Communities of Knowledge and Knowledge of Communities
An appreciative inquiry into rural wellbeing
Kathleen May Kevany
Gateways: International Journal of Community Research and Engagement
Vol 7 (2014): 34–51
© UTSePress and the authors
International Journal of Community Research and Engagement 2014. © 2014 Kathleen May Kevany et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 Unported (CC BY 4.0) License (https://creativecommons.org/licenses/by/4.0/), allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material for any purpose, even commercially, provided the original work is properly cited and states its license.
Citation: International Journal of Community Research and Engagement (IJCRE) 2014, 7, 3392, http://dx.doi.org/10.5130/ijcre.v7i1.3392
This article is an examination of the suitability of appreciative inquiry (AI) as an approach to investigate rural wellbeing. It endeavours also to reveal attributes of AI that are conducive to bolstering community and university partnerships. A team of researchers and community members designed a research study to explore elements of community life that contributed to rural wellbeing from the perspective of the community. The team became an ad hoc inquiry group that discussed and established the details of the study, including who, where, when and why. The researchers facilitated the study logistics, while the community members became aids in establishing the meeting location, ordering refreshments and encouraging participation. The members of the inquiry group also became participants in the study.
Through AI and other evolving and emerging forms of participatory or relational research methods, researchers and community partners actively forge co-generative relational processes and outcomes. These relatively recent research approaches encourage consideration of not only what the study reveals, but also with whom and how research relationships are developed and how power is shared within the research process itself.
With input from the community members, the university researchers selected AI as a helpful approach for enabling participants to articulate and researchers to extract what is included in community members’ experiences of wellbeing.
Communities of knowledge, like communities of practice, are groups of people informed about or curious about discovering and sharing relevant knowledge. Communities of knowledge may convene virtually or in person for a specified time, with a focus being on exchanging knowledge. Knowledge of communities, as in the title, is identified as pivotal to participatory research (PR) and community-university partnerships. The best understanding of communities and of the lives of the community members can be obtained from the community members themselves. They are the experts about their lives and communities and are aware of what is working and what is not. Universities looking to embark upon community research are guided by an ethic of mutual learning based on the expertise all parties bring to the project. Referring to the work of Biggs in the field of agricultural research, Cornwall and Jewkes (1995, p. 1669) summarised four modes of engaging in participatory research:
contractual – people are contracted into the projects of researchers to take part in their enquiries or experiments;
consultative – researchers are asked for their opinions and consulted by researchers before interventions are made;
collaborative – researchers and local people work together on projects designed, initiated and managed by researchers;
collegiate – researchers and local people work together as colleagues with different skills to offer, in a process of mutual learning where local people have control over the process.
In practice, the form of community engaged scholarship and participatory research moves through the different modes of PR at different stages.
This article is a critical reflection on and analysis of the use of AI as a PR approach to a qualitative study of rural community wellbeing. In considering the four PR modes as revealed by Biggs, the study involved elements of both consultative and collaborative approaches. In essence, the article is an examination of the interface between communities of knowledge and knowledge of communities.
First, we examine the existing literature on AI and why it was chosen as the preferred approach. Next, we consider the rationale for selecting this method under four themes: relational dynamics, positivity, multivocality and social construction, and generativity and action orientation. We then discuss AI as a research methodology and its limitations. To contextualise our discussion, we provide a brief overview of our rural wellbeing case study design. The results of this case study are then examined, providing examples and evidence of the outcomes of using AI in this type of research. And finally, its impacts on community-university relationships are discussed and conclusions drawn.
For the purposes of this article, our review of the literature forms the basis of our analysis of the suitability of AI as a research methodology to inquire into and distil the factors contributing to rural wellbeing.
Reflection on the Selection of AI
Participatory research approaches, such as AI, are designed to undertake research with people who are part of the design team while also being subjects of the study. Participatory design is built around engaging with local agents rather than carrying out research on them. ‘The participatory research process enables co-researchers to step back cognitively from familiar routines, forms of interaction, and power relationships in order to fundamentally question and rethink established interpretations of situations and strategies’ (Bergold & Thomas 2012, p. 2). The feature distinguishing AI from other participatory approaches is its orientation towards examining and appreciating what is already working in the area of inquiry. Cooperrider & Srivastva (1987) developed AI to counter the hostility and cantankerousness that had characterised much of community study and engagement and to seek out what was working well in communities. AI endeavours to modify or transform the power dynamic often inherent in research by democratising the process and sharing study design and decision-making powers. Instead of community members being viewed as subjects, they become instead research designers, participants and analysers. ‘Appreciative Inquiry proposes reawakening collaborative action research so that it is grounded on a deep kind of participative, intuitive and appreciative ways of knowing, and so that it includes generative theory as a prime mover in organizational innovation’ (Heron 1996, p. 8). By its nature, living within a community entails cooperative action and collective existence, so AI can be a purposeful approach to examining and illuminating these processes.
Undertaking an AI process is personal, relational and communal. These three dimensions can be found also in the collectivity of voices that generate and critically reflect upon ideas and possibilities (Kevany & MacMichael 2014, in press). AI necessitates being personally engaged with both process and content. It invites being present to and in relationship with others, perhaps in new ways, through new contexts and new conversations. It also involves consideration of community issues, and has the potential to ripple out to positively impact the community through the resultant findings and actions. As previously elucidated, appreciative inquiry stimulates a ‘generative capacity’ that may serve to challenge the guiding assumptions of the culture, raise probing questions regarding contemporary social life and examine what is often taken for granted, thereby stimulating possible alternatives (Gergen 1978). For our study, our use of the AI approach was intended to shine light on under-appreciated aspects of community life, particularly those that, in the experience of community members, were operating discreetly and tenaciously, and yet were life giving.
Our modality for this research project was prominently consultative and collaborative. It did not involve contractual or collegial frameworks. Our design for AI involved both community and researchers collaborating on many aspects of the research. The identification of the research focus and the formation of the questions were initiated by the researchers and then brought to the inquiry group for adjustment. The data was collected and interpreted by the researchers. The draft report was shared with the community participants to solicit a fuller analysis and any required corrections. The design included the findings of members of the community. The action on the findings remains the domain of all parties.
Using the AI process affords community-university partnerships opportunities for communicative processes and relational practices that have the potential to increase civic deliberation and transformation (Gergen & Gergen 2000). Doig and Muller (2011) and Gergen and Gergen (2000) prompted examination of approaches to research design by asking critically reflective questions. We asked participants to consider aspects of their quality of life that often are overlooked or devalued: for example, how can researchers best serve society and foster progressive social policy or move citizens to greater engagement, democratisation and criticality? Exercising scientific rigour necessitated our selecting a study design that enabled an examination of the phenomena in question without situational impediments or undue influence from community members or researcher bias. This included ensuring that participants’ contributions were without constraint.
AI was conceived of as a tool to enhance organisational profitability, sustainability and overall effectiveness (Cooperrider & Whitney 2001), yet the literature provides valuable evidence of it being successfully adapted for community applications. Our literature review revealed the potential of AI as an effective and critical method of research for our study, as AI uses an appreciative, rather than a problematic, lens. The positive or appreciative lens of AI is one of its central tenets and as such was influential in our selection of it as a suitable methodology, as explored further below.
AI Themes Aligning with Wellbeing
AI seeks to identify, consider and document the forces that stimulate creativity and vibrancy in, and give voice and life to, a community. Applying AI in a collaborative community approach was deemed appropriate for our inquiry into factors mediating rural wellbeing. We intended to build upon the recent and growing interest in wellbeing (Aked et al. 2011; Beckley 1995; Cox et al. 2010; Huppert 2009) by investigating what fosters rural wellbeing and whether participants in the inquiry believed that attributes like resiliency, prosperity, sustainability and vibrancy could be nurtured. We chose AI because of the qualities and characteristics it afforded this inquiry, particularly its suitability in prompting participants to share their opinions and experiences of what was working well. Our rationale for selecting this method is summarised under the four themes of (1) relational dynamics, (2) positivity, (3) multivocality and social construction, and (4) generativity and action orientation. These themes are discussed below.
1. Relational dynamics. AI design is purposeful as it invites consideration of individual perceptions and shared meaning-making within a research initiative. The use of AI enables both the researchers and the community participants, as agents of social analysis and social construction, to mutually engage in constructing relations, realities and outcomes. Utilising AI has been found to enhance relationships and bolster enthusiasm and creativity in workplaces and communities, and it may serve to re-energize engagement (Ryan et al. 1999).
2. Positivity. With its focus on what is working, AI was considered appropriate for and consistent with our inquiry into rural wellbeing. As Ludema, Wilmot & Srivastva (1997) and van der Haar & Hosking (2004) purport, researchers focus their questions through their ontological, epistemological and methodological lens. How the nets are cast and the lines of inquiry defined largely determine what is sought, noticed, discovered, valued and captured as worthwhile knowledge. ‘AI’s approach counteracts exclusive preoccupation with problems that all too often de-energize …’ (Ryan et al. 1999, p. 167). The line of questioning invariably influences the construction of the study and its outcomes. ‘Based upon the belief that organizations grow in the direction of what is studied (inquiry is constructive), the choice of a positive [emphasis in original] topic for inquiry is proposed – as a way to construct positive social realities’ (van der Haar & Hosking 2004, p. 1025).
By selecting the AI approach, we intended to consider and document the ‘life-giving forces’ that can be identified and extracted from cooperative action and collective existence (Troxel 2002). The positive framing of questions has been found by other researchers to often surprise participants and cause them to view things as they had not before (Bushe 2012; Michael 2005). A couple of examples of questions we used were: ‘What local environmental actions bolster your community pride?’ ‘And what further cultural resources or events may add to community wellbeing?’ Such positive framing may have prompted participants to respond with less reluctance and reticence and with more honesty than they would to traditional styles of questions focused on problems, most of which they would have heard time and time again (Michael 2005). Often change frameworks seek to highlight problems, with problems frequently becoming the primary focus and consumer of time and energy (Cooperrider & Srivastva 1987; Gonzales & Leroy 2011). In contrast, AI seeks to illuminate the factors contributing to successes and focuses on fostering more of them. According to Bushe (2012, p. 50), ‘a central tenet of AI is that positive thoughts and feelings create more generative relationships’ and generativity is a necessary component for ‘transformational change’.
3. Multivocality and social construction. AI invites multivocality and affords dialogue around diverse and complex interpretations that often leads to the construction of new views and creative options. A facilitated AI process encourages the articulation and mining of unique local insights that build upon the interpretations each participant brings to the community-university engagement process. If AI facilitators properly frame the questions and engage the right people, it allows ‘members to construct a new and better future’ (Bushe 2012). AI helps members to critically discuss and hold contrasting views (Ospina & Dodge 2005), and the conversations sparked by AI can change the way that participants view their circumstances and their community (Aldred 2009). The participants, along with the researchers, are valued as contributors and co-producers of knowledge (Ospina & Dodge 2005). When participants consider themselves and others as local experts, as Michael (2005) suggested, passionate narratives emerge, which are inclusive of voices from a variety of backgrounds and able to generate disparate and synergistic ideas. AI provides opportunity for ongoing dialogue (Gergen & Gergen 2000), strengthening the relationships between community members and between community and academy. Northmore & Hart (2011) intimated that the careful use of AI may be instrumental in forming a collaborative, practical and purposeful partnership between researcher and resident/participant. Appreciating and respecting the knowledge that the community brings to the partnership is an essential attribute of a genuine community-university research partnership and strengthens its integrity (Netshandama 2010).
4. Generativity and action orientation. The generative and action-oriented nature of AI supported the rationale for selecting AI as a suitable modality for our community-university research project. As Gergen and Gergen (2000) stated, advances in the social sciences that were informed by and applied such research approaches were found to enhance creativity, intellectual curiosity and purposeful action. AI, as part of the larger movement of participatory engagement (Aldred 2009), encourages looking for what works and ways to achieve more of the desired outcomes. This places a value on research with community input that is applicable to and inspires action to be taken in the lives of community or organisational members and of those within the academy.
Michael (2005) postulated that positiveness used as an interview technique may reinvigorate interviewees and spark more openness, and may also reveal latent potential for change and enhancement (Ryan et al. 1999). AI has been found to inspire energy and hope by catalysing discussions that changed the participant’s viewpoint to one of positivity (Michael 2005). As AI holds the potential to motivate communities to take action towards an improved future state, it provides a useful lens to study community wellbeing.
We now turn to a summary of the critiques of AI and an analysis of the evidence for the rigour and relevance of AI as a research method.
Critiques and Limitations of AI
Critics of AI often apply critiques of positive psychology and other participatory methods to Appreciative Inquiry theory and approaches (Aldred 2009; Fineman 2006), although we have also highlighted AI-specific potential limitations. One of the most common criticisms found in the literature is that focusing on positive experiences and emotions may cause researchers to turn a blind eye to very real and potentially useful negative experiences (Bushe 2011, Fineman 2006; Michael 2005; Oliver, Fitzgerald & Hoxsey 2011), and that if a biased view towards the positive were strictly applied, a partial version of the truth would emerge (Aldred 2009; Michael 2005). Also, a complete focus on positiveness may ignore factors such as complex emotions or ambiguous social situations or the diversity presented by AI study participants (Fineman 2006). Positivity is not a universal truth. What is positive to some may be viewed as neutral or even negative by others. While AI does give researchers an alternative to traditional problem-based methods, ignoring ‘the shadow’ (Oliver, Fitzgerald & Hoxsey 2011) could cause the analysis to be ‘divorced from reality’ (Doig & Muller 2001, p. 31). Such emerging research could be viewed as producing less relevant or applicable insights. Unaddressed concerns about such research design questions could impede meaningful engagement in both the development of the research and in the use of the findings.
Another thrust of the criticism is that focusing on positiveness and attempting to recreate or replicate past success is based on false assumptions about social context (Aldred 2009; Fineman 2006) and may not work to deconstruct power structures (Boje 2010). Success is not always repeatable or reproducible as it may have been achieved under exceptional circumstances or within a different social and economic context. Knowledge of external environmental factors is critical to the full understanding of participants’ narratives. And finally, as with other participatory methods, AI is criticised for embodying problematic assumptions about empowerment and thus the process could give a false sense of control to participants (Aldred 2009). These criticisms will be further addressed in the discussion on limitations.
Bushe (2012) proposed that, rather than using the term ‘positive’, new terminology could be found. He proposed meaningful and important as helpful terms and gave an example of how, through a positively framed process, negative responses could be incorporated into the generating capacity and strengthening of communities. The questions about community health could be broad and inclusive of a variety of perspectives of the community members. For example, ‘What do you think are the most important factors for contributing to the physical and mental health of local residents?’ and ‘What approaches are used to reach out to youth or marginalized persons in the area?’ This framing would enable any responses that the members might want to share. ‘Avoiding or actively repressing “negative” thoughts and feelings, in the name of “staying positive”, can also make AI a form of oppression that leads to non-generativity’ (Bushe 2012, p. 50). Indeed, some negative or grave experiences could be, in themselves, informative and motivating to the community.
RESEARCH CASE STUDY DESIGN
The objective of the study in which we applied AI was to discover factors contributing to rural wellbeing from the perspectives of community members. The research approach included organising focus groups in two rural communities in Nova Scotia, Canada. Tatamagouche and Advocate Harbour were chosen as pilot study sites and one focus group was conducted in each. They were chosen as they met the criterion for evidence of vitality: continuation of particular institutions in the community (i.e. schools and healthcare facilities, and an active community gathering space), when many communities similar in size and distance from urban centres were losing theirs.
The research plan was initiated by the university principal researcher. It was brought to a planning and brainstorming session with local persons termed ‘community bridgers’. These agents of the communities were identified through existing networks of colleagues or collaborators. For the purposes of our study, community bridgers were defined as individuals with formal and informal roles as leaders, educators and community builders who would act as initial intermediaries with the communities. They would help bridge the community and the university and enable contact between the community participants and the principal researcher by identifying potential participants. Many of the community bridgers also participated in the focus groups. Once introduced to the principal researcher by the bridgers, the community participants were then able to assume their own leadership role and to voice ideas about the design and date of the research. Consequently, the times and places suitable for focus groups and interviews were established by the participants. This was important in building the relationship between the principal researcher and community members because, as Netshandama (2010, p. 80) found, ‘quality partnerships respect community members’ time and have some level of organization that is considerate of community wellbeing’.
Efforts were made to include participants from different sectors and backgrounds in the hope of representing a diversity of voices in the rural wellbeing study design. While focus group sizes was designed to be 7–10 in number to encourage optimal participation, all community members who indicated interest were encouraged to participate. This quite small number would enable participants to contribute ‘experientially generated data’ (Heron 1996, p. 87). The experiences, interpretations, stories and examples shared by participants were collected on flip-chart pages by a research assistant, while another took notes and captured a recording of each focus group session. This was proposed by the bridgers as a transparent way to capture the information. All present could hear and see the individual input, appreciate it and consider the input of the whole group, and then collectively construct additional ideas and directions. The notes gathered at the session were formed into a record of the focus groups’ discussions. Participants were offered the opportunity to provide the principal researcher with comments about the record of the focus group. The notes from the two focus groups were then consolidated. The input from the focus groups was analysed for insights to inform a larger regional study on the factors contributing to community vibrancy in rural Atlantic Canada. While only small numbers of community members participated in the focus groups and interviews, as mentioned above, some representation of voices from diverse age groups was achieved.
Although questions to be used in the AI study were prepared in advance to accommodate the university’s research ethics review, these questions merely guided, and did not bind, the ‘appreciative’ discussions. Further examples of focus group questions were: Can you describe what ‘food literacy’ might mean in this community? What are the benefits the community has obtained from farming, fisheries or forestry? The opinions of community members directly impacted which research questions would be emphasised and whether a more in-depth or different line of inquiry would be helpful to the participants in the study. The sessions were conducted as brainstorming and open discussion sessions, during which participants built upon one another’s ideas.
CASE STUDY RESULTS
Our Appreciative Inquiry in two rural Nova Scotian communities proved effective in uncovering some of the aspects of rural wellbeing exhibited in Tatamagouche and Advocate Harbour. Citizens, politicians, community business owners, leaders, community planners and institutions all fulfil important and interdependent roles in fostering and/or thwarting community strength and vitality.
In the field study, community members suggested qualities and actions that could serve to enhance wellbeing. In addition to systemic issues like the availability of health services or environmental conditions, including accessible, clean and beautiful natural features, community members spoke of the ways in which community exchanges helped to nurture the human qualities of being hard working, hospitable, compassionate, justice-oriented, economical, entrepreneurial, self-reliant, stewardship-minded and playful. Community members also expressed the importance of being grateful and being ready to receive the generosity and hospitality of others.
… you wanna talk about fair trade when we’re buying coffee, I think we have to think locally as well, if we want to be food secure, we have to ensure that the people growing food are making enough to keep up with the costs …
Diverse researchers have investigated elements of and factors contributing to wellbeing. Davis et al. (2012) found it was important to maintain health systems to ensure healthy individuals and communities. MacKendick and Parkins (2004) found in their study in rural British Columbia that there were many elements necessary for rural wellbeing, for example, the community’s ability to maintain a healthy and thriving economy, society and environment. We also found in our study of rural wellbeing that the capacities to adapt to external and internal stresses were factors identified by community as important characteristics (Kevany & MacMichael 2014, in press). Prominent themes that emerged from the study were community members’ appreciation of the degree of community engagement, along with citizen efficacy and their sense of shared responsibility. Participants also were attentive to and proud of the area’s natural magnificence, the vibrant arts scene, and the emerging entrepreneurial spirit and attractiveness of their community to newcomers. In both of the rural Nova Scotian communities that were visited, the members spoke about the commitment of residents towards self-sufficiency, giving many illustrations. As one resident stated:
One of the things we did at the Advocate Harbour Development Association and the hospital board was we sent a letter together to the Department of the Environment … we wanted them to come to this area, check the harbor and tell us what is causing the problems with the clams. They did not come.
In addition to identifying strategies for enhancing local results, participants in our study discussed impediments to a higher quality of community life. While these ideas could be viewed as having a ‘negative’ lens, conscious efforts were made not to discount such experiences. The research discussions enabled them to speak about strategies to enhance some weaknesses in the community around healthy living, environmental sustainability and economic innovativeness.
While not all were happy that physical isolation and lack of local services imposed a necessity for self-directedness, many stated that it was a central driver for qualities in their towns that might be termed resiliency and sustainability. In these communities, isolation, while it could be perceived as a detriment, became a spark for self-reliance, creativity and community sharing. An additional quality identified by participants was the tendency to be compassionate; many members spoke of being concerned about justice not only locally, but globally as well. In general, they found community members to be hard working, hospitable and generally playful.
A distilled summary of the lengthy discussions would be that members of the community generally were self-sufficient and good stewards who were grateful for and happy with their community. Two community members in Tatamagouche captured this notion in the following exchange:
‘You can patch it together and not on the government dime.’
‘Yah I mean they’re patching it together. They’ve got a job but maybe it’s just on minimum wage but they’ve got a cow on the side and they’re fixing engines you know what I mean and it’s amazing to me when you watch how people will knit together a number of income streams you know to make a go of it. And they also learn to live below their means in order to do that.’
This generative nature of discourse served to encourage connections and the evolution of ideas. While in the focus group it appeared that participants attended to the comments of others and built upon the emerging ideas around the factors affecting wellbeing, this may have been an illustration of ‘shared meaning making’ underway. Discussions were recorded to ensure individual input was captured, appreciated and seen to be of value. The recordings were transcribed for data analysis. The community input was then analysed for insights that might reveal factors contributing to vibrancy in rural Nova Scotia. The researchers paid particular attention to issues in which the opinions of participants converged, or diverged. Shared ideas were noted, but distinct or unique ideas were given special attention to ensure that such ideas were not overlooked. A frequent notion that participants shared is reflected in the quote by a longer term resident from Advocate Harbour:
That’s the big thing. But you know it’s a good place to live here. It’s uh, you can still leave your doors unlocked. You know. Not much crime. Taxes and things are cheap. You have lots of privacy if you want. We have 88 acres here more or less and I don’t have a computer. I do have a cell phone but its only pay as you go. And uh you know you can slow life down a little.
Once the focus groups’ report had been summarised, participants were invited to offer any further remarks or reflections on the report developed from the community input. This reflective community input was incorporated into the study report. The principal researcher then invited community members from both rural settings to collaborate on a paper to be submitted to the Community-University Exposition of 2013. Members enthusiastically agreed to participate and the paper was accepted for the conference. While representatives from only one of the rural communities were available to attend the conference, there was still representation from the partnership. Together, we set out to articulate our appreciative process and demonstrate an example of rural community wellbeing. Having the participants involved throughout the research process was intended to enhance a sense of reciprocity with participants and to appreciate and encourage more civic participation. Such positive outcomes from community engagement have been documented in many places and one of note here is Christens’ (2010) work on how community-organising processes helped to facilitate changes in individuals and their relationships, which in turn fostered system changes.
Community members spoke of many dimensions of quality of life as essential factors in mediating wellbeing. Through their critical thinking and creative contributions, they were contributing to new notions of community identity. This value arising from AI is well summarised by van der Haar and Hosking (2004, p. 1031) in the following statement: ‘Reflection upon local constructions, confronting other local constructions, helping people to become aware that they are part of the realities they create … makes these assumptions more explicit and opens up to other possibilities’. When describing their experiences of rural wellbeing, community members were interested in talking about ways of enhancing quality of life; community and cultural engagement; prosperity and economy; learning, education, and communications; political influence and democratic engagement; community sustainability and environmental wellness; vitality and health; food and resource security. The following is an example of the collaborative community spirit that emerged using AI: ‘… while we have our differences … in deep disagreements there is a capacity to maintain decent relationships because we have to work together in the community’.
In the following examples, we provide insights from community members that substantiate our selection of AI for this positive appreciation of rural wellbeing. The following four points of discussion relate to our earlier rationale for selecting this method.
1. An example of the importance of relational links became evident around social connections and the initial reception of new members to the community. The come-from-aways indicated that they found residents cautious and less welcoming initially, but then high degrees of acceptance and inclusion soon replaced this. This familiarity quickly led to community members readily challenging the notions of others while also showing themselves to be receptive to divergent ideas. This substantiates a similar observation by Ospina & Dodge (2005) that participants valued as well as challenged contributions by other community members and the researchers. Thus, through the use of AI, more participants were inclined to engage and become co-contributors and co-producers of knowledge (Ospina & Dodge 2005). The degree of influence community members sense in their lives is largely a product of local politics. In order to investigate this sense of meaning, influence, relationship and impact, another focus group question was: ‘In your opinion, does your participation in democratic process such as voting or community consultations make a difference?’ Some participants shared the following sentiments: ‘Tatamagouche is a place where people come together; money is not the criteria to keep this place afloat …’ and ‘There is more vitality than funding; that’s the way you want it’.
The role of the principal researcher in working to form relationships also deserves some discussion. The researchers travelled to the communities and, where distances necessitated, stayed overnight and engaged in additional activities in the area. This helped members of the communities to be exposed to the character of the researchers and in their own way to assess the motives and nature of the research initiative. It also afforded the researcher opportunities to develop more friendly relationships with the study participants and other community members. Our presence, as researchers observing and interacting with local residents, provided opportunities to appreciate and discern the complexity of relational and situational elements of both communities, as well as allowing community members to have casual conversations with us and to come and learn more about our work and life pursuits.
2. Regarding the practice of positivity, we found that participants in the study viewed themselves as generally positive and interconnected. This orientation was evident from participants’ comments when discussing the focus group question: What are the benefits the community has obtained from farming, fisheries or forestry? ‘I grew up here as a kid. But it was nothing like this. I mean everything’s close by. I can buy fish from the harbor, right off the boats, uh scallops, lobsters, uh and there’s blueberries. There’s nothing missing! I can’t think of a better place to be.’ Another contributed a similar idea when talking about the small but diversified economy: ‘I mean everybody sort of seems to be specialized in something, so it’s almost self-supporting. I mean you have to go outside for a few things, but generally speaking, this community here has everything.’ However, they also recognised that not everyone in the community enjoyed this positive sense of connectedness and supportiveness: they talked about some residents experiencing isolation and loneliness during our discussion. In their work on ‘renegotiating community life’, Mulligan, Scanlon & Welch (2008) found that some community members experienced mediated ‘inclusionat-a-distance’ relationships in their community that could be simultaneously described as inclusive and exclusive. Some concern was also expressed about the disproportionate power people with money can wield: ‘Money can buy you a name on a building even though local contributors of significance may not even have the least of recognition’. Participants were encouraged to speak freely, and comments about ‘negative’ aspects of rural life were valued equally.
3. Within the theme of multivocality, the complementary, as well as the distinctive, diverse and divergent, points of view that were readily shared by community participants could be viewed as assets and strengths of communities that in themselves contributed to positive identity formation. How people ought to live out their values in relation to the environment or democracy, for example, was contested territory. Members were mixed on the degree to which they could or had opportunities to influence government. A couple of divergent views in Tatamagouche offered these perspectives: ‘And all government’s subsidies, government’s programs, they all go from the county lines, back to the city. So, we are stuck up here and we are nothing. We are too far from centres for certain things. It’s a problem.’ and ‘Perhaps we’re politically and geographically isolated, and that’s our advantage. That helps. That’s a good issue. We are far enough away from the centre that we have to fend for ourselves.’ The inclusion of such divergent opinions in the data analysis allowed for a fuller picture of the community and its residents.
Striving to achieve diverse input and to inspire respectful dialogue helped to guard against potential monologicality or monovocality. Criticism of the potential for the latter has been levied against AI methods by Boje (2010). This weakness did not apply in our study as all participants were attentive to, respectful of and built upon the input contributed by other research participants. An example of how ‘weaving and expanding of ideas’ was achieved is demonstrated in the way the following focus group question was posed: Do people become involved in this community? What are the signs of engagement? The question invited open discussion about people’s sense of levels and signs of community engagement, and the persons contributing their answers would know that their evidence would be scrutinised by other participants and could be either corroborated or challenged. In both rural communities, participants spoke of the high levels of demonstrated social connection and inclusion. They also supported each other’s examples of how the vibrancy of the communities was nurtured through volunteering and participation. They shared concerns about burn-out of volunteers and other barriers to community engagement. One participant stated, ‘There is ongoing engagement in a large number of organisations. This indicates to me that there is a high level of engagement.’ Another member added, ‘On the other hand, there can be difficulty in getting new people engaged or in sustaining engagement or re-engaging people’. A similar openness to hearing and building on one another’s responses is exemplified in the remark, ‘I would like to add, that I was a ‘come from away’ probably 6 years ago. So my husband and I came here to start a business in sea kayaking which wasn’t done here before. And we wouldn’t have come here other than for the holiday we took initially, if it hadn’t been for the people.’ This weaving and expanding of ideas enabled the study to take on a polyvocality quality. The pursuit of and respect for polyvocality, as encouraged by Gergen and Gergen (2000), were integral to this inquiry. Polyvocality recognises the importance of a multiplicity of competing and complementary values, impulses and interpretations. Participants were given the opportunity not only to shape the research process, but to co-author and participate in the articulation and dissemination of the findings.
4. Further mining of the rural wellbeing focus groups and interview transcripts revealed the willingness of community members to generate ideas and build on one another’s proposals, particularly in relation to taking action for the environment. This does not suggest that action was produced by the study, but rather the climate and space to purposefully inquire appreciatively provided an enabling environment towards generativity and action orientation. One such example was the attention paid to reducing energy use. Many ideas that were generated related to actions to care for the environment, such as more car pooling and insulating and using less space in large homes, along with having one’s own garden, walking and cycling. Also, both communities practised preserving produce in season, storing produce in root cellars and sharing seasonal excess. Participants were not reticent in raising obstacles to sustainability, such as extensive commuting, reliance on non-renewables and the amount of consumption of all goods. As with the other topics, residents had varying points of view on the willingness and capability of members to reduce, reuse and take appropriate action. One Tatamagouche participant said, ‘But I think people are environmentally concerned here’. Another added, ‘But what can we do collectively as a community that moves all of us a little bit further? I’m not totally sure that the whole community is on board with environmental stuff.’
The use of Appreciative Inquiry could be purposefully directed to consider power issues and strategies for communal engagement and action planning. For example, in this multigenerational discussion, some needs of youth were voiced and interest was shown to attend to these needs in more creative ways. Our experience of AI revealed participants’ expanding views of each other, as noted as an outcome by Aldred (2009).
Criticism that issues of access to and distribution of power are not discussed during AI is often raised. However, in the above study, concerns with power and justice were discussed around access to healthy foods at fair prices and the desire to protect and sustain the natural resources. In future research, AI could be employed in a more in-depth analysis and critique of power dynamics. Such inquiry may be instructive for systemic change that may be beneficial to rural communities.
As well, AI, as a research method, has many of the attributes required for the creation of strong community-university partnerships. The noted attributes are: a focus on what matters to the community, value and respect for local knowledge, continued dialogue between community participants and the researcher, participation in the research process and clear criteria for participation (Dong et al. 2011; Netshandama 2010). Like other relationships, the maintenance of community-university partnerships requires nurturing of the needs and interests of all parties and the acknowledgement of the value of one another’s objectives. The researchers involved in this study continue to communicate with members of the communities and to invite further engagement, including making the research findings accessible to others in the community and possibly undertaking further action to bolster wellbeing as a result of the study. This speaks to the function some researchers recommend of maintaining and monitoring the progress of the community after the original research project has been completed (Netshandama 2010).
In this critical reflection and analysis we argued for the suitability and actionability of AI as an approach to investigate rural wellbeing and to bolster community-university engagement.
A review of the literature, substantiated with evidence from the focus group discussions, supports this central argument. Examination of our approach to the study of rural wellbeing revealed that the elements of AI – relational dynamics, positivity, multivocality and social construction, and generativity and action orientation – can be recalibrated. AI can contribute to the building of strengthened relationships that may be leveraged to bring to fruition more of the qualities and conditions for rural wellbeing.
While the role of community-university partnerships was not largely discussed in the community focus groups, it was illuminated as a sub-theme of this article – appreciating the importance of communities of knowledge and respecting the knowledge of communities. Appreciating and extracting the profound and extensive knowledge of communities was a central pillar of this inquiry into community views of wellbeing. Additionally, respecting the knowledge that community practitioners and university researchers cultivated and shared proved a valuable process development to add to the practical tools for enhancing rural wellbeing.
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