The PILAR model of collaboration contains five perceptions of collaboration held by group members: prospects, involved, liked, agency and respect that occur in idealised collaboration, which presumes voluntary membership, sincere commitment and equal member power. PILAR is used to explain the strengths and weaknesses of two contrasting perspectives on collaboration: qualitative (Appreciative Inquiry: AI) and quantitative (organisational and industrial psychology: OIP).
Delirium is common amongst older people in hospital, but up to a third of new cases may be prevented by introducing simple preventative protocols. Addressing these preventable risk factors can seem like stating the obvious to staff: ‘But aren’t we doing that already?’ An AI approach provided a way to encourage staff to reflect and identify changes themselves. In a small-scale demonstration project, the AI process led to a significant improvement in awareness amongst the staff.
Delirium is a common and serious illness amongst older people in hospital, but it is be able to be prevented in many cases. An Appreciative Inquiry approach offered us a way to respectfully find out what was working well and how this could inform and inspire improvements in delirium prevention.
Delirium is sudden confusion which develops over hours to days. People with a delirium have trouble thinking clearly, focusing their thoughts and paying attention. This tends to fluctuate across the course of the day. It is different from dementia, which is a progressive cognitive decline that develops and progresses over time. Delirium is under-recognised but surprisingly common, particularly among older people who are hospitalised. Delirium can have lasting and grave consequences for a patient’s recovery, with an increased risk of staying in hospital longer, having more complications, being discharged to long-term care, and dying. (National Institute for Health and Clinical Excellence, 2010).
A considerable proportion of delirium affecting older people in hospital develops after admission. These are the cases that we can often prevent with better care.
Anything that impacts on the brain can increase the risk of delirium. Conversely, anything that can be done to reduce a cause or risk factor could help prevent delirium. The focus is to target the triggers that can occur during the episode of care. There is considerable consensus about these preventable risk factors to guide prevention protocols. International evidence shows that new cases of delirium developing during admissions can be reduced by a third or more using interventions that target and reduce identified risk factors (Martinez, Tobar and Hill, 2015; Hshieh et al., 2015). Indeed there is more evidence to support the effectiveness of making changes to prevent delirium than there is to support making changes to better treat or manage delirium once it occurs (Francis, 2014).
The components of these interventions are simple but the key is consistency. Despite the knowledge that delirium can be reduced, hospitals are indeed in a persistent “know-do” gap:
… as the evidence grows, the state of implementation is not keeping up … Why, then, are these protocols not more widely implemented? … there may be an assumption held by many physicians and hospital leaders along the lines of “aren’t we doing all this stuff already?” Perhaps because most of the interventions to reduce delirium are protocol-based and nursing-driven, it may be easy to assume that such measures are already in place and running in the background at many hospitals. Numerous components of these interventions may simply seem too simple to question that they are not being done already. (Greyson, 2015, p. 521).
In previous studies, the push to change practice to a more preventative approach was supported by a considerable investment of resources in the form of a designated position and/ or a team of volunteers. In the absence of these types of resources, the gap between the potential and actual practice remains.
The AI fit
It seemed to us that an AI approach offered a powerful way to overcome the “but aren’t we doing that already” hurdle, to encourage staff to reflect and identify changes themselves. AI shares many of the values of nursing and allied health professions, and provides an approach that is supportive and respectful (Reed, 2010). Rather than raising defensiveness by a problem-focused or didactic approach, AI recognises the health professionals involved as experts in their own experience who have much to offer, with specialised skills and knowledge that we can access and utilise (Reed, 2010).
Reflective practice is a key skill for nursing and allied health professionals. Natius Oelofsen (2012) discusses how reflective practice starts with curiosity about a puzzling situation and hopefully concludes with a sense of clarity and understanding. This is achieved through a process of looking closer and finding new ways of answering the questions that were raised. AI can be seen as scaffolding health professionals’ self-reflection and providing a way to focus on how best practice can be maintained and further developed (Bellinger & Elliot, 2011; Stefaniak, 2007). The group nature of AI enables conversations that help health professionals to focus on and strengthen their skills and positive experiences, but also to imagine possibilities together that may go beyond what any one individual might create themselves (Wasserman and Namee, 2010).
The participants were recruited through invitations at Older Person’s Health Specialist Service education days and learning networks. The fifteen volunteers were spread amongst various wards and outpatient teams and twelve nurses, a social worker, an occupational therapist and a physiotherapist.
A key feature of an AI approach is that the methods of inquiry are themselves part of the intervention. Inquiry and change are simultaneous and, by stimulating reflection, inquiry can lead to different ways of thinking (Reed 2007, 2010). To document this we introduced a repeated measures design: a baseline individual interview, a guided AI group discussion, and then a follow-up individual interview.
While an AI discovery phase often begins with the participants interviewing each other, we began with a separate one-to-one interview with a member of the research team. This interview began the appreciative discovery process by framing the conversation with positive questions, but also provided a baseline measure of awareness of the status of risk factors in day-to-day practice. The interviewer asked participants to think about the last older patient in their case load that they interacted with. The preventable risk factors were simplified by a PINCHES ME Kindly mnemonic: Pain, Infection, Nutrition and Constipation, Hydration, Exercise, Sleep, Medication, Environment and person-centred care (kindly). The discussion went through each of the risk factors in turn, for example “Was Mr X in pain? How did you know?” We also asked participants to talk about what was working well to help to prevent, identify or manage each risk.
We gathered the participants together into small groups for a guided AI discussion or “brainstorming session” with the research team. The brain storm was semi-structured to lead the discussion through an iteration of the AI cycle, but remain flexible and fluid (Reed, 2007). The core questions that guided the discussion (see Table 1) opened with sharing what participants were proud of, and flowed on to how things could be even better, and the steps that could be taken to get there. Again, we used the PINCHES ME Kindly mnemonic to help collate the tips and ideas during the discussion about what people were proud of and the magic wand question, with a large sheet of paper for each risk factor alongside broader environmental factors.
Follow-up interviews a month later allowed us to gauge how much had changed since the first interview and what the participants thought of the process. Staff were again asked to talk individually about a specific case to provide a post-intervention measure of awareness of risk factors.
To document the impact of the reflective process on the individuals involved, the awareness of risk factors from each interviews was categorised as 1: cursory, 2: moderate, or 3: thorough by each of the three researchers. The coding was then compared and any discrepancies resolved by consensus, with evolving coding notes to clarify future decisions.
To help embed and sustain change, specific projects that emerged from the discussion were dovetailed into the work stream of the existing Dementia and Delirium group. A link group was set up to enable participants and other interested staff to remain involved.
Key questions / activities
Sharing the positive
One-to-one interviews: What is working well…?
Group, opening question: You’ve all had the opportunity to reflect about what’s happening with delirium prevention for you currently, and we’d like to start by gathering together some ideas about what is being successful. Thinking back about your interviews – what parts made you feel proud to talk to share. What’s working well for you for delirium prevention?
Sharing a vision
Group, magic wand question: … We’ve gathered together some of the good things that you are doing that really work for delirium prevention. How might we make it better? If you could transform the way you work so that you are more focused on delirium prevention, what would it look like, what would it take to happen? Imagine that you can wave your magic wand and anything is possible – for you, your team, your environment. You’ve got three wishes for delirium prevention in your own work setting – what are they going to be?
Sharing what we think should be
Group, developing provocative propositions (Bridging dream and design): We are going to come up with some statements – our vision, our goal for what we are going to look like in a couple of years when we “think delirium” in our work setting. They are going to be positive. And they going to be bold: “if you are good we want better, if you are great we want outstanding…”
Group: Identifying specific “projects” for the service that would make a difference
Sharing a commitment to change
Group, personal goals: Now our last thing is that we want you to think about your own goals in the short term. We’ve got a sheet here for you to fill in three goals – what are you are to aim to do when you walk out that door?
One-to-one follow-up interview
Action planning and implementation: Specific projects were followed through with the support of delirium groups and the participants could remain involved through a link group
Part of the Appreciative Inquiry process is to take the ideas that have been generated about what is working and visions for the future, and to turn them into concrete actions about what could be done differently. The AI process was an effective kick-starter for sparking a number of further specific projects.
The vision for a service that “thinks delirium prevention” that emerged had three overarching themes: committed, consistent and a great care environment. We used the simple PINCHES ME Kindly mnemonic to summarise risk factors during the project. The participants strongly embraced this and wanted it to be the core of the ongoing actions.
A committed environment included being proactive about delirium prevention with PINCHES ME Kindly ingrained throughout the service. Identified actions included education, reminders and resources to encourage this.
Collating the Discovery and Dream phase questions can be seen as forming the basis of a good practice guide (Bellinger and Elliott, 2011). A user-friendly fun booklet was created to share the tips and possibilities from participants to inspire other staff (Gee, Bergman, Hawkes & Croucher, 2016). This well-received book provides a useful bridge between abstract international guidelines and how they can be operationalised in daily practice.
A poster/handout summary version of PINCHES ME Kindly was created for publicity and education.
The THINKdelirium resources are being used by CDHB nurses in orientation and in-house education, and there are moves to spread this, given that delirium prevention requires a multidisciplinary approach. A multidisciplinary working group has been set up to integrate the THINKdelirium booklet into an online education package.
An annual “Spring Into Delirium” day has been initiated to raise awareness of the THINKdelirium messages and resources and where to locate them.
The consistent theme focused on consistently and comprehensively assessing and addressing the risk factors. Identified actions included having documentation resources available to accurately monitor relevant risk factors.
A THINKdelirium webpage has been created on the staff intranet. This provides an interactive summary of PINCHES ME Kindly, with links to download the THINKdelirium and associated documentation resources.
A summary of the PINCHES ME Kindly message has been added to the routine documentation for hospital aids when they are providing close observation.
Staff are considering how the PINCHES ME Kindly message can be included in records and discharge documentation.
A great care environment focused on providing person-centred care. Identified actions included more resources for activities, and a prevention-focused brochure for care-partners.
The staff were keen to be supported to encourage meaningful activity for older people on the wards. A follow-up project is trialling the introduction of “activity trolleys” with activity resources and tips alongside a staff-education package.
Staff wanted ways to engage care partners to help prevent older people getting delirium. Based on international exemplars, a prevention-focused brochure for those supporting older people in hospital has been developed.
The impact for participants
The AI approach was effective in engaging staff and overcoming the “aren’t we doing that already” hurdle. A nonparametric Wilcoxon signed-rank test was used to test the difference between the sum pre-test and post-test scores, and an effect size was calculated (Field, 2009). There was a significant improvement in the awareness of the status of the target risk factors for older people in their care when the follow-up interviews were compared with the baseline interviews (z = 3.21, p. <.001,) with a moderate effect size of .59. This change is illustrated in Figure 1 which shows the increase in the proportion of the participants being given the highest rating of awareness for each of the risk factors.
All of the participants gave positive qualitative feedback about being involved in the project during the follow-up interviews, with the predominant themes being that they “enjoyed the process”, and “learned a lot”. A six-month follow-up survey was completed by fourteen of the fifteen participants, and again all were positive about the project. In particular, all the participants agreed that the project inspired them to do more to pro-actively prevent delirium, and that it was worthwhile taking part in the project. Twelve of the respondents identified specific changes in their personal practice and thirteen identified activities or changes that they were inspired to champion in their workplace.
Examples of feedback included
Everyone on our ward has thought the booklet is fantastic. We have found it really useful for our nursing students also. We also love the brochure for families … What a fantastic project to be involved in! It challenged my thinking and made me think about my practice.
My awareness of delirium [now] makes me ask more specific questions regarding risk factors in my assessments. I also provide information/education to clients/families where appropriate. I have provided an in-service session to my immediate team … and [plan to] discuss with the team to include [delirium tool] in our initial assessment tools. I very much appreciated being able to participate in the project.
There are well-established guidelines for delirium prevention (e.g., National Institute for Health and Clinical Excellence, 2010), with good evidence of the benefits when they are followed (Martinez, Tobar and Hill, 2015; Hshieh et al., 2015). However, while guidelines are commonly produced as a tool to promote best practice, it has been recognised that on their own they often have little impact on behaviour (Greyson, 2015) and that strength of evidence is not a good predictor of the likelihood of adherence (Ricart et al., 2003). AI was a very effective tool to help to bridge the gap between the abstract guidelines (but aren’t we doing that already?) and what that could look like in the real world. AI gave a way of basing our projects and resources on listening, which has helped to increase engagement and ownership. While we have not yet achieved our ideal vision for the organisation we are excited about the collaborative steps we are taking toward it.
We were drawn to the AI approach for this project because of our positive experiences using an AI approach with our adult students, and our recognition of the synergy between an AI approach and reflective professional practice. Appreciative Inquiry holds that at its best, change is a process of inquiry grounded in affirmation and appreciation (Whitney and Trosten-Bloom, 2010). The AI approach was a chance to provide recognition of the staff and help them work from their strengths (Whitney and Trosten-Bloom, 2010). The staff were respected as experienced professionals, and their contributions were recognised and valued as a resource to be shared. The approach encouraged the participants to recognise the importance of what they do and their potential to make a difference. The AI approach was about asking questions, not providing solutions.
Reflective practice itself can be seen as a process of inquiry: the process of asking questions helps us to look at what we are doing and reflect on how we can do things even better. The use of positive questions and constructing positive images of the future in the AI process helped to inspire positive change. The AI process worked so well because it was both visionary and pragmatic. It helped to inspire a larger vision of an ideal environment while also exploring the small practical steps that could be taken (Loveday, 2011). The AI process was not just a constructive process for generating actions for the teams, but also a positive and effective learning experience for the individual participants.
Bellinger, A. and Elliott, T. (2011) What are You Looking At? The potential of Appreciative Inquiry as a research approach for social work, British Journal of Social Work, 41, 708–725.
Kadi-Hanifi, K., O. Dagman, J. Peters, E. Snell, C. Tutton, and T. Wright. (2014) Engaging Students and Staff with Educational Development Through Appreciative Inquiry, Innovations in Educational and Teaching International, 51(6), 584–594.
Field, A. (2009) Discovering Statistics Using SPSS (3rd Ed.). London, UK: Sage.
Francis, J. (2014) Delirium and Confusional States: Prevention, treatment, and prognosis. In M. J. Aminoff and K. E. Schmader, (Eds), UpToDate, Waltham, MA: Wolters Kluwer Health.
Gee, S., J. Bergmann, T. Hawkes and M. Croucher. (2016) THINKdelirium Preventing Delirium Amongst Older People in Our Care. Tips and Strategies from the Older Persons’ Mental Health THINKDelirium Prevention Project. Christchurch, NZ: Canterbury District Health Board. Available from: www.cdhb.health.nz/delirium
Greyson, S. R., (2015) Delirium and the “Know-do” Gap in Acute Care for Elders, JAMA Internal Medicine, 175(4), 521–522.
Hshieh, T. T., J. Yue, E. Oh, M. Puelle, S. Dowal, T. Travison and S. K. Inouye. (2015) Effectiveness of Multicomponent Nonpharmacological Delirium Interventions: A meta-analysis, JAMA Internal Medicine, 175(4), 512–520.
Loveday, B. (2011. Dementia Training in Care Homes. In T. Dening and A. Milne (Eds.) Mental Health and Care Homes (pp. 327–344). Oxford, UK: Oxford University Press.
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National Institute for Health and Clinical Excellence. (2010) Delirium: Diagnosis, prevention and management (Clinical guideline 103). London, UK: NICE. Available from www.nice.org.uk/CG103.
Oelofsen N. (2012) Using Reflective Practice in Frontline Nursing. Nursing Times, 108(24), 22–24.
Reed, J. (2007) Appreciative Inquiry: Research for Change. Thousand Oaks, CA: Sage.
Reed, J. (2010). Appreciative Inquiry and Older People–Finding the Literature. International Journal of Older People Nursing, 5(4), 292–298.
Ricart, M., C. Lorente, E. Diaz, M. H. Kollef and J. Rello. (2003) Nursing Adherence with Evidence-based Guidelines for Preventing Ventilator-associated Pneumonia, Critical Care Medicine, 31(11), 2693–2696.
The purpose of the field experiment was to study the effectiveness of a generative Appreciative Inquiry versus ‘traditional’ AI and a problem-solving approach in fostering generativity among organizational members. The results empirically supported the thesis that synergenesis was the most generative of all the approaches.
Generativity, as defined by Kenneth Gergen in 1978, is the capacity to challenge what is taken for granted and propose fresh alternatives for future action. Appreciative Inquiry (AI) is generative in several ways: it values the strengths of the organisation rather than looking at its weaknesses; uses a collaborative approach to facilitate dialogue; creates new possibilities for the future; and energises people to take action. As there have been very few, if any, studies systematically exploring the process of AI and what makes it generative, I was motivated to undertake this research. The research was conducted in a government agency with an attempt to help facilitate change in the organisation at the same time. Conducting AI-based research was both exciting and challenging, because the organisation was unfamiliar with the AI process and had traditionally depended on a deficit-based approach. It is also the organisation where I work.
Begin with the positive
Since it was a field experiment that I conducted in my organisation, I had to approach the study as a consulting project. For any change initiative to be successful, it is important to have the support and approval of the senior leadership. When I first started describing the research methodology, there was an immediate resistance to the idea of research within because of the apprehension around uncovering of internal secrets. I was able to convince the senior management about my research only when I told them that AI implies working on strengths of the people and the organisation. This focus on the positive and appreciation made my research sound harmless, enabling its acceptance.
I realised that, although I was researching how AI is more than positive, it is better to begin with the focus on positive, especially in an organisation that is new to the AI process or has always relied on deficit-based problem-solving approaches to change. The positive and strength-focused approach provides a new lens for organisational members who are accustomed to problem-finding ways of change.
The next step in the research process was identifying stakeholders. Getting the whole system in the room is essential for change (Weisbord, 2004). For everyone in the organisation to support the change program, it is important for individuals to understand their unique contribution and how it fits in the overall success of the organisation (Ludema et al, 2003). AI literature provides guidelines on how to select stakeholders or participants for research. The rule of thumb is “anyone affected by the study”.
I thought I had included everyone, until I realised that a manager who had not been included was very upset and trying to impede the progress of the study. I learned that it was hard and sometimes impractical to bring the whole organisation together in a room, but good practice was asking the people in the room to identify who was missing and who else should be included in the study.
This helps in getting a good representation of the organisation. Also, consistent communication to the organisation about the change and getting feedback from them at various stages of the change process can be an alternative to including the whole system.
Logical positivist vs. social constructionist
I had used surveys to measure some of the constructs of my research. As a researcher trained in this positivistic mindset, I was very excited about administering surveys. But when participants have undergone an interesting AI session and are energised by the power of dialogue and ideas generated from the intervention, surveys can kill the spirit of AI. When I administered the surveys after conducting AI sessions, the energy level suddenly dropped. This may or may not affect the results, but the difference between story mode and a logical comprehensive mode is considerable.
The logical positivist approach considers knowledge to be valid when it is quantitatively measured and validated. However as AI is based on the social constructionist paradigm, I feel it is essential to consider methods other than quantitative surveys to describe the effectiveness of AI. The narrative method that is ingrained in the AI approach needs to be kept alive and be utilised for this purpose. Asking participants to keep diaries, writing stories about theirexperiences or drawing pictures could be alternatives means of measuring the effectiveness of AI.
It is hard to think of these approaches without worrying about the potential threats of subjective evaluations and biases. However there are ways to manage them, for example using multiple rater evaluations, rater training and so on. It puts the onus on us as AI researchers and practitioners to be more creative and rigorous in the ways we measure AI without affecting the enthusiasm of the participants.
Storytelling and working with stories
According to Yaeger and Sorensen (2001), one of the factors that matters most in the effectiveness of AI is storytelling. In my research, I found that storytelling is essential to make AI generative though I did not measure storytelling as a variable in the study. I was anxious about how a group of serious, analytically minded engineers would receive AI and synergenesis sessions.
To my surprise, they loved telling stories. I realised that, if the intervention is structured well, the topic of inquiry is of interest to participants and they want to do something about it, everyone enjoys storytelling. This taught me the value and art of creating a good balance.
As for the approach to working with stories, I compared (1) synergenesis (Bushe, 2010), (2) the classical AI Discovery phase and (3) a problem-solving approach. On the surface I did not find classical AI and synergenesis to be very different. Both engaged participants equally. But the results communicated a different story. I found that synergenesis was more generative: the listener writes the story, adding his/her own reflections, thus empathising more with the stories. The stories are used as a catalyst to generate ideas in the group, unlike classical AI where stories are used to understand mainly the root causes of success. The storytelling in synergenesis helps not only to generate new and innovative ideas but also changes the narrative held by the group.
As a researcher and practitioner of AI, I realised that not everything goes as planned. As an AI researcher, I faced emerging challenges, such as: gaining the support of the stakeholders at every stage; finishing the research in the given amount of time and resources; organisational structures, processes and other change initiatives that hampered the progress of the study.
My first session was so successful that I was confident that rest of the process would go without a glitch but, sooner rather than later, my illusion was shattered! In the next session, the participants refused to sign the confidentiality agreement, so I had to wrap up the session without any data. However, even though I was not getting data for my research, I used the opportunity to use AI questions to focus the attention on the positive aspects of the organisation and also introduce them to the change process.
Similarly, there were times when I wanted the inquiry to be longer than the allocated time, just because of the participants’ enthusiasm, but as a researcher I had to be consistent about the time given to all the AI sessions. I made up by having follow-ups after I finished my research. This taught me the value and art of creating a good balance between the required research rigour and the desired flexibility of AI sessions.
May 2017: The research revisited
In 2013, I conducted Appreciative Inquiry and problem-solving sessions in a large public transportation organization to understand if AI was a more generative process than problem solving. Seventy-six members participated in the study. The groups were divided among three interventions (synergenesis, classical AI and problem solving). In order to provide heterogeneity, each intervention was composed of employees from the four divisions of the company: operations, non-operations, exempt employees and non-exempt employees.
Identification of the need and design of the interventions
The senior management identified a need for an employee recognition program at the organization in order to engage employees, reduce absenteeism and improve performance. After the need was identified, I formed an advisory committee of twenty employees across the organization who had experience in designing such programs. The purpose of forming the committee was threefold.
First, the committee was important for increasing awareness among employees about the need for a recognition program and for reducing resistance about conducting an organization-wide change. Second, the advisory committee helped define the protocols for the three interventions. Third, the committee was used to encourage employees to participate in the intervention. The questionnaire consisted of:
Best experiences of employee recognition at the organization
Experience of communication that connects all levels of the organization
Most inspiring team experience, and
Dreaming an organization.
The first three themes were also used for synergenesis. The main themes for the problem-solving approach were:
Reasons for failure of employee recognition program
Possible ways of recognition
Removing communication barriers, and
Decreasing absenteeism to engage employees.
In other words, the affirmative approach of inquiry in the classical AI and synergenesis was converted to a deficit-based approach in the problem-solving intervention. The results indicated that compared to problem solving, AI was more generative, i.e., participants were able to come up with new ideas, challenge old ways of thinking and foster possibilities of a collective future.
The positivity of AI makes it attractive as an organization design (OD) intervention, particularly in an organization that is riddled with negativity and disgruntled employees. The culture of the organization at the time was that it was undergoing major changes, there were layoffs, the senior leadership was a revolving door and there was a fair degree of skepticism and distrust regarding leaders.
On the positive side, many employees had been with the company for more than 20 years and had historical knowledge of the company. The employees were proud of the old days, and “how it was before”. This unique mix of negativity regarding the present and pride in the past, I believe, made it ideal for an AI intervention. The questions in AI are focused around describing the “best” in the past. In the AI sessions, the participants had an opportunity to relive some of those best moments together with people with similar historical knowledge.
When the organization was undergoing change, the morale was low, but AI gave an opportunity to rethink, to view things in a positive light. Specifically, AI sessions helped them realize what they most appreciated about the company, why they were still with the company, and which positive aspects from the past could be brought to the present.
Three main lessons learned
You have to be mindful of the questions asked during any intervention. My research showed that a question focused on problems results in deficit thinking, whereas questions focused on finding the positive help people redefine the present and imagine a new social reality. I had asked participants to respond in writing to “What are your thoughts on having an employee recognition program in the organization? What should it include?” and repeated the same question after the sessions. Not knowing if I would find anything of significance, to my astonishment, when I analyzed the results, I discovered that majority of the participants had much more favorable opinions after AI than after problem solving for having an employee recognition program.
Also, after problem-solving session people did not write as much, and what they wrote had a pronounced “negative” sentiment. As David Cooperrider has said, change begins with the first question you ask. Problem solving does have a time and place, but if the goal of the initiative is for participants to reimagine the present and be more hopeful about the future, it is important to include generative questions.
It is possible to produce many generative ideas in short of amount of time with AI.Critics of Appreciative Inquiry complain that it takes several days to conduct an AI session and, for the same reason, sometimes companies are hesitant to commit to long sessions. My research used only a two-hour AI Discovery session and produced a significant number of generative ideas. In a classical AI, stories are generated in the Dream stage and then brainstormed in Design stage. I used the AI approach “synergenesis” (a term coined by Gervase Bushe), in which stories were a catalyst to provoke ideas for the topic of inquiry. People read each story aloud and brainstormed ideas until they produced an exhaustive list of ideas, and then moved on to the next story and repeated the same process. This approach helped not only generate new ideas in a shorter time but also shifted the narrative held by group members in the direction of the new ideas and new future imagined during the conversations.
There is more to AI than positivity. People are drawn to the positive aspects of AI, but more than being limited to being positive, my research was able to prove that AI can be generative, i.e. it changes people’s mindset and compels them to act in new ways. There are instances where an AI intervention is conducted and employees feel good during the intervention, but the excitement wanes in a few months because of lack of outcome as a result of the intervention. Therefore, it is important that practitioners define clearly the purpose of the inquiry, the nature of the questions and the design of the intervention in order to make it more generative, and to have a sustainable effect.
Although I was not able to follow through with the research, the company successfully launched an employee recognition initiative from the ideas generated during the AI sessions. Appreciative Inquiry’s focus on storytelling is a compelling aspect and, during challenging times, practitioners should find ways to engage people in stories that bring the best from the past and rethink the future.
Bushe, G.R. (1995) Advances in Appreciative Inquiry as an Organization Development Intervention. Organization Development Journal, 13, 14–22.
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Ludema, J. D., Whitney, D., Mohr, B. J., and Griffin, T. J. (2003) The Appreciative Inquiry Summit: A Practitioner’s Guide for Leading Large-Group Change. San Francisco, CA: Berrett-Koehler Publishers.
Weisbord, M. (2004). Productive Workplaces Revisited. San Francisco, CA: Jossey-Bass, Inc.
Yaeger, T. and Sorensen Jr, P. (2001) What Matters Most in Appreciative Inquiry: Review and ThematicAssessment. Ed. D. Cooperrider, P. Sorensen, Jr T. Yaeger, D. Whitney. Appreciative Inquiry: An Emerging Direction for Organization Development, 129–142.
Neelima Paranjpey, PhD Neelima is an I/O psychologist with a doctorate degree in OD. She works as a consultant in a talent management consulting firm in Chicago. She has a passion for helping organizations identify their strengths and leverage those to engage employees and increase productivity.
Appreciative Inquiry combines a wide variety of philosophies, theories and paradigms. With its increasing popularity, AI is being used in more and different sectors, in different ways, and with different emphases. In order for AI to use this enormous potential for growth, it becomes ever more important to ensure a constant, constructive, critical exchange between practitioners, researchers and clients. Fostering a discussion about weak signals may be an essential success factor for AI in the future.
It has been a little more than a year since I first got in touch with AI. Until then –having followed a rather classical management education with internships in business consultancies – my idea of organisational change and development had been dominated by the paradigm: analyse it, fix it, control it.
My masters’ thesis, which followed, then gave me the chance to talk to a variety of inspiring, open-hearted, intelligent people – many of whom have already written for this journal – who helped me in shaping a clear picture about the status quo of Appreciative Inquiry in all its diversity, and its potential future. Considering my short experience with AI I am very thankful for this opportunity to share my first impression of AI and the resulting findings with you. I would therefore also like to invite you to read this article not only on the content level but also as a description of a first encounter with AI and its community.
Seeing AI as a patchwork
When I started my analysis of the status quo of AI, I realized the domination of AI literature by success stories and decided to base my research on a critical analysis of existing literature, actively looking for weak signals.
By doing so, I quickly realized the broad variety of AI’s underlying philosophies, theories and paradigms, a fact that in my opinion has an important influence not only on the status quo but also on the potential future of AI. These include essentials such as social constructionism, dating back as far as Heraclitus of Epheseus (Mahoney, 2005) and adapted to modern times by Berger and Luckmann (1991), as well as Lewin’s (1947) action research, Beckhard’s (1969) ideas about organisational development, positive social psychology by Fromm (1962), and Seligman and Csikszentmihalyi (2000), as well as more recent influences such as: generative theories by Gergen (1993), Theory U by Scharmer (2007), or overarching fields of influence such as interpersonal neurobiology.
Without going into too much detail, this broad spectrum of influencing and influenced theories gave me a first indication of the extensive applicability of AI, motivating me to interview practitioners and researchers from different field of application and different parts of the world.
The perceived status quo of AI
A further deep-dive into literature in combination with an analysis of interview results led to a number of interesting findings about the perceived status quo of AI and its potential future.
Practitioners and researchers currently see the main strength of AI in its generative capacity, fostering responsibility-taking and commitment by creating connections and stimulating communication. Furthermore, AI allows for applications in a wide range of situations and can be combined with other change methods.
On a more critical note, members of the AI community see incorrect applications of AI as a high risk for the method, reducing its credibility, especially when applied through a simplified positivity-focus, without sufficient background knowledge on the part of the practitioners. Moreover, AI has a too optimistic, naïve view of organisations, lacking self-reflection and self-criticism; not only in the literature but also in considering the possible negative effects of AI, such as creating a “shadow” or in supporting undesirable power relations.
The lack of measurability of results and the difficulty of achieving sustainable effects through AI, are also potential weaknesses.
In order to achieve AI’s full potential, practitioners must overcome a variety of obstacles which, when handled successfully, can be important success factors. These include an early integration of client management to ensure support from the client’s side, overcoming power relations and control issues, assuring a consistent communication in order to achieve trusting relationships, defining an energising topic at the outset of an intervention, and clearly defining expectations in the contracting phase in order to avoid disappointment.
The practitioner’s knowledge of underlying theory and philosophy is essential for a successful intervention.Overall, practitioners and researchers share a wide range of assumptions and convictions about the role of AI as a change method. Inconsistencies in the status quo are:
a potential lack of compatibility between Social Constructionism and planned change;
the role of practitioners in the clients’ construct of power relations;
resistance or disagreement about an underlying spirituality and mindset of AI;
and finally, differing perceptions about the role of positivity in theapplication of AI.
Recent trends and the potential future of Appreciative Inquiry Recent trends in the field of AI include an on-going increase of awareness of and openness toward the method, leading to the application of AI in formerly atypical fields, such as conflict resolution or evaluation studies. Moreover, AI is spreading into and integrating elements of related fields, such as positive psychology, neuroscience or complexity theory on a theoretical level; and other dialogic OD methods, such as World Cafés and Future Search; and, on a practical level, process-improvement methods such as Lean Management. The most discussed and most important ongoing trend, however, is a change of awareness about the core of AI, shifting from a positivity-focused to a generativity-focused approach, allowing for an integration of negative aspects into the AI process.
According to my interview results, the potential futures for AI are manifold. One potential future consists of an increased focus on the emotional and spiritual elements of AI. Another sees a return of AI as a tool for academic knowledge creation. Practitioners predict a further evolution of AI into other fields of practice, a stronger link between AI, strategy and core processes in organisations, as well as the development of new tools and frameworks.
Appreciating the weak signals
Many of these results will not come as a surprise to those who have been involved in the AI community for many years. However, these findings also indicate that only a limited number of topics, such as the discussion about generativity versus positivity, are often at the core of discussions within the AI community; thereby leaving less room for “weak signals”. These weaker signals can be found in theoretical discussions (e.g. the combination of planned change with social constructionism), as well as in practical application (e.g. regional differences depending on cultural backgrounds, the use of new frameworks such as the integration of a “drenching” phase into the 4D cycle, or the use of the SOAR framework for personal development). Several interviewees also pointed out the importance of seeing practitioners of AI themselves as a subject for further research; thereby allowing for a more selfcritical reflection of AI, which may ultimately provide new insights to the entire AI community.
Shaping the future of AI – together
Overall, the results of this research stress the importance of an ongoing increase of communication and exchange among different stakeholders in the field of AI. As one of my interviewees phrased it: “How we grow in our lives is entirely mediated by the quality of our relationships.”
For AI to flourish in the future, this exchange has to take place among practitioners, in order to exchange new forms of application and develop new models and tools. Between researchers and practitioners this exchange is necessary to clarify underlying philosophies to new practitioners and to provide researchers with new trends from the field to help them refocus their research.
Considering the vast variety of current trends, and seeing them as an indication of the future direction of AI, the general outlook is very optimistic and diverse, if collaboration and exchange among different stakeholder groups further increases.
Personal thoughts and reflection
After finishing my thesis and taking a step back from the entire research and writing process, I quickly realised how easy it was to feel a part of the AI community; being welcomed with open arms, supported and congratulated for becoming part of it.
This made me ask whether I had fallen victim to some form of self-censorship during my research. Only allowing myself to think in a way that was generative, thereby gaining a deeper understanding of what AI is all about, and turning a blind eye to some relevant aspects of research persuaded me that this much-discussed idea of the “shadow” might be relevant not only for client interventions, but also for self-reflection for those within the AI community.
I felt like there might be a risk for a feeling of superiority, especially when AI is seen as a state of being or practised with tight bonds to religious beliefs, preventing AI practitioners from proactively trying to connect with other areas of practice or research. This relates to the final thought I would like to share: considering the ongoing growth of AI and its dissemination into other fields of practice, should the AI community foster a rapid distribution of AI at the potential expense of the quality of its application? Opinions from interviews differed in this regard from seeing the fast growth of AI as a risk to its core values, if done too quickly, to considering this growth as a unique opportunity to spread AI.
I myself am certain that the AI community will continuously grow in the upcoming years. How fast and in which direction will be up to all of us. To rephrase the words of one of my interview partners: “The biggest challenge for AI is building practices of common ground to solve problems together, thereby overcoming the ongoing fragmentation of the world.”
Based on my experience within this community for the last twelve months, I am convinced that there is a real chance to succeed.
References Beckhard, R. (1969) Organisation Development: Strategies and Models. Reading, MA: Addison-Wesley. Berger, P. and Luckmann, T. (1991) The Social Construction of Reality: A Treatise in the Sociology of Knowledge. London, UK: Penguin Books. Cooperrider, D. and Srivastva, S. (1987) Appreciative Inquiry in Organisational Life. Research in Organisational Change and Development, 1(1), 129–169. Fromm, E. (1962) Beyond the Chains of Illusion: My Encounter with Marx and Freud. New York, NY: Simon and Schuster. Gergen, K. (1993) Refiguring Self and Psychology. Aldershot: Dartmouth. Lewin, K. (1947) Frontiers in Group Dynamics. Human Relations 1(1), 5–41. Mahoney, M. and Granvold, D. (2005) Constructivism and Psychotherapy. World Psychiatry 4(2), 74–77. Scharmer, O. (2007) Theory U: Leading from the Future as It Emerges. Cambridge, MA: Society for Organisational Learning. Seligman, M. and Csikszentmihalyi, M. (2000) Positive Psychology: An Introduction. American Psychologist, 55(1), 5–14.
Alexander Röndigs Alexander Röndigs is an alumnus of esB Business school (Germany) and neOMA Business school (france). He recently graduated from erasmus University (the netherlands) with a focus on organisational change. His master thesis examined the “status quo and potential future of AI”. He works as change management consultant at Timmermann Partners, Munich. contact: email@example.com
Using an Appreciative Inquiry Approach to Enhance Intrinsic Motivation in Higher Education Courses
A pre/post-test control group mixed methods study explored the effectiveness of using an Appreciative Inquiry approach to enhance intrinsic motivation in online instruction. The findings of the study showed that there were statistically significant increases in overall intrinsic motivation, confidence and satisfaction for the treatment group, but no significant changes for the control group.
The results of a pre/post control group mixed-methods study showed that an Appreciative Inquiry (AI) approach used as an online assignment in an undergraduate college course increased intrinsic motivation for students in the treatment group, especially among the students who were least motivated at the beginning of the course. The purpose of the study was to determine if an AI approach could be used effectively as a strategy to positively influence student motivation and achievement in an upper-division early childhood education course at a private Western university. Online modules were created using the Analysis, Design, Development, Implementation, Evaluation (ADDIE) instructional design model (Molenda, 2003; Lohr, 2008; Morrison, Ross, Kalman & Kemp, 2011) while simultaneously employing Appreciative Inquiry as the framework for relating course content to students’ personal and professional goals, strengths and dreams for the future. The study results indicated that integration of even a partial list of the AI phases enhanced student motivation and attitudes.
In 2014, the early childhood education program at a private undergraduate university in the western United States requested that the Instructional Development Department at the university conduct a satisfaction survey among the students enrolled in an upper-division early childhood education course required by three bachelor degree programs and an associate degree program. This course, typically taken during the sophomore, junior or senior year of study (second, third or fourth year, depending on a student’s major), focused on designing, developing, implementing and evaluating preschool curriculum in on-campus laboratory preschool classrooms. The survey results revealed the students disliked the course, and particularly disliked the major assignment associated with the course, writing a series of preschool lesson plans.
In response to these findings, a research study was proposed to determine if an AI approach could be used to enhance intrinsic student motivation and improve the overall subjective experience for the students. Following approval from the Institutional Review Board, it was determined that the population for the study would include all students registered for an undergraduate early childhood education methods course taught during the Winter 2015 and Spring 2015 terms. The students in these courses were expected to develop preschool lesson plans according to national, state and program standards. This assignment required a comprehensive application of discipline-related concepts and a high level of intrinsic motivation. The study sample (n =74) consisted of 37 students randomly assigned to the control group, which received traditional instruction, and 37 students randomly assigned to the treatment group, which received the AI-based instruction modules. One student in the treatment group failed to complete the AI modules and was therefore eliminated from the study during the data analysis phase, reducing the treatment group to 36 students and the total population of students in the study to 73.
Description of the process
During the fourth week of each of the fourteen-week semesters, all students enrolled in the designated course were informed of the study and invited to participate. Only students enrolled in this specific course were included in the study, and written informed consent was obtained from all study participants. Any students who declined to give informed consent or who failed to complete the surveys or the lesson plan assignment were excluded from the study, although they did complete the course. After providing consent, study participants were randomly assigned to either the treatment group or the control group each semester. Students in the control group experienced the course exactly as originally designed by the course instructors. Students in the treatment group likewise participated in the course as originally designed, but for three weeks received online modules incorporating the first three phases of AI respectively, in lieu of the standard weekly online reflection assignment.
The Course Interest Survey
Students assigned to the control and treatment groups completed the Course Interest Survey (CIS), which measured intrinsic motivation in the areas of Attention, Relevance, Confidence and Satisfaction (ARCS), before and after the treatment. The CIS is a valid and reliable instrument, developed by Keller and Subhiyah (1993), which assesses situation-specific motivation by using a Likert-type scale to “measure how motivated students are with respect to a particular course” (Keller, 2010, p. 277). Permission was granted from Dr. John Keller to use the CIS to collect and measure intrinsic motivation (Keller, 2010). The independent variable in this study was the Appreciative Inquiry modules. The dependent variables included the four motivation subscales of attention, relevance, confidence and satisfaction, as well as the overall motivation score, as measured by the CIS.
The pre-test CIS was administered in hard copy during class time to all study participants at the beginning of the fourth week of the semester. Any students who were not participating in the study were allowed to use the survey time for course-related work. Following the pre-test CIS, the treatment group began the first AI module during the fifth week of the semester. This module replaced the weekly online reflection journal assignment completed by the control group. The control group continued with the course as assigned by their instructors. The treatment group subsequently received the second and third online modules during the sixth and seventh weeks of the semester, respectively. As with the first module, the second and third modules replaced the weekly online reflection journals completed by the control group.
The modules: Define, Discover and Dream
The treatment consisted of three online modules that corresponded with the first three phases of AI: Define, Discover and Dream. Each module was developed using the ADDIE instructional design model for the purpose of ensuring quality instruction. Throughout the design process, formative assessments – involving course instructors, focus group student volunteers, subject matter experts and instructional designers – were iteratively conducted to ensure that the goals and objectives targeted the identified instructional problem of low intrinsic motivation and adhered to the AI philosophy. During the eighth week of the semester, after the last AI module had been completed, the post-test CIS survey was administered to all participants.
After collecting the results of the pre-test and post-test CIS, an independent t-test was performed to analyze the differences between the mean overall motivation scores on both the pre-test and post-test CIS for the treatment group and the control group. In addition, the post-test motivation subscale scores for ARCS were analyzed by performing a multivariate analysis of variance (MANOVA) using the Statistical Package for Social Sciences (SPSS) program to compare the mean scores between the treatment group and the control group for each of the four dependent variables (Leech, Barrett, & Morgan, 2011; Mitchell & Jolley, 2010).
Descriptive statistics for the study were calculated in SPSS. The mean score for the overall motivation scale from the CIS pre-test for the control group was M = 3.97, SD =.48, which was similar to the mean pre-test score for the treatment group, M = 3.99, SD =.47 (see Table 1). A t-test revealed no statistically significant difference between the mean overall motivation pre-test scores for the two groups, t (72) =.281, p =.779. Interestingly, the mean score for the overall motivation scale post-test decreased for the control group to M = 3.92, SD = .68, while the mean score for the treatment group increased to M = 4.17, SD = .38. A t-test comparing the control and treatment groups post-test scores indicated a significant difference, t (56.606) = 2.02, p = .049.
Table 1: Descriptive Statistics for CIS Pre- and Post-Test Overall and Subscale Results
Overall Group Mean
Attention Group Mean
Relevance Group Mean
Confidence Group Mean
Satisfaction Group Mean
As indicated in Table 1, the mean scores on the relevance, confidence,and satisfaction subscales increased for the treatment group from pre-test to post-test; however, the relevance, confidence and satisfaction subscale scores for the control group decreased. Only on the subscale of attention did the mean score for the control group increase, while the mean score for the treatment group remained unchanged. An independent samples t-test revealed no significant differences on the overall motivation score (t = .281, df = 71, p = .779) prior to initiating the treatment.
On the post-test scores, an independent groups t-test (t = 2.015, df = 71, p = 0.049) revealed a statistically significantly higher motivation score for the treatment group. A MANOVA statistical analysis, conducted on the subscale scores, indicated significant post-treatment differences in two motivation subscales between the control and the treatment groups. In both the confidence and satisfaction subscales, the AI treatment group scored higher (see Table 2).
Table 2: MANOVA Statistical Analysis Results
F = .157
p = .693
F = 3.467
p = .067
F = 6.788
p = .011*
F = 5.740
p = .019*
This study found that a statistically significant difference existed between the treatment and control groups in overall post-test intrinsic motivation following the application of the treatment. Moreover, it was determined that a statistically significant difference, favoring the Appreciative Inquiry group, existed in the post-test scores for the CIS subscales of confidence and satisfaction. Perhaps the most surprising finding in the study was the observed effect of the control and treatment conditions on the least-motivated students in each group (see Table 3). Within the control group, the lower end of the range dropped even lower, while within the treatment group, the lower end of the range was raised.
Table 3: Range of Individual Mean Scores Pre-Test to Post-Test
For the overall motivation scale, the lowest scores in the control group decreased from 2.88 to 1.97, while the lowest scores in the treatment group increased from 2.85 to 3.35. This pattern – a drop in the lowest scores for the control group and a rise in the lowest scores for the treatment group – was also found for each of the motivation subscales (see Table 3). These differences resulted in a sizeable gap in motivation scores following treatment between the least-motivated students in the control group and the least-motivated students in the treatment group. . These data suggest the potential power of using an Appreciative Inquiry approach in an instructional setting to enhance intrinsic motivation and/or prevent the reduction of intrinsic motivation, especially among students who may be less motivated than their peers at the start of the class.
Perspective from participants
The AI approach made a significant difference in the intrinsic motivation of students included in this study, as shown by both the quantitative data reported above and the following qualitative data. One participant commented, “I liked the type of questions we were asked to reflect on. It gave me a chance to reflect on why I chose this major and go deeper on how it has benefited my life” (McQuain, 2015, p. 97 ). Another participant remarked, “I really liked how I was able to see the connection between what I was most proud of in the first question, to how it related to my major in the last few questions” (McQuain, 2015, p. 98). Wrote a third, “I also really appreciated the questions because it helped me to reflect on why I am in this major and what my purpose is. I want to remember that purpose as I move forward in this class” (McQuain, 2015, p. 98). A fourth participant described the experience in this way:
I have changed in that I found a motivation to continue my education that I haven’t had before. This experience has given me a new resolve to do the best I can in my remaining time here on campus and do the best that I can and apply the knowledge I am gaining in all areas of my life. It has also reminded me of why I started on this journey in my major. I was studying in a different concentration and was not happy with all of the material I was asked to absorb and when I switched I found joy in doing my school work for the first time in a long time. With the course work load I sometimes lose sight of that joy. In regards to the principles of appreciative inquiry, I realize that all of the parts put together do create one great whole. All aspects of my life effect [sic] each other and if one aspect is out of equilibrium then everything seems to be. I also realized how questions can truly bring about change and how self-reflection impacts our attitudes, behaviors, and relationships. When we question why we love someone and reflect on those reasons, then we tend to have a greater appreciation for that person and how we treat them tends to change. This experience has made me aware of the importance of regular self-evaluation and how it can truly positively effect [sic] our lives, relationships, goals, and overall outlook on life (McQuain, 2015, p. 103–104).
The quantitative data collected through the use of the CIS motivation measurement instruments, as well as the qualitative data collected in the responses to the AI-inspired reflective prompts embedded in the modules, demonstrated that an Appreciative Inquiry approach can be effectively incorporated into online instruction to impact student motivation in an higher education context. Results suggest that the AI modules increased intrinsic student motivation in the treatment group and were positively received by the students. In summary, the use of an Appreciative Inquiry approach as a motivational design strategy yielded positive intrinsic motivation outcomes and improved the overall subjective experience for the students in the treatment group in this study.
Keller, J. M. (2010) Motivational Design for Learning and Performance: The ARCS Model Approach. New York, NY: Springer.
Keller, J. M. and Subhiyah, R. (1993) Course Interest Survey. Tallahassee, FL: Instructional Systems Program, Florida State University.
Leech, N. L., Barrett, K. C. and Morgan, G. A. (2011) IBM SPSS for Intermediate Statistics: Use and Interpretation. New York, NY: Routledge.
Lohr, L. L. (2008) Creating Graphics for Learning and Performance. Upper Saddle River, NJ: Pearson Education.
McQuain, B. (2015) Using an Appreciative Inquiry Approach to Enhance Student Motivation and Achievement in Higher Education Courses. PhD Dissertation, Idaho State University.
Mitchell, M. L. and Jolley, J. M. (2010) Research design explained (7th Ed.). Belmont, CA: Wadsworth.
Molenda, M. (2003) In Search of the Elusive ADDIE Model, Performance Improvement, 42(5): 34–37. doi: http://dx.doi.org/10.1002/pfi.4930420508
Morrison, G. R., Ross, S. M., Kalman, H. K. and Kemp, J. E. (2011) Designing Effective Instruction (6th Ed.). Hoboken, NJ: Wiley.
In 2014, a preliminary review of literature found that Appreciative Inquiry (AI) practitioners indicated a need for further research into AI success and failure, identifying the processes and levers that lead to an outcome (Bushe, 2011; Head, 2005), and to fill the gaps in AI literature (Bushe, 2011; Messerschmidt, 2008). Schooley (2008) examined the viability of public administrators using AI to improve government effectiveness, through interviewing 20 managers from large cities (not exceeding populations of 250,000.) Schooley’s study found that negative environments (due to political context) were a barrier, hindering a successful outcome. The specific issue addressed in the present study was to determine why AI outcomes fail and succeed, specifically in US municipalities.
First, it was necessary to examine existing literature to understand the AI methodology and how it could be used in organizations. Secondly, it was necessary to organize a theoretical framework for further exploration on the successes and failures with emphasis on AI processes and levers. This study explored the use of AI as a methodology for change by US municipalities. The research questions (RQ) that guided the study are:
What are the Appreciative Inquiry key processes and levers that led to application success and failure in those city governments that adopted the methodology in the past ten years and the three highest populated municipalities (populations identified by the US Census Bureau in 2013)?
What is the success and failure rate of Appreciative Inquiry initiation in US municipalities that adopted the methodology in the past ten years?
To address the two primary RQs, the study utilized a mixed methods exploratory sequential design, consisting of two phases (Figure 1). In essence, this approach addressed the RQs through review of the AI literature and survey research. To build a theoretical basis for exploring and understanding US municipalities’
use of AI and causes for its outcomes, the fundamental steps of this mixed methods research were used to gather data for this study, including specifying the problem, engaging in a systematic process of inquiry, and analyzing data for understanding the nature of the problem (Creswell, 2013).
From conducting a qualitative data analysis of the literature, findings were used to help build two instruments, a survey questionnaire and an interview protocol. A sample was taken from three population groups. A nonprobability purposive sampling technique known as judgment sampling was utilized.
The survey targeted two populations and consisted of members from the web-based LinkedIn social networking community. Many of their members are also members of various LinkedIn groups who identify with their work-related background, which provided an opportunity to tap into people with specialized knowledge and experience in AI. Four AI LinkedIn groups represented population group one, and one municipal LinkedIn group represented population group two:
Population group one: AI Practitioners, AI Facilitators, other AI Professionals (and has US municipality AI implementation experience within last 10 years), and
Population group two: HR personnel (with US municipality employment and knowledge of AI within last 10 years, current or former employees).
Survey participants from LinkedIn reside worldwide to include a gender dyad composition. Group one and two population sizes were determined by analyzing the targeted LinkedIn group statistics. To apply the survey questionnaire, a discussion was crafted requesting participation and posted to the five LinkedIn groups. Respondents proceeded to a researcher-created website for prescreening and informed consent, and then to the survey site.
The survey consisted of 20 logically driven questions. There were 16 survey respondents, eight from each group.
Only US municipalities with populations of 600,158 and smaller were identified as having utilized AI. To find out if larger US municipalities utilized AI (New York, Los Angeles and Chicago), the interview protocol was applied to a third population group consisting of:
Current HR personnel with specialized informed inputs, senior or otherwise.
The interview protocol was semi-structured, consisting of three primary open-ended questions (including sub-questions dependent upon answers), and defining AI to ensure understanding. Group three’s population came from the three cities’ official websites. There were 43 attempts to conduct an interview by phone and 16 interviews were conducted: eight with personnel working for the City of New York, and four each for the cities of Chicago and Los Angeles.
Dr Anthony H. Schmidt Jr. recently completed the Doctorate of Business program at the National Graduate School of Quality Management, and served as co-faculty. He served for 20 years in the US Navy as a certified Navy instructor. After transitioning from the Navy, he served as a safety and training officer in a US municipality.
This Research Review and Notes column explores the recent development and research into the use of Appreciative Inquiry in the Gulf States, both in organisations and in academic settings.
I’ve been reading Be Heard Now by Lee Glickstein, who shares his ideas and processes for developing skills and confidence as a public speaker. Glickstein’s process is to build speaker confidence by focusing on the positives rather than pointing out the negatives. Familiar message, right?
AI in the little league
Glickstein’s book reminds me of some of the sport coaches I’ve seen over the years. Like teachers and other influential people in our lives, sport coaches can have profound impact, which can begin at very young ages. I am reminded of one incident in particular, in which my oldest son was playing little league baseball in a local tournament. We were thrilled with our coach because he was both knowledgeable about the game and a tremendous supporter of each player on the team. I wasn’t familiar with Appreciative Inquiry (AI) at the time, but in retrospect I can see that his coaching style was AI in action. He coached via the five generic process of AI, and the results were fantastic. Our boys learned a lot about the game and about how to behave as people – and they enjoyed the learning. They also did quite well in the league.
On the day in question, our team was playing another highly ranked team. The contrast between the two coaching styles was extreme. As our coach was yelling encouragement to our team and pointing out what they were doing well, the other coach berated his players for what he thought they were doing incorrectly or poorly. We parents talked in the stands about the contrast, our shock at how the other team’s coach was behaving, and our deep gratitude for our coach. Our team won the tournament, but the joys of victory and accomplishment were lessened as we heard the coach from the other team ranting and cussing at his young players after the game. Our coach walked over to the other team to see if he could get the other coach to calm down and treat the players with more respect. To the other coach’s credit, he seemed to understand he had run off the rails: he did calm down and apologised to his players for losing control. Regardless, we were appalled.
My wife and I had always maintained that we would pull our sons from any team with leaders who behaved as the coach on that other team had done. Berating and focusing on the negatives are the antithesis of what we wanted in coaches for our children. We wanted, and still want, the best for our children, and that means we want coaches (and now employers) who will treat our offspring with respect and help them build skills while also building a healthy awareness of personal strength, self-confidence and inclusivity. As you and I know, this is where AI is so powerful, and it extends far beyond the playgrounds of local neighborhoods and places of business. It extends all over the world.
Living in the Arab Gulf
I lived and worked in Doha, Qatar from July 2008 through June 2015. For those seven years, I led the Liberal Arts & Sciences program for the first American international branch campus invited to Qatar, Virginia Commonwealth University in Qatar. It was during this time I trained and certified as an Appreciative Inquiry facilitator and applied AI at my workplace. For example, I participated in a very successful AI-focused strategic planning process that involved everyone on our campus, from caretakers and facilities people to students to the top levels of administration. I’ll talk more about that a bit later, but for now, my point is that AI is alive and well in many locations around the world. There isn’t space in this column to discuss every AI activity, nor even every country where we can find AI in action, but I can briefly outline some recent AI activities in the Arab Gulf countries of Qatar, Bahrain and the United Arab Emirates.
AI in 2009
In early 2009, Martha Robinson and Mohamed Ally of Canada’s Athabasca University published an article about their application of AI in a girls-only middle school in Qatar. Utilising AI’s dialogical and inclusive process, Robinson and Ally investigated the emotional and intellectual reactions of a dozen Grade 7 students who received e-Schoolbags with Tablet PCs as part of a pilot project involving a new and innovative e-Learning platform. The AI process enabled these researchers to find the information they needed in order to assess the impacts of this program.
AI in 2012
Bonnie Joan Milne’s doctoral research, completed in 2012, focused on the creation of social capital the United Arab Emirates (UAE). Milne combined AI’s 4D method with action-research in her look at 15 Emirati women living in the UAE. Milne attempted to help her participants identify and generate life changes by focusing on strengths and past successes through Discovery, Dream, Design and Destiny/Delivery. Her work showed these women are not the submissive, passive and weak individuals so often portrayed in Western media, but rather, their narratives speak of strengths and accomplishments.
Another successful 2012 adoption of AI in the UAE is seen in the case study published by Vicki Culpin and Judith Scott. This case focused on the efforts of one manager in a government office in Dubai who experimented with AI methodologies to see if he could enhance leadership development amongst his staff. Culpin and Scott found that the inclusive and strengths-based AI process put into play was initially met with resistance by some because it was seen as contrary to the traditionally authoritarian and hierarchical culture. Cultural insights such as this are crucial in understanding optimal uses of AI in non-Western cultures. The authors noted that it took persistence and patience, but the manager was able to use AI to change his organisational culture and develop leadership across the spectrum of his staff. This innovatively inclusive and strengths-based approach resulted in a positive influence on the organisational culture as a whole.
My third example from 2012 comes from Bahrain Gulf Air, who reported in the local media that their organisation hosted the 54th Annual Conference of Worldwide Airlines Customer Relations Associations. Held in Kuala Lumpur, the conference featured a session on using AI as a means to help them improve their branding. This shows us some of the breadth of AI application.
AI in 2013
2013 was a busy year for AI in the Arab Gulf. Paul MacLeod’s article in conference proceedings from the Q-Science conference held at College of the North Atlantic-Qatar presented his use of AI to facilitate student engagement amongst a group of 100 students. His objective was to move these students from a deficit-based mindset, in which they are mere recipients of information, to the more dynamic, participatory and empowering mindset of engaged learners. The process was highly successful. Another educator, Cliff Oswick of the Cass Business School and Cass Dubai Centre, reported in The Gulf that AI provides a meaningful and viable change process for contemporary organisations and he recommended AI for 21st century leadership.
My third example from 2013 is another doctoral dissertation. Susanne Bauer found the AI dialogic process enabled her to better understand dialogues of cooperation, competitiveness, development and cultural changes across the Middle East and North Africa (MENA). Instances of low capacity building were linked to ineffective (non-AI) processes of communication. Bauer’s findings informed her recommendation of AI communicative processes as a method for achieving possible paradigm shift in MENA’s international development cooperation.
The fourth and final example from 2013 comes from Nancy Fuchs Kreimer and her study of relationship building in a series of religious retreats. Fuchs Kreimer and her colleagues led three retreats to foster relationships amongst emerging Muslim and Jewish religious leaders. Participants built connections through personal stories that were individual but also reflected substantive shared realities. The strengths-base and inclusivity of AI was pivotal in enabling these narratives to emerge, be heard, be understood and be accepted by participants regardless of religious faith. Communal bonding was further enhanced through group activities and open but respectful discussion of challenges (including race, class, gay sexuality and the Israeli-Palestinian conflict!).
AI in 2014
I have two examples of AI work in 2014. First, AI featured prominently in the ninth national conference of Assessing Medical Professionalism, hosted by Salim El-Hoss Bioethics and Professionalism Program at the University of Beirut Faculty of Medicine. Brownell Anderson, Senior Academic Officer of International Programs at the National Board of Medical Examiners, USA, presented an AI workshop as a vehicle for conference participants to examine and address the question of professionalism in medicine as it is practised in the Arab Gulf.
The second example from 2014 is the project I was involved in at Virginia Commonwealth University in Qatar (VCUQatar). This project was published in the May 2014 edition of AI Practitioner, so I will share just a brief outline here. In the article, Rob Bianchi and I discuss the use of AI in a strategic planning process at VCUQatar throughout 2013/2014. My co-facilitator and I worked closely with the upper administration and developed a team to facilitate the planning process.
This team included representation from all sectors of the campus population and enabled us to collect extensive data from individuals across the campus community. The AI process enabled us to garner valuable information and inform future planning. The campus community was new to the AI philosophy and process, but the project was successful.
AI in 2015
My final example is from 2015. Joanne Rowe and Sarah Hyde wrote a white paper outlining their use of AI to develop effective and sustainable teams at the Higher Colleges of Technology in the UAE. As a result of their findings, these authors recommended AI as one of the key factors in successfully improving religious and cultural sensitivity amongst team leaders. Positive team development resulted in enhanced team effectiveness, heightened levels of individual satisfaction and, subsequently, improved organisational success.
In other words
From public speaking to little league baseball to accessing student input to campus-wide strategic planning to effective team leadership, AI has been proven effective. AI can be found improving the activities and interactions of individuals and groups from a Canadian college to an American university to a girls’ middle school in Qatar, to a group of Emirati women and a national college system in the UAE, to a Bahraini national airline. The world is a small place these days, and AI spans the globe. This makes for a positive future for AI practitioners and for the world we live in.
Assessing Medical Professionalism. (January 17–18, 2014). Ninth National Conference, Salim El-Hoss Bioethics and Professionalism Program at the University of Beirut Faculty of Medicine. doi:https://www.aub.edu.lb/fm/cme/activities/Documents/Assessing%20Professionalism%20Save-%20January%2026-27.pdf
Bauer, Susanne. (2013) Dialogues for Knowledge and Development. Unpublished PhD dissertation. Tilburg: Tilburg University. doi:http://www.taosinstitute.net/Websites/taos/images/PhDProgramCurrentDissertations/Suzanne_Bauer_updated_-Dialogues_for_Knowledge_MENA_excerpt2013.pdf
Culpin, Vicki and Scott, Judith. (Autumn 2012) A leadership experiment in the UAE. Retrieved from The Ashbridge Journal. doi: https://ashridge.org.uk/Media Library/Ashridge/PDFs/Publications/ALeadershipExperimentInTheUAE.pdf
Gulf Air Leads Debate on Airline Customer Relations Challenges. (October 7, 2012). In Air Transport.Retrieved from Arabian Aerospace Online News Service. doi: http://www.arabianaerospace.aero/gulf-air-leads-debate-on-airline-customer-relations-challenges.html
Fuchs Kreimer, Nancy. (October 2013) Relationship Building through Narrative Sharing: A Retreat for Muslim and Jewish Emerging Religious Leaders. Teaching Theology and Religion, 16(4), 371–380.
Glickstein, Lee. (1998). Be Heard Now. New York: Broadway Books.
MacLeod, Paul. (2013). Giving students a voice to achieve positive change: Using appreciative inquiry to maximize student engagement. QScience Conference Proceedings. Global Innovators Conference 2013, College of the North Atlantic-Qatar, April 4–7, 2013. doi: http://www.qscience.com/doi/pdf/10.5339/qproc.2013.gic.8
Milne, Bonnie J. (2012). Creating Social Capital: Inspiring Stories of Emirate Women. Unpublished PhD dissertation. Tilburg: Tilburg University. doi:ttp://www.taosinstitute.net/Websites/taos/images/PhDProgramsCompletedDissertations/Milne_Creating_Social_Capital_Inspiring_Stories_of__Emirati_Women.pdf
Oswick, Cliff. (May 2013). A new perspective on change. Labour Markets: News Analysis. Retrieved from The Gulf. doi: http://www.thegulfonline.com/Articles.aspx?ArtID=5372
Robinson, Martha and Ally, Mohammed. (2009). Transition to e-Learning in a Gulf Arab Country. International Journal of Excellence in e-Learning 2. doi:https://www.academia.edu/12585195/Transition_to_eLearning_in_a_Gulf_Arab_Country
Rowe, Joanne and Hyde, Sarah. (2015). Leading Sustainable Positive and Effective Teams. Higher Colleges of Technology UAE. doi: http://www.chairacademy.com/conference/2015/conpap/Rowe%20and%20Hyde%20-%20Leading%20sustainable%20positive%20and%20effective%20teams.pdf
Yyelland, Byrad and Bianchi, Rob. (May 2014). Strategic Planning Using Appreciative Inquiry in Qatar’s First American University Campus. Retrieved from AI Practitioner, 16(2), 54–59. doi: dx.doi.org/10.12781/978-1-907549-19-9
Authors Kaplan and Kaiser have postulated that too much of a good thing – such as being too appreciative – can actually be detrimental to both the individual and the organisation. However, a more recent study by Charlotte Crisp has found there cannot be too much appreciative leadership if the aim is to increase work engagement.
Can a Leader Be Too Appreciative?
In a 2014 literature study, a former Master’s student of mine, Charlotte Crisp, found that many organisations which strive towards high employee engagement do so by encouraging those in leadership positions to employ appreciative behaviours alongside their task-orientated leadership behaviours. As such, the regression line between appreciative leadership and employee engagement should be linear, suggesting that as appreciative leadership increases, so will employee engagement.
Then we stumbled upon the work of Kaplan and Kaiser (2009; 2013), which suggests that an individual (e.g. a manager or leader) may demonstrate too little optimal, or too much, appreciation – each of which may have a unique effect on the organisation’s employees. Moreover, the authors postulate that too much of a good thing – such as being too appreciative – can actually be detrimental to both the individual and the organisation.
My colleague Deon de Bruin and I subsequently challenged Charlotte to test Kaplan and Kaiser’s suggestion that the relationship between appreciative leadership and work engagement is curvilinear, meaning that too much or too little appreciative leadership is negatively related to work engagement, while optimal appreciative leadership is positively related to work engagement.
She took up the challenge and developed the Appreciative Leadership Questionnaire with the aim of measuring five strategies of appreciative leadership: inquiry, illumination, inspiration, inclusion and integrity (Whitney, Trosten-Bloom & Rader, 2010). Each strategy was measured using two items; the scale therefore consisted of a total of ten items. Each item presented the participants with a scenario, and the questionnaire required the participants o select one of three responses (in each case) that best described how their leader would most likely react in a given situation. The three possible responses were structured to represent a team leader/floor manager who is respectively unappreciative, optimally appreciative, or overly appreciative. An example of an item from this questionnaire is: “I feel overwhelmed, because I believe that I do not have the skills required to successfully complete my work tasks. I approach my team leader to explain this. My boss is most likely to…”.
The participants were required to choose one of the following responses:
(a) highlight my weaknesses or skill deficiencies;
(b) highlight my strengths, and align them with compatible and attainable target outcomes; or
(c) overemphasise my strengths.
For the measurement of work engagement the UWES-9 (Schaufeli, Bakker & Salanova, 2006), which is a shortened version of the Utrecht Work Engagement Scale (UWES) developed by Schaufeli and Bakker (2004), was used. The scale, which has sound metric properties, measures work engagement in terms of three factors: vigour, dedication and absorption.
Charlotte conducted her research at a call centre in the Gauteng province of South Africa. Her sample (n = 171) consisted of call centre agents and their team leaders. The results of Charlotte’s study do not support Kaplan and Kaiser’s claim, indicating that the relationship between appreciative leadership and work engagement is not curvilinear, but linear: as appreciative leadership increases, so does work engagement. Furthermore, the results show that there are, indeed, only two significant categories of appreciative leadership, namely under-appreciation and over-appreciation. Charlotte’s study implies that there cannot be too much appreciative leadership if the aim is to increase work engagement. Moreover, as far as her findings are concerned, there appears to be no optimal level of appreciative leadership.
Freddie is professor of Industrial Psychology at theUniversity of Johannesburg, where he co-chaired the 2015 World Appreciative Inquiry Conference with Anastasia Bukashe.
References of this article you can find in the issue of February 2016.