A multidisciplinary journal that focuses on public health and technology, public health informatics, mass media campaigns, surveillance, and innovation in public health practice and research. Also dedicated to rapid open data sharing during epidemics.

The effects of drinking alcohol extend beyond the individuals concerned to the wider community. While there is recognition of such a global implication, currently no study has quantified the impact of alcohol consumption on aggregate wellbeing. This study aims to address this gap and attempts to investigate the impact of various levels of alcohol consumption on aggregate happiness. The study was carried out on a random selection of participants (n = 1,817) drawn from the 3Di consumer panel, comprising over 170,000 New Zealanders aged 18 and above. Using a subjective happiness scale (SHS) in conjunction with the Alcohol Use Disorders Identification Test (AUDIT), investigation was carried out to find whether drinking behaviour affected aggregate happiness. SHS and AUDIT scores were negatively correlated and the strength of the correlation increased with the intensity of problematic drinking. Regression analysis showed that the beta coefficient was positive for the low-risk (.074) and negative for the high-risk (−.081) category, suggesting approaches to intervene with the growing problem of alcohol consumption in modern societies. Measurements of happiness can explain the global implication of alcohol in wellbeing terms. The findings of this study indicated that low-risk drinkers affected aggregate happiness positively, whereas high-risk drinkers affected aggregate happiness negatively. While the latter observation is not new, the former raises the need to promote moderation in drinking alcohol for the common good of everyone.

Aims: Social media, because of its broad coverage, is an attractive option for communicating public health messages. However, the lack of a theoretical framework, supporting the two-way communication of social media, is holding back its development and use. This study investigated the suitability of a dynamic transactional model (DTM) of communication for explaining the use of social media for communicating public health messages. Methods: The study was carried out on ‘Don’t know? Don’t drink’, a Facebook campaign against drinking alcohol during pregnancy that targeted women of childbearing age in New Zealand. The comments generated were analysed for two features of DTM, namely inter- and intra-transaction, by examining the plurality of topics and polarity of sentiments, using text-mining techniques. Results: The analysis of the textual data revealed nine independent topics, confirming the plurality of topics. The conversation contained both positive and negative terms, establishing the polarity of sentiment. Conclusions: This study verified the two DTM features (inter- and intra-transaction) in the comments produced by the ‘Don’t know? Don’t drink’ campaign implemented on Facebook. DTM exhibited the potential to be a theoretical framework for recommending and evaluating social media sites like Facebook for communicating public health messages.

Content marketing has become a mainstream channel for brands to engage the market with value propositions. Through content, brands control, instigate and co-create value with its target audience. However, the nature of value propositions embedded within the cycle of content co-creation and their impact on eWOM outcomes has received scant attention from an empirical perspective. In this paper, we propose a value proposition-based framework around content marketing using established business perspectives. We employ bidirectional Twitter data from brands and customers to unearth descriptive, diagnostic and predictive insights into value propositions. Using a sample of marketer- and user-generated data from 10 Coffee (n_(MGC) = 290,n_(UGC) = 8811) and Car brands (n_(MGC) = 635,n_(UGC) = 7035) in 2018, a taxonomy of value propositions based on the literature was proposed and validated. The results of our study identify (a) descriptive insights explaining differentiation of brand value propositions, (b) diagnostic insights relating to consumer sentiments in response to the value proposition mix and (c) predictive insights of models predicting brand-specific values’ influencing Like, Share, Comment and Positive/Negative valence. Our results show that an effective social media marketing strategy selectively uses elements of the marketing mix (i.e. 4 P’s) within value propositions to attract favourable eWOM outcomes.