Bibliographic record
Abstract
The consequences of missing targets can be found on a daily basis in many organizations. As such, targets and target setting in an extremely important topic to companies and one that should receive more attention. Although the vast amount of reasons for missing targets are difficult to study, the process of setting the target which includes budgeting has been proven to affect performance and achievement through goal setting theory (Locke & Latham, 2002). Thus, targets are an important element in almost every organization (Chenhall, 2003). We focus this review of literature exclusively in the relationship between target setting and firm performance and as such consolidate, organize, and synthesize past literature in this field and provide a clear direction for future research. We further identify two impactors found to affect firm and management performance but never researched as an impactor of the relationship between target setting and firm performance. Those impactors are Transparency of targets and length of management experience. In this paper, we fill the gaps identified above and inform the study of target setting in order to spark future research on this topic. We also identify the dimensions affecting the relationship between target setting and firm performance as well as the different measurement approaches in target setting literature.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".