Understanding Consumers’ Attitudes Toward Controversial Information Technologies: A Contextualization Approach
Bibliographic record
Abstract
Controversial information technologies, such as biometrics and radio frequency identification, are perceived as having the potential to both benefit and undermine the well-being of the user. Given the type and/or amount of information these technologies have the capability to capture, there have been some concerns among users and potential users. However, prominent technology adoption models tend to focus on only the positive utilities associated with technology use. This research leverages net valence theories, which incorporate both positive and negative utilities, and context of use literature to propose a general framework that can be used for understanding consumer acceptance of controversial information technologies. The framework also highlights the importance of incorporating contextual factors that reflect the nuances of the controversial technologies and their specific context of use. We apply the framework to consumer acceptance of biometric identity authentication for banking transactions through automated teller machines. To that end, we contextualize the core construct of perceived benefits and concerns to this domain in a qualitative study of 402 participants, determine the appropriate contextual factors that are antecedents of the contextualized core constructs by examining relevant past research, and then develop and validate a contextualized research model in a quantitative study of 437 participants. Findings support the validity of our framework, with the model explaining 77.6% of the variance in consumers’ attitudes toward using biometrics for identity authentication at automated teller machines. The online appendix is available at https://doi.org/10.1287/isre.2017.0706 .
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".