Influencing Factors of Retweet Intention of Branded Tweet Message

Main Article Content

Nazmul Kabir Rony
Aditi Shams
Rahnuma Ahmed

Abstract

This study examined how Twitter (officially renamed to X) is accepted among Gen-Z users as a marketing tool. This study extended the technology adoption model by including two core motivational factors -intrinsic and extrinsic motivations and examined how these factors influence users’ retweet intention. This study used survey instruments and structural equation modeling to explore and test the factors influencing users' retweet intention of branded tweet messages of 263 participants in 2022. The survey results support that perceived usefulness and ease of use are significantly associated with users' attitudes toward using Twitter (AT) as a marketing tool. The study also significantly validated the addition of intention to retweet (IRT), which is influenced by the attitude toward using Twitter (AT) as a marketing tool. These findings highlight the evolving role of social media platforms in shaping consumer behavior and provide a refined model for analyzing content-sharing motivations, which will help marketers maximize the impact of digital campaigns.

Metrics

Metrics Loading ...

Downloads

Download data is not yet available.

Article Details

Section

Articles

Author Biographies

Nazmul Kabir Rony, Slippery Rock University

Associate Professor of Communication, Department of Communication, College of Business, Slippery Rock University, PA, USA.

Aditi Shams, University of Dhaka

Department of International Business, University of Dhaka. 

Rahnuma Ahmed, University of Dhaka

Independent Researcher, PA, USA. 

How to Cite

Rony, N. K., Shams, A. S., & Ahmed, R. (2025). Influencing Factors of Retweet Intention of Branded Tweet Message. Bangladesh Journal of MIS, 10(02), 65-82. https://doi.org/10.61606/BJMIS.V10N2.A4

Plaudit

References

Agarwal, R., & Karahanna, E. (2000). Time flies when you're having fun: Cognitive absorption and beliefs about information technology usage. MIS Quarterly, 665–694. https://doi.org/10.2307/3250951

Asur, S., & Huberman, B. A. (2010). Predicting the future with social media. In Web Intelligence and Intelligent Agent Technology (WI-IAT), 2010 IEEE/WIC/ACM International Conference on (Vol. 1, pp. 492-499). IEEE. https://doi.org/10.1016/j.apenergy.2013.03.027

Balolong, F. R. (2013, July 26). How social media has changed the world. Retrieved from http://socialbarrel.com/how-social-media-has-changed-the-world- infographic/52618/

Bauer, H. H., Barnes, S. J., Reichardt, T., & Neumann, M. M. (2005). Driving consumer acceptance of mobile marketing: A theoretical framework and empirical study. Journal of Electronic Commerce Research, 6(3), 181-192. http://www.ebusinessforum.gr/old/content/downloads.

Beck, H. (2009, October 21). New way to gauge popularity. New York Times. Retrieved from http://query.nytimes.com/gst/fullpage.html?res=9C02E6DF1F31F932A15 753C1A96F9C8B63

Boyd, D. G., & Golder, S. S., & Lotan, G. (2010). Tweet, tweet, retweet: Conversational aspects of retweeting on twitter. Proceedings of HICSS-43, 1-10.

Byrne, B. M. (2016). Structural equation modeling with AMOS: Basic concepts, applications, and programming (3rd ed.). Routledge.

Chow, M., Herold, D. K., Choo, T. M., & Chan, K. (2012). Extending the technology acceptance model to explore the intention to use Second Life for enhancing healthcare education. Computers & Education, 59(4), 1136–1144. https://doi.org/10.1016/j.compedu.2012.05.011

Chu, S. C., & Kim, Y. (2011). Determinants of consumer engagement in electronic word-of-mouth (eWOM) in social networking sites. International Journal of Advertising, 30(1), 47-75. https://doi.org/10.2501/IJA-30-1-047-075

Cooper, B. B. (2011, July 13). 6 ways to socially reward your customers. Retrieved from http://www.socialmediaexaminer.com/6-ways-to-socially-reward- your-customers/

Davenport, S. W., Bergman, S. M., Bergman, J. Z., & Fearrington, M. E. (2014). Twitter versus Facebook: Exploring the role of narcissism in the motives and usage of different social media platforms. Computers in Human Behavior, 32, 212-220. https://doi.org/10.1016/j.chb.2013.12.011

Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results (Doctoral dissertation, Massachusetts Institute of Technology).

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319–340. https://doi.org/10.2307/249008

Deci, E. L. (1971). Effects of externally mediated rewards on intrinsic motivation. Journal of Personality and Social Psychology, 18(1), 105–115. https://doi.org/10.1037/h0030644

Di Pietro, L., & Pantano, E. (2012). An empirical investigation of social network influence on consumer purchasing decision: The case of Facebook. Journal of Direct, Data and Digital Marketing Practice, 14(1), 18-29. https://doi.org/10.1057/dddmp.2012.10

Dickey, I. J., & Lewis, W. F. (2009). Consumer generated media: Evolving marketing opportunity for consumer engagement. Society for Marketing Advances Proceedings, 181–85. https://ecommons.udayton.edu/mgt_fac_pub/34

Duggan, M., & Brenner, J. (2013). The demographics of social media users, 2012 (Vol. 14). Washington, DC: Pew Research Center's Internet & American Life Project. Retrieved from http://www.pewinternet.org/2013/02/14/the-demographics-of-social-media-users- 2012/

Ellison, N. B., Steinfield, C., & Lampe, C. (2007). The benefits of Facebook “friends:” Social capital and college students’ use of online social network sites. Journal of Computer-Mediated Communication, 12(4), 1143-1168. https://doi.org/10.1111/j.1083-6101.2007.00367.x

Gao, H., Mahmud, J., Chen, J., Nichols, J., & Zhou, M. (2014). Modeling User Attitude toward Controversial Topics in Online Social Media. In the Eighth International AAAI Conference on Weblogs and Social Media (ICWSM 2014). https://doi.org/10.1609/icwsm.v8i1.14513

Gefen, D. (2003). Assessing unidimensionality through LISREL: An explanation and an example. Communications of the Association for Information Systems, 12(1), 2. https://doi.org/10.17705/1CAIS.01202

Gefen, D., Straub, D., & Boudreau, M.-C. (2000). Structural equation modeling and regression: Guidelines for research practice. Communications of the Association for Information Systems, 4(7), 1–70. https://doi.org/10.17705/1CAIS.00407

GWI (Q3, 2024), GWI Core, Q3, 2024, Individual Internet Users aged 16+. Accessed on 17 February 2025. https://datareportal.com/social-media-users

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Canonical Correlation: A Supplement to Multivariate Data Analysis. Multivariate data analysis: a global perspective. 7th ed. Pearson Prentice Hall Publishing, Upper Saddle River.

Hampton, G. M. (1979). Students as subjects in international behavioral studies. Journal of International Business Studies, 94–96. https://doi.org/10.1057/palgrave.jibs.8490788

Handy, T. (2012, October 4). Social media virality: Why do people retweet? Retrieved from http://www.socialmediatoday.com/content/social-media-virality-why-do-people- retweet

Hanna, R., Rohm, A., & Crittenden, V. L. (2011). We’re all connected: The power of the social media ecosystem. Business Horizons, 54(3), 265-273. https://doi.org/10.1016/j.bushor.2011.01.007

Hooper, D., Coughlan, J., & Mullen, M. (2008). Structural equation modeling: Guidelines for determining model fit. Electronic Journal of Business Research Methods, 6(1), 53–60.

Igbaria, M., Parasuraman, S., & Baroudi, J. J. (1996). A motivational model of microcomputer usage. Journal of Management Information Systems, 127-143. http://www.jstor.org/stable/40398206

Jansen, B. J., Zhang, M., Sobel, K., & Chowdhury, A. (2009a). Twitter power: Tweets as electronic word of mouth. Journal of the American society for information science and technology, 60(11), 2169–2188. https://doi.org/10.1002/asi.21149

Jansen, B. J., Zhang, M., Sobel, K., & Chowdhury, A. (2009b). Micro-blogging as online word of mouth branding. In CHI'09 Extended Abstracts on Human Factors in Computing Systems (pp. 3859–3864). ACM. https://doi.org/10.1145/1520340.1520584

Kenny, D. A. (2014, October 6). Measuring model fit. Retrieved from http://davidakenny.net/cm/fit.htm

Kim, T. (2014). Observation on copying and pasting behavior during the Tohoku earthquake: Retweet pattern changes. International Journal of Information Management. 34(4), 546–555. https://doi.org/10.1016/j.ijinfomgt.2014.03.001

Kwon, E. S., & Sung, Y. (2011). Follow me! Global marketers’ twitter use. Journal of Interactive Advertising, 12(1), 4–16. https://doi.org/10.1080/15252019.2011.10722187

Kwon, O., & Wen, Y. (2010). An empirical study of the factors affecting social network service use. Computers in Human Behavior, 26(2), 254–263. https://doi.org/10.1016/j.chb.2009.04.011

Laudon, K. C. & Traver, C. G. (2010). E-commerce: business, technology, society. (6th Ed). Prentice Hall: Upper Saddle River, NJ

Malhotra, A., Malhotra, C. K., & See, A. (2012). How to get your messages retweeted. MIT Sloan Management Review, 53(2), 61-66.

Mangold, W. G., & Faulds, D. J. (2009). Social media: The new hybrid element of the promotion mix. Business Horizons, 52(4), 357–365. https://doi.org/10.1016/j.bushor.2009.03.002

Moon, J. W., & Kim, Y. G. (2001). Extending the TAM for a World-Wide-Web context. Information & Management, 38(4), 217-230. https://doi.org/10.1016/S0378-7206(00)00061-6

Moran, G. (2012, December 31). 4 reasons why retweeting needs to be a part of your content marketing strategy. Retrieved from http://www.business2community.com/content- marketing/4-reasons-why-retweeting-needs-to-be-a-part-of-your-content-marketing- strategy-0366334#!bfEDfH

Pantano, E., Tavernise, A., & Viassone, M. (2010). Consumer perception of computer-mediated communication in a social network. In New Trends in Information Science and Service Science (NISS), 2010 4th International Conference on (pp. 609-614). IEEE.

Petrosyan, A. (2024). Worldwide digital population, 2024. Accessed on 29 November 2024 https://www.statista.com/statistics/617136/digital-population worldwide/

Pinho, J. C. M. R., & Soares, A. M. (2011). Examining the technology acceptance model in the adoption of social networks. Journal of Research in Interactive Marketing, 5(2/3), 116- 129. https://doi.org/10.1108/17505931111187767

Rauniar, R., Rawski, G., Yang, J., & Johnson, B. (2014). Technology acceptance model (TAM) and social media usage: an empirical study on Facebook. Journal of Enterprise Information Management, 27(1), 6-30. https://doi.org/10.1108/JEIM-04-2012-0011

Satell, G. (2014, January 18). If you doubt that social media has changed the world, take a look at Ukraine. Retrieved from http://www.forbes.com/sites/gregsatell/2014/01/18/if-you-doubt-that-social-media-has- changed-the-world-take-a-look-at-Ukraine/

Shi, Z., Rui, H., & Whinston, A. B. (2014). Content sharing in a social broadcasting environment: evidence from twitter. Mis Quarterly, 38(1), 123–142. https://dx.doi.org/10.2139/ssrn.2341243

Shin, D. H. (2010). The effects of trust, security and privacy in social networking: A security- based approach to understand the pattern of adoption. Interacting with Computers, 22(5), 428–438. https://doi.org/10.1016/j.intcom.2010.05.001

Stelzner, M. (2009, October 12). The marketing power of the retweet: An interview with Dan Zarrella. Retrieved from http://www.socialmediaexaminer.com/the-marketing-power-of- the-retweet-an-interview-with-dan-zarrella

Tabachnick, B. G., & Fidell, L. S. (2007). Multivariate analysis of variance and covariance. Using multivariate statistics, 3, 402-407.

Tiago, M. T. P. M. B., & Veríssimo, J. M. C. (2014). Digital marketing and social media: Why bother? Business Horizons, 57(6), 703-708. https://doi.org/10.1016/j.bushor.2014.07.002

Toubia, O., & Stephen, A. T. (2013). Intrinsic vs. image-related utility in social media: Why do people contribute content to twitter? Marketing Science, 32(3), 368-392. https://doi.org/10.1287/mksc.2013.0773

Venkatesh, V., & Davis, F. D. (1996). A model of perceived ease of use antecedents: Development and test. Decision Sciences, 27(3), 451-481. https://doi.org/10.1111/j.1540-5915.1996.tb00860.x

Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: four longitudinal field studies. Management Science, 46(2), 186–204. http://www.jstor.org/stable/2634758

Westland, J. C. (2010). Lower bounds on sample size in structural equation modeling. Electronic Commerce Research and Applications, 9(6), 476-487. https://doi.org/10.1016/j.elerap.2010.07.003

Wolf, E. J., Harrington, K. M., Clark, S. L., & Miller, M. W. (2013). Sample size requirements for structural equation models an evaluation of power, bias, and solution propriety. Educational and Psychological Measurement, 73(6), 913–934. https://doi.org/10.1177/0013164413495237

Yavas, U. (1994). Research Note: students as subjects in advertising and marketing research. International Marketing Review, 11(4), 35–43. https://doi.org/10.1108/02651339410069236

Zhou, L., Xue, S., & Li, R. (2022). Extending the Technology Acceptance Model to explore students’ intention to use an online education platform at a University in China. Sage Open, 12(1), 21582440221085259. https://doi.org/10.1177/21582440221085259