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Online privacy pertains to an individual’s capacity to regulate and oversee the gathering and distribution of online information. Conversely, online privacy concern (OPC) pertains to the protection of personal information, along with the worries or convictions concerning potential risks and unfavorable outcomes associated with its collection, utilization and distribution. With a holistic approach to these relationships, this study aims to model the relationships between digital literacy (DL), digital data security awareness (DDSA) and OPC and how these relationships vary by gender.

The participants of this study are 2,835 university students. Data collection tools in the study consist of personal information form and three different scales. Partial least squares (PLS), structural equation modeling (SEM) and multi-group analysis (MGA) were used to test the framework determined in the context of the research purpose and to validate the proposed hypotheses.

DL has a direct and positive effect on digital data security awareness (DDSA), and DDSA has a positive effect on OPC. According to the MGA results, the hypothesis put forward in both male and female sub-samples was supported. The effect of DDSA on OPC is higher for males.

This study highlights the positive role of DL and perception of data security on OPC. In addition, MGA findings by gender reveal some differences between men and women.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-03-2023-0122

Hospitals recently search for more accurate forecasting systems, given the unpredictable demand and the increasing occurrence of disruptive incidents (mass casualty incidents, pandemics and natural disasters). Besides, the incorporation of automatic inventory and replenishment systems – that hospitals are undertaking – requires developed and accurate forecasting systems. Researchers propose different artificial intelligence (AI)-based forecasting models to predict hospital assets consumption (AC) for everyday activity case and prove that AI-based models generally outperform many forecasting models in this framework. The purpose of this paper is to identify the appropriate AI-based forecasting model(s) for predicting hospital AC under disruptive incidents to improve hospitals' response to disasters/pandemics situations.

The authors select the appropriate AI-based forecasting models according to the deduced criteria from hospitals' framework analysis under disruptive incidents. Artificial neural network (ANN), recurrent neural network (RNN), adaptive neuro-fuzzy inference system (ANFIS) and learning-FIS (FIS with learning algorithms) are generally compliant with the criteria among many AI-based forecasting methods. Therefore, the authors evaluate their accuracy to predict a university hospital AC under a burn mass casualty incident.

The ANFIS model is the most compliant with the extracted criteria (autonomous learning capability, fast response, real-time control and interpretability) and provides the best accuracy (the average accuracy is 98.46%) comparing to the other models.

This work contributes to developing accurate forecasting systems for hospitals under disruptive incidents to improve their response to disasters/pandemics situations.