Mercenaries: The Paradoxical Effect of Pay-for-performance on Unethical Tasks
Bibliographic record
Abstract
While some organizations, like charities and government agencies, engage in prosocial behavior that upholds the highest prescriptive moral values in our society; others, like those in the tobacco, fossil fuel, gambling, and weapons industries, operate within the confines of the legal system while nevertheless behaving antisocially and violating core moral proscriptions against harming others. Unfortunately our understanding of the psychology that enables collective moral violations is still very limited because scholarship in behavioural business ethics has tended to focus on individual-level antecedents of unethical behaviour as opposed to the interpersonal and organizational factors that lead ordinarily ethical people astray. This dissertation attempts to rectify this imbalance by asking how it is possible to motivate employees to comply with instructions that violate society’s ethical principles. Given that money is generally considered the most powerful motivational tool available to most organizations, this research examines how different financial incentive structures motivate performance of dirty work that is technically legal but violates society’s ethical norms. Surprisingly, and contrary to its well-documented positive effect on the performance of ethically-neutral work tasks, I predict that pay-for-performance incentive structures will have a negative effect on performance of unethical tasks. Existing motivational theories are unable to make similar predictions, so I contribute to our understanding of unethical behavior on behalf of organizations by considering the embedded nature of ethical decision-making and argue that pay-for-performance alters employees’ perceived employment relationships and thereby inhibits moral disengagement from societal norms. Through a series of pilot studies I identify two appropriate experimental paradigms to test my hypotheses. The first reported experiment involves a novel paradigm in which online workers are recruited as part of a marketing study to compose social-media advertising messages in support of a tobacco company. The second experiment adapts an established bug-killing paradigm in which laboratory participants are asked to place either bugs or inanimate objects into a modified coffee grinder that they subsequently activate. In both experiments, participants are either paid for their performance of this moderately unethical task or receive a comparable flat fee. This dissertation reports the results of these experiments and discusses their scholarly contribution.
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 | 0.005 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".