Effectiveness of the forced usage of alternative digital platforms during the COVID-19 pandemic in project communication management.

The COVID-19 pandemic social distancing forced a shift from Face-to-Face (F2F) to virtual work sessions, applying innovative digital tools. These tools have previously been neglected, mainly due to a conservative approach or prioritization. Consequently, their effectiveness was never tested in depth. While applying these innovative digital tools during the COVID-19 pandemic was clearly preferable to shutting down organizational activity, managers and workers recognized the advantages of these alternatives and tended to apply them in the post-COVID-19 period. However, in this post-pandemic period, which is free from social distancing limitations, a relatively full space of choices was introduced again, which raised the question whether these alternatives should be kept. Therefore, this study examines whether digital communication tools can adequately substitute F2F sessions in project management. We conducted an experiment with participants (n=269),askingthem to perform project-oriented tasks on four platforms: as individuals, in an F2F group meeting, on Zoom, or using WhatsApp. The results indicate that while an F2F meeting is more effective than individual work, Zoom and WhatsApp are not. These findings appear surprising and may contradict the concept of group empowerment. The use of digital sharing platforms did not affect the tasks' performances nor create synergy. This raises the issue of whether these digital means are here to stay, should be discarded, or must be upgraded.

Prediction model for the spread of the COVID-19 outbreak in the global environment.

COVID-19 has long become a worldwide pandemic. It is responsible for the death of over two million people and posed an economic recession. This paper studies the spread pattern of COVID-19, aiming to establish a prediction model for this event. We harness Data Mining and Machine Learning methodologies to train regression models to predict the number of confirmed cases in a spatial-temporal space. We introduce an innovative concept ‒ the Center of Infection Mass (CoIM) ‒ adapted from the field of physics. We empirically evaluated our model on western European countries, based on the CoIM index and other features, and showed that a relatively high accurate prediction of the spread can be obtained. Our contribution is twofold: first, we introduced a prediction methodology and proved empirically that a prediction can be made even to the range of over a month; second, we showed promise in adopting the CoIM index to prediction models, when models that adopt the CoIM yield significantly better results than those that discard it. By applying our model, and better controlling the inherent tradeoff between life-saving and economy, we believe that decision-makers can take close to optimal measures. Thus, this methodology may contribute to public welfare.