Knowledge Management - Crossref Bibliographic Metadata

This dataset provides detailed bibliographic metadata records for scholarly publications related to 'Knowledge Management' (KM), as retrieved from Crossref.org. This metadata corpus facilitates in-depth exploration of the academic discourse surrounding the strategies and practices for leveraging organizational knowledge. Contextual Overview of Knowledge Management: 1. Definition and Context: Knowledge Management (KM) encompasses the processes of creating, sharing, using, and managing the knowledge and information of an organization. It aims to improve organizational performance by leveraging intellectual assets. Gaining significant attention from the 1990s, KM emerged from the recognition that intangible assets, particularly knowledge, are critical sources of competitive advantage in increasingly information-intensive economies and across diverse organizational settings. 2. Strengths and Weaknesses: Effective KM can lead to improved decision-making, enhanced innovation, increased efficiency, better employee skills, and a stronger learning culture. Strengths include fostering collaboration and retaining tacit knowledge. Challenges involve motivating knowledge sharing (cultural barriers, "knowledge is power" mentality), implementing effective KM systems and technologies, measuring the ROI of KM initiatives, and ensuring knowledge is relevant and up-to-date. Success often depends more on cultural and people factors than on technology alone. 3. Relevance and Research Potential: KM remains crucial for organizations navigating complexity, fostering innovation, and managing talent in the digital age. It is a key area in information science, organizational behavior, and strategic management. Research opportunities include the role of AI and big data in KM, managing knowledge in virtual and distributed teams, the impact of social media and collaboration platforms on knowledge sharing, measuring intellectual capital, and strategies for fostering continuous learning and unlearning in dynamic environments. Dataset Structure and Content: The dataset consists of one or more archives. Each archive contains a series of approximately 850 monthly folders (e.g., spanning from January 1950 to January 2025), reflecting a granular month-by-month process of metadata retrieval and curation for Knowledge Management. Within each monthly folder, users will find several JSON files documenting the search and filtering process for that specific month: term_results/: A subfolder containing JSON files for results of initial broad keyword searches related to Knowledge Management. merged_results.json: Aggregated results from these individual term searches before advanced filtering. filtered_results.json: Results after applying a more specific, complex Boolean query (e.g., ("knowledge management" OR "intellectual capital" ...) AND ("organizational" OR ...)) and exact phrase matching to refine relevance. The exact query used is detailed within this file. final_results.json: This is the primary file of interest for most users. It contains the curated, deduplicated (by DOI) list of unique publication metadata records deemed most relevant to 'Knowledge Management' for that specific month. Includes fields like Title, Authors, DOI, Publication Date, Source Title, Abstract (if available from Crossref). statistics_results.json: Summary statistics of the search and filtering process for the month. This granular monthly structure allows researchers to trace the evolution of academic discourse on Knowledge Management and identify relevant publications with high temporal precision. For an overview of the general retrieval methodology, refer to the parent Dataverse description (Management Tool Bibliographic Metadata (Crossref)). Users interested in aggregated publication counts or trend analysis for Knowledge Management should consult the corresponding datasets in the Raw Extracts Dataverse and the Comparative Indices Dataverse.