The Influence of Personal Innovativeness on ChatGPT Continuance Usage Intention among Students

Authors

  • Satrio Tegar Sadewo Universitas Tidar
  • Shinta Ratnawati Universitas Tidar
  • Axel Giovanni Universitas Tidar
  • Ipuk Widayanti Universitas Tidar

DOI:

https://doi.org/10.54259/satesi.v5i1.4117

Keywords:

Personal Innovativeness, Technology Acceptance Model, Continuance Usage Intention, Artificial Intelligence, ChatGPT

Abstract

The rapid advancement of generative AI technologies, such as ChatGPT, has introduced significant innovations across various sectors. However, the factors influencing the continuance usage intention of these technologies remain underexplored, particularly among university students in Indonesia. This study investigates the role of Personal Innovativeness within the framework of the Technology Acceptance Model (TAM) in determining students' intentions to continue using ChatGPT. The study used a quantitative method, involving a survey of 252 Indonesian university students. The survey measured Personal Innovativeness, Perceived Ease of Use, Perceived Usefulness, and Continuance Usage Intention using validated measures on a Likert scale with five points. Partial Least Squares Structural Equation Modeling (SEM-PLS) was utilized to evaluate the evidence. The results demonstrate that all three factors have a favorable effect on the desire to continue utilizing ChatGPT. Theoretically, this study expands the Technology Acceptance Model (TAM) by incorporating Personal Innovativeness, offering new insights into factors that sustain long-term technology engagement. This integration contributes to the growing body of knowledge on technology adoption and its continued use, especially within educational contexts. Practically, the results underscore the importance of developing user-friendly and beneficial AI tools and fostering an innovative mindset among students to enhance sustained engagement. Future research should consider longitudinal studies and more diverse populations to further elucidate the factors influencing the continuance usage intention of AI technologies in educational contexts. These insights have significant implications for educators and developers aiming to improve the adoption and sustained use of AI tools like ChatGPT in education.

Downloads

Download data is not yet available.

References

E. Faisal, “Unlock the potential for Saudi Arabian higher education: a systematic review of the benefits of ChatGPT,” Front. Educ., vol. 9, no. April, pp. 1–12, 2024, doi: 10.3389/feduc.2024.1325601.

I. J. Agbong-Coates, “ChatGPT integration significantly boosts personalized learning outcomes: A Philippine study,” Int. J. Educ. Manag. Dev. Stud., vol. 5, no. 2, pp. 165–186, 2024, doi: 10.53378/353067.

X. Li, H. Guo 2nd, D. Li 3rd, and Y. Zheng, “Engine of Innovation in Hospital Pharmacy: Applications and Reflections of ChatGPT (Preprint),” J. Med. Internet Res., 2023, doi: 10.2196/51635.

M. Bektaş, J. K. Pereira, F. Daams, and D. L. van der Peet, “ChatGPT in surgery: a revolutionary innovation?,” Surg. Today, vol. 54, no. 8, pp. 964–971, 2024, doi: 10.1007/s00595-024-02800-6.

E. K. Fishman, W. B. Weeks, J. M. Lavista Ferres, and L. C. Chu, “Watching Innovation in Real Time: The Story of ChatGPT and Radiology,” Can. Assoc. Radiol. J., vol. 74, no. 4, pp. 622–623, 2023, doi: 10.1177/08465371231174817.

M. Bouteraa et al., “Open Innovation in the Financial Sector: A Mixed-Methods Approach to Assess Bankers’ Willingness to Embrace Open-AI ChatGPT,” J. Open Innov. Technol. Mark. Complex., vol. 10, no. 1, p. 100216, 2024, doi: 10.1016/j.joitmc.2024.100216.

H. R. Prameswari, N. M. Habibah, N. M. Chorer, and M. A. S. M. Mun, “The Role of ChatGPT in Knowledge Management: Its Impact on Innovation and Performance of Micro, Small, and Medium Enterprises (MSMEs),” J. Contemp. Adm. Manag., vol. 2, no. 2, pp. 530–538, 2024, [Online]. Available: https://doi.org/10.61100/adman.v2i2.196.

R. S. Al-Maroof, K. Alhumaid, A. Q. Alhamad, A. Aburayya, and S. Salloum, “User acceptance of smart watch for medical purposes: An empirical study,” Futur. Internet, vol. 13, no. 5, pp. 1–19, 2021, doi: 10.3390/fi13050127.

Y. S. Yen, “Channel integration affects usage intention in food delivery platform services: the mediating effect of perceived value,” Asia Pacific J. Mark. Logist., vol. 35, no. 1, pp. 54–73, Jan. 2023, doi: 10.1108/APJML-05-2021-0372/FULL/XML.

E. J. Kim, J. J. Kim, and S. H. Han, “Understanding student acceptance of online learning systems in higher education: Application of social psychology theories with consideration of user innovativeness,” Sustain., vol. 13, no. 2, pp. 1–14, 2021, doi: 10.3390/su13020896.

B. Setiawan, D. P. Nugraha, A. Irawan, R. J. Nathan, and Z. Zoltan, “User innovativeness and fintech adoption in indonesia,” J. Open Innov. Technol. Mark. Complex., vol. 7, no. 3, pp. 1–18, 2021, doi: 10.3390/joitmc7030188.

H. Bang and J. Su, “Who Uses Virtual Wardrobes? Investigating the Role of Consumer Traits in the Intention to Adopt Virtual Wardrobes,” Sustain., vol. 14, no. 3, 2022, doi: 10.3390/su14031209.

D. P. Nugraha et al., “Role of Financial Literacy and Saving Habits on Fintech Adoption post Covid-19,” Etikonomi, vol. 23, no. 1, pp. 63–80, 2024, doi: 10.15408/etk.v23i1.37856.

A. M. Al-Zahrani and T. M. Alasmari, “Exploring the impact of artificial intelligence on higher education: The dynamics of ethical, social, and educational implications,” Humanit. Soc. Sci. Commun., vol. 11, no. 1, pp. 1–12, 2024, doi: 10.1057/s41599-024-03432-4.

A. Polyportis, “A longitudinal study on artificial intelligence adoption: understanding the drivers of ChatGPT usage behavior change in higher education,” Front. Artif. Intell., vol. 6, 2023, doi: 10.3389/frai.2023.1324398.

S. Zhang, X. Zhao, T. Zhou, and J. H. Kim, “Do you have AI dependency? The roles of academic self-efficacy, academic stress, and performance expectations on problematic AI usage behavior,” Int. J. Educ. Technol. High. Educ., vol. 21, no. 1, 2024, doi: 10.1186/s41239-024-00467-0.

E. Çela, M. M. Fonkam, and R. M. Potluri, “Risks of AI-Assisted Learning on Student Critical Thinking,” Int. J. Risk Conting. Manag., vol. 12, no. 1, pp. 1–19, 2024, doi: 10.4018/ijrcm.350185.

S. A. D. Popenici and S. Kerr, “Exploring the impact of artificial intelligence on teaching and learning in higher education,” Res. Pract. Technol. Enhanc. Learn., vol. 12, no. 1, 2017, doi: 10.1186/s41039-017-0062-8.

R. S. Pasupuleti and D. Thiyyagura, “An empirical evidence on the continuance and recommendation intention of ChatGPT among higher education students in India: An extended technology continuance theory,” Educ. Inf. Technol., vol. 29, no. 14, pp. 17965–17985, 2024, doi: 10.1007/s10639-024-12573-7.

S. R. Tummalapenta, R. S. Pasupuleti, R. M. Chebolu, T. V. Banala, and D. Thiyyagura, “Factors driving ChatGPT continuance intention among higher education students: integrating motivation, social dynamics, and technology adoption,” J. Comput. Educ., 2024, doi: 10.1007/s40692-024-00343-w.

C. D. Duong, “Modeling the determinants of HEI students’ continuance intention to use ChatGPT for learning: a stimulus–organism–response approach,” J. Res. Innov. Teach. Learn., vol. 17, no. 2, pp. 391–407, 2024, doi: 10.1108/JRIT-01-2024-0006.

A. A. Daneji, M. N. M. Khambari, and A. F. Mohd Ayub, “Influence of students’ perceived ease of use, perceived usefulness and time spent towards students’ continuance intention using mooc among public university students,” ICCE 2018 - 26th Int. Conf. Comput. Educ. Work. Proc., vol. 115, no. Icems 2017, pp. 576–581, 2018, doi: 10.2991/icems-17.2018.50.

A. Strzelecki, “To use or not to use ChatGPT in higher education? A study of students’ acceptance and use of technology,” Interact. Learn. Environ., pp. 1–14, 2023, doi: 10.1080/10494820.2023.2209881.

H. Jo, “Decoding the ChatGPT mystery: A comprehensive exploration of factors driving AI language model adoption,” Inf. Dev., 2023, doi: 10.1177/02666669231202764.

Q. Pang, M. Zhang, K. F. Yuen, and M. Fang, “When the winds of change blow: an empirical investigation of ChatGPT’s usage behaviour,” Technol. Anal. Strateg. Manag., pp. 1–15, 2024, doi: 10.1080/09537325.2024.2394783.

B. Foroughi et al., “Determinants of Intention to Use ChatGPT for Educational Purposes: Findings from PLS-SEM and fsQCA,” Int. J. Hum. Comput. Interact., vol. 40, no. 17, pp. 4501–4520, 2024, doi: 10.1080/10447318.2023.2226495.

A. Biloš and B. Budimir, “Understanding the Adoption Dynamics of ChatGPT among Generation Z: Insights from a Modified UTAUT2 Model,” J. Theor. Appl. Electron. Commer. Res. , vol. 19, no. 2, pp. 863–879, 2024, doi: 10.3390/jtaer19020045.

R. A. Mahury and Neneng Nurlaela Arief, “The Factors Affecting Continuance Intention of ChatGPT as An AI Chatbot in Indonesia,” Soc. Sci. Stud., vol. 4, no. 2, pp. 103–116, 2024, doi: 10.47153/sss42.8472024.

D. Ramdhony, F. Liébana-Cabanillas, V. D. Gunesh-Ramlugun, and F. Mowlabocus, “Modelling the determinants of electronic tax filing services’ continuance usage intention,” Aust. J. Public Adm., vol. 82, no. 2, pp. 194–209, 2023, doi: 10.1111/1467-8500.12559.

C. F. Y. Chen, T. J. Chan, and N. H. Hashim, “Factor Influencing Continuation Intention of Using Fintech from the Users’ Perspectives: Testing of Unified Theory of Acceptance and Use of Technology (UTAUT2),” Int. J. Technol., vol. 14, no. 6, pp. 1277–1287, 2023, doi: 10.14716/ijtech.v14i6.6636.

L. Nie, B. Oldenburg, Y. Cao, and W. Ren, “Continuous usage intention of mobile health services: model construction and validation,” BMC Health Serv. Res., vol. 23, no. 1, pp. 1–14, 2023, doi: 10.1186/s12913-023-09393-9.

J. Wang, X. Shen, X. Huang, and Y. Liu, “Influencing Factors of the Continuous Usage Intention of Consumers of Online Food Delivery Platform Based on an Information System Success Model,” Front. Psychol., vol. 12, no. August, pp. 1–12, 2021, doi: 10.3389/fpsyg.2021.716796.

N. P. Ghaisani, R. Kannan, F. Basbeth, and J. Selatan, “Consumers’ Intention to Continue Using Cryptocurrency Mobile Wallets in Malaysia,” Int. J. Manag. Financ. Account., vol. 3, no. 2, 2022, doi: 10.1111/j.1468-2370.2006.00130.x.

M. Al Amin, M. S. Arefin, M. R. Alam, T. Ahammad, and M. R. Hoque, “Using Mobile Food Delivery Applications during COVID-19 Pandemic: An Extended Model of Planned Behavior,” J. Food Prod. Mark., vol. 27, no. 2, pp. 105–126, 2021, doi: 10.1080/10454446.2021.1906817.

F. D. Davis, “User acceptance of information systems: the technology acceptance model (TAM),” Management Science, vol. 35, no. 8. pp. 982–1003, 1987.

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

D. Gefen and D. Straub, “The Relative Importance of Perceived Ease of Use in IS Adoption: A Study of E-Commerce Adoption,” J. Assoc. Inf. Syst., vol. 1, no. 1, pp. 1–30, 2000, doi: 10.17705/1jais.00008.

V. Venkatesh and F. Davis, “A theoretical extension of the tecgnology acceptance model: Four longitudinal field studies University of Maryland at College Park,” Manage. Sci., vol. 46, no. 2, pp. 186–204, 2000.

C. Nadal, C. Sas, and G. Doherty, “Technology acceptance in mobile health: Scoping review of definitions, models, and measurement,” J. Med. Internet Res., vol. 22, no. 7, pp. 1–17, 2020, doi: 10.2196/17256.

F. W. Hutomo, “Technology Acceptance Model (TAM) methodology system analysis on mobile banking users,” J. Earth Kingdom, vol. 1, no. 1, pp. 26–37, 2023, doi: 10.61511/jek.v1i1.2023.32.

C. S. Said et al., “Exploring University Students’ Acceptance in Online Learning Using Technology Acceptance Model (TAM),” Int. J. Acad. Res. Progress. Educ. Dev., vol. 11, no. 4, pp. 81–89, 2022, doi: 10.6007/ijarped/v11-i4/15066.

N. Kripanont, “Using a Technology Acceptance Model to Investigate Academic Acceptance of the Internet,” J. Bus. Syst. Gov. Ethics, vol. 1, no. 2, pp. 13–30, 2006, doi: 10.15209/jbsge.v1i2.72.

V. Venkatesh and H. Bala, “Technology Acceptance Model 3 and a Research Agenda on Interventions; Decision Sciences, Vol. 39 (2) pp 273 – 315.,” Decis. Sci. Inst., vol. 39, no. 2, pp. 273–315, 2008.

Sutresno and A. Affandi, “Technology Acceptance Model of ATM Machine Services: A Case Study of MyGraPARI within Analytical Hierarchy Process,” Airlangga J. Innov. Manag., vol. 4, no. 2, pp. 147–157, 2023, doi: 10.20473/ajim.v4i2.49238.

C. Gardner and D. L. Amoroso, “Development of an instrument to measure the acceptance of internet technology by consumers,” Proc. Hawaii Int. Conf. Syst. Sci., vol. 37, no. C, pp. 4143–4152, 2004, doi: 10.1109/hicss.2004.1265623.

R. Fayad and D. Paper, “The Technology Acceptance Model E-Commerce Extension: A Conceptual Framework,” Procedia Econ. Financ., vol. 26, no. 961, pp. 1000–1006, 2015, doi: 10.1016/s2212-5671(15)00922-3.

D. Leyton, J. A. Pino, and S. F. Ochoa, “Modeling technology acceptance in e-Business environments,” Proc. - 2013 IEEE 10th Int. Conf. E-bus. Eng. ICEBE 2013, pp. 90–97, 2013, doi: 10.1109/ICEBE.2013.14.

A. Kemp, E. Palmer, and P. Strelan, “A taxonomy of factors affecting attitudes towards educational technologies for use with technology acceptance models,” Br. J. Educ. Technol., vol. 50, no. 5, pp. 2394–2413, 2019, doi: 10.1111/bjet.12833.

M. R. Mahaputra and M. R. Mahaputra, “The Influence of Perceived Usefulness, Perceived Ease Of Use and Social Influence on Mobile Commerce Usage Activities,” Greenation Int. J. Tour. Manag., vol. 1, no. 1, pp. 8–15, 2023, doi: 10.38035/gijtm.v1i1.17.

Emi Amelia and R. Hurriyati, “ANALISIS PERCEIVED USEFULNESS DAN PERCEIVED EASE OF USE TERHADAP MINAT MENGGUNAKAN MOBILE PAYMENT (Studi pada Pengguna Aplikasi Pembayaran OVO),” Airlangga J. Innov. Manag., vol. 3, no. 2, pp. 123–135, Nov. 2022, doi: 10.20473/ajim.v3i1.39463.

D. M. R. Fachrulamry and H. Hendrayati, “Perceived Ease of Use on Purchase Intention of Mobile Commerce Application,” in Proceedings of the 5th Global Conference on Business, Management and Entrepreneurship (GCBME 2020), 2021, vol. 187, no. Gcbme 2020, pp. 512–516, doi: 10.2991/aebmr.k.210831.100.

F. Effendy, R. Hurriyati, and H. Hendrayati, “Perceived Usefulness, Perceived Ease of Use, and Social Influence: Intention to Use e-Wallet,” in Proceedings of the 5th Global Conference on Business, Management and Entrepreneurship (GCBME 2020), 2021, vol. 187, no. Gcbme 2020, pp. 311–315, doi: 10.2991/aebmr.k.210831.060.

J. Wang and Y. Cao, “Factors Influencing Continuous Intention to Use Telemedicine after the COVID-19 Pandemic in China: An Extended Technology Acceptance Model,” Open J. Soc. Sci., vol. 10, no. 12, pp. 344–359, 2022, doi: 10.4236/jss.2022.1012023.

Y. Song, L. Gui, H. Wang, and Y. Yang, “Determinants of Continuous Usage Intention in Community Group Buying Platform in China: Based on the Information System Success Model and the Expanded Technology Acceptance Model,” Behav. Sci. (Basel)., vol. 13, no. 11, 2023, doi: 10.3390/bs13110941.

H. Jo, “Antecedents of Continuance Intention of Social Networking Services (SNS): Utilitarian, Hedonic, and Social Contexts,” Mob. Inf. Syst., vol. 2022, 2022, doi: 10.1155/2022/7904124.

R. Roslan, A. F. M. Ayub, N. Ghazali, N. N. Zulkifli, S. N. H. M. Latip, and S. S. A. Hanifah, “INVESTIGATING FACTORS THAT AFFECT THE CONTINUANCE USE INTENTION AMONG THE HIGHER EDUCATION INSTITUTIONS’ LEARNERS TOWARDS A GAMIFIED M-LEARNING APPLICATION,” J. Inf. Technol. Educ. Res., vol. 22, pp. 97–128, 2023, doi: 10.28945/5080.

D. Thalia, S. Aliya, M. Gunarto, and S. Helmi, “The Influence of Employee Engagement and Work Environment on Employee Performance at PT. Agronusa Alam Perkasa,” J. Ekon., vol. 13, no. 1, pp. 2278–2290, 2024, doi: 10.54209/ekonomi.v13i01.

M. T. A. Simarmata and I. J. Hia, “the Role of Personal Innovativeness on Behavioral Intention of Information Technology,” J. Econ. Bus., vol. 1, no. 2, pp. 18–29, 2020, doi: 10.36655/jeb.v1i2.169.

N. T. B. Dao and D. N. T. Anh, “the Impact of Personal Innovativeness on the Behavioral Intention To Using Tourism Mobile Applications of Generation Z in Ho Chi Minh City, Vietnam,” Geoj. Tour. Geosites, vol. 55, no. 3, pp. 1186–1197, 2024, doi: 10.30892/gtg.55319-1291.

S. C. Jeong and B. J. Choi, “Moderating Effects of Consumers’ Personal Innovativeness on the Adoption and Purchase Intention of Wearable Devices,” SAGE Open, vol. 12, no. 4, pp. 1–14, 2022, doi: 10.1177/21582440221134798.

A. A. Fauzi and M. L. Sheng, “Ride-hailing apps’ continuance intention among different consumer groups in Indonesia: the role of personal innovativeness and perceived utilitarian and hedonic value,” Asia Pacific J. Mark. Logist., vol. 33, no. 5, pp. 1195–1219, 2020, doi: 10.1108/APJML-05-2019-0332.

A. Amid and R. Din, “Acceptance and Use of Massive Open Online Courses: Extending Utaut2 With Personal Innovativeness,” J. Pers. Learn., vol. 4, no. 1, pp. 57–66, 2021.

J. F. Hair, G. T. Hult, C. Ringle, and M. Sarstedt, A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) - Joseph F. Hair, Jr., G. Tomas M. Hult, Christian Ringle, Marko Sarstedt. 2017.

D. J. Ketchen, “A Primer on Partial Least Squares Structural Equation Modeling,” Long Range Plann., vol. 46, no. 1–2, pp. 184–185, 2013, doi: 10.1016/j.lrp.2013.01.002.

M. A. Memon, T. Ramayah, J. H. Cheah, H. Ting, F. Chuah, and T. H. Cham, “Pls-Sem Statistical Programs: a Review,” J. Appl. Struct. Equ. Model., vol. 5, no. 1, pp. i–xiv, 2021, doi: 10.47263/JASEM.5(1)06.

I. Etikan, “Comparison of Convenience Sampling and Purposive Sampling,” Am. J. Theor. Appl. Stat., vol. 5, no. 1, p. 1, 2016, doi: 10.11648/j.ajtas.20160501.11.

C. H. Huang, “Using pls-sem model to explore the influencing factors of learning satisfaction in blended learning,” Educ. Sci., vol. 11, no. 5, 2021, doi: 10.3390/educsci11050249.

J. F. Hair, W. C. Black, B. J. Babin, and R. E. Anderson, Multivariate Data Analysis (7th ed.). 2014.

G. W. Cheung, H. D. Cooper-Thomas, R. S. Lau, and L. C. Wang, Reporting reliability, convergent and discriminant validity with structural equation modeling: A review and best-practice recommendations, vol. 41, no. 2. Springer US, 2024.

A. Achsa, D. M. Verawati, and I. Novitaningtyas, “Implementation of Marketing Strategy Innovation and Business Model Development: Study of SMEs in Tourism Village,” Airlangga J. Innov. Manag., vol. 4, no. 2, pp. 172–184, 2023, doi: 10.20473/ajim.v4i2.49859.

V. T. T. Nha, “Understanding validity and reliability from qualitative and quantitative research traditions,” VNU J. Foreign Stud., vol. 37, no. 3, pp. 1–10, 2021, doi: 10.25073/2525-2445/vnufs.4672.

J. Henseler, G. Hubona, and P. A. Ray, “Using PLS path modeling in new technology research: Updated guidelines,” Ind. Manag. Data Syst., vol. 116, no. 1, pp. 2–20, 2016, doi: 10.1108/IMDS-09-2015-0382.

M. Sarstedt, J. F. Hair, J. H. Cheah, J. M. Becker, and C. M. Ringle, “How to specify, estimate, and validate higher-order constructs in PLS-SEM,” Australas. Mark. J., vol. 27, no. 3, pp. 197–211, 2019, doi: 10.1016/j.ausmj.2019.05.003.

A. Saxena and T. Doleck, “A structural model of student continuance intentions in ChatGPT adoption,” Eurasia J. Math. Sci. Technol. Educ., vol. 19, no. 12, 2023, doi: 10.29333/ejmste/13839.

C. Yu, J. Yan, and N. Cai, “ChatGPT in higher education: factors influencing ChatGPT user satisfaction and continued use intention,” Front. Educ., vol. 9, no. May, pp. 1–11, 2024, doi: 10.3389/feduc.2024.1354929.

P. Fan and Q. Jiang, “Exploring the Factors Influencing Continuance Intention to Use AI Drawing Tools: Insights from Designers,” Systems, vol. 12, no. 3, pp. 1–27, 2024, doi: 10.3390/systems12030068.

L. Y. K. Wang, S. L. Lew, S. H. Lau, and M. C. Leow, “Usability factors predicting continuance of intention to use cloud e-learning application,” Heliyon, vol. 5, no. 6, p. e01788, 2019, doi: 10.1016/j.heliyon.2019.e01788.

H.-J. Chen, “Assessing the Influence of Optimism on Users’ Continuance Use Intention of ChatGPT: An Expectation-Confirmation Model Perspective,” Int. J. Manag. Stud. Soc. Sci. Res., vol. 06, no. 01, pp. 347–353, 2024, doi: 10.56293/ijmsssr.2024.4831.

M. A. Hafidhuddin and N. Azizah, “The Influence of Perceived Usefulness, Perceived Ease of Use, and Service Quality on Continuance Intention with Satisfaction as an Intervening Variable (Study of Indrive Application Users in Surabaya),” Int. J. Econ., vol. 2, no. 2, pp. 2961–712, 2023, doi: 10.55299/ijec.v2i2.717.

R. U. Ashraf, F. Hou, and W. Ahmad, “Understanding Continuance Intention to Use Social Media in China: The Roles of Personality Drivers, Hedonic Value, and Utilitarian Value,” Int. J. Hum. Comput. Interact., vol. 35, no. 13, pp. 1216–1228, 2019, doi: 10.1080/10447318.2018.1519145.

J. Lu, “Are personal innovativeness and social influence critical to continue with mobile commerce?,” Internet Res., vol. 24, no. 2, pp. 134–159, 2014, doi: 10.1108/IntR-05-2012-0100.

A. Luqman, R. C. Razak, M. Ismail, and M. A. M. Alwi, “The Influence of Individual Characteristics in Predicting Mobile Commerce Usage Activities’ Continuance Intention,” J. Entrep. Bus., vol. 4, no. 2, pp. 54–69, 2016, doi: 10.17687/jeb.0402.05.

A. Ruangkanjanases, S. L. Hsu, Y. J. Wu, S. C. Chen, and J. Y. Chang, “What drives continuance intention towards social media? Social influence and identity perspectives,” Sustain., vol. 12, no. 17, pp. 1–15, 2020, doi: 10.3390/su12177081.

M. A. Kaium, Y. Bao, M. Z. Alam, and M. R. Hoque, “Understanding continuance usage intention of mHealth in a developing country: An empirical investigation,” Int. J. Pharm. Healthc. Mark., vol. 14, no. 2, pp. 251–272, 2020, doi: 10.1108/IJPHM-06-2019-0041.

D. T. Sasongko, P. W. Handayani, and R. Satria, “Analysis of factors affecting continuance use intention of the electronic money application in Indonesia,” Procedia Comput. Sci., vol. 197, no. 2021, pp. 42–50, 2021, doi: 10.1016/j.procs.2021.12.116.

F. Huang and S. Liu, “If I Enjoy, I Continue: The Mediating Effects of Perceived Usefulness and Perceived Enjoyment in Continuance of Asynchronous Online English Learning,” Educ. Sci., vol. 14, no. 8, 2024, doi: 10.3390/educsci14080880.

J. Lu, J. E. Yao, and C. S. Yu, “Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology,” J. Strateg. Inf. Syst., vol. 14, no. 3, pp. 245–268, 2005, doi: 10.1016/j.jsis.2005.07.003.

J. D. Jackson, M. Y. Yi, and J. S. Park, “An empirical test of three mediation models for the relationship between personal innovativeness and user acceptance of technology,” Inf. Manag., vol. 50, no. 4, pp. 154–161, 2013, doi: 10.1016/j.im.2013.02.006.

H. Gangwar, H. Date, and R. Ramaswamy, “Understanding determinants of cloud computing adoption using an integrated TAM-TOE model,” J. Enterp. Inf. Manag., vol. 28, no. 1, pp. 107–130, 2015, doi: 10.1108/JEIM-08-2013-0065.

Downloads

Published

2025-04-20

How to Cite

Satrio Tegar Sadewo, Ratnawati, S. ., Giovanni, A., & Widayanti, I. (2025). The Influence of Personal Innovativeness on ChatGPT Continuance Usage Intention among Students. SATESI: Jurnal Sains Teknologi Dan Sistem Informasi, 5(1), 88–98. https://doi.org/10.54259/satesi.v5i1.4117

Issue

Vol. 5 No. 1 (2025): April 2025

Section

Articles

License

Copyright (c) 2025 Satrio Tegar Sadewo, Shinta Ratnawati, Axel Giovanni, Ipuk Widayanti

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).