Establishing Profitable Customer Loyalty for Multinational Companies in the Emerging Economies: A Conceptual Framework
Bibliographic record
Abstract
It has been observed that some firms succeed in their attempts to achieve business goals in emerging economies, whereas others fail. To understand the reasons for this phenomenon, the authors conduct a qualitative study where they interview 42 managers of multinational companies from the United States, Canada, Europe, Asia, and Australia. From the insights gleaned from these interviews and the available literature, they propose a conceptual framework that identifies the possible factors that would drive the creation of both a profitable and a loyal customer base (termed “profitable customer loyalty” in this study) in the emerging economies. The influencing factors are categorized as customer-specific variables, marketing-mix variables, and firm-specific variables. From these factors, the authors advance research propositions that discuss the potential relationships with profitable customer loyalty. One of this study's key contributions is the proposal that multinational companies monitor the suggested factors and assess a degree of comfort before formulating strategies in the emerging economies. Further research can focus on the empirical validation of the proposed framework.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".