The Role of Social Media Engagement in Shaping Blockchain Adoption: Insights from Thai Users

Authors

  • Alfonso Pellegrino Columbia University, School of International and Public Affairs, 420 W 118th St, New York, NY 10027, United States Author
  • Alessandro Stasi Business Administration Division, Mahidol University International College Mahidol University, Salaya, 73170, Nakhon Pathom, Thailand Corresponding Author

DOI:

https://doi.org/10.47654/v30y2026i1p261-296

Keywords:

Social Media Engagement, Blockchain Technology, Information Systems Management, Structural Equation Modeling (SEM), Digital Communication

Abstract

Purpose – This study examines the effect of social media engagement (SME) on blockchain technology adoption, focusing on awareness, trust, and perceived usefulness among Thai social media users.

Design/methodology/approach – Structural equation modeling (SEM) was employed to test six hypotheses using data from 400 Thai social media users collected via an online survey.

Findings – SME significantly increases blockchain awareness, which strongly influences adoption. However, SME did not directly affect trust or perceived usefulness. Trust moderately influenced adoption, while perceived usefulness was a key driver. Awareness is a critical mediator, indicating SME's limited direct impact on trust and usefulness.

Research limitations/implications – The cross-sectional design limits causal claims. Findings may not generalize beyond Thailand due to cultural and regulatory variations.

Practical implications – Marketers and policymakers should complement social media campaigns with deeper educational initiatives and trusted institutional backing to foster blockchain adoption.

Social implications – Insights inform strategies for responsible blockchain promotion, ensuring public understanding and trust in emerging digital technologies.

Relevance to Decision Sciences – The findings directly inform Decision Sciences by modeling the cognitive and social factors that drive user adoption decisions for complex, high-risk technologies, offering a new framework for predicting technology uptake in uncertain environments. Within the broader Decision Sciences field, this research specifically advances Information Systems and Management by demonstrating how digital engagement and behavioral data can be integrated into technology adoption models, providing actionable insights for system designers and managers seeking to enhance trust, awareness, and perceived usefulness in digital innovation ecosystems.

Originality/value – This study integrates social media engagement into TAM/UTAUT frameworks in an emerging market, refining adoption theory and offering evidence-based guidance for digital engagement in technology uptake.

Author Biographies

  • Alfonso Pellegrino, Columbia University, School of International and Public Affairs, 420 W 118th St, New York, NY 10027, United States

    Alfonso Pellegrino, Ph. D. is an academic researcher at the Columbia University, United States. His work focuses on various aspects of management and business education, providing valuable insights into contemporary business challenges in Asia. In addition to his academic role, Dr. Pellegrino serves as an individual contractor for the United Nations Economic and Social Commission for Asia and the Pacific (ESCAP), where he contributes to projects aimed at sustainable development and regional cooperation in the Asia-Pacific region.

  • Alessandro Stasi, Business Administration Division, Mahidol University International College Mahidol University, Salaya, 73170, Nakhon Pathom, Thailand

    Alessandro Stasi, Ph.D., is an Associate Professor of Business Law at Mahidol University International College in Thailand. He holds two PhDs, one from the University of Naples Federico II and another from the University of Nice Sophia-Antipolis. His research interests focus on technology law, with an emphasis on the legal and regulatory frameworks governing biotechnology, blockchain, and other emerging technologies. Dr. Stasi's work examines the intersection of law and technology, exploring how legal systems can adapt to innovations in artificial intelligence, digital transformation, and biotechnologies, while addressing the ethical implications of these advancements in both developed and emerging markets.

References

Alalwan, A. A., Dwivedi, Y. K., Rana, N. P., & Williams, M. D. (2016). Consumer adoption of mobile banking in Jordan: Examining the role of usefulness, ease of use, perceived risk and self-efficacy. Journal of Enterprise Information Management, 29(1), 118-139. https://doi.org/10.1108/JEIM-04-2015-0035

Alzubaidi, H., Slade, E. L., & Dwivedi, Y. K. (2021). Examining antecedents of consumers’ pro-environmental behaviours: TPB extended with materialism and innovativeness. Journal of Business Research, 122, 685-699. https://doi.org/10.1016/j.jbusres.2020.01.017

Beran, T. N., & Violato, C. (2010). Structural equation modeling in medical research: a primer. BMC research notes, 3(1), 267. https://doi.org/10.1186/1756-0500-3-267

Charoensereechai, C., Nurittamont, W., Phayaphrom, B., & Siripipatthanakul, S. (2022). Understanding the effect of social media advertising values on online purchase intention: A case of Bangkok, Thailand. Asian Administration and Management Review, 5(2), 1-11. http://dx.doi.org/10.2139/ssrn.4103522

Chen, C., Wu, J., Lin, H., Chen, W., & Zheng, Z. (2019). A secure and efficient blockchain-based data trading approach for internet of vehicles. IEEE Transactions on Vehicular Technology, 68(9), 9110-9121. https://doi.org/10.1109/TVT.2019.2927533

Cheng, Y., Hui, Y., Liu, S., & Wong, W.-K. (2022). Could significant regression be treated as insignificant: An anomaly in statistics? Communications in Statistics: Case Studies, Data Analysis and Applications, 8(1), 133–151. https://doi.org/10.1080/23737484.2021.1986171

Cheng, Y., Hui, Y., McAleer, M., & Wong, W. K. (2021). Spurious relationships for nearly non-stationary series. Journal of Risk and Financial Management, 14(8), 366. https://doi.org/10.3390/jrfm14080366

Davis, F. D. (1987). User acceptance of information systems: The technology acceptance model (TAM) (Working Paper No. 529). University of Michigan, School of Business Administration. https://quod.lib.umich.edu/b/busadwp/images/b/1/4/b1409190.0001.001.pdf

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

Di Gangi, P. M., & Wasko, M. M. (2016). Social media engagement theory: Exploring the influence of user engagement on social media usage. Journal of Organizational and End User Computing (JOEUC), 28(2), 53-73. https://doi.org/10.4018/JOEUC.2016040104

Dolan, R., Conduit, J., Fahy, J., & Goodman, S. (2016). Social media engagement behaviour: a uses and gratifications perspective. Journal of strategic marketing, 24(3-4), 261-277. https://doi.org/10.1080/0965254X.2015.1095222

Dowelani, M., Okoro, C., & Olaleye, A. (2022). Factors influencing blockchain adoption in the South African clearing and settlement industry. South African Journal of Economic and Management Sciences, 25(1). https://doi.org/10.4102/sajems.v25i1.4460

Dutta, P., Choi, T. M., Somani, S., & Butala, R. (2020). Blockchain technology in supply chain operations: Applications, challenges and research opportunities. Transportation research part e: Logistics and transportation review, 142, 102067. https://doi.org/10.1016/j.tre.2020.102067

Emerson, R. W. (2015). Convenience sampling, random sampling, and snowball sampling: How does sampling affect the validity of research?. Journal of visual impairment & blindness, 109(2), 164-168. https://doi.org/10.1177/0145482X1510900215

Falwadiya, H., & Dhingra, S. (2022). Blockchain technology adoption in government organizations: A systematic literature review. Journal of Global Operations and Strategic Sourcing, 15(3), 473-501. https://doi.org/10.1108/jgoss-09-2021-0079

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104

Gaskin, J. E., Lowry, P. B., Rosengren, W., & Fife, P. T. (2025). Essential Validation Criteria for Rigorous Covariance-Based Structural Equation Modelling. Information Systems Journal, 35(6), 1630–1661. https://doi.org/10.1111/isj.12598

Guidi, B. (2021). An overview of blockchain online social media from the technical point of view. Applied Sciences, 11(21), 9880. https://doi.org/10.3390/app11219880

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning.

Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (1998). Multivariate data analysis (5th ed.). Prentice-Hall.

Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Practice, 19(2), 139-152. https://doi.org/10.2753/MTP1069-6679190202

Hajli, M. N. (2014). A study of the impact of social media on consumers. International journal of market research, 56(3), 387-404. https://doi.org/10.2501/IJMR-2014-025

Harman, H. H. (1976). Modern factor analysis. University of Chicago press.

Hasan, M., & Sheikh, M. R. (2018). Factors affecting attitude towards social marketing through social media. Pacific Business Review International, 10(12), 20-28. http://www.pbr.co.in/2018/2018_month/June/2.pdf

Hisseine, M., Chen, D., & Yang, X. (2022). The application of blockchain in social media: a systematic literature review. Applied Sciences, 12(13), 6567. https://doi.org/10.3390/app12136567

Hong Kong Monetary Authority & Bank of Thailand. (2020). Project Inthanon–LionRock: Report on a retail central bank digital currency prototype. https://www.hkma.gov.hk/media/eng/doc/key-functions/financial-infrastructure/Report_on_Project_Inthanon-LionRock.pdf

Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural equation modeling: a multidisciplinary journal, 6(1), 1-55. https://doi.org/10.1080/10705519909540118

Hubona, G. S., & Whisenand, T. G. (1995). External variables and the technology acceptance model. In Proceedings of the Americas Conference on Information Systems (AMCIS 1995) (Paper 85). Association for Information Systems. https://aisel.aisnet.org/amcis1995/85

Hui, Y., Wong, W. K., Bai, Z., & Zhu, Z. Z. (2017). A new nonlinearity test to circumvent the limitation of Volterra expansion with application. Journal of the Korean Statistical Society, 46, 365-374. https://doi.org/10.1016/j.jkss.2016.11.006

Ibrahim, M. (2024). The effect of blockchain technology in enhancing ethical sourcing and supply chain transparency: evidence from the cocoa and agricultural sectors in Ghana. African Journal of Empirical Research, 5(2), 55-64. https://doi.org/10.51867/ajernet.5.2.6

Janssen, M., Weerakkody, V., Ismagilova, E., Sivarajah, U., & Irani, Z. (2020). A framework for analyzing blockchain technology adoption: Integrating institutional theory and the technology-organization-environment framework. Government Information Quarterly, 37(1), 101401. https://doi.org/10.1016/j.giq.2019.101401

Jöreskog, K. G., & Sörbom, D. (1996). LISREL 8: User's reference guide. Scientific Software International.

Kananukul, C., Jung, S., & Watchravesringkan, K. (2015). Building customer equity through trust in social networking sites: A perspective from Thai consumers. Journal of Research in Interactive Marketing, 9(2), 148-166. https://doi.org/10.1108/JRIM-03-2014-0019

Katchapakirin, K., Wongpatikaseree, K., Yomaboot, P., & Kaewpitakkun, Y. (2018, July). Facebook social media for depression detection in the Thai community. In 2018 15th international joint conference on computer science and software engineering (JCSSE) (pp. 1-6). IEEE. https://doi.org/10.1109/JCSSE.2018.8457362

Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). Guilford Press.

Klingler, K. (2015). Structural Equation Modelling with Latent Variables-Evidence from a Monte Carlo Study (Doctoral dissertation, Düsseldorf, Heinrich-Heine-Universität, Diss., 2015).

Lei, M., & Lomax, R. G. (2005). The effect of varying degrees of nonnormality in structural equation modeling. Structural equation modeling, 12(1), 1-27. https://doi.org/10.1207/s15328007sem1201_1

Li, C., & Palanisamy, B. (2019, June). Incentivized blockchain-based social media platforms: A case study of steemit. In Proceedings of the 10th ACM conference on web science (pp. 145-154). https://doi.org/10.1145/3292522.3326041

Liang, T.-P., Ho, Y.-T., Li, Y.-W., & Turban, E. (2011). What drives social commerce: The role of social support and relationship quality. International Journal of Electronic Commerce, 16(2), 69-90. https://doi.org/10.2753/JEC1086-4415160204

Liao, Z., Shi, X., & Wong, W. K. (2012). Consumer perceptions of the smartcard in retailing: An empirical study. Journal of International Consumer Marketing, 24(4), 252-262. https://doi.org/10.1080/08961530.2012.728503

Liao, Z., Shi, X., & Wong, W. K. (2014). Key determinants of sustainable smartcard payment. Journal of Retailing and Consumer Services, 21(3), 306-313. https://doi.org/10.1016/j.jretconser.2014.02.001

Liao, Z., & Wong, W. K. (2008). The determinants of customer interactions with internet-enabled e-banking services. Journal of the Operational Research Society, 59(9), 1201-1210. https://doi.org/10.1057/palgrave.jors.2602429

Liu, N., & Ye, Z. (2021). Empirical research on the blockchain adoption–based on TAM. Applied Economics, 53(37), 4263-4275. https://doi.org/10.1080/00036846.2021.1898535

Malhotra, N. K. (2006). Questionnaire design. In R. Grover & M. Vriens (Eds.), The handbook of marketing research: Uses, misuses, and future advances (pp. 83–94). Sage Publications.

McKenzie, J., Castellón, R., Willis-Grossmann, E., Landeros, C., Rooney, J., & Stewart, C. (2024). Digital divides, generational gaps, and cultural overlaps: a portrait of media use and perspectives of media in Thailand. Media Psychology, 27(1), 106-134. https://doi.org/10.1080/15213269.2023.2222533

McKnight, D. H., Choudhury, V., & Kacmar, C. (2002). Developing and validating trust measures for e-commerce: An integrative typology. Information systems research, 13(3), 334-359. https://doi.org/10.1287/isre.13.3.334.81

Mittal, S., Kumar, S., & Ahmadi, M. H. (2024). Role of Internship quality and Proactive Personality in Job Search Success: a moderated-mediation model through career adaptability. Advances in Decision Sciences, 28(2), 137-164. https://doi.org/10.47654/v28y2024i2p137-164

Moslehpour, M., Altantsetseg, P., Mou, W., & Wong, W. K. (2019). Organizational climate and work style: The missing links for sustainability of leadership and satisfied employees. Sustainability, 11(1), 125. https://doi.org/10.3390/su11010125

Moslehpour, M., Pham, V. K., Wong, W. K., & Bilgiçli, İ. (2018). E-purchase intention of Taiwanese consumers: Sustainable mediation of perceived usefulness and perceived ease of use. Sustainability, 10(1), 234. https://doi.org/10.3390/su10010234

Moslehpour, M., Wong, W. K., Pham, K. V., & Aulia, C. K. (2017). Repurchase intention of Korean beauty products among Taiwanese consumers. Asia Pacific Journal of Marketing and Logistics, 29(3), 569-588. https://doi.org/10.1108/APJML-06-2016-0106

Norbu, T., Park, J. Y., Wong, K. W., & Cui, H. (2024). Factors affecting trust and acceptance for blockchain adoption in digital payment systems: A systematic review. Future Internet, 16(3), 106. https://doi.org/10.3390/fi16030106

Norman, G. (2010). Likert scales, levels of measurement and the “laws” of statistics. Advances in Health Sciences Education, 15(5), 625–632. https://doi.org/10.1007/s10459-010-9222-y

Nunnally, J. C. (1975). Psychometric theory—25 years ago and now. Educational Researcher, 4(10), 7-21. https://doi.org/10.3102/0013189X004010007

Nunnally, J. C. (1978). An overview of psychological measurement. Clinical diagnosis of mental disorders: A handbook, 97-146. https://doi.org/10.1007/978-1-4684-2490-4_4

Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of applied psychology, 88(5), 879. https://doi.org/10.1037/0021-9010.88.5.879

Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012). Sources of method bias in social science research and recommendations on how to control it. Annual review of psychology, 63(1), 539-569. https://doi.org/10.1146/annurev-psych-120710-100452

Puriwat, W., & Tripopsakul, S. (2021). Customer engagement with digital social responsibility in social media: a case study of COVID-19 situation in Thailand. The Journal of Asian Finance, Economics and Business (JAFEB), 8(2), 475-483. https://doi.org/10.13106/JAFEB.2021.VOL8.NO2.0475

Rana, N. P., Dwivedi, Y. K., Williams, M. D., & Weerakkody, V. (2017). Adoption of online public grievance redressal systems. International Journal of Information Management, 37(4), 273–283. https://doi.org/10.1016/j.chb.2016.02.019

Razali, N. M., & Wah, Y. B. (2011). Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors and Anderson-Darling tests. Journal of Statistical Modeling and Analytics, 2(1), 21–33.

Rhemtulla, M., Brosseau-Liard, P. É., & Savalei, V. (2012). When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods. Psychological Methods, 17(3), 354–373. https://doi.org/10.1037/a0029315

Ruangkanjanases, A., Hariguna, T., Adiandari, A., & Alfawaz, K. (2022). Assessing blockchain adoption in supply chain management, antecedent of technology readiness, knowledge sharing and trading need. Emerging Science Journal, 6(5), 921-937. https://doi.org/10.28991/esj-2022-06-05-01

Schivinski, B., & Dabrowski, D. (2016). The effect of social media communication on consumer perceptions of brands. Journal of Marketing Communications, 22(2), 189-214. https://doi.org/10.1080/13527266.2013.871323

Securities and Exchange Commission. (2018a). Emergency Decree on Digital Asset Businesses B.E. 2561. https://www.sec.or.th/EN/Documents/EnforcementIntroduction/digitalasset_decree_2561_EN.pdf

Securities and Exchange Commission. (2018b). Notification of the Securities and Exchange Commission No. GorThor.22/2561: Prescription of an application form and procedure for applying for a license in undertaking digital asset businesses under the Notification of the Ministry of Finance regarding licensing of digital asset businesses B.E. 2561. https://www.sec.or.th/EN/pages/lawandregulations/digitalassetbusiness.aspx

Securities and Exchange Commission. (2024, August 9). SEC launches Digital Asset Regulatory Sandbox. https://www.sec.or.th/EN/Pages/News_Detail.aspx?SECID=11004

Sharif, M. H. M., Troshani, I., & Davidson, R. (2015). Public sector adoption of social media. Journal of Computer Information Systems, 55(4), 53-61. https://doi.org/10.1080/08874417.2015.11645787

Sharif, S. P., & Khanekharab, J. (2017). Identity confusion and materialism mediate the relationship between excessive social network site usage and online compulsive buying. Cyberpsychology, Behavior, and Social Networking, 20(8), 494-500. https://doi.org/10.1089/cyber.2017.0162

Sirgy, M. J. (1999). Materialism: The construct, measures, antecedents, and consequences. Academy of Marketing Studies Journal, 3(2), 78-110. https://www.researchgate.net/profile/Newell-Wright-2/publication/309076034_Materialism_The_construct_measures_antecedents_and_consequences/links/5818ec4308ae1f34d24aafec/Materialism-The-construct-measures-antecedents-and-consequences.pdf

Statista. (2022). Distribution of worldwide social media users in 2022, by region. https://www.statista.com/statistics/295619/regional-distribution-of-social-media-users-worldwide/

Tabachnick, B. G., & Fidell, L. S. (1996). Using multivariate statistical. New York: Harper Collins College Publishers.

Thongmak, M. (2024). Twitter content strategies to maximize engagement: The case of Thai Banks. Computers in Human Behavior, 152, 108081. https://doi.org/10.1016/j.chb.2023.108081

True Digital Park. (n.d.). One roof, all possibilities [Website]. https://www.truedigitalpark.com/en

United Nations Conference on Trade and Development. (2021). Contribution to the CSTD 2021–2022 priority themes on “Industry 4.0 and the fourth industrial revolution” – Thailand. https://unctad.org/system/files/non-official-document/CSTD2021-22_c36_IU_Thailand_en.pdf

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540

Voorveld, H. A. M., van Noort, G., Muntinga, D. G., & Bronner, F. (2018). Engagement with social media and social media advertising: The differentiating role of platform type. Journal of Advertising, 47(1), 38-54. https://doi.org/10.1080/00913367.2017.1405754

Wang, Y., Han, J., & Beynon‐Davies, P. (2019). Understanding blockchain technology for future supply chains: a systematic literature review and research agenda. Supply Chain Management an International Journal, 24(1), 62-84. https://doi.org/10.1108/scm-03-2018-0148

Wong, W. K., Cheng, Y., & Yue, M. (2024). Could regression of stationary series be spurious?. Asia-Pacific Journal of Operational Research, 2440017. https://doi.org/10.1142/S0217595924400177

Wong, W.-K., & Pham, M. T. (2022). Could the test from the standard regression model could make significant regression with autoregressive noise become insignificant? The International Journal of Finance, 34, 1–18. https://tijof.scibiz.world/ijof-2022_01

Wong, W.-K., & Pham, M. T. (2023). Could the test from the standard regression model could make significant regression with autoregressive Yt and Xt become insignificant? The International Journal of Finance, 35, 1–19. https://tijof.scibiz.world/ijof-2023_01

Wong, W.-K., & Pham, M. T. (2025). Could the correlation of a stationary series with a non-stationary series obtain meaningful outcomes? Annals of Financial Economics, 20(3), Article 2550015. https://doi.org/10.1142/S2010495225500150

World Bank. (2022). Mobile cellular subscriptions (per 100 people) [Data set]. https://data.worldbank.org/indicator/IT.CEL.SETS.P2

Published

2026-03-18

How to Cite

Pellegrino, A., & Stasi, A. (2026). The Role of Social Media Engagement in Shaping Blockchain Adoption: Insights from Thai Users . Advances in Decision Sciences, 30(1), 261-296. https://doi.org/10.47654/v30y2026i1p261-296