Generative AI is increasingly woven into organisational information practice, challenging established assumptions about trust, accountability, and professional responsibility. Contemporary AI ethics frameworks emphasise transparency and provenance but often assume discrete, visible instances of AI use. This paper argues that the central ethical question is not where information originates but how much verification it requires before it can responsibly inform action. Drawing on foundational theories of information behaviour, including Belkin's anomalous state of knowledge, Taylor's question negotiation, and Kuhlthau's information search process, it reconceptualises interaction with generative AI as a process of task specification rather than content generation. Building on this perspective, the paper links informational tasks and contextual risk factors to differing verification burdens. Provenance remains important, but as a diagnostic indicator of likely forms of error rather than a guarantor of reliability. Human and generative systems are understood as complementary information production systems characterised by distinct patterns of failure requiring different forms of evaluative scrutiny. The paper concludes by distinguishing first-order AI literacy, concerned with the practical competence needed to use generative systems effectively, from second-order literacy, concerned with understanding how information acquires meaning, authority, and trust within social and organisational contexts. It argues that second-order literacy constitutes the key professional capability for ethical information work in AI-mediated environments.
Libraries are facing many challenges to remain relevant actors in the present cultural, educational and digital environment, and to achieve long-term success in an ever-evolving ecosystem. Looking for holistic approaches to strategic management, libraries have explored some Operational Excellence philosophies such as Lean, Agile, and Six Sigma. These approaches, originally developed in industrial and software development contexts, offer principles and tools that can be adapted to the unique context of libraries. Besides theoretical frameworks, also some empirical research has been carried out in this field, especially by academic libraries. This systematic literature review identifies and categorizes relevant research studies to provide an overview of the strategies adopted by libraries over the past decade to enhance their operations. By examining Operational Excellence within the library context, the study offers actionable insights to support the development and implementation of Operational Excellence practices tailored specifically to academic libraries.
Artificial intelligence (AI) is increasingly embedded in organizational information environments, reshaping how knowledge is created, interpreted, and applied. As AI systems become active participants in organizational information processes, employees are required not only to operate digital technologies but also to interpret algorithmic outputs and collaborate effectively with intelligent systems. While prior research has extensively examined digital literacy, human–AI collaboration, and AI literacy, these streams of literature remain largely fragmented, providing limited conceptual integration regarding the competencies required for knowledge work in AI-enabled organizations. This study develops a conceptual framework that positions AI literacy as an extension of digital literacy within contemporary knowledge work environments. Drawing on human capital theory and sociotechnical systems theory, the paper explains how digital literacy provides foundational capabilities for navigating digital infrastructures, while AI literacy enables employees to critically evaluate algorithmic outputs and collaborate effectively with AI systems. The framework further proposes that human–AI collaboration acts as a key mechanism through which digital and AI-related competencies enhance knowledge work outcomes. The study contributes to the literature in three ways. First, it integrates fragmented research on digital literacy, AI literacy, and knowledge work into a unified conceptual perspective. Second, it reconceptualizes AI literacy as an emerging capability that extends digital literacy in AI-mediated organizational environments. Third, it proposes a theoretical framework explaining how human capabilities interact with intelligent technologies to support knowledge creation, decision-making, and organizational learning. By highlighting AI literacy as a strategic organizational capability, this study provides implications for information professionals and managers seeking to develop human competencies required for effective human–AI collaboration. The framework also provides a foundation for future empirical research examining how AI-related competencies influence knowledge work performance in digitally transformed organizations.
Smart library research is expanding quickly, but evidence remains fragmented across technologies, metrics, and evaluation methods. Many studies focus on individual tools, such as RFID shelves or occupancy sensors, while governance, user capacity, and contextual constraints receive limited attention. This fragmentation restricts cross-site learning and slows the development of coherent smart library strategies. Addressing this means the study reviews literature using Google Scholar as the sole database. Iterative keyword clusters covered IoT, RFID, AI/ML, AR wayfinding, environmental sensing, governance, and user capacity. Eligible sources included conceptual papers, empirical studies, systematic reviews, and practitioner reports, all mapped using the People, Place, Platform, and Policy (P 4 ) framework. Structured templates guided data extraction and quality appraisal, and findings were synthesized narratively due to diverse methodologies. The review finds consistent evidence that RFID-enabled shelves enhance inventory efficiency, occupancy systems improve space management, and IoT–ML environmental solutions increase user comfort. AR navigation tools show emerging potential for inclusive wayfinding. However, human capacity limitations, weak governance structures, infrastructure gaps, and immature AI safety practices repeatedly constrain implementation. The review concludes that smart libraries are socio-technical ecosystems and calls for baseline KPIs, embedded governance, and capacity development, supported by phased, context-sensitive roadmaps for scalable, equitable adoption.
The rapid emergence of agentic artificial intelligence (AI) has outpaced scholarly discussions of its implications for library reference services, creating a significant conceptual gap within the broader discourse on the Fifth Industrial Revolution (5IR). Existing studies have focused largely on chatbots, robotic technologies, and conventional AI applications, with limited attention to autonomous AI agents capable of reasoning, planning, and executing complex information tasks. This conceptual review addresses that gap by examining the transformative potential of agentic AI for reference services within the human-centred philosophy of the 5IR. Drawing on recent literature on agentic AI, Industry 5.0, and library and information science, the paper synthesises current knowledge to identify the defining characteristics, emerging applications, opportunities, challenges, and strategic implications of agentic AI for librarians. The review indicates that agentic AI can significantly enhance reference services through autonomous literature searching, personalised research support, intelligent knowledge discovery, multilingual assistance, and complex query resolution. Nevertheless, concerns relating to algorithmic bias, hallucination, transparency, data privacy, intellectual property, and professional accountability require careful governance. The paper argues that successful implementation depends on AI literacy, ethical oversight, human–AI collaboration, institutional governance frameworks, equitable access, and continuous evaluation. It provides a strategic framework for advancing innovative, ethical, and sustainable AI-enabled reference services.
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.
The emergence of agentic artificial intelligence (AI) systems capable of initiating actions, coordinating tasks, and adapting decisions with minimal human intervention marks a significant inflexion point for the library sector. Unlike earlier forms of automation and assistive AI, agentic systems increasingly participate in organisational routines and decision-making processes, raising fundamental questions about professional authority, accountability, and ethical stewardship in information work. This paper critically examines the adoption of agentic AI in libraries through the normative lens of Industry 5.0, a paradigm that prioritises human-centricity, ethical governance, sustainability, and resilience over purely efficiency-driven automation. Drawing on recent scholarship and practice, the paper argues that agentic AI should not be understood as a neutral technical upgrade, but as a strategic and policy intervention with far-reaching institutional implications. It highlights both the strategic opportunities of agentic AI, such as enhanced organisational intelligence and reduced administrative burden and the emerging risks, including deskilling, accountability diffusion, and over-reliance on algorithmic judgement. The paper contends that without deliberate governance frameworks, agentic AI may inadvertently undermine the professional values that underpin librarianship. To address this tension, the paper outlines key policy and strategic considerations for library leaders and decision-makers, emphasising calibrated autonomy, ethical oversight, data governance, and continuous professional development. As a result, this paper positions agentic AI as compatible with the promise of Industry 5.0 only when human judgment remains central to library governance and service delivery.
The advent of big data analytics in universities libraries information management offers enormous potential as well as formidable obstacles. This study therefore examined perceptions and use of big data analytics for information management among librarians in selected universities libraries in Kwara State, Nigeria. The study adopts cross-sectional research design and questionnaire was adopted for data collection. Using purposive sampling technique, the sample size of the study is forty-eight (48). The results revealed that big data analytics can be used in planning, organising, and structuring information management. Findings showed that more than half of the librarians lowly used QlikView to process large datasets and Splunk platform for machine data analysis for information management. The results indicated that Tableau, Apache Spark, SAS Visual Analytic, and Apache Hadoop were moderately used for information management. Results demonstrated that BDA are used to analyse user data and research data. Results showed that data quality issues, complex data, epileptic power supply and diversity data types are challenges associated with usage of big data analytic. The study provides insights into how big data analytics is perceived and utilised by librarians in university libraries, while addressing unique challenges such as local technological limitations and specific usage patterns of big data tools. The study highlights a functional perspective of big data analytics, which demonstrates its practical applications in organising, structuring, and improving the quality of information handled by university libraries.
There has been a continuous increase and interest in the discussion regarding gamification in libraries and information centers. This necessitates the importance of discussing the ethical issues that concerns the adoption and use of gamified library services. This study adopts the narrative review approach to provide a discursive review of evidence of past literature, which were consulted both in libraries and the Internet. The study findings showed that ethical issues relating to the adoption of gamified library services include safety of data to be stored in the cloud, data integrity, prevention of manipulation and exploitation of librarians, avoidance of psychological discomfort, and so on. The study showed that ethical issues regarding the use of gamified library services include game overloading, cheating the game system, advantage for technology-savvy users, librarians’ autonomy and privacy, and so on. Future research directions in ethical issues in gamified library services were discussed.
Digital repositories have become increasingly important for university libraries to store and provide access to scholarly materials in the digital age. However, concerns about the trustworthiness of these repositories persist. This study aims to investigate the trustworthiness of digital repositories for university libraries. A systematic review methodology was employed to systematically search, select, and analyse 45 relevant studies addressing the trustworthiness of digital repositories in university libraries. The findings indicate that while most university libraries strive to maintain trustworthy repositories, challenges such as inadequate infrastructure, lack of technical skills, financial constraints, a lack of management awareness, and copyright issues still exist.
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.
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.
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.
