TEM Journal. Volume 9, Issue 3, Pages 1221‐1226, ISSN 2217‐8309, DOI: 10.18421/TEM93-50, August 2020. TEM Journal – Volume 9 / Number 3 / 2020. 1221 How Technology Acceptance Model (TAM) Factors of Electronic Learning Influence Education Service Quality through Students’ Satisfaction Mar’atus Sholikah 1 , Sutirman Sutirman 2 1 Universitas Negeri Yogyakarta, Yogyakarta, Indonesia 2 Faculty of Economics, Universitas Negeri Yogyakarta, Yogyakarta, Indonesia Abstract – The objectives of the study is figuring out of the results of adoption of electronic learning systems (BeSmart) using Technology Acceptance Model (TAM) factors and servqual models to measure the education services. The data was obtained using SEM with AMOS 2.4. From the findings of the study, note that EoU and U of electronic learning systems hava a positive and significant effect on ESQ through student satisfaction. These findings contribute to university management to understand that service quality needs to pay attention to Ease of Use, Usefulness, and student satisfaction. This research provides insights into the importance of improving the quality of service in education and students’ satisfaction, especially in the provision of service in learning and teaching field. As such, the study has implications for teaching and learning practice in higher education institution, and suggests recommendations for further research. Keywords – service quality, student satisfaction, convenience, usefulness 1. Introduction The development of technology and information greatly affect the quality of education. Therefore, DOI: 10.18421/TEM93-50 https://doi.org/10.18421/TEM93-50 Corresponding author: Mar’atus Sholikah, Postgraduate Student, Universitas Negeri Yogyakarta, Yogyakarta, Indonesia. Email: maratussholikah.2019@student.uny.ac.id Received: 25 June 2020. Revised: 11 August 2020. Accepted: 17 August 2020. Published: 28 August 2020. © 2020 Mar’atus Sholikah & Sutirman Sutirman; published by UIKTEN. This work is licensed under the Creative Commons Attribution‐ NonCommercial‐NoDerivs 4.0 License. The article is published with Open Access at www.temjournal.com educators are encouraged to utilize technology in their teaching as a tool to facilitate learning or as a means for formative assessment [1], [2]. Judging from the current educational trends, many universities in Indonesia use online learning systems such as e-learning with the aim of improving student learning outcomes, student satisfaction, and the quality of educational services [3]. E-learning is defined as a system in education that applies electronic applications to encourage the process of learning so that what is taught is fully conveyed to students who receive it. E-learning applications use internet, computer networks or standalone computers as an operating tool with the lecturer as the main actor, so that lecturers in this case must understand how to operate it [4]. The adoption of electronic learning like BeSmart has been shown to improve student performance [5]. However, not all learning processes carried out by lecturers and students use the portals that have been provided. In fact, Cheung & Hew (2015); Geng, Law & Niu (2019) revealed that online lectures can enhance the quality of educational services and outcomes of student learning [6], [7]. Improving the quality of service through the use of BeSmart must be supported by the intention to use the learning media. Intention is defined as the desire to conduct behavior [8]. Skiner defines behavior as a response or reaction to a stimulus (stimulation from outside). This is in accordance with Planned Behavior Theory (PBT) which affirms that behavior is an action carried out based on the factors that influence it. Thus, user behavior in this case is the key to success in implementing the use of the system or technology. Much of the literature has discussed the factors associated with the process of adoption of information technology. The model of technology acceptance or TAM is one of the most dominating models of research. TAM consists of several variables that explain behavioral intentions and the use of technology both directly and indirectly. Schepers and Wetzels (2007) divide the TAM