Analisis Faktor- Faktor Penentu Efektifitas Pemanfaatan Integrasi Meta AI Dalam Sosial Media Whatsapp: Pendekatan Expectation Confirmation

Timoti James Kaway; Ratna Juita; Dedi Iskandar Inan

doi:10.35760/ik.2025.v30i2.14558

Analisis Faktor- Faktor Penentu Efektifitas Pemanfaatan Integrasi Meta AI Dalam Sosial Media Whatsapp: Pendekatan Expectation Confirmation

Timoti James Kaway
Universitas Papua
Indonesia

Ratna Juita
Universitas Papua
Indonesia

Dedi Iskandar Inan
Universitas Papua
Indonesia

DOI: http://dx.doi.org/10.35760/ik.2025.v30i2.14558

Article Submitted: 01 July 2025

Article Published: 09 September 2025

Abstract

Penelitian ini mengkaji faktor-faktor yang berkontribusi terhadap integrasi yang efektif dari teknologi Generative Artificial Intelligence (GenAI) ke dalam penyampaian pesan di media sosial. Meskipun pemanfaatan AI dalam komunikasi digital semakin berkembang, dan banyak digunakan dalam komunikasi digital, kajian yang membahas persepsi pengguna dan faktor-faktor yang mendukung integrasi yang efektif ke dalam pesan media sosial, khususnya melalui pendekatan Konfirmasi Harapan, masih jarang ditemukan. Penelitian ini bertujuan mengidentifikasi faktor-faktor kunci yang mempengaruhi keberlanjutan penggunaan GenAI dalam strategi komunikasi media sosial Meta (Instagram, WhatsApp, Messenger) dan menguji hubungan antar variabel Expectation Confirmation Theory (ECT ) yang diperluas. Penggunaan pendekatan kuantitatif berbasis ECT , data dikumpulkan melalui survei daring terhadap 131 responden dan dianalisis menggunakan Partial Least Squares Structural Equation Modeling (PLS-SEM). Hasil menunjukkan bahwa Continuance intention memiliki koefisien determinasi tertinggi (R² = 0,464). Perceived Usefulness (R² = 0,41; p < 0,001) dan Satisfaction (R² = 0,38; p < 0,001) berpengaruh langsung dan signifikan terhadap continuance intention, diikuti Perceived Enjoyment (R² = 0,33; p < 0,01) dan Perceived Ease of Use (R² = 0,27; p < 0,05). Temuan ini menekankan pentingnya kesesuaian antara kinerja GenAI dan harapan pengguna dalam menciptakan pengalaman komunikasi digital yang bernilai. Studi ini memberikan wawasan strategis bagi praktisi komunikasi dan pengembang AI untuk memperkuat adopsi teknologi GenAI di ranah media sosial.

Keywords

Integrasi Teknologi; Kecerdasan Buatan Generatif; Media Sosial; Persepsi Pengguna; Teori Konfirmasi Ekspektasi.

References

A. G. Møller, D. M. Romero, D. Jurgens, and L. M. Aiello, “The Impact of Generative AI on Social Media: An Experimental Study,” Arxiv, pp. 1–48, Jun. 2025.

M. Mariani and Y. K. Dwivedi, “Generative artificial intelligence in innovation management: A preview of future research developments,” J. Bus. Res., vol. 175, no. 114542, pp. 1–21, Mar. 2024, doi: 10.1016/j.jbusres.2024.114542.

C. K. Y. Chan, “AI as the Therapist: Student Insights on the Challenges of Using Generative AI for School Mental Health Frameworks,” Behav. Sci. (Basel)., vol. 15, no. 3, pp. 1–22, Mar. 2025, doi: 10.3390/bs15030375.

F. D. Davis, “Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology,” MIS Q., vol. 13, no. 3, pp. 319–340, Sep. 1989, doi: 10.2307/249008.

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., vol. 36, no. 1, pp. 157–178, 2012, doi: 10.2307/41410412.

A. Bhattacherjee, “Understanding Information Systems Continuance: An Expectation-Confirmation Model,” MIS Q., vol. 25, no. 3, pp. 351–370, Sep. 2001, doi: 10.2307/3250921.

M. Arce-Urriza, R. Chocarro, M. Cortiñas, and G. Marcos-Matás, “From familiarity to acceptance: The impact of Generative Artificial Intelligence on consumer adoption of retail chatbots,” J. Retail. Consum. Serv., vol. 84, no. 104234, pp. 1–17, May 2025, doi: 10.1016/j.jretconser.2025.104234.

D. Gamage, D. Sewwandi, M. Zhang, and A. K. Bandara, “Labeling Synthetic Content: User Perceptions of Label Designs for AI-Generated Content on Social Media,” in Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, New York, NY, USA: ACM, Apr. 2025, pp. 1–29. doi: 10.1145/3706598.3713171.

A.-K. E. Onjewu, N. Walton, and I. Koliousis, “Blockchain agency theory,” Technol. Forecast. Soc. Change, vol. 191, no. 122482, pp. 1–10, Jun. 2023, doi: 10.1016/j.techfore.2023.122482.

L. Pan, C.-Y. Wang, F. Zhou, and L. Lü, “Complexity of social media in the era of generative AI,” Natl. Sci. Rev., vol. 12, no. 1, pp. 1–3, Dec. 2024, doi: 10.1093/nsr/nwae323.

M. G. de Blanes Sebastián, A. Antonovica, and J. R. Sarmiento Guede, “What are the leading factors for using Spanish peer-to-peer mobile payment platform Bizum? The applied analysis of the UTAUT2 model,” Technol. Forecast. Soc. Change, vol. 187, no. 122235, pp. 1–16, Feb. 2023, doi: 10.1016/j.techfore.2022.122235.

A. M. Kaplan and M. Haenlein, “Users of the world, unite! The challenges and opportunities of Social Media,” Bus. Horiz., vol. 53, no. 1, pp. 59–68, Jan. 2010, doi: 10.1016/j.bushor.2009.09.003.

J. H. Kietzmann, K. Hermkens, I. P. McCarthy, and B. S. Silvestre, “Social media? Get serious! Understanding the functional building blocks of social media,” Bus. Horiz., vol. 54, no. 3, pp. 241–251, 2011, doi: 10.1016/j.bushor.2011.01.005.

R. L. Oliver, “A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions,” J. Mark. Res., vol. 17, no. 4, pp. 460–469, Nov. 1980, doi: 10.2307/3150499.

D. Shin, “The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI,” Int. J. Hum. Comput. Stud., vol. 146, no. 102551, pp. 1–10, Feb. 2021, doi: 10.1016/j.ijhcs.2020.102551.

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., vol. 31, no. 4, pp. 705–737, 2007, doi: 10.2307/25148817.

E. D. Putra, S. Athaullah, and A. Yusuf, “Pengaruh Perceived Ease of Use, Perceived Enjoyment, Perceived Usefulness, dan Satisfaction terhadap Continued IT Usage Intention: Expected-Confirmation Model (ECM),” J. Maksipreneur Manajemen, Koperasi, dan Entrep., vol. 13, no. 1, pp. 1–19, Oct. 2023, doi: 10.30588/jmp.v13i1.1051.

M. Y. Ubaidillah, E. Pramana, and F. H. Chandra, “Continuance Intention Pada Aplikasi Mobile Payment Dengan Menggunakan Extended Expectation Confirmation Model,” JTIM J. Teknol. Inf. dan Multimed., vol. 5, no. 2, pp. 149–161, Aug. 2023, doi: 10.35746/jtim.v5i2.359.

C. Liao, P. Palvia, and J.-L. Chen, “Information technology adoption behavior life cycle: Toward a Technology Continuance Theory (TCT),” Int. J. Inf. Manage., vol. 29, no. 4, pp. 309–320, Aug. 2009, doi: 10.1016/j.ijinfomgt.2009.03.004.

I. Elmiradewi, I. W. Mustika, and D. Adhipta, “PEMANFAATAN PENCARIAN LITERATUR KEDOKTERAN CLINICALKEY (STUDI KASUS: FAKULTAS KEDOKTERAN UNIVERSITAS KRISTEN DUTA WACANA YOGYAKARTA),” Berk. Ilm. Kedokt. Duta Wacana, vol. 2, no. 2, pp. 403–415, May 2017, doi: 10.21460/bikdw.v2i2.49.

Y. Lee and O. Kwon, “Intimacy, familiarity and continuance intention: An extended expectation–confirmation model in web-based services,” Electron. Commer. Res. Appl., vol. 10, no. 3, pp. 342–357, May 2011, doi: 10.1016/j.elerap.2010.11.005.

B. H. Wixom and P. A. Todd, “A Theoretical Integration of User Satisfaction and Technology Acceptance,” Inf. Syst. Res., vol. 16, no. 1, pp. 85–102, Mar. 2005, doi: 10.1287/isre.1050.0042.

G. Wiwoho, “PENGARUH PERCEIVED USEFULNESS DAN PERCEIVED EASE OF USE TERHADAP CUSTOMER SATISFACTION DAN REPURCHASE INTENTION PENGGUNA APLIKASI OVO,” J. Fokus Bisnis, vol. 17, no. 01, pp. 53–61, 2018.

A. K. Wulandari and R. Asmara, “Persepsi Mahasiswa terhadap Penggunaan Meta AI sebagai Sumber Informasi untuk Tugas Akademik,” MASALIQ, vol. 5, no. 3, pp. 1101–1113, May 2025, doi: 10.58578/masaliq.v5i3.5688.

L. F. Rodrigues, A. Oliveira, and C. J. Costa, “Does ease-of-use contributes to the perception of enjoyment? A case of gamification in e-banking,” Comput. Human Behav., vol. 61, no. August, pp. 114–126, Aug. 2016, doi: 10.1016/j.chb.2016.03.015.

V. Venkatesh, “Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model,” Inf. Syst. Res., vol. 11, no. 4, pp. 342–365, Dec. 2000, doi: 10.1287/isre.11.4.342.11872.

R. Aditya and A. Wardhana, “Pengaruh perceived usefulness dan perceived ease of use terhadap behavioral intention dengan pendekatan Technology Acceptance Model (TAM) pada pengguna Instant Messaging LINE di Indonesia,” J. Siasat Bisnis, vol. 20, no. 1, pp. 24–32, Jan. 2016, doi: 10.20885/jsb.vol20.iss1.art3.

M.-H. Hsu and C.-M. Chiu, “Internet self-efficacy and electronic service acceptance,” Decis. Support Syst., vol. 38, no. 3, pp. 369–381, Dec. 2004, doi: 10.1016/j.dss.2003.08.001.

A. Gofur, “Pengaruh Kualitas Pelayanan Dan Harga Terhadap Kepuasan Pelanggan,” J. Ris. Manaj. dan Bisnis Fak. Ekon. UNIAT, vol. 4, no. 1, pp. 37–44, 2019, doi: 10.36226/jrmb.v4i1.240.

C. F. Risdiyanto, D. I. Inan, R. N. Wurarah, and O. A. Fenetiruma, “Analisis Faktor-faktor Pendukung dan Penghambat Beralih Mengadopsi Mobile Banking di Papua Barat Memanfaatkan PLS-SEM dan Perspektif Status Quo Bias,” MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 4, no. 2, pp. 637–646, Mar. 2024, doi: 10.57152/malcom.v4i2.1289.

F. Faul, E. Erdfelder, A.-G. Lang, and A. Buchner, “G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences,” Behav. Res. Methods, vol. 39, no. 2, pp. 175–191, May 2007, doi: 10.3758/BF03193146.

Y. D. Pratiwi and R. Purbojo, “Pengaruh Kompetensi Digital dan Disiplin Kerja terhadap Kinerja Guru dengan Mediasi Motivasi Berprestasi di SD XYZ,” J. Inovasi, Eval. dan Pengemb. Pembelajaran, vol. 5, no. 1, pp. 7–13, Apr. 2025, doi: 10.54371/jiepp.v5i1.631.

J. F. Hair, J. J. Risher, M. Sarstedt, and C. M. Ringle, “When to use and how to report the results of PLS-SEM,” Eur. Bus. Rev., vol. 31, no. 1, pp. 2–24, Jan. 2019, doi: 10.1108/EBR-11-2018-0203.

J. Henseler, C. M. Ringle, and M. Sarstedt, “A new criterion for assessing discriminant validity in variance-based structural equation modeling,” J. Acad. Mark. Sci., vol. 43, no. 1, pp. 115–135, Jan. 2015, doi: 10.1007/s11747-014-0403-8.

M. Sarstedt, J. F. Hair, C. Nitzl, C. M. Ringle, and M. C. Howard, “Beyond a tandem analysis of SEM and PROCESS: Use of PLS-SEM for mediation analyses!,” Int. J. Mark. Res., vol. 62, no. 3, pp. 288–299, 2020, doi: 10.1177/1470785320915686.

J. F. Hair, G. T. M. Hult, C. M. Ringle, M. Sarstedt, N. P. Danks, and S. Ray, Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R. in Classroom Companion: Business. Cham: Springer International Publishing, 2021. doi: 10.1007/978-3-030-80519-7.

M. Sarstedt, C. M. Ringle, and J. F. Hair, “Partial Least Squares Structural Equation Modeling,” in Handbook of Market Research, Cham: Springer International Publishing, 2022, pp. 587–632. doi: 10.1007/978-3-319-57413-4_15.

D. Gefen, E. Karahanna, and D. W. Straub, “Trust and TAM in Online Shopping: An Integrated Model,” MIS Q., vol. 27, no. 1, pp. 51–90, 2003, doi: 10.2307/30036519.

C.-M. Chiu, M.-H. Hsu, S.-Y. Sun, T.-C. Lin, and P.-C. Sun, “Usability, quality, value and e-learning continuance decisions,” Comput. Educ., vol. 45, no. 4, pp. 399–416, Dec. 2005, doi: 10.1016/j.compedu.2004.06.001.

T. Zhou, “An empirical examination of continuance intention of mobile payment services,” Decis. Support Syst., vol. 54, no. 2, pp. 1085–1091, Jan. 2013, doi: 10.1016/j.dss.2012.10.034.

H.-W. Kim, H. C. Chan, and S. Gupta, “Value-based Adoption of Mobile Internet: An empirical investigation,” Decis. Support Syst., vol. 43, no. 1, pp. 111–126, Feb. 2007, doi: 10.1016/j.dss.2005.05.009.

Dosen S1 Teknik Informatika Universitas Papua