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This paper aims to advance understanding of value co-creation and value co-destruction in artificial intelligence (AI)-enabled services by introducing the Refined Interaction Value Framework (R-IVF). The R-IVF explains how interaction value depends not only on technical performance, but also on how users interpret machine initiative, calibrate trust, experience meaningful exploration and evaluate contextual fit.This paper develops the R-IVF conceptually by integrating three AI-era constructs – Agentic Perception (AP), Dynamic Trust Calibration (DTC) and Serendipitous Exploration (SE) – into the original framework and by reconceptualizing Configuration Value as Contextual Intelligence, an integrative mechanism that situates these constructs within the user’s evolving context. The framework is illustrated through two contrasting AI-enabled service contexts and extended through cross-functional managerial implications and an illustrative diagnostic monitoring approach.The paper shows that interaction value in AI-enabled services is shaped by three recurrent tensions: increasing machine agency within interaction, the need for dynamically calibrated trust and the challenge of preserving meaningful exploration under increasingly personalized service conditions. The R-IVF explains how these tensions may either support value co-creation or contribute to value co-destruction, depending on whether AI-enabled interactions are experienced as supportive, trustworthy, contextually appropriate and open to meaningful discovery.The R-IVF offers managers a practical lens for designing, governing and monitoring AI-enabled service interactions. It highlights cross-functional priorities across design, operations and customer communication and supports the use of illustrative diagnostic indicators to identify emerging vulnerabilities such as overreach, trust erosion, constrained exploration and contextual mismatch.This paper extends interaction value theory into the AI era by introducing AP, DTC and SE as AI-specific mechanisms shaping service interaction and by reconceptualizing Configuration Value as Contextual Intelligence. In doing so, it reframes interaction value as an adaptive, context-sensitive process rather than a fixed set of service attributes. It contributes a conceptually grounded framework that clarifies how AI-enabled interactions may sustain, undermine, or reshape value over time.

Capacitive swipe gestures have recently emerged as a promising behavioral biometric modality for user authentication and identification on smartphones. While prior studies have demonstrated their effectiveness using classical machine learning techniques, the potential of deep learning in this domain remains largely unexplored, particularly under limited training data conditions. In this paper, we investigate the use of deep learning for capacitive swipe–based user identification and address the challenge of data scarcity through an image fusion strategy. Experiments on a dataset of 30 users show that the proposed "deep learning plus image fusion" approach significantly improves performance over a standalone deep learning baseline and achieves results comparable to state-of-the-art classical machine learning methods. These findings highlight the importance of data size and demonstrate the potential of image fusion for enhancing deep learning–based user identification.

Background: The Fourth Industrial Revolution (4IR) has introduced transformative technologies into academic libraries, offering opportunities to enhance access, efficiency and service delivery. The increasing reliance on digital platforms, data-driven tools and third-party services raises challenges regarding privacy, data protection and ethical information management. Objectives: This study was guided by two research objectives. Firstly, it sought to investigate the current and projected adoption of 4IR technologies in academic libraries to enhance service provision. Secondly, it aimed to examine academic librarians’ concerns regarding user privacy within these evolving technological practices. Method: Drawing on a case study of two academic libraries in Cape Town, this study examines librarians’ awareness, perceptions, and practices concerning data privacy, cybersecurity and regulatory compliance with the Protection of Personal Information Act. Using a qualitative case study of two anonymised academic libraries in Cape Town, the study examined librarians’ awareness, perceptions and practices regarding data privacy, cybersecurity and compliance with the POPI Act. The study also used thematic analysis to interpret the data and to provide insight into how institutional policies and technological infrastructures shape librarians’ responses to data protection obligations. Furthermore, the study highlights emerging challenges and opportunities for strengthening privacy practices within academic libraries in the context of evolving digital environments. Results: Findings reveal that while libraries acknowledge the importance of privacy and confidentiality, gaps remain in institutional policies, technical capacity and staff training. The findings suggest that libraries must adopt a multidimensional approach, strengthening cybersecurity, enhancing ethical governance, fostering collaboration and investing in continuous staff development. Conclusion: The study argues for a multidimensional approach that combines robust cybersecurity measures, ethical governance, collaboration and continuous staff development to safeguard patron confidentiality while advancing innovation. Contribution: Libraries must navigate the intersection of technological advancement and ethical responsibility, while ensuring that emerging technologies reinforce, rather than undermine the values of access, equity and privacy.

This cross-sectional study examines how age-group differences in work motivation and basic psychological needs relate to preferences between self- and wage-employment. Drawing on Self-Determination and Lifespan Development Theories, age groups are conceptualized as life-stage categories rather than birth cohorts, and motivational profiles are compared in the post-COVID US labor market. Online survey data were collected from 1,484 adult Americans across five age groups, from Gen Z to Traditionalist. Multi-group CFA for measurement invariance, ANOVA, latent mean comparisons, and latent profile analysis with distal outcome comparisons were performed. Results indicated significant, albeit small, age-group differences in employment preferences and motivational constructs. Gen Z-young adults reported relatively stronger preference for self-employment than older age-groups, whereas Gen Y (Millennials), early to mid-career, exhibited the highest autonomous motivation, the lowest amotivation, and greater satisfaction of autonomy and competence needs at work. Five distinct motivational profiles were identified from latent profile analyses; low-intensity and controlled–amotivated profiles were most strongly associated with a preference for self-employment, whereas a high-involvement yet conflicted profile showed the strongest preference for wage-employment. Younger participants, particularly Gen Z and Gen Y, were more likely to fall into globally elevated and psychologically complex profiles. Overall, the findings suggest that age-linked life stages are associated with nuanced motivational configurations that only modestly translate into employment preferences, highlighting the value of developmentally sensitive, person-centered approaches to understanding contemporary work, employment preferences, and entrepreneurial orientations.

The COVID-19 pandemic constituted an unprecedented exogenous shock to many nonprofit organizations, testing their ability to sustain operations and adapt to crises. Building on population ecology and nonprofit organizational resilience framework, this study conceptualizes COVID-19 as a dual-structured crisis and examines how organizational factors, coupled with resilience tactics, shape nonprofit resilience. Results from 169 nonprofits highlight that specialist organizations (art and culture) are more vulnerable to external crisis than generalist nonprofits (human services). While donor support was critical for sustaining human-service organizations, peer support from other nonprofits emerged as a key factor for arts and culture organizations. In addition, system-wide constraints muted the effects of traditional strategies such as technology adoption and federal funding access on nonprofit resilience. Our findings underscore the context-dependent effectiveness of resilience tactics and offer new insights into managing nonprofits in future crises.

Economic recessions reshape consumer expectations and amplify scrutiny, altering how firms’ capabilities translate into performance. This study examines how recession-driven shifts in consumer expectations intensify the vulnerability of firms with high preexisting consumer satisfaction and how their innovation choices shape the extent to which this capability becomes a liability. We argue that recessions heighten negative reactions to perceived performance deviations, making high pre-recession satisfaction—typically a strategic strength—especially vulnerable. Using data from the U.S. automobile industry spanning the 2008 financial crisis, we find that consumer satisfaction declines significantly during recession and that firms with higher preexisting satisfaction experience disproportionately larger losses. We further demonstrate that innovation strategy plays a decisive moderating role: incremental product redesigns, which preserve familiarity and signal stability, mitigate the recession penalty for high-satisfaction firms, whereas new product introductions do not offer comparable protection. The study provides a theoretical account of how economic downturns can invert the value of demand-side capabilities and identifies conditions under which incremental innovation outperforms more transformative approaches. Managerially, the findings offer guidance on how firms can align innovation portfolios with shifting consumer psychology to protect reputational assets and sustain performance during an economic disruption.

This article examines the linguistic landscape of Paris’s 2024 Marche des fiertés LGBTQ+ through an analysis of 122 participant-produced signs. Drawing on linguistic landscape theory, it explores how language choice shapes political, social, and cultural expression in this highly visible public event. The findings show that language use is structured rather than random. French predominates in signs expressing political critique, satire, and wordplay, positioning it as the primary medium for engaging domestic audiences, particularly in the context of the 2024 legislative elections. English, by contrast, is frequently used for messages of solidarity, identity affirmation, and pop-cultural reference, aligning with transnational activist discourse. Multilingual signs often bridge these functions. The article argues that this distribution reflects the strategic deployment of language to address distinct yet overlapping publics, demonstrating how Pride participants use written language to negotiate identity, politics, and belonging in contemporary France.This article was published open access under a CC BY-NC-ND licence: https://creativecommons.org/licenses/by-nc-nd/4.0/.

Background/Objectives: Obstructivesleep apnea (OSA) is one of the most widespread forms of sleep disease, affecting over 936 million adults globally. The health consequences of obstructive sleep apnea (OSA) are well documented; however, it remains largely underdiagnosed because the current gold-standard diagnostic method, polysomnography (PSG), is often costly, time-consuming, and unavailable in many healthcare settings. To address these challenges, this study presents a novel explainable deep learning (DL) framework for automated multi-class OSA severity classification using single-lead electrocardiogram (ECG) signals. Methods: The proposed framework integrates a hybrid CNN–BiLSTM architecture with explainable artificial intelligence (XAI) techniques to generate clinically meaningful predictions and explanations across four OSA severity classes: Normal, Mild, Moderate, and Severe. The framework was evaluated using the publicly available PhysioNet Apnea-ECG dataset (70 recordings) together with an institutional ECG dataset (150 recordings), resulting in a combined cohort of 220 recordings. Results: The proposed framework achieved an overall classification accuracy of 94.7%, with sensitivity and specificity values of 92.3% and 96.1%, respectively. Furthermore, the proposed model consistently outperformed conventional machine learning algorithms, including Support Vector Machine (SVM), Random Forest, and XGBoost, by 5.5%, 4.2%, and 2.9%, respectively. To enhance transparency and clinical trust, SHAP (SHapley Additive exPlanations) was employed to identify the most influential physiological predictors driving model decisions. Heart rate variability features, particularly RMSSD and pNN50, emerged as the strongest indicators of OSA severity. Moreover, computational efficiency analysis revealed that the model required only 0.23 s to process a 60 s ECG epoch on a standard computing platform, supporting its suitability for real-time deployment. Conclusions: The findings demonstrate that explainable deep learning applied to ECG signals can provide accurate, interpretable, and computationally efficient assessment of OSA severity. The proposed framework may support OSA screening, clinical triage, and early intervention, particularly in resource-constrained healthcare environments.

This research presents a comparative performance analysis of three prominent hypervisor architectures—KVM, Xen, and VMware Workstation—to evaluate their scalability and resource efficiency under increasing virtual machine (VM) densities. Utilizing a standardized benchmarking framework, this study quantifies performance regressing across CPU throughput (GFLOPS), memory bandwidth, disk I/O, and initialization latency. The experiment results reveal that architectural design significantly dictates scalability limits: KVM’s kernel-integrated Type-1 model achieved the highest efficiency, maintaining a near-native CPU baseline and superior I/O resilience at peak density. In contrast, Xen’s microkernel architecture demonstrated stable but lower throughput due to Domain 0 (Dom0) management overhead, while VMware’s Type-2 hosted model exhibited the most acute performance degradation. These results demonstrate that while hosted hypervisors are suitable for low-density personal use, kernel-level integration is a mechanical necessity for maintaining performance in high-concurrency server environments. This work provides a reproducible methodology and critical data for system architects and researchers seeking to optimize resource orchestration in distributed and parallel systems.

A bstract We report on the first measurement of the Central Exclusive Production process in proton-proton collisions: pp → ph + h − p (where h = π , K , p ) at the center-of-mass energy $$\sqrt{s}=510$$ GeV with the STAR experiment at RHIC. At this energy, the process is dominated by a double Pomeron exchange mechanism. Hence, it provides a clean environment for investigating Pomeron interactions by measuring fully reconstructed final states involving only two hadrons and two forward scattered protons. The oppositely charged hadron pairs are measured within the central detector of STAR. The forward scattered protons are measured in the Roman Pot system allowing the verification of the event’s exclusivity. Differential fiducial cross sections within the STAR acceptance are presented as a function of the difference in the azimuthal angle between the outgoing protons. The invariant masses of the charged hadron pairs are measured up to approximately 3 GeV and the square of the four-momentum transfer ( t 1 and t 2 ) of the two forward-scattered protons in the range 0.3 GeV 2 < − t 1 , − t 2 < 1.6 GeV 2 . The differential fiducial cross sections of the forward protons as a function of the | t 1 + t 2 | are also presented. All results for the π + π − pair are presented in three mass ranges. A comparison with GRANIITTI Monte Carlo predictions are also presented, where the spectra include continuum and resonant contributions. The observed spectra are consistent with double Pomeron exchange, including resonances seen in previous studies.

Objective: Previous research has found bullying present across the library profession, with documented instances in school, public, and university literature, but not in academic medical and hospital library literature. This study aims to determine the prevalence of workplace bullying among academic medical and hospital librarians. Methods: An anonymous survey was distributed to members of the Medical Library Association, the Association of Academic Health Sciences Libraries, and the Association of College & Research Librarians Health Sciences Interest Group containing the Short Negative Acts Questionnaire. Quantitative analysis was conducted using the Real Statistics Resource Pack software (Release 8.9.1). Results: One-hundred eighteen survey responses met inclusion criteria. 49.2% of the sample met the threshold for self-identifying as victims of workplace bullying. This was significantly higher than the prevalence among the general librarian population (40.1%) in 2017 (p = 0.029) and consistent with findings in 2022 (51.8%; p = 0.31). More participants perceived themselves as not experienced bullying (69%) than the prevalence rate found. Those who perceived themselves as victims identified colleagues as the most frequent perpetrators (35%), followed immediate superiors (22%) and other superiors/managers within the organization (25%). Conclusions: This study showed nearly half (49.2%) of academic medical and hospital librarians included in the sample experience bullying. These findings highlight the need for an increased understanding of bullying behaviors, education on prevention, and for library leaders to develop interventions to mitigate bullying in their institutions.

In recent years, universities have extended lactation protections to students that have been traditionally afforded to paid employees only. Absent federal- and state-level regulation, this expansion has generally been piecemeal, taking place only when “breastfeeding advocates” request (or demand) those protections within their universities. Little research exists on these initiatives or about the breastfeeding advocates themselves. This article describes three examples of student lactation support initiatives on diverse U.S. university campuses. Using a comparative case study approach, the researchers examined the impact of positionality within higher education—and relative power afforded to different positions—on the initiatives and experiences of breastfeeding advocates. It is necessary to raise awareness and critically reflect on power structures to bring about change. The researchers found benefits of empowering students to engage in collective advocacy toward these ends, with practical implications for future U.S. lactation protection in academia. © 2026 NASPA—Student Affairs Administrators in Higher Education.

This article presents measurements of inclusive J / ψ production at midrapidity (| y | < 1.0) in Au+Au collisions at sNN=54.4 GeV with the STAR detector at the Relativistic Heavy Ion Collider. A suppression of the J / ψ yield, quantified using the nuclear modification factors ( R AA, R CP), is observed with respect to the scaled production in p+p collisions. The dependence of R AA on collision centrality and J / ψ transverse momentum is measured with improved precision compared to previous measurements at 39 and 62.4 GeV, while the centrality dependence of R CP is measured and compared to the same results at 39, 62.4, and 200 GeV. In central collisions, no significant collision energy dependence of R AA is found within uncertainties for collision energies between 17.3 and 200 GeV. Two transport model calculations that include dissociation and regeneration contributions are consistent with the experimental results within uncertainties. Although no significant collision energy dependence of the J / ψ suppression in high energy heavy-ion collisions up to sNN=200 GeV is observed within uncertainties, the newly measured results at 54.4 GeV Au+Au collisions provide additional constraints on theoretical calculations of the hot medium evolution and cold nuclear matter effects. © 2026 The Authors.

The COVID-19 vaccine supply chain faced unprecedented disruption from information disorder (collectively, misinformation, disinformation, and malinformation (MDM)). While prior research has examined the societal and health consequences of information disorder, little is known about how it disrupts supply chain management (SCM) or how supply chain actors respond. Guided by organizational information processing theory (OIPT) and stakeholder theory (ST), this study employs an embedded qualitative case study of the U.S. COVID-19 vaccine supply chain. We find that crisis-induced MDM functioned as an exogenous shock that heightened uncertainty and equivocality, overwhelming information-processing capacity, and fragmenting stakeholder responsibility. Downstream actors (i.e., pharmacies and clinicians) were disproportionately burdened in their efforts to combat MDM, relying largely on reactive tactics. Additionally, SC actors prioritized correcting false claims while deferring attribution of intent. This led to the conflation of misinformation and disinformation and the underdiagnosis of malinformation. By integrating OIPT and ST, we develop a framework that explains how information-processing misfit and shifting stakeholder salience jointly produce operational disruption and uneven accountability. Our findings extend SCM theory by conceptualizing MDM as a systemic supply chain risk and offer actionable guidance to improve resilience and trust. © 2026 The Author(s). Journal of Business Logistics published by Wiley Periodicals LLC.