Narrative Paradigms: Emotional Intelligence and Strategic Imperatives in HR Professional Designation Preparation
Bibliographic record
Abstract
This study explores the emotional dimensions of HR analytics education among MBA students preparing for the Certified Professional in Human Resources (CPHR) designation. Using qualitative data from faculty narratives at University Canada West (UCW) and insights from prior research, the study examines students’ emotional responses to People Analytics Platforms (PAPs) and the integration of emotional intelligence and cultural competence into HR curricula. Grounded in Bourdieu’s theory of capital, Critical Social Justice Theory, and the Technology Acceptance Model (TAM) extension, the research highlights how emotional intelligence, cultural capital, and social justice considerations shape students’ attitudes toward HR analytics tools. Findings reveal a range of emotional reactions—from curiosity and enthusiasm to frustration and apprehension—underscoring the role of emotional intelligence in managing technological challenges and enhancing decision-making. The integration of the Attitude, Behavior, Knowledge (ABK) model and Emotional Intelligence (EI) Theory further emphasizes emotional awareness and regulation as critical skills for future HR leaders. Practical implications suggest curriculum enhancements that foster emotional competence alongside technical proficiency. The study contributes to HR analytics education by highlighting the interplay between emotional dynamics and technological adoption, offering recommendations for MBA educators to create supportive learning environments. This holistic framework aims to develop students’ analytical capabilities, emotional intelligence, and cultural fluency, equipping them to address the complexities of modern HR practice.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".