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This study aims to investigate how message sidedness affects the impact of fake news posted on social media on consumers' emotional responses.

The study involves a face-tracking experiment in which 198 participants were exposed to different fake news messages concerning the COVID-19 vaccine. Specifically, participants were exposed to fake news using (1) a one-sided negative fake news message in which the message was entirely unfavorable and (2) a two-sided fake news message in which the negative message was mixed with favorable information. Noldus FaceReader 7, an automatic facial expression recognition system, was used to recognize participants' emotions as they read fake news. The authors sampled 17,450 observations of participants' emotional responses.

The results provide evidence of the significant influence of message sidedness on consumers' emotional valence and arousal. Specifically, two-sided fake news positively influences emotional valence, while one-sided fake news positively influences emotional arousal.

The current study demonstrates that research on fake news posted on social media may particularly benefit from insights regarding the potential but often overlooked importance of strategic design choices in fake news messages and their impact on consumers' emotional responses.

Patient-centric exchanges, a major type of Health Information Exchange (HIE), empower patients to aggregate and manage their health information. This exchange model helps patients access, modify and share their medical information with multiple healthcare organizations. Although existing studies examine patient engagement, more research is required to investigate patients' attitudes and willingness to play an active role in patient-centered information exchange. The study's main objective is to develop a model based on the belief-attitude-intention paradigm to empirically examine the effects of patients' attitudes toward engagement in care on their willingness to participate in patient-centric HIE.

The authors conducted an online survey study to identify the antecedents and consequences of patients' attitudes toward engagement in care. To empirically test the research model, the authors collected data from a national sample (n = 357) of individuals in the United States. The data were analyzed using structural equation modeling (SEM).

The proposed model categorizes the antecedents to patients' attitudes toward engagement in patient-related and healthcare system factors. The results show that patient-related factors (perceived health literacy and perceived coping ability) and health system factors (perceived experience with the healthcare organization and perceived patient-provider interaction) significantly shape patient attitude toward care management engagement. The results indicate that patients' attitudes toward engaging in their healthcare significantly contribute to their willingness to participate in medical information sharing through patient-centric HIE initiatives. Moreover, the authors’ findings also demonstrate that the link between patient engagement and willingness to participate in HIE is stronger for individuals who perceive lower levels of privacy and security concerns.

The authors validate the proposed model explaining patients' perceptions about their characteristics and the healthcare system significantly influence their attitude toward engaging in their care. This study also suggests that patients' favorable attitude toward engagement can bring patient-centric HIE efforts onto a path to success. The authors’ research attempts to shed light on the importance of patients' roles in adopting patient-centric HIE initiatives. Theoretical and practical contributions of this study are noticeable since they could result in a deeper understanding of the concept of patient engagement and how it may affect healthcare services in an evolving digital world. The authors’ findings can help healthcare organizations provide public citizen-centric services by introducing user-oriented approaches in healthcare delivery systems.

This study aims to contribute to the field of computer systems for sustainability research. It proposes a framework for sustainability awareness using online social networks (OSNs) by analyzing major research streams of the current state of knowledge and different bibliometric variables, and identifies a future research agenda in the field.

The preferred reporting items for systematic review and meta-analysis (PRISMA) methodology, content analysis and bibliometric tools were employed to identify, select, collect, synthesize, analyze and evaluate all research published on sustainability awareness using OSNs to provide complete insight into this research area.

This study proposed a framework comprising four categories for sustainability awareness using OSNs. These four categories are: the key factors to success, analysis of existing tools, proposal of new methods, approaches and theoretical frameworks, and case examples. In addition, this study synthesized the future research challenges for each category of the proposed framework.

Fostering sustainability awareness and sustainable behavior using OSNs is a growing area of research that seeks cultural change in society to achieve sustainable development. Through OSNs, people can discover and become aware of the consequences of unsustainable practices and habits in society, and learn how to develop sustainable behavior.

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

A semiparametric stochastic frontier model is proposed for panel data, incorporating several flexible features. First, a constant elasticity of substitution (CES) production frontier is considered without log-transformation to prevent induced non-negligible estimation bias. Second, the model flexibility is improved via semiparameterization, where the technology is an unknown function of a set of environment variables. The technology function accounts for latent heterogeneity across individual units, which can be freely correlated with inputs, environment variables, and/or inefficiency determinants. Furthermore, the technology function incorporates a single-index structure to circumvent the curse of dimensionality. Third, distributional assumptions are eschewed on both stochastic noise and inefficiency for model identification. Instead, only the conditional mean of the inefficiency is assumed, which depends on related determinants with a wide range of choice, via a positive parametric function. As a result, technical efficiency is constructed without relying on an assumed distribution on composite error. The model provides flexible structures on both the production frontier and inefficiency, thereby alleviating the risk of model misspecification in production and efficiency analysis. The estimator involves a series based nonlinear least squares estimation for the unknown parameters and a kernel based local estimation for the technology function. Promising finite-sample performance is demonstrated through simulations, and the model is applied to investigate productive efficiency among OECD countries from 1970–2019.

One of the most marginalized communities in India is the Lesbian, Gay, Bisexual and Transgender (LGBT) community which commonly experiences discrimination. Many studies have countered that the LGBT community faces high discrimination in the banking and financing industry. As a result, this study concentrates on this marginalized community and its acceptance and continuation habit regarding mobile wallets. Consequently, this study has considered continuance intentions as a response to confirm the progress of the mobile-wallet industry. Also, this study tried to study the relationship between behavioral intention (BI) and continuous intention (CI) which is seriously lacks in the library of literature. The research operationalized the stimulus–organism–response (SOR) framework for the conceptual model and surveyed 100 self-proclaimed members of the LGBT community in India. The analysis has been done using the partial least structure (PLS). The findings demonstrate that variables like perceived trust (PT) directly influence the BI. On the other hand, variables like perceived ease of use (PEoU), social influence (SI), and satisfaction (S) doesn’t influence BI of the LGBT Community. The main outcome was a favorable association between BI and CI. It will help the stakeholders to understand how important this new market avenue is and how it can be explored. To ensure safe and secure transactions, a group think tank composed of important parties (financial institutions, mobile-wallet providers, the government, security specialists, etc.) should make recommendations. Mobile-wallet providers will attain benefit from this study’s understanding of user categories and ability to tailor their service offers as per the community.

The UK government has taken the lead in accelerating the capacity of higher education to engage with sustainability accounting and adopting a novel systematic approach toward a collective implementation of and contribution to Sustainable Development Goals (SDGs). The UN SDG 16 “Peace, Justice & Strong Institutions” promotes the (re)building of effective and accountable institutions. In line with the institutional logics metatheory, we provide empirical evidence on how the alignment between social mechanisms alongside the reputation of higher education institutions (HEIs) and SDGs on transparent and responsible service (SDG 16) affect the students' overall experience. Using a sample of 142 UK HEIs, interpretative content analysis and ordinary least squares, the results show that integrating HEIs' responsible-oriented research agenda proactively with high sustainability reputation adds significantly to greater student satisfaction.

Research on the role of women in Public Relations (PR) has seen a notable increase in recent years (Topić et al., 2019), yet exploratory studies on the subject remain limited, particularly concerning the role of women in the academic sphere of PR in Spain. Guided by the imperative to reflect on the role of PR professionals operating within the Spanish academic landscape, this chapter aims to identify and reflect on the role of women in PR academia in Spain to discover the extent to which women are leading the communication faculties where PR is taught; to identify the presence of women in the departments where communication studies take place and to determine women's leadership in national research activity. This study follows a combined approach with two dimensions, exploratory and interpretative, combining quantitative and qualitative techniques. The interpretative approach is developed using a focus group with women currently working in PR education and leading projects and institutions in the Spanish academia. Through this research, we discover the struggles and opportunities they face and identify possible solutions and ideas to improve the situation of women in academia and, specifically, in PR.

Knowledge management (KM) is an important topic in the age of big data, and this study adds to the existing body of literature by providing a novel KM perspective on the technological phenomenon of artificial intelligence (AI). This study aims to discover how AI might facilitate knowledge-based business-to-business (B2B) marketing. In this chapter, the authors take a close look at the building blocks of AI and the relationships between them. Future research directions and also the effects of the various market information building components on B2B marketing are discussed. The study’s approach is theoretical; it tries to provide a framework for characterising the phenomenon of AI and its constituent parts. Additionally, this chapter provides a methodical analysis of the three categories of market information crucial to B2B marketing: knowledge of customers, knowledge of users, and knowledge of external markets. This research looks at AI through the lens of the conventional data processing framework, analysing the six pillars upon which AI systems are founded. It also explained how the framework’s components work together to transform data into actionable information. In this chapter, the authors will look at how AI works and how it can benefit B2B knowledge-based marketing. It’s not aimed at AI experts but rather at general marketing managers. In this chapter, the possible effects of AI on B2B marketing are discussed using examples from the real world.