Date of Award

Spring 2013

Degree Type

Open Access Dissertation

Degree Name

Management, PhD

Program

Peter F. Drucker and Masatoshi Ito Graduate School of Management

Advisor/Supervisor/Committee Chair

Joseph Maciariello

Dissertation or Thesis Committee Member

Mihaly Csikszentmihalyi

Dissertation or Thesis Committee Member

Michelle C. Bligh

Dissertation or Thesis Committee Member

David L. Cooperrider

Terms of Use & License Information

Terms of Use for work posted in Scholarship@Claremont.

Abstract

The purpose of this exploratory study is to develop alternative models for analyzing the systems dynamics of a large group conference format called appreciative inquiry (AI) summits. I apply Luhmann’s social systems theory to the strategizing activities of AI summits to examine how this particular format is capable of generating organizational knowledge. An AI summit is a strategic planning conference in which hundreds of internal and external stakeholders collectively design the future of the organization through structured activities. It applies the principles of AI, a consulting method used in organizational development that attends to the positive aspects of an organization as opposed to its problems. Critics challenge this unconditional focus on the positive, questioning the validity of its methods and techniques. Indeed, very few rigorous evaluations of AI methods including AI summits exist.

I propose a new approach for assessing the effectiveness of AI summits. I focus on knowledge creation as the dependent variable. Previous studies have shown that successful AI interventions generate new knowledge, not just transformational change. I conceptualize an AI summit as a strategic episode that allows an organization to temporarily suspend its routines and structures for strategic reflection.

According to social systems theory, organizations are autopoietic (self-reproducing) systems that maintain their identity through an ongoing production of decision communications. An AI summit consists of three different types of systems that co-evolve and are structurally coupled: an organization system, interaction system and the individual participants’ psychological systems. I propose a typology for analyzing episodes during an AI summit as a starting point for determining the structural dynamics inherent in an AI summit system.

Using illustrative examples from a case study, I identify five structural features of an AI summit that facilitate organizational knowledge creation, including reduced communication barriers and the production of decisions during the conference. The study contributes to the existing literature by identifying the important but understudied role of self-organizing project teams in the knowledge creation process at an AI summit. Limitations and implications are discussed.

DOI

10.5642/cguetd/80