Business Information Review

In today’s work environments, work-life balance has grown in importance, especially for those with irregular schedules. The work-life balance experiences of librarians working afternoon and night shifts in a few academic libraries in Port Harcourt, Nigeria, were examined in this study. The study addressed lack of scholarly focus on librarians who work night shifts in urban environments that are marked by flooding, traffic jams, transportation unreliability, insecurity, and other socio-environmental stressors. The study used a qualitative design within a constructivist-interpretivist paradigm, guided by the Job Demands–Resources (JD–R) model. Semi-structured interviews with purposefully chosen library staff members (8) from three Port Harcourt university libraries, the University of Port Harcourt, Ignatius Ajuru University of Education, and Rivers State University, were used to gather data. These interviews were complemented by confirmatory follow-up interactions. Thematic analysis was done on the data, and the results show that although participants thought the university environment was generally safe during working hours, they had significant anxiety and stress due to late-night commuting, urban insecurity, transportation challenges, long travel distances, bad weather, and the removal or lack of shift-related financial incentives. While some respondents viewed advance notice and rotational scheduling as coping strategies, others characterised night duty as physically, emotionally, and financially taxing. The study comes to the conclusion that the interplay of institutional support systems, environmental vulnerabilities, and occupational demands shapes the work-life balance of nocturnal librarians in metropolitan university contexts. In order to promote healthier and more sustainable working circumstances for library employees working non-traditional shifts, it suggests specific policy changes, such as transportation assistance, staff housing choices, the reinstatement of shift allowances, and more responsive welfare and safety measures.

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.

In the current business environment shaped by artificial intelligence, the relevance of information is increasingly limited by time. This paper examines the concept of expiring knowledge, defined as information whose usefulness decreases as conditions change. We identify four main types of expiries: event triggered, continuous decay, context dependent, and structural. A temporal knowledge framework is proposed to classify business knowledge according to how quickly it expires and how frequently new knowledge is created. Case examples from finance, technology, public policy, and healthcare illustrate how expiry patterns differ across sectors. The study also presents strategies for managing expiring knowledge, including temporal tagging, decay monitoring, context aware reframing, structural decoupling, and automated invalidation. These approaches aim to help organizations move from static knowledge storage toward systems that are responsive to time. By aligning knowledge management practices with the changing value of information, organizations can reduce risk, improve decision making, and maintain strategic advantage in the era of artificial intelligence.

This paper argues that the retraction crisis in scholarly publishing is increasingly entering large language model (LLM) training datasets through commercial publisher–AI licensing agreements that provide entire journal archives without retraction filtering. Using a conceptual-analytical approach, the paper synthesizes three strands of empirical literature: studies on retraction growth and post-retraction citation, research examining LLM interactions with retracted papers, and documented publisher–AI licensing agreements. The paper advances three theoretical findings. First, it identifies a direct and commercially formalised pipeline through which retracted papers enter LLM training corpora: bulk licensing agreements in which publishers sell entire journal archives to AI developers without retraction-filtered exports, thereby bypassing the retraction notification infrastructure that already exists and functions for human readers. Second, it introduces the contamination-absence asymmetry: citation decay removes valid evidence from AI knowledge while retraction propagation inserts invalid evidence into it, producing a compound failure mode in which AI systems simultaneously know less than they should and believe more than they should. Third, it proposes the governance gap hypothesis: that retraction infrastructure — Retraction Watch, Crossref retraction flags, PubMed retraction notices — constitutes a functioning information system that stops at the human reader and was never designed to intercept AI ingestion, representing a gap in information governance whose closure is a library and information science responsibility. The study concludes that ensuring the integrity of AI-generated scientific knowledge requires stronger information governance and the active involvement of library and information science professionals in the oversight of AI training datasets.

This opinion article examines how managers can integrate AI, coaching, and the Four Functions of Management—Planning, Organizing, Leading, and Controlling—to build a learning organization and prepare their workforce for rapid change, drawing on positive psychology, strengths-based development, and reflective practices to offer a practical, evidence-informed framework for cultivating adaptability, engagement, and resilience.