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Mobile health (mHealth) service is an Australian Government initiative aiming to improve the quality of health-care services. However, little is known about Australian health consumers’ willingness to adopt mHealth. The purpose of this paper is to study the usage behavior of mHealth service users in Australia. While various factors may impact users’ willingness to accept mHealth, this research investigates factors influencing the mHealth adoption decisions of the Australian health-care consumers, and the moderating impact of demographic factors on the usage behavior (UB) of mHealth services which has been rarely addressed in an Australian setting in the past.

The theoretical framework is based on the technology acceptance model (TAM). Data were collected from residents of the Australian Capital Territory using a survey questionnaire and examined using partial least squares structural equation modeling.

The proposed mHealth usage model demonstrated a good fit and indicated that perceived usefulness, perceived ease of use, social influence, service quality and government influence are critical issues shaping mHealth UB. The moderation analysis revealed that users’ demographics, namely, gender, age and education are instrumental in broadening the understanding of UB of mHealth service in Australia.

The findings will inform health-care service providers about the critical importance of the key factors driving the usage of mHealth services. Health-care providers and relevant authorities can develop targeted communication strategies that maximize the acceptance of mHealth services. Furthermore, deeper understanding of users’ demographic profiles would enable health-care service providers to promote their services to the right clients.

The above findings on the factors and user demographics informing the usage of mHealth services have unique practical, contextual and theoretical implications.

This paper aims to identify the variables responsible for classifying the investors into risk takers (RT) and risk avoiders (RA) across their economic perspectives.

The research offers a novel and unobtrusive measure of classifying investors into RT and RA based on a set of financial risk tolerance (FRT) questions. The authors have investigated the causes of discrimination across economic perspectives over a sample of 552 investors exposed to market risk.

The authors identify that out of the total of 11 risk assessment variables, only three are responsible for classifying investors into RA and RT. The variables are risk return trade-off, comfort level dealing with risk, and understanding short-term volatility. Financial literacy is considered as an emerging cause of discrimination. Further, the authors highlight the most striking finding to be the discriminating factors across wealth and source of income of the investors.

Existing research on FRT can be loosely segregated into three groups: the relationship between an individual's financial and non-FRT, estimation of FRT score (FRTS), and perceived self-assessed FRTS. The current research roughly falls into the third category of study where the authors have not only studied the self-assessed risk tolerance but also evaluated the predictors. Most of the studies have focussed on estimating self-assessed FRT with the help of one direct question to the respondent. However, the uniqueness of this study is that the researchers have used an instrument comprising a series of direct and indirect questions that can easily estimate the self-assessed risk perception and also discriminate the role of the economic factors that have any impact on self-assessed FRTS.