Appreciative inquiry: models & applications.
This study looks at the Appreciative Inquiry literature and intervention methods and also considers three cases from private and government organizations to critically examine its applicability/ implementation methods. The study also compares Appreciative Inquiry with the traditional Action Research approach and arrived at a model which is a combination of both.

Organizational change (Analysis)
Corporate culture (Analysis)
Organizational research
Mishra, Pavitra
Bhatnagar, Jyotsna
Pub Date:
Name: Indian Journal of Industrial Relations Publisher: Shri Ram Centre for Industrial Relations and Human Resources Audience: Academic Format: Magazine/Journal Subject: Economics Copyright: COPYRIGHT 2012 Shri Ram Centre for Industrial Relations and Human Resources ISSN: 0019-5286
Date: Jan, 2012 Source Volume: 47 Source Issue: 3
Geographic Scope: India Geographic Code: 9INDI India
Accession Number:
Full Text:
Appreciative Inquiry

Most organizational change processes are based on problem-solving. The focus is on 'what is the problem or what went wrong'. In these traditional change processes, one invests energy in finding out or rectifying something which may not be very important (Knippen & Green 1997). Traditional approach has been criticized by many researchers over the years and a new approach called Appreciative Inquiry (AI) has been developed which treats social and psychological reality as a product of the moment, open to reconstructions (Gergen 1982, Gergen 1990, Bushe & Coetzer 1995).

AI which works orthogonal to a traditional change process has come up in the last one decade. AI is an approach where instead of focusing on problems, one focuses on things that are right in the organization. AI works on the fundamental assumption that organizations move towards

what they study (Cooperrider et al 2003). In fact, AI is not (just) about the positive aspects, it is the quest for new ideas, images, theories, and models that liberate our collective aspirations, alter the social construction of reality and, in the process, make available decisions and actions that were not available or did not occur to us before (Bushe 2007). AI should not be considered just another organizational development intervention or tool but a philosophy and orientation to change that can fundamentally reshape the practice of organizational learning, design and development (Watkins & Mohr 2001). AI was developed primarily for private sector organizations. However, over the years, this intervention has been used in nonprofit and government organizations as well. Education was one of the first sectors outside private organizations where AI was used (Schooley 2008).

Emergence of AI

The first outline of AI was developed by David Cooperrider during his doctoral study (completed in 1986) in organizational dynamics at Case Western Reserve University. The purpose of his study was to find out physician leadership at Cleveland Clinic, which is one of the most highly, regarded medical centres in the United States. He asked physician leaders to describe their stories of successes and failures. He was attracted by the level of positive cooperation, innovation, and egalitarian governance when they were most effective among physician leaders. This led him to coin a new approach to develop organizations i.e. considering only those data that described the physician's leadership and organization when it was most effective rather than concentrating and sorting failures and identified problems.

Cooperrider's idea was also supported by numerous research findings from the fields of medicine, sports, behavioural sciences, and anthropology that demonstrated the power of positive images. Some of the research finding which influenced Cooperrider were: (i) Research done on the "placebo effect", in which one to two thirds of the patients showed improvement in health by believing that they have received effective treatment, though the treatments given to them were "dummy" i.e. could not influence their health. The placebo effect demonstrated the power of mind and belief, (ii) The "Pygmalion studies" in which teachers were given information about the abilities of their pupils. These descriptions were randomly assigned to pupils, regardless of their actual performance. With time, it was found that pupil's test scores began to match up with the statements that their teachers had been given, (iii) Effects of both positive and negative thinking on the outcome of surgery: patients with more positive thoughts recovered at a faster rate, and (iv) Research on learned helplessness i.e. in response to the suggestion that people were unable to change their situations, it was found that they became apathetic and hopeless (Cooperrider et al 2003).

The above cited studies showed that people can be highly affected by what they and others believe and expect from them, thus supporting basic AI thought that focus on positive behaviour leads to success. Since AI is concerned with meaning and interpretation rather than measurable facts (Reed 2007), it shares a conceptual link with social constructionist research. The notion of words are negotiated and co-constructed i.e. the truth is arrived by a rational and value free process of discovery. However the challenge is in terms of ensuring that the meanings are understood as they have been used by the participants.

AI Principle

AI stands on five principles that make the link between theoretical developments across a range of disciplines. These principles were propounded by Cooperrider & Srivastva (1987), and are given in his initial description of AI. These principles are:

i. Constructivist Principle: This principle is related to social construction theory (Gergen 1982) and on the notion that our thoughts about the world and its realities are based upon interpretation and construction, rather than by just recording of phenomena. Since the interpretation and understanding will differ from person to person there will be multiple realities. In AI, this principle is used in the way that people can tell various stories in the past, present and future and the way these stories have the power to shape and reflect the way people think and act (Reed 2007).

ii. Principle of Simultaneity states that inquiry and change are simultaneous. This is because to make any change happen inquiry is required. Inquiry act as an intervention as the various questions asked helps people to think and discover various ways of doing things, this must be appreciated throughout AI process.

iii. Poetic Principle points out that an organization's member continually frame their world on the basis of which parts of their stories interest them more and which they will like to experiment. Thus what is chosen makes a difference.

iv. Anticipatory Principle explains that an organization member's current behavior is formulated on the basis of what they perceive about the future of the organization. If, for example, they see that the future is full of prospects, it will give them hope and energy thus influencing their behaviour in a positive manner and vice-versa. AI therefore emphasizes on what is right and then further builds on it.

v. Positive Principle emerges out from various researches done by practitioners of AI. It was found that the more positive question was asked, the more engaged and excited the members were and more successful and long lasting the change efforts were. By focusing on people's interest it is easier to involve them in the process and the best way to capture their interest is by asking and inviting positive questions.

Over the years, primarily two models of conducting AI have emerged: (i) 4-D model and (ii) 4-I model. (Cooperrider & Srivastava 1987, Watkins & Mohr 2001)

4-D Model

This cycle has four phases, which are as follows:

I. Discovery: People talk to one another through structured interviews and also by sharing stories about their peak experiences. In the process, they discover the times when the organization was at its best.

II. Dream: On the basis of the information collected in the discovery phase, members are requested to think broadly and envision a desired future of them as well as of their organization.

III. Design: As the name suggests, in this phase focus is on the creation of agreed-upon concepts and principles in the dream phase. The participants propose strategies, processes, and systems; make decisions and develop collaborations that will create and support positive change. They then develop a detailed, concrete vision statement.

IV. Destiny: This is the final phase which delivers the dream as well as design. This is an ongoing phase as participants continue to implement changes, monitor their progress, and engage in new dialogue and Appreciative Inquiries.


4-I Model

4-I model was developed by Mohr & Jacobsgaard (Watkins & Mohr 2001). This model also has four phases.

I. Initiate: In this phase, key stakeholders are introduced to AI. These stakeholders understand the positives of AI as compared to traditional change methods. After introducing AI, temporary structures are introduced for implementation of AI.

II. Inquire: In the second phase, generic interviews are conducted to identify what each of the people think. This phase is very similar to the discovery phase of 4 D model.

III. Imagine: In the third phase, interview data is collaged and shared with other members of the team. Based on the interview, a set of provocative propositions are developed regarding the future of the firm.


IV. Innovate: In this stage, provocative statements are shared with all the possible stakeholders and action level statements are prepared for implementation. This phase is similar to the two combined phases i.e. design and destiny.

There are two differences between the 4-D and 4-I models. These differences pertain to the way in which AI is administered:

i. In the 4-I model there is an initiate phase where details regarding AI are shared with the participants.

ii. 4-I model subsumes design and destiny phases are combined to form one phase called innovate phase.

The two models are otherwise very similar in the implementation of AI. We now look at three cases from the private and government organizations where AI has been implemented. While looking at these case studies the challenges in conducting them must be appreciated. These challenges as identified by Reed (2007) include: (i) supporting people--engaged stance rather than disengaged, (ii) people getting together--collective interaction to share and explore experiences, (iii) telling stories--the telling process and the language used, and (iv) positive development - innovation and generation of plans for future. AI is about positive engagement of self and organization. It weaves the dreams, vision to the destiny statement through a discovery process as elaborated by the first model. In the second model we see the linkage between inquiry and innovation. What may be evident to the reader is that AI may be going on within our organizations at a subconscious level. We need to bring it to the conscious level and practice it in our daily routines, in order to integrate this form of learning and change, and evolve as learning communities (Kauffman & Senge 1993).

Case 1: British Broadcasting Corporation (BBC)

The British Broadcasting Corporation (BBC) was established in 1922 and has been operating eight TV channels, 53 radio stations, and BBC website. It employed more than 5,000 journalists and a large number of independent minded producers and editors. The culture of BBC was to question, challenge and critically analyze any issue. There was mistrust among colleagues and a very high level of personal competition. Anything was done based on persuasion and influence.

The prevalent culture encouraged personal level creativity but as a group there was no cohesion. Also, the group as a whole was not seen to be very creative and constructive towards the business of BBC.

The Director General of BBC, Greg Dyke, in 2002 felt that there was a strong need to change the culture of this organization. The low level of team work and poor leadership had to be reduced. This whole exercise was carried out through a program called 'Making It Happen' using AI principles. This exercise started with a complex consultation exercise called 'Just Imagine'. The project was led from the front by Greg Dyke who associated himself strongly and personally with the exercise. During this exercise, he was also able to develop a strong emotional connection with a large number of BBC staff in a reciprocative manner.

The whole exercise was very well planned. The formats for generating stories, quotes, and ideas were well defined before the start of the exercise. The standardization helped in keeping record of all the ideas that were generated in the six months of 2002 where more than 10,000 employees participated in about 200 meetings across the globe. Attendance was voluntary. The number of people participating varied from 25 to 200 at one point of time. People were asked to pair up and have reciprocal interviews about their positive, successful, and proud moments at BBC. The specific questions were:

a. What have been the most creative/valued experiences in your time at the BBC?

b. What were the conditions that made that experience possible?

c. If those experiences were to become the norm, how would the BBC have to change?

Each of the pairs discussed their stories with smaller groups of 10 colleagues to filter our most powerful and affecting stories. These shortlisted stories were shared with the whole team. In the process nearly 98,000 ideas were captured, analyzed, and fed back to the divisional and pan-BBC change team for evaluation and implementation. Some of the generated ideas were easy to implement with little cost and effort. These were implemented immediately. Others were quite complex and difficult to address e.g. demand for more feedback and development, a flexible holiday scheme, job shadowing, and enhanced induction program for new juniors. The exercise also generated many comments and suggestions regarding the value system and the behaviour in the organization. These were classified into six aspirational values:

a. Trust is the foundation of the BBC: we are independent, impartial and honest.

b. Audiences are at the heart of everything we do.

c. We take pride in delivering quality and value for money.

d. Creativity is the lifeblood of our organization.

e. We respect each other and celebrate our diversity so that everyone can give their best.

f. We are one BBC: great things happen when we work together.

This helped BBC in creating a new culture over a period of time. The initiatives continued even after Dyke left the office.

Case 2: Federal Research Organization

Office of Research and Development (ORD) of Environmental Protection Agency was established in 1972 with a mission to provide environmental protection with sound scientific support to regulatory decision making. An independent congressionally mandated assessment, in 1994, concluded that the ORD's laboratories, which are discipline based, are not optimally placed to support the rest of the mission based organization. The organization needed to be far more responsive to changing environmental research and requirements of regulatory decision makers. This could not have been achieved in a regimented organizational setup as it existed.

In 2002, ORD concluded that it has to move away from the traditional hierarchical structure towards a flexible, open, and knowledge based organizational structure. ORD decided to use AI as an organizational development (OD) intervention method. The program was managed by internal OD practitioners. In the first stage, a workshop 'Optimize your interaction and expand your sphere of influence' was conducted. This introduced the participants to the AI tools and its application in the work related settings. As described by Elstein & Driver (2007), the workshop was divided into seven components to make the exercise memorable, efficient, and effective and at the same time force them to think about themselves, their colleagues, and the organization differently: (i) Introduction to both the strengths based model and examples of its application in workplace setting. (ii) Instruction on the art of 'progressive inquiry cycle', which was modelled on the basis of 4-D approach. (iii) Physical experimental learning activities as a shared reference for appreciative debriefings. (iv) Brief instruction on Myers-Briggs personality type model to enhance participant's appreciation for diverse thinking. (v) Exposure to a framework for ensuring progressive dialog. (vi) Meeting techniques for enhancing individual, group awareness, fostering, divergent thinking, and promoting convergence. (vii) Group role plays to provide practical experience applying the philosophy and techniques in work-related settings. Second, workshop graduates were brought together in an ever expanding community of practice to increase understanding of and skill in applying the model and to build a disseminated network of practitioners to parallel ORD's distributed power base.

During the intervention, more than 200 ideas were generated and discussed in detail with all the employees. The employees prioritized the ideas based on the shared vision created by them in the workshop. This helped in evolving top five research agenda which made sense for all the stakeholders. Thus, the AI process in this case was also able to streamline the process of selection of research agenda for the group. Since individuals were involved in defining the top five research agenda, they started owning them and hence became more responsive.

Case 3: Vancouver School District

The Vancouver School District used AI model to lead the change process and make the school as one of the best places for learning. For this purpose, the school started a district wide planning process involving all the stakeholders (students, parents, teachers, administrators, and all other employee groups). These stakeholders discussed and developed a common understanding of the school's core objective over the next one year. The following year (2004-05) planning process was sustained and refined (Filleul & Rowland 2006). This was done to facilitate congruence among roles and functions of the organization and make them more consistent with the broader organizational goals.

In 2005-06, nine inquiry sites (three single schools, five groups of combined schools, and one district school) participated in the process. These were selected based on the proposal that they had submitted and the presence of strong team at the site. District support was provided in the form of coordinators, who facilitated interviews and management of the projects in general. These coordinators were given two days training on what is AI and how will it be useful in achieving the overall goal of the school.

Each team was asked to develop answers, through stories of their own and their colleagues, to two questions: (i) what do educators do that create exceptional learning experiences and (ii) what choices and options offered in the educational setting enhance the learning experiences (Filleul & Rowland 2006). These stories became the basis of further discussion in smaller groups. These discussions led to filtering of important stories which were shared with everyone. The filtered stories became the basis of imagining future by each of the participants and inspiring them.

In the next phase, the imagined future was to be translated to practical action by each of the participants by preparing design statements. The participants experienced what involved in making their dream true. It also helped in making them focused towards achieving their dream.

In the last phase, these participants developed innovating action plans for ensuring the implementation of their design statements. Since these statements were made by the stakeholders, they were far more committed towards achieving them.

Overall, this change process helped in creating a group of enthusiastic, com mitted, and focused group of individuals working towards a common goal.

Within &Cross-Case Analysis

The previous section discussed three cases where AI has been implemented. BBC was a commercial organization where as Federal Research and District Schools were like government organizations. Federal research was focused on research while district education was focused on social objectives.

In the case of BBC, participants were not given any training on AI in the beginning. BBC was a commercial organization driven by profit and each of the individuals were also driven by personal incentives. This helped in going through the classic 4-D model without any discussion on the benefits of AI to motivate. Rather, employees were motivated to participate through 'Just Imagine' program. Standard formats were prescribed which enabled management of large volume of data generated in the process.

In the second case of Federal Research Organization, participants were given initial training on AI before asking them to share their positive stories. A workshop was organized over two days to take them through the various stages of AI, which was the 4-I model of introducing AI. Within 30 minutes, they shortlisted five research agendas from a list of more than 200. This was achieved without any substantial conflict among different groups.

In the third case, employees at the Vancouver School experienced a mix of 4-D and 4-I models. First, some of the selected employees were given training on the benefits of AI and how it functions before embarking on the 4-D model. The training was useful in making them appreciate the whole process much better.

The three cases, which represent three different types of organizations (profit making corporate sector, social sector responsible organization, and research organization), used AI method effectively for their change process. However, their method of applying AI varied. In some cases, initial workshop/ training was conducted to apprise the participants regarding the positives of AI before getting into the AI process.

Based on the above cases and theoretical discussions, we can develop a set of propositions related to implementation of AI method.

Proposition 1: For implementing the change process and as part of setting the vision context AI may provide strong foundation for innovative ideas.

In all the three cases it has been found that AI has helped in generating number of pioneering ideas to deal with a particular issue and in setting up of a new culture. It was facilitated as AI supports the idea of a complex organization where ideas are generated organically rather than the mechanistic approach as reported by Hamel (2002).

Proposition 2: In firms where there is climate of distrust or hyper competition an OD intervention of AI will yield higher employee engagement.

In the BBC there was a culture of mistrust, low level of team work and high level of competition. It was through OD intervention of AI that higher level of employee engagement became possible. Among the various range of approaches for managing change and development reported by Johnson & Leavitt (2001), AI involves wide participation across organizational members rather than a notion of 'line of command' (Reed 2007). Such participations are expected to yield better results in a climate of distrust or hyper competition.

Proposition 3: AI triggers high level of employee engagement in employees, leading to higher ownership of the change process.

In the Federal Research case it was found that since individuals were involved in defining the top five research agenda, they started owning them and hence be came more responsive. In BBC the initiatives of AI continued even after the project champion, Greg Dyke (the then the Director General of the BBC), left the organization. Also in Vancouver, AI helped in formation of a group of committed individuals working towards a common goal.

Proposition 4: Providing a format for individual inputs enables creativity in the workplace leading to reduction in complexity.

In the case of Vancouver, a template was given to each individual at the beginning of the process. This helped in receiving details in a given format making the analysis very simple. The whole analysis was completed within a short span. The AI process led to positive changes in the organization while satisfying each of the participants regarding their input. This project appears to be as effective as the other two, where no format was used.

Contrasting with Action Research

AI has been compared with Action Research (AR) quite often by researchers. In fact Action Research has become an umbrella term for a range of orientations which shape particular research practices (Reason & McArdle 2004). The research activity undertaken is done in conjunction with the participants. It is the study for the participants (rather than be a study of them), often with the aim of achieving social change (Bradbury & Reason 2003).

In AR, organizational members and practitioners work in collaboration throughout in order to analyze, implement, and evaluate system level changes. The main purpose of any intended change in AR is the re-education of its clients i.e. changes in the patterns of thinking and action that are currently well established in individuals and groups. A change intended by change agents is typically at the level of norms and values expressed in action. Effective re-education depends on participation by clients in diagnosis, fact finding and free choice to engage in new kinds of action (Coghlan & Brannick 2001). AR has developed as an important tool in facilitating social and organizational change (Goldstein 1992).


Although there are several AR approaches varying from five to fourteen steps (McLean & Sullivan 1989, Argyris 1993, Barker & Barker 1994, Davis & Cook 1998, De Poy, Hartman & Haslett 1999, Cummings & Worley 2004). The general approach involves data gather ing, diagnosis, implementation, and evaluation of the intervention. All AR models appear to be comparable to the basic premises found in the Shewart cycle (Shewart 1939) and the AR cycle developed by Lewin (1946).

Commonalities between AI & AR

Both AI and AR engage real social systems and conducted in real time. There are no retrospective or advance data gathering possible in either of the methods. Both the processes are value, action, and reflection oriented. Both these methods can be used for theory building. Both the methods are aimed at creating changes and improving organizational performance.

AI and AR both require interaction and involvement of all the stakeholders and therefore respect the concept of empowerment and individual difference. The interactions need to be repetitive as these processes tend to be cyclic in nature. The processes are applicable to small as well as large organizations.

Differences between AI & AR

AR is a problem-centric method and is based on rational empiricist assumption whereas AI is an opportunity-centric method and is based on socio-rationalist assumptions. Two of the differentiating factors between AI and AR can be found in the assessment & feedback and the evaluation stages, presented in the eight-step AR model (Fig. 3). Boyd and Bright (2007) have differentiated the two methods on the dimensions such as basic processes, underlying metaphor, role of facilitator, role of stakeholders, role of leaders, dominant motivation for change, possibility of change, and appropriate application.

Mixed Model for AI Application

While AR and traditional methods of change are criticized for being problem centric, AI is seen as opportunity centric. However, in real life, just taking positive aspects and creating future action plans based on the collective feelings of all the stakeholders may not take an organization to its best performance. As the critiques of AI suggest, a look at the problems and diagnosis would also help in taking the organization forward.

Proposition 5: A judicious mix of AI and AR process interventions can be more effective in a change implementation process than either of the interventions.

As discussed in proposition 1 and 2, knowledge about the method that is being applied helps participants utilize the method better than without having an idea of it. Hence, it may be desirable to provide initial introduction regarding AI methods to all the stakeholders as was in the Vancouver school case. At least a training of some of the change leaders may be desirable to take the project for ward in the intended direction as was done in the case of District school.

Based on this thinking, we propose a model in Fig. 4 for implementing the change processes in any organization. The proposed process takes the positive side of the traditional model, AI models and AR models to develop this model.


In the first step, introduction of AI provides an opportunity to bring all the stakeholders to the same level in terms of what is the tool and what is desired out of them in this process. The instrument preparation phase allows for standardization of the AI story sharing processes. This allows for easy reference across smaller teams as well as larger teams. When the number of employees may be very large, standardization would help in collating them together. Inquire and imagine phases are the same as in 4-I model phases. Innovate includes only development of innovative methods for the implementation of ideas at the operational level. At the implement level, ideas are implemented with all the participants. The improvement phase allows not only for modifying or re-looking at the processes which were initiated but also for looking at the change in a traditional manner within the ambit of AI. Thus, it brings a flavour of AR apart from addressing some of the criticisms of AI methods.


One of the most important parts of any change process is 'the people' involved in it. It has been often found that people's feelings and emotions are ignored while conducting a change process because it is difficult to control them. However, research has found that the effective handling of various psychological and emotional dynamics is a key to attain a transformational change rather than a temporary change. AI process has proven itself to be able to attain this key, as it believes in people to be able to act as real change agents. Since it involves people from all the levels of the organization it automatically takes their commitment to facilitate the change process. It is a process in which people are asked to live their dreams and make them come true. The power of addressing issues from a positive lens brings hope in people and boosts their morals. It not only creates a knowledge sharing and knowledge rich culture but also lead to openness towards learning. This was also reflected in a study done by Michael (2005) in which she applied AI to figure out power of local African NGOs and found that interviewees: (i) were more eager to tell their stories, (ii) offered dynamic and unrehearsed information and (iii) spoke more openly, with less defensiveness or fear of reprisal. Thus, asking people to change might prompt resistance but involving them in creating change and making them responsible for better future of their organization evoke a spirit of cooperation and coordination.

AI not only smoothens the change process but also helps in creating a shared meaning of ethics in an organization. Vuuren and Crous (2005) found that AI is a useful and powerful intervention to initiate ethical change as it is a collaborative process that creates meaning and shared dreams of an ethical future through participation and dialogue by focusing on the positive, assumes system wide involvement, and views ethics as an opportunity to be embraced rather than a problem to be solved.

Lewis, Passmore and Cantore (2008) demonstrated extreme flexibility and versatility of the AI approach. They applied AI on one of the four area sales teams which covered the UK for Marley Plumbing and Drainage, part of the Aliaxis Group the result of which was strong team building, and more cross-boundary cooperative behaviour in the team. Also, this team received the maximum bonus at the end of the year.

However, this article has also tried to address the approach's deficiencies in comparison with AR. We hope that the proposed model can help in combining the strengths of both the approaches as well as deal with some of their weaknesses. Cady & Caster (2000), McLean (1996), Golembiewski (1999), and others have suggested that there is a lot more scope to study these two OD approaches and their integration. There have been sustained attempts to study the OD strand of AI (Reed 2007) and significant research output has been achieved. However, AI needs to respond creatively and responsibly to stand against the traditional frameworks of research.


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Pavitra Mishra is a Doctoral Student, Human Behaviour & Organizational Development, Management Development Institute, Gurgaon 122001.Email: Jyotsna Bhatnagar is Associate Professor, Human Resource Management in the same institute. Email:
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