This paper aimed to alleviate the disparity in the literature regarding social media use for collaboration and communication and its influence on the performance of students at higher education. A questionnaire survey on constructivism theory, technology acceptance model, and communication theory were utilized as the key method for collecting data and was circulated among a total of 863 university students. The obtained outcomes of students' behavioral intention to utilize social media to collaborate learning and online communication indicates a positive effect on their academic works in higher education institutes, while male students were not completely satisfied with interaction with peers for collaboration learning. The study indicates that collaboration learning, as well as online communication over social media enhances, the students learning activities and enable to sharing knowledge, information, and discussions, and hence, we recommend students to utilize social media for education purpose and should have encouraged them through lecturers at higher level education institutions.

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This is an exploratory study to model the determinants of actual use of a digital library system. To do so, a research model was developed using Delone and McLean’s information system success model and explained as an empirical study. Data were collected from 978 respondents using a structured questionnaire from four different universities of Malaysia. The findings showed that the quality factors of digital library systems have a strong influence on satisfaction, behavioral intention, and variance in actual use. Information quality is the strongest predictor to measure user satisfaction, and satisfaction has a strong effect on students’ behavioral intention to use the system. In addition, user satisfaction and behavioral intention to use the system also have a strong positive relationship with the actual use of a digital library system. In brief, behavioral intentions are greatly influenced by system quality, information quality and service quality.

In the IoT (Internet of Things) environment, smart homes, smart grids, and telematics constantly generate data with complex attributes. These data have low heterogeneity and poor interoperability, which brings difficulties to data management and value mining. The promising combination of blockchain and the Internet of things as BCoT (blockchain of things) can solve these problems. This paper introduces an innovative method DCOMB (dual combination Bloom filter) to firstly convert the computational power of bitcoin mining into the computational power of query. Furthermore, this article uses the DCOMB method to build blockchain-based IoT data query model. DCOMB can implement queries only through mining hash calculation. This model combines the data stream of the IoT with the timestamp of the blockchain, improving the interoperability of data and the versatility of the IoT database system. The experiment results show that the random reading performance of DCOMB query is higher than that of COMB (combination Bloom filter), and the error rate of DCOMB is lower. Meanwhile, both DCOMB and COMB query performance are better than MySQL (My Structured Query Language).