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Once a corporate crisis is entangled with a social issue, how consumers make sense of the crisis can be impacted by issue-based opinion polarization. This study investigates the underlying mechanisms as consumers go through this process. This study also examines whether corporate social advocacy (CSA) can be an effective crisis-response strategy for mitigating reputational loss.

Theoretical inquiries were empirically tested using an online experiment (N = 792). The experiment set the context in China, in a working-overtime-issue-related crisis. It had a 2 (online exposure: anti-issue opinion vs. pro-issue opinion) × 2 (CSA: absence vs. presence) between-subject design with a continuous variable (pre-existing issue attitudes) measured before the manipulation.

This study found that pre-existing issue attitudes can be directly and indirectly associated with corporate reputation, for the issue attitudes influence how consumers attribute crisis blame. Such a direct effect of pre-existing issue attitudes varies depending on which polarized opinion consumers were exposed to on social media. This study also found CSA to be a robust crisis response strategy, through multiple mechanisms, in protecting the corporate reputation.

Scholars are scarcely aware of the threats that issue-based opinion polarization poses to corporate reputation. This study serves as an early attempt to provide theoretical explanations. In addition to this, this study extends the current conceptual understandings of CSA during corporate crises that involve social issues while adding fresh insights into the established typology of crisis-response strategies.

In the face of increasing political polarization worldwide, this study explores whether people create biased perceptions of political knowledge and how this affects their selection and evaluation of political content on YouTube.

For this study, an online experiment was conducted with 441 panels of South Korean respondents. In the first phase, participants answered 10 questions designed to capture their level of objective political knowledge, and for each question, they indicated whether they had responded to that question correctly as a means of measuring their subjective political knowledge. In the second phase, two types of YouTube thumbnails were presented to represent progressive and conservative claims on two controversial political issues, and participants rated and selected the content they would like to see.

Participants with low political knowledge perceived their knowledge as more than it really was. In contrast, participants with high political knowledge perceived their political knowledge as less than it really was. This biased perception of political knowledge influences respondents' choice and evaluation of political YouTube channel videos.

At a time when political polarization is increasing around the world, this study sought to explore how perceptions of political knowledge differ from actual political knowledge by applying the Dunning-Kruger effect. The authors also used political YouTube channels, whose role in forming public opinion and political influence is rapidly growing, to study the behavior and attitudes of a group of Korean respondents in the media according to their actual and perceived level of political literacy.

This paper highlights an assignment in a combination upper-division undergraduate and graduate civic leadership class at a Midwestern state comprehensive university. The three-part assignment challenges students’ critical thinking skills and research capabilities while simultaneously necessitating the exploration of contrasting viewpoints on contentious issues.

Intentionally exposing students to diverse perspectives in a controlled environment.

We posit that the severity and frequency of these issues can be mitigated through focused efforts.

Students are better prepared to engage in civil debate on controversial topics, which continuously divide our communities, after completing a class using this pedagogical strategy.

Web users rely heavily on online content make decisions without assessing the veracity of the content. The online content comprising text, image, video or audio may be tampered with to influence public opinion. Since the consumers of online information (misinformation) tend to trust the content when the image(s) supplement the text, image manipulation software is increasingly being used to forge the images. To address the crucial problem of image manipulation, this study focusses on developing a deep-learning-based image forgery detection framework.

The proposed deep-learning-based framework aims to detect images forged using copy-move and splicing techniques. The image transformation technique aids the identification of relevant features for the network to train effectively. After that, the pre-trained customized convolutional neural network is used to train on the public benchmark datasets, and the performance is evaluated on the test dataset using various parameters.

The comparative analysis of image transformation techniques and experiments conducted on benchmark datasets from a variety of socio-cultural domains establishes the effectiveness and viability of the proposed framework. These findings affirm the potential applicability of proposed framework in real-time image forgery detection.

This study bears implications for several important aspects of research on image forgery detection. First this research adds to recent discussion on feature extraction and learning for image forgery detection. While prior research on image forgery detection, hand-crafted the features, the proposed solution contributes to stream of literature that automatically learns the features and classify the images. Second, this research contributes to ongoing effort in curtailing the spread of misinformation using images. The extant literature on spread of misinformation has prominently focussed on textual data shared over social media platforms. The study addresses the call for greater emphasis on the development of robust image transformation techniques.

This study carries important practical implications for various domains such as forensic sciences, media and journalism where image data is increasingly being used to make inferences. The integration of image forgery detection tools can be helpful in determining the credibility of the article or post before it is shared over the Internet. The content shared over the Internet by the users has become an important component of news reporting. The framework proposed in this paper can be further extended and trained on more annotated real-world data so as to function as a tool for fact-checkers.

In the current scenario wherein most of the image forgery detection studies attempt to assess whether the image is real or forged in an offline mode, it is crucial to identify any trending or potential forged image as early as possible. By learning from historical data, the proposed framework can aid in early prediction of forged images to detect the newly emerging forged images even before they occur. In summary, the proposed framework has a potential to mitigate physical spreading and psychological impact of forged images on social media.

This study focusses on copy-move and splicing techniques while integrating transfer learning concepts to classify forged images with high accuracy. The synergistic use of hitherto little explored image transformation techniques and customized convolutional neural network helps design a robust image forgery detection framework. Experiments and findings establish that the proposed framework accurately classifies forged images, thus mitigating the negative socio-cultural spread of misinformation.

This study aims to elucidate institutional analysis as an effective approach to investigating and designing the multilevel policymaking system of online social networks (OSN) for achieving a participatory model.

The institutional mapping approach has been used to analyze Iran’s OSN multilevel policymaking system. A combination of two matrices, including institutions-institutions and institutions-functions, was used to perform the institutional mapping. Two main steps were taken to draw the mentioned matrices. First, a review of related studies in Iran’s OSN policymaking system was conducted and the policy functions mentioned in these studies were identified and categorized using the meta-synthesis. Second, based on analyzing two policy documents of Iran’s OSN, institutions and their interactions were identified and policy functions were allocated to institutions.

Based on the results, the most important policy functions in the current OSN policymaking system in Iran are support, regulatory, monitoring and evaluation, business environment development, culture building and promotion, organizing licenses and permissions, policymaking and legislation. Also, the results show that there are shortcomings in this system, some of the most important of which are lack of transparency in regulatory, little work in culture building and promotion, neglect of the training of specialized human resources and research and development, slow development of the business environment and neglecting the role of nongovernmental organizations in policymaking.

By examining and analyzing how different institutions operate within a multilevel policymaking system, the policymaking process and its overall effectiveness can be enhanced. This analysis helps identify any inconsistencies, overlaps or conflicts in the roles and policies of these institutions, leading to a better understanding of how a multilevel policymaking system is organized.

This paper aims to investigate a condition under which identity salience effects are weakened. By examining how identity salience influences individuals’ product judgment in a domain of trade-offs, the current research demonstrates that the utilitarian value of a product is an important determinant of the effectiveness of identity salience on product judgment.

This research consists of two experiments. In Experiment 1, the authors examined whether identity salience effects were mitigated when the level of the perceived utilitarian value of an identity-incongruent product was greater than that of an identity-congruent product. In Experiment 2, the authors examined the effectiveness of internal attribution as a moderator that strengthens identity salience effects when the perceived utilitarian value of an identity-incongruent (vs. identity-congruent) product is higher.

In Experiment 1, the authors show that when the utilitarian value of a product with an attribute congruent (vs. incongruent) with one’s salient identity is lower, individuals do not show a greater preference for the identity-congruent (vs. identity-incongruent) product, mitigating the identity salience effects. Experiment 2 demonstrates that when individuals with a salient identity attribute a decision outcome to the self, they display a greater preference for the identity-congruent product even when its utilitarian value is lower compared to that of the identity-incongruent product.

The research contributes to previous research examining conditions under which identity salience effects are weakened [e.g. social influence by others (Bolton and Reed, 2004); self-affirmation (Cohen et al., 2007)] by exploring the role of the utilitarian value of a product, which has not been examined yet in prior research. Also, by doing so, the current research adds to the literature on identity salience in a domain of trade-offs (Benjamin et al., 2010; Shaddy et al., 2020, 2021). Finally, this research reveals that when a decision outcome is attributed to the self, identity salience effects become greater. By finding a novel determinant of identity salience effects (i.e. internal attribution), the present research contributes to the literature that has examined factors that amplify identity salience effects [e.g. cultural relevance (Chattaraman et al., 2009); social distinctiveness (Forehand et al., 2002); different types of groups (White and Dahl, 2007)].

The findings provide managerial insights on identity-based marketing by showing a condition under which identity-based marketing does not work [i.e. when the utilitarian value of an identity-congruent (vs. identity-incongruent) product is lower] and how to enhance the effectiveness of identity-based marketing by using internal attribution.

By exploring the role of utilitarian value, not yet examined in prior research, the present research adds to the knowledge of the conditions under which identity salience effects are weakened. Furthermore, by finding a novel determinant of identity salience effects (i.e. internal attribution), the research contributes to the literature on factors that amplify identity salience effects.

The effects of board composition on performance have long been discussed in management research using fractionalization measures. In this study, we propose an alternative measurement approach based on board polarization.

Using an exploratory analysis and applying the polarization measure to German Deutscher Aktienindex (DAX)-, Midcap-DAX (MDAX)- and Small Cap-Index (SDAX)-listed companies, this paper applies the polarization index to examine the relationship between board diversity and performance.

The results show that the polarization concept is well suited to measure principal-agent problems between the members of the management and supervisory boards. We reveal that board polarization is negatively associated with firm performance, as measured by return on investment (ROI).

This exploratory study shows that the measurement of board polarization can be linked to performance differences between companies, which offers promising starting points for further research.

The ballooning health misinformation on social media raises grave concerns. Drawing upon the S-O-R (Stimulus-Organism-Response) model and the information processing literature, this study aims to explore (1) how social media health information seeking (S) affects health misinformation sharing intention (R) through the channel of health misperceptions (O) and (2) whether the mediation process would be contingent upon different information processing predispositions.

Data were collected from a survey comprising 388 respondents from the Chinese middle-aged or above group, one of China's most susceptible populations to health misinformation. Standard multiple linear regression models and the PROCESS Macro were adopted to examine the direct effect and the moderated mediation model.

Results bolstered the S-O-R-based mechanism, in which health misperceptions mediated social media health information seeking's effect on health misinformation sharing intention. As an indicator of analytical information processing, need for cognition (NFC) failed to moderate the mediation process. Contrarily, faith in intuition (FI), an indicator reflecting intuitive information processing, served as a significant moderator. The positive association between social media health information seeking and misperceptions was stronger among respondents with low FI.

This study sheds light on health misinformation sharing research by bridging health information seeking, information internalization and information sharing. Moreover, the authors extended the S-O-R model by integrating information processing predispositions, which differs this study from previous literature and advances the extant understanding of how information processing styles work in the face of online health misinformation. The particular age group and the Chinese context further inform context-specific implications regarding online health misinformation regulation.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2023-0157.

This study aims to explore the dissemination and evolutionary path of online public opinion from a crisis management perspective. By clarifying the influencing factors and dynamic mechanisms of online public opinion dissemination, this study provides insights into attenuating the negative impact of online public opinion and creating a favorable ecological space for online public opinion.

This research employs bibliometric analysis and CiteSpace software to analyze 302 Chinese articles published from 2006 to 2023 in the China National Knowledge Infrastructure (CNKI) database and 276 English articles published from 1994 to 2023 in the Web of Science core set database. Through literature keyword clustering, co-citation analysis and burst terms analysis, this paper summarizes the core scientific research institutions, scholars, hot topics and evolutionary paths of online public opinion crisis management research from both Chinese and international academic communities.

The results show that the study of online public opinion crisis management in China and internationally is centered on the life cycle theory, which integrates knowledge from information, computer and system sciences. Although there are differences in political interaction and stage evolution, the overall evolutionary path is similar, and it develops dynamically in the “benign conflict” between the expansion of the research perspective and the gradual refinement of research granularity.

This study summarizes the research results of online public opinion crisis management from China and the international academic community and identifies current research hotspots and theoretical evolution paths. Future research can focus on deepening the basic theories of public opinion crisis management under the influence of frontier technologies, exploring the subjectivity and emotionality of web users using fine algorithms and promoting the international development of network public opinion crisis management theory through transnational comparison and international cooperation.

Managers must make numerous strategic decisions in order to initiate and implement a business model innovation (BMI). This paper examines how managers perceive the management team interacts when making BMI decisions. The paper also investigates how group biases and board members’ risk willingness affect this process.

Empirical data were collected through 26 in-depth interviews with German managing directors from 13 companies in four industries (mobility, manufacturing, healthcare and energy) to explore three research questions: (1) What group effects are prevalent in BMI group decision-making? (2) What are the key characteristics of BMI group decisions? And (3) what are the potential relationships between BMI group decision-making and managers' risk willingness? A thematic analysis based on Gioia's guidelines was conducted to identify themes in the comprehensive dataset.

First, the results show four typical group biases in BMI group decisions: Groupthink, social influence, hidden profile and group polarization. Findings show that the hidden profile paradigm and groupthink theory are essential in the context of BMI decisions. Second, we developed a BMI decision matrix, including the following key characteristics of BMI group decision-making managerial cohesion, conflict readiness and information- and emotion-based decision behavior. Third, in contrast to previous literature, we found that individual risk aversion can improve the quality of BMI decisions.

This paper provides managers with an opportunity to become aware of group biases that may impede their strategic BMI decisions. Specifically, it points out that managers should consider the key cognitive constraints due to their interactions when making BMI decisions. This work also highlights the importance of risk-averse decision-makers on boards.

This qualitative study contributes to the literature on decision-making by revealing key cognitive group biases in strategic decision-making. This study also enriches the behavioral science research stream of the BMI literature by attributing a critical influence on the quality of BMI decisions to managers' group interactions. In addition, this article provides new perspectives on managers' risk aversion in strategic decision-making.