Dimensions of Online Behavior: Toward a User Typology
Bibliographic record
Abstract
Online behavior refers to organized (e.g., search) and unorganized (e.g., browse) interactions with both human (e.g., chat) and nonhuman (e.g., database) elements in online environments. The salient features of online behavior are conceptualized as sociability (human connection motives), utility (efficiency orientation), and reciprocity (cognitive stimulation and active involvement). Recently published factor analytic studies support the validity of these three dimensions of online behavior. The proposed Brief Test of Online Behavior (BTOB) contains five rating scale items that determine user position on each dimension of online behavior (i.e., 15 items in total). A typology of online behavior emerges as BTOB scores position users in the three-dimensional space created by the intersection of sociability, utility, and reciprocity. Subsequent research may validate the proposed dimensions of online behavior, establish practical applications of the BTOB, and connect type of user with cognitive, social, and emotional outcomes. For example, users who score high on sociability and reciprocity but low on utility may learn best in interactive and stimulating online environments, which necessarily include self-regulating software.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".