unlearning dimension

7/23/2019 Unlearning Dimension

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The unlearning dimension of organizational learning in construction projects

Peter S.P. Wong a,, Sai On Cheung b, Regina L.Y. Yiu b, Mary Hardie c

a School of Property Construction & Project Management, RMIT University, Australiab Construction Dispute Resolution Research Unit, Department of Building and Construction City, University of Hong Kong, Hong Kong

c School of Engineering, University of Western Sydney, Australia

Received 5 August 2010; received in revised form 4 April 2011; accepted 14 April 2011

Abstract

It has been suggested that contracting organizations in construction projects do not seem to adapt resiliently under changing market conditions.

Interestingly, recent organizational management literature reveals the essential role of practicing unlearning for generating more resilient

performance improvement actions. This paper reports on a study that aims to test empirically the contingent effect of unlearning on the

relationship between organizational learning (OL) and organizational success. A conceptual model which depicts the hypothesized relationship

among OL, unlearning and organizational success is presented. Data were obtained from a questionnaire survey. To test the conceptual model,

Pearson Correlation Analysis and Multiple Moderated Regression Analysis were employed. The study hypothesized that interaction between the

practice of OL and unlearning have a moderating effect on organizational success. The hypothesis was only partially supported by the results of

the MMR analysis. Only the practice of double-loop learning was found to be symbiotic with the practice of unlearning for achieving

organizational success. The effect was found to be more significant when organizational success was evaluated in terms of meeting the client's

expectations on project cost.

2011 Elsevier Ltd. APM and IPMA. All rights reserved.

Keywords: Unlearning; Organizational learning; Project success

1. Introduction

Organizational learning (OL) is recognized as vital for a

contracting organization's1 enhanced performance. Therefore, it

is hardly surprising that OL has become topical in the project

management literature (Jashapara, 2003; Kululanga et al.,1999;

Wong and Cheung, 2008). One focus of attention in the researcharea is the effect of OL on project outcomes (Love and

Josephson, 2004; Murray and Chapman, 2003). Based on case

studies of construction projects in Sweden,Love and Josephson

(2004)found that project cost savings can be attained by those

contractors who are able to actively learn from experience.

Research findings derived from a questionnaire survey con-

ducted by Murray and Chapman (2003) in Australia indicate

that an organization's performance in construction projects is

positively linked to its learning competencies.Nevertheless, some scholars have argued about whether

contracting organizations, under the current project environment,

can be prudent learners (Chan et al.,2005; Love et al., 2004).

Based on a literature review,Chan et al. (2005) observed that few

contracting organizations are able to systematically convert their

lessons learned into improvement actions. However, possible

reasons that disengaged contracting organizations from learning

were not discussed. In this regard, Love et al. (2004) reported that

the transient nature of construction projects offers no guarantee of

future dealing among team members and, consequently,

contracting organizations thus often lack the necessary degree

Corresponding author at: School of Property Construction & Project

Management, RMIT University, City Campus, 360 Swanston Street (Bldg 8.

Lvl 8), PO Box 2476, Melbourne, 3001 VIC, Australia. Tel.: +613 99253978;

fax: +613 99251939.

E-mail address: [email protected](P.S.P. Wong).1 Contracting organization in this study refers to the organizations

collaborating in a construction project. This includes the architect, engineering

and surveying consultants employed by the developers, the main contractors and

the sub-contractors.

0263-7863/$36.00 - see front matter 2011 Elsevier Ltd. APM and IPMA. All rights reserved.doi:10.1016/j.ijproman.2011.04.001

Available online at www.sciencedirect.com

International Journal of Project Management 30 (2012) 94104www.elsevier.com/locate/ijproman
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of proximity to derive learning from each other. They contended

that the way of coupling organizations in construction projects

can be one of the barriers of learning and thus organizational

success. Nonetheless, the work ofLove et al. (2004)falls short of

empirical evidence to unveil how the barriers are affecting the

effect of learning on success. Ruuska and Vartiainen (2005)

suggest that learning from feedback is vital for a contractingorganization's performance improvement. Nevertheless, they

noted that many project monitoring systems are not well designed

to provide useful feedback in order to facilitate learning from

mistakes.Jashapara (2003), in extending the work ofArgyris and

Schn (1978), identified the fact that contractors typically exhibit

two types of OL: single-loop learning (SLL) and double-loop

learning (DLL). SLL refers to a detection and correction of errors

without scrutinizing the organizational basic premises and norms

that had led to the divergence between the expected and the actual

outcomes (Argyris and Schn, 1978). DLL is attained when

organizations detect and correct errors by inquiring into, and

modifying if necessary, their underlying norms and assumptions(Argyris and Schn, 1978). Based on a questionnaire survey

conducted in the U.K. with 181 valid responses collected from the

contracting organizations,Jashapara (2003)found that practicing

DLL has a more significant effect on organizational performance

than practicing SLL. Nevertheless, he criticized contracting

organizations as generally not competent at generating improve-

ment through practicing DLL.Similar findings were also reported

by Kululanga et al. (1999) who found that contracting

organizations in construction mainly practice SLL only. The

authors argued that without moving to DLL, performance

improvement actions generated by contracting organizations

may no longer be effective when market needs change. However,

the reasoning behind the organizations' incompetence inDLLpractice was not discussed in these studies. Based on a

questionnaire survey conducted in Hong Kong, Wong et al.

(2009) reported that contracting organizations often improve

performance through detecting and correcting errors (i.e.

practicing SLL) and yet rarely look into the root causes of these

errors or identify the new behaviors needed to prevent

reoccurrences (i.e. practicing DLL). Despite supporting the con-

tention that the practices of SLL and DLL are both imperative for

performance improvement, Wong et al. (2009) argued that

practicing SLL only is not sufficient for contracting organizations

to sustain performance in response to the changes of market

demands What has caused hindrance to the practice of DLL, andthus success was then described as valuable for further research.

The above review indicates that contracting organizations do

not seem to learn in a manner that fosters organizational resilience

in coping with changes of market demand. However, while

drawing a conclusion that contracting organizations should learn

more effectively, the possible conditions that are detrimental to

the practice of OL were rarely delved into in previous studies.

From the non-construction field,McGill and Slocum (1993)

argued that not many organizations are capable of learning in an

introspective manner. Supported by a literature review, they noted

that organizations rarely accept an operational change that does

not fit their core values. Such organizations are often prone to limit

themselves to acquiring knowledge that fits for achieving their

pre-determined goals and pre-defined best performance. Never-

theless, the organization's understanding about the client's

requirements may no longer be valid when the market envi-

ronment changes. Thus, if knowledge was processed under rigid

sets of beliefs and core values, the possible improvement actions

derived may have outlived their effectiveness to meet changing

market demand.Akgn et al. (2006)pinpointed the fact that withcumulative experience, organizations often develop a set of beliefs

and routines in their operations. Performance change may become

difficult if the required response to the environmental change

clashes with their core values. As such, the rigidities of attitude

formed in acquiring new knowledge hinder the organization's

adaptation to changing conditions.

The above studies found from the non-construction field

collectively support the idea that OL is not merely a detection and

correction of errors system for attaining a pre-determined per-

formance standard. Instead, OL involves a process of identifying

and discarding obsolete beliefs and routines (Akgn et al.,2006).

Hedberg (1981) used the term unlearning

to describe suchprocesses and emphasized that organizations may not learn

effectively without first unlearning irrelevant ideas from the past.

Researchers have also pointed out that it may be an uncomfortable

process to unlearn those beliefs and routines that may have taken

years to establish (Akgn et al., 2006; Mezias et al., 2001). In

particular, the established beliefs and routines may have led the

organizations to business success in the past and the organizations

may also have invested a lot of effort in developing these routines

(Akgn et al., 2006; Mezias et al., 2001). This reluctance may

help to explain why the inability to unlearn has been highlighted

as a criticalweakness of many organizations (Akgn et al., 2007a,

b). While OL has emerged as a popular research topic in con-

struction project management, the above literature review reveals

that previous studies about the concept of OL were rarely

addressed from an unlearning perspective (Love et al., 2000;

Wong and Cheung, 2008). This paper reports a study that aims to

examine the effect of learning on the contracting organization's

success in an unlearning perspective. It seeks to investigate if

effective learning is contingent on the contracting organization's

practice of discarding obsolete beliefs and routines (i.e. unlearn-

ing). From an academic point of view, this study contributes to a

deeper understanding of the underlying dynamics of OL. Fur-

thermore, it complements existing research on factors fostering

and supporting OL (Schilling and Kluge, 2009). For practitioners,

it is believed that a better understanding of the inter-relationshipamong OL, unlearning and organizational success can provide

valuable insights for management wishing to devise ways and

means of enhancing a contracting organization's performance.

To accomplish the research objective, the following hy-

potheses are tested in this study:

H1. The practice of OL is contingent on the contracting

organizations' practice of unlearning;

H2. The interactions between the practice of OL and unlearning

have moderating effect on organizational success.

This paper is organized as follows: Firstly, a conceptual model

which depicts the hypothesized relationships among learning,

95P.S.P. Wong et al. / International Journal of Project Management 30 (2012) 94104


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unlearning and organizational success is presented; Secondly,

research methodologies used to examine the hypothesized relation-

ships are introduced; Finally, the findings and the implications

thereof are discussed.

2. Conceptual model development

Summarizing from the literature review, a conceptual model

which depicts the energizing effect of unlearning on the practice

of learning and organizational success is developed and presented

in this paper. The conceptual model is underpinned by three

streams of studies: Practice of OL, Practice of unlearning and

Organizational success.

2.1. Practice of OL

The first stream of literature is related to the contracting

organization's practice of OL. The concept of OL was first

derived from work ofCyert andMarch (1963) who used AdaptiveLearning theory to describe organizations as anthropomorphic

entities (Chan et al.,2005; Pawlowsky, 2001).Cyert and March

(1963) assumed that the learning process for an organization is

similar to that for an individual. They described OL as a mech-

anistic stimulusresponse process. Consequently, an organiza-

tion's improvement actions are based on its memory of the

stimulusresponse combinations (Cyert and March, 1963). Such

conceptualizations were later challenged byArgyris and Schn

(1978) who argued that OL should not be conceived as a self-

productive process.Argyris and Schn (1978) emphasized that

members of an organization are not merely a storage bin of the

past rational experiences. They suggested that an organization's

knowledge exists through integrations of its member's knowl-edge.Argyris and Schn (1978)conceptualized OL as a process

that imbibes and applies knowledge integrated among the mem-

bers in an organization, in order to produce performance change.

Such performance change may include improvements in prof-

itability, project delivery times or project quality. In the time since

Argyris and Schn's study, a number of researchers have further

advanced the study of OL.Duncan and Weiss (1979)reaffirmed

Argyris and Schn's (1978) concepts and defined OL as a

cognitive system that is developed and shared by members of an

organization. Fiol and Lyles (1985) further defined OL as a

modification of a knowledge system that facilitates collective

learning. Based on conceptualizations from Argyris and Schn(1978), Inglis (1994) defined OL as a process of knowledge

development and implementation for business performance

improvement. Pedleret al. (1997) defined OL as an organization's

process of cognitive and behavioral change for performance

improvement. The current study benefited from this wealth of

studies and defines the OL process of contracting organizations as

a process of imbibing and applying knowledge integrated from

individuals for organizational success (Wong et al.,2009).

Given that organizations may display different practice pat-

terns of knowledge acquisition and integration, some researchers

have attempted to distinguish these practice patterns by OL types

(Argyris and Schn, 1978; Fiol and Lyles, 1985; Senge, 1990).

Argyris and Schn (1978) reported that organizations mainly

exhibit two major types of learning: Single-loop learning (SLL)

and Double-loop learning (DLL).Fiol and Lyles (1985)proposed

Behavioural Learning and Cognitive Learning as the major

learning styles of organizations. Behavioural Learning can be

viewed as new responses or actions based on existing inter-

pretations. Cognitive Learning refers to the continuous review

and modification of ways of working for performance improve-ments.Senge (1990)suggested two types of learning: adaptive

learning (knowledge gained through small-scale adjustment of the

past decisions) and generative learning (knowledge gained

through challenging, questioning, and repudiating decision

making assumptions). Despite the divergence in terminologies,

Pawlowsky (2001)found that the underlying construct of various

OL types are similar and indeed compatible. This may due to the

fact that many OL type taxonomies are indeed an extension on the

work ofArgyrisand Schn (1978). In thisconnection, Pawlowsky

(2001) proposed two types of OL practice: SLL and DLL.

Likewise, similar findings werealso reported by Chan et al. (2005)

who reported thatArgyris and Schn's (1978)conceptualizationof OL was cited widely as describing the contracting organiza-

tion's learning practice in construction. This study employs the

work ofArgyris and Schn (1978) and addresses contracting

organization's learning practice by two OL types: SLL and DLL.

Previous studies have suggested that the practice of SLL can

be identified by the following responses: (i) Working under a set

of clearly identified project goals (L1); and (ii) Referring the

firm's past experience to interpret the performance feedback

(L2) (Kululanga et al., 1999; Murray and Chapman, 2003;

Wong and Cheung, 2008). Furthermore, the practice of DLL

can be characterized by: (i) Identifying the root of the problem

before taking improvement action (L3); and (ii) Seeking and

adopting new management and working approaches throughevaluation of current practice (L4)(Table 1refers).

2.2. Practice of unlearning

The second stream of literature is related to the contracting

organization's practice of unlearning. Many of these studies have

reported that the concept of unlearning was firstly introduced by

Hedberg (1981) (Akgn et al., 2006; Tsang, 2008; Tsang and

Zahra, 2008).Hedberg (1981)theorized that knowledge grows,

and simultaneously becomes obsolete as reality changes. He

argued that knowledge that becomes obsolete must be renewed or

organizations may lose their competitiveness under changing andturbulent environments (Hedberg, 1981). In his study, the term

unlearningis used for describing a process of clearing out old

routines and beliefs that no longer meet the current challenges

(Hedberg, 1981).Nystrom and Starbuck (1984), in extending the

work ofHedberg (1981), proposed that, before organizations will

trynew ideas, they must first unlearn old ones by discovering their

inadequacies and then discarding them. They further conceptu-

alized unlearning as a process preceding learning.Klein (1989)

described unlearning as a process of replacing the new response

by eliminating the old one.De Holan and Phillips (2004)defined

unlearning as a process of discarding old routines and un-

derstandings that are no longer usefuland may be blocking much-

needed learning. Likewise,Navarro and Moya (2005)described

96 P.S.P. Wong et al. / International Journal of Project Management 30 (2012) 94104


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unlearning as a dynamic process that identifies and removes

ineffective and obsolete knowledge and routines which block thecollective appropriation of new knowledge and opportunities.

While much work has been done with regard to the concept of

unlearning, some researchers have argued that few attempts have

been made to develop measures of unlearning (Akgn et al.,

2006). Based on an extensive literature review, Akgn et al.

(2006) found that previous studies generally share a common

view that unlearning refers to a process of discarding the obsolete

beliefs and routines. The term organization's beliefsrefers to a

general and common understanding that directs members in an

organization to a specified course of action under different sit-

uations. Organization's routinesrefer to a repetitive pattern of

actions used by an organization to tackle problems'. The authors,

therefore, proposed to identify the practice of unlearning in two

aspects: (1) change of beliefs, (2) change of routines. Table 2

summarizes the attributes proposed for identifying the organiza-

tional change of beliefs and routines.

The proposed attributes were subsequently adopted in a

number of studies (Akgn et al., 2007a,b; Becker, 2010). The

most recent one was conducted by Becker (2010) who

investigated the effect of unlearning on individuals and

organizations during technology implementation. In view of the

fact that the set of attributes proposed byAkgn et al. (2006)has

been validated in a number of research studies (Akgn et al.,

2007a,b; Becker, 2010), this study employed the same set ofattributes to evaluate the contracting organization's practice of

unlearning in two aspects: (1) change of beliefs, (2) change of

routine.

2.3. Organizational success

The third stream of studies identified organizational success

as the ultimate goal of OL. Organizational success can be de-

scribed as the value and the level of service provided to meet both

the organization's and the client's demands (Cooper and

Kleinschmidt, 1987). In terms of evaluating the organization's

business success, researchers have suggested identifying the

extent to which the organizations can meet their targeted service

quality (P1) and profitgoals (P2) (Akgn et al., 2007a; Al-JiBouri,

2003; Cooper and Kleinschmidt, 1987). In terms of meeting the

developer's demands, researchers typically relied on evaluating

the organization's compliance with predetermined criteriaon time

(P3), cost (P4) and quality (P5) (Al-JiBouri, 2003; Law and Chuah,

2004; Wong et al.,2009; Xiao and Proverbs, 2003). In summary,

previous studies have suggested that organizational success of

contracting organizations can be assessed by the attributes as

shown inTable 3.

Table 1

Attributes for identifying the practice of SLL and DLL.

Practice of OL in terms of: Kululanga

et al. (1999)

Pawlowsky

(2001)

Jashapara

(2003)

Kurtyka

(2003)

Murray and

Chapman

(2003)

Love and

Josephson

(2004)

DeVilbiss

and Leonard

(2000)

Wong and

Cheung

(2008)

Practice of Single-loop learning (SLL)

Working (and considering correctiveactions if required) under a clearly

identified project goal. (L1)

* * * * * *

Referring the firm's past experience to

interpret the performance feedback. (L2)

* *

Practice of Double-loop learning (DLL)

Identifying the root of the problem before

taking improvement action. (L3)

* * * * * *

Seeking and adopting new management and

working approach through evaluation of

current practice. (L4)

* * * * * * *

Table 2

Attributes for evaluating the unlearning practice.

Practice of unlearning in terms of Akgn et al.

(2007a,b)

Akgn et al.

(2006)

De Holan and

Phillips (2004)

Leonard-

Barton (1995)

Meyers and

Wilemon (1986)

I. Belief change

The developer's concerns about project time/cost control/quality

control. (U1)

* *

The project features that the end users demanded. (U2) * *

The technology/strategies available for use in practice.(U3) * *

II. Routine change

Pace of project development.(U4) * * * *

Development budget and cost plans. (U5) * * *

The information sharing mechanisms (memos, e-mails, teleconferencing)

among collaborating firms in the project. (U6)

* * * *

The use of performance feedback derived from the project

monitoring system. (U7)

* * * * *



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2.4. The conceptual model

Based on thehypotheses and theattributes identified in Tables1

to 3, a conceptual model is developed and shown in Fig. 1. The

arrows represent the direction of the hypothesized influence.The conceptual model is underpinned by the work ofArgyris

and Schn (1978). The practice of OL is identified by the practice

of the two OL types: Single-loop learning and Double-loop

learning (i.e. L1 to L4 as summarized inTable 1). Contracting

organization's practice of unlearning is identified by the

organizational change in beliefs and routines (i.e. U1 to U7 as

summarized inTable 2). Organizational success is evaluated in

terms of business success and the extent to which they meet the

developer's demands (i.e. P1to P5as summarized inTable 3).

3. Research methodology

3.1. Data collection

A questionnaire was designed to collect data to test the con-

ceptual model. A sample of the questionnaire is given in Fig. 2.

3.2. Data analysis methods

Two types of data analysis methods were employed in this

study: Pearson correlation analysis and Multiple Moderated

Regression (MMR) analysis. To examine the relationship be-

tween the organization's practice of learning and unlearning (i.e.

Hypothesis H1 in this study), Pearson correlation analysis is

applied. Multiple Moderated Regression (MMR) Analysis is

used for testingH2(i.e. the interactions between the practice of

OL and unlearning have moderating effect on organizational

success). MMR is a frequently used technique in management,

social and behavioral science research to test for the existence of

the moderating effect of an independent variable (Aguinis, 1995;Choe, 2004; Snell and Dean, 1994). The technique has also been

applied in construction research. Yiu and Cheung (2007) applied

MMR to investigate the moderating effect of construction

dispute sources on the mediator tactics and the respective

mediation outcomes. Wong and Cheung (2008) employed

MMR analysis to investigate whether the effect of practicing

intra-organizational learning on performance improvement is

contingent on the contracting organization's engagement in

inter-organizational learning.

This study employed MMR analysis to examine the effect of

the practice of unlearning (U1 to U7) on the relationship

between practicing OL (L1to L4) and organizational success (P1to P5). The solving equations for the MMR analysis weredeveloped as follows:

Pi = a + b1Lj+ b2Uk + 1

Pi = a + b1Lj+ b2Uk + b3LjUk + 2

Where

Pi is theith attribute for assessing organizational success

andi =1,2,3,4,5

Lj is the jth attribute for identifying the practice of OL

andj=1,2,3,4

Table 3

Attributes for evaluating the organizational success.

Evaluating organizational success in terms of: Wong et al.

(2009)

Akgn et al.

(2007a)

Law and Chuah

(2004)

Xiao and Proverbs

(2003)

Al-JiBouri S.H.

(2003)

Cooper and

Kleinschmidt

(1987)

Business success

Meet the firm's service quality in delivering the project. (P 1) * * *Meet the firm's targeted profit in delivering the project. (P 2) * * *

Meeting developer's demand

Achieve the predetermined project progress. (P3) * * * * *

Achieve the predetermined project cost. (P4) * * * * *

Achieve the predetermined end product quality.(P5) * * * * *

Practice of OL in terms ofpracticing SLL (L1to L2)

&DLL (L3to L4)

Organizational Success in terms ofbusiness success (P1to P2)

&meeting developer s demand (P3to P5)

Practice of Unlearning in terms of changein belief (U1to U3)

&change in routines (U4to U7)

+ve

Fig. 1. Conceptual model about the relationships between practice of organizational learning, practice of unlearning and organizational success.

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Uk is the kth attribute for identifying the practice of

unlearning and k=1,2,3,4,5,6,7

Where Pi is the dependent variable, Lj and Uk are the

independent variables, a, b1, b2are the unknown constant and

is the random error for any given set of values for Ljand Uk.

The coefficient of determination, R2, records the proportion of

variation in the dependent variable explained by the independent

variables. The possiblevalue of the measure falls between 0 and1.

When R2=1, the independent variables completely account for

variation in the dependent variable. When R2= 0, the independent

variables do not account for variation of the dependent variable

(Lewis-Beck, 1993).

Multiple Regression equation (Eq.(1)) assumes that Ljand Ukhave independent effects on predicting the dependent variable Pi,

whereas the equation of the MMR (Eq. (2) refers) takes the

Part 1-Personal Information:

Q1.1 Type of your company: a: Developer b: Contractor

c: Consultant d: Others: ___________

Q1.2 Working experience in

the construction field:

a: 20 years

With reference to one construction project that you have been (either fully or partly: involved for at least 1 year

and provide the following particulars:Q1.3 Project

nature

1) Residential 2) Office 3) Hotel 4) Shopping

centre

and/or

carpark

5) Infrastructure

6) Complex:

comprising 1)

and 4)

7) Complex:

comprising 2)

and 4)

8) Complex:

comprising

3) and 4

9) Others:_________

Q1.4 Project Name: _________________________________________________

Part 2- Measure of organizational unlearning

During theprojectas stated above, a change is observed regarding the followings:

(1 = Disagree strongly, 7= Agree strongly)

Q2.1 Your firms belief about the developers concerns in terms of

project time/cost control/ quality control.

1 2 3 4 5 6 7

Q2.2 Your firms belief about the project features demanded by the end

users

1 2 3 4 5 6 7

Q2.3 Your firms belief about the technology/strategies available for

use in practice.

1 2 3 4 5 6 7

Q2.4 Pace of project development. 1 2 3 4 5 6 7

Q2.5 Development budget and cost plans. 1 2 3 4 5 6 7

Q2.6 The information sharing mechanisms (memos, e-mails,

teleconferencing) among collaborating firms in the project.

1 2 3 4 5 6 7

Q2.7 The use of performance feedback derived from the project

monitoring system

1 2 3 4 5 6 7

Part 3- Measure of organizational learning

Do you agree that your firm practiced the followings during the project

Q3.1 Working (and considering corrective actions if required) under a

set of clearly identified project goals.

1 2 3 4 5 6 7

Q3.2 Referring the firms past experience to interpret the performance

feedback.

1 2 3 4 5 6 7

Q3.3 Identifying the root of the problem before taking improvement

action.

1 2 3 4 5 6 7

Q3.4 Seeking and adopting new management and working approach

through evaluation of current practice.

1 2 3 4 5 6 7

Part 4- Measure of organizational success

Q4.1 Your firms service quality in delivering the project met or

exceeded the senior managements expectation.

1 2 3 4 5 6 7

Q4.2 Your firms targeted profit in delivering this project is met or

exceeded the senior managements expectation.

1 2 3 4 5 6 7

Q4.3 Met the developers pre-determined project progress 1 2 3 4 5 6 7

Q4.4 Met the developers pre-determined project cost 1 2 3 4 5 6 7

Q4.5 Met the developers pre-determined end-product quality 1 2 3 4 5 6 7

Fig. 2. The sample questionnaire.



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moderated effect of the independent variables (i.e. LjUk) into

account (Cohen et al., 2003;Jaccard et al., 1990; Yiu and Cheung,

2007). Two criteria can be used to determine whether the mod-

erating effect is significant or not:

(1) The inclusion of the moderator variable (LjUk) in Eq.(2)

produces a significant increase in the squared multiplecorrelation (R2) when compared with the simple regres-

sion model (Eq.(1)); and

(2) The standardized coefficient of the moderator variable

(i.e. b3) in Eq.(2) is significant in explaining the depen-

dent variable (Cohen et al.,2003; Jaccard et al.,1990).

Fisher Z test (F-test hereafter) is used to determine the

significance of the R2 (Choe, 2004; Cohen et al., 2003;

Jaccard et al., 1990). R2 is considered as significant if the

calculated F-value for R2 is significant at b0.10 (Cohen

et al.,2003; Jaccard et al., 1990; Yiu and Cheung, 2007). The

F-value can be calculated by the following equation:

Fvalue = R 22R21

. n2n1

h i. 1R22 .

Sn21 h i

3

Where

n1 is the number of predictors in Eq.(1);

n2 is the number of predictors in Eq.(2);

S is the total sample size;

(Sn21) is the degree of freedom;

R21 is the R2 value for Eq.(1); and

R22 is R2 value for Eq.(2).

A T-test for Regression Coefficient is used to examine thesignificance of the moderator variable (LjUk) in explaining the

dependent variable (Pi) (Cohen et al.,2003; Jaccard et al.,1990;

Wong et al., 2008; Yiu and Cheung, 2007). The moderating

effect of LjUk on Pi is considered as significant when the

probability of error (-value) is lower than 0.05 (Cohen et al.,

2003; Jaccard et al.,1990; Wong et al.,2008; Yiu and Cheung,

2007).

In this study, a total of 140 MMR analyses, which are

devised from the different combinations of the Pi, Lj and Ukwere conducted. The significance of the moderating effects was

examined by both F-test andt-test. Both the Pearson correlation

analysis and the MMR analysis of this study were done by usingStatistical Package for Social Sciences (SPSS)Version 17.0.

4. The response rate

The questionnaires were distributed to the target respondents

who are practicing professionals in consultant offices and con-

tractor firms in Hong Kong. To enhance the validity of the survey

results, a list of potential respondents was assembled from

official webpages of professional institutes such as the Hong

Kong Institute of Architects (HKIA), Hong Kong Institution of

Engineers (HKIE) and the Hong Kong Institute of Surveyors

(HKIS) as well as the latest edition of the Hong Kong Builder's

Directory (Far East Trade, 2003). The targeted respondents were

randomly selected from the list. A total of 200 questionnaires

were sent out and 95 usable responses were received and used in

the analysis. This represents a 47.5% valid response rate (Table 4

refers).Easterby-Smith et al. (1991) observed that the response

rate to questionnaire survey studies conducted in the construction

industry typically ranged from 25% to 30%. The response rate of

the research study about the effect of OL on contractors' per-formance conducted by Jashapara (2003) was 14.1%. Further-

more, recent research studies about OL in construction conducted

byWong et al. (2009)have been based on 84 usable responses

(equivalent to a 42% response rate). In this regards, both the

sample size and the valid response rate of this study are con-

sidered reasonable. As shown inFig. 3, 75% of respondents have

over 5 years working experience. Thus, the reliability of the

results to accurately reflect the opinion of the industry is strongly

indicated.

5. Findings and discussions

5.1. Correlations between practice of learning and unlearning

The results of the Pearson correlation analysis indicate sig-

nificant relationships between the practice of unlearning and DLL

(Table 5 refers). In particular, among the attributes identifying the

practice of OL, Seeking and adopting new management and

working approach through evaluation of current practice (L4) is

found to have a relatively stronger relationship with the

organization's changes in beliefs (U1to U3) and routines (U4to

U7). This echoes the findings reported by Akgn et al. (2006)

who, supported by a questionnaire survey, pinpointed the idea

that changing beliefs and routines are preconditions fororganizations to improve by developing new strategies. Double-

loop learning (DLL) refers to a change of performance improve-

ment actions taken after reviewing the need to change the

underlying assumptions that have caused errors or deficiencies

(Argyris and Schn, 1978). In this regard, discarding obsolete

beliefs and routinesi.e. unlearning becomes constructive to the

DLL process (Akgn et al.,2007a,b; Ozorhon et al.,2005).

While researchers have criticized contracting organizations in

construction for being averse to practicing DLL (e.g.Holt et al.,

2000; Jashapara, 2003; Kululanga et al., 1999), the reasons

behind such behavior has yet been explored. In this regard, the

results of this study reveal that there is a significant relationshipbetween unlearning and the practice of DLL.Love et al. (2004)

criticized the transient nature of construction projects may

discourage the practice of DLL that is instrumental in de-

riving solution by comprehensively reviewing the problems.

The findings of this study extend our knowledge about OL in

Table 4

Questionnaire sent and received.

Consultants Contractors and others Total

Sent (No.) 100 100 200

Received (No.) 51 44 95

% Received 51% 44% 47.5%



8/11

construction by indicating that the practice of unlearning can

affect the contracting organizationspractice of DLL.

5.1.1. Moderating effect of the capability and practice of unlearning

Moderated regression analyses (MMR) were performed to

validate hypotheses H2. Whether the interactions between the

practice of OL and unlearning have significant contributions to

the organizational success were examined. If such an interaction

effect is statistically significant, this would mean that the

relationship between practicing learning and organizational

success might be energized by the contracting organization's

practice of unlearning.

The F-testand t-testresults suggest that 15 out of these 140 sets

of MMR results showed significant moderating effects. To save

space and preserve clarity, only the results that showed the sig-

nificant moderating effects are presented in Table 6. As anillustration, the effect of Identifying the root of the problem

before taking improvement action (L3) on organizational success

in terms of meeting the service quality expectation (P1)is per-

ceived to be contingent on the The use of performance feedback

derived from the project monitoring system (U7). The R2

values obtained from the MMR analyses, as well as the results

obtained from the respective F-tests and T-tests are reported in

Columns A to C ofTable 6.

From Table 6, it is worth noting that not all interactionsbetween the learning and unlearning attributes are perceived to

have significant moderating effect on organizational success.

As such, the hypothesisH2is only partially supported.

Interestingly, L1: Considering a corrective action under clear

goals and L2: Referring past experience to interpret feedback

which characterize the practice of SLL are found to have no

significant moderating effect on the organizational success when

interacted with the practice of unlearning (U1 to U7). SLL has

been conceptualized as an alteration of actions without scru-

tinizing the underlying assumptions leading to the difference

between the expected and the actual outcomes (Kurtyka, 2003).

The findings derived from the MMR analyses are in agreementwithAkgn et al. (2007a,b) who reported that reinforced by the

previous success derived from adopting old routines, organiza-

tions typically preserve predetermined routines and mindsets

throughout a project, inhibiting improvement actions conflict

with conventional wisdom. In this regard, a process of discarding

the existing beliefs and routines (i.e. unlearning) seems to be less

desirable for formulating quick-fix improvement actions derived

from the practice of SLL (Wong et al.,2008). Analogously, our

knowledge about what hampers unlearning, as well as the ways of

facilitating unlearning practice is limited and further investigation

in this connection is suggested for further research. The results of

this study suggest that the practice of unlearning and the practice

of SLL represent two contrary approaches to foster organizationalsuccess. DLL refers to the alterations of performance im-

provement actions taken after reviewing the need to change the

5-

10years

51%

20

years

9%

16-20

years

4%

11-15

years

11%

Fig. 3. Working experience of the respondents.

Table 5

Correlation between the practice of learning and practice of unlearning.

Practice of organizational learning in terms of:

Single Loop Learning (SLL) Double Loop Learning (DLL)

L1: Working (and

considering corrective

actions if required)

under a set of clearly

identified project goals.

L2: Referring

the firm's past

experience to

interpret the

performancefeedback.

L3: Identifying the

root of the problem

before taking

improvement action.

L4: Seeking and

adopting new

management and

working approach

through evaluationof current practice.

Practice of unlearning U1: The developer's concerns about project

time/cost control/quality control.

.155 .292 .319 .515

U2: The project features that the end

users demanded.

.123 .182 .362 .574

U3: The technology/strategies available for

use in practice.

.232 .175 .324 .398

U4: Pace of project development. . 219 .302 .557 .558

U5: Development budget and cost plans. .159 .222 .337 .436

U6: The information sharing mechanisms

(memos, e-mails, teleconferencing)

among collaborating firms in the project.

.310 .103 .273 .273

U7: The use of performance feedback derived

from the project monitoring system

.223 .197 .243 .327

* Correlation is significant at the 0.05 level (1-tailed), 0.01 level (2-tailed).

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underlying assumptions that had caused the errors or deficiencies

(Wong et al., 2008). In this aspect, unlearning seems to be fa-

cilitating the practice of DLL as this involves a process of dis-carding obsolete beliefs and routines (Akgn et al., 2007b).

Indeed, scholars have long observed that if obsolete beliefs and

routines were not effectively discarded, organizations may have

insufficient room to acquire new and useful knowledge (Akgn

et al.,2003, 2007b; Meyers and Wilemon, 1986). The results of

this study echoed these findings and indicated that the effect of

practicing DLL (i.e. L3 and L4) on meeting the targeted profit (P2)

can be energized by a wide range of unlearning attributes (U1to

U4,and U7). As such, the attributes of unlearning (U1to U4and

U7) are confirmed to be significant moderators of the DLL-

organizational success relationship when organizational success

is evaluated in terms of meeting the targeted profit of the con-tracting organizations. Nevertheless, no significant result is found

from the MMR analyseswhenthe unlearning practiceis evaluated

by the organizations' change of routines in terms of U5 : the

development budget and cost plans and U6: The information

sharing mechanisms (memos, e-mails, teleconferencing) among

collaborating firms in the project. The results indicate that to

energize the effect of practicing DLL on organizational success in

the construction projects, appropriate unlearning practice should

be employed.

Scholars in previous studies have typically suggested that

practicing DLL, instead of SLL, is more conducive to continual

performance enhancement (Jashapara, 2003; Kululanga et al.,

1999; Wong et al., 2009). While they noted that the performance

of contracting organizations was rarely enhanced through

practicing DLL (Holt et al., 2000; Jashapara, 2003; Kululanga

et al.,1999), possible reasons for contracting organizations notbeing able to improve through practice DLL have yet been

explored. This study adds new insights to the organizational

learning behavior of the contracting organizations in construc-

tion. It reveals that the effect of practicing DLL on organizational

success is contingent on the contracting organization's practice

of unlearning. In particular, the effect is found to be more

significant when organizational success is evaluated in terms of

meeting the firm's targeted profit and the developer's expecta-

tions on project cost.

5.1.2. Limitations and recommendations

This study has a number of limitations. The first limitation isassociated with the sampling issue. The questionnaire survey as

reported in this study was conducted in Hong Kong. This study's

conclusions may be limited to a representation of the situation in

Hong Kong.

Secondly, although the 95 valid responses used in this study

are considered reasonable, a larger number is preferred. Using

greater sample size for analyses and collecting data from other

countries can therefore be considered for further studies. These

may, or may not, identify that cultural differences have a sig-

nificant impact on the research findings.

Thirdly, throughout the study, a seven point Likert scale was

employed in the questionnaire surveys to collect the perceptive

views of the respondents. Bias may possibly exist in some of the

Table 6

Results of the MMR analyses.

Variables of the MMR analysis (A) (B) (C)

Dependent(Pi) Predictor (Lk) Moderator (Uj) Predictormoderator product (LkUj) R2 F-value forR2 Sig. Std. coefficient of variable LkUj Sig.

P1 L3 U7 L3X U7 0.05 5.71 * 1.39 *

P1 L4 U3 L4X U3 0.03 3.41 + 1.29 +

P1 L4 U4 L4X U4 0.04 4.41 * 1.88 *P2 L3 U2 L3X U2 0.06 6.23 * 2.26 *

P2 L3 U3 L3X U3 0.06 6.41 * 1.93 *

P2 L4 U1 L4X U1 0.07 7.06 ** 1.80 **

P2 L4 U3 L4X U3 0.10 10.43 ** 2.37 **

P2 L4 U4 L4X U4 0.09 8.95 ** 2.85 **

P2 L4 U7 L4X U7 0.03 2.81 + 1.00 +

P3 L4 U7 L4X U7 0.04 4.54 * 1.28 *

P4 L4 U4 L4X U4 0.08 8.35 ** 1.83 **

P5 L4 U4 L4X U4 0.02 3.07 + 1.04 +

P5 L3 U7 L3X U7 0.04 4.79 * 1.18 *

P5 L4 U7 L4X U7 0.04 4.71 * 1.31 *

Remarks:+b .1, b .05 (one-tailed), b .01(two-tailed).

P1: Your firm's service quality in delivering the project met or exceeded the senior management's expectation.

P2: Your firm's targeted profit in delivering this project is met or exceeded the senior management's expectation.P3: Met the developer's pre-determined project progress.

P4: Met the developer's pre-determined project cost.

P5: Met the developer's pre-determined end-product quality.

L3: Identifying the root of the problem before taking improvement action.

L4: Seeking and adopting new management and working approach through evaluation of current practice.

U1: The developer's concerns about project time/cost control/quality control.

U2: The project features that the end users demanded.

U3: The technology/strategies available for use in practice.

U4: Pace of project development.

U7: The use of performance feedback derived from the project monitoring system.



10/11

responses. Notwithstanding, responses of these questionnaire

surveys were collected from those key persons (including the

project managers, engineers, surveyors, clerks of works and

project coordinators) in contractor and consultant firms. Many

of them have over 5 years of industry experience. Therefore, the

respondents are assumed to have sufficient knowledge of their

company's practices. The reliability of the responses is thusstrongly indicated.

While organizational learning and organizational success are

widely published research topics in the construction manage-

ment sector (Chan et al.,2005; Kululanga et al.,1999; Murray

and Chapman, 2003; Wong et al., 2009), the concept of

unlearning is not quite widespread. It is prudent to be mindful

that the reported findings in this study cannot be viewed as

exhaustive but a kick-start of investigation about unlearning in

construction. Notwithstanding, the reported study provides an

extended perspective on the effect of OL on organizational

success in construction. Further study can be extended through

exploring possible ways to enhance the unlearning capability ofthe contracting organizations.

6. Concluding remarks

The present study seeks to examine the role of unlearning for

fostering effective OL under construction project environment.

The results of the Pearson correlation analysis provide an

empirical support to hypothesis H1 (i.e. the practice of OL is

contingent on the contracting organizations' practice of

unlearning). Nevertheless, hypothesisH2is partially supported

as suggested by the results of the MMR analysis. The results

suggest that organizational success may not be significantly

energized by the interactions between the practices of SLL andunlearning. Instead, contracting organizations' practice of DLL

is better to be supported by the practice of unlearning to attain

organizational success. It is noted that DLL refers to a change of

performance improvement actions taken after reviewing the

need to change the underlying assumptions that have caused

errors or deficiencies (Argyris and Schn, 1978; Wong et al.,

2008). In this aspect, unlearning can be viewed as a pre-

requisite of a successful practice of DLL as this involves a

process of discarding the obsolete beliefs and routines (Akgn

et al., 2007b).

While researchers have reported that contracting organiza-

tions in construction are often incapable of practicing DLL (e.g.Holt et al., 2000; Jashapara, 2003; Kululanga et al.,1999), the

role of unlearning within the DLL process has mostly been

overlooked. The results of this study provide empirical support

for the proposition that the effect of practicing DLL on

organizational success can be significantly moderated by the

practice of unlearning. As such, this is a timely reminder about

the importance of unlearning on the learning process as well as

on the success of the contracting organizations in construction.

Seeking possible ways to enhance the unlearning capability of

the contracting organizations is thus recommended as a future

research topic.

The objective of this study is not to argue that organizational

learning should be supported by the practice of unlearning in all

cases. Despite demonstrating that unlearning and OL are closely

related processes and conceptually they may be contributive to

each other, it is also agreedthat OL may occurwithout unlearning,

in particular, when the existing beliefs and routines are still fit for

sustaining performance in the current market condition (Akgn

et al., 2007b). Nevertheless, in face of the global financial turmoil,

turbulence in the construction market is expected to be intensifiedin the foreseeable future. It is suggested that contracting orga-

nizations should promptly discard obsolete beliefs and routines

i.e. unlearn in response to the change of the market environment.

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