Bibliographic record
Abstract
Purpose The purpose of this paper is to explore how small firms in the tattooing industry actively shape institutional expectations of value for consumers in a changing industry. Design/methodology/approach The paper draws upon interviews with key actors in the firms under study to explore their experiences with consumers and other constituents in determining how competitive advantage is constructed in this environment. These data are complemented data with interviews with governmental representatives and material from secondary sources. Findings The results reveal efforts of firms to construct and increase organizational legitimacy through the prominence of discourses of professionalism based on artistry and medicine/public health. These bases of competitive differentiation are not the clear result of exogenous pressure, rather they arise through the active efforts of the firm to construct value guidelines for consumers and other constituents. Practical implications Strategic management in small firms is a complex and dynamic process that does not necessarily mirror that of large organizations. Constructing competitive advantage is an interacting process between key actors of small firms and various constituents. Originality/value The paper extends the application of institutional theory in strategic management by illuminating the active role that firms play in creating industry norms, especially in industries where norms are not well established or no longer entrenched. Moreover, exploring an alternative site of study offers a means through which to see well‐studied issues in new ways.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.044 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.007 |
| 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".