Need for cognitive closure and mobile personalization: a cluster analysis
Bibliographic record
Abstract
Purpose This study aims to profile mobile users based on their need for cognitive closure (NFC) (preference for order, preference for predictability, discomfort with ambiguity, close-mindedness and decisiveness) and identify differences among the groups regarding their perceptions of personalized preferences and privacy concerns. Design/methodology/approach Based on the data from 285 participants, the authors seek to identify and profile unique consumer segments (mobile users) generated based on their NFC. Second, once the segments are established, the authors analyze how the segments differ across their personalized preferences and privacy concerns. Findings The data generated three distinct consumer segments: equivocal users, structured users and eclectic users. Across the segments, there were differences in their mobile personalization (experience, value and actions) and preference for information privacy (perceived risks and fabrication of personal information). Research limitations/implications United States (US)-based sample may restrict the generalizability of this research. Thus, future research should include participants from other geographic regions to increase external validity. Practical implications Retail managers can apply this knowledge to implement appropriate personalization strategies for these distinct target groups. Originality/value Segmenting clusters based on differences in consumption trait (NFC) provides key insights to retailers looking to deliver personalized customer experience, particularly in a mobile shopping context.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".