2.1. Eligibility criteria

The eligibility criteria are divided into two sections. The first section includes inclusion criteria that mainly focus on titles and keywords as metadata. Specifically, it looks for the combination of terms such as ’mobile learning’ and ’university’ in various forms of citation, including variations such as ’m-learning’ and ’mobile learning’. These criteria allow for an exhaustive and precise search for relevant studies that address the adoption of mobile learning in university environments, ensuring the inclusion of the most relevant literature for analysis.

The exclusion process involves three phases. The first phase excludes all records with erroneous indexing or those not directly related to the study’s topic. The second phase of exclusion aims to eliminate all documents for which full text access is not available. This phase applies only to Systematic Literature Reviews since the review in question focuses exclusively on the analysis of metadata. Finally, the third phase, the Exclusion phase, is responsible for discarding documents that do not present a clearly defined or explicit mobile learning adoption model. These exclusion criteria ensure the rigor and quality of the ongoing systematic literature review’s study selection process.

2.2. Source of information

The Scopus and Web of Science databases were chosen as the primary sources of information. Scopus and Web of Science are considered the main bibliometric databases today due to their wide coverage and reputation in the academic and scientific fields. Research, such as that conducted by [14], has compared the quality and coverage of different bibliometric databases, concluding that Scopus and Web of Science are two of the most complete and reliable platforms available. Similarly, Tennant [15] conducted a study comparing the quality and coverage of different bibliometric databases, contributing to the understanding of the scope of platforms such as Scopus and Web of Science in the field of scientific knowledge collection. Although it is important to acknowledge that no database is entirely comprehensive, both Scopus and Web of Science provide a broad selection of academic and scientific journals, along with advanced search and analysis tools, making them ideal options for conducting a systematic literature review in a university setting.

2.3. Search strategy

To facilitate the search for relevant studies in the Scopus and Web of Science databases, two specialized search equations were designed. These equations were adapted to the defined inclusion criteria and the search characteristics of each platform. They were meticulously developed to ensure comprehensiveness and precision in identifying relevant articles on the adoption of mobile learning in the university context. The search equations were materialized on January 30, 2024, taking advantage of the advanced search functionalities of both databases to maximize the collection of relevant literature in the field of study.

For the Scopus database: (TITLE (("mobile learning") OR (mlearning) OR (m-learning)) AND TITLE (student OR scholar OR undergraduate OR learner) AND TITLE ((adoption) OR (use) OR (acceptance) OR tam OR tpb OR utaut)) OR (KEY (("mobile learning") OR (mlearning) OR (m-learning)) AND KEY (student OR scholar OR undergraduate OR learner) AND KEY ((adoption) OR (use) OR (acceptance) OR tam OR tpb OR utaut))

For the Web of Science database: (TI = ((“mobile learning”) OR (mlearning) OR (m-learning)) AND TI = (student OR scholar OR undergraduate OR learner) AND TI = ((adoption) OR (use) OR (acceptance) OR TAM OR TPB OR UTAUT)) OR (AK = ((“mobile learning”) OR (mlearning) OR (m-learning)) AND AK = (student OR scholar OR undergraduate OR learner) AND AK = ((adoption) OR (use) OR (acceptance) OR TAM OR TPB OR UTAUT))

2.4. Data management

The study utilized the Microsoft Excel® tool to extract, store, and process information from selected databases. This tool provided an organized structure to record relevant data from identified studies, allowing for efficient subsequent analysis. Each article obtained from the databases underwent an extensive and thorough full-text review to identify its relevance, contributions, and findings regarding the adoption of mobile learning in university settings. This systematic and detailed approach ensured completeness and quality in the collection and analysis of scientific literature relevant to the study’s topic.

2.5. Selection process

Following the PRISMA 2020 statement guidelines, it is crucial to utilize internal automatic classifiers to facilitate the systematic literature review study selection process [16]. This practice helps to mitigate the risk of missing studies or incorrect classifications when applying inclusion and exclusion criteria more efficiently. Additionally, it is essential to validate these classifiers internally or externally to understand and control the risk of bias in study selection. In this study, we utilized an automation tool created in Microsoft Excel® as an internal classifier. All researchers involved in the study independently applied this tool during the study selection process, using predefined inclusion and exclusion criteria. This approach helped to minimize the risk of missing studies or incorrect classifications by converging the results and carefully reviewing the extracted metadata.

Furthermore, we used a specific Microsoft Excel® tool to homogenize all the articles extracted from both sources of information. This facilitated the process of excluding duplicates and applying the predefined inclusion and exclusion criteria. This ensures clear and unambiguous identification of the texts that will be analyzed in-depth for this systematic literature review. It guarantees consistency in the selection process mentioned earlier and contributes to the study’s integrity and validity by minimizing the risk of missing relevant studies or making incorrect classifications.

2.6. Data collection process

As per the guidelines of [16], it is essential to specify the methods employed for collecting data from reports in a systematic literature review. In this study on the adoption of mobile learning in the university context, we used Microsoft Excel® as an automated tool for data collection from the selected databases, Scopus and Web of Science. The authors acted as reviewers for data validation, with each author conducting an independent evaluation to ensure an objective and thorough assessment of the information extracted from the studies. Subsequently, the authors collectively confirmed the data, comparing and contrasting the results obtained by each reviewer. The process was developed until achieving absolute convergence in the results, ensuring the reliability and integrity of the data collected in the literature review systematics.

2.7. Data elements

The objective was to gather data from all articles that met the research objective, which required adherence to the specialized search equation created for each database. This involved searching for results related to the implementation of mobile learning in the university context. The selected texts covered relevant measurements, time points, and analyses. However, if any information was missing or unclear, it was excluded as ’non-relevant texts’ since they do not contribute to the understanding of knowledge on the topic. The purpose and scope of the research were considered to ensure consistency, allowing for the inclusion of significant and relevant results for the analysis of the adoption of mobile learning in the university context.

2.8. Assessment of the risk of bias of the study

The process of assessing the risk of bias in the included studies was a collaborative effort among all authors. The authors used the same automated Microsoft Excel® tool for data collection and evaluation of included studies. Each author independently assessed the studies using predefined criteria to identify potential sources of bias. The use of this automated tool standardized the evaluation process, ensuring the quality and integrity of the results. This comprehensive and rigorous approach contributed to the validity and reliability of the systematic literature review on the adoption of mobile learning in the university context.

2.9. Measures of effect

It is relevant to specify that the effect measures traditionally used in primary research, such as the risk ratio or the difference in means, are not applicable in the analysis of secondary research sources. In this study, variables related to the data collection instruments, the geographical context of application of the study, the target population, the psychobehavioral theory used and latent variables within each evaluated model are analyzed. These aspects are addressed through the use of Microsoft Excel® to organize and analyze the data, as well as the use of VOSviewer® to determine thematic associations between the selected studies, this allows a deeper and more holistic understanding of the adoption of mobile learning in the university context, expanding the scope beyond conventional effect measures and providing a comprehensive view of the factors that influence this educational phenomenon.

2.10. Synthesis methods

It was established that all the studies included in the analysis had to be open access to ensure the availability of the full text and facilitate a thorough examination of each article. The data extracted from the selected studies were then stored in Microsoft Excel®. This tool provided a centralized platform to systematically tabulate and organize information, allowing for the comparison of study characteristics, preparation of data for presentation and synthesis, and efficient and coherent display of results. The use of Microsoft Excel® as a data management tool contributed to the rigorous organization and structured analysis of the information collected in this systematic review.

2.11. Assessment of reporting bias

When conducting a systematic literature review, it is important to be aware of potential biases towards certain synonyms found in thesauri, such as the IEEE. These biases may influence inclusion criteria, search strategy, and data collection, which could result in the exclusion of relevant studies that use alternative terms to describe the concept of mobile learning adoption. Additionally, excluding texts without a defined adoption model may lead to the omission of valuable information that could contribute to the understanding and construction of knowledge on the subject. Therefore, it is essential to take steps to mitigate the impact of these potential biases on the systematic literature review process.

2.12. Certainty evaluation

As part of this systematic investigation, we comprehensively and exhaustively evaluated the certainty of the body of evidence. We applied inclusion and exclusion criteria to each study to determine the suitability of the selected articles. Additionally, we conducted an individual evaluation of each article to identify any possible methodological biases or limitations of the study. These aspects were mentioned in both the description of the methodological designs and the discussion of the study’s limitations. This contributed to a comprehensive evaluation of the certainty of the body of evidence, ensuring the transparency and reliability of the results obtained in the systematic review of literature on the adoption of mobile learning in university contexts (see Fig 1).

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Fig 1. PRISMA flowchart.

Own elaboration based on Scopus and Web of Science.

https://doi.org/10.1371/journal.pone.0304116.g001

In this systematic literature review, the selection and exclusion of studies were carried out in several stages. First, we conducted an exhaustive search in selected information sources to identify relevant studies. Then, we eliminated duplicate records to ensure data integrity. Finally, we proceeded with the selection and exclusion of studies based on pre-defined criteria. Three exclusion phases were carried out, applying predefined criteria to discard studies that did not meet the research objectives and scope. After this rigorous selection process, 44 articles were included as pertinent and relevant to address the issue of mobile learning adoption in the university context.

4.1. Analysis of data collection instruments

The results section indicates that the primary data collection instruments used were questionnaires and surveys. Several studies have contributed significantly to the understanding of questionnaires as data collection instruments. For example, Kumar et al. (2022) examined the behavioral change among university engineering students in the acceptance of mobile learning after the pandemic, providing an insightful view on how students perceive and adopt this educational modality. Similarly, Camilleri and Camilleri [35] explored the utilitarian motivations and facilitating conditions for mobile learning, delving into the factors that influence the adoption of this technology.

Baghcheghi et al. [23] analyzed the factors that affect the adoption of mobile learning in health professional students using the Technology Acceptance Model. The study provided valuable information on students’ perceptions and attitudes towards the use of mobile devices for learning. This research, along with others in the literature, has significantly contributed to the understanding of the factors that influence the adoption of mobile learning in the university setting. It has been a key reference in this field of research.

4.2. Analysis of the geographical context of the adoption of mobile learning in the university context

The results section reveals that the theme has mainly occurred in Asia, including countries such as China, Turkey, Iran, India, Saudi Arabia, and Malaysia. It has also been observed in Europe, specifically in Sweden, Spain, the United Kingdom, and Romania. In China, Peng et al. [10] conducted a study on improving students’ English language learning through mobile learning, integrating the Technology Acceptance Model and the SOR Model. Kucuk et al. [1] proposed a model for medical students’ behavioral intention towards mobile learning in Turkey, examining their perceptions and attitudes in this educational field.

Azizi and Khatony [32] explored the factors that influence the intention of medical science students to adopt mobile learning in Iran, providing a detailed view of the variables that affect this decision. Gupta et al. [42] investigated Indian students’ perception of mobile learning as a tool for education during the COVID-19 pandemic. Alturki and Aldraiweesh [44] analyzed the use of mobile learning in higher education during the pandemic in Saudi Arabia, providing valuable insights into students’ experiences.

Saroia and Gao [34] investigated university students’ intention to use mobile learning management systems in Sweden, exploring their attitudes and perceptions towards this emerging technology. Additionally, Andujar et al. [31] examined the integration of flipped learning through mobile devices in Spain, exploring technological acceptance and the flipped learning experience. Abu-Al-Aish and Love [25] investigated the factors that influence the acceptance of mobile learning among students in the United Kingdom. Their study provides a detailed view of the elements that affect the adoption of this educational modality.

4.3. Analysis of the target population in the adoption of mobile learning in the university context

As previously mentioned, the topic has gained prominence in several countries including Jordan, Malaysia, Saudi Arabia, China, Spain, India, and Indonesia. The results section indicates that research on this topic has primarily focused on university students and students in general in these countries. Ismiyati et al. [48] conducted a study to investigate Semarang State University students’ intention to use mobile learning as an alternative to in-person learning during the COVID-19 pandemic. Alturki and Aldraiweesh [44] examined students’ perceptions of the actual use of mobile learning in higher education during the pandemic, providing a detailed view of their experiences.

Lo et al. [47] conducted a study on augmented reality-based learning for natural science inquiry activities in primary schools in Taiwan, from the perspective of sustainable development. The study focused on students in general. Research sheds light on the effectiveness and sustainability of AR-based learning in the Taiwanese school context. These investigations represent significant contributions to understanding the adoption and benefits of mobile learning in different educational contexts.

4.4. Analysis of psychometric theories in the adoption of mobile learning in the university context

The results section reveals that the main theories used to understand the factors that determine the adoption of mobile learning in the university context are TAM, UTAUT, Own Models, Extended UTAUT, and TPB. Within the scope of Technology Acceptance Theory (TAM), Almaiah et al. [24] examined the factors that affect the acceptance of a mobile learning application in higher education during the COVID-19 pandemic, using the Ann-Sem modeling technique. Alghazi et al. [29] developed an extended model to examine the effect of technical factors on the sustainable use of mobile devices as a learning tool, based on the Unified Technology Acceptance Model (UTAUT).

Pramana [11] investigated the determinants of mobile learning system adoption among university students in Indonesia. Similarly, Alfalah [26] explored the factors influencing the adoption and use of mobile learning management systems among students in Saudi Arabia. Azizi and Khatony [32] investigated the factors that affect students’ intention to adopt mobile learning in medical sciences, using the Extended UTAUT model and the Theory of Planned Behavior (TPB). The study provides a deep understanding of the underlying theories that influence the adoption of mobile learning in the university context.

4.5. Analysis of the main variables of adoption of mobile learning in the university context

The results indicate that the main latent variables used to determine adoption of mobile learning in the university context are Behavioral Intention, Attitude, Expectation of Effort, and Current Use. Regarding the Behavioral Intention variable, Andujar et al. [31] explored the integration of foreign language learning through mobile devices, focusing on technological acceptance and the flipped learning experience. As for the Attitude variable, Azizi and Khatony [32] investigated the factors that affect medical science students’ intention to adopt mobile learning, analyzing the influence of their attitude towards this educational modality.

Dahri et al. [3] investigated teachers’ acceptance of mobile learning technology, focusing on the influence of mobile self-efficacy and training based on 21st century skills, while Almaiah et al. [24] examined the factors affecting the adoption of a mobile learning application in higher education during the COVID-19 pandemic using the Ann-Sem modeling technique. These studies provide a deeper understanding of the latent variables that influence the adoption of mobile learning in the university environment.

In addition to the previously mentioned variables, other factors have emerged as significant in analyzing the adoption of mobile learning in university contexts. One such factor is Academic Relevance, which pertains to students’ perception of the usefulness and relevance of mobile learning for their academic training. The influence of the perception of usefulness and academic relevance on students’ intention to use learning management systems has been explored in studies such as Alfalah [26] and Saroia and Gao [34]. This study examines the use of mobile learning management systems in different university environments.

Another important factor to consider is engagement, which refers to the level of dedication and emotional connection that students have with mobile learning. This variable has been analyzed by Imlawi et al. [2] and Andujar et al. [31] to understand how student engagement influences their intention to use mobile learning management systems in university environments. It is important for the active involvement of students in the educational process.

Compatibility is a relevant variable that has been extensively studied in the context of mobile learning adoption. It refers to students’ perception of the agreement between mobile learning and their needs, skills, and technological environment [29]. This variable has been explored in studies such as those by Wang, Zhao, and Cheng [19] and Alghazi et al. The language used in the text is clear, concise, and objective, with a formal register and precise word choice. The text follows a logical structure with causal connections between statements. The grammar, spelling, and punctuation are correct. No changes were made to the content of the original text. Masa’deh et al. [45] and Chen [8] have investigated the impact of perceived compatibility on students’ adoption of mobile learning. They analyzed how technical factors, such as technological stress and available resources, affect students’ perceptions of mobile learning. It is important to consider the compatibility of mobile learning with your individual learning needs and expectations.

Finally, confirmation is another variable that has gained importance in the literature on the adoption of mobile learning in higher education. It is defined as the continuous evaluation that students make of the usefulness and effectiveness of mobile learning after its initial implementation. Mobile learning applications have been extensively studied to explore the factors that influence students’ confirmation of continuing to use them. These studies, such as those by Roslan et al. and Alowayr and Al-Azawei, highlight the impact of these factors on user satisfaction and intention to continue using these technologies. The language used is clear, objective, and value-neutral, with a formal register and precise word choice. The text follows conventional structure and adheres to formatting features and style guides. The grammar, spelling, and punctuation are correct. No changes in content have been made.

From another perspective, it is crucial to take into account the perspectives provided by Al-Adwan, Al-Adwan and Berger [50] and Al-Adwan, Al-Madadha and Zvirzdinaite [51], highlighting the importance of analyzing other factors that influence the adoption of mobile learning in higher education, highlighting the relevance of delving into factors such as student disposition and the enigmatic nature of adoption to unravel the complexities surrounding the adoption of mobile learning.

4.6. Main research gaps

Table 3 presents the main research gaps identified in the field of mobile learning in the university context that need to be addressed in future research. These gaps highlight areas where the existing literature may be insufficient or where greater depth is needed to fully understand the adoption of mobile learning and its implications in the university context.

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Table 3. Main research gaps identified.

https://doi.org/10.1371/journal.pone.0304116.t003

The identified gaps in research on the adoption of mobile learning in university contexts provide an opportunity for further exploration to enhance our understanding of this dynamic. One of the gaps identified is the lack of studies in Latin American countries, which highlights the need for specific investigation into the factors that influence the adoption of mobile learning in universities in this region. How do these factors differ from those identified in other geographic contexts? Examining these differences could provide insight into the cultural and socioeconomic factors that impact the adoption of these technologies in various regions of the world.

Additionally, there is a lack of application of emerging theories in the study of mobile learning adoption. While traditional theories like TAM and UTAUT have been extensively utilized, incorporating emerging theories could provide a more comprehensive and current understanding of the phenomenon. How do these new theoretical models compare to traditional ones in explaining the adoption of mobile learning in university contexts? Investigating this question could reveal new facets of the adoption process and provide innovative insights for the design of implementation and promotion strategies.

Another significant gap identified is the lack of consideration of accessibility in studies on mobile learning adoption. Accessibility is a crucial factor in ensuring the effectiveness and equity of technologies for all students. To design more inclusive and effective strategies, it is important to explore how the accessibility of mobile technologies affects the adoption of mobile learning among diverse student groups. What strategies can be implemented to improve the accessibility of mobile learning platforms? Investigating this question could lead to identifying practices and policies that encourage broader and more equitable adoption of mobile learning in university settings.

The gaps in research on the adoption of mobile learning in university settings not only highlight areas where current knowledge is limited but also point to the importance of addressing these gaps to promote more complete and balanced development in this field. By exploring and closing these gaps, we can advance our theoretical understanding of mobile learning adoption. This will enable us to more accurately inform educational policies and practices that encourage the effective and equitable integration of these technologies into teaching and learning at universities.

4.7. Theoretical implications

The evaluation of the data collection instruments used in the studies allows us to identify trends, methodological approaches, and possible biases in the measurement of key variables. Additionally, analyzing the geographical context of each study reveals regional patterns in the implementation and acceptance of mobile learning. This may suggest the influence of cultural, economic, and technological factors in the adoption of this educational modality.

Considering the target population of the studies provides valuable information on the demographic, academic, and socioeconomic characteristics of the university students involved in the adoption of mobile learning. Additionally, exploring the theoretical models used to understand the adoption phenomenon offers insights into the predominant theoretical perspectives and their applications in different educational contexts. Identifying the key factors used to approach the understanding of mobile learning in the university environment allows for a critical evaluation of the determinants that influence adoption and the effective use of this technology.

The systematic literature review, conducted using the PRISMA-2020 methodology, reveals the research gaps in the adoption of mobile learning in university contexts. Identifying gaps in research is crucial for future studies on mobile learning adoption in universities. These gaps may be due to a lack of research in certain geographic areas, scarcity of studies using emerging theories, absence of consideration of relevant factors, or the need to delve into specific aspects of the phenomenon. By identifying these gaps, efforts can be focused on areas where greater theoretical and empirical development is required to comprehensively understand mobile learning adoption in the university environment.

4.8. Practical implications

The current study has significant practical implications for both academics and decision-makers in the field of education. The evaluation of data collection instruments enables identification of the most effective ones for capturing relevant information on the adoption of mobile learning. This can guide academics in designing future research and developing evaluation tools for more accurate monitoring and measuring of progress in implementing this technology.

The analysis of the geographical context of each study provides a global view of the trends and specific challenges associated with the adoption of mobile learning in different regions of the world. This information is invaluable for decision-makers in the educational field as it allows them to identify geographic areas where greater support and resources are needed to promote the successful adoption of this educational modality, as well as to adapt implementation strategies to the local needs and realities of each context.

Consideration of the target population is crucial for understanding the specific characteristics and needs of university students in relation to mobile learning. This insight allows decision-makers to design educational programs and policies that best suit the preferences and abilities of the students, promoting greater participation and commitment to this learning modality.

The analysis of theoretical models and factors used to understand the adoption of mobile learning in the university context provides academics and decision-makers with a solid conceptual framework to design intervention strategies and training programs that encourage successful and sustainable adoption of this technology. Identifying research gaps is also essential as it highlights areas where further research and development are required to address specific challenges and maximize the impact of mobile learning in university education.

It provides educators and educational policy makers with a deep understanding of the trends, challenges, and best practices related to the integration of mobile learning in university environments. This allows them to make informed decisions about the implementation of educational technologies and design teaching strategies that make the most of the potential of mobile learning to improve the learning experience of students.

Furthermore, a systematic review can impact the allocation of resources and strategic planning in educational institutions. It identifies priority areas for investment in technological infrastructure, teacher training, and development of digital content. Additionally, it can guide the formulation of policies and support programs that promote equity of access and digital inclusion, particularly for students who may face socioeconomic or geographic barriers to accessing digital educational resources.

Understanding the factors that influence the adoption and effective use of mobile learning in university environments can inform training and professional development strategies in companies and organizations. This can help design mobile learning programs that align with the needs and expectations of today’s workforce. The practical implications can be extended to the industry and business sector.

Additionally, the review’s findings and recommendations can inform government decision-makers in formulating education and technology-related public policies. This can promote effective integration of mobile learning into national or regional educational systems, fostering innovation and continuous improvement in higher education.

4.9. Limitations

One limitation of this systematic literature review is the potential for publication bias, as only studies available in the Scopus and Web of Science databases were included. It is possible that relevant studies not indexed in these databases or available in other languages were not considered, leading to a limited selection of literature. Additionally, restricting articles to English may have excluded significant research conducted in other languages, potentially biasing the results towards a specific linguistic perspective.

Another limitation of this study is related to the search process. Although we used broad search criteria and explored multiple combinations of terms related to mobile learning adoption in university environments, it is possible that some relevant studies were not identified due to the complexity and diversity of the terminology used in this field. Additionally, the exclusion of studies not available in full text may have limited the inclusion of relevant research that was only available in abstract format or with restricted access. These limitations may have affected the exhaustiveness and representativeness of the systematic review, which could impact the generalization of the results and conclusions obtained.

Finally, a limitation of this study is that important databases, such as ERIC, were excluded. ERIC is recognized as one of the main sources of information in the field of education. The omission of this database may have resulted in a lack of access to relevant studies that could have further enriched the analysis and understanding of the topic. Therefore, future studies should aim to include a wider range of databases to ensure a comprehensive review of the literature and a more complete representation of available research.

4.10. Agenda for future research

Several recommendations for future research can be derived from the obtained results, which could enhance the understanding of this emerging field. Firstly, longitudinal studies are suggested to track the evolution of mobile learning adoption over time and assess its long-term impact on academic achievement and the student experience. These investigations could provide a more complete understanding of how attitudes and behaviors towards mobile learning change over time and identify predictors of sustained adoption.

Furthermore, it is recommended to expand the geographical scope of research to include less explored contexts, such as Africa, Latin America, and Eastern Europe. Comparative studies between different geographical regions could help identify common patterns and significant differences in the factors that influence mobile learning adoption.

To improve the impact of mobile learning on diverse university student populations, it is recommended to conduct research that analyzes the needs and preferences of different demographic groups, including students from various disciplines, educational levels, and socioeconomic backgrounds. This will enable the design of more personalized interventions that are better adapted to the needs of each group.

Regarding theoretical models, researchers are encouraged to validate new conceptual frameworks that accurately capture the underlying processes that influence mobile learning adoption. It is also recommended to integrate multiple theoretical models and approaches to obtain a more holistic and multidimensional understanding of this complex phenomenon.

Finally, it is recommended to investigate emerging and under-researched variables that may impact the adoption of mobile learning, such as digital accessibility, technological inclusion, data privacy, and cybersecurity. These aspects are crucial to ensure fair and sustainable adoption of mobile learning in university environments and can lead to new areas of research that address emerging needs and concerns in this constantly evolving field.

4.11. Main adoption model of mobile learning in the university context

Fig 6 shows the main theoretical models and variables used to understand or predict the adoption of mobile learning in the university context. The TAM and the UTAUT are consolidated conceptual frameworks that have been widely used to understand the attitudes, perceptions, and behaviors of university students towards the use of mobile technologies in their educational processes. Theoretical models, along with associated variables such as perceived ease of use, perceived usefulness, attitude toward use, social influence, and intention to use, have been crucial in contextualizing and analyzing the adoption of mobile learning in various university environments.

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Fig 6. Proposed theoretical model on adoption of mobile learning in the university context.

https://doi.org/10.1371/journal.pone.0304116.g006

This systematic literature review examines the adoption of Mobile Learning in the university context and proposes a comprehensive model that combines the UTAUT Model and the TAM. The model incorporates external variables for a holistic understanding of Mobile Learning adoption in university environments.

The proposed model integrates additional variables, such as Academic Relevance, Confirmation, and Compatibility factors, into the conceptual pillars of UTAUT and TAM. These variables are of vital importance in the academic field as they evaluate the relevance of Mobile Learning in the university educational context and determine the acceptance and effective use of this technology. This theoretical contribution provides a comprehensive and contextualized analytical framework for understanding the determinants that influence the adoption of Mobile Learning in the university environment. It can aid in the formulation of informed and relevant strategies in the higher education field.