How Do Multinational Suppliers Formulate Mechanisms of Global Account Coordination? An Integrative Framework and Empirical Study
Bibliographic record
Abstract
Existing global account management (GAM) studies mostly focus on multinational suppliers’ decision on which customers to serve with GAM programs, while paying limited attention to how to formulate mechanisms of global account coordination to best serve a chosen account. The issue of making optimal choices on specific coordination modes is pressing to global account managers because GAM programs are both costly and consequential. First, building on prior research that suggests the existence of two modes of global account coordination—namely, interorganizational coordination (IOC) and intercountry coordination (ICC)—the authors propose a formal framework that delineates their similarities and differences along the domain (internal vs. external), level (horizontal vs. vertical), and foci (strategic execution vs. relationship maintenance). Second, following the resource-based view and power-dependency theory, the authors propose an integrative framework on how various supplier, interorganizational, and customer factors influence global marketing managers’ coordination mechanism choices. They further advance several hypotheses on the curvi-linear nature of influences of several antecedents on coordination mode choices and examine the impacts of forms of global account coordination modes on suppliers’ market and relational performance. They test the conceptual model using data collected from a cross-national sample of more than 200 global account managers. The findings show that the two coordination modes have different sets of antecedents and exert independent, different influences on supplier performance. Therefore, they should be treated separately in research as well as practice.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".