FAKTOR-FAKTOR YANG MEMENGARUHI BEHAVIORAL INTENTION TO USE GENERATIVE AI UNTUK PEMBUATAN KONTEN MEDIA SOSIAL PADA MAHASISWA DI JAKARTA

DAMAR LINTANG, . (2026) FAKTOR-FAKTOR YANG MEMENGARUHI BEHAVIORAL INTENTION TO USE GENERATIVE AI UNTUK PEMBUATAN KONTEN MEDIA SOSIAL PADA MAHASISWA DI JAKARTA. Sarjana thesis, UNIVERSITAS NEGERI JAKARTA.

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Abstract

Penelitian ini bertujuan untuk menganalisis faktor-faktor yang memengaruhi behavioral intention to use generative AI, khususnya Google Gemini, untuk pembuatan konten media sosial pada mahasiswa di Jakarta. Penelitian ini mengintegrasikan kerangka Unified Theory of Acceptance and Use of Technology (UTAUT) dengan konstruk information quality. Pendekatan kuantitatif digunakan dengan pengumpulan data melalui kuesioner daring kepada 320 mahasiswa di Jakarta. Analisis data dilakukan menggunakan metode Partial Least Squares Structural Equation Modeling (PLS-SEM). Temuan penelitian menunjukkan bahwa effort expectancy, information quality, dan attitude berpengaruh positif dan signifikan terhadap behavioral intention. Sebaliknya, performance expectancy tidak memiliki pengaruh yang signifikan terhadap behavioral intention. Attitude ditemukan sebagai prediktor yang paling dominan dalam memengaruhi behavioral intention to use generative AI. Hasil penelitian ini memberikan kontribusi teoretis bagi literatur penerimaan teknologi serta wawasan praktis berupa usulan rancangan antarmuka bagi pengembang teknologi dan institusi pendidikan untuk mengoptimalkan adopsi generative AI di kalangan kreator muda Indonesia. ***** This study aims to analyze the factors influencing the behavioral intention to use generative AI, specifically Google Gemini, for social media content creation among university students in Jakarta. This research integrates the Unified Theory of Acceptance and Use of Technology (UTAUT) framework with the information quality construct. A quantitative approach was employed, with data collected through online questionnaires from 320 university students in Jakarta. Data analysis was performed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) method. The research findings indicate that effort expectancy, information quality, and attitude have a positive and significant effect on behavioral intention. Conversely, performance expectancy does not have a significant effect on behavioral intention. Attitude was found to be the most dominant predictor influencing the behavioral intention to use generative AI. The results of this study provide theoretical contributions to the technology acceptance literature, as well as practical insights in the form of interface design proposals for technology developers and educational institutions to optimize the adoption of generative AI among young creators in Indonesia.

Item Type:	Thesis (Sarjana)
Additional Information:	1). Diena Noviarini, S.E., M.MSi. ; 2). Meta Bara Berutu, S.E., M.M.
Subjects:	Manajemen > Manajemen , Business
Divisions:	FE > S1 Bisnis Digital
Depositing User:	Damar Lintang .
Date Deposited:	18 Aug 2026 03:38
Last Modified:	18 Aug 2026 03:38
URI:	http://repository.unj.ac.id/id/eprint/71277

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