Talent & Employee Engagement - Crossref Bibliographic Metadata

This dataset provides detailed bibliographic metadata records for scholarly publications related to 'Talent & Employee Engagement', encompassing concepts like talent management, employee engagement strategies, and corporate ethics, as retrieved from Crossref.org. This metadata corpus facilitates in-depth exploration of the academic discourse surrounding these critical human capital management areas. Contextual Overview of Talent & Employee Engagement: 1. Definition and Context: Talent Management refers to the strategic attraction, identification, development, engagement, retention, and deployment of individuals who are considered particularly valuable to an organization. Employee Engagement describes the level of an employee's psychological investment in their organization and their commitment to its goals. These concepts gained prominence as organizations recognized human capital as a key differentiator and driver of performance, especially in knowledge-based economies. 2. Strengths and Weaknesses: Effective talent and engagement strategies can lead to higher productivity, improved retention, greater innovation, and enhanced employer branding. Strengths include fostering a positive work environment and aligning individual capabilities with organizational needs. Challenges involve accurately identifying and developing talent, creating a genuinely engaging culture (beyond superficial perks), measuring engagement effectively, and addressing diverse employee expectations in a multi-generational workforce. 3. Relevance and Research Potential: Managing talent and fostering engagement are paramount for organizational success and sustainability in competitive labor markets. These are central themes in Human Resource Management, Organizational Behavior, and Leadership studies. Research opportunities include the impact of remote/hybrid work models on engagement, the role of technology (HR tech, AI) in talent processes, strategies for inclusive talent management, linking engagement to tangible business outcomes, and the influence of ethical leadership and corporate purpose on employee commitment. 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 Talent & Employee Engagement. 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 Talent & Employee Engagement. 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., ("employee engagement" OR "talent management" ...) AND ("human resources" 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 'Talent & Employee Engagement' 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 Talent & Employee Engagement 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 Talent & Employee Engagement should consult the corresponding datasets in the Raw Extracts Dataverse and the Comparative Indices Dataverse.