Why and When Consumers Prefer Products of User-Driven Firms: A Social Identification Account
Bibliographic record
Abstract
Companies are increasingly drawing on their user communities to generate promising ideas for new products, which are then marketed as “user-designed” products to the broader consumer market. We demonstrate that nonparticipating, observing consumers prefer to buy from user- rather than designer-driven firms because of an enhanced identification with the firm that has adopted this user-driven philosophy. Three experimental studies validate a newly proposed social identification account underlying this effect. Because consumers are also users, their social identities connect to the user-designers, and they feel empowerment by vicariously being involved in the design process. This formed connection leads to preference for the firm’s products. Importantly, this social identification account also effectively predicts when the effect does not materialize. First, we find that if consumers feel dissimilar to participating users, the effects are attenuated. We demonstrate that this happens when the community differs from consumers along important demographics (i.e., gender) or when consumers are nonexperts in the focal domain (i.e., they feel that they do not belong to the social group of participating users). Second, the effects are attenuated if the user-driven firm is only selectively rather than fully open to participation from all users (observing consumers do not feel socially included). These findings advance the emerging theory on user involvement and offer practical implications for firms interested in pursuing a user-driven philosophy. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2014.1999 . This paper was accepted by Pradeep Chintagunta, marketing.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".