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Peer-reviewed

Research Article

Adoption of mobile learning in the university context: Systematic literature review

Roles Conceptualization, Data curation, Visualization, Writing – review & editing

* E-mail: [email protected]

Affiliation School of Industrial Engineering, Universidad Señor de Sipán, Chiclayo, Perú

Roles Data curation, Investigation, Methodology, Writing – original draft, Writing – review & editing

Affiliation Centro de investigaciones, Institución Universitaria Escolme, Medellín, Colombia

Roles Formal analysis, Resources, Software, Writing – review & editing

Affiliation Coordinación de Investigaciones e Innovación, Fundación Universitaria Católica del Norte, Medellin, Colombia

Roles Conceptualization, Methodology, Validation, Writing – original draft, Writing – review & editing

Affiliation Facultad de Ciencias Económicas Administrativas y Contables, Fundación Universitaria Católica del Norte, Medellín, Antioquía, Colombia

Roles Investigation, Software, Visualization, Writing – review & editing

Affiliation Instituto de Investigación y Estudios de la Mujer, Universidad Ricardo Palma, Lima, Peru

• Alejandro Valencia-Arias, 
• Sebastian Cardona-Acevedo, 
• Sergio Gómez-Molina, 
• Rosa María Vélez Holguín, 
• Jackeline Valencia

• Published: June 7, 2024
• https://doi.org/10.1371/journal.pone.0304116
• Peer Review
• Reader Comments

The study on the adoption of mobile learning in university education reveals a growing interest in mobile technologies to improve the learning process; both the acceptance and rejection of these tools among students have been analyzed. However, there are gaps in the research that require a deeper exploration of the factors that influence the adoption and use of these technologies. Understanding these aspects is crucial to optimize mobile learning strategies and improve the educational experience in the university setting. The objective is to examine research trends regarding the topic. PRISMA-2020 is used in the Scopus and Web of Science databases. The results show the questionnaires as the main collection instruments; geographical contexts show that it has been researched predominantly in Asia; The studies have focused on university students; the most applied theories are TAM and UTAUT; and latent variables such as behavioral intention and attitude. The conclusions summarize the trends and patterns observed in the reviewed literature, as well as the research gaps identified, providing a solid foundation for future research and highlighting the importance of addressing this issue in the current context of digital education. The systematic review identifies key models and factors in the adoption of mobile learning in university settings, revealing both theoretical and practical implications. Furthermore, this text provides practical guidance for selecting effective data collection tools and making informed educational and policy decisions. However, it acknowledges limitations such as potential publication and language bias in the search process.

Citation: Valencia-Arias A, Cardona-Acevedo S, Gómez-Molina S, Vélez Holguín RM, Valencia J (2024) Adoption of mobile learning in the university context: Systematic literature review. PLoS ONE 19(6): e0304116. https://doi.org/10.1371/journal.pone.0304116

Editor: Eric Amankwa, Presbyterian University College, GHANA

Received: February 13, 2024; Accepted: May 6, 2024; Published: June 7, 2024

Copyright: © 2024 Valencia-Arias et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data underlying the results presented in the study are available from https://doi.org/10.5281/zenodo.10655493

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

The integration of mobile learning platforms in the university context is a significant topic of interest in contemporary educational research. With the growing prevalence of mobile devices and digital technologies, these platforms have been adopted to improve accessibility and flexibility of learning. Mobile learning is the use of mobile devices to facilitate the teaching and learning process. It has transformed educational dynamics by allowing students to access educational resources at any time and place [ 1 ]. This modality of education not only offers flexibility in terms of time and location but also provides opportunities for personalized learning, peer collaboration, and active student participation [ 2 ].

The acceptance and adoption of mobile learning among students and teachers is a crucial research topic that has generated a significant body of literature. Theoretical models have been proposed to understand the factors that influence students’ intention to use mobile learning. These models highlight elements such as previous experience with technology, perceived usefulness, and ease of use [ 3 ]. Recent research has also explored how factors such as mobile self-efficacy and 21st-century skills influence the willingness of teachers to adopt mobile learning technologies in their pedagogical practices [ 4 ].

Empirical analysis has identified reasons and perceptions that influence the adoption of mobile learning applications among students [ 5 ]. Investigating the changing dynamics and emerging contexts in the use of mobile learning is crucial, particularly in light of external events such as the COVID-19 pandemic, to understand students’ response to and experience with educational technologies [ 6 ].

The integration of mobile devices and digital technologies in university learning environments has made mobile learning an increasingly popular topic. This is due to its potential to improve the accessibility, flexibility, and effectiveness of learning. Mobile learning allows students to access educational resources conveniently and personalize their learning experience anytime and anywhere. Its adoption is important because it can transform traditional teaching methods and facilitate the creation of more dynamic and interactive learning experiences [ 7 ].

Recent research has investigated different aspects of mobile learning adoption in the university context. These studies have analyzed the factors that affect students’ perception of mobile learning and its impact on enhancing learning. For instance, researchers have analyzed mobile learning adoption models that consider student perceptions as key determinants for improving the learning process [ 8 ]. Additionally, studies have identified socioeconomic and cultural factors that influence students’ attitudes towards the use of mobile devices in learning, highlighting the importance of understanding contextual differences in the adoption of these technologies [ 9 ].

Understanding the factors that influence the adoption of mobile learning systems among university students is crucial for designing effective implementation and promotion strategies. Previous studies have examined the impact of theoretical models, such as the Technology Acceptance Model (TAM) and the SOR (Stimulus-Organism-Response) Model, on enhancing learning through mobile learning [ 10 ]. In 2023, the adoption of mobile learning systems in the Indonesian educational context was examined, emphasizing the significance of cultural and contextual factors in their implementation [ 11 ]. These studies underscore the importance of researching and understanding the adoption processes of mobile learning in the university context to optimize its potential as an educational tool.

The topic analyzed in this study has gaps that require attention and systematic analysis. Although various systematic reviews have been carried out in the field of mobile learning adoption, there is a need to delve into current trends and the factors that influence the acceptance and use of these technologies in specific university environments. For instance, while studies like Kumar and Chand [ 12 ] and Alsharida et al. [ 13 ] have explored the general adoption of mobile learning, further research is needed to examine how factors such as technostress and compatibility can impact the adoption of mobile learning among foreign language learners, as suggested by Wang et al. (insert year here). These gaps in the literature justify conducting a systematic review in 2022 that integrates and critically analyzes the available evidence. This will allow for the identification of emerging research areas and contribute to the theoretical and practical development of mobile learning adoption in specific university contexts. The purpose of this study is to analyze research trends in the adoption of mobile learning in the university context from 2013 to 2024. The following questions will guide the research:

• RQ1: What are the primary data collection instruments utilized in articles regarding the implementation of mobile learning in university settings?
• RQ2: In what geographical contexts has the implementation of mobile learning in university settings been studied?
• RQ3: What are the various population segments that have been the focus of research on the implementation of mobile learning in university settings?
• RQ4: What psychobehavioral theories are used to understand the adoption of mobile learning in the university context?
• RQ5: What are the primary latent variables or constructs used to comprehend the adoption of mobile learning in the university context?

This study compiles and synthesizes various theories, variables, and models used to understand the adoption of mobile learning in university educational environments. The aim is to identify predominant trends and approaches in research and offer a comprehensive vision of the factors that influence the acceptance and use of mobile learning in different university contexts worldwide. The study provides a solid foundation for building a unified mobile learning adoption model.

This study aims to identify the countries and populations that have been researched in this field. Recognizing geographical and demographic variations in the implementation and acceptance of this educational modality is important. The goal is to develop a conceptual framework based on the unified model that is applicable and relevant in various cultural and socioeconomic contexts. This integrative approach enables us to advance the theoretical understanding of mobile learning adoption in higher education and inform more effective and contextualized educational implications.

2. Methodology

Exploratory research was conducted using secondary sources. The methodology was based on the parameters and guidelines established by the PRISMA-2020 declaration, which provides a rigorous and transparent framework for conducting and presenting systematic reviews. Relevant studies were carefully selected, and key data were extracted to explore the factors that influence the adoption of mobile learning in specific university environments. This allowed for the identification of trends, research gaps, and areas of interest for future research in this emerging field of digital education.

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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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.

The results section provides a comprehensive overview of the findings obtained from the systematic analysis of the relevant literature. This section presents the main emerging results of the review in an organized and structured manner. It addresses key aspects such as the instruments used for data collection, the geographical contexts where the phenomenon has been studied, the population segments under investigation, the theoretical models used, and the latent variables or constructs identified.

This systematic literature review examines the adoption of mobile learning in university environments, following the parameters established by the PRISMA-2020 declaration. The summary of the articles included in the study is presented in Table 1 , which includes only those that passed the inclusion phase and the three exclusion phases. This summary provides a clear and transparent understanding of the evidence base used in the analysis of the adoption of mobile learning in the university environment.

https://doi.org/10.1371/journal.pone.0304116.t001

Table 2 details the identification and classification of the data collection instruments used in the included studies. These instruments are essential for understanding how information related to the adoption of mobile learning is collected in the university context. The analysis reveals that questionnaires and surveys are the main data collection instruments used by researchers to gather information on the adoption of mobile learning in university environments. This provides a clearer understanding of the phenomenon.

https://doi.org/10.1371/journal.pone.0304116.t002

The study provides a comprehensive review of the geographical contexts in which various populations have been analyzed in relation to the adoption of mobile learning. Fig 2 presents these geographical contexts. The research highlights those Asian countries, including China, Turkey, Iran, India, Saudi Arabia, and Malaysia, among others, have been the most prominent in this field. Similarly, the topic has been extensively studied in Europe, with research conducted in countries such as Sweden, Spain, the United Kingdom, and Romania. This information offers a global perspective on the geographical distribution of research on mobile learning adoption in universities, highlighting worldwide areas of interest.

https://doi.org/10.1371/journal.pone.0304116.g002

In addition to the geographical analysis, the study provides context about the population that the different authors have researched to understand the determining factors of mobile learning adoption. This context is presented in Fig 3 . Research on the adoption of mobile learning in the university setting has primarily focused on university students in general, as well as students in various classifications. This suggests a broad and varied approach to understanding the topic. The information provides a clear vision of the interest groups in this field of research.

https://doi.org/10.1371/journal.pone.0304116.g003

Fig 4 outlines the theoretical frameworks and psychobehavioral models utilized by researchers to forecast the factors that influence the adoption of mobile learning. The authors predominantly use the Technology Acceptance Theory (TAM) followed by others such as the Unified Model of Acceptance and Use of Technology (UTAUT), Proprietary Models, Extended UTAUT, and Theory of Planned Behavior (TPB). This highlights the diversity of theoretical approaches used in research on the adoption of mobile learning in the university setting, contributing to a more complete understanding of this phenomenon.

https://doi.org/10.1371/journal.pone.0304116.g004

Fig 5 presents the main latent variables, factors, or constructs that different authors have adopted to understand the adoption of mobile learning among university populations in various geographical contexts. Researchers exploring the adoption of mobile learning in university settings have identified several key variables, including Behavioral Intention, Attitude, Expectation of Effort, Current Use, Compatibility, Confirmation, Academic Relevance, and Commitment. This information provides a deeper and more holistic understanding of the phenomenon.

https://doi.org/10.1371/journal.pone.0304116.g005

4. Discussion

This section provides a detailed analysis of the research results, presenting their relevance and meaning. The findings are discussed, and the theoretical and practical implications derived from the results are presented. The study’s limitations are also identified. The study identifies the main research gaps and proposes a research agenda based on the results. Additionally, a theoretical model on the adoption of mobile learning in university environments is presented, utilizing the main theories and variables identified. This section is crucial for contextualizing and providing meaning to the review results, as well as guiding future research in the field of university mobile learning.

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.

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.

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.

5. Conclusions

The research has produced significant conclusions that address the research questions. The analysis indicates that questionnaires are the primary data collection instruments used in the studies, indicating a preference for quantitative methods to gather information on the adoption of mobile learning.

In terms of geographical contexts, research in the field of mobile learning has been primarily focused on Asia and Europe. Countries such as Saudi Arabia, China, the United Kingdom, and Spain have been leading this research. This finding emphasizes the global nature of mobile learning adoption and the importance of considering diverse contexts in future research.

Research on mobile learning adoption focuses mainly on university students, highlighting the importance of understanding the needs and perceptions of this demographic group when integrating mobile technologies in education. The identified theoretical models were TAM, UTAUT, and own models. This highlights the importance of understanding users’ attitudes and perceptions towards mobile learning from a consolidated theoretical framework.

The main variables used to understand the adoption of mobile learning in university contexts are behavioral intention, attitude, effort expectation, current use, and compatibility. The article highlights the need to develop integrative theoretical models that address the factors influencing the adoption of mobile learning. It is also recommended to explore new variables and geographical contexts to enrich the understanding of the phenomenon.

Regarding future research, it is recommended to investigate the impact of mobile learning in various fields of study and educational contexts. Additionally, it is important to examine the influence of contextual and cultural factors on the adoption of these technologies.

A theoretical model is presented that integrates the main theoretical models and variables identified in the review, providing a conceptual structure for future research in the field of mobile learning adoption in the university context.

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Mobile Phones for Agricultural and Rural Development: A Literature Review and Suggestions for Future Research

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• Volume 28 , pages 213–235, ( 2016 )

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This article provides a systematic review of the potential and limitations of mobile phones in the delivery of rural services for agricultural and rural development in developing countries. The review indicates a rapid expansion of research in recent years, and a growing number of primary research studies that have developed rigorous methodologies for data collection and analysis, with welcome contribution from developing country institutions and researchers. Gaps in the literature suggest areas where future research priorities may lie. These include the provision of agricultural data sources that can provide the basis for effective planning and policymaking, and the assessment of information and service needs that take into account gender differences and the potential for user involvement in the design of service provision. Research is also needed to assess the potential for financial market integration, sustainable business models, consideration of indicators of sector performance and productivity, and assessment of broader impacts at the community and societal level.

Cet article apporte une revue systématique sur le potentiel et des limites des téléphones portables dans la prestation de services ruraux pour le développement agricole et rural dans les pays en développement. La revue indique qu’il y a eu une expansion rapide de la recherche ces dernières années, ainsi qu’un nombre croissant de recherches primaires qui ont développé une méthodologie rigoureuse pour la collection et l’analyse des données, avec la contribution d’institutions et de chercheurs de pays en développement. Les lacunes du corps de recherche suggèrent les domaines dans lesquels de futures recherches pourraient être priorisées. Ces domaines incluent des sujets tels que: l’obtention de sources de données agricoles permettant une plannification et des politiques efficaces; l’évaluation des besoins en information et en services qui prend en compte les différences liées au genre ainsi que le potentiel de l’implication du client dans le design de la prestation de service; le potentiel de l’intégration des marchés financiers; des modèles d’affaires durables; la considération d’indicateurs de performance et de productivité du secteur et l’évaluation qualitative des impacts généraux au niveau sociétal.

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Duncombe, R. Mobile Phones for Agricultural and Rural Development: A Literature Review and Suggestions for Future Research. Eur J Dev Res 28 , 213–235 (2016). https://doi.org/10.1057/ejdr.2014.60

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Using Mobile Phone Data for Emergency Management: a Systematic Literature Review

Yanxin wang.

1 School of Management, Xi’an Jiaotong University, Xi’an, 710049 China

2 The Key Lab of the Ministry of Education for process control & Efficiency Engineering, Xi’an, 710049 China

Gengzhong Feng

Xin (robert) luo.

3 Anderson School of Management, University of New Mexico, Albuquerque, NM USA

Emergency management (EM) has always been a concern of people from all walks of life due to the devastating impacts emergencies can have. The global outbreak of COVID-19 in 2020 has pushed EM to the top topic. As mobile phones have become ubiquitous, many scholars have shown interest in using mobile phone data for EM. This paper presents a systematic literature review about the use of mobile phone data for EM that includes 65 related articles written between 2014 and 2019 from six electronic databases. Five themes in using mobile phone data for EM emerged from the reviewed articles, and a systematic framework is proposed to illustrate the current state of the research. This paper also discusses EM under COVID-19 pandemic and five future implications of the proposed framework to guide future work.

Introduction

Emergency situations such as terrorist attacks or earthquakes occur at different scales daily around the world. They may be natural or human-caused events that occur suddenly, affect public order, and disrupt the regularity of an area’s political, economic, and social life (Fogli et al. 2017 ; Seba et al. 2018 ). Such an emergency causes great losses and widespread impacts on society, and “requires a prompt intervention by all involved stakeholders” (Fogli et al. 2017 ; Lauras et al. 2015 ). To gain public support and maintain regular social order, authorities should pay special attention to the effective management of such situations. In this study, emergency management (EM) is defined as the effective organization, direction, and management of both emergency-related humanitarian and material resources (Othman and Beydoun 2013 ). Traditionally, it comprises four phases: mitigation, preparedness, response, and recovery (Othman and Beydoun 2013 ). EM is generally considered to have undergone three stages (Phillips et al. 2011 ), including passive response (before the 1950s), active preparation and prediction (1960s–1990s), and whole community response based on integrated information systems (after the 2000s). To align with such developments, scholars have swifted their attention from solving a single issue to focusing on efficient intra-organizational collaboration (Janssen et al. 2010 ).

Lack of collaboration is the chief culprit in major failures in disaster response and takes the form of a lack of available crisis information or poorly managed information flow (Valecha 2019 ; Beydoun et al. 2018 ). Information and communication technology (ICT) and information systems (ISs) are considered as crucial means to enhance the collaboration process and information flow management (Sagun et al. 2009 ; Ipe et al. 2010 ). However, a lack of informative and appropriate data hinders further development and practical use of emergency management information systems (EMISs) (Ghosh et al. 2018 ; Roberts 2011 ). First, data reflecting human behaviors on a large scale are required for each phase of EM, since managing affected people is a crucial part of EM. Second, due to the rapid changes of challenges encountered in EM, data collected promptly and timely are required to support various EMISs for corresponding responses. Finally, EMISs require accurate and objective data to reflect emergencies, while traditional emergency-related data heavily rely on surveys. These three requirements create obstacles for the practical use of EMISs in dealing with real-world emergencies.

Many studies have regarded mobile phone data as a potential data source to fulfill these requirements, because these data reflect human behavior richly and ubiquitously. Globally, in 2017, mobile phones attained a registration number of 103.5 per 100 people, as reported by the International Telecommunication Union (ITU) (Sanou 2017 ). They have been transformed from a simple communication tool to a multifunctional ‘mobile-computer’ with the rise of apps on mobile platforms. Mobile phone data such as CDRs and app data can be applied to the analysis of human mobility (Stefania et al. 2018 ; Duan et al. 2017 ; Gao et al. 2014 ; Lwin et al. 2018 ), social networks (Poblet et al. 2018 ; Trestian et al. 2017 ; Ghurye et al. 2016 ; Dobra et al. 2015 ), mobile phone usage patterns (Jia et al. 2017 ; Steenbruggen et al. 2016 ; Gundogdu et al. 2016 ; Gao et al. 2014 ), and geographic location (Lwin et al. 2018 ; Poblet et al. 2018 ; Dong et al. 2017 ; Šterk and Praprotnik 2017 ); these themes are discussed in additional detail in Section 3. The results can further be developed to address various issues encountered during emergencies, such as predicting epidemic transmission (Bengtsson et al. 2015 ; Panigutti et al. 2017 ) and developing pre-warning systems (Zhang et al. 2016 ; Dong et al. 2017 ).

Although many efforts have been made to investigate the application of mobile phone data in EM, the knowledge and understanding in this field are still fragmented. Therefore, a systematic framework that synthesizes the fragmented knowledge and provides insights into the-state-of-art of using mobile phone data for EM is needed. This study aims to propose such a framework and provide guidance for further research in this field.

This study is related to two streams of literature reviews, which are using ICT in EM and mobile phone data analysis. On the first stream of using ICT in EM (Martinez-Rojas et al. 2018 ; Tan et al. 2017 ), Martinez-Rojas et al. ( 2018 ) have reviewed 158 related articles from 2009 to 2018 to discuss current opportunities and challenges of using Twitter for EM. Tan et al. ( 2017 ) have summarized the involvement of mobile apps in the crisis informatics literature by reviewing 49 related articles. On the second stream of mobile phone data analysis, Blondel et al. ( 2015 ) and Naboulsi et al. ( 2016 ) have summarized some studies on mobile data analysis, some of which can be applied in EM. These two literature reviews focused on the method of data mining, while the current study focuses on EM. To sum up, there is a lack of a literature review that considers both the characteristics of mobile phones and using such data for EM.

Three research objectives are undertaken to achieve the goal of synthesizing the fragmented knowledge and providing research guidance: (i) extract basic knowledge (e.g. types of mobile phone data, situations) of EM from the selected studies; (ii) break the boundaries of different disciplines and aggregate each analysis perspective; and (iii) study the identified knowledge and integrate it into a single framework that draws a comprehensive map of existing findings under this subject, and provides future implications.

To attain these objectives, this study follows a methodology of systematic literature review (SLR) and synthesized the results of the reviewed studies into a framework, thus allowing a discussion of future implications obtained from the framework. The next section introduces the research method applied in this systematic literature review, which is followed by an illustration of the five major themes in using mobile phone data for EM. Section 4 presents the proposed framework of using mobile phone data for EM based on the five themes. Section 5 discusses current EM under the COVID-19 pandemic. Finally, this paper discusses future implications and provides a conclusion.

Systematic Literature Review

Based on the systematic research methodology (Ghobadi 2015 ) which is considered as a means of identifying, evaluating and interpreting all available research relevant to a particular research question, or topic area, or phenomenon of interest (Budgen and Brereton 2006 ), this study processes three phases of work, including planning, conducting, and reporting. These phases are graphically exhibited along with their specific steps and objectives in Fig.  1 . The research questions and relevant studies have been identified in phase 1, and the studies we reviewed in this paper have been selected through a specific research strategy and inclusion/exclusion criteria illustrated in phase 2. As a result, we have selected 65 papers from six document databases for this review. Note that phases 1 and 2 are both explained in Appendix 1 , while the results of Phase 3 are detailed in Sections 3 and 4.

Three phases of the literature review

Five Themes in Using Mobile Phone Data for EM

Emergency situations.

To identify the trends of research on emergency situations during the selected period, the 65 reviewed articles have been statistically analyzed by their published years and the types of situations mentioned in each article. The results are exhibited in Fig.  2 .

Numbers of studies classified by emergency situation and by year

Emergencies can be divided into two categories, natural (41 papers) and man-made (16 papers). ‘Natural’ refers to the emergencies that occurred due to processes of the earth (such as weather) that can hardly be avoided, while ‘man-made’ refers to the emergencies that occurred specifically due to human action or inaction. To be more specific, this paper considers that ‘natural’ emergencies include ‘natural disaster’ (19 papers) and ‘disease disaster’ (22 papers), such as earthquakes and the Ebola virus disease, respectively. Meanwhile, ‘man-made’ emergencies cover various types, among which ‘traffic accident’ (six papers), ‘violence and terror incident’ (six papers), and ‘other’ (four papers) have been regarded as three major subcategories based on the articles reviewed. The ‘others’ category includes ‘sudden strikes’ (Garroppo and Niccolini 2018 ), ‘damages to pipelines’ (Dong et al. 2017 ), and ‘refugee problem’ (Andris et al. 2019 ). The remaining studies (eight papers) cannot be clustered into any of the aforementioned categories or subcategories in the course of our review, as they are not clearly related to any particular type of emergency, so we have categorized them as ‘general emergency.’

Studies paid considerably more attention to ‘natural’ emergencies compared with ‘man-made’ emergencies, and this was particularly evident in terms of disease disasters. We also note that the number of studies peaked in 2015 (13 papers; the other years contained 9, 8, 11, 13, and 11 papers, respectively), and ‘disease disaster’ has been studied in 2015 (seven papers) almost twice as much as in other years. The phenomenon may have been the result of attention drawn by the outbreak of the Ebola virus.

Phases of the EM Process

EM generally goes through four phases, namely mitigation, preparedness, response, and recovery. Most of the studies investigated the response phase (39 papers). This phase involves activities like organizing an evacuation, mobilization, and assisting victims to manage the emergency appropriately. Then, 27 papers focused on the preparedness phase, and 22 papers focused on the mitigation phase. The preparedness phase in EM comprises a sequence of activities including planning, training, warning, and updating solutions by learning previous emergencies, which can help enhance response abilities (Oberg et al. 2011 ). The mitigation phase aims to prevent a disaster or lessen its impacts by modifying the causes and vulnerabilities or distributing the losses (Oberg et al. 2011 ). Finally, the recovery phase received the least attention from scholars (14 papers). This phase consists of both short-term and long-term activities that are designed for reestablishing and returning disaster areas to normal conditions. Note that the total sum according to these phases (102 papers) is greater than the number of reviewed articles (65 papers) since some studies focused on more than one phase.

Types of Mobile Phone Data

Mobile phone data consist of information collected by mobile carriers, sensors, and apps on mobile phones. Mobile phone data collected by carriers include CDRs, SMS (short message service) information, traffic volume, etc., and data collected by phone sensors, such as GPS records, Bluetooth-sensed interaction data, etc.

Most of the studies applied CDRs (34 papers) for emergency analysis. This type of mobile phone data contains details about each call such as “the location, call duration, call time, and both parties involved in the conversation” (Trestian et al. 2017 ). CDRs containing both users’ spatial and temporal information can support research on modeling human mobility during emergencies. Moreover, information about caller and recipient IDs reveals individuals’ social networks, which correlate to infectious disease dissemination (Gundogdu et al. 2016 ; Wesolowski et al. 2014 ).

Some studies combined CDRs with other data sources to obtain more comprehensive emergency-related information (Bharti et al. 2015 ; Pastor-Escuredo et al. 2014 ). Bharti et al. ( 2015 ) used both CDRs and nighttime lighting data from satellite imagery to analyze population sizes and human mobility (the CDRs for short-term and the satellite data for long-term assessment), which helped in making policies and understanding emergency impacts in the response phase. Pastor-Escuredo et al. ( 2014 ) adopted both CDR and rainfall-level data in Mexico to help discover anomalous mobile phone usage patterns in seriously affected areas and assess infrastructure damage and casualty populations in time.

SMS (seven papers) is considered as another category of mobile phone data (separate from CDR in this study), which contains both passively collected information like communication details and actively collected information. GPS data (11 papers) can provide the location information of individuals with higher accuracy than CDRs and can be helpful in the identification of individual locations as well as the study of human dynamics. In addition, there were some studies being developed based on app data (six papers) and others (nine papers), such as Bluetooth-sensed interaction data, mobile-phone-usage data, mobile-traffic data, etc.

Analysis Perspectives

When applying mobile phone data to practical problems, scholars have to gather and extract useful information from the raw mobile phone data. We have categorized different information processing paths from six analysis perspectives: human mobility, geographic location, social networks, mobile phone usage patterns, collected information, and information diffusion (Blondel et al. 2015 ). Detailed definitions for these analysis perspectives are exhibited in Table ​ Table2 2 (refer to Appendix 2) with specific examples.

Illustration of analysis perspectives

As depicted in Fig.  3 , spatial-temporal information extracted from CDR and GPS data has been mostly investigated by scholars, which is reflected as human mobility (30 papers). The spatial and temporal information can benefit the tracking of individuals’ trajectories and modeling their movement patterns. Since the relationship between human movement patterns and infectious transmission routes were found (Blondel et al. 2015 ), the analysis of human mobility contributes to the understanding of the disease dissemination process.

Breakdown of studies by analysis perspectives

Mobile phone usage patterns perspective (10 papers) refers to various individual behavior gathered from one’s mobile phone data, such as normal call volume. Arai et al. ( 2015 ) found clues to the whereabouts of unobserved populations by analyzing individuals’ usage behaviors from CDRs, especially for the whereabouts of children, who are vulnerable to epidemics. This result contributes to tracking epidemic dissemination and the deployment of various resources.

Social networks (10 papers) are constructed based on the communication information gathered from mobile phone data. The analysis of social networks helped to reproduce disease dissemination models and predicted epidemic transmission for the preparedness phase (Y. Chen et al. 2017 ; Farrahi et al. 2015 ).

Scholars also used spatial information extracted from mobile phone data (11 papers) at both the individual and aggregate level. The analysis of individual geographic location benefited in tracking anomalous individual locations, and could be further applied to develop pre-warning systems, such as systems to lessen potential damage on natural gas pipelines (Dong et al. 2017 ).

The collected information (seven papers) perspective refers to the process of gathering multiple types of information content from mobile phones, such as using a specific app to collect public opinions. Deng et al. ( 2016 ) collected public opinions from an online opinion governance app to assist in government decision-making.

In addition, other studies focused on a more general perspective of gathering information from mobile phone data, and this kind of information was defined as information diffusion (five papers). Diffusion strategies of prevention knowledge could be developed to mitigate the impacts of emergencies by analyzing information diffusion networks (Lima et al. 2015 ).

Applications

Research in EM mainly focused on five kinds of problems: resource management, evacuation, pre-planning, decision-making, and education and training (Mingliang et al. 2006 ). Within these five types, various issues (such as making evacuation plans, optimizing resource allocation, predicting epidemic transmission, etc.) are defined as applications in this study.

Although the selected studies developed their unique applications, this study provides a relatively broad classification, five general categories and 13 subcategories to help create a map of the existing applications. This classification is exhibited in Table ​ Table3 3 (refer to Appendix 3) with a detailed definition and a specific example for each category.

Illustration of applications

Most studies focused on the decision-making problem (42 papers), among which ‘conducting public health intervention’ (12 papers) and ‘processing real-time detection’ (12 papers) had been mostly developed. It was followed by presenting emergency impact (seven papers), ‘stating policy/regulations’ (five papers), ‘making construction plans’ (four papers), and ‘developing emergency-related platforms’ (two papers). The decision-making problem aims to give guidance for the relevant work in the EM process.

The pre-planning problem has been studied by 15 papers during these five years, among which eight papers focused on ‘predicting epidemic transmission’ and seven papers focused on ‘developing pre-warning system.’ This kind of application aims to anticipate what might happen and to provide an early warning.

Within the evacuation problem (14 papers), ‘finding victims’ (five papers) and ‘making evacuation plans’ (nine papers) were studied. It aims to solve the issues of finding and rescuing disaster victims.

Within the education and training problem (seven papers), ‘delivering emergency announcement’ (five papers) and ‘guiding psychological recovery’ (two papers) were explored. This kind of problem focuses on spreading emergency-related knowledge for the public and helping the public to face the emergency rationally.

The remaining problem type was the resource management problem (five papers). This kind of problem focuses on the appropriate allocation of both material and human resources according to the distributions of the victims and rescuers.

A Framework of Using Mobile Phone Data for EM

Our framework for using mobile phone data for EM is depicted in Fig. ​ Fig.4 4 and the detailed process is described in Section A.2. The framework synthesizes the five aforementioned themes (i.e., emergency situations, EM phases, types of applications, analysis perspectives, and types of mobile phone data) and illustrates two logical routes with potential correspondences between each theme. The boxes in the framework represent the categories under each theme, and the lines with arrows represent the existing correlation between each theme according to the 65 reviewed studies.

Framework of using mobile phone data for EM. Note: Mit = Mitigation; Pre = Preparedness; Res = Response; and Rec = Recovery

The first type of logical route is the decision-making route (represented as blue lines with arrows in the framework). It starts with the emergency situation theme, goes through the phases of EM, and ends with the application theme. This route assists managers to make emergency-related decisions comprehensively and conveniently during the emergency. They can make specific emergency plans involved in each phase of EM by following the steps: (i) judging what types of emergency the public are encountering; (ii) determining the phases of the emergency in different regions; (iii) figuring out general categories of applications related to the determined phase, and then referring to Table ​ Table3 3 and Fig.  6 (in Appendix 3) for sub-applications ought to be taken; (iv) determining procedure and activities that can be implemented in practice by considering both the characteristics of the encountered emergency and referred applications. For example, during COVID-19, relevant managers can identify the situation as a disease disaster. If the managers judge that the phase of the pandemic is in the ‘mitigation,’ he/she can take applications in pre-planning, education and training, and decision-making categories into account according to our framework. After referring to Table ​ Table3 3 and Fig. ​ Fig.6, 6 , they can consider implementing applications, like predicting epidemic transmission, as part of their emergency plans to protect communities and reduce loss.

Framework of using mobile phone data for EM (with sub-applications)

The second type of logical route is the problem-solving route (represented as red lines with arrows in the framework). It ends with the application theme, but starts from the mobile phone data theme while goes through the analysis perspective. This route assists technicians to devise solutions for emergency plans. They can employ mobile technology to implement the identified activities by following the steps: (i) understanding the activity involved in emergency plans given by the decision-maker, i.e. what application is this activity; (ii) identifying appropriate mobile technology that can assist in implementing the application; (iii) gathering information and making appropriate analysis (refer to Table ​ Table2) 2 ) by using the identified mobile technology; (iv) applying the results of analysis into real-world EM. For example, to predict epidemic transmission during COVID-19 pandemic, technicians can find CDR and GPS data appropriate for gathering individuals’ spatial-temporal information and analyzing human mobility. They can apply the analysis to technical solutions development and decision-support system.

The framework also contributes to extending and enriching our understanding of the evolving literature of EM. Links in the framework represent existing studies in this field, thereby illuminating the established correlations between the themes and indicating potential research gaps. For example, the studies on education and training applications mainly focus on the disease disaster and man-made disaster, while the focus on natural disasters is limited. Meanwhile, studies only analyze this application from four perspectives while information diffusion and geographic location are missing. Researchers may further consider the feasibility of these two perspectives to analyze this application.

EM under the COVID-19 Pandemic

The outbreak of the COVID-19 pandemic in early 2020 has caused huge impacts on global political, economic, and social development (WHO 2020d ; W. Chen and Bo 2020 ). Both emergency-related managers and technicians can refer to the proposed framework for potential decision-making and problem-solving.

Drawing on the decision-making route, emergency managers can determine emergency plans involved in each phase of managing the COVID-19 pandemic. Existing activities under the pandemic fit four categories of applications including predicting epidemic transmission (Jia et al. 2020 ; Iacus et al. 2020 ; Gatto et al. 2020 ), public health intervention (Magklaras and Nikolaia Lopez Bojorquez 2020 ; Ekong et al. 2020 ; Canada 2020 ), delivering emergency announcement (Barugola et al. 2020 ; Shi and Jiang 2020 ; Xinhua 2020b ), and stating policy/ regulations (Speakman 2020 ; Hu and Zhu 2020 ; Canada 2020 ; Singapore 2020 ). Specifically, in the mitigation phase, related organizations have predicted epidemic transmission through monitoring population flows to reduce potential risks (Jia et al. 2020 ; Iacus et al. 2020 ; Gatto et al. 2020 ). In the preparedness phase, inspectors and the public have been trained and mobilized to use mobile phone security codes, which help track infection cases and contacts (AGDH 2020 ; Guinchard 2020 ; Zastrow 2020 ). In the response phase, the World Health Organization has announced global objectives including conducting public health intervention by rapidly tracing, finding, and isolating all cases and contacts (WHO 2020b ; Cozzens 2020 ; Magklaras and Nikolaia Lopez Bojorquez 2020 ). And in the recovery phase, some governments have used mobile technology to aid business resumption with contact tracing applications (Thompson 2020a , 2020b ; Sunil 2020 ; Devonshire-Ellis 2020 ; Hu and Zhu 2020 ).

Drawing on the problem-solving route in our framework, relevant technicians can analyze various collected information from mobile devices to implement the aforementioned applications. First, researchers have used CDR data (Jia et al. 2020 ) and mobile positioning data (Gatto et al. 2020 ; Iacus et al. 2020 ) to predict epidemic transmission by capturing and simulating population movements. They have mentioned that their prediction model can conduct risk assessments and plan limited resources allocation as well. Second, contact tracing requires various mobile tools including GPS (Cozzens 2020 ), Bluetooth (WHO 2020c ; Xinhua 2020a ), apps (Zhang et al. 2020 ; Singapore 2020 ; AGDH 2020 ), and SMS technology (WHO 2020c ) to gather individuals’ geographic and health-related information. The analysis of this information can be further applied to conduct public health intervention such as isolation strategies and travel restrictions. Finally, as for business resumption, history trajectories and health status of individuals collected through specific mobile phone apps (Zastrow 2020 ), SMS (WHO 2020a ; McCabe 2020 ) and GPS technology (Elliott 2020 ) helps to evaluate their infection risk, which can be used by governments to resume business and study.

Our framework not only covers applications that have already been adopted in this EM, but also provides a reference for future EM. For example, it is possible to consider other applications in our framework such as guiding psychology recovery in the context of COVID-19. The use of hedonic apps after an earthquake has been identified to reduce perceived risk effectively (Jia et al. 2017 ). Therefore, decision-makers can learn from this knowledge and implement appropriate applications in the recovery phase of managing this pandemic.

Future Implications

Future research directions can be implied based on the proposed framework from five perspectives: (1) a focus on man-made emergencies, (2) a focus on the recovery phase, (3) exploring new applications, (4) creating better comprehension of the analysis perspective, and (5) combining other data with mobile phone data.

A Focus on Man-Made Emergencies

It is reasonable to put forward further research on man-made emergencies with mobile phone data. The results shown in Section 3.1 indicate a greater emphasis on natural emergencies (41 articles reviewed in total) and less emphasis on man-made emergencies (16 articles reviewed in total). However, managing violence and terror incidents is also very vital, because these emergencies happen frequently around the world. For such a crisis situation, it is challenging to gather their information due to the dark side and dynamic nature (Roberts 2011 ; Skillicorn 2011 ; Chen et al. 2011 ).

In view of these challenges, current literature has explored the suitability of using social media data (Oh et al. 2011 ; Cheong and Lee 2011 ; Prentice et al. 2011 ; Qin et al. 2011 ). However, lacking individuals’ real-time location and objective behavior information makes social media data limited. Some studies have identified the feasibility of using mobile phone data in man-made emergencies, such as detecting terrorism attacks and monitoring traffic conditions (Blondel et al. 2015 ). However, the application of mobile phone data in this domain still has great potential.

A Focus on the Recovery Phase

A focus on the recovery phase would also be a future direction. The occurrence of emergencies will have both physical and psychological impacts on the victims. However, most scholars focused on the timely and effective response to emergencies, while fewer studies focused on the psychological recovery of people after emergencies. With the popularity and development of 4G/5G communication technology, many people use their mobile phones for obtaining emergency-related information and entertainment to ease their anxiety. For example, Jia et al. ( 2017 ) discovered that the use of hedonic apps after an earthquake can help people reduce their perceived risk. Therefore, there is the potential to use mobile phone data for the study of developing positive psychological structures in the recovery phase.

Exploring New Applications

Additional applications can be explored and expanded according to the two ways demonstrated in the framework. With a further understanding of analysis perspectives (which are discussed in Section 6.4), scholars can explore additional applications. For example, with a more comprehensive notion of human social interaction, scholars may not only investigate approaches in predicting epidemic transmission, but also apply this knowledge to develop systems in predicting crimes. In addition, with the upgrading of mobile phones and ICT (Bandyopadhyay et al. 2018 ; Palshikar et al. 2018 ), new applications can be explored under the proposed framework. As a result, additional applications can be similarly explored within and beyond the existing applications in the future.

Creating Better Comprehension of the Analysis Perspective

Future work should also make efforts to complement the theoretical foundation of emergency studies with theories from other fields. Current literature has developed theories about information transmitted among various stakeholders (Wang et al. 2018 ; Weidinger et al. 2018 ; Liu and Xu 2018 ; Abedin and Babar 2018 ; Fedorowicz and Gogan 2010 ), and theories of coordination and political science (Maldonado et al. 2010 ). Moreover, a better comprehension of the analysis perspective can be made by learning constructs about human behavior, social networks, etc. For example, scholars have found similarities between human communication and infectious disease dissemination (Blondel et al. 2015 ), which indicates the benefits of drawing on theories from social networks to apply to EM. Meanwhile, the current understanding of human mobility is mainly based on data analysis. If scholars can learn and apply aspects from relevant psychological and behavioral theories, the analysis of human mobility can be further deepened.

In addition, with a better comprehension of analysis perspectives and applications, it is possible to develop new correspondences between analysis perspectives and applications. The current framework was developed based on the 65 articles reviewed, and cannot reveal every possible relationship between analysis perspectives and applications. For example, human mobility can probably be analyzed in order to construct pre-warning systems. According to the study of daily movement patterns of individuals, a detected anomaly of movement may indicate a coming disaster, which would suggest the need to construct correlations between human mobility and pre-warning systems. Consequently, the correspondence between analysis perspectives and applications should be considered and expanded upon in future research.

Combining Other Data with Mobile Phone Data

Although mobile phone data have been successfully applied to EM, combining other micro and macro data can help the development of EM research more efficiently (Ghosh et al. 2018 ). Martinez-Rojas et al. ( 2018 ) have reviewed 158 articles about using Twitter to manage emergencies, indicating the significant roles and value of data from social media in EM. Under what circumstances it is appropriate and how to combine mobile phone data with micro (e.g., individual locations) and macro (e.g., public opinions) data from social media requires further exploration. Moreover, combining data from other platforms is worth consideration as well. For instance, Pastor-Escuredo et al. ( 2014 ) believe there is potential in combining information from officially monitored sensors like traffic video cameras, which can provide a fine-grained validation for the existing measures.

Conclusions

Emergencies have great impacts on the whole of society, affecting material facilities and social order in terms of economic losses and human casualties. Unlike traditional management measures based on data sources with limited adaptability and low accuracy, applications for handling emergencies that utilize ubiquitous and real-time mobile phone data have greatly improved EM mechanisms and minimized their negative impacts on society. This systematic literature review analyzes 65 studies concerning the use of mobile phone data for EM, and proposes a framework to synthesize the fragmented knowledge of existing studies. The framework comprises five themes, among which six analysis perspectives and five general types of applications are put forward to explain the EM process, which includes two logical routes. The framework can support stakeholders, such as emergency managers and technicians, and is used to suggest five future research directions in the field for scholars. In addition, this study discusses EM under the COVID-19 pandemic and provides a reference for future management of the pandemic.

Despite all the contributions mentioned above, this study still possesses some inevitable limitations. First, the common limitation of literature reviews related to keywords exists in this study as well. Making the best efforts to alleviate this drawback, this study draws on keywords about EM from previous reviews and research works in the field of EM as well as keywords about mobile phone data. The second limitation is that the framework develops in this study is only based on the articles reviewed, meaning that relationships not mentioned in the articles are not considered. Although this review is intended to be both wide and deep in coverage, it should not be considered as a complete or final summary of the topic. Reviews, no matter how current, by definition focus on the past and cannot fully anticipate novel approaches or new developments. Finally, this study does not focus on the detailed introduction of data processing and analyzing techniques. Nevertheless, we still believe this study makes a strong contribution to the field, especially toward emergency managers and scholars who are looking for direction to develop this field in the future.

Acknowledgments

The corresponding author is a Tang Scholar. He would like to acknowledge the support from National Natural Science Foundation of China (Grant No. 91746111), The Key Research and Development Program of Shaanxi Province (Grant No. 2020ZDLGY09-08).

Biographies

received the bachelor degree in Industrial engineering at the school of management from Xi’an Jiaotong University, Xi’an, China, in 2019. She is currently pursuing the Ph.D. degree in Management Science and Engineering at the school of Management, Xi’an Jiaotong University. Her main research interests are big data and behavior analysis, social media and gamification.

received the bachelor degree in Industrial engineering at the school of management from Xi’an Jiaotong University, Xi’an, China, in 2019. He is currently pursuing the Ph.D. degree in Management Science and Engineering at the School of Management, Xi’an Jiaotong University. His main research interests are massive human behavior analysis and blockchain finance exploration.

received the Ph.D. (Hons.) degree in computer science from the Ecole Centrale de Lyon, Lyon, France, in 2010. He conducted research in the fields of data analytics and pattern recognition as a Research Assistant Professor in the Department of Computer Science, University of Houston, USA. He iscurrently a Professor with Xi’an Jiaotong University, Xi’an, China. His research interests include behavior computing, mobile computing. His research has been published in the journals such as Information & Management, Decision Support System, Tourism Management, ACM Knowledge Discovery and Data Mining and IEEE Transactions on PAMI and Cybernetics.

is a Professor of Information Management and EBusiness at the School of Management, Xi’an Jiaotong University, P. R. of China. He obtained the B.S. degree in computer science in 1987, the M.S. degree in systems engineering in 1990, and the Ph.D. degree in management engineering in 1993, all from Xi’an Jiaotong University of China. His research interests include logistics and supply chain management, information system management, big data and information quality. His research has been published in the journals such as Journal of the Association for Information Systems, European Journal of Operational Research, Omega-International Journal of Management Science, International Journal of Production Research and Expert Systems with Applications.

is an Endowed Regent’s Professor and a Full Professor of MIS and Information Assurance in the Anderson School of Management at the University of New Mexico, Albuquerque, USA. He received his Ph.D. in MIS from Mississippi State University. He has published research papers in leading journals, including Decision Sciences, Decision Support Systems, European Journal of Information Systems, Journal of the Association for Information Systems, Journal of Strategic Information Systems, Information & Management, and Information Systems Journal. He is currently serving as an ad hoc Associate Editor for MIS Quarterly and an Associate Editor for Decision Sciences, European Journal of Information Systems, Information & Management, Electronic Commerce Research, and Journal of Electronic Commerce Research. He currently also sits on the Editorial Board of Journal of the Association for Information Systems. His research interests center around information assurance, innovative technologies for strategic decision-making, and global IT management. He is the Co-Editor-in-Chief for the International Journal of Accounting and Information Management.

Appendix 1. Systematic Literature Review

Identifying the research questions . Our research questions were as follows: (1) What types of mobile phone data—with respect to its applied phases and use to cope with practical issues—have been studied? (2) What is the state of the art of this field? and (3) What can future works develop to facilitate the understanding of this subject?

Regarding the first question, a statistical analysis was performed on the aspects of emergency situations, phases of EM, types of mobile phone data, analysis perspectives, and applications. Next, a framework of using mobile phone data for EM to address the second question was proposed. Finally, five future implications based on the proposed framework are presented in order to address the third question.

Identifying relevant studies . The second step of planning the research was identifying relevant studies, which defines the scope of this review study. Six document databases were searched to find related studies between 2014 and 2019. These were: ScienceDirect, Scopus, Web of Science, IEEE Xplore, ACM, and Springer. IEEE Xplore and ACM are two specialty article databases that provide extensive coverage of the literature in computer science and related areas, and Scopus (SciVerse Scopus) is the largest abstract and citation database. The other three were additional comprehensive and widely searched databases.

To draw the boundaries of what articles would be included and reviewed in the study, the phenomenon of interest was identified as ‘research that applies mobile phone data to manage emergencies.’ We developed an initial list of research keywords to match the definition with published documents and considered various literature expressions that represented the same terminology by considering both aspects of emergencies and mobile phone data. This method is illustrated at the beginning of the conducting phase and further guided the search for related articles.

Generating a research strategy . The first step was to generate a research strategy by finding and filtering studies from the six databases. Two iterations were processed: (1) searching 26 terms in the keywords list (“mobile phone data” OR “short message service” OR “call detail record” OR “phone GPS data” OR “cellular network data” OR “app data” OR “application data” OR “Bluetooth data”) AND (“emergency” OR “extreme situation” OR “extreme event” OR “large-scale event” OR “special event” OR “special situation” OR “anomalous event” OR “anomalous situation” OR “unusual event” OR “unusual situation” OR “crisis” OR“disaster” OR “catastrophe” OR “traffic accident” OR “epidemics” OR “infectious disease”) AND (2013 < PUBYEAR<2019); (2) searching papers in the reference list of the five previously identified review articles and including additional studies.

In the first iteration, 4784 papers (1880 ScienceDirect +1542 Springer +101 IEEE +715 ACM + 276 Web of Science +170 Scopus) were initially identified. In the second iteration, 13 additional articles were included as relevant for further selection by searching the reference lists. Accordingly, 4797 articles were found through the process of this stage (Fig.  5 ).

Study selection process

Selecting primary studies . The second step was to select the studies to be reviewed through a standard inclusion and exclusion criteria. The specific items are depicted in Table ​ Table1. 1 . The inclusion criteria I1 and I2 ensured that the selected studies were in accord with mobile phone data and applying it in EM, which aligned with the objectives of this study. The exclusion criterion E1 eliminated studies that were found by the keyword “emergency,” but discussed emergency-related activities instead of an emergency situation itself or did not discuss a specific situation at all, for example, studies of efficient deployment of emergency departments or the contents and answers of emergency calls. E2 excluded studies that analyzed the use of mobile phones for self-rescue when individuals encountered difficulties, like heart attacks. Such situations were considered to be emergencies for a single person, which did not fall within the scope of this study. E3 excluded studies with a discussion of non-emergency situations, which were planned events like concerts, festivals, and football matches. All of these criteria were considered to point out the most relevant studies for the topic and improved the reliability and validity of the review study. Finally, in order to mitigate bias, two of the authors conducted this step using the above criteria. After discussion, 65 articles were chosen for the analysis and report in this review (refer to the process illustrated in Fig. ​ Fig.5 5 ).

Inclusion/Exclusion criteria

Extracting data . The third step was to extract data from the studies that were selected. The following information was extracted from the 65 studies: (i) document demographic information, including the title, year of publication, journal name or conference name, and authors; (ii) information about emergency situations, including the general types of emergency and specific events; (iii) types of mobile phone data used in emergency situations; (iv) methods to apply such data in the situations and objectives of the study (what perspectives it analyzed and to which process of EM the study belongs). The detailed results are presented in Section 3.

Based on the information extracted, the analysis perspectives and applications that were proposed were summarized to create a clearer idea of how to apply mobile phone data to effective management (refer to SectionS 3.4 and 3.5 for details). For example, individual movement patterns were studied by Stefania et al. ( 2018 ) and Vogel et al. ( 2015 ) to either establish new models or develop existing models, and thus help prevent disease dissemination, while aggregated population mobility was analyzed by Sekimoto et al. ( 2016 ) to benefit government policymaking. In this study, the phrases ‘individual movement patterns’ and ‘aggregated population mobility’ were combined and expressed as ‘human mobility,’ which represents a kind of analytic perspective. In addition, objectives (or final applications) of the reviewed articles like ‘making rescue plans,’ ‘making traffic regulations,’ and ‘helping in policy decisions’ were paraphrased as ‘making policies’. In this way, the current study transferred the meta-information extracted from the reviewed studies into a collective and scientific form for the conclusions and future discussion.

Preparing the framework for using mobile phone data for emergency management . The final step in the conducting phase was to propose the plan for developing a framework that could illustrate the current state of research. First, we drew on the typology of mobile data sources, types, and uses in the disease disaster management cycle proposed by Cinnamon et al. ( 2016 ), and we planned to adjust the framework to adapt to the data from this study. Second, we considered “emergency situations” and “mobile phone data” as two starting points, as both aspects were key features of the subject. Third, considering the practicality of this framework, it was desired to expand the framework to the perspectives of stakeholders from both managerial and technical layers, respectively. Therefore, five themes (situations, phases in EM, mobile phone data, analysis perspectives, and applications) were identified as components in the framework. Based on these themes, we realized that “applications” served as the final consideration regardless of the perspective. Finally, we developed a draft of the framework which started from “emergency situations” and “mobile phone data” and ended in “applications.” It consisted of two logical routes: the decision-making, or managerial, route (“emergency situation”, “phases in emergency management”, and “applications”); and the problem-solving, or technical, route (“mobile phone data”, “analysis perspective”, and “applications”).

Appendix 2. Illustration of Analysis Perspectives

Appendix 3. illustration of applications, appendix 4. characteristic matrix of the reviewed studies.

Characteristic matrix (data types, analysis perspectives, applications, EM phases)

‘AP’ stands for ‘Analysis perspective’ and ‘EM phases’ represents ‘phases of emergency management’. Six analysis perspectives are respectively human mobility (HM), social networks (SN), mobile phone usage pattern (MPUP), information diffusion (ID), geographic location (GL) and collected information (CI). The definitions of AP and Applications are consistent with Tables  2 and ​ and3 3 .

Authors’ Contributions

YW, JL, XZ, GF, XL conceived the study. YW, JL carried out the review, synthesized and analyzed the evidence, and drafted the manuscript. XZ, XL, GF supervised the review process and revised the manuscript. All authors read and approved the final manuscript.

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Contributor Information

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Gengzhong Feng, Email: nc.ude.utjx.liam@gnefzg .

Xin (Robert) Luo, Email: ude.mnu@oulnix .

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