The application of hybrid Partial-Least-Square-Structural-Equation-Modeling-Artificial-Neural-Network in Information Systems (IS) research has surged over the years. Grounded on a systematic literature review from the list of premier and other promising IS journals, we found several concerns and issues. Hitherto, there are no guidelines for IS researchers for the hybrid PLS-SEM-ANN approach. We unlocked the potential of the hybrid PLS-SEM-ANN in providing better insight and understanding for IS researchers. In addition, best practices and recommendations for the adoption of PLS-SEM-ANN are discussed. The study contributes to advancing IS research by conducting a systematic literature review with Biblioshiny apps from the R studio’s Bibliometrix package and then proposing a comprehensive and robust approach to address the duality nature through the linear-nonlinear and compensatory-non-compensatory relationships. We proposed a guideline and suggested the minimum sample size, best practices and recommendations for reporting the results. We discuss the opportunities and prospects of the hybrid approach.

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.

Mobile grocery shopping has gained traction recently due to the COVID-19 pandemic. Hence, this study aims to investigate the antecedents that influence consumers’ buying intention through mobile grocery shopping. This was achieved through the use of a multi-perspective research model that includes the consumers’ positive and negative valences as well as consumer characteristics. Data was collected from 400 Malaysian consumers and analyzed using the Partial Least Squares-Structural Equation Modeling (PLS-SEM) and Artificial Neural Network (ANN) analysis. The finding of the study indicated the significant effects of positive valences (e.g., hedonic and utilitarian value) but not negative valences (e.g., privacy concern and security risk) on consumer buying intention through mobile grocery shopping. This study among the first to examine the intricacies of varying consumer characteristics in the context of mobile grocery shopping. The implications derived from the findings of this study are discussed.

The term metaverse is described as the next iteration of the Internet. Metaverse is a virtual platform that uses extended reality technologies, i.e. augmented reality, virtual reality, mixed reality, 3D graphics, and other emerging technologies to allow real-time interactions and experiences in ways that are not possible in the physical world. Companies have begun to notice the impact of the metaverse and how it may help maximize profits. The purpose of this paper is to offer perspectives on several important areas, i.e. marketing, tourism, manufacturing, operations management, education, the retailing industry, banking services, healthcare, and human resource management that are likely to be impacted by the adoption and use of a metaverse. Each includes an overview, opportunities, challenges, and a potential research agenda.

Although mobile payment services are unlikely to become obsolete, their market dominance has been challenged following the intensified market competition. A research model is developed to uncover the drivers of users’ hedonic and utilitarian motivations (i.e., application, marketing, and internal stimuli) as the key to fostering satisfaction and commitment toward mobile payment services. Data were gathered from a survey and analyzed via the dual-stage Structural Equation Modeling-Artificial Neural Network (SEM-ANN) technique to capture both linear and non-linear relationships. The findings suggested that information value, monetary value, self-congruence, and reward and recognition drive hedonic and/or utilitarian motivations, which in turn promote users’ satisfaction and commitment toward mobile payment services. The ANN analysis reveals the existence of a non-linear hidden attribute. The research provides theoretical insights and practical implications for the field of mobile payment services.

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.

Tourism shopping via mobile applications has revolutionized the tourism industry. The research examined the antecedents to the adoption of mobile tourism shopping (MTS) via an integrated model, consisting of the Mobile Technology Acceptance Model, consumer perception of merchant ethics, consumer decision-making styles, and characteristics of mobile applications. The results were empirically validated via the PLS-SEM-ANN method. The findings indicated that mobile usefulness, mobile ease of use, application quality, and service quality have significant positive correlations with the intention to use MTS, whereas privacy concern has displayed a significant negative relationship. Moreover, source reliability, application reputation, perfectionism, and novelty-fashion consciousness were revealed to be statistically insignificant to the development of adoption intention toward MTS. Furthermore, the ANN results have indicated the existence of a non-linear hidden attribute in the prediction of ANN Model A. The research has generated valuable theoretical implications and practical insights. For one, the advantages that come with the adoption of MTS and the security measures taken by the service providers should be highlighted to the prospects via effective marketing messages to nurture confidence, which can subsequently drive adoption.