Social Preferences in Decision Making Under Cybersecurity Risks and Uncertainties

The most costly cybersecurity incidents for organizations result from the failures of their third parties. This means that organizations should not only invest in their own protection and cybersecurity measures, but also pay attention to that of their business and operational partners. While economic impact and real extent of third parties cybersecurity risks is hard to quantify, decision makers inevitably compare their decisions with other entities in their network. This paper presents a theoretically derived model to analyze the impact of social preferences and other factors on the willingness to cooperate in third party ecosystems. We hypothesize that willingness to cooperate among the organizations in the context of cybersecurity increases following the experience of cybersecurity attacks and increased perceived cybersecurity risks. The effects are mediated by perceived cybersecurity value and moderated by social preferences. These hypotheses are tested using a variance-based structural equation modeling analysis based on feedback from a sample of Norwegian organizations. Our empirical results confirm the strong positive impact of social preferences and cybersecurity attack experience on the willingness to cooperate, and support the reciprocal behavior of cybersecurity decision makers. We further show that more perception of cybersecurity risk and value deter the decision makers to cooperate with other organizations

Understanding Factors Influencing the Usage Intention of Mobile Pregnancy Applications

Part 8: Healthcare Information TechnologyInternational audienceAdvancement in digital technology and the need to provide alternate healthcare delivery channels to individuals in developing countries has led to the boom in mobile Health (mHealth). A wide range of mHealth applications (apps) and services are available today to combat the maternal and newborn health disparities in India. Yet, there is scant research in understanding the predictors of pregnant women’s adoption towards pregnancy apps in developing countries. The objective of this study is to identify the most significant predictors influencing behavioural intention to use pregnancy apps. To meet this objective, a conceptual model was developed and empirically tested by extending UTAUT with relevant constructs namely personal innovativeness in IT and perceived risk. A conceptual model along with the hypothesized causal paths among the constructs are empirically validated with the help of structural equation modeling using Smart PLS 3.0 with a sample of 220 pregnant women. Results showed that intention to use pregnancy apps by women was predicted by six influencing factors: performance expectancy, effort expectancy, facilitating conditions, social influence, personal innovativeness and attitude. Perceived risk had no significant effect on the behavioural intention to use pregnancy apps