Bibliographic record
Abstract
Purpose The study conceptualizes how firms’ strategic motives interact with the heterogeneity of host country institutional environments in determining the subsidiary ownership. The author hypothesizes and tests two interaction effects. The study found that firms with market-seeking motives are more affected by the heterogeneity of host country institutional environments, while firms with resource-seeking motives are less affected by the heterogeneity. The empirical findings are based on a sample of overseas subsidiaries reported in the annual reports of listed firms in China. Design/methodology/approach In this study, an empirical investigation was conducted using a sample of subsidiaries of listed firms in China. The data were compiled from 2012 annual reports of listed firms in China. The sample consists of 2,270 subsidiaries of these firms. Findings The study conceptualizes that firms with market-seeking motives and resource-seeking motives are influenced differently by the heterogeneity of host country institutional environments in determining their subsidiary ownership. We hypothesize two interaction effects. Firms with market-seeking motives are more subject to the heterogeneity of host country institutional environments in determining their FDI ownership level. In contrast, firms with resource-seeking motives are less subject to this heterogeneity. The findings largely supported the study’s hypotheses. Originality/value This study fills an important gap in the literature by incorporating the interaction between strategic motives and host country environments in the analysis of subsidiary ownership. The findings of the study suggest that firms with a market-seeking motive are more particular about the host country institutional environments. They will acquire a high level of ownership in host countries with attractive institutional environments. In contrast, firms with resource-seeking motive are less concerned with the host country institutional environments. Their decision on subsidiary ownership is less affected by the variance in host country institutional environments. This study adds to the stream of studies that have examined outward investments of firms from emerging economies, particularly the outward expansion of Chinese firms with different strategic motives.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".