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The purpose of this study is to identify critical demographic variables that would significant influence each dimension of patient safety culture. Understanding nurses' attitudes toward patient safety is important for healthcare organizations to relentlessly improve medical quality and services for patients.

The internal survey data sets in 2015 and 2016 from nurses' viewpoints are used. Linear regression with forward selection is applied where nine demographic variables are the input variables, while each dimension of the Chinese version of safety attitudes questionnaire (SAQ) is the dependent variable.

Supervisor/manager is the most essential demographic variable that has significant impacts on six dimensions. Experience in organization is the other critical demographic variable.

Nurses who are in charge of supervisors/managers are more satisfied in six of eight dimensions. Nurses who have much experience in an organization tend to have less satisfaction in three dimensions. Therefore, hospital management should enhance the leader's effectiveness in engaging their subordinates' commitment.

The results enable the hospital management to pay much attention to two major demographic variables, namely supervisor/manager and experience in organization, in order to improve the patient safety culture based on the Chinese version of SAQ in this hospital. Moreover, supervisor/manager is a more critical demographic variable for nurses due to larger absolute values of standardized coefficients by linear regression with forward selection.

With the advancement of digital transformation, it is important for e-retailers to use artificial intelligence (AI) for customer engagement (CE), as CE enables e-retail brands to succeed. Essentially, AI e-marketing (AIeMktg) is the use of AI technological approaches in e-marketing by blending customer data, and Retail 4.0 is the digitisation of the physical shopping experience. Therefore, in the era of Retail 4.0, this study investigates the factors influencing the use of AIeMktg for transforming CE.

The primary data were collected from 305 e-retailer customers, and the analysis was performed using a quantitative methodology.

The results reveal that AIeMktg has tremendous applications in Retail 4.0 for CE. First, it enables marketers to swiftly and responsibly use data to anticipate and predict customer demands and to provide relevant personalised messages and offers with location-based e-marketing. Second, through a continuous feedback loop, AIeMktg improves offerings by analysing and incorporating insights from a 360-degree view of CE.

The main contribution of this study is to provide theoretical underpinnings of CE, AIeMktg, factors influencing the use of AIeMktg, and customer commitment in the era of Retail 4.0. Subsequently, it builds and validates structural relationships among such theoretical underpinning variables in transforming CE with AIeMktg, which is important for customers to expect a different type of shopping experience across digital channels.