critical analysis of key determinants and barriers to digital innovation adoption among...

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CRITICAL ANALYSIS OF THE KEY DETERMINANTS AND BARRIERS

IN DIGITAL INNOVATION ADOPTION AMONG ARCHITECTURAL

ORGANIZATIONS

RUNDDY RAMILO AND MOHAMED RASHID BIN EMBI

Department of Architecture, Faculty of Built Environment

University of Technology Malaysia

[email protected] and [email protected]

Abstract. As modern world develops and utilizes design technology for architecture,

different design methodologies have emerged. Current design research have focused on

computationally mediated design process in which essentially concerned with form finding

and building performance simulation i.e. structural, environmental, constructional and cost

performance through the integration of physics and algorithms. Since its emergence, design

practices are increasingly aided by and dependent on the technology and have resulted to

major paradigm shift. However, while technological advancement of the new technology has

the potential for dramatically improving design and productivity, related literature shows that

substantial technical and organizational barriers exist that inhibits the effective adoption of

these technologies. It happens in architectural practice and the impact varies differently on

the size of architectural organization. To gain insights of the problem, an in-depth research

study was conducted from several small, medium and big architectural organizations. This

involves in-depth evaluation of technological, financial, organizational governmental,

psychological and process barriers when digital innovation is adopted. It has found out

relevant attributes and pattern of variables that can be used in establishing a framework for

digital innovation adoption. Valuable findings of this study reveal that smaller architectural

organizations present more barriers to digital innovation than those of bigger architectural

organizations. This study is very important because research in digital innovation in

architecture that address barriers in different size of architectural organization still very

limited.

Keywords: Digital Innovation, Architectural Organizations, Technologies, Digital Innovation

Barriers

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1. INTRODUCTION

While technological advancement of the new technology has the potential for

dramatically improving design and productivity, related literature shows that substantial

technical and organizational barriers exist that inhibits the effective adoption of these

technologies (Leach & Gou, 2007), (Johnson & Laepple 2004) & (Intrachooto, 2002).

Despite of the abundant availability of digital technologies, digital innovation does not occur

because few knowledge and resources are transferred from one project to another. This

occurs when the purpose between projects is dissimilar or projects do not include members

of the previous team who has relevant skills or knowledge of the technology. Additionally,

Cory & Bozell (2001) found out that though architects and designers have acknowledged

that the advent of computer aided architectural design in the design process can save an

abundance of time and energy, these tools are not being utilized to their full potential. The

benefits of intelligent modeling to the design process are to increased productivity, reduced

cycle time, better work flow and life cycle applications but these technologies are not fully

utilized (Fallon, 2004).

While technological advancement of the new technology has the potential for

dramatically improving design and productivity, related literature shows that substantial

technical and organizational barriers exist that inhibits the effective adoption of these

technologies (Leach & Gou, 2007), (Johnson & Laepple 2004) & (Intrachooto, 2002).

Despite of the abundant availability of digital technologies digital innovation does not occur

because few knowledge and resources are transferred from one project to another. This

occurs when the purpose between projects is dissimilar or projects do not include members

of the previous team who has relevant skills or knowledge of the technology. Additionally,

Cory & Bozell (2001) found out that though architects and designers have acknowledged

that the advent of computer aided architectural design in the design process can save an

abundance of time and energy, these tools are not being utilized to their full potential. The

benefits of intelligent modeling to the design process are to increased productivity, reduced

cycle time, better work flow and life cycle applications but these technologies are not fully

utilized (Fallon, 2004). Undeniably, the digitalization of design practices has not been

trouble-free. Business profits as one of the major goals of design practice are at risk when

implementing digital innovation.

Innovation implies to newness of process, or new way of doing something, and

therefore businesses are at risk of failure (Davila et al, 2006). While innovation typically

adds value, innovation may also have a negative or destructive effect as new developments

clear away or change an old organizational forms and practices. The negative impact varies

from the size of organization (Davila et al, 2006). This area of research has not fully been

explored yet. This highlights the purpose of this paper.

To gain insights of the problem, this research paper investigates the key

determinants that impede the effective adoption of digital innovation in architectural

practices whereby projects are computationally and digitally driven. Specifically it seeks to

answer the following research questions: (1) What are the barriers that architectural

organization encountered when adopting digital innovation? (2) How crucial are these

barriers to the adoption of digital innovation? (3) Which among the barrier is the most

significant when adopting digital innovation? (4) Is there a significant relationship between

the size of architectural organization and barriers in digital innovation adoption?

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2. UNDERSTANDING DIGITAL INNOVATION IN ARCHITECTURE

Digital innovation on the area of information science refers to new combinations of

digital and physical components to produce novel products or services or to the embedding

of digital computer and communication technology into a traditionally non-digital product or

service (Yoo et al, 2010), (Henfridsson et al, 2009), (Svensson, 2012).

While the definition of digital innovation in information science is developed, there

is no well-established definition of digital innovation in the context of architecture.

Literature of the subject shows that theories of adopting digital innovation are very limited.

It is evident that digital innovation in architectural organizations is already happening. These

are focused on process innovation on the use of digital technology to find novel solutions of

important problems in order to improve building design and organization’s productivity.

Despite the rapid changes in digital technology are revolutionizing specific types of digital

innovation, this is not recognized as digital innovation but a user-end application of

computer aided design technology. Since there is the possibility of confusion of the term, it

is necessary to first define innovation, types of innovation and what kind of innovation is

digital innovation in architecture because of the lack of literature of the subject.

Innovation is a new way of doing something that is made useful. It may also refer to

incremental, emergent or radical and revolutionary changes in thinking, products, processes,

or organizations. It is defined as the introduction of new elements or a new combination of

old elements in industrial organizations (Schumpeter 1934). Innovations are not represented

solely of breakthroughs. Generally, innovation is the process where a good idea or creation

of new knowledge concerning a product or process begins to affect its context.

To summarize the meaning of innovation, it can be generally defined as the

successful implementation of new ideas from which commercial values are generated. It is

pervasive and diverse, and is usually associated with, but not limited to, technological

advancement. Innovations may be introduced in the market (product innovation) or used

within a production process (process innovation). Innovations therefore involve a series of

scientific, technological, organizational, financial and commercial activities. All these

innovation activities share common goals that are to promote economic growth through

improved performance and enhanced competitiveness.

In other words “innovation” includes both technical innovations, which consist of

application of scientific advancement or other research, and process innovations, which

constitute a novel and improved way of doing things without necessarily including any

scientific advance. These two are intimately linked and interdependent (Thomas & Bone,

2000). Yet innovations are not limited to product innovation or “objects” or technological

innovations.

In architecture, engineering and construction research (Male and Stocks, 1989) they

have coined that there are four distinct types of innovation (1) Technological innovation,

which utilizes new knowledge or techniques to provide a product or service at lower cost or

higher quality, (2) Organization innovation, which does not require technological advances

but involves “social technology” that is changing the relationship between behaviors,

attitudes and values. (3) Product innovation, which may have a low hardware dependency,

provides better utilization of resources and involves advances in technology resulting in

superior products or services. (4) Process innovations, which substantially increase

efficiency without significant advances in technology.

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Relevant definition of process innovation is an implementation of a new or

significantly improved production or delivery method including significant changes in

techniques, equipment and or software (Svensson, 2011). Likewise, process innovation is

defined as the introduction of new elements into a firm’s production or service operation to

produce a product or render a service (Rosenberg, 1982), (Utterback and Abernathy, 1975)

with the aim of improving productivity, capacity, flexibility, quality, reducing costs,

rationalizing production processes (Edquist, 2001), (Simonetti et al., 1995) and lowering

labor costs (Vivarelli and Toivanen, 1995), (Vivarelli and Pianta, 2000). Following

innovation research of Reichstein and Salter, (2006) process innovation is related to new

capital equipment (Salter, 1960) and the practices of learning-by-doing and learning-by-

using (Cabral and Leiblein, 2001), Similarly, (Hollander, 1965) defines process

development as “the implementation of new or significantly improved production or delivery

methods. This includes significant changes in techniques, equipment and/or software”. By

reviewing the literature of process innovation (Svensson, 2011), (Edquist, 2001), (Simonetti

et al., 1995), (Vivarelli and Toivanen, 1995), (Vivarelli and Pianta, 2000), digital innovation

in architecture is one that can be considered as process innovation.

Therefore, digital innovation in the context of architecture can be defined as “the

use of new digital channels, digital tools and relevant methodologies to improve the

operation of the architectural organizations, delivery of services and building design”. In

other words, new design processes refers to computationally mediated processes and differs

from the conventional paper based architecture by independently using parametric modelling

tools, building performance simulation tools, scripting or algorithms that can be reinforced

by the conventional non-parametric tools and other relevant methodologies. Its attribute is to

improve but not limited to (1) architectural design through form finding, facade

optimization, digital fabrication, material assembly, cost optimization (2) sustainability by

using the building performance simulation tools by evaluating energy efficiency, airflows,

daylighting, wind analysis and the implication of climate to architectural forms, (3)

structural conceptualization by finite element analysis to investigate structural behavior and

stability (4) improving productivity to achieve less time and cost.

3. REVIEW OF DIGITAL TOOLS AND DIGITAL INNOVATION IN

ARCHITECTURAL ORGANIZATIONS

3.1 NON-PARAMETRIC GEOMETRIC MODELLING

Non-parametric computer-aided design (CAD) belongs and credited to Ivan

Sutherland, who in 1963 developed special graphics hardware and a program called

‘Sketchpad’ for his PhD dissertation (Eastman, 1999). Through the years, computer-aided

design system evolves and has been developed in an evolutionary manner in which the

development of algorithms for drawing curves, panning and scaling images are further

intensely research. Many of the research were focused on the development for building use

such as representation and manipulation of geometry. Following on early 1980s architects

began using computer-based CAD. The familiar layer metaphor that originated with pin-bar

drafting was easily adapted to the layer-based CAD systems, and within a few years a large

percentage of construction documents and shop drawings were plotted from computers

rather than being manually drafted on drawing boards. (Autodesk, 2006).

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https://www.researchgate.net/publication/4992920_Adoption_of_a_Process_Innovation_With_Learning-by-Doing_Evidence_From_the_Semiconductor_Industry?el=1_x_8&enrichId=rgreq-3c58fb96-b92c-4a63-867f-3fe6add15db0&enrichSource=Y292ZXJQYWdlOzI2MzE4NDExMztBUzoxMjI4Mjk5NDA5ODk5NTNAMTQwNjI5NjM0ODQzMg==

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Non-parametric modelling can be considered as conventional digital tools. Through

the years it was proven efficient tools for construction detailing and visualization. It is

dominated by four high-end packages SketchUp from Last Software, AutoCAD from

Autodesk, 3D Studio Max from Kinetix and Maya from Alias that is owned by Autodesk.

Some of the modeling tools with wider spread use are listed below (Table 3.1).

Table 3.1 Popular non-parametric modeling tools edited by the researcher

3.2 PARAMETRIC MODELLING

One that can be considered as digital innovation is parametric modeling. Parametric

Modeling is an approach to CAD that leaves the traditional two- dimensional

approach. According to Burry (1999) in parametric modelling, it is the parameters of a

particular design that are declared, not its shape. By assigning different values to parameters,

different objects or configurations can be created. Equations can be used to describe the

relationships between objects, thus defining an associative geometry. As also points out by

Hoffmann (2005), a parametric object can be defined as a solid whose actual shape is a

function of a given set of parameters and constraints. A ‘parametric solid modelling’ is also

referred to as ‘ parametrics’ to represent an object and is not only govern by single technique

but a set of techniques with its own advantages and limitations (figure 3.1). There are three

claimed benefits of parametrics (Shah, 1998). Automatic change propagation, geometry re-

use and embedding of design/manufacturing knowledge with geometry.

Figure 3.1 Representation of reciprocal trusses that is parametrically modelled (Burry, 1999)

Non-Parametric Modeling Tools

Website

AutoCAD www.autodesk.com

SketchUp http://sketchup.google.com

Maya www.alias.com

3D Studio Max www.discreet.com

Houdini www.sidefx.com

Rhinoceros www.rhino3d.com

Cinema4D www.maxon-computer.com

Lightwave www.newtek.com

Caligari Truespace www.caligari.com

SoftImage www.softimage.com

Parametric models are variations of pre

dimensions. Using this approach, data and parameters could be set to parametrically change

the size and shape of a model. With this method, one is required for two steps of geometr

modelling process (figure

simple topologically method. The second step is to link its variants and data to any basic

computational tools like Microsoft Excel. Once the data is exported to E

and manipulate the geometric models by changing the data or number in Excel and

subsequently will automatically change the models when changes of data in Excel are made.

A new parametric generative tools called ParaCloud Modeler is rec

using this method. The table 2.1 below is a list of parametric

in the market and is widely used in practice.

Figure 3.2 The first model is created in Rhino, the other models are parametrically

manipulated in Paracloud and Excel

Parametric Modeling Tools

Autodesk Revit

GenerativeComponents

Rhino-Grashopper

ParaCloud Modeler

CATIA

Table 3.2 High-end parametric modeling tools that are commonly used in design practice

Gehry and Partners is the pioneer of using parametric modelling in architecture. The

firm uses powerful 3-dimensional representation tool, CATIA, to model the complex

geometry of their buildings

owners, contractors, subcontractors, engineers and fabricators. On their project they usually

used CATIA 3-D representation tools focused on how the tool enable

complex buildings.

According to Yoo et al (

(figure 3.3) that can be used by all consultant and contractor in carrying their work. This

process evokes collaboration of team o

interactively. In many instances, subcontractors and fabricators collaborated with architects

and general contractors during the design stage with the result that key players are involved

in the design process much earlier than they normally might be. Such tightly coupled

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Parametric models are variations of pre-define shapes that is driven by key


the size and shape of a model. With this method, one is required for two steps of geometr

(figure 3.2): First, the initial model is parametrically created by any


computational tools like Microsoft Excel. Once the data is exported to Excel, one can change



A new parametric generative tools called ParaCloud Modeler is recently developed and is

using this method. The table 2.1 below is a list of parametric-based software that is available

in the market and is widely used in practice.

.2 The first model is created in Rhino, the other models are parametrically

anipulated in Paracloud and Excel (Nir, 2006).

Parametric Modeling Tools

Website

Autodesk Revit www.autodesk.com

GenerativeComponents www.bently.com

Grashopper www.grasshopper.com

ParaCloud Modeler www.paraclouding.com

www.3ds.com

end parametric modeling tools that are commonly used in design practice


dimensional representation tool, CATIA, to model the complex

geometry of their buildings (Yoo et all, 2006). It has influenced the way the firm works with


D representation tools focused on how the tool enabled Gehry to design more

According to Yoo et al (2006) the firm uses of a centralized 3d model and database

that can be used by all consultant and contractor in carrying their work. This

process evokes collaboration of team of a project from a series exchange of information



cess much earlier than they normally might be. Such tightly coupled

define shapes that is driven by key


the size and shape of a model. With this method, one is required for two steps of geometric

: First, the initial model is parametrically created by any


xcel, one can change



ently developed and is

based software that is available

.2 The first model is created in Rhino, the other models are parametrically

end parametric modeling tools that are commonly used in design practice


dimensional representation tool, CATIA, to model the complex

It has influenced the way the firm works with


d Gehry to design more

the firm uses of a centralized 3d model and database

that can be used by all consultant and contractor in carrying their work. This

f a project from a series exchange of information



cess much earlier than they normally might be. Such tightly coupled

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collaboration patterns not only enhanced the quality of communications, thus reducing errors

and redundant communication, but also enabled the design team to tap into the expertise of

various trades and specialists in a much more meaningful way.

Such collaboration at the early stage of design process enabled Gehry and his

associates to experiment with new materials and constructions methods for their projects,

and at the same time push contractors, subcontractors and fabricators to innovate in their

own domains, which in turn inspired others, including Gehry himself, to pursue further

innovations. In this way, 3-D tools play an important role in connecting all the players and

stimulating their joint and separate innovations. As a result, the project became a nexus of

emergent and distributed innovations. In this process, the use of 3-D representations became

a part of a larger process in which the whole project network became a vibrant source of

innovations.

Figure 3.3 Full 3d model of Gehry’s project using CATIA (Shelden, 2002)

3.4 BUILDING INFORMATION MODELLING (BIM)

Likewise Building Information Modeling (BIM) is another form of parametric

modeling. It represents the process of development and use of a computer generated model

to simulate the planning, design, construction and operation of a facility (Azhar et al, 2006).

The resulting model, a Building Information Model, is a data-rich, object-oriented,

intelligent and parametric digital representation of the facility. Moreover, it is where the data

appropriate to the needs of the users can be used to make decisions and improve the process

of delivering the facility (AGC, 2005).

The noted main difference of BIM and 2D CAD is that, the last describes a building

by independent 2D views like plans, sections and elevations. Furthermore, editing one of

these views requires that all other views must be checked or updated. This process is known

to be error-prone and is considered as one of the major causes of poor documentation. In

addition, data in 2D drawings are graphical entities only unlike the intelligent contextual

semantic of BIM models wherein object are defined in terms of building elements and

systems (CRC Construction Innovation, 2007).

According to Azhar et al (2006), the key benefit of BIM is its accurate geometrical

representation of the parts of a building in an integrated data environment. Other known

benefits of BIM are; faster and more effective process, better design, controlled whole-life

costs and environmental data, better production quality, automated assembly, better

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customer service and lifecycle data. An example of project that have employed BIM was the

Royal London Hospital, (figure 3.4) the largest new hospital in the UK which has a 905-bed

facility. The building is being configured as a three tower containing 6,225 rooms across

110,00 square meters of floor space.

Figure 3.4 3d models through BIM by HOK and Skanska (Walker, 2008)

HOK and Skanska made a working strategy based on sharing a BIM dataset via a

virtual ‘portal’. The business case for BIM built around costs versus perceived value, which

shows that this working method should considerably reduce the typical 10 per cent

overspend attributed to poor spatial coordination rework and waste for an investment of

around 0.5 per cent of the total tender sum. Skanska benefited an increased construction

margin. A fewer requests for information (RFIs) and of course an increased fee margin were

the benefits for HOK. And the benefit to the client is a better quality, more robust building.

Early investment in BIM is therefore important. Cost benefits are already starting to come

through even though construction has barely begun. Moreover, the simplicity of BIM data

reuse will save £230,000 on the cost of producing an operations and maintenance manual

alone, as announced by business model.

HOK and Skanska have both agreed in advance to standardize around Autodesk’s

Architectural Desktop (ADT) modeling tool thus allowing ADT compatible programs to be

used by the key team members, as well as subcontractors. Project participants including

architects, engineers, contractors, facilities management team and client, have agreed to feed

into, and off, a single portal set up and managed by Skanska’s central 3-D CAD and Data

Management Group.

The central model contains all the data from which, for example, lighting and

acoustic studies can be sourced, and verified views generated, structures and cladding

systems analyzed, and services mapped out. All computer packages used in the development,

analysis, visualization and management of the central 3-D model have been factored into the

Data Management Group’s ‘roadmap’.

The linked between the principal ADT models and the Codebook, the software

which encapsulates key UK government-sanctioned medical information and requirements

saves a considerable amount of design and revision time. However, the links between ADT

and Codebook do not generate designs automatically. Instead, the Codebook links allow

designers to constantly check that room layouts and services fulfill the requirements of

medical need.

3.5 BUILDING PERFORMANCE MODELLING

Building Performance Modelling

another kind of architecture that is emerging is using building performance as guiding design

principle and adopting new performance

architecture, interrogates a broadly defined performative design above form making. It

utilizes the digital technologies of quantitative and qualitative techniques and simulation to

offer a comprehensive new approach to the design of the built environment

2003).

This new design methodology uses new information and simulation driven design

process. Performance-based design is primary used to simulate environmental, thermal,

climatic, acoustical etc. as the emphasis of the design like the City Hall, London that was

designed by Foster and Partners and ARUP

strategies using building performance tools or 4d modelling tools that is available in the

market. 4d digital technologies are those

unseen such as air flows, energy efficiency, indoor humidity etc. This provides design teams

with the high quality information needed to quantify and inform iterative decisions, so the

project team can effectively develop creative sustainable

project.

Figure 3.5 3d models City Hall, London by Foster and Partners and ARUP

Building performance simulation is already available from major CADD companies

Autodesk and Bently as listed in table 3.3 They provide plug

products. Some of these products can be downloaded from their website and can be used for

30-days trial period.

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BUILDING PERFORMANCE MODELLING

Building Performance Modelling can be also considered as digital innovation. It


principle and adopting new performance-based priorities for the design. This new kind of

ogates a broadly defined performative design above form making. It


offer a comprehensive new approach to the design of the built environment


based design is primary used to simulate environmental, thermal,


designed by Foster and Partners and ARUP (figure 3.5). This involves finding sustainable


market. 4d digital technologies are those software’s that can simulate and



project team can effectively develop creative sustainable solutions at the early stage of the

3d models City Hall, London by Foster and Partners and ARUP

(Kolarevic, 2005)


as listed in table 3.3 They provide plug-inn that is linked to their


can be also considered as digital innovation. It is


based priorities for the design. This new kind of

ogates a broadly defined performative design above form making. It


offer a comprehensive new approach to the design of the built environment (Kolarevic,


based design is primary used to simulate environmental, thermal,


This involves finding sustainable


that can simulate and analyze the



solutions at the early stage of the

3d models City Hall, London by Foster and Partners and ARUP


inn that is linked to their


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Table 3.3 Building performance modelling tools for environmental analyses

3.6 SCRIPTING

"Script" is another form of digital innovation. It is derived from written dialogue in

the performing arts, where actors are given directions to perform or interpret (Schnabel,

2007). Scripting languages are typically not technical but mathematical solutions that are

define by set of rules and based on parameters. It is a programming language that controls a

software application and often treated as distinct from programs which execute

independently from any other application. The use of scripting in architecture was taken

from the ‘Theory of L-System’ in which the simulation of plant growth is further

experimented and applied as a process to generate objects (Allen et al, 2004).

As design computing is becoming powerful, scripting as a form of programming for

architects have widespread in research and popularly known as ‘Emergence’ . The ‘Theory

of Emergence’ looks up at natural phenomena (Leach, 2007) of any kinds like ‘Genetic

Space’ (Chu, 2000) from biology and extracts their morphogenetic process and

morphological formations as generator of design. Several applications of this theory are

‘Morpho-Ecologies’ (Hensel, Menges et al 2004), ‘Biothing and Continuum’ (Andraseck,

2007) and ‘L-System in Architecture’ (Hansmeyer, 2003) in which morphogenetic process is

applied as form generators to architectural design. Its approach takes up from biological

morphogenesis for which the process of evolutionary development and growth of organism

is observe and apply as generative morphogenetic process to model building forms.

There are already available major programming language and plug-inn that can be

used for scripting (Table 3.4). Some of these products can be downloaded from their website

and can be used for 30-days trial period or can be commercial bought from IT companies.

Building Performance

Simulation Tools

Website

IES www.iesve.com

Radiance http://wapedia.mobi/en/Radiance

Ecotect http://ecotect.com

Green Building Studio www.autodesk.com/greenbuildingstudio.com

Hevacomp www.bentley.com

Energy Plus http://apps1.eere.energy.gov

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Table 3.4 Scripting tools and programming languages used by architectural

organizations

One project that have used scripting was Foster + Partners in their Great Canopy

Project, a proposal for the West Kowloon Cultural District is to create a unique landmark of

collection of visual arts, performance and leisure venues, on a dramatic harbour-front site in

the heart of Hong Kong (figure 3.6). The canopy flows over the various spaces that

contained within the development to create a unique landmark. The sinuously flowing form

of the site contours and the canopy produce a memorable effect.

Figure 3.6 Digital models of the Great Canopy project used by Fosters and Partners,

(Whitehead et al, 2008)

Scripting was used throughout the project to develop architectural ideas. They

created algorithms and generative scripts to quickly create multiple structures and cladding

options, it has proven to be a hugely successful and adaptable tool for skilled architectural

designers to create their own design tools. A modular system was used to elaborate the

complex design of the surfaces height, width and curvature that varies over its length,

presenting smooth that can be viewed from above and beneath. Investigation determined that

a space truss solution suited to the varying figure of the canopy. In modular component

based system minimal cost varies and provides a structural solution that provides varying

measures in the head and tail areas. Computation design techniques used to design the roof

results a thorough structure that has many components, but with this new technique we were

able to create a digital models, new ones was essential for the fabrication of the model.

Using generative scripts the canopy’s structures produced three dimensions that was laser

cut from digital files that produced seven glazing component, these elements was assembled

by our in-house model shop.

Programming software and languages

C+ Programming Genetic Programming

Visual Basic Generative Scripts

DOX Software Python

Component Distribution Diagramming (CDD)

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3.7 SUMMARY OF DIGITAL INNOVATION TOOLS

Summarizing the four (4) basic categories of digital tools (chart 3.1) i.e. non-

parametric, parametric, building simulation tools and scripting, it has its common goal but

different semantics, approaches and techniques that designer could use. Each method has its

own weakness, strengths, and representational limitations and different capability in ways of

generating different shapes-linear, curvilinear and free-forms. Non-parametric tools is noted

to be purely for drafting, object modeling, visualizations and the logic or the underlying

factors of the 3d models are less prioritized. However for the purpose of other disciplines

like graphics, movies industrial design etc. wherein the underlying logic of the 3d models is

not critical, it is proven to be useful.

On the other hand, parametric tools is rigid and sometimes computationally tedious

than traditional digital applications. However, the synergy and flexibility of using the data to

parametrically create and change a model is very powerful (Hoffman, 2005). In parametric

modeling, it is the logic of the object that is being prioritized such as those of generating a

performative architecture as building performance as guiding principles (Burry, 1999). The

principal advantage of parametric tools is that it allows a level of flexibility to perform

transformations that would result to different configurations of the same geometrical

components. The different configurations are called ‘instances’ of the parametric model.

Each instance represents a unique set of transformations based on the values assigned to the

parameters. As a result, variations on the design are obtained by assigning different values

that yield different configurations. In simple terms, a parametric model allows the designer

to perform changes and reconfigurations of the geometry without erasing and redrawing.

Building simulation tools is made not for form making and modeling but is mainly

for to evaluate simulate environmental, thermal, climatic, acoustical etc. It supports the

understanding of how a given building operate according to certain criteria and enables

comparisons of different design alternatives. It is very helpful to quantify data to provide

sustainable solutions in the early phase of the project.

Scripting is form finding technique or computational procedure to address a problem

in a finite number of steps. role of the designer can shift from "space programming" to

"programming space"; the designation of software programs to generate space and form

from the rule-based logic inherent in architectural programs, typologies and building code

(Terzidis, 2003). While it is considered tedious and distinctly different from the three tools, it

offers enormous benefits to design a project, from form generation, structural system

integration to components fabrication.

Chart 3.1 Categorization diagram of current CAAD tools that are used by architectural

practices

13

4. UNDERSTANDING BARRIERS AND IMPEDIMENTS TO DIGITAL

INNOVATION ADOPTION

Digital innovation is happening in architectural practices but some architectural

practices are facing challenges. This is because of the increasing new technology and the

current demands of topologically non-linear building design and the issue of sustainability.

Some architectural practices are indeed experiencing the challenges triggered by digital

innovation. Constant introduction of new digital technology, increased global competitions,

increasing client’s demand and limited costs, limited software knowledge are among the

challenges..

Understanding digital innovation adoption in architecture is difficult, and

developing a single framework or model for innovation adoption is even more challenging.

The reason of the difficulty for developing a single framework is that there is no available

framework that discusses how architectural practices will be able to adopt the innovation

process and how it affects the organization. Digital innovation is already happening in

architectural practices but there is not enough research in digital innovation that tackles the

barriers, impediments and challenges particularly in architectural practices. There has been

relatively limited critical analysis of the practices of using digital technologies in building

and infrastructure projects (Whyte, 2011). Literature in this subject is still very limited.

For the purpose of understanding the barriers and challenges that affect architectural

organization in digital innovation adoption, a literature review of innovation in allied fields

such as information science, business and organizational management, manufacturing,

product design, engineering and construction was conducted. Though it is not specifically in

architecture, it reveals several challenges and barriers in adopting digital innovation both of

which has different views but shared common attributes that can be used for establishing the

variables this research.

In digital innovation research focused on management information technology

(Whyte, 2011) he elucidates that the new digital processes present a ‘technological black

box’ with little visibility of the completeness of the design work represented in models and

drawings. According to his research, this is a challenge because it makes it difficult to

manage client expectations, especially where the completeness of design may have

contractual implications. Boland et al. (2007) explores digital innovation on a project,

arguing that the use of 3D digital technologies allows waves of innovation to propagate

across the firms and have identified challenges relating to the use of digital technology and

related processes.

On management perspective it is coined that organizations that are engaged in the

design of buildings are often complex, having non-linear and multiple interdependencies

between their sub-systems. They are considered as complex organizations whereby

efficiency is important when using innovation. Digital technologies enable new forms of

interaction and coupling (Kallinikos 2005), increasing the interactive complexity in complex

organizations. Dossick and Neff (2008) have argued that it is a set of leadership skills that

enables managers in design organizations to deal with the increasing tight coupling of

technological solutions within loosely coupled organizational structures, and according to

them it is important to analyze digital technologies, organizational structures and processes.

On digital innovation research of Whyte (2011), he added that the way in which

digital innovation processes is configured and organized has a major impact on delivery.

Organizational challenges related to process or performance management becomes an issue.

14

He coined that on new digital processes, the team is under pressure to deliver to traditional

timescales though it took longer to develop 3D information that could then add benefit at

later stages. The new digital tools and processes implied wider organizational changes across

firm boundaries. Whyte (2011) added, there is an implication for the deliverables to the

client and regulatory bodies and for the nature and duration of different stages of design

work. On the other hand, while much of the technological infrastructure of practice is taken

for granted by practitioners (Edwards et al. 2007), new technologies become salient in day-

to-day work as organizations require significant learning, this could be a barriers to adopting

digital innovation. Furthermore in digital innovation management research of Whyte (2011),

he coined several barriers in adopting digital innovation such as coordination of digital

package had a consequential problem, limitations of the 3D modelling package and

challenges in finding competent staff that has combined practical construction experience

and digital technology.

Whyte (2011) added that information management has a strong relationship with the

management of projects. His research suggests (1) that new digital tools and processes

increase the coupling between the various disciplines involved in design, implying wider

organizational changes across firm boundaries, (2) the process change challenges the

currently institutionalized understandings of design stages and effective processes. He

concluded that design organization operates within a wider set of institutionalized practices,

which include formats for delivery, building regulations, local authority permissions and

construction schedules. His research suggest that that to be effective 3D modes of working

require a wider process of change, as this digital infrastructure for delivery challenges

institutionalized understandings of the activities in and duration of different stages of the

process (Whyte, 2011). Likewise as explained by Whyte (2011), this work has implications

for practitioners, as work flow is changing the nature of professions. Figure 4.1 is the

conventional process, and figure 4.2 is a useful of representation of new digital work flow in

3D centric way.

Figure 4.1 and 4.2 Idealized information flows between different professionals on a project,

a) without a central project model and b) with a central model (Whyte, 2011).

In digital innovation research in architecture, construction and engineering industry

(Johnson & Laepple, 2003), it was argued that there is an interrelationship between business

goals, work processes, and the adoption of information technology. That is, changes in

business goals generally require revising work processes which can be enhanced further by

the introduction of information technology. They concluded that organization structures of

architecture and construction have not changed very much despite the introduction of

computer aided design. The reasons for this are varied and complex. According to Johnson

& Laepple, (2003), it might be because the nature of work processes of the building design

and construction industry is fragmented. On their research they concluded significantly

15

issues or barriers related to organizations, expertise and software for instance acquisition of

architecture and real estate expertise, changes in company leadership and culture, R&D

software investment and work process changes.

Likewise in the white paper published by Autodesk (Bernstein & Pittman, 2004)

they have pointed out three significant barriers relating to process management of building

information modeling, (1) Transactional business process evolution, refers to the array of

technologies available on today’s designer or constructor creates process possibilities that far

exceed norms of practice and well-understood business protocols, (2) Computability of

digital design information, which is digital data exist in a variety of forms and many of

which are not computable and (3) Meaningful data interoperability, some digital data is not

accessible to the relevant parties involved in the building process.

In innovation research in engineering and construction (Shabanesfahani & Tabrizi,

2012), they concluded that one of the major issues in innovation in engineering and

construction industry are knowledge transfer endeavors that are being harshly hindered by

the lack of proper understanding of such knowledge transfer and the interrelationships to

both firm skills and procedures, and the vision characteristics of the technologies being

transferred (Sexton & Barrett, 2003) Second, is the present knowledge transfer mechanisms

are not sufficiently notified by or involve with firm crucial association and organizational

skills and procedures vital to enable them to absorb technologies and to coil them into

appropriate innovations (Shabanesfahani & Tabrizi, 2012). In summary according to

Shabanesfahani & Tabrizi (2012), the attributes of these barriers boils down to traditional

organization processes, taxation on innovative products going to be commercialized, lack of

expertise and technical support, knowledge transfer, investing in R &D.

Following with innovation research of Bogacheva (2011), that success of

underproduction innovations in the various organizations shows, that it is defined by a

condition of two groups (1) are forces assisting deduction in the existing system condition,

(2) there are forces aspiring to change. This is because innovation is doing something new

and some members are reluctant to change because they are anxious failure. The attributes of

these are personal interests of workers and managers, attitudes with other workers and

managers, attitudes during variations, attitudes between initiators, innovation introduction

organizers and heads which can be considered as organizational psychological barriers.

According to Bogacheva (2011), organizational psychological barriers arise from the

innovations rejection because of their diverging from the individual value orientation. This is

the most powerful influence on the behavior of the individual barrier, which eventually

develops into active and persistent negative attitudes towards innovation. The reasons of this

psychological barrier on impeding innovation are economical, afraid of absence in sufficient

volume of necessary resources, material, financial, labor, technical, characteristics of

available materials, the equipment and devices which mismatch requirements of innovations,

organizational-psychological, unknown facts on how to organize sequential innovation

penetration and operation, organizational- administrative, there is no effective way for

coordination of different business units interests in the whole entity.

A survey in manufacturing and product design innovation that was conducted by

O’Sullivan (2002) reveals to several causes of failure in organizations are cited such as

poor leadership, poor organization, poor communication, poor empowerment and poor

knowledge management. Another factors are failure is an inevitable part of the innovation

process, and most successful organization factor in an appropriate level of risk, the impact of

failure goes beyond the simple loss of investment, failure can also lead the loss of morale

16

among employees, an increase in cynicism, and even higher resistance to change in the

future, some causes are external to the organization and outside its influence of control.

Similar studies on AEC made by Cory and Bozell (2001) claim that while the advent

of digital technology have benefited the profession, practical issues occurs in utilizing new

technologies and company should consider are design costs and time, software learning

curve, software costs, ability of the software to handle complex geometry performance of the

software, level of detail needed and what the software can deliver, partition the model

among multiple users, integrate model from multiple sources, tools for model review and

web publishing, speed and working drawing extraction and maintenance both of which affect

the profitability of the company.

A research made by Civil Engineering Research Foundation (1996) reveals several

barriers to innovation in the building industry such as risk and liability, financial

disincentives, high equipment cost, inadequate technology transfer, Inadequate basic and

industrial R & D, adversarial relationships, poor leadership, inflexible building codes and

standards and construction based initial costs.

Inchachoto (2002) on his technological innovations research coined some important

pointers on his research about technological innovation such as innovation is best fostered by

team members with prior work experience, as opposed to an assembly of individuals

selected solely on the basis of expertise, collaboration in innovation is useful and

distinctively serves multiple functions such as technical-risk reduction, financial security,

and psychological assurance, project logistics is also important such as external funding,

research collaboration, technical evaluation, demonstration and validation, and allocated

budgets for research plays an integral role for technological innovations.

Yoo et al (2010) on their digital innovations research focused on the process they

coined some several barriers like prerformance of software, ability of software to handle

complex geometry, integrations of model to multiple sources, speed and drawings

extractions. Such list of barriers is similar to the organizational barriers that have elucidated

by Jones and Saad (2003) in their innovation research in construction that are inherent

problem in the innovation, lack of mutual recognition of the need for innovation, insufficient

technical capabilities and skill levels, reluctance to change, inexperienced team members,

lack of training, weak commitment and support by top, inadequate resources, lack of

integration and collaboration, lack of learning environment, lack of incentives and difficult

to comply with the existing regulations and established standards.

4. COMMON ATTRIBUTES OF BARRIERS FOUND

Though, the application and multitude categorization of issues and barriers presented

earlier are from different practices, common attributes are found (table 4.1) from various

research of innovation in different allied fields. These barriers of innovation coincide with

process barriers elicited by Walcoff et al (1983) which are summarized into six distinct

group such as technological barriers, financial barriers, organizational barriers, governmental

barriers, psychological barriers, and process barriers. Recognizing these barriers as

‘challenges’ to the innovation process, highlights the purpose of establishing a framework of

this research.

17

Innovation research from

allied fields

Common Barriers

A B C D E F

Digital innovation management

(Whyte, 2007) o o o

Digital innovation in AEC

(Johnson & Laepple, 2002) o o o o

Building Information Modelling

(Bernstein & Pittman, 2004) o o

Innovation in Construction

(Shabanesfahani & Tabrizi, 2012) o o

Innovation Research

(Bogacheva, 2007) o o o o

AEC Innovation

(O’Sullivan, 2002) o

AEC Innovation

(Cory & Bozell, 2001) o o o

Engineering Innovation

(Civil Eng’g. Research, 1996) o o o

Architectural Innovation

(Inchachoto, 2002) o o o

Digital Innovation Management

(Yoo et al, 2010) o o

Construction Innovation

(Jones & Saad, 2003) o o o o o

Innovation Research

(Walcoff et al, 1981) o o o o

Table 4.1 Common determinants and barriers from innovation research in different allied

fields, where is A-Technological barriers, B-Financial barriers, C-Organizational barriers, D-

Governmental barriers, E-Psychological barriers, F-Process barriers

5. RESEARCH METHODOLOGY

The scope of this study is focused in evaluating the barriers and key determinant

factors that impede digital innovation adoption in different size of architectural

organizations. Through its research questions mentioned in chapter 1, six general variables

(technological, financial, organizational, governmental, psychological and process barriers)

were tested. From the literature review, each of these group has specific variables

summarized in table 5.2, 5.3, 5.4, 5.5, 5.6 & 5.7 based on innovation research in

construction, engineering, product design, industrial and manufacturing studies elicited by

(O’Sullivan, 2002), (Johnson and Laepple, 2002), (Cory and Bozell ,2001), (Civil

Engineering Research Foundation, 1996), ( Inchachoto, 2002), (Yo et al, 2010), (Jones &

18

Saad, 2003), (Bernstein & Pittman, 2002), (Shabanesfahani & Tabrizi, 2012), (Bogacheva,

2007) and (Walcoff et al, 1983) in chapter 4.

5.1 SURVEY RESPONDENTS

Forty five architectural practices were selected on the basis of digital innovation

experience and size of architectural organization. Singapore was chosen as the model for the

study because the country has the availability of resources such as digital tools, complexity

of projects, skills, knowledge transfer and presence of variety of sizes of architectural

practices employing digital innovation. Through experience and observations, digital

innovations exist in several architectural organizations in Singapore.

5.1.1 EXPERIENCE IN DIGITAL INNOVATION

Architectural practices with experience in digital innovation were only selected

hence the relevant data needed on this study can be efficiently answered by those

organization who has the experienced in digital innovation or at least have tried to adopt

digital innovation in their project. They are those companies that have employed digital

innovations for the purpose of form finding, BIM, optimization or different process of design

using digital tools as stated in the review of literature.

5.2.2 SIZE OF ARCHITECTURAL ORGANIZATION

To ensure that correlation of size of architectural organization and barriers of digital

innovation adoption is evaluated, forty five firms were selected in the basis of size of the

organization in which (1) small size architectural practices that has 5-10 staffs, (2) medium

size architectural practices that has 11-50 staffs and (3) big size architectural practices that

has 51 or more staff. The purpose of grouping them is to evaluate the fourth objective of this

study which is to find out whether the size of organization has a significant relationship to

barriers in digital innovation adoption.

Size of architectural organization N

Small (1-10 staff) 15

Medium (11-50 staff) 15

Big (50 or more staff) 15

Table 5.1 Number of firms as respondents of the study

5.2 VARIABLES

Upon analysis of the challenges and impediments presented earlier in chapter 4,

these challenges were distinctively group into six categories such as technological barriers,

financial barriers, organizational barriers, governmental barriers, psychological barriers,

19

process barriers and were used as variables (table 5.2, 5.3, 5.4, 5.5, 5.6 & 5.7) of this

research. It is taken from innovation research from allied fields elicited by Whyte (2011),

Johnson & Laepple (2002), Bernstein & Pittman (2004), Shabanesfahani & Tabrizi (2012),

Bogacheva (2007), O’Sullivan (2002), Cory & Bozell (2001), Civil Eng’g. Research (1996),

Inchachoto (2002), Yoo et al (2010), Jones and Saad (2003), Walcoff et al (1981). Using

these ‘challenges’ as variables is significant in this research.

Technological Barrier

1 Lack of equipment or computers

2 Insufficient knowledge of team members

3 Lack of training for technology

4 Lack of interest for the knowledge of digital technology

5 Lack of technical demonstration

6 Inadequate maintenance

7 Inadequate technology transfer

8 Inadequate R and D knowledge

9 Insufficient skills on the technology

10 Insufficient skills on the maintenance of technology

11 Unavailability of new digital tools

Table 5.2 Variables for technological barriers used in the survey questionnaire

Financial Barrier

1 Inadequate design fee to support digital innovation

2 Insufficient budget for digital innovation

3 The practice doesn’t want to spend much for digital tools

4 Digital tools are expensive

5 Expensive to set-up equipment

6 Lack of budget for training the team

7 Financial disincentive

8 High equipment maintenance cost

9 Practice-based cost doesn’t support digital innovation

10 Lack of R and D budget

11 Expensive salary to hire knowledgeable staff that know the new digital

tools

Table 5.3 Variables for financial barriers used in the survey questionnaire

20

Organizational Barrier

1 Poor leadership towards digital innovation

2 Poor organization attitude to innovation

3 Lack of empowerment and support to digital innovation

4 Poor knowledge management

5 Lack of manager to manage digital innovation

6 Inadequate personnel to carry out 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

Table 5.4 Variables for organizational barriers used in the survey questionnaire

Process Barrier

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 of 3D models to multiple source

5 Limited availability of digital tools to deliver digital innovation

6 Inadequate level of detail needed for the 3d

7 Slow data processing of 3d models

Table 5.5 Variables for process barriers used in the survey questionnaire

21

Psychological Barrier

1 Fear of work changes

2 Lack of psychological assurance

3 Fear of product change

4 Fear of failure

5 Afraid of process change

6 Fear of new marketing changes

7 Fear of increase of labor cost

8 Fear of profit loss

9 No trust to digital technology

Table 5.6 Variables of psychological barriers used in the survey questionnaire

Governmental Barrier

1 Inflexible building codes

2 Submission of drawings is still hard copy

3 Submission of drawings doesn’t use digital copy from digital

innovations

4 High standard of digital modelling and procedure established by

government for drawing submission

Table 5.7 Variables of governmental barriers used in the survey questionnaire

5.3 DATA COLLECTION AND GATHERING

The data were primarily collected through structured survey questionnaire. This

method is efficient means gathering the information from selected architectural firms and

simultaneously validates the data and gain immediate feedback. At least 1 manager or senior

architect in managerial level involved as a key player of the project was interviewed.

5.4 METHOD OF ANALYSIS

For the first, second and third research questions which are obtaining the data

relative to barriers in digital innovation adoption and how crucial and significant are the

barriers, the mean scores were computed using set of ranges with their qualitative

description scoring such as 0 if barrier is not encountered, and relative importance is 1-5 that

is low to high (table 5.8)

22

Mean ranges

Qualitative Interpretation

0 Barrier was not encountered

1 Slightly crucial

2 Moderately crucial

3 Often crucial

4 Very crucial

5 Extremely crucial

Table 5.8 Relative with their qualitative description (Score is 0 if barrier is not encountered.

Relative importance is 1-5 that is low to high)

For the fourth research question which is evaluating whether there is a significant

relationship between digital innovation barriers and size of organization, multiple regression

analyses was used. The data were treated through statistical Multiple Regression Analysis

and Shefee post hoc respectively for accuracy and reliability of results.

6. PRESENTATION OF DATA AND ANALYSIS

6.1 BARRIERS TO DIGITAL INNOVATION ADOPTION

Distribution of firms answering research question -“What are the barriers that architectural

organizations encountered when adopting digital innovation?” and “How crucial are these

barriers when digital innovation is implemented”?

6.1.1 TECHNOLOGICAL BARRIERS TO INNOVATION ADOPTION

Size of

architectural

organization

How crucial is technological barriers in digital innovation adoption?

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

Small 4.4 4.3 4.3 4.5 3.8 1.1 1.3 4.5 4.9 4.6 1.9 3.2

Medium 3.7 3.2 4.3 4.8 3.5 1.2 1.5 4.3 4.9 4.7 1.7 3

Big 1 1 1.1 1.1 1.5 1.5 1.5 1.2 1.5 1.5 1.6 0.9

Table 6.1 Mean score of technological barrier according to size of firm. The higher the mean

score, the higher is the observed barrier and the lower the mean score, is the lower barrier.

23

Variables: (1) Lack of equipment or computers, (2) Insufficient knowledge of team members, (3)

Lack of training for technology, (4) Lack of interest for the knowledge of digital technology, (5) Lack

of technical demonstration, (6) Inadequate maintenance of equipment, (7) Inadequate technology

transfer, (8) Insufficient skills on the technology, (9) Insufficient skills on the maintenance

technology, (10) Unavailability of new digital tools, (11) Inadequate in-house technical support.

Chart 6.1 Chart showing how crucial technological barriers according to size of firm based

on the variables above.

In summary, descriptive statistics in table 6.2 have shown that technological barriers

are significantly present and observed to be very crucial in small and medium architectural

organizations but not in big architectural organizations. Pattern of results shows that it is

essential to note that with the eleven variables, three variables which are inadequate

maintenance of equipment, inadequate technology transfer and inadequate technical supports

are uniformly not crucial in all group of architectural organization. Holistically, overall

results can be gleaned that technological barriers is more crucial in smaller architectural

organizations.

.

0

1

2

3

4

5

1 2 3 4 5 6 7 8 9

10

11

OV

ER

AL

L

TECHNOLOGICAL BARRIER

SMALL

MEDIUM

BIG

24

6.1.2 ORGANIZATIONAL BARRIERS TO DIGITAL INNOVATION ADOPTION

Size of

architectural

organization

How crucial is organizational barriers to digital innovation adoption?

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

Small 4.2 4.2 4.1 6.4 4.3 4.2 4.7 4.1 4.4 4.7 5 4.2

Medium 4.1 4.3 4.2 2.3 2.1 4.3 1.1 1.1 1.4 1.7 1.5 2.2

Big 0.6 0.6 0.9 1 1.9 0.9 0.7 1.1 1.5 1.6 1.3 0.7

Table 6.2 Mean score of organizational barrier according to size of firm. The higher the

mean score, the higher is the observed barrier and the lower the mean score, is the lower

barrier.

Variables: (1) Poor leadership towards digital innovation, (2) Poor organization attitude to innovation,

(3) Lack of empowerment and support to digital innovation, (4) Poor knowledge management, (5)

Lack of manager to manage digital innovation, (6) Inadequate personnel to carry out 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.

Chart 6.2 Chart showing how crucial organizational barrier according to size of firm based


Holistically as shown in table 6.2, organizational barriers are observed to be more

crucial in small architectural organizations. Organizational barriers boils down to the support

of staff and managers for digital innovation and willingness to adopt change. It can be noted

that bigger architectural organizations having enough resources and support from the

organization are less affected by organizational barriers. However, when staff and managers

who does not support digital innovation as in the case of smaller architectural organizations

0

1

2

3

4

5

1 2 3 4 5 6 7 8 9

10

11

OV

ER

AL

L

ORGANIZATIONAL BARRIER

SMALL

MEDIUM

BIG

25

thus, digital innovation is hardly to be implemented. With the result presented in table 6.2

and chart 6.2, it can be gleaned that the smaller architectural organization, is highly affected

by the organizational barriers.

6.1.3 FINANCIAL BARRIERS TO DIGITAL INNOVATION ADOPTION

Size of

architectural

organization

How crucial is financial barriers 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 3.3

Medium 4.4 4.3 4.4 4.5 4.5 4.4 0.9 1.3 4.9 4.7 4.8 3.5

Big 1 0.7 1 4.7 1.2 1.1 0.9 1.3 4.9 1.7 1.1 1.4

Table 6.3 Mean score of financial barrier according to size of firm . The higher the mean


Variables: (1) Inadequate design fee to support digital innovation, (2) Insufficient budget for digital

innovation, (3) The practice doesn’t want to spend much for digital tools, (4) Digital tools are

expensive (5) Expensive to set-up equipment (6) Lack of budget for training the team, (7) Financial

disincentive, (8) High equipment (computer) maintenance cost, (9) Practice-based cost doesn’t

support digital innovation, (10) Lack of R and D budget, (11) Expensive salary to hire knowledgeable

staff that know the new digital tools

Chart 6.3 Chart showing how crucial the financial barrier is according to size of firm based


0

1

2

3

4

5

1 2 3 4 5 6 7 8 9

10

11

OV

ER

AL

L

FINANCIAL BARRIER

SMALL

MEDIUM

BIG

26

Overall results have been shown in table 6.3 the mean scores of each group. It can

be concluded that financial barriers are signicantly present and observed to be extremely

crucial in small and medium architectural organizations rather than big architectural

organizations. It can be noted that financial barriers are not crucial in big architectural

organization because big architectural organizations have substantial professional fees for

the big projects, are economically adequate and could support financial needs to adopt

innovation. Smaller architectural organizations with smaller projects which have lesser fees

are less able to afford the cost of digital innovation.

6.1.4 PROCESS BARRIERS TO DIGITAL INNOVATION ADOPTION

Size of

architectural

organization

How crucial is 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 3.7

Medium 4.6 4.1 4.3 4.6 0.9 4.4 4.9 3.7

Big 4.6 4.4 4.1 4.8 1.3 4.4 4.7 3.8

Table 6.4 Mean score of process barrier according to size of firm. The higher the mean


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 source, (5) Limited availability of digital tools to deliver

digital innovation, (6) Inadequate level of detail needed for the 3D, (7) Slow data processing of 3d

models

Chart 6.4 Chart showing how crucial is process barrier according to size of firm based on

the variables above.

0

1

2

3

4

5

1 2 3 4 5 6 7

OV

ER

AL

L

PROCESS BARRIER

SMALL

MEDIUM

BIG

27

As shown in chart 6.4, all the the variables revealed to be crucial except for one

variable which is the limited availability of digital tools to deliver digital innovation. It can

be concluded that process barriers boils down to performance of computers, fragmentation of

3d models and data processing is obviously not correlated to the size of the organizations.

Overall results revealed in table 6.4 that process barriers are observed to be slightly

equally crucial on all architectural organization with a mean scores of of 3.7-3.8. It can be

gleaned that size of architectural organizations doesn’t correlates with process barriers in

digital innovation adoption.

6.1.5 PSYCHOLOGICAL BARRIERS TO DIGITAL INNOVATION ADOPTION

Size of

architectural

organization

How crucial is 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 4.3

Medium 4.1 3.9 4.6 4.1 3.4 1.3 4.1 3.2 4.5 3.3

Big 0.3 0.7 1 0.8 0.9 1.1 1.2 1.1 1.1 0.6

Table 6.5 Mean score of psychological barrier according to size of firm. The higher the

mean score, the higher is the observed barrier and the lower the mean score, is the lower

barrier.

Variables: (1) Fear of work changes, (2) Lack of psychological assurance, (3) Fear of product change,

(4) Fear of failure, (5) Afraid of productivity loss, (6) Fear of new marketing changes, (7) Fear of

increase of labor cost, (8) Fear of profit loss, (9) No trust to digital technology

Chart 6.5 Chart showing how crucial is psychological barrier according to size of firm based


0

1

2

3

4

5

1 2 3 4 5 6 7 8 9

OV

ER

AL

L

PSYCHOLOGICAL BARRIER

SMALL

MEDIUM

BIG

28

Holistically it is shown in table 6.5 that psychological barriers is more crucial in

small architectural organization (mean score 4.3) followed by medium architectural

organization (mean score of 3.3) and big architectural organizations which has mean score of

0.60. With this result, it can be gleaned that big architectural organizations are observed to

have support from the organization and have the resources available, is perceived to be

psychologically secured compared to smaller organizations with just limited resources. One

significant result that is important to note is that the smaller the architectural organization the

more that they are affected by psychological barriers.

6.1.6 GOVERNMENTAL BARRIERS TO DIGITAL INNOVATION ADOPTION

Size of architectural

organization

How crucial is governmental barriers to digital innovation

adoption?

1 2 3 4 Overall

Small 0.67 0.53 0.80 4.67 1.5

Medium 1.40 0.67 1.20 4.73 1.8

Big 1.1 0.8 0.87 1.2 0.8

Table 6.6 Mean score of governmental barrier according to size of firm. The higher the mean


Variables: (1) Inflexible building codes, (2) Submission of drawings is still hard copy, (3)

Submission of drawings doesn’t use digital copy from digital innovations, (4) High standard of

digital modelling and procedure established by government for drawing submission

Chart 6.6 Chart showing how crucial is governmental barrier according to size of firm based


0.00

1.00

2.00

3.00

4.00

5.00

1 2 3 4 OVERALL

GOVERNMENTAL BARRIER

SMALL

MEDIUM

BIG

29

Result of descriptive statistics in table 6.6 shows that governmental barrier is not

crucial in digital innovation adoption among architectural organizations. However, one

variable that is high standard of digital modelling and procedure established by government

for drawing submission is found to be crucial. This is because the model of this study is

taken in Singapore for which the authority is strictly regulating the standard of submitting

BIM models. However, in other country where authorities are not regulating the BIM

models, this variable is not crucial. In a holistic sense, governmental barriers are not crucial

in digital innovation adoption.

6.2 MOST SIGNIFICANT BARRIER IN DIGITAL INNOVATION ADOPTION

Distribution of firms answering research question -“Which among the barriers is the most

significant in digital innovation adoption?

6.3.1 MOST SIGNIFICANT BARRIER IN DIGITAL INNOVATION IN SMALL

ORGANIZATION

This part is presented and evaluated to analyze and satisfy the research question –

“Which among the digital innovation barriers is the most significant?”

Digital Innovation Barriers N Mean

Standard Deviation

Technological barrier 15 35.3333 4.48277

Organizational barrier 15 45.9333 6.9741

Financial barrier 15 36.0000 4.42396

Process barrier 15 26.1333 2.13363

Psychological barrier 15 38.2667 3.71227

Governmental barrier 15 6.0000 1.88982

Note: The higher the mean score is the more likely to be the most significant barrier in

digital innovation adoption

Table 6.7 Chart showing descriptive statistics of the most significant barrier in small

architectural organizations

The result of descriptive statistics shown above in table 6.7 revealed that in small

architectural organizations, the most significant barrier is organizational barrier, while

government barrier is the least significant among all the barriers presented.

30

6.2.2 MOST SIGNIFICANT BARRIER IN DIGITAL INNOVATION IN MEDIUM SIZE

ARCHITECTURAL ORGANIZATION

Digital Innovation Barrier N Mean

Standard Deviation






Governmental barrier 15 7.3333 1.83874

Note: The higher the mean score is likely to be the most significant barrier in digital

innovation adoption

Table 6.8 Chart showing descriptive statistics of the most significant barrier in medium


Overall result of descriptive statistics shown above in table 6.9 has shown that in

medium architectural firms, the most significant barrier is financial barrier, while

governmental barrier is found to be the least significant among all the barriers presented.

6.2.3 MOST SIGNIFICANT BARRIER IN DIGITAL INNOVATION IN BIG

ARCHITECTURAL ORGANIZATION

Digital Innovation Barriers N Mean Standard Deviation






Government barrier 15 3.2667 3.17280

Note: The higher the mean score is the more likely to be the most significant barrier in

digital innovation adoption

Table 6.9 Chart showing descriptive statistics of the most significant barrier in big


Specifically in big architectural firms as shown in table 6.10, the most significant

barrier is process barrier. Typically like other small and medium architectural organizations,

governmental barriers are observed to be the least significant.

31

Chart 6.7 Summary of distribution of mean scores showing degree of digital innovation

barriers each architectural firm

The chart 6.7 above indicates a result that significant barriers vary from the size of

architectural organizations. Significant result that is worth noting is that organizational

barriers are observed to the most significant in small firms, while financial barriers and

process barriers is very significant in medium and big architectural firms respectively. It

varies from size of architectural organization.

6.2.4 OVERALL RESULT FOR THE MOST SIGNIFICANT BARRIER IN DIGITAL

INNOVATION

Digital Innovation Barriers N Mean Standard Deviation

Technological 45 26.3111 12.37817

Organizational 45 25.7333 16.71336

Financial 45 29.9556 11.13748

Process 45 26.1556 2.48592

Psychological 45 24.5556 14.54078

Government 45 5.5333 2.88885

Table 6.10 Chart showing overall results of descriptive statistics of the most significant

barrier

45.93

38.60

26.47

0

5

10

15

20

25

30

35

40

45

50

SMALL MEDIUM BIG

ME

AN

SC

OR

E

Technological

Organizational

Financial

Process

Psychological

Governmental

32

Chart 6.8 Overall result for the most significant barrier in digital innovation

Holistically, descriptive statistics in chart 6.8 revealed a very significant result in

evaluating the most significant barrier among the six digital innovation barriers presented.

It can be noted that out of the six digital innovation barriers presented in this study, financial

barrier is observed to be the most significant among all the barriers in digital innovations

adoption.

6.4 SIGNIFICANT RELATIONSHIP BETWEEN DIGITAL INNNOVATION

BARRIER AND SIZE OF ARCHITECTURAL ORGNIZATION

Distribution of firms answering research question - “Is there a significant relationship

between size of architectural organization and barrier in digital innovation adoption?”

Size of architectural

firms Mean Standard Deviation N

Big 11.3447 1.75841 15

Medium 26.4993 2.13441 15

Small 31.2780 2.32134 15

Total 23.0407 8.83137 45

Table 6.11 Dependent Variable: Total mean score for digital innovation barriers

26.31 25.73

29.96

26.1624.56

5.53

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

SUMMARY

ME

AN

SC

OR

E Technological

Organizational

Financial

Process

Psychological

Governmental

33

Source

Type III Sum of

Squares df Mean Square F Sig.

Corrected Model 3249.187a 2 1624.593 373.860 .000

Intercept 23889.254 1 23889.254 5497.527 .000

Size of Architectural Firm 3249.187 2 1624.593 373.860 .000

Error 182.509 42 4.345

Total 27320.950 45

Corrected Total 3431.696 44

a. R Squared = .947 (Adjusted R Squared = .944) 94%

Table 6.12 Test between size of organizations and digital innovation barrier

The table 6.12 above shows a significant relationship between size of architectural

organization and digital innovation barrier with F = 373 and significance level at p = .000.

The relationship of the two variables showed that bigger architectural firms have lower

digital innovation barriers while smaller firms have higher digital innovation barriers. About

94% of digital innovation barrier can be explained by size of architectural organization.

(I) Firm (J) firm

Mean

Difference (I-J) Std. Error Sig.

95% Confidence Interval

Lower Bound Upper Bound

Big Medium -15.1547* .76118 .000 -17.0863 -13.2230

Small -19.9333* .76118 .000 -21.8650 -18.0017

Medium Big 15.1547* .76118 .000 13.2230 17.0863

Small -4.7787* .76118 .000 -6.7103 -2.8470

Small Big 19.9333* .76118 .000 18.0017 21.8650

Medium 4.7787* .76118 .000 2.8470 6.7103

Based on observed means, the error term is Mean Square (Error) = 4.345.

*. The mean difference is significant at the .05 level.

Table 6.13 Multiple comparisons between size of organizations and barrier in digital

innovation using Shefee post hoc

Shefee post hoc test showed in table 6.13 that all comparisons showed significant

difference. Significance is at p = .000 level, significant results shows that big architectural

firms have lower digital innovation barriers compared to medium and small architectural

organizations. A correlation between the size of architectural organization and barriers of

digital innovation adoption is found. It can be concluded that the bigger the architectural

organization, the lower the barrier, and the smaller architectural organization, the higher the

barrier in digital innovation adoption.

34

8. CONCLUSIONS

The findings of the study were summarized based on the data presented. An analysis

of the said findings led the researcher to draw conclusions. In evaluating the six subsequent

barriers (technological barriers, financial barriers, organizational barriers, governmental

barriers, psychological barriers, and process barriers) among architectural organizations the

finding revealed interconnected barriers but varies from the size of architectural

organization.

Small architectural organizations is extremely affected by financial, organizational

and psychological barriers that led to technological problems with consequential effect of the

organization . The main cause of this interwined barriers is mainly due to the small projects

that small architectural organizations oftenly have with limited design fee which is not

substantial to provide the cost of technology, software and other logistics that is needed for

digital innovation. With the limited resources, the consequential effect is that they are

psychologically affected and afraid of profit loss. In effect, managers are being reluctant to

support digital innovation. Aside from financial, organizational and psychological barriers,

process barriers are also present in small architectural organization. Process barriers boils

down to 3d modelling and processing. It has to do with the upgrade of the equipment and

software that is too expensive for small architectural organizations due to limited funds. In

conclusion, technological, financial, organizational, psychological and process barriers is

very crucial in small architectural organizations.

Similarly, medium architectural organizations is also affected by series of barriers.

However, they are able to cope with the changing needs of the advent of digital technology

hence that the organization is able to support digital innovation because they are less

affected by psychological barriers. One reason for this is that medium architectural

organization is capable to get bigger projects with better design fees that can afford to

provide technology, software and other logistics that is necessary for digital innovation.

Holistically, medium architectural organizations is moderately affected by financial barriers

and with the support of the managers they are not extremely affected by technological

organizational and phsycological barriers.

Big architectural organizations are less affected by barriers rather than small and

medium architectural organizations. The main reason of being less affected is they have big

projects with considerable design fee that can support digital innovation. One reason of fully

implementing digital innovation in big architectural organizations is to be competitive with

other architectural firm. In addition, it is important to note that more often big architectural

organizations are in collaboration with universities in computational design research. This is

helpful in such a way that the transfer knowledge from leading research institutes can be

applied and useful to its actual projects. Though descriptive statistics have shown that the

most crucial barriers in big architectural organizations is process barriers, this is not really

the case. Big architectural organization is able to cope with process barriers. Complex

projects have heavier 3d models and slower data processing, but with the support from

organizations and providing more powerful equipment and collaboration with computational

design research, data processing become at ease.

In a holistic sense, financial barriers is the most crucial among the six barriers

presented. This has a consequential effect to the other barriers such as technological,

organizational and psychological barriers. When architectural organization is financially

incapable, the more that they are psychologically affected and the more they are not

35

supporting digital innovation. With this, it could be concluded that financial, technological,

organizational and psychological barriers are interelated. While these four barriers are

interelated, it can be also gleaned that process barriers which boils down to 3d modelling and

processing, can be also crucial. However, doesn’t correlates with financial, technological,

organizational and psychological barriers. In addition, governmental barrier like building

codes and other compliance is observed to be not crucial.

One of the most valuable results of this research is evaluating the correlation

between size of organization and barriers in digital innovation adoption. It was found out

that the size of architectural organization and barriers to digital innovation are significantly

correlated. This means that bigger architectural organization have lesser barriers, while

smaller architectural organization have more barriers in digital innovation. Therefore, the

bigger the architectural organization, the lesser it is affected by barriers in digital innovation

adoption.

Research for digital innovation in architectural organization is still very limited and

evaluating the challenges and barriers in related fields are significant. The wide variety of

barriers presented earlier, indicates series of problems in adopting digital innovations that

varies in different size of architectural organization. It should be considered and supported

by the architectural organizations. The new digital technology is proven to improve

efficiency, productivity and design quality but has not been used to its potential. These

barriers for digital innovation should be taken into consideration by architectural

organization so new technologies can be used to its full advantage.

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