Ambivalence Is Better than Indifference: A Behavioral and Neurophysiological Assessment of Ambivalence in Online Environments
Bibliographic record
Abstract
Information representations such as ratings and reviews play an important role in assisting users in making decisions in online environments. Prior information systems (IS) research has mostly focused on the role of extreme valence, i.e., the positivity/negativity of information, portrayed by such representations. Yet this bipolar approach discounts how the coexistence of positivity and negativity (i.e., ambivalence) or their absence (i.e., indifference) is formed and leads to distinct attentional processes and outcomes such as purchase decisions. We theorize how and why the valence of information projected through such representations may elicit mixed feelings and influence decision-making in online environments. We conducted four randomized controlled experiments, including an electroencephalography (EEG) study, to disentangle the influence of ambivalence and indifference on decision-making in an online shopping context. We found that ambivalence and indifference to online information distinctly influenced attention and purchase decisions relative to positivity and negativity. Our findings further suggest the inability of incumbent bipolar representations, such as the widely implemented star rating system, to capture the mixed feelings expressed in online content. We propose a bivariate intervention that overcomes the limitations of bipolar representations by not only discerning ambivalence from indifference but also amplifying purchase decisions for products with ambivalent information by at least 50%, compared to incumbent bipolar representations. Our findings advance ongoing research on the role of information valence in online environments and offer implications for practice.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".