Exploring the influence of intangible resources on firm value across major Indonesian industrial sectors: An RBV perspective
Bibliographic record
Abstract
The development of Resources Based-View in 2021 states that the firm's value creation is influenced by unique resources. This research aims to explore the use of intangible resources in three main industrial sectors in Indonesia to create firm value. The research method begins with the mapping of resources that meet valuable, rare, imperfect imitability, and non-substitution, forming a research model. These internal resources are measured using the company's financial ratio. The research data is in the form of secondary data for the period 2012-2022 which is analyzed using the panel data regression method and robustness test. The results found that each industry has different resources to increase the firm value. Internal resources; Intangible assets, firm innovation and managerial ability can create firm value in the basic materials industry. Meanwhile, in the Consumer Noncyclicals industry, only managerial ability affects the firm's value. Meanwhile, intellectual capital is not able to create firm value in Indonesia. The research implies that physical resources are still the main factor in creating a competitive advantage to achieve sustainable corporate value in Indonesia. The theoretical contribution is that there are still other applications of the RBV concept to create firm value.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".