The issue of credibility between customers’ perceptions and the attitude toward Facebook advertising
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DOIhttp://dx.doi.org/10.21511/im.21(3).2025.10
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Article InfoVolume 21 2025, Issue #3, pp. 128-141
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Creative Commons Attribution 4.0 International License
Type of the article: Research Article
Abstract
Among marketing professionals, the use of Social Networking Sites (SNSs) as a platform for advertising is on the rise, despite ongoing concerns regarding credibility and trust in privacy related to advertising on these sites. Consequently, this study explored how customer perception factors, namely Perceived Interactivity, Advertising Avoidance, and Privacy, affect attitudes towards Facebook advertisements and how these perception factors moderate these attitudes. This research was conducted in Amman, Jordan, and data were collected from undergraduate students enrolled in the Digital Marketing program at Applied Science Private University (ASU) in 2024 using purposive sampling methods via Google Forms. This sample was chosen due to students’ high engagement with social media and their relevance as future marketing professionals, yielding 277 valid responses to be analyzed, and the hypotheses were tested using smart PLS through Structural Equation Modelling. The analysis revealed that Perceived Interactivity, Advertising Avoidance, and Privacy positively impacted attitudes towards Facebook advertisements, with coefficients and significance levels as follows: β = 0.122, t = 5.545, p < 0.003, β = 0.324, t = 2.643, p < 0.002, and β = 0.046, t = 3.833, p < 0.003, respectively. Moreover, credibility was identified as a moderating factor influencing the relationships among Perceived Interactivity, Advertising Avoidance, and Privacy, with significant results of β = 0.043, t = 3.909, p < 0.003, β = 0.055, t = 2.291, p < 0.004, and β = 0.072, t = 2.067, p < 0.001), respectively. This research provides advertising agencies with essential insights for effectively managing online advertising strategies.
- Keywords
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JEL Classification (Paper profile tab)C38, L81, M30, M37
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References68
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Tables6
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Figures3
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- Figure 1. Research model
- Figure 2. Convergent validity results
- Figure 3. Hypothesis results
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- Table 1. Participants’ demographics
- Table 2. Measured constructs and related items
- Table 3. Skewness and kurtosis for the variables
- Table 4. Results of convergent validity for the proposed model
- Table 5. HTMT results
- Table 6. Hypothesis testing
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- Ahmad, A., Shattal, M., Rawashdeh, L., Ghasawneh, J., & Nusairat, N. (2022). Corporate social responsibility and brand equity of operating telecoms: Brand reputation as a mediating effect. International Journal of Sustainable Economy, 14(1), 78-97.
- Akhmedov, B. (2022). Use of information and communication technologies in higher education: Trends in the digital economy. Ijtimoiy Fanlarda Innovasiya Onlayn Ilmiy Jurnali [Innovation in Social Sciences Online Scientific Journal], 5, 71-79.
- Al Sokkar, A. A. (2014). Multimodal human-computer interaction for enhancing customers’ decision-making and experience on B2C e-commerce websites (Doctoral dissertation). University of Leicester.
- Al Sokkar, A. A., & Law, E. L.-C. (2013). In situ observations of non-verbal emotional behaviours for multimodal avatar design in e-commerce. In Proceedings of the International Conference on Multimedia, Interaction, Design and Innovation (pp. 1-12).
- Al-Gasawneh, J. A., AlSokkar, A. A., Alamro, A. S., Binkhamis, M., Khalaf, O. I., & AbdElminaam, D. S. (2025). The cutting edge of AI in E-Marketing: How the use of digital tools boosts performance in Jordan. SN Computer Science, 6(1), 82.
- Al-Gasawneh, J., Alsoud, M., AlSokkar, A., Warrad, L., Saputra, J., & Daoud, M. (2023). Internet advertisements and brand equity amongst user-generated content and purchase intention. Migration Letters, 20(S8), 467-478.
- Alsmadi, A., Alfityani, A., Alhwamdeh, L., Al_hazimeh, A., & Al-Gasawneh, J. (2022). Intentions to use FinTech in the Jordanian banking industry. International Journal of Data and Network Science, 6(4), 1351-1358.
- AlSokkar, A. A., & Law, E. (2013). Validating an episodic UX model on online shopping decision making: A survey study with B2C e-commerce. In Proceedings of the 5th ACM SIGCHI Symposium on Engineering Interactive Computing Systems (pp. 296-306).
- AlSokkar, A. A., Al-Gasawneh, J. A., Alamro, A., Binkhamis, M., AlGhizzawi, M., & Abu Hmeidan, T. (2025). The effectiveness of E-Marketing on marketing performance in Jordanian telecommunications companies: Exploring the mediating role of the competitive environment. SN Computer Science, 6(1), 58.
- AlSokkar, A., Al-Gasawneh, J. A., Otair, M., Alghizzawi, M., Alarabiat, D., & Al Eisawi, D. (2024). Online marketing campaigns’ aesthetics: Measuring the direct effect on customers’ decision-making. Innovative Marketing, 20(4), 206-218.
- Andrews, M. (2021). Quality indicators in narrative research. Qualitative Research in Psychology, 18(3), 353-368.
- Anić, I.-D., Škare, V., & Milaković, I. (2019). The determinants and effects of online privacy concerns in the context of e-commerce. Electronic Commerce Research and Applications, 36, 100868.
- Ashraf, A., Hameed, I., & Saeed, A. (2023). How do social media influencers inspire consumers’ purchase decisions? The mediating role of parasocial relationships. International Journal of Consumer Studies, 47(4), 1416-1433.
- Auxier, B., Rainie, L., Anderson, M., Perrin, A., Kumar, M., & Turner, E. (2019). Americans and privacy: Concerned, confused and feeling lack of control over their personal information. Pew Research Center.
- Bartoloni, S., & Ancillai, C. (2024). Twenty years of social media marketing: A systematic review, integrative framework, and future research agenda. International Journal of Management Reviews, 26(3), 435-457.
- Bond, C., Ferraro, C., Luxton, S., & Sands, S. (2010). Social media advertising: An investigation of consumer perceptions, attitudes, and preferences for engagement. In P. Ballantine & J. Finsterwalder (Eds.), Proceedings of the Australian and New Zealand Marketing Academy (ANZMAC) Conference 2010 – ‘Doing More with Less’ (pp. 1-7). University of Canterbury.
- Bozzola, E., Spina, G., Ruggiero, M., Vecchio, D., Caruso, C., Bozzola, M., ... & Peroni, D. (2019). Media use during adolescence: The recommendations of the Italian Pediatric Society. Italian Journal of Pediatrics, 45.
- Brinson, N., & Lemon, L. (2023). Investigating the effects of host trust, credibility, and authenticity in podcast advertising. Journal of Marketing Communications, 29(6), 558-576.
- Can, U., & Alatas, B. (2019). A new direction in social network analysis: Online social network analysis problems and applications. Physica A: Statistical Mechanics and Its Applications, 535, 122372.
- Chenavaz, R., Feichtinger, G., Hartl, R., & Kort, P. (2020). Modeling the impact of product quality on dynamic pricing and advertising policies. European Journal of Operational Research, 284(3), 990-1001.
- Chin, W. (2009). How to write up and report PLS analyses. In V. Esposito Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of Partial Least Squares: Concepts, Methods and Applications (Vol. II, pp. 655-690). Springer.
- Connell, R., Wallis, L., & Comeaux, D. (2021). The impact of COVID-19 on the use of academic library resources. Information Technology and Libraries, 40(2).
- Dinner, I. M., van Heerde, H. J., & Neslin, S. A. (2014). Driving online and offline sales: The cross-channel effects of traditional, online display, and paid search advertising. Journal of Marketing Research, 51(5), 527-545.
- Ha, L., & James, E. L. (1998). Interactivity reexamined: A baseline analysis of early business web sites. Journal of Broadcasting & Electronic Media, 42(2), 457-474.
- Hadija, Z. (2008). Perceptions of advertising in online social networks: In-depth interviews (Master’s thesis, Rochester Institute of Technology).
- Haenlein, M., Anadol, E., Farnsworth, T., Hugo, H., Hunichen, J., & Welte, D. (2020). Navigating the new era of influencer marketing: How to be successful on Instagram, TikTok, & Co. California Management Review, 63(1), 5-25.
- Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2013). Multivariate data analysis (7th ed.). Pearson Education.
- Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24.
- Hammouri, Q., Altaher, A., Al-Gasawneh, J., Rabaai, A., Aloqool, A., & Khataybeh, H. (2022). Understanding the determinants of digital shopping features: The role of promo code on customer behavioral intention. International Journal of Data and Network Science, 6(3), 641-650.
- Hammouri, Q., Nusairat, N. M., AlSokkar, A. A., Jdaitawi, A. M., Mistarihi, A. M., Akhuirshaideh, D. A., & AlFraihat, S. F. (2025). Engaging Gen Z through personalized social media content: The mediating role of perceived relevance on platform engagement. Data and Metadata, 4(9), 1-8.
- Henseler, J., & Chin, W. W. (2010). A comparison of approaches for the analysis of interaction effects between latent variables using partial least squares path modeling. Structural Equation Modeling, 17(1), 82-109.
- Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115-135.
- Hijjawi, M., Al Shinwan, M., Qutqut, M. H., Alomoush, W., Khashan, O. A., Alshdaifat, M., ... & Abualigah, L. (2023). Improved flat mobile core network architecture for 5G mobile communication systems. International Journal of Data & Network Science, 7(3), 1421-1434.
- Hill, K. (2022). The secretive company that might end privacy as we know it. In Ethics of Data and Analytics (pp. 170-177). Auerbach Publications.
- Ivie, E. J., Pettitt, A., Moses, L. J., & Allen, N. B. (2020). A meta-analysis of the association between adolescent social media use and depressive symptoms. Journal of Affective Disorders, 275, 165-174.
- Jadhav, G. G., Gaikwad, S. V., & Bapat, D. (2023). A systematic literature review: Digital marketing and its impact on SMEs. Journal of Indian Business Research, 15(1), 76-91.
- Jain, S., & Purohit, H. C. (2022). Consumer acceptance of online behavioural advertising: Role of persuasion knowledge and protection motivation. International Management Review, 18(2), 48-54.
- Katz, H. (2022). The media handbook: A complete guide to advertising media selection, planning, research, and buying (8th ed.). Routledge.
- Kelly, L., Kerr, G., Drennan, J., & Fazal-E-Hasan, S. (2021). Feel, think, avoid: Testing a new model of advertising avoidance. Journal of Marketing Communications, 27(4), 343-364.
- Kim, E., Shoenberger, H., Kwon, E., & Ratneshwar, S. (2022). A narrative approach for overcoming the message credibility problem in green advertising. Journal of Business Research, 147, 449-461.
- Kočišová, L., & Štarchoň, P. (2023). The role of marketing metrics in social media: A comprehensive analysis. Marketing Science & Inspirations, 18(2), 40-49.
- Kozyreva, A., Lewandowsky, S., & Hertwig, R. (2020). Citizens versus the internet: Confronting digital challenges with cognitive tools. Psychological Science in the Public Interest, 21(3), 103-156.
- Krishen, A. S., Dwivedi, Y. K., Bindu, N., & Kumar, K. S. (2021). A broad overview of interactive digital marketing: A bibliometric network analysis. Journal of Business Research, 131, 183-195.
- Lo, F.-Y., & Peng, J.-X. (2022). Strategies for successful personal branding of celebrities on social media platforms: Involvement or information sharing? Psychology & Marketing, 39(2), 320-330.
- Masa’deh, R., AlMajali, D., AlSokkar, A. A., Alshinwan, M., & Shehadeh, M. (2023). Antecedents of intention to use e-auction: An empirical study. Sustainability, 15(6), 4871.
- Memon, M. A., Cheah, J.-H., Ramayah, T., Ting, H., Chuah, F., & Cham, T. H. (2019). Moderation analysis: Issues and guidelines. Journal of Applied Structural Equation Modeling, 3(1), 1-11.
- Miech, R. A., Johnston, L. D., O’Malley, P. M., Bachman, J. G., Schulenberg, J. E., & Patrick, M. E. (2020). Monitoring the Future national survey results on drug use, 1975–2019: Volume I, Secondary school students. Institute for Social Research, University of Michigan.
- Milas, J., & Lesinger, G. (2022). The relationship between the perception of advertising, FOMO, social network fatigue and privacy concerns among social network users. Communication Management Review, 7(1), 26-47.
- Mukherjee, K., & Banerjee, N. (2017). Effect of social networking advertisements on shaping consumers’ attitude. Global Business Review, 18(5), 1291-1306.
- Niu, X., Wang, X., & Liu, Z. (2021). When I feel invaded, I will avoid it: The effect of advertising invasiveness on consumers’ avoidance of social media advertising. Journal of Retailing and Consumer Services, 58, 102320.
- Nguyen, C., Nguyen, H., Doan, T., Nguyen, M., & Le, M. (2023). Viewing advertisements in social networks: The attitude-intention inconsistency revisited. Online Information Review, 47(7), 1248-1263.
- Nguyen, D. (2022). Television–Social network sites multiplatform identity credibility: A bibliographic analysis. RES MILITARIS, 12(2), 1263-1287.
- Obar, J. A., & Oeldorf-Hirsch, A. (2020). The biggest lie on the internet: Ignoring the privacy policies and terms of service policies of social networking services. Information, Communication & Society, 23(1), 128-147.
- Qin, X., & Jiang, Z. (2019). The impact of AI on the advertising process: The Chinese experience. Journal of Advertising, 48(4), 338-346.
- Rahman, M., Abir, T., Yazdani, D. M. N., Hamid, A. B. A., & Al Mamun, A. (2020). Brand image, eWOM, trust and online purchase intention of digital products among Malaysian consumers. Journal of Xi’an University of Architecture & Technology, 12(3), 4935-4946.
- Rehman, F., & Zeb, A. (2023). Translating the impacts of social advertising on Muslim consumers buying behavior: The moderating role of brand image. Journal of Islamic Marketing, 14(9), 2207-2234.
- Rezaei, R., Sharifnia, H., Nazari, R., & Saatsaz, S. (2023). Bedtime massage intervention for improving infant and mother sleep condition: A randomized controlled trial. Journal of Neonatal-Perinatal Medicine, 16(2), 271-278.
- Sekaran, K., Varghese, R. P., Zayed, H., El Allali, A., & George Priya Doss, C. (2023). Single-cell transcriptomic analysis reveals crucial oncogenic signatures and its associative cell types involved in gastric cancer. Medical Oncology, 40(10).
- Sharabati, A.-A. A., Ali, A. A. A., Allahham, M. I., Abu Hussein, A., Alheet, A. F., & Mohammad, A. S. (2024). The impact of digital marketing on the performance of SMEs: An analytical study in light of modern digital transformations. Sustainability, 16(19), 8667.
- Shirazi, F., Hajli, N., Sims, J., & Lemke, F. (2022). The role of social factors in purchase journey in the social commerce era. Technological Forecasting and Social Change, 183, 121861.
- Sides, J., Vavreck, L., & Warshaw, C. (2022). The effect of television advertising in United States elections. American Political Science Review, 116(2), 702-718.
- Singh, R., Dwivedi, A., Srivastava, G., Chatterjee, P., & Lin, J.-W. (2023). A privacy-preserving Internet of Things smart healthcare financial system. IEEE Internet of Things Journal, 10(21), 18452-18460.
- Sokkar, A., & Musa, A. (2014). Multimodal human-computer interaction for enhancing customers’ decision-making and experience on B2C e-commerce websites (Doctoral dissertation). University of Leicester.
- Stratton, S. J. (2021). Population research: Convenience sampling strategies. Prehospital and Disaster Medicine, 36(4), 373-374.
- Weismueller, J., Harrigan, P., Wang, S., & Soutar, G. (2020). Influencer endorsements: How advertising disclosure and source credibility affect consumer purchase intention on social media. Australasian Marketing Journal, 28(4), 160-170.
- Yaakop, A., Anuar, M., & Omar, K. (2013). Like it or not: Issue of credibility in Facebook advertising. Asian Social Science, 9(3), 154.
- Youn, S., & Kim, S. (2019). Newsfeed native advertising on Facebook: Young millennials’ knowledge, pet peeves, reactance and ad avoidance. International Journal of Advertising, 38(5), 651-683.
- Zhu, G., Cheng, F., Lian, D., Yuan, C., & Huang, Y. (2022). NAS-CTR: Efficient neural architecture search for click-through rate prediction. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 332-342).