Firm‐ and Individual‐Level Determinants of Balanced Scorecard Usage*/DÉTERMINANTS DE L'USAGE DU TABLEAU DE BORD ÉQUILIBRÉ AU DOUBLE ÉCHELON ORGANISATIONNEL ET INDIVIDUEL
Bibliographic record
Abstract
ABSTRACT The factors influencing the organizational as well as the individual decision to utilize the balanced scorecard (BSC) approach have not been widely researched. In the first part of this paper, we study BSC adoption at the organizational level while utilizing a multifaceted perspective of socio‐psychological, economic, and resource‐based influences; specifically, we investigate the perceptions of desirability, urgency, and feasibility of BSC adoption. Our findings show that customer norms, competitor norms, and organizational resources are significant predictors of BSC adoption. In the second part of the paper, we discuss individual‐level aspects of utilization decisions. Here, we explore the impact of perceived ease of use, perceived usefulness, and awareness on the intentions to use the BSC approach. Our findings show that both awareness of BSC capabilities and perceived ease of use are significantly related to perceived usefulness. However, only perceived usefulness is significantly related to intentions to use the BSC. Implications for research and practice are discussed.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".