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Title: Strategic learning in design contests
Author: Jha, Pushkar P.
ISNI:       0000 0001 3590 4972
Awarding Body: City University London
Current Institution: City, University of London
Date of Award: 2007
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This dissertation examines strategic learning as learning from events and experiences that have significant consequences for organisational survival in a competitive environment. The study is centred on design contest as repeat event systems that in their time bracketed generational progression, provide for an ideal setting to analyse such learning. Convergence that constrains experimentation to a few elements that define the strategic configuration of organisations is posited as a natural consequence of performance feedback. Strategic learning is seen to be manifested in the interplay of behavioural and cognitive attributes that moderate such convergence. Effective strategic learning is seen as key to distinguishing `winners' from the `also-rans' where the former counter overt convergence by striking a balance between `searching for the competitive edge' and `creating the competitive edge'. In its conceptualisation of strategic learning, study design, and selection of research sites, the study successfully navigates most of the problems that have confounded research in the relatively nascent area of strategic learning. The dissertation comprises three empirical studies. The first two are based in the quasi-experimental settings of a robotic design contest called Robot Wars where the strategic learning model emerges by an examination of convergence as consequence of performance feedback, and the factors that moderate it. The last study is based on the sequential event system of movie-sequels to provide external validity to the study. The study presents the first comprehensive examination of strategic learning in repeat event systems. It provides empirical evidence for the effect of performance feedback on convergence, and the consequences this has for future performance. Evidence for the interplay of behavioural and cognitive forces in moderating convergence for effective strategic learning, completes the strategic learning model that this dissertation delivers as a contribution to research and managerial practice.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: HD28 Management. Industrial Management