joint problem solving meaning

Collaborative Negotiation Done Right

October 10, 2014

Selena McLachlan

Collaborative negotiation – 6 important reminders about this win-win approach.

Getting to Yes: How To Negotiate Agreement Without Giving In, by Roger Fisher, was written in 1981, yet remains a best-seller. Why? Because it’s brilliant. Because it’s straightforward. Because it speaks to us leaders who value relationships. It’s a universally applicable method for negotiating personal and professional disputes without getting taken – and without getting angry. The book offers a concise, step-by-step, proven strategy for coming to mutually acceptable agreements in every sort of conflict. And as leaders, we know how invaluable this practice is.

If you’re like many, you’ve probably already read the book. But if you’re like most, you’d probably also benefit from a periodic refresher. If you don’t have several hours to spend, I’ve taken some liberties to summarize the most salient points below.

Collaborative negotiation in a nutshell

Collaborative negotiation – also called constructive, principled or interest-based negotiation – is an approach that treats the “relationship” as an important and valuable element of what’s at stake, while seeking an equitable and fair agreement. As opposed to always conceding in order to sustain the relationship.

A “competitive” approach to negotiation assumes a fixed pie, zero-sum, win-lose situation. In collaborative negotiation, it’s essentially assumed that the pie can be enlarged by finding things of value to both parties, creating a win-win situation, so that everyone leaves the table feeling like they’ve gained something of value.

Fair-process

Unlike most of the animal kingdom, we humans have a profound and deep need for fairness. And when this doesn’t happen – even if we’re the ones emerging as “winners” from a competitive negotiation – the end result is often not truly satisfying. A better feeling, and result, occurs when our needs are met; including the need for fairness.

Joint problem-solving

A collaborative approach to negotiation strives to convert individual wants into a single problem, bringing both parties together to work on solving the problem. The theory stems from the notion that by converting individual positions, wants and desires into separated problems, the negotiators are able to free themselves of any jealously or personal attachment to their requirements, in order to take a more objective and equitable position to collaborate from.

Transparency and trust

While it may not be possible or necessary to give away all of your information, there’s little tolerance for deceptive practices in collaborative negotiation. Moreover, gaining trust will be next to impossible. A simple way to eliminate suspicion is to be open and transparent, giving out most or all of your information (i.e. your wants, desires, end goal) before the other party requests it. The exact opposite of playing your best poker hand!

Dealing with competitive negotiators

So what happens when not everyone is playing by the same rules? Indeed, a huge challenge can occur if the other party takes a competitive approach, and tries to take advantage of your desire to collaborate. Sometimes we’re even perceived by competitive negotiators, to be weak. A proven way to deal with this type of situation is to be assertive and remain calm. Fend off your fight-or-flight reaction, recap your interests and summarize what you heard as their interests. Offer up a bit of an olive branch, while staying strong. And perhaps most importantly, know in advance what your BATNA is (back-up alternative to negotiated agreement), and demonstrate that you’re prepared to use it.

Remember, being a collaborative leader does not mean being weak or giving in. On the contrary, a collaborative approach seeks to gain the best possible solution for all. A true win-win situation. As educators, this means that our teachers, parents, students and school boards can all walk away feeling like they’ve come out winners. Kind of like a good haggle over a cup of tea at a middle-eastern carpet bazaar!

Think about the next time you need to engage your stakeholders in a collaborative negotiation. What’s your starting position? What are you prepared to give up? What are you not? And what’s your fall-back plan?

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Industrial and Commercial Training

ISSN : 0019-7858

Article publication date: 1 February 1978

The joint problem solving process is not just a matter of using a good logical system, or just a matter of effective interaction and sound group processes. It is a complex interplay between ‘social’ and ‘rational’ processes. Kepner and Tregoe, examined a number of successful problem solvers — and found that there was a consistent logical pattern in which they moved from problem definition, to a comparison of the problem situation with the non‐problem situation then on to locating the cause and finally on to some form of positive decision and action plan. Another social scientist, Norman Maier has suggested that effective group processes are important, but that an effective group solution depends largely on the nature of the actual problem; he also gave an account of the rational and group processes in joint problem solving. Others, such as Rensis Likert, believe that problem solving effectiveness is due primarily to supportive group relationships. Another writer, William Gore, attributes successful problem solving to a type of ‘unconscious’ non‐rational process which has to be surfaced and accepted in order to get the best solutions. Alex Osborn pioneered the creative element in problem solving and laid emphasis on brainstorming where the group generates a wide range of alternatives in an unrestricted manner prior to deciding on the best solution to a problem. All these writers have made a valuable contribution to understanding the joint problem solving process and any effective approach to problem‐solving should take serious account of this wide range of approaches. But the approaches are nevertheless very different and may be difficult to reconcile in a unified approach.

MISSELHORN, H. (1978), "Joint problem solving: Building better relationships and better solutions", Industrial and Commercial Training , Vol. 10 No. 2, pp. 60-70. https://doi.org/10.1108/eb003654

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The process of exploring options, developing strategy, identifying barriers, and ultimately solving problems jointly is the most exciting, and often the most challenging aspect of the collaborative governance process. This is the stage that everyone has been preparing for, the reason for the proper framing, working principles, relationship building, and joint discovery that have come before it. Because the problems and challenges that agreement-seeking is trying to address are quite different from those in collective action, the joint problem-solving steps for each type of process will be discussed separately below.

As set forth in chapter 3, policy agreement-seeking processes are focused on reconciling diverse interests to reach agreement on the question of what is to be done. Agreement-seeking is used most often, though not exclusively, when there is a conflict. The major challenges it must overcome include positional bargaining, disparities in power, and trust issues.

When trying to resolve conflicts, parties often resort to positional bargaining, staking out their opposing positions before engaging in a struggle to see who can compromise the least. A developer wants to build 120 housing units, for example, while the neighborhood organization’s position is a maximum of 65. Each may have a less extreme position they would be willing to accept, but they begin the negotiation with their optimum number, giving themselves room to eventually compromise. The problem with this positional bargaining approach in a collaborative governance setting is that it frames the problem as win-lose, making mutual benefit—and agreement—more difficult. Consensus may eventually be reached, but opportunities for greater mutual gain go unrealized. Focusing instead on underlying interests, that is, why the positions were taken, expands the range of potential solutions and opportunities for agreement.

In our example of the proposed residential development, residents’ interests in retaining the neighborhood’s more rural character may conflict with the developer’s interests. However, the neighborhood residents may have other interests as well, such as maintaining a neighborly atmosphere, preserving easy access to commercial services such as groceries and pharmacies, protecting pedestrian spaces, easing traffic circulation, and so forth. Packaging solutions that address multiple interests is one way of finding agreement when a group finds itself stuck on a particular issue.

Another challenge for agreement-seeking processes is disparity in power among participants. These disparities are often created by asymmetries in the interdependence between the parties (Coleman et al. 288). Some parties are simply more dependent on the actions or resources of others. Put another way, if the negotiations do not work out, some parties may have better options than others. When this happens, two conditions often follow that create barriers to agreement. The first is that those who perceive themselves as having relatively good alternatives to a collaborative agreement are likely to be less committed to the process. They will be less willing to work hard to find mutually-beneficial outcomes; in other words, they will be less cooperative. Then, as we discussed in chapter 4, this less cooperative behavior will elicit distrust and, therefore, less cooperative behavior in return. Addressing this challenging cycle is important to success.

Some would say the biggest challenge to agreement-seeking, particularly in situations requiring resolution of historic conflicts, is distrust. We previously discussed in chapter 4 the role that trust plays as social capital, enabling exchanges to happen between parties. When there is an atmosphere of distrust, however, the exchanges necessary for agreement-seeking become far more difficult. Therefore, the joint problem-solving approach needs to take into account the levels of trust between parties, and incorporate procedural elements that can either repair trust or create situational trust through contingencies, joint monitoring, and other measures. We discuss some of these procedural elements below, and delve into them further in chapter 9.

To solve the problem of reconciling diverse interests, and addressing the challenges presented above, we focus on three problem-solving steps for agreementseeking projects: developing criteria together, exploring potential options, and jointly evaluating those options against the criteria.

The problem-solving process can often be aided by first developing objective criteria for evaluating solutions. These criteria should derive from the parties’ interests and incorporate legal and other constraints for potential solutions. They will be most helpful if they are as objective, straightforward, and measurable as possible. Subjective criteria, such as “best looking design,” may elicit entirely different responses, depending on the stakeholder making the judgment. More helpful and measurable criteria might be a ranking based upon cost or greenhouse gas emissions.

The establishment of objective criteria, as Carpenter and Kennedy point out, contrasts with the often common approach of every party evaluating each option based upon how closely it resembles their own proposal (53). These objective criteria, however, should be used to help the group reach consensus, rather than to rigidly constrain it, or prohibit creative solutions. The criteria should be intended to clarify trade-offs and develop the best solution, or package of solutions, rather than dictate what the solutions should be.

The joint development of criteria is also an opportunity to shift the focus to underlying interests by incorporating the various interests in the criteria. In the Columbia River project discussed earlier, for example, the agreed-upon criteria for the placement of dredged material included economic development impacts and ecological impacts, as well as other technical criteria. Particularly in high conflict situations, when each party sees their interests reflected in the criteria adopted by the group, the belief that “we’re in this together” is reinforced. Similarly, when there are disparities in power among the participants, having all interests reflected in the criteria can help foster a sense of belonging and can help ensure more equitable outcomes.

Before a group begins the process of moving toward a decision, it is often helpful to engage in creative thinking, opening up the range of possibilities by brainstorming ideas. Susskind and Cruikshank call this process “inventing.” It is important to refrain from jumping to evaluation during this brainstorming step to discourage group members from immediately responding with—that will never work! Get as many ideas out on the table as possible, expanding the range of possible solutions, before beginning the process of winnowing them down. As Susskind and Cruikshank say, “the more good ideas, the better” (90).

In the Tillamook flooding project described in chapter 1, the group consisted of federal and state agencies, community groups, landowners, environmental groups, and local governments. All participants, it seemed, had different ideas about the best solution. They developed an early list of eighteen actions, from wetland restoration and channel widening to dredging the bay. That list of actions served a unifying function, representing everyone’s ideas. While they subsequently identified clear priorities for action, and some of the ideas have yet to be implemented, none of the original ideas were taken off the list.

As the joint problem-solving process moves toward the evaluation of options and developing agreements, a second phase of brainstorming may be needed, this time to brainstorm possible packages of solutions.

As noted previously, the evaluation of options is intended to aid the decisionmaking process rather than constrain it, to focus the discussion so that decisions can be made. In the Eastern Oregon water policy process discussed in chapter 6, where the group was attempting to improve conditions for migrating fish as well as increase irrigation water for farmers, the brainstorming phase created a full range of options, based upon the group’s fact-finding and technical analysis. After the group went through the first round of evaluations, however, there was a clear separation between the top nine options and the others that followed, and the group chose to focus their deliberation on the nine that had the best chance of being approved by the whole group.

Evaluating the potential options using the adopted criteria, as described above, is one of several techniques that can be used for winnowing options. Another method for moving toward agreement is to agree first on a general plan or principles, and then to dive into a deeper round of negotiation on the details. A third approach is to develop a single negotiating text, which becomes the starting point for parties to make revisions and additions until they find agreement. As we stated at the beginning of this chapter, there is no unified approach that fits all collaborative processes.

As we have previously emphasized, the people at the collaborative governance table are representatives of organizations or constituencies. As such, they need to take the critical step of keeping their constituencies informed and up-to-date. When moving toward agreement, it is critical that the representatives have checked in with their constituencies along the way to ensure that those constituencies understand the new information gathered in the fact-finding stage, their interdependence with others at the table and in the community, and the alternatives to a collaborative outcome. The representatives at the table should also bring interests, information, and concerns back from their constituencies to the collaborative group so that those interests and concerns can be considered as the group moves toward agreement. A reminder of this responsibility by the group’s convener or facilitator can ensure that a broad range of interests is considered and that the agreement will not be unraveled because the essential constituencies were not fully consulted and represented.

The basic problem that a collective action process is trying to solve is fundamentally different than an agreement-seeking process. The goal of collective action is to create a public good. The problem is that no one party has the authority, expertise, or resources to do so on its own. Because the basic problem to be solved is quite different than that for agreement-seeking, it is not surprising that the principal challenges are different as well.

The classic and most well-documented challenge of collective action is the problem of free riders. Because a public good can, by definition, be enjoyed by everyone, every party has an incentive to take a free ride, that is, enjoy the benefits while leaving others to step up and contribute to those benefits. The paradox is that if everyone acts on that incentive, no one contributes, and no public good is produced. Think about a public transit system that depends upon the honor system for riders to pay their fare before riding. Each rider may ultimately think they will get to ride whether they pay or not, and, therefore, choose to ride for free. But if all riders do that, the transit system itself will not survive, and nobody will get to ride. This problem of free riders becomes more pronounced the larger the group and the smaller each party’s relative contribution.

Another key challenge to collective action stems from the horizontal nature of collaborative relationships. Even when parties understand they need the help and cooperation of others, they are often wary of entering into an enterprise where their success is dependent upon the actions of those they can’t control. This can result in either a lack of commitment to the collaborative process and reduction in their own contribution or an attempt to control the process and others who are participating in it. Both become challenges to the success of the enterprise.

One of the more difficult challenges that can face a group trying to initiate collective action is the lack of what we call a principal implementing party. Nearly all projects or programs require someone to step up to take on a kind of principal administrative or coordinating role, convening the other partners when needed to address an unanticipated problem, for example. Without someone playing this role, there is no foundation for others to add to. We discuss this particular challenge in more detail in the next chapter.

Given this different set of challenges, the joint problem-solving phase for collective action, therefore, involves a slightly different series of steps and questions designed to solve the specific problem and challenges surrounding the creation of a public good.

The first step is usually development of a preliminary strategy for solving the problem. Once a group agrees on the initial strategic approach, it can then make an initial assessment of what potential resources are represented at the table. We normally help groups make this assessment by simply asking each party at the table in turn why they support the project and what they might be able to contribute. For example, in a project to improve the structural integrity of the Columbia River levee system in Portland, Oregon, participants first agreed upon a general strategy of conducting engineering studies and sharing the costs of levee improvements among a number of public jurisdictions.

The initial strategy and commitments provide the group with a starting point, and a road map for the work ahead. Depending upon the resources available, the problem-solving strategy may need to be revised over time. Most often, the details of the strategy need to be filled into more specifically identify the resource needs (discussed further in the section below). More important, the group needs to identify remaining barriers, challenges, and what resources are missing or might be added to complete or improve the project. Adding details to the problem-solving strategy, identifying gaps and opportunities, and finding missing resources then become the collaborative problem-solving work for the group.

While there is often broad agreement about the general strategy, moving to implementation usually requires greater detail to better clarify the actions and resources actually needed to make implementation successful. Groups will often charge committees with tackling the detail of various parts of the initial strategy, bringing recommendations or options back to the larger group. The collaborative group working on the Portland levee project may have found agreement on the general strategy relatively easy, but the devil was in the details. A committee of key stakeholders subsequently spent months detailing, negotiating, and vetting the cost-sharing formula before finally bringing it back to the larger group for approval.

Depending upon the type of collective action (fixed goal, incremental improvement, or coordinated interdependent actions) required, the group must identify what resources or actions are needed. What are the political or resource challenges? Potential resources at the table may have been identified earlier, but they now need to be quantified and reaffirmed. As the group approaches success and gets more resources committed, the effort to fill the gap gets more and more targeted. In the Vernonia school project, the known price tag was $38 million to replace three schools in the community that had been destroyed by flooding. At every meeting, the group would report new commitments, and the gap would get smaller. In the rural transportation project discussed in chapter 4, every meeting would include reports of additional resource commitments, but the need for a principal implementing party was still reported as a gap, until ultimately that gap was filled. In addition to filling gaps, a collective-action group should also identify opportunities. There may be ways to magnify the impact of a project by adding additional resources or actions.

Finding and aligning the needed resources and other commitments to create a public good is the essential problem of collective action. A good starting point is to identify who might particularly benefit from the public good, as these are the stakeholders who have a vested interest in making sure the effort succeeds. Economists would argue that the degree to which a party benefits should be relatively proportional to the degree to which the party is willing to contribute to ensure that benefit.

In one project that we facilitated, the City of Eugene, Oregon, approached our center to convene a collaborative process to transform unsightly riverside gravel pits into an urban natural area with pedestrian trails and viewing platforms surrounding scenic ponds that fill during the high-water months. When the project—which became known as the Delta Ponds project—began, one of the first steps was to identify and contact the owners of property abutting the ponds. If the project was successful, the city reasoned, those property owners would not only enjoy the improved amenities, but also have the value of their own properties substantially increased. Each of those property owners had an incentive to help make that project successful, and many of them ended up contributing to the project’s success.

Successful groups also look for how they can combine their individual assets to create greater public value. If one party has already committed actions or invested resources to address a problem, for example, others at the table may be able to add additional resources to boost that effort. By piggy-backing on the existing commitments, group members can produce something bigger, better, faster, or cheaper.

The challenge, as we described above, is the free-rider problem, and each party’s fear that others will be free riders. If one party steps forward, they risk having others take less responsibility for the solution. This risk is real, and often prevents parties from stepping forward to fill the necessary gaps. One way to deal with this problem is to arrange for joint or simultaneous commitments. The transparency of the collaborative governance process, with its face-to-face interactions that encourage accountability to the group, can foster these joint commitments. It is, in fact, one key advantage of transparency. When multiple parties make a commitment in the same meeting, it starts to become a group norm, and other commitments are likely to follow. The research is clear that creating a group norm of contributing is an effective way to solve the free-rider problem (Ostrom 9). It is one of the reasons we recommend against having a significant number of interested stakeholders with no incentive or ability to contribute participate in collective action projects. The more non-contributors are involved, the more difficult it becomes to create a group expectation that everyone contributes, paving the way for free riders.

Another strategy for reducing the incidence of free riders is for participants to make contingent offers. For example, I will commit my organization’s staff time, other organizations can commit financial resources to pay for the materials. We have seen parties successfully leverage their resources by making them contingent on a commitment by others. In the Lakeview project discussed in chapter 4, one company offered to construct a new mill that would process small-diameter logs, creating needed jobs in the community. That offer, however, was contingent upon federal agencies guaranteeing a supply of small-diameter logs. Both commitments were ultimately kept.

One of the most effective antidotes to the free-rider problem is creating a sense of momentum. One way to engender belief in potential success is to recognize and celebrate resource commitments as they are made rather than waiting until the end of the process. This approach not only provides greater belief in the enterprise, it reinforces the group norm that everyone contributes. We have observed that when a certain critical mass of resources and support starts to accumulate, increasing the chances of success, other parties become more willing to contribute. For example, in the project in which the neighborhood was working with the city to build a community bike park, the neighbors were raising money to build the park with mostly small donations obtained through a crowdfunding platform. As they got closer to their goal, they held a press conference, and the media reported their success. Then, seemingly out of the blue, a major corporation—with no previous connection to the project—made a $25,000 contribution. Everyone, it seems, wants to be associated with a winner.

These unexpected synergies are why it is important to mark success along the way and to publicly recognize the contributions or efforts of various parties. We’ve seen project teams utilize news articles, widely-distributed newsletters, joint appearances, or almost any opportunity to celebrate and recognize their collaborative success. The project to repair the Portland Columbia River levee, described earlier, began with a relatively narrow geographic scope. After nearby jurisdictions learned of the successful initial stage of the project, however, they soon petitioned to join the group. They did not want to be left out of a good thing.

What should be done if the needed actions and resources are not found around the table? If needed resources can be identified outside the group, the parties that control those resources should be invited to join the group. Indeed, one of the questions the group should ask itself in the early stages of the collective action process is: Who else can help? Celebrating the resources that have already been harnessed can help in that regard. The project to rebuild the Vernonia schools began with a number of substantial commitments, not least of which was the passage of a local bond measure that raised $13 million. Still, the group faced a major gap, and began to approach private foundations and businesses that had not previously been at the collaborative table. Eventually, this effort to enlarge the circle of contributors filled the gap.

Whether for agreement-seeking or collective action joint problem-solving is truly the heart of the collaborative process, one which hopefully leads to a group decision. It is to that decision-making process that we next turn our attention.

Collaborative Problem Solving: What It Is and How to Do It

What is collaborative problem solving, how to solve problems as a team, celebrating success as a team.

Problems arise. That's a well-known fact of life and business. When they do, it may seem more straightforward to take individual ownership of the problem and immediately run with trying to solve it. However, the most effective problem-solving solutions often come through collaborative problem solving.

As defined by Webster's Dictionary , the word collaborate is to work jointly with others or together, especially in an intellectual endeavor. Therefore, collaborative problem solving (CPS) is essentially solving problems by working together as a team. While problems can and are solved individually, CPS often brings about the best resolution to a problem while also developing a team atmosphere and encouraging creative thinking.

Because collaborative problem solving involves multiple people and ideas, there are some techniques that can help you stay on track, engage efficiently, and communicate effectively during collaboration.

Set Expectations. From the very beginning, expectations for openness and respect must be established for CPS to be effective. Everyone participating should feel that their ideas will be heard and valued.
Provide Variety. Another way of providing variety can be by eliciting individuals outside the organization but affected by the problem. This may mean involving various levels of leadership from the ground floor to the top of the organization. It may be that you involve someone from bookkeeping in a marketing problem-solving session. A perspective from someone not involved in the day-to-day of the problem can often provide valuable insight.
Communicate Clearly.  If the problem is not well-defined, the solution can't be. By clearly defining the problem, the framework for collaborative problem solving is narrowed and more effective.
Expand the Possibilities.  Think beyond what is offered. Take a discarded idea and expand upon it. Turn it upside down and inside out. What is good about it? What needs improvement? Sometimes the best ideas are those that have been discarded rather than reworked.
Encourage Creativity.  Out-of-the-box thinking is one of the great benefits of collaborative problem-solving. This may mean that solutions are proposed that have no way of working, but a small nugget makes its way from that creative thought to evolution into the perfect solution.
Provide Positive Feedback. There are many reasons participants may hold back in a collaborative problem-solving meeting. Fear of performance evaluation, lack of confidence, lack of clarity, and hierarchy concerns are just a few of the reasons people may not initially participate in a meeting. Positive public feedback early on in the meeting will eliminate some of these concerns and create more participation and more possible solutions.
Consider Solutions. Once several possible ideas have been identified, discuss the advantages and drawbacks of each one until a consensus is made.
Assign Tasks.  A problem identified and a solution selected is not a problem solved. Once a solution is determined, assign tasks to work towards a resolution. A team that has been invested in the creation of the solution will be invested in its resolution. The best time to act is now.
Evaluate the Solution. Reconnect as a team once the solution is implemented and the problem is solved. What went well? What didn't? Why? Collaboration doesn't necessarily end when the problem is solved. The solution to the problem is often the next step towards a new collaboration.

The burden that is lifted when a problem is solved is enough victory for some. However, a team that plays together should celebrate together. It's not only collaboration that brings unity to a team. It's also the combined celebration of a unified victory—the moment you look around and realize the collectiveness of your success.

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Make Negotiating Easier by Approaching It as “Joint Problem Solving”

For some people, negotiating comes naturally. For the rest of us, it can feel intimidating, awkward, and slightly confrontational. If this rings true for you, and you have trouble negotiating, try approaching it as “joint problem solving” instead.

Harvard Business Review says that approaching a negotiation as a confrontation actually helps ensure that it will be confrontational. Here’s the alternative they suggest:

Instead, approach it as an act of joint problem-solving: What are the critical issues at hand, what are my interests and their interests, and what are some different possible options for satisfying those various interests?

Instead of focusing on what either of you will have to give up, focus on a creative solution. Of course, you don’t want to be a complete pushover during the negotiating process, either. Just keep in mind—joint problem solving includes your needs, too. This perspective can make it a little easier to negotiate when you’re not a fan of it in the first place. For more detail, check out the full post, below.

How to Negotiate Nicely Without Being a Pushover | HBR

Photo by Flazingo Photos .

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Collaborative Problem Solving and Consensus Building

By Cate Malek

Based on a longer essay on Consensus Building written by Brad Spangler and Heidi Burgess for the Intractable Conflict Knowledge Base Project

Updated February 2013 by Heidi Burgess

Definition:

Collaborative problem solving and consensus building are processes that groups use to make plans, solve problems, develop recommendations, or make decisions in a collaborative (or cooperative) way, rather than in isolated, competitive, or confrontational way.

Collaborative processes can be used with any group of people who face a common problem. They are often used in schools, businesses, communities, organizations, and all levels of government agencies to set policy and to resolve disputes.

Description:

In a collaborative problem solving and consensus building process, representatives of all the necessary parties with a stake in an issue work together collaboratively. Participants make a good faith effort to meet the interests of all participants and to make plans, recommendations, and decisions, that if not unanimous, at least everyone can live with. These processes are usually designed by the participants themselves (often with the help of a facilitator or a mediator). This allows for considerable flexibility about who can participate, how, and what outcomes will be considered. However, these processes tend to move through common stages.

They begin with an assessment of the situation. The goal is to gather information on what the issues are, what the barriers and incentives are, which parties should be included, the time frame for the process, how people will work together, and the desired outcomes.
The parties then decide who will lead the process and how the meetings will be managed. They may decide they can do this themselves, or they may seek the assistance of an outside, impartial third party to manage the process.
They develop ground rules.
They decide what information they need to plan, solve problems, develop recommendations, or make decisions.
They collect, exchange, verify, and assess information, frame the issues, and discuss points of view.
They generate and evaluate different options, negotiate, and move toward the desired goal of the process.
Finally, parties check whether the plans, agreements, recommendations, or decisions will work for all authorized decision makers and whether they are implementable.
At this stage the plan, the decision or agreement is formalized and the group develops plans to implement and monitor the outcome.

There are 10 ingredients common to all successful collaborative problem solving and consensus building processes. These are:

inclusion of all affected stakeholders,
incentives to participate,
effective representation and clear accountability,
agreement on the scope of the process,
establishment of clear objectives,
provision of sufficient resources,
opportunities for learning and capacity building,
full participation and communication,
careful, consistent and continual process management, and
clear connections between the process and how outcomes are implemented.

Collaborative problem solving processes are often used in natural resource planning. In the United States, the U.S. Forest Service has to formulate forest management plans. These plans are frequently controversial, as different forest user groups: timber companies, jeepers, snowmobilers, hikers, skiers, and environmental protection advocates (among many others) have very different interests and concerns. Increasingly the Forest Service has brought these groups together in collaborative processes in an effort to develop plans that are accepted -- even embraced -- by all these groups. Another example is the National Wind Coordinating Collaborative which is a collaborative group that seeks to collaboratively develop environmentally, economically, and politically sound wind power in the US.

Next Steps:

People interested in pursuing a consensus process should do some reading to learn more about how these processes work. They then might approach leaders of different interest groups to try to assess their level of interest in such an endeavor. If some people are interested, it is often possible to start talking with them, and then enlist their help to bring more people into a process.

Links to Related Articles:

Environmental Policy Dispute Mediation Facilitation

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Phases of collaborative mathematical problem solving and joint attention: a case study utilizing mobile gaze tracking

Original Paper
Open access
Published: 09 June 2021
Volume 53 , pages 771–784, ( 2021 )

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Jessica F. A. Salminen-Saari ORCID: orcid.org/0000-0003-1329-5912 1 ,
Enrique Garcia Moreno-Esteva 1 ,
Eeva Haataja ORCID: orcid.org/0000-0003-2466-4576 1 ,
Miika Toivanen ORCID: orcid.org/0000-0003-1258-4237 2 ,
Markku S. Hannula ORCID: orcid.org/0000-0003-4979-7711 1 &
Anu Laine ORCID: orcid.org/0000-0003-3881-8134 1

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Given the recent development of mobile gaze-tracking devices it has become possible to view and interpret what the student sees and unravel the associated problem-solving processes further. It has also become possible to pinpoint joint attention occurrences that are fundamental for learning. In this study, we examined joint attention in collaborative mathematical problem solving. We studied the thought processes of four 15–16-year-old students in their regular classroom, using mobile gaze tracking, video and audio recordings, and smartpens. The four students worked as a group to find the shortest path to connect the vertices of a square. Combining information on the student gaze targets with a qualitative interpretation of the context, we identified the occurrences of joint attention, out of which 49 were joint visual attention occurrences and 28 were attention to different representations of the same mathematical idea. We call this joint representational attention. We discovered that ‘verifying’ (43%) and ‘watching and listening’ (35%) were the most common phases during joint attention. The most frequently occurring problem solving phases right after joint attention were also ‘verifying’ (47%) and ‘watching and listening’ (34%). We detected phase cycles commonly found in individual problem-solving processes (‘planning and exploring’, ‘implementing’, and ‘verifying’) outside of joint attention. We also detected phase shifts between ‘verifying’, ‘watching and listening’, and ‘understanding’ a problem, often occurring during joint attention. Therefore, these phases can be seen as a signal of successful interaction and the promotion of collaboration.

A Historical Review of Collaborative Learning and Cooperative Learning

Making Sense of Generative Learning

Cognitive Architecture and Instructional Design: 20 Years Later

Avoid common mistakes on your manuscript.

1 Introduction

Collaborative learning affects achievement, attitudes, and perceptions positively (Kyndt et al., 2013 ). Joint attention, that is the ability to focus attention on the same thing simultaneously with other people and to acknowledge it, develops already in infancy (Corkum & Moore, 1995 ). This ability allows us, for example, to engage in meaningful interactions and to learn from others (Tomasello, 1995 ). The recent development of mobile gaze-tracking devices has made it possible to study nonverbal interaction more precisely in an authentic context. Mobile gaze-tracking allows us to view what the participant sees and focuses on. With mobile gaze-tracking it is easier to determine, for example, which gestures are relevant and catch the attention of the participant. It also gives exact information on the direction of gaze during interaction. There are studies concerning campus walks (Foulsham et al., 2011 ), science learning center visits (Magnussen et al., 2017 ), teacher attention in classrooms (Haataja et al., 2021 ; McIntyre et al., 2017 ; Prieto et al., 2017 ) and collaboration in student dyads (Schneider et al., 2018 ).

Even though many have studied collaborative problem-solving process over the years (e.g., Artzt & Armour-Thomas, 1992 , 1997 ; Roschelle & Teasley, 1995 ), there is still uncharted territory left to investigate. The quality of interaction is important for successful collaborative problem solving (Barron, 2003 ). Gaze is an important part of non-verbal interaction, for the purposes of observing what the others are doing, and of communicating one’s own intentions (Cañigueral & Hamilton, 2019 ). Joint attention is one part of fruitful collaboration, and we need to learn more about how and when it happens. Mobile gaze tracking lets us analyze in detail the eye movements of participants during mathematical problem-solving processes. This allows us to pinpoint the phases of collaborative problem-solving during which joint attention arises. Joint attention allows us to assess the importance of different phases of the collaborative problem-solving process. During which phases do students share joint attention, e.g., during which phases do they learn from each other, and engage in scaffolding together.

Educational researchers have studied joint attention mostly in the context of visual attention. Joint visual attention requires a shared target of visual attention. However, mathematics is full of abstract ideas and representations. In fact, Kaput ( 1987 ) claimed that mathematics can be seen as the discipline that studies representation of one structure with another one. As such, representations are also an integral part of mathematical problem-solving. Because representations inevitably are also part of the interaction during collaborative mathematical problem solving, we might need not only to focus on visual attention but also to acknowledge the role of multiple representations when studying joint attention in the context of mathematical problem solving. In this paper, we examine the nature of joint attention in interactions that evolved in doing mathematics. We then examine which phases of collaborative problem solving occurred during and right after joint attention.

1.1 Phases of mathematical problem-solving processes

For problem-solving, we use the definition commonly used in mathematics education: problem-solving happens when the solver does not know how to carry out a task with familiar or routine procedures (Schoenfeld, 1983 ). In collaborative problem solving, the collaborators need to build a shared space of understanding in a joint problem-solving space (Roschelle & Teasley, 1995 ). A joint problem-solving space includes socially negotiated sets of knowledge elements, such as goals, problem-state descriptions, and problem-solving actions (Roschelle & Teasley, 1995 ). Roschelle ( 1992 ) argued that one of the key factors in the creation of a joint problem-solving space is the presence of repeated cycles of displaying, confirming, and repairing understandings.

Whereas the individual problem-solving process can be described as cyclic (e.g., Carlson & Bloom, 2005 ; Pólya, 1945 ; Schoenfeld, 1985 ), the collaborative problem-solving process is more unpredictable (Artzt & Armour-Thomas, 1992 ). In collaborative problem-solving, individuals bring ideas into a collaborative space. The group constructs knowledge via interaction (Roschelle, 1992 ). The interaction helps the individual through ideas, structuring the problem, and verifying the correctness of the plausible solutions. However, it also disrupts the cyclic process of the individual (Artzt & Armour-Thomas, 1992 ).

Various researchers have identified somewhat similar, yet different phases in a problem-solving process. Table 1 shows these phases according to various authors, together with our version, and their relation to each other.

The earlier model for studying collaborative problem solving by Artzt and Armour-Thomas ( 1992 ) was based on Schoenfeld ( 1985 )’s framework, which in turn was founded on Pólya’s ( 1945 ) model. Based primarily on Artzt and Armour-Thomas’s ( 1992 ) framework, but also adopting ideas from other frameworks, we built a framework that is better suited for analyzing the phases appearing during engagement with a geometric mathematical problem-solving task in a small group. In what follows, we describe the framework.

Orienting The first stage in Pólya’s ( 1945 ) framework was understanding the problem , which he later (Pólya, 1973 ) divided into getting acquainted and working for better understanding . Other researchers call the first stage orienting to the problem (Carlson & Bloom, 2005 ) or reading the problem (Artzt & Armour-Thomas, 1992 ; Schoenfeld, 1985 ). As the problem used in this study includes hardly any textual information, we decided to call this opening stage orienting . During this phase, the problem solver gets acquainted with the problem. Thus, it does not entail any collaboration and is unlikely to lead to joint attention.

Understanding the problem As did Pólya ( 1973 ) and Artzt and Armour-Thomas ( 1992 ), we also separated the phase working for better understanding of the problem from the phase getting acquainted with the problem . Whereas orienting can happen only when the problem-solver sees or hears the problem for the very first time, the phase understanding the problem does not have a fixed position in the problem-solving process timeline. This phase occurs when the problem-solver considers linguistic, semantic, and schematic attributes of the problem in his or her own words, and represents the problem in a different form (Artzt & Armour-Thomas, 1992 ).

Planning and exploring Whereas Pólya called the planning phase devising a plan , we call this phase planning and exploring, combining the terminology used by Schoenfeld ( 1985 ), Carlson and Bloom ( 2005 ), and Artzt and Armour-Thomas ( 1992 ). Artzt and Armour-Thomas ( 1992 ) considered calculations and diagrams as reference points to separate the phases of analyzing , planning , and exploring . We expected the problem-solvers mainly to discuss and produce drawings. Due to the nature of our data, it was impossible to separate these phases similarly to Artzt and Armour-Thomas ( 1992 ).

Implementing Pólya ( 1945 ) called this phase carrying out a plan . We call it implementing, similarly to Schoenfeld ( 1985 ) and Artzt and Armour-Thomas ( 1992 ). During this phase, the student carries out the plan and comes up with a possible solution, a drawing.

Verifying Even though Pólya ( 1973 ) did not have separate phases for verifying and watching and listening , they are present in the description of the phase looking back . He defined this phase as reviewing the solution and possibly discussing it. We have divided this phase into two phases: verifying (defined as reviewing) and watching and listening (defined as following a discussion about the problem or solution), as was done by Artzt and Armour-Thomas ( 1992 ). During the verifying phase, the problem-solver checks to see if the solution satisfies the problem’s conditions or explains to others how he or she obtained the solution.

Watching and listening The phase watching and listenin g happens when the problem-solver is attending to others’ ideas and work (Artzt & Armour-Thomas, 1992 ). During this phase, the problem-solver is attentive towards a fellow collaborative problem-solver actively participating in the collaborative problem-solving process. The observed problem-solver is actively trying to communicate his or her thoughts to the group.

1.2 Interaction during mathematical collaborative problem-solving

Interaction is necessary for the collaborative problem-solving process (Barron, 2003 ). Without successful interaction, it is nearly impossible to construct a shared space of understanding in a joint problem space (Roschelle & Teasley, 1995 ).

During interaction, people observe others' non-verbal signals, using them to estimate their level of attention (Clark & Schaefer, 1987 ). People use non-verbal signals to communicate understanding and they use gestures especially when explaining abstract ideas (McNeill, 1992 ). We define gestures following McNeill ( 1992 ): gestures are the movements of the hands or arms in space or on objects.

Non-verbal interaction, such as nods, smiles, and gestures, can enhance learning during problem-solving (Rouinfar et al., 2014 ). McNeill ( 1992 ) identified four types of hand gestures: beat, deictic, iconic, and metaphoric gestures. Kita and Davies ( 2009 ) combined the categories iconic and deictic, calling all those gestures representational. They observed that representational gestures could change their forms flexibly depending on the communicative and linguistic context. They also found that the representational gestures' occurrence rate was higher the more challenging the mathematical representation (the diagram) was to describe.

Representations have a central role in collaborative mathematical problem solving. Mathematical ideas are abstract in nature (Radford, 2008 ), and to communicate an abstract idea requires a reference to a representation. External representations are the external embodiments of participants’ internal conceptualizations (Lesh et al., 1987 ) that other people can observe. Understanding the role of representations and representation systems in mathematical problem solving has interested many researchers over the years (e.g., Goldin, 1998 ; Lesh et al., 1987 ). Later research has highlighted that also non-verbal signs, such as gestures, are important forms of representation.

In learning interaction, the students interpret the learning of content meanings by following the teacher’s verbal instructions, gaze cues, and gestures simultaneously (Jarodzka et al., 2013 ; Shvarts, 2018 ). Tracking the participants’ gaze allows us to follow gaze cues and gestures of the participants accurately.

1.3 Joint attention

Joint attention is a social phenomenon. When two or more individuals know that they are attending to something in common, they experience joint attention (Tomasello, 1995 ). In group work, joint attention supports interaction (Barron, 2003 ; Mercier et al., 2017 ). As joint attention entails the capacity to coordinate attention with a social partner, it is fundamental for learning (Mundy & Newell, 2007 ). However, we have not found any studies on how joint attention affects the collaborative mathematical problem-solving process, nor on what the characteristics of joint attention are in the context of interaction evolved around mathematics.

Joint visual attention is understood traditionally as attending to the same target and acknowledging this shared perception (Emery, 2000 ). For example, the participants are looking at an apple and discussing it (Fig. 1 ). In mathematics, instead of an apple, the target could be a diagram, a problem, or a solution in a particular place, e.g., on the board or in a notebook.

Joint visual attention (based on Emery, 2000 )

Joint attention need not be only joint visual attention. Moore ( 2013 ) has written about how the onset of symbolic linguistic representation during the second year of life enables interactions around absent or even nonexistent objects. Such interaction is possible when the participants understand the non-visible object roughly the same way, e.g., they have a similar image of it in their minds, for example, an apple. He refers to this mental image as representation. During this kind of interaction, the participants do not share joint visual attention. However, their attention is on the same thing around the same time, and they acknowledge it. Moore ( 2013 ) refers to this kind of interaction around non-visible objects as joint representational attention . We suggest that joint representational attention can be found also in the interaction focusing on mathematics.

We think about Tomasello’s ( 1995 ) “something in common” through representations and limit ourselves to a collaborative mathematical problem-solving situation where students work with different diagrams, some of which may represent the same mathematical idea. We hypothesize four possible joint attention situations where the participants do not look at the same diagram:

the participants are discussing a solution and looking at the representation of this solution, each in their own notebook (Fig. 2 );

the participants are discussing a possible solution that each can visualize in their minds in similar ways (Fig. 3 );

one participant explains a solution through representational gestures to others, for example, by drawing a diagram into air, and they are visualizing the solution in their minds (Fig. 3 );

one participant looks at a diagram and another is talking about the diagram.

In this study we were interested in finding out if these situations are part of the joint representational attention phenomena described by Moore ( 2013 ).

Attention to the same representation and acknowledging it

Attention to the same representation through discussion or representational gesture

1.4 Observing joint attention

Visual attention is often intentional (Tomasello, 1995 ). Therefore, joint visual attention offers information also about the intentions of the participants. Gullberg and Holmqvist ( 1999 ) emphasized that unless a visual target falls in our field of foveal vision, which is the small area of acute visual perception (Campbell & Green, 1965 ), we are not able to read symbols or to detect facial expressions or gestures. Gaze patterns are not the only indicator of cognitive attention, but as such, they are necessary (Gullberg & Holmqvist, 1999 ).

Emerging verbal and kinetic practices affect the direction of the gaze. For example, speech can direct the observer’s visual attention towards the speaker, after which gestures and other body movements become detectable. They can further direct the observer’s gaze. When the observer is already fine-tuned to the subject, speech is not necessary for directing the observer’s gaze (Stukenbrock, 2018 ). Student gaze direction is an essential indicator of the target of their attention when they are silent (Hannula & Williams, 2016 ).

The methods to study joint visual attention in small groups are only developing. However, some methods have already been developed for dyadic joint visual attention. Jermann et al. ( 2011 ) introduced a method that utilizes what they call cross recurrence graphs for tracing joint attention. This method works best when there are only two participants, and therefore, it is not suitable for groups with more than two people. Schneider and Pea ( 2014 ) developed this method further and introduced network representations to study gaze in collaborative learning. This method is also optimal for only two participants. We have developed a new method to study joint visual attention with more than two people, which we describe in the methods section, and further in the electronic supplementary material.

1.5 Research aim

While most of the previous research has usually examined collaborative problem solving through the discourse in the group and questionnaires (Greiff et al., 2013 ), we see that non-verbal interaction is also relevant. Therefore, we investigate both verbal and non-verbal interaction during collaborative problem solving in a regular classroom. To make joint attention visible for our investigation, we gave students a drawing problem.

We see the importance of investigating the idea of joint representational attention in mathematical problem-solving in order better to understand joint attention in the context of mathematics. As previous research on the topic is limited, our study is explorative. Our research aim is to understand the nature of joint attention in mathematical problem solving and its effect on the collaborative problem-solving process:

What characterizes joint attention in the context of mathematical problem solving?

Which phases of the collaborative problem-solving process occur during and right after joint attention?

2.1 Participants

The data were collected during a grade nine mathematics lesson in a Finnish comprehensive school from a class of 22 students. The participants were four 15–16-year-old students. The rest of their class (18 students) was present and participated actively in the lesson but did not wear the gaze tracking devices. The four participants, three boys and one girl, were selected among volunteers.

2.2 Apparatus

We recorded the lesson using audio recording and three video cameras in the classroom. Two of the video cameras were pointed towards the students, and one camera followed the teacher. The teacher wore a mobile gaze-tracking device and a personal mobile microphone. Ambient microphones placed in the classroom recorded student voices. The four participants used a smartpen that recorded their drawings as a video and served as a personal microphone. We recorded students’ eye movements with mobile gaze tracking devices.

The gaze tracking devices, the algorithms, and software were developed in the Finnish Institute of Occupational Health (Toivanen et al., 2017 ) and manufactured in a lab at the University of Helsinki. The accuracy of the device is approximately 1.5 degrees of the visual angle.

The device consists of a glasses-like frame equipped with electronics and three mini-cameras connected to a computer that was carried in a backpack (see Fig. 4 ), allowing the participants to move. The software in the computer records the video frames and produces a video of the scene camera, superimposed with a gaze point.

Mobile gaze tracking gear and research setting. The light around the eyes in the picture is infrared light and invisible to the naked eye

The frame rate of the video camera varies according to the amount of light; optimally, it is 30 fps. The video frames of each device were recorded with synchronized time stamps.

2.3 Procedure for data collection

Before the lesson started, the teacher was instructed not to provide hints about the optimal solution, but instead, to encourage students to keep trying and to ask questions that could help students articulate their ideas.

The students were asked to find the shortest possible way to connect four cities located at the vertices of a square (Fig. 5 ), first on their own, then with a partner, and finally in groups of four. However, our target group started spontaneously collaborating already during the instructions, skipping the stages when they work alone and when they work with a partner.

The illustration shown on the whiteboard to pose the problem

This problem is the four-point version of the Steiner tree problem . For this study, we wanted a mathematics task that (1) would work as a collaborative task for Grade 9 students, i.e., the task should be accessible and provide meaningful solution alternatives; (2) would be challenging enough to generate opportunities for novel insights during the process, and (3) would generate interesting visual representations as potential targets for visual attention. Our piloting of the task indicated that people trying to solve this task generate alternative solution versions (see examples in the Figs. 6 , 7 , 8 ) quite easily, but that the optimal solution is challenging to find (Fig. 9 ). This kind of problem task provided opportunities for Aha! experiences and opportunities to collaborate.

The solution ‘X’

The solution ‘Z’

The solution ‘H’

The optimal solution

2.4 Analysis procedure

To study the similarity of two or more students’ attentional behavior, we developed a gaze synchrony measure. It is a numerical measure that identifies moments when a gaze target overlaps among two or more subjects (see details in the electronic supplementary material). We developed the gaze synchrony measure further, creating an improved and less error prone measure, the Garcia Moreno–Esteva–Salminen-Saari measure of joint attention (GMESS; see details in the electronic supplementary material) to identify joint representational attention. Both methods rely on annotated targets for fixations. Fixations stabilize the retina over a stationary gaze target (Duchowski, 2007 ).

Collaboration can be detected from the gaze, gestures, and speech of the collaborators (Gullberg & Holmqvist, 1999 ; Radford, 2008 ). We identified the periods of collaboration, which we call task-focused sections, based on whether students engaged in discussion about the problem, viewed each other’s diagrams or calculations, or produced diagrams or calculations. These sections were identified from the moment the teacher introduces the task until the moment when the collaborative problem solving in groups of four was over. In these task-focused sections, we annotated the gaze target for all the occurring fixations for each participant, fixations having been detected to within an accuracy of 30 ms. For recording and analyzing annotations, we used the software package ELAN ( 2019 , September). We used the annotated gaze videos to determine the possible occurrences of joint visual attention and joint representational attention during these task-focused sections using GMESS.

We used the gaze-tracking videos and smartpen recordings to determine the phases of collaborative mathematical problem solving for each individual. Our coding scheme follows the model presented in Table 2 . The multimodal data provided us with detailed and accurate access to the microanalysis of the collaborative problem-solving process. The stationary video cameras informed us about the general learning process of the group and the actions of the teacher. We could follow the student’s gaze in detail through gaze-tracking videos. Additionally, the audio recordings from the personal microphones offered us information about the thinking processes of the students through their verbal communication and argumentation. From the smartpen recordings, we were able to track when the solutions were drawn.

In joint visual attention, the participants are looking at the same gaze target. In joint representational attention, the participants are looking at representations of the object or discussing them. In each type of joint attention, it is necessary to map out in time first the gaze patterns of the participants, and then to determine from those the possible occurrences of joint attention. We studied qualitatively the possible occurrences of joint attention. With the external videos, we checked if the participants in the possible joint attention occurrence truly acknowledged the shared perception. Moreover, we used student gestures and speech as indicators for their acknowledgement of the shared perception (see McNeill, 1992 ).

Using the framework justified in Sect. 1.1 . we identified 180 phase changes in the data. We then determined the lengths of the collaborative problem-solving phases for each student using the gaze-tracking videos and smartpen recordings. The gaze tracking videos give detailed information about the students’ gaze. As such, they reveal more about the students’ thinking than the external videos of the situation do. Gaze patterns do indicate cognitive attention, even though they cannot solely be used as an indicator of cognitive attention (Gullberg & Holmqvist, 1999 ). Elan makes it possible to mark the phase changes without a need to categorize them immediately. The first author determined the positions of the phases first without categorizing them. They were interpreted as starting from the moment there was evidence of a particular phase and as ending when there was evidence of another phase or the student disengaged from the problem-solving process. The first author and the third author each categorized the phases independently after discussing the criteria (see Table 2 ). They then discussed phases and their length. In the end, we combined the phases that were categorized to be the same ones occurring without a gap between them. We reached a consensus, and we were left with 166 phases.

During the phase orientin g, the problem solver gets acquainted with the problem and looks at the assignment right after it is presented, before engaging in conversation. The student may look at the problem again also in the later stages, but this is interpreted as understanding the problem or planning, depending on the verbal context around the situation. Due to the solitary nature of this phase, we did not include this phase in the joint attention analysis.

If one of the students asks a question concerning the nature of the problem, looking at the assignment is interpreted as understanding the problem . If there are no questions concerning the nature of the problem, and the student draws a possible solution in his or her notebook shortly after looking at the assignment, the act of looking at the assignment is interpreted as planning and exploring. Note that in these phases, understanding the problem and planning and exploring , the student might be staring at the assignment in his or her notebook. In other words, the student has drawn the four points that are given at the onset of the assignment in his or her notebook. For the phase understanding the problem , the verbal context provides evidence of working towards understanding. Even though understanding can happen also without the verbal context, it is impossible to observe this externally. Only from the comments of the students can we make this interpretation with certainty (Artzt & Armour-Thomas, 1992 ).

The phase planning and exploring is not dependent on the verbal context. During this phase, the student may be silent and look at what others in the group have tried out already, or a student might look at his or her notebook seeking answers. This phase should not be confused with the phase verifying . For the phase verifying , it is necessary that either the student has drawn a possible solution immediately before discussing or comparing it to other solutions, thus shifting to the phase verifying from the phase implementing , or that another student draws attention to his or her own suggestion for a solution. During implementation , the student makes an idea visible by drawing a solution suggestion. For this phase, it is not relevant how correct the solution suggestion is. During verifying , the student explains these ideas to others or silently compares the solution to other solutions.

An important component of interaction and therefore also collaboration is the ability to watch and listen to others. We did not interpret a student as being in the phase watching and listening unless they simultaneously watched another student speaking. If the students did not fulfill any of these criteria (Table 2 ), they were not placed in any of these phases.

Our focus group started working as a group of four from the onset, even though the teacher instructed them to first work alone. Our analysis ends at the time when the teacher made a call for attention and the class started to ponder different solution options collectively. From this timeline, we identified seven sections when the students were focused on the task. We analyzed these task-focused sections in detail. The total length of these sections was 8 min and 30 s and they occurred within 22.5 min. The distribution and the length of each task-focused section is illustrated in Fig. 10 .

Timeline of analyzed task-focused sections 1–7 starting from the first task-focused section

We identified 3903 fixations from the task-focused sections. With GMESS, we identified 77 joint attention occurrences. Of all the identified occurrences of joint attention, 49 (64% of all) were joint visual attention and 28 (36% of all) joint representational attention. The high frequency of joint representational attention suggests that joint representational attention is a common phenomenon during collaborative mathematical problem solving.

Both joint visual attention, and joint representational attention occurrences had very similar mean lengths, suggesting their similar nature. Even though the mean length of joint representation attention occurrence ( M = 5.91 s, SD = 3.51) was slightly longer than joint visual attention occurrence ( M = 5.68 s, SD = 4.63), the joint visual attention occurrences varied more in length (range 1.32–31.67 s) than joint representational attention occurrences (range 2.32–17.67 s).

We identified 166 problem-solving phases in the task-focused sections. The distribution, the mean length, and standard deviation of the phase categories for each student is presented in Table 3 . It was interesting that the phase implementing was clearly shorter than other phases. Its mean length was only 4.34 s (SD = 3.19), which was a little over half of the length of the second shortest phase planning and exploring ( M = 8.05, SD = 5.57). The phase implementing was also least common ( f = 14) in the dataset. The phase verifying was clearly the longest lasting phase ( M = 18.89, SD = 28.19). This phase was over 2/3 longer than the second longest and most common ( f = 63) phase watching and listening ( M = 11.42, SD = 15.58).

We noticed that the durations of the different kinds of phases (see Table 3 ) were mainly longer on average than joint attention occurrences. The number of the occurrences of different phases among the students did not vary much. All the students spent most time in the phase verifying and watching and listening, and least in implementing .

Joint attention occurrences were mainly shorter than the phases. Therefore, it is likely for the student to stay in the same phase before, during, and after participating in joint attention. This happened in our data 147 times. During the joint attention occurrence, it is not uncommon for a student to change the phase they are in. This happened in our data 73 times. (See an interactive illustration of the phases and the joint visual and representational attention occurrences during the first task-focused section in https://www.geogebra.org/m/ymyscvqj .) In the illustration, the timeline is on the $y$ -axis and proceeds from bottom to top. As the illustration in the link shows, joint visual attention and joint representational attention can exist simultaneously, and a student may participate in each of them at the same time. For example, let us imagine a situation where three students, A, B and C, are discussing a solution. A and B look at a diagram in B’s notebook and the third student, C, looks at the same diagram in her own notebook. In this situation A and B are participating in joint visual attention together, but also in joint representational attention with C. In other words, a student A is included in joint visual attention with student B and at the same time in joint representational attention with students B and C.

If a student participated in each type of joint attention at the same time, we counted that student into each type of joint attention. Therefore, the total number of phases is greater in Tables 4 and 5 than in Table 3 . Also, it was possible for a student to stay in the same problem-solving phase during and after joint attention and continue to be in this phase also during the next joint attention occurrence (see Fig. 11 , student 1). When joint attention occurred twice during the same continuous phase, that phase was counted twice in Table 4 .

The beginning of the first task-focused section. Time progresses from down to upwards on the y-axis. Each column indicates the phase changes of each student during that time and when they attend to joint visual attention

Figure 11 demonstrates the progression of phases of problem solving and joint visual attention occurrences at the beginning of the first task-focused section. (See the full version of the first task-focused section as an interactive graph in color in https://www.geogebra.org/m/ymyscvqj .)

Table 4 shows which phases the researchers observed during each type of joint attention. There were also students participating in joint attention who were in none of the problem-solving phases. In these cases, they were not focused at first, but during joint attention they either rejoined the problem-solving process, or they gradually lost focus, or joint attention was requested successfully by a student outside of the focus group. These occurrences are shown in Table 4 in the None-column.

As can be seen from Table 4 , the two phases occurring most often during joint attention were verifying and watching and listening. The phase implementing was more common during joint visual attention ( f = 5) than during joint representational attention ( f = 1) as was planning and exploring (JVA f = 11, JRA f = 3). Overall, the problem-solving phases occurred with similar frequency during each type of joint attention. For example, implementing occurred the least during both joint visual and representational attention whereas verifying was the most common phase in both joint visual and representational attention. Figure 12 is an illustration of phase shifts during joint visual attention, joint representational attention, and during other times. The phase shifts occurred similarly during joint visual and joint representational attention, also suggesting their similar nature.

Phase shifts during joint visual attention, joint representational attention, and during other times. The weight of the arrows indicates how many times that phase change was observed in the data. This graph is also available as an interactive illustration in https://www.geogebra.org/m/byvz3cgx

In Fig. 12 the weight of the arrows indicates how many times that phase shift was observed in the data. The most often occurring phase shifts were between the phases watching and listening and verifying . This was observed in the data more often towards the end of the problem-solving process. The phase shift between watching and listening and verifying was observed often during joint visual attention, during joint representational attention, and during other times. It suggests that when the students enter the phase verifying , they soon after seek approval from the group. Also, it entails that many discussions in the group evolve around verifying a solution. The phase shift from planning and exploring to implementing occurred often at times other than during joint attention. This suggests that the phase shift to implementing is more solitary in nature than a direct product of group interaction.

Table 5 shows which phases occurred right after each type of joint attention. In a manner which is similar to what Table 4 indicates, a student can simultaneously attend to joint visual attention with one student, and to joint representational attention with other students. Hence, the total number of the phases is greater in Table 4 than in Table 3 . But unlike Table 4 which shows all the phases experienced during joint attention occurrence, Table 5 shows only the phase they were at or entered right after an occurrence of joint attention ended.

Table 5 shows that the phases verifying (46.70% of the total) and watching and listening (33.92% of the total) occurred most often right after joint attention. The phase implementing (0.44% of the total) was the rarest of the phases right after a joint attention occurrence. Some of the students turned to doing other things, for example, doodling. In these cases, the students were not categorized in any of the problem-solving phases. Table 5 also shows that the problem-solving phases occur somewhat similarly after each type of joint attention.

It was also interesting to notice that participants in joint attention were not experiencing the same phases of problem-solving, neither during nor after joint attention with each other.

4 Discussion and conclusions

We identified 77 joint attention occurrences, and 28 of these were joint representational attention occurrences. Already the shared number of joint representational attention occurrences implies that the definition of joint attention should also include attention to separate representations of the same idea in the context of collaborative mathematical problem solving. The results also showed that phases of problem solving occurred in the same proportion during joint visual and representational attention.

There were phase shifts during joint attention (73 times in our data) but more often there was no phase change (146 times). In addition, the students often continued to be in the same phase after joint attention as they were in during joint attention. During both joint visual and representational attention, the two most often occurring phases were verifying and watching and listening. Our results are in line with those of Roschelle ( 1992 ), who argued that one of the key characteristic features in the creation of joint problem-solving spaces is repeating cycles involving the display, confirmation, and repairing of understandings. The phase shifts during joint attention in our case study were mainly between verifying, watching and listening, and understanding the problem, which are the ones that correspond to displaying, confirming, and repairing understandings. Both Roschelle ( 1992 ) and Barron ( 2003 ) also observed that increasingly, the students expected to get evidence from each other that they understood one another as the conversation progressed. In our data the phase shifts between verifying and watching and listening increased clearly towards the end. Right after joint attention, the phases occurring most often were verifying and watching and listening.

This study showed that a student did not necessarily need to be in any of the phases during, or after entering joint attention. In such a case, the student was not focused on the problem at first but got back into the problem-solving process during joint attention. Alternatively, they either gradually lost their focus or joint attention was requested successfully by a student outside of the focus group. Additionally, the phases of the problem-solvers attending to joint attention were not synchronized during or after joint attention.

Carlson and Bloom ( 2005 ) showed that individual problem-solving processes progress in cycles of planning, executing, and checking. We showed that this cycle (planning and exploring, implementing, and verifying) is also present in collaborative problem solving, but it is less likely to appear during joint attention than at other times.

Reflecting about methodological implications and limitations of this study, we consider mobile gaze tracking a useful way to complete a problem-solving phase analysis. For example, Arzt and Armour-Thomas ( 1992 ) used videotapes in determining the phases of the students. They and their research assistant observed a videotape together in one-minute intervals. They then recorded the observations and noticed that the participants exhibited several behaviors during the one-minute interval. Gaze tracking allows us to view what the participant sees, and the current technology allows one researcher to observe several synchronized videos simultaneously. Visual attention is often intentional (Tomasello, 1995 ). Hence, being able to view exactly what the students see makes it easier to understand their problem-solving strategies and to pinpoint more precisely when a phase begins and ends. Nevertheless, it remains characterized by a third person instead of the actual problem solver.

Relying on gaze target annotations has its difficulties. There are moments when the annotator cannot see clearly what the participant is looking at. The drawings can be small and relatively far, and the lights may cause reflections. In addition, the gaze targeted on a pen pointing at a drawing could be interpreted as gaze focusing on pen, hand, or gesture. The new method GMESS for tracing joint attention proved to be of value also in those kinds of situations. It made it easy to detect not only joint visual attention occurrences but also joint representational attention occurrences. Before checking the audio—which is necessary for identifying joint attention—we had identified by using GMESS seven possible joint attention occurrences, which, however, did not have the verbal context required for joint attention. We discarded two additional occurrences of joint attention from the analysis because attention was not on the task. Yet, we found 77 occurrences of joint attention, which we consider to be a lot for 8 min and 30 s of task-focused time. With GMESS, even those difficult to detect occurrences become detectable. Therefore, we recommend the use of GMESS, especially in the context of mathematics education.

Even though we analyzed only one group solving one problem, the data had many fixations ( n = 3903) and occurrences of joint attention ( n = 77). The high frequency of joint attention occurrences can be seen as a sign of successful interaction (Barron, 2003 ). Coding the gaze targets for fixations is extremely time consuming (approximately an hour per minute of data). For this reason, only the first author annotated the fixations. Use of the GMESS method also made some errors in the coding visible, thus allowing us to correct them. The analysis of problem-solving phases, on the other hand, was much quicker to do. The first and the third author annotated the phases first separately and then discussed their coding where it differed, until they reached a full consensus. Whereas we could detect joint attention within an accuracy of 30 ms, the time when a student transitioned from one phase to another could not be determined as exactly. This did not cause problems for our analysis, as the problem-solving phases were relatively long. We were not interested in when exactly during a phase joint attention occurred. Instead, we focused on finding out during which phases joint attention occurred.

Joint attention as such is fundamental for collaborative learning (Mundy & Newell, 2007 ). Our research introduces the concept of joint representational attention to mathematical problem-solving research. By including joint representational attention in the analysis of joint attention in collaborative problem solving, the analysis gives more holistic information about the process. It also emphasizes the fact that interaction and collaboration do not necessarily require a concrete joint visual target. To establish successful interaction and to initiate joint attention, it is enough that the collaborators share an understanding of the concept through representations. Adding the concept of joint representational attention in the repertoire of mathematical problem-solving research opens also new opportunities for research. A more in-depth analysis of the fixations is needed to understand gaze behavior during joint attention. One important future direction of work is to study why and when joint attention is initiated during collaborative problem solving, and what determines the success of the request for joint attention. Also, a more qualitative analysis is needed to understand why a student skips a phase, during collaborative problem-solving, that would normally occur in the individual problem-solving process.

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Acknowledgements

This research was funded by the Academy of Finland Grant no. 297856. We thank the participants that made this study possible, and also Mr. Visajaani Salonen for his contribution in the data collection.

This research was funded by the Academy of Finland Grant no. 297856. Open access funding provided by University of Helsinki including Helsinki University Central Hospital.

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Salminen-Saari, J.F.A., Garcia Moreno-Esteva , E., Haataja, E. et al. Phases of collaborative mathematical problem solving and joint attention: a case study utilizing mobile gaze tracking. ZDM Mathematics Education 53 , 771–784 (2021). https://doi.org/10.1007/s11858-021-01280-z

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Stop Complaining About Your Colleagues Behind Their Backs

Deborah Grayson Riegel

It makes it harder to give feedback.

Many of us believe that we’re above workplace gossip, and that we never engage in it. But, if you’ve ever participated in a “confirmation expedition” — whereby you 1) ask a colleague to confirm their own negative or challenging experience with a third colleague who is not present, or 2) welcome a similar line of confirmation inquiry from another colleague about a third colleague who is not present, you are in fact engaging in gossip. By talking to anyone, everyone, or even one person about another colleague who isn’t there to hear the feedback, provide his or her perspective, and engage in joint problem solving, you are undermining the benefits of an open, honest relationship and a feedback-rich culture. To stop this kind of behavior, we have to first call gossip “gossip” to stop it in its tracks. Most people will step back at hearing a colleague say, “This sounds like gossip. Is that what you intended?” Then, pivot the conversation by asking, “How can I help you get a better outcome?” Only engage in coaching, brainstorming, and problem-solving conversations — not in problem-confirming expeditions.

In my coaching work with leaders and teams, I often ask my clients whether they engage in workplace gossip. More often than not, they respond, “of course not!” with a look on their faces that indicates that they are insulted to have been asked such a question.

Deborah Grayson Riegel is a professional speaker and facilitator, as well as a communication and presentation skills coach. She teaches leadership communication at Duke University’s Fuqua School of Business and has taught for Wharton Business School, Columbia Business School’s Women in Leadership Program, and Peking University’s International MBA Program. She is the author of Overcoming Overthinking: 36 Ways to Tame Anxiety for Work, School, and Life and the best-selling Go To Help: 31 Strategies to Offer, Ask for, and Accept Help .

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More From Forbes

How to adopt a collaborative problem-solving approach through 'yes, and' thinking.

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After more than 24 years of coaching, I've noticed that teams and organizations still use traditional problem-solving techniques despite these being either obsolete or ineffective. For example, individuals still attempt to focus and dissect problems on their own with the hope of coming up with a solution by themselves.

I also notice a pattern of clients operating in silos. They have a tendency to equate the ability to solve problems by themselves as a form of independence and initiative. This works only to a certain degree. As the problem becomes more complex, this solo-solving technique becomes ineffective. Instead, teams should tap into the increasingly diverse and multidisciplinary pool that makes up the workforce. Not only is this useful for performance and productivity but also for problem solving.

I have found the collaborative problem-solving approach, by Alexander Hancock , to be an effective approach to achieving clients’ objectives. Collaborative problem solving occurs as you collaborate with other people to exchange information, ideas or perspectives. The essence of this type of collaboration is based on “yes, and” thinking – building on and valuing each other’s ideas.

Any individual, team or company can take advantage of this approach. I have found this approach to be most effective for companies facing problems that involve team members from different departments, backgrounds and personalities. This is also an approach that is usually unique to the coaching profession.

In any situation, when someone comes to you as a leader with a problem to discuss, your role is to help him or her look for the causes and discover solutions. Your role is not to resolve the problem alone but to guide them through collaborative problem-solving approach.

Attitudes For Collaborative Problem Solving

Hancock provides the list below of attitudes that are best paired with the approach:

• Win-win abundance thinking: Collaboration allows you to work with others to develop solutions that will benefit you both. The key concept is to believe that it is possible to create a synergistic solution before you create them. It is not "you vs. me" — we can both succeed. Develop an "abundance mentality" — there is enough for everyone. “If you win, we all win.”

• Patience: Collaboration takes time. You need to recognize that you are both helping one another to reach a resolution, and it may take more than one meeting to discuss. You will often need to work together over time to reach a satisfying solution that you will both agree on.

• “Yes, and” thinking: Move away from polarized (either/or) thinking, and develop a “yes, and” way of thinking. This thinking is supporting a suggested idea and building on the idea to make it better.

Benefits Of Collaborative Problem Solving

Collaborative problem solving opens communication and builds trust in the relationship as you and your co-collaborator discover that you are both working together toward a shared outcome. This increases a joint commitment to the relationship and to the organization. It also indicates a commitment to helping others reach their goals and objectives, and to improve everyone’s performance for the company or the organization. Collaborative communication also encourages finding creative solutions. This increases the likelihood that others will take ownership of an issue and its solution.

Collaborative Problem-Solving Techniques

There are techniques that can help you engage in collaborative communication. Here are a few examples:

• Build on and connect ideas, rather than discarding one idea and looking for another one.

• Explore the strengths and drawbacks of each idea, compare and balance the pluses and drawbacks of each idea.

• Convert drawbacks to new possibilities. Try to find ways to integrate and combine new possibilities into an existing idea.

• When sharing your own opinion, make sure you offer it as a suggestion and not as a directive. The intention of collaborative problem solving is to provide a catalyst for exploration and consideration, instead of having the other person accept your advice or direction.

The collaborative problem-solving approach paves ways to open communication, trust, better planning and smooth implementation of a plan or strategy.

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November 9, 2023

Joint Problem-Solving While Moving A Couch

This post is adapted from Wiring the Winning Organization: Liberating Our Collective Greatness Through Slowification, Simplification, and Amplification .

In our book Wiring the Winning Organization , we present two vignettes to introduce the key concepts of wiring an organization to move from the danger zone to the winning zone through the mechanisms of slowification, simplification, and amplification. These two vignettes are simple models to illustrate fundamental concepts.

The first vignette is about two people moving a couch (which we share below). It reveals that even “brawn work” involves significant “brain work.” Even two people moving a couch requires joint problem-solving and cognition. This is to help leaders recognize that everyone is doing “knowledge work” of some form, regardless of the nature of their work in Layers 1 and 2.

We will show how leaders can help or hinder knowledge work by the decisions they make in Layer 3 (the social circuitry). It is insufficient to focus primarily on the flow of materials or information through machines, with people merely as bystanders. Rather, leaders must shape the social circuitry so that people can best engage their ingenuity and problem-solving capabilities.

In the first vignette, we use the act of moving a couch to describe how the boundary of a group solving problems must be large enough that it is coherent, having all the people and resources needed to solve the problem. However, the boundary must also be small enough to not require large amounts of coordination. We also show how leaders must ensure the communication channels are sufficiently direct and have sufficient bandwidth to support joint problem-solving.

Vignette One: Moving a Couch, Together

Gene and Steve are trying to move a couch. This may seem like a problem that involves physical labor only. However, in order to succeed, they must collaborate to solve many important problems. These include: Where should they place their hands to lift the couch? How do they keep the couch balanced when they move? To get through a narrow doorway, do they orient the couch vertically or horizontally? To get down a narrow and winding staircase, who should go first? And should they face forward or backward?

Gene and Steve don’t need to conduct elaborate studies to answer these questions. They assess the couch and the room it’s in, lift it to get a feel for its weight and balance, and work together so their efforts are coordinated. Through trial and error and fast feedback, as well as by communicating and coordinating, Gene and Steve are able to generate the information they need to solve their problems.

As they go, there are unforeseen problems, such as balance, positioning, and pace. They resolve some issues by talking, but some are communicated by gestures—nodding in which direction to move, shifting a grip, vocalizing when the effort is too great. Regardless of how problem-solving occurs, it must be a team effort. Gene can’t just change his grip without risking Steve losing his. And Steve can’t speed up the stairs without putting Gene at risk.

Of course, their ability to collaborate can be compromised. When the sun sets, the room where they are working gets darker. Because Gene and Steve are no longer able to see and sense what’s around them, everything takes more time. Furthermore, someone may trip over something on the floor, or someone’s finger might get pinched.

Their work may also become even more difficult when a fire alarm goes off or a car alarm starts blaring outside. This is because they are no longer able to hear each other’s concerns and corrections, reducing their ability to communicate and coordinate.

It is important to note that the added noise does not make the task at hand more difficult, unlike with the loss of light. In this situation, it is Gene and Steve’s inability to communicate that makes it more difficult to solve problems and complete their task.

Yet another way for their work to become more difficult is if an intermediary is introduced. Let’s say a friend tries to help, relaying messages between Gene and Steve, telling them what’s going on, what to do, how to do it, and why. Despite their best efforts, the friend may actually make matters worse. This is because the friend cannot convey information with nearly the frequency, speed, detail, or accuracy as compared with when Gene and Steve communicate directly.

Key Concepts

Two people moving a couch together is different from two people each moving a chair. When moving the chairs, the two people can work independently. However, two people moving a couch is collaborative, requiring communication, coordination, and interaction. And when their ability to collaborate degrades (e.g., the room becomes too dark to see, too noisy to hear, or the friend intermediates their communication), their task becomes increasingly difficult.

In the beginning, Gene and Steve worked together in a coherent environment. The conditions for doing the brain work were hospitable, which enabled them to succeed in the brawn work. Conversely, when the conditions became incoherent, the brain work was more difficult, and so too was the brawn work.

By coherent , we mean having the quality of a unified whole. The elements that interact frequently and intensely (e.g., Gene and Steve) are in the same group, and they can communicate directly and with needed frequency, speed, accuracy, and detail. This is necessary for the performance of the whole to be logical and consistent. In this case, a well-lit, relatively quiet room meant Gene and Steve could solve problems as they arose. On the other hand, a poorly lit and noisy room with an intermediary degraded that coherence, which made moving the couch much more difficult.

For now, let us state that leaders make many Layer 3 decisions about the social circuitry of their organization that create or destroy coherence. For Gene and Steve, diminishment in lighting, increase in noise, and intermediation in communications were all arbitrary events. However, in more complex situations, leaders often make decisions that deliberately or accidentally improve or impede people’s ability to make sense of their situation (e.g., the lighting), to exchange information (e.g., the noise), or communicate and collaborate directly (e.g., the intermediating friend).

Related to coherence, we’ll introduce another term: coupling . Elements in a system are coupled when changes in one affect the other. Gene and Steve are coupled through the couch. Gene’s actions affect not just the couch but Steve as well, and vice versa. For instance, if Gene twists his end of the couch, Steve has to adjust to compensate.

How much coupling there is determines how much coherence leaders must create so that people can collaborate. Two people moving a couch are coupled; two people each moving a chair are not (unless, of course, they have to go through the same narrow door at the same time).

Depending on conditions, even people in the same situations can have different degrees of coupling, necessitating a different drawing of the boundaries to maintain coherence.

All Work is Knowledge Work

Leaders must appreciate that all the work they are managing is knowledge work. At times, some of this work is loosely coupled, while at other times, it is tightly coupled. It is not arbitrary. Instead, it depends on how much coherence has to be provided to whom, in which working groups, and the type of problem they are trying to solve. This, in turn, determines how leaders must configure the social circuitry of their organization (Layer 3). This includes the design of roles, routines, processes, and procedures. For instance, the social circuitry to support normal air traffic control operations is different from the circuitry needed to ensure the safe landing by a student pilot in a damaged aircraft.

Coupling and coherence are important, not just for Gene and Steve trying to move a couch or Maggie Taraska landing safely. Look around your own work environment and assess whether you are wired to win or not. Have many people have been placed into the same group arbitrarily, when the problems they’re dealing with are not tightly coupled? If so, this is likely a couch team that is actually moving chairs. This social circuitry design error creates the predictable consequence of people being drawn into situations where they are not needed and for which they will not be affected by the outcomes. This creates more meetings, memos, status updates, and the like, which adds work and time but does not add value.

Conversely, as you look around your work environment, are there people who are responsible for some portion of a larger problem scattered around the organization, not taking into account how coupled their work is? If so, this is likely because a couch problem is being solved by multiple chair teams. People who should be solving problems together can’t. Collaboration should be frequent, fast, and rich but becomes occasional, slow, and imprecise. Instead of conversation, there are forms, work orders, tickets, intermittent meetings, and convoluted reporting channels.

Wired this way, people with tightly coupled work are not in a coherent working group. They don’t have everything they need to do their work easily and well, which includes people, skills, resources, decision rights, and so forth. This makes it more difficult to find solutions, and those solutions are worse than they otherwise would have been. This is also a social circuitry design error, one of breaking things into such small pieces that coherence is lost. That’s both coherence of completeness and coherence in terms of being able to act logically and reliably.

In the first case, the system was over-coupled and under-partitioned. In the second case, the system was under-coupled and over-partitioned. Later in the book, we’ll describe how leaders can address both of these situations to be wired to win.

This first vignette demonstrated the effects of cohesion and coupling to make it easier or more difficult to jointly solve a problem. I n the second vignette , we’ll illustrate how management systems can make it easier or more difficult to integrate different functional specialties to achieve a common goal, what can go wrong, and what we can do about it.

Learn more about Wiring the Winning Organization here.

Gene Kim is a best-selling author whose books have sold over 1 million copies. He authored the widely acclaimed book "The Unicorn Project," which became a Wall Street Journal bestseller. Additionally, he co-authored several other influential works, including "The Phoenix Project," "The DevOps Handbook," and the award-winning "Accelerate," which received the prestigious Shingo Publication Award. His latest book, “Wiring the Winning Organization,” co-authored with Dr. Steven Spear, was released in November 2023.

Dr. Steven J. Spear

Dr. Steven J. Spear (DBA MS MS) is principal for HVE LLC, the award-winning author of The High-Velocity Edge, and patent holder for the See to Solve Real Time Alert System. A Senior Lecturer at MIT’s Sloan School and a Senior Fellow at the Institute, Dr. Spear’s work focuses on accelerating learning dynamics within organizations so that they know better and faster what to do and how to do it. This has been informed and tested in practice in multiple industries including heavy industry, high tech design, biopharm R&D, healthcare delivery and other social services, US Army rapid equipping, and US Navy readiness.

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Project Convergence: Achieving Overmatch by Solving Joint Problems

By John Michael Murray and Richard E. Hagner Joint Force Quarterly 103

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A s the United States confronts Great Power competition (GPC), incremental improvements to individual Service capabilities will not produce a military able to decisively win on the battlefield. Although important, the enhanced range, precision, and survivability of our weapons systems are just one part of achieving overmatch. When employed effectively, advancements in artificial intelligence (AI) and machine learning, robotics, and autonomy improve our weapons systems’ effectiveness by boosting the decisionmaking pace of our commanders and reducing the options for our adversaries. Success on the battlefield depends on whether we leverage these new technologies to create simultaneous dilemmas across multiple domains.

This article describes what Army Futures Command, in cooperation with the Air Force, Navy, Marine Corps, and coalition partners, is doing to advance emerging technologies and ensure that we achieve convergence —that is, the full integration of effects across all domains to reach overmatch on the battlefield. Project Convergence is the Army’s contribution to the Combined Joint All-Domain Command and Control (CJADC2) concept and will help inform the joint warfighting concept.

GPC and the Need for Overmatch

National security experts agree that gaps in military capability are closing. Better China-Russia relations and accelerated innovations in defense are “eroding U.S. military advantage.” 1 Russia and China are quickly closing in on American military superiority. A Department of Defense report to Congress in 2020 describes China’s goal “to become a ‘world-class’ military by the end of 2049” and outlines the steps the People’s Liberation Army has taken to achieve that objective, including investments in emerging AI and cloud computing technologies. 2 This investment in emerging technologies could result in an asymmetric advantage—an ability to achieve an advantage in one domain through sheer speed of data processing.

The National Defense Strategy (NDS) and National Military Strategy (NMS) address the reemergence of GPC. The NDS points to “military modernization” by China and “use of emerging technologies” by Russia to achieve their respective regional goals. 3 A summary of the NMS states that “the reemergence of Great Power competition with China and Russia represent[s] the most difficult challenges facing the Joint Force.” 4 The NDS and NMS acknowledge and address what policy experts have stated: the military gap between the United States and its near-peers is closing. The result is a complex and dynamic environment the likes of which the U.S. military has not faced since the end of World War II.

The challenge of GPC will likely persist for decades as countries develop and employ new systems and technologies, driving competition for information and military superiority. The goal of the United States is to deter through competition but, if needed, win in conflict. Overmatch is the key. Chairman of the Joint Chiefs of Staff General Mark Milley has called for a new modernization approach to deliver “capabilities that are 10 times more lethal than those they replace.” 5 But achieving the 10 times overmatch in individual systems is cost-prohibitive and inefficient. Experts in defense modernization efforts and processes have rightly criticized the lack of integration of these systems—the lack of convergence to accelerate the kill chain. 6

Army Futures Command leads persistent Army modernization and was created to “regain overmatch in MDO [multidomain operations]” and “provide the ‘10x’ capability with increased range, lethality, reliability and survivability.” 7 To enable true overmatch, we must expand that concept of the kill chain and develop “sensor-to-shooter webs” via a new model that shifts away from postdelivery interdependence to prerequirement integration. 8 We will accomplish this overmatch, with our partners, through Project Convergence.

A Campaign of Learning

Project Convergence is a campaign of learning designed to inform how we fight, how we organize, what we fight with, and even who we are. It incorporates the Army’s modernization efforts and culminates in an annual capstone event. The approach monitors the progress of emerging technologies and science and technology investments, which allows us to assess those relatively immature technologies ripe for development and include them into the capstone event. It also shows us the technical challenges or problems we need to address to maximize the collective capability of our signature systems. In this sense, the 10 times overmatch requires only 4 times modernization for the signature programs—the remainder is accomplished through integration of emerging technologies and results in a capability greater than the sum of its parts. This assessment informs the technologies and objectives included in the capstone event. The first event, Project Convergence 20, was held at Yuma Proving Ground in August and September 2020.

Project Convergence 20 was designed as a proof of concept for a new way of advancing technologies. The value of Project Convergence 20, and the catalyst for its success, was the ability to bring together Soldiers and scientists from our various laboratories, program executive offices, and cross-functional teams. For 5 weeks, these teams worked together to solve interoperability problems and advance science and technology efforts, operating outside of the traditional stovepiped model. This collaboration included nightly revisions of code—an effort that would have taken months of back and forth between the engineers and scientists working on systems in our labs. The process of identifying integration barriers and immediately addressing them also highlighted the need for an open architecture design, an observation well documented by those with experience in the defense industrial complex and those in Congress. 9

The result of this focused collaboration was the acceleration of certain programs along the technology readiness level (TRL) stages depicted in the figure. The most striking case may be that of Firestorm, a government-owned target deconfliction platform enabled by AI. Firestorm not only deconflicts airspace but also recommends the best shooter for a given target. It employs AI and machine learning to assess the target and friendly capabilities and determine the priority of the target. The Firestorm example is significant for three reasons. First, from a technology development perspective, Firestorm was able to advance from TRL 3 to TRL 6 because of the experimental conditions established at Yuma. Second, the AI aspect of Firestorm reduced from minutes to seconds the time from sensor to shooter. Whereas a traditional call-for-fire mission takes anywhere from 10 to 20 minutes, Firestorm accomplished it in less than 30 seconds in Yuma. Such a reduction in time will have a significant operational impact.

Finally, and perhaps most important, the process of integrating sensors and shooters through Firestorm allowed us to reassess objectives. By demonstrating our ability to connect sensors to shooters in a way that dramatically reduced the time from target identification to engagement, we were able to reevaluate what the joint kill web requires to be effective. We went into Project Convergence 20 with the objective of connecting “any sensor, any shooter, and any C2 node.” Through the weeks of resolving technical issues and contemplating the implications of what we had accomplished, we adjusted that objective to “all sensors, the best shooter, and the right C2 node.” Although we want to utilize all sensors available, convergence requires that we identify the best shooter and right C2 node at the speed of relevance.

We approach this AI-enabled objective attentive to the concerns policy experts have expressed about ensuring there is always a person making the decision—this is Army policy. 10 Though the discussion of human-in-the-loop and human-on-the-loop is important for determining how we employ AI, robotics, and autonomy, we first need to prove that we can develop the loop. Future war will occur at machine speed. Militaries able to engage at that speed will have a decisive advantage. Project Convergence allows us to test our ability to employ these technologies across the joint force.

AI is just one emerging military technology the Army and its adversaries are pursuing. Policy experts advising Congress have identified autonomous weapons, hypersonics, directed energy, biotechnology, and quantum technology as areas of both opportunity and concern. 11 Project Convergence is a venue to test and conduct analysis on these technologies. Project Convergence 20 set the foundation for Army modernization efforts moving forward. Convergence, however, is not just about Army systems; a common concern among policymakers is how we integrate with joint and coalition partners. 12 We began to address this concern at Yuma, when the Marine Corps provided an opportunity to include an F-35B. Initially, the F-35B could not communicate with ground troops. By the end of the exercise, the F-35B integrated into the kill web as a sensor for ground shooters and a shooter for ground observers. This example presents just one type of problem that we want to work with the joint force to solve.

Informing Joint Concepts by Solving Joint Problems

The Army Modernization Strategy offers guidance on such matters as what we fight with, how we fight, and who we are. 13 Project Convergence puts that guidance into action by establishing a systematic sequence of events designed to integrate the systems we fight with, inform how we fight, and develop the force required to win in the age of GPC. The table shows Army Futures Command’s approach to executing the Army Modernization Strategy through Project Convergence. Building on Project Convergence 20, next year’s capstone event will focus on joint integration by using joint mission threads to test and evaluate emerging technologies. In 2022, the capstone event will include British and Australian technologies that we and coalition partners will begin to integrate.

Winning matters—but winning together matters more. As we turn to Project Convergence 21, we will focus specifically on the joint force. Project Convergence 21 will build on Convergence 20 in two substantial ways. First, it is set as a U.S. Indo-Pacific Command scenario and will incorporate the multidomain task force (MDTF), a division headquarters, and a brigade combat team. This scenario will better inform the joint warfighting concept as well as MDTF functions and requirements. The inclusion of the Air Force’s Advanced Battle Management System and fifth-generation fighters provides opportunities to identify and resolve barriers to effective sensor-shooter connectivity at the joint level. This cooperation is the result of recent Army–Air Force talks and a signed memorandum of understanding between General Charles Brown, chief of staff of the Air Force, and General James McConville, chief of staff of the Army, and the need for both Services to inform the Joint Staff–led JADC2 effort.

There is also increased understanding that “JADC2 cannot be a single approach to achieving convergence but must be a composite of several solutions tailored to the several different environments comprising the expanded battlefield.” 14 Therefore, Project Convergence is the Army’s contribution to JADC2, providing a tailored solution for the land domain and a way to test integration into the “expanded battlefield.” This effort is similar to the Air Force approach for Advanced Battle Management System. Initially developed as an on-ramp model, the Air Force effort is now structured as “Architecture Evaluation Events” complementing Project Convergence. The Navy’s integration endeavors, Project Overmatch and the Naval Introductory Flight Evaluation program, take comparable approaches to informing JADC2 requirements. These Service-driven efforts, however, are not mutually exclusive. For example, to address the challenge of linking sensors and shooters across domains, Project Convergence 21 will include the Air Force’s F-35 and Navy’s Aegis systems. In addition to contributing to JADC2, this interservice cooperation in Project Convergence allows us to identify and address the technical hurdles spotted in the multidomain battle concept of General David Perkins, USA, and General James Holmes, USAF. 15

Project Convergence 22 will build on the momentum gained in 2021, continuing to contribute to JADC2 and informing the joint warfighting concept. Coalition participation in Project Convergence 22 will further develop these concepts and expand the battlefield—and introduce the Combined JADC2 concept. Our position going in is that we will always fight with a coalition, and thus interoperability must be fundamental to our C2 systems. Given the significant data-sharing challenges among coalition units, we are already working with our British and Australian counterparts to identify the technical and policy barriers that must be addressed prior to and during the 2022 capstone event.

Learning from the Past

Project Convergence is an ambitious endeavor. Observers have already cautioned that including too many systems too quickly could derail the new modernization effort and lead the Army astray from its goals. 16 These concerns are valid and should be kept in mind as we move forward. Fortunately, we have several historical examples to inform our approach. Some of these examples—for instance, Future Combat Systems (FCS) and Network Integration Evaluations (NIE)—illustrate how modernization efforts can become too ambitious, be ahead of emerging technology, and not meet the needs of Soldiers and commanders. Less often discussed are the success stories, such as the Louisiana and Tennessee maneuvers prior to World War II and more recent 9 th Infantry Division (ID) and 4 th ID modernization efforts prior to 9/11. The success and failures of these efforts not only have informed our approach but also provide a way ahead for joint force modernization.

It is natural to form opinions of a new initiative or approach by looking to past efforts meant to accomplish the same goals. When discussing Project Convergence, observers typically mention two predecessors: FCS and NIE. While both FCS and NIE ultimately failed to achieve their objectives of a modernized and network-centric force, both have critical lessons to teach us. Perhaps the most important takeaway deals with the requirements process. In the case of FCS, requirements were defined with the anticipation that promising technologies would mature along a predictable timeline. As the RAND autopsy of FCS found:

The Army’s combat developers set out to design an entire brigade of networked systems and subsystems from the ground up, taking advantage of advanced technologies that were largely underdeveloped, untested, and unknown, but were assumed eventually to be capable of achieving revolutionary levels of interoperability and tactical coordination. 17

A key component of Project Convergence is to test emerging technologies before they become a requirement in a program of record. The experimentation conducted at Project Convergence then determines which promising technologies are “capable of achieving revolutionary levels of interoperability and tactical coordination” 18 and which need more time to develop.

The Army’s NIE design likewise relied on preset requirements. At NIE, new systems were put in the hands of operational units to test interoperability and usability; unlike those at FCS, the technologies enabling these systems were already mature. The flaw resulted from the requirements of each individual system being established prior to testing its interoperability or putting it in the hands of Soldiers. The result was multiple high-profile programs being identified as unable to either integrate into a system of systems or meet the needs of the Soldiers and commanders employing them. Project Convergence tests interoperability and leverages the Army’s Soldier-centered design to inform the requirements process. This approach ensures delivery of a desirable capability able to seamlessly integrate with other systems.

Incorporating this two-pronged approach, assessing emerging technology and getting it in the hands of Soldiers and commanders, is critical to the success of Army and joint force modernization. As the RAND report on FCS astutely pointed out, “Any acquisition program faces the dual risks that the future capabilities envisioned today may not meet the actual operational needs of tomorrow and that technological progress simply may not occur as quickly as anticipated.” 19 Project Convergence addresses both threats by using real-world vignettes to inform future operational requirements and evaluating emerging technology to determine what is viable.

There are, of course, examples of successful military modernization efforts that properly considered the emerging technologies and forecasted operating environment. In the leadup to World War II, General George Marshall and General George Patton led the Louisiana and Tennessee maneuvers, respectively. At the time, the emerging technologies were aircraft, tanks, and radios, and the operating environment was Europe. These exercises not only tested the new capabilities but also identified scenarios that replicated the operational needs for war in Europe, to change how the Army fought. Today, the emerging technology is AI, robotics, and autonomy, and the future operating environment will be asymmetric, highly lethal, and hyperactive across all domains.

More recent examples of the 9 th ID and 4 th ID modernization reinforce the benefit of including Soldiers and command nodes in modernization efforts. Such inclusion informs how we fight and the force structure required to effectively use new systems. Incorporating headquarters at echelon (MDTF, Data and Information Viewpoint, and brigade) and Soldiers into the Project Convergence design allows us to do more than experiment with emerging technology; we can test how we employ that technology effectively through force structure, concepts, and doctrine across the joint force. At its core, Project Convergence is a process of “discovery experimentation”—that is, “a deliberately crafted and planned approach for addressing an issue long before it becomes a pressing problem” and one that “allows operators to interact with new or potential concepts and capabilities to explore their military utility.” 20 This tactic, built on lessons from past modernization efforts, provides a framework to identify joint warfighting problems; evaluate potential technological solutions; contribute to joint interoperability, via CJADC2; and inform the joint warfighting concept. Project Convergence allows us to create our own “Yuma Maneuvers” to apply the pre–World War II objectives of the Louisiana maneuvers to today’s joint force.

Great Power competition requires overmatch—and thus a transformation of the joint force to ensure it. General McConville has stated, “In the face of determined adversaries and accelerating technological advances, we must transform today to meet tomorrow’s challenges.” 21 Tomorrow’s challenges are rapidly approaching, and through Project Convergence, Army Forces Command is spearheading the required changes. By leveraging joint mission threads to test and evaluate emerging technology, Project Convergence establishes a process to identify and solve joint problems. This approach to persistent modernization ensures that all efforts build toward eventual and recurring demonstration of joint force capabilities and that we remain grounded in the operational problems we are trying to solve. Collaboration widens our view and expands the collective appreciation of the challenges ahead, specifically those that our respective Services cannot solve alone. Resolving these technical challenges together, and applying new technology to known mission sets, allows us to establish a common architecture (CJADC2) and approach the new joint warfighting concept with an understanding of how we fight, how we organize, and what we fight with. JFQ

1 Andrea Kendall-Taylor and Jeffrey Edmonds, “Addressing Deepening Russia-China Relations,” Center for a New American Security, August 31, 2020, available at < https://www.cnas.org/publications/commentary/addressing-deepening-russia-china-relations >.

2 Military and Security Developments Involving the People’s Republic of China: Annual Report to Congress 2020 (Washington, DC: Department of Defense, 2020).

3 Summary of the 2018 National Defense Strategy of the United States of America: Sharpening the American Military’s Competitive Edge (Washington, DC: Department of Defense, 2018).

4 Description of the National Military Strategy 2018 (Washington, DC: The Joint Staff, 2018).

5 Daniel Gour é , “Winning Future Wars: Modernization and a 21 st Century Defense Industrial Base,” Heritage Foundation, October 4, 2018, available at < https://www.heritage.org/military-strength-topical-essays/2019-essays/winning-future-wars-modernization-and-21st-century >.

6 Christian Brose, The Kill Chain: Defending America in the Future of High-Tech Warfare (New York: Hachette Books, 2020).

7 Daniel S. Roper and Jessica Grassetti, Seizing the High Ground —United States Army Futures Command , ILW Spotlight 18-4 (Arlington, VA: Institute of Land Warfare, Association of the U.S. Army, October 2018), available at < https://www.ausa.org/sites/default/files/publications/SL-18-4-Seizing-the-High-Ground-United-States-Army-Futures-Command.pdf >.

8 David G. Perkins and James M. Holmes, “Multidomain Battle: Converging Concepts Toward a Joint Solution,” Joint Force Quarterly 88 (1 st Quarter 2018), 54–57.

9 Brose, The Kill Chain .

10 Paul Scharre, Army of None: Autonomous Weapons and the Future of War (New York: Norton, 2018).

11 Kelley M. Sayler, Emerging Military Technologies: Background and Issues for Congress , R46458 (Washington, DC: Congressional Research Service, 2020).

12 John Hoehn, Joint All-Domain Command and Control (JADC2) (Washington, DC: Congressional Research Service, updated June 4, 2021).

13 U.S. Army, “Army Modernization Strategy: Investing in the Future,” 2019, available at < https://www.army.mil/e2/downloads/rv7/2019_army_modernization_strategy_final.pdf >.

14 J.P. Clark et al., “Command in Joint All-Domain Operations: Some Considerations,” Carlisle Scholars Program, 2020.

15 Perkins and Holmes, “Multidomain Battle.”

16 Thomas Spoehr, “Project Convergence: Its Success Could Draw Army Astray,” Breaking Defense , November 13, 2020, available at < https://breakingdefense.com/2020/11/project-convergence-its-success-could-draw-army-astray/ >.

17 Christopher G. Pernin et al., Lessons from the Army’s Future Combat Systems Program (Santa Monica, CA: RAND, 2012), available at < https://www.rand.org/pubs/monographs/MG1206.html >.

20 Kevin M. Woods and Thomas C. Greenwood, “Multidomain Battle: Time for a Campaign of Joint Experimentation,” Joint Force Quarterly 88 (1 st Quarter 2018), 14–21.

21 James C. McConville, “AUSA Noon Report,” Association of the U.S. Army, January 19, 2021.

ORIGINAL RESEARCH article

Joint problem-solving orientation, mutual value recognition, and performance in fluid teamwork environments.

1 Harvard TH Chan School of Public Health, Harvard University, Cambridge, MA, United States
2 School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, United States

Introduction: Joint problem-solving orientation (JPS) has been identified as a factor that promotes performance in fluid teamwork, but research on this factor remains nascent. This study pushes the frontier of understanding about JPS in fluid teamwork environments by applying the concept to within-organization work and exploring its relationships with performance, mutual value recognition (MVR), and expertise variety (EV).

Methods: This is a longitudinal, survey-based field study within a large United States healthcare organization n = 26,319 (2019 response rate = 87%, 2021 response rate = 80%). The analytic sample represents 1,608 departmental units in both years (e.g., intensive care units and emergency departments). We focus on departmental units in distinct locations as the units within which fluid teamwork occurs in the hospital system setting. Within these units, we measure JPS in 2019 and MVR in 2021, and we capture EV by unit using a count of the number of disciplines present. For a performance measure, we draw on the industry-used measurement of perceived care quality and safety. We conduct moderated mediation analysis testing (1) the main effect of JPS on performance, (2) mediation through MVR, and (3) EV as a moderator.

Results: Our results affirm a moderated mediation model wherein JPS enhances performance, both directly and through MVR; EV serves as a moderator in the JPS-MVR relationship. JPS positively influences MVR, irrespective of whether EV is high or low. When JPS is lower, greater EV is associated with lower MVR, whereas amid high JPS, greater EV is associated with higher MVR, as compared to lower EV.

Discussion: Our findings lend further evidence to the value of JPS in fluid teamwork environments for enabling performance, and we document for the first time its relevance for within-organization work. Our results suggest that one vital pathway for JPS to improve performance is through enhancing recognition of the value that others offer, especially in environments where expertise variety is high.

Introduction

In today’s specialized and fast-paced world, organizations increasingly rely on fluid teamwork. Individuals often come together quickly and change frequently based on the needs of the organization or the nature of the task at hand ( Bushe and Chu, 2011 ; Li and van Knippenberg, 2021 ). This is common in industries from engineering to healthcare, where networks of diverse experts must be drawn upon to accomplish complex work in the moment ( Burke et al., 2004 ; Edmondson and Nembhard, 2009 ; Retelny et al., 2014 ). Teamwork in these settings can offer advantages of expertise pooling, knowledge integration, and shared accountability ( Cummings, 2004 ; Dahlin et al., 2005 ). Doing so fluidly may enable more efficient and adaptive use of expertise than stable team membership because individuals with distinct expertise can rapidly come and go as the need for their input arises or dissipates. This helps to address issues like urgency ( Klein et al., 2006 ), complexity ( Huckman et al., 2009 ), schedule shifts ( Valentine and Edmondson, 2015 ), and surprises ( Bechky and Okhuysen, 2011 ). However, fluid teamwork environments present challenges for organizational leaders who must establish conditions that enable effective teamwork, calling for new research to identify and understand the factors that are supportive ( Salas et al., 2018 ; Kerrissey et al., 2020 ).

Teams are groups of individuals who interact to pursue a common goal ( Salas et al., 2008a ). Richard Hackman described “real” teams as teams that have stable and bounded membership, such that it is clear who is on the team and membership does not shift dramatically over time ( Hackman, 2002 ). Research has since identified stable teams as being advantageous for performance, suggesting that stable teams’ members gain a familiarity over time that confers a better understanding of one another’s strengths, weaknesses, backgrounds, and habits, which can stimulate both cognitive and social benefits ( Muskat et al., 2022 ). For this reason, it is often exhorted that teams be designed to remain relatively stable in order to derive the benefits of familiarity for performance.

However, scholars over the past decade have noted that many dynamic work settings make stable and fixed team membership hard to achieve ( Edmondson, 2012 ; Tannenbaum et al., 2012 ; Mortensen and Haas, 2018 ; Li and van Knippenberg, 2021 ). Fluidity has been described as the presence of shifting team members, i.e., individuals moving on or off the team, though the term at times is also used to refer to ad hoc or short-duration teams ( Huckman and Staats, 2011 ; Dibble and Gibson, 2018 ). For clarity, we use fluidity to refer to membership change and “short duration” to refer to brief team lifespans (though note that, in real world settings, fluidity and short duration often overlap considerably). At the extreme of fluidity in teamwork, individuals may team up together in pursuit of shared goals on the fly or with such a short duration or high degree of individual turnover that ongoing familiarity as a coherent unit becomes elusive—what has been called teaming ( Edmondson, 2012 ) or dynamic participation ( Mortensen and Haas, 2018 ).

Fluid teamwork can offer advantages in adaptiveness and efficiency because individuals are able to come and go as their contributions to a task or goal are needed, but it can also undermine familiarity and its potential benefits to teamwork. For example, it can limit the development of shared mental models and cohesion, which ordinarily help individuals to see joint work similarly and help them to depend upon and reciprocate with one another ( Bushe and Chu, 2011 ). The challenges of fluid teamwork are especially notable in the presence of varied expertise, as familiarity is important for bridging the knowledge differences that separate experts ( Kerrissey et al., 2021 ). In such circumstances, behaviors and orientations to collaboration may be especially valuable because they set expectations about whether and how to team up with others to share information, coordinate, and pursue overlapping goals, even when the structural conditions for ongoing, stable teamwork are not present ( Edmondson and Harvey, 2017 ). Amid calls for research on fluid teamwork for years ( Cronin et al., 2011 ; Wageman et al., 2012 ; Dibble and Gibson, 2018 ), there is a particular need for research that explores the contexts in which highly fluid teamwork transpires and that identifies factors that aid performance in the face of considerable barriers.

In this study, we explore the unit conditions that may enable fluid teamwork to thrive, focusing on units within a context known for highly fluid teamwork: health care delivery. Many have written about the challenges of fluid teamwork in health care ( Bushe and Chu, 2011 ), such as shifting task needs due to the emergent nature of many health conditions, the presence of multi-disciplinarity (and its increase with expansion of medical expertise and the addition of new allied health roles), patient-centered frameworks that build unique clinician and staff teams around each patient’s needs, and increasing policy emphasis on team-based care ( Andreatta, 2010 ; Bedwell et al., 2012 ; Kerrissey et al., 2023 ). We focus on hospital-based care across units, such as emergency departments, medical intensive care units, surgical intensive care units, and transplant units, because of the common occurrence of shifting sets of individuals teaming up in service of a specific patient during their stay at the hospital. For instance, one study found that the average patient sees 17.8 professionals during a hospitalization (with a range of 5–44), and these individuals come and go throughout the stay as needed and available ( Whitt et al., 2007 ).

Past research in such settings has detailed how fluid teamwork manifests. For example, teamwork shifts around patients in the emergency department with rotating work schedules as nurses and attending physicians clock in and out or specific consulting expertise is brought in for a unique need ( Valentine and Edmondson, 2015 ). Other research has described how intensive care units rely on shifting teamwork across core and peripheral members who may experience brief synchronous periods of work ( Mayo, 2022 ). Aligning with the perspective that health care teamwork often entails fluidity, we sought to examine organizational units (departments in distinct locations) to explore factors that managers and leaders may find useful in promoting effective teamwork in fluid settings and that would be measurable across a large number of work units in future research.

Specifically, we focus on joint problem-solving orientations (JPS)—defined as emphasizing problems as shared and viewing solutions as requiring co-production—as a factor that has been found to promote performance in fluid teamwork settings and for which empirical and theoretical development remains nascent ( Kerrissey et al., 2021 ). Connecting with traditional research that has illuminated the value of shared orientations in more stable teams ( Driskell and Salas, 1992 ; Eby and Dobbins, 1997 ), the concept of JPS is especially relevant for fluid teamwork because it captures both the perceived jointness of the problem faced and the willingness to resolve it together, even without the luxury of stable team membership or fully aligned goals. Initial research on JPS was conducted in unique fluid teamwork settings—cross-sector, cross-organizational teams, and, later, in a computer simulation about a shopping task ( Kerrissey et al., 2021 ). Other research on fluid teams has been conducted in unique contexts, such as crowdsourced software coding ( Retelny et al., 2014 ). However, much fluid teamwork is more mundane, occurring within the bounds of organizations day-to-day, from software development ( Huckman et al., 2009 ) to health care delivery ( Bedwell et al., 2012 ). In these settings, establishing mechanisms and conditions that enable fluid teamwork to yield performance is vital for overcoming challenges and improving performance. This is especially important for work that relies on experts, who are known to face challenges in moving beyond their individual expertise to fully collaborate ( Reyes and Salas, 2019 ). However, we know little about both the mechanisms through which JPS affects performance and the boundary conditions that shape its effectiveness within organizational units where fluid teamwork is common.

Drawing on multi-year field data from over 1,600 organizational units within a large, geographically-distributed healthcare organization, this paper makes two primary extensions. First, we apply JPS within work units in which fluid teamwork is common, examining its shared presence within organizational units and exploring its relationship with performance in this context. This perspective aligns with the conceptual claim that organizational environments affect teamwork ( Salas et al., 2018 ), alongside the practical reality that departments in hospitals are cogent entities that are used internally to structure work. This makes measurement of JPS within and across departments plausibly informative. Second, we test hypotheses about how JPS affects performance, proposing mutual value recognition (MVR) as a mediator and expertise variety (EV) as a moderator. We focus on MVR as describing the extent to which people recognize (i.e., respect, trust, and listen to) the value that others bring to collaboration. This may be vital for producing performance in fluid teamwork environments where diverse experts draw on distinct languages and norms ( Hall, 2005 ) and may face differences in near-term goals and commitments ( Bushe and Chu, 2011 ). In facilitating a shared focus on solving joint problems, JPS may allow individual experts to better and more rapidly recognize the value that others offer. Our specific hypotheses are detailed in the sections that follow.

We build out a set of hypotheses to propose a moderated mediation model ( Figure 1 ), beginning with a main effect of JPS on performance, followed by a set of hypotheses pertaining to MVR as a mediator of that relationship. We conclude with moderation by expertise variety, hypothesizing that more variety heightens the positive relationship between JPS and both MVR and performance.

Figure 1 . Hypothesized research model.

Joint problem-solving orientation

Past research has found that joint problem-solving orientation (JPS) is associated with improved work quality in fluid cross-boundary teams ( Kerrissey et al., 2021 ). This has two interrelated aspects: problem-solving and jointness. For problem-solving, seeking help with problem-solving tasks is central to knowledge-intensive work because it enables employees to address and complete complex tasks, thereby directly enhancing performance ( Hargadon and Bechky, 2006 ). The focus not only on problems but also on solving them further emphasizes the value of capturing the willingness and tendency for collaborators to move beyond venting ( Rosen et al., 2021 ) and toward solutions.

The aspect of jointness, though related, is distinct, as individuals may seek help and advice for problem-solving, but that does not guarantee that they do so in a way that implies a shared sense of problem ownership among the asker and receiver of problem-solving assistance. The jointness aspect of JPS refers to this shared emphasis and understanding that problems are mutually faced and require solving together. Jointness is important because of a tendency toward separation among loosely affiliated people; for example, social categorization theory suggests that individuals tend to view others with shared goals, motivations, and priorities as the ingroup and to categorize those who do not as the outgroup ( Harrison and Klein, 2007 ). In related literature, establishment of collective orientation among team members, even in stable teams, has been identified as an important factor in team effectiveness ( Driskell and Salas, 1992 ; Eby and Dobbins, 1997 ; Hagemann and Kluge, 2017 ). For instance, research on computer-based simulations of complex teamwork tasks (e.g., extinguishing forest fires and protecting houses) found that, among a set of variables including trust and cohesion, only joint orientation of team members positively affected team performance ( Hagemann and Kluge, 2017 ). Other research on creative teams has found that teams that overcome asymmetries in psychological ownership of their ideas to generate collective ownership have more early successes ( Gray et al., 2020 ).

In line with Kerrissey et al. (2021) , we posit that a joint orientation toward problem-solving is particularly relevant for fluid, knowledge-intensive teamwork contexts when diverse experts come together rapidly to solve problems. Kerrissey and co-authors examined this phenomenon in the extreme context of cross-sector, cross-organization teams that form ad hoc to solve pressing societal problems. We adapt their logic here to hypothesize that the relationship holds even in more ordinary work contexts (i.e., organizational units in health care). Our first hypothesis thus seeks to replicate the finding that JPS is positively associated with performance, but in the context of organizational work units where fluid teamwork occurs.

H1: JPS is positively associated with performance.

Mutual value recognition as a mediator

Amid fluid teamwork, the need to swiftly establish a common understanding of what others offer becomes paramount ( Bushe and Chu, 2011 ), especially in the presence of expertise differences ( Reyes and Salas, 2019 ). Beyond a direct effect of JPS on performance through the concrete solving of organizational problems that would otherwise directly hinder performance, we hypothesize that the relationship of JPS to performance is also mediated through a greater recognition of the value that others in their environment offer. Extensive research shows that different expertise areas bring different values, perspectives and technical languages ( Carlile, 2004 ), including in health care ( Hall, 2005 ). Gaining familiarity with one another by working together over time can improve performance ( Huckman and Staats, 2011 ). However, in fluid expertise-driven work contexts where individuals fill roles in shifting sets based on their training (e.g., a nurse acting as a nurse across several teams, and being replaced by other nurses as needed), we posit that JPS enables people to better recognize the value in what other roles and expertise areas have to offer. In spurring problem and solution-focused collaborative work through shared recognition of problems as joint, JPS may help highly trained experts gain real-world experience with and respect for others’ work contributions.

H2: JPS is positively associated with MVR. H3: MVR relates positively to performance. H4: MVR mediates the relationship between JPS and performance.

EV as a moderator

Expertise variety (EV) refers to heterogeneity among members of an interdependent work group who have each accumulated domain specific-knowledge, encompassing variations in functional role or educational background and skill ( Ericsson and Smith, 1991 ). On the one hand, the presence of varied expertise offers the advantage of a more heterogeneous pool of task-relevant perspectives and informational resources to draw from, which serves to enhance team performance ( Van Knippenberg et al., 2004 ). On the other hand, the presence of differing training or functional backgrounds can create communication and cooperation barriers and heighten relational conflicts, damaging interpersonal relationships and negatively affecting performance ( Cronin and Weingart, 2007 ).

We hypothesize that EV in departments moderates the relationship of JPS with both MVR and performance. When there is high expertise variety within a department, we posit that the effect of JPS on performance and MVR is strengthened, as JPS can enable diverse experts to come together and mutually solve problems despite their differing backgrounds. When there is lower EV, we expect that JPS is still positively related to performance but less essentially so, as individuals with similar backgrounds may not need to rely on and value others to address problems collaboratively. Similarly, the benefit of JPS for performance that flows through MVR is likely especially important amid EV because the more experts present the more important it is likely to be that individuals value what others offer.

H5: JPS in the presence of greater EV is related to greater MVR (H5a) and greater performance (H5b). H6: There is a moderated mediation that explains the relationship between JPS and performance, with MVR mediating the JPS-performance relationship and EV moderating the JPS-MVR and JPS-performance relationships.

We collected data from a large, United States-based organization with over 20 hospitals, over 200 outpatient locations, and over 13 million patient encounters in 2022. It is commonly accepted that teamwork is central to most care delivery environments ( Rosenbaum, 2019 ) and that it is typically fluid ( Bedwell et al., 2012 ), in part because healthcare teams often engage varied expertise in response to patient needs ( Rosen et al., 2018 ). This makes a hospital-based healthcare organization an ideal setting for this study.

Sample and administration

The organizational survey was sent to 45,471 staff. We excluded individuals from our study who were in purely administrative departments to retain a focus on teamwork in patient-serving care, resulting in n = 26,319. The sample was composed of an array of expertise areas including patient-facing caregivers and their managers within the organization, which includes senior management, middle management, physicians, nurse practitioners, registered nurses, licensed practice nurses, nursing assistants, and other clinical professionals such as speech, physical and occupational therapists, alongside some security, service and clerical personnel supporting patient-facing departments. The survey was administered to staff electronically in English at two time points (May 2019 response rate = 87%; May 2021 response rate = 80%). The staff respondents were attributed to 1,608 departmental units, which were defined as being within the same department and physical location (in this organization, a single department can cut across several locations). These departmental units were obtained from human resources files. Table 1 presents the sample characteristics (presented for 2019). The study sample had a predominantly female composition (77.25%) and an age distribution with a large proportion in the 30–49 years age group (48.62%). A slight majority had 1–10 years of tenure (52.38%). There was a range of expertise areas present, with Registered Nurse being the most frequent (23.85%).

Table 1 . Demographic statistics of the sample ( n = 26,319).

All measures were assessed using five-point Likert scales and converted to domain means using the mean of the composite items.

Joint problem solving

Through iterative input sessions with organizational staff, we modified the joint problem-solving orientation measure developed by Kerrissey et al. (2021) for relevance within a single organization (the original measure was framed to ask about teamwork across two organizations). The adapted measure retained the theoretical emphasis of problems being seen as shared and solutions being seen as requiring co-production, but the language was modified to reflect departments as the referent unit. It included three items: (1) we view addressing problems as a team effort in this department, (2) when a problem arises, we routinely involve whomever is needed to address it, regardless of their unit or role, and (3) we can rely on people in other departments to address problems with us when needed, ( α = 0.85). JPS was measured in 2019.

Mutual value recognition

We measured MVR using relevant items from a validated survey developed for use in care delivery environments to capture affective teamwork across roles, the Primary Care Team Dynamics instrument, which includes 29 items all measured on Likert agreement scales and that are allocated across seven conceptual domains, including conditions for team effectiveness, shared understanding, accountability processes, communication processes, acting and feeling like a team, and perceived team effectiveness ( Song et al., 2015 , 2017 ). For the purpose of our hypothesizing in this study, we focused on the teamwork items used to capture valuing, trusting, and respecting others in expertise-diverse healthcare environments, which in the instrument’s measurement scheme fell under the broader theme of “acting and feeling like a team” (this theme also included two other aspects, one pertaining to using team skills and another on communicating information, which were not related to our hypothesizing and thus not measured in this study). In line with our interest in this study on mutually recognizing the value that others can offer, we focused on the three items describing aspects of valuing others, namely, respecting other roles and expertise, trusting each other’s work contributions, and listening to each other. MVR is thus distinct from the adjacent concept of transactive memory systems (TMS), which describes the shared division of cognitive labor in encoding, storage, retrieval, and communication of information ( Hollingshead, 2001 ). MVR focuses not on the cognitive representation and assignment of information from different domains but rather on the recognition that the information from other domains is valuable (respectable, trustable, and worth listening to).

To measure this concept, we used the following items from Song et al. (2015) : (1) “People in this department show respect for each other’s roles and expertise,” (2) “People in this department trust each other’s work and contributions,” and (3) “Most of the time people in this department listen to the information that I communicate to them,” ( α = 0.86). We made slight updates to the original items to reflect the department as the referent entity rather than “team.” We used these items as measured in 2021 to mitigate common method bias concerns with their measurement alongside JPS.

Outcome measure

We captured performance in the context of healthcare delivery by measuring staff-perceived care quality and safety, leveraging practical measures used widely within the industry to inform operations and managerial decision-making. Specifically, we used measures from a survey conducted by the health system we studied through a national vendor (Press Ganey), which implements validated employee experience surveys in healthcare. Press Ganey’s industry-oriented research has found that employee perceptions derived from these surveys are related to patient ratings of care as well as hospital financial performance ( Buhlman and Lee, 2019 ). We draw on three measures from their survey, as captured in 2021: (1) “[This organization] provides high-quality care and service,” (2) “[This organization] makes every effort to deliver safe, error-free care to patients,” and (3) “I would recommend [this organization] to family and friends who need care.” These items are conceptually related as markers of performance in healthcare (i.e., that care is both high quality and safe, alongside the general measure of perceived performance based on likelihood of recommending their services to others); they were also empirically related with a high Cronbach’s alpha ( α = 0.92). For parsimony in presenting our results, we thus operationalize performance as a mean across the three interrelated items; sensitivity analyses examining each item separately yielded similar results.

Aggregation of constructs

Because we are interested in the organizational conditions in which fluid teamwork transpires, our measurement is within the department as the local environment that exists within physically located departments, with common management and workers who team up in shifting but overlapping configurations day after day. This approach has been used in prior research on psychological constructs, such as in the study of team climates using psychological safety, which has often been conducted at the departmental level in healthcare e.g., (see Nembhard and Edmondson, 2006 ).

To justify this aggregation, we calculated within-team agreement parameters and intraclass correlations, and performed a one-way ANOVA for JPS, MVR, and team performance. All scales exhibited significant between-group variance ( F = 2.36, p < 0.01, F = 2.61; p < 0.01; and F = 3.28, p < 0.01, respectively). Intraclass correlations were: ICC 1 = 0.10 and ICC 2 = 0.57 for JPS; ICC 1 = 0.11 and ICC 2 = 0.62 for MVR; and ICC 1 = 0.11 and ICC 2 = 0.62 for performance. All scales showed moderate levels of agreement ( rwg = 0.70 for JPS; rwg = 0.71 for MVR; and rwg = 0.82 for performance). The ICC and rwg values were consistent with those in team research and considered acceptable for justifying aggregation ( Chen and Bliese, 2002 ; LeBreton and Senter, 2008 ). The typical values for ICC (1) are 0.01–0.45 and for ICC (2) are 0.45–0.90; values of rwg of 0.51–0.70 show moderate agreement, values of 0.71–0.90 show strong agreement, and 0.91–1.00 show very strong agreement ( LeBreton and Senter, 2008 ).

Expertise variety

Expertise variety was assessed as a sum of all professional/disciplinary title types in the department [job titles were provided through human resources records and were presented in a consistent fashion such that similar expertise and functional roles were labeled in the same way (e.g., Licensed Practice Nurse, Physician, etc.)]. This variable captures the variety of expertise and reflects the range of specialized knowledge and skills represented among individuals in each department.

Control variable

As a control measure, we included department size in our analysis, recognizing its established association with performance ( Salas et al., 2008b ). This was calculated as a sum of all individual people attached to a department-location in the human resources record.

Analytic procedure

We conducted CFA using structural equation modeling in Stata 15.1 for the individuals answering each item for JPS and MVR ( N = 24,563), examining root mean squared error of approximation (RMSEA), the chi-squared for the model vs. saturated, the Akaike’s information criterion (AIC), the comparative fit index (CFI), the Tucker Lewis Index (TLI), and the standardized root mean squared residuals (SRMR). We reviewed the descriptive means, standard deviations and correlations of the measures (as depicted in Table 2 ).

Table 2 . Means, standard deviations, and bivariate correlations for the research variables.

To test our hypotheses, we conducted a set of regression analyses using SPSS version 27, including a baseline regression, a mediation model, and moderated mediation using two models; we present the underlying regressions for these models in a stepwise fashion for clarity, in a series of Estimated Models (EM), which are each labeled within Table 3 . EM1 is a baseline model that includes the control variable of department size only. We then used the PROCESS macro for SPSS developed by Hayes (2018) to estimate the remaining models. To test hypotheses 1 through 4 pertaining to direct effects and mediation, we used a mediation model based on Hayes (2018) mediation “Model 4,” drawing on 5,000 random bootstrap samples. EM2 through EM4 in the results ( Table 3 ) build up the mediation model stepwise.

Table 3 . Analytic results for baseline, mediation and moderated mediation models.

For the moderated mediation analysis and testing of Hypotheses 5 and 6, we began with the Hayes mediation “Model 8,” which includes two moderating relationships for the moderator term (one with the mediator and one with the outcome). We used performance as the dependent variable, JPS as the independent variable, MVR as the mediator, and EV as the moderator (presented across EM5 and EM6 in Table 3 ).

After finding that only one of the hypothesized moderating relationships was statistically significant (between EV and JPS with MVR), we then tested a moderated mediation model that used only that one moderating relationship (excluding the non-significant moderation between EV and JPS with performance) in order to check that the statistically significant moderated mediation holds with one moderating relationship between EV and JPS on MVR ( Hayes, 2018 ; “Model 7”). We present the findings from the moderated mediation with this single moderating relationship across EM5 and EM7. To interpret the form of the interactions in the moderated mediation analysis, we plotted the relationships between JPS, EV and performance/MVR using high and low levels of JPS and expertise at one standard deviation above and below their means ( Aiken et al., 1991 ).

Confirmatory factor analysis

Confirmatory factor analysis yielded a significant model with satisfactory goodness of fit ( Hu and Bentler, 1999 ; x 2 N = 24,563, p < 0.01, CFI = 0.997, TLI = 0.995, RMSEA = 0.038, SRMR = 0.016, AIC = 302377.974) suggesting that JPS and MVR loaded onto two factors as expected. The two-factor structure yielded a substantially better fit than when JPS and MVR were collapsed into one factor ( N = 24,563, p < 0.01, CFI = 0.701, TLI = 0.501, RMSEA = 0.361, SRMR = 0.182, AIC = 3309). These findings suggest that JPS and MVR are two distinct constructs ( Cangur and Ercan, 2015 ).

Descriptive statistics

Table 2 summarizes the descriptive statistics and the correlations among the control, independent, and dependent variables at the department level.

Hypothesis testing

Table 3 presents the results of the baseline, mediation, and moderated mediation models, all of which include department size as a control variable. Consistent with hypothesis 1, we found a significant relationship between JPS and performance ( b = 0.33, SE = 0.02, p < 0.01; see EM 2). We also found evidence consistent with hypothesis 2 relating JPS to MVR ( b = 0.62, SE = 0.02, p < 0.01; see EM 3). When regressing, JPS and MVR on performance (see EM 4), we found a significant relationship between JPS and performance ( b = 0.10, SE = 0.02, p < 0.01), and MVR and performance ( b = 0.37, SE = 0.02, p < 0.01). The effects of the mediation pathways are significant as follows: the total effect of JPS on performance = 0.33 (Bootstrapped SE = 0.02, with 95% CI [0.29, 0.36]), the direct effect of JPS on performance = 0.10 (Bootstrapped SE = 0.02, with 95% CI [0.06, 0.14]), and the indirect effect of JPS to performance through MVR is = 0.23 (Bootstrapped SE = 0.02, with 95% CI [0.20, 0.26]). Taken together, these results support Hypothesis 4 pertaining to the presence of mediation.

For the first part of moderated mediation analysis, we regressed JPS, EV, and the interaction between JPS and EV on MVR (see EM 5). We found a significant interaction ( b = 0.05, SE = 0.02, p < 0.05), which supports hypothesis 5a. Figure 2A visually presents the form of the interaction, plotting the relationship between JPS, EV, and MVR using high and low levels of JPS and expertise at one standard deviation above and below their means ( Aiken et al., 1991 ).

Figure 2 . (A) Two-way interaction between JPS and EV on MVR as the dependent variable. (B) Two-way interaction between JPS and EV on performance as the dependent variable.

For the second part of moderated mediation model, we regressed JPS, MVR, and EV and the interaction between JPS and EV on performance (see EM 6). The interaction between JPS and EV was not statistically significant ( b = 0.02, SE = 0.01, p = 0.16). Hypotheses 5b and 6 were thus not supported. Figure 2B visually presents the form of the interaction, plotting the relationship between JPS and EV and performance using high and low levels of JPS and EV at one standard deviation above and below their means ( Aiken et al., 1991 ).

We then re-tested the moderated mediation model while excluding the non-significant moderation between JPS and EV on performance from the model (Model 7, Hayes, 2018 ). We found a significant relationship between JPS and performance ( b = 0.10, SE = 0.02, p < 0.01; Table 2 , EM 7), and MVR and performance ( b = 0.37, SE = 0.02, p < 0.01). The index of moderated mediation (PROCESS, Model 7; Hayes, 2018 ) support a moderated mediation model (indirect effect = 0.02, Boot SE = 0.01, with 95% CI [0.01, 0.03]). The results support a moderated-mediation model with EV moderating the relationship between JPS and MVR, and MVR mediating the relationship between JPS and performance.

This study sought to extend understanding of JPS within an organizational context characterized by fluid teamwork. We found evidence in support of a moderated mediation model, in which JPS was associated with performance directly and through MVR as a mediator, and in which JPS was most strongly related to MVR when expertise variety was high. These findings advance the nascent theory and research on JPS in fluid teamwork environments. They highlight JPS as valuable for organizations seeking to improve performance.

Building upon the recently identified concept of JPS in research on fluid cross-sector teams ( Kerrissey et al., 2021 ), we found that JPS was also associated with performance within organizational work units that rely upon highly fluid teams of experts to conduct complex work. Our results show that this relationship held when controlling for departmental size, and the results indicate that a substantial proportion of the variance was explained even in the parsimonious models that we used (i.e., observing the r-squared terms ranging from 0.34 to 0.35). As an orientation, JPS is focused on the presence of a shared emphasis, focusing on the interpersonal rather than informational aspects of fluid teamwork—particularly, how people approach one another in reference to the work they are doing together and the problems they face. Though connected conceptually and likely empirically, it is thus distinct from other measures that focus on factors like transactive memory systems and information sharing (e.g., Hollingshead, 2001 ; Mesmer-Magnus and DeChurch, 2009 ). Our results lend evidence to JPS as a factor worth examining.

Through mediation analysis, we found that part of the relationship between JPS and performance occurred through an enhanced recognition of the value that others can offer in collaborative work. This aligns with the perspective that experts must be able to swiftly establish a common understanding ( Reyes and Salas, 2019 )—and our findings lend evidence to the idea that JPS may help experts to do this more readily as they team up day-to-day with others. Put practically in an example, this represents the notion that a physician may not necessarily only need to learn afresh what a nurse “knows” but also to recognize that what a nurse knows about a patient from serving at their bedside is an important and valid input to the care process that is worth deliberately incorporating. This type of recognition may often come through familiarity in stable teams; it appears that JPS may also enable it, even without the luxury of stable teamwork over time.

Our findings lend support to EV as a moderator. However, it was only statistically significant for the relationship between JPS and MVR and not for the direct relationship between JPS and performance. The pattern for the moderating relationship between JPS and MVR was notable. As Figure 2A depicts, we found that when JPS was low, high EV resulted in less MVR. As JPS increased, MVR increased for all levels of EV. When JPS was high, organizational units with more EV showed higher MVR than units with lower EV. This suggests that in units with less EV, a little JPS may go a long way to foster MVR, but when there is substantial EV, a relatively high amount of JPS may be needed to expand MVR. This underscores the importance of JPS in highly expertise-varied environments for rapidly establishing awareness of what other expertise domains can contribute. This is especially notable in contrast to the moderation of the direct relationship between JPS and performance, which though not statistically significant implied the potential of a notably different pattern, in which greater EV always strengthened the relationship between JPS and performance, regardless of the level of JPS ( Figure 2B ). This contrast seems plausible, as having more expertise to draw from extends the pool of task-relevant perspectives and informational resources to draw from, which serves to enhance team performance directly ( Van Knippenberg et al., 2004 ). Our findings suggest that for MVR this advantage is likely to differ, requiring substantial JPS to engender MVR when EV is high.

Implications for theory and future research

Our findings contribute to the emerging literature on fluid teamwork, for which there have been calls for more research ( Tannenbaum et al., 2012 ; Wageman et al., 2012 ; Mortensen and Haas, 2018 ). In exploring JPS within organizational units where highly fluid teamwork is dominant, we found that a factor that was initially studied in the unique context of cross-sector teams remained relevant, and our analyses enhanced our understanding of how it yields performance through the moderated mediation model we test. We nonetheless view our study with an exploratory lens, given the nascency of research on fluid teamwork and the empirical difficulty of studying fluid teamwork at scale within organizations, which often leads to “glimpses” rather than comprehensive pictures of this dynamic phenomenon ( Kerrissey et al., 2020 ). There is a great deal more to explore and learn.

A main contribution of this research is to extend JPS to the organizational work unit context and to better understand how JPS operates and the boundary conditions that might shape its effectiveness in organizational contexts. While we find support for our conceptual model of moderated mediation, there are likely other important boundary conditions and mechanisms that can be proposed and explored in future research. For example, future research might examine how hierarchy and team climate measures such as psychological safety might relate to JPS and performance ( Nembhard and Edmondson, 2006 ). Research is also needed to identify ways to prompt JPS and to do so in a way that further facilitates MVR. One promising avenue may be through interventions focused on reflection; research on interprofessional collaboration, for instance, has underscored the value of reflection in helping individuals loosen the dominance of their tacitly acquired professional identities that prevent them from collaborating more effectively ( Wackerhausen, 2009 ).

While we examine JPS and MVR across a 2-year timeframe, there is great opportunity to examine these relationships with more longitudinal time points and a greater focus on temporal developments. Both theory on teams and theory on problem solving present development as a temporal process; for example, the model of problem solving of Mac Duffie (1997) articulates problem definition, analysis, generation and selection of solutions, testing and evaluation of solutions, and routinization. Future theoretical work might integrate the temporal models around team development and problem-solving fruitfully to conceptualize how JPS unfolds. Further, there is opportunity for in-depth ethnographic research to examine JPS in real-world settings to identify its antecedents. This kind of in-depth longitudinal work may be particularly valuable given the likelihood of mutually reinforcing relationships; although our measurement timeframe and theory suggest that JPS generates MVR, it is also plausible that MVR further reinforces JPS. Indeed, the decision to ask for problem-solving assistance is enhanced by a cognitively-based appraisal of that person ( Nebus, 2006 ). Studies to investigate how team processes dynamically unfold are needed, for instance, event and time-based behavioral observation ( Kolbe and Boos, 2019 ).

Because our purpose in this paper was to explore and extend the concept of JPS for fluid teamwork environments, we focused on JPS alongside a mediator and moderator rather than comparing JPS to alternative factors. We thus do not attempt to make a comprehensive model of team fluidity and performance—there are other factors that matter, and future research can explore how they compare to or interact with JPS. For example, our hypothesizing and findings in support of MVR as a key mediator differ from common explanations in more stable teamwork environments, where for instance collective psychological ownership is thought to be valuable for prompting effort, commitment and sacrifice among members, rather than elements of mutual value recognition ( Pierce and Jussila, 2011 ; Gray et al., 2020 ). It may be that in highly fluid teamwork among experts commitment mechanisms, though likely present to some degree, are less central because high fluidity may make commitment to any particular team entity less essential. This would be interesting to test in future research. A second, related area of extension could examine positive affective relationships, which are often cited as important factors in intact teams. However, research on problem-solving work has found a performance benefit to seeking out problem solving assistance from “dissonant ties,” i.e., difficult colleagues with whom a relationship may be fraught ( Brennecke, 2020 ). This points to potentially interesting and important differences in the role of positive affective bonds, relative to MVR, in highly fluid teamwork among experts; for instance, it is possible that MVR can develop effectively among dissonant ties, helping to value contributions even when other bonds remain suboptimal. Future research could compare and test these ideas.

Implications for practice

For practice, our findings suggest that organizational leaders and managers might look to joint problem-solving orientations as a key factor to promote performance within their organizational units where fluid teamwork occurs. This is important given that fluid teamwork is a reality in many highly dynamic, expertise-driven work settings ( Mortensen and Haas, 2018 ). Our research suggests that when fluid teamwork prevents people from gaining in-depth familiarity with other individuals—a key to performance in stable teams ( Hackman, 2002 )—they may nonetheless through joint problem-solving orientations come to better recognize the value of others’ contributions and thereby generate performance.

For organizations, looking for ways to hire for, foster, measure and reward JPS may be highly valuable. Our measurement of JPS at the departmental level in this study suggests that organizations may use this level of measurement to inform and improve their fluid teamwork in practice, as they may find it onerous or infeasible to track such measures at the team level amid such high fluidity (e.g., in healthcare, it might otherwise require surveying staff for each of the many teams they interact with per day). Moreover, that a department-level measure of JPS has predictive power for performance in a fluid teamwork environment may also offer to practitioners a pragmatic entity for intervention. Consider the alternative for a highly dynamic organizational environment: even if an organization were able to collect data on each fluid team that formed, if those teams are so fluid, distinct and often short-lived as they are in healthcare, then it would nonetheless not be clear who would be responsible for intervening, when, or with whom. That organization might then have beautiful data on which teams perform best, and which need help, only to realize that none of the teams still exist. For this reason, a departmental or similar unit-based measurement approach may be advantageous to intervention within organizations.

Limitations and future research

This study has limitations. First, while the analysis was conducted across a large number of work units (i.e., departments), it was conducted within one overarching healthcare delivery organization. Future efforts to further test these measures and relationships in other organizations and industries where fluid teamwork is central are needed to inform the generalizability of our findings. There are aspects of healthcare delivery, such as the overarching shared mission of delivering high quality patient care across disciplines, that may make JPS more salient for performance and MVR more relevant as a mediator in this context than others. Second, there are limitations in measuring these concepts at a departmental level. We did not have access to measures of variation by degree of fluidity in teamwork, and presumably there is some variation in degree of fluidity across departments in our study that would be fruitful to explore. Moreover, we recognize that measuring constructs at the departmental unit is imperfect in healthcare, especially as additional teamwork occurs across departments. However, because the preponderance of work occurs within departments (i.e., within an intensive care unit) and because JPS influences the interactions among members teaming up within the department, we believe it is a useful and reasonable simplification. In addition, the consistency and agreement measures (e.g., ICCs and RWGs) we analyzed provided empirical evidence that the key constructs were similar within and different across the departments. Third, future research can further develop the concept and refine the measurement of MVR, given its significant relationship to JPS and performance in our exploratory analysis. Fourth, because we measured JPS and MVR within the department rather than measuring these factors in each fluid team that occurred, our results address the department environment rather than the fluid team as the unit of analysis. While this offers advantages for pragmatism and practice, it is also a limitation for understanding team-level orientations and processes. Future research could fruitfully extend theory in this area by observing JPS as it forms in the moment within fluid teams.

Data availability statement

The datasets presented in this article are not readily available because this is organizational data with sensitive employee information that authors have access to under condition of not sharing it. Requests to access the datasets should be directed to [email protected] .

Ethics statement

The study involving humans was approved by Harvard University Institutional Review Board. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required from the participants or the participants’ legal guardians/next of kin in accordance with the national legislation and institutional requirements.

Author contributions

MK: Conceptualization, Data curation, Investigation, Methodology, Project administration, Writing – original draft, Writing – review & editing. ZN: Conceptualization, Data curation, Formal analysis, Methodology, Visualization, Writing – review & editing.

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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PubMed Abstract | Google Scholar

Keywords: teamwork, fluid teamwork, healthcare, joint problem-solving, survey

Citation: Kerrissey M and Novikov Z (2024) Joint problem-solving orientation, mutual value recognition, and performance in fluid teamwork environments. Front. Psychol . 15:1288904. doi: 10.3389/fpsyg.2024.1288904

Received: 05 September 2023; Accepted: 02 January 2024; Published: 13 February 2024.

Reviewed by:

Copyright © 2024 Kerrissey and Novikov. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Michaela Kerrissey, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Joint problem-solving

Transparency and trust

Dealing with competitive negotiators

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Collaborative Problem Solving: What It Is and How to Do It

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A Historical Review of Collaborative Learning and Cooperative Learning

Making Sense of Generative Learning

Cognitive Architecture and Instructional Design: 20 Years Later

1 Introduction

1.1 Phases of mathematical problem-solving processes

1.2 Interaction during mathematical collaborative problem-solving

1.3 Joint attention

1.4 Observing joint attention

1.5 Research aim

2.1 Participants

2.2 Apparatus

2.3 Procedure for data collection

2.4 Analysis procedure

4 Discussion and conclusions

Acknowledgements

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Corresponding author

Ethics declarations

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Supplementary Information

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ORIGINAL RESEARCH article

Introduction

Joint problem-solving orientation