A study on the level of customers satisfaction towards counter services of Sabah Electricity Sdn. Bhd. (SESB) / Norliana Matasan
Bibliographic record
Abstract
This thesis is submitted to the Faculty of Business Management, Universiti Teknologi MARA, Sabah Campus as part of requirement for the fulfillment of Marketing Research subject (MKT 660). This paper is entitled "A STUDY ON THE LEVEL OF CUSTOMER SATISFACTION TOWARDS SABAH ELECTRICITY SDN BHD". Consequently, a survey was performed focus on the customers who came to the counter of SESB to pay their bills and how employees served them. This research's objective is to identify the problem faced by customers at the counter and evaluate the customer's satisfaction toward the services that provided by SESB . Last part is to recommend the most suitable practice to implement by SESB in satisfying their customers. These were selected through customer satisfaction in analyzing the data, frequency distribution, percentage and cross tabulation were used. The data of information was gathered from the questionnaires which were divided into both internal and external. The data was systematically analyzed and processed by using the SPSS 15.0. Finally, the results of the survey are converted into findings that are presented in term of tables and charts that are easy to understand. All objectives stated by the researcher were achieved through this research. The research identified the major problem faced by customers being dissatisfied with the environment at the counter services that is lack of facilities including not enough counters to serve them. The researcher also found that customers are satisfied with the staff of S E S B that treated them nicely and are willing to assist them when they come to the SESB counter. Lastly the researcher was able to find the solution from the findings through the suggestions and recommended to be implemented by SESB in the future.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".