Retaining the green workforce, employees driving sustainability and environmental innovation, is essential for organizational resilience and long-term environmental goals. While prior Green HRM research has primarily relied on survey-based methodologies and theoretical frameworks to examine retention factors, these approaches lack predictive capability and fail to provide actionable, employee-specific insights. This study advances beyond descriptive and correlational analyses by employing explainable artificial intelligence (XAI) to develop a transparent, data-driven framework for identifying attrition drivers and quantitatively evaluating retention strategies. Unlike existing studies that rely on self-reported perceptions, our approach leverages objective HR data and machine learning to predict individual-level attrition risk with calibrated probabilities. Leveraging the IBM HR Analytics dataset as a proxy for sustainability-focused roles, we construct an interpretable logistic regression model with strong predictive performance and isotonic regression calibration. Global and local interpretability techniques, including SHAP, LIME, and permutation importance, show that non-monetary factors, such as excessive overtime, frequent business travel, and limited promotion opportunities, have a greater impact on turnover risk than salary levels. These findings align with Green Human Management (Green HRM) principles, which emphasize work-life balance and employee well-being. Crucially, our policy simulation framework, absent from prior Green HRM studies, demonstrates that eliminating overtime could reduce predicted attrition probability by 17.35% for affected employees, potentially retaining 31 staff members, substantially outperforming modest salary adjustments. This work expands the value of predictive AI into HR analytics by consolidating HR analytics with Green HRM through a novel methodology that bridges the gap between prediction and actionable intervention. It represents the first systematic integration of XAI-based predictive modeling with counterfactual policy simulation in environmentally conscious sustainable organizations.
Examining generative AI through the lens of transformative learning theory, this article conceptualizes AI as a potential disorienting dilemma for adult learners and educators. Critiquing AI applications using Mezirow's (Cranton & Taylor, 2012) learning theory, this article shows that generative AI simultaneously short-circuits the kinds of cognitive engagement needed for transformative learning and upsets the learners' most basic assumptions about expertise, intelligence, and learning. This article reworks the logic of transformative learning theory and establishes specific conditions that make AI-mediated learning possible as well as certain conditions in which AI-mediated learning precludes perspective transformation. The article has three theoretical contributions: (1) transformational learning necessitates irreducible personal engagement with premise-level complexity, (2) technological disruption triggers transformational learning only when it upends ontological, as opposed to procedural, assumptions, and (3) AI-mediated learning can only scaffold but not replace critical reflection, situating it as a "sophisticated" disorienting dilemma.
The fast-paced progress in quantum computing introduces significant new challenges for digital forensics by undermining classical cryptographic mechanisms that protect digital evidence. Algorithms such as Shor's and Grover's threaten the long-term reliability of traditional hash functions, digital signatures, and encryption schemes, thereby compromising the integrity, authenticity, and confidentiality of evidence. This paper investigates how quantum entanglement can be leveraged to enhance the security of digital forensic evidence in the post-quantum era. A hybrid quantum-classical forensic framework is proposed, integrating three entanglement-based components: an entanglement-assisted quantum hashing mechanism for integrity assurance, a CHSH nonlocality-based protocol for authenticity verification, and a BBM92 quantum key distribution scheme for confidentiality and secure chain-of-custody management. All components are implemented using IBM Qiskit and evaluated with the AerSimulator under realistic Noisy Intermediate-Scale Quantum conditions. Experimental results measured using Hamming distance, CHSH S-values, and Quantum Bit Error Rate demonstrate improved tamper detection, reliable authenticity validation, and strong overall confidentiality.
In today’s digital environment, social media platforms offer many free services that have become integral to users’ daily lives. However, there are serious privacy concerns associated with this convenience, especially given how artificial intelligence (AI) is being incorporated into user data management. This study explores the social and ethical dimensions of the trade-off between the benefits of free platform use and the privacy concerns emerging from AI-powered targeted advertising. Users are generally more concerned about how AI algorithms use their data for advertising and personalization than they are about the collection of their data. AI technologies have significant issues with surveillance, behavioral profiling, and data commodification, even though they improve user experience through personalization. The study presented is thorough literature-based research that explores user knowledge gaps and perceptions of risk and awareness of AI-based features in the use of social media. It has been discovered that there has been an increase in attention to data collection, but a lack of knowledge about the AI mechanisms, thus leading to an overall problem of the so-called privacy paradox, in which continued use is promoted despite the lack of trust in it. The study suggests the proposed framework model of the study to guide the evaluation of the user attitudes and intentions. These lessons are meant to help social media companies; policy initiators and digital rights activists understand how to strike a balance between technological advances and strong privacy safeguards. Study phases that will be applied later will consist of a Delphi-based qualitative analysis of the internet privacy experts.
The microexplosive processes occurring on the surface of a copper electrode that accompany a radiofrequency vacuum breakdown have been numerically simulated. The use of a wide-range equation of state for matter in the calculations has made it possible to study the dynamics of change in the phase state of the electrode material during the breakdown. The conditions for the formation of microprotrusions on the rim of the crater formed due to the displacement of liquid metal from the microexplosion zone have been determined. The microprotrusions contribute to self-sustaining of the discharge or to the initiation of a new breakdown when the electrode is exposed to the subsequent pulse of the electromagnetic wave.
