Abstract—The intelligent green building (IGB) industry has

received considerable recognition globally due to the rapid

development of advanced technology, intelligent materials,

innovative products and services in recent years. Although

various cross-domain experiments and practices with respect to

IGB projects are ready for the full swing, the notion and

benefits of IGB are still ambiguous and debatable. The purpose

of this study is to apply a Kano quality model and a customer

satisfaction matrix to evaluate the satisfaction, preferences,

acceptability and marketability of these IGB design strategies.

The result reveals that there is indeed a preference gap between

designers and users in implementing certain strategies and

these findings could be useful to support design communication,

while leading to effectively understand discrepant concerns in

IGB design.

Index Terms—Customer satisfaction matrix, design

strategies, intelligent green building, Kano two-dimensional

quality model, Taiwan.

I. INTRODUCTION

During the last decade, advances in information,

communication and technology (ICT) have prompted a

revolution in the building industry [1]. One of the most

significant breakthroughs is the emergence of the so-called

“Intelligent green buildings” (IGB), in which an application

of ICT, intelligent materials and products can simultaneously

increase intelligence and sustainability of buildings [2].

Considering the potentials of embodying ICT components in

green buildings, some of multidisciplinary experiments and

practices have been deployed to evaluate the feasibility of

these IGB projects [3], [4], [5].

Taiwan, with a population of only 23 million, has been

regarded as a “Technology Island” carving out a huge niche

in the global ICT industry. The Taiwanese government is also

one of the relatively few governments that launched the green

building certification system before the year of 2000. In 2010,

the government further proposed the “Intelligent Green

Building Promotion Program” promising that more than

US$ 100 million dollars would be invested in supporting ICT

applications for green buildings and enhancing industrial

competitiveness in the following six years.

Since the concept of IGB is still a relatively new and

interdisciplinary field in the building industry, limited studies

have been attempted to explore its benefits, market demands,

and user acceptability toward IGB projects, meaning that the

assessment for ICT-green technologies and strategies,

Manuscript received November 9, 2012; revised December 21, 2012.

The authors are with the Department of Architecture, National Taiwan

University of Science and Technology, Taipei, Taiwan (e-mail: rik@

mail.ntust.edu.tw, 1976sjhh@gmail.com ).

namely IGB design strategies, is full of ambiguity and

paradoxes [6]. Considering the customer‟s requirements and

marketability of technology, some questions gradually come

to mind. Are customers aware of the potentials of IGB design

strategies? Are customers satisfied with these promising

strategies? Is there any conscious gap between designers and

users when adopting these strategies?

The objective of this research is to apply a Kano

two-dimensional quality model to classify feasible IGB

design strategies into five categories, namely, “Attractive”,

“One-dimensional”, “Must-be”, “Indifferent”, and “Reverse”,

based on customers‟ preferences [7]. By adopting the

customer satisfaction coefficient developed by Matzler and

Hinterhuber [8], these strategies can be further quantified to

establish a customer satisfaction matrix for evaluating

satisfied and unsatisfied preferences. Two groups comprised

of thirty professional designers and thirty general users are

invited to test proposed IGB design strategies. The

preference similarities and discrepancies between the two

groups and potentials of the application of these strategies

will be also discussed.

II. INTELLIGENT GREEN BUILDING (IGB)

Green Building (GB) is a design which focuses on

increasing the efficiency of resources use, energy, water and

materials while reducing building and environment impacts

during the building‟s lifecycle [9]. In order to mitigate and

measure these impacts, many GB rating systems, such as

LEED, BREEAM, GBTools, etc., have been developed [10],

[11], [12]. In Taiwan, the government established the

„„Green Building Evaluation and Labeling System‟‟ in 1999

to promote the concept of green buildings. This system

includes nine indicators: Biodiversity, Greenery, Soil Water

Content, Daily Energy Saving, CO2 Emission Reduction,

Waste Reduction, Indoor Environment, Water Resource, and

Sewage & Garbage Improvement [13].

The term of “Intelligent Building” (IB) was first used in

the United States in the early eighties and its original

definition is a building which can integrate various systems

to effectively manage resources in a coordinated way to

maximize technical and operating performance [14]. As the

development of automatic and interactive technologies

between users and environments, different understanding and

inconsistent interpretation of building intelligence indeed

exist [15].

Based on extensive implementing experiences with respect

to GB and IB, as well as promising ICT applications, the

Taiwan government has been aware that the vision of

developing “Intelligent Green Building” (IGB) will be one of

Kano-Based Quality Model for Differencing Intelligent

Green Building Design Strategies

Yi-Kai Juan and Sheng-Jhao Hsu Huang

32

IACSIT International Journal of Engineering and Technology, Vol. 5, No. 1, February 2013

DOI: 10.7763/IJET.2013.V5.504

the major policies for emerging intelligent industries in the

next generation. The term “IGB” is widely defined as

adoptions of ICT, intelligent materials, products and services

on green buildings to make buildings safer, healthier, more

convenient, and more energy-saving [16].

The concept of IGB is basically an integration of IB and

GB. Unlike numerous studies regarding design strategies of

IB and GB that many researchers, academics, and

professionals have been involved in, there has been a very

limited amount of research into the IGB.

Through a review of seven green and intelligent building

projects completed in 2010 and an in-depth interview of six

professionals, twenty-seven design strategies are collected as

shown in Fig. 1.

A

D

G

B

E

H

C

F

I

GB IB

B1 Intelligent vegetal wall systemB2 Intelligent roof greening systemC1 ICT-based permeable pavementC2 Rainwater infiltration technologiesD1 Intelligent energy monitoring systemD2 Inductive shutterD3 Solar photoelectric glassD4 Nano-coating solar panelsD5 Inductive ventilating towerD6 Earth cooling tube systemD7 Intelligent VRV systemD8 Intelligent heat exchanger systemD9 Intelligent cold storage system D10 Solar-tracking light-guide platesD11 Smart roof light-guide systemE1 Intelligent CO2 monitoring systemF1 BIM-based management systemF2 RFID-based material checkingG1 IEQ monitoring systemG2 Automatic humidity controlling systemG3 Adaptive lighting systemG4 Intelligent fire alert systemG5 Intelligent fire compartment systemG6 Build damage monitoring systemH1 Intelligent graywater recycling systemH2 Inductive rainwater recycling systemH3 Inductive water-saving equipment

Fig. 1. Design strategies of IGB

IV. METHODOLOGY

A. Kano Two-Dimensional Quality Model

The Kano two-dimensional quality model is a useful tool

to classify and prioritize customer needs. It can also address

the non-linear relationship between quality attributes

performance of a product (or service) and overall customer

satisfaction [7]. These quality attributes, as shown in Fig. 2,

can be classified into five categories.

With quality

attribute

Without

quality

attribute

Satisfied

Dissatisfied

Must-be

One-dimensional

Attractive

Indifferent

Reverse

Fig. 2. Kano two-dimensional quality model

(1) must-be attributes are expected by the customers and

will result in dissatisfaction when these attributes are not

fulfilled; (2) one-dimensional attributes are those for which

better fulfillment leads to linear increment of customer

satisfaction; (3) attractive attributes are usually unexpected

by the customers and can result in great satisfaction if they

are available; (4) indifferent attributes are those that the

customers is not interested in the level of their performance;

and (5) reverse attributes refer to a high degree of

achievement resulting in dissatisfaction [7], [8], [17]. By

adopting this Kano model, customer needs, such as

preferences of these IGB design strategies, can be effectively

classified and indentified.

B. Research Process

The traditional Kano method can not be equipped with

quantitative assessment [18]. Therefore, a three-stage

quantitative assessment process based on improving the

Kano model is further developed [8, 17].

First, a questionnaire that consists of positive/functional

and negative/dysfunctional is established. The customer can

answer a pair of questions in one of five different ways,

“Like”, “Must-be”, “Neural”, “Live with”, and “Dislike” for

each attribute of a product (or service). The first question

concerns the reaction of the customer if the product (or

service) has that attribute (functional form); the second

involves the reaction if the product (or service) does not have

that attribute (dysfunctional form) [8].

Second, the questionnaire is deployed to various

customers, and each answer pair is aligned with the Kano

evaluation table [17], as shown in Table 1, which can reveal

each customer‟s perception toward attributes of a product (or

service) [18, 19]. If the customer answers, for example, “I

like it that way” as regards a specific attribute from the

functional side, and answers “I am neural” for the same

attribute from the dysfunctional side, the combination of the

question in the evaluation table will be the category of “A”,

indicating that this attribute is attractive to customer needs.

TABLE I: KANO EVALUATION TABLE

Dysfunctional:

Like Must-be Neural Live with Dislike

Fu

nct

ion

al:

Like Q A A A O

Must-be R I I I M

Neural R I I I M

Live with R I I I M

Dislike R R R R Q

Note: Q, A, R, I, O, and M denote “Questionable”, “Attractive”,

“Reverse”, “Indifferent”, “One-dimensional”, and “Must-be” attributes,

respectively

Third, the use of the customer satisfaction coefficient

(CSC), as show in equation (1) and (2), is applied to

understand how strongly a product (or service) attribute may

affect satisfaction or, in the case of its non-fulfillment,

customer dissatisfaction. The positive CSC ranges from 0 to

1. The closer the value is to 1, the higher the influence on

customer satisfaction. On the contrary, if the negative CSC

approaches -1, the influence on customer dissatisfaction is

especially strong if the attribute of the product (or service) is

33

IACSIT International Journal of Engineering and Technology, Vol. 5, No. 1, February 2013

III. IGB DESIGN STRATEGIES

not fulfilled. A value of 0 implies that this attribute has low

influence which does not cause dissatisfaction if it is not met

[8].

Extent of satisfaction coefficient (SC)

= (A+O)/(A+O+M+I) (1)

Extent of dissatisfaction coefficient (DSC)

= -(O+M)/(A+O+M+I) (2)

V. KANO TWO-DIMENSIONAL QUALITY MODEL FOR IGB

DESIGN STRATEGIES

A. Experimental Groups

In order to understand preferences of IGB design strategies

for different customers, two groups comprised of thirty

professional designers and thirty general users were invited

to test these strategies. The group of the designers consists of

the principal architects, project managers, design directors,

and senior architectural designers. The group of the users

comprises the directors of community committee and facility

managers of office buildings. Among these respondents, four

(6.7%) had never heard of the concept of IGB and therefore

data analysis from these respondents was removed. Twelve

general users and four professional designers had a very

limited knowledge of IGB strategies. More explanations and

illustrations regarding benefits, features, and applications of

these strategies were introduced in advance before answering

the questionnaire.

B. Data Analysis

The questionnaire was first tested by the internal

consistency based on the notion of the Cronbach‟s α

reliability coefficient. The results show that the average

coefficient for the group of professional designers and

general users are 0.812 and 0.724, respectively, above the

benchmark of 0.7 suggested by Nunnally (1978) [20],

interpreting that the proposed questionnaire has a high

reliability suitable for data analysis. The quality attribute for

each IGB design strategy and its corresponding SC/DSC for

these two groups are separately illustrated in Table II and

Table III.

VI. RESULTS AND DISCUSSIONS

According to the result shown in Table 2 and Table 3,

several findings with respect to the customer‟s preferences

and satisfaction for IGB design strategies are worth further

discussion

A. Same Opinions on Quality Attributes for Professional

Designers and General Users

For some IGB design strategies related to human safety

issues, such as “G4 Intelligent fire alert system”, “G5

Intelligent fire compartment system”, and “G6 Build damage

monitoring system”, there is no doubt that these two groups

have the same opinion and regard requirements of these

strategies as “Must-be” attributes, revealing that customers

will be extremely dissatisfied if these basic requirements are

not fulfilled.

TABLE II: QUALITY ATTRIBUTE AND SATISFACTION COEFFICIENT OF

PROFESSIONAL DESIGNERS

IGB design strategies A O M I Quality

attribut

e

SC DSC

B1 Intell. vegetal wall sys. 54

%

7% 7% 29

% A 0.63

-0.1

5

B2 Intell. roof greening sys. 57

%

14

%

14

%

14

% A 0.71

-0.2

9

C1 ICT-based permeable pave. 48

%

7% 11

%

33

% A 0.56

-0.1

9

C2 Rainwater infiltration tech. 25

%

7% 25

%

43

% I 0.32

-0.3

2

D1 Intell. energy monitoring sys. 29

%

21

%

36

%

14

% M 0.50

-0.5

7

D2 Inductive shutter 50

%

7% 7% 36

% A 0.57

-0.1

4

D3 Solar photoelectric glass 59

%

11

%

0% 30

% A 0.70

-0.1

1

D4 Nano-coating solar panels 43

%

4% 4% 50

% I 0.46

-0.0

7

D5 Inductive ventilating tower 46

%

11

%

7% 36

% A 0.57

-0.1

8

D6 Earth cooling tube sys. 56

%

7% 11

%

26

% A 0.63

-0.1

9

D7 Intell. VRV sys. 32

%

20

%

44

%

4% M 0.52

-0.6

4

D8 Intell. heat exchanger sys. 35

%

15

%

42

%

8% M 0.50

-0.5

8

D9 Intell. cold storage sys. 29

%

11

%

7% 54

% I 0.39

-0.1

8

D10 Solar-track. light-guide

plates

48

%

7% 11

%

33

% A 0.56

-0.1

9

D11 Smart roof light-guide sys. 52

%

12

%

4% 32

% A 0.64

-0.1

6

E1 Intell. CO2 monitoring sys. 24

%

8% 16

%

52

% I 0.32

-0.2

4

F1 BIM-based management sys. 32

%

4% 11

%

54

% I 0.36

-0.1

4

F2 RFID-based material

checking

29

%

11

%

25

%

36

% I 0.39

-0.3

6

G1 IEQ monitoring sys. 44

%

16

%

0% 40

% A 0.60

-0.1

6

G2 Auto. humidity controlling

sys.

35

%

15

%

8% 42

% I 0.50

-0.2

3

G3 Adaptive lighting sys. 23

%

27

%

15

%

35

% I 0.50

-0.4

2

G4 Intell. fire alert sys. 11

%

11

%

43

%

36

% M 0.21

-0.5

4

G5 Intell. fire compartment sys. 14

%

18

%

39

%

29

% M 0.32

-0.5

7

G6 Build damage monitoring

sys.

39

%

4% 46

%

11

% M 0.43

-0.5

0

H1 Intell. graywater recycling

sys.

50

%

7% 0% 43

% A 0.57

-0.0

7

H2 Inductive rainwater recyl.

sys.

32

%

11

%

14

%

43

% I 0.43

-0.2

5

H3 Inductive water-saving

equip.

25

%

21

%

4% 50

% I 0.46

-0.2

5

Average 0.49 -0.2

8

Strategies focusing on applying ICT to external

environments and components of buildings, such as “B1

Intelligent vegetal wall system”, “D2 Inductive shutter”, “D3

Solar photoelectric glass”, “D10 Solar-tracking light-guide

plates”, “D11 Smart roof light-guide system”, and “H1

Intelligent graywater recycling system”, are classified into

“Attractive” attributes. The finding discloses that these

strategies are more explicit and fulfilling these requirements

leads to more than proportional satisfaction. If they are not

met, however, there is no feeling of dissatisfaction. The

reason might be that customers have an intimate knowledge

34

IACSIT International Journal of Engineering and Technology, Vol. 5, No. 1, February 2013

of these explicit strategies. Without adopting these strategies,

customers can still find other alternative solutions to fulfill

their requirements.

TABLE III: QUALITY ATTRIBUTE AND SATISFACTION COEFFICIENT OF

GENERAL USERS

IGB design strategies A O M I Quality

attribut

e

SC DS

C

B1 Intell. vegetal wall sys. 43% 18% 4% 36% A

0.6

1

-0.2

1

B2 Intell. roof greening sys. 32% 21% 4% 43% I

0.5

4

-0.2

5

C1 ICT-based permeable pave. 32% 18% 7% 43% I

0.5

0

-0.2

5

C2 Rainwater infiltration tech. 32% 14% 7% 46% I

0.4

6

-0.2

1

D1 Intell. energy monitoring sys. 32% 14% 7% 46% I

0.4

6

-0.2

1

D2 Inductive shutter 57% 11% 7% 25% A

0.6

8

-0.1

8

D3 Solar photoelectric glass 43% 11% 11

%

36% A

0.5

4

-0.2

1

D4 Nano-coating solar panels 39% 4% 7% 50% I

0.4

3

-0.1

1

D5 Inductive ventilating tower 29% 14% 11

%

46% I

0.4

3

-0.2

5

D6 Earth cooling tube sys. 25% 14% 7% 54% I

0.3

9

-0.2

1

D7 Intell. VRV sys. 21% 14% 18

%

46% I

0.3

6

-0.3

2

D8 Intell. heat exchanger sys. 29% 21% 11

%

39% I

0.5

0

-0.3

2

D9 Intell. cold storage sys. 32% 18% 7% 43% I

0.5

0

-0.2

5

D10 Solar-track. light-guide

plates

50% 14% 18

%

18% A

0.6

4

-0.3

2

D11 Smart roof light-guide sys. 39% 14% 18

%

29% A

0.5

4

-0.3

2

E1 Intell. CO2 monitoring sys. 18% 25% 11

%

46% I

0.4

3

-0.3

6

F1 BIM-based management sys. 29% 7% 11

%

54% I

0.3

6

-0.1

8

F2 RFID-based material checking 29% 18% 7% 46% I

0.4

6

-0.2

5

G1 IEQ monitoring sys. 11% 36% 25

%

29% O

0.4

6

-0.6

1

G2 Auto. humidity controlling

sys.

18% 46% 21

%

14% O

0.6

4

-0.6

8

G3 Adaptive lighting sys. 39% 25% 18

%

18% A

0.6

4

-0.4

3

G4 Intell. fire alert sys. 11% 18% 54

%

18% M

0.2

9

-0.7

1

G5 Intell. fire compartment sys. 21% 18% 39

%

21% M

0.3

9

-0.5

7

G6 Build damage monitoring sys. 25% 18% 32

%

25% M

0.4

3

-0.5

0

H1 Intell. graywater recycling

sys.

39% 14% 29

%

18% A

0.5

4

-0.4

3

H2 Inductive rainwater recyl. sys. 7% 43% 18

%

32% O

0.5

0

-0.6

1

H3 Inductive water-saving equip. 25% 36% 18

%

21% O

0.6

1

-0.5

4

Average 0.4

9

-0.3

5

These two groups also have the same opinion on several

strategies such as “C2 Rainwater infiltration technology”,

“D4 Nano-coating solar panels”, “D9 Intelligent cold storage

system”, “E1 Intelligent CO2 monitoring system”, “F1

BIM-based management system”, and “F2 RFID-based

material checking”. They express their indifferences towards

these strategies, leading to “Indifferent” attributes. The

reason might be that the benefits and advantages of these

strategies are not easily perceived by customers, and

therefore they do not result in either customer satisfaction or

dissatisfaction.

B. Discrepant Opinions on Quality Attributes for

Professional Designers and General Users

There is an interesting finding that these two groups show

discrepant opinions on some IGB design strategies related to

energy-saving issues, such as “D1 Intelligent energy

monitoring system”, “D5 Inductive ventilating tower”, “D6

Earth cooling tube system”, “D7 Intelligent VRV system”,

and “D8 Intelligent heat exchanger system”. Professional

designers generally regard requirements of these strategies as

“Must-be” or “Attractive” attributes, while general users take

these strategies for indifference. The result implies that there

is still much room for improving energy awareness and

environmental consciousness education for users.

The second cluster of discrepant opinions between these

two groups appears to be the strategies of indoor environment

issues, such as “G1 IEQ monitoring system”, “G2 Automatic

humidity controlling system”, and “G3 Adaptive lighting

system”. Unlike professional designers who usually pay

more attention to daily energy-saving issues, general users,

on the contrary, are more sensitive to these indoor

environment quality problems. These strategies acquired

from general users are classified into “Attractive” or

“One-dimensional” attributes, showing that customer

satisfaction is proportional to the level of fulfillment of these

strategies.

The similar situation also occurs over the last two

strategies: “H2 Inductive rainwater recycling system” and

“H3 Inductive water-saving equipment”. These two

strategies seem to be the basic design requirements for

general users (“One-dimensional” attribute), whereas

professional designers hold negative attitude (“Indifferent”

attribute). The reason might be that professional designers

have taken these strategies for granted and easily overlooked

the basic requirements for users.

C. Customer Satisfaction Matrix

Based on the concept of CSC, a customer satisfaction

matrix can be developed to quantitatively reflect the priority

of adopting these IGB design strategies. This matrix,

depicted in Fig. 3, is comprised of four quadrants divided by

an X-axis, representing the level of satisfaction, and a Y-axis,

representing the level of dissatisfaction. The origin of this

matrix is the average of SC and DSC. Strategies located in the

quadrant I, therefore, imply that these strategies have greater

impacts on highly increasing customer‟s satisfaction and

dissatisfaction and should be implemented firstly. Strategies

in the quadrant III, on the contrary, could be suspended due

to their low influence on customer‟s satisfaction and

dissatisfaction. Considering the efficiency of budget

utilization, decision-makers can decide whether they would

like to invest in strategies located in quadrant II and IV since

these strategies can only improve either customer‟s

satisfaction or customer‟s dissatisfaction and do not have

much effect upon the result.

The observation from the Fig. 3 also reveals insightful

35

IACSIT International Journal of Engineering and Technology, Vol. 5, No. 1, February 2013

information for both professional designers and general users.

For example, designers give the priority to the strategies of

“D1 Intelligent energy monitoring system” and “D7

Intelligent VRV system”, while users regard them as

unimportant strategies. Similarly, the strategies, such as “H2

Inductive rainwater recycling system” and “H3 Inductive

water-saving equipment”, are critical to users but

insignificant to designers. These two groups have the same

preferences to some strategies, such as “G3 Adaptive lighting

system” for a prior execution, and “F1 BIM-based

management system” and “D4 Nano-coating solar panels”

for a stay of implementation. The matrix is a useful tool for

design communication to discover the real requirements and

expectations, providing a good opportunity to re-examine

and decrease this design gap between the designers and users.

0.49

Satisfaction

coefficient (SC)

-0.28

Dissatisfaction

coefficient

(DSC)B1

B2

C1

C2

D1

D2 D3

D4

D5

D6

D7D8

D9

D1

0D1

1

E1

F1

F2

G1

G2

G3

G4G5 G6

H1

H2H3

Customer satisfaction matrix for professional designers

0.49

Satisfaction

coefficient (SC)

-0.35

Dissatisfaction

coefficient

(DSC)B1

B2

C1C2D1

D2D3

D4

D5D6

D7D8

D9

D1

0

D1

1

E1

F1

F2G1

G2

G3

G4

G5 G6

H1

H2H3

Customer satisfaction matrix for general users

G1

III

III IV

III

III IV

Fig. 3. Customer satisfaction matrix.

VII. CONCLUSION

Embodying ICT components, intelligent materials,

products and technologies into green buildings to develop the

concept of Intelligent Green Buildings (IGB) has prompted a

revolution in the building industry. In view of the benefits of

transforming from traditional buildings into IGBs, many

global organizations including Taiwan are making efforts to

create better living environments which are both sustainable

and intelligent. However, the assessment for ICT-green

technologies and strategies, namely IGB design strategies, is

still in a preliminary stage and all participants in the related

industries are at a loss as to how to find the right path to

implement these technologies and strategies.

This study attempts to apply a Kano two-dimensional

quality model to classify possible IGB design strategies and

adopt the customer satisfaction coefficient to quantitatively

evaluate the satisfaction and preferences of these strategies

from professional designers and general users. This is the

first step towards a successful implementation of these

strategies by learning about market demands and customer‟s

acceptability. The experimental result further proves that the

proposed approach could be a useful tool to explore

similarities and discrepancies of strategy preferences

between designers and users, and accordingly to support

design communication for decreasing the gap between these

two sides. Lessons with respect to IGB issues learned from

investigating experiences in Taiwan might be also referable

to other organizations that are facing similar research

requirements and problems. Continuous updates of these

strategies and strategic development from the proposed

approach to be an efficient decision support tool could be a

future focus for further research.

REFERENCES

[1] X. Xue, Q. Shen, H. Fan, H. Li, and S. Fan, “IT supported collaborative

work in A/E/C projects: A ten-year review,” Automation in

Construction, vol. 21, pp. 1-9, 2012.

[2] M. J. Skibniewski, “Optimal buildings through automation technology:

Toward systems unification for secure, intelligent, green, and immune

buildings,” The 25th International Symposium on Automation and

Robotics in Construction, Vilnius, Lithuania, pp. 1-4, June 2008.

[3] H. Adeli, “Smart structures and building automation in the 21st

century,” The 25th International Symposium on Automation and

Robotics in Construction, Vilnius, Lithuania, pp. 5-10, June 2008.

[4] L. R. Yang, J. H. Chen, and H. W. Wang, “Assessing impacts of

information technology on project success through knowledge

management practice,” Automation in Construction, vol. 22, pp.

182-191, 2012.

[5] P. Raju and V. Ahmed, “Enabling technologies for developing

next-generation learning object repository for construction,”

Automation in Construction, vol. 22, pp. 247-257, 2012.

[6] C. K. Chau, M. S. Tse, and K. Y. Chung, “A choice experiment to

estimate the effect of green experience on preferences and

willingness-to-pay for green building attributes,” Building and

Environment, vol. 45, no. 11, pp. 2553-2561, 2010.

[7] N. Kano, N. Seraku, F. Takahashi, and S. Tsuji, “Attractive quality and

must-be quality,” The Journal of the Japanese Society for Quality

Control, vol. 14, no. 2, pp. 39-48, 1984.

[8] K. Matzler and H. H. Hinterhuber, “How to make product development

projects more successful by integrating Kano‟s model of customer

satisfaction into quality function deployment,” Technovation, vol. 8, no.

1, pp. 25-38, 1998.

[9] V. W. Y. Tam, J. L. Hao, and S. X. Zeng, “What affects implementation

of green buildings? An empirical study in Hong Kong,” International

Journal of Strategic Property Management, vol. 16, no. 2, pp. 115-125,

2012.

[10] USGBC (United States Green Building Council) (1999).

LEED-Leadership in Energy and Environmental Design: Green

Building Rating System, US Green Building Council. [Online].

Available: http://www.usgbc.org/DisplayPage.aspx?CMSPageID=222

[11] BRE Global (2008). BREEAM Offices 2008 Assessor Manual. [Online].

Available: http://www.breeam.org/page.jsp?id=109.

[12] GBC Green Building Tool (2005). International Initiative for a

Sustainable Built Environment, IISBE. [Online]. Available:

http://iisbe.org/gbc2k/gbc-start.htm

[13] K. F. Chang, C. M. Chiang, and P. C. Chou, “Adapting aspects of GB

tool 2005-searching for suitability in Taiwan,” Building and

Environment, vol. 42, no. 1, pp. 310-316, 2007.

[14] T. D. J. C. Croome, “What do we mean by intelligent buildings?”

Automation in Construction, vol. 6, no. 5, pp. 395-400, 1997.

[15] J. K. W. Wong, H. Li, and S. W. Wang, “Intelligent building research:

A review,” Automation in Construction, vol. 14, no. 1, pp. 143-159,

2005.

[16] M. C. Ho (2011). Green Building Promotion: Current Development

and Future Perspectives in Taiwan. [Online]. Available:

http://unfccc.saveoursky.org.tw/2011cef/images/cadiis/presentation/s1

t1.pdf

36

IACSIT International Journal of Engineering and Technology, Vol. 5, No. 1, February 2013

[17] C. Berger et al., “Kano‟s methods for understanding customer-defined

quality,” Center for Quality Management Journal, vol. 2, no. 4, pp.

3-35, 1993.

[18] Q. Xu, R. J. Jiao, X. Yang, M. Helander, H. M. Khalid, and A. Opperud,

“An analytical Kano model for customer need analysis,” Design

Studies, vol. 30, no. 1, pp. 87-110, 2009.

[19] S. P. Lin, C. L. Yang, Y. H. Chan, and C. Sheu, “Refining Kano‟s

quality attributes–satisfaction‟ model: A moderated regression

approach,” International Journal of Production Economics, vol. 126,

no. 2, pp. 255-263, 2010.

[20] J. C. Nunnally, Psychometric Theory. McGraw-Hill, New York. 1978

Yi-Kai Juan is an Assistant Professor at Department

of Architecture, National Taiwan University of

Science and Technology (NTUST), Taipei, Taiwan.

He received a B.S. degree in Architecture from

National Cheng Kung University in 2000 and M.S.

and Ph.D. degrees in architecture from NTUST in

2004 and 2006, respectively, specializing in facility

and project management, and development of

decision support system in A/E/C industries. Dr. Juan

was a post-doctoral research fellow at Building Construction Program,

Georgia Institute of Technology in 2008 and a visiting scholar at Center for

Sustainable Development and Global Competitiveness, Stanford University,

California in 2009. He has published several articles regarding the

applications of decision support systems in A/E/C industries in various

international journals. His recent research focuses on cross-domain

technology integration, and strategy optimization for sustainable built

environments.

Sheng-Jhao Hsu Huang is an architect in Taiwan

and a Ph.D. student at National Taiwan University of

Science and Technology (NTUST), Taipei, Taiwan.

He received a B.S. degree from Department of

Architecture, Chung Yuan University in 1999 and

M.S. degree in Architecture from NTUST in 2005,

specializing in architectural and interior design, and

project management. Mr. Huang is a principal

architect of SFT Interiors

(http://www.sftid.com.tw/). His recent professional work focuses on

architectural and interior design for residential buildings, office buildings,

and public exhibition halls. His research interests include strategic use of

management methods on building projects and optimization for business

strategies.

37

IACSIT International Journal of Engineering and Technology, Vol. 5, No. 1, February 2013