📣[Call for papers] on AI-enabled accommodation platforms and destination governance!
Accommodation platforms may no longer be simply considered as places where travellers book a room, bearing in mind that algorithms rank properties; AI recommends destinations; dynamic pricing affects perceived value; reviews and generated content influence destination images; AND platforms redirect tourist flows across cities and neighbourhoods.
I am pleased to invite submissions to our Special Issue in the Journal of Destination Marketing & Management [CS 13.5; IF 8]:
“Digital Transformation and AI-Enabled Accommodation Platforms: Implications for Destination Marketing, Governance and Development”.
My colleagues and I welcome theoretically grounded, empirically robust research that examines how AI, algorithms and accommodation platforms are reconfiguring destination ecosystems.
🔎 Plausible research areas include: • Generative AI and destination marketing • AI-powered recommendation systems • Algorithmic visibility and destination branding • Dynamic pricing and demand forecasting • Reviews, ratings and AI-generated content • Hotels versus P2P accommodation platforms • Overtourism, undertourism and spatial redistribution • Platform power, regulation and destination governance • Sustainable and inclusive destination development
💡 Here are a few questions worth investigating: 🔴 What if AI decides which destinations tourists see and visit? 🔴 Who controls destination visibility in the age of AI? 🔴 Who shapes a destination’s image today: marketers, travellers or algorithms? 🔴 Could algorithms determine which destinations become more competitive? 🔴 When AI recommends where to stay, does it also influence where we travel? 🔴 Are booking platforms just selling rooms, or are they transforming destinations? 🔴 What happens to destination governance when platforms manage data and direct tourist demand?
📅 Submissions open: 1 October 2026 ⏳ Deadline: 31st August 2028
We particularly encourage mixed-method, longitudinal and multi-level empirical research.
The special issue will focus on issues surrounding strategic governance and ecosystem management in AI-enabled digital marketplaces. Digital marketplaces have become central infrastructures through which firms design, implement, and revise competitive strategies. Across industries, platforms such as Amazon, Alibaba, and Booking.com increasingly shape how firms access markets, coordinate interactions, and capture value. These marketplaces are no longer simply transactional venues: they operate as strategic environments in which rules, data, and intermediation structures redefine the competitive landscape and reshape ecosystem-level outcomes (Bourai et al., 2024; Loonam & O’Regan, 2022). A key reason for the strategic relevance of marketplaces is that they embed governance directly into market processes. Pricing rules, commission systems, access regimes, ranking and recommendation logics, and enforcement routines influence participation incentives and competitive conduct. Marketplace governance thus becomes a strategic design issue rather than an operational detail. This aligns with research conceptualizing platforms as hybrid governance systems that blend market coordination, hierarchical control, and network interdependence, where governance mechanisms continuously evolve in response to ecosystem dynamics (Cuypers et al., 2021; McIntyre et al., 2020). This governance perspective also brings boundary decisions and distribution strategy back to the center of strategic management. For vendors—particularly SMEs—marketplace participation is a fundamental strategic choice affecting dependence, autonomy, capability development, and long-term positioning. Firms increasingly experiment with direct, indirect, and hybrid routes to market, integrating proprietary infrastructures with third-party marketplaces and offline channels. Recent evidence suggests that SME performance depends on how platform adoption interacts with commitment and organizational routines (Ballerini et al., 2023). These choices reflect strategic trade-offs between control and efficiency and are shaped by how firms design and govern multichannel systems (Homburg et al., 2020). Marketplace governance is also deeply intertwined with organizational learning, alliances, and transformation processes. Digital strategy effectiveness depends on cultural and organizational alignment (Cyfert et al., 2025), while dynamic capabilities shape firms’ ability to adapt and transform in digital contexts (Ellström et al., 2022). In turbulent environments, SMEs rely on agility and transformation capabilities to remain competitive (Troise et al., 2022). Moreover, alliances and tacit learning can enable recovery and resilience under constrained governance conditions, particularly in emerging markets (Aditchere et al., 2025). These dynamics highlight that governance is not only imposed by platforms but also shaped by how ecosystem actors learn, adapt, and coordinate over time (Öberg, 2024). At the same time, governance complexity is increasingly amplified by AI-enabled systems embedded in marketplace architectures. Algorithmic ranking, automated monitoring, fraud detection, dynamic pricing, and AI-supported customer management systems increasingly mediate competitive interactions and decision-making. Recent research suggests that generative AI can enhance market effectiveness through CRM-related applications, particularly under technological turbulence and with strong top management support (Kumar et al., 2025). Yet adoption is shaped by managerial cognition and organizational culture: technophobia, self-regulated learning, and open cultures influence managerial intentions to adopt generative AI (Zhao et al., 2025). Marketplace-based business models may also create competitive advantage by reducing time-related frictions for ecosystem participants, reinforcing the strategic role of platform architectures in enabling efficiency and coordination (Santoro et al., 2025). In practice, major marketplaces have already introduced generative AI tools for sellers and advertising optimization (e.g., Amazon’s AI listing tools and ad automation), illustrating how AI is increasingly integrated into governance and ecosystem management logics. These developments raise not only managerial and strategic issues but also broader societal and ethical questions. AI-enabled governance may increase efficiency and scalability, yet it may also intensify opacity, reinforce asymmetries, and raise concerns around accountability, contestability, and fairness. Understanding how governance mechanisms evolve under AI-enabled coordination therefore represents a timely and consequential research agenda. In addition, digital marketplace configurations may shape firm growth and internationalization trajectories in ways that depend on the strategic balance between platform reach and strategic autonomy (Ballerini et al., 2024).
We welcome submissions to this special issue. The special issue seeks a mix of theoretical, conceptual, and empirical cases and is open to various methods (e.g., qualitative case studies, quantitative analysis of platform data, or formal modeling, etc.). It welcomes theoretically grounded and empirically rich contributions that advance strategy and management research on governance and ecosystem management in AI-enabled digital marketplaces. We encourage conceptual and theory-building contributions, qualitative and process-based studies, large-scale empirical analyses (including digital trace and platform data), mixed-method research, and comparative cross-country work. In line with emerging methodological developments, we also explicitly welcome innovative approaches such as agent-based modeling, longitudinal ecosystem mapping, and multi-level designs that connect governance mechanisms to ecosystem-level outcomes. Topics covered include (but are not limited to):
1. Designing and revising governance architectures in digital marketplaces
How governance systems are deliberately designed, experimented with, and revised over time
Strategic trade-offs between openness, control, and scalability across platform life cycles
Prof. Camilleri is supported by internationally recognised experts in services marketing and management, comprising Prof. Levent Altinay, Editor of The Services Industries Journal; Prof. Sang M. Lee, Editor of Service Business and Prof. Cheng Lu (Charles) Wang, Editor of the Journal of Research in Interactive Marketing.
The full text of the Call for Papers is presented below. It provides background to the special issue. It outlines the theoretical perspectives that prospective contributors may adopt. It highlights illustrative research topics and also includes the submission guidelines for authors.
Introduction
Generative Artificial Intelligence (GenAI) and Agentic Artificial Intelligence (Agentic AI) are transforming how services are designed, delivered, experienced and led. While GenAI refers to systems, such as large language models (LLMs), that produce content in response to human prompts; Agentic AI technologies may be considered as active agents that can implement tasks (rather than merely functioning as passive generators) (Acharya et al., 2025). The latter can monitor situations, allocate resources, initiate and manage processes as well as co-ordinate multiple activities (Gonzalez et al., 2026). Hence, Agentic AI algorithms and their governance affect service outcomes.
Generative AI capabilities often constitute the communicative and cognitive foundations of Agentic AI. In other words, many Agentic AI systems rely on GenAI models to reason, communicate and interact. Together, these AI technologies challenge conventional assumptions about agency, control, responsibility and value creation in service environments (Ferraro et al., 2024; Wirtz & Stock-Homburg, 2025). Unlike earlier forms of automation and analytics, these AI systems can engage in social interactions, reason in a contextual manner and may dynamically adapt to changing situations. As such, they raise profound theoretical questions about anthropomorphism, social presence, trust, autonomy, creativity, emotion, accountability, responsibility and moral agency (Banh & Strobel, 2023; Ng et al., 2026; Sun et al., 2026).
These capabilities indicate that Generative and Agentic AI represent more than incremental advances in automated technologies. They introduce different forms of interaction and agency that cannot be fully explained by utility-driven adoption frameworks (Camilleri, 2024).
Consequently, there is a growing need for theory-driven and conceptually rigorous research that explains how, why and under what conditions Generative and Agentic AI are deployed, adapted, governed, or even resisted in service environments.
This special issue seeks to advance services marketing research by encouraging scholars to utilise, extend, integrate or critically evaluate existing theories to investigate user engagement with Generative and Agentic AI across diverse service settings. In this light, the guest editorial team particularly welcomes submissions that move beyond descriptive accounts. Prospective contributions are expected to offer strong theoretical explanations of AI acceptance and usage in services.
Theoretical perspectives
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).
Behavioural 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 (organisational 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 behaviour (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 anthropomorphise 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 organisations 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.
List of topic areas
Theoretical perspectives on Generative and Agentic AI adoption in service environments.
Comparative or multi-theoretical frameworks for studying human-AI interaction in services.
Anthropomorphism, social presence and human-AI relationships.
Perceived affordances, interface design and service experiences.
Emotions, expectations and psychological responses to AI.
Adoption, acceptance and continued use of AI in services.
Trust, ethics, accountability and relational governance.
AI as a service actor within socio-technical systems.
Contextual and contingency-based perspectives.
Value co-creation, value co-destruction and service outcomes.
Organisational, strategic and policy implications of Generative and Agentic AI in services.
Submissions Information
Submissions are made using ScholarOne Manuscripts. Registration and access are available here.
Author guidelines must be strictly followed which are available online.
Authors should select (from the drop-down menu) the special issue title at the appropriate step in the submission process, i.e. in response to ““Please select the issue you are submitting to”.
Submitted articles must not have been previously published, nor should they be under consideration for publication anywhere else, while under review for this journal.
Key deadlines
Opening date for manuscripts submissions: 23 June 2026
Closing date for manuscripts submission: 26 February 2027
In January 2026, Professor Camilleri launched another call for papers for a special issue focused on ethical AI. The latter one, entitled: ‘Ethical implications of artificial intelligence (AI) and automation in service industries’, will be published by The Service Industries Journal. In this case, the deadline for submission will be on 31 January 2027.
Educational technology has evolved far beyond digital textbooks and online quizzes. Today’s learners are increasingly engaging with edutainment mobile applications that combine learning with leisure activities through storytelling, game mechanics and immersive audiovisual experiences. These technologies are transforming how people learn both inside and outside of the classroom, from language-learning apps to quiz-based platforms and via interactive games.
Moving beyond traditional technology adoption models
For many years, researchers have relied on models such as the Theory of Planned Behaviour (TPB) and the Technology Acceptance Model (TAM) to understand why people adopt digital technologies. These frameworks typically focus on factors such as attitudes, social influences, perceived usefulness and ease of use.
While these theories have usually proven to be valuable in some contexts, they do not fully capture the unique characteristics of educational games. Unlike many traditional educational technologies, edutainment applications blend learning and entertainment. Their users are influenced by practical considerations and by the enjoyment and quality of the experience itself.
To address this knowledge gap in the extant academic literature, the researchers of this study have developed the Experiential Design-Engagement Model, a framework that combines established behavioural factors with two important dimensions of game design, namely, game narratives and game aesthetics.
Game narratives refer to the stories, characters, themes and progression that create meaningful and engaging experiences for players. Game aesthetics, on the other hand, encompass the visual design, graphics, animations, sound effects and other sensory elements that enhance the overall gaming experience. Together, these factors provide a more comprehensive understanding of what drives users to adopt and what triggers them to continue engaging with edutainment games.
The result is a more comprehensive explanation of why learners choose to engage with certain educational gaming applications.
What did the study investigate?
Drawing on responses from 186 university students with experience in edutainment applications, this research explored the factors that are influencing the players’ ongoing engagement with educational games. Specifically, it examined the roles of game narratives, aesthetics, attitudes, social influences and perceived behavioural control. The results highlight the importance of experiential designs. They show that well-crafted gaming experiences can significantly enhance the learners’ willingness to keep using edutainment applications.
They report that the learners’ attitudes towards edutainment apps are the strongest predictor of their intention to continue using them. In simple terms, students who find these games to be enjoyable, tend to develop an emotional connection with them. As a result, they are more likely to return to these games and to engage with them on a regular basis.
This finding suggests that sustained engagement is not solely driven by functionality and/or by convenience. Rather, positive feelings such as enjoyment, excitement, satisfaction and emotional connection play a decisive role in determining whether learners return to an educational app or not.
For educators and developers, this means that creating positive learning experiences should be a central objective. Interestingly, design matters more than they realise. One of the most significant contributions of the study is that it confirmed that game design features have a powerful influence on user attitudes.
This research found that game aesthetics exerted one of the strongest effects on learner attitudes. Participants clearly appreciated high-quality audiovisual experiences, immersive graphics, expressive characters and engaging soundscapes.
These design elements do much more than make a game look attractive. They create emotional engagement, increase immersion and enhance the overall learning experience.
Hence, educational technologies should not treat design as an afterthought. Well-crafted aesthetics can significantly influence the learners’ willingness to engage with educational content.
Game narratives also played a significant role in shaping positive attitudes. Strong stories help learners connect emotionally with educational content. Notwithstanding, educational games can transform abstract concepts into engaging activities, by embedding learning objectives within meaningful adventures, challenges and character-driven experiences.
The study confirms that compelling narratives make educational experiences more enjoyable and memorable. Learners are more likely to remain engaged when they feel that they are part of a meaningful journey rather than by simply completing isolated tasks.
Moreover, this research also examined two established factors drawn from the Theory of Planned Behavior, including, perceived behavioural control (in plain words, this construct measures the ease of use of the app) and subjective norms (this is related to the influence of friends, family, peers, educators, et cetera, on the individuals’ perceptions, beliefs and interpretations of the world around them).
In this case, neither perceived behavioural control nor the subjective norms were having a direct impact on the learners’ intentions to continue using edutainment apps. However, both had important indirect effects, as the ease of use as well as social encouragement first shaped the learners’ attitudes. Afterwards, the latter factor (attitudes) had a significant effect on the students’ intentions to engage with edutainment games.
This finding emphasises that: making a game easy to use or receiving recommendations from other gamers are not enough on their own. The students must also develop positive emotional responses toward their gameplay experience. In other words, technical usability and social endorsement are valuable, but they only become effective when they can contribute to create favourable attitudes towards the game.
Why the Experiential Design-Engagement Model matters?
One of the strongest aspects of this research is the robustness of the proposed Experiential Design-Engagement Model. The model explained: 64.5% of the variance in learner attitudes as well as 43.2% of the variance in behavioural intentions. These results are substantial explanatory levels for behavioural research. They clearly demonstrate the model’s strong predictive power.
Arguably, Experiential Design-Engagement Model provides a practical bridge between educational technology research and game design theory. Rather than viewing educational games as learning tools, this model recognises them as experiential products. This research indicates that students are emotionally engaged with edutainment apps. They appreciate their gaming design elements, in terms of their aesthetics, narratives and storytelling, among other factors.
This integrated perspective offers a richer understanding of learner engagement than traditional technology acceptance models alone.
Implications for media and education
The findings carry important implications for educational institutions, developers and policymakers.
For developers, the message is clear. They need to invest in immersive designs, compelling storytelling and high-quality audiovisual experiences, as this research reported that these features directly contribute to learner engagement and continued usage.
For educators, the study suggests that selecting educational apps should involve evaluating both pedagogical value and experiential quality. Even the most educationally sound platform may struggle to sustain engagement if it lacks emotional appeal.
For policymakers, the research proves that successful educational technologies require more than content delivery. Therefore, funding and evaluation frameworks ought to encourage the development of engaging, evidence-based learning experiences that combine educational effectiveness with strong user-centred designs.
A new direction for educational gaming research
The study’s most important contribution is its recognition that learner engagement emerges from the interaction between behavioural psychology and experiential design.
This contribution’s Experiential Design-Engagement Model offers a powerful new framework for understanding why individuals (including students) adopt and continue using educational games. This framework provides valuable guidance for the next generation of edutainment applications by raising awareness of gaming narratives, aesthetics, the players’ attitudes and their emotional engagement.
As educational technologies continue to evolve, this research delivers a clear message: The most effective learning games do more than simply impart knowledge. They captivate learners, spark their curiosity and foster meaningful emotional connections. They transform learning into an insightful experience that is not only educational, but also engaging, enjoyable and memorable.
Ultimately, the true measure of success lies in creating learning experiences that learners willingly return to, not because they have to, but because they want to.
Suggested citation: Camilleri, M.A. & Camilleri, A.C. (2026). User Acceptance of Edutainment Mobile Applications: Advancing an Experiential Design-Engagement Model (EDEM). Technology, Knowledge and Learning, https://doi.org/10.1007/s10758-026-09991-6
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.
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.
The basic idea is simple. AI systems should not only give answers; they should also explain how they arrived at them. Users deserve understandable reasons behind decisions that affect their lives. Transparency builds trust. Without it, people may fear that AI is unfair, biased or unreliable.
Notwithstanding, the world around us never stands still. Economies shift, behaviours evolve and social conditions change. As a result, the AI models that are trained on old data may become inaccurate over time. Hence, an AI system that worked well two years ago may suddenly start making poor or unfair decisions today. Experts call this “data drift” or “concept drift”.
This is why explainability matters so much. When AI systems can be examined and understood, it is much easier to detect and correct their errors and biases.
Researchers and technology companies are already developing tools to make AI more understandable. Some of these diagnostic tools have unusual names such as SHAP and LIME. Despite the technical labels, their purpose is quite straightforward: These interpretability frameworks can help identify which factors have influenced an AI decision the most.
For example, if an AI system denies someone a bank loan, these tools can show whether income, employment history or debt level has played the biggest role in the decision. This allows humans to review whether the outcome was fair and reasonable.
In this day and age, explainable AI has moved beyond the lab; it is now a concern for everyone, not just for tech experts. Regulators, governments and businesses are demanding for more transparency. In Europe, the new AI Act and existing privacy laws such as GDPR are pushing organisations to become more accountable for how AI systems operate.
There is also growing recognition that humans must remain involved in important decisions. Experts often refer to this as the “human-in-the-loop” approach. In simple terms, AI is meant to support human judgement. It should not replace it. A doctor, teacher, judge or manager should still be able to question and override an AI recommendation when necessary.
This balance is essential because AI systems are powerful, but they are not infallible. They can make mistakes, misunderstand situations, hallucinate or fail to recognise unique human circumstances.
We simply cannot afford to trust algorithms blindly. This is where explainable AI steps in. It helps ordinary users feel more confident about the technology they use every day. When people understand how a system works, they are far more likely to accept it. Thus, transparency will replace fear and confusion.
However, the challenge is that there is often a trade-off between power and simplicity. The most advanced AI systems, including modern generative AI tools, are often the hardest to explain. Simpler systems are easier to understand but may not perform as well. Therefore, researchers are striving in their endeavours to find the right balance between accuracy and transparency.
Arguably, the future of AI hinges on trust. Society is unlikely to fully embrace technologies that appear secretive or uncontrollable. Businesses and governments must therefore ensure that AI systems are fair, explainable and aligned with human values.
Debatably, explainable AI is more than a technical upgrade. It is a moral safeguard. It ensures that humans don’t get sidelined as machines become smarter.
In this new information era, explainable AI isn’t just a technological upgrade; it’s a moral boundary. It ensures that as machines get smarter, humans don’t get left in the dark. In a world shaped by intelligent machines, we must hold on to one simple rule: if an algorithm makes a choice that changes your life, you have every right to know how it reached its conclusion, why that decision was made, where the data came from and when the logic was applied.
📍It explains key concepts related to XAI research.
📍It provides clear insights into widely used techniques like SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), among others.
📍It presents a comparison matrix of XAI tools. It specifies their key metrics, strengths, weaknesses/limitations and domain fit.
📍It puts forward a conceptual framework to support responsible AI implementation.
📍It provides practical, actionable guidance for developers of AI solutions, as well as for professionals, who are responsible for managing data-driven strategies and governance policies.
📍It serves as a valuable resource for those aiming to move beyond black-box reliance toward more informed, responsible and accountable AI oversight.
📍It outlines future research directions related to XAI and discusses on their potential impact.
Suggested Citation: Camilleri, M.A. (2026). Opening the black box: Operational principles, tools and frameworks that advance explainable artificial intelligence (XAI) models, Technological Forecasting and Social Change, https://doi.org/10.1016/j.techfore.2026.124710
Mark Anthony Camilleri is an Associate Editor of Bus. Strat. & the Environ. of the Int. J. of Hosp. Mgt.| Fulbrighter| Listed among top 2% of scientists (Elsevier)| Expert Reviewer for research councils| Principal Investigator| Statistician| PhD Mentor
📌 ICETT2025’s conference proceedings were published through the Institute of Electrical and Electronics Engineers (IEEE). This underlines the international standing and scholarly credibility of this conference.
📌 All accepted papers will be peer-reviewed, presented during the conference and published via ICETT’s 2026 Conference Proceedings. They will be indexed by EI Compendex and Scopus, among other recognised academic databases.
Conference topics include (but are not limited to):
E-learning and online learning
Game-based learning
Learning analytics and education big data
MOOCs (Massive Open Online Courses)
Mobile & ubiquitous learning
Online platforms and environments
Open educational resources
Podcasting and broadcasting
Social media for teaching and learning
Virtual reality for teaching and learning
The papers’ submission deadline is the 20th of December, 2025.
Erbatax (14) mill-membri akkademiċi ta’ l-Universita’ ta’ Malta, li jinkludu lil Daphne Attard, Fakultà tax-Xjenza; Everaldo Attard, Istitut tax-Xjenzi Rurali; Godfrey Baldacchino, Fakultà ta’ l-Arti; Jean Calleja-Agius, Fakultà tal-Mediċina u s-Servizzi Kirurġiċi; Mark Anthony Camilleri, Fakultà tal-Media u x-Xjenza ta’ l-Għarfien; Albert R. Caruana, Fakultà tal-Media u x-Xjenza ta’ l-Għarfien; Sarah Cuschieri, Fakultà tal- Mediċina u s-Servizzi Kirurġiċi; Michael Galea, Fakultà tal-Inġinerija; Ruben Gatt, Fakultà tax-Xjenza; Joseph N. Grima, Fakultà tax-Xjenza; Jackson Levi-Said, Istitut tax- Xjenzi tal-Ispazju u l-Astronomija; Peter Mayo, Fakultà tal-Edukazzjoni; Eleanor M.L. Scerri, Fakultà ta’ l-Arti; u Brendon P. Scicluna, Fakultà tax-Xjenzi tas-Saħħa (dawn l- ismijiet huma mqassmin skont l-ordni alfabetiku), ġew inklużi f’lista magħrufa bħala l- klassifika tal-aqwa 2% riċerkaturi fid-dinja, li hi ikkompilata mill-Universita’ ta’ Stanford, b’kollaborazzjoni ma’ publikatur internazzjonali ta’ kotba u rivisti akkademiċi, Elsevier. Dawn l-akkademiċi ġew rikonoxxuti għall-għadd ta’ ċitazzjonijiet li rċevew il- pubblikazzjonijiet tagħhom, matul is-sena 2024.
Barra minn hekk, disgha (9) kollegi Maltin iddistingwew ruħhom ghal publikazzjonijiet akkademiċi tul il-karriera tagħhom. Dawn ta’ l-aħħar jinkludu lil:
Professur Mark A. Camilleri (c-score 3.7352); Professur Albert R. Caruana (c-score 3.6561); Professur Godfrey Baldacchino (c-score 3.6299); Professur Sarah Cuschieri (c-score 3.5770); Professur Joseph N. Grima (c-score 3.3261); Professur Donia R. Baldacchino (c-score 3.0595); Professur Norman Poh (c-score 2.9874); Professur Michael Galea (c-score 2.7935); u Professur Antonios Liapis (c-score 2.7905).
Din l-aħbar tenfasizza l-impenn ta’ l-Università ta’ Malta sabiex tikseb eċċellenza fil- qasam tar-riċerka u l-innovazzjoni, f’dixxiplini varji, li jinkludu n-negozju u l-immaniġġjar, l-inġinerija, il-mediċina u x-xjenzi tas-saħħa, kif ukoll fix xjenzi umanistici u dawk soċjali. Il-kontributur ta’ dan l-istudju globali huwa l-Professur John P.A. Ioannidis mill-Università ta’ Stanford. L-għażla tiegħu hija “ibbażata fuq l-aqwa 100,000 xjenzati skont il-punteġġ hekk imsejjah ‘c-score’ jew fuq rank percentwali ta’ 2% jew aktar fl-oqsma rispettivi tagħhom”. L-indikaturi taċ-ċitazzjonijiet u l-metodoloġija tal-klassifiki tiegħu jinsabu f’ dan il-portal: https://elsevier.digitalcommonsdata.com/datasets/btchxktzyw/8
Ċitazzjoni suġġerita: Ioannidis, John P.A. (2025), “Updated science-wide author databases of standardized citation indicators”, Elsevier Data Repository, V8, doi: 10.17632/btchxktzyw.8
University of Malta has recently appraised its academic members of staff, who were listed among the world’s top 2% scientists, in Elsevier-Stanford University’s 2025 ranking: https://lnkd.in/dQmrPqz5
In this media release, the University of Malta has identified those who were recognized for their “career-long” (lifetime) high-impact publications, including:
Professor Camilleri, Mark A. (c-score 3.7352); Professor Caruana, Albert R. (c-score 3.6561); Professor Baldacchino, Godfrey (c-score 3.6299); Professor Cuschieri, Sarah (c-score 3.5770); Professor Grima, Joseph N. (c-score 3.3261); Professor Baldacchino, Donia R. (c-score 3.0595); Professor Poh, Norman (c-score 2.9874); Professor Galea, Michael (c-score 2.7935); and Professor Liapis, Antonios (c-score 2.7905).
The contributor of this global study is Stanford University Professor, John P.A. Ioannidis. His selection is “based on the top 100,000 scientists by c-score or a percentile rank of 2% or above in the sub-field”.
The citation indicators and the methodology of his rankings are available here: https://lnkd.in/dpxrCJtx
Suggested citation: Camilleri, M.A. (2025). Cocreating Value Through Open Circular Innovation Strategies: A Results-Driven Work Plan and Future Research Avenues, Business Strategy and the Environment, https://doi.org/10.1002/bse.4216
This research raises awareness of practitioners’ crowdsourcing initiatives and collaborative approaches, such as sharing ideas and resources with external partners, expert consultants, marketplace stakeholders (like suppliers and customers), university institutions, research centers, and even competitors, as the latter can help them develop innovation labs and to foster industrial symbiosis (Calabrese et al. 2024; Sundar et al. 2023; Triguero et al. 2022). It reported that open innovation networks would enable them to work in tandem with other entities to extend the life of products and their components. It also indicated how and where circular open innovations would facilitate the sharing of unwanted materials and resources that can be reused, repaired, restored, refurbished, or recycled through resource recovery systems and reverse logistics approaches. In addition, it postulates that circular economy practitioners could differentiate their business models by offering product-service systems, sharing economies, and/or leasing models to increase resource efficiencies and to minimize waste.
Arguably, the cocreation of open innovations can contribute to improve the financial performance of practitioners as well as of their partners who are supporting them in fostering closed-loop systems and sharing economy practices. They enable businesses and their stakeholders to minimize externalities like waste and pollution that can ultimately impact the long-term viability of our planet. Figure 1 presents a conceptual framework that clarifies how open innovation cocreation approaches can be utilized to advance circular, closed-loop models while adding value to the businesses’ financial performance.
The collaborative efforts between organizations, individuals, and various stakeholders can lead to sustainable innovations, including to the advancement of circular economy models (Jesus and Jugend 2023; Tumuyu et al. 2024). Such practices are not without their own inherent challenges and pitfalls. For example, resource sharing, the recovery of waste and by-products from other organizations, and industrial symbiosis involve close partnership agreements among firms and their collaborators, as they strive in their endeavors to optimize resource use and to minimize waste (Battistella and Pessot 2024; Eisenreich et al. 2021). While the open innovation strategies that are mentioned in this article can lead to significant efficiency gains and to waste reductions, practitioners may encounter several difficulties and hurdles, to implement the required changes (Phonthanukitithaworn et al. 2024). Different entities will have their own organizational culture, strategic goals, and modus operandi that may result in coordination challenges among stakeholders.
Organizations may become overly reliant on sharing resources or on their symbiotic relationships, leading to vulnerabilities related to stakeholder dependencies (Battistella and Pessot 2024). For instance, if one partner experiences disruptions, such as operational issues or financial difficulties, it can adversely affect the feasibility of the entire network. Notwithstanding, organizations are usually expected to share information and resources when they are involved in corporate innovation hubs and clusters. Their openness can lead to concerns about knowledge leakages and intellectual property theft, which may deter companies from fully engaging in resource-sharing initiatives, as they pursue outbound innovation approaches.
Other challenges may arise from resource recovery, reverse logistics, and product-life extension strategies (Johnstone 2024). The implementation of reverse logistics systems can be costly, especially for small and micro enterprises. The costs associated with the collection, sorting, and processing of returned products and components may outweigh the benefits, particularly if the market for recovered materials is not well established (Panza et al. 2022; Sgambaro et al. 2024). Moreover, the effectiveness of resource recovery methodologies and of product-life extension strategies would be highly dependent on the stakeholders’ willingness to return products or to participate in recycling programs. Circular economy practitioners may have to invest in promotional campaigns to educate their stakeholders about sustainable behaviors. There may be instances where existing recovery and recycling technologies are not sufficiently advanced or widely available, in certain contexts, thereby posing significant barriers to the effective implementation of open circular innovations. Notwithstanding, there may be responsible practitioners and sustainability champions that may struggle to find reliable partners with appropriate technological solutions that could help them close the loop of their circular economy.
In some scenarios, emerging circular economy enthusiasts may be eager to shift from traditional product sales models to innovative product-service systems. Yet, such budding practitioners can face operational challenges in their transitions to such circular business models. They may have to change certain business processes, reformulate supply chains, and also redefine their customer relationships, to foster compliance with their modus operandi. These dynamic aspects can be time-consuming, costly, and resource intensive (Eisenreich et al. 2021). For instance, the customers who are accustomed to owning tangible assets may resist shifting to a product-service system model. Their reluctance to accept the service providers’ revised terms and conditions can hinder the adoption of circular economy practices. The former may struggle to convince their consumers to change their status quo, by accessing products as a service, rather than owning them (Sgambaro et al. 2024). In addition, the practitioners adopting products-as-a-service systems may find it difficult to quantify their performance outcomes related to resource savings and customer satisfaction levels and to evaluate the success of their product-service models, accurately, due to a lack of established metrics.
In a similar vein, the customers of sharing economies and leasing systems ought to trust the quality standards and safety features of the products and services they use (Sergianni et al. 2024). Any negative incidents reported through previous consumers’ testimonials and reviews can undermine the prospective customers’ confidence in the service provider or in the manufacturer who produced the product in the first place. Notwithstanding, several sharing economy models rely on community participation and localized networks, which can pose possible challenges for scalability. As businesses seek to expand their operations, it may prove hard for them to consistently maintain the same level of trust and quality in their service delivery. Moreover, many commentators argue that the rapid growth of sharing economies often outpaces existing regulatory frameworks. The lack of regulations, in certain jurisdictions, in this regard, can create uncertainties and gray areas for businesses as well as for their consumers.
I have just returned back to base after a productive two-day foreign expert meeting.
Once again, it was a positive experience to connect with European academic colleagues, to review and discuss research proposals worth thousands of Euros.
My big congratulations go to the successful scholars who passed the shortlisting phase, based on our evaluation scores.
The best proposals will eventually receive national government funds for transformative projects that will add value to society and the natural environment.
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