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Based on existing research in media economics, media management, as well as media history, this paper analyzes the characteristics of media innovations. These media-specific attributes help distinguish media innovation from other types of innovation and justify the necessity to establish a distinct field of research on media innovation. As a result, eight attributes are presented. These attributes refer to media innovations both as products and as processes. They characterize media innovations as multidimensional and risky products and highlight the importance of approaching media innovation development as interactive, long-term processes that go beyond the control of particular media organizations. In conclusion, implications with respect to studying media innovations from an interdisciplinary perspective are derived.

Decision-making theories have argued that many daily decisions are the result of heuristic rather than systematic processes. Given the ubiquity of smartphones as mobile communication and computing devices along with the vast smartphone app market, our exploratory study aimed to understand how heuristics guide smartphone app selection. Observing 49 smartphone users from the US and Germany viewing 189 total apps from three predetermined categories, the current study identified five decision-making heuristics used to download a variety of smartphone apps. Of these, four were variants of a "Take the First" (TtF) heuristic that allowed smartphone users to quickly navigate the app market, by passing a good deal of other informational cues in order to download apps that were simply highly rated or ranked. Reliance on heuristic processing is useful in helping navigate the app market, but it also results in smartphone users overlooking potentially important app information.

In light of the widespread use of big data analytics, internet users are increasingly confronted with algorithmic decision-making. Developing algorithm literacy is thus crucial to empower users to successfully navigate digital environments. In this paper, we present the development and validation of an algorithm literacy scale that consists of two interrelated dimensions: 1) awareness of algorithms use and 2) knowledge about algorithms. To validate the scale, we use item response theory and report findings from two studies.In study 1, we tested 46 items among N = 331 participants, resulting in a 32item pool. These items were tested in a second study among N = 1,041 German internet users. The final scale consists of each 11 items measuring algorithm awareness and knowledge. Both subscales correlated positively with participants' subjective coding skills and proved to be an appropriate predictor for participants' handling of algorithmic curation in three testscenarios.