Consumer acceptance of m-wellbeing services : a social marketing perspective

Purpose This study aims to employ the Model of Goal-Directed Behaviour (MGB) to examine the consumer acceptance of technology-based self-service (TBSS) for a credence service instrumental to a social goal. Credence services are increasingly delivered via self-service technology and in social marketing, the achievement of social goals can be contingent on consumer acceptance of these services. However, little is known about the determinants of acceptance and extant marketing literature fails to account for emotional and goal influences which are likely to be important. Design/methodology/approach The authors interviewed 30 young adults with self-reported stress, anxiety or depression as potential users of a self-help mental health service delivered via mobile phone. The data were analysed deductively and inductively with the assistance of NVivo. Findings The findings generally support using the MGB to enhance understanding of consumers' acceptance of TBSS. The paper also found evidence of the importance of maintenance self-efficacy, the self-evaluation of the ability to continue using the service, and a previously ignored element of consumer level competition that arises between alternatives that achieve the same goal. Originality/value This study is the first to examine factors that influence consumers' acceptance of TBSS for credence services aimed at achieving a social goal. It builds on understanding of consumer decision making in social marketing, particularly the influence of self-efficacy and competition. It also contributes to attitudinal research by providing initial evidence for deepening and broadening the MGB in the context of TBSSs.

Identify disorders in health records using Conditional Random Fields and Metamap: AEHRC at ShARe/CLEF 2013 eHealth Evaluation Lab Task 1

The Australian e-Health Research Centre (AEHRC) recently participated in the ShARe/CLEF eHealth Evaluation Lab Task 1. The goal of this task is to individuate mentions of disorders in free-text electronic health records and map disorders to SNOMED CT concepts in the UMLS metathesaurus. This paper details our participation to this ShARe/CLEF task. Our approaches are based on using the clinical natural language processing tool Metamap and Conditional Random Fields (CRF) to individuate mentions of disorders and then to map those to SNOMED CT concepts. Empirical results obtained on the 2013 ShARe/CLEF task highlight that our instance of Metamap (after ltering irrelevant semantic types), although achieving a high level of precision, is only able to identify a small amount of disorders (about 21% to 28%) from free-text health records. On the other hand, the addition of the CRF models allows for a much higher recall (57% to 79%) of disorders from free-text, without sensible detriment in precision. When evaluating the accuracy of the mapping of disorders to SNOMED CT concepts in the UMLS, we observe that the mapping obtained by our ltered instance of Metamap delivers state-of-the-art e ectiveness if only spans individuated by our system are considered (`relaxed' accuracy).