Journal of International DBA Studies - GGU

AI-Enabled Training Micro-Agents Longitudinal Effects on Adoption, Learning Efficiency, and Human Oversight

2026-01-26T19:21:31+00:00

This study reports a longitudinal assessment of micro-agents facilitated by AI in a front-line hospitality environment. It compares a supervised micro-agent deployment in 2024 with a scaled organizational deployment in 2025. Deployment maturity is the independent variable, while objective learning platform trace metrics - completion rate, assessment performance, and time-on-task - that comprise the dependent variables of this study. An exposure-adjusted active employee model is utilized to reduce potential biases due to a high employee turnover rate. A decrease in completion rate is found from 100% (656/656) in the supervised micro-agent deployment to 86.82% (8,505/9,796) in the scaled micro-agent deployment. A two-proportion z-test shows a significant difference (z = 11.52, p < .001) with a decrease in completion rate by 13.18% (95% CI [12.51, 13.85]). This decline is consistent with normalization effects commonly observed when controlled pilot interventions transition to scaled operational environments. Assessment performance increases from M = 80.10 (SD = 21.55) to M = 83.38 (SD = 23.14) with a small effect size (Welch's t (1108.70) = 4.29, p < .001; Cohen's d = 0.14; 95% CI [1.78, 4.79]). A decrease in mean time-on-task is found from 10.30 minutes (SD = 11.53) to 5.98 minutes (SD = 7.82) with a moderate efficiency effect (Welch's t(971.64) = -10.88, p < .001; Cohen's d = -0.52; 95% CI [-5.09, -3.53]). The findings provide empirical evidence regarding the effectiveness of AI-enabled micro-agent frameworks within frontline organizational learning environments. It links longitudinal behavioral traces with micro-agent frameworks, providing a replicable model for assessing the effectiveness of AI-enhanced organizational learning systems.

Reciprocal Enablement of Data Centers and AI Agents: From Silicon Foundations to Sentient Operations

2025-11-24T17:13:24+00:00

Artificial intelligence (AI) agents and data centers are in a symbiotic relationship of mutual enablement. AI agents and autonomous goal-oriented systems, which are able to perceive, reason, and act, are becoming more and more coordinated and specifically as related to mechanical, electrical, controls, and IT (MECIT). Simultaneously, hyperscale and edge data centers deliver the silicon, networks, storage hierarchies, thermal envelopes, and governance needed to scaffold and enable agentic systems to operate at scale in real time. To address this newer phenomenon, this article consolidates a cross-knowledge base (computer science, operations research, energy systems, and international business policy) and cross checks it with current industry facts to (a) explain the processes through which AI agents achieve efficiency, resiliency, sustainability, and security of data centers; (b) analyze how data center structures, supply chains, and instituting frameworks facilitate increasingly capable AI agents; and (c) appraise managerial, financial, and policy implications over the global digital infrastructure. Examples of case studies related to reinforcement-learning (RL) cooling optimization, carbon-aware scheduling, liquid-cooled AI clusters, multi-agent enterprise orchestration, and carrier-neutral interconnection fabrics are provided. Through these sample cases, we posit that agentic automation and carbon-conscious compute are complementary and facility-scale innovations (liquid cooling, power-dense racks, and edge-to-cloud fabrics). The paper ends with research and practice agenda implications based on quantifiable KPIs (e.g., PUE, WUE, partial PUE, embodied carbon per server, outage rates) and governance anchors (NIST AI RMF, ISO/IEC 42001, EU AI act).

A Dual Strategy for Digital Market Integrity: Content Credentials and Consumer Trust

2026-02-11T21:47:50+00:00

The rapid advancement of generative AI has raised concerns about visual trust in digital commerce, as highly realistic synthetic images make it challenging to distinguish between authentic and artificial content. This article examines implications for e-commerce and online food delivery, using India’s fast-growing digital marketplace as context and extending to global policy trends. Grounded in signaling theory, it argues that AI-generated visuals act as low-cost, deceptive signals in information-asymmetric environments, eroding consumer confidence and inflating post-purchase dissatisfaction. Detection-only strategies fall short due to adversarial adaptation and model drift. It proposes the Verifiable Authenticity Framework: a phased playbook that (1) embeds Content Credentials via the C2PA standard across the media supply chain; and (2) ensures consumer-facing transparency at purchase, with optional cryptographic notarization for high-value categories. This enables brands to send costly, auditable signals of authenticity, restoring trust in image-driven markets. An experimental design is outlined to assess its impact on return rates, ratings, and repurchase intent. By embedding provenance and transparency, firms can reduce operational risk, align with emerging cross-jurisdictional mandates, and build sustainable competitive advantage in the global AI-driven economy.

From AI Hype to Agentic Reality: A Readiness Lens for Sustainable Enterprise Adoption

2026-03-17T23:08:19+00:00

This conceptual article examines organizational readiness as a critical but under-theorized condition for sustainable enterprise adoption of artificial intelligence (AI) agents. While AI agents are increasingly touted as the next stage of enterprise AI, many organizations are pursuing deployment without adequately preparing the governance, accountability, and organizational conditions that are required for responsible scale. This conceptual paper investigates the above by relying upon socio-technical systems theory, dynamic capabilities, and accountability theories needed to explore the organizational preparedness as a critical, but also as an under-theorized state of enterprise adoption of AI agents. As such, this paper introduces a new term: organizational readiness debt and defines it as an accrual of hidden governance, accountability, and legitimacy risks incurred due to the hasty implementation of agentic systems. To address this gap, the paper also includes the introduction of the Agentic Organizational Readiness Framework (AORF). AORF is a multi-dimensional diagnostic framework, which consists of strategic, governance, risk, workforce, architectural, and ethical-legitimacy dimensions. Using an integrative conceptual review and illustrative case-based analytic examples, the paper argues that organizational readiness, rather than technical capability alone, is a key determinant of agentic outcomes. As such, the paper contributes to the emerging literature on AI agents and offers practical guidance for senior leaders responsible for enterprise AI transformation.

Multimodal Generative AI Agents for Biomedical Document Classification: Architecture, Ethical Boundaries, and Human-in-the-Loop Governance

2026-02-09T14:36:18+00:00

The rapid increase in biomedical research publications has made it difficult for researchers, clinicians, and policymakers to efficiently review and interpret scientific information. Traditional manual review methods and rule-based automation tools are no longer sufficient to manage the growing volume, complexity, and multimodal nature of modern biomedical literature, where important insights need to be presented through both written text and visual elements such as figures and images. To address this challenge, this study proposes and evaluates a multimodal generative AI agent for biomedical document classification and image captioning, which combines an instruction-tuned language model with a vision encoder to process abstract text and related visual content together. The agent operates within a controlled framework that includes human oversight to ensure responsible and ethical use. To study its effect a mixed-method approach was used, including quantitative performance evaluation and qualitative expert review. The model was tested on open-access biomedical papers from arXiv across four subject areas. Results indicate that the multimodal approach performs better than text-only systems in classification accuracy and contextual understanding. However, the findings also show that human supervision remains important in order to reduce risks related to bias and incorrect outputs. This study therefore offers practical and theoretical guidance for developing ethical and reliable AI systems in biomedical research settings.