Dynamic Content Marketing Model Based on Social Sentiment and Real-Time Trends: A Cross-Sectoral Study on Generation Z in Southeast Asia
DOI:
https://doi.org/10.38035/dijemss.v7i4.6349Keywords:
Dynamic Content Marketing, Social Sentiment, Real-Time Trends, Generation Z, Consumer EngagementAbstract
Generation Z in Southeast Asia is increasingly shaping the digital consumer ecosystem, creating demand for adaptive and data-informed content marketing strategies. However, existing studies often examine social sentiment and real-time trends separately and rarely integrate them within a dynamic content framework. Addressing this gap, this study proposes an exploratory dynamic content marketing model that combines social sentiment analysis and real-time trend monitoring to explain engagement dynamics among Generation Z. Positioned as a pilot exploratory study, the research employed a quantitative survey of 82 respondents in Indonesia and Malaysia and sentiment analysis of 3,500 social media posts collected over two months. The data were analysed using descriptive statistics and Partial Least Squares Structural Equation Modelling (PLS-SEM). The findings indicate that social sentiment positively influences perceived content relevance, while real-time trend signals show unstable effects on personalisation and engagement, suggesting a volatility risk in trend-driven content strategies. The results also reveal that engagement operates as a proximal outcome of dynamic content strategies, whereas trust and purchase intention exhibit weaker structural relationships. Rather than confirming a predictive model, the study provides preliminary evidence that integrating sentiment monitoring and trend analytics can support more adaptive content strategies while highlighting the limitations of trend dependency. This research contributes by proposing a sentiment-calibrated dynamic content framework, identifying the volatility effect of real-time trends, and distinguishing engagement outcomes from transactional intentions in digital marketing contexts. Future studies with larger samples and refined measurement constructs are needed to validate and extend this preliminary framework.
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