managing the implementation of construction innovations

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Managing the implementation of constructioninnovationsFlorence Yean Yng Ling aa Department of Building , National University of Singapore , 4 Architecture Drive,Singapore 117566Published online: 13 May 2010.

To cite this article: Florence Yean Yng Ling (2003) Managing the implementation of construction innovations, ConstructionManagement and Economics, 21:6, 635-649, DOI: 10.1080/0144619032000123725

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Managing the implementation of construction innovations 635

Construction Management and EconomicsISSN 0144-6193 print/ISSN 1466-433X online © 2003 Taylor & Francis Ltd

http://www.tandf.co.uk/journalsDOI: 10.1080/0144619032000123725

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Introduction

Innovation is the productive use of knowledge manifestedin the successful development and introduction of newproducts, processes and/or services (Dodgson et al., 2002).Innovation may result from the everyday problem-solvingcarried out by construction professionals – ‘practitioner-research’ (Groak and Krimgold, 1989) – or throughformal research and development processes (Freeman,1974; Gann, 2000). Gann (2000) argued that the pres-sures for innovation are often strongest in constructionwhen there is demand-pull. For example, he observedthat significant innovation is more likely to take placewhen clients require the following: buildings that needto be provided with enhanced information services;flexible spaces that need to accommodate information-intensive activities; and buildings that need to havereduced environmental impact.

In this paper, an innovation is defined as a new ideathat is implemented in a construction project with the

Managing the implementation of constructioninnovations

FLORENCE YEAN YNG LING*

Department of Building, National University of Singapore, 4 Architecture Drive, Singapore 117566

Received 28 March 2003; accepted 30 June 2003

This study investigates some factors that need to be considered in the management of innovation imple-mentation within construction project organization. The main objective was to find explanatory variablesthat significantly affect the benefits of an innovation to project team members and the project as a whole. Aspart of a larger study on innovation in Singapore’s construction industry, a survey was conducted and datafrom 58 projects that had adopted innovations in Singapore were collected via mailed questionnaire. Thequestionnaire requested respondents to provide specific data about their projects, the type of innovationsadopted and the enablers and barriers to innovation. Four main categories of factors were found to signifi-cantly affect the extent to which an innovation will benefit project team members and the project. Theseare: the level of interest of project team members; working environment; formation of task groups; and thecapabilities of the people involved in the innovation. Research questions relating to modelling innovationsuccess are posed for further study.

Keywords: Construction innovation, innovation implementation, innovation management

intention of deriving additional benefits although theremight have been associated risks and uncertainties. Thenew idea may refer to new design, technology, materialcomponent or construction method deployed in a project.Following this definition, ‘innovation’ in this studyfocuses on project-based problem solving.

There are many net benefits in fostering innovation inthe construction sector, especially at the project level. Inno-vation in construction may become a fourth competitivedimension to be added to the existing dimensions of cost,quality and time (Newton, 1999). Innovation is requiredbecause there are pressures from clients to improve qual-ity, reduce costs and speed up construction processes(Gann, 2000). To realize these benefits, it is necessaryto manage and control significant factors that affectinnovation success. However, it is not easy to pin downwhat innovation success is because the nature of innova-tion is multi-faceted. It includes the innovation activity,sources of innovation, innovation process and innovationsystems (Dodgson et al., 2002). This study focuses onthe innovation process, in particular management actionsthat may bring about benefits to construction projects.

Construction Management and Economics (September 2003) 21, 635–649

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The objectives of this paper are to (1) find explanatoryvariables that significantly affect the benefits of an inno-vation to project team members and the project, and(2) propose a set of questions for further research inmodelling and predicting innovation success. The scopeof this study covers the extent to which the parties in aconstruction project organization (project team members)and the project as a whole benefited from the innovation(see Table 1). The issues are addressed in the context ofmanaging and implementing the innovation process.

The first objective is important because clients,consultants and contractors will know the importantvariables that they must manage properly in order thatthey and the project may obtain benefits out of the inno-vation that is introduced. This is because innovationcan only create competitive advantage when managedproperly (Pries and Janszen, 1995). The second objectiveis important because it throws up questions for furtherresearch, to assist in the search for the ‘holy grail’ ofmodelling and predicting innovation success.

After this introduction, the literature is briefly reviewed.This is followed by a description of the research methodand presentation of the survey results. The next sectionis the analysis of results and discussion. Finally, conclusionsand recommendations are presented.

Innovation management

Many studies on how innovation could be implementedin construction projects have been undertaken (Tatum,1987; Slaughter, 1993a, 1993b, 1998, 1999, 2000;Winch, 1998). These studies usually focused on howinnovation is managed within one firm.

Some studies compared the production of constructedfacility with manufacturing (Gann, 1996). Gann andSalter (2000) put forth the main arguments regardinginnovation in these two industries. According to them, themanufacturing industry generally focuses on individualfirms with clear boundaries and transactions betweenthem are usually straightforward buy–sell relationships.In the construction industry, the linkages between firmsand other institutions differ from the manufacturing sector.

In addition, construction activities are project based, anddifferent firms’ technical competencies are applied inassociation with technical capabilities from other firms.Construction production is often triggered in response touser needs and, in this sense, projects are demand-drivenrather than the result of arm’s-length market transactions,which typify consumer-goods industries.

Dulaimi et al. (2002a) proposed a theoretical frame-work on how to manage the implementation of innovationin a construction project organization that comprises manyplayers. In their framework, they divided the players intotwo blocks: origin organization and supporting organi-zations. Origin organizations are firms that propose orinitiate the innovation; and supporting organizations arestakeholders (other project team members), whose supportis needed for the innovation to be successfully imple-mented. In their framework, they proposed several factorsto be controlled when players are attempting to initiate,implement or support an innovation to ensure that itis properly managed. Dulaimi et al.’s (2002a) model,which concurrently looked at many parties, is consistentwith Gann’s (2000) recommendation that for an innovationto be successful, it would be necessary for firms to worktogether, erode boundaries between professions and forproject-based firms to embrace new roles and developnew capabilities (Gann, 2000).

In their later study, Dulaimi et al. (2003) tested andfound some evidence that high-expected goals andfavourable innovation results lead to increased commit-ment and hence higher organizational motivation. Interms of inter-organizational interaction, they foundsome correlation between effort of the origin organizationand supporting organizations with interest level of allparties during implementation stage, and this in turnleads to project team members deriving benefits.

Potential factors affecting innovationimplementation

Many barriers and enablers affect the implementation ofinnovation. Bowley (1960, 1966) found several barriersthat hinder progress of innovation: form of contract, cost

Table 1 Operationalization of benefit of innovation

Rate the benefit level of the innovation after its implementation in the project

The team members benefited from the innovationThe customer/client realized his/her expected benefitsThe main consultant realized his/her expected benefitsThe main contractor realized his/her expected benefitsThe sub contractor realized his/her expected benefitsThe other relevant parties in the project realized their expected benefitsThe whole project benefited from the innovation

Ref

Y1Y11

Y12

Y13

Y14

Y15

Y2

1 = strongly disagree7 = strongly agree

1 2 3 4 5 6 71 2 3 4 5 6 71 2 3 4 5 6 71 2 3 4 5 6 71 2 3 4 5 6 71 2 3 4 5 6 71 2 3 4 5 6 7

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of carrying out research, lack of information on costsavings arising from the innovation and restrictionsimposed by regulations. Gann (2000) identified otherimpediments to construction innovation: contractors andconsultants are isolated from one another; contractorsare often of small size and fragmented; there is absenceof competitive pressures on non-innovating firms andcontractors may be unable to specify their products.

Several works on factors necessary for successfulinnovation were undertaken by observing examples ofinnovation in construction projects and in firms (Stewartand Tatum, 1988; Tatum and Funke, 1988; Tatumet al., 1989; Nam et al., 1991). These are now reviewedunder five headings, following Dulaimi et al. (2002a):goals, constraints and capabilities, effort, challenges andcommitment.

Goals of innovation

Tatum (1984) investigated the conditions for innovationby extracting data qualitatively from several power plantprojects in the USA. He discovered that conditions thatfavour innovation include having high technical orschedule requirements. The team must then work toachieve the goals of overcoming technical difficulties orreducing the normal time required for completing theproject.

Slaughter (1993a) found that innovative products andtechnologies must aim to integrate into the total buildingsystem so that these innovations enhance the performanceof the whole project. Learning-by-doing and user inno-vation are tightly linked to successful implementation ofinnovation (Slaughter, 1993b). Therefore, the design ofinnovative products and technologies must aim to explicitlyaccommodate the need for changes on-site.

Mitropoulos and Tatum (1999) examined the decision-making and implementation process of innovationthrough eight case studies. They found that to increasethe probability of successful innovation, implementationshould be preceded by searching for alternatives, evalu-ating them and justifying the cost. The goal of the inno-vation should be to manage or incorporate technologicalchange. This may enable the innovation to fulfil a marketdemand.

Constraints and capabilities

Tatum (1989) found that to foster innovation, there mustbe implicit vertical integration. This is confirmed byDulaimi et al. (2002b), who found that procuring morecontracts based on design–build method would enablecompanies to increase their innovation, compared todesign–bid–build, which is known to be one of the causesof fragmentation. In order for the innovation to besuccessful, there must be strong co-operation from the

relevant parties and minimal contractual and statutoryconstraints to implement the innovation.

Firms that are proposing and supporting the innovationmust also have relevant capabilities. Gann (2000) recom-mended that firms devote more resources to developingtheir technical and managerial competencies. Tatum(1989) found that firms need to build a diverse technicalcapability. Nam and Tatum (1992a) also stressed theimportance of technology in construction innovation. Theyfound that effective implementation of technology-pushstrategy requires continuous research and developmentefforts that are integrated but informal in nature.

Effort exerted at management and project levels

Tatum (1989) found that firms that foster innovationhave organizational structures that maintained flexibilityin unit size and grouping to allow attention to innovation.These organizations made the effort to have special linkagesfor both internal and external co-ordination.

Dulaimi et al. (2002b) found that successful innovationmay come about if companies establish a reward systemto recognize innovators and to promote innovation. Theyalso found that increased co-operation among organiza-tions involved in construction to co-ordinate innovationeffort within a particular project may lead to successfulinnovation.

Challenges faced by the firms

Culture plays an important part in innovation implemen-tation. At the industry level, Gann (2000) proposed aculture of innovation in which different professionalswork together in new ways, with the aim of meeting users’needs and improving performance of the constructionsector. At the firm level, Tatum (1989) found that firmsthat have construction innovations have an organizationalculture that have supportive policies and priorities.Dulaimi et al. (2002b) discovered that companies shouldcreate a ‘no blame’ culture to encourage staff to experimentwith new ideas.

Winch (2000) studied the extent to which design of theorganization facilitates or inhibits innovation of Frenchand British project organizations, using the ChannelTunnel as data for comparative analysis. The Frenchdisplayed competitive group behaviour, less job involve-ment, individualistic personality and have a stressfulworking environment. On the other hand, the Britishadopted collegial group behaviour and have high level ofjob involvement, supportive interpersonal relations and aworking environment that is less stressful. The Britishalso have a more systematic and disciplined approach towork than French. He found that the British approach toproject organization has less capacity for innovation thanthe French.

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Conditions that favour innovation include those thatminimize challenges that firms will face at the manage-ment and project levels. Tatum (1984) found that thesefavourable conditions include having sufficient lead timeto develop a detailed construction schedule, planningconstruction methods and providing input to activities,and having a schedule that allows adequate time forplanning and evaluating alternatives.

Dulaimi et al. (2002b) found that companies shouldgive staff a slack in their workload so that they have anopportunity to develop and experiment with new ideas.There is also a need to provide sufficient tendering periodto allow tenderers and consultants to develop new ideas.

Commitment of the firms

To enable the innovation to be successfully implemented,it is necessary that the stakeholders are committed.Tatum (1984) found that having a planning team that hasan energetic champion, team members who are willing toevaluate alternatives and interdisciplinary co-ordinationwithin design teams are important manifestations ofcommitment for innovation success. There must also bekey individuals, who act as gatekeepers to identify externaltechnology, and champions who absorb the risks anddrive the innovation (Tatum, 1989). Gann (2000) referredto them as innovation directors, technology managersand facilitators.

Nam and Tatum (1997) studied 10 successful innova-tions in the US construction industry with regard to therole of individuals who exert great influence in the pro-cess of technological innovation. They found that effectiveleadership is essential for technological innovation. Inaddition, technological competence is a prerequisite foreffective leadership for innovation. Lastly, the role oftechnical champion can be delegated only with slackresources and adequate power.

Conclusion of literature review

From the brief review above, the uniqueness of construc-tion innovation is highlighted. A framework explaininghow innovation can be initiated and implemented in aconstruction project is also reviewed. It is also shown thatfor innovations to be successfully implemented, a few keyconditions must be present. Many of the previous workswere based on observing examples of innovation in con-struction projects and in firms to provide some insightsregarding the nature of innovation processes in construc-tion. The studies did not quantitatively measure the extentto which the innovation benefited the firms in the projectteam and the whole project. Based on the above, thereappears to be a need to find variables that influencemanagement effectiveness of innovation implementation.This study therefore aims to fill this gap.

The literature review above is used as the basis forquestionnaire design. This was done by laying down thedisparate factors identified in many studies and summa-rizing them as a set of propositions for testing in thefieldwork. To ensure that the innovation is effectivelyimplemented, the following conditions must be fulfilled:

• expected goals of the innovations are clearly laidout (Tatum, 1984; Slaughter, 1993a; Mitropoulosand Tatum, 1999);

• capabilities are maximized and constraintsminimized (Tatum, 1989; Gann, 2000; Dulaimiet al., 2002b; Nam and Tatum, 1992a);

• effort exerted at the management and projectlevels are maximized (Tatum, 1989; Slaughter,1993b; Dulaimi et al., 2002b);

• challenges faced at the management and projectlevels are minimized (Tatum, 1984, 1989; Dulaimiet al., 2002b; Winch, 2000); and

• commitment exerted at the management andproject levels are maximized (Tatum, 1984; Namand Tatum, 1997).

These five conditions are operationalized and shown inTable 2, and tested in the fieldwork.

Research method

This paper presents part of the findings of a majorresearch project that investigated the enabling and inhi-biting factors of innovation in Singapore’s constructionindustry. The research method had the aim of identifyingthe important factors that affect innovation implementa-tion. To achieve this, a questionnaire (data collectioninstrument) was designed. The questionnaire requestedrespondents to provide certain information about theirproject that had adopted innovation and they were free toreport on any project. The definition of an innovation, asgiven in the introduction section, was provided in thequestionnaire. The returned questionnaires were checkedand none of the projects were reported by more than onerespondent.

To measure the benefit of the innovation, respondentswere asked to rate the extent to which the projectteam members realized their expected benefits after theinnovation was implemented on a seven-point scale,where 1 = strongly disagree and 7 = strongly agree. Thebenefit of the innovation to project team members (Y1)is the average score of the five measures (Y11 to Y15) shownin Table 1. The benefit of the innovation to the wholeproject (Y2) is the mean score given by respondents onthe same seven-point Likert scale.

The main difference between Y1 and Y2 is that Y1measures benefits realized by participants in the con-struction project organization (players) while Y2 focuses

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Variable description

Expected goalsExtent to which the purpose of the innovation is to enhance the quality

of the project (1 = not applicable; 7 = highly applicable)Extent to which the purpose of the innovation is to minimize the cost of

the project (1 = not applicable; 7 = highly applicable)Extent to which the purpose of the innovation is to reduce the normal

time required for completing the project (1 = not applicable;7 = highly applicable)

Extent to which the purpose of the innovation is to overcome technicaldifficulties in the project (1 = not applicable; 7 = highly applicable)

Extent to which the purpose of the innovation is to maximize company’soverall profit from the project (1 = not applicable; 7 = highlyapplicable)

Extent to which the purpose of the innovation is to strengthen company’sstake or influence in the project (1 = not applicable; 7 = highlyapplicable)

Extent to which the purpose of the innovation is to enhance theperformance of the whole project(1 = not applicable; 7 = highlyapplicable)

Extent to which the purpose of the innovation is to fulfil a marketdemand (1 = not applicable; 7 = highly applicable)

Extent to which the purpose of the innovation is to secure a goodbusiness relationship with the customer (1 = not applicable;7 = highly applicable)

Extent to which the purpose of the innovation is to secure a goodbusiness relationship with other stakeholders (1 = not applicable;7 = highly applicable)

Extent to which the purpose of the innovation is to secure goodmarket reputation in the construction industry (1 = not applicable;7 = highly applicable)

Perceived constraints and capabilitiesExtent of co-operation that may be obtained from the other parties

in the project (1 = strong co-operation; 7 = no co-operation)Extent of general capability of the other parties in the project

(1 = strong capability; 7 = no capability)Extent of general capability of the party who is proposing or supporting

the innovation (1 = strong capability; 7 = no capability)Extent of technical capability to implement the innovation (1 = strong

capability; 7 = no capability)Extent of managerial capability to implement the innovation (1 = strong

capability; 7 = no capability)Extent of contractual constraint on the project to implement the

innovation (1 = no constraint; 7 = strong constraint)Extent of constraints imposed by statutory regulations to implement

the innovation (1 = no constraint; 7 = strong constraint)Management level effortInvestment of additional resources to implement the

innovation(1 = least emphasized; 7 = highly emphasized)Formation of separate task group within the company(1 = least

emphasized; 7 = highly emphasized)Formulation of incentive and reward plans for employees who

implement the innovation (1 = least emphasized; 7 = highlyemphasized)

Formulation of monitoring plans to track the implementation progressof the innovation (1 = least emphasized; 7 = highly emphasized)

Table 2 Potential factors affecting innovation implementation

Label

EG1

EG2

EG3

EG4

EG5

EG6

EG7

EG8

EG9

EG10

EG11

CNST1

CNST2

CNST3

CNST4

CNST5

CNST6

CNST7

MLE1

MLE2

MLE3

MLE4

Y1 players Y2 project

Coeff.

0.127

0.171

0.088

0.047

0.065

0.238

0.459

0.015

0.180

0.182

0.341

−0.165

−0.199

−−−−−0.294

−−−−−0.340

−−−−−0.367

−0.249

−0.113

0.216

0.321

−0.014

0.231

Sig.

0.369

0.231

0.536

0.745

0.656

0.096

0.001*

0.917

0.221

0.220

0.014*

0.251

0.166

0.039*

0.014*

0.007*

0.078

0.431

0.120

0.020*

0.921

0.103

Coeff.

0.198

0.341

0.257

0.214

0.195

0.283

0.489

0.019

0.143

0.140

0.326

−0.250

−0.220

−−−−−0.314

−−−−−0.354

−−−−−0.348

−−−−−0.434

−−−−−0.352

0.083

0.208

−0.042

0.396

Sig.

0.186

0.022*

0.084

0.159

0.206

0.060

0.0010.0010.0010.0010.001*

0.901

0.359

0.360

0.029*

0.097

0.146

0.036*

0.016*

0.018*

0.003*

0.018*

0.577

0.165

0.787

0.007*

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Formulation of co-ordination plan with the parties involved(1 = leastemphasized; 7 = highly emphasized)

Formulation of separate incentive plan for the other parties in theproject to get their co-operation (1 = least emphasized; 7 = highlyemphasized)

Management level challengesFavourableness of contractual relationships with origin or

supporting organizations (1 = very favourable; 7 = veryunfavourable)

Favourableness of contractual relationships with other parties(1 = very favourable; 7 = very unfavourable)

Extent to which employees adopted and implemented the innovation(1 = very willing; 7 = highly unwilling)

Team spirit among the project parties at managerial level(1 = verystrong; 7 = very weak)

Management level commitmentInterest level of the client when the innovation was first proposed

(1 = not interested; 7 = highly interested)Interest level of the main consultant when the innovation was first

proposed (1 = not interested; 7 = highly interested)Interest level of the main contractor when the innovation was first

proposed (1 = not interested; 7 = highly interested)Interest level of the subcontractor when the innovation was first

proposed (1 = not interested; 7 = highly interested)Interest level of the supplier when the innovation was first proposed

(1 = not interested; 7 = highly interested)Project level effortExtent to which separate implementation team was formed which

included members from all concerned parties (1 = least emphasized;7 = highly emphasized)

Extent to which special training opportunities were provided formembers of the implementation team (1 = least emphasized;7 = highly emphasized)

Extent to which co-ordination mechanisms are established at projectlevel to implement/support the innovation (1 = least emphasized;7 = highly emphasized)

Extent to which monitoring mechanisms are established to identify andsolve problems encountered by the implementation team (1 = leastemphasized; 7 = highly emphasized)

Project level challengesSkills level of the implementation team to implement the innovation

(1 = very skilful; 7 = no skill)Team spirit in the project for implementing the innovation at project

level (1 = very strong; 7 = very weak)Flexibility of schedule of activities to allow for the innovation to be

worked on (1 = very flexible; 7 = not flexible)Extent of co-ordination problems with other parties in checking the

related works done before innovation can be implemented (1 = noproblems; 7 = many problems)

Extent of co-ordination problems with other parties in checking therelated works done after innovation can be implemented (1 = noproblems; 7 = many problems)

Project level commitmentInterest level of the client when the innovation is being implemented

(1 = not interested; 7 = highly interested)

Table 2 (cont’d)

Label

MLE5

MLE6

MLCH1

MLCH2

MLCH3

MLCH4

SO1-MLC

SO2-MOC

SO3-MLC

SO4-MLC

SO5-MLC

PLE1

PLE2

PLE3

PLE4

PLCH1

PLCH2

PLCH3

PLCH4

PLCH5

SO1-PLC

Y2 project

Coeff.

0.354

0.133

−0.211

−0.063

−−−−−0.652

−−−−−0.505

0.214

0.036

0.399

0.331

0.236

0.055

0.229

0.472

0.313

−−−−−0.425

−−−−−0.495

−−−−−0.627

−−−−−0.368

−0.289

0.498

Sig.

0.017*

0.390

0.164

0.680

0.000*

0.000*

0.205

0.847

0.019*

0.074

0.179

0.718

0.125

0.001*

0.032*

0.003*

0.001*

0.000*

0.012*

0.054

0.001*

Y1 players

Coeff.

0.347

0.208

−0.137

−0.090

−−−−−0.563

−−−−−0.398

0.340

0.237

0.621

0.462

0.371

0.191

0.242

0.478

0.321

−−−−−0.338

−−−−−0.518

−−−−−0.622

−−−−−0.329

−0.218

0.720

Sig.

0.013*

0.148

0.343

0.529

0.000*

0.004*

0.030*

0.177

0.000*

0.008*

0.028*

0.175

0.084

0.000*

0.019*

0.014*

0.000*

0.000*

0.020*

0.132

0.000*

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on the output of the construction project (product). It ispossible that players may gain in terms of, say higherprofitability, but the project as a whole may not be abetter product. Bowley (1960) found that there are sometypes of innovations, which she called ersatz innovations,which merely acted as substitutes or alternatives to amaterial or component. These materials do not havesignificant impact on the building performance. Theseinnovations may not have high Y2, but may have high Y1especially for material suppliers. Hence, the need for twoyardsticks.

Admittedly, Y1 is not a refined measure becauseit averages the benefits of innovation to differentstakeholders. In reality, the benefit to client/customermay be more important than say, subcontractors, becausethe former is the project sponsor and the other partiesshould aim to satisfy him. In addition, it may not alwaysbe possible to have different parties’ benefits realizedsimultaneously because of goal incongruity in thefragmented construction process (Dulaimi et al., 2003).

The questionnaire also listed potential factors that mayhave affected the benefit of innovation in the reportedprojects (see Table 2). These potential factors werebased on Dulaimi et al.’s (2002a) framework thatexplains how innovation can be implemented in aproject, and other past studies on construction innovation(Paulson and Fondahl, 1980; Tatum, 1984, 1986, 1989;Tatum and Funke, 1988; Nam and Tatum, 1989, 1992a,1992b, 1997; Slaughter, 1993a, 1993b, 1998, 1999, 2000;Winch, 1998, 2000; Mitropoulos and Tatum, 1999)reviewed earlier and innovation in the manufacturingindustry (Sterman et al., 1997; Repenning, 2002).

The factors affecting successful innovation implemen-tation were categorized into attributes relating to thefollowing in initiating, implementing or supporting theinnovation: expected goals (EG); constraints faced(CNST); management level effort (MLE); managementlevel challenges (MLCH); interest level when innovationwas proposed (MLC); project level effort (PLE); projectlevel challenges (PLCH) and interest level when innovationwas implemented (PLC).

Respondents were required to rate each attribute on aseven-point Likert scale. The anchors are shown inTable 2. A pilot study was first carried out, to identifypossible inadequacies in the data collection instrument.The finalized form provided the basis for the implemen-tation of a questionnaire survey, which constituted theprimary data collection method for this study.

In this study, there were five population frames com-prising clients, consultants, contractors, subcontractorsand suppliers. The different groups were targeted becausethey occupy the different positions in the constructionvalue chain and their perspectives were needed for thisresearch. Respondents were randomly selected from eachof the groups. However, since they could choose anysuitable project to report, this means that the ‘actors’ inthe process were sampled, but not the projects.

Two groups of clients were surveyed: public sectorclients and private sector property developers. The publicsector clients were obtained from the Singapore Govern-ment Telephone Directory. The private sector propertydevelopers were the members of the Real Estate Devel-opers Association of Singapore. The consultants whowere surveyed comprised architects, civil engineers,mechanical and electrical (M&E) engineers and quantitysurveyors who were listed in the directories of their respec-tive professional institutions. The target populations forcontractors, subcontractors and suppliers were based oncompanies that were registered with the Building andConstruction Authority (BCA) under the relevant work-heads (BCA, 2000). Samples were randomly selected fromall the relevant categories of contractors and suppliers.

Data were collected through postal survey. The surveypackage comprised a cover letter, the questionnaire anda pre-stamped, self-addressed envelope.

Results

A total of 1286 questionnaires were sent to the randomlyselected firms based in Singapore. Sixty were returnedbecause the companies have changed their addresses.


Interest level of the main consultant when the innovation is beingimplemented (1 = not interested; 7 = highly interested)

Interest level of the main contractor when the innovation is beingimplemented (1 = not interested; 7 = highly interested)

Interest level of the subcontractor when the innovation is beingimplemented (1 = not interested; 7 = highly interested)

Interest level of the supplier when the innovation is being implemented(1 = not interested; 7 = highly interested)

Table 2 (cont’d)

Label

SO2-PLC

SO3-PLC

SO4-PLC

SO5-PLC

Y2 project

Coeff.

0.335

0.533

0.502

0.374

Sig.

0.061

0.001*

0.006*

0.038*

Y1 players

Coeff.

0.654

0.817

0.644

0.584

Sig.

0.000*

0.000*

0.000*

0.001*

*Significant correlation at 0.05 level.

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Seventy-six questionnaires were returned within onemonth of sending out (response rate of 6%). Out ofthese, 18 respondents indicated that they do not haverelevant experience in innovation. Of the remaining 58usable questionnaires, eight of them were obtained aftertelephoning potential respondents whom the researcherknew personally. The low response rate may be due tothe relatively low level of innovation in the constructionindustry or that the postal survey method was not entirelysuitable for a complex topic such as innovation. Datafrom the 58 usable returned questionnaires were checked,edited, coded and analysed.

The profiles of respondents and their companies aresummarized in Table 3. The respondents had an averageconstruction experience of approximately 16 years. Themajority of them hold senior positions in their firms.Most of the respondents are from indigenous companiesand they initiated the innovation. Innovation was reportedin both traditional and design–build projects. Table 3shows that contractors made up the majority of therespondents, and this population also had the highestresponse rate. Therefore, the results and findings may bemore applicable to contracting organizations. There was agood mix of process and product innovations (see Table 4).The largest group of innovation was new processes anddesign (48%). This is consistent with Slaughter andShimizu’s (2000) finding that a large proportion of

innovation relates to design innovation. Tatum (1988)also found that technological innovation is importantbecause of increasing technical complexity in projectsand increasing international competition.

While the survey respondents were sampled from dif-ferent populations, the low response rates and relativelysmall sample sizes of the individual populations pre-cluded a statistical treatment of the different populationsseparately. However, aggregating the replies from all therespondents together to deliver statistically significantresults reveals some inherent limitations. This is becausecontradictions may arise from different stakeholders. Forexample, a contractor may propose a new idea in orderto improve profitability, but no other members of theproject organization or the client may receive any benefitsout of it. Another example is a supplier proposing anersatz innovation (Bowley, 1960) described earlier. It isalso possible that the origin organization achieved benefitfrom an innovation, but to the detriment of other mem-bers in the team or the project. A check of the responsesto the questions shown in Table 1 revealed that this didnot happen, as the ratings were 4 and above.

Correlation and factor analyses

To achieve the first objective of this study, Spearman’scorrelation analysis was undertaken with the help of theStatistical Package for Social Sciences software (SPSS).The correlations between the hypothesized variables andthe ‘Benefit of Innovation to Project Team Members(Y1)’ and ‘Benefit of Innovation to the Whole Project(Y2)’ are shown in Table 2: 24 and 23 variables aresignificantly correlated with Y1 and Y2, respectively; 19variables affect both Y1 and Y2 significantly.

Due to the large number of variables, factor analysisbased on principal component analysis was carried out

Table 3 Profile of respondents and their firms

No.

1

2

3

4

Description

Response ratesSample sizeUsable responseResponse rate (%)

DesignationSenior managementProfessionals

Experience in construction industryMaximumAverageMinimum

Turnover (US$)MaximumAverageMinimum

Client

34110

2.9

55

2516.57

3

$8 bn$4 bn$100 m

Consultant

444214.7

183

3515.894

$40 m$5 m$60 000

Contractor

50127

5.4

225

3816.85

2

$10 bn$450 m$600 000

Table 4 Reported innovations

Innovation category

New productsNew processes and designNew organizationsNew management practicesUnknownTotal

Frequency

8285

134

58

Percentage

13.848.38.6

22.46.9

100

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using SPSS to reconstruct the variables that are corre-lated with Y1 and Y2 to obtain a smaller set of para-meters. Tables 5 and 6 show the factor loadings,communalities (h2) and eigenvalues for the extractedfactors. The factor analysis revealed five and sevencomponents to explain Y1 and Y2, respectively.

The results are further examined in the discussionsection later. The usefulness of the results is that factorsthat affect innovation implementation are identified.This means that when the innovation is being imple-mented, the relevant attributes can be properly control-led and managed, so as to ensure that more benefits maybe derived from the innovation.

Multivariate analysis

Multivariate linear regression (MLR) analysis wasused to develop models to determine the statisticalrelationship between benefit level of innovation and theexplanatory variables. The models were developed usingtraditional regression techniques with the help of theSPSS. In this research, the independent/predictorvariables are the attributes identified from past studiesand presented in Table 2. The dependent variables areY1 and Y2, defined in Table 1.

Given the large number of predictor variables and thelimited sample size, there was a need to reduce thenumber of predictor variables, before embarking onregression modelling. In Step 1, predictor variables thatcorrelate significantly (p ≤ 0.05) with Y1 and Y2 wereidentified separately through Spearman’s correlationanalysis (see Table 2). The other independent variablesthat displayed weak associations (p > 0.05) with benefitof innovation were eliminated and, therefore, the numberof predictor variables was reduced. This eliminationprocess is equivalent to the removal of statistical outliers.The predictor variables that are significantly correlatedwith a dependent variable were then used to construct anMLR model.

In Step 2, Spearman’s correlation coefficients betweenevery two significant predictor variables were calculatedto identify instances of multicollinearity among thevariables. When two predictor variables were found tobe significantly correlated, the one that is correlated toa lesser extent to the dependent variable was excludedfrom the model construction during regression modelling.

The details of the 58 projects were inputted into theSPSS software and two models were produced to predictthe benefit of innovation to project participants andthe whole project (Tables 7 and 8). The models wereselected by the stepwise procedure and other accepted

Table 5 Factor analysis of attributes affecting benefit to project team members

h2

0.5300.8700.8080.6670.7670.7780.8430.9270.942

0.6290.6530.5800.7170.7530.556

0.3850.7330.6570.518

0.3320.8120.855

0.7200.720

Label

SO1-MLCSO3-MLCSO4-MLCSO5-MLCSO1-PLCSO2-PLCSO3-PLCSO4-PLCSO5-PLC

MLCH3MLCH4PLCH1PLCH2PLCH3PLCH4

MLE2MLE5PLE3PLE4

CNST3CNST4CNST5

EG7EG11

Category (eigenvalue)

Project team members are highly interested inthe innovation (7.132)

Working conditions for innovation mustbe conducive (3.887)

Form task group to co-ordinate and monitorthe innovation (2.293)

General, technical and managerial capabilitiesmust be strong (1.491)

Goal of innovation is to enhance performanceof project and market reputation (1.439)

Factor loading

0.7280.9330.8990.8170.8760.8820.9180.9630.971

0.7930.8080.7620.8470.8680.745

0.6210.8560.8110.719

0.5760.9010.925

0.8480.848

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Table 7 Regression results for predicting benefit of innovation to project participants

σ

0.5910.068

0.085

0.074

β

1.9520.430

0.342

−0.153


ConstantInterest level of the main consultant when the

innovation is being implemented (SO2-PLC)Extent to which co-ordination mechanisms are

established at project level to implement/support the innovation (PLE3)

Extent of managerial capability to implement theinnovation (CNST5)

b

NA0.580

0.354

−0.196

Notes:1. Regression coefficient (β), calculated using ordinary least square method.2. Standard error (σ) of variable regression coefficient, measures the dispersion of regression coefficient over sampling distribution.3. Standardized regression coefficient (b), allows for equal comparison of coefficient weights, when the constant is removed.4. Value of t-statistic, to be compared to the theoretical t-distribution for accuracy.5. Significance of t-statistic. For significance < 0.05, the null hypothesis that b = 0 is rejected. There is less than 5% chance that t-statistic is

wrong due to random occurrence.

t

3.3036.361

4.006

−2.076

Sig.

0.0020.000

0.000

0.046

Table 6 Factor analysis of attributes affecting benefit to the whole project

h2

0.7470.7750.8280.8310.855

0.6290.6530.5800.7170.7530.556

0.2870.8560.877

0.4890.7320.5630.609

0.988

0.6950.857

0.7270.725

Label

SO3-MLCSO1-PLCSO3-PLCSO4-PLCSO5-PLC

MLCH3MLCH4PLCH1PLCH2PLCH3PLCH4

CNST3CNST4CNST5

MLE4MLE5PLE3PLE4

EG2

CNST6CNST7

EG7EG11

Category (eigenvalue)

Project team members are highly interestedin the innovation (4.036)

Working conditions for innovation mustbe conducive (3.887)

General, technical and managerial capabilitiesmust be strong (2.530)

Form task group to co-ordinate and monitorthe innovation (2.393)

Goal of innovation is to minimize cost (1.421)

Constraints imposed by statutory regulationsand contract conditions are weak (1.043)

Goal of innovation is to enhance performanceof project and market reputation (1.009)

Factor loading

0.8640.8800.9100.9120.925

0.7930.8080.7620.8470.8680.745

0.5360.8490.820

0.6990.8550.7510.781

0.993

0.7480.752

0.8430.847

Table 8 Regression results for predicting benefit of innovation to the whole project

σ

0.8520.139

0.080

0.133

β

2.6610.356

−0.276

0.344


ConstantExtent to which the purpose of the innovation is to

enhance the performance of the whole project (EG7)Extent to which the schedule of activities can allow the

innovation to be worked on at the project level (PLCH3)Extent to which co-ordination mechanisms are established

at project level to implement/support the innovation(PLE3)

b

NA0.338

−0.389

0.340

Note: see Table 7.

t

3.1222.569

−3.432

2.596

Sig.

0.0040.016

0.002

0.015

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regression modelling practices. Model Y1 is robust as ithas R2 and adjusted R2 of 0.807 and 0.789, respectively.Model Y2 has R2 and adjusted R2 of 0.672 and 0.639,respectively.

Model construction

Using MLR technique, it was found that 79% of inno-vation benefit to project team members can be explainedby three variables (see model in Table 7). The formulato predict Y1 is given in Eq. 1 below.

Y1 = 1.952 + 0.43 (SO2−PLC) + 0.342 (PLE3)− 0.153 (CNST5) (1)

where:

• Y1 is given on a seven-point scale, where 1 = nobenefit derived by project team members from theinnovation and 7 = maximum benefit derived byteam members from the innovation;

• SO2-PLC is the interest level of the main consult-ant when the innovation is being implemented,mea-sured on a seven-point scale, where 1 = notinterested and 7 = highly interested;

• PLE3 is the extent to which co-ordination mecha-nisms are established at project level to implement/support the innovation, measured on a seven-pointscale, where 1 = least emphasized and 7 = highlyemphasized; and

• CNST5 is the extent of managerial capability toimplement the innovation, measured on a seven-point scale, where 1 = very strong capability and7 = very weak capability.

Using MLR technique, it was found that 64% ofbenefit that the innovation can bring to the project maybe explained by three variables (see Table 8). Theformula to predict Y2 is given in Eq. 2 below.

Y2 = 2.661 + 0.356 (EG7) − 0.276 (PLCH3)+ 0.344 (PLE3) (2)

where:

• Y2 is given on a seven-point scale, where 1 = nobenefit derived by the project from the innovationand 7 = maximum benefit derived by the projectfrom the innovation;

• EG7 is the extent to which the purpose of the inno-vation is to enhance the performance of the wholeproject, measured on a seven-point scale, where1 = not applicable and 7 = highly applicable;

• PLCH3 is the extent to which the schedule ofactivities could allow the innovation to be workedon at the project level, measured on a seven-pointscale, where 1 = schedule of activities are highlyflexible and 7 = schedule of activities are not at allflexible; and

• PLE3 is the extent to which coordination mecha-nisms are established at project level to implement/support the innovation, measured on a seven-pointscale, where 1 = least emphasized and 7 = highlyemphasized.

Model validation

Model validation was carried out on the two modelspresented earlier. A diagnostic test was conducted, andthe residual plots of R (being actual Y less predicted Y)versus ‘predicted Y’ show a random distribution. Thisconfirms that the assumption about normality is valid. Asdescribed earlier, when two predictor variables werefound to be significantly correlated, one of them wasexcluded from the model. Therefore, multicollinearity isabsent from the models. To validate these predictionmodels further, six new project data were collected. Thepredicted innovation benefit levels (predicted Y1 and Y2)were derived mathematically from the models. Thesepredicted results were compared to the actual innovationbenefit levels (actual Y1 and Y2) of the projects.

The percentage error for Y1 ranged from −4.49% to8.26% with mean percentage error (MPE) and meanabsolute percentage error (MAPE) at 0.86% and 4.76%,respectively. Percentage error for Y2 ranged from −8.5%to 9.78% with MPE and MAPE at 0.24% and 9.34%,respectively. These results show that the models achievedrelatively accurate representation of the actual results.

Discussion

It should be noted that since each respondent was askedto provide information on one project that adopted a newidea, the study is situated in an analysis of innovation indiscrete projects. It is possible that these innovations didnot have benefit for clusters of projects (for example forthe same client or for similar building types) and thosethat might improve performance of business processes.The findings and analysis of this study are therefore moreapplicable to practitioner-research (Groak and Krimgold,1989) type of innovation within a specific project. Thefindings are now discussed.

Factors that correlated significantly withbenefit of innovation

Table 2 shows the correlation between explanatoryvariables and innovation benefit. Using factor analysistechnique, the many significant variables were reducedto several categories as shown in Table 5 (for Y1) andTable 6 (for Y2).

The most important factor for the innovation to bebeneficial to project team members and the project is that

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the members themselves must be highly interested in theinnovation (Y1 eigenvalue 7.132; Y2 eigenvalue 4.036).Being interested in an innovation was operationalized asbeing committed. This agrees with Tatum and Funke(1988) and Slaughter (2000) who found that manage-ment must be determined to commit resources requiredfor the innovation. It is also consistent with Tatum’s(1984) finding that the team must display a willingnessand positive approach regarding evaluation of alternativeapproaches. There must be competent interdisciplinarypeople capable of working in teams (Gann, 2000). It isimportant for senior management’s direct participationin the formation, specification and evaluation of innova-tion (Mitropoulos and Tatum, 1999). Tatum (1989)found that firms with construction innovation establishsupportive policies and priorities. The finding of thisstudy through a survey questionnaire confirms the resultsobtained from case studies conducted by previousresearchers. The implication of the result is that teammembers should not adopt an arm’s length approachwhen trying to initiate, implement or support an innova-tion. They must be fully committed, to the point ofproviding resources and having champions and visionar-ies to lead in the innovation. The commitment shouldnot be confined to the initiation stage, but be maintainedthroughout the project until the innovation has beensuccessfully implemented.

The second most important factor is that there shouldbe conducive working conditions (eigenvalue 3.887 forboth Y1 and Y2). Positive environment at the companylevel include the presence of team spirit and acceptanceof the innovation by employees. At the project level, theimplementation team must have the necessary skills. Thefinding confirms Gann and Senker’s (1998) conclusionthat new technologies can be implemented effectivelyonly by properly trained and skilled workers. Tatum(1989) also found that it is important to have a diversetechnical capability. It is also important to have flexiblework schedules and good co-ordination among thedifferent parties involved in the innovation. This isconsistent with Tatum’s (1989) study, which showed thatfirms must have organization structures that have speciallinkages for both internal and external co-ordination.The implication of the finding is that before proceedingto initiate innovation, it is important to study howcohesive the project participants are. It they haveadversarial relationships, are fragmented and employeesare uninterested or unconvinced by the innovation, itmay be better not to embark on it, as the benefits derivedmay be lower.

Table 2 shows some potential factors are less impor-tant than might be expected. For example, there isno significant correlation between innovation benefitand the goal of innovation to enhance quality of theproject, reduce construction time, and overcome technical

difficulties. This contradicts Tatum’s (1984) study thatfound that high schedule and technical requirementsfavour innovation. One possible explanation for this isthat the majority of the respondents are contractors, whoare more accustomed to design–bid–build contracts.They may have felt that it is the consultants’ responsibilityto achieve those goals in the design. Another possibilityis that the majority of the cases investigated in this studyare buildings while Tatum (1984) studied power plantprojects, which are more complex in nature.

It was also found that favourable contractual relation-ships do no significantly affect innovation benefit. Thiscontradicts Nam and Tatum’s (1992b) finding thatsuccessful product innovation needs good procurementpolicies implemented by clients. This may be because inSingapore, standard conditions of contracts are used and,therefore, not much can be changed to improve the situ-ation to enable innovation. This accords with Bowley(1966), who found that the form of contract hindersinnovation progress.

Previous studies promoted the concept of rewardinginnovators (Dulaimi et al., 2002b). This study departsfrom Dulaimi et al.’s (2002b) results as there is no sig-nificant correlation between the benefit of innovation andincentive plans for employees who implement the inno-vation. This may be because construction projects areusually secured on thin profit margins and there is justnot enough money to mete out to employees as rewardsand incentives.

Managing construction innovation effectively

Two models were produced in this study. The first is topredict benefits of innovation to project team members(players) Y1, and the second to the project as a whole(product) Y2. While Table 2 shows the variables that arecorrelated with Y1 and Y2, the two models show only thevariables that need to be used to predict Y1 and Y2. Ascan be seen from Tables 7 and 8, only three variables areused to predict each model. This does not mean thatthe other significant variables identified in Table 2 areunimportant. It is just that those variables need not beused to predict the outcome of Y1 and Y2.

Model Y1 shows that the interest level of the mainconsultant when the innovation is being implemented(SO2-PLC) plays a major role in predicting the benefitof innovation to team members. This is consistent withNam and Tatum (1989), who found that designers andconsultants play an important role, especially in productinnovation. They need to possess relevant technology, inthe form of objects (tools and equipment), knowledge (inconstruction materials, construction processes, restric-tions, design science) and the combination of both toproduce innovative designs. The consultants should alsohave a tendency toward innovation (Nam et al., 1991).

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The implication of this finding is that firms that wish topropose an innovation should first convince consultantsof its merits. If the consultant is not persuaded, it maybe better to abandon the innovation as the benefit toproject team members may be minimal.

Interestingly, consultants’ interest in the innovationwhen it is being implemented (SO2-PLC) is not a signifi-cant variable that affects Y2 (see Table 2). This may bebecause consultants, at implementation stage, take asmaller role compared to contractors and, therefore,their effect on innovation benefit to the whole project islesser.

The extent to which coordination mechanisms areestablished at project level (PLE3) is found to be a pre-dictor of Y1 and Y2. This finding is in accordance withSlaughter (1999), who found that as construction pro-cesses are dynamic and responsive systems using flexi-ble resources, the design and construction organizationsneed to coordinate their activities explicitly to obtainthe expected benefits from an innovation (Slaughter,1999). Tatum (1989) also emphasized the importanceof planning for the innovation. The implication of thisfinding is that innovation must be implemented in awell-planned and coordinated manner. Haphazard andcrisis management may not lead to innovation success,notwithstanding some parties proclaiming the benefits ofchaotic thinking.

The extent of managerial capability to implement theinnovation (CNST5) is a valid predictor of Y1. Whilethis variable did not appear in Y2’s equation, Table 2shows that this is nevertheless a significant variable(r = −0.348; p = 0.018). This means that CNST5 affectsY2 significantly, but need not be used to predict Y2.Managerial capability is very important, and this includeseffective or strong leadership, as advocated by Gann(2000). Tatum and Funke (1988) also found that man-agement must have the vision and ability to recognizeforces and opportunities for innovation. Nam and Tatum(1997) found that effective leadership, in particular, aproactive technology leadership is essential for technicalinnovation.

To ensure that the innovation will benefit the wholeproject (Y2), it is important to explicitly state that thegoal of the innovation is to enhance the performance ofthe whole project (EG7). This apparent tautology in thefindings may be explained by looking at the 11 possiblegoals that cause project organizations to want to initiateand implement innovation (see Table 2, EG1 to EG11).There are so many reasons for doing so, but for theinnovation to be beneficial, there must be a ‘right’ rea-son; which is to improve the performance of the project(EG7) and not merely to, say, increase profitability(EG5). Again, this finding agrees with previous studies.Slaughter and Shimizu (2000) discovered that innova-tion is not developed and used in isolation, but as a

co-ordinating means of improving the performance ofthe project.

The flexibility of the schedule of activities to allow theinnovation to be worked on at the project level (PLCH3)is another variable to predict Y2. Even though it is notused to predict Y1, it is nevertheless a significant variablethat affects Y1 (r = −0.622, p = 0.000). This discovery isconsistent with Tatum’s (1984) finding that one of theconditions that favour innovation is having sufficient leadtime to develop, plan and design the innovation. Theschedule must allow adequate time for detailed planningand evaluation of several alternatives. The implication ofthis finding is that projects that are on tight schedulesmay want to avoid implementing innovative practices.There is just not enough time to initiate and implementinnovation in such a way that it leads to success.

Practical applications

Individual team members (origin organizations) in aconstruction project organization may be motivated toinitiate an innovation at certain points of the constructionvalue chain. These origin organizations must havewanted to initiate the innovation because they think thatthey will benefit from it and/or the project will benefitfrom it. This study provides some inkling on howbeneficial the innovation may be.

To predict how much benefit the origin organizationwill reap from the innovation, it should use model Y1,and input the necessary ratings for SO2-PLC, PLE3 andCNST5 into Eq. 1. Generally, the benefit to the firmmay be high if the main consultant is very interestedin the innovation when it is being implemented, thereare coordination mechanisms in place and managerialcapability is high.

Clients are expected to be more interested in ensuringthat the innovation will benefit the project as a whole,as they are the ones who are users or owners of theconstructed facility. When they wish to introduce aninnovation, or if an innovation is being presented to themfor approval, they may use model Y2 to predict the extentto which the project will benefit from that proposedinnovation. The necessary ratings for EG7, PLCH3 andPLE3 should be inputted into Eq. 2 and the benefit levelcalculated. Generally, the project may benefit from theinnovation if the true intention of the innovation is toimprove project performance and not other spuriousreasons, there is enough float in the programme to allowfor the innovation to be worked on, and coordinationmechanisms are in place to implement the innovation.

Questions for further research

This study has several limitations. The first and obviousone is the low response rate and the amalgamation of the

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different groups of respondents in the statistical analysis.The second is that the innovations are reported and ana-lysed within discrete projects. The third is the lack oflinkage between project processes and business processesin firms. These limitations suggest the need for furtherresearch in this area. Future study in this area is importantbecause there is an urgent need for firms to improve theircapabilities in managing innovation, so that they canbuild reputations for technical excellence, to give themcompetitive advantage (Gann, 2000).

Many academic studies have been conducted in manydifferent sectors with the aim of developing models topredict innovation success levels. However, robustmodels have not yet been found. Based on the findingsof this study on factors affecting innovation implemen-tation, some questions are now proposed for furtherresearch, with the aim of making a small contribution tothe search for the ‘holy grail’.

The first question for further research is how thefindings would differ if more samples are obtained andthe results from each population analysed separately. Infuture research, the response rate may be improved byinviting potential respondents to a seminar or round-table discussion on innovation and, then, persuadingthem to respond to the questionnaire. Alternatively,potential respondents may be contacted first and per-suaded to respond. More tangible benefits could also beoffered to those who are participating in the survey.

The types of innovations reported in this study belongto the practitioner-research category. The second questionfor further research is how the findings would differ if theinnovation is the product of a formal research as opposedto practitioner-research. This issue is worth exploringbecause innovation arising from a formal research mayhave a larger impact compared to practitioner-research,which usually gives small incremental improvements.

In this study, each respondent reported on oneinnovation in a discrete project. It may be necessary tosee how the findings would differ if innovation is appliedto a cluster of projects instead of discrete projects asreported in this study.

Conclusion

As mentioned in the Introduction, innovation in con-struction may come about from practitioner-research ormore formal R&D. The nature of the responses showsthat the findings would be more applicable to project-basedproblem solving, which accords with the practitioner-research concept. The findings of this study would moreappropriately address issues relating to implementationin discrete construction projects.

The essence of this study is that significant variablesthat affect innovation implementation were identified in

Table 2, thus achieving the first objective. The implica-tion of the findings is that practitioners could go aboutcontrolling and managing the significant factors affectinginnovation implementation. In particular, innovationshould only be embarked upon if team members arehighly interested in it, both during initiation and imple-mentation stages. In addition to management beinginterested in the innovation, the commitment to theinnovation should also be strong at the project level.Otherwise, the innovation should not be started, or stepsmust first be taken to enthuse the relevant players.

This study also found that it is important that workingconditions for innovation must be conducive. The impli-cation of this finding is that the challenges faced by themanagement, and at the project level, must be minimizedthrough the presence of strong team spirit. Innovationshould also not be implemented in a haphazard way, butplanned and coordinated among the relevant parties.

It is recommended that those who are pursuing researchin modelling innovation success investigate the set ofquestions identified in this study. Suggestions on how theresponse rate may be increased have been given and it ishoped that a robust prediction model can be constructedin the near future.

Acknowledgement

This study was funded by the National University ofSingapore (R-296-000-042-112). Mohammed F. Dulaimi,Mohan Kumaraswamy and Arun Bajracharya’s participa-tion in various parts of the research project is gratefullyacknowledged. The comments and suggestions by DavidGann and the anonymous reviewers to improve this paperare acknowledged with grateful thanks. The authorremains responsible for any errors and omissions.

References

BCA (2000) Building and Construction Authority. Availableonline: http://www.bca.gov.sg (accessed 20 November2000).

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managing the implementation of construction innovations

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