information sharing problem solving

5 Shared Information Bias

Original content remixed from fema’s guide on group decision making, the noba project, and wikipedia, the free encyclopedia..

Group decision-making is complex. There are certain conditions that make group decision-making more desirable:

The situation is complex.

Consequences are significant.

Commitment and buy-in are important.

There is time for deliberation and consensus building.

When there is sufficient motivation for group decision-making, it is critical to be aware of some of the biases or complexities that group conversation can invite. This chapter briefly reviews Shared Information Bias and the Hidden Profile Task. As you read, try to consider why this bias is important to keep in mind.

One of the advantages of making decisions in groups is the group’s greater access to information. When seeking a solution to a problem, group members can put their ideas on the table and share their knowledge and judgments with each other through discussions. But all too often groups spend much of their discussion time examining shared information —information that two or more group members know in common—rather than unshared information. This shared information bias will result in a bad outcome if something known by only one or two group members is very important.

Researchers have studied this bias using the  hidden profile task . On such tasks, information known to many of the group members suggests that one alternative, say Option A, is best. However, Option B is definitely the better choice, but all the facts that support Option B are only known to individual groups members—they are not common knowledge in the group. As a result, the group will likely spend most of its time reviewing the factors that favor Option A, and never discover any of its drawbacks. In consequence, groups often perform poorly when working on problems with nonobvious solutions that can only be identified by extensive information sharing (Stasser & Titus, 1987).

Sharing information with other group members is associated with group member perceptions of competence, knowledge, and credibility (Wittenbaum & Park, 2001). According to Broderick et al., (2007), information known to only a single member of a group prior to group discussion will be mentioned less often and evaluated less favorably compared with information known to multiple group members prior to a group discussion.This phenomenon describes shared information bias (Baker, 2010). Shared information bias (or the hidden profile problem) is thus a tendency for group members to spend more time and energy discussing information that multiple members are already familiar with (i.e., shared information). Researchers predict poor decision-making can arise when the group does not have access to unshared information for making well-informed decisions. The result of inaccessible unshared information is called hidden profiles. Hidden profiles describe group decision tasks in which different (but correct) possible solutions exist, but no group member detects it based on his or her individual information prior to the discussion (Stasser, 1988).

Although discussing unshared information may be enlightening, groups are often motivated to discuss shared information in order to reach group consensus on some course of action. According to Postmes et al., (2001), when group members are motivated by a desire to reach closure (e.g., a desire imposed by time constraints), their bias for discussing shared information is stronger. However, if members are concerned with making the best decision possible, this bias becomes less salient.

Stewart and Stasser (1998) have asserted that the shared information bias is strongest for group members working on ambiguous, judgment-oriented tasks because their goal is to reach consensual agreement than to distinguish a correct solution.  The shared information bias may also develop during group discussion in response to the interpersonal and psychological needs of individual group members. For example, some group members tend to seek group support for their own personal opinions. This psychological motivation to garner collective acceptance of one’s own initial views has been linked to group preferences for shared information during decision-making activities (Greitemeyer & Schulz-Hardt, 2003; Henningsen & Henningsen, 2003).

Lastly, the nature of the discussion between group members reflects whether biases for shared information will surface. According to Wittenbaum et al. (2004), members are motivated to establish and maintain reputations, to secure tighter bonds, and to compete for success against other group members.  As a result, individuals tend to be selective when disclosing information to other group members.

Focusing on shared information leads teams to make poorer decisions and often to ignore critical information that might help facilitate better decision outcomes. Wittenbaum et al. (1999) examined mutual enhancement during various discussions about job candidates (i.e., interactions between two people). Participant dyads were assigned to one of two conditions. Before meeting to discuss candidate profiles, researchers had dyads in the first condition look at same information about candidates, while the second condition had dyads receive different information. Participants in condition one evaluated both their partner and self as more component and credible.

Teams which engage in shared information bias are often unable to come to the best conclusion because they do not share all relevant information. The shared information bias demonstrates the importances of thoughtful and sustained deliberation. Shared information bias also highlights how we often are unsure what we need to share to help solve the problem. Thus, sharing openly any potentially helpful information is essential if teams expect to succeed. If you think it might be useful–share it with the group!

Avoidance strategies

Several strategies can be employed to reduce group focus on discussing shared information:

• Make effort to spend more time actively discussing collective decisions. Given that group members tend to discuss shared information first,  longer meetings increase likelihood of reviewing unshared information as well.
• Make effort to avoid generalized discussions by increasing the diversity of opinions within the group (Smith, 2008).
• Introduce the discussion of a new topic to avoid returning to previously discussed items among members (Reimer, Reimer, & Hinsz, 2010).
• Avoid time pressure or time constraints that motivate group members to discuss less information (Kelly & Karau, 1999; Bowman & Wittenbaum, 2012).
• Clarify to group members when certain individuals have relevant expertise (Stewart & Stasser, 1995).
• Include more group members who have task-relevant experience (Wittenbaum, 1998).
• Technology (e.g., group decision support systems, GDSS) can also offer group members a way to catalog information that must be discussed. These technological tools (e.g., search engines, databases, computer programs that estimate risk) help facilitate communication between members while structuralizing the group’s decision-making process (Hollingshead, 2001).
• Baker, D. F. (2010). Enhancing group decision making: An exercise to reduce shared information bias. Journal of Management Education , 34 , 249-279.
• Brodbeck, F.C., Kerschreiter, R., Mojzisch, A., & Schulz-Hardt, S. (2007). Group decision making under conditions of distributed knowledge: The information asymmetries model. Academy of Management Review, 32 , 459-479.
• Hollingshead, A. B. (2001). Cognitive interdependence and convergent expectations in transactive memory. Journal of Personality and Social Psychology , 8 , 1080-1089. doi: 10.1037/0022-3514.81.6.1080
• Stasser, G. (1988). Computer simulation as a research tool: The DISCUSS model of group decision making. Journal of Experimental Social Psychology, 24 , 393–422.
• Stasser, G., & Titus, W. (1985). Pooling of unshared information in group decision making: Biased information sampling during discussion. Journal of Personality and Social Psychology, 48, 1467–1478
• Wittenbaum, G. M., Hubbell, A. P., & Zuckerman, C. (1999). Mutual enhancement: Toward an understanding of the collective preference for shared information. Journal of Personality and Social Psychology , 77 , 967-978. doi: 10.1037/0022-3514.77.5.967
• Wittenbaum, G. M., & Park, E. S. (2001). The Collective Preference for Shared Information. Current Directions in Psychological Science , 10 , 70–73. https://doi.org/10.1111/1467-8721.00118

information that two or more group members know in common

A complex group problem in which one (or more) member possesses unique information which can aid the other group members in solving the problem.

a tendency for group members to spend more time and energy discussing information that multiple members are already familiar with (i.e., shared information)

Shared Information Bias Copyright © 2021 by Cameron W. Piercy, Ph.D. is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License , except where otherwise noted.

Share This Book

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Published: 18 June 2021

The benefits of group-learning and information-sharing

• Kate Peysner 1  

BDJ Team volume  8 ,  pages 8–10 ( 2021 ) Cite this article

958 Accesses

1 Altmetric

Metrics details

You have full access to this article via your institution.

By reader panellist Kate Peysner , a dental hygienist and therapist and a clinical tutor at the University of Sheffield

©Vasyl Dolmatov / iStock / Getty Images Plus

Many, if not all of us within the dental team, will have some prior experience of group-learning and information-sharing. Be that from vocational training, to in-house mandatory training, to staff meetings and beyond. Joining two or more members of a team, cohort, or profession together to disseminate knowledge is a multi-faceted method which harnesses time-efficiency, analysis and reflection, and the chance to learn from one another. Working with others to pool ideas enables participants to become cognisant of problems from different perspectives. Additionally, by combining a variety of skills and expertise, tasks may be attempted that could not be accomplished by an individual and more complex and larger scale problems tackled.

The General Dental Council (GDC) previously acknowledged these types of interactions in their various forms by allowing for submission of these into individuals' CPD cycles as 'non-verifiable CPD'. Reading a dental journal, or participation in a discussion in a professional online forum, for example, were exactly the types of informal interactions taking place then, just as they do now, informing practice and, not least, providing a network of support, be that theoretical or practical. The advent of the Enhanced CPD Scheme 2018 removed formal recording of non-verifiable CPD; however, the GDC encourages continuation of these types of activity if they contribute to individuals' personal development plans (PDPs) ( Table 1 ).

Types of group-learning and information-sharing

Information sharing takes place frequently, often daily in practice through written and verbal notices, emails, guidelines, instructions, and informally over lunchtime discussions and online forums, etc. Engaging with a number of information sources is essential for - amongst others - maintaining a high standard of evidence-based practice, keeping up to date with latest guidelines and standard operating procedures, and fulfilling health and safety obligations.

Group learning involves engagement in solving problems and making meaning where each person learns autonomously, and through the ways of learning of others, as opposed to 'whole group learning', which is the standard mode of instruction where a teacher leads a class and the group learns as a collective.

Group learning can take a great number of forms. Many people will be familiar with and have participated in small revision or seminar groups at college or university. Learning in this manner, when all aspects of the group dynamics are optimal, is known to increase depth of learning. This may be due to accountability and the opportunity to go over concepts a number of times to gain understanding and alternative perspectives.

Webinars and, increasingly, Zoom-type meetings with participatory elements offer similar shared learning opportunities, though with the ability to switch off both camera and microphone, the impetus to contribute may be lessened.

Getting out of it what you put in: a case study

Sandra is a 43-year-old dental therapist. She works across three different practices over five days. On Monday, practice number one asks her if she can work an extra day next week. She is unable to because she already works full time, however she engages with a forum for therapists on social media and manages to find a practitioner who can provide locum services. On Wednesday, Sandra attends CPR training at her second practice. Afterwards there is a staff meeting to discuss surgery provision for the coming month, and any untoward incidents or complaints. On Friday, Sandra sees a patient at her third practice with an unusual dental complaint. At lunchtime she looks through some back copies of the BDJ as she is sure she has seen an article on this condition. She finds it, and has a discussion with a dentist colleague about it. Together they make a referral to the local hospital for the patient. Sandra tables the case study (omitting all patient identifiers) at her BSDHT Regional Group Meeting the following month.

Working with others to pool ideas enables participants to become cognisant of problems from different perspectives.

Sandra participated in both formal and informal group-learning, plus information-sharing in a number of different circumstances in the week in question. By engaging with others, and conducting personal research, Sandra learned, shared, escalated and problem-solved. She felt supported, worthwhile and diligent, which increased her job satisfaction.

It is clear to see in this case study that the benefits of group-learning and information-sharing are felt not just by the individual, but by colleagues and patients alike.

Toxic collaborators

But what about group situations where one or more individuals are not participating equally, or worse, are deliberately sabotaging efforts to move forward with the task in hand?

Known as 'toxic collaborators', these individuals have the ability to undermine all of the work of those determined to achieve success. Modern platforms such as Zoom present new challenges by allowing participants licence to sit as silent partners simply by the click of a button. If you have ever presented a live online session of any kind, you will know how dispiriting it is to be greeted by silence at the point that you require input in order to progress. Evaluation and assessment forms are one way of validating participation by checking understanding, however this may be a rather rudimentary measure unreflective of the whole potential of learning that might have taken place if collaboration had occurred.

The dynamics of an effective working group need to include a willingness to participate equally, an ability to overcome conflict, team-working skills and dedication to the task. Often a leader needs to be elected if the group is not formally led in order to avoid domination of the task by individual needs. Equally, the needs of the group must not dominate or overshadow an individual's needs as, if by addressing these, group learning and perspective is also enhanced.

Wider challenges to group working can include cultural differences, motivational differences, varying learning styles and logistics - how will a consensus be reached? Has enough time been allocated? What is the broadband connection like? However, these challenges can also be beneficial when those involved have the skills to turn them into positive aspects of the group-work experience. Development of accountability, leadership, communication and negotiation skills adds to personal satisfaction and team spirit. When things go well, naturally we want to return to that setting for further collaboration.

Formalising informal group-learning and information-sharing sessions

When removing 'unverifiable CPD', the GDC installed the ability to make verifiable those activities undertaken that enhance knowledge and evidence-based practice according to your field/s of practice. The CPD activity must align with one or more GDC development or learning outcome(s) ( Table 2 ).

As an example, Sandra, who we met previously, would have been able to collect the following verifiable CPD - providing she had planned in advance - for the sessions detailed in Table 3 . Prior planning needn't be arduous but should involve outlining the development outcomes you wish to and expect to cover and compiling [or accessing a pre-written] evaluation form in order to reflect on the efficacy of the learning that has taken place.

Networking & support mechanisms

With all this focus on qualifying the time we spend collaborating with CPD points, it would be completely remiss not to acknowledge the plentiful networking opportunities that such meetings provide. Connecting with team members from outside of your own work environment, be that locally or nationally, can provide the basis for future employment, locum cover, equipment recommendations - the list goes on. Working in often quite isolated environments (how often do you get to leave your surgery and communicate with other colleagues during a session?), or transient settings can be deleterious to one's sense of inclusion, and for this reason engaging with others of the same profession, or likewise those of the wider dental team, is paramount.

Not least, sharing and communicating with others can prove to be an immense source of information and confidence-boosting support as and when it is required.

Useful resources

Inner Drive. 10 advantages and disadvantages of group work in the classroom. Available at: https://blog.innerdrive.co.uk/advantages-disadvantages-group-work (accessed June 2021).

Lore Central. Advantages and disadvantages of group work. Available at: https://www.lorecentral.org/2019/10/advantages-and-disadvantages-of-group-work.html (accessed June 2021).

General Dental Council. Enhanced CPD scheme 2018. Available at: https://www.gdc-uk.org/education-cpd/cpd/enhanced-cpd-scheme-2018 (accessed June 2021).

Jackson D, Hickman L D, Power T et al. Small group learning: graduate health students' views of challenges and benefits. Contemp Nurse 2014; 48: 117-128.

Author information

Authors and affiliations.

Kate Peysner

You can also search for this author in PubMed   Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article.

Peysner, K. The benefits of group-learning and information-sharing. BDJ Team 8 , 8–10 (2021). https://doi.org/10.1038/s41407-021-0653-5

Download citation

Published : 18 June 2021

Issue Date : June 2021

DOI : https://doi.org/10.1038/s41407-021-0653-5

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Be your organization's hero.

Encourage collaboration and teamwork with a recognition program that is effective and enjoyable! Book a demo now to take advantage of some incredible offers!

How to Share Information with Team Members Effectively

Learn about the importance of information sharing in the workplace and discover effective strategies for exchanging knowledge

Press the button to generate random icebreaker questions.

There are 300 more icebreaker questions at the bottom of the article.

In today's fast-paced business environment, effective communication is vital for the success of any organization. One critical aspect of this communication is information sharing. Sharing relevant insights, data, or knowledge can help team members and departments achieve their goals, improve their performance, and foster a culture of collaboration and teamwork. In this article, we will discuss the importance of sharing information and the different types of communication methods organizations can use to implement effective information sharing. We will also explore the five ways of sharing information in the workplace and how to create a culture of trust and transparency that encourages effective communication and collaboration.

What are the types of information sharing?

There is a vast range of communication methods that organizations can use to share information. These include face-to-face conversations, instant messaging services, email, video conferencing, phone calls, and even social media. In addition, many companies use a company wiki, which is an online platform that serves as a central repository of information that can be accessed by all team members.

Why is sharing intelligence important?

Sharing intelligence, or the act of disclosing information that may be critical to the organization's future success, is particularly important. It can help to identify potential risks or threats that may be lurking in the background and ensure that the organization is prepared to address them. It can also help to uncover opportunities for growth and innovation that might otherwise have gone unnoticed.

What is a synonym for sharing information?

Sharing information is also commonly referred to as knowledge sharing. It involves the exchange of insights, expertise, resources and ideas that can help team members learn from one another and improve their performance. It is a crucial component of building a culture of continuous learning and development.

What is the meaning of share information?

To share information means to communicate relevant information with others in a way that is clear, concise, and easy to understand. for example: It can involve conveying data, insights, or knowledge that can help team members make better decisions, solve problems, or improve their performance.

What is sharing information in communication?

Sharing information in communication refers to the act of exchanging relevant information between team members, departments, or even organizations. It involves conveying valuable insights, data, or knowledge that can help the group or organization achieve its goals and improve its performance.

What is an act of sharing information?

An act of sharing information involves communicating relevant information to others in a way that is clear, concise, and easy to understand. It can involve sharing insights, expertise, and ideas that can help team members learn from one another and improve their performance. This act can be done through various communication methods such as face-to-face conversations, instant messaging, email, video conferencing, or company wikis.

What is meant by information sharing?

Information sharing refers to the process of exchanging relevant information between team members, departments government agencies, or even organizations. It can involve conveying data, insights, or knowledge that can help team members make better decisions, solve problems, or improve their performance. The goal of information sharing is to create a culture of collaboration and teamwork that fosters continuous improvement and promotes the well-being of the organization.

What are the different types of information sharing?

There are many different types of information sharing that organizations can use depending on their specific needs and goals. Some of the most common types include sharing knowledge, best practices, lessons learned, project updates, and performance metrics. Each type of information sharing has its unique benefits and can help team members collaborate effectively and achieve their objectives.

Why is information sharing so important?

Information sharing is critical for the success of any organization. It helps to create a feedback loop that fosters continuous improvement and promotes the well-being of the entire organization. It also helps to build trust and transparency, which are crucial for creating a positive work environment. Effective communication and information sharing can lead to increased productivity, higher employee engagement, and better overall performance.

Information sharing is a crucial aspect of effective communication in any workplace. It involves exchanging relevant information between team members, departments, or even organizations in a way that is clear, concise, and easy to understand. The act of sharing information can be done through various communication methods such as face-to-face conversations, instant messaging, email, video conferencing, or company wikis. Information sharing is essential for creating a feedback loop that fosters continuous improvement, promoting transparency and accountability, and building trust and collaboration. By implementing effective communication strategies and encouraging information sharing, organizations can improve their performance, increase productivity, and create a positive work environment that promotes the well-being of their team members.

What are the five ways of sharing information?

The five ways of sharing information in the workplace are:

• Verbal communication: This involves face-to-face conversations, meetings, and presentations.
• Written communication: This involves emails, memos, reports, and other forms of written communication.
• Visual communication: This involves the use of graphs, charts, diagrams, and other visual aids to convey information.
• Digital communication: This involves the use of digital tools such as instant messaging, video conferencing, and social media to communicate.
• Knowledge management: This involves the use of tools such as company wikis, databases, and other knowledge management systems to share information and knowledge.

Sharing information refers to the act of exchanging relevant information between team members, departments, or even organizations. It can involve conveying data, insights, or knowledge that can help team members make better decisions, solve problems, or improve their performance. The goal and importance of sharing information is to create a culture of collaboration and teamwork that fosters continuous improvement and promotes the well-being of the organization.

Sharing information in communication refers to the act of exchanging relevant information between team members, departments, or even organizations. It can involve conveying data, insights, or knowledge that can help team members make better decisions, solve problems, or improve their performance. Effective communication and information sharing can lead to increased productivity, higher employee engagement, and better overall business performance.

Effective information sharing requires clear communication, active listening, and a willingness to collaborate and learn from one another. It is important to create a culture of trust, transparency, and openness that encourages team members to share their ideas, feedback, and concerns without fear of judgment or reprisal.

To ensure effective information sharing, organizations should also invest in knowledge management systems that enable team members to access and contribute to a centralized database of information. This can help to capture and share valuable insights, best practices, and lessons learned, which can improve performance and promote innovation.

In summary, information sharing is critical for effective communication, collaboration, and performance in the workplace. By fostering a culture of trust, transparency, and openness, and investing in the appropriate communication and knowledge management tools, organizations can reap the benefits of improved productivity, engagement, and well-being.

Start your FREE workflow automation today!

Tour Assembly now with no credit card or – book a demo

Browse our Free Employee Recognition Guide

Get the foundational knowledge on creating an employee recognition program that boosts employee engagement and helps them feel valued.

Latest articles

Top Performer Award: Celebrating the Pinnacle of Achievement

Explore the value and impact of the Top Performer Award and how to use it to improve productivity in your workplace.

Employee Milestones and the Significance of Long-term Recognition

Celebrate long-term employees. Discover how recognizing years of service boosts morale and employee engagement.

• Search Menu
• Browse content in Arts and Humanities
• Browse content in Archaeology
• Anglo-Saxon and Medieval Archaeology
• Archaeological Methodology and Techniques
• Archaeology by Region
• Archaeology of Religion
• Archaeology of Trade and Exchange
• Biblical Archaeology
• Contemporary and Public Archaeology
• Environmental Archaeology
• Historical Archaeology
• History and Theory of Archaeology
• Industrial Archaeology
• Landscape Archaeology
• Mortuary Archaeology
• Prehistoric Archaeology
• Underwater Archaeology
• Urban Archaeology
• Zooarchaeology
• Browse content in Architecture
• Architectural Structure and Design
• History of Architecture
• Residential and Domestic Buildings
• Theory of Architecture
• Browse content in Art
• Art Subjects and Themes
• History of Art
• Industrial and Commercial Art
• Theory of Art
• Biographical Studies
• Byzantine Studies
• Browse content in Classical Studies
• Classical History
• Classical Philosophy
• Classical Mythology
• Classical Literature
• Classical Reception
• Classical Art and Architecture
• Classical Oratory and Rhetoric
• Greek and Roman Papyrology
• Greek and Roman Epigraphy
• Greek and Roman Law
• Greek and Roman Archaeology
• Late Antiquity
• Religion in the Ancient World
• Digital Humanities
• Browse content in History
• Colonialism and Imperialism
• Diplomatic History
• Environmental History
• Genealogy, Heraldry, Names, and Honours
• Genocide and Ethnic Cleansing
• Historical Geography
• History by Period
• History of Emotions
• History of Agriculture
• History of Education
• History of Gender and Sexuality
• Industrial History
• Intellectual History
• International History
• Labour History
• Legal and Constitutional History
• Local and Family History
• Maritime History
• Military History
• National Liberation and Post-Colonialism
• Oral History
• Political History
• Public History
• Regional and National History
• Revolutions and Rebellions
• Slavery and Abolition of Slavery
• Social and Cultural History
• Theory, Methods, and Historiography
• Urban History
• World History
• Browse content in Language Teaching and Learning
• Language Learning (Specific Skills)
• Language Teaching Theory and Methods
• Browse content in Linguistics
• Applied Linguistics
• Cognitive Linguistics
• Computational Linguistics
• Forensic Linguistics
• Grammar, Syntax and Morphology
• Historical and Diachronic Linguistics
• History of English
• Language Evolution
• Language Reference
• Language Acquisition
• Language Variation
• Language Families
• Lexicography
• Linguistic Anthropology
• Linguistic Theories
• Linguistic Typology
• Phonetics and Phonology
• Psycholinguistics
• Sociolinguistics
• Translation and Interpretation
• Writing Systems
• Browse content in Literature
• Bibliography
• Children's Literature Studies
• Literary Studies (Romanticism)
• Literary Studies (American)
• Literary Studies (Asian)
• Literary Studies (European)
• Literary Studies (Eco-criticism)
• Literary Studies (Modernism)
• Literary Studies - World
• Literary Studies (1500 to 1800)
• Literary Studies (19th Century)
• Literary Studies (20th Century onwards)
• Literary Studies (African American Literature)
• Literary Studies (British and Irish)
• Literary Studies (Early and Medieval)
• Literary Studies (Fiction, Novelists, and Prose Writers)
• Literary Studies (Gender Studies)
• Literary Studies (Graphic Novels)
• Literary Studies (History of the Book)
• Literary Studies (Plays and Playwrights)
• Literary Studies (Poetry and Poets)
• Literary Studies (Postcolonial Literature)
• Literary Studies (Queer Studies)
• Literary Studies (Science Fiction)
• Literary Studies (Travel Literature)
• Literary Studies (War Literature)
• Literary Studies (Women's Writing)
• Literary Theory and Cultural Studies
• Mythology and Folklore
• Shakespeare Studies and Criticism
• Browse content in Media Studies
• Browse content in Music
• Applied Music
• Dance and Music
• Ethics in Music
• Ethnomusicology
• Gender and Sexuality in Music
• Medicine and Music
• Music Cultures
• Music and Media
• Music and Religion
• Music and Culture
• Music Education and Pedagogy
• Music Theory and Analysis
• Musical Scores, Lyrics, and Libretti
• Musical Structures, Styles, and Techniques
• Musicology and Music History
• Performance Practice and Studies
• Race and Ethnicity in Music
• Sound Studies
• Browse content in Performing Arts
• Browse content in Philosophy
• Aesthetics and Philosophy of Art
• Epistemology
• Feminist Philosophy
• History of Western Philosophy
• Metaphysics
• Moral Philosophy
• Non-Western Philosophy
• Philosophy of Language
• Philosophy of Mind
• Philosophy of Perception
• Philosophy of Science
• Philosophy of Action
• Philosophy of Law
• Philosophy of Religion
• Philosophy of Mathematics and Logic
• Practical Ethics
• Social and Political Philosophy
• Browse content in Religion
• Biblical Studies
• Christianity
• East Asian Religions
• History of Religion
• Judaism and Jewish Studies
• Qumran Studies
• Religion and Education
• Religion and Health
• Religion and Politics
• Religion and Science
• Religion and Law
• Religion and Art, Literature, and Music
• Religious Studies
• Browse content in Society and Culture
• Cookery, Food, and Drink
• Cultural Studies
• Customs and Traditions
• Ethical Issues and Debates
• Hobbies, Games, Arts and Crafts
• Lifestyle, Home, and Garden
• Natural world, Country Life, and Pets
• Popular Beliefs and Controversial Knowledge
• Sports and Outdoor Recreation
• Technology and Society
• Travel and Holiday
• Visual Culture
• Browse content in Law
• Arbitration
• Browse content in Company and Commercial Law
• Commercial Law
• Company Law
• Browse content in Comparative Law
• Systems of Law
• Competition Law
• Browse content in Constitutional and Administrative Law
• Government Powers
• Judicial Review
• Local Government Law
• Military and Defence Law
• Parliamentary and Legislative Practice
• Construction Law
• Contract Law
• Browse content in Criminal Law
• Criminal Procedure
• Criminal Evidence Law
• Sentencing and Punishment
• Employment and Labour Law
• Environment and Energy Law
• Browse content in Financial Law
• Banking Law
• Insolvency Law
• History of Law
• Human Rights and Immigration
• Intellectual Property Law
• Browse content in International Law
• Private International Law and Conflict of Laws
• Public International Law
• IT and Communications Law
• Jurisprudence and Philosophy of Law
• Law and Politics
• Law and Society
• Browse content in Legal System and Practice
• Courts and Procedure
• Legal Skills and Practice
• Primary Sources of Law
• Regulation of Legal Profession
• Medical and Healthcare Law
• Browse content in Policing
• Criminal Investigation and Detection
• Police and Security Services
• Police Procedure and Law
• Police Regional Planning
• Browse content in Property Law
• Personal Property Law
• Study and Revision
• Terrorism and National Security Law
• Browse content in Trusts Law
• Wills and Probate or Succession
• Browse content in Medicine and Health
• Browse content in Allied Health Professions
• Arts Therapies
• Clinical Science
• Dietetics and Nutrition
• Occupational Therapy
• Operating Department Practice
• Physiotherapy
• Radiography
• Speech and Language Therapy
• Browse content in Anaesthetics
• General Anaesthesia
• Neuroanaesthesia
• Clinical Neuroscience
• Browse content in Clinical Medicine
• Acute Medicine
• Cardiovascular Medicine
• Clinical Genetics
• Clinical Pharmacology and Therapeutics
• Dermatology
• Endocrinology and Diabetes
• Gastroenterology
• Genito-urinary Medicine
• Geriatric Medicine
• Infectious Diseases
• Medical Toxicology
• Medical Oncology
• Pain Medicine
• Palliative Medicine
• Rehabilitation Medicine
• Respiratory Medicine and Pulmonology
• Rheumatology
• Sleep Medicine
• Sports and Exercise Medicine
• Community Medical Services
• Critical Care
• Emergency Medicine
• Forensic Medicine
• Haematology
• History of Medicine
• Browse content in Medical Skills
• Clinical Skills
• Communication Skills
• Nursing Skills
• Surgical Skills
• Browse content in Medical Dentistry
• Oral and Maxillofacial Surgery
• Paediatric Dentistry
• Restorative Dentistry and Orthodontics
• Surgical Dentistry
• Medical Ethics
• Medical Statistics and Methodology
• Browse content in Neurology
• Clinical Neurophysiology
• Neuropathology
• Nursing Studies
• Browse content in Obstetrics and Gynaecology
• Gynaecology
• Occupational Medicine
• Ophthalmology
• Otolaryngology (ENT)
• Browse content in Paediatrics
• Neonatology
• Browse content in Pathology
• Chemical Pathology
• Clinical Cytogenetics and Molecular Genetics
• Histopathology
• Medical Microbiology and Virology
• Patient Education and Information
• Browse content in Pharmacology
• Psychopharmacology
• Browse content in Popular Health
• Caring for Others
• Complementary and Alternative Medicine
• Self-help and Personal Development
• Browse content in Preclinical Medicine
• Cell Biology
• Molecular Biology and Genetics
• Reproduction, Growth and Development
• Primary Care
• Professional Development in Medicine
• Browse content in Psychiatry
• Addiction Medicine
• Child and Adolescent Psychiatry
• Forensic Psychiatry
• Learning Disabilities
• Old Age Psychiatry
• Psychotherapy
• Browse content in Public Health and Epidemiology
• Epidemiology
• Public Health
• Browse content in Radiology
• Clinical Radiology
• Interventional Radiology
• Nuclear Medicine
• Radiation Oncology
• Reproductive Medicine
• Browse content in Surgery
• Cardiothoracic Surgery
• Gastro-intestinal and Colorectal Surgery
• General Surgery
• Neurosurgery
• Paediatric Surgery
• Peri-operative Care
• Plastic and Reconstructive Surgery
• Surgical Oncology
• Transplant Surgery
• Trauma and Orthopaedic Surgery
• Vascular Surgery
• Browse content in Science and Mathematics
• Browse content in Biological Sciences
• Aquatic Biology
• Biochemistry
• Bioinformatics and Computational Biology
• Developmental Biology
• Ecology and Conservation
• Evolutionary Biology
• Genetics and Genomics
• Microbiology
• Molecular and Cell Biology
• Natural History
• Plant Sciences and Forestry
• Research Methods in Life Sciences
• Structural Biology
• Systems Biology
• Zoology and Animal Sciences
• Browse content in Chemistry
• Analytical Chemistry
• Computational Chemistry
• Crystallography
• Environmental Chemistry
• Industrial Chemistry
• Inorganic Chemistry
• Materials Chemistry
• Medicinal Chemistry
• Mineralogy and Gems
• Organic Chemistry
• Physical Chemistry
• Polymer Chemistry
• Study and Communication Skills in Chemistry
• Theoretical Chemistry
• Browse content in Computer Science
• Artificial Intelligence
• Computer Architecture and Logic Design
• Game Studies
• Human-Computer Interaction
• Mathematical Theory of Computation
• Programming Languages
• Software Engineering
• Systems Analysis and Design
• Virtual Reality
• Browse content in Computing
• Business Applications
• Computer Security
• Computer Games
• Computer Networking and Communications
• Digital Lifestyle
• Graphical and Digital Media Applications
• Operating Systems
• Browse content in Earth Sciences and Geography
• Atmospheric Sciences
• Environmental Geography
• Geology and the Lithosphere
• Maps and Map-making
• Meteorology and Climatology
• Oceanography and Hydrology
• Palaeontology
• Physical Geography and Topography
• Regional Geography
• Soil Science
• Urban Geography
• Browse content in Engineering and Technology
• Agriculture and Farming
• Biological Engineering
• Civil Engineering, Surveying, and Building
• Electronics and Communications Engineering
• Energy Technology
• Engineering (General)
• Environmental Science, Engineering, and Technology
• History of Engineering and Technology
• Mechanical Engineering and Materials
• Technology of Industrial Chemistry
• Transport Technology and Trades
• Browse content in Environmental Science
• Applied Ecology (Environmental Science)
• Conservation of the Environment (Environmental Science)
• Environmental Sustainability
• Environmentalist Thought and Ideology (Environmental Science)
• Management of Land and Natural Resources (Environmental Science)
• Natural Disasters (Environmental Science)
• Nuclear Issues (Environmental Science)
• Pollution and Threats to the Environment (Environmental Science)
• Social Impact of Environmental Issues (Environmental Science)
• History of Science and Technology
• Browse content in Materials Science
• Ceramics and Glasses
• Composite Materials
• Metals, Alloying, and Corrosion
• Nanotechnology
• Browse content in Mathematics
• Applied Mathematics
• Biomathematics and Statistics
• History of Mathematics
• Mathematical Education
• Mathematical Finance
• Mathematical Analysis
• Numerical and Computational Mathematics
• Probability and Statistics
• Pure Mathematics
• Browse content in Neuroscience
• Cognition and Behavioural Neuroscience
• Development of the Nervous System
• Disorders of the Nervous System
• History of Neuroscience
• Invertebrate Neurobiology
• Molecular and Cellular Systems
• Neuroendocrinology and Autonomic Nervous System
• Neuroscientific Techniques
• Sensory and Motor Systems
• Browse content in Physics
• Astronomy and Astrophysics
• Atomic, Molecular, and Optical Physics
• Biological and Medical Physics
• Classical Mechanics
• Computational Physics
• Condensed Matter Physics
• Electromagnetism, Optics, and Acoustics
• History of Physics
• Mathematical and Statistical Physics
• Measurement Science
• Nuclear Physics
• Particles and Fields
• Plasma Physics
• Quantum Physics
• Relativity and Gravitation
• Semiconductor and Mesoscopic Physics
• Browse content in Psychology
• Affective Sciences
• Clinical Psychology
• Cognitive Psychology
• Cognitive Neuroscience
• Criminal and Forensic Psychology
• Developmental Psychology
• Educational Psychology
• Evolutionary Psychology
• Health Psychology
• History and Systems in Psychology
• Music Psychology
• Neuropsychology
• Organizational Psychology
• Psychological Assessment and Testing
• Psychology of Human-Technology Interaction
• Psychology Professional Development and Training
• Research Methods in Psychology
• Social Psychology
• Browse content in Social Sciences
• Browse content in Anthropology
• Anthropology of Religion
• Human Evolution
• Medical Anthropology
• Physical Anthropology
• Regional Anthropology
• Social and Cultural Anthropology
• Theory and Practice of Anthropology
• Browse content in Business and Management
• Business Ethics
• Business Strategy
• Business History
• Business and Technology
• Business and Government
• Business and the Environment
• Comparative Management
• Corporate Governance
• Corporate Social Responsibility
• Entrepreneurship
• Health Management
• Human Resource Management
• Industrial and Employment Relations
• Industry Studies
• Information and Communication Technologies
• International Business
• Knowledge Management
• Management and Management Techniques
• Operations Management
• Organizational Theory and Behaviour
• Pensions and Pension Management
• Public and Nonprofit Management
• Strategic Management
• Supply Chain Management
• Browse content in Criminology and Criminal Justice
• Criminal Justice
• Criminology
• Forms of Crime
• International and Comparative Criminology
• Youth Violence and Juvenile Justice
• Development Studies
• Browse content in Economics
• Agricultural, Environmental, and Natural Resource Economics
• Asian Economics
• Behavioural Finance
• Behavioural Economics and Neuroeconomics
• Econometrics and Mathematical Economics
• Economic History
• Economic Systems
• Economic Methodology
• Economic Development and Growth
• Financial Markets
• Financial Institutions and Services
• General Economics and Teaching
• Health, Education, and Welfare
• History of Economic Thought
• International Economics
• Labour and Demographic Economics
• Law and Economics
• Macroeconomics and Monetary Economics
• Microeconomics
• Public Economics
• Urban, Rural, and Regional Economics
• Welfare Economics
• Browse content in Education
• Adult Education and Continuous Learning
• Care and Counselling of Students
• Early Childhood and Elementary Education
• Educational Equipment and Technology
• Educational Strategies and Policy
• Higher and Further Education
• Organization and Management of Education
• Philosophy and Theory of Education
• Schools Studies
• Secondary Education
• Teaching of a Specific Subject
• Teaching of Specific Groups and Special Educational Needs
• Teaching Skills and Techniques
• Browse content in Environment
• Applied Ecology (Social Science)
• Climate Change
• Conservation of the Environment (Social Science)
• Environmentalist Thought and Ideology (Social Science)
• Natural Disasters (Environment)
• Social Impact of Environmental Issues (Social Science)
• Browse content in Human Geography
• Cultural Geography
• Economic Geography
• Political Geography
• Browse content in Interdisciplinary Studies
• Communication Studies
• Museums, Libraries, and Information Sciences
• Browse content in Politics
• African Politics
• Asian Politics
• Chinese Politics
• Comparative Politics
• Conflict Politics
• Elections and Electoral Studies
• Environmental Politics
• European Union
• Foreign Policy
• Gender and Politics
• Human Rights and Politics
• Indian Politics
• International Relations
• International Organization (Politics)
• International Political Economy
• Irish Politics
• Latin American Politics
• Middle Eastern Politics
• Political Behaviour
• Political Economy
• Political Institutions
• Political Methodology
• Political Communication
• Political Philosophy
• Political Sociology
• Political Theory
• Politics and Law
• Public Policy
• Public Administration
• Quantitative Political Methodology
• Regional Political Studies
• Russian Politics
• Security Studies
• State and Local Government
• UK Politics
• US Politics
• Browse content in Regional and Area Studies
• African Studies
• Asian Studies
• East Asian Studies
• Japanese Studies
• Latin American Studies
• Middle Eastern Studies
• Native American Studies
• Scottish Studies
• Browse content in Research and Information
• Research Methods
• Browse content in Social Work
• Addictions and Substance Misuse
• Adoption and Fostering
• Care of the Elderly
• Child and Adolescent Social Work
• Couple and Family Social Work
• Developmental and Physical Disabilities Social Work
• Direct Practice and Clinical Social Work
• Emergency Services
• Human Behaviour and the Social Environment
• International and Global Issues in Social Work
• Mental and Behavioural Health
• Social Justice and Human Rights
• Social Policy and Advocacy
• Social Work and Crime and Justice
• Social Work Macro Practice
• Social Work Practice Settings
• Social Work Research and Evidence-based Practice
• Welfare and Benefit Systems
• Browse content in Sociology
• Childhood Studies
• Community Development
• Comparative and Historical Sociology
• Economic Sociology
• Gender and Sexuality
• Gerontology and Ageing
• Health, Illness, and Medicine
• Marriage and the Family
• Migration Studies
• Occupations, Professions, and Work
• Organizations
• Population and Demography
• Race and Ethnicity
• Social Theory
• Social Movements and Social Change
• Social Research and Statistics
• Social Stratification, Inequality, and Mobility
• Sociology of Religion
• Sociology of Education
• Sport and Leisure
• Urban and Rural Studies
• Browse content in Warfare and Defence
• Defence Strategy, Planning, and Research
• Land Forces and Warfare
• Military Administration
• Military Life and Institutions
• Naval Forces and Warfare
• Other Warfare and Defence Issues
• Peace Studies and Conflict Resolution
• Weapons and Equipment

• < Previous chapter
• Next chapter >

8 Information Sharing Within Groups in Organizations: Situational and Motivational Influences

Loyola University Chicago, Chicago, IL, USA

• Published: 08 August 2018
• Cite Icon Cite
• Permissions Icon Permissions

Information sharing is essential for learning and performance in groups and organizations. This chapter examines factors that either encourage or discourage information sharing, particularly during group meetings convened for the purpose of solving a problem or making a decision. Such purposes are usually best served when members share with one another the task-relevant information they hold that others in the group do not possess (i.e., uniquely held information). Yet meetings are often dominated by discussion of information that most members already know (i.e., commonly held information), to the relative exclusion of their uniquely held information. We examine in some detail the information sampling process that gives rise to this phenomenon, and we consider a range of situational and motivational factors that moderate it, including features of the information held, the task performed, and the group performing it. Finally, we offer recommendations for how information sharing during group meetings might be improved.

Information sharing—the active communication of what one knows—is enormously important for learning and performance at all levels of analysis in organizations. It can benefit individual employees by facilitating goal-directed behavior and sparking creativity (e.g., Locke & Latham, 2002 ; Zhou, 2008 ). It can benefit groups and teams by improving coordination, problem solving, and decision making ( Lu, Yuan, & McLeod, 2012 ; Mesmer-Magnus & DeChurch, 2009 ; Okhuysen & Bechky, 2009 ). And it can benefit whole organizations by enhancing efficiency, market competitiveness, and overall financial performance (e.g., Gibson, Porath, Benson, & Lawler, 2007 ; Li, Ye, & Sheu, 2014 ; Wu, 2008 ).

Our particular focus in the present chapter is information sharing within groups in organizations. “Groups,” as we will use the term here, are simply small collections of employees who work together in some fashion to achieve an identifiable goal. The information sharing that occurs within groups can take many forms. It can be one-to-one or one-to-many, oral or written, face-to-face or electronically mediated, and synchronous or asynchronous. No matter how it occurs, information sharing is critical for group learning, performance, and adaptation to changing circumstances ( Argote, Gruenfeld, & Naquin, 2001 ; Burke, Stagl, Salas, Pierce, & Kendall, 2006 ; Edmondson, 1999 , 2003 ; Kolbe et al., 2014 ). Given its importance, and in light of the rapid growth during the past half-century in the use of groups and teams to accomplish all manner of organizational objectives (cf. Humphrey & Aime, 2014 ; Weiss & Hoegl, 2015 ), there would seem to be great practical value in understanding the situational and motivational factors that either encourage or discourage information sharing within groups.

We will concern ourselves primarily with the sharing of declarative knowledge, 1 particularly those facts, data, truths, and principles that are directly associated with the task at hand, as these are what can most demonstrably benefit the learning and performance of groups within organizations. Suppositions, speculations, evaluations, and opinions also qualify as declarative knowledge, but only insofar as they convey something about the individuals who hold them. That a company’s chief information officer (CIO) prefers one software vendor over another is, strictly speaking, information about the CIO, not about the vendors in question. Likewise, expressing one’s own attitude or judgment is to share declarative knowledge about one’s self. But such expressions are not our primary emphasis.

All sorts of declarative information might be shared within groups in organizations, from the technical, objective, and strictly task relevant (e.g., the lifting capacity of a particular hydraulic pump) to the highly personal, subjective, and task irrelevant (e.g., regarding a sports team, fragrance, or ex-boss). We focus on the former, but we recognize that the distinction between the two domains is not as sharp as it might seem. It is easy to imagine a piece of just-shared information that, while irrelevant for the task being performed today, will be essential for accomplishing tomorrow’s task, even though at present we may be unaware of what tomorrow’s task will be. Still, we concentrate in this chapter on information that is understood at the moment it is shared to be at least potentially relevant to performing a current work-related task.

Communication is a two-party affair, of course, involving both a sender and receiver. But it is not the full communication episode that is of interest here. Rather, our treatment of information sharing is confined to the sender’s side of the transaction; we focus on those situational and motivational factors that either increase or decrease the probability that a sender will initiate the communication. 2 What, for instance, determines whether or not a group member will speak up during a meeting in order to mention a potentially useful piece of information? Learning in groups depends critically on such actions. Although it depends as well on a range of other factors (e.g., the recipients’ existing state of knowledge and their ability to comprehend the meaning of what was just said), these additional matters are beyond the scope of the present chapter.

Finally, we ignore in this chapter those situations in which people might communicate information inadvertently and without awareness, for example via nonverbal channels during face-to-face interactions (e.g., Bond & DePaulo, 2006 ; Morris et al., 2016 ). Instead, we are concerned exclusively with deliberate information sharing that is intended to benefit group learning and performance. Further, for the most part we will assume that the shared information is true, or is believed to be true, by the person communicating it. We deal only very briefly with the intentional dissemination of misinformation.

The chapter is organized as follows. We begin by examining in detail a key situational factor that influences information sharing and learning when group members meet to discuss a problem or make a decision: the way in which the task-relevant information they hold is initially distributed among them. We consider this first under a set of constraints that require the fewest possible assumptions about member motivation in order to gain an appreciation for the relatively “pure” effect of this important determinant of information sharing within groups. After that, we gradually relax these constraints and consider other factors that are likely to affect information sharing precisely because they do engage various motives. The latter include other features of the information held besides its distribution among members (its importance, interdependence, and relationship to members’ individual solution preferences), features of the task as a whole (the degree to which it requires interdependent action by members, whether it has a demonstrably correct solution, the information load it imposes, the virtuality with which members must interact, and time pressure), and features of the group performing that task (its social and reward structure, member solution preference diversity, and group norms). We also touch briefly on stable individual differences among group members that can influence information sharing (their epistemic motivation, social value orientation, and relevant leader characteristics such as dominance and supportiveness). Finally, we conclude by considering the practical implications of what is known about information sharing, and we suggest several things that can be done to promote information sharing—and so learning—within groups in organizations.

The Insidious Effects of Distributed Information

One place we expect to see a great deal of information sharing and learning in organizations is in group meetings. Indeed, information sharing and learning are two reasons frequently given for holding meetings. Regularly scheduled (e.g., daily, weekly, monthly) meetings are often a venue for the dissemination of information by meeting leaders and for the formal reporting of information by attendees. But meetings also provide an opportunity for more spontaneous forms of information sharing, particularly when they are convened for the purpose of solving a problem or making a decision. 3 Such meetings might be arranged in advance and have a formal agenda, or they might occur spontaneously and be quite informal (e.g., three engineers at a construction site having an impromptu conversation about how best to solve a recently discovered technical problem). Whether formally scheduled or spontaneous, meetings generally take place under the assumption that more learning and better results can be obtained by pooling the diverse information and perspectives of those in attendance. Indeed, meeting attendees from different functional areas within an organization (e.g., R&D, manufacturing, marketing) are sometimes invited precisely because they are presumed to have valuable information bearing on the matter at hand that others do not possess. But even when they are all from the same functional area, differences in work roles, personal background, and day-to-day experience often mean that attendees may hold useful information that others in the group do not have. This uniquely held information stands in contrast to the information that most or all attendees were aware of prior to the meeting. We refer to the latter as commonly held information . Thus, at the outset of a meeting, attendees may have a mix of unique and commonly held information relevant to the issue at hand. The hope is that during the course of their discussion they will share what they know, particularly their uniquely held information, and that by doing so the group as a whole will become better informed and so more able to find an optimal solution to their problem or to make the best possible decision.

Unfortunately, research indicates that this hope, especially with regard to the sharing of uniquely held information, often goes unrealized. The particular way in which the information bearing on an issue is distributed among attendees prior to their meeting—what exactly they share in common versus hold uniquely—turns out to be an important feature that can dramatically impact what subsequently gets discussed during that meeting, what is learned, and ultimately what course of action is taken. Specifically, other things being equal, uniquely held information is (a) less likely than commonly held information to be shared at all, and (b) when it is shared, it often surfaces later and is discussed less thoroughly than is the case for commonly held information. Both tendencies put the decisional implications of uniquely held information at a distinct disadvantage relative to those of information that attendees hold in common. We consider these two phenomena in some detail, beginning with a description of a methodology that is often used to investigate information sharing within groups.

The Prediscussion Distribution of Common and Uniquely Held Information

To study the sharing of common and uniquely held information during group problem-solving and decision-making meetings, it is essential to know what information members held in common prior to their meeting and what information they held uniquely. The most effective way to do this is to conduct a controlled experiment in which participants are asked to discuss and decide as a group how to resolve some issue, and to supply them individually with information about that issue in advance of their discussion. In this way, each participant can be given some information that every other member of the group also receives, and some that he or she alone receives. Importantly, however, while no group member may individually hold all of the relevant information about the issue to be decided, all of that information nevertheless is given to the group as a whole. Thus, what is being manipulated is simply how the task-relevant information is distributed among group members prior to discussion.

Table 8.1 illustrates three possible ways in which task-relevant information might be distributed that conform to these requirements. These distributions, or profiles, all represent a situation in which a three-person group must decide between two choice alternatives, A and B , and where there are 6 pieces of information that favor A , and 12 that favor B . Let us suppose that all 18 pieces of information are equally valid, important, and memorable. Thus, what distinguishes the two choice alternatives is simply the amount of information favoring them, with two thirds of it favoring B , and one third favoring A . As such, alternative B would seem a better choice than A , because it is supported by more of the available evidence.

Note : Each table entry represents one piece of decision-relevant information. Letters identify the decision alternative, A or B , supported by the information, and subscripts identify separate pieces of information. Thus, within each profile there are exactly 6 pieces of information favoring alternative A and 12 pieces favoring B . Based on the weight of the evidence, alternative B is therefore the better choice alternative in each case.

Consider first the “ Manifest ” profile shown in the left-hand panel of that table. Here we see that every group member is given 12 pieces of information in advance of the group’s meeting. However, they do not receive all of the same information. Rather, each member receives 9 pieces of information that every other member also receives (A 1 , A 2 , A 3 , B 1 , B 2 , B 3 , B 4 , B 5 , and B 6 ), but a different 3 pieces of the remaining information (A 4 , A 5 , A 6 , B 7 , B 8 , B 9 , B 10 , B 11 , and B 12 ). Thus, as intended, each possesses a mix of common and uniquely held information prior to the start of discussion. Even so, the pattern of support for the two choice alternatives that is evident in the information received by each member is identical to the pattern that exists in the full set of information: two thirds of it favors alternative B , whereas only one third favors A . Consequently, even before discussion begins we should expect each member to prefer the choice alternative that objectively is best.

Next consider the “Hidden (Weak)” profile shown in the center panel of Table 8.1 . Again, we see that each group member is given 12 pieces of information. In this case, however, the information is distributed in such a way that the superiority of the best choice alternative, B , should not be apparent to members prior to discussion. This is because half of the information they each receive favors alternative A , while half favors B . This is accomplished by giving all of the information favoring A , but only one quarter of the information favoring B , to all of the group members. The rest of the information favoring B is divided among them. Thus, each member once again possesses a mix of common and uniquely held information. But in this case, prior to the start of discussion we should expect each member to be indifferent to the two choice alternatives, because the strength of the objectively best alternative, B , has been hidden from them.

Finally, consider the “Hidden (Strong)” profile shown in the right-hand panel of the table. Here too the information is distributed in a way that hides the superiority of the best choice alternative. But in this case the information held by each member prior to discussion actually favors the poorer of the two choice alternatives, alternative A . This occurs because all of the information that favors A , but none of the information that favors B , is distributed to all group members, while each of them receives a different third of the information that favors B . As a result, more of the information they each hold prior to the start of their meeting favors A than favors B , which is opposite to what exists in the full set of information. Thus, not only is the superiority of the better choice alternative, B , hidden from members prior to discussion, we should expect them each to prefer the alternative that objectively is inferior, alternative A . 4

Thus, the three information distribution profiles shown in Table 8.1 are similar in some respects but different in others. They are similar in that they all involve the same total amount of information favoring the two choice alternatives, with some of that information held in common by all members and some held uniquely. But they differ with respect to whether, prior to discussion, members are apt to prefer the alternative that objectively is best (the manifest profile) and, if not, whether they will simply be indifferent to the two alternatives (the weak hidden profile) or actually prefer the one that is inferior (the strong hidden profile). We consider next both the discussion and decisional consequences of these similarities and differences.

The Discussion Advantage of Common Relative to Uniquely Held Information

The uneven distribution of decision-relevant information among group members has two important implications for the content of group problem-solving and decision-making discussions. The first is that during discussion, group members will often share significantly more of their commonly held than uniquely held information (e.g., Brodbeck, Kerschreiter, Mojzisch, & Schulz-Hardt, 2007 ; Lu et al., 2012 ; Mesmer-Magnus, & DeChurch, 2009 ; Reimer, Reimer, & Czienskowski, 2010 ; Sohrab, Waller, & Kaplan, 2015 ). The magnitude of this counterintuitive effect is quite large. For example, Lu et al. (2012) conducted a meta-analytic review of 33 independent studies involving 5,885 participants and 1,703 different groups, and found that the proportion of commonly held information that was shared during group decision-making discussions exceeded that of uniquely held information by an average of 2 standard deviations ( d = 2.03). Moreover, they found that this effect did not vary by profile type.

The second implication that an uneven distribution of information has for group decision-making discussions is that groups will often share their commonly held information earlier than their uniquely held information ( Larson, 1997 ; Larson, Christensen, Abbott, & Franz, 1996 ; Larson, Christensen, Franz, & Abbott, 1998 ; Larson, Foster-Fishman, & Franz, 1998 ; Larson, Foster-Fishman, & Keys, 1994 ). As a consequence, their commonly held information frequently dominates the early portions of a meeting, with much of their uniquely held information—to the extent that it is shared at all—being discussed later.

These two information-sharing tendencies are well illustrated in a pair of studies of medical decision-making teams conducted by Larson and his colleagues ( Larson et al., 1996 , Larson, Christensen, et al. 1998 ; see also Christensen, Larson, Abbott, Ardolino, Franz, & Pfeiffer, 2000 ). In both studies, three-person physician teams met twice to diagnose two complex patient cases. Prior to discussing each case, the physicians each privately viewed one of three video recordings of an interview with the patient. During the interview the patient either displayed or verbally reported relevant signs, symptoms, and other diagnostic information. However, the three interviews differed with respect to the specific information they contained. Each contained some information that was common to all three interviews, and some that was unique to that one interview alone. Importantly, however, every piece of case-relevant information was present in at least one of the interviews. Further, care was taken in these studies to counterbalance across teams which specific pieces of information were common and which were unique. In this way, conclusions could be drawn about information sharing that were not confounded by the specific content of that information.

After privately viewing one of these patient interviews, the three physicians in each team met in a conference room to discuss each case and render their diagnosis. The content of their discussions was recorded and later analyzed. The results reported by Larson et al. (1998) are typical, and they are reproduced in Figure 8.1 . The X-axis in that figure represents the serial order in which case-relevant information was first mentioned (i.e., the first, second, third, etc., piece of information shared). Each data point indicates the percentage of teams in which either commonly held or (separately) uniquely held information was shared at the indicated item serial position. As can be seen, the percentage of teams in which commonly held information was shared was high initially but steadily declined, whereas the percentage in which uniquely held information was shared followed the opposite trend. 5 Further, all of these teams ended their discussions prematurely, before sharing with one another all of the case-relevant information they had seen. As a result, while they shared on average 81% of the information that they held in common prior to discussion, they shared only 51% of the information they held uniquely. Finally, the amount of uniquely held (but not commonly held) information that was shared during discussion predicted the diagnostic accuracy of these teams: Sharing more uniquely held information resulted in more accurate diagnoses, whereas sharing less resulted in less accurate diagnoses.

Percentage of teams sharing common and uniquely held information at each item serial position during discussion (from Larson, Christensen, Franz, & Abbott, 1998 ).

It is tempting to imagine that these results reflect the operation of underlying motivational forces that somehow led members preferentially to share the information that they held in common with others rather than the information they held uniquely. However, motivation cannot explain these findings. This is because although the physicians in these teams knew that they had seen different interviews with the patients, and so were likely to have received somewhat different information about each case, none of them knew in advance which of the information they saw was common to all of the interviews and which was unique to their interview alone. Thus, because they had no way of knowing which items of information they held were also held by others, they also had no way of preferentially sharing that information.

Rather, the tendency of teams in these studies to share more of their common than uniquely held information, and to share their commonly held information first, is most parsimoniously explained as a sampling bias that resulted from there having been three members who potentially could have recalled and mentioned any given piece of commonly held information, but only one member who potentially could have recalled and mentioned a piece of uniquely held information. This notion was first suggested by Stasser and Titus (1985) , and then formalized by Stasser and Titus (1987) in what has become known as the collective information sampling (CIS) model. Their model is expressed as follows:

where n is the number of group members who were exposed to a given piece of information prior to discussion, p(R ) is the probability that any one member who was exposed to that information can recall and mention it during discussion, and p(S ) is the resultant probability that that information will actually be shared. For example, if the members of a three-person group each have a p(R ) = .75 probability of recalling during discussion a given piece of decision-relevant information to which they were all exposed beforehand (i.e., the information is commonly held, so n = 3), then there is a p(S ) = .98 probability that that information will be shared. By contrast, if just one member was exposed to that information beforehand (i.e., the information is uniquely held, so n = 1), then there is only a p(S ) = .75 probability that it will be shared. Thus, the CIS model predicts that, other things being equal, the more members there are who encountered a given piece of information prior to discussion, the greater the likelihood that that information will actually be shared during discussion. Importantly, the model makes this prediction without relying on any assumptions about motivation other than that members will share during discussion whatever task-relevant information they are able to recall.

Despite its simplicity, the CIS model has been relatively successful in predicting the overall tendency of groups in controlled experimental studies to share more of their common than uniquely held information (but see Reimer, Reimer, & Czienskowski, 2010 ). However, as expressed earlier, the model is silent about the parallel observation that common information tends to be shared earlier than uniquely held information. But this latter tendency is predicted by the ideas underlying the CIS model if account is taken of the sequential nature of group discussion.

Such an account was first suggested by Larson et al. (1994) , who developed a dynamic version of the information sampling model that focuses on the number of opportunities a group collectively has at any given moment during discussion to sample (and so share) decision-relevant information that has not already been mentioned. 6 Importantly, it considers how these opportunities change over the course of discussion as more and more information is brought to light. The number of opportunities that a group has to sample information of a specific type (common or unique) is defined as the amount of not-yet-shared information of that type multiplied by the number of group members who potentially can mention it. 7 These sampling opportunities change each time a new item of information is shared, and do so according to the number of members who originally held that just-shared information. Specifically, when a piece of uniquely held information is shared, the number of opportunities to sample additional pieces of not-yet-mentioned uniquely held information decreases by 1 (because only one member could have mentioned the just-shared unique information). By contrast, when a piece of commonly held information is shared, the number of opportunities to sample additional pieces of not-yet-mentioned commonly held information decreases by n , the size of the group (because every member of the group potentially could have mentioned the just-shared commonly held information). Thus, sampling opportunities are lost at a faster rate for common than for uniquely held information. This, in turn, causes the compound probability of sharing commonly held information to decrease over time, and that of sharing uniquely held information to increase, at least until members reach the limits of what they can recall. 8

These ideas were formalized by Larson (1997) in a computational model referred to as the Dynamic Information Sampling Model of Group Discussion (DISM-GD). 9 That model considers not only group size and the overall amounts of common and uniquely held decision-relevant information originally available to the group but also the ability of group members to recall that information during discussion, the possibility that members may not recall exactly the same commonly held information, and the likelihood that members might not contribute equally to discussion. The predictions generated by DISM-GD account for 73% of the variance in the sequential pattern of information sharing shown in Figure 8.1 , and an average of 52% of the variance in the sequential pattern of information sharing observed in all four of the independent empirical studies against which the model has been tested ( Larson, 1997 ).

Thus, the CIS and DISM-GD models have been quite successful in forecasting the sharing of common and uniquely held information during unstructured group decision-making discussions. Together they predict a de facto bias favoring commonly held information. That bias leads groups to share more of what they hold in common than of what they hold uniquely, and it leads them as well to share their commonly held information earlier than their uniquely held information.

Why (and When) These Information-Sharing Biases Matter

These biased information-sharing tendencies of groups during problem-solving and decision-making meetings would be little more than a curiosity if common and uniquely held information were always distributed among members as they are in the Manifest profile shown in the left-hand panel of Table 8.1 . That panel is so named because the better choice alternative—the one for which there is more favorable information overall, regardless of how it is distributed—should be apparent to each group member even before discussion. As described previously, this is because the pattern of support for the two choice alternatives found in the subset of information initially held by each group member mirrors the pattern observed in the full set of information. That same pattern is also found within the common and unique information subsets: in both cases, there is twice as much information favoring B as there is favoring A . Consequently, even though groups confronted with this profile are apt to share more of their common than uniquely held information, and to share their commonly held information earlier, the information they do share should still tend to favor the choice alternative that is objectively best, alternative B . Therefore, if it can be assumed that group decisions follow at least in part from the content of their deliberations (cf. Burnstein, 1982 ), then it is likely that, despite a biased discussion that favors their commonly held information, under conditions of a manifest profile information distribution, groups will nevertheless choose the decision alternative that objectively is best. And there is strong empirical evidence that this in fact occurs (e.g., Lu et al., 2012 ; Mesmer-Magnus, & DeChurch, 2009 ).

But there is no guarantee in any given situation that the task-relevant information held by members will in fact be distributed among them so as to create a manifest profile. Indeed, it is likely that decision-relevant information will sometimes be distributed in ways more similar to what is shown in the center and right-hand panels of Table 8.1 , the two hidden profile information distributions. These profiles are so named because the better choice alternative (still alternative B ) should not be readily apparent to anyone in the group before discussion, either because the information that each member holds does not clearly favor one alternative over the others (a weak hidden profile) or because it actually favors an alternative that objectively is inferior (a strong hidden profile).

Although profile type (manifest vs. weak hidden vs. strong hidden) does not appear to affect the propensity of groups to discuss more common than uniquely held information ( Lu et al., 2012 ), it does significantly affect the quality of the decisions they eventually make. Groups are much less likely to select the objectively best choice alternative when the decision-relevant information they hold is distributed so as to create a hidden profile—particularly a strong hidden profile—compared to when it is distributed in a way that creates a manifest profile ( Lu et al., 2012 ). This occurs, because choosing correctly in a hidden profile situation requires that group members share their uniquely held information ( Henningsen & Henningsen, 2003 ; Larson, Christensen et al., 1998 , Winquist & Larson, 1998 ). Unfortunately, the information sampling opportunities tend to favor instead the sharing of commonly held information, especially early in a meeting. When a hidden profile exists, this means that group discussion is initially likely to focus on information that supports an inferior alternative. If a consensus favoring that alternative begins to build, members are apt to pay less heed to whatever unique information favoring the objectively best alternative might subsequently come to light (cf. Hogarth & Einhorn, 1992 ), and so in the end they are even less likely to choose that superior alternative. Thus, other things being equal, in the context of a hidden profile information distribution, there is reason to be pessimistic about the ability of groups to learn the true state of affairs and so to decide wisely.

Situationally Motivated Information Sharing

So far, we have considered information sharing under very limited circumstances. We have focused exclusively on how information sharing is affected by the premeeting distribution of information among group members, ignoring other pertinent features of that information, of the task being performed, and of the group performing it. Further, we explicitly assumed that members are motivated to share whatever task-relevant information they can recall, and that that information is all equally valid and important. But what can be expected if we relax these restrictions? In particular, what happens if the information distribution factor is overlaid with other features of the situation that prompt various motivational concerns among members? It is to this question that we turn next.

Characteristics of the Information Held

One obviously important set of features that should impact whether or not information is shared concerns the substance of that information. Next we examine three such features: the importance or diagnosticity of the information, the degree to which the meaning of various pieces of information depend upon one another, and the extent to which the information in question is consistent or inconsistent with the members’ initial solution preferences. We address each of these in turn.

Importance/Diagnosticity

First, let us relax the assumption that the information members hold is all equally important, as this seldom matches reality. Even in the simplest decision-making situations, not all information about the various choice alternatives is necessarily relevant to choosing between them. Thus, if they are to be effective, it is necessary for group members to judge the potential value of the information they hold for accomplishing their task and to give priority to sharing that which is most important, relevant, and useful (cf. Gigone, 1996 ; Grice, 1975 ; Littlepage, Perdue, & Fuller, 2012 ; Stasser, Abele, & Parsons, 2012 ).

The particular way in which more versus less important information is distributed among group members prior to discussion can either exaggerate or mask completely the general tendency of groups to share more of their common than uniquely held information. That tendency will be exaggerated if most of the important information happens to be commonly held by members, but it can be masked if most of it is uniquely held.

This latter point is nicely demonstrated in a study by Kelly and Karau (1999) , who asked three-person groups to decide which of two cholesterol-lowering drugs would be the better product for a pharmaceutical company to market. These groups were to base their decision on 30 facts about each drug that they had read in advance. Some of these facts were positive (e.g., suggesting that the drug in question was effective or safe to use), others were negative (e.g., indicating that it might be expensive or difficult to manufacture), while still others were neutral (e.g., describing the form in which the drug would be manufactured or the name under which it would be sold). Pretesting ensured that the valence (positive, negative, or neutral) of each fact was perceived as intended, and that both the positive and negative facts were seen as more important for making the decision than were the neutral facts. These facts were distributed among the information packets read by members prior to discussion such that some of them were commonly held and some were uniquely held. Further, this was done in a way that created two different hidden profile conditions, with more of the important facts and fewer of the unimportant facts held uniquely by members in one condition than in the other. Thus, the unique information that members held was, on average, more diagnostic in one condition than in the other, while the opposite was true for their commonly held information.

As might be expected, Kelly and Karau (1999) found that when these groups subsequently met to discuss the two drugs and make their decision, they shared more of the important (positive or negative) than unimportant (neutral) facts about the two drugs. This difference was particularly strong when they were under time pressure to make their decision. Indeed, it was so strong that in the condition in which the members’ uniquely held information was most diagnostic, the overall tendency to share more common than uniquely held information completely disappeared (see also Bowman & Wittenbaum, 2012 ). On the other hand, when the diagnosticity of the drug information was statistically controlled in that condition, a robust tendency to share more common than uniquely held information re-emerged. Thus, having made the uniquely held information more diagnostic simply masked the usual tendency of groups to share more of their common than uniquely held information.

Interdependent Information

As in many experimental studies of information sharing in groups, the correct choice alternative in the experiment described earlier by Kelly and Karau (1999) was defined as the drug with the most positive and fewest negative attributes or, more formally, the one with the greatest expected value, ∑ p i v i , where p is the probability that the choice alternative actually possesses attribute i (typically assumed to be 1), and v is the valence or importance of that attribute. This definition presumes that the value of each piece of decision-relevant information is—and can be judged—independent of every other piece of available information. But in the real world this is not always the case. Sometimes the implications of one piece of information become apparent only when it is considered in relation to other available information (cf. Pennington & Hastie, 1993 ; Trabasso & Sperry, 1985 ).

When the implications of two important items of task-relevant information can be accurately assessed only by considering them together, those items are said to have interdependent meaning ( Fraidin, 2004 ). 10 Once that meaning is appreciated and the true value of the information is understood, the likelihood of those items being shared with others should increase. But the ability to recognize the importance of information with interdependent meaning hinges critically on how it is distributed among group members: That meaning is unlikely to be perceived unless the two pieces of information are held simultaneously by the same member so that they can be considered together. Consequently, two items of interdependent information are more apt to be shared with others in the group when both items are held by the same rather than by different group members. This is true even though both items might be uniquely held in each case.

To illustrate, Fraidin (2004) conducted a study in which he had participants work in two-person teams on either a murder mystery task or a personnel selection task. Each task involved 20 distinct items of relevant information that were distributed between the members so as to create a strong hidden profile. Among those items were four pairs with interdependent meaning that all favored the objectively correct choice alternative and that were all distributed as uniquely held information. Importantly, however, in one condition each member received both items from two of the interdependent pairs (jointly held pairs), whereas in a second condition each received just one item from all four interdependent pairs (separately held pairs). As expected, prior to discussion participants rated the interdependent items as being less important for solving the mystery or making the personnel decision in the separately-held-pairs condition than in the jointly-held-pairs condition. Moreover, when they were separately held, the usual tendency to share more common than uniquely held information was observed. But that tendency disappeared when the two items from each interdependent pair were jointly held. The perceived importance of the interdependent items mediated this effect: When the items from each of the interdependent pairs were jointly held, they were judged to be more important, which in turn led to them more often being shared.

In a conceptually related study, Reimer, Kuendig, Hoffrage, Park, and Hinsz (2007) found that information that differentiated multiple choice alternatives simultaneously (a form of jointly held, interdependent information) was shared at a higher rate overall than information that pertained only to a single choice alternative. This presumably occurred because the former information was perceived to be of greater diagnostic value than the latter. Nevertheless, Reimer et al. (2007) still found a significant tendency for groups in their study to share more of each type of information when it was commonly held relative to when it was uniquely held.

If the inability to see the true value of a pair of separately held items of interdependent information really does hinder their being shared during a decision-making meeting, then we should expect that if one of those items does somehow get shared, this will prompt the group member holding the other item in that pair to share it as well. This is because once the first item is shared, the member holding the second item can consider both simultaneously, and so should be better able to appreciate their interdependent meaning. In support of this, Deiglmayr and Spada (2010) found that when one item in a separately held interdependent pair was shared, the member holding the other item in that pair shared it very soon thereafter more than 60% of the time, and nearly always did so with an accompanying statement about the pairs’ interdependent meaning. Thus, the member who held the second item appeared to share it precisely because he or she recognized its interdependent meaning when combined with the first, already shared item.

Preference-Consistent Information

The information that group members individually possess prior to a problem-solving or decision-making meeting will often lead them to form in advance of that meeting a clear preference for one choice alternative over the others. This should be the case, for example, when information is distributed among members so as to create a strong hidden profile. And as we have seen, when information is distributed in this way, members are predicted to share more of their common than uniquely held information. But doing so also means sharing more preference-consistent information. For example, in the strong hidden profile shown in the right-hand panel of Table 8.1 , members individually should prefer alternative A before the meeting starts, and then during the meeting they should share more information favoring A . This is because in a strong hidden profile most or all of the commonly held information is preference consistent (in this case, consistent with the members’ preference for alternative A ), whereas most or all of the uniquely held information is preference inconsistent. As such, the predicted tendency to share more common than uniquely held information might be due here either to (a) the unmotivated, probabilistic sampling advantage enjoyed by common relative to uniquely held information (as both the CIS and DISM-GD models suggest) or to (b) a motivated propensity of members to share information that is consistent with their prediscussion preference. 11

There is good evidence that information sharing in decision-making groups indeed does tend to favor not only the information that members hold in common but also information that supports their initial decision preferences (e.g., Faulmüller, Mojzisch, Kerschreiter, & Schulz-Hardt, 2012 ; Greitemeyer, Schulz-Hardt, & Frey, 2003 ; Mojzisch, Grouneva, & Schulz-Hardt, 2010 ; Mojzisch, Kerschreiter, Faulmüller, Vogelgesang, & Schulz-Hardt, 2014 ; Schulz-Hardt, Brodbeck, Mojzisch, Kerschreiter, & Frey, 2006 ; Toma & Butera, 2009 ). Dennis ( 1996a , 1996b ), for example, had participants perform a personnel selection task in either 6-person or 10-person groups, with decision-relevant information distributed among them so as to create a strong hidden profile (though with a degree of dissent built in as well; to be discussed later). As expected, groups shared more of their common than uniquely held information overall. On the other hand, the unique information they shared was more likely to support than oppose their prediscussion choice preference. This was true regardless of whether the group members communicated with one another face to face or electronically.

In part, the tendency to share more preference-consistent than -inconsistent information may reflect a general social norm of advocacy ( Stasser, 1988 ; Stasser & Titus, 1985 ; see also Greitemeyer, Schulz-Hardt, Brodbeck, & Frey, 2006 ), an expectation that during discussion one should actively promote one’s preferred choice alternative in an attempt to persuade others of its superiority. Almost by definition this seems to require the selective sharing of favorable information about that alternative and unfavorable information about competing alternatives.

Beyond advocacy, however, it has also been shown that group members judge their preference-consistent information to be stronger, more important, and more reliable than their preference-inconsistent information (e.g., Edwards & Smith, 1996 ; Greitemeyer & Schulz-Hardt, 2003 ; Koehler, 1993 ; Mojzisch et al., 2010 ; Rothmund, Mojzisch, & Schulz-Hardt, 2011 ; Russo, Medvec, & Meloy, 1996 ; Russo, Meloy, & Wilks, 2000 ; Toma, Gilles, & Butera, 2013 ; Van Swol, 2007 ). Thus, even in the absence of an advocacy motive, members should still exhibit a tendency to selectively share their preference-consistent information, because doing so means communicating the information they perceive to be most valuable and diagnostic.

In support of this idea, Faulmüller et al. (2012) had participants engage in a personnel selection task in which they were given equal amounts of positive and negative information about each of two job candidates. After studying that information carefully, but before discussing it with anyone, participants indicated which candidate they personally preferred. Then they discussed the two candidates with a partner in order to reach consensus about which one they would jointly select. It was found that participants on average shared twice as much of their preference-consistent information as they did of their preference-inconsistent information. Interestingly, this effect was significantly reduced—though not completely eliminated—when the partner indicated that he or she understood (vs. did not understand) the reason why the participant held his or her preference. This pattern of results suggests that, beyond wanting to persuade others to adopt their preferred choice alternative, the tendency of members to share more preference-consistent than -inconsistent information may also be motivated simply by their desire to make themselves understood.

Characteristics of the Task as a Whole

Information sharing in groups is animated not just by the importance, interdependence, and preference consistency of the information itself but also by the overarching nature of the task being performed. There are a great many ways in which tasks differ that might potentially affect information sharing within the groups performing them. Here we briefly touch on those that have received the most research attention. These include the level of behavioral interdependence required by the task, whether there is a solution (in the case of problem-solving tasks) that can be conclusively demonstrated by members to be correct, the amount of information that must be processed, the extent to which members must interact virtually while performing the task, and the amount of time pressure involved.

Behavioral Interdependence

Perhaps the most obvious task characteristic affecting information sharing in groups is the degree to which members are behaviorally interdependent. Generally speaking, more information sharing can be expected when the task calls for more interdependent action by group members, where each member’s behavior vis-à-vis the task both affects and is affected by the behavior of others. Behavioral interdependence increases, for example, when the resources needed to perform a task are divided among members, yet those resources must be combined in order to complete the task successfully (Johnson & Johnson, 1989 , 1992 ; Wageman, 1995 , 1999 ). Hidden profile decision-making tasks are an example. Here the most critical resources are informational, but for other kinds of tasks they might be material in nature (e.g., supplies, tools), in which case information sharing is apt to focus more on issues of coordination as members work together to identify and use one another’s resources in the service of task accomplishment. Interdependence can also arise from the sharing of a common resource, for example when a single piece of equipment needed by everyone in the group can be used by only one member at a time. A shared budget creates interdependence in the same way. Finally, interdependence can also result simply from the flow of work within the group, as when one member’s work output is work input for another. Regardless of how it arises, tasks that involve higher levels of behavioral interdependence should prompt greater information sharing among group members (e.g., Courtright, Thurgood, Stewart, & Pierotti, 2015 ).

Solution Demonstrability

Focusing more narrowly on problem-solving tasks, more information sharing is also likely to occur when the problem in question has a demonstrably correct solution, meaning that it is possible for members to demonstrate conclusively to one another that the correct or best solution is in fact correct or best. Problems with demonstrably correct solutions should more strongly engage members’ epistemic motivation—their willingness to expend effort to achieve a full and accurate understanding of a given situation—than problems in which it is difficult or impossible to demonstrate the correctness of one proposed solution over others (cf. De Dreu, Nijstad, & van Knippenberg, 2008 ). Stronger epistemic motivation implies greater interest in bringing task-relevant information to bear (e.g., Kaplan, Schaefer, & Zinkiewicz, 1994 ), which in a group problem-solving context generally implies more information sharing. Consistent with these ideas, a number of studies have found higher levels of information sharing in groups that work on tasks with (vs. without) demonstrably correct solutions (e.g., Huang & Wei, 2000 ; Kaplan & Miller, 1987 ; Mesmer-Magnus & DeChurch, 2009 ; Stasser & Stewart, 1992 ; Stewart & Stasser, 1998 ; but see also Campbell & Stasser, 2006 ; Schittekatte, 1996 , Experiment 3). This effect appears to be independent of how the task-relevant information is distributed, as the meta-analytic review by Lu et al. (2012) found that whether groups worked on tasks with high versus low solution demonstrability did not moderate their tendency to share more of their common than uniquely held information.

Information Load

The information load imposed by a group’s task also has a reliable impact on information sharing. Information load is simply the total amount of task-relevant information that a group must process in order to perform its task. 12 Thus, a decision-making task that involves 50 pieces of information imposes a greater load than does one that involves just 5 pieces. Not surprisingly, in absolute terms, groups share more information when performing tasks that impose a higher information load, simply because there is more information to share. But when the amount of information shared is expressed as a percentage of the total amount of information available, that percentage often decreases with increasing load. For example, a group that shares 4 pieces of information when performing a decision-making task that involves 5 pieces of information overall (80% sharing) might share 30 pieces of information when performing a task that involves 50 pieces of information overall (60% sharing). Importantly, the percentage decrease tends to be more severe for unique than for commonly held information. As a result, as information load increases, so too does the bias in groups to discuss more of their common than uniquely held information ( Lu et al., 2012 ; but see Reimer, Reimer, & Czienskowski, 2010 ).

Communication Virtuality

The tasks that groups perform are defined not only by what is accomplished but also by how what is accomplished gets done (cf. Hackman, 1969 ; Larson 2010 ), which includes how group members communicate with one another. There has been a good deal of research on information sharing in groups whose members communicate face to face versus electronically. The latter category is increasingly being differentiated in the literature according to its “virtuality.” Communication virtuality is the extent to which groups rely on electronic tools to coordinate and execute their activities, in combination with the informational richness and interaction synchrony afforded by those tools ( Kirkman & Mathieu, 2005 ). 13 Tools that enable richer, more synchronous communication (e.g., video conferencing) more closely mirror what occurs in a face-to-face meeting, and so are considered less virtual than those that do not (e.g., email).

In a recent meta-analytic review of the effect of virtuality on information sharing in groups, Mesmer-Magnus, DeChurch, Jimenez-Rodriguez, Wildman, and Shuffler (2011) found that a high level of virtuality hinders information sharing generally (what they refer to as the openness dimension of information sharing), but it may actually improve the sharing of uniquely held information. They explain this latter result by noting that, compared to meeting either face to face or via a rich, synchronous, low-virtuality electronic tool (e.g., video conferencing), highly virtual modes of communication (e.g., email) minimize the salience of status differences among members that can inhibit the sharing of information contrary to an emerging group consensus (cf. Siegel, Dubrovsky, Kiesler, & McGuire, 1986 ). Further, the asynchronous nature of these tools offers greater opportunity to reflect on information shared by others, to consider responses carefully before making them, and, if necessary, to gather additional data on an issue before responding. As a result, group members might be more likely to share uniquely held information that they otherwise would have been hesitant to share—or may simply not have thought to share—in a faster paced, more interpersonally risky, face-to-face or video conference meeting.

It is worth noting here that in contrast to the results reported earlier, the meta-analytic review by Lu et al. (2012) , which also examined the effects of working virtually versus face to face, concluded that this variable has no impact on the sharing of uniquely held information. However, these authors did not distinguish between high and low levels of virtuality. Mesmer-Magnus et al. (2011) also observed no differences in the sharing of unique information when they ignored the level of virtuality found in the various forms of electronically mediated communication they studied. It was only when they distinguished between groups that relied on tools with high versus low virtuality that an effect on the sharing of uniquely held information was observed. Thus, it seems clear that virtual communication tools do not all have the same impact on information sharing within groups.

Time Pressure

Occasionally, problems must be solved and decisions made in time-pressured circumstances. As a solution or decision deadline draws near, group members tend to focus on the most diagnostic, task-relevant information they hold ( Karau & Kelly, 1992 ; Kelly & Karau, 1999 ; Kelly & Loving, 2004 ), which may also be information that supports their initial solution or decision preference. Time pressure, particularly when it is acute, engages members’ need for closure and their associated consensus seeking tendencies (e.g., Kruglanski, 2004 ; Kruglanski, Dechesne, Orehek, & Pierro, 2009 ; Kruglanski, Orehek, Dechesne, & Pierro, 2010 ; Kruglanski & Webster, 1991 , 1996 ; see also Nijstad & De Dreu, 2012 ). Need for closure refers to an urgent desire for a definite solution to a problem or answer to a question, and it manifests itself as a tendency to seize on whatever information is most readily available at the moment in order to reach a firm assessment of the situation at hand, and then to freeze on that assessment so as to prevent backsliding into a state of ambiguity. The latter implies a reticence to consider additional problem- or decision-relevant information once a solution has been found or a decision reached. Thus, time pressure can be expected not only to narrow the content of the information shared to that which is perceived to be most task relevant but also to reduce the overall amount of information shared ( Bluedorn, Turban, & Love, 1999 ; Bowman & Wittenbaum, 2012 ; Reimer, Reimer, & Hinsz, 2010 ) and to increase a group’s reliance on normative influence tactics in order to reach and maintain consensus ( Kelly, Jackson, & Hutson-Comeaux, 1997 ).

Other things being equal, a reduction in the overall amount of information shared in response to time pressure can be expected to impact disproportionately the sharing of uniquely held information. This expectation derives from the DISM-GD model ( Larson, 1997 ), which predicts that during a problem-solving or decision-making meeting, uniquely held information will tend to be shared later than commonly held information (e.g., see Figure 8.1 ). If so, then when there is an overall reduction in the amount of information shared because discussion is cut short—for any reason, including the lack of time—it is members’ uniquely held information that is more apt to be left unshared. Consistent with this, Parks and Cowlin (1995) found that decreasing the amount of time allotted for discussion reduced the amount of unique but not commonly held information that group members shared, and so accentuated the discussion advantage of the latter relative to the former. 14

Characteristics of the Group

Information sharing within groups is also likely to be affected by certain characteristics of the group itself. Below we consider four such characteristics: the group’s social structure, the reward structure under which it operates, the degree of diversity that exists among members’ initial solution preferences, and group norms. The first three of these speak to the mixed-motive nature of many information sharing situations. Members generally want their group to do well, but they also want to do well themselves, which includes seeing their own preferred solution adopted by the group, receiving recognition and rewards, gaining status, and maintaining good relationships with other members ( De Dreu et al., 2008 ; Wittenbaum, Hollingshead, & Botero, 2004 ). The fourth characteristic, group norms, concerns the expectations that members have for one another’s behavior, and it reflects a type of “behavioral inertia” that can affect information sharing.

Social Structure

As it applies to groups within organizations, social structure refers to the relatively enduring patterns of social relationships that exist among members. There are several different dimensions on which a group’s social structure might be characterized. Here we briefly touch on three: status, power, and the special role of group leaders.

Perhaps the most fundamental and universally relevant aspect of social structure is the degree to which group members are stratified according to status. Status refers to the amount of recognition, respect, and deference accorded to members by others in the group (cf. Fiske, 2010 ; Mannix & Sauer, 2006 ). In nearly every group, members can be differentiated according to status. Differences among members in socially valued physical and demographic characteristics (e.g., age, race, gender), background and organizational roles (e.g., leader vs. subordinates), and task-specific experience and expertise can all give rise to status stratification. Members can also gain or lose status over time as a consequence of group interaction, for example when they demonstrate (or fail to demonstrate) competence vis-à-vis the task at hand (cf. Leary, Jongman-Sereno, & Diebels, 2014 ; Owens & Sutton, 2001 ; Ridgeway, 1982 ). The degree to which a group is stratified by status—roughly, the difference in the amount of recognition and respect given to its highest versus lowest status members—can vary widely. In general, the greater the degree of stratification, the greater the potential influence of status on information sharing during group meetings.

Because high-status members are often given (and take) more opportunities to participate in group problem-solving and decision-making discussions, other things being equal, they can be expected to share more of what they know than low-status members. Further, compared to low-status members, high-status members may be more willing to share the unique information they hold, particularly when a strong hidden profile exists ( Hollingshead, 1996 ; Wittenbaum, 1998 , 2000 ; Wittenbaum & Bowman, 2005 ). This is because sharing uniquely held information can be risky in such a circumstance, since the implications of that information are likely at odds both with the initial solution preferences expressed by others in the group (e.g., see Table 8.1 ) and with much of the information that others will have already shared (i.e., their commonly held information, which will tend to support their initial preferences and be shared first). This prediction requires only that members understand the decisional implications of the various pieces of information they hold, along with the initial preferences of other group members. It does not presume that they know which pieces of information they hold are common versus uniquely held.

On the other hand, there are often circumstances in which members do know (or strongly suspect) which of the pieces of information they hold they hold uniquely. In this case, high-status members may be more likely than low-status members to share such information regardless of the type of profile involved. This is because unlike commonly held information, when a piece of uniquely held information is shared, it cannot be socially validated—no one else in the group can independently attest to its truthfulness or accuracy. Sharing such information is therefore likely to require more confidence and credibility than is the case when sharing commonly held information ( Wittenbaum & Park, 2001 ; Wittenbaum & Stasser, 1996 ). Thus, it should be easier for high-status members to share their uniquely held information, given the affordances typically accorded to them by others in the groups (cf. Hollander, 1958 ; Hollingshead, 1996 ; Stone & Cooper, 2009 ). Low-status members, on the other hand, may be more strongly motivated to share their commonly held information, because others in the group can affirm that information and so are more likely to evaluate them positively ( Wittenbaum, Hubbell, & Zuckerman, 1999 ). In other words, sharing commonly held information can be a status-enhancing strategy, which should be of greater benefit to those who currently have low status.

A related but conceptually distinct aspect of group social structure concerns the power relationships that exist among members. Power refers to an asymmetric control over valued outcomes or the resources needed to achieve those outcomes (cf. Anderson & Brion, 2014 ; Emerson, 1962 ; Sturm & Antonakis, 2015 ). Thus, like status, power is an inherently relational phenomenon—one group member has power over another only to the extent that the latter is dependent on the former to obtain desired outcomes or to avoid outcomes that are undesired (i.e., punishments). Within organizations, formal role assignments are a common source of power ( Pfeffer, 1992 ). The leader of a group, for example, typically has power over other group members because he or she has the ability to provide or withhold valued resources and to administer rewards and punishments.

Power can have a pervasive impact on the behavior of power holders and on the information sharing of low-power others. Keltner, Gruenfeld, and Anderson (2003) argue that power engages the behavioral approach system, causing power holders to attend more closely to opportunities and to potential rewards than to threats and potential punishments (see also Higgins, 1998 ; Pickering & Gray, 1999 ). This, in turn, causes them to be more goal-directed and action-oriented than those who do not have power ( Fast, Gruenfeld, Sivanathan, & Galinsky, 2009 ; Galinsky, Gruenfeld, & Magee, 2003 ; Guinote, 2007 ). Consistent with these ideas, leaders with a high subjective sense of power have been shown to be more verbally dominant during group meetings than leaders with a lower subjective sense of power, and as a result to curtail information sharing by other members (e.g., Bass, 2008 ; Kilduff & Galinsky, 2013 ; Tost, Gino, & Larrick, 2013 ).

However, reduced information sharing by other group members is not an inevitable consequence a leader’s power. Galinsky, Magee, Rus, Rothman, and Todd (2014 , Experiment 3) found that when leaders were encouraged to take the perspective of other members—to imagine themselves in the place of those members, and to try to understand what they are thinking, what their viewpoints are, and what their interests and purposes are—there was significantly more sharing of uniquely held information by both the leader and the other members. Interestingly, these benefits did not accrue when the same perspective-taking induction was applied to nonleaders. This suggests that perspective taking by leaders not only makes them open to input from others in the group, but it also causes them to signal their openness, either verbally or nonverbally, and it is ultimately the perceived openness of the leader that encourages information sharing by other members (cf. Hirak, Peng, Carmeli, & Schaubroeck, 2012 ; Locke & Anderson, 2015 , Morrison, See, & Pan, 2015 ; van Ginkel & van Knippenberg, 2012 ). Thus, while power differentials within groups are apt to make the sharing of uniquely held information seem risky to low-power members, leaders can reduce that perceived risk, and so encourage greater information sharing, by signaling a genuine openness to the ideas of others.

Beyond simply conveying their openness to the ideas of others, information sharing within groups is also benefited when leaders take an active “information management role” during problem-solving and decision-making meetings ( Larson, 2010 ; Maier, 1967 ). During such meetings, it is important that someone keep the discussion focused on the matter at hand, promote the sharing of task-relevant information, and ensure that that information is appropriately integrated into the group’s eventual decision. Regarding the last of these, because the full meaning and value of certain pieces of uniquely held information may not be apparent when they first come to light (e.g., interdependent information, as discussed earlier), it is important that someone “keep that information alive” so that the group remains mindful of what has already been shared and can properly connect it with information shared later. This and other related information management functions would seem naturally to fall to the group’s leader.

Leaders often take on this role quite spontaneously, as was observed in the previously described experiments by Larson and his colleagues ( Larson et al., 1996 ; Larson, Christensen et al. 1998 ). Recall that they had three-person physician teams meet twice to diagnose two complex patient cases, with the relevant information about each case distributed among the team members so as to create a hidden profile. When the physicians met to discuss each case, one of them was assigned by the researcher to serve as the team’s leader. The leader was handed a diagnosis report form to complete and was told that he or she had ultimate responsibility for the accuracy of the team’s diagnosis. It was found that team leaders were more likely than other group members to repeat already mentioned case information, especially during the latter two thirds of discussion. This was true for both common and uniquely held information, although they tended to repeat commonly held information more often. These repetitions served to keep the information alive and under active consideration by the team. Further, the leaders also asked more questions than other group members about the information that had already been shared. Questions not only keep information alive, they also help to elicit additional, related information that might improve the group’s understanding the problem or situation it faces. Questions are apt to be most effective in eliciting such information when the leader knows, broadly, what types of special expertise others in the group may hold (e.g., as a consequence of their current role, prior task assignments, training, or other distinct experiences; cf. Mell, van Knippenberg, & van Ginkel, 2014 ). The repetition of previously shared information, and asking questions that help to draw out additional new information, are both part of what van Knippenberg, De Dreu, and Homan (2004) refer to as information elaboration (see also Okhuysen & Eisenhardt, 2002 ).

Reward Structure

In much of what has been covered so far, we have implicitly assumed that group members are all motivated to work cooperatively with one another in order to find an optimal solution to a problem or to make the best possible decision. This assumption is undoubtedly realistic in a great many circumstances. But it also seems likely that there are frequent exceptions, and that sometimes members’ information-sharing behavior is driven by self-interest and competitive motives ( Toma & Butera 2015 ; Wittenbaum et al., 2004 ).

Whether or not members are motivated to work cooperatively with others depends partly on the nature of the task and partly on the prevailing reward structure within the group. Regarding the latter, a primary consideration is whether rewards are administered collectively or individually. When a task demands interdependent action by members—as decision-making and problem-solving tasks that involve distributed information certainly do—the task itself seems naturally to elicit cooperative behavior and information sharing. Such cooperation is supported when rewards are administered collectively, with outcomes accruing to the group as a whole and shared equally by members regardless of their individual contributions. In contrast, rewarding members separately according to the quality of their individual contributions can undermine cooperation, and it can foster instead a tendency toward strategic information sharing designed to gain an advantage with respect to obtaining those rewards (cf. DeMatteo, Eby, & Sundstrom, 1998 ; Wageman, 2001 ; but see Pearsall, Christian, & Ellis, 2010 ).

For example, Toma and her colleagues ( Hayek, Toma, Oberlé, & Butera, 2015 ; Toma & Butera, 2009 ) have conducted a series of experiments involving hidden profile decision-making tasks in which members’ uniquely held information was more important for making a correct decision than was their commonly held information. In each of these experiments it was found that evaluating and rewarding members based on their individual performance in the group, compared to evaluating and rewarding the group collectively, led to significantly less sharing of their valuable, uniquely held information. Similar results have been obtained with other types of tasks in which information sharing is crucial for effective group performance (e.g., Beersma et al., 2003 ). Indeed, Steinel, Utz, and Koning (2010) found that when group members were rewarded individually rather than collectively, they were motivated not only to share less of their uniquely held information but also to distort whatever uniquely held information they did share in order to misdirect and impede others (cf. Poortvliet, Anseel, Janssen, Van Yperen, & Van de Vliert, 2012 ; Poortvliet, Janssen, Van Yperen, & Van de Vliert, 2007 ).

It should be noted that the study cited earlier by Steinel et al. (2010) assessed participants’ information-sharing intentions , not their actual information-sharing behavior. Further, in the studies by Toma and Butera (2009) , Hayek et al. (2015) , and Beersma et al. (2003) , where actual information-sharing behavior was assessed, the research employed ad hoc student groups that existed only for the duration of that one experimental session. In none of these studies was there much risk that participants would be discovered by others to have withheld or distorted information, nor were there any long-term negative consequences for having done so. This is obviously different from what is found in most groups in organizations, where the consequences of being branded “untrustworthy,” “not a team player,” or worse can be significant. As such, intentionally withholding and/or distorting information may occur less frequency in real-world groups than these studies seem to suggest. What is likely to occur more often is simply a dampening of members’ enthusiasm for engaging in the kind of thoughtful, deep processing of information that sometimes is necessary in order to realize that there are things one knows that might be useful if shared (cf. De Dreu et al., 2008 ; Nijstad & De Dreu, 2012 ).

Preference Diversity

When we introduced the concept of a hidden profile information distribution earlier in this chapter, we considered only those situations in which group members are like-minded in their initial solution preferences. Thus, the weak hidden profile shown in Table 8.1 anticipates that all of the members will initially be indifferent toward the choice alternatives they face, while the strong hidden profile anticipates that they will all initially prefer the same suboptimal choice alternative. Under both scenarios, groups have been shown to have great difficulty choosing the alternative that objectively is best, mainly because they tend not to discuss all of the unique decision-relevant information they hold.

But preference homogeneity is not a defining characteristic of hidden profiles. A hidden profile can exist even when members initially have very diverse preferences. Such diversity can arise from either of two sources. First, it might arise as a consequence of the benefits that are likely to accrue to different group members if various choice alternatives are selected. For example, one member might gain more if the group chooses alternative A , while another might benefit only if they choose alternative B . Here it is the reward structure that drives preference diversity within the group. Second, when there are more than two choice alternatives under consideration, the information bearing on them may be distributed in such a way that different members prefer different alternatives and yet none prefers the one that objectively is best. In this case, preference diversity—perhaps in the form of dissent from a majority opinion—arises simply as a function of the subset of information to which members were initially exposed.

It is important to distinguish these two sources of preference diversity—different rewards and different information—because they are likely to yield rather dissimilar patterns of information sharing in groups. On the one hand, consistent with the ideas presented earlier, if members are rewarded differently for different decision outcomes, they will be tempted to share information in a strategic, egocentric manner, preferentially volunteering information that supports the choice alternative they prefer, withholding information that might favor another alternative, and even distorting the information that they do share (cf. Steinel, & De Dreu, 2004 ; Toma & Butera, 2009 ; Toma, Vasiljevic, Oberlé, & Butera, 2013 ). This type of self-serving information sharing can be expected whenever the reward structure encourages members to pursue different, negatively interdependent goals.

On the other hand, when different members prefer different choice alternatives simply because they initially were exposed to different subsets of relevant information, but they all still share the common goal of choosing the alternative that objectively is best, greater information sharing can be expected (e.g., Brodbeck, Kerschreiter, Mojzisch, Frey, & Schulz-Hardt, 2002 ; Hightower & Sayeed, 1996 ; Klocke, 2007 ; Rijnbout & McKimmie, 2014 ; Scholten, van Knippenberg, Nijstad, & De Dreu, 2007 ; Schulz-Hardt et al., 2006 ; but see Lu et al., 2012 ). For example, Brodbeck et al. (2002) had three-person groups perform a personnel selection task involving three job candidates, with the information about those candidates distributed among members so as to create a hidden profile. When they met to discuss the three candidates, the group members shared significantly more of their uniquely held information if at least one of them initially preferred a different candidate than was preferred by the other two. And when all three members each preferred different candidates, they shared three times as much of their uniquely held information compared to when all of them preferred the same candidate.

The presence of an initial, information-driven diversity of opinion about which solution alternative is best is apt to hold in check—at least temporarily—the consensus-seeking pressures that inevitably arise in decision-making groups, and so provide more opportunity for information sharing. Decision-making meetings often begin with members spontaneously exchanging their solution preferences, so they learn rather quickly the initial distribution of preferences in the group ( Pavitt, 2014 ; Schulz-Hardt & Mojzisch, 2012 ). This is particularly true when the group is working with a well-defined set of choice alternatives, its members believe that they each are already aware of most of the decision-relevant information, and the meeting is informal and unstructured. Should members discover that a strong majority favors one choice alternative, those few with a dissenting opinion will soon likely feel normative pressure from the rest of the group to change their opinion ( Brodbeck et al., 2007 ). In such a situation, members are apt to focus more on one another’s opinions than on the information underlying them, and their collective decision will be made rather quickly and with relatively limited information sharing and discussion of facts. On the other hand, if members are prevented from exchanging their initial solution preferences ( Mojzisch & Schulz-Hardt, 2010 , Experiment 4), or if they do exchange preferences but discover a diversity of opinion about which alternative is best, significantly more information sharing is apt to occur.

In sum, an initial diversity in member solution preferences seems capable of prompting either more or less information sharing, depending on whether that diversity is the result of different reward contingencies or differences in the information to which members were first exposed. We are unaware of any studies that have directly pitted these two root causes of preference diversity against one another. This would therefore seem a worthwhile avenue for future research.

Group Norms

The last group characteristic we consider here is their norms, particularly those that bear on information sharing. Group norms are expectations that members hold concerning appropriate modes of thought and behavior that apply to all members of the group, including themselves. Norms specify how members should think and how they should behave. Norms can be established explicitly, for example by directives from a group’s leader, but very often they emerge as an implicit product of group interaction. In the latter case, they reflect a group’s history of working together. When a group’s norms are made salient to members, for example by calling attention to the level of norm-related behavior that typically has occurred in the group in the past, the members’ own subsequent behavior often follows suit (e.g., Cress & Kimmerle 2007 , Experiment 2).

Postmes, Spears, and Cihangir (2001) suggest that group norms can influence information sharing to the extent that members are expected to behave in ways that either (a) promote solidarity and consensus within the group or (b) display independent, critical thinking (cf. Janis, 1982 ). Specifically, they argue that because everyone in the group can affirm, and so agree on, the accuracy and correctness of their commonly held information but cannot do so for the information they each hold uniquely, if the prevailing group norms emphasize the importance of achieving consensus, then commonly held information should be valued more highly than uniquely held information. By contrast, this difference should not emerge when the prevailing norms emphasize the importance of critical thinking.

In two separate experiments, Postmes et al. (2001) demonstrated empirically the impact of such norms. They had four-person groups engaged in one of two preliminary tasks designed to establish either a consensus norm (make a poster) or a critical thinking norm (debate a policy proposal), with subsequent questionnaire measures confirming that participants perceived the norms as intended. All groups then performed a personnel selection task in which the information about several job candidates was initially distributed among members so as to create a hidden profile. Before meeting to discuss these candidates, members reported which one they personally thought was best based on the subset of information that they had just read. Next, every member received a complete list of all of the candidate information, including the unique information given to others that they themselves had not previously read. They then met as a group to discuss that information and to decide collectively which candidate was best. Thus, during discussion every member had full access to all of the candidate information. Afterward, members individually rated how valuable each piece of information had been for making their group decision.

As predicted, Postmes et al. (2001) found that groups with a consensus norm, but not those with a critical thinking norm, rated the information that initially was held uniquely by members as being less valuable than the information they initially held in common. Further, consensus norm groups were also less likely than critical-thinking norm groups to select the objectively best candidate. Although information sharing was not assessed in these studies, given that group members are generally less likely to share information that they perceived to be less important (as discussed earlier), it seems reasonable to conclude that the consensus norm groups were less successful in choosing correctly because they discussed less of their uniquely held information.

The findings reported by Postmes et al. (2001) suggest that the pattern of interaction—and so group norms—established under one set of conditions may sometimes inhibit group behavior change and effective adaptation to new circumstances. The results of an interesting series of studies by Hollenbeck, Humphrey, Ilgen, and colleagues ( Beersma et al., 2009 ; Johnson et al., 2006 ; Moon et al., 2004 ) support this conclusion. In each of these studies participants worked in four-person teams on a command-and-control task in which dynamic, task-relevant information was widely distributed among team members, and where it was necessary for members to share information with one another in order for the team as a whole to perform well. Beersma et al. (2009) and Johnson et al. (2006) both found that teams that had historically worked under an individual reward structure (i.e., members were rewarded according to their individual accomplishments) demonstrated less effective information sharing and coordination following a shift to a collective reward structure (i.e., where teams were rewarded as a whole according to their collective accomplishments) compared to teams that had worked under a collective reward structure all along. Moon et al. (2004) observed similar adaptation difficulties when the operating structure under which teams were organized (functional vs. divisional) changed.

Thus, norms promote a degree of “inertia” in group behavior. Interaction patterns that evolve in one context give rise to normative expectations that encourage the persistence of those patterns in the future both in that same context and in others. Often this is beneficial for the group, as it creates stability and predictability. But, as the research cited earlier suggests, it can be dysfunctional when existing norms encourage less information sharing than is needed for a new task or in a new performance context.

Characteristics of the Individual Group Members

Our goal in this chapter has been to highlight the situational determinants of information sharing within groups in organizations and to call attention to the motivations that mediate some of their effects. As such, we have ignored stable individual differences among group members that also might influence information sharing. Indeed, compared to situational determinants, the impact of individual differences on information sharing in groups has received relatively little research attention. What research has been done can be organized into three main areas, and it is briefly summarized next.

Epistemic Motivation

The individual difference variable that is most broadly relevant to information sharing in groups concerns members’ epistemic motivation—the degree to which they are willing to expend effort in order to achieve a thorough, rich, and accurate understanding of the world, including whatever task or challenge is facing the group at the moment ( De Dreu et al., 2008 ). Some people are chronically high in epistemic motivation, whereas others are chronically low. Stable individual differences in epistemic motivation can be detected by measuring group members’ need for cognition, which is defined as a general tendency to engage in and enjoy effortful cognitive activity ( Cacioppo, Petty, Feinstein, & Jarvis, 1996 ). There is evidence that members who score high in need for cognition, compared to those who score low, more readily share information with others when working on a problem-solving task with distributed information (e.g., Henningsen & Henningsen, 2004 ). Epistemic motivation is also reflected (negatively) in the need for cognitive closure, which is defined as members’ eagerness for quick, firm answers to questions, and their desire to avoid confusion and ambiguity. High need for cognitive closure indicates low epistemic motivation. Those high in need for cognitive closure tend to seize on early evidence, freeze upon whatever solution, judgment, or decision that evidence might suggest, and resist pressure to consider the problem further ( Kruglanski, 2004 ; Kruglanski et al., 2009 , 2010 ). Thus, once they have latched onto a solution, those who are chronically high in need for cognitive closure are apt to engage in less information sharing than others in the group. The impact of both needs (for cognition and, oppositely, for cognitive closure) on information sharing is likely to be stronger to the extent that the situation at hand is more complex, involving more—and more interdependent—information that demands greater effort to process and interpret. Time pressure should also magnify the effect of these needs.

Social Value Orientation

A second individual difference variable, social value orientation, comes into play when information sharing involves a mix of costs and benefits, and especially when the interests of the group are at odds with those of its individual members. Three social value orientations can be distinguished, according to the goals that members typically pursue in interdependent outcome situations. Prosocials strive to maximize outcomes for both themselves and others, individualists try to maximize outcomes for themselves without regard for others, and competitors seek to maximize outcomes for themselves relative to others ( Messick & McClintock, 1968 ; Van Lange, 1999 ; see also Utz, Muscanell, & Goeritz, 2014 ). Prosocials generally contribute more resources in social dilemma situations than either individualists or (especially) competitors, and consistent with this, they have been shown to share more information with others (e.g., Kimmerle, Wodzicki, Jarodzka, & Cress, 2011 ; Utz et al., 2014 ). De Dreu et al. (2008) suggest that the highest level of information sharing in groups is apt to be found among prosocial members who are also high in epistemic motivation. These individuals should be the ones who not only are most strongly oriented toward achieving the group’s collective goal but also are most willing to expend cognitive effort in pursuit of it.

Leadership Style

Finally, there is a substantial literature that focuses on how a leader’s personality and/or behavior can affect the functioning and performance of his or her group (e.g., Bass, 2008 ). Most relevant to information sharing within groups is research on leader directiveness and dominance. This work indicates that a controlling, directive, or dominant leadership style tends to suppress information sharing by other members of the group (e.g., Maner & Mead, 2010 ). By contrast, greater information sharing occurs in groups whose leaders have an open, participative style (e.g., Hoch, 2014 ; Larson, Foster-Fishman, & Franz, 1998 ; Srivastava, Bartol, & Locke, 2006 ). Information sharing is also facilitated by a supportive leadership style ( Cavaliere, Lombardi, & Giustiniano, 2015 ), whereas an abusive style tends to inhibit information sharing ( Kim, Kim, & Yun, 2015 ; Kim & Yun, 2015 ). Finally, narcissistic leaders can also stifle information sharing ( Nevicka, Ten Velden, De Hoogh, & Van Vianen, 2011 ).

Promoting Information Sharing Within Groups in Organizations

The body of research reviewed in this chapter contains a number of hints and suggestions that can be put to practical use in order to promote information sharing and learning within groups in organizations. We therefore close by offering the following evidence-based recommendations, and we do so focusing on the role that group leaders play in implementing them.

First, every problem-solving and decision-making group should have an assigned leader, someone who is responsible for the quality of the group’s product. Even in a very informal meeting, having someone present who is ultimately accountable for the outcome of that meeting is apt to result in greater information sharing (cf. van Swol, 2009 ), particularly if that individual has an open, participative leadership style.

Second, leaders can promote information sharing by the manner in which they introduce and frame the group’s task ( van Ginkel & van Knippenberg, 2012 ). The experimental manipulations employed in a number of the studies reviewed earlier hinged entirely on how the task was described to—and so perceived by—participants (e.g., Campbell & Stasser, 2006 ; Kelly & Karau, 1999 ; Kelly & Loving, 2004 ; Rothmund et al., 2011 ; Schittekatte, 1996 ; Stasser & Stewart, 1992 , Stewart & Stasser, 1998 ; Tsai, & Bendersky, 2016 ). In the same way that researchers are able to frame the nature of experimental tasks for study participants, leaders can frame for their groups the nature of many of the problem-solving and decision-making tasks that they undertake. This need not be complex or elaborate—simply calling attention to the importance of information sharing can be helpful ( van Ginkel, Tindale, & van Knippenberg, 2009 ). Further, because members often assume that others in the group are privy to the same information they are, they may be unaware of the possibility—and even likelihood—that a significant amount of important but uniquely held information may exist within the group (cf. Phillips, Northcraft, & Neale, 2006 ). Leaders would do well to underscore this possibility. And if certain members are known by the leader to have special areas of expertise, bringing this to the attention of the entire group can also benefit information sharing ( Stasser, Stewart, & Wittenbaum, 1995 ; Stewart & Stasser, 1995 ; see also Mell et al., 2014 ).

Third, in most cases, leaders should avoid starting problem-solving and decision-making meetings by asking members to share their initial solution preferences with the rest of the group, as doing so often stifles information sharing and degrades the quality of the group’s problem-solving and decision-making performance ( Mojzisch & Schulz-Hardt, 2010 , Exp. 4). An exception is when the leader is reasonably certain that there is a strong diversity of opinion among members about the desirability of various solution alternatives, and members are all committed to the same goal of finding the one solution that objectively is best. In this case, having members share their initial solution preferences at the start of a meeting may actually fuel information sharing within the group ( Brodbeck et al., 2002 ; Lu et al., 2012 ).

Fourth, under certain circumstances leaders might consider employing one or more formal techniques specifically designed to improve information sharing and processing within groups. For example, someone in the group, preferably a high-status member (but not the leader), might be assigned to play the role of “devil’s advocate,” and so to question assumptions made by the group and attempt to show why the group’s consensus solution, once it has emerged, should not be adopted (e.g., Waddell, Roberto, & Yoon, 2013 ). Alternatively, before beginning their main task, members might be asked to engage in a preliminary reflexivity exercise wherein they deliberately discuss the group’s goals and processes ( Konradt, Schippers, Garbers, & Steenfatt, 2015 ; Schippers, Edmondson, & West, 2014 ; but see also Moreland & McMinn, 2010 ). Or members might be asked to prepare themselves by first performing either a counterfactual thinking or a perspective-taking exercise. The former involves thinking about something that might have occurred, but in fact did not (e.g. Galinsky & Kray, 2004 ; Liljenquist, Galinsky, & Kray, 2004 ), whereas the latter asks members to imagine themselves in the place of the other attendees and to try to understand the viewpoints and perspectives that those others may have (e.g., Galinsky et al., 2014 , Experiment 3). All of these techniques have been shown or suggested to improve the sharing of uniquely held information when groups perform problem-solving tasks that involve distributed information.

Fifth, leaders have two important process management roles to perform during the course of group discussion that can benefit information sharing. One is to ensure that everyone in the group is heard, and that the information shared by low-status members and by those who hold minority opinions is given full consideration. The other is to make certain that all of the information that is shared by members is kept alive and available for further examination as discussion moves forward ( Larson, 2010 ; Maier, 1967 ). Meetings have an obvious temporal dimension to them, with different pieces of information surfacing at different points in time, often in haphazard fashion. It is important that all of that information be properly considered, and in particular that facts with interdependent meaning be evaluated in light of one another ( Fraidin, 2004 ). Although there is evidence that interdependent facts held uniquely by different group members are often shared in close temporal proximity during group meetings ( Deiglmayr & Spada, 2010 ), this clearly does not happen in every instance. Consequently, systematic efforts must be made to ensure that earlier mentioned information—including details that might initially seem insignificant—are not forgotten, as those details may prove crucial when set alongside other information that surfaces much later on.

Finally, and also with respect to the temporal dimension of group meetings, there is a strong tendency for commonly held information to be shared before uniquely held information (Larson, Christensen, et al., 1996 , 1998 ). This means that group discussions will often be dominated early on by information that everyone was aware of already, and that uniquely held information will come to light more slowly and only after much attention has already been paid to that commonly held information. Thus, it is important for leaders to ensure that discussion is not terminated early, before members have had ample opportunity to share all of the unique information they hold. At minimum, this means scheduling meetings so that there is plenty time for discussion. But it also means resisting members’ natural inclination to coalesce on whatever consensus solution might be suggested by the easily surfaced information that they hold in common. It is precisely when such a consensus starts to build that further probing by the leader may be most beneficial for eliciting potentially valuable uniquely held information, and so ensuring that no important facts or points of view are overlooked.

Thus, there would seem to be much that leaders can do to promote information sharing within their groups. Still, the task of eliciting information, particularly that which is uniquely held, is not easy. In the real world, it is usually impossible to know exactly how information is distributed among group members, or when all of the important information bearing on a problem or decision has in fact been shared. Even so, if leaders are vigilant to the possibility that a hidden profile might exist, and make persistent efforts to surface whatever relevant information members may hold, the benefits of information sharing for learning within groups in organizations will have the best chance of being realized.

Declarative knowledge is knowledge about how the world is and is often contrasted in cognitive science with procedural knowledge, which is knowledge about how to do something (e.g., Ohlsson, 2011 ). Knowing how to ride a bicycle is an example of the latter. Procedural knowledge frequently requires multiple trials to attain, and, because it is often implicit and not easily codified, can be surprisingly difficult to communicate to others. As a result, attempts to share procedural knowledge may sometimes be limited simply to creating circumstances that facilitate observation by others, as when an artisan arranges for an apprentice to observe her as she plies her craft. The sharing of procedural knowledge is clearly important in many organizational settings (e.g., for job training). However, we do not consider it in any depth here.

This one-sided focus obliges us to define information sharing (“the active communication of what one knows”) in a way that is independent of the recipient’s state of knowledge. In particular, we do not define information sharing in terms of the recipient’s knowledge gain. Although incompatible with classical information theory (e.g., Shannon & Weaver, 1949 ), this approach allows us to consider potentially useful behavior that otherwise would have to be ignored. For example, communicating a piece of task-relevant information that the recipient already knows might still have utility if it serves an attention-focusing function or acts as a substitute for recall (cf. Larson & Christensen, 1993 ).

For the sake of simplicity, in this chapter we make no distinction between problem solving and decision making, and so use the terms interchangeably. See Larson (2010) for separate treatments of the two.

Hidden profiles that are intermediate in strength between those shown in the center and right-hand panels of Table 8.1 can also be created, for example, by expanding the number of choice alternatives (e.g., to 3, 4, or more) and then distributing the information in ways that create varying degrees of diversity in member preferences for those alternatives (cf. Brodbeck, Kerschreiter, Mojzisch, Frey, & Schulz-Hardt, 2002 ; Schulz-Hardt, Brodbeck, Mojzisch, Kerschreiter, & Frey, 2006 ). Thus, hidden profile strength is a continuum, not a dichotomy. Circumstances involving diverse member preferences of this sort are considered later in the chapter.

Because Figure 8.1 considers only information that was experimentally manipulated to be either common or uniquely held, the results shown for uniquely held information are necessarily a mirror image of those shown for commonly held information.

Repetitions of already shared information, whether by the person who shared it initially or by others, are ignored.

For example, in the Manifest profile shown in Table 8.1 , there are 9 pieces of commonly held information (3 favoring A and 6 favoring B ) and 9 pieces of uniquely held information (also 3 favoring A and 6 favoring B ). Thus, at the outset of discussion, before any information has been shared, the group collectively has 9 × 3 = 27 opportunities to sample (share) a piece of commonly held information, but only 9 × 1 = 9 opportunities to sample a piece of uniquely held information.

To illustrate, and again using as an example the Manifest profile shown in Table 8.1 , if a piece of commonly held information is the very first thing shared during discussion, then the number of opportunities to sample a second piece of commonly held information would drop by 3, from 27 to 24. Thus, whereas at the outset of discussion the probability of sharing commonly held information is 27/(27 + 9) = .75 and the probability of sharing unique information is 9/(27 + 9) = .25, if a piece of commonly held information is in fact shared at the outset, then the conditional probability of sharing a second piece of commonly held information would drop to 24/(24 + 9) = .73, while the conditional probability of sharing a piece of uniquely held information would rise to 9/(24 + 9) = .27. By contrast, if a piece of uniquely held information had been shared initially, the number of opportunities to sample a second piece of uniquely held information would drop by only 1, from 9 to 8, and the conditional probabilities of the second piece of information shared being either common or uniquely held would be .77 and .23, respectively. But the former scenario is more likely than the latter, so the compound probability of the second item shared being commonly held information is actually (.75 × .73) + (.25 × .77) = .74, whereas that of it being unique information is (.75 × .27) + (.25 × .23) = .26. These latter values are only slightly different from what existed initially (.75 and .25, respectively), but the differences grow increasingly larger as more and more information is sampled and shared with the group as discussion proceeds. For example, after two thirds of the available information has been shared, the compound probability of the next (13th) item shared being commonly held information will have dropped to .40, whereas the compound probability of it being uniquely held information will have risen to .60.

DISM-GD has recently been reimplemented as an agent-based model using the NetLogo modeling environment ( Larson, 2017 ), and it is available upon request from the first author.

For the sake of simplicity, we consider only the case of two pieces of information having interdependent meaning. But in principle, the meaning of any number of items might be interdependent.

This same interpretational ambiguity does not exist for either the manifest or weak hidden profiles shown in Table 8.1 . Regarding the manifest profile (left-hand panel), half of the information about each choice alternative is common and half is unique. Thus, although the information is distributed there in a way that encourages a prediscussion preference for one choice alternative, whether the information is held in common or held uniquely by members is independent of whether it is preference consistent or inconsistent. Consequently, the predicted tendency to share more common than uniquely held information can be clearly distinguished from a motivated tendency to share preference-consistent information. Regarding weak hidden profiles (e.g., center panel of Table 8.1 ), the decision-relevant information is distributed among members in a way that should not encourage the formation of a strong prediscussion preference in the first place. Nevertheless, in both manifest and weak hidden profile situations, groups have been shown to share more of their common than uniquely held information (e.g., Greitemeyer, Schulz-Hardt, Brodbeck, & Frey, 2006 ; Lavery, Franz, Winquist, & Larson, 1999 ; Winquist & Larson, 1998 ; see also Sohrab, 2014 ). Such results cannot be explained by a motivated propensity of members to share preference-consistent information. Instead they seem to be due simply to the probabilistic sampling advantage of commonly held information.

Information load can also vary at the individual level, independently of task-level information load, simply by varying the amount of information that members hold in common (e.g., compare the center- and right-hand panels of Table 8.1 ). Doing so is likely to affect the information sharing bias favoring commonly held information (e.g., Cruz, Boster, & Rodriguez, 1997 , Schittekatte, 1996 , Experiment 1; Stasser, Taylor, & Hanna, 1989 ; Stasser & Titus, 1987 ).

Note that physical proximity is not part of the definition of virtuality. Although groups whose members are physically dispersed are highly likely to communicate virtually, even those that are colocated and can easily communicate face to face if they wish may nevertheless often choose to communicate and coordinate their activities in a highly virtual manner.

It should be noted that the meta-analytic review by Reimer et al. (2010) found that the overall amount of time allotted for a decision-making discussion had a moderating effect just opposite to what is suggested here, with shorter (less than 30-minute) discussions yielding smaller differences in the amount of common versus uniquely held information shared than longer discussions (30 minutes or more). The meaning of that result is unclear, however, as having less time for discussion does not, by itself, necessarily imply greater time pressure, particularly when the amount of time available for discussion covaries with other factors such as the number of decision alternatives and group size, as was the case in that meta-analysis. Simpler decisions made by smaller groups do not necessary require as much time to resolve as more complex problems involving larger groups.

Anderson, C. , & Brion, S. ( 2014 ). Perspectives on power in organizations.   Annual Review of Organizational Psychology and Organizational Behavior , 1 , 67–97.

Google Scholar

Argote, L. , Gruenfeld, D. , & Naquin, C. ( 2001 ). Group learning in organizations. In M. E. Turner (Ed.), Groups at work: Theory and research (pp. 369–411). Mahwah, NJ: Erlbaum.

Google Preview

Bass, B. M. ( 2008 ). The Bass handbook of leadership: Theory, research, and managerial applications (4th ed.). New York, NY: The Free Press.

Beersma, B. , Hollenbeck, J. R. , Conlon, D. E. , Humphrey, S. E. , Moon, H. , & Ilgen, D. R. ( 2009 ). Cutthroat cooperation: The effects of team role decisions on adaptation to alternative reward structures.   Organizational Behavior and Human Decision Processes , 108 , 131–142.

Beersma, B. , Hollenbeck, J. R. , Humphrey, S. E. , Moon, H. , Conlon, D. E. , & Ilgen, D. R. ( 2003 ). Cooperation, competition, and team performance: Toward a contingency approach.   Academy of Management Journal , 46 , 572–590.

Bluedorn, A. C. , Turban, D. B. , & Love, M. S. ( 1999 ). The effects of stand-up and sit-down meeting formats on meeting outcomes.   Journal of Applied Psychology , 84 , 277–285.

Bond, C. F., Jr. , & DePaulo, B. M. ( 2006 ). Accuracy of deception judgments.   Personality and Social Psychology Review , 10 , 214–234.

Bowman, J. M. , & Wittenbaum, G. ( 2012 ). Time pressure affects process and performance in hidden-profile groups.   Small Group Research , 43 , 295–314.

Brodbeck, F. C. , Kerschreiter, R. , Mojzisch, A. , Frey, D. , & Schulz-Hardt, S. ( 2002 ). The dissemination of critical, unshared information in decision-making groups: The effects of pre-discussion dissent.   European Journal of Social Psychology , 32 , 35–56.

Brodbeck, F. C. , Kerschreiter, R. , Mojzisch, A. , & Schulz-Hardt, S. ( 2007 ). Group decision making under conditions of distributed knowledge: The Information Asymmetries Model.   Academy of Management Review , 32 , 459–479.

Burke, C. S. , Stagl, K. C. , Salas, E. , Pierce, L. , & Kendall, D. ( 2006 ). Understanding team adaptation: A conceptual analysis and model.   Journal of Applied Psychology , 91 , 1189–1207.

Burnstein, E. ( 1982 ). Persuasion as argument processing. In H. Bandstatter , J. H. Davis , & G. Stocker-Kreichgauer (Eds.), Group decision making (pp. 103–124). New York, NY: Academic Press.

Cacioppo, J. T. , Petty, R. E. , Feinstein, J. A. , & Jarvis, W. B. G. ( 1996 ). Dispositional differences in cognitive motivation: The life and times of individuals varying in need for cognition.   Psychological Bulletin , 119 , 197–253.

Campbell, J. , & Stasser, G. ( 2006 ). The influence of time and task demonstrability on decision-making in computer-mediated and face-to-face groups.   Small Group Research , 37 , 271–294.

Cavaliere, V. , Lombardi, S. & Giustiniano, L. ( 2015 ). Knowledge sharing in knowledge-intensive manufacturing firms: An empirical study of its enablers.   Journal of Knowledge Management , 19 , 1124–1145.

Christensen, C. , Larson, J. R., Jr. , Abbott, A. , Ardolino, A. , Franz, T. , & Pfeiffer, C. ( 2000 ). Decision-making of clinical teams: Communication patterns and diagnostic error.   Medical Decision Making , 20 , 45–50.

Courtright, S. H. , Thurgood, G. R. , Stewart, G. L. , & Pierotti, A. J. ( 2015 ). Structural interdependence in teams: An integrative framework and meta-analysis.   Journal of Applied Psychology , 100 , 1825–1846.

Cress, U. , & Kimmerle, J. ( 2007 ). Guidelines and feedback in information exchange: The impact of behavioral anchors and descriptive norms in a social dilemma.   Group Dynamics: Theory, Research, and Practice , 11 , 42–53.

Cruz, M. G. , Boster, F. J. , & Rodriguez, J. I. ( 1997 ). The impact of group size and proportion of shared information on the exchange and integration of information in groups.   Communication Research , 24 , 291–313.

De Dreu, C. K. W. , Nijstad, B. A. , & van Knippenberg, D. ( 2008 ). Motivated information processing in group judgment and decision making.   Personality and Social Psychology Review , 12 , 22–49.

Deiglmayr, A. , & Spada, H. ( 2010 ). Collaborative problem-solving with distributed information: The role of inferences from interdependent information.   Group Processes & Intergroup Relations , 13 , 361–378.

DeMatteo, J. S. , Eby, L. T. , & Sundstrom, E. S. ( 1998 ). Group rewards and group effectiveness: A critical review.   Research in Organizational Behavior , 20 , 141–183.

Dennis, A. R. ( 1996 a). Information exchange and use in group decision making: You can lead a group to information, but you can’t make it think.   MIS Quarterly , 20 , 433–457.

Dennis, A. R. ( 1996 b). Information exchange and use in small group decision making.   Small Group Research , 27 , 532–550.

Edmondson, A. C. ( 1999 ). Psychological safety and learning behavior in work teams.   Administrative Science Quarterly , 44 , 350–383.

Edmondson, A. C. ( 2003 ). Speaking up in the operating room: How team leaders promote learning in interdisciplinary action teams.   Journal of Management Studies , 40 , 1419–1452.

Edwards, K. , & Smith, E. E. ( 1996 ). A disconfirmation bias in the evaluation of arguments.   Journal of Personality and Social Psychology , 71 , 5–24.

Emerson, R. M. ( 1962 ). Power dependence relations.   American Sociological Review, 27, 30–41.

Fast, N. J. , Gruenfeld, D. H. , Sivanathan, N. , & Galinsky, A. D. ( 2009 ). Illusory control: A generative force behind power’s far-reaching effects.   Psychological Science , 20 , 502–508.

Faulmüller, N. , Mojzisch, A. , Kerschreiter, R. , & Schulz-Hardt, S. ( 2012 ). Do you want to convince me or to be understood? Preference-consistent information sharing and its motivational determinants.   Personality and Social Psychology Bulletin , 38 , 1685–1697.

Fiske, S. T. ( 2010 ). Interpersonal stratification: Status, power, and subordination. In S. T. Fiske , D. T. Gilbert , & G. Lindzey (Eds.), Handbook of social psychology (5th ed., Vol. 2, pp. 941–982). New York, NY: Wiley.

Fraidin, S. N. ( 2004 ). When is one head better than two? Interdependent information in group decision making.   Organizational Behavior and Human Decision Processes , 93 , 102–113.

Galinsky, A. D. , Gruenfeld, D. H. , & Magee, J. C. ( 2003 ). From power to action.   Journal of Personality and Social Psychology , 85 , 453–466.

Galinsky, A. D. , & Kray, L. J. ( 2004 ). From thinking about what might have been to sharing what we know: The role of counterfactual mind-sets in information sharing in groups.   Journal of Experimental Social Psychology , 40 , 606–618.

Galinsky, A. D. , Magee, J. C. , Rus, D. , Rothman, N. B. , & Todd, A. R. ( 2014 ). Acceleration with steering: The synergistic benefits of combining power and perspective-taking.   Social Psychological and Personality Science , 5 , 627–635.

Gibson, C. B. , Porath, C. L. , Benson, G. S. , & Lawler, E. E. ( 2007 ). What results when firms implement practices: The differential relationship between specific practices, firm financial performance, customer service, and quality.   Journal of Applied Psychology , 92 , 1467–1480.

Gigone, D. M. ( 1996 ). Group discussion and small group decision making: Effects of task and subjective meaning of information (Unpublished doctoral dissertation). University of Colorado, Boulder.

Greitemeyer, T. , & Schulz-Hardt, S. ( 2003 ). Preference-consistent evaluation of information in the hidden profile paradigm: Beyond group-level explanations for the dominance of shared information in group decisions.   Journal of Personality and Social Psychology , 84 , 322–339.

Greitemeyer, T. , Schulz-Hardt, S. , Brodbeck, F. C. , & Frey, D. ( 2006 ). Information sampling and group decision making: The effects of an advocacy decision procedure and task experience.   Journal of Experimental Psychology: Applied , 12 , 31–42.

Greitemeyer, T. , Schulz-Hardt, S. , & Frey, D. ( 2003 ). Preference consistency and sharedness of information as predictors of information evaluation and intended behavior in group discussions.   Zeitschrift fur Sozialpsychologie , 34 , 9–23.

Grice, H. P. ( 1975 ). Logic and conversation. In P. Cole & J. Morgan (Eds.), Syntax and semantics 3: Speech acts (pp. 41–58). New York, NY: Seminar Press.

Guinote, A. ( 2007 ). Power and goal pursuit.   Personality and Social Psychology Bulletin , 33 , 1076–1087.

Hackman, J. R. ( 1969 ). Toward understanding the role of tasks in behavioral research.   Acta Psychologica , 31 , 97–128.

Hayek, A.-S. , Toma, C. , Oberlé, D. , & Butera, F. ( 2015 ). Grading hampers cooperative information sharing in group problem solving.   Social Psychology , 46 , 121–131.

Henningsen, D. D. , & Henningsen, M. L. M. ( 2003 ). Examining social influence in information-sharing contexts.   Small Group Research , 34 , 391–412.

Henningsen, D. D. , & Henningsen, M. L. M. ( 2004 ). The effect of individual difference variables on information sharing in decision-making groups.   Human Communication Research , 30 , 540–555.

Higgins, E. T. ( 1998 ). Promotion and prevention: Regulatory focus as a motivational principle. In M. P. Zanna (Ed.), Advances in experimental social psychology (Vol. 30, pp. 1–46). New York, NY: Academic Press.

Hightower, R. , & Sayeed, L. ( 1996 ). Effects of communication mode and prediscussion information distribution characteristics on information exchange in groups.   Information Systems Research , 7 , 451–465.

Hirak, L. R. , Peng, A. C. , Carmeli, A. , & Schaubroeck, J. M. ( 2012 ). Linking leader inclusiveness to work unit performance: The importance of psychological safety and learning from failures.   The Leadership Quarterly , 23 , 107–117.

Hoch, J. E. ( 2014 ). Shared leadership, diversity, and information sharing in teams.   Journal of Managerial Psychology , 29 , 541–564.

Hogarth, R. M. , & Einhorn, H. J. ( 1992 ). Order effects in belief updating: The belief-adjustment model.   Cognitive Psychology , 24 , 1–55.

Hollander, E. A. ( 1958 ). Conformity, status, and idiosyncrasy credit.   Psychological Review , 65 , 117–127.

Hollingshead, A. B. ( 1996 ). Information suppression and status persistence in group decision making: The effects of communication media.   Human Communication Research , 23 , 193–219.

Huang, W. W. , & Wei, K. K. ( 2000 ). An empirical investigation of the effects of group support systems (GSS) and task type of group interactions from an influence perspective.   Journal of Management Information Systems , 17 , 181–206.

Humphrey, S. E. , & Aime, F. ( 2014 ). Team microdynamics: Toward an organizing approach to teamwork.   Academy of Management Annals , 8 , 443–503.

Janis, I. L. ( 1982 ). Groupthink: Psychological studies of policy decisions and fiascoes (2nd ed.). Boston, MA: Houghton Mifflin.

Johnson, D. W. , & Johnson, R. T. ( 1989 ). Cooperation and competition: Theory and research . Edina, MN: Interaction.

Johnson, D. W. , & Johnson, R. T. ( 1992 ). Positive interdependence: Key to effective cooperation. In N. Miller & R. Hertz-Lazarowitz (Eds.), Interaction in cooperative groups: The theoretical anatomy of group learning (pp. 174–199). New York, NY: Cambridge University Press.

Johnson, M. D. , Hollenbeck, J. R. , Humphrey, S. E. , Ilgen, D. R. , Jundt, D. , & Meyer, C. J. ( 2006 ). Cutthroat cooperation: Asymmetrical adaptation to changes in team reward structures.   Academy of Management Journal , 49 , 103–119.

Kaplan, M. F. , & Miller, C. E. ( 1987 ). Group decision making and normative versus informational influence: Effects of type of issue and assigned decision rule.   Journal of Personality and Social Psychology , 53 , 306–313.

Kaplan, M. F. , Schaefer, E. G. , & Zinkiewicz, L. ( 1994 ). Member preferences for discussion content in anticipated group decisions: Effects of type of issue and group interactive goal.   Basic and Applied Social Psychology , 15 , 489–508.

Karau, S. J. , & Kelly, J. R. ( 1992 ). The effects of time scarcity and time abundance on group performance quality and interaction process.   Journal of Experimental Social Psychology , 28 , 542–571.

Kelly, J. R. , Jackson, J. W. , & Hutson-Comeaux, S. L. ( 1997 ). The effects of time pressure and task differences on influence modes and accuracy in decision-making groups.   Personality and Social Psychology Bulletin , 23 , 10–22.

Kelly, J. R. , & Karau, S. J. ( 1999 ). Group decision making: The effects of initial preferences and time pressure.   Personality and Social Psychology Bulletin , 25 , 1343–1354.

Kelly, J. R. , & Loving, T. J. ( 2004 ). Time pressure and group performance: Exploring underlying processes in the attentional focus model.   Journal of Experimental Social Psychology , 40 , 185–198.

Keltner, D. , Gruenfeld, D. H. , & Anderson, C. ( 2003 ). Power, approach, and inhibition.   Psychological Review , 110 , 265–284.

Kilduff, G. J. , & Galinsky, A. D. ( 2013 ). From the ephemeral to the enduring: How approach-oriented mindsets lead to greater status.   Journal of Personality and Social Psychology , 105 , 816–831.

Kim, S. L. , Kim, M. , & Yun, S. ( 2015 ). Knowledge sharing, abusive supervision, and support: A social exchange perspective.   Group & Organization Management , 40 , 599–624.

Kim, S. L. , & Yun, S. ( 2015 ). The effect of coworker knowledge sharing on performance and its boundary conditions: An interactional perspective.   Journal of Applied Psychology , 100 , 575–582.

Kimmerle, J. , Wodzicki, K. , Jarodzka, H. , & Cress, U. ( 2011 ). Value of information, behavioral guidelines, and social value orientation in an information-exchange dilemma.   Group Dynamics: Theory, Research, and Practice , 15 , 173–186.

Koehler, J. J. ( 1993 ). The influence of prior beliefs on scientific judgments of evidence quality.   Organizational Behavior and Human Decision Processes , 56 , 28–55.

Kolbe, M. , Grote, G. , Waller, M. J. , Wacker, J. , Grande, B. , Burtscher, M. J. , & Spahn, D. R. ( 2014 ). Monitoring and talking to the room: Autochthonous coordination patterns in team interaction and performance.   Journal of Applied Psychology , 99 , 1254–1267.

Konradt, U. , Schippers, M. C. , Garbers, Y. & Steenfatt, C. ( 2015 ) Effects of guided reflexivity and team feedback on team performance improvement: The role of team regulatory processes and cognitive emergent states.   European Journal of Work and Organizational Psychology , 24 , 777–795.

Kirkman, B. L. , & Mathieu, J. E. ( 2005 ). The dimensions and antecedents of team virtuality.   Journal of Management , 31 , 700–718.

Klocke, U. ( 2007 ). How to improve decision making in small groups: Effects of dissent and training interventions.   Small Group Research , 38 , 437–468.

Kruglanski, A. W. ( 2004 ). The psychology of closed mindedness . New York, NY: Psychology Press.

Kruglanski, A. W. , Dechesne, M. , Orehek, E. , & Pierro, A. ( 2009 ). Three decades of lay epistemics: The why, how and who of knowledge formation.   European Review of Social Psychology , 20 , 146–191.

Kruglanski, A. W. , Orehek, E. , Dechesne, M. , & Pierro, A. ( 2010 ). Lay epistemic theory: The motivational, cognitive, and social aspects of knowledge formation.   Social and Personality Psychology Compass , 4 , 939–950.

Kruglanski, A. W. , & Webster, D. M. ( 1991 ). Group members’ reactions to opinion deviates and conformists at varying degrees of proximity to decision deadline and of environmental noise.   Journal of Personality and Social Psychology , 61 , 212–225.

Kruglanski, A. W. , & Webster, D. M. ( 1996 ). Motivated closing of the mind: “Seizing” and “freezing.” Psychological Review , 103 , 263–283.

Larson, J. R., Jr. ( 1997 ). Modeling the entry of shared and unshared information into group discussion: A review and BASIC language computer program.   Small Group Research , 28 , 454–479.

Larson, J. R., Jr. ( 2010 ). In search of synergy in small group performance . New York, NY: Psychology Press.

Larson, J. R., Jr. ( 2017 ). A re-implementation of the Dynamic Information Sampling Model of Group Discussion (DISM-GD): An agent-based model . Unpublished software.

Larson, J. R., Jr. , & Christensen, C. ( 1993 ). Groups as problem-solving units: Toward a new meaning of social cognition.   British Journal of Social Psychology , 32 , 5–30.

Larson, J. R., Jr. , Christensen, C. , Abbott, A. S. , & Franz, T. M. ( 1996 ). Diagnosing groups: Charting the flow of information in medical decision making teams.   Journal of Personality and Social Psychology , 71 , 315–330.

Larson, J. R., Jr. , Christensen, C. , Franz, T. M. , & Abbott, A. S. ( 1998 ). Diagnosing groups: The pooling, management, and impact of shared and unshared case information in team-based medical decision making.   Journal of Personality and Social Psychology , 75 , 93–108.

Larson, J. R., Jr. , Foster-Fishman, P. G. , & Franz, T. M. ( 1998 ). Leadership style and the discussion of shared and unshared information in decision-making groups.   Personality and Social Psychology Bulletin , 24 , 482–495.

Larson, J. R., Jr. , Foster-Fishman, P. G. , & Keys, C. B. ( 1994 ). Information sharing in decision making groups.   Journal of Personality and Social Psychology , 67 , 446–461.

Lavery, T. A. , Franz, T. M. , Winquist, J. R. , & Larson, J. R., Jr. ( 1999 ). The role of information exchange in predicting group accuracy on a multiple judgment task.   Basic and Applied Social Psychology , 21 , 281–289.

Leary, M. R. , Jongman-Sereno, K. P. , & Diebels, K. J. ( 2014 ). The pursuit of status: A self-presentational perspective on the quest for social value. In J. T. Cheng , J. L. Tracy , & C. Anderson (Eds.), The psychology of social status (pp. 159–178.). New York, NY: Springer.

Li, Y. , Ye, F. , & Sheu, C. ( 2014 ). Social capital, information sharing, and performance: Evidence from China.   International Journal of Operations & Production Management , 34 , 1440–1462.

Liljenquist, K. A. , Galinsky, A. D. , & Kray, L. J. ( 2004 ). Exploring the rabbit hole of possibilities by myself or with my group: The benefits and liabilities of activating counterfactual mind-sets for information sharing and group coordination.   Journal of Behavioral Decision Making , 17 , 263–279.

Littlepage, G. , Perdue, E. B. , & Fuller, D. K. ( 2012 ). Choice of information to discuss: Effects of objective validity and social validity.   Small Group Research , 43 , 252–274.

Locke, C. C. , & Anderson, C. ( 2015 ). The downside of looking like a leader: Power, nonverbal confidence, and participative decision-making.   Journal of Experimental Social Psychology , 58 , 42–47.

Locke, E. A. , & Latham, G. P. ( 2002 ). Building a practically useful theory of goal setting and task motivation.   American Psychologist , 57 , 705–717.

Lu, L. , Yuan, Y. C. , & McLeod, P. L. ( 2012 ). Twenty-five years of hidden profiles in group decision making: A meta-analysis.   Personality and Social Psychology Review , 16 , 54–75.

Maier, N. R. F. ( 1967 ). Assets and liabilities in group problem solving: The need for an integrative function.   Psychological Review , 74 , 239–249.

Maner, J. K. , & Mead, N. L. ( 2010 ). The essential tension between leadership and power: When leaders sacrifice group goals for the sake of self-interest.   Journal of Personality and Social Psychology , 99 , 482–497.

Mannix, E. A. , & Sauer, S. J. ( 2006 ). Status and power in organizational group research: Acknowledging the pervasiveness of hierarchy. In S. R. Thye & E. J. Lawler (Eds.), Advances in group processes (Vol. 23, pp, 149–182) Bingley, UK: Emerald Group.

Mell, J. N. , van Knippenberg, D. , & van Ginkel, W. P. ( 2014 ). The catalyst effect: The impact of transactive memory system structure on team performance.   Academy of Management Journal , 57 , 1154–1173.

Mesmer-Magnus, J. R. , & DeChurch, L. A. ( 2009 ). Information sharing and team performance: A meta-analysis.   Journal of Applied Psychology , 94 , 535–546.

Mesmer-Magnus, J. R. , DeChurch, L. A. , Jimenez-Rodriguez, M. , Wildman, J. & Shuffler, M. ( 2011 ). A meta-analytic investigation of virtuality and information sharing in teams.   Organizational Behavior and Human Decision Processes , 115 , 214–225.

Messick, D. M. , & McClintock, C. G. ( 1968 ). Motivational bases of choice in experimental games.   Journal of Experimental Social Psychology , 4 , 1–25.

Mojzisch, A. , Grouneva, L. , & Schulz-Hardt, S. ( 2010 ). Biased evaluation of information during discussion: Disentangling the effects of preference consistency, social validation, and ownership of information.   European Journal of Social Psychology , 40 , 946–956.

Mojzisch, A. , Kerschreiter, R. , Faulmüller, N. , Vogelgesang, F. , & Schulz-Hardt, S. ( 2014 ). The consistency principle in interpersonal communication: Consequences of preference confirmation and disconfirmation in collective decision making.   Journal of Personality and Social Psychology , 106 , 961–977.

Mojzisch, A. , & Schulz-Hardt, S. ( 2010 ). Knowing others’ preferences degrades the quality of group decision.   Journal of Personality and Social Psychology , 98 , 793–808.

Moon, H. , Hollenbeck, J. R. , Humphrey, S. E. , Ilgen, D. R. , West, B. , Ellis, A. P. J. , & Porter, C. O. L. H. ( 2004 ). Asymmetric adaptability: Dynamic team structures as one-way streets.   Academy of Management Journal , 47 , 681–695.

Moreland, R. L. , & McMinn, J. G. ( 2010 ). Group reflexivity and performance. In S. R. Thye & E. J. Lawler (Eds.), Advances in group processes (Vol. 27, pp. 63–95). Bingley, UK: Emerald Group.

Morris, W. L. , Sternglanz, R. W. , Ansfield, M. E. , Anderson, D. E. , Snyder, J. L. H. , & DePaulo, B. M. ( 2016 ). A longitudinal study of the development of emotional deception detection within new same-sex friendships.   Personality and Social Psychology Bulletin , 42 , 204–218.

Morrison, E. W. , See, K. E. , & Pan, C. ( 2015 ). An approach-inhibition model of employee silence: The joint effects of personal sense of power and target openness.   Personnel Psychology , 68 , 547–580.

Nevicka, B. , Ten Velden, F. S. , De Hoogh, A. H. B. , & Van Vianen, A. E. M. ( 2011 ). Reality at odds with perceptions: Narcissistic leaders and group performance.   Psychological Science , 22 , 1259–1264.

Nijstad, B. A. , & De Dreu, C. K. W. ( 2012 ). Motivated information processing in organizational teams: Progress, puzzles, and prospects. In A. P. Brief & B. M. Staw (Eds.), Research in organizational behavior: An annual series of analytical essays and critical reviews (vol. 32, pp. 87–111). New York, NY: Elsevier.

Ohlsson, S. ( 2011 ). Deep learning: How the mind overrides experience . Cambridge, UK: Cambridge University Press.

Okhuysen, G. A. , & Eisenhardt, K. M. ( 2002 ). Integrating knowledge in groups: How formal interventions enable flexibility.   Organizational Science , 13 , 370–386.

Okhuysen, G. A. , & Bechky, B. A. ( 2009 ). Coordination in organizations: An integrative perspective.   The Academy of Management Annals , 3 , 463–502.

Owens, D. A. , & Sutton, R. I. ( 2001 ). Status contests in meetings: Negotiating the informal order. In M. E. Turner (Ed.), Groups at work: Theory and research (pp. 299–316). Mahwah, NJ: Lawrence Erlbaum.

Parks, C. D. , & Cowlin, R. ( 1995 ). Group discussion as affected by number of alternatives and by a time limit.   Organizational Behavior and Human Decision Processes , 62 , 267–275.

Pavitt, C. ( 2014 ). An interactive input-process-output model of social influence in decision-making groups.   Small Group Research , 45 , 704–730.

Pearsall, M. J. , Christian, M. S. , & Ellis, A. P. J. ( 2010 ). Motivating interdependent teams: Individual rewards, shared rewards, or something in between?   Journal of Applied Psychology , 95 , 183–191.

Pennington, N. , & Hastie, R. ( 1993 ). Reasoning in explanation-based decision making.   Cognition , 49 , 123–163.

Pfeffer, J. ( 1992 ). Managing with power: Politics and influence in organizations . Cambridge, MA: Harvard Business School Press.

Phillips, K. W. , Northcraft, G. B. , & Neale, M. A. ( 2006 ). Surface-level diversity and decision making in groups: When does deep-level similarity help?   Group Processes and Intergroup Relations , 9 , 467–482.

Pickering, A. D. , & Gray, J. A. ( 1999 ). The neuroscience of personality. In L. A. Pervin & O. P. John (Eds.), Handbook of personality: Theory and research (2nd ed., pp. 277–299). New York, NY: Guilford.

Poortvliet, P. M. , Anseel, F. , Janssen, O. , Van Yperen, N. W. , & Van de Vliert, E. ( 2012 ). Perverse effects of other-referenced performance goals in an information exchange context.   Journal of Business Ethics , 106 , 401–414.

Poortvliet, P. M. , Janssen, O. , Van Yperen, N. W. , & Van de Vliert, E. ( 2007 ). Achievement goals and interpersonal behavior: How mastery and performance goals shape information exchange.   Personality and Social Psychology Bulletin , 33 , 1435–1447.

Postmes, T. , Spears, R. , & Cihangir, S. ( 2001 ). Quality of decision making and group norms.   Journal of Personality and Social Psychology , 80 , 918–930.

Reimer, T. , Kuendig, S. , Hoffrage, U. , Park, E. , & Hinsz, V. ( 2007 ). Effects of the information environment on group discussions and decisions in the hidden-profile paradigm.   Journal Communication Monographs , 74 , 1–28.

Reimer, T. , Reimer, A. , & Czienskowski, U. ( 2010 ). Decision-making groups attenuate the discussion bias in favor of commonly held information: A meta-analysis.   Communication Monographs , 77 , 121–142.

Reimer, T. , Reimer, A. , & Hinsz, V. B. ( 2010 ). Naive groups can solve the hidden-profile problem.   Human Communication Research , 36 , 443–467.

Ridgeway, C. L. ( 1982 ). Status in groups: The importance of motivation.   American Sociological Review , 47 , 76–88.

Rijnbout, J. S. , & McKimmie, B. M. ( 2014 ). Deviance in organizational decision making: Using unanimous decision rules to promote the positive effects and alleviate the negative effects of deviance.   Journal of Applied Social Psychology , 44 , 455–463.

Rothmund, T. , Mojzisch, A. , & Schulz-Hardt, S. ( 2011 ). Effects of consensus information and task demonstrability on preference-consistent information evaluation and decision quality in group decision making.   Basic and Applied Social Psychology , 33 , 382–390.

Russo, J. E. , Medvec, V. H. , & Meloy, M. G. ( 1996 ). The distortion of information during decisions.   Organizational Behavior and Human Decision Processes , 66 , 102–110.

Russo, J. E. , Meloy, M. G. , & Wilks, T. J. ( 2000 ). Predecisional distortion of information by auditors and salespersons.   Management Science , 46 , 13–27.

Schippers, M. C. , Edmondson, A. C. , & West, M. A. ( 2014 ). Team reflexivity as an antidote to team information-processing failures.   Small Group Research , 45 , 731–769.

Schittekatte, M. ( 1996 ). Facilitating information exchange in small decision-making groups.   European Journal of Social Psychology , 26 , 537–556.

Scholten, L. , van Knippenberg, D. , Nijstad, B. A. , & De Dreu, C. K. W. ( 2007 ). Motivated information processing and group decision-making: Effects of process accountability on information processing and decision quality.   Journal of Experimental Social Psychology , 43 , 539–552.

Schulz-Hardt, S. , Brodbeck, F. C. , Mojzisch, A. , Kerschreiter, R. , & Frey, D. ( 2006 ). Group decision making in hidden profile situations: Dissent as a facilitator for decision quality.   Journal of Personality and Social Psychology , 91 , 1080–1093.

Schulz-Hardt, S. & Mojzisch, A. ( 2012 ). How to achieve synergy in group decision making: Lessons to be learned from the hidden profile paradigm.   European Review of Social Psychology , 23 , 305–343.

Shannon, C. E. , & Weaver, W. ( 1949 ). The mathematical theory of communication . Urbana: University of Illinois Press.

Siegel, J. , Dubrovsky, V. , Kiesler, S. , & McGuire, T. W. ( 1986 ). Group processes in computer-mediated communication.   Organizational Behavior and Human Decision Processes , 37 , 157–187.

Sohrab, G. ( 2014 ). Team interaction patterns under asymmetric information distribution. (Unpublished doctoral dissertation). York University, Toronto.

Sohrab, S. G. , Waller, M. J. , & Kaplan, S. ( 2015 ). Exploring the hidden-profile paradigm: A literature review and analysis.   Small Group Research , 46 , 489–535.

Srivastava, A. , Bartol, K. M. , & Locke, E. A. ( 2006 ). Empowering leadership in management teams: Effects on knowledge sharing, efficacy, and performance.   Academy of Management Journal , 49 , 1239–1251.

Stasser, G. ( 1988 ). Computer simulation as a research tool: The DISCUSS model of group decision making.   Journal of Experimental Social Psychology , 24 , 393–422.

Stasser, G. , Abele, S. , & Parsons, S. V. ( 2012 ). Information flow and influence in collective choice.   Group Processes & Intergroup Relations , 15 , 619–635.

Stasser, G. , & Stewart, D. ( 1992 ). Discovery of hidden profiles by decision-making groups: Solving a problem versus making a judgment.   Journal of Personality and Social Psychology , 63 , 426–434.

Stasser, G. , Stewart, D. D. , & Wittenbaum, G. M. ( 1995 ). Expert roles and information exchange during discussion: The importance of knowing who knows what.   Journal of Experimental Social Psychology , 31 , 244–265.

Stasser, G. , Taylor, L. A. , & Hanna, C. ( 1989 ). Information sampling in structured and unstructured discussions of three- and six-person groups.   Journal of Personality and Social Psychology , 57 , 67–78.

Stasser, G. , & Titus, W. ( 1985 ). Pooling of unshared information in group decision making: Biased information sampling during group discussion.   Journal of Personality and Social Psychology , 48 , 1467–1478.

Stasser, G. , & Titus, W. ( 1987 ). Effects of information load and percentage of shared information on the dissemination of unshared information during group discussion.   Journal of Personality and Social Psychology , 53 , 81–93.

Steinel, W. , & De Dreu, C. K. W. ( 2004 ). Social motives and strategic misrepresentation in social decision making.   Journal of Personality and Social Psychology , 86 , 419–434.

Steinel, W. , Utz, S. , & Koning, L. ( 2010 ). The good, the bad and the ugly thing to do when sharing information: Revealing, concealing and lying depend on social motivation, distribution and importance of information.   Organizational Behavior and Human Decision Processes , 113 , 85–96.

Stewart, D. D. , & Stasser, G. ( 1995 ). Expert role assignment and information sampling during collective recall and decision making.   Journal of Personality & Social Psychology , 69 , 619–628.

Stewart, D. D. , & Stasser, G. ( 1998 ). The sampling of critical, unshared information in decision-making groups: The role of an informed minority.   European Journal of Social Psychology , 28 , 95–113.

Stone, T. H. , & Cooper, W. H. ( 2009 ). Emerging credits.   Leadership Quarterly , 20 , 785–798.

Sturm, R. E. , & Antonakis, J. ( 2015 ). Interpersonal power a review, critique, and research agenda.   Journal of Management , 41 , 136–163.

Toma, C. , & Butera, F. ( 2009 ). Hidden profiles and concealed information: Strategic information sharing and use in group decision making.   Personality and Social Psychology Bulletin , 35 , 793–806.

Toma, C. , & Butera, F. ( 2015 ). Cooperation versus competition effects on information sharing and use in group decision-making.   Social and Personality Psychology Compass , 9 , 455–467.

Toma, C. , Gilles, I. , & Butera, F. ( 2013 ). Strategic use of preference confirmation in group decision making: The role of competition and dissent.   British Journal of Social Psychology , 52 , 44–63.

Toma, C. , Vasiljevic, D. , Oberlé, D. , & Butera, F. ( 2013 ). Assigned experts with competitive goals withhold information in group decision making.   British Journal of Social Psychology , 52 , 161–172.

Tost, L. P. , Gino, F. , & Larrick, R. P. ( 2013 ). When power makes others speechless: The negative impact of leader power on team performance.   Academy of Management Journal , 56 , 1465–1486.

Trabasso, T. , & Sperry, L. L. ( 1985 ). Causal relatedness and importance of story events.   Journal of Memory and Language , 24 , 612–630.

Tsai, M.-H. , & Bendersky, C. ( 2016 ). The Pursuit of information sharing: Expressing task conflicts as debates vs. disagreements increases perceived receptivity to dissenting opinions in groups.   Organization Science , 27 , 141–156.

Utz, S.   Muscanell, N. , & Goeritz, A. S. ( 2014 ). Give, match, or take: A new personality construct predicts resource and information sharing.   Personality and Individual Differences , 70 , 11–16.

van Ginkel, W. , Tindale, R. S. , & van Knippenberg, D. ( 2009 ). Team reflexivity, development of shared task representations, and the use of distributed information in group decision making.   Group Dynamics: Theory Research and Practice , 13 , 265–280.

van Ginkel, W. P. , & van Knippenberg, D. ( 2012 ). Group leadership and shared task representations in decision making groups.   The Leadership Quarterly , 23 , 94–106.

van Knippenberg, D. , De Dreu, C. K. W. , & Homan, A. C. ( 2004 ). Work group diversity and group performance: An integrative model and research agenda.   Journal of Applied Psychology , 89 , 1008–1022.

Van Lange, P. A. M. ( 1999 ). The pursuit of joint outcomes and equality in outcomes: An integrative model of social value orientation.   Journal of Personality and Social Psychology , 77 , 337–349.

Van Swol, L. M. ( 2007 ). Perceived importance of information: The effects of mentioning information, shared information bias, ownership bias, reiteration, and confirmation bias.   Group Processes & Intergroup Relations , 10 , 239–256.

Van Swol, L. M. ( 2009 ). Discussion and perception of information in groups and judge-advisor systems.   Communication Monographs , 76 , 99–120.

Waddell, B. D. , Roberto, M. A. , & Yoon, S. ( 2013 ). Uncovering hidden profiles: Advocacy in team decision making.   Management Decision , 51 , 321–340.

Wageman, R. ( 1995 ). Interdependence and group effectiveness.   Administrative Science Quarterly , 40 , 145–180.

Wageman, R. ( 1999 ). Task design, outcome interdependence, and individual differences: Their joint effects on effort in task-performing teams (Commentary on Huguet et al., 1999).   Group Dynamics: Theory, Research, and Practice , 3 , 132–137.

Wageman, R. ( 2001 ). The meaning of interdependence. In M. E. Turner (Ed.), Groups at work: Theory and research (pp. 197–217). Mahwah, NJ: Erlbaum.

Weiss, M. , & Hoegl, M. ( 2015 ). The history of teamwork’s societal diffusion: A multi-method review.   Small Group Research , 46 , 589–622.

Winquist, J. R. , & Larson, J. R., Jr. ( 1998 ). Information pooling: When it impacts group decision making.   Journal of Personality and Social Psychology , 74 , 371–377.

Wittenbaum, G. M. ( 1998 ). Information sampling in decision-making groups: The impact of members’ task-relevant status.   Small Group Research , 29 , 57–84.

Wittenbaum, G. M. ( 2000 ). The bias toward discussing shared information: Why are high-status group member immune?   Communication Research , 27 , 379–401.

Wittenbaum, G. , & Bowman, J. ( 2005 ). Member status and information exchange in decision-making groups. In M. C. Thomas-Hunt (Ed.), Research on managing groups and teams: Status and groups (Vol. 7, pp 143–168). Bingley, UK: Emerald Group.

Wittenbaum, G. M. , Hollingshead, A. B. , & Botero, I. C. ( 2004 ). From cooperative to motivated information sharing: Moving beyond the hidden profile paradigm.   Communication Monographs , 71 , 286–310.

Wittenbaum, G. M. , Hubbell, A. P. , & Zuckerman, C. ( 1999 ). Mutual enhancement: Towards an understanding of the collective preference for shared information.   Journal of Personality and Social Psychology, 77, 967–978.

Wittenbaum, G. M. , & Park, E. S. ( 2001 ). The collective preference for shared information.   Current Directions in Psychological Science , 10 , 70–73.

Wittenbaum, G. M. , & Stasser, G. ( 1996 ). Management of information in small groups. In J. L. Nye & A. M. Brower (Eds.), What’s social about social cognition? Research on socially shared cognition in small groups (pp. 3–28). Thousand Oaks, CA: Sage.

Wu, W.-P. ( 2008 ). Dimensions of social capital and firm competitiveness improvement: The mediating role of information sharing.   Journal of Management Studies , 45 , 122–146.

Zhou, J. ( 2008 ). Promoting creativity through feedback. In J. Zhou & C. E. Shalley (Eds.), Handbook of organizational creativity (pp. 125–145). New York, NY: Erlbaum.

• About Oxford Academic
• Publish journals with us
• University press partners
• What we publish
• New features  
• Open access
• Institutional account management
• Rights and permissions
• Get help with access
• Accessibility
• Advertising
• Media enquiries
• Oxford University Press
• Oxford Languages
• University of Oxford

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide

• Copyright © 2024 Oxford University Press
• Cookie settings
• Cookie policy
• Privacy policy
• Legal notice

This Feature Is Available To Subscribers Only

Sign In or Create an Account

This PDF is available to Subscribers Only

For full access to this pdf, sign in to an existing account, or purchase an annual subscription.

How to improve knowledge sharing among teams

A true marker of successful teamwork is how fluidly your team shares knowledge with one another . Unfortunately, this isn’t something you can simply hire for — it’s part of the culture of teamwork you have to create and cultivate on your own. 

In the age of hybrid and remote work , sharing knowledge is more important than ever. When teams are distributed, team leaders need to take extra care to make sure valuable knowledge gets recorded and shared openly.

So let’s take a closer look at why, exactly, knowledge sharing is so beneficial, and how you can make it an indelible part of your team.

What is knowledge sharing?

At its most basic level, knowledge sharing is when people, teams, or organizations exchange‌ information with each other. This can take many forms. It can be as formal as a company Wiki, mentoring program, or a presentation — or as informal as a conversation or email. It may even be knowledge shared through more abstract or diffuse methods, such as via direct experience or interactions with social networks.

Effective and fluid knowledge sharing is typically a sign of a healthy and high-functioning team. It not only increases expertise across an organization, but also helps create trust, leads to more creative solutions, and empowers employees to do their jobs.

What types of knowledge should be shared?

Depending on how it's shared and the form it takes, knowledge sharing in an organization can come in several different types. The following two are the most common:

• Explicit knowledge: Any information that can be easily articulated through simple instruction, documents, or other types of procedures. This might include company strategy, processes, workflows, or statistics.
• Tacit knowledge: Any information that must be learned through experience or inference because it can't be easily written down or communicated. This might include historical context, company culture, or learned skills.

Why is knowledge sharing in the workplace important?

Any company with a robust culture of knowledge sharing is set up for successful teamwork over the long-term. Here are a few reasons why.

Preserve important information

An organization can lose vital knowledge in many ways. A team may disband, leaving useful workflows behind. Or, even worse, an individual employee may retire or depart the company, taking years of invaluable knowledge and experience with them. 

But when knowledge sharing is firmly embedded within the organization, this same knowledge can be transferred to other teams and new employees, providing that essential policies, skills, and other information live on.

Break down silos

Silos happen when teams or individuals are working in isolation from one another. Whether because the structure of their organization makes sharing information difficult, or there's a lack of trust for some reason, this means employees may be missing out on valuable insights and expertise from other areas of the company. 

But by promoting and supporting knowledge sharing, companies can break down these silos , start building a culture of cooperation, and build collective knowledge among their teams.

Foster a culture of collaboration

Creating a collaborative culture — one in which individuals and teams regularly come together to solve problems, share ideas, and work toward common goals — first requires a few key ingredients. For instance, employees must be comfortable communicating with each other. 

They should also have a healthy amount of trust and respect for one another , as well as a simple willingness to work together. Fortunately, when knowledge sharing is a regular part of the culture, all of this should already be firmly in place.

Improve organizational alignment

Organizational alignment is when everyone understands your company’s mission and is working together to achieve its goals. This kind of cohesion is essential to success, yet it can be difficult to achieve. 

Different teams may have competing priorities; individuals may be unwilling to share certain information with others; or there may be geographic or technical barriers that make it challenging to align. 

However, when teams and employees are used to sharing knowledge with each other, these issues tend to resolve themselves. Everyone has the same information, which makes it much easier for them to stay on the same page.

Increase productivity and efficiency

No one likes roadblocks. But when employees have to spend their time searching around for information or waiting for the few internal SMEs who can help, they’re unavoidable. And that eats into their productivity. 

When knowledge is more widely available and accessible, though, the chances of this happening become much smaller. Employees can find information whenever they need it, as well as share the responsibility for common tasks, such as employee onboarding — helping raise efficiency and productivity across the board.

How to create a culture of knowledge sharing among teams

The benefits of knowledge sharing are clear, yet many organizations still struggle to make it a regular part of their culture. This is typically because they’re approaching it as if it’s another skill to learn. Instead, it’s better to think of knowledge sharing as a habit that builds with consistent practice. And as with any habit, it’s all about creating an environment that will set you up for success. 

Here are some tips for doing just that.

1. Set an example for your team

If you want to change how something's done in your organization, why not start by taking the lead yourself? This strategy can be especially effective if the current culture is skeptical about making knowledge sharing more pervasive. 

You can begin by regularly sharing status updates, company news, and other helpful information. Encourage other team members to send their own updates for you to disseminate, helping establish a rhythm of knowledge sharing. You could even schedule a weekly meeting dedicated to sharing updates and communicating core pieces of knowledge for everyone’s benefit. 

By making it a normal event, you can demystify knowledge sharing and start establishing it within your culture.

2. Make knowledge sharing a team or organization value

Along with demonstrating knowledge sharing itself, you can help further lend it legitimacy by cementing it as part of the organization itself. In other words, make it clear that this isn’t simply a best practice or ideal, but an essential quality you expect from all your employees.

One effective way to do this is by incorporating knowledge sharing into your team charter . While the exact way you’ll want to do this will depend on your organization’s particular needs, try to make it clear what you expect from both teams and individuals when it comes to sharing and communicating information. 

Pro-tip: Team charters can also help new hires understand team norms to make the knowledge transfer easier. Get started with the team charter template from Mural.

Even better, solicit input from your own employees on how they’d like to share and receive knowledge from each other. This will help increase accountability. By defining and codifying knowledge sharing, you’ll help clarify and legitimize it across your organization.

3. Create rituals and opportunities that encourage knowledge sharing

Sometimes, your team may be willing or even enthusiastic about sharing knowledge with each other, but they just don’t know where to start. After all, old habits can be hard to break. If this is the case, then you can ease this shift by actively creating opportunities and rituals for sharing knowledge and information with each other. 

There are many ways of doing this. For instance, you could require team members to take part in retrospectives after each project is complete, or even on a weekly recurring basis. 

You could also set up a Slack channel where employees could share useful insights or answer questions other employees post. 

Or, to use a more visual approach, you could use the What’s on Your Radar template asynchronously or in team meetings to share, plot, and prioritize regular updates with each other.

You can even try simple exercises or introduce habits to foster team building , which helps teams feel more comfortable and open with each other.

Regardless of your approach, your goal should be to make the shift to knowledge sharing as easy and seamless as possible. Your team should know where and how to access information easily.

4. Create a knowledge sharing system for your teams

If your organization is large or your company’s knowledge base is complex, sharing information may sound great in theory, but in practice, it‌ can turn out to be chaotic. 

Employees may not know where to go to find the information they need or to document their own learnings. Instead, they need a centralized system in place that helps organize and streamline the flow of knowledge for everyone.

The first step should be to agree on a platform where employees will share issues, information, and documentation. The best tools will integrate into your existing workflows and provide your employees with an intuitive interface. You should also try to document and formalize any processes for sharing knowledge, whenever possible, hard coding and automating them to further streamline workflows . This way, sharing and receiving knowledge will be as effortless as possible.

The end goal here should be to have a system of knowledge management in place that makes sure every person and team across your organization (even remote teams) has access to the knowledge they need to do their job successfully.

5. Incentivize and reward individuals and teams that share knowledge

If you’ve set an example, created numerous opportunities for effective knowledge sharing, and even gone ahead and systemized the knowledge management process, but still aren’t seeing results, then it may be time to consider incentives. Knowledge hoarding can sometimes be a hard habit to break, so don’t be afraid to reward employees who make the effort.

Start by simply encouraging individuals and teams who are doing a good job of sharing information with each other. Single them out at meetings and call out how their efforts have helped. 

If this sort of soft motivation still isn’t leading to the transparency you want, try offering tangible rewards. These could be anything from a shout-out and extra time off, to full-fledged bonuses — just as long as they get the attention of everyone else and start turning knowledge sharing into a company-wide habit.

Improve knowledge sharing and break down silos with Mural

A culture of sharing know-how can effectively transform all your employees into experts, improving your team’s problem-solving and decision-making ability. Information will flow freely and everyone will have easy access to the answers they need. But implementing this culture may mean contending with numerous roadblocks that exist in the status quo, such as information silos, embedded competition, and a lack of trust. 

Fortunately, there’s no reason you have to overcome this all on your own. Mural can help make information sharing easier by giving you the knowledge-sharing tools and templates you need for better real-time and asynchronous collaboration . Whether you need to hold a quick brainstorming session or want to build a long-term system for exploring problems and exchanging information, we have something for you and your teams.

Get started with the free, forever plan with Mural to create a workspace and start collaborating with your team.

About the authors

David Young

Tagged Topics

Related blog posts

How to create better team alignment [+ templates]

Visual collaboration: What it is & how to get started

Shaping how we work: 6 digital transformation trends to watch

Related blog posts.

How to make a digital vision board: A complete guide

5 ways visual task management benefits your team

11 top tips for facilitating strategic planning sessions

Get the free 2023 collaboration trends report.

Extraordinary teamwork isn't an accident

• Scholarly Community Encyclopedia
• Log in/Sign up

Video Upload Options

• MDPI and ACS Style
• Chicago Style

Information problem solving (IPS) is a complex cognitive process considered as an important 21st century skill in combination with critical thinking [15].

1. Introduction

The impact of the Internet Age has prompted a paradigm shift in education. Nowadays most of our everyday learning is characterized by drawing knowledge from a wide variety of electronic resources. Learners from different levels are required to search, collect, and understand information from digital external sources and construct a solution to solve a task. This shift has never been more noticeable than amidst the current coronavirus pandemic. In this context, it is important to remember that educational research has identified information problem solving (henceforth IPS) as a complex process that requires the unfolding of complex higher-order cognitive skills, e.g., [ 1 ] [ 2 ] [ 3 ] .

Although it is undeniable that younger generations of students appear to master the skills needed to navigate online digital resources, educational research confirms that, without explicit instruction, students underuse or even lack the IPS skills to find correct and reliable online resources and construct knowledge from them [ 4 ] [ 5 ] [ 6 ] [ 7 ] . Therefore, educational research sees the need to provide students with adequate IPS skills to learn from online and digital resources. Furthermore, [ 8 ] claim that IPS skills instruction is crucial to promote quality, equality, and sustainable education because it has been found that students’ performance in digital skills is initially associated with their socio-economic background, academic achievement and residence location.

Various theoretical models have been proposed to characterize the phases and the cognitive processes involved in IPS that are needed to transform the retrieved web information into knowledge [ 9 ] . However, these models describe the stages and cognitive competences involved in the process, but fail to show which the students’ specific activities are in each stage and how to best support them. As a consequence, educational institutions and teachers find it difficult to teach the key IPS skills that could help students take full advantage of the opportunities the Internet provides for learning and building knowledge autonomously from online digital resources and in finding a suitable place and time in the curriculum [ 3 ] [ 5 ] .

In recent years, research has been carried out to analyse the effectiveness of teaching IPS using the Internet, e.g. [ 10 ] [ 11 ] [ 12 ] [ 13 ] . However, further research is still needed to tailor the existing IPS models to specific groups of students and in specific learning contexts [ 3 ] [ 9 ] and, by so doing, promote quality and sustainable education for all students. 

Information problem solving (IPS) is a complex cognitive process considered as an important 21st century skill in combination with critical thinking [ 14 ] . [ 1 ] [ 2 ] [ 3 ] have defined a five-step approach to solving information problems based on a decomposition of the IPS process into constituent skills and subskills. This approach highlights the fact that during the implementation of all skills it is essential to activate regulation activities, such as orientation, monitoring, steering, and evaluating [ 15 ] [ 16 ] .

Figure 1 shows this IPS model. Basically, it represents that, when students are confronted with an information problem or challenge. Considering that the resolution of the task as the solution to an information problem from online sources implies a complex cognitive process [ 12 ] [ 15 ] , in which secondary students face many challenges, it is essential for them to receive guidance and supervision through a well-designed educational intervention.

2.  Types of Information Problem Solving Instruction

It is often claimed that the IPS skills are underdeveloped or absent without explicit instruction, even among “digital natives” [ 1 ] [ 3 ] [ 4 ] [ 5 ] [ 6 ] [ 15 ] . However, educational research shows that students can be instructed to define better the problem and the information needed, generate more relevant search queries, adopt more evaluation criteria, select higher quality resources and deeply processed and presented information to answer an informational problem [ 10 ] [ 17 ] .

Over the last decades much effort has been made to investigate efficient instructional approaches for IPS and incorporate effective support for guiding students’ activity in searching, retrieving, evaluating  and integrating  information from multiple web sources (e.g., [ 3 ] [ 4 ] [ 10 ] [ 11 ] [ 12 ] [ 13 ] [ 15 ] [ 18 ] ). However, despite the researchers’ efforts made so far, their attempts have proved insufficient and further research is still needed in order to face and shed light on how formal IPS skills training could be designed in order to have a positive impact on student’s learning.

Our study is built on the basis of the four-component instructional design (4C/ID, for short) model [ 19 ] to design, implement and empirically test an innovative IPS instruction in secondary education. The 4C/ID model advocates the design of four components:

• Learning tasks are understood as authentic real-life tasks and their solution requires the integration and coordination of skills, knowledge and attitudes.
• Both supportive information and guidance are needed to develop cognitive models and strategies in order to complete the learning task.
• Procedural information has to be carefully designed by providing step-by-step instruction and explicit skills and procedures.
• Part-task practice should be included to provide enough training for recurrent skills.

In the arena of IPS instruction these four components have been translated according to the following principles: whole-task, embedded, and long-term instruction.

2.1. Whole-Task IPS Instruction

Whole-task instruction proposes the resolution of ill-structured, authentic and complex real-life situations in which students have to perform all the steps of the IPS process, from beginning to end, and students can find different ways to solve the task. A whole-task instruction has proved more effective to teach IPS complex skills than part-task fragmented instruction [ 10 ] [ 19 ] . Whole-task instruction offers the possibility to provide support for all the IPS skills and practise them as a whole process in which one skill relates to and impacts on the others. By contrast, instructional approaches that focus only on practising specific searching or evaluating skills, e.g. [ 20 ] , offer students very few occasions to coordinate and integrate all of the five IPS skills [ 19 ] , and also to transfer [ 21 ] .

Regarding the support needed, research provides evidence that it is possible to build on whole-task support to improve students’ IPS skills in demanding learning and learning that is difficult to be achieved successfully [ 11 ] [ 12 ] [ 22 ] [ 23 ] [ 24 ] . The main approaches for giving support in IPS instruction and the outcomes obtained are the following five: driving questions, prompting, content representations tools, processing worksheets, and writing and communicating support.

2.2. Embedded Instruction

Embedding IPS training within a meaningful context with domain-specific instruction has proved more effective than standalone courses [ 25 ] [ 26 ] [ 27 ] . Embedding instruction has the potential to increase engagement, motivation, transfer, and deep learning [49]. Previous studies investigating embedded instruction have shown good results in primary education [ 28 ] [ 29 ] , secondary education [ 12 ] [ 21 ] [ 30 ] , and higher education [ 13 ] [ 31 ] .

A literature review offers theoretical and empirical evidence on the effectiveness of whole-task and embedded IPS instruction. However, there are still scarce studies combining these two key instructional approaches. For instance, [ 10 ] investigated an embedded IPS course designed according to a whole-task approach and instructed ten student teachers in a quasi-experimental intervention research, finding positive results in the development of IPS skills and task performance. In another study, [ 20 ] successfully applied an embedded IPS instruction with psychology students. In this study, students obtained good learning outcomes and increased their frequency in some of their IPS constituent skills and regulation activities. More recently, [ 32 ] investigated student teachers' IPS skills through an embedded whole-task instruction in a 20-week course and reported that the instruction succeeded in developing cognitive strategies to tackle an information problem.

2.3. Long-Term Instruction

Long-term instruction for learning has been considered as the instruction that lasts over a quarter of the academic year [ 33 ] , or even as the instructional course that may take place over two or three weeks [ 34 ] . In the specific field of IPS, a long-term instruction has been related with a curriculum-wide approach [ 6 ] [ 18 ] [ 32 ] . Most IPS intervention studies apply short term instructions and these studies report that some of the improvements on IPS skills reached by the participants disappeared after completing the course [ 10 ] . In this vein, researchers claimed the need of “a scaled-up version with more content, more task classes containing tasks of increasing complexity, offered over a longer period of time and embedded in a multitude of contexts, might prove very effective.” [ 3 ] (p. 101). This claim is also shared by other studies, in which it is assumed that the whole-task approach to complex learning requires more learning tasks over longer periods than other kinds of instruction, but such practice will lead to better transfer to new settings when designed and conducted  adequately [ 10 ] [ 35 ] .

In summary, despite the existence of studies confirming that embedded whole-task IPS instruction improves the students’ IPS skills, there is still the need to know to what an extent the period of instruction of the IPS skills might have a positive impact on students’ learning and performance results [ 36 ] [ 22 ] [ 37 ] . Furthermore, while most educational institutions acknowledge that IPS is an essential academic skill in this digital and knowledge era, they struggle with its implementation, and specifically in finding a suitable place and sizeable time in the curriculum for IPS integration [ 3 ] [ 38 ] . IPS skills require domain-specific knowledge and in order to guarantee their transfer to daily activities, long-term, embedded, and supported IPS practice throughout the whole curriculum is needed [ 13 ] [ 39 ] .

Notwithstanding this necessity, most IPS instruction is often implemented as a separate course and loosely connected to the curricular contents (e.g. [ 11 ] ) and secondary education students still face difficulties in their daily school activities [ 40 ] [ 41 ] . Therefore, it is desirable to further investigate how to embed IPS research and instruction in real secondary classrooms and learn curricular contents to provide best practices, approaches and conclusive results of quality education for all students. To this end, this paper tackles this objective and provides answers to this educational challenge by discussing the design, development and empirical testing of a long-term, embedded, whole-task IPS instruction in secondary education. Specifically, our research investigates the longitudinal effects of a three-year IPS instruction on students’ task-performance when solving complex digital problems.

• Brand-Gruwel, S.; Wopereis, I.; Vermetten, Y. Information problem solving by experts and novices: Analysis of a complex cognitive skill. Comput. Hum. Behav. 2005, 21, 487–508.
• Brand-Gruwel, S.; Wopereis, I.; Walraven, A. A descriptive model of information problem solving while using internet. Comput. Educ. 2009, 53, 1207–1217.
• Frerejean, J.; van Strien, J.L.H.; Kirschner, P.A.; Brand-Gruwel, S. Completion strategy or emphasis manipulation? Task support for teaching information problem solving. Comput. Hum. Behav. 2016, 62, 90–104.
• Kirschner, P.A.; van Merriënboer, J.J. Do learners really know best? Urban legends in education. Educ. Psychol. 2013, 48, 169–183.
• Van Deursen, A.J.A.M.; van Diepen, S. Information and strategic Internet skills of secondary students: A performance test. Comput. Educ. 2013, 96, 218–226.
• Rosman, T.; Mayer, A.-K.; Krampen, G. A longitudinal study on information-seeking knowledge in psychology undergraduates: Exploring the role of information literacy instruction and working memory capacity. Comput. Educ. 2016, 96, 94–108.
• Spisak, J. Secondary Student Information Literacy Self-efficacy vs. Performance. Ph.D. Dissertation, University Richmond, Richmond, VA, USA, November 2018.
• Hinostroza, J.E.; Ibieta, A.; Labbé, C.; Soto, M.T. Browsing the internet to solve information problems: A study of students’ search actions and behaviours using a ‘think aloud’protocol. Int. J. Inf. Educ. Technol. 2018, 23, 1933–1953.
• Dinet, J.; Chevalier, A.; Tricot, A. Information search activity: An overview. Eur. Rev. Soc. Psychol. 2012, 62, 49–62.
• Frerejean, J.; Velthorst, G.J.; van Strien, J.L.; Kirschner, P.A.; Brand-Gruwel, S. Embedded instruction to learn information problem solving: Effects of a whole task approach. Comput. Hum. Behav. 2019, 90, 117–130.
• Mason, L.; Junyent, A.A.; Tornatora, M.C. Epistemic evaluation and comprehension of web-source information on controversial science-related topics: Effects of a short-term instructional intervention. Comput. Educ. 2014, 76, 143–157.
• Raes, A.; Schellens, T.; De Wever, B.; Vanderhoven, E. Scaffolding information problem solving in web-based collaborative inquiry learning. Comput. Educ. 2012, 59, 82–94.
• Wopereis, I.; Brand-Gruwel, S.; Vermetten, Y. The effect of embedded instruction on solving information problems. Comput. Hum. Behav. 2008, 24, 738–752.
• Donnelly, A.; Leva, M.C.; Tobail, A.; Valantasis Kanellos, N. A Generic Integrated and Interactive Framework (GIIF) for Developing Information Literacy Skills in Higher Education. PG Diploma in Practitioner Research Projects, DIT. 2018. Available online: https://api.semanticscholar.org/CorpusID:69560549 (accessed on 8 August 2020).
• Walraven, A.; Brand-Gruwel, S.; Boshuizen, H.P. Information-problem solving: A review of problems students encounter and instructional solutions. Comput. Hum. Behav. 2008, 24, 623–648.
• Winne, P. Enhancing self-regulated learning and information problem solving with ambient big data. In Contemporary Technologies in Education: Maximizing Student Engagement, Motivation, and Learning; Adesope, O.O., Rud, A.G., Eds.; Palgrave Macmillan: New York, NY, USA, 2019; pp. 145–162.
• Brand-Gruwel, S.; Kammerer, Y.; van Meeuwen, L.; van Gog, T. Source evaluation of domain experts and novices during Web search. J. Comput. Assist. Learn. 2017, 33, 234–251.
• Frerejean, J.; van Strien, J.L.; Kirschner, P.A.; Brand-Gruwel, S. Effects of a modelling example for teaching information problem solving skills. J. Comput. Assist. Learn. 2018, 34, 688–700.
• Van Merrienböer, J.J.G.; Kirschner, P.A. Ten Steps to Complex Learning: A Systematic Approach to Four-Component Instructional Design, 3rd ed.; Routledge: New York, NY, USA, 2008.
• Gerjets, P.; Kammerer, Y.; Werner, B. Measuring spontaneous and instructed evaluation processes during Web search: Integrating concurrent thinking-aloud protocols and eye-tracking data. Learn. Instr. 2011, 21, 220–231.
• Walraven, A.; Brand-Gruwel, S.; Boshuizen, H.P. Fostering students’ evaluation behaviour while searching the internet. Instr. Sci. 2013, 41, 125–146.
• Badia, A.; Becerril, L. Collaborative solving of information problems and group learning outcomes in secondary education. Int. J. Educ. Dev. 2015, 38, 67–101.
• Walhout, J.; Brand-Gruwel, S.; Jarodzka, H.; van Dijk, M.; de Groot, R.; Kirschner, P.A. Learning and navigating in hypertext: Navigational support by hierarchical menu or tag cloud? Comput. Hum. Behav. 2015, 46, 218–227.
• Wedderhoff, O.; Chasiotis, A.; Mayer, A.K. Information Preferences when Facing a Health Threat-The Role of Subjective Versus Objective Health Information Literacy. In Proceedings of the Sixth European Conference on Information Literacy (ECIL), Oulu, Finland, 24–27 September 2018.
• Stadtler, M.; Bromme, R. Effects of the metacognitive computer-tool met. a. ware on the web search of laypersons. Comput. Hum. Behav. 2008, 24, 716–737.
• Farrell, R.; Badke, W. Situating information literacy in the disciplines: A practical and systematic approach for academic librarians. Ref. Serv. Rev. 2015, 43, 319–340.
• Tricot, A.; Sweller, J. Domain-specific knowledge and why teaching generic skills does not work. Educ. Psychol. Rev. 2014, 26, 265–283.
• Spink, A.; Danby, S.; Mallan, K.; Butler, C. Exploring young children’s web searching and technoliteracy. J. Doc. 2010, 66, 191–206.
• Wang, C.H.; Ke, Y.T.; Wu, J.T.; Hsu, W.H. Collaborative action research on technology integration for science learning. J. Sci. Educ. Tech. 2012, 21, 125–132.
• Argelagós, E.; Pifarré, M. Improving information problem solving skills in secondary education through embedded instruction. Comput. Hum. Behav. 2012, 28, 515–526.
• Squibb, S.D.; Mikkelsen, S. Assessing the value of course-embedded information literacy on student learning and achievement. Coll. Res. Libr. 2016, 77, 164–183.
• Frerejean, J.; van Merriënboer, J.J.; Kirschner, P.A.; Roex, A.; Aertgeerts, B.; Marcellis, M. Designing instruction for complex learning: 4C/ID in higher education. Eur. J. Educ. 2019, 54, 513–524.
• Koni, I. The perception of issues related to instructional planning among novice and experienced teachers. Ph.D. Dissertation, University of Tartu, Tartu, Estonia, 2017.
• Rohrer, D. Student instruction should be distributed over long time periods. Educ. Psychol. Rev. 2015, 27, 635–643.
• Van Merrienboer, J.J.; Kester, L.; Paas, F. Teaching complex rather than simple tasks: Balancing intrinsic and germane load to enhance transfer of learning. Appl. Cogn. Psychol. 2006, 20, 343–352.
• Argelagós, E.; Pifarré, M. Key Information-Problem Solving Skills to Learn in Secondary Education: A Qualitative, Multi-Case Study. Int. J. Educ. Learn. 2016, 5, 1–14.
• Tran, T.; Ho, M.-T.; Pham, T.-H.; Nguyen, M.-H.; Nguyen, K.-L.P.; Vuong, T.-T.; Nguyen, T.-H.T.; Nguyen, T.-D.; Nguyen, T.-L.; Khuc, Q.; et al. How Digital Natives Learn and Thrive in the Digital Age: Evidence from an Emerging Economy. Sustainability 2020, 12, 3819.
• Lazonder, A.W.; Rouet, J.F. Information problem solving instruction: Some cognitive and metacognitive issues. Comput. Hum. Behav. 2008, 24, 753–765.
• Salmerón, L.; Kammerer, Y.; García-Carrión, P. Searching the Web for conflicting topics: Page and user factors. Comput. Hum. Behav. 2014, 29, 2161–2171.
• Crary, S. Secondary Teacher Perceptions and Openness to Change Regarding Instruction in Information Literacy Skills. Sch. Libr. Res. 2019, 22, 1–26.
• Stubeck, C.J. Enabling Inquiry Learning in Fixed-Schedule Libraries: An Evidence-Based Approach. Knowl. Quest 2015, 43, 28–34.

• Terms and Conditions
• Privacy Policy
• Advisory Board

The Six Most Common Types of Meetings

Identify your meeting type to plan for success.

The first step towards planning a meeting is defining what type of meeting it is. While every meeting is unique, being familiar with the six most common types of meetings will help you better identify the goals, structure, and activities best suited for your meetings.

The six general types of meetings:

• Status Update Meetings
• Information Sharing Meetings
• Decision Making Meetings
• Problem Solving Meetings
• Innovation Meetings
• Team Building Meetings

Here is a break-down of the six general types of meetings with examples of the main activities involve in each type. Knowing what type of meeting you are planning will increase the success of your meeting.

Meeting Type 1: Status Update Meetings

Check out our post about  how to run status update meetings .

Meeting Type 2: Information Sharing Meetings

At information sharing meetings the attendees have historically been passive listeners. With new technologies like MeetingSift they can use their smart devices to go from passive spectators to active participants, making the meeting more engaging and productive for all.

Check out our post about  how to run information sharing meetings .

Meeting Type 3: Decision Making Meetings

Check out our post about  how to run decision making meetings .

Meeting Type 4: Problem Solving Meetings

Check out our post about  how to run problem solving meetings .

Meeting Type 5: Innovation Meetings

Check out our post about  how to run innovation sharing meetings .

Meeting Type 6: Team Building Meetings

Check out our post about  how to run team building meetings .

How to run successful meetings

Meetings represent a huge value to both companies and employees, so when planning and running meetings you should not wing it and hope for the best. Instead, earning a reputation for running efficient and successful meetings is good for you and your career. To help you make good use of your meeting participants’ valuable time, our meeting scientists have put together a road map on  how to run successful meetings .

More Meeting Resources

Meeting Basics Meeting How Tos Meeting Leader Tips Innovation Decisions Making Team Building Communications Group Dynamics Just for Fun

Better host any type of meeting

With MeetingSift you can easily plan and run any type of meeting.

• Use our best practice meeting agenda template library to get started quickly.
• Send email invites to meeting participants from within MeetingSift.
• Our slide integration and interactive group activities lets you build and customize the agenda that fits your needs.
• Have your meeting participants collaboratively take notes from the meeting using the MeetingSift minutes and task assignment functionality.
• Once the meeting is over you immediately receive a meeting report, including all agenda items, notes, and tasks.

Download and share:

Download free ebook:, quadrant analysis for strategic decision making, export your meeting data, send meeting invitations with meetingsift, capture meeting minutes & task assignments, create or import slides to go with your activities, linked activities – take your group from ideas to decision, brainstorm – capture & visualize ideas as word clouds, evaluate & compare – collaboratively evaluate competing options, free account.

Use MeetingSift for free. Unlimited meetings with up to 25 participants in each.

See our plans and pricing to learn about all options.

“This is like running meetings on steroids. More engagement, better feedback, instant polling of opinions or to make decisions and more.”

“MeetingSift saved us probably 90 minutes over the 9ish hours of agenda, which left us more time for the deep-dive, deep-content conversations leadership needs to have”

“How to engage every participant ? How to leverage collective intelligence ? Meetingsift provides us a fantastic answer to these key requirements during events.”

“MeetingSift really changed the dynamic of our meetings and presentations. It instantly allowed us to engage a broad audience and gain feedback that we wouldn’t have otherwise been able to hear.”

“MeetingSift is easy to use, embraces BYOD, gives more structure to meetings and provides great ways to evaluate information.”

“MeetingSift has not only engaged but produced usable results to make the meeting productive and the followup easier.”

“MeetingSift provides all participants with an opportunity to share their views in their own words in real time…including contributions from those who don’t usually speak up.”

“Especially for larger groups, we get better quality feedback, from more participants, in less time, with less effort than with any other facilitation strategy we have used.”

“We cannot say enough about how effective MeetingSift is in bringing together people to address mutual interests.”

MeetingSift's easy to use collaboration platform for meetings helps you run more productive meetings, with higher engagement, better decision making, and more consistent follow up.

• How It Works
• Customer Login
• Terms of Use
• Privacy Policy

Stay Connected

• Cause and Effect Diagrams: A Problem-Solving Technique
• Debate Activities and Role-Play Scenarios
• Negotiation and Compromise
• Gantt Charting: A Primer for Problem Solving & Planning Techniques
• Analytical problem solving
• Identifying root causes
• Analyzing consequences
• Brainstorming solutions
• Heuristic problem solving
• Using analogies
• Applying existing solutions
• Trial and error
• Creative problem solving
• Mind mapping
• Brainstorming
• Lateral thinking
• Research skills
• Interpreting information
• Data collection and analysis
• Identifying patterns
• Critical thinking skills
• Recognizing bias
• Analyzing arguments logically
• Questioning assumptions
• Communication skills
• Negotiation and compromise
• Listening skills
• Explaining ideas clearly
• Planning techniques
• SWOT analysis
• Gantt charting
• Critical path analysis
• Decision making techniques
• Force field analysis
• Paired comparison analysis
• Cost-benefit analysis
• Root cause analysis
• Five whys technique
• Fault tree analysis
• Cause and effect diagrams
• Brainstorming techniques
• Brainwriting
• Brainwalking
• Round-robin brainstorming
• Creative thinking techniques
• Serendipity technique
• SCAMPER technique
• Innovation techniques
• Value innovation techniques
• Design thinking techniques
• Idea generation techniques
• Personal problems
• Deciding what career to pursue
• Managing finances effectively
• Solving relationship issues
• Business problems
• Increasing efficiency and productivity
• Improving customer service quality
• Reducing costs and increasing profits
• Environmental problems
• Preserving natural resources
• Reducing air pollution levels
• Finding sustainable energy sources
• Individual brainstorming techniques
• Thinking outside the box
• Word association and random word generation
• Mind mapping and listing ideas
• Group brainstorming techniques
• Synectics technique
• Online brainstorming techniques
• Online whiteboarding tools
• Virtual brainstorming sessions
• Collaborative mind mapping software
• Team activities
• Group decision making activities
• Debate activities and role-play scenarios
• Collaborative problem solving games
• Creative activities
• Creative writing exercises and storyboards
• Imagination activities and brainstorming sessions
• Visualization activities and drawing exercises
• Games and puzzles
• Crossword puzzles and Sudoku
• Logic puzzles and brain teasers
• Jigsaw puzzles and mazes
• Types of decisions
• Structured decisions
• Simple decisions
• Complex decisions
• Problem solving skills
• Interpreting Information: A Problem-Solving and Research Skills Primer

Learn how to interpret information, a critical problem-solving and research skill, with this comprehensive guide.

Interpreting information is an essential problem-solving and research skill. In today's world, we are constantly bombarded with vast amounts of data from multiple sources. To make sense of it all, we need to be able to interpret the information with accuracy, objectivity, and relevance. This article will provide a primer on how to interpret information. We will cover the importance of understanding the context of the information, the types of sources that can be used to interpret it, and the various methods and tools available to help make sense of the data.

With the right approach, understanding and interpreting information can be made much easier. Interpreting information is an important skill for problem solving and research. It involves taking data or written material, understanding the meaning behind it, and then making decisions or formulating conclusions based on that understanding. Interpreting information can be used in a variety of real-life scenarios, such as interpreting data from research studies, understanding the implications of a legal document, or making decisions in business. In order to interpret information correctly, it is important to have a thorough understanding of the context in which it exists.

This means considering all the relevant facts and details surrounding the material and how it might be interpreted. Additionally, it is important to consider any potential biases that may be present in the material and how those might affect its interpretation. It is also important to remain objective when interpreting information. This means being open to different interpretations and being willing to consider alternative points of view.

Additionally, it is important to be aware of any potential misperceptions or inaccuracies that might be present in the material that could lead to misinterpretation or drawing inaccurate conclusions. Finally, there are several techniques that can be used to improve one's ability to interpret information accurately. These include actively listening to the material, questioning assumptions, and using critical thinking skills to analyze the material. Additionally, taking the time to reflect on what has been learned can help ensure that any interpretations are accurate.

Why Interpreting Information Is Important

By understanding how to interpret information, we can become better problem-solvers and researchers. Interpreting information involves taking raw data and making sense of it in order to draw conclusions or make decisions. It requires us to recognize patterns, identify relationships between variables, and critically evaluate the data in order to draw accurate conclusions. Accurate interpretation of information can lead to better decision-making and improved problem-solving skills. For example, consider a business trying to make a decision on whether to invest in a new product. By interpreting the data related to the product's potential market, its development costs, and its expected revenue, the business can make an informed decision on whether or not to invest in the product. Inaccurate interpretation of information can also have serious consequences.

If a business incorrectly interprets the data related to the potential market for a new product, they may invest money in a product that ends up being unsuccessful. Similarly, if a researcher incorrectly interprets data from an experiment, their results could be unreliable. In summary, interpreting information is an important skill for problem-solving and research. It is essential for drawing accurate conclusions and making informed decisions. Accurate interpretation of information can lead to better problem-solving and research outcomes, while inaccurate interpretation can have serious implications. Interpreting information is a key problem-solving and research skill that can be used to make better decisions and develop new ideas.

Being able to accurately interpret information is essential in today’s world, where the amount of data available can be overwhelming. In order to stay ahead of the competition, it is important to be able to quickly identify trends and make informed decisions. To become better at interpreting information, it is important to develop a good understanding of the data and have an analytical approach to problem solving. Additionally, it is important to practice interpreting information from multiple sources, such as news articles, government documents, and surveys.

With practice, interpreting information will become easier and more accurate. By mastering the skill of interpreting information, individuals can become more effective problem solvers and researchers. This article has provided an overview of what interpreting information is, how it can be used, and how to become better at it. With the right knowledge and skills, anyone can become a better problem solver and researcher.

• information

• Applying Existing Solutions for Problem Solving Strategies

Learn how to use existing solutions to solve problems in heuristic problem solving

Learn how debate activities and role-play scenarios can help develop problem solving skills and create an engaging team environment.

• Crossword Puzzles and Sudoku: A Problem-Solving Exploration

This comprehensive guide covers all you need to know about crossword puzzles and Sudoku - from the history to the types of puzzles, strategies, and more.

Learn the key strategies for successful negotiation and compromise, and how to use them to effectively solve problems. Suitable for all skill levels.

• Maximizing Efficiency and Productivity
• Fault Tree Analysis: A Comprehensive Overview
• Analyzing Arguments Logically
• Mind Mapping: A Creative Problem Solving Tool
• Virtual Brainstorming Sessions: A Comprehensive Overview
• Cost-benefit Analysis: A Guide to Making Informed Decisions

How to Explain Ideas Clearly

• Finding Sustainable Energy Sources
• Mind Mapping - Creative Problem Solving and Creative Thinking Techniques
• Round-robin brainstorming: Exploring a Group Brainstorming Technique
• Exploring Trial and Error Problem Solving Strategies
• Recognizing Bias: A Problem Solving and Critical Thinking Skills Guide
• Choosing the Right Career: Problem-Solving Examples

Exploring the Serendipity Technique of Creative Problem Solving

• Critical Path Analysis: A Comprehensive Guide
• Imagination Activities and Brainstorming Sessions
• Word Association and Random Word Generation
• Exploring Lateral Thinking: A Comprehensive Guide to Problem Solving Strategies
• Managing Your Finances Effectively
• Brainstorming: A Comprehensive Look at Creative Problem Solving
• Round-robin Brainstorming: A Creative Problem Solving Tool
• Exploring the SCAMPER Technique for Creative Problem Solving
• Design Thinking Techniques: A Comprehensive Overview
• Simple Decisions - An Overview
• Analyzing Consequences: A Problem Solving Strategy
• Value Innovation Techniques
• Jigsaw Puzzles and Mazes: Problem Solving Activities for Fun and Learning
• Brainwriting: A Creative Problem-Solving Technique
• Creative Writing Exercises and Storyboards
• Force Field Analysis for Problem Solving and Decision Making
• Group Decision Making Activities
• Data Collection and Analysis - Problem Solving Skills and Research Skills
• Idea Generation Techniques: A Comprehensive Overview
• Using Analogies to Solve Problems

Paired Comparison Analysis: A Comprehensive Overview

• Exploring Online Whiteboarding Tools for Brainstorming
• Collaborative Mind Mapping Software
• Reducing Costs and Increasing Profits: A Problem Solving Example
• Exploring Brainwalking: A Creative Problem-Solving Technique
• Listening Skills: A Comprehensive Overview
• Logic Puzzles and Brain Teasers: A Comprehensive Overview
• Reducing Air Pollution Levels
• Preserving Natural Resources
• SWOT Analysis: A Comprehensive Overview
• Brainwriting: A Group Brainstorming Technique
• Identifying Patterns: A Practical Guide
• Identifying Root Causes
• Mind Mapping and Listing Ideas
• Solving Relationship Issues
• Five Whys Technique: A Comprehensive Analysis
• Making Complex Decisions: A Comprehensive Overview
• Brainstorming Solutions: A Problem-Solving Guide
• Thinking Outside the Box: An Overview of Individual Brainstorming Techniques
• Structured Decisions: An Overview of the Decision Making Process
• Improving Customer Service Quality
• Collaborative Problem Solving Games: Exploring Creative Solutions for Teams
• Exploring Synectics Technique: A Comprehensive Guide
• Visualization Activities and Drawing Exercises
• Questioning Assumptions: A Critical Thinking Skill

New Articles

Which cookies do you want to accept?

Interview Questions

Comprehensive Interview Guide: 60+ Professions Explored in Detail

26 Good Examples of Problem Solving (Interview Answers)

By Biron Clark

Published: November 15, 2023

Employers like to hire people who can solve problems and work well under pressure. A job rarely goes 100% according to plan, so hiring managers will be more likely to hire you if you seem like you can handle unexpected challenges while staying calm and logical in your approach.

But how do they measure this?

They’re going to ask you interview questions about these problem solving skills, and they might also look for examples of problem solving on your resume and cover letter. So coming up, I’m going to share a list of examples of problem solving, whether you’re an experienced job seeker or recent graduate.

Then I’ll share sample interview answers to, “Give an example of a time you used logic to solve a problem?”

Problem-Solving Defined

It is the ability to identify the problem, prioritize based on gravity and urgency, analyze the root cause, gather relevant information, develop and evaluate viable solutions, decide on the most effective and logical solution, and plan and execute implementation. 

Problem-solving also involves critical thinking, communication, listening, creativity, research, data gathering, risk assessment, continuous learning, decision-making, and other soft and technical skills.

Solving problems not only prevent losses or damages but also boosts self-confidence and reputation when you successfully execute it. The spotlight shines on you when people see you handle issues with ease and savvy despite the challenges. Your ability and potential to be a future leader that can take on more significant roles and tackle bigger setbacks shine through. Problem-solving is a skill you can master by learning from others and acquiring wisdom from their and your own experiences. 

It takes a village to come up with solutions, but a good problem solver can steer the team towards the best choice and implement it to achieve the desired result.

Watch: 26 Good Examples of Problem Solving

Examples of problem solving scenarios in the workplace.

• Correcting a mistake at work, whether it was made by you or someone else
• Overcoming a delay at work through problem solving and communication
• Resolving an issue with a difficult or upset customer
• Overcoming issues related to a limited budget, and still delivering good work through the use of creative problem solving
• Overcoming a scheduling/staffing shortage in the department to still deliver excellent work
• Troubleshooting and resolving technical issues
• Handling and resolving a conflict with a coworker
• Solving any problems related to money, customer billing, accounting and bookkeeping, etc.
• Taking initiative when another team member overlooked or missed something important
• Taking initiative to meet with your superior to discuss a problem before it became potentially worse
• Solving a safety issue at work or reporting the issue to those who could solve it
• Using problem solving abilities to reduce/eliminate a company expense
• Finding a way to make the company more profitable through new service or product offerings, new pricing ideas, promotion and sale ideas, etc.
• Changing how a process, team, or task is organized to make it more efficient
• Using creative thinking to come up with a solution that the company hasn’t used before
• Performing research to collect data and information to find a new solution to a problem
• Boosting a company or team’s performance by improving some aspect of communication among employees
• Finding a new piece of data that can guide a company’s decisions or strategy better in a certain area

Problem Solving Examples for Recent Grads/Entry Level Job Seekers

• Coordinating work between team members in a class project
• Reassigning a missing team member’s work to other group members in a class project
• Adjusting your workflow on a project to accommodate a tight deadline
• Speaking to your professor to get help when you were struggling or unsure about a project
• Asking classmates, peers, or professors for help in an area of struggle
• Talking to your academic advisor to brainstorm solutions to a problem you were facing
• Researching solutions to an academic problem online, via Google or other methods
• Using problem solving and creative thinking to obtain an internship or other work opportunity during school after struggling at first

You can share all of the examples above when you’re asked questions about problem solving in your interview. As you can see, even if you have no professional work experience, it’s possible to think back to problems and unexpected challenges that you faced in your studies and discuss how you solved them.

Interview Answers to “Give an Example of an Occasion When You Used Logic to Solve a Problem”

Now, let’s look at some sample interview answers to, “Give me an example of a time you used logic to solve a problem,” since you’re likely to hear this interview question in all sorts of industries.

Example Answer 1:

At my current job, I recently solved a problem where a client was upset about our software pricing. They had misunderstood the sales representative who explained pricing originally, and when their package renewed for its second month, they called to complain about the invoice. I apologized for the confusion and then spoke to our billing team to see what type of solution we could come up with. We decided that the best course of action was to offer a long-term pricing package that would provide a discount. This not only solved the problem but got the customer to agree to a longer-term contract, which means we’ll keep their business for at least one year now, and they’re happy with the pricing. I feel I got the best possible outcome and the way I chose to solve the problem was effective.

Example Answer 2:

In my last job, I had to do quite a bit of problem solving related to our shift scheduling. We had four people quit within a week and the department was severely understaffed. I coordinated a ramp-up of our hiring efforts, I got approval from the department head to offer bonuses for overtime work, and then I found eight employees who were willing to do overtime this month. I think the key problem solving skills here were taking initiative, communicating clearly, and reacting quickly to solve this problem before it became an even bigger issue.

Example Answer 3:

In my current marketing role, my manager asked me to come up with a solution to our declining social media engagement. I assessed our current strategy and recent results, analyzed what some of our top competitors were doing, and then came up with an exact blueprint we could follow this year to emulate our best competitors but also stand out and develop a unique voice as a brand. I feel this is a good example of using logic to solve a problem because it was based on analysis and observation of competitors, rather than guessing or quickly reacting to the situation without reliable data. I always use logic and data to solve problems when possible. The project turned out to be a success and we increased our social media engagement by an average of 82% by the end of the year.

Answering Questions About Problem Solving with the STAR Method

When you answer interview questions about problem solving scenarios, or if you decide to demonstrate your problem solving skills in a cover letter (which is a good idea any time the job description mention problem solving as a necessary skill), I recommend using the STAR method to tell your story.

STAR stands for:

It’s a simple way of walking the listener or reader through the story in a way that will make sense to them. So before jumping in and talking about the problem that needed solving, make sure to describe the general situation. What job/company were you working at? When was this? Then, you can describe the task at hand and the problem that needed solving. After this, describe the course of action you chose and why. Ideally, show that you evaluated all the information you could given the time you had, and made a decision based on logic and fact.

Finally, describe a positive result you got.

Whether you’re answering interview questions about problem solving or writing a cover letter, you should only choose examples where you got a positive result and successfully solved the issue.

Example answer:

Situation : We had an irate client who was a social media influencer and had impossible delivery time demands we could not meet. She spoke negatively about us in her vlog and asked her followers to boycott our products. (Task : To develop an official statement to explain our company’s side, clarify the issue, and prevent it from getting out of hand). Action : I drafted a statement that balanced empathy, understanding, and utmost customer service with facts, logic, and fairness. It was direct, simple, succinct, and phrased to highlight our brand values while addressing the issue in a logical yet sensitive way.   We also tapped our influencer partners to subtly and indirectly share their positive experiences with our brand so we could counter the negative content being shared online.  Result : We got the results we worked for through proper communication and a positive and strategic campaign. The irate client agreed to have a dialogue with us. She apologized to us, and we reaffirmed our commitment to delivering quality service to all. We assured her that she can reach out to us anytime regarding her purchases and that we’d gladly accommodate her requests whenever possible. She also retracted her negative statements in her vlog and urged her followers to keep supporting our brand.

What Are Good Outcomes of Problem Solving?

Whenever you answer interview questions about problem solving or share examples of problem solving in a cover letter, you want to be sure you’re sharing a positive outcome.

Below are good outcomes of problem solving:

• Saving the company time or money
• Making the company money
• Pleasing/keeping a customer
• Obtaining new customers
• Solving a safety issue
• Solving a staffing/scheduling issue
• Solving a logistical issue
• Solving a company hiring issue
• Solving a technical/software issue
• Making a process more efficient and faster for the company
• Creating a new business process to make the company more profitable
• Improving the company’s brand/image/reputation
• Getting the company positive reviews from customers/clients

Every employer wants to make more money, save money, and save time. If you can assess your problem solving experience and think about how you’ve helped past employers in those three areas, then that’s a great start. That’s where I recommend you begin looking for stories of times you had to solve problems.

Tips to Improve Your Problem Solving Skills

Throughout your career, you’re going to get hired for better jobs and earn more money if you can show employers that you’re a problem solver. So to improve your problem solving skills, I recommend always analyzing a problem and situation before acting. When discussing problem solving with employers, you never want to sound like you rush or make impulsive decisions. They want to see fact-based or data-based decisions when you solve problems.

Next, to get better at solving problems, analyze the outcomes of past solutions you came up with. You can recognize what works and what doesn’t. Think about how you can get better at researching and analyzing a situation, but also how you can get better at communicating, deciding the right people in the organization to talk to and “pull in” to help you if needed, etc.

Finally, practice staying calm even in stressful situations. Take a few minutes to walk outside if needed. Step away from your phone and computer to clear your head. A work problem is rarely so urgent that you cannot take five minutes to think (with the possible exception of safety problems), and you’ll get better outcomes if you solve problems by acting logically instead of rushing to react in a panic.

You can use all of the ideas above to describe your problem solving skills when asked interview questions about the topic. If you say that you do the things above, employers will be impressed when they assess your problem solving ability.

If you practice the tips above, you’ll be ready to share detailed, impressive stories and problem solving examples that will make hiring managers want to offer you the job. Every employer appreciates a problem solver, whether solving problems is a requirement listed on the job description or not. And you never know which hiring manager or interviewer will ask you about a time you solved a problem, so you should always be ready to discuss this when applying for a job.

Related interview questions & answers:

• How do you handle stress?
• How do you handle conflict?
• Tell me about a time when you failed

About the Author

Read more articles by Biron Clark

Continue Reading

15 Most Common Pharmacist Interview Questions and Answers

15 most common paralegal interview questions and answers, top 30+ funny interview questions and answers, 60 hardest interview questions and answers, 100+ best ice breaker questions to ask candidates, top 20 situational interview questions (& sample answers), 15 most common physical therapist interview questions and answers, 15 most common project manager interview questions and answers.