Australasian Journal of Educational Technology, 2021, 37(3). 38 Predicting mobile learning acceptance: An integrated model and empirical study based on the perceptions of higher education students Ali Alowayr Albaha University, Saudi Arabia Ahmed Al-Azawei University of Babylon, Iraq This research extends the unified theory of acceptance and use of technology (UTAUT) model based on the expectation-confirmation and self-determination theories. It is aimed at exploring students’ perspectives regarding the acceptance of mobile learning (m-learning) in higher education. Although UTAUT receives considerable attention in technology acceptance research, the present study is unlike previous work, in that it is among the first to integrate the self-determination and expectation-confirmation theories with this model to better understand m-learning adoption, particularly in developing countries. A total of 246 undergraduate students responded voluntarily to an online questionnaire. The resulting findings suggest that integrating the UTAUT model with variables that represent learners‘ basic psychological needs can highly affect their acceptance of m-learning technology. These findings are discussed further for their theoretical and practical implications. Implications for practice or policy:  In mobile learning, teachers need to create meaningful learning tasks to facilitate students’ learning.  Teachers should be aware of the importance of confirming learners’ initial expectations of mobile learning’s benefits, to ensure greater learning effectiveness.  Students should feel no pressure in their decision to adopt mobile learning.  Higher education institutes need to exploit the benefits of mobile learning, given that students have high willingness to accept it, but adequate training and facilities should be provided. Keywords: UTAUT, expectation-confirmation theory, self-determination theory, mobile learning adoption, Saudi Arabia, higher education Introduction The integration of educational technologies into the teaching and learning process is becoming ubiquitous in contemporary education. It has been demonstrated that such technologies can help improve learning outcomes and learners’ perceptions (Al-Azawei, 2019a, 2019b; Orús et al., 2016). A variety of electronic media have been used as vehicles for learning, such as the Internet, audiotape, videotape, CD-ROMs, intranet, interactive TV and smart devices. However, the “rapid growth of mobile and wireless technologies [has] resulted in increasing use of mobile devices in education” (Nikou & Economides, 2017, p. 56). According to Al-Emran et al. (2016), mobile learning (m-learning) may be defined according to three perspectives. First, it refers to learning that takes place via small smart devices. Moreover, it is a specific form of learning, which has evolved from the broader term of distance learning. Finally, it represents the next generation of e-learning, which is based on mobile devices. Thus, m-learning may represent a more innovative means of communication and knowledge sharing for learners and educators, in addition to the inherent features of mobile devices such as mobility and flexibility (Kumar Basak et al., 2018). Nevertheless, m-learning is not without its limitations. In terms of the input, output, processing and memory capabilities of mobile devices, it is often evident that they lack features such as a user-friendly keyboard and screen. Moreover, they can be slow and have low processing and storage memory (Asabere, 2012; Kumar Basak et al., 2018). Aside from this, parents may have negative attitudes to their children using this Australasian Journal of Educational Technology, 2021, 37(3). 39 technology (Kumar Basak et al., 2018), and the users themselves may have low willingness to accept it (Abu-Al-Aish & Love, 2013). Therefore, the acceptance of m-learning receives considerable attention as it is still an evolving topic (Briz-Ponce et al., 2017; Nikou & Economides, 2017). In particular, the adoption of m-learning has been examined based on the perceptions of students and teachers (Al-Emran et al., 2016; Aliaño, et al., 2019; Althunibat, 2015; Gómez-Ramirez et al., 2019; Nikou & Economides, 2017; Thomas et al., 2013). To elaborate on the above, there is currently a dearth of research dedicated to identifying m-learning acceptance in higher education, based on an integrated model. The key aims of this study were threefold:  Highlight the predictability of the unified theory of acceptance and use of technology (UTAUT) model (Venkatesh et al., 2003) for m-learning.  Shed light on the role of the expectation-confirmation theory (Oliver, 1981) and self-determination theory (Ryan & Deci, 2000) in explaining m-learning acceptance.  Investigate the integration of the UTAUT model with the expectation-confirmation and self- determination theories. The rationale underpinning this extension is that these theories can complement each other. To clarify, while UTAUT ignores the role of initial expectation and its confirmation when predicting technology acceptance, this role is taken into consideration in the expectation-confirmation theory, as well as the way in which initial expectation is formed by users, and confirmation after use can influence their choice of whether to enact a particular behaviour. Moreover, UTAUT fails to consider the influence of psychological needs in its investigation of technology acceptance. Conversely, the self-determination theory suggests three psychological needs that affect intrinsic motivation. In turn, these can influence users’ decisions to accept a particular technology. To the best of our knowledge, this has not been investigated in the earlier literature. Accordingly, the present study attempts to bridge the identified research gap and extend previous work on m-learning acceptance, especially in developing countries. Theoretical background M-learning The rapid development of mobile technology is evidence of the wireless revolution and its role in many sectors, including education (Rosman, 2008). Its adoption in education could be due to enhanced network infrastructure capacity, high bandwidth Internet and advancement in connecting such technologies wirelessly. M-learning offers the possibility of delivering learning anywhere and at any time. Over the past century, there has been a dramatic increase in the use of mobile devices in the education sector. As a result, many international universities have applied this technology, for example, Aoyama Gakuin University in Japan, where the effectiveness of m-learning for enhancing learners’ performance has been confirmed (Anzai, 2013). This approach to teaching and learning is interesting, in that it enables access to learning materials anywhere and at any time, including outside working hours, as well as in multiple formats, such as audio, visual, or textual modes, according to learners’ preferences. In turn, the recent adoption of m-learning in education requires further investigation to identify the factors potentially affecting its acceptance. Although there have been numerous studies examining the importance of mobile education, very few have focused on applying it to serve educational programs in developing countries (Hoi, 2020). Mobile use and acceptance in Saudi education Over the last decade, interest in mobile education has significantly increased in Saudi universities (Abdulrahman & Benkhelifa, 2017). This trend is attributed to various factors, such as the availability of wireless networks and the rapid growth of mobile technology (Hoi, 2020). Moreover, Internet and smartphone penetration has spread throughout the Saudi population, due to its affordability, resulting in 28.8 million users in 2019 (Statista, 2020). Hence, educators and academic staff need to harness these multiple mobile applications for teaching and learning. To date, there has been an essential investment in m-learning in many of Saudi Arabia’ s universities, such as Albaha University, the Saudi Electronic Australasian Journal of Educational Technology, 2021, 37(3). 40 University and King Abdul-Aziz University in Jeddah (Alkhaldi & Abualkishik, 2019). As a result, the Saudi government has established the requisite infrastructure for projects such as the National Center for E-learning and Distance Learning (Al-Shehri, 2010) and the Saudi Digital Library (Taala et al., 2019). Currently, the use of educational technologies such as e-learning and m-learning is on the rise in Saudi Arabia, due to the international COVID-19 crisis (Alarifi, 2020). Hence, Saudi universities have transferred their activities to the channels afforded by various educational technologies, so that learning content can be delivered to students while they self-isolate at home. These channels include learning management systems (LMSs), which can be navigated and used on different electronic devices, for example, computers, tablets, and/or smartphones. AlEid (2019) investigated the use of mobile devices in learning at Princess Nourah University in Saudi Arabia, using a semi-experimental approach. Overall, the research findings supported the important effect of m-learning adoption on learners’ perceptions. Moreover, the study demonstrated that the success of m- learning depends on Internet availability, human resources and the willingness of teachers and students to use it. Moreover, Al-Fahad (2009) conducted a research study on female undergraduate students at King Saud University, revealing their perceptions and attitudes towards the effectiveness of m-learning. The findings suggest that the availability of m-learning would improve students’ retention and enrich their learning experience. More recently, Saleem (2017) investigated the use of m-learning for teaching English at Taibah University in Saudi Arabia, with results indicating that m-learning could enhance self-learning and provide practice opportunities. These studies, therefore, concluded that the use of m-learning can positively affect the teaching and learning process. The proposed research model Ignoring learners’ perceptions can negatively influence the acceptance of educational technologies. UTAUT was used as a theoretical framework in this study to understand learners’ perceptions regarding m- learning acceptance. UTAUT has attracted significant attention in m-learning research (Aliaño et al., 2019; Gómez-Ramirez et al., 2019; Nikou & Economides, 2017). However, the ability of this model to predict technology acceptance is not high (Mtebe et al., 2016), suggesting that other factors should be integrated to improve its overall predictability power. This may indicate that the four constructs of UTAUT (effort expectancy, performance expectancy, social influence, and facilitating conditions) may be inadequate to cover all significant components in determining technology adoption. Moreover, another debate sparked by UTAUT is that it does not consider users’ expectations, the confirmation of those expectations in relation to a specific technology or what affects users’ psychological needs. This research, therefore, adapted UTAUT to address these limitations by proposing an integrated model based on three well-known theories. The UTAUT model Technology acceptance signifies the clear willingness of users to adopt technology to perform the tasks and activities for which it was intended (Walldén et al., 2015). To examine such acceptance, a large number of models have been developed to determine users’ adoption of information systems. The technology acceptance model (TAM) proposed by Davis (1986) represents one of the most commonly used models for explaining technology acceptance. The updated TAM model suggests that perceived usefulness and perceived ease of use are direct predictors of behavioural intention (Venkatesh & Davis, 2000). Perceived usefulness (performance expectancy) refers to people’s beliefs that a particular technology can help enhance their job performance, whereas perceived ease of use (effort expectancy) is the user’s belief that implementing the technology does not require great mental effort (Davis, 1986). However, due to the criticisms directed at TAM, other technology acceptance models have been proposed (Al-Azawei, 2017). One of the most recent of these is the UTAUT model, developed by Venkatesh et al. (2003) (see Figure 1). UTAUT also suggests two other predictors of technology acceptance, namely facilitating conditions and social influence. Moreover, Venkatesh et al. (2003) proposed gender, age, experience and voluntariness of use as moderators of the relationship between the model’s variables. Facilitating conditions refer to the support that users receive from their institutes or organisations to assist their technology use, while social influence consists of social pressures on a user’s decision to perform or refrain from performing certain actions, depending on the behaviour in question. Australasian Journal of Educational Technology, 2021, 37(3). 41 Figure 1. The UTAUT model (Venkatesh et al., 2003, p. 447) Concerning the use of UTAUT variables in m-learning acceptance research, Briz-Ponce et al. (2017) found that social influence significantly affects m-learning adoption. In line with the above research, Gómez- Ramirez et al. (2019) also showed social influence to be a significant antecedent of m-learning acceptance. Moreover, Althunibat (2015) and Nikou and Economides (2017) highlighted that both effort expectancy and performance expectancy are predictors of behavioural intention to use m-learning. In a study conducted by Aliaño et al. (2019), the findings indicated that social influence, effort expectancy, performance expectancy and facilitating conditions are significantly associated with the behavioural intention to use m- learning. Furthermore, Althunibat (2015) supported that facilitating conditions are a predictor of effort expectancy, and Davis (1986) confirmed that perceived ease of use (effort expectancy) is a determinant of perceived usefulness (performance expectancy). Based on such outcomes, the hypotheses for the current study were as follows: H1: Social influence has a significant effect on the behavioural intention to adopt m-learning. H2: Facilitating conditions have a significant effect on the behavioural intention to adopt m-learning. H3: Effort expectancy has a significant effect on the behavioural intention to adopt m-learning. H4: Performance expectancy has a significant effect on the behavioural intention to adopt m-learning. H5: Facilitating conditions have a significant effect on effort expectancy in the adoption of m-learning. H6: Effort expectancy has a significant effect on performance expectancy in the adoption of m-learning. Expectation-confirmation theory The expectation-confirmation theory was first suggested in marketing research to investigate consumers’ post-purchase behaviour and satisfaction (Oliver, 1981). The theory assumes that before purchasing a product or using a service, consumers form an initial expectation. After purchasing and using the product for a period of time, they form particular perceptions of the product performance. Accordingly, consumers draw a comparison, based on their initial expectation and perceived performance of the product, thereby indicating the extent to which that expectation is confirmed. Repurchase behaviour is affected by the level of expectation and confirmation, which can be called satisfaction. This is very similar to information system use, where users also make an initial decision, which is affected by their experience of use and, finally, either sustained or changed negatively (Bhattacherjee, 2001). Although this theory is still referred to in terms of expectation, empirically, such pre-consumption is transformed by post-consumption expectation, namely performance expectancy or perceived usefulness (Dwivedi et al., 2012). The expectation-confirmation theory is widely applied in information systems research (Dwivedi et al., 2012). For example, Venkatesh et al. (2011) integrated the variables suggested in this theory with the UTAUT model, in order to investigate the behavioural intention to accept e-government technologies, Use Behaviour Behavioural Intention Performance Expectancy Effort Expectancy Social Influence Facilitating Conditions Gender Age Experience Voluntariness of Use UTAUT Australasian Journal of Educational Technology, 2021, 37(3). 42 whereupon its overall effectiveness was highly supported. Moreover, Bhattacherjee (2001) proposed an information systems continuance model based on the expectation-confirmation theory. The resulting findings showed confirmation to be a predictor of satisfaction, whereas perceived usefulness (performance expectancy) was influenced by confirmation. Although previous studies suggest confirmation as a predictor of satisfaction, whereby the latter then influences the intention to use (Lai & Zhao, 2019; Wijaya et al., 2019), the current study hypothesised a direct relationship between expectation-confirmation and behavioural intention. The philosophy behind this assumption stems from the empirical research confirming that identical constructs can be adopted to predict perceived satisfaction and technology acceptance (Al-Azawei et al., 2017; Capece & Campisi, 2013; Weng et al., 2015). According to this discussion, we assumed that: H7: Confirmation has a significant effect on the behavioural intention to adopt m-learning. H8: Confirmation has a significant effect on performance expectancy to adopt m-learning. Self-determination theory Self-determination theory consists of two key elements: intrinsic and extrinsic motivation, as well as a group of psychological needs, collectively referred to as motivation (Gagné & Deci, 2005). Motivation is defined as the reason for undertaking an activity. Extrinsic motivation is not self-determined, unlike intrinsic motivation, which is self-determined. The self-determination theory suggests that intrinsic motivation is based on three key elements of psychological need: competence, the need for relatedness, and autonomy (Roca & Gagné, 2008). According to Ryan and Deci (2000), competence refers to the user’s desire to feel effective in attaining important outcomes, whereas relatedness is the user’s desire to feel a sense of connection with others. Finally, autonomy is the user’s desire to self-regulate and self-initiate behaviour. The theory suggests that these three items (competence, relatedness, autonomy) can be satisfied when engaging in many different behaviours, which also vary among individuals and may differ between dissimilar cultures. Self-determination theory has been applied to determine the acceptance of information systems. Roca and Gagné (2008) extended TAM by including these three psychological items to determine e-learning adoption. In their model, Roca and Gagné proposed that autonomy and competence are predictors of perceived usefulness, perceived playfulness, and perceived ease of use, whereas perceived relatedness is purely a determinant of perceived usefulness and perceived playfulness. The empirical findings supported all suggested hypotheses, except for the influence of perceived relatedness on perceived usefulness. In a similar context, Sørebø et al. (2009) integrated the self-determination and expectation-confirmation theories into an understanding of teachers’ continuance behaviour in e-learning use. Sørebø et al. suggested that these three items are predictors of perceived usefulness and intrinsic motivation. The research outcomes suggested that perceived relatedness was not a significant predictor of perceived usefulness and intrinsic motivation, while the two other variables were determinants of confirmation and performance expectancy. Hence, perceived relatedness was not included in the present study. Consequently, we proposed that: H9: Perceived competence has a significant effect on the behavioural intention to adopt m-learning. H10: Perceived autonomy has a significant effect on the behavioural intention to adopt m-learning. Figure 2 presents the proposed research model. Australasian Journal of Educational Technology, 2021, 37(3). 43 Figure 2. The proposed research model Research methods This research adopted a methodology based on a quantitative approach, which can be used to generate statistical findings within the scope of the study, using a systematic and empirical examination. An online questionnaire was distributed to around 300 students in a public university in the Kingdom of Saudi Arabia, during the first semester of the academic year 2019–2020. As the key focus of this study was on extending UTAUT to understand m-learning adoption, the questionnaire targeted computer science students, because they had more opportunities to take advantage of m-learning. The research participants used a mobile device in several different ways, such as to access and read course materials, take notes, search for information relating to their discipline on the Internet, survey new studies in the Saudi Digital Library, share ideas, carry out assignments and sit online exams hosted in their university’s LMS. Overall, a total of 246 valid responses were received. Thus, the response rate was 82%, as students were highly engaged by their lecturers to take part in the research. The data collection period was about 2 weeks. The participants’ demographic information is presented in Table 1. Performance Expectancy Social Influence Facilitating Conditions Confirmation Behavioural Intention Effort Expectancy Perceived Autonomy Perceived Competence H1 H2 H3 H4 H6 H9 H7 H8 H10 H5 Australasian Journal of Educational Technology, 2021, 37(3). 44 Table 1 Participants’demographic information (N = 246) Factor Number % Gender: - Male 96 39 - Female 150 61 Age group: - 18 to 22 years 192 78 - 23 years and over 54 22 Study year: - First 45 18.3 - Second 1 0.4 - Third 72 29.3 - Fourth 99 40.2 - Postgraduate Experience of mobile use: - Low 2 0.8 - Moderate 91 37 - High 153 62.2 In possession of a smartphone: - No 7 2.8 - Yes 239 97.2 Years of smartphone use: - Have not used it 3 1.2 - One year 1 0.4 - Two to three years 12 4.9 - Four years or more 230 93.5 The research measurement was in two parts. The first encompassed six questions about the participants’ demographic features, whereas the second was intended to measure the proposed model constructs. The items used to operationalise the factors in the proposed research model were adapted from previous studies (Al-Azawei, 2018; Sørebø et al., 2009). Some changes were made to the wording of certain items to correspond to the technology targeted in this study. All items relating to the research model variables were measured based on a 5-point Likert scale, ranging from 1 for strongly disagree to 5 for strongly agree. The research factors were first measured using 29 items. However, four items were excluded from the analysis phase due to their low factor loadings. It is worth mentioning that all items were translated into Arabic, because the use of English could lead to arbitrary answers, as most undergraduate students in Arabic countries have either weak or moderate English ability. Table 2 shows the research survey items and some statistical analysis. Table 2 Research item means, standard deviations (SD) and internal consistencies Code Item Mean SD Loadings Outer weights t stat Performance Expectancy (PE) PE1 I find mobile-learning technology useful for my daily studies. 0.346 0.023 0.807 0.346 15.37 1 PE2 Using mobile-learning technology increases my chances of achieving tasks that are important to me in my studies. 0.294 0.021 0.789 0.294 14.14 0 PE3 Using mobile-learning technology helps me accomplish tasks more quickly. 0.285 0.020 0.785 0.286 14.46 7 Australasian Journal of Educational Technology, 2021, 37(3). 45 PE4 Using mobile-learning technology increases my productivity during my studies. 0.326 0.022 0.816 0.324 14.60 1 Effort Expectancy (EE) EE1 It is easy for me to learn how to use mobile-learning technology. 0.277 0.016 0.871 0.278 17.954 EE2 My interaction with mobile-learning technology is clear and easy to understand. 0.356 0.026 0.849 0.354 13.514 EE3 I find mobile-learning technology easy to use. 0.299 0.020 0.838 0.298 15.069 EE4 It is easy for me to become skilled in using mobile-learning technology. 0.261 0.017 0.794 0.261 15.701 Social Influence (SI) SI1 People who are important to me think I should use mobile-learning technology 0.380 0.019 0.915 0.379 19.631 SI2 People who influence my behaviour think I should use mobile-learning technology. 0.355 0.017 0.916 0.355 20.932 SI3 People whose opinions I value prefer me to use mobile-learning technology. 0.377 0.024 0.871 0.377 15.755 Facilitating Conditions (FC) FC1 I have the necessary resources to use mobile-learning technology. 0.391 0.034 0.841 0.391 11.654 FC2 I have the necessary knowledge to use mobile-learning technology. 0.412 0.031 0.868 0.411 13.233 FC3 Mobile-learning technology is compatible with other technologies that I use. 0.383 0.030 0.818 0.383 12.934 Confirmation (Con) Con1 My experience of using mobile- learning technology was better than I expected. 0.430 0.018 0.917 0.429 23.955 Con2 The service level provided by mobile- learning technology was better than I expected. 0.359 0.014 0.899 0.360 25.580 Con3 Overall, most of my expectations of using mobile-learning technology were confirmed. 0.335 0.017 0.846 0.335 19.750 Perceived Competence (PC) PC1 I have been able to learn useful new skills in mobile-learning technology through my studies. 0.467 0.033 0.844 0.468 13.984 PC2 Most days, I feel a sense of accomplishment from studying with mobile-learning technology. 0.656 0.036 0.924 0.654 18.115 Perceived Autonomy (PA) PA1 I feel that I can form many opinions in deciding how to use mobile-learning technology during my studies. 0.557 0.038 0.854 0.557 14.845 PA4 I feel that I can pretty much use mobile-learning technology as I want in my studies. 0.598 0.042 0.875 0.599 14.197 Australasian Journal of Educational Technology, 2021, 37(3). 46 Behavioural Intention (BI) BI1 I intend to use mobile-learning technology in the future. 0.272 0.009 0.884 0.271 30.769 BI2 I will always try to use mobile- learning technology in my daily studies. 0.298 0.014 0.860 0.299 21.725 BI3 I plan to use mobile-learning technology in future. 0.285 0.010 0.911 0.284 29.788 BI4 I will recommend other students to use mobile-learning technology. 0.287 0.011 0.848 0.288 27.094 Data analysis and results As an analysis technique, this study used partial least squares (PLS) with Smart PLS 3.2.8 (Ringle et al., 2015) to identify determinants of m-learning in higher education. The data analysis encompassed two key steps: the first examining the measurement model to ensure that the gathered data achieved a good fit, and the second proceeding to the structural model as a measurement of hypotheses. Measurement analysis Before analysing the structural equation modelling, a pivotal step is to investigate the unidimensionality of the research model variables. The unidimensionality of each factor is confirmed if its Cronbach’s alpha (internal consistency) and composite reliability (CR) values are equal to or greater than 0.7 (Garver & Mentzer, 1999). However, for exploratory studies such as this research, an internal consistency of 0.6 is also acceptable (Hair et al., 2006). The average variance extracted (AVE) refers to the convergent validity of reflective factors that should not be less than 0.5 (Fornell & Larcker, 1981). The outer loadings of all items used to measure the research variables were greater than 0.7 as a threshold (Hulland, 1999). The data analysis also indicates that the multicollinearity assumption was not violated, as confirmed by the variance inflation factor (VIF) values. The discriminant validity of a research measurement is confirmed where the square root of the AVE for each factor in the research model is greater than the associations between the variable itself and other variables in the model (Fornell & Larcker, 1981). Based on the findings reported in Tables 2, 3 and 4, it is clear that the research measurement has met all recommended thresholds to support its validity and reliability. To examine the relationship between the research model variables and their association with behavioural intention for m-learning, Pearson’s correlation coefficient matrix was generated first. As proposed, Table 5 presents all variables that were significantly associated with the behavioural intention to use m-learning, in order to provide preliminary support for the research assumptions. Table 3 Descriptive statistics, AVE, CR, VIF, Cronbach’s alpha, outer weight and loadings of estimates made using the measurement model Mean SD VIF Cronbach’s alpha CR AVE Behavioural Intention 4.1220 .87065 - 0.899 0.930 0.768 Confirmation 3.9513 .82245 3.179 0.866 0.918 0.788 Effort Expectancy 4.2734 .70870 2.741 0.860 0.904 0.703 Facilitating Conditions 4.0840 .75631 2.825 0.796 0.880 0.710 Perceived Autonomy 3.8374 .84514 2.174 0.663 0.856 0.748 Perceived Competence 3.9776 .89472 2.135 0.730 0.878 0.783 Performance Expectancy 4.2449 .71730 2.465 0.812 0.876 0.639 Social Influence 3.4648 .86483 1.574 0.884 0.928 0.811 Australasian Journal of Educational Technology, 2021, 37(3). 47 Table 4 Discriminant validity of the research variables BI Con EE FC PA PC PE SI BI 0.876 Con 0.680 0.888 EE 0.599 0.666 0.839 FC 0.480 0.644 0.737 0.843 PA 0.607 0.633 0.569 0.673 0.865 PC 0.575 0.698 0.530 0.532 0.575 0.885 PE 0.663 0.702 0.635 0.560 0.508 0.588 0.799 SI 0.505 0.514 0.487 0.421 0.394 0.402 0.573 0.901 Note. BI (Behavioural Intention), Con (Confirmation), EE (Effort Expectancy), FC (Facilitating Conditions), PA (Perceived Autonomy), PC (Perceived Competence), PE (Performance Expectancy), SI (Social Influence). Table 5 Pearson’s correlation analysis Con EE FC PA PC PE SI BI 0.680** 0.599** 0.480** 0.607** 0.575** 0.663** 0.505** Con 0.666** 0.644** 0.633** 0.698** 0.702** 0.514** EE 0.737** 0.569** 0.530** 0.635** 0.487** FC 0.673** 0.532** 0.560** 0.421** PA 0.575** 0.508** 0.394** PC 0.588** 0.402** PE 0.573** Note. BI (Behavioural Intention), Con (Confirmation), EE (Effort Expectancy), FC (Facilitating Conditions), PA (Perceived Autonomy), PC (Perceived Competence), PE (Performance Expectancy), SI (Social Influence). **. Correlation is significant at the 0.01 level (2-tailed). PLS analysis In the first phase, the original constructs of UTAUT in predicting m-learning acceptance were investigated. As shown in Table 6 and Figure 3, UTAUT presents a good explanation of the variance of behavioural intention (R2 = 0.509). Three out of four of the UTAUT constructs were significant determinants of m- learning, namely effort expectancy, performance expectancy, and social influence. This result provides good support for the original UTAUT in a non-Western context. Table 6 Predictability of the UTAUT model Relation β t value p value Findings EEBI 0.285 3.077 0.002 Supported FCBI -0.019 0.218 0.827 Rejected PEBI 0.416 6.126 0.000 Supported SIBI 0.137 2.479 0.015 Supported Note. BI (Behavioural Intention), EE (Effort Expectancy), FC (Facilitating Conditions), PE (Performance Expectancy), SI (Social Influence). Australasian Journal of Educational Technology, 2021, 37(3). 48 Figure 3. Predictability of the UTAUT model In the second phase, the integrated model was examined. Table 7 and Figure 4 illustrate the findings from the PLS structural model findings. Standardised path coefficients are presented, and R2 is depicted in combination with each endogenous construct. Overall, eight out of 10 hypotheses were confirmed (H2 to H9), where the effect of the integrated variables on m-learning acceptance was established. The p value is set as significant at 0.05. Concerning the UTAUT constructs after extension, social influence displayed an insignificant effect on behavioural intention, thereby rejecting H1. Moreover, facilitating conditions had a significant impact on behavioural intention, consequently supporting H2. Moreover, as assumed in H3 and H4, effort expectancy and performance expectancy were significantly related to m-learning adoption. The analysis also confirmed that facilitating conditions predicted effort expectancy (H5), while effort expectancy had a significant effect on performance expectancy, thereby supporting H6. Regarding the extended variables, confirmation was found to be a significant determinant of m-learning use and performance expectancy, confirming H7 and H8. Furthermore, perceived competence was a predictor of behavioural intention, supporting H9. Conversely, perceived autonomy was an insignificant predictor of m-learning adoption, thereby rejecting H10. Significant predictors accounted for 54.3%, 54.3% and 60.5% of the variance of effort expectancy, performance expectancy and behavioural intention respectively (R2 = 0.543, R2 = 0.543 and R2 = 0.605). Australasian Journal of Educational Technology, 2021, 37(3). 49 Table 7 Analysis of the research model hypotheses Hypothesis β t value p value Findings H1: Social Influence  Behavioural Intention 0.094 1.823 0.068 Unsupported H2: Facilitating Conditions  Behavioural Intention -0.245 2.811 0.005 Supported H3: Effort Expectancy  Behavioural Intention 0.206 2.245 0.025 Supported H4: Performance Expectancy  Behavioural Intention 0.259 3.463 0.001 Supported H5: Facilitating Conditions  Effort Expectancy 0.737 20.45 0.000 Supported H6: Effort Expectancy  Performance Expectancy 0.298 4.201 0.000 Supported H7: Confirmation  Behavioural Intention 0.230 2.665 0.008 Supported H8: Confirmation  Performance Expectancy 0.504 7.770 0.000 Supported H9: Perceived Autonomy  Behavioural Intention 0.299 4.149 0.000 Supported H10: Perceived Competence  Behavioural Intention 0.074 1.097 0.272 Unsupported Figure 4. Structural model results Australasian Journal of Educational Technology, 2021, 37(3). 50 Discussion and contributions This research aimed to investigate m-learning acceptance by integrating three theories into a single model. The proposed research framework included different variables, perfectly applied from the adopted theories to identify the antecedents of m-learning acceptance. The results suggested that the original UTAUT had good explanatory power for behavioural intention variance (50.9%). However, this was significantly improved after introducing other constructs from the integrated theories to explain 60.4% of behavioural intention variance. It was found that most of the factors included in the proposed model had a significant effect on m-learning adoption. Moreover, only facilitating conditions explained 54.3% of the variance of effort expectancy, whereas effort expectancy and confirmation accounted for 54.3% of the variance of performance expectancy. The first aim of this research was to investigate the predictive ability of the original UTAUT model. This was achieved through a PLS approach to examining the cause and effect associations between the model’s constructs. The findings indicated that UTAUT explained 50.9% of the variance of behavioural intention. Effort expectancy, performance expectancy and social influence were predictors of m-learning, whereas facilitating conditions had no significant effect on behavioural intention. Overall, such outcomes may provide empirical evidence of this model’s potential to be used in different cultural contexts with different technologies. Although the model demonstrated good predictability power, we suggest that its extension could lead to better results. Regarding the UTAUT variables after integrating the expectation-confirmation and self-determination theories, the findings support the overall effect of its factors on learners’ behaviour to accept m-learning. However, social influence was found to be a weak predictor of behavioural intention. Thus, hypothesis H1 was rejected. Although this result is inconsistent with the findings of Briz-Ponce et al. (2017) and Aliaño et al. (2019) that social influence significantly affected m-learning acceptance, the result supported Nassuora (2013), whose study showed that social influence had an insignificant association with learners’ m-learning behaviour in Saudi Arabia. A possible explanation is that the influence of the integrated variables was higher than the effect of social influence in the proposed model. In turn, this led to a reduction in its overall effect. The above explanation can be supported if the findings for the original UTAUT are noted (see Table 6 & Figure 3), since they clearly indicate social influence as a predictor of behavioural intention in m-learning. Iqbal and Qureshi (2012) argued that the insignificant effect of social influence on m-learning adoption in developing countries could be due to the absence of supporting technology as well as to the high cost of smartphones. Moreover, Venkatesh et al. (2003) indicated that social influence has the biggest impact at an early stage of technology adoption but may decrease over time. Conversely, Al Adwan et al. (2018) declared that students do not make technology adoption decisions outside their social environment, where peers, faculties and individuals influence their m-learning adoption. Such inconsistent findings may be attributed to several factors, including overall living costs, the specific culture of the sample under investigation or the type of antecedents included in the particular theoretical model, as some variables have more impact than others. This research also shows that both performance expectancy and effort expectancy significantly affected the prediction of m-learning use, thereby supporting hypotheses H3 and H4. In fact, these results agree with the original assumption of the UTAUT model (Venkatesh et al., 2003). However, as found in the previous literature, performance expectancy had more influence than effort expectancy on technology adoption (Aliaño et al., 2019; Briz-Ponce et al., 2017). This may indicate that when users perceive the usefulness of a particular technology, they are willing to use it, regardless of their individual skill in its use. Accordingly, educational institutions should pay further attention to integrating valuable functions and a variety of services that could help fulfil the learners’anticipated needs. The findings of hypothesis H5 revealed that the facilitating conditions variable predicts effort expectancy. Moreover, effort expectancy was found to be a determinant of performance expectancy, confirming hypothesis H6. Overall, these findings support recent studies (Aliaño et al., 2019; Althunibat, 2015; Nikou & Economides, 2017), which indicate that if learners feel that an educational institution is offering them the required technological support, the effort they expend in performing a particular behaviour will increase. Moreover, when users believe that they will not need much effort to enact a particular behaviour, their perception of a technology’s usefulness (perceived usefulness) may be positively affected. Hence, universities and other educational institutions need to offer their students the required technical support to Australasian Journal of Educational Technology, 2021, 37(3). 51 ensure that they use technology successfully. Training courses or workshops are also important for enhancing learners’self-confidence and reducing the effort required in their technology use. Concerning the expectation-confirmation theory, we propose that confirmation is a predictor of behavioural intention and performance expectancy. This is a new assumption, although it has been suggested that the relationship between confirmation and behavioural intention is mediated by satisfaction (Lai & Zhao, 2019). The rationale for this assumption is that identical constructs can be used to predict satisfaction and behavioural intention. The empirical analysis in this study supports hypothesis H7, which means that when learners’ initial expectations are confirmed, their willingness to adopt technology is increased accordingly. This is a key finding in the present work, as pre-acceptance perceptions are based on cognitive beliefs – for example, effort expectancy and performance expectancy – which are perhaps based on other sources, popular media or referent others (Bhattacherjee, 2001). In light of such beliefs, users’ attitudes may be unrealistic or uncertain. On the contrary, confirmation reflects users’ satisfaction with a technology, which may be unbiased, but more realistic in influencing their final decision. In agreement with previous research (Bhattacherjee, 2001), this study also confirms the significant influence of confirmation on performance expectancy, supporting hypothesis H8. A possible explanation of this significant association is that learners may initially have a low performance expectancy of a learning technology due to their uncertainty over its use. However, confirmation will elevate learners’ performance expectancy, adjusted as a consequence of confirmation experience. Meanwhile, concerning the self-determination theory, two of its main items were used as direct predictors of the behavioural intention to adopt m-learning: perceived autonomy and perceived competence. The first assumption was confirmed (H9), whereas hypothesis H10 was rejected. These results are consistent with a recent study conducted by Lu et al. (2019), which also confirmed that while perceived competence did not predict behavioural intention, perceived autonomy was a significant determinant of this factor. It may indicate that when learners feel they can make their own choices and initiate their activities without pressure, their willingness to adopt technology is correspondingly affected. Hence, this present research proved the self-determination theory assumption (Ryan & Deci, 2000) regarding the effect of perceived autonomy. As perceived competence refers to users’ desire to feel effective in achieving important outcomes, it was expected that this factor would significantly influence behavioural intention (H10). Interestingly, the analysis rejects this assumption, suggesting instead that Saudi students care more about self-regulation than about their ability to make a pre-acceptance decision. This study contributes to different streams of information systems, and m-learning in particular. Its key contribution is the integration of three well-known and widely accepted theories, integrating UTAUT, the expectation-confirmation theory, and the self-determination theory to investigate m-learning acceptance in higher education. Thus, it extends research that has focused exclusively on UTAUT and/or the two other theories in explaining users’ behaviour towards a particular technology. Accordingly, this study establishes a broad understanding by developing a more comprehensive mode, integrating theories that originate from a quite different perspective. Based on these theories, this research has highlighted many important findings. First, social influence revealed a weak effect on behavioural intention towards m-learning, after integrating the two other theories with UTAUT. This suggests that when learners’ perceptions of confirmation and self-determination are considered, their concern for other people’s opinions will be low, because other perceptions have a stronger influence on behavioural intention. Second, confirmation was found to be a strong predictor of both behavioural intention and performance expectancy. Previous literature has proposed that satisfaction is a mediator between confirmation and behavioural intention (Lai & Zhao, 2019; Wijaya et al., 2019). The current study, however, confirms a direct association between confirmation and behavioural intention, without the mediation of satisfaction. This is an interesting outcome, which suggests that learners’ behavioural intention and perceptions of usefulness are significantly improved if their initial expectation is confirmed. However, the findings suggest that confirmation has a stronger effect on performance expectancy than on behavioural intention to support the original assumption of this theory (Bhattacherjee, 2001). Finally, an investigation of the two parameters of self-determination theory revealed that perceived autonomy was a significant factor in explaining behavioural intention for m-learning, whereas perceived competence had a low effect on this variable. Thus, when users felt no pressure to adopt technology, their individual willingness was significantly increased. These findings open up the possibility of using more Australasian Journal of Educational Technology, 2021, 37(3). 52 factors to explain m-learning adoption. Therefore, the study complements earlier literature by providing a possible explanation of the role of different theories in shedding light on technology acceptance. Conclusion This research integrated three well-known theories to predict m-learning acceptance in higher education. The PLS technique was applied to analyse the research data. The findings support the extension of the UTAUT model through the theories of self-determination and expectation-confirmation. The original UTAUT model explained 50.9% of the variance of behavioural intention, whereas the proposed model explained 60.4% of the variance of behavioural intention to accept m-learning. Thus, the present study confirms the effect of users’ basic psychological needs on their final decision to adopt a technology. Based on these outcomes, several conclusions may be drawn: 1. Although UTAUT is an effective framework for understanding technology acceptance, regardless of cultural differences or the type of technology, its overall predictability power can be improved by integrating constructs from other theories, which may have quite dissimilar assumptions. 2. Unlike other studies, this research supports a direct relationship between confirmation and the behavioural intention to adopt m-learning in the context of Saudi higher education. 3. Perceived autonomy was found to be a direct and significant predictor of the behavioural intention to use m-learning. 4. Even though social influence has a significant effect on technology adoption, its role may be reduced when considering other users’ perceptions. 5. As the learners showed high perceptions of the use of m-learning for different educational activities, universities and schools could exploit the advantages of this technology to deliver learning content and enhance the learning process. More specifically, m-learning could provide an excellent opportunity for students to learn continuously in specific times of crisis, such as the current global COVID-19 pandemic. Although this research generated significant findings, it is not without limitations. First, the data were collected from students in one discipline (computer science). Further research could focus on other specific disciplines such as medicine, engineering and the humanities to compare students’ perceptions of m- learning according to discipline. However, as discussed previously, most of the current findings are aligned with literature examining m-learning adoption among students from different disciplines and cultures. This could provide some support for the generalisability of the findings. Moreover, this study examined m- learning acceptance based on students’ perspectives, whereas identifying teachers’ adoption and use of such technologies is another research direction that should be pursued. Finally, a quantitative research design was applied in this study. Future research could, therefore, adopt a mixed methods design, integrating both quantitative and qualitative approaches to obtain findings that are more reliable and valid for a holistic understanding of m-learning acceptance. Acknowledgments The authors are indebted to Albaha University, Saudi Arabia for supporting this study as part of the research project entitled “A study of factors influencing mobile learning adoption of Higher Education students”. References Abdulrahman, R., & Benkhelifa, E. (2017). A systematic literature review on mobile learning for nursing education in Kingdom of Saudi Arabia. In Proceedings of the 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (pp. 1354–1361). The Institute of Electrical and Electronics Engineers, Inc. https://doi.org/10.1109/AICCSA.2017.211 Abu-Al-Aish, A., & Love, S. (2013). Factors influencing students’ acceptance of m-learning: An investigation in higher education. International Review of Research in Open and Distance Learning, 14(5), 82–107. https://doi.org/10.19173/irrodl.v14i5.1631 Al Adwan, A. S., Al Adwan, A., & Berger, H. (2018). Solving the mystery of mobile learning adoption in higher education. International Journal of Mobile Communications, 16(1), 24–49. https://doi.org/10.1504/ijmc.2018.10007779 https://doi.org/10.1109/AICCSA.2017.211 https://doi.org/10.19173/irrodl.v14i5.1631 https://doi.org/10.1504/ijmc.2018.10007779 Australasian Journal of Educational Technology, 2021, 37(3). 53 Alarifi, I. (2020). Readiness switching traditional learning form at Saudi Arabia University as a quick action to the COVID-19 virus pandemic. International Journal of Disaster Recovery and Business Continuity, 11(1), 3237–3259. http://sersc.org/journals/index.php/IJDRBC/article/view/30654/17012 Al-Azawei, A. (2018). Predicting the Adoption of social media: An integrated model and empirical study on Facebook usage. Interdisciplinary Journal of Information, Knowledge, and Management, 13, 233– 238. https://doi.org/10.28945/4106 Al-Azawei, A. (2019a). The moderating effect of gender differences on learning management system acceptance: A multi-group analysis. Italian Journal of Educational Technology, 27(3), 257-278. https://doi.org/10.17471/2499-4324/1088 Al-Azawei, A. (2019b). What drives successful social media in education and e-learning? A comparative study on Facebook and Moodle. Journal of Information Technology Education: Research, 18, 253– 274. https://doi.org/10.28945/4360 Al-Azawei, A., Parslow, P., & Lundqvist, K. (2017). Investigating the effect of learning styles in a blended e-learning system: An extension of the technology acceptance model (TAM). Australasian Journal of Educational Technology, 33(2), 1–23. https://doi.org/10.14742/ajet.2758 Al-Azawei, A. H. S. (2017). Modelling e-learning adoption: The influence of learning style and universal learning theories. [Doctoral Dissertation, University of Reading]. CentAUR. http://centaur.reading.ac.uk/77921/ Al-Emran, M., Elsherif, H. M., & Shaalan, K. (2016). Investigating attitudes towards the use of mobile learning in higher education. Computers in Human Behavior, 56, 93–102. https://doi.org/10.1016/j.chb.2015.11.033 Al-Fahad, F. (2009). Students’ attitudes and perceptions towards the effectiveness of mobile learning in King Saud University, Saudi Arabia. The Turkish Online Journal of Educational Technology, 8(2), 111–119. AlEid, A. (2019). The impact of using Edmodo through mobile devices on learning and access to information for Princess Nourah University. Educational Journal, (58), 1–42. Aliaño, Á. M., Hueros, A. M. D., Franco, M. D. G., & Aguaded, I. (2019). Mobile learning in university contexts based on the unified theory of acceptance and use of technology (UTAUT). Journal of New Approaches in Educational Research, 8(1), 7–17. https://doi.org/10.7821/naer.2019.1.317 Alkhaldi, A. N., & Abualkishik, A. M. (2019). The mobile blackboard system in higher education: Discovering benefits and challenges facing students. International Journal of Advanced and Applied Sciences, 6(6), 6–14. https://doi.org/10.21833 Al-Shehri, A. (2010). E-learning in Saudi Arabia:‘To E or not to E, that is the question’. Journal of Family and Community Medicine, 17(3), 147–150. https://doi.org/10.4103/1319-1683.74333 Althunibat, A. (2015). Determining the factors influencing students’ intention to use m-learning in Jordan higher education. Computers in Human Behavior, 52, 65–71. https://doi.org/10.1016/j.chb.2015.05.046 Anzai, Y. (2013). Factors to design and implement technology-enhanced global instruction. In R. McBride & M. Searson (Eds.), SITE 2013 Society for Information Technology & Teacher Education International Conference (pp. 169–173). Association for the Advancement of Computing in Education. https://www.learntechlib.org/primary/p/48087/ Asabere, N. Y. (2012). Towards a perspective of information and communication technology (ICT) in education: Migrating from electronic learning (e-Learning) to mobile learning (m-Learning). International Journal of Information and Communication Technology Research, 2(8), 646–649. Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly: Management Information Systems, 25(3), 351–370. https://doi.org/10.2307/3250921 Briz-Ponce, L., Pereira, A., Carvalho, L., Juanes-Méndez, J. A., & García-Peñalvo, F. J. (2017). Learning with mobile technologies – Students’ behavior. Computers in Human Behavior, 72, 612–620. https://doi.org/10.1016/j.chb.2016.05.027 Capece, G., & Campisi, D. (2013). User satisfaction affecting the acceptance of an e-learning platform as a mean for the development of the human capital. Behaviour & Information Technology, 32(4), 1–9. https://doi.org/10.1080/0144929X.2011.630417 Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results [Doctoral thesis, Massachussetts Institute of Technology]. DSpace@MIT. http://dspace.mit.edu/handle/1721.1/15192 Dwivedi, Y. K., Wade, M. R., & Schneberger, S. L. (Eds.). (2012). Informations systems theory (Vol. 1). http://sersc.org/journals/index.php/IJDRBC/article/view/30654/17012 https://doi.org/10.28945/4106 https://doi.org/10.17471/2499-4324/1088 https://doi.org/10.28945/4360 https://doi.org/10.14742/ajet.2758 http://centaur.reading.ac.uk/77921/ https://doi.org/10.1016/j.chb.2015.11.033 https://doi.org/10.7821/naer.2019.1.317 https://doi.org/10.21833 https://doi.org/10.4103/1319-1683.74333 https://doi.org/10.1016/j.chb.2015.05.046 https://www.learntechlib.org/primary/p/48087/ https://doi.org/10.2307/3250921 https://doi.org/10.1016/j.chb.2016.05.027 https://doi.org/10.1080/0144929X.2011.630417 http://dspace.mit.edu/handle/1721.1/15192 Australasian Journal of Educational Technology, 2021, 37(3). 54 Springer. https://doi.org/10.1007/978-1-4419-6108-2 Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312 Gagné, M., & Deci, E. L. (2005). Self-determination theory and work motivation. Journal of Organizational Behavior, 26, 331–362. https://doi.org/10.1002/job.322 Garver, M. S., & Mentzer, J. T. (1999). Logistics research methods: Employing structural equation modeling to test for construct validity. Journal of Business Logistics, 20(1), 33–57. Gómez-Ramirez, I., Valencia-Arias, A., & Duque, L. (2019). Approach to M-learning acceptance among university students: An integrated model of TPB and TAM. International Review of Research in Open and Distributed Learning, 20(3), 141–164. https://doi.org/10.19173/irrodl.v20i4.4061 Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis. Pearson Prentice Hall. Hoi, V. (2020). Understanding higher education learners’ acceptance and use of mobile devices for language learning A Rasch-based path modeling approach. Computers & Education, 146, Article 103761. https://doi.org/10.1016/j.compedu.2019.103761 Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic Management Journal, 20(2), 195–204. https://doi.org/10.1002/(SICI)1097- 0266(199902)20:2%3C195::AID-SMJ13%3E3.0.CO;2-7 Iqbal, S., & Qureshi, I. A. (2012). M-learning adoption: A perspective from a developing country. The International Review of Research in Open and Distance Learning, 13(3), 147–164. https://doi.org/10.19173/irrodl.v13i3.1152 Kumar Basak, S., Wotto, M., & Bélanger, P. (2018). E-learning, M-learning and D-learning: Conceptual definition and comparative analysis. E-Learning and Digital Media, 15(4), 191–216. https://doi.org/10.1177/2042753018785180 Lai, W., & Zhao, L. (2019). Exploring the influencing factors of undergraduates’ continuance intentions in e-reading APPs (Eifuciea). International Journal of Information and Education Technology, 9(12), 924–932. https://doi.org/10.18178/ijiet.2019.9.12.1328 Lu, Y., Papagiannidis, S., & Alamanos, E. (2019). Exploring the emotional antecedents and outcomes of technology acceptance. Computers in Human Behavior, 90, 153–169. https://doi.org/10.1016/j.chb.2018.08.056 Mtebe, J. S., Mbwilo, B., & Kissaka, M. M. (2016). Factors influencing teachers’ use of multimedia enhanced content in secondary schools in Tanzania. The International Review of Research in Open and Distributed Learning, 17(2), 65–84. https://doi.org/10.19173/irrodl.v17i2.2280 Nassuora, A. (2013). Students acceptance of mobile learning for higher education in Saudi Arabia. International Journal of Learning Management Systems, 1(1), 1–9. https://doi.org/10.12785/ijlms/010101 Nikou, S. A., & Economides, A. A. (2017). Mobile-based assessment: Investigating the factors that influence behavioral intention to use. Computers and Education, 109, 56–73. https://doi.org/10.1016/j.compedu.2017.02.005 Oliver, R. L. (1981). Effect of satisfaction and its antecedents on consumer preference and intention. Advances in Consumer Research Volumei 8, 88–93. http://acrwebsite.org/volumes/9746/volumes/v08/NA-08 Orús, C., Barlés, M. J., Belanche, D., Casaló, L., Fraj, E., & Gurrea, R. (2016). The effects of learner- generated videos for YouTube on learning outcomes and satisfaction. Computers and Education, 95, 254–269. https://doi.org/10.1016/j.compedu.2016.01.007 Ringle, C. M., Wende, S., & Becker, J.-M. (2015). SmartPLS 3 Boenningstedt: SmartPLS GmbH. http://www.smartpls.com Roca, J. C., & Gagné, M. (2008). Understanding e-learning continuance intention in the workplace: A self-determination theory perspective. Computers in Human Behavior, 24(4), 1585–1604. https://doi.org/10.1016/j.chb.2007.06.001 Rosman, P. (2008). M-learning – As a paradigm of new forms in education. Informační Management, 11(1), 119–125. http://www.ekonomie-management.cz/download/1331826665_1cdd/13_rosman.pdf Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78. https://psycnet.apa.org/doi/10.1037/0003-066X.55.1.68 https://doi.org/10.1007/978-1-4419-6108-2 https://doi.org/10.2307/3151312 https://doi.org/10.1002/job.322 https://doi.org/10.19173/irrodl.v20i4.4061 https://doi.org/10.1016/j.compedu.2019.103761 https://doi.org/10.1002/(SICI)1097-0266(199902)20:2%3C195::AID-SMJ13%3E3.0.CO;2-7 https://doi.org/10.1002/(SICI)1097-0266(199902)20:2%3C195::AID-SMJ13%3E3.0.CO;2-7 https://doi.org/10.19173/irrodl.v13i3.1152 https://doi.org/10.1177/2042753018785180 https://doi.org/10.18178/ijiet.2019.9.12.1328 https://doi.org/10.1016/j.chb.2018.08.056 https://doi.org/10.19173/irrodl.v17i2.2280 https://doi.org/10.12785/ijlms/010101 https://doi.org/10.1016/j.compedu.2017.02.005 http://acrwebsite.org/volumes/9746/volumes/v08/NA-08 https://doi.org/10.1016/j.compedu.2016.01.007 http://www.smartpls.com/ https://doi.org/10.1016/j.chb.2007.06.001 https://psycnet.apa.org/doi/10.1037/0003-066X.55.1.68 Australasian Journal of Educational Technology, 2021, 37(3). 55 Saleem, T. (2017). Mobile phone applications in the educational process and their usage obstacles in Jordan: A Field study in public schools. Cybrarians Journal, (47), 1–28. Sørebø, Ø., Halvari, H., Gulli, V. F., & Kristiansen, R. (2009). The role of self-determination theory in explaining teachers’ motivation to continue to use e-learning technology. Computers and Education, 53(4), 1177–1187. https://doi.org/10.1016/j.compedu.2009.06.001 Statista. (2020, August 26). Number of smartphone users in Saudi Arabia from 2017 to 2025 (in millions). https://www.statista.com/statistics/494616/smartphone-users-in-saudi-arabia/ Taala, W., Filoteo, F. B., & De Sagun, R. S. (2019). Impact of Saudi Digital Library (SDL) to Saudi research output: A review. Open Access Library Journal, 6, Article e5331. https://doi.org/10.4236/oalib.1105331 Thomas, T. D., Singh, L., & Gaffar, K. (2013). The utility of the UTAUT model in explaining mobile learning adoption in higher education in Guyana. International Journal of Education and Development Using Information and Communication Technology, 9(3), 71–85. https://doi.org/10.4018/978-1-4666-8358-7.ch086 Venkatesh, V., & Davis. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926 Venkatesh, Viswanath, Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540 Venkatesh, Viswanath, Thong, J. Y. L., Chan, F. K. Y., Hu, P. J. H., & Brown, S. A. (2011). Extending the two-stage information systems continuance model: Incorporating UTAUT predictors and the role of context. Information Systems Journal, 21(6), 527–555. https://doi.org/10.1111/j.1365- 2575.2011.00373.x Walldén, S., Mäkinen, E., & Raisamo, R. (2015). A review on objective measurement of usage in technology acceptance studies. Universal Access in the Information Society, 15, 713–726. https://doi.org/10.1007/s10209-015-0443-y Weng, C., Tsai, C., & Weng, A. (2015). Social support as a neglected e-learning motivator affecting trainee’s decisions of continuous intentions of usage. Australasian Journal of Educational Technology, 31(2), 177–192. https://doi.org/10.14742/ajet.1311 Wijaya, I. W. K., Rai, A. A. G., & Hariguna, T. (2019). The impact of customer experience on customer behavior intention use in social media commerce, an extended expectation confirmation model: An empirical study. Management Science Letters, 9(12), 2009–2020. https://doi.org/10.5267/j.msl.2019.7.005 Corresponding author: Ahmed Al-Azawei, ahmedhabeeb@itnet.uobabylon.edu.iq Copyright: Articles published in the Australasian Journal of Educational Technology (AJET) are available under Creative Commons Attribution Non-Commercial No Derivatives Licence (CC BY-NC-ND 4.0). 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