What question did this study set out to answer?

This research aims to understand the factors influencing Generation Z's adoption of social media platforms like Threads and the associated risks.

May 16, 2026Open Access

From usefulness to addiction: modeling the drivers and risks of threads adoption in the Gen Z context

Key Points

This research aims to understand the factors influencing Generation Z's adoption of social media platforms like Threads and the associated risks.
Analyzed data from 314 users utilizing PLS-SEM and GSCA for structural validation.
Employed machine learning techniques, specifically Random Forest, to evaluate predictive utility.
Integrated Social Impact Theory, Technology Acceptance Model, and dual-process frameworks.
Social impact significantly increases both acceptance and immersion-related risks, including internet addiction.
Perceived usefulness, ease of use, and positive perception primarily drive behavioral intention.
Machine learning confirms social impact, positive perception, and perceived usefulness as the strongest predictors.

Abstract

This study investigates why Generation Z adopts emerging social media platforms like Threads despite coexisting digital risks and benefits. Integrating Social Impact Theory, the Technology Acceptance Model, and dual-process frameworks, we propose an integrative model where social impact (SI) triggers both reflective (System 2: PU, PEU) and experiential (System 1: PP, immersion risks) pathways. Data from 314 users were analyzed using PLS-SEM and GSCA for structural validation, alongside machine learning (Random Forest) for predictive utility. Results indicate that SI significantly elevates both acceptance perceptions and immersion-related risks, including internet addiction. While PU, PEU, and PP primarily drive behavioral intention, the positive association between internet addiction and intention suggests compulsive continuance rather than voluntary motivation. In contrast, inefficiency and time distortion do not significantly deter usage. Machine learning findings converge with SEM results, identifying SI, PP, and PU as the strongest predictors. This research clarifies the dual psychological drivers of Gen Z engagement and demonstrates the methodological value of combining explanatory modeling with predictive analytics.

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