Distance factors and target market selection: the moderating effect of market potential
Why this work is in the frame
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Bibliographic record
Abstract
Purpose The paper is in the domain of marketing strategies of multinational firms. Specifically, it aims to focus on target market selection of multinational firms. Design/methodology/approach Using the cultural, administrative, geographic, and economic distance framework proposed by Ghemawat, the authors offer empirical support for the role of different distance factors on firms' foreign market acquisition behavior. In addition, they examine the moderating role of market potential of a target country on the relationship between distance factors and target market selection. The context of the paper is multinational firms from developing countries. The sample consists of cross‐border acquisitions (CBAs) completed by firms from 18 emerging countries between 1990 and 2006. The authors use ordinary least squares and moderated regression analysis to determine the main effect of distance factors and the interaction effect of market potential. Findings The authors find that while cultural and geographic distance factors have a significant, negative impact on the number of CBAs, administrative and economic distances have a significant, positive effect. They also find that the market potential of target countries significantly moderates the relation between the distance factors and the number of CBAs. Research limitations/implications The results show that the market potential of countries compensates and sometimes even overrides the role of distance. Future studies should expand this research to include industry‐specific factors. Originality/value The paper provides an empirical illustration of Ghemawat's distance framework. In addition, the paper highlights several boundary conditions of the impact of distance factors on firms' internationalization processes. Finally, the study enhances knowledge on foreign market entry behavior of firms from developing countries.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 it