Online brand communities distribute brand-related credibility across multiple human actors, yet research typically examines these sources independently or focuses on a single advocacy group. This article introduces the trust portfolio—the configuration of credible human voices within an online brand community—and a framework for how those voices collectively build brand trust. Drawing on source credibility, trust transfer, online brand community, social capital, and employee advocacy research, the framework distinguishes credibility-bearing roles by the mechanisms through which they establish credibility: expertise, lived experience, audience relationships, positional authority, independence, and procedural fairness. These roles are not interchangeable. A portfolio’s value depends on role diversity, role-question fit, and credibility’s distribution across individuals and roles. Organizational governance should be differentiated by role because transparency and oversight can strengthen affiliated actors’ credibility while undermining the independence on which other members’ credibility depends. The framework distinguishes credibility from advocacy, since credible voices may reinforce, qualify, or challenge brand claims. As trust-producing communication, validation, and governance increasingly originate within the community, marketing becomes more effortless, not because work disappears, but because responsibility shifts from the firm to the community. Illustrative cases show how these properties appear in practice and provide a foundation for future empirical research.
Artificial intelligence (AI) is reshaping occupational tasks and skill requirements, creating a human resource management (HRM) challenge around how future employees develop readiness for responsible AI-enabled work. This study examines relationships among perceived AI readiness (PAIR), perceived entrepreneurial AI value (AIV), and future AI-enabled work and entrepreneurial engagement (FAE) among 407 female business students in Kuwait. Five-category ordinal indicators were analyzed using diagonally weighted least squares confirmatory factor analysis, competing measurement models, bootstrap-based latent indirect-effect analysis, and an extended structural model incorporating AI learning exposure, trust in AI task delegation, and job-displacement concern. A three-factor readiness–value–engagement structure was retained, although discriminant validity between AIV and FAE was qualified by a high latent correlation. The retained structure showed mixed robust fit and substantial shared covariance, warranting cautious interpretation. PAIR was positively associated with AIV, which was strongly associated with FAE. The indirect association remained significant after supplementary variables were included, decreasing from β = 0.650 to β = 0.420. Formal AI learning exposure was positively associated with readiness but showed a small negative conditional association with perceived entrepreneurial AI value after readiness was held constant; this exploratory finding requires replication. Trust was positively associated with readiness, value, and engagement, while job-displacement concern coexisted with higher readiness and value. Findings suggest that AI-ready human-capital development should combine exposure with authentic business applications, critical evaluation, calibrated trust, and responsible human–AI collaboration.
This study examines whether the digitalization of tourism consumption is associated with greater access to holidays in Europe and whether this relationship depends on households’ financial conditions. Using publicly available Eurostat data, the analysis combines a 2023 cross-section of 26 European countries with a complementary pooled panel of 133 country–year observations for 2020–2024. Holiday deprivation is measured as the share of the population unable to afford one week of annual holiday away from home. Digitalization is captured through online tourism purchasing in the cross-section and general online purchasing in the panel. Results show a strong negative bivariate association between digital purchasing and holiday deprivation, but this relationship weakens and becomes statistically imprecise after socioeconomic and connectivity differences are considered. By contrast, perceived debt-repayment burden remains positively associated with holiday deprivation across specifications, while aggregate household leverage does not show the expected moderating effect. The findings distinguish digital market accessibility from economic accessibility: digitalization may facilitate transactions, but the capacity to convert these opportunities into tourism participation remains closely linked to financial conditions. This distinction extends responsible tourism marketing towards the distributive consequences of digital transformation.
Artificial intelligence (AI) is widely described as reshaping marketing administration through personalization, predictive analytics, and automated customer engagement. Whether firms describe AI as a customer-facing marketing capability or as a technology and a risk to be disclosed remains an open empirical question. This study uses mandated disclosure as an observational window on that question. Drawing on a balanced panel of 1100 firm-years from 100 U.S.-listed consumer-facing firms in 15 sectors (fiscal years 2015 to 2025), I measure technology vocabulary and marketing vocabulary in Form 10-K filings using phrase-presence indicators from the EDGAR full-text index and graded, section-located counts from the full text of each filing. Three findings emerge. First, AI disclosure diffused rapidly and converged: the share of firm-years disclosing rose from 28.7% in fiscal 2020 to 2022 to 91.7% in 2023 to 2025, and the intensity of AI language rose roughly twentyfold between 2021 and 2025. Second, within firms and net of common year shocks, adopting AI vocabulary was not accompanied by a substantively meaningful increase in marketing vocabulary (standardized β = 0.02, 95% CI [−0.09, 0.13]); graded and staggered-adoption estimates leave room for at most a small, lagged association. Only 3.2% of AI sentences written in 2023 to 2025 used marketing vocabulary, and AI language migrated from the Business section toward Risk Factors and MD&A. Third, first disclosure was ordered by sector, with digital-commerce and financial firms preceding hospitality and travel firms. I interpret the pattern as vocabulary decoupling: AI entered the technology-and-risk register of the annual report largely without being connected to the register in which firms account for value delivered to customers. The findings concern disclosure, not deployed capability.
Artificial intelligence (AI) adoption in higher education increasingly represents a challenge in workforce development and organizational transformation, rather than a purely technological issue. This study re-analyzed survey data from 311 faculty members, training instructors, and administrative staff at the Public Authority for Applied Education and Training (PAAET), Kuwait, to examine the associations of AI awareness and perceived barriers with AI usage/readiness from a human resource management perspective. AI usage/readiness was treated as a self-reported indicator of AI-related human capital readiness rather than as a measure of demonstrated competence or comprehensive organizational readiness. Ordinal confirmatory factor analysis and structural equation modeling were conducted using robust diagonally weighted least squares/weighted least squares mean and variance adjusted (DWLS/WLSMV) estimation. The refined 12-item, three-factor model demonstrated satisfactory reliability and validity, although model fit was mixed across scaled and robust indices. A near-zero eigenvalue also indicated possible numerical instability, warranting cautious interpretation and independent confirmation. AI awareness was strongly and positively associated with usage/readiness (β = 0.782, p < 0.001), whereas perceived barriers showed a small positive adjusted association (β = 0.117, p = 0.019). Together, the predictors explained 66.0% of the variance in usage/readiness. The positive association with perceived barriers may indicate that employees who engage more extensively with AI are more likely to recognize institutional and technical constraints. Building on these findings, this study proposes two conceptual extensions for future validation: a five-level AI capability maturity model for higher education and a seven-domain AI-enhanced instructor competency framework. The findings emphasize the need to integrate employee capability, institutional support, responsible AI governance, and continuous learning to develop sustainable AI readiness. Given the cross-sectional, self-reported design, these findings should be interpreted as associations rather than causal effects.
