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24th International Conference on Business Information Systems, BIS 2021 ; 444 LNBIP:161-173, 2022.
Article in English | Scopus | ID: covidwho-1826264


The global pandemic, caused by the spread of COVID-19, has altered the way people go shopping. In light of this, Social Media channels are an important means of sharing information about goods and services, and different kinds of brands. Since these channels are of considerable market significance, the authors of this paper decided to describe the results of a survey on how to use Social Media to improve customer relationship management processes in 31 companies. The focus was on digital marketing for micro and small businesses. In addition, an in-depth analysis was conducted of four companies, to determine the challenges and strategies in social customer relationship management adopted by micro and small businesses. The results show that this is still a new policy for micro and small companies, but has a great potential to boost sales, enhance customer loyalty and increase brand awareness. The lessons learned can assist policymakers in taking more suitable measures for strengthening this market sector. © 2022, Springer Nature Switzerland AG.

STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics and Health ; : 59-77, 2021.
Article in English | Scopus | ID: covidwho-1574900


The pandemic caused by the novel coronavirus, although more than a year has passed since the first case, still plagues almost the whole world. Several policies have been adopted, especially related to social distancing measures, aiming to mitigate the spread of the disease. Such decisions, in general, take into account simulations capable of providing an overview of the spread of the virus in a given location. Based on the guidelines of the World Health Organization, countries have defined their own policies to fight against the disease, considering economic and social interests. Determining strategies that are increasingly efficient in modeling and simulating such phenomena is essential to support decision making in adverse circumstances. Our objective is to provide a more comprehensive view of strategies for predicting the spread of COVID-19 in the scope of computational modeling and to analyze scenarios capable of describing the impact of social distancing measures. Two different strategies are compared to characterize the virus incubation period, using particular models. Since Italy was one of the countries most affected by the pandemic, despite taking drastic measures to reduce mobility and contact between citizens, we adopt the situation of the early stages of the disease outbreak in this country to demonstrate the numerical results. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

4th International Conference on Computer-Human Interaction Research and Applications, CHIRA 2020 ; : 319-328, 2020.
Article in English | Scopus | ID: covidwho-1279082


Sentiment Analysis techniques enable the automatic extraction of sentiment in social media data, including popular platforms as Twitter. For retailers and marketing analysts, such methods can support the understanding of customers' attitudes towards brands, especially to handle crises that cause behavioural changes in customers, including the COVID-19 pandemic. However, with the increasing adoption of black-box machine learning-based techniques, transparency becomes a need for those stakeholders to understand why a given sentiment is predicted, which is rarely explored for retailers facing social media crises. This study develops an Explainable Sentiment Analysis (XSA) application for Twitter data, and proposes research propositions focused on evaluating such application in a hypothetical crisis management scenario. Particularly, we evaluate, through discussions and a simulated user experiment, the XSA support for understanding customer's needs, as well as if marketing analysts would trust such an application for their decision-making processes. Results illustrate the XSA application can be effective in providing the most important words addressing customers sentiment out of individual tweets, as well as the potential to foster analysts' confidence in such support. Copyright © 2020 by SCITEPRESS-Science and Technology Publications, Lda. All rights reserved.