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Learning from anywhere, anytime: The use of mobile technologies for educational purposes

This contribution is a excerpt from my latest article that was published by Springer’s Technology, Knowledge and Learning (Journal). The content has been adapted for this blog post.

Suggested citation: Camilleri, M.A. & Camilleri, A.C. (2022). Learning from anywhere, anytime: Utilitarian motivations and facilitating conditions to use mobile learning applications. Technology, Knowledge and Learninghttps://doi.org/10.1007/s10758-022-09608-8


University students are using mobile technologies to improve their learning outcomes. In the past years, a number of academic authors contended that educational apps were supporting many students in different contexts Butler et al., 2021; Crompton & Burke, 2018; Hamidi & Chavoshi, 2018; Sung et al., 2016; Tosuntas et al., 2015). In the main, they maintained that ubiquitous technologies enable them to access learning management systems and to engage in synchronous conversations with other individuals (Camilleri & Camilleri, 2021).

One may argue that the m-learning paradigm is associated with the constructivist approaches (Chang et al., 2018), including those related with discovery-based learning (Camilleri & Camilleri, 2019c). Relevant theoretical underpinnings suggest that the use of mobile apps can improve the delivery of quality, student-centered education (Camilleri & Camilleri, 2021; Camilleri, 2021b; Chang et al., 2018; Crompton & Burke, 2018; Furió et al., 2015; Lameu, 2020; Nikolopoulou et al., 2021; Sung et al., 2016; Swanson, 2020). This research raises awareness on m-learning technologies that enable students to search for solutions for themselves through the Internet and via learning management systems. It also indicated that mobile apps like Microsoft Teams or Zoom, among others, allow them to engage in synchronous conversations with course instructors and with their peers, in real time.

This study explored the users’ perceptions about m-learning technologies. It validated key constructs from TAM Briz-Ponce et al., 2017; Cheung & Vogel, 2013; Granić & Marangunić, 2019; Ngai et al., 2007; Scherer et al., 2019; Thong Hong & Tam, 2002) and UTAUT (Gunasinghe et al., 2019; Yang et al., 2019), as shown in Table 1.

The descriptive statistics clearly indicated that the research participants felt that m-learning technologies were useful for them to continue their course programs. The principal component analysis confirmed that the students’ engagement with their educational apps was primarily determined by their ease of use. This is one of the main factors that influenced their intentions to engage with m-learning apps.

The findings revealed that higher education students were using m-learning apps as they considered them as useful tools to enhance their knowledge. Evidently, their perceptions about the ease of use of m-learning technologies were significantly correlated with their perceived usefulness. In addition, it transpired that both constructs were also affecting their attitudes towards usage, that in turn preceded their intentions to use m-learning apps.

The results also revealed that the respondents were satisfied by the technical support they received during COVID-19. Apparently, their university provided appropriate facilitating conditions that allowed them to engage with to m-learning programs during the unexpected pandemic situation and even when the preventative restrictions were eased.

The stepwise regression analyses shed light on the positive and significant relationships of this study’s research model. Again, these results have proved that the respondents were utilizing m-learning apps because their university (and course instructors) supported them with adequate and sufficient resources (i.e. facilitating conditions). The findings indicated that they were assisted (by their institution’s helpdesk) during their transition to emergency remote learning. In fact, the study confirmed that there was a positive and significant relationship between facilitating conditions and the students’ engagement with m-learning technologies.

On the other hand, this empirical research did not yield a statistically significant relationship between the students’ social influences and their intentions to use the mobile technologies. This is in stark contrast with the findings from past contributions, where other researchers noted that students were pressurized by course instructors to use education technologies (Camilleri & Camilleri, 2020; Teo & Zheng, 2014). The researchers presume that in this case, the majority of university students indicated that they were not coerced by educators or by their peers, to use m-learning apps. This finding implies that students became accustomed or habituated with the use of mobile technologies to continue their course programs.

This research builds on previous technology adoption models Davis et al., 1989; Venkatesh et al., 2003; 2012) to better understand the students’ dispositions to engage with m-learning apps. It integrated constructs from TAM with others that were drawn from UTAUT/UTAUT2. To the best of the researchers’ knowledge, currently, there are no studies that integrated facilitating conditions and social influences (from UTAUT/UTAUT2) with TAM’s perceived ease of use, perceived usefulness and attitudes. This contribution addresses this knowledge gap in academia. In sum, it raises awareness on the importance of providing appropriate facilitating conditions to students (and educators). This way, they will be in a better position to use educational technologies to improve their learning outcomes.

Practical implications

This research indicated that students held positive attitudes and perceptions on the use of m-learning technologies in higher educational settings. Their applications allow them to access course material (through Moodle or other virtual learning environments) and to avail themselves from video conferencing facilities from everywhere, and at any time. The respondents themselves considered the mobile technologies as useful tools that helped them improve their learning journeys, even during times when COVID-19’s preventative measures were eased. Hence, there is scope for university educators and policy makers to create and adopt m-learning approaches in addition to traditional teaching methodologies, to deliver quality education (Camilleri, 2021).

Arguably, m-learning would require high-quality wireless networks with reliable connections. Course instructors have to consider that their students are accessing their asynchronous resources as well as their synchronous apps (like Zoom or Microsoft Teams) on campus or in other contexts. Students using m-learning technologies should have appropriate facilitating conditions in place, including adequate Wi-Fi speeds (that enable access to high-res images, and/or interactive media, including videos, live streaming, etc.). Furthermore, higher education institutions ought to provide ongoing technical support to students and to their members of staff (Camilleri & Camilleri, 2021).

This study has clearly shown that the provision of technical support, as well as the utilization of user-friendly, m-learning apps, among other factors, would probably improve the students’ willingness to engage with these remote technologies. Thus, course instructors are encouraged to create attractive and functional online environments in formats that are suitable for the screens of mobile devices (like tablets and smartphones). There can be instances where university instructors may require technical training and professional development to learn how to prepare and share customized m-learning resources for their students.

Educators should design appealing content that includes a good selection of images and videos to entice their students’ curiosity and to stimulate their critical thinking. Their educational resources should be as clear and focused as possible, with links to reliable academic sources. Moreover, these apps could be developed in such a way to increase the users’ engagement with each other and with their instructors, in real time.

Finally, educational institutions ought to regularly evaluate their students’ attitudes and perceptions toward their m-learning experiences, via quantitative and qualitative research, in order to identify any areas of improvement.

Research limitations and future research directions

To date, there have been limited studies that explored the institutions’ facilitating conditions and utilitarian motivations to use m-learning technologies in higher education, albeit a few exceptions. A through review of the relevant research revealed that researchers on education technology have often relied on different research designs and methodologies to capture and analyze their primary data. In this case, this study integrated measures that were drawn from TAM and UTAUT. The hypotheses were tested through stepwise regression analyses. The number of respondents that participated in this study was adequate and sufficient for the statistical purposes of this research.

Future research could investigate other factors that are affecting the students’ engagement with m-learning technologies. For example, researchers can explore the students’ intrinsic and extrinsic motivations to use educational apps. These factors can also have a significant effect on their intentions to continue their learning journeys. Qualitative research could shed more light on the students’ in-depth opinions, beliefs and personal experiences on the usefulness and the ease of use of learning via mobile apps, including serious games and simulations. Inductive studies may evaluate the effectiveness as well as the motivational appeal of gameplay. They can possibly clarify how, where and when mobile apps can be utilized as teaching resources in different disciplines. They can also identify the strengths and weaknesses of integrating them in the curricula of specific subjects.

Prospective researchers can focus on the design, structure and content of m-learning apps that are intended to facilitate the students’ learning experiences. Furthermore, longitudinal studies may provide a better understanding of the students’ motivations to engage with such educational technologies. They can measure their progress and development, in the long term. The students’ perceptions, attitudes and intentions to use m-learning technologies can change over time, particularly as they become experienced users.

A prepublication of the full article is available here: https://www.researchgate.net/publication/360541461_Learning_from_anywhere_anytime_Utilitarian_motivations_and_facilitating_conditions_to_use_mobile_learning_applications

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The Users’ Perceptions of the Electronic Government’s (e-gov) Services

This is an excerpt from one of my latest conference papers entitled; “Exploring the Behavioral Intention to Use E-Government Services: Validating the Unified Theory of Acceptance and Use of Technology”.

How to Cite: Camilleri, M.A. (2019). Exploring the Behavioral Intention to Use E-Government Services: Validating the Unified Theory of Acceptance and Use of Technology. In Kommers, P., Hui, W., Isaias, P., & Tomayess, I. (Eds) 9th International Conference on Internet Technologies & Society, Lingnan University, Hong Kong (February 2019), International Association for Development of the Information Society.

The information and communication technologies (ICTs) as well as other web-based technologies can enhance the effectiveness, economies and efficiencies of service delivery in the public sector. Therefore, many governments are increasingly using the digital and mobile media to deliver public services to online users (Zuiderwijk Janssen & Dwivedi. 2015). The electronic or mobile government services (e-gov) are facilitators and instruments that are intended to better serve all levels of the governments’ operations, including its departments, agencies and their employees as well as individual citizens, businesses and enterprises (Rana & Dwivedi, 2015). The governments may use information and communication technologies, including computers, websites and business process re-engineering (BPR) to interact with their customers (Isaías, Pífano & Miranda, 2012; Weerakkody, Janssen & Dwivedi, 2011). E-gov services involve the transformational processes within the public administration; that add value to the governments’ procedures and services through the introduction and continued appropriation of information and communication technologies, as a facilitator of these transformations. These government systems have improved over the years.  In the past, online users relied on one-way communications, including emails. Today, online users may engage in two-way communications, as they communicate and interact with the government via the Internet, through instant-messaging (IM), graphical user interfaces (GUI) or audio/video presentations.

Traditionally, the public services were centered around the operations of the governments’ departments. However, e-governance also involves a data exchange between the government and other stakeholders, including the businesses and the general public (Rana & Dwivedi, 2015). The advances in technology have led to significant improvements in the delivery of service quality to online users (Isaías et al., 2012). As e-government services become more sophisticated, the online users will be intrigued to interact with the government as e-services are usually more efficient and less costly than offline services that are delivered by civil servants. However, there may be individuals who for many reasons, may not have access to computers and the internet. Such individuals may not benefit of the governments’ services as other citizens. As a result, the digital divide among citizens can impact their socio-economic status (Ebbers, Jansen & van Deursen, 2016). Moreover, there may be individuals who may be wary of using e-government systems. They may not trust the e-gov sites with their personal information, as they may be concerned on privacy issues. Many individuals still perceive the governments’ online sites as risky and unsecure.

This contribution addresses a knowledge gap in academic literature as it examines the online users’ perceptions on e-gov systems. It relies on valid and reliable measures from the Unified Theory of Acceptance and Use of Technology (UTAUT) (Zuiderwijk et al., 2015; Wang & Shih, 2009; Venkatesh, Morris, Davis & Davis, 2003;2012) to explore the respondents ’attitudes toward performance expectancy, effort expectancy, social influences, facilitating conditions as well as their intentions to use the governments’ electronic services. Moreover, it also investigates how the demographic variables, including age, gender and experiences have an effect on the UTAUT constructs.. In a nutshell, this research explains the causal path that leads to the online users’ acceptance and use of e-gov.


Ebbers, W. E., Jansen, M. G., & van Deursen, A. J. 2016. Impact of the digital divide on e-government: Expanding from channel choice to channel usage. Government Information Quarterly, Vol. 33, No. 4, pp. 685-692.

Isaías, P., Pífano, S., & Miranda, P. (2012). Web 2.0: Harnessing democracy’s potential. In Public Service, Governance and Web 2.0 Technologies: Future Trends in Social Media (pp. 223-236). IGI Global.

Rana, N. P., & Dwivedi, Y.K. 2015. Citizen’s adoption of an e-government system: Validating extended social cognitive theory (SCT). Government Information Quarterly, Vol. 32, No. 2, pp. 172-181.

Venkatesh, V., Morris, M.G., Davis, G.B., & Davis, F. D. 2003. User acceptance of information technology: Toward a unified view. MIS Quarterly, pp. 425-478.

Venkatesh, V., Thong, J.Y., & Xu, X. 2012. Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Quarterly, pp. 157-178.

Wang, Y.S., & Shih, Y.W. (2009). Why do people use information kiosks? A validation of the Unified Theory of Acceptance and Use of Technology. Government Information Quarterly, Vol. 26, No. 1, pp. 158-165.

Weerakkody, V., Janssen, M., & Dwivedi, Y. K. 2011. Transformational change and business process reengineering (BPR): Lessons from the British and Dutch public sector. Government Information Quarterly, Vol. 28, No. 3, pp. 320-328.

Zuiderwijk, A., Janssen, M., & Dwivedi, Y.K. 2015. Acceptance and use predictors of open data technologies: Drawing upon the unified theory of acceptance and use of technology. Government Information Quarterly, Vol. 32, No. 4, pp. 429-440.

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Filed under digital media, e government, internet technologies, internet technologies and society, online, Web