Analisis Technology Acceptance Model (TAM) Dengan Pendekatan Fernandez-Steel Skew Normal Structural Equation Modeling Bayesian (Bayesian FSSN-SEM)

Rafikasari, Elok Fitriani (2025) Analisis Technology Acceptance Model (TAM) Dengan Pendekatan Fernandez-Steel Skew Normal Structural Equation Modeling Bayesian (Bayesian FSSN-SEM). Doctoral thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Pembelajaran daring (e-learning) merupakan proses pembelajaran yang memanfaatkan media digital untuk menyampaikan materi dan memfasilitasi interaksi antara dosen dan mahasiswa. Dosen dituntut untuk tetap memberikan layanan berkualitas meskipun melalui platform daring. Salah satu bentuk implementasinya adalah pengembangan aplikasi e-learning yang telah dilengkapi berbagai fitur pembelajaran. Namun, pemanfaatan aplikasi tersebut belum optimal. Oleh karena itu, penelitian ini bertujuan untuk menganalisis persepsi dosen dan mahasiswa UIN Sayyid Ali Rahmatullah Tulungagung dalam mengimplementasikan e-learning dalam perkuliahan. Penelitian ini menggunakan pendekatan Technology Acceptance Model (TAM) yang telah dimodifikasi. Model TAM digunakan untuk mengukur penerimaan dan penggunaan teknologi, namun model standar belum mempertimbangkan peran aktif dosen sebagai pengguna sekaligus pengelola sistem. Struktur TAM dikembangkan dengan menambahkan variabel Lecture Self-Managing (LSM) sebagai moderator. LSM mencerminkan kemampuan dosen dalam mengelola konten, tugas, dan evaluasi dalam sistem e-learning. Variabel lain yang digunakan meliputi Subjective Norm (SN), Training (T), Experience (E), Facilitating Condition (FC), Perceived Usefulness (PU), Perceived Ease of Use (PE), Behavioral Intention (BI), dan Actual Use (AU). Structural Equation Modeling (SEM) digunakan untuk menganalisis hubungan antar variabel pada model TAM. Mengingat distribusi skor faktor yang cenderung skew dalam praktik nyata, pendekatan Bayesian SEM berbasis distribusi Fernandez-Steel Skew Normal (FSSN) diterapkan untuk mengatasi keterbatasan asumsi pada SEM standar. Hasil penelitian menunjukkan bahwa model Bayesian FSSN-SEM mampu menjelaskan hubungan antar skor faktor dengan lebih baik dibandingkan model Bayesian SEM berbasis distribusi normal. Struktur TAM yang dikembangkan dengan mengakomodasi variabel LSM terbukti mampu menjelaskan dengan lebih baik dan signifikan terhadap penerimaan e-learning, dibandingkan dengan struktur TAM tanpa variabel LSM. Penambahan LSM sebagai variabel moderator juga terbukti signifikan dalam memoderasi hubungan antara SN dan PE serta antara SN dan PU.
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Online learning (e-learning) is a learning process that utilizes digital media to deliver material and facilitate interaction between lecturers and students. Lecturers are required to maintain quality service, even through online platforms. One implementation is the development of e-learning applications equipped with various learning features. However, the use of these applications is not yet optimal. Therefore, this study aims to analyze the perceptions of lecturers and students at UIN Sayyid Ali Rahmatullah Tulungagung regarding the implementation of e-learning in lectures. This study uses a modified Technology Acceptance Model (TAM) approach. The TAM model is used to measure acceptance and use of technology, but the standard model does not consider the active role of lecturers as both users and system managers. The TAM structure was developed by adding the Lecture Self-Managing (LSM) variable as a moderator. LSM reflects lecturers' ability to manage content, assignments, and evaluations within the e-learning system. Other variables used include Subjective Norm (SN), Training (T), Experience (E), Facilitating Condition (FC), Perceived Usefulness (PU), Perceived Ease of Use (PE), Behavioral Intention (BI), and Actual Use (AU). Structural Equation Modeling (SEM) was used to analyze the relationships between variables in the TAM model. Given the tendency for factor score distributions to be skewed in real-world practice, a Bayesian SEM approach based on the Fernandez-Steel Skew Normal (FSSN) distribution was applied to address the limitations of the standard SEM assumptions. The results showed that the Bayesian FSSN-SEM model was able to explain the relationships between factor scores better than the Bayesian SEM model based on the normal distribution. The TAM structure developed by incorporating the LSM variable proved to be able to better and significantly explain e-learning acceptance compared to the TAM structure without the LSM variable. The addition of LSM as a moderator variable also proved significant in moderating the relationships between SN and PE and between SN and PU.

Item Type:	Thesis (Doctoral)
Uncontrolled Keywords:	Technology Acceptance Model, FSSN Distribution, Structural Equation Modeling, Bayesian FSSN-SEM, e-learning
Subjects:	L Education > L Education (General) Q Science > QA Mathematics > QA279.5 Bayesian statistical decision theory.
Divisions:	Faculty of Science and Data Analytics (SCIENTICS) > Statistics > 49001-(S3) PhD Thesis
Depositing User:	Rafikasari Elok Fitriani
Date Deposited:	06 Aug 2025 02:01
Last Modified:	06 Aug 2025 02:01
URI:	http://repository.its.ac.id/id/eprint/127614

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