Knowledge management in client–vendor partnerships

International Journal of Information Management 32 (2012) 451–458

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International Journal of Information Management journal homepage: www.elsevier.com/locate/ijinfomgt

Knowledge management in client–vendor partnerships Thompson S.H. Teo a,b a b

National University of Singapore, School of Business, Department of Decision Sciences, Singapore 119245, Singapore National University of Singapore, School of Computing, Department of Information Systems, Singapore 117417, Singapore

a r t i c l e

i n f o

Article history: Available online 22 March 2012 Keywords: Client Vendor Partnership Knowledge management Knowledge transfer Impact Singapore

a b s t r a c t Partnerships between business clients and vendors are increasingly becoming more common as firms strive to reduce cost and outsource non-core activities. Some firms proactively manage the knowledge gained from such partnership while others do so to a lesser extent. Through a questionnaire survey of business clients in Singapore, this study helps to shed some light on the nature of client–vendor partnership, factors affecting knowledge transfer (defined in terms of knowledge codifiability, client’s motivation for partnership, vendor’s willingness to share, and trust between client and vendor). In addition, we also examine mechanisms for knowledge dissemination within the client organization and the impact on the IT function. The results should be of interest to both researchers and practitioners in better understanding how such partnership could be managed more effectively. © 2012 Elsevier Ltd. All rights reserved.

1. Introduction Organizations are increasingly partnering with software vendors and service providers for a variety of information technology (IT) related services, including applications development, systems integration, and IT operations. The range of partnership varies from supporting simple operational requirements such as help desk support to becoming a strategic business partner managing all the IT services for the organization. In fact, in interviews with IT executives, Dail (2005) found that 70% wants stronger relationship with their vendors. There has been a dramatic change in organizational make and buy decisions related to IT products and services as organizations shift from in-house development and operations of their IT needs to selective outsourcing of IT products and services to supplement their internal capability and expertise. Further, the traditional reason of “cost savings” from outsourcing is increasingly complemented by the need to build long term competitive advantage, acquire knowledge and learn from their partners. In addition, effective management of both endogenous and exogenous knowledge can enhance a firm’s dynamic capabilities (Sher & Lee, 2004). Partnerships often require significant information and knowledge flow between the business client (henceforth referred to as client) and the vendor. Both the vendor and client can benefit from this knowledge flow in improving their operations and business outcomes. Basically, vendor possesses much technical knowledge

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while client possesses business knowledge (Rus & Lindvall, 2002). This may result in knowledge asymmetry and knowledge barriers (Attewell, 1992) which can be mitigated through knowledge transfer between vendor and client. Further, the knowledge exists in two primary forms: explicit (transmittable in formal, systematic language) and tacit (difficult to codify in formal, systematic language) (Nonaka, 1994). Very often, organizations do not realize the volume of knowledge it receives from various inter-organizational partnerships and perhaps, do not realize the value of the knowledge received. Hence, most organizations may not have appropriate strategies to facilitate the acquisition and assimilation of knowledge received at different points of contact between the client and vendor. This has resulted in an inability to derive maximum benefits from such partnerships. Consequently, organizations increasingly recognized that in order to build and sustain competencies, it is essential to make knowledge available to the right worker at the right time and the right place (Drucker, 1995; Kwan & Balasubramanian, 2003). Further, knowledge management is a necessity and a source of competitive advantage (du Plessis, 2005). Hence, it is not surprising that research on knowledge management (KM) has increased significantly over the years. Researchers have examined technological, managerial, and cultural factors that facilitate KM (Alavi & Leidner, 2001; Teo, 2005; Teo & Men, 2008), KM frameworks (Nonaka & Konno, 1998), relationship between KM and other business functions (Yahya & Goh, 2002), knowledge sourcing (Gray & Meister, 2006), knowledge transfer (Li & Hsieh, 2009; Schulz, 2003), knowledge sharing (Sulaiman & Burke, 2009; Teo, Nishant, Goh, & Agarwal, 2011; Wasko & Faraj, 2005) knowledge integration (Basaglia, Caporarello, Magni, & Pennarola, 2010; Grant, 1996) and absorptive capacity (Jansen, Van Den Bosch, & Volberda, 2005).

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In the area of client–vendor partnership, related research includes those in the strategic alliance literature (which tends to be mostly non-IS) as well as research on outsourcing. While the strategic alliance literature has examined knowledge transfer, learning and innovation (e.g., Eunni, Kasuganti, & Kos, 2006; Inkpen, 2005), the examination of knowledge transfer in the outsourcing literature is still relatively sparse. Most outsourcing literature tends to focus on questions of why outsource, what to outsource, which decision process to take, how to implement the sourcing decision, and what is the outcome of the sourcing decision (Dibbern, Goles, Hirschheim, & Jayatilaka, 2004). Previous research on knowledge management and outsourcing has examined outsourcing for intellectual value (Quinn, 1999), partnership quality and outsourcing success (Lee & Kim, 1999), knowledge sharing and outsourcing success (Blumenberg, Wagner, & Beimborn, 2009; Lee, 2001) and knowledge potential of outsourcing (Willcocks, Hindle, Feeny, & Lacity, 2004). This paper is an exploratory, descriptive study that attempts to understand factors affecting knowledge transfer, mechanisms for knowledge transfer, and impact on the IT function. There are at least four distinct motivations underlying this study. First, while there are numerous research on knowledge management as well as outsourcing, research on knowledge management in client–vendor partnership in the IT context is still relatively sparse. The growth in outsourcing makes it an important topic for research. This study aims to shed some light on this issue and should be of interest to both researchers and practitioners in better understanding client–vendor partnerships. Second, there are limited studies on knowledge management and outsourcing that have been conducted in the Asia-Pacific region. Most research has been done in western countries. Third, our goal is to examine, report and contribute to the knowledge management and client–vendor partnership practices in Singapore organizations. Firms outsourcing and forming partnerships with vendors are increasingly becoming more common and there is a need to document their experiences to provide guidelines to others in their knowledge management and client–vendor partnership initiatives. Note that we define client–vendor partnership as outsourcing relationship between client and external vendor in the context of IT. Our examination of knowledge management practices pertains to knowledge transfer from vendor to client. Fourth, most of the research on knowledge transfer has been done in non-IS context and usually focus on unit-to-unit transfer within the firm (Ko, Kirsch, & King, 2005). Given the increasing importance of outsourcing and knowledge management, there is therefore a need to better understand knowledge transfer from vendor to client. This study helps to further this understanding. The paper is organized as follows. First, we present our research framework which summarizes the various areas examined in this study. Second, we present the method used to collect data. Third, we analyze the data and present the results. The paper concludes by discussing the key findings and their implications for researchers and practitioners.

2. Research framework Our research framework, comprising concentric circles, is shown in Fig. 1. The inner circle indicates that the extent of knowledge transfer from vendor to client depends on four key characteristics: knowledge (codifiability), vendor (willingness to share), client (motivation for partnership) and relationship (trust between vendor and client). These four characteristics are consistent with Argote, McEvily, and Reagans (2003) classification of knowledge management context as properties of units (i.e., client’s motivation for partnership, vendor’s willingness to share knowledge),

Knowledge codifiability

Client’s motivation

Vendor’s willingness to share

Trust between client and vendor

Fig. 1. Research framework.

properties of the relationship between units (i.e., trust between client and vendor), and properties of the knowledge (i.e., knowledge codifiability). In this study, we will compare low versus high knowledge transfer groups in terms of these four factors. As transferred knowledge needs to be disseminated, we also examine mechanisms for knowledge dissemination. The outer ring shows that impact of knowledge management on the IT function. In terms of factors affecting knowledge transfer, knowledge codifiability refers to whether the knowledge can be explicitly expressed and documented. It would be difficult for knowledge transfer to take place if knowledge cannot be codified or vendor is unwilling to share their knowledge. In addition, client may have different motivations for the partnership that include gaining knowledge and expertise from vendors. Implicit in the notion of partnership is the need for trust between the client and vendor, which acts as a “lubricant” in facilitating knowledge transfer. In other words, once knowledge has been transferred from vendor to client (which are facilitated by the four factors in the inner circle), it needs to be disseminated within and outside the project team using various mechanisms, such as meetings (as shown by the middle circle). An assessment of the impact on the IT function (as shown in the outer circle) also needs to be made to evaluate the outcome of the partnership, which may serve as important learning points to enhance the effectiveness of future client–vendor partnerships.

3. Method A questionnaire survey was used to gather data for this study. The survey comprises items to measure various characteristics of knowledge management in software partnership. All items were measured using a 7-point Likert scale ranging from (1) strongly disagree to (7) strongly agree. The survey was pilot tested in four organizations with different levels of software partnerships, organizational sizes, IT budgets, and industries. They ranged from firms with very large to very small information systems (IS) departments. The industries represented by the four firms include financial services, semi-conductor manufacturing, transportation services, and industrial manufacturing. The pilot testing provided many unique insights into client–vendor partnerships and helped us refine some

T.S.H. Teo / International Journal of Information Management 32 (2012) 451–458 Table 1 Sample characteristics. Characteristics

453

Table 2 Knowledge transfer from vendor to client. Percent

Number of employees <100 22.8 100–300 29.0 301–600 15.2 10.3 601–1000 6.2 1001–2000 16.6 >2000 Number of IT employees <5 40.4 6–20 26.0 21–50 8.2 8.9 51–100 7.5 101–200 >200 8.9 Annual revenue 26.4 <50 m 9.9 $50–100 m 19.8 $101–300 m $301–600 m 13.2 10.7 $601–1 bn >1 bn 19.8

Characteristics

Percent

Industry sector 4.8 Architecture/Engineering Manufacturing 26.7 Finance/Banking/Insurance 6.8 Health/Medical 2.1 Real Estate 3.4 Retail/Trading 19.9 Travel/Tourism/Hotel 6.2 Others 29.5 Change in contract vol. over last 1–2 years Rapid decline 2.1 Moderate decline 14.6 No growth 20.8 56.9 Moderate growth Rapid growth 5.6 Proportion of IT budget to outside vendor <5% 21.0 6–10% 15.4 11–20% 17.5 21–40% 15.4 41–60% 15.4 15.4 >60%

N = 146.

of our measurement items and wordings to make it elicit the appropriate response. The survey was mailed to 732 firms in Singapore, listed in the Singapore 1000 directory (DP Information Network, 2006). This directory lists the top 1000 firms in Singapore in terms of sales. Since we did not have the contact name for the IT chief in many of these organizations, phone calls were made to get the name of the contact person who would be most qualified to complete the survey. Two rounds of mailing were carried out with a time gap of about three weeks between the mailings. A total of 146 usable responses were received giving a response rate of 20%, which is comparable with most IS studies conducting large scale field surveys of senior IT managers. We checked if there was any systematic non-response bias by comparing the sample characteristics and responses to research variables between the two mailings, assuming the later responses would be reasonable estimate of the characteristics of nonrespondents. We did not see any significant differences between the two groups in terms of annual revenue, number of employees, number of IT employees, and industry sector.

Knowledge transfer

Mean

S.D.

Ktvc2: Developed a greater understanding of what software/services can do for our firm Ktvc4: Picked up strategies for successful IT implementation Ktvc1: Acquired vendor’s knowledge on designing applications architecture Ktvc3: Used vendor’s advice for streamlining our business processes Ktvc5: Gained some design skills for developing applications Ktvc7: Obtained software testing skills from the vendor Ktvc6: Acquired programming skills from the vendor Overall mean

4.99

1.19

4.66

1.24

4.51

1.26

4.49

1.26

4.36

1.29

3.93

1.39

3.77

1.33

4.39

1.02

Scale: (1) strongly disagree to (7) strongly agree.

over the last 1–2 years and the proportion of IT budget allocated to outside vendors. Hence, in our survey, respondents were asked to choose an IT vendor that they are most familiar with and to respond to all questions with reference to that vendor. On average, respondents had worked 7.4 years with the vendors that they had chosen. These vendors provide various services to their clients ranging from applications development (54.1%), systems integration (41.8%), consulting services (35.6%), managing IT operations (27.4%) and others (7.5%). Note that the above percentage totaled to more than 100% as some vendors provide more than one type of services to their clients. Majority of respondents (56.9%) reported moderate growth in their relationship with the chosen vendor. Hence, it appears that client–vendor partnership is likely to increase over time. Overall, the proportion of the firm’s IT budget allocated to outside vendors varies from less than 5% to greater than 60%. These variations indicate that there is potential scope for further partnerships between clients and vendors. In the next few sections, we compare and discuss the mean scores of items measuring various constructs such as knowledge transfer from client to vendor, and factors affecting knowledge transfer. Next, we compare low and high knowledge transfer groups in terms of the following factors: knowledge codifiability, client’s motivation for partnership, vendor’s willingness to share knowledge and trust between client and vendor.

4. Results 4.2. Knowledge transfer from vendor to client 4.1. Sample characteristics There is a mix of small and medium enterprises (SMEs) and large firms in the sample as evident by the number of employees and annual revenue. About sixty-six percent of firms have less than 20 IT staff. This is perhaps not surprising as many of the firms in the sample have outsourced some parts of their IT operations. Respondents have worked an average of 7.6 years in their firm and 11.9 years in the industry. These results provide some assurance on the validity of the sample as experienced respondents are more likely to be knowledgeable about client–vendor partnership in their firms. In addition, respondents also come from a wide variety of industries with a predominance of manufacturing and retail/trading firms. This is perhaps expected as manufacturing and retail/trading sectors comprise a significant portion of the Singapore economy. The sample characteristics are shown in Table 1. As part of the sample characteristics, we also examine the nature of client–vendor partnership in terms of change in contract volume

Table 2 shows the different knowledge transferred from vendor to client. The items were adapted from Yli-Renko, Autio, and Sapienza (2001). The vendors typically may have more project and IT implementation experience since they are probably implementing projects in different client organizations with different environments. Consequently, clients are able to develop greater understanding of what software/services can do (mean = 4.99). As IT implementation is often a challenging and difficult task, clients are able to pick up useful strategies from their vendors (mean = 4.66), acquire vendor’s knowledge on designing applications architecture (mean = 4.51) as well as use vendor’s advice for streamlining business processes (mean = 4.49). Clients also gained some design skills for developing applications (mean = 4.36). However, the results also showed that specific skills such as programming (mean = 3.77) or software testing skills (mean = 3.93) were transferred from vendor to client to a lesser degree. Perhaps, the clients did not need those skills or did not have employees who had the ability to absorb those skills.

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Table 3 Factors affecting knowledge transfer. Factors Knowledge codifiability Code1: Much of the knowledge is formalized and documented Code2: Knowledge received is well documented Code3: Knowledge is available in easily transferable form Overall mean Client’s motivation for partnership Motiv1: To obtain technical knowledge on various technologies Motiv3: To obtain technical expertise (e.g., design, methods) Motiv2: To obtain information on best practices Motiv4: To acquire IT/project management skills Overall mean Vendor’s willingness to share knowledge Vwill2: Willing to share expertise on business best practices Vwill1: Willing to share technical knowledge and expertise Vwill3: Vendor encourages employees to share expertise Overall mean Trust between client and vendor Trust1: Both organizations are generally honest and truthful to each other Trust3: Both organizations trust each other Trust2: Both organizations treat each other fairly and justly Overall mean

Mean

S.D.

4.51

1.29

4.25 4.21

1.36 1.23

4.33

1.15

5.37

1.39

5.28

1.38

5.16 4.59 5.10

1.47 1.47 1.19

5.01

1.10

4.99

1.16

2.95

1.10

5.02

0.99

5.25

1.05

5.24 5.22

1.05 1.06

5.23

0.99

Scale: (1) strongly disagree to (7) strongly agree.

4.3. Factors affecting knowledge transfer Knowledge transfer from vendor to client is affected by the characteristics of knowledge (in terms of knowledge codifiability), characteristics of client (in terms of client’s motivation for partnership), characteristics of vendor (in terms of vendor’s willingness to share knowledge) and characteristics of client–vendor relationship (in terms of trust between the client and vendor). The results are shown in Table 3. 4.3.1. Characteristics of knowledge: codifiability of vendor’s knowledge The characteristics of the knowledge influence the ease with which it can be transferred. In general, the more easily knowledge can be communicated and understood, the shorter the time needed for its transfer (Zander & Kogut, 1995). This suggests that knowledge transfer can be impeded if the knowledge is not available in easily codified form for transfer. The organization may also have a greater awareness of transfer of codified knowledge. Further, the codifiability of knowledge will affect its costs of transfer. In general, the codifiability of knowledge can be linked to the tacitness of knowledge, namely the degree to which knowledge can be encoded. Explicit knowledge is knowledge that can be codified while tacit knowledge is difficult to be codified. The transfer of tacit knowledge is more difficult than explicit knowledge (or codifiable knowledge) (Hansen, Nohria, & Tierney, 1999; Yang & Farn, 2009). The codifiability of the knowledge is a major determinant in influencing the volume of knowledge transfer as codified knowledge is easier for transfer both for the giver and the receiver. The items used to measure knowledge codifiability were adapted from Hansen (2002). The results in Table 3 indicates that knowledge codifiability is about average (since mean is slightly above median value of 4.0). This indicates that knowledge from vendors may often be tacit knowledge which is difficult to be codified and hence knowledge

transfer may often be limited. However, clients should encourage their vendors to provide formal documentation whenever possible, so that knowledge transfer can be facilitated. Further, clients could more easily disseminate knowledge gained if formal documentation is available. 4.3.2. Characteristics of client: client’s motivation for partnership Another major requirement for knowledge transfer is the motivation of the client (Ko et al., 2005; Osterloh & Frey, 2000). Many IT partnerships may be for services that the client firms do not have expertise or are not interested in acquiring knowledge, and therefore there is less motivation for them to be open to knowledge transfer. A key trigger for knowledge flow from external sources is the organization’s motivation to acquire that knowledge, which is driven by the value it places on that knowledge. The value is determined by various factors including the type or quality of that knowledge, its usefulness to the organization, and the organization’s ability to assimilate and use the knowledge. Previous research (e.g., Osterloh & Frey, 2000) emphasized that motivation is an important factor in the transfer of knowledge. In a similar vein, Argote (1999) found a positive relationship between motivation and knowledge transfer. In this study, the items used to measure client’s motivation were adapted from Rus and Lindvall (2002). The results in Table 3 indicate that clients are generally motivated to obtain technical knowledge on various technologies (mean = 5.37), technical expertise (mean = 5.28) and information on best practices (mean = 5.16). However, acquiring IT/project management skills does not appear to be a key priority (mean = 4.59). This is perhaps not surprising as clients usually form partnership to obtain the necessary knowledge and expertise from the vendors, rather than to improve their IT/project management skills. 4.3.3. Characteristics of vendor: vendor’s willingness to share knowledge While the client may find value in the external knowledge, the transfer can only take place if the vendor is willing to share that knowledge. Hence, vendor willingness is also important since many vendors are dealing with knowledge products and therefore would be very interested in protecting their proprietary knowledge. While some of the knowledge flow could occur due to contractual requirements, much of the knowledge, particularly tacit knowledge, is transferred through contacts between people at different levels in the two organizations. Vendors may want to protect their proprietary knowledge, especially in knowledge intensive industries such as IT consulting and software development. The items used to measure vendor’s willingness to share knowledge were adapted from Ko et al. (2005). The results in Table 3 show that although vendors are generally willing to share their expertise on best practices (mean = 5.01) as well as technical knowledge (mean = 4.99), the vendor organizations generally do not explicitly encourage their employees to share knowledge (mean = 2.95). This has important implications for clients as it indicates that clients must play a proactive role in encouraging the transfer of knowledge from the vendors. 4.3.4. Characteristics of client–vendor relationship: trust between client and vendor The definition of trust varies across disciplines. Economists view trust in a calculative way (Williamson, 1993), posing that the trustee is unlikely to take opportunistic behaviors if the loss of being caught outweighs the benefit of doing so. Psychologists commonly view trust as a psychological state taken individually (Deutsch, 1962), while sociologists emphasize the nature of social collectiveness where trust resides in social relationships (Lewis & Weigert, 1985) or institutions (Zucker, 1986). Although researchers

T.S.H. Teo / International Journal of Information Management 32 (2012) 451–458 Table 4 Mechanisms for intra-firm knowledge transfer and impact on the IT function. Items Mechanisms for intra-firm knowledge transfer Mech1: Communicate new ideas to all team members Mech3: Share expertise with colleagues in project meetings Mech2: Document the things we learn on the job for others to use Mech4: In-house meetings to encourage knowledge dissemination Overall mean Impact on the IT function Imp3: Provide more effective IT solutions to our organization Imp1: Make our business processes more efficient and responsive Imp2: Provide more cost-efficient IT solutions to our organization Imp6: Increase access to key information technologies Imp5: Reduce the risk of technological obsolescence Imp4: Better control the cost of IT operations Overall mean

Mean

455

5.57

Trust between client and vendor

4.74

S.D.

5.00 4.81

1.23 1.15

4.56

1.18

5.51

Client's motivation for partnership

4.50

Vendor's willingness to share knowledge

4.41

5.43 4.65

Knowledge codifiability 4.51

1.33

4.72

1.04

5.07

1.12

4.99

1.10

4.97

1.24

4.92 4.81 4.62 4.90

1.31 1.24 1.26 1.01

3.85 1

2

3

4 Low KT

5

6

7

High KT

Fig. 2. Factors affecting low versus high knowledge transfer.

Scale: (1) strongly disagree to (7) strongly agree.

dissemination of knowledge is usually done within project groups rather than with other staff. Practitioners should realize that it is important for the whole organization to learn rather than primarily the project group involved in the partnership. In addition, top management must play an active role in motivating employees to learn and share knowledge (Hsu, 2006). 4.5. Comparing low and high knowledge transfer groups

in different disciplines define trust from different perspectives, they share one common ground: trust is necessary for a party to rely on another party when uncertainty is involved in the relationship and trust acts as a mechanism to reduce such uncertainty based on positive expectations toward the trusted party (Luhmann, 1979). As such uncertainty is particularly salient in client–vendor partnership (due to the difficulty of defining exact requirements as well as changing environment conditions), trust is necessary for such partnership. Our results in Table 3 show that trust between the client and vendor is generally high (means > 5.0), thereby providing empirical evidence that trust is important for client–vendor partnership. This result is perhaps not surprising as trust helps to facilitate successful win-win partnership for both the client and the vendor. Note that the items used to measure trust were adapted from Sarkar, Echambadi, Cavusgil, and Aulakh (2001). 4.4. Mechanisms for knowledge dissemination In a study of software development teams, Faraj and Sproull (2000) emphasized that the most critical resource for knowledge teams is expertise or specialized skills and knowledge. However, expertise by itself would not produce high quality work unless it is managed and coordinated to achieve its potential. In a similar vein, knowledge that is acquired, by itself, is not effective, unless it can be converted, transformed or integrated efficiently to produce desired results. It is possible for an organization to absorb extensive external knowledge but unable to utilize it effectively for long term competitive advantage since it lacks the internal mechanisms to disseminate knowledge and actively pursue potential opportunities that may arise due to synergies from integrating different knowledge sources. Organizations that value the knowledge received develop mechanisms to disseminate the knowledge as well as leverage the knowledge in its operations. The items used to measure mechanisms for knowledge dissemination were adapted from Grant (1996) and Tiwana and McLean (2005). The results (Table 4) show that simple mechanisms such as communicating new ideas (mean = 5.00), and sharing expertise with colleagues during project meetings (mean = 4.81) can influence the learning curve in organizations. Interestingly, documentation of things learnt for others to use (mean = 4.56) and general in-house meetings to disseminate knowledge (mean = 4.51) seem to be practiced to a lesser extent. These results indicate that

To analyze the results further, we carried out factor analysis with varimax rotation on the constructs in this study. As expected, the results loaded onto seven factors explaining 75.9% of the variance. We assessed the reliability of each factor using Cronbach’s alpha. The results indicated that all reliability values were above 0.70 as suggested by Nunnally (1971). Next, we split the sample based on the overall mean of knowledge transfer. We then computed the mean values for knowledge codifiability, client’s motivation for partnership, vendor’s willingness to share knowledge and trust between client and vendor for both the high and low knowledge transfer groups and compared them using t-test. The results (Fig. 2) indicate significant differences between the high and low knowledge transfer groups in terms of knowledge codifiability (t = 4.39, p < 0.05), client’s motivation for partnership (t = 5.09, p < 0.05), vendor’s willingness to share knowledge (t = 6.43, p < 0.05) and trust between client and vendor (t = 5.02, p < 0.05), thereby confirming that these factors are significantly associated with high knowledge transfer between the client and vendor. In the next few sections, we compare and discuss the mean scores of items measuring the impact on the IT function. Next, we created 4 cells comprising low and high knowledge transfer groups, and low and high knowledge dissemination groups so that we can compare these four cells in terms of impact on IT function. 4.6. Impact on the IT function Better utilization of IT knowledge influences both the efficiency and effectiveness of the IT organization, and IT capability to support organization’s business strategies (Sher & Lee, 2004). Very often the partnership is focused on the technological outcome, but the ability to create an IT organization that is flexible to business needs and responsive enough to provide quick solutions to business demands is a clearly a capability that organizations will find useful in the long run. Client–vendor partnership helps client organizations realize various outcomes in their IT organization as well as help IT better meet the business requirements. The items to measure the impact of partnership on the IT function were adapted from DeLone and McLean (2003) and Nelson and Cooprider (1996) (Table 4). Specifically, client–vendor partnerships tend to provide more effective (mean = 5.07) and cost-efficient IT solutions (mean = 4.97), make

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Table 5 Knowledge transfer and knowledge dissemination in low and high impact groups. Items

Mean (S.D.)

Knowledge transfer Ktvc2: Developed a greater understanding of what software/services can do for our firm Ktvc4: Picked up strategies for successful IT implementation Ktvc1: Acquired vendor’s knowledge on designing applications architecture Ktvc3: Used vendor’s advice for streamlining our business processes Ktvc5: Gained some design skills for developing applications Ktvc7: Obtained software testing skills from the vendor Ktvc6: Acquired programming skills from the vendor Overall mean Mechanisms for intra-firm knowledge transfer Mech1: Communicate new ideas to all team members Mech3: Share expertise with colleagues in project meetings Mech2: Document the things we learn on the job for others to use Mech4: In-house meetings to encourage knowledge dissemination Overall mean ***

t-Test

Low impact

High impact

4.46 (1.16) 3.91 (1.07) 4.00 (1.22) 3.87 (1.15) 3.79 (1.07) 3.30 (1.28) 3.24 (1.13) 3.80 (0.89)

5.43 (1.02) 5.30 (0.99) 4.95 (1.13) 5.03 (1.10) 4.85 (1.27) 4.47 (1.26) 4.23 (1.33) 4.89 (0.83)

5.36*** 8.16*** 4.88*** 6.22*** 5.38*** 5.55*** 4.80*** 7.69***

4.51 (1.32) 4.24 (1.18) 4.04 (1.16) 4.12 (1.46) 4.23 (1.11)

5.42 (0.97) 5.29 (0.87) 5.00 (1.01) 4.85 (1.11) 5.14 (0.77)

4.68*** 6.05*** 6.20*** 3.34*** 5.66***

p < 0.001.

High

Cell 1 Mean impact=4.99, SD=0.47,n=25

Cell 2 Mean impact=5.51, SD=0.59, n=62

Low

Cell 4 Mean impact=3.95, SD=0.98, n=35

Cell 3 Mean impact=4.61, SD=1.19, n=24

Knowledge transfer

Low

High Knowledge dissemination

Fig. 3. Knowledge transfer versus knowledge dissemination (in terms of impact on IT function).

business processes more efficient and responsive (mean = 4.99), increase access to key technologies (mean = 4.92). It appears to be less effective in controlling the cost of IT operations (mean = 4.62). Hence, while vendors have commonly marketed their services by emphasizing the reduction in the total cost of ownership (TCO), reduction in IT operational cost may be more elusive due to the increased complexity of business operations as the result of globalization and changing environmental conditions.

4.7. Examining the effect of vendor–client knowledge transfer and mechanisms for intra-firm knowledge dissemination on impact on the IT function

An Anova analysis revealed significant main effects but nonsignificant interaction effects (F = 29.17, df = 3, p < 0.05). This implies that knowledge transfer has significant effect on impact on the IT function, and knowledge dissemination also has significant effect on impact on the IT function. These results are consistent with Lee (2001) and Blumenberg et al. (2009) who found that knowledge sharing (defined in terms of knowledge transfer and dissemination) is positively associated with outsourcing success. Next, we carried out pairwise comparison of the mean values of impact on IT function in the four cells. The results indicate that all the means were significantly different from each other with the exception of Cell 1 (high KT, low KD) and Cell 3 (low KT, high KD). These results provide evidence of the impact of knowledge transfer and knowledge dissemination on the client organization. In other words, the presence of both knowledge transfer and knowledge dissemination gives rise to the greatest impact on the IT function. The results also provide support for the intuitive notion that knowledge that is transferred but not widely disseminated would have lesser impact. To analyze the results further, we split the sample into low and high impact groups and compare the groups in terms of the items measuring knowledge transfer and knowledge dissemination (Table 5). The results indicate significant differences between the low and high impact groups for all the items measuring knowledge transfer and mechanisms for knowledge dissemination. This provides further evidence that the high impact group has greater knowledge transfer and greater mechanisms to disseminate knowledge compared to the low impact group.

5. Limitations Past research has often emphasized the need for transferred knowledge to be disseminated within the firm (e.g., Tiwana & McLean, 2005). Hence, as we have done previously for knowledge transfer (KT), we decided to use the mean value of mechanisms for intra-firm knowledge dissemination to split the sample in terms of high and low knowledge dissemination (KD). This results in four groups as shown in Fig. 3. We also computed the mean values of impact on the IT function for each of the four cells. Interestingly, the mean value of impact for Cell 2 (high KT, high KD, mean = 5.51) is the highest, while the mean value of impact for Cell 3 (low KT, low KD, mean = 3.95) is the lowest, among the four cells. Further, Fig. 3 also shows that most firms with high knowledge transfer also tend to have high knowledge dissemination mechanisms and vice versa.

There are two main limitations of this study. First, the study is cross-sectional; hence, the relationships are correlational rather than causal. Nevertheless, the exploratory nature of this study makes this limitation acceptable. Further, the correlational relationships found in this study provide some insights into the interrelationships among various knowledge management constructs, which should be of interests to both researchers and practitioners. Second, we collected data only from the client. We tried to collect data from vendors by asking clients to provide the contact information of their vendors. Unfortunately, only a small percentage of clients provided such information. It is plausible that clients are not comfortable providing the names of their vendors due to competitive reasons, security reasons, or others. Future

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research can devise other ways to obtain a matched-pair sample from clients and vendors.

6. Implications and conclusions Overall, our results provide some insights on the nature of client–vendor partnership, types and extent of knowledge transferred from vendor to client, factors affecting knowledge transfer, mechanisms that facilitate knowledge dissemination as well as impact on the IT function. Understanding the nature of client–vendor partnership is important as it gives businesses and researchers an idea of the prevalence of various services (e.g., applications development, systems integration, consulting services) provided by vendors. In terms of contract volume, the results should be comforting to vendors as they indicate that for most firms, the change in contract volume with vendors is moderately increasingly over the past 1–2 years. However, the potential for further partnership is evident as firms still tend to allocate only a portion of their IT budget to outside vendors. The study also provides vendors with some idea of client’s motivations for partnership. The key motivations pertain to obtaining technical knowledge and expertise as well as best practices. By understanding these motivations, vendors will be in a better position to fulfil client’s needs. As well, vendors will be better able to market their services to clients by demonstrating how they could fulfil clients’ needs and motivations. Clients should be reassured from the results that vendors are generally willing to share their expertise, though most vendor firms do not have explicit policies to encourage their employees to share knowledge with their clients. Clients need to maintain good relationship with their vendor(s) and be proactive in acquiring knowledge from vendors. One way of acquiring knowledge is to build in knowledge transfer into the contract with the vendor so that the vendor will also be motivated to share their knowledge. Another way is to have staff work closely with the vendor in order to learn from the vendor. The factors affecting knowledge transfer includes characteristics of knowledge (knowledge codifiability), characteristics of client (client’s motivation for partnership), characteristics of vendor (vendor’s willingness to share knowledge) and the relationship between client and vendor (trust between client and vendor). In terms of the relative importance of the four factors, it appears that trust between client and vendor, client’s motivation, and vendor’s willingness to share are given greater importance than knowledge codifiability. One possible reason is that knowledge codifiability is dependent on the nature of the project and may be less controllable. By understanding these factors, clients would be better able to devise appropriate strategies to facilitate knowledge transfer from the vendor. Researchers can also investigate deeper into these four factors to determine how and why knowledge transfer and client–vendor partnership succeed or fail. As well, researchers can also investigate other factors affecting knowledge transfer between the client and vendor. Overall, our research framework provides a parsimonious view of factors affecting knowledge transfer which generally involves characteristics of knowledge, client, vendor and relationship. The results regarding the mechanisms for knowledge dissemination give some idea to clients and researchers on the various mechanisms used to encourage knowledge sharing and dissemination. It appears that communication among team members is crucial. The results also indicate that knowledge sharing is primarily within the project units rather than throughout the organization. Hence, further research needs to be done on the mechanisms to encourage knowledge sharing outside the project units to the rest of the organization. For example, future research could compare the

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various mechanisms used by different organizations to disseminate knowledge gained from client–vendor partnership. The impact of knowledge transfer is reassuring to both clients and vendors as it generally indicates positive outcomes from client–vendor partnership. This also shows that it is often worthwhile for firms to explore partnership with vendors. The key impacts pertain to obtaining efficient and effective IT solutions and streamlining of business processes. However, more research needs to be done on how to use client–vendor partnership to better control IT cost. In order to do this, vendors would need a better understanding of client’s business processes and operations. One way to do this is to transfer employees from client’s organization to the vendor. This benefits both client and vendor since both parties are assured that staff involved in the project are familiar with the idiosyncrasies of client’s operations. Our results also provide empirical evidence that both knowledge transfer from vendor to client and the presence of various mechanisms to disseminate the knowledge gained within the firm will result in the greatest impact. This should provide some assurance to client organizations that partnership with vendor is worthwhile, but they should also have in place mechanisms to disseminate knowledge gained from vendor outside the project team to the rest of the client organization. In other words, knowledge transfer and knowledge dissemination are distinct activities and clients should devise appropriate processes or mechanisms to maximize such knowledge transfer and knowledge dissemination. Overall, this study should be of interest to clients, vendors and researchers in better understanding the various knowledge management issues pertaining to client–vendor partnership. As such, it represents an ongoing stream of research that should prove worthwhile in facilitating win-win partnerships between clients and vendors. Acknowledgements The author would like to thank G. Premkumar, Amrit Tiwana and Anol Bhattacherjee for their contributions to this project. This research is funded by a research grant from the National University of Singapore. References Alavi, M., & Leidner, D. E. (2001). Review – Knowledge management and knowledge management systems: Conceptual foundations and research issues. MIS Quarterly, 25(1), 107–136. Argote, L. (1999). Organizational learning: Creating, retaining and transferring knowledge. Norwell, MA: Kluwer. Argote, L., McEvily, B., & Reagans, R. (2003). Managing knowledge in organizations: An integrative framework and review of emerging themes. Organization Science, 49(4), 571–582. Attewell, P. (1992). Technology diffusion and organizational learning: The case of business computing. Organization Science, 3(1), 1–19. Basaglia, S., Caporarello, L., Magni, M., & Pennarola, F. (2010). IT knowledge integration capability and team performance: The role of team climate. International Journal of Information Management, 30(6), 542–551. Blumenberg, S., Wagner, H-T., & Beimborn, D. (2009). Knowledge transfer processes in IT outsourcing relationships and their impact on shared knowledge and outsourcing performance. International Journal of Information Management, 29(5), 342–352. Dail, B. S. (2005). Building stronger relationships with IT vendors. McKinsey Quarterly, (Spring), 6–8. DeLone, W. D., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9–30. Deutsch, D. (1962). Cooperation and trust: Some theoretical notes. Lincoln, Nebraska: University Press. Dibbern, J., Goles, T., Hirschheim, R., & Jayatilaka, B. (2004). Information systems outsourcing: A survey and analysis of the literature. Data Base for Advances in Information Systems, 35(4), 6–102. DP Information Network. (2006). Singapore 1000. Drucker, P. F. (1995). Managing in a time of great change. New York: Truman Talley. du Plessis, M. (2005). Drivers of knowledge management in the corporate environment. International Journal of Information Management, 25(3), 193–202.

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