https://www.um.edu.mt/library/oar/handle/123456789/308 Wed, 15 Jul 2026 10:14:25 GMT 2026-07-15T10:14:25Z 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. Thu, 01 Jan 2026 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/147894 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. Sat, 30 May 2026 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/147123 2026-05-30T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/146598 Title: User acceptance of edutainment mobile applications : advancing an experiential design-engagement model (EDEM) Authors: Camilleri, Mark Anthony; Camilleri, Adriana Caterina Abstract: Mobile users are increasingly engaging with edutainment gaming apps in formal and informal contexts. They are drawn to them for their educational and entertainment aspects. In this light, this study validates the Theory of Planned Behavior’s key measures and integrates them with game narratives and game aesthetics constructs to better understand the extent to which psychological and gaming design factors are predicting the individuals’ intentions to play with these learning technologies. The data were gathered through a survey questionnaire from one hundred eighty-six (n = 186) respondents, who were higher education students in a Southern European university. The quantitative findings analyzed through partial least squares (PLS) revealed that the gamers appreciate the edutainment platforms’ audiovisual effects as well as their storylines and narratives. The results reported that mobile users enjoyed playing with entertaining learning apps. Respondents indicated that they were willing to continue their gameplay in the future. In conclusion, this contribution raises awareness on the important synergies between gaming design elements and behavioral dimensions driving the users’ engagement with edutainment apps. It puts forward a robust theoretical framework that is empirically-grounded. Sat, 16 May 2026 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/146598 2026-05-16T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/146103 Title: Opening the black box : operational principles, tools and frameworks that advance explainable artificial intelligence (XAI) models Authors: Camilleri, Mark Anthony Abstract: As artificial intelligence (AI) models are increasingly becoming permeated across various domains, there are instances where they are generating hallucinations, misinformation and erroneous outputs. Various stakeholders, particularly the regulatory ones, are encouraging the developers of machine learning (ML) systems to clarify or justify their models' decisions, actions or predictions in a way that is understandable to their users. In this light, this article raises awareness on Explainable Artificial Intelligence (XAI) principles that are intended to increase transparency, accountability and fairness about the modus operandi of machine learning algorithms. A systematic review of the extant literature identifies key tools, frameworks and best practices that enhance the interpretability of AI models, including open-source techniques like SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), among others. The synthesis of the findings also shed light on XAI challenges and limitations of black-box models. This contribution advances a conceptual framework for the responsible implementation of XAI and offers practical guidelines that promote the interpretability of AI systems, whilst addressing their opacity, as well as their biased outcomes. It puts forward theoretical and managerial implications as well as future research avenues. Thu, 01 Jan 2026 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/146103 2026-01-01T00:00:00Z