Explaining the intentions to share and reuse ...

Explaining the intentions to share and reuse knowledge in the context of IT service operations Johnny C.F. So and Narasimha Bolloju

Abstract Purpose – Aims to provide an understanding on IS/IT professionals’ intentions to share and reuse knowledge in the context of information technology service operations.

Johnny C.F. So received his BBA (Hons) in Information Systems from City University of Hong Kong in 2002. He is currently studying for his MPhil in City University of Hong Kong. His research interests include knowledge management and IT management. Narasimha Bolloju is an associate professor of Information Systems at the City University of Hong Kong. Dr Bolloju received his PhD in Computer Science from the University of Hyderabad, India. His current research interests are in web services, knowledge management, and object-oriented systems. He has published articles in Communications of ACM, Decision Support Systems, European Journal of Operational Research, Journal of Database Management, and Journal of Object-Oriented Programming.

Design/methodology/approach – The theory of planned behavior (TPB) is applied for examining IS/IT professionals’ intention to share and reuse knowledge. The data were collected from working IS/IT professionals using an online survey, and partial least squares was used for analyzing the data. Findings – The results from this study indicate that the theory of planned behavior is an adequate model for investigating behavioral intentions of knowledge sharing and reuse in the context of information technology service operations. All direct determinants of intention to share knowledge, except subjective norm regarding information technology service operations knowledge sharing, and intention to reuse knowledge were significant. Research limitations/implications – This paper is one of the first to attempt to study both knowledge sharing and knowledge reuse under the same context. The relatively small sample size has limited statistical power of the implications drawn. Practical implications – This paper attempts to highlight the importance of information technology service operations in the IS/IT industry, and study knowledge management in that context. To encourage knowledge sharing, top management is advised that they should focus on building up a positive attitude in their employees, through improving relationships and recognition of their contributions. Originality/value – This paper is the first attempt to combine both knowledge sharing and knowledge reuse in the same context, and initiates research in the area of information technology service operations. This paper offers help to both practitioners and researchers in understanding that area. Keywords Knowledge sharing, Knowledge management, Communication technologies, Libraries Paper type Research paper

Introduction Managing knowledge resources is one of the key functions in modern organizations. Knowledge is treated as a vital and significant strategic organizational resource that can influence the competitive advantages of the organization (Alavi and Leidner, 2001). A recent study reported that about 80 percent of companies in Europe consider knowledge is a strategic asset (KPMG, 2003). Managing knowledge is an extremely important activity in the IS industry where IS/IT professionals are often involved in knowledge-intensive work. From an individual perspective, knowledge is defined as a justified belief that increases an entity’s capacity for effective action (Huber, 1991; Nonaka, 1994). In the organizational context, knowledge management (KM) refers to identifying and leveraging the collective knowledge in an organization to help the organization to compete (von Krogh, 1998). In other words, KM aims at managing individual employee’s knowledge for increasing organization’s benefits. KM is critical to the organization’s success. However, KPMG’s (2003) study reported that companies estimate 6 percent, on average, of revenue as a percentage of annual turnover or budget is being missed from failing to exploit available knowledge. In addition, there are 78

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VOL. 9 NO. 6 2005, pp. 30-41, Q Emerald Group Publishing Limited, ISSN 1367-3270

DOI 10.1108/13673270510629945

percent of the companies believe they are currently missing out on business opportunities by failing to successfully exploiting available knowledge. The potential reasons for this could be: B

employees do not share their knowledge voluntarily due to the feeling of losing some of their power, reducing the chances of success (e.g. promotion, compensation), and additional workload required;

B

employees do not apply and reuse the knowledge even when it is available and accessible, due to reasons such as the source of knowledge is questionable, the feeling of risk aversion, and lack of time or opportunities; and

B

employees have difficulties in locating the information required, possibly due to information overload.

In recent years, different aspects of KM attracted researchers to study it from different perspectives (Nonaka and Konno, 1998; Rus and Lindvall, 2002; Bhatt, 2002; Mason and Pauleen, 2003). Organizations are likely to run into difficulties if the knowledge of individual employee is not well-managed. It is believed that employees’ knowledge would not be successfully exploited if either knowledge sharing or knowledge reuse is overlooked. This study attempts to explore the differences in intentions to sharing and reuse of knowledge in the context of IT service operations.

Theory In order to focus on key organizational activities that require extensive knowledge sharing and reuse, IT service operations (ITSO) is selected as a suitable context for this study. The theory of planning behavior (TPB), a well-established theory, has been applied for examining IS/IT professionals’ intention to share and reuse knowledge. Knowledge management in IS/IT industry There is no doubt that KM takes an important role in maintaining sustainable competitive advantages of an organization. Managing knowledge in IS/IT industry is an important activity because it involves many people working in different activities and phases (e.g. software development, system support and system administration). Rus and Lindvall (2002) pointed out five critical knowledge needs for achieving business goals in IS industry: 1. acquiring knowledge about new technology; 2. accessing domain knowledge from which software is being developed; 3. sharing knowledge about local policies and practices; 4. capturing knowledge and knowing who knows what; and 5. collaborating and sharing knowledge. Alavi and Leidner (2001) developed a framework of organizational knowledge management process which classified that knowledge management systems (KMS) consist of four knowledge process: creation, storage/retrieval, transfer, and application. In particular, knowledge sharing/transferring is perceived to be the most essential process for KM (Bock and Kim, 2002; Goh, 2002). In this area, researchers studied from several perspectives. Table I summarizes these studies. Most researchers have studied individual’s knowledge sharing and reuse behavior separately. It seems that the linkage and relationship between these two sides of KM process are overlooked. This study is intended to link the two sides of KM process using the context of IT services management (ITSM). IT services management Nowadays, the business focus of IS/IT industry is changing from technology management to service management by integrating people, processes and technologies. Many IS/IT

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Table I Previous studies on knowledge sharing and reuse Study

Research focus

Conceptual framework perspective Alavi and Leidner (2001) Reviewing and interpreting knowledge management literature to identify important research areas and potential role of IT in support of KM Markus (2001) Study the different knowledge reusers’ requirements for knowledge management systems and repositories, as well as the use of intermediaries Goh (2002) An integrative conceptual framework with a number of explored key factors (e.g. reward system and relationships among individuals), which influence on organizational ability to transfer knowledge, is discussed Knowledge transfer processes perspective Lee and Choi (2003) Interconnecting KM seven enablers, four processes and organizational performance into an integrated view Szulanski (2000) Introducing the process model of knowledge transfer with different stages and different difficulty factors of transferring, measures of stickiness are developed to explore the predictive power of those factors Knowledge ownership perspective Kolekofski and Heminger Exploring employees’ beliefs and attitudes about sharing (2003) organizational information Constant et al. (1994) Studying individuals’ attitudes about sharing technical work (information as product) and expertise (information as expertise) in organizations Jarvenpaa and Staples Exploring the belief of organizational ownership, which relates to (2001) whether information and knowledge created by individual knowledge worker are believed to be owned by the organization, affects information and knowledge sharing Knowledge sharing perspective Bock and Kim (2002) Understanding the factors affecting the individual’s behavior in the organizational context, expected rewards is found to discourage the formation of a positive attitude toward knowledge sharing Henningsen and Examining the influence of profile type (hidden, ambiguous and clear) Henningsen (2003) on group discussion, use of shared and unshared information and perception of normative and informational influence Ardichvili et al. (2003) Pointing out motivation and barriers to participation in (contribute knowledge to and use knowledge from) virtual knowledge-sharing communities IT support perspective Woitsch and Karagiannis (2002) Miranda and Saunders (2003)

Sussman and Siegal (2003)

Introducing concepts to model, defining and analyzing and enterprise knowledge management system during the design phase Examining the social construction of meaning in the group meeting under different meeting environment with collaborative technologies, breadth and depth of information shared during meeting, and decision quality Investigating how knowledge workers are influenced to adopt the advice that they receive in computer-mediated communication context, and addressing information usefulness as a mediator of the information adoption process

companies are becoming the strategic solution providers by aligning the IT with business goals, providing and delivering value-added as well as quality services to the customer (HP IT Service Management, 2004). Consequently, IT organizations become highly customer-orientated in how to understand and satisfy customers’ requirements in order to deliver high quality services (van Bon, 2002). Many organizations are becoming increasingly dependent on IT in order to satisfy their business aims and meet their needs. This growing dependency leads to an increased requirement for high quality IT services which can match

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the business needs and user requirements. The concern about how can IT organizations achieve this objective is increased. The emergence of Information Technology Infrastructure Library (ITIL) is a recognition of this phenomenon. ITIL was originally developed by the UK’s Office of Government Commerce (OGC) in late 1980s. The government designed a set of best practice advice and guidance on the provision of quality IT services. It is a consistent and comprehensive documentation of best practice for ITSM, and it is used by several hundreds of organizations around the world (The Information Technology Infrastructure Library, 2004). According to ITIL, ITSM is defined as: IT Service Management is concerned with delivering and supporting IT services that are appropriate to the business requirements of the organization. ITIL provides a comprehensive, consistent, and coherent set of best practices for ITSM processes, promoting a quality approach to achieving business effectiveness and efficiency in the use of information systems.

ITIL is one of the most widely accepted approaches to ITSM in the world, and it is a de facto industry standard for IT management (HP IT Service Management, 2004; van Bon, 2002). Many world-class and famous IS/IT companies adopt, embody and integrate ITIL into their business for managing their IT services. IBM described that their ITIL offerings can address a wide range of company’s IT management needs and provide real value by efficiently and effectively addressing company’s unique IT and business requirements (IBM ITIL Services, 2004). Therefore, ITIL is definitely an invaluable resource for organizations which are seeking to implement ITSM. IT service operations HP ITSM reference model provides a fundamental architecture to identify key processes which require extensive knowledge sharing and reuse in ITSM for this study. According to this reference model, IT processes are organized into five different groups that focus on different aspects of the service lifecycle, they are: 1. business-IT alignment; 2. service design and management; 3. service development and deployment; 4. service operations; and 5. service delivery assurance (HP IT Service Management, 2004). Applegate et al. (1999) have noted that some firms have reorganized IT operations in order to be more responsive to user needs, and the most critical need for IT operations success is for recruiting, training, and retaining knowledgeable people to operate, maintain, and develop IT services. Woitsch and Karagiannis (2002) have provided similar argument that the overall goal within KM is to support the daily work of employees. In other words, KM is to support the execution of business processes in a process-oriented point of view. It is believed that IT service operations (ITSO), which incorporates day-to-day operational activities, involves extensive knowledge which needs to be shared and reused among the employees. ITSO provides daily monitored services and handles customer service requests to meet agreed service levels and increase customer satisfaction. The processes identified under ITSO include incident and service request management, problem management, and operations management. The objective and activities involved of each process are summarized in Table II. These processes provide the necessary and required control, command and support capabilities to enable on-going day-to-day service provided to customers. They assist an organization in maintaining customer satisfaction and meeting agreed level of service quality. Theoretical development Ajzen’s (1991) theory of planned behavior (TPB) provides the theoretical background for examination of how IS/IT professionals share their knowledge as well as reuse others’

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Table II ITSO activities involve knowledge sharing and reuse Process

Description

Operations management

Objective: To manage and perform the normal, day-to-day processing activities required for service delivery in accordance with agreed-on service levels Typical activities: Monitoring and controlling services Administering clients, servers and networks Coordinating preventive maintenance Administering databases and data storage systems

Incident and service request management

Objective: To restore normal service operation as quickly as possible, minimize service disruptions, and respond to customer needs Typical activities: Logging incidents and service requests Categorizing incidents and service requests Prioritizing incidents and service requests Tracking incidents and service request progress

Problem management

Objective: To prevent and reduce incidents as well as to provide quick and effective problem solving to ensure a structured use of resources Typical activities: Logging problems Identifying root causes of problems Controlling known problems Resolving problems

knowledge within organizational ITSO environment. TPB is a widely accepted model in social psychology. The theory is well-established and well-supported in the IT arena, and it has been utilized in the IT context (Riemenschneider et al., 2003). Based on TPB, intentions to perform different kinds of behavior can be predicted with high accuracy from attitudes toward the behavior, subjective norms, and perceived behavioral control; and these intentions, together with perceptions of behavioral control, account for considerable variance in actual behavior (Ajzen, 1991). The theory is found to be well supported by empirical evidence (Taylor and Todd, 1995a, b; Venkatesh et al., 2003; Riemenschneider et al., 2003). According to the theory, the most important determinant of individual’s behavior is behavior intent. It is believed that the stronger intention to engage in a behavior, the more likely should be its performance. Intention to perform a behavior is the direct determinant of actual performance of such behavior. An IT professional is more likely to share problem-identification knowledge if he has a strong intention to share it. The individual’s intention to perform a behavior is a combination of attitude toward performing the behavior, subjective norm and perceived behavioral control. The research model, based on TPB to integrate intention to share knowledge and intention to reuse knowledge, used in this study is presented in Figure 1. According to TPB, Ajzen (1991, p. 188) defines attitude as ‘‘the degree to which a person has a favorable or unfavorable evaluation or appraisal of the behavior in question’’. In other words, it is an individual’s positive or negative behavioral belief about performing a specific behavior, and it is regarded as the first antecedent of behavioral intention. An individual will intend to perform a certain behavior when he or she evaluates it positively. For instance, a technical support staff has high intention to share her problem solving knowledge to other colleagues if she believes sharing such kind of knowledge is a beneficial idea. Attitudes are determined by the individual’s beliefs about the consequences of performing the behavior (behavioral beliefs), weighted by his or her evaluation of those consequences (outcome evaluations). Those attitudes are believed to have a direct effect on behavioral intention.

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Figure 1 Research model

H1a. Attitude toward ITSO knowledge sharing will have a positive effect on the intention to share ITSO knowledge. H1b. Attitude toward ITSO knowledge reuse will have a positive effect on the intention to reuse ITSO knowledge. Subjective norm is regarded as the second antecedent of behavioral intention. Ajzen (1991, p. 188) defines it as ‘‘the perceived social pressure to perform or not to perform the behavior’’. That means subjective norm is the influence of social pressure as perceived by the individual to perform or not perform a certain behavior. In other words, it is the individual’s perception that most people who are important to him or her think he or she should or should not perform the behavior in question. For instance, a programmer has high intention to share her debugging knowledge to other colleagues if she perceives her boss thinks that she should share such kind of knowledge among other programmers. Therefore, subjective norm is believed to have a direct effect on behavioral intention. H2a. Subjective norm regarding ITSO knowledge sharing will have a positive effect on the intention to share ITSO knowledge. H2b. Subjective norm regarding ITSO knowledge reuse will have a positive effect on the intention to reuse ITSO knowledge. Ajzen (1991, p. 188) defines perceived behavioral control as ‘‘the perceived ease or difficulty of performing the behavior and it is assumed to reflect past experience as well as anticipated impediments and obstacles’’. So, it refers to the degree to which an individual feels that performance or nonperformance of the behavior in questions is under his or her volitional control. For example, a system administrator has strong intention to share his knowledge on tuning system performance if he feels he has the ability and chance to share. From the theory, although people hold positive attitudes toward the behavior and believe that important others would approve of the behavior, they are not likely to form a strong intention to perform a behavior if they believe that they do not have any resources or opportunities to do so. Therefore, perceived behavioral control can influence behavior directly or indirectly through behavioral intention. The addition of this construct could enhance the TPB model from original theory of reasoned action (TRA). It is believed to have direct effect on behavioral intention. H3a. Perceived behavioral control over ITSO knowledge sharing will have a positive effect on the intention to share ITSO knowledge. H3b. Perceived behavioral control over ITSO knowledge reuse will have a positive effect on the intention to reuse ITSO knowledge.

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‘‘ Managing knowledge resources is one of the key functions in modern organizations. ’’ Since this study considers knowledge sharing and reuse behavior of IS/IT professionals, the relationship between the intentions to share knowledge and reuse knowledge needs to be investigated. If an IS/IT professional has a high intention on one side, it is more likely that he has a high intention on the other side. H4.

The intention to share ITSO knowledge and intention to reuse ITSO knowledge are positively correlated.

Research method An online survey was used to collect data for testing the proposed model. The introductory notes of the survey included the definition of ITSO and some example of activities in ITSO that requiring share and reuse knowledge. The survey consisted of two sections: 1. sharing one’s ITSO expertise; and 2. reusing others’ ITSO expertise. The respondents were asked to complete both the sections. The questionnaire employed in this study is based on prior studies. The scales developed by Taylor and Todd (1995b) and Ajzen (2002) were used for measuring the variables. Using a seven-point Likert scale (1 ¼ Strongly disagree, 7 ¼ Strongly agree) in the survey, the respondents were asked same type of question with different wordings in both sections. For example, the item ‘‘Sharing my ITSO expertise is a good idea’’ in knowledge share section corresponds to ‘‘Reusing others’ ITSO expertise is a good idea’’ in knowledge reuse section. In addition, the words ‘‘knowledge’’ and ‘‘expertise’’ are interchangeable in the meanings; and ‘‘expertise’’ was used in the questionnaire for enhancing respondents’ understanding of various questions. Tables III and IV present the questions used for measuring different constructs in this study. E-mail requests for completing the online survey were sent to 170 working IT professionals who were studying a part-time master degree program at a large university. The reason for choosing the respondents from the total population is that they had relevant working experience in IS/IT industry and familiar with the operations in their professional area. All the respondents were volunteers. The online questionnaire was kept open for 18 days. One reminder e-mail and one final reminder e-mail were sent six days and three days before the deadline respectively. A total of 40 completed questionnaires (response rate about 24 percent) were used in the subsequent analysis. Among the respondents, about 72.5 percent were male and 27.5 percent were female. The average working experience was 9.04 years (SD ¼ 4:22). The respondents are mainly working in computer and technology/internet industry (25 percent) followed by banking and finance (20 percent) and government/public utilities (15 percent). A total of 32.5 percent of respondents are system analysts, 20 percent are managers and 17.5 percent are analyst programmers. The main job duties of the respondents included development (32 percent), maintenance (24 percent) and system administration (16 percent).

Analysis The survey data were analyzed using partial least squares (PLS) which is a structural equation modeling technique that accesses the measurement model (relationships between questions and constructs) within the context of the structural model (relationships among constructs). PLS first estimates the loadings of indicators of constructs and then estimates causal relationships among the constructs iteratively (Fornell, 1982).

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Table III Operationalization of constructs Sharing of ITSO expertise Constructs

Question Measure

Source

Attitude (S-ATT)

S-ATT1 S-ATT2 S-ATT3 S-ATT4

Sharing my ITSO expertise is a good idea Sharing my ITSO expertise is a beneficial idea I like the idea of sharing my ITSO expertise Sharing my ITSO expertise would be pleasant

Taylor and Todd (1995b)

Subjective norm (S-SN)

S-SN1

People who influence my behavior (e.g. boss, Taylor and Todd (1995b), Ajzen (2002) colleague etc.) think that I should share my ITSO expertise People who are important to me (e.g. boss, colleague etc.) think that I should share my ITSO expertise People whose opinions I value (e.g. boss, colleague etc.) would approve of my ITSO expertise sharing It is expected (e.g. by boss, colleague etc.) of me that I share my ITSO expertise

S-SN2

S-SN3

S-SN4 Perceived behavioral control (S-PBC) S-PBC1 S-PBC2 S-PBC3 S-PBC4 Behavioral intention (S-BI)

S-BI1 S-BI2 S-BI3 S-BI4

I would be able to share my ITSO expertise Taylor and Todd (1995b), Ajzen (2002) Sharing my ITSO expertise is currently within my control I have the resources and the knowledge and ability to share my ITSO expertise If I wanted to, I could share my ITSO expertise I will share my ITSO expertise in the near future Taylor and Todd (1995b) I intend to share my ITSO expertise in the near future I intend to share my ITSO expertise frequently All things considered, I expect to share my ITSO expertise

Measurement model Three tests were used to assess convergent validity: 1. reliability of questions; 2. composite reliability of constructs; and 3. average variance extracted (AVE) by constructs (Fornell and Larcker, 1981). Reliability of the questions was assessed by examining the loadings of each question and it was found to be adequate because all the loadings are above 0.5. When examining the composite reliability of constructs, a score of 0.8 as an indication of adequate composite reliability (Nunnally, 1994). It was suggested that the constructs had an AVE of at least 0.5 for demonstrating an adequate level (Fornell and Larcker, 1981). All constructs in this study have an adequate convergent validity. Discriminant validity was assessed by looking at correlations among constructs and variances of and covariances among constructs. For this, each question should correlate more highly with other questions measuring the same construct than with other questions measuring other constructs (Chin, 1998). This can be examined by determining whether the average variance shared between a construct and it measures exceeded the variance shared between the construct and other constructs in the model (Barclay et al., 1995). Good discriminant validity was demonstrated because all the diagonal elements in Table V were greater than corresponding off-diagonal elements. Structural model The hypotheses, described in previous section, were tested by examining the structural model. The test of the structural model includes estimating the path coefficients (the

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Table IV Operationalization of constructs Reuse of ITSO expertise Constructs Attitude (R-ATT)

Question Measure

Source

R-ATT1 R-ATT2

Taylor and Todd (1995b)

R-ATT3 R-ATT4 Subjective norm (R-SN)

R-SN1

R-SN2

R-SN3

R-SN4 Perceived behavioral control (R-PBC) R-PBC1 R-PBC2 R-PBC3 R-PBC4 Behavioral intention (R-BI)

R-BI1 R-BI2 R-BI3 R-BI4

Reusing others’ ITSO expertise is a good idea Reusing others’ ITSO expertise is a beneficial idea I like the idea of reusing others’ ITSO expertise Reusing others’ ITSO expertise would be pleasant

People who influence my behavior (e.g. boss, Taylor and Todd (1995b), Ajzen(2002) colleague etc.) think that I should reuse others’ ITSO expertise People who are important to me (e.g. boss, colleague etc.) think that I should reuse others’ ITSO expertise People whose opinions I value (e.g. boss, colleague etc.) would approve of reusing others’ ITSO expertise It is expected (e.g. by boss, colleague etc.) of me that I reuse others’ ITSO expertise I would be able to reuse others’ ITSO expertise Taylor and Todd (1995b), Ajzen (2002) Reusing others’ ITSO expertise is currently within my control I have the resources and the knowledge and ability to reuse others’ ITSO expertise If I wanted to, I could reuse others’ ITSO expertise I intend to reuse others’ ITSO expertise in the near future I will reuse others’ ITSO expertise in the near future All things considered, I expect to reuse others’ ITSO expertise I intend to reuse others’ expertise frequently

Taylor and Todd (1995b)

Table V Construct correlations

S-ATT S-SN S-PBC S-BI R-BI R-ATT R-SN R-PBC

S-ATT

S-SN

S-PBC

S-BI

R-BI

R-ATT

R-SN

R-PBC

(0.906) 0.728 0.683 0.884 0.680 0.538 0.531 0.542

(0.807) 0.680 0.710 0.631 0.450 0.576 0.426

(0.831) 0.732 0.749 0.649 0.559 0.642

(0.895) 0.770 0.614 0.622 0.663

(0.881) 0.863 0.795 0.785

(0.905) 0.611 0.622

(0.837) 0.672

(0.811)

Note: The diagonal elements are the square roots of the average variance extracted (AVE); the off-diagonal elements are the inter-construct correlations

strengths of the relationship between the dependent and independent variables) and the R 2 value (the amount of variance explained by independent variables). The structural model explains 81.4 percent and 89.1 percent of the variance for intention to share knowledge and intention to reuse knowledge respectively. Additionally, a paired-samples T-test was performed to test the correlation between intentions to share knowledge and reuse knowledge. The correlation coefficient was 0.770 (p , 0:01), indicating that there was no significant difference between the two intentions. Table VI provides complete statistical summary of the result. All hypotheses were supported except H2a.

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Table VI Result summary R2 value Intention to share knowledge P1a: S-ATT – S-BI P2a: S-SN – S-BI P3a: S-PBC – S-BI Intention to reuse knowledge P1b: R-ATT – R-BI P2b: R-SN – R-BI P3b: R-PBC – R-BI Correlation between two intentions P4: S-BI – R-BI

Path coefficient

t-statistic

Result

0.693 0.055 0.861

3.690* 0.404 1.714**

Supported Not supported Supported

0.514 0.307 0.259

5.563* 2.968* 3.019*

Supported Supported Supported

Correlation

Result

0.770*

Supported

0.814

0.891

Notes: * p , 0:01; ** p , 0:10

Discussion and Implications Overall, the results from this study indicate that the TPB is an adequate model for investigating behavioral intentions of knowledge sharing and reuse in the context of ITSO. It can be observed from the R2 values that the model explains the intention to share knowledge as well as the intention to reuse knowledge. All direct determinants of intention, except subjective norm regarding ITSO knowledge sharing, were significant. More importantly, these results suggest that there is a significant correlation between the intentions to share and reuse knowledge. The results reveal that the H2a, which focused on the positive relationship between subjective norm and intention to share ITSO knowledge, is not supported. This could be due to the characteristics of the sample which included fairly experienced respondents (over nine years of experience) on whom the influence of subjective norm towards intention to knowledge sharing may be minimal. The results have implications for practice in promoting ITSO knowledge sharing within the organization. Since the path from attitude towards intention to share knowledge was most significant, we suggest that top management should focus on building up a positive attitude of their employees, through improving relationships and recognition of their contributions, in order to encourage sharing. It is no doubt a positive knowledge sharing culture in organization could influence employees’ attitude. Employees are more willing to offer and share knowledge when they perceive knowledge sharing is encouraged in organization. Lin and Lee (2004) suggest that senior manager is the main antecedent to establish such knowledge sharing culture. To encourage knowledge reuse, attitude, subjective norm and perceived behavioral control are important factors. Top management should put their focus on these important factors. Additionally, Markus (2001) describes some knowledge reuse situations according to diverse purpose of reuse and different type of knowledge reusers. There are some recommendations and suggestions for them on how to promote successful knowledge reuse in organization. For instance, providing appropriate incentives or motivations for reuse; and providing training and consultation to employees. The results also have implications for research in the area of KM. As noted previously, ITSM attracts more and more consideration in the industry because of changing business environment. However, researchers pay little attention on this area. This study attempts to initiate research in this area of KM for ITSM. Moreover, knowledge sharing and reuse were studied separately in prior research and those studies have not considered the relationships between them. The result suggests that the intention to share knowledge and the intention to reuse knowledge are correlated and need for considering both these aspects for a better understanding of KM in organizations. The relatively small sample size has limited statistical power of the implications drawn. The data for this study were collected based on the context of ITSO, attempts to generalize the results to other contexts must be done cautiously. In addition, the use of self-report scales to measure the

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study variables may involve the possibility of common method bias for some results obtained. This has been a criticism of tests of the TRA and TPB in other contexts (Ajzen, 1991). The relation between intention to share knowledge and reuse knowledge was tested indirectly, and significant correlation was found. A direct test of such relation should be pursued. Further research can extend this study in several ways. Investigating the relations between intention to perform knowledge sharing and reuse behavior and actual performance of such behaviors. This study can also be extended to other IT processes beside ITSO, including service design and management; service development and deployment; and service delivery assurance. Making comparison between knowledge sharing and reuse behavior under these processes is one potential research area for further studies. Additionally, further research studying the effects of cultural factors on knowledge share and reuse behavior would also be interesting.

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