Accidental thinking: the serendipitous cognition model

Serendipity captures the interaction between a skilled human agent and a fortuitous event in the environment. Although it features in many stories of invention and discovery, its antecedents remain elusive. This paper combines research from different domains of psychology to present a model of the cognitive processes required for a serendipitous episode to occur. The model describes a prepared mind that consists of an informational state and an attentional state. Both states are continually updating. An accident is considered as a trigger event that updates both of these and feeds information back into the prepared mind. If the accident is noticed, a cycle of judgement and amplification occurs, eventually leading to an output. The model generates novel predictions that point to an increased understanding of how best to scaffold serendipitous moments

National libraries' use of Facebook and Twitter and user engagement

This research investigated national libraries' use of Facebook and Twitter and the user responses to those activities. Data was collected directly from the Facebook and Twitter pages of three national libraries (Library of Congress between 30.01.18 and 24.04.18, National Library of Australia and National Library of Scotland both between 10.06.19 and 1.09.19) including the posts and user comments. Content and thematic analysis was performed on the posts to determine library behaviour and a developed toolkit utilising thematic discourse analysis was used to understand user responses. Libraries were found to post about library-centric topics such as collections, events and resources, and linked users to library controlled webspaces such as their websites or other social media. Images were used to either complement or enhance the information contained in posts, though no overall patterns emerged as the libraries varied slightly in their posting patterns. Two major differences were responses to other social media on Twitter that were not available on Facebook, and the NLS using more personable themes. The libraries also responded to users differently with LoC barely responding, and NLA and NLS liking and responding to comments. Users mostly responded to the content of posts, as well as having conversations in the NLA and NLS datasets. Common motivations for responding including liking the content, sharing the content with others, sharing relevant memories or content as well as gratitude and answering a question, with most motivations and comments positive. Response rates varied, with the NLS receiving the most comments on Twitter despite the smallest library size. The results aligned with existing research in other areas, and beyond the advice for practitioners to respond to users and use informal language, one of the main outputs of the research is a toolkit that can be used by others to gain deeper understanding of user engagements.This research investigated national libraries' use of Facebook and Twitter and the user responses to those activities. Data was collected directly from the Facebook and Twitter pages of three national libraries (Library of Congress between 30.01.18 and 24.04.18, National Library of Australia and National Library of Scotland both between 10.06.19 and 1.09.19) including the posts and user comments. Content and thematic analysis was performed on the posts to determine library behaviour and a developed toolkit utilising thematic discourse analysis was used to understand user responses. Libraries were found to post about library-centric topics such as collections, events and resources, and linked users to library controlled webspaces such as their websites or other social media. Images were used to either complement or enhance the information contained in posts, though no overall patterns emerged as the libraries varied slightly in their posting patterns. Two major differences were responses to other social media on Twitter that were not available on Facebook, and the NLS using more personable themes. The libraries also responded to users differently with LoC barely responding, and NLA and NLS liking and responding to comments. Users mostly responded to the content of posts, as well as having conversations in the NLA and NLS datasets. Common motivations for responding including liking the content, sharing the content with others, sharing relevant memories or content as well as gratitude and answering a question, with most motivations and comments positive. Response rates varied, with the NLS receiving the most comments on Twitter despite the smallest library size. The results aligned with existing research in other areas, and beyond the advice for practitioners to respond to users and use informal language, one of the main outputs of the research is a toolkit that can be used by others to gain deeper understanding of user engagements