Art of innovating in the arts: Disentangling determinants of technological and symbolic innovations in creative industries— Evidence from Canadian museums
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study investigates the factors driving innovation in museums by incorporating both technological and symbolic innovations. Unlike previous research, it employs a comprehensive set of determinants to examine their impact on technological and symbolic innovations. Based on data from 250 Canadian museums and a multivariate path model, we simultaneously estimate eight types of innovations, four types of technological innovations (product, process, organizational, marketing) and four types of symbolic innovations (artistic, aesthetic, cultural, audience). The findings indicate that innovation appears to emerge through complex interplays between internal capabilities, market responsiveness, and external relationships. Resource-related factors such as technological infrastructure, financial assets, and artistic capabilities show differentiated impacts across types of innovation, suggesting that in museums, innovation is not uniformly resource-driven. Human capital, artistic creativity, and R&D investments demonstrate more limited or selective effects. Market orientation, particularly visitor orientation, emerges as a relevant driver of symbolic innovations, while custodial orientation, collaboration, and co-creation strategies have weaker or isolated impacts. Hence, the determinants differ across types of innovation, with some being specific to particular types thereof. Moreover, the study reveals complementarities between several pairs of types of innovation including Process and Aesthetic innovation, Artistic and Cultural innovation, and Aesthetic and Audience innovation. Finally, the degrees of complementarity between technological innovations are higher than those between symbolic innovations. These findings highlight the complex and contingent nature of innovation in museums, underlining, for museum managers, the importance of resource alignment, market-driven orientation, and external engagement strategies for successful innovation.
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.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it