The effect of electronic word of mouth on online customer loyalty through perceived ease of use and information sharing
Bibliographic record
Abstract
The development of internet information technology has encouraged the presence of various application platforms that can be used for multiple needs. One application that is very widely used socially is the social media application. Through social media applications, users can share information according to their needs. The use of the internet also supports the use of streaming technology that makes it easier to carry out activities to watch movies online. The availability of information technology facilities and infrastructure makes it easier for people to get films. This study aims to study the influence of electronic worth of mouth on online customer loyalty through information sharing, perceived ease of use, and intention to use. This study collected data using questionnaires as many as 378 respondents of streaming technology users obtained from the spread of 1237 questionnaires, which means a response rate of 30.55%. Data analysis uses the partial least square technique to test the study's hypothesis. The results showed that electronic word of mouth directly affects perceived ease of use, intention to use, and information sharing. Perceived ease of use is based on the intention to use, information sharing, and customer loyalty. Information sharing directly affects the intention to use and online customer loyalty. The results also showed that intention to use impacts increasing customer loyalty. In addition to direct influence, the study's results showed that electronic worth of mouth indirectly affects online customer loyalty through information sharing, perceived ease of use, and intention to use. This research enriches the field of technology acceptance model with perceived ease of use, intention to use, and information sharing. Contributions to the theory of marketing behavior are related to electronic word of mouth and online customer loyalty. A practical contribution to companies engaged in the cinema and internet technology infrastructure providers is to sustainably apply streaming technology as a form of business that society uses.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".