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Knowledge Sharing in Large IT Organizations: A Case Study.

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Purpose Research has shown that the current knowledge management (KM) practices are developed from the standpoint of the management and do not put enough emphasis on knowledge sharing from the non‐executive employees' perspective. However, it is important for organizations to understand – from the perspective of employees – the factors that motivate employees to share knowledge for successful implementation of any KM program. In this exploratory study, willingness of employees to share knowledge is the dependent variable. The purpose of this study is to explore the knowledge sharing factors from the employees' perspective. Design/methodology/approach Using survey methodology, two large IT service and consulting organizations were included in the study to examine cultural, technological, motivational and organizational factors, which influence knowledge sharing within an organization from the perspective of non‐executive employees. Findings The study results showed that issues related to availability and usability of technology, leadership support and motivating structures were shown to have influences on knowledge sharing. The study also revealed that employees' willingness to share knowledge was not affected by their concerns about the loss of power or job insecurity. Research limitations/implications Self‐reporting bias is a limitation of the survey study. Self‐report bias occurs when individuals would bring in their experiences, self‐perception, and their work environment when completing a survey. Even though the present study clearly indicates to the participants that it is anonymous, it is possible that sometimes participants may misreport and misrepresent their perceptions to make themselves look better. The study was exploratory, and it was limited to two organizations. This would therefore restrict one from generalizing the outcomes of the study. Originality/value This exploratory study contributed to a deeper understanding of knowledge sharing with empirical data from two large IT organizations based on the non‐executive employees' perspective rather than that of management.
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Knowledge sharing in large IT
organizations: a case study
Brent M. Han
MITRE Corporation, Fairfax, Virginia, USA, and
Vittal S. Anantatmula
College of Business, Western Carolina University, Cullowhee, North Carolina,
USA
Abstract
Purpose Research has shown that the current knowledge management (KM) practices are
developed from the standpoint of the management and do not put enough emphasis on knowledge
sharing from the non-executive employees’ perspective. However, it is important for organizations to
understand from the perspective of employees the factors that motivate employees to share
knowledge for successful implementation of any KM program. In this exploratory study, willingness
of employees to share knowledge is the dependent variable. The purpose of this study is to explore the
knowledge sharing factors from the employees’ perspective.
Design/methodology/approach Using survey methodology, two large IT service and consulting
organizations were included in the study to examine cultural, technological, motivational and
organizational factors, which influence knowledge sharing within an organization from the
perspective of non-executive employees.
Findings The study results showed that issues related to availability and usability of technology,
leadership support and motivating structures were shown to have influences on knowledge sharing.
The study also revealed that employees’ willingness to share knowledge was not affected by their
concerns about the loss of power or job insecurity.
Research limitations/implications Self-reporting bias is a limitation of the survey study.
Self-report bias occurs when individuals would bring in their experiences, self-perception, and their
work environment when completing a survey. Even though the present study clearly indicates to the
participants that it is anonymous, it is possible that sometimes participants may misreport and
misrepresent their perceptions to make themselves look better. The study was exploratory, and it was
limited to two organizations. This would therefore restrict one from generalizing the outcomes of the
study.
Originality/value This exploratory study contributed to a deeper understanding of knowledge
sharing with empirical data from two large IT organizations based on the non-executive employees’
perspective rather than that of management.
Keywords Knowledge sharing, Large enterprises
Paper type Case study
Introduction
Knowledge is commonly acknowledged as a critical economic resource in the present
global economy and it is progressively becoming evident that organizations should
possess the right kind of knowledge in the desired form and context to be successful.
Knowledge is considered an important source of establishing and maintaining
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/0305-5728.htm
Brent M. Han’s affiliation with The MITRE Corporation is provided for identification purposes
only, and is not intended to convey or imply MITRE’s concurrence with, or support for, the
positions, opinions or viewpoints expressed by the author.
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sharing
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VINE: The journal of information and
knowledge management systems
Vol. 37 No. 4, 2007
pp. 421-439
qEmerald Group Publishing Limited
0305-5728
DOI 10.1108/03055720710838506
competitive advantage. Specifically, knowledge sharing and resultant knowledge
creation are crucial for organizations to gain and sustain competitiveness.
Knowledge has become an important component of competitiveness and a nation’s
economic development (Pinelli et al., 1997). Consequently, organizations are
capitalizing on knowledge in the form of patents, processes, management skills,
technologies, information about customers and suppliers, and core competency
experience. It is through this knowledge that an organization can gain a competitive
edge (Stewart, 1997).
Knowledge management (KM) is a discipline that is still evolving. Also, the KM
concept is still understood as information management and is associated with
technological solutions, such as intranets and databases (Marr, 2003). Many
organizations perceived knowledge management (KM) initiatives at the information
technology (IT) level. Consequently, these organizations would invest heavily in KM
tools and place them on their Intranet server. The underlying assumption is that when
these technologies are in place, employees will willingly share their knowledge
(Geraint, 1998). Unfortunately, this approach has lead to many disappointments.
Companies, in particular, are disappointed when the IT systems could not deliver what
they claimed. What these companies failed to realize was that other factors were not
taken into consideration when the technology was implemented (Reimus, 1997).
Geraint (1998) succinctly stated:
It should come as no surprise ... that chief among these is the realization that too much faith
has been invested in technology at the expense of people issues.
Davenport (1994) argues that though “many managers still believe that once the right
technology is in place, appropriate information sharing will follow,” the reality is that
people do not share knowledge and information easily. The common mistake
executives and organizations make is the assumption that employees from different
departments, professionals, consultants or line workers will know how to use and are
willing to use the technology to share knowledge.
Studies indicate that the focus of most KM studies was on organization culture and
technology from the executive management perspective with few studies examining
issues such as trust, interaction, rewards, and motivation system from non-executive
employee’s perspective. It is unfortunate that an unbalance emphasis of technology
over other factors such as organization culture, individual employee’s attitude, and
availability of networking facilities has led to many failures and unsuccessful
implementation of KM systems (Davenport, 1998). Garvin (1997) notes:
If people don’t want to share, they are not going to do it even if you have the best technology
in the world. People won’t share if they don’t see what’s in it for them.
Geraint (1998) asserts that what really matters in KM ... is getting employees to share
their insights and experience.” A successful KM system goes beyond using technology
to capture knowledge (Sage and Small, 2000). As such, it is important for organizations
to understand from employees’ perspective the factors that motivate them to share
before implementing any KM program.
The purpose of this study is to explore the knowledge sharing factors from the
employees’ perspective. In this study, we examine common organizational factors that
influence non-executive employees’ willingness to share their knowledge. The study
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aims to provide a deeper understanding of knowledge sharing with empirical data
based on employees’ perspective rather than that of management. In this paper, using
the literature research and analysis, we identify a list of factors that contribute to
knowledge sharing. We then present our research methodology to establish these
factors. Subsequently, we discuss research results after providing a detailed analysis of
the findings. From a research and practitioner’s perspective, we discuss how to use
these results to promote knowledge sharing in organizations. Finally, we present
limitations of the research study and suggest opportunities for future research efforts.
Research methodology
Knowledge is derived from thinking and it is a combination of information, experience
and insight. As a result, people are associated with knowledge creation because
deriving knowledge from information requires human judgment and is based on
context and experience. Tacit and explicit knowledge are found in different forms and
sources. Explicit knowledge can be found in articulated and documented forms, tacit
knowledge, which is personal and specific, can be found in people’s actions and
interpersonal communications. Much of the tacit knowledge a greater component of
organizational knowledge is found in social interactions through communication and
knowledge sharing. Underlining the importance of communication and knowledge
sharing, Nonaka and Takeuchi (1995) argued that organizations cannot create
knowledge without individuals. They argued that unless individual knowledge is
shared with other individuals and groups, the knowledge is likely to have limited
impact on organizational effectiveness.
The reviews of the knowledge frameworks (Choo, 1998; Holsapple and Joshi, 1999;
Jennex and Olfman, 2004; Leonard-Barton, 1995; Sage and Small, 2000; Stewart, 1997;
Von Krogh, 1998; Wiig, 1993) showed that each of the frameworks emphasizes
different areas. For example, Wiig’s (1993) framework focuses on managerial issues
and knowledge management activities that affect an organization. Leonard-Barton’s
(1995) framework focuses on knowledge management in direction of managing
technological capabilities, developing an organizational culture of knowledge sharing
with effective management within an organization. Choo (1998) identifies knowledge
management process in a “knowing organization” with an emphasis on organizational
culture and strong management participation. The framework of van der Spek and
Spijkervet (1997) focuses on different process stages of knowledge management in an
organization. Absent from the above knowledge management frameworks is an
emphasis on the characteristic of the difference in individual employee’s attitude and
the availability of a learning facility for knowledge sharing.
The literature review also showed that knowledge management is generally studied
from organizations’ perspective, and that empirical research in understanding the
factors that contribute to knowledge sharing among employees are not well explored or
understood. The research question of interest for this study is what are the factors that
influence employees’ willingness to share their knowledge? In order to have a better
understanding of the concept of knowledge management and how knowledge sharing
could be brought about, it is necessary to look at the organization system as a whole. A
four-KM Pillars (or components) interrelationship framework (Stankosky and
Baldanza, 2001) provides a system approach to addresses the organization as a
whole. The four components are organization, leadership, learning, and technology.
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Another research study has supported has similar findings. In an attempt to explore
the relation between KM drivers and organizational KM performance, Yu et al. (2004)
based on the study of 66 Korean firms found that KM drivers such as learning
orientation, knowledge sharing intention, knowledge management system quality,
reward, and knowledge management team activity were enabled by organizational
characteristics. These include learning and structure, information technology, and
management support in promoting learning culture.
It should be noted that, this research does not set out to validate or verify Stankosky’s
framework academic research has validated using dissertation studies (Calabrese,
2000). The purpose of applying Stankosky’s KM interrelationship components to this
study is that it provides a framework for generating questionnaires. The Jennex and
Olfman’s (2004) framework does look at the organization as a whole, but from a context of
evaluating different knowledge management systems models. Therefore, it was not
appropriate for the purpose of this study.
Questionnaire
A set of questions was developed for the four components with seven dependent and 45
independent variables using extensive literature review. First section of the
questionnaire addressed respondents’ demographic information, such as age, gender
and education. This section also asked respondents’ job title, how long they have been
in the organization, and other job related information. The 12 survey questions used to
assess leadership issues from employees’ perspectives, such as management support,
and the reward structure were adopted from O’Dell and Grayson (1998). A total of 13
technology associated questions (adopted from Von Krogh, 1998) addressed the type of
communication technology made available for employees to knowledge share, and the
issues of technology usability.
Social interaction is important for knowledge sharing. This is because social
interaction promotes sharing and the exchange of knowledge, tacit knowledge in
particular. In this study, socialization is categorized under the learning component with
the assumption that social interaction generally takes place in a physical location. Four
questions asked if employees have a physical location for social interaction to take
place, as well as addressing the issue of training availability. In addition, there were
nine questions on organization to address trust among employees, and their perception
on the level of knowledge sharing that is emphasized by the organization.
A constant five-point Likert style scale (Likert, 1932) was used for all questions to
avoid confusion. Two forms of questionnaires were used:
(1) Electronic questionnaires document e-mailed or hand delivered hard copies.
(2) Web-based questionnaires.
Subject and sample size
The targeted population of this study was employees working for two IT professional
services organizations from Washington DC Metropolitan areas. Organization A has
approximately total 4,800 employees with approximately 3,700 non-managerial staff
members through out the world. This international organization has provided support
to government agencies to address issues of critical national importance by applying
systems engineering, advanced technologies, and research and development to address
its sponsors’ challenging problems. Organization B is another leading IT service firm
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based in Washing DC metropolitan areas. This organization has more than 44,000
employees (approximately 30,000 non-managerial staff members) with offices in over
150 cities worldwide. Their primary capability is providing IT services to both
government and commercial clients.
Analysis of results
Cronbach’s alpha reliability test and correlation analysis
In this study, several items were measured for each construct. To analyze all of the
constructs in a single regression model, the Cronbach alpha statistic was used to test
its internal consistency, or reliability of the group items. According to Nunnally and
Bernstein (1994) the minimum accepted alpha level utilized was 0.7.
Table I shows the reliability of the tested scales. The reliability for items related to
leadership factors and technology were above Nunnally and Bernstein’s (1994)
recommended level of a¼0:7. The scales were lower for learning and organization
factors with reliability scale of 0.556 and 0.549 respectively. A possible reason for the
low reliability score on the learning and organization factors scale could be a result of
these questions being used for the first time as part of this research.
Correlation analysis on the dependent and independent variables was computed.
Based on the correlation analysis results, none of the independent variables was above
0.7 threshold (Johnston, 1984). Therefore, all the variables were retained for the
regression analysis.
Demographic data of survey respondents
The survey produced 235 responses (company A: 107 responses, company B: 128
responses) from 500 requests and these responses did not include the individuals
participated in the pilot study. Several responses included only demographic information,
and others were sparsely populated. These partial responses were invalid data and were
not included in the analysis, thus yielding 182 valid responses for analysis.
The respondents consisted of 40.1 percent females and 59.3 percent males. While
41.3 percent of the participants’ age ranged from 20 to 39 years, the age range from 40
to 49 years old accounted for 34.7 percent of the sampled population. There were about
22.8 percent of the respondents in the range of 51 years and older (Figure 1).
Figure 2 shows the profile of the respondents by their roles. As expected, most of the
respondents were technical staff (73.7 percent) followed by employees in the support
services (13.2 percent). Although 9.05 percent of the respondents were
manager/directors, they were not executive managers.
The sampled population represented individuals who are generally well educated,
with 38.0 percent having earned a bachelor’s degree and 53.6 percent a graduate
degree. In terms of association with the organization, data showed that about 32.4
Construct Alpha Mean Standard deviation No. of items
Leadership 0.901 34.37 7.66 12
Learning 0.556 9.69 1.84 4
Organization 0.549 29.23 3.79 9
Technology 0.780 23.88 2.77 10
Table I.
Cronbach’s alpha for
individual construct
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percent of the participants were with the organization for less than two years. There
were around 17.4 percent of the participants working with the company between two to
four years and 34.2 percent were working in the organization for more than six years
(Figure 3).
Demographic results suggested that the sampled population represented employees
of all age groups with different years of experience. However, nearly three-fourths of
the respondents are technical staff (technical staff 73.7 percent; support staff 13.2
percent; non-executive managers 9.1 percent; other 3.0 percent). Given the nature
of the business of both the organizations, one can understand that majority of the
employees are technical staff. We can conclude that the sampled population was a
representative of the total population of the organizations under study.
Research results
Descriptive statistics were performed on all the questionnaire items to provide a
general observation of performance. The number of respondent (n), mean values (
m
),
and standard deviation (
s
) were computed for each variable.
Figure 1.
Respondents by age
Figure 2.
Percent respondents by
roles
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Organization. Organizational practice issues were addressed through questions that
related to investment in technology, the organization culture that influences
individual’s attitude and rotating jobs within the organization. Over 90 percent of
the employees indicated that the organizations emphasized knowledge sharing. More
than half of the employees participated in the study indicated that the organizations
were willing to invest in technology to promote knowledge sharing. However,
approximately 70 percent of the employees responded that the organizations seldom
rotate the employees across jobs to acquire new knowledge. Thus, job rotation is not
used as means to promote knowledge sharing in these organizations.
Frequency analysis on variables related to the job security showed that over 85
percent of the employees do not feel loss of power when they share knowledge. Results
also showed employees feel secure about their job when sharing knowledge. In
addition, approximately 70 percent of the employees expressed that they need
moderate to high level of trust before they share their knowledge with others.
It was also interesting to note that employees (.70 percent) were very much willing
to share their knowledge with co-workers, but only a small number of them felt they
actually contributed to the knowledge sharing (15 percent). In addition, employees who
are either somewhat new to the company or who have been with the company for
longer period of time are more willing to share knowledge with their coworkers.
In summary, these results suggested that both the organizations promoted
knowledge sharing and invested in relevant technologies for this purpose. However,
job rotation concept is not used to promote knowledge sharing. Most of the
respondents did not feel loss of power by sharing their knowledge. The results also
suggested that trust must be present for sharing knowledge.
Technology. Issues related to usability of technology were addressed through
survey questions such as ease of use, ease of access, ease of locating information and
types of technology. Almost all the respondents (more than 95 percent) perceived the
technology to be easy to use. Over 80 percent of the employees perceived information to
be easy to locate and access for sharing knowledge.
The results showed that most employees have access to technology such as internet
(95.2 percent), intranet (94.6 percent), e-mail (98.8 percent), telephone (98.2 percent),
facsimile (93.4 percent), video conference (83.8 percent), and telephone conference (95.8
percent). The technologies that are not widely available are web conference (51.5
Figure 3.
Years of association with
organization
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percent), KM software (34.1 percent), and groupware (43.1 percent) with the
percentages in parenthesis indicating the availability
Learning. Learning factors were addressed through questions such as availability
of meeting and break rooms and how much does the available facility help employees
to share and learn knowledge as well as through formal trainings. Over 90 percent of
the respondents indicated that the availability of a place for meeting. It is interesting to
note that over 80 percent of the employees indicated that that the organizations provide
opportunity for knowledge sharing among employees. However, when employees were
asked about receiving training to learn technologies and processes, over 70 percent of
the employees indicated that they do not receive much training.
Leadership. Leadership factors are addressed through questions such as
management support, rewards and motivation systems for employees’ sharing
knowledge with other employees. When employees were asked, “How often does your
manager share his/her knowledge with you?” over 80 percent of respondents indicated
that their managers were willing to share their knowledge with them. A strong
encouragement of knowledge sharing from the management was observed with
approximately 90 percent of the respondents indicating that managers encouraged
knowledge sharing among employees. More than 90 percent of the respondents
expressed that their managers took their suggestions and proposals seriously; while
over 80 percent of the respondents also indicated that their managers were willing to
help them find solutions to difficult problems.
Frequency analysis on variables related to the type of rewards and motivations
employees received showed that over 70 percent of the respondents indicating that
they seldom received monetary awards, career promotion, pay increment, or social
recognition. Results also showed low mean value for monetary awards, career
promotion, pay increment and social recognition. More than 70 percent of the
respondents expressed that they receive verbal praise and encouragement.
Over 60 percent of the respondents indicated that the rewards they received were
not proportional to their contribution. Likewise, approximately 60 percent of the
respondents perceived that they were not appreciated for their contribution.
We can conclude that even though organizational leadership supports knowledge
sharing, receptive to suggestions, and supportive in finding solutions to difficult
problems, motivation factors are absent. Employees are rarely rewarded and even
when they are rewarded, it did not meet the expectations of employees.
Regression analysis
We used multiple regression analysis on each of the seven dependent variables.
Consistent across all regression analysis were the covariates and the independent
variables entered. In the multiple regression approach, 45 independent variables were
entered into the regression model. Independent variables that were significant from the
multiple regression analysis were further analyzed with the stepwise regression
approach (see Appendix 1).
For the dependent variable (DV1) that addressed the issue of employees’ willingness
to share their knowledge with their coworkers, the stepwise regression results showed
that all the control variables were dropped and they did not have any significant effect
on the regression model (see Appendix 2). Table II shows that, of the independent
variables entered in the model, the first significant variable accepted by stepwise
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regression was frequent interaction with co-workers and it accounted for 5.1 percent of
the variance with
b
¼20:225. The second significant variable accepted was high level
of trust and it accounted for 3.1 percent of the variance with
b
¼20:176. Feeling no
loss of power by sharing knowledge was the third variable accepted, which accounted
for 2.5 percent of the variance with
b
¼20:157. Therefore, results of the regression
analysis of the independent variables on the dependent variable that addressed the
employees’ willingness to share their knowledge with their coworkers showed that
organization factors play major role in influencing the employees’ willingness to share.
It is interesting to note that employees who were either somewhat new to the
company or have been with the company for longer period of time were more willing to
share knowledge with their coworker (Table III). This pattern was similar when the
question of “how long have you been in this position?” was asked (Table IV).
The dependent variable DV5 addressed how employees willingness to share with
coworkers who had helped them in the past. Results of the stepwise regress (Table V)
showed that of significant independent variables entered, the first variable entered by
stepwise regression was IV45 (easy access to technologies) and it accounted for 10.7
percent variance with
b
¼0:327. The second variable entered was IV27 (feeling no loss
of power by sharing knowledge) which accounted for 7.2 percent variance with
b
¼20:271. Variable IV16 (manager’s encouragement for sharing knowledge among
0 to 2 years (%) 2.1 to 4 years (%) 4.1 to 6 years (%) 6.1 and more (%) Total (%)
Sometimes 0 3.4 7.7 1.8 2.4
Often 18.5 41.4 46.2 21.1 27.7
Always 81.5 55.2 46.2 77.2 69.9
Table III.
Response to DV1 by no.
of years with the current
organization
RR-square Adjusted R-square Std error of the estimate R-square change
b
1 0.225 0.051 0.045 0.51 0.051 20.225
2 0.286 0.082 0.071 0.50 0.031 20.176
3 0.326 0.106 0.090 0.49 0.025 20.157
Table II.
Stepwise regression
summary for dependent
variable DV2
RR-square Adjusted R-square Std error of estimate R-square change
b
1 0.327 0.107 0.102 0.63 0.107 0.327
2 0.423 0.179 0.169 0.60 0.072 20.271
3 0.456 0.208 0.194 0.60 0.029 0.178
Table V.
Stepwise regression
model summary for
dependent variable DV5
0 to 2 years (%) 2.1 to 4 years (%) 4.1 to 6 years (%) 6.1 and more (%) Total (%)
Sometimes 1.2 2.6 8 0 2.4
Often 24.7 33.3 32 21.1 27.4
Always 74.1 64.1 60 78.9 70.1
Table IV.
Response to DV1 by no.
of years in the current
position
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team members) was entered the last and it accounted for 2.9 percent variance with
b
¼0:178.
Regression analysis of another dependent variable (DV7) that addressed the issue of
how willing employees are to talk with coworkers about new ideas (Table VI) showed
that the first variable entered by stepwise regression was high level of trust with 11.1
percent variance and
b
¼20:334. This is followed by encouragement as reward with
7.6 percent variance and
b
¼0:275. The third variable entered feeling no loss of power
by sharing knowledge and it accounted for 3.3 percent variance with
b
¼20:188.
Verbal praise as reward was entered as the fourth variable and it accounted for 1.8
percent variance with
b
¼20:137.
When the dependent variable that addressed the employees’ willingness to share
new ideas (Dependent variable DV2) was analyzed, the results showed that the first
variable entered by stepwise regression was manager’s willingness to help employees
in sharing knowledge and it accounted for 5.4 percent of the variance with
b
¼0:232
(Table VII). The second variable entered was frequent interaction with co-workers and
it accounted for 4.1 percent variance with
b
¼20:202. Feeling no loss of power by
sharing knowledge and easy access to technologies were entered consecutively into the
stepwise regression model and respectively, they accounted for 3.3 (
b
¼20:183), and
2.3 percent (
b
¼20:159) of the variance and
b
coefficients observed.
The significant factors resulted from multiple regression analysis of the seven
dependent variables are shown in the Table VIII, organized by the four KM
components. Based on our study, these factors from each KM components are the
factors that influence knowledge sharing by employees.
Discussion
Organization factors
Result from the study demonstrated that organization factor is an important
component to employees’ willingness to share knowledge. The organization factor
construct was based on organization culture and employees’ trust among each other.
Evidence in support of the importance of organization culture came from the fact that
majority of the employees’ indicated that they were aware of the emphasis and practice
RR-square Adjusted R-square Std error of the estimate R-square change
b
1 0.232 0.054 0.048 0.59 0.054 0.232
2 0.307 0.094 0.083 0.57 0.041 20.202
3 0.356 0.127 0.111 0.57 0.033 20.183
4 0.387 0.150 0.129 0.56 0.023 20.159
Table VII.
Stepwise regression
summary for dependent
variable DV2
RR-square Adjusted R-square Std error of the estimate R-square change
b
1 0.334 0.111 0.106 0.82 0.111 20.334
2 0.433 0.187 0.177 0.78 0.076 0.275
3 0.469 0.220 0.206 0.77 0.033 20.188
4 0.488 0.238 0.220 0.76 0.018 20.137
Table VI.
Stepwise regression
summary for dependent
variable DV7
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of knowledge sharing in their organization. Our results also showed that both the
organizations were very much willing to invest in new technology to promote
knowledge sharing.
Interestingly, results from our study clearly demonstrated that loss of personal
power as well as job security were not obstacles to knowledge sharing among
employees. These results proved to be at odds with some of the literature review
findings, which hold that employees are not willing to share knowledge because they
perceived knowledge sharing to be a threat to their personal power and job security
(Wiig, 1997; O’Dell and Grayson, 1998; Sveiby, 1997). The results of this study suggest
that employees are willing to share knowledge without feeling of losing power or job. A
possible explanation for failing to observe a loss of power and job insecurity in this
study might be that trust and social interactions were already practiced among
employees. Evidence for this explanation can be observed from the data that most of
the respondents indicating the necessity of trust and that social interaction with
co-workers are important for knowledge sharing. This finding is still consistent with
other studies that found trust to be an important component for knowledge sharing.
When employees were asked about the extent of their willingness to share
knowledge with employees who had helped them in the past, majority the respondents
indicated that they would. Consequently, if employees had previously experienced
reluctance or refusal of help, it is unlikely that they will be willing to share their
knowledge with fellow co-workers.
We conclude that the study showed no evidence of loss of personal power or
perceived threat to job security when employees share their knowledge. Level of
interaction with coworkers, however, influences knowledge sharing.
Technology factors
Developments in technology, specifically in information and communications
technologies, have played a vital role in providing the infrastructure needed to
support network structures and organizational learning. The types of technology and
channels of communication that assist in the creation, storage, sharing and transfer of
knowledge are integral parts towards building the learning organization. It is
interesting to note that the presence of telephone and facsimile and the accessibility of
KM components Significant factors
Organizational Frequent emphasis of knowledge sharing
Feeling no loss of power by sharing knowledge
High level of trust
Frequent interaction with co-workers
Technology Availability of telephone
Availability of facsimile
Easy access to technologies
Learning Amount of training received
Leadership Frequent encouragement by managers
Manager’s willingness to help employees in sharing knowledge
Verbal praise as reward
Encouragement as reward
Career promotion as reward
Table VIII.
Results of regression
analysis organized by
four KM components
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technologies were significant predictors of employees’ willingness to share knowledge.
On the other hand, the video conference, web conference, internet, intranet, telephone,
KM software and groupware were not significant predictors of knowledge sharing. It is
interesting to understand why these somewhat of more advanced technologies failed to
predict willingness to share, specifically, technologies such as Internet, intranet and
groupware. A possible explanation could be that employees were not trained to use
these technologies.
The results of this study showed that the technology was easy to use, and information
was easy to locate and access. Training, on the other hand, was not frequently provided.
Many of the employees indicated that they received little to moderate training for using
the available technology. It is important for organization to realize that making the latest
technology available is not the solution to promote knowledge sharing. There is a need for
an implemented system where employees receive training in using the available
technology for knowledge transfer. Furthermore, although the culture and structure of an
organization have the most significant bearing on KM, ongoing technological
developments have also helped make possible the transference and storage of
knowledge that were previously inconceivable. Thus, the technological infrastructure of
an organization is crucial to its learning and KM initiatives.
We sum up that the availability of advanced technology does not mean that
employees will use the technology to share knowledge. However, if the technologies are
easy to use and sufficient training is provided, employees may be persuaded and
encouraged to use the available technology to share their knowledge. Results from the
study showed that the usability of technology is important for knowledge sharing.
Learning factors
Many employees expressed that the available physical facilities were supportive in
facilitating knowledge sharing. In addition, when employees were asked how much
opportunity does the organization provide for employees to share and learn their
knowledge, most of them responded in affirmative. However, results of further analysis
showed employees felt that they do not receive enough training on the available
technologies and processes. If employees do not have the proper skill to use the
existing technology, or do not know the processes, it is very unlikely that employees
would interact with each other effectively or efficiently even if the available
technologies have the right functionality and capabilities. This observation is
supported by Kim and Lee (2005) in their study that employees are more likely to
express knowledge sharing when they perceived ease of use of information systems.
Leadership factors
Most of the respondents indicated that their managers were very much willing to share
their knowledge with them. Further, a convincing number of the employees felt that
their managers were always willing to help them find solutions to difficult problems.
The data also show that management facilitates knowledge sharing by allocation of
resources to support the transfer of knowledge. Based on our study, we can conclude
that management is encouraging knowledge sharing among employees in these
organizations. These results further showed that employees were aware of the
importance of knowledge sharing and were encouraged by the management for
knowledge transfer.
Our analysis showed that five factors related to leadership were significant predictors
to three dependent variables, which display the importance of leadership support in
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employees’ knowledge sharing. These results are consistent with the literature findings
that management support is of utmost important to knowledge sharing.
Our study shows that leadership that encourages knowledge sharing would allocate
resources to support the transference of knowledge. In other words, leadership would
support their employees by allocating paid-hours and funds for training courses,
conference attendance, and the purchase of technology to support knowledge sharing.
Leadership support as well as organization practices are unspoken messages to
employees highlighting the importance of knowledge sharing.
Although there were strong indications of management support, employees indicated
a lack of appreciation and felt that rewards were not proportional to their contributions.
Many of the employees perceived that they were not being appreciated for the time and
energy they have invested into knowledge sharing. These results suggest the
importance of acknowledgement and appreciation of employees for their contribution.
It is obvious that encouragement has been the main motivation employees received
for sharing their knowledge. This is followed closely by verbal praise and social support.
Monetary awards, career promotion and pay increments, on the other hand, were not
employed frequently. If organizations were to realize the benefits of knowledge sharing,
they must then understand and implement the motivational factors to share.
Summary
All four KM components organization, technology, learning, and leadership have
significant contributing factors to employees’ willingness to share their knowledge.
Results of both multi-regression analysis and stepwise regression analysis confirmed
that organization culture and learning factors affect employees’ willingness to share
knowledge sharing. Culture includes organizational practices, trust, usability of
technology, and leadership support. Learning factors are training opportunity and
physical facilities for social networks.
It is also interesting to note that the present study provided empirical data showing
that employees are very much willing to share their knowledge. However, failure to
observe a higher number of employees actually contributing to knowledge sharing
clearly demonstrates that certain variables are obstacles to knowledge transfer. One
key variable is employees’ motivation. The results indicate that most employees felt
under appreciated and not compensated for their contribution. If knowledge assets
were viewed as value assets, it is important that organization may want to reconsider
the rewards and motivation structure that encourages employees to share knowledge.
It will help capture the tacit knowledge of employees.
Our study showed that there was little loss of personal and perceived threat to job
security when employees share their knowledge. Interaction with coworkers, however
affects knowledge sharing. In order to increase the level of social interactions,
employees should be encouraged to work in teams. Social interaction could also be
enhanced through job rotation, thereby providing opportunity for individuals to
interact with different groups of people and form informal social networks.
Limitations of the study
Self-reporting bias is a limitation of the survey study. Self-report bias occurs when
individuals would bring in their experiences, self-perception, and their work
environment when completing a survey. Even though the present study clearly
indicates to the participants that it is an anonymous study, it is possible that
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sometimes participants may misreport and misrepresent their perceptions to make
themselves look better.
The study was an exploratory, and it was limited to two organizations. This would
therefore restrict us from generalizing the outcomes of the study. Future studies need
to increase the number of participating organizations as well as different industries
and we intend to extend this study to many more organizations.
Suggestions for future research
A comparative study of cross-organizations and within an organization will allow us to
determine factors that influence employees’ willingness to knowledge share. It would be
interesting and useful to investigate what factors of the four components are important
for different types and sizes of organizations. We can extend the study to examine what
factors motivate employees to share knowledge and what types of knowledge are
valuable and should be rewarded when shared or transferred to the organization.
Conclusion
Our exploratory study contributed to a deeper understanding of knowledge sharing
with empirical data from two large IT organizations based on non-executive
employees’ perspective rather than that of management. The present study showed
that employees show willingness to share their knowledge. The study also showed that
organizations should not be concerned with employees experiencing a loss of power or
feeling of insecurity with their job when they share knowledge with fellow employees.
The obstacles to knowledge sharing are that employees feel they are under appreciated
and their rewards were not proportional to their contribution. Lack of training to use
available technology is another notable obstacle to knowledge sharing.
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Appendix 1. List of variables
Dependent variables
.DV1 Willingness to share knowledge with coworkers.
.DV2 Willingness to share new ideas.
.DV3 Actual contribution of knowledge to coworkers.
.DV4 Willingness to discuss the gained knowledge with coworkers.
.DV5 Willingness to share knowledge with coworkers who had helped you in the past.
.DV6 Willingness to share knowledge with other team members.
.DV7 Willingness to talk to other employees about new ideas.
Independent variables
.IV1 Job title/rank.
.IV2 Position level.
.IV3 Type of business (dropped from analysis).
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.IV4 Size of company (dropped from analysis).
.IV5 Length of stay with the current company.
.IV6 Length of current position.
.IV7 Length of experience.
.IV8 Age.
.IV9 Gender.
.IV10 Education level.
.IV11 Knowledge sharing practice in the company.
.IV12 Emphasis of knowledge sharing in the company.
.IV13 Opportunity to attend training programs.
.IV14 Opportunity to be rotated around projects.
.IV15 Manager’s willingness to share knowledge with employees.
.IV16 Manager’s encouragement to share knowledge among team members.
.IV17 Manager’s willingness to help employees in sharing knowledge.
.IV18 Manager’s participation in solving employees’ problems.
.IV19 Received monetary reward or bonus for sharing knowledge.
.IV20 Received verbal praise for sharing knowledge.
.IV21 Received encouragement for sharing knowledge.
.IV22 Received career promotion for sharing knowledge.
.IV23 Received salary increase for sharing knowledge.
.IV24 Received social recognition for sharing knowledge.
.IV25 Received rewards proportional to the contribution.
.IV26 Feeling appreciated when invested time and energy to sharing knowledge.
.IV27 Feeling loss of power from sharing knowledge.
.IV28 Feeling job insecurity from sharing knowledge.
.IV29 Level of trust need before sharing knowledge.
.IV30 Affect of interaction with coworkers in sharing knowledge with them.
.IV31 Investment of technology by company to promote sharing knowledge.
.IV32 Availability of meeting place.
.IV33 Availability of place for socialization.
.IV34 Knowledge sharing opportunity provided by the company.
.IV35 Availability of video conferencing.
.IV36 Availability of web conference.
.IV37 Availability of telephone conference.
.IV38 Availability of knowledge management software.
.IV39 Availability of groupware.
.IV40 Availability of internet.
.IV41 Availability of email technology.
.IV42 Availability of telephone.
.IV43 Availability of facsimile.
.IV44 Usability of technology for sharing knowledge.
.IV45 Accessibility of technology.
.IV46 Easiness of locating information.
.IV47 Training received on the existing technology.
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Appendix 2. Results of stepwise regression models for dependent variables
About the authors
Brent M. Han is currently the Deputy Program Manager of Financial
Management and Intelligence Programs at MITRE’s Center for Enterprise
Modernization. In this capacity, he leads financial-related decision analytics
and systems engineering support to federal government agencies and
non-profit financial intelligence organizations. He also leads several strategic
internal financial intelligence research and development projects. Prior to
joining MITRE, he was a Chief Engineer at Science Applications International
Corporation, and worked for Deloitte Consulting, providing decision support systems and data
warehousing solutions. Dr Han’s expertise ranges from decision support systems, systems
engineering, modeling and simulations to knowledge management and program management.
Brent Han is the corresponding author and can be contacted at: bhan@mitre.org
Model RR-square
Adjusted
R-square
Std error of
the estimate
R-square
change Sig.
b
Dependent variable DV1
1 0.225 0.051 0.045 0.51 0.051 0.003 20.225
2 0.286 0.082 0.071 0.50 0.031 0.020 20.176
3 0.326 0.106 0.090 0.49 0.025 0.036 20.157
Dependent variable DV2
1 0.232 0.054 0.048 0.59 0.054 0.003 0.232
2 0.307 0.094 0.083 0.57 0.041 0.007 20.202
3 0.356 0.127 0.111 0.57 0.033 0.015 20.183
4 0.387 0.150 0.129 0.56 0.023 0.037 20.159
Dependent variable DV3
0.251 0.063 0.057 0.57 0.063 0.001 0.251
Dependent variable DV4
0.307 0.094 0.089 0.64 0.94 0.000 20.327
Dependent variable DV5
1 0.327 0.107 0.102 0.63 0.107 0.000 0.327
2 0.423 0.179 0.169 0.60 0.072 0.000 20.271
3 0.456 0.208 0.194 0.60 0.029 0.015 0.178
Dependent variable DV6
1 0.256 0.065 0.060 0.61 0.065 0.001 20.256
2 0.306 0.094 0.083 0.60 0.028 0.025 20.169
Dependent variable DV7
1 0.334 0.111 0.106 0.82 0.111 0.000 20.334
2 0.433 0.187 0.177 0.78 0.076 0.000 0.275
3 0.469 0.220 0.206 0.77 0.033 0.010 20.188
4 0.488 0.238 0.220 0.76 0.018 0.049 20.137
Notes: Variables entered: For DV1: 1. IV29; 2. IV21; 3. IV28; 4. IV20. For DV2: 1. IV17; 2. IV30; 3. IV27;
4. IV45. For DV3: 1. IV47. For DV4: 1. IV27. For DV5: 1. IV45; 2. IV27; 3. IV16. For DV6: 1. IV27;
2. IV30. For DV7: 1. IV29; 2. IV21; 3. IV28; 4. IV30
Table AI.
Stepwise regression
model summary table
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Vittal S. Anantatmula is currently working as Assistant Professor of Project
Management in the College of Business, Western Carolina University. Prior to
joining Western Carolina University, he worked as the program director of the
Project Management Graduate Degree Program, School of Business of the
George Washington University. He has worked in the petroleum and power
industries for several years as an electrical engineer and project manager. As a
consultant, he worked with the World Bank, Arthur Andersen, and other
international consulting firms. He has co-authored a book, Project Planning Techniques, and has
more than ten journal publications in project management and knowledge management. He has
presented more than 20 papers in prestigious and international conferences. He holds BE
(Electrical Engineering) from Andhra University, MBA from IIM-MDI, MS and DSc in
Engineering Management from the George Washington University. He is a certified Project
Management Professional and Certified Cost Engineer.
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