Correspondence to : febriwidianto22@gmail.com Received: March, 12, 2020 Accepted: March, 21, 2020 Published: March, 30, 2020 JURNAL AKUNTANSI, MANAJEMEN DAN EKONOMI Vol 22, No 1, pp.12-20 Published online in http://jos.unsoed.ac.id/index.php/jame ISSN: 1410-9336 / E-ISSN: 2620-8482 INTRODUCTION Along with the rapid development of science and technology in Indonesia, many new creative industries are emerging. One of them is the application-based or online transportation industry. According to ComScore data as of December 2017, there were 15.73 million people using online transportation applications in Indonesia. Indonesia has several companies engaged in the field of application-based or online transportation, both car, and motorcycle- based. In Purwokerto, the online transportation industry only began to develop in 2017, marked by the entry of one of the online transportation companies, namely PT Aplikasi Karya Anak Bangsa, or better known as Gojek Indonesia. In Purwokerto itself online transportation is still very new. In receiving a new information technology-based service, not all people are able to easily understand and accept the new service. Therefore it is necessary to measure the level of acceptance and understanding in using services by measuring the behavior of its users (Jogiyanto, 2007). In behavioral information systems, there are several theories that can be used to study and measure user behavior in receiving information systems. And one of the most frequently used theories is the Technology Acceptance Model (Jogiyanto, 2007). Technology Acceptance Model (TAM) is a theory that was first introduced by Fred D. Davis, Jr. in 1986. According to Davis (1986), this theory has two main constructs, that construct is perceived usefulness and perceived ease of use (perceived ease of use). This model argues that the acceptance of information technology systems is determined by these two constructs (Davis, 1986). The perception of usefulness and perceived convenience is a belief about the decision making process. Thus if someone feels that the information system is useful and easy to use, he will use it. Conversely, if someone feels that the information system is less useful and difficult to do so he will not use it. Perceived usefulness and perceived ease of use both have an influence on behavioral intention. Technology users will have an interest in using technology if they find the technology system useful and easy to use. Perceived ease of use also affects perceived usefulness but not vice versa. System users will use the system if the system is useful whether the system is easy to use or difficult to use. Systems that are difficult to use will still be used if the user feels that the system is still useful (Jogiyanto, 2007). While the quality variable is one of the external variables that was first proposed by Venkatesh & Davis (2000). Technology Acceptance Model on Gojek Applications in Purwokerto Yohanes Febri Widianto1, Haryadi2, Devani Laksmi Indyastuti3 1,2,3 Fakultas Ekonomi dan Bisnis, Universitas Jenderal Soedirman Abstract This research is a quantitative study on the application of Gojek in Purwokerto. The purpose of this study was to analyze the acceptance of Gojek applications in Purwokerto using the Technology Acceptance Model. This study uses the Structural Equation Modeling analysis method using the SmartPLS3 application. The results of the analysis show that (1) Perceived ease of use has a positive effect on perceived usefulness, (2) Perceive usefulness is not significant and has a negative effect user attitudes, (3) Perceived ease of use has a positive effect on user attitudes, (4) Quality has a positive effect on user attitudes,(5) Perceived usefulness is not significant and has a negative effect on behavior to use, (6) User attitude positively effect behavior to use. Keywords Technology Acceptance Model, Structural Equation Modeling, SmartPLS, Gojek JURNAL AKUNTANSI, MANAJEMEN DAN EKONOMI, VOL 22, N0 1, 2020, 12-20 Pada penelitian yang diterbitkan Suhud, Wibowo, Khairi, dan Wilson (2019) dan Normalini (2019). Sementara pada penelitian yang dilakukan Adhiputra (2015) dan Salloum (2018) menunjukkan pengaruh positif antara persepsi kegunaan terhadap sikap pengguna. Dari hasil penelitian yang dilakukan Li & Zhang (2002) dan Chotimah (2017) menunjukkan kualitas yang positif terhadap sikap pengguna. Penelitian sebelumnya Maharidho, Haryono, dan Wahyono (2018) dan Normalini (2019) menyatakan bahwa persepsi mengenai penggunaan positif terhadap penggunaan teknologi. Penelitian ini juga untuk mempelajari kembali penelitian Mulyani & Kurniadi (2015) dan Suhud et, al. (2019) dan juga penelitian yang dilakukan oleh Mulyani & Kurniadi (2015) dan Rahayu, Budiyanto, dan Palyama (2017). Hasil penelitian Mulyadi & Kurniadi (2015) menyatakan bahwa persepsi tersebut mengenai negatif terhadap sikap pengguna, sedangkan dalam penelitian Suhud et, al. (2019) menunjukkan perbedaan positif antara persepsi positif terhadap sikap pengguna. Hasil penelitian yang dilakukan oleh Mulyadi & Kurniadi (2015) menunjukkan tidak adanya perbedaan antara sikap terhadap penggunaan dalam teknologi, sedangkan dalam penelitian Rahayu et, al. (2017) menunjukkan pengaruh positif antara sikap terhadap hubungan dalam menggunakan teknologi. LITERATURE REVIEW Classical Growth Theory Behavior is an action carried out by someone. In the context of the use of information technology systems, behavior (use) is a reality (actual use) of technology (Jogiyanto, 2007). Factors that influence the purchase, use, of goods and services through theories that affect consumers, lifestyles, consumer personalities, consumer knowledge, consumer attitudes, demographics, and consumer environment (Sumarwan, 2004). According to Peter & Olson (2014) in the consumption process, consumers consider the environment, affection, cognition, consumer knowledge, and consumer attitudes in deciding the purchase and use of goods or services. Applications are types of software both on the computer or on the smartphone. Software is a program that contains a line of instructions (commands) sent in computer language that is understood by computer hardware (Sutarman, 2009). An application is a program that is commonly used by users to perform specific tasks; for example to make documents, manipulate photos, or make financial reports (Kadir & Triwahyuni, 2013). The Technology Acceptance Model or TAM is an acceptance model developed by Fred D. Davis, Jr. in 1986 which was adapted from the Reasoned Action Theory or TRA proposed by Ajzen and Feishbein in 1980. This Technology Acceptance Model is the most commonly used theory used if you want to measure the level of user acceptance (Jogiyanto, 2007). Davis (1986) states that the Technology Acceptance Model (TAM) places the attitude factor of each user relationship with two variables namely; usability (usability), and ease of use (ease of use). Empirically this model has been shown to provide an overview of aspects of computer use that can easily accept information technology that is in accordance with what it wants (Jogiyanto, 2007). In the Technology Acceptance Model (TAM) there are 2 construct variables that can influence user behavior, namely perceived ease of use and perceived usefulness. Both of these variables can explain aspects of user behavior. So that by seeing the ease and usability, the use of information technology can be used as a reason for someone to behave or act as a benchmark in accepting an information technology (Davis, 1986). The easier use of information technology indicates that less effort must be done to improve its performance using information technology. Similarly, the more benefits felt by users, will have a greater influence in using information technology. (Jogiyanto, 2007). Zarmpou (2012) in Maharidho et al. (2015) said that the most influential variable on user acceptance of technology especially mobile-based technology was perceived usefulness and perceived ease of use, trustworthiness, innovation, functionality, and behavioral interest. Davis (1986) argues, the definition of perceived usefulness as the extent to which an individual believes using a technology will improve his work performance. If the individual finds the information media useful then he will use it. Conversely, if the individual thinks the media information is less useful then he will not use it. According to Aditya & Wardhana (2016) percieved usefulness is defined as the usefulness of a technology so that if the usefulness of a technology is doubtful, the intention of people to use it will decrease or not even appear. JURNAL AKUNTANSI, MANAJEMEN DAN EKONOMI, VOL 22, N0 1, 2020, 12-20 According to Davis (1986), the definition of perceived ease is the extent to which individuals believe that the technology used can be used without requiring great effort. If the individual considers the information media easy to use then he will use it. Conversely, if an individual considers the media information is not easy to use then he will not use it. Perceived ease is defined as the extent to which a person believes that using a particular technology can be free from both physical and mental effort (Falode, 2018). Attitudes toward use are defined by Davis et al. (1989) in Jogiyanto (2007) as positive or negative feelings from someone if they have to do the behavior that will be determined. Attitudes toward use are also defined by Mathieson (1991) as the user's evaluation of his interest in using the system. According to Tubalawony (2015) Attitudes towards use in the Technology Acceptance model (TAM) are conceptualized as attitudes towards the use of systems in the form of acceptance or rejection as an impact when someone uses a technology in work. Quality is a dynamic condition related to products, services, human resources, processes, and the environment that meets or exceeds expectations (Tjiptono, 2005). According to Venkatesh & Davis (2000) information quality is a perception of how well the system works to achieve the goals desired by users. According to Normalini (2019) the quality of information systems focuses on the performance of information system components, namely how well the capabilities of hardware, software, human, procedures, databases, communication networks, data, activities, networks, and technology of information systems in producing information for the the user. The usefulness or benefits of a technology will not be separated from the ease of use. To maximize the benefits provided by a technology, the technology must be able to be operated without too much effort both physically and mentally. Previous studies have shown that the construct of ease of use affects perceptions of usability, attitudes, interests, and actual use (Jogiyanto 2007). Suhud et al. (2019) conducted research on the Technology Acceptance model in online transportation applications by using variables of perceived convenience, perceived usefulness, attitudes, and behavior. The study concluded that the perception of ease has a positive influence on perceived usefulness. Similar results were obtained by Normalini (2019) who conducted research with a similar model. H1: Perceived ease has a positive effect on perceived usefulness. Previous studies have shown that the construct of perceived usefulness positively and significantly influences the use of information systems. Previous studies have also shown that perceived usefulness is the most significant and important construct that influences attitudes in using technology compared to other constructs (Jogiyanto, 2007). In a study conducted by Adhiputra (2015) shows that perceived usefulness has a positive and significant effect on user attitudes. The research was conducted using the Technology Acceptance Model on bank customers using internet banking. The same result was obtained by Salloum (2018) who conducted research using the Technology Acceptance Model on e-Learning in Dubai. H2: Usability perception has a positive effect on user attitude. Ease of operation of a technology is how much the user spends effort to get what he wants from the technology. If the technology is too complex and difficult for the user to use, the user's intention will tend to reject the technology. Jogiyanto (2007) states that previous studies show that the construct of perceived ease influences user attitudes. Mulyani & Kurniadi (2015) conducted a study on the Student Information Terminal at AMIK arrowroot using a technology acceptance model and found that ease of perception variables were not proven to significantly influence user attitudes. Different results obtained by Suhud et al. (2019) which states that perceived ease has a positive effect on user attitudes. H3: Perceived ease has a positive effect on user attitudes. Zhang & Dran (2000) evaluate software quality from the perspective of user satisfaction and dissatisfaction. Their study shows that software design features can be considered cleanliness and motivating factors that contribute to dissatisfaction and user satisfaction with software. According to Li & Zhang (2002) motivational factors in using an information technology are: enjoyment, results, user empowerment, credibility, visual appearance, and the content of the information provided. Li & Zhang (2002) get results that web quality has a positive and significant effect on user attitudes. The better the quality offered by the website, it will help consumers to complete transactions smoothly JURNAL AKUNTANSI, MANAJEMEN DAN EKONOMI, VOL 22, N0 1, 2020, 12-20 and make them want to use it again. Similar results were also found by Chotimah (2017) who conducted research on mobile applications that website quality has a positive effect on user attitudes. H4: Quality has a positive effect on user attitude Users may not have a strong motivation to adopt a technology unless the service offered by the technology creates a new choice where the service actually provides more benefits than conventional methods. According Jogiyanto (2007) previous studies showed that the construct of perceived usefulness positively and significantly affected the use of information systems. In research conducted by Maharidho et al. (2018) there is a positive influence between perceived usefulness of behavior to use a technology. The research was conducted using a technology acceptance model in streaming music applications. Similar results were obtained by Normalini (2019) using the Technology Acceptance Model. H5: Perceived utility has a positive effect on behavior to use technology. Attitudes affect user behavior based on an individual's belief in a particular technology and also the evaluation of that individual. User behavior will depend on the attitude of the user. The results of previous studies indicate that attitude has a positive effect on behavior. However, some research also shows that this attitude does not have a significant influence on behavioral interest. (Jogiyanto, 2007). Fathema et al. (2015) got results after conducting research on Learning Management Systems at tertiary institutions that attitudes had a positive effect on behavior in using technology. The same results were obtained by Rahayu et al. (2019) after conducting research using the Technology Acceptance Model on e-Learning systems at Atma Jaya University Yogyakarta. Whereas different results were found Mulyani & Kurniadi (2015) which said that user attitudes had a negative effect on behavior. H6: User Attitude positively influences behavior in using technology. RESEARCH METHODS This type of research is a quantitative study, which is based on philosophy, positivism, used to examine populations or certain samples. Data collection using research instruments, analysis, statistical data with the aim of testing the hypothesis set (Sugiyono, 2005). The method used is the survey method using a questionnaire distributed to respondents as a means of collecting data for analysis. The location of this research will be conducted in the city of Purwokerto, Kab. Banyumas, Central Java. The object of this research is the effect of perceived convenience variables on perceived usefulness, the effect of perceived convenience variables, perceived usefulness, and the quality of attitudes to use technology, as well as its influence on behavior in using technology. The subjects of this study are online transportation users who have used the Gojek application in the city of Purwokerto. The population in this study are application-based or online transportation users in Purwokerto City. Indicators in this study amounted to 23 indicators, so that at least require 23 x 5 or 115 samples. To avoid mistakes like answers not being filled properly, as many as 15 questionnaires will be added. The sampling method in this study was carried out by the convenience sampling method, which is a sample technique based on the availability of elements and the ease of getting it. The sample was taken because the sample is in the right place and time and suitable as a source of data (Sugiyono, 2015). In this study the sample criteria used were respondents who had used the Gojek application in Purwokerto. The data used in this study are primary data in the form of a questionnaire about perceived ease of use, perceived usefulness, quality, attitude toward use, and behavior in using technology. Data collection is done by distributing questionnaires conducted by providing a Google Form link to the intended respondent to be filled out through the respondent's equipment. This research uses Structural Equation Modeling with the help of SmartPLS3 application. The first thing to test is factor analysis, then tests of convergent and discriminant validity and reliability testing will be conducted. Then after conducting a validity and reliability test a Path Analysis will be conducted, then after that a Goodness of Fit test will be conducted, then a hypothesis test will be conducted. DISCUSSION The total number of questionnaires from respondents to be processed amounted to 115. Respondents were dominated by respondents aged 17-30 years as many as 62 people or 53.9%. Followed by respondents aged 31-55 years as many as 47 people or 40.9%. The rest are respondents aged <17 JURNAL AKUNTANSI, MANAJEMEN DAN EKONOMI, VOL 22, N0 1, 2020, 12-20 years with a total of 6 people or 5.2%. This shows that most Gojek application users are young respondents who often use existing technologies to support their activities and always follow the development of existing technology, especially in the City of Purwokerto. Followed by respondents who were older, namely between 31-55 years who did not really follow the development of technology and rarely use it in everyday life. Factor analysis is part of the Structural Equation Modeling (SEM) stage which aims to test whether the factors used are able to describe or represent a variable. A factor or indicator is said to be able to describe a variable if the loading factor value (λ)> 0.5 or> 0.7 (Sugiyono, 2015). Following are the results of calculation of factor analysis using the SmartPLS application: Loading Factor ATT BEHAVIOR PEU PU QUALITY ATT1 0,881 ATT2 0,896 ATT3 0,742 ATT4 0,864 BV1 0,902 BV2 0,883 BV3 0,904 BV4 0,883 BV5 0,792 PEU1 0,779 PEU2 0,850 PEU3 0,837 PEU4 0,792 PEU5 0,872 PU1 0,820 PU2 0,853 PU3 0,833 PU4 0,769 PU5 0,747 Q1 0,724 Q2 0,737 Q3 0,818 Q4 0,737 From the calculation of the table above, it can be seen that each factor or indicator used is able to describe each variable that is formed properly because each loading factor value generated is above 0.7. The indicator that has the largest loading factor value is the BV3 indicator or the indicator that forms the third behavior variable of 0.904. So the BV3 indicator can describe the behavior variable of 90.4%. While the indicator with the smallest loading factor value is the Q1 indicator or the indicator that forms the first quality variable of 0.724. So the indicator Q1 can describe the quality variable of 72.4%. Convergent validity is related to the principle that the meters of a construct should be highly correlated. Convergent validity test parameters seen from the Average Variance Extracted (AVE) value must be above 0.5. Convergent validity test results are as follows: AVERAGE VARIANCE EXTRACTED (AVE) ATT 0,719 BEHAVIOR 0,763 PEU 0,684 PU 0,649 QUALITY 0,570 From the calculation results in the above table it can be concluded if the Perceived of Usefulness variable has a AVE value of 0.649 where this value is above 0.5, so the convergent validity can be said to be valid. Furthermore, the Perceived Ease of Use variable has a AVE value of 0.684 where this value is above 0.5, so convergent validity can be said to be valid. Quality variable which has a AVE value of 0.570 where this value has also exceeded 0.5, convergent validity can be said to be valid. The four variables Attitude user (Attitude) which has a AVE value of 0.719 which is already above 0.5, so the convergent validity can be said to be valid. The last variable, Behavior, has a AVE value of 0.763 where this value has also exceeded 0.5, so convergent validity can be said to be valid. It can be concluded that each construct has good convergent validity because each construct has an Average Variance Extracted (AVE) value above 0.5. Discriminant validity can be seen from the higher cross loading values for the constructs it measures compared to the cross loading values in other constructs. Cross Loading ATT BEHAVIOR PEU PU QUALITY ATT1 0,881 0,777 0,779 0,606 0,560 ATT2 0,896 0,737 0,758 0,637 0,663 ATT3 0,742 0,694 0,528 0,346 0,432 ATT4 0,864 0,731 0,715 0,538 0,638 BV1 0,769 0,902 0,745 0,526 0,525 BV2 0,788 0,883 0,781 0,550 0,558 BV3 0,773 0,904 0,750 0,452 0,514 BV4 0,771 0,883 0,697 0,475 0,559 BV5 0,675 0,792 0,616 0,521 0,581 PEU1 0,624 0,580 0,779 0,610 0,519 PEU2 0,705 0,746 0,850 0,451 0,470 PEU3 0,673 0,752 0,837 0,443 0,442 JURNAL AKUNTANSI, MANAJEMEN DAN EKONOMI, VOL 22, N0 1, 2020, 12-20 PEU4 0,677 0,640 0,792 0,603 0,598 PEU5 0,733 0,695 0,872 0,614 0,650 PU1 0,580 0,485 0,625 0,820 0,558 PU2 0,561 0,532 0,598 0,853 0,583 PU3 0,507 0,417 0,487 0,833 0,615 PU4 0,448 0,418 0,413 0,769 0,547 PU5 0,444 0,456 0,514 0,747 0,591 Q1 0,440 0,475 0,477 0,468 0,724 Q2 0,456 0,474 0,433 0,399 0,737 Q3 0,526 0,467 0,470 0,447 0,818 Q4 0,603 0,472 0,573 0,784 0,737 From the calculation results in the above table, it can be concluded that each cross loading value for the construct that is measured is higher than the cross loading value for other constructs. Then the discriminatory validity is said to be good. For the reliability test the parameter is the Cronbach’s Alpha value must be higher than 0.6 and Composite Reliability must be above 0.7. The results of the reliability test are as follows: Reliability Test CRONBACH'S ALPHA COMPOSITE RELIABILITY ATT 0,868 0,911 BEHAVIOR 0,922 0,941 PEU 0,884 0,915 PU 0,864 0,902 QUALITY 0,750 0,841 From the reliability test results in table 4.7, each construct has a Cronbach's Alpha value above 0.6 and a Composite Reliability value above 0.7. Then it can be concluded that each construct tested is reliable. The results of the path analysis or Path Analysis using SmartPLS get results as shown below: The effect of PEU variable on PU was 0.663. The effect of PU variable on ATT was 0.043. The effect of PEU variables on ATT was 0.651. The effect of the Q variable on ATT is 0.224. The influence of PU variable on Behavior is 0.044. The influence of ATT variable on Behavior is 0.838. The biggest Loading Factor value on the PU variable is on the PU2 indicator of 0.853. The smallest Loading Factor value on the PU variable is on the PU5 indicator of 0.747. The largest Loading Factor value on the PEU variable is on the PEU5 indicator of 0.872. The smallest Loading Factor value on the PEU variable is on the PEU1 indicator of 0.779. The biggest Loading Factor value on the Quality variable is in the Q3 indicator of 0.818. The smallest Loading Factor value on the Quality variable is in the Q1 indicator of 0.724. The biggest Loading Factor value on the ATT variable is on the ATT2 indicator of 0.896. The smallest Loading Factor value on the ATT variable is on the ATT3 indicator of 0.742. The biggest Loading Factor value on the Behavior variable is on the BV3 indicator of 0.904. The smallest Loading Factor value on the Behavior variable is on the BV5 indicator of 0.792. The biggest Loading Factor value of the whole model is in the BV3 indicator of 0.904. The smallest Loading Factor value of the entire model is in the Q1 indicator of 0.724. Goodness of Fit CRITERIA VALUE INFORMATION SRMR ≤ 0,100 0,100 Fit D_UL S > CI (1,5) 2,734 Fit D_G > CI (0,8) 1,299 Fit CHI- SQUA RE < Chi- Square tabel (135,5) 706,146 Not Fit NFI Between 0 and 1 0,701 Fit Goodness of Fit test results above indicate that all criteria have met the criteria except Chi-Square. Modifications to the model have been made but the results of Chi-Square still do not meet the criteria, so results that do not fit these criteria are tolerated. GoF test results for Chi-Square are tolerated because all indicators used have met the CFA criteria which must be above 0.7, so deletion of indicators is not possible because the whole has fulfilled the existing criteria. From the results of the construct validity test both convergent and discriminant and the reliability test has met the existing criteria so that the JURNAL AKUNTANSI, MANAJEMEN DAN EKONOMI, VOL 22, N0 1, 2020, 12-20 model is said to be valid and reliable. For these reasons, GoF test results for Chi-square that are not fit are tolerated. To conduct a hypothesis test, a hypothesis is accepted if the T-Statistics value from the calculation results is greater than 1.64. Here are the results of calculations to test the hypothesis: Hypothesis Test Results T STATISTICS P VALUES INFORMATION ATT -> BEHAVIOR 19,965 0,000 Accepted PEU -> ATT 9,343 0,000 Accepted PEU -> PU 12,822 0,000 Accepted PU -> ATT 0,653 0,257 Rejected PU -> BEHAVIOR 0,829 0,204 Rejected QUALITY - > ATT 3,100 0,001 Accepted H1 - Perceived Ease of Use has a positive effect on Perceived of Use Based on the results of hypothesis testing, the T-Statistics value in the table above is 12.822 and the P value is 0.000. The results of these two values conclude that Perception of ease is significant and has a positive effect on Perception of usability, because it meets the T-statistic requirements above 1.64 and P below 0.05. Thus it can be said that the results of hypothesis 1 in this study were accepted. H2 - Perceived of Use has a positive effect on user's attitude Based on the results of hypothesis testing, the T-Statistics value in the table above is 0.653 and the P value is 0.257. The results of these two values conclude that the perception of usability is not significant and negatively affects the user's attitude, because it does not meet the T-statistic requirements above 1.64 and P below 0.05. Thus it can be said that the results of the hypothesis 2 test in this study were rejected. H3 - Perceived Ease of Use has a positive effect on user's attitude Based on the results of hypothesis testing, the T-Statistics value in the table above is 9,343 and the P value is 0,000. The results of these two values conclude that Perceived ease is significant and has a positive effect on user attitudes because it meets the T-statistic requirements above 1.64 and P below 0.05. Thus it can be said that the results of hypothesis 3 in this study were accepted. H4 - Quality has a positive effect on User Attitude Based on the results of hypothesis testing, the T-Statistics value in the table above is 3.100 and the P value is 0.001. The results of these two values conclude that Quality is significant and has a positive effect on User Attitudes, because it meets the T-statistic requirements above 1.64 and P below 0.05. Thus it can be said that the results of hypothesis 4 test in this study were accepted. H5 - Perceived Usefulness has a positive effect on Behavior Based on the results of hypothesis testing, the T-Statistics value in the above table is 0.829 and the P value is 0.204. The results of these two values conclude that the perception of usability is not significant and has a negative effect on behavior to use, because it does not meet the T-statistic requirements above 1.64 and P below 0.05. Thus it can be said that the results of the hypothesis 5 test in this study were rejected. H6 - The user's attitude has a positive effect on Behavior in using (Behavior) Based on the results of hypothesis testing, the T-Statistics value in the table above is 19,965 and the P value is 0,000. The results of these two values conclude that the user's attitude is significant and has a positive effect on behavior in using, because it meets the T- statistic requirements above 1.64 and P below 0.05. Thus it can be said that the results of the hypothesis 6 test in this study were accepted. CONCLUSIONS Based on the results of the analysis and discussion of the Technology Acceptance Model on the Gojek application in Purwokerto, the conclusion is the ease in operating the Gojek application also influences the results obtained from the use of the application. The ease of use of the Gojek application affects the benefits of the application to be more effective, faster, easier, and minimize the level of error in using the application. Users of the Gojek application in Purwokerto feel that the use of the application does not provide effectiveness and efficiency to them. This is because users prefer to use a personal vehicle rather than using a motorcycle taxi because it is considered faster and more efficient. Users of the Gojek application use more of the additional facilities offered by the application such as GoFood. GoFood is an additional facility offered by the Gojek application for Delivery Orders for food or drinks ordered by users. So users tend not to accept the Gojek application when viewed in terms of the benefits provided to them. JURNAL AKUNTANSI, MANAJEMEN DAN EKONOMI, VOL 22, N0 1, 2020, 12-20 When Gojek application users in Purwokerto feel that the application is not too difficult to use and easy to learn they will have an accepting attitude towards the Gojek application. When users feel that the technology is easy to use they will feel comfortable so the attitude they give towards the technology will tend not to reject the use of the Gojek application. When the application or technology offered is of good quality in terms of the information offered and the system provided, users of the Gojek application will tend to accept the technology. When users in Purwokerto feel that the application is of good quality and not made improperly, users will feel happy and comfortable when using it so they will accept the application. Lack of interest in Gojek application users in Purwokerto to use the Gojek application to order ojeg online. Users prefer to use a private vehicle rather than order a motorcycle taxi on the Gojek application. This is considered more effective and efficient for them to carry out their activities. Behavior in using the Gojek application in Purwokerto is more influenced by his attitude, ie the user feels comfortable and happy using it, regardless of whether the application provides benefits or not. Users of the Gojek application in Purwokerto use the Gojek application on the basis of their pleasure, comfort, and love for the application. If the user feels comfortable and happy using the application, then the user will tend to use the Gojek application for other things. In addition, the Gojek application users in Purwokerto will motivate others both friends and family who may have never used the Gojek application to try to use it.. REFERENCES Adhiputra, M. W. (2015). 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