How emotions from content social relevance mediate social media engagement: evidence from European supermarkets during the COVID-19 pandemic

PurposeThe purpose of this study is to examine the antecedents of consumer engagement with supermarkets' social media accounts. Drawing on regulatory fit theory and social sharing of emotions theory, the authors test if the content posted on the social media brand pages of supermarkets dealing with a topic of high social relevance, such as the COVID-19 pandemic, stimulates social media consumer engagement and if and how the engagement is mediated by the arousal of positive and negative emotions.Design/methodology/approachThe authors retrieved data from the Facebook accounts of the top 20 European supermarkets identified in the Deloitte 2020 Global Powers of Retailing report during the first wave of the pandemic from 1 March to 30 June 2020, collecting a sample of 2,524 posts from 8 different countries. After a content analysis to classify COVID-19 content, the authors applied the Baron and Kenny (1986) methodology to verify the hypothesised relationships.FindingsThe findings highlight a positive direct relationship between the social relevance of a topic (COVID-19) and social media consumer engagement mediated by the arousal of positive and negative emotions.Originality/valueTo the best of the authors' knowledge, this is one of the earliest empirical research using Facebook data to investigate the role of the social relevance of content as an antecedent of social media consumer engagement with a specific focus on supermarkets. The paper contributes to the stream of social media literature investigating the antecedents of social media engagement behaviour, exploring the role of topics' choice and aroused emotions, which to date are both under-investigated.

How emotions from content social relevance mediate social media engagement: evidence from European supermarkets during the COVID-19 pandemic

Purpose The purpose of this study is to examine the antecedents of consumer engagement with supermarkets' social media accounts. Drawing on regulatory fit theory and social sharing of emotions theory, the authors test if the content posted on the social media brand pages of supermarkets dealing with a topic of high social relevance, such as the COVID-19 pandemic, stimulates social media consumer engagement and if and how the engagement is mediated by the arousal of positive and negative emotions. Design/methodology/approach The authors retrieved data from the Facebook accounts of the top 20 European supermarkets identified in the Deloitte 2020 Global Powers of Retailing report during the first wave of the pandemic from 1 March to 30 June 2020, collecting a sample of 2,524 posts from 8 different countries. After a content analysis to classify COVID-19 content, the authors applied the Baron and Kenny (1986) methodology to verify the hypothesised relationships. Findings The findings highlight a positive direct relationship between the social relevance of a topic (COVID-19) and social media consumer engagement mediated by the arousal of positive and negative emotions. Originality/value To the best of the authors' knowledge, this is one of the earliest empirical research using Facebook data to investigate the role of the social relevance of content as an antecedent of social media consumer engagement with a specific focus on supermarkets. The paper contributes to the stream of social media literature investigating the antecedents of social media engagement behaviour, exploring the role of topics' choice and aroused emotions, which to date are both under-investigated.

Machine and Deep Learning Algorithms, Computer Vision Technologies, and Internet of Thingsbased Healthcare Monitoring Systems in COVID-19 Prevention, Testing, Detection, and Treatment

Keywords: Internet of Things;machine and deep learning algorithm;COVID-19 1.Introduction The purpose of our systematic review is to examine the recently published literature on COVID-19 prevention, testing, detection, and treatment, and integrate the insights it configures on machine and deep learning algorithms, computer vision technologies, and Internet of Things-based healthcare monitoring systems. The manuscript is organized as following: theoretical overview (section 2), methodology (section 3), COVID19 detection and diagnostic tools (section 4), machine learning techniques, healthcare sensor devices, and computer vision (section 5), machine learning algorithms and Internet of Things-based monitoring systems (section 6), discussion (section 7), synopsis of the main research outcomes (section 8), conclusions (section 9), limitations, implications, and further directions of research (section 10). (Table 3) 5.Machine Learning Techniques, Healthcare Sensor Devices, and Computer Vision Internet of Things-based healthcare monitoring systems are pivotal in accurate and suitable patient treatment (Jain et al., 2021;Li et al., 2021;Rhayem et al., 2021;Zhang et al., 2021a) by integrating medical wearable sensors, actuators, and networked devices. (Table 4) 6.Machine Learning Algorithms and Internet of Things-based Monitoring Systems Internet of Medical Things devices and wearables can be pivotal in contact tracing, early diagnosis, and symptom tracking (Khowaja et al., 2021;Mehrdad et al., 2021;Tai et al., 2021) by use of machine learning techniques, neural network architectures, and data fusion.

Adoption of Delivery Apps during the COVID-19 Crisis: Consumer Perceived Value, Behavioral Choices, and Purchase Intentions

The purpose of this study is to examine adoption of delivery apps during the COVID-19 crisis. In this article, we cumulate previous research findings indicating that decrease in consumer risk perception may boost the adoption of food delivery apps. We contribute to the literature on customers' behavioral intentions to use food delivery apps throughout the COVID-19 outbreak by showing that shopping online on food delivery platforms can shape user perception, satisfaction, and loyalty. Throughout January 2022, we performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including "delivery app" + "COVID-19," "perceived value," "behavioral choice," and "purchase intention." As we inspected research published in 2020 and 2021, only 152 articles satisfied the eligibility criteria. By eliminating controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, we decided upon 25, generally empirical, sources. Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, Dedoose, MMAT, and SRDR.

Artificial Intelligence-enabled Wearable Medical Devices, Clinical and Diagnostic Decision Support Systems, and Internet of Things-based Healthcare Applications in COVID-19 Prevention, Screening, and Treatment

Building our argument by drawing on data collected from Accenture, GlobalWebIndex, GoMo Health, KPMG, McKinsey, Oracle, Sermo, STAT, Statista, and Workplace Intelligence, we performed analyses and made estimates regarding how predictive big data analytics, body sensor networks, medical wearable devices, decision support systems, and wireless sensing applications can be harnessed in real-time continuous remote monitoring of patients vital signs configuring clinical data in pervasive mobile patient-centric healthcare. Introduction The extensive data of COVID-19 patients can be assimilated and inspected by cutting-edge machine learning algorithms to grasp the pattern of viral transmission, optimize diagnostic swiftness and precision, advance adequate therapeutic methods, and identify the most vulnerable individuals according to personalized genetic and physiological features. Methodology and Empirical Analysis Building our argument by drawing on data collected from Accenture, Global-WebIndex, GoMo Health, KPMG, McKinsey, Oracle, Sermo, STAT, Statista, and Workplace Intelligence, we performed analyses and made estimates regarding how predictive big data analytics, body sensor networks, medical wearable devices, decision support systems, and wireless sensing applications can be harnessed in real-time continuous remote monitoring of patients' vital signs configuring clinical data in pervasive mobile patient-centric healthcare. Study Design, Survey Methods, and Materials The interviews were conducted online and data were weighted by five variables (age, race/ethnicity, gender, education, and geographic region) using the Census Bureau's American Community Survey to reflect reliably and accurately the demographic composition of the United States.

Restricting Human Rights and Increasing Discrimination through COVID-19 Vaccination Certificates: Necessity, Benefits, Risks, and Costs

We draw on a substantial body of theoretical and empirical research on restricting human rights and increasing discrimination through COVID-19 vaccination certificates, and to explore this, we inspected, used, and replicated survey data from Access Now, Ada Lovelace Institute, Associated Press, Dynata, Morning Consult, and Redpoint Global, performing analyses and making estimates regarding rational and ethical COVID-19 vaccine certification schemes. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate.

Post-Traumatic Stress Disorder, Extreme Anxiety, and Depressive Symptoms in Frontline Healthcare Workers during the COVID-19 Pandemic

Based on an in-depth survey of the literature, the purpose of the paper is to explore post-traumatic stress disorder, extreme anxiety, and depressive symptoms in frontline healthcare workers during the COVID-19 pandemic. Using and replicating data from ACHA, BMA, CMA, Commonwealth Fund, GWI, Harvard Medical School, HMN, IASC, MHA, Rek et al. (2020), Statista, and UNC School of Medicine, we performed analyses and made estimates regarding the psychological stress, anxiety and depression levels of frontline healthcare workers during the COVID-19 epidemic. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate.