Inglés (pdf)
Articulo en XML
Referencias del artículo
Traducción automática
Enviar articulo por email
Citado por SciELO 
Similares en
SciELO 
Permalink
Introduction
Due to the considerable array of choices available to consumers, organizations acknowledge that merely attaining customer satisfaction and preference are no longer enough to ensure long-term loyalty (Palusuk et al., 2019). As a result, marketers are advocated to find new ways that raise a deep emotional connection, making their brands truly loved by customers (Velmurugan & Thalhath, 2021). The grounded theory approach has been recommended for studying brand love, emphasizing the importance of phenomenological experience in consumer-brand relationships. (Batra et al., 2012; Santos & Schlesinger, 2024).
New technologies are changing people’s lives and are focusing on their attention. The idea of Marketing 4.0 encourages a transition from conventional methods to a digital strategy in developing customer relationships (Kotler et al., 2016). Marketing 4.0 notifies the usage of digital methods to understand, reach, satisfy, and keep customers loyal by fostering strong relationships (Dash et al., 2023).
In the current digital environment, the enhancement of positive word-of-mouth (WOM) and the prevention of brand switching have attracted the attention of researchers (Chen & Zhang, 2022). Furthermore, the notion of brand love involves positive emotions towards the brand, which facilitates relevant brand outcomes such as brand loyalty and WOM (Carroll & Ahuvia, 2006).
Competition in the streaming TV market is fierce. Today, more than 23 major streaming TV platforms constitute the global market, with popular brands such as Amazon Prime, Disney+, and Apple TV. This academic work focuses on the brand of Netflix to study the streaming service industry context. The study centers on the brand of Netflix as the most popular streaming service brand worldwide (Amoroso et al., 2021). Netflix has attracted attention not only among the media and practitioners in the industry but also in academia (Naranjo & Fernández-Ramírez, 2022). The investigation` focuses on the Spanish market, which is one of the most successful non-English speaking markets for Netflix (Naranjo & Fernández-Ramírez, 2022).
The concept of Marketing 4.0 presents four components: brand integrity, brand image, brand identity, and brand interaction (Kotler et al., 2016). Dash et al. (2021), while testing their model ) found that only brand identity and image had a significant impact on purchase intention and satisfaction.
Although brand love offers evident benefits, research on the topic remains scarce, and the concept itself is still underdeveloped. The gap this study aims to address is the examination of the effect of key antecedents-brand integrity, sense of brand community, and brand interactions-on brand love. Additionally, the study seeks to contribute to literature by testing two key outcomes of brand love: switching intention and word-of-mouth (WOM) intensity.
Methodologically, this research advances the field by employing partial least squares (PLS) path modelling to analyze these relationships. Furthermore, the study conducts a cluster analysis to segment brand lovers based on the passion and affection dimensions outlined in Albert and Valette-Florence’s (2010) model.
This study has three main objectives. First, it aims to analyze the effect of brand integrity, brand interactions, and sense of brand community on brand love. Second, it seeks to examine the influence of brand love on switching intention and word-of-mouth (WOM) intensity. Third, the study conducts a cluster analysis to segment Netflix consumers based on the dimensions of this construct.
This paper is organized as follows. First, a literature review presents the concepts and subsequent hypotheses that establish the study model. Second, quantitative PLS-SEM and cluster analysis were performed. Finally, this study discusses its results, theoretical contributions, implications, and upcoming research opportunities.
Theoretical Framework
Brand love
Shimp and Madden (1988) establish that individuals can experience emotions similar to love toward objects. Researchers have conceptualized brand love as a strong feeling from customers towards a brand, which is more than mere liking. Brand love is defined as the passionate and emotional attachment of a consumer with a brand’ (Joshi & Garg, 2021). For Aro et al. (2018), this concept means the emotional bond that a satisfied consumer develops with a brand, which can manifest in various ways depending on the individual but generally involves a certain level of brand identification.
Brand love was first introduced by Carroll & Ahuvia (2006) as a passionate emotional attachment to a brand; this is different from satisfaction, which involves only cognition. According to Gumparthi and Patra (2020), brand love is merely affection for, or attachment to, a particular brand. Albert and Valette-Florence (2010) define brand love as a multidimensional construct comprising two key dimensions: passion and affection. Affection refers to a strong and unique bond, coupled with a close psychological connection, between an individual and a brand. Passion, viewed as a central component of emotional attachment, plays a significant role in this relationship (Velmurugan & Thalhath, 2021), describes the consumer’s intense desire for a brand, characterized by high levels of emotional arousal (Batra et al., 2012).
Overall, brand love is a source of relevant brand outcomes and a main factor in building a competitive advantage (Joshi & Garg, 2022)
Brand integrity
Brand integrity means the realization of the promise made through the positioning and the differentiation of the brand (Kotler et al., 2010). Maintaining a brand's credibility helps establish trust and reliability among consumers. Integrity represents the ethics, honesty, and commitment firms have for its clients (Hu et al., 2016). Also known as brand credibility, means keeping promises made to customers with the help of proper differentiation techniques (Dash et al., 2021). Furthermore, this concept is related to being honest, displaying moral courage, and possessing reliability and self-awareness (Murphy et al., 2013). It also reflects brand credibility and the ability to deliver value to customers(Erdem & Swait, 2004).
Credibility is the combination of having the ability to fulfil the promise (expertise) and the willingness to do so (trustworthiness) (Dash et al., 2021). For Aaker (1996), credibility is an essential factor in a long-term relationship. According to Joshi & Garg (2022), a brand’s success relies on its proven and trusted capacity to satisfy its promise. Trust is the pillar of online shopping and e-commerce. (Boateng, 2021). Customers tend to engage in electronic WOM about a brand with brand integrity (Boateng, 2021). Having reliability also means listening to customer`s complains, which enhances perceived quality and purchase intention (Baek & King, 2011).
Sense of brand community
An individual can express her or his identity through consumption. By consumption a brand, a person could perceive he is a member to a distinct social category. For Muñiz and O’Guinn (2001), a sense of community means consciousness of being part of a different social group. Within this social group, everyone shares a similar social connection with the brand. A sense of brand community involves a feeling of belonging to the group and perceived differentiation from non-members, as well as a relational bond with other brand users (Muñiz & O’Guinn, 2001). The greater the fit between the consumer`s identity and social identity, the greater the sense of community with others linked with the brand (Carlson et al., 2008).
According to McAlexander et al. (2002), the primary function of consumers' sense of brand community is to strengthen their relationship with the brand. Essentially, a community is capable of existing merely in the mind of the consumers.
Brand interaction
Marketing 3.0 includes brand image, brand identity and brand integrity. Brand interaction is the only factor that differentiates Marketing 4.0 from Marketing 3.0. Brand interaction indicates the experience the customer obtains when engaging with the company or brand (Winarko et al., 2022). Recent technological innovations allow interactions between brands and consumers to occur continuously and in real-time (Dash et al., 2021). Web 3.0 applications, such as social media, changed interactions and motivate everyone to connect and share their views (Kamboj & Sarmah, 2017).
Brand interaction develops around customer experience and can engage customers in the product development process through participation and collaboration (Dash et al., 2021). Hollebeek et al. (2014) considers the nature of brand engagement is like brand interaction. For Dash et al. (2021), brand identity, brand image and brand integrity have a positive effect on customers merely when the brand engages with customers successfully. While interacting with the brand, consumers engage in three tasks: consumption, provision and production (Schivinski et al., 2016).
Switching intention
Given that attracting new customers is more expensive than retaining current ones, brand-switching efforts lead to reduced profitability (Sun et al., 2017). For Li & Ku (2018), switching intention means the consumer`s intention to switch between different brands within the same product and service. In the technological context, switching intention is defined as “a partial reduction or complete termination of users’ use of a particular information technology product” (Ye & Potter, 2011: 528).
According to Moon (1995), brand switching consists of three types of effects: push, pull, and mooring. Push effects, such as low quality or dissatisfaction, trigger consumers to leave or drive users away. Second, pull effects attract consumers to alternative services such as efforts from competitors. Finally, mooring effects refer to personal and social reasons (Sun et al., 2021) that either increase or reduce migration, such as switching costs and habits. Effective implemented loyalty programs could prevent consumers from switching (Quoquab et al., 2018).
WOM intensity
De Meyer & Petzer (2014) defined WOM intensity as the volume of messages expressed by consumers (i.e., how often and the amount of information expressed). This kind of communication also refers to the frequency of WOM in comparison with other preferred companies and the number of people with whom the comments are shared by the same customer/s (Bulut & Ulema, 2022). For Xu et al. (2018) is equivalent to how often people talk about the brand and the level of detail they put in their narratives. Additionally, factors such as the type of product and consumer characteristics-including personality, social class, and culture-play a role in shaping the extent of these kind of conversations(Lam et al., 2009).
Based on findings from the literature, WOM intensity increases profitability (Zhang et al., 2018), as satisfied consumers may highly engage in WOM communication (Anderson, 1998). In a survey ran by (Díaz et al., 2023), 40% of people claimed their purchase decision was based on other consumer`s opinions.
Hypotheses development
Brand integrity constitutes the differentiation factor, which is the proof a brand delivers its promise. Efforts on brand identity and brand integrity will generate a good brand image. Ultimately, a good brand image will spark consumer`s emotions positively. According to Kotler et al. (2010), consumer`s emotions are motivated through a rational evaluation (the positioning), plus something that attracts the human spirit (the differentiation).
The human centric-marketing envision consumers as individuals with mind and souls (Kotler et al., 2010). In the human-centric marketing, a brand requires a positioning (brand identity) and a differentiation (brand integrity) (Kotler et al., 2010). Brands integrity means the proof a brand is reliable and capable of delivering its promise. Likewise, brand love relates to a long-term relationship with the consumer (Carroll & Ahuvia, 2006). Hence, we hypothesize as follows:
H1: Brand integrity has a direct and positive effect on brand love.
Connectivity, driven by the latest technological advances, has transformed the consumption landscape. Traditionally, consumers formed their attitudes toward a brand individually, relying on personal experiences and direct interactions. However, with the rise of digital connectivity, social influence plays a crucial role in shaping brand perceptions (Kotler et al., 2016). In this new era, marketing has shifted from a vertical model-where brands unilaterally persuaded consumers-to a horizontal dynamic, where consumers influence each other (Kotler et al., 2016). Customer communities have become key sources of information, allowing individuals to assess brand quality through shared experiences. As Kotler et al. (2016) highlight, those who appreciate a brand often organize themselves into these communities, further amplifying their impact.
According to the Social Identity Theory (Tajfel & Turner, 1979), individuals build their identities based on the identity of the group. This social identity may lead to brand love (Vernuccio et al., 2015). Subjective norms, meaning that consumers act in accordance with their social groups, have a positive influence on brand love (Hegner et al., 2017). What’s more, literature finds that sense of community associates with emotional attachment (Mcmillan & Chavis, 1986; Sanz-Blas et al., 2019) and brand love (Bergkvist & Bech-Larsen, 2010) These consumers establish a symbolic connection with their favored brand and feel a deep sense of congruence between the brand’s identity and their personal values (Palusuk et al., 2019). Brand communities are capable of fostering brand love (Banerjee & Chaudhuri, 2022). When the level of a customer’s perceived membership in a brand community increases, their love for the brand increases (Ahuvia et al., 2022).
The psychological sense of brand community theory proposes consumers make a brand community based on shared experiences, values and beliefs. In the creation of such brand community, consumers generate an emotional connection with the brand (Audria et al., 2023). As stated by Palazon et al. (2019), when a consumer experiences a sense of belonging to a brand community, the chances of forming an emotional connection with the brand are significantly increased, with their empirical research demonstrating a positive and significant relationship between a sense of brand community and brand love. Building on prior research, we put forward the following hypothesis:
H2: Sense of brand community has a direct and positive impact on brand love.
Almost 60% of people like interacting with a brand, give their opinion and receiving a response from the brand (Díaz et al., 2023). Brand interaction is the paramount element from Marketing 4.0. In the current era of connectivity, the other three components require brand interactivity to affect customers.
Up to 50% of people claim they learned about new brands as a result of other consumer`s comments (Díaz et al., 2023). While satisfaction may occur during the first interaction with a brand, brand love materializes over time and requires several interactions (Carroll & Ahuvia, 2006). Brand love develops progressively as consumers accumulate interactions and experiences with the brand over time, and it can grow positively as long as these experiences remain favourable (Banerjee & Chaudhuri, 2022). Customers’ brand interaction triggers positive emotions (Song et al., 2019) and associates their sentiments and growth with the brand (Trudeau & Shobeiri, 2016). Additional studies have shown that brand love is a consequence of past interactions (Pontinha & Coelho do Vale, 2020), or frequent interactions (Aro et al., 2018). Moreover, Naeem & Ozuem (2021) found that brand-related interactions through social media positively affect consumers’ brand love. For these argues, we propose the following hypothesis:
H3: Brand interaction has a direct and positive effect on brand love.
In theory, every brand should aim to cultivate love among consumers. Once a consumer forms brand love, their loyalty is secured, like how an individual deeply in love with a partner would not contemplate other options. This emotional bond reduces the likelihood of switching intentions, so brand love acts as a barrier against the efforts of other brands, also known as the pull effect. In the other hand, consumers change brands because of flaws in the primary brand and the attractiveness of an alternative. Empirical research has identified brand love as a main component of loyalty (Liebl et al., 2022). In addition, brand love reduces factors that might lead consumers to migrate to alternative brands. Concerning push effects, migration theory regards as an antecedent of switching intention, brand love leads to the forgiveness of a brand’s mistakes (Ahuvia et al., 2020). Furthermore, brand love may hinder mooring effects that provoke consumers to replace a brand, such as a lack of affordability (Ahuvia et al., 2020). Arun et al. (2023) found that brand love acts as a mediator in the relationship between switching intention and switching behavior. Therefore, we propose the following hypotheses:
H4: Brand love has a direct and positive influence on preventing switching intention.
When consumers feel passion for a brand, emotional arousal drives them to inform others about the experience (Lovett et al., 2013). According to the literature, brand love is an antecedent of WOM (Liebl et al., 2022; Amaro et al., 2020). Furthermore, Albert and Merunka (2013) considered WOM to be the main outcome of brand love. Hence, when consumers love a brand, they tend to desire to influence others (Junaid et al., 2020). As highlighted by Lovett et al. (2013), brands that evoke high emotional intensity are likely to generate more eWOM than brands that elicit lower emotional engagement. Considering these findings, we suggest a positive relationship between brand love and WOM intensity.
H5: Brand love has a direct and positive effect on WOM intensity.
We summarize the set of hypotheses to be tested in Figure 1.
Methodology
Spain is one of Netflix's key markets (Naranjo & Fernández-Ramírez, 2022), as reflected in two globally successful shows (Elite and Money Heist) (Agirre, 2021). In 2019, two traditional TV companies, Atresmediaplayer and Mediaset España, entered the over-the-top (OTT) service industry by launching their streaming TV platforms (Alcolea-Díaz et al., 2022). The global Covid-19 pandemic has expanded the market in Spain, as Netflix reported a large increase in its subscriber base (Alcolea-Díaz et al., 2022).
The study population includes current Netflix subscribers who have been using the service for a minimum of six months, considering the minimum duration for experiencing the brand (Hepola et al. 2017). Since the study took place in Spain, a marketing expert assisted in the translation of the items from English to Spanish. In addition, an English expert reviewed the study to ensure linguistic consistency and an adequate translation. We carried out a pretest with 25 students and customers of Netflix to obtain an observation on the questionnaire. Their feedback assisted in the polishing of the questionnaire.
To test the model, a questionnaire was conducted. Data were gathered using CAWI (Computer Assistant Web Interviewing) using an online survey platform. 279 questionnaires were received. The collecting of the data was done during December 2021 and January 2022. All 279 participants provided their consent to contribute to the present study.
Table 1 reports the demographic data from the participants. The respondents’ ages ranged from 18 to 41 years, with an average age of 24.6 years. The sample included slightly more women than men (54% vs. 45.5%). The contestants had a minimum of high school education, with most respondents being students. (68%). 79.53% of the participants earn less than 1500€ and 64% were not responsible for paying for the service. (64.5%). Regarding consumption behaviour, most respondents had spent some time with the service (87.3% had been subscribers for more than one year), with a rather high frequency (79% watched Netflix at least once a week and 26.5% daily).
Table 1 Characteristics of the sample
| Variable | Characteristics | N | % |
|---|---|---|---|
| Age |
|
|
|
| Gender | Male | 127 | 45.5 |
| Female | 152 | 54.4 | |
| Education | High school diploma | 110 | 39.4 |
| University degree | 168 | 60.2 | |
| Occupation | Looking for work | 8 | 2.8 |
| House wife/ house husband | 4 | - | |
| Student | 191 | 68.4 | |
| In employment (full time and part time) | 76 | 27.24 | |
| Monthly net income | >1500 € | 57 | 20.4 |
| 1201-1500 € | 41 | 14.7 | |
| 901-1200 € | 28 | 10 | |
| 601-900 € | 29 | 10.39 | |
| < 600 € | 124 | 44.44 | |
| Do you pay for the service? | Yes | 99 | 35.4 |
| No | 180 | 64.5 | |
| Time of being a subscriber | < 1 year | 35 | 12.5 |
| 1-3 yrs | 175 | 62.7 | |
| 4-6 yrs | 59 | 21.1 | |
| > 7 yrs | 10 | 3.5 | |
| Frequency | Daily | 74 | 26.5 |
| 2-3 times per week | 108 | 38.7 | |
| Once per week | 61 | 21.8 | |
| Once per month | 27 | 9.6 | |
| Less than once per month | 8 | 2.8 | |
| Never | 1 | - | |
| Total simple size | 279 | 100 |
The questionnaire was separated into three parts: The first section contained an introduction regarding the participants’ familiarity with Netflix. The next section contained the questionnaire, with items that investigated respondents’ views on brand integrity, sense of brand community, brand interaction, brand love, switching intention, and WOM intensity. The third and last part included questions to gather the respondents’ demographic characteristics.
Albert & Valette-Florence (2010) scale tests brand love as a higher-order construct composed of passion and affection. Moreover, Erdem & Swait (2004) scale analyses brand integrity as a second-order construct formed by trustworthiness and expertise. To test the variable of sense of brand community, the present study utilized a four-item single-construct scale created by Carlson et al. (2008). In this existing study, Schivinski et al. (2016) scale was used to measure brand interaction as a second-order construct consisting of three lower-order constructs: consumption, contribution, and creation. Furthermore, to quantify the consequences of brand love, Anton et al., (2007) three-item, single-construct scale tested switching intention. Similar to switching intentions, this examination relies on a three-item scale used by Goyette et al. (2010) to calculate the WOM intensity variable. Additionally, this current study performs a cluster analysis based on the Albert & Valette-Florence (2010) scale of brand love, involving the two lower-order dimensions of affection and passion. Finally, all scales were fitted to the study and estimated using a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree). The items were associated to brand integrity, sense of brand community, brand interaction, brand love, WOM intensity, and switching intention.
Data analysis
We present two studies in our current case study. Study 1 conducts an empirical testing of the hypotheses through a PLS-SEM analysis. The PLS-SEM examination tests the five hypotheses. Likewise, the PLS-SEM testing assesses the impact brand love has on switching intention and WOM intensity. Study 2 develops a cluster analysis to categorize brand lovers based on (Albert & Valette-Florence, 2010) dimensions of brand love, passion and affection.
Study 1 - Evaluation of the reflective model
Being consistent to Podsakoff et al. (2003), common method bias could exist in this analysis since the collection of the data utilized a single source and a single context. To solve the previous issues, this study considered two courses of actions proposed by Podsakoff et al. (2003) for the questionnaire`s appliance. The current research implemented procedural remedies related to questionnaire design and methodological separation. Regarding questionnaire design, the study ensured that clear instructions were provided to participants, avoided complex or ambiguous items, kept the survey concise, and guaranteed anonymity of responses.
The procedure of partitioning out a “marker” variable (Podsakoff et al., 2003) was employed by detecting changes before and after implementing a marker variable. As shown in Table 2, the R2 modifications are minor, indicating a lack of common method bias in the study.
Table 2 Partial out a marker variable
| Without marker variable | With marker variable | Changes | |
|---|---|---|---|
| Brand love | 0.371 | 0.478 | 0.107 |
| WOM Intensity | 0.167 | 0.189 | 0.022 |
| Switching intention | 0.014 | 0.018 | 0.004 |
Four tests evaluate the reflective model`s convergent validity, reliability, and discriminant validity. They are: Item`s factor loadings assessment, Cronbach`s alpha, compose reliability and average variance extracted (AVE). Table 3 displays the variables and the corresponding items of the reflexive measurement model.
As for the item factor loadings assessment, an indicator`s loading above 0.7 explains 50% of the factor variance (Henseler et al., 2015). In relation to the criteria given, all items are valid and reliable except for two items, Bla1 and Blp1, which were removed. Second, Cronbach’s alpha checks the internal consistency, a Cronbach’s alpha value exceeding 0.7 is considered acceptable (Henseler et al., 2015). The lowest Cronbach’s alpha value in our model is 0.706 for the variable of Expertise, indicating an internal consistency in the reflective model. Third, composed reliability, which assesses internal consistency, necessitates a value greater than 0.7 to be acceptable. considered optimal. In our reflective model, all the factors values in regard to the composed reliability test are over 0.8. These mentioned factors values for the composed reliability test reveal reliability in our reflective model. Lastly, the AVE test examines divergent validity, for which, a value over 0.5 explains the majority of the variance (>50%) derived from the indicators (Henseler et al., 2015). Table 3 reports AVE values greater than 0.6, denoting that all the factors explain more than half of the variance of its indicators.
Table 3 Evaluation of the reflective model
| Second order factors | First order factors | Items | Factor loadings | α | CR | AVE |
|---|---|---|---|---|---|---|
| Sense of brand community | SBC1 | 0.894 | 0.912 | 0.938 | 0.792 | |
| SBC2 | 0.908 | |||||
| SBC3 | 0.912 | |||||
| SBC4 | 0.844 | |||||
| WOM intensity | WI1 | 0.899 | 0.766 | 0.907 | 0.766 | |
| WI2 | 0.904 | |||||
| WI3 | 0.820 | |||||
| Switching intention | SI1 | 0.783 | 0.609 | 0.825 | 0.726 | |
| SI2 | 0.733 | |||||
| SI3 | 0.827 | |||||
| Brand integrity | Trustworthiness | Blt1 | 0.811 | 0.855 | 0.896 | 0.687 |
| Blt2 | 0.769 | |||||
| Blt3 | 0.868 | |||||
| Blt4 | 0.802 | |||||
| Blt5 | 0.725 | |||||
| Expertise | Ble1 | 0.855 | 0.706 | 0.871 | 0.771 | |
| Ble2 | 0.901 | |||||
| Brand interaction | Consumption | Blcs1 | 0.818 | 0.862 | 0.901 | 0.693 |
| Blcs2 | 0.842 | |||||
| Blcs3 | 0.847 | |||||
| Blcs4 | 0.758 | |||||
| Blcs5 | 0.745 | |||||
| Contribution | Blct1 | 0.826 | 0.895 | 0.920 | 0..677 | |
| Blct2 | 0.809 | |||||
| Blct3 | 0.797 | |||||
| Blct4 | 0.807 | |||||
| Blct5 | 0.807 | |||||
| Blct6 | 0.815 | |||||
| Creation | Blcr1 | 0.797 | 0.908 | 0.929 | 0.685 | |
| Blcr2 | 0.840 | |||||
| Blcr3 | 0.868 | |||||
| Blcr4 | 0.836 | |||||
| Blcr5 | 0.833 | |||||
| Blcr6 | 0.789 | |||||
| Brand love | Affection | Bla2 | 0.699 | 0.833 | 0.915 | 0.752 |
| Bla3 | 0.861 | |||||
| Bla4 | 0.828 | |||||
| Bla5 | 0.871 | |||||
| Bla6 | 0.861 | |||||
| Passion | Blp2 | 0.809 | 0.863 | 0.902 | 0.647 | |
| Blp3 | 0.828 | |||||
| Blp4 | 0.798 | |||||
| Blp5 | 0.834 | |||||
| Blp6 | 0.750 |
α = Cronbach’s alpha; CR = composite reliability; AVE = average variance extracted.
Table 4 shows that the squared AVE of each construct, which is greater than its correlation with other factors. Most of the same-construct correlation`s squared AVE are larger than 0.8, whereas the highest different-construct correlation is 0.726. Therefore, regarding the Fornell-Larcker criterion, the study`s constructs possess discriminant validity.
In the reflective model, the R2 value for the variable of brand love is 0.478. The current value means that 47.8% of variation in brand love depends on the model. The variables of brand integrity, sense of brand community and brand interaction explain 47.8% of variations in brand love. The structural equation modelling (goodness of fit model) reveals the model and the constructs` level of predictability. Results are also in line with empirical works by (Dash et al., 2021).
Table 4 Discriminant validity - Fornell and Larcker criterion.
| Brand love: affection | Brand love: passion | Brand interaction: Consumption | Brand interaction: Contribution | Brand interaction: Creation | Brand integrity: Expertise | Sense of brand community | Switching intention | Brand integrity: Trustworthiness | WOM intensity | |
|---|---|---|---|---|---|---|---|---|---|---|
| Brand love: affection | 0.827 | |||||||||
| Brand love: passion | 0.508 | 0.804 | ||||||||
| Brand interaction: Consumption | 0.431 | 0.399 | 0.803 | |||||||
| Brand interaction: Contribution | 0.511 | 0.446 | 0.726 | 0.81 | ||||||
| Brand interaction: Creation | 0.412 | 0.41 | 0.555 | 0.665 | 0.828 | |||||
| Brand integrity: Expertise | 0.229 | 0.099 | 0.149 | 0.143 | 0.048 | 0.878 | ||||
| Sense of brand community | 0.526 | 0.5 | 0.434 | 0.409 | 0.396 | 0.157 | 0.89 | |||
| Switching intention | 0.107 | 0.094 | 0.168 | 0.109 | 0.05 | 0.36 | 0.079 | 0.782 | ||
| Brand integrity: Trustworthiness | 0.211 | 0.169 | 0.192 | 0.15 | 0.036 | 0.718 | 0.138 | 0.408 | 0.796 | |
| WOM intensity | 0.361 | 0.343 | 0.347 | 0.335 | 0.261 | 0.314 | 0.331 | 0.428 | 0.328 | 0.875 |
Structural model assessment and hypotheses testing
PLS-SEM is a robust modelling technique that is suitable for analysing complex predictive models and testing the strength of the relationships between latent variables (Andrés-Sánchez & Puchades, 2023). Once the reliability and validity has been tested using PLS and testing of the hypothesized paths using structural equation modelling (SEM). SEM was performed using the repeated indicators method, which is useful when a model contains high-order variables (Van Riel et al., 2017). For this purpose, the SMARTPLS software (version 4.0) was employed. The model of this current study is reflective, in which the items reflect the variables, and the variables contain meaning. Therefore, items could be omitted from a variable with no major impact on the results.
Table 5 presents the results of the structural relations analysis. Structural equation modelling analysis was performed using a bootstrapping technique with 5,000 bootstrap samples and a two-tailed test type at a significance level of 0.05.
Table 5 Path coefficients
| Original sample | Sample mean | Standard deviation | Standard error | P value | |
|---|---|---|---|---|---|
| H1. Brand integrity → Brand love | 0.113 | 0.111 | 0.054 | 0.054 | 0.035** |
| H2. Sense of brand community → Brand love | 0.401 | 0.399 | 0.053 | 0.053 | 0.000**** |
| H3. Brand interaction → Brand love | 0.372 | 0.374 | 0.053 | 0.053 | 0.000**** |
| H4. Brand love → switching intention | 0.118 | 0.117 | 0.061 | 0.061 | 0.054* |
| H5. Brand love → WOM intensity | 0.409 | 0.409 | 0.050 | 0.050 | 0.000**** |
Notes: **** p<0.001; *** p<0.01; ** p<0.05; * p<0.1
As Table 5 shows, hypotheses 1, 2, and 3 examined the effects of brand integrity, sense of brand community, and brand interaction as antecedents of brand love. The results showed a significant and positive correlation between brand love and brand integrity, sense of brand community, and brand interaction. Brand integrity has the smallest effect among the three antecedents of brand love included in the model. Hypotheses 4 and 5 focus on the effect of brand love on switching intentions and WOM intensity. The findings demonstrate that brand love hasn’t a significant impact on consumer`s switching intentions (H4). The results from the bootstrapping display show how brand love influences WOM intensity (H5). Thus, the results support 4 of 5 hypotheses.
4.2 Study 2- Evaluation of the cluster analysis
As a complementary step, a cluster analysis was performed to explore the dimensions of brand love. It would provide further insights for segmenting consumers into homogenous groups based on the (Noël Albert & Valette-Florence, 2010) dimensions of brand love. The cluster analysis was performed using IBM SPSS 30. This study`s cluster analysis identified 4 homogeneous groups based on their k-means values. The clusters’ nomenclature was designed to represent their situations and positions based on their scores on the dimensions of affection and passion. Table 6 lists these clusters and their respective values.
True brand lover: This cluster comprises 70 members (25.1%). Members of this group had the highest scores of all clusters in terms of affection and passion. Warm consumer: This cluster comprises 52 members (19.4%). Members showing above-average affection for the brand but below-average passion for the brand. Passionate consumers: This group consists of 37 members (13.3%), who exhibited above-average passion but below-average affection for the brand. Cold consumers: This group includes 118 members (42.3%), who scored below average in both affection and passion toward the brand.
Table 6 Evaluation of the cluster analysis.
|
|
|
|
|
Mean | ||
|---|---|---|---|---|---|---|
| Affection | Bla1 | 4 | 3.54 | 2.95 | 2.64 | 3.2 |
| Bla2 | 3.7 | 3.54 | 2.70 | 2.29 | 2.94 | |
| Bla3 | 3.5 | 2.98 | 2.16 | 1.5 | 2.38 | |
| Bla4 | 3.19 | 3 | 1.65 | 1.44 | 2.21 | |
| Bla5 | 3.21 | 2.22 | 1.49 | 1.2 | 1.94 | |
| Bla6 | 3.34 | 2.15 | 1.73 | 1.28 | 2.03 | |
| Passion | Blp1 | 3.4 | 1.87 | 2.81 | 1.73 | 2.32 |
| Blp2 | 2.73 | 1.24 | 2.03 | 1.12 | 1.67 | |
| Blp3 | 2.46 | 1.07 | 2 | 1.02 | 1.52 | |
| Blp4 | 2.39 | 1.24 | 2.14 | 1.07 | 1.57 | |
| Blp5 | 2.77 | 1.44 | 2.38 | 1.14 | 1.77 | |
| Blp6 | 2.53 | 1.2 | 2.27 | 1.19 | 1.67 |
Discussion
The current study validates the proposed relationships, as discussed earlier. The findings provide theoretical insights for academics and practical implications for managers.
The results of H1 confirm that brand integrity has a significantly positive impact on brand love. This finding contrasts with Dash et al. (2021), who found no effect of brand integrity on customer satisfaction or purchase intention. Since brand integrity represents the human aspect of Marketing 3.0, its connection to brand love is rectified.
The outcome of H2 validates our assumption that a sense of brand community has a significant effect on brand love. Brand communities (Banerjee & Chaudhuri, 2022) and seeking social approval (subjective norm) (Hegner et al., 2017) influence our love for certain brands. The findings demonstrate that brand communities, even those existing purely psychologically, exert an impact on brands. Shared experiences, values and beliefs with a brand, are enough for a consumer to feel part of a group. Our results are consistent with the literature on the relation between sense of brand community and brand love (Palazon et al., 2019).
The findings for H3 support our expectation that brand interactivity has a positive and significant influence on brand love. Likewise, the present study provides empirical evidence showing that brand interactions also lead to brand love. Results from the current study present consistency to the literature when it comes to the influence of interactions on brand love (Naeem & Ozuem, 2021;Pontinha & Coelho do Vale, 2020; Aro et al. 2018). However, our study contrast results from Dash et al. (2021). Perhaps, it might be the case, brand interactions merely arouse directly the emotional side of consumers (Song et al., 2019) and it is not effective with rational variables.
Furthermore, the findings indicate that brand love does not significantly influence the reduction of consumers' switching intentions (H4). This result was unexpected since loyalty is a main consequence of brand love (Song & Kim, 2022). In addition, our study mirrors the results obtained by Arun et al. (2023), who discovered a weak relationship between brand love and switching intentions. Nonetheless, (Arun et al., 2023) It was confirmed that brand love serves as a barrier between switching intentions and switching behaviors. Empirical research shows that consumers can love multiple brands in a product category (Sarkar & Sarkar, 2016). Consumers are increasingly indifferent to brand loyalty, often willing to switch brands based on factors like price, convenience, or product features. This trend is evident in various industries, including streaming services (Kumar et al., 2025; Kumar et al., 2023). Research by Kumar et al. (2025) indicates that consumers often seek variety or are interested in trying something different from their usual brand, leading to brand switching. These findings suggest that brand love may not be as influential in retaining customers as previously thought, highlighting the need for brands to continuously engage and meet the evolving preferences of consumers.
The results for H5 support our expectation that brand love will have a significant impact on WOM intensity. Our findings are in line with the literature on the effects of brand love on WOM (Liebl et al., 2022) and WOM intensity (Amaro et al., 2020). This finding suggests that consumers who are emotionally invested in a brand are inclined to tell others. According to (Díaz et al., 2023), people prefer seeing other consumers` opinions rather than sharing an opinion on their own. However, the results from this study show that brand love motivates consumers to overcome the barriers of engaging in WOM. Moreover, Anderson (1998) found that unsatisfied consumers tend to engage more in negative WOM than satisfied consumers do in positive WOM. Nevertheless, our results show that consumers who feel love for a brand are also capable of intensively participating in positive WOM.
Concerning the Study 2, the analysis identified four distinct groups that differed in the dimensions of brand love. Segmentation by latent classes provides more effective information than the simple elaboration of socio-demographic profiles by making it possible to identify the unobservable heterogeneity of the sample and further enrich the research on brand love in a specific way.
The segment that scored the highest in passion and affection, true brand lovers, represented 25% of all participants, which indicates that one out of four consumers of a brand have the potential to become a brand lover. Brand lovers experience affection, feelings of closeness with the brand, passion, and feelings of pleasure and idealization of the brand (Albert et al., 2009). Furthermore, according to our results, the segment “warm consumer” presents high affection for the brand but low passion. Although these consumers identify with the brand, they do not derive pleasure from using the service. Contrastingly, “passionate consumers” are motivated to consume the service, even though they do not feel a strong connection with the brand. Finally, the cluster with the lowest scores in both affection and passion had the most members, with 42% of all participants. The current result is consistent with the literature, which finds that most consumers do not have a strong love for most brands (Bagozzi et al., 2017).
Theoretical implications
This study makes several key contributions to the literature on brand love, addressing gaps identified in previous research. One of its main contributions is advancing the analysis of brand integrity and brand interaction. Previous studies, such as (Dash et al., 2021), found no significant relationship between these variables and satisfaction or purchase intention. However, the paper presents contrasting findings by demonstrating a positive and significant relationship between brand integrity, brand interaction, and brand love, suggesting that consumers of digital services (like streaming TV) in a developed country (Spain) may be more attuned to the latest digital marketing trends. These results affirm the importance of adapting marketing communication strategies to different contexts and consumer groups.
Another key contribution of this research lies in its examination of the sense of brand community and how it influences brand love. While prior works, such as (Palazon et al., 2019) and (Audria et al., 2023), explored this relationship, their approaches were either limited to specific contexts, like destination brands, or merely theoretical. This paper builds on that by empirically testing the influence of brand community on brand love using the PLS-SEM methodology. This provides new insights into how a sense of belonging and community within a brand’s ecosystem can foster deeper emotional connections with consumers, enriching the existing theory on brand communities.
In addition, the results reveal that while brand love has a positive and significant effect on WOM intensity, it does not significantly decrease consumers switching intention. This is an important contribution, as previous studies have rarely explored this kind of relationship between brand love and WOM intensity. Although WOM is commonly viewed as a primary outcome of brand love, this study is one of the few to quantify its intensity. In contrast, results from this study showed that love for the brand may not be sufficient to prevent consumers from switching brands.
Moreover, the study breaks new ground by conducting a cluster analysis based on (Albert & Valette-Florence, 2010) dimensions of brand love-passion and affection. This approach offers a novel way of segmenting brand lovers, identifying four distinct consumer groups based on their levels of passion and affection. The cluster analysis reveals that only one in four consumers experiences high levels of both passion and affection, providing empirical support for (Bagozzi et al., 2017) claiming that most consumers do not develop an intense love for most brands. Palusuk et al. (2019) argued that brand love should be viewed as a dynamic concept, as something that continuously develops, shaped by every interaction and new experience with the that can shape the future direction of the consumer-brand relationship. In the early stages of love, passion tends to outweigh affection. However, over time, passion diminishes while affection grows stronger, these concepts would be drawing to study within the frameworkof brand love.
In conclusion, this study provides valuable insights into how brand integrity, sense of brand community and brand interaction can foster brand love, influence WOM intensity, and segment brand lovers. These findings are particularly relevant for marketing scholars and practitioners aiming to deepen consumer engagement and advocacy in the digital age.
Practical implications
Consistent to the human-centric marketing approach, this study encounters brand integrity has a positive significant effect on brand love. While brands have to keep up with the latest technological trends, (Kotler et al., 2016), brands should also act like humans. In that sense, brands that embrace the human-centric approach view customers as friends and aim to be trustworthy. On this respect, consumers expect their brand to accomplish on their promises (Campelo et al., 2011).
There are handfuls of actions brand managers could exercise to embrace the human-centric approach on their brands. To learn about customers` anxieties and desires, firms could implement research methods such as social listening, netnography and emphatic research (Kotler et al., 2016). To build a human-centric brand, Kotler et al. (2016) list six attributes a brand should possess: physicality, intellectuality, sociability, emotionality, personability and morality. Furthermore, to manifest true brand integrity, a brand should deliver its promise with a convincing differentiation (Kotler et al., 2016). Marketers must recognize, in the social media age, the brand’s promise does not truly exist until it is validated by interconnected consumers through their conversations. A consumer could experience a sense of brand community when she or he is in tune with brand`s experiences, values and beliefs. In that sense, marketers should communicate the essence of the brand to facilitate the consumer`s identification with the brand. Brand managers could elicit a sense of brand community through targeted advertising, influencer marketing and other social media game plans (Audria et al., 2023). Considering this study`s results, managers should be aware that enhancing brand love leads consumers to engage in WOM intensity. The present study shows that brand love not only fosters WOM in consumers, but also WOM in an intensive manner.
Brand managers should be aware that only a few consumers have the potential to become brand lovers (one out of four). Nonetheless, there are two segments of consumers with some sort of emotional investment for the brand: “affective consumers” and “passionate consumers”. Brand managers could attempt to transform warm and passionate consumers into brand lovers to maximize their benefits. For affective consumers who lack passion, marketers could aim to increase their idealization and pleasure of using the brand (Albert et al., 2009). Furthermore, for passionate consumers lacking attachment to the brand, brand managers could strive to improve the feelings of duration, dream, memories, intimacy and uniqueness aroused by the brand (Albert et al., 2009).
Limitations and future research
Although this study focused on relevant issues, it was subject to various limitations. First, the present study contributed by empirically testing the effect of the variables of brand integrity, sense of brand community and brand interaction towards brand love. This existing study merely focused on two variables from the Marketing 4.0 framework (brand integrity and brand interaction). Future research could overcome this limitation by replicating the study while integrating the four elements of Marketing 4.0 (brand identity, brand integrity, brand image and brand interaction).
Second, the scope of the current study was limited to one geographical location and cultural background. To work on this limitation, the following academic works could progress a study comparing participants from different geographical locations or demographics. For example, according to Dash et al. (2021), consumers in emerging markets are not sufficiently adapted to the latest challenges presented by digital platforms.
The present study is limited to establishing only brand love as a factor who influences switching intention. Three effects constitute the variable of switching intention: pull, push and mooring effects. Research by Kumar et al. (2023) shows that switching intentions vary based on age, annual income, and education. The findings reveal that the younger the individuals, the higher their intention to switch. Future studies could address the limitations of this research by incorporating variables that represent the three factors influencing switching intention within the framework. Future research should further investigate the antecedents and moderators of brand love, considering various consumer-brand contexts. While some earlier studies exist, most offer a snapshot of brand love; it remains unclear whether the effects would endure when examined from a developmental standpoint. Regarding moderators, researchers highlight the significance of demographic factors (such as income, age, gender) and cultural influences (Palusuk et al. 2019), with further studies needed to clarify their specific influence and role.
Fourth, the current paper contributed to the literature by developing a cluster analysis on brand lovers. Our cluster analysis is limited to segment brand lovers based on Albert & Valette-Florence (2010) dimensions of brand love, passion and affection. To attain this limitation, future studies could contribute by dividing brand lovers based on another specification. Literature of brand love recommends the assessment of studies on the topic in other methodology different from PLS-SEM (Velmurugan & Thalhath, 2021).
