Australasian Journal of Educational Technology, 2023, 39(2). 47 Investigating university students’ online proctoring acceptance during COVID-19: An extension of the technology acceptance model Xinyu Jiang, School of Education, Hubei University, Wuhan, China Tiong-Thye Goh School of Information Management, Victoria University of Wellington, New Zealand Xinran Chen School of Foreign Languages, Hubei University, Wuhan, China Mengjun Liu, Bing Yang School of Education, Hubei University, Wuhan, China To ensure the normal operation of teaching and meet the needs of teaching quality assessment in the COVID-19 situation, universities in various countries have adopted online proctoring for assessment. The epidemic has accelerated the development of online education. Online proctoring, as an integral part of future online teaching, has not yet drawn sufficient attention. To understand students’ experiences and attitudes towards initial online proctoring, an extended technology acceptance model was utilised to examine the motivations and barriers that influence students’ online proctoring acceptance in terms of technology perception, presence and social influence. Structural equation models were used to analyse data from a questionnaire survey of 760 university students. Results revealed that social influence, social presence and perceived usefulness are the significant predictors of online proctoring acceptance. Social influence and social presence have significant positive effects on online proctoring acceptance through perceived usefulness, and social presence has a positive effect on perceived ease of use. However, perceived ease of use has a significant negative effect, while place presence has no significant effect. Implications, limitations and future work are discussed at the end. Implications for practice or policy: • Online proctoring organisers can bring a better exam experience to students by ensuring the flexibility and integrity of online proctoring. • Online proctoring workers can improve students' exam experience by building a positive group atmosphere in the early stages of online proctoring applications. • Social recognition and support for online proctoring can enhance students' choice and willingness to use online proctoring and increase opportunities for online proctoring development. Keywords: online proctoring, technology acceptance model (TAM), social influence, perceived usefulness, social presence Introduction In the face of the unexpected COVID-19 pandemic, online instruction can help students avoid threats to their physical health and address the challenges that have hampered traditional offline instruction. In addition to the focus on student activities and quality of instruction, measuring student performance and ensuring the integrity of assessments were the main concerns in this large-scale online distance learning (Coghlan et al., 2021). To test the effectiveness of students’ online learning and to ensure that the test results are authentic and credible, universities and institutions in higher education have adopted online proctoring for assessment (Burgess & Sievertsen, 2020). Although online proctoring remains highly Australasian Journal of Educational Technology, 2023, 39(2). 48 controversial in terms of security, privacy and ethical issues, with the ongoing COVID-19, online proctoring meets the needs of the times and undeniably offers great benefits and convenience. Online proctoring providers are increasingly offering advanced technology and high-quality services (e.g., ProctorU, https://www.proctoru.com/). Many schools have already used online proctoring tools for assessment, such as Harvard University, Massachusetts Institute of Technology, Michigan State University and Hong Kong University of Science and Technology (Raman et al., 2021). Online proctoring, as a practical and efficient idea, is expected to evolve into the global norm in higher education (Selwyn et al., 2021). This implementation of online proctoring is a challenge for schools and families, as well as for students. How to effectively organise and implement online proctoring in an unstable environment while ensuring the authenticity and validity of exam results is a test for schools and teachers. As students rely on their homes to complete their learning and assessments, families face the challenge of creating a distraction-free environment for students to take exams (Conijn et al., 2022). As test takers, students need to have self- control in an unconventional learning environment, but they also need to have a certain degree of adaptability and acceptance of online proctoring. Understanding students’ attitudes and concerns about the new examination model is helpful for online proctoring providers, proctors and students to reach a consensus and avoid misunderstandings. It is also helpful to build a good online proctoring environment and improve students’ experience and performance in online exams. Online learning has been extensively researched, and researchers have emphasised the learning activities and experiences of students in the online learning (Konstantinidou & Nisiforou, 2022; Marković et al., 2021; Yildirim & Usluel, 2022). However, research on the testing process that includes home-based online proctoring is still limited. Existing research for online proctoring includes: • System design and technological development for online proctoring (Atoum et al., 2017; Jia & He, 2022), especially the integration with smart technologies of AI (Nigam et al., 2021) and blockchain (Slusky, 2020) • Opportunities and challenges of online proctoring development, especially privacy and cheating. The privacy and security of e-proctoring are considered to be decisive factors affecting the implementation of e-proctoring in online teaching (González-González et al., 2020). To ensure the authenticity and validity of online proctoring results, some scholars have proposed combining multi-factor authentication and authorisation (Slusky, 2020), biometrics (Labayen et al., 2021), application locks (Alessio et al., 2017), and question bank randomisation (Chua et al., 2019) in online proctoring. • The challenges of online proctoring applications, including student attitudes and choices and the impact of online proctoring on student performance. Before the COVID-19 outbreak, lower cost, comfort and less anxiety and stress were found to be motivating factors for students to choose online proctoring, but technical difficulties and unreliable Internet connections were great barriers that could even outweigh the benefits of motivation (James, 2016; Milone et al., 2017). Although during the COVID-19 pandemic, Kharbat and Abu Daabes (2021) investigated and found that students’ overall satisfaction with online proctoring was lower than they expected, with major concerns about privacy and environmental and psychological factors. However, the study had not taken into account the technological contexts in online proctoring. To further examine students’ choice of online proctoring, Raman et al. (2021) found a relative advantage related to the diffusion of innovation theory, compatibility, ease of use, trialability and observability as predictors of university students’ adoption of online proctored exams. The difficulties and concerns that students have should be taken into account when implementing online proctoring, but research on the impact of online proctoring on students’ experience is still very limited, especially after the epidemic brought opportunities for online proctoring. Therefore, a more comprehensive analysis of the factors that influence students’ use of online proctoring is imperative. The large-scale application of online proctoring for the first time was a new experience for students. Thus, this study examining online proctoring acceptance through the perspective of innovation adoption can provide insight into online proctoring adoption. The technology acceptance model (TAM) is based on the https://www.proctoru.com/ Australasian Journal of Educational Technology, 2023, 39(2). 49 theory of reasoned action and the theory of planned behaviour, combined with the self-efficacy theory and the expectation confirmation theory (Davis et al., 1989). As a classic model for studying users’ behavioural intentions to use technological innovations in the field of information technology, TAM has proven to be widely applicable and credible (King & He, 2006). TAM suggests that perceived ease of use and perceived usefulness are the main reasons for technology innovation adoption (Davis et al., 1989). Useful and easy-to-use learning tools promote student engagement, satisfaction and willingness to continue learning (Al-Adwan, 2020; Esteban-Millat et al., 2018; Hsu et al., 2018; Mailizar et al., 2021). Presence theory suggests that “presence occurs when media users somehow ignore the role of technology in their experience” (Lombard et al., 2017). Social presence emphasises the salience of interactions and interpersonal relationships in human-computer interaction (Short et al., 1976). Place presence reflects the high level of engagement and immersion in a given virtual environment (Witmer & Singer, 1998). Research has focused on the impact of social presence and place presence on students’ educational experiences and learning performance in online learning environments (Bodzin et al., 2021; Bulu, 2012; Doo & Bonk, 2020; Luo et al., 2019). Through presence theory, we can improve the online proctoring experience by enhancing the presence of students. In addition, the social environment has a significant impact on technology adoption decisions (Venkatesh et al., 2003). Therefore, this study extended and refined the research model based on TAM by including social presence, sense of place presence and social influence as external factors and investigates influential motivations and barriers from the technology perception, presence, and social influence of online proctoring. Given these considerations, this study aimed to examine the challenges of implementing online proctoring by highlighting students’ experiences and attitudes during the COVID-19 pandemic and how these processes can be improved from students’ perspective. In addition, this study focused primarily on exploring the factors that facilitate and hinder student acceptance of online proctoring based on the extended TAM. This study provides important evidence for academic institutions to understand the most salient concerns of students regarding the implementation of online proctoring tools. It will not only provide some guidance for educational institutions to respond to the current epidemic emergency but also offer some practical suggestions for the future development of online proctoring. The rest of the paper is as follows: It first discusses the student online proctoring acceptance model constructed based on the extended TAM. Then, the methodology and results are discussed. The implications, limitations and future research are discussed at the end. Online proctoring Online proctoring refers to proctors monitoring the status of students during exams via webcam and Internet connection to detect and prevent any misconduct (Hylton et al., 2016). Online proctoring takes place online and allows students to participate in exams remotely from outside the physical classroom, ensuring the integrity of course assessments (Simone et al., 2021). Hussein et al. (2020) identified three types of online proctoring. Live proctoring is where professionally trained human proctors monitor the student’s real-time status via camera and microphone and flag cheating and misconduct. This mode can be implemented through better student-teacher ratios and multiple cameras to get a better understanding of the student’s exam environment. Recorded proctoring analyses students’ activities in exam footage through technology such as eye-tracking, facial detection and log analysis to generate reports for human review and intervention, but is very time-consuming and expensive. Automated proctoring has no time or place restrictions and by automatically identifying fraud and cheating through artificial intelligence or algorithms, humans do not proctor exams all the time but just review. Online proctoring tools on the market continue to evolve and are increasingly able to combine various algorithms and technologies to identify and monitor students’ exam environments and exam behaviours, performing more sophisticated monitoring functions (ProctorU, n.d.). Online proctoring has a promising future in online education (Kubiatko, 2017). However, due to considerations of technical support, service costs, and privacy and security, schools that have chosen and implemented complex functional online exam proctoring tools are still in the minority. Australasian Journal of Educational Technology, 2023, 39(2). 50 The online proctoring model used in this study was live proctoring through videoconference software. Using this method, one instructor could monitor many students at once, as shown in Figure 1. The instructor monitored multiple students in real time through an online conference webcam and microphone, with no additional software involved. A maximum of 20 students in each group of the conference are matched with one instructor. Students prepared two devices: the monitoring device and the answering device. Students presented their ID and campus card to the camera in the monitoring device for authentication. During the examination, students were not allowed to leave the camera area of the monitoring device without consent. Failure to do so was considered a violation and would affect the exam grade. Students downloaded the test questions on the answering device and answered them. The use of live proctoring during the epidemic helps teachers to respond to various emergencies in a timely manner, especially when the online proctoring mode has not been extensively tested yet. Figure 1. The online proctoring process Perceived ease of use TAM considers perceived ease of use as a direct influence on perceived usefulness and behavioural intention (Eraslan Yalcin & Kutlu, 2019). Perceived ease of use indicates how easy or difficult students perceive it is to use the online proctoring tool during the exam. Ease of use has been shown to be positively correlated with the acceptance of online proctoring (Raman et al., 2021; Sefcik et al., 2022). Unreliable systems can affect perceptions of the tool’s usefulness during an exam and impede the willingness to use the online proctoring tool. When hampered by technical difficulties or unreliable Internet connections, students choose to discontinue their online proctoring experience (James, 2016). Hence, we proposed the following hypotheses: Australasian Journal of Educational Technology, 2023, 39(2). 51 • H1a: Perceived ease of use positively influences students’ perceived usefulness. • H1b: Perceived ease of use positively influences students’ online proctoring acceptance. Perceived usefulness TAM considers perceived usefulness as an important determinant of users’ persistent intention (Baki et al., 2018). Perceived usefulness indicates the perceived functionality and usefulness of the tool, and students’ agreement of using an online proctoring tool has improved their exam experience. Due to the epidemic, students were unable to take traditional offline exams. As the first large-scale adoption of online proctoring, it can provide a better exam experience for students while meeting the requirements of integrity and fairness of the assessment. Online exams were associated with less test anxiety and test stress for students compared to traditional exams (James, 2016). When online proctoring can bring flexibility and convenience, for example, in time and location, students would be more receptive to online proctoring tools (Milone et al., 2017). Therefore, we proposed the following hypothesis: • H2: Perceived usefulness positively influences students’ online proctoring acceptance. Social presence Social presence is social interaction and cognitive exchange that involves a continuum from absence to low levels of psychological involvement to high levels of behavioural performance (Van Liere, 1978). In online learning contexts, social presence has been found to contribute to positive learning experiences, with significant positive effects on learning satisfaction, motivation and willingness to continue learning (Lim et al., 2021; Luo et al., 2019; Zuo et al., 2021). Social presence has also been found to influence users’ enjoyment of e-learning and perception of the technology, including perceived ease of use and perceived usefulness (Ogonowski et al., 2014; Salimon et al., 2021). The physical and psychological distances between teachers and students have changed from a strong relationship at zero distance in offline learning to a weak relationship at a distance in online learning. Online proctoring through cameras and microphones helps students in a separate location form a certain sense of belonging and identity that would reduce the loneliness generated in the remote exam environment and enhance students' persistence in the exam. We proposed the following hypotheses: • H3a: Social presence positively influences students’ perceived ease of use. • H3b: Social presence positively influences students’ perceived usefulness. • H3c: Social presence positively influences students’ online proctoring acceptance. Place presence Place presence is defined as a subjective and psychological sense of an individual in a particular virtual environment (Sheridan & Thomas, 1992). Place presence is related to students’ perception of immersive tendencies. In virtual world learning, place presence is positively related to students’ system satisfaction (Bodzin et al., 2021; Bulu, 2012). The online proctoring in our study was a combination of online videoconference proctoring by the instructor and offline question answering by students, a combination of a contextualised virtual world environment and a real task. By simulating a traditional offline exam situation, it provided students with a psychological sense of taking the exam proctored by the teachers in the real context. Therefore, this study suggests that students who feel a real presence in online proctoring would feel a stronger sense of intimacy and immediacy, and would have a higher willingness to use the online proctoring tool. We proposed the following hypothesis: • H4: Place presence positively influences students’ online proctoring acceptance. Social influence Australasian Journal of Educational Technology, 2023, 39(2). 52 Social influence indicates the extent to which specific people or organisations influence technological innovation, including surrounding people, mass media, and government norms. In online learning environments, social influence is an important predictor of students’ perceived usefulness (Wu & Chen, 2017) and willingness to continue learning (Hossain et al., 2019; Olasina, 2019). In our study, social influence was mainly from the calls and promotion of government and schools, including the Ministry of Education’s (2020)_advocacy of “suspending classes without stopping learning,” the promotion of completing teaching tasks on time through online learning and the standardisation and implementation of online proctoring by schools and teachers for teaching inspections and assessments. These will enhance students’ perceptions of the usefulness and importance of online proctoring tools. We proposed the following hypotheses: • H5a: Social influence positively influences students’ perceived usefulness. • H5b: Social influence positively influences students’ online proctoring acceptance. Control variables: gender, major, grade and online learning experience The characteristics of the respondents included are thought to enhance the explanatory power of TAM, such as gender and experience (Morris & Venkatesh, 2000; Sun & Zhang, 2006). In online instruction, gender was found to significantly affect university students’ e-learning satisfaction (Hsi-Peng Lu, 2010). Gender and age had significant moderating effects between technology perception and e-learning acceptance (Tarhini et al., 2014). However, studies that examined the effect of respondents’ characteristics on online learning intentions remain limited, and findings are not always consistent. To further investigate online proctoring acceptance, respondents’ characteristics were included as control variables in the proposed research model. The study hypothesised that gender, major, grade and online learning experience lead to different intentions to accept online proctoring tools. The research model is shown in Figure 2. Figure 2. The online proctoring acceptance research model Materials and methods Participants To test the proposed model, this study conducted an online questionnaire survey in China. First, in response to the epidemic’s hindrance to education, China explicitly proposed and implemented an online teaching policy (Ministry of Education, 2020). Second, for summative assessments, Chinese students generally valued traditional offline exams before the epidemic, and large-scale online proctoring was a new and profound experience for them. The studies involving human participants were reviewed and approved by Hubei University, School of Education, Ethics Committee (HREC number 20200616). Data on students’ attitudes towards the use of online proctoring were collected anonymously online from Australasian Journal of Educational Technology, 2023, 39(2). 53 university students in Hubei Province, China, from July 28 to 31, 2020. A total of 992 questionnaires were returned. After eliminating questionnaires that were filled out within 60 seconds, show discrepancies in the reverse questions or have all the same responses to the scale items, a total of 760 valid questionnaires were obtained, with a response rate of 76.6%. The respondents’ profiles were presented in Table 1. Males and females accounted for 41.97% and 58.03% respectively in the sample. Table 1 Respondents’ profile (N = 760) Profile Frequency Percentage (%) Gender Males 319 41.97% Females 441 58.03% Major Science 293 38.55% Liberal arts 166 21.84% Engineering 301 39.61% Physical education 0 0% Grade Freshman 360 47.37% Sophomore 221 29.08% Junior 153 20.13% Senior 26 3.42% Online learning experience Less than 1 year 583 76.71% 1 to 2 years 114 15% 2 to 3 years 46 6.05% More than 3 years 17 2.24% Instruments The questionnaire was divided into two parts. The first part included demographic information on gender, major, grade and online learning experience. The second part included six factors in the research model, as shown in Table 2. Overall, the six factors in the research model were measured with 21 closed-ended questions. A 5-point Likert scale was used to measure respondents’ opinions, with 5 representing strongly agree and 1 representing strongly disagree. Australasian Journal of Educational Technology, 2023, 39(2). 54 Table 2 Measurement items Constructs Items Statements Source Perceived ease of use (PEOU) PEOU1 I can easily meet the equipment and network requirements for online proctored exams. Davis et al. (1989) PEOU2 It’s easy for me to learn how to take an online proctored exam. PEOU3 It is easy for me to proficiently use the platform for online proctored exams. PEOU4 I think the interactive logic of the online proctoring platform is straightforward and easy to understand. Perceived usefulness (PU) PU1 I find the online proctoring approach useful in solving the challenge of being unable to take traditional exams during the epidemic. Davis et al. (1989) Cho et al. (2009) PU2 I think the online proctoring platform is very functional and helps me do well in online proctored exams. PU3 I feel that online proctoring provides a more flexible and convenient way to take exams. Social presence (SP) SP1 I feel comfortable taking exams on the online proctoring platform. Shea & Bidjerano (2010) SP2 I feel comfortable taking exams with my classmates on the Internet. SP3 I feel like I belonged to the test when I saw familiar teachers and classmates on the screen. SP4 In online proctored exams, even though I may not do as well as other students, I still feel a sense of closeness and trust towards them. Place presence (PP) PP1 In online proctored exams, I felt strongly that I was taking the exam. Slater (2016) PP2 In online proctored exams, I almost forgot that I was taking the exam online and felt like I was taking it in a regular classroom. PP3 When I think back to my online proctoring experience in online proctored exams during the epidemic, I feel that the process of answering and solving questions was not quite different from that in the previous exams in a regular classroom. PP4 For most of the time during the online proctored exam, I felt like I was taking the exam as usual with my classmates. Social influence (SI) SI1 The school has put in place various policies and regulations to standardise online proctored exams, which will make me more willing to try online proctored exams. Zainab et al. (2018) SI2 The course management team’s careful process design and maintenance for orderly online proctoring will make me feel comfortable with online proctoring. SI3 Online proctoring is a major trend at this particular time, and I am willing to try online proctoring. SI4 I am willing to try online proctoring because of the high praise for online proctoring. Online proctoring acceptance (OPA) OPA1 I am willing to continue to participate in online proctoring in the future. Lin & Wang (2012) OPA2 I think online proctoring is the inevitable trend in education in the future. Australasian Journal of Educational Technology, 2023, 39(2). 55 Results This study aimed to examine the influencing factors of online proctoring acceptance, and structural equation modelling was considered appropriate, as it helps to explain causal relationships among constructs (Grace et al., 2012). Internal consistency reliability, convergent validity, discriminant validity and common method bias tests were conducted to assess the measurement model. Then, to test the research hypotheses, the structural equations were modelled and analysed using Analysis of Moment Structure software. Measurement analysis J. Hair et al. (2017) indicated that both the Cronbach’s alpha and composite reliability greater than 0.7 mean high reliability of the scales. The Cronbach’s alpha and composite reliability values shown in Table 3 were both greater than 0.7, indicating that each construct exhibited strong internal reliability. All indicator factor loadings were significant and greater than 0.5, and when the average variance extracted (AVE) for each construct exceeded the variance of that construct (Fornell & Larcker, 1981a), then the convergent validity was achieved. As shown in Table 3, all item loadings were statistically significant (p < 0.001) and greater than 0.50, and all constructs had AVE values greater than 0.5. Therefore, the convergent validity condition was achieved. To achieve discriminant validity, the square of the correlation coefficient must be less than the two AVE estimates (Chin, 1998). As shown in Table 4, the square root values of all AVEs exceeded the estimated values of the correlation coefficients between the constructs, so discriminant validity was achieved. For testing common method bias that can easily occur with the same questionnaire method and data source, this study used two approaches. The Harman' s single factor test and controlling for the effects of a single unmeasured latent method factor. In the first approach, the confirmatory factor analysis test found that the fit indices of the single factor confirmatory factor analysis model (χ² = 3098.576, df = 186, χ²/df = 16.395***, root-mean-square error of approximation (RMSEA) = 0.142, CFI = 0.758, TLI = 0.731) did not meet the fit good criteria, indicating that the CMB was not severe (Williams et al., 2004). In the second approach, common methods variance was added as a latent variable to the structural equation model to compare the changes in model fitting before and after adding (Podsakoff et al., 2003). The analysis results showed no significant improvement in the model fitting (Δχ² = 276.697, Δdf = 21, Δχ²/df = 1.282), which also indicated that the common method bias was not problematic. Australasian Journal of Educational Technology, 2023, 39(2). 56 Table 3 Results of construct reliability and convergent validity Construct Items Factor loading (> 0.5) Cronbach’s alpha (> 0.7) Composite reliability (> 0.7)) Average variance extracted (> 0.5) Perceived ease of use PEOU1 0.775 0.89 0.90 0.68 PEOU2 0.817 PEOU3 0.865 PEOU4 0.766 Perceived usefulness PU1 0.726 0.89 0.89 0.72 PU2 0.723 PU3 0.741 Social presence SP1 0.745 0.89 0.89 0.68 SP2 0.792 SP3 0.683 SP4 0.564 Place presence PP1 0.625 0.88 0.88 0.65 PP2 0.846 PP3 0.710 PP4 0.705 Social influence SI1 0.724 0.90 0.90 0.70 SI2 0.740 SI3 0.740 SI4 0.660 Online proctoring acceptance OPA1 0.547 0.75 0.77 0.64 OPA2 0.839 Table 4 Results of correlation matrices and discriminant validity (Diagonal elements are square roots of average variance extracted.) Construct PEOU PU SP PP SI OPA Perceived ease of use (PEOU) 0.83 Perceived usefulness (PU) 0.60 0.85 Social presence (SP) 0.53 0.65 0.82 Place presence (PP) 0.52 0.64 0.72 0.81 Social influence (SI) 0.55 0.71 0.71 0.67 0.84 Online proctoring acceptance (OPA) 0.36 0.63 0.61 0.59 0.67 0.80 Structural model analysis The goodness-of-fit analysis was used to assess the degree of fit of the proposed model to the collected data (Fidell et al., 2013). As shown in Table 5, CFI (0.916), AGFI (0.888), NFI (0.933), CFI (0.951), RMR (0.032) and RMSEA (0.057) were within the recommended range, and although χ²/df (3.432) was greater than 3.00, it was less than 5.00 and acceptable (Kline, 2011). Therefore, the model was assumed to have a good fit. Australasian Journal of Educational Technology, 2023, 39(2). 57 Table 5 Results of model fit indices Goodness-of-it indices Observed value Recommended value Source χ²/df 3.432 ≤3.00 Kline (2011) GFI 0.916 ≥0.90 Bagozzi & Yi (1988) AGFI 0.888 ≥0.80 Fornell & Larcker (1981b) NFI 0.933 ≥0.90 J. F. Hair et al. (2009) CFI 0.951 >0.90 Fornell & Larcker (1981b) RMR 0.032 ≤0.10 Fidell et al. (2013) RMSEA 0.057 <0.08 J. F. Hair et al. (2009) To test the hypotheses, we conducted a path analysis. Figure 3 depicts the results of the analysis, and Table 6 shows the results of the direct, indirect and total effects among the variables. The results of the analysis indicated that perceived usefulness (β = .259, p < 0.01), social presence (β =.236, p < 0.01), and social influence (β = .442, p < 0.01) positively affect online proctoring acceptance. Therefore, H2, H3c and H5b were supported. Perceived ease of use (β = .253, p < 0.01), social presence (β =.240, p < 0.01) and social influence (β = .462, p < 0.01) positively influenced perceived usefulness. Therefore, H1a, H3b and H5a were supported. Social presence (β = .604, p < 0.01) positively influenced perceived ease of use, and H3a was supported. However, perceived ease of use (β =-.168, p < 0.01) negatively influenced online proctoring acceptance and place presence (β = .084, p = 0.16) had no effect on online proctoring acceptance. Therefore, H1b and H4 were not supported. Overall, the structural model explained 36.5% of perceived ease of use, 69.2% of perceived usefulness and 72.8% of online proctoring acceptance. To further examine the mediating role of perceived usefulness, the meditation test of indirect effects by performing bootstrapping indicated that the effects of social presence (β = .062, 95% CI = .025 to .117) and social influence (β = .120, 95% CI = .057 to .214) on online proctoring acceptance through perceived usefulness were significant. Figure 3. Results of hypotheses test (n = 760) (*p < 0.05, **p < 0.01, ***p < 0.001) Australasian Journal of Educational Technology, 2023, 39(2). 58 Table 6 Results of hypotheses test (n = 760) (*p < 0.05, **p < 0.01, ***p < 0.001) Hypotheses Standardised (β) Supported Direct effect Indirect effect Total effect H1a Perceived ease of use—>Perceived usefulness .253** - .253** Yes H1b Perceived ease of use—>Online proctoring acceptance -.168** .065** -.102** No H2 Perceived usefulness—>Online proctoring acceptance .259** - .259** Yes H3a Social presence—>Perceived ease of use .604** - .604** Yes H3b Social presence—>Perceived usefulness .240** .153** .392** Yes H3c Social presence—>Online proctoring acceptance .236** .001** .237** Yes H4 Place presence—>Online proctoring acceptance .084 - .084 No H5a Social influence—>Perceived usefulness .462** - .462** Yes H5b Social influence—>Online proctoring acceptance .442** .120** .561** Yes Discussion This study aimed to investigate the potential factors of students’ online proctoring acceptance. We proposed a conceptual model to study the influencing factors from technology perception, presence, and social influence of online proctoring from the student’s perspective. The whole model is significant in explaining online proctoring acceptance. Regarding the technological context, surprisingly, the results showed that perceived ease of use related to technical operation did not significantly and positively affect online proctoring acceptance. Although this finding is in contradiction with previous studies, it may be explained by familiarity with the technical requirements of online proctoring. In this study, the main technical support required for online proctoring was a reliable Internet connection, and the students surveyed generally agreed that “the device and network requirements for online proctoring can be easily reached” (M = 3.76) and “it is easy to become proficient in using the platform for online proctoring” (M = 3.77). With access to a fast and stable Internet connection and technical support already, students may not be overly concerned with the perceived technical features in online proctoring, but rather focus primarily on the answering process in exams. This suggests that in online proctoring where the technological requirements are low or easily met, students’ perceptions of technological ease of use do not affect students’ online proctoring acceptance. Similarly, Wu and Chen (2017) found no significant association between perceived ease of use and attitudes in examining MOOC continuance intention, and attributed the results to the ease of use of the MOOC, as each MOOC platform is accessible through a web browser. Also, the results revealed that perceived usefulness was an important predictor of online proctoring acceptance. Previous studies have also emphasised the convenience and completeness of online proctoring as an important motivation for choosing online proctoring (James, 2016; Milone et al., 2017). Overall, online proctoring is a good solution to the challenge of being unable to take traditional offline exams during an epidemic, it reduces the additional time and physical effort for students to take the exam and provides a more flexible and convenient exam experience. Our findings also showed that social presence had a significantly positive effect on perceived ease of use, perceived usefulness, and online proctoring acceptance. This is consistent with the study of Ogonowski et al. (2014) that higher social presence increased users’ usefulness and trust in a system when they first used it. In summative assessment in higher education, the transition from traditional offline proctoring to online proctoring was in the early phase. Students may be more accustomed to being connected with teachers and classmates at a close psychological and physical distance. Social presence theory emphasises Australasian Journal of Educational Technology, 2023, 39(2). 59 the influence of social presence on the level of interaction and frequency of use in computer-mediated communication (Huang et al., 2012). Therefore, when seeing familiar teachers and classmates on the screen in online proctoring, students with higher social presence would have stronger persistence on online proctoring use. This suggests that online proctoring requires organisations to build a good group atmosphere. However, the results showed that place presence was not a significant predictor of online proctoring acceptance. In the study, online proctoring was a combination of online videoconference proctoring and offline paper-based or online question answering, so students were able to perceive that it is “not quite different from the regular classroom exam” (M = 3.25). However, there was no effect on online proctoring acceptance. This means that the degree to which students perceive a realistic experience with traditional offline exams does not affect students’ use of online proctoring. Regarding the social influence, the results revealed that social influence played the most important role in students’ perceived usefulness and online proctoring acceptance, as it was the strongest predictor of perceived usefulness and online proctoring acceptance. This suggests that social recognition and support for online proctoring are important for students’ choice and use of online proctoring (Hossain et al., 2019; Olasina, 2019). The outbreak disrupted the normal state of learning and living, and online teaching was a good policy to help students complete their learning tasks successfully. Students appreciate the usefulness of online proctoring tools when they receive calls from organisations and positive remarks from teachers, the government and schools. Students valued the opinions of the government and schools, so they were motivated to accept and use online proctoring tools in exams by the requirements and organisations of the government and school. In addition, students’ initial decision to use online proctoring tools depended largely on the opinion of the government and the school. The finding did not observe any significant effects of gender, major, grade, or online learning experience on the acceptance of online proctoring as control variables. This could be attributed to the fact that after a period of online home learning during covid pandemic, students with diverse demographic backgrounds have accustomed to the home learning environment. Furthermore, the examination procedure for live proctoring in the study is relatively straightforward and comprehensible, with identity verification and device debugging requiring minimal technical knowledge from students. Consequently, there are no significant barriers for students with varying professional backgrounds and online learning experiences to accept the online proctoring exam format. Research implications This study has several important research implications. From a theoretical perspective, this study introduces an extended TAM on innovation adoption to explain the motivations and concerns of online proctoring acceptance. This allowed us to better understand how the technology perception, presence, and social influence of online proctoring affect students’ attitudes towards and use of online proctoring. At the same time, this study is one of the few studies on online proctoring acceptance, especially when the epidemic brought more opportunities for online proctoring. We hope that our study will provide a corresponding pre-study basis for subsequent studies and will be valuable for the future development of online proctoring standardisation. From a practical perspective, the findings provide practical implications for online proctoring providers and organisers to improve the online proctoring environment. They can focus on the technology perception, presence and social influence of online proctoring because they have a significant effect on students’ online proctoring acceptance. Among the technological characteristics, the significant effect of perceived usefulness on online proctoring acceptance provides important insights for online proctoring developers. Developers should focus on the substantive usefulness and value of online proctoring. When designing online proctoring, developers can focus on the key role of information technology, such as deep learning (Ahmad et al., 2021) and artificial intelligence (Nigam et al., 2021), to improve online proctoring functions and services. Among the perceived presences, the significant positive effect of social presence on perceived ease of use, perceived usefulness and online proctoring acceptance indicates the importance of a good group atmosphere. Online proctoring organisers can create diverse interaction channels and a trusting group atmosphere in exams, thus enhancing students’ experience and willingness Australasian Journal of Educational Technology, 2023, 39(2). 60 to use it. The study shows that social influence is the most important influencing factor. This suggests the critical role of government policy and school organisation in the implementation of online proctoring. Educational institutions and schools can facilitate the change from traditional exams to online exams and increase opportunities for online proctoring development. Online learning institutions and university teachers can also adopt online proctoring as one of the ways of summative assessment of courses. Limitations and future work There are some limitations of this study. First, the sample of the study was only from China. Because online proctoring has developed differently in different countries, students’ experiences with online proctoring may also differ. The results also need to consider the influence of the online proctoring development and cultural background. Second, this study was conducted in the specific context of emergency measures in response to a major health and safety event. The factors influencing the future development of online proctoring will have to be further refined. Also, although the variance explaining student online proctoring acceptance in this study was high, there were other variables we did not consider, such as cheating and privacy. Future research could include these variables to extend the model. In this study, perceived ease of use in technical characteristics did not significantly and positively affect online proctoring acceptance as we hypothesised, which may be explained by the low technology requirements of the online proctoring. For the future development of online proctoring systems, subsequent research needs to clarify the impact of the technological characteristics of online proctoring on students’ willingness to use it in combination with the types and functions of online proctoring, so as to build a more comprehensive research model. Author contributions Xinyu Jiang: Conceptualisation, Data analysis, Writing – original draft, review and editing; Tiong-Thye Goh: Formal analysis, Writing – review and editing; Xinran Chen: Writing-polishing and editing; Mengjun Liu: Conceptualisation, Data collection and analysis, Writing – review and editing; Bing Yang: Writing – review and editing. Acknowledgements This work was supported by the National Natural Science Foundation of China: Research on the Security Management Mechanism of Comprehensive Quality Evaluation Data for the New College Entrance Examination: Blockchain Technology Empowerment Perspective (Grant number: 72204077), the General Project of the Natural Science Foundation of Hubei Province: Based on Blockchain New College Entrance Examination Comprehensive Quality Evaluation Data Security Management Mechanism (Grant number: 2021CFB470), Hubei University Teaching Reform Research Project Research on Blockchain-Based Normal Student Course Archive Data Security Management Mechanism (Grant number: 090017168), and Hubei University Teaching Reform Research Project Supported by the reform research project Research on Blockchain Enabled Data Security Management Mechanism for Graduate Training Process (Grant number: 090014534). References Ahmad, I., AlQurashi, F., Abozinadah, E., & Mehmood, R. (2021). 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Australasian Journal of Educational Technology, 39(2), 47-64. https://doi.org/10.14742/ajet.8121 https://doi.org/10.4018/IJDET.20210401.oa3 https://doi.org/10.1518/001872098779591368 https://doi.org/10.58729/1941-6679.144 https://doi.org/10.1016/j.ijhcs.2005.04.013 https://doi.org/10.2190/EC.51.2.b https://doi.org/10.2307/2065897 https://doi.org/10.2307/30036540 https://doi.org/10.1002/9780470756669.ch18 https://doi.org/10.1162/105474698565686 https://doi.org/10.1016/j.chb.2016.10.028 https://doi.org/10.14742/ajet.7360 https://doi.org/10.22452/mjlis.vol23no1.2 https://doi.org/10.1007/s10639-021-10791-x mailto:lmj_whu@163.com https://creativecommons.org/licenses/by-nc-nd/4.0/ https://doi.org/10.14742/ajet.8121 Introduction Online proctoring Perceived ease of use Perceived usefulness Social presence Place presence Social influence Control variables: gender, major, grade and online learning experience Materials and methods Participants Instruments Results Measurement analysis Structural model analysis Discussion Research implications Limitations and future work Author contributions Acknowledgements References