https://www.um.edu.mt/library/oar/handle/123456789/307 2026-10-10T18:00:17Z https://www.um.edu.mt/library/oar/handle/123456789/149670 Title: Digital transformation and AI-enabled accommodation platforms: Implications for destination marketing, governance and development Editors: Lehto, Xinran Abstract: This special issue seeks to advance understanding of how digital platforms and AI are reshaping destination marketing and management through their influence on accommodation systems. The guest editors invite contributions that reconceptualize accommodation from a traditional infrastructural requirement for tourism growth, into a strategic and digitally mediated component of destination marketing ecosystems, where platforms: (i) Curate and prioritize destination visibility through algorithms; (ii) Influence destination image formation via reviews, ratings and content; (iii) Shape demand distribution across locations and property types; and (iv) Mediate pricing and perceived value through AI-driven tools. Therefore, prospective contributions can examine the interplay between digital platforms, AI systems and destination stakeholders, including DMOs, local authorities, hotels, hosts and residents. It is imperative that all submissions include empirical testing based on multiple sources of data (e.g., mixed datasets, multi-level data, stakeholder perspectives, longitudinal evidence, and/or triangulated methods). Studies should move beyond descriptive analysis to offer theoretically grounded insights and meaningful implications for policy, governance and managerial practice. Interdisciplinary perspectives are welcome, provided that the study maintains a clear focus on destination-level outcomes and implications. Indicative themes Topics of interest include, but are not limited to: Digital intermediation and destination marketing • The role of accommodation platforms in shaping destination visibility and image • Algorithmic curation and its impact on destination branding and positioning • User-generated content, reviews and AI-generated content in destination representation • Platform-mediated storytelling and the construction of place narratives AI and the transformation of tourism demand: • AI-driven recommendation systems and their influence on destination choice • Dynamic pricing and perceived destination value • Personalization, segmentation and demand forecasting in platform ecosystems • The role of generative AI in marketing accommodation and destinations Destination governance and strategic management • The evolving role of DMOs in platform-dominated ecosystems • Governance challenges arising from platform power and data asymmetries • Regulatory responses to platform-mediated accommodation and AI systems • Public–private coordination in destination marketing and management Spatial dynamics and demand distribution • The redistribution of tourist flows through platform algorithms • Neighbourhood-level impacts and micro-destination development • Overtourism, undertourism and spatial imbalances linked to platform visibility Competition and destination competitiveness • Competitive dynamics between hotels and P2P accommodation in digital marketplaces • The influence of platform metrics (ratings, rankings) on destination competitiveness • The effects of market concentration and platform dominance on destination offerings Sustainability and destination development • Impacts of accommodation platforms on sustainable destination development • Community well-being, housing pressures and social carrying capacity • Infrastructure and environmental implications of platform-driven demand Stakeholders and power relations • Power asymmetries between platforms, DMOs, accommodation providers and regulators • Stakeholder collaboration and conflict in destination marketing ecosystems • The impact of data access and digital skills on stakeholder’s decisionmaking Future trajectories of destination systems • The integration of AI in destination marketing strategies • The future of hotels within platform-mediated ecosystems • Future scenarios for the evolution of destination management in the context of platform-based ecosystems. In sum, this special issue seeks to develop a system-level perspective that positions AI-enabled accommodation platforms as both market mechanisms (i.e., systems that match supply and demand, set prices and govern transactions) as well as promotional channels. It calls for research that critically examines how these digitally mediated systems are reshaping destination visibility, competitiveness and governance. Moreover, prospective contributions can provide insights into how stakeholders can avail themselves of these smart technologies to ensure sustainable, inclusive and strategically coordinated destination development. 2026-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/148948 Title: Responsible food production and sustainable consumptionin Italian restaurants : a pathway to SDG 12 Authors: Basilico, Paolo; Camilleri, Mark Anthony; D'Adamo, Idiano; Di Santo, Federica; Gastaldi, Massimo; Maccalini, Anna Chiara Abstract: The pursuit of sustainable business approaches has gained increasing importance in the past years. Entrepreneurs are increasingly reassessing their strategies and operations in line with corporate social and environmental responsibility principles. Restaurants are well positioned to play a key role as agents for positive change, as they can offer nutritious, high-quality food. At the same time, they could minimise food loss and waste. They may even contribute to local economic development through the promotion of short supply chains among other responsible practices. This study examines consumer behaviours and perceptions of responsible food production and sustainable consumption within the Italian hospitality industry. It focuses on factors that influence the consumers' dining choices and their willingness to engage in environmentally responsible actions. The research adopts a social analysis perspective based on data collected from an online survey of 609 respondents in Italy. The findings indicate that consumer decisions are primarily motivated by hedonic factors, especially taste and the quality of ingredients. Awareness of sustainable practices tends to increase by time and is more evidenced among women, who also show a greater propensity to adjust their habits in more sustainable ways. Despite a general interest in sustainability and a willingness to pay a premium for certified establishments, there remains a limited understanding of restaurants' environmental impacts and low familiarity with sustainability certifications. These research implications highlight considerable opportunities for growth. This contribution postulates that enhanced communication strategies and targeted awareness campaigns could improve consumer knowledge, foster more informed choices, and encourage active support for sustainability initiatives in the restaurant sector, in line with Sustainable Development Goal (SDG) 12. 2026-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/147894 Title: Theoretical perspectives on generative and agentic AI adoption in service environments Abstract: The editors of this special issue particularly welcome submissions that explicitly draw upon, refine or combine well-established theories that have been influential in service and technology research, including (but not limited to) the following ones (as discussed in Camilleri & Troise, 2023): Anthropomorphism theory (e.g., human-likeness, emotional attachment and/or moral attributions to AI). Affordance theory (perceived action possibilities enabled or constrained by GenAI and/or Agentic AI interfaces). Assemblage theory (AI as part of dynamic socio-technical service systems). Behavioral reasoning theory (reasons for and against AI use in service encounters). Cognitive fit theory (task–AI alignment and decision quality). Commitment–consistency theory (habit formation and sustained AI use). Communication accommodation theory (linguistic and stylistic adaptation in human–AI interaction). Contingency theory (contextual conditions that can have an impact on AI effectiveness). Diffusion of innovations theory (organizational and market-level adoption trajectories). Expectancy and expectation-violation theories (surprise, delight, discomfort or distrust in AI services). Flow theory in computer-mediated environments (engagement, creativity and immersion). Functionalist theory of emotion (affective responses to AI-enabled services). Human–computer interaction / human–machine communication theories. Information systems success model (service quality, satisfaction and net benefits of AI). Politeness theory (face-management and social norms in AI communication). Self-determination theory (autonomy, competence and relatedness in AI use). Situational theories of problem-solving and publics. Social cognitive theory (learning AI use through observation and social influence). Social presence and social response theories. Structural role theory (AI as role-performing service actors). Technology acceptance model (TAM) and unified theory of acceptance and use of technology (UTAUT). Theory of conversation. Theory of planned behavior (TPB) and its related theory of reasoned action (TRA). Trust–commitment theory. Uses and gratifications theory. Submissions that integrate multiple perspectives, compare existing conceptual frameworks and develop new theoretical models specific to GenAI and Agentic AI in services are especially encouraged for this special issue. Illustrative research questions may include (but are not limited to): How and to what extent do customers and employees anthropomorphize Generative versus Agentic AI in service encounters? Which GenAI and Agentic AI affordances drive value co-creation, trust, reliance or resistance in services? How do emotional cues, social presence and politeness strategies influence engagement with AI-driven service agents? Under what contingencies does AI adoption enhance or undermine service quality, relationships and well-being? How do expectations and expectation violation aspects influence satisfaction and continued use of AI-enabled services? How do organizations implement Agentic AI within broader service systems? What ethical, relational, psychological and accountability tensions emerge from sustained human–AI interactions, particularly when AI acts autonomously? The special issue welcomes conceptual, qualitative, quantitative, experimental or mixed-methods approaches, provided that the contributing authors demonstrate strong theoretical grounding and relevance to the underlying objectives of this journal. 2026-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/147123 Title: Cracking the black box : the quest to understand the machines that run our lives Abstract: Artificial intelligence (AI) is now part of everyday life. It recommends what we watch online, helps banks approve loans, assists doctors in hospitals and even acts as a digital gatekeeper for who gets hired. Many people enjoy the convenience of these systems, yet, few truly understand how they work. That is where the ‘Explainable AI’ notion comes in. Essentially, it is a growing movement that is aimed at increasing AI transparency, to earn user trust. For years, AI systems were treated like mysterious ‘black boxes’. You feed information into them and they produce an answer. However, at times, it proves hard to clearly explain how they have reached their conclusions. Even the engineers who have built these systems sometimes struggle to fully understand the internal reasoning behind complex AI models. This becomes worrying when AI is used in areas such as healthcare, education, banking, policing or public services. Imagine applying for a loan and being rejected by an AI system without any explanation. Alternatively, consider a hospital using AI to help doctors diagnose patients without anyone being able to explain why the system recommended a particular treatment. In such situations, people may naturally ask: Why did the machine decide this? Explainable AI (XAI) tries to answer that question. 2026-05-30T00:00:00Z