A Study Of Indonesian Host Country Nationals' Perspectives: What Expatriates Should Know
Bibliographic record
Abstract
Expatriates live many paradoxical experiences while being immersed in another culture (Russell, 2006; Osland & Osland, 2006; Russell & Dickie, 2007; Russell & Aquino-Russell, 2010; 2011). This led us to wonder what it might be like for host country nationals (HCNs) to work with expatriates in their own country. There is literature describing the changing of business, communication, and cultural practices so that expatriates can be more successful and more culturally congruent (Selmer, 2000; Banuta-Gomez, 2002; Montagliani & Giacalone, 1998; Hawkins, 1983; Peppas, 2004), but is this really happening from the HCNs perspectives? This study focused on describing the lived experience of Indonesian employees using their own words. Written descriptions were analyzed/synthesized using Giorgis descriptive phenomenological method (Giorgi, 1975; 1985; 2009; Giorgi & Giorgi, 2003). The central finding points to a disconnect between two worlds and paradoxical ways of being for Indonesians while working for Western-based organizations at home. The new knowledge may enhance knowledge for managers which could in turn alter management practices in relation to valuing HCNs for their contributions to Western organizations.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| 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 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".