3. Theoretical Review
3.1. Definition and Models of Event Marketing
A gathering activity or event held to advertise a good, service, or business is known as event marketing. Potential and current consumers focus on the event. As it requires face-to-face interaction event marketing typically takes place in person. It covers a business taking part in a trade show, sponsoring an event, or organizing one themselves in
| [18] | Eckerstein, A. (2002). Evaluation of event marketing [Master’s thesis, Göteborg University]. |
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.
Kotler, P defines event marketing as occurrences designed to communicate particular messages to target audiences
| [29] | Kotler, P. (2003) Marketing Management. 11th Edition, Prentice-Hall, Upper Saddle River. |
[29]
. It is quite inclusive and says that any event can be considered a marketing event as long as there is an audience and a message is being communicated. A more constrained definition of the term is required to allow for the development of event marketing theories
| [57] | Wood, E. H. (2009). Evaluating event marketing: Experience or outcome? Journal of Promotion Management. |
[57]
. Event marketing is a launch towards coordinating communication regarding a created or sponsored event, and the event is said to be an activity that gathers the target group in time and space for a meeting in which an experience is created and a message is communicated
| [50] | Sneath, J., Finney, R., and Close, A. (2005) An IMC Approach to Event Marketing: The Effects of Sponsorship and Experience on Customer Attitudes. Journal of Advertising Research. 45(4). p. 373-381. |
[50]
. Event marketing is becoming more popular since businesses are looking for innovative ways to connect with their current and potential clients to stand out in the increasing business environment. Another factor is the overuse of conventional media and its problems.
The media becomes less effective because there is a clutter of communications as a result of to many communication messages competing for the same audiences
| [57] | Wood, E. H. (2009). Evaluating event marketing: Experience or outcome? Journal of Promotion Management. |
[57]
. The growing use and popularity of event marketing is due among other things to its flexibility in adapting to various circumstances. Regardless of the size of the target audience for the company event marketing may be employed by all sizes of businesses. By creating a marketing strategy and event aim it can be modified to meet the unique needs of businesses
| [6] | Behringer, M., & Larsson, A. (1998). Event marketing as a strategic marketing resource. IHM Göteborg University. |
[6]
.
The company wants to use event marketing to boost sales, build brand recognition and stand out from rivals when launching a new product. The best return on investment comes from event marketing because it is an effective strategy for luring in new clients and retaining existing ones. When the event makes a positive impression on everyone who attends including current and potential consumers, it can be effective. The right message must be delivered to the right audience, the company must consider the location where the target market enjoys it, the best time and place to hold the event, and other factors
| [17] | Duncan, T. (2005). Principle of advertising & IMC. Boston: McGraw-Hill. |
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are some of the elements that determine the success of event marketing. According to
| [36] | Meenaghan, T. (2001) Sponsorship and advertising: a comparison of consumer perceptions. Psychology & Marketing, 18(2), pp. 191-215. |
[36]
concept of sponsorship impact, highly engaged customers exhibit a higher level of sponsorship awareness and are more likely to show a preference for the sponsor's goods as a result of the latter's connection to the event. Positive consumer perceptions may impact their propensity to purchase from sponsors of their preferred event. The most important abilities for event planners are the ability to successfully connect with their audience and influence them to take the required action.
Event Marketing Models
There are theories within internal and external marketing communication and advertising that have been highly influential in both textbooks and in the professional sponsorship practice
| [22] | Hackley, C. (2005) Advertising and Promotion: Communicating Brands. London, Sage. |
[22]
. The existing behavioral sponsorship models will now be studied further and compared to each other. There are many numbers of sponsoring models. Insufficient models are discussed and listed below.
AIDA Models
AIDA was created by Strong in 1925 and is a behavioral model that has as purpose of making sure that an event marketing raises awareness, stimulates interest, and leads the customer to desire and eventually take action
| [22] | Hackley, C. (2005) Advertising and Promotion: Communicating Brands. London, Sage. |
[22]
. The model is seen as highly persuasive and is said to often unconsciously affect our thinking
| [25] | Kadko, D., Gronvold, K. and Butterfield, D. (2007) Application of Radium Isotopes to Determine Crustal Residence Times of Hydrothermal Fluids from Two Sites on the Reykjanes Peninsula, Iceland. Geochimica et Cosmochimica Acta, 71, 6019-6029. |
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. With the AIDA model Strong suggests that for an event marketing to be effective it has to be one of that Awareness, Interest, Desire, and Action. Attention: to capture the attention of the target audience. The goal is to make potential customers aware of the product or service.
Interest: is to generate interest in the product or service. Marketers aim to engage consumers emotionally or intellectually to keep them interested.
Desire: In this stage, the goal is to create a desire for the product or service.
Action: The final stage is encouraging consumers to take action, such as making a purchase, signing up for a newsletter, or requesting more information. So that the customer passes through all these four phases with all being equally important. The model implies that event marketing should inject memorable and believable messages that will make customers triggered to act in a certain way. The model drawbacks only focus on four variables but it’s not enough measuring the customer purchase intention.
Hierarchy-of-effects model
The model was named the hierarchy-of-effects which is the same name as some authors used on the foundation theory and will therefore go under the name Lavidge &Steiner’s. In this model customers do not change from being completely uninterested to becoming convinced to buy the product in one step. Lavidge and Steiner's Hierarchy-of-effects model is created to show the process or steps that event marketing assumes that customers pass through in the actual purchase process
| [5] | Barry, T. E. and Howard, D. J. (1990) A Review and Critique of the Hierarchy of Effects in Advertising. International Journal of Advertising, 9, 121-135. |
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The Hierarchy-of-Effects Model is a marketing communication model that outlines the stages a consumer goes through from first becoming aware of a product to making a purchase decision. This model helps marketers understand how to effectively communicate with their audience at each stage of the buying process. Awareness: consumers become aware of the product or brand. The goal is to capture attention and make consumers aware of the existence of the product. Knowledge: consumers seek information and learn more about the product. They may research features, benefits, and other relevant details that help them understand what the product is about. Liking: consumers have knowledge about the product, they form an opinion about it. This stage involves developing a positive attitude towards the product based on the information they've gathered. Preference: consumers compare the product with alternatives and begin to develop a preference for it. They may consider factors such as quality, price, and brand reputation. Conviction: consumers are convinced of their preference and are ready to make a purchase. They may seek additional reassurance through reviews, testimonials, or recommendations from others. Purchase: The final stage involves the actual purchase of the product. This is where consumers take action based on their previous evaluations and decisions.
3.2. Event Marketing as Promotional Tool
The definition of marketing communications by Philip Kotler and Kevin Lane
| [30] | Kotler, P. and Keller, K. (2009) Marketing Management. Global Edition, Pearson Education Inc., Upper Saddle River. |
[30]
is the means by which firms attempt to inform, persuade and remind their customers directly and indirectly of the products and brands they sell. According to
| [30] | Kotler, P. and Keller, K. (2009) Marketing Management. Global Edition, Pearson Education Inc., Upper Saddle River. |
[30]
Marketing communications represent the voice of the firm and its brands are how the corporation may develop a conversation and build a relationship. A marketing mix is a deliberate combination of variables that can be controlled; it is called a "mix" because one component influences the others and because the mix as a whole must be appropriate for the target market. Product, pricing, promotion, and place are referred to as "the 4 Ps" and make up the core four components of the marketing mix. The term promotional mix refers to a company's overall marketing communications strategy which initially included public relations, sales promotion, personal selling, and advertising
| [32] | Kotler, P., & Keller, K. (2009). Marketing Management (14th ed.). Pearson International Edition. |
[32]
. Event marketing is a promotional strategy that involves planning and executing events to promote a brand, product, or service. It is a way to engage with potential customers directly, create memorable experiences, and build relationships.
3.3. Effect of Event Marketing on Customer Purchase Intention
The evaluation of an event is frequently viewed as being too difficult for the individual firm, and businesses frequently struggle with determining how to evaluate the results of actions related to event marketing
| [50] | Sneath, J., Finney, R., and Close, A. (2005) An IMC Approach to Event Marketing: The Effects of Sponsorship and Experience on Customer Attitudes. Journal of Advertising Research. 45(4). p. 373-381. |
[50]
. The foundation for any review, according to
| [6] | Behringer, M., & Larsson, A. (1998). Event marketing as a strategic marketing resource. IHM Göteborg University. |
[6]
is establishing the marketing goals when the event is planned. The main goals that need to be very specific and quantifiable are those of communication and sales. Event-specific objectives are unique goals that a firm participating in event marketing might set to get the most out of the event. Stating a clear purpose and a specific objective will help the evaluation process writes
| [57] | Wood, E. H. (2009). Evaluating event marketing: Experience or outcome? Journal of Promotion Management. |
[57]
. The outcomes can then be used to determine whether future action or improvement is needed. Companies are urged to evaluate the small- and big-scale consequences of events, as well as each event component's potential impact on or detriment from marketing goals
| [57] | Wood, E. H. (2009). Evaluating event marketing: Experience or outcome? Journal of Promotion Management. |
[57]
. According to several experts, the relationship between event marketing and purchase intention has proven to be relevant for the factors of consumer involvement, brand awareness, event sponsor fit, and attitude toward the event.
According to the
| [36] | Meenaghan, T. (2001) Sponsorship and advertising: a comparison of consumer perceptions. Psychology & Marketing, 18(2), pp. 191-215. |
[36]
, customers' intentions to buy the sponsored brand are influenced by their perceptions of the sponsor, and their attitudes toward the event may also influence their decisions to do so. According to
| [35] | Meenaghan Tony, (2001), “Understanding Sponsorship Effects”, Psychology & Marketing, Vol. 18, Issue 2, pp. 95-122. |
[35]
, the primary goal of marketing strategy is to increase brand recognition cheaply and effectively by working outside of the media channels and having a large sales volume since consumers need the knowledge to be able to compare different products.
According to
| [45] | Russell, B. (2004). Knowledge by acquaintance and knowledge by des- cription (Originally published 1911). In Mysticism and logic. Mineo- la, NY: Dover Publications. |
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, patrons form more favorable event sponsor ties when they enjoy the event. The customer's opinion of the event sponsor fit will be positively impacted by their favorable attitude toward the event. The study also shows how favorable brand commitment to the sponsor brand and customer desire to purchase products is positively influenced by event sponsor fit. As a result, event marketing has a positive impact on brand building since it raises knowledge about the introduction of new products and allows for the association of customer brand personalities with those of the target market. In contrast, a good fit between an event's sponsor and its attendees inspires customers to make purchases.
According to
| [27] | Keller, K. L. (2001) Building Customer-Based Brand Equity. Marketing Management, 10, 14-21. |
[27]
there are several external factors such as perceived price and perceived quality during the purchasing process that might influence purchase intention. Before making a purchase consumers go through six stages: awareness, knowledge, interest, preference, persuasion, and purchase
| [31] | Kotler, P., & Armstrong, G. (2010). Principles of Marketing. Prentice Hall. |
[31]
. Another study on engaging the consumer through EM, by
| [4] | Angeline Close is an Assistant Professor of Marketing in the College of Business, University of Nevada, Las Vegas. 4505 Maryland Parkway, Las Vegas, NV 89154-6010. |
[4]
, suggests that when the customer is more engaged, enthusiastic, and knowledgeable about the company's involvement with the community, it has a positive influence on the attendee's perceptions of the sponsor's brand and is associated with increased intentions to buy the firm's products.
3.4. Conceptual Framework of the Study
A conceptual framework is a serious base that can indicate logical flows of assumptions to achieve the objectives of the study. It is a graphical representation of the theorized interrelationships of the variables of a study
| [28] | Kothari, C. R., & Gang, W. (2014). Research Methodology; Methods and Techniques. New Age International Publishers Ltd. |
[28]
. The conceptualization of variables in the work is important since it forms the basis for testing hypotheses and coming up with conclusions in the findings of the research, Fahy, John, Francis Farrelly, and Pascale G. Quester argue that event marketing might be a widespread promoting tool that has a large share within the marketing mix of businesses and increasing growth in sponsorship funds
| [19] | Fahy, John, Francis Farrelly, and Pascale G. Quester (2004), “Competitive Advantage Through Sponsorship,” European Journal of Marketing, 38(8), 1013–30. |
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.
The dependent variable in this study is purchase intention, whereas the independent variables are brand awareness, event sponsor fit, attitude toward the event, and customer engagement and brand image. Regarding customer by examining these components within the conceptual framework, the study aims to provide insights and recommendations to BGI Company on how to optimize its event marketing activities to enhance customer engagement, increase customer purchase intention, and ultimately boost sales in the Hawassa city.
Figure 1. Conceptual Framework.
4. Research Design & Approach
A research design serves as a strategic blueprint that outlines the procedures for collecting, measuring, and analyzing data pertinent to a research problem
| [61] | Zikmund, W. G. (2003) Business Research Methods. 7th Edition, Thomson/ South-Western. |
[61]
. It provides a structured framework to guide the research process and aids in systematically addressing the research questions and objectives. The choice of research design is largely influenced by the type of research being conducted and the nature of the data to be collected.
In the context of this study, which seeks to investigate the effect of event marketing on customer purchase intention at BGI Beer Factory in Hawassa, an explanatory research design was employed. Explanatory research is designed to examine the relationships among variables, often through hypothesis testing. It aims to clarify how and why certain phenomena occur by identifying causal relationships and underlying mechanisms
| [44] | Polit, D. F. and Beck, C. T. (2004) Nursing Research: Principles and Methods. 7th Edition, Lippincott Williams & Wilkins, Philadelphia. |
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. This design is appropriate for this study as it allows for the investigation of direct and indirect effects of variables such as brand awareness, customer engagement, and customer attitude toward the event, event-sponsor fit, and brand image on purchase intention.
Qualitative research is typically exploratory and is utilized to gain insights into complex or sensitive issues. It focuses on understanding the meanings and experiences of participants. Conversely, quantitative research applies statistical and mathematical techniques to measure and analyze variables, enabling researchers to test hypotheses and generalize findings
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.
Given the multifaceted nature of this study and the need to capture both statistical relationships and contextual insights, a mixed methods research approach was adopted. The mixed methods approach integrates both qualitative and quantitative data, allowing the researcher to benefit from the strengths of each while offsetting their individual limitations. As
| [10] | Cameron, S., & Price, D. (2009). Business Research Methods: A Practical Approach. London: CIPD. |
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emphasizes, mixed methods research provides a comprehensive understanding of research problems by combining empirical measurement with in-depth contextual analysis. In this study, the mixed approach facilitated the triangulation of data and enriched the interpretation of how event marketing influences consumer purchase intention.
4.1. Target Population
A population refers to the complete set of individuals, objects, events, or elements that possess common characteristics and are of interest to a particular study
| [55] | Walliman, N. (2011). Research Methods the Basics. Routledge. |
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. It encompasses all members of a defined group that the researcher aims to draw conclusions about. Populations can be classified as either finite or infinite, depending on the nature and countability of the sampling units involved.
In the context of this study, the population is considered infinite, as it involves an indeterminate and potentially large number of customers who attend BGI Beer Factory’s marketing events in Hawassa. These customers vary across different events and are not pre-enumerated, making exact counting impractical.
Therefore, the target population for this research includes: Customers who participated in BGI’s event marketing activities in Hawassa City, Selected BGI staff members involved in event coordination and marketing, Department managers responsible for marketing and customer engagement strategies.
4.2. Sampling Techniques
A sample refers to a subset of a population that is selected for analysis and is assumed to reflect the characteristics of the whole population
| [49] | Sekaran, U. (2001) Research Methods for Business: A Skills Building Approach. 2nd Edition, John Wiley and Sons, Inc., New York. |
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. The two primary sampling methods are probability sampling and non-probability sampling. In probability sampling, every member of the population has a known and equal chance of being selected, whereas in non-probability sampling, the probability of any particular member being chosen is unknown
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.
For this study, the researcher employed a non-probability sampling techniques to ensure a comprehensive and representative data collection process. Specifically: Researcher employed Convenience Sampling (a non-probability technique) to gather responses from event participants who were readily available and willing to participate. This method was particularly useful in approaching attendees during the event in real time. Furthermore, key informants such as Four BGI staff and One Marketing & Sales managers were selected through purposive sampling for interview, a type of non-probability sampling, based on their specialized knowledge and involvement in the organization’s event marketing strategies. These respondents were instrumental in providing in-depth qualitative insights into the role of event marketing and its impact on customer purchase intentions.
4.3. Sample Size Determination
In research involving large populations it is often impractical or impossible to study the entire population. Therefore, an appropriate sample size must be determined to ensure representativeness and accuracy. A representative sample provides reliable results that can be generalized to the entire population. For this study, the sample size was determined using the widely accepted formula developed by
| [28] | Kothari, C. R., & Gang, W. (2014). Research Methodology; Methods and Techniques. New Age International Publishers Ltd. |
[28]
for estimating proportions in large portion.
The
| [28] | Kothari, C. R., & Gang, W. (2014). Research Methodology; Methods and Techniques. New Age International Publishers Ltd. |
[28]
formula is written as follows: -
Where:
n0 is the sample size.
Z is the Z-value (the number of standard deviations from the mean corresponding to the desired confidence level). For a 95% confidence level, Z=1.96
Use p=0.5 to maximize the sample size.
n0=
n0=
n0=
n0= 384.16
Based on this calculation, the required sample size was approximately 385 respondents. This sample size ensures adequate representation of the study population and allows for generalization of the results to customers of BGI Beer Factory’s event marketing activities in Hawassa.
4.4. Data Collection Instruments
In this study, data collection instruments were carefully selected to ensure the reliability and validity of the gathered information pertinent to the effect of event marketing on customer purchase intention at BGI Beer Factory in Hawassa. The primary instruments used were structured questionnaires and semi-structured interviews.
A structured questionnaire was developed as the main quantitative data collection tool. According to
| [28] | Kothari, C. R., & Gang, W. (2014). Research Methodology; Methods and Techniques. New Age International Publishers Ltd. |
[28]
, structured questionnaires enable researchers to collect data that can be systematically quantified and analyzed statistically. The questionnaire consisted of closed-ended questions, primarily measured using a five-point Likert scale ranging from “strongly disagree” to “strongly agree,” which facilitated the measurement of key variables such as brand awareness, customer engagement, attitude toward the event, event-sponsor fit, and brand image. The questionnaire was divided into two sections: the first captured demographic characteristics of the respondents, while the second focused on variables directly related to the research objectives. To complement the quantitative data, semi-structured interviews were conducted with selected BGI staff and department managers. This qualitative instrument provided deeper insights into organizational perspectives on event marketing strategies, enabling the exploration of contextual factors and subjective experiences that quantitative methods might not fully capture
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. The semi-structured format allowed flexibility to probe further into responses, enriching the understanding of how event marketing influences purchase intention within the organizational setting.
Together, these instruments provided a comprehensive data collection framework that integrated quantitative measurement with qualitative depth, enhancing the study’s robustness and the validity of its findings.
4.5. Methods of Data Analysis
The analysis of data in this study employed a mixed-methods approach integrating both quantitative and qualitative techniques to achieve a robust understanding of the effect of event marketing on customer purchase intentions.
Quantitative data collected through structured questionnaires were subjected to descriptive statistics to summarize demographic characteristics and general trends in consumer perceptions. Measures such as frequencies, percentages, means, and standard deviations were computed to present an overview of respondents’ attitudes toward event marketing.
Further, inferential statistical techniques including correlation analysis and regression analysis were applied to examine the strength and direction of relationships between event marketing activities and customers’ purchase intentions. These analyses enabled the identification of significant predictors and the estimation of the magnitude of event marketing’s impact, supporting hypothesis testing within the conceptual framework with support of SPSS version 23.
Data from semi-structured interviews with the promotion manager, the qualitative insights provided contextual depth and explanatory power, complementing the quantitative findings and facilitating triangulation to enhance the study’s validity. This comprehensive data analysis framework allowed for a nuanced understanding of both the measurable outcomes and underlying mechanisms through which event marketing influences customer purchase intention, adhering to best practices in marketing research methodology.
5. Data Analysis & Discussion
5.1. Reliability of the Study
Reliability refers to the consistency and stability of a research instrument in measuring the intended variables over repeated trials
| [9] | Bryman, A. (2016). Social Research Methods (5th ed.). London: Oxford University Press. |
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. In this study, the reliability of the questionnaire used to assess the effect of event marketing on customers’ purchase intentions at BGI Brewery Factory in Hawassa City was ensured through pre-testing and the use of Cronbach’s alpha. According to
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, a Cronbach’s alpha value of 0.70 or higher indicates an acceptable level of internal consistency. To enhance reliability, the survey instrument was pilot-tested with a small sample before full distribution, and necessary modifications were made based on feedback. Furthermore, clear and unambiguous questions were used to minimize respondent bias and ensure uniform understanding. As suggested by
| [47] | Saunders, M. N. K., Lewis, P. and Thornhill, A. (2019) Research Methods for Business Students. 8th Edition, Pearson, New York. |
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ensuring high reliability strengthens the credibility of the study findings, making them generalizable and reproducible in similar contexts.
Table 1. Cronbach's Alpha for each variable.
Cronbach's Alpha for each variable |
| Number of items | Cronbach's Alpha |
Customer Purchase Intention | 6 | .748 |
Brand Awareness | 6 | .817 |
Event Sponsor Fit | 5 | .838 |
Attitude toward Event | 6 | .843 |
Customer Engagement | 6 | .855 |
Brand Image | 10 | .813 |
Source: Owen Survey SPSS Finding, 2025
The results showed that all constructs exhibited satisfactory to excellent reliability: Customer Purchase Intention (6 items) had an Alpha of 0.748, Brand Awareness (6 items) 0.817, Event Sponsor Fit (5 items) 0.838, Attitude toward Event (6 items) 0.843, Customer Engagement (6 items) 0.855, and Brand Image (10 items) 0.813. These findings affirm the internal consistency of the questionnaire items and support the reliability of the instruments for measuring the key variables related to event marketing and purchase intention, thereby strengthening the validity of subsequent data analyses and conclusions.
5.2. Inferential Analysis of the Study
Event marketing has become an integral promotional strategy in the competitive beverage industry, influencing customer perceptions, brand engagement, and ultimately purchase intentions. Inferential analysis in this study aims to establish statistical relationships between event marketing dimensions and customers' purchase decisions at BGI Brewery in Hawassa.
Scholars suggest that event marketing enhances consumer engagement, brand recall, and emotional attachment, leading to stronger purchase intentions
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. Empirical studies confirm that experiential marketing strategies, such as live events and sponsorships, significantly impact consumer behavior by creating interactive and memorable brand experiences
| [28] | Kothari, C. R., & Gang, W. (2014). Research Methodology; Methods and Techniques. New Age International Publishers Ltd. |
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. Furthermore, inferential statistics, particularly regression and correlation analyses, help determine the extent to which event marketing activities influence customer purchasing behaviors
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.
Prior research highlights that consumer-brand interactions at events increase brand trust and preference, ultimately driving sales (Pine & Gilmore, 1999). Similarly, studies show that brand-sponsored events create a sense of exclusivity and belonging, strengthening consumer loyalty
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. Given BGI Brewery’s strong market presence, analyzing the effect of event marketing on purchase intentions is crucial in assessing its return on investment and strategic positioning.
Thus, this study employs inferential analysis to statistically validate how event marketing efforts at BGI Brewery influence customers' purchasing decisions, contributing to the broader discourse on experiential marketing effectiveness in the brewery sector.
5.3. Coefficient of Correlation Analysis
Understanding the factors that drive customer purchase intention is critical for brands seeking to strengthen their market position. Several key variables, including brand awareness, event-sponsor fit, attitude toward the event, customer engagement, and brand image, have been identified as crucial determinants of consumer behavior
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. Coefficient of correlation analysis helps in quantifying the strength and direction of these relationships, providing insights into how event marketing and branding strategies influence consumer decisions
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.
Brand awareness plays a fundamental role in shaping purchase intention, as higher brand familiarity often translates into stronger consumer trust and preference
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. When consumers recognize a brand, they are more likely to consider its products during the decision-making process
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. Additionally, event-sponsor fit—the perceived alignment between a brand and an event—has been shown to enhance brand credibility and positively influence purchase behavior
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.
Attitude toward the event is another significant predictor of purchase intention. Consumers who perceive an event positively are more likely to transfer that positive sentiment to the sponsoring brand, leading to increased purchase likelihood
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. Similarly, customer engagement, which includes emotional, cognitive, and behavioral interactions with a brand, fosters deeper brand relationships and enhances loyalty
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. High levels of engagement often lead to higher purchase intentions, as consumers feel more connected to the brand’s values and messaging
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.
Lastly, brand image serves as a mediating factor that influences consumer attitudes and behaviors. A strong and favorable brand image enhances perceived quality and differentiation, making consumers more inclined to choose the brand over competitors
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. Empirical studies suggest that brand image positively correlates with purchase intention, reinforcing the importance of maintaining a positive brand reputation
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.
By employing coefficient of correlation analysis, this study examines the strength of these relationships, providing a data-driven understanding of how these variables collectively shape consumer purchase intentions. The findings can inform strategic marketing decisions, helping brands refine their event sponsorship and engagement strategies to maximize consumer impact.
The study employed a pearson correlation analysis to measure the strength of linear association between two variables. Correlations are perhaps the most basic and most useful measure of association between two or more variables
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. It helps in determining the strength of association in the model. Pearson correlation coefficients reveal magnitude and direction of relationships (either positive or negative) and the intensity of the relationship (-1.0 + 1.0). To interpret the direction and strengths of relationships between variables, the guidelines suggested by Field (2005) researcher followed. His Classification of the correlation coefficient (r) refers 0.1– 0.29 is weaker; 0.3 – 0.49 is moderate; and > 0.5 is strong.
Table 2. Guideline for the Pearson Correlation Analysis.
Pearson Correlation | Strength of Association |
r = 0.10 to 0.29 or r = -0.1 to -0.29 | Weak |
r = 0.30 to 0.49 or r = -0.30 to -0.49 | Moderate |
r = 0.50 to 1.00 or r = -0.50 to -1.00 | Strong |
Source: Field (2005)
Table 3. Correlations Matrix.
Correlations |
| CustomerPurchaseIntention | BrandAwareness | Event Sponsor Fit | Attitude toward-event | Customer Engagement | Brand Image |
Customer Purchase Intention | Pearson Correlation | 1 | | | | | |
Sig. (2-tailed) | | | | | | |
N | 368 | | | | | |
Brand Awareness | Pearson Correlation | .754** | 1 | | | | |
Sig. (2-tailed) | .000 | | | | | |
N | 368 | 368 | | | | |
Event Sponsor Fit | Pearson Correlation | .647** | .785** | 1 | | | |
Sig. (2-tailed) | .000 | .000 | | | | |
N | 368 | 368 | 368 | | | |
Attitude toward Event | Pearson Correlation | .616** | .289** | .158** | 1 | | |
Sig. (2-tailed) | .000 | .000 | .002 | | | |
N | 368 | 368 | 368 | 368 | | |
Customer Engagement | Pearson Correlation | .560** | .404** | .229** | .350** | 1 | |
Sig. (2-tailed) | .000 | .000 | .000 | .000 | | |
N | 368 | 368 | 368 | 368 | 368 | |
Brand Image | Pearson Correlation | .824** | .374** | .341** | .652** | .403** | |
Sig. (2-tailed) | .000 | .000 | .000 | .000 | .000 | |
N | 368 | 368 | 368 | 368 | 368 | |
Source: Owen Survey SPSS Finding, 2025
The correlation analysis between customer purchase intention and key branding factors—including brand awareness, event-sponsor fit, attitude toward the event, customer engagement, and brand image—reveals significant relationships, emphasizing their collective role in influencing consumer behavior. With an overall strong correlation (Mean = 3.37, SD = 1.05), these variables demonstrate their impact on purchase intention in marketing and branding strategies
| [33] | Kotler, P., & Keller, K. L. (2016). Marketing Management (14th edition). Shanghai: Shanghai People’s Publishing House. |
[33]
.
The strongest correlation is observed between customer purchase intention and brand image (r =.824, p <.01), indicating that a well-established brand identity significantly enhances the likelihood of consumers purchasing a brand’s products
| [1] | Aaker, D. A. (1991) Managing Brand Equity. The Free Press, New York. |
[1]
. This aligns with research suggesting that a positive brand image reinforces trust and product desirability, ultimately driving purchase behavior
| [29] | Kotler, P. (2003) Marketing Management. 11th Edition, Prentice-Hall, Upper Saddle River. |
[29]
.
Brand awareness also exhibits a strong positive correlation with purchase intention (r =.754, p <.01), affirming that consumer familiarity with a brand increases their likelihood of purchasing (Yoo et al., 2000). Event-sponsor fit (r =.647, p <.01) further supports this, emphasizing that a well-aligned sponsorship fosters credibility and strengthens consumer-brand relationships.
Similarly, attitude toward the event (r =.616, p <.01) significantly influences purchase intention, suggesting that positive experiences at branded events enhance consumer perception of the sponsoring brand, ultimately leading to increased purchasing behavior. Additionally, customer engagement (r =.560, p <.01) shows a meaningful correlation, reinforcing the idea that interactive brand experiences deepen consumer relationships and promote brand loyalty.
These findings support the integrated branding and event marketing model, where strong brand awareness, a well-matched sponsorship strategy, positive event experiences, and high customer engagement collectively contribute to a favorable brand image, which in turn strengthens purchase intention
| [33] | Kotler, P., & Keller, K. L. (2016). Marketing Management (14th edition). Shanghai: Shanghai People’s Publishing House. |
[33]
. The high significance (p <.01) across all correlations highlights the robustness of these relationships and suggests that brands should prioritize these factors to enhance consumer purchase behavior.
5.4. Multiple Linear Regression Analysis
Event marketing is a strategic tool that brands employ to create memorable experiences, connect with consumers, and ultimately influence their purchasing behaviors. The role of event marketing in shaping customer purchase intentions has garnered significant attention in academic research, emphasizing its potential to strengthen brand relationships, enhance brand recall, and increase consumer engagement. In the context of BGI Brewery, a leading brewery in Hawassa city, event marketing initiatives, such as product launches, promotional events, and sponsorship activities, have been integral to the company’s efforts to differentiate its products and create a positive brand image.
Multiple linear regression (MLR) analysis serves as an essential statistical technique for examining the relationship between event marketing and consumer behaviors, specifically purchase intentions
| [13] | Cornwell, B. T., Roy, D. P. and Steinard, E. A. (2001) Exploring Managers’ Perceptions of the Impact of Sponsorship on Brand Equity. Journal of Advertising, 30, 41-51. |
[13]
. By employing MLR, this study seeks to identify the significant predictors of customers' purchase intentions and the extent to which event marketing activities, such as exposure to advertisements, participation in promotional events, and overall brand experience, affect these intentions. MLR is particularly useful in evaluating the effects of multiple independent variables simultaneously, allowing for a comprehensive understanding of how different facets of event marketing contribute to consumer decisions
| [23] | Hair, J. F., Black, W. C., Babin, B. J. and Anderson, R. E. (2010) Multivariate Data Analysis. 7th Edition, Pearson, New York. |
[23]
. This analysis will provide valuable insights into how BGI Brewery’s event marketing activities impact the buying decisions of its consumers in Hawassa city, informing future marketing strategies and optimizing brand communication efforts.
Assumptions of the Multiple Linear Regression Model
Multiple Linear Regression (MLR) is a statistical technique used to model the relationship between one dependent variable and multiple independent variables. The assumptions underlying MLR are crucial for ensuring valid and reliable results. When applying MLR to study the effect of event marketing on customers' purchase intentions, particularly in the context of BGI Brewery in Hawassa city, understanding these assumptions becomes essential to interpreting the data correctly.
I. Assumptions of Linearity
The relationship between the dependent variable (purchase intentions) and independent variables (such as Brand Awareness, Event-Sponsor Fit, Attitude toward Event, Customer Engagement, Brand image) should be linear. The MLR model assumes that a change in the independent variables leads to a proportional change in the dependent variable. According to
| [13] | Cornwell, B. T., Roy, D. P. and Steinard, E. A. (2001) Exploring Managers’ Perceptions of the Impact of Sponsorship on Brand Equity. Journal of Advertising, 30, 41-51. |
[13]
, ensuring linearity is crucial for the model’s validity, as non-linear relationships may bias the results.
Figure 2. Assumptions of Linearity.
II. Assumptions of Homoscedasticity
The variance of errors should remain constant across all levels of the independent variables. This assumption ensures that the model's predictions are equally reliable across the range of data
| [58] | Wooldridge, J. M. (2015) Introductory Econometrics: A Modern Approach. Nelson Education, Toronto, Canada. |
[58]
. In event marketing studies, heteroscedasticity might occur if different customer segments respond differently to marketing events, which could lead to biased predictions.
Figure 3. Assumptions of Homoscedasticity.
III. Assumptions of Normality of Errors
Descriptive statistics offer a preliminary overview of the data’s distributional properties, which are crucial for determining the appropriateness of subsequent statistical analyses. In the table provided, measures of skewness and kurtosis are reported for six variables—Purchase Intention, Brand Awareness, Event Sponsor Fit, Attitude toward Event, Customer Engagement, and Brand Image—based on a sample of 368 respondents.
Table 4. Assumptions of Normality.
Descriptive Statistics |
| N | Skewness | Kurtosis |
Statistic | Statistic | Std. Error | Statistic | Std. Error |
Purchase Intention | 368 | .225 | .127 | .030 | .254 |
Brand Awareness | 368 | .730 | .127 | .141 | .254 |
Event Sponsor Fit | 368 | .651 | .127 | -.209 | .254 |
Attitude toward Event | 368 | .084 | .127 | -.583 | .254 |
Customer Engagement | 368 | .314 | .127 | -.998 | .254 |
Brand Image | 368 | .486 | .127 | -.299 | .254 |
Valid N (listwise) | 368 | | | | |
Skewness indicates the asymmetry of a distribution. A skewness value close to zero suggests that the data are approximately normally distributed. According to George and
| [21] | George, D. and Mallery, P. (2010) SPSS for Windows Step by Step: A Simple Guide and Reference 17.0 Update. 10th Edition, Pearson, Boston. |
[21]
, skewness values between -1 and +1 are considered acceptable in many social science contexts. In this case, all variables exhibit positive skewness, ranging from 0.084 (Attitude toward Event) to 0.730 (Brand Awareness), suggesting mild to moderate asymmetry with a longer tail on the right. This implies that most respondents scored lower on these measures, with fewer scoring high.
Kurtosis measures the tailedness or peakedness of a distribution. A kurtosis value near zero indicates a distribution similar to the normal curve, while positive values suggest a sharper peak and heavier tails, and negative values indicate a flatter peak and lighter tails [x]
| [52] | Tabachnick, B. G., & Fidell, L. S. (2013). Using Multivariate Statistics (6th ed.). Boston, MA: Pearson. |
[52]
In the present dataset, kurtosis values range from -0.998 (Customer Engagement) to 0.141 (Brand Awareness). These figures fall within acceptable thresholds, particularly as values between -2 and +2 are commonly accepted for assuming normality in medium-sized samples
| [56] | West, S. G., Finch, J. F., & Curran, P. J. (1995). Structural Equation Models with Nonnormal Variables: Problems and Remedies. In R. H. Hoyle (Ed.), Structural Equation Modeling: Concepts, Issues, and Applications (pp. 56-75). |
[56]
. The negative kurtosis values (e.g., for Customer Engagement and Attitude toward Event) suggest slightly flatter distributions, implying less extreme responses compared to a normal distribution.
Overall, the skewness and kurtosis statistics indicate that the data distributions are approximately normal, justifying the use of parametric statistical techniques such as regression or structural equation modeling. This initial check is a crucial step in ensuring the validity and robustness of inferential analysis
| [20] | Field, A. P. (2018) Discovering Statistics Using IBM SPSS Statistics. 5th Edition, Sage, Newbury Park. |
[20]
.
IV. Assumptions of No Multicollinearity
The independent variables in the model should not be highly correlated with each other. In the context of event marketing, if variables like promotions and sponsorship are too closely related, it can cause multicollinearity, leading to unreliable coefficient estimates
| [23] | Hair, J. F., Black, W. C., Babin, B. J. and Anderson, R. E. (2010) Multivariate Data Analysis. 7th Edition, Pearson, New York. |
[23]
. Ensuring low correlation between event marketing strategies is essential for obtaining accurate insights on their individual effects on purchase intentions.
Table 5. Collinearity Statistics.
Coefficientsa |
Model | Collinearity Statistics |
Tolerance | VIF |
1 | Brand Awareness | .320 | 3.123 |
Event Sponsor Fit | .353 | 2.831 |
Attitude toward Event | .543 | 1.841 |
Customer Engagement | .732 | 1.366 |
Brand Image | .494 | 2.024 |
a. Dependent Variable: Customer Purchase Intention
Source: Owen Survey SPSS Finding, 2025
The Tolerance value represents the proportion of variance in an independent variable that is not explained by other predictors in the model. A low tolerance value (close to 0.1 or below) indicates a potential collinearity issue
| [23] | Hair, J. F., Black, W. C., Babin, B. J. and Anderson, R. E. (2010) Multivariate Data Analysis. 7th Edition, Pearson, New York. |
[23]
. On the other hand, the Variance Inflation Factor (VIF) quantifies how much the variance of a regression coefficient is inflated due to multicollinearity. A VIF above 10 is generally considered problematic, suggesting severe multicollinearity, while values between 1 and 5 indicate moderate correlation
| [39] | O’Brien, R. M. (2007) A Caution Regarding Rules of Thumb for Variance Inflation Factors. Quality and Quantity, 41, 673-690. |
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.
In the case of event marketing and its effect on customer purchase intentions at BGI Brewery in Hawassa, the VIF values range from 1.366 to 3.123, indicating that multicollinearity is within an acceptable range. Brand Awareness (VIF = 3.123, Tolerance = 0.320) and Event Sponsor Fit (VIF = 2.831, Tolerance = 0.353) exhibit moderate correlation with other variables, but these values do not exceed the critical threshold of concern. Customer Engagement (VIF = 1.366, Tolerance = 0.732) shows the lowest collinearity risk. Since no variables exceed a VIF of 5, multicollinearity is unlikely to pose a serious problem in the regression model.
Ensuring low multicollinearity is essential for the stability and interpretability of regression models in event marketing research. The reported VIF values suggest that the predictor variables are not excessively correlated, supporting the reliability of the estimated coefficients. If collinearity concerns arise, techniques such as centering variables, stepwise regression, or Principal Component Analysis (PCA) can help mitigate its impact
| [51] | Tabachnick, B. G., & Fidell, L. S. (2007). Using Multivariate Statistics (5th ed.). New York: Allyn and Bacon. |
[51]
. Proper handling of multicollinearity ensures that event marketing strategies targeting customer purchase intentions remain analytically sound and practically applicable.
Model Summary
Table 6. Model Summary.
Model Summaryb |
Model | R | R Square | Adjusted R Square | Std. Error of the Estimate |
1 | .965a | .931 | .930 | .75264 |
a. Predictors: (Constant), Brand Image, Event Sponsor Fit, Customer Engagement, Attitude toward Event, Brand Awareness
b. Dependent Variable: Customer Purchase Intention
Source: Owen Survey SPSS Finding, 2025
The Model Summary in multiple linear regression provides key indicators of how well the independent variables explain variations in the dependent variable. The coefficient of determination (R²) measures the proportion of variance in the dependent variable that is explained by the predictors, while the Adjusted R² accounts for the number of predictors and sample size, providing a more refined measure of model fit
| [20] | Field, A. P. (2018) Discovering Statistics Using IBM SPSS Statistics. 5th Edition, Sage, Newbury Park. |
[20]
.
In the context of the effect of event marketing on customer purchase intentions at BGI Brewery in Hawassa, the R value of 0.965 indicates a strong correlation between the independent variables (Brand Image, Event Sponsor Fit, Customer Engagement, Attitude toward Event, and Brand Awareness) and the dependent variable (Customer Purchase Intention). The R² value of 0.931 suggests that 93.1% of the variation in customer purchase intention is explained by the predictors, demonstrating a highly explanatory model. Additionally, the Adjusted R² of 0.930 confirms that the model remains robust even after adjusting for the number of predictors, reinforcing its reliability
| [23] | Hair, J. F., Black, W. C., Babin, B. J. and Anderson, R. E. (2010) Multivariate Data Analysis. 7th Edition, Pearson, New York. |
[23]
. The standard error of the estimate (0.75264) further indicates the extent of deviation between actual and predicted values, with lower values suggesting better predictive accuracy.
A high R² value (above 0.90) suggests that event marketing factors significantly influence customer purchase intentions, confirming the theoretical relevance of these predictors in consumer behavior research. However, while a strong R² indicates a good fit, it is essential to check for potential overfitting, which can occur when the model captures noise rather than actual patterns
| [51] | Tabachnick, B. G., & Fidell, L. S. (2007). Using Multivariate Statistics (5th ed.). New York: Allyn and Bacon. |
[51]
. Future studies may employ techniques such as cross-validation or regularization methods to ensure generalization. Additionally, further investigation into other potential moderating variables—such as price sensitivity or brand loyalty—may enhance the model’s explanatory power in real-world marketing scenarios.
The ANOVA (Analysis of Variance)
The Analysis of Variance (ANOVA) is a statistical method used to determine whether the independent variables in a regression model significantly explain the variation in the dependent variable
| [15] | Crowther, P. (2011). Marketing event outcomes: From tactical to strategic. International Journal of Event and Festival Management. |
[15]
. In multiple linear regression, ANOVA evaluates the overall significance of the model by comparing the variance explained by the predictors (Regression Sum of Squares) to the unexplained variance (Residual Sum of Squares)
| [51] | Tabachnick, B. G., & Fidell, L. S. (2007). Using Multivariate Statistics (5th ed.). New York: Allyn and Bacon. |
[51]
. The F-statistic is derived from this comparison, and a p-value (Sig.) below 0.05 indicates that the regression model significantly predicts the dependent variable.
Table 7. ANOVA (Analysis of Variance).
ANOVAa |
Model | Sum of Squares | Df | Mean Square | F | Sig. |
1 | Regression | 2768.667 | 5 | 553.733 | 977.523 | .000b |
Residual | 205.061 | 362 | .566 | | |
Total | 2973.728 | 367 | | | |
a. Dependent Variable: Customer Purchase Intention
b. Predictors: (Constant), Brand Image, Event Sponsor Fit, Customer Engagement, Attitude toward Event, Brand Awareness
Source: Owen Survey SPSS Finding, 2025
In the effect of event marketing on customer purchase intentions at BGI Brewery in Hawassa, the ANOVA results (F = 977.523, p < 0.001) confirm that the independent variables (Brand Image, Event Sponsor Fit, Customer Engagement, Attitude toward Event, and Brand Awareness) collectively have a significant impact on Customer Purchase Intention. The Regression Sum of Squares (2768.667) is substantially larger than the Residual Sum of Squares (205.061), indicating that the model explains most of the variance in the dependent variable. The Mean Square of the regression model (553.733) compared to the residual error (0.566) further supports the strength of the model.
A highly significant ANOVA result suggests that event marketing strategies are strong predictors of customer purchase behavior. However, while a significant F-test confirms that at least one independent variable contributes to the model, it does not specify which variables are the most influential. Further analysis using standardized regression coefficients (β-values) and t-tests is necessary to determine individual predictor contributions
| [23] | Hair, J. F., Black, W. C., Babin, B. J. and Anderson, R. E. (2010) Multivariate Data Analysis. 7th Edition, Pearson, New York. |
[23]
. Moreover, given the large F-statistic, researchers should assess assumptions such as homoscedasticity and independence of errors to ensure the robustness of the findings.
5.5. The Coefficients of Regression Analysis
The coefficients of regression analysis provide insight into the relationship between independent variables and the dependent variable by measuring the strength and direction of their effects. Unstandardized coefficients (B values) indicate how much the dependent variable changes with a one-unit increase in the predictor variable, while standardized coefficients (Beta values) allow for comparisons of the relative importance of each predictor
| [23] | Hair, J. F., Black, W. C., Babin, B. J. and Anderson, R. E. (2010) Multivariate Data Analysis. 7th Edition, Pearson, New York. |
[23]
. The t-statistics and corresponding p-values (Sig.) assess the statistical significance of each independent variable, confirming whether they meaningfully contribute to the model
| [23] | Hair, J. F., Black, W. C., Babin, B. J. and Anderson, R. E. (2010) Multivariate Data Analysis. 7th Edition, Pearson, New York. |
[23]
.
Table 8. Regression Coefficients.
Coefficientsa |
Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. |
B | Std. Error | Beta |
1 | (Constant) | 1.641 | .652 | | 2.518 | .012 |
Brand Awareness | .595 | .039 | .375 | 15.385 | .000 |
Event Sponsor Fit | .165 | .030 | .128 | 5.505 | .000 |
Attitude toward Event | .199 | .037 | .102 | 5.438 | .000 |
Customer Engagement | .141 | .017 | .135 | 8.347 | .000 |
Brand Image | .588 | .022 | .519 | 26.425 | .000 |
a. Dependent Variable: Customer Purchase Intention
Source: Owen Survey SPSS Finding, 2025
In the case of event marketing’s effect on customer purchase intention at BGI Brewery in Hawassa, the regression results reveal that all five predictors—Brand Awareness (B = 0.595, p < 0.001), Event Sponsor Fit (B = 0.165, p < 0.001), Attitude toward Event (B = 0.199, p < 0.001), Customer Engagement (B = 0.141, p < 0.001), and Brand Image (B = 0.588, p < 0.001)—significantly influence Customer Purchase Intention. Among them, Brand Awareness (β = 0.375) and Brand Image (β = 0.519) have the strongest standardized effects, indicating their dominant roles in shaping purchase behavior. The high t-values, particularly for Brand Image (t = 26.425) and Brand Awareness (t = 15.385), further reinforce their substantial impact on customer purchase intentions.
The findings suggest that brand-related factors (Brand Awareness and Brand Image) play a crucial role in driving customer purchase decisions, aligning with previous studies emphasizing the significance of strong brand positioning in marketing effectiveness
| [26] | Keller, K. L. (2013). Strategic Brand Management: Building Measuring, and Managing Brand Equity, Global Edition (4th ed.). Pearson Education. |
[26]
. The significant influence of Event Sponsor Fit and Customer Engagement further supports the idea that sponsorship alignment and interactive customer involvement enhance purchase intentions. However, despite its statistical significance, Attitude toward Event (β = 0.102, t = 5.438) appears to have a relatively weaker impact, suggesting that other psychological or contextual factors may mediate its role. Future studies may explore moderating effects such as consumer demographics, price perceptions, or past purchase experiences to refine the model's explanatory power.
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