EXAMINING THE DETERMINANTS OF MOBILE TECHNOLOGIES USE IN ACADEMIC SETTINGS BY LECTURERS AND STUDENTS IN TWO SAUDI ARABIAN UNIVERSITIES

1.   Introduction

Technology-supported learning is a facet of modern pedagogy that continues to demonstrate its potential to enhance educational effectiveness and efficiency [1]. Indeed, advanced technologies help to overcome a range of classroom limitations and provide a platform for new learning materials to meet a variety of learner needs [2]. Mobile devices are examples of popular advanced technologies increasingly used as supplementary tools in pedagogical practices, including the implementation of mobile learning (m-learning) programs [3]. Such devices have the potential to support learning outcomes by providing students and educators with the ability to access, store, and exchange information in any place and at any time, and to facilitate the creation of collaborative learning communities [4], [5].

Various definitions of m-learning have emerged to characterize the integral role it plays in education service delivery in educational institutions around the world. Some definitions focus on device characteristics, others on device mobility, and others again on the technological capabilities of the device [6]. This study characterizes m-learning as the integration of all these factors: the device, the mobility, and the capabilities to use the device to learn in various contexts.

Ongoing developments in portable devices m-learning contribute to the development of new teaching and learning strategies [7]. Such technologies afford educators and students new methods and opportunities to manage and utilize learning and teaching materials. In addition, advancing technologies offer new opportunities for knowledge management and collaborative learning practices [8]. It is therefore not surprising that mobile and other advanced technologies are increasingly prevalent in educational settings.

Research studies continue to identify the benefits of mobile technologies for work and study practices [9]-[11]. Students report the use of mobile devices for learning can increase their participation and engagement in academic tasks [12]-[14], and their motivation to learn. Furthermore, educators have reported the use of mobile devices for learning can increase their level of interactivity with their students and their classroom management practices [15]. 

Even though Saudi Arabia has one of the highest rates of mobile phone use, particularly among students and young people, the implementation of mobile devices for learning by academics is still in its initial stage [16]. Nonetheless, the adoption of learning technologies in education settings in Saudi Arabia is undoubtedly on the rise [16]. There are however still many gaps in the academic understanding of how best to implement them into pedagogical practices to maximize their effectiveness. Specifically, a deeper level of understanding is required in relation to how best to respond to ongoing innovation in the field, the anxiety factors associated with their use, and the cultural factors that may hinder the progress of mobile device usage for educational purposes [17].

Therefore, the current adoption of m-learning technologies among Saudi university students and educators is an important topic of research investigation. In response, this study aims to further investigate the drivers of mobile technology acceptance and uses for learning by Saudi lecturers and students. It also aims to identify and discuss the features of mobile technologies that facilitate students and lecturers to integrate them into their academic lives. Research of this kind into m-learning practices is important to better understand the scope of the field and how technological advances can be utilized to enhance and capacity of m-learning in educational institutions. The purpose of this paper is divided into two main points:

·   To examine the determinants of mobile technology use for mobile learning by lecturers and students at two higher education institutions in Saudi Arabia.

·   To discuss the features of mobile social networks (MSNs) that lead lecturers and students to integrate them into their academic lives.

2.   Theoretical framework

To identify the determinants of mobile technology use for m-learning by lecturers and students in higher education settings, this study illustrated on the Unified Theory of Acceptance and Use of Technology (UTAUT) model developed by [18] and the Technology Acceptance Model (TAM) initially developed [19]. Implied in the UTAUT model is that a person’s intentions to use technology and his or her usage behaviors are dependent on eights key constructs that may be categorized in to two domains (see Figure 1): user-technology relationship factors including performance expectancy, effort expectancy, social influence, facilitating conditions; and individual difference factors including gender, age, experience, and voluntariness of use.

Figure 1. The unified theory of acceptance and use of technology.

Two additional factors: self-efficacy and self-directedness (related to students only) were also included for investigation in this study under individual difference factors. Self-directedness was included for students only because it is related to how the student directs his or her own learning and there is no interest in comparing this with how educators lead their learning/teaching. As stated by [18], the four user-technology relationship factors may be considered as the direct predictors of technology use intentions and behaviors, and the four individual difference factors as the moderating effects on technology use intentions and behaviors. Table 2.1 presents some Conducted in the Context of Mobile Learning Technologies

Table 2.1 Summary of Research Conducted in the Mobile Learning Technologies Context

3.   Methodology

The study employed an explanatory mixed-methods study design. As such, both quantitative and qualitative research principles were applied to generate different perspectives of, and data outcomes related to the research topic for triangulation [20]. This paper reports the quantitative data results only.

3.1 Settings and participants

Two universities in Saudi Arabia were used as settings for this study: King Abdul-Aziz University in the western part of the country; and the King Khalid University, located in a number of towns including Abha, al-Namas, and Tanomah in the ‘Asir Province in South-West Saudi Arabia.

The study sample comprised 191 lecturers (male = 99; female = 92) and 209 students (male = 109; female = 100) from both universities (King Khalid University = 222; King Abdul-Aziz University = 178). The students and lecturers were drawn relatively evenly from five faculties within each university: College of Education, Faculty of Arts and Social Science, Faculty of Computing and Information Technology, Faculty of Science, and Faculty of Law.

The ages of the students ranged from 18-26 years. Approximately 50% of the lecturers were aged between 25 and 34 years, and 37% were aged between 35 and 44 years. The majority of students (98%) reported having attained a bachelor’s degree; whereas the largest percentage of educators (50%) were found to have a Master’s degree, followed relatively closely (42%) by those holding a Doctoral degree.

3.2 Data collection

The collection of quantitative data for this study was done via an online questionnaire. The items in the questionnaire supported data collection on the demographic characteristics of the participants including gender, age-range, IT skills, enrolment status, and level of experience with the use of mobile technologies; as well as their attitudes towards, and perspectives of, the use of mobile devices to enhance teaching and learning (adoption - See Appendix A). The questionnaire was a multi-item instrument with responses measured on a 5-point Likert-type scale ranging from: 1 (Never) to 5 (Always); or a 7-point Likert-type scale ranging from 1 (Strongly disagree) to 7 (Strongly agree).

3.3 Data analysis

The quantitative data were analyzed using SPSS version 23. Exploratory Factor Analysis (EFA) was performed using the principal component extraction method. Inferential statistical analyses were also conducted on the data, including a multivariate ANOVA (MANOVA) and a series of Structural Equation Models (SEM) to explore in further detail the relationships between the independent and dependent variables. A multivariate ANOVA (MANOVA) analysis of the data was performed to examine the extent to which the demographic variables of interest (gender and role) influence the UTAUT scale scores related to mobile device user experience. Usage experience for each cohort was grouped on a spectrum of experience. The scale scores were entered as dependent variables in the MANOVA, with the two demographic variables included as independent variables. The SEM analyses were performed to explore the extent to which the UTAUT model generalizes to the larger population-based upon the study sample. The analyses served to partially replicate a testable version of the UTAUT model as well as to test an extended model, which included a range of other attitudinal- and experience-based variables. Figure 2 illustrates the first SEM conducted:

4.   Results

The questionnaire instrument contained items to collect data on the demographic characteristics of the participants and their attitudes towards and perspectives of the use of mobile devices to support the enhancement of teaching and learning strategies.

4.1 Background and experience

Participants were asked to rate the frequency at which they performed seven activities on their mobile devices (see Table 1).

Table 1. Mobile device activities: frequency of performance

The students and lecturers reported frequencies in the ‘Sometimes’ (3) to ‘Often’ (4) intervals. Table 1 shows the highest average frequencies were for ‘Follow friends’ posts on social sites’ and ‘Adding family members to your social sites’; whereas the lowest average frequencies were for ‘Comments on friends’ posts or re-tweet their tweets’. Overall, students undertook each of these activities more frequently than lecturers, with students reporting significantly higher frequencies on all activity items.

Participants were asked to rate the frequency at which they performed five learning activities on their mobile devices (see Table 2).

Table 2. Learning activities using a mobile device

The students and lecturers reported frequencies in the ‘Rarely’ (2) to ‘Often’ (4) intervals. Table 2 shows the highest average frequency was for ‘Adding your class to your personal page’; whereas the lowest average frequency was for ‘Comment on class posts or re-tweet their tweet’. Again, the students reported significantly higher frequencies on all learning activity items compared to the lecturers.

4.2 User-technology relationship factors

Participants were asked to rate five performance expectancy items related to mobile device use for learning (see Table 3).

Table 3. Performance expectancy and mobile device use for learning

The students and lecturers reported levels of agreement in the ‘Slightly agree’ (5) to ‘Strongly agree’ (7) intervals. Table 3 shows the highest average agreement was for ‘Mobile devices as useful tools for learning’; whereas the lowest average agreement was for ‘Mobile learning enables students to obtain better learning outcomes’.

Participants were asked to rate five effort expectancy items related to mobile device use for learning (see Table 4).

Table 4. Effort expectancy and mobile device use for learning

The students and lecturers reported level of agreement in the ‘Slightly agree’ (5) to ‘Agree’ (6) intervals. Table 4 reveals the highest average agreement was for ‘I can easily complete my assignment using my mobile device’, and the lowest average agreement was for ‘I understand how to develop and create mobile learning activities’.

Participants were asked to rate five social influence items related to mobile device use for learning (see Table 5)

Table 5. Social influence and mobile device use for learning

The students and lecturers reported level of agreement in the ‘Slightly disagree’ (3) to ‘Agree’ (6) intervals. Table 5 shows the highest average agreements were ‘I play the main role in the use of mobile devices for learning and should be supportive of classmates’, and ‘I am likely to be well engaged in using m-learning if class supported its use’; whereas the lowest average agreement for ‘Using mobile technologies in my learning process does not fit my learning culture’ (i.e., for a negatively worded item).

Participants were asked to rate five facilitating condition items related to mobile device use for learning (see Table 6).

Table 6. Facilitating conditions and mobile device use for learning

Students and lecturers reported agreement levels in the ‘Slightly disagree’ (3) to ‘Agree’ (6) intervals. Table 6 reveals the highest average agreement was for ‘I have the knowledge required to use mobile devices for learning’; whereas the lowest average agreement was for ‘The university has a plan to introduce and integrate mobile technologies in education’.

Notably, when scores were treated as non-parametric and ordinal, the students reported significantly higher agreement compared to the lectures for all items across the four user-technology relationship factor categories except for the item, ‘The university has a plan to introduce and integrate mobile technologies’, in ‘Facilitating conditions’.

4.3 Individual difference factors

Students were asked to rate their level of agreement for 11 self-directedness items related to mobile device use for learning (see Table 7).

Table 7. Student self-directedness related to mobile device use for learning

The students reported agreement levels in the ‘Slightly agree’ (5) to ‘Strongly agree’ (7) intervals. Table 7 shows the highest average agreements were for ‘In a mobile learning environment, I am responsible for my own action’, ‘Mobile devices for learning help me to enjoy learning new information’, and ‘Mobile learning helps me to be systematic in managing learning time. In contrast, the lowest average agreement was for ‘I am more focused on mobile learning activities.

Participants were asked to rate five self-efficacy items related to mobile device use for learning (see Table 8).

Table 8. Self-efficacy for mobile device use for learning

The students and lecturers reported agreement levels in the ‘Slightly agree’ (5) to ‘Agree’ (6) intervals. Table 8 reveals the highest average agreement was for ‘I could complete the desired task using mobile technologies if I had enough time to check the task for which the mobile technology was designed’; whereas, the lowest average agreement was ‘Someone else needs to help me to use mobile devices to prepare an academic task’.

Participants were asked to rate seven voluntariness of use items related to mobile device use for learning (see Table 9).

Table 9. Voluntariness of mobile device use for learning

The lecturers and students reported levels of agreement in the ‘Slightly agree (5) to ‘Agree’ (6) intervals. Table 9 shows the highest average agreements were for ‘I intend to use mobile devices for learning in the future’, and ‘I wish to use mobile devices for learning to improve the way of learning students learn my subject. The lowest average agreement was for ‘Students are allowed to bring their own devices and use them in the classroom’.

Participants were asked to rate five behavioral intention items related to mobile device use for learning (see Table 10).

Table 10. Behavioral intention to use mobile devices for learning

The students and lecturers reported levels of agreement in the ‘Slightly agree’ (5) to ‘Strongly agree’ (7) intervals. Table 10 reveals the highest average agreements were for ‘I intend to use mobile devices for learning in academic life’, and ‘I would like to be involved in mobile device-based courses in the future’; whereas, the lowest average agreement was for ‘I intending to use mobile devices for learning frequently’.

When scores were treated as non-parametric and ordinal, students reported significantly higher agreement than lecturers across all self-efficacy, behavioral intention to use, and voluntariness of use items, excepting; ‘Despite the benefits mobile learning provides, the use of mobile technologies is not compulsory in my class’.

4.4 Internal and external learning environment factors

Participants were asked to rate four content access items related to mobile device use for learning (see Table 11).

Table 11. Content access when using mobile devices for learning

The lecturers and students reported levels of agreement in the ‘Slightly agree’ (5) to ‘Strongly agree’ (7) intervals. Table 11 reveals the highest average agreement was for ‘Encourage student group discussion using mobile devices’; whereas the lowest average agreement was for ‘Provide notifications or lecture notes via mobile social networks instead of learning management systems’.

Participants were asked to rate six academic communication items related to mobile device use for learning (see Table 12).

Table 12. Academic communication when using mobile devices for learning

The students and lecturers reported agreement levels in the ‘Slightly agree’ (5) to ‘Agree’ (6) intervals. Table 12 shows the highest average agreement was for ‘If I could participate in mobile learning activities, I would communicate better with my classmate than via face-to-face communication’, and the lowest average agreement was for ‘Mobile learning activities allow me to communicate and interact with the students better than face-to-face communication’. Participants were asked to rate seven quality characteristics of mobile learning (see Table 13).

Table 13. Mobile device characteristics quality

Participants reported agreement levels in the ‘Agree’ (6) to ‘Strongly agree’ (7) intervals. Table 13 shows the highest average ratings were for ‘For m-learning to be effective, it is important for content to be easy to navigate’, and the lowest average ratings were for ‘For m-learning to be effective, it is important for the service to be error-free’.

Participants were asked to rate 11 skills perceptions related to mobile device use for learning (see Table 14).

Table 14. Skill perceptions for mobile device use for learning

The students and lecturers reported levels of agreement in the ‘Neither agree nor disagree’ (4) to ‘Strongly agree’ (7) intervals. Table 14 reveals the highest average rating was for ‘I find I adapt quickly to the use of new mobile technology features’; whereas the lowest average rating was for ‘Mobile technologies are sophisticated to use.

Participants were asked to rate seven characteristics of mobile devices related to uses for learning (see Table 15).

Table 15. Mobile device characteristics for m-learning

The students and lecturers reported levels of agreement in the ‘Neither agree nor disagree’ (4) to ‘Agree’ (6) intervals. Table 15 shows the highest average rating was for ‘I prefer to use mobile to access learning materials instead of desktop computer’; whereas the lowest average rating was for ‘I prefer to use a mobile device with a small screen’.

When scores were treated as non-parametric and ordinal, students reported significantly higher agreement than lecturers across all content access, academic communication, quality of m-learning service, skills perceptions, and characteristics of mobile device items.

4.5 MANOVA and SEM results

The MANOVA test results for role, gender, and the two-way interaction between role and gender to predict the scale scores were all statistically significant. Role significantly influenced scores for 10 of the 12 scale scores included as dependent variables such that students obtained significantly higher scores than lecturers on these scales. Gender significantly influenced scores for five of the 12 scale scores included as dependent variables such that females obtained significantly higher scores than males on these scales. The two-way interaction between gender and role significantly influenced scores for four of the 12 scale scores included as dependent variables such that females obtained significantly higher scores than males on these scales. In terms of skills on mobile devices, females obtained higher scores than males, and students obtained higher scores than lecturers. In terms of the quality of mobile services, male students obtained the lowest scores and female students the highest scores. In terms of behavior intention scores, female students obtained higher scores than male students, and both female and male students obtained higher scores than either male or female lecturers. In terms of effort expectancy scores, female students obtained higher scores than male students, and both obtained higher scores than either male or female lecturers.

A structural equation model (SEM) with predictors, moderators, and outcome variables indicated that role (being a student) was significantly and positively associated with the three moderator variables plus the voluntariness of use outcome such that students appeared to be more likely than lecturers to obtain higher ratings. In contrast, gender (being female) was significantly and positively associated with the moderator, effort expectancy, and the outcome variable, behavioral intention such that females appeared to be more likely than men to obtain high effort expectancies and to intend to use mobile devices.

The associations between predictors, moderators and outcome variables included both direct and indirect pathways. Direct pathways for gender (being female) included those towards effort expectancy and behavioral intention, and direct pathways for students included those towards social influence, effort expectancy, performance expectancy, and voluntariness of use. Indirect pathways for gender (being female) and role (being a student) both included pathways towards voluntariness of use and behavioral intention. The strengths of these direct and indirect pathways were compared. For gender, the indirect pathway towards voluntariness of use and the direct pathway towards behavioral intention were each greater in magnitude than the corresponding indirect/direct effect. For role (being a student), indirect pathways towards both voluntariness of use and behavioral intention trumped the corresponding direct pathways.

A follow-up test involved an extended UTAUT model that included additional variables from the present dataset related to the impact of mobile learning components, with four of the five variables related to the impact of mobile learning components entered as additional moderators. For the purposes of this analysis, it was assumed that judgments about the impact of mobile learning components were influenced by performance expectancy, effort expectancy, and social influence variables. As one might expect, the parsimonious model included only some of the possible associations between the exogenous variables, moderators and outcome variables (see Figure 3). Of interest to this study is that neither role (being a student) nor gender (being female) retained any direct pathways to the two outcome variables: voluntariness of use and behavioral intention. It follows that one would expect the indirect pathways to be greater in magnitude in both cases (by default).

Gender (being female) included direct pathways to effort expectancy, quality of mobile service, academic communication, and content access, but not to voluntariness of use or behavioral intention. Role (being a student) included direct pathways to all variables except for voluntariness of use or behavioral intention. Gender (being female) and role (being a student) both included indirect pathways to characteristics of mobile use, quality of mobile service, academic communication, content access, the voluntariness of use and behavioral intention. For gender (being female), the indirect pathways to voluntariness of use and behavioral intention were by default greater in magnitude than the corresponding (non-significant) pathways. Likewise, the indirect pathway for gender to the characteristics of mobile use was greater in magnitude. However, the direct pathways between gender and quality of mobile service, academic communication and content access were each greater in magnitude than their indirect counterparts. For role (being a student), the indirect pathways to the voluntariness of use and behavioral intention were also by default greater in magnitude than the corresponding (non-significant) pathways. Beyond these pathways of primary interest, and with the exception of the indirect pathway between role and content access, other direct pathways (to quality of mobile service, and academic communication) were greater in magnitude.

In summary, after adding four variables related to the impact of mobile learning components as additional moderators, neither gender (being female) nor role (being a student) included direct pathways to the two outcome variables. It follows then that variables associated with the impact of mobile use on learning played an important role in explaining the effect of gender and role on the two outcomes (voluntariness of use, behavioral intention).

5.   Discussion

These results reported in this study show that performance expectancy and facilitating conditions affect technology use behavior [18]. In turn, while numerous studies have reported encouraging results with respect to the integration of mobile devices into classrooms to facilitate learning and teaching [22], [23], an initial analysis of students’ and lecturers’ expectations and perceptions of the technology is still vital [24], [25].

The students’ overall perceptions of mobile devices for learning were significantly more positive than the lecturers’ perceptions. As shown in the results, significant differences between each group emerged in relation to performance expectancy, although both groups regarded m-learning devices to be user-friendly, time-saving, and highly productive, which could serve to improve their overall performance. These findings accord with previous research findings that performance expectancy acts as the strongest factor relating to learner acceptance. This suggests that students have a favorable view of mobile devices within higher education learning contexts and believe that such devices enhance their learning capabilities [26].

Moreover, students have more favorable attitudes than lecturers toward m-learning devices in relation to effort expectancy, with respondents regarding mobile devices to be user-friendly and an easy way to complete academic tasks. They also exhibited understanding with respect to using such devices to interact with others. However, some lecturers indicated concerns about their understanding of how to create and develop m-learning activities. The results accord with previous research findings that effort expectancy has a significant and positive impact on learner behavioral intentions [27], [22], with learners showing greater inclination if they regard such devices as user-friendly and understandable [28].

The present study demonstrates that social influence is much more prominent for students than for lecturers. The results indicate that students particularly have a preconceived notion that the use of mobile devices will enable them to support their classmates and significantly enhance their engagement in-class activities. Moreover, students demonstrated a greater propensity to be influenced by people around them in relation to the use of mobile devices for learning compared to lecturers. These results conform to those found in previous research [29], [26]. For instance, [26] suggested that students regard their social circle as influential in regard to the adoption of such technology and concluded that the prominent figures could act as opinion-makers while integrating mobile technology.

Overall, students appear more willing than lecturers to use m-learning technology and report having adequate resources to purchase devices, knowledge of how to use m-learning technologies, and an ability to access assistance when issues arise. These findings align with those reported in previous research, where students reported they have adequate resources to afford such devices [30], [31]. However, both groups did indicate that mobile technologies do not fit into the present learning culture and felt uncertain as to whether the university had a plan to integrate such technology into the learning programs (related to administrative resistance).

Various research studies have contended that self-efficacy and self-directedness have a significant and positive influence on learners’ willingness to use new technology. Moreover, perceived ease and productive usage have been linked directly to self-confidence and willingness to adopt the technology [32], [33]. The results for student self-directedness and whether they feel they can use mobile devices to better manage their time (e.g., search for information quickly) and to learn in a self-organized environment (i.e., more autonomous learning and increased responsibility for their own learning) suggest a positive attitude and willingness in this regard. However, the results show students’ present focus is more on face-to-face learning activities, which agree with results reported in previous research [32], [33], [26].

In terms of self-efficacy for mobile technology use, this is higher in students compared to lecturers. Students demonstrate a readiness to take the risk to use mobile technologies without assistance and are confident in their ability to implement the latest technology on their own assignments to complete assigned tasks for which the mobile technology is designed. Similarly, students appear to be more enthusiastic about using mobile technologies in the classroom compared to lecturers as they regard it as an efficient learning tool. The self-efficacy results agree with previous studies findings that prior use of mobile technology and self-efficacy influence student confidence and perceptions of usage ease for m-learning [32], [33]. Overall, a positive association between self-efficacy and willingness to adopt mobile technologies for learning is evident among both students and lecturers. Both groups agree however, that the current learning environment does not support the use of such mobile devices.

Both lecturers and students show a behavioral intention (willingness) to use mobile technology for academic purposes. In terms of frequency of use; however, the idea of frequently using mobile devices for learning is not widely supported by either group. These findings accord with those in previous research showing mobile device use in the classroom may result in unproductive behaviors, and that access to mobile devices should only be allowed when required [34].

Also of interest to this study is the internal and external factors related to m-learning (e.g., access to content, mobile device characteristics, etc.) and how willing the lecturers and students were to use mobile devices to conduct group discussions, record lectures, and provide notifications of lecture notes via mobile social network platforms, the results showed both groups had a relatively positive attitude towards these ideas. These findings accord with those reported by [34], where the researchers recommended limited access to content to avoid any possible misuse. The results also suggest that both groups regard m-learning technologies as an effective mode of academic communication. However, both groups also regard mobile technologies and face to face communication as equally important for interacting with fellow students. Reference [35] reported the same results by placing an emphasis on the effectiveness of mobile devices while communicating and interacting for educational purposes.

In relation to mobile device characteristics, both groups placed high importance on ease of navigation, followed by user-friendliness, service availability, and fast speed. However, respondents were not very concerned about the error-free demand. Other desirable characteristics included affordability, broad access, and opportunities to integrate with social networks. A small screen was not considered a desirable characteristic.  These results aligned with those reported in previous research where researchers contended that ease of use and the latest characteristics could convince learners to accept a new mode of learning [36]. However, the findings did not accord with [37], as researchers stressed the importance of both the size and the smooth touch of the device screen. These discrepancies may be due to differences in how the specific items were measured across the studies, along with differences in the samples analyzed within the studies.

Lastly, the significance of gender in m-learning usage has been studied by many authors, with mixed results reported. Reference [26] reported gender had a significant impact, whereas [38] found no such impact. Within the current study, the multivariate tests for the role, gender, and the two-way interaction between role and gender were all significant. In terms of role, students were found to indicate significantly higher scores on 10 of the 12 scales compared to lecturers. Gender (being female) was found to significantly influence 5 of the 12 scale scores: behavioral intention, content access, academic communication, quality of mobile communication, and skills on mobile devices. Analysis of the two-way interaction between role and gender found that being a student and being female significantly influenced scores for 4 of the 12 scale scores: effort expectancy, behavioral intention, the quality of mobile communication, and skills on mobile devices. Here, the research findings differ from those reported in previous research where gender did not show any significant difference [39]. A study conducted by [40] also did not report a significant role of gender regarding m-learning usage.  However, due to the limited scope of their respective studies, both researchers suggested exploring the role of gender in greater detail. The discrepancies may also be due to differences in how the specific items were measured across the studies, along with differences in the samples analyzed.

The higher student scores related to willingness and enthusiasm to engage in m-learning compared to lecturers suggest that if policymakers decide to integrate m-learning into the overall learning environment, then support from students would be greater than from lecturers. Similarly, policymakers must take into consideration that student self-directedness and both lecturers’ and students’ performance expectancies, social influences, effort expectancies, and perceptions of device characteristics were significant predictors of behavioral intention to use m-learning.

6.   Conclusion

Overall, the findings in this study were rich and insightful, adding further clarity to our understanding of the factors determining students’ and lecturers’ adoption and continued use of mobile technologies for learning in higher education settings. Most students and lecturers identified the affordance of such technologies to enhance academic, informal, and teacher-student communication pathways and for the creation and management of new knowledge as important determinants of use. However, it was also the case that aspects of the design of mobile technologies such as screen size and data storage capacity were identified by the participants as potential hindrances to use. Although both students and lecturers held generally positive perceptions of m-learning within Saudi higher education service delivery, the students overall were more receptive to the idea and to the affordances mobile technologies offer to the learning process. Regarding the integration of mobile technologies for learning into higher education settings, both students and teachers reaffirmed findings reported in previous research of the need to ensure ease of use, convenience, and the provision of organizational support for users. 

Acknowledgment:

The Ministry of Education in Saudi Arabia sponsored this study. The author is thankful to all students and educators who participated in the study voluntarily.