Critical analysis of key determinants and barriers to digital innovation adoption among architectural organizations

Frontiers of Architectural Research (2014) 3, 431–451

H O S T E D B Y Available online at www.sciencedirect.com

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Critical analysis of key determinantsand barriers to digital innovation adoptionamong architectural organizationsRunddy Ramilon, Mohamed Rashid Bin Embi1

Department of Architecture, Faculty of Built Environment University of Technology Malaysia, Johor 81310,Malaysia

Received 7 November 2013; received in revised form 13 June 2014; accepted 16 June 2014

KEYWORDSDigital innovation;Architecturalorganizations;Technologies;Digital innovationbarriers

rg/10.1016/j.foar.201414. Higher Education P

g author.: Tel.:+60 75sses: [email protected] responsibility of

AbstractThe development and use of design technology for architecture in the modern world have led to theemergence of various design methodologies. Current design research has focused on a computationallymediated design process. This method is essentially concerned with finding forms and buildingperformance simulation, i.e., structural, environmental, constructional, and cost performance, byintegrating physics and algorithms. From the emergence of this process, design practices have beenincreasingly aided by and dependent on the technology, which has resulted in a major paradigm shift.Advancement of the new technology has the potential to improve design and productivity dramatically.However, related literature shows that substantial technical and organizational barriers exist. Thesebarriers inhibit the effective adoption of these technologies. The effect of these obstacles onarchitectural practice varies depending on the size of an architectural organization. To furtherunderstand the problem, we conducted an in-depth study on several small, medium, and largearchitectural organizations. This study involves in-depth evaluation of technological, financial,organizational, governmental, psychological, and process barriers encountered in the adoption ofdigital innovation. Results reveal relevant attributes and patterns of variables, which can be used toestablish a framework for digital innovation adoption. Valuable findings of this study reveal that smallerarchitectural organizations present more barriers to digital innovation compared with their largercounterparts. This study is important because it contributes to the research on digital innovation inarchitecture and addresses the barriers faced by different sizes of architectural organizations.& 2014. Higher Education Press Limited Company. Production and hosting by Elsevier B.V. All rightsreserved.

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530611.m.my (R. Ramilo), [email protected] (M.R.B. Embi).

Southeast University.

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R. Ramilo, M.R.B. Embi432

Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4322. Understanding digital innovation in architecture. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4333. Review of digital tools and digital innovation in architectural organizations. . . . . . . . . . . . . . . . . . . . . . . . 434

3.1. Non-parametric geometric modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4343.2. Parametric modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4343.3. Building information modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4353.4. Building performance modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4363.5. Scripting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4363.6. Summary of digital innovation tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 438

4. Understanding barriers and impediments to digital innovation adoption . . . . . . . . . . . . . . . . . . . . . . . . . . 4385. Common attributes of barriers observed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4416. Research methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 441

6.1. Survey respondents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 441
6.1.1. Experience in digital innovation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4416.1.2. Size of architectural organization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 441
6.2. Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4416.3. Data collection and gathering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4426.4. Method of analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 442

7. Presentation of data and analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4437.1. Barriers to digital innovation adoption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4437.2. Technological barriers to innovation adoption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4437.3. Organizational barriers to digital innovation adoption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4447.4. Financial barriers to digital innovation adoption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4457.5. Process barriers to digital innovation adoption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4457.6. Psychological barriers to digital innovation adoption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4467.7. Governmental barriers to digital innovation adoption. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4467.8. Most significant barriers to digital innovation adoption. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 447

7.8.1. Most significant barrier to digital innovation in small architectural organizations . . . . . . . . . . . . 4477.8.2. Most significant barrier to digital innovation in medium-sized architectural organizations . . . . . . . 4477.8.3. Most significant barrier to digital innovation in large architectural organizations . . . . . . . . . . . . 4477.8.4. Overall result for most significant barrier to digital innovation . . . . . . . . . . . . . . . . . . . . . . . 447

7.9. Significant relationship between digital innovation barrier and size of architectural organization . . . . . . 4488. Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 448References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 450

1. Introduction

Technological advancement of the new technology has thepotential to improve design and productivity dramati-cally. However, related literature shows that substantialtechnical and organizational barriers exist, which inhibitthe effective adoption of these technologies (Leach andGuo, 2007; Johnson and Laepple, 2004; Inchachoto,2002). Despite the availability of digital technologies,innovation does not occur because limited knowledge andresources are transferred from one project to another.This concern occurs when projects have dissimilar objec-tives or exclude members of the previous team withrelevant skills or knowledge. Cory and Bozell (2001) foundthat although architects and designers have acknowl-edged the advent of computers as an aid in architecturaldesign, particularly in saving time and energy, these toolshave not been fully utilized. The benefits of intelligentmodeling to the design process are increased productivity,

reduced cycle time, and better work flow and life cycleapplications (Fallon, 2004).

Undeniably, the digitalization of design practices has notbeen trouble-free. Business profitability, one of the majorgoals of design practice, is at risk when digital innovation isimplemented.

Innovation implies a new process or way of doing certaintasks, which exposes businesses to the risk of failure (Davilaet al., 2006). Generally, innovation adds value, but it mayhave a negative or destructive effect because new develop-ments eliminate or change old organizational forms andpractices. The negative effect varies depending on the sizeof the organization (Davila et al., 2006). The need to fullyexplore this research area highlights the purpose of this paper.

To understand the problem, this study investigates thekey determinants that impede the effective adoption ofdigital innovation in architectural practices that are com-putationally and digitally driven. Specifically, this studyaims to answer the following research questions: (1) What

433Critical analysis of key determinants and barriers

are the barriers encountered by an architectural organiza-tion in adopting digital innovation? (2) How crucial are thesebarriers to the adoption of digital innovation? (3) Whichbarrier is the most significant in adopting digital innovation?(4) Is there a significant relationship between the size ofan architectural organization and the barriers to digitalinnovation?

2. Understanding digital innovationin architecture

Digital innovation in the area of information science refersto new combinations of digital and physical components toproduce novel products or services or to embed digitalcomputer and communication technology into a tradition-ally non-digital product or service (Yoo et al., 2010;Henfridsson et al., 2009; Svensson, 2012).

The definition of digital innovation in information science hasbeen developed, but no well-established definition of digitalinnovation in the context of architecture is available. Studies onthe subject show that theories of adopting digital innovationare extremely limited. Evidently, digital innovation in architec-tural organizations is already happening. The efforts focus onprocess innovation on the use of digital technology to find novelsolutions to important problems, as well as improve buildingdesign and organizational productivity. The rapid changes indigital technology are revolutionizing specific types of digitalinnovation. These technological changes are not yet recognizedas digital innovation but as user-end applications of computer-aided design technology. The possibility of confusion of termsand lack of information on the subject necessitate the defini-tion of innovation and its types, as well as the classification ofdigital innovation in architecture.

As mentioned, innovation is a new way of doing certaintasks more effectively. This term may also refer to incre-mental, emergent, or radical and revolutionary changes inthinking, products, processes, or organizations. Innovationis defined as the introduction of new elements or a newcombination of old elements into industrial organizations(Schumpeter, 1934). Innovations are not represented solelyby breakthroughs. Generally, innovation is the process inwhich a good idea or the creation of new knowledge about aproduct or process begins to affect its context.

Generally, innovation is defined as the successfulimplementation of new ideas from which commercialvalues are generated. Innovation is pervasive and diverse,as well as typically associated with, but not limited to,technological advancement. Innovations may be intro-duced in the market (product innovation) or used withina production process (process innovation). Therefore,innovations involve a series of scientific, technological,organizational, financial, and commercial activities. Allthese innovation activities share a common goal, which isto promote economic growth through improved perfor-mance and enhanced competitiveness.

In other words, “innovation” includes both technicalinnovations, which involves applying scientific advancementor other types of research, and process innovations, whichconstitute a novel and improved way of doing things withoutnecessarily including any scientific advancement. Both typesof innovation are intimately linked and interdependent.

However, these innovations are not limited to productinnovation or “objects” or technological innovations.

In architecture, engineering, and construction research,Male and Stocks have identified four distinct types ofinnovation, namely, (1) technological innovation, whichemploys new knowledge or techniques to provide aproduct or service at lower cost or higher quality;(2) organizational innovation, which does not requiretechnological advances but involves “social technology”that changes the relationship between behaviors, atti-tudes, and values; (3) product innovation, which mayhave low hardware dependency, provides better utiliza-tion of resources, and involves advances in technology,resulting in superior products or services; and (4) processinnovations, which substantially increase efficiency with-out significant technological advances.

Process innovation is defined as the implementation ofa new or significantly improved production or deliverymethod, including significant changes in techniques, equip-ment, and/or software (Svensson, 2012). Process innovationis also defined as the introduction of new elements into theproduction or service operation of a firm to generate aproduct or render a service (Rosenberg, 1982; Utterbackand Abernathy, 1975) with the aim of improving productiv-ity, capacity, flexibility, and quality, reducing costs, ratio-nalizing production processes (Edquist, 2001; Simonettiet al., 1995), and lowering labor costs (Vivarelli andToivanen, 1995; Vivarelli and Pianta, 2000). According tothe innovation research conducted by Reichstein and Salter(2006), process innovation is related to new capital equip-ment (Salter, 1960) as well as practices of learning-by-doingand learning-by-using (Cabral and Leiblein, 2001). Similarly,Hollander (1965) defines process development as “theimplementation of new or significantly improved produc-tion or delivery methods, [including] significant changesin techniques, equipment and/or software”. According tothe literature on process innovation (Svensson, 2012;Edquist, 2001; Simonetti et al., 1995; Vivarelli andToivanen, 1995; Vivarelli and Pianta, 2000), digital inno-vation in architecture can be considered as a form ofprocess innovation.

Therefore, digital innovation in the context of architec-ture can be defined as “the use of new digital channels,digital tools and relevant methodologies to improve theoperation of architectural organizations, delivery of ser-vices, and building design”. In other words, new designprocesses refer to computationally mediated proceduresand differ from the conventional paper-based architectureby independently using parametric modeling tools, buildingperformance simulation tools, scripting or algorithms rein-forced using conventional non-parametric tools and otherrelevant methodologies. The attributes of digital innovationinclude, but are not limited to the improvement of(1) architectural design through form finding, facade opti-mization, digital fabrication, material assembly, and costoptimization; (2) sustainability using the building perfor-mance simulation tools by evaluating energy efficiency,airflows, daylighting, wind analysis, and implication ofclimate on architectural forms; (3) structural conceptuali-zation by finite element analysis to investigate structuralbehavior and stability; and (4) productivity improvement tosave time and cost.


3. Review of digital tools and digitalinnovation in architectural organizations

3.1. Non-parametric geometric modeling

Non-parametric computer-aided design (CAD) is credited toIvan Sutherland, who developed special graphics hardwareand a program called Sketchpad for his Ph.D. dissertation in1963 (Eastman, 1999). Over the years, the CAD system hasevolved and developed in an evolutionary manner. Thissystem has developed algorithms for drawing curves, aswell as panning and scaling images, which are furtherexamined in-depth. Numerous studies focus on buildinguse development, such as geometric representation andmanipulation. In the early 1980s, architects began using

Figure 3.1 Representation of reciprocal trusses t

Figure 3.2 The first model is created in Rhino and the other mod2006).

Table 3.1 Popular non-parametric modeling tools edi-ted by the researcher.

Non-parametric modelingtools

Website

AutoCAD www.autodesk.comSketchUp http://sketchup.google.

comMaya http://www.alias.com3D Studio Max www.discreet.comHoudini www.sidefx.comRhinoceros www.rhino3d.comCinema4D www.maxon-computer.

comLightwave www.newtek.comCaligari Truespace www.caligari.comSoftImage www.softimage.com

computer-based CAD. The familiar layer metaphor thatoriginated with pin-bar drafting was easily adapted to thelayer-based CAD systems. Within a few years, a largenumber of construction documents and shop drawings wereplotted on computers rather than manually drafted ondrawing boards (Autodesk, 2006).

Non-parametric modeling can be considered as a conven-tional digital tool. Over the years, this method has becomean efficient tool of construction detailing and visualization.Non-parametric modeling is dominated by four high-endpackages, namely, SketchUp from Last Software, AutoCADfrom Autodesk, 3D Studio Max from Kinetix, and Maya fromAlias (owned by Autodesk). Several widely used modelingtools are listed in Table 3.1.

3.2. Parametric modeling

Parametric modeling, an approach to CAD that disregardsthe traditional two-dimensional approach, is considered as adigital innovation. According to Burry (1999), in parametricmodeling, requires the parameters of a particular design aredeclared rather than its shape. Different objects or config-urations can be created by assigning different values toparameters. Equations can be used to describe the relation-ships between objects, thereby defining an associativegeometry. Hoffmann (2005) points out that a parametricobject can be defined as a solid with an actual shape that isa function of a given set of parameters and constraints. A“parametric solid modeling” is also referred to as “para-metrics” to represent an object and is governed not only bya single technique but also by a set of techniques with itsown advantages and limitations (Figure 3.1). The benefits ofparametrics are automatic change propagation, geometryre-use, and embedding of design/manufacturing knowledgewith geometry (Shah, 1998).

hat are parametrically modeled (Burry, 1999).

els are parametrically manipulated in ParaCloud and Excel (Nir,

http://www.graphicon.ru/

http://www.michaelhansmeyer.com/projects


http://www.alias.com

www.discreet.com

www.sidefx.com

www.rhino3d.com

www.maxon-computer.com

www.maxon-computer.com

www.newtek.com

www.caligari.com

www.softimage.com

Figure 3.4 3D model constructed using BIM by HOK and Skanska(Walker, 2008).

Table 3.2 High-end parametric modeling tools com-monly used in design practice.

Parametric modeling tools Website

Autodesk Revit www.autodesk.comGenerativeComponents www.bently.comRhino-Grashopper www.grasshopper.comParaCloud Modeler www.paraclouding.comCATIA www.3ds.com

Figure 3.3 Full 3D model of Gehry's project using CATIA(Shelden, 2002).


Parametric models are variations of pre-defined shapesdriven by key dimensions. This approach can be used to setdata and parameters to parametrically change the size andshape of a model. This method involves two steps in thegeometric modeling process (Figure 3.2). First, the initialmodel is parametrically created by any simple topologicalmethod. Second, the variants and data of the model arelinked to any basic computational tool, such as MicrosoftExcel. Once the data are exported to Excel, the geometricmodels can be altered and manipulated by changing thedata or number in Excel. Subsequently, the models areautomatically changed when data are modified in Excel. Anew parametric generative tool called ParaCloud Modelerhas been developed recently. Table 3.2 presents a list ofparametric-based software available in the market andwidely used in the design industry.

Gehry and Partners is the pioneer in using parametricmodeling in architecture. The firm uses a powerful three-dimensional representation tool, CATIA, to model the com-plex geometry of its buildings (Yoo et al., 2010). This toolhas influenced the way the company works with owners,contractors, subcontractors, engineers, and fabricators. Byusing CATIA 3-D representation tools on projects, Gehry isable to design highly complex structures.

According to Yoo et al. (2010), the firm uses a centralized3D model and database (Figure 3.3) that can be used by allconsultants and contractors. This process necessitates teamcollaboration on a project through interactive exchange ofinformation. In numerous instances, subcontractors andfabricators worked with architects and general contractorsduring the design stage. Consequently, the key playersbecame involved in the design process earlier than they

did in the typical practice. Such close collaboration patternsnot only enhanced the quality of communications by redu-cing errors and redundant communication but also enabledthe design team to tap into the expertise of various tradesand specialists in a more meaningful manner.

Such collaboration at the early stage of the designprocess enabled Gehry and his associates to experimentwith new materials and construction methods for theirprojects while also encouraging contractors, subcontrac-tors, and fabricators to innovate in their own domains,which in turn inspired others, including Gehry himself, topursue further innovations. In this manner, 3D tools play asignificant role in connecting all participants in a largerprocess, as well as in stimulating their joint and separateinnovations. As a result, the entire project network becomesa nexus of emergent and distributed innovations.

3.3. Building information modeling

Building information modeling (BIM) is another form ofparametric modeling. BIM represents the process of devel-opment and use of a computer-generated model to simulatethe planning, design, construction, and operation of afacility (Azhar et al., 2000). The resulting building informa-tion model is a data-rich, object-oriented, intelligent, andparametric digital representation of the facility. Moreover,this model allows the data appropriate to the needs of theusers to be used in decision making and improving theprocess of project delivery (AGC, 2005).

The main difference between BIM and 2D CAD is that thelatter uses independent 2D views, such as plans, sections,and elevations, in describing buildings. Editing one of theseviews requires all other views to be checked or updated.This process is known as error-prone and considered as oneof the major causes of poor documentation. In addition,data in 2D drawings are graphical entities only, unlikethe intelligent contextual semantic of BIM models in whichobjects are defined in terms of building elements andsystems (CRC Construction Innovation, 2007).

According to Azhar et al. (2000), the main benefit of BIMis its accurate geometrical representation of building partsin an integrated data environment. Other known advantagesare faster and more effective process, better design,controlled lifetime costs and environmental data, betterproduction quality, automated assembly, and better

www.autodesk.com

www.bently.com

www.grasshopper.com

www.paraclouding.com

www.3ds.com

Table 3.3 Building performance modeling tools forenvironmental analyses.

Building performancesimulation tools

Website

IES www.iesve.comRadiance http://wapedia.mobi/en/

RadianceEcotect http://ecotect.comGreen Building Studio www.autodesk.com/

greenbuildingstudio.comHevacomp www.bentley.comEnergy Plus http://apps1.eere.energy.gov

Figure 3.5 3D models of the London City Hall by Foster andPartners and ARUP (Kolarevic, 2005).


customer service and lifecycle data. An example of aproject that employed BIM was the Royal London Hospital(Figure 3.4) the largest new hospital in the United Kingdomwith a 905-bed facility. The building is being configured as athree-tower structure with 6225 rooms across 110,000 m2 offloor space.

HOK and Skanska created a working strategy based onsharing a BIM dataset via a virtual “portal”. The businesscase for BIM built around costs versus perceived value,which shows that this working method should considerablyreduce the typical 10% excess expense attributed to poorspatial coordination rework and waste for an investment ofapproximately 0.5% of the total tender. Skanska benefitedwith an increased construction margin. HOK benefitedby receiving fewer requests for information (RFIs) andincreased fee margin. The client benefited with betterquality and more robust building. Therefore, early invest-ment in BIM is important. Cost benefits are already startingto come through even though construction has barelybegun. Moreover, the business model shows that the simpli-city of BIM data reuse can lead to savings worth £230,000on the cost of production operations and maintenancemanual alone.

HOK and Skanska both agreed in advance to standardizebased on the Architectural Desktop (ADT) modeling tool ofAutodesk. Thus, key team members and subcontractors wereallowed to use ADT-compatible programs. Project participants,including architects, engineers, contractors, members of thefacility management team, and clients, agreed to feed into andoff a single portal set up and managed by Skanska's central 3DCAD and Data Management Group.

The central model contains all of the data. For instance,the model can provide sources for lighting and acousticstudies, verify views generated, analyze structures and clad-ding systems, and map out services. All computer packagesused in the development, analysis, visualization, and manage-ment of the central 3D model have been factored into the“roadmap” of the Data Management Group.

The link between the principal ADT models and Code-book, the software that encapsulates key UK government-sanctioned medical information and requirements, saves aconsiderable amount of design and revision time. However,the links between ADT and Codebook do not generatedesigns automatically. Instead, the Codebook links enabledesigners to constantly check whether room layouts andservices fulfill the requirements.

3.4. Building performance modeling

Building performance modeling can be also considered asdigital innovation. This emerging kind of architecture usesbuilding performance as a guiding design principle, adoptsnew performance-based priorities for the design, andinterrogates a broadly defined performative design aboveform making. This kind of architecture also uses the digitaltechnologies of quantitative and qualitative techniques andsimulation to offer a comprehensive new approach to thedesign of the built environment (Kolarevic and Malkawi,2003).

This new design methodology uses new information andsimulation driven design process. Performance-based design

is primarily used to simulate environmental, thermal,climatic, and acoustic factors to emphasize the design, asin the case of the London City Hall designed by Foster andPartners and ARUP (Figure 3.5). This method involves findingsustainable strategies using building performance tools or4D modeling tools available in the market. The 4D digitaltechnologies are software that can simulate and analyzeunseen elements, such as air flows, energy efficiency, andindoor humidity. This tool provides design teams with high-quality information needed to quantify and inform iterativedecisions. Thus, the project team can effectively developcreative sustainable solutions at the early stage of theproject.

Building performance simulation software is alreadyavailable from major CADD companies, such as Autodeskand Bently, as listed in Table 3.3. These companies provide aplug-in linked to their products. Several of these productscan be downloaded from the company website and used for30-day trial periods.

3.5. Scripting

“Script” is another form of digital innovation. This form isderived from written dialog in the performing arts, whereactors are given directions to perform or interpret a story(Schnabel, 2007). Generally, scripting languages are nottechnical but mathematical solutions defined by a set of

www.iesve.com

http://wapedia.mobi/en/Radiance

http://wapedia.mobi/en/Radiance

http://ecotect.com

www.autodesk.com/greenbuildingstudio.com

www.autodesk.com/greenbuildingstudio.com

www.bentley.com

http://apps1.eere.energy.gov


rules and based on parameters. This programming languagecontrols a software application and is often treated asdistinct from programs executed independently from anyother application. The use of scripting in architecture isbased on the theory of L-systems in which the simulation ofplant growth is investigated and applied as a process togenerate objects (Allen et al., 2004).

As design computing becomes increasingly influential,scripting as a form of programming for architects hasbecome widespread in research. Scripting is popularly

Chart 3.1 Categorization diagram of current CAAD tools usedcomputers; 2, insufficient knowledge of team members; 3, lack ofdigital technology; 5, lack of technical demonstration; 6, inadequate8, insufficient skills in technology; 9, insufficient skills in techn11, inadequate in-house technical support.

Table 3.4 Scripting tools and programming languagesused by architectural organizations.

Programming software and languages

C+ Programming Genetic ProgrammingVisual Basic Generative ScriptsDOX Software PythonComponent Distribution

Diagraming (CDD)

Figure 3.6 Digital model of the Great Canopy projectdesigned by Foster and partners (Whitehead and Peters, 2008).

known as “emergence.” The theory of emergence is basedon natural phenomena (Leach, 2007) of any kind, such asgenetic space (Chu, 2004) from biology. The morphogeneticprocess and morphological formations of these phenomenaare used to generate design. Several applications of thistheory are “Morpho-Ecologies” (Hensel et al., 2004), “Bioth-ing and Continuum” (Andraseck, 2007), and “L-System inArchitecture” (Hansmeyer, 2003), in which the morphoge-netic process is applied to architectural design as formgenerators. The approach is inspired by biological morpho-genesis in which the processes of evolutionary developmentand organism growth are observed and applied as thegenerative morphogenetic process to model building forms.

Major programming language and plug-ins are alreadyavailable and can be used in scripting (Table 3.4). Several ofthese products can be downloaded from the website of thecompanies and used for 30-day trial periods or purchasedfrom information technology (IT) companies.

Foster and Partners used scripting in the Great CanopyProject, a proposal for the West Kowloon Cultural District.This project aims to create a unique landmark collection ofvisual arts, performance, and leisure venues on a dramaticharbor-front site in the heart of Hong Kong (Figure 3.6). Thecanopy flows over the various spaces within the develop-ment to create a unique landmark. The sinuously flowingsite contours and canopy produce a memorable effect.

Scripting was used throughout the project to developarchitectural ideas. Algorithms and generative scripts werecreated to quickly design multiple structures and claddingoptions. This method was proven to be a successful andadaptable tool for skilled architectural designers in creatingtheir own design tools. A modular system was used toelaborate the complex design of the surface, including itsheight, width, and curvature, which vary depending on thelength. This system presented a smooth surface that couldbe viewed above and beneath. Investigation determinedthat a space truss solution was suitable to the varyingfigures of the canopy. In the modular component-basedsystem, minimal cost varies and presents a structural

in architectural practice. Variables: 1, lack of equipment ortraining in technology; 4, lack of interest in the knowledge ofmaintenance of equipment; 7, inadequate technology transfer;ology maintenance; 10, unavailability of new digital tools;

Figure 4.1 Idealized information flows between different professionals on a project (a) without a central project model and(b) with a central model (Whyte, 2011).


solution that provides different measures in the head andtail areas. Computation design techniques used to designthe roof results in a complex structure with numerouscomponents. However, this new technique allowed thecreation of digital models. The new ones became essentialin the fabrication of the model. Using generative scripts,the canopy structures produced three dimensions that werelaser-cut from digital files to produce seven glazing compo-nents. These elements were assembled by our in-housemodel shop.

3.6. Summary of digital innovation tools

The four basic categories of digital tools (Chart 3.1), i.e.,non-parametric, parametric, building simulation tools,and scripting, have a common goal but different semantics,approaches, and techniques that may be used by thedesigner. Each method has its own weaknesses, strengths,and representational limitations, as well as different cap-abilities in generating different shapes, namely, linear,curvilinear, and free forms. Non-parametric tools are usedsolely for drafting, object modeling, and visualization.Moreover, the logic or underlying factors of the 3D modelsare less prioritized. However, this method has been provento be useful in other disciplines such as graphic arts,filmmaking, and industrial design, where the underlyinglogic of the 3D model is not critical.

Parametric tools are rigid and sometimes computationallytedious compared with traditional digital applications.However, the synergy and flexibility of using the data toparametrically create and change a model is extremelypowerful (Hoffman, 2005). In parametric modeling, thelogic of the object is prioritized, such as in performativearchitecture where building performance serves as a guidingprinciple (Burry, 1999). The principal advantage of para-metric tools is that it allows a degree of flexibility toperform transformations, which results in various configura-tions of the same geometrical components. The differentconfigurations are called “instances” of the parametricmodel. Each instance represents a unique set of transforma-tions based on the values assigned to the parameters.Consequently, design variations are obtained by assigningdifferent values that yield different configurations. Insimple terms, a parametric model allows the designer to

change and reconfigure the geometry without erasing andredrawing.

Building simulation tools are not for form-making andmodeling but are mainly for evaluating and simulatingenvironmental, thermal, climatic, and acoustical factors.Such tools support the understanding of the operation of agiven building according to certain criteria and enablecomparisons of various design alternatives. Thus, such toolsare highly beneficial in quantifying data to provide sustain-able solutions at the early phase of the project.

Scripting is a form-finding technique or computationalprocedure used to solve a problem in a finite number ofsteps. The role of the designer can shift from “spaceprogramming” to “programming space”. The designationof software programs to generate space and form from therule-based logic is inherent in architectural programs,typologies, and building code (Terzidis, 2003). Althoughscripting is considered tedious and distinctly different fromthe three tools, this technique offers enormous benefits toproject design from the form generation stage to structuralsystem integration to component fabrication.

4. Understanding barriers and impedimentsto digital innovation adoption

Digital innovation has commenced in architectural practicebut it poses certain challenges to some firms. Thesechallenges are caused by increasing new technology, currentdemands of topologically non-linear building design, and theissue of sustainability. Several architectural practices areindeed experiencing the challenges triggered by digitalinnovation. These obstacles include constant introductionof new digital technology, increased global competition,increasing demand from clients, limited costs, and limitedsoftware knowledge.

Understanding the adoption of digital innovation inarchitecture is difficult. Furthermore, developing a singleframework or model of innovation adoption is even morechallenging. The reason for the difficulty in developing asingle framework is the unavailability of studies that discusshow architectural practices can adopt to the innovationprocess, and how such a process affects the organization.Also, limited critical analysis has been conducted on the


practice of using digital technologies in building and infra-structure projects (Whyte, 2011).

To understand the barriers and challenges that affect digitalinnovation adoption in architectural organizations, we con-ducted a literature review in allied fields, such as informationscience, business and organizational management, manufac-turing, product design, engineering, and construction.Although these fields were not specifically connected toarchitecture, they revealed several challenges and barriersto adopting digital innovation. These innovations provideddifferent perspectives but shared common attributes and canbe used in establishing the variables in this study.

In digital innovation research focusing on IT management,Whyte (2011) elucidates that the new digital processespresent a “technological black box” with minimal visibilityon the completeness of the design work represented inmodels and drawings. This challenge is caused by thedifficulty in managing client expectations, particularly whenthe design completeness has contractual implications.Boland et al. (2007) explore digital innovation on a project,arguing that the use of 3D digital technologies enableswaves of innovation to propagate across the firms, andidentifying challenges to the use of digital technology andrelated processes.

From a management perspective, organizations engagedin the design of buildings are often complex; they have non-linear and multiple interdependencies among their sub-systems. They attach importance to efficiency when imple-menting innovation. Digital technologies enable new formsof interaction and coupling , increasing the interactivecomplexity in such organizations. Dossick and Neff (2008)argue that a set of leadership skills enables managers indesign organizations to deal with the increasingly tightcoupling of technological solutions within loosely coupledorganizational structures. According to them, analyzingdigital technologies, organizational structures, and pro-cesses is important.

According to Whyte (2011), the manner by which digitalinnovation processes are configured and organized has asignificant effect on delivery. Organizational challengesrelated to process or performance management becomesan issue. In new digital processes, the team is pressured todeliver under traditional timescales despite the longerperiod needed to develop 3D information that may bebeneficial at later stages of the project. The new digitaltools and processes imply widespread organizationalchanges across company boundaries. Whyte (2011) pointedout the implication of the deliverables to the client andregulatory bodies, as well as for the nature and duration ofdifferent stages of design work. Conversely, most of thetechnological infrastructure in practice is taken for grantedby practitioners. Nonetheless, new technologies havebecome salient in day-to-day work because organizationsrequire significant learning, which can hinder the adoptionof digital innovation. Furthermore, Whyte (2011) revealedseveral barriers to adopting digital innovation, such asconsequential problem of digital package coordination,limitations of the 3D modeling package, and challenges infinding competent staff with combined practical construc-tion experience and digital technology skills.

Whyte (2011) added that information management has astrong relationship with project management, suggesting

that (1) new digital tools and processes increase thecoupling between the various disciplines involved in design,implying wider organizational changes across firm bound-aries, and (2) the process change challenges the currentlyinstitutionalized understanding of design stages and effec-tive processes. Whyte concluded that a design organizationoperates within a wider set of institutionalized practices,which include formats for delivery, building regulations,local authority permissions, and construction schedules. Tobe effective, 3D modes of working require a wider processof change because this digital infrastructure for deliverychallenges the established views of the activities at differ-ent stages of the design process (Whyte, 2011). Such asituation has implications for practitioners because thework flow changes the nature of professions. Figure 4.1aillustrates the conventional process. Figure 4.1b presents auseful representation of a new digital work flow in a 3D-oriented manner.

In digital innovation research in the architecture, con-struction, and engineering industries, Johnson and Laepple(2004) argued that business goals, work processes, andadoption of IT are interrelated. That is, changes in businessgoals generally require revising work processes that areenhanced further by the introduction of IT. The researchersconcluded that organization structures of architecture andconstruction have not extremely changed despite theintroduction of computer-aided design. The reasons for thisconclusion are varied and complex. According to Johnsonand Laepple (2004), the minimal change might be caused bythe nature of work processes in the building design andfragmentation of the construction industry. They concludedthat significant issues or barriers related to organizations,expertise, and software, such as acquisition of architectureand real estate expertise, change depending on companyleadership and culture, R&D software investment, and workprocess changes.

Similarly, in a white paper published by Autodesk(Bernstein and Pittman, 2004), three significant barrierswere presented in relation to process management ofbuilding information modeling; these barriers were (1) trans-actional business process evolution, which refers to thevarious technologies available to designers or constructors,which create process possibilities that surpass norms ofpractice and well-understood business protocols; (2) com-putability of digital design information, which refers todigital data in a variety of forms, many of which are notcomputable; and (3) meaningful data interoperability, inwhich some digital data are not accessible to the relevantparties involved in the building process.

In innovation research in engineering and construction,Shabanesfahani and Tabrizi (2012) concluded that one of themajor issues is that knowledge transfer endeavors are hinderedby insufficient understanding of such knowledge transfer andinterrelationships of both firm skills and procedures, as well asthe vision characteristics of the technologies transferred.In addition, existing knowledge transfer mechanisms are notsufficiently notified by or involved in crucial association andorganizational skills and procedures that are essential in helpingthe firms to absorb technologies and use appropriate innova-tions (Shabanesfahani and Tabrizi, 2012). The attributes ofthese barriers boil down to traditional organizational processes,taxation on innovative products to be commercialized, as well


as lack of expertise, technical support, knowledge transfer, andR&D investment (Shabanesfahani and Tabrizi, 2012).

Bogacheva (2003) reveals that the success of underproduc-tion innovations in various organizations is defined by theexistence of (1) forces that assist deduction in the existingsystem condition, and (2) forces that aspire to change. Thiscondition is caused by the innovation, which involves doingsomething new. Moreover, several members are reluctant tochange because of the risk of failure. These organizationalpsychological barriers include personal interests of workersand managers, attitudes with other workers and managers,attitudes during variations, attitudes between initiators, andinnovation introduction organizers and heads. According toBogacheva (2003), organizational psychological barriers arisefrom the rejection of innovations rooted in diverging fromthe individual value orientation. This influence has thestrongest effect on the behavior of the individual barrier,which eventually develops into active and persistent negativeattitudes toward innovation. The reasons behind this psycho-logical barrier on impeding innovation may be economical,such as fear of the absence of a sufficient amount ofnecessary resources; material, financial, labor, technical,characteristics of available materials; and mismatch ofequipment and devices with innovation requirements. Otherreasons involve organizational–psychological factors, such aslack of knowledge in organizing sequential innovation pene-tration and operation, and organizational-administrativeconcerns. No effective way exists for coordination of inter-ests of different units in the whole business entity.

A survey in manufacturing and product design innovationconducted by O’Sullivan (2002) revealed several causes offailure in organizations, such as poor leadership, poor organiza-tion, poor communication, poor empowerment, and poorknowledge management. Other factors include failure, whichis an inevitable part of the innovation process. Most successfulorganizations anticipate an appropriate level of risk. Moreover,these organizations understand that the effect of failuresurpasses the simple loss of investment. Failure can also leadto the loss of morale among employees, increase in cynicism,

Table 5.1 Common determinants and barriers identified in inntechnological barriers, B indicates financial barriers, C indicatesE indicates psychological barriers, and F indicates process barrie

Innovation research from allied fields

Digital innovation management (Whyte, 2007)Digital innovation in AEC (Johnson and Laepple, 2004)Building information modeling (Bernstein and Pittman, 2004)Innovation in construction (Shabanesfahani and Tabrizi, 2012)Innovation research (Bogacheva, 2003)AEC innovation (O’Sullivan, 2002)AEC innovation (Cory and Bozell, 2001)Engineering innovation (Civil Engineering Research Foundation,Architectural innovation (Inchachoto, 2002)Digital innovation management (Yoo et al., 2010)Construction innovation (Jones and Saad, 2003)Innovation research (Walcoff et al., 1981)

and even higher resistance to change in the future. Severalcauses are external and beyond the control of the organization.

Cory and Bozell (2001) conducted similar studies, whichclaim that the benefits of digital technology on the profes-sion fail to resolve practical issues in utilizing new technol-ogies. Thus, these researchers argue that a companyshould consider design costs and time, software learningcurve, software costs, ability of the software to handlecomplex geometry performance, level of detail needed, andadvantages of using the software, as well as partition in themodel among multiple users, integration of the model frommultiple sources, tools for model review and Web publish-ing, and speed in drawing extraction and maintenance.These factors affect the profitability of the company.

A study conducted by the Civil Engineering ResearchFoundation (1996) shows several barriers to innovation inthe building industry, such as risk and liability, financialdisincentives, high equipment cost, inadequate technologytransfer, inadequate basic and industrial R&D, adversarialrelationships, poor leadership, inflexible building codes andstandards, and construction-based initial costs.

Inchachoto (2002) presented important pointers regard-ing technological innovation, as follows: Innovation is bestfostered by team members with prior work experience, asopposed to an assembly of individuals selected solely on thebasis of expertise. Collaboration in innovation is useful anddistinctively serves multiple functions, such as technical riskreduction, financial security, and psychological assurance.Project logistics is also important, such as external funding,research collaboration, technical evaluation, demonstra-tion, and validation. Allocated budgets for research has anintegral role in technological innovations.

Yoo et al. (2010), in their digital innovations research focusedon the process, identified several barriers such as performanceof the software, ability of the software to handle complexgeometry, integration of the model into multiple sources,speed, and drawing extraction. Such barriers are similar tothe organizational obstacles elucidated by Jones and Saad(2003) in their innovation research in construction, namely,

ovation research in different allied fields, where A indicatesorganizational barriers, D indicates governmental barriers,rs.

Common barriers

A B C D E F

o o oo o o o

o oo oo o o o

oo o o

1996) o o oo o oo oo o o o oo o o o

Table 6.1 Number of firms as study respondents.

Size of architectural organization N

Small (1–10 employees) 15Medium (11–50 employees) 15Large (50 or more employees) 15

Table 6.2 Variables for technological barriers used inthe survey questionnaire.

Technological barriers

1 Lack of equipment or computers2 Insufficient knowledge of team members3 Lack of training in technology4 Lack of interest in the knowledge of digital

technology5 Lack of technical demonstration6 Inadequate maintenance7 Inadequate technology transfer8 Inadequate R&D knowledge9 Insufficient skills in the technology

10 Insufficient skills in the maintenance of technology11 Unavailability of new digital tools


inherent problems in innovation, such as lack of mutualrecognition of the need for innovation, insufficient technicalcapabilities and skill levels, reluctance to change, inexper-ienced team members, lack of training, weak commitment andsupport by the top management, inadequate resources, lack ofintegration and collaboration, lack of learning environment,lack of incentives, and non-compliance with the existingregulations and established standards.

5. Common attributes of barriers observed

Although the application and multiple categorization ofissues and barriers presented previously are from differentpractices, common attributes are observed (Table 5.1) fromvarious studies on innovation in different allied fields. Thesebarriers to innovation coincide with the process barriersdetermined by Walcoff et al. (1983), which are summarizedinto six distinct groups, such as technological barriers,financial barriers, organizational barriers, governmentalbarriers, psychological barriers, and process barriers.Recognizing these barriers as “challenges” to the innovationprocess, we highlight the purpose of establishing a frame-work for this study.

6. Research methodology

The scope of this study focuses on evaluating the barriersand key factors that impede digital innovation adoption indifferent sizes of architectural organizations. Through theresearch questions mentioned in Section 1, six generalvariables (technological, financial, organizational, govern-mental, psychological, and process barriers) were tested. Asshown in the literature review, each of these groups hasspecific variables summarized in Tables 6.2–6.7 based oninnovation research in construction, engineering, and pro-duct design and on industrial and manufacturing studiesconducted by O’Sullivan (2002), Johnson and Laepple(2004), Cory and Bozell (2001), Civil Engineering ResearchFoundation (1996), Inchachoto (2002), Yoo et al. (2010),Jones and Saad (2003), Bernstein and Pittman (2002),Shabanesfahani and Tabrizi (2012), Bogacheva (2003), andWalcoff et al. (1983) previously mentioned in Section 4.

6.1. Survey respondents

Forty-five architectural practices were selected on the basisof digital innovation experience and size of the architec-tural organization. Singapore was chosen as the model forthe study because the country has the availability ofresources, such as digital tools, complexity of projects,skills, knowledge transfer, and presence of a variety of sizesof architectural practices utilizing digital innovation.Through experience and observations, digital innovationsexist in several architectural organizations in Singapore.

6.1.1. Experience in digital innovationOnly architectural practices with experience in digitalinnovation were selected. Thus, the relevant data neededin this study can be efficiently provided by organizationsthat have such experience or at least have attempted to

adopt digital innovation in their projects. These companieshave used digital innovations for the purpose of formfinding, BIM, optimization, or different design processesusing digital tools, as stated in the literature review.

6.1.2. Size of architectural organizationTo ensure that correlation of the size of the architecturalorganization and barriers to digital innovation adoption isevaluated, 45 firms were selected based on their size. Thesecompanies include (1) small architectural practices thathave 5–10 employees, (2) medium-sized architectural prac-tices that have 11–50 employees, and (3) large architecturalpractices that have 51 or more employees. The purpose ofgrouping these architectural firms is to evaluate the fourthobjective of this study, which is to determine whether thesize of the organization has a significant relationship withbarriers to digital innovation adoption (Table 6.1).

6.2. Variables

After analyzing the challenges and impediments presented inSection 4, we classified these challenges were into six cate-gories, such as technological, financial, organizational, govern-mental, psychological, and process barriers; and we used thesecategories as research variables (Tables 6.2–6.7). These chal-lenges were derived from innovation research from allied fieldsconducted by Whyte (2011), Johnson and Laepple (2004),Bernstein and Pittman1 (2004), Shabanesfahani and Tabrizi(2012), Bogacheva (2003), O’Sullivan (2002), Cory and Bozell(2001), Civil Engineering Research Foundation (1996),

Table 6.6 Variables of psychological barriers used inthe survey questionnaire.

Psychological barriers

1 Fear of work changes2 Lack of psychological assurance3 Fear of product change4 Fear of failure5 Fear of process change6 Fear of new marketing changes7 Fear of increase in labor cost8 Fear of profit loss9 Lack of trust in digital technology

Table 6.7 Variables of governmental barriers used inthe survey questionnaire.

Governmental barriers

1 Inflexible building codes2 Drawings still submitted in hard copy3 Drawings do not use digital copies from digital

innovations4 High standard of digital modeling and procedure

established by the government for drawing submissions

Table 6.8 Relative to their qualitative description, thescore is 0 if the barrier is not encountered and 1–5 ifrelative importance is low to high.

Mean ranges Qualitative interpretation

0 Barrier was not encountered1 Slightly crucial2 Moderately crucial3 Often crucial4 Very crucial5 Extremely crucial

Table 6.3 Variables for financial barriers used in thesurvey questionnaire.

Financial barriers

1 Inadequate design fee to support digital innovation2 Insufficient budget for digital innovation3 Unwillingness of firm to spend a large amount on

digital tools4 High cost of digital tools5 High cost of setting up equipment6 Lack of budget for team training7 Financial disincentive8 High cost of equipment maintenance9 Lack of practice-based cost support for digital

innovation10 Lack of R&D budget11 High salary for staff who have knowledge of new

digital tools

Table 6.4 Variables for organizational barriers used inthe survey questionnaire.

Organizational barriers

1 Poor leadership toward digital innovation2 Poor organization attitude to innovation3 Lack of empowerment and support for digital

innovation4 Poor knowledge management5 Lack of managers to supervise digital innovation6 Inadequate personnel to implement digital innovation7 Adversarial relationship among staff8 Lack of teamwork9 Lack of collaboration

10 Insufficient team commitment11 Lack of support from managers and staff

Table 6.5 Variables for process barriers used in thesurvey questionnaire.

Process barriers

1 Performance of digital tools or software2 Slow speed of computers in processing and drawing

extraction3 Mobility of software to handle complex geometry4 Disintegration of 3D models to multiple sources5 Limited availability of digital tools to deliver digital

innovation6 Inadequate level of detail needed for the 3D models7 Slow data processing of 3D models


Inchachoto(2002), Yoo et al. (2010), Jones and Saad (2003), andWalcoff et al. (1981). Using these “challenges” as variables issignificant in this study.

6.3. Data collection and gathering

The data were primarily collected through a structured surveyquestionnaire. This method is an efficient means of gatheringinformation from selected architectural firms and simulta-neously validates the data and gains immediate feedback. Atleast one manager or senior architect at managerial levelinvolved as a key project participant was interviewed.

6.4. Method of analysis

For the first, second, and third research questions, whichare used to obtain the data relative to barriers to digitalinnovation adoption and to determine how crucial and


significant these barriers are, the mean scores were com-puted using a set of ranges with their qualitative descriptionscoring, such as 0 if the barrier is not encountered and 1–5 ifrelative importance is low to high (Table 6.8).

For the fourth research question, which is evaluatingwhether a significant relationship exists between digital innova-tion barriers and organizational size, multiple regression ana-lyses were used. The data were evaluated through statisticalmultiple regression analysis and Sheffe post hoc test of theaccuracy and reliability of the results, respectively.

7. Presentation of data and analysis

7.1. Barriers to digital innovation adoption

The firms answered the following research questions:“What are the barriers that architectural organizations

Table 7.1 Mean score of technological barriers based on theobserved barrier; the lower the mean score, the lower the obse

Size of architectural organization How crucial are technologic

1 2 3 4

Small 4.4 4.3 4.3 4.5Medium 3.7 3.2 4.3 4.8Large 1 1 1.1 1.1

Table 7.2 Mean score of organizational barrier based on theobserved barrier; the lower the mean score, the lower the obse

Size of architectural organization How crucial are organizatio

1 2 3 4

Small 4.2 4.2 4.1 6.4Medium 4.1 4.3 4.2 2.3Large 0.6 0.6 0.9 1

Chart 7.1 Chart showing how crucial technological barriers arementioned variables. Variables: 1, lack of equipment or computers; 2technology; 4, lack of interest in the knowledge of digital tecmaintenance of equipment; 7, inadequate technology transfer; 8, inmaintenance; 10, unavailability of new digital tools; 11, inadequat

encountered when adopting digital innovation?” and “Howcrucial are these barriers when digital innovation isimplemented?”

7.2. Technological barriers to innovation adoption

In summary, the descriptive statistics in Table 7.1 show thattechnological barriers are significantly present and extremelycrucial in small and medium-sized architectural organizations,but not in large ones. The pattern of the results indicate that,among the 11 variables, 3 variables, namely, inadequatemaintenance of equipment, inadequate technology transfer,and inadequate technical support, are uniformly not crucial inall groups of architectural organizations. Overall, the resultsindicate that technological barriers are more crucial in smallerarchitectural organizations.

size of the firm. The higher the mean score, the higher therved barrier.

al barriers to digital innovation adoption?

5 6 7 8 9 10 11 Overall

3.8 1.1 1.3 4.5 4.9 4.6 1.9 3.23.5 1.2 1.5 4.3 4.9 4.7 1.7 31.5 1.5 1.5 1.2 1.5 1.5 1.6 0.9

size of the firm. The higher the mean score, the higher therved barrier.

nal barriers to digital innovation adoption?

5 6 7 8 9 10 11 Overall

4.3 4.2 4.7 4.1 4.4 4.7 5 4.22.1 4.3 1.1 1.1 1.4 1.7 1.5 2.21.9 0.9 0.7 1.1 1.5 1.6 1.3 0.7

, depending on the size of the firm based on the previously, insufficient knowledge of team members; 3, lack of training inhnology; 5, lack of technical demonstration; 6, inadequatesufficient skills in technology; 9, insufficient skills in technologye in-house technical support.

Table 7.3 Mean score of financial barrier based on the size of the firm. The higher the mean score, the higher the observedbarrier; the lower the mean score, the lower the observed barrier.

Size of architectural organization How crucial are financial barriers to digital innovation adoption?

1 2 3 4 5 6 7 8 9 10 11 Overall

Small 4.2 4.4 3.9 4.9 4.8 4.6 1.3 4.7 4.7 1.4 1.5 3.3Medium 4.4 4.3 4.4 4.5 4.5 4.4 0.9 1.3 4.9 4.7 4.8 3.5Large 1 0.7 1 4.7 1.2 1.1 0.9 1.3 4.9 1.7 1.1 1.4

Chart 7.3 Chart showing how crucial financial barriers are, depending on the size of the firm based on the previously mentionedvariables. Variables: 1, inadequate design fee to support digital innovation; 2, insufficient budget for digital innovation;3, Unwillingness of firm to spend a large amount on digital tools; 4, high cost of digital tools; 5, high cost of setting up equipment;6, lack of budget for training the team; 7, financial disincentive; 8, high cost of equipment (computer) maintenance; 9, lack ofpractice-based cost support for digital innovation; 10, lack of R&D budget; 11, High salary for staff who have knowledge of newdigital tools.

Chart 7.2 Chart showing how crucial organizational barriers are, depending on the size of the firm based on the previouslymentioned variables. Variables: 1, poor leadership toward digital innovation; 2, poor organizational attitude to innovation; 3, lack ofempowerment and support for digital innovation; 4, poor knowledge management; 5, lack of manager to supervise digitalinnovation; 6, inadequate personnel to implement digital innovation; 7, adversarial relationship among staff; 8, lack of teamwork;9, lack of collaboration; 10, insufficient team commitment; 11, lack of support from managers and staff.


7.3. Organizational barriers to digitalinnovation adoption

As shown in Table 7.2, organizational barriers are observed tobe more crucial in small architectural organizations. Organiza-tional barriers boil down to the support of staff and managersfor digital innovation and willingness to adopt change. Largerarchitectural organizations have sufficient resources and

support from the organization are less affected by organiza-tional barriers. However, when staff and managers do notsupport digital innovation, as in the case of smaller architec-tural organizations, digital innovation cannot be implementedeasily. Using the results shown in Table 7.2 and Chart 7.2, wededuced that smaller architectural organizations are signifi-cantly affected by the organizational barriers (Table 7.1,Chart 7.1).

Table 7.4 Mean score of process barrier based on the size of the firm. The higher the mean score, the higher the observedbarrier; the lower the mean score, the lower the observed barrier.

Size of architecturalorganization

How crucial are process barriers to digital innovation adoption?

1 2 3 4 5 6 7 Overall

Small 4.3 4.4 4.5 4.8 0.7 4.5 4.8 3.7Medium 4.6 4.1 4.3 4.6 0.9 4.4 4.9 3.7Large 4.6 4.4 4.1 4.8 1.3 4.4 4.7 3.8

Table 7.5 Mean score of psychological barrier based on the size of the firm. The higher the mean score, the higher theobserved barrier; the lower the mean score, the lower the observed barrier.


How crucial are psychological barriers to digital innovation adoption?

1 2 3 4 5 6 7 8 9 Overall

Small 4.5 4.4 4.5 4.6 4.5 4.5 4.7 4.8 4.7 4.3Medium 4.1 3.9 4.6 4.1 3.4 1.3 4.1 3.2 4.5 3.3Large 0.3 0.7 1 0.8 0.9 1.1 1.2 1.1 1.1 0.6

Chart 7.4 Chart showing how crucial process barriers are, depending on the size of the firm based on the previously mentionedvariables. Variables: 1, performance of digital tools or software; 2, slow speed of computers in processing and drawing extraction;3, mobility of software to handle complex geometry; 4, disintegration or fragmentation of 3D models to multiple sources; 5, limitedavailability of digital tools to deliver digital innovation; 6, inadequate level of detail needed for 3D models; 7, slow data processingof 3D models.


7.4. Financial barriers to digitalinnovation adoption

The overall results and mean scores of each group arereported in Table 7.3. We conclude that financial barriersare signicantly present and extremely crucial in smalland medium-sized architectural organizations rather thanlarge architectural organizations. Financial barriers arenot crucial in large architectural organizations becausethese organizations earn substantial professional fees forlarge projects, are economically stable, and can supporttheir financial needs to adopt innovation. Smaller architec-tural organizations with smaller projects and smaller feesare less able to afford the cost of digital innovation(Chart 7.3).

7.5. Process barriers to digitalinnovation adoption

In Chart 7.4, all variables are shown to be crucial exceptfor one, which is the limited availability of digital tools todeliver digital innovation. We conclude that processbarriers boil down to the performance of computers,fragmentation of 3D models, and data processing, whichare evidently not correlated with the size of theorganizations.

The overall results in Table 7.4 show that process barriersare observed to be slightly equally crucial in all architecturalorganizations, with mean scores of 3.7–3.8. We deduced thatthe size of architectural organizations does not correlatewith process barriers to digital innovation adoption.

Chart 7.5 Chart showing how crucial psychological barriers are, depending on the size of firm based on the previously mentionedvariables. Variables: 1, fear of work changes; 2, lack of psychological assurance; 3, fear of product change; 4, fear of failure; 5, fearof process change; 6, fear of new marketing changes; 7, fear of increase in labor cost; 8, fear of profit loss; 9, lack of trust in digitaltechnology.

Table 7.6 Mean score of governmental barrier based on the size of the firm. The higher the mean score, the higher theobserved barrier; the lower the mean score, the lower the observed barrier.


How crucial are governmental barriers to digital innovation adoption?

1 2 3 4 Overall

Small 0.67 0.53 0.80 4.67 1.5Medium 1.40 0.67 1.20 4.73 1.8Large 1.1 0.8 0.87 1.2 0.8

Chart 7.6 Chart showing how crucial governmental barriers are, depending on the size of the firm based on the previouslymentioned variables. Variables: 1, inflexible building codes; 2, submission of drawings in hard copy; 3, submission of drawings doesnot use digital copies from digital innovations; 4, high standards of digital modeling and procedure established by the governmentfor drawing submissions.


7.6. Psychological barriers to digitalinnovation adoption

As shown in Table 7.5, psychological barriers are more crucialto small architectural organizations (mean score of 4.3),followed by medium-sized architectural organizations (meanscore of 3.3) and large architectural organizations (mean scoreof 0.60). Considering these results, we deduced that largearchitectural organizations have support from the manage-ment, have available resources, and are perceived to be morepsychologically secured than smaller organizations with lim-ited resources. One notable result is that the smaller the

architectural organization, the more affected it is by psycho-logical barriers (Chart 7.5).

7.7. Governmental barriers to digitalinnovation adoption

In Table 7.6, the result of the descriptive statistics showthat governmental barriers are not crucial to digital innova-tion adoption among architectural organizations. However,one variable is considered crucial: high standards of digitalmodeling and procedure established by the government fordrawing submissions. We obtained this result because the


model for this study is Singapore, where the authoritiesstrictly regulate the standards for submitting BIM models.However, this variable is not crucial in other countrieswhere authorities do not regulate the BIM models. Insummary, governmental barriers are not crucial to digitalinnovation adoption (Chart 7.6).

7.8. Most significant barriers to digitalinnovation adoption

The firms answered the following research question: “Whichamong the barriers is the most significant in digital innova-tion adoption?”

7.8.1. Most significant barrier to digital innovation insmall architectural organizationsThis section is presented and evaluated to analyze andanswer the following research question: “Which among thedigital innovation barriers is the most significant?”

In Table 7.7, the result of the descriptive statisticsshowed that, in small-sized architectural organizations,the most significant barrier is organizational, whereas theleast significant is governmental.

7.8.2. Most significant barrier to digital innovation inmedium-sized architectural organizationsIn Table 7.9, the overall result of the descriptive statisticsshow that, in medium-sized architectural firms, the most

Table 7.7 Descriptive statistics of the most significantbarrier in small-sized architectural organizations.

Digital innovationbarriers

N Mean Standarddeviation

Technological barrier 15 35.3333 4.48277Organizational

barrier15 45.9333 6.9741

Financial barrier 15 36.0000 4.42396Process barrier 15 26.1333 2.13363Psychological barrier 15 38.2667 3.71227Governmental barrier 15 6.0000 1.88982

Note: The higher the mean score, the more likely that thebarrier is the most significant in digital innovation adoption.

Table 7.8 Descriptive statistics of the most significantbarrier in medium-sized architectural organizations.




barrier15 23.6667 4.53032



significant barrier is financial, whereas the least signifi-cant is governmental among all the barriers presented(Table 7.8).

7.8.3. Most significant barrier to digital innovation inlarge architectural organizationsAs shown in Table 7.10, the most significant barrier is theprocess barrier, specifically in large architectural firms.Typically, as in small and medium-sized architectural orga-nizations, governmental barriers are observed to be theleast significant.

Chart 7.7 shows that significant barriers vary dependingon the size of architectural organizations. A notable result isthat organizational barriers are the most significant in smallarchitectural firms, whereas financial and process barriersare significant in medium-sized and large architecturalfirms, respectively. These barriers vary depending on thesize of the organization.

7.8.4. Overall result for most significant barrier todigital innovationIn Chart 7.8, the descriptive statistics show a significantresult in evaluating the most significant barrier amongthe six digital innovation barriers presented. Among thesix digital innovation barriers presented in this study, thefinancial barrier is observed to be the most significant indigital innovation adoption.

Table 7.9 Descriptive statistics of the most significantbarrier in large architectural organizations.




barrier15 7.6000 3.83219



Table 7.10 Overall results of descriptive statistics ofthe most significant barrier.




barrier45 25.7333 16.71336


Chart 7.7 Summary of the distribution of mean scores showing the degree of digital innovation barriers in each architectural firm.

Chart 7.8 Overall results for the most significant barrier todigital innovation.

Table 7.11 Dependent variable: total mean score fordigital innovation barriers.

Size of architecturalfirms

Mean Standarddeviation

N

Large 11.3447 1.75841 15Medium 26.4993 2.13441 15Small 31.2780 2.32134 15Total 23.0407 8.83137 45


7.9. Significant relationship between digitalinnovation barrier and size ofarchitectural organization

The firms answered the following research question: “Isthere a significant relationship between the size of thearchitectural organization and barriers to digital innovationadoption?” (Table 7.11).

Table 7.12 shows a significant relationship between the sizeof the architectural organization and digital innovation barriers,with F=373 and significance level at p=0.000. The relationshipbetween the two variables showed that larger architecturalfirms have lower digital innovation barriers, whereas smallerfirms have higher digital innovation barriers. Approximately 94%of digital innovation barriers can be explained by the size of thearchitectural organization.

In Table 7.13, the Sheffe post hoc test showed that allcomparisons indicated significant differences. With significancelevel at p=0.000, the results show that large architectural firmshave lower digital innovation barriers compared with medium-sized and small architectural organizations. A correlationbetween the size of the architectural organization and barriersto digital innovation adoption is observed. We conclude that thelarger the architectural organization, the lower the barrierto digital innovation adoption; the smaller the architec-tural organization, the higher the barrier to digital innovationadoption.

8. Conclusions

The findings of this study are summarized based on the datapresented. An analysis of these findings led to certain conclu-sions. In evaluating the six subsequent barriers (technological,financial, organizational, governmental, psychological, andprocess barriers) among architectural organizations, the find-ings showed interconnected barriers that vary depending onthe size of the architectural organization.

Small architectural organizations are extremely affectedby financial, organizational, and psychological barriers thatresult in technological problems with significant conse-quences on the company. These interconnected barriersmainly result from the small projects of small-sized archi-tectural organizations, which earn limited design fees thatare insufficient to cover the cost of technology, software,and other logistical needs for digital innovation. The con-sequences of limited resources are the lack of psychologicalassurance and fear of profit loss. In effect, managers arereluctant to support digital innovation. Aside from financial,organizational, and psychological barriers, process barriersare also present in small architectural organizations. Pro-cess barriers boil down to 3D modeling and processing. Thisfinding is correlated with the upgrade of the equipment andsoftware, which is too expensive for small architecturalorganizations with limited funds. In conclusion, technologi-cal, financial, organizational, psychological, and processbarriers are extremely crucial in small architecturalorganizations.

Similarly, medium-sized architectural organizations areaffected by a series of barriers. However, such organizationsare able to cope with the changing needs brought about bydigital technology. Thus, the organization can support digitalinnovation because the organization is less affected by

Table 7.13 Multiple comparisons between the size of organizations and digital innovation barriers using Sheffe posthoc test.

Firm (I) Firm (J) Mean difference (I�J) Standard error Sig. 95% Confidence interval

Lower bound Upper bound

Large Medium �15.1547* 0.76118 0.000 �17.0863 �13.2230Small �19.9333* 0.76118 0.000 �21.8650 �18.0017

Medium Large 15.1547* 0.76118 0.000 13.2230 17.0863Small �4.7787* 0.76118 0.000 �6.7103 �2.8470

Small Large 19.9333* 0.76118 0.000 18.0017 21.8650Medium 4.7787* 0.76118 0.000 2.8470 6.7103

Based on observed means, the error term is Mean Square (Error)=4.345.nThe mean difference is significant at the 0.05 level.

Table 7.12 Test between size of organizations and digital innovation barriers.

Source Type III sum of squares df Mean square F Sig.

Corrected model 3249.187a 2 1624.593 373.860 0.000Intercept 23,889.254 1 23,889.254 5497.527 0.000Size of architectural firm 3249.187 2 1624.593 373.860 0.000Error 182.509 42 4.345Total 27,320.950 45Corrected total 3431.696 44

aR2=0.947 (adjusted R2=0.944) 94%.


psychological barriers. One reason for this finding is that themedium-sized architectural organization is capable of obtaininglarger projects with higher design fees that can cover the costof technology, software, and other logistical requirements ofdigital innovation. In summary, medium-sized architecturalorganizations are moderately affected by financial barriers.With the support of the managers, these architectural organi-zations are not significantly affected by technological, organi-zational, and phsycological barriers.

Large architectural organizations are less affected by barrierscompared with small and medium-sized firms because largeorganizations have substantial projects with considerable designfees that can support digital innovation. One reason for fullyimplementing digital innovation in large architectural organiza-tions is the need to be competitive with other architecturalfirms. Notably, large architectural organizations frequentlycollaborate with universities in computational design research.Thus, the knowledge transfer from leading research institutescan be applied to actual projects. Although the descriptivestatistics have shown that process barriers are the most crucialin large architectural organizations, the actual case is differ-ent. Large-sized architectural organizations are able to copewith process barriers. Complex projects have heavier 3Dmodels and slower data processing, but data processing isfacilitated by support from organizations, the use of powerfulequipment, and collaboration with computational designresearch institutions.

In summary, financial barriers are the most crucial amongthe six barriers presented. This barrier has a more

significant effect than technological, organizational, andpsychological barriers. When an architectural organizationis financially incapable, it is more psychologically affectedand does not support digital innovation. Thus, we concludethat financial, technological, organizational, and psycholo-gical barriers are interrelated. Although these four barriersare interrelated, we can also deduce that process barriers,which boil down to 3D modeling and processing, can also becrucial. However, process barriers do not correlate withfinancial, technological, organizational, and psychologicalbarriers. Governmental barriers, such as building codes andother compliance documents, are not crucial.

One of the most valuable results of this study is evaluat-ing the correlation between the size of the organization andbarriers to digital innovation adoption. We observed thatthe size of the architectural organization and barriers todigital innovation are significantly correlated. This findingmeans that larger architectural organizations face fewerbarriers, whereas smaller architectural organizations facemore barriers to digital innovation. Therefore, the largerthe architectural organization is, the less affected it is bybarriers to digital innovation adoption.

Research on digital innovation in architectural organiza-tions is limited and evaluating the challenges and barriers inrelated fields is significant. The extensive range of barrierspresented indicates a series of problems in adopting digitalinnovations that vary in different sizes of architecturalorganizations. These innovations should be considered andsupported by the organizations themselves. The new digital


technology is proven to improve efficiency, productivity, anddesign quality but its full potential has not been tapped.Therefore, the barriers to digital innovation shouldbe considered by architectural organizations so that newtechnologies can be used to their full advantage.

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Critical analysis of key determinants and barriers to digital innovation adoption among architectural organizations