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The purpose of this paper, which is based on the UTAUT2 model, is to develop and evaluate a predictive model of customer satisfaction related to mobile commerce (m-commerce) and the willingness to recommend this service to others.

The study was conducted based on a sample of 402 respondents. Confirmative factor analysis was used to evaluate the validity of the model, while structural equation modeling (SEM) was used to test the hypotheses. Finally, artificial neural networks were used to rank the influence of the significant predictors obtained by SEM.

Trust was found to be the most significant driver of customer satisfaction, followed by performance expectancy and perceived value. In addition, affective commitment and satisfaction were identified as the strongest predictors of word of mouth (WOM).

The originality/value of the paper lies in the establishment of the connection between the independent variables of the UTAUT 2 model – trust, satisfaction, affective and continence commitment and WOM. Additionally, it is one of a small number of studies investigating customer commitments and their influence on WOM in m-commerce.

The purpose of this paper is to determine statistically significant drivers of customer satisfaction in mobile commerce and to test the moderating effects of customization on the relationships between customer satisfaction and its predictors.

The sample comprised 224 respondents. Confirmatory factor analysis was used to test the validity of the model, and moderated regression analysis was applied to determine main and interaction effects.

Trust, perceived usefulness, mobility, and perceived enjoyment were found to be significant drivers of customer satisfaction. The results also indicate the statistical significance of two interaction effects: customization moderates the influence of mobility and the influence of trust on customer satisfaction.

The study was conducted in a single time period and in a developing country where m-commerce is still not widely used. Future models should include new variables. Comparison between different age or gender groups would also be useful.

The findings are useful for m-commerce providers who are developing marketing campaigns, where the focus should be on promoting the mobility aspect of m-commerce, in particular its usefulness to consumers and its security. M-commerce activities should be developed and redesigned to better meet consumers’ specific demands and needs.

M-commerce customer satisfaction studies are rare. The developed model has five potential antecedents of satisfaction: trust, social influence, perceived usefulness, mobility, and perceived enjoyment. New insights are provided into the moderating role of customization.

The purpose of this paper is to determine the significant antecedents of peer-to-peer (P2P) m-payment acceptance and explore the moderating effects of gender on the influence of these predictors with regards to intention of using the system.

The research was conducted on a sample comprised of 701 Spanish smartphone users. A multi-group structural equation modeling analysis was used to test the moderating effect of gender with a particular focus on the relationships between the latent variables of the research model.

The study identified significant differences between the two observed groups – the results show that men are more likely to use mobile payments than women and are therefore less influenced by the potential risks involved. In addition, men are more easily influenced by their social environment, whereas women are more influenced by their personal innovativeness.

The study proposes a three-level model, based on an extended TAM model. It is a pioneering study, exploring the effects of gender on P2P m-payment acceptance. Due to its novel value and the potential involved, the results of the study may be of great importance for m-payment providers, particularly in marketing strategy planning and customer segmentation.

The purpose of this study is to comprehend how AI aids marketers in engaging customers and generating value for the company by way of customer engagement (CE). CE is a popular area of research for scholars and practitioners. One area of research that could have far-reaching ramifications with regard to strengthening CE is artificial intelligence (AI). Consequently, it becomes extremely important to understand how AI is helping the marketer reach customers and create value for the firm via CE.

A detailed approach using both systematic review and bibliometric analysis was used. It involved identifying key research areas, the most influential authors, studies, journals, countries and organisations. Then, a comprehensive analysis of 50 papers was carried out in the four identified clusters through co-citation analysis. Furthermore, a content analysis of 42 articles for the past six years was also conducted.

Emerging themes explored through cluster analysis are CE concepts and value creation, social media strategies, big data innovation and significance of AI in tertiary industry. Identified themes for content analysis are CE conceptualisation, CE behaviour in social media, CE role in value co-creation and CE via AI.

CE has emerged as a topic of great interest for marketers in recent years. With the rapid growth of digital media and the spread of social media, firms are now embarking on new online strategies to promote CE (Javornik and Mandelli, 2012). In this review, the authors have thoroughly assessed multiple facets of prior research papers focused on the utilisation of AI in the context of CE. The existing research papers highlighted that AI-powered chatbots and virtual assistants offer real-time interaction capabilities, swiftly addressing inquiries, delivering assistance and navigating customers through their experiences (Cheng and Jiang, 2022; Naqvi et al., 2023). This rapid and responsive engagement serves to enrich the customer’s overall interaction with the business. Consequently, this research can contribute to a comprehensive knowledge of how AI is assisting marketers to reach customers and create value for the firm via CE. This study also sheds light on both the attitudinal and behavioural aspects of CE on social media. While existing CE literature highlights the motivating factors driving engagement, the study underscores the significance of behavioural engagement in enhancing firm performance. It emphasises the need for researchers to understand the intricate dynamics of engagement in the context of hedonic products compared to utilitarian ones (Wongkitrungrueng and Assarut, 2020). CEs on social media assist firms in using their customers as advocates and value co-creators (Prahalad and Ramaswamy, 2004; Sawhney et al., 2005). A few of the CE themes are conceptual in nature; hence, there is an opportunity for scholarly research in CE to examine the ways in which AI-driven platforms can effectively gather customer insights. As per the prior relationship marketing studies, it is evident that building relationships reduces customer uncertainty (Barari et al., 2020). Therefore, by using data analysis, businesses can extract valuable insights into customer preferences and behaviour, equipping them to engage with customers more effectively.

The rapid growth of social media has enabled individuals to articulate their thoughts, opinions and emotions related to a brand, which creates a large amount of data for VCC. Meanwhile, AI has emerged as a radical way of providing value content to users. It expands on a broader concept of how software and algorithms work like human beings. Data collected from customer interactions are a major prerequisite for efficiently using AI for enhancing CE. AI not only reduces error rates but, at the same time, helps human beings in decision-making during complex situations. Owing to built-in algorithms that analyse large amounts of data, companies can inspect areas that require improvement in real time. Time and resources can also be saved by automating tasks contingent on customer responses and insights. AI enables the analysis of customer data to create highly personalised experiences. It can also forecast customer behaviour and trends, helping businesses anticipate needs and preferences. This enables proactive CE strategies, such as targeted offers or timely outreach. Furthermore, AI tools can analyse customer feedback and sentiment across various channels. This feedback can be used to make necessary improvements and address concerns promptly, ultimately fostering stronger customer relationships. AI can facilitate seamless engagement across multiple digital channels, ensuring that customers can interact with a brand through their preferred means, be it social media, email, or chat. Consequently, this research proposes that practitioners and companies can use analysis performed by AI-enabled systems on CEB, which can assist companies in exploring the extent to which each product influences CE. Understanding the importance of these attributes would assist companies in developing more memorable CE features.

This study examines how prominent CE and AI are in academic research on social media by identifying research gaps and future developments. This research provides an overview of CE research and will assist academicians, regulators and policymakers in identifying the important topics that require investigation.