Research on Short Video Marketing Strategy of AI Smartphones under the Generation Z Consumption Behavior Map_Vol. 3 No. 2 (JNME 2026)_Journal of New Media and Economics (ISSN: 3005-5725)

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Research on Short Video Marketing Strategy of AI Smartphones under the Generation Z Consumption Behavior Map

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DOI: https://doi.org/10.62517/jnme.202610211

Author(s)

Jinyang Wang

Affiliation(s)

Hubei University of Technology Engineering and Technology College, Wuhan, China

Abstract

This study constructs a "content-cognition-emotion-behavior" theoretical framework by integrating the Technology Acceptance Model (TAM) with Fear of Missing Out (FOMO) theory and short-form video content characteristics, aiming to explore the consumption decision-making mechanism of Generation Z toward AI smartphones. Based on a survey of 345 Gen Z consumers and structural equation modeling analysis, the findings reveal that FOMO indirectly drives consumption intention by significantly enhancing perceived usefulness (β = 0.485, p < 0.001). Both perceived entertainment (β = 0.400, p < 0.001) and perceived ease of use (β = 0.282, p < 0.001) exhibit significant direct positive effects on consumption intention, with the influence of entertainment surpassing that of functional utility. However, consumption intention fails to effectively translate into actual consumption behavior (β = -0.004, p = 0.933). The research deepens the application of TAM in short-form video marketing contexts and provides theoretical and practical insights for AI smartphone brands to optimize content strategies, such as enhancing entertaining designs and embedding instant conversion mechanisms.

Keywords

Generation Z; AI Smartphones; Short-Form Video Marketing; Technology Acceptance Model (TAM); FOMO; Consumer Behavior.

References

[1] Przybylski, A. K., Murayama, K., DeHaan, C. R., & Gladwell, V. Przybylski, A. K., Murayama, K., DeHaan, C. R., & Gladwell, V. (2013).Motivational, emotional, and behavioral correlates of fear of missing out. Computers in Human Behavior, 29(4), 1841-1848 [2] Zhang, Y., Wang, L., & Chen, H.Zhang, Y., Wang, L., & Chen, H. (2023).Short-form video content and technology adoption: Evidence from AI smartphone marketing. Technological Forecasting and Social Change, 188, 122-135. [3] Davis, F. D. Davis, F. D. (1989).Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. [4] Wang X, Li Z, Zhou T (2023). The impact of AI function demonstration modes on user perceived usefulness. Acta Psychologica Sinica, 55(4),321-330 [5] Smith, J., Brown, K., & Davis, L.Smith, J., Brown, K., & Davis, L. (2022).The role of entertainment in short-form video marketing: A Gen Z perspective. Journal of Consumer Behaviour, 41(3), 45-60. [6] Li Qi, Zhang Lei, Wang Ying (2022). The Impact of Fun Presentation in Short Videos on the Acceptance of AI Technology. Journal of Marketing Science, 18(3),56-67 [7] Liu Wei, Chen Hao, Zhao Fang (2022). Empirical Analysis of Short Video Interaction Design on Technology Usability Perception. Journal of Management Engineering, 36(2),112-120. [8] Ajzen, I. Ajzen, I. (1991).The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. [9] Pei Xudong, Sun Yuwei, Huang Jing (2024). Research on the Shortening Mechanism of Short Video Marketing in the Consumer Decision-Making Chain. China Circulation Economy, 38(1),45-53. [10] Westland, J. C. Westland, J. C. (2020).Sample size determination for structural equation modeling. Journal of Business Research, 117, 432-439. [11] Liu, X., Zhang, Q., & Wu, Y.Liu, X., Zhang, Q., & Wu, Y. (2023).Snowball sampling in digital community research: Applications and limitations. Social Science Computer Review, 41(1), 77-95. [12] Chen, R., Li, T., & Zhou, M. Chen, R., Li, T., & Zhou, M. (2022).Data quality control in online surveys: A methodological review. Journal of Marketing Analytics, 10(2), 89-102 [13] Wang, J., Zhao, K., & Li, M. (2023).Incentive strategies for improving online survey response rates. Journal of Data Collection, 15(4), 203-215. [14] The SPSSAU project (2026). SPSSAU. (Version 26.0) [Online Application Software]. Retrieved from https://www.spssau.com. [15] Zhou Jun, Ma Shipeng. SPSSAU: Methods and Applications for Scientific Research Data Analysis. 1st ed. [M]. China Machine Press, 2024.