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Implications in Research – Types, Examples and Writing Guide

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Implications in Research

Implications in research refer to the potential consequences, applications, or outcomes of the findings and conclusions of a research study. These can include both theoretical and practical implications that extend beyond the immediate scope of the study and may impact various stakeholders, such as policymakers, practitioners, researchers , or the general public.

Structure of Implications

The format of implications in research typically follows the structure below:

• Restate the main findings: Begin by restating the main findings of the study in a brief summary .
• Link to the research question/hypothesis : Clearly articulate how the findings are related to the research question /hypothesis.
• Discuss the practical implications: Discuss the practical implications of the findings, including their potential impact on the field or industry.
• Discuss the theoretical implications : Discuss the theoretical implications of the findings, including their potential impact on existing theories or the development of new ones.
• Identify limitations: Identify the limitations of the study and how they may affect the generalizability of the findings.
• Suggest directions for future research: Suggest areas for future research that could build on the current study’s findings and address any limitations.

Types of Implications in Research

Types of Implications in Research are as follows:

Theoretical Implications

These are the implications that a study has for advancing theoretical understanding in a particular field. For example, a study that finds a new relationship between two variables can have implications for the development of theories and models in that field.

Practical Implications

These are the implications that a study has for solving practical problems or improving real-world outcomes. For example, a study that finds a new treatment for a disease can have implications for improving the health of patients.

Methodological Implications

These are the implications that a study has for advancing research methods and techniques. For example, a study that introduces a new method for data analysis can have implications for how future research in that field is conducted.

Ethical Implications

These are the implications that a study has for ethical considerations in research. For example, a study that involves human participants must consider the ethical implications of the research on the participants and take steps to protect their rights and welfare.

Policy Implications

These are the implications that a study has for informing policy decisions. For example, a study that examines the effectiveness of a particular policy can have implications for policymakers who are considering whether to implement or change that policy.

Societal Implications

These are the implications that a study has for society as a whole. For example, a study that examines the impact of a social issue such as poverty or inequality can have implications for how society addresses that issue.

Forms of Implications In Research

Forms of Implications are as follows:

Positive Implications

These refer to the positive outcomes or benefits that may result from a study’s findings. For example, a study that finds a new treatment for a disease can have positive implications for patients, healthcare providers, and the wider society.

Negative Implications

These refer to the negative outcomes or risks that may result from a study’s findings. For example, a study that finds a harmful side effect of a medication can have negative implications for patients, healthcare providers, and the wider society.

Direct Implications

These refer to the immediate consequences of a study’s findings. For example, a study that finds a new method for reducing greenhouse gas emissions can have direct implications for policymakers and businesses.

Indirect Implications

These refer to the broader or long-term consequences of a study’s findings. For example, a study that finds a link between childhood trauma and mental health issues can have indirect implications for social welfare policies, education, and public health.

Importance of Implications in Research

The following are some of the reasons why implications are important in research:

• To inform policy and practice: Research implications can inform policy and practice decisions by providing evidence-based recommendations for actions that can be taken to address the issues identified in the research. This can lead to more effective policies and practices that are grounded in empirical evidence.
• To guide future research: Implications can also guide future research by identifying areas that need further investigation, highlighting gaps in current knowledge, and suggesting new directions for research.
• To increase the impact of research : By communicating the practical and theoretical implications of their research, researchers can increase the impact of their work by demonstrating its relevance and importance to a wider audience.
• To enhance the credibility of research : Implications can help to enhance the credibility of research by demonstrating that the findings have practical and theoretical significance and are not just abstract or academic exercises.
• To foster collaboration and engagement : Implications can also foster collaboration and engagement between researchers, practitioners, policymakers, and other stakeholders by providing a common language and understanding of the practical and theoretical implications of the research.

Example of Implications in Research

Here are some examples of implications in research:

• Medical research: A study on the efficacy of a new drug for a specific disease can have significant implications for medical practitioners, patients, and pharmaceutical companies. If the drug is found to be effective, it can be used to treat patients with the disease, improve their health outcomes, and generate revenue for the pharmaceutical company.
• Educational research: A study on the impact of technology on student learning can have implications for educators and policymakers. If the study finds that technology improves student learning outcomes, educators can incorporate technology into their teaching methods, and policymakers can allocate more resources to technology in schools.
• Social work research: A study on the effectiveness of a new intervention program for individuals with mental health issues can have implications for social workers, mental health professionals, and policymakers. If the program is found to be effective, social workers and mental health professionals can incorporate it into their practice, and policymakers can allocate more resources to the program.
• Environmental research: A study on the impact of climate change on a particular ecosystem can have implications for environmentalists, policymakers, and industries. If the study finds that the ecosystem is at risk, environmentalists can advocate for policy changes to protect the ecosystem, policymakers can allocate resources to mitigate the impact of climate change, and industries can adjust their practices to reduce their carbon footprint.
• Economic research: A study on the impact of minimum wage on employment can have implications for policymakers and businesses. If the study finds that increasing the minimum wage does not lead to job losses, policymakers can implement policies to increase the minimum wage, and businesses can adjust their payroll practices.

How to Write Implications in Research

Writing implications in research involves discussing the potential outcomes or consequences of your findings and the practical applications of your study’s results. Here are some steps to follow when writing implications in research:

• Summarize your key findings: Before discussing the implications of your research, briefly summarize your key findings. This will provide context for your implications and help readers understand how your research relates to your conclusions.
• Identify the implications: Identify the potential implications of your research based on your key findings. Consider how your results might be applied in the real world, what further research might be necessary, and what other areas of study could be impacted by your research.
• Connect implications to research question: Make sure that your implications are directly related to your research question or hypotheses. This will help to ensure that your implications are relevant and meaningful.
• Consider limitations : Acknowledge any limitations or weaknesses of your research, and discuss how these might impact the implications of your research. This will help to provide a more balanced view of your findings.
• Discuss practical applications : Discuss the practical applications of your research and how your findings could be used in real-world situations. This might include recommendations for policy or practice changes, or suggestions for future research.
• Be clear and concise : When writing implications in research, be clear and concise. Use simple language and avoid jargon or technical terms that might be confusing to readers.
• Provide a strong conclusion: Provide a strong conclusion that summarizes your key implications and leaves readers with a clear understanding of the significance of your research.

Purpose of Implications in Research

The purposes of implications in research include:

• Informing practice: The implications of research can provide guidance for practitioners, policymakers, and other stakeholders about how to apply research findings in practical settings.
• Generating new research questions: Implications can also inspire new research questions that build upon the findings of the original study.
• Identifying gaps in knowledge: Implications can help to identify areas where more research is needed to fully understand a phenomenon.
• Promoting scientific literacy: Implications can also help to promote scientific literacy by communicating research findings in accessible and relevant ways.
• Facilitating decision-making : The implications of research can assist decision-makers in making informed decisions based on scientific evidence.
• Contributing to theory development : Implications can also contribute to the development of theories by expanding upon or challenging existing theories.

When to Write Implications in Research

Here are some specific situations of when to write implications in research:

• Research proposal : When writing a research proposal, it is important to include a section on the potential implications of the research. This section should discuss the potential impact of the research on the field and its potential applications.
• Literature review : The literature review is an important section of the research paper where the researcher summarizes existing knowledge on the topic. This is also a good place to discuss the potential implications of the research. The researcher can identify gaps in the literature and suggest areas for further research.
• Conclusion or discussion section : The conclusion or discussion section is where the researcher summarizes the findings of the study and interprets their meaning. This is a good place to discuss the implications of the research and its potential impact on the field.

Advantages of Implications in Research

Implications are an important part of research that can provide a range of advantages. Here are some of the key advantages of implications in research:

• Practical applications: Implications can help researchers to identify practical applications of their research findings, which can be useful for practitioners and policymakers who are interested in applying the research in real-world contexts.
• Improved decision-making: Implications can also help decision-makers to make more informed decisions based on the research findings. By clearly identifying the implications of the research, decision-makers can understand the potential outcomes of their decisions and make better choices.
• Future research directions : Implications can also guide future research directions by highlighting areas that require further investigation or by suggesting new research questions. This can help to build on existing knowledge and fill gaps in the current understanding of a topic.
• Increased relevance: By highlighting the implications of their research, researchers can increase the relevance of their work to real-world problems and challenges. This can help to increase the impact of their research and make it more meaningful to stakeholders.
• Enhanced communication : Implications can also help researchers to communicate their findings more effectively to a wider audience. By highlighting the practical applications and potential benefits of their research, researchers can engage with stakeholders and communicate the value of their work more clearly.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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How to Write an “Implications of Research” Section

4-minute read

• 24th October 2022

When writing research papers , theses, journal articles, or dissertations, one cannot ignore the importance of research. You’re not only the writer of your paper but also the researcher ! Moreover, it’s not just about researching your topic, filling your paper with abundant citations, and topping it off with a reference list. You need to dig deep into your research and provide related literature on your topic. You must also discuss the implications of your research.

Interested in learning more about implications of research? Read on! This post will define these implications, why they’re essential, and most importantly, how to write them. If you’re a visual learner, you might enjoy this video .

What Are Implications of Research?

Implications are potential questions from your research that justify further exploration. They state how your research findings could affect policies, theories, and/or practices.

Implications can either be practical or theoretical. The former is the direct impact of your findings on related practices, whereas the latter is the impact on the theories you have chosen in your study.

Example of a practical implication: If you’re researching a teaching method, the implication would be how teachers can use that method based on your findings.

Example of a theoretical implication: You added a new variable to Theory A so that it could cover a broader perspective.

Finally, implications aren’t the same as recommendations, and it’s important to know the difference between them .

Questions you should consider when developing the implications section:

●  What is the significance of your findings?

●  How do the findings of your study fit with or contradict existing research on this topic?

●  Do your results support or challenge existing theories? If they support them, what new information do they contribute? If they challenge them, why do you think that is?

Why Are Implications Important?

You need implications for the following reasons:

● To reflect on what you set out to accomplish in the first place

● To see if there’s a change to the initial perspective, now that you’ve collected the data

● To inform your audience, who might be curious about the impact of your research

How to Write an Implications Section

Usually, you write your research implications in the discussion section of your paper. This is the section before the conclusion when you discuss all the hard work you did. Additionally, you’ll write the implications section before making recommendations for future research.

Implications should begin with what you discovered in your study, which differs from what previous studies found, and then you can discuss the implications of your findings.

Your implications need to be specific, meaning you should show the exact contributions of your research and why they’re essential. They should also begin with a specific sentence structure.

Examples of starting implication sentences:

●  These results build on existing evidence of…

●  These findings suggest that…

●  These results should be considered when…

●  While previous research has focused on x , these results show that y …

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You should write your implications after you’ve stated the results of your research. In other words, summarize your findings and put them into context.

The result : One study found that young learners enjoy short activities when learning a foreign language.

The implications : This result suggests that foreign language teachers use short activities when teaching young learners, as they positively affect learning.

 Example 2

The result : One study found that people who listen to calming music just before going to bed sleep better than those who watch TV.

The implications : These findings suggest that listening to calming music aids sleep quality, whereas watching TV does not.

To summarize, remember these key pointers:

●  Implications are the impact of your findings on the field of study.

●  They serve as a reflection of the research you’ve conducted.              

●  They show the specific contributions of your findings and why the audience should care.

●  They can be practical or theoretical.

●  They aren’t the same as recommendations.

●  You write them in the discussion section of the paper.

●  State the results first, and then state their implications.

Are you currently working on a thesis or dissertation? Once you’ve finished your paper (implications included), our proofreading team can help ensure that your spelling, punctuation, and grammar are perfect. Consider submitting a 500-word document for free.

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How to Write Extraordinary “Managerial Recommendations” in Your Thesis!

Imagine the following scene: You go to a doctor because you have a weird pain in what you think might be a human organ, which for some unknown reason you have in your body. After arriving at the hospital, you describe to the doctor your condition:

You are not sure if the pain was caused because you have been eating too many slices of four cheese pizza (my favorite flavor) or simply because you have been drinking 5 cappuccinos per day for the last 5.913 consecutive days (which I probably also have).

The doctor asks you to follow the nurse and go through a few check-up tests: MRIs, X-rays, blood tests, 5km sprints, gardening techniques, singing “Bohemian Rhapsody” a Capella and much more. Once all check-ups are over, the doctor examines all results and tells you:

“Son, I think you have a rare form of claustrophobic pancreas. This is affecting the inner pride of your lungs and making your expansive heart feel both lonely and susceptible to storms of tears”.

After describing quite poetically your condition, the doctor simply leaves the room and goes away.

Would you think this is a good doctor? ABSOLUTELY NOT! Why? Well, apart from having bizarre methods of investigation and post-modern terms to describe one’s condition, the doctor HAS NOT TOLD YOU WHAT TO DO BASED ON THE RESULTS OF YOUR CHECK-UP!

EXACTLY! The development and interpretation of results from a patients’ diagnosis represents only 50% of a doctors’ job. The other 50% represents ACCURATELY SAYING WHAT THE PATIENT MUST DO BASED ON RESULTS to heal the negative condition.

IMPORTANT: In your thesis, we call it “managerial recommendations” or “managerial Implications”. It represents the moment where you tell the reader what should or must be done based on the findings of your thesis.

Ps: Does it also represent 50% of the work in my thesis? NO, NO, NO!!! Usually it represents the LAST section of your thesis, before listing your references and it is a fairly brief section.

Nonetheless, it is a very important section of your thesis. Students should show both creativity and expertise to interpret results and propose solutions.

Here are some personal recommendations “From me to you” (This has to be a title of a cheesy song) of what you should keep in mind when working on this section.

Video Support: Managerial Recommendations

In case you are enjoying the article, do not forget to watch the video with further support on how to develop your managerial recommendations.

HERE WE GO!

Breathe and rest before starting.

Very often, this is the last section students will work on before finishing their thesis. At this point they are tired, exhausted from working on the same topic and just want to get it done with. Tiredness will come across very easily in your work. How? The reader might clearly identify that the section is:

• Too short and without depth
• The wording is not as careful and detailed
• The ideas are superficial

And probably why? You were simply “too tired and out of breath”.

So if you think by this stage you are out of breath, I suggest the following:

• Take a day off and watch Netflix to unwind (Have you seen “Narcos” or “Black Mirror”? They are great!).
• Next day, make yourself a nice drink
• Put some nice music on (Don’t even get me started on suggesting music…), and;
• Start writing some amazing managerial recommendations !

Recommendations ONLY Based on Your Findings

This is perhaps the most common mistake I see from students . To explain this, let us go back to a medical example (Not sure why I am continuing on this topic, but hang in there will be a logic to it  ).

Imagine you go a doctor and he asks you to take an X-ray of your right foot. After you have done it, he prescribes you have surgery on your left elbow. Would that make sense? NO! He examined your right foot, so why would he suggest something on your elbow without checking it?

EXACTLY! Only propose managerial recommendations based on results of your study and that YOU HAVE DISCUSSED ON THE PREVIOUSLY!

Feasibility (Implementation, financial and non-financial return)

PS: Pay extra attention to this point. It is very important.

The recommendations that you are suggesting can be extraordinary, but you have to question yourself: Would it be possible for an organization to implement it and would it bring sufficient return to justify the investment? If the answer is NO , please reconsider including it (At least for now or provide a good explanation as to why you will keep it).

Thus, you must also consider the trade-off between investment and return!

How would you do it?

A) Consider first the INVESTMENTS

• Consider the time needed to implement
• Consider the financial investment to implement it (How much it would cost)
• Consider the non-financial investment to implement it (e.g. Human resources, time, efforts)

B) Consider the EXPECTED RETURN to the business

• Consider the financial return (e.g. Revenue generated by the implementation)
• Consider the non-financial return from the investment (e.g. Brand image, publicity)

After contrasting investment versus return , the question is: Is your recommendation feasible or not? And be prepared to defend your idea!

Example: One time a student developed a managerial recommendation that included developing a complex autonomous system to track consumer behavior in physical stores. However, the focus was on small businesses. The idea of the student was very interesting, but would it be feasible for small businesses to invest in such complex systems and would it bring enough financial return to justify the investment? Probably not… Do you get the point now?

Implementation Process

Very often students have great ideas for recommendations, but actually little knowledge as to how they would be implemented. If you were a consultant for a company, would you suggest a solution if you did not know how it could be implemented? No, right? So do not suggest it here, ok?

Also consider if it can even be implemented at all! For example:

• Are there technologies available to implement it?
• Is there enough time to implement your solution?
• Are there any limitations (e.g. governmental, societal, legal) to implement your solution?

If so, do not include the solution because it simply will not happen.

Remember: If an interesting solution cannot solve a problem because it cannot be implemented, it is not a solution.

Do you need to describe in your thesis how it would be implemented? Discuss this with your supervisors, because it will depend. Many times, at this stage students are already struggling with page numbers and word counts. Personally, I usually do not ask the students to include this, but often we discuss it in their presentations.

Finally, also remember: Implementation of a managerial recommendation you have suggested in your thesis can easily be a question from your supervisors during your colloquium.

Originality

Sometimes due to the lack of time and patience from the students, the managerial recommendation suggested by them is the first idea that comes to mind. And (surprise, surprise!) often ideas that have already been implemented or that are common practices in the industry!

What does it mean when this happens? You are informing the panel of reviewers of your thesis two main things:

• That you have little knowledge of your industry; or,
• That you did not bother to research to identify if your idea is original or not.

Do managerial recommendations need to be incredibly creative and original? No , very few things in this world actually are. However, please do not repeat common practices claiming they are your own original ideas.

Can you suggest solutions that have been used in different markets, industries or time in history to apply to your context? ABSOLUTELY! Many interesting solutions come from the observation of other industries, different contexts and even from simply observing nature!

Relevance of the Problem it Will Solve

When you are developing managerial recommendations, consider the relevance of the problem you are trying to address . Just because something is an issue to a company does not mean that all issues have the same importance.

For example, can we compare:

• A poor website usability that is giving users poor online experience and limiting the conversions from “lookers” into “bookers” on your site; with,
• The desire to update the company logo.

Not at all! Poor usability destroys conversion, reduces revenue generated online and can limit sales massively. Thus, it is crucial for any online business and must always be a top priority.

On the other hand, updating a company logo is also important. However, not as important as the consequences of having a more old fashioned logo (although a negative thing), might have lower impact for an online business.

Thus, consider the importance of the problems you are trying to solve with your managerial recommendations and focus on the most important ones. If you are only focusing on superficial problems, consider working on the section again!

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Final Thoughts

I honestly hope you have enjoyed the article and that will be helpful to you. In case you want to thank me for writing it by buying a ticket for a Formula 1 race, the closest ones for me to attend are the German Grand Prix and the Belgian Grand Prix .

Oh! Here are some music recommendations for you to listen while working on this section:

• James Taylor
• Chris Cornell
• Lisa Hannigan
• Eddie Vedder (Ukulele Songs)

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Should Our Papers Contain Managerial Implications?

Many aspects of the review process have been discussed in earlier articles and viewpoints. There is probably no shortage of discussions on how to write papers, how to communicate with reviewers and editors, and so forth. One aspect that is a part of almost all papers, however, is rarely discussed – managerial implications.

In the majority of our paper, we ultimately try to give managerial implications. In the introductions, we usually try to make a case that the phenomenon that we are looking at is managerially interesting, and that knowing more about it would help managers make better decisions. In the discussion or implications section, we write down what we believe that managers can make better based on what we have found.

In a way, I have always found it ironic that researchers communicate to other researchers what they believe managers should do differently. Even more so, it is somewhat funny that the judges on the managerial usefulness of our research are usually academics (the reviewers of the journal). At the same time, we always hope that our research is read, taken up by others, and affects textbooks, executive education and ultimately management practice. So again, other academics (the authors of textbooks or practitioner-related articles) decide on the managerial usefulness of our research.

I am unlikely to be the only researcher to have made some strange experiences in this regard. For example, when I once had worked quite closely with a company, and the managerial implications section was very strongly based on the discussions I had with their managers, a reviewer wrote back to me that “the managerial discussion seems useless and irrelevant. The suggestions for concrete action are unrealistic at best.” In another case, where a publication was coming out of a close collaboration, an editor rejected my paper, among other reasons based on his assessment that “the paper does not lead to relevant insights for marketing managers”. So again, should researchers assess the managerial usefulness of their peers’ work?

In hindsight, I would say that both assessments were probably good ones. The collaboration with the firms was very insightful for me, it resulted in interesting data and a lot of know-how (much of which was irrelevant for the publication process). But, it had blurred my own assessment of what was important in this research not only from an academic, but also from a managerial point of view. I was probably writing very specifically about the meaning of my analysis and results for this particular company. But almost all our findings have a context, and ignoring that context would lead to suboptimal decisions. For an obvious example, consider that company A can make a profitable investment by increasing service quality, whereas for other companies, the same quality level enhancement would be more costly, difficult, time consuming, or less effective because they serve other customer segments.

Does that mean we should refrain from giving managerial implications? One might think so, and remove the ironic twist that academics decide on the managerial relevance of what other academics have found. However, when I am, as a reviewer, undecided about a paper, I typically turn to the managerial implications section, and often base my final judgment of contribution on that part. The trick for authors, I believe, in writing a good managerial implications sections is not to repeat the obvious (when we find that A leads to B, and B is desirable, the manager should do more A) or the details of what they have discussed with the one firm that they have been working with (which is unlikely to base their decisions solely on this research in any case). Instead, when we find that A leads to B, good managerial implications would describe the broader view on the often very specific A and B in our research. What other variables could replace A and B in the context of companies in other industries and contexts? This would make our findings meaningful beyond the context that we have studied. Ironically, this may prove more difficult when we have worked very closely with a partner firm in our research.

Not giving managerial implications is a no-go for an applied discipline, such as service management. But we may have to rethink the kind of implications we want to give.

Note: There is already an active conversation going on about the topic. Let us know your perspective and join the conversation on Facebook https://www.facebook.com/groups/251270684898315/permalink/1174411969250844/

Florian von Wangenheim is  Chair of  Dept. of Management, Technology, and Economics ETH Zurich, Switzerland

One comment on “ Should Our Papers Contain Managerial Implications? ”

Yes, I agree with you prof, I think the management area should look for this issue. Indeed, managers affect adoption and use of technology (decision ) from them …top management also they have strategic scope.

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Educational resources and simple solutions for your research journey

What are Implications and Recommendations in Research? How to Write It, with Examples

Highly cited research articles often contain both implications and recommendations , but there is often some confusion around the difference between implications and recommendations in research. Implications of a study are the impact your research makes in your chosen area; they discuss how the findings of the study may be important to justify further exploration of your research topic. Research recommendations suggest future actions or subsequent steps supported by your research findings. It helps to improve your field of research or cross-disciplinary fields through future research or provides frameworks for decision-makers or policymakers. Recommendations are the action plan you propose based on the outcome.

In this article, we aim to simplify these concepts for researchers by providing key insights on the following:  

• what are implications in research 
• what is recommendation in research 
• differences between implications and recommendations 
• how to write implications in research 
• how to write recommendation in research 
• sample recommendation in research 

Table of Contents

What are implications in research

The implications in research explain what the findings of the study mean to researchers or to certain subgroups or populations beyond the basic interpretation of results. Even if your findings fail to bring radical or disruptive changes to existing ways of doing things, they might have important implications for future research studies. For example, your proposed method for operating remote-controlled robots could be more precise, efficient, or cheaper than existing methods, or the remote-controlled robot could be used in other application areas. This could enable more researchers to study a specific problem or open up new research opportunities.   

Implications in research inform how the findings, drawn from your results, may be important for and impact policy, practice, theory, and subsequent research. Implications may be theoretical or practical. 1  

• Practical implications are potential values of the study with practical or real outcomes . Determining the practical implications of several solutions can aid in identifying optimal solution results. For example, clinical research or research on classroom learning mostly has practical implications in research . If you developed a new teaching method, the implication would be how teachers can use that method based on your findings.  
• Theoretical implications in research constitute additions to existing theories or establish new theories. These types of implications in research characterize the ability of research to influence society in apparent ways. It is, at most, an educated guess (theoretical) about the possible implication of action and need not be as absolute as practical implications in research . If your study supported the tested theory, the theoretical implication would be that the theory can explain the investigated phenomenon. Else, your study may serve as a basis for modifying the theory. Theories may be partially supported as well, implying further study of the theory or necessary modifications are required.  

What are recommendations in research?

Recommendations in research can be considered an important segment of the analysis phase. Recommendations allow you to suggest specific interventions or strategies to address the issues and constraints identified through your study. It responds to key findings arrived at through data collection and analysis. A process of prioritization can help you narrow down important findings for which recommendations are developed.  

Recommendations in research examples

Recommendations in research may vary depending on the purpose or beneficiary as seen in the table below.  

Table: Recommendations in research examples based on purpose and beneficiary  

If you’re wondering how to make recommendations in research . You can use the simple  recommendation in research example below as a handy template.  

Table: Sample recommendation in research template  

Basic differences between implications and recommendations in research

Implications and recommendations in research are two important aspects of a research paper or your thesis or dissertation. Implications discuss the importance of the research findings, while recommendations offer specific actions to solve a problem. So, the basic difference between the two is in their function and the questions asked to achieve it. The following table highlights the main differences between implications and recommendations in research .  

Table: Differences between implications and recommendations in research  

Where do implications go in your research paper

Because the implications and recommendations of the research are based on study findings, both are usually written after the completion of a study. There is no specific section dedicated to implications in research ; they are usually integrated into the discussion section adding evidence as to why the results are meaningful and what they add to the field. Implications can be written after summarizing your main findings and before the recommendations and conclusion.   

Implications can also be presented in the conclusion section after a short summary of the study results.   

How to write implications in research

Implication means something that is inferred. The implications of your research are derived from the importance of your work and how it will impact future research. It is based on how previous studies have advanced your field and how your study can add to that.   

When figuring out how to write implications in research , a good strategy is to separate it into the different types of implications in research , such as social, political, technological, policy-related, or others. As mentioned earlier, the most frequently used are the theoretical and practical implications.   

Next, you need to ask, “Who will benefit the most from reading my paper?” Is it policymakers, physicians, the public, or other researchers? Once you know your target population, explain how your findings can help them.  

The implication section can include a paragraph or two that asserts the practical or managerial implications and links it to the study findings. A discussion can then follow, demonstrating that the findings can be practically implemented or how they will benefit a specific audience. The writer is given a specific degree of freedom when writing research implications , depending on the type of implication in research you want to discuss: practical or theoretical. Each is discussed differently, using different words or in separate sections. The implications can be based on how the findings in your study are similar or dissimilar to that in previous studies. Your study may reaffirm or disprove the results of other studies, which has important implications in research . You can also suggest future research directions in the light of your findings or require further research to confirm your findings, which are all crucial implications. Most importantly, ensure the implications in research are specific and that your tone reflects the strength of your findings without exaggerating your results.   

Implications in research can begin with the following specific sentence structures:  

• These findings suggest that…
• These results build on existing body of evidence of…
• These results should be considered when…
• While previous research focused on x, our results show that y…

What should recommendations in research look like?

Recommendations for future research should be:  

• Directly related to your research question or findings  
• Concrete and specific  
• Supported by a clear reasoning  

The recommendations in research can be based on the following factors:  

1. Beneficiary: A paper’s research contribution may be aimed at single or multiple beneficiaries, based on which recommendations can vary. For instance, if your research is about the quality of care in hospitals, the research recommendation to different beneficiaries might be as follows:  

• Nursing staff: Staff should undergo training to enhance their understanding of what quality of care entails.  
• Health science educators: Educators must design training modules that address quality-related issues in the hospital.  
• Hospital management: Develop policies that will increase staff participation in training related to health science.  

2. Limitations: The best way to figure out what to include in your research recommendations is to understand the limitations of your study. It could be based on factors that you have overlooked or could not consider in your present study. Accordingly, the researcher can recommend that other researchers approach the problem from a different perspective, dimension, or methodology. For example, research into the quality of care in hospitals can be based on quantitative data. The researcher can then recommend a qualitative study of factors influencing the quality of care, or they can suggest investigating the problem from the perspective of patients rather than the healthcare providers.   

3. Theory or Practice: Your recommendations in research could be implementation-oriented or further research-oriented.   

4. Your research: Research recommendations can be based on your topic, research objectives, literature review, and analysis, or evidence collected. For example, if your data points to the role of faculty involvement in developing effective programs, recommendations in research can include developing policies to increase faculty participation. Take a look at the evidence-based recommendation in research example s provided below.   

Table: Example of evidence-based research recommendation  

Avoid making the following mistakes when writing research recommendations :  

• Don’t undermine your own work: Recommendations in research should offer suggestions on how future studies can be built upon the current study as a natural extension of your work and not as an entirely new field of research.  
• Support your study arguments: Ensure that your research findings stand alone on their own merits to showcase the strength of your research paper.   

How to write recommendations in research

When writing research recommendations , your focus should be on highlighting what additional work can be done in that field. It gives direction to researchers, industries, or governments about changes or developments possible in this field. For example, recommendations in research can include practical and obtainable strategies offering suggestions to academia to address problems. It can also be a framework that helps government agencies in developing strategic or long-term plans for timely actions against disasters or aid nation-building.  

There are a few SMART 2 things to remember when writing recommendations in research. Your recommendations must be: 

• S pecific: Clearly state how challenges can be addressed for better outcomes and include an action plan that shows what can be achieved. 
• M easurable: Use verbs denoting measurable outcomes, such as identify, analyze, design, compute, assess, evaluate, revise, plan, etc., to strengthen recommendations in research .   
• A ttainable: Recommendations should offer a solution-oriented approach to problem-solving and must be written in a way that is easy to follow.  
• R elevant: Research recommendations should be reasonable, realistic, and result-based. Make sure to suggest future possibilities for your research field.  
• T imely: Time-based or time-sensitive recommendations in research help divide the action plan into long-term or short-term (immediate) goals. A timeline can also inform potential readers of what developments should occur over time.  

If you are wondering how many words to include in your research recommendation , a general rule of thumb would be to set aside 5% of the total word count for writing research recommendations . Finally, when writing the research implications and recommendations , stick to the facts and avoid overstating or over-generalizing the study findings. Both should be supported by evidence gathered through your data analysis.  

References:  

• Schmidt, F. L., & Hunter, J. E. (1998). The validity and utility of selection methods in personnel psychology: Practical and theoretical implications of 85 years of research findings.  Psychological bulletin ,  124 (2), 262.
• Doran, G. T. (1981). There’s a S.M.A.R.T. way to write management’s goals and objectives.  Manag Rev ,  70 (11), 35-36.

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Manuscripts that do not mention the implications of the study are often desk-rejected by journals. What constitutes the ‘implications’ of research, and why is it important to include research implications in your manuscript?

Research implications: An overview

Once you have laid out the key findings in your paper, you have to discuss how they will likely impact the world. What is the significance of your study to policymakers, the lay person, or other researchers? This speculation, made in good faith, constitutes your study’ implications.

A research paper that does not explain the study’s importance in light of its findings exists in a vacuum. The paper may be relevant to you, the author, and some of your co-workers. But it is unclear how others will benefit from reading it.

How can the findings of your study help create a better world? What can we infer from your conclusion about the current state of research in your field or the quality of methods you employed? These are all important implications of your study.

You cannot predict how your study will influence the world or research in the future. You can only make reasonable speculations. In order to ensure that the implications are reasonable, you have to be mindful of the limitations of your study.

In the research context, only speculations supported by data count as valid implications. If the implications you draw do not logically follow the key findings of your study, they may sound overblown or outright preposterous.

Suppose your study evaluated the effects of a new drug in the adult population. In that case, you could not honestly speculate on how the drug will impact paediatric care. Thus, the implications you draw from your study cannot exceed its scope.

Practical implications

Imagine that your study found a popular type of cognitive therapy to be ineffective in treating insomnia. Your findings imply that psychologists using this type of therapy were not seeing actual results but an expectancy effect. Studies that can potentially impact real-world problems by prompting policy change or change in treatments have practical implications.

It can be helpful to understand the difference between an implication of your study and a recommendation. Suppose your study compares two or more types of therapy, ranks them in the order of effectiveness, and explicitly asks clinicians to follow the most effective type. The suggestion made in the end constitutes a ‘recommendation’ and not an ‘implication’.

Theoretical implications

Are your findings in line with previous research? Did your results validate the methods used in previous research or invalidate them? Has your study discovered a new and helpful way to do experiments? Speculations on how your findings can potentially impact research in your field of study are theoretical implications.

The main difference between practical and theoretical implications is that theoretical implications may not be readily helpful to policymakers or the public.

How to Write Implications in Research

Implications usually form an essential part of the conclusion section of a research paper. As we have mentioned in a previous article, this section starts by summarising your work, but this time emphasises your work’s significance .

While writing the implications, it is helpful to ask, “who will benefit the most from reading my paper?”—policymakers, physicians, the public, or other researchers. Once you know your target population, explain how your findings can help them.

Think about how the findings in your study are similar or dissimilar to the findings of previous studies. Your study may reaffirm or disprove the results of other studies. This is an important implication.

Suggest future directions for research in the subject area in light of your findings or further research to confirm your findings. These are also crucial implications.

Do not try to exaggerate your results, and make sure your tone reflects the strength of your findings. If the implications mentioned in your paper are convincing, it can improve visibility for your work and spur similar studies in your field.

For more information on the importance of implications in research, and guidance on how to include them in your manuscript, visit Elsevier Author Services now!

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• DOI: 10.1007/978-3-642-33621-8_8
• Corpus ID: 150944735

Theoretical and Managerial Implications

• M. Schlickel
• Published 2013
• Business, Economics

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Management and organizational research: structural topic modeling for a better understanding of theory application, 186 references, the interplay between theory and method, theory building from cases: opportunities and challenges, a resource-based view of the firm, exploring the concept of “fit” in strategic management, the strategy process, middle management involvement, and organizational performance, contingency linkages between strategy and general manager characteristics: a conceptual examination, paradox and theorizing within the resource-based view, exploitation, exploration, and process management: the productivity dilemma revisited, 50th anniversay article: the strategy field from the perspective of management science: divergent strands and possible integration, methodological fit in management field research., related papers.

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Please note you do not have access to teaching notes, managerial implications of research on inter-organizational interfaces: the case of key account management.

IMP Journal

ISSN : 0809-7259

Article publication date: 14 March 2016

The purpose of this paper is to identify what managerial implications research related to inter-organizational interfaces has been produced in marketing. For this aim, the authors focus on a specific concept implemented in many firms that operate on business-to-business markets, which is key account management (KAM).

Design/methodology/approach

The authors used the Ebsco Database entering “account management” as a key word in the title row. The search provided 51 papers to which the authors added four MSI reports written by Moriarty and Shapiro between 1980 and 1984. The authors then identified such keywords as “managers”, “practitioners”, “marketers”, “managerial”, “business”, and their variations as well as normative words such as “should”, “must”, etc. in order to identify managerial implications.

Four main findings are provided: a clear managerial purpose is affirmed by KAM academic works whether as a central “purpose” of the works or as “implications”; these managerial implications may display different forms (dimensions to be considered, consequences to anticipate, advices); though the managerial scope of KAM works is clearly visible, the sophistication of managerial recommendations remains … limited; the identification of who is exactly “the manager” targeted by the implications remains vague.

Research limitations/implications

The authors discuss the notion of managerial relevance of academic research.

Practical/implications

The authors explore sources for practices (whether they are the ones of scholars or managers) that could help “spelling out more effectively the managerial implications.

Originality/value

To the knowledge this is the first work that reviews so precisely how academic articles address to the managerial audience on a precise issue. Furthermore, the authors believe that KAM is an interesting and appropriate field for such a review because it is widely implemented on business markets.

• Managerial implications

Ivens, B.S. and PARDO, C. (2016), "Managerial implications of research on inter-organizational interfaces: The case of key account management", IMP Journal , Vol. 10 No. 1, pp. 25-49. https://doi.org/10.1108/IMP-06-2015-0030

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Affect and Cognition in Managerial Decision Making: A Systematic Literature Review of Neuroscience Evidence

Matteo cristofaro.

1 Department of Management and Law, University of Rome ‘Tor Vergata’, Rome, Italy

Pier Luigi Giardino

2 Doctoral School of Social Sciences, University of Trento, Trento, Italy

Andrea P. Malizia

3 Molecular Mind Laboratory (MoMiLab), IMT School for Advanced Studies Lucca, Lucca, Italy

Antonio Mastrogiorgio

4 Laboratory for the Analysis of CompleX Economic Systems (AXES), IMT School for Advanced Studies Lucca, Lucca, Italy

Associated Data

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.

How do affect and cognition interact in managerial decision making? Over the last decades, scholars have investigated how managers make decisions. However, what remains largely unknown is the interplay of affective states and cognition during the decision-making process. We offer a systematization of the contributions produced on the role of affect and cognition in managerial decision making by considering the recent cross-fertilization of management studies with the neuroscience domain. We implement a Systematic Literature Review of 23 selected contributions dealing with the role of affect and cognition in managerial decisions that adopted neuroscience techniques/points of view. Collected papers have been analyzed by considering the so-called reflexive (X-) and reflective (C-) systems in social cognitive neuroscience and the type of decisions investigated in the literature. Results obtained help to support an emerging “unified” mind processing theory for which the two systems of our mind are not in conflict and for which affective states have a driving role toward cognition. A research agenda for future studies is provided to scholars who are interested in advancing the investigation of affect and cognition in managerial decision making, also through neuroscience techniques – with the consideration that these works should be at the service of the behavioral strategy field.

Introduction

Since Simon’s (1947) administrative man – featured by bounded rationality – and the conceptualization of a firm’s performance as the result of the decision makers’ collective choice ( Cyert and March, 1963 ) – a series of studies in management studies advanced the debate of how organizational actors (individually or collectively) make decisions ( Kahneman, 2011 ; Lovallo and Sibony, 2018 ; Cristofaro, 2021a ). Over the years, managerial decision making – concerning the decisional activities made at the low-, middle-, and top-management levels ( Koontz et al., 1980 ) – attracted the interest of scholars in various areas, mainly due to its cross-disciplinary nature ( Cristofaro, 2017 ; Adinolfi, 2021 ). A great advancement has been made by the Behavioral Decision Theory (BDT), that originated in the ‘60s for the study of the real behavior of people when making decisions ( Edwards, 1961 ), and then increasingly adopted/developed in managerial decision-making research stimulating reflections on bounded rationality (e.g., Edwards, 1961 ; March, 1978 ; Kahneman, 2011 ; Powell et al., 2011 ; Sibony et al., 2017 ; Abatecola et al., 2021 ; Cristofaro and Giannetti, 2021 ).

From the progress made, the role of affective states has continuously and increasingly gained momentum within managerial decision-making research (e.g., Cristofaro, 2017 , 2019 , 2021a , b ). This happened mainly because affective states are considered the first biological reaction to stimuli in a decisional environment, condensing all other irrational impulses ( Weick, 1979 ). Emotional response, however, may not only directly influence the initiation and/or the output of a decision path, but they can also influence the content and depth of thought within decision-making processes at the individual and collective levels (e.g., Damasio, 1994 ; Lerner et al., 2013 ; Cristofaro, 2019 ).

However, as reported by the Call for Papers on the research topic “Affect and Cognition in Upper Echelons’ Strategic Decision Making: Empirical and Theoretical Studies for Advancing Corporate Governance” to which this article contributes, what remains largely unknown in managerial decision-making research is “the interplay of affective states and cognition; considered by some scholars to be two parallel competitive systems of the human mind”. From that, we want to answer the following research question: how do affect and cognition interact in managerial decision making? Concerning the definition of affect, we can recall the contribution brought by Forgas (1995) , who asserted that this word depicts a broad array of various affective states (the main term we will use hereafter), among which the preeminent ones are “moods” and “emotions”. Specifically, moods are related to low intensive and lasting affective circumstances (e.g., feeling down) that cannot be identified as a reaction to a precedent situation, while emotions, on the contrary, group all those affective reactions into a specific event. With regard to the definition of cognition, Neisser (1967) has defined it as the mental procedure through which inputs, such as information, are transformed, reduced, elaborated, and gathered, and then put into practice when needed.

To answer this study’s research question in a solid and “new” way, we offer a first systematization of contributions on managerial decision making that implement neuroscience techniques/points of view; thus allowing this review to robustly inform the affect-cognition debate. In this regard, a series of position papers/reviews/commentaries have been published about the impact of neuroscience approach and techniques in management studies (e.g., Becker et al., 2011 ; Powell, 2011 ; Ashkanasy et al., 2014 ; Ward et al., 2015 ; Jack et al., 2019 ; Massaro et al., 2020 ; Cucino et al., 2021 ), but only a few looked at the decision-making processes of managers within organizations. The sole contribution that tried to look at the influence of neuroscience studies in managerial decision making was Butler et al. (2016) ; however, for their results categorized under the “organizational behavior cluster” heading, some of the 12 contributions in that sample did not deal with managerial decision-making processes (e.g., Peterson et al., 2008 ), as well as it was not focused on shedding light on the affect-cognitive debate.

The collected 23 papers have been thematically analyzed by considering the type of managerial decisions, and under the reflexive (X-) and reflective (C-) systems of our brain, which seem to be devoted differently in describing consciousness, awareness, and mental processes ( Lieberman et al., 2002 ; Lieberman, 2007 ; Lieberman-Aiden et al., 2009 ). From the analysis, affect and cognition work in a meaningful interplay that directs decision-making processes of managers, with affective states having an initial (but not exclusively) driving role. This result shed light on the possibility of a “unified” mind processing theory ( Sadler-Smith, 2016 ; Cristofaro, 2020a ; Cristofaro and Giannetti, 2021 ) for which the two Systems of our mind – System 1, devoted to operating mental processes that are fast and automatic, and System 2, devoted to operating mental processes that are consciously monitored ( Kahneman, 2003 ) – are not in conflict, but they operate dialectically. These are the main theoretical implications of this paper. Yet, the main implications for research coming from this paper are related to the need for: (i) continuing the deconstruction of the distinction between irrational/automatic vs. rational/deliberate; (ii) articulating the ecological dimension of decision-making; and (iii) trying distinguishing the brain default activity by the task-evoked one, occurring while performing decision tasks.

Theoretical Background

Affect and cognition in decision making.

Since the birth of the bounded rationality concept by Simon (1947) , scholars have been increasingly involved in identifying how management decisions are made. This endeavor has found fertile ground in Behavioral Decision Theory (BDT).

Behavioral Decision Theory was born to understand real human behavior in decision making by studying, for example, models for static risky decision making, utility function, subjective probability, variance preferences, and personality variables, mainly through experimentations and computational models ( Edwards, 1961 ). In particular, BDT tries to explain why decision makers go beyond normative assumptions, such as violating expected utility axioms ( Einhorn and Hogarth, 1981 ). In this regard, initial progress of BDT studies (e.g., Good, 1962 ) lead to depict rationality being shaped by unconscious psychological events and external forces that determine human decisions and their consistency. This updated conceptualization of rationality in organizations, rooted in Simon (1947) , stimulated other new theories within BDT. The main contribution in this direction was the Behavioral Theory of the Firm by Cyert and March (1963) , which stated that decisions in organizations are always made in the presence of scarce information and negotiated within coalitions composed of managers and other stakeholders with different preferences and interests. Another relevant advancement in BDT was made by Tversky and Kahneman (1974) who proposed and verified one hypothesis that threatened normative decisional approaches: decision makers act according to unstable and ambiguous preferences ( Slovic et al., 1977 ). This conceptualization was based on the idea that people have multiple selves with conflicting assumptions, causing them to act inconsistently with regard to their previous choices.

Developments in BDT reinforced the need to enlarge the debate on human rationality to also include unconscious “irrational forces”; in this regard, Simon later added that “to have anything like a complete theory of human rationality, we have to understand what role emotion plays in it” ( Simon, 1983 ; p. 29). Stemming from this last assumption, over the past decades, many scholars ( Barsade and Gibson, 2007 ) have attempted to analyze the impact that both affective and cognitive variables play in the decision-making process. As an example, one recent contribution toward this last direction is the one by Treffers et al. (2020) , who demonstrated that managers in a positive affective state and under high time constraints elaborate fewer original and fewer feasible strategic ideas and make their original strategic choices worse when compared with managers in a negative affective state and under high time constraints, who generated better original strategic choices.

However, despite the growth of research on the affect-cognition debate in managerial decision making, there has been a profound division among scholars concerning whether affective states influence cognition or vice versa . This can be seen in the debate on the roles of System 1 and System 2 in our mind. Indeed, within dual mind processing theories ( Kahneman, 2003 ), there are two main schools of thought that flourished over time (see Evans, 2021 for a discussion): (i) default-interventionist , whereby, as a default setting, individuals make decisions recurring to intuition/emotions and reflective thinking may intervene dependent on the task at hand (e.g., Stanovich and West, 2000 ); and (ii) parallel-competitive , whereby, intuitive/emotional and reflective processes operate in parallel, such that “in the event of conflicts between them, they literally compete for the control of thinking and behavior” ( Hodgkinson and Sadler-Smith, 2018 ; p. 483).

Stimulated by these different visions within dual mind processing theories and strongly anchored in BDT, many contributions have tried to shed light on the relationship between affect and cognition in decision making. For example, Blanchette and Richards (2010) reviewed a series of articles to identify if and how affective states have repercussions on cognitive mechanisms. In particular, these scholars concluded that cognitive biases are mainly linked with anxiety and that (high/low) risk perception is also influenced by (negative/positive) affective states. However, according to them, sometimes affective states hinder normatively correct thinking, while in other cases, they promote it. In the same vein, Lochner and Eid (2016) proved that both negative and positive affective states drastically impact individuals’ reasoning performances.

On the other hand, another group of scholars claimed the supremacy of cognition over affective states. In this regard, Grecucci et al. (2020) recently hypothesized, tested, and verified that cognitive strategies are powerful enough to alter emotional states. Nevertheless, between these two opposite points of view, there is a third group of scholars who assumed that emotions and cognition could not be analyzed separately; indeed, they claimed that the affect-cognition debate should be studied by adopting lenses of mutual interplay since cognitive and affective domains should be perceived as two faces of the same coin ( Gosling et al., 2020 ).

In this last vein, Cristofaro’s (2019 ; 2020a , b ; 2021a , b ; Cristofaro and Giannetti, 2021) recent and in-depth contributions rooted in BDT enriched the debate by discussing the role of affect in management decisions, also proposing an Affect Cognitive Theory to explain how decision-making processes occur by considering the interplay between affective states and cognition. Hence, this new theory proposes that the crucial circumstances in which emotional states influence/are influenced by cognition and its biases identify that decision makers are affected by multi-level variation of both physical and social scenarios. Under these circumstances, decision makers are perceived as “emotional cognizers”, overwhelming the thinking-feeling dichotomy often promoted in the precedent studies of management decisions.

Neuroscience in Management and Organization Studies

The first seminal contribution that tried implementing neuroscience techniques/points of view in management studies was by Taggart et al. (1985) . In particular, by analyzing the link between decision style and cerebral dominance in 71 subjects by the use of an Electroencephalogram (EEG), these scholars concluded that psychological measurement captures very little actual cerebral processing. In practice, a boost of neuroscience adoption was encouraged to arrive at more solid managerial implications when concerning decision making.

After that stroke of genius, a period of stagnation followed and contributions aimed at connecting the management and neuroscience disciplines started appearing again – but not in a continuous way – only around the 2010s. This raised interest gave light to the field of ‘‘organizational neuroscience’’, aiming at using neuroscience knowledge and approaches at different levels in organizations, as well as promoting linkages to management practice 1 . However, due to the strong epistemological and ontological differences of these two disciplines, there have been many “positioning” contributions oriented to provide a solid direction for this cross-fertilization.

With regard to the above, the work by Laureiro-Martínez et al. (2015a) , which discusses the possible merge of cognitive neuroscience and strategic management starting from the value in their complementarities (see also Ascher et al., 2018 ), is noteworthy. In particular, they suggested three pillars – task selection, sampling, and ethical issues – for a successful mutual implementation of neurosciences and strategic management and provided a research agenda about the several circumstances of synergy between management and neuroscience researchers. Yet, the authors underline the advantage of neuroscience for management research laying in the possibility to scrupulously analyze the decisions made by managers at the brain level, a locus of psychological formation that cannot be intentionally biased by the participant.

In a similar vein, Murray and Antonakis (2018) and Jack et al. (2019) also provided updated suggestions about how to advance this new area of research. Under a methodological point of view, Jack et al. (2019) have envisioned that neuroimaging procedures, particularly functional Magnetic Resonance Imaging (fMRI) and Electroencephalogram (EEG) 2 , are expected to provide a lot of support to organizational neuroscience over the following decades. However, in line with Powell (2011) ; Jack et al. (2019) highlighted that reverse inference, i.e., inferring the presence of a specific cognitive process from observed brain activation, is mandatory for neuroscience to inform scholars involved in the organizational field consciously. However, as advanced by Murray and Antonakis (2018) , the hype and the unfamiliarity with the methods made scholars cautious about adopting neuroscientific methodologies in social sciences. Notwithstanding, in terms of benefits, Murray and Antonakis (2018) have pointed out that neuroscience data are exempt from the “cheap talk” and social desirability that can bias self-reports and surveys ( Podsakoff and Organ, 1986 ). In fact, data coming from neuroscience have several advantages such as their immediate observability, impartiality, and require relatively low-cost measurement tools.

Affect and Cognition in Neuroscience

Within the last decades, due to the emergence of powerful new tools for assaying the brain, researchers in cognitive psychology and neuroscience have been able to identify and validate the foundations of the decision model (e.g., Fellows, 2004 ) while looking at affect and cognitive mechanisms.

In particular, neuroimaging studies have identified two main brain regions involved in the “cognitive” system: the Anterior Cingulate Cortex (ACC) and the dorsolateral Prefrontal Cortex (dlPFC). The ACC’s dorsal part is linked with the Prefrontal Cortex (PFC), the Parietal Cortex (PC), the motor system, and the Orbitofrontal Cortex (OFC). In terms of functions executed, the ACC processes top-down and bottom-up stimuli and assigns specific control to other areas of the brain. Regulation of norm enforcement and self-interest, and adaptive response to a changing condition, are juxtaposed with the emotions in this structure ( Knoch et al., 2006 ). The dlPFC is part of the PFC. Yet, the dlPFC has been associated with functions executed as switching attention, working memory, abstract rules, and inappropriate response inhibition ( Fehr and Krajbich, 2014 ).

Regarding the “affect” system, this has been found to include three independent areas known to serve as broad functions in emotional processing, including mind-body integration of affective information and fundamental for experience and expression of emotions: insula, amygdala, and ventromedial Prefrontal Cortex (vmPFC). The insula is a portion of the cerebral cortex folded deep within the fissure, separating the temporal lobe from the parietal and frontal lobes. The amygdala is an almond-shaped set of neurons located deep in the brain’s medial temporal lobe, which has been shown to play a critical role in processing emotions, necessary for triggering aversive emotional states from primary inducers ( Haruno and Frith, 2010 ). The vmPFC is situated in the medial portion of the PFC and has been implicated in various social, cognitive, and affective functions; for example, it is critical for generating and regulating negative emotions and the representation of reward and value-based decision making. This is why a vmPFC study is essential in encoding subjective values of perceived offers and emotion regulation ( Gilam et al., 2015 ).

The described brain areas (see Appendix 1 for a graphical illustration) can also be re-interpreted according to the largely adopted brain categorization of the X- and C-systems ( Lieberman et al., 2002 ). This view includes, in contrast to the dual-process theories, the social cognitive neuroscience perspective, which implies a social interaction to drive behavior. Indeed, the dual system, represented for example by the Kahneman systems 1 and 2, is a more individualistic view, which highlights only the personal perception of the context. Although, the managerial decision making includes the individual along with external data processing from the social context, which reinforces our statement that Lieberman’s framework is adapting to provide a more complete and complex view in which managers need to operate. Environmental, social, and cultural conditions that should not be included in the decision-making process are, instead, part of it due to the inner characteristics of individuals finding they have to choose in the event of uncertainty.

In particular, the X-system is associated with non-conscious environmental analysis, which some scholars have described as automatic processing, implicit learning, and even intuition. In practice, the X-system conducts perhaps a vast majority of everyday processing ( Reynolds, 2006 ). The X-system has many components: the ventromedial PFC (vmPFC), basal ganglia (BG), amygdala (A), lateral temporal cortex (LTC), posterior superior temporal sulcus (pSTS), temporal pole (TP), and dorsal anterior cingulate (dACC) are the most relevant to automatic cognition. On the contrary, the C-system is the mechanism by which complicated reasoning is accomplished (see Lieberman et al., 2002 ). Specifically, the C-system is capable of rule-based analysis and can be interpreted as a complex analytical tool able to take the facts of a situation and apply an abstract decision rule to determine an outcome ( Reynolds, 2006 ). Yet, when activated, the C-system performs a regulatory role over the X-system. In terms of composition, the C-system is formed by lateral PFC (LPFC), ventrolateral PFC (VLPFC), medial temporal lobe (MTL), medial parietal cortex (MPAC), lateral parietal cortex (LPAC), rostral ACC (rACC), medial PFC (MPFC), and dorsomedial PFC (DMPFC).

However, for the sake of clarity, it is noteworthy to say that despite the apparent clarity and distinctions of brain areas, neuroscience studies also advanced some different positions about the functioning of affective and cognitive mechanisms. For example, Adolphs and Damasio (2001) , by reconsidering previous laboratory findings of cognitive neuroscience, highlighted that affect and cognition are inseparable and that the former drives the latter. Specifically, they reaffirmed that the arousal of affective states is the first reaction external stimuli, and that in this process it is the amygdala that rapidly triggers physiological changes in response to emotionally salient stimuli. Thus, through the vmPFC, the influence of affective states on cognition happens through changes in the visceral state (e.g., heart rate, blood pressure, gut motility – somatic markers in general) that then affect cognitive processes (e.g., learning through failures and being aware of the future consequences of decisions). This conceptualization is at the basis of the somatic marker hypothesis ( Bechara, 2011 ).

At the center of the somatic marker theory, there is the assumption that decision makers encode the consequences of choices effectively (e.g., Pessoa, 2008 ). In particular, according to Reimann and Bechara (2010) , when making a decision, “the immediate prospects of an option may be driven by more subcortical mechanisms (e.g., via the amygdala) that do not require a PFC. However, weighing the future consequences requires the PFC for triggering somatic responses about possible future consequences. Specifically, when pondering the decision, the immediacy and prospects of an option may trigger numerous somatic responses that conflict with each other (that is, positive and negative somatic responses). The end result, though, is that an overall positive or negative signal emerges (a “go” or “stop” signal)” (p. 770). Therefore, from that theory – that is not an excerpt of criticism (see Dunn et al., 2006 ) – it emerges that complex cognitive-emotional behaviors are grounded in dynamic coalitions of brain areas’ networks.

In this vein, Pessoa (2008) has deeply highlighted that behavior should be perceived as the result of the mutual interaction of different brain areas, proposing, at the same time, the idea that emotion and cognition not only strongly interact in the brain but that they also jointly contribute to shaping human actions. In particular, Pessoa (2008) has remarked that the amygdala plays an essential role in forming individuals’ emotional aspects, while the PFC is responsible for the cognitive one. However, as recalled by this scholar, several brain regions are loci where both the affect and cognitive mechanisms interact vigorously, such as in the lPFC and the dlPFC. This hypothesis is also supported by evidence on brain structure in highly clustered synapses. Hence, brain areas cannot be considered as watertight compartments. From that, it can be said that Pessoa (2008) was one of the first who advanced that cognitive and affective mechanisms are mutually influenced.

Methodology

In order to answer the research question: “ How do affect and cognition interact in managerial decision making? ”, we implemented a Systematic Literature Review (SLR) of contributions dealing with the role of affect and cognition in managerial decisions that adopted neuroscience techniques/points of view. In this regard, we identified the SLR methodology as the suitable research design to consolidate and synthesize academic research. In particular, this method differs from the traditional narrative reviews in: (a) assisting in linking future research to the questions and concerns that have been posed by past research, and (b) being more explicit in the selection process by employing rigorous and reproducible evaluation methods. In this work, the established SLR procedure by Tranfield et al. (2003) has been followed; see also Figure 1 .

PRISMA representation of papers’ collection strategy.

(1) The databases for the identification of the studies were: (a) Business Source Premier (EBSCO); (b) ProQuest’s ABI/Inform; (c) ISI Web of Science; (d) Scopus; (e) PsycINFO; and (f) PubMed (including MedLine);

(2) Only peer-reviewed journal articles published in English have been included to enhance quality control. Furthermore, the research was not restricted to a given starting period (end period December, 31th 2021) and type of paper (qualitative or quantitative). These two criteria enhanced the value-added of this work compared to the one by Butler et al. (2016) , who just considered empirical papers published between 2007 and 2014.

(3) Only articles adopting a neuroscientific methodology/point of view have been considered by using the keywords: “neuro*” or “brain*” or “Functional Magnetic Resonance Imag*” or “FMRI” or “Electroencephalograph*” or “EEG” or “Magneto Encephalograph*” or “MEG” or “Transcranial Magnetic Stimulation*” or “TMS” or “Transcranial Electric Stimulation” or “tES” or “Positron Emission Tomograph*” or “Functional Near Infrared Spectroscop*” or “fNIRS” or “skin conductance” or “Galvanic Skin Response” or “GSR” or “GRV” or “skin conduct*” or “electrodermal activ*” or “eye track*” or “gaze track*” or “pupillometr*” or “pupil diamet*” or “heart rate*” or “HRV” or “facial expression recognition” or “emotion recognition” or “non-invasive brain stimulation” or “NIBS” or “cortisol” or “testosterone.” These keywords have been derived – and enlarged – following Butler et al. (2016) and Ascher et al. (2018) and aim to identify works related to cognitive neuroscience. 28,705 results were produced.

(4) The substantive relevance of contributions to the managerial decision-making theme has been ensured by requiring that the selected abstracts contained at least one of the following words: “decision*” or “choic*” or “preference*” or “judg*.” These keywords have been derived from the SLR by Cristofaro (2019) on affecting management decisions. 3,070 results were produced.

(5) Only articles regarding business issues have been considered by using the keyword “organization*” and its synonyms: “compan*” or “manag*” or “corporat*,” or “firm*” or “business*” or “enterprise*” or “venture*” or “start-up*” (these keywords have been derived following De Vita et al., 2013 ). 633 results were produced.

(6) Duplicates from databases were eliminated at this stage thanks to the integration operated through reference-manager software. 401 hits were produced;

(7) The resulting articles were scanned by reading all the abstracts to ensure their substantive context, mainly according to their coherence with the review’s aim. When there was doubt about the content regarding the inclusion/exclusion of an article, the full text was examined. 77 results were produced;

(8) The remaining papers were fully read to ensure their alignment with the research objective. Thus, we included in the sample only scientific contributions dealing with managerial decision-making processes that implement neuroscience techniques/points of view; 20 results were produced. In this regard, we implemented the same exclusion criteria as Butler et al. (2016) – see their online S-2 Appendix. Initially, the authors individually read the articles and then compared their evaluations; when disagreeing, the authors assessed the papers together and decided whether or not to include those papers within the sample. Cronbach’s alpha for inter-rater reliability was 0.95.

(9) Snowballing techniques have been applied to the reference lists of the resultant 20 articles. This ensured that important works in the field were included that might have been missed. Three were added arriving a final sample of 23 articles (almost double that of Butler et al., 2016 ; N = 12; yet, these two samples are very different too in terms of selected papers; see Table 1 ).

The study sample’s papers.

The asterisk (*) identifies papers that are also present in the sample by Butler et al. (2016) .

The selection at points 7 and 8 followed criteria used by Sandberg and Tsoukas (2015) ; in particular, studies have been included that explicitly: (i) aim to contribute to the development of managerial decision making, and (ii) apply neuroscience in their research. So, similarly to Butler et al. (2016) , we excluded articles “that only briefly highlighted cognitive neuroscience in a cursory way, for example, in a one-line reference to the topic” (p. 546) and that did not use the terms at point (5) in relation to organizational contexts (e.g., waste-water management).

Following Butler et al. (2016) , the 23 contributions related to the role of affect and cognition in managerial decisions that implemented neuroscience techniques/points of view have been structured into three clusters. The literature itself ( Braun and Clarke, 2006 ) defined these inductive emergent clusters and reflected the type of decision made by managers. Sample articles categorized into these three clusters have been read by looking at the assumed relationship of decisions with the reflexive (X-) and reflective (C-) systems ( Lieberman et al., 2002 ;

Lieberman-Aiden et al., 2009 ). This course of action helps build a solid neuroscientific basis for the assumed connections between neuroscience and the affect and cognition of managers proposed in the discussion section below.

Among the 23 papers in the sample, only three contributions (i.e., Krueger et al., 2009 ; Boyatzis et al., 2012 ; Hannah et al., 2013 ) are present also in the “organizational behavior cluster” of Butler et al. (2016) , substantiating originality and novelty in the systematization we propose. The majority of the 23 papers in our sample are conceptual (12; 52%), then 9 empirical articles (39%) and 2 review works (9%) complete the sample. Among these, authors who have published empirical contributions have used many techniques to test their assumptions; the main ones used have been fMRI (40%), EEG or qEEG (20%), and electrodermal activity (13%). Other techniques used for the data analysis have been: Computed Tomography scans (5%), Facial Expression Recognition (5%), Eye-tracking (5%), fNIRS (5%), and Testosterone (5%). With regard to authors, Gerard P. Hodgkinson is the only one present with two contributions (both conceptual).

The 23 selected papers cover a period of 18 years, with the distribution shown in Figure 2 . There is an average of two publications per year. Furthermore, the selected contributions were published in many different journals; among them, 4 (15%) appeared in The Leadership Quarterly .

Number of sample papers published by year.

An inductive thematic analysis of the 23 selected papers’ manuscripts has been implemented, aimed at identifying the type of decisions treated. In particular, thematic analysis is principally employed to acquire a nuanced comprehension of spontaneous and sophisticated processes ( Mills et al., 2009 ), such as decision making. Within inductive thematic analysis, there is no presence of an initial codebook and themes are free to emerge. In line with Strauss and Corbin (1998) , the coding has relied on the research question to determine themes associated with the main aspects of the analysis based on the theoretical background. The resulting themes/decision types emerging from the manuscripts dealing with managerial decision making are: (a) ethical decisions, (b) innovation decisions, and (c) data-enabled decisions.

Ethical Decisions

Our systematic review includes many publications on ethical decision-making processes (i.e., consistently evaluating and choosing ethical principles), emphasizing moral aspects. In this vein, Reynolds (2006) was the first to report the interaction of the C- and X-systems in the ethical decision-making process. In particular, according to Reynolds (2006) model, ethical decisions come from the accuracy of the neural pattern of the stimulus, i.e., prototype, which are activated in the brain of the decision maker, such that: “an effective and thorough search can facilitate ethical behavior by gathering enough accurate information either to match a prototype correctly or to apply the moral rules available effectively” ( Reynolds, 2006 ; p. 743). Prototypes are evoked according to the information reflexively collected and categorized into ethical patterns by the decision maker. The ability to structure information to match multiple prototypes will be positively associated with ethical behavior. Then, the ethical judgment is made, and it would be highly positively correlated to ethical behavior if performed by the C-system rather than by the X-system. This last avenue allows restructuring prototypes in the light of a rationalization process of moral rules and, in sum, substantiates that the C-system would exert control over the X-system to ethically manage organizations.

However, research has reported that not only the pre-frontal cortex, but also the amygdala (involved in memorizing emotional reactions) are important for a person’s moral development ( Greene, 2015 ), suggesting that “the discussion of ethics should not rest solely on a rational decision-making model” ( Robertson et al., 2017 ; p. 691). This adheres to the conceptualization of moral development by Kohlberg and Hersh (1977) that does not consider moral judgment as a necessary condition for moral action, because of the fact that also emotions, and a general of sense of will, come into play. Therefore, moral reasoning does not always lead to moral behavior. In this vein, Dedeke (2015) , in his theoretical model, suggests that ethical decision making includes five interdependent yet functionally distinct steps and proposes an intuitive view of ethical judgment, as it describes how emotion regulation, perceived moral intensity, and perceived ethical climate constructs impact the formation of moral intent ( Dedeke, 2015 ). In particular, Dedeke (2015) proposes that emotions always influence employes’ cognitive moral decisions. He assumes that automatic cognition (i.e., intuition) and automatic emotions interact within the pre-processing stage of an ethical decision. This would happen as follows: (i) the situation faced by the decision maker elicits some memories (as knowledge structures, schematic mental structures) that recall actions that have been implemented in the past and that can also have a role in ethical decisions; and (ii) concurrently, emotions provide a frame of reference for cognition; emphasizing some elements of the context over others. Moreover, the emotional reaction is used as an information point in decision making and works as a driver for cognition (i.e., somehow, the X-system drives the C-system).

In the same vein, McDonald (2018) underpins the importance of social cognitive neuroscience in sustainable management and related ethical decisions. The author reports several research insights from the literature, underscoring the essential role of social cognitive neuroscience in corporate sustainability management research. In particular, McDonald (2018) focused his attention on managers and their responsibilities on creating value in an integrated manner across ecological, economic, and social spheres. In doing that, he highlighted the importance of the amygdala, which was found to be assisting emotional learning in ethical decision-making processes. According to this author, the amygdala allows bypassing the cortex providing an automatic and unconscious reaction (X-system over C-system) in unforeseen situations and humans respond immediately to an input, such as fear. To improve ethical decisions in sustainability management, this latter needs to be communicated so that emotional tags are created within the memory to evoke a future state that will facilitate creative solutions. In this regard, the X-system of decision makers, in which the amygdala operates, should prepare the field for an oriented cognition operated by the C-system. This is aligned with the results by Lee and Yun (2019) who identified, using fNIRS, an increase in the hemodynamic responses in the dlPFC that can be linked to moral stress, caused by time constraint and that causes shifting to a proself-condition (i.e., adopting a selfish behavior). Accordingly, it could be hypothesized that activated dlPFC correlates with the capacity to handle moral stress, and can easily affect the C-system favoring the X-system in ethical decision-making processes.

Fennimore and McCue (2021) also deepen the role of stress and other motivational drivers in ethical decision making. From their model, decision makers are neurobiologically inclined to be engaged in risk-averse behaviors once they are persuaded by the fear of disrupting the status quo and seek relief by preventing punishment. Yet, other decision makers have neurobiological preferences toward risk-seeking behaviors, galvanized by the hope of reward prospects. In brief, from this study, the X-system seems to orient the risk orientation behavior (similarly to Cropanzano et al., 2017 ), who studied the neurobiological origins of deontic justice; the moral obligation to uphold norms of justice. In particular, Cropanzano et al. (2017) pointed out that the presence, or absence, of business ethics within the organizational environment are likely to affect positively, or negatively, the behaviors of those involved, since “the pernicious effects of injustice are likely to be spread rapidly through an organization, as some employees become displeased with the treatment and experiences of their coworkers” ( Cropanzano et al., 2017 ; p. 746). Consequently, in deontic justice contexts, the emotional state (X-system) seems to prevail over the rational one (C-system) for the formation of ethical choices.

Innovation Decisions

With regard of innovation decisions, i.e., the choice to adopt or not adopt an innovation, Hodgkinson et al. (2009) identified intuition as crucial for those organizations seeking innovation, such as new business opportunities (i.e., exploration). Hodgkinson and Healey (2014) , demonstrated that reflexive processes (associated with the X-system) are not relegated to a mere source of error or bias to be overcome with effort; rather, they are integral to reflective (C-system) processes of human cognition and critical for skilled processes such as intuition. In particular, they added that, to achieve a successful level of innovation and shifting strategic choices, managers must be offered the opportunity to regulate their feelings (the so-called emotional reframing); adaptive regulation stimulates the PFC.

Such adaptability may be contingent upon managers having the requisite complexity to facilitate effectiveness across various roles with different grades of intuition and expertise. Hannah et al. (2013) specifically examine managers’ self-complexity, which is based on the self’s central role in managing the interface between a manager’s internal processes (closer to the C-system) and his or her interactions with the social environment (closer to the X-system). More effective managers possess a requisite level of complexity that allows them to perceive and assess complex and changing dynamics accurately and, in turn, adapt their decision making and behaviors to enact effective responses. This was confirmed by these scholars through the measurement of the executive and cognitive-associated frontal lobe by quantitative EEG. In addition, Hannah et al. (2013) have recommended that practitioners assess managers’ self-complexity (LSC) to measure their ability to handle internal processes – i.e., goal system, self-regulation, and identity – and their synergies with the external environment. Specifically, Hannah et al. (2013) have highlighted that LSC is a reliable precursor of managers’ adaptive behavior; thus, according to these scholars, LSC can be spotted in managers that can lead them to achieve goals characterized by a higher degree of complexity.

To confirm what was proposed by Hannah et al. (2013) ; Laureiro-Martínez et al. (2015b) have used the fMRI technique in a sample of expert decision makers that exploitation – deepening the existing business – relies on brain regions (i.e., the mPFC and the hippocampus bilaterally) mainly associated with anticipation of rewards, while exploration – looking for new business opportunities – depends on regions primarily associated with attentional control (bilateral parietal and frontal regions, e.g., dACC). From that, exploitation and exploration are separate behaviors involving different mind processes. Moreover, Laureiro-Martínez et al. (2015b) found that the locus coeruleus-norepinephrine (LC-NE) system and the PFC are activated by the cognitive processes that enable decision makers to switch between exploitation and exploration processes. In particular, these brain regions can be traced back to the C-system and, thus, the LC-NE, which controls the degree of attention, affects this particular system rather than the X-system. In practice, brain circuits related to attentional control allows individuals to achieve better decision-making. Nonetheless, the rational and attentive management of exploration-exploitation processes usually leads, as for March (1991) , to selecting more reliable business actions, such as exploitation, rather than those leading to uncertain outcomes – thus, degrading, however, organizational learning in a mutual learning situation and compromising the competitive position in the long term. In brief, even if the more attentive brain processes are put into action, they cannot ensure that the produced equilibrium between exploration and exploitation is the best for successfully adapting to the changing environment.

Data-Enabled Decisions

Some neuroscience contributions ( Damasio, 1994 ; Bechara and Damasio, 2005 ) intensely stressed the idea that decision-making processes are not exempted from being affected by emotions and, in recent times, scholars started investigating the emotional responses of decision makers in data-enabled decisions (i.e., decisions facilitated by information technology (IT) systems and related produced data). In this regard, pioneers such as Dimoka et al. (2011) asserted that, in different social contexts in an office environment, exploring the potential of cognitive neuroscience and information systems (neuro-IS) research offers examples of a fertile intersection in which there is a considerable potential to optimize management activities.

Following this line of research, Leger et al. (2014) measured emotional responses, based on electrodermal activity, in two samples of managers with different expertise during the use of an enterprise resource planning system in a decision-making context led to different sourcing information. These scholars found confirmation that the more people become proficient in doing a specific task, the higher the decreasing activity in the prefrontal brain regions. Moreover, as the prefrontal brain regions are correlated with the cognitive side, it can be hypothesized that the more a person becomes confident with a task, the more intuitive his/her behavior will be and the X-system will prevail over the C-system. Hence, remarking on the contribution brought by Krueger et al. (2009) concerning the role covered by emotional intelligence (i.e., the ability of reasoning about emotions, and, in turn, to use emotions to intensify reasoning) in driving individuals’ reasoning and behavioral skills, Leger et al. (2014) shed light on the function played by emotions in IT systems’ frameworks – marking the distinction between proficient and neophyte users.

Similarly to Leger et al. (2014) , through the means of the Locarna eye tracker, Chen et al. (2016) investigated how decision quality is affected by the amount of time managers spend looking at the Balanced Scorecard (BSC)’s performance metrics, and whether understanding of a firm’s strategy and how the presentation format affect individuals’ focus. In this context Chen et al. (2016) found that: (i) managers who look more at strategic performance measures (e.g., sales margin, brand recognition rating, and employes’ satisfaction) are more likely to make decisions consistent with the achievement of their subordinates’ strategic objectives, and (ii) when managers are informed about the strategy put in place by their organizations, they are more concentrated on strategic performance measures than others. Therefore, it could be possible to conjecture that the C-system overrides the X-system in shaping managers’ approach to the results depicted in the BSC.

Generally speaking, the distinction between the X- and C-systems, building upon the lateralization of the brain, although valid, is a coarse classification of many distinct human faculties that pertain to one of two broad domains and their interaction. This general distinction oversimplifies brain lateralization and assumes absolute functional differences in management studies. Our literature review consistently shows that the distinction between reflexive and reflective systems dominates the debate with marginal articulations within each of the systems ( Laureiro-Martínez et al., 2015a , b ; Boone et al., 2020 ). In this regard, we recognize from our SLR that three different schools of thought emerged: (i) the C-system has a predominant role over the X-system; (ii) the X-system has a predominant role over the C-system; (iii) the C- and X-systems interact, and neither of the two has a primacy.

For the first cluster, Reynolds (2006) study is among the few important works. Indeed, he was the first to report the interaction of the C- and X-systems in the ethical decision-making process. Ethical judgment would be highly positively correlated to ethical behavior if performed by the C-system rather than by the reflexive one. Hence, according to Reynolds (2006) , the C-system exerts control over the X-system for the management of organizations. Recently, Boone et al. (2020) conducted a study to assess the pivotal importance of the controlled C-system over the automatic X-system for pushing Top Management Teams (TMTs) to invest in corporate social responsibility.

With reference to the second cluster, many other scholars, instead, have devoted their efforts to analyzing the influence that the X-system has on the C-system. Among them, it is noteworthy to recall the contribution brought by Leger et al. (2014) , who, using electrodermal activity to measure psychophysiological responses elicited by arousal, have confirmed what was previously proposed by Krueger et al. (2009) , thus that the more a person becomes confident with a task, the more intuitive his/her behavior will be and, therefore the X-system will prevail over the C-system. This latter idea also has found fertile support from Antonakis and Dietz (2010) , who have asserted that the decision makers’ emotional intelligence, combined with other elements such as the working memory and the intelligence quotient, plays a pivotal role in shaping leaders’ answers in critical situations, such as when they face constraints in their own cognitive resources. Particularly, leaders’ ability to handle emotional intelligence allows them to be engaged in higher quality decision-making processes since, due to the aforementioned skill, they are more competent in the creation of proactive environments where a contamination of ideas is widely supported and positively perceived ( Antonakis et al., 2009 ).

A third cluster supports the idea that there is a mutual influence between the C- and X-systems. Among them, we recall the important works by Hodgkinson et al. (2009) and Hodgkinson and Healey (2014) , proposing a theoretical framework in which the two systems of human mind have close interaction and mutual influence. According to this third cluster, lateralization of the brain appears to be more complex than how it is popularly investigated ( Toga and Thompson, 2003 ). As suggested by Hodgkinson et al. (2009) : “old models based on a simplistic left brain/right brain dichotomy are giving way to more sophisticated conceptions, in which intuitive and analytical approaches to decision making are underpinned by complex neuropsychological systems” (p. 277).

From the above, we should be aware that the traditional distinction between reflexive (X-) and reflective (C-) systems, considered alone, cannot be framed as a complete theoretical framework as the nature of the interplay defines specific paradigms. Indeed, many theories assume dual processing of information, but they radically differ in their articulation. A fundamental distinction, mainly discussed in literature with reference to System 1 and System 2, is between a parallel-competitive and a default-interventionist approach ( Evans, 2021 ). In this last regard, from the analysis of the sample contributions described above, it can be derived that the managerial decisions result as the product of an emotional-driven dialectic of affect and cognition (e.g., Damasio, 1994 ; Sadler-Smith, 2016 ; Abatecola et al., 2018 ; Cristofaro, 2020a , b , 2021a , b ), redirecting the discussion on information processing from dual-mind processing theories (e.g., Stanovich and West, 2000 ; Hodgkinson and Sadler-Smith, 2018 ) to a “unified” mind processing theory ( Sadler-Smith, 2016 ) for which the two systems of our mind are not in conflict and for which affective states have an initial (but not exclusive) primary driving role. As a consequence, the recent affect-cognitive interplay emerges, under a neuroscientific point of view, as supported, and may be considered as the fertile ground from which a renewed understanding of managerial decision making can move forward – also because its explanations are intertwined with other relevant streams of research such as the Upper Echelons Theory ( Hambrick and Mason, 1984 ; Abatecola and Cristofaro, 2020 ) and Behavioral Strategy ( Powell et al., 2011 ; Sibony et al., 2017 ; Abatecola et al., 2021 ; Cristofaro and Giannetti, 2021 ). In particular, it seems to be that the provided understanding supports the recent Affect-Cognitive Theory of management decisions by Cristofaro (2021a) ; in fact, assumptions of this theory clearly identifies an interplay of affect and cognition, with affective states having an initial (but not exclusive) primary driving role, for the formation of choices supporting the cited “unified” mind processing theory.

Limitations of the Research Field and Future Research

Neuroscience can help to deconstruct and reformulate from scratch some traditional problems – i.e., the roots of behavioral strategy ( Powell et al., 2011 ) – that connote the agenda of management studies. Notwithstanding such premises, the contributions present in our literature review seem not to follow this trend. Indeed, they are characterized more by a mere integration of neuroscientific methods than a radical reformulation of research questions based on neuroscientific evidence.

The main limits of the papers in our sample are that: (a) neuroscientific studies are often conducted on non-representative populations, because studies on practitioners are limited, and nothing can grant that the evidence found on non-representative populations (e.g., students) can be generalized; (b) when practitioners are present, samples are often limited or biased, as there is difficulty in balancing the different profiles of participating organizations and teams; and (c) studies are often based on laboratory experiments.

The above limitations are commonly connected with the intrinsic difficulty of implementing several neuroscientific tools in ecological conditions (e.g., fMRI outside a medical hospital center) and ethical problems in adopting them. In this regard, as expressed by other scholars (e.g., Tivadar and Murray, 2019 ; Zwaan et al., 2019 ), since managerial decision-making processes are filled with strong interplay between affective and cognitive contents, the use of either laboratory experiments or non-representative populations will unquestionably lead to results affected by a lower ecological validity and, in turn, to a lesser practical utility of these results to help scholars in the study and explanation of peculiar situations (e.g., by controlling, in real-time, the neurobehavioral mechanisms affecting executives’ decisions while they have to counteract sudden organizational/financial crises).

Furthermore, the neuroscientific contributions examined in this study often present limitations concerning the statistical analysis techniques. More specifically, the number of significance tests carried out in neuroimaging analyses (e.g., fMRI, EEG, and qEEG) are extremely likely to inflate the risk of Type I error (false positiveness) – in line with Jack et al. (2019) . A clear example of limitations regarding the statistical methods has been explicitly declared by Balthazard et al. (2012) who have not correctly applied multiple comparisons in their analyses, because this “would impose an overly conservative and impractical limit for exploratory studies like our own” (p. 255). To overcome this limit, Balthazard et al. (2012) have wisely improved the likelihood of any “spurious results” by the means of a second population to replicate their results. Therefore, despite having cross-validated their findings to overcome statistical limitations, this represents a common and evident barrier that many other contributions have had to face.

An Agenda for Future Research

Articulating the reflexive system.

The general distinction between reflexive/X-system and reflective/C-system dimensions seems to substitute the further distinctions among specific cognitive phenomena within the reflexive realm. Affect, intuition, insight, instinct, are quite different types of cognitive phenomena – pertaining to the reflexive system – each markedly connoted by distinctive features and neurophysiological systems. What seems to emerge from our literature review is that such a fine-grained distinction is never by scholars. With a few exceptions (such as Hodgkinson et al., 2009 ), the selected contributions rely on a coarse-grain distinction between reflexive and reflective systems, avoiding further articulations within each one of them. This theoretical choice seems to discharge the many advancements made in cognitive neuroscience in the last two decades, which tend to articulate on a neurophysiological level of the specific sub-systems involved.

Generally speaking, this review easily indicates that many reflexive processes do not necessarily present an affective dimension. For instance, numerical cognition involved in the well-known “bat and ball” like problems (e.g., Branas-Garza et al., 2019 ) relies on automatisms but does not consider any affective dimension. What seems to be relevant here is not that distinctive cognitive phenomena are all ascribed to the reflexive system, but they are quite different types whose specificities are underexplored by management scholars.

Assuming a strict functional specialization and rigid modularity between reflexive and reflective systems could be misleading, as it hides several alternative views of the human brain that have emerged in the last decade. System 1 is quite flexible and content-sensitive, as different specialized brain regions are able to contingently interact to form coalitions of brain areas to perform new tasks, instantiating neural reuse ( Gallese et al., 2021 ; Mastrogiorgio et al., 2022 ). Such considerations about the nature of the X-system are quite absent from the contributions that emerged in our literature review and should be explored further in future studies. In particular, and contrary to the idea that the reflexive system is biased, the automatic response can be a source of satisficing decisions in specific task environments ( Gigerenzer, 2007 ). For instance, the recognition heuristic is based on the idea that if one of two objects is recognized, we can infer that the recognized object has a higher value than the criterion to infer it. Generally speaking, organizations that incorporate the affective dimension in decision making are more successful than those organizations that rely solely on analytic approaches ( Hodgkinson and Healey, 2011 ).

The contribution of our literature review seems to acratically assume (with a few exceptions like Hodgkinson et al., 2009 ) a Manichean duality to overlook the idea that automatic response can generate rational outcomes, which is another avenue for future research. The reflexive system can be a significant source of correct judgments; Hodgkinson et al. (2009) discuss the distinction between insight and intuition, both characterized by the automatic response, where the anterior superior temporal gyrus region of the right hemisphere is related to insight and the orbitofrontal cortex and the amygdala are activated in intuitive judgments ( Volz and von Cramon, 2006 ). If we admit that the reflexive system is a source of rational judgments, we should be tempted to criticize most of the arguments presented in the contributions that tend to unwittingly adopt a sharp distinction between the sources of rational or irrational judgment. We think that continuing the deconstruction of the distinction between irrational/automatic vs. rational/deliberate is a fundamental domain of future research.

Articulating the Ecological Dimension

The traditional perspective of cognitive biases ( Kahneman et al., 1982 ; Stanovich and West, 2000 ) has been, in the last decades, complemented by an alternative program emphasizing that decision makers are able to deal with complex environments through the use of fast and frugal heuristics that are adapted to the structure of the environment ( Gigerenzer and Selten, 2002 ). Kahneman’s (2003) framework has been criticized for a poor specification of the role of the environment when formulating judgment, as Kahnemanian heuristics are assessed using non-ecological benchmarks (such as logic and probability calculus) (e.g., Gigerenzer and Murray, 1987 ; Gigerenzer, 1991 ). Within the framework of bounded and ecological rationality, it is impossible to assess human rationality only by looking at the cognitive phenomenon, limited-to-the-brain, as the structure of the environment specifies which cognitive process is successful ( Gigerenzer and Selten, 2002 ). Interestingly, the contributions in the sample, despite they have been selected with specific reference to Simon’s (1947) tradition, neglect the role of ecological dimension and, in particular, the fit between cognitive resources and environmental structure (also known as the scissors’ argument, Newell and Simon, 1972 ). The so-called naturalistic decision making – which analyzes how experts make decisions in ill-structured, complex environments under conditions of time pressure ( Zsambok and Klein, 2014 ) – introduces an alternative perspective on reflexivity and represents a promising, but somehow underexplored, program of research. Organizational neuroscience represents a “natural” articulation of the ecological dimension, where the workplace represents the “real-world” in which specific neuro-cognitive mechanisms can be studied.

Views of the Brain and Affective-Cognitive Interaction

Traditionally, experimental research favors a reactive view of the brain, as brain functions are studied by means task-evoked responses. The experimental perspective, though successful, leaves aside the factual consideration that brain activity is mainly intrinsic and involves functions for interpreting and predicting environmental instances, and not just reacting to them. Generally speaking, what makes the study of the intrinsic brain activity relevant is that the brain’s enormous energy consumption is not related to specific tasks, but to its default activity mainly related to the ongoing, perceptual information processing of large amounts of sensory data ( Raichle and Snyder, 2007 ; Raichle, 2010 ).

Although the idea that the brain is not primarily reactive – a default activity occurs prescinding from the responses of contingent tasks – is not new, the investigation of intrinsic brain activity (i.e., “baseline”) represents a relatively under-investigated domain of neuroscientific research, also considering its related methodological problems. Indeed, while experiments are rigorously designed (stimuli and responses can be measured with great precision), the measurement of the default activity of the brain can be elusive, as there is no specific theoretical focus. We think that the role of the intrinsic brain represents a future domain of investigation for decision-making research, which is able to shed new light on the affective-cognitive interplay. As shown in Table 2 , while in the reactive brain perspective, articulating the reflexive system and the ecological dimension (discussed in the previous section) represents the two domains respectively related to the affective and cognitive dimension, in the intrinsic brain, the focus is on the long-term default mode of brain functioning. In particular, with reference to the affective dimension, personality is reflected in the brain’s intrinsic functional architecture, where the resting-state functional connectivity is predicted by specific personality traits ( Adelstein et al., 2011 ). With reference to the cognitive dimension, when humans have ample time at their disposal to make a decision – and this is expected in upper echelons contexts – spontaneous brain activity constrains the selection of solution strategy ( Barnes et al., 2014 ), as intrinsic activity may reflect a memory system represented by an internal statistical structure of the outside world ( Sadaghiani and Kleinschmidt, 2013 ).

A potential typology of future research.

The role of the default mode in the affective-cognitive framework helps us to uncover the long-term, stable, not contingent, boundary conditions of decision making. Emerging domain of investigation could be central in organizational neuroscience which, by definition, focuses on the stable “default-experience” of individuals in structured organizational contexts. Notice, incidentally, that this focus on stable default-mode experience can be also useful to investigate the cultural issue (as we expect that specific organizations’ practices enter into the “baseline” in order to affect the way in which decisions are made).

Implications and Conclusion

How do affect and cognition interact in managerial decision making? This is the research question that we tried to answer through the SLR of contributions produced on managerial decision making, which consider neuroscience techniques/points of view. In terms of originality, this is the first contribution filling this gap, stemming from the fact that the only other SLR produced ( Butler et al., 2016 ) did not deeply focus on managerial decision making.

Results of the sample papers show alternative views about the X- and C-systems that seem differently devoted to non-conscious and complicated reasoning. Selected works are not unanimous, but, from their systematic analysis, it can be advanced that the relationship between affective states and cognition is dialectical, with affective states having a driving role toward cognition: the X-system initially drives the C-system. This is aligned with brain studies that point toward a driving role of affective states, since they come from the biochemical response of individuals to their context. In this regard, seeing the relationship between System 1 and System 2 in managerial decision making as parallel, reorients the discussion on information processing from the tradition of sequential dual-mind processing to a “unified” mind processing theory for which the two Systems are not in contrast and for which affective states have an initial (but not exclusive) primary driving role (e.g., Damasio, 1994 ; Cristofaro, 2020a , b ).

From this work, the relationship between BDT – including its developments – and neuroscience emerges as stronger, because one modifies/reinforces the other in a virtuous scientific debate. However, as advanced by Powell (2011) , management scholars must not forget that neuroscience can add significant value to the current state of the art in management research only if the former is “at the service” of the latter. Otherwise, neuroscience results could be not perceived as relevant for practitioners and for management scholars themselves, reducing the communication power of the neuroscience-management duality in decision-making research. To avoid that, traditional managerial problems must be re-articulated through a neuroscience lens. Brain imaging techniques can reveal the specific brain area involved in specific decision-making domains. But, there is more. The investigation of intrinsic brain activity could represent a future domain of organizational neuroscience research in which the default-mode of brain functioning can be considered as a “boundary condition” for decision making. This trend is also favored by the increasing use of dedicated devices (such as a stress bracelets, EEG, etc.) in organizational settings. Measuring the default-mode parameters of brain activity can inform a next-generation of practitioners on how to improve decision making.

Our study also shows that real novelty in hypothesis generation, informed by cognitive neuroscience, is somehow missing. While neuroscience allows deconstructing consolidated categories – to reformulate old management problems in a fresh manner – this is not what our literature review shows; instead of generating radically novel hypotheses, it seems that management scholars are more prone to use novel neuroscientific tools to investigate old management problems. For example, with reference to innovation decisions, the dynamics that lead to innovation are framed within the traditional paradigms such as organizational myopia ( Laureiro-Martínez et al., 2015b ) or managers’ adaptability ( Hannah et al., 2013 ). Interestingly several neuroscientific accounts of innovation-related problems – such as technical reasoning and technological culture (e.g., Osiurak and Reynaud, 2020 ) – are not explored. This type of consideration also applies to the data-enabled decision, where neuroscientific tools are used in an ecological setting that involves data manipulation (e.g., Leger et al., 2014 measured electrodermal activity during the use of an enterprise resource planning ERP system). Interestingly, despite their neuroscientific claims, such contributions seem to ignore a relevant tradition of neuroscientific evidence dealing with related problems (such as manipulating numerical magnitudes and formats, e.g., Kadosh and Walsh, 2009 ). Generally speaking, management scholars are more and more prone to neuroscientific investigations. Still, their hypotheses do not seem to be well-informed by the art of cognitive neuroscience and its related debates.

However, if we think that moving from consolidated neuroscientific evidence and debates can be the solution to a solid generation of hypotheses in management studies, we are wrong. Cognitive neuroscience is a dynamic domain characterized by different theories and views of the brain that are often incommensurable. This is particularly true for affect and cognition that, far from being distinct domains, have been shown entangled as cognition is affectively modulated ( Damasio, 1994 ; Adolphs and Damasio, 2001 ). What should guide future research is the awareness, informed by neuroscientific evidence, that the interplay between affect and cognition could be radically different from what a folk approach could suggest.

In terms of practical implications, managers should take into consideration that their decisions are the concurring product of affective and cognitive influences, with the former having an initial (but not exclusive) role. In this regard, decision makers’ course of action can be regulated acting on the perceived affective state; e.g., decision makers interested in enhancing accurate analyses for a choice should consider planning them after recognizing the dramatic impact that a wrong decision may have – this can be done by referring to the “pre-mortem” technique of Klein (2007) aimed at discovering why a project may fail – so as to insert a negative mood. From what has been said, it also suggested investigating the emotional side (by using, for example, the Positive and Negative Affect Schedule questionnaire) of potential collaborators – at all levels – for a complete evaluation of their decision-making processes. Yet, as advanced many times in this study, one of the biggest limitations of the empirical studies in this field lays in the scarce possibility to accomplish ecological testing of the neurobehavioral processes that shape managers’ decision-making choices. Consequently, this has prompted scholars to rely mainly on laboratory experiments or non-representative populations reducing the validity – given the absence of important stressors (e.g., the awareness that their choices, being taken in the laboratory, will not affect the safety or stability of their organization) – and, in turn, the practical utility of the resulting insights. Therefore, in addition to having a higher confidence toward neuroscience, as well as an improved availability of the instruments belonging to this scientific field, it is desirable to reach greater synergy between scholars and practitioners in order to produce more complete, trustworthy, and meaningful understandings of the real neurobehavioral processes that affect managerial decision-making outcomes, thus resulting in a mutual win. Doing that ensures following the recommendations by Powell (2011) in using neuroscience as the mean to explore behavioral assumptions of managerial decision making, reinforcing, in turn, behavioral strategy research ( Powell et al., 2011 ).

Data Availability Statement

Author contributions.

MC and APM equally contributed to section introduction. APM contributed to the sections: neuroscience in management and organization studies, and ethical decisions. MC contributed to the sections: affect and cognition in decision making, methodology, data-enabled decisions, and discussion. PG contributed to sections: affect and cognition in neuroscience, results, innovation decisions, and limitations of the research field and future research. AM contributed to the section: an agenda for future research. MC and AM equally contributed to the section implications and conclusion. All authors approved the submitted version.

Conflict of Interest

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

Publisher’s Note

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

Adapted from Lieberman (2007) . Social cognitive neuroscience: A review of core processes. Annual Review of Psychology , 58 , 259–289.

1 This definition is taken from the Organizational Neuroscience interest group born within the Academy of Management ( https://neu.aom.org/home ).

2 fMRI and EEG are two popular non-invasive techniques used in medical sciences to analyze brain responses to specific stimuli evoked by sensory, motor or cognitive tasks in controlled environments. Specifically, EEG uses electrodes to quantify the dynamics of the global neuronal activation with sub-millisecond resolution, while fMRI, by the means of a magnetic resonance machine, produces images of local changes in cerebral blood oxygenation, which are indirect consequences of neural activity. Therefore, given this complementarity – direct vs. indirect analysis of neuronal activation – fMRI and EEG techniques are often used simultaneously.

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Theoretical and managerial implications

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Overview : Chapter 7.1 provides the theoretical implications and resulting propositions of this study, based on the preceding historical analysis, as well as the synthesis (provided in the previous chapter) that re-evaluated innovation in current-day China from the historical perspective. In Chapter 7.2, the implications for management are outlined, based on the historical analysis and chapter 4 outlining the practical relevance of the present study.

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Thraen, J.J. (2016). Theoretical and managerial implications. In: Mastering Innovation in China. Springer Gabler, Wiesbaden. https://doi.org/10.1007/978-3-658-14556-9_7

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