Metropolitan State University

ABSTRACT This study examines how gender equality and governance conditions relate to national innovation capability, using an exploratory machine‐learning approach on cross‐national panel data. Gender equality is widely recognized as a human right and a core Sustainable Development Goal (SDG 5), yet its association with country‐level innovation capability remains insufficiently mapped in sustainability‐oriented innovation research. To address this gap, we compile a harmonized dataset for 109 countries (2007–2019) from the Global Innovation Index, the Worldwide Governance Indicators, UNDP gender indicators, and World Bank development controls. We conceptualize innovation as national innovation capability and operationalize it using the GII score, a composite index that combines innovation inputs and outputs. Across decision‐tree and ensemble models, governance quality emerges as consistently informative for predicting innovation capability. Gender equality indicators also display substantial predictive relevance, indicating that inclusion‐related conditions are systematically associated with cross‐country variation in innovation capability. The findings contribute to sustainability science and innovation policy by clarifying how gender equality and institutional quality jointly map onto innovation capability while maintaining an exploratory, non‐causal interpretation of model outputs.

This study addresses two gaps in aging consumer research: inconsistent definitions of older consumers and limited understanding of age-related behavioral change. Using multiple years of the American Time Use Survey, the study examines time spent in grocery and non-grocery shopping for adults of different ages. Guided by arousal theory and the Selection, Optimization, and Compensation model, it examines behavioral entropy (daily routine diversification) and life satisfaction as indicators of adaptive functioning related to shopping. The analysis provides a behaviorally grounded account of U.S. older shoppers and explains how shopping is embedded within day-to-day activity structure during later life. Results reveal remarkably consistent patterns for both grocery and non-grocery shopping and offer strong implications for retailing strategy and marketing theory.

We present a short review of the analytical aspects of recent progress in the study of black hole spectral instability and its potential observational consequences. This topic, inspired by earlier foundational works, has attracted considerable attention in the recent literature. It has been demonstrated that both the low-lying modes and high overtones of black hole quasinormal spectra can be substantially influenced by small deformations in the effective potential of the wave equation that describes black hole perturbations. The temporal evolution of gravitational wave signals is primarily governed by the first few low-lying quasinormal modes. In contrast, the asymptotic behavior of high overtones is closely associated with the phenomenon of black hole echoes. We review relevant studies on spectral instability in both regimes, highlighting their potential to produce substantial observational signatures in gravitational wave data. Additionally, recent proposals of Regge poles and reflectionless modes as alternative stable observables for probing black hole spacetimes are summarized.

This paper explores the transformative relationship between personal branding and artificial intelligence (AI), positioning digital literacy as the foundation of this shift. It argues that personal branding remains essential in today’s digital era, enabling individuals to establish trust and cultivate distinct identities. The research examines how AI influences personal branding through customized content creation, interactive audience engagement, and data-driven strategies. Drawing on current scholarship, the paper demonstrates how digital literacy enhances online visibility while highlighting AI tools that strengthen messaging. The paper argues that leveraging AI to showcase digital competence in personal branding strengthens career readiness and accelerates professional growth. The study demonstrates that the practical and ethical application of AI in personal branding necessitates a strong foundation in digital literacy to achieve authenticity, credibility, and sustained professional success.

ABSTRACT This study introduces an analytical model that establishes a connection between the factors that promote innovation in a country and the competitiveness of its tourism destinations. Invoking the international strategic competitiveness theory, this study is among the first to propose and empirically test the predictive roles of innovation facilitators on tourism competitiveness. Utilising longitudinal data from multiple countries from 2013 to 2022, we ran machine learning algorithms. The results show that several innovation facilitators, such as research and development and trade, diversification, and market scale, significantly predict competitiveness in tourism destinations. The results of this investigation enhance our knowledge of innovation and competitiveness in tourism locations globally.

This study evaluates the user experience (UX) of learning applications among university students in Nigeria using the user experience questionnaire (UEQ). With the rapid shift toward digital and mobile learning platforms in higher education, understanding students’ perceptions of usability, engagement, and overall satisfaction has become crucial. The study surveyed 397 university students to assess six key UX dimensions: attractiveness, perspicuity, efficiency, dependability, stimulation, and novelty. The findings revealed that the learning management system (LMS) users have a positive experience with it and use it frequently. The novelty category, on the other hand, has the lowest moodle mean score. We posit that these results are acceptable since the student aims to access the LMS to learn. The findings provide valuable insights for educators, developers, and policymakers aiming to optimize e-learning applications for improved usability and engagement. This study contributes to the broader conversation on enhancing digital learning experiences in developing regions.

This study explores how the Entrepreneurship Ecosystem (EE) fosters tourism competitiveness by analyzing international arrivals, tourism expenditures, and employment. Using data from the Global Entrepreneurship Monitor (GEM) and the World Bank (2013–2022), advanced machine learning techniques, bagging, random forest, and boosting, are applied to assess the impact of EE components. SHAP and Partial Dependence Plots (PDP) reveal key predictors and their interactions, showing that Cultural and Social Norms (CSN), Total Early-stage Entrepreneurial Activity (TEA), and Business Services Sector (BSS) are crucial drivers. The integration of the Resource-Based View (RBV) and Dynamic Capabilities Theory illustrates how EE components enhance tourism growth through innovation and adaptability. Practical recommendations focus on fostering EEs to achieve sustainable tourism development.

The continuous growth of devices of the Internet of Things (IoT) has increased the risk of ransomware attacks. Conventional detection techniques rely on more specific data sources, and isolated layers typically prove insufficient in com-plex, resource-constrained environments like IoT systems. In this work, we present CrossSentry, a cross-layer detection framework that employs metrics from perception, network, and middleware layers to enhance detection accuracy and reduce false positives. CrossSentry integrates a weighted ensemble of anomaly detection algorithms to tie up multiple indicators of file system alterations, network traffic changes, and hardware performance counters into decision-making. The system uses statistical techniques like Z-Score normalization with machine learning approaches (Isolation Forest, One-Class Support Vector Machine, and Local Outlier Factor) to detect even slight ransomware-indicative behaviors. Central to CrossSentry is the adaptive decision mechanism that accords layer-specific importance in real time; thus, ensuring op-timal performance even when computationally challenged. Using knowledge across several layers of the IoT stack, CrossSentry not only addresses the fragmentation inherent in existing detection methods but also lays a scalable foundation for future validation and practical deployment in diverse IoT systems. This paper examines the theoretical basis, architectural innovation, and empirical potential of CrossSentry, thus providing an alternative to ransomware defense in an increasingly distributed dynamic IoT environment.