Studies are in progress in a range of sectors and by various workers to discover how ‘talent and enthusiasm’ and ‘appreciation and encouragement’ can find a role in organisations, training and networking. There is also research done to find a way to give positive appreciation for the contribution of the different volunteers. The aim is to make positive use of their contributions towards the development of the organisation itself. This will raise the motivation of the volunteers and bind them more closely to the organisation, while the organisation itself will be strengthened by the talents of the many (different) volunteers.
In the text ‘9 basic assumptions for appreciative management’ (from Haslebo, M.L. & Lyngaard, D.B. (2008):Anerkendende HR og organisationsudvikling – skab mening, handlekraft og bedre resultater, Dansk Psykologisk Forlag) the authors explain ways in which a positive approach to colleagues can have a positive effect on how the organisation works.
Appreciative Inquiry (AI) fits into this approach and unlike the problem diagnostic deficit method, it looks at what does work, and it investigates which factors are responsible for this.
The method concentrates on the delivery of broadly supported, shared aspirations and ambitions by showing appreciation of what is good and looking for ways to strengthen and disseminate it. There has been an enormous growth in recent years in the use of AI as a research and development tool.
AI is an approach to organisational change by which people together look for what works, as opposed to what goes wrong. AI moves the focus away from problems, towards a broad view, and away from denial, complaints and criticism towards ownership, cooperation and taking responsibility. This results in the creativity, involvement, actions and initiatives which are necessary to achieve change successfully.
AI enables people to talk to each other about topics which are relevant. This always takes place in an appreciative manner, with respect for differences in opinion, background and ambitions. During the process of change people learn from and with each other and create new perspectives for the future. These perspectives translate themselves into actions and initiatives in which people assume complete responsibility for the future of their organisation and of themselves.
AI works well in situations where the success of change or revitalisation depends on the active involvement of the people concerned. Typical situations in which AI is applied are culture changes, product innovation, strategic planning, management development, organisational and personal development, evaluation and process and quality improvement.
AI invites people to take responsibility for their own future and to be open to discussion about their own contribution towards that. The AI process makes for a constructive and open atmosphere in which the participants feel confident that they can discuss matters with each other and where spontaneous creativity, ideas, energy and actions come into being in order to create the result that was created by collaboration throughout the process.
AI is powerful, not just because of its forward-looking and appreciative approach but especially through its basic attitude which is grounded in research. It is a qualitative research technique.
The final result of AI is a reliable plan for the future based on the active involvement of people who are able and willing to convert this vision into actions and results. Enthusiasm, energy and an entrepreneurial atmosphere are created which contribute to the realisation of changes that really work. In addition, a more open and expressive culture is produced and this promotes communication and cooperation.
The basis of the AI philosophy is formed by a cycle of four steps:
- Discovery: Finding and appreciating ‘The Best of What is’;
- Dream: Envisioning ‘What Could Be’;
- Design: Co-Constructing ‘What Should Be’;
- Destiny: Sustaining ‘What Will Be’
- Masseling R. e.a. , Waarderend Organiseren. Appreciative Inquiry: co-creatie van duurzame verandering. Gelling publishing, 2008, Part 1, p. 6 – 48.