Customer engagement behaviors on physical and virtual engagement platforms

Abstract

Purpose

This study aims to examine and compare the influence of the disposition to engage in engagement behaviors on physical and virtual engagement platforms, as well as the influence of these engagement behaviors on brand loyalty, value-in-use and word-of-mouth.

Design/methodology/approach

Data were collected using a survey distributed to a random sample of 10,000 fans of five teams in the Swedish top-division of elite football. An exploratory factor analysis was performed to derive a distinction between prevalent platforms, scales were validated through a confirmatory factor analysis and structural equation modeling was used to test the research model.

Findings

Customer disposition to engage with the sports team had a significant influence on customer engagement behaviors on both physical and virtual engagement platforms. However, engagement behaviors on virtual platforms were found to be more important than engagement behaviors on physical platforms for fostering brand loyalty and value-in-use.

Practical implications

The results highlight the importance of engagement behaviors with a brand on virtual engagement platforms. Thus, brand managers should prioritize their presence on social media to generate the positive outcomes of customer engagement behaviors.

Originality/value

By examining the effects of customer engagement behaviors on both physical and virtual engagement platforms, this study provides new insights to the emerging customer engagement literature.

Citation

Winell, E., Nilsson, J. and Lundberg, E. (2023), "Customer engagement behaviors on physical and virtual engagement platforms", Journal of Services Marketing, Vol. 37 No. 10, pp. 35-50. https://doi.org/10.1108/JSM-03-2023-0084

Introduction

In the past decade, customer engagement has become a fundamental concept in marketing and consumer research (Hollebeek et al., 2022). Customer engagement has, for instance, been identified as an important antecedent to many desirable outcomes, such as brand loyalty (Fernandes and Esteves, 2016; Yoshida et al., 2014), word-of-mouth (Vivek et al., 2012) and value-in-use (Behnam et al., 2021). Thus, it is not surprising that many brands judge customer engagement to be a top priority and invest much effort and many resources into creating structures and platforms that allow for customer engagement (Braze, 2021). In practical terms, this prioritization of customer engagement often implies a focus on communication and interaction, such as substantial investments in platforms such as Facebook and Instagram but also physical platforms such as consumer fairs and pop-up stores (Braze, 2021; Read et al., 2019).

Engagement platforms can be defined as the physical and virtual touchpoints, arenas or “places” within service ecosystems where engagement behaviors are realized (Breidbach and Brodie, 2017), and studies have shown how these play a vital role in enabling customer engagement behaviors (Blasco-Arcas et al., 2016; Blut et al., 2023). However, even though such engagement platforms are essential for realizing customers’ disposition to engage (Behnam et al., 2021), there is an evident gap of research on how different types of engagement platforms, such as physical and virtual platforms, influence customer engagement behavior and its potential outcomes. For instance, in a recent review of the topic, Blut et al. (2023) note that despite the importance of platforms,

[…] we find few studies examining the influence of platform characteristics on the effectiveness of engagement strategies, possibly because most research in this domain features a single sample on one platform, and thus cannot undertake a comparative assessment.

Thus, instead of performing comparative studies, many scholars have conducted research on engagement platforms that focuses on one type of platform. For instance, many recent studies have focused on the specifics of virtual engagement platforms (de Oliveira Santini et al., 2020; Unnava and Aravindakshan, 2021). The recent decade has indeed given birth to several virtual engagement platforms, such as Facebook and Instagram pages, online forums and online social marketplaces, such as Amazon.com (Blasco-Arcas et al., 2020; de Oliveira Santini et al., 2020). However, physical engagement platforms, such as consumer fairs, physical stores and other types of physical (face-to-face) events, remain important for physical engagement behaviors (Leipämaa-Leskinen et al., 2022).

In all, we have very little knowledge about how different types of virtual and physical platforms compare in their influence on customer engagement behaviors (Blut et al., 2023). Considering that engagement platforms facilitate customer engagement behavior, as they provide the key touch points where customers and brands meet and thereby provide the structure and limits of engagement behavior (Breidbach et al., 2014), this is concerning.

Against this background, the aim of this study is to, in an exploratory manner, examine and compare the influence of the disposition to engage in engagement behaviors on physical and virtual engagement platforms, as well as the influence of these engagement behaviors on brand loyalty, word-of-mouth and value-in-use. In accordance with this, two research questions are formulated:

RQ1.

How does customer disposition to engage with a brand influence engagement behaviors on physical and virtual platforms?

RQ2.

How do engagement behaviors on physical and virtual platforms influence brand loyalty, word-of-mouth and value-in-use?

To fulfill this aim, we focus on fans of Swedish elite football [1]. Elite football is recognized for the high levels of customer engagement (fan engagement; intense physical and virtual interactions; and intimate relationships among fans, teams and other actors (Woratschek et al., 2014, 2020), and as such, it provides an appealing context for exploring engagement behaviors on both physical and virtual platforms. Given the lack of previous research which examine customer engagement behaviors on both physical and virtual engagement platforms (Blut et al., 2023), this paper contributes much needed exploratory insights to the field. More specifically, we contribute to the engagement platform literature by empirically analyzing how engagement behaviors on physical and virtual engagement platforms have different importance in predicting outcomes of customer engagement. Thus, from both a scholarly and a managerial perspective, this study yields valuable insights in how brands should use both physical and virtual platforms to engage their customers.

Literature review

Customer engagement

Customer engagement has been approached and defined in many ways (Harmeling et al., 2017). However, in the literature on customer engagement, there are essentially two main research streams. One of these streams largely focuses on the behavioral manifestations of customer engagement with a focal brand (Carlson et al., 2018; Barari et al., 2020). Here, customer engagement is primarily viewed as the nontransactional interactions and specific activities that occur between customers and brands (Verhoef et al., 2010). These include interacting with employees at a retail store, chatting with a brand representative in an online marketplace (Behnam et al., 2021) or providing feedback to the brand on social media (Carlson et al., 2018).

The other major stream focuses on the multidimensional perspective of customer engagement (Dessart et al., 2016). Here, customer engagement is viewed as a customer’s investment of cognitive, emotional, behavioral and social resources in interactions with a brand (Brodie et al., 2011). A main rationale for this perspective is that if all four dimensions of customer engagement are included, researchers can fully capture the interactive and experiential nature of contemporary relationships with entities such as brands, products and brand communities (Morgan-Thomas et al., 2020).

In this study, we align with Hollebeek et al. (2014) and focus on the behavioral manifestations of customer engagement. This implies that customer engagement behaviors are “a customer’s positively valenced brand-related cognitive, emotional and behavioral activity during or related to focal consumer/brand interactions” (Hollebeek et al., 2014, p. 154). Hence, we consider customers’ disposition to engage as the driving force, i.e. the antecedent to engagement behaviors on physical and virtual platforms (Neghina et al., 2014). As such, customers’ disposition to engage with a brand is important for the following customer engagement behaviors with a brand: attending brand-related events or interacting with the brand on social media (Carlson et al., 2018; Unnava and Aravindakshan, 2021).

Engagement platforms: physical and virtual

Previous marketing and consumer behavior literature highlights that it is important that customer engagement is understood in relation to its specific context (Hollebeek et al., 2019; Hollebeek and Macky, 2019) or, as suggested by Breidbach et al. (2014), in relation to the virtual and/or physical platforms where customer engagement behaviors occur. Breidbach et al. (2014) introduced the concept of engagement platforms and, through this, highlighted the importance of both physical and virtual touchpoints where customers, brands and other actors engage. An engagement platform thus provides the structure for actors to engage with each other, such as through customers engaging virtually or physically with a focal brand (Breidbach and Brodie, 2017). Also, engagement platforms form the foundation to the value-co-creation which is driven by customer engagement (Chen et al., 2023). From a managerial perspective, a main purpose of an engagement platform is to create a structure that allows for continuous and transparent dialogs between the brand and its customers (Marino and Lo Presti, 2019).

Engagement platforms take on different forms, from entirely virtual platforms to traditional physical outlets such as retail stores, trade fairs and sport arenas (Sarmento and Simões, 2019; Stegmann et al., 2021). The characteristics of the platforms play an important role in determining the nature and the consequences of the engagement behavior. For instance, in comparison to physical engagement platforms, virtual platforms are (most often) not constrained by space or time (Huang et al., 2022). In many cases, this has made virtual platforms important for both customers and brands during the COVID-19-pandemic (Huang et al., 2022). Physical engagement platforms are often considered to foster human connection and direct interaction, while virtual, often digital, engagement platforms may act as catalysts for human connection and are optimal for continuous dialogues between customers, brands and other actors (Carlson et al., 2019; Tsiotsou, 2021). Alongside the rapid development of social media platforms, and not the least, the COVID-19-pandemic (Huang et al., 2022; Hollebeek et al., 2020), customers are nowadays faced with a myriad of platforms (both physical and virtual) on which to engage. As such, the rapid diffusion of many different engagement platforms has distinctively made customers engaged “value cocreators” in today’s service ecosystems (Islam et al., 2019).

Both physical and virtual engagement platforms are likely important for turning customers’ disposition to engage into actual engagement behaviors (Breidbach et al., 2014; Yoshida et al., 2014). In some contexts, virtual engagement platforms, such as online forums, may be the most relevant, while in other contexts and in other circumstances, physical engagement platforms, such as physical stores or trade fairs, may be the most relevant platforms (Sarmento and Simões, 2019). Studies have shown the importance of customer engagement behaviors to brands, e.g. brand loyalty (Rather et al., 2022). Yet there is an evident lack in the engagement literature on how engagement behaviors on physical and virtual engagement platforms compare and how the characteristics of the platform influence the nature of engagement and its outcomes (Blut et al., 2023).

Outcomes of customer engagement

Previous research has highlighted several outcomes of customer engagement with a brand. While not completely exhaustive, Table 1 provides an overview of the studied outcomes of customer engagement.

Customer engagement in elite football: research model

In this study, we focus on the context of the elite football ecosystem and examine the relationships among the disposition to engage, engagement behaviors on different platforms and several potential outcomes of fans’ engagement behavior. Relevant actors within the ecosystem include, for instance, fans, clubs, media and sponsors (Tsiotsou, 2016).

Platforms in elite sport ecosystems

In the context of elite sports, many different types of platforms exist. For instance, Buser et al. (2020) and Uhrich (2017) investigated elite football fans and identified different types of platforms where fans interact with either other fans or with clubs. Examples of physical platforms, which Uhrich (2017) identified, are stadiums, trips to away games, sports bars and official supporter meetings. In turn, examples of virtual platforms are a team’s official social media accounts, online fan forums and private chats between fans (Uhrich, 2017). In recent years, the importance of e-sports and e-sporting platforms has also been elevated, which further accentuates the multiplicity of platforms in the elite sport ecosystem (Abbasi et al., 2023).

Outcomes of customer engagement in elite sports

As shown in Table 1, many different potential outcomes of customer engagement behavior exist. While they are all relevant, the present study focuses on three particularly important outcomes for elite sports organizations: value-in-use, loyalty and word-of-mouth (Behnam et al., 2021; McDonald et al., 2022; Yoshida et al., 2014).

Value-in-use is the dimension of value cocreation that covers the experiential and relational value created through the joint interactions between customers and brands (Behnam et al., 2021; Ranjan and Read, 2016). For sports clubs, delivering a high level of value-in-use is essential, as it is a driver in fostering strong relationships both among fans and between the fans and the team (Harris and Ogbonna, 2008; Kolyperas et al., 2019). Without this, it is difficult to build the emotional bonds, atmosphere and commitment that are immensely valuable assets in the sports context.

Word-of-mouth is often a key component for fans, as conversations about the team play a central part in social media and beyond (Wakefield and Bennett, 2018). To create positive word-of-mouth, teams often create video clips and other content to drive conversations about the team among their fans. There are good reasons for teams to spend resources in doing so. For instance, fans who are engaged tend to talk about the team and are therefore more likely to introduce the team to their peers (Yoshida et al., 2014). In addition, fans often evaluate the value of sporting events based on other fans’ opinions (Asada and Ko, 2016). Thus, being the topic of conversation among fans is highly important for sports teams.

Finally, for football clubs, it is important that fans feel a strong sense of loyalty. As in other businesses, there are strictly financial aspects of loyalty. For instance, having loyal fans leads to higher attendance (McDonald et al., 2022; Yoshida et al., 2014) and more profits (Woratschek et al., 2020). However, the benefits that the team receives from having a strong loyal fan base go beyond short-term financial aspects. For instance, as the sports domain is often highly emotional and passionate, loyalty toward a team is often seen as a part of an individual, and a loyal supporter thus often becomes an ally to the team (Obiegbu et al., 2020). This loyalty can even transcend generations, as supporting a team is often passed down to children (Abosag et al., 2012). As such, having a high level of loyalty among fans is exceptionally important in the sports setting.

Research model

Against the background of the discussion above, the research model (Figure 1) consists of two major parts. In accordance with RQ1, the first part focuses on the relationship between sports consumers’ dispositions to engage and physical and virtual customer engagement behaviors with their team. To answer RQ2, the second part of the model investigates the extent to which physical and virtual customer engagement behaviors foster value-in-use, brand loyalty and word-of-mouth (from Table 1).

Hypothesis development

Disposition to engage and virtual engagement behaviors in elite sports

Virtual engagement platforms, such as social media or online marketplaces, are online touchpoints where customers and other actors virtually engage through computer-mediated and digitalized forums (Blut et al., 2023; Sarmento and Simões, 2019). Thus, social media is an important platform for customers to join online brand communities where firms and customers engage (Chi et al., 2022). The development of social media in recent decades has enabled such connecting virtual engagement behaviors (Khan et al., 2020), which include cocreating brand-driven content online (Schivinski et al., 2021) and providing feedback to the brand on social media (Carlson et al., 2018). Thus, virtual engagement platforms have made customers active cocreators of online content, driving ongoing interactions between the brand and consumers (Schivinski et al., 2021). As Carlson et al. (2018) showed, customers who want to engage, i.e. have a disposition to engage, are also more likely to engage with a brand on social media, for instance, by interacting with the brand on Instagram and/or Facebook.

Within elite sports, Yoshida et al. (2014) showed that fans who are more devoted to a team are also more likely to virtually engage in discussions of team-related issues, for instance, on Twitter (Read et al., 2019; Vale and Fernandes, 2018). Thus, when fans are more devoted to a team, they use social media and other virtual platforms to virtually engage and to further deepen their interest in the club (Vale and Fernandes, 2018). Examples of such virtual engagement behaviors, driven by dispositions to engage, are sharing information about the team to others and commenting on club-related content (Vale and Fernandes, 2018). Thus, we hypothesize the following:

H1a.

Disposition to engage with a club is positively related to customer engagement on virtual engagement platforms.

Disposition to engage and engagement on physical engagement platforms in elite sports

Sarmento and Simões (2019) found that while virtual engagement platforms are important when, for example, physical proximity between customer and brand is difficult, physical engagement platforms often provide more intense and mesmerizing structures to the customer. Studies have also suggested that engagement on physical platforms may be regarded as more meaningful to customers (Blut et al., 2023). Several scholars have identified that customers who want to interact with a brand engage physically with the brand and its employees, for instance, discussing improvements of a facility with employees (Behnam et al., 2021) or attending trade fairs (Sarmento and Simões, 2019).

Within elite sports, physical engagement platforms, especially the arenas of the teams, are often regarded as cornerstones of the club’s identity and the fan community (McDonald et al., 2022). Attending games in person, for instance, is often how fans join the experience of the club and its culture (Uhrich, 2017). Games provide a manifestation of the club’s culture, such as specific songs, chants and other routines (Heere and James, 2007). As taking part in this atmosphere can be a weekly routine for local fans or a lifelong dream for fans from other parts of the world, the driving force to physically take part is often strong (Behrens and Uhrich, 2019). Therefore, football leagues such as the English Premier League, Serie A in Italy and La Liga in Spain have given rise to large football tourism industries (Behrens and Uhrich, 2019). For the English Premier League, VisitBritain (2021) reports that 1.5 million visits to the UK included going to a football game in 2019. Of these, more than 350,000 visits to the UK were specifically conducted for the purpose of going to watch live football. A similar driving force to physically engage also exists in sports other than football, such as Formula 1 and tennis, in which fans pay a lot of money for the opportunity to travel to the major circuits, events and competitions (Kim et al., 2016; Roberts et al., 2016).

Against this background, it is not surprising that studies have found that sport fans who want to engage with a team are more likely to engage in behaviors such as attending matches, supporter meetings and other sorts of team-related physical platforms (McDonald et al., 2022; Uhrich, 2017). Thus, we hypothesize the following:

H1b.

Disposition to engage with a club is positively related to customer engagement on physical engagement platforms.

Engagement behaviors and the experiential dimension of value-in-use in elite sports

Value-in-use is the customer-oriented dimension of value cocreation and refers to the value that is specified and created by the customer through engagement and joint activities with a brand (Behnam et al., 2021). Studies have found that customers who are more engaged with a brand are more likely to perceive greater value from their consumption (Behnam et al., 2021) and that customers who are more engaged experience greater value-in-use because they put more effort into their consumption (Rather et al., 2021). Engaged customers are also more inclined to collaborate and participate in value-co-creation processes (Carlson et al., 2018; Kao et al., 2016). The positive relationship between customer engagement and value-in-use derives from the fact that customers who engage more often with a brand are more interested in it and thus derive greater value from their consumption activities (Nysveen and Pedersen, 2014).

In elite sports, the experiential dimension of value-in-use is particularly important, as sport consumption often involves the absorbing experiences of being a fan and following a team (McDonald et al., 2022). Sport consumption takes place both online and offline and often together with other fans and the team (Yoshida, 2017). This shapes a mesmerizing experience of consuming sports (Yoshida, 2017). On this topic, studies on fans of elite sports have shown that those who are more engaged with a team and, for instance, discuss the team more frequently on social media are more interested in being a supporter of the team (Yoshida et al., 2014). As such, engagement may lead to fans perceiving a greater experiential value of being a fan. Consequently, we hypothesize the following:

H2a.

Customer engagement on virtual platforms has a positive effect on the experiential dimension of value-in-use.

H2b.

Customer engagement on physical platforms has a positive effect on the experiential dimension of value-in-use.

Engagement behaviors and brand loyalty in elite sports

Brand loyalty refers to a customer’s long-standing devotion to a brand (Oliver, 1999). Hence, brand loyalty includes both a behavioral and attitudinal commitment toward the brand (Oliver, 1999), in this case a team. Because loyalty often involves repurchases and revisits, it has several important benefits from a brand perspective (Schivinski et al., 2021). Regarding customer engagement behaviors, studies have shown that engaged customers are often more loyal to a brand (Pansari and Kumar, 2017). Moreover, customers who are highly engaged are often much more eager to repurchase from a brand compared to less engaged customers (Rather et al., 2022). This implies that customers who, for instance, interact with employees or with brands on social media more often are also more likely to remain customers to the brand, i.e. be more loyal (Pansari and Kumar, 2017).

In the context of elite sports, loyalty is one of the most important assets a team can have (Abosag et al., 2012). Compared to other industries, emotional loyalty can be very strong, and being a supporter of a specific team can be an important part of self-identification and identity (Heere and James, 2007). Engagement behaviors on both physical and virtual platforms can be a means to strengthen this loyalty (Huettermann et al., 2019). Through engagement behaviors, fans have the opportunity to emerge in the teams’ culture, communicate with other fans and express belonging and supportership, all activities that may strengthen the perceived connection between the individual and the team. As such, it is not surprising that studies have shown that fans who are more engaged with a team through, for instance, attending supporter meetings more frequently, are also more motivated to attend future matches, buy more team apparel and remain supporters despite losses (Huettermann et al., 2019; Sullivan et al., 2022). Hence, we hypothesize the following:

H3a.

Customer engagement on virtual platforms has a positive effect on brand loyalty.

H3b.

Customer engagement on physical platforms has a positive effect on brand loyalty.

Engagement behaviors and word-of-mouth in elite sports

Vivek et al. (2012) found that word-of-mouth, referring to how customers recommend a brand’s service or product to others, is an important outcome of customer engagement behaviors. Customers who are more engaged and interact more intensively with a brand are often more committed to the brand and may therefore invite others to become customers of the brand (Vivek et al., 2012). Moreover, as Maslowska et al. (2022) found in their study on recommender systems, word-of-mouth is often a long-term effect of customer engagement. In short, when customers are engaged, they want to share this experience and information with others (An and Han, 2020).

Within the sports context, studies have found that fans who are more engaged with a team through, for instance, interacting with the team on social media, are also more likely to invite others (Vale and Fernandes, 2018). The same relationship has been shown for physical engagement platforms, as fans who attend more games and engage with the team on these platforms are also more eager to share their experiences of being a fan with others (Yoshida et al., 2014). Thus, as engagement platforms enable customer engagement (Ramaswamy and Ozcan, 2018) and its outcomes, we propose the following:

H4a.

Customer engagement on virtual platforms has a positive effect on word-of-mouth.

H4b.

Customer engagement on physical platforms has a positive effect on word-of-mouth.

Method

Sample and context

To examine and compare the influence of the disposition to engage on engagement behaviors on physical and virtual engagement platforms, as well as the influence of this engagement behavior on brand loyalty, word-of-mouth and value cocreation, this study focused on fans of Swedish elite football. More specifically, we surveyed individuals who had attended at least one game over the last three seasons of the Swedish top division. A total of 10,000 invitations to the online survey were distributed by email, in Fall 2021. Through the cooperation with five clubs in the Swedish top-tier football league (Hammarby, IFK Göteborg, BK Häcken, Malmö FF and Örebro SK), 2,000 fans [2] were randomly selected from the ticketing database of each club. Of the 10,000, 2,746 answered (approximately 27.5%), which Stedman et al. (2019) describe is a common response rate in recent years of mail survey research. After missing data were analyzed, a total of 2,031 full responses (20.3%) were used in the analysis. The respondents with high levels of missing data were deleted from the analysis in line with recommendations by Hair et al. (2014) (respondents who did not complete up to 50% of the items).

Of the 2,031 respondents, the average age was 58 years old, and 79.1% were men. According to a report commissioned by the Swedish Professional Football Leagues in 2022, the most frequent attendees of Swedish top-tier matches are men aged 30–44 years old. The statistics are not perfectly comparable, as it seems that the sample of the present study is older. An examination of the data revealed that the respondents have a high level of engagement and are also frequent attendees of their favorite teams’ home games.

Survey and measurements

The survey was made up of questions regarding engagement, engagement platforms, word-of-mouth, value-in-use and loyalty, all set in the football context, see Appendix for full item names. Scales were given on a five-point Likert scale (from 1 = completely disagree to 5 = completely agree). To ensure face and content validity, measurements for disposition to engage in elite sports, brand loyalty, word-of-mouth and value-in-use were based on previously validated studies and adapted to the specific context of this study (see Table 2). The scale for disposition to engage was adapted from Yoshida et al. (2014), who investigated customer engagement with a focal team. In this study, to examine how a more general stance toward cooperating/engaging with a club leads to engagement on physical and virtual platforms, we used the scales of Yoshida et al. (2014) as a proxy for the disposition to engage.

As there are no existing quantitative scales, especially within studies on elite sport customers, for customer engagement behaviors on physical and virtual engagement platforms, two indices were developed. To ensure face and content validity, these indices were based on the study by Uhrich (2017), who identified several platforms, both physical and virtual, on which German football fans commonly engaged. Examples of the included platforms were sports stadiums, supporter meetings, club meetings with supporters, sports pubs, social media and online fan forums. Based on the platforms identified by Uhrich (2017), our measurement focused on how often, compared to other fans (before the pandemic), the respondents engaged on these platforms. In this preliminary stage, we thus included the clubs’ annual meetings, away games, sport bars, supporter meetings and the physical arena as physical engagement platforms. The virtual engagement platforms include the team’s social media accounts, online supporter forum, streaming of matches and news related to the team (Table 2). Some examples of platforms, such as self-organized trips with fellow fans, independent online fan forums and private chat rooms, which Uhrich (2017) identified as value-cocreation platforms, were not included because this study focused on engagement behaviors with a team, not between fans.

The survey was pretested on small samples prior to being sent out, and some minor amendments, such as providing examples to clarify the statements, were made to better tailor the survey items to the focal context, i.e. Swedish elite football (see Appendix for full item names). Regarding COVID restrictions, the survey was sent out during a short break in the Swedish COVID-19 restrictions (in Fall 2021). At the time, it was thus possible to engage physically. However, to make the answers reliable for nonpandemic years, the respondents were asked to base their answers on the past five years, i.e. to make the answers as “normal” as possible.

Structure of the analysis

To increase the reliability and validity of the measurements and subsequent analysis, we divided the sample into three subgroups. This procedure can be referred to as a split-sample-validation method and is especially suitable for validating the estimation of a model (Hair et al., 2014). More specifically, we divided the sample into three groups of equal size using the randomize command in SPSS, version 28. To extract and derive factors, the first group (n = 677) was used for an exploratory factor analysis (Stage I). In Stage II, the second part of the sample (n = 677) was used to confirm the measurement model in a confirmatory factor analysis using SPSS AMOS 26. Finally, the final part of the sample (n = 677) was used for hypothesis testing using structural equation modeling (SEM) in SPSS Amos 26 (Stage III). In addition, in Stage III, to control for the direct effects of the disposition to engage on the tested outcomes, a fully mediated model with both direct and indirect effects of the disposition to engage on the outcomes of engagement behaviors was analyzed.

Stage I: exploratory factor analysis

To extract and validate the factors of this study, a principal component analysis with varimax rotation was performed. The purpose of this stage was to, in an exploratory way, investigate the underlying dimensions of this study, especially regarding the new scale on engagement behavior on virtual and physical platforms in elite sports. As displayed in Table 2, the factor analysis yielded a six-factor solution (eigenvalues > 1), where four of these [customer engagement in elite sports (inspired by Yoshida et al., 2014), brand loyalty in elite sports (inspired by Bauer et al., 2008), word-of-mouth (Jahn and Kunz, 2012) and value-in-use[3] (Ranjan and Read, 2016)] were based on existing scales. Except for the loyalty scale, from which two items had to be removed, all the items loaded as expected. For the new indices that measure engagement on platforms, the items loaded, as expected, on two separate dimensions (physical/virtual platforms). However, due to cross-loadings, or weak loadings on the associated factor (i.e. below .3), four items had to be removed for the forthcoming confirmatory factor analysis (Table 2) (Hair et al., 2014). Among the excluded items was the physical stadium. Given the importance of the stadium to a football team, this may be surprising. However, as attending a team’s home game may include several engagement platforms, such as certain pregame activities, bars at the stadium, engagement with other fans and activities after a game, the low loading is not surprising. As such, we decided to further examine the physical and virtual engagement platforms that were distinctively separated in the EFA.

Stage II: confirmatory factor analysis/measurement model

Having refined the scales in the EFA, the purpose of Stage II of the analysis was to test the reliability and validity of the model in a measurement model (Hair et al., 2014). Although the ratios of chi square to degrees of freedom did not meet the cutoff criteria (<3.00; Hu and Bentler, 1999), the overall assessment of the fit statistics allowed us to conclude that the measurement model was an acceptable fit to the data (χ² = 425.32; df = 131; χ²/df = 3.25 p = < 0.000; standardized RMR = 0.0519; RMSEA = 0.061; CFI = 0.956; TLI = 0.943; GFI = 0.937; AGFI = 0.908). No items needed to be correlated due to the fit of the model. In addition, as the sample size for this test was above 500 (n = 677), χ²/df = 3.25 was deemed acceptable (Cho et al., 2020). The standardized factor loadings were all above 0.50, thus indicating construct reliability (Hair et al., 2014). Statistics for composite reliability and Cronbach’s alpha were all above 0.7, indicating scale reliability (Hair et al., 2014). In addition, as no single factor accounted for more than 50% of the total variance of the items (here 38%), there were no severe issues with common method variance (Podsakoff et al., 2003). The square root of each construct’s AVE exceeded their intercorrelation, thus indicating discriminant validity (Table 3) (Hair et al., 2014). Moreover, as the HTMT ratios did not exceed 0.85 (at a sample size above 500) (Table 3), discriminant validity was further strengthened (Henseler et al., 2015).

Stage III: hypothesis testing using structural equation modeling

All hypotheses were tested using SEM (Table 4). Considering the large sample size (n = 677), the fit of the structural model was deemed acceptable (χ² = 589.406; df = 138; χ²/df = 4.27; p = 0.000*** CFI = 0.933; IFI = 0.934; RMSEA = 0.076; standardized RMR = 0.554; GFI = 0.911) [4]. To compare this partially mediated model with a fully mediated model, a second SEM, with the direct effects of disposition to engage on outcomes of engagement behaviors, was analyzed. This second model (Table 5) was used to control for direct effects and indicated that only one direct effect, i.e. disposition to engage in word-of-mouth, was significant. Thus, and as the fit statistics were somehow decreased, the following analysis is based on the first partially mediated model.

Results

The results of the structural model are shown in Table 4 and Figure 2. As seen, the disposition to engage has a significant effect on customer engagement behaviors on both physical (β = 0.65) and virtual engagement platforms (β = 0.74). The data thus indicate that the desire of elite sport consumers to engage is transformed into engagement behaviors with a team on both virtual and physical platforms. Hence, both H1a and H1b are supported by the data. As such, these results confirm previous literature on how customers’ disposition to engage leads to both physical and virtual engagement behaviors (Breidbach and Brodie, 2017; Carlson et al., 2019).

Turning to the influence of customer engagement behaviors on virtual platforms on value-in-use, word-of-mouth and loyalty, the results show a positive influence on all outcomes (β_VIR→ViUExp = 0.83; β_VIR→LOY = 0.92; β_VIR→WOM = 0.70). Thus, fans of elite football clubs who engaged with their team on virtual platforms perceived higher value of the sport experience were more loyal to the club and spoke well about the club to others to a higher extent than consumers who engaged little on virtual platforms. Hence, in line with studies such as Liu et al. (2021) and Oliveira and Fernandes (2022), H2a, H3a and H4a are supported by the data.

However, if we examine the influence of customer engagement on physical platforms on value-in-use, word-of-mouth and loyalty, the results are different. As shown in Table 4, none of the relationships were significant in the structural model (p > 0.05). Thus, consumers of elite football who frequently engage with a team on the physical platforms considered in the study were not more loyal and did not perceive a higher level of value than consumers who do not often engage on physical platforms. Hence, H2b, H3b and H4b are rejected.

Discussion and conclusions

This study has addressed the need for more research on different types of engagement platforms and their role in customer engagement behaviors (Blut et al., 2023; Breidbach and Brodie, 2017; Leipämaa-Leskinen et al., 2022).

The aim of this study was to examine and compare the influence of the disposition to engage on engagement behaviors on physical and virtual engagement platforms, as well as the influence of this engagement behavior on brand loyalty, word-of-mouth and value-in-use. Through a study on fans of Swedish elite football teams, the results indicate that(1) customers’ disposition to engage leads to customer engagement behaviors on both physical and virtual platforms. There was no major difference between the influence of the disposition to engage on engagement behavior on virtual or physical platforms. Related to the second research question, (2) the results indicate that for value-in-use, brand loyalty and word-of-mouth as outcomes, engagement behaviors with the brand on virtual platforms are more important than engagement behaviors on physical platforms.

In fact, there were no significant relationships between engagement behaviors on physical platforms and the measured outcomes.

Theoretical implications

In answering the two research questions, this study contributes to the literature on engagement platforms in several ways.

First, this study highlights that different types of platforms act as facilitators for the disposition to engage, which emphasizes that, at least in the football context, consumers tend to search out different platforms to engage with a brand. Second, this study provides much needed empirical insights to the understanding of how engagement on virtual and physical platforms may lead to different outcomes. Hence, as Blut et al. (2023) argued, this study contributes to knowledge on how various types of customer engagement behaviors (physical or virtual) have different weights in predicting outcomes of engagement, such as fostering brand loyalty, word-of-mouth and driving value-in-use.

From a scholarly perspective, this study emphasizes that in examining customer engagement, one needs to always consider the characteristics and the peculiarities of the platform where the engagement behaviors occur. Moreover, the results also stress that with digital transformation, customers use multiple touchpoints during their customer journey. It is this mix of both virtual and physical engagement platforms that can be referred to as part of the phygital experience or phygital customer journey (Mele and Russo-Spena, 2022).

Physical and virtual engagement platforms as facilitators of the disposition to engage

To date, studies have sought to explore how engagement platforms, mainly virtual ones, play a part in the relationship between the disposition to engage with a brand and the outcomes of customer engagement behaviors (Blasco-Arcas et al., 2020; Sarmento and Simões, 2019; Carlson et al., 2018).

Building upon this research, the present study contributes empirically testing engagement behaviors on physical and virtual platforms separately. As the results indicate, there are small, if any, differences between the effects of the disposition to engage on customer engagement behaviors on physical engagement platforms and the effects of the disposition to engage on customer engagement behaviors on virtual engagement platforms. This implies that customers use and search for many different places to engage with brands. This also means that within the studied context, if fans of football teams are eager to engage with their teams, they are likely to engage to the same extent on both physical and virtual platforms.

Essentially, this result highlights the importance of providing customers with different types of platforms as avenues for engagement (Blasco-Arcas et al., 2020). If more engagement platforms are available to the customers, it may as well means intensified engagement behaviors, potentially also creating closer bonds between customers and brands (Blut et al., 2023).

Virtual engagement platforms and engagement outcomes

The results of this study indicate a strong relationship between engagement behaviors on virtual engagement platforms and value-in-use, word-of-mouth and loyalty. These results were expected. After all, previous research indicates that virtual engagement platforms, such as Facebook and Instagram, allow for a continuous dialog between customers and brands (Vale and Fernandes, 2018; Tsiotsou, 2021). As such, this study shows that virtual engagement platforms provide a convenient way to interact with a company and leads to desirable outcomes of customer engagement.

In comparison to physical engagement platforms, virtual platforms are (most often) not constrained by space or time (Uhrich, 2017). Thus, fans, in the context of elite football, can interact with a team at home or at a match at almost any time of the day. Considering the case of elite football, virtual platforms also allow customers to create strong connections to a team, even if the customer does not live in the proximity of a physical engagement platform, such as an arena. From a scholarly perspective, this also means that virtual engagement platforms may be even more important considering the rapid globalization and commercialization of elite football (Winell et al., 2022). This as many modern elite football fans might never have access to the arena, or other physical engagement platforms, due to geographical distance and financial means (Winell et al., 2022).

Physical engagement platforms and engagement outcomes

Engagement behaviors on virtual engagement platforms seem more important to brand loyalty, word-of-mouth and the experiential dimension of value-in-use than those on physical platforms (see Figure 2). These findings may be surprising considering the immersive nature of physical engagement platforms, such as football arenas and fan meetings (Woratschek et al., 2014). However, there are possible explanations for these results.

First, engagement between fans and teams, i.e. customers and brands, may not be easily facilitated on physical engagement platforms. To perform engagement behaviors with a team on an away game might be difficult. Instead, fans perform different engagement behaviors, such as singing, chanting and cheering together with mainly other fans in the physical arena. These customer-to-customer engagement behaviors could lead to many positive outcomes not reflected in this exploratory study because we focus on customer-to-brand, i.e. fan-to-team, engagement behavior.

Second, our sample consists of devoted fans who attend many home games[5], i.e. they perform frequent engagement behaviors on certain physical engagement platforms. The data show that they are heavily involved in their fandom with high mean scores on, e.g. emotional attachment to the club (mean score of 4.23) and levels of identification with the club (mean score of 4.64), i.e. a negatively skewed distribution. It might be that physical engagement platforms serve as a hygiene factor in their relationship with the club (Lee et al., 2012) and do not positively influence outcomes such as word-of-mouth, loyalty and value-in-use.

Moreover, it is important to discuss which physical platforms for customer–brand engagement were included in this exploratory study. After the exploratory factor analysis, club meetings, attending and traveling to away games and supporter meetings were kept, while supporter bars and the physical arena were omitted due to cross-loadings or weak loadings. Thus, the retained engagement platforms are mostly concerned with engagement behaviors outside of the game day context. Because these factors do not positively influence the measured outcomes, it is possible that other physical engagement platforms are needed for this to occur. Previous research emphasizes the importance of the physical arena for engagement behaviors in sports (Huettermann et al., 2019). Beyond the game itself, in the arena, there are several other possible physical engagement platforms in and around the arena, such as pre- and in-game activities (e.g. pre- or postgame meets with players and coaches). Including more types of platforms might result in several categories of physical engagement platforms, for example, one category including home game engagement platforms (e.g. pregame activities, game activities and postgame activities) and another including platforms that are not game related (e.g. supporter meetings, supporter bar visits and arena tours) (Uhrich, 2017). Uhrich (2017) states that physical engagement platforms are often restricted to a certain place and time, which supports an approach with physical platforms for different places (arena – not arena) or times (game day – not game day). To conclude, this study highlights the complexity of engagement behavior on physical engagement platforms that should be explored further (see Limitations and suggestions for future research below).

Managerial implications

From a managerial perspective, this study provides several insights. Providing a mix of both virtual and physical engagement platforms to create a phygital experience or phygital customer journey is likely an important way forward. In a sports context, we can imagine that a fan at a game can perform engagement behaviors simultaneously on the physical platform (the arena) and a virtual platform (the team’s social media). However, as loyalty, word-of-mouth and value-in-use were mostly driven by engagement behaviors on virtual platforms, this highlights the importance for brands to invest in these types of platforms, such as Facebook, Twitter or other virtual touchpoints. Studies have shown that social media is often used to drive customer-to-customer engagement (Unnava and Aravindakshan, 2021); however, the results of this study show that customer–brand engagement would also benefit from investments in virtual engagement platforms. Examples of such initiatives may be allowing for more feedback regarding an offer or a service of the brand or ensuring that there is an ongoing dialog between customer and brand, for instance, through chatbot services or other messaging functions (Marino and Lo Presti, 2019). In elite sports, this may imply that clubs prioritize their presence on the virtual engagement platforms where their customers are present. This may include interacting with their fans on Twitter regarding team news or upcoming matches, for example.

As physical engagement platforms are more immersive and allow for more direct interactions with others, they are probably more important for engagement between customers (Morgan-Thomas et al., 2020). It is important to note that this study does not indicate that physical engagement platforms should be neglected by brands. Customers’ disposition to engage with a brand results in customer engagement on both physical and virtual engagement platforms, and even if there is no positive influence on the outcomes included in this study, there are other outcomes, such as brand image and trust, that might be positively influenced by these customer engagement behaviors (Blasco-Arcas et al., 2016; Hollebeek and Macky, 2019). Moreover, the nonsignificant influence of physical engagement platforms might also be a sign to management that the platforms need to be adjusted to accommodate positive outcomes, i.e. how can away games, for example, include more interaction between the fans and the team.

Limitations and suggestions for future research

This study was restricted to one specific context, i.e. Swedish men’s elite football, and the survey was distributed in the fall of 2021, when some restrictions regarding attending games and other types of physical activities were still imposed because of the pandemic. Future research should examine the relationships after the pandemic as well as in other contexts with high levels of customer engagement. These might be brands offering similarly hedonic and multisensory experiences such as theme parks, events and festivals (Fernandes and Esteves, 2016).

Moreover, this exploratory study was performed with a focus on customer engagement with a focal football team. There is also a need to go beyond the customer–brand dyad in assessing customer engagement (Storbacka et al., 2016). Hence, exploring, for instance, engagement between customers and the role of both physical and virtual engagement platforms for this purpose, is an important avenue of research. This is especially important considering that virtual engagement platforms have improved the possibilities for customer engagement behaviors between customers (Carlson et al., 2019). Furthermore, we only empirically studied a limited number of virtual engagement platforms (team social media accounts and media outlets). All are important and have a strong influence on brand loyalty, word-of-mouth and experiential value-in-use, but further studies could examine other types of virtual platforms. Perhaps, more importantly, future studies could test whether there are different types of virtual platforms related to the game day (in the context of elite football). This is also the case for physical engagement platforms, where a differentiation of platforms related to place and time, as well as a breakdown of physical arena activities, might be needed in future studies to better capture engagement behaviors on physical engagement platforms.

Related to the discussion about multiple touchpoints and the mix of virtual and physical engagement platforms (Mele and Russo-Spena, 2022), it is vital to further explore how this multiple touchpoint engagement is carried out in a sports context and beyond and how it impacts outcomes such as loyalty, value-in-use and word-of-mouth. An exploratory qualitative approach, such as participant observation studies, would pave the way for a better understanding of how customers engage on multiple platforms and how the interrelationship between platforms is carried out.

Figures

Figure 1

Research model

Figure 2

Final research model

Table 1

Consequences of customer engagement behavior

Outcomes of customer engagement
Concept	Definition	Implication	Studies
Value-in-use	The experiential, personal and relational value derived by the beneficiary (often the consumer) in value-cocreation) (Ranjan and Read, 2016)	Customer engagement covers customers interactions with others. Thus, it serves as a microfoundation to the cocreated value-in-use which stems from customer–brand interactions.	(Behnam et al., 2021; Ramaswamy and Ozcan, 2018; Storbacka et al., 2016)
Brand loyalty	A customer’s long-standing commitment to a brand, service and/or product. Covers both behavioral and attitudinal commitment (Oliver, 1999)	The more a customer engages with a brand, the closer the customer–brand relationship is likely to be. Thus, engaged customers are more likely to remain as customers to the brand (Fernandes and Esteves, 2016; Rather et al., 2022)	Fernandes and Esteves (2016), Vivek et al. (2014)
Word-of-mouth	A customer’s referrals to a brand, service or product, to others (Vivek et al., 2012)	The more a customer engages with a brand, the more likely they are to talk with others about it. Thus, customer engagement leads to word-of-mouth (Vivek et al., 2012)	(Chang et al., 2021; Vivek et al., 2012; Yoshida et al., 2014)
Price perceptions	Customers’ valuation of the price worthiness of an offer (Bergel et al., 2019)	Engaged customers tend to have more positive price perceptions than less engaged customers (Bergel et al., 2019)	(Bergel et al., 2019)
Brand image	A mental scheme of linkages and associations of a brand that creates a meaning of the brand to customers (Blasco-Arcas et al., 2016)	Customer engagement has a positive effect on brand image, as it allows customers to form positive experiences and encounters associated with the brand (Blasco-Arcas et al., 2016)	(Blasco-Arcas et al., 2016)
Trust	Customers having confidence in the reliability and integrity of a brand (Vivek et al., 2012)	As customers engage, the interactions between the customers and the brands may, if positive, lead to more trust between the two exchange partners (Vivek et al., 2012)	(Hollebeek and Macky, 2019; Vivek et al., 2012)

Source:

Authors’ own work

Table 2

Exploratory factor analysis (Stage I) and confirmatory factor analysis (Stage II)

			Study I – EFA*						Study II – CFA**
			EFA loadings after promax rotation
Factor items	Mean	SD	Disposition to engage	Value-in-use (Experience)	Team loyalty	WOM	Engagement on physical platforms	Engagement on virtual platforms	Composite reliability	Cronbach’s alpha	AVE	Mean	SD	λ
	Disposition to engage (Yoshida et al., 2014)									0.77	0.76	0.52
	DE1	3.31	1.36	0.88									3.33	1.33	0.77
DE2	3.43	1.43	0.95									3.41	1.40	0.73
DE3	4.18	1.01	0.63									4.14	1.07	0.67
Value-in-use – Experience (Ranjan and Read, 2016)									0.76	0.75	0.52
ViUE1:	4.46	0.80		0.50	0.38							4.45	0.85	0.83
ViUE2	3.63	1.19		0.98								3.64	1.21	0.68
ViUE3	3.91	1.00		0.82								3.92	0.99	0.64
Team loyalty (Bauer et al., 2008; Yoshida et al., 2014)									0.86	0.85	0.61
LOY1	4.67	0.73			0.97							4.70	0.69	0.78
LOY2	4.56	0.80			0.83							4.56	0.83	0.79
LOY3	4.69	0.70			0.80							4.71	0.69	0.71
LOY4	3.72	1.36			*							na
LOY5	4.05	1.29			*							na
LOY6	4.40	0.89			0.49							4.43	0.91	0.84
Word-of-mouth (WOM) (Jahn and Kunz, 2012)									0.93	0.93	0.81
WOM1	3.93	1.24				0.94						3.94	1.23	0.91
WOM2	3.89	1.26				0.98						3.96	1.22	0.96
WOM3	4.09	1.14				0.77						4.10	1.15	0.83
WOM4	4.39	0.85				*						na
Engagement on physical platforms (NEW SCALE)									0.77	0.76	0.53
FPFYM	2.24	1.43					0.81					2.05	1.36	0.70
FPFAWG	2.06	1.26					0.87					2.08	1.22	0.67
FPFSPB	1.91	1.07					*					na
FPFSUP	2.08	1.27					0.86					2.06	1.26	0.81
FPFHMG	4.22	1.15					*					na
Engagement on virtual platforms (NEW SCALE)									0.76	0.74	0.52
FPFSM	3.87	1.32						0.91				3.93	1.31	0.82
FPFPLSOC	2.84	1.45						0.91				2.96	1.45	0.76
FPFSOC	2.40	1.36						*				na
FPFTV	3.38	1.40						*				na
FPFNWS	4.19	1.00						0.55				4.18	1.02	0.55

Notes:

*Issues with cross-loadings and, or weak loadings (<0.3), therefore excluded for the CFA; **Rotated solution for EFA is PROMAX, total variance extracted by the six factors = 38%, loadings <0.3 are not shown; ***Model fit for CFA: χ² = 425.32; df = 131; χ²/df = 3.25 p ≤ 0.000; standardized RMR = 0.0519; RMSEA = 0.061; CFI = 0.956; TLI = 0.943; GFI = 0.937; AGFI = 0.908; n1 = 602; n2 = 677; ***See Appendix for full item names

Source: Authors’ own work

Table 3

Fornell–Larcker criterion and HTMT ratios

Construct	CE	VIUEx	LOY	WOM	PHY	VIR
CE	0.72	0.51	0.53	0.41	0.66	0.50
VIUEx	0.47	0.79	0.84	0.57	0.40	0.61
LOY	0.54	0.78	0.77	0.56	0.45	0.59
WOM	0.43	0.53	0.56	0.90	0.33	0.49
PHY	0.65	0.37	0.46	0.33	0.73	0.48
VIR	0.50	0.56	0.60	0.44	0.47	0.72

Notes:

HTMT = heterotrait–monotrait test (Henseler et al., 2015); the italcized diagonal factors are the square roots of all construct AVEs. Above the diagonal factors are the HTMT ratios, and below are the estimated correlations

Source: Authors’ own work

Table 4

Stage III: partially mediated model

Hypothesis	Path	Estimate	β	p	Result
H1a	Disposition to engage → Engagement on physical platforms	0.92	0.65	*	Supported
H1b	Disposition to engage → Engagement on virtual platforms	0.56	0.74	*	Supported
H2a	Engagement on virtual platforms → Value-in-use (Experience)	0.95	0.83	*	Supported
H2b	Engagement on physical platforms → Value-in-use (Experience)	−0.04	−0.06	0.13	Rejected
H3a	Engagement on virtual platforms → Brand loyalty	0.74	0.92	*	Supported
H3b	Engagement on physical platforms → Brand loyalty	−0.02	−0.05	0.29	Rejected
H4a	Engagement on virtual platforms → Word-of-mouth	1.16	0.70	*	Supported
H4b	Engagement on physical platforms → Word-of-mouth	−0.02	−0.02	0.59	Rejected

Notes:

*p < 0.01, model fit: χ² = 589.406; df = 138; χ²/df = 4.27; p = 0.000; ***CFI = 0.933; IFI = 0.934; RMSEA = 0.076; standardized RMR = 0.554; GFI = 0.911; n = 677; β = unstandardized estimate; p = significance

Source: Authors’ own work

Table 5

Fully mediated model

Path	Estimate	β	CR	p	Result
Disposition to engage → Engagement on physical platforms	0.92	0.65	12.97	***	Statistically significant
Disposition to engage → Engagement on virtual platforms	0.56	0.74	11.24	***	Statistically significant
Disposition to engage → Value-in-use (Experience)	−0.01	−0.07	−0.08	1.00	Not statistically significant
Disposition to engage → Brand loyalty	0.00	0.00	0.01	0.93	Not statistically significant
Disposition to engage → Word-of-mouth	0.24	0.19	2.22	0.02	Not statistically significant
Engagement on virtual platforms → Value-in-use (Experience)	0.96	0.83	10.71	***	Statistically significant
Engagement on physical platforms → Value-in-use (Experience)	−0.04	−0.06	−1.52	0.13	Not statistically significant
Engagement on virtual platforms → Brand loyalty	0.74	0.92	11.58	***	Statistically significant
Engagement on physical platforms → Brand loyalty	−0.02	−0.05	−1.06	0.29	Not statistically significant
Engagement on virtual platforms → Word-of-mouth	0.97	0.70	11.42	***	Statistically significant
Engagement on physical platforms → Word-of-mouth	−0.080	−0.02	−1.06	0.59	Not statistically significant

Notes:

*p < 0.01, model fit: χ² = 582.357; df = 135; χ²/df = 4.32; p = 0.000; ***CFI = 0.933; IFI = 0.934; RMSEA = 0.076; standardized RMR = 0.550; GFI = 0.911; n = 677; β = unstandardized estimate; p = significance

Source: Authors’ own work

Notes

1 In this study, elite football refers to “European style football” (Soccer), not “American football.”

2 Individuals who had attended at least one game of the selected teams during the last three seasons.

3 The experiential dimension of value-in-use. The two dimensions of “Personalization” and “Relationship” were excluded, as the experiences of being a customer were the focal outcome of CE.

4 The principles for the fit indices are based on Hair et al. (2014) and Cho et al. (2020). The sample size is large in this study (>500); thus, our GFI is lower than 0.9. However, as the other fit indices are acceptable the overall model is deemed as acceptable.

5 According to the survey, 1,542 of the respondents stated that they attended more home games than the average Swedish football fan.

Appendix 1. full item names

Disposition to Engage (Yoshida et al., 2014)

• DE1: I try to collaborate with the club

• DE2: I do things to make my teams event management easier

• DE3: The employees of TEAM X get my full cooperation

Value-in-use – Experience (Ranjan and Read, 2016)

• ViUE1: Following “Team X” creates memorable experiences for me

• ViUE2: My way of following “Team X” creates experiences that are unique for me (in comparison with other supporters)

• ViUE3: As a supporter there are possibilities to follow “Team X” in multiple ways

Team loyalty (Bauer et al., 2008; Yoshida et al., 2014)

• LOY1: No matter the results on the pitch, I will always follow “Team X”

• LOY2: I defend “Team X” no matter what others may think of me

• LOY3: The probability that I will attend future games of “Team X” is very big

• LOY4: The probability that I will spend more than 50% of my total event spendings on attending the teams’ games is large

• LOY5: The probability that I will continue to follow my team on social media is very big

• LOY6: I am very devoted to “Team X”

Word-of-mouth (WOM) (Jahn and Kunz, 2012)

• WOM1: I recommend others to be supporters of “TEAM X”

• WOM2: I encourage others to follow “TEAM X”

• WOM3: I encourage others to attend games of “TEAM X”

• WOM4: I say positive things about “TEAM X” to others

Engagement on physical platforms (NEW SCALE)

• FPFYM: I attend the club’s annual meetings

• FPFAWG: I travel to my teams away games

• FPFSPB: I see my teams matches on bars, pubs and, or similar sites

• FPFSUP: I attend supporter meetings for “TEAM X”

• FPFHMG: I attend my teams home games

Virtual engagement platforms (Inspired by Uhrich et al., 2014)

• FPFSM: I follow “TEAM X” on social media

• FPFPLSOC: I follow the players/managers of “TEAM X” on social media

• FPFSOC: I am active and participate in conversations about “TEAM X” on social media

• FPFTV: I see my team’s games from home, on the TV

• FPFNWS: I consume news/media of “TEAM X”

Source: Authors’ own work