Analysis of the Effect of Food Quality, Price, Location and Environment, and Service Quality on Customer Loyalty through Customer Satisfaction of Burger King Customers in Surabaya during the Pandemic
Bibliographic record
Abstract
Community needs in the food industry have decreased due to the COVID-19 pandemic. . This can be seen from the economic growth in the second quarter of 2020 which decreased by minus 5.32 percent. Restaurant companies that rely on dine-in eating habits must also pay more attention to the development of online delivery so that restaurants can survive, with good online development, making their own restaurant applications, would be able to attract more buyers, the use of technology to facilitate payments and ordering is one of the steps restaurants to survive and thrive. With the changes in habits implemented during the pandemic, this can be an opportunity for a fast food restaurant like Burger King, which has more practicality compared to other non-fast food meals. The concept of a drive-tru and delivery-order is becoming more and more due to these pandemic times, which should pay more attention to various sectors of the food and beverage industry, especially Burger King. This study aims to analyze how the influence of food quality, price, location and environment, and service quality on customer loyalty through customer satisfaction variables. The benefits expected in this study can increase knowledge in the field of management, especially how much influence customer satisfaction has so that it can increase customer loyalty and correct existing errors and ultimately this will be able to increase sustainable transactions from Burger King restaurant customers in Surabaya. The data collection in this study was carried out by distributing questionnaires to 132 respondents with the characteristics of male and female respondents, aged 18-60 years, domiciled in Surabaya, had bought and consumed ( dine in / on the spot) Burger King in Surabaya twice in the last two years. For processing and analyzing data in this study using SPSS 22.0. After that, to tabulate the results of the respondent's research, and to test the research model using the Structural Equation Model (SEM) AMOS 22.0 data analysis technique.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".