Purpose This paper examines how regular use of generative AI enhances productivity, but somehow, in contrast, reduces the opportunities for employees to utilize critical thinking skills and translate their hands-on experience into organizational learning. Design/methodology/approach The paper is a problem-based conceptual synthesis linking AI literacy, organizational learning, human-AI collaboration, evidence from knowledge workers, and knowledge collapse theory. On the other hand, human-AI augmentation and disengagement are considered opposing risks from which five workplace safeguards are derived. Findings The paper shows that the issue is not the use of GenAI, but organizations that reward flawless automation output while ignoring verification, human reasoning, and visible employee learning. The paper introduces the THINK framework interlinking organizational practices: Transparency, Human judgment, Intellectual engagement, Navigation of evidence and Knowledge construction. Research limitations/implications The THINK framework conceptual synthesis is selective and not empirically tested. Future research should validate the application effect across different sectors, industries and levels of GenAI adoption. Practical implications The THINK framework can be integrated into employee training, performance management discussions, decision-making and AI governance systems. Originality/value The paper transposes the traditional argument about GenAI-mediated cognition into a simple, auditable framework that managers can apply on the job as coaches, learning professionals, and AI governance teams.
Purpose This paper reconceptualizes green innovation as an integrative organizational capability rather than a standalone environmental or technological initiative. It examines how organizational, institutional and technological conditions shape its contribution to sustainable organizational performance. Design/methodology/approach The paper adopts a narrative literature review, drawing on the natural resource-based view (NRBV) to synthesize research on organizational capabilities, organizational learning, stakeholder relationships, institutional conditions and digital technologies. Findings The review shows that green innovation contributes to sustainable performance when supported by complementary capabilities, organizational learning, stakeholder collaboration, favorable institutional conditions and digital technologies. It further suggests that technologies alone are insufficient; outcomes depend on aligning organizational capabilities with contextual conditions. Research limitations/implications As a conceptual review, the proposed framework requires empirical validation across different organizational and institutional contexts. Practical implications Managers should embed green innovation within organizational strategy, strengthen learning, strengthen stakeholder collaboration, leverage digital technologies and adopt context-specific sustainability strategies. Originality/value The paper advances an integrative capability-based perspective by positioning green innovation capability as the mechanism through which organizational resources, learning, stakeholder relationships, institutional conditions and digital technologies are combined to generate sustainable performance.
Purpose This paper examines leadership entrenchment as a learning problem in which succession risk grows when judgment, legitimacy, and key relationships become tied to one leader. Design/methodology/approach The paper adopts a conceptual, practitioner-informed approach. It draws on repeated observations across anonymized organizational settings and integrates leadership continuity and learning-oriented leadership ideas to identify patterns of leadership entrenchment. Findings The paper shows that succession vulnerability emerges from organizational dependence, not leadership continuity alone. SHIFT enables managers to identify and loosen five dependence risks: concentrated knowledge, weak accountability for successor development, limited successor exposure, hidden single-point dependencies, and untracked renewal readiness. Research limitations/implications The paper is conceptual and practitioner-informed. Future research can examine how leadership dependence forms and how renewal readiness can be measured across organizational contexts. Practical implications Managers can use SHIFT to surface dependence risks, share knowledge, increase successor exposure, and make renewal part of everyday leadership practice. Originality/value The paper extends succession and leadership development discussions by showing how leadership entrenchment weakens organizational learning before a formal transition becomes urgent.
Purpose This paper explores cognitive offloading in higher education arising from the increasing use of Generative Artificial Intelligence (GenAI). It analyses the dual role of GenAI, recognizing its potential to enhance learning while posing risks to critical thinking, independent reasoning, and cognitive resilience. Design/methodology/approach This paper synthesizes literature on GenAI, cognitive offloading, and critical thinking, to examine the implications of GenAI-supported learning for higher education and learning and development professionals. Findings GenAI offers educational benefits, including personalized support, accessibility, and improved learning efficiency. However, excessive reliance on GenAI-generated content may encourage cognitive offloading, limiting critical reflection, reasoning, and independent problem-solving. By applying the Tool-Tutor-Crutch framework, this paper demonstrates that the educational value of GenAI depends on whether it supports rather than substitutes meaningful cognitive engagement. Research limitations/implications As a conceptual viewpoint, this paper does not present empirical evidence on the effects of GenAI on cognitive offloading. Future research should examine how GenAI use influences critical thinking, cognitive resilience, and learning outcomes across different educational contexts and levels of GenAI dependence. Practical implications Educational institutions should design learning and assessment practices that foster critical evaluation, cognitive resilience, and ethical human-GenAI collaboration, ensuring that GenAI functions as a tool and tutor rather than a crutch. Originality/value This paper moves the discussion beyond academic integrity and technology integration by positioning cognitive offloading as a pedagogical challenge. It applies the Tool-Tutor-Crutch framework to provide a practical lens for promoting cognitive resilience and responsible human-GenAI collaboration in higher education.
PurposeThis paper maps the thematic evolution of technology-focused reverse mentoring (2015–2025), specifically investigating the transition from “digital literacy” to “AI fluency” and identifying critical gaps regarding ethical governance. Design/methodology/approachA general bibliometric analysis was conducted using data from major academic databases. VOSviewer software was utilized to visualize keyword co-occurrence networks, identifying dominant conceptual clusters and temporal trends across the broader management and organizational development discipline. FindingsThe analysis reveals five distinct clusters: corporate strategy, social equity, well-being, mentoring mechanics and crisis adaption. Crucially, a “technological lag” is evident; while “digital transformation” is a central node, keywords related to “Generative AI,” “Prompt Engineering,” and “Ethics” are conspicuously absent from the core network, highlighting a disconnect between current literature and Industry 4.0 realities. Research limitations/implicationsThe findings imply an urgent theoretical need to pivot from general “digital adoption” models to “AI competency” frameworks. Practical implications HR practitioners should transition from generic digital up skilling to “Strategic AI mentoring,” where junior employees guide leaders on ethical AI application and workflow integration. Originality/valueThis study uniquely quantifies the lack of AI and ethics discourse in the general body of knowledge, offering a novel roadmap for integrating Generative AI into competency-based training assessments.
