Escalation of commitment as an antecedent to noncompliance with information security policy
Bibliographic record
Abstract
Purpose This study aims to identify antecedents to noncompliance behavior influenced by decision contexts where investments in time, effort and resources are devoted to a task – referred to as a task unlikely to be completed without violating the organization’s information security policy (ISP). Design/methodology/approach An empirical test of the suggested relationships in the proposed model was conducted through a field study using the survey method for data collection. Pre-tests, pre-study, main study and a follow-up study compose the frame of our methodology where more than 500 respondents are involved across different organizations. Findings The results confirm that the antecedents that explain the escalation of commitment behavior in terms of the effect of lost assets, such as time, effort and other resources, give us a new lens to understand noncompliance behavior; employees seem to escalate their commitments to the completion of their tasks at the expense of becoming noncompliant with ISP. Research limitations/implications One of the key areas that requires further attention from this study is to better understand the role of risk perceptions on employee behavior when dealing with value conflicts. Depending on how risk-averse or risk seeking an employee is, the model showed no significant support in either case to influence their noncompliance behavior. The authors therefore argue that employees' noncompliance may be influenced by more powerful beliefs, such as self-justification and sunk costs. Practical implications The results show that when employees are caught in tasks undergoing difficulties, they are more likely to increase noncompliance behavior. By understanding better how project obstacles result in such tasks, security managers can define new mechanisms to counter employees’ shift from compliance to noncompliance. Social implications Apart from encouraging compliance with enforcement mechanisms (using direct behavioral controls like sanctions or rewards), indirect behavior controls may also encourage compliance. The authors suggest that the ISPs should state that the organization would take positive actions toward task completion and help their employees to resolve their problems quickly. Originality/value This study is the first to tackle escalation of commitment theories and use antecedents that explain the effect of lost assets, such as time, effort and other resources can also explain noncompliance with ISP in terms of the value conflicts, where employees would often choose to forego compliance at the expense of finishing their tasks.
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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 | 0.003 | 0.019 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".