The role of market uncertainty in fostering innovation and green supply chain management on the performance of tourism SMEs
Bibliographic record
Abstract
This research was conducted to examine the extent to which market uncertainty can encourage market players, especially SMEs, to exploit innovation and environmentally friendly orientation to improve their performance. From a supply chain perspective, market uncertainty, which in this study is proxied by the Covid-19 pandemic, has great potential to reduce performance and disrupt production and distribution lines as well as consumer demand. This encourages affected SMEs, such as SMEs that focus on providing tourism products, such as fashion and merchandise, to maintain their performance with product innovation, and minimize the use of non-environmentally friendly products. The object of research is Small and Medium Enterprise (SME) producing tourism souvenirs in Yogyakarta, Indonesia. Using the analysis technique of Structural Equation Modeling (SEM) with 150 respondents, the findings indicate that market uncertainty serves as a catalyst for SMEs to maintain performance through marketing innovation and product reorientation. Specifically, the results show that there is a positive and significant influence between innovation and green orientation on SME performance, and the mediating effect of market uncertainty to increase marketing innovation and environmentally friendly orientation. These findings theoretically contribute to explaining the relationship between supply chain management in the context of market uncertainty. In practical terms, this study confirms the need for support by stakeholders to support limited domestic tourism, according to health protocols, as well as digitalization of marketing for tourism SMEs.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".