Application of theory of planned behavior to study online booking behavior
Bibliographic record
Abstract
The development of the internet has influenced the development of the world economy. Various buying and selling transactions that previously could only be done face-to-face, have now developed into transactions via the internet known as e-business or e-commerce. The hotel room online booking system was created to make it easier for consumers to book rooms 24 hours a day. With the availability of the online booking feature, consumers can access hotel information in detail and more transparently, besides that, consumers can also see reviews which can be used as their consideration in choosing hotels and planning holidays. Traveloka's significant development as an Indonesian online travel agent unicorn plays an important role in accelerating the growth rate of the online travel ecosystem, especially for the domestic market. There are many factors that must be examined in finding information, placing orders, and purchasing online. Therefore, this research is focused on online booking behavior. This study aims to determine the influence between variables based on Theory of Planned Behavior, which consists of attitude toward the online booking behavior, subjective norm, perceived behavioral control, online booking intention and online booking behavior at Traveloka. Data was collected from 133 respondents of domestic tourists who have made online bookings at Traveloka. Data were analyzed using Partial Least Square (PLS) statistics with the Smart PLS 3.0 M3 program to determine the complexity of the relationship between latent variables and their indicators. The results of this study indicate that attitude toward the behavior and subjective norms have a positive and significant effect on online booking intention. Meanwhile, perceived behavioral control has no significant effect on online booking intention. Another finding is that online booking intention and perceived behavioral control are known to have a positive and significant effect on online booking behavior. Traveloka management and marketers are also expected to be able to use the results of this research to evaluate and take corrective action on aspects that are deemed inadequate and manage the ease of use of the application to increase online booking intentions through the Traveloka application.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 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".