Consumer perception of brand equity based on environmental sustainability - development and validation of a scale

doi:10.21203/rs.3.rs-1942953/v3

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Consumer perception of brand equity based on environmental sustainability - development and validation of a scale

https://doi.org/10.21203/rs.3.rs-1942953/v3

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Brands gradually became the core dimension and strategic asset of branding for organizations of all sizes, and today many companies adopt various forms of green marketing activities as part of their strategies. The objective of this study was to investigate and develop the psychometric properties of precision and validity of a scale for brand evaluation equity based on environmental sustainability. The scale was validated by adopting the standards described in the Standards for Educational and Psychological Testing APA. As samples for validation tests, eight companies were selected from those developing their sustainable business. The data were applied to 262 potential consumers. A scale with 22 items was constructed and validated, and after evaluation by the specialists, 22 items were obtained, divided into four dimensions: Brand quality perception — BQP (6 questions); Strategic Brand Positioning – SBP (10 questions); Willingness to purchase — WtB (3 questions) and Innovation in retail — INM (3 questions). Three items were modified because they presented the CVC at 0.8 and were accepted after the adjustments. The instrument items showed good internal consistency (0.877) regarding their domains. For DIF data, the scale invariably works for older and younger people for almost all items, except item INM 04. This article fills the gap in the literature by developing a scale to assess consumer perception of brand equity and environmental sustainability.

Agricultural Engineering

Agricultural Economics & Policy

Forest sustainability

Sustainable

Validity of measures

Brand equity and sustainability have been constantly discussed in the academic scenario. Morit et al. (2018) describe the two fundamentals as symbiotic when planning to change the image of the business with a focus on innovation; other studies reinforce that sustainability is an antecedent and basic factor for a heavy equity of the brand (Arrigo, 2018; Gonzalez-Lafaysse & Lapassouse-Madrid, 2016; Ray & Sharma, 2020). Recent research points to a link between these variables that can contribute to the economic and sustainable advantages of an organization (Marín-García et al., 2020). Adjustments and changes in brand management, such as rebranding focused on the strategy of environmental responsibility in brand sustainability and environmental innovations, have been adopted to associate the factors sustainability and brand equity (Choi & Yi, 2018; Long & Lin, 2018; Marques et al., 2020).

As the main asset for business change focused on consumer perception, brands can be understood as distinctive signs of products, companies, or services (W. Liu, 2017). As a result, it has become an important economic asset for its bearer (Singh et al., 2020). However, changes in the strategic characteristics of the new business image and the implementation of environmental innovations require a real and strong engagement in the change in products and manufacturing processes and/or service of companies (W. L. Lin et al., 2021) because with the ease of access to information, consumers have increasingly sought to know the sustainable standards adopted by the brands they consume and know the real brand equity (Chen, 2019).

However, the adoption of environmentally sustainable and indeed economically viable business activities and competitive advantages based on innovation, complexity and/or product sophistication are necessary (Guimarães & Azambuja, 2018), meeting consumers' desires for environmental sustainability and maintaining competitive ability (Lucas et al., 2019). One of the ways to maintain competitive capacity and produce sustainably and value for its products and services based on consumer perception when the relationship between the form of alternative environmental exploitation and sustainable technology is adopted. A deeper understanding of consumer perception related to sustainable environmental management aspects with regard to natural resources and consumption and poverty alleviation would allow the development of effective branding that incorporates sustainable practices in creating a solver brand (Niu et al., 2020; Raufflet & Bres, 2014). This strategy may influence reliability and a subsequent preference of consumers for the product linked to this company.

Brand-related mental images are defined as preexisting understandings associated with new information contained in the brand's visual identity (Cowan & Dai, 2014). These insights can be achieved by using appropriate branding for this purpose (Crader & Santomier, 2011). The concepts contained in the brand equity directly related to the brand's business asset, if properly constructed, promote consumer affinity for the products by forming positive and ethically acceptable sensory cues (Ulian et al., 2020). Previous research shows that planned images related to consumption increase attitudes and the will to buy (Schlosser, 2003), forming concepts about products and their value and fair price.

The mental images that build the nomological network that form Brand equity can be understood through the evaluation of the first-order formative constructs (Aprile and Punzo, 2022; Cowan and Dai, 2014; Defrancesco et al. 2017). These formative constructs can be evaluated by understanding the experiences of consumers through the perception of PQM quality, strategic positioning of the brand in environmental sustainability, consumer willingness to purchase and sustainable innovation of the branding brand (J. L. Aaker, 1997; Figueiredo & Castro, 2019).

The perception of quality is at a higher level of abstraction than any specific attribute. It can be defined as the customer's judgment of overall excellence, estimating the brand's superiority over competitors (Konuk, 2018a); it is an attitudinal assessment of a brand—an overall emotional assessment of a brand's performance relative to other brands (Y. S. Chen & Chang, 2013). The perception of quality is strictly linked to the reception of functional/practical benefits that increase the reward value for the acquisition of the product, even with added premium value (Dash et al., 2021).

The concept of brand positioning is seen as a branch of differentiation in which researchers try to position a brand in relation to its competitors in the minds of consumers. However, a very important function of brand positioning is to design the product and image of an organization to occupy a unique place in the mind of the target market and convert them into trusted consumers and brand propagators (Pratihari & Uzma, 2018).

The notion of brand positioning has been seen as an offshoot of differentiation, whereby the researchers attempt to locate in the mind of the consumer the relative position of a brand regarding its competitors (McDowell, 2009; Wang & Horng, 2016). ButHowever, many studies have shown that positioning is largely determined by a brand's identity and image and its consequence in the formation of the consumer's imaginary as to the values provided by him (Bulovic & Seric, 2021; le Roux & du Plessis, 2019; Wang, 2017). The interaction between brand concept formative values helps create strong, unique and positive associations that should resonate positioning the brand in a prominent place (Koch & Gyrd-Jones, 2019). Creating these “points of difference” is considered a key point in building favorable brand equity. This can increase the perception of brand exclusivity and the range of products to compete on more than just price (Ameyibor et al., 2022), factors that significantly impact the perception of business uniqueness.

Another important factor for building a nomological network is consumers' willingness to buy a particular brand. The provision is defined as the value that a customer is willing to pay for their preferred brand versus a comparable/smaller brand for the same size/quantity of packaging (Kimura et al., 2012; Konuk, 2019). It is one of the strongest indicators of brand loyalty and may be the most reasonable summation measure of overall brand equity (Kataria & Saini, 2020; Netemeyer et al., 2004).

Therefore, we define purchase intent as consumers' preference for products manufactured and sold under fair and substantially acceptable working conditions (Belanche et al., 2021). The provision is linked to the judgment of fair value applied to the product compared to the value of a comparable competitor brand for the same size/quantity of packaging (Konuk, 2018b). Willingness to buy is one of the strongest indicators of brand loyalty and may be the most reasonable summation measure of overall brand equity (Balderjahn & Hüttel, 2019).

As competitive Competitive advantages that can be achieved through brand innovation or the relationship between innovation and market performance play an important role in the brand equity of sustainable companies (Medrano et al., 2020; Quaye & Mensah, 2019a). The definition of innovation in branding has several meanings and understandings from different domains and academic perspectives. From a broad concept, it can be defined as “a new way of doing things, or a unique combination of factors of production” (Schumpeter, 1980). However, the definition of branding innovation requires taking on a new paradigm as global business trends have changed the flow of innovative ideas partially from producer to consumer (Quaye & Mensah, 2019b). Innovating in branding requires significant changes in both products and the image presented to consumers, aesthetic designs, product packaging improvements, new mass media, new prices and sales strategies (Medrano et al., 2020).

A correct understanding of the latent variables that shape the nomological network that forms the brand equity of environmentally sustainable brands in different cultures is relevant as different populations have different social values and these are able to interfere in perception and choice (Zajkowski & Domańska, 2019).

The cultural and linguistic adaptation of tools designed to assess consumer perceptions of sustainable products is an essential step toward increasing their usefulness and relevance. In many cases, the development of a brand management model requires polycentricity to reach enough consumer markets to generate the need to apply the tool internationally (H. Liu et al., 2022; Shrestha et al., 2020). To reliably collect and analyze instrument data, instruments in different target languages must cover the same concepts, although the wording must be modified to reflect equivalent concepts in the target language (Hustvedt & Bernard, 2010).

The field of psychological perception analyses and cross-cultural adaptation has slowly reached a consensus on how to validate and use measures effectively (Grassi et al., 2021). The adaptation procedure brings together areas of expertise in different areas, including psychometric and statistical (e.g., structural equation modeling, item response models) (Clark & Watson, 2019; Y. Liu et al., 2019; McAllister et al., 2021; Mels et al., 2010). The cross-cultural adaptation process is essential when a scale is used in a different language, environment, and time to reduce the risk of bias in a study (Herdman et al., 1998; Swami & Barron, 2019). In this study, the thematic adaptation of the items did raise discrepant translation and cross-cultural adaptation adjustments, not harming the instrument.

The objective of this study was to investigate and develop the psychometric properties of precision and validity of a scale for brand evaluation equity based on the environmental sustainability of the Brazilian Amazon. The validation phases followed the Standard psychometric instrument validation standards APA. The standards have important general implications for comparative psychometric studies investigating consumer perceptions. Standardized scale assessment tools should demonstrate a similar interpretation of test items cross-culturally.

Then, this research is organized as follows: First, we present the analyzed dimensions for the development of the scale. After this description, we present the adopted methodology to validate and refine the proposed scale. Finally, we discuss the obtained results and our final remarks.

Operationalization of Variables

Brand equity comprises a series of dimensions that the brand can develop, manage and control. Different conceptual structures include different dimensions. Then, this research is considered a theoretical reference to the dimensions postulated by Aaker (1996, 1991): awareness, loyalty, perceived quality, and associations). The initial dimensions and findings from a scoping review were developed by cross-cultural translation ('forward') of selected items from the original instruments.

Two translators, one being a linguist and the other from the research area of the experiment, were selected since when translating a scale, several equivalences must be sought about the original, such as cultural, semantic, technical, content, and criteria (Guillemin et al. 1993). Furthermore, the current literature in the area highlights the need to avoid a literal translation of the items, a fact that can be avoided if they are only met through the variability of translators (Callegaro Borsa et al., 2012).

After the consensus of the translators, a structured scale initially contains 23 items to deal with the brand equity of brands related to environmental sustainability in Amazonia. The four dimensions proposed in this research were Brand quality perception — BQP (6 questions); Strategic Brand Positioning – SBP (10 questions); Willingness to purchase — WtB (3 questions) and Innovation in retail — INM (3 questions).

We investigated data from 7 brands related to sustainability that market their products/services considering the equity of the sustainable Amazon brand. The brands are classified into six product categories: cosmetics, tourism, clothing, accessories and food. To characterize the perception of the brand, we gathered images from the website, images of the products, posts on social networks, and descriptive texts, as well as the prototyping of the e-commerce website, using data provided by them.

The total sample consisted of 263 potential consumers, aged between 16 and 64 (ages 19 to 30 = 40%; 31 to 64 = 60%). Participating interviewees were selected using the quota method to guarantee homogeneity regarding age and sex. Participants included consumers from all regions of Brazil. The survey was carried out from March to July 2022.

Analyzed dimensions

The first dimension was initially composed of 10 (ten) transliterated questions from the developed scale by Grigorescu et al. (2019), which will analyze the interviewee's perception of the strategic positioning of the brand (SBP) in the Amazon region of Brazil. Additionally, we considered the level of perception of the participants regarding the importance of the company/startup's relationship with environmental sustainability in the region. Ten (10) with minor language modifications out of 21 (twenty-one) items of the original scale were used.

The second dimension presents five items and comprises the perception of product quality and the brand equity related to environmental sustainability initially proposed by each brand (PQM). This construct was evaluated using five pictures/items of the brand, five pictures of the store (digital), four pictures/items of the premium product (with a value above the average market), and five pictures representing the business intention toward environmental sustainability in the Amazon. (production/extraction medium). From the hands of the figures/products seeking to contemplate this dimension, 5 of the 11 items were adapted, dealing with the brand quality perception of the questionnaires developed by Netemeyer et al. (2004) and Yoo et al. (2017).

The third dimension contains three items, which seek to understand how much participants would be willing to purchase products from companies related to environmental sustainability through the presented data (willingness to purchase - WtB). To measure willingness to purchase, the 3 (three) arranged items according to Arnett et al. (2003).

Finally, the fourth dimension assesses how much the business presents branding innovation (INM). The dimension was set up by asking participants to answer four questions that addressed the theme of innovation, uniqueness and brand design, and the questions were adapted from the questionnaires proposed by Lin, (2015) and Netemeyer et al, (2004). Of these works, only the translation of the items was carried out.

Seeking to comply with ethical principles in research with human beings, the present study was sent to the Research Ethics Committee of the Universidade Federal de Sergipe, receiving the following code CAAE: 55999722.1.0000.5546. Participants who agreed to participate in the study were informed about the research objectives, data collection, and the information regarding the Free and Informed Consent Term (FICT).

Cross-cultural translation of the items into the instrument

The translation and adaptation of the items used from the original questionnaires followed five essential steps recommended by the literature: (1) translation of the instrument from the source language to the target language, (2) analysis of the prototypes and analysis of the synthesized version by expert judges, (4) back-translation to the source language), and (5) pilot study (Gjersing et al. 2010; Hambleton, 1993). For the convenience of this research, another task (phase 2) was performed during content validation by CVC for the full scale, and phases (4) and (5) were performed after completion of content validation.

Scale validation

After the formulation and structuring of the items to be used in the data collection scale, it was submitted for validation based on the tripartite model described in the Standards for Educational and Psychological Testing, (Frey & Association, 2018), The Standards presentspresent five validation processes to accept the instrument as reliable and replicable:

Content validation;
Validity based on internal structure;
Validity is based on relationships with external measures if
Validity based on response pattern to items.

Content validity

The content validity coefficient (CVC) was applied in the proposed instrument seeking to measure the effectiveness of the scale based on qualitative evaluations of expert judges in the area. This technique involves seeking the consensus of a community of professionals who will attribute values and opinions about the translations and adaptations made in the instrument so that it can be refined and contemplate the initial objectives of the instrument. The evaluation of the scale by the judges was performed blindly; therefore, the confidentiality of the participants guarantees greater fidelity to the results at the end of the experiment, allows better exposure to the results, and maintains their understanding Hasson al, (2000). After item validation, randomization was performed using Excel software, and data collection was carried out from March to May 2022.

Pilot public and initial application of the tool.

Seeking to contemplate a greater number of regions of the country, the instrument was applied both presecinally and digitally. According to Walter et al, (2016), due to its advantages, such as speed and capacity, the application of digital questionnaires areis being increasingly used to reach specific populations, reducing the possible costs of displacement. The selection of the participating respondents was randomly performed to ensure the population variability of the country.

Pilot validation of the tool

Validity is based on internal structure.

Validity based on internal structure reflects the degree to which the structure of correlations between items matches what the construct intends to measure (Frey & Association, 2018). Therefore, in this research, an AFE was carried out to evaluate the factor-named structure of the proposed scale. The analysis was implemented using a polychromic matrix, and the extraction method used total diagonally weighted least squares (RDWLS) (Asparouhov et al., 2010). The decision on the number of factors to be retained was performed using the technique of parallel analysis with a random permutation of the observed data (Timmerman & Lorenzo-Seva, 2011).

To determine the number of retention factors and facilitate the interpretation of the factors (Damásio, 2012), a rotation method was adopted similar to the oblique one by Robust. Promin(Lorenzo-Seva & Ferrando, 2019). Seeking a better refinement of the scale, it was assumed that, for an item to be accepted, it must have a factor loading above 0.60 and cross-loadings between items lower than 0.20 (Howard, 2016).

Seeking to evaluate the quality of the model's adequacy and to assess how well it can reproduce the covariance structure or the correlational structure of the variables, the following strategies were used: the root mean square error of approximation (RMSEA), Tuker-Lewis index (TLI) and comparative adjustment index (CFI). According to Brown (2015), the RMSEA value must be less than 0.08, and the confidence interval cannot reach 0.10. The CFI and TLI values must be greater than 0.90, preferably 0.95. The model's reliability, that is, how consistent and reproducible it is in measuring the intended latent variables, was tested using the composite reliability indicator, which must have values greater than or equal to 0.70 (Hair et al., 2009).

The factor´s stability was evaluated using the H-index (Lorenzo-Seva & Ferrando, 2019). The H-index proposes to assess how well a set of items represents a common factor. H values range from 0 to 1. High H values (> 0.80) suggest a well-defined latent variable, which is likely to be stable in different studies.

Validity based on relationships with external measures

We sought to evaluate the evidence of validity of the instrument based on the relationship with other variables arranged in other similar questionnaires(Souza et al., 2017), as well as the information asymmetry. For this, convergent and discriminant validity tests were performed.

Convergent and discriminant validity

The factors’ convergent validity was evaluated utilizing the average variance extracted (AVE) and using standardized factor loadings and the error of the item measure. The CVA estimation was obtained by the factor loadings and measurement errors obtained by confirmatory factor analysis using the weighted estimator least square mean and variance (WLSMV). AVE values greater than 0.50 indicated good convergent validity (Maroco et al., 2014).

Validity of Invariance of Measures

Multigroup confirmatory factor analysis (AFCMG) was performed to verify the invariance of the proposed model measures in two samples selected at random to the extent of 50% for each (T=Test and A=Sample) of the collected data (J. C. Borsa & de Sousa, 2018). Comparative Fit Index (CFI) tests and the chi-square difference were used to assess the scale invariance. To assume the invariance of the measure, in comparison with the previous model, the fit index of the test model in the CFI must not be greater than 0.01 (Cheung & Rensvold, 2002), and the χ ²must present a value of p > 0.05 (Milfont & Fischer, 2010; Zaiţ & Bertea, 2011).

Validity based on the item response pattern

The psychometric properties of the adapted instrument were evaluated using the Rasch model for polytomous data (Andrich, 1978). In addition, indicators of the reliability of people and items and performance deviations were evaluated through the infit and outfit indices. Regarding reliability indices, values greater than 0.70 or, preferably, values greater than 0.80 are expected (Linacre, 2012). The Infit and Outfit indices quantify residuals for the items in the model tested (Bond & Fox, 2013a; Linacre, 2012). The DIF was evaluated using the Mantel procedure (Wright, 1994). Items whose difficulty estimates were significantly different for men and women (p ≤ 0.05) were inspected. The magnitude of the DIF was interpreted through the DIF contrast: values between | 0.00 | and | 0.43 | are considered low/negligible; values between | 0.44 | and | 0.64 | are considered moderate; and values above | 0.64 | are considered high (Linacre, 2012).

Cross-cultural translation of the items into the instrument.

The process of translation and cultural adaptation generated the Brazilian Portuguese version. During the translation process, important difficulties were not found. However, form observed some idiomatic problems, resulting in some weak changes in grammatical structures. Based on the translators' consensus, 23 adapted items were accepted, 19 polyatomic (Likert scale 1 - 5), two dichotomous (Yes - No), and one qualitative question was also accepted.

Validity by internal measures

Content validity

Based on the scores attributed to the items, the CVC was calculated to evaluate the agreement that the judges perform on the scale. Based on the score assigned by the judges, items that did not reach a value greater than or equal to 0.8 were grouped and relocated for correction in at least two of the established criteria (clarity, relevance and relevance).

When implementing the validation procedures APA standards for cross-cultural adaptation in the Portuguese-speaking population, the initial qualitative study, as expected, revealed initial translation overlaps, as well as important discrepancies in the languages used. From the base instrument in its original version, five changes were accepted in the statements and submitted to new rounds. RSA 02 “Can you quickly remember the brand symbol or logo?” has been corrected to “Do you think this brand symbol/logo is easy to remember?” (clarity = 0.430); (relevance = 0.650); (relevance = 0.700); INM 01 “When imagining the product, I realized that the quality of the products is high” changed to “When imagining a product of this brand, I believe that the quality is high” (clarity = 0.896) (relevance = 0.963); (relevance = 0.963); QLM 01 “I feel that when I purchase the services, I will receive the quality I expect.” Changed to “I feel that by purchasing the services of this brand, the quality will meet my expectations.”, BQP02 (clarity = 0.830) (relevance = 0.963); (relevance = 0.896) “While viewing the services of the Brand, would you be able to differentiate of products from other companies?” changed to “After seeing the above image, would you be able to differentiate branded services from other companies?”; WTB 03 (clarity = 0.896) (relevance = 0.963); (relevance = 0.963) "If the Brand´s prices rise a little, I would buy from the competition, regardless the responsibility of environmental sustainability." Changed to "If the brand's prices went up a little, I would buy from the competition, even though I know they care about environmental sustainability." The other listed items to compose the scale met the established criteria.

The change in item INM 01 was suggested considering the change from the term "perceive" to "believe" since imagining that the item has some quality factor and is semantically closer to belief than to perception. Given that the CVC_t presented satisfactory results for all aspects judged, the change seemed appropriate, suggesting that the term was adaptable semantically.

After corrections, the Content Validity Coefficient for the complete scale was accepted for eligibility of the instrument to the new validation phase (Santos, 2018). considering the CVC_t mean for three aspects judged - clarity, coherence and relevance - was CVC_t = 0.87. Based on the values found, CVC _tfor the entire instrument presented satisfactory values of language clarity (CVC_t= 0.84), practical relevance (CVC_t= 0.91) and theoretical relevance (CVC_t= 0.90), having a content validity coefficient for the test as a whole of CVC_t=0.87 and an error Pe_{J =}0.003. The instrument was considered eligible for the other phases considering values similar to those found by Daffern et al, (2017).

Validity based on internal structure

Bartlett's (2926.2, gl = 210; p < 0.001) and KMO (0.89) tests of sphericity suggested the interpretability of the items' correlation matrix. For stability, Cronbach's alpha presented acceptable values (Cronbach's = 0. 925). The parallel analysis suggested four more representative factors for the data (Table 1).

Table 1. Results of the parallel analysis of the scale to consumers' perception of sustainable brands on the Amazon

Percentage of explained variance of actual data	Percentage of explained variance of random data (95% CI)
49.527 *	10.780
8.888 *	9.832
7.545 *	9.026
5.293 *	8.476

Note: The number of factors to retain is four, as four factors from actual data have a higher % explained variance than random data.

The factor loadings of the items can be seen in Table 2. In addition, the composite reliability indices are reported, as well as estimates of replicability of the factor scores (H-index) (Ferrando & Lorenzo-Seva, 2018). From the factor´s analysis, it was possible to identify which of them belong to what dimension, that is, to reorganize them by counting the items that best explained them, as well as which items did not reach factor loadings to be kept in the instrument.

Table 2. Factor structure of consumers' perception of sustainable brands on the Amazon

Items	POM	PQM	INM	DCM
POM 01	0.451	0.041	0.244	-0.166
POM 02	0.837	-0.240	0.222	0.061
POM 03	0.921	-0.107	-0.054	0.059
POM 04	0.899	-0.134	0.044	0.053
POM 05	0,750	0.136	-0.149	0.132
POM 06	0.823	0.141	-0.294	0.080
POM 07	0.829	-0.045	0.212	-0.128
POM 08	0.929	-0.007	0.094	-0.110
POM 09	0.569	0.093	0.202	-0.245
PQM 01	-0.210	0.407	-0.083	-0.048
PQM 02	-0.270	0.706	-0.007	0.166
PQM 03	0.055	0.728	-0.041	0.064
PQM 04	-0.031	0.577	0.835	0.077
PQM 05	0.277	0.117	-0.013	-0.013
INM 02	-0.054	0.131	0.806	0.084
INM 03	-0.014	0.297	0.512	-0.125
INM 04	0.111	0.013	0.688	0.124
DCM 01	0.071	0.136	-0.047	0.464
DCM 02	0.133	0.263	-0.112	0.495
DCM 03	0.069	-0.045	0.013	0.813
DCM 04	-0.018	-0.021	0.207	0.833
DCM 05	0.251	0.041	0.244	0.464
Composite Reliability	0.937	0.703	0.714	0.760
H- latent	0.965	0. 916	0. 916	0. 905

Note: Quality perception – PQM; Strategic Brand Positioning – POM; Willingness to buy — DCM and Branding Innovation — INM.

The structure factor yielded few cross-loadings, and the loading factor was higher for the initially expected factors than the others. There was only one item (Branding Innovatio): "The brand offers more innovative products than other companies I know") that did not project a fair value on the expected factor (i.e., items with factor loadings above 0.30), based on Nunnally, (1978) items with loading factor below 0.30 were omitted. In the others, only one item showed a pattern of cross loads (19); this item is allocated to the class with the highest load value and corroborates the findings in the literature. All items presented values close to the load of the dimension itself, also corroborating the discriminant validity, a behavior similar to that found by Castro et al., (2020).

The composite reliability of the factors was acceptable (above 0.70) for the SBP factor and marginally acceptable for the other. Second Ferrando and Lorenzo-Seva, (2018), the data suggest that all factors may be replicable in future studies (H < 0.80). In the composite reliability, the factors PQM, WtB, and INM presented values marginally accepted by the literature. According to Valentini and Damásio, (2016), the change in the homogeneity of the factor loadings may be due to the smaller number of items in the factor to limit the interpretation of the results of the composite reliability.

The scale adjustment indices were adequate (c ²= 86,460, gl = 132; p < 0.001; RMSEA = 0.027; BCa 95% [0.00 – 0.037]; CFI = 0.998; BCa 95% [0.998 – 1.002]; TLI = 0.997; BCa 95% [0.997 - 1.002]). In the Root Mean Square Error of Approximation (RMSEA), a value lower than 0.08 for the proposed instrument indicates a good statistical fit for the model (Tucker and Lewis, 1973; West et al. 2012). Comparative adjustment indices (CFI) and Tucker-Lewis index (TLI) above 0.95 also indicate a good instrument adjustment.

It is essential to highlight that the indicators of unidimensionality Unidimensional Congruence (UniCo) 0.845, Explained Common Variance (ECV) 0.886, and Mean of Item Residual Absolute Loadings 0.177 did not support the hypothesis of unidimensionality of the scale, since the values of UniCo, ECV, and MIRAEL that were greater than 0.95 suggest that the data can be treated as essentially unidimensional; otherwise, the null hypothesis is accepted, (Ferrando & Lorenzo-Seva, 2018), as for the MIREAL (Mean of Residual Item Absolute Loads) less than 0.300 suggests that the data can be treated as essentially one-dimensional. Therefore, this implies that our proposed instrument satisfies the benchmarks for the theoretical expression of formative constructs (Sarker et al., 2021).

Table 3 breaks down the quality and effectiveness of the factors. The factor determinacy index (FDI) can be interpreted as the number of different factor levels that can be differentiated based on the factor score estimates. Values above 0.90 are considered good for the FDI (DiStefano et al., 2018; Shi & Maydeu-Olivares, 2020).

Table 3. Quality and Effectiveness of Factor Score Estimates

methods

pet

SBP

PQM

WTB

INM

FDI

0.974

0.951

0.950

0.948

ORION

0.951

0.904

0.902

0.878

Note: FDI = Factor Determinacy Index; ORION = Overall Reliability of fully Informative Prior Oblique N-EAP scores; Brand quality perception – PQM; Strategic Brand Positioning – SBP; Willingness to purchase – WtB and Branding Innovation – INM.

The values obtained by the exploratory factor structure confirm the initial prediction proposed in this research for the construction of the instrument, establishing the final model with four factors. The perception of quality factor (PQM) was assigned five questions describing the perception of the potential consumer interviewed when the evaluated brand presented the product. For the strategic brand positioning factor SBP, ten questions were assigned. The SBP dimension is theoretically based on the concept of strategic positioning described by López and Alcañiz, (2000). The authors define the brand´s strategic positioning as their performance in the search for factors that make up for the success of the business, such as technological innovation and relation to the ethical concepts of the consumer and the environment. Finally, the willingness-to-buy factor - WtB was composed of 2 questions, and the innovation factor in retail - INM was composed of 4 questions.

Validity based on relationships with external measures.

Convergent and discriminant validity

The proposed model from the validity by internal measures comprises a total of 22 items divided into four dimensions, namely, SBP (0.845), BQP (0.964), WtB (0.889) and INM (0.677). According to Maroco et al. (2014), all dimensions reached acceptable values (> 0.50). The analyzed validity pairwise showed good values to attest to the validity (Table 4). According to Maroco et al. (2014), the VEM value for each dimension to be accepted must be greater than 0.50. Furthermore, the operational measures obtained by the factors when analyzed pairwise showed satisfactory values to attest to the validity hypothesis when analyzing the discriminant validity of the instrument.

Table 4. Discriminant validity of scale factors.

Correlation of dimensions	Z correlation coefficient	r ²
SBP ↔ PQM	0.699	0.488
SBP ↔ WtB	0.846	0.715
SBP ↔ INM	0.857	0.734
BQP↔ WtB	0.596	0.355
BQP↔ INM	0.672	0.451
WtB ↔ INM	0.756	0.571

According to Fornell and Larcker (1981), when one of two factors (fej) is greater than or equal to the square of the correction between these factors, discriminant validity is attested. Then, the factors explain more variability of the items that are manifestations of each of the factors than the variability that only one factor explains (J. Borsa & Damasio, 2018). Reaching satisfactory indices, the instrument was considered eligible for the other validations.

Measurement invariance

Table 5 shows the configural, metric, and scalar multigroup invariance analysis. These are the results of the scale in the different investigated groups. The data of different tests of the Comparative Fit Index (ΔCFI) indicate that the measure presents fine invariance adjustments. To assume measurement invariance, the value assigned to (ΔCFI) must not be greater than 0.01. It was observed, through the ΔCFI values, that the scale structure is stable, with no response bias in the samples for ~~the~~ different groups (Lee & Smith, 2020).

Table 5. Comparison of models by ΔCFI for the applied scale Multigroup Confirmatory Factor Analysis

Quiz groups	X ²	df	P Value	RMSEA (90% CI)	CFI	TLI	ΔCFI
configurable invariance	2413,245	421	0.00	0.079 (0.062 0.086)	0.952	0.968	---
metric invariance	1177,096	383	0.00	0.072 (0.065 0.079)	0.951	0.997	0.001
scalar invariance	1186,748	400	0.00	0.065 (0.059 0.072)	0.953	0.926	-0.001

The invariance data by multigroup confirmatory factor analysis (AFCMG), through the values of configurable, metric, and scalar invariance, shows that the structures measured between the groups and the scalar units are the same for the groups studied since the values of difference from the Comparative Fit Index (ΔCFI) are below 0.01. Factor invariance is an essential component of the iterative process of demonstrating the measurement equivalence of latent constructs across groups, including gender and age subpopulations.

When measurement equivalence is present, the relationship between the latent variable and the observed variable remains unchanged across the analysis’s populations, (Raju et al., 2002). This premise succinctly demonstrates the importance of measurement equivalence in psychometric instruments, suggesting that it is possible to apply this scale when the objective is to analyze, in different groups, the perceptions of consumer´s concerning brands related to environmental sustainability in the Amazon.

Validity based on the item response pattern

The validity based on the response pattern to items using item IRT when associated with invariance analyses allows the evaluation of the similarity of items in a given instrument developed for different groups (Sireci, 2021). The initial analyses showed adequate reliability indices for the items (Reliability = 0.98; Separation Index = 7.48) and for people (Reliability = 0.90; Separation Index = 2.93), which suggests that the estimates obtained tend to be replicable. Regarding the performance deviations (infit and outfit), it is noteworthy that the instrument presents acceptable values, (Bond & Fox, 2013b; Boone, 2016), both for the items (Infit MNSQ =1.00, ZSTD = -0.70; Outfit MNSQ = 1.06; ZSTD = -0.13) and for the participants (Infit MNSQ =1.10, ZSTD = -0.10; Outfit MNSQ = 1.06; ZSTD = -0.20).

The results of IRT support the validity and reliability of the instrument. Furthermore, the values of reliability of the items (0.98) and the separation index of the items (7.48), as well as the index of the reliability of the participants = 0.90 and the index of separation of the participants (2.93), suggest that the estimates obtained tend to be replicable. Infit s and outfit s values of the instrument of the items and the participants prove to be acceptable (Bond & Fox, 2013b; Boone, 2016).

The item-person map (Figure 1) shows that in the presented instrument, the level of latent trait was greater than the difficulty of the items (mean theta = 0.67; mean difficulty of the items = 0). As seen, the most difficult items were POQ 02: “After seeing the image above, can you differentiate the services of this company from other companies?” (d = 1.83) and BRI 01: "Do you believe that this brand symbol/logo is easy to remember?" (d = 1.43), and the most specific item was item SBP 07, “I would consume this mark” (d = -0.96).

The majority of the items are grouped between – 1.5 and approximately 1.5 logi (Figure 1), so there is greater precision in the theta estimation of the participants allocated in this continuum range, and the Test Information Curve supports this information. Regarding the statistics of the items individually, Table 6 shows the threshold indicators when all items showed an increasing structure, as theoretically expected.

Table 6. Thresholds of the items used in the perception questionnaire

Items	Thresholds _1-2	Thresholds _2-3	Thresholds _3-4	Thresholds _4-5
POM 01	-7.690	-2,420	-1,360	-0,400
POM 02	-8.420	-3,150	-2,090	-1,130
POM 03	-8.110	-2,840	-1,780	-0.810
POM 04	-7.910	-2,640	-1,580	-0.620
POM 05	-7.290	-2020	-0.960	0,000
POM 06	-7.700	-2,430	-1,370	-0.410
POM 07	-8.520	-3,250	-2,190	-1,230
POM 08	-8.210	-2,930	-1,870	-0,900
POM 09	-8.000	-2,730	-1,670	-0.710
PQM 01	-5.740	-0.460	.590	1,560
PQM 02	-7.280	-2010	-0.950	.0100
PQM 03	-6.140	-0.870	0.190	1,160
PQM 04	-7.910	-2,630	-1,570	-0.610
PQM 05	-7.350	-2,080	-1,020	-0.060
INM 02	-7.320	-2,050	-0.990	-0.020
INM 03	-7.900	-2,630	-1,570	-0,600
INM 04	-8.020	-2,750	-1,690	-0.730
DCM 01	-6.550	-1,270	-0.210	0,750
DCM 02	-7.540	-2,270	-1,210	-0.250
DCM 03	-7.610	-2,340	-1,280	-0.320
DCM 04	-7.720	-2,450	-1,390	-0.430
DCM 05	-7.690	-2,420	-1,360	-0,400

Regarding the DIF for age groups, Appendix - Table 7 presents the results found. As seen, only INM 04 (“Do you think this brand symbol/logo is easy to remember?”) showed significant DIF with moderate effect size (DIF Contrast = 0.48). The items with greater difficulty (POQ 02 and BRI 04) reflect that potential consumers need a greater latent trait for positive confirmation. This result shows that, for consumers, the visual signs used by brands do not reflect the strategic positioning sought.

To better understand this difficulty in items POQ 02 ("After seeing the image above, would you be able to differentiate the services of this company from other companies?) and BRI 04 ("Do you believe that this brand symbol/logo is easy to remember? "), the results of the item functioning differential analysis (DIF) support the premise that this INM 04 item presents varying DIF values when q values are observed for subjects with different age groups (Callegaro Borsa et al., 2012). The literature suggests that short-term recognition losses in adults may be linked to the activity performed in situations where attention is divided (Craik, 2018), a fact that may have affected the participants of the older age group during the questionnaire in a digital environment.

Table 7. Differential Functioning of the Item (DIF) of the proposed instrument.

Items	mantel chi-square	Sig.	DIF Contrast
POM 01	.058	.8084	0 .26
POM 02	.090	.7630	0.00
POM 03	2,000	.1573	-0.63
POM 04	.272	.6015	0.12
POM 05	2,000	.1573	-0.19
POM 06	.2727	.6015	-0.25
POM 07	1,000	.3173	1.00
POM 08	.024	.8759	-0.23
POM 09	2,000	.1573	-0.55
PQM 01	.960	.3270	0.23
PQM 02	1,058	.3035	-0.32
PQM 03	2,454	.1172	0.46
PQM 04	.058	.8084	-0.28
PQM 05	.615	.4328	-0.16
INM 02	.510	.4751	0.08
INM 03	3,240	.0719	0.21
INM 04	4,840	.0278	-0.48
DCM 01	.250	.6171	0.10
DCM 02	3,240	.0719	-0.10
DCM 03	.6207	.4308	-0.63
DCM 04	.9245	.3363	0.33
DCM 05	9,680	.8084	0.26

Note: sig. = p value.

Figure 2 illustrates the relationship between the instrument's characteristic curve and the standard errors of the items in the instrument. The solid Instrument Information line (θ) represents the scale information function. The overall scale provided more information in the range 0 ≤ θ -2 ≤ +4, dropping to values close to 6. The instrument's standard error dotted line provides a visual reference on how the estimation precision varies in θ, with smaller values corresponding to better estimation accuracy.(Bean & Bowen, 2021a). For errors, the accuracy of the estimated score of the proposed instrument is adjusted in the range between 0 ≤ θ ≤ +6.

Another required item that asked a higher level of q for positive endorsement (agree and agree) was WtB01, where the consumer is willing to pay a higher price (premium) up to the 10% limit. The results are close to those obtained because of D'Amico et al. (2016) when analyzing the probability that a consumer will pay (or not) a premium for a product with sustainable certification and that the brand equity sustainability individually does not promote the consumer's interest in paying more for the product. corroborating the findings by Rahmani et al. (2019).

The information functions for each item are best illustrated by the item characteristic curves (ICCs), as shown in Figure 3a. These curves show that item information is not a static quantity but conditional on the item's difficulty levels. For example, as shown in the item-person map, item WtB 01 shows more significant progress toward the respondent's endorsement level (q) for positive responses to the questionnaire (I agree and agree).

The study contributes to the branding literature by proposing and verifying a model that examines the associations of latent variables that form brand equity (brand quality perception, strategic brand positioning, willingness to purchase and Innovationinnovation in retail) in brands that develop sustainable activities. From a model thus proposed, it is possible to understand the nomological network that connects the aspects of branding and sustainability of ecologically responsible enterprises.

Figure 3b describes the characteristic curve of the CCE scale. The curve shows that, for the analyzed instrument, a score of q = -1 translates into an expected score of 30 points, whereas a value of q = 3.4 translates into an expected valid score of 90 points.

On the other hand, item SBP 07, which presented the lowest level for positive endorsement (I agree and agree), shows that the consumer after knowing all the information about the brand and agreeing with the value of the products, plus a maximum of 10% premium increase, shows a tendency to consume the brands presented in the survey. It is important to note that the items SBP 07 (q = -1.28) WtB 02 (q = -1.18) presented a low level q, which signals positive endorsements on the Likert scale (agree and totally agree) and shows that, after facing all brand information added to the agreement with the prices of the products, this potential consumer lus a maximum of a 10% premium increase, showing desire to consume the brands presented in the survey.

The instrument information curve briefly explains how well items, in general, provide statistical information about the latent trait. The curves are mathematical functions of each other where Standard Error (θ) = 1 – √ Information (θ). Higher information along the θ scale leads to lower standard errors, resulting in more accurate θ estimates. In addition, the scale information values can be used to calculate which level of θ the instrument reliably fits best. The range of information obtained by the validated items being between 0 ≤ θ ≤ +4 indicates that the instrument best applies to potential consumers who tend to respond moderately (between disagree and agree).

The transformation of the endorsement estimates q present in the Characteristic Curve of the Instrument makes it possible to verify that the test participants, on average, responded 50% above the positive score of the scale > 4 (I agree on the Likert scale). The transformation to the original scale metric provides a more familiar frame of reference to interpret the sum of scores, (Bean & Bowen, 2021b), having properties favorable to reliability in the analysis of the latent trait, improving and making the approach in scores more accurate (Edwards & Wirth, 2009). Regarding the effects of the ceiling and floor, no participants were identified or questions that corroborated this occurrence for this instrument.

The final scale with 22 items, available in electronic version, has good psychometric properties. Pilot questionnaires based on the work of Netemeyer et al. (2004), Yoo et al. (2017), Grigorescu et al. (2019), and Lin (2015) in a total of 32 items covering five dimensions were reduced to 20 items covering four dimensions. Each dimension contains items about the perception of product quality, the perceived positioning of the brand, the willingness of the potential consumer to buy, and the innovation perceived in the branding of the analyzed brand.

The initial results suggest that, among the dimensions of sustainable brands, they exert a positive influence on the variable result of brand value for the consumer when it is aimed to add premium value to products. The study findings are in line with the results of previous authors (Ng et al., 2014; Shanti & Joshi, 2021) suggesting that this scale is capable of assessing data similar to those proposed in previous scales in English.

Although this research has the objective of developing and validating a psychometric scale, the present findings contribute to the scientific findings that studies sustainable brands, as effective branding plans require broad knowledge about the perception of consumers and potential consumers regarding the dimensions responsible for the strategic positioning of brand equity.

The proposed scale proved to be psychometrically solid and showed the expected relationships with known variables when associated with the perception of potential consumers regarding validity and reliability by standards. Therefore, the validated instrument can be a valuable research tool in the observation of the perception of Brazilian consumers in different sets of their population and in the evaluation of branding management for future sustainable brands.

This scale proved to be useful for company managers in order to develop their business in a sustainable way and continuously, as well as brand managers aimed at consumers of forest products. It is recommended that future studies should investigate further evidence of validity, including additional measures of cognitive functioning, modeling by structural equations and qualitative comparative analysis (QCA) with qualitative data from stakeholders involved in the brands, applying them in group exams to further test its usefulness.

This study yields several recommendations related to sustainable brand branding research. These include the positive perception of sustainable products, the acceptance of paying a premium price of up to 10% for products from sustainable brands, and the differentiation of logo memorization for different ages.

Therefore, we hope that this study will serve as a starting point for future research to build and expand our knowledge on this scientifically interesting and management-relevant topic.

POTENTIAL COMPETING INTERESTS

The authors declare no competing interests.

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Consumer perception of brand equity based on environmental sustainability - development and validation of a scale

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