During the COVID-19 pandemic, unusual consumer behavior, such as hoarding toilet paper, was reported globally. We investigated this behavior when fears of consumer market disruptions started circulating, to capture human behavior in this unique situation. Based on the stimulus-organism-response (S-O-R) framework, we propose a structural model connecting exposure to online information sources (environmental stimuli) to two behavioral responses: unusual purchases and voluntary self-isolation. To test the proposed model, we collected data from 211 Finnish respondents via an online survey, and carried out analysis using PLS-SEM. We found a strong link between self-intention to self-isolate and intention to make unusual purchases, providing empirical evidence that the reported consumer behavior was directly linked to anticipated time spent in self-isolation. The results further revealed exposure to online information sources led to increased information overload and cyberchondria. Information overload was also a strong predictor of cyberchondria. Perceived severity of the situation and cyberchondria had significant impacts on people's intention to make unusual purchases and voluntarily self-isolate. Future research is needed to confirm the long-term effects of the pandemic on consumer and retail services.

The World Health Organisation has emphasised that misinformation – spreading rapidly through social media – poses a serious threat to the COVID-19 response. Drawing from theories of health perception and cognitive load, we develop and test a research model hypothesising why people share unverified COVID-19 information through social media. Our findings suggest a person’s trust in online information and perceived information overload are strong predictors of unverified information sharing. Furthermore, these factors, along with a person’s perceived COVID-19 severity and vulnerability influence cyberchondria. Females were significantly more likely to suffer from cyberchondria, with males more likely to share news without verifying its reliability. Our findings suggest that to mitigate the spread of COVID-19 misinformation and cyberchondria, measures should be taken to enhance a healthy scepticism of health news while simultaneously guarding against information overload.

Background: During the coronavirus disease (COVID-19) pandemic, governments issued movement restrictions and placed areas into quarantine to combat the spread of the disease. In addition, individuals were encouraged to adopt personal health measures such as social isolation. Information regarding the disease and recommended avoidance measures were distributed through a variety of channels including social media, news websites, and emails. Previous research suggests that the vast amount of available information can be confusing, potentially resulting in overconcern and information overload. Objective: This study investigates the impact of online information on the individual-level intention to voluntarily self-isolate during the pandemic. Using the protection-motivation theory as a framework, we propose a model outlining the effects of cyberchondria and information overload on individuals’ perceptions and motivations. Methods: To test the proposed model, we collected data with an online survey (N=225) and analyzed it using partial least square-structural equation modeling. The effects of social media and living situation were tested through multigroup analysis. Results: Cyberchondria and information overload had a significant impact on individuals’ threat and coping perceptions, and through them on self-isolation intention. Among the appraisal constructs, perceived severity (P=.002) and self-efficacy (P=.003) positively impacted self-isolation intention, while response cost (P<.001) affected the intention negatively. Cyberchondria (P=.003) and information overload (P=.003) indirectly affected self-isolation intention through the aforementioned perceptions. Using social media as an information source increased both cyberchondria and information overload. No differences in perceptions were found between people living alone and those living with their families. Conclusions: During COVID-19, frequent use of social media contributed to information overload and overconcern among individuals. To boost individuals’ motivation to adopt preventive measures such as self-isolation, actions should focus on lowering individuals’ perceived response costs in addition to informing them about the severity of the situation.

Purpose: Inscrutable machine learning (ML) models are part of increasingly many information systems. Understanding how these models behave, and what their output is based on, is a challenge for developers let alone non-technical end users.Design/methodology/approach: The authors investigate how AI systems and their decisions ought to be explained for end users through a systematic literature review.Findings: The authors’ synthesis of the literature suggests that AI system communication for end users has five high-level goals: (1) understandability, (2) trustworthiness, (3) transparency, (4) controllability and (5) fairness. The authors identified several design recommendations, such as offering personalized and on-demand explanations and focusing on the explainability of key functionalities instead of aiming to explain the whole system. There exists multiple trade-offs in AI system explanations, and there is no single best solution that fits all cases.Research limitations/implications: Based on the synthesis, the authors provide a design framework for explaining AI systems to end users. The study contributes to the work on AI governance by suggesting guidelines on how to make AI systems more understandable, fair, trustworthy, controllable and transparent.Originality/value: This literature review brings together the literature on AI system communication and explainable AI (XAI) for end users. Building on previous academic literature on the topic, it provides synthesized insights, design recommendations and future research agenda.

This study explores how using social networking sites (SNSs) to cope with stressors induced by a global pandemic (in this case, COVID-19) can have negative consequences. The pandemic has imposed particular stressors on individuals, such as the threats of contracting the virus and of unemployment. Owing to the lockdowns and confinements implemented to limit the spread of the pandemic, SNS use has surged worldwide. Drawing on Lazarus and Folkman’s theory of stress and coping, we consider COVID-19 obsession to be an adverse emotional response to the stressors brought about by the pandemic and emotional support seeking through SNS as a coping strategy. Furthermore, we identify SNS exhaustion as an adverse outcome of this form of coping. Finally, we analyze the intention to reduce SNS use as a corrective behavioral outcome to mitigate the negative effect of SNS-mediated coping. The findings indicate that: 1) the threat of the COVID-19 disease and the threat of unemployment drive COVID-19 obsession; 2) COVID-19 obsession contributes to emotional support seeking through SNS; 3) emotional support seeking through SNS exerts a positive effect on SNS exhaustion; 4) SNS exhaustion contributes to the intention to reduce SNS use. Our results advance Information Systems (IS) research by focusing on the use of Information Technology (IT) to cope with stressors that are essentially not IT-related; such research is largely absent from previous literature. Furthermore, our paper contributes to the increasing amount of literature on IT-mediated coping with stressors and reduced social media use.