The Effect of Social Media Engagement on Consumer Purchase Behaviour in the Food and Beverage Industry

The Effect of Social Media Engagement on Consumer Purchase Behaviour in the Food and Beverage Industry

Amizatulhawa Mat Sani^1*, Nur Syuhadah Umairah Hasri², Nurhayati Kamarudin³, Tiara Turay⁴

^1,2,3 Faculty of Technology Management and Technopreneurship / University Technical Malaysia Melaka, Malacca, Malaysia,

⁴Universitas Dharma Andalas

^*Corresponding Author

DOI: https://dx.doi.org/10.47772/IJRISS.2025.908000482

Received: 13 August 2025; Accepted: 18 August 2025; Published: 17 September 2025

ABSTRACT

The increasing adoption of social media platforms was under-researched, with limited understanding of which specific engagement strategies have the most significant impact on purchasing behavior. Previous studies show the decreasing numbers of purchasing in social media particularly related to the food and beverage industry. The increasing adoption of social media platforms was under-researched, with limited understanding of which specific engagement strategies have the most significant impact on purchasing behavior. Previous studies show the decreasing numbers of purchasing in social media particularly related to the food and beverage industry. Pearson correlation results indicated strong, positive, and significant relationships between current engagement, valuable content, collaboration with influencer and partner, monitoring trends and news and consumer purchase behavior. Multiple regression analysis revealed that monitoring trends and news had the most substantial predictive influence, followed by valuable content, collaboration with influencers and partners, and current engagement. Practically, the results suggest that marketers in the food and beverage industry should focus on monitoring trends, creating valuable content, and collaborating with influencers to boost sales and enhance consumer engagement.

Keywords: Social media engagement, consumer purchase behaviour, food and beverage industry

INTRODUCTION

The rapid growth of the Internet has greatly changed consumer behavior, especially in the food and beverage (F&B) industry. Social media platforms have become key channels for consumers to find product information, with more than half of users (53.4%) considering them valuable sources (Md. Shawmoon Azad, 2022). Social media advertising is seen as more effective at grabbing attention than traditional advertising, with 47% of respondents agreeing it has a greater impact (Md. Shawmoon Azad, 2022).

Social media engagement impacts various phases of the purchase decision-making process. Consumers are more likely to seek additional information when promotional keywords, such as discounts, are present (49%), and more than half (50.3%) report buying products they first encountered through social media (Md. Shawmoon Azad, 2022). Social influence also plays a role, with family members influencing 41% of purchase decisions, and frequent exposure to product promotions raises the likelihood of purchase for 40% of consumers (Md. Shawmoon Azad, 2022). Earlier studies highlight that user-generated content, including reviews and ratings, along with interactive marketing strategies, greatly influence purchase intentions in the F&B industry (Amoah, Radder, & Van Eyk, 2021; Vishnu, 2024).

Despite the recognized importance of social media, the links between engagement and actual purchase behavior are still not well understood. Businesses in the F&B sector often invest heavily in social media marketing without clearly knowing which engagement strategies, such as influencer partnerships, creating valuable content, or trend monitoring, generate the highest returns (Smith, 2020). Additionally, a lack of distinction in engagement strategies has resulted in uniform marketing approaches, which reduce consumer interest and lower campaign effectiveness (Jones & Brown, 2021).

This study fills these gaps by analyzing how different types of social media engagement, including current engagement, valuable content, influencer collaboration, and trend monitoring, affect consumer purchasing behavior in Malaysia’s food and beverage (F&B) sector. By pinpointing the most impactful engagement areas, the results aim to enhance both digital marketing theory and practical strategies for improving social media initiatives.

LITERATURE REVIEW

Consumer Purchase Behaviour in the Food Beverage Industry

More than half (50.3%) report buying products they first encountered on social media (Md. Shawmoon Azad, 2022). Family members influence 41% of purchase decisions, and frequent exposure to product promotions increases the likelihood of purchase for 40% of consumers (Azad, 2022). Previous studies have shown that user-generated content, such as reviews and ratings, as well as interactive marketing strategies, greatly influence purchase intentions in the food and beverage (F&B) industry (Amoah, Radder, & Van Eyk, 2021; Vishnu, 2024).

By identifying the most impactful engagement areas, the study aims to enhance digital marketing theory and practical strategies for improving social media initiatives. In the food and beverage industry, this behavior encompasses what to eat or drink, where to shop, and the factors that influence these choices. According to Wang et al. (2020), purchasing decisions in this sector depend on several factors, including product quality, brand reputation, price, and promotions. Health and taste awareness are strong drivers of consumer choices (Luna-Nevarez & McGovern, 2021).

Cultural background and heritage also shape consumer preferences because different cultural groups tend to have distinct eating and drinking habits (Liu, Liu, & Lee, 2020). These findings emphasize the importance of F&B companies crafting targeted engagement strategies that reflect their customers’ preferences, lifestyles, and behaviors to maximize marketing effectiveness in a competitive digital environment.

Stratification of Social Media Engagement

This classification provides a clearer picture of the quality and depth of online interactions between brands and consumers. Engagement behavior can range from passive consumption of content to active sharing, commenting, and creation of content. As de Vries, Gensler, and Leeflang (2021) point out, stratification is also important for describing the various levels of user participation and how they can influence consumer decisions and choice.

Conceptually, there are three levels of social media involvement: passive, medium, and high participation. Passive participation involves actions such as viewing and liking content, which indicate exposure but minimal interaction (Peters, Chen, Kaplan, Ognibeni, & Pauwels, 2021; Hudson, Huang, Roth, & Madden, 2021). Moderate involvement encompasses actions such as commenting and sharing, indicating greater interest and a willingness to engage in public discussions about a brand (Muntinga, Moorman, & Smit, 2019; Gvili & Levy, 2021).

High involvement is the most active type of participation, such as creating original brand-related content or participating in community-based brand discussions (Lim, Hwang, Kim, & Biocca, 2020; Goh, Heng, & Lin, 2020). Lim et al. (2020) argue that identifying these various levels allows marketers to create targeted approaches that align with specific engagement behaviors, thereby enhancing message relevance and effectiveness

Effectiveness of Social Media Engagement

Social media interaction effectively measures how meaningful social media engagements lead to desired business outcomes, such as increased brand awareness, loyalty, and sales. Its effectiveness depends not only on how often customers engage but also on the depth and impact of these interactions on consumer behavior (Hollebeek, Sprott, & Andreassen, 2019).

Current Engagement

Current engagement includes a wide range of social media user behaviors, from passive actions such as  viewing and liking to active ones such as commenting, sharing, and creating content. High levels of such engagement are usually linked to better brand performance, stronger customer relationships, and more advocacy. For example, De Vries, Gensler, and Leeflang found that consistent, active engagement with an audience is associated with increased customer loyalty and brand advocacy, resulting in more favorable marketing results (2023).

H1: Current social media engagement metrics positively influence consumer purchase behaviour.

Valuable Content

Creating valuable content is crucial for increasing social media engagement. Content that is relevant, visually attractive, informative, or emotionally touching tends to generate stronger responses from users, such as likes, shares, and repeated visits. Ashley and Tuten (2015) emphasize that visually appealing and meaningful branded content greatly enhances consumer engagement and purchase intent.

 H2: Creating valuable content on social media positively influences consumer purchase behaviour.

Collaboration with Influencers and Partners

Partnering with influencers and strategic allies boosts engagement by harnessing their authenticity, credibility, and reach. In the food and beverage (F&B) industry, influencer endorsements can heavily sway consumer preferences. Lou and Yuan (2019) highlight that successful influencer marketing depends on message value and credibility when these are high, influencer-driven content greatly boosts consumer trust and interaction with brands.

H3: Collaborating with influencers and partners on social media positively influences consumer purchase behaviour.

Monitoring Trends and News

It is essential for brands to stay informed about social media trends and current news to stay relevant and engaging. Aligning content with trending topics or viral narratives allows brands to attract consumer interest more effectively. While there is limited detailed academic evidence in the food and beverage (F&B) context, Wang, Yu, and Fesenmaier (2002) suggest that social media offers multifunctional benefits, including functional, hedonic, social, and psychological, that can boost engagement when used at the right moment..

H4: Monitoring trends and news on social media positively influences consumer purchase behaviour.

Based on the discussions above, a conceptual framework is developed, as shown in Figure 1.

Conceptual Framework

This study highlights Malaysia as an example of rapid technology adoption and innovation among SMEs. The country has demonstrated a strong commitment to becoming a developed nation by integrating AI into business operations. Significant investments have been made to modernize services, leading Malaysia to achieve a high ranking in AI adoption. This progress reflects not only technological advancements but also a competitive market environment supported by increasing digital literacy. This study proposed a conceptual framework that focuses on two technological factors: relative advantage and compatibility.

Fig. 1. Conceptual framework

RESEARCH METHODOLOGY

Methodology

This study uses the research onion model proposed by Saunders, Lewis, and Thornhill (2019) as the main framework, ensuring a structured and systematic approach to research design. An experimental research design is used to explore the causal link between social media engagement (independent variable) and consumer purchase behavior (dependent variable) in the food and beverage (F&B) industry. Taking a positivist philosophy, the study takes a deductive approach, deriving hypotheses from existing theory and testing them empirically with data. The study design entails a quantitative survey method, supported by secondary data in the form of academic articles, statistical reports, government records, and peer-reviewed journals. The time period is cross-sectional, in which data is gathered at a single point to analyze current engagement levels and their immediate effect on purchasing behavior.

A stratified random sampling method is used to obtain representativeness by segregating the target population into subgroups with similar traits. The respondents are Melaka residents in Malaysia, a state with an ethnically diverse population of approximately 1,027,500, comprising various age groups, educational backgrounds, and socio-economic statuses. Melaka’s mix of urban and rural residents, along with its vibrant F&B sector, offers an ideal location for examining the effect of social media use on purchasing behavior. Based on the Krejcie and Morgan (1970) sample size determination table, 384 respondents were identified as the sample size to achieve a statistically acceptable degree of precision.

The main research tool is a formal questionnaire with three sections: Section A (demographic profile), Section B (social media engagement effectiveness), and Section C (consumer purchase behavior). The items are measured on a five-point Likert scale, ranging from 1 (“Strongly Disagree”) to 5 (“Strongly Agree”), with multiple-choice questions as an option to solicit additional information. This tool was selected based on its utility in collecting large amounts of quantitative data that can be subjected to statistical analysis (Creswell & Creswell, 2020). Data will be collected online and analyzed using regression analysis to establish the strength and significance of the relationship between social media engagement and consumer purchasing behavior.

TABLE I Construct of Stratification of Consumer Purchase Behaviour

A total of 384 respondents took part in this study, with the sample size determined using the Krejcie and Morgan (1970) method for calculating the population size. Data were collected through online surveys targeting individuals in the food and beverage industry in Malaysia, resulting in a 100% response rate. The demographic analysis offers an overview of the respondents’ characteristics, including gender, age, ethnicity, occupation, monthly income, and social media usage patterns.

Regarding gender distribution, female respondents made up a slightly higher percentage at 53.9%, compared to 46.1% for males. The age distribution showed the largest group was between 25 and 34 years old (42.7%), followed by those aged 35–44 years and 18–24 years. The smallest group consisted of respondents aged 45 and older (9.4%). Ethnically, the Chinese community represented the largest portion at 48.4%, with Malays, Indians, and other ethnicities following. The Bajau and Indonesian groups had the smallest representation, each making up only 0.3% of all respondents.

Regarding occupational status, most respondents were employed (58.9%), followed by unemployed individuals (14.8%) and students (14.1%). Self-employed respondents made up 10.4%, while smaller groups included housewives (1.3%), retirees (0.3%), and individuals who identified as both master’s degree students and employed (0.3%). The monthly income distribution indicated that the largest portion of respondents earned between RM3,000 and RM4,999 (35.4%), followed by RM1,000 to RM2,999 (19.8%) and RM5,000 or more (19.3%). An additional 12.2% chose not to disclose their income, 12.0% earned less than RM1,000, and 1.3% reported having no income. Social media use was widespread among respondents, with 83.6% identifying as active users. Notably, 71.4% reported using social media to search for food and beverage products, 17.7% held a neutral stance, and 10.9% were non-users in this context.

RESULT

Reliability Analysis

The table above shows Cronbach’s Alpha reliability analysis for independent and dependent variables. The purpose of this reliability test is to check whether the data obtained is reliable for the research or not. The results show a higher reliability collaboration with influencers and partners for this test, which is 0.928 (excellent in reliability). Followed by monitors trends and news and current engagement which is 0.926. For knowledge and belief obtained in a low reliability scale which is valuable content 0.925. For the dependent variable (stratification of consumer purchase behaviour), managed to obtain about 0.924 and on a very good scale as well. The overall results that have been used by SPSS, show that the overall Cronbach’s Alpha is 0.940 and on a very good scale in the reliability test.

TABLE II Summary Of Reliability Analysis On Of Independent And Dependent Variables

Correlation Analysis

The table above shows the correlation and relationship between the independent variable (Monitor Trends and News, Valuable Content, Collaboration with Influencers and Partners and Current Engagement) and the dependent variable (Stratification of Consumer Purchase Behaviour). As a result, the significant value (2-tailed) for all variables is p<0.001.

For monitoring trends and news, the value of Pearson’s Correlation is 0.784, which is moderate and positively significant. For valuable content, the Pearson Correlation is 0.782 which is moderately significant and positive and for the collaboration with influencers and partners, the Pearson Correlation is 0.769 which is moderately significant and positively significant. While for current engagement, the Pearson Correlation is 0.766, which is moderate and positively significant. It shows that all the Pearson Correlation in this research is moderate and significantly positive 0.7 above (strong).

Table III Pearson Correlation Analysis

The table above shows the correlation and relationship between the independent variable (Monitor Trends and News, Valuable Content, Collaboration with Influencers and Partners and Current Engagement) and the dependent variable (Stratification of Consumer Purchase Behaviour). As a result, the significant value (2-tailed) for all variables is p<0.001.

For monitoring trends and news, the value of Pearson’s Correlation is 0.784, which is moderate and positively significant. For valuable content, the Pearson Correlation is 0.782 which is moderately significant and positive and for the collaboration with influencers and partners, the Pearson Correlation is 0.769 which is moderately significant and positively significant. While for current engagement, the Pearson Correlation is 0.766, which is moderate and positively significant. It shows that all the Pearson Correlation in this research are moderate and significantly positive 0.7 above (strong).

Multiple Regression Analysis

Multiple regression analysis is known as a technique for estimating values based on two independent variables and a dependent variable. The effect of the independent variable on the dependent variable was analysed by multiple regression analysis in this study with four independent variables and using only one dependent variable. Using SPSS, the results show a multiple regression analysis with four independent variables (Monitor Trends and News, Valuable Content, Collaboration with Influencer and Partner and Current Engagement) and one dependent variable (Stratification of Consumer Purchase Behaviour).

Table IV

MODEL SUMMARY

Based on the table, the regression model explains that the value correlation coefficient (R- value) is 0.860, equal to 0.86, showing a moderate relationship between monitoring trends and news, valuable content, current engagement, and collaboration with influencers and partners on consumer purchase behaviour. The R-Square value is 0.739 and the results explain that 73.0% of the variance in all independent variables can influence consumer purchase behaviour. The standard error of the estimate is 2.660, indicating the average distance that the observed values fall from the regression line.

Table V ANOVA

The results of the study show the relationship between the independent variable (monitoring trends and news, valuable content, current engagement, collaboration with influencers and partners) and the dependent variable (stratification of consumer purchase behaviour). The regression model is significant with an F value of 268.411 and a p value of 0.001. The regression sum of squares (7597,266) accounts for the majority of the total variance (10279,122), while the residual sum of squares (2681,856) represents the unexplained variance. This shows that independent variables contribute significantly to the influence of purchase behaviour. The results show that the model has more systematic variation than unsystematic variation because the p-value is 0.01 which is less than 0.05.

Table VI Coefficients

Based on the results, showing the effectiveness of monitoring trends and news, valuable content, current engagement, and collaboration with influencers and partners on consumer purchase behaviour. Monitor trends and news has the greatest positive influence on consumer purchase behaviour (B = 0.333, Beta = 0.333, p < 0.001), followed by valuable content (B = 0.305, Beta = 0.305, p < 0.001), next is a collaboration with influencer and partner (B = 0.280, Beta = 0.280, p < 0.001). Current engagement also has a positive effect on consumer purchase behaviour (B = 0.206, Beta = 0.206, p < 0.001), although the effect is relatively smaller. The constant term is not significant (p = 0.000), indicating that without the independent variable, brand loyalty cannot be adequately predicted.

Table VII Hypothesis

DISCUSSION

The reliability test indicates that all constructs within this study exhibited high internal consistency, with Cronbach’s alpha values greater than the recommended 0.90 (Nunnally & Bernstein, 1994). The most reliable of all the independent variables was working with partners and influencers (α = 0.928), followed by monitoring news and trends, as well as current involvement (both α = 0.926), and quality content (α = 0.925). The dependent variable, consumer purchase behavior stratification, also yielded a high reliability value (α = 0.924). Cronbach’s alpha for the dataset was found to be 0.940, confirming that the measurement tool used in this research was reliable and acceptable for statistical analysis (Hair et al., 2019). The findings reveal that the scale items were well-written and consistently measured the targeted constructs with confidence, making the findings credible.

The analysis of correlation revealed positive and strong correlations between all independent variables and the dependent variable, with Pearson’s correlation coefficients exceeding 0.70 (p < 0.001) in all cases. Monitor trends and news were most highly correlated with consumer buying behavior (r = 0.784), followed by highly valued content (r = 0.782), influencer and partner collaboration (r = 0.769), and real-time engagement (r = 0.766). According to Cohen’s (1988) criteria, these correlation values indicate strong positive associations, suggesting that greater exposure to popular content, high-quality content, influencer collaborations, and social media activity are all strongly linked to increased consumer purchasing activity. They validate previous research showing that social media trend consciousness increases consumer buying intent and interest (Ferreira & Silva, 2021; Xiao et al., 2021), and that relevant, informative content fosters brand engagement and trust (Minton & Liu, 2020; Pentina et al., 2021). Similarly, influencer collaboration was also found to positively influence purchase intent and brand perception through social proof, as well as credibility (Ki et al., 2020; Lou & Yuan, 2021).

Multiple regression analysis also reinforced these associations by determining the predictive power of each of the independent variables. The model had an extremely high coefficient of determination (R² = 0.739), indicating that the four predictors accounted for 73.9% of the variance in consumer buying behavior. All the predictors were significant (p < 0.001), and news and trends on the screen were the strongest factor (β = 0.333), followed by helpful content (β = 0.305), influencer and partner cooperation (β = 0.280), and continuous interaction (β = 0.206). ANOVA results (F = 268.411, p < 0.001) confirmed the overall significance of the model, i.e., that the independent variable set provided a more reliable prediction of consumer purchase behavior than could be expected by chance (Hair et al., 2019). The relevance of monitoring trends and news is supported by evidence from Balakrishnan and Boorstin (2023), who claim that trending topics and live news create a sense of urgency and influence consumer decisions. Similarly, the relevance of high-quality content is supported by evidence from Rahman and Yu (2023), who claim that transparency and high-quality content create trust and influence purchase behavior.

Finally, all four hypotheses proved to be correct, validating the fact that following news and trends, creating helpful content, collaborating with influencers and partners, and measuring existing engagement metrics on social media have a significant and positive impact on food and beverage shopper purchasing behavior. These findings align with the Technology Acceptance Model (TAM) and social influence theory, which emphasize the importance of perceived relevance, trust, and social proof in shaping consumer choice in virtual environments (Davis, 1989; Venkatesh & Davis, 2000).

CONCLUSIONS

However, the cross-sectional design and small sample size impose limitations on the findings. Future research with larger longitudinal samples is recommended to examine the changing social media landscape and its long-term effects on purchasing habits.

ACKNOWLEDGMENT

Special thanks are extended to all personnel and individuals who contributed to this research. The author also wishes thanks to University Technical Malaysia Melaka (UTeM), Centre for Tehnopreneurship Development (C-TeD) and Centre for Research and Innovation Management (CRIM) for their support.

REFERENCES

1. Alalwan, A. A. (2018). Investigating the impact of social media advertising features on customer purchase intention. International Journal of Information Management, 42, 65-77.
2. Alalwan, A. A. (2020). Investigating the impact of social media advertising features on customer purchase intention. International Journal of Information Management, 50, 280- 292. https://doi.org/10.1016/j.ijinfomgt.2019.05.011
3. Alalwan, A. A., Rana, N. P., Dwivedi, Y. K., & Algharabat, R. S. (2021). Social media in marketing: A review and analysis of the existing literature. Telematics and Informatics, 54, 101273.
4. Amoah, J., Radder, L., & Van Eyk, M. (2021). Influence of social media engagement on consumer purchase intention in the food and beverage industry. Journal of Consumer Behaviour, 20(5), 1265-1280. https://doi.org/10.1002/cb.1924
5. Anderson, R. (2019). The dynamics of the food and beverage industry. Food Industry Review, 15(4), 78-92. https://doi.org/10.1016/j.fir.2019.04.010
6. Ashley, C., & Tuten, T. (2018). Creative strategies in social media marketing: An exploratory study of branded social content and consumer engagement. Psychology & Marketing, 35(2), 95-112. https://doi.org/10.1002/mar.20761
7. Azad, M. S., Khan, S. S., Hossain, R., Rahman, R., & Momen, S. (2023). Predictive modelling of consumer purchase behaviour on social media: Integrating the theory of planned behaviour and machine learning for actionable insights. PLoS ONE, 18(12), e0296336. https://doi.org/10.1371/journal.pone.0296336
8. Balakrishnan, J., & Boorstin, A. (2023). Role of real-time news on social media in shaping purchase behavior. Journal of Business Research, 152, 239–251. https://doi.org/10.1016/j.jbusres.2022.09.041
9. Barger, V. A., Peltier, J. W., & Schultz, D. E. (2021). Social media and marketing: A review of the recent literature. Journal of Marketing Communications, 27(2), 205-225. https://doi.org/10.1080/13527266.2020.1767341
10. Bay, S. W. (2018). The effects of social media on consumer behaviour in tourism: A study among university students (Unpublished undergraduate thesis). Universiti Tunku Abdul Rahman, Malaysia.
11. Chukwuma, D. U. (2020). The influence of social media on buying behaviour of consumers, in Nigeria (Unpublished master’s thesis). National College of Ireland, Dublin.
12. Clark, P. (2020). Influencer marketing strategies for brands. Journal of Brand Management, 27(5), 501-517. https://doi.org/10.1057/s41262-020-00201-7
13. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
14. Creswell, J. W., & Creswell, J. D. (2020). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (5th ed.). Sage Publications.
15. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
16. Davis, L. (2021). The effectiveness of sponsored content. Advertising Today, 8(3), 6679. https://doi.org/10.1016/j.advt.2021.07.005
17. De Veirman, M., Cauberghe, V., & Hudders, L. (2017). Marketing through Instagram influencers: The impact of influencer marketing on brand attitude and purchase intention. International Journal of         Advertising,     36(5),  798-828. https://doi.org/10.1080/02650487.2017.1348035
18. de Vries, N. J., Gensler, S., & Leeflang, P. S. (2021). Effects of traditional advertising and social messages on brand-building metrics and customer acquisition. Journal of Marketing, 85(1), 29-44. https://doi.org/10.1177/0022242920958050
19. DeVellis, R. F. (2017). Scale Development: Theory and Applications (4th ed.).Sage Publications.
20. Dwivedi, Y. K., Ismagilova, E., Hughes, D. L., Carlson, J., Filieri, R., Jacobson, J., … & Wang, Y. (2021). Setting the future of digital and social media marketing research: Perspectives and research propositions. International Journal of Information Management, 59, 102168.
21. Etikan, I., & Bala, K. (2017). Sampling and sampling methods. Biometrics & Biostatistics International Journal, 5(6), 00149. doi: 10.15406/bbij.2017.05.00149
22. Ferreira, C., & Silva, S. (2021). Understanding consumer behavior in social media context: The influence of trends on purchase intentions. Journal of Consumer Marketing, 38(4), 345–357. https://doi.org/10.1108/JCM-05-2020-3882
23. Field, A. (2020). Discovering Statistics Using IBM SPSS Statistics (5th ed.). Sage Publications.
24. Frost, J. (2020). Regression Analysis: An Intuitive Guide for Using and Interpreting Linear Models. Statistics by Jim Publishing.
25. Goh, K. Y., Heng, C. S., & Lin, Z. (2020). Social media brand community and consumer behaviour: Quantifying the relative impact of user-and marketer- generated content. Information Systems Research, 31(1), 124-145. https://doi.org/10.1287/isre.2019.0808
26. Gvili, Y., & Levy, S. (2021). Investigating antecedents and outcomes of brand engagement in social media: A meta-analytic approach. Journal of Interactive Marketing, 53, 69-90. https://doi.org/10.1016/j.intmar.2021.02.001
27. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning.
28. Hanna, R., Rohm, A. J., & Crittenden, V. L. (2017). We’re all connected: The power of the social media ecosystem. Business Horizons, 60(3), 361-369. https://doi.org/10.1016/j.bushor.2017.01.007
29. Hollebeek, L. D., Glynn, M. S., & Brodie, R. J. (2021). Consumer brand engagement in social media: Conceptualization, scale development, and validation. Journal of Interactive Marketing, 28(2), 149-165. https://doi.org/10.1016/j.intmar.2013.12.002
30. Huang, S. L., & Kao, T. W. (2020). Understanding the antecedents of social media acceptance in the B2B context: The empirical study of Taiwan. Technology in Society, 63, 101425. https://doi.org/10.1016/j.techsoc.2020.101425
31. Hudson, S., Huang, L., Roth, M. S., & Madden, T. J. (2021). The influence of social media interactions on consumer-brand relationships: A three-country study of brand perceptions and marketing behaviours. International Journal of Research in Marketing, 38(1), 90-103. https://doi.org/10.1016/j.ijresmar.2020.05.008
32. Jain, V., Kumar, A., & Sharma, A. (2021). Social media use and consumer behaviour: An empirical analysis. Journal of Retailing and Consumer Services, 59, 102398. https://doi.org/10.1016/j.jretconser.2020.102398
33. Johnson, A., & Brown, M. (2021). Consumer purchase behaviour in the digital age.
34. Jones, A., & Brown, M. (2021). Challenges of differentiation in social media marketing within the food and beverage sector. Journal of Digital Marketing, 10(2), 123-135. https://doi.org/10.1016/j.jdigital.2021.02.005
35. Ki, C. W., Cuevas, L. M., Chong, S. M., & Lim, H. (2020). Influencer marketing: Social media influencers as human brands and their effects on consumers. Business Horizons, 63(4), 491–499. https://doi.org/10.1016/j.bushor.2020.03.002
36. Kim, S., & Lee, J. (2019). Market segmentation and consumer targeting.
37. Koch, J., Frommeyer, B., & Schewe, G. (2020). Online shopping motives during the COVID-19 pandemic—lessons from the crisis. Sustainability, 12(24), 10247. https://doi.org/10.3390/su122410247
38. Lim, X. J., Cheah, J. H., & Wong, M. W. (2020). A critical review of consumer behaviour and social media: Theoretical frameworks and future directions. Journal of Cleaner Production, 273, 122897. https://doi.org/10.1016/j.jclepro.2020.122897
39. Lipsman, A., Mudd, G., Rich, M., & Bruich, S. (2020). The impact of social media on consumer behaviour: Insights from the latest research. Journal of Business Research, 118, 132-140. https://doi.org/10.1016/j.jbusres.2020.06.054
40. Liu, P., Liu, L., & Lee, S. H. (2020). The impact of cultural differences on food and beverage preferences of consumers. Journal of Retailing and Consumer Services, 54, 101961. https://doi.org/10.1016/j.jretconser.2019.101961
41. Lopez, E. (2022). Engaging consumers through interactive campaigns.
42. Lou, C., & Yuan, S. (2019). Influencer marketing: How message value and credibility affect consumer trust of branded content on social media. Journal of Interactive Advertising, 19(1), 58-73. https://doi.org/10.1080/15252019.2019.1573516
43. Lou, C., & Yuan, S. (2019). Influencer marketing: How message value and credibility affect consumer trust of branded content on social media. Journal of Interactive Advertising, 19(1), 58-73. https://doi.org/10.1080/15252019.2018.1533501
44. Lou, C., & Yuan, S. (2021). Influencer marketing: How message value and credibility affect consumer trust of branded content on social media. Journal of Interactive Advertising, 21(1), 27–42. https://doi.org/10.1080/15252019.2020.1826882
45. Luna-Nevarez, C., & McGovern, E. (2021). The role of social media influencers in shaping healthy eating behaviour among college students. Journal of Marketing Management, 37(3-4), 317-337. https://doi.org/10.1080/0267257X.2020.1865481
46. Minton, E. A., & Liu, L. (2020). The effect of authenticity in social media marketing on consumer engagement. Journal of Business Research, 115, 101–112. https://doi.org/10.1016/j.jbusres.2020.05.002
47. Montgomery, D. C., Peck, E. A., & Vining, G. G. (2021). Introduction to Linear Regression Analysis (6th ed.). Wiley. https://doi.org/10.4324/9781003117452
48. Muntinga, D. G., Moorman, M., & Smit, E. G. (2019). Introducing COBRAs: Exploring motivations for brand-related social media use. International Journal of Advertising, 38(3), 428- 446. https://doi.org/10.1080/0267257X.2019.1573516
49. National Center for Biotechnology Information. (2023). Designing and validating a research questionnaire – Part 2. National Center for Biotechnology Information. Retrieved from https://pmc.ncbi.nlm.nih.gov/articles/PMC10810057/
50. Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
51. Pallant, J. (2020). SPSS Survival Manual: A Step by Step Guide to Data Analysis using IBM SPSS (7th ed.). Routledge. https://doi.org/10.4324/9781003117452
52. Pentina, I., Zhang, L., & Bataineh, M. T. (2021). The role of social media content in driving customer engagement and purchase intentions. Journal of Retailing and Consumer Services, 61, 102585. https://doi.org/10.1016/j.jretconser.2021.102585
53. Peters, K., Chen, Y., Kaplan, A. M., Ognibeni, B., & Pauwels, K. (2021). Social media metrics—A framework and guidelines for managing social media. Journal of Interactive Marketing, 28(4), 282-298. https://doi.org/10.1016/j.intmar.2021.02.001
54. Phua, J., Lin, J. S., & Lim, D. J. (2020). Understanding consumer engagement with social media content using the Theory of Planned Behavior. International Journal of Advertising, 39(1), 30-58. https://doi.org/10.1080/02650487.2019.1648322
55. Rahman, M., & Yu, X. (2023). Consumer trust in social media brand communication and purchase intentions. Journal of Business Research, 152, 281–289. https://doi.org/10.1016/j.jbusres.2022.06.045
56. Saunders, M., Lewis, P., & Thornhill, A. (2016). Research methods for business students (7th ed.). Pearson Education.
57. Schober, P., Boer, C., & Schwarte, L. A. (2018). Correlation Coefficients: Appropriate Use and Interpretation. Anesthesia & Analgesia, 126(5), 1763-1768. https://doi.org/10.1213/ANE.0000000000002864
58. Smith, J. (2020). The impact of social media engagement on consumer behaviour in the food and beverage industry. Journal of Marketing Research, 58(3), 456-472. https://doi.org/10.1016/j.jmr.2020.03.005
59. Solomon, M. R. (2019). Consumer Behaviour: Buying, Having, and Being (13th ed.). Pearson.
60. Tandon, A., Dhir, A., Kaur, P., & Mäntymäki, M. (2021). Social media-induced fear of missing out (FoMO) and phubbing: Behavioral, relational and psychological outcomes.
61. Tavakol, M., & Dennick, R. (2021). Making Sense of Cronbach’s Alpha. International Journal of Medical Education, 2, 53-55. https://doi.org/10.5116/ijme.4dfb.8dfd
62. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
63. Wang, E., An, N., Gao, Z., Kiprop, E., & Geng, X. (2020). Consumer food stockpiling behaviour and willingness to pay for food reserves in COVID-19. Food Security, 12(4), 739- 748. https://doi.org/10.1007/s12571-020-01092-1
64. Wang, Y., McKee, M., Torbica, A., & Stuckler, D. (2020). Systematic literature review on the spread of health-related misinformation on social media. Social Science & Medicine, 240, 112552. https://doi.org/10.1016/j.socscimed.2020.112552
65. Williams, K. (2020). Demographic factors in marketing strategy. Journal of Marketing Strategies, 22(2), 201-218. https://doi.org/10.1177/jms.2020.1048
66. Xiao, M., Yang, S., & Iqbal, Q. (2021). Social media’s impact on brand image and consumer buying intentions. Journal of Retailing and Consumer Services, 59, 102396. https://doi.org/10.1016/j.jretconser.2020.102396
67. Yadav, R., & Pathak, G. S. (2017). Determinants of consumers’ green purchase behaviour in a developing nation: Applying and extending the Theory of Planned Behavior. Ecological Economics, 134, 114-122. https://doi.org/10.1016/j.ecolecon.2016.12.019