Oliveira et al. (2022) conducted an extensive bibliometric review and pointed out that the analy- sis of digital communication tools is one of the issues of institutional communication most studied by universities. In addition, another review of the literature (with a temporal analysis over 30 years) on universities' digital commu- nication (Zeler et al., 2023) pointed out that the posting strategy implemented by institutions with their followers on social networks remains an understudied topic. Far from conducting studies on their posting strategies on social networks, scholars have focused on recognising the main communication resources and content. But the analysis of the different aspects in isolation does not allow studying the strategies developed by universities on their social networks. The literature review also indicates that most studies investigate a single social network (Arevalo et al., 2018; Fähnrich et al., 2020; Kimmons et al., 2017; Peruta & Shields, 2016) and focus on small sam- ples of universities (Alonso-­Flores et al., 2020) and countries (Eger et al., 2020). The aim of the present study is to examine the influence of universities' social network posting strategy on engagement by their publics (reaction, virality and conversation) by measuring their level of activity and type of presence on three social networks (Facebook, LinkedIn and Twitter). This research will contribute to determining which social media posting strategy dimensions affect their followers' interactions. It enriches the field of insti- tutional communication by deepening the knowledge of the strategic management of social media and by inte- grating the key dimensions (since they have usually been studied separately). This will also enable practitioners to 14682273, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/hequ.12439 by Readcube (Labtiva Inc.), Wiley Online Library on [29/09/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License CAPRIOTTI et al. | 3 improve their digital communication to boost the reach of their posts and encourage interaction with their users on social media. 2 | LITE R AT U R E R E V I E W A N D TH EO R E TI C A L FR A M E WO R K Digital platforms are playing an increasingly preponderant role in universities' institutional communication (Cancelo Sanmartín & Almansa Martínez, 2013). Social networks enable higher education institutions to con- nect with their publics, extend their reach and prove their value to society (Aguilera Moyano et al., 2010; Brech et al., 2017; Kimmons et al., 2017; Peruta & Shields, 2016). They enable organisations to understand their stake- holders better, learn about users' opinions and feelings, promote their activities and enhance their reputation (Fuertes-­C allén et al., 2014). They also enable users to talk directly to the institutions in an easier, more direct and fluid manner (Capriotti, Zeler, & Camilleri, 2021). Thus, universities must develop strategies in the digital field that foster good institutional visibility as well as dialogue and interaction with their followers. They can adopt a proactive presence on their digital platforms by properly managing two key aspects or components of their posting strategy: the frequency and intensity of the activity in which they engage on social networks and the type of presence they adopt in each case. Thus, the chosen type of presence and the level of activity undertaken will enable defining universities' posting strategies, which, in turn, may, or may not, influence the degree of engagement they achieve with their target audiences (Capriotti, Zeler, & Camilleri, 2021). This paper assesses the mediation/moderation effects of the two main di- mensions of institutions’ posting strategy (activity and presence) on users’ engagement levels on social media platforms (Figure 1). 2.1 | Activity Activity is the proactive, continuous use of social networks that enables information-­sharing and encourages interaction between an organisation and its users (Brubaker & Wilson, 2018). Several authors have investigated universities’ activity on social networks (Fähnrich et al., 2020; Gori et al., 2020;Guzmán Duque & Del Moral, 2013; Peruta & Shields, 2016). These studies show that universities implement very different posting strategies and detect important differences in levels of activity (Guzmán Duque & Del Moral, 2013; Peruta & Shields, 2016). Ideal posting frequency must be taken into account (Capriotti & Zeler, 2020). There is, however, little consen- sus on this point. Therefore, in the present study, a position is assumed based on studies conducted on posting frequency on the three social networks under study (McLachlan, 2021; Newberry, 2021; Williams, 2020). The aforementioned authors suggest that the ideal frequencies would be: Facebook (between 1 and 2 posts per day), Twitter (between 3 and 5 tweets per day) and LinkedIn (between 0.5 and 1 post per day). Various authors (Brubaker & Wilson, 2018; Capriotti, Zeler, & Camilleri, 2021; Simancas-­González & García-­ López, 2017) suggest that an active presence on social media is a key factor for successful interaction with users since it increases the chances of engaging in dialogue. It is, therefore, proposed that a higher level of activity will have a positive impact on the level of engagement. Thus, a primary research question can be formulated as fol- lows: Does the level of activity influence universities’ engagement on social networks? This question aims to analyse the statistically significant differences between regions and social networks, leading to the formulation of a first hypothesis (Figure 1): H1 = A higher level of activity will have a positive impact on the level of engagement on social networks. 14682273, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/hequ.12439 by Readcube (Labtiva Inc.), Wiley Online Library on [29/09/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 4 | CAPRIOTTI et al. FIGURE 1 The influence of posting strategy on engagement. 2.2 | Presence Presence refers to determining the type of posting that should be promoted on each platform. Having suitable profiles on social networks that best adapt to their communication needs (Kemp, 2022) enables organisations to present and disseminate their own public identity more effectively (Cho et al., 2014). Several authors have studied the digital presence of universities on social networks (Brech et al., 2017; Cancelo Sanmartín & Almansa Martínez, 2013; Guzmán Duque & Del Moral, 2013; Peruta & Shields, 2016; Sharma et al., 2022) and have concluded that institutions use digital platforms proactively to present their content. In addition to the institutional profile, some studies not only yield profiles linked to the various activities and spe- cific areas of universities (i.e. faculties, departments, master's degrees, sport, university libraries and vice-­rectors’ offices, etc.) (Brech et al., 2017; Cancelo Sanmartín & Almansa Martínez, 2013), but these authors also find the profiles of universities’ leaders and administrators (Sharma et al., 2022). This suggests that higher education cen- tres are harnessing the advantages provided by social networks to build an institutional digital presence from a global perspective. The literature identifies firm-­created content as one of the social media communication tools that involves an organisation's effort to design customer-­oriented content (Ibrahim et al., 2022). Some studies (Capriotti & Zeler, 2020; Guzmán Duque & Del Moral, 2013; Martínez-­C ardama & Pacios, 2020) recognise two main types of university presence on social networks: (a) university-­created content (UCC), in which the institution creates and disseminates its content on its profiles and (b) university-­shared content (USC), in which the institution shares con- tent from other users on its profiles (with or without adding any further customised information). Enjoying an acceptable presence on social networks enables an institution to present its attributes, activities and idiosyncrasies while giving users the option of sharing them and/or commenting on them, thus increasing ex- posure and institutional reach (Fähnrich et al., 2020; Simancas-­González & García-­López, 2017). Thus, we propose that UCC type of presence will have a more positive impact on the level of engagement. Accordingly, a second research question can be formulated: Does the type of presence influence the engagement achieved by universities on their social networks? This question aims to study the statistically significant differences between regions and social networks, leading to the formulation of a second hypothesis (Figure 1): 14682273, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/hequ.12439 by Readcube (Labtiva Inc.), Wiley Online Library on [29/09/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License CAPRIOTTI et al. | 5 H2 = Higher dissemination of university-­created content (UCC) will have a positive impact on the level of engagement on social networks. 2.3 | Engagement Engagement on social networks significantly affects relationships between organisations and their stakehold- ers. For engagement to take place, the organisation's presence and activity on digital platforms need to be good enough to generate likes from users, foster the sharing of its content or elicit comments to strike up conversations (Brubaker & Wilson, 2018). Interaction by universities on social networks has been studied by various authors (Atarama-­Rojas & Vega-­Foelsche, 2020; Eger et al., 2019; Fähnrich et al., 2020; Gori et al., 2020). Engagement is determined by measuring the various forms of interaction between organisations and users: likes, shares and comments, each of which can be considered to constitute a direct manifestation of ‘social media engagement’ by users with institutional posts (Ballesteros, 2018; Fähnrich et al., 2020). Likes suggest that people are—­in some way—­reacting to the posted content, albeit in a basic or minimal manner (Abitbol & Lee, 2017; Cho et al., 2014; Lappas et al., 2022). Awarding a like is considered to constitute affectively driven behaviour (Eger et al., 2019). Likes on social media networks would symbolise support for the organisation's content (Swani & Labrecque, 2020) and clearly denote the online user's reaction to the posted content (Anderson et al., 2016; Macnamara, 2014). Shares enable followers (or third parties in general) to become volunteer spokespersons since they promote the organisation's content (Abitbol & Lee, 2017; Cho et al., 2014). They can be regarded as constituting affective or cognitive behaviour or a combination of the two (Eger et al., 2019). Shares boost visibility by enabling users to share content at the click of a button (Swani & Labrecque, 2020). They show the virality achieved on social net- works by the digital content of the institutional communication (Anderson et al., 2016; Macnamara, 2014). Comments are the most direct form of online user interaction and dialogue on social networks (Abitbol & Lee, 2017) as they enable two-­way communication (Lappas et al., 2022; Swani & Labrecque, 2020). While com- ments and shares can be considered a more reliable source for measuring engagement (Chugh et al., 2019), com- ments are particularly employed by social media users to reply to organisations and make their opinions public (Lappas et al., 2022). Conversation on social networks is usually manifested in comments (Anderson et al., 2016; Macnamara, 2014; Romenti et al., 2016) by means of which the organisation and its users can engage in direct dialogue. Taken together, these three forms of interaction represent an institution's General Engagement on its social networks (Ballesteros, 2018; Capriotti & Zeler, 2020; Voorveld et al., 2018). Some studies point to significant differences in the recommended or acceptable levels of engagement on social networks (Adobe Express, 2022; Martinez, 2022). There is a certain consensus that optimal engagement would be equal to or greater than 1% on Facebook, equal to or greater than 0.5% on Twitter and equal to or greater 2% on LinkedIn. However, the mean of engagement on social networks is actually well below these figures. On Facebook the general mean is between 0.06% and 0.18%, on Twitter from 0.04% to 0.07%, and on LinkedIn between 0.25% and 0.5% (Adobe Express, 2022; Dixon, 2022; Feehan, 2022; Martinez, 2022). Measuring and evaluating the level of engagement will help to properly manage posts on social networks and elicit a higher degree of participation from the target audience. Based on these three types of interactions (likes, shares and comments), the Level of Engagement (the degree to which users interact on social networks) could be obtained, combining the engagement of the universities’ posts with the volume of posts published (interactions per post) in relation to the number of followers of each institution (Abitbol & Lee, 2017; Ballesteros, 2018; Capriotti & Zeler, 2020; Cho et al., 2014). Therefore, four rates of engage- ment could be identified: reaction rate (RR, related to likes), virality rate (VR, related to shares), conversation rate (CR, related to comments) and general engagement rate (GER, involving all three prior rates) (Capriotti & Zeler, 2020; 14682273, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/hequ.12439 by Readcube (Labtiva Inc.), Wiley Online Library on [29/09/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 6 | CAPRIOTTI et al. Capriotti, Zeler, & Oliveira, 2021). Then, a third research question could be established: What Level of Engagement do universities’ posts generate on their social networks? 3 | M E TH O D A content analysis was conducted to answer these questions. This technique enables the researchers to dissect the messages, identify certain aspects of the content of a text and quantify the frequency with which they occur (Gheyle & Thomas, 2017). Universities’ posts on their institutional social network profiles were studied, and in- formation on the number and type of posts and the number of followers and interactions on each social network was collected. The sample includes seventy universities that were selected based on their position in the three most pres- tigious international rankings: ARWU Ranking of World Universities, The Times Higher Education Rankings and the QS World University Rankings. Universities from three large geographical areas were chosen to make the sample more representative: Twenty from Europe (because it is a clear international benchmark), twenty-­f ive from the United States (due to the number and importance of the country's universities in the rankings and its geographical dimension), and twenty-­f ive from Latin America (due to the high level of university development in the region). All the universities from the United States and Europe were among the top 100 entities in the aforementioned rankings. The Latin American universities, while not ranked in the top 100 positions, were selected based on their general location in global rankings and by region. Geographic diversity was prioritised in the case of Latin America and Europe to represent as many countries from each region as possible in the sample (Appendix 1). The social networks selected for analysis are three of the most important at the present time: Facebook is the social network with the highest number of active monthly users worldwide—­2.91 billion users (Kemp, 2022), Twitter is deemed to be one of the main platforms for disseminating information—­436 million users (Kemp, 2022) and LinkedIn, with 900 million users, is the largest social network devoted entirely to professional activity (LinkedIn, n.d.). The universities’ official institutional profiles were identified, and any that could not be located or verified were discarded. The units of analysis were the posts published by the selected universities on their institutional Facebook, Twitter and LinkedIn accounts. All posts during a six-­month period in 2021 were recorded: three months in the first semester (13 weeks, 91 days) and three months in the second semester (13 weeks, 92 days). All posts uploaded by the universities were recorded: the final sample consisted of 90,241 posts (27,356 from Facebook, 9439 from LinkedIn and 53,446 from Twitter). The posts were manually searched and collected by a team of three people (a supervisor and two technicians) who, on a weekly basis, checked the profiles on each social network and copied and pasted the link in an Excel sheet. Three categories were defined for this study: Level of Activity, Type of Presence and Level of Engagement. These categories have been tested in prior studies (Capriotti & Ruesja, 2018; Capriotti & Zeler, 2020). Level of Activity determines the intensity or frequency of posts by the institutions on their social networks (Capriotti & Zeler, 2020; McLachlan, 2021; Williams, 2020). Thus, the total volume of the universities’ posts during the period under study was analysed to obtain the mean daily posting rate of each one. An equivalence table was developed (from 0 to 5 points at intervals of 0.05 points for LinkedIn, 0.1 points for Facebook and 0.2 points for Twitter, rounded up to the nearest whole number) since each social network has a specific recommended posting frequency according to the authors (Adobe Express, 2022; Dixon, 2022; Feehan, 2022; Martinez, 2022). This harmonises the frequency values and thus enables comparing universities’ level of activity on their social networks. Based on the mean frequencies recommended by the authors (Feehan, 2022; McLachlan, 2021; Newberry, 2021; Williams, 2020), three general activity levels were established: ‘low’ (when the daily mean is 14682273, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/hequ.12439 by Readcube (Labtiva Inc.), Wiley Online Library on [29/09/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License CAPRIOTTI et al. | 7 below the recommended level), ‘medium’ (when the daily mean reaches the suggested frequency), and ‘high’ (when the mean number of posts exceeds the recommended level). Type of Presence establishes the type of posts that the institutions upload to their social networks to distinguish their own content from content they share (Capriotti, Zeler, & Camilleri, 2021; Cho et al., 2014). Two options were identified to do so: university-­created content (UCC) and university-­shared content (USC). The latter may be shared directly (USC) or after adding a certain amount of in-­h ouse content (university-­hybrid content—­U HC). Due to the internal regime of each social network and the information publicly available at the time the research data was collected, it was not possible to determine the number of interactions obtained by USC (because the platform attributed these interactions to the original post). For this reason, when relating type of presence to engagement, USC was discarded and only UCC and UHC were considered since only they clearly enabled the researchers to identify some degree of interaction with the posts (likes, shares and comments). Level of Engagement establishes the degree to which users interact on social networks (Abitbol & Lee, 2017; Ballesteros, 2018; Capriotti & Zeler, 2020; Cho et al., 2014). The total series of interactions (likes, shares and comments) obtained by the universities was then analysed and related to the number of posts they published and their volume of followers. This enabled the researchers to obtain homogeneous results, avoiding deviation due to the effect of over-­representation of the volume of activity and the number of followers. Thus, four standardised indicators (engagement rates) were created that will enable assessing the engagement of the universities’ posts and the (possible) influence that their activity and presence could have on engage- ment: reaction rate (RR), virality rate (VR), conversation rate (CR) and general engagement rate (GER), (Capriotti & Zeler, 2020; Capriotti, Zeler, & Oliveira, 2021). They are measured as follows: • Reaction Rate (RR): the total number of likes divided by the total number of posts divided by the number of followers and multiplied by 1000. • Virality Rate (VR): the total number of shares divided by the total number of posts divided by the number of followers and multiplied by 1000. • Conversation Rate (CR): the total number of comments divided by the total number of posts divided by the num- ber of followers and multiplied by 1000. • General Engagement Rate (GER): the sum of the above three rates. The information was harvested using the platform and massive data collection system of the media analysis company Noticias Perú (www.notic​iaspe​ru.pe) by a team of three people (a supervisor and two technicians) who searched for and collected the posts. An analysis team of three (a supervisor and two trained analysts) was set up to extract the information systematically and objectively. The two analysts performed a test using a random procedure on a sample of 300 posts to assess the intercoder reliability and agreement of the method. This sample is highly suitable for properly assessing agreement and reli- ability between two analysts (Lombard et al., 2002). The percentage of calculation agreement between the two analysts is determined using 2 × 2 contingency tables and a confidence interval of 95% as the bases for the statistical analysis to find out if they obtain similar results. Cohen's kappa coefficient (k) is also calculated to assess the reliability of the categorical variables. The measurement ranges proposed by Landis and Koch (1977) were used to interpret the results. The equivalent per- centages are applied to assess the level of agreement. Agreement of 99% was obtained for the ‘Type of presence’ parameter (Kappa value of 0.99), 97% (Kappa value of 0.96) for the ‘Level of Activity’ and 91% (Kappa value of 0.83) for the ‘Level of Engagement’, proving a high degree of agreement in the tool's criteria. Consequently, the measurement can be considered valid. 14682273, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/hequ.12439 by Readcube (Labtiva Inc.), Wiley Online Library on [29/09/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 8 | CAPRIOTTI et al. Content analysis was first conducted to obtain raw data from the collected posts, and it was entered in an Excel sheet. Subsequently, it was coded and input into the IBM SPSS Statistics 25 statistical program for analysis and statistical processing by the research team. The statistical tests used in the analysis are non-­parametric: in the case of correlations, Spearman's Rho test was applied. The Kruskal–­Wallis H test was chosen to contrast means and ranges. 4 | R E S U LT S The seventy universities analysed generated 90,241 social network posts in the period under study with an overall mean of 7.04 posts per day, which, however, are unevenly distributed by region and network. There are some statistically significant differences by region. Latin American institutions (48.7% of all posts and 9.61 posts/day) are clearly the most active, followed by North American universities (27.4% of the total and 6.76 posts/day) and finally European institutions (23.9% of the posts and only 4.71 posts/day). Statistically significant differences were also observed by social network. The most used platform is Twitter (59.2% of all posts and 4.17 posts/day) followed by Facebook (with 30.3% of the posts and 2.14 posts/day), while LinkedIn (10.5% of all posts and 0.74 post/day) lags a considerable way behind the other two. 4.1 | Universities' level of engagement The 75,404 UCC and UHC generated total interaction (the sum of likes, shares and comments) of 12,681,800 hits, with a mean of 168.2 interactions/post (USC was not studied since interactions could not be attributed to them). Reaction (likes), accounting for 84.8% (n = 10,749,721) of the total, is the main type of interaction of the three that can be generated. It is followed at a great distance by virality (shares) (11.8%, n = 1,494,721) and conversation (comments) (3.4%, n = 437,364). By social network, Facebook (7,419,763; 58.5%) accounts for the majority of interactions, followed by LinkedIn (3,032,683; 23.9%), which only just exceeds a fifth of all the interactions, while Twitter (2,229,354; 17.6%) brings up the rear. Latin America (with 6,447,450; 50.9%) leads the field by region regarding the number of interactions, while the United States (3,755,780; 29.6%), with almost one-­third of the total volume, is a distant second and Europe (2,478,570; 19.5%) comes last. With the sole exception of the reaction rate of Latin American universities, analysis by social network in each region (Table 1) shows an association and statistically significant differences between posts published by the uni- versities and the engagement rates they generate. Latin America is the clear leader on Facebook with a high mean of posts per day (3.91) and a high overall en- gagement rate (1.151), while Europe and the United States obtain far more modest results: both regions yield the same mean of posts (1.15 and 1.15) and a fairly similar engagement rate (0.720 and 0.863). Latin America also achieves the highest daily mean (5.09) on Twitter, but its engagement rate (0.636) is quite similar to that of Europe (0.540), which, however, records almost half the posts. The United States universities issue a high mean of posts (4.83, very close to Latin America), but their engagement rate is by far the lowest of all three regions (0.197). The mean of posts on LinkedIn is quite similar in all three regions (EUR = 0.82; USA = 0.79; LAT = 0.61). The interaction rate, however, is very different: it is highest in Europe (1.448), scores a good mean in the United States (1.049) and is poor in Latin America (0.655). 14682273, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/hequ.12439 by Readcube (Labtiva Inc.), Wiley Online Library on [29/09/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License CAPRIOTTI et al. | 9 TA B L E 1 Comparative analysis of mean activity and engagement rates by region and social network. Typical Posts deviation GER Region x /daily σ RR rate VR rate CR rate rate EUR Twitter 2.73 2.108 0.411 0.113 0.016 0.540 Facebook 1.15 0.825 0.629 0.055 0.036 0.720 LinkedIn 0.82 0.754 1.428 0.001 0.02 1.448 H (sig) 0.00 0.001 0.001 0.008 0.003 USA Twitter 4.83 3.472 0.16 0.033 0.004 0.197 Facebook 1.15 0.528 0.751 0.061 0.051 0.863 LinkedIn 0.79 0.593 1.018 0 0.031 1.049 H (sig) 0.00 0.001 0.001 0.001 0.001 LAT Twitter 5.09 4.954 0.483 0.143 0.01 0.636 Facebook 3.91 3.915 0.883 0.225 0.043 1.151 LinkedIn 0.61 0.669 0.637 0 0.018 0.655 H (sig) 0.00 0.57 0.001 0.001 0.026 Note: H = Kruskal–­Wallis H test Sig .05. Reaction Rate (RR) = Likes/Followers/Posts*1000; Virality Rate (VR) = Shares/ Followers/Posts*1000; Conversation Rate (CR) = Comments/Followers/Posts*1000; General Engagement Rate (GER) = ∑ likes, shares, comments/∑ followers/∑ posts*1000. Italics refers to the significance of the results (not the results themselves). 4.2 | Influence of the level of activity on the level of engagement A series of significant outcomes were observed at both the general social network level and in the regions under study to find out if the universities' level of activity on social networks influences the engagement rates generated by their posts. By social network (Table 2), it can be clearly seen that universities with a lower level of activity manage to gen- erate higher interaction rates. A moderate increase in interaction rates is observed on all three social networks in universities with a moderate level of activity compared to those with a high level. However, there is a very signifi- cant increase in engagement rates (in many cases double or triple) among low-­engagement institutions compared to medium-­and high-­activity universities. By region, the correlation analysis between the activity level and the interaction rates through social networks (Table 3) enables us to observe several important aspects that must be taken into consideration. The most signifi- cant is the largely negative correlation between two activity levels (medium and high) and the various engagement rates on Twitter and Facebook, while on LinkedIn the association is somewhat more positive. In Europe, the high negative correlation on Facebook (rs = −.846; sig. .01 bilateral) that occurs between the level of activity and the overall interaction rates obtained stands out. This means that the engagement rate drops when more posts are uploaded to the platform. A similar trend is detected on Twitter, although in this case, the association is weaker. In the United States, a moderately negative association is also observed on Twitter for all engagement rates, especially the virality rate (rs = −.722; sig. .01 bilateral), in which a high, inverse correlation can be observed rather than a moderate one in the opposite direction. 14682273, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/hequ.12439 by Readcube (Labtiva Inc.), Wiley Online Library on [29/09/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 10 | CAPRIOTTI et al. TA B L E 2 Level of engagement by level of activity on social media. Universities Level of activity RR CR GER Social media No. Level Media posts/day rate VR rate rate rate TOTAL 70 Medium 7.04 0.199 0.030 0.008 0.237 23 High 13.34 0.112 0.018 0.004 0.134 21 Medium 6.00 0.125 0.012 0.005 0.142 26 Low 2.32 0.335 0.057 0.013 0.405 Twitter 70 Medium 4.17 0.365 0.101 0.010 0.476 23 High 8.33 0.152 0.046 0.004 0.202 14 Medium 3.70 0.241 0.056 0.006 0.303 33 Low 1.47 0.566 0.159 0.016 0.741 Facebook 70 Medium 2.14 0.755 0.117 0.043 0.915 20 High 5.01 0.460 0.086 0.025 0.571 25 Medium 1.36 0.789 0.111 0.039 0.939 25 Low 0.60 0.956 0.149 0.061 1.166 LinkedIn 70 Medium 0.90 1.043 0.00040 0.023 1.066 21 High 1.57 0.770 0.00044 0.018 0.789 18 Medium 0.74 0.826 0.00020 0.024 0.851 18 Low 0.29 1.638 0.00081 0.041 1.679 13 Inactive 0.00 In Latin America, universities' activity level on Twitter also displays a moderate, negative association between the reaction rate and the general engagement rate. On LinkedIn, however, the association becomes positive and there is a moderate correlation in all cases that suggests that an increase in posts may be related to higher en- gagement rates. 4.3 | Influence of type of presence on level of engagement With respect to the type of presence, UCC predominates over sharing by a large margin whereas there are no statistically significant differences in the results by network or region (Table 4). Of the 90,241 posts, 81.2% are UCC, and of the 18.8% of USC, 2.4% are UHC. No notable differences are observed in distribution by region. Of the total of 12,681,800 interactions, 97.9% involved UCC and only 2.1% UHC. USC was discarded from the analysis because it failed to record any interaction. To determine the existence of statistically significant homogeneity between interaction level and type of pres- ence, the mean contrast analysis at the between-­group level (Table 4) clearly confirms that UCC produces more interactions than USC, with a mean of 169.41 per post. This figure is much higher than the mean of interactions produced by UHC, which stands at 126.18. The similarities and differences between the two types of posts were also analysed by region and social network based on the mean values observed for reaction, virality, conversation and general engagement rates (Table 5). Broadly speaking, the general engagement rate shows statistically significant differences by type of post across the three regions. The rate is higher for UCC in both the European and in the Latin American universities, but 14682273, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/hequ.12439 by Readcube (Labtiva Inc.), Wiley Online Library on [29/09/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License CAPRIOTTI et al. | 11 TA B L E 3 Analysis of the correlation between level of activity and interaction rates by social network and region. RR rate VR rate CR rate GER rate R R R R EUR Twitter −.568** −.507** −.563** −.561** Facebook −.859** −.748** −.709** −.846** LinkedIn −0.177 0.263 0.003 −0.179 USA Twitter −.557* −.722** −.669** −.605** Facebook 0.024 −0.116 −0.093 −0.018 LinkedIn −0.331 0.319 −0.209 −0.334 LAT Twitter −.460* −0.372 −0.337 −.452* Facebook −0.388 −0.315 −0.271 −0.389 LinkedIn .567** .677** .554** −.561** EUR R (next) −.596** 0.205 −.477** −.584** USA R (next) −.646** .293* −.617** −.644** LAT R (next) −0.052 .426** 0.054 −0.018 Twitter R (next) −.516** −.507** −.515** −.530** Facebook R (next) −.434** −0.041 −.316** −.377** LinkedIn R (next) 0.109 .409** 0.168 0.104 Note: R = Spearman's Rho test. *Sig. .05 (bilateral); **Sig. .01 (bilateral). TA B L E 4 Contrast of the between-­group mean of total interaction by type of presence. N total Effect Presence x 𝝈 interaction Sig. 1−β size UCC 169.41 343.823 12,410,755 .001** 0.99 d = 0.12 UHC 126.18 300.803 271,045 USC*** 0.00 0.00 **Mann–­Whitney U test/Effect size: d = 0.20 (small); 0.50 (medium); 0.80 (large); ***No interaction by university-­shared content presence was recorded. this effect is reversed in the North American universities where UHC (x = 1010; σ = 2456) obtains greater inter- action than its UCC counterpart. Something similar occurs with respect to the reaction rate (x = .901; σ = 2.179). Differences in virality rates are only observed in North American and Latin American universities and, again, UHC 14682273, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/hequ.12439 by Readcube (Labtiva Inc.), Wiley Online Library on [29/09/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 12 | CAPRIOTTI et al. obtains higher virality in North American institutions (x = .103; σ = .739). Finally, there are significant differences between European and Latin American universities with respect to the conversation rate, but in this case, it is UCC that records the highest rates. The effect observed differs marginally between social networks (Table 5). On Twitter, higher interaction rates are obtained for UHC in North American universities and for UCC in European and Latin American universities. The aforesaid effect on North American institutions is even more pronounced on Facebook. The result for UHC is marginally higher for all rates among European institutions, although the differences are not statistically signifi- cant, while UCC continues to obtain higher interaction rates in Latin America. Significant differences are observed on LinkedIn for European and Latin American universities where UCC records higher interaction rates. In US insti- tutions, UHC continues to record higher engagement rates. 5 | D I S CU S S I O N In this article, we have studied followers' interactions with posts by universities in Europe, the United States and Latin America on Twitter, Facebook and LinkedIn to try to understand users' behaviour. The data obtained enables us to broach certain topics. Concerning their Level of Engagement, universities' general activity level (mean of daily posts) on their social media could be considered moderate (approximately seven posts per day) and barely adequate based on recom- mendations by the experts (Feehan, 2022; McLachlan, 2021; Newberry, 2021; Williams, 2020). However, the interaction rate is very low both overall and by social network and region, far below the levels of engagement recommended in the studies we analysed (Adobe Express, 2022; Feehan, 2022; Martinez, 2022). This would be in line with the results obtained in other studies (Fähnrich et al., 2020; Gori et al., 2020) that also remark on the low level of interaction obtained by university posts. The differences with respect to social networks, however, enable us to draw some important considerations (Table 6). On the one hand, LinkedIn only generated 12.4% of the posts but elicited 23.9% of the interactions, with a mean of 325.11 interactions/post. Moreover, similar results were achieved on Facebook, which scored a higher proportion (35.9% of posts and 58.5% of interactions) but a lower mean of interactions per post (273.66 interactions/post). Twitter, in stark contrast, accounted for 51.5% of the total posts but only 17.6% of the interac- tions. These data enable us to reflect on the particularities of how each social network is used and how this can condition the results. LinkedIn is designed to inform its users about professional activities and establish work-­ related contact networks (Arevalo et al., 2018; IAB Spain and ELOGIA, 2022) by means of which it stimulates in- formation exchange and person-­to-­person interaction. Facebook prioritises sharing content and narratives related to personal values and activities (Capriotti & Zeler, 2020; IAB Spain and ELOGIA, 2022) that foster interaction with the community. Twitter, on the other hand, is characterised by intense dissemination of information and retweeting (people sharing each other's content) (Capriotti & Ruesja, 2018; IAB Spain and ELOGIA, 2022; Kimmons et al., 2017) and, therefore, basically fosters a one-­way informative stream, mainly about current affairs. Some differences can be observed concerning the regions (Table 6), although they are moderate. The North American universities obtained better results than those in other regions: they issued 24.7% of the posts and ob- tained 29.6% of all interactions with a mean of 201.55 interactions/post. Latin America made 53.4% of the posts but yielded 50.9% of the interactions and ended up with a mean of 160.34 interactions/post. And the European universities generated 21.9% of the posts and yielded only 19.5% of the interactions, obtaining a mean of 149.65 interactions/post. Figure 2 shows the general orientation of the results concerning the influence of the level of activity on the level of engagement. A bivariate correlation analysis using Spearman's Rho test between the number of posts and the overall interaction rate shows that the correlation is significant at the two-­t ailed .001 level with a low-­ moderate correlation coefficient (−.373): the level of engagement decreases as the level of activity increases. TA B L E 5 Mean contrast analysis of type of presence and engagement rates by region and social network. RR rate VR rate CR rate GER rate Social media Region Type x σ U x σ U x σ U x 𝝈 U CAPRIOTTI et al. Twitter EUR UCC .465 5.844 .004 .137 1.024 .001 .018 .150 .001 .620 6.889 .001 UHC .294 .574 .076 .166 .007 .022 .377 .723 USA UCC .195 .599 .001 .039 .098 .002 .005 .025 .063 .238 0.689 .001 UHC .534 1.147 .052 .099 .005 .010 .591 1.43 LAT UCC .272 1.701 .001 .089 .629 .001 .006 .064 .917 .367 2.324 .001 UHC .188 .853 .049 .188 .008 .091 .245 1.052 Facebook EUR UCC .393 .957 .363 .033 .127 .112 .022 .134 .219 .448 1.117 .348 UHC .462 1.316 .034 .102 .025 .100 .521 1.450 USA UCC .709 1.475 .001 .056 .202 .001 .042 .178 .001 .807 1.691 .001 UHC 2.384 4.040 .235 .515 .110 .203 .730 4.628 LAT UCC .576 2.442 .001 .134 .891 .001 .029 .186 .001 .740 3.179 .001 UHC .153 .387 .022 .047 .006 .040 .181 .448 LinkedIn EUR UCC .909 2.959 .001 .0004 .008 .408 .019 .070 .001 .928 2.996 .001 UHC .492 1.195 .011 .029 .503 1.221 USA UCC .898 2.100 .458 .0001 .003 .766 .027 .108 .798 .925 2.170 .455 UHC 1.479 2.934 .052 .102 1.531 3.036 LAT UCC .702 1.482 .001 .001 .018 .612 .019 .054 .001 .723 1.523 .001 UHC .138 .298 .0001 .0005 .001 .004 .139 .301 Total EUR UCC .543 4.210 .001 .072 .694 .900 .019 .131 .001 .634 4.886 .001 UHC .352 .861 .058 .146 .011 .048 .421 .973 USA UCC .421 1.221 .001 .037 .124 .001 .017 .098 .217 .474 1.330 .001 UHC .901 2.179 .084 .246 .025 .097 1.010 2.456 LAT UCC .436 2.055 .001 .103 .739 .001 .017 .133 .001 .556 2.701 .001 UHC .171 .679 .035 .143 .007 .072 .214 .830 | Note: Reaction Rate (RR) = Likes/Followers/Posts*1000; Virality Rate (VR) = Shares/Followers/Posts*1000; Conversation Rate (CR) = Comments/Followers/Posts*1000; General 13 Engagement Rate (GER) = ∑ likes, shares, comments/∑ followers/∑ posts*1000. H = Kruskal–­Wallis H test Sig .05. 14682273, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/hequ.12439 by Readcube (Labtiva Inc.), Wiley Online Library on [29/09/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 14682273, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/hequ.12439 by Readcube (Labtiva Inc.), Wiley Online Library on [29/09/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 14 | CAPRIOTTI et al. TA B L E 6 Comparison of between-­group total interaction means by region and social network. Posts Interaction Interaction/post Region % % x Sig. 1−β Effect size EUR 21.9 19.5 149.65 0.001* 1 f = 0.04 USA 24.7 29.6 201.56 LAT 53.4 50.9 160.34 TOTAL 100 100 168.18 TWT 51.7 17.6 57.21 0.001* 1 f = 0.28 FBK 35.9 58.5 273.66 LKN 12.4 23.9 325.11 *Kruskal–­Wallis H test/Effect size: f = .10 (small); .25 (medium); .40 (large). FIGURE 2 Level of engagement by level of activity. These results confirm the conclusions obtained in other research (Fähnrich et al., 2020; Gori et al., 2020; Zamora & Battung, 2020), which also observe that a lower level of activity by universities on social networks is associated with a higher level of engagement in their posts. It can also be seen through a moderate association on the social networks (Figure 3) that universities that have a lower level of activity (mean of daily posts) on Twitter and Facebook also manage to generate a higher level of engagement (general engagement rate), whereas LinkedIn does not show such a direct and clear relationship and there are no statistically significant differences. Consequently, these results verify that by increasing their level of activity on social networks, universities do not achieve greater engagement by their followers, rather there is an inverse relationship between the two. In view of this, Hypothesis 1 would be refuted (H1 = A higher level of activity will have a positive impact on the level of engagement on social networks). These results are in line with other recent studies (Brubaker & Wilson, 2018; Gori et al., 2020; Zamora & Battung, 2020). Concerning the influence of the type of presence on the level of engagement, the general results (Figure 4) indicate that UCC achieves a higher level of engagement than USC, although the influence is only moderate (UCC 14682273, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/hequ.12439 by Readcube (Labtiva Inc.), Wiley Online Library on [29/09/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License CAPRIOTTI et al. | 15 FIGURE 3 Level of engagement by level of activity by social network. FIGURE 4 Level of engagement by type of presence. produces 169 interactions/post compared to 126 for UHC). The better rates of UCC obtained are in line with other studies (Arevalo et al., 2018; Brech et al., 2017; Peruta & Shields, 2016). It can also be seen (Figure 5) that UCC obtains higher levels of engagement than USC on all three social networks. The differences, however, are moderate on Twitter and Facebook and much stronger on LinkedIn. Although universities recognise the potential of LinkedIn, suitable strategies to generate connection and collabo- ration with stakeholders are still lacking (Arevalo et al., 2018). By region, broadly speaking, there are clear statistically significant differences: in European and Latin American universities the level of engagement is higher for UCC, but in North America it is UHC that achieves a higher de- gree of interaction. There are also differences between regions on the basis of social networks. North American universities obtain higher interaction rates with USC on all the three social networks. Latin American institutions obtain a higher level of engagement with their UCC across all three social networks. However, the behaviour is more diverse in Europe: UCC elicits more interaction on Twitter and LinkedIn, while USC yields better engagement results on Facebook. The data, therefore, enable us to conclude that the type of presence universities transmit on their social networks influences their followers' engagement since UCC generates a higher level of interaction than USC. However, there are significant differences between regions and social networks. For these reasons, Hypothesis 2 could only be partially confirmed (H2 = Higher dissemination of university-­created content (UCC) will have a positive 14682273, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/hequ.12439 by Readcube (Labtiva Inc.), Wiley Online Library on [29/09/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 16 | CAPRIOTTI et al. FIGURE 5 Level of engagement by type of presence on social networks. impact on the level of engagement on social networks). These results reinforce the conclusions of other studies (Brech et al., 2017; Cancelo Sanmartín & Almansa Martínez, 2013; Guzmán Duque & Del Moral, 2013; Peruta & Shields, 2016), which indicate that information disseminated by universities on social networks about their core activities achieves a higher level of engagement than posts on general topics related to the university's environment. The outcomes of this research could have clear practical and managerial implications since they can provide practitioners with relevant results that will help them to manage the active presence (or posting strategy) of social media strategic communication. Thus, this study offers communication practitioners some key findings for carry- ing out their posting strategy for their social networks: • Our results show that the best presence (combination of posts) should be with a clear majority of UCC posts. An effective strategy for generating engagement on social media is for universities to implement the 4-­1-­1 rule (Beese, 2019): four posts by the university with significant topics for its followers (UCC), one post by the uni- versity of its own interest (UCC) and one post sharing information of other accounts (USC). Therefore, a good combination of posts is around 85% UCC and 15% USC, although these proportions should be slightly modified for each platform. • Moreover, our findings also suggest that universities should have a more passive profile on social media, with a total mean of 2–­3 posts per day (around 1–­2 tweets/day on Twitter, 0.5–­1 posts/day on Facebook and 0.25–­0.5 posts/day on LinkedIn). However, the posts should be significant and engage their followers. 6 | CO N C LU S I O N S The above results and reflections enable us to draw some important conclusions concerning the impact of univer- sities' posting strategy on the level of user engagement. The low interaction results they obtained lead us to ponder the reasons why universities' followers are somewhat passive on social networks. The differences between the regions in terms of engagement are moderate rather than substantial, so it could be said that universities, in general, are not properly analysing the particular characteristics of followers or perhaps they are not responding to users' interests, disseminating information that lacks relevance to them. We have also noted that the idiosyncrasies of each social network play an important part in posting strategy. LinkedIn and Facebook seek to promote interaction through their predominantly relational and interactive nature, while Twitter tends to promote the dissemination of information with a more unidirectional approach. Two important ideas can be brought to bear concerning the key aspects of posting strategy (level of activity and type of presence). On the one hand, we can conclude that a higher level of activity by universities on social networks does not necessarily lead to a higher level of user engagement (H1): the lower the volume of posts, the higher the 14682273, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/hequ.12439 by Readcube (Labtiva Inc.), Wiley Online Library on [29/09/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License CAPRIOTTI et al. | 17 interaction rates with certain slight nuances between the different social networks (more pronounced on Twitter and Facebook and more moderate on LinkedIn) and no significant differences are found between the regions. Consequently, future research could investigate whether this relationship also holds for different sectors (only higher education institutions were analysed) and for other social networks (three social networks were studied). Moreover, as a general rule, the type of presence affects the users' level of engagement (H2) on all three social networks: UCC yields better interaction results than USC. Therefore, universities would seem to be focusing their posting strategy on their UCC and wasting the opportunity to promote engagement by sharing posts by other users. However, these results are not fully conclusive since there are some significant variations by regions and social networks (such as in the United States and on Facebook). It would, therefore, be advisable to perform fur- ther studies to verify or reject the results presented above. This article enables us to recognise the key dimensions of posting strategy and assess whether it influences the level of engagement. This will enable other researchers to use the method to develop their projects on these subjects since it will be important to assess the results in other types of organisations and social networks to consolidate this area of knowledge. This study's main limitations are that it analyses only one sector and a certain type of institution (universities) on specific social networks (Facebook, Twitter and LinkedIn). In addition, other aspects that may influence or be relevant to interaction are not studied or taken into account in this work, such as the communication resources used in social media posts by higher education institutions or the types of post content (Fähnrich et al., 2020; Peruta & Shields, 2016). Thus, future research could be complemented to assess how other aspects influence engagement. It would be interesting to find out how the type of content affects these factors since it is difficult to get users sufficiently involved to interact if the content fails to include participation strategies (Brubaker & Wilson, 2018). Using attractive resources for users would also arouse more engagement (Fähnrich et al., 2020; Peruta & Shields, 2016). This points to the option of studying and analysing the types of resources applied in the institutions' posts. Another aspect to assess would be the influence of universities' popularity or reputation (Harvard, Oxford, etc.) on the interactions they elicit. Finally, it would be interesting to apply this method to other increasingly consolidated social networks (i.e. Instagram or TikTok). AU T H O R C O N T R I B U T I O N S Paul Capriotti: Conceptualization; Methodology; Writing -­original draft; Writing -­review & editing; Supervision; Funding acquisition; Project administration; Validation. Rodolfo Martínez-­Gras: Methodology; Software; Formal analysis; Visualization; Writing -­ original draft. Ileana Zeler: Investigation; Conceptualization; Data curation; Resources; Writing -­original draft; Writing -­review & editing. AC K N O​W L E D ​ G E ​M E N T S This article is part of the competitive R&D&I project on ‘La Comunicación Institucional Digital 2.0 de las Universidades’ (Digital Institutional Communication 2.0 of Universities) (PID2019-­106053GB-­I 00), funded by the Spanish Ministry of Science and Innovation. C O N FL I C T O F I N T E R E S T S TAT E M E N T The authors declare that they have no known conflict of interest or personal relationships that could appear to influence the work reported in this paper. DATA AVA I L A B I L I T Y S TAT E M E N T The data that support the findings of this study are available from the corresponding author upon reasonable request. 14682273, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/hequ.12439 by Readcube (Labtiva Inc.), Wiley Online Library on [29/09/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 18 | CAPRIOTTI et al. ORCID Paul Capriotti https://orcid.org/0000-0002-9398-5886 Rodolfo Martínez-­Gras https://orcid.org/0000-0002-7423-4374 Ileana Zeler https://orcid.org/0000-0002-5550-1000 REFERENCES Abitbol, A., & Lee, S. Y. (2017). Messages on CSR-­dedicated Facebook pages: What works and what doesn't. Public Relations Review, 43(4), 796–­8 08. https://doi.org/10.1016/j.pubrev.2017.05.002 Adobe Express. (2022). Your guide to social media engagement rates. Learn target engagement rates for Facebook, LinkedIn, Instagram, and Twitter. www.adobe.com. https://www.adobe.com/expre​ss/learn/​blog/what-­is-­a-­good-­ socia​l-­media​-­engag​ement​-­rate#what-­is-­a-­good-­socia​l-­media​-­engag​ement​-­rate Aguilera Moyano, M., Farías Batlle, P., & Baraybar Fernández, A. (2010). 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Higher Education Quarterly, 00, 1–21. https://doi.org/10.1111/ hequ.12439 14682273, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/hequ.12439 by Readcube (Labtiva Inc.), Wiley Online Library on [29/09/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License CAPRIOTTI et al. | 21 APPENDIX 1 Sample of Universities Europe United States Latin America University of Oxford Harvard University Universidad de Buenos Aires University of Cambridge Stanford University Universidad Nacional de Córdoba University College London MIT (Massachusetts Institute of Universidad Nacional de La Plata Technology) Imperial College London Princeton University Universidad Austral University of Edinburgh Columbia University Universidad de Sao Paulo University of Manchester California Institute of Technology Universidad de Campinas (Caltech) King's College London University of Chicago Universidad Federal de Rio de Janeiro University of Bristol Yale University Universidad Federal de Minas Gerais London School of Economics John Hopkins University Universidad Católica de Rio de Janeiro and PS University of Warwick University of Pennsylvania Universidad Católica de Rio Grande do Sul Sorbonne University University of Michigan—­Ann Arbor Universidad de Chile Paris Science et Lettres—­PSL University of North Carolina—­Chapel Pontificia Universidad Católica de Hill Chile Paris Saclay University of California—­Berkeley Universidad de Concepción Heidelberg University University of Washington—­Seattle Universidad de Santiago de Chile University of Munich (LMU) Purdue University—­West Lafayette Universidad Nacional de Colombia Technical University of Munich University of Illinois—­Urbana Universidad de Antioquia Champaign Swiss Federal Institute of University of Texas—­Austin Pontificia Universidad Javeriana Technology Zurich University of Zurich University of Wisconsin—­Madison Universidad de Los Andes (Colombia) Swiss Federal Institute of University of Maryland—­College Park Universidad Nacional Autónoma de Technology Lausanne México Utrech University University of Minnesota—­Twin Cities Universidad Autónoma Metropolitana University of Amsterdam Benemérita Universidad Autónoma de Puebla Karolinska Institute TEC de Monterrey University of Oslo Universidad Nacional Mayor de San Marcos University of Helsinki Universidad San Francisco de Quito University of Copenhagen Universidad de la República