Beyond lurking and posting: segmenting the members of a brand community on the basis of engagement, attitudes and identification
Bibliographic record
Abstract
Purpose Brand communities are an increasingly important way for brands to interact with their customers, as they give brands an opportunity to learn from and interact with people with a demonstrated interest in the brand. Literature has explored the difference between lurkers and posters within these brand communities. However, there are other ways to segment members, just as there are many ways to segment customers of products and services – and this paper aims to be a step toward going beyond simple lurking vs posting behavior as a means of differentiating community members. As such, the purpose of this paper is to segment brand communities based on not only their participation behavior but also their identification with the brand community, loyalty and benefits gained from membership. Design/methodology/approach This study used a cross-sectional survey of members of various brand communities in North America. Partial least squares structural equation modeling together with finite mixture partial least squares and prediction-oriented segmentation was used to discover the distinct segments of brand community members. Findings The findings indicate that there are two distinct segments that behave differently regarding their behavior, attitudes and motives. Segment one has a stronger relationship between identification and other outcomes and is also more motivated by social enhancement than segment two. Thus, it is clear that brand community members can be segmented and served based on more than their posting behavior. Originality/value The members of brand communities have often been thought of as homogeneous. This paper is unique in identifying heterogeneity among the members of the brand community and demonstrates the need for brand community managers to identify these differences and manage the brand community accordingly.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".