Dying to Understand How Electronic Word of Mouth Legitimates Sustainable Innovations in Stigmatized Markets
Bibliographic record
Abstract
Entrepreneurs entering stigmatized markets face barriers to entry beyond those encountered in traditional markets. Yet, little research examines factors influencing the diffusion of these goods and services. Through the lens of institutional theory, this paper proposes and demonstrates the application of a conceptual model outlining the process by which stigmatized innovations become (de-)institutionalized. We combine mixed methods by blending qualitative with quantitative tools to analyze the legitimating influence of electronic word-of-mouth (eWOM) over time. Our findings suggest that dichotomized consumer preferences stem from normative (natural and benevolent versus artificial and malevolent), cultural-cognitive (ecological health and sustainable services versus public health and traditional services), and regulatory (government rule versus market rule) binaries that influence the deinstitutionalization of orthodoxy (utopian versus dystopian worldviews). Notwithstanding, we show that, in stigmatized markets, consumers look to eWOM to inform their choices, which can aid in deinstitutionalizing rational myths and help perpetuate service innovation. We also find that in stigmatized markets, the existing industry does not show a predictable response to societal pressures for service innovations that promote social wellbeing and sustainability.
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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 | 0.013 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".