As the use of artificial intelligence (AI) grows, so do the questions regarding this new technology and its potential uses. Among the various possibilities and employment that could be offered by AI is personalized news technology. Nowadays, it is already possible to produce journalistic content through AI (Carlson, 2014; Graefe & Haim, 2018). Digital storytelling has become a reality through automated journalism powered by AI (Caswell & Dörr, 2018; Galily, 2018; Linden, 2017; Thorne, 2020). “Artificial intelligence applies advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decisions, and take actions” (Gartner Group, 2019). In personalized news technology, algorithms are responsible for selecting content and sorting it according to the personalization criteria (Powers, 2017). So far, AI has been studied in different fields with distinct research focuses (Loureiro et al., 2021). Studies of news-personalization technologies have mainly focused on research engines and filtering mechanisms (Darvishy et al., 2020; Haim et al., 2017; Manoharan & Senthilkumar, 2020). Few studies examine news aggregators (Haim et al., 2018; Kwak et al., 2021) and the effects of news personalization on audiences (Merten, 2021; Swart, 2021; Thurman et al., 2019), thus demanding further research. AI is an imminent reality for the future, reshaping the news media (Brennen et al., 2022; Linden, 2017; Thorne, 2020). Hence, it is still necessary to investigate the impacts that this technology potentially offers to users. Therefore, the current study seeks to respond to this need to deepen research into the area of news personalization through AI, by analyzing the response of audiences toward current and future technological tendencies. The main aim of this research is to investigate the levels of trust that users have in AI-generated personalized video news.

Technologies, such as Artificial Intelligence (AI) and robotics are emerging as a new way to improve services, readjusting and impacting all business industries and relationships among people (Loureiro et al., 2021; Makridakis, 2017; Mingotto et al., 2020). The hospitality industry is no exception to this (Mingotto et al., 2020) since a quick growth in the use of robots and AI in this industry has registered a turnover of 249 million U.S. dollars (International Federation of Robots, 2021). However, very few of the existing studies highlight the customers’ perspective and sentiments on service robots (Luo et al., 2021) or the robot-human interactions/ customer service experience (Choi et al., 2021). Thus, further studies in the enhancement of human well-being through transhumanistic technologies, close relationship marketing capabilities, and the evolution of the engagement process between humans and AI-enabled machines are necessary (Loureiro et al., 2021). This research intends to understand how different types of robots influence customers’ perception of the service provided. Therefore, the following research questions are proposed; Can humans develop feelings of identification with a service robot? Can the identification that customers perceive between themselves, and service robots be strong enough to influence the creation of a close relationship between both parties? What are the features of service robots that heighten customer well-being?

Virtual Reality is based on three key characteristics: immersion, interactivity (Boyd & Koles, 2018). Firstly, when exposed to a virtual environment, the individual experiences the sense of immersion or presence within that environment. The user feels like being there and escaping or becoming isolated from the real world. Beside immersion, VR provides a very dynamic environment (Loureiro et al., 2019), which is important to create consumer involvement. Hence, the current study explores antecedents of emotions and purchase intention in virtual supermarket setting

This research seeks to unveil how YouTube influencers and digital interaction can contribute to the process of customer-brand relationship and engagement. Based on in-depth interviews of female Youtubers devoted to the lifestyle categories, we aim to comprehend the engagement factors that influencers should rely on to promote engagement between their followers and the brands they advocate.

The study aims to analyze and compare how fashion brands of different categorization communicate in Instagram. Six global brands (Zara, H&M, Prada, Gucci, Nike, and Adidas) are chosen to be analyzed due to their different type/category of brands and their worldwide recognition. Netnography concept and method is used to conduct the data collection and data analyze during a period of time of six mouth. The results show that overall fast fashion brands (Zara and H&M) emerge to be more effective than other fashion categories in online communication. The Haute-de-couture brands (Prada and Gucci) reveal to be very similar in the way they communicate, demonstrating a good level of interactivity with consumers. The Sports brand (Nike and Adidas) have a low level of communication with the consumers and low number of photos and videos uploaded, which results in an average online communication of the brands in Instagram. This research highlights that to be successful in the online communication, fashion brands must be always updating photos and videos, they need to interact with consumers and make them feel a part of the brand, use celebrities to give more notoriety to the brand and be always present in the latest trends.

The evolution of the internet led to a shift in the business operations environment, giving rise to a plethora of challenges and opportunities for companies. Social networks have become attractive to companies due to their interactive nature, not only facilitating conversations with consumers, but also increasing the possibility of enhancing the online consumer brand engagement. Additionally, social networks and online brand communities increased consumers’ possibility of developing an active role in companies’ decision-making process, through the creation of user generated content, together with the opinion sharing and directly information exchange with brands and other internet users. The main objective of this research is to ascertain whether the active listening practice can contribute, in some way, to the improvement of the relationship maintained between consumers and brands. The current study suggests the adaptation of the active listening practice on the online field, as an attempt to enhance the communication strategies held by brands. Hence, this research seeks to demonstrate that this practice can improve the consumer-brand relationship through the development of two qualitative studies, as main approach, where the findings extracted in the first study will be used as inputs to the second one.

Customers’ opinions on social network platforms are known to influence peer behaviour (Bai, 2011; Eirinaki, Pisal, & Singh, 2012). Customers are also known to be more engaged in sharing their experiences by writing online reviews and recommendations that may be useful to others (Cantallops & Salvi, 2014; Tang & Guo, 2015; Xu & Li, 2016). Actually, user-generated content (UGC) on social network platforms has emerged as an important source for understanding and managing consumers’ expectations, particularly using automated and semi-automated knowledge extraction techniques from text such as text mining and sentiment analysis (Zhang, Zeng, Li, Wang, & Zuo, 2009). This research analyses dimensions of online customer engagement and associated concepts in customers’ reviews through (i) a global sentiment analysis using positive, neutral and negative sentiments and (ii) a topic-sentiment analysis to capture latent topics in online reviews. Furthermore, it examines what influences customers to contribute their online reviews, beyond the features of each focal company or brand. The research methodology is based on a text mining approach, using the MeaningCloud tool. The study focuses on Yelp.com reviews and includes a random sample of 15,000 unique reviews of restaurants, hotels and nightlife entertainment in eleven cities in the USA. An innovative customer engagement dictionary is created, based on previously validated scales using known dimensions of engagement, experience, emotions and brand advocacy, and extended using WordNet 2.1 lexical database. The research findings reveal a high impact of the engagement cognitive processing dimension and hedonic experience on customers’ review endeavour. The study results further indicate that customers seem to be more engaged in positively advocating a company/brand than the contrary. The findings will help social network managers to reinforce their platforms.