Customer-Based Brand Equity Drivers: A Leading Brand of Beer in Estonia

Abstract

Based on the trust/commitment theory and the customer-based brand equity theory, this study aims to ascertain which of the brand equity drivers of A. Le Coq beer have an impact on attachment and its overall brand equity in the Estonian brewery market. In order to achieve this goal, an empirical study was conducted based on the 17 customer-based/consumer-based brand equity models: the 15 brand equity models, including the beer/beverage brand equity models, the 2 internal brand equity models, as well as 3 other related models. The study utilised a sample of convenience of 120 University of Tartu students. The questionnaire was placed on Google’s online survey administration service. Confirmatory factor analysis (CFA) through AMOS29 was used for testing the fit of the model and covariances (through AMOS29) were used for testing the hypotheses. Additionally, t-test analysis was used for the differences in the means between the demographic characteristics and the items of the model. The results show that brand meaning has a strong positive effect on attachment strength, which significantly influences relationship factors—commitment, trust, and satisfaction. Another major finding is that the relationship factors—commitment, trust, and satisfaction—play a significant role in the development of the brand equity of A. Le Coq beer. This study provides useful insights for brewery marketing managers by exploiting the strong positive relationships found between beer brand equity drivers, such as the strong positive relationships found within consumers of beer, i.e., the relationships between brand reputation and brand image, brand meaning and attachment strength, attachment strength and commitment, attachment strength and satisfaction, attachment strength and trust, satisfaction and brand equity, commitment and brand equity, and trust and brand equity. This finding contributes to the literature on brand equity related to the Estonian environment. Five differences in demographic characteristics seem to play a role in designing strategies by the management teams of different brands for increasing the consumption of their competing brands of beer. A replication of a model previously used for a non-product is part of the novelty of this paper. In addition, all the examined relationships are found to be positive and significant, which provides a contribution to the existing literature.

1. Introduction

Although customer-based brand equity drivers have attracted a lot of attention from scholars in recent years, the amount of research which analyses the impact of brand equity components on overall brand equity based on empirical data is quite different. One particular study was conducted by Aaker (1996, p. 118), who measured brand equity across products and markets and concluded that brand equity consisted of loyalty (price premium and satisfaction/loyalty among those who have used the brand), perceived quality/leadership (perceived quality and leadership), associations/differentiation (perceived value, personality, organisation, and differentiation), awareness (brand awareness), and market behaviour (market share and price and distribution indices). Park et al. (2010, p. 33) showed “the uniquely strong effects of brand attachment”, and, later, this paper was established as the main reference for the brand attachment construct. Allaway et al. (2011, Table 15, p. 198) developed customer-based brand equity drivers in the supermarket industry, which consisted of effort, service level, product, programme prices, layout location, and community. Furthermore, Jillapalli and Jillapalli (2014) constructed, advanced, and empirically tested the customer-based brand equity of professors. Both customer-based brand equity models by Dennis et al. (2016), who subsequently investigated the brand equity drivers of a chosen higher education institute (HEI), and Jillapalli and Jillapalli’s (2014) customer-based brand equity model for professors are considered as starting points for this research. Finally, Keller (2016) revealed other important branding topics, such as “understanding brand purpose, narratives, and story-telling, understanding brand architecture and brands more holistically, understanding how to develop timeless, inclusive brands, understanding how brand elements can work together, and understanding how to effectively and efficiently track brands” (Keller 2016, pp. 13–14).

This study aims to ascertain whether the brand equity components of A. Le Coq beer have an impact on its overall brand equity in the Estonian brewery market.

Regarding the research gaps of this study, our investigation is based on a single brand, specifically A. Le Coq beer, which is the leading brand in Estonia and in the Baltic countries. A previous empirical investigation of the antecedents of brand equity in the beverage industry in Turkey (Atilgan et al. 2005) found only the relationship between perceived quality and brand equity to be significant. Moreover, it is worth mentioning the two previous studies focusing on CEE’s brewing industry by Larimo et al. (2006) and Larimo et al. (2011). Another study focused on the Estonian beer market was performed by Larimo et al. (2013), but not on the antecedents of the brand equity of beers in Estonia. Therefore, the main research gap of the current study is to empirically investigate the antecedents of the brand equity of the A. Le Coq brand of beer in Estonia. Moreover, there are five studies on the dimensions of beer brand equity in different countries, such as in Thailand by Aimkij and Mujtaba (2010), in Spain by Porral et al. (2013), in Uganda by Agaba and Emenike (2019), in Nigeria by Odeleye (2021), and in Italy by Francioni et al. (2022). The study by Agaba and Emenike (2019) tested the five dimensions of brand equity introduced by Aaker (1991), including perceived quality, brand awareness, brand associations, brand loyalty, and other proprietary brand assets, in a regression analysis. Both studies by Atilgan et al. (2005, p. 246) and Porral et al. (2013, p. 86) using structural equation modelling empirically tested the four antecedents of beer brand equity, namely, perceived quality, brand loyalty, brand awareness, and brand associations. The study by Francioni et al. (2022, p. 510) used structural equation modelling to test three antecedents of overall brand equity, namely, brand awareness/associations, perceived quality, and brand loyalty. The three studies by Atilgan et al. (2005), Porral et al. (2013), and Francioni et al. (2022) showed that the impact of brand loyalty to brand equity was important, significant, and positive. Bearing in mind these results, the present study uses the internal brand equity model by Dennis et al. (2016) to empirically test the antecedents of the beer brand equity of A. Le Coq by utilising the structural equation modelling of the CFA. Through these investigations, competing brands of beer can find a lot of interesting results when modifying their strategy in these markets. None of the existing research has focused on the successful brand of A. Le Coq beer in the Estonian market and/or in Baltic countries.

Beer has become the most popular alcoholic beverage in Estonia, where its consumption was 80 million litres in 2018 or the per capita consumption was 60.51 litres in 2018 (based on the total population of Estonia in 2018—https://www.worldometers.info/world-population/estonia-population/), which was higher than the EU-27 average (Brewers of Europe 2020). Estonia held second place in the Baltic Sea region and outranked Finland in beer consumption in 2016 (Brewers of Europe 2020). As reported by the Brewers of Europe, in 2018, 93.5 million litres of beer was consumed in Estonia, which was 90.5 million and 88.8 million litres of beer in 2017 and 2016, respectively. The per capita consumption of beer in Estonia has remained quite stable over the years between 2015 and 2018, with around 81 million litres of beer on average (Brewers of Europe 2020).

The Estonian brewery market is the smallest both in Central and Eastern Europe (CEE) and the Commonwealth of Independent States (CIS), and is highly concentrated, with two major players—A. Le Coq and Saku—together possessing around 80 percent of the market share (Larimo et al. 2013).

Market research by Williams and Marshall Strategy 2020 indicates that the beer market in Estonia, which has a high degree of competition, was worth USD 269 million in 2014. Recent market trends indicate that the local beer market is expanding, with the number of local microbreweries increasing by around 20 percent per year (Invest in Estonia 2019). Even though the market share of these microbreweries is only around 1.5% of the overall Estonian beer market, they can still trigger competition in the market, and thus benefit customers in the long run (Invest in Estonia 2019).

A. Le Coq has survived already for more than 200 years and through many wars. During this period, A. Le Coq invested a lot into new technology, innovation, and machinery. Therefore, it can be called a sustainable brewery, managing the two world wars, Russian rule, and, later, the difficult transition years after the new independence in 1991.

The A. Le Coq brewery received its name from Albert Le Coq, whose roots historically go back to Germany and England. Imperial Stout beer export from London to St. Petersburg was replaced by production in the then Russian Tsarist province of Tartu, Estonia, in 1913 (Cornell 2017). With the independence of Estonia in 1918, A. Le Coq’s market became Estonia. Having survived Soviet annexation in 1940, German occupation in 1941–1944, and Soviet rule again, the former name of the state-owned brewery subsequently disappeared from use. When Estonia regained its independence in 1991, the state-owned brewery was soon afterwards privatised, in 1995. Since 1997, A. Le Coq has belonged to the Olvi Group (Finland) (Cornell 2017). Other sister companies under their local names in the Baltics also belong to the same group. The trademark of the brewery A. Le Coq was restored in 1999. Soft drinks and mainly light beer are now produced. The annual sales volume is close to one hundred million euros. Exports account for approximately a quarter of A. Le Coq’s production (A. Le Coq 2021).

Even though beer has maintained its leadership position as the most popular alcoholic beverage in Estonia, there is a lack of academic research on beer brand value from the customer’s perspective. A case study conducted by Larimo et al. (2013) investigated the factors that affect the market share of Estonian breweries, mainly concentrating on companies’ marketing strategies and discussing their marketing-mix activities. At the same time, it must be taken into consideration that consumers’ purchasing behaviour is strongly affected not only by the price or quality of a product, but also its brand equity (Porral et al. 2013). Therefore, investigating the customer-based brand equity drivers of A. Le Coq beer will lead to insightful findings.

The acquisition of A. Le Coq in 1995 by the Finnish company Olvi Oyj brought back not only the company’s trademark, which had been changed to Tartu Õlletehas during the Soviet era, but also its heydays, which then seemed far away because of a long period of unsuccessful privatisation attempts. This handover was considered a milestone for A. Le Coq, since it played a major role in regaining its market share and competitiveness. As a result of immediate investments by the parent company, Olvi Oyj, beer production capacity hit 30 million litres in 1999, up from 13.5 million litres in 1995 before the acquisition (Larimo et al. 2013).

Therefore, the reason behind the selection of A. Le Coq for the present study is the company’s long-term existence of more than two centuries, its strong reputation for being the most innovative beer manufacturer, and being the market leader in Estonia. However, in this paper, the authors concentrate solely on the brewery side of A. Le Coq, and the findings do not have relevance to the corporate brand of A. Le Coq and other individual brands of A. Le Coq for different types of products. According to Larimo et al. (2011), “CEE has played a significant role in the growth strategies of European breweries. This process has been largely helped by the economic and political changes taking place in the region since 1989” (Larimo et al. 2011, p. 89). Furthermore, the dramatic increase in the consumption of beer after the collapse of the USSR in Central–Eastern Europe (see Larimo et al. 2006, Table II, p. 374), specifically in Estonia, has helped A. Le Coq to increase its production of beer alongside the European brands which entered the country.

The following two research questions are set for this study: (i) What are the brand equity drivers of a beer brand, i.e., A. Le Coq beer? and (ii) Which are the significant and important relationships?

In order to answer the above research questions, an empirical study was conducted based on Jillapalli and Jillapalli’s (2014) and Dennis et al.’s (2016) customer-based brand equity models. The objectives of this paper were firstly to compare the possible differences between different segments of the sample. Secondly, by performing a regression analysis, we unveil the relationships between brand equity components and the formation of the brand equity of a beer brand, and indicate whether these relationships are significant or not. Thirdly, the confirmatory factor analysis (CFA) approach was employed for testing the badness-of-fit of the model to the data and evaluating the reliability versus the validity.

This research contributes to the existing literature on beer brand equity, and specifically on the internal brand equity. The purpose of this study is to explore the significant and important factors of brand equity in the beer industry in Estonia. In particular, we focused on the A. Le Coq brand in the Estonian market, and we have added valuable insights to the current body of knowledge in this field. The management team of A. Le Coq beer brand should think more constructively about how to improve the customer’s commitment to, trust in, and satisfaction with the A. Le Coq beer brand. One can think that trust interlinks with commitment, and there are ways to boost this relationship by advertising within the relationships of the attachment strength–commitment–trust–satisfaction–beer brand equity of A. Le Coq. This study reveals that there are important, positive, and significant relationships between brand reputation and brand meaning, trust and beer brand equity, commitment and beer brand equity, and satisfaction and beer brand equity. This study shows the importance of the four antecedents of attachment strength, feelings towards the beer brand, commitment/trust relationship of consumers towards the beer brand, and consumers’ satisfaction with the beer brand and their important, positive, and significant relationships towards the beer brand equity.

In addition, this research tests the ecological validity of the customer-based brand equity model devised by Jillapalli and Jillapalli (2014) in order to see whether the results would hold in a different context—the Estonian brewery market. Since a case study approach is employed for the example of A. Le Coq, this research contributes to the scarcely investigated area of the brand equity of breweries, as well as providing practical implications for brewery marketing managers, such as employing policies with useful insights into the development of strong positive relationships between some customer-based brand equity drivers. Additionally, a further contribution of this study lies in the fact that it uses established constructs from other contexts—specifically, measuring both the brand equity of universities, Dennis et al. (2016), and the brand equity of professors, Jillapalli and Jillapalli (2014)—and transferring them to the case of A. Le Coq beer in Estonia.

The rest of the paper is structured as follows. A literature review and conceptual model are presented and discussed in Section 2, where the main brand equity concepts are presented and hypotheses are introduced. Then, we explain the adopted methodology in Section 3, alongside data description and descriptive statistics. Section 4 is dedicated to the findings and their interpretation. Moreover, Section 5 discusses the findings and, in particular, that the tested model reveals some internal strong positive relationships that managers should exploit. Finally, the paper concludes by providing an overview of the study in Section 6, and theoretical and managerial implications together with limitations and future research are discussed.

2. Literature Review

2.1. Theoretical Background

The theory of trust/commitment and the customer-based brand equity theory are used in this study. The study considers that brand equity is variable, depending on the trust/commitment relationship between the consumers of A. Le Coq beer and the specific firm.

2.1.1. Trust/Commitment Theory

What is most important is how weak or strong the trust/commitment relationship is between consumers and the firm. In a study by Hwang and Burgers (1997, p. 70) on the properties of trust, it is pointed out that trust is “a necessary but not sufficient condition for cooperation and that trust supports cooperation through easing two very different types of risks, namely, the risk of being victimized and the risk of losing a trustworthy partner”. Additionally, the authors conclude that: “while trust eliminates all fear, full trust does not eliminate all greed” (Hwang and Burgers 1997, p. 70).

Furthermore, Hwang (2006, pp. 423–38) argues that the trust and time horizon are important in a relationship. According to that study, the time horizon depends on the existing and future environmental parameters that the relationship may encounter.

Morgan and Hunt (1994) were two of the authors who used the theory of trust to commitment affect, meaning that “successful relationship marketing requires relationship commitment and trust”, and they considered that “commitment and trust are key mediating variables in their model” (Morgan and Hunt 1994, p. 20). The model by Morgan and Hunt (1994) shows that there is a positive and significant effect of trust on commitment. According to Morgan and Hunt (1994), trust and commitment are at the centre of any successful relationship with customers. Similar relationships exist in another model developed by Money et al. (2007, Figure 2.4, p. 42), showing that non-material benefits are a mediator between commitment and trust. This study shows a positive and significant relationship between trust, non-material benefits, and commitment. In a recent study, Brown et al. (2019, p. 155) found that while trust enhances commitment, commitment can also erode trust.

2.1.2. Customer-Based Brand Equity Theory versus Consumer-Based Brand Theory

In the existing literature of brand equity, there are two similar theories, namely, the customer-based brand equity theory and the consumer-based brand equity theory. The difference between them is that the customers are specific persons with some distinct characteristics, whereas the consumers include persons from a wide range of characteristics.

The customer-based brand equity theory was developed by Aaker (1991) and Keller (1993). The conceptual model by Aaker (1991) includes four constructs/dimensions, namely, brand awareness, perceived brand quality, brand associations/differentiation, and brand loyalty. Brand loyalty is considered the most effective dimension for brand equity.

Keller (1993), based on Aaker’s (1991) work, offers an alternative model. The conceptualisation of CBBE by Keller (2003) suggests that “customer-based brand equity occurs when the consumer has a high level of awareness and familiarity with the brand and holds some strong, favourable, and unique brand associations in memory” (Keller 2003, p. 67). Keller’s (2003) CBBE is defined as “the differential effect of brand knowledge on consumer response to the marketing efforts of the brand”. Brand knowledge consists of two dimensions, namely, brand awareness and brand image. Park and Srinivasan (1994) argued that brand associations are the foundations of brand equity, and they divided brand equity into attribute-based and non-attribute-based factors. According to Erdem and Swait (1998), CBBE is defined as the value of a brand signal to consumers.

In their model, Tolba and Hassan (2009) linked customer-based brand equity with brand market performance based on data from the US automotive market. Their findings showed that customer-based brand equity constructs correlated with brand market performance.

From the point of view of Christodoulides and De Chernatony (2010), two research streams, namely, cognitive psychology and information economics, are complementary, and they proposed a definition of CBBE that contained elements from both, i.e., “a set of perceptions, attitudes, knowledge, and behaviors on the part of consumers that results in increased utility and allows a brand to earn greater volume or greater margins than it could without the brand name” (Christodoulides and De Chernatony 2010, p. 48).

A study by Allaway et al. (2011) concluded that the drivers of customer-based brand equity for supermarkets are the service level, product quality and assortment, programmes for rewarding patronage, effort expended in keeping customers, prices, layout, and location.

Furthermore, Nam et al. (2011) introduced a CBBE model for services based on seven dimensions, namely, physical quality, staff behaviour, ideal self-congruence, brand identification, lifestyle congruence, brand satisfaction, and brand loyalty. This model excluded brand awareness, although previous models by Aaker (1991) and Keller (1993) used this dimension.

In his reflections on customer-based brand equity, Keller (2016) put emphasis on the online and digital developments that have happened since his article in 1993 (Keller 1993).

A study by Veloutsou et al. (2013) revealed a taxonomy of measures/dimensions for consumer-based brand equity among 13 studies during the period 1993–2010. Moreover, in a study by Christodoulides et al. (2015), the dimensions of CBBE, specifically, brand awareness, brand associations, and brand loyalty, could not always be clearly identified in all national contexts, i.e., the UK, Germany, and Greece. Furthermore, in another study by Pham (2019), companies in Vietnam showed that creating brand awareness, as well as brand loyalty and increasing perceived quality, were the most influential antecedents of the consumer-based brand equity of consumer goods retailers.

According to Chatzipanagiotou et al. (2016), “Consumer-based brand equity focuses on consumers and represents positive business outcomes” (Chatzipanagiotou et al. 2016, p. 5479). The fit of their configural model (Chatzipanagiotou et al. 2016, Figure 1, p. 5481), which was composed of a brand building block, brand understanding block, brand relationship block, and CBBE, was not tested, and one wonders if it is practically possible to test it with one or many sets of data, and what the reliability and validity is of this model. Additionally, Chatzipanagiotou et al. (2019) tested, in Greece and Germany, their five-construct conceptual framework from 2016 (Chatzipanagiotou et al. 2016), including the brand building block (brand personality, brand nostalgia, brand heritage, brand quality, brand competitive advantage, and brand leadership), brand understanding block (brand awareness, brand associations, brand reputation, and brand self-connection), brand relationship block (brand trust, brand relevance, brand intimacy, and brand partner quality), overall brand equity, and consumers’ behavioural outcomes (intention to pay more for the brand, intention to recommend the brand, and intention to repurchase the brand). More recently, Veloutsou et al. (2020) tested their initial model from 2016 (Chatzipanagiotou et al. 2016) consisting of four dimensions, i.e., the brand building block (brand personality, brand nostalgia, brand heritage, brand quality, brand competitive advantage, and brand leadership), the brand understanding block (brand awareness, brand associations, brand reputation, and self-brand connection), the brand relationship block (brand trust, brand relevance, brand intimacy, and brand partner quality), and the overall brand equity, for unliked brands. Their findings showed that self-brand connection and partner quality were the key links for the deconstruction and restoration of CBBE.

In recent years, various studies have investigated product categories and brands, and commented on consumer-based brand equity (Hakala et al. 2012; Çifci et al. 2016; Chatzipanagiotou et al. 2016; and Sarker et al. 2021).

Hakala et al. (2012) explored the relationships between consumers’ awareness of brands, attitudes related to brand equity, and changes in cultural context. They found that the four dimensions of brand equity co-vary depending on the cultural context.

Çifci et al. (2016) compared the validity of the models of Yoo and Donthu (2001) and Nam et al. (2011) and found that Nam et al.’s (2011) model had better validity than Yoo and Donthu’s (2001) model. Kumar (2013) found that the brand experience dimensions (sensory, affective, behavioural, and intellectual) positively influenced the five brand equity dimensions (brand awareness, brand association, perceived quality, brand trust, and brand loyalty).

Finally, three authors, namely, Veloutsou, Christodoulides, and Chatzipanagiotou, have, in recent years, developed a five-construct model indicating the dimensions of CBBE (Chatzipanagiotou et al. 2016, 2019; Veloutsou et al. 2020). It is worth noting that the study by Veloutsou et al. (2013) includes a table with the various dimensions of CBBE in relation to 13 studies during the period 1993–2010.

2.2. CBBE in Different Conceptual and Empirical Models during the Period 1993–2021 and Other Related Constructs

Table 1. Determinants of models on customer-based brand equity versus consumer-based brand equity, internal brand equity, and related constructs during the period 1993–2023 *.

A/A	Authors and Year of Publication	Name of Journal	Sample Size	Key Constructs in the Model	Comments on the Model
1.	Veloutsou (2023)	Journal of Brand Management	Conceptual	Brand-building and audience response framework	Consumer-based brand equity: brand-building block
2.	Francioni et al. (2022)	British Food Journal	401 Italian student beer consumers	Dimensions of overall brand equity were brand awareness/associations, perceived quality, and brand loyalty. COO image, WOM, and brand distinctiveness were influencing brand awareness/associations, perceived quality, and brand loyalty	Brand loyalty was the most important dimension impacting significantly and positively on overall brand equity. The COO image was the most important construct, impacting significantly and positively on the perceived quality
3.	Odeleye (2021)	International Journal of Business, Economics & Management	175 employees of Guinness Nigeria Plc (100), Edo State (52), and Nigerian Breweries Plc (23)	The effect of brand equity on marketing performance	Brand loyalty and brand associations were positive and significant factors of marketing performance
4.	Sarker et al. (2021)	Journal of Retailing and Consumer Services	A self-administered questionnaire was distributed to airline passengers; 778 surveys were returned	A consumer-based service brand equity (CBSBE) model in the airline industry	Service brand equity
5.	Veloutsou et al. (2020)	Journal of Business Research	300 questionnaires	Consumer-based brand equity	Fifteen measures of brand constructs, namely, brand heritage, brand personality, brand nostalgia, brand perceived quality, brand leadership, brand competitive advantage, brand awareness, brand associations, brand reputation, brand self-connection, partner quality, brand intimacy, brand trust, brand relevance, and overall brand equity
6.	Zollo et al. (2020)	Journal of Business Research	326 followers of luxury fashion brands on social media	Social media marketing to consumer-based brand equity relationship	Social media marketing activities, brand experience, and social media benefits associate with consumer-based brand equity
7.	Agaba and Emenike (2019)	International Journal of Management and Network Economics	312 respondents using different beer brands (Eagle, Nile, Club, Senator, and local beers)	The effect of brand equity (brand awareness, brand association, perceived quality, brand loyalty, and other proprietary brand assets) on competitive advantage	Brand awareness, brand association, perceived quality, and brand loyalty have significant and positive effects on the competitive advantage
8.	Iglesias et al. (2019)	Journal of Business Research	1739 customers	Sensory brand experience influence brand equity	Sensory brand experience, customer affective commitment, and customer satisfaction associate with brand equity
9.	Chatzipanagiotou et al. (2019)	International Business Review	Samples from Greece (312) and Germany (301)	Consumer-based brand equity process in different countries	The model includes a brand building block, a brand understanding block, a brand relationship block, the overall brand equity, the intention to pay more, brand recommendation, and the intention to repurchase
10.	Augusto and Torres (2018)	Journal of Retailing and Consumer Services	280	A full mediation of consumer-based brand equity between brand attitude and the willingness of customers to pay premium prices	The model includes brand attitude, electronic word-of-mouth, consumer–brand identification, and consumer-based brand equity, which influence the willingness to pay a premium price
11.	Keller (2016)	Academy of Marketing Science Review	Conceptual	Customer-based brand equity	Brand resonance model: it includes, in a pyramid, resonance, judgments, feelings, performance, imagery, and salience
12.	Çifci et al. (2016)	Journal of Business Research	Samples from Turkey (285) and Spain (236)	Consumer-based brand equity	It compares three CBBE models, namely, Yoo and Donthu (2001), Nam et al. (2011), and the extended model of Nam et al.
13.	Chatzipanagiotou et al. (2016)	Journal of Business Research	15 semi-structured interviews with senior brand managers and consultants	This study identifies CBBE as an overall system with three major blocks, namely, brand building, brand understanding, and brand relationship	All the six elements of brand building are core causes leading CBBE Only two elements of brand understanding, namely, brand associations and self-connection, contribute as core causes to CBBE All four elements of brand relationships are core causes leading to CBBE
14.	Dennis et al. (2016)	Journal of Business Research	Online survey of 605 students and graduates	The role of brand attachment strength in education	Internal brand equity
15.	Sandbacka et al. (2013)	Journal of Services Marketing	Single case study	Company brand identity and company brand image	External branding process
16.	Veloutsou et al. (2013)	Journal of Product & Brand Management	15 interviews in three countries (5 from each country), namely, Greece, Britain, and Germany	Dimensions of CBBE used in academic research	Four categories of measures can define brand equity. These are as follows: the consumers’ understanding of brand characteristics, consumers’ brand evaluation, consumers’ affective response towards the brand, and consumers’ behaviour towards the brand
17.	Porral et al. (2013)	European Research Studies	346 questionnaires were sent randomly to people residing in Spain	Dimensions used as antecedents of beer brand equity were perceived quality, awareness, associations/image, and loyalty. Dependent variables of beer brand equity were both the purchase intention and the willingness to pay a premium price	Beer brand image as a dimension had the most significantly positive impact on beer brand equity. A significant positive impact on beer brand equity was found for all the dimensions analysed, namely, brand awareness, perceived quality, and loyalty
18.	Hakala et al. (2012)	The Journal of Product & Brand Management	University students, as follows: USA (198), Finland (129), France (231), and Sweden (185)	Dimensions of consumer-based brand equity	The four dimensions of brand equity co-vary depending on the cultural context. There is a relationship between top-of-mind awareness and the national context
19.	Nam et al. (2011)	Annals of Tourism Research	378 customers	Consumer-based brand equity, brand loyalty, and consumer satisfaction	The study investigates the mediating effects of consumer satisfaction between CBBE and brand loyalty
20.	Baumgarth and Binckebanck (2011)	Journal of Product & Brand Management	201	Salesperson’s personality, salesperson’s behaviour, product quality, non-personal communication, brand perceptions, brand strength, and brand loyalty	Sales force impact on B2B brand equity. There are significant positive relationships
21.	Marquardt et al. (2011)	Journal of Services Marketing	Two case studies	Brand management and brand equity	B2B services–branding process
22.	Allaway et al. (2011)	Journal of Product & Brand Management	659 usable questionnaires	Two brand equity outcome factors and eight brand equity drivers	Brand equity outcome factors: emotional loyalty and fanaticism. Drivers of brand equity: service level, product quality and assortment, programmes for rewarding patronage, effort expended in keeping customers, prices, layout, location, and community involvement
23.	Aimkij and Mujtaba (2010)	Chinese Business Review	379 males and females who had at least occasionally had a beer regularly and in larger quantities	Five factors of brand equity are examined, including brand awareness, brand liking, brand purchase intention, brand satisfaction, and brand loyalty	Brand equity measurement in the beer industry of Thailand
24.	Baumgarth and Schmidt (2010)	Industrial Marketing Management	93	Brand orientation, internal brand commitment, internal brand knowledge, internal brand involvement, internal brand equity, and customer-based brand equity	Internal brand equity
25.	Park et al. (2010)	Working paper MKT 16-10	Study 2: 108 undergraduate marketing students; Study 3: 140 undergraduate marketing students; Study 4: Pretest: 41 telephone interviews and 52 customers; Sampling effort generated 701 responses	Brand attachment and brand attitude strength	Two critical brand equity drivers
26.	Rauyruen et al. (2009)	Journal of Services Marketing	294 firms	(a) Drivers of customer loyalty: habitual buying, trust in the service provider, and perceived service quality; (b) Service loyalty: purchase intentions and attitudinal loyalty; (c) Brand equity: customer share of wallet, price premium 1, and price premium 2	A conceptual model of service loyalty and brand equity
27.	Burmann et al. (2009)	Marketing Theory	Conceptual model	Identity-based branding: major constructs: brand identity, brand image, brand promise, brand behaviour, brand expectations, and brand experience	Interaction between internal and external stakeholders
28.	Tolba and Hassan (2009)	Journal of Product & Brand Management	5598 usable observations	Attitudinal loyalty and satisfaction were the strongest predictors of brand preference and intention to purchase	Customer-based brand equity constructs, except knowledge equity and value, were correlated with brand market performance
29.	Kuhn et al. (2008)	Qualitative Market Research: An International Journal	Two studies: Study 1: 5 Eastern Australian councils; Study 2: 30 Eastern Australian local authorities	A revised customer-based brand equity pyramid for B2B	Suitability and limitations of Keller’s customer-based brand equity model and its applicability in B2B markets
30.	Dawes (2008)	Industrial Marketing Management	142 service providers and 71 customers	Brand awareness, brand image, relationship role, and brand equity	A conceptual model among service providers and customers. Significant positive relationships
31.	Atilgan et al. (2005)	Marketing Intelligence & Planning	255 usable questionnaires of students at a local university	Brand equity dimensions: perceived quality, brand loyalty, brand associations, and brand awareness. Brand equity was the dependent construct	The strongest path was the brand loyalty to brand equity relationship. Brand loyalty had a positive and significant effect on brand equity. However, the effects of perceived quality, brand awareness, and brand associations on brand equity were positive but very weak
32.	Lynch and de Chernatony (2004)	Brand Management	Conceptual model	Internal brand development and communication, external brand communication, brand information processing in the buying centre, and marketing variables	Building B2B brands with balanced functional and emotional values
33.	Yoo and Donthu (2001)	Journal of Business Research	1530 American, Korean-American, and Korean participants	Multidimensional CEBE scale	Four-dimensional model: brand loyalty, perceived quality, awareness, and associations. Three-dimensional model: brand loyalty, perceived quality, and awareness/associations
34.	Berry (2000)	Journal of the Academy of Marketing Science	4 cases	Company’s brand, external brand communications, and customer experience with company–brand awareness and brand meaning–brand equity	A service-branding model
35.	Aaker (1996)	California Management Review	Conceptual model	Ten measures of brand equity: loyalty measures: price premium, satisfaction/loyalty, perceived quality; leadership measures: perceived quality, leadership, associations; differentiation measures: perceived value, brand personality, organisational associations; awareness measures: brand awareness; market behaviour measures: market share, price, and distribution indices	The ten measures are structured and motivated by the four dimensions of brand equity, namely, loyalty, perceived quality, associations, and awareness (Aaker 1991). They are influenced by the Brand Asset Valuator of Young and Rubicam and EquiTrend of Total Research
36.	Chaudhuri (1995)	Journal of Product & Brand Management	199 shoppers at a campus store	Brand attitudes and habit–brand loyalty; brand equity outcomes	A model of attitudes, habit, loyalty, and brand equity outcomes
37.	Keller (1993)	Journal of Marketing	Conceptual model	Brand knowledge and its constructs, i.e., brand awareness and brand image, associate with customer-based brand equity	Customer-based brand equity

Note *: Compiled by the authors.

reveals the evolution of customer-based brand equity versus consumer-based brand equity since the seminal work of Keller (1993) on customer-based brand equity. Among the 37 articles included in Table 1, there are at least 17 which refer to CBBE (see A/A: 1, 4–6, 8–13, 16, 18–19, 28–29, 33, and 37), 9 on brand equity (see A/A: 20–22, 25–26, 30, and 34–36), 5 on beer brand equity (2–3, 7, 17, and 23), 1 on beverage brand equity (31), 2 on internal brand equity (see A/A: 14 and 23), and 3 on related issues (see A/A: 15, 27, and 32). In our research in Table 1, there are four research streams, including studies on CBBE, internal brand equity, brand equity, and others on related issues. However, none of them have studied the successful brand of A. Le Coq in the Estonian market.

2.3. Brand Equity Conceptualisation and Its Dimensions

This study defines the brand concept as a combination of components that helps us to recognise and distinguish the products and services of one company from its competitors in the market (Kotler and Keller 2012). Nowadays, products and services are not the only subjects of branding efforts, and branding people, places, events, and political parties represents a new trend which has gained a substantial amount of success.

Being a popular concept, the definition of a brand has been explained in different ways over the years. The American Marketing Association (2021) defines a brand as “a name, term, design, symbol or any other feature that identifies one seller’s goods or service as distinct from those of other sellers” (American Marketing Association 2021). Roper and Fill (2012) approach the notion of the brand from an emotional point of view, and describe it as a combination of sentiments that comes to one’s mind when the brand is mentioned or remembered. Payne et al. (2009) emphasise the customer experience in the creation of value in the service industry and equate the brand with customer experience.

Branding activities are considered important in order to be competitive in the marketplace, since strong brands positively affect companies in the long run and help them to acquire a bigger market share and more profit. Powerful brands can be built up through robust marketing campaigns in the long term and thus cause the creation of competitive advantage in the market (Yoo et al. 2000). Evaluation of a brand in order to indicate how much value it brings to customers is required in making strategic marketing decisions and has thus led company managers and marketing researchers to emphasise the brand equity concept (Aaker 2009; Kotler and Keller 2012; Porral et al. 2013). Brand equity is a well-investigated concept by scholars from both marketing and other disciplines, where two main motivations are noticed in most of the conducted research (Keller 2012). From a financial point of view, brand equity is utilised for assessing the brand’s overall monetary value for merger and acquisition transactions, and is also used as an accounting element, more precisely, as a substantial asset in the balance sheet (Keller 2012). The second perspective, which is also the main focus in this research, examines the brand equity concept from the customer’s standpoint while emphasising its value in the customer’s mind. Keller’s (2012) comprehensive work is considered very important, since the author proposes a conceptual framework for brand equity from the customer’s point of view in drawing attention to how a customer reacts to marketing-mix efforts.

Scholars agree that the brand equity concept has a multidimensional nature, whereby Aaker (1991, 1996) indicates four dimensions, namely, brand loyalty, brand awareness, perceived quality, and brand associations, while Keller (1993) emphasises the role of brand knowledge in association with brand awareness and brand image (Yoo et al. 2000).

Even though the concept of brand equity is well known and widely recognised in marketing circles, not everybody is in agreement, for example, Ehrenberg et al. (1990) with their theory of double jeopardy. Mitchell (1992) suggests that firms should focus on increasing their market share if they want to have a strong brand with a high frequency of repeat buying and many customers, since the concept of brand equity does not exist (as cited in Chaudhuri 1995, p. 26). The theory of double jeopardy can be defined as a situation where popular brands with bigger market shares are chosen more frequently by more customers because of the attention and distribution advantages they receive, whilst small market share brands receive less attention, and therefore only occasional purchases with fewer buyers (Chaudhuri 1995; Fader and Schmittlein 1993). Brand equity in the brewery market has been explored by Dawes (2008) in focusing on empirical generalisations—repertoire buying, double jeopardy, and duplication of purchase—which were also investigated by Ehrenberg and his colleagues in 1990. In order to test the empirical generalisations of Ehrenberg et al. (1990), field research was performed with face-to-face interviews conducted among the attendees of football matches at a stadium in Australia. Dawes (2008) reaches the conclusion that consumers should not be treated as very loyal, since they purchase several beer brands from a bundle of brands. These brands mainly keep large market shares with high levels of loyalty, and instead of competing against only one competitor, they direct all their marketing efforts to the whole market.

However, these two concepts—brand equity and double jeopardy—contradict each other, and Chaudhuri (1995) suggests that both of them have a significant impact on market share and other brand equity outcomes, while drawing attention to the relationship between customer-based outcomes and brand equity outcomes. In summary, the author argues that the “double jeopardy” theory can be observed as a direct relationship, whereas the “brand equity” theory contains intervening factors, namely, brand loyalty, while showing an indirect relationship. Yoo et al. (2000) investigated the impact of selected marketing-mix efforts on brand equity by indicating the relationship between marketing activities and brand equity dimensions, thus the overall brand equity. Meanwhile, a comparative study by Chaudhuri (2002) concentrated mainly on brand reputation and its intermediary role in order to examine the effect of marketing efforts such as advertising on brand equity outcomes. Both studies confirm the tremendous impact of marketing-mix elements on creating brand equity, whilst Chaudhuri (2002) states that brand reputation can be used as an important tool for assessing the brand’s value and overall marketing activities in order to make a strategic managerial decision. Mongkol’s (2014) empirical research suggests that integrated marketing communication (ICM) tools play a crucial role in building strong brand equity by examining a beverage company from Thailand.

Marketing efforts to create strong and positive brand equity do not always result in desirable outcomes, since the recipe for successful branding is not simple and straightforward, but in fact is very complex and requires a comprehensive approach. Burmann et al. (2009) introduce a two-dimensional concept in order to demonstrate the two-sided relationship of branding, which has a dynamic character, while focusing on brand identity and brand image by using Erikson’s (1994) theory of identity. Goi et al. (2014) introduce empirical research for examining branding activities, particularly the brand identity of higher education institutes (HEIs), where the findings suggest a two-dimensional model where visual and verbal identity are emphasised.

Consumers quite often make their purchasing decision influenced by their social environment (Fischer et al. 2010; Grubb and Grathwohl 1967). In their empirical research, Escalas and Bettman (2005) discuss the importance of brand meaning on an individual’s self-perception and how one expresses him/herself in the surroundings by using brands as an instrument. It is suggested that belonging to a certain community can significantly affect the perception of brand image, as well as the independence level and certain characteristics of a brand. Dennis et al. (2016) conducted empirical research aiming to unveil the brand equity of a chosen higher education institute (HEI) by evaluating the relationship between both current and graduated students and the brand attributes of HEIs. The authors emphasise the impact of brand meaning on the brand equity of the higher education institutes in using Jillapalli and Jillapalli’s (2014) empirical study on the professor–brand equity relationship.

Jillapalli and Jillapalli (2014) established their framework based on Keller’s (1993) customer-based brand equity (CBBE) model and relationship marketing theory, aiming to unveil the professor–student relation in the light of branding concepts. They concluded their findings by emphasising the importance of the professor’s brand-building effort and its long-term benefits on both higher education institutes and professors.

A comparative analysis performed by Park et al. (2010) conceptualises the brand attachment notion by defining its elements—brand self-connections and prominence—while mentioning the dissimilarities between brand attachment and brand attitude strength. The authors empirically argue the weight of these elements on overall brand attachment by formulating a measurement method and concluding that both brand self-connection and prominence are vital for brand attachment. Their results emphasise the fact that brand attachment plays a more prominent and effective role as a predictor of consumer behaviour than does brand attitude strength.

Porral et al. (2013) performed empirical research in order to examine the brand equity of local and imported beers in the Spanish market while testing Aaker’s (1991) brand equity model. The authors define brand equity as “an intangible asset, being a source of long-term competitive advantage in the marketplace”. They emphasise that brand equity sources—brand awareness, perceived quality, brand associations, and loyalty—are vital for understanding the brand equity concept clearly, and that they have a significant impact on it, and therefore also on consumer behaviour. Structural equation modelling (SEM) was applied, aiming to identify the possible effects of brand equity on Spanish beer consumers’ behaviour regarding purchase intention and the willingness to pay a premium price for the product. Their findings are in line with Aaker’s brand equity model, where brand equity sources have a significant and positive impact on it, while the brand image is considered to be the most powerful influence.

The empirical research performed by Atilgan et al. (2005), based on Aaker’s (1991) brand equity model, examines the brand equity of specific products in the Turkish beverage market. Similar research was conducted by Vinh (2017) in investigating the brand equity of Heineken in the Vietnamese beer market. The results from both studies seem to be in line with preceding ones, as the impact of brand equity dimensions on the overall brand equity is noteworthy. Vinh et al. (2019) investigated empirically the impact of social media communication on brand equity. According to them, brand awareness/association, perceived quality, and brand loyalty directly influence overall brand equity.

Research by Berry (2000) on the branding activities in service companies stressed how crucial it is to have strong “brand equity”, as these companies do not have any tangible assets that can be used to communicate with potential consumers. The author demonstrated the brand-building efforts by using the “service-branding model”, which displays the building blocks of the service brand and how they are connected to each other, emphasising that brand building is not specific to only tangible products, whilst service companies benefit from branding activities in order to reach customers in providing them with assurance about the service.

While most conceptual and empirical studies concentrate on the product side of the branding, some scholars try to draw attention to the concept of “corporate brand” (Balmer 1995; Hatch and Schultz 2001; Ind 1997; Syed Alwi and Da Silva 2007). The corporate brand seems to have a significant effect on the customer’s purchasing behaviour, where it can be a sign of high quality and satisfaction (Balmer and Gray 2003; Syed Alwi and Da Silva 2007). Syed Alwi and Da Silva (2007) conducted an empirical study in order to find out the locomotive factors of corporate brand image in the online environment by using De Chernatony and Christodoulides’s (2004) “triangle framework of corporate brands”. The authors summarise their research by emphasising the importance of personalisation and security, which can help the company to enhance the corporate brand image in an online setting, while also mentioning the notable impact of customer care and ease of use. In another study, Da Silva and Syed Alwi (2008) revealed that corporate brand image can also have a direct positive relationship with consumer loyalty. Moreover, Sandbacka et al. (2013) discussed corporate brand-building activities in the business-to-business context, and modelled these activities in separate but related blocks. The main focus of the research (Sandbacka et al. 2013) is micro-industrial service companies, where the contribution and participation of all stakeholders in the corporate brand-building process are emphasised.

Brand-building activities are applicable not only to products or services, but also people, events, political parties, places, or even concepts or visions (Kotler and Keller 2012; Kuhn et al. 2008). While most of the literature focuses on branding strategies in the business-to-consumer (B2C) context, Kuhn et al. (2008) endeavoured to uncover the importance of branding in a business-to-business setting and its feasibility. This paper attempts conceptualise the brand equity model in a B2B context using Keller’s (2003) customer-based brand equity model as a basis. We emphasise the observed differences in the purchasing behaviours of organisational buyers, and present a modified version of the CBBE model in order to contribute to branding efforts in the B2B context.

In order to perform successful branding activities, managers should answer the following question: Will these activities positively affect the consumer’s purchasing behaviour, or will there be any significant improvement in the brand–buyer relationship? This is because not every brand responds in the same way to branding efforts depending on its category characteristic, as argued in the research by Fischer et al. (2010). These authors discuss whether branding-making activities hold the same degree of importance for every brand, and meanwhile examine their (referring to brand-making activities) impact on the company’s economic situation. While explaining why brands are important for both consumer and company, two elements are emphasised: the brand’s risk reduction and self-expression aspects. Fischer et al. (2010) suggest a new conceptual framework, namely, BRiC (brand relevance in category), and strengthen it with empirical data relevant for 20 different product categories. Based on the findings, some managerial recommendations are set out that can be beneficial for positioning the company’s brand-building efforts.

2.4. Hypotheses Building and the Conceptual Model

The presented conceptual model for unveiling the brand equity of A. Le Coq beer, which is based on the study by Dennis et al. (2016) and partly on Jillapalli and Jillapalli (2014), contains 12 hypotheses and 9 constructs. Jillapalli and Jillapalli (2014) built this conceptual model on the basis of Keller’s CBBE model (Keller 1993, 2001) and relationship marketing theory, and performed it in the context of professor brands in order to elicit its antecedents and the relationships between brand characteristics. In the research of this study, we utilise an adaptation of this conceptual model by Dennis et al. (2016), where the brand characteristics—brand image, brand meaning, and brand identity—are added as antecedents of the attachment strength and emphasise the impact of reputation on brand characteristics. Of course, the model by Dennis et al. (2016) was not assessed on products. However, our study adapted the model of Dennis et al. (2016) on the product of the leading brand of beer in the Estonian market. Also, the connections between the attachment strength and relationship factors, namely, commitment, trust, and satisfaction, are depicted in our conceptual model and are easy to discern. The conceptual model shown in Figure 1 is finalised by displaying the effect of the relationship factors on the brand equity of A. Le Coq beer.

2.4.1. The Impact of Reputation on Brand Characteristics

Reputation can be defined as a favourable view that customers hold, where they differentiate a certain brand from another by evaluating its overall value and utility (Bhattacharya and Elsbach 2002; Jillapalli and Jillapalli 2014). Roper and Fill (2012) explain reputation as a summary that combines various views and perceptions shared by different people. In other words, brand reputation can be seen as an important indicator that displays how well the brand performs in the market in comparison with its competitors (Dennis et al. 2016). Dennis et al. (2016) found positive effects of reputation to brand image, brand meaning, and brand identity. It is worth mentioning that Dennis et al. (2016, Table 4, p. 3054) found that reputation had a strong positive effect on brand meaning. Chaudhuri (2002) emphasises the importance of brand reputation and refers to it as an important tool, since it plays an immense role in making strategic managerial decisions. It is mentioned that cultivating a positive reputation is vital in order to become successful and profitable in the market (Herbig and Milewicz 1993) whilst requiring proper branding and overall marketing activities. Reputation has an impact on the development of brand image by creating brand awareness, which boosts brand image (Jillapalli and Jillapalli 2014; Keller 2001). Another explanation of brand reputation is given by Van Vught (2008), who talks about it as a cumulative outcome of several activities in order to generate an external image. Reputation is discussed by Bosch et al. (2006) as a variable that creates a brand identity, where the positive impact of reputation on brand identity is empirically tested and confirmed. Veloutsou and Moutinho (2009) stress the importance of brand meaning in a decision-making process where customers develop the symbolic meaning of brand through its reputation. Escalas and Bettman (2005) also suggest that the reputation of a certain brand among the reference group plays a significant role in its selection, where the authors regard reference groups as a source of brand meaning. Furthermore, the study by Jillapalli and Jillapalli (2014, Table 3, p. 35) revealed that reputation was negatively related to attachment strength, and therefore this relationship was not supported. Additionally, the study by Dennis et al. (2016, Table 4, p. 3054) found that reputation to the brand image, the brand meaning, and brand identity were positive and significant.

Therefore, the following hypotheses are posited:

H1a: 

A. Le Coq beer’s brand reputation is significantly and positively related to the brand image of A. Le Coq beer.

H1b: 

A. Le Coq beer’s brand reputation is significantly and positively related to the brand meaning of A. Le Coq beer.

H1c: 

A. Le Coq beer’s brand reputation is significantly and positively related to the brand identity of A. Le Coq beer.

2.4.2. Brand Characteristics and Their Impact on Attachment Strength

As is already clear, brand characteristics have the ability to affect and change customer behaviour significantly, and this influence can start before the actual purchasing process and last afterwards.

It has been observed that customers make their purchasing decisions not only based on rational arguments, such as the product’s functionality and utility, but also their subjective understanding of what a brand means to them (Levy 1959). In their empirical research, Escalas and Bettman (2005) discuss the importance of brand meaning on an individual’s self-perception and how he/she expresses him/herself in the surroundings by using brands as an instrument. The authors also draw attention to the fact that customers develop their self-identities based on their brand selection and build up strong brand–customer relationships (Escalas and Bettman 2005).

The next brand characteristic, which is also part of the conceptual model, is brand identity, which is defined as a set of distinctive brand associations that marketing managers endeavour to develop in order to differentiate their product from others in the market (Aaker 1996; Kapferer 2008). The main purpose behind identity-building activities is to secure a competitive advantage and to survive in the marketplace by showing what the brand is capable of offering to customers (Da Silveira et al. 2013). The development of a strong and clear brand identity can enable deep brand–customer attachment and accelerate the overall brand equity (Ghodeswar 2008).

Brand image is a concept which has been well researched by scholars over time, and it is described as a customer’s perception of a brand that is strongly affected by brand associations that have emerged and developed in their mind over time (Keller 2003). These brand associations include all kinds of beliefs, ideas, visuals, and thoughts (Kotler and Fox 1995) which are held in the customers’ memory and have a notable impact on customer behaviour (Kuhn et al. 2008). Companies prioritise their brand image activities in order to develop a strong relationship with the customers in the market, thus increasing brand loyalty and maximising profit.

After defining and explaining the brand characteristics in brief, the significant effect of them on brand–self connection can be understood, and therefore on attachment strength. Park et al. (2010) suggest that customers develop an attachment to the brand, whereby they are connected emotionally and cognitively to the brand, eventually considering it as part of themselves. It can be argued that accurate and complete branding activities strengthen the attachment between the brand and the self. The study by Dennis et al. (2016) showed that the relationship between brand meaning and attachment strength was important, positive, and significant. However, the relationships between brand identity and attachment strength and brand image and attachment strength were positive, but not significant. These considerations are captured in the following hypotheses:

H2a: 

A. Le Coq’s brand meaning is significantly and positively related to a customer’s attachment strength to the brand of A. Le Coq beer.

H2b: 

A. Le Coq’s brand identity is significantly and positively related to a customer’s attachment strength to the brand of A. Le Coq beer.

H2c: 

A. Le Coq’s brand image is significantly and positively related to a customer’s attachment strength to the brand of A. Le Coq beer.

2.4.3. The Impact of Attachment Strength on Relationship Factors

The notion of attachment strength plays a significant role in order to unveil the quality of the relationship between a customer and brand (Thomson 2006). Feelings that emerge alongside attachment are essential for the brand–customer relationship (Fournier et al. 1998), and the more this attachment intensifies, the more enduring the relationship becomes cultivated. In the study by Park et al. (2010), the authors mention the importance of attachment strength that can lead to positive emotions, such as commitment, satisfaction, etc. In the study by Jillapalli and Jillapalli (2014, Table 3, p. 35), it was found that attachment strength was positively related to satisfaction, commitment, and trust. However, attachment strength was significantly related to only satisfaction and commitment, but not to trust. Additionally, the study found that attachment strength had a strong positive effect on commitment. It can be stated that attachment to the brand is vital for developing a brand–customer relationship which is trustful, committed, and satisfied. The study by Dennis et al. (2016, Table 4, p. 3054) showed that both relationships between attachment strength and commitment and attachment strength and trust were positive and significant. However, for the same study, the relationship between attachment strength and satisfaction was negative and non-significant. In addition, both studies by Jillapalli and Jillapalli (2014, Table 3, p. 35) and Dennis et al. (2016, Table 4, p. 3054) revealed that the relationship between attachment and brand equity was positive and significant. Therefore:

H3a: 

The customer’s attachment strength to A. Le Coq beer is significantly and positively related to his/her commitment to the brand of A. Le Coq beer.

H3b: 

The customer’s attachment strength to A. Le Coq beer is significantly and positively related to his/her trust in the brand of A. Le Coq beer.

H3c: 

The customer’s attachment strength to A. Le Coq beer is significantly and positively related to his/her satisfaction in the brand of A. Le Coq beer.

2.4.4. Relationship Factors and Their Role in the Formation of the Brand Equity of A. Le Coq Beer

After reviewing the literature, we define trust as the willingness of one party to enter the situation of being open to the actions of another party, whether or not his/her actions can be inspected (Mayer et al. 1995). Confidence plays an important role in the process of developing trust, and Morgan and Hunt (1994) describe trust as having confidence in another party’s credibility and fairness. Furthermore, it can be said that trust emerges in the circumstance where the trusting person expects positive outcomes from the counterpart’s actions (Anderson and Narus 1990) and holds a belief that he or she will not be taken advantage of. In conclusion, the value of trust in the brand–customer relationship should be emphasised, since credibility and trustworthiness accelerate brand loyalty and brand advocacy, which are indicators of strong and desired brand equity (Jillapalli and Jillapalli 2014; Keller 2001).

Commitment can be conceptualised as a process of preserving an existing relationship which is valuable and worth sustaining. One of the most well-known definitions of commitment is introduced by Morgan and Hunt (1994), where they describe it as one party’s utmost efforts and desire to protect an ongoing relationship with an exchange partner. Moreover, attention can be drawn to the fact that strong commitment can lead to brand loyalty (Hennig-Thurau et al. 2002), whereby customers tend to perform repeated purchasing. Since it is known that brand loyalty is one of the essential blocks of the CBBE model in order to develop strong brand equity (Keller 2001), the following statement can make sense: the more strongly commitment is developed between customer and brand, the greater is the growth of brand equity.

Sense of satisfaction refers to the customer’s perception of the difference in expected and experienced performance resulting from the utilisation of certain products or services (Hennig-Thurau et al. 2002; Jillapalli and Jillapalli 2014). The satisfaction level of a customer is related to how well his or her needs are fulfilled. Satisfied customers tend to be more engaged in the relationship with the brand, and they are also keen to share their positive experience with others through word-of-mouth (File et al. 1994; Yi 1990). There are several instances of research stating that the notion of satisfaction plays an important role in the emergence of brand loyalty and in developing an emotional bond with the brand (Anderson et al. 1994; Rust and Zahorik 1993). Thus, the conclusion can be made that satisfied customers are likely to exhibit stronger levels of brand equity.

Keller (1993) stresses the necessity of customers having positive, strong, and distinctive brand associations in order to maintain favourable customer-based brand equity. Desirable brand associations are developed when customers genuinely believe that the brand is capable of meeting their expectations and fulfilling their needs (Keller 1993). Therefore, if customers are satisfied with the brand, show a strong commitment to the brand, and believe that the brand can be trusted, it will lead to strong and desired brand equity. The study by Jillapalli and Jillapalli (2014, Table 3, p. 35) found that the relationships between trust and brand equity, commitment and brand equity, and satisfaction and brand equity were positive and significant. Similarly, the study by Dennis et al. (2016, Table 4, p. 3054) found that the same three relationships were positive and significant. Since these aforementioned relationship factors have a significant effect on the occurrence of brand equity, we present the following hypotheses:

H4a: 

The customer’s trust in A. Le Coq beer is significantly and positively related to the brand equity of A. Le Coq beer.

H4b: 

The customer’s commitment to A. Le Coq beer is significantly and positively related to the brand equity of A. Le Coq beer.

H4c: 

The customer’s satisfaction with A. Le Coq beer is significantly and positively related to the brand equity of A. Le Coq beer.

3. Methodology

3.1. Scale Development

We assessed 9 constructs with the help of 7-point multi-item Likert-type scales to examine the presented hypotheses and conceptual model. These multi-item scales were adopted from various sources and tailored to this research. Even though the study uses established scales taken from research in a higher education context by Dennis et al. (2016), they fit appropriately into this research, since they were not developed specifically for certain industry contexts, except for the scales for brand identity by Goi et al. (2014), which also show compatibility.

Appendix A (

Table A1. Constructs and their definitions.

Constructs and Contributors	Items in Each Construct *	Definitions
Reputation (Chaudhuri 2002)	X7: A. Le Coq beer has a good status X8: A. Le Coq beer has a good reputation	In order to assess A. Le Coq beer’s reputation, a two-item scale (Chaudhuri 2002) is utilised which reveals the customer’s attitude towards the brand.
Brand image (Syed Alwi and Da Silva 2007)	X9: The brand image of A. Le Coq beer is reassuring X10: The brand image of A. Le Coq beer is straightforward X11: The brand image of A. Le Coq beer is open X12: The brand image of A. Le Coq beer is supportive	The four-item scale (Syed Alwi and Da Silva 2007) captures the customer’s perception of the brand of A. Le Coq beer and measures the brand image.
Brand meaning (Escalas and Bettman 2005)	X13: A. Le Coq beer reflects who I am X14: I feel a personal connection to A. Le Coq beer X15: I consume A. Le Coq beer to communicate who I am to other people X16: I think A. Le Coq beer helps me become the type of person I want to be	The four-item scale is sourced from Escalas and Bettman (2005) and measures the brand meaning by linking tangible and intangible brand associations.
Brand identity (Goi et al. 2014)	X17: A. Le Coq has a helpful website X18: A. Le Coq has an outstanding mission and vision X19: This is visible brand name with personality X20: The members of the staff are well trained in their roles	The four-item brand identity scale (Goi et al. 2014) describes how well customers distinguish the brand of A. Le Coq beer from its competitors.
Attachment strength (Park et al. 2010)	X21: A. Le Coq beer is part of me and who I am X22: I feel personally connected to A. Le Coq beer X23: I feel emotionally bonded to A. Le Coq beer X24: A. Le Coq beer is part of me X25: A. Le Coq beer says something to other people about how I am	The five-item scale (Park et al. 2010) measures how strongly customers are attached to A. Le Coq beer and gives insight into the intensity of the brand–customer relation.
Commitment (Jillapalli and Jillapalli 2014)	X26: I am very committed to A. Le Coq beer X27: A. Le Coq beer is very important to me X28: I really care about A. Le Coq beer X39: I believe that A. Le Coq beer deserves my effort in maintaining a relationship	The four-item commitment scale (Jillapalli and Jillapalli 2014) determines the importance of the relationship to the customer of the brand of A. Le Coq beer and the dedication to preserve it.
Trust (Jillapalli and Jillapalli 2014)	X30: A. Le Coq beer can be trusted X31: A. Le Coq beer is expected to do what is right X32: A. Le Coq has high integrity X33: A. Le Coq beer keeps its promises	The four-item scale (Jillapalli and Jillapalli 2014) shows how confident the customers are about their relationship with the brand of A. Le Coq beer and measures trust in the brand.
Satisfaction (Jillapalli and Jillapalli 2014)	X34: I am delighted with A. Le Coq beer, as it satisfies my thirst X35: Overall, I am satisfied with consuming A. Le Coq beer X36: I think I did the right thing when I decided to consume A. Le Coq beer	The three-item satisfaction scale (Jillapalli and Jillapalli 2014) measures the customers’ response to how well A. Le Coq beer does in meeting customer expectations.
Brand equity (Yoo et al. 2000)	X37: Even if another beer had the same features as this one, I would prefer to purchase A. Le Coq beer X38: If there was another beer as good as this one, I would still prefer to purchase A. Le Coq beer X39: If another beer was similar to A. Le Coq beer in any way, it would still seem smarter to purchase A. Le Coq beer	The final three-item scale (Yoo et al. 2000) measures whether customers respond positively to the marketing activities of A. Le Coq beer and captures the brand associations of A. Le Coq beer that customers keep in their minds.

Note *: Adapted from Dennis et al. (2016, p. 3053).

) shows the operationalisation of the study, for example, the measurement scales and their sources as well as the constructs and items utilised for the evaluation of the brand equity of A. Le Coq beer. For the sake of clarity, the 9 multi-item scales that are mentioned in the conceptual model will now be focused on and a brief explanation of each of them is given in the same Appendix A (Table A1).

3.2. Exploration for Biases

The study checks for different biases, such as the non-response bias, the common method bias, and the endogeneity bias. First, the non-response bias is the method followed by Armstrong and Overton (1977). According to them, late responses are expected to be similar to non-respondents. When performing a t-test under the assumption of equal and unequal group variances for three groups (early and late participants), the study found no significant differences between the means of any of the variables associated with early and late responses.

Based on the findings of the t-test, there were no significant differences between the early cases (the first 60 cases) and late participants (the final 60 cases). Within the total sample of 120 participants and the two groups (early and late participants), there were no significant differences between the means of the items in the t-test analysis. Therefore, the non-response bias was not an issue.

Second, the study explored the common method of variance. A CFA was performed, in which all indicators included in the structural model were restricted to load on a single factor (Podsakoff and Organ 1986; Podsakoff et al. 2003; Podsakoff et al. 2012). The test of the fit of the model showed a poor model fit, which implies that the common method of variance was not a problem in this study.

Third, based on the two-stage least-squares method, the study checked for the endogeneity bias. The instrumental variables, i.e., reputation, brand image, brand meaning, brand identity, attachment strength, commitment, trust, and satisfaction, were correlated with their respective endogenous explanatory variables, but not with brand equity. F-tests (Stock and Watson 2011) revealed the strength of instrumental variables, and we computed another model. The findings of the test of Durbin–Wu–Hausman indicated that reputation, brand image, brand meaning, brand identity, attachment strength, commitment, trust, and satisfaction were exogenous to brand equity, with all variables having F statistics greater than 10 (Stock and Watson 2011). Therefore, it was evident that there was no endogeneity bias.

3.3. Sample Selection and Fieldwork

In order to collect the empirical data for this research, the authors conducted self-administered structured online and offline surveys among consumers of A. Le Coq beer in Estonia. The study used a sample of convenience from bachelor’s, master’s, and Ph.D. students, as well as graduates from the University of Tartu, who are consumers of A. Le Coq beer. Google’s online survey administration service—Google forms—was used for the online response gathering process, since it has several of the following benefits: the responses can be exported to Google sheets, which can be downloaded as .xlsx files for future usage, the survey can be accessed and modified easily in case of need, there is a self-made summary mode for better view, and so on.

Initially, we performed a pilot study with 10 questionnaires among students at the University of Tartu to ensure the reliability and validity of the scales. These questionnaires were used in the analysis. The online survey was sent out to both students and graduates living in Estonia and received 100 valid responses. An offline survey was also conducted among students at that time in Tartu, and 20 valid answers were obtained. For both of these surveys, live assistance was provided to the participants, if needed, in order to make sure that every question was well understood. The gender breakdown of the respondents was almost equal, representing the general population very well. Even though it is believed that student samples cannot represent the general population and there is a question mark over the validity of these samples, nevertheless, student samples can be used for some situations where they actually are recognised as major consumers of the chosen product (Atilgan et al. 2005; Yoo et al. 2000). Several studies (Karam et al. 2007; Stock et al. 2009) have discussed the alcohol consumption habits of students, and a high level of alcohol consumption was observed. A survey of university students in the USA about their alcohol consumption concluded that 70% of the respondents had consumed alcohol during the last 30 days (O’Malley and Johnston 2002), and Kidorf et al. (1995) observed that this consumption mostly took the form of drinking beer (as cited in Barth 2013). Additionally, empirical studies on the demographics of beer consumption show that younger people between the ages of 19 and 34 drink more beer per month than older people (Kerr et al. 2004). Considering the fact that all of the respondents—both Estonians and foreigners—are consumers of A. Le Coq beer and have lived or are living in Tartu, where A. Le Coq has significant popularity, this sample group can be regarded as appropriate.

The survey instrument consisted of two parts: the respondents answered questions about their socio-demographic and economic status in the first part, and the second part consisted of 9 constructs with 33 variables/items/statements related to various aspects and consequences of the brand equity of A. Le Coq beer. In the second part, the respondents were asked to state their agreement or disagreement with questions based on a 7-point Likert scale, with 1 meaning strongly disagree and 7 meaning strongly agree.

Instead of a simple yes/no-type of assessment, we chose a Likert-type scale in order to obtain more sophisticated and specific data, since this type of scale helps to measure feelings more precisely. In the end, we managed to obtain 120 valid responses in total. The investigation of this study, and, in particular, the collection of data through a survey instrument, took place during the first six months of 2021.

Table 2. Profile of the participants.

Variables		Frequency	Percentage
Gender	Male	62	51.7
	Female	58	48.3
	Total	120	100.0
Age	From 18 to 24	90	75
	25–34	30	25
	Total	120	100.0
Location	Tartu	84	70
	Tallinn	22	18.3
	Other	14	11.7
	Total	120	100.0
Education (Highest level completed)	Ph.D.	1	0.83
	Master’s	60	50
	Bachelor’s	53	44.1
	Secondary	5	4.24
	Other	1	0.83
	Total	120	100.0
Occupation	Employed	26	21.7
	Non-employed	94	78.3
	Total	120	100.0
Nationality	Estonian	26	21.7
	Foreigners	94	78.3
	Total	120	100.0

Note: Compiled by the authors.

indicates the frequencies and the percentages related to gender, age, location, education level, occupation, and nationality.

4. Findings

4.1. Profile of the Participants1

Analysis of the participants in the survey showed that the majority were male (62) compared to female (58). The participants were young, all between 18 and 34 years old. The majority were in the age group 18–24 (90) as compared to 25–34 (30). In addition, most of the participants were from Tartu (84) as compared to Tallinn (22) and other places (14). The majority of the participants had a master’s degree (60) compared to a bachelor’s degree (53), secondary certificate (5), Ph.D. (1), and other education (1). The majority of the participants were university students (94) compared to employees (26). Finally, there were 26 Estonians and 94 participants from other countries.

4.2. Differences between Gender Groups, Age Groups, Location, Education Level, Employment/Non-Employment, and Nationality

An independent samples t-test was performed to reveal whether there were statistically significant differences between different populations based on the tested variables. The t-test analysis was performed on six different population characteristics, which are shown in

Table 3. T-test analysis of differences between different population segments.

Variables	Item	p-Value *	Significant Differences between Different Segments
X1, Gender	X15	0.015	Where 1: male, 2: female
	X37	0.037
X2, Age	-	no statistically significant differences	Where 1: 18–24, 2: 35–34
X3, Location	X7	0.072	Where 1: Tartu, 2: Tallinn
	X8	0.003
	X10	0.077
	X12	0.082
	X33	0.048
X4, Education	X23	0.087	Where 1: bachelor’s, 2: master’s
X5, Occupation	X20	0.084	Where 1: student, 2: employed
	X33	0.035
	X35	0.038
X6, Nationality	X16	0.027	Where 1: Estonian, 2: other
	X19	0.016
	X28	0.055
	X37	0.084
	X38	0.080

Note *: Significant at p < 0.10.

, among 9 constructs including 33 items that belong to the conceptual model.

Table 4. Mean values of variables revealed from t-test for different pairs of groups *.

Population Characteristics	Items Revealed from the t-Test	Male vs. Female	Tartu vs. Tallinn	Bachelor’s vs. Master’s	Unemployed vs. Employed	Estonians vs. Other
X1, Gender	X15	2.98 MD/2.22 D
	X37	3.44 MD/4.09 N
X2, Age	None
X3, Location	X7		5.19 MA/ 4.41 N
	X8		5.44 MA/ 4.46 N
	X10		5.08 MA/ 4.5 N
	X12		4.69 MA/ 4.09 N
	X33		4.46 N/ 3.68 N
X4, Education	X23	.		3.32 MD/2.73 MD
X5, Occupation	X20				4.35 N/3.92 N
	X33				4.52 MA/3.73 N
	X35				4.70 MA/3.89 N
X6, Nationality	X16					3.23 MD/2.23 D
	X19					5.0 MA/4.12 N
	X28					3.35 MD/2.58 MD
	X37					4.31 N/3.60 N
	X38					4.27 N/3.61 N

Note *: Based on a scale of the range strongly disagree (1) to strongly agree (7) where D = disagree = 2; MD = mildly disagree = 3; N = neutral = 4; MA = mildly agree = 5.

shows which items/variables are statistically significantly different between each of the six different population characteristics, i.e., males vs. females, 18–24 vs. 25–34 age groups, Tartu vs. Tallinn, Ph.D. vs. master’s vs. bachelor’s vs. secondary education, employed vs. non-employed, and Estonians vs. foreigners.

The independent samples test was performed with the use of SPSS 29, and the study revealed the following significant differences, as follows.

Regarding gender groups, there was a significant difference between males and females regarding the following two statements: I consume A. Le Coq beer to communicate who I am to other people (X15), and even if another beer had the same features as this one, I would prefer to purchase A. Le Coq beer (X37). Females agreed with both statements as compared to males, who disagreed with both statements.

Regarding age groups, there were no significant differences among the statements of the model between the two age groups, 18–24 and 25–34 years old. Therefore, all age groups behaved in the same way.

Regarding location, there were some significant differences in relation to statements X7, X8, X10, X12, and X33 between participants from Tartu vs. Tallinn. Persons from Tartu agreed with the following statements: A. Le Coq beer has a good status (X7), A. Le Coq beer has a good reputation (X8), the brand image of A. Le Coq beer is supportive (X12), and A. Le Coq beer keeps its promises (X33), as compared to persons from Tallinn, who disagreed with them. Persons from Tartu disagreed with the statement that the brand image of A. Le Coq beer is straightforward (X10), as compared to persons from Tallinn, who agreed with this statement.

Regarding the education level, persons with a master’s degree agreed with the statement to what extent do you feel emotionally bonded to A. Le Coq beer (X23), as compared to persons with a bachelor’s degree, who disagreed with the statement.

Regarding occupation engagement, students (non-employees) agreed with the statement that the members of the staff are well trained in their roles (X20), as compared to employees, who disagreed with this. Additionally, students disagreed with the statement that A. Le Coq beer keeps its promises (X33), and with the statement that, overall, I am satisfied with consuming A. Le Coq beer (X35), as compared to employees, who agreed with both statements.

Finally, regarding nationality, Estonians agreed with the following three statements, namely, this is a visible brand name with personality (X19), even if another beer had the same features as this one, I would prefer to purchase A. Le Coq beer (X37), and if there was another beer as good as this one, I would still prefer to purchase A. Le Coq beer (X38), as compared to other nationalities, who disagreed with these statements. In addition, Estonians disagreed with the statement, I think A. Le Coq beer helps me become the type of person I want to be (X16), and the statement, I really care about A. Le Coq beer, as compared to other nationalities, who agreed with both statements (X28).

Initially, the study split the data files into different data files, i.e., male and female, 18–24 years and 25–34 years, Tartu and Tallinn, bachelor’s and master’s students, unemployed and employed, and Estonians and others.

In addition, the study calculated the means of the variables found in the t-test based on descriptive statistics, and the results are compared between the dual groups in Table 4.

Table 4 shows that, for females, the mean value of X15 was lower compared to males, and the mean value of X37 was higher compared to males. Additionally, it shows that there is no statistically significant difference regarding age groups of 18–24 years old and 25–34 years old. Regarding the location, the mean values of X7, X8, X10, X12, and X33 were lower for participants coming from Tallinn compared to those from Tartu. Additionally, in terms of education, the mean value of X23 was lower for participants having a bachelor’s degree compared to those with a master’s degree. Furthermore, regarding the occupation of the participants, the mean values of X20, X33, and X35 were lower for employed persons compared to unemployed ones. Finally, in terms of nationality, the mean values of X16, X19, X28, X37, and X38 were lower for other nationalities compared to Estonians.

4.3. Variance Inflation Factor and Confirmatory Factor Analysis Using AMOS 29

4.3.1. Variance Inflation Factor

In the following paragraphs, the study uses confirmatory factor analysis through AMOS 29 in order to (a) examine the collinearity statistics, (b) test the model fit, (c) illustrate the path coefficients, total effects, and outer weights, and (d) evaluate the construct reliability and validity, as well as the discriminant validity.

Initially, the study runs the collinearity statistics—the Variance Inflation Factor (VIF) (Hair et al. 2014, pp. 157, 200)—to assess the collinearity issues of the model. Performing collinearity statistics is crucial, since it tells us whether any variables should be extracted or combined together in order to avoid multicollinearity problems (Wong 2013).

The value of the VIF needs to be less than five for each variable in the model to prevent multicollinearity (Hair et al. 2011). Based on the values of the VIF (see

Table 5. Collinearity statistics (VIF).

Variables	VIF	Variables	VIF	Variables	VIF	Variables	VIF
X7	2.586	X16	3.057	X25	3.218	X34	2.578
X8	2.586	X17	1.525	X26	2.454	X35	3.980
X9	1.486	X18	2.004	X27	3.834	X36	3.071
X10	1.761	X19	1.483	X28	3.721	X37	3.429
X11	1.605	X20	1.799	X29	2.613	X38	4.431
X12	1.898	X21	3.693	X30	2.295	X39	3.333
X13	2.781	X22	5.376	X31	2.471
X14	2.446	X23	5.398	X32	2.405
X15	2.788	X24	4.341	X33	2.136

Note: Compiled by the authors.

), the study reveals that two variables, X22 and X23, face the problem of multicollinearity. However, the study has not extracted these two variables from the model due to the high standardised regression weights of all items of the model, which were all above the critical value of 0.500 (see Table 6). It is worth mentioning that we have calculated the VIF statistic for all items, revealing that items X22 and X23 had values of 5.376 and 5.398, respectively. The remaining items had VIF values of between 1.483 and 4.431.

4.3.2. Confirmatory Factor Analysis Using AMOS 29

A confirmatory factor analysis (CFA) using AMOS 29 was performed to test the model fit of the model. A CFA has about the same findings as the partial least squares–structural equation modelling (PLS-SEM) SmartPLS3, the findings of which are not included, as this method does not calculate the covariances for testing the hypotheses. Furthermore, the CFA statistics are satisfactory for the model fit to the data. The final improved version of the model showed satisfactory results in terms of the model fit. However, the 16th run of the final output of the structural equation modelling (SEM) using the CFA revealed a significant final chi-square statistic of 749.504 (Hair et al. 2019). In particular, the initial chi-square was 806.922, the number of parameters for the model (NPAR) was 135, and the degrees of freedom (df) was 459 with a p = 0.000. Additionally, the initial run revealed a CMIN/df of 1.758l, a CFI of 0.899, an NFI of 0.797, an RFI of 0.766, an IFI of 0.901, a TLI of 0.884, an RMSEA of 0.080, an LO90 of 0.071, an HI90 of 0.089, and a PCLOSE of 0.000 The unidimensional model (meaning all items had loadings of less than one) was found at the tenth run, revealing an NPAR of 126 and a chi-square value of 820.587 with a df of 468 and a p of 0.000.

The potential improvement of the fit of the model can be shown by some issues with the modification indices through the CFA. Therefore, we performed another six runs for the fit of the model by correlating the errors of the items within a construct (see e9 to e10, e3 to e4, e17 to e18, e21 to e22, e20 to e21, and e7 to e8 in Figure 2).

The last run (the 16th run of the fit of the model) resulted in an NPAR of 132 and a chi-square value of 749.504 with a df of 462 and a p of 0.000. In addition, in the final run CMIN/DF was 1622, the CFI was 0.916, the NFI was 0.811, the RFI was 0.784, the IFI was 0.918, the TLI was 0.905, the RMSEA was 0.072, the LO90 was 0.063, the HI90 was 0.082, and the PCLOSE was 0.000. Moreover, the 94th case of the sample had a Mahalanobis d-squared value of 69.902, which was acceptable to keep it within the fit of the model. Therefore, this case was not eliminated from the sample. Other cases did not show significant Mahalanobis d-squared values and were below 67.625. Finally, the values of the standardised regression weight of all the items were found through a CFA above 0.500, which were above the threshold of 0.5 (see Table 6 below).

Table 5 shows the values of the key important statistics of the nine constructs of the model, where the means of the items using SPSS, skewness and kurtosis using SPSS, the EFA values of the items using SPSS, Cronbach’s alpha using scales of SPSS, standardised regression weights using the CFA findings, and the AVEs and composite reliabilities (CRs) were calculated. They show that there are no issues of skewness and kurtosis, as their values are below +1 and less than −1. Regarding the AVEs, the discriminant validity criterion of Fornell and Larcker (1981) was satisfied, as all constructs separately had AVE values above 0.5. The mean of the AVEs was 0.681, which is close to the criterion value of 0.7. Therefore, this value of 0.681 suggests adequate convergent validity (Bagozzi and Yi 1988). However, we performed convergent validity by calculating the heterotrait–monotrait (HTMT) ratio to test the discriminant validity. Anderson and Gerbing’s (1988) criterion was applied to prove the existence or non-existence of the discriminant validity. For this purpose, we used the chi-square difference test to compare a single-factor model with a two-factor model. Additionally, the HTMT ratios were used to evaluate the discriminant validity (Henseler et al. 2015). The acceptable criterion for the HTMT ratios between the constructs of this study is less than 0.85, showing the non-existence of the discriminant validity.

With respect to the reliability, Table 6 shows that all the values of Cronbach’s alpha are very good and above the criterion value of 0.7; the mean average value of Cronbach’s alpha is 0.879. In addition, the composite reliability values for all nine constructs are above 0.5 and the average composite reliability is 0.782. Both Cronbach’s alpha and CRs show high reliability related to all six constructs.

The exploratory factor analysis (EFA) in Table 5 shows that the items of five constructs are well identified by their items, namely, F1: reputation, F2: brand image, F3: brand meaning, F4: brand identity, and F7: trust. Neither of the two constructs F8: satisfaction and F9: brand equity have any of their items identifiable (factor loadings are less than 0.5), and two constructs, namely, the construct F5: attachment strength and construct F6: commitment, are not identified in all their items (factor loadings are below 0.5).

Table 6. Completely standardised factor loadings, variances extracted, estimates of construct reliability, and EFA results (N = 120) *.

Items	Mean (Using SPSS)	Skewness (Using SPSS)	Kurtosis (Using SPSS)	EFA Factor Loadings (Using SPSS) **	Standardised Regression Weights (Based on CFA Findings)										Ʃ(Li)² n	CR	δ = 1-Item Reliability
					F1	F2	F3	F4	F5	F6	F7	F8	F9
X7	5.075	−0.685	0.760	0.829	0.889									0.889			0.111
X8	5.283	−0.578	−0.242	0.844	0.880									0.880	0.760	0.868	0.120
X9	4.808	−0.629	0.554	0.228		0.798								0.798			0.202
X10	4.983	−0.519	0.242	0.637		0.676								0.676			0.324
X11	4.892	−0.326	0.028	0.858		0.639								0.639			0.361
X12	4.592	−0.379	0.410	0.758		0.719								0.719	0.505	0.634	0.281
X13	2.875	0.579	−0.591	0.307			0.766							0.766			0.234
X14	2.758	0.912	−0.284	−0.072			0.791							0.791			0.209
X15	2.617	0.835	−0.291	0.393			0.801							0.801			0.199
X16	2.450	0.984	−0.351	0.366			0.813							0.813	0.629	0.687	0.187
X17	3.825	0.038	0.120	0.854				0.618						0.618			0.382
X18	3.783	−0.022	−0.255	0.622				0.785						0.785			0.215
X19	4.308	−0.206	−0.520	0.371				0.652						0.652			0.348
X20	4.258	−0.015	0.405	0.607				0.798						0.798	0.515	0.642	0.202
X21	2.958	0.469	−0.518	0.843					0.872					0.872			0.128
X22	2.833	0.674	−0.324	0.890					0.889					0.889			0.111
X23	2.933	0.556	−0.770	0.893					0.886					0.886			0.114
X24	2.892	0.480	−0.776	0.878					0.910					0.910			0.090
X25	2.933	0.383	−0.910	0.838					0.883					0.883	0.789	0.876	0.117
X26	2.817	0.486	−0.896	0.107						0.850				0.850			0.150
X27	2.800	0.663	−0.566	−0.024						0.869				0.869			0.131
X28	2.742	0.659	−0.744	0.100						0.805				0.805			0.195
X29	2.808	0.671	−0.682	0.201						0.818				0.818	0.699	0.809	0.182
X30	4.175	−0.417	−0.421	0.752							0.802			0.802			0.198
X31	4.067	−0.256	−0.780	0.795							0.799			0.799			0.201
X32	4.250	−0.361	−0.408	0.758							0.827			0.827			0.173
X33	4.350	−0.390	−0.213	0.701							0.817			0.817	0.658	0.778	0.183
X34	4.100	−0.236	−0.661	0.735								0.818		0.818			0.182
X35	4.525	−0.453	−0.670	0.666								0.910		0.910			0.090
X36	4.267	−0.312	−0.690	0.589								0.896		0.896	0.763	0.859	0.104
X37	3.750	0.019	−0.695	0.762									0.884	0.884			0.116
X38	3.750	−0.093	−0.734	0.769									0.934	0.934			0.066
X39	3.850	−0.064	−0.862	0.734									0.879	0.879	0.809	0.889	0.121
Average Variance Extracted					0.760	0.505	0.629	0.515	0.789	0.699	0.658	0.763	0.809	MAVE = 0.681
Construct Reliability					0.868	0.634	0.687	0.642	0.876	0.809	0.778	0.859	0.889	ACR = 782.
Cronbach’s alpha					0.878	0.790	894	0.807	0.912	0.917	0.886	0.905	0.926	MCα = 0.879

* Note: The following formulae are used for calculating the AVEs and CRs of the constructs: the AVE is computed as the total of all squared standardised factor loadings (squared multiple correlations) divided by the number of items (Hair et al. 2019, p. 676) or AVE = Ʃ (standardised regression weights)²/n or Σ(Li)²/n. CR = (Ʃ of standardised regression weights)²/[(Ʃ of standardised regression weights)² + (Ʃδ)]; MAVE = mean average variance extracted, ACR = average construct reliability, and MCα = mean Cronbach’s α. Constructs: F1: reputation, F2: brand image, F3: brand meaning, F4: brand identity, F5: attachment strength, F6: commitment, F7: trust, F8: satisfaction, and F9: brand equity ** The extraction method used was Principal Component Analysis; Rotation method: Varimax with Kaiser Normalisation. Rotation converged in 18 iterations.

Table 6. Completely standardised factor loadings, variances extracted, estimates of construct reliability, and EFA results (N = 120) *.

Items	Mean (Using SPSS)	Skewness (Using SPSS)	Kurtosis (Using SPSS)	EFA Factor Loadings (Using SPSS) **	Standardised Regression Weights (Based on CFA Findings)										Ʃ(Li)² n	CR	δ = 1-Item Reliability
					F1	F2	F3	F4	F5	F6	F7	F8	F9
X7	5.075	−0.685	0.760	0.829	0.889									0.889			0.111
X8	5.283	−0.578	−0.242	0.844	0.880									0.880	0.760	0.868	0.120
X9	4.808	−0.629	0.554	0.228		0.798								0.798			0.202
X10	4.983	−0.519	0.242	0.637		0.676								0.676			0.324
X11	4.892	−0.326	0.028	0.858		0.639								0.639			0.361
X12	4.592	−0.379	0.410	0.758		0.719								0.719	0.505	0.634	0.281
X13	2.875	0.579	−0.591	0.307			0.766							0.766			0.234
X14	2.758	0.912	−0.284	−0.072			0.791							0.791			0.209
X15	2.617	0.835	−0.291	0.393			0.801							0.801			0.199
X16	2.450	0.984	−0.351	0.366			0.813							0.813	0.629	0.687	0.187
X17	3.825	0.038	0.120	0.854				0.618						0.618			0.382
X18	3.783	−0.022	−0.255	0.622				0.785						0.785			0.215
X19	4.308	−0.206	−0.520	0.371				0.652						0.652			0.348
X20	4.258	−0.015	0.405	0.607				0.798						0.798	0.515	0.642	0.202
X21	2.958	0.469	−0.518	0.843					0.872					0.872			0.128
X22	2.833	0.674	−0.324	0.890					0.889					0.889			0.111
X23	2.933	0.556	−0.770	0.893					0.886					0.886			0.114
X24	2.892	0.480	−0.776	0.878					0.910					0.910			0.090
X25	2.933	0.383	−0.910	0.838					0.883					0.883	0.789	0.876	0.117
X26	2.817	0.486	−0.896	0.107						0.850				0.850			0.150
X27	2.800	0.663	−0.566	−0.024						0.869				0.869			0.131
X28	2.742	0.659	−0.744	0.100						0.805				0.805			0.195
X29	2.808	0.671	−0.682	0.201						0.818				0.818	0.699	0.809	0.182
X30	4.175	−0.417	−0.421	0.752							0.802			0.802			0.198
X31	4.067	−0.256	−0.780	0.795							0.799			0.799			0.201
X32	4.250	−0.361	−0.408	0.758							0.827			0.827			0.173
X33	4.350	−0.390	−0.213	0.701							0.817			0.817	0.658	0.778	0.183
X34	4.100	−0.236	−0.661	0.735								0.818		0.818			0.182
X35	4.525	−0.453	−0.670	0.666								0.910		0.910			0.090
X36	4.267	−0.312	−0.690	0.589								0.896		0.896	0.763	0.859	0.104
X37	3.750	0.019	−0.695	0.762									0.884	0.884			0.116
X38	3.750	−0.093	−0.734	0.769									0.934	0.934			0.066
X39	3.850	−0.064	−0.862	0.734									0.879	0.879	0.809	0.889	0.121
Average Variance Extracted					0.760	0.505	0.629	0.515	0.789	0.699	0.658	0.763	0.809	MAVE = 0.681
Construct Reliability					0.868	0.634	0.687	0.642	0.876	0.809	0.778	0.859	0.889	ACR = 782.
Cronbach’s alpha					0.878	0.790	894	0.807	0.912	0.917	0.886	0.905	0.926	MCα = 0.879

* Note: The following formulae are used for calculating the AVEs and CRs of the constructs: the AVE is computed as the total of all squared standardised factor loadings (squared multiple correlations) divided by the number of items (Hair et al. 2019, p. 676) or AVE = Ʃ (standardised regression weights)²/n or Σ(Li)²/n. CR = (Ʃ of standardised regression weights)²/[(Ʃ of standardised regression weights)² + (Ʃδ)]; MAVE = mean average variance extracted, ACR = average construct reliability, and MCα = mean Cronbach’s α. Constructs: F1: reputation, F2: brand image, F3: brand meaning, F4: brand identity, F5: attachment strength, F6: commitment, F7: trust, F8: satisfaction, and F9: brand equity ** The extraction method used was Principal Component Analysis; Rotation method: Varimax with Kaiser Normalisation. Rotation converged in 18 iterations.

Table 7. Correlation matrix based on CFA output *.

	AS	BE	BID	BIM	BM	C	R	S	T
Attachment Strength	0.888
Brand Equity	0.643	0.899
Brand Identity	0.451	0.451	0.718
Brand Image	0.278	0.441	0.607	0.711
Brand Meaning	0.681	0.653	0.661	0.458	0.793
Commitment	0.695	0.695	0.497	0.327	0.665	0.836
Reputation	0.153	0.381	0.513	0.695	0.278	0.237	0.872
Satisfaction	0.555	0.687	0.646	0.506	0.665	0.638	0.568	0.873
Trust	0.476	0.633	0.688	0.438	0.556	0.562	0.489	0.651	0.811

Note *: Diagonal values are the square root of the AVEs and are in italics.

below shows the correlation matrix related to the last run of the CFA. The squared values of the average variances extracted (AVEs) for all constructs were higher than the values of the correlations horizontally and vertically. Therefore, there was no issue of multicollinearity between the items. Table 7 below compares the square root of the AVEs (diagonal values) with the correlations among the reflective constructs. All the constructs were more strongly correlated with their own measures than with any other of the constructs, suggesting good convergent and discriminant validity. In fact, the square root of the AVEs were higher than the correlations, horizontally and vertically. All the constructs explain more information through their items than through their inter-relationships. Based on Hu and Bentler (1999), all the constructs performed well, suggesting that the conceptual model is valid (see Figure 1). In addition, Table 6 shows that there is no multicollinearity problem, as the correlations are below the threshold of 0.7.

4.4. Important and Significant Relationships Based on Hypotheses Testing

Table 8. Test of hypotheses and their status based on covariances through CFA (N = 120) *.

Research Hypotheses	Importance of Relationship	Estimate Beta	Critical Ratio (t)	Sig/(p-Value)	Status of Hypothesis
H1a: A. Le Coq’s brand reputation (F1) is significantly and positively related to the brand image (F2) of A. Le Coq beer (F1 to F2)	8	0.921	5.584	0.000	Supported
H1b: A. Le Coq’s brand reputation (F1) is significantly and positively related to the brand meaning (F3) of A. Le Coq beer (F1 to F3)	11	0.494	2.495	0.013	Supported
H1c: A. Le Coq’s brand reputation (F1) is significantly and positively related to the brand identity (F4) of A. Le Coq beer (F1 to F4)	10	0.642	4.338	0.000	Supported
H2a: A. Le Coq’s brand meaning (F3) is significantly and positively related to a customer’s attachment strength to the brand (F5) of A. Le Coq beer (F3 to F5)	2	2.098	6.678	0.000	Supported
H2b: A. Le Coq’s brand identity (F4) is significantly and positively related to a customer’s attachment strength to the brand (F5) of A. Le Coq beer (F4 to F5)	9	0.724	3.947	0.000	Supported
H2c: A. Le Coq’s brand image (F2) is significantly and positively related to a customer’s attachment strength to the brand (F5) of A. Le Coq beer (F2 to F5)	12	0.413	2.480	0.013	Supported
H3a: The customer’s attachment strength (F5) to A. Le Coq beer is significantly and positively related to his/her commitment to the brand (F6) of A. Le Coq beer (F5 to F6)	1	2.343	7.094	0.000	Supported
H3b: The customer’s attachment strength (F5) to A. Le Coq beer is significantly and positively related to his/her trust in the brand (F7) of A. Le Coq beer (F5 to F7)	7	0.985	4.243	0.000	Supported
H3c: The customer’s attachment strength (F5) to A. Le Coq beer is significantly and positively related to his/her satisfaction in the brand (F8) of A. Le Coq beer (F5 to F8)	5	1.397	4.949	0.000	Supported
H4a: The customer’s trust (F7) to A. Le Coq is significantly and positively related to the brand equity (F9) of A. Le Coq beer (F7 to F9)	6	1.330	5.296	0.000	Supported
H4b: The customer’s commitment (F6) to A. Le Coq is significantly and positively related to the brand equity (F9) of A. Le Coq beer (F6 to F9)	4	1.662	5.738	0.000	Supported
H4c: The customer’s satisfaction (F8) to A. Le Coq is significantly and positively related to the brand equity (F9) of A. Le Coq beer (F8 to F9)	3	1.858	6036	0.000	Supported

Note *: Compiled by the authors.

shows the results of the hypotheses testing based on the covariances of the last run of the CFA. All hypotheses are supported, being positive in direction and statistically significant. All hypotheses are statistically significant at the 99% confidence level, except the hypotheses H1b and H2c, which are statistically significant at the 95% confidence level.

Table 8 also reveals some positive and strong relationships. The positive and strong relationships are between the customer’s attachment strength and commitment (2.343), brand meaning and attachment strength (2.098), satisfaction to brand equity (1.858), commitment to brand equity (1.662), customer’s attachment strength to satisfaction (1.392), customer’s trust to brand equity (1.330), customer’s attachment strength to trust in the brand (0.985), and brand reputation to brand image (0.923). The remaining four relationships between the brand identity to customer’s attachment strength (0.724), brand reputation to brand identity (0.642), brand reputation to brand meaning (0.494), and brand image to customer’s attachment strength (0.413) are not as important.

4.5. Reliability versus Validity of the Model

Table 9. Construct reliability through CFA and Cronbach’s alpha through SPSS, and convergent validity through CFA.

	Cronbach’s Alpha	Composite Reliability	Average Variance Extracted (AVE) *
Attachment Strength (F5)	0.912	0.876	0.789
Brand Equity (F9)	0.926	0.889	0.809
Brand Identity (F4)	0.807	0.642	0.515
Brand Image (F2)	0.790	0.634	0.505
Brand Meaning (F3)	0.894	0.687	0.629
Commitment (F6)	0.917	0.809	0.699
Reputation (F1)	0.878	0.868	0.760
Satisfaction (F8)	0.905	0.859	0.763
Trust (F7)	0.886	0.778	0.658

Note *: AVEs were calculated in Table 6 above. Source: Compiled by the authors.

below shows the reliability versus the validity of the model. The values of Cronbach’s alpha are above 0.7 between 0.790 and 0.926, which avoids the problem of unidimensionality (Tenenhaus et al. 2005). Based on Fornell and Larcker (1981), it is acceptable for the composite reliability to be higher than 0.7, and the Average Variance Extracted (AVE) should be higher than 0.5.

In this study, the composite reliability was between 0.634 and 0.889, and the AVE for the CFA was between 0.505 and 0.809, whereby both statistics were above the minimum thresholds set by Hair et al. 2019 (p. 776), i.e., 0.7 and 0.5, respectively. The mean AVE estimate suggests an adequate convergent validity. Since the mean composite reliability has a value of 0.782, which is more than 0.7, this suggests good reliability (Hair et al. 2019, p. 775). A high latent construct reliability indicates that there is an adequate convergence or internal consistency, which means that all the measures are consistently representing something.

5. Discussion

The model (Figure 1), which has been tested in terms of its fit, has a very good fit. However, it is a very complicated model with nine constructs, which makes it difficult to be utilised in practice. The model’s usefulness can be focused on the central relationships of brand equity. For example, the relationships between brand meaning, attachment strength, commitment, trust, and satisfaction produce strong positive relationships that managers should exploit. More recent conceptualisations by Chatzipanagiotou et al. (2016) show a similar structure, although they argue about the co-existence of three models together, a very complicated thing to be understood by simple brand managers. One of their models shows trust to intervene, but nobody has tested their models and their usefulness. In theory, you can claim many models. However, in practice, only few constructs will work for the benefit of brand managers and for the maximisation of the performance of firms.

In terms of theoretical implications, this study shows the importance of the three relationships, namely, the satisfying relationship with brand equity, the trust relationship with brand equity, and the commitment to brand equity. Another theoretical implication relates to brand characteristics, as brand reputation has a significant impact on brand meaning. Finally, the attachment strength has an impactful influence on commitment. These theoretical implications can provide an opportunity for future research and can also give valuable insights to breweries, leading to several managerial implications.

Since the CFA model proved to be a significant one, the managers of competitive Estonian breweries can think about ways to advertise their beer by paying more attention to developing trust with consumers and to providing more satisfaction to them. A satisfying and trusting relationship between customers and brands can lead to brand loyalty (Atilgan et al. 2005; Porral et al. 2013), and thus to several benefits for a company, such as increased market share, loyal customers, and stronger brand equity.

In addition, managers should focus on how to extend brand meaning, which has an impactful influence on attachment strength. Breweries can enhance their brand meaning by improving both performance-related functional attributes and extrinsic attributes (Escalas and Bettman 2005). Another important issue is that the managers of competitive brands should further promote their reputation, whether this be in terms of their firm or their brands. By promoting their reputation, companies can distinguish their brands from competitors and attract more customers (Kuhn et al. 2008).

Regarding the practical implications, A. Le Coq beer should exploit the strong positive relationships found among consumers of beer, i.e., the relationships between brand meaning and attachment strength, attachment strength and trust, and commitment and satisfaction, by further advertising the brand meaning of A. Le Coq beer.

The results of this study and the recent study by Dennis et al. (2016) show that the successful brand of A. Le Coq beer has high scores of overall brand equity in both studies, and both found that the relationship between brand meaning and attachment strength was important and significant. In addition, the relationship between brand reputation and brand meaning was important and significant. However, the relationship between attachment strength and commitment was found in both models to be significant and important, and the relationship between attachment strength and satisfaction was found to be negatively related in the model by Dennis et al. (2016). Moreover, both the relationships between brand image and attachment strength as well as brand identity and attachment strength were significant in the current study, but non-significant in the model by Dennis et al. (2016). In addition, in both models, the relationship between brand reputation and brand identity was significant. Furthermore, the present study showed that the relationships between attachment strength and commitment, attachment strength and trust, and attachment strength and satisfaction were significant. However, in the model by Dennis et al. (2016), only the relationship between attachment strength and commitment was important and significant.

Additionally, the current study found that there were no significant differences among the statements of the model between the two age groups of 18–24 and 25–34 years old. However, some significant differences were found (through the t-test analysis) between gender groups, location, education level, employees/non-employees, and nationality, which are discussed in Section 4.2 of the results.

The following differences related to five demographic characteristics are found in this study. Firstly, regarding gender groups, females compared to males agreed that they consume A. Le Coq beer to communicate who they are, and they prefer to purchase this brand even if another brand has the same characteristics. Secondly, regarding location differences, people from Tartu compared to persons from Tallinn agreed that A. Le Coq beer has a good status and a good reputation, the brand image of A. Le Coq beer is supportive, and A Le Coq beer keeps its promises. People from Tartu compared to people from Tallinn disagreed that the brand image of A. Le Coq beer is straightforward. Thirdly, regarding education level, master’s graduates compared to bachelor’s graduates feel emotionally bonded to A. Le Coq beer. Fourthly, non-employees (students) compared to employees agreed that the members of staff are well trained, and they disagreed that A. Le Coq beer keeps its promises. In addition, they disagreed that they are satisfied with consuming A. Le Coq beer. Finally, regarding nationality, the Estonians compared to other nationalities agreed that A. Le Coq beer is a visible brand name with personality, and they would purchase A. Le Coq beer even if other beers have the same characteristics or are as good as this brand. Furthermore, the Estonians disagreed with other nationalities that A. Le Coq beer helps them become the type of persons they want to be, and they disagreed that they really care about A. Le Coq beer.

The above findings on the five categories of demographic differences could assist the CEOs of A. Le Coq beer and of other brands to set up appropriate strategies to attract different groups of consumers to purchase their brands of beer.

6. Conclusions, Limitations, and Future Research

6.1. Conclusions

The notion of brand equity has received significant attention from scholars in the past two decades and many concepts and models concerning brand equity have been proposed. However, there are few studies that analyse the impact of brand equity components on overall brand equity based on empirical data. This paper has empirically examined the brand equity components of A. Le Coq beer in order to contribute to this sparsely examined area. Referring to the theoretical underpinnings of the study, we adopted Jillapalli and Jillapalli’s (2014) customer-based brand equity model, which is, in turn, based on Keller’s (1993; 2001) brand resonance model, and also employed Dennis et al.’s (2016) empirical research. The focus of this study was in unveiling an understanding of A. Le Coq beer in the minds of customers. In this section, we provide a summary of the results obtained from the multiple analyses and comment on the hypothesised relationships of the brand equity of A. Le Coq beer.

The present research contributes to the existing literature on beer brand equity, and specifically internal brand equity. We did not follow Aaker’s construct structure in the model, but used, for the first time, a product such as the alcoholic beverage of A. Le Coq beer, part of the structure of the constructs followed by Jillapalli and Jillapalli (2014), and the whole structure of constructs used by the study of Dennis et al. (2016). Both models have engaged the important four constructs of attachment strength, commitment, trust, and satisfaction. It is worth mentioning that the model of Jillapalli and Jillapalli (2014, Figure 2, p. 34) includes the construct of competence as one of the three dimensions of brand characteristics (perceived quality, competence, and reputation). In comparison to this study, the investigation by Dennis et al. (2016, Figure 1, p. 3051) included perceived quality, reputation, brand image, brand meaning, and brand identity, with the last three being new, as well as competence, which was not included. Both studies by Jillapalli and Jillapalli (2014) and Dennis et al. (2016) used three of the constructs utilised by Aaker (1991), namely, perceived quality, reputation, and brand equity, as the dependent variable. Additionally, the present study finds that all the examined relationships are positive and significant. Compared to both studies by Jillapalli and Jillapalli (2014, Table 3, p. 35) and Dennis et al. (2016, Table 4, p. 3054), which found some relationships that were negative and non-significant or positive and non-significant, the current study found only positive and significant relationships. Therefore, the application of the model by Dennis et al. (2016, Figure 1, p. 3051) on the current set of data in relation to the beer brand equity of A. Le Coq beer is very successful, as all hypotheses are supported. All the hypotheses are found to be positive and significant.

The study revealed that some relationships are strong and positive, and identified the strong brand equity drivers of beer in Estonia. These strong and positive relationships are between brand meaning and attachment strength, attachment strength and trust, attachment strength and commitment, and attachment strength and satisfaction.

In addition, the study revealed some significant differences between gender, i.e., male vs. female, adequate convergence or location, i.e., Tartu vs. Tallinn, education, i.e., participants with a bachelor’s degree vs. a master’s degree, occupation, i.e., university students vs. employees, and nationality, i.e., Estonians vs. others. There was no significant difference between the participants of the different age groups, i.e., 18–24- and 25–34-year-olds.

In the conceptual model, there is the suggestion that A. Le Coq’s reputation has a significant and positive impact on the brand characteristics (brand image, brand meaning and brand identity) of A. Le Coq beer, which is validated by the findings. Furthermore, the results of the study reveal that brand meaning as one of the brand characteristics causes a sense of attachment, whereas the other two brand characteristics—brand image and brand identity—do not have an impact on attachment strength. As anticipated, the attachment strength has an impact on customers in developing committed, trustful, and satisfying relationships with A. Le Coq beer. One of the major findings is that the relationship factors—commitment, trust, and satisfaction—play a significant role in the development of the brand equity of A. Le Coq beer. Committed, trusting, and satisfying relationships shape the customer’s perception of the brand equity of A. Le Coq beer and strengthen the overall brand equity.

The goodness of fit of the CFA model reveals that, generally, the model fits very well with the data, and, based on the calculations, the model has very good reliability and validity. In addition, the important relationships of the model that the CEOs of brands should pay attention to are specifically six relationships in terms of importance based on their path coefficients, including attachment strength to commitment, brand meaning to attachment strength, reputation to brand image, attachment strength to satisfaction, reputation to brand identity, and attachment strength to trust.

The results indicate that the conceptual model of brand equity of A. Le Coq beer is valid and mostly in line with the findings of Dennis et al. (2016) and parallel to the customer-based brand equity model of Jillapalli and Jillapalli (2014). The results are congruent with those of Dennis et al. (2016), who concluded that reputation has a positive and significant impact on brand characteristics but a non-significant impact on attachment strength. Additionally, the relationship between brand characteristics and attachment strength in this study shows consistency with Dennis et al.’s (2016) findings. On the other hand, Dennis et al. (2016) discovered a negative relationship between attachment strength and satisfaction; however, this study shows the relationship between them to be positive and statistically significant. Meanwhile, Jillapalli and Jillapalli (2014) indicated that the impact of attachment strength on trust is non-significant, which is contrary to the findings of both Dennis et al. (2016) and this study. Finally, Jillapalli and Jillapalli (2014) found relationship factors to have a positive and significant impact on brand equity, whereas this study indicates that commitment has a non-significant impact on brand equity, which parallels the findings of Dennis et al. (2016).

Aaker (2009) emphasises the role of cultivating brand equity in developing a strong brand in order to differentiate a product from its competitors and gain competitive advantage in the marketplace. Strong brand equity can be achieved in the long term through rigorous marketing efforts, and thus leads to competitive barriers against competitors (Yoo et al. 2000). Breweries in Estonia, therefore, should put more emphasis on cultivating and managing brand equity, since it plays a significant role in the emergence of strong brands.

6.2. Theoretical and Managerial Implications

Regarding the theoretical implications, one could say that the fit of the model is very good, showing the model to be quite satisfactory, with all relationships being significant and positive. On the other hand, regarding managerial implications, this study provides important insights to the management teams of competitive brands of beer, which should focus on the important relationships, namely, attachment strength and commitment, brand meaning and attachment strength, satisfaction to brand equity, commitment to brand equity, attachment strength to satisfaction, trust to brand equity, attachment strength to trust in the brand, and brand reputation to brand image. It is worth mentioning that both trust/commitment theory and CBBE theory are supported by the findings; for example, there are strong positive relationships within the tested model between trust, commitment, and satisfaction to brand equity and the determinants of CBBE are associated with strong positive relationships.

Additionally, regarding the managerial implications of this study, the management team of A. Le Coq beer brand should think about how to boost the quadratic relationship of attachment strength–commitment–trust–satisfaction, which in turn positively influences the beer brand equity. This can be achieved to a high degree by advertising the strategic characteristics of the beer brand and the positive reactions of consumers to the beer brand, being a leader in the Estonian market and Baltic countries’ markets. What is new to the literature is that the quadratic relationship of attachment strength–commitment–trust–satisfaction positively influences the beer brand equity, and what managers can learn from these finding is that the management team should apply different methods/proposals to boost these relationships. Such methods could include the application of a package of advertising techniques to achieve this objective.

6.3. Limitations and Future Research

This research contributes to the sparsely examined area of brand equity and is useful for understanding the brand equity of A. Le Coq beer, even though there are several limitations regarding the research. The sample size could be considered one of the limitations, since the conceptual model was tested with a sample size of 120 people. Additionally, the brand equity of A. Le Coq beer was studied with reference only to home country customers and not export markets.

Based on the characteristics of the profile of the participants discussed earlier, the sample was based on young consumers aged 18 to 34 years, located in Tartu and Tallinn, with the majority of participants being from foreign countries (94) as compared to Estonians (24), and most of the participants being university students (94) as compared to employees (26). All these characteristics limit the generalisability of the study. A similar limitation was found in another study focused on another context, namely, that of Katsikari et al. (2020). Therefore, a future survey should be based on a stratified sample to include older participants aged 35 to 65 years, a higher percentage of employees, a more representative sample of Estonians vs. foreigners, and a more geographically representative sample based on the population of the counties of Estonia. The size of the sample (120 surveys) constitutes another limitation of the study. More reliable and comprehensive results can be achieved by simply employing a bigger and more diverse sample size. The issue of a small target population (based on a sample of students from the University of Tartu) is another limitation of this study. Furthermore, the convenience sampling used in the study can lead to the lack of generalizability of the data and the results. Moreover, we have used the scale developed for HEI rather than others tested in products, as we could not use many of other approaches in same investigation due to a time limitation. Another possible limitation of this study was the fact that we have performed gender group and age group differences with the use of a t-test analysis instead of using ANOVA, which is a multigroup analysis. Therefore, there was no need to run ANOVA using SPSS29. Furthermore, since we have obtained a good result for the EFA and CFA by using SPSS29 and AMOS29, respectively, and we were familiar with them, there was no need to invent and learn to use new software, for example, using Marzi et al.’s (2023) software. According to McNeish and Gordon (2020), the issue of a misalignment in the representativity of the constructs can be effectively assessed by using Marzi et al.’s (2023) validity measure of latent variables by utilising the CLC estimator.

Another limitation is related to the cross-sectional nature of this research, where causal relationships can be derived. This limitation can provide an opportunity for future research, where a longitudinal study would benefit the in-depth understanding of the dynamic nature of brand-building activities. And, lastly, a comparative study involving another major beer brand in Estonia may reveal different aspects of brand equity formation in the brewery market.

Finally, one of the limitations of this study is that the results of the research are useful only to the management of A. Le Coq and to the CEOs of the competing brands. Moreover, local competing brands could possibly find the results of this study very interesting in terms of the decision-making of their own management.

Future research should focus on the findings of the EFA, in which both the constructs of satisfaction and brand equity were not identified. Research should be carried out in other countries to find out whether different cultures can influence the structure of the suggested model. Furthermore, researchers should assess models which have been tested on products.

It is worth mentioning that future studies should focus on a systematic literature review (see Coudounaris and Arvidsson 2019, 2022), on content analysis to find the potential themes (see Coudounaris et al. 2009; Leonidou et al. 2010), on a meta-analysis to find the meta-analytic correlations (see Coudounaris 2017, 2018; Coudounaris et al. 2020), or on a bibliometric analysis of the dimensions of CBBE, which are missing from the existing literature.

As the history of A. Le Coq in Estonia is intriguing and potentially specific, future researchers should consider the heritage of the brand (Hudson 2011; Rose et al. 2016; Pecot et al. 2018) in the context of the profound change in the Estonian market since 1912, involving World War I, the first independence, World War II, Soviet rule, and the new independence. This company is one of the few which have managed to survive. In this context, there are interesting paths to be tested, e.g., brand heritage, brand personality, brand nostalgia, brand quality, brand leadership, and brand competitive advantage (Chatzipanagiotou et al. 2016, Figure 1, Brand Building Block, p. 5481). Finally, brand authenticity (Prayag and Del Chiappa 2021) could be another antecedent factor of the brand building block.

Finally, future researchers should think to engage, in their future models, with the brand building block, brand understanding block, and brand relationship block, as discussed by Chatzipanagiotou et al. (2016), Chatzipanagiotou et al. (2019), Veloutsou et al. (2020), and Veloutsou (2023).