COVID-19 and telemedicine: A netnography approach.

The COVID-19 pandemic has consolidated some trends that already existed in our society. Perhaps one of the most visible is the transformation of society towards greater digitisation. Digitalisation has gained weight in all aspects of our lives, and from the point of view of the health system we find an example in the slow historical adoption of telemedicine, which contrasts sharply with the massive conversion to this technology as a tool for social distancing. In this sense, the homebound population is the one most affected by the pandemic and the one that could benefit the most from the use of telemedicine. Using a netnography approach and based on the stimulus-organism-response paradigm, this study proposes to analyse the evolution of perception about telemedicine using the opinions expressed on Twitter. The primary technical tasks of the study incorporate the analysis of topics and the review of emotions and positive image perception using natural language processing. Specifically, tweets about telemedicine generated by the Spanish community are analysed in this work. The findings show that the COVID-19 pandemic has affected emotions and topics of interest related to telemedicine. This has changed the image that it had and the behaviour of the Twitter community in Spain. In conclusion, the study results suggest that changes in health systems affect people's emotions and behaviours.

Explaining the Consumption Technology Acceptance in the Elderly Post-Pandemic: Effort Expectancy Does Not Matter.

Consumer technology has been enormously boosted by the COVID-19 pandemic, with one of the primary consumers being the elderly. In this scenario, it is necessary to consider the impact of technologies on different older generational cohorts to understand the future of a data-driven digital society fully. This research aims to explain the acceptance of social networking sites, a particular consumer technology, in the post-pandemic elderly population. Data were obtained from 1555 older adults in Chile based on a consumer technology acceptance model. The respondents were grouped according to their technological predisposition and their generation into three groups. Applying a multigroup analysis based on structural equation modelling reveals significant differences in the explanatory variables of the intention and use of this technology between the groups. And more remarkably, the effort expectancy is not statistically significant as a variable to explain this acceptance globally in either of the three groups. There are two principal contributions of this study. First, it shows why adults adopt consumer technology after the pandemic. Second, it validates a classification of elderly adults who use consumption technologies that are useful in understanding the heterogeneity of this phenomenon.

Acceptance of Social Networking Sites by Older People before and after COVID-19 Confinement: A Repeated Cross-Sectional Study in Chile, Using the Theory of Planned Behaviour (TPB).

This study aims to examine the capacity of the Theory of Planned Behaviour (TPB) to explain the intention to use social networking sites by older people in two time periods, before and after confinement due to the COVID-19 epidemic, as well as the evolution of effects (paths) over time of TPB's determinants. Based on interviews from samples of 384 and 383 elderly Chilean adults collected before and after confinement, the evolution of the effects (paths) was analysed using the TPB model applying the PLS-SEM technique. The intention to use social networks and its association with three factors were evaluated: attitude toward the behaviour, subjective norms, and perceived control over the behaviour. The model explains the intention to use social networks by 27% before confinement, increasing its magnitude to 50% after confinement. After the period of confinement, their attitudes become more significant, their perceptions of control become less important, and social pressures remain permanent in predicting the behaviour. In conclusion, better access and greater use of social networks by older people during the lockdown period increased the predictive strength of the attitude towards these technologies.

Percepciones de personas mayores chilenas respecto del uso de las redes sociales digitales y su relación con el bienestar subjetivo, en contexto de pandemia SARS CoV-2

Keywords: aging, elderly, social networking site, qualitative study. 1.Introducción El proceso de transición demográfica ha develado un aumento notable y acelerado de la población mayor de 60 años. La noción de bienestar subjetivo comprende una dimensión afectiva, relativa a las respuestas emocionales de las personas, pudiendo ser éstas positivas o negativas;así como también, una dimensión cognitiva o de percepción global respecto de la satisfacción general con la vida (Diener et al. 1999)the authors review current evidence for Wilson's conclusions and discuss modern theories of SWB that stress dispositional influences, adaptation, goals, and coping strategies. The next steps in the evolution of the field are to comprehend the interaction of psychological factors with life circumstances in producing SWB, to understand the causal pathways leading to happiness, understand the processes underlying adaptation to events, and develop theories that explain why certain variables differentially influence the different components of SWB (life satisfaction, pleasant affect, and unpleasant affect. In this paper, we present a literature review of Information Systems research on the digital divide within settings with advanced technological infrastructures and economies over the last decade (2010-2020.

Consumer Ethnocentrism and Country of Origin: Effects on Online Consumer Purchase Behavior in Times of a Pandemic

Electronic commerce has shown exponential growth over the past decade, but the impact of COVID-19 has exceeded all expectations. Based on the theory of planned behavior, this paper aims to investigate the relationship between consumer ethnocentrism and internet purchase behavior in times of pandemics. Data was collected from 294 online purchasers, and the analysis was conducted utilizing a Partial Least Squares Structural Equation Modeling approach. The results indicate no significant impact on the relationship between the planned online purchase behavior and the country of origin when consumers face a health crisis. Additionally, the outcomes show that attitude toward online shopping positively affects online purchase behavior.

Identifying Engineering Undergraduates’ Learning Style Profiles Using Machine Learning Techniques

In a hybrid university learning environment, the rapid identification of students’ learning styles seems to be essential to achieve complementarity between conventional face-to-face pedagogical strategies and the application of new strategies using virtual technologies. In this context, this research aims to generate a predictive model to detect undergraduates’ learning style profiles quickly. The methodological design consists of applying a k-means clustering algorithm to identify the students’ learning style profiles and a decision tree C4.5 algorithm to predict the student’s membership to the previously identified groups. A cluster sample design was used with Chilean engineering students. The research result is a predictive model that, with few questions, detects students’ profiles with an accuracy of 82.93%;this prediction enables a rapid adjustment of teaching methods in a hybrid learning environment.

Predicting the Intention to Donate Blood among Blood Donors Using a Decision Tree Algorithm

The blood donation process is essential for health systems. Therefore, the ability to predict donor flow has become relevant for hospitals. Although it is possible to predict this behaviour intention from donor questionnaires, the need to reduce social contact in pandemic settings leads to decreasing the extension of these surveys with the minimum loss of predictivity. In this context, this study aims to predict the intention to give blood again, among donors, based on a limited number of attributes. This research uses data science and learning concepts based on symmetry in a particular classification to predict blood donation intent. We carried out a face-to-face survey of Chilean donors based on the Theory of Planned Behaviour. These data, including control variables, were analysed using the decision tree technique. The results indicate that it is possible to predict the intention to donate blood again with an accuracy of 84.17% and minimal variables. The added scientific value of this article is to propose a more simplified way of measuring a multi-determined social phenomenon, such as the intention to donate blood again and the application of the decision tree technique to achieve this simplification, thereby contributing to the field of data science.

Telemedicine Acceptance during the COVID-19 Pandemic: An Empirical Example of Robust Consistent Partial Least Squares Path Modeling

The explanation of behaviors concerning telemedicine acceptance is an evolving area of study. This topic is currently more critical than ever, given that the COVID-19 pandemic is making resources scarcer within the health industry. The objective of this study is to determine which model, the Theory of Planned Behavior or the Technology Acceptance Model, provides greater explanatory power for the adoption of telemedicine addressing outlier-associated bias. We carried out an online survey of patients. The data obtained through the survey were analyzed using both consistent partial least squares path modeling (PLSc) and robust PLSc. The latter used a robust estimator designed for elliptically symmetric unimodal distribution. Both estimation techniques led to similar results, without inconsistencies in interpretation. In short, the results indicate that the Theory of Planned Behavior Model provides a significant explanatory power. Furthermore, the findings show that attitude has the most substantial direct effect on behavioral intention to use telemedicine systems.