Pricing strategy with customers’ privacy concerns in Smart-X systems

The Moderating Effects of Social Responsibility Climate and Safety Climate in Keeping Healthcare Workers' Engagement during COVID-19

OBJECTIVE

The outbreak of COVID-19 brings an overload of physical and mental demands to healthcare professionals. Keeping healthcare professionals sustainable, engaged, and performing at their highest levels becomes critical and nonetheless difficult. The objective of this research is to link the literature on organizational climates, corporate social responsibility, safety science, and work engagement, and propose a research framework that investigates the factors influencing healthcare professionals' engagement during COVID-19.

METHODOLOGY

We propose that when healthcare workers' career callings are triggered by COVID-19, it influences their perceptions of the work's meaningfulness, which ultimately enhances their work engagement. We argue that creating a social responsibility climate and a safety climate inside the hospital facilitates the process of turning healthcare workers' perceived work meaningfulness into work engagement. We collected data from 112 healthcare professionals, including nurses, doctors, and executive staff, from 16 wards in a public hospital in China to test our hypotheses.

RESULTS

Hierarchical linear regression analysis provided empirical support for our research model. We find that healthcare professionals' career callings during COVID-19 enhanced their perceived work meaningfulness, which results in increased work engagement. Moreover, a social responsibility climate and a safety climate strengthens the link between work meaningfulness and work engagement among participants.

CONCLUSIONS

Creating a social responsibility climate and a safety climate in the workplace are effective management approaches to realize healthcare workers' feelings of work meaningfulness and turn them into work engagement.

A basic framework for privacy protection in personalized information retrieval

Personalized information retrieval is an effective tool to solve the problem of information overload. Along with the rapid development of emerging network technologies such as cloud computing, however, network servers are becoming more and more untrusted, resulting in a serious threat to user privacy of personalized information retrieval. In this paper, we propose a basic framework for the comprehensive protection of all kinds of user privacy in personalized information retrieval. Its basic idea is to construct and submit a group of well-designed dummy requests together with each user request to the server, to mix up the user requests and then cover up the user privacy behind the requests. Also, the framework includes a privacy model and its implementation algorithm. Finally, theoretical analysis and experimental evaluation demonstrate that the framework can comprehensively improve the security of all kinds of user privacy, without compromising the availability of personalized information retrieval.