Technology Adoption and Peer Influence on Student AI Research Tools Purchase Intention

Authors

  • Zidane Ramadhan State University Jakarta
  • Dr. Osly Usman State Jakarta University

DOI:

https://doi.org/10.21009/ISC-BEAM.013.59

Keywords:

technology adoption, Artificial Intelligence (AI), Consumer Behavior

Abstract

Technological advancement has brought changes into how researchers conduct their research, Artificial Intelligence (AI) is one of the most impactful technologies that change the process. Through the lens of Technology Adoption Model (TAM) Framework, this research is aimed to uncover the relationship between technology adoption factors and peer influence in students’ intention toward buying AI research tools. Previous research is still limited toward explaining technology adoption toward students' purchase intention and neglected social factors such as peer influence, therefore reinforcing the importance of the research. The study will be conducted on 100 State University of Jakarta students and Analysis of the data will use Partial Least Square (PLS) Structural Equation Model to explain the connection between variables.

Author Biography

Dr. Osly Usman, State Jakarta University

Mr. Dr. Osly Usman, M.Bus.Syst is the head of digital business department at state jakarta university. He led this research by guiding the author on ideas, structures, and analysis of the research itself. 

References

Al-Baity, H. (2023). The artificial intelligence revolution in digital finance in saudi arabia: a comprehensive review and proposed framework. Sustainability, 15(18), 13725. https://doi.org/10.3390/su151813725

Al–shami, S., Mamun, A., Ahmed, E., & Rashid, N. (2021). Artificial intelligent towards hotels’ competitive advantage. an exploratory study from the uae. Foresight, 24(5), 625-636. https://doi.org/10.1108/fs-01-2021-0014

Frank, D., Jacobsen, L., Søndergaard, H., & Otterbring, T. (2023). In companies we trust: consumer adoption of artificial intelligence services and the role of trust in companies and ai autonomy. Information Technology and People, 36(8), 155-173. https://doi.org/10.1108/itp-09-2022-0721

Fu, H., Chang, T., Lin, S., Teng, Y., & Huang, Y. (2023). Evaluation and adoption of artificial intelligence in the retail industry. International Journal of Retail & Distribution Management, 51(6), 773-790. https://doi.org/10.1108/ijrdm-12-2021-0610

Ghandour, A. (2021). Opportunities and challenges of artificial intelligence in banking: systematic literature review. Tem Journal, 1581-1587. https://doi.org/10.18421/tem104-12

Huang, W. (2023). Analysis of promotional online shopping behavior based on machine learning. Highlights in Science Engineering and Technology, 56, 65-72. https://doi.org/10.54097/hset.v56i.9817

Lazo, M. (2023). Artificial intelligence adoption in the banking industry: current state and future prospect. Journal of Innovation Management, 11(3), 54-74. https://doi.org/10.24840/2183-0606_011.003_0003

Lee, H. (2023). Ready for robot assistance? exploring gender influences on service robot adoption in luxury vs. economy hotels. Journal of Marketing Development and Competitiveness, 17(4). https://doi.org/10.33423/jmdc.v17i4.6663

Pillai, R. and Sivathanu, B. (2020). Adoption of ai-based chatbots for hospitality and tourism. International Journal of Contemporary Hospitality Management, 32(10), 3199-3226. https://doi.org/10.1108/ijchm-04-2020-0259

Qin, M., Zhu, W., Zhao, S., & Yu, Z. (2022). Is artificial intelligence better than manpower? the effects of different types of online customer services on customer purchase intentions. Sustainability, 14(7), 3974. https://doi.org/10.3390/su14073974

Shin, H. and Jeong, M. (2020). Guests’ perceptions of robot concierge and their adoption intentions. International Journal of Contemporary Hospitality Management, 32(8), 2613-2633. https://doi.org/10.1108/ijchm-09-2019-0798

Yazdani, A. (2023). The impact of ai on trends, design, and consumer behavior. aitechbesosci, 1(4), 4-10. https://doi.org/10.61838/kman.aitech.1.4.2

Rathor, K. (2024). Exploring the challenges and opportunities of implementing artificial intelligence in supply chain management: a survey-based study in asian manufacturing sector. International Research Journal of Modernization in Engineering Technology and Science. https://doi.org/10.56726/irjmets49724

Almarashdeh, I., Sahari, N., Zin, N., & Alsmadi, M. (2011). Acceptance of learning management system: a comparison between distance learners and instructors. International Journal on Advances in Information Sciences and Service Sciences, 3(5), 1-9. https://doi.org/10.4156/aiss.vol3.issue5.1

Askari, M., Klaver, N., Gestel, T., & Klundert, J. (2020). Intention to use medical apps among older adults in the netherlands: cross-sectional study. Journal of Medical Internet Research, 22(9), e18080. https://doi.org/10.2196/18080

Baizal, Z., Widyantoro, D., & Maulidevi, N. (2016). Factors influencing user’s adoption of conversational recommender system based on product functional requirements. Telkomnika (Telecommunication Computing Electronics and Control), 14(4), 1575. https://doi.org/10.12928/telkomnika.v14i4.4234

Dou, K., Yu, P., Deng, N., Liu, F., Guan, Y., Li, Z., … & Duan, H. (2017). Patients’ acceptance of smartphone health technology for chronic disease management: a theoretical model and empirical test. Jmir Mhealth and Uhealth, 5(12), e177. https://doi.org/10.2196/mhealth.7886

Hasan, R., Shams, S., & Rahman, M. (2021). Consumer trust and perceived risk for voice-controlled artificial intelligence: the case of siri. Journal of Business Research, 131, 591-597. https://doi.org/10.1016/j.jbusres.2020.12.012

Jeong, B. and Yoon, T. (2013). An empirical investigation on consumer acceptance of mobile banking services. Business and Management Research, 2(1). https://doi.org/10.5430/bmr.v2n1p31

Li, W. (2024). Social media use and attitudes toward ai: the mediating roles of perceived ai fairness and threat. Human Behavior and Emerging Technologies, 2024, 1-11. https://doi.org/10.1155/2024/3448083

Mafi, S. (2023). Insight into the embrace of artificial intelligence appraisal systems in montessori pedagogy in thailand.. International Journal of Research Publication and Reviews, 4(7), 1503-1510. https://doi.org/10.55248/gengpi.4.723.48878

Na, S., Heo, S., Choi, W., Han, S., & Kim, C. (2023). Firm size and artificial intelligence (ai)-based technology adoption: the role of corporate size in south korean construction companies. Buildings, 13(4), 1066. https://doi.org/10.3390/buildings13041066

Ozturk, A. (2016). Customer acceptance of cashless payment systems in the hospitality industry. International Journal of Contemporary Hospitality Management, 28(4), 801-817. https://doi.org/10.1108/ijchm-02-2015-0073

Yan, H. and Wang, M. (2012). What factors affect physicians’ decisions to use an e-health care system?. Health, 04(11), 1023-1028. https://doi.org/10.4236/health.2012.411156

Van Noorden, R., & Perkel, J. M. (2023). AI and science: what 1,600 researchers think. Nature, 621(7980), 672–675. https://doi.org/10.1038/d41586-023-02980-0OECD (2011), Greening Public Budgets in Eastern Europe, Caucasus and Central Asia, OECD Publishing, Paris, https://doi.org/10.1787/9789264118331-en.

Arief, A. (2023). Age-dependent user perception analysis of web application using technology acceptance model approach: a case study. Technium Romanian Journal of Applied Sciences and Technology, 17, 95-99. https://doi.org/10.47577/technium.v17i.10052

Gefen, D. and Straub, D. (2000). The relative importance of perceived ease of use in is adoption: a study of e-commerce adoption. Journal of the Association for Information Systems, 1(1), 1-30. https://doi.org/10.17705/1jais.00008

Genoveva, G., Syahrivar, J., & Ariestiningsih, E. (2023). Technology readiness during the covid-19 pandemic: lessons learned from indonesia. Commit (Communication and Information Technology) Journal, 17(1), 93-102. https://doi.org/10.21512/commit.v17i1.8068

Hermawan, A., Hurriyati, R., & Hendrayati, H. (2022). Technology acceptance model (tam): an analysis on user of digital statistic platform (lapangbola.com).. https://doi.org/10.2991/aebmr.k.220701.064

Honein-AbouHaidar, G., Antoun, J., Badr, K., Hlais, S., & Nazaretian, H. (2020). Users’ acceptance of electronic patient portals in lebanon. BMC Medical Informatics and Decision Making, 20(1). https://doi.org/10.1186/s12911-020-1047-x

Lee, H., Lee, Y., & Kwon, D. (2005). The intention to use computerized reservation systems: the moderating effects of organizational support and supplier incentive. Journal of Business Research, 58(11), 1552-1561. https://doi.org/10.1016/j.jbusres.2004.07.008

Mustapha, B. and Obid, S. (2015). Tax service quality: the mediating effect of perceived ease of use of the online tax system. Procedia - Social and Behavioral Sciences, 172, 2-9. https://doi.org/10.1016/j.sbspro.2015.01.328

Nofirda, F. and Ikram, M. (2023). The use of artificial intelligence on indonesia online shopping application in relation to customer acceptance., 642-651. https://doi.org/10.2991/978-94-6463-158-6_56

Nurhayati, E., Nurfatimah, S., Syarifudin, S., Nurhayati, N., & Suhendar, D. (2022). Technology acceptance model analysis on software e-financial solutions.. https://doi.org/10.4108/eai.2-12-2021.2320358

Paper, D. and Fayad, R. (2015). E commerce extended tam instrument development.. https://doi.org/10.15224/978-1-63248-081-1-42

Setiawan, M. and Setyawati, C. (2020). The influence of perceived ease of use on the intention to use mobile payment: attitude toward using as mediator. Journal of Accounting and Strategic Finance, 3(1), 18-32. https://doi.org/10.33005/jasf.v3i1.67

Setiawati, E., Trisnawati, R., & Diana, U. (2019). The analysis of acceptance of hospital information management system (hims) using technology acceptance model method. Riset Akuntansi Dan Keuangan Indonesia, 4(2), 186-195. https://doi.org/10.23917/reaksi.v4i2.8652

Sidanti, H., Murwani, F., & Wardhana, E. (2021). Online purchasing intention using the technology acceptance model (tam) approach. Economic Annals-Ххi, 193(9-10), 85-91. https://doi.org/10.21003/ea.v193-10

Widayanto, M., Wang, L., & Syarifah, L. (2023). Analisis faktor-faktor yang mempengaruhi keputusan pembelian. Manajemen Dan Kewirausahaan, 4(1), 29-40. https://doi.org/10.53682/mk.v4i1.5821

Anisafirli, R. and Lusia, A. (2023). Faktor - faktor yang mempengaruhi keputusan pembelian produk di seira skincare. Sentri Jurnal Riset Ilmiah, 2(4), 1322-1337. https://doi.org/10.55681/sentri.v2i4.750

Imelda, Y. (2020). Faktor-faktor yang mempengaruhi penggunaan generalized audit software di kap. Jurnal Paradigma Akuntansi, 2(2), 845. https://doi.org/10.24912/jpa.v2i2.7667

Marfuah, D., Noviyanti, R., & Khotimah, F. (2022). Hubungan suhu makanan dan cara penyajian makanan dengan tingkat kepuasan makanan di catering betty karanganyar. Jurnal Kesehatan Dan Kedokteran, 1(1), 1-8. https://doi.org/10.56127/jukeke.v1i1.570

Olii, N. and Yusuf, N. (2021). Faktor-faktor yang memengaruhi keputusan nasabah menggunakan pegadaian syariah pada cabang pegadaian syariah (cps) datoe binangkang. Kunuz Journal of Islamic Banking and Finance, 1(1), 35-43. https://doi.org/10.30984/kunuz.v1i1.24

Hadewia, S. (2022). Analisis kesulitan belajar siswa pada mata pelajaran kimia kelas xi di man 2 kota palu. Jurnal Kolaboratif Sains, 5(10), 701-705. https://doi.org/10.56338/jks.v5i10.2834

Jordy dan Laksmidewi. (2022). Faktor-faktor pendorong intensi membeli produk vegan Jurnal manajemen maranatha, doi:10.28932/jmm.v22i1.5162 2.

Marsyain Adaptasi Penggunaan Teknologi E-Commerce Produk Segar dan Olahan Organik pada Generasi X, Y, dan Z (Studi Kasus Generasi X, Y, dan Z di Kota Bandung dan DKI Jakarta), Mimbar agribisnis jurnal pemikiran

Downloads

Published

2025-04-24

How to Cite

Ramadhan, Z., & Usman, O. (2025). Technology Adoption and Peer Influence on Student AI Research Tools Purchase Intention. International Student Conference on Business, Education, Economics, Accounting, and Management (ISC-BEAM), 3(1), 852–871. https://doi.org/10.21009/ISC-BEAM.013.59

Most read articles by the same author(s)

1 2 3 4 > >>