Bibliographic record
Abstract
Abstract Performance goals are one of the most prevalent management controls used in practice to address motivational concerns. Goals influence employees' affective state by enabling them to evaluate their performance against a standard and determine if their performance is satisfactory. The objective of this paper is to understand and extend the current management accounting literature on goals and their effect on employee effort via affect. I address this objective in three ways. First, I conduct a systematic review of the management accounting literature to determine what we know about goals and determine if the literature acknowledges the important effects that goals have on affect. From the literature review, I note the paucity of research that examines the affective consequences of using performance goals. Second, I discuss some of the prominent theories from psychology that explain the relationship between goals and affect and provide suggestions for research questions. Third, I develop an experimental manipulation using online participants to demonstrate that goal attainment and goal failure lead to significant positive and negative affective reactions, respectively. By sharing my research method, I provide a starting point that accounting researchers can employ to examine how affect can influence effort, which has an important causal linkage with performance outcomes.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".