Wanted: Knowledge workers for foreign firms in developed markets
Bibliographic record
Abstract
Competition in attracting talented employees can be tough, particularly for firms looking for knowledge workers. How will foreign firms fare in this competition, if potential employees assess their own level of person-organization fit, based on the firms’ country of origin (COO), long before any job offer is made? In order to explore the question of what influences an individual’s attraction to a foreign firm, I conducted a mixed-methods investigation of the organizational attractiveness (OA) of foreign firms that enter developed markets, from the perspective of person-organization fit. First, I developed a model that illustrates the effect of various factors on the OA of foreign firms in developed markets. The first of the factors that may influence a potential applicant’s assessment of their fit with a generic foreign firm was predicted to be the applicant’s perception of tone of news coverage of the firm’s COO in the media. A second factor is the prior experience that potential applicants may have had with people from the foreign firm’s COO. Both of these factors were predicted to be moderated by the potential applicants’ level of cultural intelligence and their affinity with the firm’s COO. A third factor was the potential applicants’ preference for a type of psychological contract. An online survey of knowledge workers in Canada tested this model. The perception of tone of news coverage showed a positive, significant relationship with OA. Many firms utilize the assistance of labour market intermediaries to attract potential employees. Thus, my second study in this dissertation investigated how the foreignness of firms affected the work of headhunters as they sought to attract candidates for the firms. Due to the exploratory nature of this research, I conducted interviews to address these questions.
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 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".