Factors Influencing the Extent of the Ethical Codes: Evidence from Slovakia
Bibliographic record
Abstract
Even though formalization of ethical principles is a must in today’s business, research and evidence in the Slovak conditions remain scarce. Yet, creating an ethical business climate and especially the formalization of ethics through codes of ethics incorporated in corporate standards is a particularly interesting phenomenon in the conditions of transit economies due to the significant role of multinationals in this process. Therefore, the purpose of this study was to examine main factors influencing the extent of ethical codes in 225 subsidiaries of multinational companies operating in Slovakia. The conducted questionnaire study containing items focused on area and extent of ethical code, number of employees, economic performance, regional and industrial scope, ownership structure, and nationality of executive director was used as a tool for data collection. Factor analysis was processed to identify the interdependencies between observed variables and to find the latent variables. Further, the Kruskal–Wallis test was applied to identify the differences among the variables along with the Bonferroni correction test, which specified the items between which the significant difference occurred. The following findings emerged. First, companies with lower extent of ethical code use general phrases. When they want to specialize on any ethics problems, extent must be wider. Second, companies with a lower number of employees do not need extensive ethical code due to clear rules with which they are familiar in a direct way by owners. In multinational companies, the communication of ethical rules is realized via ethical codes with specific purposes because the direct way is impossible. Third, companies with foreign ownership used different managerial approaches, and therefore ethical codes differ in extent and content.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".