Task-technology fit and technology acceptance model application to structure and evaluate the adoption of social media in academia

NurulAmalinMohamad2020_VocationalEducationforAutismSpectrumThe purpose of this article was to reduce the dissimilarities in the literature regarding the use of social media for training and its impact on students' academic performance in higher education institutions. The main method of data collection for task-technology fit (TTF) and the technology acceptance model (TAM) was a questionnaire survey. This research hypothesizes that TTF applied to social media for learning will affect technology, task, and social characteristics that in turn improve students' satisfaction and students' academic performance. It also posits that the behavioral intent to use social media for learning will affect comprehension efficiency, ease of use, and enjoyment, all of which also improve students' satisfaction and students' academic performance. The data collection questionnaire was conducted with 162 students familiar with social media. Quantitative structural equation modeling was employed to analyze the results. A significant relationship was found between technology, task, and social features with TTF for utilizing social media for academic purposes, all of which fostered student enjoyment and improved outcomes. Similarly, a clear relationship was found between comprehension efficiency, ease of use, and enjoyment with behavioral intentions to utilize social media for academic purposes that positively affected satisfaction and achievement. Therefore, the study indicates that TTF and behavioral intentions to use social media improve the active learning of students and enable them to efficiently share knowledge, information, and discussions. We recommend that students utilize social media in pursuit of their educational goals. Educators should also be persuaded to incorporate social media into their classes at higher education institutions

High-Order Multivariate Spectral Algorithms for High-Dimensional Nonlinear Weakly Singular Integral Equations with Delay

One of the open problems in the numerical analysis of solutions to high-dimensional nonlinear integral equations with memory kernel and proportional delay is how to preserve the high-order accuracy for nonsmooth solutions. It is well-known that the solutions to these equations display a typical weak singularity at the initial time, which causes challenges in developing high-order and efficient numerical algorithms. The key idea of the proposed approach is to adopt a smoothing transformation for the multivariate spectral collocation method to circumvent the curse of singularity at the beginning of time. Therefore, the singularity of the approximate solution can be tailored to that of the exact one, resulting in high-order spectral collocation algorithms. Moreover, we provide a framework for studying the rate of convergence of the proposed algorithm. Finally, we give a numerical test example to show that the approach can preserve the nonsmooth solution to the underlying problems. © 2022 by the authors.King Saud University, KSUM. A. Zaky and A. Aldraiweesh extend their appreciation to Distinguished Scientist Fellowship Program (DSFP) at King Saud University (Saudi Arabia)

Big data adoption and knowledge management sharing: an empirical investigation on their adoption and sustainability as a purpose of education

The aim of this paper to develop a model to measure sustainability for education and incorporate the literature big data adoption and knowledge management sharing in the educational environment. This paper hypothesizes that perceived usefulness, perceived ease of use, perceived risk, and behavioral intention to use big data should influence adoption of big data, while age diversity, cultural diversity, and motivators should impact knowledge management sharing. Therefore, knowledge management sharing influences behavior intention to use technologies and big data adoption would be positively associated with sustainability for education. This paper employed a version of TAM and motivation theory as the research framework and adopted quantitative data collection and analysis methods by surveying 214 university students who were chosen through stratified random sampling. Student's responses were sorted into the 11 study constructs and analyzed to explain their implication of sustainability on education. The data were then quantitatively analyzed using structural equation modeling (SEM). The results showed that perceived usefulness, perceived ease of use, perceived risk, and behavioral intention to use big data were significant determinants of big data adoption, while age diversity, cultural diversity, and motivators were significant determinants of knowledge management sharing. The knowledge management sharing, behavior intention to use technologies, and big data adoption succeeded in explaining 66.7% of sustainability on education. The findings and implications of this paper are provided

Social media–based collaborative learning: the effect on learning success with the moderating role of cyberstalking and cyberbullying

Social media (SM) provide new opportunities to foster collaboration and engagement between students. However, the moderating effect of cyberstalking and cyberbullying on the relationship between students’ academic performance and collaborative learning has not yet been addressed. Therefore, this study aims to bridge the literature gap concerning the use of SM and explore its effect on student performance through Cyberstalking and cyberbulling. A questionnaire was designed based on both the Technology Acceptance Model and Constructivism Theory for data collection. It was handed to 538 university students. This study found a significant relationshipbetween social presence, perceived usefulness, perceived ease of use, and perceived enjoyment with SM use. As shown by the use of communication and communication indicated by the results, SM is a powerful tool for developing and enhancing educational settings. However, this study found a negative relationship between student interactions and SM use. A positive relationship was found from SM use on collaborative learning and student performance that was dampened by Cyberstalking, which is considered a dampening factor and a moderator. Moreover, collaborative learning was reported to be negatively influenced by perceived usefulness as Cyberbullying was found to dampen the relationship between student performance and collaborative learning

Integrating technology acceptance model with innovation diffusion theory: an empirical investigation on students' intention to use e-learning systems

This paper aims to explore and investigate the potential factors influencing students' behavioral intentions to use the e-learning system. This paper proposes an extended technology acceptance model (TAM) that has been tested and examined through the use of both innovation diffusion theory (IDT) and integrating TAM. This paper was conducted on 1286 students utilizing systems of e-learning in Malaysia. The findings were obtained via a quantitative research method. The findings illustrate that six perceptions of innovation characteristics, in particular, have impacts on students' e-learning system behavioral intention. The influences of the relative advantages, observability, trialability, perceived compatibility, complexity, and perceived enjoyment on the perceived ease of use is noteworthy. Moreover, the effects of the relative advantages, complexity, trialability, observability, perceived compatibility, and perceived enjoyment on the perceived usefulness have a strong impact. Therefore, the empirical results provide strong backing to the integrative approach between TAM and IDT. The findings suggest an extended model of TAM with IDT for the acceptance of the e-learning system used to improve the students' learning performance, which can help decision makers in higher education, universities, as well as colleges to evaluate, plan and execute the use of e-learning systems

Social media based collaborative learning: the effect on learning success with the moderating role of cyberstalking and cyberbullying

Social media (SM) provide new opportunities to foster collaboration and engagement between students. However, the moderating effect of cyberstalking and cyberbullying on the relationship between students’ academic performance and collaborative learning has not yet been addressed. Therefore, this study aims to bridge the literature gap concerning the use of SM and explore its effect on student performance through Cyberstalking and cyberbulling. A questionnaire was designed based on both the Technology Acceptance Model and Constructivism Theory for data collection. It was handed to 538 university students. This study found a significant relationshipbetween social presence, perceived usefulness, perceived ease of use, and perceived enjoyment with SM use. As shown by the use of communication and communication indicated by the results, SM is a powerful tool for developing and enhancing educational settings. However, this study found a negative relationship between student interactions and SM use. A positive relationship was found from SM use on collaborative learning and student performance that was dampened by Cyberstalking, which is considered a dampening factor and a moderator. Moreover, collaborative learning was reported to be negatively influenced by perceived usefulness as Cyberbullying was found to dampen the relationship between student performance and collaborative learning

The influence of information system success and technology acceptance model on social media factors in education

The current study explores the students’ behavioral intention to use social media and actual social media use in higher education, specifically the perception of their academic performance and satisfaction. The study is theoretically based on the technology acceptance model (TAM) with evaluation information system success models (ISSM). Theoretically, five independent constructs were identified as contributory to behavioral intention to use social media, and actual social media use towards the students’ satisfaction and performance impact was analyzed. A questionnaire survey based on the technology acceptance model (TAM) and information system success model (ISSM) was utilized as the key method for collecting data and disseminated to 1200 students from four public universities of Malaysia chosen through a random sampling technique. For data analysis, the SPSS and structural equation modeling (SEM-Amos) were used. Outcomes obtained from the students’ behavioral intention to use and actual social media usage indicates a positive and constructive influence on satisfaction and academic performance in higher education. In addition, both male and female students were satisfied with perceived usefulness (ß = 0.095, t-value = 3.325, p < 0.001 and ß = -0.045, t-value = -2.079, p < 0.001, respectively), perceived ease of use (ß = 0.108, t-value = 3.29, p < 0.001 and ß = 0.307, t-value = 12.365, p < 0.001, respectively), perceived technology fit (ß = 0.14, t-value = 4.769, p < 0.001 and ß = 0.277, t-value = 12.358, p < 0.001, respectively), information quality (ß = 0.108, t-value = 3.825, p < 0.001 and ß = 0.109, t-value = 5.087, p < 0.001, respectively), and system quality (ß = 0.232, t-value = 7.573, p < 0.001 and ß = 0.176, t-value = 7.429, p < 0.001, respectively). Therefore, we encourage students to use social media for educational purposes and encourage more interactions with peers at higher education institutions. The study’s empirical findings present strong support for the integrative association between the TAM and the ISSM in using online learning platforms to improve students’ academic achievements and satisfaction. This could help decision makers in universities, higher education institutions, and colleges to plan, evaluate, and implement online learning platforms in their institutions

The effect of air conditioner sound on sleep latency, duration, and efficiency in young adults

BACKGROUND: Many individuals complain of disturbed sleep during the wintertime when their air conditioner (AC) is off. Therefore, we conducted this study to objectively assess the impact of AC sound on sleep latency, sleep duration, and sleep efficiency. METHODS: An experimental study was conducted on 48 healthy young adults, in their homes, to assess the effect of a standardized AC white noise, on sleep latency, duration, and efficiency, while simultaneously monitoring light intensity and room temperature. The study was conducted during the winter months. Sleep quality was objectively assessed using sleep actigraphy. Participants were monitored for two nights, during which two different, randomized sets of conditions were used: During one of the nights, the adults were exposed to 43 dB AC white noise; during the other night, adults were not exposed to the AC white noise. RESULTS: Actigraphy results showed that the mean sleep duration during the AC sound nights (ASNs) was 466.8 ± 60.8 min, compared to 478.8 ± 55.4 min during the non-AC sound nights (NASNs) (P = 0.6). Sleep-onset latency was 10.8 ± 15.2 min and 15.1 ± 18.2 min during the ASNs and the NASNs, respectively (P = 0.8). Moreover, there was no difference in sleep efficiency, 81% ± 7.8% vs. 78.8% ± 15.4% in the ASNs and NASNs, respectively (P = 0.9). CONCLUSION: AC sound had no significant positive effect on sleep duration, latency, and efficiency