Consumer Adoption of Net-Enabled Infomediaries: Theoretical Explanations and an Empirical Test
Bibliographic record
Abstract
The emergence of infomediaries ?which allow online consumers to search for, and provide comparisons among, many online retailers ?is a prominent trend in e-commerce. However, little research has been done on consumer reactions to this new e-commerce tool. To explain why and how online shoppers adopt a new infomediary website, this study proposes a conceptual model with insights obtained from literatures on the technology acceptance model (TAM), the economics of intermediation, and transaction cost analysis (TCA). Infomediaries provide powerful search capabilities to online shoppers to provide them with a list of potential retailers (efficiency benefits), and then provide information to aid in selecting from this list of retailers (effectiveness benefits). Accordingly, the proposed model posits that infomediaries offer two major types of utilitarian benefits to online customers: namely, perceived efficiency and perceived effectiveness. In addition, the model predicts that one's willingness to adopt an infomediary is a function of his/her evaluation of the two types of utilitarian benefits of using the infomediary, which are in turn determined by the subjective interpretation of his/her e-commerce transaction environment. The model was tested using data collected from an online questionnaire administered to 367 online shoppers. Online shoppers?intention to use the infomediary was found to be a function of the two types of utilitarian benefits and perceived ease of use. In addition, our findings suggest that online shoppers who are low on asset specificity (e.g., consumers who have not made a high transaction-specific investment toward a specific online retailer) and who also are high on uncertainty (e.g., consumers who believe that online retailers in general are opportunistic) tend to appreciate the benefits of using an infomediary more than other online shoppers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".