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Social distancing is an effective preventative policy for the coronavirus disease (COVID-19) that is enforced by governments worldwide. However, significant variations are observed in following the policy across individuals and countries. Arguably, differences in citizens' adherence actions will be influenced by their perceptions about government's plans and the information available to guide their behaviors-more so in the digital age in the realm of mass influence of social media on citizens. Insights into the underlying factors and dynamics involved with citizens' adherence process will inform the policy makers to follow appropriate communication and messaging approaches to influence citizens' willingness to adhere to the recommendations. The aim of this study is a comparative evaluation of citizens' adherence process to COVID-19-relevant recommendations by the government. The focus is on how three different countries' (United States, Kuwait, and South Korea) citizens, randomly sampled, respond to governments' pandemic guidance efforts. We draw insights into two categories of perceived government roles in managing the pandemic: (1) citizens' perceptions of government's role in responding to the pandemic and (2) citizens' perceptions of government's business reopening efforts. Undoubtedly, the internet and social media have burgeoned, with differing effects on shaping individuals' views and assessments of the COVID-19 situation; we argue and test for the effects of information sources, social media use, and knowledge on the adherence actions. We randomly sampled web-based survey data collected by a global firm in May 2020 from citizens of the United States, Kuwait, and South Korea. A nonlinear ordered probit regression, controlling for several counterfactuals, was used for analysis. The focal estimated effects of the study were compared across countries using the weighted distance between the parameter estimates. The total sample size was 482 respondents, of which 207 (43%) lived in the United States, 181 (38%) lived in Kuwait, and 94 (20%) lived in South Korea. The ordered probit estimation results suggest that overall, perception of government response efforts positively influenced self-adherence (P<.001) and others' adherence (P<.001) to social distancing and sheltering. Perception of government business reopening efforts positively influenced others' adherence (P<.001). A higher intensity of general health information source for COVID-19 had a positive effect on self-adherence (P=.003). A higher intensity of social media source use for COVID-19 positively influenced others' adherence (P=.002). A higher intensity of knowledge on COVID-19 positively influenced self-adherence (P=.008) and negatively influenced others' adherence (P<.001). There were country-level variations-broadly, the United States and Kuwait had better effects than South Korea. As the COVID-19 global pandemic continues to grow and governmental restrictions are ongoing, it is critical to understand people's frustration to reduce panic and promote social distancing to facilitate the control of the pandemic. This study finds that the government plays a central role in terms of adherence to restrictions. Governments need to enhance their efforts on publicizing information on the pandemic, as well as employ strategies for improved communication management to citizens through social media as well as mainstream information sources.

Generative artificial intelligence tools and applications (GenAI) are being increasingly used in health care. Physicians, specialists, and other providers have started primarily using GenAI as an aid or tool to gather knowledge, provide information, train, or generate suggestive dialogue between physicians and patients or between physicians and patients' families or friends. However, unless the use of GenAI is oriented to be helpful in clinical service encounters that can improve the accuracy of diagnosis, treatment, and patient outcomes, the expected potential will not be achieved. As adoption continues, it is essential to validate the effectiveness of the infusion of GenAI as an intelligent technology in service encounters to understand the gap in actual clinical service use of GenAI. This study synthesizes preliminary evidence on how GenAI assists, guides, and automates clinical service rendering and encounters in health care The review scope was limited to articles published in peer-reviewed medical journals. We screened and selected 0.38% (161/42,459) of articles published between January 1, 2020, and May 31, 2023, identified from PubMed. We followed the protocols outlined in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to select highly relevant studies with at least 1 element on clinical use, evaluation, and validation to provide evidence of GenAI use in clinical services. The articles were classified based on their relevance to clinical service functions or activities using the descriptive and analytical information presented in the articles. Of 161 articles, 141 (87.6%) reported using GenAI to assist services through knowledge access, collation, and filtering. GenAI was used for disease detection (19/161, 11.8%), diagnosis (14/161, 8.7%), and screening processes (12/161, 7.5%) in the areas of radiology (17/161, 10.6%), cardiology (12/161, 7.5%), gastrointestinal medicine (4/161, 2.5%), and diabetes (6/161, 3.7%). The literature synthesis in this study suggests that GenAI is mainly used for diagnostic processes, improvement of diagnosis accuracy, and screening and diagnostic purposes using knowledge access. Although this solves the problem of knowledge access and may improve diagnostic accuracy, it is oriented toward higher value creation in health care. GenAI informs rather than assisting or automating clinical service functions in health care. There is potential in clinical service, but it has yet to be actualized for GenAI. More clinical service-level evidence that GenAI is used to streamline some functions or provides more automated help than only information retrieval is needed. To transform health care as purported, more studies related to GenAI applications must automate and guide human-performed services and keep up with the optimism that forward-thinking health care organizations will take advantage of GenAI.

Social distancing is an effective preventative policy for COVID-19 that is enforced by governments worldwide. However, significant variations are observed in adherence to social distancing across individuals and countries. Due to the lack of treatment, rapid spread, and prevalence of COVID-19, panic and fear associated with the disease causes great stress. Subsequent effects will be a variation around the coping and mitigation strategies for different individuals following different paths to manage the situation. This study aims to explore how threat and coping appraisal processes work as mechanisms between information and citizens' adherence to COVID-19-related recommendations (ie, how the information sources and social media influence threat and coping appraisal processes with COVID-19 and how the threat and coping appraisal processes influence adherence to policy guidelines). In addition, this study aims to explore how citizens in three different countries (the United States, Kuwait, and South Korea), randomly sampled, are effectively using the mechanisms. Randomly sampled online survey data collected by a global firm in May 2020 from 162 citizens of the United States, 185 of Kuwait, and 71 of South Korea were analyzed, resulting in a total sample size of 418. A seemingly unrelated regression model, controlling for several counterfactuals, was used for analysis. The study's focal estimated effects were compared across the three countries using the weighted distance between the parameter estimates. The seemingly unrelated regression model estimation results suggested that, overall, the intensity of information source use for the COVID-19 pandemic positively influenced the threat appraisal for the disease (P<.001). Furthermore, the intensity of social media use for the COVID-19 pandemic positively influenced the coping appraisal for the disease (P<.001). Higher COVID-19 threat appraisal had a positive effect on social distancing adherence (P<.001). Higher COVID-19 coping appraisal had a positive effect on social distancing adherence (P<.001). Higher intensity of COVID-19 knowledge positively influenced social distancing adherence (P<.001). There were country-level variations. Broadly, we found that the United States had better results than South Korea and Kuwait in leveraging the information to threat and coping appraisal to the adherence process, indicating that individuals in countries like the United States and South Korea may be more pragmatic to appraise the situation before making any decisions. This study's findings suggest that the mediation of threat and coping strategies are essential, in varying effects, to shape the information and social media strategies for adherence outcomes. Accordingly, coordinating public service announcements along with information source outlets such as mainstream media (eg, TV and newspaper) as well as social media (eg, Facebook and Twitter) to inform citizens and, at the same time, deliver balanced messages about the threat and coping appraisal is critical in implementing a staggered social distancing and sheltering strategy.

Travel photos inform and inspire consumers by conveying a first-hand destination experience. Despite the proliferation of consumer-generated travel photos in online travel review sites, deconstructing the effects of photos on consumer engagement remains a challenge to the tourism industry. We provide a framework to process and interpret various photographic elements on user engagement using deep learning algorithms. We posit that a photo can invoke consumers’ subjective interpretations of photos representing authentic, creative, or emotional dimensions of the destination experience. A structured crowdsourced categorization process was deployed to measure the interpretive dimensions of the photos. The objects in photographs are identified using a novel deep learning algorithm for controls. We use narrative framing concepts to theorize their influence on user engagement in an online travel review site setting. Relevant three sets of hypotheses are tested using a large dataset of photo-based travel reviews sampled between 2012 and 2014. A negative zero-inflated binomial regression is used to estimate the effect of subjective interpretation of photos on user engagement, accounting for overdispersed excess zeros associated with count outcomes. Results support the hypotheses. The additional analyses explore other plausible influential attributes to user engagements to complement our main findings. We discuss the theoretical contributions to the online-image-persuasion stream of research and practical implications for online tourist review sites.

Infodemic exacerbates public health concerns by disseminating unreliable and false scientific facts to a population. During the COVID-19 pandemic, the efficacy of hydroxychloroquine as a therapeutic solution emerged as a challenge to public health communication. Internet and social media spread information about hydroxychloroquine, whereas cable television was a vital source. To exemplify, experts discussed in cable television broadcasts about hydroxychloroquine for treating COVID-19. However, how the experts' comments influenced airtime allocation on cable television to help in public health communication, either during COVID-10 or at other times, is not understood. This study aimed to examine how 3 factors, that is, the credibility of experts as doctors (DOCTOREXPERT), the credibility of government representatives (GOVTEXPERT), and the sentiments (SENTIMENT) expressed in discussions and comments, influence the allocation of airtime (AIRTIME) in cable television broadcasts. SENTIMENT pertains to the information credibility conveyed through the tone and language of experts' comments during cable television broadcasts, in contrast to the individual credibility of the doctor or government representatives because of the degree or affiliations. We collected transcriptions of relevant hydroxychloroquine-related broadcasts on cable television between March 2020 and October 2020. We coded the experts as DOCTOREXPERT or GOVTEXPERT using publicly available data. To determine the sentiments expressed in the broadcasts, we used a machine learning algorithm to code them as POSITIVE, NEGATIVE, NEUTRAL, or MIXED sentiments. The analysis revealed a counterintuitive association between the expertise of doctors (DOCTOREXPERT) and the allocation of airtime, with doctor experts receiving less airtime (P<.001) than the nonexperts in a base model. A more nuanced interaction model suggested that government experts with a doctorate degree received even less airtime (P=.03) compared with nonexperts. Sentiments expressed during the broadcasts played a significant role in airtime allocation, particularly for their direct effects on airtime allocation, more so for NEGATIVE (P<.001), NEUTRAL (P<.001), and MIXED (P=.03) sentiments. Only government experts expressing POSITIVE sentiments during the broadcast received a more extended airtime (P<.001) than nonexperts. Furthermore, NEGATIVE sentiments in the broadcasts were associated with less airtime both for DOCTOREXPERT (P<.001) and GOVTEXPERT (P<.001). Source credibility plays a crucial role in infodemics by ensuring the accuracy and trustworthiness of the information communicated to audiences. However, cable television media may prioritize likeability over credibility, potentially hindering this goal. Surprisingly, the findings of our study suggest that doctors did not get good airtime on hydroxychloroquine-related discussions on cable television. In contrast, government experts as sources received more airtime on hydroxychloroquine-related discussions. Doctors presenting facts with negative sentiments may not help them gain airtime. Conversely, government experts expressing positive sentiments during broadcasts may have better airtime than nonexperts. These findings have implications on the role of source credibility in public health communications.

For many products, particularly digital content, consumers base purchase-related decisions on not only others’ evaluations (e.g., online reviews) but also their own direct experiences (e.g., previews). Many of them therefore combine “seeing” through their own encounters with “believing” the assessments of others, often being confronted with a situation wherein the former contradicts the latter. This study investigates the dynamics underlying the interactive relationships between online reviews and previews to shed light on how consumers collectively use these pre-buying resources as guidance in purchasing e-books. Our research reveals that consumers wisely leverage previews in accordance with the characteristics of reviews (e.g., volume, valence, and variance). The positive effects of previews increase with decreasing review volume and average valence. Consumers also rely heavily on previews in situations wherein a high variance exists among the indirect evaluations of reviewers. We detect the presence of order effects with respect to exposure to previews and reviews; exposure to previews after accessing reviews more effectively drives sales than when exposure to reviews precedes the encounters. These results provide content providers with useful managerial insights into how they can maximize consumers’ overall prepurchase experiences and enhance content sales through the excellent leveraging of previews and reviews. Online reviews offer consumers the indirect experience of products through others’ consumption evaluations, whereas previews afford them direct experience through product trials. Although conceptual and empirical studies on the business ramifications of online reviews abound, little is known about how online previews moderate the effects of online reviews on sales. To cast light on this issue, the current research investigated the interactive effects of exposure to online previews and reviews on individual purchase decisions. We analyzed a unique two-month panel data set on 270,260 sessions that comprise clickstream data on consumers’ exposure to previews and reviews and data on their subsequent purchase behaviors. On the basis of analyses underlain by a two-stage hierarchical Bayesian framework, we found that online previews positively influence individual purchase decisions. More importantly, significant interactions exist between previews and reviews, as evidenced by the decreasing positive effect of previews with increasing review volume and average review rating. In addition, previews can complement reviews when a high variance in the latter renders purchase decisions difficult. We further examined the sequence effect of exposure to previews and reviews and discovered that exposure to previews following the experience of reviews may exert a considerable positive influence on individual purchase decisions. The results from an additional field experiment and a text-based sentiment analysis reinforced the validity of our main findings by mitigating concerns regarding the endogeneity and the accuracy of the review quality, respectively. The findings provide practical implications with regard to the design of optimal strategies for releasing preview information to digital platforms.

As a corporate social responsibility (CSR) initiative, firms are increasingly disclosing sustainability indicators on online platforms to attract stakeholders’ interests. It is vital to understand what indicators reflect more on a firm’s performance and valuations. This study focuses on deriving value-oriented business intelligence from the voluntary disclosure of sustainability reports. The analysis in this study involves a three-stage approach: (1) Latent Dirichlet allocation (LDA) based topic modeling algorithm to identify and summarize typical contents expressed in various documents, (2) firm’s sustainability maturity modeled as a function of its strategic intent using a latent Markov model (LMM) to estimate the statistical significance and the extent of their relationships, and (3) empirical analysis using random effect linear and non-linear probit models to explore the impact of antecedents and firm performance consequences of three strategic intents. This study highlights using an advanced business analytics approach, specifically with latent Dirichlet allocation (LDA) topic modeling, to codify intangible knowledge embedded in annual sustainability reports to infer a firm’s strategic intent behind voluntary disclosure. In addition, this study aims to analyze the influence of the firm’s sustainability strategic intent on its financial performance. A secondary panel dataset consisting of information on 680 firms in 3 years was constructed by matching the text mined data with information from other sources. Results indicate that, on the one hand, while external stakeholder engagement is the primary motivation behind voluntary disclosure of sustainability reporting, firms are starting to engage internal stakeholders through workforce practices. On the other hand, internal employee-oriented intent has more influence on firm performance than external customer-oriented intent. This study demonstrates a toolset to index firms’ sustainability indicators and evaluates firms’ sustainability practice as an intangible asset and its impact on firms’ financial performance.

Increasingly, people are turning to the internet to access health information despite reports that sites vary in terms of their quality, especially when the health practice is emerging or exclusive, such as stem cell and umbilical cord blood therapy. Given the controversy, patients have to depend on available sources to validate their knowledge prior to going for these practices as treatments. This study explores how the internet supports the spread of stem cell therapy practices, viewing it from a knowledge validation theoretical perspective. The study posits hypotheses differentiating digital and human sources, trust in the media source, and exploratory and verification sources on knowledge validation for exclusive practices. Primary survey data was collected from the US and Kuwait. Key findings suggest that knowledge verification and trust in the internet influences knowledge conversion and the practice decision of patients for less practice-oriented knowledge, and this effect is higher for Kuwait than USA, and more so for stem cell than umbilical cord blood practice.

Mitigating the greenhouse gas (GHG) emission problem is one efficient way to respond to climate change challenges. Firms must proactively manage GHG emissions, with increasing pressure from various stakeholders to be environmentally responsible. GHG Protocol Tools help in managing GHG emissions. However, besides responsibility, the factors that influence the adoption and implementation of GHG Protocol Tools is sparsely investigated in empirical research, although studies point to different benefits and pressures influencing adoption. This study examines the factors affecting GHG Protocol Tool usage in organizations in China and South Korea. We consider two contrasting perspectives, affordance-based perceived benefits and constraint-based perceived pressures through imitating others, for GHG Protocol Tool adoption. Survey data from samples of firms from both countries are used for analysis. Results of empirical analyses indicate that perceived benefits and pressures have a positive relationship with the usage intention of GHG Protocol Tools. In comparison, the perceived benefits play a more critical role than the perceived pressures. Comparative analysis is conducted to explore the differences between Chinese and Korean firms, and study implications are discussed.

Information artifacts incorporate cognitive elements in their design to inform users about and entice them to consume relevant content. Sparse research has examined how to design cognitive elements in information artifacts in the digital news platforms context. This study investigates how information artifacts’ semantic and sentiment elements convey meaning and emotion to elicit users to share online news. We propose a dissonant framework and hypothesize that three dissonance dimensions (namely, semantic dissonance, textual sentiment dissonance, and visual sentiment dissonance) influence news sharing. We tested the hypotheses using real-world data from 2013 to 2015 from Mashable—a popular digital news platform. We used novel machine-learning techniques to extract topics and sentiments from text and photos in news articles. Findings from our econometric analysis support that textual sentiment and visual sentiment dissonance positively affect news sharing.