Generative Artificial Intelligence is reshaping marketing practice, yet little is known about whether Large Language Models (LLMs) can produce email templates that are directly editable inside campaign builders. This study reports a technical feasibility evaluation of AI-generated email templates compatible with the Stripo template builder, conducted with a hospitality CRM partner. Few-Shot Prompting and Fine-Tuning of an LLM were compared on the generation of Stripo-compatible, non-standard HTML components and of complete templates. Fine-Tuning outperformed Few-Shot Prompting in Stripo compatibility (88%vs. 69%, p = 0.002), generation speed, and cost per output. End-to-end template generation proved feasible but not production-ready, confirming that deployment requires human review of every campaign-critical element. The study serves as an evaluation protocol for platform compatibility of AI-generated email HTML, providing an empirical comparison of adaptation strategies for this niche code-generation task and design implications for CRM vendors and hospitality marketing teams.
The purpose of this research is to map the customer experience (CX) literature in the metaverse using a dual approach, bibliometric analysis and topic modelling. While both methods are individually known, their combined application to specifically isolate CX within metaverse research has been limited. Our objective is not to claim a breakthrough method but to offer a structured, reproducible synthesis of existing knowledge. We analysed 1460 peer-reviewed articles from the Scopus database using bibliometric techniques followed by Latent Dirichlet Allocation (LDA) topic modelling. The analysis revealed seven dominant themes: Customer Experience Enhancement, Brand Engagement & Virtual Services, Marketing Research Trends in CX, Digital Marketing Impact & NFT Adoption, AI-Driven Engagement & Mediation, Immersive Business & Value Creation, and Consumer Behavior in Virtual Retail. When interpreted collectively, these themes reveal three important patterns, a strong orientation toward positive outcomes, a notable silence on ethical and sustainability issues, and fragmentation between technical and psychological research streams. The findings call for the evolution of traditional CX frameworks to reflect immersive, co-created, and avatar-mediated experiences. Practitioners are encouraged to adopt customer-centric strategies that leverage AI, gamification, and virtual branding. The study also provides strategic guidance on where academic inquiry and innovation in CX design should be focused next.
The diffusion of Artificial Intelligence and Generative-AI is reshaping labour market demand, occupational structures, and skills-sets configurations. This study addresses skills complementarity, defined as the joint request of pairs of skills, within about 240 million job vacancies published in Italy and Germany in 2022–2023. Leveraging Natural Language Processing and Network Theory, skills-sets are mapped in four clusters: fundamental analytical and practical skills, computational linguistics, Industry 4.0 skills, and marketing-and-sales-related skills. This work demonstrates that AI skills are not isolated but show a pervasive interdependence with transversal skills. NetworkComparisonTest confirms statistically significant structural differences in configurations across the European Qualifications Framework. A growing trend in specialization emerges within specific levels: EQF3 (vocational training), EQF5 (higher technician) and EQF8 (PhD). The cross-country comparison reveals in Germany technical training is the core hub of the industrial system, while in Italy the landscape is polarized with technological adoption primarily driven by advanced formal education.
This study advances the design and evaluation of Human–AI Interaction Service Systems (HAI-SS) through a design science research approach in collaboration with a global high-tech manufacturer. Two artifacts were developed. The first, a multi-agent prototype, demonstrates how hybrid intelligence can be operationalized to support diagnostic work, standardize knowledge reuse, and address service-quality objectives across support, customization, and commissioning. The second, a HAI-SS Assessment Model, consolidates 51 metrics and 40 evaluation methods into 8 clusters, linking goals, metrics, and methods through conditional rules to enable context-sensitive evaluation. Together, these artifacts contribute methodological rigor, empirical grounding, and actionable tools for both research and practice. By introducing HAI-SS as a socio-technical category and providing a replicable evaluation framework, the study advances research in service science and information systems and directs the responsible incorporation of AI into complex service systems.
Alternative finance platforms, including crowdfunding, peer-to-peer lending, equity-based platforms, and token-based fundraising mechanisms, have become important channels for financing entrepreneurial, social, and investment-oriented initiatives. Yet their reliance on digital intermediation, dispersed participation, and information asymmetry creates opportunities for fraud, undermining trust, investor protection, and platform sustainability. This study provides a systematic review of fraud detection and prevention in alternative finance, with crowdfunding emerging as the most extensively represented empirical domain. Methodologically, the paper combines a PRISMA-guided systematic literature review with a hybrid topic-modeling strategy that integrates neural topic modeling and probabilistic refinement, thereby supporting both transparent corpus selection and data-driven thematic synthesis. The findings show that Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), and blockchain-based mechanisms are recurrently discussed as promising tools for detecting, preventing, or mitigating fraud. AI and ML approaches are mainly used to identify anomalies, suspicious textual patterns, behavioral signals, and transaction irregularities, while blockchain-based approaches are associated with transparency, traceability, smart contracts, and conditional fund release. The review also shows that fraud differs across alternative finance models, ranging from campaign misrepresentation and intentional and premeditated non-delivery in crowdfunding to borrower or platform misreporting in lending-based models and misleading disclosures or white-paper manipulation in ICO/STO contexts. A central challenge across the literature is the scarcity of labeled fraud data, which limits the use and benchmarking of supervised ML models. Overall, this study contributes by linking a reproducible hybrid SLR methodology to a structured synthesis of fraud types, platform-specific vulnerabilities, and AI-, ML-, and blockchain-based detection strategies in alternative finance.
Despite the growing practical emphasis on data management, limited empirical research has examined the role of its capabilities. This study addresses this gap by exploring the relationships among data management, data-driven decision-making, decision-making performance, and innovation capability. Additionally, it explores the mediating roles of data governance and data ethics. We propose that data management strengthens an organization’s data-driven decision-making, thereby improving decision-making performance. Furthermore, it enhances both incremental and radical innovation capabilities. To test our research model, we conducted a survey study and applied partial least squares structural equation modeling. Our findings indicate that data-driven decision-making strongly supports incremental innovation, while its effect on radical innovation is not supported. Additionally, decision-making performance appears to positively influence radical innovation and, to a certain extent, incremental innovation. However, while data governance does not mediate the relationship between data management and data-driven decision-making, data ethics shows a marginally significant negative mediating effect.
This study examines the impact of cyberloafing on employee performance in public higher education institutions (HEIs), focusing on the mediating role of work engagement. Using an explanatory quantitative research design, structured questionnaires were administered to 332 administrative staff members from public HEIs in the Amhara Regional State, Ethiopia, who were proportionally and randomly selected. The reliability and validity of the measurement instruments were rigorously assessed before hypothesis testing. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS software. The findings indicate that cyberloafing has a statistically significant negative effect on both work engagement and employee performance, while work engagement positively and significantly influences employee performance. Moreover, work engagement partially mediates the relationship between cyberloafing and employee performance. This study fills a gap in the literature by revealing the psychological mechanism through which cyberloafing influences employee performance via work engagement, thereby contributing to the limited empirical research on cyberloafing in public higher education institutions (HEIs)
This study employs a text-mining analysis to generate data-driven insights on how the communication of risk management information impacts the cost of capital in tourism firms across 22 European countries. To construct risk management information, our paper relies on a lexicon-based model to extract and quantify firm-level risk management information from annual reports. Our empirical analysis, using a Generalized Method of Moments (GMM) approach, indicates that greater transparency in risk management is significantly linked to a reduced cost of capital, suggesting that information transparency enhances investors’ risk assessment and reduces financing costs. We also explored how this relationship manifested during the COVID-19 pandemic, given that firms may have varied their risk management information. Our results indicate that firms providing more extensive and consistent risk management information maintained a comparatively lower cost of capital, highlighting the informational value of risk communication under uncertainty. Our findings demonstrate that text-based analytics provide reliable insights into how information conveyed through corporate reports influences financial decision-making.
In the post-COVID-19 era, agricultural services face significant challenges in adopting smartphone-based technologies, which impact their effectiveness in extension activities. Previous studies in the agricultural domain have primarily focused on technology acceptance theories, lacking a comprehensive view of the interaction and interplay of constructs on the ultimate performance of smartphone usage. This study aims to: (1) identify the key determinants influencing Agricultural Extension Agents' (AEAs) behavioral intention to adopt smartphone-based applications (SBAs) for agricultural services in Khuzestan Province, Iran; and (2) examine how facilitating conditions and behavioral intention translate into actual usage behavior. This research employs a quantitative approach, integrating established models for technology acceptance to explore the behavior of agricultural extension agents in Khuzestan Province, a key agricultural region in southwestern Iran. The data were examined through the application of Partial Least Squares Structural Equation Modeling (PLS-SEM) and Importance-Performance Map Analysis (IPMA). PLS-SEM results indicate that Performance Expectancy, Social Influence, Perceived Uncertainty, and Trust significantly influence Behavioral Intention, whereas Effort Expectancy and Facilitating Conditions do not. Facilitating Conditions and Behavioral Intention significantly predict Actual Usage behavior. IPMA results reveal that while Performance Expectancy and Behavioral Intention exhibit high performance scores, Facilitating Conditions and Trust represent high-priority areas requiring targeted improvement. This study contributes theoretically by validating an integrated acceptance framework in an underexplored agricultural extension context and methodologically by demonstrating the complementary value of combining PLS-SEM with IPMA for actionable insight generation. Practically, findings suggest that policymakers and developers should prioritize infrastructure investment, localized application design, and trust-building mechanisms to enhance SBA adoption among extension agents. The knowledge generated supports evidence-based strategies to overcome technology adoption barriers and promote sustainable digital transformation in agricultural communities.
Spreading processes in a population, such as the adoption of a novel innovation or product, may be driven by both viral interactions and external factors. The social graph of the population is usually used to model peer-to-peer interactions, but in practice, such graphs are never completely observed. In particular, certain interactions may be missing. We conduct a simulation study to determine how missing interactions affect the inference and predictions of the graph-based Poisson process model introduced by Parviero et al. (2022), modelling both peer-to-peer and external influences. If the social graph is only partly observed, simulations show that parameter estimation will be biased. The external share of the overall cumulative intensity is seen to be overestimated, while the viral share is underestimated. The estimated parameters are seen to increase. Simulations suggest that the bias will be relatively small even for high proportions of missing interactions. In addition, the simulations suggest that resulting predictions are not particularly affected.
In this study, we examine the effect of data sovereignty, digital maturity, and stakeholder engagement on the quality of policy formulation and the subsequent effect on the trust of employees in Palestinian e-government using X-Road as an emergent interoperability platform functioning within a politically restricted developing environment. Our data were gathered using surveys distributed among 88 employees in 46 Palestinian public entities and were subsequently analyzed using two-stage PLS-SEM–ANN techniques. Our findings suggest that both digital maturity and data sovereignty have positive effects on the quality of policy formulation, while stakeholder engagement does not have a statistically significant effect. Moreover, our results demonstrate that better policy formulation quality improves employees’ trust in the Palestinian e-government system. In other words, in the case of emerging digital platforms operating in politically restricted environments, having a more secure control over data and increased digital maturity becomes a much more important factor affecting perceived quality of policy formulation than stakeholder involvement. This paper adds to the literature by going beyond the citizen-centric adoption frameworks and advanced digital infrastructures to explore how governance capabilities affect policy formulation quality and trust formation within an emerging Palestinian e-government infrastructure based on X-Road. Overall, our analysis presents a new perspective on X-Road and repositions it not as a general-purpose citizen-centric platform, but rather as an emergent back-end infrastructure where governance capabilities affect policy formulation quality and trust formation within a politically restricted developing country.
In the luxury fashion industry, where quality directly impacts brand reputation and customer satisfaction, detecting product defects is crucial. This paper introduces FashionDSS: a Human-in-the-Loop Decision Support System designed to facilitate proactive quality management during the design phase. The system integrates three structured data sources, Product Registry, Bill of Materials (BOM) and Claim Records, into a unified predictive framework. Using machine learning models that are tailored to moderate-sized, information-rich datasets and validated through expert interaction, FashionDSS can perform two tasks: forecasting prototype production time (Task T1) and predicting potential defects in newly designed products (Task T2). Experimental results on real-world luxury fashion data demonstrate the feasibility of extracting meaningful predictive signals from structured complaint and production information despite data constraints. Although validation is currently limited to a single company’s dataset, the findings emphasise the potential benefits of combining predictive analytics with human expertise for design-oriented quality management.
Individuals with both mobility and visual impairment faces difficulty to ambulate into a room that can only be recognized through the writing of the room nameplate. Room nameplate recognition system on autonomous smart wheelchair can overcome the problem. Deep learning that offers accurate prediction capabilities, has extended to various intelligent devices. However despite its remarkable potential, deep learning poses significant risks in term of computation time. Accidents can occur if the deep learning model used in smart wheelchair has high complexity computation which can make the system inference time longer. The aims of this study is to reduce time and complexity of YOLOv8n model to detect room nameplate objects. We proposed YOLOv8n-GSM, an improved YOLOv8n model to detect room nameplate using ghost module and modules pruning to reduce model complexity. YOLOv8n-GSM successfully reduces the number of parameters by 65,36%, GFLOPs by 50%, and model size by 61,47% which resulting 28,55% shorter inference speed on NUC mini computer, and 35,71% shorter inference speed on Jetson TX2 NX, while maintain the detection accuracy on 98,9%. We hope our research will be further utilized by smart wheelchair developers. Code is available at: https://github.com/ainandafiq55/YOLOv8-GSM.
This paper examines digital transformation outcomes in public administration, focusing on how the digitalization of information-intensive service processes may generate value for different stakeholders. While existing studies have largely assessed digital transformation outcomes from the end-users' perspective, limited attention has been paid to how digitally mediated information processes affect public administration personnel, particularly front-office employees. To address this gap, this study tests a framework that investigates how organizational support shapes employees’ perceived outcomes of digital transformation, including relational quality, process control, and job productivity. The empirical analysis focuses on student services in Italian public universities, a context characterized by highly standardized and data-intensive service processes. The findings show that organizational support may play a critical role in enabling front-office staff to effectively manage digitally transformed information flows, thereby enhancing perceived process control and service-related performance. These results contribute to information management and digital transformation research by opening the “black box” linking digital systems, organizational support, and employee-level outcomes in public sector service delivery.
Trustworthy AI has recently emerged as a composite construct combining the legal, ethical and technical conditions relevant to mitigating the risks of AI systems. The methods used to assure the trustworthiness of AI systems have typically taken the form of a hierarchical approach and assurance strategies. These aim to provide documentary evidence demonstrating an AI system’s compliance with pre-defined legal, ethical, and technical criteria. Much less attention has been accorded to the use of a relational approach and assurance strategies that aim to increase stakeholders’ confidence in the trustworthiness of AI systems. Drawing on a directed content analysis of 90 research and policy documents, this article presents an integrative review of the hierarchical and relational strategies used to assure AI trustworthiness. Based on the content analysis, a hierarchical-relational classification is presented of the strategies that currently exist to assure the ethical principles, governance, accountability, transparency and explainability of AI systems. This confirms the prevalence of hierarchical assurance strategies with limited attention paid to relational assurance strategies. The significance of this disparity and the theoretical and practical implications for assuring the trustworthiness of AI systems are discussed. The article concludes by presenting a researchable framework embedding the combined use of hierarchical and relational assurance strategies within the AI life cycle and beyond.
This study analyzes the adoption of Geographic Information Systems (GIS) in five Costa Rican public universities -UCR, TEC, UNA, UTN and UNED- examining institutional integration, barriers and opportunities to improve the governance of all geospatial data. Drawing on successful international experiences (Australia, North Carolina, California, Saudi Arabia and Bahrain), it uses a mixed-methods approach combining interviews with Vice-Rectory officials and data analysis of software acquisitions from the SICOP platform. The results reveal significant disparities: only UCR and TEC have achieved high levels of GIS integration, while others show limited or almost no adoption. The main obstacles encountered are the lack of clear institutional policies, fragmented data infrastructure and gaps in professional training. Although the average investment in GIS licenses reached USD $18,892 per institution (2021–2023), the use is still concentrated in academic programs without using them in strategic management and planning. Adoption varies from open source tools to licensed platforms, affecting operational efficiency and decision making. This research highlights the need to align GIS with university planning and provides a replicable framework for digital transformation in higher education.
Small and medium-sized enterprises (SMEs) face unique challenges in leveraging data and analytics for business value. Resource constraints, skill shortages, and uncertainty often complicate the decision of whether to develop in-house capabilities or outsource analytics projects. This paper develops an initial decision framework tailored to SMEs, bridging established make-or-buy theories with the specific requirements of analytics adoption. Drawing on a literature review and 24 expert interviews, we identify key triggers and influencing factors across four dimensions: criticality, capability, cost, and compatibility. The resulting framework provides structured guidance for navigating outsourcing decisions, balancing strategic importance and data sensitivity against resource limitations, cost trade-offs, and vendor risks. For practice, the framework supports SME managers in moving beyond ad hoc decisions towards more transparent and strategic choices. For research, it contributes an empirically grounded foundation for future work on analytics sourcing and capability building in SMEs.
With the increasing adoption of AI, there is a growing focus on understanding how the agile cultural context of organizations influences AI adoption and how collective human intelligence drives it, thereby fostering the development of augmented intelligence within agile organizations. To investigate it, we analyzed a sample of 1871 knowledge workers from Poland and Finland, comprising 936 females and 935 males, representing the IT sector (943 participants) and various other sectors (928 participants). So, the sectoral view in this research is purposefully free of a gender bias. We employed structural equation modeling (SEM) utilizing AMOS software version 26 to analyze the data.Findings show that agile organization benefits cannot be achieved without enhanced organizational intelligence, driven by the development of collective human and artificial intelligence (augmented), and without agile culture support.Regarding AI adoption, women and respondents from non-IT sectors perceive it as riskier than men and those in the IT sector do. However, women also view AI adoption as contributing more significantly to organizational intelligence than men do. Moreover, in the IT sector, professionals prioritize collective human intelligence in shaping organizational intelligence over AI input, as do females. Trust and a willingness to accept risks are vital for AI adoption. Critical thinking also plays a crucial role, but primarily in the IT sector. Notably, Finland and Poland share similar perspectives on achieving augmented intelligence and creating agile organizations, reflecting the European view.
This study examines the interplay between technological innovation, regulatory compliance, and public perception surrounding Meta's AI-powered smart glasses within the EU's GDPR framework. Applying NLP methods—including VADER sentiment analysis, thematic mapping, and n-gram analysis—to a multi-source corpus of 334 articles from eight English-language outlets, the research reveals that positive sentiment clusters around product innovation while significant negative sentiment is tied to GDPR compliance challenges, a pattern statistically confirmed across outlets through chi-square testing (p = 0.0119). Thematic co-occurrence analysis identifies strong intersections between technology and privacy discourse, reflecting persistent concerns about data collection and surveillance. A comparative analysis of EU and UK GDPR frameworks highlights how post-Brexit regulatory divergence under the Data (Use and Access) Act 2025 adds compliance complexity for AI wearables. To interpret these dynamics, the paper develops an Innovation–Compliance–Perception (ICP) framework, demonstrating how governance simultaneously constrains and stimulates technological advancement.
The rapid digitalization of correctional systems globally has attracted significant attention from both scholars and policymakers. However, digital transformation (DT) within correctional environments remains conceptually fragmented and empirically underexplored. The purpose of this study is to develop a Smart Prisons Transformation Framework (SPTF) that supports responsible and context-appropriate digital reform within Indonesia’s correctional system. Using a mixed-method research design, the study integrates an adapted Grounded Theory approach to inductively construct the framework's conceptual foundation and applies Q methodology to capture and validate diverse stakeholder perspectives. The resulting SPTF comprises four dimensions: people, process, technology, and organization operationalized into 16 components and 45 measurable factors. To assess practical feasibility, a supporting prototype was developed and piloted across selected prisons. Empirical findings indicate that structured, ethically guided DT implementation can enhance institutional efficiency, accountability, and rehabilitative outcomes. Overall, the findings suggest that digital transformation in correctional environments requires a context-sensitive framework that balances technological innovation, security, ethical governance, and organizational readiness. The proposed Smart Prisons Transformation Framework (SPTF) demonstrates initial practical feasibility and positive usability, although further validation across diverse correctional contexts is necessary to confirm its long-term effectiveness and generalizability. This study contributes to theory and practice by offering actionable guidance for responsible digital reform, uniquely embedding ethical governance and human-rights considerations into digital transformation design for high-regulation correctional environments.
