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|Title:||Knowledge agent theory (KAT) for knowledge activation in the leagile manufacturing environment of the soft goods industry||Authors:||Chan, Yan Yu||Degree:||Ph.D.||Issue Date:||2012||Abstract:||Many organisational learning researchers have been working on finding critical factors that allow organisations to learn better. Discussions on the ontology of learning activists within organisations, however, are limited. It is asserted that the knowledge agent (KA) is the simplest unitary entity. The Knowledge Agent Theory (KAT) has therefore been conceptualised, in which the KA operates in an autopoietic closed system. Cross-case participatory observation research has been employed to verify the constructs of the KAT. The soft goods industry is selected for study. The replication logic is used along with the participation of four firms in the textiles and clothing industry and a consultant in this study. To validate the primary data, multiple sources of evidence are collected from naturalistic environment to observe learning behaviours. More than one hundred and nine informants were involved. The analysis identifies three types of learning groups: pseudo-, quasi- and proper KAs. The findings confirm that members who have the cognitive ability to learn, willingness and an action plan for change potentially have the components to become KAs. The commonality between members and tolerance of differences allow individuals to establish double neighbourhood relations at the individual and organisational levels to activate the functions of KAs to effectively handle collective problems in organisations. The ontology of KAs will be explained. This will contribute to empirical studies for future research in the domains of autopoietic properties that will allow practitioners to apply the theory in practice.||Subjects:||Knowledge management.
Hong Kong Polytechnic University -- Dissertations
|Pages:||xx, 296 p. : ill. ; 30 cm.|
|Appears in Collections:||Thesis|
View full-text via https://theses.lib.polyu.edu.hk/handle/200/6974
Citations as of Oct 1, 2023
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