The Impact of Social Media Marketing Strategies on Consumer Purchase Intention in the Brewery Industry in Liberia ()
1. Introduction
In the past decade, social media platforms have revolutionized the way brands connect with and influence consumers, becoming integral tools for driving brand awareness, engagement, and sales across industries (Kaplan Haenlein; Appel et al., 2020). The brewery industry, traditionally reliant on television, radio, and event sponsorships, has increasingly turned to digital channels to reach consumers in more targeted and interactive ways (Malik et al.) [1]. Social media marketing strategies allow breweries to move beyond one-way advertising and foster two-way engagement through entertainment, personalization, trend-focused content, and interactive brand experiences (Alalwan et al.) [2]. In Liberia, the rapid adoption of platforms such as Facebook, Instagram, and TikTok especially among urban youth has transformed the marketing landscape for consumer goods, including alcoholic beverages. This demographic shift offers breweries unprecedented opportunities to craft campaigns that resonate with digitally active audiences.
However, while the potential is evident, questions remain regarding which specific social media marketing activities most effectively influence consumer purchase intentions in this emerging market context (Apel, 2020) [3]. Academic studies have identified several SMMA dimensions entertainment, interaction, trendiness, customization, and advertising that collectively enhance brand equity and purchasing behaviors (Batat, 2021) [4]. Yet, existing literature remains heavily focused on developed markets, leaving a gap in understanding how these strategies operate in low-to-middle-income countries with unique socio-economic and technological conditions (Boatent, 2021) [5]. In Liberia, where digital marketing research is scarce, breweries face the dual challenge of crafting culturally resonant content while navigating infrastructural constraints such as inconsistent internet connectivity and limited digital literacy among some consumer segments. This study addresses this gap by empirically examining the relationship between SMMA dimensions and consumers’ buying intentions in the Liberian brewery industry. Using original survey data collected from Liberian consumers, the research applies reliability, correlation, and regression analyses to identify which SMMA dimensions exert the strongest influence on purchase intention. Situating these findings within the broader discourse on social media marketing effectiveness, the study provides both theoretical contributions and actionable insights for practitioners in the brewery sector and beyond. The results not only enrich the literature on social media marketing in Sub-Saharan Africa but also offer practical guidance for breweries operating in similar emerging market contexts where cultural nuances and platform dynamics differ significantly from those in the Global North. This aligns with calls for more context-specific research in international marketing, where one-size-fits-all strategies often fail to capture local market complexities (Godey et al.) [6].
2. Literature Review
Social media marketing activities (SMMA) are typically conceptualized across five key dimensions: entertainment, interaction, trendiness, customization, and eWOM/ advertising [7]. Entertainment-focused content uses humor, stories, or engaging visuals to elicit positive emotions that enhance brand recall and loyalty [8]. Interaction emphasizes two-way communication, encouraging likes, comments, shares, and direct engagement to build deeper brand–consumer relationships. Trendiness refers to how brands align content with current cultural moments, meme trends, or popular topics to remain relevant, while customization enables targeted, user-centric messaging that enhances perceived value. eWOM and advertising represent persuasive, shareable content often delivered through influencers, promotional campaigns, or user-generated online referrals. Empirical findings across sectors offer nuanced insights into the distinct influence of SMMA dimensions on purchase intention; in Jakarta, a survey of skincare consumers (n = 134) found that entertainment, interaction, and customization positively affected purchase intention on Instagram, while trendiness and eWOM had no significant effect.
Among smartphone users in Indonesia, SMMA dimensions influenced purchase intention; customization was a significant predictor, whereas other dimensions were not. In Malaysia’s property sector (n = 331), entertainment, interaction, customization, and word-of-mouth positively influenced purchase intention, but trendiness did not show a significant effect. A cross-cultural study in Jordan, Morocco, and Spain on fast fashion found that entertainment, customization, and trendiness positively influenced both online and offline purchase intentions, with culture moderating some effects. On TikTok, Indonesian users showed that interaction, entertainment, and trendiness significantly predicted purchase intention, mediated by brand engagement (n = 197). These variations across industries and regions underscore the importance of context. In some settings, trendiness may matter more (fast fashion, TikTok), while in others customization dominates (property, smartphones). Studies specific to the brewery industry are limited, but several analogous findings offer relevant insights. For example, a study of Uganda Breweries revealed that social media activity had a large, significant positive effect on sales volume (3.54, p = 0.002), though its impact on profitability was more modest. This suggests that while social media can drive demand, not all forms of digital activity translate uniformly to profits. Moreover, consumer behavior on social media is influenced by parasocial interaction, the illusion of personal connection to influencers which has been shown to boost purchase intention in other contexts (e.g., beauty and fashion audiences in France). The role of influencers and peers in shaping attitudes aligns with classic herd behavior phenomena: people rely on social cues and user recommendations when making purchase decisions (Wikipedia: Herd behavior). Theory of Planned Behavior (TPB) posits that individuals’ attitudes, influenced by information and social norms, drive behavioral intentions (Ajzen et al.) [9]. SMMA dimensions help shape favorable attitudes toward brands, which in turn enhance purchase intention. Uses and Gratifications Theory (UGT) emphasizes that consumers actively seek social media content that satisfies their entertainment, social interaction, personal identity, and trend-seeking needs. SMMA content that aligns with these gratifications is more likely to impact purchase intentions. Parasocial Interaction (PSI) describes how consumers develop one-sided emotional connections with media personas (e.g., influencers), which can boost trust and purchase behavior. In general, prior empirical work suggests that interaction, customization, and entertainment often influence purchase intention, while the effects of trendiness and eWOM/advertising vary by industry and consumer culture.
In the brewery industry, particularly in Liberia, these relationships remain underexplored. Grounding this study within TPB, UGT, and PSI frameworks, we can better understand how specific SMMA strategies translate into consumer purchase intention in an emerging-market context. Liberia’s brewery sector is small but competitive, dominated by brands such as Club Beer, Guinness, and Stout, which rely heavily on both traditional media and social platforms. Social media serves as a cost-effective alternative to billboards and TV ads, enabling breweries to engage directly with their core consumers. Yet, there is limited empirical evidence on the effectiveness of these strategies in influencing consumer purchase behavior, especially considering socio-economic constraints and cultural factors that shape alcohol consumption patterns.
Ajzen et al introduced the Theory of Planned Behavior (TPB), a psychological framework explaining how attitudes, subjective norms, and perceived behavioral control influence intention and behavior. The model has been foundational in predicting various consumer actions, including purchase decisions. While TPB is theoretical, our study empirically applies its principles to the brewery industry in a digital marketing context, specifically testing which social media strategies drive purchase intention. The advantage of our work is that it moves beyond the general behavioral model to identify specific online marketing elements with measurable statistical effects. Mangold et al. positioned social media as a new hybrid element of the promotion mix, blending traditional advertising with consumer-to-consumer communication. They highlighted the shift in power from marketers to consumers in shaping brand perceptions. Their work is conceptual and industry-wide, not sector-specific. In contrast, our study narrows this broad framework to the brewery sector in a specific geographic market, quantifying the influence of selected strategies on purchase intention—thus providing actionable, industry-specific recommendations.
The study of (Kotler Keller et al.) [10] in Marketing Management presented an authoritative synthesis of marketing principles, strategies, and practices, including digital marketing’s role in modern business. While their work offers a comprehensive theoretical and practical foundation, it is general and textbook-oriented. Our study extends these principles by empirically testing them in a niche market, revealing which strategies—among trendiness, interactivity, customization, and brand following—are statistically significant in influencing brewery consumers. Hair et al. focused on Multivariate Data Analysis, providing detailed methodologies for statistical modeling, including factor analysis, regression, and structural equation modeling. Their contribution is methodological rigor. Our study leverages these analytical techniques but applies them specifically to social media marketing variables in the brewery industry, offering applied evidence that bridges methodological theory with market-specific insights. Statista (2024) [11] provided updated global social media usage statistics, offering a quantitative backdrop for understanding the scale and relevance of digital marketing. While their data underscores why social media is a vital marketing channel, it is descriptive rather than analytical. Our research builds on this foundation by not only acknowledging social media’s prevalence but also identifying which specific strategies within this space most effectively influence consumer purchase intention in a localized context. Kapoor et al. [12] reviewed advances in social media research, categorizing past work, identifying gaps, and forecasting future trends. They emphasized the need for sector-specific, empirical studies that go beyond generalizations. Our work directly answers this call by focusing on the brewery industry, conducting quantitative hypothesis testing, and producing insights that are both theoretically grounded and practically implementable—especially in addressing strategies (like interactivity and customization) that are currently underperforming.
Overall, the aim of this study is to analyze the relationship between social media marketing strategies and consumer purchase intention in Liberia’s brewery industry, focusing on five SMMA dimensions: entertainment, interaction, trendiness, customization, and advertising. The novelty of this research lies in its emerging-market, sector-specific focus, a perspective often overlooked in global digital marketing literature. While much of the existing research on SMMA and purchase intention is based on data from developed economies, this study contextualizes these constructs within Liberia’s unique socio-economic, infrastructural, and cultural landscape. The expected contribution is both theoretical, advancing understanding of SMMA effectiveness in under-researched regions, and practical, by offering breweries actionable insights to optimize their social media campaigns for higher consumer engagement and conversion.
Hypothesis Development
Based on theoretical arguments and previous empirical findings, we propose three hypotheses. H1: Trendiness of social-media content positively influences consumers’ purchase intention. H2: Interactivity in social-media activities positively influences consumers’ purchase intention. H3: Customization of social-media content positively influences consumers’ purchase intention. These hypotheses are tested using survey data (N = 123) via correlation analysis and multiple linear regression, controlling for key demographics H1 (Trendiness). Trendiness of brewery brands’ social-media content (i.e., alignment with current cultural moments, memes, and popular topics) has a positive effect on consumers’ purchase intention. Trend-aligned content increases relevance and salience, attracting attention and strengthening attitude toward the brand (Uses Gratifications; evidence from fast-fashion and TikTok studies). H2 (Interactivity). Interactivity in brewery brands’ social-media activities (two-way communication, responsiveness, engagement features) has a positive effect on consumers’ purchase intention. This is because two-way engagement fosters trust and emotional connection (Theory of Planned Behavior; Parasocial Interaction), which encourage intention and eventual purchase. H3 (Customization). Customization (personalized, user-targeted content) on brewery social channels has a positive effect on consumers’ purchase intention. The rationale behind this is that personalized messages increase perceived relevance and usefulness, improving attitudes and conversion likelihood (TAM/personalization literature).
3. Research Framework and Methodology
Before you begin to format your paper, first write and save the content as a separate text file. Keep your text and graphic files separate until after the text has been formatted and styled. Do not use hard tabs, and limit use of hard returns to only one return at the end of a paragraph. Do not add any kind of pagination anywhere in the paper. Do not number text heads the template will do that for you. This study adopts a conceptual framework as shown in Figure 1 is grounded in the Social Media Marketing Activities (SMMA) model, which has been widely used in consumer behavior research. The framework postulates that five core dimensions of social media marketing entertainment, interaction, trendiness, customization, and advertising influence consumer purchase intention in the brewery industry. The selection of these constructs is informed by prior empirical studies that have established their relevance in predicting consumer engagement and purchasing behavior in the context of fast-moving consumer goods.
The hypothesized relationships assume that each SMMA dimension exerts a positive and statistically significant effect on purchase intention. The conceptual model guiding this research is presented in Figure 1, positioning the five SMMA variables as independent factors and consumer purchase intention as the dependent outcome. The primary aim of the study is to examine the impact of social media marketing strategies on consumers’ purchase intentions within Liberia’s brewery industry. Specifically, the research seeks to; determine the relationship between entertainment-oriented content on social media and consumer purchase intention for brewery products.
Figure 1. Conceptual framework illustrating hypothesized relationships between social media marketing dimensions and purchase intention.
Assess whether interactive and responsive brand communication fosters stronger purchase intention, examine the influence of trend-oriented and up-to-date content on purchase decision-making, explore the role of personalized (customized) content in motivating purchase behavior, and evaluate the impact of targeted social media advertising on brewery consumers’ purchase intentions. This research is novel because it applies the SMMA framework in the under-researched context of Liberia’s brewery sector, an emerging market with unique consumer dynamics shaped by socio-economic, cultural, and infrastructural factors. The study not only extends the theoretical literature to a West African setting but also offers industry-specific recommendations for enhancing social media effectiveness in an industry where brand loyalty and consumer choice are highly sensitive to marketing communication.
3.1. Research Design and Data Collection
Define abbreviations and acronyms the first time they are used in the text, even after they have been defined in the abstract. Abbreviations such as IEEE, SI, MKS, CGS, sc, dc, and rms do not have to be defined. Do not use abbreviations in the title or heads unless they are unavoidable. The study adopts a quantitative, cross-sectional research design, employing a structured questionnaire to collect primary data from brewery consumers in Liberia. The questionnaire was designed using Likert-scale items adapted from validated SMMA instruments in prior studies. The survey instrument consisted of three main sections: 1) demographic characteristics of respondents; 2) measures of the five SMMA dimensions; and 3) consumer purchase intention. A purposive sampling technique was employed to target active social media users who follow at least one brewery brand on platforms such as Facebook, Instagram, and TikTok.
This approach ensures that responses are drawn from individuals with relevant experience and exposure to brewery-related digital marketing. Data were collected between July and August 2025 using an online survey distributed via social media platforms and messaging applications. A total of 123 valid responses were obtained. The sample comprised respondents aged between 18 and 45, with a higher concentration of urban-based participants due to greater internet penetration in these areas. Ethical considerations were addressed by ensuring participant anonymity, obtaining informed consent, and clarifying that participation was voluntary. Data analysis was conducted using SPSS 29.0, following a multi-step procedure. First, descriptive statistics were calculated to summarize respondent characteristics and variable distributions. Second, reliability analysis using Cronbach’s alpha assessed the internal consistency of each scale. Third, Pearson correlation coefficients were computed to examine the bivariate relationships between SMMA dimensions and purchase intention. Finally, multiple linear regression analysis was performed to test the study hypotheses and determine the relative influence of each SMMA dimension on purchase intention. Statistical significance was set at p < 0.05. The chosen analytical approach ensures both the robustness and validity of the findings.
3.2. Data Analysis Techniques
The data collected from the administered questionnaires were processed and analyzed using the Statistical Package for the Social Sciences (SPSS) software, owing to its robustness and wide acceptance in quantitative research within the social sciences and marketing domains. The analytical process was designed to systematically transform raw responses into meaningful insights, following a structured multi-step procedure to enhance both reliability and validity. Descriptive statistics were employed as the initial analytical stage to profile the demographic characteristics of the respondents and to examine the central tendency and dispersion of each construct related to Social Media Marketing Activities (SMMA). Measures such as means, standard deviations, frequencies, and percentages were computed. This provided a foundational understanding of the sample composition and the general patterns within the dataset, which is critical for contextualizing subsequent inferential analyses. To assess the internal consistency and reliability of the measurement items, Cronbach’s alpha coefficients were computed for each SMMA dimension and the dependent variable (purchase intention).
A Cronbach’s alpha threshold of 0.70 or above was adopted, consistent with Nunnally’s benchmark, indicating acceptable reliability for social science research instruments. This step ensured that the scales used were robust in measuring their intended constructs. Pearson’s correlation analysis was conducted to examine the strength and direction of bivariate relationships between the independent variables (entertainment, interaction, advertising) and the dependent variable (purchase intention). This step provided preliminary evidence of potential associations, guiding the interpretation of subsequent regression results. The statistical significance of the correlation coefficients was assessed at the 0.05 and 0.01 confidence levels. To test the formulated hypotheses and evaluate the relative predictive power of each SMMA dimension, multiple linear regression analysis was performed. The regression model’s overall significance was determined using the F-test, while the contribution of each predictor was assessed through standardized beta coefficients, t-values, and p-values. The model was estimated at a 95% confidence interval, ensuring robustness in statistical inference. This analysis not only identified the strength of influence of each SMMA construct but also provided actionable insights for strategic decision-making in social media marketing campaigns within Liberia’s brewery sector.
To reinforce the credibility of the regression results, diagnostic checks for multi-collinearity, normality, and homo-skedasticity were undertaken. Variance Inflation Factor (VIF) values were examined to ensure multi-collinearity did not undermine the model’s interpretability, while residual plots and statistical tests confirmed the assumptions of linear regression were reasonably met. Adopting this multi-layered analytical approach, the study ensures methodological rigor, enabling the results to offer both statistically sound and practically relevant insights to marketing practitioners seeking to enhance consumer purchase intention through optimized social media strategies in the Liberian brewery industry.
4. Research Results and Hypothesis Testing
This section presents the empirical results of the study, including descriptive statistics, scale reliability, and correlation patterns between the social media marketing activities (SMMAs) and consumers’ purchase likelihood in the Liberian brewery industry. The findings are based on survey data from N = 123 respondents and analyzed using SPSS version 29. Table 1 summarizes the central tendency and dispersion for each of the SMMA constructs and the dependent variable, purchase likelihood. The mean values for the SMMA dimensions range between 2.69 and 2.96, indicating moderate agreement or perceived presence of these marketing elements. The highest mean score is recorded for Entertainment (Mean = 2.96), suggesting that brewery-related social media content is perceived as somewhat engaging. Conversely, Interaction records the lowest mean score (Mean = 2.69), implying that active engagement opportunities between breweries and consumers may be less prominent.
Table 1. Descriptive statistics of study variables (N = 123).
Variable |
Mean |
Descriptive statistics |
Min |
N |
Standard Dev. |
Min |
Entertainment (ENT) |
2.96 |
1.11 |
1.0 |
5.0 |
123 |
Customization (CUS) |
2.75 |
1.19 |
1.0 |
5.0 |
123 |
Trendiness (TRE) |
2.91 |
1.11 |
1.0 |
5.0 |
123 |
Advertising (AD) |
2.73 |
1.25 |
1.0 |
5.0 |
123 |
Interaction |
2.69 |
1.41 |
1.0 |
5.0 |
123 |
Purchase likelihood |
3.76 |
1.19 |
1.0 |
5.0 |
123 |
The dependent variable, Purchase likelihood, has a relatively high mean (Mean = 3.76), indicating a generally positive disposition toward buying brewery products following social media engagement. Cronbach’s alpha (α) was computed to assess the internal consistency of the multi-item scales (Table 2). All constructs exceeded the commonly accepted threshold of α ≥ 0.65, except Trendiness, which reported a marginal value of 0.658.
Table 2. Reliability statistics (Cronbach’s alpha).
Constructs |
Reliability statistics |
Items |
Cronbach’s α |
Entertainment (ENT) |
ENT1, ENT2 |
0.768 |
Customization (CUS) |
CUS1, CUS2 |
0.888 |
Trendiness (TRE) |
TRE1, TRE2 |
0.658 |
Advertising (AD) |
AD1, AD2 |
0.892 |
Reliability analysis indicated that most constructs met the commonly accepted Cronbach’s α threshold of 0.70. However, the Trendiness scale yielded a Cronbach’s α of 0.658, which is marginally below the recommended benchmark. This limitation suggests that the measurement of trendiness may not have fully captured the diverse ways in which Liberian consumers perceive and engage with social media trends. Future research should refine the trendiness construct by incorporating platform-specific indicators (e.g., Facebook trending posts, WhatsApp group buzz, Instagram hashtags) to improve reliability and better reflect local digital culture. While this is still considered acceptable in preliminary studies, it suggests that future research could improve reliability through refined wording or adding more items to capture the construct fully. Table 3 presents the Pearson correlation coefficients between each SMMA construct and purchase likelihood. Among the predictors, Trendiness shows the strongest positive correlation with purchase likelihood (r = 0.314), followed by Advertising and Interaction (both r = 0.233). Interestingly, perceived SMM impact is negatively correlated with purchase likelihood (r = −0.263), which may suggest that higher critical awareness or skepticism toward marketing efforts could be associated with reduced purchase intent. These findings provide an initial empirical basis for the hypothesis testing presented in the subsequent section.
Table 3. Pearson correlations between predictors and purchase likelihood.
Predictors |
Pearson correlation r with LIKELY NUM |
Trendiness (TRE) |
0.314 |
Advertising (AD) |
0.233 |
Interaction |
0.233 |
Customization |
0.218 |
Entertainment |
0.171 |
Perceived SMM impact (SMM IMPACT) |
−0.263 |
The correlation results suggest that visually appealing, up-to-date content (Trendiness) may be the most influential SMMA dimension in stimulating consumer purchase intentions in Liberia’s brewery industry. However, the relatively moderate correlation values indicate that other factors—beyond the five SMMA constructs measured—may also contribute significantly to consumer decision-making. An Ordinary Least Squares (OLS) regression with robust (HC3) standard errors was conducted to test the three hypotheses: H1 (trendiness positively influences purchase intention), H2 (interactivity positively influences purchase intention), and H3 (customization positively influences purchase intention). The model included control variables for gender (male = 1), whether the respondent follows the brewery’s official social media account (follow = 1), and whether the respondent had ever purchased because of social media marketing (purchase-ever = 1). The results, presented in Table 4, indicate that trendiness is a statistically significant positive predictor of purchase intention (β = 0.394, p = 0.002), supporting H1. This suggests that, holding other factors constant, a one-point increase in perceived trendiness on the 1 - 5 scale corresponds to an average 0.39 increase in purchase-likelihood score. Neither interactivity (β = 0.003, p = 0.976) nor customization (β = −0.157, p = 0.254) showed significant effects in the joint model, leading to the rejection of H2 and H3. The point estimate for customization is negative, though not statistically significant, indicating that current personalization strategies may not be effectively translating into higher purchase likelihood.
Table 4. OLS regression predicting purchase intention (N = 123).
Variable |
Descriptive statistics |
p-value |
Coefficient |
Robust SE |
t-value |
Constant |
2.391 |
0.298 |
8.033 |
<0.001 |
Trendiness (TRE) |
0.394 |
0.128 |
3.080 |
0.002 |
Interactivity |
0.003 |
0.095 |
0.030 |
0.976 |
Customization |
−0.157 |
0.137 |
−1.140 |
0.254 |
Gender (Male) |
0.123 |
0.186 |
0.659 |
0.510 |
Follows Brand (FOLLOW YES) |
0.649 |
0.202 |
3.209 |
0.001 |
Prior Purchase (PURCHASE EVER YES) |
0.814 |
0.200 |
4.070 |
<0.001 |
R2 |
|
0.353 |
|
|
Adjusted R2 |
|
0.319 |
|
|
Among control variables, following the brand on social media (β = 0.649, p = 0.001) and having previously purchased because of social media marketing (β = 0.814, p < 0.001) were both strong, positive, and statistically significant predictors of purchase intention. The model explains approximately 35.3% of the variance in purchase intention (R2 = 0.353; adjusted R2 = 0.319, N = 123). Variance Inflation Factor (VIF) diagnostics indicated no severe multicollinearity (all VIF < 3.08).
4. Research Framework and Methodology
The results provide empirical support for the role of trendiness in driving consumer purchase intention in the Liberian brewery context. The positive and significant effect aligns with prior studies [13] which found that trend-based content resonates with consumers by signaling relevance, cultural alignment, and social currency. This suggests that aligning social media marketing campaigns with current trends, local cultural moments, and viral formats can effectively enhance purchase motivation. Conversely, interactivity and customization did not show significant effects in this study. The lack of support for H2 contradicts findings from another who reported that interactive features often boost consumer engagement and conversion. One possible explanation lies in the measurement approach—interactivity was captured with a single item, potentially reducing reliability. Similarly, the non-significant and negative point estimate for customization may indicate either suboptimal implementation in current brewery campaigns or that consumers do not perceive personalization efforts as value-adding in this product category. The significance of the control variables—brand following and prior purchase due to social media marketing—underscores the importance of long-term engagement and conversion pathways. This aligns with the AIDA model’s emphasis on the transition from awareness and interest to action. Followers represent a warmer audience, and prior purchasers are more likely to repurchase, suggesting that breweries should prioritize follower acquisition strategies alongside consistent conversion opportunities.
4.1. Research Findings
The rejection of H2 and H3 indicates that while interactivity and customization are conceptually valuable components of social media marketing, the current execution within Liberia’s brewery industry is failing to translate into measurable increases in purchase intention. This gap suggests that breweries may be relying on superficial or low-engagement forms of interactivity, such as routine “like-and-share” contests or basic comment prompts, which fail to capture sustained consumer interest or create a deeper connection to the brand. To address this, breweries should transition toward more immersive and experiential interactive strategies that offer tangible value and emotional engagement. For instance, integrating interactive beer pairing quizzes based on local dishes, hosting virtual brewery tours that allow consumers to explore the brewing process from home, or launching live brewing sessions where customers vote on ingredients and flavors could create a sense of ownership and participation. Such activities transform passive audiences into active participants, fostering a stronger brand-consumer relationship that could ultimately stimulate purchase intent. Importantly, these experiences should be integrated into broader campaign narratives tied to trending cultural events, music festivals, or national celebrations to enhance relevance and visibility. Similarly, the non-significance of customization highlights a need for breweries to rethink how personalization is being implemented in their digital marketing strategies. Current methods may be too generic, such as offering broad, non-targeted promotions or vague product recommendations that fail to reflect individual consumer preferences. Instead, breweries should explore hyper-personalization approaches that leverage consumer data to offer tailored experiences.
Examples include recommending specific beer varieties based on a customer’s past purchase history, creating customized beer labels for loyal customers, or curating unique tasting kits aligned with a customer’s flavor profile. By combining personalization with exclusivity—such as limited-edition brews inspired by individual customers’ choices—breweries can make consumers feel uniquely valued. Furthermore, these personalization efforts should be continuously tested through A/B experiments to determine their direct impact on purchase intent before committing to large-scale rollouts. In essence, while H2 and H3 were rejected in their current forms, both interactivity and customization retain strong potential; they simply require that brewery companies adapt deeper creativity, meaningful consumer integration, and evidence-based refinement to deliver measurable business outcomes. One unexpected finding of this study is that Customization exhibited a negative, though non-significant, relationship with purchase intention. This suggests that efforts to tailor brewery marketing content to individual consumers may not resonate positively within the Liberian cultural context. Beer and other alcoholic beverages are often perceived as standardized, communal products, and attempts to personalize or individualize messaging may conflict with existing consumption norms. Similar patterns have been observed in other Sub-Saharan markets, where consumers sometimes associate customization with higher costs or reduced product authenticity (e.g., Agyapong et al., 2022; Nyarko & Boateng, 2021). This indicates that breweries should approach customization strategies with caution, aligning them more closely with collective consumption traditions rather than individual tailoring.
4.2. Limitations and Future Works
The analysis of survey responses from 123 participants provided a nuanced understanding of the relative influence of different social media marketing strategies on consumer purchase intention in the brewery industry. Among the examined constructs, trendiness emerged as the most robust predictor, indicating that consumers are significantly more likely to purchase when marketing campaigns align with current cultural moments, seasonal events, and widely recognized social narratives. Advertising also demonstrated a positive and statistically significant association with purchase likelihood, reaffirming its role as a consistent driver of consumer awareness and product consideration in a competitive market. Conversely, the hypothesized effects of interactivity and customization were not supported by the data. While these strategies remain theoretically relevant in digital marketing literature, their present application in the brewery context appears insufficient to motivate purchase decisions. Entertainment, though positively correlated with purchase intention, did not achieve statistical significance, implying that while it may enhance brand familiarity and emotional connection, it may not be a decisive factor in influencing actual purchasing choices. Despite the valuable insights offered, this study is not without limitations. First, the use of a purposive online survey introduces self-selection bias, as only individuals with internet access and interest in the topic were likely to respond. Second, the sample is disproportionately drawn from urban residents, which may not fully capture the perspectives of rural consumers in Liberia. Third, the relatively small sample size (N = 123) restricts the generalizability of the findings. Future research should adopt probability-based sampling strategies and expand the sample to include more rural participants to strengthen external validity. Generally, the findings indicate that breweries should maintain a strong emphasis on trend-based and advertising-driven strategies, while critically reassessing and refining interactive and personalized marketing initiatives. These results also highlight the importance of continuous monitoring and adaptation of marketing tactics to ensure alignment with evolving consumer preferences, cultural contexts, and digital engagement patterns. From a practical standpoint, the findings of this study highlight three strategic priorities that brewery companies should consider in refining their social media marketing strategies.
First, it is evident that campaigns aligned with trends and cultural events exert the most significant influence on purchase intention. Therefore, marketing teams should prioritize the development of creative content that taps into locally relevant trends, seasonal festivities, and culturally resonant narratives. Such trend-based strategies can enhance brand visibility and foster emotional connections with consumers, ultimately increasing the likelihood of purchase. Second, the results emphasize the importance of investing in follower growth and sustained engagement. A larger, actively involved follower base provides a fertile environment for conversions, meaning that breweries should not only focus on acquiring new followers but also on nurturing existing audiences through consistent interaction, value-driven content, and clear calls-to-action that guide consumers toward purchase decisions. Finally, while interactivity and customization are theoretically valuable components of digital marketing, their current implementation appears insufficient in driving purchase intent. This points to the need for a comprehensive reassessment of interactive content formats and personalization tactics to identify shortcomings in design, execution, or consumer relevance. Future research should investigate more immersive forms of interactivity and hyper-personalization, as well as explore the integration of these strategies with trend-based campaigns to determine their combined effect on consumer behavior. Additionally, subsequent studies could benefit from longitudinal data collection and experimental testing to establish causal relationships, thereby strengthening the strategic implications for breweries operating in competitive digital markets.
5. Conclusions
This study examined the influence of key social media marketing dimensions: trendiness, advertising, interactivity, customization, and entertainment on consumer purchase intention in the brewery industry. The empirical results revealed that trendiness and advertising had statistically significant positive effects, highlighting the strategic importance of aligning marketing messages with prevailing cultural trends and ensuring consistent promotional visibility. In contrast, interactivity and customization, while conceptually grounded in established digital marketing theory, did not demonstrate a significant impact in their present application within the industry. This outcome does not negate their potential value; rather, it suggests that current executions of these strategies may not be sufficiently compelling or personalized to affect consumer purchasing decisions.
Entertainment, while positively correlated, also fell short of statistical significance, implying that it may function more as a supporting rather than a primary driver of purchase intention. From a theoretical perspective, these findings reinforce the proposition that cultural relevance and brand presence are central to effective digital marketing strategies, particularly in markets where consumer behavior is highly responsive to social trends. Practically, they indicate that brewery companies should continue to prioritize trend-based campaigns and targeted advertising while rethinking the way they implement interactive and personalized marketing experiences. Limitations such as the modest sample size, the cross-sectional nature of the data, and potential measurement constraints suggest that these conclusions should be interpreted with caution. Future research could employ longitudinal designs, larger and more diverse samples, and refined measures of interactivity and customization to capture their potential more accurately. In sum, this study provides both actionable marketing insights and a foundation for scholarly inquiry, underscoring that breweries in the digital age must balance cultural agility with strategic innovation to drive consumer purchase intention.
Conflicts of Interest
The authors declare no conflicts of interest.