The Role of Design Characteristics in Shaping Perceptions of Similarity: The Case of Online Shopping Assistants
Bibliographic record
Abstract
This research proposes that technological artifacts are perceived as social actors, and that users can attribute personality and behavioral traits to them. These formed perceptions interact with the user’s own characteristics to construct an evaluation of the similarity between the user and the technological artifact. Such perceptions of similarity are important because individuals tend to more positively evaluate others, in this case technological artifacts, to whom they are more similar. Using an automated shopping assistant as one type of technological artifact, we investigate two types of perceived similarity between the customer and the artifact: perceived personality similarity and perceived behavioral similarity. We then investigate how design characteristics drive a customer’s perceptions of these similarities and, importantly, the bases for those design characteristics. Decisional guidance and speech act theory provide the basis for personality manifestation, while normative versus heuristic-based decision rules provide the basis for behavioral manifestation. We apply these design bases in an experiment. The results demonstrate that IT design characteristics can be used to manifest desired personalities and behaviors in a technological artifact. Moreover, these manifestations of personality and behavior interact with the customer’s own personality and behaviors to create matching perceptions of personality and behavioral similarity between the customer and the artifact. This study emphasizes the need to consider technological artifacts as social actors and describes the specific ways in which technology design can manifest social attributes. In doing so, we show that it is possible to match the social attributes of a technological artifact with those of the user.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".