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.
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