An investigation of mobile learning readiness in higher education based on the theory of planned behavior

This study investigated the current state of college students' perceptions toward mobile learning in higher education. Mobile learning is a new form of learning utilizing the unique capabilities of mobile devices. Although mobile devices are ubiquitous on college campuses, student readiness for mobile learning has yet to be fully explored in the United States. The paper describes a conceptual model, based on the theory of planned behavior (TPB), which explains how college students' beliefs influence their intention to adopt mobile devices in their coursework. Structural equation modeling was used to analyze self-report data from 177 college students. The findings showed that the TPB explained college students' acceptance of m-learning reasonably well. More specifically, attitude, subjective norm, and behavioral control positively influenced their intention to adopt mobile learning. The results provide valuable implications for ways to increase college students' acceptance of mobile learning.     

Exploring students' awareness and perceptions: Influencing factors and individual differences driving m-learning adoption

This study investigates students' awareness and perceptions of m-learning and examines the factors affecting students' behavioral intention to adopt m-learning, by using a modified research model that integrate technology acceptance model (perceived usefulness and perceived ease of use) and unified theory of acceptance and use of technology (social influence) along with other factors (m-learning services and mobile limitations). In addition, control (gender, field of study, study level) and moderator variables (mobile capabilities, level of mobile usage, and frequent use of m-services) were introduced to verify the individual differences between respondents on the key factors affecting the adoption and usage of m-learning. Structural equations modeling and path analysis were used to test the hypotheses and the proposed model. The results revealed that perceived usefulness and perceived ease of use were found to be the primary factors driving students' intentions to use m-learning. Both m-learning services and social influence have positive effects on the acceptance of m-learning, while mobile limitations were found to be the main obstacle restraining students' participation in a m-learning environment. Most of the control variables yield no significant differences between students, but all the moderator variables were found to be significant determinants that can influence students to adopt m-learning. Overall, students have great potential to engage and integrate mobile technology into their educational environment.     

It's not only what I think but what they think! The moderating effect of social norms

The current research extends our knowledge of the main effects of attitude, subjective norm, and perceived control over the individual's technology adoption. We propose a critical buffering role of social influence on the collectivistic culture in the relationship between attitude, perceived behavioral control, and Information Technology (IT) adoption. Adoption behavior was studied among 132 college students being introduced to a new virtual learning system. While past research mainly treated these three variables as being in parallel relationships, we found a moderating role for subjective norm on technology attitude and perceived control on adoption intent. Implications and limitations for understating the role of social influence in the collectivistic society are discussed.     

An Identification of Factors Motivating Individuals’ Use of Cloud-Based Services

The purpose of this study is to develop a better understanding of the factors motivating individuals who use cloud-based services despite the privacy and security risks associated with it. The authors developed a research model that incorporates the theory of planned behavior (TPB) and constructs from previous research to explain individuals’ intentions to use cloud-based services. Our analysis shows support for the relationships among predictor variables (attitude, subjective norm, perceived behavioral control, and information privacy) and the outcome variable (behavioral intention). Additionally, our analysis shows IT leadership and trust as moderating variables between the TPB predictor variables (attitude and perceived behavioral control), but not subjective norm. The results indicate that IT leadership support of cloud-based systems can have a positive effect on cloud adoption and individuals are likely to continue their use of cloud computing despite the privacy and security risks associated with it.     

An empirical investigation of anti-spyware software adoption: A multitheoretical perspective

We performed an empirical investigation of factors affecting an individual's decision to adopt anti-spyware software. Our results suggested that an individual's attitude, subjective norm, perceived behavioral control, and denial of responsibility significantly affected anti-spyware adoption intention. Also, relative advantage and compatibility showed a significant effect on attitude, visibility, and image on subjective norm, and trialability, self-efficacy, and computing capacity on perceived behavioral control. Interestingly, moral obligation, ease of use, and perceived cost were not as significant as was originally expected.     

Do learners’ characteristics matter? An exploration of mobile-learning adoption in self-directed learning

This paper aims to identify individual characteristics that motivate learners to use mobile-learning. It sheds light on our current knowledge by a) examining an m-learning adoption model which accounts for learners’ characteristics (learning style and personal innovativeness) in addition to previously studied mobile platform characteristics and b) considering the context in which learning occurs (formal and informal). A framework has been introduced and empirically tested. Results suggest that individuals’ learning style and perceived playfulness influence m-learning usage in both learning situations; while performance expectancy and personal innovativeness are only influential in specific learning contexts. This study highlights the role of learners’ characteristics in m-learning adoption and emphasizes the importance of distinguishing between various types of m-learning. This multi-disciplinary research enriches m-learning literature and offers practical implications for educators using mobile technologies as well as developers of virtual learning platforms.     

Psychosocial predictors of the use of enhanced podcasting in student learning

The current study examined the influence of psychosocial constructs, from a theory of planned behavior (TPB) perspective, to predict university students’ (N = 159) use of a newly offered on-line learning tool, enhanced podcasts. Pre-semester, students completed questionnaires assessing the TPB predictors (attitude, subjective norm, perceived behavioral control) related to intended enhanced podcast use until the middle of semester. Mid-semester, students completed similar items relating to podcast use until the end of semester. Self-report measures of podcast use were obtained at the middle and end of semester. At both time points, students’ attitudes predicted their intentions and, at the initial time point, subjective norm also predicted intended podcast use. An examination of the beliefs underlying attitudes, the only construct to predict intentions at both time points, revealed differences between those students higher, rather than lower on intentions to use the podcasts, especially for the perceived educational benefits of podcast use later in the semester. Intentions to use enhanced podcasting only predicted self-reported use in the second half of the semester. Overall, this study identified some of the determinants which should be considered by those aiming to encourage student use of novel on-line educational tools.     

Mobile learning: A framework and evaluation

Wireless data communications in form of Short Message Service (SMS) and Wireless Access Protocols (WAP) browsers have gained global popularity, yet, not much has been done to extend the usage of these devices in electronic learning (e-learning). This project explores the extension of e-learning into wireless/handheld (W/H) computing devices with the help of a mobile learning (m-learning) framework. This framework provides the requirements to develop m-learning applications that can be used to complement classroom or distance learning. A prototype application was developed to link W/H devices to three course websites. The m-learning applications were pilot-tested for two semesters with a total of 63 students from undergraduate and graduate courses at our university. The students used the m-learning environment with a variety of W/H devices and reported their experiences through a survey and interviews at the end of the semester. The results from this exploratory study provide a better understanding on the role of mobile technology in higher education.     

Comparing the social knowledge construction behavioral patterns of problem-based online asynchronous discussion in e/m-learning environments

In recent years, researchers have conducted various studies on applying wireless networking technology and mobile devices in education settings. However, research on behavioral patterns in learners' online asynchronous discussions with mobile devices is limited. The purposes of this study are to develop a mobile learning system, mobile interactive teaching feedback system (MITFS), linked to both mobile devices and the internet, to support learners with online asynchronous discussion, and combine content analysis and sequential analysis to compare and contrast the social knowledge construction behavioral patterns of problem-based asynchronous discussion in e-learning and m-learning environments. This study investigated four weeks of online discussions in an “Introduction to Computer Science” course involving forty first year university students. The control group (online asynchronous discussion without mobile devices) and the experimental group (online asynchronous discussion with mobile devices) in the group discussions were explored. By using content analysis and sequential analysis for the problem-based online asynchronous discussion of the behavioral patterns and differences between students in control and experimental group, the results showed that using mobile devices in online asynchronous discussion influenced students' learning performance. Some interesting results were found. Firstly, when the students used mobile devices in discussion situations, they could more engage in reflecting thinking, sharing more information, and further facilitating social knowledge construction among group members. Secondly, the experimental group performed better than the control group in terms of participation and diversity in knowledge construction behavioral patterns. Finally, based upon the findings, some implications are proposed for further research.     

Investigating e-learning system usage outcomes in the university context

This paper investigates the outcomes of e-learning systems adoption and use by conceptualizing three e-learning systems adoption outcome constructs namely perceived learning assistance, perceived community building assistance and perceived academic performance. Utilizing these constructs, the paper proposes a research model for assessing the possible outcomes of e-learning systems adoption and use. The study collected longitudinal survey data from 249 university students participating in hybrid courses using a popular learning management system, Moodle. Partial least squares (PLS) approach was then used to test the research model.The findings suggest that beliefs about perceived usefulness and perceived ease of use, and how an e-learning system is used influence students' perceived learning assistance and perceived community building assistance. In turn, perceived learning assistance and perceived community building assistance influence the students' perceived academic performance.     

Factors driving the adoption of m-learning: An empirical study

Even if m-learning is spreading rapidly in many regions of the world, research addressing the driving factors of m-learning adoption is in short supply. Built on the Technology Acceptance Model, this paper proposes a hypothesized model of m-learning adoption. Employing structural equation modeling technology, the model was assessed based on the data collected from 230 participants using a survey questionnaire. Results indicate that perceived near-term/long-term usefulness and personal innovativeness have significant influence on m-learning adoption intention, while perceived long-term usefulness significantly affects the perceived near-term usefulness. Personal innovativeness is a predictor of both the perceived ease of use and perceived long-term usefulness as well. Of all variables, the perceived long-term usefulness contributes to the most influential predictor of m-learning adoption. The model accounts for approximately 60.8% of the variance of behavioural intention. The results indicted that offing high-quality contents complying with students’ future targets is key to the success of m-learning in China. Both theoretical and practical implications are discussed.     

Applying the Technology Acceptance Model to Social Networking Sites (SNS): Impact of Subjective Norm and Social Capital on the Acceptance of SNS

With their heavy traffic and technological capabilities, social networking sites (SNS) introduced a new means of building and maintaining perceived social capital. This study aims to identify underlying factors and causal relationships that affect behavioral intention to use SNS. For this purpose, this research developed an extended technology acceptance model, incorporating subjective norm and perceived social capital for predicting SNS acceptance and usage. Exploratory correlation and path analyses were conducted to identify the relationships between five constructs: perceived usefulness, perceived ease of use, subjective norm, perceived social capital, and intention to use. The results showed that perceived usefulness and perceived ease of use had robust effects on the user's intention to use SNS. The research findings also demonstrated that subjective norm and perceived social capital were significant predictors of both perceived usefulness and perceived ease of use and therefore should be considered as potential variables for extending the technology acceptance model.     

A model of organizational employees’ e-learning systems acceptance

This study examines the factors that influence employees’ adoption and use of e-learning systems and tests the applicability of the technology acceptance model (TAM) in the organizational context. We examined the relationship of employees’ perceptions of their behavioral intention to use e-learning systems in terms of four determinants (individual, organizational, task characteristics, and subjective norm), to further explore the effects of management and organizational support on the subjective norm. Data were 357 valid questionnaires from four industries in Taiwan. The findings indicate that organizational support and management support significantly affected perceived usefulness and intention to use. Individuals’ experience with computers and computer self-efficacy had significantly positive effects on perceived ease of use. Task equivocality significantly influenced perceived usefulness. Organizational and management supports significantly impacted the subjective norm, perceived usefulness, perceived ease of use, and intention to use. Additionally, the results suggest that external variables that affect perceived usefulness, perceived ease of use, and intention to use, need to be considered as important factors in the process of designing, implementing, and operating e-learning systems. The results provided a more comprehensive insight of individual, organizational, and task characteristics in predicting e-learning acceptance behavior in the organizational contexts, rarely tested in previous studies. By considering these identified factors, practitioners can take corresponding measures to predict or promote organizational employees’ e-learning systems acceptance more effectively and efficiently. Furthermore, by explaining employees’ acceptance behavior, the findings of this research help to develop more user-friendly e-learning systems and provide insight into the best way to promote e-learning systems for employees.     

Understanding and predicting students' intention to use mobile cloud storage services

The challenges of mobile devices such as limited bandwidth, computing, and storage have led manufacturers and service providers to develop new value-added mobile services. To address these limitations, mobile cloud computing, which offers on-demand services including platforms, infrastructure, and software have been developed. This study attempts to build a significantly improved research framework based on the Technology Acceptance Model in order to identify factors that affect students' attitudes toward and intentions in using mobile cloud storage services. A structural equation model was used to assess the proposed model based on the data collected from 262 undergraduate students. Results show that perceived usefulness, subjective norm, and trust have a significantly positive effect on the attitude, which in turn is a significant predictor of behavioral intentions. The research model, which explains 82% of the variance in attitudes toward using mobile cloud storage services has a strong predictive power. The findings have both theoretical and practical implications for academics, managers, and educational institutions.     

An exploratory study on mobile banking adoption in Indian metropolitan and urban areas: a scenario-based experiment

Security concerns have thwarted the widespread adoption of mobile banking in India. To respond to the concerns of Indian banks and their customers, we present in this paper our exploratory attempts to understand how the levels of security affect perceived risk and control and ultimately, adoption of mobile banking by Indian customers. This study also examines the moderating influence of the type of city on the relationship between security levels and risk/control perceptions associated with mobile banking. Using a scenario-based experiment, we classified security-enhancing approaches into three categories and examined their effectiveness in decreasing Indian customers’ perceived risk, increasing their perceived control, and then in turn, facilitating mobile banking adoption. Our findings reveal the important role of perceived risk and control in influencing customers’ intention to adopt mobile banking. Moreover, perceived risk and control significantly influenced mobile banking adoption by customers in urban areas, but only perceived control significantly influenced mobile banking adoption by metropolitan customers. Additional analyses show that customers’ risk and control perceptions differ according to the level of security; however, these perceptions do not have a significant influence on risk and control.     

Electronic versus traditional print textbooks: A comparison study on the influence of university students' learning

University students are increasingly choosing to purchase e-textbooks for their mobile devices as an alternative to traditional textbooks. This study examines the relationship between textbook format and 538 university students' grades and perceived learning scores. Results demonstrate that there was no difference in cognitive learning and grades between the two groups, suggesting that the electronic textbook is as effective for learning as the traditional textbook. The mean scores indicated that students who chose e-textbooks for their education courses had significantly higher perceived affective learning and psychomotor learning than students who chose to use traditional print textbooks.     

The impact of m-learning technology on students and educators

This paper addresses the notion of the impact of mobile learning technology from the learner’s as well as educator’s point of view. The authors have outlined the application of the e-learning in smart classes, which is followed by a similar argument with regard to the m-learning technology. This is followed by a statistical evaluation of the m-learning which through multiple surveys is conducted among the undergraduate and postgraduate students as well as the academics. In conclusion, the outcomes of these surveys are presented in graphical forms that highlight the merits and demerits of the m-learning technology.     

Mobile Technology in the Classroom: What Drives Student-Lecturer Interactions?

Online learning when combined with mobile technology transforms the traditional classrooms from teacher-centered to student-centered classrooms. Despite the widespread use of mobile technology among students and educators today, limited researches have been conducted to study the effects of using mobile technology to enhance student–lecturer interactions. In addition, existing theories of technology acceptances, chiefly Information System Success Model (ISSM), Motivational Model (MM), Social Cognitive Theory (SCT), Technology Acceptance Model (TAM), and Cultural Dimension Theory (CDT) are widely recognized for their predictive power in determining adoption intentions. In this study, determinants from all five theories were unified and examined, namely system quality and information quality from ISSM, enjoyment from MM, perceived usefulness and perceived ease of use from TAM, self-efficacy from SCT, and uncertainty avoidance from CDT as predictors of adoption intention in the context of predicting student–lecturer interactions. This empirical study was conducted using an online survey. Data collected from the samples (n = 328) were analyzed using PLS-SEM. Results obtained exhibited adequate explanatory power, where information quality, system quality, enjoyment, and uncertainty avoidance significantly predict adoption intention, while perceived usefulness, perceived ease of use, and self-efficacy were insignificant. Secondly, each theory was independently analyzed, and the predictive power and relevance of ISSM, MM, TAM, and UDT confirmed the importance and relevance of these theories. Results obtained provided a comprehensive understanding of the factors that significantly affect students’ intentions to use mobile technology to interact with their lecturers on academic matters. The discussions and implications of this study are crucial for researchers and practitioners of educational technologies in higher education.     

Determining the factors influencing students’ intention to use m-learning in Jordan higher education

M-learning is characterized as a powerful element of learning and education for facilitating the learning experiences. With enhanced and rapid advancements in technologies of ICTs (Information and Communication Technologies) and mobile, numerous innovative services and applications are being developed. Therefore, it becomes significant to investigate the factors influencing the intentions of m-learning to be used among the students of higher education institution. This study examines the “Technology Acceptance Model” (TAM), “Theory of Reasoned Action” (TRA) and “Unified Theory of Acceptance and Use of Technology” (UTAUT). The study is based on a survey being conducted across diverse groups of students, belonging to different communities and universities. The survey questionnaire was utilized for collecting the relevant data from 250 respondents. The results analyzed yields the impact that the proposed model of m-learning is comprehensive to study in the institutions of higher education.