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Facilitated by image retouch tools, social media influencers can digitally enhance their self-image in product recommendation posts. This paper proposes that image enhancement may serve as a cue for the audience to assess the authenticity of the influencer (“true to oneself”), which further affects the influencer's product recommendation effectiveness (i.e. attitudes toward the post and recommended product).

Experiment 1 examines the effect of image enhancement on consumers' perceived influencer authenticity and product recommendation effectiveness. Experiment 2 considers the moderating role of post type, examining the effects in informational versus storytelling posts.

Consumers perceived an influencer to be more authentic when the image is not enhanced; in turn, consumers reported more favorable attitudes toward the post and the recommended product upon reading the post. The effects are moderated by post type: the effect of image enhancement (through perceived influencer authenticity) exists in posts using an informational message format but is attenuated for those using a storytelling message format.

This research enriches the literature on authenticity cues by documenting a novel visual cue and contributes to influencer marketing by identifying a nuanced interactive effect between image enhancement and post type on recommendation effectiveness.

This study aims to analyze a conspicuous corpus of literature related to the field of technology-based intensive care research and to develop an architecture model of the future smart intensive care unit (ICU).

Papers related to the topics of electronic health record (EHR), big data, data flow and clinical decision support in ICUs were investigated. These concepts have been analyzed in combination with secondary use of data, prediction models, data standardization and interoperability challenges. Based on the findings, an architecture model evaluated using MIMIC III is proposed.

Research identified issues regarding implementation of systems, data sources, interoperability, management of big data and free text produced in ICUs and lack of accuracy of prediction models. ICU should be treated as part of a greater system, able to intercommunicate with other entities.

The research examines the current needs of ICUs in interoperability and data management. As environment changes dynamically, continuous assessment and evaluation of the model with other ICU databases is required.

The proposed model improves ICUs interoperability in national health system, ICU staff intercommunication, remote access and decision support. Its modular approach ensures that ICUs can have their own particularities and specialisms while ICU functions provide ongoing expertise and training to upgrade its staff.