An Exploratory Study of Behavior-Based Segmentation Typology of Facebook Users in Thailand
Bibliographic record
Abstract
The purpose of this research is to demonstrate the importance behaviors as well as demographics in developing an effective consumer behavior segmentation strategy of Facebook users in Thailand. The questionnaire which comprised a twenty nine items intended user-behaviors scale. The data was collected from 503 potential respondents with valid responses received. There were 173 males respondents (34.4%) and 330 females (65.6%). The majority of the respondents were 21 years old (n=142, 28.2%). Data were initially analysed by factor analysis to develop the type of user-behaviors solution. The results indicated five distinct types of Facebook user-behaviors: Update and share, Shopping and learning, Prefer uncomplicated, Sociable, and Fast distribution. The relationship between behavior types and demographic variables was investigated through ANOVA. The results revealed that gender had no impact for all types. As for age, there was significant difference for “shopping and learning” type. The author interpreted to mean that younger people using Facebook for more shopping and learning than the other age group. These five distinct types were validated by examining their individual behavior type regarding frequency of access to Facebook and network size, there were significant differences for all of the types. The author interpreted that frequency of log in Facebook, and a large number of network size can drive Facebook usage. The empirical findings of this research indicated that 29.8% of Thai teenagers visit Facebook 2-3 times per day and 21.5% visit to Facebook more than 16 times per day. The result also indicated that the majority of the young (54.5%) have more than 181 friends on Facebook.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".