Business Information Review

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.

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.

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.

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.

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.

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.