Sustainable HRM in Tradition-Based Enterprises: Bridging Heritage and Modern Business Models
Bibliographic record
Abstract
Abstract This paper seeks to understand how Human Resource Management (HRM) contributes to sustaining small, tradition-based enterprises such as handloom weaving, organic farming, and forest-based livelihoods. These sectors depend on traditional knowledge, community relationships, and the transmission of skills from one generation to the next. Handloom weaving, for example, is one of the many sectors of the economy that operate as living examples of highly sustainable “natural systems”. Ethical work behaviour, production choices that are mindful of the environment, community-responsive decision-making, and the use of natural or repurposed materials characterize their everyday functioning. Secondary literature review and qualitative research reveal that a committed and resilient workforce can be created when HRM responds to local realities. Informal recruitment networks, the use of apprenticeship models, the guidance of experienced practitioners, and community-based welfare systems nurture talent and ensure the survival of cultural practices. These systems, however, are threatened by the speed of modernisation, the dispersal of knowledge due to migration, the diminishing support of institutions, and unequal access to technology. This paper proposes a combination of HRM practices in which structured managerial practices are supplemented by ethical principles and cultural values that have been transmitted over time. This combination can contribute to local economies, identity, and ecologically sound production management. Finally, this paper contributes to the field of study by showing how HRM can act as a mediator between tradition and modernity, facilitating sustainable transformation and people-centred development, while providing new perspectives for scholars and policy-makers.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".