User perceptions of the technology characteristics in a cloud-based collaborative learning environment: a qualitative study

The purpose of this study was to assess user perceptions of technology characteristics, which is a complicated construct in task technology fit model, in a cloud-based collaborative learning environment. For this purpose, cloud computing characteristics cited in the previous related research, were categorised into cost saving, ease of implementation, flexibility, mobility, scalability, sustainability, personalization, processing capabilities, agility, collaboration, usability, risk reduction, measured service, on demand self-service, and resource pooling. Interviews were then conducted with students who had some experience in using cloud-based applications for collaborative learning. Directed content analysis was performed using ATLAS.ti software to organise the coding process. The results of coding data showed that collaboration, mobility, and personalization, which resulted from previous related literature, are also cited by a large number of participants in interviews as being significant characteristics of cloud-based collaborative learning applications. Organisational cost saving, ease of implementation, flexibility (elasticity), scalability, sustainability, processing capabilities, agility, usability, risk reduction, measured service, on demand self-service, and resource pooling were not mentioned by any of the participants at all. However, easy monitoring and assessment, time control and saving, cost saving, accessibility, ease of use, and easy connection to other applications were new themes that emerged inductively during data analysis

A Survey on Trend and Classification of Internet of Things Reviews

The Internet of Things (IoT) is shaping the current and next generation of the Internet. The vision of IoT is to embed communication capabilities with a highly distributed, ubiquitous and dense heterogeneous devices network. This vision includes the adaptation of secure mobile networks, anytime, anywhere, by anyone or anything with new intelligent applications and services. Many efforts have been made to review the literature related to the IoT for the benefit of IoT development. However, many issues need to be addressed to overtake the full potential of the IoT. Therefore, this paper aims to classify and standardize IoT research areas by considering review papers that were published between 2010 and 2019. This paper analyzes a total of 95 related reviews, which were manually selected from databases based on 6 chosen areas. This paper presents the trends and classification of IoT reviews based on 6 research areas, namely, application, architecture, communication, challenges, technology, and security. IoT communication research has been dominating the trends with 21% of total reviews and more than 100% research growth in the last 10 years. Hence, this paper can provide useful insights into specific emerging areas of IoT to assist future research

Neuromarketing: a review of research and implications for marketing

In this research, we reviewed existing studies which used neuromarketing techniques in various fields of research. The results revealed that most attempts in neuromarketing have been made for business research. This research provides important results on the use of neuromarketing techniques, their limitations and implications for marketing research. We hope that this research will provide useful information about the neuromarketing techniques, their applications and help the researchers in conducting the research on neuromarketing with insight into the state-of-the-art of development methods

Investigating factors influencing decision-makers’ intention to adopt green IT in Malaysian manufacturing industry

Green IT has attracted policy makers and IT managers within organizations to use IT resources in cost-effective and energy-efficient ways. Investigating the factors that influence decision-makers’ intention towards the adoption of Green IT is important in the development of strategies that promote the organizations to use Green IT. Therefore, the objective of this study stands to understand potential factors that drive decisions makers in Malaysian manufacturing sector to adopt Green IT. This research accordingly developed a model by integrating two theoretical models, Theory of Planned Behavior and Norm Activation Theory, to explore individual factors that influence decision’ makers in manufacturing sector in Malaysia to adopt Green IT via the mediation of personal norms. Accordingly, to determine predictive factors that influence managerial intention toward Green IT adoption, the researchers conducted a comprehensive literature review. The data was collected from 183 decision-makers from Malaysian manufacturing sector and analyzed by Structural Equation Modelling. This research provides important preliminary insights in understanding the most significant factors that determined managerial intention towards Green IT adoption. The model of Green IT adoption explained factors which encourages individual decision-makers in the Malaysian organizations to adopt Green IT initiatives for environment sustainability

An adoption model for cloud-based collaborative learning applications from top Malaysian universities’ experience

Cloud-based collaborative learning applications are new computing paradigms which facilitate collaborative activities in a centralized location. These applications offer various benefits to higher education. However, even though previous research have discussed cloud computing in general, there is still lack of studies considering students’ intention to adopt cloud-based collaborative learning applications in university settings especially in the context of Malaysian universities. Therefore, this research aims to develop and test an adoption model for cloud-based collaborative learning applications for Malaysian universities by integrating Unified Theory of Acceptance and Use of Technology (UTAUT) and Task Technology Fit (TTF). A preliminary investigation using face-to-face interviews with directors of Information Technology centers and administrators of students email in four selected top Malaysian universities was conducted to understand the current adoption status of cloud-based collaborative learning applications. Next, using purposive sampling, a survey which involved 209 students was conducted to collect data from students who have had experience in using cloud-based collaborative learning applications such as Google Apps and/or Office 365. Partial Least Squares (PLS) method based on Structural Equation Modelling (SEM) was used for analyzing the survey data. Smart PLS 2.0M3 was applied to validate the research model. The overall analysis results showed that characteristics of cloud computing and collaborative task significantly predict the fit between these constructs. Furthermore, Task Technology Fit together with, Performance Expectancy, Social Influence, and Facilitating Conditions significantly influenced intention to adopt cloud-based collaborative learning applications. Findings confirmed that individual and group characteristics were significant drivers of Performance Expectancy and Effort Expectancy. Finally, this research develops a Cloud-Based Collaborative Learning Applications Adoption Model that can serve as a tool to assist the Ministry of Education, university administrators, and cloud service providers to plan their strategies and provide supportive adoption environment for cloud-based collaborative learning applications in universities

M learning adoption model for UTM

M-learning is the use of electronic learning (E-learning) materials on mobile devices such as personal digital assistants (PDAs), Tablet PCs, mobile phones, Pocket PCs and in general every devices that are small and autonomous enough to help us in every moment of our life. With this new technology, learning will become more learner-centered and informal, rather than teacher-centered and formal. Adoption of M-learning refers to the interest of students and lecturers to use mobile devices in order to help them in their teaching and learning processes. But it is very considerable that decision of both students and lecturers to adopt M-learning is a long-term and complicated process and there are many factors that influence this adoption. In order to have successful adoption of M-learning determining these factors, eliminating problems, and highlighting the profits of this new technology for users are very essential. The aims of this project are to identify the factors that influence adoption of M-learning by users in UTM and to propose suitable M-learning adoption model for UTM. In order to reach this aim an interview is conducted by IT manager of CTL and two sets of questionnaire are distributed among students and lecturers. Analyzing these information shows that factors like Perceived Ease of Use, Perceived Usefulness, perceived Mobility Value, Prior Use of E-learning, Self-efficacy, Attitude Toward Using are main factors influencing adoption of M-learning in UTM . Furthermore, faculty and age differences are two moderators that also can impact this adoption. Finally, some recommendations are given to help CTL to have successful M-learning adoption in UTM

The policy as repudiation factors of adopting cloud computing in university administration

Cloud computing (CC) technology can be described as the next generation of Information technology for companies, educational institutions and governmental agencies; which provides easy and affordable access to state of the art technology; IT, technological application and services. Due to the growing needs for information technology (IT) and the current dwindling global financial stability, many higher education institutes including universities, are facing problems in providing the essentials of IT supports for administrative, educational, and research activities. However, CC is rarely implemented in universities in Malaysia. This research thus aims to find out the causal factor for the poor implementation and usage of CC in Malaysia universities. To achieve our aim, CC published works was reviewed to identify the staff positions as well as their required services. The outcomes of the review were applied to find out the models, services, and applications that are available in educational environments. Afterwards, an explorative case study was used to explore the factors that have caused the negligence of CC applications at universities. A semi-structured interview was used to collect data and samples were randomly chosen among administrators, IT staffs, technicians and clerks at one of the biggest public university in Malaysia. The data were analyzed both quantitatively and qualitatively. The findings showed that policy is the main reason to reject of using CC in administrative activities. Thus, with amendment of the existing policy, authorities can benefit from CC and as well as prevent the risk associated with CC

A Model for Decision-Makers’ Adoption of Big Data in the Education Sector

Big Data Adoption (BDA) has already gained tremendous attention from executives in various fields. However, it is still not well explored in the education sector, where a large amount of academic data is being produced. Therefore, integrating Technology Organization Environment (TOE) and Diffusion of Innovation (DOI), this study aims to develop a theoretical model to identify the factors that influence BDA in the higher education sector. To do so, significant technology-, organization-, and environment-related factors have been extracted from previous BDA studies. Meanwhile, the moderating effects of the university size and the university age are added into the developed model. A sample of 195 data was collected from the managerial side of virtual university (VU) campuses in Pakistan using an online survey questionnaire. Structural equation modeling (SEM) was used to test the research model and developed hypotheses. The results showed that relative advantage, complexity, compatibility, top management support, financial resources, human expertise and skills, competitive pressure, security and privacy, and government policies are significant determinants of BDA. However, the results did not support the influence of IT infrastructure on BDA. Based on the findings, this study provides guidelines for the successful adoption of big data in higher education sector. This study can serve as a piece of help to the ministry of education, administrators, and big data service providers for the smooth adoption of big data

The Coursera Community Framework: exploring the MOOC as a Community of Practice

Massive open online courses (MOOCs) have increasingly become an important element for individuals’ learning and development. However, MOOCs mainly concentrate on duplicating knowledge instead of constructing it. This research aims to explore the structure of the MOOCs for fostering the knowledge construction in which educational professional build, develop, share one another’ learning and reflections. This research focused on Coursera, a particular MOOC community, by drawing on the concepts of community of practice (CoP) as a theoretical lens. Three types of data were collected. The archival data consisted of the top and selected posts from online discussion forums, and the elicited data which was derived from over 60 interviews with Coursera learners. Meanwhile, field note data was extracted from 160 days of interaction with the participants. A qualitative research method using a netnographic methodology was employed. The findings contribute to the body of knowledge construction and online communities by providing an understanding of the domain, community and practice elements. The study on other elements such as the reinforcement of identity, formation of warrants and identification of mechanisms for legitimate peripheral participation can help to interpret the constitution of CoPs in MOOC. This research developed a Coursera community framework that generally makes a MOOC community more energetic to construct knowledge

Success factors influencing the adoption of M–learning

In today’s educational world delivering teaching and learning materials anywhere/any time has became a major focus of interest. Mobile learning is a new pedagogy/technology that breaks the limitations of classrooms and shifts educational processes from lecturer-centred to learner-centred without any limitations. As mobile learning is in its infancy, identifying success factors influencing the adoption of this pedagogy/technology is very important. The present study aims to determine the factors contributing to the adoption of M-learning. Three external variables (perceived mobility value, self-efficacy and prior use of electronic learning) were added to technology acceptance model and tested in Universiti Teknologi Malaysia. Quantitative research approach was conducted to collect required data. The sample was composed of 350 undergraduate and postgraduate students of Universiti Teknologi Malaysia who were selected by stratified sampling to answer questionnaires. Finally, the results of data analysis indicate that perceived mobility value is a more significant factor while other factors – perceived ease of use, perceived usefulness, attitude toward using, self-efficacy, prior use of e-learning – are also accepted as contributing factors for mobile learning adoption. In addition, some recommendations are given to act on these factors and to accelerate the process of M-learning adoption in higher education institutions