To remain competitive in the present dynamic environment, ‘governance for agility’ has become a key solution. Past literature paid little attention to understanding how governance for agility, particularly in regard to the delivery of Information Technology (IT) implementation. Using agile organisation and IT-governance theory as lenses to analyse data from a hospital case study, a strategic process model of governance for agility is empirically derived. This model suggests that agile healthcare information technology implementation is achievable via phase-based IT-governance strategies and forms which authorise decision makers to maneuver resources strategically in a dynamic environment. Theoretically, this study contributes to the dearth of empirical understanding of IT governance in the Healthcare IT literature and advances knowledge by making a conceptual distinction through introducing the use of phase-based IT-governance strategies and forms to generate agile organisational capabilities to achieve agile Healthcare IT implementation. The findings serve as a foundation for future research within the information systems (IS) discipline. Practitioners could plan an agile Healthcare IT implementation by referring to the model—a systematic roadmap for governing and strategising hospital resources and capabilities.

Abstract In this paper, a new method for fuzzy clustering is proposed that combines generative topographic mapping (GTM) and Fuzzy c‐means (FCM) clustering. GTM is used to generate latent variables and their posterior probabilities. These two provide the distribution of the input data in the latent space. FCM determines the seeds of clusters, as well as the resultant clusters and the corresponding membership functions of the input data, based on the latent variables obtained from GTM. Experiments are conducted to compare the results obtained using FCM and the Gustafson‐Kessel (GK) algorithm with the proposed method in terms of four cluster‐validity indexes. Using simulated and benchmark data sets, it is observed that the hybrid method (GTMFCM) performs better than FCM and GK algorithms in terms of these indexes. It is also found that the superiority of GTMFCM over FCM and GK algorithms becomes more pronounced with the increase in the dimensionality of the input data set.