Navigating ethical decision-making in digital transformation: ethical climate, digital competence, and person-organization fit in China’s banking sector
Bibliographic record
Abstract
Abstract In the context of rapid digital transformation, Chinese commercial banks face growing pressure to uphold ethical standards while adopting advanced technologies. This study explores the factors shaping employees’ ethical decision-making intentions in this setting. Grounded in Person–Environment Fit Theory and Ethical Decision-Making Theory, it examines how ethical climate and digital competence influence ethical intentions, and how person–organization (P–O) fit mediates these relationships. Drawing on a two-wave survey of 678 bank employees, the study finds that P–O fit plays a pivotal mediating role in linking ethical climate and digital competence to three types of ethical decision-making intentions: procedural, relational, and innovative. A supportive ethical climate enhances P–O fit by aligning organizational and individual values, while higher digital competence enables employees to manage the complexities of digital work environments. Moreover, P–O fit significantly amplifies the effects of ethical climate and digital competence across all dimensions of ethical intent. Moderating analyses show that organizational digital ethical culture and employees’ educational levels further shape these dynamics. This research contributes new insights by positioning digital competence as an ethical capacity, not just a technical skill, and by proposing an integrated framework connecting digital ethics and person–organization alignment. The findings provide theoretical and practical implications for promoting ethical conduct in digitally evolving organizations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".