The Adoption of Online Shopping Assistants: Perceived Similarity as an Antecedent to Evaluative Beliefs
Bibliographic record
Abstract
In recent work, researchers have supplemented traditional IS adoption models with new constructs that capture users’ relational, social, and emotional beliefs. These beliefs have given rise to questions regarding their antecedents and the nature of the user-artifact relationship. This paper sheds light on these questions by asserting that users perceive and respond to information technology (IT) artifacts as social partners and form perceptions about their social characteristics. Subsequently, users’ perceptions of the similarity of these characteristics to their own affect evaluations of these artifacts. Within the context of online shopping and using an automated shopping assistant, our paper draws upon social psychology and human-computer interaction research in developing hypotheses regarding the effects of perceived personality similarity (PPS) and perceived decision process similarity (PDPS) on a number of beliefs (enjoyment, social presence, trust, ease of use, and usefulness). The results indicate that PDPS acts as an antecedent to these beliefs, while the effects of PPS are largely mediated by PDPS. Furthermore, the results reveal that the effects of perceived similarity, in general, exceed those of the effects of the individual assessments of the user’s and the assistant’s personalities and decision processes. These results have important implications for IS design. They highlight the importance of designing artifacts that can be matched to users’ characteristics. They also underscore the importance of considering similarity perceptions rather than solely focusing on perceptions of the IT artifact’s characteristics; a common approach in IS adoption research.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".