Digital Sustainability for Human Resource Management Canvas Meta-Synthesis Approach
Bibliographic record
Abstract
In the era of Digital Transformation (DT) and Sustainable Development (SD), the pivotal role of Human Resources (HR) in addressing business challenges has become increasingly evident. These challenges directly impact different HR departments and processes, prompting a need for a strategic overhaul. HR management should continuously adapt its service delivery model to align with the business model and create a balance between internal and external organizational expectations and employee needs, considering the two fundamental challenges of digital transformation and sustainable development, in an agile manner. The critical role of sustainable human resource management in fostering overall business sustainability is evident. However, in many cases, HR management has failed to recognize and demonstrate its cost structure, revenue flow, and social and environmental benefits transparently for the business. This research focuses on presenting the sustainable digital human resource management canvas and its connection to the business canvas to achieve sustainability in the digital era. Employing a qualitative research methodology, this study conducts a meta-synthesis of two pivotal concepts-sustainable human resource management and digital human resource management- leveraging data from reputable databases including Science Direct, Scopus, and Web of Science. Following meticulous screening and examination, a total of 17 articles from Q1-ranked journals were selected as the final articles for a more detailed and in-depth review. The findings from the meta-synthesis results consist of 5 dimensions and 23 components, ultimately presenting the sustainable digital human resource management canvas by modeling it after the business canvas. The distinct feature of this canvas, compared to other similar and conventional models, is the inclusion of individual sustainability in the value proposition and the creation of a digital value component, including artificial intelligence, the Internet of Things, and big data.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".