International Journal of Interactive Mobile Technologies (iJIM) – eISSN: 1865-7923 – Vol. 15, No. 12, 2021 Paper—Exploring Factors Influencing Mobile Learning in Higher Education – A Systematic Review Exploring Factors Influencing Mobile Learning in Higher Education – A Systematic Review https://doi.org/10.3991/ijim.v15i12.22503 Yuvika Gupta IMS Unison University, Dehradun, India Farheen Mujeeb Khan (*) The ICFAI University, Dehradun, India farheenk121@gmail.com Sonal Agarwal Datalink Institute of Business and Technology, Tema, Ghana Abstract—To facilitate compelling learning experiences for the students, mobile learning (m-learning) has evolved as a powerful component of education – learning and teaching. Unlike electronic learning (e-learning), m-learning uses small and portable devices and laptops and desktops, primarily small and porta- ble technological tools. M-learning helps impart knowledge focusing on the learner's need, accessibility, infrastructure, and interaction, irrespective of the place and time. With rapid advancements in Information and Communication Technologies (ICT) and mobile devices, myriad applications (Apps) and inno- vative m-learning and e-learning services are being developed and launched at an unprecedented pace. For students representing different demographics (age, education level, class, socioeconomic status, location) and enrolled in higher education, m-learning has quickly become the modern style and preferred for- mat of learning and accessing knowledge to integrate different modes of learn- ing. Motivated by this novel m-learning learning movement, this study synthe- sizes existing research on m-learning technologies and technological platforms that cater to synchronous and asynchronous learning/teaching modalities for students and teachers at higher education institutions. Keywords—M-learning, TAM, UTAUT, Diffusion of innovation, Higher edu- cation 1 Introduction Revolutionary change in technological progress has generated profound changes in individual living and working activities. Moreover, the relentless high-tech advance- ment and decrease in the price of mobile devices and services have made it a conven- tional usage device. The rapid and continuous need to access the information necessi- tates mobile devices used in the education sector, which has further transformed learning mode as mobile learning [1-3]. The extensive ownership of mobile gadgets 140 http://www.i-jim.org Paper—Exploring Factors Influencing Mobile Learning in Higher Education – A Systematic Review has helped make it an educational tool, as it offers a focused learning environment to learners by negating the constraint of time and place [4-6]. Thus, mobile learning (m- learning) has widened the extent of educational opportunities in higher education (HE) [7]. M-learning empowers the learners with innovative learning opportunities, like personalized data, context-awareness, interactivity, communication, and collabo- ration. It also helps in linking both formal and informal education from different per- spectives [8-12]. M-learning enables access to educational content globally, empowering interaction between individuals that cannot easily communicate face-to-face, which is believed to increase education effectiveness [13]. Furthermore, m-learning allows the reusing of educational materials and enables individually enhanced learning. On the contrary, researchers have claimed that students are less inclined to use e-learning systems [14] in their education even after the teachers' influence. The factors affecting mobile de- vices' usage among students are its reasonable price, assertiveness towards usage, determination, and focus on achievement and information controls [15],[16]. As stat- ed in reference [17], mobile devices like palmtops, tablets, laptops, personal digital assistants, and mobile phones act as a learning tool with acceptable capability in lec- ture rooms and outside learning. Thus, m-learning has proven to be an essential component of HE; its acceptance and adoption are of growing interest for researchers. There exist a massive variety of literature review studies concerning mobile-based learning. In conformity with the new inspired movement of learning, this review of prior research commences explor- ing the factors affecting students' acceptance of m-learning and their behavioral inten- tion to use it in an integrated way. Research has been conducted by going through existing literature and examining different approaches and models of m-learning. 2 Methodology The well-known PRISMA statement by Moher et al. [18] was used for performing literature searches and exploring the most relevant articles (Fig. 1). The main criteria for inclusion of studies considered in the present research are: • Only articles published in peer-reviewed journals are studied. • Leading publications in the area of education technology were selected. Five-year h-index criteria of Google Scholar metrics were used to explore the top journal in education technology. From the following journals, three or more articles relating to the study were identified and considered for the present study: Education and In- formation Technologies, Telematics and Informatics, Computers & Education, Computers in Human Behaviour. The journal's name and the number of articles in- cluded in the present study are mentioned in Table 1. Thesis dissertations and con- ference proceedings were not considered in the study due to not being subject to peer review. The last condition was that the studies selected must be published be- tween January 2013 to September 2020, as this period represented the significant trends in the m-learning domain. iJIM ‒ Vol. 15, No. 12, 2021 141 Paper—Exploring Factors Influencing Mobile Learning in Higher Education – A Systematic Review • Students should have used mobile devices such as laptops, smartphones, and tab- lets for learning purposes in the included studies. Studies relating to specialized polling systems and electronic response systems were excluded from the study. Table 2 presents the inclusion and exclusion criteria incorporated in the current study. Table 1. Educational technology journals considered. S.No. Name of Journal Number of Articles 1. Journal of Librarianship and Information Science 2 2. International Journal of Business and Management 1 3. Journal of Critical Reviews 1 4. Education and Information Technologies 4 5. Technology in Society 2 6. International Journal of Networking and Virtual Organisations 1 7. Educational Technology Research and Development 2 8. Frontiers in Psychology 1 9. Universal Access in the Information Society 1 10. Computers in the Schools 1 11. Australasian Journal of Educational Technology 1 12. Telematics and Informatics 3 13. International Journal of Interactive Mobile Technologies 2 14. Journal of Systems and Information Technology 1 15. Computers & Education 4 16. Interactive Technology and Smart Education 1 17. Computers in Human Behaviour 3 18. Computer Science & Information Technology 1 19. World Applied Sciences Journal 1 20. International Review of Research in Open and Distance Learning 1 21. SSRN Electronic Journal 1 Table 2. Inclusion and exclusion criteria. Inclusion criteria Exclusion criteria Articles published in peer-reviewed journals are considered Book chapters, conference papers, review studies, dissertations are not considered The study must be published between January 2013 to September 2020 Clicker device studies are excluded Students must use mobile devices such as tablets, smartphones, and laptops. Non-English articles 142 http://www.i-jim.org Paper—Exploring Factors Influencing Mobile Learning in Higher Education – A Systematic Review Fig. 1. PRISMA diagram (Moher et al., 2009) 3 Literature Review The following study investigates the factors that influence HE's m-learning via a systematic literature review (SLR). The study comprises articles searched using the following query for titles, abstracts, and keywords section: ("Mobile learning" OR "m-learning") AND "Students" AND "higher education." The selected studies and research titles, methodologies, factors, and results are present in Table 3. iJIM ‒ Vol. 15, No. 12, 2021 143 Paper—Exploring Factors Influencing Mobile Learning in Higher Education – A Systematic Review Table 3. Review of studies table Studies Research Title Methodology Factors/ Variables Results Zhang et al. (2020) [19] Medical stu- dents’ attitudes and perceptions towards the effectiveness of mobile learning: A comparative information- need perspective Data were collected from 150 respondents through questionnaires. The collected data was examined through SPSS with the help of descrip- tive statistics. TAM model was analyzed. Perceived use- fulness (PU), perceived ease of use (PEOU), attitude (ATT), behavioral intention (BI) Medical students use their mobile phones for networking through social media, relaxation, and enter- tainment activities easily accessible. Societal standards and medical discipline disparities are strongly linked with PU, PEOU, ATT, and BI. The easy access to the system was the main driving force behind the positive influence of PEOU. Social norms represent most promi- nent aspect that positively correlates to PU, ATT, and BI. Alotaibi et al. (2020) [20] Factors influ- encing ac- ceptance to use m-learning in learning the Arabic language for non-native speakers in Saudi universi- ties Data was collected from 460 respondents through paper-based question- naires. SmartPLS 3.0 was used for analysis. The ex- tended UTAUT model was analyzed. FC, EE, per- ceived enjoy- ment, perceived interactivity, expected per- formance, con- tent quality, BI The extended factors, i.e., language interactivity, system enjoyment, and content quality, are significantly related to BI. FC and EE had a significant impact on BI to use m- learning in education. Hu et al. (2020) [21] Exploring factors affecting academics’ adoption of emerging mo- bile technolo- gies-an extended the UTAUT perspective Data was collected from 638 participants using a questionnaire of a seven-point Likert scale. SPSS 24.0 and SmartPLS-SEM 3.0 were used for analysis Independent sample t- test and one-way ANO- VAs were conducted to compare academics' perceptions of mobile technologies' adoption factors. Performance expectancy (PE), EE, social influ- ence (SI), FC, hedonic motiva- tion (HM), Price value (PV), habit (HB), BI PE, FC, HM, HB, and BI signifi- cantly impact user behavior towards mobile technologies. HB has been proved to be the most significant predictor of BI. EE and PV were not significant predictors of BI. The moderators' age, gender, and experience have a negative impact on the relationship between EE and BI. Hoi (2020) [22] Understanding higher education learners' ac- ceptance and use of mobile devices for language learn- ing: A Rasch- based path modeling ap- proach Data was collected through an online questionnaire from 293 respondents. Structural Equation Modelling (SEM), AMOS, and Rasch- based path model were for analysis. The modi- fied version of the UTAUT model was applied. PE, EE, SI, FC, ATT, BI ATT toward Mobile assistant lan- guage learning (MALL) was found to be the most potent predictor of learners' BI. PE was a significant predictor of students' attitudes toward the use of MALL. EE has no direct effect on BI. FC has no direct effect on user behavior. Limited access to high-speed wire- less networks, insufficient budget, and a technical assistant's absence prevent the respondents from using mobile devices to learn a foreign 144 http://www.i-jim.org Paper—Exploring Factors Influencing Mobile Learning in Higher Education – A Systematic Review language on a continuous and regular basis. Nawaz et al. (2020) [23] Acceptance of mobile learning by higher educa- tional institu- tions in Sri Lanka: An UTAUT2 approach Data was collected from 453 respondents through printed hard copies and online forms. SPSS and AMOS analyzed the data. UTAUT2 model was analyzed. PE, EE, SI, FC, HB, HM PE, EE, HB, FC, and HM influ- enced Sri Lankan students' BI to use m-learning Ameri et al. (2020) [24] Acceptance of a mobile-based educational application (LabSafety) by pharmacy students: An application of the UTAUT2 model Data was collected from 241 pharmacy students through a questionnaire designed considering the UTAUT2 model. PLS-SEM was used to analyze the data. PE, EE, SI, FC, HB, BI, use behavior (UB) PE, SI, and HB have a significant effect on BI of pharma students. BI has a significant impact on UB. The use of information technology in education and learning affected the students' BI. Ni- kolopou- lou et al. (2020) [25] Acceptance of mobile phone by university students for their studies: an investigation applying UTAUT2 model Data was collected from 540 university students through Google Forms. Variance-Based Struc- tural Equation Modeling (VB-SEM) and PLS- SEM were applied for analysis. PE, EE, SI, FC, HM, PV, HB, BI, UB EE and PV have no significant impact on BI. HB was the most significant driver of BI to use a mobile phone by the students. PE has a significant impact on the BI of students towards the use of the mobile phone. Students perceive that using mobile phones will help them in improving their academic performance. Chatter- jee et al. (2020) [26] Adoption of mobile applica- tions for teach- ing-learning process in rural girls’ schools in India: an empir- ical study The data was collected from 271 respondents, including both students and teachers, through convenience sampling. PLS-SEM was used for the analysis of data. PU, PEOU, Perceived Risk, EE, PV, BI, Adoption of mobile applica- tion (AMA) PEOU, EE, and PU have a signifi- cant positive impact on the BI. Perceived Risk has a negative but significant impact on the BI. PV has no direct impact on the BI. Al- Azawei & Alowayr (2020) [27] Predicting the intention to use and hedonic motivation for mobile learning: A comparative study in two Middle Eastern countries Data was collected from 469 students through a questionnaire designed on a five-point Likert scale. Convenience sampling was used for data collection. SPSS was used for descriptive and inferen- tial statistics. SmartPLS was used for analysis, Pearson corre- lation, one-way ANOVA test was applied. BI, EE, HM, PE, PV, SI, Trust The trust factor was incorporated in the UTAUT2 model. PE was the most significant predic- tor influencing the BI towards the use of m-learning. PV was the most significant predic- tor influencing the BI. Trust and HM had a significant impact on BI. Al- Na- wayseh, Mobile learning adoption in Jordan: Tech- Data was collected from 300 respondents through questionnaires designed BI, EE, PE, FC, SI PE, EE, and SI have a significant positive impact on BI to adopt m- learning. iJIM ‒ Vol. 15, No. 12, 2021 145 Paper—Exploring Factors Influencing Mobile Learning in Higher Education – A Systematic Review et al. (2020) [28] nology influenc- ing factors on a five-point Likert scale. UTAUT model was analyzed. FC did not have a significant impact on BI. Hwang et al. (2020) [29] A long-term experiment to investigate the relationships between high school students’ perceptions of mobile learning and peer interac- tion and higher- order thinking tendencies Data was collected from 658 respondents with the help of a question- naire designed on a 5- point Likert scale. EFA, CFA, and SEM are used for the data analysis. Ease of use (EU); continuity (CN); Adaptive content (AC); Collaboration (CL) Communi- cation (CO) Problem-solving (PS) Critical thinking (CT) Creativity (CA) CA and CT had a strong relation- ship with CO and CL in the aspect of interaction with learners. CL and AC are highly interconnect- ed; to assist students in collabora- tive m-learning, multiple relevant learning material is essential. The ease-of-use dimension of interaction with technologies was not related to any dimension of interaction with learners. Al-Emran et al. (2020) [30] Towards a conceptual model for examining the impact of knowledge management factors on mobile learning acceptance Data was collected from 416 students using the convenience sampling technique. PLS-SEM was used for the analysis of data. TAM model was ana- lyzed. Actual Use, BI, Knowledge Acquisition, Knowledge Application, Knowledge Protection, Knowledge Sharing, PEOU, PU Knowledge acquisition, application, and protection positively impact PU and PEOU, affecting the behavioral intention to use and actual use. PEOU was the most significant driver of BI. Chao (2019) [31] Factors deter- mining the behavioral intention to use mobile learning: An application and extension of the UTAUT model Data was collected through a questionnaire from 2,000 students using a 5-point Likert scale. PLS-SEM was used to validate structured data. UTAUT model was analyzed. Perceived En- joyment, EE, PE, Satisfaction; Trust; Mobile Self-efficacy; Perceived Risk (PR); BI The moderating effect of PR on the EE and BI relationship is insignifi- cant. PR negatively moderated the rela- tionship between PE and BI. Chavoshi & Hamidi (2019) [32] Social, individ- ual, technologi- cal and peda- gogical factors influencing mobile learning acceptance in higher educa- tion: A case from Iran The data was collected from 257 respondents using snowball sam- pling. Partial Least Squares Artificial Neural Net- works (PLS-ANN) were used to analyze the collected data. TAM and UTAUT combination models are used. User Interface, Mobile Device Limitations, Government Support, Social Influence, Per- sonal Innova- tiveness (PI), Self-efficacy, Trust, PU, PEOU Individual, pedagogical, social, and technological factors significantly affect selecting mobile devices for m-learning. Perceived usefulness was the most significant driver for acceptance of m-learning in Iran. PI has proved to be insignificant in impacting the acceptance of m- learning in HE. Pedagogy of teaching had a consid- erable impact on the PU while technological and individual factors are significant on PEOU. Social influence has a positive effect on PU and PEOU. Arain et al. (2019) [33] Extending UTAUT2 toward ac- ceptance of mobile learning in the context of higher education The data were collected by stratified random sampling method using a 7-point Likert scale. SEM was used to ana- lyze the collected data. The extended UTAUT2 model was analyzed. PE, EE, FC, SI, HM, HB, BI, Ubiquity, Infor- mation quality (IQ), System quality (SQ), Appearance quality, HB, PE, and HM have a significant impact on the BI of students. The new constructs ubiquity and satisfaction were significant drivers which impact the BI of students. EE, FC, and SI were not statistically significant predictors of the stu- dents' BI toward m-learning ac- 146 http://www.i-jim.org Paper—Exploring Factors Influencing Mobile Learning in Higher Education – A Systematic Review Satisfaction ceptance. Ubiquity, IQ, SQ, appearance quality, and satisfaction as a media- tor significantly impact m-learning acceptance. Fagan (2019) [34] Factors Influ- encing Student Acceptance of Mobile Learning in Higher Edu- cation The data was collected from 171 participants through a survey shared by email link online. PLS-SEM was used to analyze the data, and the UTAUT model was analyzed. PE, EE, SI, HM, and BI HM and SI were not significant on BI. PE fully mediates the effect of HM and SI toward students’ BI. Stu- dents in the study perceived iPads as valuable and enjoyable tools for accomplishing educational tasks and improving learning outcomes. Moorthy et al. (2019) [35] Habit and hedonic motivation are the strongest influences in mobile learning behaviors among higher education students in Malay- sia. Data was collected from 358 respondents through questionnaires shared on the internet (Google Form) PLS-SEM was used to analyze the data. PE, EE, FC, SI, HM, HB, PV, BI Amongst all the factors, HB act as the most significant predictor influ- encing students' intention to adopt m-learning. Hamidi & Ja- hansha- heefard (2019) [36] Essential factors for the applica- tion of educa- tion information system using mobile learning: A case study of students of the university of technology Data was collected from 300 students selected randomly. SPSS and AMOS were used for the analysis of collected data. Ease of use, Culture of using m-learning, BI, Student trust, Usefulness, M- learning The results of the study reflect m- learning has a significant impact on student satisfaction. The study reported that the educa- tion level of students impacts the expectations and satisfaction of students towards m-learning. Hamidi & Chavoshi (2018) [37] Analysis of the essential factors for the adoption of mobile learn- ing in higher education: A case study of students of the University of Technology Data was collected from 300 students with the help of a questionnaire designed on a 5-point Likert scale. SEM, CFA, and AMOS were used to test the adoption of m-learning Trust, Characters and personal qualities, BI, Context, Ease of use, Usefulness, Culture of using Trust has a direct and positive relationship with the BI. Personal qualities do not have a significant impact on BI. Kim & Rha, (2018) [1] Predicting the Drivers of the Intention to Use Mobile Learning in South Korea Data was collected through a questionnaire from 580 people, using 5- point Likert scale. Quota sampling was applied. Reliability (Cronbach’s alpha) was 0.743. SPSS 19.0 was used for descriptive statistics, factor analysis, k-means cluster analysis, chi- squared test, ANOVA, and multiple regression analysis. The diffusion of innova- tion model was ana- Optimism, Innovativeness, Discomfort, Insecurity, Relative Ad- vantage, Com- patibility, Com- plexity, Observ- ability, Trialabil- ity, Mobile learning self- efficacy, Mobile learning re- sistance, Status quo bias, inten- tion to use (ITU) Innovation is seen as following the skills and requisites. M-learning improves consumers’ reassurance towards innovation. In contrast, if the latest technology is irreconcilable with consumers' lives, they might endure anxiety or even suffer from technophobia. Observability towards innovation has a significant impact on the ITU towards m-learning. Individual resistance act as a predic- tor for adoption and barriers of innovation diffusion. The intention to use m-learning is directly related to the learning costs involved by using mobile phones. iJIM ‒ Vol. 15, No. 12, 2021 147 Paper—Exploring Factors Influencing Mobile Learning in Higher Education – A Systematic Review lysed. Wai et al. (2018) [38] Exploring undergraduate students’ usage pattern of mo- bile apps for education Data were collected from 150 subjects, 50 each from each faculty concerned, through an online questionnaire. χ2-test and ANOVA were used for data analysis. TAM model was analysed using SmartPLS. Perception of ease of use, perception of usefulness, Overall attitude (OA), Actual Use Perception of the ease of use, per- ception of the usefulness, and OA of students was significant for actual use. Issara- manoros et al. (2018) [39] Auto Mechanic Students’ Per- ceptions and Readiness toward Mobile Learning in Thailand Data were collected from 384 auto mechanic students selected via multistage sampling. SPSS was used for the analysis of data. PE, EE, SI, FC, HM, and PI EE proved to be the most significant factor towards BI. PE was an insignificant predictor of intention to accept m-learning. Thongsri et al. (2018) [17] Integrating UTAUT and UGT to explain behavioural intention to use m-learning: A developing country’s per- spective Data was collected from 359 respondents through a questionnaire designed on a five-point Likert scale. UTAUT and UGT model were analysed. Intention to use m-learning, EE, PE, SI, Cogni- tive need, Affec- tive need, Social need Results revealed that PE, cognitive need, affective need, and social need significantly affected using m- learning. Significant effect of cognitive need on PE and social need on EE was found. PE, EE, and SI had a significant influence on respondents' BI. Research findings showed that there is a high correlation between UGT & UTAUT. EE and SI were insignificant on respondents’ BI, while PE was found to be the most critical factor for predicting BI. Fadzil (2018) [40] A Study on Factors Affect- ing the Behav- ioural Intention to Use Mobile Apps in Malay- sia 100 fully completed questionnaires; through a self-administered questionnaire using email; respondents selected are experts in the m-device industry; analysed using Statistical Package for Social Sciences (SPSS); Pearson correlation is used to examine the association between variables; multiple regression analyses (MRA) were conducted to analyse the direct relationship BI, EE, FC, HB, HM, PE, PV, SI PV, HB, SI, and HM were found to be significant with BI. PE, EE, and FC were not significant with BI. Briz- Ponce et al. (2017) [41] Learning with mobile technol- ogies students behaviour Data were collected from 160 students through convenience sampling, and a 5-point Likert scale was used. The software SmartPLS and the SPSS were used PU, PEOU, SI, Attitude toward the use of technology, Self-efficacy, Anxiety, BI to use new technology, Reliability & PU, PEOU, and SI have a positive impact on user's Attitudes towards the use of technology. User's Attitude has a significant favourable influence on the degree of recommendation or the necessity of a pp certification. 148 http://www.i-jim.org Paper—Exploring Factors Influencing Mobile Learning in Higher Education – A Systematic Review to computerize and analyse the data. TAM and UTAUT model were analysed. Recommendation Reliability and recommendation were the most significant drivers of BI towards using apps for learning. Kim et al. (2017) [2] Understanding the role of user resistance on mobile learning usage among university students Data was collected from 493 students by online survey. Innovation diffusion theory (IDT) and model of innovation resistance (MIR) were analysed. Relative ad- vantage, Com- plexity, Inertia, Innovativeness, Mobile learning resistance, Intention to use mobile learning The relative advantage was found to be a highly prominent predictor of intention to use m-learning. Complexity was not significant in the intention to use m-learning. Personal innovativeness was dis- covered to increase the students' intention to use m-learning. Isaias et al. (2017) [42] Empathic tech- nologies for distance/mobile learning: An empirical re- search based on the unified theory of ac- ceptance and use of technolo- gy (UTAUT) Data was collected via an online questionnaire from 79 students. SmartPLS was used for the analysis of data. The extended UTAUT model was analyzed. PE, EE, FC, Social factors, Attitudes toward empathic fo- rums, BI PE and EE have a significant effect on the students’ attitudes towards empathic forums. SI and FC have no significant effect on the student’s attitudes towards empathic forums. Karimi (2016) [43] Do learners' characteristics matter? An exploration of mobile learning adoption in self- directed learning Data were collected from 130 undergraduate students using a 7-point Likert scale. Regression analysis was used for the analysis of data. Innovativeness, learning style inventory, EE, PE, Perceived playfulness; Intention to adopt The learning style of students in m- learning plays an essential role in its adoption. Playfulness and performance expec- tancy acts as a vital sign of m- learning adoption. PE has a considerable impact on the usage of mobile phones in the formal education system. EE was insignificant on m-learning adoption amongst undergraduate students. Han & Shin (2016) [44] The use of a mobile learning management system and academic achievement of online students Data was collected from 1,604 respondents: five- point Likert scale TAM and ISS model is analysed Self-Efficacy, Innovativeness, Attitude toward technology, PU, PEOU, Subjec- tive Norm, Perceived system accessibility Self-efficacy, innovativeness, and perceived ease of use are significant predictors of the adoption of new technology. Personal innovation influenced the adoption of m-learning. Hao et al. (2016) [45] Influential factors for mobile learning acceptance among Chinese users Data was collected from 282 undergraduate students using the snowball sampling method. Exploratory factor analysis & path analysis was used to test m- learning acceptance model. PU, PEOU, FC, Image, Subjec- tive norm, Voluntariness, PI, BI Pedagogy acts as a significant driver to influence the adoption of m-learning. SI, social image & subjective norm were significant factors that influ- ence usage of m-learning. PI did not have a direct effect on m- learning adoption Althu- nibat (2015) [46] Determining the factors influenc- ing students' intention to use Data was collected from 239 students across universities through a questionnaire. Multiple PU, PEOU, BI, Self-efficiency, FC, Services quality PU, PEOU, and services quality has a significant impact on BI of using m-learning iJIM ‒ Vol. 15, No. 12, 2021 149 Paper—Exploring Factors Influencing Mobile Learning in Higher Education – A Systematic Review m-learning in Jordan higher education regression analysis was used for analysis. TAM, TRA & UTAUT model were analysed. Joo et al. (2014) [47] Factors Influ- encing Actual Use of Mobile Learning Con- nected with E- Learning Data was collected from 238 people using a 5- point Likert scale. SEM was used to ana- lyse structural relation- ships between relevant variables and actual use of m-learning; AMOS was used as a measure- ment model. UTAUT model was analysed. Self-efficacy, PE, EE, SI, FC, Intention of use, and Actual Behaviour PE, EE, SI, and FC did not have a significant effect on the BI. Mobile self-efficacy and perfor- mance expectancy directly impact intention of use, and intention of use affects actual behaviour. Viberg & Grönlund (2013) [48] A cross-cultural analysis of users' attitudes toward the use of mobile devic- es in second and foreign language learning in higher educa- tion: A case from Sweden and China Online questionnaires & paper forms were both used to collect the data from 345 respondents. Hofstede’s cultural constructs were evaluat- ed. Power distance; Individualism– collectivism; Uncertainty avoidance; Long vs. short-term orientation; Mascu- linity-femininity; Indulgence vs. restraint; Personali- zation authenticity; collaboration The findings show respondents’ attitudes toward m-learning are highly positive, with individualiza- tion being most optimistic (83%) followed by collaboration (74%) and authenticity (73%). The statistical analysis indicates that Hofstede's factors cannot explain the differences in mobile-assisted language learning (MALL) attitudes in the chosen sample. Jambu- lingam (2013) [49] Behavioural Intention to Adopt Mobile Technology among Tertiary Students Data was collected from 351 respondents through a questionnaire using a 5-point Likert scale. For analysis, confirmative factor analysis (CFA), composite reliability (CR), and Average Extracted Variance (AVE), SEM, and AMOS were used. UTAUT model was analysed. PE, EE, FC, SI, Affordability, Pedagogy Affordability and Pedagogy have a significant effect on student adoption of Mobile technology learning environ- ments (MTLE). PE has a significant effect on student adoption towards MTLE. EE and SI was an insignificant predictor of BI. Affordability is the most significant predictor of BI that influences MTLE adoption. There is no significant effect of modera- tors' age and gender on MTLE adoption. Abu-Al- Aish & Love (2013) [13] Factors Influ- encing Students’ Acceptance of m-learning: An Investigation in Higher Educa- tion Data was collected from 174 respondents through questionnaire. Principal components extraction with varimax rotation using SPSS 16, CFA using AMOS-16 was used to analyse it. PE, EE, Lectur- ers’ influence, Quality of ser- vice (QS), PI, BI PE, EE, lecturers, QS, and PI, were all significant factors that affect BI to use m-learning. QS and personal innovativeness were added to the structure of UTAUT & provide practitioners and educators with valuable rec- ommendations for designing an implied m- learning system. 4 Discussion The main objective of this research is to identify the factors influencing m-learning in higher education. The thirty-five studies discussed in this paper are a few relevant studies contributed by different authors in identifying factors influencing higher edu- 150 http://www.i-jim.org Paper—Exploring Factors Influencing Mobile Learning in Higher Education – A Systematic Review cation students towards m-learning. The essential behavioural theories in literature which suggest investigating the user's willingness to accept or adopt new innovative technology are Theory of Reasoned Action (TRA), Theory of Planned behaviour (TPB), Theory of Interpersonal behaviour (TIB). Also, there are the Technology Ac- ceptance Model (TAM), Extended Technology Acceptance Model (ETAM), Social Cognitive Theory (SCT), Diffusion of Innovation Theory (DOI), Uses and Gratifica- tion Theory (UGT), Unified Theory of Acceptance Use of Technology (UTAUT), extended UTAUT model and many more. The initial theories (SCT, TIB, and TRA) were psychosocial theories that have proven their efficacy in predicting and explaining numerous human behaviours in varying situations. Further, the DOI focused on describing individuals' behaviour while TRA and TPB concentrate on adopting an organization's decisions. Another model rooted in the theory of human behaviour is TAM, introduced by Davis (1989). According to this theory, the factors influencing the user to adopt new technology are PU and PEOU. The opinion of an individual depends on age and gender. Contrary to it, pre-service teachers' research positively responds to mobile devices for learning, irrespective of age and gender [50]. TAM is regarded as the most signifi- cant paradigm in literature for assessing the adoption of new technology by users. Conversely, despite frequent use, it has been criticized by various authors on the ground of little explanation, heuristic values, triviality, and lack of practical value [51]. TAM has been widely criticized, despite its frequent use, leading researchers to redefine it. As per the reference [48] extended the original TAM model as TAM2 to explain perceived usefulness and usage intentions in terms of social influence (subjective norms, voluntariness, image) and cognitive instrumental processes (job relevance, output quality, result demonstrability, perceived ease of use). Further, as per the refer- ence [52], Venkatesh developed the Unified theory of acceptance and use of technol- ogy (UTAUT) by integrating all the eight models applied to an individual's usage behaviour. According to the reference [53] Two new dependent variables were intro- duced, i.e., behavioural intention and usage behaviour, eight independent variables included in the theory are performance expectancy, effort expectancy, social influ- ence, facilitating condition, gender, age, experience, and voluntariness of use. Amongst UTAUT constructs, the researchers discovered that EE and PE influenced SI in m-learning, while on the other hand, FC and intention of use affected users' actual user behaviour. In addition to this, few researchers found that self-efficacy also affects the actual use behaviour [17]. According to the literature, the UTAUT model has been proved as the most precise model for assessing user acceptance towards a technology. The UTAUT is considered the modern model for analysing user BI factors towards any new technology and the actual use. This paper mainly discussed the DOI, TAM, UTAUT, and extended UTAUT mod- els of user acceptance theory. The papers considered in the present study are mainly of the previous five years; hence, most researchers have discussed UTAUT and ex- tended UTAUT models in their study. iJIM ‒ Vol. 15, No. 12, 2021 151 Paper—Exploring Factors Influencing Mobile Learning in Higher Education – A Systematic Review 5 Conclusion In recent years, a rapid increase in mobile phone usage has been witnessed; many users belong to higher education students. M-learning provides them an opportunity to embed learning in both formal and informal education settings [54],[5]. In the current technological environment, parents intend to boost their children towards using m- learning [55]. Studies discussed in the paper demonstrated how mobile could be used as a communication tool to support teaching and learning in today's technologically advanced environment. M-learning promotes collaborative learning, easy sharing of audio and video contents; record-keeping features aid learners to study the contents quickly and easily, when and where required [56]. In addition to it, as per the refer- ence [57], coding accelerates learning and thinking capability in young age children who are apt to a range of technological tools. The significant features of m-learning like flexibility for learning, access to brief materials, multimedia learning, and content review help enhance traditional education methods. Furthermore, as per the reference [58], educational apps attain the requisite content and resources to reinforce active learning and productive learning activities among young children. 6 Scope for Further Research Further studies can focus on one of the biggest challenges associated with m- learning: the availability, or lack thereof, of internet services and its cost-prohibitive nature, especially for people residing in remote areas and with limited financial and other resources along with compromised infrastructure. Future studies can also ex- plore accessible evaluation tools for learning and emphasize their strengths, limita- tions, and integrity. They can also study instructors' prerequisites for an appropriate, fast, and easy-to-use tool while assessing educational apps. Researchers can think in the direction of reference [59-61], whereby they emphasized the importance of new technology usage in the early childhood education system and its impact on young children's learning. Further to enhance students' knowledge, digital education peda- gogy can be designed to build their content and share amongst themselves. This in- creases students' motivation, and their learning goals can be achieved via fun and enjoyable activities [62],[7],[63]. In support of the study mentioned previously, dis- cussed the pertinent purposes of the educational apps, young students' potentials, and teachers' implied demands for evaluating educational apps for children considering four factors: usability, efficiency, parental control, and security [58]. The role of gov- ernment, non-governmental organizations (NGO), not-for-profit (NFP) organizations, public-private partnerships (PPP), and institutions (universities, colleges, and schools) in the context of m-learning and e-learning resources can be studied considering K-12 students. 152 http://www.i-jim.org Paper—Exploring Factors Influencing Mobile Learning in Higher Education – A Systematic Review 7 References [1] H.-J. 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Kalogiannakis, V. Orfanakis, and N. Zaranis, “The appropriateness of Scratch and App Inventor as educational environments for teaching introductory pro- gramming in primary and secondary education,” in Early Childhood Development, IGI Global, pp. 797–819, 2019. https://doi.org/10.4018/978-1-5225-7507-8.ch039 8 Authors Ms. Yuvika Gupta is an Assistant Professor at the School of Management, IMS Unison University, Dehradun, India. She is an academician with over 11 years of teach- ing experience and MBA and UGC-NET(JRF) qualified. As a focused researcher, she enjoys writing research papers and interacting with fellow researchers at various nation- al and international seminars and conferences. Ms. Yuvika has published over 20+ peer- reviewed research papers in leading management journals indexed in ABDC (Australian Business Deans Council) "B" Category, SCOPUS, Web of Science, and UGC Care. She is also serving as a reviewer in Journals of repute published by Inderscience. Dr. Farheen Mujeeb Khan is an Assistant Professor at ICFAI Business School (IBS), the ICFAI University, Dehradun, India. Believing that the agricultural sector is the backbone of India’s economy, she conducted her doctoral research on "Green Mar- keting in the Processed Food Industry of India" in 2018. She continues to pursue her post-doctoral fellowship (PDF) research on a Senior Research Fellowship awarded competitively by the prestigious Indian Council of Social Science Research (ICSSR) in 2019. Dr. Khan has to her credit over five research papers published in national and international refereed journals. Dr Sonal Agarwal is an Associate Professor at the School of Business Management, Datalink Institute of Business and Technology, Tema, Ghana. She has a teaching expe- rience of more than 20 years in India and abroad (Nigeria, Ghana, Benin). She has earned her Doctorate in Commerce from India, entitled “Appraisal of Housing and Ur- ban Development Programme in India” in the Department of Commerce, M.J.P. Ro- hilkhand University, Bareilly, India in 2004. She holds double Master’s degree in Commerce and Economics. She is examiner for renowned institutions like International Baccalaureate and Cambridge International Examination. She has more than 25 research papers published in National and International Journals. She is reviewer for may reputed journals. Article submitted 2021-03-08. Resubmitted 2021-04-13. Final acceptance 2021-04-13. Final version published as submitted by the authors iJIM ‒ Vol. 15, No. 12, 2021 157