ANALISIS PENGARUH PERILAKU KONSUMEN TERHADAP CUSTOMER MISBEHAVIOR DALAM INDUSTRI RETAIL DI INDONESIA (Studi pada Hypermart)
Bibliographic record
Abstract
The retail industry is one of the fastest growing industries in Indonesia, with Hypermart as one of the largest retail companies. As a company engaged in retail then Hypermart provides a variety of consumer goods that the majority of Indonesian people shopping with high frequency at Hypermart. The purpose of this study was to understand the effect of customer behavior in the past and the future customer behavior. In previous studies in various research on consumer behavior, this relationship has not been studied adequately using personality variables were complete. Additional variables used in this study are five variables based on consumer personality is Consumer alienation, Machiavellianism, Sensation seeking, aggressiveness, self-esteem, Past misbehavior, Future misbehavior Furthermore, this study also aimed as a consideration for the managerial or practitioners Hypermart in the decision-making process, in an effort to reduce misbehavior future intentions of customers Hypermart through past efforts to reduce customer misbehavior. The sampling method used in the study to be performed are non-probability sampling. This study used a questionnaire as a major tool in data collection. This research will be used purposive sampling technique. The number of samples in this research are 300 respondents were evaluated, and the data were analyzed using multiple regression with SPSS 16. The results of this study can be used by companies that are facing the problem of customer misbehavior in shaping strategies to reduce customer misbehavior.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".