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Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Theoretical Background
  5. Research Method
  6. Data Analysis and Results
  7. Discussion and Conclusion
  8. References
  9. Appendix A

Recently, several researchers have questioned the predicting power of intention to actual system usage (Burton-Jones & Straub, 2006; Jasperson, Carter, & Zmud, 2005; Kim & Malhotra, 2005; Kim, Malhotra, & Narasimhan, 2005; Limayem & Hirt, 2003). In this article, we report a study that investigates the gap between intention and usage by observing an Internet-based knowledge management system, SCTNet, from the perspective of volitional control. Relying on the theory of planned behavior and the theory of action control, we investigate four types of volitional control mechanisms that may impact people's knowledge-sharing practices. Our results show that in knowledge-management-based knowledge sharing, people do not always perform in a manner consistent with their espoused beliefs. This intention–action inconsistency can be explained by perceived self-efficacy, but not by intention and controllability. In addition, a person's action/state orientation moderates his or her enactment of intention toward knowledge sharing into behaviors. The main theoretical implication of this study is that the study of knowledge-management-based knowledge sharing has to go beyond intention to include the investigation of both the actual behaviors of knowledge sharing and the volitional control constructs that predict these behaviors. Furthermore, previous research has shown that the environment interacts reciprocally with individuals' psychological control mechanisms in regulating their behaviors. Thus, the management must focus on the social and cultural attributes of organizational settings that may strengthen people's volitional control in practicing knowledge sharing.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Theoretical Background
  5. Research Method
  6. Data Analysis and Results
  7. Discussion and Conclusion
  8. References
  9. Appendix A

“Knowledge has become the key economic resource and the dominant—and perhaps even the only—source of comparative advantage” (Drucker, 1995, p. 5). In this thrust, some theorists see knowledge management (KM) as a matter of extracting the right knowledge from a person's memory and storing it in networked computers for later distribution (Tiwana, 2001). The emphasis is normative, mainly on codification and discovery of law-like relationships between knowledge parts. The review by Schultze and Leidner (2002) has confirmed this bias toward normative research in KM.

Yet, the contribution of various codified KM systems to business profitability has not lived up to expectation. McDermott (1999) concluded that information technology could inspire, but could not deliver, KM and that many KM systems were “an expensive and useless information junkyard” (p. 104). Ruggles (1998) indicated that information technologies (ITs) would not bring down organizations' greatest knowledge-sharing barriers. Another study, by KPMG Consulting (2000), found that in knowledge sharing, employees often were required to reveal their own or colleagues' mistake, resulting in such unforeseen consequences as deteriorations in colleagues' relations or negative evaluations by managers. Finally, Michailova and Husted's research (2003) on attempts to challenge well-established norms and to break existing social networks revealed a consequence of attacks by the situated society.

The present article reports a study of an Internet-based KM system, SCTNet, from the perspective of volitional control. SCTNet has been put in place to facilitate teachers' knowledge-sharing tasks in Taiwan since 1999. With the aid of the government, approximately 90,000 registered members, most of whom are teachers, joined SCTNet over a period of 7 years. Even though most members have indicated their desire to create, store, distribute, and discuss their teaching knowledge, the actual activities on SCTNet have been lacking. The teachers undoubtedly possess a certain knowledge that is potentially useful to others, and they do desire to learn from others to fulfill their ever-demanding, daily teaching assignments, but they simply fail to enact their desires to conduct knowledge-sharing tasks. In seeking to explain the teachers' lack of volitional strength, which results in the inconsistency between intention and behavior, we investigated four volitional control constructs relevant to knowledge-sharing practices: intention, controllability, self-efficacy, and action/state control (see the research model in Figure 1). Relying on the research model couched in the theory of planned behavior and the actual log of SCTNet's knowledge-sharing activities, we are able to show that in knowledge sharing, people do not always perform in a manner consistent with their espoused attitudes and intentions. This intention–action inconsistency can be explained by people's volitional control mechanisms. Specifically, both perceived self-efficacy (Bandura, 1997) and action control (Kuhl & Bechmänn, 1985) play roles in motivating individuals to share and use knowledge whereas perceived intention and controllability do not.

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Figure 1. Research model.

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Theoretical Background

  1. Top of page
  2. Abstract
  3. Introduction
  4. Theoretical Background
  5. Research Method
  6. Data Analysis and Results
  7. Discussion and Conclusion
  8. References
  9. Appendix A

Knowledge-Sharing Practices

Knowledge sharing has been portrayed as the key component of successful KM practices (Alavi & Leidner, 2001; Earl, 2001; Hendriks, 1999). KM researchers adopt various terms such as knowledge exchanging, knowledge diffusion, knowledge distribution, and knowledge transaction to describe the content of knowledge sharing (Dixon, 2000). These alternative terms present knowledge as a medium flowing between knowledge possessors and knowledge acquirers in the development of new capacities for action. Davenport and Prusak (1998, pp. 101–102) depicted the main activities involved in knowledge sharing as transmission and absorption. The transmission activity includes effectively sending and correctly presenting knowledge to the potential knowledge recipients, and the absorption activity is the effectiveness of knowledge use. Wijnhoven (1998) stated that knowledge sharing should incorporate knowledge interpretation into knowledge-sharing activities. Similarly, in Hendriks' (1999) model of knowledge sharing, knowledge owners externalize the knowledge through the behaviors of codifying, showing, and describing whereas knowledge reconstructors internalize and absorb knowledge through learning, reading, and interpretation.

Thus, a KM system includes not only the technology but also the social system in which the technology is embedded and through which knowledge is shared (Tuomi, 1999–2000). For example, Hansen and Avital (2005) proposed that formal and informal features of organizational contexts, such as organizational incentives structure, organizational cooperative culture, and executive management and professional peers' expectations, can promote or discourage knowledge-sharing behavior. Lin (2007), in a survey of managers, showed that both extrinsic factors such as expected organizational rewards and reciprocal benefits, intrinsic factors such as knowledge self-efficacy and enjoyment in helping others may explain employee's attitudes toward knowledge sharing and knowledge-sharing intention. In addition, Cabrera et al. (2006) demonstrated that psychological, organizational, and system-related variables may determine individual engagement in intraorganizational knowledge sharing while Hsu et al.'s (2007) research on knowledge-sharing behavior in virtual communities showed that environmental and individual factors may support or hinder people's knowledge-sharing behavior. Similarly, Chu et al. (2006) revealed that environmental factors such as social-interaction ties, norm of reciprocity, shared vision, and community-related outcome expectation may influence what and how often members of a virtual community share information. Thus, knowledge and the social system from which it derives meaning are considered inextricably related. Knowledge is what is shared within discourse, within a “textual” social system (Bruner, 1996, p. 57). From the perspective of social cognition, knowing involves complex interactions among prior knowledge; the perception of events, intents, actions, and observations; and reflections attendant to ongoing thoughts and actions (Hannafin & Land, 1997). Actions, goals, and processes are initiated as a result of both previous experience and intuitive assumptions about the concepts to be studied. Knowing, then, is a dynamic “reflection-in-action” course, where action is used to extend thinking, and reflection is governed by the results of action (Schön, 1983).

Theory of Planned Behavior

In information systems research, the theory of planned behavior (TPB) stands out as the most preferred of the intention–action models. Intention refers to the degree to which people are willing to try or how much of an effort they are planning to exert to perform the behavior (Ajzen, 1991). Recently, Ajzen (2002a) postulated that an individual intention to perform an action has three basic antecedents: attitude, subjective norms, and perceived behavioral control, which can be further decomposed into controllability and self-efficacy. Attitude towards a behavior is defined as the degree to which a person has a favorable or an unfavorable evaluation or appraisal of the behavior in question. The subjective norms toward a behavior are defined as the perceived social pressure to perform or not perform the behavior in question. The construct of perceived behavioral control refers to the amount of control over the achievement of personal goals that is introduced to deal with situations in which people may lack complete volitional control over the behavior in question (Ajzen, 1985, 1988). Previous research has revealed several control factors that can influence a person's control over a given behavior (Ajzen, 1988). These include individual differences such as abilities and skills, and the degree that individuals have control over their actions in the form of will power. The former (i.e., individual differences) is generally recognized as perceived self-efficacy, and the latter as controllability (Ajzen, 2002a). Note that in the formulation of the TPB, perceived self-efficacy and controllability serve as antecedents to both intention and actual behavior (Ajzen, 2002a).

Attitude.

In KM practice, Jarvenpaa and Staples (2000) and Constant et al. (1994) reported that people's favorable attitude towards knowledge sharing reflects their sharing behaviors. Kolekofski and Heminger (2003) also concluded that an individual's attitude about sharing organizational information is an important factor in explaining knowledge-sharing activities. Conversely, people are unlikely to cooperate when their actions have no discernible effect on the value of the shared good (Drucker, 2001; Husted & Michailova, 2002; McDermott & O'Dell, 2001; Michailova & Husted, 2003). Thus:

H1: The more favorable the individual's attitude towards knowledge-sharing practices, the stronger his or her intention to share knowledge.

Subjective norms.

Subjective norms also are important in KM practice. Knowledge-sharing practices among individuals in an organization are considered as valuable social acts. For example, sharing knowledge occurs when people in an organization are genuinely interested in helping one another to develop new capacities for action (Senge, 1998). Constant et al. (1994) and Jarvenpaa and Staples (2000) discovered that sharing information in the workplace is aimed at maintaining the well-being and integrity of others and oneself. Bock et al. (2005) proposed that organizational norm has a positive influence on the formation of subjective norms concerning knowledge-sharing practice; it also directly affects the individual's intention toward knowledge-sharing behaviors. Conversely, an organization with knowledge-rejecting behavior as its norm might summarily reject new ideas from outsiders (Husted & Michailova, 2002). Accordingly:

H2: The stronger the individual's perceived subjective norms toward knowledge-sharing practices, the stronger his or her intention to share knowledge.

Controllability.

For KM-based knowledge-sharing behaviors, as in other IT-based behaviors, perceived ease of use or facilitating conditions related to the controllability of a particular technology has been shown to be a major factor predicting intention (Compeau & Higgins, 1995, Venkatesh et al., 2003). Jarvenpaa and Staples's (2000) study confirmed this by showing that computer comfort is positively correlated to the use of collaborative media. In addition, Husted and Michailova (2002) concluded that the lack of a sense of control leads people to suppress themselves and remain at a distance from undertaking effective knowledge sharing. Thomas et al.'s (2001) study also showed that an uncertain level of controllability leads people to change their approach to sharing knowledge with a person in need. Thus:

H3: The greater the individual's controllability of knowledge-sharing practices, the stronger his or her intention to share knowledge.

H4: The greater the individual's controllability of knowledge-sharing practices, the more likely it is that he or she will perform knowledge-sharing behavior.

Perceived self-efficacy.

Perceived self-efficacy refers to people's judgment of their own capabilities to organize and execute the course of action required to attain a designated type of performance (Bandura, 1997). Perceived self-efficacy is concerned “not with the number of skills you have, but with what you believe you can do with what you have under a variety of circumstances” (Bandura, 1997, p. 37). Because knowledge-sharing practices involve various forms of social learning (Senge, 1998), perceived self-efficacy is suitable for measuring one's control over KM practices. For example, Cabrera et al. (2006) and Lin (2007) showed that knowledge self-efficacy significantly influences the individual's intention toward knowledge sharing while Hsu et al. (2007) also demonstrated that individual knowledge self-efficacy is a predictor of knowledge-sharing behavior. In addition, the success of knowledge-sharing practices in BP Amoco is attained through coaching and training individuals to use KM systems so that individuals believe in their ability to perform knowledge-sharing tasks through KM systems (Husted & Michailova, 2002). In contrast, a lack of confidence causes employee failure in integrating KM into the everyday workload (KPMG, 2000, pp. 1–23). Thus:

H5: The greater the individual's perceived knowledge-sharing self-efficacy, the stronger his or her intention to share knowledge.

H6: The greater the individual's perceived knowledge-sharing self-efficacy, the more likely that he or she will share knowledge.

Finally, the TPB prescribes that intention influences one's behavior. Therefore:

H7: The stronger the individual's intention to share knowledge, the more likely it is that he/she will share knowledge.

The Theory of Action Control

Kuhl's (1981, 1982, 1994a, 1994b; Kuhl & Bechmänn, 1985) work on state versus action orientation is a part of his more general theory of action control. State versus action orientation indicates a person's general tendency to approach or avoid things in a static (i.e., passive) or dynamic (i.e., active) fashion. Individuals with a strong action orientation are able to devote their cognitive resources to the task at hand, thus enabling themselves to move expediently from a present goal state to the desired future goal state. These individuals flexibly allocate their attention for task execution and goal attainment, and are characterized by enhanced performance efficiency and the ability to complete tasks after minor failures (Kuhl, 1994a). Conversely, individuals with a state orientation tend to have ruminative thoughts about alternative goals, and this reduces the cognitive resources available for goal striving. This reduction of available resources impairs state-oriented individuals' ability to initiate activities and to follow tasks through to completion, especially when the activities are difficult, nonroutine, or both (Goschke & Kuhl, 1993; Kuhl, 1981; Kuhl & Bechmänn, 1985).

Many previous studies have shown that action/state orientation may moderate the influence of attitudes and norms on intention as well as the influence of intention on task performance. For example, Bagozzi et al. (1992) indicated that attitudes influence intentions more strongly for action-oriented people than for state-oriented people. We can think of an attitude as an indicator of one's evaluation function for an action. But there also needs to be an explicit motivational force that transforms the attitude into the will for action (Bagozzi et al., 1992). State orientation implies low motivation or an absence of motivation to act. Hence, we expect that attitudes will have a stronger effect on intentions for action-oriented people than for state-oriented people. Bagozzi et al. (1992) also found that subjective norms influence intentions more strongly for state-oriented than for action-oriented people. In addition, Kuhl (1982) reported that the correspondence between intentions and actual behavior is significantly greater for action-oriented than for state-oriented people.

Although little previous research has assessed the influence of action/state orientation on KM-based knowledge-sharing practices, several studies have suggested that such an effect might exist. For example, McDermott and O'Dell (2001) suggested that organizations should recruit those people who are autonomous (i.e., action-oriented) in sharing ideas and insights as the core members to encourage colleagues to share their knowledge and assure the successful implementation of KM. At Siemens, mid-level employees have actively and autonomously practiced KM-based tasks such as creating knowledge repositories and sharing their knowledge, without any suggestions or provocation from top managers (Davenport & Völpel, 2001). This success suggests that action orientation on the part of employees is critical for the success of knowledge sharing. Impediments to individuals' control in action enactment were revealed in Husted and Michailova's (2002) research. They reported that an individual may have the intention to perform knowledge-sharing activities in the organization, yet his or her action may be impeded by uncertainty about colleagues' reactions or fear of career damage. Those who are action-oriented can overcome impediments in decision conflicts, move to the desired future goal state, and are able to complete tasks soon after minor failures. In contrast, people who are state-oriented immerse themselves in the impediment and become ruminative and hesitant to move themselves toward the final goal. Thus:

H8a: The effect of attitude on an individual's intention to share knowledge is invariant across the action- and state-oriented groups.

H8b: The effect of subjective norms on an individual's intention to share knowledge is invariant across the action- and state-oriented groups.

H8c: The effect of an individual's intention to share knowledge on his or her knowledge-sharing practices is invariant across the action- and state-oriented groups.

Based on the previous review, Figure 1 depicts our TPB-based research model. Table 1 lists the research hypotheses.

Table 1. Research hypotheses.
Hypotheses
H1The more favorable the individual's attitude towards knowledge-sharing practices, the stronger his or her intention to share knowledge.
H2The stronger the individual's perceived subjective norms toward knowledge-sharing practices, the stronger his or her intention to share knowledge.
H3The stronger the individual's intention to share knowledge, the more likely it is that he or she will share knowledge.
H4The greater the individual's controllability of knowledge-sharing practices, the stronger his or her intention to share knowledge.
H5The greater the individual's controllability of knowledge-sharing practices, the more likely it is that he or she will perform knowledge-sharing behavior.
H6The greater the individual's perceived knowledge-sharing self-efficacy, the stronger his or her intention to share knowledge.
H7The greater the individual's perceived knowledge-sharing self-efficacy, the more likely it is that he or she will share knowledge.
Moderating hypotheses
H8aThe effect of attitude on an individual's intention to share knowledge is invariant across the action- and state-oriented groups.
H8bThe effect of subjective norms on an individual's intention to share knowledge is invariant across the action- and state-oriented groups.
H8cThe effect of an individual's intention to share knowledge on his or her knowledge- sharing practice is invariant across the action- and state-oriented groups.

Research Method

  1. Top of page
  2. Abstract
  3. Introduction
  4. Theoretical Background
  5. Research Method
  6. Data Analysis and Results
  7. Discussion and Conclusion
  8. References
  9. Appendix A

Participants and Procedure

SCTNet (http://SCTNet.edu.tw), the virtual professional community of teachers in Taiwan, served as the source of participants for this study. More than 90% of the SCTNet members are preliminary and junior high school teachers. SCTNet provides several forums for facilitating knowledge sharing among members: discussion, professional workshops, resource sharing, and coffee shop forums. Teachers have to register to become SCTNet members to post work-related issues, participate in discussions, and upload or download material. All knowledge-sharing contents and activities are administrated by the knowledge managers, who are selected from SCTNet members who show an active history and expertise in some specific teaching domain.

Data collection started at the beginning of the fall semester in 2002 and lasted for 1 month. For members who agreed voluntarily to participate in the study, a computer program provided guidance to complete the Web-based survey on SCTNet. During this data-collection period, approximately 2,000 individual members visited SCTNet, and a total of 304 unique individual participants completed the questionnaire. Of these, 264 were considered valid responses. Thus, the response rate was approximately 15.2%, which matched the average response rate of 15% in several pop-up Web-based surveys (Comley, 2000; McLaughlin, 2000). SCTNet maintained a log of all members' knowledge-sharing activities such as posting work-related issues, discussion and critiques of these issues, and uploading and downloading instructional material. After participants filled out the questionnaires, a 2-week activity log was collected for later use to measure the behavior of knowledge-sharing practices for the respondents in this research.

Measures

In this study, we conducted a pilot study to develop measurement items. First, we adopted measurement items from the literature and translated them into Chinese. We then asked several SCTNet experts and information management professionals to make sure that our translation was correctly interpreted. The comments collected from these experts led to a few minor modifications of the wording. Afterward, we conducted an online pilot test in which 235 individuals participated. The resulting data were analyzed to further modify the questionnaire items. Finally, we invited 30 elementary and junior high school teachers to participate in another test on the revision to attain the final questionnaire items for the formal study.

The Appendix presents the details of all items in our study. Intention refers to the degree to which people are willing to try or how much of an effort they are planning to exert to perform the behavior (Ajzen, 1991). Ajzen used “I intend to” and “I will try to” to capture the individual's intention that she or he “will do” a behavior (Ajzen 1985, 1988, 1991, 2001, 2002a, 2002b; Ajzen & Fishbein, 1980)”. In this study, we use two Likert-scale items, “I intend to share knowledge on SCTNet” (INT1) and “I will try to share knowledge on SCTNet” (INT2), to measure on a scale of 1 (totally disagree) to 7 (totally agree) respondents' knowledge-sharing intentions. Attitude toward a behavior is defined as the degree to which a person has a favorable or an unfavorable evaluation or appraisal of the behavior in question. Ajzen used “good,” “beneficial,” and “valuable” to measure a summary evaluation of a psychological object captured in such attribute dimensions. Thus, we assess the attitudes toward knowledge sharing by asking three 7-point Likert-scale items: “For me to perform knowledge sharing on SCTNet is good (A1), beneficial (A2), and valuable (A3) (Ajzen, 1988; Ajzen & Fishbein, 1980). The measure of subjective norms consists of three 7-point Likert-scale items: “The principal or colleagues in my school, or peers in my field of expertise expect(s) me to perform knowledge sharing on SCTNet.” The measure of controllability includes two 7-point Likert-scale items: “I believe that I have full control of using SCTNet” (C1), and “It is mostly up to me whether or not I use SCTNet” (C2).

To capture the respondents' self-efficacy in using SCTNet for knowledge-sharing practices (e.g., using search engines for resource queries, postings and/or discussions, and sharing teaching materials through different forums), we rely on a six-item measure that asks participants to judge the level of their capabilities as of now. Therefore, items are phrased in terms of “can do” rather than “will do.” This reveals the level of strength in doing the activity specified in the item. Note that the efficacy ratings are on a 100-point scale ranging, in 10-unit intervals, from 0 (cannot do) to 10 (can certainly do), indicating complete assurance. Such scales could reveal both magnitude (i.e., the respondent can or cannot do an activity of a certain difficulty) and strength (i.e., the level of conviction in doing that activity) (Bandura, 1995).

We measure knowledge-sharing behavior via aggregating the frequency of two types of knowledge-sharing activities: transmission (i.e., sending or presenting knowledge to a potential recipient) and absorption by that person or group (Davenport & Prusak, 1998). The transmission and absorption actions include activities such as posting work-related issues, sharing individual classroom experiences, downloading resources posted by peers on SCTNet, participating in discussions, and responding to questions requests by other teachers. Table 2 shows the aggregated frequency of transmission and absorption logs for all respondents. This use of logged frequency to measure knowledge sharing is consistent with the mechanisms for capturing individual knowledge sharing proposed by Earl (2001), and resembles that in the studies by Morris and Venkatesh (2000) and Venkatesh, Morris, and Ackerman (2000).

Table 2. Aggregated frequency of transmission and absorption logs for all respondents.
Knowledge-sharing behaviorsPosting work-related issues that individual encounteredSharing individual teaching experiences, know-how from work, or uploading teaching resources that are designed by individualDownloading teaching experiences, know-how, or teaching resources that are shared by other peers on SCTNetParticipating in work-related issues' discussionsGiving feedback as individual reads or implement those thoughts that shared (i.e., posted) by other peers
Frequency4,7672,2991,5563071,819

We measure the action control with a 24-item that is divided into two subscales: Preoccupation and Hesitation (Kuhl, 1994b). For each item, the participant is given an alternative between two possible response alternatives: one oriented towards action and the other towards state. The grading of the participants' responses for these items is based on the count of the total of action-oriented responses. Here, we follow the same method employed by previous researchers in computing individual action/state orientation (Antoni & Beckmann, 1990; Bagozzi et al., 1992; Javier et al., 2003; Kendzierski, 1990). For example: Item AC1 (see the Appendix) states: “When I have lost something that is very valuable to me and I can't find it anywhere. Two alternatives are presented: (a) I have a hard time concentrating on something else, and (b) I put it out of my mind after a little while.” We assign a score of 0 to a participant who selects (a), which indicates a state orientation, and a score of 1 to those who select (b), which indicates an action orientation. All scores for the 24 items are then summed up for each respondent. Finally, based on a median split of this score (Mdn = 14), those who have a score greater than or equal to 14 are classified as having an action orientation, and those whose score is less than 14 are classified as having a state orientation (Antoni & Beckmann, 1990; Kendzierski, 1990).

Data Analysis and Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Theoretical Background
  5. Research Method
  6. Data Analysis and Results
  7. Discussion and Conclusion
  8. References
  9. Appendix A

Table 3 describes the participants' profiles. Most participants owned a personal computer and were between 20 and 40 years of age at the time of the study. Around 84% of the participants held bachelor's degrees, and 16% held master's degrees. There is 78% of the participants had more than 3 years of computer experience and spent an average of 4 to 36 hr per week on the Internet.

Table 3. Profile of respondents.
VariableValue (%)
Age (years) 
20–3058.7
30–4023.1
40–5015.2
>50a3.0
Education
Bachelor's degree or equivalent84.5
Master's degree15.5
Own a personal computer
Yes95.8
No4.2
Experience of using a computer
≤1 year2.7
1–2 years7.2
2–3 years12.1
>3 years78.0
Average use of Internet per week
≤3 hr9.8
4–12 hr36.7
13–36 hr35.2
>37 hr18.1

The Measurement Model

Our data analysis consisted of two stages: the measurement model and the structural model. For the measurement model, we assessed the construct validity with LISREL confirmatory factor analysis. As shown in Table 4, reliability was examined using Cronbach's alpha values. All values were above 0.8, representing an acceptable level for confirmatory research. We also tested convergent validity using three criteria: (a) All indicator factor loadings should be significant and exceed 0.7, (b) construct reliabilities should exceed 0.7, and (c) the average variance extracted (AVE) by each construct should exceed 0.5 (Fornell & Larker, 1981 Hair, Anderson, Tatham, & Black, 1998; Segars, 1997). In our model, all factor loadings and composite reliabilities exceeded 0.7 and were significant at p = .05; the AVE ranged from 0.54 to 0.91 (Tables 4 & 5). Therefore, our models meet the convergent validity criteria. Discriminant validity of the resulting measures was assessed using the guideline suggested by Fornell and Larcker (1981): The square root of AVE for each construct should exceed the correlation shared between the construct and other constructs in the model. Table 5 lists the correlation matrix, with correlation among constructs, and the square root of AVE on the diagonal. The diagonal values exceed the interconstruct correlations; thus, the test of discriminant validity is acceptable.

Table 4. Measurement Model and scale properties.
Construct & itemsMSDItem-to-construct loadingCronbach's α
Attitude    
A16.250.820.900.945
A26.190.860.93 
A36.090.930.92 
Subject norms    
SN14.921.240.840.87
SN24.881.160.93 
SN35.311.100.74 
Intention    
INT15.890.910.970.94
INT25.860.890.92 
Controllability    
C15.821.040.850.81
C26.061.050.79 
Self-efficacy    
SE19.371.200.680.94
SE28.651.690.91 
SE38.701.610.97 
SE48.551.740.94 
SE58.661.590.87 
SE68.101.890.78 
Table 5. Correlation and AVE.
ConstructAVECRASNSECI
  1. AVE is average variance extracted. AVE should be larger than the squared correlation between any pair of constructs.

  2. A = Attitude; SN = Subjective Norms; SE = Self-efficacy; C = Controllability; I = Intention. CR = Composite reliability.

A0.880.950.938
SN0.640.840.3060.800
SE0.540.870.1170.0320.735
C0.650.790.4280.1250.3590.806
I0.910.950.6090.3120.2450.4420.954

The Structural Model

The structural model was tested using the structural equation modeling approach by using LISREL 8.30. The overall model fit was assessed by using multiple fit criteria, as suggested in the literature (Hair et al., 1998). Table 6 summarizes the values observed in the study together with recommended values of common model fit indexes. As shown, all of them meet the recommended minimum levels, suggesting adequate model fit.

Table 6. Analysis of overall model goodness-of-fit using common fit indexes.
Model goodness-of-fit indexesRecommended valueResults obtained from this study
χ2 significance (p)p > .05p = .21
χ2/df≥3.0114.33/103 = 1.11
Goodness-of-fit index (GFI)≥0.900.95
Adjusted goodness-of-fit index (AGFI)≥0.900.93
Normalized fit index (NFI)≥0.900.97
Nonnormalized fit index (NNFI)≥0.900.99
Comparative fit index (CFI)≥0.901.00
Root mean square residual (RMSR)≥0.100.02

Next, we evaluated the path significance in the research model, and examined the variance (R2 value) explained by each path. As summarized in Table 7 and Figure 2, the paths from attitude, subjective norm, controllability, and self-efficacy to intention were all significant (p < .05); together, they explain 49% of the knowledge-sharing intention variance. For behavior prediction, only the path from self-efficacy to knowledge-sharing behavior was statistically significant (p < .05), with a 0.19 path coefficient (see Tables 7 and 8). Thus, self-efficacy is a significant direct predictor of knowledge-sharing behavior, explaining 4% of the behavior variance. This indicates that individuals who have a conviction in their ability to perform knowledge-sharing tasks through knowledge-management systems can actually carry out the knowledge-sharing behaviors.

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Figure 2. Results of model test.

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Table 7. Significance of individual paths.
PathPath coefficient (t value)Hypotheses
  1. **p < .05.

Attitude [RIGHTWARDS ARROW] Intention0.50(7.34)**H1 (Supported)
Subject norms [RIGHTWARDS ARROW] Intention0.14(2.66)**H2 (Supported)
Intention [RIGHTWARDS ARROW] Behavior0.08(1.07)H3 (Not supported)
Controllability [RIGHTWARDS ARROW] Intention0.18(2.55)**H4 (Supported)
Controllability [RIGHTWARDS ARROW] Behavior− 0.11(− 1.35)H5 (Not supported)
Self-efficacy [RIGHTWARDS ARROW] Intention0.13(2.40)**H6 (Supported)
Self-efficacy [RIGHTWARDS ARROW] Behavior0.19(2.75)**H7 (Supported)
Table 8. Strengths of individual factors.
Effect on behaviorEffect size
Direct effect 
Self-efficacy0.19
Total effect 
Self-efficacy0.19

The Moderating Effect of Action Versus State Orientation

To test the moderating effect, two stages of data processing were performed. First, we used a median split criterion (Act Mdn = 14): Subjects were classified as action- (Act ≥ 14) versus state-oriented (Act ≤ 14) group (Antoni and Beckmann, 1990; Kendzierski, 1990).

Then, we conducted a subgroup analysis of the model-data fit. For each of the two subgroups, Figures 3a and 3b show theresults of the model test for action- and state-orientation groups, respectively, and Table 9 shows that both groups have a good model-data fit. These results indicate that the same pattern of estimated parameters are able to fit the data for each group, but the parameters estimated for the paths do not take on the same values for the different groups.

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Figure 3a. Results of the model test for the action-orientation group.

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Figure 3b. Results of the model test for the state-orientation group.

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Table 9. Subgroup analysis of goodness of fit.
DimensionsSubgroupsχ2dfpGFIAGFINFINNFICFI
  1. GFI = goodness-of-fit index; AGFI = adjusted goodness-of-fit index; NFI = normalized fit index; NNFI = nonnormalized fit index; CFI = comparative fit index.

ActionAction119.69107.190.910.870.940.990.99
control(n = 138)        
 State111.69104.290.900.860.930.990.99
 (n = 126)        

At the second stage, a two-group stacked model was used to test whether the individual gamma and beta coefficients are equal between the action-oriented and state-oriented groups. Tables 10 and 11 show the key path loadings and the results of the moderating effects of action control. For the action-orientation group, attitude and subjective norms had significant effects on knowledge-sharing intention, and intention to perform knowledge sharing in SCTNet had a significant effect on knowledge-sharing practices. This means that in the action-oriented group, as long as teachers had a favorable evaluation of the knowledge-sharing behavior and perceive that important others expect them to perform knowledge sharing, they showed their intention towards the behavior and actually performed it. Conversely, for the state-orientation group, only attitude had a significant effect on knowledge-sharing intention. The effect of perceived knowledge-sharing self-efficacy in social cognitive theory was statistically significant. This indicates that in the state-oriented group, teachers showed their intention for knowledge sharing when they had a positive evaluation of that activity. Their intentions toward knowledge sharing, however, did not predict their actual behaviors. Only self-efficacy to perform knowledge sharing can directly predict their actual behaviors.

Table 10. Key path for action-oriented and state-oriented group.
 Action orientation group (n = 138)State orientation group (n = 126)
 βtβt
  1. **p < .05.

Attitude [RIGHTWARDS ARROW] intention0.47(4.69)**0.51(5.36)**
Subjective norms [RIGHTWARDS ARROW] intention0.21(2.83)**0.09(1.15)
Intention [RIGHTWARDS ARROW] behavior0.22(2.11)**− 0.05(− 0.47)
Controllability [RIGHTWARDS ARROW] intention0.20(1.87)0.17(1.70)
Controllability [RIGHTWARDS ARROW] behavior− 0.18(− 1.37)− 0.06(− 0.59)
Self-efficacy [RIGHTWARDS ARROW] intention0.08(1.00)0.16(1.95)
Self-efficacy [RIGHTWARDS ARROW] behavior0.13(1.32)0.25(2.54)**
Table 11. Tests for invariance of key paths.
 Fit of the model with the pathTest of invariance 
PathFreeFixed to be equalΔχ2(df) testHypotheses
  1. **p < .05.

Attitude [RIGHTWARDS ARROW] intentionχ2(211) = 231.39χ2(212) = 231.31χ2(1) = − 0.08H8a
   p = 1 
Subjective norms [RIGHTWARDS ARROW] intentionχ2(211) = 231.39χ2(212) = 233.31χ2(1) = 1.92H8b
   p = 1.17 
Intention [RIGHTWARDS ARROW] behaviorχ2(211) = 231.39χ2(212) = 235.63χ2(1) = 4.24H8c**
   p = .04** 

Finally, we tested the equity constraint model for H8a, H8b, and H8c to make sure the invariance test was significant. The test of the equality constraint model showed that gamma coefficients (a) between the attitude and knowledge-sharing intention and (b) between the subjective norms and knowledge-sharing intention do not differ between the action- and state-orientation groups. Therefore, H8a and H8b are not supported by the data. But the path coefficient between knowledge-sharing intention and actual behavior in the equality constraint model differs between action- and state-oriented groups, thereby supporting H8c. This indicates that action or state orientation moderates the influence of intention on task performance.

Discussion and Conclusion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Theoretical Background
  5. Research Method
  6. Data Analysis and Results
  7. Discussion and Conclusion
  8. References
  9. Appendix A

In this study, we have investigated four types of volitional control mechanisms that may impact people to transfer their intentions into knowledge-sharing practice. Our results show that in knowledge sharing, people do not always perform in a manner consistent with their espoused attitudes and intentions. Specifically, the perceived controllability of using a KM platform is not critical, confirming many previous findings that perceived ease or difficulty related to IT usage does not play a central role in successful KM implementations. However, perceived self-efficacy (i.e. one's conviction of his or her ability to conduct KM tasks), is an important factor directly influencing KM practice. In addition, one's action/state orientation moderates his or her enactment of subjective norms and self-efficacy beliefs into intentions, and his or her enactment of controllability into behaviors. Overall, the result indicates that people of high self-efficacy and an action orientation are more likely to overcome the impediment in knowledge sharing. But, more importantly, the low R2 value of 0.04 indicates that the overall explanatory power of the quantitative study is rather low. This weak R2 value may be caused by the limitation of the quantitative approach to study a fundamentally social act such as knowledge sharing.

The results have important theoretical and managerial implications. Theoretically, one possible cause of the weak R2 value is that items such as “I intend to” and “I will try to” fail to capture the participants' evaluation when behaviors become automatic. Thus, for habitual behaviors that become automatic, this lack of predicting power may be improved by introducing “implementation intention” into the TPB research model (Gollwitzer, 1993). Via the formation of plans specifying where and when to get started (Orbell et al., 1997), implementation intention provides a mechanism that facilitates the retrieval of intentions in memory and reduces the capacity of past behavior to predict future behavior. It has been empirically tested in the healthcare domain and has shown that forming an implementation intention increases the likelihood of translating an intention into action (Orbell et al., 1997).

However, this is not the case in the current study, in which we have observed that participants failed to enact their intention in sharing their knowledge in SCTNet. Our research indicates that to research knowledge sharing in a virtual-community environment, the study has to go beyond the investigation of intention, which has been the most commonly employed dependent variable when applying theories such as the theory of reasoned action (TRA) and TPB to study knowledge-sharing behaviors (Bock et al., 2005; Hansen & Avital, 2005; Lin et al., 2006; Ryu et al., 2003; So & Bolloju, 2005). This is particularly significant for KM researchers who wish to be not only rigorous in research but also relevant to practice. KM usage differs from other IT usages in that there exists both environmental and psychological barriers to prevent people from sharing their knowledge despite their intention to do so. This gap between intention and action is not unique. In fact, Ajzen (2002b), the TRA/TPB models' original author, indicated that the gap between intentions and actions may occur due to factors such as detrimental unanticipated consequences, negative reactions from important referents, underestimation of the behavior's difficulty, and lack of resolve or willpower. In this study, we incorporate psychological mechanisms of control to address these factors in the study of knowledge-sharing practices. As in studies of technology-mediated learning (Alavi & Leidner, 2001; Alavi et al., 2005–2006), this incorporation enhances our understanding of how specific social and technological arrangements may have an impact on people's psychological processes, thereby resulting in certain KM outcomes. This idea may be traced back to Vygotsky (1978, pp. 52–57), who suggested that learning new knowledge is centrally a mediation process—where an external stimulus triggers learners' psychological processes and learners transfer the external activities inward so as to internalize these developmental events into higher order mental functions. Thus, the demonstrated importance of self-efficacy and action-control beliefs points to the need for researching their anteceding determinants. Both types of beliefs are highly and reciprocally dependent on the environment. In fact, self-efficacy is at the center of social cognitive theory (Bandura, 1997), which adopts a cognitive interactionist perspective on personal behavior. Within SCTNet, personal factors in the form of thoughts and affections, environmental factors such as social norms and peer encouragement, and personal behavior all operate as interacting determinants which influence each other bidirectionally (Bandura, 1997). A substantial body of research on the diverse effects of self-efficacy has been conducted over the past 20 years (Bandura, 1997). The findings suggest that people with a low sense of self-efficacy tend to have low aspirations and weak commitment to pursuing their goals, and feeble adherence to their values. Conversely, people with high self-efficacy approach difficult tasks as challenges to be mastered rather than threats. They quickly recover their efficacy perception from their failures and heighten their efforts in the face of difficulties. Thus, the important tasks for KM researchers are to investigate (a) how efficacy and action-control beliefs can be raised through organizational policies and technological designs, and (b) what possible adverse effects may be exerted by low efficacy and action-control beliefs on a particular KM implementation. In doing this, researchers should be equipped to interpret more clearly whether certain organizational policies or KM designs lead to effective KM outcomes.

For managers who wish to carry out KM projects, our research confirms the finding of Davenport and Völpel (2001) that “managing knowledge is managing people; managing people is managing knowledge” (p. 218). Past research has shown that beliefs concerning self-efficacy and action/state orientation can be strengthened by vicarious observation, positive feedback, enactive mastery, and appropriate psychological arousal (Bandura, 1997). For example, managerial support in encouraging subordinates to contribute their thoughts may significantly impact effort and frequency of individuals in sharing their knowledge via knowledge management systems (King & Marks, 2008). This is critically important for those who have low knowledge self-efficacy and strong state orientation and who may develop a tendency to perform routinized and externally controlled behaviors to obviate heavy demands on their relatively low volitional capacities (Kuhl, 1985). In addition, to save oneself from the risk of making a mistake, one may choose to keep quiet or say what the manager wants to hear in responding to organizational knowledge-sharing policy (Blain, 1994; Deter & Edmondson, 2007; Voelpe & Han, 2005). Yet, these passive enactments can impede the organizational knowledge innovation. Managers should therefore focus on these general areas if they wish their KM investment to be worthwhile.

In addition, management should be sensitive to the interaction of information culture and technology. In Taiwan, sharing knowledge publicly may be interpreted as an arrogant act and is discouraged. In addition, Voelpel and Han's (2005) study of Siemens' Web-based KMS (called ShareNet) in China has shown that in- and out-group distinction causes subordinates to hesitate in sharing their knowledge in Web-based KMS. Most individuals would rather share their knowledge with good friends who they call “in-group” members. This is especially so for people who are not confident in presenting themselves in public. Sharing with “in-group” members can secure themselves from unanticipated detrimental consequences or negative reactions from unfamiliar/unknown others in a Web-based setting. Thus, managers should be sensitive to the interaction of information culture and technology in a Web-based or public setting if they wish their KM practices to be successful.

Our research has several limitations. First, Ajzen (2002a) showed that controllability has been found to be insignificant in many studies. Ajzen suggested that the more specific the target behavior, the better the predicting power of the perceived behavioral-control construct. Yet, the two terms “knowledge” and “sharing” are interpreted very differently by different people. Consequently, in our study, knowledge-sharing behaviors may be reflected ambiguously by the items of controllability. The same also may be true for items measuring the intention construct (e.g., “I intend to share knowledge on SCTNet”) due to the vagueness of the wording “sharing knowledge.” Future research needs to address this problem. At the same time, the measurement of knowledge-sharing practices needs to be expanded to include self-reporting. In the current study, we have found that the use of knowledge-sharing statistics documented by SCTNet is too limited. We believe that temporal and qualitative statistics may be added to enhance our understanding. Another limiting factor is that we were not able to collect actual knowledge-sharing practices such as browsing the SCTNet and their private exchanges of ideas through e-mail. This may contribute to the low R2 value of 0.04 observed here. Finally, a longitudinal study may be better suited than a sectional study to reveal the characteristics of the mechanisms of psychological control. We have therefore planned a follow-up field study for this purpose.

References

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  2. Abstract
  3. Introduction
  4. Theoretical Background
  5. Research Method
  6. Data Analysis and Results
  7. Discussion and Conclusion
  8. References
  9. Appendix A
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Appendix A

  1. Top of page
  2. Abstract
  3. Introduction
  4. Theoretical Background
  5. Research Method
  6. Data Analysis and Results
  7. Discussion and Conclusion
  8. References
  9. Appendix A

Study Measures

  • Intention: 7-point Likert scale, 1 (totally disagree) and 7 (totally agree)

    • INT1 I intend to share knowledge on SCTNet.

    • INT2 I will try to share knowledge on SCTNet.

  • Attitude: 7-point Likert scale, 1 (totally disagree) and 7 (totally agree)

    • A1 For me to perform knowledge sharing on SCTNet is good.

    • A2 For me to perform knowledge sharing on SCTNet is beneficial.

    • A3 For me to perform knowledge sharing on SCTNet is valuable.

  • Subjective Norm: 7-point Likert scale, 1 (totally disagree) and 7 (totally agree)

    • SN1 The principal in my school expects me to perform knowledge sharing on SCTNet.

    • SN2 The colleagues in my school expect me to perform knowledge sharing on SCTNet.

    • SN3 The peers in my field of expertise expect me to perform knowledge sharing on SCTNet.

  • Controllability: 7-point Likert scale, 1 (totally disagree) and 7 (totally agree)

    • C1 I believe that I have full control of using SCTNet.

    • C2 It is mostly up to me whether or not I use SCTNet.

  • Self-Efficacy: 100-point scale ranging, in 10-unit intervals, from 0 (cannot do) to 10 (can certainly do)

    • SE1 I am confident that I can find all the resources that I want by using SCTNet's search engine.

    • SE2 I am confident that I can post new issues on SCTNet's Discussion Forum.

    • SE3 I am confident that I can give a response to a specific issue on SCTNet's Discussion Forum.

    • SE4 I am confident that I can discuss work-related issues on SCTNet's Professional Forum.

    • SE5 I am confident that I can perform resource sharing on SCTNet's Resource Sharing Forum.

    • SE6 I am confident that I can chat on a specific topic on SCTNet's Coffee Shop.

  • Action Control: binary scale, 0 (state-oriented tendency) and 1 (action-oriented tendency)

    • AC1 When I have lost something that is very valuable to me and I can't find it anywhere: (a) I have a hard time concentrating on something else; (b) I put it out of my mind after a little while.

    • AC2 If I've worked for weeks on one project and then everything goes completely wrong with the project: (a) it takes me a long time to adjust myself to it; (b) it bothers me for a while, but then I don't think about it anymore.

    • AC3 When I'm in a competition and have lost every time: (a) I can soon put losing out of my mind; (b) the thought that I lost keeps running through my mind.

    • AC4 If I had just bought a new piece of equipment (e.g., a tape deck) and it accidentally fell on the floor and was damaged beyond repair: (a) I would manage to get over this quickly; (b) it would take me a long time to get over it.

    • AC5 If I have to talk to someone about something important and repeatedly can't find him or her at home: (a) I can't stop thinking about it, even while I'm doing something else; (b) I easily forget about it until I see the person.

    • AC6 When I've bought a lot of stuff at the store and realize when I get home that I've paid too much—but I can't get my money back: (a) I can't usually concentrate on anything else; (b) I easily forget about it.

    • AC7 When I am told that my work has been completely unsatisfactory: (a) I don't let it bother me for too long; (b) I feel paralyzed.

    • AC8 If I'm stuck in traffic and miss an important appointment: (a) at first, it's difficult for me to start doing anything else; (b) I quickly forget about it and do something else.

    • AC9 When something is very important to me, but I can't seem to get it right: (a) I gradually lose heart; (b) I just forget about it and do something else.

    • AC10 When something really gets me down: (a) I have trouble doing anything at all; (b) I find it is easy to distract myself by doing other things.

    • AC11 When several things go wrong on the same day: (a) I usually don't know how to deal with it; (b) I just keep on going as though nothing has happened.

    • AC12 When I have put all my effort into doing a really good job on something and the whole thing doesn't work out: (a) I don't have too much difficulty starting something else; (b) I have trouble doing anything else.

    • AC13 When I know I must finish something soon: (a) I have to push myself to get started; (b) I find it easy to get it done and over with.

    • AC14 When I don't have anything in particular to do and I am getting bored: (a) I have trouble getting up enough energy to do anything; (b) I quickly find something to do.

    • AC15 When I'm getting ready to tackle a difficult problem: (a) it feels like I am facing a big mountain that I don't think I can climb; (b) I look for a way to approach the problem in a suitable manner.

    • AC16 When I have to solve a difficult problem: (a) I usually don't have any trouble getting started on it; (b) I have trouble sorting things out in my head so that I can get down to working on the problem.

    • AC17 If I have to make up my mind about what I am going to do when I get some unexpected free time: (a) it takes me a long while to decide what I should do during this free time; (b) I can usually decide on something to do without having to think about it for very long.

    • AC18 When I have work to do at home: (a) it is often hard for me to get the work done; (b) I usually get it done right away.

    • AC19 When I have a lot of important things to do and they must all be done soon: (a) I often don't know where to begin; (b) I find it easy to make a plan and stick to it.

    • AC20 When there are two things that I really want to do, but I can't do both of them: (a) I quickly begin one thing and forget about the other thing I can't do; (b) it's not easy for me to put the other thing I can't do out of my mind.

    • AC21 When I have to take care of something important which is also unpleasant: (a) I do it and get it over with; (b) it can take a while before I can bring myself to do it.

    • AC22 When I am facing a big project which has to be done: (a) I often spend too long thinking about where I should begin; (b) I don't have any problems getting started.

    • AC23 When I have a boring assignment: (a) I usually don't have any problem getting through it; (b) sometimes I can't get moving on it.

    • AC24 When I have an obligation to do something which is boring and uninteresting: (a) I do it and get it over with; (b) it can take a while before I can bring myself to do it.