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Article

Psychometric Properties, Measurement Invariance, and Construct Validity of the Italian Version of the Brand Hate Short Scale (BHS)

by
Silvia Platania
*,
Martina Morando
and
Giuseppe Santisi
Department of Educational Sciences, Section of Psychology, University of Catania, 95124 Catania, Italy
*
Author to whom correspondence should be addressed.
Sustainability 2020, 12(5), 2103; https://doi.org/10.3390/su12052103
Submission received: 23 January 2020 / Revised: 20 February 2020 / Accepted: 5 March 2020 / Published: 9 March 2020
(This article belongs to the Special Issue New Patterns in Consumer Behavior)
Versions Notes

Abstract

:
Brand hate can be defined as the consumer’s dissatisfaction with the product or service performance. The consumer’s hatred of the brand is related to the desire for revenge and avoidance. This kind of emotion does not remain only a desire; it is often transformed into real actions that lead to consumer behavior. Although the analysis of the literature provides useful insights and interesting suggestions about the phenomenon of brand hate, to date, very few studies and scales that are capable of measuring this phenomenon have been developed, especially in the Italian context. The present work investigated the psychometric properties of an Italian adaptation of the BHSs (N = 422) with the aim of verifying the same factor structure found in the original German version. Results confirmed a one-factor structure (first order, 6 items). Moreover, the scale was found to be invariant across gender. This suggests that companies must first have adequate systems and mechanisms in place to understand the reasons that drive consumers to hate the brand and implement recovery strategies to address this hatred. The achievement of sustainability or failure to do so can add to or detract from a brand’s value in the marketplace.

1. Introduction

Our current society, dominated by the spread of the internet and the hyper connectivity of human relationships, has made online debate and communication a usual and uninterrupted “exchange” that permeates every area of human society and enriches it for better or for worse. In particular, with the development of social media and sites in general, the web became a fertile terrain for the so-called “hate speech” phenomenon, which is a phenomenon that transcends virtual reality and affects real life on several levels, included brands and its companies [1,2,3].
In Italy, the brand hate online theme, even though debated, is a rather tangible reality that scares most of the companies, which have gradually lost control of their brand image and of their brand value. To date, the increasing spread of anti-branding sites [4] is one of the clearest manifestations of the increased power of consumers [5]. Brand hate is a negative intense emotional state capable of influencing the consumers’ relationship and attitude toward certain brands and their related companies [6]. Although today the analysis of the literature provides useful insights and interesting suggestions about the phenomenon of brand hate, especially in the Italian context, there are very few studies and scales that are capable of measuring this phenomenon. Indeed, the Italian SWG’s (Standard Wire Gauge) study indicates the necessity to delve into this phenomenon [7], which shows that 81% of managers consider companies as the target of hatred and fake news, which blemish the brand image online.
From the managerial point of view, the analysis and comprehension of this phenomenon bring important implications; therefore, both our research and data in the literature confirm that it is important for all companies to wisely exploit this knowledge and to study this phenomenon in order to early identify those potential antecedents sources of risk, strengthen and defend itself, and at the same time favor consumers’ satisfaction [8,9].
In the field of consumer behavior studies, there is a lack of studies into negative brand relationships. People remember negative events more than positive ones. Furthermore, the negative word pass has more effect than the positive one. The little attention paid to negative consumer emotions has produced studies of a qualitative nature. The present study aims to verify the psychometric properties of the brand hate short scale in the Italian context; it underlines the importance of outlining a tool that is able to detect the perceptions and negative emotions of consumer respect toward the brand. It is also believed that it is more convenient to use a short scale because, in accordance with the literature on very large samples, the short scale is particularly efficient and effective. When scales are intended to be used for individual assessments, it is indeed important to have a reliable score interpretation [10] (p. 4). Using structural equation models, it is possible to guarantee short measuring scales that have the same construct and criterion validity as long scales, which have the advantage of guaranteeing effective individual measurements. In the case of the brand hate construct, it is evident how emotion drives consumer behavior. However, is fundamental to understand its determinants because its attitude is not necessarily stable and long lasting. The postmodern consumer is flexible; the reference values are no longer constant: even sustainable choices could not be dictated so much by “believing” in the validity type of choice. For companies, it is important to understand the determinants that push the consumer to make different consumption choices that are based on new behavioral patterns, but it must be done with effective and efficient measuring scales that guarantee the correct measurement validity of the construct in question.

1.1. Conceptualizations of Brand Hate Psychology and Consumer Research

It should be emphasized that the construct of brand hate is not born as a simple opposite of the construct of brand love but has its own specific multidimensional and complex nature. As claimed by Eagly and Chaiken [11], both positive and negative judgments contribute equally but in the opposite sense to determine preferences for the choice of product. Usually, the base cognitive constructs of both types of evaluation are different and tendentially not equivalent. At the same time, many individuals in reference to a specific product can have positive and negative judgments and merge them in order to have a remarkable attitude at the beginning and then a specific purchasing behavior [12] (p. 2). The most interesting contributions on brand hate are those of Sternberg [13] and of Opotow and McClelland [14]. Sternberg [13], with his Triangular Theory of Hate, identifies three primary components that comprise hate: the negation of intimacy, passion, and commitment. This “triangle of hate” seems to be composed by the particular emotions and actions associated with each of the three components.
Although Sternberg [13] in his studies of hate does not refer directly to the consumer psychology or to the construct of brand hate, his model seems to fit perfectly and is also able to explain the typical consumer behavior of a hater.
Opotow and McClelland [14] instead focused on the dynamic nature of the concept of hate, identifying it as a process that develops from specific antecedents mediated by different variables such as emotions, rationalizations, beliefs, and moral values to transform them into specific behaviors. Key elements of this model are antecedents, affect, cognitions, morals, and behaviors. The antecedents show those particular life experiences of the subject as an individual or as a member of a group, which contributed to the creation of his own vision of the world; such experiences are able to develop just a predisposition to hate. Antecedents on the individual level are simply negative experiences or unconscious painful memories; on the group level, they are stories and experiences with a very intense negative meaning [14] (p. 81). In the context of consumer psychology and in the theory of brand hate, this is perfectly linked with the concepts of symbolic incongruity and ideological incompatibility [1,9]. Behavior can have a totally different aspect: it can be quiescent or it can lead to violent actions, both verbal and physical. Referring to the theorization of brand hate, these behaviors can be translated into exit and rejection, negative WOM (Word Of Mouth), online public complaining, and marketplace aggression.
One of the first conceptualizations of brand hate was provided by Grégoire et al. [15], which in detail focused on the description of some possible outcomes generated by hate toward a brand: consumer desire for revenge and consumer desire for avoidance.
Rotman et al.’s [16] contribution goes in line with this concept; indeed, it is focused on the consumer who perceives a brand as harmful and performing unethical behaviors, such as lying, cheating, or stealing. Other research has established that when companies and brands do specific transgressions (such as corporate social irresponsibility and negative brand reputation), it typically results in punishment by consumers in the form of brand hate [1,9], fewer repurchases [17], negative word of mouth (N-WOM), and boycotts [18].
Johnson et al. [19], which elaborate their study starting from the reflection on the dissolution of consumer–brand relationships, also theorize brand hate. The hate is considered one of the outcomes triggered by this break, and it is also presented as strong opposition from the consumer toward the brand. The two constructs of revenge and guilt, which contribute to the process that brings consumers to behave hatefully, assume importance in this conceptualization. Furthermore, Dalli et al. [20] proposed their conceptualization on brand hate, defining it in detail as brand dislike. This study is very interesting because it focuses on the analysis and description of the possible antecedents of brand hate; precisely, they identify a product brand level (dislike depends on unsatisfaction with some product or service characteristics), a user brand level (dislike depends on negative stereotypes and poor self-expression properties), and finally a corporate brand level (dislike depends on companies’ unethical practice). Another Italian contribution is also provided by Romani et al. [21] based on the study of negative emotions toward the brand. They describe hate as an emotion descriptor (along with contempt and revulsion) that “implies consumers’ rejection of the brand based on unappealing evaluations” [21] (p.12). In their study on luxury brands, Bryson et al. [6] define brand hate as “an intense negative emotional affect toward the brand” (p. 395); they also identified four potential antecedents: a brand’s country-of-origin, customer dissatisfaction with the product, consumers’ negative stereotypes about the brand, and corporate social performance.
According to the approach of Zarantonello et al. [8], brand hate is described as the different emotions that consumers feel when they hate a brand. They considered different emotions (anger, contempt, disgust, fear, disappointment, shame, and dehumanization), and they also identified antecedents (corporate wrongdoings, violation of expectation…) and outcomes (complaining, negative WOM, protest, and patronage reduction/cessation). Finally, the contribution of Hegner et al. [1], which our theorization is based on, defines brand hate as “a more intense emotional response that consumers have toward a brand than brand dislike” (p. 14). They also analyzed and described three potential antecedents (negative past experience, symbolic incongruity, and ideological incompatibility) and four behavioral outcomes (exit and rejection, negative WOM, online public complaining, and marketplace aggression).

1.2. Emotions’ Role in Consumer Behavior

Considering a deeper and more complete understanding of the brand hate phenomenon, it is indispensable to focus on the important role fulfilled by emotions. Buying behavior moves within a complex dynamic that singles out a considerable variety of modulating factors [22]. Emotional states motivate and are associated with consumption choices: individuals may feel guilty for having bought something very expensive or regret and be ashamed for possessing an embarrassing product, or they might even feel joy and are proud of the fashionable product’s ostentation [23].
The consumer emotional sphere stimulates the interest of companies and marketing and encourage scholars in the study of this phenomenon.
Other researchers over the years [24,25] have dealt with studying the relationship between consumers and brand, identifying a specific emotional linked between consumers and consumer objects, brand included. Regarding this “emotional attachment”, relevant and interesting analogies are emerged with the attachment that develops between human beings, which has been widely described and studied by Bowlby: exactly as an individual closely related to another is willing to make sacrifices for this person and remain faithful, in the same way a consumer emotionally tied to a brand will show brand loyalty and will also be willing to pay more to have it [26,27,28,29].
In the study of emotions, we have to consider both negative and positive emotions because they affect the consumer behavior [30].
Consumers may indeed be satisfied with a product or service, but this does not automatically guarantee consumer loyalty and devotion to the brand. They trust the brand and its performance and are satisfied with the benefit they earn, but they do not reach the level that triggers a stronger bond, which is typical of brand love [27,31]. Brand love is one of the strongest consumer-brand relationships since it includes all the variables mentioned above, and it also contributes to the adoption of the specific behavior: passionate desire to use, willingness to invest resources, self-brand integration, and positive emotional connection [32,33].
From a negative emotions point of view, brand avoidance emerges as a weak form of a bad relationship between consumers and a brand. This reaction shows up after dissatisfaction, undesired self, self-concept incongruity, and organizational misidentification [34]. It appears to be similar to the concept of boycotting, and they may occur simultaneously. However, under certain conditions, in boycott behavior, there exists the possibility of re-entering the relationship [34,35]; meanwhile in brand avoidance, there is no guarantee of re-establishing this relationship. In conclusion, the strongest negative relationship is represented by brand hate [1,8,36,37,38], brand divorce [39], and brand dislike [20,40]. This relationship demonstrates its strength not only in its definition of “a more intense emotional response that consumers have toward a brand than dislike” [1] (p. 14) but above all in the specific behaviors’ generation. The following are related to brand hate: a desire for vengeance [41]; negative WOM; vindictive complaining [42]; retaliation [43]; perceived discrepancy; a lack of fit with personal values; switching intention [44,45]; and all the above-mentioned negative variables.

1.3. Antecedents and Outcomes of Brand Hate: A Brief Description

One of the main reasons behind brand hate and of vindictive and/or avoidance behaviors is the perceived gap between consumers’ expectations of brands and products and their actual performance. As a result, today’s digitally empowered consumers’ expectations lead to greater dissatisfaction and wrongdoing [46].
Altogether, consumer brand hate antecedents can be analyzed through two major components: [1] company-related antecedents and [2] consumer-related antecedents. The anti-branding factors are dissatisfaction as a consequence of product or service failures [6,34,38,47]; disappointment with a brand or discontentment with irresponsible company practices [48,49,50]; and ideological contrast, or a failure to perfectly fit personal and company values [34,51,52]. Regarding consumer-related antecedents, it emerges that brand hate is probably affected by consumers’ own personality. An important example of psychological research indicates that narcissistic disorders and entitlement are the major consumer-related antecedents of brand hate. These researches underline the fact that people showing these personality features “are prone to get easily into conflict with others and hence potentially feel more hate than others” [46] (p. 49), also for consumption situations [51,53,54,55,56].
Among the most important antecedents are past negative experience, symbolic inconsistency, and ideological incompatibility.
Negative past experience posits that the negative consumption experiences that consumers have with certain product or brands have powerful negative effects, which first entails a sense of general dissatisfaction and then evolves into brand avoidance. Whenever the expectations of consumers are belied by the reality of the brand, the usual behavior is avoidance [6,34,57]. This is particularly connected to product failure, the level of provided service, poor performance, and unpleasant store environment.
Symbolic incongruity is one of the main goals that all brands present and aim for; it allows their consumers, through the direct purchase of their products, to better express their personal style or a part of themselves [58]. Individuals buy and consume the products that most fit the image of the ‘Real Self’, but above all their ‘Ideal Self’; while they reject all those products and services that can be associated with an image of self that does not coincide with the desired one [34]. This perceived inconsistency between the personal self and the brand image generates negative emotions and attitudes toward the brand, such as brand hate.
As for ideological incompatibility, Portwood-Stacer [59] (p.96) supports the existence of a real anti-consumption morality; this concept is based on the idea that to act morally in the context of consumption, the individual needs to be able to always keep faith with one’s own personal values, but at the same time be aware that their own actions can have a concrete and meaningful impact on others. Whenever a discrepancy is perceived between his own values and those of the company or brand designated, intense negative emotions emerge that then affect the development of brand hate [6,34,57].
The current literature, although rather poor, specifies that hate as an emotion and attitude toward the brand can assume behavioral aspects that are very different from one another. Some scholars have indicated exit and rejection as the most pertinent and useful answers for consumers in the current market [36]. In addition, Gregoire et al. [15] thanks to their theorization, allow us to identify important behavioral outcomes, distinguishing them in direct and indirect vindictive behavior. The first group includes all those vindictive behaviors with a more aggressive form such as the so-called “marketplace aggression” [60], which is a particular behavior whose main goal is to cause damage to the company; in detail, these behaviors include damage to a company’s properties, denigration of the policies used, and excessive frustration or anger toward the employees [47]. Among the indirect vindictive behaviors, negative word of mouth and online complaints have emerged. Negative WOM in detail is one of the most used and effective tools, which consumers use to share their negative experiences with relatives and friends in order to denigrate the specific company or to persuade others to reconsider the relationship with the company itself [42,61]. Another similar behavior is represented by online complaints, which is a phenomenon that is so popular in the current era and represents a strong risk for companies [42,62]; this behavior is very similar to the N-WOM but it is more mass-oriented.

2. Materials and Methods

2.1. The Present Study: Brand Hate Short Scale (BHS)

Brand hate is not simply developed as a reaction to a lack of brand love; brand hate influences people and society in general and it constructs brand attachment and/or brand avoidance. Hegner et al. [1] define brand hate as “a more intense emotional response consumers have toward a brand than brand dislike” (p. 3), but it is hard to measure the negative emotions of the consumer toward the brand. As a result, it is not possible for researchers and marketing professionals to understand the nature of these negative emotions and to predict possible negative consumer behavior toward a brand [21]. This suggests that companies must first have adequate systems and mechanisms in place to understand the reasons that drive consumers to hate the brand and implement recovery strategies to address this hatred. In order to provide answers to these questions in our study, we hypothesized that the BHS would show the same factor structure found in the Hegner, Fetscherin, and Delzen [1] study (Hypothesis 1). Furthermore, we hypothesized that the BHS would show significant correlations with some criterion-related variables (Hypothesis 2). Finally, we hypothesized (Hypothesis 3) that the Italian version of the BHS will show measurement equivalence [63] across gender (men versus women).

2.2. Participants and Procedure

Data collection was conducted from March 2018 to October 2019. We used convenience sampling. Participants were recruited through a link published in several social media groups; the survey was carried out in the Italian language. In order to align the same validation procedure as Hegner et al. [1], a short introductory text and a link to our survey were provided on the website targeting people who were interested in brands. The study involved a total of 422 participants (179 men, 42.4%; 243 women, 57.6%). The age of participants ranged between 18 and 58 (Mage = 29.1, SD = 6.7). The participants replied that the most hated brands are Nestlé (17.5%), Apple (15.4%), McDonald’s (9.8%), Desigual (8.2%), O’Bag (7.6%), and Trenitalia (4.2%). It is interesting to note that the responses given by Italian consumers to the brand follow the same trend as German consumers [1] (p.16) and that our results also confirm studies on the dispersion of the brand, according to which there is a polarization for brand lovers and haters [4,64].

2.3. Measures

2.3.1. Avoidance Scale

Also, for the Motivations scale, we refer to two different studies: the first one is from Lee et al. [34] and the second one is from Salvatori [57]. We decided to consider and use both scales because they add value to each other but especially because they focused on two different aspects: Lee et al. [34] in fact are focusing specifically on avoidance, while Salvatori [57] analyzed brand hate in general. Therefore, the final scale consisted of 17 items divided into:
  • Experiential Avoidance (6 items) [57], which measures negative experiences with a certain product can be a basis for brand hate.
  • Identity Avoidance (7 items) [34], which measures when people avoid brands that do not match their identity, or that when a brand is closely linked to certain groups, identity avoidance can occur.
  • Moral Avoidance (4 items) [34], which measures avoiding a brand due to ideological reasons.
All the items were represented on a 5-point Likert scale with 1 as Absolutely disagree and 5 as Absolutely agree.

2.3.2. Direct and Indirect Revenge Behaviors Scale

The items related to behavioral outcomes adopted by consumers have been developed from different studies and methods and depend on different type of actions. Regarding exit and rejection, items are designed from the definition of the construct, while regarding indirect revenge and direct revenge, we refer to the study carried out by Grégoire et al. [42]. Consequently, the final Behavioral Scale consists of 18 items, where we find: 4 items for Rejection, 7 items for Negative Word-of-Mouth, 3 items for Online Complaining, and 4 items for Marketplace Aggression. All the items were represented on a 5-point Likert scale with 1 as Absolutely disagree and 5 as Absolutely agree.

2.4. Data Analysis

Linear structural equations models were used to verify the hypothesized model. Tests were executed in AMOS 25.0 [65] through the maximum likelihood method. Firstly, a series of confirmatory factor analyses (CFA) were carried out on the dataset to establish if the short version (six items) one-factor model was that which best fit the data. Next, a sequence of multiple-group CFA was run on the whole sample grouped by gender, through which different forms of equivalence were tested [63,66]. In addition to configural invariance, the following forms of equivalence were tested: Metric invariance [67], Measurement error invariance [68,69], Scalar invariance [70,71], and Structural invariance [72,73]. The models’ goodness of fit was evaluated through the Tucker Lewis Index (TLI), the Comparative Fit Index (CFI), the Root Mean Square Error of Approximation (RMSEA), and the Standardized Root Mean Square Residual (SRMR). Furthermore, χ2 values and Δχ2 values between the competing models are shown, but they are sensitive to sample size [74], so Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) were also presented (lower values indicate better fit). ΔCFI was also used with values not exceeding 0.01, indicating that the models are equivalent in terms of fit [75]. To test reliability for the multiple-indicator construct, the use of the Cronbach’s Alpha coefficient is restricted [76], so convergent validity was tested by calculating the average variance extracted (AVE) and the construct reliability (CR). The AVE needs to be > 0.50 [77] and the CR needs to be > 0.60 [78]. Finally, to test the correlations, we used SPSS 25.0.

3. Results

3.1. Confirmatory Factor Analysis

At first, a four-factor second-order model (Model 1, 22 items) with covariances among the latent variables was tested on the calibration sample, showing that the fit indices were not good was demonstrated, and the following fit indexes were obtained: [χ2(203) = 1386.24, SRMR = 0.09, RMSEA = 0.10, CFI = 0.83, TLI = 0.82, AIC = 1486.242, BIC = 1688.492]. So, we test a new model (Model 2, 22 items), a one-factor first order. These decisions were based on an examination of the modification indices and standardized factor loading (not significant and adequate). The results showed a poor model fit for the original form of the BHS χ2(203) = 1459.78, SRMR = 0.08, RMSEA = 0.10, CFI = 0.83, TLI = 0.82, AIC = 1486.242, BIC = 1688.492]. According to Hegner et al. [1], the short form of BHS (Model 3, 6 items) achieved better model fits and smaller AIC and BIC values, which was an improvement on the original full scale (see Table 1). Based on modification indices, the short form with a correlated error achieved the best model fit [χ2(9) =21.894, SRMR = 0.03, RMSEA = 0.05, CFI = 0.98, TLI= 0.97, AIC=45.894, BIC=94.434]; these results supported our Hypothesis 1.
Moreover, all factor loadings were significant at p <0.001 and varied between 0.68 and 0.97, with a mean of 0.87. Fit indexes for the tested models are presented in Table 1.

3.2. Convergent and Discriminant Validity

We next examined correlations between each Avoidance and Revenge behavior (antecedents and outcomes) and the Brand Hate short scale. Descriptive statistics and correlation matrix for the study variables shown in Table 2.
Cronbach’s Alpha was computed for each factor to test reliability, and it showed good internal consistency of the scale: BHSs 0.85, Experiential Avoidance 0.71, Identity Avoidance 0.85, Moral Avoidance 0.87, Rejection 0.88, Online Complaining 0.91, Marketplace aggression 0.72, and Negative Word-of-Mouth 0.86.
Composite reliability and average variance extracted were CR 0.93, AVE 0.68 for BHSs; CR 0.90, AVE 0.61 for Experiential Avoidance; CR 0.92, AVE 0.62 for Identity Avoidance; for Moral Avoidance, CR 0.93, AVE 0.75; for Rejection, CR 0.94, AVE 0.75; for Online Complaining, CR 0.96, AVE 0.78; for Marketplace aggression, CR 0.90, AVE 0.61; and for Negative Word-of-Mouth, CR 0.91, AVE 0.64.
The high positive relationship (r = 0.75, p < 0.001) between BHSs and Moral Avoidance confirms that these two constructs are similar but not identical; Moral avoidance indicates that consumers perceive an ideological incompatibility with the brand because of legal, moral, or social concerns when a brand is suspected of corporate irresponsibility [1,8].
Another important positive correlation is between BHSs and Negative Word-of-Mouth (r = 0.72, p < 0.001); recent studies have shown that emotional response to product or service performance evokes Negative Word-of-Mouth directly [79]. Those results are in line with Hegner and colleagues’ findings as they prove both the discriminant and convergent validity of the scale [1].
Overall, these results did support our first hypothesis, i.e., that the Italian version of the BHS scale would show the same factor structure found in Hegner and colleagues’ study [1] (Hypothesis 2).

3.3. Multiple-Group Confirmatory Factor Analysis (MCFA)

The first multiple-group analysis tested a model of configural invariance (Model 1) by simultaneously assessing the fit of male and female samples. The fit indices (χ2(18) = 53.050, p < 0.001; TLI = 0.95; CFI = 0.97; SRMR = 0.038; RMSEA = 0.068) showed a good fit for this model, supporting an equivalent solution made of one factor for BHSs in the datasets for both men and women (Table 3). “The fit of this configural model provides the baseline value against which all subsequently specified equivalence models are compared” [72] (p. 873).
Model 2 was tested for metric invariance (Table 3). More importantly, Δχ2M1-M2(5) = 9.64 and ΔCFI = 0.002 suggested that Model 2 is equivalent to Model 1. Hence, metric invariance was supported.
Moreover, scalar invariance (as tested by Model 3) and measurement error invariance (Model 4) were found (Δχ2M2-M3(4) = 5.90, ΔCFI = 0.001; Δχ2M3-M4(4) = 5.51, ΔCFI = 0.001.
Subsequently, the equivalence in factor variances was tested (Model 5) (Δχ2M4-M5(7) = 15.68, ΔCFI = 0.000). Finally, the equivalence in factor covariances was tested (Model 6), and the result was that it was supported (Δχ2M5-M6(6) = 7.59, ΔCFI = 0.000). Results were totally satisfactory, as the model fit proved to be invariant across both populations and they supported Hypothesis 3 (Table 3).

4. Discussion

The aim of the present research was to provide psychometric evidence for the use of the BHSs in Italy. Results were consistent with the original findings [1]. Different statistics procedures were used: Confirmative Factor Analyses and Multigroup Confirmative Factor Analyses using Structural Equation Modeling (AMOS 25.0) were performed to verify the factorial structure of the scale (Hypothesis 1). Results showed a one-factor structure composed of six items. Therefore, the study revealed that the Italian version of the BHSs confirmed some structure found in the study of Hegner and colleagues (Hypothesis 2) [1].
Moreover, we performed a multigroup confirmatory factor analysis to test whether the scale is invariant across gender. In the original study, the measurement invariance was not assessed as only when such equivalence is established can researchers proceed with examining the mean group differences. So far, no study conducted on the validity of BhSs has investigated its psychometric properties to such an extent. This happens because the phenomenon of brand hate is a construct studied both from the field of psychology and from the economic field; therefore, a psychometric study of the scale was missing. Results from the multigroup confirmatory factor analysis showed that the same factor solution was invariant for both men and women (Hypothesis 3). This indicates that Italians conceptualize the Brand Hate Short Scale in the same way [72]; also, the present research found evidence for metric invariance, uniqueness invariance, scalar invariance, and structural invariance, which means that the relationship between the constructs was the same across the groups. The reliability of the scale, as evaluated through Cronbach’s alpha, composite reliability, and average variance extracted showed very good values. The discriminant validity between latent factors [80] was tested using a technique of Fornell and Larcker [77], and we found that it was fully supported. In correspondence with the study conducted by Hegner and colleagues in 2017 [1] and in accordance with the literature, we have tested the correlation of the scale with its antecedents and its outcomes. The results revealed that there is a high positive correlation between BHSs and Moral Avoidance, confirming that these two constructs are similar but not identical. In the literature, the Moral Avoidance construct indicates that consumers perceive an ideological incompatibility with the brand because of legal, moral, or social concerns when a brand is suspected of corporate irresponsibility [1,8]. Furthermore, another important result regards the correlation of BHSs with Negative Word-of-Mouth; most studies agree that the negative relationship with the brand is perceived as a relevant factor for Negative Word-of-Mouth [8,81].
The results corroborate the possibility of using the Brand Hate Short Scale in further studies.
In addition, these results suggest two important reflections. The first of a psychometric type confirms that the use of a short scale is valid and effective for studies regarding correlational patterns within large and population representative samples. In studies relating the analysis of consumer behavior, they are revealed as support and orientation tools that allow companies to develop successful strategies toward the consumer.
The second aspect regards the brand hate construct itself and how it can affect the role of brands in the creation of sustainable markets. According to Lehner and Halliday [82], the relationship between brands and sustainability can lead to promising alliances between business and consumers and make consumption more sustainable [82] (p.14). Therefore, in order for the brand itself to create an effective relationship with the consumers, the company must be a "bearer" of sustainability. Studies on brand hate are still few, but they can make an important contribution to understanding the choices made by the consumer, the emotions that drive him or take him away from the brand, but above all to the new consumption patterns they implement toward a specific brand.

5. Conclusions, Limitations, and Future Studies

The present study is part of a recent line of research concerning the negative emotions that consumers develop toward brands. The innovation of our study concerns the enhancement and adaptation of BHSs in the Italian context. The importance of adding this research contribution is due to the need to give answers and indications to companies that want to find their way around consumer behavior and who want to do it through validated and reliable tools.
A limitation of our study is to have used a sample only online; in the future, it would be useful to test the scale on a sample detected in order to allow verification of the predictive validity and test–retest reliability of the instrument, particularly in relation to similar constructs.
Future research aimed at further establishing the psychometric properties of BHSs should particularly focus on test–retest reliability and within-subject variability implementing true longitudinal designs [83].
Another potential limitation of our research is the use of convenience samples, which restricts the generalizability of the results.
Despite these limitations, the results point to important suggestions for future studies and interventions; the present findings show BHSs to display an excellent fit plus good discriminant and convergent validities, as well as good reliability. The high measure’s correlations between moral avoidance and negative word of mouth support the Brand Hate Short Scale to become a focus of research in consumer behavior. Moreover, this study proves fundamental for the future understanding between brands and sustainability. A brand is perceived as sustainable by the consumer only if it has credibility; its central tenet concerns the relationship of trust among consumers and brands. If our results mainly suggest that the predictive and outcomes factors with brand hate are Moral Avoidance and Negative Word-of-Mouth, it can be assumed that the construct is certainly linked to the moral engagement that the consumer must perceive toward the brand [84,85].

Author Contributions

Conceptualization, S.P., M.M. and G.S-; Methodology, S.P..; Software, S.P.; Investigation, M.M., and G.S.; Data Curation, S.P. and M.M.; Writing—original draft preparation, M.M., G.S., and S.P.; Writing—Review and Editing, S.P. and G.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Hegner, S.M.; Fetscherin, M.; van Delzen, M. Determinants and outcomes of brand hate. J. Prod. Brand Manag. 2017, 26, 13–25. [Google Scholar] [CrossRef]
  2. Dessart, L.; Morgan-Thomas, A.; Veloutsou, C. What Drives Anti-Brand Community Behaviours: An Brand Hate Examination of Online Hate of Technology Brands. In Let’s Get Engaged! Crossing the Threshold of Marketing’s Engagement Era; Obal, M.W., Krey, N., Bushardt, C., Eds.; Springer International Publishing: New York, NY, USA, 2016; pp. 473–477. [Google Scholar]
  3. Kucuk, S.U. Exploring the legality of consumer anti-branding activities in the digital age. J. Bus. Ethics 2015, 139, 77–93. [Google Scholar] [CrossRef]
  4. Kucuc, S.U. Negative double jeopardy. The role off anti-brand sites on the internet. J. Brand Manag. 2008, 15, 209–222. [Google Scholar] [CrossRef] [Green Version]
  5. Pei-I Yu, A. The Emergence of Anti-Brand Communities and Their Influence on Companies and the Other Consumers. In Proceedings of the Academy of Marketing Conference, Liverpool, UK, 5–7 July 2011; p. 2. [Google Scholar]
  6. Bryson, D.; Atwal, G.; Hulten, P. Towards the conceptualisation of the antecedents of extreme negative affect towards luxury brands. Qual. Market. Res. Int. J. 2013, 16, 393–405. [Google Scholar] [CrossRef]
  7. Hate Speech e Fake News nel Lavoro e nel Business. Available online: https://paroleostili.it/wp-content/uploads/2019/04/SWG-Hate-speech-e-fake-news-business-5.pdf (accessed on 8 January 2020).
  8. Zarantonello, L.; Romani, S.; Grappi, S.; Bagozzi, R. Brand hate. J. Prod. Brand Manag. 2016, 25, 11–25. [Google Scholar] [CrossRef]
  9. Platania, S.; Morando, M.; Santisi, G. The phenomenon of brand hate: Analysis of predictors and outcomes. Qual. Access Success 2017, 18 (Suppl. S1), 342–347. [Google Scholar]
  10. Ziegler, M.; Kemper, C.J.; Kruyen, P.M. Short Scales—Five Misunderstandings and Ways to Overcome Them. J. Individ. Differ. 2014, 35, 185–189. [Google Scholar] [CrossRef]
  11. Eagly, A.H.; Chaiken, S. Attitude Structure and Function. In The Handbook of Social Psychology; Gilbert, D.T., Fiske, S.T., Lindzey, G., Eds.; McGraw-Hill: New York, NY, USA, 1998; pp. 269–322. [Google Scholar]
  12. Dalli, D. Le Marche Sgradite: L’altra Faccia Delle Preferenze del Consumatore. In Proceedings of the Le Tendenze del Marketing, Paris, France, 21–22 January 2005. [Google Scholar]
  13. Sternberg, R.J. A duplex theory of hate: Development and application to terrorism, massacres, and genocide. Rev. Gen. Psychol. 2003, 7, 299–328. [Google Scholar] [CrossRef]
  14. Opotow, S.; McClelland, S. The Intensification of Hating: A Theory. Soc. Justice Res. 2007, 20, 68–97. [Google Scholar] [CrossRef]
  15. Grégoire, Y.; Tripp, T.; Legoux, R. When customer love turns into lasting hate: The effects of relationship strength and time on customer revenge and avoidance. J. Mark. 2009, 73, 18–32. [Google Scholar] [CrossRef] [Green Version]
  16. Rotman, J.D.; Khamitov, M.; Connors, S. Lie, Cheat, and Steal: How Harmful Brands Motivate Consumers to Act Unethically. J. Consum. Psychol. 2018, 28, 353–361. [Google Scholar] [CrossRef] [Green Version]
  17. Huber, F.; Volhardt, K.; Matthes, I.; Vogel, J. Brand misconduct: Consequences on consumer-brand relationships. J. Bus. Res. 2010, 63, 1113–1120. [Google Scholar] [CrossRef]
  18. John, A.; Klein, J.G. The Boycott Puzzle: Consumer Motivations for Purchase Sacrifice. Manag. Sci. 2003, 49, 1196–1209. [Google Scholar] [CrossRef] [Green Version]
  19. Johnson, A.; Matear, M.; Thomson, M. A coal in the heart: Self-relevance as a post-exit predictor of consumer anti-brand action. J. Consum. Res. 2011, 38, 108–125. [Google Scholar] [CrossRef]
  20. Dalli, D.; Romani, S.; Gistri, G. Brand dislike: Representing the Negative Side of Consumer Preferences. In Advances in Consumer Research; Connie Pechmann, C., Price, L., Eds.; Association for Consumer Research: Duluth, MN, USA, 2006; pp. 87–95. [Google Scholar]
  21. Romani, S.; Grappi, S.; Dalli, D. Emotions that drive consumers away from brands: Measuring negative emotions toward brands and their behavioral effects. Int. J. Res. Mark. 2012, 29, 55–67. [Google Scholar] [CrossRef]
  22. Pravettoni, G.; Lucchiari, C. Psicologia del consumo e neuroscienze. Leben. Aesth. Phil. Exp. 2014, 4, 89–118. [Google Scholar]
  23. Mileti, A.; Prete, M.I.; Guido, G. Brand emotional credibility: Effects of mixed emotions about branded products with varying credibility. Psychol. Rep. 2013, 113, 404–419. [Google Scholar] [CrossRef]
  24. Belk, R. Possessions and the extended self. J. Consum. Res. 1988, 15, 139–168. [Google Scholar] [CrossRef] [Green Version]
  25. Thomson, M.; MacInnins, D.J.; Park, C.W. The ties that bind: Measuring the strength of consumers’ emotional attachment to brands. J. Consum. Psychol. 2005, 15, 77–91. [Google Scholar] [CrossRef]
  26. Price, L.L.; Arnould, E.J.; Curasi, C.F. Older consumers’ disposition of special possessions. J. Consum. Res. 2000, 27, 179–201. [Google Scholar] [CrossRef]
  27. Yuksel, A.; Yuksel, F.; Bilim, Y. Destination attachment: Effect on customer satisfaction and cognitive, affective and conative loyalty. Tour. Manag. 2010, 31, 271–284. [Google Scholar] [CrossRef]
  28. Thomson, M. Human brands: Investigating antecedents to consumers’ strong attachment to celebrities. J. Mark. 2006, 70, 104–119. [Google Scholar] [CrossRef]
  29. Platania, S.; Vullo, C.; Morando, M. Resident attitude toward: The mediation effect of quality of life in the Hyblaean District. Qual. Access Success 2018, 19 (Suppl. S1), 400–405. [Google Scholar]
  30. Fetscherin, M.; Heinrich, D. Consumer brand relationships: A research landscape. J. Brand Manag. 2014, 21, 366–371. [Google Scholar] [CrossRef]
  31. Loureiro, S.M.C.; Ruediger, K.H.; Demetris, V. Brand emotional connection and loyalty. J. Brand Manag. 2012, 20, 13–27. [Google Scholar] [CrossRef]
  32. Santisi, G.; Platania, S.; Vullo, C. The valueof brands of the typical Sicilian wine in the connoisseurs and outdoor. Qual. Access Success 2017, 18 (Suppl. S1), 382–387. [Google Scholar]
  33. Batra, R.; Ahuvia, A.C.; Bagozzi, R.P. Brand love. J. Mark. 2012, 76, 1–16. [Google Scholar] [CrossRef]
  34. Lee, M.S.; Motion, J.; Conroy, D. Anti-consumption and brand avoidance. J. Bus. Res. 2009, 62, 169–180. [Google Scholar] [CrossRef]
  35. Friedman, M. Consumer Boycotts in the United States, 1970–1980: Contemporary Events in Historical Perspective. J. Consum. Aff. 1985, 19, 96–117. [Google Scholar] [CrossRef]
  36. Hirschman, A.O. Exit, Voice, and Loyalty: Responses to Decline in Firms, Organizations, and States; Harvard University Press: Cambridge, MA, USA, 1970. [Google Scholar]
  37. Van Delzen, M. Identifying the motives and behaviors of Brand Hate. Master’s Thesis, University of Twente, Enschede, The Netherlands, 24 February 2014. [Google Scholar]
  38. Zarantonello, L.; Romani, S.; Grappi, S.; Fetscherin, M. Trajectories of brand hate. J. Brand Manag. 2018, 25, 549–560. [Google Scholar] [CrossRef]
  39. Sussan, F.; Hall, R.; Meamber, L.A. Introspecting the spiritual nature of a brand divorce. J. Bus. Res. 2012, 65, 520–526. [Google Scholar] [CrossRef]
  40. Romani, S.; Sadeh, H.; Dalli, D. When the Brand is Bad, I’m Mad! An Exploration of Negative Emotions to Brands. Adv. Consum. Res. 2009, 36, 494–501. [Google Scholar]
  41. Bechwati, N.N.; Morrin, M. Outraged Consumers: Getting Even at the Expense of Getting a Good Deal. J. Consum. Psychol. 2003, 13, 440–453. [Google Scholar] [CrossRef]
  42. Grégoire, Y.; Laufer, D.; Tripp, T. A comprehensive model of customer direct and indirect revenge: Understanding the effects of perceived greed and customer power. J. Acad. Mark. Sci. 2010, 38, 738–758. [Google Scholar] [CrossRef]
  43. Thomson, M.; Whelan, J.; Johnson, A.R. Why brands should fear fearful consumers’ how attachment style predicts retaliation. J. Consum. Psychol. 2012, 22, 289–298. [Google Scholar] [CrossRef]
  44. Bougie, R.; Pieters, R.; Zeelenberg, M. Angry customers don’t come back, the get back: The experience and behavioral implications of anger and dissatisfaction in services. J. Acad. Mark. Sci. 2003, 31, 377–393. [Google Scholar] [CrossRef]
  45. Jamieson, L.F.; Bass, F.M. Adjusting Stated Intention Measures to Predict Trial Purchase of New Products: A comparison of models and methods. J. Mark. Res. 1989, 26, 336–345. [Google Scholar] [CrossRef]
  46. Kucuk, S.U. Macro-level antecendents of consumer brand hate. J. Consum. Mark. 2018, 35, 555–564. [Google Scholar] [CrossRef]
  47. Funches, V.; Markley, M.; Davis, L. Reprisal, retribution and requital: Investigating customer retaliation. J. Bus. Res. 2009, 62, 231–238. [Google Scholar] [CrossRef]
  48. Bhattacharya, C.B.; Sen, S. Doing better at doing good: When, why, and how consumers respond to corporate social initiatives. Calif. Manag. Rev. 2004, 47, 9–24. [Google Scholar] [CrossRef]
  49. Berens, G.; van Riel, C.B.M.; van Bruggen, G.H. Corporate associations and consumer product responses: The moderating role of corporate brand dominance. J. Mark. 2005, 69, 35–48. [Google Scholar] [CrossRef] [Green Version]
  50. Luo, X.; Bhattacharya, C.B. Corporate Social Responsibility, Customer Satisfaction, and Market Value. J. Mark. 2006, 70, 1–18. [Google Scholar] [CrossRef]
  51. Kucuk, S.U. Consumer empowerment model: From unspeakable to undeniable. Direct. Mark. Int. J. 2009, 3, 327–342. [Google Scholar] [CrossRef]
  52. Khan, M.A.; Lee, M.S.W. Prepurchase determinants of brand avoidance: The moderating role of country-of origin familiarity. J. Glob. Mark. 2014, 27, 329–343. [Google Scholar] [CrossRef]
  53. Campbell, W.K.; Bonacci, A.M.; Shelton, J.; Exline, J.J.; Bushman, B.J. Psychological entitlement: Interpersonal consequences and validation of a self-report measure. J. Pers. Assess. 2004, 83, 29–45. [Google Scholar] [CrossRef] [PubMed]
  54. Moeller, S.J.; Crocker, J.; Bushman, B.J. Creating hostility and conflict: Effects of entitlement and self-image goals. J. Exp. Soc. Psychol. 2009, 45, 448–452. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  55. Lee, S.; Jude, B.C. Consumer complaint channel choice in online and offline purchases. J. Consum. Stud. 2012, 36, 90–96. [Google Scholar] [CrossRef]
  56. Kucuk, S.U. Can consumer power lead to market equalization? J. Res. Consum. 2012, 21, 1–25. [Google Scholar]
  57. Salvatori, E. Brand Hate: The “Dark Side” of Consumer Attitudes towards a Brand. Bachelor’s Thesis, Bocconi University, Milan, Italy, 2007. [Google Scholar]
  58. Sinha, N.; Ahuja, V.; Medury, Y. Corporate blogs and internet marketing—Using consumer knowledge and emotion as strategic variables to develop consumer engagement. J. Database Mark. Cust. Strategy Manag. 2011, 18, 185–199. [Google Scholar] [CrossRef] [Green Version]
  59. Portwood-Stacer, L. Anti-consumption as tactical resistance: Anarchists, subculture, and activist strategy. J. Consum. Cult. 2012, 12, 87–105. [Google Scholar] [CrossRef]
  60. Douglas, S.C.; Martinko, M.J. Exploring the Role of individual differences in the prediction of workplace aggression. J. Appl. Psychol. 2001, 86, 547–559. [Google Scholar] [CrossRef] [PubMed]
  61. Grégoire, Y.; Fisher, R.J. The effects of relationship quality on customer retaliation. Mark. Lett. 2006, 17, 31–46. [Google Scholar] [CrossRef]
  62. Ward, J.C.; Ostrom, A. Complaining to the masses: The role of protest framing in customer-created complaint web sites. J. Consum. Res. 2006, 33, 220–230. [Google Scholar] [CrossRef]
  63. Cheung, G.W. Testing equivalence in the structure, means, and variances of higher order constructs with structural equation modelling. Organ. Res. Methods 2008, 11, 593–613. [Google Scholar] [CrossRef]
  64. Luo, X.; Raithel, S.; Wiles, M. The impact of brand rating dispersion on firm value. J. Mark. Res. 2013, 50, 399–415. [Google Scholar] [CrossRef] [Green Version]
  65. Craparo, G.; Magnano, P.; Paolillo, A.; Costantino, V. The Subjective Risk Intelligence Scale. The Development of a New Scale to Measure a New Construct. Curr. Psychol. 2018, 37, 966–981. [Google Scholar] [CrossRef]
  66. Arbuckle, J.L. IBM® SPSS® Amos™ 21: Users Guide; IBM: Armonk, NY, USA, 2012. [Google Scholar]
  67. Horn, J.L.; McArdle, J.J. A practical and theoretical guide to measurement invariance in aging research. Exp. Aging Res. 1992, 18, 117–144. [Google Scholar] [CrossRef]
  68. Mullen, M.R. Diagnosing measurement equivalence in cross national research. J. Int. Bus. Stud. 1995, 26, 573–596. [Google Scholar] [CrossRef]
  69. Singh, J. Measurement issues in cross-national research. J. Int. Bus. Stud. 1995, 26, 597–619. [Google Scholar] [CrossRef]
  70. Meredith, M. Measurement invariance, factor analysis and factorial invariance. Psychometrika 1993, 58, 525–543. [Google Scholar] [CrossRef]
  71. Steenkamp, J.-B.; Baumgartner, H. Assessing measurement invariance in cross-National Consumer Research. J. Consum. Res. 1998, 25, 78–107. [Google Scholar] [CrossRef] [Green Version]
  72. Byrne, B.M. Testing for multigroup equivalence of a measuring instrument: A walk through the process. Psicothema 2008, 20, 872–882. [Google Scholar] [PubMed]
  73. Byrne, B.M. Structural Equations Modeling with AMOS: Basic Concepts, Applications and Programming, 2nd ed.; Routledge: New York, NY, USA, 2010. [Google Scholar]
  74. Meade, A.W.; Johnson, E.C.; Braddy, P.W. Power and sensitivity of alternative fit indices in tests of measurement invariance. J. Appl. Psychol. 2008, 93, 568–592. [Google Scholar] [CrossRef] [PubMed]
  75. Cheung, G.W.; Rensvold, R.B. Evaluating goodness-of-fit indexes for testing measurement invariance. Struct. Equ. Model. 2002, 9, 233–255. [Google Scholar] [CrossRef]
  76. Raykov, T. Coefficient alpha and composite reliability with interrelated nonhomogeneous items. Appl. Psychol. Meas. 1998, 22, 375–385. [Google Scholar] [CrossRef]
  77. Fornell, C.; Larker, D.F. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
  78. Bagozzi, R.P.; Yi, Y. On the evaluation of structural equation models. J. Acad. Mark. Sci. 1988, 16, 74–94. [Google Scholar] [CrossRef]
  79. Berger, J.; Iyengar, R. Communication channels and word of mouth: How the medium shapes the message. J. Consum. Res. 2013, 40, 567–579. [Google Scholar] [CrossRef]
  80. Farrell, A.M. Insufficient discriminant validity: A comment on Bove, Pervan, Beatty, and Shiu (2009). J. Bus. Res. 2010, 63, 324–327. [Google Scholar] [CrossRef] [Green Version]
  81. Ruiz-Mafe, C.; Aldas-Manzano, J.; Veloutsou, C. Understanding and managing negative eWom: The role of trust, dissatisfaction and negative brand relationships. In Proceedings of the 10th Global Brand Conference, Turku, Finland, 27–29 April 2015. [Google Scholar]
  82. Lehner, M.C.; Halliday, S.V. Branding sustainability: Opportunity and risk behind a brand-based approach to sustainable markets. Ephemer. Theory Polit. Organ. 2014, 14, 13–34. [Google Scholar]
  83. Dawkins, S.; Martin, A.; Scott, J.; Sanderson, K. Building on the positives: A psychometric review and critical analysis of the construct of Psychological Capital. J. Occup. Organ. Psychol. 2013, 86, 348–370. [Google Scholar] [CrossRef]
  84. Amatulli, C.; Guido Carli, L.; Costabile, M.; Guido, G. Sustainable Luxury Brands Evidence from Research and Implications for Managers; Palgrave Macmillan: London, UK, 2017. [Google Scholar]
  85. Platania, S.; Santisi, G.; Morando, M. Impact of emotion in the choice of eco-luxury brands: The multiple mediation role of the brand love and the brand trust. Qual. Access Success 2019, 20 (Suppl. S2), 501–506. [Google Scholar]
Table
Table 1. Fit indexes for models tested in CFA.
Table 1. Fit indexes for models tested in CFA.
Modelχ2 dfSRMRRMSEARMSEA 90%-C.I.CFITLIAICBIC
Model 1 a1386.24 *2030.090.0970.089–0.100.830.821486.2421688.492
Model 2 b1459.78 *2090.080.0790.065–0.0870.880.861147.5411321.236
Model 3 c21.894 *90.030.0480.027–0.0700.980.9745.89494.434
Note: a Model 1= four-factor second order 22 items; b Model 2= one-factor first order 22 items; c Model 3= one-factor first-order 6 items. CFI=Comparative Fit Index; TLI= Tucker Lewis Index; AIC=Akaike Information Criterion; RMSEA= Root Mean Square Error of Approximation; SRMR= Standardized Root Mean Square Residual; BIC= Bayesian Information Criterion.
Table
Table 2. Descriptive statistics, reliability, composite reliability, average variance extracted, and intercorrelations for study 1 (N = 422).
Table 2. Descriptive statistics, reliability, composite reliability, average variance extracted, and intercorrelations for study 1 (N = 422).
VariablesMSDαAVECR1234567
1. BHSs3.460.990.850.680.93
2. EA3.560.830.710.610.900.55 **
3. IA3.750.860.850.620.920.58 **0.43 **
4. MA3.461.220.870.750.930.75 **0.31 **0.53 **
5. Rejection4.250.910.880.750.940.52 **0.43 **0.52 **0.33 **
6. OC2.111.230.910.780.960.50 **0.30 **0.26 **0.50 **0.09
7. MPA1.860.860.720.610.900.48 **0.30 **0.26 **0.48 **0.040.66 **
8. NWM2.930.960.860.640.910.72 **0.48 **0.35 **0.58 **0.39 **0.60**0.55 **
Note. BHSs = Brand Hate Short Scale; EA = Experiential Avoidance; IA = Identity Avoidance; MA = Moral Avoidance; OC = Online Complaining; MPA = Marketplace Aggression; NWM = Negative Word-of-Mouth. ** the correlations are significant at p < 0.001.
Table
Table 3. Fit statistics for measurement invariance by gender.
Table 3. Fit statistics for measurement invariance by gender.
Modelχ2(df)CFISRMRRMSEAΔCFI
Model 1. Configural Invariance 53.050(18)0.960.040.07 (0.047–0.088)
Model 2. Metric Invariance 62.686(23)0.960.040.06 (0.045–0.083)0.002
Model 3. Scalar Invariance68.589 (27)0.960.030.06 (0.045–0.083)0.001
Model 4. Measurement Error Invariance 74.096 (31)0.960.030.06 (0.045–0.083)0.001
Model 5. Structural Variance Invariance89.781 (38)0.960.030.06 (0.045–0.083)0.000
Model 6. Structural Covariance Invariance97.369 (44)0.960.030.06 (0.045–0.083)0.000

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Platania, S.; Morando, M.; Santisi, G. Psychometric Properties, Measurement Invariance, and Construct Validity of the Italian Version of the Brand Hate Short Scale (BHS). Sustainability 2020, 12, 2103. https://doi.org/10.3390/su12052103

AMA Style

Platania S, Morando M, Santisi G. Psychometric Properties, Measurement Invariance, and Construct Validity of the Italian Version of the Brand Hate Short Scale (BHS). Sustainability. 2020; 12(5):2103. https://doi.org/10.3390/su12052103

Chicago/Turabian Style

Platania, Silvia, Martina Morando, and Giuseppe Santisi. 2020. "Psychometric Properties, Measurement Invariance, and Construct Validity of the Italian Version of the Brand Hate Short Scale (BHS)" Sustainability 12, no. 5: 2103. https://doi.org/10.3390/su12052103

APA Style

Platania, S., Morando, M., & Santisi, G. (2020). Psychometric Properties, Measurement Invariance, and Construct Validity of the Italian Version of the Brand Hate Short Scale (BHS). Sustainability, 12(5), 2103. https://doi.org/10.3390/su12052103

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