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Programmer perceptions of knowledge-sharing behavior under social cognitive theory
Expert Systems with Applications 37 (2010) 8479–8485
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Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa
Programmer perceptions of knowledge-sharing behavior under social cognitive theory Ming-Ten Tsai, Nai-Chang Cheng * Department of Business Administrator, National Cheng-Kung University, Tainan, Taiwan
a r t i c l e
i n f o
Keywords: Knowledge sharing Social cognitive theory Self-efficacy Organizational climate Intention Behavior
a b s t r a c t Despite the importance of the software industry, little research using social cognitive perspective has focused on the software industry. This study thus examines key factors, including self-efficacy, expectancy theory and organizational climate, on the software workers to intent to share knowledge, using a social cognitive framework. Programmers and software workers in Taiwan were surveyed to test the proposed research model. Confirmatory factor analysis (CFA) and structural equation modeling (SEM) technique were used to analyze the data and evaluate the research model. Results showed that that the research model fit the data well and the main determinants of knowledge-sharing behavior were the encouraging intentions of knowledge intensive workers. We confirm our hypothesis that knowledge sharing self-efficacy and outcome expectancy, as well as organizational climate, will affect individual intentions to share knowledge. Additionally, organizational climate and perceived managerial incentive were found to positively encourage knowledge-sharing behavior. Research and practical implications are described. Ó 2010 Elsevier Ltd. All rights reserved.
1. Introduction Organizations recognized that knowledge constitutes a valuable intangible asset for creating and sustaining competitive advantage (Crone & Roper, 2001; Howells, 2002; Lee, 2001; Miller & Shamsie, 1996; Warkentin, Sugumaran, & Bapna, 2001). Knowledge sharing is thus a key component of knowledge management in organizations, and knowledge sharing activities are generally supported by knowledge management systems (Walsham, 2001). Many studies have identified the many factors that affect the sharing of knowledge in organizations, such as organizational culture, trust, and incentives (Cabrera & Cabrera, 2002). In this research, we consider knowledge sharing to be both an emotional expression and a behavioral reaction. Social cognitive theory (SCT) provides the fundamental framework for this concept. SCT posits that the outcome expectation will be affected by personal behavior, especially in organizational learning. Bandura (1986) proposes self-efficacy theory (SET), which argues that belief in one’s capabilities to organize and execute courses of action is required to produce given attainments. Selfefficacy plays a central role in the cognitive regulation of motivation, because people regulate the level and the distribution of effort
* Corresponding author. E-mail address: [email protected] (N.-C. Cheng). 0957-4174/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2010.05.029
they will expend in accordance with the effects they are expecting from their actions (Bandura, 1986; Igbaria & Iivari, 1995). SET can handle a wide range of behaviors through its focus on cognitive factors (Bandura, 2005). Knowledge sharing in the Information Technology (IT) industry can be the key to organizational effectiveness. An attempt is made here to understand the need for knowledge management and knowledge sharing in software industries (P.S. & P.T., 2007; Rus & Lindvall, 2002). The paper examines the role of social cognitive perspective in promoting knowledge sharing within software industries. The objective of this study is to empirically examine the knowledge sharing behavior among computer software worker using existing theories of social psychology, such as social cognitive theory (SCT) (Bandura, 1986) and the theory of reasoned action (TRA) (Fishbein & Ajzen, 1975). We argue that social cognition explains the knowledge sharing process more robustly when studies of the processes of learning are integrated into social cognition. We use SCT as the basic theoretical framework to illustrate and explain how to form individual knowledge sharing cognition. Self-efficacy and outcome expectations are seen as predictors of personal factors since both of them are considered major influences on individual behavior (Bandura, 1982; Bandura, 1986; Bandura, 1997; Igbaria & Iivari, 1995). Expected benefits and costs of performing a behavior (outcome expectations), and individual beliefs that he or she is capable of performing a behavior and obtaining the de-
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sired result (self-efficacy) are also constructs utilized from established cognitive-based theories to understand activity at the individual level. 2. Theoretical background 2.1. Knowledge management and knowledge sharing Organizations recognize that knowledge constitutes a valuable intangible asset for creating and sustaining competitive advantage (Miller & Shamsie, 1996). Knowledge management is a management field which emerged in the 1990s. It was introduced to help organizations create, use, and share knowledge effectively. Knowledge that resides within individuals often is referred to as tacit knowledge. (Nonaka & Takeuchi, 1995) argue that successful knowledge management needs to convert internalized tacit knowledge into explicit codified knowledge in order to share it. Knowledge sharing is the behavior of disseminating acquired knowledge to other members of the organization (Ryu, Ho, & Han, 2003). It constitutes a major challenge because some employees tend to resist sharing their knowledge with the rest of the organization (Bock & Kim, 2003; Ciborra & Patriotta, 1998). Knowledge sharing activities are generally supported by knowledge management systems. Researchers have found that many factors affect knowledge sharing in organizations, including organizational culture, climate, trust, and incentives (Cabrera & Cabrera, 2002). Few papers explore the relationship between knowledge-sharing behavior and personal cognition from the social cognitive perspective (Bandura, 1982; Bandura, 1986; Bandura, 1997). In this study, social cognition theory will be used to explore knowledgesharing behavior in knowledge intensive organizations. 2.2. Theory of reasoned action (TRA) Ajzen and Fishbein’s (1975) theory of reasoned action (TRA) is one of the most well known and commonly-used models of human action, used in predicting a wide range of behavior (Madden, Ellen, & Ajzen, 1992). The TRA is based on the premise that intention is the best predictor of behavior. TRA posits that human behavior is quite rational and makes use of the limited information available to individuals. Behavioral intention measures a person’s relative strength of intention to perform a behavior, and has been found to be a good predictor of behavior. Intention to behave is an individual’s intention to perform or not perform a specific behavior. This study measured behavioral intention rather than actual behavior, which is consistent with much of the research in knowledge sharing. Intention is thought to capture the motivational factors that affect behavior. Ajzen, Kuhl and Beekmann (1985) found intention to be a very accurate variable when it came to predicting behavior. Therefore, based on TRA, cognition will affect intention, in turn affecting actual behavior. We thus construct our first hypothesis: H1. Intention to knowledge share will have a positive effect on individual knowledge-sharing behavior. The next hypothesis refers to the level of managerial support. Ajzen and Fishbein (1980) argued that several external variables could have an effect when an intention was realized to perform a behavior. Since managerial support is considered as an important enabler in knowledge management (Davenport & Prusak, 1997), we examine how the level of managerial support affects the knowledge-sharing behavior. H2. Managerial support will have a positive effect on the individual’s knowledge-sharing behavior.
2.3. Self-efficacy theory and social cognitive theory (SCT) SCT is a widely accepted model for validating individual behavior (Compeau & Higgins, 1995). In the SCT model (Fig. 1), personal factors, environmental influence, and behavior are interacting determinants that influence each other bidirectionally (Wood & Bandura, 1989). This study is concerned with the role of personal factors and environmental influences in individual behavior. Self-efficacy and outcome expectations are seen as predictors of personal factors since both of them are considered major influences on behavior (Bandura, 1982; Bandura, 1986; Bandura, 1997). In this study, organizational climate is treated as a major environmental factor affecting individual characteristics and behavior. Organizational climate identifies the variables which moderate an organization’s ability to mobilize its workforce in order to achieve business goals and maximize performance (Hart, Griffin, Wearing, & Cooper, 1996). Based on SCT, we assume that the organizational environment should have an influence on personal factors and behavior. Self-efficacy theory (SET) theory suggests that expectations are major factors determining affective and behavioral reactions in numerous situations, including employee behavior in organizations (Bandura, 1986; Martinko, Henry, & Zmud, 1996). Self-efficacy theory maintains that all processes of psychological and behavioral change operate through the alteration of the individual’s sense of personal mastery or efficacy (Bandura, 1977; Bandura, 1982; Bandura, 1986). Self-efficacy is best understood in the context of social cognitive theory (Maddux & Gosselin, 2005). Bandura (1977), Bandura (1986) separated expectations into two distinct categories that affect individual behavioral and affective outcomes. He labeled these expectancies, self-efficacy and outcome expectancy. In general, self-efficacy is the belief that one possesses the skills and abilities to successfully accomplish a specific task (Ormrod, 2006). Self-efficacy determines the individual’s level of persistence in learning a task and influences their perception of future outcomes. Perceived self-efficacy plays an important role in influencing individual motivation and behavior (Bandura, 1982; Bandura, 1986; Igbaria & Iivari, 1995). People who have high self-efficacy will be more likely to perform related behavior than those with low self-efficacy. Bandura (1977), Bandura (1982) pointed to four factors affecting self-efficacy. He proposed four groups of variables or experiences that affect an individual’s self-efficacy beliefs concerning a o particular task (Schaub & Tokar, 2005). These are: (1) Performance accomplishments: The individual’s personal mastery or accomplishments regarding the task have the greatest effect of any factor on self-efficacy. Prior successes performing the task increase self-efficacy regarding the task. Repeated failures when performing the task lower these expectations (Gist & Mitchell, 1992). (2) Vicarious experience: Vicarious experience involves modeling the behavior of others who successfully complete the
Person
Environment
Behavior
Fig. 1. Interaction between the environment, the individual, and behavior.
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task. Through observing others successfully complete the task, the observers can improve their own performance (Bandura, 1977; Gist & Mitchell, 1992). In this study, we view vicarious experience as listening to and watching other people share in organizations. (3) Social persuasion: Social persuasion occurs when someone tells an individual that they can successfully complete the task in question. Common forms of social persuasion are verbal encouragement, coaching, and providing performance feedback (Bandura, 1977). In this study, we used encouragement of knowledge sharing and helping others share their knowledge as forms of social persuasion. We consider the support and concern of people in sharing knowledge as one of the variables in social persuasion. (4) Emotional arousal: Physiological arousal and emotional states affect a person’s expectancy judgments regarding specific tasks (Bandura, 1977). Negative emotions, such as anxiety, regarding a specific task can produce negative judgments of one’s efficacy, whereas arousal, such as intellectual interest in a task, can improve perceptions of selfefficacy (Bandura, 1986). The anxiety and emotional discomfort felt by individuals when knowledge is not shared in organizations represents emotional arousal. Social-cognitive models of behavior change include the construct of perceived self-efficacy either as predictors, mediators, or moderators. Self-efficacy is supposed to facilitate the forming of behavioral intentions, the development of action plans, and the initiation of action. As a moderator, self-efficacy can support the translation of intentions into action. Therefore, this study construct Hypothesis 2 and 3: H2. Knowledge sharing self-efficacy has a positive effect on intention to share knowledge. H3. Knowledge sharing self-efficacy has a positive effect on knowledge-sharing behavior.
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H5. Outcome expectancy has a positive effect on intention to share knowledge 2.5. Organizational climate and social cognitive theory In the SCT model, personal factors, environmental influences, and behavior act as interacting determinants that influence each other bidirectionally (Wood & Bandura, 1989). Social cognitive theory also explains human behavior in terms of continuous reciprocal interaction between cognitive, behavioral, and environmental influences. In addition to the factors of organizational culture, a few scholars use organizational climate to explore knowledge-sharing behavior. Cabrera and Cabrera (2005) pointed out that organizations tend to arouse innovational thinking if they offer safe and non-judgmental organizational climates. Many scholars hold similar views (Zarraga & Bonache, 2003; Bock, Zmud, Kim & Lee, 2005), arguing comfortable organizational climate can encourage personal knowledge sharing and creating new knowledge. Because the complexity of knowledge sharing is typically underestimated in organizations, at the organizational level factors affecting knowledge sharing must be considered carefully. These include trust between the people and the organization, organizational climate, and practice communities. Many management strategies, incentives, or methods thus attempt to promote knowledge sharing in organizations (Choo & Bontis, 2002; Davenport & Prusak, 1997). Organizational climate surveys can provide concrete evidence of how such programs work in practice. A number of studies have found a very strong link between organizational climate and employee reactions (Bondarouk & Sikkel, 2001; Davenport, De Long, & Beers, 1998; Kong, 2005). In this paper, we explore how environment affects behavior through personal factors using organizational climate. Thus, we hypothesize: H6. Organizational climate has a positive effect on self-efficacy. H7. Organizational climate has a positive effect on knowledge sharing outcome expectancies.
2.4. Outcome expectancy Outcome expectancy refers to an individual’s belief that task accomplishment leads to a desired outcome. We argue that outcome expectancies positively affect a given individual’s behavioral intentions to share the knowledge. Outcome expectancy is defined as the consequence of an act and not the act itself. We argue that knowledge sharing self-efficacy in turn impacts outcome expectancy (see Fig. 2). Based on the literature (Stone & Bailey, 2007; Stone & Henry, 2003; Williams & Bond, 2002), this study propose the following hypothesis. H4. Knowledge sharing self-efficacy has a positive effect on individual outcome expectancy.
3. Conceptual model Base on the above-mentioned literature, theory development and the conceptual model of this study is shown in Fig. 3 below. 4. Research methodology 4.1. Sampling and data collection Knowledge sharing is a key component of an organization’s knowledge management strategy. Software development is often considered to be an intense cognitive activity that requires
Outcome
Behavior
Person
Efficacy beliefs
Outcome expectancy
Fig. 2. Relationships between efficacy beliefs and outcome expectancy (Bandura, 1997).
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Self-efficacy H6 (+)
H3 (+) H2 (+)
Organizational climate
Intention to share knowledge
H4 (+)
H1 (+)
Knowledge sharing Behavior
H5 (+)
H7 (+) Outcome expectancy
Fig. 3. Research model.
collaborative problem solving. This study population consisted of software engineers and workers in Taiwanese companies. The target participants were software engineers in Taiwan. Five hundred questionnaires were mailed to software companies. A total of 225 usable questionnaires were returned, for a response rate of 56.2 percent. The respondents consisted of junior engineers (79.6%), senior engineers (16%), and managers (4.4%). On an average, the responding engineers had 3.5 years of experience in their areas. The descriptive characteristics of the respondents are shown in Table 1. 4.2. Measurement Table 2 lists the construct definitions of instruments and the relevant literature. In this study, items used to operationalize the constructs included in each investigated model were mainly adapted from previous studies and modified for use in the knowledge-sharing context. This study measured six constructs: knowledge-sharing behavior, intentions to sharing knowledge, knowledge sharing self-efficacy, outcome expectancy, and organizational climate. Multiple items were used to measure all constructs, and all items were measured using a five-point Likert-type scale (ranging from 1 = strongly disagree to 5 = strongly agree). The Appendix lists all of the survey items. Knowledge-sharing behavior was measured using five items adapted from the work of Ajzen (1991) and Bock and Kim (2003). Intentions to share knowledge were measured using a four-item scale adapted from Bock and Kim (2002). Terms such as ‘‘likely”, ‘‘acceptable”, and ‘‘needed” were used to assess software engineers’ intentions to share knowledge.
4.3. Statistical analysis The research model shown in Fig. 3 was analyzed primarily using structural equation modeling, supported by AMOS 16 software. Numerous researchers have proposed a two-stage modelbuilding process for applying structural equation modeling (Hair, Anderson, Tatham, & Black, 1998; Hoyle & Panter, 1995; Joreskog & Sorbom, 1996; Maruyama, 1998), in which the measurement models (or confirmatory factor models) were tested before testing the structural model. The measurement models specify how hypothetical constructs are measured in terms of the observed variables (such as organizational climate, self-efficacy, outcome expectancy, intentions, and behavior). Furthermore, the structural models specify causal relationships among the latent variables. This study is employed to describe the causal effects and amount of unexplained variance (Anderson & Gerbing, 1988). 5. Analysis and result 5.1. Analysis of reliability and validity Composite reliability (CR) in the present study consists of the validity of the latent variables. Higher CR values of the indices indicate better the construct reliability of the latent variables. According to the suggestion of Fornell and Larcker (1981) and Bagozzi and Yi (1988), the CR value should exceed 0.6. As shown in Table 3, the validity of each construct of the present study is good. In addition, the measurement items of the questionnaire are based on relevant literature and theories, modified in accordance with the properties of the information industry, thus conforming to content validity.
Table 1 Characteristics of respondents. Measure
Items
Frequency
Percent (%)
Measure
Items
Frequency
Percent (%)
Gender
Male Female
108 117
48.0 52.0
Position
Junior engineer Senior manage Manager
179 36 10
79.6 16.0 4.4
Age
21–29 30–39 40–44 45–50
122 88 12 3
54.2 39.1 5.3 1.3
Career
Electric Machinery and Electronics
102
45.3
24 36 63
10.7 16.0 28.0
High school College University Master Doctor
35 42 125 18 5
15.6 18.7 55.6 8.0 2.2
Work Experience (in years)
185 22 12 4 2
82.2 9.8 5.3 1.8 0.9
Education
Network communication Conventional industries Marketing service Under 5 years 6–10 years 11–15 years 16–20 years Over 21 years
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M.-T. Tsai, N.-C. Cheng / Expert Systems with Applications 37 (2010) 8479–8485 Table 2 Operational definitions. Constructs
Operational definition
References
Knowledge-sharing behavior
The degree to which workers within a company actually shares knowledge with others The degree to which software workers believe they will adopt knowledge-sharing actions The belief that one is capable of performing knowledge- sharing (1) Enactive attainment (2) Vicarious experience (3) Verbal persuasion (4) Emotional factors A person’s estimation that a given behavior will lead to certain outcomes (1) Expected rewards (2) Expected associations (3) Expected contributions The feeling conveyed in a group by the physical layout and the way in which members of the organization interact with each other, including: (1) Clarity of organizational goals (2) Flexibility (3) Innovation (4) Reflexivity
Ajzen (1991), Lee (2001)
Intention to share knowledge Knowledge sharing self-efficacy
Outcome expectancy
Organizational climate
Ajzen (2002), Constant, Kiesler, and Sproull (1994), Feldman and March (1981), Fishbein and Ajzen (1981) Stone and Bailey (2007)
Ardichvili, Cardozo, and Ray (2003), Balkin and Gomez-Mejia (1990), Bock and Kim (2003), Kolekofski and Heminger (2003), Lesser (2000), Malhotra and Galletta (1999)
Patterson et al. (2005), Quinn and Rohrbaugh (1983)
Table 3 Internal reliability and convergent validity test results. Latent variable
a
Item
Internal reliability
Convergent validity Factor loadinga
Composite reliability
Variance extracted
Knowledge-sharing behavior
BE1 BE2 BE3
0.914
0.781 0.858 0.851
0.84 0.90 0.92
0.917
0.787
Knowledge sharing intention
IN1 IN2 IN3
0.888
0.785 0.813 0.746
0.84 0.87 0.84
0.887
0.723
Organizational climate
OC1 OC2 OC3 OC4
0.936
0.808 0.884 0.878 0.824
0.85 0.93 0.92 0.86
0.939
0.794
Outcome expectancy
OE1 OE2 OE3
0.919
0.830 0.872 0.808
0.89 0.93 0.86
0.923
0.799
Self-efficacy
SE1 SE2 SE3
0.895
0.742 0.832 0.808
0.79 0.91 0.88
0.896
0.742
Cronbach a
Item-total correlation
Factor loadings are come from confirmatory factor analysis.
Since the average variance extracted (AVE) is over 0.5, they appear to have construct validity (Anderson & Gerbing, 1988). Table 4 presents the means and standard deviations of construct. It also shows that the variances extracted by constructs are greater than any squared correlation among constructs; this implies that constructs are empirically distinct. In summary, the results of the measurement mode test, including convergent and discriminant validity measures, are satisfactory.
Table 4 Correlations among the latent-variables.
5.2. Results of hypothesis testing In this study, we investigated the social cognition affecting an individual’s knowledge-sharing behavior, applying Bandura’s theory in the knowledge-sharing context to understand how these factors affect knowledge-sharing behavior. The hypothesized relationships depicted in Fig. 4 were tested using SEM. Table 5 presents a summary of the hypothesis tests. Hypothesis 1 examined Fishbein and Ajzen’s model in the knowledge-sharing context. An individual’s actual knowledge-
Diagonal elements (shaded) are the square root of the variance shared between the constructs and their measures. Off diagonal elements are the correlations among constructs. For discriminant validity, diagonal elements should be larger than offdiagonal elements.
sharing behavior is highly correlated with the behavioral intention to share knowledge. Hypotheses 2–5 examine the links between self-efficacy, outcome expectancy, and intention toward knowledge sharing. This
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SE1
SE2
SE3
Self-efficacy 0.34***
OC1
0.41***
0.53***
OC2
BE1
Organizational climate
OC3
0.73***
Knowledge sharing intention
0.57***
Knowledge sharing behavior
BE2 BE3
0.17*
OC4 0.11*
***p OE1
OE2
IN2
IN1
Outcome expectancy
0.001 **p
0.05 *p
0.1
OE3
Fig. 4. Results of SEM analysis.
Table 5 Path coefficients and strengths of individual paths. 0.53*** 0.11* 0.73*** 0.44*** 0.28*** 0.17* 0.57***
Organizational climate ? Self-efficacy Organizational climate ? Outcome expectancy Self-efficacy ? Outcome expectancy Self-efficacy ? Intention Self-efficacy ? Behavior Outcome expectancy ? Intention Intention ? Behavior *
p < 0.1. p < 0.001.
***
6. Conclusions
Table 6 Overall fit indices of the CFA model. Fit index Absolute fit measures
v2 df
v2/df GFI RMR RMSEA
* **
For the last hypothesis, we investigated the effect of organizational climate on the cognitive model of knowledge-sharing behavior. We found that organizational climate does not show a significant effect on individual cognition toward knowledge-sharing behavior. Thus, Hypotheses 6 and 7 were supported. Table 6 shows the SEM analysis has a good fit, as seen from the goodness of fit indices (GFI = 0.91; AGFI = 0.87; CFI = 0.97; RMSEA = 0.07), and the chi-square index is significant (v2 = 175.04; df = 83; v2/df = 2.10).The results indicate that the research model exhibited a satisfactory overall fit to the collected data and was capable of providing a reasonable explanation of knowledge-sharing behavior.
Scores
Recommended cut-off value
175.04 83 2.10** 0.91** 0.027** 0.07**
Near to degree of freedom The greater, the better 63 P0.90 60.05 60.1
Incremental fit measures
NFI AGFI CFI IFI
0.94** 0.87* 0.97** 0.97**
P0.9 P0.9 P0.9 P0.9
Parsimonious fit measures
PCFI PNFI
0.77** 0.75**
The higher, the better The higher, the better
Acceptability: (marginal). Acceptability: (acceptable).
study finds self-efficacy is a strong and significant predictor of outcome expectancy, behavioral intention, and behavior. Both selfefficacy and outcome expectancy have a significant positive effect on intention to share knowledge. Self-efficacy similarly has a strong significant positive effect on outcome expectancy and knowledge-sharing behavior. Therefore, Hypothesis 2, 3, 4 and 5 were supported.
The main contribution of this study is that it is the first to explore software engineers’ knowledge-sharing behavior using existing theories of social psychology. In this study, the applicability of SCT to explaining the knowledge-sharing behavior of engineers was demonstrated, and engineers’ social cognition was found to have the strongest overall effect on their behavior and intentions to share knowledge. Further, organizational climate was found to have a significant effect on software engineers’ knowledge-sharing behavior. Managerial and technological implications can be drawn from this study. From the managerial perspective, the managers and chief knowledge officers of companies should place greater emphasis on creating an environment where engineers can have positive self-efficacies and outcome expectancies towards knowledge sharing. This study has a few limitations. First, the relevance of this study remains confined to the area of knowledge-sharing behavior among one particular professional group: software engineers. Thus, the findings and implications drawn from this study cannot be readily generalized to other professional groups. Second, despite the rigorous examination of the credibility and appropriateness of the collected data, this study may have common method bias, as is often the case with survey research studies. There is a need for further research efforts focused on accumulating empirical data and surmounting the limitations of the present
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study. These efforts should involve studies identifying the cultural factors affecting such independent variables as individual cognition. Special attention should be paid to finding differences in knowledge-sharing behaviors of engineers that may stem from the varying task structures and leadership styles across industries and countries. Finally, because we considered knowledge sharing to be an individualistic behavior, we focused only on those aspects of social cognitive theory which affected behavior toward knowledge sharing. However, behavioral intention is determined by social factors. Therefore, the other social factors such as organizational culture and the organizational citizenship behavior (OCB) should be considered in the future research to increase the explanatory power of the research model. References Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211. Ajzen, I. (2002). Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior. Journal of Applied Social Psychology, 32(4), 665–683. Ajzen, I., & Fishbein, M. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. MA: Addison-Wesley. Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Prentice-Hall. Ajzen, I., Kuhl, J., & Beckmann, J. (1985). From intentions to actions: A theory of planned behavior. New York: Springer. Anderson, J., & Gerbing, D. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423. Ardichvili, A., Cardozo, R., & Ray, S. (2003). A theory of entrepreneurial opportunity identification and development. Journal of Business Venturing, 18(1), 105–123. Bagozzi, R., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94. Balkin, D., & Gomez-Mejia, L. (1990). Matching compensation and organizational strategies. Strategic Management Journal, 11(2), 153–169. Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215. Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist, 37(2), 122–147. Bandura, A. (1986). Social foundations of thought and action : A social cognitive theory. Englewood Cliffs, NJ: Prentice-Hall. Bandura, A. (1997). Self-efficacy: The exercise of control. New York: Freeman. Bandura, A. (2005). The evolution of social cognitive theory. Great Minds in Management: The Process of Theory Development, 9–35. Bock, G., & Kim, Y. (2002). Breaking the myths of rewards: An exploratory study of attitudes about knowledge sharing. Information Resources Management Journal, 15(2), 14–21. Bock, G., & Kim, Y. (2003). Exploring the influence of rewards on attitudes towards knowledge sharing. Advanced Topics in Information Resources Management. Bock, G., Zmud, R., Kim, Y., & Lee, J. (2005). Behavioral intention formation in knowledge sharing: Examining the roles of extrinsic motivators, socialpsychological forces, and organizational climate. MIS Quarterly, 29(1), 87–111. Bondarouk, T., & Sikkel, K. (2001). Implementation of collaborative technologies as a learning process. Centre for Telematics and Information Technology, University of Twente. Cabrera, A., & Cabrera, E. (2002). Knowledge-sharing dilemmas. Organization Studies, 23(5), 687–710. Cabrera, A., & Cabrera, E. (2005). Fostering knowledge sharing through people management practices. International Journal of Human Resource Management, 16(5), 720–735. Choo, C., & Bontis, N. (2002). The strategic management of intellectual capital and organizational knowledge. USA: Oxford University Press. Ciborra, C., & Patriotta, G. (1998). Groupware and teamwork in R&D: Limits to learning and innovation. R&D Management, 28(1), 43–52. Compeau, D., & Higgins, C. (1995). Computer self efficacy: development of a measure and initial test. Management Information Systems Quarterly, 19(1), 9. Constant, D., Kiesler, S., & Sproull, L. (1994). What is mine is ours, or is it. Information Systems Research, 5(4), 400–422. Crone, M., & Roper, S. (2001). Local learning from multinational plants: knowledge transfers in the supply chain. Regional Studies: The Journal of the Regional Studies Association, 35(6), 535–548. Davenport, T., De Long, D., & Beers, M. (1998). Successful knowledge management projects. Sloan Management Review, 39(2), 43–57. Davenport, T., & Prusak, L. (1997). Information ecology: mastering the information and knowledge environment. USA: Oxford University Press. Feldman, M., & March, J. (1981). Information in organizations as signal and symbol. Administrative Science Quarterly, 26(2), 171–186.
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Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa
Programmer perceptions of knowledge-sharing behavior under social cognitive theory Ming-Ten Tsai, Nai-Chang Cheng * Department of Business Administrator, National Cheng-Kung University, Tainan, Taiwan
a r t i c l e
i n f o
Keywords: Knowledge sharing Social cognitive theory Self-efficacy Organizational climate Intention Behavior
a b s t r a c t Despite the importance of the software industry, little research using social cognitive perspective has focused on the software industry. This study thus examines key factors, including self-efficacy, expectancy theory and organizational climate, on the software workers to intent to share knowledge, using a social cognitive framework. Programmers and software workers in Taiwan were surveyed to test the proposed research model. Confirmatory factor analysis (CFA) and structural equation modeling (SEM) technique were used to analyze the data and evaluate the research model. Results showed that that the research model fit the data well and the main determinants of knowledge-sharing behavior were the encouraging intentions of knowledge intensive workers. We confirm our hypothesis that knowledge sharing self-efficacy and outcome expectancy, as well as organizational climate, will affect individual intentions to share knowledge. Additionally, organizational climate and perceived managerial incentive were found to positively encourage knowledge-sharing behavior. Research and practical implications are described. Ó 2010 Elsevier Ltd. All rights reserved.
1. Introduction Organizations recognized that knowledge constitutes a valuable intangible asset for creating and sustaining competitive advantage (Crone & Roper, 2001; Howells, 2002; Lee, 2001; Miller & Shamsie, 1996; Warkentin, Sugumaran, & Bapna, 2001). Knowledge sharing is thus a key component of knowledge management in organizations, and knowledge sharing activities are generally supported by knowledge management systems (Walsham, 2001). Many studies have identified the many factors that affect the sharing of knowledge in organizations, such as organizational culture, trust, and incentives (Cabrera & Cabrera, 2002). In this research, we consider knowledge sharing to be both an emotional expression and a behavioral reaction. Social cognitive theory (SCT) provides the fundamental framework for this concept. SCT posits that the outcome expectation will be affected by personal behavior, especially in organizational learning. Bandura (1986) proposes self-efficacy theory (SET), which argues that belief in one’s capabilities to organize and execute courses of action is required to produce given attainments. Selfefficacy plays a central role in the cognitive regulation of motivation, because people regulate the level and the distribution of effort
* Corresponding author. E-mail address: [email protected] (N.-C. Cheng). 0957-4174/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2010.05.029
they will expend in accordance with the effects they are expecting from their actions (Bandura, 1986; Igbaria & Iivari, 1995). SET can handle a wide range of behaviors through its focus on cognitive factors (Bandura, 2005). Knowledge sharing in the Information Technology (IT) industry can be the key to organizational effectiveness. An attempt is made here to understand the need for knowledge management and knowledge sharing in software industries (P.S. & P.T., 2007; Rus & Lindvall, 2002). The paper examines the role of social cognitive perspective in promoting knowledge sharing within software industries. The objective of this study is to empirically examine the knowledge sharing behavior among computer software worker using existing theories of social psychology, such as social cognitive theory (SCT) (Bandura, 1986) and the theory of reasoned action (TRA) (Fishbein & Ajzen, 1975). We argue that social cognition explains the knowledge sharing process more robustly when studies of the processes of learning are integrated into social cognition. We use SCT as the basic theoretical framework to illustrate and explain how to form individual knowledge sharing cognition. Self-efficacy and outcome expectations are seen as predictors of personal factors since both of them are considered major influences on individual behavior (Bandura, 1982; Bandura, 1986; Bandura, 1997; Igbaria & Iivari, 1995). Expected benefits and costs of performing a behavior (outcome expectations), and individual beliefs that he or she is capable of performing a behavior and obtaining the de-
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sired result (self-efficacy) are also constructs utilized from established cognitive-based theories to understand activity at the individual level. 2. Theoretical background 2.1. Knowledge management and knowledge sharing Organizations recognize that knowledge constitutes a valuable intangible asset for creating and sustaining competitive advantage (Miller & Shamsie, 1996). Knowledge management is a management field which emerged in the 1990s. It was introduced to help organizations create, use, and share knowledge effectively. Knowledge that resides within individuals often is referred to as tacit knowledge. (Nonaka & Takeuchi, 1995) argue that successful knowledge management needs to convert internalized tacit knowledge into explicit codified knowledge in order to share it. Knowledge sharing is the behavior of disseminating acquired knowledge to other members of the organization (Ryu, Ho, & Han, 2003). It constitutes a major challenge because some employees tend to resist sharing their knowledge with the rest of the organization (Bock & Kim, 2003; Ciborra & Patriotta, 1998). Knowledge sharing activities are generally supported by knowledge management systems. Researchers have found that many factors affect knowledge sharing in organizations, including organizational culture, climate, trust, and incentives (Cabrera & Cabrera, 2002). Few papers explore the relationship between knowledge-sharing behavior and personal cognition from the social cognitive perspective (Bandura, 1982; Bandura, 1986; Bandura, 1997). In this study, social cognition theory will be used to explore knowledgesharing behavior in knowledge intensive organizations. 2.2. Theory of reasoned action (TRA) Ajzen and Fishbein’s (1975) theory of reasoned action (TRA) is one of the most well known and commonly-used models of human action, used in predicting a wide range of behavior (Madden, Ellen, & Ajzen, 1992). The TRA is based on the premise that intention is the best predictor of behavior. TRA posits that human behavior is quite rational and makes use of the limited information available to individuals. Behavioral intention measures a person’s relative strength of intention to perform a behavior, and has been found to be a good predictor of behavior. Intention to behave is an individual’s intention to perform or not perform a specific behavior. This study measured behavioral intention rather than actual behavior, which is consistent with much of the research in knowledge sharing. Intention is thought to capture the motivational factors that affect behavior. Ajzen, Kuhl and Beekmann (1985) found intention to be a very accurate variable when it came to predicting behavior. Therefore, based on TRA, cognition will affect intention, in turn affecting actual behavior. We thus construct our first hypothesis: H1. Intention to knowledge share will have a positive effect on individual knowledge-sharing behavior. The next hypothesis refers to the level of managerial support. Ajzen and Fishbein (1980) argued that several external variables could have an effect when an intention was realized to perform a behavior. Since managerial support is considered as an important enabler in knowledge management (Davenport & Prusak, 1997), we examine how the level of managerial support affects the knowledge-sharing behavior. H2. Managerial support will have a positive effect on the individual’s knowledge-sharing behavior.
2.3. Self-efficacy theory and social cognitive theory (SCT) SCT is a widely accepted model for validating individual behavior (Compeau & Higgins, 1995). In the SCT model (Fig. 1), personal factors, environmental influence, and behavior are interacting determinants that influence each other bidirectionally (Wood & Bandura, 1989). This study is concerned with the role of personal factors and environmental influences in individual behavior. Self-efficacy and outcome expectations are seen as predictors of personal factors since both of them are considered major influences on behavior (Bandura, 1982; Bandura, 1986; Bandura, 1997). In this study, organizational climate is treated as a major environmental factor affecting individual characteristics and behavior. Organizational climate identifies the variables which moderate an organization’s ability to mobilize its workforce in order to achieve business goals and maximize performance (Hart, Griffin, Wearing, & Cooper, 1996). Based on SCT, we assume that the organizational environment should have an influence on personal factors and behavior. Self-efficacy theory (SET) theory suggests that expectations are major factors determining affective and behavioral reactions in numerous situations, including employee behavior in organizations (Bandura, 1986; Martinko, Henry, & Zmud, 1996). Self-efficacy theory maintains that all processes of psychological and behavioral change operate through the alteration of the individual’s sense of personal mastery or efficacy (Bandura, 1977; Bandura, 1982; Bandura, 1986). Self-efficacy is best understood in the context of social cognitive theory (Maddux & Gosselin, 2005). Bandura (1977), Bandura (1986) separated expectations into two distinct categories that affect individual behavioral and affective outcomes. He labeled these expectancies, self-efficacy and outcome expectancy. In general, self-efficacy is the belief that one possesses the skills and abilities to successfully accomplish a specific task (Ormrod, 2006). Self-efficacy determines the individual’s level of persistence in learning a task and influences their perception of future outcomes. Perceived self-efficacy plays an important role in influencing individual motivation and behavior (Bandura, 1982; Bandura, 1986; Igbaria & Iivari, 1995). People who have high self-efficacy will be more likely to perform related behavior than those with low self-efficacy. Bandura (1977), Bandura (1982) pointed to four factors affecting self-efficacy. He proposed four groups of variables or experiences that affect an individual’s self-efficacy beliefs concerning a o particular task (Schaub & Tokar, 2005). These are: (1) Performance accomplishments: The individual’s personal mastery or accomplishments regarding the task have the greatest effect of any factor on self-efficacy. Prior successes performing the task increase self-efficacy regarding the task. Repeated failures when performing the task lower these expectations (Gist & Mitchell, 1992). (2) Vicarious experience: Vicarious experience involves modeling the behavior of others who successfully complete the
Person
Environment
Behavior
Fig. 1. Interaction between the environment, the individual, and behavior.
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task. Through observing others successfully complete the task, the observers can improve their own performance (Bandura, 1977; Gist & Mitchell, 1992). In this study, we view vicarious experience as listening to and watching other people share in organizations. (3) Social persuasion: Social persuasion occurs when someone tells an individual that they can successfully complete the task in question. Common forms of social persuasion are verbal encouragement, coaching, and providing performance feedback (Bandura, 1977). In this study, we used encouragement of knowledge sharing and helping others share their knowledge as forms of social persuasion. We consider the support and concern of people in sharing knowledge as one of the variables in social persuasion. (4) Emotional arousal: Physiological arousal and emotional states affect a person’s expectancy judgments regarding specific tasks (Bandura, 1977). Negative emotions, such as anxiety, regarding a specific task can produce negative judgments of one’s efficacy, whereas arousal, such as intellectual interest in a task, can improve perceptions of selfefficacy (Bandura, 1986). The anxiety and emotional discomfort felt by individuals when knowledge is not shared in organizations represents emotional arousal. Social-cognitive models of behavior change include the construct of perceived self-efficacy either as predictors, mediators, or moderators. Self-efficacy is supposed to facilitate the forming of behavioral intentions, the development of action plans, and the initiation of action. As a moderator, self-efficacy can support the translation of intentions into action. Therefore, this study construct Hypothesis 2 and 3: H2. Knowledge sharing self-efficacy has a positive effect on intention to share knowledge. H3. Knowledge sharing self-efficacy has a positive effect on knowledge-sharing behavior.
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H5. Outcome expectancy has a positive effect on intention to share knowledge 2.5. Organizational climate and social cognitive theory In the SCT model, personal factors, environmental influences, and behavior act as interacting determinants that influence each other bidirectionally (Wood & Bandura, 1989). Social cognitive theory also explains human behavior in terms of continuous reciprocal interaction between cognitive, behavioral, and environmental influences. In addition to the factors of organizational culture, a few scholars use organizational climate to explore knowledge-sharing behavior. Cabrera and Cabrera (2005) pointed out that organizations tend to arouse innovational thinking if they offer safe and non-judgmental organizational climates. Many scholars hold similar views (Zarraga & Bonache, 2003; Bock, Zmud, Kim & Lee, 2005), arguing comfortable organizational climate can encourage personal knowledge sharing and creating new knowledge. Because the complexity of knowledge sharing is typically underestimated in organizations, at the organizational level factors affecting knowledge sharing must be considered carefully. These include trust between the people and the organization, organizational climate, and practice communities. Many management strategies, incentives, or methods thus attempt to promote knowledge sharing in organizations (Choo & Bontis, 2002; Davenport & Prusak, 1997). Organizational climate surveys can provide concrete evidence of how such programs work in practice. A number of studies have found a very strong link between organizational climate and employee reactions (Bondarouk & Sikkel, 2001; Davenport, De Long, & Beers, 1998; Kong, 2005). In this paper, we explore how environment affects behavior through personal factors using organizational climate. Thus, we hypothesize: H6. Organizational climate has a positive effect on self-efficacy. H7. Organizational climate has a positive effect on knowledge sharing outcome expectancies.
2.4. Outcome expectancy Outcome expectancy refers to an individual’s belief that task accomplishment leads to a desired outcome. We argue that outcome expectancies positively affect a given individual’s behavioral intentions to share the knowledge. Outcome expectancy is defined as the consequence of an act and not the act itself. We argue that knowledge sharing self-efficacy in turn impacts outcome expectancy (see Fig. 2). Based on the literature (Stone & Bailey, 2007; Stone & Henry, 2003; Williams & Bond, 2002), this study propose the following hypothesis. H4. Knowledge sharing self-efficacy has a positive effect on individual outcome expectancy.
3. Conceptual model Base on the above-mentioned literature, theory development and the conceptual model of this study is shown in Fig. 3 below. 4. Research methodology 4.1. Sampling and data collection Knowledge sharing is a key component of an organization’s knowledge management strategy. Software development is often considered to be an intense cognitive activity that requires
Outcome
Behavior
Person
Efficacy beliefs
Outcome expectancy
Fig. 2. Relationships between efficacy beliefs and outcome expectancy (Bandura, 1997).
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Self-efficacy H6 (+)
H3 (+) H2 (+)
Organizational climate
Intention to share knowledge
H4 (+)
H1 (+)
Knowledge sharing Behavior
H5 (+)
H7 (+) Outcome expectancy
Fig. 3. Research model.
collaborative problem solving. This study population consisted of software engineers and workers in Taiwanese companies. The target participants were software engineers in Taiwan. Five hundred questionnaires were mailed to software companies. A total of 225 usable questionnaires were returned, for a response rate of 56.2 percent. The respondents consisted of junior engineers (79.6%), senior engineers (16%), and managers (4.4%). On an average, the responding engineers had 3.5 years of experience in their areas. The descriptive characteristics of the respondents are shown in Table 1. 4.2. Measurement Table 2 lists the construct definitions of instruments and the relevant literature. In this study, items used to operationalize the constructs included in each investigated model were mainly adapted from previous studies and modified for use in the knowledge-sharing context. This study measured six constructs: knowledge-sharing behavior, intentions to sharing knowledge, knowledge sharing self-efficacy, outcome expectancy, and organizational climate. Multiple items were used to measure all constructs, and all items were measured using a five-point Likert-type scale (ranging from 1 = strongly disagree to 5 = strongly agree). The Appendix lists all of the survey items. Knowledge-sharing behavior was measured using five items adapted from the work of Ajzen (1991) and Bock and Kim (2003). Intentions to share knowledge were measured using a four-item scale adapted from Bock and Kim (2002). Terms such as ‘‘likely”, ‘‘acceptable”, and ‘‘needed” were used to assess software engineers’ intentions to share knowledge.
4.3. Statistical analysis The research model shown in Fig. 3 was analyzed primarily using structural equation modeling, supported by AMOS 16 software. Numerous researchers have proposed a two-stage modelbuilding process for applying structural equation modeling (Hair, Anderson, Tatham, & Black, 1998; Hoyle & Panter, 1995; Joreskog & Sorbom, 1996; Maruyama, 1998), in which the measurement models (or confirmatory factor models) were tested before testing the structural model. The measurement models specify how hypothetical constructs are measured in terms of the observed variables (such as organizational climate, self-efficacy, outcome expectancy, intentions, and behavior). Furthermore, the structural models specify causal relationships among the latent variables. This study is employed to describe the causal effects and amount of unexplained variance (Anderson & Gerbing, 1988). 5. Analysis and result 5.1. Analysis of reliability and validity Composite reliability (CR) in the present study consists of the validity of the latent variables. Higher CR values of the indices indicate better the construct reliability of the latent variables. According to the suggestion of Fornell and Larcker (1981) and Bagozzi and Yi (1988), the CR value should exceed 0.6. As shown in Table 3, the validity of each construct of the present study is good. In addition, the measurement items of the questionnaire are based on relevant literature and theories, modified in accordance with the properties of the information industry, thus conforming to content validity.
Table 1 Characteristics of respondents. Measure
Items
Frequency
Percent (%)
Measure
Items
Frequency
Percent (%)
Gender
Male Female
108 117
48.0 52.0
Position
Junior engineer Senior manage Manager
179 36 10
79.6 16.0 4.4
Age
21–29 30–39 40–44 45–50
122 88 12 3
54.2 39.1 5.3 1.3
Career
Electric Machinery and Electronics
102
45.3
24 36 63
10.7 16.0 28.0
High school College University Master Doctor
35 42 125 18 5
15.6 18.7 55.6 8.0 2.2
Work Experience (in years)
185 22 12 4 2
82.2 9.8 5.3 1.8 0.9
Education
Network communication Conventional industries Marketing service Under 5 years 6–10 years 11–15 years 16–20 years Over 21 years
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Operational definition
References
Knowledge-sharing behavior
The degree to which workers within a company actually shares knowledge with others The degree to which software workers believe they will adopt knowledge-sharing actions The belief that one is capable of performing knowledge- sharing (1) Enactive attainment (2) Vicarious experience (3) Verbal persuasion (4) Emotional factors A person’s estimation that a given behavior will lead to certain outcomes (1) Expected rewards (2) Expected associations (3) Expected contributions The feeling conveyed in a group by the physical layout and the way in which members of the organization interact with each other, including: (1) Clarity of organizational goals (2) Flexibility (3) Innovation (4) Reflexivity
Ajzen (1991), Lee (2001)
Intention to share knowledge Knowledge sharing self-efficacy
Outcome expectancy
Organizational climate
Ajzen (2002), Constant, Kiesler, and Sproull (1994), Feldman and March (1981), Fishbein and Ajzen (1981) Stone and Bailey (2007)
Ardichvili, Cardozo, and Ray (2003), Balkin and Gomez-Mejia (1990), Bock and Kim (2003), Kolekofski and Heminger (2003), Lesser (2000), Malhotra and Galletta (1999)
Patterson et al. (2005), Quinn and Rohrbaugh (1983)
Table 3 Internal reliability and convergent validity test results. Latent variable
a
Item
Internal reliability
Convergent validity Factor loadinga
Composite reliability
Variance extracted
Knowledge-sharing behavior
BE1 BE2 BE3
0.914
0.781 0.858 0.851
0.84 0.90 0.92
0.917
0.787
Knowledge sharing intention
IN1 IN2 IN3
0.888
0.785 0.813 0.746
0.84 0.87 0.84
0.887
0.723
Organizational climate
OC1 OC2 OC3 OC4
0.936
0.808 0.884 0.878 0.824
0.85 0.93 0.92 0.86
0.939
0.794
Outcome expectancy
OE1 OE2 OE3
0.919
0.830 0.872 0.808
0.89 0.93 0.86
0.923
0.799
Self-efficacy
SE1 SE2 SE3
0.895
0.742 0.832 0.808
0.79 0.91 0.88
0.896
0.742
Cronbach a
Item-total correlation
Factor loadings are come from confirmatory factor analysis.
Since the average variance extracted (AVE) is over 0.5, they appear to have construct validity (Anderson & Gerbing, 1988). Table 4 presents the means and standard deviations of construct. It also shows that the variances extracted by constructs are greater than any squared correlation among constructs; this implies that constructs are empirically distinct. In summary, the results of the measurement mode test, including convergent and discriminant validity measures, are satisfactory.
Table 4 Correlations among the latent-variables.
5.2. Results of hypothesis testing In this study, we investigated the social cognition affecting an individual’s knowledge-sharing behavior, applying Bandura’s theory in the knowledge-sharing context to understand how these factors affect knowledge-sharing behavior. The hypothesized relationships depicted in Fig. 4 were tested using SEM. Table 5 presents a summary of the hypothesis tests. Hypothesis 1 examined Fishbein and Ajzen’s model in the knowledge-sharing context. An individual’s actual knowledge-
Diagonal elements (shaded) are the square root of the variance shared between the constructs and their measures. Off diagonal elements are the correlations among constructs. For discriminant validity, diagonal elements should be larger than offdiagonal elements.
sharing behavior is highly correlated with the behavioral intention to share knowledge. Hypotheses 2–5 examine the links between self-efficacy, outcome expectancy, and intention toward knowledge sharing. This
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SE1
SE2
SE3
Self-efficacy 0.34***
OC1
0.41***
0.53***
OC2
BE1
Organizational climate
OC3
0.73***
Knowledge sharing intention
0.57***
Knowledge sharing behavior
BE2 BE3
0.17*
OC4 0.11*
***p OE1
OE2
IN2
IN1
Outcome expectancy
0.001 **p
0.05 *p
0.1
OE3
Fig. 4. Results of SEM analysis.
Table 5 Path coefficients and strengths of individual paths. 0.53*** 0.11* 0.73*** 0.44*** 0.28*** 0.17* 0.57***
Organizational climate ? Self-efficacy Organizational climate ? Outcome expectancy Self-efficacy ? Outcome expectancy Self-efficacy ? Intention Self-efficacy ? Behavior Outcome expectancy ? Intention Intention ? Behavior *
p < 0.1. p < 0.001.
***
6. Conclusions
Table 6 Overall fit indices of the CFA model. Fit index Absolute fit measures
v2 df
v2/df GFI RMR RMSEA
* **
For the last hypothesis, we investigated the effect of organizational climate on the cognitive model of knowledge-sharing behavior. We found that organizational climate does not show a significant effect on individual cognition toward knowledge-sharing behavior. Thus, Hypotheses 6 and 7 were supported. Table 6 shows the SEM analysis has a good fit, as seen from the goodness of fit indices (GFI = 0.91; AGFI = 0.87; CFI = 0.97; RMSEA = 0.07), and the chi-square index is significant (v2 = 175.04; df = 83; v2/df = 2.10).The results indicate that the research model exhibited a satisfactory overall fit to the collected data and was capable of providing a reasonable explanation of knowledge-sharing behavior.
Scores
Recommended cut-off value
175.04 83 2.10** 0.91** 0.027** 0.07**
Near to degree of freedom The greater, the better 63 P0.90 60.05 60.1
Incremental fit measures
NFI AGFI CFI IFI
0.94** 0.87* 0.97** 0.97**
P0.9 P0.9 P0.9 P0.9
Parsimonious fit measures
PCFI PNFI
0.77** 0.75**
The higher, the better The higher, the better
Acceptability: (marginal). Acceptability: (acceptable).
study finds self-efficacy is a strong and significant predictor of outcome expectancy, behavioral intention, and behavior. Both selfefficacy and outcome expectancy have a significant positive effect on intention to share knowledge. Self-efficacy similarly has a strong significant positive effect on outcome expectancy and knowledge-sharing behavior. Therefore, Hypothesis 2, 3, 4 and 5 were supported.
The main contribution of this study is that it is the first to explore software engineers’ knowledge-sharing behavior using existing theories of social psychology. In this study, the applicability of SCT to explaining the knowledge-sharing behavior of engineers was demonstrated, and engineers’ social cognition was found to have the strongest overall effect on their behavior and intentions to share knowledge. Further, organizational climate was found to have a significant effect on software engineers’ knowledge-sharing behavior. Managerial and technological implications can be drawn from this study. From the managerial perspective, the managers and chief knowledge officers of companies should place greater emphasis on creating an environment where engineers can have positive self-efficacies and outcome expectancies towards knowledge sharing. This study has a few limitations. First, the relevance of this study remains confined to the area of knowledge-sharing behavior among one particular professional group: software engineers. Thus, the findings and implications drawn from this study cannot be readily generalized to other professional groups. Second, despite the rigorous examination of the credibility and appropriateness of the collected data, this study may have common method bias, as is often the case with survey research studies. There is a need for further research efforts focused on accumulating empirical data and surmounting the limitations of the present
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