Motivation for women’s football: a competing scales study

Abstract

Purpose

The purpose of this study is to compare measurement scales of sports fans’ motivations applied to women’s football.

Design/methodology/approach

A survey research approach was used to collect 574 valid responses from participants in Brazil and the USA. Three prominent scales – Sport Interest Inventory (SII), Sport Fan Motivation Scale (SFMS) and Motivation Scale for Sport Consumption (MSSC) are were compared using the structural equation modeling technique.

Findings

The results indicate that the SII scale demonstrates superior predictive power for variables such as “purchase intention,” “electronic word of mouth,” “identification as a fan” and “interest in women’s football” compared to the SFMS and MSSC scales. The primary motivation among followers and spectators of women’s football in the study was “supporting women’s opportunities” in sport.

Research limitations/implications

While the study is grounded in the most relevant scales pertinent to the theme, the limited academic production on the subject hinders direct comparisons with prior research.

Practical implications

Leveraging the insights from the SII scale, football team managers can refine their marketing strategies by understanding the primary motivations driving women’s football consumption. This knowledge can inform targeted efforts to enhance women’s football consumption, subsequently expanding opportunities for women in the sport.

Social implications

This study provides valuable information that can inform initiatives aimed at boosting women’s soccer consumption, thereby contributing to increased opportunities for women in the sport.

Originality/value

To the best of the authors’ knowledge, this study represents the first attempt to compare scales in the specific context of women’s soccer, contributing with a unique perspective to the development of women’s sports.

Introduction

The 2019 FIFA Women’s World Cup in France garnered substantial media attention, surpassing previous editions (Coche, 2022). Publicis Sport and Entertainment’s 2019 study for FIFA reported an audience exceeding 1.1 billion viewers across TV, digital platforms and out-of-home, doubling from the 2015 edition (Publicis Sport & Entertainment, 2019). Digital platforms notably rose from 86 million viewers in 2015 to 481 million in 2019, with the 2023 edition perceiving increased engagement on social media surpassing 2019 totals (FIFA, 2023).

Various studies have explored fan motivations in sports content consumption (Funk, Mahony, Nakazawa, & Hirakawa, 2001; Mahony, Nakazawa, Funk, James, & Gladden, 2002; Sloan, 1989; Trail & James, 2001; Wann, 1995). Giulianotti (2002) categorizes football spectators into four identities: Supporters, Followers, Fans and Flaneurs, each exhibiting different levels of club identification. Supporters strongly identify with a particular club, actively participating in club-organized activities such as cheering sections, showcasing profound loyalty and a shared collective identity with fellow supporters. Followers, conversely, exhibit a weaker attachment to a specific club but maintain a close emotional connection without involvement in organized club activities. Fans, with even less club identification, enjoy watching matches and staying updated on club-related news, though lacking a strong commitment or shared collective identity among peers. Finally, Flaneurs represent spectators who primarily view football matches as entertainment without solid club-specific identification. They may watch matches of various clubs, emphasizing the experiential aspect of football games over specific match outcomes (Giulianotti, 2002).

The landscape of sports consumption has undergone a transformative shift since the 2000s with the pervasive influence of the internet, as Seo and Green (2008) indicated. Initially centered on specialized websites, live streaming and fan communities, the evolution of online sports consumption reached a turning point with the rise of various digital platforms. Recent research by Petersen-Wagner (2022), focusing on FIFA’s YouTube channels, reveals a compelling trend: women’s football videos demonstrate higher engagement ratios than male-focused content, highlighting a dedicated and enthusiastic audience. This shift in consumption patterns underscores the digital realm’s role in reshaping the dynamics of sports engagement, particularly in fostering inclusivity and recognition of women’s sports in the online space.

Despite gender coverage disparities in sports (Martin et al., 2016; Pegoraro, Comeau, & Frederick, 2017; Giachino, Valenti, Bonadonna, & Bollani, 2023), some women’s teams, like the Portland Thorns, have drawn attention. As an illustration, the Thorns – a team in the National Women’s Soccer League, the professional women’s football league in the USA, boasted an average attendance of 16,945 spectators per game in 2016, surpassing the figures of any team in the Major League Soccer, the men's professional football league (Guest & Luijten, 2018).

According to the “World Football Report” conducted by Nielsen Sports (2018) across more than 30 countries, football is the most popular sport in these nations, with over 40% of respondents expressing their following.

In Brazil, football is the predominant and most financially lucrative sport, generating R$52.9bn (around US$10bn) in 2018 and providing about 156,000 jobs (Ernst & Young Global Limited, 2019). This economic impact encompasses men’s and women’s football, as well as the junior football base. Women’s competitions constitute 11% of all competitions in Brazil, reflecting a growing presence and recognition for women’s football. Notably, during the 2019 Women’s World Cup quarterfinal match between France and Brazil, around 30 million viewers tuned in, as reported by Rede Globo, Brazil’s influential media giant (Globoesporte, 2019). To further support this upward trajectory, it is crucial to comprehend the motivations driving viewers to engage with women's football championships, broadcasts, team merchandise and related products.

Previous studies explored motivations in women's sports (Funk et al., 2001, Funk, Ridinger, & Moorman, 2003; Guest & Luijten, 2018). Funk et al. (2003) noted the emergence of women’s sports leagues in the 1990s, prompting studies on spectator motivations. Subsequently, various scales have been created and independently applied in diverse contexts. Nevertheless, it remains essential to identify the scale with the most robust psychometric properties for measuring motivations and determine which scale is most closely linked to the behavioral intentions of female football spectators.

This research aims to identify the most suitable scale for measuring women’s football spectators’ motivations, comparing three widely used scales:

• the Sport Fan Motivation Scale (SFMS) by Wann (1995);

• the Motivation Scale for Sport Consumption (MSSC) by Trail and James (2001); and

• the Sport Interest Inventory (SII) scale by Funk et al. (2001).

The study also explores the significance of motivations for women’s football followers and their predictive capabilities for crucial variables: purchase intention, electronic word of mouth (eWOM), fan identification and interest in women’s football.

Literature review

The following sections delve into prominent scales developed over the years for gauging sports fans' motivations and their correlations with key variables in this study: purchase intention, fan identification, eWOM and interest in women’s football.

Measurement of motivations for sports fans

Scholars have increasingly focused on fan motivation in sports over the past decades. Sloan (1989) initiated this exploration by categorizing fan motivations into five areas: salubrious effects, stress release, stimulation seeking, aggression and catharsis, entertainment and achievement. Subsequent authors, including Wann (1995), Trail and James (2001) and Funk et al. (2001), approached the theme from the spectator’s perspective. Wann (1995) defines spectators as enthusiasts of a particular sport or athlete, engaging in person or through various media channels, including radio, television and the internet (Seo & Green, 2008).

Wann (1995) introduced the Sport Fan Motivation Scale (SFMS), comprising eight factors: eustress, self-esteem, escape, entertainment, economic considerations, aesthetics, group affiliation and family needs. This groundbreaking study empirically tested spectator motivations, unveiling factors not previously measured.

Another influential scale for measuring sports fans’ motivation is the Sport Interest Inventory (SII) by Funk et al. (2001), first validated during the 1999 Women’s Football World Cup matches in the USA. The scale encompasses ten factors: football, vicarious achievement, excitement, team identification, support for women’s opportunities in sport, aesthetics, socialization, national pride, drama and interest in the player.

Similarly, Trail and James (2001) proposed the Motivation Scale for Sport Consumption (MSSC) to address the deficiencies of earlier scales. This scale, grounded in psychometric criteria, consists of nine factors: achievement, knowledge acquisition, aesthetics, drama, escape, family, physical attractiveness of participants, quality of physical skills and social interaction.

Several studies applied these motivation scales validated across different cultures and sports. For instance, Cohen and Avrahami (2005) used Wann’s (1995) SFMS scale for football in Israel, demonstrating its replicability beyond North America.

Wang, Zhang, and Tsuji (2011) highlighted SII (Funk et al., 2001) and MSSC (Trail & James, 2001) as scales with reliable psychometric properties. They emphasized that only the SII has undergone testing in diverse cultures and sports, both female and male. Therefore, for Wang et al. (2011), SII (Funk et al., 2001) proved more consistent in measuring the motivation of fans of the Chinese Baseball Professional League.

In their 2017 study, Pu and James investigated the motivations of long-distance fans supporting NBA teams. The research, involving 281 Chinese fans, analyzed factors influencing their engagement and differences in motivations based on involvement levels. The study used three instruments – SII (Funk, Mahony, & Ridinger, 2002), MSSC (Trail & James, 2001) and the Motivation Factors for Spectators and Participants scale (McDonald, Milne, & Hong, 2002). Ten motivations for sports spectators were identified: interest in basketball, team, league, player, involvement, aesthetics, escape, knowledge acquisition, athletes’ physical skills and social affiliation. However, the study did not employ a specific methodology or statistical test to determine the best-fit scales and factors.

Yenilmez, Ersöz, Çınarlı, and Sarı (2020) validated the SII scale (Funk et al., 2001) in Turkey, incorporating additional factors from the 2003 SII. They identified ten motivations for football fans in Turkey (namely, bonding with family, socialization, entertainment value, role model, drama, wholesome environment, vicarious achievement, community support, escape and excitement), excluding certain factors like support for women’s opportunities in sports.

This study opted for the three most cited and replicated fan motivation scales in major academic databases, including Scopus, Web of Science and Google Scholar: SFMS (Wann, 1995), MSSC (Trail & James, 2001) and SII (Funk et al., 2001). Despite being over 20 years old, these scales remain the most frequently cited in recent literature.

Table 1 introduces each selected scale for this study, offering a comprehensive overview of their dimensions and highlighting the distinct features that define each scale. This table serves as a concise reference point for readers to understand the key attributes of the scales under consideration. An extended version of this table, providing detailed insights into each dimension and its specific objectives, can be found in the supplementary table for a more in-depth exploration. This extended version aims to offer a thorough understanding of the nuances and intricacies associated with the measurement criteria, facilitating a more nuanced comprehension of the selected scales and their relevance to the study’s overarching objectives. The subsequent sections delve into factors influencing the attitudes and behaviors of sports fans, drawing insights from prior studies focused on measuring the motivations of sports enthusiasts (Wann, 1995; Funk et al., 2001; Trail & James, 2001).

Purchase intention

Duan and Liu (2021) revealed that the event's image and perceived social impact significantly shape spectator satisfaction, influencing not just behavioral intentions for the event but also the product purchase intentions of event sponsors. Silveira, Cardoso, and Quevedo-Silva (2018) utilized Chandran and Morwitz (2005) Purchase Intention Scale, establishing that sports followers' engagement positively affects the intention to attend stadium events. Given the close relationship between purchase intention and attendance, we included this variable in our study for its marketing relevance in women's football, in which fans actively engage with various related items and products. In addition, Schreyer and Ansari (2021) noted the oversight of stadium attendance in women's sports literature. Thus, incorporating this variable is vital to assess whether specific motivations wield stronger predictive power concerning sports fans' purchase intentions.

Electronic word of mouth

Word of mouth (WOM) is essential in consumer behavior studies (Santos, Freire, de Oliveira, & Lourenço, 2017). More recently, this phenomenon has evolved into digital WOM, specifically eWOM (Hennig-Thurau, Gwinner, Walsh, & Gremler, 2004). The internet enables consumers to share their product and service experiences with a broad audience (Hennig-Thurau et al., 2004) since, through digital platforms, consumers can express positive emotions or voice concerns, turning these platforms into advocates for consumer rights (Kozinets, Ferreira, & Chimenti, 2021).

Given that eWOM has been linked to increased product sales (Duan, Gu, & Whinston, 2008) and influences the attitudes, intentions, and behaviors of other consumers (Reichelt, Sievert, & Jacob, 2014), this variable is pertinent to the present study. The study aims to determine which motivations significantly predict fans' and spectators' recommendations of the sport. Therefore, the dependent variable “electronic word of mouth” is utilized based on the framework established by Santos et al. (2017).

Fan identification

Greater fan identification has a more pronounced impact on fan cognition (Funk & James, 2001). This connection suggests a heightened engagement with the team's sport, consequently leading to a greater inclination to consume team products (Wann & Branscombe, 1993). Prior research indicates a strong correlation between team identification and consumption intention (James & Trail, 2008), serving as a robust predictor of match attendance and licensed merchandise purchases (Kwon, Pyun, & Lim, 2022).

Theodorakis, Wann, Sarmento, and de Carvalho (2010) contend that the Sport Spectator Identification Scale (SSIS) (Wann & Branscombe, 1993) serves as a valuable tool to discern the various attachment levels fans have to a team. This insight allows sports marketers to refine market segmentation strategies, influencing future behaviors by understanding fans' desires and needs as consumers.

Interest in women's football

The dependent variable under investigation, “interest in women's football,” is based on Funk et al. (2001). Coche (2014) also examined “interest in women's football” through a social media survey, acknowledging methodological biases such as sample overrepresentation, demographic bias, and small sample size. Despite these limitations, the study drew attention to the scarcity of research on women's football audiences – a gap that has persisted over the years (Hallmann, Oshimi, Harada, Matsuoka, & Breuer, 2016; Schreyer & Ansari, 2021), with only a few notable exceptions.

For instance, Giachino et al. (2023) measured “interest in women's football” but did not use any of the scales assessed in the present study. Instead, they employed a binary approach, asking a yes or no question:

Do you follow women's football?

Their analysis revealed that gender and age are not significant predictors of interest in women's football, indicating a diverse audience interested in the sport, contrary to the findings of Woratschek, Preuss, and Durchholz (2011).

In summary, the studies above highlight that the described variables are robust predictors of sports followers' behavior. This suggests their potential contribution to managerial applications in the sports industry, indicating some degree of interconnection. Figure 1 illustrates how the present study aims to measure the relationship between motivations and the four proposed variables.

Method

Data collection occurred in two stages across different cultural contexts. The first stage, conducted in Brazil via SurveyMonkey, was initiated due to the country's rich football history and passion. Recognized as a football powerhouse, Brazil holds a significant place for the sport in the nation's culture (Buarque de Holanda, 2014). The choice allowed exploration in a country where women's football has been on the rise, albeit facing challenges such as lower salaries, limited sponsorship and resource shortages for female players compared to their male counterparts (Da Costa, 2014; Hallmann, Giel, Beermann, Herold, & Breuer, 2022; Knijnik, 2013; Valenti, Peng, & Rocha, 2021).

We meticulously translated the scales, preserving their original meaning in Portuguese for data collection. The translation, involving back-translation and expert review, ensured data validity and consistency in this specific cultural and linguistic context. The second data collection stage was conducted in the USA, distinct due to its soccer culture, and aimed to complement our findings (Allison, 2018).

No translation was needed for US participants. Both countries were included not for comparison but to enhance generalizability. Incorporating data from the USA was not aimed at comparing motivations but bolstering the robustness and generalizability of our findings. We aimed to ensure that identified motivations for football followers hold true across diverse cultural and sporting contexts in Brazil and the USA, contributing to a broader understanding of fan motivations in women's football.

In selecting Brazil and the USA as our data collection locations, we purposefully chose two distinct contexts to delve into the motivations of women's football fans. Brazil, renowned for its deep-rooted football history and passion, provided an environment of high sports engagement but with persistent gender disparities in the sporting realm, such as lower salaries and limited resources for female players compared to males. On the other hand, the USA, where soccer has been gaining prominence steadily, was selected due to its evolving landscape in gender equality within sports, reflecting a context where the sport was not a national passion but has been progressively gaining traction. This deliberate choice allowed us to investigate fan motivations in a context of intense sports involvement with lingering gender differences and another context marked by advancements in reducing such disparities. Our aim was not merely to compare but to illuminate how motivations for women's football followers resonate in diverse cultural and sporting landscapes, offering a nuanced understanding beyond a simple cross-cultural contrast.

This study randomly administered the three scales to respondents via the SurveyMonkey platform to prevent order bias. The order of items within each scale was randomized so that each participant encountered a different sequence. The instrument incorporated 23 items from Wann's (1995) SFMS, distributed across eight factors, 29 items from Trail and James,' (2001) MSSC scale spanning nine factors and 30 items from SII (Funk et al., 2001) covering ten factors. Notably, Trail and James' MSSC had not been previously applied to female football spectators. To the best of our knowledge, our study is the first to use MSSC (Trail & James, 2001) in the context of women's football followers. Other studies (Guest & Luijten, 2018; Coche, 2014; Funk et al., 2001) used both the SII (Funk et al., 2001) and the SFMS (Wann, 1995). The Likert scale for scale items ranged from 1 (strongly disagree) to 7 (strongly agree).

Purchase intention was assessed using a three-item scale adapted from Silveira et al. (2018), which originated from Chandran and Morwitz (2005). Fan identification used the seven-item SSIS scale developed by Wann and Branscombe (1993) and translated into Portuguese by Theodorakis et al. (2010), encompassing team identification and place attachment. eWOM was gauged using a four-item scale proposed by Santos et al. (2017). Interest in women's football was measured through a single-factor scale introduced by Funk et al. (2001). While single-item scales may be perceived as having lower reliability and validity, Kwon and Trail (2005) argue that this is not universally true, advising caution and suggesting their use in specific scenarios. In our study, the decision to retain “interest in women's football” as a single item was driven by the unique dimensions present in women's football, with no other scale aside from Funk et al. (2001) capturing these aspects.

Both data collections were conducted online due to the COVID-19 pandemic, which severely restricted global sporting events from March 2020. Surveys were distributed on Twitter, Facebook and Instagram to women's football spectators, filtered based on their engagement in the sport. This included attending matches, watching them on TV, reading, sharing online content or buying related products in the past 18 months (February 2019 to August 2020 for the first collection and February 2020 to August 2021 for the second). This timeframe covered significant events such as the Women's World Cup in June 2019 (first collection) and the 2020 Olympic Games (held in 2021) (second collection), as well as local events like national championships and other women's football leagues globally.

A pre-test was conducted for instrument validation from August 28 to September 03, 2020, yielding 15 responses via the SurveyMonkey platform. Respondents addressed issues regarding wording, question arrangement and platform visuals. The survey was also promoted on social networks and women's football blogs like “Dibradoras.” The pre-test took place between September 4 and 22, 2020.

The survey garnered 764 responses. Excluding incomplete or inconsistent surveys (n = 388) following Freire, Senise, Reis, and Ono (2017) approach resulted in a final sample of 376 valid responses. The second data collection occurred from August 20 to September 04, 2021, producing 406 responses, 198 validated and 208 excluded using the same criteria as the first stage. In the initial phase, analyses were conducted separately, given that the data collection in Brazil occurred approximately one year before the one carried out in the USA. The first collection allowed for verifying the data's reliability and ensuring the certainty of significant results. The second collection only reinforced the findings of the first one, and the multi-group analysis, which will be described in detail below, allowed this study to consider a single sample for the presented results.

In total, both stages generated 574 valid responses. Surveys in Brazil and the USA included a question about respondents' state of residence, and SurveyMonkey's Internet Protocol (IP) data ensured location accuracy.

Data analysis

In the sample, 74.4% are female, 74% are single and 50.9% have completed both higher education and post-graduation. Most respondents (57.3%) belong to the low-middle and middle-income classes, determined by country-specific income criteria.

To validate the proposed model, we conducted a structural equation modeling (SEM) analysis, allowing for observing relationship patterns among model variables akin to multiple regression (Hair, Black, Babin, Anderson, & Tatham, 2009). In this study, SEM was executed using partial least squares (PLS) with SmartPLS software (v. 3.2.2) (Ringle, Wende, & Becker, 2015). The decision to employ PLS was grounded in considerations such as the non-normal distribution of data, model complexity, a small sample size, limited theoretical support and the inappropriateness of covariance-based SEM (Ringle, Silva, & Bido, 2014). Given the observed non-normal distribution in the data for this study, PLS was deemed a suitable choice.

As observed in Table 2, all dimensions across the three scales exhibited average variance extracted (AVE) values greater than 0.5. Following the guidance of Hair, Black, Babin, and Anderson (2019), the researcher should prioritize composite reliability when dealing with reflective constructs in SEM. Here, all dimensions demonstrated composite reliability values exceeding 0.7. Both AVE and composite reliability measures ensure internal consistency and, consequently, affirm the convergent validity of the model. Regarding the SFMS scale, out of the eight dimensions, seven did not yield satisfactory alpha values, highlighting the scale's fragility compared to the other two examined in this study.

Cross-loadings for each dimension were assessed following Fornell and Larcker (1981) correlation matrix, ensuring proper discriminant validity. The final model, refined through item adjustments and exclusions, confirmed discriminant validity. The structural model identified which scale better predicted the dependent variables.

The comparative analysis of the scales considered each scale's second-order constructs. The complete scale included all items from each dimension that composed the first-order constructs. This technique, common in competing scale studies, comprehensively assessed women's football followers' motivations from each scale. By adhering to this methodology, aligned with established practices in similar research (Lopes, de Lamônica Freire, & Lopes, 2019; Freire, Quevedo-Silva, Frederico, Vils, & Junior, 2021), we aimed to ensure a robust and comparative analysis, capturing the complexity of the dimensions and their interactions in the specific context of our study.

Table 3 reveals that the SII scale demonstrated the most significant predictive values for the four dependent variables among the three measured scales. In contrast, the SFMS scale exhibited problematic results, displaying a lack of a significant relationship with the SSIS and a significant negative relationship with all other dependent variables. The MSSC scale outperformed the SFMS, showing a significant relationship with both eWOM and SSIS but a nonsignificant relationship with purchase intention and interest in women's football. Finally, the SII scale performed the best among all three scales tested, demonstrating a significant relationship with all four dependent variables. Moreover, SII paths were notably more robust than those from the competing scales (Table 3).

The final model was validated after assessing the convergent, discriminant and nomological validities, as per Figure 2.

As a post hoc test, a multigroup analysis was performed using SmartPLS 3.0 software to test path differences between the Brazilian and the US samples. Results are shown in Table 4.

No significant difference is observed in the dependent variables e-WOM, SISS and interest in women's football for the three scales. Purchase intention was nonsignificant for the MSSC and SFMS scales, whereas for the SII scale, the difference was significant (0.038). As indicated by Table 4, this result reveals that only one path among the 12 possible ones has a statistically significant difference between the two collections, which can be reliable for data analysis. It is worth noting that the central focus of the research lies in the intrinsic comparison of competing scales. In this context, the multigroup analysis serves as an instrumental tool to ensure that the observed groups exhibit similar behavior without delving too deeply into this specific realm. To the best of the authors' knowledge, this represents the first study of competing scales that includes data collection in two distinct cultural contexts/countries.

Discussion

The study assessed and compared three different scales of motivations of women's football followers (SFMS, MSSC and SII), and SII clearly demonstrated superior explanatory power for all dependent variables – purchase intention, eWOM, identification as a fan and interest in women's football. In contrast, the other two scales exhibited lower explanatory power. Hence, the study concludes that SII is the most appropriate scale for measuring the motivations of women's football followers, aligning with Yenilmez et al. (2020) findings on male football fans in Turkey.

Considering women's football, SII may have higher predictive power due to its “support for women's opportunities” dimension, which is linked to brand and societal issues. The other two scales have a strong direction toward sports consumption motivation, but when it comes to women's sports, they may have failed to understand the activism for spectators in supporting women's opportunities. The distinction in brand loyalty and satisfaction between male and female spectators is documented (Lamberti, Rialp, & Simon, 2021).

Another important aspect of finding the “support for women's opportunities” as the primary motivation among followers and spectators of women's football is its connection to the UN Sustainable Development Goal #5 (gender equality), which emphasizes “achieve gender equality and empower all women and girls.” It clearly shows that followers and spectators are willing to consume more women's football, so companies and clubs should direct their efforts for filling the gender gap, both for economic and social reasons. Previous studies show that promoting women's football has been prioritized by football bodies (Meier, Konjer, & Strauß, 2020), but despite the efforts, salaries, visibility and training conditions are still a major gap when it comes to gender comparisons between male and female athletes (Euronews, 2023).

In summary, the results indicate that the SII is the most effective scale for understanding the motivations of women's football followers, outperforming the other two tested scales. The “support for women's opportunities” dimension stands out as the primary motivation linked to brand and societal issues. This motivation, aligned with UN Sustainable Development Goal #5 for gender equality, suggests that followers are willing to support women's football. Thus, it underscores the need for targeted efforts by companies and clubs to reduce gender disparities, not only for economic reasons but also in the interest of equality and sustainable development. These questions align with research emphasizing critical examination of the differences between women's and men's football (Tang, Schallhorn, Guo, & Coombs, 2022, p. 10).

In this study, it was not expected that a scale would demonstrate predictive power for women's football fans as the SII did in comparison to the other two scales. Nevertheless, several factors explain this unexpected result, one of which is that this specific scale was unique in having a variable related to support for opportunities for women in sports. This motivation emerged as the primary consideration for women's football fans, indicating that these data may have behaved differently in the male sports context.

Conclusion

The primary aim of this study was to determine the most suitable scale for assessing the motivations of women's football followers, selected from SFMS by Wann (1995), MSSC by Trail and James (2001) and SII by Funk et al. (2001).

In the realm of women's sports, limited studies have delved into the motivations of these spectators (Coche, 2014; Funk et al., 2001, 2003; Guest & Luijten, 2018). This study's significance lies in charting a cohesive path for measuring the motivations of these spectators, particularly in women's football, which gained substantial attention in 2019 with over 1 billion viewers for the FIFA Women's World Cup. The research contributes to our understanding of this market, where conventional marketing strategies persist (Johnson & Zhang, 2018), potentially contributing to the sporadic nature of fan engagement (Giachino et al., 2023). Moreover, the study fills a gap in academia, as the literature review indicates limited interest in studying women sports fans' engagement (Johnson & Zhang, 2018), despite recent research revealing a growing fan base advocating for equality in sports (Martins, Silva, & Delarmelina, 2022).

In addition, dimensions such as “supporting women's opportunities in sports” emerge as pivotal factors for purchase intention, e-WOM, and interest in women's football. Consequently, marketing efforts should strongly highlight clubs' commitment to creating opportunities for women in professional sports, fostering conducive working conditions that empower them to gain recognition and assume central roles.

While our analysis focused on motivations for women's football consumption, exploring barriers to such consumption could predict further behavior, as Mayer and Hungenberg (2021) demonstrated. Future studies might combine these aspects. Qualitative approaches could delve into the cultural aspects of women's football consumption, enhancing the understanding of motivations crucial to women's football followers. Another avenue for future studies is understanding gender and age differences in fans' motivations to consume women's football. Demographic variables have been explored in previous studies, showing bias in sports consumption (Giachino et al., 2023), and it could be interesting to analyze how these differences are shown in the three scales, especially in the SII one.

Notably, two out of the three scales (SII and MSSC) directly address female players but overlook inquiries about the team coach. Contrarily, Hallmann et al. (2016) identified that the “coach attachment” dimension was a reliable predictor of the dependent variable “intention to attend a match of the league” in Japan and Germany. Coach gender does not predict club results, but the underrepresentation of women coaches in professional women's football leagues persists (Gomez-Gonzalez, Dietl, & Nesseler, 2019). Could fans be motivated to watch women's football to support a female coach in a leadership role?

Finally, it is noteworthy to highlight that the study exclusively gathered data from Brazilian and US women's football fans, responding to the call for cross-country research in fandom studies (Tang et al., 2022). However, extending this comparison across scales to other cultural contexts could validate the study's results.

The author would like to thank Valerio de Souza-Neto for valuable comments on the first translated version of the paper. We would also like to thank all the professors on the master’s board of Anderson Filipe Rosa’s dissertation; Andres Veloso, Ludmila Mourão and Ary Rocco, as this article is the result of contributions from the master’s dissertation.

Disclosure statement: No potential conflict of interest was reported by the author(s).

Figure 1.

Competing model of the study

Figure 2.

Structural model

Table 1.

Scales of motivations of sports followers selected for this study

Scales
SFMS		SII		MSSC
Eustress	The Sports Fan Motivation Scale (SFMS) was developed to assess the motivations behind sports fandom. It quantifies various factors that drive sports fans, offering, according to the author, a reliable tool for understanding the underlying motivations of sports enthusiasts	Football	The Sport Interest Inventory (SII) is a scale designed to measure various individual difference factors related to spectator interest in sports, particularly women's competitive sports. It has been used to understand consumer support for specific sports properties and provides insights into spectator behavior in different sports contexts	Achievement	The Motivation Scale for Sport Consumption (MSSC) was developed to measure the motivations that influence the behavior of sports spectatorsAccording to the authors, it addresses previous weaknesses in the measurement of spectator motivations and has demonstrated strong psychometric properties, becoming an effective tool for understanding why people engage in sport consumption activities
Self-esteem		Vicarious achievement		Acquisition of knowledge
Escape		Excitement		Aesthetics
Entertainment		Team identification		Drama
Economic		Supporting women's opportunities in sport		Escape
Aesthetics		Aesthetics		Family
Group affiliation		Socialization		Physical attractiveness of participants
Family needs		National pride		Quality of the physical skill of the participants
		Drama		Social interaction
		Interest in the player

Source: Table by authors

Table 2.

Reliability of constructs

Dimension	Cronbach’salpha	rho_A	Compositereliability	Average varianceextracted (AVE)
E-WOM	0.849	0.857	0.898	0.689
Purchase intention	0.868	0.871	0.919	0.792
Interest in women’s soccer	1.000	1.000	1.000	1.000
Siss	0.610	0.616	0.792	0.560
MSSC_acuisition of knowledge	0.837	0.838	0.902	0.755
MSSC_aesthetic	0.763	0.767	0.894	0.808
MSSC_drama	0.585*	0.620*	0.779	0.544
MSSC_escape	0.851	0.853	0.909	0.770
MSSC_physical	0.832	0.843	0.898	0.747
MSSC_social interaction	0.845	0.853	0.906	0.763
MSSC_vicarious achievement	0.853	0.856	0.911	0.773
MSSSC_enjoyment of aggression	0.414*	0.572*	0.750	0.611
SFMS_aesthetic	0.581*	0.591*	0.783	0.549
SFMS_economic	0.658*	0.684*	0.810	0.588
SFMS_entertainment	0.593*	0.603*	0.830	0.710
SFMS_escape	0.886	0.889	0.929	0.814
SFMS_eustress	0.589*	0.589*	0.785	0.550
SFMS_family	0.582*	0.620*	0.824	0.701
SFMS_group affiliation	0.604*	0.664*	0.784	0.553
SFMS_self esteem	0.600*	0.599*	0.790	0.558
SII_aesthetic	0.273*	0.285*	0.730	0.577
SII_comunity	0.763	0.772	0.863	0.677
SII_drama	0.639*	0.687*	0.796	0.568
SII_excitment	0.805	0.805	0.911	0.837
SII_fan identification	0.844	0.861	0.905	0.762
SII_interest in player	0.777	0.913	0.855	0.664
SII_soccer	0.792	0.796	0.905	0.827
SII_social	0.748	0.772	0.856	0.665
SII_vicarious achievement	0.849	0.861	0.909	0.770
SII_women's sports opportunities	0.697	0.716	0.829	0.618

Note:*Below critical threshold 0.7

Source: Table by authors

Table 3.

Path for each dependent variable

Path	Pathcoefficient	R2	T-statistics(|O/STDEV|)	P-value
MSSC → E-WOM	0.115	0.576	2.559	0.011
SFMS → E-WOM	−0.153		4.559	0.000
SII → E-WOM	0.783		13.033	0.000
MSSC → SISS	0.208	0.463	3.510	0.000
SFMS → SISS	−0.022		1.062	*0.288
SII → SISS	0.517		7.965	0.000
MSSC → Purchase intention	0.033	0.246	1.407	*0.159
SFMS → Purchase intention	−0.078		2.842	0.005
SII → Purchase intention	0.530		5.353	0.000
MSSC → Interest in women’s soccer	−0.113	0.452	0.788	*0.431
SFMS → Interest in women's soccer	−0.100		2.929	0.003
SII → Interest in women's soccer	0.838		12.913	0.000

Note:*>0.05 significant value

Source: Table by authors

Table 4.

Multigroup analysis

Path	Structure-diff coefficients(Brazil vs USA)	Original one-sidedp-value (Brazil vs USA)	New p-values(Brazil vs USA)
MSSC → E-WOM	0.279	0.052	0.104
MSSC → Interest in women's soccer	−0.026	0.542	0.917
MSSC → Purchase intention	−0.109	0.700	0.600
MSSC → SISS	−0.032	0.583	0.834
SFMS → E-WOM	−0.192	0.927	0.146
SFMS → Interest in women's soccer	0.199	0.092	0.184
SFMS → Purchase intention	−0.119	0.744	0.512
SFMS → SISS	−0.196	0.913	0.174
SII → E-WOM	0.034	0.411	0.822
SII → Interest in women's soccer	0.075	0.357	0.714
SII → Purchase intention	0.340	0.019	*0.038
SII → SISS	0.136	0.169	0.338

Note:*Significant difference between Brazil and US respondents

Source: Table by authors