Method for analyzing the knowledge collaboration effect of R&D project teams based on Bloom’s taxonomy

Accepted Manuscript Method for analyzing the knowledge collaboration effect of R&D project teams based on Bloom’s taxonomy Jun Wang, Wei Wei, Liting Ding, Junpeng Li PII: DOI: Reference:

S0360-8352(16)30426-0 http://dx.doi.org/10.1016/j.cie.2016.11.010 CAIE 4526

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Computers & Industrial Engineering

Received Date: Revised Date: Accepted Date:

8 April 2016 26 August 2016 12 November 2016

Please cite this article as: Wang, J., Wei, W., Ding, L., Li, J., Method for analyzing the knowledge collaboration effect of R&D project teams based on Bloom’s taxonomy, Computers & Industrial Engineering (2016), doi: http:// dx.doi.org/10.1016/j.cie.2016.11.010

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Method for analyzing the knowledge collaboration effect of R&D project teams based on Bloom's taxonomy Jun Wang a,*, Wei Wei a, Liting Ding a, Junpeng Li a a

School of Economics and Management, Beihang University, Beijing 100191, PR China Corresponding author: Jun Wang Corresponding Author’s Email: [email protected]

E-mail addresses: [email protected] (Jun Wang), [email protected] (Wei Wei), [email protected] (Liting Ding)

*Corresponding author.

Method for analyzing the knowledge collaboration effect of R&D project teams based on Bloom's taxonomy Ab st ract Knowledge collaboration is a method for organization to create value in the institutionalized process of knowledge creation, knowledge acquisition, knowledge sharing and knowledge reuse, i.e., team members gathering distributed knowledge resources and supplementing and sharing their knowledge, is an important activity in R&D projects. Studying the effect of knowledge collaboration is an important means to evaluate R&D project teams. To address the effect of knowledge collaboration, this paper, which is based on knowledge management, the theory of knowledge collaboration and the theory of collaboration effects, studied the factors that influence the knowledge collaboration effect between members. The method of Bloom's taxonomy was used to quantify those factors. In addition, this study defined collaboration activity with formalized language and proposed a method to evaluate the collaboration effects of R&D project teams and a model of team knowledge collaboration effects. Finally, through a case study, this paper analyzed the optimal allocation of resources of team knowledge collaboration and provided a basis and standards for organizational managers to design an incentive mechanism. Keywords: Knowledge collaboration effect; Bloom's taxonomy; R&D team; Knowledge management

1. Int rod uct i on Knowledge has become the most competitive key resource for enterprises. In the era of the knowledge-driven economy, enterprises are forced to attach great importance to knowledge innovation and regard knowledge and information as major breakthroughs (Alavi & Leidner, 2001). The importance of knowledge management within business communities in particular has been intensively discussed (Bose, 2004). Knowledge collaboration was put forward by Karlenzig et al (Karlenzig, 2002; Karlenzig, Markovich & Borromeo et al, 2002). Anklam (2003) pointed out collaboration is the development tendency of knowledge management. Gloge et al (2005) defined knowledge collaboration as the capacity that organizations convey the right information to the right people at the right time, while McKlvey, Almb & Riccaboni (2003) defined knowledge

collaboration as activity, such as collaborative development, collaborative authoring, etc. It is also defined in general as the sharing, transfer, accumulation, and transformation of knowledge, which involves individual acts of offering knowledge to others as well as recombining, modifying, and integrating knowledge that others have contributed (Faraj, Jarvenpaa & Majchrzak, 2011). Proper knowledge management requires collaborative working modes to come into use. Enterprises realize knowledge integration by mining the internal relation of knowledge resources to improve the speed and efficiency of knowledge innovation. The development of new products is crucial for modern enterprises to develop sustainability. The R&D project team plays an important role in the project development of modern industry design. The intellectual property and knowledge creation of companies is coming from its R&D activity, so that it’s necessary to evaluate the R&D project (Imoto, Yabuuchi & Watada, 2008). In the interpersonal interaction of R&D project teams, the main elements affecting knowledge (Gong & Zhang, 2014). Collaboration are individual motivation and the incentive mechanism of teams. Only when teams provide a reasonable incentive mechanism and both sides of the interaction have active communication do knowledge collaboration and sharing have the best environment for development. Through knowledge collaboration, knowledge transfer and knowledge sharing in the R&D project team can be more efficient. The knowledge collaboration effects between team members are regarded as an evaluation index of team knowledge innovation, which can help managers design incentive mechanism and reward measures preferably, thereby further inspiring knowledge collaboration between staff and achieving the goal of team knowledge innovation. At present, knowledge collaboration research mainly focuses on the application of knowledge collaboration in enterprises and scientific collaboration, the modeling of knowledge collaboration and knowledge collaboration areas in a network. Many scholars also paid attention to the collaborative situation and model of Industry-university, academia-industry. (Massay, Udoka & Ram, 1995; Greitzer et al, 2010; Rentzos, Mavrikios & Chryssolouris, 2015). Van Leijen & Baets (2003) analyzed knowledge collaboration among multiple agents in depth when he studied reengineering knowledge-intensive processes, and put forth a framework for improving knowledge-intensive processes. Adam et al. (2005) presented a cross-enterprise collaboration framework, which was based on the differentiation of global knowledge within a network and the local knowledge of each participating company. This framework could guide cooperation for

process orientation, setting up and operation. Yamamoto (2013) analyzed the methods of knowledge collaboration through enterprise information services, and proposed a sustainable knowledge collaboration service analysis methodology. Vazquez-Brust, Sarkis & Cordeiro (2014) studied cross-border scientific collaboration. Many scholars have researched knowledge collaboration from the view of applications. These studies presented certain scenarios and case analyses but there also needs an in-depth and systematized analysis for the rule of knowledge collaboration. Petrescu, Rus and Negrusa (2014) studied how enterprises strengthen cooperation and information sharing in networks and lead to better development and innovation through cooperation. Pirkkalainen & Pawlowski (2014) researched the troubles of global social knowledge management that utilized social software. Quinton & Allen (2014) discussed how virtual collaborative learning environments help learners effectively. We can see that knowledge collaboration through networks is becoming more and more widespread. However, many aspects need change, such as the new communication barriers brought by social software. Beckmann (1995) studied the behavior of members in knowledge collaborative processes from the view of economics of utility. Nissen, Evald & Clarke (2014) researched how collaboration and cooperation in heterogeneous teams affect knowledge sharing and innovation. Mishra, Chand-rasekaran & MacCormack (2015) studied the effect of partnering scale and scope on collaboration in Multi-Partner R&D Projects. It’s important for the success of knowledge management implementation to identify the critical factors of knowledge management (Wu, 2012). Mehta, Hall &Byrd (2014) discussed the impact effect of information technology and project uncertainty on the knowledge-sharing process in software development teams. Tohidinia and Mosakhani (2010) examine

potential

influences

on

knowledge-sharing behavior

with TPB

framework.

Camarinha-Matos et al (2009) studied collaborative networked organizations theory, and provided a classification of collaborative networks. Some scholars figured out that effective management of knowledge should be achieved within organizations and networks among collaborative enterprises. However, in reality, many enterprises do not obtain benefits from knowledge management. Thus, knowledge sharing across collaborative networks is very necessary. Based on knowledge collaboration, Bloom's taxonomy, R&D staff appraisal and the collaboration effect, the factors affecting the knowledge collaboration effect among R&D project

members are analyzed in this paper. By designing an algorithm and application case, we study these factors’ degree of importance and relevant relations and then provide management suggestions. Section 2 discusses the factors affecting the knowledge collaboration effect among R&D project members. In section 3, those factors are quantized according to Bloom's taxonomy. An algorithm and a model of the knowledge collaboration effect are then designed. Section 4 describes the case study and the contributions and limitations of this study, and implications for future research are discussed in section 5.

2. Th eoret i cal Backgroun d The original intention of Bloom's taxonomy was assisting teachers in making teaching goals clear, thereby improving educational quality (Bloom, 1956). However, many later scholars have improvement on Bloom's taxonomy. Krathwohl, Bloom & Masia defined the attitude category in their book published in 1964. Simpson (1966) developed a classification system, the psychomotor domain of which covered the movement of physical, coordination and motor skills. Harrow (1972) considered 6 classifications: reflex action, basic motion, perception, physical abilities, advanced skills and effective communications. Reeves (1990) used Bloom's taxonomy for education research. Today, Bloom's taxonomy has been used as a type of scoring method that is usually applied to evaluate the individual’s professional quality. In particular, Krathwohl (2002) uncovered a new classification in term of knowledge area and cognitive process. Knowledge collaboration depends on information technology in a certain extent. The design of knowledge collaboration models has become the research orientation of some scholars. Ho (2004) designed a distributed knowledge management model to improve the knowledge management and collaboration implementation within the enterprise. Yesilbas and Lombard (2004) studied the conflict management of the process of collaborative design and developed a conflict management support system. Robin, Rose & Girard (2007) concerned the knowledge exchange and knowledge sharing in the collaborative design process and built a model of design context to make the design process and knowledge exchange more reasonable. Yang & Chen (2008) applied Bloom's taxonomy to rate personal professional knowledge in P2P network. The existing literature focus on the research that consider knowledge collaboration as a kind of new collaborative environment or a system aiming to support collaborative knowledge activities rather than designing model to reflect the characteristics of knowledge collaboration.

Some scholars analysed the operational mechanism of knowledge collaboration, and researched the measurement and evaluation methods of knowledge collaboration. Newman (2004) studied the relationship of collaborative authoring network and partnership. Mason & Lefrere (2003) analysed the relation of trust, collaboration and organizational reform. Karlenzig (2002) considered the performance measurement of knowledge collaboration. Ye (2005) pointed out that the detailed evaluation of information should be provided in knowledge collaboration. Nummela and Saarenketo (2004) designed a measuring system with six factors of knowledge collaboration, used to measure the international growth of knowledge intensive companies. Nagurney & Qiang (2010) put forward an optimization model of the collaboration effect in a knowledge collaboration network. Kozlov & Große (2016) studied how the dyads of online learners with symmetrical prior knowledge and asymmetrically distributed prior knowledge impact the collaborative outcome, and identified which partner can benefit most from the collaboration. The existing literature of the measurement of knowledge collaboration stress on the measurement of implementation technology instead of the evaluation of the efficiency and effect of knowledge collaboration from the level of management. The content of assessments for R&D staff which involves six personality scales and ten short cognitive abilities (Moser, Schuler & Funke, 1999). Del Rosario (2003) defined the evaluation index of R&D staff, which included loyalty, legal compliance, work performance, sense of community, sense of responsibility, leadership, honesty and initiative. Miller & Thornton (2006) also found that to improve the accuracy of the evaluation, there are at least 10 to 14 evaluation indicators of R&D staff. The evaluation methodology of R&D staff can be classified into three types of methods based on the staff character, work behavior and work outcome. A relatively famous evaluation methodology based on the staff character is called “Five-Factor Model (FFM)”. In addition, the performance measurement of R&D staff can be measured throughout a work log, work performance and work unit. The method based on work outcome is applied at different levels, but it may encounter the problems of the outcome being difficult to quantify and lag time. The engineering approach also can be used to evaluate the human resources performance to find the best performing employee (Gürbüz & Albayrak, 2014). In addition, the research of group performance has attracted widespread attention in recent years. (Kramer, Bhave & Johnson, 2014; Zhan et al, 2015; Ladley, Wilkinson & Young, 2015).

In conclusion, the impact factors of knowledge collaboration are divided into 3 classes: the impact factors of knowledge collaboration between teams, the impact factors between two members, and the impact factors of the external environment. The impact factors of knowledge collaboration between teams include both the organizational structure and work process of the team. The organizational structure of a team is divided into structural features, size and the member composition of the team. The work process of a team is divided into communication between team members, cohesion and the organizational culture of the team. With the development of internet, the members of a team have the aid of all types of network media for knowledge transfer, assimilation and integration. Impact factors between two members mainly include personal ability, communication time and the reputation of members, which have an effect on group knowledge collaboration based on the effect on small groups. The external environment includes support from leadership, incentive mechanisms, membership training, etc. DeShon et al. (2004) thought that team members should be provided more team performance feedback, through which members could exert more effort in improving their team’s performance. Personal knowledge can be training. The knowledge of team work training of team members is beneficial to improving team performance (Hirschfeld et al, 2006). In summary, team size, team structure and team culture mainly affect the knowledge collaboration effect between teams. Personal ability, members’ prestige and time efficiency mainly affect the knowledge collaboration effect between members. Organizations training and support of the leadership have an impact on the knowledge collaboration effect of both teams and members within the teams. Figure 1 displays the factors that influence knowledge collaboration.

Figure 1 Influence factors of knowledge collaboration

3. K nowled ge Collab orat ion Algori th m Taking interdependent R&D project teams for example, in the course of collaborative work, each member in these teams is in close touch with other members, communicates and cooperates widely, and finishes team tasks through joint consultation. The knowledge collaboration process between members can be summarized as a process where one member raises problem and another member solves the problem. Figure 2 shows the interdependent R&D project team’s knowledge collaboration network.

Figure 2 Interdependent R&D project team’s knowledge collaboration network To study the knowledge collaboration effect of R&D project team members more intuitively, we developed the following three hypotheses. H1a The influence of external environment on the team insider members is homogeneous, we ignore the influence of the external environment when we study knowledge collaboration within the team in this paper and pay attention to personal ability, members’ prestige and the time efficiency of members. H1b The method of knowledge collaboration between members is one member asks a question and another figures it out. Only when both sides achieve the original target or reach an agreement will this knowledge collaboration be considered effective. Therefore, we are concerned with effective knowledge collaboration in this study. H1c The knowledge collaboration process is limited to two persons. Three or more persons’ knowledge collaboration processes are not considered in this paper. The research object of this paper is an interdependent R&D project. Assume that there are N members in the interdependent R&D project, which can be described as ‘A,’ ‘B,’ etc. Every member has their own knowledge background, and they can be interested in two or more

professional fields. For example, ‘A’ is an expert of the computer realm; ‘B’ is an expert of mathematics. In addition, there are many interactive modes between members of R&D projects, including email, face to face and instant messaging. A knowledge collaboration activity ( TR & D ) is defined as follows:

TR&D  U , KC, K , KP  Here U  {u1 , u2 ,

un }, n  Z means there are n team members taking part in the

knowledge collaboration activity of the R&D team.

KC  {kc1 , kc2 , kc3

kcm }, m  Z is used to indicate that there are m methods of

knowledge collaboration.

K  {k1 , k2

k },   Z reflects the types of collaborative knowledge.

KP  {kp1 , kp2

kp },  Z shows the effect of knowledge collaboration.

Based on the knowledge collaboration process between team members in an interdependent R&D project, the impact factors of knowledge collaboration are discussed in the following.

3.1. The quantification of impact factors 1. Personal knowledge ability evaluation based on Bloom's taxonomy The personal knowledge ability of R&D projects team members is reflected in the process of knowledge collaboration, their own knowledge background and the degree of knowledge matching of unresolved problems. The higher the degree of knowledge matching, the more able members will be in the knowledge collaboration. In addition, personal knowledge ability is relevant to the complexity of questions. The more difficult the questions are, the more able members will be. Thus, personal knowledge ability can be reflected by the matching degree of problem solver and unresolved problem as well as how difficult the unresolved problem is. (1) The degree of knowledge matching of the problem solver and unresolved problem We use the revised Bloom taxonomy in the form of matrix (Anderson et al., 2001). We define numerical knowledge dimension referring to the literature of Yang & Chen (2008). In table 1, knowledge of a field is divided into 24 classes, with the rows representing 6 cognitive process

dimensions and columns representing 4 knowledge dimensions. The knowledge dimensions include Factual Knowledge, Conceptual Knowledge, Procedural Knowledge, Metacognitive Knowledge. The cognitive process dimensions include Remember, Understand, Apply, Analyze, Evaluate and Create. Each combination of cognitive dimension and knowledge dimension is a type of knowledge, such as [Factual knowledge, Remember], which represents factual knowledge one needs to remember, such as common sense regarding the software engineering domain. The value of each cell in figure 1 reflects the level of expertise of member in corresponding knowledge categories, which ranges from 0 to 1. Table1 Bloom's taxonomy matrix in a knowledge area

Factual Knowledge Conceptual Knowledge Procedural Knowledge Metacognitive Knowledge

Remember

Understand

Apply

Analyze

Evaluate

Create

0.8

0.7

0.5

0.6

0.2

0

0.2

0.2

0.4

0.3

0

0.3

0.4

0.3

0.6

0.1

0.1

0

0.2

0.1

0.2

0

0

0.1

Experts in Bloom's taxonomy can use a number of ways to evaluate R&D project members, but the most common way is a questionnaire survey, which is tested by experts and answered by team members. Experts need to sort out questions ahead of time. Each question corresponds to only one type of knowledge, which means one of the 24 cells in figure 1. The value depends on members’ answer. For example, if there are 5 questions belonging to [Procedural knowledge, Apply], of which 3 questions are answered correctly, then the value of [Procedural knowledge, Apply] is 0.6. A46 matrix is used to represent the professional level of members.

BGrc represents each element of matrix. r  1, and c  1,

4 describes the 4 knowledge levels,

6 describes the 6 cognitive levels. For instance, BG22 =0.4 in a member’s

Bloom's taxonomy means the member’s professional level is 0.4 in [Conceptual Knowledge, Apply]. It is worth noting that Bloom's taxonomy is only one type of research method and a general classification method that can be used to any knowledge. The experts of each area can design their

own knowledge classification table according to their area’s characteristics. (2) Difficulty Level of question Bloom's taxonomy can classify unresolved questions in terms of the knowledge collaboration process.

Vrc  Nrc  Drc

(1)

As before, Vrc represents each element of matrix. r  1, levels, and c  1,

4 describes the 4 knowledge

6 describes the 6 cognitive levels.

Table2 Bloom's taxonomy matrix in difficulty level of question

Factual Knowledge Conceptual Knowledge Procedural Knowledge Metacognitive Knowledge

Remember

Understand

Apply

Analyze

Evaluate

Create

1

2

3

4

5

6

2

4

6

8

10

12

3

6

9

12

15

18

4

8

12

16

20

24

Here N rc is used to show the number of questions belonging to the knowledge level of r and cognitive level of c . Drc describes the difficulty level of a question belonging to knowledge level of r and cognitive level of c . The difficulty level of the question also can be judged by Bloom's taxonomy. The Bloom's taxonomy knowledge level becomes deeper and deeper from factual knowledge, conceptual knowledge, and procedural knowledge to metacognitive knowledge. What’s more, the cognitive level also increases form memory, comprehension, application, analysis, evaluation and creation. Thus, the difficulty level and value of each question are different. The higher the knowledge level and cognitive level are, the more difficult the question is. Thus, we can have Drc  r  c . For example, for [Procedural knowledge, Evaluate], the difficulty level is 3×5=15. The personal knowledge ability of members can be described into BM (ij ) , which is also a 4×6 matrix. Here BM rc represents the matrix element of BM (ij ) . r  1, knowledge levels, and c  1,

6 describes the 6 cognitive levels.

4 describes the 4

BM (ij )  BG( j )V(i )

(2)

What calls for special attention is that BG( j )V(i ) is the product of the degree of knowledge matching matrix( BG( j ) ) and the element of question matrix ( V( i ) ) rather than matrix multiplication, that is BM rc  BGrc Vrc . 2. Members’ reputation Mui, Mohtashemi & Halberstadt (2002) built a computational model of credibility, which mentioned an evaluation method of members’ reputation. The reputation of member u j in the eyes of member ui is also very important. That indicates how can u j promise ui to solve problem. Two hypotheses are given when calculating the reputation of u j in the eyes of the ui : (1) The knowledge collaboration network of the R&D project stable. Neither new members join nor old members exit when the reputation is calculated. (2) There are only two behaviors, agree and disagree, which means when member ui asks a question, member u j can choose only to agree or not agree to solve the question. In real cooperation, the values of cooperation can be discrete values in a certain range. However, to simplify the calculation, only integer values are adopted. Because ui is the problem originator, ui has a cooperative attitude every time. In this equation, X ( ji ) (t ) is on behalf of the time t knowledge collaboration situation. Thus, X（ji）(t ) can be described as follows:

1, X ( ji ) (t )   0,

j agree to cooperative j disagree to cooperative



(3)



Assume that ui asks n questions to u j . D( ji )  X ( ji ) (1), X ( ji ) (2),...... X ( ji ) (n) . Here we make p agreed to n times. The estimated value of reputation of u j in the eyes of ui , R（jim ) is p n . 3. Time efficiency of Knowledge Collaboration The time of Knowledge Collaboration between members mainly indicates the basic time of knowledge transfer in the media. The times of knowledge transfer in different media are unequal.

Nagurney & Qiang (2010) argued that because the communication methods of R&D projects are different, the times of the knowledge collaboration process are also different. In reality, face-to -face communication is not necessarily better than online communication in terms of saving time. However, to simply calculate, we adopt the following time setting. For example, e-mail communication between members is better than a fax in terms of saving time. We divide the communication method of knowledge collaboration into three types: face-to-face communication, communication based on the network, and communication based on other communication modes. Here Tm is used to show the time of the knowledge transfer process through communication mode m , which is defined as follows:

1, R e a l - t i m e c o m m u n i c a t i o n , s u c, hr eaas l f- at icme et on feat cweo r k e t c .  (4) Tm  2, Co m m u n i c a t i o n n e e d a ,c es ur tcahi na s t ei m ea i l . 3, Co m m u n i c a t i o n n e e d ea, ls ou nc gh taism l e t t e r .  Assume that ui ask 4 questions, including 3 questions of [conceptual knowledge, comprehension] and 1 question of [metacognitive knowledge, application]. 1 of the 3 questions about [conceptual knowledge, comprehension] requires face to face communication, and the remaining two require communicate through network. The question about [metacognitive knowledge, application] raised by ui also requires communicate face-to-face. The time spent on the 3 questions of the category [conceptual knowledge, comprehension] is (1×1+2×2), whereas the time spent on the question of [metacognitive knowledge, application] is 1×1.

3.2. Knowledge collaboration effect algorithm between members The knowledge collaboration effect algorithm between members can be described as follows: The measure of degree of knowledge matching of members can be show by the difficulty level of questions and the degree of mastery of members in terms of this knowledge area. 1) BM ( ij ) is used to describe the degree of knowledge matching matrix of u j in the knowledge area of the question raised by ui . For example, the question raised by ui belongs to the software engineering domain. Thus, BG( j ) is the professional level matrix of in u j in the software engineering domain.

BM (ij )  BG( j )V(i )

(5)

V( i ) is matrix in knowledge area m of question raised by, ui which not only reflects the numbers of questions raised by ui but also difficulty level of questions.

BM ( ij ) is the degree of professional knowledge matching matrix, which is used to represent the personal ability of the questioner and question solver. 2) The knowledge collaboration effect matrix can be described by the product of the degree of knowledge matching and time efficiency.

CE( ij )  BM ( ij )T( ij )

(6)

Here, T( ij ) is the time efficiency of knowledge collaboration between ui and u j .

CE( ij ) is the knowledge collaboration effect matrix concerning the degree of knowledge matching and time efficiency. 3) Formula (7) is used to calculate the sum of the knowledge effect matrix among all the members. 4

6

DE(ij )   CErc

(7)

r 1 c 1

DE( ij ) is numerical, which is the sum of all the elements of matrix . 4) KP( ij ) reflects the knowledge collaboration effect between two members, which is the product of the knowledge collaboration effect matrix and reputation.

KP(ij )  DE(ij ) R( ji )

(8)

R( ji ) is the reputation of u j in the eyes of ui in the knowledge area m . KP( ij ) represents the knowledge collaboration effect between ui and u j .

3.3. Optimizing The application condition of optimizing The research object is knowledge collaboration process in interdependent R&D project teams.

Assume that there are members and types of communication modes in the R&D project teams. 5 hypotheses are presented clearly. H2a The knowledge collaboration process is limited to two persons. Three or more persons’ knowledge collaboration processes are not considered in this paper. H2b Both sides of knowledge collaboration have a cooperative attitude. None of their attitudes are negative. H2c Both sides of knowledge collaboration can adopt all types of communication modes, including face to face and email. H2d The time spent on the knowledge collaboration process is composed of two parts. One part is because of knowledge gap of two sides, which can be measured by the aforementioned degree of knowledge matching. The higher the degree of knowledge matching is, the smaller the knowledge gap is. The other part is decided by the modes of communication of both sides. H2e The knowledge collaboration effects between members accumulate into the whole knowledge collaboration effect of the team. The knowledge collaboration process will eventually produce a terminal effect, whether the effect is the innovation of knowledge, an increase in the knowledge stock, or an improvement in personal ability and change in the organizational structure. Thus, the Douglas production function is used to describe the knowledge collaboration effect between members.

E(ijm )

    A(ijm ) ( X (ijm ) ) ( X ( jim ) )      A(iim ) ( X (iim ) )

i j i j

(9)

Here E( ijm) is used to define the knowledge collaboration output that member ui and member u j communicate through mode m . A( ijm ) represents the influence factor of the external environment for knowledge collaboration between members ui and u j , such as managerial and administrative expertise and incentive mechanisms. Because the influence of the external environment on the same team members is homogeneous, A( ijm ) is always a constant.

X ( ijm ) reflects ui ’s work in the knowledge collaboration process between member ui and u j ,whereas X ( jim ) reflects u j ’s work. Assume the aforementioned Douglas production function

has constant return. That is to say, the increasing proportion of E( ijm) is equal to the increasing proportion of X ( ijm ) or X ( jim ) . Then     1 .

 and  respectively represent the

influence of ui and u j ’s work on the knowledge collaboration output because the knowledge collaboration output is also a type of effect for members ui and u j . Thus, the output is shared by members ui and u j . Well, because E( ijm )  E( jim ) , we know that

    0.5 . When

members take part in knowledge collaboration activities by themselves, the output is

A(iim ) ( X (iim ) )   , of which X ( iim ) reflects work paid by member. Project schedule is a key element of R&D teamwork. Every project has some important time nodes. Before a time node, their time is limited for each member. Distributing limited time reasonably in knowledge collaboration activities can maximize the team knowledge collaboration effect. Solving the maximization of knowledge collaboration can be described as a single objective programming problem. Nagurney & Qiang (2010) put forward an optimization model of the collaboration effect in a knowledge collaboration network, which is improved in this paper and described as follows: N

Max

N

K

 E i 1 j 1 m 1

N

s.t.

K

 ( T j 1 m 1

v ( ij )

( ijm )

( X ( ijm ) , X ( jim ) )

  T(cijm )  1) X ( ijm )  LT( i ) , i  1,..., N X  R N N K

(10)

(11)

In formula (10), E(ijm) ( X (ijm) , X ( jim) ) is used to reflect the knowledge collaboration output that members ui and u j communicate via mode m , that is the knowledge collaboration effect N

of member ui and u j .

N

K

 E i 1 j 1 m1

( ijm )

( X (ijm ) , X ( jim ) ) represents the knowledge collaboration

effect of the whole R&D project team, which is the sum of the knowledge collaboration effects of all members. In formula (11), LTi is the time budget of member ui , namely all the disposable time of member ui in the project schedule. X (ijm ) reflects how many units of labor are paid by members

ui and u j communicating through mode m . T(vij ) is used to show time consumption because of the knowledge gap between members ui and u j in the knowledge collaboration process. 4

6

T(vij )  V( j ) / BG( i ) . V( j ) is the difficulty level matrix of the question raised by member r 1 c 1

u j . BG(i ) is used to describe the degree of knowledge matching matrix of the question raised by member ui and u j . Here, there is a normalization process on V( j ) . That is, each element of the matrix is divided by the maximum of the elements in the matrix.

T(vij )  V(ij )  p(ijm) . V(ij ) is

the knowledge distance of members ui and u j , which is measured by units of time. For example, V(ij )  20 reflects that there are 20 units of time between members ui and u j , which means members ui and u j need to spend 20 units of time on every communication because of the knowledge gap. p(ijm ) expresses the communication times when ui pays out one unit of c labor. T( ijm ) is the time spent on communication through modes between members ui and u j in c the knowledge collaboration process. T(ijm)  C(ijm )  p(ijm ) . C( ijm ) is the communication

distance between members ui and u j , which is also measured by unit of time. In addition, assume that there is no knowledge distance or communication distance between two the sides. They should spend one time unit on communication at a time. Thus,

 T(vij )   T(cijm)  1 reflects

how much time it takes to contribute a unit of labor when members ui and u j communicate through mode m .  and

 respectively represent the effect degree of T(vij ) and T(cijm ) in

terms of the time needed for the whole knowledge collaboration process.   (0,1] ,   (0,1] , and     1 . The values of  and

 are provided by experts. As for the process of

optimization, it is similar to Nagurney and Qiang (2010), which we will not describe in further detail. From the perspective of economics, all the rational members of the R&D project team hope they can gain the maximal utility with the shortest time. Assume that all the members of the R&D

project team are rational. The most suitable partner of knowledge collaboration can be chose by the following optimization method.

Max N

s.t

K

 ( T

v ( ij )

j 1 m 1

A(i j m)( X

) X (



( i j )m

(

T(ij v)  T ijm( c ) 1

)

j i) m

 j  1 , 2 .N. .

(12)

  T(cijm )  1) X ( ijm )  LT( i ) , i  1,..., N X  R N N K

In the formula (12),

A(ijm ) ( X (ijm ) ) ( X ( jim ) )  T(ij ) v  T(ijm ) c  1

(13)

is used to reflect the utility acquired by ui

in the per unit time when member ui and u j have a knowledge collaboration activity.

4. Case S tud y 4.1. Analytical example of the knowledge collaboration effect between R&D project team members A case study of knowledge collaboration effect between team members is carried out in a software house with the core development team consisting of 6 staff who was building the enterprise knowledge management system to improve the ability of knowledge innovation and management. In the project with multiple knowledge subjects participating, the knowledge collaboration process includes knowledge sharing, knowledge integration, knowledge collision and interactive activity. In particular, raising and solving problems in the course of project development embody knowledge collaborative activities, which are quantified in table 3. Table 3. Statistics regarding the amount of problems to be raised/solved in the project

uA

uB

uC

uD

uE

uF

Total

uA uB uC uD uE uF

140

5

26

6

2

17

196

0

32

1

0

0

0

33

4

3

25

9

1

4

46

1

5

1

57

3

0

67

1

0

3

3

52

0

59

0

1

5

1

1

17

25

Total

146

46

61

76

59

38

426

According to the above definition, there are 6 members. U  {u1, u2

u6} . So there are 24

kinds of knowledge according the Bloom's taxonomy, K  {k1, k2

k24} , and 2 kinds of

communication mode, KC  {kc1, kc2} , which are face to face and communication via internet. The knowledge collaboration effects are KP  {kp1, kp2

kp24} . Take the knowledge

collaboration process u A and u D for example. We can calculate the knowledge collaboration effect between u A and u D . Because it is an information system development team, the problem belongs to the area of software development. The knowledge collaboration between u A and u D can be divided into two scenarios: u A raises a problem and u D solves it and vice versa. Next, we calculate the knowledge collaboration between u A and u D taking the first scenario as an example. Step 1: V( A) is used to show the problems raised by u A . According to the matrix, u A raises 6 problems to u D . In that way, the value matrix of the 6 problems can be described as follows. 0 0 1  1 0  0 3 4 0 0  0 0 1  12  0  0 0 1  12 0 

0 0 0 0

0 0  0  0

The 6 problems can be divided into 4 types: 1 problem belonging to [Factual knowledge, Remember], 3 problems belonging to [Conceptual knowledge, Understand], 1 problem belonging to [Metacognitive knowledge, Apply] and 1 problem belonging to [Procedural Knowledge, Analyze]. The professional knowledge matrix of u D in the area of software development is shown in

BM ( AD ) . Therefore, BM ( AD )  BG( D ) V( A)  0.8 0.2 = 0.4  0.2

0.7 0.2 0.4 0.6

0.5 0.6 0.2 0  1  1 0 0 0   0.4 0.3 0 0.3 0 3  4 0 0  0.6 0.1 0.1 0   0 0 0 1  12  0.2 0 0 0.1  0 0 1 12 0

0 0 0 0

0 0.8 0 0 0 0   0 0 2.4 0 0 0 = 0  0 0 0 1.2 0   0  0 0 2.4 0 0

0 0  0  0

Step 2: T( AD ) reflects the time efficiency matrix of the knowledge collaboration process

between u A and uD .The 3 problems belonging to [Factual knowledge, Remember], [Procedural knowledge, Analyze] and [Metacognitive knowledge, Apply] are based on the internet, whereas 2 of the remaining 3 problems are based on the internet and the other is not. 0.8 0 0 0 0  0 24 0 0 0 =  0 0 0 1.2 0   0 0 24 0 0

0 0 0 0 1/11(1/0.8)1   0 1/(1/0.2)4(1 21) 0 0 0   0 0 0 1/[21(1/0.1)12] 0   0 0 1/11(1/0.2)12 0 0 

0 0 0 0 1 0 0.75 0 0 0 = 0 0 0.005 0 0  0 0.04 0 0 0

0 0 0 0

0  0 0  0

0 0  0  0

Step 3: DE( AD ) is the sum of each element of matrix CE( AD ) . 4

6

DE( AD )   CErc  1  0.75  0.005  0.04  1.795 r 1 c 1

Step 4: According to the previous statistics, the reputation of u D in the eyes of u A is 0.8.

KP( AD)  DE( AD)  R( DA)  1.795  0.8  1.436 Thus, in the knowledge collaboration process where u A raises a problem and uD solves it, the knowledge collaboration effect between u A and uD is 1.436. The larger KP( AD ) is, the better the knowledge collaboration effect between u A and uD is.

4.2. Analytical example of the knowledge collaboration effect between R&D project teams Assume that the utility function is a Douglas production function:

E(ijm) ( X (ijm) , X ( jim) )  A(ijm) ( X (ijm) )1/2 ( X ( jim) )1/2 , i, j, m Suppose that there are two mathematicians, a computer expert and a behaviorist expert in a knowledge collaboration network, U  {u1, u2 , u3 , u4} , where members can communicate face to face or via the internet. The mode of face-to-face communication is defined as mode 1, and internet is mode 2. That is to say, there are three types of knowledge areas and two types of knowledge collaboration modes in the knowledge collaboration network, KC  {kc1, kc2} . So

there are 24 kinds of knowledge according the Bloom's taxonomy, K  {k1, k2

k24} . Make

u A and uB represent the mathematicians, uC represent the computer expert and uD represent the behaviorist expert. N  4，M  2 . There are 36 variables. The amount of variable X is 32 The opportunity cost of variable is 4. The time budget of each member is set to LT( i )  120 . In addition,  = =0.5 , which is the incidence of time spent on the whole knowledge collaboration process. The parameters of the knowledge collaboration network are shown as follows. Table 4 Parameters of the knowledge collaboration effect members

m

T(vij )

T(cijm )

T( ijm )

LT( i )

A( ijm )

u A , uB

1

13

1

7

120

8

u A , uC

1

31

1

16

120

8

u A , uD uB , uC uB , uD uC , uD

1

25

1

13

120

8

1

37

1

19

120

8

1

31

1

16

120

8

1

25

1

13

120

8

u A , uB u A , uC u A , uD

2

24

2

13

120

8

2

36

2

19

120

8

2

30

2

16

120

8

uB , uC uB , uD uC , uD

2

36

2

19

120

8

2

36

2

19

120

8

2

42

2

22

120

8

After calculating, we obtain the following results: X ( AB1)  1.4631 , X (CD 1)  0 ,

X ( BC1)  0 , X ( AC1)  0 , X ( AD1)  2.8529 , X ( BD1)  0 , X ( AB 2)  0 , X ( AC 2)  0 , X ( AD 2)  4.9344 , X ( BC 2)  0 , X ( BD 2)  0 ,and X (CD 2)  0 .The remaining X (ijm ) equals 0. The results show that: (1) The marginal utility of the knowledge collaboration between experts from diffident areas is larger than that from the same area. (2) When members prefer a knowledge collaboration mode that wastes time, the marginal utility of their knowledge collaboration activity can make up for the extra time cost. (3) The low output side in knowledge collaboration requires spend more time than the high output side.

Only when the marginal utility is equal to the opportunity cost acquired by ui , ui will like to work on knowledge collaborative activities with u j , while when the marginal utility is smaller than the opportunity cost acquired by ui , ui will not conduct the knowledge collaborative activities with u j . Besides, if the opportunity cost acquired by ui is a positive number, then ui will just use up all the time, that is ui will participate in an on-going knowledge collaborative activity, while if the opportunity cost acquired by ui is 0, that means ui won’t use up all the time. The necessary condition that ui and u j take part in the interdisciplinary knowledge collaboration is the marginal utility of ui is bigger than the marginal utility of ui when ui and

u j taking part in the collaboration activity in the same knowledge domain. When member ui and u j taking part in the knowledge collaborative activities through two modes, the necessary condition that both two modes are adopted is the ratio of member’s marginal utility is equal to the time of knowledge collaboration under the two modes. Otherwise,

ui and u j will choose only one mode. The necessary condition that member ui and u j choose the time-wasting communication mode is that the marginal utility per unit time through the time-wasting mode is bigger than the other mode. In the real teamwork, some difficult problems need resolve by the more time-consuming way, such meeting. So most time-saving mode doesn’t always mean best.

5. Con clu sion This paper, which is based on the theory of knowledge collaboration, the theory of collaboration effects and Bloom's taxonomy, studied the factors that influence the knowledge collaboration effect between members and discussed a model of the knowledge collaboration effect of R&D project teams. The impact factors of the knowledge collaboration of R&D project teams include structural features, team size, the task allocation and member combination of the team, cohesion and the organizational culture of the team. The impact factors of knowledge collaboration between team members include personal ability, communication time and the

reputation of members. In addition, an algorithm is designed to solve for the optimal solution of the knowledge collaboration effect in R&D project teams. Finally, through case analysis, the resource allocation plan is given for reaching the maximum effect of the R&D project team. The research method of the knowledge collaboration effect in this paper is novel. However, there are also some limitations. Any future study plan should include the following points: (1) Some factors of knowledge collaboration effect of R&D are perhaps not considered in this paper. The impact factors of members’ behaviors can be classified and detailed further, and the influence of the external environment on members will be considered in further research. (2) In the process of solving, it is difficult to obtain truthful data, in terms of both the professional knowledge matrix of team members and the time of knowledge collaboration. The assumed conditions will be broadened in the future study to describe the real knowledge collaboration situation. (3) The knowledge collaboration among multi-members or organizations is more difficult, which will be studied in our future research. Taking knowledge collaboration teams as a network, social network theory and complex network theory can be used to analyze the collaborative relationship. We also try to study the cooperation and evolution of knowledge collaboration combining the game theory and optimization theory. In our further work, we will build the framework of the evaluation of knowledge collaboration effect using the theories mentioned above, and applying the relative research to the fields of knowledge management, enterprise management and industrial engineering, etc.

Ackn owled gement s This research was supported in part by the National Natural Science Foundation of China under Grant No.71271018 and No.71531001.

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Acknowledgements This research was supported in part by the National Natural Science Foundation of China under Grant No.71271018 and No.71531001.

Highlights (1) Quantify the factors that influence the knowledge collaboration effect. (2) Design an algorithm to evaluate the collaboration effects of R&D project teams (3) Build an optimizing model of the knowledge collaboration effects of R&D teams (4) Give the resource allocation plan of maximal effect of knowledge collaboration.

Knowledge Collaboration The method of assessments for R&D staff Bloom's taxonomy collaboration effect

Method

Measurement Personal knowledge ability evaluation based on Bloom's taxonomy Difficulty Level of question based on Bloom's taxonomy Members’ reputation

Time efficiency of Knowledge Collaboration

The knowledge collaboration effect algorithm between members Step1:compute the degree of knowledge matching matrix Step2:compute the degree of knowledge matching and time efficiency Step3: knowledge collaboration effect matrix concerning the degree of knowledge matching and time efficiency Step4:compute the knowledge collaboration effect

R&D Project Team knowledge management performance optimizing

R&D Project Team Knowledge Management Performance Evaluation

Application provide a basis and standards for organizational managers to Improve the knowledge collaboration in R&D project teams