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Brand authenticity influence on young adults’ luxury sneakers brand preference: the mediating role of brand image


It is of interest to examine how consumers perceive luxury brands with the different elements of authenticity, leading to brand image, for companies to create and build a brand that is conducive to influence purchase intention. Through a proposed conceptual research model, brand authenticity and brand image as the predictor variables, with brand preference as the outcome variable, the study investigates the causal relationship of these constructs, where eight (8) determinants of brand authenticity are assessed. Findings of the research support all nine proposed hypotheses, as a result, indicated that brand authenticity and brand image influence consumers’ brand preference. The same model development for the current study can be applied in other geographical zones and in different contexts to prove its generalisability. This study assists fashion marketing practitioners and brand managers to remain sustainable and better identify the factors that influence brand authenticity and brand image on brand preference of luxury sneakers among young adults in South Africa. The rapid globalization and increase in competition has caused a shift in the luxury industry so marketers need to focus on the authenticity of a brand to influence consumers’ brand preference. Theoretically, this study adds to the limited academic literature on the theory of reasoned action and the brand equity theory, with regard to branding.


There is an estimated increase in 2020 from US$285.1 billion to US$388 billion in 2025, for the luxury goods market globally [33]. The three biggest markets for luxury goods in 2020, are forecast as America, China and Japan, respectively. However, the COVID-19 pandemic has impacted the market growth as revenues for this industry were expected to drop drastically, in comparison to 2019. The industry is further disturbed by reductions in discretionary spending, the uncertain economic and political environments, as well as reduced international travel, with decreased GDPs and employability levels. In 2021, the growth for this market is expected to continue, supported by China’s recovery with the digital platforms gaining maturity, generated by the demand created from Generation Y and Z. It is assumed that Asia will witness the most amount spent, followed by Europe, North America, South America, Middle East and Africa [35]. Although there is a lack of buying power compared to the affluent customers, even the middle-class customers are participating more in luxury usage. This is shown in the significant growth of luxury brands.

Africa’s powerful economic growth and increasing population have resulted in a constantly increasing affluent class that falls under the expanding demand for luxury goods. The rapid growth of luxury brands has also resulted in a side effect, increasing counterfeit products, both deceptive (buyer is not aware) and non-deceptive. (Buyer is aware but buys it anyway because it is sold at a cheaper price) [26]. South Africa’s footwear producers, wholesalers and retailers are suffering because of difficult economic conditions. The South African Footwear and Leather Industry Association numbers indicate that local requests for footwear in 2018 was 264 million pairs, with local production making up less than 1/4 of the demand.

The objective of the study is to develop a deeper understanding of brand authenticity and how it influences young adults’ brand preference on luxury sneaker brands. There is still a gap in this area of study due to the various possibilities and influencing factors of buying behaviour, resulting in purchase intentions and brand loyalty, especially in South Africa, a developing country. The study is based on the brand equity theory and the theory of reasoned action [3, 12]. It applies a quantitative research methodology. Practically, for fashion marketers, the study touches on and provides recommendations on the value of brand authenticity, image and preference.

Review of literature

Theoretical framework

Brand equity theory

Brand equity refers to the value creation as a result of distinctive attributes towards a brand from marketing. In other words, if a good or service does not hold the given name (brand), certain results from marketing would not be evident [8]. The brand equity concept has been investigated since the early 1990s by several researcher. Brand equity forms a part of the foundation for differentiation. As a result, based on non-price competition, there will be a competitive advantage which can also lead to profitability in the long run. The brand equity theory is based on three elements, namely brand awareness, brand loyalty, brand association, brand image and perceived quality. Brand awareness is the power of a brand’s existence in the consumers’ mind, with regard to brand recognition and brand recall. The core of a brand’s value is based on brand loyalty. Therefore, the aim for loyalty segments is to be strengthened in terms of the level of intensity, as well as the size. L Brand association refers to the information retained in the consumer’s memory. The brand image refers to the symbolic meaning created in the consumer’s mind by attaching pictures to given product offerings. The extent to which a certain product or service can meet the customers’ desired expectations is related to the perceived quality [2].

Theory of reasoned action (TRA)

The theory of reasoned action (TRA) proposes that one’s viewpoint towards behaviour and subjective norms is a function that influences behavioural intention, resulting in certain behaviour [12]. In other words, the way a person behaves in a particular manner is predicted through their intention whereby the person’s intention will be stronger to carry out the behaviour if the attitude and subjective norms are more favourable. The theory takes into consideration that the influence of attitude on behaviour is limited by other elements. Behavioural intention can be influenced by two main elements to predict attitude and behaviour, namely attitude towards behaviour and subjective norms. The elements that influence behaviour are separated from behavioural intention [26]. Moreover, [12] looked at attitude as two separate components by looking at the behavioural beliefs, as well as the outcome evaluation, where the other element influencing behavioural intention is referred to as subjective norms. Normative beliefs and motivation are the next two components that are formed under subjective norms [8].

Hypotheses development

Originality and brand image

Originality is one of the dimensions that best encompasses brand authenticity and can be incorporated with an image [29]. Hence, an authentic brand is known to be very clear in what it stands for and in differentiating itself from competing brands. The desired good impression of a brand could come up if the brand has a special advantage, positive reputation, recognition, trust and the willingness to give the best service to its consumers in comparison to the competitors [24]. Genuine brands are perceived to be difficult to imitate and are an intangible asset for companies as they can be differentiated and are superior to other competitors. Young adults’ external perception of brands is usually based on product benefits such as the originality of the sneaker brand. This is determined, based on marketing activities, social influences, as well as personal experiences [7]. The present study, therefore, proposes the following hypothesis:


There is a positive relationship between originality and brand image.

Genuineness and brand image

Genuineness is a dimension most commonly used to describe authenticity; it is indicated with the congruence of concepts, benefits and actions through honest behaviour. It is being seen as “condensed and judgmental perceptions about a brand fixed in the psyche of relevant external target groups”, for example, young adults’ preference for luxury goods. Consumers perceive luxury sneaker brands as being original. Originality is based on a consumer’s impression of the brand based on their experiences or perception. Therefore, as a result, the present study proposes the following hypothesis:


There is a positive relationship between genuineness and brand image.

Continuity and brand image

Brand authenticity is dependent on the view of its antecedents, such as congruity, continuity, uniqueness, and can be included with the image. Continuity is an integral part of creating authentic brands. As brands portray consistency when their communication and experiences are the same as their identity, inspiration and value [11]. Consistency is seen with configuration; it is the visual identity of the brand, including the name, logo, slogan, fonts, as well as the colours of the brand [6]. A brand’s image becomes established when this is memorable and creates a lasting influence on the customer by showing authenticity. Therefore, continuity influences brand image. Hence, the following hypothesis is proposed:


There is a positive relationship between continuity and brand image.

Perceived value and brand image

The perceived value presents a close correlation with the core value and organizational value and is an important connection between internal and external brand procedures, whereas brand image refers to the customers’ interaction with a product. In this particular instance, it is their interactions with luxury sneakers or the acquired experiences in the past which forms their attitudes and beliefs towards the brand. Brand image comprises all tangible and intangible perceptions, inferences and beliefs about a brand that the customers have. Further, Jeng [25] suggest a successful brand image as the creation of value for customers. As a result, a customer’s preference for luxury sneaker brands is normally dependent on the perceived value acquired, i.e. customer’ trade-off between perceived benefits and perceived costs. Thus, this study proposes the following hypothesis:


There is a positive relationship between perceived value and brand image.

Brand logo and brand image

Brand logo is usually the first aspect that comes to customers’ minds when thinking about a brand. It forms a part of the visual equity contributing significantly to brand image. Kaur and Kaur [23], as well as several other researchers, suggest in their findings, that the organization’s brand logo enhances the brand image [20]. A logo that is memorable and easily identified is more likely to create an impression in the consumer's mind. Therefore, as a result, the present study proposes the following hypothesis:


There is a positive relationship between brand logo and brand image.

Brand integrity and brand image

The relationship between brand integrity and brand image should be taken into consideration as well. Djailani and Tan [10] studied the structure between both brand integrity and brand image; it was found that there is a relationship between the two constructs. Further, the connection between the brands to the consumers is authenticity and transparency, as a reflection of brand integrity. Therefore, the brand’s unique selling proposition can be created around the brand image through honesty. Thus, this study proposes the following hypothesis:


There is a positive relationship between brand integrity and brand image.

Brand signature and brand image

Foroudi [13] recommends that brand signature is a helpful tool to manage brand image and performance, according to research. Designers can represent their personality and standards through the brand signature. Hence, the aspirational categorization with authenticity, class and exclusivity of brand signature. Additionally, brands can be recalled easily with the aid of their specific signatures. Therefore, it is proposed that the following hypothesis is relevant to this study:


There is a positive relationship between brand signature and brand image.

Brand heritage and brand image

Strong customer relationships can be developed and maintained through heritage because of the strategic value in the long run. It is important to know the significant impact that brand heritage has on a brand’s overall image. Based on attitudinal elements of brand strength, the research indicates the relationship and importance of brand heritage, as well as the development towards its brand image. Corporate brand image has gained interest and been created through the link from the past. The present study thus proposes the following hypothesis:


There is a positive relationship between brand heritage and brand image.

Brand image and brand preference

Brand preference can result due to superiority, although in the luxury segment, all brands consider themselves to be superior. Therefore, it has become increasingly important to understand the determinants of brand preference. Brand image and brand preference have been proven to have a strong positive relationship in several studies [21]. Moreover, luxury sneaker brands appeal to individuals who are usually very self-conscious and desire the attention when showing off status. This is a result of brand certainty (such as quality), which provides a sense of security or trust for and towards the brand. Hence, the following hypothesis is proposed:


There is a positive relationship between brand image and brand preference.

Conceptual model

The conceptual model utilized proposes brand authenticity and brand image as the predictor variables, with brand preference as the outcome variable. A total of eight determinants of brand authenticity include originality, genuineness, continuity, perceived value, logo, integrity, brand signature and brand heritage were investigated (Fig. 1).

Fig. 1
figure 1

Conceptual model


Study design

The research design provides a framework for how marketing research data are collected and analysed. To ensure that the study achieves its intended purpose, the selected research design should be suitable for the research problem and the objectives of the study. A positivist research approach was applied for the study with a deductive approach to test the proposed hypotheses. The paradigm chosen for this study is the positivism research philosophy. It suggests an independent outlook on the researcher and the area of the study because it examines the relationship between variables. The research started with a detailed investigation of related literature, accompanied by the constructed research framework. Subsequently, to evaluate the relationships between the dependent and independent variables, research hypotheses were developed. This study is based on empirical research using quantitative data, it followed a positivist research philosophy paradigm and used a deductive research approach. During a specific time, to test the sample population of consumers, it is often cross-sectional and includes observations of the participants [5]. A quantitative research method has been selected as a suitable approach for this research, as it allows a large sampling of data to be collected by using a questionnaire [9]. For this study, by collecting quantitative data, the conceptual model was tested empirically using a cross-sectional research method. As a result, the deductive research approach requires the researcher to initiate the measurable constructs to be tested empirically [5]. For this reason, the current study takes on a deductive method, in which the hypotheses that have been adapted from the existing literature are empirically assessed using quantitative data.

Study sample characteristics

The sample that is chosen through the totality of units is known as the population of interest. In other words, it can also be referred to as the target population of the study, which is the entire group on whom research is being conducted. With regard to the subject of study and analysis of units, it can range from human beings to the different characterized items. It is important for the researchers of the study to clearly state the specific requirements and elements that are necessary for the selected target. For this study, the population of interest is the young adults in Generation Y and Generation Z, specifically focusing on those between the ages of 18 to 35, of all genders, races and income groups who have owned a Nike sneaker over the past two years. The reason behind this target population is simply because it is assumed that these young adults are most likely to buy and wear luxury branded sneakers. The majority (97%) of the participants who completed the survey met this requirement. The demographics that were used to undertake the study included the respondent’s gender, age, financial status, the number of Nike sneakers owned, as well as the reason for buying or owning. Most respondents were female, comprising 51% of the total sample (100 respondents), while 49% of the respondents were male. The gender of the population who took part in this study was equally distributed with only a 2% difference. The majority of the respondents were somewhat financially well-off, making up 58% of the sample. 20% of the respondents were not so well-off, while 18% were very well-off and 4% were not well-off at all. Based on the results, it can be concluded that the majority of the sample can afford the sneakers from the luxury brand (Nike). The reasoning behind 54% of the respondents for buying or owning Nike sneaker(s) is based on both functional and symbolic reasons.

Data collection strategy, population, sampling and sample size

The number of respondents who are surveyed is referred to as the sample size. In the sampling process, the determination of the sample size is one of the most essential aspects to outline. Studies from different researchers suggest that the larger the sample size, the more accurate the study will be, because it will more correctly represent the respondents of the specified target population. An empirical study and quantitative technique were used in the collection of data from 100 respondents, by distributing online surveys through Google Forms. However, for this study, partial least squares is used to perform the analysis, with the smart partial least squares (SmartPLS) software. The “10-times rule” method is widely used to determine the estimated minimum sample size in partial least squares (PLS-SEM/PLS-PM) thus, assuming that the sample size should be more than ten times the maximum number of the structural model’s latent variable links. After data have been collected, the results are examined to determine the reliability, validity and quality of the measurement scale to test the proposed hypotheses. Online surveys for research are known to be more flexible, visual and engaging.

Research instrument

The five-item Likert scales (strongly disagree to strongly agree) as the measuring instrument with 48 items were adapted from existing scales to suit this study. Section A compromised demographic data and Section B was to test the variables that make up the conceptual model, namely, brand image, brand preference, brand authenticity and the eight determinants of brand authenticity: Originality, genuineness, continuity, perceived value, logo, integrity, brand signature and brand heritage (as indicated in Table 1). With regard to measurement scales, several different ones were adopted in this study, including a Perceived Brand Authenticity (PBA) scale measuring three out of four dimensions: integrity, brand signature, and continuity adapted from Bruhn et al. [6]. Akbar and Wymer’s [4] scale was adapted to suit Genuineness. The brand signature (BS) scale is adapted from Foroudi et al. [14] and Klink [25], and Fritz et al. [15] was used for Brand Heritage (BH). Salinas and Pérez’s [30] scale was adapted to suit Brand Image (BI).

Table 1 Measurement instrument items and statements

Reliability and validity

Reliability was executed to check for consistency of the measurement instrument, so that it yields consistent results without random error. Validity of the scale was measured using the average variance extracted (AVE). The inter-construct correlation matrix is utilized to assess the existence of discriminant validity, whereby high and accurate results of discriminant validity are less than 0.8. In this present study, an HTMT approach was adopted to determine the discriminant validity [19].

Data analysis

The proposed hypotheses in the conceptual model were tested using a structural equation modelling (SEM) approach through the smart partial least squares (PLS) statistical software. The research model and the hypotheses for this study was statistically tested and examined in this section. The data collected and conducted for this study were analysed using SmartPLS, a software that measures and assesses the structural model. The structural equation modelling technique for this method is partial least squares (PLS-SEM/PLS-PM), it is the second generation. It is a variance-based approach and instead of the maximum likelihood estimation procedure, PLS-SEM utilizes an ordinary least-squares regression method.

Data analysis strategy

The Statistical Package for the Social Sciences (SPSS) is used by different types of researchers to process and analyse complicated statistical data drawn from surveys. The structural equation modelling analyses the causal relationship through smart partial least squares (PLS). SmartPLS is software with a method of structural equation modelling (SEM). Complicated cause-effect relationship models with latent variables can be estimated using SmartPLS with the regression analysis. This statistical test was used to help determine that the sample size does not require a large sample; it operates efficiently with limited sample sizes and complicated models. In addition, there was no identification problems using PLS-SEM with single-item constructs, and it can simply operate with measurement models that are reflective, as well as formative. In comparison to other structural equations modelling techniques, it has a larger statistical strength when estimating the parameter and the estimated yields of mediation effects are more precise. The requirements for the PLS sample size indicates that the minimum sample should be ten times greater than the number of indicators of the construct, as well as the links pointing towards specific latent variables in the developed structural model.

Ethical considerations

A formal ethical clearance process was ensured, and the researchers abided by the ethical standards. The researchers used a consent form for voluntary informed consent from the respondents. Researchers must follow the rules and regulations required to research responsibly and ethically. Privacy and consent must remain a top priority throughout the research. The respondents would have given consent to their participation and were adequately informed about the current study. The participation to partake in the survey is voluntary and could be withdrawn at any time during the research. The data are kept safe in a password protected computer or laptop and will be deleted three years after conducting the study. The researchers attempted, by all means, to abide by the ethical principles prescribed for research so that no harm to the respondents' wellbeing was done.


This section discusses the conceptual research model by looking at the measurement model in assessing the reliability and validity of variables, as well as the quality of the model.

Measurement model assessment

As illustrated in Table 2 all constructs have been tested, justified and analysed indicating the reliability and validity results.

Table 2 Measurement model assessment

Each variable in the measurement instrument was examined, using the standardized Cronbach alpha coefficient to ensure reliability. The Cronbach alpha value that is known to be reliable is generally more than 0.7. From the results, it shows that all of the variables indicate reliability at satisfactory levels. In relation, the lowest Cronbach coefficient was detected to be 0.710 (GEN), while the remaining values are between 0.721 and 0.923. In other words, the measures that were utilized in this study are confirmed as reliable because all variables tested using Cronbach’s alpha surpassed the recommended threshold of 0.7. The internal reliability was also evaluated, using the composite reliability (CR) index. The acceptable index for CR should be greater than 0.7. The results for the CR test indicate that the indices were between 0.823 and 0.943, surpassing the approximate criteria used in the literature. The total number of variance accounted by the latent variables in the indicators shows the average value extracted (AVE) estimate. The standardized factor loading values in the CFA results are utilized to calculate AVE. The desired value for AVE to be valid should be more than 0.5. Table 2 indicates acceptable levels of scale reliability because the AVE for all the variables is between 0.536 and 0.811. The AVE and factor loadings are used to test the convergent validity [17]. According to Schwab [32], convergent validity indicates high integration of more than one differential procedure of the same construct. That is, it provides an interpretation of the correlating scales in the same direction, with other measures of the same construct, and thus offers an indication of the validity of the construct and hence, the item loadings should be greater than 0.5 for validity acceptability [17]. The following SmartPLS output, generated as indicated in Fig. 2, depicts the conceptual model and its outer loadings graphically.

Fig. 2
figure 2

Conceptual model

In terms of the analysis and the interpretation of the reliability and validity tests conducted, it is important to compare the results to the threshold values, to determine if the tests are reliable and valid. However, several influencing factors affect the results of the reliability and validity tests. One is required to take all the possible options into consideration when interpreting the results. The acceptable thresholds are as follows: Cronbach alpha > 0.7 (derived from SPSS 26); CR > 0.7; AVE > 0.5, HTMT < 0.85.

Heterotrait–Monotrait Ratio (HTMT)

The Heterotrait–Monotrait ratio (HTMT) has a set standard of less than 0.85. It is used to assess the discriminant validity, in terms of inter-item correlations. In other words, the HTMT ratio is used in the model if a latent construct shares greater variance with its indicators in comparison to another. Based on the SmartPLS output, the discriminant validity was insufficient for all the variables because some of them did not meet the criteria of having a HTMT value of below 0.85 but were, instead, higher. Discriminant validity looks at the uniqueness of a construct, whether the situation represented by a construct is unique and not represented by the other constructs in the model [19]. Thus, it is commonly used to examine the relationship between latent variables. Consequently, it proves the heterogeneity between the different constructs. The inter-construct correlation matrix is utilized to assess the existence of discriminant validity, where high and accurate results of discriminant validity are less than 0.8. In this present study, an HTMT approach was adopted to determine the discriminant validity [19]. According to Henseler et al. [18], the HTMT score should be between the confidence interval values of -1 and 1, to achieve discriminant validity. It can be evaluated by assessing the cross-loadings among constructs, by using Fornell–Larcker criterion and Heterotrait–Monotrait ratio of correlation (HTMT).

PLS Algorithms–R-Squared (R2)

The PLS algorithms in terms of R-squared (R2), looks at the dependent variables to explain the variance for a linear model, depending on the study’s context. Generally, 0.75, 0.50 and 0.25 are known as strong, moderate and weak, respectively. Partial least squares structural equation modelling (PLS-SEM) is an advanced next-generation analysis approach that has rapidly gained attention in management, marketing, management information systems and other social science disciplines. R‐squared and adjusted R‐squared are statistics that come from evaluations that form the basis of the general linear model. It signifies the proportion of variance in the outcome variable, explained by the predictor variables in the sample (R‐squared) and an estimate in the population (adjusted R‐squared). More specifically, R-squared provides the percentage variation in y, explained by x-variables. The range is zero to one (i.e. 0–100% of the variation in y can be explained by the x-variables. According to Hair et al. [16], the acceptable level of R2 is dependent on the area on research with greater values, showing a higher level of estimated accuracy. In some areas such as consumer behaviour, values of 0.20 are considered high, while in scholarly research that focuses on marketing issues, 0.75, 0.50 and 0.25 are described as significant, moderate and low in respective [16]. By utilizing the PLS longitudinal dimension reduction, it can fully take into account the level of correlation between feature variables and the dependent variables and can solve the small sample problems caused by cluster analysis. Through cluster analysis, similar samples are classified as one close subclass, and it is easy to train more precise neural network models.

Bootstrapping (t-statistics)

Bootstrapping refers to the t-statistics calculated from the data collected to evaluate the significance level of the structural relationships. An approach to validating a multivariate model is by drawing a large number of subsamples and estimating models for each subsample. Estimates from all the subsamples are then combined, providing not only the “best” estimated coefficients (For example, the means of each estimated coefficient across all the subsample models) but their expected variability and thus, their likelihood of differing from zero, that is, are the estimated coefficients statistically different from zero or not. This approach does not rely on statistical assumptions about the population to assess [26]. Moreover, bootstrapping entails taking random samples and randomly replacing dropped values and will give slightly different standard error estimates on each run. Also, it estimates the point variance and the entire distribution and thus bootstrapping is required when the research purpose is distribution estimation. Some PLS packages such as SmartPLS makes use of bootstrapping [19].

Bootstrapping uses resampling methods to compute the significance of PLS coefficients. It may be used with the traditional PLS estimation algorithm. For bootstrapped significance, a probability level of 0.05 means there is one chance in 20 that a result as strong or stronger in absolute terms will occur due to chance of sampling (taking another sample from the data). Although bootstrapping will handle any distribution, the researcher cannot generalize to the population unless the sample is randomly drawn from the population. Otherwise, the researcher can generalize only to the data at hand. The bootstrapped estimates the addressed problem of the non-normal distribution of data but does not address the problem of non-random sampling [19]. Figure 3 illustrates the bootstrapping results, as summarized in Table 3.

Fig. 3
figure 3

Bootstrapping results

Table 3 Structural relationships and hypothesis testing

Path coefficient (\(\upbeta\))

Path coefficient values are extracted from the PLS-SEM algorithms to examine possible causal relationships in the structural equation modelling approach between statistical variables. The path coefficient is derived from Wright [34], through the application of a diagram-based method to determine the association between variables in an assorted system. Convergent validity is set when the association between the latent variables is 0.80 or more. For the path coefficients in the structural models to be statistically significant, the values should be 1.96 at 0.05 level or more. It is mainly utilized to examine causal relationships where the criteria are formed, based on values interpreted from grounded theory. From the perspective of construct interrelationships, the degree or strengths of the relationships between the four constructs are reflected by the total effects and path coefficients in the structural models. For the results to be statistically significant, the path coefficient values should be more than 1.96 at 0.05 level. The results are presented in Table 3.

Upon analysing the results in Table 3, it was indicated that all nine hypotheses are supported however, not all of them are significant. The relationship in hypothesis 9 that tested the relationship between brand image and brand preference was the strongest (0.869), indicating a strong positive influence. Moreover, hypotheses 1, 2, 4 and 7 also indicated that there is a significant relationship between the variables. The path coefficients show a weak to moderate relationship of 0.470, 0.212, 0.150 and 0.205, respectively.

The weakest relationships were found to be hypotheses 3 (continuity and brand image), 5 (logo and brand image), 6 (integrity and brand image) and 8 (brand heritage and brand image) with path coefficients of − 0.073, − 0.029, 0.010 and 0.079, respectively. The significant levels for these four hypotheses are greater than 0.05, indicating that they are not significant and there is a negative relationship between the variables. To conclude, the results are supported by brand image and brand preference indicating the strongest relationship. Furthermore, hypothesis 1, 2, 4, 7 and 9 are significant and hypothesis 3, 5, 6 and 8 are not significant.


Originality has been demonstrated to have a strong relationship with brand image. The analysis of the data collected indicated that originality has a supported and significant influence on brand image. This was also confirmed by Morhart et al.’s [29] research findings that proved that originality is a dimension of brand authenticity. In other words, brands like Nike are entrenched in the minds of young adults and are difficult to imitate. Genuineness significantly influences brand image. The relationship between genuineness and brand image was found to be strong and significant. These findings are consistent with literature, where Meffert et al. [28] posit, in their study, how originality is an external view of a brand that is being seen as hypercritical perceptions about a brand embedded in the minds of consumers. The weakest relationships were found to be continuity and brand image. The results are contrary to Schallehn et al. [31] and Eggers et al. [11] on continuity being an integral part of creating authentic brands. The relationship between perceived value and brand image has proven to be significant. These results corroborate Aaker’s [1] research findings which measured dimensions of brand authenticity through three dimensions that found perceived value as a dimension of brand image. The research findings have shown that brand image is indeed a creation of value for customers. The influence brand logo has on brand image was found to be supported but not significant. The results contradict studies by several other researchers’ [27, 20] findings that suggest brand logo enhances brand image. The findings further contradict Foroudi et al.’s [14] empirical research which investigated the relationship between brand logo on brand image in a financial corporate context.


The rationale for this study was to investigate the crucial role brand authenticity plays in young adults’ purchase intention. In addition, how a positive brand image and optimistic attitude towards the brand itself results in brand preference of luxury sneakers. Since the concept of authenticity has no specific definition with several contributing attributes, this study has expanded on those attributes. This research is important because luxury brands are being held at higher regard when it comes to being authentic, thus, indicating superiority in every aspect. Further, young adults prefer luxury brands for their value creation, which is both product-related and non-product-related. Brand authenticity has been postulated to having product-related and non-product-related attributes. The findings of this study will contribute to how brands can use the different attributes of brand authenticity to connect with young adults at an emotional level.

Practical implications

Young adults are looking for real experiences, they want brands they support and that resonate with who they are. In a digital world when we are bombarded with marketing messages from competing brands, the elements that make a brand stand out are their authenticity and honesty. Product-related attributes are simply not enough. Therefore, young adults can be proud of being associated with a specific brand. In addition, brand authenticity is an important factor in brand choice for young adults. It is always crucial for brands to bridge the gap between consumer expectations and the experience of the brand. Luxury sneakers’ brand preference among young adults provides an opportunity for marketers when it comes to understanding the practical aspects of branding that lead to brand preference. Additionally, it can also assist brands and other service companies in terms of what to implement to enhance their brand image. This study includes both academic and practical marketing implications. Previous literature has proven that brand association leads to brand loyalty, brand authenticity leads to brand trust and brand image leads to brand preference. Therefore, this study provides empirical evidence, suggesting that brand authenticity and positive brand image leads to brand preference.

Theoretical implications

The theory of reasoned action suggests that a person’s attitude towards behaviour and subjective norms is a function that influences behavioural intention, resulting in certain behaviour. This study has found that brand preference is influenced by behavioural intentions, how a person behaves is predicted through their intention. The theoretical contributions helped ground the hypothesis, concerning how brand authenticity and brand image are factors that affect the brand preference of luxury sneakers among young adults, where the person’s intention to perform the behaviour will be stronger if the attitude and subjective norms are more favourable concerning brand preference. This study supports that young adults’ purchase intention is a result of favourable brand attitudes towards luxury sneakers. Young adults’ buying behaviour in terms of luxury sneakers from Nike is a result of a combination of attitudes, decisions, intentions and preferences in the marketplace. The study has proven that it is essential for people to have strong intentions through positive evaluations, resulting in stronger brand preference [3, 12]. Aaker’s brand equity theory suggests that brand equity is the value created as a result of distinguishing attributes towards a brand from marketing efforts, a set of brand assets and liabilities linked to a brand. The study found that Nike’s brand name and symbol collectively add to the value provided by the symbolic meaning of luxury sneakers created in the young adults’ minds, by attaching pictures to the given product offerings.

Future research directions

Theories on brand image, particularly those that comprise subjective constructs, such as brand authenticity, are constantly evolving due to ongoing research. Therefore, there is a gap in literature in terms of the widely accepted measurement scale for brand authenticity. The findings of this study have indicated that there are relationships between brand authenticity and five other constructs. Future research could measure these constructs in a different context. In other words, to address other limitations mentioned, they can be performed in a qualitative approach. There can be more human-to-human interaction in the approach of this study.

Availability of data and materials

Not applicable.



Theory of reasoned action


  1. Aaker DA (1996) Measuring Brand Equity across Products and Markets. California Management Review. 38:102–120.

  2. Aaker DA, Biel AL (2013) Brand equity & advertising: advertising’s role in building strong brands. Psychology Press, New York

    Book  Google Scholar 

  3. Ajzen I (1991) The theory of planned behaviour. Organ Behav Hum Decis Process 50:179–211

    Article  Google Scholar 

  4. Akbar MM, Wymer W (2017) Refining the conceptualization of Brand Authenticity. J Brand Manag 24:14–32.

  5. Babbie E (2008) The basics of social science research. Thomson Wadsworth, New York

    Google Scholar 

  6. Bruhn M, Schoenmüller V, Schäfer D, Heinrich D (2012) Brand authenticity: towards a deeper understanding of its conceptualization and measurement. Adv Consum Res 40. Available at SRN:

  7. Burmann C, Zeplin S (2005) Building brand commitment: a behavioral approach to internal brand management. J Brand Manag 12:279–300

    Article  Google Scholar 

  8. Cobb-Walgren CJ, Ruble CA, Donthu N (1995) Brand equity, brand preference, and purchase intent. J Advert 24(3):25–40

    Article  Google Scholar 

  9. Creswell JW, Creswell JD (2017) Research design: qualitative, quantitative, and mixed methods approaches, 4th edn. Sage, Newbury Park

  10. Djailani I, Tan CC (2015) Studying the Interrelationship Structure between Brand 3i (Brand Identity, Brand Integrity, and Brand Image) and Brand Trust and Attitude in Islamic Marketing Context: A Case with Rumah ZAKAT Indonesia, 3rd SUIC International Conference: The Trend of Global Business in the New Digital Era. Silpakorn University International College, Bangkok, Thailand.

  11. Eggers F, O’Dwyer M, Kraus S, Vallaster C, Güldenberg S (2013) CEO Brand Authenticity Measure [Database record]. APA PsycTests.

  12. Fishbein M, Ajzen I (1975) Belief, attitude, intention, and behaviour: an introduction to theory and research. Addison-Wesley, Reading

    Google Scholar 

  13. Foroudi P (2019) Influence of brand signature, brand awareness, brand attitude, brand reputation on hotel industry’s brand performance. Int J Hosp Manag 76:271–285

    Article  Google Scholar 

  14. Foroudi P, Melewar TC, Gupta S (2014) Linking corporate logo, corporate image, and reputation: an examination of consumer perceptions in the financial setting. J Bus Res 67(11):2269–2281

    Article  Google Scholar 

  15. Fritz S, See L, Perger C et al (2017) A global dataset of crowdsourced land cover and land use reference data. Sci Data 4:170075

  16. Hair JF, Black WC, Babin, BJ, Anderson RE (2010) Multivariate Data Analysis. 7th Edition, Pearson, New York.

  17. Hair JF, Sarstedt M, Ringle CM, Mena JA (2012) An assessment of the use of partial least squares structural equation modelling in marketing research. J Acad Mark Sci 40(3):414–433

    Article  Google Scholar 

  18. Henseler J, Ringle CM, Sinkovics RR (2009) The use of partial least squares path modeling in international marketing. Sinkovics RR and Ghauri PN (Ed.) New Challenges to International Marketing (Advances in International Marketing, Vol. 20), Emerald Group Publishing Limited, Leeds, pp. 277–319.

  19. Henseler J, Ringle CM, Sarstedt M (2015) A new criterion for assessing discriminant validity in variance-based structural equation modeling. J Acad Mark Sci 43(1):115–135

    Article  Google Scholar 

  20. Hofstede A, van Hoof J, Walenberg N, de Jong M (2007) Projective techniques for brand image research. Qual Market Res Int J

  21. Hu J, Liu X, Wang S, Yang Z (2012) The role of brand image congruity in Chinese consumers' brand preference. J Product Brand Manage

  22. Jeng W, He D, Oh JSO (2016) Toward a conceptual framework for data sharing practices in social sciences: A profile approach. Proc Assoc Inf Sci Technol.

  23. Kaur H, Kaur K (2019) Connecting the dots between brand logo and brand image. Asia-Pac J Bus Adm 11(1);68–87.

  24. Keller KL (1993) Conceptualizing, measuring, and managing customer-based brand equity. J Mark 57(1):1–22

    Article  ADS  MathSciNet  Google Scholar 

  25. Klink RR (2003) Creating meaningful brands: the relationship between brand name and brand Mark. Mark Lett 14:143–157.

  26. Ligaraba R, Nyagadza B, Dorfling D, Zulu Q (2022) Factors influencing re-usage intention of online and mobile grocery shopping amongst young adults in South Africa. Arab Gulf J Sci Res.

    Article  Google Scholar 

  27. Maziriri ET, Nyagadza B, Mabuyana B, Rukuni TF, Mapuranga M (2023) Marketing cereal to the generation Z cohort: what are the key drivers that stimulate consumer behavioural intentions in South Africa? Young Consumers 24(5):615–648.

    Article  Google Scholar 

  28. Meffert H, Burmann C, Kirchgeorg M (2015) Marketing: Grundlagen marktorientierter Unternehmensführung Konzepte - Instrumente – Praxisbeispiele, Springer Gabler Wiesbaden. 978-3-658-02344-7.

  29. Morhart F, Malär L, Guèvremont A, Girardin F, Grohmann B (2015) Brand authenticity: an integrative framework and measurement scale. J Consum Psychol 25(2):200–218

    Article  Google Scholar 

  30. Salinas EM, Pérez JMP (2009) Modelling the brand extensions influence on brand image. J Bus Res 62(1): 50–60.

  31. Schallehn M, Burmann C, Riley N (2014) Brand authenticity: model development and empirical testing. J Prod Brand Manag 23(3):192–199.

  32. Schwab K (2017) The fourth industrial revolution. Portfolio Penguin.

  33. Statista (2020) 2020 in numbers: Statista trend report on major events shaping 2020. Accessed 21 Feb 2024

  34. Wright S (1921) Correlation and causation. J Agric Res 20:557–585

  35. Zhang L, Cude BJ, Zhao H (2020) Determinants of Chinese consumers’ purchase intentions for luxury goods. Int J Market Res

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The researchers express unwavering gratitude to the respondents who provided responses during the surveys. Their invaluable efforts cannot be ignored.


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NL analysed and interpreted the data regarding brand authenticity influence on young adults’ luxury sneakers brand preference. JC and NFN performed the interpretation of analysed data, and BN was a major contributor in writing the manuscript. All authors read and approved the final manuscript.

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Correspondence to Brighton Nyagadza.

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Ligaraba, N., Cheng, J., Ndungwane, N.F. et al. Brand authenticity influence on young adults’ luxury sneakers brand preference: the mediating role of brand image. Futur Bus J 10, 33 (2024).

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