Considering the impact of the unprecedented worldwide changes on employee well-being; well-being will increasingly become a competitive edge for organisations. The focus of this study was on appreciative inquiry (AI) as intervention for well-being; to hypothesise why and under what circumstances AI could be effective, and what can be performed to enhance its effectiveness, especially in respect of improving the well-being of employees.
The purpose of this study was to valuate and refine AI as an intervention aimed at facilitating well-being in the workplace, using a neuropsychotherapeutic approach as framework.
Appreciative inquiry is well known in the management and organisational sciences as an approach and a method to facilitate change. Despite the importance of well-being for organisations, in the academic literature, reporting on the use of AI as an intervention to facilitate well-being remains largely limited. Furthermore, no evidence of a similar study using a neuropsychoterapeutic framework could be found in the English literature.
This was a conceptual analysis with theory adaption as an approach. Appreciative inquiry was chosen as a domain theory and neuropsychotherapy as a method theory. Firstly, AI was discussed, after which the focus fell on neuropsychotherapy with the focus on well-being. Neuropsychotherapy was then used to evaluate and refine AI as an intervention directed at well-being.
It was found that neuropsychotherapy served as a valuable method theory to refine AI for enhancing well-being.
Appreciative inquiry in its more traditional form can be used to facilitate employee well-being in general but will probably be less effective in changing hard-wired neural circuits for the better. For employees who experience high levels of stress, a refinement of AI is needed, in line with neuropsychotherapy.
This study contributed to the literature on well-being interventions.
Considering the impact of the unprecedented worldwide changes on employee well-being; well-being will increasingly become a competitive edge for organisations. With the influence of appreciative inquiry (AI) on the development of positive organisational behaviour (POB) concept and on the strengths-based movement (Bushe,
Bushe (
Here, an argument is made for the relevance of using a neuropsychotherapeutic framework. Grawe (
It is argued that applied neuroscience as an interdisciplinary field of study offers a scientific theoretical framework that serves as a benchmark for assessing, expanding on, or refining existing theories, and even proposing new theories and practices aimed at facilitating well-being in the workplace. More precisely, for the purpose of this article, neuropsychotherapy offers a method for valuating and refining AI as a strengths-based intervention. The premises of this argument is that neuropsychotherapy is regarded not only as a stand-alone theoretical method through which to comprehend and facilitate wellness, but also as a meta-theory that suggests baseline principles for other methods of well-being (Rossouw,
The purpose of this article is thus to apply neuroscientific principles – as embedded in neuropsychotherapy – to the domain of AI as a strengths-based intervention. It, therefore, constitutes an attempt to contribute to the refinement of an intervention and thereby to bridge any disparities that may occur between theory and practice (Nielsen, Taris, & Cox,
This was a conceptual analysis with theory adaption as an approach (Jaakkola,
Firstly, AI is discussed as domain theory, after which the focus falls on neuropsychotherapy as a method theory with the focus on well-being. The method theory is used to evaluate AI as an intervention directed at well-being, indicating the value as well as the shortcomings thereof. The discussion concludes with recommendations on refining the intervention, with a view to enhancing well-being.
Scholarly books of seminal authors were primarily consulted. These sources offer integrated, consolidated perspectives on the concepts. To identify current developments, the literature was then augmented with e-journals retrieved from EBSCOHOST, Emerald, Google Scholar, Science Direct and ProQuest. Access was obtained through
Inclusion and exclusion criteria were applied to the e-journals. Only peer-reviewed articles, published in English, were included. Articles regarded as irrelevant were excluded. Keywords used in the search included AI and well-being (19 articles) and AI and neuroscience (2 articles). No articles on a concept analysis of AI using neuropsychotherapy as method theory could be found.
Links were made between the domain theory and the method theory, and theoretical explanations for the links were offered to serve as a ground for the claims being made on the value and refinement of AI as intervention to well-being in the workplace (Hirschheim,
Appreciative inquiry emerged as a research method that is primarily based on social constructionism (Watkins, Mohr, & Kelly,
With no universally accepted definition of AI and even variances between academics and practitioners (Bushe
The unique strength of AI can be attributed to the integration of a new paradigmatic perspective grounded in social constructionism and the offering of a practical process in facilitating transformation in complex human systems (Watkins et al.,
Social constructionism, with its generative view on the role of dialogues, is fundamental to AI. According to the constructionist principle, conversation is at the core of human functioning; it creates knowledge and meaning and is the seed from which action sprouts. Words are not used only to describe objects: reality is created through the words people use when engaging in dialogue.
The simultaneity principle postulates that change is not linear; the process starts with the very first question we ask. The manner in which questions are crafted is thus crucially important, and hence implies a moment of choice. Asking unconditionally positive questions helps to inspire positive future images and action (Stratton-Berkessel,
According to the poetic principle, what we focus on, grows. When we study a piece of art or listen to music with endless possibilities of interpretation, we all bring our own interpretations of our experiences to conversations, thereby contributing to different perspectives, learning and inspiration. The metaphors we use shape our beliefs.
The positive principle resembles the ‘broaden-and-build theory’ (Fredrickson,
The anticipatory principle states that the images we form about the future direct our behaviour. In line with the placebo effect (Chaplin,
With the evolvement of AI, more principles were added: the wholeness principle that refers to the interconnectedness of systems, people and even dichotomous ideas (Watkins et al.,
The above-mentioned principles of AI are integrated in practice. The typical AI process known as the 4-D cycle, consists of four different phases. An alternative 5-D cycle differs from the 4-D cycle by including a ‘define phase’ (see below), as an equivalent to the affirmative topic choice (Barret & Fry,
As human beings are inclined to focus on problems, an AI intervention often commences with the process of reframing the problem in such a manner that it ‘most captures what people are really curious about, what they really want to see as a desired outcome of working together’ (Barrett & Fry,
The discovery phase of the AI process begins with appreciative interviews through one-on-one dialogue, to uncover the positive core of the system or the life-giving forces that are evident when the system is functioning at its best. These dialogues are based on the topic of investigation, and the questions posed are formulated in an unconditionally positive manner. Stories that are deeply valued, persistent and durable not only become powerful sources for future reference but sharing them also initiates creative interaction and assists in building powerful relationships (Barret & Fry,
This phase involves the creation of a results-oriented vision, based on the strengths and potential that were discovered during the interviews. Participants are invited to use their imagination to discuss what life would look like, if their strengths and aspirations were fully aligned. The envisioned dream of what might be is often expressed as a form of art (Cooperrider & Whitney,
The design phase refers to the social architecture of the envisioned system. Possibility propositions of what the ideal should be, and the enabling mechanisms needed to facilitate change, are co-created. These possibility propositions serve as a bridge between the best of what is and the collective aspirations of what might be (Barrett & Fry,
During the destiny phase, people self-organise and commit to action that will enable the whole system to move towards to realise its dreams and sustain the change process (Barrett & Fry,
Although the approach is still applied at large-scale organisational systems, such as the use of AI summits, group and individual levels, often for team building and coaching purposes (Lewis,
The discovery phase of AI begins with appreciative interviews, often following a stage-setting introduction (Lewis et al.,
The purpose of appreciative interviewing is to not only to provide rationally derived ideas, but also to produce rich embodied descriptions of personal experiences of past events (the discovery phase), future possibilities (dream phase), transition (design phase) and intent (destiny phase) (Lewis et al.,
The use of language plays a crucial role during appreciative interviews as the questions asked during the interview already impact the dynamics of the relationship in the current moment. ‘The questions we ask determine what we find, and what we find escalates in our language, in our dialogue, in our conceptualizations and very much takes part in the social construction of reality’ (Grieten et al., 2017, p. 104).
The experience of positive emotions as supported by Martin Seligman’s classification of happiness (Seligman,
Also, asking positive questions does not mean ‘mindless happy talk’ whereby problems are ignored – they are just approached from the other side. Issues presented by clients are therefore paraphrased to demonstrate an understanding of the issue before the conversation is directed towards what they want more of (Cooperrider & Whitney,
Furthermore, by acknowledging the critique on AI’s focus on the positive (Barge & Oliver,
Despite case studies where AI was not successful (Bushe,
For AI to be transformative, the focus should not be on only sharing those stories that we want to hear. For instance, according to Bushe (
New images direct behaviour as if it is already happening. Social constructionism plus the power of image equals AI in organisational change (Watkins et al.,
Regarding the improvement of health or well-being, Moore and Charvat (
Since the ground-breaking research of Eric Kandel (
Current neuropsychotherapeutic approaches have seen three networks or operating systems emerge as critically important for healthy mental functioning (Arden,
The executive network (EN), being the last part of the brain to develop, is only fully formed in the individual’s mid-twenties (Rossouw,
When the EN has not fully developed yet or does not function optimally, attention disorders, a lack of spontaneity or resilience in respect of decision-making might be experienced (Arden,
The experiential nature of AI allows for stimulating and exercising EN functions throughout the process. Working memory, a core component of the EN is, for example, used when themes are identified during the discovery phase and goal-directed behaviour is of relevance, especially during the design and destiny phases of AI when people self-organise and commit to action.
Furthermore, it seems that during the design and destiny phases, the focus shifts form self-reflection to the outside world. The safe space created during the preceding phases serves to enhance the activation of the EN, resulting in better planning, decision-making and more goal-directed behaviour (Dahlitz,
The default-mode network (DMN), also referred to as the story brain (Arden,
The over-activation of the DMN might, however, lead to rumination on negative past experiences, instead of considering positive future possibilities. This, in turn, may adversely impact a person’s sense of control, self-efficacy and self-esteem, and hence lead to rigidity and even depression (Arden,
The DMN derives its information from both the explicit and the implicit, but mainly from the latter memory systems (Arden,
However, implicit memories are profound, difficult to change and provide a template for new learning. Feelings related to past experiences are thus reactivated in the current moment and also used to project future experiences (Arden,
By discovering and aligning best practices and using these to create better futures, AI seems to focus on changing these memory systems related to the DMN. Opportunities for reflection on and sharing positive or strength-based stories can thus be regarded as an attempt to develop a more coherent DMN. The mutual sharing and combining of strengths could enhance the development of the DMN even more.
The more recent focus of AI on inquiry instead of the over focus on appreciation (Grieten et al., 2017) that allows for sharing and appreciating ‘dark’ stories, may create an awareness of these unwanted memories, and sharing different stories can provide different perspectives that is also conducive for learning and change. ‘The mutual exploration of values, commitments, and moralities as well as relational communities that give them sustenance – can allow participants to collaborate even when they differ over those values, commitments and moralities’ (Hoskin & McNamee,
The salience network (SN) is also known as the feeling network (Arden,
The SN assists the EN in choosing and maintaining the focus of attention on internal or external stimuli, to guide our emotional and interpersonal responses (Cozolino,
The SN derives its information from an implicit, unconscious, emotional memory system and is hence especially activated when potential threats to safety are detected or when the fulfilment of basic human needs such as the need for control and orientation, attachment, pleasure maximisation or pain avoidance and self-esteem is impeded (Grawe,
The SN is largely activated when personal experiences are shared during the AI process. Telling a story is an embodied experience that activates emotions. When a person shares a memory of a personal nature, neural pathways similar to the original experience are activated, and therefore, the original emotions, state of arousal and muscle tone are reactivated and re-experienced in the body (Lewis et al.,
To ensure well-being, specialisation of the systems in their unique functioning as discussed above, together with maintaining a balance and co-ordination between these different systems through both positive and negative feedback loops into a functional whole, is crucial (Arden,
During optimal functioning, the activation of the DMN assists in providing stability by reflecting on the past and envisioning the future, while the activation of the EN creates the capacity to dynamically engage with the challenges of the environment in the present moment. The SN serves as a ‘switch’ between these two networks by determining the relevance and urgency of dealing with outside challenges or personal needs. ‘A secure adult flexibly shifts from the DMN to the EN to focus attention on cognitively demanding tasks while maintaining activity in the salience network for self-awareness’ (Arden,
At times, changing rigid implicit memories such as those related to routine behaviour or unwanted emotional responses requires unlearning. These memories can in most instances be unlearnt by activating or developing preferred memories through new learning in areas such as the EN. This type of learning and unlearning is especially relevant for skills development and resource utilisation (Ecker, Ticic, & Hulley,
However, especially during times when the functioning of the EN is compromised by strong past or present emotional memories, a cognitive understanding of the inappropriate feelings alone is not enough for change to happen (Ecker,
It therefore stands to reason that balancing between the networks will best be facilitated in those instances by the simultaneous activation of, and oscillation between, the different networks (Ecker,
Visual images also play an important role in facilitating the integration of and balancing between neural networks (Cozolino,
Another form of neural plasticity, known as memory reconsolidation (Alberini,
The purpose of this article is to evaluate and refine AI as a strengths-based intervention aimed at enhancing well-being. The discussion will offer propositions on the strengths of the AI approach for facilitating well-being, after which possible refinements to enhance the process will be proposed.
The AI process, to a large extent, relies on the use of conversations or story-telling about positive experiences or current strengths, values about the self and wishes for the future, elicited during the discovery phase of the intervention. With this in mind, it is postulated that AI creates relational spaces of psychological safety ‘where curiosity reigns, where new possibilities are considered and embraced’ (Barrett & Fry,
More specifically, sharing autobiographical memories in the present moment could aid in enhancing the connections between the EN and the DMN as it permits outside experiences to be connected with self-reflection, thereby opening up new, future possibilities and planting the seeds of creativity (Arden,
Furthermore, focusing on positives and strengths might enhance well-being by enabling the experience of control and the development of self-esteem (Arden,
Sharing stories also enables interpersonal integration, as stability and growth development in response to relationships (Arden,
The dream phase is used to envision the system when it is functioning at its very best. Using forms of art as an activity to encapsulate that vision builds on the strengths and wishes identified during the discovery phase. It is therefore reasoned that this activity similarly contributes to the development of a more coherent experience, with options for the future leading to a sense of control being experienced (Grawe,
The design and destiny phases of AI probably also serve to integrate the SN with its focus on the saliency of emotions and the goal-directed behaviour of the EN, and hence to enhance well-being. As the development of goals during the design phase is based on positive personal experiences and amplified during the dream phase, participants will be committed to enact these goals. Theories of subjective well-being suggest that well-being can be enhanced when the goals are self-chosen and of intrinsic value, and that the process of moving towards personal aspirations may be more important to well-being than the end state of goal attainment (Diener, Sue, Lucas & Smith,
Although emphasised to a lesser extent in well-being, it can be postulated that AI also facilitates the integration of left- and right-brain regions. As left-brain regions are primarily associated with positive emotions and approach motivation and right brain with negative emotions and avoidance motivation (Cozolino,
Notwithstanding the above, it is argued that if the focus of AI is only on the sharing of positive experiences and strengths, an overly safe environment can be created, without establishing that controlled incongruency (or optimal stress) which serves as a stimulus for change (Cozolino,
Similarly, not allowing for reflection on negative feelings, sensations and thoughts might possibly sustain (or lead to the development of) avoidance behaviour as a mechanism to control negative feelings and thoughts. Even worse – the EN can be used to rationalise and reinforce avoidance behaviour (Cozolino,
Using AI might also not be conducive in situations where sub-cortical systems are over-activated to such an extent that the capacity of the EN to down-regulate these systems is compromised. If a major imbalance between the EN and the SN exists, and if there is no integration, people might find it difficult to focus on the task at hand, to control their thoughts and feelings or even use their imagination (Cozolino,
In light of the above, it is proposed that AI could enhance well-being, because the process, to a significant extent, serves to integrate (and create a balance between) different neural networks. It will thus assist in developing whole-brain functioning, particularly if the more recent developments in AI is incorporated.
To facilitate AI in organisations for well-being purposes, the process should be refined not to improve the functioning of the different networks only, but also to balance and integrate the different networks.
When facilitating AI in situations where the well-being of participants is already compromised to such an extent that the DMN is biased towards the SN, they might find it difficult to engage the EN. It is recommended that, in these situations, reframing during the define phase be emphasised, to become a core phase of the AI process. The purpose of this phase is not to deal with, or solve, the problems that are presented. Rather, its goal should be to activate the neural networks involved in responding to the impact of the environment and making the emotional circuit malleable.
As well-being is mainly a function of the environment in the neuropsychotherapeutic literature (Grawe,
Reframing will set the scene for appreciating the functional role of negative reactions to the outside world as responses that are instrumental to survival. The activation of negative emotions should thus unfold within a safe, supportive environment, where control is still experienced and basic human needs, such as the need for control and orientation, attachment, pleasure maximisation or pain avoidance and self-esteem needs, are met (Grawe,
Following on from the above, a reframed topic choice can serve as the focus for the discovery phase with strength-based questions formulated in terms of well-being constructs in positive psychology, for example, resilience, self-efficacy, happiness and gratitude. Care should nonetheless be taken not to view the process as linear. Provision should be made for dealing with any negative emotions which surface during the process. ‘The ebb and flow of emotion over the course of therapy reflects the underlying neural rhythms of growth and change’ (Cozolino,
Furthermore, the dream phase of the AI approach, as discussed, will most probably influence the development of the DMN, enhance the connections between the different networks and serve to reinforce learning. However, opportunities for working with feelings could play a more prominent role during the AI process, especially during the dream phase. Identifying, naming and sharing positive and negative feelings that are activated in the body, similar to the practice of focused attention in mindfulness training (Alberts & Hülsheger,
Additionally, to enhance the integration of the SN and the connection between a healthy SN and EN, introducing ‘drench’ as a phase of
Setting goals for developing well-being after ‘drenching’ would probably play an important role in the integration of the different networks. Individualised goals could be formulated in line with the constructs covered during the interviews as these would most probably have been embodied at this stage. The importance of goal setting for life satisfaction, self-esteem and dealing with anxiety has already been established in research on subjective well-being (Diener & Suh,
About the destiny phase, it is recommended that the facilitator purposefully includes an opportunity for participants to reflect on the possible external realities that might impact on their well-being. As a form of anticipatory learning (Grieten et al., 2017, p. 104), it can assist in priming the brain for possible future challenges that might be experienced post-intervention. In this sense, it could provide stability through time in the DMN, and hence, assist with building resilience proactively, allowing the EN to deal with those challenges more conductively. The process can be facilitated by using negative images of expected challenges while planning how to deal with those challenges. Visualising negative scenarios, as well as actions to mitigate such challenges, help build neural pathways that could assist individuals with retaining control when affronting the challenges presented by the real world (Rossouw & Rossouw,
Lastly, with the operating context in constant flux, stories that are durable with multilayered social contexts are needed to increase the flexible zone between rigidity and the experience of chaos (Arden,
Using AI effectively to enhance well-being will depend on the current state of integration between the different brain systems, and hence the state of participants’ well-being at that moment in time. Practising AI as originally conceptualised with its focus on the positive only will most probably enhance the well-being of participants who do not experience stress, by developing their well-being proactively, and building resilience through positive experiences. When using the AI process where participants already experience stress or are traumatised, the intervention needs to be adapted by first appreciating the survival function of the brain and then oscillating between positive and negative experiences, within a safe environment.
The academic literature on AI at times differs with the way AI interventions are practised, making it difficult to account for both. Furthermore, neuropsychotherapy is constantly adapting to new findings in the neuroscientific research, influencing the reliability of the method theory. As this study points to a refinement of AI as intervention per se, it is recommended that empirical studies be conducted on facilitating the refined method and determine the impact thereof, especially during current stressful times.
To conclude, the author sought to provide an answer to questions around the evaluation and refinement of AI as a domain theory, for improving well-being in organisations, using neuropsychotherpy as a method theory. Considering the analysis, it is hypothesised that neuropsychotherpy offers a novice contribution to the evaluation and refinement of AI as an intervention to facilitate well-being in the workplace. Regarding the relevancy of AI, the answer might align with the more recent developments in AI and the advent of the second wave of positive psychology, and its deviation from a sole focus on positive emotions, traits and institutions (Wong,
Appreciative inquiry in its more traditional form can thus be used constructively to manage employee well-being but will probably be less effective in changing hard-wired neural circuits for the better. To sustain the well-being of employees, change, repetition and practice are needed. The more recent developments in AI allow us to conclude that, through AI, we are not co-constructing new realities of well-being; we are co-constructing each other to ‘becoming’ more healthy human beings.
The author declares that no competing interest exist.
I declare that I am the sole author of this research article.
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
This article followed all ethical standards for carrying out research.
The author confirms that the data supporting the findings of this study are available within the article.
The views and opinions expressed in this article are those of the author and do not necessarily reflect the official policy or position of any affiliated agency of the author.