INTEGRASI UTAUT DAN TTAT DALAM KERANGKA IS DUAL FACTOR PADA NIAT PENGGUNAAN TEKNOLOGI AI DI JABODETABEK

MANDA RAIHANA LAKSITA, . (2026) INTEGRASI UTAUT DAN TTAT DALAM KERANGKA IS DUAL FACTOR PADA NIAT PENGGUNAAN TEKNOLOGI AI DI JABODETABEK. Sarjana thesis, UNIVERSITAS NEGERI JAKARTA.

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Abstract

Penelitian ini bertujuan untuk menguji pengaruh variabel-variabel dalam kerangka Unified Theory of Acceptance and Use of Technology (UTAUT) dan Technology Threat Avoidance Theory (TTAT) yang diintegrasikan melalui pendekatan IS Dual Factor terhadap Generative AI Usage Intention pada pengguna di wilayah Jabodetabek. Sebagian besar penelitian terdahulu mengenai adopsi teknologi AI cenderung berjalan di lintasan terpisah antara faktor pendorong (enablers) dan faktor penghambat (inhibitors), padahal dalam praktiknya pengguna mengalami tarikan ganda secara bersamaan. Penelitian ini hadir untuk mengisi celah tersebut dengan membuktikan relevansi kerangka IS Dual Factor yang memandang kedua gugus determinan sebagai entitas yang berbeda namun bekerja secara simultan. Penelitian menggunakan pendekatan kuantitatif dengan metode survei terhadap 200 responden yang berdomisili di Jabodetabek dan memiliki pengetahuan dan/atau pengalaman menggunakan teknologi Generative AI. Pengumpulan data dilakukan melalui kuesioner daring pada periode September 2025 hingga April 2026. Analisis data menggunakan pendekatan Covariance-Based Structural Equation Modeling (CB-SEM) melalui perangkat lunak IBM AMOS, mencakup tahap Confirmatory Factor Analysis (CFA) dan pengujian model struktural. Hasil penelitian menunjukkan bahwa seluruh enam hipotesis diterima. Performance Expectancy (β = 0,564) dan Facilitating Conditions (β = 0,566) terbukti sebagai dua prediktor terkuat yang mendorong niat penggunaan Generative AI. Effort Expectancy (β = 0,379) dan Social Influence (β = 0,243) juga berpengaruh positif dan signifikan. Di sisi penghambat, Perceived Threats (β = −0,277) terbukti mengurangi niat penggunaan secara signifikan, sementara Perceived Avoidability (β = 0,287) berfungsi sebagai buffer yang melemahkan efek negatif persepsi ancaman. Secara keseluruhan, penelitian ini memberikan bukti empiris bahwa integrasi UTAUT dan TTAT dalam kerangka IS Dual Factor menghasilkan model prediktif yang lebih komprehensif dibandingkan pendekatan unidimensional, dan relevan dalam menjelaskan dinamika niat penggunaan Generative AI pada populasi urban Indonesia. ***** This study aims to examine the influence of variables within the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Technology Threat Avoidance Theory (TTAT), integrated through an IS Dual Factor approach, on Generative AI Usage Intention among users in the Jabodetabek region. Most prior research on AI technology adoption has tended to examine enabling and inhibiting factors along separate trajectories, whereas in practice users experience these dual pulls simultaneously. This study addresses that gap by demonstrating the relevance of the IS Dual Factor framework, which treats the two sets of determinants as distinct yet concurrently operating constructs. A quantitative approach with a survey method was employed, involving 200 respondents domiciled in Jabodetabek who possess knowledge of and/or experience using Generative AI technology. Data were collected through an online questionnaire from September 2025 to April 2026 and analyzed using Covariance-Based Structural Equation Modeling (CB-SEM) via IBM AMOS, encompassing Confirmatory Factor Analysis (CFA) and structural model testing. The results indicate that all six hypotheses were supported. Performance Expectancy (β = 0.564) and Facilitating Conditions (β = 0.566) emerged as the two strongest predictors driving Generative AI usage intention. Effort Expectancy (β = 0.379) and Social Influence (β = 0.243) also exerted significant positive effects. On the inhibiting side, Perceived Threats (β = −0.277) significantly reduced usage intention, while Perceived Avoidability (β = 0.287) functioned as a buffer that attenuated the negative effect of perceived threat. Overall, this study provides empirical evidence that integrating UTAUT and TTAT within an IS Dual Factor framework yields a more comprehensive predictive model than unidimensional approaches, and is relevant for explaining the dynamics of Generative AI usage intention among Indonesia's urban population.

Item Type:	Thesis (Sarjana)
Additional Information:	1). Diena Noviarini, M.M.Si.; 2). Adnan Kasofi, S.Pd., MBA.
Subjects:	Manajemen > Manajemen , Business
Divisions:	FE > S1 Bisnis Digital
Depositing User:	Manda Raihana Laksita .
Date Deposited:	18 Aug 2026 03:49
Last Modified:	18 Aug 2026 03:49
URI:	http://repository.unj.ac.id/id/eprint/71652

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