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Social media has played a pivotal role in polarizing views on Russia–Ukraine conflict. The effects of polarization in online interactions have been extensively studied in many contexts. This research aims to examine how multiple social media sources may act as an integrator of information and act as a platform for depolarizing behaviors.

This study analyzes the communications of 6,662 tweets related to the sanctions imposed on Russia by using textual analytics and predictive modeling.

The research findings reveal that the tweeting behavior of netizens was depolarized because of information from multiple social media sources. However, the influx of information from non-organizational sources such as trending topics and discussions has a depolarizing impact on the user’s pre-established attitude.

For policymakers, conflict mediators and observers, and members of society in general, there is a need for (1) continuous and consistent communication throughout the crisis, (2) transparency in the information being communicated and (3) public awareness of the polarized and conflicting information being provided from multiple actors that may be biased in the claims being made about the conflict crisis.

While previous research has examined Russia–Ukraine conflict from a variety of perspectives, this is the first study to examine how social media might be used to reduce attitude polarization during times of conflict.

This paper aims to identify, prioritise and explore the relationships between the various barriers that are hindering the machine learning (ML) adaptation for analysing accident data information in the Indian petroleum industry.

The preferred reporting items for systematic reviews and meta-analysis (PRISMA) is initially used to identify key barriers as reported in extant literature. The decision-making trial and evaluation laboratory (DEMATEL) technique is then used to discover the interrelationships between the barriers, which are then prioritised, based on three criteria (time, cost and relative importance) using complex proportional assessment (COPRAS) and multi-objective optimisation method by ratio analysis (MOORA). The Delphi method is used to obtain and analyse data from 10 petroleum experts who work at various petroleum facilities in India.

The findings provide practical insights for management and accident data analysts to use ML techniques when analysing large amounts of data. The analysis of barriers will help organisations focus resources on the most significant obstacles to overcome barriers to adopt ML as the primary tool for accident data analysis, which can save time, money and enable the exploration of valuable insights from the data.

This is the first study to use a hybrid three-phase methodology and consult with domain experts in the petroleum industry to rank and analyse the relationship between these barriers.

Deepfakes are fabricated content created by replacing an original image or video with someone else. Deepfakes have recently become commonplace in politics, posing serious challenges to democratic integrity. The advancement of AI-enabled technology and machine learning has made creating synthetic videos relatively easy. This study explores the role of political brand hate and individual moral consciousness in influencing electorates' intention to share political deepfake content.

The study creates and uses a fictional deepfake video to test the proposed model. Data are collected from N = 310 respondents in India and tested using partial least square–structural equation modelling (PLS-SEM) with SmartPLS v3.

The findings support that ideological incompatibility with the political party leads to political brand hate, positively affecting the electorates' intention to share political deepfake videos. This effect is partially mediated by users' reduced intention to verify political deepfake videos. In addition, it is observed that individual moral consciousness positively moderates the effect of political brand hate on the intention to share political deepfake videos. Intention to share political deepfake videos thus becomes a motive to seek revenge on the hated party, an expression of an individual's ideological hate and a means to preserve one's moral self-concept and strengthen their ideologies and moral beliefs.

The study expands the growing discussion about disseminating political deepfake videos using the theoretical lens of the negative consumer-brand relationship. It validates the effect of political brand hate on irrational behavior that is intended to cause harm to the hated party. Further, it provides a novel perspective that individual moral consciousness may fuel the haters' desire to engage in anti-branding behavior. Political ideological incompatibility reflects ethical reasons for brand hate. Therefore, hate among individuals with high moral consciousness serves to preserve their moral self.

The purpose of this paper is twofold: first, to present a bibliometric analysis and systematic literature review of industry convergence and value innovation to understand the current research status; second, to provide a coherent theoretical research framework for future research.

This study adopts a two-step analysis approach by combining bibliometric analysis and systematic literature review to explore the research topic of industry convergence and value innovation. Besides, two bibliometric tools, HistCite and VOSviewer, were applied to this study.

This study found that Stefanie Bröring and Fredrik Hacklin are the top two most influential authors among all authors in the sample publications. Technological Forecasting and Social Change is one of the top-ranking journal that often publishes this topic of articles. Germany and the University of Munster are the most influential country and institutions, respectively. Besides, five core research themes were identified based on keywords co-occurrence map, theoretical lenses, factors promoting industry convergence, indicators of industry convergence, the impact of industry convergence and emerging research directions. Based on the above analysis, this paper constructed a theoretical research framework of industry convergence and value innovation.

This paper only draw data from one database – Web of Science – which cannot provide broad coverage of the research topic. Besides, the bibliometric method of this paper is based on high local citation score and high-frequency words, articles in the skirting subjects’ area may not be analyzed.

With the rapid development of technology, such as nanotechnology, radio - frequency identification (RFID), etc., the iterative upgrading of products also comes. As a result, the boundary between industries is gradually blurred, and the phenomenon of industry convergence appears. Therefore, managerial decision-makers are facing challenges of how to respond to the convergence phenomena. From the firm level, firms are facing the problem of value innovation of the existing product, new product development and core competence improvement. Industries are facing the problem of transformation and upgrading. This paper provides certain theoretical insights for both firms and industries to guide the practice accordingly.

This paper is the first to use a bibliometric method to examine the topic of industry convergence and value innovation. In addition, this paper presents an in-depth analysis of this topic and provides a comprehensive theoretical research framework for future study.

This paper aims to conduct an interdisciplinary systematic literature review (SLR) of fake news research and to advance the socio-technical understanding of digital information practices and platforms in business and management studies.

The paper applies a focused, SLR method to analyze articles on fake news in business and management journals from 2010 to 2020.

The paper analyzes the definition, theoretical frameworks, methods and research gaps of fake news in the business and management domains. It also identifies some promising research opportunities for future scholars.

The paper offers practical implications for various stakeholders who are affected by or involved in fake news dissemination, such as brands, consumers and policymakers. It provides recommendations to cope with the challenges and risks of fake news.

The paper discusses the social consequences and future threats of fake news, especially in relation to social networking and social media. It calls for more awareness and responsibility from online communities to prevent and combat fake news.

The paper contributes to the literature on information management by showing the importance and consequences of fake news sharing for societies. It is among the frontier systematic reviews in the field that covers studies from different disciplines and focuses on business and management studies.

Following the remarkable debut of ChatGPT and its rapid adoption by a global audience since its launch in November 2022, this study delves into educators' perceptions of ChatGPT within the specialized domains of tourism and hospitality education. While acknowledging ChatGPT’s swift rise and advanced capabilities, this research aims to comprehensively explore educators' perspectives, advantages and concerns regarding its integration into academic settings.

A qualitative approach was utilized to reveal dominant themes from in-depth, semi-structured face-to-face interviews with twenty lecturers in tourism faculties in North Cyprus. Collected responses from respondents were subjected to analysis using Leximancer software.

Our analysis unearthed seven significant themes encapsulating educators' perceptions of ChatGPT: 1 – “reliability and accuracy concerns”; 2 – “dependency”; 3 – “student engagement”; 4 – “ethical considerations”; 5 – “time efficiency and productivity”; 6 – “teacher-student interaction” and 7 – “learning flexibility”. Noteworthy positive perceptions emerged regarding “student engagement,” “time efficiency and productivity,” and “learning flexibility.”

This study contributes to the originality of research by addressing the underexplored aspect of educators' perceptions of ChatGPT within the domains of tourism and hospitality education, shedding light on its potential implications, advantages and drawbacks in a specialized educational context. Furthermore, it aims to offer insights into educators' recommendations for the effective incorporation of ChatGPT technologies into this specific educational setting, filling a crucial gap in understanding the integration of artificial intelligence (AI) in specialized fields of study.

The purpose of this study is to examine the trends and developments of subscription business models (SBMs) over the past two decades.

The study extracted 469 documents (articles and reviews) from the Scopus database during 2000–2022 and analysed 132 documents (articles and reviews). A bibliometric methodology of scientific mapping was employed, including a cluster analysis based on the bibliographic coupling of documents. Content analysis was also conducted to reveal emerging trends in SBMs.

The study revealed six emerging themes in SBMs related to consumer behaviour, digital advertising, online news media, journal publications, circular economy and sustainability strategies.

The results of this study provide new and unique insights into the development and trends of SBMs over the past two decades and offer guidance for future researchers to investigate further the phenomenon of SBMs in emerging areas.

The purpose of this paper is to identify the factors determining students’ attitude toward using newly emerged artificial intelligence (AI) tool, Chat Generative Pre-Trained Transformer (ChatGPT), for educational and learning purpose based on technology acceptance model.

The recommended model was empirically tested with partial least squares structural equation modeling using 375 student survey responses.

The study revealed that students have a favorable view of the instructional use of ChatGPT. Usefulness, social presence and legitimacy of the tool, as well as enjoyment and motivation, contribute to a favorable attitude toward using this tool in a learning environment. However, perceived ease of use was not found to be a significant determinant in the adoption and utilization of ChatGPT by the students.

This research is intended to benefit enterprises, academic institutions and the global community by offering light on how students perceive the ChatGPT service in an educational setting. Furthermore, the application enhances confidence and interest among learners, leading to improved literacy and general awareness. Eventually, the outcome of this research will help AI developers to improve their product and service delivery, as well as benefit regulators in regulating the usage of AI-based bots.

Due to its novelty, the current research on AI-based ChatGPT usage in the education sector is rather restricted. This study provides the adoption aspects of ChatGPT, a new AI-based technology for students, thereby contributing significantly to the existing research on the adoption of advanced education technologies. In addition, the literature lacks research on the adoption of ChatGPT by students for educational purposes; this study addresses this gap by identifying adoption determinants of ChatGPT in education.