The Analysis of Online Worship Services Acceptance using the UTAUT 2 Method and Clustering k-Means

Gilang Jonathan Phita, Yessica Nataliani


The characteristics and background of the church congregation and the acceptance of technology in online worship services were one of the parameter to determine user attitudes or behavior in accepting technology. The objective of this research to is to find out the acceptance of church congregation's technology toward the online worship services using the Unified Theory of Acceptance and Use of The Technology 2 (UTAUT 2), and answers of respondents from various backgrounds and ages were grouped using k-Means clustering. Data were collected through a questionnaire with a total of 220 respondents. From the UTAUT 2 method using multiple linear regression, it was found that the Habit variable on the use of online worship has a high influence toward the Behavioral Intention variable, and the Habit variable on the use of online worship has a high influence toward the Use Behavior variable. Furthermore, clustering with k-Means was carried out to see the age group that was satisfied with online worship services, based on four clusters obtained using the Elbow method and six age groups. The results of the k-Means clustering of all UTAUT 2 variables, the age group 12-16 years and 26-35 years are the most satisfied group and accept online worship services.

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V. Venkatesh, M. G. Morris, G. B. Davis, and F. D. Davis, “User Acceptance Of Information Technology: Toward A Unified View,” MIS Q. Manag. Inf. Syst., vol. 27, no. 3, pp. 425–478, 2003.

T. Handayani and S. Sudiana, “Analisis Penerapan Model UTAUT (Unified Theory of Acceptance and Use of Technology) Terhadap Perilaku Pengguna Sistem Informasi (Studi Kasus: Sistem Informasi Akademik Pada Sttnas Yogyakarta),” Angkasa J. Ilm. Bid. Teknol., vol. 7, no. 2, pp. 165–179, 2017.

V. Venkatesh, J. Y. L. Thong, and X. Xu, “Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology,” MIS Q. Manag. Inf. Syst., vol. 36, no. 1, pp. 157–178, 2012.

P.-N. Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining. 2006.

S. Andayani, “Formation of clusters in Knowledge Discovery in Databases by Algorithm K-Means,” 2007.

A. Nur Khormarudin, “Teknik Data Mining: Algoritma K-Means Clustering,” J. Ilmu Komput., pp. 1–12, 2016.

L. Agustina, “Live Video Streaming Sebagai Bentuk Perkembangan Fitur Media Sosial,” Diakom J. Media dan Komun., vol. 1, no. 1, pp. 17–23, 2018.

A. Garone et al., “Clustering University Teaching Staff Through UTAUT: Implications for the Acceptance of a New Learning Management System,” Br. J. Educ. Technol., vol. 50, no. 5, pp. 2466–2483, 2019.

A. A. Onibala, Y. Rindengan, and A. S. Lumenta, “Analisis Penerapan Model UTAUT 2 (Unified Theory Of Acceptance And Use Of Technology 2) Terhadap E-Kinerja Pada Pemerintah Provinsi Sulawesi Utara,” E-journal Tek. Inform., vol. 2, pp. 1–13, 2021.

N. W. D. M. Y. Pertiwi and D. Ariyanto, “Penerapan Model UTAUT 2 Untuk Menjelaskan Minat Dan Perilaku Penggunaan Mobile Banking Di Kota Denpasar,” E-Jurnal Akunt., vol. 18, no. 2, pp. 1369–1397, 2017.

W. Dhuhita, “Clustering Menggunakan Metode K-Means Untuk Menentukan Status Gizi Balita,” J. Inform. Darmajaya, vol. 15, no. 2, pp. 160–174, 2015.

Asroni and R. Adrian, “Penerapan Metode K-Means Untuk Clustering Mahasiswa Berdasarkan Nilai Akademik Dengan Weka Interface Studi Kasus Pada Jurusan Teknik Informatika UMM Magelang,” J. Ilm. Semesta Tek., vol. 18, no. 1, pp. 76–82, 2015.

Sugiyono, Metode Penelitian Kombinasi (Mixed Methods). Bandung: Alfabeta, 2017.

M. A. Memon, H. Ting, J.-H. Cheah, R. Thurasamy, F. Chuah, and T. H. Cham, “Sample Size for Survey Research: Review and Recommendations,” J. Appl. Struct. Equ. Model., vol. 4, no. 2, pp. i–xx, 2020.

S. A. Brown and V. Venkatesh, “Model of adoption of technology in households: A baseline model test and extension incorporating household life cycle,” MIS Q. Manag. Inf. Syst., vol. 29, no. 3, pp. 399–426, 2005.

W. B. Dodds, K. B. Monroe, and D. Grewal, “Effects of Price, Brand, and Store Information on Buyers’ Product Evaluations,” J. Mark. Res., vol. 28, no. August, pp. 307–319, 1991.

M. Limayem, S. G. Hirt, and C. M. K. Cheung, “How Habit Limits the Predictive Power of Intention: The Case of Information Systems Continuance,” MIS Q. Manag. Inf. Syst., vol. 31, pp. 705–737, 2007.

J. O. Ong, “Implementasi Algotritma K-Means Clustering untuk Menentukan Strategi Marketing President University,” J. Ilm. Tek. Ind., vol. 12, pp. 10–20, 2013.

W. Siswaningsih, H. Firman, Zackiyah, and A. Khoirunnisa, “Development of Two-Tier Diagnostic Test Pictorial-Based for Identifying High School Students Misconceptions on the Mole Concept,” J. Phys. Conf. Ser., vol. 755, no. 1, 2017.


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