Moderating Effect of Organizational Learning Type on Performance Improvement

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Moderating Effect of Organizational Learning Typeon Performance Improvement

Peter Shek Pui Wong1; Sai On Cheung2; and Ka Yan Leung3

Abstract: It has been advocated that information available from a performance measurement system �PMS� is an invaluable source ofknowledge for contracting organizations. As such, it has been suggested that performance improvement can be achieved if contractingorganizations can capitalize on the learning opportunities accorded from feedback derived from a PMS. Literature on learning alsosuggests that organizations display three types of learning: �1� single loop, double loop and Deutero. This paper reports a study that aimsto �1� empirically test the positive effect derived performance feedback on performance; and �2� identify the extent to which the learningtypes have significant impact on performance improvement. Data on performance, performance feedback available, and responses werecollected through a questionnaire survey. Pearson correlation and multiple moderated regression were used to accomplish researchobjectives �1� and �2�, respectively, as mentioned. The findings suggested that there is a significant correlation between performanceimprovement and feedback from a PMS. It was found that all three forms of organizational learning can contribute to performanceimprovement and are not mutually exclusive. Nonetheless, double-loop learning practices that addresses the root causes of underperfor-mance were found to be more versatile in facilitating improvement in efficiency and effectiveness.

DOI: 10.1061/�ASCE�0742-597X�2008�24:3�162�

CE Database subject headings: Organizations; Performance characteristics; Contracts; Construction industry.

Introduction

Construction contracting organizations have been criticized asbeing incapable of solving unprecedented problems, graspingunexpected opportunities, and adapting to the dynamic businessenvironment �Egan 1998; CIRC 2001; Love et al. 2000�. In re-sponse to these criticisms, a quest for strategies to improve per-formance has been advocated in several industry reviews �Latham1994; Egan 1998; CIRC 2001; Mottahedin 2003�. Various studiesaimed to enhance the contracting organizations’ performancehave been conducted �Cheung et al. 2004; Love et al. 2004�.From a policing perspective, performance measurement systems�PMS� have been developed as a means to guard against inferiorproducts �Wong and Cheung 2005�. Moreover, PMS can also beidentified as a system that records information reflecting projectperformance �Wong and Cheung 2005�. Huemer and Ostergren�2000� advocated that performance records can be a source offeedback from which lessons can be learned. In this regard, the

1Associate Director, Construction Dispute Resolution Research Unit,Dept. of Building and Construction, City Univ. of Hong Kong, 83 TatChee Ave., Hong Kong �corresponding author�. E-mail: [email protected]

2Director, Construction Dispute Resolution Research Unit, Dept. ofBuilding and Construction, City Univ. of Hong Kong, 83 Tat Chee Ave.,Hong Kong.

3Member, Construction Dispute Resolution Unit, Dept. of Buildingand Construction, City Univ. of Hong Kong, 83 Tat Chee Ave., HongKong.

Note. Discussion open until December 1, 2008. Separate discussionsmust be submitted for individual papers. To extend the closing date byone month, a written request must be filed with the ASCE ManagingEditor. The manuscript for this paper was submitted for review and pos-sible publication on September 13, 2006; approved on July 13, 2007. Thispaper is part of the Journal of Management in Engineering, Vol. 24, No.
3, July 1, 2008. ©ASCE, ISSN 0742-597X/2008/3-162–172/$25.00.
162 / JOURNAL OF MANAGEMENT IN ENGINEERING © ASCE / JULY 2008

J. Manage. Eng. 2008

development concept of a PMS can be extended beyond policingand includes providing feedback to contracting organizations.This feedback is a vital source for learning. In fact, Crawford andBryne �2003� described PMS as a system that provides an invalu-able “source of knowledge” for contracting organizations to affectimprovement actions. Furthermore, some studies advocated thatby providing sufficient feedback, contracting organizations’ learn-ing can be facilitated and subsequently performance improve-ments can be achieved �Ibbs et al. 2001; Franco et al. 2004�. Asummary of descriptions of PMS is given in Table 1.

Moreover, project performance improvement is not a guaran-teed result with the installation of a PMS �Kululanga et al. 1999;Love et al. 2000; Choe 2004�. Instead, contracting organizationscould only improve project performance if knowledge can be im-bibed from the feedback. Kululanga et al. �1999� described theprocess of applying the imbibed knowledge for improvement asorganizational learning �OL�.

Nevertheless, Wong and Cheung �2005� contended that previ-ous studies had paid little attention to the learning aspect betweenPMS development and performance improvement. This may helpto explain why contracting organizations fail to take advantage ofthe knowledge embedded in performance feedback �Jashapara2003; Sense and Antoni 2003�.

Notwithstanding, several attempts have been reported to ex-amine the relationship between contracting organizations’ learn-ing and performance improvement. Ford et al. �2000� noted thatthe lack of OL ability is one of the primary constraints againstcontracting organizations’ performance improvement after ana-lyzing some United Kingdom based case studies. Based on aliterature review on OL, Ozorhon et al. �2005� echoed the propo-sition of Ford et al. �2000�. Siriwardena and Kagioglou �2005�recognized the importance of facilitating OL by instilling a suit-able project environment. Tjandra and Tan �2002� conducted a
questionnaire survey in Indonesia and suggested that the contract-
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ing organizations’ capability of learning is a possible way to ad-dress the problem of declining project productivity.

As for learning, researchers also noted that organizations dis-play different practice patterns described as OL type in this study�Kululanga et al. 1999, 2002; Murray and Chapman 2003�. OLtype can be defined as the manner in which an organization ap-plies the imbibed knowledge for improvement actions �Kululangaet al. 1999�. Sense and Antoni �2003� suggested that organiza-tions’ responses to the performance feedback are derived fromtheir OL types. Kolb and Kolb �2005� further suggested that un-derstanding the relationship between OL types and performanceimprovement would enhance a contracting organization’s capac-ity for meta-cognitive control of their learning process and enablethem to monitor and select appropriate approaches contingent tothe situations.

Nevertheless, supporting notes on the relationship between thepractice of OL types and performance improvement were mainlyanecdotal �Kululanga et al. 1999; Jashapara 2003; Murray andChapman 2003�. Notwithstanding the voluminous case studies re-ported, very little is backed with quantitative analysis �Tjandraand Tan 2002; Ozorhon et al. 2005�. This paper reports a studythat aims to: �1� empirically test the positive effect derived fromperformance feedback on performance improvement; and �2�identify the OL type that has the most significant impact on per-formance improvement �Sense and Antoni 2003�. In other words,the second research objective aims to study the moderating effectsof different OL types in response to feedback from the PMS�Kolb and Kolb 2005�. The moderating effect on performanceimprovement means that when a particular OL type is practiced,the performance feedback gives rise to a higher degree of perfor-mance improvements. In this study, performance feedback is de-scribed as the information that can be obtained from the PMS.This study reminds us of the importance of designing a PMS withan OL perspective in a timely fashion in order to achieve perfor-mance improvement. Furthermore, identifying the moderating ef-fects should provide insight into choosing the appropriate type of

Table 1. Development of Project Monitoring Systems �PMS� in Constru

PMS

Workflow technology-based monitoring andcontrol system �Shih and Tseng 1996�

A system applyingas the utilization an

Life cycle project management system�Chaaya and Jaafari 2001�

A system that evalu

Balanced score card performance evaluationsystem �Landin and Nilsson 2001�

A system that evaluand learning perspepast performance. Cchanges of customethe demand change

Web-based construction project managementsystem �Chan and Leung 2004�

A system developedthe demarcation ofrequirements and oconferencing and e-

Project performance monitoring system�Cheung et al. 2004�

The system evaluatpeople, cost, time, qThe monitoring protechnology. The autadjustments of perf

learning for facilitating performance improvements.

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Study

To accomplish the research objectives, a questionnaire was de-signed for data collection. The questionnaire has three parts. Thefirst part contains questions aimed at soliciting the respondents’demographic information. In the second part, the respondentswere asked to evaluate the degree of attainment of performanceoutcome. The third section addresses the responses of an organi-zation upon receipt of performance feedback. A sample question-naire is given in the Appendix.

Performance Feedback

A review of the PMSs listed in Table 2 suggests typical informa-tion performance feedback obtainable from a PMS. The respon-dents were asked to assess the degree of agreement on theperformance feedback in their respective construction projectsagainst a Likert scale of 1 �strongly disagree� to 7 �stronglyagree�.

OL Types

OL type can be defined as a manner in which an organizationimbibes and applies the acquired knowledge for performanceimprovements �Kululanga et al. 1999�. Various OL types wereidentified in a number of studies �Argyris and Schön 1978;Francis 1997; Hayes and Allison 1998; Easterby-Smith et al.2000; Kurtyka 2003�. Argyris and Schön �1978� noted that orga-nizations mainly exhibited three major types of learning: single-loop learning, double-loop learning, and Deutero learning. Thisclassification is subsequently used by Kululanga et al. �1999� intheir study to assess the extent of the OL types practiced by con-tractors in United Kingdom. Murray and Chapman �2003� sug-gested that adaptive learning and generative learning were theprincipal OL types. In fact, their descriptions of adaptive learningand generative learning match well the definitions of single-loopand double-loop learning, respectively. Jashapara �2003� identi-

Descriptions

k-based technology to track the flow of work and information, as wellmitment of resources along the project period.

e contracting organizations’ achievement of the preagreed project goals

e contracting organizations’ performance in financial, process, customerAs such, financial and process evaluate the contracting organizations’er and learning evaluates contracting organizations’ adaptability to theand. A feedback loop is provided for effective communication about

g the contracting organizations, consultants, and the clients.

a web-based environment that enables interactive communication onponsibility and the scope of work upon the changes of working

bulletin board is developed in this system to enable on-lineg among all organizations in the construction supply chain.

contracting organizations’ performance by eight major aspects namely,safety & health, environment, client satisfaction and communication.automated through the use of the world wide web and databasemonitoring process of PPMS affords ease of setup and further

e indicators to adapt to the change of the clients’ demands

ction

netword com

ates th

ates thctives.ustomrs’ dems amon

underthe resrders. Amailin

es theuality,

cess isomatedormanc

fied behavioral learning and cognitive learning as the major

AL OF MANAGEMENT IN ENGINEERING © ASCE / JULY 2008 / 163

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project learning types of construction contracting organizations.Behavioral learning can be viewed as “new responses or actionsbased on existing interpretations.” Cognitive learning refers to thecontinuous review and modification of ways of working towardperformance improvements. He further described cognitive learn-ing and behavioral learning as single-loop and double-loop learn-ing, respectively. Notwithstanding the difference in terminology,many OL type taxonomies are developed based on the work ofArgyris and Schön �1978�, Kululanga et al. �1999; 2002�, Loveet al. �2000�, Holt et al. �2000�, and Jashapara �2003�. This studyalso employs this taxonomy.1. Single-loop learning (SLL). SLL refers to the alteration of

behaviors and actions taken when mismatch between inten-tions and actual happenings is discovered �Argyris andSchön 1978�. This entails detection and correction of errorsto ensure the accomplishment of the anticipated outcomes.However, Kurtyka �2003� noted that SLL enables alterationof actions without scrutinizing the underlying assumptionsthat had led to the difference between the expected and theobtained outcomes. As such, SLL is regarded as a lower-levellearning type which reposits the organizations’ assumptionsdespite the fact that this may be one of the reasons that hasled to unsatisfactory performance �Kululanga et al. 1999�.Previous studies reported that organizations often seek im-provement actions through SLL �McGill and Slocum 1993;Kululanga et al. 1999�. Nevertheless, some researchers ar-gued that practicing SLL is less effective than practicingother OL types in sustaining organizations’ competitive ad-vantages, in particular, under a rapidly changing businessenvironment �McGill and Slocum 1993; Kululanga et al.1999�.

Previous studies suggested that the practice of SLL canbe identified by the following responses: �1� seeking andtaking immediate corrective actions; �2� distributing suchfeedback to the colleagues involved; and �3� referring to thefirm’s past experience to interpret the feedback received�Table 3�.

2. Double-loop learning (DLL). DLL refers to the alterations ofperformance improvement actions taken after reviewing theneed to change the underlying assumptions that had causedthe errors or deficiencies. DLL enables organizations to de-tect and address the root causes of underperformance and toassist in reforming their ways of working �Argyris and Schön1978�. Kurtyka �2003� distinguished the difference betweenDLL and SLL by the ways that improvement actions areformulated. In this regard, SLL formulates improvement ac-

Table 2. Typical Performance Feedback Obtainable from PMS

Performancefeedback�notation� Example

Love et al.�2000, 2003

Time �F1� Project progress versus originalprogram

*

Cost �F2� Project expenditure versus originalcost plan

*

Quality �F3� Project quality versus specification *Profitability �F4� Updated forecast profit versus

original anticipated profit*

Environment �F5� Actual versus anticipated influenceto the environment surrounding theconstruction sites

tions without questioning the root causes of the problem,



which is more like symptomatic treatment of disease,whereas for DLL, symptoms are treated as indicators andcorrective actions are formulated after reviewing the under-lying assumptions �Kululanga et al. 1999; Kurtyka 2003�.Hayes and Allinson �198� succinctly distinguished SLL andDLL by its goals. Thus, SLL seeks to ensure that organiza-tions “do things right,” whereas DLL seeks in assist organi-zations in “doing things right,” for achieving the projectgoals.

DLL emphasizes the need to look for thorough rather thandiscrete solutions in order to avoid recurrence of the sameproblem �Kululanga et al. 1999�. In response to the feedbackfrom a PMS, the practice of DLL can be characterized by: �1�identifying the root causes of the problem before taking ac-tion; �2� based on the feedback, evaluate the need to changethe current working performance; and �3� seeking and adopt-ing alternative performance improvement methods fromsuggestions/advice of the collaborating companies workingin the same project �Table 3�.

3. Deutero-learning (DeuL). DeuL refers to the ability oflearning to learn �Argyris and Schön 1978�. Francis �1997�described DeuL as a mechanism or system development“which forces learning to become explicit, and it is the av-enue for organizations to leverage a continuing commitmentto learning.” In order to accomplish DeuL, organizationsshould map out all areas that contribute to its improvementas an entity and set out to improve its ability to learn effec-tively in each of the areas that constitutes its total learning�Pedler et al. 1997; Kululanga et al. 1999�. Kululanga et al.�1999�emphasized the necessity of DeuL for organizations toattain sustainable performance improvements. While bothSLL and DLL are concerned with the operational events thatare the subject of learning, DeuL conceptualizes learning asan event in its own right, treating the learning process asmuch more conceptual and self-sustaining �Francis 1997�.Thus upon receipt of performance feedback, organizationspracticing DeuL display the following learning-based re-sponses: �1� not only seeking prompt actions, but also adjust-ing the long term strategy if required and �2� keeping asystematic record of performance feedback for future refer-ences �Table 3�.

Performance Improvement

Performance improvement is one of the ultimate goals of re-sponding to performance feedback. In this regard, a definition of

l-Jibouri�2003�

Xiao andProverbs�2003�

Koehn andDatta�2003�

Low andTeo

�2004�

Cheunget al.

�2004�

Wong andCheung�2005�

* * * *

* * * *

* * * ** *

* * *

�A

performance improvement for this study is thus essential. Some

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studies defined project performance as the organizations’ com-pliance with predetermined criteria on time, cost, and quality�Proverbs and Holt 2000; Soetanto et al. 2001; Baloi and Price2003�. Project performance improvement was then regarded as achange of action that minimizes the deviations between actual andpredetermined standards �Al-JiBouri 2003�.

Others argued that the above definition merely described per-formance in terms of project efficiency, without due regard to theimportance of project effectiveness �Ramo 2002; Crawford andBryne 2003�. They adopted the classical work of Drucker �1974�who defined project performance in terms of efficiency �doingthings the right way� and effectiveness �doing things right�.Mintzberg �1983� further described efficiency as how well re-sources are optimized to achieve the measurable benefits, andeffectiveness as “the consistency between the situational factorsand the design parameters.” As such, construction project perfor-mance should be gauged by both efficiency and effectiveness.Either failure in achieving project efficiency or effectivenesswould hamper the ability of an organization to improve perfor-mance �Drucker 1974; Mintzberg 1983�.

In this study, project performance improvement is identifiedby the organizations’ betterment in terms of project efficiencyand project effectiveness. Table 4 presents a summary of at-tributes for identifying the improvement of project efficiency and

Table 3. Learning-Based Responses

Responses �notation�RelevantOL type

Huber�1991�

Dodgson�1993�

Gav�199

Seeking and takingimmediate corrective actionsif required �L1�

Single looplearning�SLL�

* * *

Distributing such feedback tothe colleagues involved �L2�

*

Referring the firm’s pastexperience to interpret thereceived feedback �L3�

*

Identifying the root causes ofproblem before taking action�L4�

Double looplearning�DLL�

* *

Based on the feedbackreceived to evaluate the needto change the current workingperformance �L5�Seeking and adoptingalternative performanceimprovement methods fromsuggestions/advice of thecollaborating companiesworking in the same project�L6�

Not only seeking promptactions, but also adjusting thelong term strategy if required.�L7�

Deuterolearning�DeuL�

Keeping systematic record ofperformance feedback forfuture reference�L8�

*

effectiveness.

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Research Methodology

Pearson Correlation Analysis

Pearson correlation analysis was used to investigate the degree ofassociation between the perceptive assessment on the extent ofimprovement and the responses to performance feedback. Jas-hapara �2003� employed Pearson correlation analysis to investi-gate the degree of association of various learning types withperformance. The strength of relationship between variables canbe expressed by a correlation coefficient which could range from−1 �which indicates the perfect negative correlation between vari-ables� to 1 �which indicates the perfect positive correlation be-tween variables� �Fig. 1�.

In this study, project performance in terms of efficiency �P1�and effectiveness �P2� are computed using the following formula:

Pi =

�j=1

n

Aij

n�1�

where i=1,2; Aij�mean score of the jth attribute of Pi; andj=1,2 ,3 ,4, and 5.

im93�

Francis�1997�

Pedler et al.�1997�

Kululanga et al.�1999�

Kululanga et al.�2002�

Jashapara�2003�

* * *

* * *

* * *

* * *

* * *

* * *

* * * * *

* * *

in3�

K�19

*

For example, project efficiency �P1� is computed as follows:


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P1 = �4.27 �achievement of the predetermined project progress�

+ 4.14 �achievement of the predetermined project cost�

+ 4.82 �achievement of the predetermined project quality�

+ 4.89 �the level of meeting the customer requirements�

+ 4.51 �the extent of maintaining the anticipated profit�/5

= 4.53

.

Multiple Moderated Regression „MMR… Analysis

MMR is a frequently used technique in management, social, andbehavioral science research to test for the existence of a moder-ating effect on an independent variable �Snell and Dean 1994;Aguinis 1995; Choe 2004�. In other words, “the existence of amoderating effect implies that the relationship between two vari-ables �e.g., X1 and Y� varies as a function of the value of the thirdvariable �e.g., X2�, labeled as moderator” �Aguinis 1995�. MMRanalysis had also been applied in construction research. Yiu andCheung �2007� applied MMR to investigate the moderating effectof construction dispute sources on the mediator tactics and therespective mediation outcomes. In this study, MMR was used toexamine whether there is any difference in performance improve-ment derived from the performance feedback if the organizationsadopt different OL types.

The principle of the MMR analysis is founded on multipleregression analysis �MRA� �Hair et al. 1998�. Assuming that a

Table 4. Attributes for Identification of Project Performance Improveme

Attributes �notation�

Efficiency�P1�

Achievement of the predetermined projectprogress �A11�Achievement of the predetermined project cost �A12�Achievement of the predetermined project quality �A13�Level of meeting the customer requirements �A14�Extent of maintaining the anticipated profit �A15�

Effectiveness�P2�

Ability to address reasons for not meeting thepredetermined targets �A21�Competence to change in order to meetthe changing project requirements �A22�Ability to address forthcoming riskand consequences �A23�Competence to derive improvement actionsfrom previous mistakes �A24�Effectiveness of the communication channels amongcontracting organizations �A25�

Fig. 1. Correlation expressed from −1 to +1



dependent variable Y can best be predicted by two independentvariables X1 and X2, the general equation of multiple regressioncan be presented as

Y = a + b1X1 + b2X2 + � �2�

where Y�dependent variable; X1 X2�independent variables; a,b1, b2�unknown constant; and ��random error for any given setof values for X1, X2.

The coefficient of determination, R2, records the proportion ofvariation in the dependent variable explained by the independentvariables. The possible value of the measure falls between 0 and1. When R2=1, the independent variables completely account forvariation in the dependent variable. When R2=0, the independentvariables do not account for variation of the dependent variable�Lewis-Beck 1993�.

Multiple regression equation �Eq. �2�� assumes that inde-pendent variables have independent effects on predicting the de-pendent variable Y, whereas the equation of the MMR �Eq. �3��takes the moderated effect of the independent variable into ac-count �Jaccard et al. 1990; Cohen et al. 2003; Yiu and Cheung2007�. Assuming that the predictive power of X1 on Y is depen-dent on X2, the general equation of the MMR can be presented asfollows:

Y = a + b1X1 + b2X2 + b3X1X2 + � �3�

In the above equation, X1 and X2�independent variables for pre-dicting the dependent variable Y. The compound variable X1X2 isknown as the predictor-moderator product which is formed bymultiplying X1 �the predictor� by X2 �the moderator�. As such,the moderating effect is considered significant if the inclusion ofthe predictor-moderator product in Eq. �3� produces a significantchange of the R2 value ��R2� between Eqs. �2� and �3� �Jaccardet al. 1990; Cohen et al. 2003; Yiu and Cheung 2007�. Fisher Ztest �F test hereafter� is used to determine the significance ofthe moderating effect �Jaccard et al. 1990; Cohen et al. 2003;Choe 2004�. �R2 is considered significant if the calculated Fvalue exceeds the critical value as shown in the F distri-

misal.01�

Al-Jibouri�2003�

Xiao andProverbs�2003�

Koehn andDatta�2003�

Low andTeo

�2004�

Cheunget al.

�2004�

Wong andCheung�2005�

* * *

* * ** * * * *

* *

* *

*

*

* * *

* *

nt

Yasaet

�20

*

*

*

*

*

bution table �Jaccard et al. 1990; Cohen et al. 2003; Yiu and

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Cheung 2007�. The F value can be calculated by the followingequation:

F value = ��R22 − R1

2�/�n2 − n1��/��1 − R22�/�S − n2 − 1�� 4�

where n1�number of predictors in Eq. �2�; n2�number of predic-tors in Eq. �3�; S�total sample size; �S−n2−1��degree of free-dom; R1

2=R2 value for Eq. �2�; and R22=R2 value for Eq. �3�.

The MMR analyses for this study were developed followingthe above procedures. The equations are solved as shown inEqs. �5� and �6�

Pi = a + b1Fj + b2Lk + � �5�

Pi = a + b1Fj + b2Lk + b3FjLk + � �6�

where Pi�ith performance outcome scale and i=1,2; Fj�extentof the jth performance feedback and j=1,2 ,3 ,4 ,5; and Lk�levelof practice of the kth learning-based response andk=1,2 ,3 ,4 ,5 ,6 ,7 ,8.

In this study, performance improvement, extent of perfor-mance feedback available, and the response thereto are used asthe dependent, predictor, and moderator variables, respectively,as summarized in Fig. 2 �Jaccard et al. 1990; Cohen et al. 2003;Yiu and Cheung 2007�.

The significance of the moderating effects was examined bythe F test. The �R2 values were the differences of R2 values fromEqs. �5� and �6�. In this study, a total of 80 MMR analyses �de-vised from the combination of the performance improvement �interms of efficiency and effectivenss�, five types of performancefeedback, and the practice of the three OL types� were conducted.

Response Rate

The questionnaires were distributed to the target respondentsworking in private and public-sector developers, consultants, con-tractors, and supply firms in Hong Kong. To enhance the validityof the study, the list of respondents was assembled from webpages of the local professional institutes as well as the HongKong Builder Directory. A total of 200 questionnaires were sentand 84 usable responses were received and used in the analysis,representing a 42% valid response rate. Easterby-Smith et al.�1991� observed that the response rate of questionnaire surveystudies conducted in the construction industry typically rangedfrom 25 to 30%. Similar research studies on OL in constructionconducted by Kululanga et al. have been based on 31 responses�equivalent to a 34% response rate�. The response rate of the

Fig. 2. Conceptual relationships between performance improvement,receipt of performance feedback, and practice of OL types

research study on the effect of OL on contractors’ performance

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conducted by Jashapara �2003� was 14.1%. Therefore, bothsample size and valid response rate for this study are consideredreasonable.

As shown in Fig. 3, more than 60% of the respondents haveover 5 years working experience. Nearly 70% of the respondentsare currently involved in building projects. The project types forwhich the respondents are affiliated are shown in Fig. 4.

Findings and Discussion

Descriptive Statistics

Respondents were asked to indicate the degree of agreementagainst each of the statements. The statements in Parts 2 and 3address performance improvement and responses to performancefeedback, respectively. Both mean scores and the standard devia-tions of the above are shown in Tables 5–7.

Generally, the mean scores for all questions asked are higherthan the midpoint of the seven-point scale �i.e., 3.5�. These sug-gest that the respondents generally agree that in response toperformance feedback, their organizations had exercised all threetypes of learning practices although to a different extent. Further-more, respondents were asked to express their degree of agree-ment on the achievement of the ten attributes representing pro-ject efficiency and project effectiveness. The means scores ofproject efficiency and effectiveness were then computed usingEq. �1� and shown in Table 5. The respondents generally agreethat their projects are satisfactory in terms of project efficiency�mean score�4.53� and project effectiveness �mean score�5.00�.

Fig. 3. Working experience of respondents

Fig. 4. Project nature of respondents involved


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Correlation between Performance Feedbackand Performance Improvement

Pearson correlation analysis results are shown in Table 8. Thereceipt of all types of performance feedback was found to bestrongly correlated �with correlation coefficient ranging from

Table 6. Performance Feedback Received

Performance feedback received—from 1�strongly disagree� to 7 �strongly agree� Mean Std. dev.

Time �project progress versus original programm� 4.35 1.35

Cost �project expenditure versus original cost plan� 4.54 1.28

Quality �project quality versus specification� 4.45 1.16

Profitability �updated forecast profit versus originalanticipated profit�

4.15 1.31

Environment �actual versus anticipated influence tothe environment�

4.34 1.31

Table 5. Project Performance Evaluation

Project performance evaluation—from 1 �strongly disagree� to 7 �strongl

Achievement of the predetermined project progress

Achievement of the predetermined project cost

Achievement of the predetermined project quality

The level of meeting the customer requirements

The extent of maintaining the anticipated profit

Project efficiencyAbility to address reasons for not meeting the predetermined targets

Competence to change in order to meet the changing project requiremen

Ability to address forthcoming risk and consequences

Competence to derive improvement actions from previous mistakes

Effectiveness of the communication channels among contracting organiza

Project effectiveness

Table 7. Practice of the OL types

Practice of the OL types in response to the performancefeedback:—From 1 �strongly disagree� to 7 �strongly agree�

Tim

Me�std. d

�

Seeking and taking immediate corrective actions if required 4.99 �

Distributing such feedback to the colleagues involved 5.02 �

Referring to the firm’s past experience to interpret thefeedback received

4.93 �

�b

Identifying the root causes of problem before taking action 4.74 �

Based on the feedback received to evaluate the need tochange the current working performance

4.55 �

Seeking and adopting alternative performance improvementmethods from suggestions/advice of the collaboratingcompanies working in the same project

4.92 �

�c

Not only seeking prompt actions, but also adjustingthe long term strategy if required

4.63 �

Keeping systematic record of performance feedbackfor future references

5.04 �



0.427 to 0.673� with performance improvement in terms of effi-ciency and effectiveness at the significant level of p�0.01. Thefindings are in agreement with Al-JiBouri’s �2003� study whichemphasized that providing sufficient performance feedback tocontracting organizations can facilitate their performance im-provements. In other words, establishing a PMS shall pave thepathways for a learning organization to improve performance�Crawford and Bryne 2003; Wong and Cheung 2005�.

Practice of OL Types—Moderating Effects

To empirically investigate whether the practice of different OLtype is a significant contribution to the relationship between theextent of performance feedback available and project perfor-mance improvement, MMR was employed. The significance ofthe moderating effect is tested by the F test. Critical values forsuch tests were obtained from F-distribution table with significantlevel at �=0.05 �Jaccard et al. 1990; Cohen et al. 2003; Yiu and

� Means Std. dev.

4.27 1.44

4.14 1.50

4.82 1.34

4.89 1.29

4.51 1.25

4.53 1.095.02 1.11

5.16 1.15

4.77 1.28

5.10 1.16

4.90 1.22

5.00 0.99

Cost Quality Profitability Environment

Mean�std. dev.�

Mean�std. dev.�

Mean�std. dev.�

Mean�std. dev.�

4.85 �1.22� 4.86 �1.24� 4.62 �1.16� 4.83 �1.36�

4.83 �1.54� 4.90 �1.41� 4.42 �1.47� 4.66 �1.47�

4.88 �1.33� 4.81 �1.38� 4.62 �1.28� 4.69 �1.42�

4.67 �1.48� 4.71 �1.42� 4.48 �1.45� 4.71 �1.39�

4.60 �1.24� 4.63 �1.30� 4.48 �1.21� 4.44 �1.39�

4.76 �1.39� 4.94 �1.40� 4.42 �1.42� 4.78 �1.34�

4.73 �1.36� 4.65 �1.45� 4.50 �1.40� 4.48 �1.45�

5.02 �1.37� 4.81 �1.42� 4.70 �1.48� 4.87 �1.35�

y agree

ts

tions

e

anev.�

a� SLL

1.24�

1.48�

1.30�

� DLL

1.47�

1.30�

1.43�

� DeuL

1.42�

1.43�

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Cheung 2007�. Referring to the F-distribution table, the MRRresult with an F value �3.96 would be treated as statisticallysignificance �Jaccard et al. 1990�. As such, the higher the F valuethe more significant the contribution of a particular OL type to-wards performance improvement respective to the extent of per-formance feedback available. The results of the MMR analysesare reported in Table 9. Learning-based responses �which are de-rived from the OL types� offering significant moderating effects�i.e., F value �3.96� are highlighted with an asterisk �*�.

From Table 9, F values from many of the results were found tobe greater than 3.96. As such, the higher the F value, the moresignificant the learning-based response towards performance im-provement �Yiu and Cheung 2007�.

Higher F values were found where performance feedback ontime, cost, and quality were used as the predictor variable. Thisindicates that greater performance improvements can be achievedby taking the learning-based responses. As such, performancefeedback on time, cost, and quality as mentioned in this studywere also recognized as the indicators of project efficiency�Mintzberg 1983�. Moreover, smaller F values were found when“Effectiveness” was used as the dependent variable instead of“efficiency.” It can be seen that performance feedback is per-

Table 8. Correlation between Receipt of Performance Feedback andPerformance Improvement

Projectefficiency

Projecteffectiveness

Time �project progress versus originalprogram�

0.626a 0.599a

Cost �project expenditure versus originalcost plan�

0.673a 0.664a

Quality �project quality versus specification� 0.572a 0.533a

Profitability �updated forecast profit versusoriginal anticipated profit�

0.500a 0.545a

Environment �actual versus anticipatedinfluence to the environment�

0.439a 0.427a

aCorrelation is significant at the 0.01 level �2-tailed�.

Table 9. Summary of the MMR Analyses Results

Dependentvariable—projectperformance

Predictorvariable—

performancefeedback

sufficiency

M

Derived from SLL type

L1 L2 L3

Efficiency Time 30.30* 40.80* 18.95*Cost 31.21* 26.89* 17.59*

Quality 38.19* 52.68* 36.83*Profitability 15.15* 15.63* 14.88*Environment 4.55* 16.91* 3.83

Effectiveness Time 7.41* 11.37* 3.06

Cost 8.20* 6.06* 2.51

Quality 19.93* 34.40* 22.26*Profitability 5.53* 5.94* 4.17*Environment 3.82 6.93* 2.13

Note: Notation: SLL�single loop learning; DLL�double loop learning; Drequired; L2�distributing such feedback to the colleagues involved;L4�identifying the root causes of problem before taking action; L5�basinperformance; L6�seeking and adopting alternative performance improvemin the same project; L7�not only seeking prompt actions, but also adju
performance feedback for future reference; *�significant moderating effect at p
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ceived to have a greater effect on improving project efficiency inrelation to the learning-based responses. This supports the find-ings of the study by Wong and Cheung �2005� who noted thatPMSs in construction typically focused on arousing users’ atten-tion about project efficiency. This also supported some studieswhich suggested advancement of PMS in facilitating organiza-tions’ learning for greater improvement in terms of project effec-tiveness �Rodney and Mohammed 2001; Wilson et al. 2003;Wong and Cheung 2005�.

Furthermore, the results of this study suggested that somelearning-based responses are more “versatile” than the others. Theterm “versatile” is used to identify a moderator variable that has asignificant effect on several dependent variables �Yiu and Cheung2007�. For example, “base the feedback received on evaluatingthe need to change the current working performance” is found tobe the most versatile response against performance feedback. Thisshowed significant moderating effects on improvement of projectefficiency when different types of performance feedback wereused as the predictor in the MMR analyses. For improvement inproject effectiveness, “identifying the root causes of a problembefore taking action” is found as the most versatile responseagainst performance feedback.

Referring to Table 2, the above two learning-based responsescharacterize DLL. As DLL aims to address deep-rooted causes ofthe underperformance and explores new ways of working for bet-terment �Kululanga et al. 1999; Jashapara 2003�, it has long beenidentified as a more effective OL type to facilitate performance�Jashapara 2003�. Notwithstanding, this does not imply excludingthe roles that single-loop learning and deutero learning can playon project performance improvement. All these types of learningare not mutually exclusive. In fact, “distributing such feedback tothe colleagues involved” �response that characterizes single-looplearning� and “not only seek prompt actions, but also adjust thelong term strategy if required” �response that characterizes deu-tero learning� ranked high in terms of their F values in differentscenarios. Nevertheless, their versatilities are comparativelylower than the above mentioned two responses that characterize

tor variables—learning-based responses

Derived from DLL type Derived from DeuL type

L4 L5 L6 L7 L8

28.86* 45.29* 27.65* 36.49* 22.75*29.07* 32.66* 24.46* 33.33* 20.22*43.07* 52.74* 40.97* 49.15* 36.24*19.95* 25.79* 14.43* 16.84* 14.26*4.19* 5.47* 3.82 4.62* 5.25*

11.16* 8.02* 7.58* 13.12* 3.94

18.79* 10.05* 4.19* 6.97* 4.47*33.99* 17.39* 23.16* 26.10* 22.53*10.09* 6.64* 4.27* 7.24* 5.84*4.57* 2.68 3.23 2.28 4.65*

Deutero learning; L1�seeking and taking immediate corrective actions iferring the firm’s past experience to interpret the received feedback;he feedback received to evaluate the need to change the current workingethods from suggestions/advice of the collaborating companies workinge long term strategy if required; and l8�keeping systematic records of

odera

euL�L3�refg on tent m

sting th
�0.01.

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double-loop learning. As Kululanga et al. �2002� emphasized, theeffect of OL on project performance improvement does not comeby chance. Furthermore, contracting organizations’ learning fromthe performance feedback could not simply be assumed as “cere-bral, unproblematic, a process of absorbing the given, and as amatter of transmission and assimilation” �Sense 2004�. Findingsin this study support that practicing OL type responsive to perfor-mance feedback may help in facilitating greater project success interms of both project efficiency and effectiveness. Furthermore,the study provided an empirical support to the notion that PMSdesign should embrace an OL dimension.

Limitations and Recommendations

There are several limitations to this study. First, the project naturemay affect the findings. In this study, 66% of the respondentsbased their responses on building works, thus the findings maynot be strictly applicable to civil and maintenance projects. Sec-ond, the data used in this study were collected in Hong Kong andgeographical differences should be noted. Third, although 84valid responses used in this study are considered reasonable, thefindings may still be limited to the specific situation in HongKong. Using greater sample size for analyses and collectingsamples in other countries can therefore be considered for furtherstudies.

Concluding Remarks

This study investigates the effects of OL practices on performance
improvement in consideration of the extent of performance feed-


back available. Data were collected from a questionnaire surveyand analyzed by MMR. The findings suggested that the existenceof moderating the effect of OL practices results in project perfor-mance improvement.

“Base feedback received on evaluating the need to change thecurrent working performance” and “identifying the root causesof problems before taking action” were found as the most versa-tile learning-based responses to improve project performance interms of efficiency and effectiveness, respectively. It is worthynoting that these learning-based responses characterize the prac-tice of double-loop learning. This echoes the finding by Jashapara�2003� who found that double-loop learning is a more effectiveOL type in facilitating performance improvement �Jashapara2003�.

In the early studies on PMS, the provision of performancefeedback was perceived as an effective means in facilitating con-tracting organizations’ performance improvement �Al-JiBouri2003; Wong and Cheung 2005�. The importance of facilitatingcontracting organizations’ learning upon receiving the perfor-mance feedback seems to attract less attention than it deserves, inparticular, in view of the impact on performance improvement�Sense and Antoni 2003�. The findings in this study remind us ofthe importance of designing a PMS with an OL dimension in atimely fashion to make performance improvement sustainable.Future research is suggested to focus on the ways to facilitateorganizations’ learning in a PMS framework, and on the use oforganizations’ learning outcomes as a benchmark for evaluation
of PMS success.
Appendix. Sample of Questionnaire

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References

Aguinis, H. �1995�. “Statistical power problems with moderated multipleregression in management research.” J. Manage., 21�6�, 1141–1158.

Al-JiBouri, S. H. �2003�. “Monitoring systems and their effectiveness forproject cost control in construction.” Int. J. Proj. Manage., 21�3�,145–154.

Argyris, C., and Schön, D. �1978�. Organisational learning: A theory ofaction perspective, Addison-Wesley, Reading, Mass.

Baloi, D., and Price, A. D. F. �2003�. “Modeling global risk factors af-fecting construction cost performance.” Int. J. Proj. Manage., 21�4�,261–267.

Chaaya, M., and Jaafari, A. �2001�. “Cognizance of visual design man-agement in life-cycle project management.” J. Manage. Eng., 17�1�,49–57.

Chan, S. L., and Leung, N. N. �2004�. “Prototype web-based constructionproject management system.” J. Constr. Eng. Manage., 130�6�, 935–943.

Cheung, S. O., Suen, C. H. H., and Cheung, K. W. K. �2004�. “PPMS:A web-based construction project performance monitoring system.”Autom. Constr., 13�3�, 361–376.

Choe, J. M. �2004�. “The relationships among management accountinginformation, organizational learning and production performance.”J. Strategic Inf. Syst., 13, 61–85.

Cohen, J., Cohen, P., West, S. G., and Aiken, L. S. �2003�. Appliedmultiple regression/correlation analysis for the behavioral sciences,L. Erlbaum Associates, Mahwah, N.J.

Construction Industry Review Committee �CIRC� of Hong Kong. �2001�.“Construct for excellence.” Construction Industry Review Committee,Hong Kong.

Crawford, P., and Bryne, P. �2003�. “Project monitoring and evaluation:A method for enhancing the efficiency and effectiveness of aid projectimplementation.” Int. J. Proj. Manage., 21�5�, 363–373.

Dodgson, M. �1993�. “Organizational learning: A review of some litera-ture.” Organ. Stud., 14�3�, 375–394.

Drucker, P. F. �1974�. Management—Tasks, responsibilities, practices,Heinemann, London.

Easterby-Smith, M., Crossan, M., and Nicolini, D. �2000�. “Organisa-tional learning: Debates past, present and future.” J. Manage. Stud.(Oxford), 37, 783–796.

Easterby-Smith, M., Thorpe, R., and Lowe, A. �1991�. Management re-search: An introduction, Sage, London.

Egan, J. �1998�. “Rethinking construction: The report of the constructiontask force.” Rep. to the Department of the Environment, Transportand the Regions, London.

Ford, D. N., Voyer, J. J., and Wilkinson, J. M. G. �2000�. “Buildinglearning organizations in engineering cultures: Case study.” J. Man-age. Eng., 16�4�, 72–83.

Francis, S. �1997�. “A time for reflection: Learning about organizationallearning.” The Learning Organization, 4�4�, 168–179.

Franco, L. A., Cushman, M., and Rosenhead, J. �2004�. “Project reviewand leaning in construction industry: Embedding a problem structur-ing method within a partnership context.” Eur. J. Oper. Res., 152�6�,586–601.

Gavin, D. �1993�. “Building a learning organization.” Harvard Bus. Rev.,71�4�, 78–91.

Hair, J. F., Anerson, R. E., Tatham, R. L., and Black, W. C. �1998�.Multivariate data analysis, 5th Ed., Prentice-Hall, Upper SaddleRiver, N.J., 88–92.

Hayes, J., and Allinson, C. W. �1998�. “Cognitive style and the theoryand practice of individual and collective learning in organisations.”Hum. Relat., 51, 847–871.

Holt, G. D., Proverbs, D., and Love, P. E. D. �2000�. “Survey findings onUK construction procurement: Is it achieving lowest cost, or value?”Asia-Pacific Build. Constr. Manage. J., 5�2�, 13–20.

Huber, G. �1991�. “Organizational learning: The contributing process andliterature.” Org. Sci., 2�2�, 88–115.

Huemer, L., and Ostergren, K. �2000�. “Strategic change and organiza-

JOURN


tional learning in two Swedish construction firms.” Constr. Manage.Econom., 18�6�, 635–642.

Ibbs, C. W., Wong, C. K., and Kwak, Y. H. �2001�. “Project changemanagement system.” J. Manage. Eng., 17�3�, 159–165.

Jaccard, J., Turrisi, R., and Choi, K. W. �1990�. Interaction effects inmultiple regression, Sage, Thousand Oaks, Calif.

Jashapara, A. �2003�. “Cognition, culture and competition: An empiricaltest of the learning organization.” Learn. Organ. Int. J., 10�1�, 31–50.

Kim, D. H. �1993�. “The link between individual and organizationallearning.” Sloan Manage. Rev., 35�1�, 37–50.

Koehn, E., and Datta, N. K. �2003�. “Quality, environmental, and healthand safety management systems for construction engineering.”J. Constr. Eng. Manage., 129�5�, 562–569.

Kolb, A. Y., and Kolb, D. A. �2005�. The Kolb learning style inventory–version 3.1 2005 technical specification, Experience Based LearningSystems Inc., Boston.

Kululanga, G. K., McCaffer, R., Price, A. D. F., and Edum-Fotwe, F.�1999�. “Learning mechanisms employed by construction contrac-tors.” J. Constr. Eng. Manage., 125�4�, 215–233.

Kululanga, G. K., Price, A. D. F., and McCaffer, R. �2002�. “Empiricalinvestigation of construction contractors’ organizational learning.”J. Constr. Eng. Manage., 128�5�, 385–391.

Kurtyka, J. �2003�. “Implementing business intelligence systems: Anorganizational learning approach.” DM Review Magazine, �http://www.dmereview.com/editorial/dmreview/print_action.cfm?articleId�7610� �Nov. 2003�.

Landin, A., and Nilsson, C. H. �2001�. “Do quality system really make adifference?” Build. Res. Inf., 29�1�, 12–20.

Latham, M. �1994�. Constructing the team, Department of Environment,HMSO, London.

Lewis-Beck, M. S. �1993�. “Applied regression: An introduction.” Quan-titative applications in the social sciences, Vol. 22, Sage, London.

Love, P. E. D., Huang, J. C., Edwards, D. J., and Irani, Z. �2004�. “Nur-turing a learning organization in construction: A focus on strategicshift, organizational transformation, customer orientation and qualitycentred learning.” Constr. Innovation, 4�2�, 113–126.

Love, P. E. D., Li, H., Irani, Z., and Faniran, O. �2000�. “Total qualitymanagement and the learning organization: A dialogue for change inconstruction.” Constr. Manage. Econom., 18�3�, 321–331.

Low, S. P., and Teo, J. A. �2004�. “Implementing total quality manage-ment in construction firms.” J. Manage. Eng., 20�1�, 8–15.

McGill, M., and Slocum, J. W. �1993�. “Unlearning the organisation.”Organisational Dyn., 22�2�, 67–79.

Mintzberg, H. �1983�. Structure in fives—Designing effective organiza-tions, Prentice-Hall, Englewood Cliffs, N.J., 121–125.

Mottahedin, P. �2003�. “Coordinating the supply chain: A strategy forsuccessful construction projects.” Master’s thesis, Univ. of AppliedSciences, Stuttgart, Germany.

Murray, P., and Chapman, R. �2003�. “From continuous improvementto organizational learning: Developmental theory.” Learn. Organ. Int.J., 10�5�, 272–282.

Ozorhon, B., Dikmen, I., and Birgonul, M. T. �2005�. “Organizationalmemory formation and its use in construction.” Build. Res. Inf.,33�1�, 67–79.

Pedler, M., Burgoyne, J., and Boydell, T. �1997�. “The Learning Com-pany: Strategies for sustainable development, McGraw-Hill, London.

Proverbs, D. G., and Holt, G. D. �2000�. “A theoretical model for opti-mum project �time� performance based on European best practice.”Constr. Manage. Econom., 18�6�, 657–665.

Ramo, H. �2002�. “Doing things right and doing the right things—Timeand timing in projects.” Int. J. Proj. Manage., 20�6�, 569–574.

Rodney, A. S., and Mohammed, S. �2001�. “Utilizing the balanced score-card for IT/IS performance evaluation in construction.” Constr. Inno-vation, 1�2�, 147–163.

Sense, A. J. �2004�. “An architecture for learning in projects?” J. Work-place Learn., 16�3�, 123–145.

Sense, A. J., and Antoni, M. �2003�. “Exploring the politics of project
learning.” Int. J. Proj. Manage., 21�7�, 487–494.

.24:162-172.

Dow

nloa

ded

from

asc

elib

rary

.org

by

CL

AR

KSO

N U

NIV

ER

SIT

Y o

n 10

/06/

13. C

opyr

ight

ASC

E. F

or p

erso

nal u

se o

nly;

all

righ

ts r

eser

ved.

Shih, H. M., and Tseng, M. M. �1996�. “Workflow technology-basedmonitoring and control for business process and project manage-ment.” Int. J. Proj. Manage., 14�6�, 373–378.

Siriwardena, M. L., and Kagioglou, M. �2005�. “An integrative reviewof organisational learning research in construction.” Proc., QUTResearch Week 2005, Queensland Univ. of Technology, Queens-land, Australia, �http://www.rics.org/NR/rdonlyres/5A225548-8EDD-487D-9F4E-65EBAAB88B65/0/Integrative_review_organisational_learning20051129.pdf�.

Snell, S. A., and Dean, J. W. �1994�. “Strategic compensation forintegrated manufacturing: The moderating effects of jobs and organi-zational inertia.” Acad. Manage J., 37�5�, 1109–1140.

Soetanto, R., Proverbs, D. D., and Holt, G. D. �2001�. “Achieving qualityconstruction projects based on harmonious working relationships:Clients’ and architects’ perceptions of contractor performance.” Int. J.Qual. Reliab. Manage., 18�4–5�, 528–548.

Tjandra, I. K., and Tan, W. �2002�. “Organisational learning in construc-tion: The case of construction firms operating in Jakarta, Indonesia.”



Proc., 1st Int. Conf. of CIB W107, Creating a Sustainable Construc-tion Industry in Developing Countries, Cape Town, South Africa,530–539.

Wilson, C., Hagarty, D., and Gauthier, J. �2003�. “Resulting using thebalanced scorecard in public sector.” J. Corporate Real Estate, 6�1�,53–63.

Wong, P. S. P., and Cheung, S. O. �2005�. “From monitoring to learning:A conceptual framework.” Proc., 21st Annual Conf. of the Associationof Researchers in Construction Management, SOAS, London, 1037–1051.

Xiao, H., and Proverbs, D. �2003�. “Factors influencing contractor per-formance: An international investigation.” Eng., Constr., Archit. Man-age., 10�5�, 322–332.

Yasamis, F., Arditi, D., and Mohammadi, J. �2001�. “Assessing contractorquality performance.” Constr. Manage. Econom., 20�3�, 211–223.

Yiu, T. W., and Cheung, S. O. �2007�. “A study of construction mediatortactics—Part II: The contingent use of tactics.” Build. Environ., 42�2�,762–769.

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Documents

Moderating Effect of Organizational Learning Type on Performance Improvement