Background: Social media is changing the modern academic landscape; this study sought to explore how organizational structures support or inhibit the harnessing of social media use in academic contexts and knowledge translation. Methods: A qualitative study was conducted using framework analysis based on the Bolman and Deal’s Four-Frame Model—structural, human resources, political and symbolic. The research team used the snowball sampling technique to recruit participants following the completion of each participant’s semi-structured interview. A member check was completed to ensure rigour. Results: 16 social media educators and experts from several countries participated in the study. Study findings showed that within the Structural Frame, participants’ organizations were reported to have with diverse hierarchical structures, ranging hospital-based (strict), education institutional-based and online only groups (malleable). The Human Resources Frame revealed that most participants’ social media organizations operated on unpaid volunteer staff. The training of these staff was primarily via role-modeling and mentorship. Regarding the Political Frame, social media helped participants accumulate scholarly currency and influence within their field of practice. The Symbolic Frame showed a wide range of traditional to non-traditional organizational supports, which interacted with both intrinsic to extrinsic motivation. Conclusions: Bolman and Deal’s Four-Frame Model framework may serve as an effective guideline for academic leaders who wish to strategically implement or enhance social media use into their organizations. The key insights that we have gained from our participants are how new emerging forms of scholarly pursuits can be more effectively enabled or hindered by the attributes of the organization within which these are occurring.

Background: Social media is changing the modern academic landscape; this study sought to explore how organizational structures support or inhibit the harnessing of social media use in academic contexts and knowledge translation. Methods: A qualitative study was conducted using framework analysis based on the Bolman and Deal’s Four-Frame Model—structural, human resources, political and symbolic. The research team used the snowball sampling technique to recruit participants following the completion of each participant’s semi-structured interview. A member check was completed to ensure rigour. Results: 16 social media educators and experts from several countries participated in the study. Study findings showed that within the Structural Frame, institution types were reported to have with diverse hierarchical structures, ranging from strict to malleable: hospital-based (strict), education institutional-based and online only groups (malleable). The Human Resources Frame revealed that most participants’ social media organizations operated on unpaid volunteer staff. The training of these staff was primarily via role-modeling and mentorship. Regarding the Political Frame, social media helped participants accumulate scholarly currency and influence within their field of practice. Symbolic Frame showed a wide range of traditional to non-traditional organizational supports, which interacted with both intrinsic to extrinsic motivation. Conclusions: Bolman and Deal’s Four-Frame Model framework may serve as an effective guideline for academic leaders who wish to strategically implement or enhance social media use into their organizations. The key insights that we have gained from our participants are how new emerging forms of scholarly pursuits can be more effectively enabled or hindered by the attributes of the organization within which these are occurring.

The paper considers the stochastic modelling of radar returns. In particular, returns from a typical airport surveillance radar (ASR) system have been modelled as autoregressive-moving average (ARMA) processes. Both maximum-likelihood (ML)-and autocorrelation-based techniques have been used. Order selection algorithms were studied and modified to optimise their performance for short-data records necessitated by the nonstationary radar environment. Distinctively different models have been found for typical combinations of ground, weather and aircraft returns.