Appreciative Inquiry as an Alternative Methodology in Team Development

Appreciative Inquiry as an Alternative Methodology in Team Development

Teams are a critical part of one's everyday life and may take numerous forms.  Teams may be defined as groups of people coming together to accomplish common goals with established roles and activities (Weis, 1991).  Identifying and understanding the stages of team development—how a group evolves into an effective, cohesive, collaborative team—has been the focus of decades of research.  Of importance is discovering how to enable individuals and teams to reach the final stage of team development (high performance) while also having positive social experiences.  Therefore, a key question is to what degree can Appreciative Inquiry (AI) serve as an alternative methodology in creating and sustaining the team development process? 

To answer the question this paper poses, foundational information about AI is provided first—What is AI? How is AI being applied?  Next, traditional team development models are discussed.  Then, traditional teambuilding methods and the AI method are compared.  Finally, an outline for future research is presented.

Appreciative Inquiry

AI goes by many names depending on the practitioner.  AI  is called a concept, a theory, a mindset, and an approach to analysis (Wakins & Cooperrider, 2000); a form of action research and theory of how to develop social systems (Bushe, 1998a); and possibly a spiritual principle or a way of living (Sorensen, Yaeger, & Nicoll, 2000).  Yet, these writers and researchers agree that AI is a process by which one or more individuals create positive images for the future and strategically move towards creating that change. 

In 1987, Cooperrider and Srivasta introduced AI as “…a way of living with, being with, and directly participating in…social organizations…[one which] engenders a reverence for life” (Cooperrider & Srivasta, 1987, cited in Sorensen et al., 2000).  The dominant theoretical basis of AI comes from Constructionism—a post-modern European philosophy wherein one's reality is socially constructed—and is supported by four principles:  Principle of Simultaneity, Poetic Principle, Anticipatory Principle, and Positive Principle. 

The Principle of Simultaneity relates to the Constructionist theory in which language and words are the building blocks of the social reality (Bushe, 1998c; Srivastva, Cooperrider, & Associates, 1990).  So, in the first step of a discovery process—initiating an AI—asking questions is not a neutral event.  Each question asked, and how it is worded, simultaneously sets the stage for what will be found. 

The Poetic Principle comes from the belief that all human systems (e.g., cultures, communities, organizations, teams, and dyads) are ever-changing social entities.  In AI, the Poetic Principle is revealed through the sharing of stories and reflections with themes such as creativity or innovation.  These anecdotes are used to interpret the past and present to become the poetic inspiration for the next evolution. 

The Anticipatory Principle simply means that what one anticipates is what one will create and is based on the placebo and Pygmalion studies (Srivastva et al., 1990).  Zemke (1999) described this principle as “positive mental imagery turned up a notch” (p. 30).  This principle is applied throughout the AI process through discussion, imagery, and written form.

Lastly, the Positive Principle, widely seen as the antithesis of the problem-solving principle (Bushe, 1998c; Srivastva et al., 1990; Watkins & Cooperrider, 2000; Zemke, 1999), focuses on positive past and current successes to create the future.  In AI, the Positive Principle means choosing a path that is strengthened by achievements and victories (improving on what works) versus one that focuses on issues and errors (solving only what is wrong). 

There are four phases in the AI process: discovery, dream, design, and destiny, and the process is iterative.  Each phase is briefly described as follows:

·      Discovery.  "What gives life?" – explores the themes of the stories and reflections and includes consensus-building about the strengths for the entity desiring change.

·      Dream.  "What might be?" – used as foundation on which to build possible futures and results in a draft statement that summarizes the vision and purpose.  

·      Design.  "What should be the ideal?" – uses the dream statements to build agreement on the future concepts and principles and to imagine how the entity (e.g., organization, team) will look. 

·      Destiny.  "How to empower, learn, and adjust?"  – originally called delivery phase, this phase is used in any way the entity needs.  As the process is iterative, this phase may be "the beginning of an organization redesign or a new strategic plan, …a quest to form a diversity-friendly culture or to create ideas for building closer customer-company collaborations" (Zemke, 1999).

Given that there are four phases to an AI process and these phases are anchored in the theory of Constructionism, how is AI being applied? 

AI Applications

AI as a concept is not new, and its elements may be noted in many domains, such as psychology and philosophy.  Originally, AI was researched and applied in U.S.-based corporations (social systems) as a methodology for culture change and targeted to leadership and management (Bushe, 1998a, 1998c; Srivastva & Cooperrider, 1990; Watkins & Cooperrider, 2000; Zemke, 1999).  For example, Zemke (1999) describes how GTE won the 1997 American Society Training & Development award for exemplifying outstanding organizational development practices during which process the "zealous" AI practitioners became known as "positive change agents" (p. 31).  Watkins and Cooperrider (2000) discuss how this inquiry method is used as a benchmarking (identifying best practices in a given field) strategy. 

Over time, AI practitioners have extended the method to have a global and diverse impact (Case Western University, 2002).  In the Myrada Appreciative Inquiry Project, the International Institute for Sustainable Development (IISD) (2001) uses AI as a community development tool in rural India.  In Becoming a Visible Force for Peace, Cooperrider (1999) presents his joint effort with the Dali Lama and other global religious leaders (as cited in Case Western University, 2002).  Even though the AI method is being demonstrated by such diverse cases, little quantitative research has been conducted with AI. 

In the late 1990s, team development became a focus for AI research (Bushe, 1998a, 1998b, 1998c, Head, 2001).  Team development models illustrate the stages through which a team passes on its evolutionary path to high performance (Bradford, 1961, 1978; Syer & Connolly, 1996; Weis, 1991).  Team interventions are those methods applied to the team or used by team members to move through the development stages (Bradford, 1961, 1978; Bushe, 1998a, 1998b, 1998c, Head, 2001; Syer & Connolly, 1996).  To consider how the AI method may be applicable to the team development process, let us first review the team development models. 

Team Development Models

Developmental models diagram the progression of change that is thought to occur in a team.  There are many recognized team development models and the quantity of stages identified range from three—Bion's (1948) model: dependence, subgroup pairing, commitment or battle—to multi-layered—Bennis' and Shepard's (1956) theory of team development (Bradford, 1961; Head, 2001). Charrier's (1972) Cog's Ladder (polite stage, why are we here stage, constructive stage, esprit stage) is still a research model, while, other models such as Weis's (1991) six stages (introducing, stage setting, probing/testing, creating, producing, maintaining) have not received much research attention (Head, 2001).  Of popular use today, is the Tuckman model: forming, storming, norming, performing. 

These models share common characteristics:  (a) the models themselves are of a process system-based design, that is, task-focused (input to output); (b) the stages' progressions are step-by-step; (c) the teams are problem-based; (d) and the members are expected to experience conflict in order to reach the final stage of development (Bradford, 1978; Head, 2001; Leonard & Freedman, 2000; Yeats & Hyten, 1998).  Based upon the literature review, the models have not greatly varied over time.

Methodologies for Team Interventions

Methodologies for team development were originally developed to move the team from stage to stage.  To better understand the span of differences in team development methodologies, a continuum (Table 1) was designed with potential characteristics of the two diametrical ends.  

Table 1 

Continuum of Team Development Methodologies

Process system-based Task focused Problem solving Applied to team		Social-system based Future focused Positive imaging Applied by team
Team placed within model Team goes step-by-step Objectivism		Team creates model Team may skip step(s) Constructionism & Constructivism

Moving from left to right on this continuum, let us discuss the methodologies. Considering the common characteristics of the models, it is not surprising that the majority of methods reviewed reflect a process-oriented, conflict-preparedness stance (Bradford, 1961, 1978; Leonard & Freedman, 2000; Syer & Connolly, 1996).  Tuckman's widely used methodology, with techniques similar to other methods, provides an example of the left side of the continuum and is based on his four stages (forming, storming, norming performing).

Tuckman infers that members will be able to make decisions by consensus after they have first completed two initial stages that include dominating members, deadlocks, and power plays; members will fully accept their roles in the third stage; and in stage four, members will finally feel comfortable to challenge relationships (Catalyst Consulting, 2002).  The role of leadership in methodologies is of notable interest.  In this regard, Tuckman suggests that one of the leader's responsibilities is to identify when the team enters a stage and then direct the team through that stage of the process.

Over time, as the number of team types have increased (e.g., virtual teams, peer-based learning groups, self-directed work teams, cross-functional teams, as well as team-based organizations), new techniques have been added to methodologies.  Yet, for the most part, the majority of methods have maintained a process system-based approach to team development (Leonard & Freedman, 2000).  The trend for methodologies to be located on the left side of the continuum is historically strong. 

One center-continuum method was found in the literature review.  Gibb and Gibb (1967) presented the group as a "growing organism" and, although there are many similarities between this method and Tuckman's, the authors believed that the formation of trust was the foundation of teams—"as trust grows, people are able to eliminate much of the structure" (Gibb & Gibb, 1967, as cited in Bradford, 1978, p. 109).  Another important component in this method is balancing a team's social aspect (personal growth and group growth) with the team's system structure (forming goals and productivity).  A further literature review would be necessary to locate additional center-continuum methods.

As mentioned above, little research has been conducted with Appreciative Inquiry and team development.  Taking the lead in this new area, Bushe (1998a, 1998b, 1998c) has experimented with newly formed teams, newly merged teams, and established teams.  One result of his research is that Bushe believes complimentary roles may be defined and accepted in the first stage and that this accomplishment may allow the team to skip a difficult stage.  Bushe describes this observation as follows:

Much of the 'forming' to 'storming' dynamics come out of the clash of establishing personal identity and the role complimentarities these create (Srivastva, Obert & Neilsen, 1977). …Role complementarity refers to the fact that for any person to take on a role (e.g., leader) others have to be willing to take on a complimentary role. (Bushe, 1998a)

If on-going research validates that teams' using AI may bypass a difficult development stage, it will be the first time that a methodology countermands the traditional team development models. 

Other observations from Bushe's research include the following:  (a) AI was an effective method for established teams experiencing difficulties; (b) in one experiment, groups using AI scored significantly higher in performance outcomes than groups without AI; and (c) team members valued the discovery phase of stories and reflections (Bushe, 1998a, 1998b).  From Bushe's research one might also conclude that AI has a profound affect on those teams having the opportunity to experience the method.

The final example of AI and team development research comes from an award-winning dissertation by Robert L. Head (2000), “Appreciative Inquiry as a team-development intervention for newly formed heterogeneous groups.”  Head focused on testing the Anticipatory Principle of AI and used three groups:  one group experienced traditional team building interventions, one had AI interventions, and one group had no interventions.  The overall findings of Head's (2000) research were as follows:  (a) Images—future expectations and visions for the team—held by participants - AI team "statistically and significantly outperformed" the other groups, (b) Group performance - AI team "statistically and significantly outperformed" the other groups, and (c) Group's process - AI team outperformed, "but not to the level of significant statistical difference" the other groups (p. 90).

Head's (2000) dissertation results also supported Bushe's (1998a) findings concerning eliminating a development stage and is described as follows:

By its very design, traditional team-building includes storming.  While team-building is considerably more advantageous than no structured intervention…the absence of storming makes AI more advantageous to organizations than team-building for new groups.  Adopting AI as the intervention of choice is a sound strategy… (p. 99).

Considering the results of Bushe's and Head's initial research, it is hoped that other researchers will follow in their footsteps. 

Comparison of Traditional and AI Methodologies

Having gained an understanding of AI, the team development models, and traditional and AI methods for the team development process, let us put that information together.  How might traditional methodologies, with their process-system designs, look contrasted with AI's social-system methodology?  Using a generic team development model, Table 2 provides this view.

Table 2 

Comparison of Traditional Team Development Methodology and AI Methodology

Generic Team Development Stages	Traditional Team Development Methodology	Appreciative Inquiry Team Development Methodology
Stage 1.  Baseline Team	Goals, Tasks & Rules: Discover relevant  parameters of team purpose and goals. Build shared mission. Establish group roles, statuses, and relations. Define rewards and recognition structure.  Identify problem/goal and work together on common tasks. Next: Stage 2	Imaging a Positive and Creative Future:  Reflect and explore best prior team experiences  (examples, stories, metaphors).  Collaboratively envision and create exemplar team model with key attributes—provocative propositions. Develop joint statement or picture of concepts and principles based on model.  Team consistently tracks and increases occurrences of “more” of what they want.  Next: Stage 3
Stage 2.  Discordant Team	Problem Root Cause Analysis: Acknowledge differing opinions and find connections between these diverse perspectives. “Raise issues, confront deviations… allow conflict to occur.” (Catalyst Consulting, 2002). Build rules for proper team behavior.  Monitor for inappropriate behavior. Next: Stage 3	(Research indicates that teams using AI may be able to avoid the traditional discordant stage altogether.  If established or newly-merged teams are experiencing points of discordance, they may choose to begin their intervention process at AI stage one or four.)
Stage 3.  Established Team	Closing the Gap:  Discuss how to make team complete tasks and work towards goals. Clarify and accept roles, including sharing leadership.  Establish team norms. Evaluate team against performance goals. Establish stretch goals by taking new risks. Work towards closing the gap between issues and performance.  Next: Stage 4	Rejuvenation: Reflect and explore best experiences inside and/or outside of the team. Share examples of members modeling these attributes. Collaboratively envision and create exemplar team model with key attributes—provocative propositions.  Promote positive growth by strengthening the best of what is.  Next: Stage 4
Stage 4.  High-performing Team	Action Planning: Team members accept and recommit to goals.	Iterative process; use Stage 3 method as needed

Future Research

Due to the findings in the literature review conducted for this paper, a future research question may be posed:  To what degree can an AI methodology increase the quality of a student’s team experience and performance as exemplified within the George Mason University Graduate School of Education Immersion environment?  The purpose of this research would be to apply AI to a specialized team development experience—Immersion—wherein both newly formed teams and newly merged teams occur for a limited time. How might the research be conducted?  An outline of research considerations is as follows:

General points

§  Teams would include members and project team leader(s) (Professors)

§  Ethical standards of research protocol would be maintained

§  Research process would include third-party assistance and collaboration in data gathering and analysis to minimize bias from primary researcher

Specific points

§  Evaluation of Immersion team development process is within two areas: experience (participant) and performance (product and customer satisfaction)

§  Participants divided into two groups: Group 1 (Traditional Group) uses traditional team development interventions (e.g., Tuckman’s model and methodology), and Group 2 (AI Group) experiences the AI interventions

§  Research criteria for evaluating the team building experience must be defined

Data Gathering (iterative)

Data that is Available

• Feelings about the Immersion team experience – individual reflections
• Team’s operational guidelines – self-generated norms and goals
• Performance – matching product delivery to project timeline

Data to be Generated

• Team self-evaluation survey (quantitative) – experience
• Individual participant interviews by researcher (qualitative) – experience
• Project team leader team-evaluation survey (quantitative) – performance
• Project team leader interview by researcher (qualitative) – experience
• Customer team-evaluation survey (quantitative) – performance

Conclusion

Inevitably one will be a member of many types of teams, and so it is important that a methodology be established to make that experience a positive and fruitful one for both the individual and the team.  The research indicates that traditional team development models and their corresponding methodologies may not fully answer this need.  "Creating self-managed teams requires transformative, perhaps revolutionary, thinking, and it will, in most environments, require at least some management reform.  Before a self-managed team can be created, a manager must see the team not as it is but rather as it could be."  (Weis, 1999, p. 95).  AI may be at the cusp of facilitating that "transformation" experience. 

References

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