Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7857
Title: Understanding creativity competency for organizational learning: A study of employees’ assumptions on creativity competency in creative industry
Authors: Prof. LAU Kung Wong, Nick 
Issue Date: 2016
Source: Journal of Management Development, 2016, Vol. 35 No. 10, pp. 1198-1218.
Journal: Journal of Management Development 
Abstract: Purpose Employees’ creativity competency is indeed an unstable, but powerful tool in any development and enhancement of business in creative industry. To understand employee’ assumptions on creativity is crucial for creative industry in recruiting creative personnel, developing organizational creativity training programmes as well as nurturing a creative organization. The paper aims to discuss these issues. Design/methodology/approach This paper aims to explore some basic assumptions of creativity competency by analysing literatures and creative employees’ feedback from a pilot study. This paper is neither going to define creativity accurately nor measuring employees’ creative outcomes for creative organizations, but rather to understand some basic assumptions of creativity competency in order to trigger further studies in organizational studies. Findings A pilot investigation has been done to investigate employees’ assumptions of creativity competency, and their expectation of the design of organizational creativity training programmes. The findings of this investigation have contributed to furthering the discussion and development in organizational studies and professional training programmes in creative industry. Originality/value Creativity and its training is getting important in developing learning organization nowadays. This paper is a study of the assumptions of employees in creative industry. This fundamental understanding of their assumption is essential for developing organization training and learning models in the future.
Type: Peer Reviewed Journal Article
URI: http://hdl.handle.net/20.500.11861/7857
ISSN: 0262-1711
DOI: 10.1108/JMD-12-2015-0174
Appears in Collections:Applied Data Science - Publication

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