Peran CSR Dalam Mempengaruhi Pembelian Impulsif di Masa Pandemik
Bibliographic record
Abstract
<p><em>Data collection from world meters shows 6 months after announcement of the corona pandemic, several countries began to experience handling of corona cases which began to decline, however this was not the same as the results for several other countries, some countries showed cases that were increasing from day to day. data from the Central Statistics Agency (BPS) reported that the country's gross domestic product (GDP) in the second quarter of 2020 was minus to 5.32 percent. On a quarterly basis, the economy contracted 4.19 percent and cumulatively contracted 1.26 percent. Because of this, many companies are looking for ways to increase purchases. Scrutinized the data, the increase in the use of CSR has soared, proactively many companies have begun to engage in various CSR activities. One of the companies that are aggressively implementing CSR programs in helping others is online transportation providers; gojek. By providing the option of providing food to ojol partners (online motorcycle taxis) several options are offered to provide assistance. The sampling technique used in this study is to use a non-probability sampling method, which is a sampling technique that does not provide equal opportunities for each member of the population to selected to be the sample. Measurement of data in this study was carried out using a Likert scale. The development of this instrument contains a total of 16 attributes developed from each variable as a measurement basis for data collection with the sample used in this study being consumers who have the Gojek application. The data collected will be processed with SPSS software for data analysis in testing the validity, reliability, classical assumptions as verification of the strength of the research.</em><em></em><em></em></p>
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".