Digital Revolution: Exploring the E-HRM Landscape
Bibliographic record
Abstract
Electronic Human Resource Management (e-HRM) stands at the intersection of Information Technology (IT) and Human Resource Management (HRM), shaping the way organizations manage their human capital. The evolution of e-HRM has been profoundly influenced by advancements in technology, defining a new era in how HR practices are carried out within the organizations. Over the past few decades, e-HRM has become an essential component in effectively supporting organizational processes through various HR practices such as recruitment, selection, training, and more (Bissola & Imperatori, 2013; Shah et al., 2020). The concept of e-HRM encompasses the strategic use of web-based technologies to implement HRM policies and practices, thereby improving HRM effectiveness and efficiency (Bondarouk, et. al., 2017). It comprises operational, relational, and transformational practices, each serving a distinct purpose in the HRM landscape (Bondarouk, et. al., 2017). Digital tools and platforms for supporting human resources include HR information systems (HRIS), employee self-service portals, data analytics, and various online communication channels. These tools contribute to the automation and integration of human resource management processes to make them more easily accessible and responsive (Sharma & Aggarwal, 2018). Because e-HRM is evolving rapidly, traditional practices are replaced with a more employee-centric approach. The purpose of this symposium is to explore the current discourse on the role of e-HRM in organizations. Fitting with the conference theme of “Innovating for the Future – Policy, Purpose, and Organizations” this collection of papers explores different ways to leverage technology for improving recruitment, well-being and retention of employees. By looking at the different aspects of human resource management, this symposium offers an integrative perspective on the applications of technology for improving organizational performance and employee experience. Navigating Uncharted Territories: Implementing Digital Interventions for Employee Wellbeing Author: Anurag Misra; - Author: Ashish Pandey; Indian Institute of Technology, Bombay Author: Ajinkya Vijay Navare; S P Jain Institute of Management and Research Newcomers’ Social Capital Development and Organizational Socialization in a Hybrid Context Author: Mara Hilmy; U. of Glasgow Are the Differences not that Different? Age-Based Employee Preferences on Review Platforms Author: Ann Sophie Lauterbach; U. of Konstanz Author: Katharina Radermacher; Paderborn U. e-HRM Innovations in Emerging Markets, a Systematic Review Author: Miguel R. Olivas-Lujan; PennWest U. Author: Sergio Madero; Tecnologico de Monterrey Author: Yusliza Mohd-Yusoff; U. Sains Malaysia
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.009 | 0.022 |
| Scholarly communication | 0.024 | 0.029 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".