technical problem recognition & resolution

35 problem-solving techniques and methods for solving complex problems

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All teams and organizations encounter challenges as they grow. There are problems that might occur for teams when it comes to miscommunication or resolving business-critical issues . You may face challenges around growth , design , user engagement, and even team culture and happiness. In short, problem-solving techniques should be part of every team’s skillset.

Problem-solving methods are primarily designed to help a group or team through a process of first identifying problems and challenges , ideating possible solutions , and then evaluating the most suitable .

Finding effective solutions to complex problems isn’t easy, but by using the right process and techniques, you can help your team be more efficient in the process.

So how do you develop strategies that are engaging, and empower your team to solve problems effectively?

In this blog post, we share a series of problem-solving tools you can use in your next workshop or team meeting. You’ll also find some tips for facilitating the process and how to enable others to solve complex problems.

Let’s get started! 

How do you identify problems?

How do you identify the right solution.

• Tips for more effective problem-solving

Complete problem-solving methods

• Problem-solving techniques to identify and analyze problems
• Problem-solving techniques for developing solutions

Problem-solving warm-up activities

Closing activities for a problem-solving process.

Before you can move towards finding the right solution for a given problem, you first need to identify and define the problem you wish to solve. 

Here, you want to clearly articulate what the problem is and allow your group to do the same. Remember that everyone in a group is likely to have differing perspectives and alignment is necessary in order to help the group move forward. 

Identifying a problem accurately also requires that all members of a group are able to contribute their views in an open and safe manner. It can be scary for people to stand up and contribute, especially if the problems or challenges are emotive or personal in nature. Be sure to try and create a psychologically safe space for these kinds of discussions.

Remember that problem analysis and further discussion are also important. Not taking the time to fully analyze and discuss a challenge can result in the development of solutions that are not fit for purpose or do not address the underlying issue.

Successfully identifying and then analyzing a problem means facilitating a group through activities designed to help them clearly and honestly articulate their thoughts and produce usable insight.

With this data, you might then produce a problem statement that clearly describes the problem you wish to be addressed and also state the goal of any process you undertake to tackle this issue.  

Finding solutions is the end goal of any process. Complex organizational challenges can only be solved with an appropriate solution but discovering them requires using the right problem-solving tool.

After you’ve explored a problem and discussed ideas, you need to help a team discuss and choose the right solution. Consensus tools and methods such as those below help a group explore possible solutions before then voting for the best. They’re a great way to tap into the collective intelligence of the group for great results!

Remember that the process is often iterative. Great problem solvers often roadtest a viable solution in a measured way to see what works too. While you might not get the right solution on your first try, the methods below help teams land on the most likely to succeed solution while also holding space for improvement.

Every effective problem solving process begins with an agenda . A well-structured workshop is one of the best methods for successfully guiding a group from exploring a problem to implementing a solution.

In SessionLab, it’s easy to go from an idea to a complete agenda . Start by dragging and dropping your core problem solving activities into place . Add timings, breaks and necessary materials before sharing your agenda with your colleagues.

The resulting agenda will be your guide to an effective and productive problem solving session that will also help you stay organized on the day!

Tips for more effective problem solving

Problem-solving activities are only one part of the puzzle. While a great method can help unlock your team’s ability to solve problems, without a thoughtful approach and strong facilitation the solutions may not be fit for purpose.

Let’s take a look at some problem-solving tips you can apply to any process to help it be a success!

Clearly define the problem

Jumping straight to solutions can be tempting, though without first clearly articulating a problem, the solution might not be the right one. Many of the problem-solving activities below include sections where the problem is explored and clearly defined before moving on.

This is a vital part of the problem-solving process and taking the time to fully define an issue can save time and effort later. A clear definition helps identify irrelevant information and it also ensures that your team sets off on the right track.

Don’t jump to conclusions

It’s easy for groups to exhibit cognitive bias or have preconceived ideas about both problems and potential solutions. Be sure to back up any problem statements or potential solutions with facts, research, and adequate forethought.

The best techniques ask participants to be methodical and challenge preconceived notions. Make sure you give the group enough time and space to collect relevant information and consider the problem in a new way. By approaching the process with a clear, rational mindset, you’ll often find that better solutions are more forthcoming.  

Try different approaches  

Problems come in all shapes and sizes and so too should the methods you use to solve them. If you find that one approach isn’t yielding results and your team isn’t finding different solutions, try mixing it up. You’ll be surprised at how using a new creative activity can unblock your team and generate great solutions.

Don’t take it personally 

Depending on the nature of your team or organizational problems, it’s easy for conversations to get heated. While it’s good for participants to be engaged in the discussions, ensure that emotions don’t run too high and that blame isn’t thrown around while finding solutions.

You’re all in it together, and even if your team or area is seeing problems, that isn’t necessarily a disparagement of you personally. Using facilitation skills to manage group dynamics is one effective method of helping conversations be more constructive.

Get the right people in the room

Your problem-solving method is often only as effective as the group using it. Getting the right people on the job and managing the number of people present is important too!

If the group is too small, you may not get enough different perspectives to effectively solve a problem. If the group is too large, you can go round and round during the ideation stages.

Creating the right group makeup is also important in ensuring you have the necessary expertise and skillset to both identify and follow up on potential solutions. Carefully consider who to include at each stage to help ensure your problem-solving method is followed and positioned for success.

Document everything

The best solutions can take refinement, iteration, and reflection to come out. Get into a habit of documenting your process in order to keep all the learnings from the session and to allow ideas to mature and develop. Many of the methods below involve the creation of documents or shared resources. Be sure to keep and share these so everyone can benefit from the work done!

Bring a facilitator 

Facilitation is all about making group processes easier. With a subject as potentially emotive and important as problem-solving, having an impartial third party in the form of a facilitator can make all the difference in finding great solutions and keeping the process moving. Consider bringing a facilitator to your problem-solving session to get better results and generate meaningful solutions!

Develop your problem-solving skills

It takes time and practice to be an effective problem solver. While some roles or participants might more naturally gravitate towards problem-solving, it can take development and planning to help everyone create better solutions.

You might develop a training program, run a problem-solving workshop or simply ask your team to practice using the techniques below. Check out our post on problem-solving skills to see how you and your group can develop the right mental process and be more resilient to issues too!

Design a great agenda

Workshops are a great format for solving problems. With the right approach, you can focus a group and help them find the solutions to their own problems. But designing a process can be time-consuming and finding the right activities can be difficult.

Check out our workshop planning guide to level-up your agenda design and start running more effective workshops. Need inspiration? Check out templates designed by expert facilitators to help you kickstart your process!

In this section, we’ll look at in-depth problem-solving methods that provide a complete end-to-end process for developing effective solutions. These will help guide your team from the discovery and definition of a problem through to delivering the right solution.

If you’re looking for an all-encompassing method or problem-solving model, these processes are a great place to start. They’ll ask your team to challenge preconceived ideas and adopt a mindset for solving problems more effectively.

• Six Thinking Hats
• Lightning Decision Jam
• Problem Definition Process
• Discovery & Action Dialogue

Design Sprint 2.0

• Open Space Technology

1. Six Thinking Hats

Individual approaches to solving a problem can be very different based on what team or role an individual holds. It can be easy for existing biases or perspectives to find their way into the mix, or for internal politics to direct a conversation.

Six Thinking Hats is a classic method for identifying the problems that need to be solved and enables your team to consider them from different angles, whether that is by focusing on facts and data, creative solutions, or by considering why a particular solution might not work.

Like all problem-solving frameworks, Six Thinking Hats is effective at helping teams remove roadblocks from a conversation or discussion and come to terms with all the aspects necessary to solve complex problems.

2. Lightning Decision Jam

Featured courtesy of Jonathan Courtney of AJ&Smart Berlin, Lightning Decision Jam is one of those strategies that should be in every facilitation toolbox. Exploring problems and finding solutions is often creative in nature, though as with any creative process, there is the potential to lose focus and get lost.

Unstructured discussions might get you there in the end, but it’s much more effective to use a method that creates a clear process and team focus.

In Lightning Decision Jam, participants are invited to begin by writing challenges, concerns, or mistakes on post-its without discussing them before then being invited by the moderator to present them to the group.

From there, the team vote on which problems to solve and are guided through steps that will allow them to reframe those problems, create solutions and then decide what to execute on. 

By deciding the problems that need to be solved as a team before moving on, this group process is great for ensuring the whole team is aligned and can take ownership over the next stages. 

Lightning Decision Jam (LDJ)   #action   #decision making   #problem solving   #issue analysis   #innovation   #design   #remote-friendly   The problem with anything that requires creative thinking is that it’s easy to get lost—lose focus and fall into the trap of having useless, open-ended, unstructured discussions. Here’s the most effective solution I’ve found: Replace all open, unstructured discussion with a clear process. What to use this exercise for: Anything which requires a group of people to make decisions, solve problems or discuss challenges. It’s always good to frame an LDJ session with a broad topic, here are some examples: The conversion flow of our checkout Our internal design process How we organise events Keeping up with our competition Improving sales flow

3. Problem Definition Process

While problems can be complex, the problem-solving methods you use to identify and solve those problems can often be simple in design. 

By taking the time to truly identify and define a problem before asking the group to reframe the challenge as an opportunity, this method is a great way to enable change.

Begin by identifying a focus question and exploring the ways in which it manifests before splitting into five teams who will each consider the problem using a different method: escape, reversal, exaggeration, distortion or wishful. Teams develop a problem objective and create ideas in line with their method before then feeding them back to the group.

This method is great for enabling in-depth discussions while also creating space for finding creative solutions too!

Problem Definition   #problem solving   #idea generation   #creativity   #online   #remote-friendly   A problem solving technique to define a problem, challenge or opportunity and to generate ideas.

4. The 5 Whys 

Sometimes, a group needs to go further with their strategies and analyze the root cause at the heart of organizational issues. An RCA or root cause analysis is the process of identifying what is at the heart of business problems or recurring challenges. 

The 5 Whys is a simple and effective method of helping a group go find the root cause of any problem or challenge and conduct analysis that will deliver results. 

By beginning with the creation of a problem statement and going through five stages to refine it, The 5 Whys provides everything you need to truly discover the cause of an issue.

The 5 Whys   #hyperisland   #innovation   This simple and powerful method is useful for getting to the core of a problem or challenge. As the title suggests, the group defines a problems, then asks the question “why” five times, often using the resulting explanation as a starting point for creative problem solving.

5. World Cafe

World Cafe is a simple but powerful facilitation technique to help bigger groups to focus their energy and attention on solving complex problems.

World Cafe enables this approach by creating a relaxed atmosphere where participants are able to self-organize and explore topics relevant and important to them which are themed around a central problem-solving purpose. Create the right atmosphere by modeling your space after a cafe and after guiding the group through the method, let them take the lead!

Making problem-solving a part of your organization’s culture in the long term can be a difficult undertaking. More approachable formats like World Cafe can be especially effective in bringing people unfamiliar with workshops into the fold. 

World Cafe   #hyperisland   #innovation   #issue analysis   World Café is a simple yet powerful method, originated by Juanita Brown, for enabling meaningful conversations driven completely by participants and the topics that are relevant and important to them. Facilitators create a cafe-style space and provide simple guidelines. Participants then self-organize and explore a set of relevant topics or questions for conversation.

6. Discovery & Action Dialogue (DAD)

One of the best approaches is to create a safe space for a group to share and discover practices and behaviors that can help them find their own solutions.

With DAD, you can help a group choose which problems they wish to solve and which approaches they will take to do so. It’s great at helping remove resistance to change and can help get buy-in at every level too!

This process of enabling frontline ownership is great in ensuring follow-through and is one of the methods you will want in your toolbox as a facilitator.

Discovery & Action Dialogue (DAD)   #idea generation   #liberating structures   #action   #issue analysis   #remote-friendly   DADs make it easy for a group or community to discover practices and behaviors that enable some individuals (without access to special resources and facing the same constraints) to find better solutions than their peers to common problems. These are called positive deviant (PD) behaviors and practices. DADs make it possible for people in the group, unit, or community to discover by themselves these PD practices. DADs also create favorable conditions for stimulating participants’ creativity in spaces where they can feel safe to invent new and more effective practices. Resistance to change evaporates as participants are unleashed to choose freely which practices they will adopt or try and which problems they will tackle. DADs make it possible to achieve frontline ownership of solutions.

7. Design Sprint 2.0

Want to see how a team can solve big problems and move forward with prototyping and testing solutions in a few days? The Design Sprint 2.0 template from Jake Knapp, author of Sprint, is a complete agenda for a with proven results.

Developing the right agenda can involve difficult but necessary planning. Ensuring all the correct steps are followed can also be stressful or time-consuming depending on your level of experience.

Use this complete 4-day workshop template if you are finding there is no obvious solution to your challenge and want to focus your team around a specific problem that might require a shortcut to launching a minimum viable product or waiting for the organization-wide implementation of a solution.

8. Open space technology

Open space technology- developed by Harrison Owen – creates a space where large groups are invited to take ownership of their problem solving and lead individual sessions. Open space technology is a great format when you have a great deal of expertise and insight in the room and want to allow for different takes and approaches on a particular theme or problem you need to be solved.

Start by bringing your participants together to align around a central theme and focus their efforts. Explain the ground rules to help guide the problem-solving process and then invite members to identify any issue connecting to the central theme that they are interested in and are prepared to take responsibility for.

Once participants have decided on their approach to the core theme, they write their issue on a piece of paper, announce it to the group, pick a session time and place, and post the paper on the wall. As the wall fills up with sessions, the group is then invited to join the sessions that interest them the most and which they can contribute to, then you’re ready to begin!

Everyone joins the problem-solving group they’ve signed up to, record the discussion and if appropriate, findings can then be shared with the rest of the group afterward.

Open Space Technology   #action plan   #idea generation   #problem solving   #issue analysis   #large group   #online   #remote-friendly   Open Space is a methodology for large groups to create their agenda discerning important topics for discussion, suitable for conferences, community gatherings and whole system facilitation

Techniques to identify and analyze problems

Using a problem-solving method to help a team identify and analyze a problem can be a quick and effective addition to any workshop or meeting.

While further actions are always necessary, you can generate momentum and alignment easily, and these activities are a great place to get started.

We’ve put together this list of techniques to help you and your team with problem identification, analysis, and discussion that sets the foundation for developing effective solutions.

Let’s take a look!

• The Creativity Dice
• Fishbone Analysis
• Problem Tree
• SWOT Analysis
• Agreement-Certainty Matrix
• The Journalistic Six
• LEGO Challenge
• What, So What, Now What?
• Journalists

Individual and group perspectives are incredibly important, but what happens if people are set in their minds and need a change of perspective in order to approach a problem more effectively?

Flip It is a method we love because it is both simple to understand and run, and allows groups to understand how their perspectives and biases are formed. 

Participants in Flip It are first invited to consider concerns, issues, or problems from a perspective of fear and write them on a flip chart. Then, the group is asked to consider those same issues from a perspective of hope and flip their understanding.  

No problem and solution is free from existing bias and by changing perspectives with Flip It, you can then develop a problem solving model quickly and effectively.

Flip It!   #gamestorming   #problem solving   #action   Often, a change in a problem or situation comes simply from a change in our perspectives. Flip It! is a quick game designed to show players that perspectives are made, not born.

10. The Creativity Dice

One of the most useful problem solving skills you can teach your team is of approaching challenges with creativity, flexibility, and openness. Games like The Creativity Dice allow teams to overcome the potential hurdle of too much linear thinking and approach the process with a sense of fun and speed. 

In The Creativity Dice, participants are organized around a topic and roll a dice to determine what they will work on for a period of 3 minutes at a time. They might roll a 3 and work on investigating factual information on the chosen topic. They might roll a 1 and work on identifying the specific goals, standards, or criteria for the session.

Encouraging rapid work and iteration while asking participants to be flexible are great skills to cultivate. Having a stage for idea incubation in this game is also important. Moments of pause can help ensure the ideas that are put forward are the most suitable. 

The Creativity Dice   #creativity   #problem solving   #thiagi   #issue analysis   Too much linear thinking is hazardous to creative problem solving. To be creative, you should approach the problem (or the opportunity) from different points of view. You should leave a thought hanging in mid-air and move to another. This skipping around prevents premature closure and lets your brain incubate one line of thought while you consciously pursue another.

11. Fishbone Analysis

Organizational or team challenges are rarely simple, and it’s important to remember that one problem can be an indication of something that goes deeper and may require further consideration to be solved.

Fishbone Analysis helps groups to dig deeper and understand the origins of a problem. It’s a great example of a root cause analysis method that is simple for everyone on a team to get their head around. 

Participants in this activity are asked to annotate a diagram of a fish, first adding the problem or issue to be worked on at the head of a fish before then brainstorming the root causes of the problem and adding them as bones on the fish. 

Using abstractions such as a diagram of a fish can really help a team break out of their regular thinking and develop a creative approach.

Fishbone Analysis   #problem solving   ##root cause analysis   #decision making   #online facilitation   A process to help identify and understand the origins of problems, issues or observations.

12. Problem Tree 

Encouraging visual thinking can be an essential part of many strategies. By simply reframing and clarifying problems, a group can move towards developing a problem solving model that works for them. 

In Problem Tree, groups are asked to first brainstorm a list of problems – these can be design problems, team problems or larger business problems – and then organize them into a hierarchy. The hierarchy could be from most important to least important or abstract to practical, though the key thing with problem solving games that involve this aspect is that your group has some way of managing and sorting all the issues that are raised.

Once you have a list of problems that need to be solved and have organized them accordingly, you’re then well-positioned for the next problem solving steps.

Problem tree   #define intentions   #create   #design   #issue analysis   A problem tree is a tool to clarify the hierarchy of problems addressed by the team within a design project; it represents high level problems or related sublevel problems.

13. SWOT Analysis

Chances are you’ve heard of the SWOT Analysis before. This problem-solving method focuses on identifying strengths, weaknesses, opportunities, and threats is a tried and tested method for both individuals and teams.

Start by creating a desired end state or outcome and bare this in mind – any process solving model is made more effective by knowing what you are moving towards. Create a quadrant made up of the four categories of a SWOT analysis and ask participants to generate ideas based on each of those quadrants.

Once you have those ideas assembled in their quadrants, cluster them together based on their affinity with other ideas. These clusters are then used to facilitate group conversations and move things forward. 

SWOT analysis   #gamestorming   #problem solving   #action   #meeting facilitation   The SWOT Analysis is a long-standing technique of looking at what we have, with respect to the desired end state, as well as what we could improve on. It gives us an opportunity to gauge approaching opportunities and dangers, and assess the seriousness of the conditions that affect our future. When we understand those conditions, we can influence what comes next.

14. Agreement-Certainty Matrix

Not every problem-solving approach is right for every challenge, and deciding on the right method for the challenge at hand is a key part of being an effective team.

The Agreement Certainty matrix helps teams align on the nature of the challenges facing them. By sorting problems from simple to chaotic, your team can understand what methods are suitable for each problem and what they can do to ensure effective results. 

If you are already using Liberating Structures techniques as part of your problem-solving strategy, the Agreement-Certainty Matrix can be an invaluable addition to your process. We’ve found it particularly if you are having issues with recurring problems in your organization and want to go deeper in understanding the root cause. 

Agreement-Certainty Matrix   #issue analysis   #liberating structures   #problem solving   You can help individuals or groups avoid the frequent mistake of trying to solve a problem with methods that are not adapted to the nature of their challenge. The combination of two questions makes it possible to easily sort challenges into four categories: simple, complicated, complex , and chaotic .  A problem is simple when it can be solved reliably with practices that are easy to duplicate.  It is complicated when experts are required to devise a sophisticated solution that will yield the desired results predictably.  A problem is complex when there are several valid ways to proceed but outcomes are not predictable in detail.  Chaotic is when the context is too turbulent to identify a path forward.  A loose analogy may be used to describe these differences: simple is like following a recipe, complicated like sending a rocket to the moon, complex like raising a child, and chaotic is like the game “Pin the Tail on the Donkey.”  The Liberating Structures Matching Matrix in Chapter 5 can be used as the first step to clarify the nature of a challenge and avoid the mismatches between problems and solutions that are frequently at the root of chronic, recurring problems.

Organizing and charting a team’s progress can be important in ensuring its success. SQUID (Sequential Question and Insight Diagram) is a great model that allows a team to effectively switch between giving questions and answers and develop the skills they need to stay on track throughout the process. 

Begin with two different colored sticky notes – one for questions and one for answers – and with your central topic (the head of the squid) on the board. Ask the group to first come up with a series of questions connected to their best guess of how to approach the topic. Ask the group to come up with answers to those questions, fix them to the board and connect them with a line. After some discussion, go back to question mode by responding to the generated answers or other points on the board.

It’s rewarding to see a diagram grow throughout the exercise, and a completed SQUID can provide a visual resource for future effort and as an example for other teams.

SQUID   #gamestorming   #project planning   #issue analysis   #problem solving   When exploring an information space, it’s important for a group to know where they are at any given time. By using SQUID, a group charts out the territory as they go and can navigate accordingly. SQUID stands for Sequential Question and Insight Diagram.

16. Speed Boat

To continue with our nautical theme, Speed Boat is a short and sweet activity that can help a team quickly identify what employees, clients or service users might have a problem with and analyze what might be standing in the way of achieving a solution.

Methods that allow for a group to make observations, have insights and obtain those eureka moments quickly are invaluable when trying to solve complex problems.

In Speed Boat, the approach is to first consider what anchors and challenges might be holding an organization (or boat) back. Bonus points if you are able to identify any sharks in the water and develop ideas that can also deal with competitors!   

Speed Boat   #gamestorming   #problem solving   #action   Speedboat is a short and sweet way to identify what your employees or clients don’t like about your product/service or what’s standing in the way of a desired goal.

17. The Journalistic Six

Some of the most effective ways of solving problems is by encouraging teams to be more inclusive and diverse in their thinking.

Based on the six key questions journalism students are taught to answer in articles and news stories, The Journalistic Six helps create teams to see the whole picture. By using who, what, when, where, why, and how to facilitate the conversation and encourage creative thinking, your team can make sure that the problem identification and problem analysis stages of the are covered exhaustively and thoughtfully. Reporter’s notebook and dictaphone optional.

The Journalistic Six – Who What When Where Why How   #idea generation   #issue analysis   #problem solving   #online   #creative thinking   #remote-friendly   A questioning method for generating, explaining, investigating ideas.

18. LEGO Challenge

Now for an activity that is a little out of the (toy) box. LEGO Serious Play is a facilitation methodology that can be used to improve creative thinking and problem-solving skills. 

The LEGO Challenge includes giving each member of the team an assignment that is hidden from the rest of the group while they create a structure without speaking.

What the LEGO challenge brings to the table is a fun working example of working with stakeholders who might not be on the same page to solve problems. Also, it’s LEGO! Who doesn’t love LEGO! 

LEGO Challenge   #hyperisland   #team   A team-building activity in which groups must work together to build a structure out of LEGO, but each individual has a secret “assignment” which makes the collaborative process more challenging. It emphasizes group communication, leadership dynamics, conflict, cooperation, patience and problem solving strategy.

19. What, So What, Now What?

If not carefully managed, the problem identification and problem analysis stages of the problem-solving process can actually create more problems and misunderstandings.

The What, So What, Now What? problem-solving activity is designed to help collect insights and move forward while also eliminating the possibility of disagreement when it comes to identifying, clarifying, and analyzing organizational or work problems. 

Facilitation is all about bringing groups together so that might work on a shared goal and the best problem-solving strategies ensure that teams are aligned in purpose, if not initially in opinion or insight.

Throughout the three steps of this game, you give everyone on a team to reflect on a problem by asking what happened, why it is important, and what actions should then be taken. 

This can be a great activity for bringing our individual perceptions about a problem or challenge and contextualizing it in a larger group setting. This is one of the most important problem-solving skills you can bring to your organization.

W³ – What, So What, Now What?   #issue analysis   #innovation   #liberating structures   You can help groups reflect on a shared experience in a way that builds understanding and spurs coordinated action while avoiding unproductive conflict. It is possible for every voice to be heard while simultaneously sifting for insights and shaping new direction. Progressing in stages makes this practical—from collecting facts about What Happened to making sense of these facts with So What and finally to what actions logically follow with Now What . The shared progression eliminates most of the misunderstandings that otherwise fuel disagreements about what to do. Voila!

20. Journalists  

Problem analysis can be one of the most important and decisive stages of all problem-solving tools. Sometimes, a team can become bogged down in the details and are unable to move forward.

Journalists is an activity that can avoid a group from getting stuck in the problem identification or problem analysis stages of the process.

In Journalists, the group is invited to draft the front page of a fictional newspaper and figure out what stories deserve to be on the cover and what headlines those stories will have. By reframing how your problems and challenges are approached, you can help a team move productively through the process and be better prepared for the steps to follow.

Journalists   #vision   #big picture   #issue analysis   #remote-friendly   This is an exercise to use when the group gets stuck in details and struggles to see the big picture. Also good for defining a vision.

Problem-solving techniques for developing solutions 

The success of any problem-solving process can be measured by the solutions it produces. After you’ve defined the issue, explored existing ideas, and ideated, it’s time to narrow down to the correct solution.

Use these problem-solving techniques when you want to help your team find consensus, compare possible solutions, and move towards taking action on a particular problem.

• Improved Solutions
• Four-Step Sketch
• 15% Solutions
• How-Now-Wow matrix
• Impact Effort Matrix

21. Mindspin  

Brainstorming is part of the bread and butter of the problem-solving process and all problem-solving strategies benefit from getting ideas out and challenging a team to generate solutions quickly. 

With Mindspin, participants are encouraged not only to generate ideas but to do so under time constraints and by slamming down cards and passing them on. By doing multiple rounds, your team can begin with a free generation of possible solutions before moving on to developing those solutions and encouraging further ideation. 

This is one of our favorite problem-solving activities and can be great for keeping the energy up throughout the workshop. Remember the importance of helping people become engaged in the process – energizing problem-solving techniques like Mindspin can help ensure your team stays engaged and happy, even when the problems they’re coming together to solve are complex. 

MindSpin   #teampedia   #idea generation   #problem solving   #action   A fast and loud method to enhance brainstorming within a team. Since this activity has more than round ideas that are repetitive can be ruled out leaving more creative and innovative answers to the challenge.

22. Improved Solutions

After a team has successfully identified a problem and come up with a few solutions, it can be tempting to call the work of the problem-solving process complete. That said, the first solution is not necessarily the best, and by including a further review and reflection activity into your problem-solving model, you can ensure your group reaches the best possible result. 

One of a number of problem-solving games from Thiagi Group, Improved Solutions helps you go the extra mile and develop suggested solutions with close consideration and peer review. By supporting the discussion of several problems at once and by shifting team roles throughout, this problem-solving technique is a dynamic way of finding the best solution. 

Improved Solutions   #creativity   #thiagi   #problem solving   #action   #team   You can improve any solution by objectively reviewing its strengths and weaknesses and making suitable adjustments. In this creativity framegame, you improve the solutions to several problems. To maintain objective detachment, you deal with a different problem during each of six rounds and assume different roles (problem owner, consultant, basher, booster, enhancer, and evaluator) during each round. At the conclusion of the activity, each player ends up with two solutions to her problem.

23. Four Step Sketch

Creative thinking and visual ideation does not need to be confined to the opening stages of your problem-solving strategies. Exercises that include sketching and prototyping on paper can be effective at the solution finding and development stage of the process, and can be great for keeping a team engaged. 

By going from simple notes to a crazy 8s round that involves rapidly sketching 8 variations on their ideas before then producing a final solution sketch, the group is able to iterate quickly and visually. Problem-solving techniques like Four-Step Sketch are great if you have a group of different thinkers and want to change things up from a more textual or discussion-based approach.

Four-Step Sketch   #design sprint   #innovation   #idea generation   #remote-friendly   The four-step sketch is an exercise that helps people to create well-formed concepts through a structured process that includes: Review key information Start design work on paper,  Consider multiple variations , Create a detailed solution . This exercise is preceded by a set of other activities allowing the group to clarify the challenge they want to solve. See how the Four Step Sketch exercise fits into a Design Sprint

24. 15% Solutions

Some problems are simpler than others and with the right problem-solving activities, you can empower people to take immediate actions that can help create organizational change. 

Part of the liberating structures toolkit, 15% solutions is a problem-solving technique that focuses on finding and implementing solutions quickly. A process of iterating and making small changes quickly can help generate momentum and an appetite for solving complex problems.

Problem-solving strategies can live and die on whether people are onboard. Getting some quick wins is a great way of getting people behind the process.   

It can be extremely empowering for a team to realize that problem-solving techniques can be deployed quickly and easily and delineate between things they can positively impact and those things they cannot change. 

15% Solutions   #action   #liberating structures   #remote-friendly   You can reveal the actions, however small, that everyone can do immediately. At a minimum, these will create momentum, and that may make a BIG difference.  15% Solutions show that there is no reason to wait around, feel powerless, or fearful. They help people pick it up a level. They get individuals and the group to focus on what is within their discretion instead of what they cannot change.  With a very simple question, you can flip the conversation to what can be done and find solutions to big problems that are often distributed widely in places not known in advance. Shifting a few grains of sand may trigger a landslide and change the whole landscape.

25. How-Now-Wow Matrix

The problem-solving process is often creative, as complex problems usually require a change of thinking and creative response in order to find the best solutions. While it’s common for the first stages to encourage creative thinking, groups can often gravitate to familiar solutions when it comes to the end of the process. 

When selecting solutions, you don’t want to lose your creative energy! The How-Now-Wow Matrix from Gamestorming is a great problem-solving activity that enables a group to stay creative and think out of the box when it comes to selecting the right solution for a given problem.

Problem-solving techniques that encourage creative thinking and the ideation and selection of new solutions can be the most effective in organisational change. Give the How-Now-Wow Matrix a go, and not just for how pleasant it is to say out loud. 

How-Now-Wow Matrix   #gamestorming   #idea generation   #remote-friendly   When people want to develop new ideas, they most often think out of the box in the brainstorming or divergent phase. However, when it comes to convergence, people often end up picking ideas that are most familiar to them. This is called a ‘creative paradox’ or a ‘creadox’. The How-Now-Wow matrix is an idea selection tool that breaks the creadox by forcing people to weigh each idea on 2 parameters.

26. Impact and Effort Matrix

All problem-solving techniques hope to not only find solutions to a given problem or challenge but to find the best solution. When it comes to finding a solution, groups are invited to put on their decision-making hats and really think about how a proposed idea would work in practice. 

The Impact and Effort Matrix is one of the problem-solving techniques that fall into this camp, empowering participants to first generate ideas and then categorize them into a 2×2 matrix based on impact and effort.

Activities that invite critical thinking while remaining simple are invaluable. Use the Impact and Effort Matrix to move from ideation and towards evaluating potential solutions before then committing to them. 

Impact and Effort Matrix   #gamestorming   #decision making   #action   #remote-friendly   In this decision-making exercise, possible actions are mapped based on two factors: effort required to implement and potential impact. Categorizing ideas along these lines is a useful technique in decision making, as it obliges contributors to balance and evaluate suggested actions before committing to them.

27. Dotmocracy

If you’ve followed each of the problem-solving steps with your group successfully, you should move towards the end of your process with heaps of possible solutions developed with a specific problem in mind. But how do you help a group go from ideation to putting a solution into action? 

Dotmocracy – or Dot Voting -is a tried and tested method of helping a team in the problem-solving process make decisions and put actions in place with a degree of oversight and consensus. 

One of the problem-solving techniques that should be in every facilitator’s toolbox, Dot Voting is fast and effective and can help identify the most popular and best solutions and help bring a group to a decision effectively. 

Dotmocracy   #action   #decision making   #group prioritization   #hyperisland   #remote-friendly   Dotmocracy is a simple method for group prioritization or decision-making. It is not an activity on its own, but a method to use in processes where prioritization or decision-making is the aim. The method supports a group to quickly see which options are most popular or relevant. The options or ideas are written on post-its and stuck up on a wall for the whole group to see. Each person votes for the options they think are the strongest, and that information is used to inform a decision.

All facilitators know that warm-ups and icebreakers are useful for any workshop or group process. Problem-solving workshops are no different.

Use these problem-solving techniques to warm up a group and prepare them for the rest of the process. Activating your group by tapping into some of the top problem-solving skills can be one of the best ways to see great outcomes from your session.

• Check-in/Check-out
• Doodling Together
• Show and Tell
• Constellations
• Draw a Tree

28. Check-in / Check-out

Solid processes are planned from beginning to end, and the best facilitators know that setting the tone and establishing a safe, open environment can be integral to a successful problem-solving process.

Check-in / Check-out is a great way to begin and/or bookend a problem-solving workshop. Checking in to a session emphasizes that everyone will be seen, heard, and expected to contribute. 

If you are running a series of meetings, setting a consistent pattern of checking in and checking out can really help your team get into a groove. We recommend this opening-closing activity for small to medium-sized groups though it can work with large groups if they’re disciplined!

Check-in / Check-out   #team   #opening   #closing   #hyperisland   #remote-friendly   Either checking-in or checking-out is a simple way for a team to open or close a process, symbolically and in a collaborative way. Checking-in/out invites each member in a group to be present, seen and heard, and to express a reflection or a feeling. Checking-in emphasizes presence, focus and group commitment; checking-out emphasizes reflection and symbolic closure.

29. Doodling Together  

Thinking creatively and not being afraid to make suggestions are important problem-solving skills for any group or team, and warming up by encouraging these behaviors is a great way to start. 

Doodling Together is one of our favorite creative ice breaker games – it’s quick, effective, and fun and can make all following problem-solving steps easier by encouraging a group to collaborate visually. By passing cards and adding additional items as they go, the workshop group gets into a groove of co-creation and idea development that is crucial to finding solutions to problems. 

Doodling Together   #collaboration   #creativity   #teamwork   #fun   #team   #visual methods   #energiser   #icebreaker   #remote-friendly   Create wild, weird and often funny postcards together & establish a group’s creative confidence.

30. Show and Tell

You might remember some version of Show and Tell from being a kid in school and it’s a great problem-solving activity to kick off a session.

Asking participants to prepare a little something before a workshop by bringing an object for show and tell can help them warm up before the session has even begun! Games that include a physical object can also help encourage early engagement before moving onto more big-picture thinking.

By asking your participants to tell stories about why they chose to bring a particular item to the group, you can help teams see things from new perspectives and see both differences and similarities in the way they approach a topic. Great groundwork for approaching a problem-solving process as a team! 

Show and Tell   #gamestorming   #action   #opening   #meeting facilitation   Show and Tell taps into the power of metaphors to reveal players’ underlying assumptions and associations around a topic The aim of the game is to get a deeper understanding of stakeholders’ perspectives on anything—a new project, an organizational restructuring, a shift in the company’s vision or team dynamic.

31. Constellations

Who doesn’t love stars? Constellations is a great warm-up activity for any workshop as it gets people up off their feet, energized, and ready to engage in new ways with established topics. It’s also great for showing existing beliefs, biases, and patterns that can come into play as part of your session.

Using warm-up games that help build trust and connection while also allowing for non-verbal responses can be great for easing people into the problem-solving process and encouraging engagement from everyone in the group. Constellations is great in large spaces that allow for movement and is definitely a practical exercise to allow the group to see patterns that are otherwise invisible. 

Constellations   #trust   #connection   #opening   #coaching   #patterns   #system   Individuals express their response to a statement or idea by standing closer or further from a central object. Used with teams to reveal system, hidden patterns, perspectives.

32. Draw a Tree

Problem-solving games that help raise group awareness through a central, unifying metaphor can be effective ways to warm-up a group in any problem-solving model.

Draw a Tree is a simple warm-up activity you can use in any group and which can provide a quick jolt of energy. Start by asking your participants to draw a tree in just 45 seconds – they can choose whether it will be abstract or realistic. 

Once the timer is up, ask the group how many people included the roots of the tree and use this as a means to discuss how we can ignore important parts of any system simply because they are not visible.

All problem-solving strategies are made more effective by thinking of problems critically and by exposing things that may not normally come to light. Warm-up games like Draw a Tree are great in that they quickly demonstrate some key problem-solving skills in an accessible and effective way.

Draw a Tree   #thiagi   #opening   #perspectives   #remote-friendly   With this game you can raise awarness about being more mindful, and aware of the environment we live in.

Each step of the problem-solving workshop benefits from an intelligent deployment of activities, games, and techniques. Bringing your session to an effective close helps ensure that solutions are followed through on and that you also celebrate what has been achieved.

Here are some problem-solving activities you can use to effectively close a workshop or meeting and ensure the great work you’ve done can continue afterward.

• One Breath Feedback
• Who What When Matrix
• Response Cards

How do I conclude a problem-solving process?

All good things must come to an end. With the bulk of the work done, it can be tempting to conclude your workshop swiftly and without a moment to debrief and align. This can be problematic in that it doesn’t allow your team to fully process the results or reflect on the process.

At the end of an effective session, your team will have gone through a process that, while productive, can be exhausting. It’s important to give your group a moment to take a breath, ensure that they are clear on future actions, and provide short feedback before leaving the space. 

The primary purpose of any problem-solving method is to generate solutions and then implement them. Be sure to take the opportunity to ensure everyone is aligned and ready to effectively implement the solutions you produced in the workshop.

Remember that every process can be improved and by giving a short moment to collect feedback in the session, you can further refine your problem-solving methods and see further success in the future too.

33. One Breath Feedback

Maintaining attention and focus during the closing stages of a problem-solving workshop can be tricky and so being concise when giving feedback can be important. It’s easy to incur “death by feedback” should some team members go on for too long sharing their perspectives in a quick feedback round. 

One Breath Feedback is a great closing activity for workshops. You give everyone an opportunity to provide feedback on what they’ve done but only in the space of a single breath. This keeps feedback short and to the point and means that everyone is encouraged to provide the most important piece of feedback to them. 

One breath feedback   #closing   #feedback   #action   This is a feedback round in just one breath that excels in maintaining attention: each participants is able to speak during just one breath … for most people that’s around 20 to 25 seconds … unless of course you’ve been a deep sea diver in which case you’ll be able to do it for longer.

34. Who What When Matrix 

Matrices feature as part of many effective problem-solving strategies and with good reason. They are easily recognizable, simple to use, and generate results.

The Who What When Matrix is a great tool to use when closing your problem-solving session by attributing a who, what and when to the actions and solutions you have decided upon. The resulting matrix is a simple, easy-to-follow way of ensuring your team can move forward. 

Great solutions can’t be enacted without action and ownership. Your problem-solving process should include a stage for allocating tasks to individuals or teams and creating a realistic timeframe for those solutions to be implemented or checked out. Use this method to keep the solution implementation process clear and simple for all involved. 

Who/What/When Matrix   #gamestorming   #action   #project planning   With Who/What/When matrix, you can connect people with clear actions they have defined and have committed to.

35. Response cards

Group discussion can comprise the bulk of most problem-solving activities and by the end of the process, you might find that your team is talked out! 

Providing a means for your team to give feedback with short written notes can ensure everyone is head and can contribute without the need to stand up and talk. Depending on the needs of the group, giving an alternative can help ensure everyone can contribute to your problem-solving model in the way that makes the most sense for them.

Response Cards is a great way to close a workshop if you are looking for a gentle warm-down and want to get some swift discussion around some of the feedback that is raised. 

Response Cards   #debriefing   #closing   #structured sharing   #questions and answers   #thiagi   #action   It can be hard to involve everyone during a closing of a session. Some might stay in the background or get unheard because of louder participants. However, with the use of Response Cards, everyone will be involved in providing feedback or clarify questions at the end of a session.

Save time and effort discovering the right solutions

A structured problem solving process is a surefire way of solving tough problems, discovering creative solutions and driving organizational change. But how can you design for successful outcomes?

With SessionLab, it’s easy to design engaging workshops that deliver results. Drag, drop and reorder blocks  to build your agenda. When you make changes or update your agenda, your session  timing   adjusts automatically , saving you time on manual adjustments.

Collaborating with stakeholders or clients? Share your agenda with a single click and collaborate in real-time. No more sending documents back and forth over email.

Explore  how to use SessionLab  to design effective problem solving workshops or  watch this five minute video  to see the planner in action!

Over to you

The problem-solving process can often be as complicated and multifaceted as the problems they are set-up to solve. With the right problem-solving techniques and a mix of creative exercises designed to guide discussion and generate purposeful ideas, we hope we’ve given you the tools to find the best solutions as simply and easily as possible.

Is there a problem-solving technique that you are missing here? Do you have a favorite activity or method you use when facilitating? Let us know in the comments below, we’d love to hear from you! 

thank you very much for these excellent techniques

Certainly wonderful article, very detailed. Shared!

Your list of techniques for problem solving can be helpfully extended by adding TRIZ to the list of techniques. TRIZ has 40 problem solving techniques derived from methods inventros and patent holders used to get new patents. About 10-12 are general approaches. many organization sponsor classes in TRIZ that are used to solve business problems or general organiztational problems. You can take a look at TRIZ and dwonload a free internet booklet to see if you feel it shound be included per your selection process.

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Problem Resolution: A Comprehensive Guide to Effective Strategies

In the landscape of everyday challenges, the art of problem resolution stands as a pivotal skill, empowering individuals and organizations to navigate the complexities of diverse situations. This introductory exploration lays the foundational understanding of what constitutes problem resolution and underscores its paramount importance in various facets of life.

As someone who has faced numerous challenges throughout my personal and professional journey, I can attest to the transformative power of effective problem resolution. From resolving conflicts in relationships to tackling complex projects at work, the ability to identify, analyze, and resolve issues has been a game-changer for me.

Definition of Problem Resolution

Problem resolution can be thought of as both a process and a skill set—a methodical approach to identifying, analyzing, and resolving issues that impede progress or cause disruption. Central to this process is the capacity for critical thinking, which enables individuals to dissect problems and create effective solutions with a strategic edge.

I remember a time when my team was faced with a seemingly insurmountable obstacle during a crucial project. By employing a structured problem-solving approach, we were able to break down the issue, identify the root causes, and develop a tailored solution that not only resolved the immediate challenge but also prevented similar issues from arising in the future.

Importance of Problem Resolution in Different Aspects of Life

Be it in personal life, educational settings, or the business arena, the benefits of proficient problem resolution are manifold. The ability to confront and navigate obstacles not only fosters resilience and adaptability but also acts as a catalyst for learning and personal development.

In my own life, I've found that honing my problem-solving skills has been invaluable. From navigating complex family dynamics to overcoming academic hurdles, the ability to approach challenges with a strategic mindset has been a key driver of my personal growth and success.

Understanding the Essence of Problem Resolution

Brief insight into the concept of problem resolution.

Delving into the essence of problem resolution involves an understanding of its multidimensional nature. It is an intellectual dance that balances analytical scrutiny with creative thinking, blending logical reasoning with intuition. Each problem presents its unique context and array of implications, necessitating an approach that is tailored yet flexible, structured yet fluid.

Different Types of Problems that Require Resolution

Problems are as diverse as the contexts in which they arise. They can be:

Simple or complex : A simple problem, such as a malfunctioning appliance, may require a straightforward troubleshooting approach, while a complex problem, like declining sales in a business, may demand a multi-faceted strategy.

Technical or emotional : Technical problems often have clear-cut solutions based on expertise and logical reasoning, while emotional problems, such as interpersonal conflicts, require a more nuanced, empathetic approach.

Immediate or chronic : Some problems, like a sudden IT outage, require swift action, while others, like improving company culture, may necessitate a long-term, sustained effort.

Importance of Identifying the Type of Problem

The accurate identification of a problem is a critical step in the problem resolution process. It influences the selection of tools and strategies and determines the path of inquiry and action. Misidentification can lead to flawed solutions, wasted resources, and additional issues.

I once witnessed a company implement a costly rebranding campaign to address declining sales, only to realize later that the root cause was actually a flawed product design. This experience taught me the crucial importance of thorough problem identification before jumping into solution mode.

Steps Involved in Problem Resolution

Identification of the problem, importance of accurately defining the problem.

The foundation of effective problem resolution is a clear, concise definition of the problem. A properly defined problem sets boundaries for the resolution process and provides clarity on what success looks like. Without this clarity, efforts can be misguided or dispersed ineffectively.

Techniques for Identifying Problems

Techniques such as root cause analysis, brainstorming sessions, and the five whys method are employed to peel back the layers of a problem and pinpoint the core issues. Effective communication also plays an essential role, allowing for various perspectives to surface and facilitate a holistic understanding of the problem.

Common Challenges in Problem Identification

Obstacles to problem identification often include cognitive biases, lack of information, and an over-reliance on assumptions. Overcoming these challenges requires an open-minded approach, rigorous questioning, and the gathering of sufficient data to inform the identification process.

Analysis of the Problem

How to analyze a problem effectively.

Once the problem is identified, the analysis begins with breaking down the problem into its constituent parts. This involves examining the underlying causes, the context, and the stakeholders involved. An effective analysis seeks to understand the problem in depth, paving the way for viable solutions to emerge.

Tools and Techniques for Problem Analysis

There are numerous analytical tools at one's disposal, such as:

SWOT analysis  (Strengths, Weaknesses, Opportunities, Threats)

Fishbone diagrams

These tools help in organizing information, revealing patterns, and illuminating the relationships between different elements of the problem.

Challenges in Problem Analysis and their Remedies

Challenges in this stage often stem from incomplete or inaccurate data, resistance to change from stakeholders, and preconceived notions about the problem. These can be mitigated by promoting a culture of transparency, encouraging diverse viewpoints, and committing to a thorough and unbiased analysis.

Setting up Possible Solutions

Formulating potential alleged solutions.

With a comprehensive analysis complete, the focus shifts toward generating a range of possible solutions. These should be diverse, creative, and practical, with an eye toward meeting the goals established during the identification and analysis stages.

Tools and Techniques to Aid Solution Formulation

Brainstorming

Lateral thinking exercises

Simulation models

Such techniques encourage out-of-the-box thinking and help to prevent the premature dismissal of innovative options.

Pitfalls to Avoid during Solution Formulation

During the formulation phase, it's critical to avoid tunnel vision or falling back onto familiar, yet suboptimal, solutions. Diverse teams, external consultations, and a willingness to experiment can combat these pitfalls.

Selection and Implementation of the Best Solution

Criteria for selecting the most effective solution.

Selecting the best solution involves evaluating the proposed options against criteria such as feasibility, sustainability, cost, and impact. A thorough cost-benefit analysis often aids in this evaluation, ensuring a rational basis for decision-making.

I once had to decide between two software solutions for my company. By carefully weighing factors like scalability, user-friendliness, and long-term costs, we were able to select the option that best aligned with our needs and goals.

Steps to Implementing the Chosen Solution

Implementing a solution typically requires planning, resource allocation, and change management. Clear communication and the establishment of milestones are also crucial to tracking progress and maintaining momentum.

Common Challenges in Solution Implementation and Possible Solutions

Resistance to change, unexpected obstacles, and resource constraints are common challenges during implementation. Addressing these effectively may require adaptive leadership, stakeholder engagement, and contingency planning.

Review and Assessment of Implemented Solution

Importance of reviewing solutions.

Once a solution is implemented, it is imperative to review and assess its effectiveness. This helps in confirming that the problem has been adequately resolved and provides insights for future problem-solving endeavors.

Tools for Solution Review and Assessment

Feedback surveys

Performance metrics

Review meetings

These tools provide quantifiable and qualitative data that inform whether the solution has met its intended objectives.

How to Leverage the Findings from the Review Process

Findings from the review process should be documented, analyzed, and communicated to all relevant stakeholders. This allows for continuous improvement and learning, honing the collective problem-solving capabilities.

Problem Resolution in Different Sectors

Business sector, role of problem resolution in business.

In the business world, problem resolution is crucial for survival and growth. It enables businesses to adapt to market changes, address client needs effectively, and optimize operations.

Real-life Example of Problem Resolution in a Business Setting

I once worked with a company facing intense competition in their industry. By engaging in a comprehensive problem-solving process, we identified key areas for differentiation and developed a targeted marketing strategy. This not only helped the company stand out but also led to a significant increase in market share.

Education Sector

Implication of problem resolution in education.

Educational institutions benefit greatly from problem resolution by enhancing the learning environment and addressing various administrative and operational challenges.

Example of Problem Resolution in an Educational Setting

As a teacher, I often employ problem-solving techniques to address individual student needs. By carefully analyzing each student's unique challenges and strengths, I can develop personalized learning plans that optimize their educational experience.

Personal Life

How problem resolution can help improve personal lives.

In personal contexts, effective problem resolution can lead to more satisfactory relationships, improved financial management, and healthier lifestyles.

Personal Life Example where Problem Resolution was Effectively Applied

In my own life, I used problem-solving skills to navigate a difficult career transition. By assessing my skills, researching the job market, and developing a strategic action plan, I was able to successfully pivot into a new field that aligned with my passions and goals.

Recap on the Importance of Problem Resolution

Problem resolution is an invaluable skill with broad applications. Whether addressing personal, educational, or business challenges, its strategic application can lead to improved outcomes and promote an environment of continuous learning and adaptation.

Encouragement for Continuous Learning and Improvement in Problem Resolution Skills

In a world of ever-evolving challenges, investing time in honing problem resolution skills remains a prudent choice. Pursuing courses, learning from diverse scenarios, and incorporating reviews into every problem-solving cycle contribute to a robust skill set that can make a marked difference in any sector.

As I reflect on my own journey, I am grateful for the problem-solving skills I have developed over the years. They have been instrumental in helping me navigate the complexities of life and work, and have opened doors to opportunities I never thought possible. I encourage everyone to embrace the art of problem resolution, for it is a powerful tool that can transform challenges into stepping stones towards success.

What are the key distinctions between different problem resolution strategies in conflict management

Understanding conflict management.

Conflict management involves various strategies. Each caters to distinct situations and outcomes. Clarity on these differences enhances conflict resolution effectiveness. Here we dissect key problem-resolution strategies.

Competing Strategy

Competing involves assertive and uncooperative behavior. Parties pursue their interests, disregarding others. It echoes a win-lose situation. This method suits quick decision needs. But, it can damage relationships.

Accommodating Strategy

Accommodating contrasts with competing. Here, one party yields to another's needs. This approach values the relationship over personal victory. It works well for minor concessions. Long-term, it might cause resentment.

Avoiding Strategy

Avoiding indicates withdrawal from conflict. Parties neither address their concerns nor others'. This strategy buys time or deflects minor issues. However, avoiding can escalate unresolved conflicts.

Collaborating Strategy

Collaborating merges assertiveness with cooperation. Parties aim for a win-win solution. They search for options satisfying all involved. This intricate process fosters understanding. It requires time and trust.

Compromising Strategy

Compromising strikes a balance between competing and accommodating. Each party concedes somewhat. This method seeks a fast, fair solution. It may not satisfy everyone fully. Yet, it maintains relationships and progress.

- Competing : Quick, assertive, potentially damaging.

- Accommodating : Yielding, preserving relationships, can foster resentment.

- Avoiding : Detachment, temporary relief, might worsen later.

- Collaborating : In-depth, time-consuming, builds mutual respect.

- Compromising : Balance, moderately satisfies, preserves relationships.

Different conflicts need different approaches. Consider each strategy's impact. Align them with your conflict resolution goals. Effective management requires flexibility and discernment.

How can efficient problem resolution strategies impact organizational success

Efficient problem resolution in organizations.

Problem resolution strategies prove critical. They define organizational success. Poor methods can lead to failure. Efficient strategies, conversely, enhance performance. They streamline operations. They foster a healthier work environment. Most importantly, they drive growth.

The Impact on Performance

Efficiency reduces waste. It minimizes downtime. Efficient problem-solving speeds resolution time. It ensures continuity in operations. Critically, it increases customer satisfaction. Resolution speed often dictates customer retention.

Workers stay productive. They rely on effective systems. Such systems aid in problem-solving. Without them, frustration can ensue. This can diminish morale. Productivity usually suffers as well. Efficient strategies keep morale high. This improves overall productivity.

Operational Streamlining and Innovation

Efficient problem resolution demands process understanding. It calls for innovation. Organizations must constantly adapt. They must refine their approaches. Streamlining results from ongoing process improvement. This increases operational efficiency.

Such constant refinement leads to innovation. Workers become more proactive. They suggest improvements. They help refine processes. Organizations grow more competitive. They grow more agile. They better navigate market changes.

Creating a Positive Work Environment

Stress pervades problem-laden environments. Efficient strategies can alleviate it. They promote a positive atmosphere. Workers feel more empowered. They tackle issues confidently. This confidence promotes job satisfaction. It also fosters loyalty.

Team collaboration often improves. Colleagues work together. They solve problems. This cultivates a united workforce. It may also inspire leadership. Efficient problem resolution empowers workers. It creates leaders.

Driving Organizational Growth

Growth links to problem resolution. Efficient strategies enable scalability. They help meet increasing demands. They allow for expansion. Without them, organizations stagnate.

Customer trust hinges on issue management. Efficient resolution bolsters this trust. It encourages repeat business. It enhances the brand's reputation. Both are vital for growth.

Healthy financial management also depends on efficiency. Fast problem resolution saves money. It prevents costly delays. It preempts larger issues. It reduces the need for damage control. Financial resources can thus promote development.

Organizations thrive on efficiency. Efficient problem resolution stands central to this. It touches every aspect of an entity. It influences performance, morale, innovation, and customer relations. Ultimately, it shapes success. Organizations must invest in it. They must prioritize it. They must refine it. Only then can they solidify their place in the market.

How do sociocultural factors influence the choice of problem resolution strategies?

Sociocultural factors and problem resolution.

Understanding culturally driven decision-making is complex. Societies differ widely. Individuals within them thus adhere to distinct norms. These norms often guide conflict resolution strategies.

The Role of Culture

Culture shapes perceptions of appropriate behavior. It provides a framework for problem-solving. Cultural protocols determine acceptable responses to conflicts. For example, some cultures value community harmony. Such values may promote mediation over confrontation. Meanwhile, individualistic cultures might stress personal rights. These societies often favor direct and assertive approaches. Egalitarian cultures might encourage collaborative strategies. Hierarchical societies might defer to authority figures.

Social Influence

Social influence is significant. It affects how people perceive problems. Peers, family, and leaders suggest suitable strategies. They model either competitive or cooperative problem-solving. Sociocultural norms dictate which influences are most potent. They also shape the cost of deviating from prescribed behavior.

Conflict Resolution in Context

Different contexts yield different strategies. Familial disputes might use conciliatory tactics. Workplace conflicts could necessitate formal mediation. Sociocultural context influences the resolution method chosen.

Gender Roles

Gender roles can dictate distinct approaches. Traditionally, societies expect men to be assertive. Conversely, women might be expected to avoid conflict. Such roles guide gender-specific resolution strategies. These expectations can hinder effective problem solving across genders.

Education and Socialization

Education instills conflict resolution tactics. School systems reflect the broader sociocultural values. They impart the skills deemed important by society. Children learn either competition or collaboration. The society's emphasis determines which skills are honed.

Religion and Belief Systems

Religious beliefs influence resolution methods. Religious tenets might promote forgiveness and reconciliation. In some religions, faith-based arbitration is preferred. These systems add a moral dimension to conflict resolution.

Economic Factors

Economic systems play a role. Capitalist societies might encourage competitive dispute resolution. Socially oriented economies might favor collective problem-solving. The economic framework often indicates the preferred approach.

Legal Systems

Legal systems embody sociocultural values. They often suggest formal processes. These processes can either be adversarial or restorative. Societies with strong legal traditions might favor structured resolution.

Sociocultural factors profoundly influence problem resolution. Understanding these influencers is vital. It ensures more culturally sensitive and effective approaches. Recognizing these nuances can lead to better outcomes in conflict resolution.

He is a content producer who specializes in blog content. He has a master's degree in business administration and he lives in the Netherlands.

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Are You Solving the Right Problem?

• Dwayne Spradlin

Most firms aren’t, and that undermines their innovation efforts.

Reprint: R1209F

The rigor with which a problem is defined is the most important factor in finding a good solution. Many organizations, however, are not proficient at articulating their problems and identifying which ones are crucial to their strategies.

They may even be trying to solve the wrong problems—missing opportunities and wasting resources in the process. The key is to ask the right questions.

The author describes a process that his firm, InnoCentive, has used to help clients define and articulate business, technical, social, and policy challenges and then present them to an online community of more than 250,000 solvers. The four-step process consists of asking a series of questions and using the answers to create a problem statement that will elicit novel ideas from an array of experts.

• Establish the need for a solution. What is the basic need? Who will benefit from a solution?
• Justify the need. Why should your organization attempt to solve this problem? Is it aligned with your strategy? If a solution is found, who will implement it?
• Contextualize the problem. What have you and others already tried? Are there internal and external constraints to implementing a solution?
• Write the problem statement. What requirements must a solution meet? What language should you use to describe the problem? How will you evaluate solutions and measure success?

EnterpriseWorks/VITA, a nonprofit organization, used this process to find a low-cost, lightweight, and convenient product that expands access to clean drinking water in the developing world.

“If I were given one hour to save the planet, I would spend 59 minutes defining the problem and one minute resolving it,” Albert Einstein said.

• DS Dwayne Spradlin is the president and CEO of InnoCentive , an online marketplace that connects organizations with freelance problem solvers in a multitude of fields. He is a coauthor, with Alpheus Bingham, of The Open Innovation Marketplace: Creating Value in the Challenge Driven Enterprise (FT Press, 2011).

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What is Troubleshooting? A Complete Guide

Dive into the world of Troubleshooting and master the art of resolving issues efficiently. This comprehensive blog covers Troubleshooting methodologies, techniques, and best practices, enabling you to tackle challenges effectively in various domains

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Troubleshooting is a critical skill and process in a myriad of fields, from information technology to customer service. At its core, Troubleshooting is about diagnosing and resolving problems, understanding why they occurred, and implementing solutions to prevent future issues. This introductory blog aims to demystify the concept of "what is Troubleshooting," presenting it not just as a technical necessity but as a fundamental approach to solving problems in our daily lives and work environments. By exploring what Troubleshooting truly entails, we can appreciate its value and how it applies to various scenarios. 

The question "What is Troubleshooting?" often arises when unexpected challenges disrupt our routines or workflows. It's more than just a technical term; it's a methodical approach to identifying, understanding, and resolving issues efficiently and effectively. In this blog, we'll delve into the systematic process of Troubleshooting, uncovering its methods, benefits, and practical applications. By the end, you'll have a comprehensive understanding of what Troubleshooting is and how mastering this skill can lead to improved outcomes in both professional and personal contexts. 

Table of Contents 

1) Understanding Troubleshooting 

2) Comprehensive Troubleshooting methods 

3) Step-by-step Troubleshooting process 

4) Benefits of effective Troubleshooting 

5) Real-life applications and case studies of Troubleshooting 

6) Advanced Troubleshooting techniques 

7) Conclusion 

Understanding Troubleshooting 

Troubleshooting is essentially a problem-solving method, often used in diagnostic processes to identify, analyse, and resolve issues in a system, whether it be in technology, business processes, or everyday scenarios. The essence of "what is Troubleshooting" lies in its systematic approach to finding the root cause of a problem and then applying knowledge and reasoning to solve it. It's not just about fixing immediate issues but also about understanding why those issues arose in the first place and how similar problems can be prevented or mitigated in the future. 

The process begins with recognising and defining the problem clearly, followed by a thorough analysis of potential causes. This is where critical thinking and experience come into play, allowing troubleshooters to hypothesise possible faults based on known symptoms. The next steps involve testing these hypotheses through experimentation or logical deduction and then implementing the most viable solution. However, "what is Troubleshooting" isn't just about the resolution; it's also about learning from the issue. Effective Troubleshooting involves documenting the problem and its solution, providing a valuable reference for preventing future occurrences. By understanding Troubleshooting as a structured yet flexible approach, individuals and organisations can foster a proactive mindset, reducing downtime and promoting efficiency. 

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Comprehensive Troubleshooting methods 

Troubleshooting is an art as much as it is a science, requiring a blend of analytical thinking, systematic investigation, and sometimes a bit of creativity. The following methods represent different facets of this multifaceted discipline, each providing a unique approach to diagnosing and resolving problems.  

Diagnostic or Failure Analysis 

Diagnostic or Failure Analysis is a critical initial step in the Troubleshooting process, focusing on identifying the exact nature and origin of the problem. This method involves gathering data, reviewing system logs, or examining hardware to pinpoint where and why the failure occurred. Technicians use various tools and techniques, such as diagnostic software or physical inspections, to analyse the symptoms and trace them back to the source. By thoroughly understanding the problem's specifics, one can develop a more accurate and effective resolution strategy, making this method a cornerstone of effective Troubleshooting. 

Elimination Process 

The Elimination Process is a systematic method where potential causes of a problem are listed and tested one by one until the actual cause is identified. It's based on the principle of ruling out possibilities through a process of elimination. This approach is particularly useful when dealing with complex systems where multiple factors could be at play. By methodically eliminating each potential cause, troubleshooters can narrow down their focus to the real issue. Though it can be time-consuming, the elimination process ensures a thorough examination and is highly effective in isolating the root cause of a problem. 

Restoration of Process or Product 

Restoration of Process or Product involves returning a system or product to its original or optimal state after identifying and fixing the underlying issue. This method is not just about patching up a problem but restoring functionality and performance to ensure long-term reliability and efficiency. It may involve replacing defective components, updating software, or revising operational procedures. The goal is to not only solve the immediate problem but also strengthen the system against future issues, emphasising preventive measures and continuous improvement as integral parts of the Troubleshooting process. 

Additional methods 

Beyond the primary techniques, there are additional methods of Troubleshooting that cater to specific scenarios or systems. These include the Comparative Analysis method, where a functioning system is compared to a malfunctioning one to spot differences, and the Top-Down or Bottom-Up approach, where one starts from the highest or lowest point of the system hierarchy, respectively. There's also the Cause and Effect Diagram, which helps visualise the relationship between various factors and the problem. Each method has its unique advantages and can be chosen based on the problem's nature, the system's complexity, and the troubleshooter's expertise. 

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Step-by- s tep Troubleshooting p rocess

Troubleshooting is a systematic process that, when followed diligently, can lead to the resolution of even the most complex issues. This step-by-step guide provides a structured approach to diagnosing and resolving problems, ensuring that no stone is left unturned.  

Step 1: Collect relevant information 

The first step is gathering all pertinent information about the issue at hand. This includes understanding the symptoms, the environment in which the problem occurs, and any recent changes or actions that might have contributed to the issue. Accurate information is crucial for effective diagnosis. 

Step 2: Clearly define the problem 

Once the information is collected, the next step is to define the problem clearly. What exactly is going wrong? Be as specific as possible in describing the issue, as a well-defined problem is much easier to solve. This might also involve distinguishing between symptoms and the actual problem. 

Step 3: Identify the most likely cause 

With the problem clearly defined, begin hypothesising what the most likely causes could be. Based on the information gathered, use logical reasoning to deduce the most probable sources of the issue. This step might involve some trial and error, but educated guesses will guide you in the right direction. 

Step 4: Develop an action plan and test potential solutions 

Develop a plan to test your hypotheses. This should involve step-by-step actions to either confirm or eliminate each potential cause. Begin with the simplest and most likely solutions before moving on to the more complex ones. Each test should be controlled and its results documented. If a solution seems to resolve the issue, don't stop there. Confirm that it truly is the root cause by understanding why and how it caused the problem. 

Step 5: Apply the chosen solution 

Once a solution has been identified and tested, it's time to apply it. This might involve repairing, replacing, or modifying a component or process. Ensure that the solution is implemented carefully and monitor the system closely to ensure that the problem is truly resolved. 

Step 6: Evaluate the outcomes 

After implementing the solution, evaluate the results. Has the problem been completely resolved? Are there any unintended consequences or new issues that have arisen? This step ensures that the solution is not only effective in resolving the initial issue but also doesn't create additional problems. 

Step 7: Document the entire Troubleshooting process 

Finally, document every step taken during the Troubleshooting process. This includes the initial problem description, all the hypothesised causes, the tests conducted, and the final solution. Documentation is invaluable for several reasons. It helps in understanding what went wrong and how it was resolved, which is essential for preventing future occurrences. It also serves as a guide for similar issues in the future, making subsequent Troubleshooting efforts more efficient. Additionally, it contributes to a knowledge base that can be beneficial for training purposes or for other team members who might encounter similar issues.  

Benefits of effective Troubleshooting 

Effective Troubleshooting offers a multitude of benefits that extend beyond the immediate resolution of issues. By mastering this skill, individuals and organisations can experience significant advantages.  

a) Cost efficiency: Effective Troubleshooting minimises downtime and associated losses by quickly identifying and resolving problems. This proactive approach prevents the need for more expensive solutions later, leading to significant long-term savings. 

b) Continuity and reliability: Troubleshooting effectively ensures that minor problems are resolved before they impact overall performance. This maintains the flow of operations and ensures consistent delivery of services and products, which is crucial for operational reliability. 

c) Reputation management: A business's ability to competently manage and resolve issues enhances its reputation. Demonstrating effective Troubleshooting skills shows commitment to quality and competence, building trust with customers and stakeholders. 

d) Continuous improvement: Each resolved issue provides insights into system and process refinement. Effective Troubleshooting fosters a culture of learning and adaptation, encouraging innovation and proactive strategies for ongoing growth and improvement. 

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Real-life applications and case studies of Troubleshooting 

Troubleshooting is a critical skill that finds application in a wide array of real-world scenarios, often preventing minor issues from escalating into major crises. Here are five illustrative examples demonstrating how effective Troubleshooting can lead to improved outcomes and efficiency: 

a) Healthcare system glitch: In a hospital, a critical patient-monitoring system started giving erratic readings. Technicians used the elimination process to pinpoint the issue of a recent software update that conflicted with existing hardware. Quick Troubleshooting ensured patient safety and care continuity. 

b) Automotive assembly line halt: An automotive company faced repeated halts on their assembly line. Through detailed failure analysis, they discovered a minor fault in the robotic arm's programming. Correcting this issue enhanced production efficiency and reduced unexpected downtimes. 

c) Telecommunications network failure: A major telecom provider experienced a sudden network failure affecting thousands of users. Using a top-down approach, engineers quickly isolated the problem to a damaged central relay station. The rapid response restored service and maintained the provider's reputation for reliability. 

d) Retail checkout errors: A retail chain encountered persistent errors in its checkout system during peak hours. Through a process of elimination, IT support traced the problem to an overloaded server. Upgrading the server capacity resolved the checkout issues, leading to improved customer satisfaction and sales. 

e) School's online learning platform crash: When a school's online learning platform crashed repeatedly, IT staff used comparative analysis with a previously stable version. They found that a recent feature addition was causing the crashes. Removing the feature until a fix was available ensured uninterrupted online learning for hundreds of students. 

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Advanced Troubleshooting techniques 

Advanced Troubleshooting techniques represent the cutting-edge of problem-solving, integrating sophisticated technology and innovative methodologies to address and predict complex issues more effectively. Here are several such techniques that are transforming the landscape of Troubleshooting: 

a) Predictive Troubleshooting: Utilising machine learning and artificial intelligence, this technique analyses historical data and operational patterns to predict and prevent issues before they arise, enhancing system reliability and reducing downtime. 

b) Root Cause Analysis with data analytics: Advanced data analytics tools dive deep into systems to uncover the underlying causes of complex issues. This approach goes beyond treating symptoms, focusing on eradicating the source of problems for long-term solutions. 

c) Remote Troubleshooting tools: Modern remote Troubleshooting tools allow experts to diagnose and resolve issues from afar. This includes remote desktop access, real-time monitoring, and advanced diagnostics, significantly reducing the need for on-site visits and expediting the resolution process. 

d) Network Behavior Analysis (NBA): Especially useful in IT, NBA tools monitor network traffic to identify anomalies or unusual patterns. By analysing these patterns, technicians can proactively address potential security threats or performance issues. 

e) Automated diagnostic systems: These systems use sophisticated algorithms to automatically diagnose and sometimes even rectify issues without human intervention. They're particularly useful in high-volume or critical environments where speed and accuracy are paramount. 

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Conclusion 

In conclusion, effective Troubleshooting is an indispensable skill, blending analytical prowess with systematic processes to resolve issues and improve systems. Embracing both traditional and advanced techniques ensures efficient problem-solving, enhances reliability, and fosters continuous improvement, making it a critical component in the landscape of operational success. 

Frequently Asked Questions

Troubleshooting is a systematic approach to diagnosing and resolving problems within a system or process aimed at restoring functionality and preventing recurrence.  

Effective Troubleshooting minimi se s downtime, saves costs by preventing larger issues, ensures system reliability, and maintains customer satisfaction.  

Yes, Troubleshooting principles can be applied to a variety of contexts, including non-technical problems in business processes, logistics , and everyday life scenarios.  

Troubleshooting begins with identifying and clearly defining the problem, then gathering all relevant information about the issue and its context.  

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Struggling to overcome challenges in your life? We all face problems, big and small, on a regular basis.

So how do you tackle them effectively? What are some key problem-solving strategies and skills that can guide you?

Effective problem-solving requires breaking issues down logically, generating solutions creatively, weighing choices critically, and adapting plans flexibly based on outcomes. Useful strategies range from leveraging past solutions that have worked to visualizing problems through diagrams. Core skills include analytical abilities, innovative thinking, and collaboration.

Want to improve your problem-solving skills? Keep reading to find out 17 effective problem-solving strategies, key skills, common obstacles to watch for, and tips on improving your overall problem-solving skills.

Key Takeaways:

• Effective problem-solving requires breaking down issues logically, generating multiple solutions creatively, weighing choices critically, and adapting plans based on outcomes.
• Useful problem-solving strategies range from leveraging past solutions to brainstorming with groups to visualizing problems through diagrams and models.
• Core skills include analytical abilities, innovative thinking, decision-making, and team collaboration to solve problems.
• Common obstacles include fear of failure, information gaps, fixed mindsets, confirmation bias, and groupthink.
• Boosting problem-solving skills involves learning from experts, actively practicing, soliciting feedback, and analyzing others’ success.
• Onethread’s project management capabilities align with effective problem-solving tenets – facilitating structured solutions, tracking progress, and capturing lessons learned.

What Is Problem-Solving?

Problem-solving is the process of understanding an issue, situation, or challenge that needs to be addressed and then systematically working through possible solutions to arrive at the best outcome.

It involves critical thinking, analysis, logic, creativity, research, planning, reflection, and patience in order to overcome obstacles and find effective answers to complex questions or problems.

The ultimate goal is to implement the chosen solution successfully.

What Are Problem-Solving Strategies?

Problem-solving strategies are like frameworks or methodologies that help us solve tricky puzzles or problems we face in the workplace, at home, or with friends.

Imagine you have a big jigsaw puzzle. One strategy might be to start with the corner pieces. Another could be looking for pieces with the same colors. 

Just like in puzzles, in real life, we use different plans or steps to find solutions to problems. These strategies help us think clearly, make good choices, and find the best answers without getting too stressed or giving up.

Why Is It Important To Know Different Problem-Solving Strategies?

Knowing different problem-solving strategies is important because different types of problems often require different approaches to solve them effectively. Having a variety of strategies to choose from allows you to select the best method for the specific problem you are trying to solve.

This improves your ability to analyze issues thoroughly, develop solutions creatively, and tackle problems from multiple angles. Knowing multiple strategies also aids in overcoming roadblocks if your initial approach is not working.

Here are some reasons why you need to know different problem-solving strategies:

• Different Problems Require Different Tools: Just like you can’t use a hammer to fix everything, some problems need specific strategies to solve them.
• Improves Creativity: Knowing various strategies helps you think outside the box and come up with creative solutions.
• Saves Time: With the right strategy, you can solve problems faster instead of trying things that don’t work.
• Reduces Stress: When you know how to tackle a problem, it feels less scary and you feel more confident.
• Better Outcomes: Using the right strategy can lead to better solutions, making things work out better in the end.
• Learning and Growth: Each time you solve a problem, you learn something new, which makes you smarter and better at solving future problems.

Knowing different ways to solve problems helps you tackle anything that comes your way, making life a bit easier and more fun!

17 Effective Problem-Solving Strategies

Effective problem-solving strategies include breaking the problem into smaller parts, brainstorming multiple solutions, evaluating the pros and cons of each, and choosing the most viable option. 

Critical thinking and creativity are essential in developing innovative solutions. Collaboration with others can also provide diverse perspectives and ideas. 

By applying these strategies, you can tackle complex issues more effectively.

Now, consider a challenge you’re dealing with. Which strategy could help you find a solution? Here we will discuss key problem strategies in detail.

1. Use a Past Solution That Worked

This strategy involves looking back at previous similar problems you have faced and the solutions that were effective in solving them.

It is useful when you are facing a problem that is very similar to something you have already solved. The main benefit is that you don’t have to come up with a brand new solution – you already know the method that worked before will likely work again.

However, the limitation is that the current problem may have some unique aspects or differences that mean your old solution is not fully applicable.

The ideal process is to thoroughly analyze the new challenge, identify the key similarities and differences versus the past case, adapt the old solution as needed to align with the current context, and then pilot it carefully before full implementation.

An example is using the same negotiation tactics from purchasing your previous home when putting in an offer on a new house. Key terms would be adjusted but overall it can save significant time versus developing a brand new strategy.

2. Brainstorm Solutions

This involves gathering a group of people together to generate as many potential solutions to a problem as possible.

It is effective when you need creative ideas to solve a complex or challenging issue. By getting input from multiple people with diverse perspectives, you increase the likelihood of finding an innovative solution.

The main limitation is that brainstorming sessions can sometimes turn into unproductive gripe sessions or discussions rather than focusing on productive ideation —so they need to be properly facilitated.

The key to an effective brainstorming session is setting some basic ground rules upfront and having an experienced facilitator guide the discussion. Rules often include encouraging wild ideas, avoiding criticism of ideas during the ideation phase, and building on others’ ideas.

For instance, a struggling startup might hold a session where ideas for turnaround plans are generated and then formalized with financials and metrics.

3. Work Backward from the Solution

This technique involves envisioning that the problem has already been solved and then working step-by-step backward toward the current state.

This strategy is particularly helpful for long-term, multi-step problems. By starting from the imagined solution and identifying all the steps required to reach it, you can systematically determine the actions needed. It lets you tackle a big hairy problem through smaller, reversible steps.

A limitation is that this approach may not be possible if you cannot accurately envision the solution state to start with.

The approach helps drive logical systematic thinking for complex problem-solving, but should still be combined with creative brainstorming of alternative scenarios and solutions.

An example is planning for an event – you would imagine the successful event occurring, then determine the tasks needed the week before, two weeks before, etc. all the way back to the present.

4. Use the Kipling Method

This method, named after author Rudyard Kipling, provides a framework for thoroughly analyzing a problem before jumping into solutions.

It consists of answering six fundamental questions: What, Where, When, How, Who, and Why about the challenge. Clearly defining these core elements of the problem sets the stage for generating targeted solutions.

The Kipling method enables a deep understanding of problem parameters and root causes before solution identification. By jumping to brainstorm solutions too early, critical information can be missed or the problem is loosely defined, reducing solution quality.

Answering the six fundamental questions illuminates all angles of the issue. This takes time but pays dividends in generating optimal solutions later tuned precisely to the true underlying problem.

The limitation is that meticulously working through numerous questions before addressing solutions can slow progress.

The best approach blends structured problem decomposition techniques like the Kipling method with spurring innovative solution ideation from a diverse team. 

An example is using this technique after a technical process failure – the team would systematically detail What failed, Where/When did it fail, How it failed (sequence of events), Who was involved, and Why it likely failed before exploring preventative solutions.

5. Try Different Solutions Until One Works (Trial and Error)

This technique involves attempting various potential solutions sequentially until finding one that successfully solves the problem.

Trial and error works best when facing a concrete, bounded challenge with clear solution criteria and a small number of discrete options to try. By methodically testing solutions, you can determine the faulty component.

A limitation is that it can be time-intensive if the working solution set is large.

The key is limiting the variable set first. For technical problems, this boundary is inherent and each element can be iteratively tested. But for business issues, artificial constraints may be required – setting decision rules upfront to reduce options before testing.

Furthermore, hypothesis-driven experimentation is far superior to blind trial and error – have logic for why Option A may outperform Option B.

Examples include fixing printer jams by testing different paper tray and cable configurations or resolving website errors by tweaking CSS/HTML line-by-line until the code functions properly.

6. Use Proven Formulas or Frameworks (Heuristics)

Heuristics refers to applying existing problem-solving formulas or frameworks rather than addressing issues completely from scratch.

This allows leveraging established best practices rather than reinventing the wheel each time.

It is effective when facing recurrent, common challenges where proven structured approaches exist.

However, heuristics may force-fit solutions to non-standard problems.

For example, a cost-benefit analysis can be used instead of custom weighting schemes to analyze potential process improvements.

Onethread allows teams to define, save, and replicate configurable project templates so proven workflows can be reliably applied across problems with some consistency rather than fully custom one-off approaches each time.

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7. Trust Your Instincts (Insight Problem-Solving)

Insight is a problem-solving technique that involves waiting patiently for an unexpected “aha moment” when the solution pops into your mind.

It works well for personal challenges that require intuitive realizations over calculated logic. The unconscious mind makes connections leading to flashes of insight when relaxing or doing mundane tasks unrelated to the actual problem.

Benefits include out-of-the-box creative solutions. However, the limitations are that insights can’t be forced and may never come at all if too complex. Critical analysis is still required after initial insights.

A real-life example would be a writer struggling with how to end a novel. Despite extensive brainstorming, they feel stuck. Eventually while gardening one day, a perfect unexpected plot twist sparks an ideal conclusion. However, once written they still carefully review if the ending flows logically from the rest of the story.

8. Reverse Engineer the Problem

This approach involves deconstructing a problem in reverse sequential order from the current undesirable outcome back to the initial root causes.

By mapping the chain of events backward, you can identify the origin of where things went wrong and establish the critical junctures for solving it moving ahead. Reverse engineering provides diagnostic clarity on multi-step problems.

However, the limitation is that it focuses heavily on autopsying the past versus innovating improved future solutions.

An example is tracing back from a server outage, through the cascade of infrastructure failures that led to it finally terminating at the initial script error that triggered the crisis. This root cause would then inform the preventative measure.

9. Break Down Obstacles Between Current and Goal State (Means-End Analysis)

This technique defines the current problem state and the desired end goal state, then systematically identifies obstacles in the way of getting from one to the other.

By mapping the barriers or gaps, you can then develop solutions to address each one. This methodically connects the problem to solutions.

A limitation is that some obstacles may be unknown upfront and only emerge later.

For example, you can list down all the steps required for a new product launch – current state through production, marketing, sales, distribution, etc. to full launch (goal state) – to highlight where resource constraints or other blocks exist so they can be addressed.

Onethread allows dividing big-picture projects into discrete, manageable phases, milestones, and tasks to simplify execution just as problems can be decomposed into more achievable components. Features like dependency mapping further reinforce interconnections.

Using Onethread’s issues and subtasks feature, messy problems can be decomposed into manageable chunks.

10. Ask “Why” Five Times to Identify the Root Cause (The 5 Whys)

This technique involves asking “Why did this problem occur?” and then responding with an answer that is again met with asking “Why?” This process repeats five times until the root cause is revealed.

Continually asking why digs deeper from surface symptoms to underlying systemic issues.

It is effective for getting to the source of problems originating from human error or process breakdowns.

However, some complex issues may have multiple tangled root causes not solvable through this approach alone.

An example is a retail store experiencing a sudden decline in customers. Successively asking why five times may trace an initial drop to parking challenges, stemming from a city construction project – the true starting point to address.

11. Evaluate Strengths, Weaknesses, Opportunities, and Threats (SWOT Analysis)

This involves analyzing a problem or proposed solution by categorizing internal and external factors into a 2×2 matrix: Strengths, Weaknesses as the internal rows; Opportunities and Threats as the external columns.

Systematically identifying these elements provides balanced insight to evaluate options and risks. It is impactful when evaluating alternative solutions or developing strategy amid complexity or uncertainty.

The key benefit of SWOT analysis is enabling multi-dimensional thinking when rationally evaluating options. Rather than getting anchored on just the upsides or the existing way of operating, it urges a systematic assessment through four different lenses:

• Internal Strengths: Our core competencies/advantages able to deliver success
• Internal Weaknesses: Gaps/vulnerabilities we need to manage
• External Opportunities: Ways we can differentiate/drive additional value
• External Threats: Risks we must navigate or mitigate

Multiperspective analysis provides the needed holistic view of the balanced risk vs. reward equation for strategic decision making amid uncertainty.

However, SWOT can feel restrictive if not tailored and evolved for different issue types.

Teams should view SWOT analysis as a starting point, augmenting it further for distinct scenarios.

An example is performing a SWOT analysis on whether a small business should expand into a new market – evaluating internal capabilities to execute vs. risks in the external competitive and demand environment to inform the growth decision with eyes wide open.

12. Compare Current vs Expected Performance (Gap Analysis)

This technique involves comparing the current state of performance, output, or results to the desired or expected levels to highlight shortfalls.

By quantifying the gaps, you can identify problem areas and prioritize address solutions.

Gap analysis is based on the simple principle – “you can’t improve what you don’t measure.” It enables facts-driven problem diagnosis by highlighting delta to goals, not just vague dissatisfaction that something seems wrong. And measurement immediately suggests improvement opportunities – address the biggest gaps first.

This data orientation also supports ROI analysis on fixing issues – the return from closing larger gaps outweighs narrowly targeting smaller performance deficiencies.

However, the approach is only effective if robust standards and metrics exist as the benchmark to evaluate against. Organizations should invest upfront in establishing performance frameworks.

Furthermore, while numbers are invaluable, the human context behind problems should not be ignored – quantitative versus qualitative gap assessment is optimally blended.

For example, if usage declines are noted during software gap analysis, this could be used as a signal to improve user experience through design.

13. Observe Processes from the Frontline (Gemba Walk)

A Gemba walk involves going to the actual place where work is done, directly observing the process, engaging with employees, and finding areas for improvement.

By experiencing firsthand rather than solely reviewing abstract reports, practical problems and ideas emerge.

The limitation is Gemba walks provide anecdotes not statistically significant data. It complements but does not replace comprehensive performance measurement.

An example is a factory manager inspecting the production line to spot jam areas based on direct reality rather than relying on throughput dashboards alone back in her office. Frontline insights prove invaluable.

14. Analyze Competitive Forces (Porter’s Five Forces)

This involves assessing the marketplace around a problem or business situation via five key factors: competitors, new entrants, substitute offerings, suppliers, and customer power.

Evaluating these forces illuminates risks and opportunities for strategy development and issue resolution. It is effective for understanding dynamic external threats and opportunities when operating in a contested space.

However, over-indexing on only external factors can overlook the internal capabilities needed to execute solutions.

A startup CEO, for example, may analyze market entry barriers, whitespace opportunities, and disruption risks across these five forces to shape new product rollout strategies and marketing approaches.

15. Think from Different Perspectives (Six Thinking Hats)

The Six Thinking Hats is a technique developed by Edward de Bono that encourages people to think about a problem from six different perspectives, each represented by a colored “thinking hat.”

The key benefit of this strategy is that it pushes team members to move outside their usual thinking style and consider new angles. This brings more diverse ideas and solutions to the table.

It works best for complex problems that require innovative solutions and when a team is stuck in an unproductive debate. The structured framework keeps the conversation flowing in a positive direction.

Limitations are that it requires training on the method itself and may feel unnatural at first. Team dynamics can also influence success – some members may dominate certain “hats” while others remain quiet.

A real-life example is a software company debating whether to build a new feature. The white hat focuses on facts, red on gut feelings, black on potential risks, yellow on benefits, green on new ideas, and blue on process. This exposes more balanced perspectives before deciding.

Onethread centralizes diverse stakeholder communication onto one platform, ensuring all voices are incorporated when evaluating project tradeoffs, just as problem-solving should consider multifaceted solutions.

16. Visualize the Problem (Draw it Out)

Drawing out a problem involves creating visual representations like diagrams, flowcharts, and maps to work through challenging issues.

This strategy is helpful when dealing with complex situations with lots of interconnected components. The visuals simplify the complexity so you can thoroughly understand the problem and all its nuances.

Key benefits are that it allows more stakeholders to get on the same page regarding root causes and it sparks new creative solutions as connections are made visually.

However, simple problems with few variables don’t require extensive diagrams. Additionally, some challenges are so multidimensional that fully capturing every aspect is difficult.

A real-life example would be mapping out all the possible causes leading to decreased client satisfaction at a law firm. An intricate fishbone diagram with branches for issues like service delivery, technology, facilities, culture, and vendor partnerships allows the team to trace problems back to their origins and brainstorm targeted fixes.

17. Follow a Step-by-Step Procedure (Algorithms)

An algorithm is a predefined step-by-step process that is guaranteed to produce the correct solution if implemented properly.

Using algorithms is effective when facing problems that have clear, binary right and wrong answers. Algorithms work for mathematical calculations, computer code, manufacturing assembly lines, and scientific experiments.

Key benefits are consistency, accuracy, and efficiency. However, they require extensive upfront development and only apply to scenarios with strict parameters. Additionally, human error can lead to mistakes.

For example, crew members of fast food chains like McDonald’s follow specific algorithms for food prep – from grill times to ingredient amounts in sandwiches, to order fulfillment procedures. This ensures uniform quality and service across all locations. However, if a step is missed, errors occur.

The Problem-Solving Process

The problem-solving process typically includes defining the issue, analyzing details, creating solutions, weighing choices, acting, and reviewing results.

In the above, we have discussed several problem-solving strategies. For every problem-solving strategy, you have to follow these processes. Here’s a detailed step-by-step process of effective problem-solving:

Step 1: Identify the Problem

The problem-solving process starts with identifying the problem. This step involves understanding the issue’s nature, its scope, and its impact. Once the problem is clearly defined, it sets the foundation for finding effective solutions.

Identifying the problem is crucial. It means figuring out exactly what needs fixing. This involves looking at the situation closely, understanding what’s wrong, and knowing how it affects things. It’s about asking the right questions to get a clear picture of the issue. 

This step is important because it guides the rest of the problem-solving process. Without a clear understanding of the problem, finding a solution is much harder. It’s like diagnosing an illness before treating it. Once the problem is identified accurately, you can move on to exploring possible solutions and deciding on the best course of action.

Step 2: Break Down the Problem

Breaking down the problem is a key step in the problem-solving process. It involves dividing the main issue into smaller, more manageable parts. This makes it easier to understand and tackle each component one by one.

After identifying the problem, the next step is to break it down. This means splitting the big issue into smaller pieces. It’s like solving a puzzle by handling one piece at a time. 

By doing this, you can focus on each part without feeling overwhelmed. It also helps in identifying the root causes of the problem. Breaking down the problem allows for a clearer analysis and makes finding solutions more straightforward. 

Each smaller problem can be addressed individually, leading to an effective resolution of the overall issue. This approach not only simplifies complex problems but also aids in developing a systematic plan to solve them.

Step 3: Come up with potential solutions

Coming up with potential solutions is the third step in the problem-solving process. It involves brainstorming various options to address the problem, considering creativity and feasibility to find the best approach.

After breaking down the problem, it’s time to think of ways to solve it. This stage is about brainstorming different solutions. You look at the smaller issues you’ve identified and start thinking of ways to fix them. This is where creativity comes in. 

You want to come up with as many ideas as possible, no matter how out-of-the-box they seem. It’s important to consider all options and evaluate their pros and cons. This process allows you to gather a range of possible solutions. 

Later, you can narrow these down to the most practical and effective ones. This step is crucial because it sets the stage for deciding on the best solution to implement. It’s about being open-minded and innovative to tackle the problem effectively.

Step 4: Analyze the possible solutions

Analyzing the possible solutions is the fourth step in the problem-solving process. It involves evaluating each proposed solution’s advantages and disadvantages to determine the most effective and feasible option.

After coming up with potential solutions, the next step is to analyze them. This means looking closely at each idea to see how well it solves the problem. You weigh the pros and cons of every solution.

Consider factors like cost, time, resources, and potential outcomes. This analysis helps in understanding the implications of each option. It’s about being critical and objective, ensuring that the chosen solution is not only effective but also practical.

This step is vital because it guides you towards making an informed decision. It involves comparing the solutions against each other and selecting the one that best addresses the problem.

By thoroughly analyzing the options, you can move forward with confidence, knowing you’ve chosen the best path to solve the issue.

Step 5: Implement and Monitor the Solutions

Implementing and monitoring the solutions is the final step in the problem-solving process. It involves putting the chosen solution into action and observing its effectiveness, making adjustments as necessary.

Once you’ve selected the best solution, it’s time to put it into practice. This step is about action. You implement the chosen solution and then keep an eye on how it works. Monitoring is crucial because it tells you if the solution is solving the problem as expected. 

If things don’t go as planned, you may need to make some changes. This could mean tweaking the current solution or trying a different one. The goal is to ensure the problem is fully resolved. 

This step is critical because it involves real-world application. It’s not just about planning; it’s about doing and adjusting based on results. By effectively implementing and monitoring the solutions, you can achieve the desired outcome and solve the problem successfully.

Why This Process is Important

Following a defined process to solve problems is important because it provides a systematic, structured approach instead of a haphazard one. Having clear steps guides logical thinking, analysis, and decision-making to increase effectiveness. Key reasons it helps are:

• Clear Direction: This process gives you a clear path to follow, which can make solving problems less overwhelming.
• Better Solutions: Thoughtful analysis of root causes, iterative testing of solutions, and learning orientation lead to addressing the heart of issues rather than just symptoms.
• Saves Time and Energy: Instead of guessing or trying random things, this process helps you find a solution more efficiently.
• Improves Skills: The more you use this process, the better you get at solving problems. It’s like practicing a sport. The more you practice, the better you play.
• Maximizes collaboration: Involving various stakeholders in the process enables broader inputs. Their communication and coordination are streamlined through organized brainstorming and evaluation.
• Provides consistency: Standard methodology across problems enables building institutional problem-solving capabilities over time. Patterns emerge on effective techniques to apply to different situations.

The problem-solving process is a powerful tool that can help us tackle any challenge we face. By following these steps, we can find solutions that work and learn important skills along the way.

Key Skills for Efficient Problem Solving

Efficient problem-solving requires breaking down issues logically, evaluating options, and implementing practical solutions.

Key skills include critical thinking to understand root causes, creativity to brainstorm innovative ideas, communication abilities to collaborate with others, and decision-making to select the best way forward. Staying adaptable, reflecting on outcomes, and applying lessons learned are also essential.

With practice, these capacities will lead to increased personal and team effectiveness in systematically addressing any problem.

 Let’s explore the powers you need to become a problem-solving hero!

Critical Thinking and Analytical Skills

Critical thinking and analytical skills are vital for efficient problem-solving as they enable individuals to objectively evaluate information, identify key issues, and generate effective solutions. 

These skills facilitate a deeper understanding of problems, leading to logical, well-reasoned decisions. By systematically breaking down complex issues and considering various perspectives, individuals can develop more innovative and practical solutions, enhancing their problem-solving effectiveness.

Communication Skills

Effective communication skills are essential for efficient problem-solving as they facilitate clear sharing of information, ensuring all team members understand the problem and proposed solutions. 

These skills enable individuals to articulate issues, listen actively, and collaborate effectively, fostering a productive environment where diverse ideas can be exchanged and refined. By enhancing mutual understanding, communication skills contribute significantly to identifying and implementing the most viable solutions.

Decision-Making

Strong decision-making skills are crucial for efficient problem-solving, as they enable individuals to choose the best course of action from multiple alternatives. 

These skills involve evaluating the potential outcomes of different solutions, considering the risks and benefits, and making informed choices. Effective decision-making leads to the implementation of solutions that are likely to resolve problems effectively, ensuring resources are used efficiently and goals are achieved.

Planning and Prioritization

Planning and prioritization are key for efficient problem-solving, ensuring resources are allocated effectively to address the most critical issues first. This approach helps in organizing tasks according to their urgency and impact, streamlining efforts towards achieving the desired outcome efficiently.

Emotional Intelligence

Emotional intelligence enhances problem-solving by allowing individuals to manage emotions, understand others, and navigate social complexities. It fosters a positive, collaborative environment, essential for generating creative solutions and making informed, empathetic decisions.

Leadership skills drive efficient problem-solving by inspiring and guiding teams toward common goals. Effective leaders motivate their teams, foster innovation, and navigate challenges, ensuring collective efforts are focused and productive in addressing problems.

Time Management

Time management is crucial in problem-solving, enabling individuals to allocate appropriate time to each task. By efficiently managing time, one can ensure that critical problems are addressed promptly without neglecting other responsibilities.

Data Analysis

Data analysis skills are essential for problem-solving, as they enable individuals to sift through data, identify trends, and extract actionable insights. This analytical approach supports evidence-based decision-making, leading to more accurate and effective solutions.

Research Skills

Research skills are vital for efficient problem-solving, allowing individuals to gather relevant information, explore various solutions, and understand the problem’s context. This thorough exploration aids in developing well-informed, innovative solutions.

Becoming a great problem solver takes practice, but with these skills, you’re on your way to becoming a problem-solving hero. 

How to Improve Your Problem-Solving Skills?

Improving your problem-solving skills can make you a master at overcoming challenges. Learn from experts, practice regularly, welcome feedback, try new methods, experiment, and study others’ success to become better.

Learning from Experts

Improving problem-solving skills by learning from experts involves seeking mentorship, attending workshops, and studying case studies. Experts provide insights and techniques that refine your approach, enhancing your ability to tackle complex problems effectively.

To enhance your problem-solving skills, learning from experts can be incredibly beneficial. Engaging with mentors, participating in specialized workshops, and analyzing case studies from seasoned professionals can offer valuable perspectives and strategies. 

Experts share their experiences, mistakes, and successes, providing practical knowledge that can be applied to your own problem-solving process. This exposure not only broadens your understanding but also introduces you to diverse methods and approaches, enabling you to tackle challenges more efficiently and creatively.

Improving problem-solving skills through practice involves tackling a variety of challenges regularly. This hands-on approach helps in refining techniques and strategies, making you more adept at identifying and solving problems efficiently.

One of the most effective ways to enhance your problem-solving skills is through consistent practice. By engaging with different types of problems on a regular basis, you develop a deeper understanding of various strategies and how they can be applied. 

This hands-on experience allows you to experiment with different approaches, learn from mistakes, and build confidence in your ability to tackle challenges.

Regular practice not only sharpens your analytical and critical thinking skills but also encourages adaptability and innovation, key components of effective problem-solving.

Openness to Feedback

Being open to feedback is like unlocking a secret level in a game. It helps you boost your problem-solving skills. Improving problem-solving skills through openness to feedback involves actively seeking and constructively responding to critiques. 

This receptivity enables you to refine your strategies and approaches based on insights from others, leading to more effective solutions. 

Learning New Approaches and Methodologies

Learning new approaches and methodologies is like adding new tools to your toolbox. It makes you a smarter problem-solver. Enhancing problem-solving skills by learning new approaches and methodologies involves staying updated with the latest trends and techniques in your field. 

This continuous learning expands your toolkit, enabling innovative solutions and a fresh perspective on challenges.

Experimentation

Experimentation is like being a scientist of your own problems. It’s a powerful way to improve your problem-solving skills. Boosting problem-solving skills through experimentation means trying out different solutions to see what works best. This trial-and-error approach fosters creativity and can lead to unique solutions that wouldn’t have been considered otherwise.

Analyzing Competitors’ Success

Analyzing competitors’ success is like being a detective. It’s a smart way to boost your problem-solving skills. Improving problem-solving skills by analyzing competitors’ success involves studying their strategies and outcomes. Understanding what worked for them can provide valuable insights and inspire effective solutions for your own challenges. 

Challenges in Problem-Solving

Facing obstacles when solving problems is common. Recognizing these barriers, like fear of failure or lack of information, helps us find ways around them for better solutions.

Fear of Failure

Fear of failure is like a big, scary monster that stops us from solving problems. It’s a challenge many face. Because being afraid of making mistakes can make us too scared to try new solutions. 

How can we overcome this? First, understand that it’s okay to fail. Failure is not the opposite of success; it’s part of learning. Every time we fail, we discover one more way not to solve a problem, getting us closer to the right solution. Treat each attempt like an experiment. It’s not about failing; it’s about testing and learning.

Lack of Information

Lack of information is like trying to solve a puzzle with missing pieces. It’s a big challenge in problem-solving. Because without all the necessary details, finding a solution is much harder. 

How can we fix this? Start by gathering as much information as you can. Ask questions, do research, or talk to experts. Think of yourself as a detective looking for clues. The more information you collect, the clearer the picture becomes. Then, use what you’ve learned to think of solutions. 

Fixed Mindset

A fixed mindset is like being stuck in quicksand; it makes solving problems harder. It means thinking you can’t improve or learn new ways to solve issues. 

How can we change this? First, believe that you can grow and learn from challenges. Think of your brain as a muscle that gets stronger every time you use it. When you face a problem, instead of saying “I can’t do this,” try thinking, “I can’t do this yet.” Look for lessons in every challenge and celebrate small wins. 

Everyone starts somewhere, and mistakes are just steps on the path to getting better. By shifting to a growth mindset, you’ll see problems as opportunities to grow. Keep trying, keep learning, and your problem-solving skills will soar!

Jumping to Conclusions

Jumping to conclusions is like trying to finish a race before it starts. It’s a challenge in problem-solving. That means making a decision too quickly without looking at all the facts. 

How can we avoid this? First, take a deep breath and slow down. Think about the problem like a puzzle. You need to see all the pieces before you know where they go. Ask questions, gather information, and consider different possibilities. Don’t choose the first solution that comes to mind. Instead, compare a few options. 

Feeling Overwhelmed

Feeling overwhelmed is like being buried under a mountain of puzzles. It’s a big challenge in problem-solving. When we’re overwhelmed, everything seems too hard to handle. 

How can we deal with this? Start by taking a step back. Breathe deeply and focus on one thing at a time. Break the big problem into smaller pieces, like sorting puzzle pieces by color. Tackle each small piece one by one. It’s also okay to ask for help. Sometimes, talking to someone else can give you a new perspective. 

Confirmation Bias

Confirmation bias is like wearing glasses that only let you see what you want to see. It’s a challenge in problem-solving. Because it makes us focus only on information that agrees with what we already believe, ignoring anything that doesn’t. 

How can we overcome this? First, be aware that you might be doing it. It’s like checking if your glasses are on right. Then, purposely look for information that challenges your views. It’s like trying on a different pair of glasses to see a new perspective. Ask questions and listen to answers, even if they don’t fit what you thought before.

Groupthink is like everyone in a group deciding to wear the same outfit without asking why. It’s a challenge in problem-solving. It means making decisions just because everyone else agrees, without really thinking it through. 

How can we avoid this? First, encourage everyone in the group to share their ideas, even if they’re different. It’s like inviting everyone to show their unique style of clothes. 

Listen to all opinions and discuss them. It’s okay to disagree; it helps us think of better solutions. Also, sometimes, ask someone outside the group for their thoughts. They might see something everyone in the group missed.

Overcoming obstacles in problem-solving requires patience, openness, and a willingness to learn from mistakes. By recognizing these barriers, we can develop strategies to navigate around them, leading to more effective and creative solutions.

What are the most common problem-solving techniques?

The most common techniques include brainstorming, the 5 Whys, mind mapping, SWOT analysis, and using algorithms or heuristics. Each approach has its strengths, suitable for different types of problems.

What’s the best problem-solving strategy for every situation?

There’s no one-size-fits-all strategy. The best approach depends on the problem’s complexity, available resources, and time constraints. Combining multiple techniques often yields the best results.

How can I improve my problem-solving skills?

Improve your problem-solving skills by practicing regularly, learning from experts, staying open to feedback, and continuously updating your knowledge on new approaches and methodologies.

Are there any tools or resources to help with problem-solving?

Yes, tools like mind mapping software, online courses on critical thinking, and books on problem-solving techniques can be very helpful. Joining forums or groups focused on problem-solving can also provide support and insights.

What are some common mistakes people make when solving problems?

Common mistakes include jumping to conclusions without fully understanding the problem, ignoring valuable feedback, sticking to familiar solutions without considering alternatives, and not breaking down complex problems into manageable parts.

Final Words

Mastering problem-solving strategies equips us with the tools to tackle challenges across all areas of life. By understanding and applying these techniques, embracing a growth mindset, and learning from both successes and obstacles, we can transform problems into opportunities for growth. Continuously improving these skills ensures we’re prepared to face and solve future challenges more effectively.

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A Guide to Problem Resolution in Product Development

The end-goal when bringing a product to market is having a fully developed, defect free product. To achieve this goal, problem resolution is an excellent way to eliminate issues that arise during development. Because every problem is different, formulating a procedure that ensures quality and consistency through the application of identifying and resolving such issues is critical. Procedures focused on problem resolution are best formulated around the Plan-Do-Study-Act (PDSA) methodology. This is an overview to guide you through the PDSA methodology as it relates to hardware/software systems.   1. Plan: Experiment for Problem Resolution Begin by creating a series of test experiments aimed at identifying and resolving a problem reported or experienced in your product. Gather information by asking questions to determine the criticality and urgency of the issue. Identify the exact product and version in which the problem is occurring – this includes associated hardware, software and/or firmware. Review the results of prior experiments to drill down to the next logical step to identify the root cause and appropriate resolution.

Utilize the information you’ve gathered to define the problem. Assemble a team familiar with the product and issue. Define the issue as a gap between the current condition and the target condition. Gather and analyze source material to determine how this issue occurred. This information may include log files, diagnostic reports, witness accounts, environment, and actions taken by the product user.

Identify a method in which the team can recreate the issue, replicating the environment in which the condition occurred. With random or infrequent failures, this step is vital as a solution may not be found otherwise. Keep in mind that all failures may not be replicable on demand and an environmental setup that can produce a failure with some reasonable frequency may have to suffice.  

2. Do: Implement the Experiment In a controlled, methodical, well documented manner, begin to implement the experiment you created. Set up the experiment using the base software/firmware and hardware that is used. Name each experiment with the date version and test number, or description. Document each experiment in an organized manner, i.e. spreadsheet or table, allowing for quick tracking and analysis. Each test should be fully documented siting successes, failure, and grey areas. Don’t trust your memory! You’d be surprised how easy it is to forget how many clicks were needed or an exact sequence of events.

Unsuccessful experiments can be just as important as a successful one. It may imply that an assumption was false and can therefore be eliminated. Add these findings to the test documentation and continue to reanalyze. Successful experiments typically imply that the assumptions are correct, possibly identifying partial solutions or the complete resolution.

  3. Study: Analyze Experiment Results After a few rounds of experiments, pause to analyze the results that were performed. This will help in determining if additional rounds of experiments are required, or if the root-cause of the issue was found. Many times, a solution to a problem requires multiple corrections in multiple locations, therefore continual monitoring of the effects and results will help narrow down a resolution. This iterative process will identify partial solutions which can lead to additional experiments that identify areas required to fully implement a solution. At minimum, each experiment should lead the team to determine a simple true or false. No experiment is a failure. Each experiment offers information that was previously unknown.

Once a solution is found, another iteration is performed with just the solution. Upon final resolution, all enhancements must be tested as a complete solution. Many times, solutions to problems are found in various components of the system that appear to be totally unrelated. When combining these solutions that appear to be unrelated, new issues may occur. Therefore, testing of the complete solution is necessary and may reenter the process for final testing and study. Stress testing may be necessary and long-term testing is certainly required. Depending on the criticality of the product, the issue identified may be tested anywhere from days to months, possibly under extreme conditions.

  4. Act: Implement and Adjust Act is the adoption of change(s) to the software base on resolution of the issue or problem. There are two major components based on the results of experimentation, results, conclusions reached, and verification that a solution corrects the original issue without introducing new issues. First, introduce the correction, adjust the plan, and reconfirm through the plan the issue was corrected. Next, identify any additional issues, steps, and processes related to the original problem that should be addressed. Finally, document and report the root cause of the issue to all stakeholders including industry regulators, management, and investors, offering peace of mind that the issue is now resolved.

  For more details on problem resolution, contact Precision Systems, Inc. today. One of our experienced engineering consultants will be happy to review this guide and provide direction as necessary.

Precision Systems Contact Info: 215-672-1860 | [email protected]

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Technological problem solving: an investigation of differences associated with levels of task success

• Open access
• Published: 02 June 2021
• Volume 32 , pages 1725–1753, ( 2022 )

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• David Morrison-Love   ORCID: orcid.org/0000-0002-9009-4738 1  

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Research into technological problem solving has shown it to exist in a range of forms and draw upon different processes and knowledge types. This paper adds to this understanding by identifying procedural and epistemic differences in relation to task performance for pupils solving a well-defined technological problem. The study is theoretically grounded in a transformative epistemology of technology education. 50 pupils in small groups worked through a cantilever problem, the most and least successful solutions to which were identified using a Delphi technique. Time-interval photography, verbal interactions, observations and supplementary data formed a composite representation of activity which was analysed with successively less contrasting groups to isolate sustained differences. Analyses revealed key differences in three areas. First, more successful groups used better and more proactive process-management strategies including use of planning, role and task allocation with lower levels of group tension. Second, they made greater use of reflection in which knowledge associated with the technological solution was explicitly verblised. This was defined as ‘analytical reflection’ and reveals aspects of pupils’ qualitative technical knowledge. Third, higher-performing groups exhibited greater levels of tacit-procedural knowledge within their solutions. There was also evidence that less successful groups were less affected by competition and not as comprehensive in translating prior conceptual learning into their tangible technological solutions. Overall findings suggest that proactive management, and making contextual and technical connections, are important for pupils solving well-defined technological problems. This understanding can be used to support classroom pedagogies that help pupils learn to problem solve more effectively.

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Investigating Preservice Teachers’ Educational Technology Skills: A Problem-Solving Process

Problem-Based Learning in Technology Education

Avoid common mistakes on your manuscript.

Introduction

Problem solving is an activity, a context and a dominant pedagogical frame for Technology Education. It constitutes a central method and a critical skill through which school pupils learn about and become proficient in technology (Custer et al., 2001 ). Research has, among other things, been able to identify and investigate sets of intellectual and cognitive processes (Buckley et al., 2019 ; Haupt, 2018 ; Johnson, 1997 ; Sung & Kelly, 2019 ) and shown there to be conceptual, procedural, relational and harder-to-get-to forms of ‘technological knowledge’ involved when pupils develop technological solutions (de Vries, 2005 ; McCormick, 1997 , 2004 ; Rauscher, 2011 ). Some authors argue that technological problem solving (and design) is a situated activity (Jackson & Strimel, 2018 ; Murphy & McCormick, 1997 ; Liddament, 1996 ), but with social and context-independent processes also playing an important role (e.g. Jones, 1997 ; Winkelmann & Hacker, 2011 ). Within and across this vista, there has been strong interest in more open-ended, creative and design-based problem-solving (Lewis, 2005 , 2009 ), which Xu et al. ( 2019 ) notes became particularly prominent after 2006. These studies have helped to understand some of the challenges and pedagogies of design (Gómez Puente et al., 2013 ; Lavonen et al., 2002 ; Mioduser & Dagan, 2007 ; Mawson, 2003 ) including those that mitigate effects such as cognitive fixation (e.g. McLellan & Nicholl, 2011 ). Problem solving, it seems, is a pervasive idea in technology education research and policy. Middleton ( 2009 ) notes that problem solving is found in almost all international technology education curricula.

The pace, nature and complexity of contemporary societal challenges make it more critical than ever that technology classrooms prepare people who can think through and respond to technological problems effectively. It requires that we strengthen our understanding in ways that will ultimately be powerful for shaping classroom learning. One way of contributing to this is to learn more about the differences between learners who are more and less successful at technological problem solving. Studies that share a comparative perspective and/or a focus upon task success are relatively few. Doornekamp ( 2001 ) compared pupils (circa 13 years old) who solved technological problems using weakly structured instructional materials with those using strongly structured materials. It was shown that the latter led to statistically significant improvements in the quality of the technical solutions. More recently, Bartholomew & Strimel ( 2018 ) were able to show that, for open-ended problem solving, there was no significant relationship between prior experience and folio creation, but that more in-school school experience of open-ended problem solving corresponded to higher ranked solutions.

This paper contributes to this work by reporting on a study that compares groups of pupils during technological problem solving in order to identify areas of difference and the factors associated with more successful outcomes. Specifically, it addresses the question: ‘In terms of intellectual processes and knowledge, what are the differences in the modi operandi between groups of pupils that produced more and less successful technological solutions to a well-defined problem?’ Theoretically grounded in a transformative epistemology of technology education (Morrison-Love, 2017 ), the study identifies prominent procedural and epistemic differences in pupils’ thinking and technical solutions. Groups of pupils engaged with a structures problem requiring them to develop a cantilever bridge system which would facilitate safe travel across a body of water.

The paper begins by setting out the theoretical basis and conceptual framework for investigation before describing the comparative methodological and analytical approaches that were adopted. Following an analysis and presentation of key findings, conclusion and implications are discussed.

A theoretical basis for the study of technological problem solving

Despite there being no single comprehensive paradigm for technological problem solving, a theoretical grounding and conceptual framework necessary for this study are presented. At the theoretical level, this study is based upon a ‘transformative epistemology’ for technology education (Morrison-Love, 2017 ). From this, a ternary conceptual framework based upon mode, epistemology and process is developed to support study design and initiate data analysis.

A transformative epistemology for technology education (Morrison-Love, ibid) proposes that pupils’ technological knowledge and capability arises from the ontological transformation of their technical solution from ‘perdurant’ (more conceptual, mutable, less well-defined, partial) in the early stages, to ‘endurant’ (comprehensive, tangible, stable over time) upon completion. It proposes that technical outcomes exist in material and tangible forms and that to be technological (rather than, for example, social, cultural or aesthetic) these must somehow enhance human capabilities in their intended systems of use. For this study, the ideas of transformative epistemology support problem solving in which pupils build technological knowledge by iteratively moving from concept to tangible, material solution. Moreover, it means pupils are successful in this when their solutions or prototypes: (1) enhance existing human capabilities in some way, and (2) are sufficiently developed to be stable over time, beyond the problem-solving activity that created it.

A conceptual framework for technological problem solving

A ternary conceptual framework (Fig. 1 ) of mode, process and epistemology was developed from the literature in which the knowledge and cognitive/intellectual processes used by pupils are enacted in the ‘process application block’. This is like the ‘problem space’ described in a model proposed by Mioduser ( 1998 ). Collectively, the goal of creating a physical artefact, the solution itself, the epistemic and procedural dimensions reflect the four dimensions of technology identified by Custer ( 1995 ).

‘A conceptual framework for technological problem solving’

Mode and forms dimension

Although problem solving may be ‘technological’, several classifications of both problem type and problem solving are found in the literature. Ill-defined and well-defined problems build upon the earlier work of information processing and cognitive psychology (see Jonassen, 1997 ). Typically, these two forms reflect different extents to which the outcome is specified to the solver at the outset. Ill-defined problems are strongly associated with design and creativity, and Twyford and Järvinen ( 2000 ) suggest that these more open briefs promote greater social interaction and use by pupils of prior knowledge and experience. Additionally, two forms of troubleshooting were identified in the literature: emergent troubleshooting and discrete troubleshooting. MacPherson ( 1998 ) argues that ‘troubleshooting’ constitutes a particular subset of technological problem solving—something earlier recognised by McCade ( 1990 ), who views it as the identification and overcoming of problems encountered during the production or use of a technical solution. In this study, emergent troubleshooting occurs in the process of developing solutions in response to emergent problems (McCormick, 1994 ). Discrete troubleshooting is a process in which significant technical understanding is applied in a structured way (Schaafstal et al., 2000 ) to resolve something about an existing artefact.

Intellectual and cognitive process dimension

Studies often conceptualise cognitive processes discretely rather than hierarchically, and different studies employ different process sets. Williams ( 2000 ), identifies evaluation, communication, modelling, generating ideas, research and investigation, producing and documenting as important to technological problem solving, while DeLuca ( 1991 ) identifies troubleshooting, the scientific process, the design process, research and development, and project management. There are also studies that employ specific, or more established, coding schemes for sets of intellectual and cognitive processes. A detailed analysis of these is given Grubbs et al. ( 2018 ), although the extent to which a particular process remains discrete or could form a sub-process of another remains problematic. In DeLuca’s ( 1991 ) break down for example, to what extent are research and investigation part of design and does this depend on the scale at which we conceptualise different processes?

Regardless of the processes a study defines, it is typically understood that pupils apply them in iterative or cyclic fashion. This is reflected across several models from Argyle’s ( 1972 ) ‘Motor Skill Process Model’ (perception-translation-motor response) through to those of Miodusre and Kipperman ( 2002 ) and Scrivener et al. ( 2002 ) (evaluation-modification cycles) which pertain specifically to technology education. All these models bridge pupils’ conceptual-internal representations with their practical-external representations as they move towards an ontologically endurant solution and this is captured by the ‘Re-Application/Transformation Loop’ of the conceptual framework. Given that little is known about where differences might lie, the process set identified by Halfin ( 1973 ) was adopted due to its rigour and the breadth of thinking it encompasses. This set was validated for technology classrooms by Hill and Wicklein ( 1999 ) and used successfully by other studies of pupils technological thinking including Hill ( 1997 ), Kelley ( 2008 ) and Strimel ( 2014 ).

Epistemology dimension

The nature and sources of knowledge play a critical role for pupils when solving technological problems, but these remain far from straightforward to conceptualise. McCormick ( 1997 ) observes that the activity of technology education, and its body of content, can be thought of as ‘procedural knowledge’ and ‘conceptual knowledge’ respectively. Vincenti ( 1990 ), in the context of Engineering, makes the case for descriptive knowledge (things as they currently are) and prescriptive knowledge (of that with is required to meet a desired state) but also recognises knowledge can take on implicit, or tacit forms relating to an individual’s skill, judgement, and practice (Polanyi, 1967 ; Schön, 1992 ; Sternberg, 1999 ; Welch, 1998 ). Arguably, moving from concept to physical solution will demand from pupils a certain level of practical skill and judgement, and Morgan ( 2008 ) observes that procedural knowledge which is explicit in the early stages becomes increasingly implicit with mastery. Notably, in addition to conceptual, procedural and tacit forms of knowledge, there is also evidence that knowledge of principles plays a role. Distinct from impoverished notions of technology as ‘applied science’, Rophol ( 1997 ) shows that it is often technological principles, laws and maxims that are applied during problem solving rather than scientific ones. Frey ( 1989 ) makes similar observations and sees this form of knowledge arising largely from practice. In this study, knowledge of principles involves knowledge of a relationship between things. It is not constrained to those that are represented scientifically.

The conceptual framework finally accounts for pupils’ sources of knowledge during problem solving, building principally on a design knowledge framework of media, community and domain presented by Erkip et al. ( 1997 ). In this study, media includes task information, representations and materials; community includes teachers and peers, and domain relates to prior technological knowledge from within technology lessons and prior personal knowledge from out with technology lessons. Finally, the developing solution is itself recognised a source of knowledge that pupils iteratively engage with and reflect upon, even when it appears that limited progress in being made (Hamel & Elshout, 2000 ).

Methodology

The research question in this study is concerned with differences in the knowledge and intellectual processes used by pupils in moving from a perdurant to an endurant technical solution. From an exploratory stance, this elicits a dualistic activity system involving pupils’ subjective constructions of reality as well as the resultant tangible and more objective material solution. The study does not aim to investigate pupils’ own subjective constructions from an emic perspective, but rather seeks to determine the nature and occurrences of any differences during observable real-time problem-solving activity. As such, content rather than thematic analysis was used (Elo & Kyngäs, 2008 ; Vaismoradi et al., 2013 ) with concurrent data collection to build a composite representation of reality (Gall et al., 2003 , p.14). Complementary data provided insights into study effects, the classrooms and contexts within which problem-solving took place.

This study assumes that should differences exist, these will be discernible in the inferred cognitive processes, external material transformations, interactions and verbalisation (even though this tends to diminish as activity becomes more practical). Absolute and objective observation is not possible. This study also accepts that data gathering and analysis are influenced by theory, researcher fallibility and bias which will be explicitly accounted for as far as possible. Finally, while the conceptual framework provides an analytical starting point, it should not preclude the capture of differences that may lie elsewhere in the data including, for example, process that lie out with those identified by Halfin ( 1973 ).

Participants, selection and grouping

To support transferability, a representative spread of pupils from low, medium and high socio-economic backgrounds took part in this study. Purposeful, four-stage criterion sampling was used (Gall et al., 2003 , p.179). Stage one identified six schools at each socio-economic level from all Scottish secondary schools that presented pupils for one or more technology subjects with the Scottish Qualifications Authority. This was done using socio-economic data from the Scottish Area Deprivation Index, the Carstairs Index and pupil eligibility for subsidised meals. Secondary school catchment areas were used to account for pupil demographics as accurately as possible. All eighteen schools were subsequently ranked with one median drawn from low, medium and high bands of socio-economic deprivation (School 1: Low, School 2: Medium, School 3: High).

One class in each school was then selected from the second year of study prior to pupils making specific subject choices to minimise variations in curricular experience. In total, 3 class teachers and 50 pupils (20 female, 30 male) aged between 12 and 13 years old took part in the study. The group rather than the individual was defined as unit of study to centralise verbal interaction.

None of the pupils participating in this study had experience of group approaches such as co-operative learning and it was likely that groups might experience participation effects including inter-group conflict and interaction effects (Harkins, 1987 ; Sherif et al., 1961 ), social loafing (Salomon & Globerson, 1989 ), free-rider (Strong & Anderson, 1990 ) and status differential effects (Rowell, 2002 ). Relevant also to this study is evidence suggesting that gender effects can take place in untrained groups undertaking practical/material manipulation activities. To maximise interaction between group members and the material solution, thirteen single sex groups averaging four pupils were formed in order to: (1) minimise the marginalisation of girls with boys’ tendency to monopolise materials and apparatus in groups (Conwell et al., 1993 ; Whyte, 1984 ); (2) recognise boys’ tendency to respond more readily to other boys (Webb, 1984 ) and, (3) maximise girls’ opportunities to interact which is seen to erode in mixed groups (Parker & Rennie, 2002 ; Rennie & Parker, 1987 ). Hong et al. ( 2012 ) examines such gender differences in detail specifically within the context of technological problem solving. Teacher participation in group allocation minimised inter-group conflict and interaction effects although groups still experienced naturally fluctuating attrition from pupil absences (School 1 = 17.6%; School 2 = 2.5% and School 3 = 8.8%).

Identification of most and least successful solutions

The research question requires differences to be identified in terms of levels of success. The overall trustworthiness of any differences therefore depends upon the credible identification of the most and least successful solutions from the thirteen produced. Wholly objective assessment of the pupils’ solutions is not possible, and material imperfections in different solutions negated reliable physical testing across the three classes. Moreover, because the researcher earlier observed pupils while problem solving, neutrality of researcher judgement in establishing a rank order of group solutions was equally problematic. Everton and Green ( 1986 ) identify this biasing risk between and early and later stages of research as a form of contamination.

To address these limitations, a Delphi technique was design using the work of Gordon ( 1994 ), Rowe and Wright ( 1999 ) and Yousuf ( 2007 ). This was undertaken anonymously prior to any analysis and, in conjunction with the results of physical testing, enabled the four most successful and four least successful solutions to be confidently identified independently of the researcher. A panel of eight secondary school teachers was convened from schools out with the study. All panel members had expertise in teaching structures with no dependent relationships or conflicts of interest. Following Delphi training, and a threshold level of 75%, the four most and four least successful solutions on outcome alone were identified after two rounds. Qualitative content validity checks confirmed that panel judgements fell within the scope of the accessible information. 37/43 reasons given were ‘High’, with six considered ‘Medium’ because the reasoning was partially speculative. When triangulated with additional evidence from physical testing, two cohorts of four groups were identified and paired to form four dyads (Table 1 ).

Study design

As noted, ‘Structures’ was chosen as a topic area and was new to all participants. It was appropriate for setting well-defined problems and allowed pupils to draw upon a sufficiently wide range of processes and knowledge types in developing a tangible, endurant solution. In discussion with the researcher, teachers did not alter their teaching style and adopted pedagogy and formative interactions that would support independent thinking, reasoning and problem solving. This study involved a learning phase, followed by a problem-solving phase.

In the learning phase, groups engaged over three fifty-minute periods with a unit of work on structures which was developed collaboratively with, and delivered by, the three classroom teachers. This allowed pupils to interact with materials and develop a qualitative understanding of key structural concepts including strength, tension and compression, triangulation, and turning moments. During this time, pupils also acclimatised to the presence of the researcher and recording equipment which helped to reduce any potential Hawthorne effect (Gall et al., 2003 ). Structured observations, teacher de-briefs and questionnaires were used in this phase to capture study effects, unit content coverage and environmental consistency between the three classrooms. Content coverage and environmental consistency were shown to be extremely high. Scores from the unit activity sheets that pupils completed were used to gauge group understanding of key concepts.

The problem-solving phase took place over two circa 50-minute periods (range: 40–52 m) in which pupils responded to a well-defined problem brief. This required them to develop a cantilever bridge enabling travel across a body of water. This bridge would enhance people’s ability to traverse terrain (conditions for being ‘technological’) with maximal span rigidity and minimal deflection (conditions for an ontologically ‘endurant’ solution). All groups had access to the same range and number of materials and resources and were issued with a base board showing water and land on which to develop their solutions.

While video capture was explored in depth (Lomax & Casey, 1998 ), challenges in reliably capturing solution detail resulted in group verbalisation being recorded as audio. This was synchronised with time interval photography and supplemented with structured observer-participant observation that focused on a sub-set of observable processes from the conceptual framework (Halfin, 1973 ). The developing technical solutions were viewed as manifestations of the knowledge and intellectual processes used by pupils at different points in time through their cognitive and material interactions. Photographs captured the results of these interactions in group solutions every 3–4 min but did not capture interactions between pupils. The structured observational approach adopted continuous coding similar to that found in the Flanders System of Interaction analysis (Amatari, 2015 ) and was refined through two pilot studies. During each problem-solving session, groups were observed at least twice between photographs and, following each session, pupil questionnaires, teacher de-briefs and solution videos (360° panoramic pivot about the solution) were completed to support future analysis. Reflexive accounts by the researcher also captured critical events, observer and study effects.

Analytical approach

All data were prepared, time-synchronised and analysed in three stages. Core verbal data (apx. 12h) and photographic data (n = 206) were triangulated with observational and other data against time. The problem-solving phase for each class was broken into a series of 3–4 min samples labelled S = 1, S = 2, S = 3…with durations in each recorded in minutes and seconds. Verbal data were analysed using NVivo software using digital waveforms rather than transcribed files to preserve immediacy, accuracy and minimise levels of interpretation (Wainwright & Russell, 2010 ; Zamawe, 2015 ). Photographic data were coded for the physical developments of the solutions (e.g. adding/removing materials in particular places) allowing solution development to be mapped for different groups over time. Triangulation of data also allowed coding to capture whether individual developments enhanced or detracted from the overall function efficacy of the solution.

The first stage of analysis was immersive, beginning with an initial codebook derived from the conceptual framework. In response to the data this iteratively shifted to a more inductive mode. To sensitise the analysis to differences, the most successful and the least successful groups were compared first as is discussed by Strauss 1987 (Miles & Huberman, 1994 , p.58). Three frameworks of differences emerged from this: (1) epistemic differences, (2) process differences, and (3) social and extrinsic differences. These were then applied to dyads of decreasing contrast and successively  refined in response to how these differences were reflected in the wider data set. Seven complete passes allowed non-profitable codes to be omitted and frameworks to be finalised. A final stage summarised differences across all groups.

Analysis and findings

The analysis and findings are presented in two main parts: (1) findings from the learning phase, and (2) findings from the problem-solving phase. Verbal data forms a core data source throughout and coding includes both counts and durations (in minutes and seconds). Direct quotations are used from verbal data, although the pupils involved in the study were from regions of Scotland with differing and often very strong local dialects. Quotations are therefore presented with dialect effects removed:

Example data excerpt reflecting dialect: “See instead-e all-e-us watchin’, we could all be doin’ su-hum instead-o watchin’ Leanne..” Example data excerpt with dialect removed: “See instead of all of us watching, we could all be doing something instead of watching Leanne..”

Part 1: Findings from the Learning Phase

Both teacher and researcher observation confirmed that pupils in all three classes engaged well with the unit of work (50 pupils across 13 groups) with all 40 content indicators covered by each class. Teachers of classes 1 and 3 commented that the lesson pace was slightly faster than pupils were used to. As expected, different teaching styles and examples were between classes, but all pupils completed the same unit activity sheets. The teacher of class 2, for example, used man-made structures and insect wings to explore triangulation; and the teacher in class 3 talked about the improved stability pupils get by standing with their feet apart. The understanding reflected in activity sheets was very good overall and Table 2 illustrates the percentage of correct responses for each class in relation to each of the three core concept areas.

Though unit activity sheets are not standardised tests, the conditions of administration, scoring, standards for interpretation, fairness and concept validity discussed by Gall et al. ( 2003 , p.xx) were maintained as far as possible. Evidence did not show that representational/stylistic variations by teachers had any discernible effect on pupil understanding and was seen to maintain normality from the pupils’ perspective. Class 3 scored consistently highly across all conceptual areas, although the qualitative understanding of turning moments was least secure for all three classes. Non-completion of selected questions in the task sheets partially explains lower numerical attainment for this concept in class 1 and 2, however, it is unknown if omissions resulted from a lack of understanding. The figures in Table 2 are absence corrected to account for fluctuating pupil attendance at sessions: (17.6% pupil absence across sessions for class 1, compared with 8.8% and 2.5% for classes 3 and 3 respectively). Table 3 illustrates the percentage scores for activity sheets completed by groups in the more and less successful cohorts.

Observational and reflexive data highlighted evidence of some researcher and recorder effects. These were typically associated with pupils’ interest in understanding the roles of the researcher and class teacher, and discussion around what they could say while being recorded. These subsided over time for all but two groups in Class 1, but with no substantive effect on pupils’ technological thinking.

In summary, findings from the learning phase show that: (1) Pupils engagement was high, and all classes covered the core structural concepts in the unit; (2) pupil knowledge and understanding, as measured by activity sheet responses, was very good overall but scores for turning moments were comparatively lower, and (3) study effect subsided quite quickly for all but two groups and there was no evidence showing these to be detrimental to technological thinking. These differences are considered epistemic and are captured in the framework of difference in Fig. 5 .

Part 2: findings from the problem-solving phase

Part 2 begins by describing the differences from comparing the material solutions produced by the most and least successful groups (dyad 1). Subsequent sections report upon the three areas in which difference were found: epistemic differences, process differences and social and extrinsic differences. Each of these sections lead with the analysis from the most contrasting groups (dyad 1) before presenting the resultant framework of difference. They conclude by reporting on how the differences in these frameworks are reflected across the wider data set. As with findings across all sections, findings only account for areas of the conceptual framework in which differences were identified. For processes such as measuring and testing, no difference was found and other processes, such as computing, did not feature for any of the groups.

Differences in the solutions produced by the most & least successful groups (dyad 1)

Group 5′s solution was identified as the most successful and Group7′s solution was identified as the least successful. Overall, both of these groups engaged well with the task and produced cantilevers that are shown in Figs. 2 and 3 . The order in which different parts of the solutions were developed is indicated by colour with the lighter parts appearing earlier in problem solving than the darker parts. Figure 4 shows this cumulative physical development of each solution over time. Both groups shared a similar conceptual basis and employed triangulation above and below the road surface. Figure 4 shows that Group 5′s solution evolved through 36 developments, while Group 7 undertook 23 developments and chose to strip down and restart their solution at the beginning of the second problem solving session. Similarly, groups 6, 11 and 13 removed or rebuilt significant sections of their solution. Neither group 5 or 7 undertook any developments under the rear of the road surface, and the greatest percentage of developments applied to the road surface itself (Group 7: 30.6%; Group 5: 47.8%). For Group 5, it was only developments 5 and 6 (Fig. 2 ) which offered little to no functional structural advantage. All other developments contributed to either triangulation, rigidity or strength through configuration and material choice with no evidence of misconception, which was also noted by the Delphi panel. The orientation, configuration and choice of materials by Group 7 share similarities with Group 5 insofar as each reflected knowledge of a cognate concept or principle (e.g. triangulation). Delphi Panel Member 8 described Group 7′s solution as having a good conceptual basis. Key differences, however, lay in the overall structural integrity of the solution and the underdevelopment of the road surface (Fig. 3 , Dev.1 and Dev.5) which mean that Group 5 achieved a more ontologically endurant solution than Group 7 did. Evidence from Group 7′s discussion (S = 3, 3.34–3.37; S = 3, 3.38–3.39; S = 16, 3.26–3.30) suggests this is partly because of misconception and deficits in knowledge about materials and the task/cantilever requirements. This was also reflected in the group’s activity responses during structures unit in the learning phase. Alongside the photographic evidence and reflexive notes of the researcher, this suggest that there was  some difficultly in translating concepts and ideas into a practical form. This constitutes a difference in tacit-procedural knowledge between Group 5 and Group 7.

‘Group 5 solution schematic’

‘Group 7 solution schematic’

‘Cumulative development of tangible solutions’

Epistemic differences during problem solving

As well as the knowledge differences in the learning phase and the physical solutions, analysis of the most and least successful groups revealed epistemic differences in problem solving activity related to ‘task knowledge’ and ‘knowledge of concepts and principles’. The extent to which ‘knowledge’ can be reliably coded for in this context is limited because it rapidly becomes inseparable from process. Skills are processes which, in turn, are forms of enacted knowledge. Consequently, although Halfin ( 1973 ) defines idea generation as a knowledge generating process using all the senses, attempts to code for this were unsuccessful because it was not possible to ascertain with any confidence where one idea ended, and another began. Coding was possible, however, for ‘prior personal knowledge’, ‘task knowledge’ and ‘prior technological knowledge’. The analysis of these is present along with the resulting framework of epistemic difference with prior personal knowledge omitted on the basis that no differences between groups was found. The final section looks at how epistemic differences are reflected in the activity of the remaining groups.

Epistemic differences between the most & least successful groups (dyad 1)

Task knowledge is the knowledge pupils have of the problem statement and includes relatively objective parameters, conditions, and constraints. One key difference was the extent to which groups explicitly used this to support decision making. Group 5 spent considerably more time than Group 7 discussing what they knew and understood of the task prior to construction (1m10s vs. 8 s) but during construction, had more instances where their knowledge of the task appeared uncertain or was questioned (n = 6 for Group 5 vs. n = 2 for Group 7). Differences were also found in the prior technological knowledge used by groups. This knowledge includes core structural concepts and principles explored in the learning phase. As with task knowledge, Group 5 verbalised this category of knowledge to a far greater extent than Group 7, both apart from, and as part of, formative discussions with the class teacher (18:59 s vs. 14:43 s). In only one instance was the prior technological knowledge of Group 5 incorrect or uncertain compared with four instances for Group 7. These included misconceptions about triangulation and strength despite performing well with these in the learning phase. Furthermore, some instances of erroneous knowledge impacted directly upon solution development. In response to a discussion about rigidity and the physical performance of the road surface, one pupil stated: “Yes, but it is supposed to be able to bend in the middle..” (Group 7, S = 3, 3.34–3.37) meaning that the group did not sufficient attend to this point of structural weakness which resulted in a less endurant solution. No such occurrences took place with Group 5. More prominent and accurate use of this type of knowledge supports stronger application of learning into the problem-solving context and appeared to accompany greater solution integrity.

From these findings, and those from the learning phase, the framework of difference shown in Fig. 5 was developed:

‘Framework of epistemic differences from comparative analysis of Group 5 and 7’

Epistemic differences across all groups (dyads 1–4)

As with dyad 1, the more successful groups in dyads 3 and 4 scored higher (+ 14% and + 20.7%, respectively) in the learning phase compared with their less successful partner groups. This, however, was not seen with dyad 2. The less successful group achieved a higher average score of 86.3% compared with 71% and, despite greater fluctuations in pupil attendance, scored 100% for turning moments compared with 58% for the more successful group. Although comparatively minimal across all groups, more successful groups in each dyad tended to explicitly verbalise technological and task knowledge more than less successful groups. Furthermore, it was more often correct or certain for more successful groups. This was particularly true for dyad 2, although there was some uncertainty about the strongest shapes for given materials in, for example, Group 12 which was the more successful group of dyad 3. The greatest similarity in verbalised task knowledge was observed with the least contrasting dyad, although evidence from concept sketching (Figs. 6 , 7 ) illustrated a shared misunderstanding between both groups of the cantilever and task requirements.

‘Group 2 concept sketch’

‘Group 8 concept sketch’

The differences in tacit-procedural knowledge between Group 5 and 7 were reflected quite consistently across other dyads, with more successful groups showing greater accuracy, skill and judgement in solution construction. The more successful groups in dyads 2 and 3 undertook three material developments that offered little to no functional advantage, and each of the developments these groups undertook correctly embodied knowledge of cognate structural concepts and principles. Notably, Group 8 of dyad 4 was able to achieved this with no structural redundancy at all. Less successful groups, however, were not as secure in their grasp of the functional dependencies and interrelationships between different parts of their structural systems. The starkest example of this was with Group 4 of dyad 3, who explicitly used triangulation but their failure to physically connect it with other parts of the structure rendered the triangulation redundant. Group 2 of dyad 4 were the only group not to triangulate the underside of the road surface. Less successful groups tended to focus slightly more of their material developments in areas of the bridge other than the road surface, whereas the opposite tended to be true for the other groups. Significantly, while all groups in the study included developments that offered little to no functional advantage, it was only in the case of less successful groups that these impaired the overall functional performance of solutions in some way. Table 4 summarises the sustained epistemic difference across all four dyads.

Process differences

Analysis of the most contrasting dyad yielded process differences in: (1) managing (Halfin, 1973 ), (2) planning, and (3) reflection. Groups managed role and task allocation differently, as well as engaging in different approaches to planning aspects of solution development. Reflection, as a process of drawing meaning or conclusions from past events, is not explicitly identified by Halfin or the conceptual framework. Two new forms of reflection for well-defined technological problem solving (declarative reflection and analytical reflection) were therefor developed to account for the differences found. The analysis of the process differences is presented with the resulting framework for this dyad. The final section presents sustained process differences across all groups.

Difference in managing—role & task allocation & adoption (dyad 1)

The autonomous creation of roles and allocation of tasks featured heavily in the activity of Group 5. These typically clustered around agreed tasks such as sketching (S = 2, 1.46), and points where group members were not directly engaged in construction. In total, Group 5 allocated or adopted roles or task on 31 occasions during problem solving compared with only 7 for Group 7. Both groups did so to assist other members (Group 5, S = 16, 3.33–3.38; Group 7, S = 3, 0.37–0.41), to take advantage of certain skills that group members were perceived to possess (Group 5, S = 2, 1.47- 1.49; Group 7, S = 2, 2.03–2.06) and, for one instance in Group 7, to prevent one group member from executing something incorrectly (S = 16, 2.11–2.13). There was evidence, however, that Group 5 moved beyond these quite pragmatic drivers. Member often had more of a choice and, as shown in Excerpt 5, allocation and adoption is mediated by sense of ownership and fairness.

Excerpt 5: Idea Ownership (Sketching) Pupil ?: “You can’t draw on them..” Pupil 1: “You draw Chloe, I can’t draw..” Pupil 2: “I know I can’t draw on them, that’s why I doing them; no, because you, you had the ideas… because you had…” Pupil ?: “(unclear)” Pupil 3: “Just draw your own ideas, right, you can share with mine right…. Right, you draw the thread one, I’ll do the straw thing…” (Group 5, S  =  2, 1.46–1.59)

The effective use of role and task allocation appeared to play an important role in realising an effective technical solution, however, negative managerial traits were perhaps more significant.

Difference in managing—negative managerial traits (dyad 1)

Evidence of differences between Group 5 and 7 were found in relation to: (1) group involvement, and (2) fragmentation of group vision, which were found to be highly interrelated. Negative group involvement accounted for traits of dominance and dismissiveness. For Group 7, this was more prevalent earlier in the problem-solving activity where one group member tended to dominate the process. This pupil tabled 9 out of 11 proposals prior to working with physical materials and, at times, readily dismissed suggestions by other group members (See Excerpt 1). Moreover, ideas and proposals within the group were sometimes poorly communicated (Excerpt 2), which led to a growing level of disenfranchisement for some group members and a fragmented group vision for solution development.

Excerpt 1 Pupil 1:“We could do it that way…” (Pupils continue discussion without acknowledgement) Pupil 1:“You could do that..” Pupil 2:“Shut up, how are we going to do that?” Pupil 1:“Well you’re allowed glue, and you’re allowed scissors..” Several group members: “Shut-up!” (Group 7, S = 1, 2.07–2.28) Excerpt 2 “(Loud inhalation) Watch my brilliant idea… I need scissors.. Are you allowed scissors?” (Group 7, S = 1, 1.36–1.41)

The was some evidence of dismissiveness present with Group 5 also (e.g. S = 9, 1.32–1.46), however, group members were able to voice their ideas which appeared to support a better shared understanding among group members. Notably, Group 5 reached a degree of consensus about what they would do prior to constructing anything, whilst Group 7 did not. Even in these early stages, two of the four members of Group 7 made it very clear that they did not know what was happening (Excerpt 3).

Excerpt 3 Pupil 1: “What are you all up to?” Pupil 2: “Move you” Pupil 4: “No idea” Pupil 2: “You’re allowed to say hell are you not?” Pupil ?: “Helli-yeh” Pupil 2: “Hellilouya” (slight laughter) Pupil 3: “Right so were going to..(unclear) and do that..” Pupil 1: “What are you all up to?” Pupil 2: “Just… I know what he’s thinking of..” Pupil 4: “I don’t have a clue what you’re thinking of..” Pupil 3: “Neither do I..” (Group 7, S = 2, 0.15–0.33)

Occurrence like these contributed to a growing sense of fragmentation in the group. Verbal and observational data show this to have been picked up by the class teacher who tried to encourage and support the group to share and discussed ideas more fully. Despite this, the group lost their sense of shared vision about how to approach a solution and, part way through the first session, two group members attempted to begin developing a separate solution of their own (S-3, 2.52).

The final managerial difference between Group 5 and 7 was the way in which efforts were made to increase the efficiency of solution development. Seen as a positive managerial trait, both groups did this, but it was more frequent and more developed with Group 5. There were four examples of this with Group 7 in the form of simple prompts to speed the process up (E.g. S = 5:3.02–3.04; S = 6:2.22–2.23; S = 11: 1.34–1.35) and 25 examples with Group 5 involving prompts and orchestrating parallel rather than successive activity.

Differences in planning (dyad 1)

Differences emerged in how Group 5 and 7 thought about and prepared for future problem-solving activity. While the complexity of the pupils’ problem-solving prevented cause and effect from being attributed to planning decisions, four areas of difference were identified: (1) determining use of/amount of materials/resources, (2) sequencing, ordering or prioritising, (3) identification of global solution requirements, and (4) working through how an idea should be practically executed. Across both problem-solving sessions, Group 5 spent over three times as long as Group 7 did, engaging in these forms of planning (8m17s vs. 2.23 s), but Group 7 planned on almost twice as many occasions (n = 98 vs. n = 56). Both groups considered the availability of materials for, and matching of materials to, given ideas (e.g. Group 5, S = 5:3.38–3.48; Group 7, S = 4:2.20–2.34; S = 12:1.53–2.00) and both identified global solution requirements. At the start, Group 5 engaged in 12 min of planning in which they read task instructions (S = 1, 0.49–1.49), explored, tested, and compared the available materials (S = 1, 1.49–2.10), and agreed on a starting point. As shown in Excerpt 4, these discussions attempted to integrate thinking on materials, joining methods, placement. As the class teacher observed, Group 7 were eager to begin construction after 4m45s and did so without an agreed starting point. Pupils in this group explored materials in a more reactive way in response to construction.

Excerpt 4 “..a tiny bit of cardboard, right, this is the cardboard, right.. (picks up part) put glue on it so that’s on that, right.. (modelling part orientation) then put glue on it there so it sticks down.. something to stick it down, do you know what I mean?” (Group 5, S = 9, 2.10–2.20)

Despite similar types of planning processes, the planning discourse of Group 5 was more proactive, and this may have minimised inefficiencies and avoidable errors. For Group 7, two group members unintentionally drew the same idea (S = 2, 3.19–3.26), parts were taped in the wrong place (S = 17, 1.26–1.40) and others glued in the wrong order (S = 5, 1.28–1.30 and 1.48–1.56). Such occurrences, however, notably reduced after the group re-started their solution in the second session which also mirrored a 73% drop in poor group involvement. Communication played an important role in planning and there was no evidence of avoidable errors with Group 5.

Differences in reflection (dyad 1)

The most prevent differences in this study were found in how Group 5 and Group 7 reflected upon their developing solutions. Analysis revealed two main forms of reflection that were used differently by groups. ‘Declarative reflection’ lies close to observation and is defined by this study as reflection that does not explicitly reveal anything of a pupil’s knowledge of technical relationships within their solution, e.g.: “that’s not going to be strong…” (Group 7, S = 2, 0.49–0.51). This form of reflection was critical for both groups who used it heuristically to quality assure material developments, but it was used slightly more often by Group 7 (n = 164:4m30s vs. n = 145:4m07s). By contrast, ‘analytical reflection’ is defined as that which does reveal something of a pupil’s knowledge of technical relationships between two or more parts of a solution. Examples of this are shown in Excerpts 5 and 6 where pupils are reflecting upon an attempt made to support the underside of the road surface.

Excerpt 5: “It’s not going to work because it’s in compression and straws bend..” (Group 5, S = 9, 2.3–2.35) Excerpt 6: “no, that’ll be… oh, aye, because that would weight it down and it would go into the water.” (Group 5, S = 14, 3.35–3.38)

Looking across verbal and observational data, there was no consistent pattern to the use of declarative reflection but analytical reflection for both groups was almost exclusively anchored around, and promoted by, the practical enactment of an idea and could be associated with predictions about the future performance of their solution. Overall, both Group 5 and 7 reflected a similar number of times (n = 216 and n = 209, respectively) although the total amount of time spent reflecting was 17% longer for Group 5. This difference in time was accounted for by comparatively more analytical reflection in Group 5 (n = 75:3m47s vs. n = 45:2m10s for Group 7), particularly during the first half of problem solving. It was also interesting that Group 7 engaged with no analytical reflection at all prior to construction.

Findings from process management, planning and reflection led to the framework of difference in Fig. 8 . This also accounts for differences in the amount of time each group reflected upon the task detail, but this was extremely limited (Group 5: n = 7, 26 s; Group 7: n = 5, 10 s).

‘Framework of process differences from comparative analysis of Group 5 and 7’

Process differences across all groups (dyads 1–4)

Task reflection, attempts at increasing efficiency and differences of fragmented vision found with the most contrasting dyad were not sustained across remaining groups. The only sufficiently consistent difference in patterns of solution development was that more successful groups, on average, spent 18% longer in planning and discussion before beginning to construct anything.

Overall, the nature and patterns of good and poor group involvement from dyad 1 were reflected more widely, with some instances of deviation. The more successful group in dyad 4 had more significant and numerous examples of poor group involvement than did the less successful group (n = 16 vs. n = 10), although they made more effective use of roles and task allocation and spent longer engaged in planning processes. Dyad 2 deviated also insofar as the less successful group (13) actually had fewer avoidable errors than Group 6 who accidentally cut the incorrect parts (e.g. S = 15, 2.44–2.47), undertook developments that were not required (e.g. S = 6, 2.11–2.16) and integrated the wrong parts into their solution (e.g. S = 7, 1.10–1.13).

Differences in the nature and use of reflection was one of the most consistently sustained findings between the most and least successful cohorts. All four of the more successful groups engaged more heavily in reflective processes and more of this reflection was analytical in nature. This shows that reflection which explicitly integrates knowledge of technical relationships between different aspects of a solution plays an important role in more successful technical outcomes. Whilst declarative reflection remained important for all groups, it was also less prominent for groups in the less successful cohort. Table 5 summarises the sustained process difference across dyads 1, 2, 3 and 4.

Social & extrinsic differences (dyad 1)

Differences reported in this section lie out with the formal conceptual framework of the study but, nonetheless, were shown to play a role in the technological problem-solving activity of dyad 1. Differences between Group 5 and 7 emerged in three areas: (1) group tension, (2) effects of the classroom competitive dynamic, and (3) study effects. Group tension, which relates to aspects of interaction such as argumentative discourse, raised voices and exasperation, were negligible for Group 5 (n = 4, 0m24s) when compared with Group 7 (n = 38, 2m38s) and related exclusively to pupils having their voiced heard. For group 7, tension was evident during both sessions, but was more significant in the first session before re-starting the solution in session 2 and purposeful attempts to work more collaboratively with the support of the teacher (Group 7, S = 10, 0.36–1.29). Observations revealed that tension was typically caused by pupils failing to carry out practical processes to the standard of other group members, or breaking parts such as the thread supporting the road surface in the 36 th minute of Session 2.

Despite collaborative efforts within groups, there was a sense of competitive dynamic which appeared either to positively bias, negatively bias, or to not affect group activity. This competitive dynamic was present in groups comparing themselves to other groups in the class. Group 7 had 3.7 times as many instances of this as Group 5 with 73% of these negatively affecting the group. These included interference from and with other groups (S = 7, 0.07–0.12), attempting to copy other groups (S = 7, 1.14–1.22) and comparing the solutions of other groups to their own (S = 8, 2.55–2.59). In contrast, Group 5 appeared to be far less affected by perceptions of competition. Around a third of instances were coded as neutral, however, Group 7 experienced more instances of positive competitive effects than Group 5 did (n = 5 vs. n = 1).

Study effects were present for both groups often triggered by the arrival of the researcher at their table to observe or take photographs. The biggest difference in study effects was associated with the audio recorder. Recorder effects for Group 7 were three and half times that of Group 5 involving discussion about how it worked (Group 7, S = 10, 3.04–3.17), or about what was caught or not caught on tape (Group 7, S = 14, 1.01–1.45). Although questionnaire data showed that pupils in Group 5 felt that they talked less in the presence of the recorder, this was not supported by observations, verbal data, or the class teacher. From these findings, the framework of social and extrinsic difference in Fig. 9 was developed.

‘Framework of social & extrinsic differences from comparative analysis of Group 5 and 7’

Social & extrinsic differences across all groups (dyads 1–4)

Most of the social and extrinsic differences identified with Groups 5 and 7 were reflected to greater or lesser extents in other dyads. In addition to less successful groups being more susceptible to researcher and recorder effects, two specific points of interest emerged. Firstly, group tension was considerably more prominent for less successful groups than it was for more successful groups. Although no evidence of a direct relationship was established, tension appeared to accompany poor managerial traits and the changing of group composition (e.g. Group 8, Group 13). The most significant differences in tension were found with dyad 3. No occurrences were found for the most successful group and 29 were seen with the least successful group including aggressive and abrupt communication between pupils involving blame for substandard construction (S = 10, 2.28–2.38), through to name calling (S = 12, 0.20–0.22), arguing (S = 6, 1.46–2.10) and threats of physical violence (S = 11, 3.25–3.29).

Secondly, the more successful groups were influenced by the competitive class dynamic more than the less successful groups were. This is the only sustained finding that directly opposes what was found with dyad 1. These took the form of neutral or negative inter-group effects involving comparing and judging other groups (e.g. Group 6), espionage, copying or suspicion thereof (e.g. Group 6, 8 and 12). Table 6 summarises the sustained social and extrinsic differences across the more and less successful cohorts.

Discussion and Conclusions

This study established and applied three frameworks to capture the epistemic, procedural, and social and extrinsic differences between groups of pupils as they developed solutions to a well-defined technological problem. Social & extrinsic findings revealed higher levels of group tension for the less successful cohort, but that more successful groups elicited more negative responses to the competitive class dynamic created by different groups solving the same problem. Major findings about differences in knowledge and process are discussed. Thereafter, a three-part characterisation of thinking for well-defined technological problem solving is presented in support of pedagogy for Design & Technology classrooms.

The most important of those knowledge differences uncovered were found in: (1) the material development of the solution itself, and (2) the reflective processes used by groups during problem solving. The conceptual framework characterises ‘tacit-procedural knowledge’ as the implicit procedural knowledge embodied in technical skill, accuracy and judgement, and this was further refined in the solutions of more successful groups. Linked to this was the fact that several of the material developments for triangulation and strength were improperly realised by less successful groups which negatively impacted on the functional performance of their solutions. Often, this was despite evidence of a good conceptual understanding of triangulation, tension, and compression in the learning phase. An ontologically endurant solution requires stability over time and lesser developed aspects of tacit-procedural knowledge and knowledge application meant that this was not realised as fully as possible for some groups.

This can be partly explained by the challenge of learning transfer, or more accurately, learning application. Several notable studies have explored these difficulties in technology education (Brown, 2001 ; Dixon & Brown, 2012 ; Kelly & Kellam, 2009 ; Wicklein & Schell, 1995 ), but typically at a subject or interdisciplinary level. The findings of this study suggest that, even when the concepts in a learning unit are tightly aligned with a well-defined problem brief, some pupils find difficulty in applying them within a tangible, material context. It could be argued that more successful groups were better at connecting learning between different contexts associated with the problem-solving task and could apply this with more developed skill and judgement.

The second important knowledge difference arose in the various forms of reflection that groups engaged with. Reflection in this study supports pupils in cycling through the re-application/transformation loop in a similar way to the perception/translation/evaluation blocks of the iterative models of problem solving (Argyle, 1972 ; Miodusre & Kipperman, 2002 ; Scrivener et al., 2002 ). Surprisingly few studies explore ‘reflection’ as a process in technological thinking (Kavousi et al., 2020 ; Luppicini, 2003 ; Lousberg et al., 2020 ), and fewer still in the context of school-level technological problem solving. This study found that more successful groups reflected more frequently, and that more of this reflection was analytical insofar as it explicitly revealed knowledge of technical relationships between different variables or parts of their solution. Such instances are likely to have been powerful in shaping the shared understanding of the group. This type of reflection is significant because it takes place at a deeper level than declarative reflection and is amalgamated with pupils’ subject knowledge and qualitative understanding of their technical solution. This allowed pupils to look back and to predict by explicitly making connections between technical aspects of their solution.

The final area in which important differences were found was management of the problem-solving process which is accounted for by Halfin ( 1973 ) in his mental process set. When analysed, the more successful cohort exploited more positive managerial strategies, and fewer negative traits. They made more extensive and effective use of role and task allocation, spent more time planning ahead and longer in the earlier conceptual phase prior to construction. Other studies have also captured aspects of these for technology education. Hennessy and Murphy ( 1999 ) discuss peer interaction, planning, co-operation and conflict, and changing roles and responsibilities as features of collaboration with significant potential for problem solving in technology. Rowell ( 2002 ), in a study of a single pair of technology pupils, demonstrated the significance of roles and participative decisions as enablers and inhibiters of what pupils take away from learning situations. What was interesting about the groups involved in this study, was that the managerial approaches were collectively more proactive in nature for more successful groups. Less successful groups were generally more reactive to emergent successes or problems during solution development.

The problem-solving activity of pupils in this study was exceptionally complex and a fuller understanding of how these complexities interacted would have to be further explored. Yet, key differences in knowledge and process collectively suggest that effectively solving well-defined technological problems involves a combination of proactive rather than reactive process management, and an ability to make two different types of technology-specific connections: contextual connections and technical connections. Proactively managing is generic and involves planning, sequencing, and resourcing developments beyond those that are immediately in play to minimise avoidable errors with reference to problem parameters. It involves group members through agreed roles and task allocation that, where possible, capitalise on their strengths. Contextual connections involve effectively linking and applying technological knowledge, concepts, and principles to the material context that have been learnt form other contexts out with solution development. This is supported by skill and judgement in the material developments that embody this knowledge. Finally, technical connections appear to be important for better functioning solutions. These are links in understanding that pupils make between different parts of the developing solution that reveal and build knowledge of interrelationships, dependencies and how their solution works. In addition to helping pupils developing effective managerial approaches in group work, this suggests that pedagogical approaches should not assume pupils are simply able to make contextual and technical connections during technological problem solving.  Rather, pedagogy should actively seek to help pupils make both forms of connection explicit in their thinking.

This study has determined that proactive management, contextual and technical connections are important characteristics of the modus operandi of pupils who successfully solve well-defined technological problems. This study does not make any claim about the learning that pupils might have taken from the problem-solving experience. It does, however, provide key findings that teachers can use to support questioning, formative assessment and pedagogies that help pupils in solving well-structured technological problems more effectively.

Ethical approval

Ethical approval for this study was granted by the School of Education Ethics Committee at the University of Glasgow and guided by the British Educational Research Association Ethical Code of Conduct. All necessary permissions and informed consents were gained, and participants knew they could withdraw at any time without giving a reason. The author declares no conflicts of interest in carrying out this study.

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Acknowledgements

I would like to thank Dr Jane V. Magill, Dr. Alastair D. McPhee and Professor Frank Banks for their support in this work as well as the participating teachers and pupils who made this possible.

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Morrison-Love, D. Technological problem solving: an investigation of differences associated with levels of task success. Int J Technol Des Educ 32 , 1725–1753 (2022). https://doi.org/10.1007/s10798-021-09675-5

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6: Problem Solving and Need Recognition Techniques

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• 6.0: Prelude to Problem Solving and Need Recognition Techniques
• 6.1: Problem Solving to Find Entrepreneurial Solutions
• 6.2: Creative Problem-Solving Process
• 6.3: Design Thinking
• 6.4: Lean Processes
• 6.5: Key Terms
• 6.6: Summary
• 6.7: Review Quetions
• 6.8: Discussion Questions
• 6.9: Case Questions
• 6.10: Suggested Resources

ITSM for high-velocity teams

Problem management roles and responsibilities.

Problem management is about more than just finding and fixing incidents. Real problem management lies in identifying and understanding the underlying causes of an incident as well as identifying the best method to eliminate that root cause. 

That’s why effective problem management efforts consist of teams operating under clear roles and responsibilities. Team members understand what the roles are, what each person is responsible for, and who is in each role during a problem investigation.

Here are a few of the most common problem management roles. It’s important to understand that not every team will operate with every role on this list. And these aren’t necessarily permanent roles. Instead, think of them as designations for a team that comes together to work on a particular problem.

Problem manager

The problem owner manages the overall process for a specific problem. They coordinate and direct all facets of the problem management effort, including bringing the right teams, tools, and information together. The problem manager may also delegate subtasks to other team members as they see fit.

Also called : Problem owner, Major Incident Manager

Process owner

The process owner is responsible for the overall health and success of the team’s problem management process. They oversee evolution and development of the process, as well as team member training and onboarding.

Also called : Process manager, process coordinator

Service owner

The service owner is responsible for defining ongoing operations and health of the service. This can include measuring and reporting on the value of changes, enhancements, planned downtime, training, documentation, and more.

Also called : Service leader, product manager

Service desk agent

Front-line support for your service desk . The service desk agent is often the first to notice and report an incident or problem. The agent is often also the first person to notice that several unique incidents all relate to a greater problem.

Also called : Agent, Service agent, Support Agent, Help desk agent, Service desk analyst

An individual familiar with the impacted service experiencing a problem or incident. Often a developer or engineer, the tech lead can dive into recent code changes to see what root causes may be contributing to the problem.

Also called : Technical lead, subject matter expert, on-call engineer, developer, software developer, Site Reliability Engineer (SRE)

Stakeholders

Stakeholders can be whoever needs high-level info on the problem but isn’t directly involved in the problem management process. This can be anyone from adjacent teams, to customers, and organizational leaders.

Also called : Customers, executive teams, vendors, end users, business teams

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Problem Recognition - Meaning, Importance, Types & Example

What is problem recognition.

Problem recognition is the initial step in the consumer decision making journey when a consumer recognizes a need or a want which is not being fulfilled by any of the existing products or services available. It starts when a customer is unable to fulfil current or future needs with the products at disposal and starts to identify the gap which now has to be filled with a purchase of a product or a service. It is also defined as a gap between the current state and the desired state from the customer's perspective.

Problem recognition is followed by information search, evaluation of options, actual purchase and post-purchase.

Importance of Problem Recognition

Problem recognition is the most important part of the consumer decisioning process as this is the point where a person becomes a potential customer and can start the process of buying a new product or a service. This is not only important for customers but also for the organizations, manufacturers and marketers. The entire product lifecycle revolves around the problem statement of the customers. If the problems ceases to exist so would be the need for the product.

Features phone still function but not many people want them as now the problem recognition is just not about being able to talk but also to have a multimedia device which can give many additional features like internet connectivity, social media, apps. If a company does not recognize the changing customer behavior they would never be able to define the problem statement and may become obsolete. 

With technological advancements the problem recognition keeps getting refined and now may be solved with an improved product or an entirely new product. Paper based documentation was very critical few years back and many products like printers, scanners etc. were solving the problem. As technology improved and automated solutions cut down the need for papers and work started happening digitally, for the same need, paper based documentation became not a problem anymore.

Types of Problem Recognition

There are multiple types of problem recognition. The two most important are:

1. Expected and Active Problems

These are the problems about which the customers are actively aware and plan to solve it themselves by looking out for a potential product or a solution which resolved the problem. These are expected like broadband plan getting expired, Need to enroll for a college course after schooling, buying a refill for printer, buying a bus ticket to travel to another town to meet a friend. These are examples of expected and active problems which are to be solved by the customer while being aware of them. 

The marketers normally present the product which can solve the problem without defining the problem again to customer as he or she is already aware. The benefits and resolution is what the customer is interested in.

2. Unexpected and Inactive Problems

These are the ones where the customer does not know if they require to solve them or not. An example can be insurance policy in which a customer has to be made aware that there is a need which is fulfilled by buying an insurance policy and will eventually solve a future problem if it arises. 

In B2B sales especially in technology, we see this problem recognition state. Many customers have been working in the same way since many years but the new technology sellers convince them that the new digital and automated solutions are much better for them as that would help in cutting a lot of costs and increase efficiency. The customers were not aware until explained and also were not expecting to solve them immediately. But once a customer is convinced about solving the issue, then it becomes an active problem.

Many times, there can be some unexpected events in life which can lead to immediate problems that require you to buy new products or services 

Examples of Problem Recognition

The problem recognition might be due to:

1. A product being out of stock like Oil, floor, raw materials can lead to a problem 2. Dissatisfaction with the current product or state 3. New needs/wants based on the lifestyle and hierarchy in life 4. Related products/purchases e.g. After buying an expensive phone, people look to buy a case immediately to protect the phone 5. Marketer induced problem recognition which are inactive problems 6. New products and categories e.g. When an iPad was launched, people were working on phones and desktops. After the launch, a new category got created in the market called Tablet PCs.

Hence, this concludes the definition of Problem Recognition along with its overview.

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31 examples of problem solving performance review phrases

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You're doing great

You should think of improving

Tips to improve

Use these practical examples of phrases, sample comments, and templates for your performance review , 360-degree feedback survey, or manager appraisal.

The following examples not only relate to problem-solving but also conflict management , effective solutions, selecting the best alternatives, decision making , problem identification, analyzing effectively, and generally becoming an effective problem-solving strategist. Start using effective performance review questions to help better guide your workforce's development. 

Problem solving appraisal comments: you're doing great

• You always maintain an effective dialogue with clients when they have technical problems. Being clear and articulate makes sure our customers' faults are attended to promptly.
• You constantly make sure to look beyond the obvious you never stop at the first answer. You’re really good at exploring alternatives. Well done!
• Keeping the supervisors and managers informed of status changes and requests is important. You’re really good at communicating the changes to the projects at all times. Keep it up!
• You stay cool and collected even when things aren’t going according to plan or up in the air. This is a great trait to possess. Well done!
• You’re excellent at giving an honest and logical analysis. Keep it up! Effectively diagnosing complex problems and reaching sustainable solutions is one of your strong points.
• Your ability to ability to make complex systems into simple ones is truly a unique skill to possess. Well done!
• You often identify practical solutions to every roadblock. You’re a real asset to the team! Great job.
• You always listen actively and attentively to make sure you understand what the exact problem is and you come up with solutions in an effective manner.
• You have an amazing ability to clearly explain options and solutions effectively and efficiently. Well done!
• When driving projects, you can shift to other areas comfortably and easily. making sure the project runs smoothly. Great job!

Problem solving performance review phrases: you should think of improving

• You always seem too overwhelmed when faced with multiple problems. Try to think of ways to make problems more manageable so that they can be solved in a timely and effective manner.
• Avoiding conflicts constantly with people is not a good idea as you will only build up personal frustration and nothing will be done to remedy the situation. Try to face people when there are problems and rectify problems when they occur.
• Don’t allow demanding customers to rattle your cage too much. If they become too demanding, take a step back, regulate your emotions , and try to make use of online support tools to help you rectify problems these tools can help a lot!
• It’s necessary that you learn from your past mistakes . You cannot keep making the same mistakes , as this is not beneficial to the company.
• You tend to ask the same questions over and over again. Try to listen more attentively or take notes when colleagues are answering!
• Providing multiple solutions in an indirect and creative approach will allow you to be more effective at problem-solving . if you struggle with this typically through viewing the problem in a new and unusual light.
• You fail to provide staff with the appropriate amount of structure and direction. They must know the direction you wish them to go in to achieve their goals .
• You need to be able to recognize repetitive trends to solve problems promptly.
• You tend to have problems troubleshooting even the most basic of questions. As a problem solver and customer support person, it’s imperative that you can answer these questions easily.
• Read through your training manual and make sure you fully understand it before attempting questions again.

Performance review tips to improve problem solving

• Try to complain less about problems and come up with solutions to the problems more often. Complaining is not beneficial to progression and innovation.
• As a problem solver, it’s important to be able to handle multiple priorities under short deadlines.
• You need to be able to effectively distinguish between the cause and the symptoms of problems to solve them in an efficient and timely manner.
• Try to anticipate problems in advance before they become major roadblocks down the road.
• Try to view obstacles as opportunities to learn and thrive at the challenge of solving the problem.
• Remember to prioritize problems according to their degree of urgency. It's important that you spend the majority of your time on urgent tasks over menial ones.
• When putting plans into place, stick to them and make sure they are completed.
• When solving problems, try to allocate appropriate levels of resources when undertaking new projects. It is important to become as efficient and as effective as possible.
• Try to learn to pace yourself when solving problems to avoid burnout . You’re a great asset to the team and we cannot afford to lose at this point.
• Meeting regularly with your staff to review results is vital to the problem-solving process.
• Staff that has regular check-ins understand what it is that is required of them, what they are currently achieving, and areas they may need to improve. Try to hold one-on-one meetings every week.

Enhance your problem-solving skills

Discover personalized coaching to navigate challenges effectively and boost your performance.

Madeline Miles

Madeline is a writer, communicator, and storyteller who is passionate about using words to help drive positive change. She holds a bachelor's in English Creative Writing and Communication Studies and lives in Denver, Colorado. In her spare time, she's usually somewhere outside (preferably in the mountains) — and enjoys poetry and fiction.

How a performance review template improves the feedback process

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Problem Solving: 40 Useful Performance Feedback Phrases

Problem Solving: Use these sample phrases to craft meaningful performance evaluations, drive change and motivate your workforce.

Problem Solving is the skill of defining a problem to determine its cause, identify it, prioritize and select alternative solutions to implement in solving the problems and reviving relationships.

Problem Solving: Exceeds Expectations Phrases

• Actively listens when others are talking and seek clarification on issues not well understood.
• Seeks to brainstorm on problems to try to find the right solutions.
• Evaluates all possible solutions and chooses the one that will deliver the best results.
• Knows how well to collaborate with others to find solutions to problems.
• Knows how to resolve any outstanding client issues and problems amicably.
• Communicates views and thoughts in a very distinct and understandable manner.
• Is decisive when it comes to making decisions and sticks by the decisions made.
• Gathers all the necessary facts and information first before making any decision.
• Monitors all outcomes of all actions undertaken to take full responsibility for any problem.
• Breaks a problem down before starting to analyze it in a more detailed manner.

Problem Solving: Meets Expectations Phrases

• Is always open-minded and readily accepts what others have to contribute.
• Has an inquisitive nature and tries to analyze all that is happening around.
• Always asks the right questions and raises any relevant issue when necessary.
• Keeps things calm even when required to make quick decisions under high pressure.
• Communicates or articulates issues in an obvious and concise way that people can easily understand.
• Shows strong level-headedness when assessing situations and coming up with solutions.
• Tries to be accommodative of other people's views and accepts them easily.
• Always portrays enough knowledge of the problem and its feasible solutions.
• Shows the willingness to change tact whenever the conditions change.
• Creates opportunities to evaluate and implement the decisions that are arrived at properly.

Problem Solving: Needs Improvement Phrases

• Not willing to be accommodative of other people's ideas and opinions.
• Does not know how to present a problem in ways that people can understand.
• Finds it difficult to articulate issues in a clear and understandable manner.
• Not decisive and assertive when it comes to coming up with solutions.
• Does not take the time to listen keenly to what others have to say or contribute.
• Always in a hurry to make decisions and does not think things through.
• Does not always monitor the decisions made to ensure that they have a positive impact.
• When faced with a high-pressure problem, does not maintain a cool head to be able to solve it properly.
• Not willing to collaborate with other people to come up with solutions to problems.
• Does not manage client-related issues in a professional manner and customers are left unsatisfied.

Problem Solving: Self Evaluation Questions

• How well do you solve issues and are you confident in your abilities?
• Give an instance you solved a problem, and it was successful.
• Give a situation that you solved a problem, and it was unsuccessful.
• How well do you accommodate other people ideas and opinions when trying to solve a problem?
• How do you manage high-pressure situations that require fast and urgent attention?
• Do you involve other people when trying to solve any particular problem?
• How well do you brainstorm before setting out to solve a problem?
• Do you research well enough to get proper facts and information?
• Are you in most cases conversant with what the problem is before you solve it?
• How well are you keen on everything that is happening around you?

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Automated Face Recognition: Challenges and Solutions

Submitted: 05 April 2016 Reviewed: 27 September 2016 Published: 14 December 2016

DOI: 10.5772/66013

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Automated face recognition (AFR) aims to identify people in images or videos using pattern recognition techniques. Automated face recognition is widely used in applications ranging from social media to advanced authentication systems. Whilst techniques for face recognition are well established, the automatic recognition of faces captured by digital cameras in unconstrained, real‐world environment is still very challenging, since it involves important variations in both acquisition conditions as well as in facial expressions and in pose changes. Thus, this chapter introduces the topic of computer automated face recognition in light of the main challenges in that research field and the developed solutions and applications based on image processing and artificial intelligence methods.

• face recognition
• face identification
• face verification
• face authentication
• face labelling in the wild
• computational face

Author Information

Joanna isabelle olszewska *.

• School of Computing and Technology, University of Gloucestershire, Cheltenham, UK

*Address all correspondence to: [email protected]

1. Introduction

Automated face recognition (AFR) has received a lot of attention from both research and industry communities since three decades [ 1 ] due to its fascinating range of scientific challenges as well as rich possibilities of commercial applications [ 2 ], particularly in the context of biometrics/forensics/security [ 3 ] and, more recently, in the areas of multimedia and social media [ 4 , 5 ].

Face recognition is the field trying to bring an answer to the question: ‘Whose face it is?’ For this purpose, people have natural abilities through their human perceptive and cognitive systems [ 6 ], whereas machines need complex systems involving multiple, advanced algorithms and/or large, adequate face databases. Studying, designing and developing such methods and technologies are the domain of automated face recognition (AFR).

AFR could be distinguished further into the computer automated face identification and the computer automated face verification. Hence, on the one hand, automated face identification consists in a one‐to‐many (1:N) search of a face image among a database containing many different face images in order to answer questions such as ‘ Is it a known face? ’ [ 7 ]. On the other hand, automated face verification is a one‐to‐one (1:1) search to solve the matter of ‘ Is it the face of …? ’ search [ 8 ].

Moreover, AFR could be the basis to the solution of the ‘ Who is in the picture? ’ problem, leading to the computer automated face labelling/face naming [ 9 ].

The general AFR process is illustrated in Figure 1 . Usually, it first applies techniques addressing questions such as ‘ Is there a face in the image? ’ (face detection) and ‘ Where is the face in the image? ’ (face location) and next, it handles the computer‐automated recognition mechanism itself [ 10 ].

Figure 1.

Overview of the face detection and recognition processes.

In particular, this chapter is dedicated to the ‘why’ and ‘how’ of the computer‐automated face recognition in constrained and unconstrained environments. The remaining parts of this chapter are structured as follows: in Section 2, we describe AFR's today challenges, while corresponding scientific solutions and industrial applications are presented in Sections 3 and 4, respectively. Section 5 draws up new trends and future directions for automated face recognition performance improvements and evolution.

2. Challenges

The study and analysis of faces captured by digital cameras address a wide range of challenges, as detailed in Sections 2.1–2.7, which all have a direct impact on the computer automated face detection and recognition.

2.1. Pose variations

Head's movements, which can be described by the egocentric rotation angles, i.e. pitch, roll and yaw [ 11 ], or camera changing point of views [ 12 ] could lead to substantial changes in face appearance and/or shape and generate intra‐subject face's variations as illustrated in Figure 2 , making automated face recognition across pose a difficult task [ 13 ].

Figure 2.

Illustration of pose variations around egocentric rotation angles, namely (a) pitch, (b) roll and (c) yaw.

Since AFR is highly sensitive to pose variations, pose correction is essential and could be achieved by means of efficient techniques aiming to rotate the face and/or to align it to the image's axis as detailed in reference [ 13 ].

2.2. Presence/absence of structuring elements/occlusions

The diversity in the intra‐subject face's images could also be due to the absence of structuring elements (see Figure 3a ) or the presence of components such as beard and/or moustache (see Figure 3b ), cap (see Figure 3c ), sunglasses (see Figure 3d ), etc. or occlusions of the face (see Figure 3e ) by background or foreground objects [ 14 ].

Figure 3.

Illustration of (a) absence or (b‐d) presence of structuring elements, i.e. (b) beard and moustache, (c) cap, (d) sunglasses or (e) partial occlusion.

Thus, face's images taken in an unconstrained environment often require effective recognition of faces with disguise or faces altered by accessories and/or by occlusions, as dealt by appropriate approaches such as texture‐based algorithms [ 15 ].

2.3. Facial expression changes

Some more variability in face appearance could be caused by changes of facial expressions induced by varying person's emotional states [ 16 ] which are displayed in Figure 4 .

Hence, efficiently and automatically recognizing the different facial expressions is important for both the evaluation of emotional states and the automated face recognition. In particular, human expressions are composed of macro‐expressions, which could express, e.g., anger, disgust, fear, happiness, sadness or surprise, and other involuntary, rapid facial patterns, i.e. micro‐expressions; all these expressions generating non‐rigid motion of the face. Such facial dynamics can be computed, e.g., by means of the dense optical flow field [ 17 ].

Figure 4.

Illustration of varying facial expressions that reflect emotions such as (a) anger, (b) disgust, (c) sadness or (d) happiness.

2.4. Ageing of the face

Another reason of face appearance's changes could be engendered by the ageing of the human face, and could impact on the entire AFR process if the time between each image capture is significant [ 18 ], as illustrated in Figure 5 .

Figure 5.

Illustration of the human ageing process, where the same person has been photographed (a) at a younger age and (b) at an older age, respectively.

To overcome face ageing issue in AFR, methods need to take properly into account facial ageing patterns [ 18 ]. Indeed, over time, not only face characteristics such as its shape or lines are modified [ 19 ], but other aspects are changing as well, e.g. hairstyle [ 20 ].

2.5. Varying illumination conditions

Large variations of illuminations could degrade the performance of AFR systems. Indeed, for low levels of lighting of the background or foreground, face detection and recognition are much harder to perform [ 21 ], since shadows could appear on the face and/or facial patterns could be (partially) indiscernible. On the other hand, too high levels of lights could lead to over‐exposure of the face and (partially) indiscernible facial patterns (see Figure 6 ).

Robust automated face detection and recognition in the case of (close‐to‐) extreme or largely varying levels of lighting apply to image‐processing techniques such as illumination normalization, e.g. through histogram equalization [ 22 ]; or machine‐learning methods involving the actual image global image intensity average value [ 21 ].

Figure 6.

Illustration of camera lighting variations, leading to (a) over‐exposure of the face, (b) deep shadows on the face or (c) partial backlight.

2.6. Image resolution and modality

Other usual factors influencing AFR performance are related to the quality and resolution of the face image and/or to the set‐up and modalities of the digital equipment capturing the face [ 23 ]. For this purpose, ISO/IEC 19794‐5 standard [ 24 ] has been developed to specify scene and photographic requirements as well as face image format for AFR, especially in the context of biometrics. However, real‐world situations of face image acquisition imply the use of different photographic hardware, including one or several cameras which could be omnidirectional or pan‐tilt‐zoom [ 25 ], and which could include, e.g. wide‐field sensors [ 25 ], photometric stereo [ 26 ], etc. Cameras could work in the range of the visible light or use infra‐red sensors, leading to multiple modalities for AFR [ 6 ]. Hence, faces acquired in real‐world conditions lead to further AFR challenges.

Figure 7.

Illustration of variations of the image scale and resolution, with (a) a large‐scale picture, (b) a small‐scale picture and (c) a low‐resolution picture.

For example, as shown in Figure 7 , in some situations, a face could be captured at distance resulting in a smaller face region image compared to the one in a large‐scale picture. On the other hand, some digital camera could have a low resolution [ 27 ] or even very low resolution [ 28 ], if the resolution is below 10 × 10, leading to poor quality face images, from which AFR is very difficult to perform. To deal with this limitation, solutions have been proposed to reconstruct a high‐resolution image based on the low‐resolution one [ 28 ] using the super‐resolution method [ 29 , 30 ].

2.7. Availability and quality of face datasets

Each AFR technology requires an available, reliable and realistic face database in order to perform the 1:N or 1:1 face search within it (see Figure 1 ). Hence, the quality such as completeness (e.g. including variations in facial expressions, in facial details, in illuminations, etc.) as well as accuracy (e.g. containing ageing patterns, etc.) and the characteristics (e.g. varying image file format and colour/grey level, face resolution, constrained/unconstrained environment, etc.) of a face dataset are crucial to the AFR process [ 31 ]. Moreover, when dealing with face data, people's consent and privacy should be respected as AFR systems should comply with the Data Protection Act 2010 [ 32 ].

ORL [ 33 ] is a 400‐picture dataset of 40 distinct subjects, in portable grey map ( pgm ) format and with a 92 × 112 pixel resolution, 8‐bit grey level. Men and women's faces are taken against a dark homogeneous background, under varying illumination conditions. The subjects are in up‐right, frontal position, with variations in face expressions, facial details and poses within ±20% in yaw and roll.

Caltech Faces [ 34 ] dataset consists of 450 jpeg images with a resolution of 896 × 592 pixels. Each image shows the frontal view of a face (single pose) of one out of 27 unique persons, under different lighting, expressions and backgrounds.

The Face Recognition Technology (FERET) [ 35 ] database has been built with 14,126 face images from 1199 individuals, defining sets of 5–11 greyscale images per person. Each set contains mugshots with different facial expressions and facial details, acquired using various cameras and varying lighting.

BioID Face database [ 36 ] has 1521 frontal face images of 23 people. Images of 384 × 286 pixel resolution are in pgm format and have been captured in real‐world conditions, i.e. with a large variety of illumination, background and face size.

Yale face database [ 37 ] has 165 greyscale, gif images of 15 individuals. There are 11 images per subject, one per different facial expression or configuration, i.e. left/centre/right‐light, with or without glasses and with different expressions.

Caltech 10,000 web faces [ 38 ] have collected 10,524 human faces of various resolutions and in different settings (e.g. portrait images, group of people, etc.) from Google Image . Coordinates of eyes, nose and the centre of the mouth for each frontal face are provided in order to be used as ground truth for face detection algorithms, or to align and/or crop the human faces for AFR.

Some databases contain both 2D and 3D face data, e.g. Face Recognition Grand Challenge (FRGC) dataset [ 39 ] recorded such 50,000 un‐/controlled images from 4003 subject sessions.

Other datasets have multiple modalities such as XM2VTSDB multi‐modal face database [ 40 ] which is the Extended M2VTS database. It is a large, multi‐modal database captured onto high‐quality, digital video. It contains four recordings, each with a speaking head shot and a rotating head shot, of 295 subjects taken over a period of 4 months. This database includes high‐quality colour images, 32 kHz 16‐bit sound files, video sequences and also a 3D model.

Another multi‐modal database is the Surveillance Cameras Face (SCFace) [ 41 ] dataset. It has recorded 4160 static human faces of 130 subjects, in the visible and infrared spectrum, in an unconstrained indoor environment, using a multi‐camera set‐up consisting of five video‐surveillance cameras which various qualities mimic real‐world conditions.

Recent developments of face databases focus on capturing faces in the wild, i.e. in unconstrained environments. For example, Face Detection Data Set and Benchmark (FDDB) [ 42 ] is a dataset of 2845 images, both greyscale and colour ones, with 5171 faces in the wild, which could include occlusions, poses variations, low resolution and out‐of‐focus faces.

Labelled Faces in the Wild (LFW) [ 43 ] database is a popular dataset for studying multi‐view faces in an unconstrained environment. It has recorded 13,233 foreground face images; other faces in the images being assimilated to the background. It has targeted 5749 different individuals, which could have one or more images in the database, and presents variations in pose, lighting, expression, background, race, ethnicity, age, gender, clothing, hairstyles, camera quality, colour saturation, focus, etc. Images have a 250 × 250 pixels resolution and are in jpeg format; they are mostly in colour, although few are greyscale only.

Some other available face datasets have been designed for specific purposes. Hence, Spontaneous MICro‐expression database (SMIC) [ 44 ] is used for facial micro‐expressions recognition, while the Acted Facial Expression in the Wild (AFEW) database [ 45 ], which has semi‐automatically collected face images with acted emotions from movies, is dedicated to macro‐expression recognition in close‐to‐real conditions. On the other hand, FG‐NET Ageing database (FG‐NET) [ 46 ] could be applied for age estimation, age‐invariant face recognition and age progression.

3. Solutions

Major pattern recognition techniques as well as main machine‐learning methods used for AFR systems are presented in Section 3.1, while classic approaches for AFR in still images or video databases/live video streams are mentioned in Section 3.2.

3.1. Face recognition systems

Most of the AFR systems consist in a two‐step process (see Figure 8 ) based firstly on facial feature extraction, as explained in Section 3.1.1, and second, on facial feature classification/matching against an available face database, as mentioned in Section 3.1.2.

3.1.1. Feature extraction

Facial features are representing the face in a codified way which is computationally efficient for further processes such as matching, classification or other machine‐learning techniques, in order to perform AFR. On the other hand, computing facial features in an image could serve to detect a face and to locate it within the image, as illustrated in Figure 9 .

Figure 8.

Schematic representation of the automated face recognition system.

Figure 9.

Face location via (a) a bounding box and (b) an ellipse.

Facial feature representations could be of different nature from sparse to dense ones, and could be focused on face appearance, face texture or face geometry [ 15 ].

Figure 10.

Results of facial feature modelling using different approaches, e.g. (a-b) Haar-like features; (c) Linear Binary Patterns (LBP); (d) Edge map; (e) Active shape; (f) SIFT points.

Commonly computed facial features are Haar‐like features [ 47 ] ( Figure 10(a, b) ); linear binary patterns (LBP) [ 48 ] ( Figure 10(c) ), which have been extended to local directional pattern (LDP) [ 49 ] for micro‐expressions recognition in particular; edge maps ( Figure 10(d) ) and their extension to line edge maps (LEM) [ 50 ]; active shape or active contours [ 51 ] ( Figure 10(e) ); SIFT points [ 52 ] ( Figure 10(f) ), etc.

The detected facial features, e.g. with SIFT points usually correspond to some or all elements of the set of facial anthropometric landmarks, i.e., facial fiducial points (FPs) (see Figure 11 ), which are defined as follows: FP1—top of the head, FP2—right eyebrow right corner, FP3—right eyebrow left corner, FP4—left eyebrow right corner, FP5—left eyebrow left corner, FP6—right eye right corner, FP7—right eye centre of pupil, FP8—right eye left corner, FP9—left eye right corner, FP10—left eye centre of pupil, FP11—left eye left corner, FP12—nose right corner, FP13—nose centre bottom, FP14—nose left corner, FP15—mouth right corner, FP16—mouth left corner, FP17—chin corner, FP18—right ear top corner, FP19—right ear bottom corner, FP20—left ear top corner and FP21—left ear bottom corner [ 53 ].

Figure 11.

Illustration of the 21 facial landmarks.

Computer automated face recognition relies on facial features, in the same way forensic examiners focus their attention not only on the overall similarity of two faces regarding their shape, size, etc. [ 54 ], but also on morphological comparisons region by region, e.g. nose, mouth, eyebrows, etc. [ 53 ]. Some AFR methods evaluate also discriminative characteristics such as the distance from people’s mouth to the nose, nose to eyes, mouth to eyes, etc. [ 55 ]. This adds robustness into AFR systems in the case of modification of some facial patterns over the course of time or occlusions.

Once the face is detected/located and the facial features are extracted, actions to crop the face, to correct its alignment by rotating it, etc., could be performed to address the challenges mentioned in Section 2, before passing the facial features into the next stage described in Section 3.1.2.

3.1.2. Feature classification/matching

For the recognition stage itself of the face recognition process, classification is often used as shown in Figure 12 . Indeed, it is a machine‐learning technique [ 56 ] that has the task of first learning and then applying a function that maps the facial features of an individual to one of the predefined class labels, i.e. class 1 (face of the individual) or class 2 (not the face of the individual), leading in this case to a binary classifier. Classifiers could be applied to the entire set of the extracted facial features or to some specific face attributes, e.g. gender, age, race, etc. [ 57 ]. More recently, methods like neural networks are used as classifiers [ 58 ].

Figure 12.

Overview of the model computation.

On the other hand, some AFR systems use the matching technique that could be applied on facial geometric features or templates [ 59 ]. This approach is also useful for multimodal face data [ 60 ].

3.2. Examples of methods

Among hundreds of techniques developed in this field [ 1 – 10 ], Sections 3.2.1–3.2.4 explain briefly some well‐established methods for automated face recognition.

3.2.1. Eigenfaces

The eigenface approach [ 61 ] is a very successful AFR method. It involves pixel intensity features and uses the principal component analysis (PCA) of the distribution of faces, or eigenvectors , which are a kind of set of features characterizing faces’ variations where each face image contributes more or less to each eigenvector. Thus, an eigenvector can be seen as a ghostly face, or eigenface . Recognition of a test face is determined by applying the nearest‐neighbour technique to the probe face projection in the face space [ 13 ]. Fisherfaces extend the eigenface approach by using linear discriminant analysis (LDA) instead of PCA [ 62 , 63 ].

3.2.2. Active appearance models

The active appearance model (AAM) [ 64 ] combines shape and texture features; thus it is slower but more robust for AFR than active shape models (ASM). AAM is built as a multi‐resolution model based on a Gaussian‐image pyramid. For each level of the pyramid, a separate texture model is computed using 400 face images. Each face is labelled with 68 points around the main features, and the facial region is sampled by c. 10,000 intensity values. AFR is performed by matching the test face with the AAM, following a multi‐resolution approach that improves speed and robustness of this method [ 64 ].

3.2.3. Local binary patterns

In reference [ 48 ], local binary patterns (LBP), which are texture features, have been introduced for AFR. In particular, the face image is divided into independent regions where the LBP operator is applied to codify every pixel of each region by thresholding the 3 × 3‐neighbourhood of each pixel with the centre pixel value and by binarizing it, and then, creating a local texture descriptor with the histogram of the codes for each face region. A global description of the face is formed by concatenating the local descriptors. Next, the nearest‐neighbour classifier is used [ 48 ]. LBP approach has been widely adopted for AFR, and several enhancements have been proposed, e.g. the local directional patterns (LDP) [ 49 ].

3.2.4. SIFT

The discriminative deep metric‐learning (DDML) [ 52 ] approach for AFR in unconstrained environment uses facial features such as SIFT descriptors and trains a deep neural network as a classifier to learn a Mahalanobis distance metric in order to maximize face's inter‐class variations and minimize face's intra‐class variations, simultaneously [ 52 ].

4. Applications

Nowadays, industry integrates cutting‐edge, face recognition research into the development of the latest technologies for commercial applications such as mentioned in Sections 4.1–4.2.

4.1. Security

Face recognition is one of the most powerful processes in biometric systems [ 8 ] and is extensively used for security purpose in tracking and surveillance [ 65 , 66 ], attendance monitoring, passenger management at airports, passport de‐duplication, border control and high security access control as developed by companies like Aurora [ 67 ].

AFR is applied in forensics for face identification [ 68 ], face retrieval in still image databases or CCTV sequences [ 69 ], or for facial sketch recognition [ 70 ]. It could also help law enforcement through behaviour and facial expression observation [ 71 ], lie detection [ 72 ], lip tracking and reading [ 73 ].

Moreover, AFR is now used in the context of ‘Biometrics as a Service’ [ 74 ], within cloud‐based, online technologies requiring face authentication for trustworthy transactions. For example, MasterCard developed an app which uses selfies to secure payments via mobile phones [ 75 ]. In this MasterCard ’s app, AFR is enhanced by facial expression recognition as the application requires the consumer blinks to prove that s/he is human.

4.2. Multimedia

In our today's life, AFR engines are embedded in a number of multi‐modal applications such as aids for buying glasses or for digital make‐up and other face sculpting or skin smoothing technologies, e.g. designed by Anthropics [ 76 ].

In social media, many collaborative applications within Facebook [ 77 ], Google [ 78 ] or Yahoo! [ 79 ] are calling upon AFR. Applications such as Snapchat require AFR on mobile [ 80 ]. With 200 million users of which half of those engage on daily basis [ 81 ], Snapchat is a popular image messaging and multimedia mobile application, where ‘snaps’, i.e. a photo or a short video, can be edited to include filters and effects, text caption and drawings. Snapchat has features such as the ‘Lens’, which allows users to add real‐time effects into their snaps by using AFR technologies, and ‘Memories’ which searches content by date or using local recognition systems [ 82 ].

Other multimedia applications are using AFR, e.g. in face naming to generate automated headlines in Video Google [ 83 ], in face expression tracking for animations and human‐computer interfaces (HCI) [ 84 ], or in face animation for socially aware robotics [ 85 ]. Companies such as Double Negative Visual Effects [ 86 ] or Disney Research [ 87 ] propose also AFR solutions for face synthesis and face morphing for films and games visual effects.

5. Conclusions

Since constraints shape the path for innovative solutions, we focused this chapter on scientific and technical challenges brought by computer automated face recognition, and we explained current solutions as well as potential applications. Moreover, there are a number of challenges ahead and plenty of room for innovations in this field of automated face recognition. In particular, three emerging directions are discussed in Sections 5.1–5.3.

5.1. Deep face

On the one hand, the proliferation of mobile devices such as smartphones and tablets, which are world‐widely available for consumers and which allow users to easily record digital pictures, and on the other hand, the outbreak of mobile and web applications, which manipulate and store thousands of pictures, have paved the way to the Big Data, and, among others, to the necessity to analysis large‐scale, face databases. This phenomenon has given rise to questions such as AFR technology scalability and computational power, and it has led to the development of a new AFR approach called deep face recognition [ 88 ], which involves deep‐learning techniques using convolutional neural networks [ 89 ], well fitted for big datasets [ 90 ]. Indeed, deep face methods are using large databases for training their models, as by biomimetics, they rely on the familiarity concept [ 91 ], which is based on the fact that more people are familiar with a person's face, more easily they recognized his/her face, even in complex situations like occlusions or low resolution. Moreover, the recent development of the deep face approach has benefited from progress in parallel computing tools for acceleration and enhancement of distributed computing techniques for scalability. In particular, for deep face recognition, graphics processing units (GPUs), which are specialized processors for real‐time, high‐resolution 3D graphics, are used as highly parallel multi‐core systems for big data [ 92 ], together with the Compute Unified Device Architecture (CUDA), which provides a simple and powerful platform [ 93 ], making easier for specialists in parallel programming to utilize GPU resources without advanced skills in graphics programming. Since the above‐mentioned, iterative computation consists of local parallel processing, CUDA implementation is employed for reducing the computation time of the AFR system [ 93 ]. However, deep face‐based methods generate themselves further challenges, e.g. face frontalization [ 94 ] that is the process of synthesizing frontal facing views of faces appearing in single unconstrained photos, in order to boost AFR performance within intelligent systems.

5.2. Wild face

Another challenge that has appeared with the generation of a large amount of visual data captured ‘in the wild’, i.e. in an unconstrained environment, by commercial cameras is the automated recognition of faces in the wild. It involves the enhancement of AFR methods [ 95 ] in order they efficiently deal with complex, real‐world backgrounds [ 96 ], multiple‐face scenes [ 51 ], skin‐colour variations [ 97 ], gender variety [ 98 ] and with inherent challenges such as image quality, resolution, illumination or facial pose correction [ 23 , 27 , 99 ].

5.3. Dynamic face

In the recent years, handling facial dynamics efficiently is crucial for AFR systems, because people have recorded a large amount of faces as still digital images, e.g. selfies or as video streams, e.g. CCTV sequences or online movies. Indeed, on the one hand, the different variations in facial micro/macro expressions [ 100 ], which generate fast, facial dynamics and the different processes such as ageing, which is an extremely slow, dynamic problem since the face evolves over large periods of time [ 18 ], have all an impact on AFR techniques. On the other hand, face acquisition in videos intrinsically creates facial dynamics due to camera motion, change of point of view, as well as head's movements or pose variations. Such situations require AFR engines perform in real time [ 84 ], apply image/frames pre‐processing such as face alignment [ 101 ], cope with intra‐class variations/inter‐class similarities [ 102 ] and are able to process single/multiple camera views [ 41 ] or synthesize a 3D face model from a single camera [ 103 ], leading to the wider study of the computational face.

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An Overview of Problem Recognition in Consumer Behavior

Table of Contents

Problem recognition is the pivotal first step in the consumer decision-making process. It’s the moment when a consumer identifies a need or issue that requires a solution. In this blog, we’ll provide an overview of problem recognition, its significance, and how it sets the stage for the entire consumer decision journey.

Understanding Problem Recognition

1. defining problem recognition.

• Problem recognition is the process of perceiving a difference between a current state (how things are) and a desired state (how we want things to be). This gap between the two states triggers the recognition of a problem or need.

2. Sources of Problem Recognition

• Internal Stimuli: These originate from within the individual, such as physiological needs (hunger, thirst), psychological needs (desire for status), or cognitive dissonance (conflict between beliefs and actions).
• External Stimuli: These come from the external environment and can include advertisements, social influences, and unexpected events.

3. Types of Problem Recognition

• Active Problem Recognition: This occurs when a consumer is acutely aware of a problem or need. For example, realizing that your smartphone battery is running low and needs charging.
• Passive Problem Recognition: Passive problems are latent or subconscious issues that consumers may not be consciously aware of until something triggers recognition. For instance, discovering a new smartphone with longer battery life makes you realize that your current phone’s battery is inadequate.

The Significance of Problem Recognition

1. initiates the decision-making process.

• Problem recognition serves as the spark that ignites the entire consumer decision journey. Without recognizing a need or problem, consumers have no motivation to consider solutions.

2. Guides Information Search

• Once a problem is recognized, consumers actively seek information to address it. This information search phase presents opportunities for businesses to engage with consumers and provide solutions.

3. Influences Decision Speed

• The urgency and severity of a recognized problem can significantly impact the speed of decision-making. More pressing problems often lead to quicker decisions.

4. Shapes Product Development

• Businesses rely on insights from problem recognition to develop and improve products and services that address consumers’ needs and issues.

Marketing Implications

1. consumer research.

• Understanding consumer needs and pain points through research is essential for identifying opportunities for problem recognition.

2. Product Development

• Products and services should be designed with a clear focus on solving specific problems or meeting recognized needs. Tailored solutions are more likely to resonate with consumers.

3. Effective Messaging

• Marketing messages should emphasize how a product or service addresses a problem or fulfills a need. Clear and compelling messaging is crucial to capture consumer attention.

4. Anticipating Problems

• Forward-thinking businesses can anticipate potential problems and proactively offer solutions, positioning themselves as problem solvers.

Problem recognition is the cornerstone of consumer decision-making. It’s the trigger that prompts consumers to seek solutions and make purchase decisions. Recognizing the importance of problem recognition allows businesses to align their strategies, products, and marketing efforts with consumer needs, ultimately fostering strong connections with their target audience.

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Consumer Behaviour

1 Consumer Behaviour-Nature, Scope, Models and Applications

• Introduction
• Nature of Consumer Behaviour
• Who is a Consumer?
• What is a Consumer Decision?
• Scope of Consumer Behaviour
• Decision Process
• Individual and Group Determinants of Consumer Decisions
• Models of Consumer Behaviour including Online Buying Behaviour
• Applications of Consumer Behaviour in Marketing

2 Consumer Behaviour and Lifestyle Marketing

• Demographics, Psychographics and Lifestyle
• Characteristics of Lifestyle
• Influences on Lifestyle
• Approaches to Study Lifestyle
• Application of AIO Studies
• Lifestyle Profiles in Indian Context
• VALS System of Classification
• Applications of Lifestyle Marketing

3 Organisational Buying Behaviour

• What is Organisational Buying Behaviour?
• Organisational Buying Behaviour: Important Features and Typical Characteristics
• Who are the Organisational Customers?
• What Influences Organisational Buying?
• The Organisational Buying Situations
• Organisational Buying Behaviour: Some Models
• The Selection of a Supplier
• Recent Developments that have Impacted Organisational Buying

4 Perceptions

• The Concept of Perception
• Stages in the Perceptual Process
• Sensory System
• Sensory Thresholds
• Perceptual Selection

5 Learning and Memory

• Concept of Learning
• Theories of Learning
• The Two Complex Issues of Learning
• Memory: Structure and Functioning
• Retrieving Information
• Measuring Memory for Advertising
• Marketing Applications

6 Attitude and Attitude Change

• Attitude and Consumer Decision-Making
• The Constituents of Consumer Attitude
• The Functions of Consumer Attitude
• Consumer Attitude: The Models
• The Marketing Response to the Consumer Attitude

7 Personality and Self-Concept

• An overview of Personality: Its Nature & Their Application to Consumer Behaviour
• The Concept of Personality
• Theories of Personality
• The Psychoanalytic Theory of Freud
• Social-Psychological or Neo-Freudian Theory
• Trait Theory of Personality
• The Theory of Self-concept
• The Related Concepts
• Consumption and Self-concept

8 Consumer Motivation and Involvement

• The Concept and Typology of Needs
• Theories of Consumer Needs
• Motives: The Basis of Motivation
• The Concept of Motivation
• Motivational Conflicts
• Consumer Involvement
• The Facets of Involvement

9 Reference Group Influence and Group Dynamics

• The influence of Reference Groups
• Types of Reference Groups
• Reference Group Influence on Products and Brands
• The Role of Opinion Leadership in the Transmission of Information
• The Dynamics of the Opinion Leadership Process
• The Personalities and Motivations of Opinion Leaders
• The Concept of Social Class: Its Nature and Meaning
• Social Class and Social Stratification
• Social Class and Social Influences
• Social Class Categorisation
• Relationship of Social Class to Lifestyles
• Social Class and Buying Behaviour
• Social Class and Market Segmentation

10 Family Buying Influence, Family Lifecycle and Buying Roles

• Introduction: The Family as a Consuming Unit
• Family Buying Influences: Nature and Types of Influences
• Consumer Socialisation
• Intergenerational Influences
• Family Decision-Making
• Family Role Structure and Buying Behaviour
• The Dynamics of Family Decision-Making: Purchase Influences and Role specialization
• The Influence of Children
• The Family Life Cycle Concept
• Implications of Family Decision-Making for Marketing Strategy

11 Cultural and Subcultural Influences

• Culture: Meaning and Significance
• The Characteristics of Culture
• Cultural Values
• Cultural Values and Change
• The Need for Cross-cultural Understanding of Consumer Behaviour
• Subcultures and their Influence

12 Problem Recognition and Information Search Behaviour

• Importance of Problem Recognition
• An Overview of Problem Recognition
• Threshold level in Problem Recognition
• Problem Recognition in the Industrial Buying Process
• Information Search

13 Information Processing

• Concept of Information Processing
• Comprehension
• Acceptance/Yielding
• The Imaginal Processing
• The Influencing Factors
• Marketing Implications of Information Processing

14 Alternative Evaluation in Buying Decisions

• Alternative Evaluation: The Four Components
• Formation of Brand Sets for Alternative Evaluation
• The Choice-Making Rules
• The Basic Choice Heuristics
• The Marketing Response to the Choice Heuristics

15 Purchase Process and Post-Purchase Behaviour

• An Overview of Purchase Process
• Buying Stage and Situational Influences
• Steps to Benefit from Situational Influences
• An Anatomy of Non-store Buying
• Routes of Non-store Buying
• Developing an Attitude to Post-purchase Behaviour
• Theories of Post-purchase Evaluation
• Marketers’ Response Strategies