18
This article was downloaded by: [Northeastern University] On: 06 October 2014, At: 09:02 Publisher: Taylor & Francis Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK Theoretical Issues in Ergonomics Science Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/ttie20 Collaboration and meaning analysis process in intense problem solving teams Joan R. Rentsch a , Abby L. Mello a & Lisa A. Delise a a Department of Management , The University of Tennessee , Knoxville, TN 37996, USA Published online: 23 Jun 2010. To cite this article: Joan R. Rentsch , Abby L. Mello & Lisa A. Delise (2010) Collaboration and meaning analysis process in intense problem solving teams, Theoretical Issues in Ergonomics Science, 11:4, 287-303, DOI: 10.1080/14639221003729151 To link to this article: http://dx.doi.org/10.1080/14639221003729151 PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/terms- and-conditions

Collaboration and meaning analysis process in intense problem solving teams

  • Upload
    lisa-a

  • View
    217

  • Download
    1

Embed Size (px)

Citation preview

Page 1: Collaboration and meaning analysis process in intense problem solving teams

This article was downloaded by: [Northeastern University]On: 06 October 2014, At: 09:02Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registeredoffice: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK

Theoretical Issues in ErgonomicsSciencePublication details, including instructions for authors andsubscription information:http://www.tandfonline.com/loi/ttie20

Collaboration and meaning analysisprocess in intense problem solvingteamsJoan R. Rentsch a , Abby L. Mello a & Lisa A. Delise aa Department of Management , The University of Tennessee ,Knoxville, TN 37996, USAPublished online: 23 Jun 2010.

To cite this article: Joan R. Rentsch , Abby L. Mello & Lisa A. Delise (2010) Collaboration andmeaning analysis process in intense problem solving teams, Theoretical Issues in ErgonomicsScience, 11:4, 287-303, DOI: 10.1080/14639221003729151

To link to this article: http://dx.doi.org/10.1080/14639221003729151

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all the information (the“Content”) contained in the publications on our platform. However, Taylor & Francis,our agents, and our licensors make no representations or warranties whatsoever as tothe accuracy, completeness, or suitability for any purpose of the Content. Any opinionsand views expressed in this publication are the opinions and views of the authors,and are not the views of or endorsed by Taylor & Francis. The accuracy of the Contentshould not be relied upon and should be independently verified with primary sourcesof information. Taylor and Francis shall not be liable for any losses, actions, claims,proceedings, demands, costs, expenses, damages, and other liabilities whatsoever orhowsoever caused arising directly or indirectly in connection with, in relation to or arisingout of the use of the Content.

This article may be used for research, teaching, and private study purposes. Anysubstantial or systematic reproduction, redistribution, reselling, loan, sub-licensing,systematic supply, or distribution in any form to anyone is expressly forbidden. Terms &Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions

Page 2: Collaboration and meaning analysis process in intense problem solving teams

Theoretical Issues in Ergonomics ScienceVol. 11, No. 4, July–August 2010, 287–303

Collaboration and meaning analysis process

in intense problem solving teams

Joan R. Rentsch*, Abby L. Mello and Lisa A. Delise

Department of Management, The University of Tennessee, Knoxville, TN 37996, USA

(Received 6 August 2009; final version received 12 August 2009)

A set of testable propositions based on the collaboration and meaning analysisprocess (C-MAP) are presented. The C-MAP involves the conscious externali-sation of knowledge to support knowledge transfer, the development of innovatedknowledge and the development of cognitive similarity in intense problem solvingteams (Rentsch, J.R., Delise, L.A., and Hutchison, S., 2008a. Transferringmeaning and developing cognitive similarity in decision making teams:collaboration and meaning analysis process. In: M.P. Letsky, et al., eds.Macrocognition in teams. Burlington, VT: Ashgate Publishing, 127–142).Innovated knowledge is collaboratively created knowledge not initially possessedby the team when it was composed. Intense problem solving teams are distributedteams in which each member possesses unique expert information that must beintegrated to achieve a viable solution. The teams work in difficult contexts andtheir decisions have high risk implications. The cognitive processes included inthe C-MAP may be regarded as macrocognitive processes (e.g. Klein, G., et al.,2003. Macrocognition. IEEE Intelligent Systems, May/June, 81–85; Letsky, M.P.,et al., 2007. Macrocognition in complex team problem solving. In: 12thinternational command and control research and technology symposium (12thICCRTS), Newport, RI, June 2007. Washington, DC: U.S. Department ofDefense Command and Control Research Program). The role of knowledgeobjects and schema-enriched communication as two mechanisms for externalisingcognition to promote innovated knowledge are described.

Keywords: macrocognition; team cognition; distributed teams; team problemsolving; team member schema similarity; team mental models

1. Introduction

The set of testable propositions articulates a process to promote the development ofinnovated knowledge and cognitive similarity among team members through collaborativeexternalisation of internal knowledge. The use of knowledge objects and schema-enrichedcommunication as mechanisms to support the effectiveness of externalisation ofinternalised knowledge are described.

The purpose of the present article is to offer a set of propositions based on thecollaboration and meaning analysis process (C-MAP). The C-MAP involves the consciousexternalisation of knowledge to support knowledge transfer, the development of innovatedknowledge and the development of cognitive congruence in intense problem solving teams(Rentsch et al. 2008a). The cognitive processes included in the C-MAP may be regarded

*Corresponding author. Email: [email protected]

ISSN 1464–536X online

� 2010 Taylor & Francis

DOI: 10.1080/14639221003729151

http://www.informaworld.com

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

09:

02 0

6 O

ctob

er 2

014

Page 3: Collaboration and meaning analysis process in intense problem solving teams

as macrocognitive processes (e.g. Klein et al. 2003, Letsky et al. 2007), because theyare high level cognitive processes associated with the creation of innovated congruentknowledge within teams. These processes include the transfer and integration of uniqueknowledge and they influence knowledge flow among individual, interindividual andteam levels.

The propositions presented here are expected to be most relevant to intense problem-solving teams in which each member possesses unique expert information that must beintegrated in order for the team to achieve a viable problem solution. These teams faceimpediments to their ability to transfer and integrate knowledge due to such contextualfeatures as technology mediated communication; time constraints; excessive, dynamic anduncertain information; and heterogeneous and rotating membership. We refer to theseteams as intense problem-solving teams (intense teams) because the complexity of theirproblems and the high risk implications associated with their problem solutions and thenature of their contexts and compositions demand that these teams be resolute,persevering, strong and focused.

The primary challenge facing intense problem solving teams is to exploit the knowledgepossessed by the team in order to generate innovated knowledge essential for achievinga high quality problem solution. Innovated knowledge is collaboratively createdknowledge not possessed by the team when it was composed. Innovated knowledgeforms as an integration, synthesis, or synergism of the knowledge initially available inthe team.

Transferring knowledge among team members is vital to the creation of innovatedknowledge. Although the information sharing research offers some understanding of theknowledge transfer and integration challenges faced by intense teams, it has beenconducted using relatively simple tasks and offers impractical remedies (Davenport et al.2007). More importantly, the information sharing research does not directly address thecognitive processes that occur in teams. Therefore, the set of testable propositionspresented here advance the information sharing research by articulating a process ofexternalising and internalising knowledge to promote knowledge transfer and integration.The propositions address how knowledge interoperability, innovated knowledge andcognitive similarity develop among team members as they work to achieve high qualityproblem solutions. Also described is the use of schema-enriched communication andknowledge objects as mechanisms for externalising knowledge to support the collabora-tion process.

2. Process impediments for intense problem solving teams

The information sharing research provides consistent and robust empirical findingsindicating that teams have difficulty eliciting and expressing uniquely held information(e.g. Campbell and Stasser 2006). Team members tend to discuss commonly heldinformation at the expense of discussing uniquely held information (e.g. Stasser andTitus 2003) and teams tend to make higher quality decisions when all members share theirunique information than when they merely discuss and repeat common information(Stasser and Stewart 1992, Larson et al. 1998). In the case of intense teams, which arecomposed of members who possess unique expert knowledge necessary to reach a solution,this sampling bias will severely inhibit the team’s ability to achieve a high quality solution.Poor contextual conditions (e.g. high temporal pressure, high cognitive load, andtechnology mediated communication) add forces that impede the sharing of unique

288 J.R. Rentsch et al.

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

09:

02 0

6 O

ctob

er 2

014

Page 4: Collaboration and meaning analysis process in intense problem solving teams

information and engender poor quality decisions and team performance (e.g. Campbelland Stasser 2006).

For example, technology mediated communication, characterised by low informationrichness, associated with negative effects including decreased accountability, inhibition,self-regulation, trust, and commitment, and increased self-absorption and counter-normative behaviour (Savicki et al. 1996, Martins et al. 2004). Research has shown thatrelative to communication within co-located teams, communication within distributedteams is characterised by lower frequency and effectiveness (e.g. Walther 1997).Furthermore, in technology mediated contexts team members experience difficultyexpressing themselves, interpreting communications from teammates, and drawinginferences about and predicting other team members’ responses (e.g. Martins et al.2004). In short, technology mediated communication impedes team members’ ability toexternalise their expert knowledge and to internalise and make useful the knowledgeof others.

Suggested remedies to combat the bias against sharing unique information in teamsinclude making all informational resources available to team members; warning teammembers that all information is not held in common; and designing tasks that havea perceived correct answer, low information load, and a low percentage of uniqueinformation (Stasser and Stewart 1992, Stewart and Stasser 1995, Schittekatte 1996,Larson et al. 1998; Franz and Larson 2002). Clearly, these remedies are impractical andoutside the control of teams working in natural settings (Klein et al. 2003). The mostpractical remedy for intense problem solving teams is to inform all team members of theareas of expertise (unique knowledge) each team member possesses (Stewart and Stasser1995, Franz and Larson 2002) and to inform team members that they must work to extractexpert knowledge from and supply it to each other.

In addition, the information sharing research does not explicitly address cognitiveprocesses that occur in teams. For example, it lacks an explanation of how team membersunderstand the information their teammates share. Information sharing is functional onlyto the extent that the information translates into knowledge that is useful to all teammembers. In order for an intense team to exploit the knowledge it possesses, teammembers must transfer knowledge to each other rather than simply share information.The amount of information shared has typically been determined by assessing the extent towhich unique information has been pooled during the team’s discussion. The entire teamor individual members provide a statement or recitation of the facts from the taskmaterials (Stewart and Stasser 1995, Campbell and Stasser 2006) or taped team discussionsare content coded for a count of facts mentioned (Stasser and Stewart 1992, Stewart andStasser 1995). However, intense teams require more than merely pooled information togenerate high quality solutions. They require knowledge transfer - the conveyance ofunderstanding.

Knowledge transfer requires explanation of the meaning or understanding ofinformation (e.g. what it means, why it is relevant) and will likely require some degreeof perspective taking in order for knowledge to be presented in a manner such that othersmay access and utilise it (Rentsch et al. 2008a). Each team member on an intense team isan expert in a unique domain and very likely is essentially a novice in the other teammembers’ expert domains (Rentsch et al. 2008a). Transferring expert knowledge to relativenovices is particularly challenging, because experts and novices tend to articulateknowledge differently (Hinds et al. 2001). Experts explain tasks using broad termsand relative novices (i.e. those who have some exposure to a domain, but whohave not achieved expert status) tend to explain tasks using concrete statements.

Theoretical Issues in Ergonomics Science 289

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

09:

02 0

6 O

ctob

er 2

014

Page 5: Collaboration and meaning analysis process in intense problem solving teams

Furthermore, novices who have no domain exposure and who are instructed by relativenovices tend to perform better than novices, who are instructed by experts (Hinds et al.2001). The differential schema structures of experts and novices create difficulties for teammembers in transferring their knowledge to one another.

In summary, intense teams face impediments including bias against sharing uniqueknowledge and difficulties associated with conveying expert knowledge to novices. Thepropositions presented below are based on the collaboration and meaning analysis processand were designed to serve as a foundation for interventions to aid teams, particularlyintense teams, in overcoming these types of impediments. The propositions presentrelations among externalised and internalised cognitive processes.

3. Overview of the C-MAP

The collaboration and meaning analysis process (C-MAP) is an intervention intended toincrease a team’s capacity to exploit its available knowledge by externalising knowledgeusing two mechanisms, schema-enriched communication and the creation of knowledgeobjects. Schema-enriched communication involves team members communicating theirinformation to one another to disclose deep understanding of the information (i.e. conveycontent and structure of one’s schema). Creation of knowledge objects enables teammembers to work collaboratively to develop a representation of their understanding of thetask information (Fischer and Mandl 2005). Using these mechanisms, team members learnor discover how their unique information affects the team’s overall understanding of theproblem. Through their joint efforts team members can create innovated knowledge aboutthe problem to generate a high quality solution. The use of these mechanisms forexternalising cognition should improve the team’s decision quality by increasingknowledge transfer, knowledge interoperability, innovated knowledge, and cognitivesimilarity with respect to the task (Rentsch et al. 2008a). Below, several propositionsunderlying the collaboration and meaning analysis process are presented.

4. Externalising cognition

Macrocognition involves internalised and externalised cognitive processes (Letsky et al.2007). Internal cognitive processes are unexpressed and must be assessed indirectly.External cognitive processes are observable and are evidenced through the interactionsamong team members. In order for intense problem solving teams to capitalise on theknowledge they possess, team members must externalise their internal expert knowledgethereby making it accessible to one another. Knowledge transfer occurs only whenknowledge is externalised in a manner that imparts understanding to the recipient suchthat the recipient is able to make enough sense of the knowledge to internalise it (Rentschet al. 2008a).

Proposition 1: Team members must externalise their knowledge to transfer knowledge.

An illustration of knowledge transfer is shown in Figure 1a. Member A has organisedexpert knowledge structure represented by interconnected circles, and Member B hasorganised expert knowledge structure represented by interconnected triangles. Member Bis a relative novice with respect to Member A’s knowledge (the circles). Knowledgetransfer has occurred when Member A’s understanding about a piece of information,represented by A’s large circle, is internalised by Member B. Notice that A’s internalunderstanding of the knowledge transferred may not be exactly what Team Member B

290 J.R. Rentsch et al.

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

09:

02 0

6 O

ctob

er 2

014

Page 6: Collaboration and meaning analysis process in intense problem solving teams

Figure 1. Internal and external cognitive processes in teams.

Theoretical Issues in Ergonomics Science 291

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

09:

02 0

6 O

ctob

er 2

014

Page 7: Collaboration and meaning analysis process in intense problem solving teams

internalises, which is indicated by the slightly different patterns of the circles forMember A and Member B. These differences in internalisation are due to the differencesin pre-existing knowledge (Bromme 2000, Stahl 2005). In addition, the receiver’s newlyacquired knowledge may not be tightly associated or assimilated into the receiver’spre-existing knowledge. This is represented in Figure 1a by the circle in Member B’s space(mind) having no linkages to the triangles that represent Member B’s pre-existingknowledge. A team member making his or her knowledge available through externali-sation initiates the knowledge transfer process. Two mechanisms by which knowledge maybe externalised are schema-enriched communication and the creation of knowledge objects(Rentsch et al. 2008a).

4.1. Schema-enriched communication

Experts have difficulty conveying their knowledge to relative novices due to the differencesin expert and novice schema structures. In addition, expert knowledge tends to be tacit,and therefore, is difficult for experts to articulate (Lord and Maher 1991). However, theuse of schema-enriched communication may enable experts to externalise knowledge suchthat it will be transferred to other team members. Schema-enriched communication refersto communication guides that enable team members to reveal how they organise or makesense of information. As defined in Table 1, schema-enriched communication is a formof externalised cognition by which team members provide and elicit cognitivestructure, organisation, assumptions, meaning, rationale and interpretations. Schema-enriched communication will support the flow of knowledge primary between individuals(i.e. interindividual flow).

Team members who engage in schema-enriched communication augment theirexternalised knowledge by providing their deep understanding of the knowledge to beimparted. Communicating in this way, team members reveal the content and structureof their expert knowledge (i.e. they reveal aspects of their schemas) enabling them totransfer expert knowledge to their teammates (Rentsch et al. 2008a). Examples of schema-enriched communication include explaining one’s logic, revealing deep-level information in

Table 1. Macrocognitive processes in intense problem solving teams.

Process Primary cognition flow Definition

Schema-enriched communication Individual to individual Providing and eliciting structure,organisation, rationale,interpretations, assumptions,meaning

Creation of knowledge objects Individual to team Externally combining, connecting,and negotiating individualknowledge to create new teamknowledge

Knowledge transfer Individual to individual Expressing one’s expert knowledgein a manner that others caninternalise it

Knowledge interoperability Intraindividual Internalising new knowledge so it ismeaningful and useable

Cognitive similarity Individual to team Internalising team knowledgesimilarly to teammates’internalisations

292 J.R. Rentsch et al.

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

09:

02 0

6 O

ctob

er 2

014

Page 8: Collaboration and meaning analysis process in intense problem solving teams

addition to or rather than surface-level information, and providing information by relatingexplicitly how it connects to other information in a path of logic (Rentsch et al. 1998).Schema-enriched communication also involves eliciting deep understanding from othersin order to extract the content and structure of their expert knowledge. Problem solvingteams in which team members engaged in schema-enriched communication tended to havea higher degree of problem space identification and were more likely to consider multiplesolution alternatives (both indicators of effective problem solving) than teams in whichmembers did not communicate in this way (Rentsch et al. 1998).

Indirect empirical evidence also supports the usefulness of schema-enriched commu-nication (e.g. Fischer and Mandl 2005). Other evidence indicates that the kind ofconceptual elaboration schema-enriched communication provides is associated withimproved memory, increased understanding of the relevance of the conveyed knowledge,and increased learning (e.g. Craik and Lockhart 1972, Stein and Bransford 1979), all ofwhich are indicators of transferred knowledge.

Intense problem solving teams that engage in schema-enriched communication willtransfer knowledge to each other rather than simply sharing information. Transferredknowledge will be used to develop high quality solutions.

Proposition 2: Teams that engage in schema-enriched communication will transfer moreknowledge than teams that do not use schema-enriched communication.

4.2. Creation of knowledge objects

Cognition may also be externalised using knowledge objects, which are depictionsdeveloped by team members about the problem (Warner and Letsky 2008). Knowledgeobjects take many forms ranging from concrete, such as a drawing, to abstract, such as anidea itself (Swan et al. 2007). Similar to learning nets, knowledge objects are externalrepresentations of knowledge created through the collaborative effort of team members(Wessner et al. 2001). Nosek (2004) defined boundary objects (a subset of knowledgeobjects) as anything observable that can convey meaning to another person, includingnonverbal behaviours, documents, or physical objects. As external representations ofknowledge, knowledge objects facilitate communication between team members withdiffering expertise (e.g. Star 1989, Carlile 2004).

While collaboratively creating a knowledge object, team members are able to develop arepresentation of the problem space and actively manipulate the contents of the problemspace thereby conveying their internal cognitions into externalised cognitions (Lee andKim 2005). Through the process of creating knowledge objects, team members imparttheir knowledge to teammates. The definition in Table 1 identifies the creation ofknowledge objects as externally combining, connecting and negotiating individualknowledge to create new team knowledge. This is an external cognition process with aprimary knowledge flow from individuals to the team (Table 1).

Proposition 3: Teams that create knowledge objects will transfer more knowledge thanteams that do not create knowledge objects.

5. Internalised cognition

On intense teams, each member’s internal knowledge must be externalised in order tobecome internalised by other members. To maximise the utility of relevant knowledge

Theoretical Issues in Ergonomics Science 293

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

09:

02 0

6 O

ctob

er 2

014

Page 9: Collaboration and meaning analysis process in intense problem solving teams

possessed by the team, transferred knowledge must become interoperable and congruent.

Recipients of expert knowledge are likely to be novices with respect that knowledge.

Therefore, they will receive the new knowledge as declarative knowledge. Declarative

knowledge is associated with rote memory and represents an early stage of learning.

However, as recipients gain deeper understanding about the problem, they will move

beyond accumulating declarative knowledge and will build meaningful relations between

the new knowledge and their pre-existing knowledge, thereby increasing the likelihood

of generalisable (some say adaptable) understanding (e.g. Leinhardt and Smith 1985,

Jonassen et al. 1993, Kozlowski 1998). Knowledge becomes increasingly useful as it

becomes organised and related to existing knowledge structures or schemas. The result

of recipients integrating new knowledge into their cognitive structures is knowledge

interoperability.Warner and Letsky (2008) defined the process of developing knowledge interopera-

bility as the ‘act of exchanging and modifying a mental model or knowledge object

such that agreement is reached among team members with respect to a common

understanding of the topic’s meaning’. One notion of interoperable knowledge is that it

equates to coordinating representations as defined by Alterman et al. (2001) (Fiore

et al. 2008).As noted in Table 1, the process of developing knowledge interoperability is defined

here as internalising new knowledge so it is meaningful and useable. Knowledge becomes

interoperable when it is meaningful and therefore available for use by more than one team

member. The interoperability of knowledge increases in relation to the number of team

members who can use it. Interoperable knowledge is that which can be utilised by multiple

team members, albeit each team member may understand and use it somewhat differently.Knowledge interoperability will develop when team members engage in active cognitive

manipulation, deconstruction and reconstruction of the domain material at different levels

of consciousness (e.g. Bereiter and Scardamalia 1996). Such cognitive manipulations

enable team members to organise novel knowledge and to assimilate it with their own

and other’s expert knowledge or collaboratively use it to innovate knowledge. As team

members internally and externally manipulate and organise the knowledge through

successive iterative integration and differentiation, they will develop a schema for

understanding and remembering it (Langfield-Smith 1992). As schemas affect how

information is integrated into memory and how it is recalled (Lilienfeld et al. 2008),

knowledge interoperability is evidenced by team members’ ability to recall knowledge

originally possessed by another team member and their ability to apply it to generate the

team’s problem solution.Knowledge interoperability is depicted in Figure 1b. The knowledge transferred

from Member A to Member B becomes interoperable to the extent is it integrated

with Member B’s pre-existing knowledge structure. In Figure 1b, the interoperable

knowledge is represented by the circle that has a loose connection with Member B’s

existing knowledge structure that is represented by the interconnected triangles. Recall

that Member A possesses different expert knowledge (represented by circles) than the

knowledge Member B possesses (represented by triangles), therefore the knowledge that

was transferred is connected to different pre-existing knowledge for each member

(Bromme 2000, Stahl 2005). Although the knowledge is useful to both members, each

may understand and use it differently. Thus, the process of developing knowledge

interoperability involves primarily intraindividual processing (Table 1). Knowledge

between expert domains must be transferred in order for it to become interoperable.

294 J.R. Rentsch et al.

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

09:

02 0

6 O

ctob

er 2

014

Page 10: Collaboration and meaning analysis process in intense problem solving teams

Proposition 4: Knowledge must be transferred to become interoperable.

Interoperable knowledge provides some degree of common ground enabling teammembers to identify relevant links between their unique or expert knowledge with that ofothers. Through a collaborative interactive and negotiation process (Daft and Weick1984, Mohammed and Ringseis 2001, Warner and Letsky 2008) team members developcognitive similarity, defined as ‘forms of related meanings or understandings attributedto and utilised to make sense of and interpret internal and external events includingaffect, behaviour, and thoughts of self and others that exist amongst individuals’(Rentsch et al. 2008b). As noted in Table 1, the process of developing cognitivesimilarity in teams is the process of internalising knowledge similarly to teammates’internalisations. Cognitive similarity among team members is related to high teamperformance (e.g. Stout et al. 1999, Marks et al. 2000, 2002, Mathieu et al. 2000).Warner and Letsky (2008) suggested that team shared understanding, which they defineas ‘the synthesis of essential data, information, or knowledge, held collectively by some(complementary understanding) and/or all (congruent understanding) team membersworking together to achieve a common task’ (p. 23) is important to the functioning ofintense teams.

Various types of cognitive similarity based on the intersection of the content domain(e.g. task, team member, communication process), form of cognition (e.g. structured,perceptual, interpretive), and the form of similarity (e.g. accuracy, congruent, comple-mentary), may exist (Rentsch et al. 2008b). Members of intense teams will likely developseveral types of cognitive similarity (e.g. similar teamwork mental models, accurate teammember schemas). However, with respect to the problem space, members of intense teamsare expected to develop congruent schemas regarding the knowledge relevant to solvingthe problem (i.e. congruent task schemas).

Knowledge interoperability developed with regard to information relevant to solvingthe team’s problem is expected to support the development of task schema congruence.As mentioned above, knowledge interoperability, which provides some degree of commonground, will enable team members to begin linking their unique information togetheras they work toward a problem solution. The common ground enables team members tonegotiate common understandings (e.g. Liang et al. 1995, Beers et al. 2005). This processof developing cognitive similarity primarily involves knowledge flowing from individualto team level. Team members must work together to negotiate or develop similar schemasfor understanding the information.

This process is illustrated in Figures 1a, 1b, 1c, and 1d. As noted above, Figure 1arepresents knowledge transfer between Member A and Member B. Figure 1b illustrates thetransferred knowledge from Member A (represented by a large circle) becoming integratedwith Member B’s pre-existing knowledge, thereby becoming interoperable (i.e. meaningfuland useful to both members). As the knowledge becomes operable for Member B, MemberB recognises some of her pre-existing expert knowledge, represented by the large triangle,as relevant to the problem. Therefore, Member B transfers that knowledge to Member A(Figure 1c). Member A receives the novel knowledge that has been externalised byMember B and integrates it with his pre-existing knowledge (Figure 1d). In the case of theillustration, Member A connects that novel knowledge (large triangle) to the knowledge hepreviously transferred to Member B (the large circle). Now, both members have aconnection between the two pieces of different expert knowledge making that knowledgeinteroperable. After multiple transfers, in iterative discussion cycles in which teammembers articulate their knowledge, provide feedback to one another, and manipulate and

Theoretical Issues in Ergonomics Science 295

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

09:

02 0

6 O

ctob

er 2

014

Page 11: Collaboration and meaning analysis process in intense problem solving teams

negotiate the common ground, their collaborative ‘problem representation’ (i.e. contentand structure of the problem space) will become increasingly similar.

Proposition 5: Knowledge transfer and knowledge interoperability contribute to thedevelopment of cognitive similarity among team members.

The development of knowledge interoperability and cognitive similarity are primarilyinternal cognitive processes supported by external cognitive processing. Interplay amongthe internal and external cognitive processes serve to create innovated knowledge withinintense problem solving teams.

6. The interplay between internal and external cognitive processes

The interplay among internal and externalised knowledge is illustrated in Figure 2.Schema-enriched communication and the creation of knowledge objects are mutuallyenhancing externalisation mechanisms (Rentsch et al. 2008a). For example, an informa-tion board visible and available to all team members on which they can post andmanipulate knowledge may serve as a knowledge object (Rentsch et al. 2007). The creationof the knowledge object will stimulate schema-enriched communication as the teamdecides whether to add or omit new knowledge, how the knowledge is related, and so on.The knowledge object will afford opportunities for team members to impart and elicitmeaning and organisation of information (e.g. Roschelle 1994, Suthers and Hundhausen2003, Fischer and Mandl 2005). Moreover, schema-enriched communication will aid theteam in creating the knowledge object. For example, team members may articulate theirrationale for how pieces of knowledge are connected and then represent these connectionson the knowledge object. Communications will stimulate the team to alter the knowledgeobject (Carlile 2002, Levina 2005). The schema-enriched communication aids in makingtacit knowledge accessible and the knowledge object provides a tangible representationof that knowledge. Externalised cognition using knowledge objects and schema-enrichedcommunication engages all team members and focuses their attention on achieving

Figure 2. The interplay among external and internal cognitive processes.

296 J.R. Rentsch et al.

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

09:

02 0

6 O

ctob

er 2

014

Page 12: Collaboration and meaning analysis process in intense problem solving teams

a solution. Thus, the development of a knowledge object affords schema-enrichedcommunication and vice versa. Together they enable team members to express the contentand structure of their expert knowledge in relation to other members’ expert knowledge,thereby increasing the team’s ability to exploit its knowledge to innovate knowledge andgenerate high quality solutions.

Proposition 6: Schema-enriched communication and knowledge objects enable teammembers to transfer knowledge by conveying the content and structure of expert knowledge.

Knowledge externalised using schema-enriched communication and knowledge objectsprovides a point of common ground that enables members of intense problem solvingteams to integrate knowledge across their specialisations (Carlile 2004, Swan et al. 2007).Knowledge that is externalised using the two mechanisms becomes available for allteam members to collaboratively recontextualise, negotiate, organise and integrate(e.g. Chen and Hung 2002). Team members possess knowledge internally and byexternalising it, they afford their teammates the opportunity to internalise the knowledge.Team members who internalise the novel knowledge will evaluate it and externalisetheir understanding of it thereby making their internal knowledge accessible to the team(Beers et al. 2005). Some (e.g. Fiore et al. 2008) suggest that the impetus for this processis uncertainty reduction. Nevertheless, the process of collaborative internalisation andexternalisation that occurs through iterative cycles develops knowledge interoperability.

Therefore, team members can use these mechanisms not only to articulate their owntacit expert knowledge but also to express the relations between it and the externalisedexpert knowledge of their teammates. Collaboratively utilised externalised knowledge willcreate interoperable knowledge.

Proposition 7: Engaging in schema-enriched communication and the creation of knowledgeobjects enable team members to develop interoperable knowledge.

The dynamic and reciprocal relationships between externalised cognitive processes andinternal cognitive processes are most clear in the use of the knowledge objects and schema-enriched communication to develop innovated knowledge and cognitive similarity.

Innovated knowledge is represented in Figure 1e. Members A and B developedinteroperable knowledge depicted by the connections between the large circles andtriangles. Using that interoperable knowledge in combination with other knowledgeaccessible in the team, they can collaboratively create knowledge that is new to both teammembers through integration, synthesis and synergism produced by the iterative processesof externalisation and internalisation. This innovated knowledge is represented by thediamond in Figure 1e.

Externalised cognition is necessary for teams to combine, visualise and aggregateinformation in order to establish patterns and relationships (Klein et al. 2003, Crandallet al. 2006, Letsky et al. 2007) and thereby generate new understandings (e.g. Lee andNelson 2005). Collaboratively developed interoperable knowledge will likely representnew knowledge for one or more team members. In addition, team members can generatenew knowledge using a knowledge object by combining externalised knowledge with theirown internalised knowledge or with other teammates’ externalised knowledge. Schema-enriched communication promotes meaningful explanation, debate and rationale neededto develop insight and new knowledge. (We distinguish new knowledge from innovatedknowledge, which was defined previously. New knowledge is that which may be novel toa subset of team members, but was knowledge that at least one member possessed whenthe team was composed.)

Theoretical Issues in Ergonomics Science 297

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

09:

02 0

6 O

ctob

er 2

014

Page 13: Collaboration and meaning analysis process in intense problem solving teams

Teams may create knowledge that is different from the expert knowledge possessed byany team member and different from an amalgamation of individual members’ knowledge(e.g. Stahl 2005). We refer to this as innovated knowledge. Innovated knowledge emergesthrough the process of grounding, whereby the team members collaboratively androutinely update their understandings and achieve new understandings. Communicationsurrounding knowledge object creation supports this process (Fischer et al. 2005).For example, knowledge externalised in a knowledge object may aid team members to‘co-construct jointly new knowledge and crystallise it into an innovation’ (Smeds et al.2006, p. 1). Knowledge objects support the transformation of knowledge into new or moremeaningful knowledge (Carlile and Rebentisch 2003). Knowledge objects and schema-enriched communication can facilitate the creation of innovated knowledge.

Proposition 8: Schema-enriched communication and the creation of knowledge objectsenable teams to develop innovated knowledge.

Team members can use knowledge objects and schema-enriched communication in theprocess of developing cognitive similarity regarding the problem space (task schemacongruence). The external representations serve as a point of reference available to all teammembers and promote the use of common terminology. In general, the externalrepresentations promote the development of common ground upon which the team canhave more complete and meaningful collaboration (Lee and Kim 2005, Stahl 2006),because team members can jointly deconstruct and reconstruct the content and structureof the knowledge. The creation of a knowledge object enables team members to articulateand negotiate a common understanding of the problem space.

External representations of knowledge ease the elaboration of the problem space(Fiore et al. 2003). The team can transform its task knowledge by using a knowledge objectand schema-enriched communication to identify points of convergence and divergenceamong team members’ understanding (Nosek 2004). Team members can collaborativelycorrect misunderstandings, mistakes and misrepresentations (Carlile 2002). In doingso, they collaboratively construct a problem solution and establish similar cognitionsregarding the content and structure of the problem space that includes relevant expertknowledge and innovated knowledge.

Cognitive similarity is represented in Figure 1f. After multiple iterations of transferringknowledge and developing interoperability regarding that knowledge, Members A and Bhave linkages between knowledge represented by two small circles and the large circle,between the large circle and the large triangle, between both large circles and the innovatedknowledge represented by the diamond, and so on. It is worth noting that these teammembers possess cognitions with the same structure but with slightly different content as isrepresented by the different patterns of represented knowledge components.

Proposition 9: Teams that use schema-enriched communication and knowledge objectsdevelop cognitive similarity.

Figure 2 provides a summary illustration of several propositions highlighting theinterplay between external and internal cognition. By way of example, use Figure 2 tofollow knowledge. In the knowledge transfer process, knowledge is originally internal(i.e. sender’s knowledge), it becomes externalised (using knowledge objects and schema-enriched communication), and it is internalised by the recipient. Once internalised, it isavailable to become interoperable by becoming integrated with the receivers’ pre-existingknowledge. Internalised interoperable knowledge that provides common ground (andsome degree of cognitive similarity) will likely be externalised making it available for all

298 J.R. Rentsch et al.

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

09:

02 0

6 O

ctob

er 2

014

Page 14: Collaboration and meaning analysis process in intense problem solving teams

members to manipulate. Thus, knowledge flows iteratively through internal and external

processes culminating in innovated knowledge understood similarly by team members,

which resides where all knowledge ultimately exists – internally in individuals.

7. Future research

Given the difficulties associated with sharing information in intense problem solving

teams, external macrocognitive processes such as the creation of knowledge objects

and the use of schema-enriched communication should facilitate teams through a sequence

of internalised macrocognitive processes (knowledge transfer, knowledge interoperability

and cognitive similarity). These components of the C-MAP are intended to be integrated

into technology to support the cognitive processes of intense problem solving teams.

The propositions presented above articulate the basic premises of the C-MAP interven-

tion, which involves the application of schema-enriched communication and the use

of knowledge objects to improve knowledge transfer, knowledge interoperability and

cognitive congruence in intense problem solving teams. The initial research is being

conducted in a laboratory in order to maximise internal validity and is producing

successful results (e.g. Rentsch et al. 2007).Future research should evaluate the effects of these processes on problem solution

quality in real world teams operating under natural environmental and internal constraints

(Klein et al. 2003). Researchers tend to agree that the study of macrocognition is an

important next step (e.g. Klein et al. 2003, Crandall et al. 2006, Letsky et al. 2007) to

understanding how teams utilise their members’ expertise and cognitive skills to

collaboratively synthesize innovated knowledge and generate optimal problem solutions.

The investigation of the roles of macrocognitive processes in intense problem solving

teams performing in natural environments where the stakes and information load are high,

goals are conflicting or uncertain, and an element of time pressure exists will ultimately be

most useful theoretically and practically.In addition to testing the propositions presented here, many related questions remain

to be examined. For example, the most effective method for creating knowledge objects

has yet to be determined. One approach suggests examining optimistic and pessimistic

processes. Optimistic processes are those in which all ideas added to the knowledge object

are assumed to be agreed upon unless otherwise stated. In pessimistic processes, all new

concepts must be actively debated and the knowledge object may be altered only when

team members achieve consensus (Wessner et al. 2001).Combining the use of knowledge objects and schema-enriched communication with

the most practical interventions suggested by information sharing literature (e.g. making

areas of expertise public) might also be examined. Some evidence suggests that the

sampling bias against unique information diminishes over time (Larson et al. 1998),

but results also indicate the impact of unique information entered near the end of the

discussion is minimal (Stasser and Titus 2003). These findings suggest that knowledge

transfer should occur relatively early in the team decision making process. Perhaps

interventions based on the two externalisation mechanisms in the C-MAP intervention

will encourage team members to transfer knowledge early in the team’s discussion.

In summary, we have offered propositions suggesting how knowledge objects and schema-

enriched communication operate to facilitate macrocognitive processes and ultimately

improve the performance of intense problem solving teams.

Theoretical Issues in Ergonomics Science 299

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

09:

02 0

6 O

ctob

er 2

014

Page 15: Collaboration and meaning analysis process in intense problem solving teams

Acknowledgements

The authors’ work on this chapter was funded in part by a grant to the first author from the Officeof Naval Research (Award Number N00014-05-1-0624). The views, opinions, and findings containedin this article are the authors’ and should not be construed as official or as reflecting the views of theDepartment of Defense or The University of Tennessee.

References

Alterman, R., et al., 2001. Coordinating representations in computer-mediated joint activities.

In: Proceedings of the 23rd annual conference of the cognitive science society, 1–4 August 2001

Edinburgh, Scotland. Hillside, NJ: Erlbaum, 43-48.Beers, P.J., et al., 2005. Computer support for knowledge construction in collaborative learning

environments. Computers in Human Behavior, 21, 623–643.

Bereiter, C. and Scardamalia, M., 1996. Rethinking learning. In: D. Olson and N. Torrance, eds.

Handbook of education and human development. Cambridge, MA: Basil Blackwell, 485–513.Bromme, R., 2000. Beyond one’s own perspective: the psychology of cognitive interdisciplinarity.

In: P. Weingart and N. Stehr, eds. Practicing inter disciplinarity. Toronto, Canada: University

of Toronto Press, 115–133.Campbell, J. and 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

(3), 271–294.Carlile, P.R., 2002. A pragmatic view of knowledge and boundaries: boundary objects in new

product development. Organization Science, 13 (4), 442–455.Carlile, P.R., 2004. Transferring, translating and transforming: an integrative framework for

managing knowledge across boundaries. Organization Science, 15 (5), 555–568.

Carlile, P.R. and Rebentisch, E., 2003. Into the black box: the knowledge transformation cycle.Management Science, 49, 1180–1195.

Chen, D. and Hung, D., 2002. Personalised knowledge representations: the missing half of online

discussions. British Journal of Educational Technology, 33 (3), 279–290.Craik, F.I.M. and Lockhart, R.S., 1972. Levels of processing: a framework for memory research.

Journal of Verbal Learning and Verbal Behavior, 11, 671–684.Crandall, B., Klein, G., and Hoffman, R.R., 2006. Thinking about cognition. In: B. Crandall,

C. Klein, and R.R. Hoffman, eds. Working minds: a practitioner’s guide to cognitive task

analysis. Cambridge, MA: MIT Press, 131–148.Daft, R.L. and Weick, K.E., 1984. Toward a model of organizations as interpretation systems.

Academy of Management Review, 9 (2), 284–295.

Davenport, S.W., et al., 2007. Information sharing and schema accuracy in team decision making.In: 22nd Annual conference of the society for industrial and organizational psychology, 27–29

May 2007, New York.

Fiore, S.M., Cuevas, H.M., and Oser, R.L., 2003. A picture is worth a thousand connections:

the facilitative effects of diagrams on mental model development and task performance.Computers in Human Behavior, 19, 185–199.

Fiore, S.M., et al., 2008. Processes in complex team problem-solving: parsing and defining the

theoretical problem space. In: M.P. Letsky, et al., eds. Macrocognition in teams: theories and

methodologies. Burlington, VT: Ashgate Publishing, 143–163.Fischer, F. and Mandl, H., 2005. Knowledge convergence in computer-supported collaborative

learning: the role of external representation tools. The Journal of the Learning Sciences, 14 (3),

405–441.Fischer, G., et al., 2005. Beyond binary choices: integrating individual and social creativity.

International Journal of Human-Computer Studies, 63, 482–512.Franz, T.M. and Larson, J.R., 2002. The impact of experts on information sharing during group

discussion. Small Group Research, 33 (4), 283–411.

300 J.R. Rentsch et al.

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

09:

02 0

6 O

ctob

er 2

014

Page 16: Collaboration and meaning analysis process in intense problem solving teams

Hinds, P.J., Patterson, M., and Pfeffer, J., 2001. Bothered by abstraction: the effect of expertise on

knowledge transfer and subsequent novice performance. Journal of Applied Psychology, 86 (6),

1232–1243.Jonassen, D.H., Beissner, K., and Yacci, M., 1993. Structural knowledge: techniques for representing,

conveying, and acquiring structural knowledge. Hillsdale, NJ: Lawrence Erlbaum.

Klein, G., et al., 2003. Macrocognition. IEEE Intelligent Systems, May/June, 81–85.Kozlowski, S.W., 1998. Training and developing adaptive teams: theory, principles, and research.

In: J.A Cannon-Bowers and E. Salas, eds. Making decisions under stress: Implications

for individual and team training. Washington, DC: American Psychological Association,

115–153.Langfield-Smith, K., 1992. Exploring the need for a shared cognitive map. Journal of Management

Studies, 29, 349–368.Larson, J.R., et al., 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 (1), 93–108.Lee, M. and Kim, D., 2005. The effects of the collaborative representation supporting tool on

problem-solving processes and outcomes in web-based collaborative problem-based learning

(PBL) environments. Journal of Interactive Learning Research, 16 (3), 273–293.Lee, Y. and Nelson, D.W., 2005. Design of a cognitive tool to enhance problem-solving

performance. Educational Media International, 42 (March), 3-18.Leinhardt, G. and Smith, D.A., 1985. Expertise in mathematics instruction: subject matter

knowledge. Journal of Educational Psychology, 77, 247–271.Letsky, M.P., et al., 2007. Macrocognition in complex team problem solving. In: 12th international

command and control research and technology symposium (12th ICCRTS), Newport, RI,

June 2007. Washington, DC: U.S. Department of Defense Command and Control Research

Program.

Letsky, M.P., et al., 2008. Macrocognition in teams. Burlington, VT: Ashgate Publishing.Levina, N., 2005. Collaborating on multiparty information systems development projects:

a collective reflection-in-action view. Information Systems Research, 16 (2), 109–130.

Liang, D.W., Moreland, R.L., and Argote, L., 1995. Group versus individual training and group

performance: the mediating role of transactive memory. Personality and Social Psychology

Bulletin, 21, 384–393.Lilienfeld, S.O., et al., 2008. Psychology: From inquiry to understanding. Boston, MA: Pearson/Allyn

& Bacon.Lord, R.G. and Maher, K.J., 1991. Cognitive theory in industrial and organizational psychology.

In: M.D. Dunnette and L.M. Hough, eds. Handbook of industrial and organizational

psychology. Vol. 2, Palo Alto, CA: Consulting Psychologists Press, 1–62.

Marks, M.A., Zaccaro, S.J., and Mathieu, J.E., 2000. Performance implications of leader briefings

and team-interaction training for team adaptation to novel environments. Journal of Applied

Psychology, 85 (6), 971–986.Marks, M.A., et al., 2002. The impact of cross-training on team effectiveness. Journal of Applied

Psychology, 87 (1), 3–13.

Martins, L.L., Gilson, L.L., and Maynard, M.T., 2004. Virtual teams: what do we know and where

do we go from here? Journal of Management, 30, 805–835.Mathieu, J.E., et al., 2000. The influence of shared mental models on team process and performance.

Journal of Applied Psychology, 85 (2), 273–283.

Mohammed, S. and Ringseis, E., 2001. Cognitive diversity and consensus in group decision making:

the role of inputs, processes, and outcomes. Organizational Behavior and Human Decision

Processes, 85, 310–335.Nosek, J.T., 2004. Group cognition as a basis for supporting group knowledge creation and sharing.

Journal of Knowledge Management, 8 (4), 54–64.Rentsch, J.R., Delise, L.A., and Hutchison, S., 2008a. Transferring meaning and developing

cognitive similarity in decision making teams: collaboration and meaning analysis process.

Theoretical Issues in Ergonomics Science 301

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

09:

02 0

6 O

ctob

er 2

014

Page 17: Collaboration and meaning analysis process in intense problem solving teams

In: M.P. Letsky, et al., eds. Macrocognition in teams. Burlington, VT: Ashgate Publishing,

127–142.Rentsch, J.R., Delise, L.A., and Hutchison, S., 2008b. Cognitive similarity configurationsc

in teams: In search of the team mindmeldTM. In: E. Salas, G.F. Goodwin, and C.S. Burke,

eds. Team effectiveness in complex organizations. New York: Psychology Press, 241–266.Rentsch, J.R., Delise, L.A., and Letsky, M.P., 2007. Improving cognitive congruence and knowledge

interoperability in decision making teams. In: INGRoup: Interdisciplinary network for group

research 2nd annual conference, 12–14 July 2007, East Lansing, MI.

Rentsch, J.R., et al., 1998. Testing the effects of team processes on team member

schema similarity and task performance: examination of the team member schema

similarity model. AFRL-TR-98-0070 (Air Force Research Laboratory, Wright-Patterson

Air Force Base, OH).Roschelle, J. 1994. Designing for cognitive communication: epistemic fidelity or mediating

collaborating inquiry? The Arachnet Electronic Journal of Virtual Culture, 2 (2). Available

from http://www.kovacs.com/EJVC/ejvc.htm [Accessed May 1994].Savicki, V., Kelley, M., and Lingenfelter, D., 1996. Gender, group composition, and task type in

small task groups using computer-mediated communication. Computers in Human Behavior,

12 (4), 549–565.

Schittekatte, M., 1996. Facilitating information exchange in small decision-making groups.

European Journal of Social Psychology, 26, 537–556.Smeds, R., et al., 2006. Multidisciplinary research on simulation methods and educational games

in industrial management. In: 10th international workshop on experimental interactive learning

in industrial management, 11–13 June 2006 Trondheim, Norway.Stahl, G., 2005. Group cognition in computer-assisted collaborative learning. Journal of Computer-

Assisted Learning, 21, 79–90.

Stahl, G., 2006. Supporting group cognition in an online math community: a cognitive tool for

small-group referencing in text chat. Journal of Educational Computing Research, 35 (2),

103–122.Star, S.L., 1989. The structure of ill-structured solutions: boundary objects and heterogeneous

distributed problem solving. In: M. Huhns and L. Gasser, eds. Readings in distributed artificial

intelligence. MenloPark, CA: Carlile, 37–54.Stasser, G. and Stewart, D.D., 1992. Discovery of hidden profiles by decision-making

groups: Solving a problem versus making a judgment. Journal of Personality and Social

Psychology, 63 (3), 426–434.

Stasser, G. and Titus, W., 2003. Hidden profiles: a brief history. Psychological Inquiry, 14,

304–313.Stein, B.S. and Bransford, J.D., 1979. Constraints on effective elaboration: effects of precision and

subject generation. Journal of Verbal Learning and Verbal Behavior, 18, 769–777.Stewart, D.D. and Stasser, G., 1995. Expert role assignment and information sampling during

collective recall and decision making. Journal of Personality and Social Psychology, 69 (4),

619–628.

Stout, R.J., et al., 1999. Planning, sharing mental models, and coordinated performance: an

empirical link is established. Human Factors, 41, 61–71.Suthers, D.D. and Hundhausen, C.D., 2003. An experimental study of the effects of representational

guidance on collaborative learning processes. The Journal of the Learning Sciences, 12 (2),

183–218.Swan, J., et al., 2007. The object of knowledge: the role of objects in biomedical innovation.

Human Relations, 60 (12), 1809–1837.

Walther, J.B., 1997. Group and interpersonal effects in international computer-mediated

collaboration. Human Communication Research, 23, 342–369.Warner, N.W. and Letsky, M.P., 2008. Empirical model of team collaboration focus on

macrocognition. In: M.P. Letsky, et al., eds. Macrocognition in teams. Burlington, VT:

Ashgate Publishing, 15–33.

302 J.R. Rentsch et al.

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

09:

02 0

6 O

ctob

er 2

014

Page 18: Collaboration and meaning analysis process in intense problem solving teams

Wessner, M., Holmer, T., and Pfister, H., 2001. The learning net- an interactive representation of

shared knowledge. In: Proceedings of the 13th annual world conference on educational

multimedia, hypermedia & telecommunications. Tampere, Finland. June, 2001. Charlottesville,

VA: Association for the Advancement of Computing in Education.

About the authors

Joan R. Rentsch is Professor of Management, Director of the Industrial/Organizational PsychologyProgram, and Director of the Organizational Research Laboratory at The University of Tennessee.She earned her Ph.D. and M.A. in industrial/organizational psychology from the University ofMaryland and her B.S. in psychology from The Ohio State University. Her research interests focuson psychological processes in organizations including cognition in teams and organizations and onthe measurement of cognition. Her research appears in such journals as Journal of AppliedPsychology, Personnel Psychology, Journal of Organizational Behavior, Academy of ManagementJournal, and Organizational Research Methods. Dr. Rentsch’s current research program includesexamining team cognition, team processes and team performance. Her research is aimed at providingscientific foundations for technological developments supporting collaborative decision-making insituations characterized by such features as complex, dynamic, time sensitive and multinational.

Abby L. Mello is a doctoral candidate and the Lab Manager of the Organizational ResearchLaboratory at The University of Tennessee, Knoxville. Her research interests focus primarily on thedevelopment of cognitive congruence, team processes, information sharing and communication invirtual teams.

Lisa A. Delise is a doctoral candidate and the Senior Researcher of the Organizational ResearchLaboratory at The University of Tennessee, Knoxville. She earned her B.S. in psychology at TulaneUniversity. Her research interests focus primarily on team processes, including development ofcognitive congruence, information sharing, communication through distributed media and exchangeperceptions among team members.

Theoretical Issues in Ergonomics Science 303

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

09:

02 0

6 O

ctob

er 2

014