How Market Orientation Impacts Customer’s Brand Loyalty and Buying Decisions
Bibliographic record
Abstract
As retail management has become increasingly demanding, it is imperative that retailers use market orientation to promote and increase loyalty to their private labels. This can be important in efforts to differentiate themselves from their competition. The focus of this study is to understand how these factors impact the loyalty of customer purchase decisions, through the link between the potential for brand risk and brand commitment, in order to facilitate customer orientation and brand loyalty. An online survey was conducted with a sample of 2900 consumers in Portugal and Spain. This study analyzed two distinct and high involvement product categories: Denomination of Origin (DOC) wine and anti-wrinkle cream. Structural equation modeling methodology was used to analyze the relationship between different constructs. It was found that there is no direct correlation between customer orientation and brand loyalty. However, this connection is critical when the two mediating variables of brand risk and brand commitment are accounted for. Another important finding relates to the values and differences identified between the two product categories. The results obtained show the importance of risk and commitment for high involvement products. In practice, this justifies brands explicitly managing these factors, because they can translate into loyalty behaviors. The results also contribute to demystifying the market for more complex products, particularly when the choice and risk process is more complex.
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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 | 0.001 | 0.003 |
| 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 | 0.002 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".