https://www.um.edu.mt/library/oar/handle/123456789/308 2026-06-04T01:04:11Z 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. 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. 2026-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/146090 Title: Drivers of managements’ behaviour intention and expectation to adopt blockchain technology Authors: Chohen, R.; Konietzny, Jirka; Caruana, Albert Abstract: Blockchain technology offers significant potential for business applications through improved information sharing and decentralized validation. However, many managers remain reluctant to adopt digital trade via blockchain. This research proposes a model to understand the drivers of managers' behavioral intentions and expectations regarding blockchain adoption. By integrating interorganizational factors (competitive pressure and trading partner readiness) and intraorganizational factors (individual technological readiness, interdepartmental conflict/connectedness, and organizational structure), the study differentiates between rational "Type 2" thinking (intention) and more intuitive "Type 1" thinking (expectation). The model suggests that while profit-driven rationales dominate management decisions, technological readiness and organizational context significantly influence the likelihood of adoption. 2022-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/145905 Title: Consumer endorsements in the business-to-business (B2B) context : adapting consumer theories and proposing a research agenda Authors: Konietzny, Jirka; Caruana, Albert Abstract: This conceptual paper addresses the gap in Business-to-Business (B2B) literature regarding how endorsements from high-status "celebrity organizations" influence organizational buying behavior. While traditional B2C theories focus on individual celebrities, this framework explores the multi-person Decision Making Unit (DMU) and identifies three distinct influence mechanisms: status-based signals for risk reduction, cognitive fit for perceived competence, and B2B parasocial relationships that bolster champion confidence. The paper also considers the moderating role of Power Distance Belief (PDB) in how these endorsements are processed. 2026-01-01T00:00:00Z