Analyzing the influence of physical posture on audience perception in mass media presentations

  • Yihan Wu School of Media Arts and Communication, Nanjing University of the Arts, Nanjing 210003, China
Keywords: Non-verbal communication; physical posture; audience perception; cellular signaling; mass media presentations
Article ID: 622

Abstract

Non-verbal communication, especially physical posture, affects audience perception. From a cellular and molecular biomechanics angle, different postures may trigger unique intracellular responses. Upright or leaning forward postures might activate neural pathways that enhance neurotransmitter release related to positive perception. In contrast, a slouched posture could disrupt normal cellular signaling, potentially leading to a less favorable audience perception. This study explores the impact of four postures on audience views in a media setting, aiming to offer data on how posture shapes key perceptions and provide valuable insights for Mass-media Presentations (MMP), despite limited prior research on this aspect. A within-subject experimental design was employed, with 34 participants observing media presentations under four posture conditions. Posture was the independent variable, while credibility, trustworthiness, engagement, and authority were the dependent variables. Data were collected using surveys, posture monitoring devices, and eye-tracking data. Statistical analyses, including Analysis of Variance (ANOVA) and paired t-tests, were conducted to determine significant differences between posture conditions. Upright and leaning forward postures were associated with the highest audience ratings for credibility, trustworthiness, engagement, and authority. Slouched posture consistently led to the lowest ratings across all measures. The ANOVA results revealed significant differences in perceptions of engagement (F = 10.21, p = 0.0008) and credibility (F = 8.67, p = 0.0013). Paired t-tests and post-hoc analyses confirmed that upright posture significantly outperformed slouched posture across all metrics, with large effect sizes (Cohen’s d > 1.0). Posture significantly influences audience perceptions in mass media presentations. Upright and leaning forward postures enhance credibility, trustworthiness, engagement, and authority, while slouched posture diminishes these perceptions. These findings provide practical insights for media professionals, suggesting that careful attention to posture can improve the effectiveness of media presentations. Future research could investigate how gestures and facial expressions interact with these cellular and molecular mechanisms to shape audience engagement.

References

1. Seiter, J. S., & Weger, H. (2020). Nonverbal communication in political debates. Rowman & Littlefield.

2. Remland, M. S., & Mahoney, L. M. (2020). Reassessing the Importance of Nonverbal Communication in the Age of Social Media. In Reimagining Communication: Experience (pp. 64-79). Routledge.

3. Štěpánková, A. (2021). Emotions in non-verbal communication at pre-election debates: a review of resources on the importance of politicians’ mimics at TV political debates and other forms of media messages. The Journal of International Communication, 27(1), 126-147.

4. Rouse, M. N., Schafer, K. R., Griffin, D. J., & Duncan, C. Verbal and Nonverbal Communication: Creating Inclusion and Accessibility. The Routledge Handbook of Public Speaking Research and Theory, 133-143.

5. Khajanchi, Y. ANALYZING THE TYPES OF NON-VERBAL COMMUNICATION. COMMUNICATION MEDIA AND SOCIETY, 26.

6. Azemi, I. (2021). Non-Verbal Communication in Public Appearance. International Journal of Arts and Social Science, 4(4), 256-267.

7. Loecherbach, F., Moeller, J., Trilling, D., & van Atteveldt, W. (2020). The unified framework of media diversity: A systematic literature review. Digital Journalism, 8(5), 605-642.

8. Maares, P., Banjac, S., & Hanusch, F. (2021). The labour of visual authenticity on social media: Exploring producers’ and audiences’ perceptions on Instagram. Poetics, 84, 101502.

9. Bassey-Duke, V. NON-VERBAL CUES FOR EFFECTIVE NARRATION IN TV. DOCUMENTARIES.

10. Pereira, M., & Hone, K. (2021, May). Communication skills training intervention based on automated recognition of nonverbal signals. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (pp. 1-14).

11. Lee, M. (2019). Forward and up: An exploration of implementations of the Alexander Technique in post-secondary music institutions (Doctoral dissertation, The University of Western Ontario (Canada)).

12. Abdulghafor, R., Turaev, S., & Ali, M. A. (2022, July). Body language analysis in healthcare: an overview. In Healthcare (Vol. 10, No. 7, p. 1251). MDPI.

13. Ezeh, N. G., Anidi, O. C., & Nwokolo, B. O. (2021). Body Language as a Communicative Aid amongst Language Impaired Students: Managing Disabilities. English Language Teaching, 14(6), 125-134.

14. Konkov, V. I., & Solomkina, T. A. (2021). Professional journalistic speech in the media environment. Russian Language Studies, 19(4), 419-435.

15. Rossette-Crake, F. (2020). ‘The new oratory’: Public speaking practice in the digital, neoliberal age. Discourse Studies, 22(5), 571-589.

16. Morelock, J., & Narita, F. Z. (2021). The society of the selfie. University of Westminster Press.

17. Ranschaert, R. (2020). Authority and carnival: Preservice teachers’ media literacy education in a time of truth decay. Educational Studies, 56(5), 519-536.

18. Clough, S., & Duff, M. C. (2020). The role of gesture in communication and cognition: Implications for understanding and treating neurogenic communication disorders. Frontiers in Human Neuroscience, 14, 323.

19. Larrouy-Maestri, P., Kegel, V., Schlotz, W., van Rijn, P., Menninghaus, W., & Poeppel, D. (2023). Ironic twists of sentence meaning can be signaled by forward move of prosodic stress. Journal of Experimental Psychology: General, 152(9), 2438.

20. Indumathi N et al., Impact of Fireworks Industry Safety Measures and Prevention Management System on Human Error Mitigation Using a Machine Learning Approach, Sensors, 2023, 23 (9), 4365; DOI:10.3390/s23094365.

21. Parkavi K et al., Effective Scheduling of Multi-Load Automated Guided Vehicle in Spinning Mill: A Case Study, IEEE Access, 2023, DOI:10.1109/ACCESS.2023.3236843.

22. Ran Q et al., English language teaching based on big data analytics in augmentative and alternative communication system, Springer-International Journal of Speech Technology, 2022, DOI:10.1007/s10772-022-09960-1.

23. Ngangbam PS et al., Investigation on characteristics of Monte Carlo model of single electron transistor using Orthodox Theory, Elsevier, Sustainable Energy Technologies and Assessments, Vol. 48, 2021, 101601, DOI:10.1016/j.seta.2021.101601.

24. Huidan Huang et al., Emotional intelligence for board capital on technological innovation performance of high-tech enterprises, Elsevier, Aggression and Violent Behavior, 2021, 101633, DOI:10.1016/j.avb.2021.101633.

25. Sudhakar S, et al., Cost-effective and efficient 3D human model creation and re-identification application for human digital twins, Multimedia Tools and Applications, 2021. DOI:10.1007/s11042-021-10842-y.

26. Prabhakaran N et al., Novel Collision Detection and Avoidance System for Mid-vehicle Using Offset-Based Curvilinear Motion. Wireless Personal Communication, 2021. DOI:10.1007/s11277-021-08333-2.

27. Balajee A et al., Modeling and multi-class classification of vibroarthographic signals via time domain curvilinear divergence random forest, J Ambient Intell Human Comput, 2021, DOI:10.1007/s12652-020-02869-0.

28. Omnia SN et al., An educational tool for enhanced mobile e-Learning for technical higher education using mobile devices for augmented reality, Microprocessors and Microsystems, 83, 2021, 104030, DOI:10.1016/j.micpro.2021.104030 .

29. Firas TA et al., Strategizing Low-Carbon Urban Planning through Environmental Impact Assessment by Artificial Intelligence-Driven Carbon Foot Print Forecasting, Journal of Machine and Computing, 4(4), 2024, doi: 10.53759/7669/jmc202404105.

30. Shaymaa HN, et al., Genetic Algorithms for Optimized Selection of Biodegradable Polymers in Sustainable Manufacturing Processes, Journal of Machine and Computing, 4(3), 563-574, https://doi.org/10.53759/7669/jmc202404054.

31. Hayder MAG et al., An open-source MP + CNN + BiLSTM model-based hybrid model for recognizing sign language on smartphones. Int J Syst Assur Eng Manag (2024). https://doi.org/10.1007/s13198-024-02376-x

32. Bhavana Raj K et al., Equipment Planning for an Automated Production Line Using a Cloud System, Innovations in Computer Science and Engineering. ICICSE 2022. Lecture Notes in Networks and Systems, 565, 707–717, Springer, Singapore. DOI:10.1007/978-981-19-7455-7_57.

Published
2024-12-17
How to Cite
Wu, Y. (2024). Analyzing the influence of physical posture on audience perception in mass media presentations. Molecular & Cellular Biomechanics, 21(4), 622. https://doi.org/10.62617/mcb622
Section
Article