A comparison of adoption and service quality between large and small broadband internet service providers in Thailand
Bibliographic record
Abstract
This research aims to compare the influence of service quality on intention to use broadband internet through attitudes and technology acceptance, as mediator variables between large and small broadband internet service providers (ISPs) in Thailand. A comprehensive review of the literature has modified the development of this behavioral model that explains intention to use of broadband internet. A data set from two groups: large and small broadband ISPs, nationwide survey was conducted in Thailand (n = 928 consumers). The theoretical model was tested using structural equation modeling. The findings show that integrated models have good explanatory power (78.3 percent) to predict customer’s intention to use broadband internet. The results of this study are as follows. Firstly, service quality: tangible, reliability, responsiveness, assurance and empathy, supports second-order factor analysis. Service quality has an influence on attitude, perceived usefulness, perceived ease of use, and intention to use broadband internet both two types of service providers. Secondly, perceived usefulness has a significant effect on attitude, and intention to use only in small companies. Thirdly, attitude, perceived usefulness, and perceived ease of use as mediator variables have a positive effect between service quality and intention to use broadband internet. This study proposes customer acceptance on broadband internet using a modified TAM.to reveal the impact of service quality on intention to use. The results of this study can be replicated and extended to ASEAN countries. The conclusions and implications for management provide alternatives for companies to increase the number of internet users in order to improve overall quality of life.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".