Knowledge leadership to improve project and organizational performance

JPMA-01505; No of Pages 14

Available online at www.sciencedirect.com

International Journal of Project Management xx (2013) xxx – xxx www.elsevier.com/locate/ijproman

Knowledge leadership to improve project and organizational performance Li-Ren Yang a,⁎, Chung-Fah Huang b , Ting-Jui Hsu

a

a

b

Department of Business Administration, Tamkang University, New Taipei City, Taiwan Department of Civil Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, 807, Taiwan Received 30 August 2012; received in revised form 16 January 2013; accepted 22 January 2013

Abstract Conceptualizing knowledge leadership and customer knowledge management (CKM) in the project context is still rudimentary. Thus, the first objective of this study is to assess the associations among knowledge leadership, customer knowledge management, the performance of a precision instrument sales (PIS) project, and organizational performance. The second objective is to determine whether project performance may mediate the effect of customer knowledge management on organizational performance. The third objective is to examine the moderating role of data complexity in the relationship between customer knowledge management and project performance. This study empirically investigated a sample of precision instrument sales projects in Taiwanese high-tech industry. The findings indicate that adoption of knowledge leadership is associated with customer knowledge management. In addition, these analyses suggest that implementation of customer knowledge management influences organizational performance via project performance. The results also show that the positive relationship between customer knowledge management and project performance depends on data complexity. © 2013 Elsevier Ltd. APM and IPMA. All rights reserved. Keywords: Knowledge leadership; Customer knowledge management; Data complexity; Project performance; Organizational performance

1 . Introduction Understanding customers and their needs is important. Many companies attempt to align its processes and products to develop stronger customer relationships. Although some of the companies have knowledge of their customers, most of the knowledge exists in fragmented form. In addition, it is difficult to share the knowledge with the organization because it is usually incomplete. Thus, customer knowledge management (CKM) is becoming an important strategic issue. Customer knowledge concerns not only understanding the customer's viewpoints, but also collection of information and insight that a company needs to have to build greater customer relationships. The role of customer knowledge management is to acquire and organize the data and allows them to be shared and applied throughout the organization. A company needs to develop

⁎ Corresponding author. Tel.: + 886 2 26215656; fax: + 886 2 26209742. E-mail address: [email protected] (L.-R. Yang).

processes and systems to gather comprehensive data and information about the customers. Leaders have a significant position of influence within their organizations. Knowledge leadership has increasingly been recognized as an essential element for organizations to enhance customer knowledge management. While many studies have promoted knowledge leadership and CKM as a means to enhance organizational performance, conceptualizing knowledge leadership and CKM in the project context is still rudimentary. Previous studies have not examined the role of project leaders in knowledge management, and their effect on project and organizational performance. Thus, developing such support will illustrate the benefits of knowledge leadership and CKM adoption. The analysis of this study also shows the relationship between CKM and project performance for different levels of data complexity. The analyses of knowledge leadership and CKM and relationships with project and organizational performance are based on an industry-wide survey performed between December 2011 and April 2012. A data collection tool was developed to

0263-7863/$36.00 © 2013 Elsevier Ltd. APM and IPMA. All rights reserved. http://dx.doi.org/10.1016/j.ijproman.2013.01.011 Please cite this article as: Yang, L.-R., et al., Knowledge leadership to improve project and organizational performance, International Journal of Project Management (2013), http://dx.doi.org/10.1016/j.ijproman.2013.01.011

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L.-R. Yang et al. / International Journal of Project Management xx (2013) xxx–xxx

assess knowledge leadership and CKM levels on precision instrument sales (PIS) projects in the Taiwanese high-tech industry, yielding 216 project responses. The data analyzed in this study are project-specific, meaning the data are representative of the levels of knowledge leadership and CKM used in projects. 2. Conceptual framework and research hypotheses Knowledge is the appropriate collection of information, such that its intent is to be useful (Ackoff, 1989). It pertains to information given meaning and integrated with other contents of understanding (Bates, 2005). Davenport and Prusak (1998) defined knowledge as “a fluid mix of framed experience, contextual information, values and expert insight that provides a framework for evaluating and incorporating new experiences and information.” The knowledge management literature has shown that knowledge management plays an important role in the performance of organizations (Alavi and Leidner, 2001; Cameron, 2004; Sabherwal and Becerra-Fernandez, 2003; Srisa-ard, 2006; Zack, 1999). In recent years, companies have integrated their customer relationship management (CRM) and knowledge management (KM) efforts because they realize that KM plays a key role in CRM success (Garrido-Moreno and Padilla-Meléndez, 2011). The relationship between CRM and KM is an important issue (Campbell, 2003; Shi and Yip, 2007; Stefanou et al., 2003). Such is the synergy potential of both concepts that have emerged theoretical models from the integration of both concepts: the models of customer KM (Gebert et al., 2003; Morgan, 2007). Customer knowledge refers to structured information about customers (Campbell, 2003; Li and Calantone, 1998), knowledge about customers (Rowley, 2002, 2004), and knowledge from customers (Garcia-Murillo and Annabi, 2002; Gibbert et al., 2002). Customer knowledge has increasingly been recognized within marketing as a significant resource that can be managed to support research and development and to improve innovation (Gibbert et al., 2002). It can facilitate sensing of emerging market opportunities and support the management of long-term customer relationships (Darroch and McNaughton, 2003). Managing knowledge residing in customers requires collaborating with customers (Garrido-Moreno and Padilla-Meléndez, 2011). Gibbert et al. (2002) have compared the knowledge from customers and the knowledge about the customers in order to receive an integrated outlook for the knowledge transfer. Customer knowledge management means creating a valuable leverage and direct interaction with the customers (Dimitrova et al., 2009). The main challenge for the development of CKM is to catch these customers' perceptions which are innovative for the enterprise's business future (Dimitrova et al., 2009). The role of leaders in managing knowledge is important to organizations (Crawford, 2005; Davenport et al., 1998; Sarabia, 2007; Singh, 2008). However, knowledge management as a key leader function has not been explored (Bell De Tienne et al., 2004). Many researchers have emphasized the lack of leadership support for the failure of many knowledge management projects (Lakshman, 2007). Thus, the potential for integrating the

leadership literature with information and knowledge management literature is likely to be beneficial for both theory and practice (Bryant, 2003). In addition, although Mintzberg (1973) identified the informational role of top managers, previous studies have not focused on the management of knowledge as key leadership roles (Bell De Tienne et al., 2004; Bryant, 2003; Lakshman, 2005; Politis, 2001; Viitala, 2004). For the early attempts on the role of leadership in knowledge management, Fleishman et al. (1991) focused on the information search and acquisition, and information use in problem solving behaviors of leaders. Day and Lord (1988) identified the building of information systems as a key leader activity leading to improved organizational performance. However, a review of the literature suggests that systematic research on the role of leaders in information and knowledge management is lacking (Lakshman, 2007). Four approaches to the examination of leadership have evolved over the past several decades: the trait approach, the behavior approach, the contingency approach, and the transformational and charismatic approach (Yukl, 1998). Although some of the approaches addressed the role of leaders in information and knowledge management (Cleveland, 1985; Lakshman, 2007; Vroom and Jago, 1988), previous studies did not stress the knowledge management aspects of leadership. The trait approach focused on leaders' traits, such as their physical appearance and personalities. It identified business knowledge as an essential quality of effective leaders (Kirkpatrick and Locke, 1991). From another point of view, the behavioral and contingency approaches suggested that leader behavior should involve information search and acquisition and information use which are critical to performance (Fleishman et al., 1991). In addition, Vroom and Jago (1988) contended that information and knowledge requirements of situations are key contingencies that influence leader behavior. The behavior of the leaders in facilitating the existence and availability of required information and knowledge can have a positive effect on organizational effectiveness (Lakshman, 2007). For the charismatic approach, information acquisition and analysis is critical to the development of vision in organizations (Kotter, 1990). In order for companies to succeed in business, knowledge leadership and knowledge management capabilities are two of the important components that companies must possess (Earl and Scott, 1999; Liebowitz, 2000; Saint-Onge, 1999). Companies worldwide have responded by creating or re-evaluating their knowledge leadership development programs and knowledge management initiatives (Davenport and Prusak, 1998; Malhotra, 2000). Knowledge leadership is an essential element for organizations (Brown and Duguid, 2000). It is an important business development activity (KPMG, 2000). The role of knowledge leadership is to promote a positive cultural orientation toward knowledge acquisition and knowledge sharing; one that values continuous learning, where experience, expertise, and innovation supersede hierarchy (Davenport et al., 1998). Human resource executives are frequently the best candidates for knowledge leadership positions within organizations (Bukowitz and Williams, 1999; Horibe, 1999; Koulopoulos and Frappaolo, 1999). They are typically technologically advanced, having

Please cite this article as: Yang, L.-R., et al., Knowledge leadership to improve project and organizational performance, International Journal of Project Management (2013), http://dx.doi.org/10.1016/j.ijproman.2013.01.011

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selected and implemented sophisticated human resource information systems focused on aligning their organizations' human, cultural, and structural capital with its business goals (Roberts-Witt, 2001). Thus, knowledge leaders should possess a broad range of interpersonal and organizational development skills and need to enable cultural change, build relationship, and facilitate knowledge transfer (Ali and Yusof, 2006). Knowledge management has been promoted as a means to enhance organizational performance. While the focus of management is often on business issues, leadership tends to concentrate more on people issues (Bass, 1990). When organizations are viewed as learning systems, the manager's role can be viewed as one of providing leadership in the learning process (Carlsson et al., 1979). The roles of leaders in a learning organization have been specified as being coaches, facilitators, teachers, leaders of learning, and developers (Macneil, 2001). In contrast to knowledge management, knowledge leadership refers to constant development and innovation of information resources, individual skills, and knowledge and learning networks (Skyrme, 2000). Knowledge leadership is defined as a process whereby an individual supports other group members in learning processes needed to attain group or organizational goals (Stogdill, 1974). On the other hand, customer knowledge management means creating a valuable leverage and direct interaction with the customers (Dimitrova et al., 2009). It is defined as the acquisition and use of customer-related knowledge to create value for the organization. Cleveland (1985) highlighted the importance of the role of leadership in managing knowledge. The study suggested that the use of teams and communities of people is important for leaders in managing information and knowledge. Prior research has also indicated that knowledge leadership plays an important role in CKM (Bukowitz and Williams, 1999; Davenport et al., 1998; Horibe, 1999; Koulopoulos and Frappaolo, 1999; Roberts-Witt, 2001). The literature on knowledge management has essentially described a new context in which modern leaders operate (Viitala, 2004). Knowledge leadership may help develop mechanisms for accountability and control, as well as for customer knowledge sharing. In addition, effective knowledge leadership may support efficient customer knowledge integration, sharing, and management. A knowledge leader is the catalyst for a knowledge-sharing culture, owner of the infrastructure specifications that facilitate customer knowledge transfer and storage, and maintainer of the closed-loop learning system (Rasmus, 2000). The study measured three knowledge leadership dimensions identified in the literature as being potentially relevant to customer knowledge management: leadership skills, cooperation and trust, and knowledge integration and innovation. Leadership is the ability to influence groups for purposes of goal accomplishment (Koontz and Weihrich, 1990). The literature emphasized developing leadership skills that are required for most jobs or particular occupations or job roles (Delamare Le Deist and Winterton, 2005). Many conceptions of leadership skill include knowledge alongside attitudes, behaviors, work habits, and personal characteristics (Green, 1999). Leadership skills are considered important in being able to increase knowledge sharing among subordinates. Prior research indicated that leadership skills may enhance levels

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of knowledge management, as well as mutual influence, more open and honest communication, and greater access to resources (Gerstner and Day, 1997). On the other hand, cooperation and trust plays a critical role in team operations (Solomom, 2001). Cooperation and trust may result in uniformity of team members and make the team more effective. In previous research, team collaboration and trust is a highly influential factor in knowledge acquisition, sharing, and application (Gladstein, 1984). Effective knowledge management may derive from successful collaboration and trust between team members (Kotlarsky and Oshri, 2005). Finally, knowledge integration and innovation has been shown to play an important role in knowledge management. In previous research, knowledge integration was found to be associated with a critical determinant of successful knowledge management (Davenport and Prusak, 1998). It facilitates knowledge sharing and application among team members. In addition, team members' learning behavior and innovative ability are important to create new knowledge and ideas (Davenport et al., 1998). Thus, effective knowledge management requires effective knowledge integration and innovation to support it. The literature supports knowledge leadership adoption as a means to enhance CKM activities. Additionally, knowledge leadership application may positively relate to customer knowledge management. This study extends previous research by addressing the effects of knowledge leadership use on customer knowledge management of projects. Based on the relevant literature, this study develops the following research hypothesis: H1. Knowledge leadership adoption positively influences project levels of customer knowledge management. Project performance is defined as the achievement of some pre-determined project goals (Lim and Mohamed, 1999). On the other hand, according to Richard et al. (2009), organizational performance comprises the actual output or results of an organization as measured against its intended outputs (or goals and objectives). Achieving the organization's long-term ultimate objective (e.g., profits) will depend on the degree to which its organizational performance is reached (Katou and Budhwar, 2007). Organizational performance is a multifaceted concept, which is usually indicated by indices such as: 1) effectiveness: if the organization meets its objectives, 2) efficiency: if the organization uses the fewest possible resources to meet its objectives, 3) development: if the organization is developing in its capacity to meet future opportunities and challenges, 4) satisfaction: of all participants; stakeholders, employees, and customers, 5) innovation: for products and processes, and 6) quality: percent of products of high quality (Katou and Budhwar, 2007). In this study, organizational performance was measured with six items that required respondents to indicate the extent to which the company had improved its sales knowledge and useful ideas, new market and product opportunities, cost efficiencies, cost reduction, awareness of the firm's other products, and sales and customer use of the firm's other products. These items represent organizational performance at the efficiency, development, and innovation levels. Organizational performance is a multifaceted concept. Although the success of one project may

Please cite this article as: Yang, L.-R., et al., Knowledge leadership to improve project and organizational performance, International Journal of Project Management (2013), http://dx.doi.org/10.1016/j.ijproman.2013.01.011

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not contribute to overall organizational performance, it can set a good example for other projects and improve organization performance in certain areas (such as efficiency, development, and innovation). Previous studies identified leadership as an important factor in the relationship between knowledge management and organizational effectiveness (Bell De Tienne et al., 2004). The positive impact of knowledge management on organizations is in the form of operational improvements (Davenport et al., 1998). A review of the literature suggests that CKM provides significant benefits to organizations. Customer knowledge is recognized as an important strategic resource for organizational success. CKM may bring a number of potential benefits to a firm and further improve firm performance (Darroch and McNaughton, 2003; Gibbert et al., 2002). The results of previous studies also confirmed that KM practice adoption may contribute to project performance (Adenfelt, 2010; Vandaie, 2008). In this study, customer knowledge management was measured by the four dimensions of knowledge acquisition, knowledge storage, knowledge sharing, and knowledge application. The knowledge that can be gained in projects refers to a variety of different contents ranging from technical knowledge concerning the product, its parts and technologies to procedural knowledge concerning production technologies and processes (Kasvi et al., 2003). Additionally, outside project partners present an important source of knowledge on customer needs (Maurer, 2010). Acquiring such knowledge increases the firm's understanding of market needs which may trigger the development of better products (Yli-Renko et al., 2001). It also increases the likelihood of project success (Maurer, 2010). Knowledge that can be articulated and codified can be documented more easily than noncodifiable knowledge (Zander and Kogut, 1995). Documentation and storage of knowledge are important when considering transferability of knowledge (Renzl, 2008). Documentation and storage of knowledge may not be dependent only on ability, but rather also on willingness to document knowledge (Hansen, 2002). An atmosphere of trust may facilitate knowledge documentation and storage (Renzl, 2008). It is evident that once knowledge is stored and updated periodically, knowledge can be shared smoothly. Thus, knowledge storage facilitates knowledge sharing within teams and leads to project success. Knowledge sharing is a prerequisite for developing new technologies and products (Nonaka and Takeuchi, 1995). Performance can be enhanced, when people communicate information, best practices, lessons learned, experiences, insights, as well as common and uncommon sense (von Krogh, 2002). Accordingly, the ability to share knowledge between units contributes significantly to the project success (Revilla et al., 2009). Product development is a knowledge intensive process that involves both knowledge creation and knowledge application to develop highly successful new products (Revilla et al., 2009). It enhances firms' renewal by exploiting the existing knowledge and exploring new (Katila and Ahuja, 2002). Using past knowledge and experience reduces the likelihood of errors and false starts and enhances project performance (Levinthal and March, 1993). Prior research indicated that organizational performance represents the external success of the new product, while project

performance reflects internal success (Garcia et al., 2008). In this study, project performance was measured by the three dimensions of sales performance, delivery performance, and training and education performance. Better sales performance can help open new market and product/service opportunities for the firm (Schmidt and Calantone, 1998). Good delivery performance also contributes to organizational performance (Chen et al., 2005). In addition, excellent training and education performance may raise awareness of the firm's other products and further enhance sales and customer use of the firm's other products (Tatikonda and Montoya-Weiss, 2001). As such, project performance may act as an internal success, which subsequently leads to improved organizational performance (Chen et al., 2005; Schmidt and Calantone, 1998; Tatikonda and Montoya-Weiss, 2001). Integrating these arguments, the following hypotheses were postulated and tested: H2. Customer knowledge management has a positive effect on project performance. H3. Project performance has a positive effect on organizational performance. H4. Customer knowledge management has a positive effect on organizational performance. H5. Project performance mediates the relationship between customer knowledge management and organizational performance. Previous studies indicated that KM practice plays an important role in project outcomes (Kasvi et al., 2003; Lindner and Wald, 2011; Pollack, 2012; Reich et al., 2012). As such, project performance may derive from KM adoption. Several researchers have also stated that task characteristics play a moderating role in the relationship between practice use and project performance (Low and Quek, 2006; Müller and Turner, 2007; Oya and Walter, 1998; Pheng and Chuan, 2006). O'Connor and Won (2001a, 2001b) developed six categories of task characteristics (task procedures, time/space/cost, data complexity, task management, nature of task product, and human resource) to classify tasks by their attributes. However, because this study addresses the issue of knowledge management, only data complexity was considered. Based on prior research on knowledge management, particularly related to performance outcomes and task characteristics, this study proposes the following hypothesis: H6. Task characteristics (in terms of data complexity) moderate the relationship between customer knowledge management and project performance. 3 . Methodology 3.1 . Research instrument The survey instrument was developed to measure knowledge leadership adoption on PIS projects and its associations with CKM and project performance in the Taiwanese high-tech industry. Study participants were first asked to identify a recent

Please cite this article as: Yang, L.-R., et al., Knowledge leadership to improve project and organizational performance, International Journal of Project Management (2013), http://dx.doi.org/10.1016/j.ijproman.2013.01.011

L.-R. Yang et al. / International Journal of Project Management xx (2013) xxx–xxx

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project that they were familiar with for assessment. The survey was composed of seven sections: 1) knowledge leadership adoption, 2) customer knowledge management, 3) task characteristics (in terms of data complexity), 4) project performance, 5) organizational performance, 6) project information, and 7) personal information.

of the survey questions. Project responses were collected via paper and online surveys. The projects were examined to ensure that no duplicate project information was collected. Ultimately, 216 survey responses were used in the analysis. Table 1 presents the characteristics of sampled projects. In addition, the profile of respondents is shown in Table 2.

3.2. Sampling method and sample description

3.3 . Survey design and construct measurement

Individuals interested in participating in the study were identified by a search from various industry associations. A survey of PIS projects was conducted in the Taiwanese high-tech industry between December 2011 and April 2012. The data collection tool was developed to collect project-based data. The targeted respondents were identified as the senior individuals who were familiar with adoption of knowledge leadership, customer knowledge management, and project and organizational performance. In order to obtain a truly representative sample, the geographic mix of projects was intentionally diverse. Additionally, a specified mix of project type was targeted in order to obtain a representative sample of the industry. All of the companies were contacted via phone or email to identify the person involved in projects by name and title. The investigators then contacted the respondents to confirm their participation in this study. This study attempted to use phone or email to identify the persons with adequate background and experience. This approach helped the investigators select the right respondents who possess adequate knowledge to properly evaluate the subjective project and are capable of answering all

Multi-item scales were developed for each of the variables included in the theoretical model. The items used to measure knowledge leadership were based on Davenport and Prusak (1998). The study measured three knowledge leadership dimensions: leadership skills, cooperation and trust, and knowledge integration and innovation. On the other hand, a 15-item multidimensional scale was used to evaluate customer knowledge management. The scales developed by Kiessling et al. (2009), Lee et al. (2005), Huang and Li (2009), and Lin and Lee (2005) were adapted to evaluate customer knowledge management. In this study, customer knowledge management was rated by the four dimensions of knowledge storage, knowledge sharing, knowledge application, and knowledge acquisition. They were measured with four, three, four, and four items respectively. Additionally, items used to rate task characteristics were based on the studies developed by O'Connor and Won (2001a, 2001b). They proposed six categories of task characteristics (task procedures, time/space/cost, data complexity, task management, nature of task product, and human resource) to classify tasks by their attributes. However, because this study addresses the issue of knowledge management, only items

Table 1 Characteristics of sampled projects. Characteristic

Class

Number

Percent of projects

Owner regulation Owner regulation Project size Project size Project size Project size Project size Number of team members Number of team members Number of team members Number of team members Number of team members Project complexity Project complexity Project complexity Environmental uncertainty Environmental uncertainty Environmental uncertainty Time availability Time availability Time availability Project duration Project duration Project duration Project duration Project duration

Public Private b$33.33 thousand $33.33–166.67 thousand $166.67–333.33 thousand $333.33 thousand − 1.67 million N1.67 million b5 5–8 9–12 13–16 N16 High Medium Low High Medium Low Realistic duration Medium Unrealistic duration b3 months 3–5 months 6–11 months 12–23 months N23 months

62 154 26 82 42 26 40 32 86 34 10 54 84 114 18 62 132 22 6 154 56 36 62 40 18 60

28.7 71.3 12.0 38.0 19.4 12.0 18.5 14.8 39.8 15.7 4.6 25.0 38.9 52.8 8.3 28.7 61.1 10.2 2.8 71.3 25.9 16.7 28.7 18.5 8.3 27.8

Please cite this article as: Yang, L.-R., et al., Knowledge leadership to improve project and organizational performance, International Journal of Project Management (2013), http://dx.doi.org/10.1016/j.ijproman.2013.01.011

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Table 2 Profile of respondents. Variable

Category

Number

Percentage

Age Age Age Age Age Age Education Education Education Position Position Position

N 45 41–45 36–40 31–35 26–30 b 26 Master's or Ph.D. degree Bachelor's degree Associate's degree Managers Director Senior specialist

28 30 64 62 30 2 74 102 40 70 26 120

13.0 13.9 29.6 28.7 13.9 0.9 34.2 47.2 18.5 32.5 12.0 55.6

associated with data complexity were considered. Questions from Deephouse et al. (1996), Wang et al. (2006), Na et al. (2007), and Garcia et al. (2008) were adapted to measure project performance. Project performance was measured by the three dimensions of sales performance, delivery performance, and training and education performance. Finally, items used to rate organizational performance were based on the surveys developed by Atuahene-Gima (1995). The survey used these items because the literature and recommendations of five project management (PM) practitioners have shown that these items are closely linked to precision instrument sales projects. These professionals averaged 15 years of experience. Each item was rated on a 7-point scale, where 1 represented strongly disagree and 7 represented strongly agree. 3.4. Content validity Previous studies have tended to form a panel of several practitioner or experts to assess content validity (Lin et al., 2010; Lopez-Cabrales et al., 2009). The content validity of the survey used in this study was tested through a literature review and interviews with five PM practitioners. The criteria for selecting the experts include: 1) Job position, 2) Expertise, 3) Years of relevant experience, and 4) Education. Thus, the professionals were identified as the senior executives who were familiar with knowledge leadership and customer knowledge management. They need to have more than 10 years of experience in project management and hold a bachelor's degree. The recommendations of the five senior PM practitioners have also shown that number of the items in each scale were applicable to measure the variables. Finally, copies of a draft survey were also sent to three professors in the PM discipline to pre-test for the clarity of questions. Their insights were also incorporated into the final version of the survey. 4 . Results and analysis 4.1. Measurement model test results Prior to estimating the structural model, a confirmatory factor analysis (CFA) was conducted to verify the measurement model.

Multiple fit criteria were used to assess the overall fit of the model. In the proposed model, knowledge leadership, customer knowledge management, and project performance are a second order construct. The data were analyzed using the AMOS/SPSS statistical package. The model refinement was performed to improve the fit to its recommended levels as shown in Fig. 1. Based on several trials resulting in elimination of some of the items, all of the scales met the recommended levels. Furthermore, the composite reliability for all constructs was above the 0.7 level suggested by Hair et al. (2006), indicating adequate reliability for each construct. Thus, the results provide evidence that the scales are reliable (see Table 3). All of the factor loadings are statistically significant at the five percent level and exceed the 0.5 standard (Fornell and Larcker, 1981), as shown in Table 3. In addition, all constructs have an average variance extracted (AVE) greater than 0.5. Thus, these constructs demonstrate adequate convergent validity. Discriminant validity evaluates whether the constructs are measuring different concepts (Hair et al., 2006). The procedure requires comparing the set of models where each pair of latent constructs has a constrained correlation of one with the correspondent models where such pairs of constructs are freely estimated (Bagozzi and Phillips, 1982). The results show that the chi-square values are significantly lower for the unconstrained models at the five percent level, which suggests that the constructs exhibit discriminant validity. 4.2. Structural model test results Fig. 2 presents results of the overall model fit in the structural model. A feasible model was selected based on the recommended Goodness-Of-Fit (GOF) measures and the model that satisfies both theoretical expectations and GOF was finally selected for structural equation modeling (SEM) analysis (Molenaar et al., 2000). Thus, the model refinement was performed to improve the fit to its recommended levels. Based on several trials resulting in elimination of some of the items (including the items associated with cooperation and trust, knowledge storage, knowledge acquisition, and project sales performance variables), the overall fit statistics indicated a very good fit for the model. The chi-square statistic for the full measurement model was nonsignificant (p N 0.05), indicating a good fit between the data and the proposed model. The normed fit index (NFI), comparative fit index (CFI), and goodness of fit index (GFI), with values of 0.943, 0.968, and 0.940 respectively, were all above the recommended acceptable 0.90 level (Chau, 1997). In addition, the adjusted goodness of fit index (AGFI = 0.896) was above the 0.80 minimum recommended value. Finally, the root mean square error of approximation (RMSEA) was 0.075, which was below the cut-off level of 0.08 recommended by Browne and Cudeck (1993). The test of H1, H2, H3, H4, and H5 was based on the direct effects (structural coefficients) among the constructs as shown in Fig. 2. H1 proposed a positive relationship between adoption of knowledge leadership and customer knowledge management. This hypothesis was supported since the standardized coefficient was 0.98 and statistically significant (p b 0.001). H2

Please cite this article as: Yang, L.-R., et al., Knowledge leadership to improve project and organizational performance, International Journal of Project Management (2013), http://dx.doi.org/10.1016/j.ijproman.2013.01.011

L.-R. Yang et al. / International Journal of Project Management xx (2013) xxx–xxx

(a) CFA measurement model for knowledge leadership

(b) CFA measurement model for customer knowledge management

r1

Leadership skills

0.90

r1 LS1

e1

LS2

e2

LS3

e3

Knowledge storage

r2

0.97

Knowledge leadership

Cooperation and trust

CT1

e4

CT2

e5

CT3

e6

CT4

e7

KI1

e8

Knowledge integration and innovation

KI2

e9

KI3

e10

KI4

e11

Knowledge sharing

e2

ST3

e3

SH1

e4

SH2

e5

SH3

e6

AP1

e7

AP2

e8

AP3

e9

AC1

e10

AC2

e11

AC3

e12

r3

0.91 Knowledge application r4

0.98

r1 SP1

0.70

e1

ST2

0.71

Customer knowledge management

(c) CFA measurement model for project performance Sales

ST1

r2

0.68

r3

0.89

7

Knowledge acquisition

e1

SP2

e2

SP3

e3

DP1

e4

DP2

e5

DP3

e6

TE1

e7

TE2

e8

TE3

e9

TE4

e10

r2

0.98

Project performance

Delivery

r3

0.96 Training and education

Note: (a) NFI=0.959>0.9; CFI =0.978>0.9; GFI=0.932>0.9; AGFI =0.885>0.8; RMSEA=0.073<0.08 (b) NFI=0.916>0.9; CFI =0.950>0.9; GFI=0.933>0.9; AGFI =0.883>0.8; RMSEA=0.077<0.08 (c) NFI=0.934>0.9; CFI =0.966>0.9; GFI=0.945>0.9; AGFI =0.896>0.8; RMSEA=0.079<0.08

Fig. 1. CFA measurement models.

proposed a positive relationship between customer knowledge management and project performance. This hypothesis was supported by a statistically significant structural coefficient of 0.73 (p b 0.001). H3 proposed a positive relationship between project performance and organizational performance. This hypothesis was also supported with a standardized coefficient of 0.32 (p b 0.001). In addition, the direct impact from customer knowledge management to organizational performance is significant (coefficient = 0.61; p b 0.001), and therefore H4 is supported.

variable (organizational performance, OP) was also exhibited (coefficient = 0.83 and p b 0.001). Finally (Model 4), the links between CKM and organizational performance, between CKM and project performance, and between project performance and organizational performance were simultaneously considered. The significant relationship between CKM and organizational performance was not significant after including project performance, that is, the existence of complete mediation by project performance in the effect of customer knowledge management on organizational performance (H5 is supported).

4.3. Mediating roles of project performance

4.4. Moderating role of task characteristics in terms of data complexity

To test the mediating role of project performance, four additional analyses recommended by Frazier et al. (2004) were conducted. The results are presented in Table 4. First (Model 1), a direct positive relationship between the independent variable (customer knowledge management, CKM) and the dependent variable (organizational performance, OP) was established with a coefficient value of 0.69 (p b 0.001). Second (Model 2), the direct link between the independent variable (customer knowledge management, CKM) and the mediating variable (project performance, PP) was found with a coefficient value of 0.72 (p b 0.001). Third (Model 3), the direct link between the mediating variable (project performance, PP) and the dependent

Hierarchical regression analysis was employed to examine whether data complexity has a moderator effect on the relationship between CKM and project performance. In agreement with Aiken and West (1991), this study centered any variable which was used as a component of an interaction term. Table 5 summarizes the regression results for delivery performance. Particularly, at steps 1 through 4, this study entered the control variables, CKM, data complexity, and the interaction of CKM and data complexity. Step 4 indicates a significant interaction of knowledge storage (ST) and data complexity (DC) for delivery performance. There is also a

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L.-R. Yang et al. / International Journal of Project Management xx (2013) xxx–xxx

Table 3 Results of CFA. Construct and item

Standardized factor loading

Composite reliability/AVE

Knowledge leadership–leadership skills (LS) LS1: The project manager understood the importance of customer knowledge acquisition and practiced what he/she preached. LS2: The project manager always tried to gain new knowledge to set an example to the others. LS3: The project manager demonstrated excellent knowledge leadership skills. Knowledge leadership–cooperation and trust (CT) CT1: The project manager understood needs and expectations of the team members and provided necessary resources. CT2: The project manager and the team members cooperated to solve problems. CT3: The project manager built an environment of trust. CT4: The project manager encouraged the team members to share and apply customer knowledge. Knowledge leadership – Knowledge integration and innovation (KI) KI1: The project manager took action to enhance the team members' innovative ability. KI2: The project manager developed a reward system to stimulate the team members' learning behavior. KI3: The project manager integrated practical experience from different departments to create new knowledge. KI4: The project manager led the team members to execute innovative ideas. Customer knowledge management–Knowledge storage (ST) ST1: Customer knowledge was well documented on the project. ST2: Customer knowledge was stored and updated periodically on the project. ST3: Different sources and types of customer knowledge were managed effectively on the project. Customer knowledge management–Knowledge sharing (SH) SH1: Customer knowledge was shared through codified forms like manuals or documents on the project. SH2: Customer knowledge was shared across the units on the project. SH3: Informal dialogues and meetings were used for customer knowledge sharing on the project. Customer knowledge management–Knowledge application (AP) AP1: The project utilized customer knowledge into practice use. AP2: The project members utilized existing customer knowledge to develop new knowledge. AP3: The project members utilized existing customer knowledge to propose new ideas. Customer knowledge management–knowledge acquisition (AC) AC1: The project periodically utilized formal procedure to obtain customer knowledge. AC2: The project contained considerable valuable customer knowledge. AC3: Information technology was used to support for obtaining customer knowledge. Data complexity (DC) DC1: The project tasks relied on industry technical standards. DC2: Data of the project tasks were in many different formats. DC3: Security of related data was very important. DC4: The project tasks involved significant amount of data updating. Project performance–sales performance (SP) SP1: The project had better competitive advantage than competing products. SP2: The project substantially lowered proxy costs. SP3: The project increased market share for the equipment. Project performance–delivery performance (DP) DP1: All project assignments were proceeding as planned. DP2: The schedule for each phase of the project complied with the contractual requirements. DP3: The project was delivered on time. Project performance–training and education performance (TE) TE1: The customer complaint rate was low. TE2: The customer was satisfied with the project deliverables. TE3: The customers understood how to properly use the equipment. TE4: The customers used the equipment proficiently. Organizational performance (OP) OP1: Members of the firm obtained valuable sales knowledge and useful ideas. OP2: Open new market and product/service opportunities for the firm. OP3: Achieve important cost efficiencies for the firm. OP4: Substantially lower costs for the firm OP5: Raise awareness of the firm's other products. OP6: Enhance the sales and customer use of the firm's other products.

– 0.841 0.924 0.816 |– 0.762 0.859 0.877 0.919 – 0.824 0.740 0.890 0.807 – 0.711 0.687 0.751 – 0.606 0.793 0.720 – 0.744 0.832 0.758 – 0.715 0.657 0.773 – 0.755 0.607 0.745 0.735 – 0.645 0.611 0.885 – 0.763 0.790 0.682 – 0.789 0.878 0.830 0.633 – 0.606 0.759 0.854 0.780 0.718 0.845

0.896/0.742 – – – 0.916/0.733 – – – – 0.889/0.668 – – – – 0.760/0.514 – – – 0.751/0.505 – – – 0.822/0.607 – – – 0.759/0.514 – – – 0.804/0.508 – – – – 0.763/0.524 – – – 0.790/0.557 – – – 0.866/0.621 – – – – 0.893/0.585 – – – – – –

significant interaction of knowledge acquisition (AC) and data complexity (DC) for delivery performance. Similarly, as shown in Table 6, the results indicate a significant interaction of knowledge sharing (SH) and data complexity (DC) for training and education performance. Data complexity has a moderating

effect on the relationship between knowledge sharing and training and education performance. Thus, H6 is supported. Fig. 3 demonstrates the predicted relationship of the two-way interaction using the procedures outlined by Aiken and West (1991).

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L.-R. Yang et al. / International Journal of Project Management xx (2013) xxx–xxx

SH1

SH2

SH3

AP1

Knowledge sharing

AP2

OP1

AP3

9

OP2

OP4

OP5

OP6

Organizational performance

Knowledge application 0.61 *** 0.32 ***

Knowledge leadership

0.98 ***

LS2

LS3

KI1

KI2

KI3

Project performance

0.73 ***

Knowledge integration and innovation

Leadership skills

LS1

Customer knowledge management

Training and education

Delivery

KI4

DP1

DP2

DP3

TE1

TE2

TE3

TE4

Notes: *** Significant at the 0.001 level Fig. 2. Research model estimation results.

5. Limitations While this study offers important insights into adoption of knowledge leadership, there are some limitations. Like many previous studies (Lau et al., 2007; Rahim et al., 2002; Wei and Morgan, 2004; Weng et al., 2010), this study is also based on the perceptions of a single respondent. Thus, the reliability of the survey relies on the truthfulness of the respondents and the expertise of the investigators in questionnaire design. A total of 216 projects were investigated in the study. Thus, it is not an efficient way to collect the necessary data from all team members participating in a project. However, this study was designed to eliminate mono-source bias. The mono-source bias was not a threat in this study for the following reasons: 1) this study attempted to select the right respondents who possess adequate knowledge and are capable of answering all of the survey questions, 2) third-party reports have been shown to be moderately consistent with self-reported measure (Van Dyne and LePine, 1998), and 3) the confirmatory factor analysis showed that the one-factor measurement model was a poor fit to the data (Podsakoff and Organ, 1986). In addition, because all the research data are self-reported and collected through the same questionnaire during the same period of time, a common method variance (CMV) may result in a systematic measurement error and may further bias the estimates of the true Table 4 Mediating effect of project performance. Structural path

Model 1

Model 2

Model 3

Model 4

CKM → OP CKM → PP PP → OP

0.69 a – –

– 0.72 a –

– – 0.83 a

0.12 0.74 a 0.75 a

a

Significant at the 0.001 level.

relationship between the theoretical constructs (Podsakoff and Organ, 1986). This study used the Harman's one-factor test to investigate the potential problem of common method variance, suggesting that common method bias was not considered a serious threat to this study. In addition, future research may use Table 5 Moderating effect for delivery performance. Variable

Control variable Owner regulation Budget Team size Project complexity Environmental uncertainty Time availability Project duration Independent variable Knowledge storage (ST) Knowledge sharing (SH) Knowledge application (AP) Knowledge acquisition (AC) Moderating variable Data complexity (DC) Interaction ST × DC SH × DC AP x DC AC × DC F-test R-squared R-squared increased a b c

Delivery performance Model 1

Model 2

Model 3

Model 4

− 0.030 0.376 a − 0.096 − 0.119 0.008 − 0.384 a − 0.335 a

− 0.097 0.184 b − 0.013 0.002 − 0.001 − 0.264 a − 0.227 a

− 0.101 0.187 b 0.014 0.039 − 0.014 − 0.243 a − 0.259 a

− 0.087 0.136 0.039 0.051 − 0.023 − 0.190 a − 0.208 c

0.103 0.141 b 0.025 0.333 a

0.087 0.101 − 0.056 0.308 a

0.104 0.078 − 0.069 0.364 a

0.215 c

0.278 a

13.270 a 0.440 9.023

0.306 b 0.159 0.169 − 0.479 c 11.443 a 0.479 0.039

7.518 a 0.202

13.287 a 0.417 0.215

Significant at the 0.001 level. Significant at the 0.05 level. Significant at the 0.01 level.

Please cite this article as: Yang, L.-R., et al., Knowledge leadership to improve project and organizational performance, International Journal of Project Management (2013), http://dx.doi.org/10.1016/j.ijproman.2013.01.011

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L.-R. Yang et al. / International Journal of Project Management xx (2013) xxx–xxx

Table 6 Moderating effect for training and education performance. Variable

Training and education performance

Control variable Owner regulation Budget Team size Project complexity Environmental uncertainty Time availability Project duration Independent variable Knowledge storage (ST) Knowledge sharing (SH) Knowledge application (AP) Knowledge acquisition (AC) Moderating variable Data complexity (DC) Interaction ST × DC SH × DC AP × DC AC × DC F-test R-squared R-squared increased b c

Model 1

Model 2

Model 3

Model 4

0.073 0.336 a − 0.187 b − 0.227 c 0.076 − 0.202 c − 0.293 a

− 0.003 0.132 − 0.092 − 0.090 0.061 − 0.091 − 0.187 c

− 0.006 0.134 − 0.077 − 0.069 0.054 − 0.079 − 0.205 c

0.020 0.085 − 0.060 − 0.063 0.033 − 0.025 −0.153 b

0.036 0.131 0.104 0.327 a

0.027 0.109 0.059 0.313 c

0.032 0.085 0.047 0.357 a

0.119

0.152

9.976 a 0.371 0.007

0.049 0.336 c 0.096 − 0.340 8.554 a 0.408 0.037

5.209 a 0.149

10.623 a 0.364 0.215

Significant at the 0.001 level. Significant at the 0.05 level. Significant at the 0.01 level.

broader cross-functional approach and measure the constructs from the viewpoints of customers. It may focus on the joint consideration of the various perspectives.

(a)

(b) High data complexity

Delivery Performance

This research provides empirical evidence that supports the expectation of gaining significant benefits from adoption of knowledge leadership. It reveals the importance of adopting knowledge leadership to improve project and organizational performance. This study adds to the literature in three valuable ways. First, it validates a framework for assessing the associations among knowledge leadership, customer knowledge management, project performance, and organizational performance and provides implications for project knowledge leadership. Second, it offers important results on identification of the mediating role of project performance in the relationship between customer knowledge management and organizational performance. Third, this research evaluates the relationship between customer knowledge management and project performance for different data complexity. The research findings indicate that adoption of knowledge leadership is associated with customer knowledge management, which supports H1. These results are in line with previous studies (Bukowitz and Williams, 1999; Davenport et al., 1998; Horibe, 1999; Koulopoulos and Frappaolo, 1999; Roberts-Witt, 2001), which have shown that knowledge leadership plays a crucial role in knowledge management. Additionally, in agreement with previous studies (Adenfelt, 2010; Vandaie, 2008), the research findings imply that implementation of customer knowledge management may improve project performance, which supports H2. The research results also show that improvement in project performance may enhance organizational performance, which supports H3. The positive relationship is in line with previous findings (Darroch and McNaughton, 2003; Gibbert et al., 2002). In addition, customer knowledge management has a positive

Low data complexity

5.5 5.0 4.5 4.0

Low

High

High data complexity

Delivery Performance

a

6 . Conclusions and implications

Low data complexity

5.5 5.0 4.5 4.0 3.5

Knowledge Storage

Low

High

Knowledge Acquisition

(c) Training and Education

High data complexity

Low data complexity

Note: (a) Moderating role of data complexity between knowledge storage and delivery performance

5.5

(b) Moderating role of data complexity between knowledge acquisition and delivery performance

5.0

(c) Moderating role of data complexity between

4.5 4.0

knowledge sharing and training and education performance Low

High

Knowledge Sharing Fig. 3. Moderating role of data complexity. Please cite this article as: Yang, L.-R., et al., Knowledge leadership to improve project and organizational performance, International Journal of Project Management (2013), http://dx.doi.org/10.1016/j.ijproman.2013.01.011

L.-R. Yang et al. / International Journal of Project Management xx (2013) xxx–xxx

influence on organizational performance. Thus, the positive relationship suggests that H4 is supported. However, the results of this research further prove that project performance fully mediates the effects of customer knowledge management on organizational performance, which supports H5. The findings indicate that implementation of customer knowledge management influences organizational performance via project performance, which is one of the main contributions of this research. This result also confirms the suggestion in literature that mediators may exist between knowledge management practices and final examined outcomes (Chen et al., 2005; Schmidt and Calantone, 1998; Tatikonda and Montoya-Weiss, 2001). In this study, knowledge leadership was measured by the three dimensions of leadership skills, cooperation and trust, and knowledge integration and innovation. The research findings imply that cooperation and trust may not contribute significantly to customer knowledge management. The results are not in agreement with previous studies, which indicated that trust stimulates the team members' learning behavior and allows one member to understand skills or knowledge of one another (Cross and Baird, 2000; Davenport and Prusak, 1998). The prior research suggested that cooperation and trust may improve customer knowledge sharing. However, based on the analysis results, leadership skills and knowledge integration and innovation are more closely associated with overall customer knowledge management than is cooperation and trust. This may be the reason why cooperation and trust does not contribute significantly to customer knowledge management. In addition, customer knowledge management was measured by the four dimensions of knowledge acquisition, knowledge storage, knowledge sharing, and knowledge application. The research results show that knowledge acquisition and knowledge storage may not contribute significantly to project performance, which are not in line with previous research (Vandaie, 2008). Similarly, knowledge sharing and knowledge application are more closely associated with overall project performance than are knowledge acquisition and knowledge storage. This may explain why knowledge acquisition and knowledge storage do not contribute significantly to project performance. Because project success factors are not universal for all types of projects, several studies have identified critical success factors in certain fields or domains. For example, for construction projects, weather conditions can be considered as a critical factor for completing the project on time (Belassi and Tukel, 1996). On the other hand, for product development projects, the project life span and its cost are critical factors for the immediate release of a product to the market (Belassi and Tukel, 1996). While certain factors exist in one domain, they may not be applicable to other fields. Thus, Dvir et al. (1998) argued that different projects exhibit different sets of success factors, suggesting the need for a more contingent approach in project management theory and practice. According to the data analysis results, the influence of customer knowledge storage on project delivery performance increases in projects with a high level of data complexity, due to the moderating effect of data complexity (H6 is supported). In other words, the influence of customer knowledge storage on

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project delivery performance, for projects with a higher level of data complexity, is more than the same effect in the case of projects with a lower level of data complexity. It is also clear that projects with a high level of data complexity are more likely to be successful in training and education performance when they experience a high level of knowledge sharing than projects with a low level of data complexity. Projects with a high level of data complexity may involve more complicated tasks, uncertainty, and high risk. This type of project usually involves diverse and complex information and knowledge. It is not easy to manage the knowledge for projects with high complexity and uncertainty. However, knowledge storage and sharing is important to incorporate all the key knowledge and to integrate the complicated tasks which are influential on project performance. In addition, knowledge storage and sharing may produce associations that create bridges between entities within a project. It is also a key to team communication and an essential element for integrating knowledge from different sources. These may be the reasons why knowledge storage and sharing is more closely associated with project performance for projects with a high level of data complexity. On the other hand, projects with a low level of data complexity are more likely to be successful in delivery performance when they experience formal methods for knowledge acquisition than projects with a high level of data complexity. It is easier to improve project delivery performance with formal knowledge acquisition approach for projects with a low level of data complexity. This study has clear implications for knowledge leadership adoption. With respect to leadership skills, project managers need to understand the importance of customer knowledge acquisition and practice what they preach. They should try to gain new knowledge to set an example to the others and develop excellent knowledge leadership skills. For knowledge integration and innovation, project managers must take action to enhance team members' innovative ability and develop a reward system to stimulate team members' learning behavior. More importantly, project managers should integrate practical experience from different departments to create new knowledge and lead team members to execute innovative ideas.

References Ackoff, R.L., 1989. From data to wisdom. Journal of Applies Systems Analysis 16, 3–9. Adenfelt, M., 2010. Exploring the performance of transnational projects: shared knowledge, coordination and communication. International Journal of Project Management 28 (6), 529–538. Aiken, L.S., West, S.G., 1991. Multiple regression: testing and interpreting interactions. Sage Publications, Thousand Oaks, California. Alavi, M., Leidner, D.E., 2001. Knowledge management and knowledge management systems: conceptual foundations and research issues. MIS Quarterly 25 (1), 107–136. Ali, H.M., Yusof, Z.M., 2006. Knowledge leadership vs. knowledge management: the Malaysian bankers' view. In: Pour, M.K. (Ed.), Emerging Trends and Challenges in Information Technology Management. Idea Group Inc., Pennsylvania. Atuahene-Gima, K., 1995. An exploratory analysis of the impact of market orientation on new product performance - a contingency approach. The Journal of Product Innovation Management 12 (4), 275–293.

Please cite this article as: Yang, L.-R., et al., Knowledge leadership to improve project and organizational performance, International Journal of Project Management (2013), http://dx.doi.org/10.1016/j.ijproman.2013.01.011

12

L.-R. Yang et al. / International Journal of Project Management xx (2013) xxx–xxx

Bagozzi, R.P., Phillips, L.W., 1982. Representing and testing organizational theories: a holistic construal. Administrative Science Quarterly 27 (3), 459–489. Bass, B.M., 1990. From transactional to transformational leadership: learning to share the vision. Organizational Dynamics 18 (3), 19–31. Bates, M.J., 2005. Information and knowledge: an evolutionary framework for information science. Information Research 10 (4) (paper 239). Belassi, W., Tukel, O.I., 1996. A new framework for determining critical success/failure factors in projects. International Journal of Project Management 14 (3), 141–151. Bell De Tienne, K., Dyer, G., Hoopes, C., Harris, S., 2004. Toward a model of effective knowledge management and directions for future research: culture, leadership, and CKOs. Journal of Leadership and Organizational Studies 10 (4), 26–43. Brown, J.S., Duguid, P., 2000. The social life of information. Harvard Business School Press, Boston, Massachusetts. Browne, M.W., Cudeck, R., 1993. Alternative ways of assessing model fit. Sage Publications, Newbury Park, California. Bryant, S.E., 2003. The role of transformational and transactional leadership in creating, sharing, and exploiting organizational knowledge. Journal of Leadership and Organizational Studies 9 (4), 32–44. Bukowitz, W.R., Williams, R.L., 1999. The knowledge management field book. Prentice-Hall, Upper Saddle River, New Jersey. Cameron, N., 2004. Trust in Knowledge Management and Systems in Organizations. Online Information Review 28 (3), 243–244. Campbell, A., 2003. Creating customer knowledge: managing customer relationship management programs strategically. Industrial Marketing Management 32 (5), 375–383. Carlsson, B., Keane, P., Martin, J.B., 1979. R & D organizations as learning systems. In: Kolb, D., Rubin, I., McIntyre, J. (Eds.), Organizational Psychology. A Book of Readings. Prentice-Hall, Englewood Cliffs, New Jersey, pp. 36–46. Chau, P., 1997. Re-examining a model for evaluating information center success using a structural equation modelling approach. Decision Sciences 28 (2), 309–334. Chen, J., Reilly, R., Lynn, G., 2005. The impacts of speed-to-market on new product success: the moderating effects of uncertainty. IEEE Transactions on Engineering Management 52 (2), 199–211. Cleveland, H., 1985. The knowledge executive. E.P. Dutton, New York. Crawford, C.B., 2005. Effects of transformational leadership and organizational position on knowledge management. Journal of Knowledge Management 9 (6), 6–16. Cross, R., Baird, L., 2000. Feeding organizational memory: improving on knowledge management's promise to business performance. In: Robert, L., Cross, J., Israelit, S.B. (Eds.), Strategic Learning in a Knowledge Economy: Individual Collective and Organizational Learning Process. ButterworthHeinemann, Oxford, UK, pp. 69–90. Darroch, J., McNaughton, R., 2003. Beyond market orientation – knowledge management and the innovativeness of New Zealand firms. European Journal of Marketing 37 (3/4), 572–593. Davenport, T.H., Prusak, L., 1998. Working knowledge: how organizations manage what they know. Harvard Business School Press, Boston, Massachusetts. Davenport, T.H., De Long, D.W., Beers, M.C., 1998. Successful knowledge management projects. Sloan Management Review 39 (2), 43–57. Day, D.V., Lord, R.G., 1988. Executive leadership and organizational performance: suggestions for a new theory and methodology. Journal of Management 14 (3), 453–464. Deephouse, C., Mukhopadhyay, T., Goldenson, D.R., Kellner, M.I., 1996. Software processes and project performance. Journal of Management Information Systems 12 (3), 187–205. Delamare Le Deist, F., Winterton, J., 2005. What is competence? Human Resource Development International 8 (1), 17–36. Dimitrova, V., Kaneva, M., Gallucci, T., 2009. Customer knowledge management in the natural cosmetics industry. Industrial Management & Data Systems 109 (9), 1155–1165. Dvir, D., Lipovetsky, S., Shenhar, A., Tishler, A., 1998. In search of project classification: a non-universal approach to project success factors. Research Policy 27 (9), 915–935. Earl, M.J., Scott, I.A., 1999. What is a chief knowledge officer? Sloan Management Review 40 (2), 29–38.

Fleishman, E.A., Mumford, M.D., Zaccaro, S.J., Levin, K.Y., Korotkin, A.L., Hein, M.B., 1991. Taxonomic efforts in the description of leader behavior: a synthesis and functional interpretation. The Leadership Quarterly 2 (4), 245–287. Fornell, C., Larcker, D.F., 1981. Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research 18 (1), 39–50. Frazier, P.A., Barron, K.E., Tix, A.P., 2004. Testing moderator and mediator effects in counseling psychology research. Journal of Counseling Psychology 51, 115–134. Garcia, N., Sanzo, M.J., Trespalacios, J.A., 2008. Effect of the interfunctional climate on internal and external new product performance. The moderator role of innovation type. International Congress “Marketing Trends”, Venice, Italy. Garcia-Murillo, M., Annabi, H., 2002. Customer knowledge management. The Journal of the Operational Research Society 53 (8), 875–884. Garrido-Moreno, A., Padilla-Meléndez, A., 2011. Analyzing the impact of knowledge management on CRM success: The mediating effects of organizational factors. International Journal of Information Management 31 (5), 437–444. Gebert, H., Geib, M., Kolbe, L., Brenner, W., 2003. Knowledge-enabled customer relationship management: integrating customer relationship management and KM concepts. Journal of Management 7 (5), 107–123. Gerstner, C.R., Day, D.V., 1997. Meta-analytic review of leader-member exchange theory: correlates and construct ideas. The Journal of Applied Psychology 82 (6), 827–844. Gibbert, M., Leibold, M., Probst, G., 2002. Five styles of customer knowledge management, and how smart companies use them to create value. European Management Journal 20 (5), 459–469. Gladstein, D.L., 1984. Groups in context: a model of task group effectiveness. Administrative Science Quarterly 29 (4), 499–517. Green, P.C., 1999. Building Robust Competencies. Jossey-Bass, California. Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E., Tatham, R.L., 2006. Multivariate data analysis, 6th ed. Prentice-Hall, Upper Saddle River, New Jersey. Hansen, M.T., 2002. Knowledge networks-explaining effective knowledge sharing in multiunit companies. Organization Science 13 (3), 232–248. Horibe, F., 1999. Managing knowledge workers: new skills and attitudes to unlock the intellectual capital in your organization. John Wiley & Sons, New York. Huang, J.W., Li, Y.H., 2009. The mediating effect of knowledge management on social interaction and innovation performance. International Journal of Manpower 30 (3), 285–301. Kasvi, J.J.J., Vartiainen, M., Hailikari, M., 2003. Managing knowledge and knowledge competences in projects and project organizations. International Journal of Project Management 21 (8), 571–582. Katila, R., Ahuja, G., 2002. Something old, something new: a longitudinal study of search behaviour and new product introduction. The Academy of Management Journal 45 (6), 1183–1194. Katou, A., Budhwar, P., 2007. The effect of human resource management policies on organizational performance in Greek manufacturing firms. Thunderbird International Business Review 49 (1), 1–35. Kiessling, T., Richey, R.G., Meng, J., Dabic, M., 2009. Exploring knowledge management to organizational performance outcomes in a transitional economy. Journal of world business 44 (4), 421–433. Kirkpatrick, S.A., Locke, E.A., 1991. Leadership: do traits matter? The Academy of Management Executive 5 (2), 48–60. Koontz, H., Weihrich, C., 1990. Essentials of management, 5th ed. McGraw Hill, New York. Kotlarsky, J., Oshri, I., 2005. Social ties, knowledge sharing and successful collaboration in globally distributed system development projects. European Journal of Informational Systems 14 (1), 37–48. Kotter, J.P., 1990. What leaders really do. Harvard Business School Press, Boston, Massachusetts. Koulopoulos, T.M., Frappaolo, C., 1999. Smart things to know about knowledge management. John Wiley, New York. KPMG, 2000. Knowledge management research report. KPMG Consulting, LLP, Annapolis, Maryland. Lakshman, C., 2005. Top executive knowledge leadership: managing knowledge to lead change at General Electric. Journal of Change Management 5 (4), 429–446.

Please cite this article as: Yang, L.-R., et al., Knowledge leadership to improve project and organizational performance, International Journal of Project Management (2013), http://dx.doi.org/10.1016/j.ijproman.2013.01.011

L.-R. Yang et al. / International Journal of Project Management xx (2013) xxx–xxx Lakshman, C., 2007. Organizational knowledge leadership: a grounded theory approach. Leadership and Organization Development Journal 28 (1), 51–75. Lau, A.K.W., Yam, R.C.M., Tang, E., 2007. The impacts of product modularity on competitive capabilities and performance: an empirical study. International Journal of Production Economics 105 (1), 1–20. Lee, J.C., Lee, S., Kang, I.W., 2005. KMPI: measuring knowledge management performance. Information Management 42 (3), 469–482. Levinthal, D.A., March, J.G., 1993. The myopia of learning. Strategic Management Journal 14, 95–112. Li, T., Calantone, R., 1998. The impact of market knowledge competence on new product advantage: conceptualization and empirical examination. The Journal of Marketing 62 (4), 13–29. Liebowitz, J., 2000. Building organizational intelligence: a knowledge management primer. CRC Press, Boca Raton, Florida. Lim, C.S., Mohamed, M.Z., 1999. Criteria of project success: an exploratory reexamination. International Journal of Project Management 17 (4), 243–248. Lin, H.F., Lee, G.G., 2005. Impact of organizational learning and knowledge management factors on e-business adoption. Management Decision 43 (2), 171–188. Lin, R., Chen, R., Chiu, K.K., 2010. Customer relationship management and innovation capability: an empirical study. Industrial Management & Data Systems 110 (1), 111–133. Lindner, F., Wald, A., 2011. Success factors of knowledge management in temporary organizations. International Journal of Project Management 29 (7), 877–888. Lopez-Cabrales, A., Pérez-Luño, A., Cabrera, R.V., 2009. Knowledge as a mediator between HRM practices and innovative activity. Human Resource Management 48 (4), 485–503. Low, S.P., Quek, T.C., 2006. Environmental factors and performance of project managers in the construction industry. International Journal of Project Management 24 (1), 24–37. Macneil, C., 2001. The supervisor as a facilitator of informal learning in work teams. Journal of Workplace Learning 13 (6), 246–253. Malhotra, Y., 2000. Knowledge management and new organization forms: a framework for business model innovation. Information Resources Management Journal 13 (1), 5–14. Maurer, I., 2010. How to build trust in inter-organizational projects: the impact of project staffing and project rewards on the formation of trust, knowledge acquisition and product innovation. International Journal of Project Management 28 (7), 629–637. Mintzberg, H., 1973. The nature of managerial work. Harper & Row, New York. Molenaar, K., Washington, S., Diekmann, J., 2000. Structural equation model of construction contract dispute potential. Journal of Construction Engineering and Management 126 (4), 268–277. Morgan, J., 2007. Customer information management (CIM): the key to successful CRM in financial services. Journal of Performance Management 20 (2), 47–65. Müller, R., Turner, J.R., 2007. Matching the project manager's leadership style to project type. International Journal of Project Management 25 (1), 21–32. Na, K.S., Simpson, J.T., Li, X., Singh, T., Kim, K.Y., 2007. Software development risk and project performance measurement: evidence in Korea. Journal of Systems and Software 80 (4), 596–605. Nonaka, I., Takeuchi, H., 1995. The knowledge-creating company-how Japanese companies create the dynamics of innovation. Oxford University Press, New York. O'Connor, J.T., Won, S., 2001a. Understanding demand for technology via work function characteristics. Specialty Conference on Fully Integrated and Automated Project Processes. Virginia Polytechnic and State University, Blacksburg, Virginia. O'Connor, J.T., Won, S., 2001b. Work function-level technology use metrics for capital facility projects. Report No. 18, Center for Construction Industry Studies. University of Texas at Austin, Austin, Texas. Oya, I.T., Walter, O.R., 1998. Analysis of the characteristics of projects in diverse industries. Journal of Operations Management 16 (1), 43–61. Pheng, L.S., Chuan, Q.T., 2006. Environmental factors and work performance of project managers in the construction industry. International Journal of Project Management 24 (1), 24–37.

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Podsakoff, P.M., Organ, D.W., 1986. Self-reports in organizational research: problems and prospects. Journal of Management 12 (4), 531–544. Politis, J.D., 2001. The relationship of various leadership styles to knowledge management. Leadership and Organizational Development Journal 22 (8), 354–364. Pollack, J., 2012. Transferring knowledge about knowledge management: Implementation of a complex organisational change programme. International Journal of Project Management 30 (8), 877–886. Rahim, M.A., Psenicka, C., Polychroniou, P., Zhao, J., 2002. A model of emotional intelligence and conflict management strategies: a study in seven countries. International Journal of Organizational Analysis 10 (4), 302–326. Rasmus, D., 2000. Knowledge management’s role in a merger. Knowledge Management Review 3 (4), 30–32. Reich, B.H., Gemino, A., Sauer, C., 2012. Knowledge management and project-based knowledge in IT projects: A model and preliminary empirical results. International Journal of Project Management 30 (6), 663–674. Renzl, B., 2008. Trust in management and knowledge sharing: the mediating effects of fear and knowledge documentation. Omega 36 (2), 206–220. Revilla, E., Rodríguez-Prado, B., Prieto, I., 2009. Information technology as knowledge management enabler in product development: empirical evidence. European Journal of Innovation Management 12 (3), 346–363. Richard, P.J., Devinney, T.M., Yip, G.S., Johnson, G., 2009. Measuring organizational performance: towards methodological best practice. Journal of Management 35 (3), 718–804. Roberts-Witt, S.L., 2001. Reinventing HR. Knowledge Management 4 (9), 34–41. Rowley, J., 2002. Reflections on customer knowledge management in e-business. Qualitative Market Research: An International Journal 5 (4), 268–280. Rowley, J., 2004. Relationship marketing and knowledge management: partnering paradigms? Industrial Management & Data Systems 104 (2), 149–157. Sabherwal, R., Becerra-Fernandez, I., 2003. An empirical study of the effect of knowledge management processes at individual, group, and organizational levels. Decision Sciences 34 (2), 225–260. Saint-Onge, H., 1999. Developing an effective knowledge strategy. Chief Learning Officer Conference, Boston, Massachusetts. Sarabia, M., 2007. Knowledge leadership cycles: an approach from Nonaka's viewpoint. Journal of Knowledge Management 11 (3), 6–15. Schmidt, J.B., Calantone, R., 1998. Are really new product development projects harder to shut down? The Journal of Product Innovation Management 15 (2), 111–123. Shi, J., Yip, L., 2007. Driving innovation and improving employee capability: the effect of customer knowledge sharing on CRM. The Business Review 7 (1), 107–112. Singh, S.K., 2008. Role of leadership in knowledge management: a study. Journal of Knowledge Management 12 (4), 3–15. Skyrme, D., 2000. Developing a knowledge strategy: from management to leadership. In: Morey, D., Thuraisingham, B. (Eds.), Knowledge Management. Classic and Contemporary Works. MIT Press, Cambridge, Massachusetts. Solomom, C., 2001. Managing virtual teams. Workforce 80 (6), 60–65. Srisa-ard, S., 2006. Knowledge management for teams and projects. Online Information Review 30 (3), 320–321. Stefanou, C.J., Sarmaniotis, C., Stafyla, A., 2003. CRM and customer-centric KM: an empirical research. Business Process Management Journal 9 (5), 617–634. Stogdill, R.M., 1974. Handbook of leadership. Free Press, New York, A survey of theory and research. Tatikonda, M.V., Montoya-Weiss, M.M., 2001. Integrating operations and marketing perspectives of product innovation: the influence of organizational process factors and capabilities on development performance. Management Science 47 (1), 151–172. Van Dyne, L., LePine, J.A., 1998. Helping and voice extra-role behaviors: evidence of construct and predictive validity. The Academy of Management Journal 41 (1), 108–119. Vandaie, R., 2008. The role of organizational knowledge management in successful ERP implementation projects. Knowledge-Based Systems 21 (8), 920–926.

Please cite this article as: Yang, L.-R., et al., Knowledge leadership to improve project and organizational performance, International Journal of Project Management (2013), http://dx.doi.org/10.1016/j.ijproman.2013.01.011

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L.-R. Yang et al. / International Journal of Project Management xx (2013) xxx–xxx

Viitala, R., 2004. Towards knowledge leadership. Leadership and Organizational Development Journal 25 (6), 528–544. von Krogh, G., 2002. The communal resource and information systems. The Journal of Strategic Information Systems 11 (2), 85–107. Vroom, V.H., Jago, A.G., 1988. The new leadership: managing participation in organizations. Prentice-Hall, Englewood Cliffs, New Jersey. Wang, E.T.G., Wei, H.L., Jiang, J.J., Klein, G., 2006. User diversity impact on project performance in an environment with organizational technology learning and management review processes. International Journal of Project Management 24 (5), 405–411. Wei, Y., Morgan, N.A., 2004. Supportiveness of organizational climate, market orientation, and new product performance in Chinese firms. The Journal of Product Innovation Management 21 (6), 375–388.

Weng, R., Huang, C., Tsai, W., Chang, L., Lee, M., Lin, S., 2010. Exploring the impact of mentoring functions on job satisfaction and organizational commitment of new staff nurses. BMC Health Services Research 10 (1), 240–249. Yli-Renko, H., Autio, E., Sapienza, H.J., 2001. Social capital, knowledge acquisition, and knowledge exploitation in young technology-based firms. Strategic Management Journal 22 (6/7), 587–613. Yukl, G., 1998. Leadership in organizations, 4th ed. Prentice-Hall, Englewood Cliffs, New Jersey. Zack, M.H., 1999. Managing codified knowledge. Sloan Management Review 40 (4), 45–58. Zander, U., Kogut, B., 1995. Knowledge and the speed of the transfer and imitation of organizational capabilities: an empirical test. Organization Science 6 (1), 76–92.

Please cite this article as: Yang, L.-R., et al., Knowledge leadership to improve project and organizational performance, International Journal of Project Management (2013), http://dx.doi.org/10.1016/j.ijproman.2013.01.011