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This paper aims to increase our understanding of the nature and role of communities within organizations with regard to innovation management, the drivers of community innovation and macro-processes of community innovation management.

The authors first use an inductive qualitative technique to analyze data gathered from a UK university to build up the concept of communities of innovation and then refine the concept of communities of innovation by contrasting it to the more established literature on communities of practice. Finally, with the aid of existing literature on collaborative innovation and the innovation processes, the authors induce from the data the drivers of community innovation and the three macro-processes of community innovation management.

The research findings suggest communities of innovation play a central and pivotal role in contributing to the generation of innovations within organizations. Drivers of innovation included corporate culture, money and time, intellectual property management, motivation, knowledge facilitators, activists and maintenance and opportunities to interact. The three macro-processes of community innovation management are identified as divergence management, gateway management and convergence management.

As this is an exploratory research into communities of innovation, all the 11 communities of innovation analyzed belong to ABC University. It is necessary to expand on this research within the education industry, as well as into other industries to further test the reliability of the findings in this paper.

Business executives who have a better understanding of communities of innovation, the drivers of community innovation and the macro-processes of community innovation management will be better able to promote innovation within their organizations.

Governments that have a better understanding of communities of innovation, the drivers of innovation and the macro-processes of community innovation management will be better able to promote innovation within their countries.

To the best of the authors’ knowledge, this is one of the first research studies attempting to understand communities of innovation and the macro-processes of community innovation management.

The purpose of this paper is to assess the safety and efficiency of, and acceptance by, patients for an express refill service (ERS).

A pilot uncontrolled, cross-sectional, single-centred study was conducted at the outpatient pharmacy of a tertiary acute care hospital. Under ERS, prescriptions were dispensed without clinical review and counselling for patients refilling prescription medications. Efficiency was assessed by comparing processing times of ERS prescriptions with regular prescriptions. Safety was assessed by independent review of prescriptions by two pharmacists. Patient acceptance was assessed by a survey.

ERS reduced processing time of prescriptions by more than 30 per cent compared to the regular fill process. ERS was generally safe for patients, with drug-related problems identified in only one prescription which may have warranted closer monitoring. It was accepted by patients who opted for the service, as 91.4 per cent agreed or strongly agreed that they were satisfied with the service.

The study was conducted on a small convenience sample of patients in a single centre, with no control group.

Results showed that ERS was efficient, safe and well-accepted for select patients refilling their prescriptions. This leads to shorter waiting times and greater patient satisfaction.

This is the first published study that has explored the feasibility of an express prescription refill service. Despite some limitations, this study showed that omitting prospective clinical review and patient counselling for a defined population segment is safe, and can reduce processing time and improve patient satisfaction.

The purposes of this paper are to (1) explore how personality traits pertaining to the dominance influence steadiness compliance model manifest themselves in terms of user interaction behavior on social media and (2) examine whether social interaction data on social media platforms can predict user personality.

Social interaction data was collected from 198 users of Sina Weibo, a popular social media platform in China. Their personality traits were also measured via questionnaire. Machine learning techniques were applied to predict the personality traits based on the social interaction data.

The results demonstrated that the proposed classifiers had high prediction accuracy, indicating that our approach is reliable and can be used with social interaction data on social media platforms to predict user personality. “Reposting,” “being reposted,” “commenting” and “being commented on” were found to be the key interaction features that reflected Weibo users' personalities, whereas “liking” was not found to be a key feature.

The findings of this study are expected to enrich personality prediction research based on social media data and to provide insights into the potential of employing social media data for the purpose of personality prediction in the context of the Weibo social media platform in China.