Based on approaches on community collaboration found in the literature, what are two that appeal to you? Which approach do you feel most comfortable with based on your skills, your abilities, and your values? Which approach seems to be the best fit for the community problem that your new initiative is to address? Support your choices with literature.
Chapter 12( Title of book>> Interorganizational collaboration: complexity, ethics, and communication) Authors: Renee Guarriello Heath and Matthew G. Isbell, waveland press inc, 2017 pg 269-294
Given that a primary reason people collaborate is for the purpose of arriving at a solution and/or innovation that advances a vision or solves a problem, you might be wondering why we place solution-oriented communication in our model as the small, top of the pyramid as opposed to the base. Indeed, we advocate a solution orientation only after stakeholders in the collaboration have already demonstrated a strong praxis of collaborative communication grounded in the other orientations. Some groups may already be very skilled in collaborative praxis and may move very quickly to solutions. Others may need more time to build a communicative foundation—getting to know and understand one another then prioritizing group interests and values before directing resources toward solutions. The point is not how fast groups move toward solutions but whether or not they skipped important communicative steps. This chapter addresses the processes and talk geared toward solution and idea generation—determining what the collaboration wants to do (i.e., what are our strategies and tactics for addressing the problem domain?). Communication behavior at this stage in collaborating is task-oriented and geared generally toward strategic planning (i.e., what are our goals and measurable objectives?). Our communication praxis model depicts visioning around group values (i.e., shared values list, chapter 9) before visioning around tasks. As we have repeated multiple times, however, collaboration is not a linear process but a contingent one. Collaborative groups may find themselves revisiting their values and interests as their focus shifts and they accomplish goals and projects. We believe the key to movement through different stages of collaboration is mastering communication that comprises the language of collaborative praxis.
The remainder of this chapter asserts that solution-oriented communication emerges most successfully when grounded in the other orientations of communication—dialogue, interests, conflict, and consensus—that compose collaborative praxis. We begin with a discussion of why stake-holders will want to hold off on solution-talk until they have mastered the foundational components of a language of collaborative praxis. Next, we introduce a transformative process, appreciative inquiry, that purposely avoids the problem-solution framing of accomplishing shared visions as a viable methodology for interorganizational collaboration. Appreciative inquiry honors the cornerstone ethical assumptions that underlay collaborative praxis. The final part of the chapter touches on general guidelines for brainstorming as a method that most groups utilize in some form or another as they move toward operationalizing their shared vision.
The Problem with Shortcutting to Solutions
Groups that shortcut to solutions—that is, make decisions without an understanding of stakeholders’ interests and values—run the risk of having their solutions viewed as compromise (a lose-lose model of conflict resolution)1 by some members because the solutions fail to meet the collective values of the group or because the collective values were not articulated. An interpersonal example illustrates the problem with shortcutting to solutions. Kate is a senior in college who wants to spend her spring break in Mexico with her closest friends. Kate’s mom is adamant that Kate not go. Kate and her mom compromise; she can go as long as she calls her mother every single morning so that her mom knows she is safe— and if she picks up the cell phone no matter what time her mother calls. Without a discussion about the interests and values driving each person’s position, the solution is vulnerable, teetering on an unstable pyramid of assumptions regarding what motivates their positions. Kate might view her mother as not trusting her and ultimately resent the fact that she has to “check in” every day, even though she is a legal adult.
If Kate and her mother spend some time exploring the interests driving their preferred solutions and integrating each other’s interests into their plan, their shared solution is less likely to be seen as a compromise. For example, Kate could learn through dialogue and interest-oriented talk that it’s not that her mother does not trust her, but that she is very worried for her daughter’s safety. In other words, her mother’s interests do not come from a place of control but from a place of security. Such a discussion would allow Kate to reframe her thinking around the solution of calling every morning as a small act that she can do to assure her mother she is safe. On the other hand, through discussion, Kate’s mother may come to understand her daughter’s natural interest in exerting control over her own life. She may see that requiring her daughter to be accessible by phone every minute of the day violates this important value of Kate’s. The solution to this dilemma in the end may be similar to the one they decided on initially with very important differences. Each believes their values are being met, and because they value the other’s interests too, they no longer feel like they are compromising. They have integrated one another’s interests into a solution that will not breed resentment, regret, or possibly sabotage. They have a solution that strengthens their relationship over time because it is grounded in understanding and acknowledgement fostered by a solid base built on the communication orientations of collaborative praxis.
The hope for collaboration is to integrate one another’s interests into solutions that will strengthen relationships, foster understanding, and cultivate trust. Our interpersonal example is not nearly as complicated as interorganizational collaborative solutions will be, but the same principles apply. Imagine that a community is working on its relationships between those suffering from mental illness and the police department. Should the group shortcut to solutions and decide the answer is in sensitivity training for the police, it is likely the police will resent the implication that the solution involves “fixing them.” They may oppose the solution or half-heartedly implement it. Some may view it as an empty “public relations” tactic. However, the police department may be very amenable to agreeing, and perhaps even helping to design, a sensitivity training if they had the opportunity to listen to stories that explain some of the experiences community members with mental illness have had in their encounters with the police. With this broader understanding, the stakeholders from the police department will work with mental health advocates in their community to determine their shared values. With joint ownership of the group’s shared values, the police department is more likely to support solutions that are in line with those values. As such they can reframe the solution of training from “fixing the police department” to seeing it as one mechanism toward accomplishing their shared vision of better community-police relationships.
A radical process that can be used to move groups toward innovation is found in appreciative inquiry (AI).2 In the chapter on dialogue, we mentioned the power of appreciation as a mechanism for listening and understanding. AI approaches an area of interest from a different lens. Instead of using a problem-solution structure that roots out what is wrong and needs to be fixed, AI takes for granted that all organizations and communities have successes and processes they do well. These strengths can be the starting point for change that value the collective and the will of the collaboration.3 Instead of paying attention to a problem and thus highlighting it, AI focuses on what works, assuming that an organization (or a collaboration) is “a solution to be embraced.”4 As creators David Cooper-rider and Suresh Srivastva argue, AI’s purpose is to gain “knowledge to promote egalitarian dialogue leading to social-system effectiveness and integrity,”5 making it a well-suited communication methodology for uncovering solutions in interorganizational collaboration. This positive shift in thinking as a driver of solutions and innovation is visible in the case that opens this chapter. Many of us arrive at strategic planning meetings expecting to focus on problems, so much so that encountering this positive shift in thinking feels unfamiliar at first—“blank stares” as Matt recalled. But as Matt experienced, a focus on things that work can ultimately be liberating for groups.
Our purpose for introducing AI in this text is to enhance a language of collaborative praxis: First, we believe the principles and assumptions of AI compose an appreciative ethic, which is complementary to the communication orientations presented thus far and can be practiced throughout collaboration at every stage. This ethic is vital to understanding those who hold different perspectives; it directs us toward each other’s strengths and contributions. It reminds us of the power of our language and questions as we move toward resolutions. An ethic of AI constitutes a worldview for how to approach others. Second, the well-developed methodology of AI and its four stages—discovery, dreaming, design, and destiny (4-D Cycle)—can be used as a facilitation tool to move groups toward tangible innovations that are rooted in past successes.6 After introducing AI principles and explaining how they relate to collaborative praxis, we will briefly explore the 4-D methodology, highlighting communication techniques that can be used to help stakeholders innovate toward their shared vision.
Appreciative inquiry is a mode of discovery that fosters innovation in a group. Cooperrider and Diana Whitney emphasize that a focus on value is the first part of adopting an ethic of AI. “Valuing [is] the act of recognizing the best in people or the world around us; affirming past and present strengths, successes, and potential; to perceive those things that give life (health, vitality, excellence to living systems).”7 Cooperrider and Whitney further define valuing as prizing, esteeming, and honoring. We believe this inspires a fundamental shift in thinking as stakeholders encounter those who hold different perspectives than themselves. The act of valuing is very different than the act of “tolerating”—language that is
You may still be wondering, if the solutions arrived at through dialogue, constructive conflict, and consensus around values and interests may end up being similar to solutions that are generated without those labor-intensive processes. Isn’t it more efficient to just skip to the solution part of collaboration? While collaborative groups might think they are being efficient in the short run, shortcutting to solutions will likely unravel because the solutions are disconnected from shared values. In the long run, groups are likely to recycle through the same arguments and debates and may ultimately be less committed to implementing solutions. Our communication model takes into account the need to build trust and to develop strong relationships among stakeholders, increasing the likelihood that solutions will be implemented and have enduring results. Solutions that do not honor the individual and shared interests of the group are untethered to a foundation of understanding and prioritizing; they are therefore less likely to galvanize stakeholder action and accountability. Having positioned solutions as a particular type of talk that should follow dialogue, constructive conflict, and consensus around values and interests, we next introduce an all-inclusive process that shares this book’s basic cornerstone ethical assumptions, appreciative inquirycommonly associated with encountering diversity. Tolerating has etymological roots dating back to European religious freedom in the sixteenth and seventeenth centuries. The derivation is from Latin, meaning “bearing, supporting, enduring,” as well as, “to sustain, support, suffer.”8 Whereas valuing calls for honoring and affirming, and adding value, tolerating suggests we are sacrificing something as we endure or suffer. Appreciating is a decidedly different lens from which to see the other.
The second profound shift generated by an AI perspective is the reverence granted to questioning. AI questions can be transformative as they turn the focus of groups toward what works. The core of an AI ethic can be further understood by considering its five theoretical principles: constructionist, simultaneity, poetic, anticipatory, and positive.9
The constructionist principle is the basis for AI and understanding how our realities are created. Rooted in the power of interaction, the constructionist principle articulates how the relationships we have with others create our sense of what is—what is true.10 Consequently, we must master the ability to listen and interpret organizations as living entities evolving through our interactions with one another. In doing so, we come to see that questioning is just a part of the evolution of the organization and that AI is a generative process of moving forward the best parts of the organization’s realities through our communal knowledge of what works.
Core Theoretical Principles of Appreciative Inquiry
• Constructionist principle: understanding organizing as living, human constructions
• Simultaneity principle: inquiry and change are not necessarily separate activities
• Poetic principle: (collaborative) organizing as a coauthored book to be studied
• Anticipatory principle: images of the future guide our present behavior
• Positive principle: affirmative language begets positivity
The simultaneity principle builds on the generative nature of AI by emphasizing that there is no difference between questioning and change; in doing one, you are already creating the other.11 As we question and communicate, we are already discovering and thinking about a future different than our current vision. Simultaneity focuses us on the importance of the question and the effect the question has on the respondent and the person asking the question. Crafting the question becomes just as important as the answer. Questions become part of the change, and asking questions that get to the strengths of the individuals (and thus the organization) allows for visions based on the positive core of what we can do together.
The third principle, poetic, posits that our organizational scripts are constantly being “coauthored” and rewritten.12 As the question is also part of the change, our pasts, presents, and futures are a constant source of information and inspiration. Highlighting this ever-present source of knowledge reminds AI participants that all parts of our lives can be explored for positive growth and strengths. The poetic principle encourages us to seek new ways of understanding continually and to avoid recreating what we already know. Inquiry should be about moving forward, not reaffirming the present or past; otherwise, growth can be stunted.
The fourth principle of AI, the anticipatory principle, emphasizes that taking a positive look forward helps create positive action toward that future.13 The principle could also be labeled the self-fulfilling prophesy; it is an important part of AI interventions. When we think about change and creating a joint vision for what we can do collectively, the basis of that dreaming needs to be positive. Why envision a negative scenario when envisioning positive results sets the tone and action for a road map to success?
The positive principle is the fifth and final theoretical principle. Affirmative language begets positivity. Thus, stakeholders in collaboration are encouraged to talk openly about what excites them or brings them hope. This type of communication builds camaraderie. When we ask questions, we need to craft those questions to elicit strengths and positive thinking. Hope and relationship building are important elements in successfully implementing AI in collaboration. They help create solutions for which all members will feel the pride of accomplishment.
Eight basic assumptions of AI are derived from these five principles.
1. In every organization [or community] something works.
2. What we focus on becomes our reality.
3. Reality is created in the moment, and there are multiple realities.
4. The act of asking questions of an organization influences the group in some way.
5. People have more confidence and comfort in their journey to the future (unknown) when they carry forward parts of the past ((known).
6. If we carry parts of the past forward, they should be what is best about the past.
7. It is important to value difference.
8. The language we use creates our realities.14
Consider how the five principles inform collaborative practice. For example, a collaboration focused on the health of a local watershed could take a more traditional approach to solution generation by first focusing on defining the problem—pollution and the unhealthy state of the water-shed. If the stakeholders were to instead take an AI approach to generating solutions, they would begin with different assumptions and thus a different set of questions. They would start by considering what makes the watershed (and perhaps other watersheds) healthy? The language frame is positive and begins to focus the group on success. With the AI approach, the group would ask questions that build upon best practices when it comes to watershed restoration. The solutions around which they coalesce as a group would eventually grow out of a methodological process of collecting ideas that have already been known to work but are not yet systemically applied. As the group continues to focus on the things that work in their community and in other locations, they begin to build a hopeful dream for watershed restoration that is grounded in concrete experience. AI questions move the group forward toward invention rather than backward in dissecting problems.
QUESTION: How can you adopt an AI ethic in your personal and professional experience? Do some of your relationships fall in the category where you can only see what is wrong rather than what works well? How could you alter your frame for thinking about those relationships or experiences to one that focuses on the positive things that work well?
APPLICATION: Take an appreciative frame to your most challenging relationships for a couple of days. Shift your thinking from tolerating to appreciating. You may be surprised by the results.
Innovating as Collaborative Praxis
This section will explore how AI can be implemented as a part of our collaborative praxis. The section concludes with a review of the well-known method of brainstorming. This tool may be included as a part of AI, or it can be a stand-alone method for generating solutions.
Appreciative Inquiry as Methodology
Earlier we mentioned the 4-D process of AI.15 The AI methodology is a specific cycle that can move groups toward idea generation and planning. In this section, we expand the discussion of discovery, dreaming, designing, and destiny to decrease ambiguity about the stages of AI and to help you visualize how a collaborative group might utilize the process. After discussing the stages of the process, we will consider the challenges of adopting a strict AI methodology before offering an example of an AI process that loosely followed the methodology. AI can be useful even if not strictly applied in community decision making.
Discovery. The discovery phase of AI is about crafting positive questions that can get to the strengths of the group or community—the best of what is.16 Always rooted in the positive core elements of the collaboration, the discovery phase is an in-depth data-collection moment for the collaboration to uncover strengths and ideas already working. The discovery phase frames the future in a positive context—in contrast to a deficit reduction conceptualization that is an issue with the problem/solution format. This phase seeds participants’ excitement about what is to come as members of the collaboration begin to share stories and craft a future that is rooted in previous successes. The discovery phase is the jumping off point for the entire AI process; it is imperative that everyone participating is actively listening to each other and that data are recorded as accurately as possible. One of the most powerful communicative practices associated with the discovery phase is the appreciative interview. Appreciative interviews could be used in strict fidelity with the AI methodology, or they could be a practice adopted by any collaboration that is interested in generating solutions rooted in each other’s experience.
The appreciative interview is based on the idea that our questions will evoke the positives that take place within the domain of the collaboration, leading to positive actions. This is why creating questions that have a positive bias are so important. Depending on the size of the collaboration, stakeholders can interview one another or choose to engage constituents from their respective communities. Some guidelines for producing appropriate appreciative questions include: invite participants to use storytelling and narratives; phrase in rapport talk, not report talk; allow ambiguity; value what is; help people locate experiences worth valuing; convey unconditional positive regard; evoke essential values, aspirations, and inspirations.17 The questions should help the participants tell a narrative story. These questions also give the interviewer the ability to probe the narrative and the respondent to go further and connect their past experiences with the future topics and vision of the collaboration. Soon after completing appreciative interviews, stakeholders will need to make sense of what they heard and the data they collected; the interviews launch the next phase of the process, dreaming.
APPLICATION: As a group, take a “problem” that you see in your community or university community—for example, you may think your university lacks diversity. Think of ways to rephrase the problem so that you can generate ideas for what is working rather than what is not working regarding the problem. Write as many positive-oriented questions as possible. Now construct an interview questionnaire that you can take to other people in the community that will elicit positive stories and examples about what is already working toward strengthening campus diversity.
Dream. When we teach public speaking in the basic communication course, one of the speaking patterns we discuss is Monroe’s Motivated Sequence. One of the reasons it has been found to be such a successful pattern for speaking is that it adds a “vision” step where the orator asks the audience to envision the future. The dreaming part of the AI process uses a similar idea to get the group members to start thinking forward based on the stories and narratives told in the appreciative interview. As with Monroe’s sequence, when we start to see ourselves doing something in the future, we find that we start naturally moving in that direction.
In the dreaming phase, we are asked to talk about what might be in terms of what can be done in the organization and the world.18 While we are dreaming, the goal is to take the positive parts of the collaborative core discussed in the discovery phase and start bringing those aspects forward when talking about the future. The dreaming phase is both practically grounded and a generative process of building on the skills and strengths of each other moving forward.19 The two main goals in this part of the AI process are to: (1) get people talking in the larger group, retelling some of their stories from appreciative interviews, and (2) to allow everyone in the group to start processing the common themes of each other’s stories. By accomplishing these two goals, the stakeholders are sharing their own skills and experiences while also anchoring them in the topics and joint vision of the collaboration. This creates an energy and collective identity that points toward the future and what can be done.
Experts recommend that whenever possible dreaming should start soon after the discovery phase.20 At this time, stories are fresh in people’s minds, and stakeholders are less likely to forget or edit those stories. As part of the methodology, dreaming is formally facilitated with all of the relevant stakeholders, sometimes in the forum of what is called an AI Summit.21 Individual stories should be retold to the larger group to build bonds between the collaboration members. Creativity helps stakeholders explore the collective dreams of the group.22 Groups may choose to draw aspiration trees that grow toward topics and future visions, or they may draw a river of discovery that places emerging themes along a river that terminates at the sea of accomplishment, such as the river drawn in the case that opens this chapter. These creative exercises stimulate people’s thought processes in different and important ways. When the dreaming is done, the larger dream narrative is composed for the group (AI avoids using the word “report” to encourage sharing rich data in a narrative format). Common themes and ideas expressed by everyone during the dream dialogue are made more explicit. The result of dreaming is that ideas and stories about the future of the collaboration are all based on the history of stakeholders’ previous successes. Collaboration members will find “there is no question as to whether the new vision is achievable; the participants have already demonstrated their desire, willingness, and ability to make it possible.23
Considerations of Appreciative Inquiry
AI as a methodology calls for a very specific commitment to positive language regarding change. As such, collaborative stakeholders must consider how this approach might work based on a number of contingencies, which include: the level of skilled facilitation at their disposal; the history of stakeholder partner relationships; and the conflict that may already exist among participants. As with all communicative processes, we do not offer a panacea or prescription for working toward solutions. Appreciative Inquiry is just one technique for generating actionable ideas, and it is sometimes critiqued for its emphasis on the positive. For example, an AI project conducted across the city of Cupertino (California) focused on “cultural richness” but was critiqued for brushing over deep problems of racial prejudice and discrimination that existed in the city.34 In another case study, AI practitioners encountered what they refer to as a “shadow”—when organizational members, who did not feel they could speak freely, rejected focusing on “strengths.”35 Rather than dismiss the process outright, these skilled consultants drew upon the theoretical assumptions of AI to address the concerns of the organizational members. They asked affirming questions (such as, “When do you feel the freest to offer opposing opinions?”) to craft a process that would feel authentic to those involved.36 At first glance, it might appear that the AI process was hijacked with concerns and problems, but the authors remind us that positivity is just one of five principles of AI that can be used to move groups toward innovation.
Another consideration regarding adapting the AI methodology is the need to tailor the inquiry to the interorganizational context. AI as an approach has been very successful as an organizational development tool. For example, employees at British Airways used AI to build a vision for the organization out of their best customer service experiences.37 It also has a successful history in interorganizational contexts. For example, a partnership among Pennsylvania community hospitals and medical centers effectively implemented AI to spark cultural change within the hospitals and local industry.38 However, in interorganizational collaboration, organizations are likely to have disparate experiences in the domain they are trying to affect. It may be hard to identify what works if the collaborative members have not ever explored what they can do together. Appreciative questions will thus need to be designed to fit the interorganizational collaboration context. The most powerful parts of AI—its principles and assumptions—can be applied in interorganizational collaboration, but it may look different than the process described above as it plays out, as in the text box case below.
The case shared by Paul Harris (see text box) tells the story of a collaborative community effort that adopted an appreciative ethic in its approach to community visioning, and adapted the AI methodology to fit its needs. Mr. Harris, a developer and city councilor in Red Deer, Canada, narrates the story of The Red Deer Culture Vision, which engaged AI processes to accomplish city planning with multiple stakeholders.
The Red Deer process demonstrates how effective AI can be in moving toward creative and innovative ideas for a community. Paul’s story illustrates that AI processes are successful in creating a strategic plan; it also illustrates how the process positively affected and inspired the community, built trust, and fed other tangible plans. The Red Deer facilitators utilized the principles and methods of AI as they best fit their community needs.
We now shift our focus from the AI world to brainstorming—one of the most familiar techniques groups use to generate solutions. Brainstorming is a feature of many informal and formal group-visioning processes. Whether used in an AI process or as a stand-alone method, brainstorming is part of the vocabulary that composes a praxis of collaborative solution-generation. The idea of brainstorming is common, and most of us have practiced it in one form or another. It is actually accompanied by an ethos that facilitates its success. Several characteristics of brainstorming are well-documented, including the need for a creative and open environment, the necessity of matching the brainstorming process with the group’s needs, and the practice of avoiding the premature critique of ideas.40 We introduce these features of brainstorming and ponder more recent research on dissent and debate as perhaps counterintuitive, but arguably helpful, in some brainstorming situations.
A Creative Environment. As a faculty member of the Communication Studies department at The University of Portland, Renee annually participated in a strategic planning day for the department. The first thing faculty members did was to “get out of Dodge,” so to speak. They met on a day when no one had any other commitments; they wore casual clothes and gathered in a comfortable, homey space off-campus, replete with culinary treats. Brainstorming requires preparation—and more so if a collaborative group has experienced prolonged conflict. One way to foster a creative mind-set is to remove stakeholders from their usual meeting spaces and get into a physical space that will help participants think out of the box. If you look up brainstorming on youtube.com, you will find hundreds of vivid examples of meetings. You will note noisy, energetic spaces. People might be standing, or laying on the floor; the meetings do not look like typical business meetings. You will note the use of color through props such as markers and sticky notes. Mentioned in the case that opens this chapter, it has also become a trend in business to hire graphic artists or to have participants create visual presentations of the outcomes of the brainstorming session. Sometimes the graphics are displayed on a banner of paper that covers the entire length of the room. You might hear music during different times of the brainstorming activity as a way of escalating the energy in the room. You may have people move around and brainstorm at different tables with different partners. Many facilitators hand out candy and sometimes hand-held brainteasers are positioned on the tables to cultivate the creativity needed to think outside of the box. The point is that creative brainstorming requires attention to the setting and environment to stimulate thinking.
Match the Process with the Group Need. Matching processes to group needs is another good rule to follow. How many people will be participating in the brainstorming activity? What type of space do you have? Will the brainstorming take place synchronously, or will it happen throughout the day? Is conflict or transparency an issue with the group? These are a few of the considerations in matching processes to group needs. We teach three brainstorming techniques; for all of them, To Critique or Not. Finally, a fundamental rule to brainstorming is rooted in the early theories of advertising executive, Alex Osborn: avoid critiquing ideas too early in the process so as not to stifle creativity.49 This rule endures today and remains a central part of the process for organizations in the business of innovating.50 It makes sense that a premature critique of ideas runs the risk of quieting participants who will be less likely to contribute if they anticipate they will encounter judgment. The act of premature critiquing not only can change the energy in the room from positive to negative but it can also limit potentially good ideas by stifling the silly. Silly ideas actually play a positive role in creative brainstorming.51 They can be a great tension reliever when passions and preferences surface during the process. If stakeholders feel stuck, a silly idea can propel them forward. They also can spark ideas in stakeholders whose contributions would have never been prompted if not for that silly idea. Accordingly, an important part of the brainstorming process is to express all ideas publicly because they have the potential to “prime” subsequent ideas.”52
However, more recent research has questioned the efficacy of the “no critique rule” and has found that more ideas are actually produced when groups are given the permission to debate contributions openly.53 Before we dismiss the “no critique” rule, let’s consider the context of the argument and the context of interorganizational collaboration. The recent findings regarding the benefits of allowing debate in the brainstorming process are predicated on the assumption that creativity is equivalent to quantity, not quality, of ideas.54 This finding has also been tested in experimental studies using zero-history groups—those participating in the studies have no social or historical relationship of any relevance to the context in which they are brainstorming. This is a very different situation than the one stakeholders in interorganizational collaboration experience. Studies in this area have also clarified that conflict or debate as it is related to the group’s task may be effective in eliciting productivity—provided that criticism is not directed at people.55 In other words, conflict that revolves around ideas may be useful, but conflict embedded in historical relationships would not be. Therefore, whether a group decides to implement the no-critique rule hinges on the level of trust and patterns of relationships experienced by the stakeholders at the time of the brainstorming.
From Solutions to Action
So what happens after idea generation? How do ideas get turned into action? The answer lies in the communication skills of collaborative praxis already developed. As stakeholders come up with ideas, they will eventually have to decide on how to proceed. Those ideas will be measured against the group values (“objective criteria” from chapter 9) and may invoke a new decision-making process. If the solutions that collaborative groups are considering already honor their shared values and interests, it may not be necessary to use consensus-decision making, as the solutions already respect the group’s identity and priorities. In this case, the group may trust a small subcommittee or vote on the solutions that will be implemented. Groups at this stage can divide and conquer the tasks that need to be implemented. Members are likely to feel confident that the group’s values will be fostered by individual stakeholders. However, if the solution generation phase prompts a questioning of group priorities, the iterative process of collaboration calls for the group to engage in consensus decision-making processes to decide on what strategies and tactics can and should be implemented. Thus, groups will draw on their collaborative skills developed throughout the stages of collaboration to move ideas toward action.
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