Building organizational innovation through HRM, employee voice and engagement
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose To investigate how HRM in an established organization can support employee voice and engage employees to be innovative in their everyday lived experience. Design/methodology/approach The research is based on a case study of an innovation event in an organization, where 27 employees were interviewed about the emotional, cognitive and behavioral aspects of their engagement in innovation. Findings Findings highlight the importance of continuing HRM's involvement during an entire event process to connect innovation events with everyday work. HRM has a central role in initiatives that intend to support voice and stimulate the engagement of diverse employees in innovation in established firms. Research limitations/implications This was a qualitative and cross-sectional case study of one organization and one event offered two years in a row. Practical implications In order to promote innovation, HR and senior management should foster an environment that motivates employees and promotes voice behavior (Morrison, 2014). HRM can create multiple methods of engagement, acknowledging the diversity of the workforce profile and its specific needs. HRM has an important role within an innovation strategy; as it can, together with other areas, create, develop and maintain actions that support and recognize innovative ideas and encourage employees to become actively engaged with the inclusion of innovation in their daily work life. Specifically, innovation exercises are an activity with much potential to foster voice and promote engagement towards innovation. Originality/value We develop a model proposing relationships between HRM, employee voice, employee engagement, cross-department collaboration and innovation. The study also considers the engagement of a diverse group of employees in an established company context.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 it