In a short span of time since its introduction, generative artificial intelligence (AI) has garnered much interest at both personal and organizational levels. This is because of its potential to cause drastic and widespread shifts in many aspects of life that are comparable to those of the Internet and smartphones. More specifically, generative AI utilizes machine learning, neural networks, and other techniques to generate new content (e.g. text, images, music) by analyzing patterns and information from the training data. This has enabled generative AI to have a wide range of applications, from creating personalized content to improving business operations. Despite its many benefits, there are also significant concerns about the negative implications of generative AI. In view of this, the current article brings together experts in a variety of fields to expound and provide multi-disciplinary insights on the opportunities, challenges, and research agendas of generative AI in specific industries (i.e. marketing, healthcare, human resource, education, banking, retailing, the workplace, manufacturing, and sustainable IT management).

This study is one of the few studies that aimed to examine, first, the effect of social media usage for news consumption on users’ cognitive notions in terms of focal and affiliate social media trust, and second, the effect of social media trust, news-finds-me perception (NFMP), and fake news’ deception detection self-efficacy (DDSE) on news credibility, brand (dis)trust, and consumers’ propensity to believe and act on fake news. The analysis findings indicated that: (i) social media usage induces focal and affiliate social media trust, (ii) NFMP and DDSE influence news credibility, which in turn determines brand (dis)trust and the propensity to believe in and act on fake news, and (iii) the impact of focal and affiliate social media trust on news credibility is moderated by persuasive knowledge. The present study’s findings contribute to the existing consumer behavior literature by offering insights to counteract the impact of fake news on brands.

Despite the high utilization of mobile payment during the COVID-19 pandemic, this situation may change in the post-pandemic era. Therefore, great value can be derived from determining the significant antecedents of mobile payment continuance intention. This study looks to do so by introducing a Multi-Dimensional Nomological Network of Mobile Payment Continuance. A two-stage Partial Least Square-Structural Equation Modeling and Artificial Neural Network was utilized for the data analysis. The results provided empirical support to establish the overall nomological network. In addition, more than 70% of the variance in continuance intention was captured. Overall, this study provides practitioners with detailed insights to develop strategies for sustainable utilization and academics with a dynamic framework to look into users’ mobile payment continuance intention.

Wearable payment, which is the use of wearable technology to make payment, is anticipated to be the future of proximity mobile payment. However, the acceptance and use of wearable payment in Malaysia leave much to be desired. Additionally, this area of research is currently under-addressed. Thus, this study looks into the elements that influence the intention to adopt wearable payment in Malaysia. The results are robustly insightful in revealing the intention to adopt wearable payment in Malaysia. Hence, there are numerous implications to wearable technology companies, wearable payment system designers, merchants and other stakeholders. Overall, this research is anticipated to serve as one of the pioneering research for future studies related to this area.

This paper looks to determine the antecedents of Near Field Communication (NFC) mobile payment from a developing country’s perspective. This was done with the use of a conceptual framework based on the Mobile Technology Acceptance Model (MTAM) that consists of mobile usefulness (MU), mobile ease of use (MEU) while integrating mobile self-efficacy (MSE), and system and service quality (SSQ). A self-administered questionnaire developed from measurement items adapted from past studies yielded 352 usable responses from smartphone users who were patronizing popular shopping malls in the Klang Valley region. The analysis was done using SmartPLS 3.0 which found that other than MSE, all other independent variables were significant determinants of the intention to adopt mobile payment. Moreover, MSE and SSQ have significantly positive relationships with MU and MEU. This study successfully applied the MTAM model as 72% of the intention to adopt mobile payment can be explained by all factors in the research model.