Generative AI (GenAI) is now part of everyday organisational knowledge work, affecting how information is gathered, interpreted, argued over, and stored. It should not be treated as a neutral aid: in many settings it can function as an active participant in collective intelligence, shaping what groups notice and how issues are framed and settled. This paper sets out a dependency-structured framework that connects information acquisition, sensemaking and framing, shared reasoning, coordinated action, and organisational memory. The framework is intended as a diagnostic and explanatory lens for organisational analysis, rather than a predictive or causal model. A key implication of the framework is that weaknesses in early stages can propagate upward and distort later decisions, even when outputs appear faster or more coherent. A hypothetical case of a mid-sized financial advisory firm illustrates how GenAI can strengthen performance while risking increased epistemic fragility when foundational processes are compressed or bypassed. The paper ends with diagnostic prompts and governance principles for information professionals, arguing that GenAI tends to amplify existing organisational tendencies rather than reliably augment intelligence.

Librarianship is evolving from a custodial and preservation-focused profession into a dynamic, innovation-driven practice that integrates technology, user-centred services, and ethical stewardship. Traditionally, libraries have upheld principles of intellectual freedom, accessibility, preservation, and neutrality, serving as trusted custodians of knowledge and cultural memory. Today, digital technologies including artificial intelligence, extended reality, blockchain, and generative AI are reshaping how information is created, curated, and accessed, extending libraries’ reach into hybrid physical–digital ecosystems. This transformation is driven by technological innovation, globalisation, and changing user behaviours, which demand seamless access, personalisation, and participatory learning. Modern libraries are emerging as innovation hubs, incorporating makerspaces, immersive learning, and collaborative knowledge creation, supported by a workforce that is digitally literate, adaptable, and ethically grounded. Strategic pathways, such as policy frameworks, hybrid service models, collaborative partnerships, and continuous professional development, are critical to sustaining library relevance and resilience. By balancing traditional values with technological advancement, libraries can remain inclusive, interactive, and ethically accountable knowledge ecosystems, navigating the complexities of the twenty-first-century information landscape.

This opinion paper examines the transformative relationship between Generation Z and business information, arguing that this cohort is pioneering a bottom-up, information-driven approach to economic self-reliance. Unlike previous generations, Gen-Z leverages a diverse digital ecosystem from social media platforms to specialized forums to gather, synthesize, and apply business intelligence for career advancement and venture creation. While this represents a significant democratization of entrepreneurial opportunity, it also presents critical challenges, including information overload, credibility assessment, and a potential foundational skills gap. This paper discusses these promises and perils, contending that the current institutional support systems are lagging behind these informal learning models. The conclusion offers a multi-stakeholder call to action, proposing that educators, policymakers, and business leaders must collaboratively build a scaffolded ecosystem to effectively harness Gen-Z’s unique information sensibilities for sustainable economic growth.

AI disruptions will bring vast benefits and challenges to companies. One key question remains: How can companies overcome corporate accountability challenges in the AI age? To answer this question, the article explores how to assign accountability when artificial intelligence systems are involved in decision-making. As AI becomes more widespread, who should be held responsible if these systems make poor choices is unclear. The traditional top-down accountability model, from executives to managers, faces challenges due to AI’s black-box nature. Approaches such as holding developers or users liable have limitations as well. It is argued that shared accountability across multiple stakeholders may be optimal but supported by testing, oversight committees, guidelines, regulations, and explainable AI Concrete finance, customer service, and surveillance examples illustrate AI accountability issues. The paper summarizes perspectives from academia and business practice on executives’ and boards’ roles, including mandating audits and transparency. It concludes that while AI accountability models remain debated, decision-makers must take responsibility for the technologies deployed. The article suggests combining prescriptive accountability rules and data quality evaluation frameworks can optimize resources to enhance AI-assisted decision-making, align regulatory requirements, respect stakeholders, and exploit competitive advantage using advanced technology.

Organizations continue to invest heavily in knowledge management (KM), yet many fail to realize measurable business value. The primary cause is not technological immaturity but a persistent mismatch between how knowledge is managed and how work is actually performed. This article argues that meaningful cost reduction and operational durability emerge only when three elements are combined: (1) AI-enabled knowledge management platforms, (2) Radical Knowledge Management (Radical KM) principles that give precedence to workflow and decision logic over documentation volume, and (3) Knowledge Retention Boards (KRBs) as a structured mechanism for capturing institutional knowledge. Drawing on applied KM practice within the broader information management tradition, the article reframes knowledge not as static content but as operational infrastructure, capable of reducing training costs by 20–40%, mitigating risk, and preserving expertise at scale.

This paper evaluates the bibliographic and full-text coverage of 15 resources that can be used to discover and access the economics literature. It compares the coverage of conventional library databases such as Scopus and EconLit with that of 10 free, alternative discovery/access mechanisms: a scholarly search engine (Google Scholar), two web-based scholarly databases (Dimensions and OpenAlex), five academic social networks (Academia.edu, arXiv, RePEc, ResearchGate, and SSRN) and two pirate sites (Anna’s Archive and Sci-Hub). The analysis, based on known-item searches for 125 works cited in the Journal of Economic Literature , reveals that the most comprehensive alternative discovery/access mechanisms offer more complete bibliographic and full-text coverage than any of the conventional databases. Google Scholar, OpenAlex, and ResearchGate are among the most comprehensive sources of bibliographic records, while Google Scholar, OpenAlex, and the two pirate sites provide free, full-text access to more of the target documents than any other resources. Although several of the alternative discovery/access mechanisms are deficient in terms of their user interfaces, search capabilities, and metadata, they nonetheless provide excellent bibliographic and full-text coverage of the economics literature. In contrast, no conventional single-subject database covers more than 62% of the target documents.

The growing emphasis on sustainable business performance has driven organizations towards the adoption of innovative practices and technologies. The leads to an inclination towards integration of Artificial Intelligence (AI) into Knowledge Management Systems (KMS). A new paradigm of smart knowledge-driven decision-making is emerging and reshaping the business with enhanced sustainability. Despite the growing importance of the topic, there is a lack of comprehensive review encompassing all the three dimensions. This research paper is based on bibliometric analysis of existing literature to explore the relationship between KMS, AI adoption, and sustainability. Documents for analysis were retrieved from the database of Scopus published between the year 2005 to 2024. In total 66 documents were reviewed to investigate the key research trends, influential studies and emerging themes in the field. To analyse the growth of knowledge domain, author contributions and to explore the collaborative networks, Vos Viewer was used as a research tool. By visualizing the trends, the study illustrates the role of AI in enhancing KMS and advancing environmental, social and economic sustainability. Besides providing a quantitative overview of the academic literature, our results also outline the framework of the existing literature on AI and KMS.

Digital storytelling has revolutionized the preservation and dissemination of cultural heritage by integrating Virtual Reality (VR) and Augmented Reality (AR). These technologies provide immersive experiences that engage diverse audiences, particularly the younger generation accustomed to digital media. VR offers fully immersive environments, transporting users to different times and places, while AR overlays digital information onto the real world, enhancing interaction with physical surroundings. This dual approach enriches storytelling, fostering emotional connections and active participation, thereby transforming users from passive observers into co-creators of cultural narratives. The application of VR and AR supports cultural institutions facing resource limitations, offering innovative solutions without extensive physical infrastructure. Theoretical frameworks like the Narrative Immersion Model and transmedia storytelling emphasize the shift from passive consumption to interactive engagement, highlighting the role of digital storytelling in enhancing cultural identity and memory. By fostering inclusivity and diversity, these technologies ensure multiple voices are represented, promoting social justice and community engagement. This paper explores the roles of VR and AR in digital storytelling for cultural heritage, analyzing their impact on audience engagement and effectiveness in preserving cultural narratives across various contexts.

With the rapid development of artificial intelligence (AI), the importance of AI-driven enterprises has become increasingly prominent. This paper selects 32 core documents from the Web of Science database from 2020 to 2025, uses a systematic literature review and scientometric methods to explore the impact of AI on enterprise agility and performance, and analyzes annual publishing trends, key contributions and important journals. The study found that AI is shifting from isolated applications to promoting digital transformation and industrial upgrading. Through keyword, co-author, country and document co-citation analysis, four major research themes were identified: the integration of AI and digital technology, AI empowering enterprise agility and performance, AI-driven project and supply chain management, and the strategic value and sustainability of AI. At the same time, future research directions are pointed out, including AI management challenges, digital transformation roles and performance improvement paths. This study provides a systematic review and research guidance for AI-enabled enterprises.