Attainment Discrepancy Level, Firm Resources Slack, and Sticky Cost
Bibliographic record
Abstract
The aim of this study is to further develop the behavioral theory of the firm into the context of sticky cost research. The company’s actions in managing resources can be explained through the concept of attainment discrepancy level and resource slack in the behavioral theory of the firm explaining the company’s sticky costs. This study also examines the effect of attainment discrepancy levels, both historical and social, on cost behavior between slack dimensions and overall slack. To examine it, this study used 2,416 observations data from 302 companies listed on the Indonesian Stock Exchange during 2009-2017. Using Eviews 10, the estimation results of the regression model based on HAC find that the attainment of discrepancy level and resource slack affects sticky costs. Specifically, this study found that historical attainment discrepancy level causes sticky cost behavior to decrease, whereas social attainment discrepancy level increases cost behavior to become more sticky cost. The effect of resource slack on sticky cost behavior is reduced, both for each slack dimension and for the overall slack. Furthermore, the results show that the existence of certain types of slack, namely unabsorbed slack, increases the company’s sticky cost behavior when it is associated with historical attainment discrepancy levels. To sum up, these results indicate that the firm makes internal business processes as the focus of attention in managing the company’s resources. As a consequence, this situation can be used as an alternative explanation for the company’s asymmetric cost behavior.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".