Group behavior in social media: Antecedents of initial trust formation

This study investigates the influence of antecedents of user trust amongst peers on social media platforms that they apply without sufficient cognitive judgment. Based on extensive literature review and analysis of social theories, we propose a theoretical framework for social media trust (SMT). Based on the SMT model, we conduct empirical analysis using structural equation modeling to verify the cause-effect relationships hypothesized in this study. The findings reveal that initial formation of SMT relies on five predictors - fulfilled expectations, predictability, familiarity, monitor, and norms. These findings have significant theoretical and practical implications. The study finds that peers are likely to invest blind faith in the content shared on social media groups without subjecting it to verification. It also identifies the threat of biased peers, who spread irresponsible content with predetermined motives to influence members of certain social media groups. Policymakers can use insights from this study to highlight the ills of non-verification, and its potential to cause harm at the very extreme

“So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy

Transformative artificially intelligent tools, such as ChatGPT, designed to generate sophisticated text indistinguishable from that produced by a human, are applicable across a wide range of contexts. The technology presents opportunities as well as, often ethical and legal, challenges, and has the potential for both positive and negative impacts for organisations, society, and individuals. Offering multi-disciplinary insight into some of these, this article brings together 43 contributions from experts in fields such as computer science, marketing, information systems, education, policy, hospitality and tourism, management, publishing, and nursing. The contributors acknowledge ChatGPT’s capabilities to enhance productivity and suggest that it is likely to offer significant gains in the banking, hospitality and tourism, and information technology industries, and enhance business activities, such as management and marketing. Nevertheless, they also consider its limitations, disruptions to practices, threats to privacy and security, and consequences of biases, misuse, and misinformation. However, opinion is split on whether ChatGPT’s use should be restricted or legislated. Drawing on these contributions, the article identifies questions requiring further research across three thematic areas: knowledge, transparency, and ethics; digital transformation of organisations and societies; and teaching, learning, and scholarly research. The avenues for further research include: identifying skills, resources, and capabilities needed to handle generative AI; examining biases of generative AI attributable to training datasets and processes; exploring business and societal contexts best suited for generative AI implementation; determining optimal combinations of human and generative AI for various tasks; identifying ways to assess accuracy of text produced by generative AI; and uncovering the ethical and legal issues in using generative AI across different contexts

“So what if ChatGPT wrote it?”:Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy

Transformative artificially intelligent tools, such as ChatGPT, designed to generate sophisticated text indistinguishable from that produced by a human, are applicable across a wide range of contexts. The technology presents opportunities as well as, often ethical and legal, challenges, and has the potential for both positive and negative impacts for organisations, society, and individuals. Offering multi-disciplinary insight into some of these, this article brings together 43 contributions from experts in fields such as computer science, marketing, information systems, education, policy, hospitality and tourism, management, publishing, and nursing. The contributors acknowledge ChatGPT’s capabilities to enhance productivity and suggest that it is likely to offer significant gains in the banking, hospitality and tourism, and information technology industries, and enhance business activities, such as management and marketing. Nevertheless, they also consider its limitations, disruptions to practices, threats to privacy and security, and consequences of biases, misuse, and misinformation. However, opinion is split on whether ChatGPT’s use should be restricted or legislated. Drawing on these contributions, the article identifies questions requiring further research across three thematic areas: knowledge, transparency, and ethics; digital transformation of organisations and societies; and teaching, learning, and scholarly research. The avenues for further research include: identifying skills, resources, and capabilities needed to handle generative AI; examining biases of generative AI attributable to training datasets and processes; exploring business and societal contexts best suited for generative AI implementation; determining optimal combinations of human and generative AI for various tasks; identifying ways to assess accuracy of text produced by generative AI; and uncovering the ethical and legal issues in using generative AI across different contexts

Artificial intelligence (AI): multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research and practice

As far back as the industrial revolution, great leaps in technical innovation succeeded in transforming numerous manual tasks and processes that had been in existence for decades where humans had reached the limits of physical capacity. Artificial Intelligence (AI) offers this same transformative potential for the augmentation and potential replacement of human tasks and activities within a wide range of industrial, intellectual and social applications. The pace of change for this new AI technological age is staggering, with new breakthroughs in algorithmic machine learning and autonomous decision making engendering new opportunities for continued innovation. The impact of AI is significant, with industries ranging from: finance, retail, healthcare, manufacturing, supply chain and logistics all set to be disrupted by the onset of AI technologies. The study brings together the collective insight from a number of leading expert contributors to highlight the significant opportunities, challenges and potential research agenda posed by the rapid emergence of AI within a number of domains: technological, business and management, science and technology, government and public sector. The research offers significant and timely insight to AI technology and its impact on the future of industry and society in general

Investigating the effect of advertising irritation on digital advertising effectiveness: a moderated mediation model

The exponential growth of mobile phone usage has shifted the attention of marketers toward digital advertising as a tool for targeted communication. However, the increased usage of digital advertising exposes consumers to excessive amounts of advertisements, which may trigger their annoyance and raise concerns about overall advertising effectiveness. In a moderated mediation model, this study examined how digital advertising value influences consumers' attitudes toward advertising and purchase intention at different levels of advertising irritation using the S-O-R framework and the advertising value model. Data were collected from 272 university students to test the conceptual model, and a multi-analytic approach combining partial least squares structural equation modeling and necessary condition analysis (NCA) was adopted. Results show that advertising value has a stronger effect on purchase intention than attitude toward advertising and that advertising irritation is a strong negative moderator that significantly reduces overall advertising effectiveness. NCA results also show varying degrees of predictor necessity. These findings can help advertisers improve their strategies to maximize the effectiveness of digital advertising