Purpose – The purpose of this paper is to explore the effects of ethical climate types on two components of organizational trust, i.e. trust in supervisor and trust in organization. Design/methodology/approach – A sample of 178 managerial employees from seven hospitals in Poland was used to investigate the specific relationships between ethical climates (i.e. egoistic, benevolent, and principled) and trust in supervisor and trust in organization. Structural equation modeling was used to explore the relationship between ethical climates and the two trust components. Findings – It was found that egoistic climates were negatively associated with trust in organization and trust in supervisor, whereas benevolent climates were positively associated with trust in supervisor and trust in organization. No support was obtained for any sort of association between principled climates and either of the two trust components. Research limitations/implications – Future research should examine the role of trust as a mediating variable in the relationship between ethical climates and variables such as commitment or productivity or satisfaction. Future research should also examine different national and work contexts to test out these relationships. Practical implications – Managers and organizations should try and establish benevolent ethical climates as opposed to egoistic ones, in order to bolster levels of trust among their employees. Originality/value – The findings of this paper are unique and original because this is the first study to suggest a relationship between ethical climate types and the two trust components. The value of this study is that it provides managers and organizations with a way by which they could potentially increase levels of trust among their employees.

This study investigates the effects of ethical climates on organizational corruption. Data from 200 employees from seven hospitals in Poland was used to test the specific relationships between the five empirically occurring ethical climate types (i.e. caring, instrumental, independence, law and code, and rules) and organizational corruption. Law and code climates were negatively associated with organizational corruption, while instrumental and caring climates were positively associated with organizational corruption.

PurposeThe objective of this study was to examine the significant factors leading to employee alienation in post-merger integration (PMI).Design/methodology/approachData were collected from 482 middle- and low-level employees in two organizations in the real estate and banking sectors in the United Arab Emirates. The analysis was carried out using structural equation modeling (SEM).FindingsOrganizational justice, employee commitment, organizational trust, perceived effectiveness of human resource (HR) initiatives and employee communication strategy played an important role in developing or mitigating a feeling of alienation among employees during PMI. Employee tenure in the organization affected individual work performance.Research limitations/implicationsThe study was limited to middle- and low-level employees and did not consider other organizational variables important in mergers. This study will help merger strategists to deliver appropriate HR practices during PMI, facilitating mitigation of uncertainties among employees and maximizing their trust and commitment.Originality/valueThe study results will help organizations understand employee trust, commitment and determinants in an emerging economy.

The purpose of this study is to prioritize the challenges of adopting Artificial Intelligence (AI) in the healthcare sector of the United Arab Emirates (UAE). An Analytic Hierarchy Process (AHP) method was used, and the data were collected from the managerial-level executives ( n = 27) involved in AI adoption in their respective healthcare organizations. The results prioritized the AI main criteria and sub-criteria based on their priority weights in the healthcare sector. The results also revealed that accuracy, privacy and security criteria are the most important factors to optimize the healthcare sector with AI. The research findings shall help policymakers formulate suitable strategies with current adoption and acceptance of AI in the healthcare sector. The findings will help policymakers utilize this study’s outcomes to create a well-defined picture of AI’s actual adoption and acceptance in the healthcare sector.