Complex assessment model for advanced technology deployment.
Kildiene, Simona ; Zavadskas, Edmundas Kazimieras ; Tamosaitiene, Jolanta 等
Introduction
As suggested by experience of some successful countries, economic
growth based on traditional factors of production is inevitably
transient; meanwhile, long-lasting high productivity of a national
system can be guaranteed by operation of innovation-based enterprises.
Innovation in businesses is based on the development of
technologies/products (Oliveira, Lino 2013). Technological development -
innovation activities based on adoption of technologies and innovations
designed in other enterprises, usually by way of procuring technological
lines or production know-how licences, etc. (Skibniewski, Zavadskas
2013). The technology adoption lifecycle describes how a market develops
for a new product category (Moore 1991).
Shift in the production and business paradigm, development of new
materials and emerging new needs of the public can generate an immense
innovation potential in any industrial or business area (Testa et al.
2011). Most scientific researches underline that in terms of
construction firms, the technological progress not only requires the
development of new technologies and solutions but also their
implementation as well as diffusion of technological advancements in
products and production proces ses (Akadiri et al. 2013; Cavico et al.
2013; Hakansson, Ingemansson 2013; Tamosaitiene, Zavadskas 2013).
Uptake of new technologies and products by construction firms is a
complex process (Mazurkiewicz, Poteralska 2012). First, it is related to
great risk and reorganisation of existing production processes and
organisational systems (Dunovic et al. 2013). Criteria pertaining to the
process of new technology uptake should be analysed assessing internal
financial criteria of a company as well as aspects related to the
external business environment and development of the technology market.
This means a complex assessment of the effects brought by micro, meso
and macro environments (Kaklauskas et al. 2012).
The likelihood that construction firms adopt or generate
innovations is affected by firm specific as well as market related
criteria. On the one hand, firm characteristics as firm size, type of
activity, location and managers' quality (including age and
education) indeed affect innovation and technology adoption. On the
other hand, very important are market-related features as market growth,
profit margins, price of financing, risk, intellectual property rights
(IPR), market structure, codification patterns, regulations, and type of
clients--high-end versus low-end (Blackley, Shepard III 1996). The
fragmented structure of the sector contributes to deter diffusion (von
Hippel 1988), as does the complex and network-like structure of the
construction production process. As the end-product is the outcome of
the coordinated inputs of different subcontractors the transfer of
ideas--and even more the R&D process itself--may become difficult
and expensive. In any case, and regardless of firm size,
enterprises' success in implementing project-based innovations
depends on firms' capabilities, the environment in which they
operate, and the characteristics of the innovation itself (Manley 2008).
Firm capabilities include core competences and the methods used to build
and exploit them (Montalvo, van der Giessen 2012).
No decision to invest into new technologies is made quickly. Mostly
decision-making is affected by uncertainty brought by new technologies
and their developmental trends. Uncertainty of newly implemented
technologies encompass unknown future market conditions, internal
capacity of the company (accurate investment costs for acquisition of a
new technology, requirement for new specialists for work with the new
technology) and many other factors
The importance to assess technological uncertainty becomes even
more obvious as a company strives for competitive advantage and
successful continuation of its activities. This process is inseparable
from technology uptake solutions as it considers future change of the
technology over a certain period of time. In sectors that encounter
especially rapid technologic change, firms rarely receive full return on
investments; in any case, new technologies are unavoidable in such
business structure. Not only does importance of such strategic decisions
arise from substantial investment costs but also from their impact on
the future operations of the firm. Investments into a new technology not
only result in acquisition of a certain piece of equipment that enables
a new process or provides an opportunity to improve existing or create
new products or services; in addition, they shape competence and
intellectual potential, which in time contributes to competitive edge of
the company (Vasauskaite et al. 2011). This necessitates models for
assessment of new technology integration and adjustment in the market
that would facilitate corporate decision-making related to market demand
for a new technology, product or service.
[FIGURE 1 OMITTED]
1. Complex model of technology deployment in construction
Aiming to satisfy standards for sustainable construction sector
products, processes and works; apply strategies for efficient use of
energy in buildings; observe requirements on energy performance of
buildings; and use certification of energy performance of buildings,
sustainable technologies must be integrated into the construction
sector.
Technology integration is inseparable from the corporate strategy
(Fig. 1). The strategy becomes especially relevant under current market
economy conditions, as companies need to project their business
development trends and service demand as well as survive on markets and
make a profit (Teece 2010). The corporate strategy accurately defines
the niche and trend selected by the firm. Assessment of a corporate
strategic decision to invest into a new technology requires forecasting
the growth strategy adopted by the company.
In a construction firm, the following strategic fields of activity
could be underlined: construction technology and product,
model-laboratory conditions, modelsimulation of an operating
environment, prototype, final product, manufacture preparation, and
construction technology process. Based on previous research results
(Shadiya, High 2012; Green et al. 2012; Schiederig et al. 2012; Kim et
al. 2011; Mat, Razak 2011; Kanapeckiene et al. 2010; Ghassan et al.
2010), authors of the article offered a new assessment model for
companies that want to introduce a sustainable technology, product or
material to a market. The model comprises three complex basic
environments on the macro, meso and micro levels and four most important
advanced technology development levels: ecological, sustainable,
environmental and green (Kaklauskas et al. 2012; Tamosaitiene et al.
2013).
[FIGURE 2 OMITTED]
These days, to stay competitive, many organisations have shifted
their focus to becoming socially and environmentally responsible as more
and more consumers demand and support only environmentally friendly
products and services (Cavico et al. 2013; Bakar et al. 2011). According
to Akadiri et al. (2013), current building technology selection methods
fail to provide adequate solutions for two major issues: assessment
based on sustainability principles, and the process of prioritizing and
assigning weights to relevant assessment criteria. For these reasons,
the article authors suggest the complex assessment model for new
technology development genered (Fig. 2 and Fig. 3).
The suggested model is focused on sustainable technologies and
comprises environmental issues, technical efficiency, functional
requirements and social aspects as well as satisfies existing needs of
the public aiming to:
--Create conditions conducive to sustainable development of
entrepreneurship and business;
--Create sustainable and effective economic infrastructure;
--Promote sustainable use of resources;
--Ensure stability of ecosystems;
--Ensure that regulatory environment is conducive to business
growth;
--Promote entrepreneurship and business development including
direct foreign investments;
--Implement sustainable development principles in business;
--Use natural resources sustainably, preserve biodiversity and
landscape.
The model created by the article authors may be described as a
systematic data processing aimed at assessment of adjustment of an
innovative technology in construction market. The procedures of model
are presented in Figure 4.
2. Empirical case study for advanced technology deployment
Decision-making is the process of defining the decision goals,
gathering relevant criteria and possible alternatives, evaluating the
alternatives for advantages and disadvantages and selecting the optimal
alternatives (Wu et al. 2008). Finding the right decision for a
complicated problem is one of the most important tasks of today.
Consequently, it is crucial to develop a multi-stage decisionmaking
system that would consider multiple efficiency criteria and enable
solving complicated problems. Such problems can hardly be solved with
the help of decision aiding methods based on a single criterion. Figure
3 presents the created multi-stage MCDM application model, which is
suitable for solving a wide range of complicated problems in macro-,
meso- and micro-environment stages (Tamosaitiene, Gaudutis 2013).
As authors or earlier articles focused on the effect of macro- and
meso-levels (Brauers et al. 2012; Kildiene 2013), this case study
demonstrates the model on the micro level.
The suggested model is used for a small or medium construction
company that aims to introduce innovative sustainable
technologies/products to the market. Next, a real example on the use of
the model in the Lithuanian market is presented.
A small and medium enterprise (SME), mainly operating in sale, rent
and maintenance of construction machinery, vehicles and construction
materials. The company follows the product development strategy that is
aimed at business growth within the existing market by developing new
advanced areas of activity. These areas require the use of effective
materials, new production methods, advance technologies, etc. The SME
has regular suppliers but also seeks for new attractive offers for
machinery or products to be sold on the local market. For the empirical
case study, three technological alternatives were selected: innovation
for facade insulation; building environment innovation; and innovative
building structures and technology design.
[FIGURE 3 OMITTED]
[FIGURE 4 OMITTED]
Management of the company is considering an offer to distribute the
products on Lithuanian market. Therefore, the most important criteria
need to be assessed to take a rational decision. The suggested
technology deployment model is used for multi-stage assessment.
3. Determination of criteria and alternatives
Summary of earlier research results suggests that numerous factors
determine the suitability of a technology/product. Some of them are
directly related to technological features, others have more to do with
the company, and some--with a user or other environmental conditions. A
technology is selected objectively only provided it is assessed
considering various aspects as a complex, using a set of criteria.
Usually, such complex assessment based on a set of criteria produces
suitability results for each technology alternative that differ
according to various criteria, which does not allow ranking, i.e.
selecting the best alternative in terms of various aspects. It is
exactly the need to compare technology alternatives that determines
treatment of the search for a solution as a multi-criteria assessment
task.
Differentiation of criteria for assessment of sustainable
technologies serves a certain purpose--to determine the most important,
essential criteria and define their limit values. Any criterion that
loses at least one technological feature is treated as unsuitable.
Comparison of three different technologies is only effective if the
designed system of criteria can be used to define all alternatives. The
assessment focused on three different innovative technologies:
Alternative [a.sub.1]--innovation for facade insulation;
Alternative [a.sub.2]--building environment innovation;
Alternative [a.sub.3]--and innovative building structures and
technology design.
It aimed to determine possible distribution of the alternatives on
Lithuanian market, for which general assessment criteria were selected.
The literature review indicated the lack of a uniform system of
criteria that could be used for assessment of innovative technologies.
Various literature sources on assessment of construction technologies
suggest different criteria. Based on criteria selection system by
Akadiri et al. (2013), the article authors selected the following
criteria:
(1) Comprehensiveness. The chosen criteria should cover four
categories--economic, environmental, social and technical--in order to
ensure that account is being taken of progress towards sustainability
objectives. The criteria need to have the ability to demonstrate
movement towards or away from sustainability according to these
objectives.
(2) Applicability. The chosen criteria should be applicable across
the range of options under consideration. This is needed to ensure the
comparability of the options.
(3) Transparency. The criteria should be chosen in a transparent
way, so as to help stakeholders to identify which criteria are being
considered, to understand the criteria used and to propose any other
criteria for consideration.
(4) Practicability. The set of chosen criteria must form a
practicable set for the decision to be assessed, the tools to be used
and the time and resources available for analysis and assessment.
Considering these four rules, 12 criteria were selected (Table 1).
The expert method is suggested for definition of the significance
of assessment criteria on the micro level, as the majority of criteria
on this level depend on views of a stakeholder or capabilities of the
company.
4. Methodology
One of the most important steps for multi-criteria decision-making
is to identify the weight for each criterion. It was carried out with
the help of AHP, which in concisely can be expressed as the relative
values of a set of criteria.
Using the AHP method (Saaty 1980; Wu et al. 2008; Maskeliunaite,
Sivilevicius 2012), expert evaluations are expressed in numerical values
according to the assessment scale (Table 2), which is the equivalent of
abstract linguistic assessment sets and the set of integers.
Although reliability of qualitative criteria is lower than that of
qualitative criteria, usually the selection of multi-criteria
decision-making analysis cannot be successful without qualitative
criteria. Consequently, there is a need to ensure logical and reliable
assessment involving qualitative criteria. For this reason, consistency
ration CR is calculated for each pairwise comparison. If CR is less or
equals 10%, the pairwise comparison is regarded appropriate. If CR is
more than 10%, the pairwise comparison needs to be repeated to reduce
the inconsistency of the evaluation.
For the solution of the problem, the permutation method was
selected. The method was developed by Paelnick (1976). The permutation
method uses Jaquet-Lagreze's successive permutations of all
possible rankings and alternatives (Hwang, Yoon 1981). When applying
this MCDM method, all permutations of alternatives according to their
preferability are checked and compared among themselves (Turskis 2008).
With m alternatives, m! permutations are available. Let's suppose
that the number of alternatives ([a.sub.i], i = 1, 2..., m) should be
assessed according to the criterion ([x.sub.j] = 1, 2 n). As the best
alternative from among the three available should be selected, there are
m = 3! alternatives, for which m = 3 * 2 * 1 = 6 combinations are made.
[[pi].sub.1] = [a.sub.1] > [a.sub.2] > [a.sub.3];
[[pi].sub.2] = [a.sub.1] > [a.sub.2] > [a.sub.3]; [[pi].sub.3] =
[a.sub.1] > [a.sub.2] > [a.sub.3]; [[pi].sub.4] = [a.sub.1] >
[a.sub.2] > [a.sub.3]; [[pi].sub.5] = [a.sub.1] > [a.sub.2] >
[a.sub.3]; [[pi].sub.6] = [a.sub.1] > [a.sub.2] > [a.sub.3];
The method allows defining the best priority ranking for the use of
alternatives. It can be used with cardinal and ordinal indicators
(Zavadskas et al. 2011).
5. Practical application
During the first stage, an expert evaluates the importance of
criteria using pairwise comparison. Technology alternatives are assessed
according to selected criteria and their significance as defined by
experts (Table 3).
During the second stage, the matrix for assessment of technology
alternatives is designed based on selected criteria and their
significance as defined by experts (Table 4).
During the third stage, the permutation method is used to compare
combinations of alternatives, which defines the priority ranking of best
alternatives. The best permutation has the greatest a [[beta].sub.g]
value, i.e. the permutation [[pi].sub.5]. The evaluation of ordering of
the alternatives evaluation criterion [[beta].sub.g](g = [bar.1,m!]), is
carried out in the following way: suppose there is the gth permutation
[[pi].sub.g] = {...,[a.sub.k]...,[a.sub.e]} [for all]g,g = [bar.1,m!]
where [a.sub.k] is preferable to [a.sub.e]. Then, to this permutation
the following estimate [[beta].sub.g] is assigned is given as Eqn (1):
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (1)
where: [C.sub.ke] {j/[x.sub.kj] [greater than or equal to]
[x.sub.ej]}, k, e = [bar.1, m]; k [equivalent to] e;
[H.sub.ke] = {j/[x.sub.kj] < [x.sub.ej]}, k, e = [bar.1 m]; k
[equivalent to] e.
Then the following evaluation criterion is given to the
permutation. In this case, the alternative [a.sub.3] is the most
suitable according to selected criteria, as [[pi].sub.5] = [a.sub.3]
> [a.sub.1] > [a.sub.2] (Table 5).
The suggested algorithm may be used as means for a decision-maker
aiding the selection of the best alternative from among their number
described using quantitative and qualitative criteria.
Conclusions
The methodology suggested by the article authors allows combining
components of the processes for technology implementation in the
construction market: assessment of the external market, assessment of
the internal market, state of the company and technology solutions into
one complex solution. This complex assessment methodology corresponds to
the concept of sustainable construction.
The multi-stage model demonstrates that effective decisions can be
made only subsequent to complex analysis and assessment of relations
between criteria that belong to all--macro, meso and
micro--environments.
The offered case study and algorithm for assessment of the
situation on the micro level formulate a new complex view to effective
implementation of new technologies/products in the construction sector.
The suggested multi-stage system may be effectively used in
operation of construction companies.
References
Akadiri, P. O.; Olomolaiye, P. O.; Chinyio, E. A. 2013.
Multicriteria evaluation model for the selection of sustainable
materials for building projects, Automation in Construction 30: 113-125.
http://dx.doi.org/10.1016/j.autcon.2012.10.004
Bakar, K. A.; Sam, M. F. M.; Tahir, M. N. H.; Rajiani, I.; Muslan,
N. 2011. Green technology readiness in Malaysia: sustainability for
business development, in Proc. of The 2nd International Conference on
Business and Economic Research (2nd Icber 2011), 14-16 March, 2011,
Langkawi, Kedah, Malaysia, 1120-1129.
Blackley, D. M.; Shepard III, E. M. 1996. The diffusion of
innovation in home building, Journal of Housing Economics 5(4): 303-322.
http://dx.doi.org/10.1006/jhec.1996.0016
Brauers, W. K. M.; Kildiene, S.; Zavadskas, E. K.; Kaklauskas, A.
2012. The construction sector in twenty European countries during the
recession 2008-2009-country ranking by MULTIMOORA, International Journal
of Strategic Property Management 17(1): 58-78.
http://dx.doi.org/10.3846/1648715X.2013.775194
Dunovic, I. B.; Radujkovic, M.; Vukomanovic, M. 2013. Risks
register development and implementation for construction projects,
Gradevinar 65(1): 23-35.
Cavico, F.; Mujtaba, B. G.; McClelland, B.; Williamson, P. 2013. A
study of bribery and wealth in the ASEAN community, International
Journal of Management, IT and Engineering 3(1): 576-597.
Ghassan, A.; Beliz, O.; Carl, A. 2010. Facilitating innovation in
construction: directions and implications for research and policy,
Construction Innovation: Information, Process, Management 10(4):
374-394.
Green, K. W.; Zelbst, P. J.; Meacham, J.; Bhadauria, V. S. 2012.
Green supply chain management practices: impact on performance, Supply
Chain Management: An International Journal 17(3): 290-305.
Hakansson, H.; Ingemansson, M. 2013. Industrial renewal within the
construction network, Construction Management and Economics 31(1):
40-61. http://dx.doi.org/10.1080/01446193.2012.737470
Hwang, C. L.; Yoon, K. S. 1981. Multiple attribute decisionmaking.
Methods and applications. Berlin, Heidelberg, New York: Springer-Verlag.
259 p. http://dx.doi.org/10.1007/978-3-642-48318-9
Kaklauskas, A.; Rute, J.; Zavadskas, E. K.; Daniunas, A.; Pruskus,
V.; Bivainis, J.; Gudauskas, R.; Plakys, V. 2012. Passive house model
for quantitative and qualitative analyses and its intelligent system,
Energy and Buildings 50: 7-18.
http://dx.doi.org/10.1016/j.enbuild.2012.03.008
Kanapeckiene, L.; Kaklauskas, A.; Zavadskas, E. K.; Seniut, M.
2010. Integrated knowledge management model and system for construction
projects, Engineering Applications of Artificial Intelligence 23(4):
1200-1215. http://dx.doi.org/10.1016/j.engappai.2010.01.030
Kildiene, S. 2013. Assessment of opportunities for construction
enterprises in European Union Member States using the MULTIMOORA method,
11th International Conference on Modern Building Materials, Structures
and Techniques, Procedia Engineering 57: 557-564.
Kim, S. K.; Lee, B. G.; Park, B. S.; Oh, S. K. 2011. The effect of
R&D, technology commercialization capabilities and innovation
performance, Technological and Economic Development of Economy 17(4):
563-578. http://dx.doi.org/10.3846/20294913.2011.603481
Manley, K. J. 2008. Against the odds: small firms in Australia
successfully introducing new technology on construction projects,
Research Policy 37(10): 1751-1764.
http://dx.doi.org/10.1016/j.respol.2008.07.013
Maskeliunaite, L.; Sivilevicius, H. 2012. Expert evaluation of
criteria describing the quality of travelling by international passenger
train: technological, economic and safety perspectives, Technological
and Economic Development of Economy 18(3): 544-566.
http://dx.doi.org/10.3846/20294913.2012.710178
Mat, A.; Razak, C. R. 2011. Technological innovation
implementation: a proposed model on organizational learning capability
with moderating effect of knowledge complexity, Full Length Research
Paper 7(12): 926-935.
Mazurkiewicz, A.; Poteralska, B. 2012. System of a complex
assessment of technological innovative solutions, Problemy
Eksploatacji--Maintenance Problems (4): 5-21.
Montalvo, C.; van der Giessen, A. 2012. Sectoral innovation
watch--synthesis report, Europe INNOVA Sectoral Innovation Watch, for DG
Enterprise and Industry, European Commission, May 2012.
Moore, G. A. 1991. Crossing the chasm: marketing and selling
high-tech products to mainstream customers. New York: Harper Business
Essentials. 227 p.
Oliveira, S. R. M.; Lino, J. 2013. Model for technological
innovation integration and new product development in high tech
environments, International Journal of Innovation and Applied Studies
3(4): 919-930.
Paelnick, J. H. P. 1976. Qualitative multiple criteria analysis,
environmental protection and multiregional development, Papers of the
Regional Science Association 36(1): 59-74.
http://dx.doi.org/10.1007/BF01944375
Saaty, T. L. 1980. The analytic hierarchy process: Planning,
priority setting, resources allocation. London: McGraw Hill. 287 p.
Shadiya, O. O.; High, K. A. 2012. Sustainability evaluator: tool
for evaluating process sustainability, Environmental Progress &
Sustainable Energy 32(3): 749-761.
Schiederig, T.; Tietze, F.; Herstatt, C. 2012. Green innovation in
technology and innovation management--an exploratory literature review,
R&D Management 42(2): 180-192.
http://dx.doi.org/10.1111/j.1467-9310.2011.00672.x
Skibniewski, M. J.; Zavadskas E. K. 2013. Technology development in
construction: a continuum from distant past into the future, Journal of
Civil Engineering and Management 19(1): 136-147.
http://dx.doi.org/10.3846/13923730.2012.756060
Tamosaitiene, J.; Gaudutis, E. 2013. Complex assessment of
structural systems used for high-rise buildings, Journal of Civil
Engineering and Management 19(2): 305-317.
http://dx.doi.org/10.3846/13923730.2013.772071
Tamosaitiene, J.; Zavadskas, E. K. 2013. The multi-stage decision
making system for complicated problems, World Conference on Psychology
and Sociology 2012, Procedia--Social and Behavioral Sciences 82:
215-219.
Tamosaitiene, J.; Sipalis, J.; Banaitis, A.; Gaudutis, E. 2013.
Complex model for the assessment of the location of high-rise buildings
in the city urban structure, International Journal of Strategic Property
Management 17(1): 93109. http://dx.doi.org/10.3846/1648715X.2013.781968
Teece, D. J. 2010. Business models, business strategy and
innovation, Long Range Planning 43(2): 172-194.
http://dx.doi.org/10.1016/j.lrp.2009.07.003
Testa, F.; Iraldo, F.; Frey, M. 2011. The effect of environmental
regulation on firms' competitive performance: the case of the
building & construction sector in some EU regions, Journal of
Environmental Management 92(9): 2136-2144.
http://dx.doi.org/10.1016/j.jenvman.2011.03.039
Turskis, Z. 2008. Multi-attribute contractors ranking method by
applying ordering of feasible alternatives of solutions in terms of
preferability technique, Technological and Economic Development of
Economy 14(2): 224-239.
http://dx.doi.org/10.3846/1392-8619.2008.14.224-239
Vasauskaite, J.; Snieska, V.; Draksaite, A. 2011. Nauju
technologiju diegimas Lietuvos pramoneje: sprendimai ir ju veiksniai,
Economics and Management 16: 418-427.
Von Hippel, E. 1988. The sources of innovation. New York, Oxford:
Oxford University Press. 218 p.
Wu, C. R.; Lin, C. T.; Chen, H. C. 2008. Is FHC the preferable
bancassureance alliance model from supervisory authorities'
perspectives in Taiwan using AHP and GRA, The Journal of Grey System 1:
39-52.
Simona KILDIENE (a, b), Edmundas Kazimieras ZAVADSKAS (a, b),
Jolanta TAMOSAITIENE (a, c)
(a) Department of Construction Technology and Management, Faculty
of Civil Engineering, Vilnius Gediminas Technical University, Sauletekio
al. 11, 10223 Vilnius, Lithuania
(b) Research Institute of Smart Building Technologies, Faculty of
Civil Engineering, Vilnius Gediminas Technical University, Sauletekio
al. 11, 10223 Vilnius, Lithuania
(c) Laboratory of Construction Technology and Management, Faculty
of Civil Engineering, Vilnius Gediminas Technical University, Sauletekio
al. 11, 10223 Vilnius, Lithuania
Received 16 Jan 2013; accepted 28 Feb 2014
Corresponding author: Jolanta Tamosaitiene
E-mail: jolanta.tamosaitiene@vgtu.It
Zavadskas, E. K.; Turskis, Z.; Tamosaitiene, J. 2011. Selection of
construction enterprises management strategy based on the SWOT and
multi-criteria analysis, Archives of Civil and Mechanical Engineering
11(4): 1063-1082.
Simona KILDIENE. PhD student at the Department of Construction
Technology and Management of Vilnius Gediminas Technical University,
Vilnius, Lithuania. Master of Science (Construction Engineering), VGTU,
2010. Bachelor of Science (construction management), VGTU, 2008.
Research interests include construction economics, construction
management, multiple criteria analysis and decision-making theories.
Edmundas Kazimieras ZAVADSKAS. PhD, DSc, h.c.multi. Prof., the Head
of the Department of Construction Technology and Management of Vilnius
Gediminas Technical University, Lithuania. Senior Research Fellow at the
Research Institute of Smart Building Technologies. PhD in Building
Structures (1973). Dr Sc. (1987) in Building Technology and Management.
A member of Lithuanian and several foreign Academies of Sciences.
Doctore Honoris Causa from Poznan, Saint Petersburg and Kiev
universities. The Honorary International Chair Professor in the National
Taipei University of Technology. A member of international
organizations; a member of steering and programme committees at many
international conferences; a member of the editorial boards of several
research journals; the author and co-author of more than 400 papers and
a number of monographs in Lithuanian, English, German and Russian.
Editor-in-chief of journals Technological and Economic Development of
Economy and Journal of Civil Engineering and Management. Research
interests: building technology and management, decision- making theory,
automation in design and decision support systems.
Jolanta TAMOSAITIENE. Assoc. Prof. Dr, she is a Vice-Dean of Civil
Engineering Faculty and working at the Department of Construction
Technology and Management of Vilnius Gediminas Technical University,
Lithuania. Since 2013, she is a member of the Editorial Board of the
Journal of Engineering, Project, and Production Management; since 2011,
she is a member of the Editorial Board of the j ournal Technological and
Economic Development of Economy. Since 2009, she is a member of EURO
Working Group OR in Sustainable Development and Civil Engineering,
EWG-ORSDCE. She published 50 scientific papers. Research interests:
various management areas (enterprise, construction project and etc.),
risk assessment, construction project administration, building
life-cycle, construction technology and organisation, decision-making
and grey system theory, Decision Making (DM), statistics, optimization,
strategies, game theory, intelligent support system, Sustainable
Development: developing of alternative construction processes, economic
and other aspects, sustainable development challenges for business and
management in construction enterprises, environmental impact processes,
etc.
Table 1. Objectives used to assess the micro environment for
technology deployment
Characteristics Measurement
of criteria set units
TECHNICAL ASSESSMENT CRITERIA - [X.sub.1]
[X.sub.11] Novelty of (score)
the product Saaty scale
/process in (1980)
the sector
[X.sub.12] Ecology (score)
Saaty scale
(1980)
[X.sub.13] Recycling (score)
Saaty scale
(1980)
[X.sub.14] Longevity (score)
compared to Saaty scale
analogues (1980)
[X.sub.15] Criterion (score)
indicating the Saaty scale
quality of use (1980)
ASSESSMENT CRITERIA FOR INNOVATION IMPLEMENTATION
PROCESS - [X.sub.2]
[X.sub.21] Production (score)
differentiation Saaty scale
(1980)
[X.sub.22] Period of (score)
warranty Saaty scale
(1980)
[X.sub.23] Labour costs Person/hour
CRITERIA FOR ECONOMIC ASSESSMENT OF THE
INNOVATION - [X.sub.3]
[X.sub.31] Direct costs EU/[m.sup.2]
[X.sub.32] Indirect costs EU/month
[X.sub.33] Expected %
profit
[X.sub.34] Technology (score)
effectiveness Saaty scale
compared to (1980)
analogues
Characteristics Description
of criteria set
TECHNICAL ASSESSMENT CRITERIA - [X.sub.1]
[X.sub.11] Assessment focuses on novelty of the product/
technology, which by certain features or purpose
greatly differs from goods or services earlier
offered on the market (or by a certain company).
An innovative product can be of two types:
technologically novel or technologically advanced
[X.sub.12] A product made following EU and national
legislation regulating eco-production. Those
wanting to label their products as organic must
correspond to requirements that apply to the
entire production process, which is assessed by an
independent controlling institution.
[X.sub.13] Is a process to change (waste) materials into new
products to prevent waste of potentially useful
materials, reduce the consumption of fresh raw
materials, reduce energy usage, reduce air
pollution (from incineration) and water pollution
(from landfilling) by reducing the need for
"conventional" waste disposal, and lower
greenhouse gas emissions as compared to plastic
production.
[X.sub.14] Product longevity is its ability to keep the
required features of a set period of time or a
long time under expected impact. Maintained
normally and used in an appropriately design and
constructed building must correspond to
requirements of the building for an economically
sound period of time.
[X.sub.15] Design and user interface.
ASSESSMENT CRASSESSMENT CRITERIA FOR INNOVATION IMPLEMENTATION
PROCESS - [X.PROCESS - [X.sub.2]
[X.sub.21] Production and sale of similar but different
products in the same branch of economy. It is
particular to monopolistic competition. It allows
reducing competition and increasing prices.
[X.sub.22] Period of warranties in comparison to analogues
offered on the market.
[X.sub.23] Labour costs per one unit of production shows
changes in wages and salaries paid by the company.
CRITERIA FOR CRITERIA FOR ECONOMIC ASSESSMENT OF THE
INNOVATION - INNOVATION - [X.sub.3]
[X.sub.31] Costs, which according to general principles
pertaining to eligibility of costs could be
perceived as specific costs that are directly
related to implementation of the project. These
costs are listed in the detailed budget of the
project.
[X.sub.32] These costs are not regarded as directly related
to the project. Indirect costs are listed in the
detailed budget of the project.
[X.sub.33] Expected profit is gross profit less alternative
(direct and expected) costs.
[X.sub.34] Quality in general and compared to analogues.
Possibilities of technological development.
Reliability of equipment.
Table 2. AHP method: scale for assessment of qualitative criteria
(Saaty 1980)
Importance level Linguistic importance level
1 Alternatives are equal
3 Weakly superior alternative
Important superiority
5 of the alternative
7 Obviously superior alternative
9 Absolutely superior alternative
2,4,6,8 Interim values
If alternatives are assessed according
1/3, 1/5, 1/7, 1/9 to criterion x, and alternative A has
one of above-stated result, compare
it to alternative B ([R.sup.x] AB), then
alternative B will have an inverse
result compared to the alternative A
([R.sup.x]BA or 1/[R.sup.x]AB).
Importance level Description of importance
1 Both alternatives are equal in terms of a
criterion.
3 Based on experience and opinion of an expert
(in respect of the assessed alternative),
the alternative is weakly superior compared
to another alternative.
Based on experience and opinion of an expert
5 (in respect of the assessed alternative),
the alternative has an important superiority
compared to another alternative.
7 The alternative has an obvious superiority
(in respect of the assessed alternative) and
the superiority has been proved in practice.
9 The alternative has an absolute superiority
(in respect of the assessed alternative).
2,4,6,8 When a compromise among previously named
assessment is required.
If alternatives are assessed according
1/3, 1/5, 1/7, 1/9 to criterion x, and alternative A has
one of above-stated result, compare
it to alternative B ([R.sup.x] AB), then
alternative B will have an inverse
result compared to the alternative A
([R.sup.x]BA or 1/[R.sup.x]AB).
Table 3. Significance of the criteria matrix
[X.sub.11] [X.sub.12] [X.sub.13] [X.sub.14]
[X.sub.11] 1 2 2 2
[X.sub.12] 1/2 1 1 1
[X.sub.13] 1/2 1 1 1
[X.sub.14] 1/2 1 1 1
[X.sub.15] 1 3 3 3
[X.sub.21] 1 3 3 3
[X.sub.22] 1/3 1/3 1/3 1/2
[X.sub.23] 1/3 1 1/3 1/2
[X.sub.31] 5 6 7 7
[X.sub.32] 1 2 2 2
[X.sub.33] 5 7 7 7
[X.sub.34] 1/5 6 8 6
[X.sub.15] [X.sub.21] [X.sub.22] [X.sub.23]
[X.sub.11] 1 1 3 3
[X.sub.12] 1/3 1/3 3 1
[X.sub.13] 1/3 1/3 3 3
[X.sub.14] 1/3 1/3 2 2
[X.sub.15] 1 1 3 2
[X.sub.21] 1 1 3 3
[X.sub.22] 1/3 1/3 1 1
[X.sub.23] 1/2 1/3 1 1
[X.sub.31] 6 6 7 7
[X.sub.32] 1 2 2 2
[X.sub.33] 5 5 7 7
[X.sub.34] 5 5 7 7
[X.sub.31] [X.sub.32] [X.sub.33] [X.sub.34]
[X.sub.11] 1/5 1 1/5 5
[X.sub.12] 1/6 1/2 1/7 1/6
[X.sub.13] 1/7 1/2 1/7 1/8
[X.sub.14] 1/7 1/2 1/7 1/6
[X.sub.15] 1/6 1 1/5 1/5
[X.sub.21] 1/6 1/2 1/5 1/5
[X.sub.22] 1/7 1/2 1/7 1/7
[X.sub.23] 1/7 1/2 1/7 1/7
[X.sub.31] 1 5 1 2
[X.sub.32] 1/5 1 1/4 1/4
[X.sub.33] 1 4 1 2
[X.sub.34] 0.5 4 1/2 1
Table 4. Initial decision-making matrix
Criteria Weight Alternatives
[a.sub.1] [a.sub.2] [a.sub.3]
Optimum - maximum
[X.sub.11] 0.073 7 7 8
[X.sub.12] 0.030 6 10 5
[X.sub.13] 0.031 10 10 10
[X.sub.14] 0.030 7 2 7
[X.sub.21] 0.059 5 5 5
[X.sub.22] 0.057 7 9 9
[X.sub.33] 0.020 3 4 4
[X.sub.34] 0.023 3 5 5
Optimum - minimum
[X.sub.15] 0.241 268.8 72.9 1.43
[X.sub.23] 0.057 235.36 477.97 98.26
[X.sub.31] 0.233 26.37 122.32 7.25
[X.sub.32] 0.147 12.46 4.93 17
Table 5. Permutations and calculations of evaluation criteria
[a.sub.1]
[a.sub.1] 0
[a.sub.2] 0.030 + 0.057 + 0.020 + 0.023
+ 0.241 + 0.147 = 0.518
[a.sub.3] 0.073 + 0.030 + 0.031 + 0.030 + 0.059
+ 0.057 + 0.020 + 0.023 = 0.796
Evaluation criterion [[beta].sub.1]
[a.sub.1]
[a.sub.1] 0
[a.sub.2] 0.073 + 0.030 + 0.031 + 0.030 + 0.059
+ 0.057 + 0.020 + 0.023 = 0.796
[a.sub.3]
0.030 + 0.057 + 0.020 + 0.023 +
0.241 + 0.147 = 0.518
Evaluation criterion [[beta].sub.2]
[a.sub.2]
[a.sub.2] 0
[a.sub1] 0.073 + 0.031 + 0.030 + 0.059 + 0.020
+ 0.023 + 0.057 + 0.233 = 0.525
[a.sub.3] 0.073 + 0.030 + 0.031 + 0.030 + 0.059 +
0.057 + 0.020 + 0.023 + 0.241 + 0.057 +
0.233 = 0.853
Evaluation criterion [[beta].sub.3]
[a.sub.2]
[a.sub.2] 0
[a.sub.3] 0.073 + 0.030 + 0.031 + 0.030 +
0.059 + 0.057 + 0.020 + 0.023 +
[a.sub.1] 0.241 + 0.057 + 0.233 = 0.853
0.073 + 0.031 + 0.030 + 0.059 + 0.020
+ 0.023 + 0.057 + 0.233 = 0.525
Evaluation criterion [[beta].sub.4]
[a.sub.3]
[a.sub.3] 0
[a.sub.1] 0.057 + 0.147 = 0.204
[a.sub.2] 0.147
Evaluation criterion [[beta].sub.5]
[a.sub.3]
[a.sub.3] 0
[a.sub.2] 0.147
[a.sub.1] 0.057 + 0.147 = 0.204
Evaluation criterion [[beta].sub.6]
[[pi].sub.1] = [a.sub.1] > [a.sub.2] > [a.sub.3]
[a.sub.2]
[a.sub.1] 0.073 + 0.031 + 0.030 + 0.059 + 0.020
+ 0.023 + 0.057 + 0.233 = 0.525#
[a.sub.2] 0
[a.sub.3] 0.073 + 0.030 + 0.031 + 0.030 +
0.059 + 0.057 + 0.020 + 0.023 +
0.241 + 0.057 + 0.233 = 0.853
Evaluation criterion [[beta].sub.1] 0.525 + 0.204 + 0.147#
[[pi].sub.2] = [a.sub.1] > [a.sub.2] > [a.sub.3]
[a.sub.3]
[a.sub.1] 0.057 + 0.147 = 0.204#
[a.sub.2] 0
[a.sub.3]
0.147
Evaluation criterion [[beta].sub.2] 0.204 + 0.525 + 0.853#
[[pi].sub.3] = [a.sub.1] > [a.sub.2] > [a.sub.3]
[a.sub.1]
[a.sub.2] 0.030 + 0.057 + 0.020 + 0.023 +
0.241 + 0.147 = 0.518#
[a.sub1] 0#
[a.sub.3] 0.073 + 0.030 + 0.031 + 0.030 + 0.059
+ 0.057 + 0.020 + 0.023 = 0.796
Evaluation criterion [[beta].sub.3] 0.518 + 0.147 + 0.204#
[[pi].sub.4] = [a.sub.1] > [a.sub.2] > [a.sub.3]
[a.sub.3]
[a.sub.2] 0.147#
[a.sub.3] 0#
[a.sub.1]
0.057 + 0.147 = 0.204
Evaluation criterion [[beta].sub.4] 0.147 + 0.518 + 0.796#
[[pi].sub.5] = [a.sub.1] > [a.sub.2] > [a.sub.3]
[a.sub.1]
[a.sub.3] 0.073 + 0.030 + 0.031 + 0.030 + 0.059
+ 0.057 + 0.020 + 0.023 = 0.796#
[a.sub.1] 0
[a.sub.2] 0.030 + 0.057 + 0.020 + 0.023
+ 0.241 + 0.147 = 0.518
Evaluation criterion [[beta].sub.5] 0.796 + 0.853 + 0.525#
[[pi].sub.6] = [a.sub.1] > [a.sub.2] > [a.sub.3]
[a.sub.2]
[a.sub.3] 0.073 + 0.030 + 0.031 + 0.030 +
0.059 + 0.057 + 0.020 + 0.023 +
0.241 + 0.057 + 0.233 = 0.853#
[a.sub.2] 0#
[a.sub.1] 0.073 + 0.031 + 0.030 + 0.059 + 0.020
+ 0.023 + 0.057 + 0.233 = 0.525
Evaluation criterion [[beta].sub.6] 0.853 + 0.796 + 0.518#
[a.sub.3]
[a.sub.1] 0.057 + 0.147 = 0.204#
[a.sub.2] 0.147#
[a.sub.3] 0
Evaluation criterion [[beta].sub.1] 0.518 + 0.796 + 0.853
[a.sub.2]
[a.sub.1] 0.073 + 0.031 + 0.030 + 0.059 + 0.020
+ 0.023 + 0.057 + 0.233 = 0.525#
[a.sub.2] 0.073 + 0.030 + 0.031 + 0.030 +
0.059 + 0.057 + 0.020 + 0.023 +
[a.sub.3] 0.241 + 0.057 + 0.233 = 0.853#
0
Evaluation criterion [[beta].sub.2] 0.796 + 0.518 + 0.147
[a.sub.3]
[a.sub.2] 0.147#
[a.sub1] 0.057 + 0.147 = 0.204#
[a.sub.3] 0#
Evaluation criterion [[beta].sub.3] 0.525 + 0.853 + 0.796
[a.sub.1]
[a.sub.2] 0.030 + 0.057 + 0.020 + 0.023
+ 0.241 + 0.147 = 0.518#
[a.sub.3] 0.073 + 0.030 + 0.031 + 0.030 +
0.059 + 0.057 + 0.020 +
[a.sub.1] 0.023 = 0.796#
0#
Evaluation criterion [[beta].sub.4] 0.853 + 0.525 + 0.204
[a.sub.2]
[a.sub.3] 0.073 + 0.030 + 0.031 + 0.030 +
0.059 + 0.057 + 0.020 + 0.023 +
0.241 + 0.057 + 0.233 = 0.853#
[a.sub.1] 0.073 + 0.031 + 0.030 + 0.059 + 0.020
+ 0.023 + 0.057 + 0.233 = 0.525#
[a.sub.2] 0#
Evaluation criterion [[beta].sub.5] 0.204 + 0.147 + 0.518
[a.sub.1]
[a.sub.3] 0.073 + 0.030 + 0.031 + 0.030 + 0.059
+ 0.057 + 0.020 + 0.023 = 0.796#
[a.sub.2] 0.030 + 0.057 + 0.020 + 0.023
+ 0.241 + 0.147 = 0.518#
[a.sub.1] 0#
Evaluation criterion [[beta].sub.6] 0.147 + 0.204 + 0.525
Regular font--concordance values; Bold font--non-concordance values
Note: Bold font--non-concordance values indicated with #
