Search results

Misinformation (i.e. information identified as false) spreads widely and quickly on social media – a space where crowds of ordinary citizens can become leading voices – during a crisis when information is in short supply. Using the theoretical lenses of socially curated flow and networked gatekeeping frameworks, we address the following three aims: First, we identify emergent opinion leaders in misinformation-related conversations on social media. Second, we explore distinct groups that contribute to online discourses about misinformation. Lastly, we investigate the actual dominance of misinformation within disparate groups in the early phases of mass shooting crises.

This paper used network and cluster analyses of Twitter data that focused on the four most prevalent misinformation themes surrounding the El Paso mass shooting.

A total of seven clusters of users emerged, which were classified into five categories: (1) boundary-spanning hubs, (2) broadly popular individuals, (3) reputation-building hubs, (4) locally popular individuals and (5) non-opinion leaders. Additionally, a content analysis of 128 tweets in six clusters, excluding the cluster of non-opinion leaders, further demonstrated that the opinion leaders heavily focused on reiterating and propagating misinformation (102 out of 128 tweets) and collectively made zero corrective tweets.

These findings expand the intellectual understanding of how various types of opinion leaders can shape the flow of (mis)information in a crisis. Importantly, this study provides new insights into the role of trans-boundary opinion leaders in creating an echo chamber of misinformation by serving as bridges between otherwise fragmented discourses.

The goal of this study is to examine how tweets containing distinct emotions (i.e., emotional tweets) and different information types (i.e., misinformation, corrective information, and others) are prevalent during the initial phase of mass shootings and furthermore, how users engage in those tweets.

The researchers manually coded 1,478 tweets posted between August 3–11, 2019, in the immediate aftermath of the El Paso and Dayton mass shootings. This manual coding approach systematically examined the distinct emotions and information types of each tweet.

The authors found that, on Twitter, misinformation was more prevalent than correction during crises and a large portion of misinformation had negative emotions (i.e., anger, sadness, and anxiety), while correction featured anger. Notably, sadness-exhibiting tweets were more likely to be retweeted and liked by users, but tweets containing other emotions (i.e., anger, anxiety, and joy) were less likely to be retweeted and liked.

Only a portion of the larger conversation was manually coded. However, the current study provides an overall picture of how tweets are circulated during crises in terms of misinformation and correction, and moreover, how emotions and information types alike influence engagement behaviors.

The pervasive anger-laden tweets about mass shooting incidents might contribute to hostile narratives and eventually reignite political polarization. The notable presence of anger in correction tweets further suggests that those who are trying to provide correction to misinformation also rely on emotion. Moreover, our study suggests that displays of sadness could function in a way that leads individuals to rely on false claims as a coping strategy to counteract uncertainty.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-03-2021-0121/

The purpose of this study is to introduce new tools to develop a more precise and focused bibliometric analysis on the field of digitalization in healthcare management. Furthermore, this study aims to provide an overview of the existing resources in healthcare management and education and other developing interdisciplinary fields.

This work uses bibliometric analysis to conduct a comprehensive review to map the use of the unified theory of acceptance and use of technology (UTAUT) and the unified theory of acceptance and use of technology 2 (UTAUT2) research models in healthcare academic studies. Bibliometric studies are considered an important tool to evaluate research studies and to gain a comprehensive view of the state of the art.

Although UTAUT dates to 2003, our bibliometric analysis reveals that only since 2016 has the model, together with UTAUT2 (2012), had relevant application in the literature. Nonetheless, studies have shown that UTAUT and UTAUT2 are particularly suitable for understanding the reasons that underlie the adoption and non-adoption choices of eHealth services. Further, this study highlights the lack of a multidisciplinary approach in the implementation of eHealth services. Equally significant is the fact that many studies have focused on the acceptance and the adoption of eHealth services by end users, whereas very few have focused on the level of acceptance of healthcare professionals.

To the best of the authors’ knowledge, this is the first study to conduct a bibliometric analysis of technology acceptance and adoption by using advanced tools that were conceived specifically for this purpose. In addition, the examination was not limited to a certain era and aimed to give a worldwide overview of eHealth service acceptance and adoption.

The purpose of this paper is to investigate the enabling factors and the customers’ acceptance of ride-hailing in Indonesia.

The authors adopt some constructs from the unified theory of acceptance and use of technology (UTAUT) 2 as the framework for the study to derive factors that influence the acceptance of ride-hailing in Indonesia. Samples through a convenience sampling method were collected from an online survey and were transformed into data through coding and subsequently processed using SPSS for descriptive analysis, reliability test, correlation and multiple regression analysis for hypothesis testing.

Ride-hailing started in 2015 in Indonesia. Five enabling factors make digital ride-hailing possible, the internet, smartphone, broadband wireless network, digital map and global positioning system. The authors found that performance expectancy, social influence and habit positively influence customers to accept ride-hailing in Indonesia.

Although this research has a small sample, it is still relevant to understand people’s acceptance to the ride-hailing platform. As a ride-hailing platform is now transformed to a multisided markets platform, adoption studies or other studies on each market to cover the whole picture of the platform influence to the society, and its contribution to the national economy will be very interesting. The authors’ future research will cover various services covered by ride-hailing companies.

This study proposes and argues that four main enabling factors make digital ride-hailing a viable business. The study contributes to three significant factors that influence the acceptance of ride-hailing in Indonesia.

Misinformation on social media has become a great threat across the globe. Therefore, the authors aim to provide a comprehensive understanding of social media users' misinformation combating behavior, especially during the COVID-19 pandemic. Specifically, the authors merged the uses and gratifications theory, social cognitive theory and theory of prosocial behavior into one theoretical framework (e.g. information seeking, status seeking, entertainment and norms of reciprocity) to understand their effect on users' prosocial media sharing experience and misinformation self-efficacy to combat misinformation.

The authors collected data from 356 social media users through “Google Forms” during the third wave of coronavirus in Pakistan. Further, the authors applied structural equation modeling for hypotheses testing.

The authors noted that entertainment and perceived norms of reciprocity positively affect social media users' prior experience and misinformation self-efficacy to enhance their misinformation combating intention. However, information seeking positively affects social media users' prior experience and insignificantly affects their misinformation self-efficacy. Similarly, status seeking was noted to be insignificantly associated with social media users' prior experience and misinformation self-efficacy.

The authors tested this model of misinformation combating intention in a developing country during the COVID-19 pandemic and noted that entertainment and status seeking motives are context-specific. Therefore, this study may likely benefit researchers, academicians and policymakers to understand the causal relationship between motivations and the behavior of combating misinformation on social media within a developing country.

In this study the authors merged three theories (e.g. uses and gratifications theory, social cognitive theory and theory of prosocial behavior) to understand information seeking, status seeking, entertainment and norms of reciprocity as the main motives for social media users' misinformation combating intention.