Modeling and analysis of project performance factors in an extended project-oriented virtual organization (EProVO)

Expert Systems with Applications 37 (2010) 1143–1151

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Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa

Modeling and analysis of project performance factors in an extended project-oriented virtual organization (EProVO) Hyeongon Wi a, Mooyoung Jung b,* a b

Technology Commercialization Group, Research Institute of Industrial Science and Technology (RIST), Hyoja San 32, Pohang 790-330, Republic of Korea School of Technology Management, Ulsan National Institute of Science & Technology (UNIST), Banyeon-ri 100, Ulsan 689-798, Republic of Korea

a r t i c l e

i n f o

Keywords: Project performance factors Project performance index Knowledge Collaboration Social network

a b s t r a c t Project performance is significantly affected by the collaboration of project team members and by the levels of their knowledge. While many qualitative and quantitative studies on project performance have been conducted, only few studies have analyzed the effects of knowledge and collaboration. This paper proposes an extended project-oriented virtual organization (EProVO) model that consists of a total of 10 factors: 2 time availability factors, 3 cost factors, and 5 capability factors that are useful in evaluating the knowledge competence and collaboration competence of project members. Defining the project performance index that generally assesses project goals – quality, time and budget, and using the data from an actual R&D institute, the effects of 10 factors on project performance were analyzed, and the performance of the project team members was predicted. The predicted project performance of team members helped the decision-making process of the project team manager by providing information required for organizing the team. Furthermore, the coefficient of each factor was useful in figuring out the problems to be encountered in the behavioral styles in terms of knowledge, collaboration, time availability, and cost of running the current organization. Consequently, directions for organizational improvement of a research institute could be offered. Ó 2009 Published by Elsevier Ltd.

1. Introduction For project-oriented R&D organizations or enterprises, human resource management is strategically crucial because the core of project management has shifted to the issue of human resource management from technical concerns (Huemann, Keegan, & Turner, 2007). In project management, human resource management is divided into eight areas – human resource planning, reception of necessary human resource, selection of project team members, job analysis, remuneration, education and training, performance assessment, and career planning (Pettersen, 1991) (see Fig. 1). When a business opportunity is found, the long-term human resource planning is established, and reception of necessary human resource is processed. For a project, a project manager and team members are selected from the personnel pool after analyzing the jobs involved in carrying out the business opportunity. After the project is completed, lack of human resources is complemented through education and training, and career planing of each human resource is established. The remuneration corresponding to project performance assessment is given to each human resource.

* Corresponding author. Tel.: +82 52 217 1011; fax: +82 52 217 1058. E-mail address: [email protected] (M. Jung). 0957-4174/$ - see front matter Ó 2009 Published by Elsevier Ltd. doi:10.1016/j.eswa.2009.06.051

The nature of the project may differ from project to project depending on its subject (for example, R&D, construction, manufacturing, etc.), but the success of a project is the common goal for any organization that performs the project. From the perspective of human resource management, the success of a project is directly linked to the formation of the project team composed of the members who are selected from the candidates available in the organization. In other words, project performance depends on the selection of a project team manager and project members who are equipped with the level of knowledge required by the projects. When combined with efficient collaboration, this will eventually lead to the success of the project. Many studies have analyzed factors that affect project success (Beck, Jiang, & Klein, 2006; Belassi & Tukel, 1996; Cheung, Suen, & Cheung, 2004; Fryer & Fryer, 1985; Jiang, Klein, Hwang, Huang, & Hung, 2004; Martin, 1976; Morris & Hough, 1987; Ojanen, Piippo, & Tuominen, 2002; Pheng & Chuan, 2006; Pinto & Slevin, 1989; Sayles & Chandler, 1971; Thamhain, 2004; Wang, Wei, Jiang, & Klein, 2006). However, most of the studies have analyzed the questionnaire-based data which lack in objectivity and quantifiable results. Moreover, there have been few models that offered analyzed factors that could predict the success or failure of a project. Extending the project-oriented virtual organization (ProVO) of a previous work (Wi, Mun, Oh, & Jung, 2009), this study develops a

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Fig. 1. Human resource management in project management.

model that predicts project performance through a quantitative analysis of the factors affecting the success of a project. When a team is formed for a new project using the model proposed in this study, the performance of a newly formed project team will be predictable. Based on the predicted result, the project team can be organized to maximize its project performance. This paper is organized as follows. Section 2 surveys related works on factors affecting project performance and points out the problems in ProVO. Section 3 proposes an extended ProVO model to resolve the problems specified in Section 2. Section 4 reports on the case studies conducted in this study and discusses the meaning of the case studies for the future directions of a research institution. Lastly, Section 5 evaluates the approach of this paper and presents our conclusions.

project performance. However, this study lacks in objectivity because the data obtained via questionnaire are used for the analysis. Cheung, Wong, Fung, and Coffey (2006) has proposed an artificial neural network based project performance prediction model for selecting a competent contractor for the construction project. However, his model’s input variables are bid and capital budget oriented. So, this study is weak in assessing the factors of quality and time. Lipke, Zwikael, Henderson, and Anbari (2009) has also proposed a reliable forecasting method of the final cost and duration. Analyzing the schedule performance and using the mathematics of statistics, earned value management and earned schedule are achieved. However, this model lacks the factors that are necessary for quality evaluation. 2.2. Challenges in modeling the extended ProVO (EProVO)

2. Related works 2.1. Project performance factors Project performance is a crucial issue for the success of a project, which has attracted much attention. Most studies have attempted to analyze the factors that determine the project success; quality (Beck et al., 2006; Belassi & Tukel, 1996; Fryer & Fryer, 1985; Ling & Liu, 2004; Morris & Hough, 1987; Sayles & Chandler, 1971; Thamhain, 2004; Wang et al., 2006), time (Cheung et al., 2004; Martin, 1976; Morris & Hough, 1987; Ojanen et al., 2002; Pinto & Slevin, 1989), budget (Jiang et al., 2004; Martin, 1976). These factors of quality, time, and budget are organized in Table A1 in Appendix A for a convenient overview of their meanings as interpreted by various researchers. Most studies have defined various factors that affect project performance and then selected effective factors based on statistical analysis of the data from questionnaires. The items in the questionnaires expressed in quantitative figures (for instance, a project size assessed in terms of the amount and the number of employees) hold objectivity. However, the items such as level of authority or complexity of project are very subjective. They are limited in ensuring the objectivity of evaluation because different evaluators apply different subjective criteria. Furthermore, only statistically significant factors have been selected, and further prediction was not made on project performance using the chosen factors. In Ling (Ling & Liu, 2004)’s study, an artificial neutral network model has been proposed, which predicts the measures associated with the

The project success is generally defined as ‘‘the completion of a project within an acceptable quality, time, cost, and achieving the client’s satisfaction” (Pheng & Chuan, 2006). In this paper, ‘‘budget” will be used instead of the cost. Here, let us suppose that the client’s satisfaction can be naturally attained if project goals – quality, time, and budget – are achieved. Eventually, project performance can be expressed as follows.

Project performance ¼ f ðquality; time; budgetÞ

ð1Þ

If the factors that affect project performance are sub-classified, they are equal to the factors that affect quality, time, and budget, which are the project goals. The various factors associated with the quality of a project can be classified into two groups: knowledge-associated factors and collaboration-associated factors as in Appendix A. The knowledge factors include the project manager’s competence, innovation of technical uncertainty, problem solving, organizational knowledge, and organizational technology learning. The collaboration factors include organizational philosophy, social skills, communications, interpersonal relations, leadership, user diversity, and team maturity. Now, project-oriented virtual organization (ProVO) can regroup these quality-determining factors into knowledge competence and collaboration competence. This study proposes an extended project-oriented virtual organization(EProVO) which is composed of the capability of ProVO, VO’s factors of time and budget, and a measure of project performance as shown in Fig. 2. In EProVO, VO’s factors of time and bud-

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Fig. 2. Conceptual diagram of EProVO.

get and a measure of project performance that generally considers project goals – quality, time, budget – are defined. The effects of the factors of quality, time and budget on project performance are analyzed and interpreted in terms of organizational behavior. The project performance depending on the project team formation is predicted. 3. Extended project-oriented virtual organization (EProVO) EProVO (extended project-oriented virtual organization) is a model that quantitatively analyzes the effect of the VO’s factors which are essential in carrying out a project to meet the goals of project performance. This paper proposes an extended project-oriented virtual organization (EProVO) model that consists of a total of 10 factors: 2 time availability factors, 3 cost factors, and 5 capability factors that are useful in evaluating the knowledge competence and collaboration competence of project members. The relationship of individual entities is shown in Fig. 3. In general, a project consists of three components – a project performer VO, goals, and performance. The project goals consist of three elements – quality, time and budget. The quality is the required level of final product or process which is an outcome of project. The time means the project should be completed within a due date. The budget means the project should be completed within a limited budget. The assessment result including the achievement degree of each component and other evaluation factors is project performance. A project performer VO is made of three elements – capability, time availability and cost that correspond to project goals. Capability contains knowledge competence and collaboration competence. Knowledge competence contains individual knowledge and knowledge from social networks, and collaboration competence includes density, degree centrality and closeness centrality. Time availability contains project duration and sum of manmonth (MM). Cost contains labor cost, indirect cost and direct cost. This section provides a detailed description on project performance, time availability and cost. 3.1. Project performance The formation of the project team must vary according to the project goal. The project team must be formed in such a way that

would maximally achieve the project goals which vary depending on the quality of work expected by the contractors, the time frame in which the project is to be completed, and the amount of budget allocated to the project. Based on the analysis of project result, the success of the project can be determined by the extent of achievement of each goal. The project performance of a VO is as follows in formula (2).

Project performanceðVOÞ ¼ xquality ESquality ðVOÞ þ xtime EStime ðVOÞ þ xbudget ESbudget ðVOÞ

ð2Þ

where VO is the target organization for the performance evaluation, x is the importance of each goal, ESðVOÞ is the defuzzified performance of VO for each goal. 3.2. Project performance factors The capability of a VO is divided into knowledge competence and collaboration competence to derive their values, and time availability is divided into project duration and sum of MM, and cost is divided into labor cost, indirect cost, and direct cost. The levels of knowledge and collaboration, which are the capability factors of a project team, are influenced by the way in which the project team is formed. The project duration and MM composition, which are the time availability factors, differ depending on the project team formation. Furthermore, the composition of labor cost, indirect cost and direct cost, which are the cost factors, differ depending on MM composition. Ultimately, project performance is influences by project team formation. Now, let us examine capability, time availability and cost one by one – which are project performance factors related to the three project goals of quality, time and budget. 3.2.1. Capability Each individual has his/her own capability. This capability refers to the capability of achieving the goals of a project. In this paper, we determine the capability of an individual by dividing it into knowledge and collaboration. Knowledge competence is divided into individual knowledge and knowledge from the social network of the individual. Collaboration competence is divided into density,

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degree centrality and closeness centrality, which are represented by three factors of collaboration – communication, cooperation and coordination. For detailed formula, refer to the previous study (Wi et al., 2009). 3.2.2. Time availability Time availability is divided into project duration and the sum of MM of a project. Project duration is the entire performance period of a project. Project can be completed by or before the due date. This project duration relies on the outcome of project team forma-

tion. For example, if a team is formed with competent members, the project can be completed with success within a short project duration. The sum of MM of a project indicates the sum of MM generally allocated. Likewise, a project that can ensure a lot of MM can select competent team members, thus leading to a successful project performance. 3.2.3. Cost Cost is divided into labor cost, indirect cost and direct cost. Labor cost is the fees during MM that are allocated for project team

Project

accomplish

Performance

1

+super goal 1..*

satisfy

Virtual Organization

Goal +sub goal

Individual

Team

Period

has

Time Availability

Cost

Sum of MM

Labor Cost

Quality

Capability

Knowledge Competence

Indirect Cost

Density

Degree Centrality

Knowledge-based Social Network

Individual Knowledge

Familarity

1 1 0..* Patent

1 0..* Paper

Budget

Collaboration Competence

KSN

Direct Cost

Time

0..* Project Report

Fig. 3. UML model of the EProVO.

Fig. 4. VO creation considering the project performance.

Closeness Centrality

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members. Indirect cost is management fees for an organization in which project team members belong. Commonly it is proportionate to labor cost, but it complies with the standard of an organization that orders the project. Direct cost includes device manufacturing cost, material cost and component cost, which are required for performing a project. The proportion of labor cost, indirect cost and direct cost may depend on the nature of a project and the character of its team, and has a direct and significant effect on project performance. For example, if the weight of labor cost increases, you can choose team members who excel in knowledge competence and collaboration competence, eventually enhancing the quality of a project. 3.3. VO creation

2

0

B aIK

M P

IK ij xij þ aSNK

M P

SNK ij xij þ adensity

M P

densityij xij

13

6P C7 6 N B i¼1 i¼1 i¼1 C7 6 B C7 M M 6 j¼1 @ A7 P P 6 þadc degree centralityij xij þ adc closeness centralityij xij 7 6 7 Max6 7 i¼1 i¼1 6 7 6 7 N P M N M N M P P P P P 6 þa PD þ a 7 yij þ aLC bj xij yij þ aIC k bj xij yij SMM 6 PP 7 4 5 j¼1 i¼1 j¼1 i¼1 j¼1 i¼1 þaDC h

ð4Þ s:t:

N X

xij ¼ 1; for i ¼ 1;. .. ;M

j¼1 N X

K ij xij i0; for i ¼ 1;. .. ;M

j¼1

The project-oriented VO generation process consists of goal generation, creation of a project performance regression model, and partner generation mathematical model as shown in Fig. 4. Once the VO that looks for a project opportunity analyzes the quality, time, and budget, sets the goals, and designs a project performance regression model, the process thereafter is automatic. First, it transforms the goals expressed in language into a fuzzy set. This paper deals with goals related to keywords of required knowledge, time, and budget. Once a goal is generated, we define the capability, time availability, and cost connected to each goal. The capability of a VO is defined as knowledge and collaboration, and time availability is defined as project duration and the sum of MM. Cost is defined as labor cost, indirect cost, and direct cost. Once capability, time availability and cost are defined in terms of their relations to the data of the 10 factors of an existing project and the project performance data, the coefficient of each factor is calculated as shown in formula (3) using a regression model.

project performance ¼ aIK IK þ aSNK SNK þ adensity density þ adc degree centrality þ acc closeness centrality þ aPP project period þ aSMM sum of MM

M X

yij 6 PD; for j ¼ 1;. .. ;N

i¼1 N X j¼1

bj

M X

xij yij þ k

i¼1

N M X X bj xij yij þ h 6 B j¼1

i¼1

xij ¼ binary for all i;j yij ¼ integer for all i; j

xij yij IK ij SNK ij

1 if keyword i is assigned to individual j; 0 otherwise man-month assigned to individual j on keyword i individual j0 s individual knowledge score on keyword i individual j0 s knowledge score of social network on keyword i densityij individual j0 s density score on keyword i degree centralityij individual j0 s degree centrality score on keyword i closeness centralityij individual j0 s closeness centrality score on keyword i a coefficient of regression model labor cost of individual j bj k rate of overhead cost h direct cost PD acceptable maximum project duration B acceptable maximum budget M number of keywords N number of individuals

þ aLC labor cost þ aIC indirect cost þ aDC direct cost

ð3Þ

where a is the coefficient of regression model. Once the coefficient of each factor is calculated, the virtual organization – which holds a high level of knowledge and collaboration competence for successful project performance within a limited budget and timeline – can be formed using a mathematical model such as the formula (4). The first line formula of the mathematical model indicates that one VO should be allocated to representative keywords of required knowledge. The second line indicates that a selection should be made on a VO that holds the knowledge of the keywords. The third line indicates that MM allocated to a VO should be less than project duration. Lastly, the fourth line suggests that the sum of labor cost and indirect/direct costs of the selected VOs should be less than project budget. There are many VOs to be considered in Eq. (4) in order to find the final VO, and we have an active method and passive method of selecting from the many candidate VOs only those that optimize the project performance and satisfy the restraining conditions. In active partner selection, once a mathematical model is designed, a VO is formed by selecting from all researchers. And then the final formation of VO is done after negotiating with those selected. In passive partner selection, a mathematical model is designed, targeting interested applicants only, and then the final formation of VO is done. This study does not deal with the project scheduling because it is beyond the scope of this paper.

4. Case study In order to examine the effect of VO formation on project performance, we conducted a case study to determine the effects of capability, time availability, and cost on project performance, and then implemented the EProVO based on the results of this case study. Here, we defined an individual as the smallest unit of EProVO. This case study used the data of a research institution that has been in active service for 20 years. One research center was selected from the institution, which consists of 2 teams and 45 researchers. In that research center, a researcher who has been granted a project opportunity from an enterprise or a government agency used to determine whether he or another researcher within the institution will serve as the project manager. Once a project manager is selected, the project manager starts negotiating with other researchers at his liberty to select the final team members who can meet the requirements of project goals in terms of the quality, time and budget of the project. Because this formation method agrees with the EProVO model described in Section 3, we analyzed the effects of capability, time availability, and cost on the project performance. 4.1. Measure of project performance For measurement of project performance for each project, the target institution has been using an evaluation sheet that uses a

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scale of 100 as a full score per project, based on the assessment of quality, time and budget. Quality is assessed in terms of achievement, technology, economy and site applicability. Technology criteria consist of originality and improvement. Economy criteria consist of expected profit and investment efficiency. Time and budget are assessed in terms of achievement. Each assessment criterion’s full score is shown in Table 1. This evaluation sheet is used when the marked score evaluated by a 5-specialist panel including the manager of the team to which the project manager belongs is input into a fuzzy model. Then the final score of project performance is drawn. 4.2. Measures of project performance factors As the measures of the factors that affect project performance, we used knowledge competence, collaboration competence, project duration, sum of MM, labor cost, indirect cost, and direct cost, which correspond to the capability, time availability, cost of the EProVO as specified in Section 3.3. The measures of knowledge competence include individual knowledge scores and knowledge scores from the knowledge-based social network. The measures of collaboration competence include density, degree centrality, and closeness centrality.

Fig. 5. Conceptual framework for knowledge competence and collaboration competence.

4.3. Research methodology A research institute was selected as the basis of data collection for the prototype. The publications of 45 researchers who belong to one of the research centers in the institution, including articles, project reports, and patents published from 2001 to 2006 were selected. The tools used in the development of the prototype are VC++ 6.0, Xfuzzy 3.0, and LabWindows/CVI. 6.0. Fig. 5 shows how to calculate the knowledge competence and collaboration competence of a project. Using a total of 526 publications by the 45 researchers from 2001 to 2006, the individual researcher’s knowledge is calculated via a fuzzy model. Then, from the data on the publications, e-mails and phone calls, a social network was formed among the researchers who have been involved in the related projects, and familiarity among them was calculated using a fuzzy model. Now, based on the calculated individual knowledge and familiarity, a knowledge-based social network was formed. Subsequently, knowledge from social network (SNK), density, degree centrality and closeness centrality were calculated. The detailed procedure for calculating knowledge competence and collaboration is described in Wi et al. (2009). Similarly, the project duration, sum of MM, labor cost, indirect cost, direct cost and project performance of each project were drawn after the calculation of knowledge competence and collaboration competence for each project. Once these 10 project performance factors were calculated, the final data format, as shown in Table 2,

Table 1 A sample of the evaluation of project performance. Goals

Assessment criteria

Full score

Quality

Achievement Technology

20 5 20 10

Economy

Time Budget

Site applicability Achievement Achievement

Originality Improvement Expected profit Investment efficiency

5 10 15 15

The evaluation score of team manager and other 4 specialists

was completed so as to analyze the effect of 10 factors on project performance. 4.4. Results and discussion To evaluate the effect of the 10 factors on project performance, a regression analysis was conducted. As shown in Table 3, the independent variables were significant at significance level a ¼ 0:05 were 6, which were degree centrality, closeness centrality, labor cost, indirect cost, project duration and sum of MM. This result was obtained presumably because the research institution carried out the evaluation of researchers based on the project size. In the project size, the proportion of the labor cost and indirect cost for the evaluation of researchers is 60%. For this reason, the researchers desire to participate in a project for which larger amounts are allocated for labor cost and indirect cost. Furthermore, they prefer a relatively longer project duration for a decent level of project progress in addition to the bigger allocation of labor cost and indirect cost. Because a project manager preferred to choose the researchers he is familiar with and those who can collaborate well with him, the degree centrality and closeness centrality are significant. Since the proportion of direct cost for the evaluation of researchers is very low, direct cost is not significant. Since only 20% is allocated for the assessment of the researcher’s knowledge, individual knowledge, knowledge from social network, and density on project performance are not significant. Let us examine the coefficient of the 10 factors that affect project performance. Those causing a positive effect were the 5 factors of individual knowledge, degree centrality, labor cost, direct cost, and project duration. Those causing a negative effect were the 5 factors of knowledge from social network, density, closeness centrality, indirect cost, and sum of MM. To satisfy the quality, which is one of the project goals, knowledge is required. Hence, individual knowledge causes a positive effect. Since the project manager selects team members who have greater familiarity with him in order to enhance project performance, degree centrality causes a positive effect. In evaluating a researcher, not the evaluation of project, the labor cost causes a positive effect since it takes the highest proportion. Direct cost is a positive factor because it is a required budgeting item to perform a project. Longer project dura-

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H. Wi, M. Jung / Expert Systems with Applications 37 (2010) 1143–1151 Table 2 Data format to analyze the factors that affect project performance. Project code

Density

IK

SNK

Degree centrality

Closeness centrality

Labor cost

Indirect cost

Direct cost

Period

Sum of MM

Project performance

01

0.46186

3.5

6.852

4.781

184.2

1.05E+08

1.36E+08

78,902,370

12

24

81.73

02

0.64986

1

2.12

2.028

51.4

7,339,000

4,050,000

37,457,000

12

15

82.3

03

0.53944

4

4.929

4.817

178.7

0

4,921,316

24,606,582

4

0

83.37

.. .

.. .

.. .

.. .

.. .

.. .

.. .

.. .

.. .

.. .

.. .

.. .

147

0.93561

0

3.245

3.036

61.8

2.91E+08

80,000,000

1.85E+09

12

61

91.76

148

0.54255

2.5

4.318

3.699

153.8

0

0

66,456,760

13

0

91.76

149

0.64554

137

459.35

385.29

7491.1

3.77E+08

4.9E+08

4.65E+08

12

92

95.62

Table 3 The result of regression analysis. Independent variable

Coefficient

SE coefficient

T

P

Individual knowledge Knowledge from social network Density Degree centrality Closeness centrality Labor cost Indirect cost Direct cost Project duration Sum of MM

25.11 17.01 0.622 65.05 62.78 26.453 16.467 7.206 10.8 22.249

14.99 21.88 2.238 26.83 20.38 8.76 6.045 4.453 2.502 8.575

1.68 0.78 0.28 2.43 3.08 3.02 2.72 1.62 4.32 2.59

0.105 0.444 0.783 0.022 0.005 0.005 0.011 0.117 0 0.015

tion is a positive factor because greater labor cost and indirect cost can be allocated for good project performance without a tight schedule. Knowledge from social network and density are negative factors. Because these two variables exert positive influence on an organization with good communication, positive effect should be drawn. However, with this specific research institution, its individual team members failed to share their knowledge, and even those who had good communication competence could not capitalize on it to a full extent. Closeness centrality indicates a coordination power among researchers. Since it is less direct than degree cen-

trality, which indicates cooperation, it is a negative factor on project performance. In the simultaneous analysis of degree centrality and closeness centrality of this research institution, the project manager has a very strong influencing power among team members. Indirect cost is a crucial factor for the evaluation of a researcher. However, for a project manager, the labor cost factor takes the greatest proportion, followed by direct cost, because it is required for project performance. Hence, the indirect cost factor, which is the last consideration, causes a relatively negative effect. With regard to the sum of MM, since a greater sum is likely to be a bigger project, the researchers are apt to shun it because of the expected burden on project performance. For this reason, the sum of MM is seen as a negative factor. If this research institution can form a project team more significantly based on researcher’s knowledge, a better project performance can be expected. For this reason, a quantitative assessment on researcher’s knowledge level of required knowledge should be provided to a project manager. Furthermore, because the factor of knowledge from social networks failed to be fully exercised for the applicable project in this research institution, more effort should be made to encourage information exchange among researchers who do not know each other. Likewise, those who had a high score on density, which is a communication skills criterion, failed to exercise it to a full extent. For an improved researcher

Fig. 6. The result of the project team formation of EProVO.

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Table A1 Outlined project performance factors related to the goals of some studies. Authors

Goals Quality

Analysis method Time

Budget

Sayles and Chandler (1971) Project manager’s competence Control systems and responsibilities Monitoring of project Continual involvement

Scheduling of activities

Questionnaire and statistical analysis

Morris and Hough (1987) Project objectives Technical uncertainty innovation Community involvement Solving of problems Politics

Schedule duration urgency Finance Contracting Legal agreement

Questionnaire and statistical analysis

Martin (1976)

Defined goals Organizational philosophy Proper delegation of duties Information mechanism

Proper allocation of resources Questionnaire and statistical analysis Selection of team Planning reviews

Fryer and Fryer (1985)

Social skills Decision making skills Problem handling skills Opportunity recognizing skills Management of changes

Questionnaire and statistical analysis

Pinto and Slevin (1989)

Project mission Client consultation Technical tasks Client acceptance Monitoring and feedback Communication Trouble-shooting

Project schedule

Resource recruitment

Questionnaire and statistical analysis

Cheung et al. (2004)

Quality Communication People Client satisfaction

Time

Cost

Questionnaire and statistical analysis

(Jiang et al. (2004)

Quality of product Communication Interpersonal relations Control the resources Organizational knowledge

Budgeted time

Money and cost

Questionnaire and statistical analysis

Ojanen et al. (2002)

Leadership Customer and market focus Information and analysis Human resource focus Business results

Strategic planning Process management

Questionnaire and statistical analysis

Wang et al. (2006)

User diversity

Ling and Liu (2004)

Project characteristics Owner and consultant characteristics DB organization form characteristics

Contractor characteristics

Questionnaire and artificial neural network

Questionnaire and statistical analysis

Thamhain (2004)

Project visibility and popularity Compensatory time-off Team maturity and tenure Project duration Stable project requirements Stable organizational structures and processes Tech complexity and interdependency Project size and complexity

Salary and bonus

Questionnaire and statistical analysis

Belassi and Tukel (1996)

Project factors Project manager factors Project team members factors

Organization factors

Questionnaire and statistical analysis

Pheng and Chuan (2006)

Availability of information Complexity of project Project team relationship Level of authority

Salary Project size Materials and suppliers

Questionnaire and statistical analysis

Beck et al. (2006)

Prototyping Organizational technology learning

Working hours Time availability Duration of project

assessment and culture, the research institution needs to create a policy that rewards those who have a high level of communication competence. This improvement can be achieved by decreasing the current rate, 60%, of the labor cost and indirect cost and also increasing the current knowledge competence rate of 20%.

Questionnaire and statistical analysis

4.5. Prototype for the VO creation We developed a VO creation prototype using the mathematical model shown in Eq. (4) and also the coefficients of the 10 project performance factors through the regression analysis. The best can-

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didates who meet the project goals and simultaneously maximize project performance were selected via a genetic algorithm. When the required keywords of the new project are ‘‘nano powder, carbon nanotube, thermal spray”, and when the parameters were set as shown in Fig. 6, the best result of the genetic algorithm was displayed. In our experiments, the project team was organized as a set of teams of 7, 21, and 42 researchers, and their project performance was 78.12. The results helped the decision-maker to organize a project team which is based on the current project team formation behavior of the organization. The team members who will perform a new project as described above by using the project performance that considers knowledge competence, collaboration competence, time availability, and cost can be presented to the project manager. The team formation time can be reduced on the basis of quantified information by contacting the candidates who are capable of high performance in the projects if they are selected. If the size of the organization is large, the project team formation using the EProVO can offer a significant help with respect to time and accuracy. 5. Conclusion EProVo was developed by extending the ProVO in which time availability and cost factors were added to knowledge competence and collaboration competence. Project performance is another component added to the ProVO, which generally assesses project goals in terms of quality, time, budget. Under this EProVO environment, an analysis was performed on the 10 factors that affect project performance. This analysis enabled the formation of a project team that predicts the project performance. Using a mathematical model, a VO was formed for a maximized project performance within the limits of the budget and timeframe. Since the solution of a model that can calculate this kind of a VO cannot be drawn mathematically, a genetic algorithm was used to calculate a VO that has a maximum project performance, and then the calculation is given to the decision maker who makes the final decision for project team formation. EProVo makes it possible to form a project team in a more quantitative method, ultimately helping the team maximize its project performance and subsequently earning client satisfaction. With some modifications, the proposed EProVO is applicable not only to construction projects and e-commerce fields but also to research institutions. The basic unit of VO for construction projects would be the construction company, and the previously implemented projects of the company would take the place of publications. Following the EProVO procedures, an optimal construction consortium may be organized which can lead to maximized performance of new construction projects.

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