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Social customer relationship management (SCRM) is an evolving strategy gaining prominence in the hotel industry by cultivating new, improved relationships through engaging customers on social media (SM) platforms. Accordingly, this study aims to assess the effect of SCRM on customer service and customer loyalty (CL) in the hotel industry. This study also explores the moderating effect of COVID-19 (EC) on the relationship between (customer engagement [CE] and improved customer service [ICS]) and (CE and trust [TR]).

This study develops and tests the SCRM model using structural equation modelling on a sample size of 214 responses. The questionnaire was administered online to the customers of five preselected global hotel chains. The criteria for selecting the participants were that they must have tweeted from their Twitter handle by using # (hashtag) hotel name to resolve any customer service issues.

Results denote that CE significantly impacts ICS. CE was also found to exert a substantial effect on TR. The moderating EC was also found to be significant, but the effect was weak. Although the customers were extensively impacted by the pandemic and were initially hesitant to visit hotels, SCRM proved to be a powerful tool to gain back customer trust (CT) and develop CL by upsurging the shadows of COVID-19.

This study suggests that viable enforcement of the SCRM system can assist in real-time monitoring and tracking of customers' activities. This can develop a more profound connection with customers through CE which can boost the co-innovation process.

This study denotes a pioneer attempt to investigate the relationships between SCRM, CE, CT, ICS, CL and COVID-19 in the same framework in a SM context.

This paper aims to explore the role of blockchain (BCT) and Artificial Intelligence (AI) technologies in enhancing and incentivizing environmentally responsible, socially inclusive and economically viable tourism practices within the hospitality and tourism (H&T) industry.

This study is based on a critical reflection research approach that enables a synthesis of information derived from existing literature's insights and the authors' experiences and observations. By examining frameworks and theories in the literature, critical reflection also helps develop a more comprehensive understanding of the topic.

This study portrays how BCT could be used to track the sustainability credentials of tourism providers and how AI can optimize energy usage in hotels. This study depicts how adopting technology-driven sustainable practices in the H&T industry can increase profitability, improve reputation, compliance with regulations, efficiency and a better guest experience.

The study suggests targeted actions and policy frameworks that can be tailored to different stakeholder groups (hospitality businesses, policymakers and tourists) to overcome barriers and maximize the positive societal and environmental impacts of adopting BCT and AI for sustainable tourism.

The originality of this study lies in its ability to offer new perspectives and novel recommendations on the diverse uses of AI and BCT in the context of sustainable tourism. Furthermore, the study provides strategic and policy elements (Targeted actions and policy frameworks) for stakeholders to integrate sustainable tourism practices using BCT and AI successfully. This study differs from earlier review studies that primarily focused on adopting emerging technologies and ignoring the sustainability angle in the use of technology.

With over-the-top (OTT) streaming services rapidly transforming the media industry and saturating the market, the authors' study seeks to enrich the goal-directed behaviour model by exploring how perceived risks and descriptive norms influence OTT consumption.

Survey data from OTT subscribers were collected online to assess their risk behaviours. The 353 responses obtained were analysed with SmartPLS, validating the structural equation modelling (SEM) through structural and measurement model verification.

The authors' findings illustrate that descriptive norm, perceived behavioural control, as well as positive and negative anticipated emotion (NEM) and attitude, contribute positively to the desire to engage with OTT streaming services. Interestingly, the authors' study contradicts common assumptions, revealing that subjective norms do not significantly impact the propensity to utilise OTT services. This counterintuitive finding necessitates a reconsideration of prevalent theories and contributes to a nuanced understanding of OTT adoption determinants.

The data gathering for this study were conducted from the perspective of a single nation. Therefore, caution must be exercised when generalising this study's results.

The practical ramifications of this research are vast, providing OTT service providers and marketers with actionable insights to maximise user engagement and navigate perceived risks related to OTT service adoption and consumption.

This study's exploration of perceived risks and descriptive norms enhances the goal-directed behaviour model's breadth, facilitating a holistic comprehension of the constructs shaping OTT consumption behaviours. It would be the first attempt to combine perceptual, affective and behavioural factors and perceived risks to understand the user's predisposition to engage in OTT streaming services.

Research on food tourism has a significant impact on destination attractiveness. However, components interfacing food experiences and memory are under-researched topics in food tourism literature. Therefore, this study aims to present a framework based on the components of rememberable food experiences while travelling through the lens of the diffusion of sensory stimulation.

This study adopted a qualitative application of “Memory-Work”, a social constructionist archetype suggested for food tourism-related research. A survey was conducted, and the respondents were asked an open-ended question.

The analysis found the components instigating these food experiences: Peculiar food and drink experience, setting/geographical location, companions and social interactions, celebrating occasions and touristic components (e.g. serendipitous travel experience and food nostalgic memory). Predominantly, rememberable food tourism experiences are more explicit than memorable tourism experiences.

The components mentioned in this framework illustrate that various food-related experiences should be involved in destination marketing. Service providers could use these components to create unique destination stories.

This study is the first to present a newly developed framework for food tourism service providers that incorporates sensory impressions with food memories to explore the connection between food memories associated with a destination.

This study defines a three-angled research plan to intensify the knowledge and development undergoing in the retail sector. It proposes a theoretical framework of the customer journey to explain the customers' intent to adopt artificial intelligence (AI) and machine learning (ML) as a protective measure for interaction between the customer and the brand.

This study presents a research agenda from three-dimensional online search, ML and AI algorithms. This paper enhances the readers' understanding by reviewing the literature present in utilizing AI in the customer journey and presenting a theoretical framework.

Using AI tools like Chatbots, Recommenders, Virtual Assistance and Interactive Voice Recognition (IVR) helps create improved brand awareness, better customer relationships marketing and personalized product modification.

This study intends to identify a research plan based on investigating customer journey trends in today's changing times with AI incorporation. The research provides a novel model framework of the customer journey by directing customers into different stages and providing different touchpoints in each stage, all supported with AI and ML.

The hospitality industry experienced an unanticipated challenge from the COVID-19 pandemic. However, research in this area is scarce. Accordingly, this study aims to unfold a three-angled research agenda to intensify the knowledge advancement in the hospitality sector. It proposes a theoretical framework by extending the protection motivation theory (PMT) to explain the guest’s intent to adopt artificial intelligence (AI) and robotics as a protective measure in reaction to COVID-19.

The research is centered on outlining the pertinent literature on hospitality management practices and the guest’s transformed behavior during the current crisis. This study intends to identify a research agenda based on investigating hospitality service trends in today’s changing times.

The study sets out a research agenda that includes three dimensions as follows: AI and robotics, cleanliness and sanitation and health care and wellness. This study’s findings suggest that AI and robotics may bring out definite research directions at the connection of health crisis and hospitality management, taking into account the COVID-19 crisis.

The suggested research areas are anticipated to propel the knowledge base and help the hospitality industry retrieve the COVID-19 crisis through digital transformation. AI and robotics are at the cusp of invaluable advancement that can revive the hotels while re-establish guests’ confidence in safe hotel practices. The proposed research areas are likely to impart pragmatic lessons to the hospitality industry to fight against disruptive situations.

This study stands out to be pioneer research that incorporated AI and robotics to expand the PMT and highlights how behavioral choices during emergencies can bring technological revolution.