Dynamic Client Portfolios as Sources of Ambidexterity: Exploration and Exploitation Within and Across Client Relationships

A concept that has captured the imagination of organisational scholars over the last two decades is that of 'organisational ambidexterity'. This has been described as the 'concurrent pursuit of exploitation and exploration', or how companies manage what they know (their current business) and develop what they as yet don't know (their business of tomorrow). It is considered critical to an organisation's success but it is also recognised as something extremely difficult to achieve within a single organisation. As a result there has been a lot of interest in how strategic alliances can help achieve this goal. Yet there are other sources of ambidexterity, such as client relationships, that merit further study.

Academic research into Knowledge-Intensive firms has developed some understanding of how firms can use client relationships for their own knowledge development. These firms rely on their ability to create, preserve and replenish valuable knowledge that they apply in their knowledge-based services. Therefore they offer a useful context in which to study the potential of client relationships to enable organisational ambidexterity.

This paper explains how dynamic client portfolios can be a source of ambidexterity for knowledge intensive firms (KIFs). Drawing from a unique qualitative dataset of firms in the global reinsurance market, we show how different types of client relationships underpin a dynamic client portfolio and become a source of ambidexterity for a KIF. We develop a process model to show how KIFs attain knowledge by segmenting their client portfolios, use that knowledge to explore and exploit within and across their client relationships, and dynamically adjust their client portfolios over time. This study contributes to the literature on external sources of ambidexterity and dynamic management of client knowledge within KIFs.

A draft version of the research paper is available for download below. This research has been accepted for publication at Long Range Planning

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