Utilizing innovation and collective organizational engagement into SMEs’ sustainable competitive advantage
Bibliographic record
Abstract
The present study centered on the mediation of innovation and the moderation of collective organizational engagement in affecting human capital (a combination of skill and knowledge) on sustainable competitive advantage and further examined human capital, innovation, and collective organizational engagement impacts on sustainable competitive advantage. Subsequently, this study utilized a quantitative approach, engaging 270 sample frame SME units in Indonesia using a Likert scale questionnaire and examining it using SEM-PLS. The findings demonstrate that human capital positively and significantly impacted innovation and sustainable competitive advantage, indicating that a high level of business skill, business orientation, perception of risk, and know-how management enhances the organization’s innovation capability, creating distinctive, exceptional, and invaluable resources as core competencies of sustainable competitive advantage. This study confirms and advances the RBV theory and previous studies by examining intangible resources' mediating and moderating role, in which innovation contributes to mediating the effect of human capital on sustainable competitive advantage and collective organizational engagement reinforces (moderates) the impact of human capital on establishing sustainable competitive advantage. So, the results of this research illuminate the significance of human capital, innovation, and collective organizational engagement as the organization’s superior resources in manifesting sustainable competitive advantage.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".