Analysis of knowledge sharing behaviour inconstruction teams in Hong Kong

PEIHUA ZHANG* and FUNG FAI NG

Department of Real Estate and Construction, The University of Hong Kong, Room 533, Knowles Building, The

University of Hong Kong, Pok Fu Lam, Hong Kong

Received 2 May 2011; accepted 22 February 2012

Knowledge sharing in construction teams is important for improved project performance and successful pro-

ject delivery. The purpose of this study is to analyse psychological motivations underlying individual knowl-

edge sharing behaviour in Hong Kong construction teams using the theory of planned behaviour (TPB). A

questionnaire survey was conducted among professionals from 172 construction companies in Hong Kong.

A total of 231 usable questionnaires were collected. Structural equation modelling (SEM) is applied to test

the research model and hypotheses. The research results indicate that professionals’ knowledge sharing

behaviour in construction teams is only significantly predicted by their intention to share knowledge rather

than perceived behavioural control over knowledge sharing, implying that knowledge sharing behaviour is

largely under the professionals’ volitional control. The research results also indicate that professionals’

knowledge sharing intention is dominantly affected by attitude and perceived behavioural control but weakly

influenced by subjective norm, which is different from other groups of professionals in prior studies. Several

managerial implications are suggested for construction companies to manage employees’ knowledge sharing

behaviour in construction teams. It is one of the first studies to employ social psychological theory to exam-

ine knowledge sharing behaviour in the construction context. However, the research model only shows pre-

dictive power and lacks explanatory power. Nevertheless, it provides a starting point for future researchers

to further explore the salient beliefs underlying attitude and perceived behavioural control so as to explain

knowledge sharing behaviour in the construction sector.

Keywords: Construction teams, Hong Kong, knowledge sharing, theory of planned behaviour.

Introduction

In the modern knowledge economy, knowledge is rec-

ognized as a critical asset for organizations to gain

competitive advantage (Grant, 1996; Nahapiet and

Ghoshal, 1998; Spender, 1998; Martın-de-Castro,

2011) and to maintain long-term success (Nonaka

and Takeuchi, 1995). Therefore, knowledge manage-

ment (KM) becomes a core business concern for

many organizations. KM is the process of identifying,

sharing and utilizing knowledge and good practice to

help organizations to compete (O’Dell and Grayson,

1998). Researchers point out that employee knowl-

edge sharing is the heart of knowledge management

(Dainty et al., 2005; Riege, 2005). Knowledge is

fundamentally created and applied by individuals

(Nonaka, 1994). Knowledge sharing is the key pro-

cess to transform individual knowledge into organiza-

tional knowledge (Foss et al., 2010; Nonaka, 1994). If

individuals are not willing to share what they know,

then implementation of knowledge management

would be out of the question. Knowledge sharing is

crucial to organizational outcomes (Foss et al., 2010).

For example, knowledge sharing could enable individ-

uals to jointly create new knowledge that is beyond

what one individually owns (van den Hooff and

Hendrix, 2004), thus giving rise to improved organi-

zational capability of innovation (Choi et al., 2008).

Knowledge sharing also could lead to a greater

individual problem-solving capacity, which is func-

tional to the organizational level problem-solving

capacity (Nickerson and Zenger, 2004). Therefore,

*Author for correspondence. E-mail: h0799029@graduate.hku.hk

Construction Management and Economics (July 2012) 30, 557–574

Construction Management and EconomicsISSN 0144-6193 print/ISSN 1466-433X online � 2012 Taylor & Francis

http://www.tandfonline.comhttp://dx.doi.org/10.1080/01446193.2012.669838

organizations that aim to launch knowledge manage-

ment initiatives should motivate employees to increase

their willingness to share their knowledge for organi-

zational use (Marshall and Sapsed, 2000).

The construction industry is a project-based indus-

try, where a number of companies form a temporary

multidisciplinary organization to construct various

facilities based on contracts and specifications. After

the completion of a project, the contractual relation-

ships are usually terminated and the parties involved

move on to other projects. The temporary nature

therefore leads to ineffectiveness in managing project

knowledge. Much project knowledge is lost due to the

failure to share and record personal tacit knowledge,

the lessons learned and good practices (Kivrak et al.,

2008). The construction industry is also criticized for

the low level of innovation activities, which results

from factors such as temporary alliances, pressure of

deadlines and pursing short-term goals (Drejer and

Vinding, 2006). Further, the industry is facing new

challenges as clients are becoming more sophisticated

and they require better value for money, and the

required products are becoming more complex (Egan,

1998; Kamara et al., 2002). There is a need for

change and continuous improvement in the construc-

tion industry. KM is one of the initiatives to address

the demands of innovation, improved project perfor-

mance and client satisfaction (Carrillo et al., 2000;

Kamara et al., 2002). In Hong Kong, the Construc-

tion Industry Review Committee (CIRC) has claimed

that the Hong Kong construction industry lacks a cli-

ent-focused approach, extensively uses traditional

construction methods and has a short-term attitude to

business development (CIRC, 2001). A transforma-

tion is desired for the Hong Kong construction indus-

try to pursue the vision of ‘an integrated industry that

is capable of continuous improvement toward excel-

lence’ (CIRC, 2001, p. 2). The CIRC suggests that

the Hong Kong construction industry should seek to

be efficient, innovative and client-oriented. The com-

mittee advocates that sharing of learning and knowl-

edge should be encouraged to pursue continuous

project improvement.

For construction companies, a large amount of

valuable knowledge is embedded in construction team

members, who create and apply expert knowledge in

the construction processes. Thus, effectively leverag-

ing individuals’ knowledge in construction teams is

critical for improving project performance and suc-

cessful project delivery. A typical construction team

constitutes professionals from different disciplines

(e.g. building engineers, surveyors, structural engi-

neers, safety engineers). It is important for team

members to share their diverse knowledge to establish

mutual understanding, achieve collaboration, jointly

seek effective solutions, and improve work efficiency.

A construction team usually disbands for other pro-

jects once the current project is completed. Important

knowledge identified and learned by team members

through knowledge sharing in the current project

team can also be transferred and applied in the next

project, thus avoiding ‘reinventing the wheel’ and

reducing repetition of previous mistakes in the con-

struction process (Bresnen et al., 2003; Ma et al.,

2008; Senaratne and Sexton, 2008).

Researchers have increasing awareness that promot-

ing knowledge sharing would significantly contribute

to the improvements of project performance and orga-

nizational performance in the construction industry

(Dainty et al., 2005; Robinson et al., 2005). However,

there are few studies that explore how to motivate

people to engage in knowledge sharing within the

construction industry (Woo et al., 2004; Dainty et al.,

2005). This knowledge gap makes organizations in

the construction industry uninformed about how they

should manage employees’ knowledge sharing prac-

tice. Foss et al. (2010) comprehensively review recent

knowledge sharing research, and claim that existing

knowledge sharing literature is preoccupied with con-

structs, processes and phenomena defined as macro

(collective, organizational) level and pay compara-

tively little attention to micro (individual) level con-

structs. The aim of this study is to use Ajzen’s (1991)

theory of planned behaviour (TPB) to empirically

examine knowledge sharing behaviour in construction

teams at the individual level. TPB has been employed

by researchers to predict a wide range of behaviours

in social psychology. Recently, it has also been

employed by researchers to successfully examine

knowledge sharing behaviour of different professional

groups such as physicians in hospitals (Ryu et al.,

2003), bank employees in Greece (Chatzoglou and

Vraimaki, 2009), and employees in the oil industry

(Tohidinia and Mosakhani, 2010). This is one of the

first studies to examine professionals’ knowledge shar-

ing behaviour using TPB in the construction sector.

Literature review

A widely accepted working definition of knowledge is

‘a fluid mix of framed experience, values, contextual

information, and expertise insight that provides a

framework for evaluating, and incorporating new

experiences and information. It originates and is

applied in the minds of knowers (Davenport and

Prusak, 1998, p. 5). Strictly speaking, individual

knowledge cannot be shared directly because it

resides in the human mind and cannot be separated

from the person who knows it (Hendriks, 1999;

558 Zhang and Ng

Wasko and Faraj, 2000). In order to communicate

knowledge to others, the person who owns the knowl-

edge needs to perform the action of ‘externalization’

to codify his/her knowledge into an explicit form (e.g.

speech, articles, formulas) that can be accessed by

others (Hendriks, 1999). The explicit form of knowl-

edge is regarded as information, which is objective in

nature and could be stored in knowledge repositories

(e.g. database, documents) or circulated among peo-

ple (Firestone, 2003). On the other hand, people who

seek knowledge need to perform the action of ‘inter-

nalization’ to make sense of and absorb the informa-

tion received (Hendriks, 1999). This process entails

the action of reconstruction, where a knowledge recei-

ver builds up his/her own knowledge by digesting the

received information. The internalization can take dif-

ferent forms such as learning by doing, and trying to

understand the information based on their previous

knowledge base. The act of ‘thinking’ taking place in

the human mind is the key to transforming informa-

tion to knowledge (McDermott, 1999).

The above illustration of a simplified knowledge

sharing process indicates that knowledge sharing may

entail costs to knowledge contributors as the expenses

of time and codification effort (Kankanhalli et al.,

2005). People may not be willing to share their knowl-

edge unless they think it is worthwhile and important

(Ryu et al., 2003). Consequently, organizations should

try to understand how employees can be motivated to

engage in knowledge sharing, as Robertson (2002,

p. 307) suggests that ‘knowledge sharing is a human

activity, and understanding the humans who will do it

is the first step in successfully supporting the activity’.

Efforts have been made by researchers to understand

motivations underlying individual knowledge sharing

behaviour from different perspectives. Some research-

ers consider knowledge sharing behaviour as a form of

social exchange (Hall, 2001; Bock and Kim, 2002;

Kankanhalli et al., 2005; Wasko and Faraj, 2005; Lin,

2007; Huang et al., 2008). They find that people will

evaluate potential costs (e.g. loss of knowledge power,

codification effort) and benefits (e.g. organizational

reward, reciprocal relationship, sense of self-worth)

associated with knowledge sharing. People are more

likely to engage in knowledge sharing if they perceive

that the benefits obtained from knowledge sharing

override the costs incurred in knowledge sharing.

Researchers have also studied the motivations of

knowledge sharing behaviour by considering contex-

tual factors. They observe that employees are moti-

vated to share knowledge by perceived peer and

supervisor support (MacNeil, 2003; Cabrera et al.,

2006; Sveiby, 2007), top management support

(Connelly and Kelloway, 2003), trust among

colleagues (Ma et al., 2008), group identification

(Cabrera and Cabrera, 2005), social network and

shared goals (Chow and Chan, 2008), organizational

culture (McDermott and O’Dell, 2001), etc.

In recent years, researchers have started to use the-

ories in social psychology to understand psychological

motivations associated with individual knowledge

sharing behaviour. Ajzen and Fishbein’s (1980) the-

ory of reasoned action (TRA) has been employed by

many researchers to examine knowledge sharing

behaviour, e.g. Bock and Kim (2002), Bock et al.

(2005), Ding and Ng (2009), So and Bolloju (2005).

TRA suggests that a person’s behaviour is determined

by his/her intention to perform the behaviour, which

in turn is determined by the person’s attitude towards

and subjective norm regarding the behaviour. One

assumption underlying TRA is that most social

related actions are under volitional control (Ajzen and

Fishbein, 1980). Volitional control means that with

relevant intention, an individual is able to feel free to

choose whether or not to act in a certain way (Hansen

and Avital, 2005). Thus TRA has limitations in deal-

ing with behaviours over which people do not have

complete volitional control. When there are certain

external constraints (e.g. lack of necessary opportuni-

ties and resources) on a behaviour, the mere forma-

tion of intention is not sufficient to predict the

behaviour (Armitage and Conner, 2001). Thereafter,

Ajzen (1991) extends the TRA model by incorporat-

ing perceived behavioural control (PBC) as an addi-

tional predictor of intention and behaviour, and

establishes the model of theory of planned behaviour

(TPB). TPB proposes that individuals’ intention to

perform a behaviour is determined by three con-

structs: attitude towards the behaviour, subjective

norm regarding the behaviour, and perceived behav-

ioural control over the behaviour. The behavioural

intention and perceived behavioural control then

jointly determine performance of the behaviour. Per-

ceived behavioural control acts as a predictor of both

intention to perform a behaviour and actual perfor-

mance of the behaviour, enabling TPB to deal with

behaviours over which people have incomplete voli-

tional control. Prior research provides empirical evi-

dence that TPB is superior over TRA in explaining

individual intention to share knowledge and shows

better overall model fit than TRA (Ryu et al., 2003).

Accordingly, TPB is adopted as the theoretical frame-

work in this study to examine individual knowledge

sharing behaviour in construction teams.

Research methodology

Research design is largely determined by the research

problem under investigation (Creswell, 2003; Fellows

Knowledge sharing behaviour 559

and Liu, 2008). The purpose of this research is to

test the existing theory of TPB in examining knowl-

edge sharing behaviour in construction teams.

Researchers claim that quantitative research, which

starts from theories and concepts, is best to test a the-

ory or explanation (Bryman, 2001; Creswell, 2003).

Quantitative research uses a deductive way to make

inquiry, i.e. deducing hypotheses based on existing

theories and empirically testing the hypotheses. Con-

cepts within the hypotheses are translated into vari-

ables that are measurable.

Regarding the approaches for quantitative research,

experiment and survey are commonly used by research-

ers (Bryman, 2001; Creswell, 2003; Fellows and Liu,

2008). Experiment is conducted by changing one vari-

able and observing the effect of the change while hold-

ing the other variables and external conditions constant

(Nardi, 2003). Experiment mainly deals with observa-

ble variables, which may be quantified and changed

(Fellows and Liu, 2008). Since the variables in this

research are latent variables and related to individuals’

perceptions, they are unlikely to be manipulated and

controlled. However, surveys allow researchers to mea-

sure people’s perceptions and attitudes by asking

respondents to indicate their evaluations against a mea-

surement scale (Punch, 1998). Thus a survey is used to

collect quantitative data from a large number of

respondents in this study. Specifically, a self-adminis-

tered questionnaire survey, which was sent by mail,

was employed to reduce the researcher’s intervention

and achieve cost-effectiveness. The following sections

will describe the hypotheses deduced, the measurement

instrument developed, sampling strategy and question-

naire administering method.

Theoretical framework, research model and

hypotheses

Figure 1 shows the research model and hypotheses

formulated on the basis of TPB. A central construct

in TPB is individuals’ intention to perform a behav-

iour. According to Ajzen (1991, p. 181), intention is

‘indications of how hard people are willing to try, of

how much of an effort they are planning to exert, in

order to perform the behavior’. TPB suggests that

intention to perform a behaviour is a crucial predictor

of the actual performance of the behaviour. In knowl-

edge sharing literature, a number of studies have

empirically reported a strong and significant causal

link between knowledge sharing intention and knowl-

edge sharing behaviour, e.g. Tohidinia and

Mosakhani (2010), Jeon et al. (2011), Choi et al.

(2008). Further, Ryu et al. (2003) even use

knowledge sharing intention as a dependent variable

to examine physicians’ knowledge sharing behaviour

given the strong link between intention and behav-

iour. Based on TPB and the assertions of previous

studies, it is hypothesized that individuals’ knowledge

sharing intention in construction teams also signifi-

cantly determines their knowledge sharing behaviour.

Thus:

Hypothesis 1: Individuals’ intention to share knowledge

has a positive effect on their knowledge sharing

behaviour in construction teams.

According to TPB, in circumstances where

individuals have incomplete volitional control over a

behaviour, the actual behaviour also depends on some

non-motivational factors such as availability of requisite

opportunities, resources and tools (Ajzen, 1991). An

evaluation of those factors produces the perceived

behavioural control (PBC), which refers to people’s

perception of the ease or difficulty of performing the

behaviour of interest (Ajzen, 1991). PBC is found to

play an important role in determining knowledge shar-

ing intention of members in a community of practice

(CoP) (Jeon et al., 2011), employees in the oil industry

(Tohidinia and Mosakhani, 2010), physicians (Ryu

et al., 2003), etc. In the context of construction teams,

individuals may evaluate PBC against availability of

time, communication channels, interaction opportuni-

ties, etc. (Fong and Lee, 2006; Kazi and Koivuniemi,

2006; Styhre, 2008). It is expected that if individuals in

construction teams have a high perceived behavioural

control (PBC) concerning knowledge sharing, they are

more likely to share knowledge with teammates. Thus:

Hypothesis 2: Individuals’ perceived behavioural con-

trol has a positive effect on their knowledge sharing

behaviour in construction teams.

TPB proposes three independent determinants of

intention: attitude, subjective norm and perceived

behavioural control. Attitude towards a behaviour

concerns the degree to which a person has a favour-

able or unfavourable evaluation of the behaviour

(Ajzen, 1991). Attitude has been tested to be a signifi-

cant antecedent of organizational behavioural inten-

tions. Chang (1998) observes that peoples’ attitude

towards moral behaviour significantly affects their

moral behavioural intention. Bock and Kim (2002)

find that attitude towards knowledge sharing exerts a

strong influence on employees’ knowledge sharing

intention in large public organizations. For profes-

sionals in construction teams, it is also expected that

a positive evaluation of knowledge sharing would lead

to a higher tendency to share knowledge. For

instance, an engineer in a construction team is likely

560 Zhang and Ng

to share his knowledge to resolve a problem if he/she

appraises knowledge sharing behaviour as beneficial

to him/her. Thus:

Hypothesis 3: Individuals’ attitude towards knowledge

sharing has a positive effect on their intention to share

knowledge in construction teams.

Subjective norm is defined as perceived social pres-

sure to perform or not to perform a given behaviour

(Ajzen, 1991). The perceived social pressure is

formed by evaluating expectations of relevant impor-

tant referents. Sveiby (2007) argues that employees’

behaviour is influenced by perceived behaviours, atti-

tudes and atmosphere that characterized the life in a

working environment. People are likely to behave in

accordance with the prevailing norms in the working

environment. Subjective norm has received consider-

able empirical support as an important predictor of

behavioural intention regarding knowledge sharing in

previous studies, e.g. Bock et al. (2005), Ryu et al.

(2003), Ding and Ng (2009). In a construction team,

if a person perceives that knowledge sharing behav-

iour is supported and valued by important members

such as colleagues, supervisors and managers, he/she

would have a greater intention to share knowledge.

Thus:

Hypothesis 4: Individuals’ subjective norm regarding

knowledge sharing has a positive effect on their

intention to share knowledge in construction

teams.

TPB suggests that perceived behavioural control

not only affects an individual’s performance of a

behaviour but also influences the individual’s inten-

tion to perform the behaviour. Even if a person

has a favourable attitude towards knowledge sharing

and has positive subjective norm regarding knowl-

edge sharing, he/she may still have little intention

to share knowledge because of lack of necessary

opportunities or resources. For example, Fong and

Chu (2006) find that time constraints as a result of

a heavy workload and the busy nature of work

reduces employees’ willingness to share knowledge

in tendering departments of contracting companies.

It is conjectured that individuals’ intention is also

predicted by their perceived behavioural control

over knowledge sharing in construction teams.

Thus:

Hypothesis 5: Individuals’ perceived behavioural con-

trol over knowledge intention to share knowledge in

construction teams.

Attitude toward knowledge

sharing

Subjective norm regarding knowledge

sharing

Perceived behavioral control over knowledge

sharing

Intention to share

knowledge

Knowledge sharing

behavior

H1

H2

H3

H4

H5

Figure 1 Research model and hypotheses

Knowledge sharing behaviour 561

Research method

Measurement development

The measures for constructs in the research model

were developed according to Ajzen (2002), who

suggests the scope and content that should be mea-

sured for each construct in TPB. In addition, a

number of existing measures in previous studies

with TPB context were used as references, including

Ajzen and Driver (1992), Taylor and Todd (1995),

Bock et al. (2005) and So and Bolloju (2005). The

existing measures are consistent with Ajzen’s (2002)

suggestions and have been tested to show adequate

reliability and validity. Following Bock et al. (2005),

the types of knowledge shared were specified for

constructs of knowledge sharing behaviour and

intention to share knowledge with reference to Ma

et al.’s (2008) description of knowledge involved in

construction project teams. The item wordings in all

constructs were carefully written to reflect knowl-

edge sharing behaviour in construction teams. In

accordance with Ajzen and Fishbein’s (1980) recom-

mendation, items in the attitude construct were

measured with semantic differential scale. Items in

other constructs were measured by a seven-point

bi-polar scale following Hanson (1997). Then the

items were compiled into a questionnaire for data

collection.

Pre-testing of questionnaire

Though the measures in the newly developed survey

instrument were adapted from prior studies where

they have been tested and validated, they have not

been validated in the context of construction teams.

Therefore, a pre-testing was conducted to test the

adequacy of the questionnaire so as to identify issues

for revision. To address the content validity of the

questionnaire, several academics were invited to

review the questionnaire to identify any errors,

ambiguities, redundancies and difficult questions.

The deficient items identified were either modified

or discarded. With the content validity established,

then a pilot study was carried out to further assess

the adequacy of the questionnaire. Forty-eight pro-

fessionals working in construction teams were con-

tacted through personal networks. They were invited

to fill in the questionnaire and further assess the

questions in terms of content, wording, clarity, etc.

Data collected from the pilot study were used to

perform item analysis to preliminarily assess the

internal consistency of construct. The pilot study led

to the elimination of two items and further modifi-

cations of wording. The final items are listed in

Appendix 1.

Sampling and data collection

The research population consists of individuals work-

ing in construction teams in Hong Kong. It is difficult

to approach the individuals directly owing to lack of

personal contact details. However, information on their

companies is usually accessible. So the companies

where they work were first identified. Based on

Neuman’s (2003) recommendation, a sampling frame

was developed by searching various sources, including

The HKSAR Government List of Approved Contrac-

tors for Public Works (Development Bureau, 2010),

the list of Registered General Building Contractors

from Hong Kong SAR Buildings Department

(Buildings Department, 2010), and the Hong Kong

Builder Directory (Ho, 2004). Then the HKIE

Yearbook (HKIE, 2009) published by the Hong Kong

Institution of Engineers was used to identify the

research sample. The HKIE Yearbook listed all

members’ basic information (e.g. names, education

qualification and membership history) and provides

some members’ additional information (i.e. employer

companies, office telephone numbers and e-mails).

Members with additional information were the main

search focus. Their companies were checked against

the sampling frame. Finally, a list of 430 individuals

from 172 organizations was compiled.

The sample size of 430 individuals was considered to

be inadequate. Therefore, the method of key contact

person was used in this study. The 430 individuals were

invited to be the key contact persons. They were

requested to fill in the questionnaire and help to find

another three persons in their teams to fill in the ques-

tionnaire. The survey was conducted from March 2010

to June 2010. A total of 430 packages were sent to the

key contact persons by mail. In each package, there was

one invitation letter, four questionnaires and four free-

post envelopes. When the survey was closed, a total of

238 questionnaires were collected from 97 key contact

persons, producing a response rate of 28.4% in respect

of key contact persons, and 17.4% in respect of the

total sample. Among the 238 returned questionnaires,

seven questionnaires were ineligible and excluded from

data analysis. Table 1 summarizes the demographic

information of respondents.

Data analysis

Structural equation modelling (SEM) is selected as

the data analysis method as it has several notable

advantages over traditional data analysis methods, e.g.

multiple regression. First, it is superior in dealing with

latent variables. SEM can show the function of each

indicator on a corresponding latent construct.

Secondly, it is able to estimate a series of multiple

562 Zhang and Ng

regression equations simultaneously. Kline’s (2005)

two-step modelling method is employed, i.e. testing

the measurement model with confirmatory factor

analysis (CFA) first and then testing the structural

model with path analysis. The software of AMOS

18.0 is used to process the SEM analysis.

Measurement model

Scale reliability is first assessed by internal consistency

measured with Cronbach’s alpha and item-total coeffi-

cient. Table 2 shows that all the alpha values exceed

the threshold of 0.7 suggested by Nunnally (1978) and

all the item-total correlations are higher than the crite-

ria of 0.4 recommended by Spector (1992). It is con-

cluded that all the scales have satisfactory scale

reliability.

Confirmatory factor analysis (CFA) is then used to

assess construct validity and test the measurement

model fit (Kline, 2005; Schumacker, 2010). Following

the approach suggested by Hair et al. (1998) and Ryu

et al. (2003), construct validity is assessed by examin-

ing factor loadings of indicators, composite reliability

and average variance extracted (AVE) produced by

CFA. Hair et al. (2010) recommend that factor load-

ing of 0.5 is minimally accepted and factor loading of

0.7 is satisfactory. Table 2 shows that except KSB3

(0.510) which is minimally accepted, all the other fac-

tor loadings either approximate to or exceed the satis-

factory level. In addition, all the composite reliabilities

are higher than the cut-off level of 0.7 suggested by

Hair et al. (1998). Concerning AVE, the threshold of

0.5 is recommended by researchers (Fornell and Larc-

ker, 1981; Hair et al., 1998; Ryu et al., 2003). There-

fore, AVE for the construct of knowledge sharing

behaviour (i.e. 0.427) is below acceptable level. Since

KSB3 has the lowest factor loading, it is removed from

the construct. Then AVE for knowledge sharing

behaviour increases to 0.480 which is marginally

accepted. Because the intention scale and the knowl-

edge sharing behaviour scale are designed uniformly in

terms of the types of knowledge (see Appendix 1),

INT3 is also discarded to maintain the uniformity.

The overall measurement model fit is assessed by

absolute fit measures (i.e. v2=df , Root Mean Square

Error of Approximation (RMSEA), Standard Root

Table 1 Demographic information of respondents

Variable Categories Number of cases Frequency (%)

Gender Female 26 11.3

Male 203 87.9

Missing 2 0.9

Education High school graduate 5 2.2

Certificate or associate degree 33 14.3

Bachelor degree 142 61.5

Postgraduate 49 21.2

Missing 2 0.9

Job position Project manager 60 30.0

Site agent 17 7.4

Engineer 67 29.0

Quantity surveyor 28 12.1

Safety manager 4 1.7

Other 51 22.1

Missing 4 1.7

Working experience in current company (years) <5 93 40.3

5–10 60 26.0

10–15 29 12.6

15–20 18 7.8

>20 27 11.7

Missing 4 1.7

Working experience in construction industry (years) <5 33 14.3

5–10 44 19.0

10–15 46 20.0

15–20 27 11.7

>20 79 34.2

Missing 2 0.9

Knowledge sharing behaviour 563

Mean Square Residual (SRMR)), incremental fit

measures (i.e. Nonnormed Fix Index (NNFI), Com-

parative Fit Index (CFI)) and parsimonious fit mea-

sures (i.e. Akaike Information Criterion (AIC))

recommended by Hair (1998) and Schermelleh-Engel

et al. (2003). Table 3 shows that all the goodness-of-

fit indices achieve desired levels of values, suggesting

that the measurement model fits the data well.

Structural model

To test the research hypotheses, a structural model is

developed as shown in Figure 2. However, SEM

results suggest that the model should be rejected

because several goodness-of-fit indices fail to achieve

the desired values. Therefore, an alternative structural

model needs to be developed. Modification indices

(MI) in AMOS text output are used to modify the

structural model.

The revised structural model with standardized

path coefficients is illustrated in Figure 3. Table 4

indicates that the revised structural model is sup-

ported with most goodness-of-fit indices accomplish-

ing the desired level of values. Table 5 lists the

significant levels of path coefficients and the regres-

sion weights before standardization.

SEM results reveal that among the three determi-

nants of intention to share knowledge, perceived

behavioural control has the most significant impact

on intention (path coefficient 0.60, p < 0.001). The

next is attitude (path coefficient 0.33, p < 0.001), and

subjective norm has no significant influence on inten-

tion (path coefficient 0.04, p value 0.378). Regarding

determinants of knowledge sharing behaviour, inten-

tion significantly influences knowledge sharing behav-

iour (path coefficient 0.48, p < 0.01) while perceived

behavioural control is proved to be a poor predictor

of knowledge sharing behaviour (path coefficient

0.13, p value 0.430). The percentage of variance

explained for knowledge sharing behaviour is 78%

and for intention to share knowledge is 36%. Table 6

summarizes the results of hypotheses testing. The

Table 2 Scale reliability and validity

Construct Item

Reliability Validity

Cronbach’s

alpha

Item-total

correlation

Factor

loading

Composite

reliability AVE

Knowledge sharing behaviour (KSB) KSB1 0.737 0.601 0.711 0.746 0.427

KSB2 0.599 0.726

KSB3 0.440 0.510

KSB4 0.492 0.645

KSB1 0.730 0.596 0.703 0.735 0.480

KSB2 0.587 0.715

KSB4 0.493 0.660

Intention to share knowledge (INT) INT1 0.867 0.742 0.847 0.874 0.637

INT2 0.750 0.824

INT3 0.597 0.652

INT4 0.799 0.852

INT1 0.880 0.783 0.869 0.882 0.713

INT2 0.791 0.838

INT4 0.739 0.826

Attitude towards knowledge sharing

(ATT)

ATT1 0.892 0.787 0.863 0.896 0.687

ATT2 0.829 0.904

ATT3 0.803 0.856

ATT4 0.639 0.672

Subjective norm of knowledge

sharing (SN)

SN1 0.930 0.836 0.879 0.932 0.822

SN2 0.901 0.963

SN3 0.836 0.875

Perceived behavioural control (PBC) PBC1 0.927 0.826 0.881 0.928 0.720

PBC2 0.814 0.817

PBC3 0.760 0.760

PBC4 0.851 0.888

PBC5 0.809 0.890

564 Zhang and Ng

results also show that significant correlations exist

among the three antecedents to intention, i.e. 0.45

between attitude and subjective norm, 0.44 between

subjective norm and perceived behavioural control,

and 0.70 between attitude and perceived behavioural

control.

Discussion of results

Research results confirm the causal relationship

between intention and actual behaviour, which is

specified in Ajzen’s (1991) TPB model. The positive

relationship between intention to share knowledge

and actual knowledge sharing behaviour is also sup-

ported in many recent studies, most of which are car-

ried out in the framework of TRA, e.g. Bock and

Kim (2002), Choi et al. (2008). However, perceived

behavioural control over knowledge sharing is found

to impose very weak influence on knowledge sharing

behaviour. The result implies that when considering

knowledge sharing behaviour, people in construction

teams have more concerns about their personal psy-

chological interests (i.e. intention) than actual behav-

ioural control, as Ajzen (1991, p. 185) argues that

the relative importance of intentions and perceived

behavioral control in the prediction of behavior is

expected to vary across situations and across different

behaviors. When the behavior/situation affords a per-

son complete control over behavioral performance,

intentions alone should be sufficient to predict behav-

ior.

Thus, the insignificant impact of behavioural con-

trol on knowledge sharing behaviour found in this

study suggests that knowledge sharing behaviour is

largely under people’s volitional control in Hong

Kong construction teams. In fact, the most frequently

used method for knowledge sharing is face-to-face

communication in construction teams (Fong and Lee,

2006; Kivrak et al., 2008; Styhre, 2008). Therefore,

people do not rely heavily on external tools or

resources to conduct knowledge sharing.

Ajzen (1991) asserts that the relative importance of

attitude, subjective norm and perceived behavioural

control in the prediction of intention changes in dif-

ferent situations. In this study, it is found that inten-

tion is dominantly influenced by attitude and

perceived behavioural control in predicting knowledge

sharing behaviour but has no significant association

with subjective norm. A positive attitude towards

knowledge sharing leading to strong intention to share

knowledge has been reported by many researchers,

e.g. Bock et al. (2005), Bock and Kim (2002),Table

3Goodness-of-fitindices

formeasuremen

tmodel

Index

Calculationofmeasures

Accep

table

level

Accep

tability

Absolute

fitmeasures

w2/df

2.521

<3

Accep

ted

RM

SEA

0.081

<0.10

Accep

ted

SRM

R0.054

<0.10

Accep

ted

Increm

entalfitmeasures

NNFI

0.927

>0.90

Accep

ted

CFI

0.941

>0.90

Accep

ted

Parsim

oniousfitmeasures

AIC

407.140>

342.000407.140<3393.665

Smaller

thanAIC

forco

mparisonmodel

Marginal

Knowledge sharing behaviour 565

Lin (2007), Huang et al. (2008), Chow and Chan

(2008). Therefore, it is concluded that people with a

favourable attitude towards knowledge sharing are

more likely to share their knowledge with teammates

in construction teams.

It is interesting to note that perceived behavioural

control has indirect impact on actual behaviour (i.e.

through intention) but has no direct impact on actual

behaviour. Godin et al. (1993) find a similar phenom-

enon in their study and explain that the phenomenon

is a product of the volitional aspect of the behaviour

being examined. Again, it is indicated that knowledge

sharing behaviour in construction teams is under peo-

ple’s volitional control. However, the presence of

opportunities and resources for knowledge sharing

can enhance the formation of strong intention to

share knowledge, which in turn influences knowledge

sharing behaviour.

The research results indicate that subjective norm

of knowledge sharing does not significantly influence

e13

e14

e15

e16

e17

e18

e1 ATT1

INT1

INT INT2

INT4

KSB1

KSB KSB2

KSB4

ATT

e2 ATT2

e3 ATT3

e4 ATT4

e5 SN1

SNe6 SN2

e7 SN3

e8 PBC1

PBC

e9 PBC2

e10 PBC3

e11 PBC4

e12 PBC5

res1

res2

1

1

1

1

1

1

1

1

1

11

1

11

1

1

1

1

1

1

1

1

1

1

1

Figure 2 Proposed structural model

Notes: ATT = attitude; SN = subjective norm; PBC = perceived behavioural control; ATT1 to ATT4 = items measuring

attitude; SN1 to SN3 = measuring subjective norm; PBC1 to PBC5 = items measuring perceived behavioural control; INT1

to INT4 = items measuring intention; KSB1 to KSB4 = items measuring knowledge sharing behaviour; e1 to e18 =

measurement error associated with each observed variable; res1 to res2 = residual error associated with each latent

endogenous variable.

566 Zhang and Ng

intention to share knowledge. The result is inconsis-

tent with some researchers’ assertion that subjective

norm is crucial in determining intention in Confucian

cultural (e.g. Chinese) background, where collectiv-

ism and social pressure to comply with collective

norms are stressed (Lee and Green, 1991). In fact,

the role of subjective norm in predicting knowledge

sharing intention varies a lot in the existing literature.

Some researchers report that subjective norm signifi-

cantly affects individuals’ intention to share knowl-

edge (Ryu et al., 2003; Bock et al., 2005; Chow and

Chan, 2008; Chatzoglou and Vraimaki, 2009; Jeon

et al., 2011), while some researchers find that there is

no significant relationship between subjective norm

and intention to share knowledge (So and Bolloju,

2005; Huang et al., 2008). According to Ajzen and

Fishbein (1980), the variations in weights for predic-

tors of intention may result from changes in the ele-

ments that define a behaviour (i.e. action, target,

context and time) and individual differences (e.g.

demographic variables, personality differences). Sev-

eral plausible explanations are suggested for the insig-

nificant impact of subjective norm on intention by

considering these issues. First, the characteristics of

e1

e2

e3

e4

.77

.77

.88

.88

.93.96

SN

.45

ATT

.88

.80

.73.54

.78.88

.90

.81

PBC

.13

.44

.70

.60

.04

.33 res1

.78.87

.84

.83

.48

.50

res2

KSB

.36 .70

.72

.65.43

KSB2

KSB1

KSB4

.52

e16

e17

e18

e13

e14

e15

.75

.70

.68

INT2

INT4

INT1

INT

.77

.63

.74

.86

.90

.82

.73 .86

.67.45

ATT4

ATT1

ATT2

ATT3

SN1

SN2

SN3

e6

e7

e8

e9

e10

e11

e12

.35

PBC5

PBC4

PBC3

PBC2

PBC1

e5

Figure 3 Revised structural model with standardized path coefficients

Notes: ATT = attitude; SN = subjective norm; PBC = perceived behavioural control; ATT1 to ATT4 = items measuring

attitude; SN1 to SN3 = measuring subjective norm; PBC1 to PBC5 = items measuring perceived behavioural control; INT1

to INT4 = items measuring intention; KSB1 to KSB4 = items measuring knowledge sharing behaviour; e1 to e18 =

measurement error associated with each observed variable; res1 to res2 = residual error associated with each latent

endogenous variable.

Knowledge sharing behaviour 567

respondents may contribute to the insignificance. As

indicated in Table 1 (demographic information on

respondents), a large proportion of the respondents

are experienced and in senior positions so they may

have less concern about subjective norm than junior

team members (So and Bolloju, 2005). Secondly,

subjective norm has different levels of influences on

employees in different research contexts. For instance,

subjective norm has a strong effect on physicians’

behavioural intention to share knowledge owing to

hospitals’ active organizational learning mechanisms

and physicians’ highly self-regulatory professional

characteristics (Ryu et al., 2003). In construction

teams, the knowledge sharing culture may not be as

strong as to make professionals feel obliged to share

knowledge (Huang et al., 2008). Thirdly, the collec-

tivism orientation in Hong Kong may not be as strong

as in other Asian countries such as Korea. Subjective

norm plays an important role in determining knowl-

edge sharing intention in the Korean context; see e.g.

Jeon et al. (2011), Bock et al. (2005), Ryu et al.

(2003). Though both Korea and Hong Kong are

Asian countries influenced by a collective cultural

background, Hong Kong has also been influenced by

Western culture because of its history as a British col-

ony. Therefore, people may have less concern regard-

ing others’ expectations than people in Korea. As a

result, for people in construction teams, ‘personal

considerations tended to overshadow the influence of

perceived social pressure’ (Ajzen, 1991, p. 189).

High correlations are found to exist among three

proposed antecedents to intention. The correlation

between attitude and subjective norm implies that the

stronger the knowledge sharing norm is in construc-

tion teams and organizations, the more positive the

construction team members’ attitude is toward knowl-

edge sharing. This implication is supported by many

prior studies, e.g. Bock et al. (2005), Chow and Chan

(2008), Ding and Ng (2009) and Ryu et al. (2003).

Similarly, the correlation between subjective norm

and perceived behaviour control indicates that people

may feel that they have more control over knowledge

sharing if they perceive a supportive environment for

knowledge sharing. Furthermore, the correlation

between attitude and perceived behavioural control

suggests that if people perceive there are sufficient

resources, opportunities and tools for knowledge shar-

ing, they may develop a more favourable attitude

towards knowledge sharing.

Conclusion

The purpose of this study is to examine individual

knowledge sharing behaviour based on the theory ofTable

4Goodness-of-fitindices

forrevised

structuralmodel

Index

Calculationofmeasures

Accep

table

level

Accep

tability

Absolute

fitmeasures

w2/df

2.315

<3

Accep

ted

RM

SEA

0.076

<0.10

Accep

ted

SRM

R0.054

<0.10

Accep

ted

Increm

entalfitmeasures

NNFI

0.937

>0.90

Accep

ted

CFI

0.948

>0.90

Accep

ted

Parsim

oniousfitmeasures

AIC

381.727>342.000381.727<3393.665

Smaller

thanAIC

forco

mparisonmodel

Marginal

568 Zhang and Ng

planned behaviour (TPB). It is one of the first studies

to employ existing social psychological theories to

examine professionals’ knowledge sharing behaviour

in construction teams. The research results show that

individuals’ knowledge sharing behaviour is signifi-

cantly affected by their intention to share knowledge

but not perceived behavioural control over knowledge

sharing, indicating that knowledge sharing behaviour

is largely under professionals’ volitional control in

construction teams in Hong Kong. The research

results also reveal that professionals’ intention to

share knowledge is dominantly predicted by their atti-

tude towards knowledge sharing and perceived behav-

ioural control over knowledge sharing, but weakly

related to subjective norm of knowledge sharing.

The findings of this study provide some managerial

implications for construction companies to manage

employees’ knowledge sharing practices in construc-

tion teams. The research results indicate that it is crit-

ical for managers to maintain among employees a

favourable and positive attitude towards knowledge

sharing. Prior research suggests that employees’ atti-

tudes towards knowledge sharing could be driven by

organizational extrinsic reward (Huang et al., 2008),

Table 5 Regression weights of revised structural model

Estimate S.E. C.R. P Label

INT <— ATT 0.467 0.105 4.452 ⁄⁄⁄ par_11

INT <— SN 0.038 0.043 0.881 0.378 par_12

INT <— PBC 0.600 0.076 7.864 ⁄⁄⁄ par_15

KSB <— INT 0.403 0.140 2.875 0.004 par_10

KSB <— PBC 0.107 0.136 0.789 0.430 par_14

ATT4 <— ATT 1.000

ATT3 <— ATT 1.401 0.122 11.503 ⁄⁄⁄ par_1

ATT2 <— ATT 1.360 0.115 11.778 ⁄⁄⁄ par_2

ATT1 <— ATT 1.379 0.120 11.467 ⁄⁄⁄ par_3

SN3 <— SN 1.000

SN2 <— SN 1.081 0.049 22.020 ⁄⁄⁄ par_4

SN1 <— SN 1.013 0.054 18.803 ⁄⁄⁄ par_5

PBC2 <— PBC 1.000

PBC1 <— PBC 0.962 0.061 15.667 ⁄⁄⁄ par_6

PBC3 <— PBC 0.984 0.065 15.070 ⁄⁄⁄ par_7

PBC4 <— PBC 1.011 0.064 15.697 ⁄⁄⁄ par_8

PBC5 <— PBC 1.039 0.066 15.839 ⁄⁄⁄ par_9

INT1 <— INT 1.000

INT2 <— INT 1.067 0.066 16.234 ⁄⁄⁄ par_18

INT4 <— INT 0.936 0.061 15.232 ⁄⁄⁄ par_19

KSB1 <— KSB 1.000

KSB2 <— KSB 1.114 0.129 8.650 ⁄⁄⁄ par_20

KSB4 <— KSB 0.792 0.109 7.249 ⁄⁄⁄ par_21

Note: ⁄⁄⁄ p < 0.001.

Table 6 Summary of hypotheses testing results

Hypotheses Path Path coefficient Result

H1 Intention ? behaviour 0.48⁄⁄ Supported

H2 Perceived behavioural control? behaviour 0.13 Not supported

H3 Attitude ? intention 0.33⁄⁄⁄ Supported

H4 Subjective norm ? intention 0.04 Not supported

H5 Perceived behavioural control ? intention 0.60⁄⁄⁄ Supported

Notes: ⁄⁄⁄ p < 0.001, ⁄⁄ p < 0.01.

Knowledge sharing behaviour 569

anticipated reciprocal relationships (Bock and Kim,

2002; Bock et al., 2005; Tohidinia and Mosakhani,

2010), knowledge self-efficacy (Lin, 2007; Huang

et al., 2008; Tohidinia and Mosakhani, 2010) and

social networks and shared goals (Chow and Chan,

2008). Given the suggestions, managers could consider

addressing knowledge sharing in a performance evalua-

tion system. They could also establish a positive social

culture in construction teams to reinforce interpersonal

interactions and foster reciprocal relationships among

team members. In addition, managers should provide

useful feedback to improve team members’ knowledge

self-efficacy, e.g. notifying knowledge contributors

what difference has been made to the project as a result

of their knowledge sharing (Husted and Michailova,

2002; Ye et al., 2006).

The research findings provide clear evidence that

managers need to enhance team members’ perception

of perceived behavioural control, which is the most

important predictor of individuals’ intention to share

knowledge. Construction companies are characterized

by an ‘oral culture’ whereby face-to-face communica-

tion is the main medium for communicating knowl-

edge (Fong and Lee, 2006; Kivrak et al., 2008;

Styhre, 2008). In such a working environment, man-

agers could reinforce social capital to provide the nec-

essary conduits for professionals to network and share

their knowledge (Subramaniam and Youndt, 2005).

Professionals are more likely to engage in knowledge

sharing if such social interaction opportunities are

provided (Kazi and Koivuniemi, 2006; Styhre, 2008).

Because of the oral culture, much valuable know-how

knowledge is confined in professionals’ minds, and

does not surface until someone seeks the knowledge

for problem solving. In Styhre’s (2008) investigation,

a site manager reported that his colleagues are ‘not

reluctant to share their insights but they don’t really

share without being asked to’ (Styhre, 2008, p. 948).

Therefore, coffee break conversations (Styhre, 2008)

and meetings (Fong and Lee, 2006) are found to be

important sources of knowledge sharing in construc-

tion teams. Researchers also suggest that coaching

and mentoring are good ways for senior professionals

to share knowledge with junior professionals (Dainty

et al., 2005). Most of construction professionals’

knowledge is tacit and embedded in practical experi-

ence, thus it may be difficult for professionals to artic-

ulate the ‘sticky’ knowledge. Coaching or mentoring

allows junior professionals to imitate actions and

acquire knowledge through ‘learning by doing’.

Though subjective norm is found to have weak

influence on knowledge sharing intention, strong sub-

jective norm regarding knowledge sharing may lead to

individuals’ more favourable attitude towards and

more perceived behavioural control over knowledge

sharing. Therefore, managers should build up the

knowledge sharing norm within construction teams

and even in the whole organization. Managers could

address the importance of knowledge sharing in com-

pany manuals or promote knowledge sharing in news-

letters, brochures or other publications. Leaders’

behaviour usually exerts normative pressure on

employees. So managers could establish the knowl-

edge sharing norm in construction teams by role

modelling, i.e. they actually participate in knowledge

sharing activities and share their own knowledge

(Cabrera et al., 2006; Sveiby, 2007).

There are a number of limitations in this study.

However, those limitations also provide direction for

future studies. TPB suggests that each of attitude,

subjective norm and perceived behavioural control is

further predicted by a set of salient beliefs. The

research model in this study only examines the predic-

tion power of TPB regarding knowledge sharing

behaviour without investigating underlying beliefs.

The research model could not explain individuals’

underlying mental processes for formations of attitude,

subjective norm and perceived behavioural control. In

future studies researchers could make efforts to

develop an explanatory model for knowledge sharing

behaviour by inaugurating TPB with various beliefs.

In addition, only individuals in Hong Kong construc-

tion teams are studied, therefore the results may not

be applicable to other regions because of different con-

struction practices and different cultural characteris-

tics. Nevertheless, a starting point is provided for

researchers to conduct similar research in other

regions to find out the determinants of professionals’

knowledge sharing behaviour in the construction con-

text. Owing to limited resources, a cross-sectional

research design is used in this study, which limits the

extent of causality that can be inferred from results. In

future, the study can be extended to collect longitudi-

nal data to investigate the casual relationships between

constructs of TPB regarding professionals’ knowledge

sharing behaviour in the construction context.

References

Ajzen, I. (1991) The theory of planned behavior. Organiza-

tional Behavior and Human Decision Processes, 50(2),

179–211.

Ajzen, I. (2002) Constructing a TPB questionnaire: concep-

tual and methodological considerations, available at

http://people.umass.edu/aizen/pdf/tpb.measurement.pdf

(accessed 28 February 2010).

Ajzen, I. and Driver, B.L. (1992) Application of the theory

of planned behavior to leisure choice. Journal of Leisure

Research, 24(3), 207–24.

570 Zhang and Ng

Ajzen, I. and Fishbein, M. (1980) Understanding Attitudes

and Predicting Social Behavior, Prentice Hall, Englewood

Cliffs, NJ.

Armitage, C.J. and Conner, M. (2001) Efficacy of the the-

ory of planned behaviour: a meta-analytic review. British

Journal of Social Psychology, 40, 471–99.

Bock, G.W. and Kim, Y.G. (2002) Breaking the myths of

rewards: an exploratory study of attitudes about knowl-

edge sharing. Information Resources Management Journal,

15(2), 14–21.

Bock, G.W., Zmud, R.W., Kim, Y.G. and Lee, J.N. (2005)

Behavioral intention formation in knowledge sharing:

examining the roles of extrinsic motivators, social-psycho-

logical forces, and organizational climate. MIS Quarterly,

29(1), 87–111.

Bresnen, M., Edelman, L., Newell, S., Scarbrough, H. and

Swan, J. (2003) Social practices and the management of

knowledge in project environments. International Journal

of Project Management, 21(3), 157–66.

Bryman, A. (2001) Social Research Methods, Oxford Univer-

sity Press, Oxford.

Buildings Department (2010) Registered General Building Con-

tractors, Hong Kong Administrative Region, available at

http://www.bd.gov.hk/english/inform/e_gbc_1.html (accessed

24 February 2010).

Cabrera, A., Collins, W.C. and Salgado, J.F. (2006) Deter-

minants of individual engagement in knowledge sharing.

International Journal of Human Resource Management, 17

(2), 245–64.

Cabrera, E.F. and Cabrera, A. (2005) Fostering knowledge

sharing through people management practices. Interna-

tional Journal of Human Resource Management, 16(5),

720–35.

Carrillo, P.M., Anumba, C.J. and Kamara, J.M. (2000)

Knowledge management strategy for construction: key IT

and contextual issues. Paper presented at the Interna-

tional Conference on Construction Information Technol-

ogy, Reykjavik, Iceland, 28–30 June.

Chang, M.K. (1998) Predicting unethical behavior: a com-

parison of the theory of reasoned action and the theory of

planned behavior. Journal of Business Ethics, 17(16),

1825–34.

Chatzoglou, P.D. and Vraimaki, E. (2009) Knowledge-shar-

ing behaviour of bank employees in Greece. Business Pro-

cess Management Journal, 15(2), 245–66.

Choi, S.Y., Kang, Y.S. and Lee, H. (2008) The effects of

socio-technical enablers on knowledge sharing: an explor-

atory examination. Journal of Information Science, 34(5),

742–54.

Chow, W.S. and Chan, L.S. (2008) Social network, social

trust and shared goals in organizational knowledge sharing.

Information and Management, 45(7), 458–65.

CIRC (2001) Construct for Excellence: Report of the Construc-

tion Industry Review Committee, Construction Industry

Review Committee, Hong Kong.

Connelly, C.E. and Kelloway, E.K. (2003) Predictors of

employees’ perceptions of knowledge sharing cultures.

Leadership and Organization Development Journal, 24(5/6),

294–301.

Creswell, J.W. (2003) Research Design: Qualitative, Quantita-

tive, and Mixed Methods Approaches, Sage, Thousand

Oaks, CA.

Dainty, A.R.J., Qin, J. and Carrillo, P.M. (2005) HRM

strategies for promoting knowledge sharing within con-

struction project organizations: a case study, in Kazi, A.S.

(ed.) Knowledge Management in the Construction Industry:

A Socio-Technical Perspective, Idea Group, Hershey, PA,

pp. 18–23.

Davenport, T.H. and Prusak, L. (1998) Working Knowledge:

How Organizations Manage What They Know, Harvard

Business School Press, Boston, MA.

Development Bureau (2010) List of Approved Contractors for

Public Works, Hong Kong Special Administrative Region,

available at http://www.devb.gov.hk/contractor.aspx?

section=80&lang=1&textonly=false (accessed 24 February

2010).

Ding, Z.K. and Ng, F.F. (2009) Knowledge sharing among

architects in a project design team: an empirical test of

theory of reasoned action in China. Chinese Management

Studies, 3(2), 130–42.

Drejer, I. and Vinding, A.L. (2006) Organization, ‘anchor-

ing’ of knowledge, and innovative activity in construction.

Construction Management and Economics, 24(9), 921–31.

Egan, J. (1998) Rethinking Construction, Construction Task

Force Report for Department of the Environment, Trans-

port and the Regions, HMSO, London.

Fellows, R. and Liu, A. (2008) Research methods for construc-

tion, 3rd edn, Wiley-Blackwell, Oxford.

Firestone, J.M. (2003) Key Issues in the New Knowledge

Management, Butterworth-Heinemann, Boston, MA.

Fong, P.S.W. and Chu, L. (2006) Exploratory study of

knowledge sharing in contracting companies: a sociotech-

nical perspective. Journal of Construction Engineering and

Management, 132(9), 928–39.

Fong, P. and Lee, K. (2006) Sharing knowledge in a con-

tractor’s team: a case study of Disney’s Adventureland

project. Paper presented at the 2nd Specialty Conference

on Leadership and Management in Construction, Grand

Bahama Island, Bahamas, 4–6 May.

Fornell, C. and Larcker, D.F. (1981) Evaluating structural

equation models with unobservable variables and measure-

ment error. Journal of Marketing Research, 18(1), 39–50.

Foss, N.J., Husted, K. and Michailova, S. (2010) Govern-

ing knowledge sharing in organizations: levels of analysis,

governance mechanisms, and research directions. Journal

of Management Studies, 47(3), 455–82.

Godin, G., Valois, P. and Lepage, L. (1993) The pattern of

influence of perceived behavioral control upon exercising

behavior: an application of Ajzen’s theory of planned

behavior. Journal of Behavioral Medicine, 16(1), 81–102.

Grant, R.M. (1996) Toward a knowledge-based theory of

the firm. Strategic Management Journal, 17, 109–22.

Hair, J.F., Anderson, R.E., Tatham, R.L. and Black, W.C.

(1998) Multivariate Data Analysis, Prentice Hall, Upper

Saddle River, NJ.

Hair, J.F., Black, W.C., Babin, B.J. and Anderson, R.E.

(2010) Multivariate Data Analysis, 7th edn, Prentice Hall,

Upper Saddle River, NJ.

Knowledge sharing behaviour 571

Hall, H. (2001) Social exchange for knowledge exchange.

Paper presented at the conference on Managing Knowl-

edge: Conversations and Critiques, University of Leices-

ter, Management Centre, 10–11 April, available at

http://www.soc.napier.ac.uk/publication/op/getpublication/

publicationid/321908 (accessed 28 February 2010).

Hansen, S. and Avital, M. (2005) Share and share alike: the

social and technological influences on knowledge sharing

behavior. Sprouts: Working Papers on Information Systems,

5(13), 1–19, available at http://sprouts.aisnet.org/5-13

(accessed 19 October 2011).

Hanson, M.J.S. (1997) The theory of planned behavior

applied to cigarette smoking in African-American, Puerto

Rican, and non-Hispanic white teenage females. Nursing

Research, 46(3), 155–62.

Hendriks, P. (1999) Why share knowledge? The influence

of ICT on the motivation for knowledge sharing. Knowl-

edge and Process Management, 6(2), 91–100.

HKIE (2009) Yearbook, The Hong Kong Institute of Engi-

neers, Hong Kong.

Ho, K.K.Y. (2004) Builder Directory, Far East Trade Press,

Hong Kong.

Huang, Q., Davison, R.M. and Gu, J. (2008) Impact of

personal and cultural factors on knowledge sharing in

China. Asia Pacific Journal of Management, 25(3), 451–71.

Husted, K. and Michailova, S. (2002) Diagnosing and

fighting knowledge-sharing hostility. Organizational

Dynamics, 31(1), 60–73.

Jeon, S., Kim, Y.-G. and Koh, J. (2011) An integrative

model for knowledge sharing in communities-of-practice.

Journal of Knowledge Management, 15(2), 251–69.

Kamara, J.M., Augenbroe, G., Anumba, C.J. and Carrillo,

P.M. (2002) Knowledge management in the architecture,

engineering and construction industry. Construction Inno-

vation, 2, 53–67.

Kankanhalli, A., Tan, B.C.Y. and Wei, K.K. (2005) Contrib-

uting knowledge to electronic knowledge repositories: an

empirical investigation. MIS Quarterly, 29(1), 113–43.

Kazi, A.S. and Koivuniemi, A. (2006) Sharing through

social interaction: the case of YIT Construction Ltd, in

Kazi, A.S. and Wolf, P. (eds) Real-Life Knowledge Man-

agement: Lessons from the Field, KnowledgeBoard, Finland,

pp. 65–79.

Kivrak, S., Arslan, G., Dikmen, I. and Birgonul, M.T.

(2008) Capturing knowledge in construction projects:

knowledge platform for contractors. Journal of Manage-

ment in Engineering, 24(2), 87–95.

Kline, R.B. (2005) Principles and Practice of Structural Equa-

tion Modeling, 2nd edn, Guilford Press, New York.

Lee, C. and Green, R.T. (1991) Cross-cultural examination

of the Fishbein behavioral intentions model. Journal of

International Business Studies, 22(2), 289–305.

Lin, H.F. (2007) Effects of extrinsic and intrinsic motiva-

tion on employee knowledge sharing intentions. Journal of

Information Science, 33(2), 135–49.

Ma, Z.Z., Qi, L.Y. and Wang, K.Y. (2008) Knowledge

sharing in Chinese construction project teams and its

affecting factors: an empirical study. Chinese Management

Studies, 2(2), 97–108.

MacNeil, C.M. (2003) Line managers: facilitators of knowl-

edge sharing in teams. Employee Relations, 25(3),

294–307.

Marshall, N. and Sapsed, J. (2000) The limits of disembodied

knowledge: challenges of inter-project learning in the pro-

duction of complex products and systems. Paper presented

at the conference on Knowledge Management: Concepts

and Controversies, Warwick, UK, 10–11 February.

Martın-de-Castro, G., Delgado-Verde, M., Lopez-Saez, P.

and Navas-Lopez, J.E. (2011) Towards ‘an intellectual

capital-based view of the firm’: origins and nature. Jour-

nal of Business Ethics, 98, 649–62.

McDermott, R. (1999) Why information technology

inspired but cannot deliver knowledge management. Cali-

fornia Management Review, 41(4), 103–17.

McDermott, R. and O’Dell, C. (2001) Overcoming cultural

barriers to sharing knowledge. Journal of Knowledge Man-

agement, 5(1), 76–85.

Nahapiet, J. and Ghoshal, S. (1998) Social capital, intellec-

tual capital, and the organizational advantage. Academy of

Management Review, 23(2), 242–66.

Nardi, P.M. (2003) Doing Survey Research: A Guide to

Quantitative Methods, 2nd edn, Allyn and Bacon, Boston,

MA.

Neuman, W.L. (2003) Social Work Research Methods: Quali-

tative and Quantitative Approaches, Allyn and Bacon, Bos-

ton, MA.

Nickerson, J.A. and Zenger, T.R. (2004) A knowledge-

based theory of the firm: the problem-solving perspective.

Organization Science, 15(6), 617–32.

Nonaka, I. (1994) A dynamic theory of organizational

knowledge creation. Organization Science, 5(1), 14–37.

Nonaka, I. and Takeuchi, H. (1995) The Knowledge-Creating

Company: How Japanese Companies Create the Dynamics of

Innovation, Oxford University Press, New York.

Nunnally, J.C. (1978) Psychometric Theory, 2nd edn,

McGraw-Hill, New York.

O’Dell, C. and Grayson, C.J. (1998) If only we knew what

we know: identification and transfer of internal best prac-

tices. California Management Review, 40(3), 154–174.

Punch, K. (1998) Introduction to Social Research: Quantita-

tive and Qualitative Approaches, Sage, London.

Riege, A. (2005) Three-dozen knowledge-sharing barriers

managers must consider. Journal of Knowledge Manage-

ment, 9(3), 18–35.

Robertson, S. (2002) A tale of two knowledge-sharing sys-

tems. Journal of Knowledge Management, 6(3), 295–308.

Robinson, H.S., Carrillo, P.M., Anumba, C.J. and Al-Ghas-

sani, A.M. (2005) Knowledge management practices in

large construction organisations. Engineering, Construction

and Architectural Management, 12(5), 431–45.

Ryu, S., Ho, S.H. and Han, I. (2003) Knowledge sharing

behavior of physicians in hospitals. Expert Systems with

Applications, 25(1), 113–22.

Schermelleh-Engel, K., Moosbrugger, H. and Muller, H.

(2003) Evaluating the fit of structural equation models:

tests of significance and descriptive goodness-of-fit mea-

sures. Methods of Psychological Research Online, 8(2),

23–74.

572 Zhang and Ng

Schumacker, R.E. (2010) A Beginner’s Guide to Structural

Equation Modeling, 3rd edn, Routledge, New York.

Senaratne, S. and Sexton, M. (2008) Managing construc-

tion project change: a knowledge management perspec-

tive. Construction Management and Economics, 26(12),

1303–11.

So, J.C.F. and Bolloju, N. (2005) Explaining the intentions

to share and reuse knowledge in the context of IT service

operations. Journal of Knowledge Management, 9(6), 30–41.

Spector, P.E. (1992) Summated Rating Scale Construction:

An Introduction, Sage, Newbury Park, CA.

Spender, J.C. (1998) Pluralist epistemology and the knowl-

edge-based theory of the firm. Organization, 5(2), 233–56.

Styhre, A. (2008) The role of social capital in knowledge

sharing: the case of a specialist rock construction com-

pany. Construction Management and Economics, 26(9),

941–51.

Subramaniam, M. and Youndt, M.A. (2005) The influence

of intellectual capital on the types of innovate capabilities.

The Academy of Management Journal, 48(3), 450–63.

Sveiby, K.E. (2007) Disabling the context for knowledge

work: the role of managers’ behaviours. Management Deci-

sion, 45(10), 1636–55.

Taylor, S. and Todd, P.A. (1995) Understanding informa-

tion technology usage: a test of competing models. Infor-

mation Systems Research, 6(2), 144–76.

Tohidinia, Z. and Mosakhani, M. (2010) Knowledge shar-

ing behaviour and its predictors. Industrial Management

and Data Systems, 110(4), 611–31.

van den Hooff, B. and Hendrix, L. (2004) Eagerness and

willingness to share: the relevance of different attitudes

towards knowledge sharing. Paper presented at the Con-

ference on Organizational Knowledge, Learning and

Capabilities, Innsbruck, 2–3 April.

Wasko, M.M. and Faraj, S. (2000) ‘It is what one does’:

why people participate and help others in electronic com-

munities of practice. Journal of Strategic Information Sys-

tems, 9(2–3), 155–73.

Wasko, M.M. and Faraj, S. (2005) Why should I share?

Examining social capital and knowledge contribution in

electronic networks of practice. MIS Quarterly, 29(1),

35–57.

Woo, J.H., Clayton, M.J., Johnson, R.E., Flores, B.E. and

Ellis, C. (2004) Dynamic knowledge map: reusing

experts’ tacit knowledge in the AEC industry. Automation

in Construction, 13(2), 203–7.

Ye, S., Chen, H. and Jin, X. (2006) Exploring the moderat-

ing effects of commitment and perceived value of knowl-

edge in explaining knowledge contribution in virtual

communities. Paper presented at the Tenth Pacific Asia

Conference on Information Systems (PACIS), Kuala

Lumpur, Malaysia, 6–9 July.

Appendix

Measurement items developed

Constructs Measurement items Sources

Knowledge sharing

behaviour(1) I share my technical skills (e.g. construction methods) with

teammates (rarely/frequently)

(2) I share my managerial expertise (e.g. progress control exper-

tise) with teammates (rarely/frequently)

(3) I share official documentations or manuals with teammates

(rarely/frequently)

(4) I share project knowledge (e.g. site conditions, project sta-

tus, client requirements) with teammates (rarely/frequently)

Ajzen (2002); Bock

et al. (2005);

Ma et al. (2008)

Intention to share

knowledge(1) I intend to share my technical skills (e.g. construction

method) with teammates (disagree/agree)

(2) I would share my managerial expertise (e.g. progress con-

trol) with teammates (disagree/agree)

(3) I would always share official documentations or manuals

with teammates (disagree/agree)

(4) I would try to share project knowledge (e.g. site conditions,

project status or client requirements) with teammates (dis-

agree/agree)

Taylor and Todd

(1995); Ajzen (2002);

Bock et al. (2005)

(Continued)

Knowledge sharing behaviour 573

Appendix (Continued)

Constructs Measurement items Sources

Attitude towards

knowledge sharing(1) My knowledge sharing with teammates is (bad/good)

(2) My knowledge sharing with teammates is

(harmful/beneficial)

(3) My knowledge sharing with teammates is

(worthless/harmful)

(4) My experience in sharing knowledge with teammates is

(unpleasant/pleasant)

Taylor and Todd

(1995);

Ajzen (2002); Bock

et al. (2005)

Subjective norm of

knowledge sharing(1) People who are important to me think that I should share

knowledge with my teammates (unlikely/likely)

(2) People who may influence my behaviour think that I should

share knowledge with my teammates (unlikely/likely)

(3) People whose opinions I value think that I should share

knowledge with my teammates (unlikely/likely)

Ajzen and Driver

(1992)

Taylor and Todd

(1995)

Perceived behavioural

control(1) I am able to share knowledge with teammates (disagree/

agree)

(2) Sharing my knowledge with teammates is within my control

(disagree/agree)

(3) I have the resources to support my knowledge sharing with

teammates (disagree/agree)

(4) I have the opportunities to share knowledge with teammates

(disagree/agree)

(5) I have the ability to share knowledge with teammates

(disagree/agree)

Taylor and Todd

(1995);

Ajzen (2002);

So and Bolloju (2005)

574 Zhang and Ng

Copyright of Construction Management & Economics is the property of Routledge and its content may not be

copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written

permission. However, users may print, download, or email articles for individual use.