Technologies and Their Stakeholders in HRM: Insights Derived From Worker-Centered Experiences
Bibliographic record
Abstract
Technologies are transforming all facets of human resource management in organizations, from hiring processes to training programs to the management and structure of workflows. These changes affect not only the work and decision-making of managers and other organizational leaders, but also of employees. Employees face a wide range of impacts, from reconfigured opportunity structures in hiring, to demand for new skills sets and training, and to changed discretion and autonomy in undertaking their work. At the same time, technologies also bring new stakeholders into the fold. Developers and vendors are designing artificial intelligence applications to recruit and screen job applications, training protocols to ensure sustained use of their products, and algorithmic management applications to automate decisions regarding labor allocation and workflow. This symposium takes these two trends – the transformation of various HRM applications through technology and the addition of new stakeholders to HRM practices – as a starting point. Specifically, we focus on three processes and their technological transformations: AI-enabled recruitment and hiring; training; and algorithmic management. Empirical studies on each emphasize the experience of employees with such technologies and illustrate how the role of additional stakeholders—namely, the developers and vendors of technology—are integral to this experience. The proposed symposium thus offers a range of insights as to how various stakeholders may collaborate to derive equitably distributed value from technologies designed to improve organizations’ human resource management practices and explores challenges and limitations to doing so. From Resumes to Algorithms: Employers' Use of Algorithm-Driven Recruitment and Worker Implications Author: Xiangmin Liu; Rutgers U., New Brunswick Author: Adrienne E. Eaton; - Author: Liang Zhang; New York U. Author: Todd Vachon; Rutgers U., School of Management and Labor Relations Trained and Constrained: How Vendors Shape Technology Use during Digital Transformation Author: Jenna E. Myers; U. Of Toronto-Ind Rel Lbr Brokerage from the Bottom Up: Workplace Leaders as Algorithmic Brokers in Hotel Housekeeping Work Author: Christine A. Riordan; U. of Illinois at Urbana-Champaign Author: Hye Jin Rho; Michigan State U. Author: Yeaseul Hur; U. of Illinois at Urbana-Champaign Author: Patricia Tabarani; U. of Illinois at Urbana-Champaign
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".