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Managing alliance networks : capability development through experiential learning in the Aurora preferred suppliers alliance network
thesisposted on 2023-05-27, 14:22 authored by Megan WoodsMegan Woods
This thesis explored the development of organisational capabilities in managing and participating in strategic alliance networks. Firms develop capabilities in successful alliance use by acquiring and leveraging knowledge about alliance management (Draulans, deMan & Volberda, 2003). This study investigated two distinct subsets of alliance capabilities: those developed by the network broker, who established and managed the network's operation, and those developed by the network members. Building on concepts from the strategic alliance and organisational learning literatures, the research explored capability development as an adaptive learning process. It examined the learning conditions and learning-assessment-action cycles which underpinned alliance development and alliance partners' learning experiences. Doz's (1996) adaptive learning model of dyadic alliance development conceptually framed the investigation of the following research questions: Research Question One: How does a network broker learn through alliance conditions to manage an alliance network? Research Question Two: How do network members learn through alliance conditions to operate under the broker's network management? The study addressed the research questions through a longitudinal case study of the Aurora Preferred Suppliers alliance network, which operates in the Tasmanian residential electric heating market. The Aurora Preferred Suppliers network is a hub-and-spoke network of alliances between Aurora Energy, Tasmania's only electricity retailer, and fourteen retailers of residential electric heating appliances. The study examined the network's history from its formation in 1992 until 2005. The study combined secondary data from the company and from news archives with primary data collected through interviews with the entire Aurora alliance management team and representatives from every network member organisation. N-Vivo, a qualitative data analysis software program, was used to undertake a systematic, transparent and comprehensively-recorded analysis of the network's history and of participants' network experiences. A chronological timeline and historical narrative of the network's history was developed, and then participants' motives for establishing and adapting to network conditions were explored. The broker's learning about network management and network members' learning about network participation were analysed, and the adaptive learning cycles that underpinned network evolution were graphically modelled. The study identified the adaptive learning cycles which characterised the broker's and network members' learning through the creation, adaptation, development and escalation of the alliance network. The findings expanded co-evolutionary approaches to alliance development by identifying that alliance partners' learning experiences co-evolved over the course of their collaboration. The broker's learning was mediated by network members' learning about network participation. Members' learning was mediated by learning about the broker and about other network members. The study also extended adaptive learning perspectives of alliance development by adapting Doz's (1996) adaptive learning model of dyadic alliance development to an alliance network context. The learning conditions, learning topics and evaluative criteria identified by Doz (1996) were found to characterise the development of the alliance network, and to be complemented by additional conditions, learning topics and evaluative criteria. As expected, broker and member learning was facilitated by the conditions of task definition, partner routines, interface structures, expectations about alliance performance, expectations about partner motives, and expectations about partner behaviour. The additional conditions of the network's environment, design, resource pool and capacity also facilitated broker and member learning. As anticipated, broker and member learning was informed by learning about the environment, the collaboration task and process, and the skills and goals of alliance partners, and was also informed by learning about additional characteristics of their alliance partners, and about their own organisations. Consistent with Doz's (1996) model, adaptations to alliance conditions were informed by evaluations of equity, efficiency and adaptability, and were also informed by evaluations of network performance, network effectiveness and collaboration value. The study findings established two future research areas: exploring the learning processes and outcomes associated with different collaborative forms, and exploring the interactions between network conditions and learning outcomes under competitive and non-competitive network contexts.
Rights statementCopyright 2009 the author No access or viewing without the approval of the author. Thesis (PhD)--University of Tasmania, 2009. Includes bibliographical references