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This study aims to examine values derived from apps and their relationship with continual intention using reviews from the Google Play Store.

This paper delves deep into the determinants of mobile health apps’ (MHAs) value offering (functional, social, epistemic, conditional and hedonic value) using automatic content analysis and text mining of user reviews. This paper obtained data from a sample of 45,019 MHA users who have posted reviews on the Google Play Store. This paper analyzed the data using text mining, ACA and regression techniques.

The findings show that values moderate the relationship between review length and ratings. This paper found that the higher the length, the lower the ratings and vice versa. This paper also demonstrated that the novelty and perceived reliability of the app are the two most essential constructs that drive user ratings of MHAs.

This is one of the first studies, to the best of the authors’ knowledge, that derives values (functional, social, epistemic, conditional and hedonic value) using text mining and explores the relationship with user ratings.

The purpose of this paper is to design organization message content strategies and analyse their information diffusion on the microblogging website, Twitter.

Using data from 29 brands and 9392 tweets, message strategies on twitter are classified into four strategies. Using content analysis all the tweets are classified into informational strategy, transformational strategy, interactional strategy and promotional strategy. Additionally, the information diffusion for the developed message strategies was explored. Furthermore, message content features such as text readability features, language features, Twitter-specific features, vividness features on information diffusion are analysed across message strategies. Additionally, the interaction between message strategies and message features was carried out.

Finding reveals that informational strategies were the dominant message strategy on Twitter. The influence of text readability features language features, Twitter-specific features, vividness features that influenced information diffusion varied across four message strategies.

This study offers a completely novel way for effectively analysing information diffusion for branded tweets on Twitter and can show a path to both researchers and practitioners for the development of successful social media marketing strategies.