Knowledge sharing in information systems development projects: Explicating the role of dependence and trust

JPMA-01510; No of Pages 13

Available online at www.sciencedirect.com

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

Knowledge sharing in information systems development projects: Explicating the role of dependence and trust Jun-Gi Park a

a, 1

, Jungwoo Lee b,⁎

IT Policy & Strategy Research Institute, Yonsei University, 50 Yonsei Ro, Sudaemun Gu, Seoul, Republic of Korea b Graduate School of Information, Yonsei University, 50 Yonsei Ro, Sudaemun Gu, Seoul, Republic of Korea Received 3 October 2012; received in revised form 10 February 2013; accepted 14 February 2013 Available online xxxx

Abstract This study provides empirical evidence for the role of dependence and trust in knowledge sharing in information systems projects. As critical antecedents of dependence and trust among team members, four constructs are included in the study—environmental complexity, domain expertise, similarity of project value, and communication frequency. Partial least square analyses are conducted, using data collected from 135 project teams in two large IT firms. The results confirm that dependence and trust maintain a strong impact on knowledge sharing, leading to good team project performance. This study uses a cross-sectional survey as a research method. Longer term exploration seems necessary to further explore how trust and dependence are actually formed among team members. Findings indicate that team members share their knowledge when they trust their partners and when they feel dependent. Feelings of dependence and trust are influenced by the communication frequency, perceived similarity of the project's value, and the perceived expertise. Project managers need to pay attention to these variables in order to increase the level of knowledge sharing among team members especially in information systems development projects where primary tasks are critically knowledgeintensive. © 2013 Elsevier Ltd. APM and IPMA. All rights reserved. Keywords: Systems development; Knowledge sharing; Knowledge management; Dependence; Trust; Project management; IT services; Information systems

1. Introduction As organizations encounter the need to develop information systems (IS) for novel business applications and new problem domains, the need for knowledge sharing in IS development projects is increasingly emphasized in practice (Tiwana and McLean, 2005). IS development is comprised of different knowledge-intensive activities that require different types of knowledge (Xia and Lee, 2004). IS projects often use a combination of complex technologies that pose a high knowledge burden and that are difficult for project members to grasp. In many cases, the ability of project members to learn and ⁎ Corresponding author. Tel.: + 82 10 5398 7751; fax: + 82 2 363 5419. E-mail addresses: [email protected] (J.-G. Park), [email protected] (J. Lee). 1 Tel.: + 82 10 5878 8862; fax: + 82 2 363 5419.

use technology-related knowledge as well as domain knowledge effectively is critical for successful IS implementation. Knowledge sharing is the most valuable activity. It is valuable because knowledge sharing encourages project participants to maintain social capital (Chiu et al., 2006; Kotlarsky and Oshri, 2005; Nelson and Cooprider, 1996; Tiwana and McLean, 2005; Yang and Farn, 2009), sustain high performance in IS projects (Hsu et al., 2012; Patnayakuni et al., 2007; Pee et al., 2010), become more innovative (Lind and Zmud, 1991) and become more creative (Tiwana and McLean, 2005). Thus, the sharing of knowledge in an IS project has become a requirement for the completion of a successful IS project. A stream of knowledge sharing research in project management has recently focused on how to transfer and share knowledge within a project (Pee et al., 2010). Each project has internal sources of knowledge such as project

0263-7863/$36.00 © 2013 Elsevier Ltd. APM and IPMA. All rights reserved. http://dx.doi.org/10.1016/j.ijproman.2013.02.004 Please cite this article as: Park, J.-G., Lee, J., Knowledge sharing in information systems development projects: Explicating the role of dependence and trust, International Journal of Project Management (2013), http://dx.doi.org/10.1016/j.ijproman.2013.02.004

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members, project deliverables, and a project form of ecology. To maximize the use of internal knowledge, the knowledge must be shared among individuals or teams (Chang et al., 2013). Effective knowledge sharing can result in accelerating the relationship between the business clients and the IS consultants that are involved in the IS project. The time spent on problem solving can be reduced significantly because the project participants' benefit from the shared and accumulated knowledge. According to the theory of relationship maintenance, trust and dependence between parties are central factors that motivate each party to participate or engage in successful and mutually beneficial exchange relationships (Hewett and Bearden, 2001). Knowledge sharing based on exchange relationships is fundamental to foster collaboration between members and to achieve the goals of an IS implementation (Xu and Ma, 2008). Thus, trust and dependence play a central role in building and maintaining the relationship between the participants of an IS project and in promoting knowledgesharing activities. While both dependence and trust are important drivers of relational behaviors, the evidence is that dependence and trust must partner in the processes involved in IS projects. A successful implementation of an IS project often requires extensive customization to configure the methodology to the specific client and their marketplace (Pan et al., 2007). IS system customization involves critical decision-making activities (Vandaie, 2008). Users should be willing to trust the customization vendor because the user determines the utility that they expect from the IS system and they depend upon the vendor's knowledge of the IS system (Sarker et al., 2005). The objective of this study is to better understand the behavioral mechanism that encourages project partners to share knowledge in IS development projects. IS projects demand creative efforts that involve in-depth exchange of expertise and insights among partners, and partners need frequent communications with each other, dealing with complex project management issues. In this regard, following two research questions are formulated for this study. (1) What are the critical antecedents for building trust and dependence in IS development projects that may lead to knowledge sharing and team performance? (2) What are the roles of trust and dependence in this behavioral mechanism? Thus, critical antecedents of trust and dependence are identified from the literature on project management and knowledge sharing. Relationship among antecedent factors (environmental, partner and interaction), mediating factors (trust and dependence), and outcome factors (knowledge sharing and project team performance) are hypothesized based on research findings from related areas. In the following sections, the research model and hypotheses are presented with literature review. Subsequently, the research methods are presented with details of data collection followed by the data analysis results. At the end, implications of our findings are discussed with limitations and directions for future research.

2. Research model The research model posits that the trust–dependence relationship influences the extent of knowledge sharing during the IS development process. The model of trust and dependence identifies the environment, partner and interaction as the basic elements of the relationship between the parties. The supplier perspective is relevant for understanding knowledge sharing because these elements are inherent in collaboration for a mutually beneficial exchange relationship (Bendapudi and Berry, 1997; Morgan and Hunt, 1994). In the context of knowledge sharing, the project environment corresponds to the complexity of the project, which affects the project participant's relationships owing to the limited resources (Xia and Lee, 2005). The project partner corresponds to the expertise and the similarity of the clients involved in the provision and acquisition of knowledge from the perspectives of IS consultants. The project interaction corresponds to the communication through which the knowledge is shared. The effect refers to the outcome of the sharing of knowledge, such as performance, creativity and outcome expectations. The prior studies of knowledge sharing have provided insight into its antecedents. Organizing the antecedents provides us with a better understanding of the state of research on knowledge sharing and identifies different subjects in prior research. According to earlier research (Bendapudi and Berry, 1997), the antecedents of relationship exchanges are the environment factor, partner and customer factors, and interaction factors in relationship environments. Because this study focuses on the partner characteristics, we study the use of the three antecedents of the environment factor, partner factors and interaction factors, which account for trust and the dependence mechanism of sharing knowledge during IS projects. The project environment antecedent considers IS complexity. IS development projects have more issues and problems than other business task projects when adapting to the ever-changing business environment on account of the complexity of IT tasks (Roberts et al., 2004). By collaborating and cooperating to reduce the risk of complexity when undertaking new product development tasks, the project team enhances organizational learning (Sorenson, 2003) and accumulates the project knowledge that stems from the development process (Brookes et al., 2006). The project partner antecedents include expertise and the similarity of project value. The partner's competency and expertise levels, indicating their ability to encode and decode knowledge clearly, have been identified as key characteristics of the participants in the project (Tiwana and McLean, 2005; Xu and Ma, 2008). Other influential knowledge source attributes include the participants' experience and credibility (Faraj and Sproull, 2000; Joshi et al., 2007). The project members are able to obtain ideas and concepts from outside of their knowledge domains, often drawing different implications from the same ideas. The similarity between parties leads to interaction and influences the choice of service partners (Johnson and Grayson, 2005). When the target goals are similar to existing values, the IS system is more easily implemented and more effectively operated (Wasko and Faraj, 2005). The customer will also be

Please cite this article as: Park, J.-G., Lee, J., Knowledge sharing in information systems development projects: Explicating the role of dependence and trust, International Journal of Project Management (2013), http://dx.doi.org/10.1016/j.ijproman.2013.02.004

J.-G. Park, J. Lee / International Journal of Project Management xx (2013) xxx–xxx

more motivated to transfer knowledge if there is a common work experience created by partner similarity. In particular, similarity in strategy and value, referred to as the vision or the goals, should have a greater impact on knowledge transfer than customer and geographic similarity (Darr and Kurtzberg, 2000). Project interaction refers to frequent communication with partners inside and outside of the project. Communication frequency is one of the binding factors to enhance the relationship between the service provider and the customer. Given that IS development is a process involving frequent communication and negotiation, often among collocated stakeholders, communication activities such as email, face-toface meetings, and verbal and non-verbal expressions from a source will lead to a more satisfying relationship with the recipient, thereby facilitating the transfer of knowledge (Joshi et al., 2007). The interaction factors, which are represented by trust and dependence, between participants in the project improve the sharing of knowledge. Trust is known as a key antecedent of knowledge sharing (Jones et al., 2006; Joshi et al., 2007; Sarker et al., 2005). Building trust with the provider in an outsourcing environment helps to enhance the success of the IS outsourcing project (Lee and Kim, 1999). The other indicator is dependence, which has been conceptualized in terms of mutual influence (Nelson and Cooprider, 1996). The dependence on partners between IS consultant and clients can also impact knowledge sharing (Pee et al., 2010). The research model is presented in Fig. 1. 2.1. Knowledge sharing and project team performance The literature on information systems suggests that knowledge sharing will have a positive impact on team performance. Choi et al. (2010) argue that knowledge sharing can lead to better team performance due to improved decision making and coordination. Stasser and Titus (1985) found that accumulated

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knowledge sharing leads to a greater understanding of the unsolved issues and the existing knowledge between team members, which contributes to improved decisions. Given experience in sharing knowledge, team members are able to understand tiny cues from others and fix errors. Knowledge sharing improves performance by leading to innovative solutions to business problems (Hansen, 2002). Tesch et al. (2009) also state that IS development is a knowledgeintensive process requiring the integration of business domain knowledge and technical development knowledge for success. Collecting complete and detailed requirements and solving the issues of system design with other project members play a key role in increasing IS development efficiency and influencing the performance of IS development (Jewels and Ford, 2006). Patnayakuni et al. (2007) demonstrate that higher levels of knowledge integration are associated with improved IS development performance. Therefore, we propose the following: H1. Knowledge sharing is positively related to the project team's performance.

2.2. Dependence and trust for knowledge sharing On the basis of the predictions of social exchange theory, certain features have repeatedly been found to be important when building quality relationships. Specifically, trust and dependence between the service provider and customer have been proposed as central factors in motivating each party to participate or engage in a successful and mutually beneficial exchange relationship (Hewett and Bearden, 2001). Other researchers (Bendapudi and Berry, 1997) consider that trust and dependence can play a role in relationship maintenance. While both dependence and trust are important drivers of relational behaviors, the evidence shows that dependence and trust partner in the process of implementing large and complex systems such as enterprise resource planning

Fig. 1. Research model. Please cite this article as: Park, J.-G., Lee, J., Knowledge sharing in information systems development projects: Explicating the role of dependence and trust, International Journal of Project Management (2013), http://dx.doi.org/10.1016/j.ijproman.2013.02.004

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(ERP). Successfully implementing ERP often requires extensive customization to configure it for the specific client and their marketplace (Pan et al., 2007). Information system customization requires critical decision-making activities because the final customized system can be an expensive failure that can take a long time to fix (Vandaie, 2008). During the implementation phase, clients should be willing to trust the customization vendor because they have determined the utility that they expect from the IS system and depend on their business knowledge of IS systems (Sarker et al., 2005). In a service relationship, a successful relationship is built by creating and maintaining a reciprocal profitable relationship with the partner and showing cooperative behaviors such as exchanging information and knowledge with the partner (Lawson et al., 2009). If there is high joint dependence in a client–vendor relationship, dependence plays a key role in creating a strong partnership (Yilmaz et al., 2005). Partnerships often have mutually required information and knowledge for the business client and the vendor. Clients can profit from the knowledge flow by improving their operations and business performance (Teo, 2012). When there is higher dependence on the partner, the firm tends to care more about the interests of the partner and is willing to help the partner (Yang and Peterson, 2004). If one firm is highly dependent on its partner, it will devote significant effort to fulfill the partner's requests so that it does not lose its status as a valuable partner. Thus, dependence on a partner has a strong effect on the information exchange in relational behavior (Sezen and Yilmaz, 2007). Therefore, we propose the following:

condition of a high level of trust, the project members are more likely to receive project-related knowledge (Xu and Ma, 2008). Therefore, we propose the following: H3. Trust in the partner is positively related to knowledge sharing. The dependence of one party on another defines the extent to which the dependent party relies on the relationship for the fulfillment of important needs (Rusbult and Van Lange, 1996). In the project environment, dependence refers to the extent to which participants believe that they depend on the other to carry out their work (Van der Vegt and Van de Vliert, 2005). Gao et al. (2005) suggested that the buyer's dependence on the provider influences the buyer's trust in the provider because the dependence on the provider allows the buyer to conduct complicated tasks, such as decision making using information from the provider, and leads to reduced conflict. According to Anderson and Weitz (1992), when there is a low level of dependence between parties, there is no effort to enhance interaction and the development of mutual trust between the partners is reduced. In contrast, high-dependency relationships entail information exchanges and extensive interaction. Wells and Kipnis (2001) empirically show that a manager's dependence on an employee is related to the employee's trust in the manager. Grant and Baden-Fuller (2004) found that mutual dependence has a positive impact on trust in an IT outsourcing relationship. Thus, the following hypothesis is proposed: H4. Dependence on the partner is positively related to trust in the partner.

H2. Dependence on the partner is positively related to knowledge sharing.

2.3. Environmental antecedent: project complexity

Earlier studies provide considerable evidence that trust plays a positive role in knowledge sharing. In the literature on relationship management, trust has been recognized as an important factor affecting knowledge sharing in virtual communities (Lin et al., 2009; Ridings et al., 2002), in team work (Chowdhury, 2005), in inter-organizational alliances (Panteli and Sockalingam, 2005), and in supply chain relationships (Cheng et al., 2008). Dyer and Chu (2003) demonstrate that the buyer's perceived trustworthiness reduces the transaction costs and is correlated with greater information sharing in vendor–client relationships. In the information systems literature, trust is considered to be a determinant of the effectiveness of the knowledge sharing activities (Staples and Webster, 2008). Trust in management plays a critical role in facilitating knowledge sharing within and between teams (Renzl, 2008). Kanawattanachai and Yoo (2007) examine the role of trust in the project setting and highlight the fact that trust affects knowledge sharing in a virtual team over time. If system developers from different groups trust each other, they will be likely to share their knowledge with the developers within the teams because they expect to make good use of the knowledge. Additionally, Maurer (2010) identifies how trust between project participants affects knowledge acquisition in a project context. Under the

IS development projects typically coincide with technological and business innovation (Xia and Lee, 2004). Through collaborating and cooperating to reduce the risk of complexity when conducting new product development, the project team enhances organizational learning (Sorenson, 2003) and accumulates the project knowledge that stems from the development process (Brookes et al., 2006). Thus, collaboration in complicated tasks is dependent on the partners' knowledge and prior experience within the project teams. With the high integration complexity of an IT project, it is critical to have the client users involved in the development process. Nearly all technical tests are conducted by business users as well as developers (Little, 2005). Thus, developers can come to depend upon business users as a way to understand complicated business cases. This dependence relationship enhances the knowledge of IS development and reduces uncertainty. Bendapudi and Berry (1997) proposed that the more complex and dynamic the environment, the more dependent the customer is on the partner. IS development is generally highly complicated because it requires business process change and technological adoption. The business clients in a project need require a diverse set of solutions in a given working area. They seek an experienced and skilled IS consultants who can offer different perspectives. Competent IS consultants possessing

Please cite this article as: Park, J.-G., Lee, J., Knowledge sharing in information systems development projects: Explicating the role of dependence and trust, International Journal of Project Management (2013), http://dx.doi.org/10.1016/j.ijproman.2013.02.004

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technical skills can reduce project complexity and uncertainty (Liberatore and Wenhong, 2010). For the successful completion of a project, the client may depend on the IS consultant's knowledge. The following hypothesis is proposed: H5. Project complexity is positively related to dependence on the partner. 2.4. Partner related antecedents: expertise and shared value Perceived expertise refers to an evaluation of the relevant competencies associated with the exchange partner (Sweeney and Webb, 2002). In IS literature, expertise refers to the partner's ability to support members of project team for implementing the IS solution (Wang et al., 2007). If the customer is perceived to be an expert, the supplier will try to maintain their relationship with them (Bendapudi and Berry, 1997). When competent experts increase the value of the partner, the partner wants to strengthen and maintain the relationship with their experts (Palmatier et al., 2006). The partner's capabilities also generate loyalty by providing core offerings and operations that create benefit-based dependence or increase cost-based dependence (Scheer et al., 2010). The vendor's capabilities allow the partner to create unique value for the customer and increase the customer's benefit-based dependence (Scheer et al., 2010). The perceived expertise of the partner can also increase the partner's trust in the customer. Expertise has been found to be a determinant of trust (Abrams et al., 2003; Moorman et al., 1993) across a variety of contexts, including health care (Spake and Megehee, 2010) and other high-involvement services such as tool development (Ming-Tien et al., 2010) and IS service (Thong and Yap, 1994). Johnson and Grayson (2005) found that service provider expertise is an antecedent of cognitive trust because the assessments of expertise and cognitive trust are both considered to be components of the evaluation process. Spake and Megehee (2010) emphasize that expertise plays a key role in the level of trust in the customer–supplier relationship. Thus, the following hypotheses are proposed: H6. The partner's expertise is positively related to the dependence on the partner. H7. The partner's expertise is positively related to trust in the partner. The similarity of the partners in provider–customer relationships describes the extent to which the partners overlap on strategic and organizational dimensions relevant to achieving success (Robson et al., 2008). A similarity of business values exists when the customer believes that their business values are similar to those of their partner (Nicholson et al., 2001). Simonin (1999) suggests that an alliance relationship with common business strategies will be better aligned to transfer knowledge within inter-organizational relationships. Studies on social capital have found that people are able to detect similarities between their individual social networks and can be subjected to similar outcomes owing to their similar network positions, including access to the same resources (Nahapiet and

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Ghoshal, 1998). When the target goals are similar to existing values, the information system is more easily implemented and more effectively operated (Wasko and Faraj, 2005). Johnson and Grayson (2005) suggest that relative similarity between parties leads to interaction and influences the choice of service partners. This interaction and influence, in turn, lead to the sharing of knowledge, which is an interaction outcome. In the case of an ERP implementation, cultural similarity should increase the user's trust in the vendor by identifying shared values and similar lifestyles and appearances between the client and the vendor (Gefen, 2004). This similarity to IS consultants can provide a level of shared understanding about different behaviors in the interaction between the parties and can reduce the risk of misunderstanding individual behaviors while conducting the project. Indeed, in customer–provider relationships, perceived similarity increases trust and reliance on the partner (Doney and Cannon, 1997). Similarities between project participants have been shown to have a positive effect on relational mediators (Sha and Chang, 2010). Similarity in terms of the value of the project helps develop trust because actors readily identify with each other, enhancing the social efficiency of their interactions (Robson et al., 2008). The following hypotheses are thus proposed: H8. Similarity in terms of the project value is positively related to dependence on the partner. H9. Similarity in terms of the project value is positively related to the trust in the partner. 2.5. Interaction related antecedent: communication frequency Communication frequency is argued to influence customers' opinions about their relationships with their partners (Massey and Kyriazis, 2007). Hartwick and Barki (2001) suggest that frequent communication activities should be included as part of the user's participation in IS development. Frequent communication also helps to build and maintain the team's social capital, which is embedded in the team members' relationships (Joshi et al., 2007). The frequency with which partners communicate to solve business issues has been shown to be a key factor in the customer–provider relationship (Sweeney and Webb, 2002). Mitręga and Katrichis (2010) found that communication frequency positively influences the seller's dependence in the relationship. Frequent communication in which conflicts are solved with an organization's employees builds a strong sense of positive trust (de Ruyter et al., 2001). Anderson and Narus (1990) stated that communication is positively associated with customer trust. Morgan and Hunt (1994) found that frequent contact with clients promotes their trust. A high frequency of interaction can lead team participants to mutual beliefs (Nicholson et al., 2001). Communication frequency contributes to the improvement of trust because more frequent communication enables an understanding of personal characteristics and the organizational context (Becerra-Fernandez and Sabherwal, 2001). In sum, frequent interaction activity (e.g., communication) can reduce conflict, reduce transaction costs, strengthen

Please cite this article as: Park, J.-G., Lee, J., Knowledge sharing in information systems development projects: Explicating the role of dependence and trust, International Journal of Project Management (2013), http://dx.doi.org/10.1016/j.ijproman.2013.02.004

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inter-relational cooperation, and improve project performance. All of these benefits increase the dependence on the partner (Scheer et al., 2010) and the trust in the partner (Bendapudi and Berry, 1997). Thus, we propose the following: H10. The frequency of communication is positively related to the dependence on the partner. H11. The frequency of communication is positively related to the trust in the partner. 3. Research method The proposed research model is tested empirically with data collected through a survey. To develop the survey instrument, this study follows the step-by-step procedure recommended by Tiwana and McLean (2005) and the method of data analysis recommended by Gefen et al. (2000) and by Pasisz and Hurtz (2009). As the research model suggested, unit of analysis for this study should seem to be the team. Therefore, project-level data on each project are collected from two members from the same project: an IS consultant and the team leader in each project. This matched pair collection of data seems appropriate for this study. IS consultants answered all the questions in the survey instrument while the team leader answered questions concerning project performance and knowledge sharing related questions, vis a vis outcome variables that they are directly responsible for. Since it is possible that project performance and knowledge sharing are viewed differently by the various stakeholders in a system (Pee et al., 2010), both the IS consultant and the team leader in each project were asked to answer the questions concerning project performance and knowledge sharing. This matched-pair data collection has two advantages. It reduces potential problems arising from common method variance (CMV) when both the independent and dependent variables were answered by the same informant (Podsakoff et al., 2003), and multiple informants reduce bias by obtaining information from the most informed subject possible and increase the reliability and validity of informant reports (Tiwana and McLean, 2005). This approach is appropriate for the study, because the objective is to test the proposed model empirically, which itself is built on a synthesis of prior work that adopted an observational, qualitative approach for examining knowledge sharing in IS development projects. 3.1. Data collection An initial version of the questionnaire is developed and each subject of the questionnaire is asked to think about project members and then answer questions about knowledge sharing. Then, three project managers and four academic experts review the questionnaire. Later, this study used forty four items to conduct a pilot test of the modified version of the questionnaire. The comments on these items are used to further refine the clarity of the instructions and the questions on the questionnaire. Finally, a field study using the modified final questionnaire, consisting of

thirty nine items, is conducted to collect the data necessary for testing the causal model and the hypotheses. Data were collected from project leaders from July to September 2012. In data collection, a two-step approach was used. Survey packages included two cover letters and two questionnaires with demographics, antecedent variables, knowledge process variables, and project performance. The first step was to send only a letter requesting the participation in the research to all 243 project leaders in two major Korea IT companies. One currently operates IT manufacturing business with more than five billion dollars in turnover and another does IT outsourcing and systems development business on a large scale with more than 7000 employees. Project leaders who responded with willingness to participate in our study were then contacted by telephone and email in the second stage. In the second stage, we electronically delivered the survey packages to 195 project leaders who agreed to participate. These project leaders hand over the second part questionnaire to one of his IS consultants. We also sought the help of the organization representatives to make follow-up telephone calls or send emails to increase the response rate. A total of 139 matched samples were returned over two months. Out of those returned responses, 4 were incomplete and thus discarded. This yielded a valid response rate of 69.2%. Demographic information of the final sample is provided in Table 1.

3.2. Operationalization This study generates potential items for measuring the constructs in the proposed model from existing scales which are used or adapted to enhance validity (Hewstone et al., 2002). They are then reworded to fit the context of this study. The draft questionnaire is first reviewed by academics with extensive experience in survey methodology, after which it is pretested. The survey is then subjected to pilot testing by eighty four members on 30 project teams of a major IT manufacturing company. All constructs are measured through seven-point Likert scales anchored with “strongly disagree” and “strongly agree” or “never” and “very frequently” (communication frequency). The questionnaire is modified, resulting in thirty nine items used to measure eight constructs. Items measuring the level of IS project complexity are developed based on the four items used by Xia and Lee (2005). The scale for the expertise area is a modified form of four items from Spake and Megehee (2010). Similarity of project value is measured using items adapted from Nicholson et al. (2001). Communication frequency is measured using five items adapted from Massey and Kyriazis (2007). Trust was measured using items adapted from Park et al. (2012) who based their measures on the work of Morgan and Hunt (1994). Dependence used the scale developed and validated by Van Der Vegt et al. (2001), which has a task focus and has been used by other scholars Van der Vegt et al. (2003). Knowledge sharing was adapted from six items of Bock et al. (2005). IS project performance was measured in terms of the perceived efficiency and effectiveness of IS development projects, as in Henderson and Lee (1992).

Please cite this article as: Park, J.-G., Lee, J., Knowledge sharing in information systems development projects: Explicating the role of dependence and trust, International Journal of Project Management (2013), http://dx.doi.org/10.1016/j.ijproman.2013.02.004

J.-G. Park, J. Lee / International Journal of Project Management xx (2013) xxx–xxx Table 1 Profile of the projects. Category

Frequency Percent

Project type

31 8 46 10 3 4 5 20 8 135 39 63 1 21 10 1 135 41 26 27 22 19 135 35 41 37 22 135 64 37 10 24 135

Data/content management Business intelligence Enterprise resource planning Supply chain management Knowledge management Human resource management Customer resource management Accounting and financing system Web application development Total Industrial type Technology/network Manufacturing Shipping/transportation Banking, insurance Software Health care, entertainment Total Length of project ~5 6–10 11–15 16–20 21 ~ Total Project phase Requirement analysis Analysis and design Development and test Roll-out Total Project size (members) ~ 10 11–20 21–30 31 ~ Total

23 5.9 34.1 7.4 2.2 3 3.7 14.8 5.9 100 28.9 46.7 0.7 15.6 7.4 0.7 100 30.4 19.3 20.0 16.3 14.1 100 25.9 30.4 27.4 16.3 100 47.4 27.4 7.4 17.8 100

(Hair et al., 1995). To assess convergent validity, (1) individual item reliability and (2) construct reliability are assessed. Internal consistency is assessed by examining the loadings of the measures with their respective constructs. A generally accepted rule of thumb is to accept items with loadings of 0.70 or above, which suggests that there exists more shared variance between the construct and its measures than error variance (Gefen et al., 2000; Lohmoller, 1989). The descriptive statistics, weights, and loadings can be found in Table 2. Construct reliability is assessed utilizing two internal consistency indicators: composite reliability and the average variance extracted (AVE) scores. AVE is similar to Cronbach's alpha. All relevant composite reliability measures in this survey are higher than 0.879 (see Table 3), providing strong evidence of their reliability (Gefen et al., 2000). With respect to the AVE scores, a value of 0.5 is required to provide evidence of satisfactory construct reliability (Fornell and Larcker, 1981). All of our scores meet this standard. The reliability of the measures (items and scales) is adequate for the analysis. To evaluate discriminant validity, AVE can be used. There are two procedures for assessing discriminant validity. First, Table 2 Descriptive statistics and loadings of the measurement items. Construct Project complexity

Clients expertise

Similarity of project value

4. Analysis and results This study used partial least squares (PLS) to evaluate the relationships specified in the research model. The PLS analysis, including significance tests for path coefficients, were performed using SMARTPLS. The analyses were conducted in two stages. The measurement model was first tested to ensure that the constructs had sufficient psychometric validity. This was followed by an assessment of the structural model in which the hypotheses were tested (Patnayakuni et al., 2007). PLS has the ability to handle relatively small sample sizes, making it an appropriate choice for testing the research model (Gefen et al., 2000). With PLS, the psychometric properties of the scales used to measure the variables are tested and the strengths and directions of the pre-specified relationships are analyzed (Barclay and Osei-Bryson, 2009). PLS was used in a two-stage approach. In the first stage, all measurement models were examined for proper psychometric properties. The second stage focused on testing the research model and hypotheses–structural model. 4.1. Measurement model The assessment of the measurement model is determined by examining several tests of convergent and discriminant validity

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Frequency

Trust

Dependence

Knowledge sharing

Project performance

CPX1 CPX2 CPX3 CPX4 EXP1 EXP2 EXP3 EXP4 SIM1 SIM2 SIM3 SIM4 SIM5 FRQ1 FRQ4 FRQ5 TRU1 TRU2 TRU3 TRU4 TRU5 DEP1 DEP2 DEP3 DEP4 DEP5 KSH1 KSH2 KSH3 KSH4 KSH5 KSH6 PER1 PER2 PER3 PER4 PER5

Mean

S.D.

Factor

5.837 5.533 5.452 5.837 5.289 5.148 5.022 5.104 5.111 5.133 5.193 5.000 5.089 5.489 5.570 5.363 5.111 5.081 5.230 5.437 5.326 5.830 5.230 5.763 5.874 5.830 5.156 5.081 5.267 5.052 4.785 4.881 4.889 5.044 4.867 5.007 5.296

1.134 1.268 1.214 1.108 1.112 1.110 1.225 1.312 1.027 1.057 1.026 1.037 1.068 1.050 1.062 1.176 1.090 1.100 1.171 1.083 1.208 1.041 1.221 1.167 1.047 0.935 1.057 1.120 0.994 1.108 1.174 1.072 1.232 1.292 1.465 1.188 1.147

0.813 0.772 0.838 0.877 0.909 0.892 0.903 0.895 0.863 0.841 0.779 0.854 0.862 0.775 0.893 0.848 0.888 0.896 0.818 0.879 0.880 0.818 0.805 0.714 0.775 0.708 0.818 0.860 0.882 0.806 0.825 0.877 0.879 0.899 0.802 0.853 0.840

Please cite this article as: Park, J.-G., Lee, J., Knowledge sharing in information systems development projects: Explicating the role of dependence and trust, International Journal of Project Management (2013), http://dx.doi.org/10.1016/j.ijproman.2013.02.004

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Table 3 Results of the confirmatory factor analysis. Measures

Average variance extracted

Composite reliability

Cronbach's alpha

Project complexity Clients expertise Similarity of project value Communication frequency Dependence Trust Knowledge sharing Project performance

0.682 0.809 0.706 0.706 0.586 0.762 0.714 0.731

0.895 0.944 0.923 0.878 0.876 0.941 0.937 0.931

0.848 0.922 0.897 0.794 0.825 0.922 0.920 0.908

AVE values must be examined to determine if they are consistently greater than the off diagonal correlations. Table 4 shows the correlations among the constructs; the values in the diagonal are the square roots of the AVE. Hence, it can be concluded that the measurement model demonstrated adequate discriminant validity (Fornell and Larcker, 1981). Second, each within-construct item must load highly on the construct it is intended to measure, while the cross-loadings need to be lower than the within-construct item loadings. All constructs meet these requirements. When assessing discriminant validity, items not loading highly on their own constructs but instead loading on other constructs were deleted. All items are indicated in Appendix A. 4.2. Structural model The measurement model analysis verified the reliability and validity of the measurement items used in this study. In the next stage, an assessment of the structural model involves estimating the path coefficients and the R 2 value using PLS. Path coefficients explain the strengths of the relationships between the independent and dependent variables, whereas the R 2 value is a measure of the predictive power of a model for the dependent variables. To assess the statistical significance of the model's path estimates, a bootstrapping method (with 500 re-samples) was used (Chin, 1998). The target t-test value was 1.960 (when p b 0.05, using two-tailed tests). The sample size of 126 exceeded the recommended minimum of 90, which represented 10 times the number of independent constructs influencing a dependent construct (Gefen et al., 2000). The results of the PLS analysis are presented in Fig. 2. Path

coefficients are the standardized beta coefficients from the PLS analysis. A summary of the test results is shown in Table 5. As expected, dependence on the partner is significantly associated with project complexity (β = 0.271, p b 0.01), expertise (β = 0.261, p b 0.01), similarity of the project's value (β = 0.243, p b 0.01) and communication frequency (β = 0.163, p b 0.05), which together explain 48.8% of the variance in the dependent variable. Four paths have effects in the direction hypothesized, and hypotheses H5, H6, H8, and H10 are, therefore, supported. As also hypothesized, trust in the partner is significantly associated with expertise (β = 0.382, p b 0.01), similarity of the project's value (β = 0.180, p b 0.05), communication frequency (β = 0.139, p b 0.05) and dependence (β = 0.218, p b 0.01), which together explain 54.2% of the variance in the dependent variable. Four paths have effects in the direction hypothesized, and hypotheses H4, H7, H9 and H11 are, therefore, supported. As shown in Fig. 2, dependence (β = 0.338, p b 0.01) and trust (β = 0.414, p b 0.01) significantly influence knowledge sharing, accounting for 45.0% of the variance and providing support for hypotheses H2 and H3. However, knowledge sharing (β = 0.666, p b 0.01) significantly influences project team performance, accounting for 44.3% of the variance and providing support for hypothesis H1. 5. Conclusions The major objective of this study is to examine the role of dependence and trust, which characterize knowledge sharing in the context of information system projects. Our findings are confirming the model proposed. This study shows that dependence and trust have positive effects on knowledge sharing from the perspectives of IS consultants. The study also finds that for the environmental antecedents, project complexity affects the level of dependence. In the partner antecedents, expertise and similarity in terms of the project's value are found to affect the level of dependence and trust among project partners and members. For the interaction antecedent, communication frequency has an influence on dependence and trust. The first important finding is the positive and significant relationship between the project complexity and dependence. It suggests that dependence results from increasing the associations and linkages among the diverse and ambiguous organizational and technological elements during an information

Table 4 Correlations (with AVEs in the diagonal).

Project complexity Clients expertise Similarity of project value Communication frequency Dependence Trust Knowledge sharing Project performance

COP

EXP

SIM

FRE

DEP

TRU

KSW

PER

0.826 0.266 0.388 0.424 0.504 0.289 0.403 0.240

0.900 0.528 0.404 0.527 0.648 0.534 0.530

0.840 0.431 0.556 0.562 0.507 0.439

0.840 0.488 0.477 0.457 0.335

0.766 0.587 0.581 0.487

0.873 0.612 0.512

0.845 0.666

0.855

The bold numbers in the diagonal row are square roots of the average variance extracted. Please cite this article as: Park, J.-G., Lee, J., Knowledge sharing in information systems development projects: Explicating the role of dependence and trust, International Journal of Project Management (2013), http://dx.doi.org/10.1016/j.ijproman.2013.02.004

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Fig. 2. Structural model test results.

system development and implementation. The relationship between the project complexity and dependence implies that the IS projects can draw on their partner's abilities to conduct the project. The results imply that project complexity does influence knowledge sharing, mediated by the dependence on partners. According to Maylor et al. (2008), more complex projects increase the failure rate, and methods for managing project complexity are of significant interest to project teams. However, project complexity can encourage the participants of projects to share knowledge with each other, which can provide significant opportunities for successful IS team performance. The second interesting finding is that the partner's expertise influences dependence and trust. This study details processes within teams whereby the dependence and trust in the partner's expertise can lead to knowledge sharing in IS projects. According to Tiwana and McLean (2005), the presence of diverse expertise in project team, the members' ability to interrelate with each other's expertise, and good working relationships within the project team are important for cooperation. This result suggests that expertise Table 5 Summary of the hypothesis tests. Hypothesis H1 H2 H3 H4 H5 H6 H7 H8 H9 H10 H11

Knowledge sharing ➔ Performance Dependence ➔ Knowledge sharing Trust ➔ Knowledge sharing Dependence ➔ Trust Project complexity ➔ Dependence Expertise ➔ Dependence Expertise ➔ Trust Similarity of project value ➔ Dependence Similarity of project value ➔ Trust Communication frequency ➔ Dependence Communication frequency ➔ Trust

⁎ p b 0.05. ⁎⁎ p b 0.01.

β

T-value Supported

0.666 ⁎⁎ 17.482 0.338 ⁎⁎ 5.677 0.414 ⁎⁎ 7.107 0.218 ⁎⁎ 2.762 0.271 ⁎⁎ 4.004 0.261 ⁎⁎ 3.645 0.382 ⁎⁎ 6.137 0.243 ⁎⁎ 3.049 0.180 ⁎ 2.298 0.163 ⁎ 2.371 0.139 ⁎ 1.992

Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

play key roles in the knowledge sharing mechanism during an IS development project. Finally, the results suggest that the similarity of project values and the communication frequencies are important variables in understanding dependence and trust. Regarding the finding that the similarity of project values is more important in dependence than in trust relationships, it seems that the similarity of goals and vision motivates the sharing of knowledge in collaborative and knowledge intensive relationships such as this IS development projects (Chiu et al., 2006). In IS development projects, the similarity of project values is a critical pre-condition in achieving the project goal by increasing the level of participants' involvement. This study also finds that the frequency of communication has significant positive effects on dependence and trust. frequency of interaction encourages them to exchange their knowledge and information (Badir et al., 2012). This result also suggests that more frequent communication creates opportunities to develop and enhance knowledge sharing in IS project. 6. Implications This research extends our understanding of knowledge sharing in IS development projects. Although there has been considerable effort to understand knowledge sharing in the IS development process, previous studies tend to focus on part of knowledge sharing rather than on the entire frame. The trust– dependence mechanism provides us with the entire picture of knowledge sharing in an IS project. It does lead to future research on whether effective inter-organizational knowledge sharing has an impact on the success of the project team or how much it contributes to the success of the cooperative venture. This study also introduces the trust–dependence mechanism as a mediator of effective knowledge sharing in IS development projects. Although there appears to be an overlap between trust–dependence and the social exchange concept in prior studies, the use of the trust–dependence model to account for

Please cite this article as: Park, J.-G., Lee, J., Knowledge sharing in information systems development projects: Explicating the role of dependence and trust, International Journal of Project Management (2013), http://dx.doi.org/10.1016/j.ijproman.2013.02.004

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the relationships between clients and IS consultants in an IS project provides a fresh approach. Because these findings are derived from this research, replication and further studies need to be initiated to confirm these results. This study provides guidance on how to conduct projects for complex information systems that are being implemented with the cooperation of project members. This study suggests that it is important for project teams to try to enhance trust in the partner by utilizing diverse expertise and frequent communications because trust will affect knowledge sharing between clients and IS consultants. Meanwhile, given that dependence strongly influences knowledge sharing, the IS management skills to manage dependence appear to be more effective in increasing knowledge sharing than the trust-based approach. Expertise also has a significant impact when exchanging knowledge. The implications of these results for the project personnel are significant. From the IS consultant's perspective, insight into process innovation, experience in process innovation, and knowledge of business are the major requirements for business members of the project to be assigned to conduct IS projects. The similarity of project values is an important consideration when sharing knowledge. Short of identifying participants with similar values and goals to work with each other on a project, project teams can begin activities such as teamwork programs and problem-solving exercises. The relationship between communication frequency and knowledge sharing is mediated by trust and dependence. This finding suggests that the interactions between IS consultants and clients are associated with successful exchanges of technological knowledge. For example, communication activities such as email, face-to-face meetings, and verbal and non-verbal expressions from clients will lead to a more satisfying relationship with the recipient, thereby facilitating the sharing of knowledge. 7. Limitations and further studies As with other studies, it is necessary to consider this study's limitations. First, random sampling is not easy to achieve because a comprehensive listing of IS development projects is not available. Instead, the rationale for selecting respondents focused on maintaining internal validity, where the targeted IS projects in the sample firms made it less likely that unmonitored variables influenced all of the IS project teams. Second, to minimize retrospective bias when measuring the relationship variables, the interaction variables, and knowledge sharing, all of the data were collected during then-current projects. The participants' memory of a project in one phase can influence the following phases. Third, to alleviate the problems of common method biases, we receive the responses from the IS consultants involved in IS projects and the assessment of team performance from the project leader. Finally, future research can further explore the constructs and relationships in the proposed model. For example, it can be fruitful to consider how various levels of relationship factors (e.g., relationship benefits and relationship investment) influence knowledge sharing differently.

Appendix A. Measurement items Project complexity 1. 2. 3. 4.

The project involved multiple user units. The project team was cross-functional. The project involved multiple technology platforms. The project involved a lot of integration with other systems. Expertise

1. 2. 3. 4.

My partner possessed specialized knowledge. My partner possessed extensive, broad knowledge. My partner was experienced in solving problems like mine. My partner contributed expertise and experience in executing the IS project Similarity

1. My partner and I had similar interests. 2. My partner and I had similar values. 3. My partner member and I were similar in many ways. Communication frequency 1. 2. 3. 4. 5.

Electronic mail Online messenger (MSN, Nate on, etc.) Mobile messenger (Kakao talk, Google talk, etc.) Scheduled one-to-one meetings (face-to-face) Informal face-to-face conversations in a non-work setting (e.g., after-work drinks, barbeques) Dependence

1. 2. 3. 4. 5.

My partner was important to our IS project. My partner was crucial to our overall IS performance. It would be costly to lose my partner. My partner had a strong reputation. Our success in our IS project was largely due to the marketing efforts of my partner. Trust

1. 2. 3. 4. 5. 6.

My partner was open and honest when problems occurred. My partner helped me make critical decisions. My partner was always willing to provide assistance. My partner was always sincere. 5 My partner could be trusted completely. My partner was someone that I have great confidence in them. Knowledge sharing

1. We shared the minutes of meetings or discussion records in an effective way. 2. We always provided technical documents, including manuals, books, training materials to each other.

Please cite this article as: Park, J.-G., Lee, J., Knowledge sharing in information systems development projects: Explicating the role of dependence and trust, International Journal of Project Management (2013), http://dx.doi.org/10.1016/j.ijproman.2013.02.004

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3. We shared project plans and the project status in an effective way. 4. We always provided know-where or know-whom information to each other in an effective way. 5. We tried to share expertise from education or training in an effective way. 6. We always shared experience or know-how from work in a responsive and effective way. Project team performance 1. 2. 3. 4. 5.

Productivity of IS project team's operation IS project team's adherence to the schedule IS project team's adherence to the budget Quality of the IS project team's deliverables IS project team's achievement of project objectives

References Abrams, L.C., Cross, R., Lesser, E., Levin, D.Z., 2003. Nurturing interpersonal trust in knowledge-sharing networks. Academy of Management Executive 17, 64–77. Anderson, J.C., Narus, J.A., 1990. A model of distributor firm and manufacturer firm working partnerships. Journal of Marketing 54, 42–58. Anderson, E., Weitz, B., 1992. The use of pledges to build and sustain commitment in distribution channels. Journal of Marketing Research 29, 18–34. Badir, Y.F., Büchel, B., Tucci, C.L., 2012. A conceptual framework of the impact of NPD project team and leader empowerment on communication and performance: an alliance case context. International Journal of Project Management 30, 914–926. Barclay, C., Osei-Bryson, K.-M., 2009. Toward a more practical approach to evaluating programs: the multi-objective realization approach. Project Management Journal 40, 74–93. Becerra-Fernandez, I., Sabherwal, R., 2001. Organizational knowledge management: a contingency perspective. Journal of Management Information Systems 18, 23–55. Bendapudi, N., Berry, L.L., 1997. Customers' motivations for maintaining relationships with service providers. Journal of Retailing 73, 15–37. Bock, G.-W., Zmud, R.W., Kim, Y.-G., Lee, J.-N., 2005. Behavioral intention formation in knowledge sharing: examining the roles of extrinsic motivatiors, social–psychological forces, and organizational climate. MIS Quarterly 29, 87–112. Brookes, N.J., Morton, S.C., Dainty, A.R.J., Burns, N.D., 2006. Social processes, patterns and practices and project knowledge management: a theoretical framework and an empirical investigation. International Journal of Project Management 24, 474–482. Chang, K.-c, Yen, H.-W., Chiang, C.-C., Parolia, N., 2013. Knowledge contribution in information system development teams: an empirical research from a social cognitive perspective. International Journal of Project Management 31, 252–263. Cheng, J.-H., Yeh, C.-H., Tu, C.-W., 2008. Trust and knowledge sharing in green supply chains. Supply Chain Management: An International Journal 13, 283–295. Chin, W.W., 1998. The partial least squares approach for structural equation modeling. In: Marcoulides, G.A. (Ed.), Methodology for Business and Management. Lawrence Erlbaum Associates Publishers, Mahwah, NJ, pp. 295–336. Chiu, C.-M., Hsu, M.-H., Wang, R.T.G., 2006. Understanding knowledge sharing in virtual communities: an integration of social capital and social cognitive theories. Decision Support Systems 42, 1872–1888. Choi, S.Y., Lee, H., Yoo, Y., 2010. The impact of information technology and transactive memory systems on knowledge sharing, application and team performance: a field study. MIS Quarterly 34, 855–870.

11

Chowdhury, S., 2005. The role of affect- and cognition-based trust in complex knowledge sharing. Journal of Managerial Issues 17 (310-310-326). Darr, E.D., Kurtzberg, T.R., 2000. An investigation of partner similarity dimensions on knowledge transfer. Organizational Behavior and Human Decision Processes 82, 28–44. de Ruyter, K., Moorman, L., Lemmink, J., 2001. Antecedents of commitment and trust in customer–supplier relationships in high technology markets. Industrial Marketing Management 30, 271–286. Doney, P.M., Cannon, J.P., 1997. An examination of the nature of trust in buyer–seller relationships. Journal of Marketing 61, 35–51. Dyer, J.H., Chu, W., 2003. The role of trustworthiness in reducing transaction costs and improving performance: empirical evidence from the United States, Japan, and Korea. Organization Science 14, 57–68. Faraj, S., Sproull, L., 2000. Coordinating expertise in software development teams. Management Science 46, 1554. Fornell, C., Larcker, D.F., 1981. Structural equation models with unobservable variables and measurement error: algebra and statistics. Journal of Marketing Research 18, 382–388. Gao, T., Sirgy, M.J., Bird, M.M., 2005. Reducing buyer decision-making uncertainty in organizational purchasing: can supplier trust, commitment, and dependence help? Journal of Business Research 58, 397–405. Gefen, D., 2004. What makes an ERP implementation relationship worthwhile: linking trust mechanisms and ERP usefulness. Journal of Management Information Systems 21, 263–288. Gefen, D., Straub, D.W., Boudreau, M.-C., 2000. Structural equation modeling and regression: guidelines for research practice. Communications of AIS 4, 1–79. Grant, R.M., Baden-Fuller, C., 2004. A knowledge accessing theory of strategic alliances. Journal of Management Studies 41, 61–84. Hair Jr., J., Anderson, R., Tatham, R., Black, W., 1995. Multivariate Data Analysis: With Readings. Prentice-Hall, Inc., Upper Saddle River, NJ, USA. Hansen, M.T., 2002. Knowledge networks: explaining effective knowledge sharing in multiunit companies. Organization Science 13, 18. Hartwick, J., Barki, H., 2001. Communication as a dimension of user participation. IEEE Transactions on Professional Communication 44, 21–36. Henderson, J.C., Lee, S., 1992. Managing I/S design teams: a control theories perspective. Management Science 38, 757–777. Hewett, K., Bearden, W.O., 2001. Dependence, trust, and relational behavior on the part of foreign subsidiary marketing operations: implications for managing global marketing operations. Journal of Marketing 65, 51–66. Hewstone, M., Rubin, M., Willis, H., 2002. Intergroup Bias. Hsu, J.S.-C., Lin, T.-C., Zheng, G.-T., Hung, Y.-W., 2012. Users as knowledge co-producers in the information system development project. International Journal of Project Management 30, 27–36. Jewels, T., Ford, M., 2006. Factors influencing knowledge sharing in information technology projects. e-Service Journal 5, 99–117. Johnson, D., Grayson, K., 2005. Cognitive and affective trust in service relationships. Journal of Business Research 58, 8. Jones, M.C., Cline, M., Ryan, S., 2006. Exploring knowledge sharing in ERP implementation: an organizational culture framework. Decision Support Systems 41, 411–434. Joshi, K.D., Sarker, S., Sarker, S., 2007. Knowledge transfer within information systems development teams: examining the role of knowledge source attributes. Decision Support Systems 43, 322–335. Kanawattanachai, P., Yoo, Y., 2007. The impact of knowledge coordination on virtual team performance over time. MIS Quarterly 31, 783. Kotlarsky, J., Oshri, H., 2005. Social ties, knowledge sharing and successful collaboration in globally distributed system development projects. European Journal of Information Systems 14, 37–48. Lawson, B., Petersen, K.J., Cousins, P.D., Handfield, R.B., 2009. Knowledge sharing in interorganizational product development teams: the effect of formal and informal socialization mechanisms*. Journal of Product Innovation Management 26, 156–172. Lee, J.-N., Kim, Y.-G., 1999. Effect of partnership quality on IS outsourcing success: conceptual framework and empirical validation. Journal of Management Information Systems 15, 29–62.

Please cite this article as: Park, J.-G., Lee, J., Knowledge sharing in information systems development projects: Explicating the role of dependence and trust, International Journal of Project Management (2013), http://dx.doi.org/10.1016/j.ijproman.2013.02.004

12

J.-G. Park, J. Lee / International Journal of Project Management xx (2013) xxx–xxx

Liberatore, M.J., Wenhong, L., 2010. Coordination in consultant-assisted IS projects: an agency theory perspective. IEEE Transactions on Engineering Management 57, 255–269. Lin, M.-J.J., Hung, S.-W., Chen, C.-J., 2009. Fostering the determinants of knowledge sharing in professional virtual communities. Computers in Human Behavior 25, 929–939. Lind, M.R., Zmud, R.W., 1991. The influence of a convergence in understanding between technology providers and users on information technology innovativeness. Organization Science 2, 195–217. Little, T., 2005. Context-adaptive agility: managing complexity and uncertainty. IEEE Software 22, 28–35. Lohmoller, J.-B., 1989. Latent Variable Path Modeling With Partial Least Squares. Physica-Verlag, Heidelberg. Massey, G.R., Kyriazis, E., 2007. Interpersonal trust between marketing and R&D during new product development projects. European Journal of Marketing 41, 1146–1172. 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, 629–637. Maylor, H., Vidgen, R., Carver, S., 2008. Managerial complexity in projectbased operations: a grounded model and its implications for practice. Project Management Journal 39, S15–S26. Ming-Tien, T., Chao-Wei, C., Cheng-Chung, C., 2010. The effect of trust belief and salesperson's expertise on consumer's intention to purchase nutraceuticals: applying the theory of reasoned action. Social Behavior and Personality: An International Journal 38, 273–287. Mitręga, M., Katrichis, J.M., 2010. Benefiting from dedication and constraint in buyer–seller relationships. Industrial Marketing Management 39, 616–624. Moorman, C., Deshpande, R., Zaltman, G., 1993. Factors affecting trust in market research relationships. Journal of Marketing 57. Morgan, R.M., Hunt, S.D., 1994. The commitment–trust theory of relationship marketing. Journal of Marketing 58, 20. Nahapiet, J., Ghoshal, S., 1998. Social capital, intellectual capital and the organizational advantage. Academy of Management Review 23, 242–266. Nelson, K.M., Cooprider, J.G., 1996. The contribution of shared knowledge to IS group performance. MIS Quarterly 20, 409–432. Nicholson, C., Compeau, L., Sethi, R., 2001. The role of interpersonal liking in building trust in long-term channel relationships. Journal of the Academy of Marketing Science 29, 3–15. Palmatier, R.W., Dant, R.P., Grewal, D., Evans, K.R., 2006. Factors influencing the effectiveness of relationship marketing: a meta-analysis. Journal of Marketing 70, 136–153. Pan, S.L., Newell, S., Huang, J., Galliers, R.D., 2007. Overcoming knowledge management challenges during ERP implementation: the need to integrate and share different types of knowledge. Journal of the American Society for Information Science and Technology 58, 404–419. Panteli, N., Sockalingam, S., 2005. Trust and conflict within virtual interorganizational alliances: a framework for facilitating knowledge sharing. Decision Support Systems 39, 599–617. Park, J., Lee, J., Lee, H., Truex, D., 2012. Exploring the impact of communication effectiveness on service quality, trust and relationship commitment in IT services. International Journal of Information Management 32, 459–468. Pasisz, D.J., Hurtz, G.M., 2009. Testing for between-group differences in within-group interrater agreement. Organizational Research Methods 12, 590–613. Patnayakuni, R., Rai, A., Tiwana, A., 2007. Systems development process improvement: a knowledge integration perspective. IEEE Transactions on Engineering Management 54, 286–300. Pee, L.G., Kankanhalli, A., Kim, H.-W., 2010. Knowledge sharing in information systems development: a social interdependence perspective. Journal of the Association for Information Systems 11, 550–575. Podsakoff, P.M., MacKenzie, S.B., Lee, J.-Y., Podsakoff, N.P., 2003. Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of Applied Psychology 88, 879–903. Renzl, B., 2008. Trust in management and knowledge sharing: the mediating effects of fear and knowledge documentation. Omega 36, 206–220.

Ridings, C.M., Gefen, D., Arinze, B., 2002. Some antecedents and effects of trust in virtual communities. Journal of Strategic Information Systems 11, 271–295. Roberts, T.L., Cheney, P.H., Sweeney, P.D., Hightower, R.T., 2004. The effects of information technology project complexity on group interaction. Journal of Management Information Systems 21, 223–247. Robson, M.J., Katsikeas, C.S., Bello, D.C., 2008. Drivers and performance outcomes of trust in international strategic alliances: the role of organizational complexity. Organization Science 19, 647–665. Rusbult, C.E., Van Lange, P.A.M., 1996. Interdependence processes. Social psychology: handbook of basic principles. In: Tory, E., Kruglanski, A.W. (Eds.), Social Psychology: Handbook of Basic Principles. Press Higgins, New York, NY, US, pp. 564–596. Sarker, S., Nicholson, D.B., Joshi, K.D., 2005. Knowledge transfer in virtual systems development teams: an exploratory study of four key enablers. IEEE Transactions on Professional Communication 48, 201–218. Scheer, L., Miao, C., Garrett, J., 2010. The effects of supplier capabilities on industrial customers' loyalty: the role of dependence. Journal of the Academy of Marketing Science 38, 90–104. Sezen, B., Yilmaz, C., 2007. Relative effects of dependence and trust on flexibility, information exchange, and solidarity in marketing channels. Journal of Business and Industrial Marketing 22, 41–51. Sha, X., Chang, K., 2010. Similarity and familiarity in distributed teams: a perspective of identification on knowledge sharing. European Conference on Information Systems. Simonin, B.L., 1999. Ambiguity and the process of knowledge transfer in strategic alliances. Strategic Management Journal 20, 595–623. Sorenson, O., 2003. Interdependence and adaptability: organizational learning and the long-term effect of integration. Management Science 49, 446–463. Spake, D.F., Megehee, C.M., 2010. Consumer sociability and service provider expertise influence on service relationship success. Journal of Services Marketing 24, 314–324. Staples, D.S., Webster, J., 2008. Exploring the effects of trust, task interdependence and virtualness on knowledge sharing in teams. Information Systems Journal 18, 617–640. Stasser, G., Titus, W., 1985. Pooling of unshared information in group decision making: biased information sampling during discussion. Journal of Personality and Social Psychology 48, 1467–1478. Sweeney, J.C., Webb, D., 2002. Relationship benefits: an exploration of buyer– supplier dyads. Journal of Relationship Marketing 1, 77. Teo, T.S.H., 2012. Knowledge management in client-vendor partnerships. International Journal of Information Management 32, 451–458. Tesch, D., Sobol, M.G., Klein, G., Jiang, J.J., 2009. User and developer common knowledge: effect on the success of information system development projects. International Journal of Project Management 27, 657–664. Thong, J.Y.L., Yap, C.-S., 1994. Engagement of external expertise in information systems implementation. Journal of Management Information Systems 11, 209–231. Tiwana, A., McLean, E.R., 2005. Expertise integration and creativity in information systems development. Journal of Management Information Systems 22, 13–43. Van der Vegt, G.S., Van de Vliert, E., 2005. Effects of perceived skill dissimilarity and task interdependence on helping in work teams. Journal of Management 31, 73–89. Van Der Vegt, G.S., Emans, B.J.M., Van De Vliert, E., 2001. Patterns of interdependence in work teams: a two-level investigation of the relations of the relations with job and team satisfaction. Personnel Psychology 54, 51–69. Van der Vegt, G.S., Vliert, E.V.D., Oosterhof, A., 2003. Informational dissimilarity and organizational citizenship behavior: the role of intrateam interdependence and team identification. Academy of Management Journal 46, 715–727. Vandaie, R., 2008. The role of organizational knowledge management in successful ERP implementation projects. Knowledge-Based Systems 21, 920–926. Wang, E.T.G., Chia-Lin Lin, C., Jiang, J.J., Klein, G., 2007. Improving enterprise resource planning (ERP) fit to organizational process through

Please cite this article as: Park, J.-G., Lee, J., Knowledge sharing in information systems development projects: Explicating the role of dependence and trust, International Journal of Project Management (2013), http://dx.doi.org/10.1016/j.ijproman.2013.02.004

J.-G. Park, J. Lee / International Journal of Project Management xx (2013) xxx–xxx knowledge transfer. International Journal of Information Management 27, 200–212. Wasko, M.M., Faraj, S., 2005. Why should I share? Examining social capital and knowledge contribution in electronic networks of practice. MIS Quarterly 29, 35–57. Wells, C.V., Kipnis, D., 2001. Trust, dependency, and control in the contemporary organization. Journal of Business and Psychology 15, 593–603. Xia, W., Lee, G., 2004. Grasping the complexity of IS development projects. Communications of the ACM 47, 68–74. Xia, W., Lee, G., 2005. Complexity of information systems development projects: conceptualization and measurement development. Journal of Management Information Systems 22, 45–83.

13

Xu, Q., Ma, Q., 2008. Determinants of ERP implementation knowledge transfer. Information Management 45, 528–539. Yang, S.-C., Farn, C.-K., 2009. Social capital, behavioural control, and tacit knowledge sharing—a multi-informant design. International Journal of Information Management 29, 9. Yang, Z., Peterson, R.T., 2004. Customer perceived value, satisfaction, and loyalty: the role of switching costs. Psychology and Marketing 21, 799–822. Yilmaz, C., Sezen, B., Ozdemir, O., 2005. Joint and interactive effects of trust and (inter) dependence on relational behaviors in long-term channel dyads. Industrial Marketing Management 34, 235–248.

Please cite this article as: Park, J.-G., Lee, J., Knowledge sharing in information systems development projects: Explicating the role of dependence and trust, International Journal of Project Management (2013), http://dx.doi.org/10.1016/j.ijproman.2013.02.004