Empire State University

As cross-border e-commerce expands in emerging economies, consumer participation increasingly depends on perceived transaction risk linked to digital payments, settlement, dispute resolution, and institutional enforceability. This study reconceptualizes online purchase intention (OPI) as a decision embedded in retail international finance. Extending the Theory of Planned Behavior (TPB), it integrates Internet Trustworthiness Behavior (ITB) and Country of Origin (COO) as risk-relevant signals shaping consumer judgment under cross-border uncertainty. Survey data from 390 digitally active consumers in the Dominican Republic were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that ITB strengthens perceived behavioral control, attitudes toward online purchasing, and subjective norms, while also exerting a direct positive effect on OPI. COO emerges as a strong direct predictor of OPI, functioning as a heuristic indicator of country credibility when formal safeguards appear weak. Contrary to standard TPB expectations, perceived behavioral control negatively predicts OPI, suggesting that greater digital competence may heighten awareness of expected losses and limited recourse in high-risk environments. The findings advance international business and finance research by showing how micro-level trust practices and macro-level country signals jointly shape consumer risk management in cross-border digital markets, with implications for inclusive participation and consumer protection.

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

Service LLMs evolve without public changelogs, complicating reproducible evaluation. We present a preregistered human-anchored longitudinal study that tracks three major model families over ten weekly waves using a fixed prompt bank (N = 240) across six domains. Blinded human raters provided correctness judgments, and a bias-calibrated LLM-as-judge produced secondary pairwise preferences corrected weekly via a Bradley–Terry model. Mixed-effects modeling and change-point detection (PELT with MBIC penalty) identified significant service drift patterns. Results show divergent stability trajectories among models: one stable, one improving, and one degrading mid-study. Judge calibration increased agreement with humans (τ = 0.59–0.68) while reducing volatility. Safety metrics co-varied with drift events, suggesting behavioral shifts rather than confirmed causal changes. All data, prompts, rubrics, and parameter configurations are provided in supporting files S1–S6.

Observational studies involving over 330,000 room observations reveal a consistent mismatch between the size of rooms provided and the size of the groups that actually use them across all office buildings. This paper investigates the causes of this misalignment and presents a model for addressing it based on observational data and advanced modelling. To address this mismatch, a planning algorithm was developed that draws on observed usage patterns to determine the number and optimal size of meeting rooms required to meet the actual demand. The model provides a structured, evidence-based framework for aligning meeting room provision with real workplace behaviour, reducing spatial inefficiencies without compromising availability. Its application has demonstrated measurable reductions in total meeting room floor area, delivering financial savings while also contributing to more sustainable use of space and a lower overall footprint. Specifically, there has been a 51.6 per cent reduction in overall space use for meeting rooms. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.

The loss of manufacturing jobs is an ongoing challenge for organized labour in Canada and a trend that has been happening for several decades. The loss of full-time, unionized factory work in Canada is commonly thought to have started in the 1990s or 2000s, but the possibility of deindustrialization was already evident in the late 1960s. This article examines the closure of the Kelvinator of Canada plant in London, Ontario, in 1969. That closure illustrates the impact of industrial job loss on workers during a period when Canada’s economy was prosperous and its manufacturing sector was robust. This analysis also reveals how a branch plant opened and expanded in Canada, and why it closed. Appliance manufacturing has never been as prominent in discussions of industrial job loss as other sectors, like automotive, but the Kelvinator closure reveals, over 55 years after it happened, that losing the London plant had a lasting impact on workers and their community while serving as a harbinger of future deindustrialization.

Despite rising personal and institutional concerns about privacy and data misuse in generative AI tools, these fears often overlook the fact that widely adopted platforms like Gmail and Outlook operate with similar technologies (cloud-based storage, machine learning algorithms, and server-side processing) without raising the same alarms. This paper argues that current AI anxieties are often misplaced, rooted more in novelty and misunderstanding than in actual increased risk. This paper presents a comparative analysis of generative AI and legacy cloud tools, identifying cognitive biases and institutional inconsistencies in data privacy concerns.

Organisations that have integrated AI solutioning and established mature information management strategy are nearly twice as likely to succeed with artificial intelligence (AI) than organisations that have not.1 Given that generative AI (GenAI) is reshaping capital markets with immediate gains in automation and analytics, those who can demonstrate responsible AI use and strong data protection measures may gain a competitive edge in industries where trust and reliability are paramount. As such, there is a drastic push for the adoption of AI technologies within the trade life cycle. While there are significant opportunities that can arise from adopting AI-related technologies, emerging data privacy directives and bespoke AI regulatory frameworks make implementation difficult to navigate. This paper serves to provide its readers with the knowledge and suite of tools requisite for navigating challenges concerning AI integration, organisation and effectuation for a responsible AI experience. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.