ABSTRACT
BACKGROUND: The COVID-19 crisis led to an unprecedented acceleration of digital learning. It pushed many institutions to abruptly switch to fully online learning modes from face-to-face learning. Prior studies show that higher IT demands can cause challenge or hindrance stressors, depending on how the digital technology characteristics are perceived by the end-user. However, there is a gap in our knowledge regarding how ICT characteristics can lead to positive stress appraisals in a remote learning environment. OBJECTIVE: This paper leverages the person-environment fit and technostress literature to examine how usefulness and reliability as demand-ability stressors of ICT tools can positively impact learning outcomes among remote learning students. Techno eustress perceptions are evaluated as a crucial mechanism for theorizing the positive impact. METHODS: We used the survey method, sampling students (N = 82) during the lockdown period to test this model. RESULTS: Our findings highlight the ICT characteristic of usefulness as salient in contributing to student learning outcomes as it promotes techno eustress. CONCLUSIONS: This is the first study to demonstrate a positive impact of ICT characteristics on student learning outcomes via techno eustress perceptions.
ABSTRACT
Introduction: We introduce in this study CovMulNet19, a comprehensive COVID-19 network containing all available known interactions involving SARS-CoV-2 proteins, interacting-human proteins, diseases and symptoms that are related to these human proteins, and compounds that can potentially target them. Materials and Methods: Extensive network analysis methods, based on a bootstrap approach, allow us to prioritize a list of diseases that display a high similarity to COVID-19 and a list of drugs that could potentially be beneficial to treat patients. As a key feature of CovMulNet19, the inclusion of symptoms allows a deeper characterization of the disease pathology, representing a useful proxy for COVID-19-related molecular processes. Results: We recapitulate many of the known symptoms of the disease and we find the most similar diseases to COVID-19 reflect conditions that are risk factors in patients. In particular, the comparison between CovMulNet19 and randomized networks recovers many of the known associated comorbidities that are important risk factors for COVID-19 patients, through identified similarities with intestinal, hepatic, and neurological diseases as well as with respiratory conditions, in line with reported comorbidities. Conclusion: CovMulNet19 can be suitably used for network medicine analysis, as a valuable tool for exploring drug repurposing while accounting for the intervening multidimensional factors, from molecular interactions to symptoms.