Analysing 'change through time': a longitudinal study of HRM in three indian operating case-study organisations headquartered in US, UK and India
Bibliographic record
Abstract
Using a longitudinal qualitative research methodology this paper analyses 'change through time' (Saldana, 2003), in HRM practices within three case-study organisations. The three case-study organisations operate in the HR-offshoring (HRO) sector in India and are headquartered in US, UK and India respectively. The fieldwork for this research took approximately four years - from June 2006 to April 2010. Findings suggest that the challenges for HR in these organisations varied during the economic upturn (second-quarter of 2006 to first-quarter of 2008 and post third-quarter of 2009) in comparison to the economic downturn (second-quarter of 2008 to third-quarter of 2009). For example attrition and retention were a challenge in the upturn and so was recruitment and training. On the other hand motivation, morale, employee-engagement and skills-development were a challenge in the downturn.The paper also highlights the development and articulation of formal and structured HR activities focused on extrinsic monetary incentives, rewards and penalties to influence and regulate employee performance and behaviour but also shows that these practices are mediated by local indigenous traditions, the nature of the service-offering; organisational management style and the aspiration to 'role-model' systematic HR practices. Also evident were differences in HR practices both between and within the different locations of the three case-study organisations. These were attributed to the size, ownership, organisational life-cycle, local-culture etc. There was also evidence of some 'headquarter' influence in terms of convergence, divergence and crossvergence in different HR practices in the organisations. The above research has implications for both academics and practitioners.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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 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".