How to reduce the negative impacts of knowledge heterogeneity in engineering design team: Exploring the role of knowledge reuse

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ScienceDirect International Journal of Project Management 34 (2016) 1138 – 1149 www.elsevier.com/locate/ijproman

How to reduce the negative impacts of knowledge heterogeneity in engineering design team: Exploring the role of knowledge reuse Lianying Zhang ⁎, Xiaonan Li College of Management and Economics, Tianjin University, 92 Weijin Road, Nankai District, Tianjin 300072, China Received 26 February 2015; received in revised form 18 May 2016; accepted 24 May 2016 Available online xxxx

Abstract Typical characteristics of construction projects are uniqueness and complexities, which lead to lack of reuse of previous knowledge in engineering design team (EDT). Furthermore, with the development of construction industry, knowledge heterogeneity continues to strengthen and increases the difficulties of knowledge reuse in EDT. The aim of this paper is to explore how to reduce the negative impacts of knowledge heterogeneity by reusing knowledge effectively in EDT. The study demonstrates that knowledge heterogeneity impact EDT performance negatively in some cases, and effective knowledge reuse and good team atmosphere help alleviate the negative effects to a certain extent. Hence EDT should encourage employees' knowledge reuse behaviors or activities and create harmonious team atmosphere so as to reduce the negative effects of knowledge heterogeneity that will in turn lead to benefits for the organization as a whole. © 2016 Elsevier Ltd, APM and IPMA. All rights reserved. Keywords: Knowledge reuse; Knowledge heterogeneity; Engineering design team performance; Employee relationships

1. Introduction One of the major issues for knowledge management in a project environment is the poor project success analysis and the lack of proper documentation on the results of the previous projects (Todorović et al., 2015). Because each project is presented as a temporary, relatively short-lived, phenomenon (Sydow et al., 2004). The vast majority of knowledge is generated during the course of a construction project, but only a small fraction is reused subsequently, which leads to most of the knowledge gained from the project being lost and not shared effectively (Tan et al., 2009). Engineering design team (EDT) as a typical project-based organization, the design projects are usually unique and complex, which need to combine professionals with different backgrounds in order to complete one project. Reusing of previous project knowledge fully means

⁎ Corresponding author. Tel.: +86 13602102333 (mobile); fax: +86 22 27401731. E-mail addresses: [email protected], [email protected] (L. Zhang), [email protected] (X. Li).

http://dx.doi.org/10.1016/j.ijproman.2016.05.009 0263-7863/00 © 2016 Elsevier Ltd, APM and IPMA. All rights reserved.

saving design times and reducing design changes for EDT. For example, when designing similar projects they can draw lessons from the successful solutions before, which facilitate reducing the human capital investment and also avoid similar mistakes. In EDT, there are so many differences in professional backgrounds, skills and professional experiences. Hence all kinds of knowledge blend, collision, which make the trait of knowledge heterogeneity intensifies constantly. The heterogeneity refers to the diversity in skills and knowledge represented on the team (Atanasova and Senn, 2011), and as one of the members' personalities is a typical phenomenon for a long time (Kichuk and Wiesner, 1997). Sometimes different background experts would provide more solutions than single individuals (Devine, 1999), because the synthesis of knowledge process between knowledge heterogeneity and performance would generate new ideas (Rodan and Galunic, 2002). Knowledge heterogeneity will benefit for the innovative and creative potential of managers, which is important for managerial performance and innovation performance (Louadi, 2008; Rodan and Galunic, 2004; Tsai et al., 2014). However, knowledge heterogeneity would impact engineering design team performance negatively in some times.

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Because team performance is influenced directly by three team processes: communication and collaboration, conflict management, and proactiveness (Atanasova and Senn, 2011). When the degree of knowledge heterogeneity is too high, it will increase the difficulties of team communication and collaboration to a certain extent, thus impacting the enthusiasm of team members, then leading to underdeveloped team performance. Engineering design is a process of knowledge acquisition, sharing, application, and innovation, so as to realize the continuous knowledge reuse cycle (Fig. 1). In EDT, heterogeneous knowledge converts to homogeneous knowledge continuously (in this case, the meaning of homogeneous knowledge is easily to be understood and applied by the whole team, rather than the homogeneous knowledge in the traditional sense). Because engineering design project is complex and the heterogeneous knowledge converts into homogeneous knowledge constantly in different project cycle. In the dynamic transformation process, the ratio of heterogeneous and homogeneous knowledge changes in different project stages. There exists an optimal ratio between heterogeneous knowledge and homogeneous knowledge in the ideal state. When the combination of heterogeneous knowledge and homogeneous knowledge reach the optimum reuse, the EDT performance will be the highest in principle. Through reusing existing knowledge, an individual can receive benefits of saving time and effort and ensure the quality of knowledge (Watson and Hewett, 2006). Knowledge reuse is able to coordinate different resources from several aspects, which facilitate the designers to better analyze and solve problems, then create new solutions. Knowledge reuse also can provide a reference for similar project and improve the quality of engineering design. Therefore, for EDT, knowledge reuse improves the team ability, innovation ability, survival ability and competition ability. However, in most EDTs, the knowledge reuse rate is not high. One of the most important reasons is that knowledge heterogeneity increases the difficulties of knowledge reuse. In today's engineering design environment, designers are limited in their ability to maximize knowledge reuse by the fact that there are so many difficulties to search for, access, and integrate reusable design knowledge across multiple sources. Because knowledge reuse provides more time for innovation, and makes the organization more creative (Baxter et al., 2008). Knowledge reuse also accelerates the speed of knowledge transfer and share, and

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improves the team agility (Liu et al., 2015). Hence, one approach to improve engineering design is through reusing previous knowledge. Humans can use knowledge gained from previous experience to make decisions when face with inadequate information (Bollacker and Ghosh, 1998). Sometimes employees would not share their knowledge due to the feeling of losing competitive advantage, and leading to employees not applying and reusing the useful knowledge (So and Bolloju, 2005). The employee's willingness of sharing knowledge and the level of knowledge storage of organizers determine the frequency and quality of knowledge reuse. For EDT, the cost of developing knowledge is huge, reusing knowledge effectively is saving cost in a sense. So it is important to arouse the enthusiasm of the employee's knowledge reuse in EDT, and try best to reusing and incorporating knowledge from other projects and parts of the organization (Mohrman et al., 2003). Although academia and industry realized the positive impact of knowledge heterogeneity on team innovation performance, they did not realize that when the level of knowledge heterogeneity is too high, knowledge could not be reused fully, thus leading to the poor team performance. Nowadays, EDT should face such questions: in the context of knowledge heterogeneity, how to realize knowledge reuse effectively? How to improve team performance through knowledge reuse effectively? Previous studies have not realized the negative impacts of knowledge heterogeneity on team performance and the importance of knowledge reuse in EDT. This paper aims to explore how to overcome the disadvantage of knowledge heterogeneity, by reusing the team knowledge effectively, then saving cost for EDT maximally. 2. Research model and hypotheses development 2.1. Knowledge reuse in engineering design team Effective experience reuse and lessons learned are increasingly important assets of enterprises and represent sources of competitive advantages in various domains (Bonjour et al., 2014). Unfortunately, even in a large construction company, there is no mechanism for capturing, storing, and reusing for generating knowledge (Esmi and Ennals, 2009), which leads to not full knowledge reuse. Knowledge reuse is defined as one

Fig. 1. The knowledge reusable process of EDT.

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individual or group using knowledge produced by a different individuals or groups in order to be more effective and productive in their work (Alavi and Leidner, 1999). Knowledge reusability not only increases the entire knowledge of the organization but also improves the quality of knowledge (Harsh, 2009). Design knowledge reuse is the reuse of knowledge from previously completed projects in a current project, and both architects and structural engineers, knowledge reuse frequently takes the form of reusing standard building details (Demian and Fruchter, 2006). In EDT, the design process is also the process of knowledge reuse, and knowledge reuse is positively related to performance benefits (Kankanhalli et al., 2011). Reusable knowledge is usually matured knowledge and tested by practice, thus in EDT, the more knowledge be reused, the higher that a team performance can be obtained. Accordingly, this line of reasoning leads to the following hypothesis. H1. Knowledge reuse has a positive impact on engineering design team performance. 2.2. Knowledge heterogeneity in engineering design team In the process of engineering design, architects, structural engineers and other professionals are often responsible for one project. Because knowledge represents the intangible source of previous experiences accumulated by an expert (Costa et al., 2012; Mikos et al., 2011), consequently, the extent of knowledge heterogeneity is usually quite high in EDT. With the development of engineering design industry, the trend of knowledge heterogeneity is increasing constantly in EDT. Engineers with different backgrounds would deal with various projects in EDT. Knowledge heterogeneity virtually increases the complexities of project work and difficulties of team collaboration, thus it impacts team performance to a certain extent. Accordingly, this line of reasoning leads to the following hypothesis. H2. Knowledge heterogeneity has a direct impact on engineering design team performance.

Knowledge reuse was defined as the reuse of previously designed buildings, building subsystems, or building components, as well as the knowledge and expertise ingrained in these previous designs (Fruchter and Demian, 2002). Markus proposed four distinct styles of knowledge reuse situations according to the knowledge reuser and the purpose of knowledge reuse, which involved shared work producers, shared work practitioners, expertise-seeking novices, and second knowledge miners (Markus, 2001). Cochrane et al. described three principles of reuse, i.e., the separation of information from knowledge, the separation of product knowledge from manufacturing process knowledge, and the correct classification of manufacturing knowledge (Cochrane et al., 2008). The design process reuse was classified into four types: full reuse, reuse after tiny revision, reuse after big revision, and reuse analogously (Yu et al., 2012). The various categories of reusable project knowledge identified include process knowledge, costing knowledge, knowledge about legal and statutory requirements, knowledge of best practices and lessons learned, and knowledge of who knows what (Tan et al., 2007). Because knowledge is decentralized and distributed, it is important to point out that knowledge can be heterogeneous or homogeneous across organizational units for knowing how to structure the organization (Louadi, 2008). The knowledge search outside organizational boundaries has the highest impact on individual learning because of high heterogeneity (Weck, 2005). Hence, in EDT, with the expansion of team size and the improvement of technology, the level of knowledge heterogeneity is increasing constantly, which impacts the speed and quality of knowledge flow. Knowledge reuse accelerates the transformation process of heterogeneous knowledge to homogeneous knowledge in EDT. Knowledge reuse also promotes the speed of knowledge turnover, reduces the extent of knowledge heterogeneity, and improves the team performance indirectly. Accordingly, this line of reasoning leads to the following hypotheses. H3. Knowledge heterogeneity affects knowledge reuse negatively sometimes in EDT. H4. Knowledge reuse plays a mediate role between knowledge heterogeneity and team performance in EDT.

2.3. Knowledge reuse and knowledge heterogeneity in engineering design team

2.4. Employee relationships in engineering design team

EDT is comprised of architects, structural engineers, equipment engineers, electrical engineers and other professionals. Therefore, the knowledge heterogeneity is a typical trait of EDT. In the process of engineering design, engineers with different professional background work for the common project goal, all kinds of professional knowledge inevitably blend and have a collision in this course. Because the designer could not be an expert in all fields, there is a need to capture, store and reuse knowledge (Ahmed, 2005). Many knowledge-intensive firms have spent enormous amounts of time and money trying to find ways for better managing their knowledge resources (Watson and Hewett, 2006), and striving to achieve the effective reuse of knowledge. Sometimes, when EDT deals with the similar project, EDT would draw lessons from previous projects, and reuse or partially reuse previous design drawings.

Knowledge management refers to capture, acquire, organize, and communicate both tacit and explicit knowledge of employees so that other employees may utilize them effectively and productively (Xu and Quaddus, 2012). Nowadays, the businesses know that employees are the major assets of an organization, and it is essential that the employees are as a collective unit and contribute to a common goal (Kuzu and Özilhan, 2014). Employee relationships are an index in order to examine the satisfaction, respect, confidence, justice, and trust relationships between employee–employer and employee–business (Liao et al., 2004). Carmel et al. discussed the firm–employee relationship that should be measured by firms and details, then developed an index model of firm–employee relationship strength predicted by cooperation, balanced power, communication, attachment, shared goals and values, trust and the absence of damaging

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conflict (Herington et al., 2009). Employee relationships of EDT are the judgment and understanding of employees' special relationship with other employees according to their own memories and all kinds of information provided by the external environment in EDT. We divided employee relationships into two dimensions: friendly exchanges and communication attitude. The specific connotation of friendly exchanges contains team trust, team atmosphere and team support. And the context of communication attitude contains communication frequency, organization encourage, and communication willingness. Organizations are likely to run into difficulties owing to the knowledge of individual employee when not well-managed (Watson and Hewett, 2006). To address these issues, employee relationships play a strong influence on performance. Good relation with employees not only helps build and protect organizational reputation and image in a turbulent environment but also contributes to organizational performance. Employee relationships as being very important in the work environment, and crucial elements found were cooperation, empowerment, communication, attachment, shared goals and values, trust and respect (Herington et al., 2005). Competitive advantage has been gained by developing strong relationships with their employees, and the general viewpoint is that looking after employees is good for business (Freiberg and Freiberg, 1998). Harmonious employee relationships provide a better team atmosphere, facilitates organizational communication and knowledge reuse effectively; examples are providing appropriate incentives or motivations for knowledge reuse and providing training and consultation for employees (So and Bolloju, 2005). In the process of team work, there will emerge knowledge conflict due to different backgrounds and experiences of team members, thus influencing employee relationships to a certain extent. Good employee relationships will create a positive team atmosphere, thus alleviating the negative impact caused by knowledge heterogeneity. Accordingly, this line of reasoning leads to the following hypotheses. H5. Employee relationships have a direct effect on engineering design team performance. H6. Knowledge heterogeneity has a direct effect on employee relationships in EDT. H7. Employee relationships play a mediate role between knowledge heterogeneity and team performance in EDT.

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may wish to understand lessons from previous experiences too (Leake and Wilson, 2001). However, sometimes individuals may accumulate valuable experiences and knowledge through practice but rarely reuse such acquisition in future construction projects (Liu et al., 2004). Since most of the design knowledge is tacit knowledge that underlies the basis of organizational knowledge creation, it is hard to formalize and communicate (Al-Jayyousi, 2004). In addition, it is difficult to capture and store explicit knowledge too, and there is no systematic plan to reuse the information and learn lessons from past projects, there is also no tendency to reuse it (Esmi and Ennals, 2009). At the same time, due to the time lapse in capturing the knowledge, staff turnover, and people's reluctance to share knowledge, these factors lead to not sharing and reusing knowledge generated on construction projects (Tan et al., 2007). Team members learning from past projects can train new employees and project managers, and other project team members can learn from previous/similar projects to deal with problems (Udeaja et al., 2008). Therefore managing and reusing knowledge in architecture, engineering and construction firms can lead to greater competitive advantage, and more effective management of constructed facilities (Fruchter and Demian, 2002). In EDT, engineers with different professional background often engage in one design scheme, such as the architects, structural engineers, equipment engineers, and electrical engineers, each engaged in a different division of labor. When individuals reuse unfamiliar complex knowledge assets, they will contact the other and share a common perspective that facilitates asset reuse (Boh, 2008). Moreover, employees' knowledge would not be successfully exploited if either knowledge sharing or knowledge reuse is overlooked (So and Bolloju, 2005). Thus harmonious employee relationships facilitate transfer knowledge from heterogeneity to the homogeneity, making it beneficial to the absorption and reuse of knowledge. Accordingly, this line of reasoning leads to the following hypothesis. H8. Employee relationships have a direct impact on knowledge reuse in EDT. This paper focuses on EDT, the research emphasis of the design phase, and the relations of knowledge heterogeneity, knowledge reuse, employee relationships and engineering design team performance reflect the dynamic process of engineering design. The hypothesized linkages among knowledge heterogeneity, knowledge reuse, employee relationships and engineering design team performance are illustrated in Fig. 2.

2.5. Knowledge reuse and employee relationships in engineering design team

3. Research design and methodology

Organization's competitive advantage lies in the knowledge stored in the employees' heads and the capability to use the knowledge to meet its business goals (Tan et al., 2007). Therefore a general designer draws from a lot of previous design experience, which can be acquired by the individual or by his/her mentors or professional community, this activity is design knowledge reuse (Fruchter and Demian, 2002). In order to reuse design rationale, designers may wish to reuse design knowledge to adapt past solutions and apply these to current problems, and novice designers

The knowledge of EDT could be explicit or tacit, which includes expertise, know-how, and other knowledge forms that related to engineering design projects. Because knowledge is considered to be an entity which is at a higher level and authority than data and information (Kampmeier, 1998), so knowledge is different from data and information. Data is raw, and simply exists and has no significance beyond its existence, and can be put in storage, captured and retrieved, and also can be mined for useful information;

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Fig. 2. Research model.

information is data that has been given meaning by way of relational connection, and information is data with meaning and the knowledge-based process of decision making; knowledge is the appropriate collection of information, and knowledge is information incorporated in an agent's reasoning resources, and made ready for active use within a decision process (Aamodt and Nygård, 1995; Chen et al., 2009; Hey, 2004; Zins, 2007). In EDT, the knowledge could be stored and reused, and this process is Positivism, because Positivism has involved a commitment to a unified view of science, and the adoption of methodologies of the nature sciences to explain the social world (Smith et al., 1996), and Positivism claimed that knowledge should be positively applied (Cruickshank, 2012). Meanwhile, in EDT, knowledge could be a process, and knowledge also consists of comprehension, understanding, insights, meaning and the ability to anticipate the effect of our actions (Bennet and Bennet, 2008), hence, this process is Social Constructionism. Social constructivism is distinguished by its focus on how the individual cognitively engages in the construction of knowledge from social construction which claims that knowledge and meaning are historically and culturally constructed through social processes and action (Young and Collin, 2004). Therefore, the research approach of this paper is to be a mixture of Positivism and Social Constructionism. Accordingly, the hypotheses were tested basing on existing theories, and we adopted quantitative research method for empirical testing of data. 3.1. Survey instrument and process The research hypotheses described above were empirically tested using a survey of professionals across different EDTs of China. These sample firms mainly specialize in engineering construction industries (i.e., architectural design, structural design, and equipment design). These teams have a larger size and good stability, which ensure the stability of the samples. In order to investigate the data effectively and accurately, we first did a pilot study that involved 12 EDTs of 294 individuals from 5 construction firms in China. After the pilot survey, we

did the exploratory factor analysis, and through principal component analysis, finally determined the mount of questionnaire items. Then we added 13 sample teams and did a formal survey, which contained 25 EDTs of 469 individuals from 8 construction firms in China, and all the team sizes are bigger than 10 peoples. 3.2. Sample and data collection 3.2.1. The pilot study In the pilot study, first we communicated with several professionals to conduct a pre-test, and we also explained the research objectives briefly before the survey. Next, we asked the participants to comment on the measurement items. Furthermore, we interviewed three experts of engineering management and further discussed the measurement questionnaires. Finally we determined the questionnaire items and handed out 294 questionnaires in the pilot study. The questionnaires were divided into online questionnaire and printed questionnaire, and we recycled the questionnaires from last October 2013 until the end of 2013. In the end, we received 210 copies of online questionnaires and 84 copies of printed questionnaires. 262 usable questionnaires from 12 teams were collected, resulting in a response rate of 89.12%. The sample descriptive statistics of the pilot study can be seen in Table 1. 3.2.2. Exploratory factor analysis In order to test the construct validity of the variables in the research, we conducted an exploratory factor analysis of all reflective measures (engineering design team performance, knowledge heterogeneity, knowledge reuse, and employee relationships) using the maximum likelihood method to extract the initial factors (Pedhazur and Schmelkin, 2013). With the factor analysis we deleted items poorly loading (0.50) on the expected construct and items with cross-loadings (Murtagh and Heck, 2012). The structure of engineering design team performance is composed of 10 items, and still kept 10 items after the factor analysis. The knowledge heterogeneity is composed 8

L. Zhang, X. Li / International Journal of Project Management 34 (2016) 1138–1149 Table 1 Sample descriptive statistics of the pilot study. Variables

Categories

Count

%

Gender

Male Female ≤ 30 years old 30–40 years old N 40 years old Associate's degree Bachelor's degree Master's or higher 1 year ≤ 3 years 3–5 years 5–10 years N 10 years

134 128 106 106 24 41 144 51 112 87 36 27

51.15 48.85 40.46 40.46 91.60 15.65 54.96 19.47 42.75 33.21 13.74 10.31

Age

Education

Years of work experience

items, and through the factor analysis, 1 item is removed. The structure of knowledge reuse is composed 4 items, and still 4 items are kept after the factor analysis. The structure of employee relationships is composed 6 items, and still 6 items are kept after the factor analysis. In Table 2, we can see that all factors show a high reliability with Cronbach's alpha greater than 0.8. 3.2.3. The formal survey We handed out 469 formal questionnaires and collected questionnaires from last January to August in 2014. 437 usable questionnaires (336 online questionnaires and 101 printed

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questionnaires) from 25 EDTs were collected, resulting in a response rate of 93.18%. A high response rate of our survey was obtained due to the strong support of our sample teams. We also committed that anyone who addressed the email would receive the analysis report of the survey. The sample descriptive statistics of the formal survey can be seen in Table 3.

3.3. Measures Multi-item scales were used to operationalize all the survey constructs. A 5-point Likert scale (ranging from 1 = none to 5 = a great deal) was used to measure survey constructs, which were engineering design team performance, knowledge heterogeneity, knowledge reuse, and employee relationships. These scales were adapted from existing literature.

3.3.1. Engineering design team performance We measured engineering design team performance (TP) using ten items developed by Guinan et al. (1998), Devine (1999), Mohrman et al. (2003), Qiu et al. (2009),Lu et al. (2011), andLiu et al. (2015). We divided engineering design team performance into three dimensions: task performance, satisfaction and development ability.

Table 2 Exploratory factor analysis results. Item

Standard loading

Total variance explained (%)

Cronbach's alpha

Engineering design team performance (TP) TP1 The team can finish task on time. TP2 The team can reach the standard of task. TP3 The team's work is very efficient. TP4 Team members are satisfied with team work. TP5 Team members are satisfied with colleagues. TP6 Team members can use their expertise and skills. TP7 Team members are satisfied with the team's management style. TP8 Team members are happy to continue cooperating with the other. TP9 Team members can learn new methods quickly and use it to accomplish tasks. TP10 The team's adapting ability to external changes is strong. Knowledge heterogeneity (KH) KH1 The educational background of team members is so different KH2 The professional knowledge of team involved is in many fields. KH3 Each team member is responsible for different aspects. KH4 Each member has some skills related to the task. KH5 The values of team members are so different. KH6 The consciousness of how to finish work is so different in teams. KH7 The opinions of team members are so different. Knowledge reuse (KR) KR1 Larger proportion of knowledge reuse in work KR2 Knowledge reuse has a big influence on project performance. KR3 Knowledge reuse has a big influence on own harvest. KR4 Knowledge reuse has a big influence on team harvest. Employee relations (ER) ER1 The level of trust is high in teams. ER2 The atmosphere is good in teams. ER3 Team support is much in teams. ER4 The willingness of communication is strong in teams. ER5 Team communication frequency is high in team work. ER6 Organization encouraging communication is much in team work.

– .520 .615 .726 .832 .790 .752 .692 .784 .676 .736 – .735 .772 .846 .842 .620 .701 .582 – .574 .700 .774 .891 – .730 .834 .676 .854 .844 .698

58.694

.920

54.868

.894

66.582

.826

68.147

.905

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Table 3 Sample descriptive statistics of the formal study. Variables

Categories

Count

%

Gender

Male Female ≤ 30 years old 30–40 years old N 40 years old Associate's degree Bachelor's degree Master's or higher 1 years ≤ 3 years 3–5 years 5–10 years N 10 years

268 169 215 121 101 59 296 82 212 105 63 57

61.33 38.67 49.20 27.69 23.11 13.50 67.73 18.75 48.51 24.03 14.42 13.04

Age

Education

Years of work experience

3.3.2. Knowledge heterogeneity We measured knowledge heterogeneity using seven items developed by Pelled et al. (1999), Jackson et al. (2003), Rodan and Galunic (2004), Lanza et al. (2008), Liao et al. (2009), and Atanasova and Senn (2011). We divided knowledge heterogeneity into three dimensions: education background, knowledge skills and professional experiences. 3.3.3. Knowledge reuse We measured knowledge reuse using four items developed by Markus (2001), Majchrzak et al. (2001), and Berkani and Chikh (2010). 3.3.4. Employee relationships We measured employee relationships using six items developed by H. H. Tan (2009), Herington et al. (2009), Bidwell et al. (2013), Strohmeier (2013), Fitzsimmons and Stamper (2014), and Kuzu and Özilhan (2014). We divided employee relationships into two dimensions: friendly exchanges and communication attitude.

over the another (Bagozzi and P. L. W., 1982). Running chi-square difference tests in pairs was done to decide if the restricted model performed significantly inferior to the unrestricted model. In the unrestricted model, the chi-square is 443.821, degree of freedom (df) is 265, CFI is 0.976, GFI is 0.933, and RMSEA is 0.039. At the same time, the chi-square is 646.961, df is 284, CFI is 0.952, GFI is 0.905, and RMSEA is 0.054 in the restricted model. Thus, it can be concluded that the discriminant validity of the constructs was acceptable. 4.2. Structural model test results The structural model shows potential causal dependencies between the four construct research; we conducted an exploratory factor analysis of all reflective measures (engineering design team performance, knowledge heterogeneity, knowledge reuse, and employee relationships). Goodness-of-fit statistics (GOF) was calculated to determine whether the model is appropriate or needs further revision. Table 5 shows the standardized path coefficients and their significance test results. Table 6 presents results of the overall model fit in the structural model. Thus, the model refinement was performed to improve the fit to its recommended levels. Finally, the overall fit statistics indicate a very good fit for the model. The normed fit index (NFI), with values of 0.942, comparative fit index (CFI), with values of 0.975, and goodness-of-fit index (GFI), with value of 0.932, were above the recommended accepted 0.9 level (Chau, 1997). In addition, the adjusted goodness-of-fit index (AGFI = 0.906) was above the 0.8 minimum recommended value. Finally, the root mean square error Table 4 Results of the confirmatory factor analyses. Factor

Item

Loading factors

AVE

CR

Cronbach's alpha

1

TP1 TP2 TP3 TP4 TP5 TP6 TP7 TP8 TP9 TP10 KH1 KH2 KH3 KH4 KH5 KH6 KH7 KR1 KR2 KR3 KR4 ER1 ER2 ER3 ER4 ER5 ER6

.520 .615 .726 .832 .790 .752 .692 .784 .676 .736 .735 .772 .846 .842 .620 .701 .582 .574 .700 .774 .891 .730 .834 .676 .854 .844 .698

0.515

0.913

.915

0.539

0.890

.888

0.553

0.829

.831

0.602

0.900

.906

4. Results and analysis 4.1. Measurement model test results 4.1.1. Convergent validity The convergent validity of the scales was verified by this criteria: all indicator loadings should be significant and exceed 0.7, the composite reliability (CR) should exceed 0.7, the average variance extracted (AVE) by each factor should exceed 0.50 (Fornell and Larcker, 1981; Pavlou and Fygenson, 2006). From Table 4, the CR of the factors ranging from 0.829 to 0.913 all exceeded the benchmark of 0.7, indicating adequate reliability for each construct. In addition, the AVE ranged from 0.515 to 0.602, and the resulting alpha values ranged from 0.831 to 0.915. Thus we concluded that the convergent validity of the reflective constructs was satisfactory. 4.1.2. Discriminant validity Discriminant validity assesses whether the constructs are measuring different concepts, and the difference in chi-square statistics was used to test the superiority of one measurement model

2

3

4

L. Zhang, X. Li / International Journal of Project Management 34 (2016) 1138–1149

of approximation (RMSEA) was 0.040, which was below the cut-off level of 0.08 (Browne and Cudeck, 1992).

Table 6 Fit indices of this study's validation models. X2 (df)

4.3. Mediating effect of knowledge reuse The data of this study was all aggregated to the team level. If a variable is a full mediator, then the relation between the predictor and the outcome will turn to be insignificant after the variable is included in the model (Liu et al., 2015). The three steps based on our empirical tests are explained in detail (Fruchter and Demian, 2002). Formal mediation testing was conducted to determine whether KR mediates the relationship between KH and TP. The mediate role of KR in the relationships between KH and TP was examined based on Baron and Kenny (1986). We took three steps for confirming the mediation role, the research should meet three requirements: (1) KH should pose significance on TP. (2) KH should pose significance on KR. (3) When the KR is added to the models of KH and TP respectively, the standardized estimates of the path of KH to TP may become insignificant (whole mediation), and may weaken before adding the KR (part of mediation). At the same time, it must be noted that KR effect poses significant effect on TP. H1 and H3 have been satisfied with the requirements of (1) and (2). Thus, we constructed the Table 5 Standardized path coefficients. Paths

Estimate

S.E.

C.R.

p

Hypothesis

ER ← KH KR ← KH KR ← ER TP ← KH TP ← KR TP ← ER TP1 ← TP TP2 ← TP TP3 ← TP TP4 ← TP TP5 ← TP TP6 ← TP TP7 ← TP TP8 ← TP TP9 ← TP TP10 ← TP KH1 ← KH KH2 ← KH KH3 ← KH KH4 ← KH KH5 ← KH KH6 ← KH KH7 ← KH KR1 ← KR KR2 ← KR KR3 ← KR KR4 ← KR ER1 ← ER ER2 ← ER ER3 ← ER ER4 ← ER ER5 ← ER ER6 ← ER

0.865 0.606 0.445 0.414 0.304 0.597 0.464 0.608 0.720 0.829 0.787 0.749 0.693 0.786 0.675 0.732 0.732 0.819 0.884 0.819 0.645 0.698 0.524 0.560 0.697 0.771 0.900 0.742 0.806 0.702 0.864 0.824 0.687

0.082 0.059 0.065 0.058 0.037 0.063 0.067 0.056 0.065 0.070 0.062 0.063 0.070 0.067 0.060 0.078

10.508 9.018 8.080 7.942 5.309 10.096 8.924 12.254 14.767 15.030 15.870 15.171 14.151 15.931 15.576 7.750

H6 support H3 support H8 support H2 support H1 support H5 support

0.062 0.061 0.071 0.072 0.066 0.073

17.162 18.043 15.541 13.138 14.340 10.805

0.096 0.100 0.118

12.387 11.774 12.148

0.045 0.071 0.059 0.062 0.069

23.844 14.366 20.063 16.936 14.001

*** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** ***

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p-Value X2/df IFI RMR

458.505 0.000 (272)

TLI CFI RMSEA GFI AGFI NFI

1.686 .976 .017

.968 .975 .040

.932 .906

.942

mediation models to confirm the mediation role of KR effect. First, we confirmed the mediation role of KR effect between KH and TP. The goodness-of-fit indices of the model, RMSEA = 0.043, GFI = 0.941, CFI = 0.975, and AGFI = 0.917. All these fulfilled the requirements, which shows that the model is well suited for the sample data. We can see the mediating role test results of knowledge reuse in Table 7. In model 1a, a direct positive relationship between the independent variable (knowledge heterogeneity, KH) and the dependent variable (engineering design team performance, TP) was established with a coefficient value of 0.37 (p b 0.001). In model 2a, the direct link between the independent variable (knowledge heterogeneity, KH) and the mediating variable (knowledge reuse, KR) was found with a coefficient value of 0.60 (p b 0.001). In model 3a, the direct link between the mediating variable KR and the dependent variable TP was also exhibited (coefficient = 0.08 and p b 0.001). Finally, in model 4a, the links between KH and TP, between KH and KR, and between KR and TP were simultaneously considered. The significant relationship between KH and TP was weaken after including KR. The results demonstrate the part of mediation role of KR between KH and TP, thus proving hypothesis H4. 4.4. Mediating effect of employee relationship We can see the mediating role test results of employee relationship in Table 8. We confirmed the mediating role of ER effect between KH and TP. The goodness-of-fit indices of the model, RMSEA = 0.049, GFI = 0.929, CFI = 0.968, AGFI = 0.902, show that the model is relevant to the sample data. In model 1b, a direct positive relationship between the independent variable (knowledge heterogeneity, KH) and the dependent variable (engineering design team performance, TP) was established with a coefficient value of 0.03 (p b 0.001). In model 2b, the direct link between the independent variable (knowledge heterogeneity, KH) and the mediating variable (employee relationship, ER) was found with a coefficient value of 0.67 (p b 0.001). In model 3b, the direct link between the mediating variable ER and the dependent variable TP was also

Table 7 Mediating effect of knowledge reuse. Structural path

Model-1a (H2)

KH ➝ TP KH ➝ KR KR ➝ TP

0.43 a

a

Significant at the 0.001 level.

Model-2a (H3)

Model-3a (H1)

Model-4a (H4)

0.3 a

0.37 a 0.60 a 0.08 a

0.58 a

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Table 8 Mediating effect of employee relationship. Structural path

Model 1-1b (H2)

KH ➝ TP KH ➝ ER ER ➝ TP

0.41 b

b

Model-2b (H6)

Model-3b (H5)

Model-4b (H7)

0.58 b

0.03 b 0.67 b 0.57 b

0.65 b

Significant at the 0.001 level.

exhibited (coefficient = 0.57 and p b 0.001). Finally, in model 4b, the links between KH and TP, between KH and ER, and between ER and TP were simultaneously considered. The significant relationship between KH and TP was not significant after including ER, that is, the existence of complete mediation by ER in the effect of KH on TP. The results indicate the complete mediation role of the ER between KH and TP, thus proving hypothesis H7. 5. Discussion This paper explored how to offset the negative influences of knowledge heterogeneity in EDT, from the perspective of knowledge reuse. This empirical research is among the earliest studies attempting to address the issue of improvement of knowledge reuse in EDT. Here we have some findings below. Examining the hypotheses, it is found that knowledge reuse has an extreme impact on engineering design team performance (H1). In EDT, the reusable knowledge could be explicit knowledge or tacit knowledge, which includes data, information, and know-how. Any knowledge that is being “reused” is actually being “re-created”, and the robustness and sustainability of knowledge lies continuous learning and re-learn, then developing the processes to assimilate, integrate and apply the knowledge we need (Bennet and Bennet, 2008). And knowledge reusability is the foundation of knowledge creation and innovation, on account of knowledge reusability which is possible for both explicit and tacit knowledge (Harsh, 2009). Therefore, the purposes of knowledge reuse are to acquire new knowledge that others have created, and obtain advice on how a particularly challenging situation should be handled (Chua et al., 2006). Reuse of existing design knowledge benefits both new design and redesign (Hao et al., 2013), so the process of knowledge reuse offers designers more opportunities to improve the knowledge quality, and facilitates designers dealing with design schemes with past experiences. Knowledge reuse as one of the important knowledge cycles is considered to be the key role in improving the efficiency of organized design, reducing organized cost, and obtaining better organizing performance. In EDT, knowledge reuse can coordinate different resources from several aspects, and provide better solutions for design schemes. Owing to the fact that construction projects involve a network of multi-disciplinary organizations, leading to uniqueness in macro terms (e.g. context, site, client requirements) and similar in a micro context; thus the lessons learnt during their execution should be reused in other projects (Kamara et al., 2003). However, the engineering design industry in China is still young, and most EDTs lack practices opportunities and experience of knowledge reuse. Hence a significant challenge is

to make sure that lessons are learned and that past mistakes are not repeated (Duffield and Whitty, 2015). Knowledge heterogeneity increases team creativity and improves team innovation performance to a certain extent (Majchrzak et al., 2004). Since large team generally deals with large design schemes, EDT inevitably faces high extent of knowledge heterogeneity. Owing to large team generally deals with large design schemes, EDT inevitably faces high extent of knowledge heterogeneity. Engineering design is a process of collision and blend of knowledge. Knowledge heterogeneity increases the difficulties of communication and cooperation in this process, and also increases the ratio of knowledge conflict. Good team communication and cooperation are one of the bases of high team performance, therefore knowledge heterogeneity has a significant impact on engineering design team performance (H2). Knowledge heterogeneity exists objectively all the time, which increases the complexities of engineering design. The complexities of engineering design increase the difficulties of transformation and absorption of knowledge. Additionally, the ability of knowledge transformation and absorption is the foundation of effective knowledge reuse, so knowledge heterogeneity affects the process of knowledge reuse (H3). We also find that knowledge reuse serves as an important mediator role between knowledge heterogeneity and engineering design team performance (H4). In order to achieve successful knowledge reuse, EDT should encourage team members to engage in collaboration or activities that keep positive attitudes toward knowledge reuse. In the context of knowledge heterogeneity, how to use existing knowledge of organizations and reduce the development cost of knowledge, are the goals of EDT in long-term pursuit. The reusable knowledge is usually tested by practice, and is relatively matured knowledge. Hence the more knowledge is reused, the more heterogeneous knowledge converts to homogeneous knowledge. The more the storage of homogeneous knowledge, the less the development cost of new knowledge. Hence EDT should strive to build good team atmosphere, and facilitate knowledge reuse effectively. Nowadays, with the expansion of EDTs, sometimes several DETs comprise a temporary team for one project, and the ownership of knowledge is very important. If EDT includes personnel of the same company, the knowledge reuse is supported by the organization. When several EDTs form a temporary team, EDTs can sign the relevant contract or agreement, ensure a clear division of responsibilities, and protect the intellectual property rights respectively. Tan et al. proposed methodology and attempted to capture reusable project knowledge generated from the various learning situations once the knowledge is created or identified i.e., live through project reviews/meetings and individuals with the aid of a web-based knowledge base, and the specific methodology contains a web-based knowledge base, a project knowledge manager (PKM), and an integrated work-flow system (Tan et al., 2007). Udeaja et al. proposed that the knowledge captured in the PKF (Project Knowledge File) can be reused during or after the project (Udeaja et al., 2008). As project-based organization, EDT can design knowledge reuse schemes which refer to the abovementioned methods. In

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order to realize knowledge reuse in EDT, first, it is necessary to know the scope of knowledge. The meta-knowledge contains explicit knowledge and tacit knowledge in EDT. The explicit knowledge contains the design schemes, project documents, construction drawings, and other forms of knowledge. Tacit knowledge contains know-how, expertise, skills and so forth in EDT. Second, it is important to set knowledge reuse mechanisms according to different knowledge types. Third, the explicit knowledge of EDT can be stored in the design knowledge warehouse through verification and assessment. Tacit knowledge could convert into internal tacit knowledge through some knowledge sharing actives such as PPRs (post-project reviews), train/meeting, CoPs (the team Communication of Practices) and so forth. Through the three steps above, EDT can reach the goal of knowledge reuse. By applying these methods of knowledge reuse, EDT can save a lot of intellectual capitals and financial expenditures. Furthermore, these knowledge reuse methods also help EDT save design times and alleviate the pressure caused by the shortage of talents in engineering design industry. EDT is made up of a variety of professionals, and is a typical knowledge-intensive team. In EDT, the professionals' communication and cooperation are usually frequent for one design scheme. Because favorable employee relationships will guarantee the excellent communication and successful cooperation, such relationships have a direct effect on engineering design team performance (H5). In regard to employee relationships impact team performance in EDT, it is important to create a relaxed working atmosphere and relieve the high pressure for team members. With the extent of knowledge heterogeneity increasing constantly, the difficulties of communication and cooperation also increase continuously in EDT. Hence knowledge heterogeneity impacts employee relationship in EDT to a certain extent (H6). Furthermore, the harmonious employee relationship can create good team atmosphere, which makes the heterogeneous knowledge to be understood and accepted by team members easily. This process helps improve team performance indirectly (H7). Good employee relationships also provide sufficient conditions for knowledge reuse, thus the employee relationships are crucial for knowledge reuse in EDT (H8). Individuals will be more likely to share knowledge when working in a trusted, supportive, and shared atmosphere (Chen et al., 2012). Therefore EDT should strive to strengthen team cohesion and trust, and establish an effective mechanism for knowledge reuse. Through strengthening the team consciousness of knowledge reuse, ensuring that heterogeneous knowledge converts to homogeneous knowledge quickly in EDT, effective knowledge reuse can be achieved. Our findings on the effect of knowledge reuse, knowledge heterogeneity, employee relationships and engineering design team performance are not only consistent with those obtained in previous studies, but also indicate how knowledge reuse is significantly influenced by knowledge heterogeneity and employee relationships. The reuse of previous design knowledge is a potentially important way to improve design efficiency (Ball et al., 2001). Managing and reusing knowledge can lead to greater competitive advantage, improved designs and more effective management (Fruchter et al., 2005).

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Knowledge reuse holds the potential of propagating proven practices and averting the recurrence of similar mistakes, hence many organizations are making deliberate efforts to facilitate knowledge reuse among their employees (Chua et al., 2006). As organizations grow larger, employees may not be aware of what their colleagues know, and this may result in redevelopment existing knowledge of organization (Liu et al., 2013). The important managerial implication is that a good practice for enhancing knowledge reuse in EDT. At the same time, EDT should develop positive and active knowledge reuse common values, which offset the negative effects of knowledge heterogeneity as much as possible.

6. Conclusion From a perspective of knowledge reuse, this study explored how to offset the negative impacts of knowledge heterogeneity in EDT. Knowledge reuse, knowledge heterogeneity, employee relationships and engineering design team performance were included and examined in this study. In addition, EDT members were chosen as the survey subjects, which is more appropriate for understanding the knowledge reuse behaviors. Overall, we find that knowledge heterogeneity and employee relationships play important roles in determining knowledge reuse behaviors. Knowledge reuse is an effective way of reducing the team knowledge heterogeneity, and good employee relationships provides harmonious team atmosphere for knowledge reuse. EDT in today's global and competitive business environment is under increasing pressure, such as the requirements of low-cost, high-quality and high yield. For EDTs, one approach to improve performance is through reusing previous knowledge. For this reason, EDT should form a climate of knowledge reuse. Because the attitude of employees is a significant element of knowledge reuse, so one of the most important things is to encourage team members' knowledge reuse activities. We also found that engineering design is a process of knowledge acquisition, sharing, application, and innovation, so as to realize the continuous knowledge reuse cycle (Fig. 1.). There exists an optimal ratio between heterogeneous knowledge and homogeneous knowledge in this process. When the combination of heterogeneous knowledge and homogeneous knowledge reach the optimal ratio, the creative tension of EDT is optimal, and the team performance is highest in principle. At the same time, because the transformation process is dynamic, so the reasonable proportion of heterogeneous knowledge and homogeneous knowledge is necessary and objective. Therefore, the heterogeneous knowledge will not convert into homogeneous knowledge completely. The results of the research were verified by China's EDTs, and reflected one of the universal issues of EDT. Knowledge heterogeneity influences knowledge reuse which is common in EDT, and how to solve the problem needs long-term efforts. Thus, the universality of the study also applies to other countries and regions' EDTs. Meanwhile, other project teams also can refer to the research results.

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7. Limitation and future study In EDT, knowledge heterogeneity exists objectively all the time. With the team size enlargement, knowledge heterogeneity will continue to enhance, and knowledge reuse can offset the negative effects of knowledge heterogeneity to a certain extent. In the dynamic transformation process, the ratio of reusable heterogeneous and homogeneous knowledge is also changed accompanied by the different engineering design stages. Therefore the heterogeneous knowledge will not become zero, and the homogeneous knowledge will not reach the rate of 100% due to the heterogeneous knowledge which generates constantly in this course. As a consequence, in the process of engineering design, there exists an optimal ratio between heterogeneous knowledge and homogeneous knowledge, and the specific optimal ratio is expected to further explore in the future. But many factors impact the extent of knowledge heterogeneity, for instance, team size, team maturity, team cognition and so forth. There are also a series of factors that impact knowledge reuse. For example, team agility, team atmosphere, knowledge capture and knowledge absorptive capacity all impact the degree of knowledge reuse. This paper only considered how to reduce the negative impacts of knowledge heterogeneity from the perspective of knowledge reuse. Future research could improve our comprehension of knowledge heterogeneity and knowledge reuse constructs by testing the model in other industries, and could involve multiple areas from more aspects, to consider how to improve the efficiency of knowledge reuse. Acknowledgments The work described in this article is supported by the National NaturalScience Foundation of China (NSFC, Project No. 71272146). References Aamodt, A., Nygård, M., 1995. Different roles and mutual dependencies of data, information, and knowledge—an AI perspective on their integration. Data Knowl. Eng. 16 (3), 191–222. Ahmed, S., 2005. Encouraging reuse of design knowledge: a method to index knowledge. Des. Stud. 26 (6), 565–592. Alavi, M., Leidner, D.E., 1999. Knowledge management systems: issues, challenges, and benefits. Commun. Assoc. Inf. Syst. 1 (1), 1–37. Al-Jayyousi, O., 2004. Greywater reuse: knowledge management for sustainability. Desalination 167, 27–37. Atanasova, Y., Senn, C., 2011. Global customer team design: dimensions, determinants, and performance outcomes. Ind. Mark. Manag. 40 (2), 278–289. Bagozzi, R.P., P. L. W., 1982. Representing and testing organizational theories: a holistic construal. Adm. Sci. Q. 459–489. Ball, L.J., Lambell, N.J., Ormerod, T.C., Slavin, S., Mariani, J.A., 2001. Representing design rationale to support innovative design reuse: a minimalist approach. Autom. Constr. 10 (6), 663–674. Baron, R.M., Kenny, D.A., 1986. The moderator–mediator variable distinction in social psychological research: conceptual, strategic, and statistical. J. Pers. Soc. Psychol. 51 (6), 1173. Baxter, D., Gao, J., Case, K., Harding, J., Young, B., Cochrane, S., Dani, S., 2008. A framework to integrate design knowledge reuse and requirements management in engineering design. Robot. Comput. Integr. Manuf. 24 (4), 585–593.

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