A SERVQUAL ASSESSMENT OF INTERNET SERVICE QUALITY IN BHUTAN
Bibliographic record
Abstract
Internet and telecommunication is nowadays one of the core necessities and is in fact required in every aspect of our daily lives. Bhutan introduced the internet and television in 1999. Internet and telecommunication has taken its roots in Bhutan and in every nook and cranny of the country. Given its importance, it is important to time to time assess its quality in the country for learning and development purposes. The study is based on quantitative analysis, using the SERVQUAL instrument. The data was collected through randomly distributed questionnaires through convenience sampling method via google form survey. Sample size of 384 was determined using an online survey monkey tool. The data for this study was analyzed using excel through paired two sample t-test to compare means and to see the significant difference between expectation and perception of the services of each dimension item of all the five dimensions at hypothesized mean difference of zero, alpha value of 0.05. The average gap score is at -0.6 which means that the expectations have not been met and quality of internet service is unsatisfactory. Interesting to note that TANGIBILITY, EMPATHY and RELIABILITY aspects require immediate attention. Generally, the difference between the two means is statistically significant, and therefore there is a statistical significant difference between perceived and expected internet service quality. The study therefore, recommends the (internet service providers) ISPs to consider improvements and rethinking developments on the dimensions discussed in the paper.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.004 | 0.001 |
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".