The Relevance of Marketing Mix and Service Quality on Students' Decision-Making Factors Regarding Higher Education and Satisfaction
Bibliographic record
Abstract
This study conducted aims to examine and analyze the relevance of marketing mix and service quality on the students decision-making, the relevance of marketing mix, service quality on student satisfaction and the effect over decisions related on student satisfaction and the relevance of marketing mix and service quality on student satisfaction as a mediating role of students decision-making. Population in this study were all a new students in the study program management, education level of Bachelor degree of the academic year 2015/2016 in Makassar. There are 381 students were used as samples. Data were analyzed using Structural Equation Modelling (SEM) with support Analysis of Moment Structures (AMOS). This study provides findings that the marketing mix and service quality has a significant effect on students decision-making, marketing mix does not directly effect in student satisfaction, services quality and student’s decision-making has a significant on student satisfaction. Marketing mix and service quality has a significant effect on student satisfaction as a mediating role of student’s decision-making. These findings explain that the marketing mix directly affects decision-making, but has no direct effect on satisfaction; marketing mix can affect satisfaction if supported by the student’s decision-making. Service quality affects directly and indirectly on satisfaction as a mediating role of decision-making.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".