INTRODUCTION
The rationale of the research
Understanding consumer behavior is essential for effective marketing, particularly in competitive markets with similar products This study focuses on the factors influencing beer purchasing decisions, enabling marketing managers to develop strategies that resonate with customer preferences and enhance brand loyalty By analyzing what drives consumers to choose one brand over another, businesses can better predict market trends and tailor their approaches accordingly.
Beer-making has been a part of human history for centuries, but in Vietnam, it gained popularity only after its introduction by the French in the 19th century Since then, beer has become a national favorite, gradually replacing traditional home-brewed liquors due to its lighter alcohol content, refined taste, and affordability.
The beer industry in Vietnam continues to experience significant growth, competing with major markets like England, Japan, and Poland (FPTS, 2018) This expansion has led to a rise in both local and international breweries vying for market share This study aims to explore beer brand preferences in Hanoi, a cosmopolitan city, by examining seven intrinsic and extrinsic factors (Khongsawatvorakul, 2016) that may influence consumer behavior.
Extensive international research has explored brand preference in the beer market, revealing that it is influenced by both intrinsic factors, as noted by Aquilani (2015) and Khongsawatvorakul (2016), and extrinsic factors, highlighted by Amadi and Sunday (2014).
Despite the limited research on this topic within Vietnamese literature, this paper seeks to address the gap by establishing a conceptual framework for brand preference in Hanoi.
Objectives of the research
This research aims to investigate factors affecting customer brand preference in Hanoi beer industry
In detail, it resolves the 3 main objectives, as state below:
First of all, it sets to identify factors that can positively or negatively affect Vietnamese customer's beer brand preference.
This study employs an empirical framework to investigate the underlying factors driving the beer industry in Vietnam, with a focus on understanding the correlations between key variables and their impact on the market, providing a comprehensive overview of the industry's dynamics.
Finally, the findings may be of use to marketers and policy makers to better understand a customer's mindset and create corresponding strategies.
In order to give light to the objectives above, this research will also aim to answer some research questions:
1 Do beer characteristic, branding, beer types, situation appropriateness statements, packaging, social media and country of origin have relationships with consumer brand preference in Hanoi beer industry?
2 Do beer characteristic, branding, beer types, situation appropriateness statements, packaging, social media and country of origin a with consumer brand preference in Hanoi beer industry?
Scope of the research
A study conducted in Hanoi utilized a non-random convenience sampling method to analyze data from 300 respondents aged over 18 who had prior drinking experience The research aimed to explore the correlation between various independent variables, including beer characteristics, branding, beer types, situational appropriateness, packaging, social media influence, and country of origin, and their impact on the latent dependent variable of brand preference.
The study took place from March 5 to May 30, 2019, beginning with a month of literature review and the drafting of research outlines and questionnaires From April 1 to May 5, the focus shifted to collecting survey data, followed by the analysis phase and the development of conclusions in the remaining time.
Research methodology
Scientific research typically utilizes either qualitative or quantitative methods Qualitative research, primarily used for exploratory purposes, focuses on non-numerical data and emphasizes the use of words and language to convey observations This approach often employs various techniques, including interviews, focus groups, secondary data analysis, and direct observations, to gather insights.
Quantitative research is characterized by its numerical data, which is analyzed using mathematical and statistical methods (Mark Saunders, 2009) This type of research relies on primary data collected through surveys and observations, as well as secondary data The gathered data is then analyzed and visualized to identify correlations and assess statistical significance using specialized quantitative programs.
This research will employ a mixed-method approach for data collection and analysis The qualitative method will be applied during the survey development phase, where latent variables are represented through specific questions In contrast, the quantitative method will be utilized in the model development process, leveraging IBM SPSS and IBM AMOS for analyzing the survey results.
Structure of the research
This research will be divided into 5 distinct chapters, each with a singular purpose as detailed:
Chapter 1 - INTRODUCTION: We will discuss the rationale of the research,what this thesis aims to accomplish, its respective area of scope and also lightly touch on relevant researches on the same field.
Chapter 2 - LITERATURE REVIEW ON CUSTOMER BRAND PREFERENCE IN BEER INDUSTRY: This chapter aims to cover all the literature review on all factors, such as customer brand preference, beer characteristic, branding, beer types, situation appropriateness statements, packaging, social media and country of origin with their respective previous findings and how these will come together to build a feasible theory model.
Chapter 3 - REASEARCH METHODOLOGY: This chapter is to build research methodology and data analysis The study will discuss the data collecting procedures, questionnaires, how data would soon be analyzed and finally come up with a conclusive model to test out all my previous hypotheses.
Chapter 4 - RESEARCH FINDINGS: This chapter aims to illustrate research findings from said questionnaires, and further analysis on both individual factors and the model's regression validation, using Confirmatory Factor Analysis and Structural Equation Modeling These results are then compared to a set of indices for model fit and used to either approve or reject the hypotheses.
Chapter 5 - RECOMMENDATIONS AND CONCLUSIONS: This final chapter sums up all previous findings, and give recommendations for future further research, strategies for marketers and students who are interested in bettering the beer industry in Vietnam.
LITERATURE REVIEW ON CUSTOMER BRAND
Brand
A brand is a valuable asset that shapes identity and character, influences consumer choices, and fosters relationships among consumers Additionally, it offers numerous advantages for businesses, consumers, and society as a whole (Bahtışen Kavak, 2015).
In terms of consumers, brand is a quality indicator and creates awareness for products A highly sought after brand is great indicator that it is a brand of good quality and reputation
Brands foster customer loyalty, drive consistent sales, and enhance profit margins for firms This mutual benefit between consumers and companies underscores the importance of branding in social development, facilitating the exchange of goods, services, and cash In highly competitive markets, establishing strong brands is essential for firms to survive and achieve a competitive edge.
The definition of a "brand" is widely debated, with some viewing it as the personality of a business that encompasses attributes such as name, design, positioning, brand promise, target market, and awareness (Dib, 2016) Others see it as the perception held by customers about a company, organization, or individual (Lischer, 2018) As an intangible concept, a brand exists in the minds of various stakeholders, including employees, investors, consumers, and the media This brand personality is what makes it recognizable to loyal customers and forms the foundation for the personal relationships they develop with it.
In summary, a brand encompasses the overall perception that individuals have of an organization This perception is shaped by the brand compass, which includes a clear vision statement, mission statement, purpose statement, value proposition, and strategic objectives designed to achieve these goals.
Core values play a crucial role in shaping a brand's engagement with the world, influencing how it is perceived by the public A strong value proposition helps establish a positive reputation, impacting both customer trust and brand loyalty.
Customer brand preference in beer industry
According to Amadi and Sunday (2014), brand preference is defined as a measure of brand loyalty, indicating that a consumer will favor one brand over competing options However, if that preferred brand is unavailable, the consumer may opt for substitute products.
The concept of preference has been explored across various fields, including economics, psychology, and sociology Economists, such as Samuels (1978), argue that preferences are stable, exogenous, and revealed through choices, while others challenge this view In marketing, brand preference refers to consumers' desirability for specific alternatives, shaped by differentiation and comparison among brands This preference often leads to biases in evaluating alternatives, with customers' predispositions manifesting through affective, cognitive, and behavioral responses (Ebrahim, 2013).
A successful brand leverages positive customer attitudes to foster brand preference over competitors, but achieving brand loyalty is crucial for retaining existing customers Brand loyalty helps companies avoid the high costs of acquiring new customers, prevents brand switching, encourages additional purchases, and increases customer tolerance during product failures However, further research is needed to explore the complexities of brand loyalty, as this concept remains underrepresented in current studies.
Customer loyalty is closely linked to brand preference, which can be challenging to measure accurately However, tracking repurchases and referral programs can provide valuable insights It is essential to prioritize strategies that enhance brand preference, as building brand loyalty serves as a foundation for increasing brand equity In a competitive market with similar products, achieving higher brand equity is crucial, as it reflects the trust and confidence customers place in specific brands.
To gain a clearer understanding of brand preference, it is essential to differentiate it from related concepts like brand loyalty, brand choice, brand attachment, and brand awareness.
Brand preference refers to a consumer's tendency to choose a specific brand over competitors, while still being open to alternatives if that brand is unavailable In contrast, brand loyalty signifies a deeper commitment, reflecting a consumer's consistent repeat purchases regardless of competing marketing efforts The relationship between brand preference and brand loyalty highlights that while preference may indicate a choice, loyalty is demonstrated through sustained buying behavior over time.
Brand choice refers to the selection and consumption of a specific brand, while brand preference serves as a key motivator for this choice (Bettman & Payne, 1998) Consumers establish clear preferences that guide them in identifying the alternatives available for selection Marketing managers prioritize brand preference over brand choice, as it is indicative of potential repeat purchases; consumer preferences are generally stable across various contexts, unlike choices that may vary depending on specific situations.
Finally, the distinction between brand preference and brand awareness.
Brand awareness refers to the level of familiarity and recognition that customers have with a brand It signifies the favorable perception formed in consumers' minds after repeated exposure to the brand Consequently, brand awareness serves as the foundation upon which customers build their preferences for specific brands.
Cognitive consistency theory influences consumer preferences by highlighting the relationship between intrinsic brand benefits and satisfaction with brand value According to Rosenberg's attitude model (1956), a customer's preference for brands is shaped by these intrinsic benefits, underscoring the importance of aligning brand value with consumer expectations.
Ao = ∑ (PIi)(VIi) i=1 Ao: is the consumer preference toward an object.
PI: is the perceived instrumentally of the object; beliefs about the ability of the object to offer value i.
VI: is the value importance; the degree of satisfaction with the value i provided.
N: the number of values offered
The Fishbein model, widely recognized in marketing studies, originated from behavior theory and aims to explain the relationship between attitude and behavior By examining their impact, Fishbein developed an equation that quantifies brand preference using an algebraic scale based on brand-weighted attributes and beliefs associated with a product.
Ai = ∑ (Wi) (Bib) i=1Ai: the preference toward a particular brand.
Wi: the weight/importance of attribute i.
Bib: the belief toward attribute i for brand b.
N: the number of attributes important in selecting brand b
Customer brand preference serves as an indicator of brand loyalty, reflecting a consumer's inclination to choose one brand over competing options This preference is shaped by both intrinsic factors, such as innate characteristics, and extrinsic influences, including social, psychological, and economic cues The following section will delve deeper into these influencing factors.
Factors affecting Customer brand preference in beer industry
The unique characteristics of beer play a crucial role in influencing consumer purchasing decisions Key attributes such as aroma, foam, perceived quality, and carbonation significantly affect the selection of different beer types (Aquilani, 2015).
While the inherent characteristics of beer are important, they do not tell the entire story According to Carole Sester (2012), previous customer experiences with brand stimuli significantly influence beer choice and preference, often overshadowing the beer's innate qualities.
Taste remains a crucial factor influencing customers' repeat purchase decisions, with research by Gnel Gabrielyan (2014) indicating that consumers are more inclined to return to a flavorful beer and are often willing to pay a premium for it The four primary ingredients in beer—malt, yeast, water, and hops—allow brewers to craft a diverse range of flavors, leading to significant quality differentiation among various beer offerings.
A beer blind taste test conducted by Carole Sester in 2012 supports the theory that sensory cues—such as taste, texture, color, appearance, and flavor—significantly influence customer perceptions, leaving lasting positive or negative impressions This blind tasting method is widely utilized in scientific research to minimize mental biases and prior preferences, allowing participants to concentrate solely on the sensory attributes of the samples being evaluated.
To accurately assess product quality, it's essential to define its meaning According to Stanley Davis (2013) in "Quality Management for Organizational Excellence," product quality can be understood as either the standard performance or the expected performance of a product The disparity between customer expectations and actual product performance significantly influences customer perception and brand preference.
Quality can be assessed both individually and collectively through the five stages of the customer decision-making process: need recognition, information search, evaluation of alternatives, purchase, and post-purchase behavior (Charles Lamb & McDaniel, 2014).
To sum up: aroma, foam, perceived quality, appearance of texture and color, these innate characteristics are found to be positively influencing brand preference.
Branding serves three key purposes: product identification, encouraging repeat sales, and facilitating new product launches, all of which significantly impact consumer choices (Charles Lamb & McDaniel, 2014) Customers often link branding with quality, making it essential for businesses to effectively differentiate their products in a competitive market By strategically utilizing names, terms, symbols, and designs, firms can create a distinct identity for their products, enhancing their market presence and gaining a competitive advantage.
The beer market exemplifies the importance of branding in influencing consumer purchasing decisions, as highlighted by Siegel (2013) In this competitive landscape, a few dominant alcohol brands capture the majority of consumption When production quality is no longer a distinguishing factor and product quality reaches its peak, it becomes essential for brands to focus on exceptional customer service and a strong brand personality Ultimately, fostering customer recognition and identification is crucial for achieving success in this industry.
Brand equity is crucial for organizations, as it encompasses high brand awareness, perceived quality, and customer loyalty (Charles Lamb & McDaniel, 2014) A strong brand equity not only enhances consumer trust but also signifies the reliability of the organization itself Developing a corporate brand is essential, as a positive brand image instills confidence in consumers and stakeholders regarding the company's products and activities (Abdurrahman, 2015).
Beer is one of the most popular alcoholic beverages in Vietnam, enjoyed by both men and women A study by Ngọc & Thiêng (2018) revealed that an impressive 68.7% of participants expressed a preference for beer.
The brewing process significantly impacts customer preferences for different beer types, as highlighted by the influence of "secret ingredients" on consumer perception (Leonard Lee, 2006) These ingredients can shape expectations regarding the product Typically, beers are crafted using four primary methods: wheat, malt, fruit, or craft beer (Khongsawatvorakul, 2016).
The Vietnamese beer market faces challenges with the availability of certain types, particularly fruit beer and malt, due to the country's tropical climate Despite malt beer being a staple for many Vietnamese, producers have turned to various "filler" ingredients like corn and potatoes to reduce costs and increase accessibility The market is dominated by a few major players, with Sabeco holding a 40% market share, followed by Heineken at 25% and Habeco at 18%.
The term "draft beer" here can sometimes be used interchangeably with
Craft beer, known as Bia Tươi, differs significantly from draft beer, or Bia Hơi While both undergo fermentation, draft beer is pasteurized through rapid heating and cooling to eliminate harmful bacteria, resulting in a shorter shelf life In contrast, craft beer is typically brewed independently by restaurants or small breweries and is served fresh without pasteurization, allowing for a unique flavor profile.
Beer is typically not a staple in every household's meals and is often enjoyed during special occasions such as weddings, social gatherings, and personal downtime The choice of beer is influenced by the context and appropriateness of the situation, making it a beverage that complements the event Additionally, the food served during these occasions plays a significant role in shaping participants' beer preferences, highlighting the interplay between cuisine and beverage selection (Carlos Gomez-Corona, 2016).
People typically enjoy beer in two primary settings: at home or in restaurants When consuming beer at home, individuals often focus more on the selection of beers, as these beverages serve as a catalyst for sharing memorable moments with friends and family.
While at the restaurant, beer choices are, most of them time, affected by the food which are present (Carlos Gomez-Corona, 2016).
In order to have clearer command of situation appropriateness, (Cardello &
RESEARCH METHODOLOGY
Research process
The research process for the topic comprises of 5 basics steps which were done in order one after another.
The initial phase involves identifying research topics, which lays the groundwork for future studies by establishing the rationale, objectives, methodology, and scope This leads to the second phase, the literature review, where a comprehensive analysis of existing literature is conducted to gather relevant information and insights, including methodologies from similar studies The third phase encompasses qualitative research, aimed at developing and refining the research scale to align with the context, ultimately proposing a formal model for the study This qualitative approach is then integrated with quantitative research to yield comprehensive results.
This article outlines the essential steps for conducting effective research in the beer industry, beginning with the identification of relevant research topics and a comprehensive literature review It emphasizes the importance of both qualitative and quantitative research methods, employing statistical techniques such as Cronbach's Alpha for reliability analysis, Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), Pearson's correlation matrix, and multiple regression analysis to rigorously test hypotheses Ultimately, the findings will yield valuable recommendations and conclusions, equipping marketers with current insights to enhance their strategies in the competitive beer market.
Research method
This research aims to identify factors that positively affect brand preference, utilizing a quantitative approach for the collection and analysis of primary data Quantitative methods, commonly associated with techniques such as questionnaires and statistical analysis, are effective in generating numerical data (Mark Saunders, 2009).
Questionnaires were created based on dimensions established by prior research, focusing on key variables such as Beer Characteristics, Beer Types, Branding, Situation Appropriateness, Packaging, Social Media, Country of Origin, and Brand Preference These variables were then translated into the questionnaires using a 5-point Likert scale to effectively quantify participants' opinions Following the completion of a draft questionnaire, a pilot test was conducted with 10 friends of the author to identify any misleading or unclear questions Feedback from this pilot test will inform the development of the final questionnaire.
On the content of the questionnaire itself, it is divided into 2 main sections.
The demographics section of the study will gather personal information from participants, including gender, age, marital status, education level, monthly income, beer consumption frequency, and favorite beer brands This data serves two key purposes: to provide a foundational understanding of the research sample and to identify any potential biases, while also filtering out respondents who do not fit the target demographic, such as those who have never consumed beer Following this, the study will delve into customer preferences, exploring the reasons behind their brand loyalty in the beer market.
There are total 40 multiple choice Questions using 5 point Linkert scale, which will be used to explore more on the 8 predetermined affecting variables.
The analysis of data distribution will focus on the characteristics of survey sample groups, including gender, age, education level, marital status, income, frequency of beer consumption, and preferred beer brands.
Descriptive statistical analysis is conducted on the observed variables of the research model, followed by the application of Cronbach's Alpha to assess the reliability of these variables The study identifies eight potential variable groups influencing consumer brand choice in the beer industry in Hanoi, measured using a 5-point Likert scale.
The Scale test, ranging from (1) Strongly disagree to (5) Strongly agree, aims to identify which question items should be retained or removed from questionnaires, as outlined by Hoang Trong (2008) This process is essential for eliminating unsuitable variables and ensuring that the scales meet the necessary requirements.
Researchers agree that a Cronbach’s Alpha between 0.8 and 1 indicates an optimal scale test, while a range of 0.7 to 0.8 is considered usable Additionally, an alpha above 0.6 is acceptable for new or novel measurement concepts (Hoang and Chu, 2008) Prior to removing inconsistent variables, Cronbach's alpha coefficients are evaluated, and any item-total correlation below 0.3 is disqualified, maintaining a standard of alpha at 0.6 or higher for scale selection (Hoang and Chu).
2008) For this study, the author proposed that the level of Cronbach’s alpha of 0.6 or more is acceptable.
Exploratory Factor Analysis (EFA) is a standard procedure aimed at uncovering the fundamental relationships between calculated variables and a group of variables Researchers commonly utilize EFA when developing a scale, which consists of questions designed to assess specific research topics In EFA, 'factors' refer to continuous latent variables, while 'factor indicators' denote the observed variables These indicators can vary significantly, encompassing censored, continuous, count, ordinal, binary, or a combination of these types EFA posits that any given indicator or measured variable may be associated with either a common factor or a unique factor.
Exploratory Factor Analysis (EFA) is used to shorten the set of observed variables with close relationships without reducing the amount of initial variables.
Factor analysis is a statistical method utilized to assess the convergence of conceptual variables, ensuring convergence validity It also evaluates discriminant validity, which measures the distinctiveness of variables by confirming their lack of correlation.
Exploratory factor analysis should be based on specific and reliable standards.
The research employs the Exploratory Factor Analysis (EFA) method, utilizing Principal Components with Varimax rotation and extracting factors with Eigenvalues greater than 1 Variables that fail to meet specific criteria are excluded from analysis The selection of observed variables is based on the KMO (Kaiser-Meyer-Olkin) coefficient, which assesses the suitability of the factor analysis, requiring a range of 0.5 to 1 Additionally, an Eigenvalue greater than 1 indicates that over 50% of the total variance is accounted for, while Bartlett’s test must yield a significance level below 0.5 to confirm statistical relevance Furthermore, a factor loading exceeding 0.55 is necessary, in accordance with the sample size requirements, as detailed in Chapter 4.
The Pearson correlation coefficient measures the strength and direction of the linear relationship between two quantitative variables A positive correlation is indicated by r > 0, while r < 0 signifies a negative correlation If r = 0, it suggests no linear relationship exists between the variables The strength of the relationship is determined by the absolute value of r, with values closer to 1 indicating a stronger correlation and values closer to 0 reflecting a weaker relationship.
High correlation among independent variables can indicate the presence of multicollinearity in a linear regression model.
The regression model analyzes the impact of various independent factors on the brand preference of beer consumers in Hanoi, with brand preference serving as the dependent variable Utilizing the ordinary least squares method, the study employs the adjusted R² as a statistical measure to assess how closely the data aligns with the regression line Additionally, the F statistic is used to test the hypothesis regarding the equality of variances between two populations The relationship between the dependent and independent variables is encapsulated in a specific equation that summarizes the model's focus on brand preference.
BRAPRE = β1.PACKAG + β2.CHARAC + β3.BRANDI + β4.SITAWA + β5.BEETYP + β6.SOCMED + β7.COUORI
All qualified variables will undergo Confirmatory Factor Analysis to assess the fit of the research model and the collected data for the observed variables Additionally, Structural Equation Modeling (SEM) will be utilized to examine potential relationships between variables in line with the proposed hypotheses SEM is a statistical technique designed to analyze the inter-relationships among multiple variables within a model.
The inter-relationships among variables could be expressed in a series of single and multiple regression equations (Awang, 2012).
Confirmatory Factor Analysis (CFA) is utilized to validate the factorial structure of an established measurement tool within a specific target population When the proposed CFA model aligns well with the data, it substantiates the validity of the factorial structure for that population (Wang & Wang, 2012).
Research model
This study will investigate seven key factors influencing beer selection: beer characteristics, branding, types of beer, situational appropriateness, packaging, social media presence, and country of origin These latent variables will be examined through a questionnaire, with each factor represented by five targeted questions, following the methodology established by Khongsawatvorakul (2016) The aim is to explore the relationships among these independent variables to gain deeper insights into consumer preferences.
Beer characteristics I choose beer based on its aroma CR1
I choose beer based on its foam structure
I choose beer based on its color and texture
I buy beer based on my previous experience with its brand
I buy beer because of product quality
Branding I choose beer based on its brand's BRAN1 trustworthiness
I choose beer based on its brand's reputation
I have at least 01 favorite beer brand
I am very familiar with my favorite beer brand
I have a clear image of the type of people who drink my favorite beer brand
Beer types I can tell the difference between malt, fruit or wheat beer
I can tell the difference between manufactured beer and craft/draft beer
I prefer malt beer than any others BTY3
I prefer craft beer (Bia Tươi) than any others
I prefer draft beer (Bia Hơi) than any others
Situation appropriateness I drink beer when I am at a casual dining restaurant
I drink beer when I relax alone at home SA2
I drink beer when I am at parties SA3
I drink beer when I want to impress someone
I drink beer for a special occasion.
Packaging Buying beer with extravagant packaging makes me feel good about myself
I buy beer with sufficient information printed on its packaging
I have a strong urge to buy beer with a really well designed
Beer's packaging design can be a source of satisfaction for me
Sufficiency of printed product information on packaging can be a source of satisfaction for me
Social media I use social media to enhance my understanding of my favorite beer brand
I use social media to interact with my favorite beer brand
I use social media to catch up with my favorite brand's promotional activities
I use social media to follow up news and events regarding my favorite brand
My purchasing decision is influenced by other opinions on social media
Country of origin Beers from Vietnam have the best quality
Beers from German have the best quality
Foreign beers are generally better than domestic ones
International beer brands have the best quality
I often drink international beers more than domestic beers
Brand preference I am loyal to only a selected few of my favorite beer brands
If my favorite beer is available, I won't buy beers from another brand
I am willing to recommend others to buy my favorite beer brand
I will definitely purchase my favorite beer brand in the future
I am completely confident in the quality of my favorite beer brand BPRE5
The questionnaire was developed based on foundational research, featuring seven latent variables, each represented by five specific indicators in the form of questions To effectively quantify these observed variables, a 5-point Likert scale was utilized, which, according to my research, is the optimal method for quantitative analysis This approach ensures robust reporting on Cronbach’s alpha coefficient, demonstrating internal consistency and reliability across multiple variables (Gliem & Gliem, 2003) Further details regarding the questionnaire can be found in the appendix.
From the conclusions drawn from a variety of researches done above, I sum up the following hypotheses:
Hypothesis H1: Beer characteristics positively influence brand preference.
Hypothesis H2: Branding positively influence brand preference.
Hypothesis H3: Beer types positively influence brand preference.
Hypothesis H4: Situation appropriateness positively influences brand preference.
Hypothesis H5: Packaging positively influences brand preference.
Hypothesis H6: Social media positively influences brand preference.
Hypothesis H7: Country of origin positively influences brand preference.
Data analysis
The research focuses on able individuals residing in Hanoi who are over the legal drinking age of 18 and have consumed alcohol at least once A non-random convenience sampling method was employed to gather data from willing participants, who completed the provided questionnaires (Trochim, January 2006).
The sample size for this research was determined based on previous studies, particularly Thong's (2017) analysis of beer choice in Nha Trang, which utilized 390 respondents Additionally, Springer's (2017) findings suggest that a sample size exceeding 200, preferably around 300, is optimal for conducting Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) After eliminating incomplete responses and participants who had never consumed alcohol, the final sample size for this study was set at 300.
This research aims to establish a conceptual framework for brand preference by examining its correlation with seven key latent variables: beer characteristics, branding, beer types, situation appropriateness, packaging, social media, and country of origin.
Due to resource constraints that hindered true random sampling and comprehensive population surveys in Hanoi, this research utilized non-random sampling methods Specifically, convenience sampling was employed, where online surveys were distributed to students, office workers, and acquaintances of the surveyor throughout the city.
As mentioned, the study was conducted from March, 5 th 2019 to May 30 th
In March 2019, the literature review was completed alongside the development of a questionnaire, which was adapted from a previous study on customer beer preferences in Bangkok (Khongsawatvorakul, 2016) From April 1st to May 5th, an online questionnaire was distributed via Google Forms to ensure ease of access It was shared among students from various institutions, including Foreign Trade University, National Economics University, and Hanoi University of Science and Technology, as well as alumni from Chu Van An High School and Trung Vuong Secondary School, and a range of companies and personal networks in Hanoi.
This study will analyze primary data collected from questionnaires to explore the causal relationship between various determinants influencing brand preference in the beer industry in Hanoi Key factors under consideration include beer characteristics, branding, types of beer, situational appropriateness, packaging, social media presence, and the country of origin.
The analysis of data will be conducted using IBM SPSS and IBM AMOS, employing various statistical methods Scale reliability will be assessed through Cronbach's Alpha, while Exploratory Factor Analysis will evaluate dimensionality Confirmatory Factor Analysis will determine the extent to which measured variables accurately represent constructs, and Structural Equation Modeling in AMOS will investigate the relationships between latent and observed variables.
RESEARCH FINDINGS
An introduction about beer industry in Vietnam
Beer production has a long history in Europe, but in Vietnam, it officially began in the 19th century with the introduction of brewing by the French colonial regime Initially aimed at satisfying the thirst of French soldiers, beer quickly gained popularity among the middle-class Vietnamese, who appreciated its lighter and more refined taste compared to traditional Vietnamese liquor, along with its affordability for everyday consumption.
The beer industry in Vietnam is experiencing significant growth, with an annual capacity of 4 billion liters, driven by major domestic breweries like Sabeco and Habeco, alongside international brands such as Heineken and Carlsberg This remarkable trend highlights Vietnam's success in attracting foreign investment in a relatively short development period compared to other countries Notably, in 2017, Vietnam ranked as the 10th fastest-growing beer market, surpassing 15 other Asian nations since 2007, and boasts the highest Compound Annual Growth Rate in the industry A report from Lincoln (2015) further supports this growth narrative, indicating a rapid increase in national alcohol consumption Overall, these statistics underscore Vietnam's substantial potential for expansion in the alcoholic beverage sector.
Table 4.1: Top 10 Asian countries with highest market growth
Source: Ministry of International Trade and Industry
Despite the growth opportunities for major players in the Vietnamese beer industry, local breweries like Sabeco and Habeco are experiencing a decline in market share, while international brands such as Heineken are rapidly increasing theirs This trend highlights the diminishing advantages of local beer brands in a promising industry To effectively compete in this fast-moving consumer goods sector, there is an urgent need for well-designed research that can inform strategic marketing initiatives.
A report by FPTS (2018) suggests that changing consumer habits are driving a shift in beer preferences, with consumers increasingly seeking higher quality products and improved consumption experiences This trend has led to a rise in demand for premium and middle-class beers, moving away from low-priced options Heineken has capitalized on this shift, dominating the market through effective global branding strategies.
A significant factor contributing to the change in beverage consumption habits among Vietnamese consumers is psychological in nature Beyond simply satisfying their thirst, individuals are motivated by a strong desire to enhance their social image during gatherings This aligns with findings from previous research by DonovanR, highlighting the importance of social perception in consumer behavior.
Research indicates that enhancing interpersonal relationships significantly impacts the decision to purchase beer products This study will also investigate how these relationships influence brand preference, particularly in the context of "Situation Appropriateness Statement."
Vietnamese consumers tend to favor foreign brands, perceiving them as superior in quality and reputation This article will explore the relationship between brand preference and the "Country of Origin" factor, as suggested by Sunkamol (Khongsawatvorakul, 2016).
The topic of beer brand preference remains underexplored in Vietnamese academic literature Noteworthy contributions include Nguyen Tien Thong's 2017 study in Nha Trang, which examines the impact of brand, price, and packaging on consumer choices Additionally, Sunkamol's comprehensive research in Bangkok identifies various factors—including beer characteristics, branding, types, situational appropriateness, packaging, social media, country of origin, and practical functions—that influence beer brand preference Gabriel A Okwandu's findings from Nigeria further validate the significance of price, taste, origin, and packaging in shaping consumer preferences This research aims to investigate how these seven factors collectively affect brand preference in the Vietnamese context.
Demographics
The study incorporates demographic questions aimed at understanding the characteristics of the 300 participants responding to the questionnaires These demographic inquiries focus on key aspects such as gender, age, marital status, education level, monthly income, frequency of beer consumption, and preferred beer brands, providing valuable insights into the sample population's profile.
The sample consists of 192 male participants and 108 female participants.
While the findings suggest a potential trend of men drinking more beer than women, this does not confirm a definitive higher consumption among men in the overall population Conducting more comprehensive research with true random sampling could address the limitations of convenience sampling in this study, leading to more conclusive results.
The study's sample was categorized into five distinct age groups, revealing that individuals under 23 years old comprised the largest portion at nearly 49% Following this group, participants aged 24 to 29 accounted for 30% of the total sample, while those aged 40 and above represented a modest 7%.
This is again, the result of sampling technique and might not necessarily reflect the whole population.
The survey reveals that 55% of participants are unmarried, a trend consistent with the predominance of young individuals in the sample, who are less likely to have started families Notably, 27% of respondents selected the "Other" option, suggesting the presence of an unexplored category regarding marital status.
Table 4.5: Demographics - Level of education
The use of a nonrandom sampling technique may introduce biases in the sample representation In this survey, 82% of the 300 participants hold a college or university degree, while none have less than a high school diploma, indicating that individuals with lower educational attainment are likely underrepresented in the sample.
A majority of participants, 57%, report a monthly income exceeding 8.6 million VND In contrast, 29% earn less than 3.5 million VND, while only a small fraction falls within the middle income brackets, with 4% earning between 3.6 and 5.5 million VND and 9% earning between 5.6 and 8.5 million VND.
A recent study by Ngọc & Thiêng (2018) at the National Economics University revealed that 64% of Vietnamese individuals consume beer primarily for special occasions and celebrations Following this, 19% reported drinking several times per month, while 10% indicated they drink several times per week Notably, only 1% of respondents stated they drink beer once a month.
Table 4.8: Demographics - Favorite beer brands
Participants were asked to select their preferred beers from a list of options available in the Hanoi market With no limit on the number of choices, this data reveals the most popular beer brands among respondents Heineken emerged as the top choice, boasting an impressive 87% selection rate, followed by Sai Gon Beer at 80% Hanoi ranked third, favored by over half of the participants at 59% The least popular brands included Sư tử trắng, Halida, and Huda, with selection rates of 4%, 6%, and 6%, respectively.
Descriptive statistics
CO3 300 1 5 3.93 707 Favoritism of foreign beers (CO3)
The table above summarizes answers to the given questionnaire CR2 was deemed to have the highest mean (3.23) while CR3 was deemed the lowest (2.49).
Meanwhile CR5 has the highest deviation of opinions among 5 questions (1.051) while CR1 has the least (0.977).
In a recent analysis of branding items, BRAN4 achieved the highest average score of 3.69, while BRAN5 received the lowest score of 3.35 Additionally, BRAN5 exhibited the greatest variability in responses, with a standard deviation of 0.948, compared to BRAN4, which showed the least variability at 0.855.
In the analysis of beer types, BTY4 achieved the highest average rating of 3.53, while BTY3 recorded the lowest at 3.087 Additionally, BTY5 displayed the greatest variance in opinions among the five questions, with a standard deviation of 1.021, whereas BTY1 showed the least variation at 0.8321.
When considering the items of Situation Appropriateness, SA2 was deemed to have the highest mean (3.17) while SA1 was deemed the lowest (2.17).
Meanwhile SA2 also has the highest deviation of opinions among 5 questions (1.42) while SA1 also has the least (0.803).
In the analysis of packing items, P4 and P1 emerged with the highest average rating of 3.97, while P3 received the lowest score of 3.92 Additionally, P4 exhibited the greatest variation in responses across five questions, with a standard deviation of 0.792, whereas P2 showed the least variation at 0.764.
In the analysis of social media items, SM1 achieved the highest mean score of 3.84, indicating a strong positive reception, while SM4 recorded the lowest mean score of 3.69 Additionally, SM4 exhibited the greatest variation in responses, with a standard deviation of 0.842, whereas SM1 showed the least variation, with a standard deviation of 0.805.
The analysis of the country of origin revealed that CO1 had the highest mean score of 4.00, while CO2 received the lowest mean score of 3.85 Additionally, CO3 exhibited the greatest variability in responses among the five questions, with a standard deviation of 0.707, whereas CO1 showed the least variability, with a standard deviation of 0.652.
In the analysis of brand preference items, BPRE4 emerged with the highest mean score of 3.05, indicating strong preference, while BPRE3 recorded the lowest mean at 2.68 Additionally, BPRE4 exhibited the greatest variability in opinions among the five questions, with a standard deviation of 0.924, whereas BPRE1 showed the least variability at 0.819.
Scale test on factors affecting beer preference
The analysis of Beer Characteristics revealed a Cronbach's Alpha reliability of 0.886, exceeding the acceptable threshold of 0.6, while the Item-Total Statistics showed a Corrected Correlation greater than 0.3 for all dimensions These findings, in accordance with Nunally (1978) and Peterson (1994), confirm that dimensions CR1, CR2, CR3, CR4, and CR5 are reliable and suitable for further analysis.
In the analysis of branding, Cronbach's Alpha reliability was found to be 0.779, exceeding the acceptable threshold of 0.6 Additionally, the Item-Total Statistics revealed that the Corrected Correlation for all dimensions was above 0.3, with the exception of BRAN2, which recorded a correlation of 0.224 As a result, dimensions BRAN1, BRAN3, BRAN4, and BRAN5 will proceed to further analysis, while BRAN2 will be excluded from the study.
The reliability analysis for the Packaging dimensions yielded a Cronbach's Alpha of 0.931, surpassing the acceptable threshold of 0.6 Additionally, the Item-Total Statistics showed corrected correlations for all dimensions exceeding 0.3, confirming their validity as per the criteria established by Nunally (1978) and Peterson (1994) Consequently, the dimensions P1, P2, P3, P4, and P5 are deemed suitable for further analysis.
According to the figure once more on Beer Types, Cronbach's Alpha reliability was 0.857, which is greater than 0.6 and Item-Total Statistics's Corrected
All dimensions exhibit a correlation greater than 0.3, which is deemed acceptable according to Nunally (1978) and Peterson (1994) Consequently, the dimensions BTY1, BTY2, BTY3, BTY4, and BTY5 are confirmed for further analysis.
The Situation Appropriateness factor demonstrated a Cronbach's Alpha reliability of 0.719, exceeding the acceptable threshold of 0.6 Additionally, the Item-Total Statistics revealed that the Corrected Correlation for all dimensions was above 0.3, with the exception of SA2, which recorded a correlation of 0.071 (Nunally).
1978) (Peterson, 1994) Therefore, all of these dimensions SA1, SA3, SA4 and SA5 will pass to further analysis and SA2 is removed from the batch.
The analysis of social media dimensions revealed a Cronbach's Alpha reliability of 0.752, exceeding the acceptable threshold of 0.6 All dimensions, except for SM3, demonstrated a Corrected Correlation greater than 0.3, with SM3 showing a correlation of only 0.153 Consequently, dimensions SM1, SM2, SM4, and SM5 will proceed to further analysis, while SM3 will be excluded from the study.
The final influencing factor is the Country of Origin, which demonstrated a Cronbach's Alpha reliability of 0.657, exceeding the threshold of 0.6 Additionally, the Item-Total Statistics indicate that the Corrected Correlation for all dimensions is above 0.3, with the exception of CO4, which recorded a value of 0.1 (Nunally).
1978) (Peterson, 1994) Therefore, all of these dimensions CO1, CO2, CO3 and CO5 will pass to further analysis and CO4 is removed from the batch.
The reliability of the dependent variable was assessed using a scale test, yielding a Cronbach's Alpha of 0.863 for brand preference, which exceeds the acceptable threshold of 0.6 Additionally, the Item-Total Statistics showed a Corrected Correlation greater than 0.3 for all dimensions, confirming the adequacy of the dimensions as per Nunally's criteria.
1978) (Peterson, 1994) Therefore, all of these dimensions BPRE1, BPRE2, BPRE3, BPRE4 and BPRE5 will pass to further analysis Below is the table summarizing scale test results on different factors.
Table 4.10: Reliability test on factors
Scale Mean if Item Deleted
Scale Variance if Item Deleted
Correlation matrix
CR BRAN BTY SA P SM CO BPRE
** Correlation is significant at the 0.01 level (2-tailed).
* Correlation is significant at the 0.05 level (2-tailed).
The Pearson product-moment correlation coefficient quantifies the strength of the linear relationship between two variables, with values ranging from -1 to 1 A coefficient of -1 signifies a perfect negative relationship, 0 indicates no relationship, and 1 denotes a perfect positive relationship Analysis reveals that all factors, except for Beer Types with a minimal coefficient of 0.085, significantly and positively influence Brand Preference To further validate these findings, the factors will undergo additional analysis using Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and Structural Equation Modeling (SEM).
Exploratory factors analysis
Table 4.12: KMO and Barlett's Test
Kaiser-Meyer-Olkin Measure of
Having Kaiser-Meyer-Olkin measure of sampling adequacy between 0.5 and
Before conducting Exploratory Factor Analysis (EFA), it is essential to have a sample size of at least 1 (with a value of 0.897), as indicated by Hair et al (2006), to ensure the adequacy for factor analysis Additionally, Hair emphasizes that for the analysis to be valid, Bartlett's Test of Sphericity must yield a significance level of less than 0.5, indicating a statistically significant interrelationship among the variables.
Therefore my Significance value of 0.000 satisfies the condition.
Table 4.13: Extraction of communalities Communalities
BPRE1 517 496BPRE2 693 830BPRE3 588 614BPRE4 582 594BPRE5 430 406Extraction Method: Principal
A key step in conducting factor analysis is to evaluate the extraction of communalities, which, according to Dinh and To (2017), should exceed 0.2 My analysis confirmed that all communalities met this criterion, with the lowest value recorded for dimension BTY3 at 0.361, utilizing the principal axis factoring extraction method.
Table 4.14: Total variance explained Total Variance Explained
Extraction Sums of Squared Loadings
Rotation Sums of Squared Loadings Total % of
Eight of these extracted factors have Total Initial Eigen values greater than 1, with a cumulative percentage of variance at 66.545%, satisfying (Gerbing, et al.,
1998) requirement that this value must be greater than 50% In short, these 8 extracted factors can explain 66.545% the variability of the variables.
Table 4.15: Factor loading based on the size of a sample
The table above was established by Gerbing and Anderson (Gerbing, et al.,
In accordance with the findings from 1998, a factor loading of 0.55 is necessary for a sample size of 300 in this research Dimensions with factor loadings below 0.55 will be excluded from the Pattern Matrix analysis and removed from subsequent testing.
Table 4.16: Pattern Matrix Pattern Matrix
Extraction Method: Principal Axis Factoring
Rotation Method: Promax with Kaiser Normalization. a Rotation converged in 6 iterations.
Using a factor loading threshold of 0.55 and the Principal Axis Factoring extraction method, all dimensions have been effectively aligned in the table, avoiding any suppression to blank values Consequently, all these dimensions will be included in the Confirmatory Factor Analysis (CFA).
Confirmatory factors analysis
The model illustrated was created with IBM AMOS utilizing the maximum likelihood method, indicating that the findings from the research sample can be generalized and are representative of the entire population.
According to Singh (2017), certain preconditions must be met before conducting a Confirmatory Factor Analysis (CFA) Firstly, the observed variables should be on a continuous scale, which is satisfied in this study as it utilized a 5-point Likert scale with a sample size of 300, qualifying it as an interval/continuous scale Secondly, Singh emphasizes the importance of unidimensionality, necessitating that all factor loadings for newly developed items must have positive values greater than 0.5.
In this research, established items necessitate a factor loading of 0.6 or higher, as indicated by Awang (2012) Additionally, construct validity, confirmed by Cronbach's Alpha values exceeding 0.6, has been detailed in the Third Chapter Therefore, all prerequisites have been satisfied, allowing the factors to proceed to Confirmatory Factor Analysis (CFA).
Figure 4.2: Measurement model in Confirmatory Factor Analysis
Table 4.17: Evaluation of Model Fit Commonly used model fit indices
Fit index Recommended cut-off Model indicator
Chi-square/df Chi-square/df ≤ 3: great fit 1.158 Great fit
(Chi-square over degree of freedom)
Comparative Fit Index (CFI) CFI ≥ 0.9 0.988 Acceptable
Tucker-Lewis Index (TLI) TLI ≥ 0.9 0.986 Acceptable
Root Mean-Square Error of Approximation (RMSEA)
RMSEA ≤ 0.05: great fit 0.023 Great fit
With all tests on parsimonious fit, incremental fit and absolute fit being satisfied, it can be concluded that measurement model fits observed data.
Structural equation model
Table 4.18: Evaluation of Model Fit Commonly used model fit indices
Fit index Recommended cut-off Model indicator Conclusion
Chi-square/df Chi-square/df ≤ 3: great fit
(Chi-square over degree of freedom)
Root Mean-Square Error of Approximation (RMSEA)
Structural Equation Modeling (SEM) is utilized to explore the relationships between items and constructs, illustrating the connections among the variables of interest The analysis was performed using unstandardized beta coefficients, and the model demonstrated strong performance by meeting all criteria for parsimonious, incremental, and absolute fit indices.
Regression Results
To validate the constructs as independent and dependent variables, SEM and CFA are employed Following this, an unstandardized regression coefficients table is created to assess the hypotheses, determining whether they should be accepted or rejected based on the regression equation.
The research findings indicate acceptance of hypotheses H1, H2, and H4, as their P-values are below 0.05 and their critical ratios exceed 1.96, aligning with a two-tailed test confidence level of 0.95 Notably, hypothesis H2 demonstrates a P-value lower than 0.001, highlighting optimal factor covariance (Hair et al.).
Beer characteristics, branding, and situational appropriateness are the three most influential factors affecting beer brand preference Among these, branding stands out as the most significant factor, with a beta value of 0.327, indicating its strong impact on consumer choices.
The analysis reveals that Situation Appropriateness and Beer Characteristics significantly influence Brand Preference, with coefficients of β = 0.158 and β = 0.136, respectively Specifically, for every unit increase in Branding, Brand Preference increases by 0.327 Additionally, a unit increase in Situation Appropriateness leads to a 0.158 increase in Brand Preference, while an increase in Beer Characteristics results in a 0.136 rise in Brand Preference, assuming all other factors remain constant.
This trend perhaps demonstrates how external factors can significantly influence beer brand preference, alongside with beer characteristics, of a customer.
According to Martha (Lincoln, 2015), "drinking to impress someone" is a significant factor influencing drinking behavior in Vietnam This social context affects beer brand preferences, particularly in professional settings or upscale restaurants, where Vietnamese consumers are more likely to choose premium beers like Heineken, Truc Bach, and Saigon Special over mid-range or budget options This trend suggests that Vietnamese drinkers often link high-quality beer with strong branding, reinforcing their choices in social situations.
Packaging plays a significant role in consumer perception, with a P-value of 0.058 indicating its importance Consistent with Nguyen's research in Nha Trang (Thong, 2017), consumers often view canned beers as inferior compared to keg or glass bottled options, primarily due to negative associations with the aluminum material.
"alter the taste" of said beer However the P-value here is not significant enough to be deemed a convincing factor, therefore, removed from the equation.
The Pearson Correlation test revealed that beer type does not significantly influence Vietnamese consumers' preferences for beer brands With a P-value of 0.234, this factor is deemed unfit and subsequently excluded from consideration, confirming that beer type is not a major contributor to brand choice among consumers in Vietnam.
Vietnamese consumers spend considerable time on social media; however, a P-value of 0.142 indicates that social media is not a significant factor in influencing beer brand preference While the visibility of beer brands on social media may enhance brand awareness, it appears that Vietnamese consumers maintain a strong and consistent preference for specific brands.
The study found a P-value of 0.624 for the country of origin, indicating that it does not significantly influence consumer preferences in the Vietnamese beer market This contradicts Cristina Calvo Porral's (2015) assertion that customers prefer internationally produced beers over local brands In Vietnam, the country of origin appears to be a uniform factor, resulting in no notable difference in consumer preference between local and foreign beer brands.