IJAHP Article: Nahavandi, Homayounfar, Daneshvar/A Fuzzy Analytical Hierarchy Process for 

evaluation of knowledge management effectiveness in research centers 

 

 International Journal of the 
Analytic Hierarchy Process 

1 Vol  15 Issue 1 2023 

ISSN 1936-6744 

https://doi.org/10.13033/ijahp.v15i1.978 

A FUZZY ANALYTICAL HIERARCHY PROCESS FOR EVALUATION 

OF KNOWLEDGE MANAGEMENT EFFECTIVENESS IN RESEARCH 

CENTERS 

 
Bijan Nahavandi 

Assistant Professor of Industrial Management 

Faculty of Management and Economics, Science and Research Branch 

Islamic Azad University 

Tehran, Iran 

bijan.nahavandi@srbiau.ac.ir 

 

Mahdi Homayounfar 

Department of Industrial Management 

Rasht Branch, Islamic Azad University 

Rasht, Iran 

homayounfar@iaurasht.ac.ir 

 

Amir Daneshvar 

Department of Information Technology Management 

Electronic Branch 

Islamic Azad University 

Tehran, Iran 

a_daneshvar@iauec.ac.ir 

 

ABSTRACT 

 

 

Measuring knowledge management (KM) effectiveness is a very important issue in 

organizations today. This study aims to develop a method to evaluate the effectiveness of 

KM under uncertainty in research centers (RCs) in Iran. To develop an evaluation, the 

relevant literature was reviewed to identify KM effectiveness criteria. Next, the 

judgments of the experts, specialists, scholars, and professionals in IT and KM systems 

and senior managers of Iran’s RCs were determined using a pairwise comparison 

questionnaire. Because linguistic terms are an integral part of human judgments that will 

influence the results of the research, a fuzzy Analytic Hierarchy Process (AHP) called the 

Extent Analysis (EA) method was used for data analysis and weight determination. 

Accordingly, 34 subcriteria extracted from the literature that are important in the 

evaluation of KM effectiveness were categorized into six main criteria as follows: human 

resources, leadership and center structure, knowledge creation and acquisition, 

knowledge storage and security, knowledge sharing, and knowledge utilization and 

updating. The findings indicate that human resources is the most important criterion 

based on both the AHP and fuzzy AHP methods. The other five criteria in descending 

order of importance are knowledge sharing, leadership and center structure, knowledge 

utilization and updating, knowledge creation and acquisition and knowledge storage and 

security. Finally, to test the validity and reliability of the proposed framework, we 

evaluated the effectiveness of KM system in nine of Iran’s RCs. 

 

mailto:bijan.nahavandi@srbiau.ac.ir
mailto:homayounfar@iaurasht.ac.ir


IJAHP Article: Nahavandi, Homayounfar, Daneshvar/A Fuzzy Analytical Hierarchy Process for 

evaluation of knowledge management effectiveness in research centers 

 

 International Journal of the 
Analytic Hierarchy Process 

2 Vol  15 Issue 1 2023 

ISSN 1936-6744 

https://doi.org/10.13033/ijahp.v15i1.978 

Keywords: knowledge management; effectiveness; Fuzzy Set Theory (FST), AHP; 

research centers 

 

 

1. Introduction 

In recent decades, organizations have shifted their focus from capital, human resources, 

and technology to knowledge (Wang & Chang, 2007). Moreover, managers need 

knowledge and information to make appropriate decisions. Therefore, the knowledge and 

information that comes from human resources must be organized and accessible for 

decision-makers whenever it is needed. As a logical result, each organization should 

develop a comprehensive and effective system to manage knowledge and information. 

Knowledge management (KM) is an appropriate tool for this purpose (Barão et al., 2017; 

Ogiela, 2015). Currently, KM has become increasingly important because of the growing 

awareness of its significance for an organization's prosperity and survival (Byukusenge & 

Munene, 2017). In summary, KM allows an organization to meet its needs and 

expectations, enhance its performance and competitiveness in the market, and develop 

new ideas or ways of conducting business. 

 

KM links information supply and demand with a learning management system and 

consequently, improves organizational performance (Schniederjans et al., 2019). 

Integrating organizational knowledge from theory into practical actions is an essential 

function of KM. The successful implementation of KM in organizations can increase the 

organization's productivity by more than 30% (Kim et al., 2021). An appropriate KM 

system enables an organization to efficiently create, acquire, share, transform, and use 

knowledge, which leads to better organizational performance (Kim et al., 2021; Yap et 

al., 2022). 

 

The most cited document on the topic of KM was written and published by Alavi and 

Leidner (2001). They indicate that companies’ difficulties in maintaining, locating, and 

applying knowledge have led them to develop systematic procedures to manage it. Their 

framework of KM processes encompasses knowledge creation, knowledge 

storage/retrieval, knowledge transfer, and knowledge application to create value from the 

knowledge assets the firm possesses. 

 

Simultaneously, the establishment of KM by research centers (RCs) has been growing in 

recent years (Ermine, 2010). Numerous RCs expect to achieve the capability of managing 

their intellectual and valuable resources and reinforce existing advantages by initiating 

KM. The strength of any RC highly depends on the knowledge that is in the hands of 

their researchers. An organization will be exposed to risk when a researcher leaves that 

organization. Indeed, tacit knowledge of employees at RCs is one of the most essential 

factors that affect business performance (Adachi, 2009; Smits & De Moor, 2004). 

However, the reduction in strength is preventable for RCs, like any other business, by 

establishing an effective KM system. Measuring the effectiveness of KM operations is 

one of the most challenging issues for RCs in Iran, and is also an extremely complicated 

matter in other countries around the world. 

 

One limitation of the classic Analytical Hierarchy Process (AHP) approach is the use of 

deterministic values as the equivalent of verbal values, which removes the subjective 

nature of the comparisons from the calculation process. Therefore, researchers have used 



IJAHP Article: Nahavandi, Homayounfar, Daneshvar/A Fuzzy Analytical Hierarchy Process for 

evaluation of knowledge management effectiveness in research centers 

 

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fuzzy AHP because fuzzy ratios help respondents include imprecise and ambiguous 

answers in pairwise comparisons in weight calculations which helps reduce respondents’ 

uncertainty about their choices (Ohoitimur et al., 2021). Fuzzy AHP is one of the most 

popular and famous MCDM methods that has been used in various fields such as 

dynamic vendor selection (Koul & Verma, 2009), warehouse location selection 

(Kahraman et al., 2016), industrial maintenance strategy (Ohta et al., 2018), strategic 

management (Ohoitimur et al., 2021), software requirements selection (Nazim et al., 

2022), and public transportation (Buran & Erçek, 2022). 

 

The aim of this study is to provide a framework for evaluating KM in RCs. Nevertheless, 

models for evaluating and measuring the effectiveness of KM in the literature are very 

general and include a wide range of businesses and organizations. In this article, a 

specific framework for measuring the effectiveness of KM at RCs is presented. Then, 

both fuzzy and crisp Analytical Hierarchy Process (AHP) methods are used to prioritize 

the criteria and subcriteria of the proposed framework and their results are compared. The 

applied methods are able to assess quantitative and qualitative criteria. Among the other 

weight determining methods in the literature, the AHP is the most frequently applied 

method which indicates its critical importance. It is believed that the proposed evaluation 

framework is very useful for research-based organizations. This study adds precise and 

practical knowledge to the available literature on the evaluation and measurement of KM 

effectiveness by providing experts with suitable criteria to evaluate and measure KM 

effectiveness. These criteria would be useful especially in RCs with a fuzzy standpoint 

for dealing quantitatively with imprecision and uncertainty. It also helps to obtain a fuzzy 

prioritization of KM effectiveness measurement indicators. RCs, which are knowledge 

producers,  have a greater need to establish a KM system at different stages in their work. 

The existence of a system that creates synergy in conducting research projects and 

optimal use of the resources of knowledge-based organizations is a matter that is directly 

related to the efficiency and effectiveness of conducting research projects in these 

centers. Therefore, in this study an attempt has been made to evaluate the influence 

factors of KM in RCs by presenting a model to improve the performance of the 

organization. 

 

 

2. Literature review 
2.1 Knowledge and knowledge management  

Rapid changes in today’s environment have led organizations to adjust and update the 

knowledge they possess to maintain their competitive advantage (Ale et al., 2014). 

Knowledge is recognized as a core competency and a primary source of value creation 

for organizations around the world (Liu et al., 2020). It is the only asset in an 

organization that completes technology, strategy, process, and structure as a whole 

(Breznik, 2018). Knowledge has an inherent value that needs to be managed, applied, 

developed, and exploited. It can be seen as an asset that raises traditional asset questions 

to management, such as when, how much, and what to invest in (Mardani et al., 2018). 

Knowledge has different meanings and concepts and is referred to by different terms, 

such as expertise, experience, skill, intelligence, and insight, depending on the subject 

area. The KM literature has not reached a consensus on the definition of knowledge 

(Anjaria, 2020). As the combination of experiences, values, contextual information and 

expert insights, knowledge provides a framework for combining experiences and 



IJAHP Article: Nahavandi, Homayounfar, Daneshvar/A Fuzzy Analytical Hierarchy Process for 

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inferring new knowledge (Donate & Sánchez de Pablo, 2015; Kusumastuti et al., 2021; 

Kokkaew et al., 2022). 

 

In the literature, knowledge is divided into tacit and explicit forms. Tacit knowledge is 

produced by information processing in an individual’s mind and acquired through 

experience (Ale et al., 2014; Schniederjans et al., 2020). It is deeply rooted in action, 

commitment, and involvement and thus is very difficult to formalize, communicate, and 

share with other people. In contrast, explicit knowledge can be expressed in words and 

numbers. It is produced by the articulation and communication of tacit knowledge and 

captured in a code or language that facilitates its communication in libraries, archives, 

and databases (Schniederjans et al., 2020). This knowledge is codified and transmittable 

in formal, systematic language and thus is able to be captured (Ale et al., 2014). There is 

a permanent interaction between tacit and explicit knowledge that moves across 

individuals, groups, organizations, and back to individuals (Nonaka & Toyama, 2003).  

 

The theory of knowledge creation proposes that knowledge is created over an endless 

cycle and, thus, increases organizational knowledge via externalization (conversion of 

tacit to explicit knowledge), socialization (creation of tacit knowledge from shared tacit 

knowledge), combination (creation of knowledge through the combination and exchange 

of explicit knowledge), and internalization (conversion of explicit to tacit knowledge) 

(Ale et al., 2014; Curado & Bontis, 2011). In the current knowledge economy, knowledge 

is an essential strategic resource that enables firms to sustain a competitive advantage in a 

dynamic market environment. Because it is intangible, knowledge is a complex concept 

to understand and share among the departments of the organization. Using knowledge 

effectively and consistently is an important way to succeed (Rabeea et al., 2019; Martins 

et al., 2019). 

 

As the necessary intangible resource for any organization, knowledge should be 

effectively managed (Mardani et al., 2018). In the literature, there are many authors who 

point out the role played by KM as an increasingly important capability for an 

organization to be successful in both public and private sectors (Al Ahbabi et al., 2018; 

Gaviria-Marin et al., 2019; Gonzaga de Albuquerque et al., 2018; Martins et al., 2019). 

Duhon (1998) defined KM as a discipline for promoting the identifying, capturing, 

evaluating, retrieving, and sharing of the organizations’ information, systematically. To 

this aim, some assets such as procedures, policies, expert and experienced workers, 

documents, databases and computerized systems must be used. Since its introduction in 

1990s, the concept of KM has become an important area of research in modern 

management and leadership for academics and practitioners. There is a consensus among 

researchers that KM can be seen as a collaborative and integrated approach that facilitates 

the creation, capture, organization, access, and use of the intellectual asset for long-term 

sustainability and strategic advantage in organizations (Al Saifi, 2015; Hussinki et al., 

2017; Peng et al., 2007; Prusak, 2014; Martins et al., 2019). KM bridges information 

demand and supply on behalf of learning processes, and consequently, organizational 

performance improvement (Curado & Bontis, 2011; Schniederjans et al., 2020). 
 

2.2 Knowledge management effectiveness 

In the 21
st
 century, business environments have become increasingly knowledge-based, 

leveraging this knowledge to innovate (Urban & Matela, 2022). Knowledge is essential to 

the organization because it is essential to its survival in the competitive market. It is the 



IJAHP Article: Nahavandi, Homayounfar, Daneshvar/A Fuzzy Analytical Hierarchy Process for 

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main engine of economic growth and the catalyst for technological progress and 

productivity (Abusweilem & Abualous, 2019). Knowledge creates value only when it is 

applied by organizations to create capabilities and take effective actions. Therefore, 

support and enhancement of knowledge use in organizations should be one of the major 

focuses of KM initiatives (Yun, 2013). 

 

KM refers to the range of practices and techniques used by organizations to identify and 

distribute knowledge, know-how, expertise, intellectual capital and other forms of 

knowledge for leveraging, reusing and transferring knowledge and learning across the 

organization. It can be defined as the practice of using prior knowledge to make decisions 

that affect the current and future effectiveness of the organization (Ale et al., 2014). 

Indeed, KM directs organizational innovation processes toward a competitive advantage 

(Mardani et al., 2018; Vaio et al., 2021) and helps organizations improve their 

effectiveness by strengthening their decision-making capabilities. The view that regards 

knowledge as a source of competitive advantage comes from the resource-based 

perspective of organizations, which stems primarily from the theory of internal resources 

and capabilities. Scholars have argued for the relevance of KM in increasing 

organizational effectiveness. Indeed, KM plays a significant role in organizations that are 

seeking ways to reach their goals and strategic plans more efficiently and effectively. 

Therefore, it is a key factor for survival in the turbulent international market because KM 

makes knowledge delivery at all layers of an organization possible (Ping et al., 2009; 

Albassam, 2019). 

 

Innovative firms such as RCs need sophisticated KM that gives close attention to the 

special requirements of interactive knowledge. Particularly in knowledge-based 

organizations such as RCs, effectiveness is highly dependent on how well knowledge is 

shared between individuals, teams, and units (Alavi & Leidner, 2001). The incentive for 

the search, absorption, and sharing of knowledge has contributed considerably to the 

achievement of organizational goals (Martin et al., 2019). It has been argued that 

knowledge-sharing behaviors contribute to the generation of various organizational 

capabilities such as innovation, which is vital to a firm's performance (Mardani et al., 

2018). 

 

According to Wu and Lee (2007) and Kamara et al. (2002), KM is the organizational 

optimization of knowledge to achieve superior productivity through a variety of 

techniques. KM is a systematic approach to managing knowledge in organizations for 

competitiveness (Wu & Lee, 2007). It is useful to consider the relationship between data, 

information, knowledge, and wisdom to have a clear view of KM. Data and information 

are the foundation of knowledge and have hierarchical relationships based on the studies 

of Arthur Anderson Business Consultant in 1999 (Wen, 2009). Dóci et al. (2022) studied 

KM in transfer management, and the results showed that the problem is due to the lack of 

knowledge integration. 

 

Wen (2009) established a model with five major constructs including staff, information, 

data, knowledge, wisdom, and 30 indices to measure the effectiveness of the KM process. 

According to the research, several problems would be solved by establishing an 

assessment model for KM effectiveness such as multiple objectives, evaluation 

difficulties, and fuzzy behavior of KM. In other research, Tseng (2008) proposed a new 

metric measure for assessing the performance of a KM system based on financial and 



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non-financial criteria. The study presented the critical factors to improve the quality of 

the KM system. Furthermore, the study claimed that appropriate IT can significantly 

enhance the effectiveness and efficiency of implementing a KM system. 

 

As previous studies indicate, many factors affect the prosperity of KM projects with 

leadership and center structure being the most important (Chang et al., 2009; Cabrera & 

Cabrera, 2005; Hislop, 2003). Moreover, among these factors, human resources is 

regarded as a key lever of competitive advantage in the current global, dynamic, and 

complex business environment, particularly in the context of KM (e.g., Chen & Huang, 

2009; Oltra, 2005; Hislop, 2003; Martinsons, 1997; Davis & Botkin, 1994; Ulrich, 1998; 

Winch & Schneider, 1993). Ode and Ayavoo (2020) investigated the relationship 

between KM practices and firm innovation by the mediating role of knowledge 

application. Their findings showed that knowledge generation, storage, and application 

have significant effects on firm innovation. Kim et al. (2021) studied the impact of KM 

strategies on firm performance by categorizing KM portfolios into four patterns. The 

findings indicate that unrelated diversity portfolio strategies show substitutable effects on 

firm performance, while the effects of related diversity portfolios strategies are 

complementary. Abbas and Sağsan (2019) studied the impact of KM practices on green 

innovation and corporate sustainable development. Based on the results, KM significantly 

impacts green innovation and corporate sustainable development activities. 

 

Na et al. (2017) developed a knowledge-based advisory expert system using the AHP to 

investigate the late-life structural ambiguity of fixed jacket platforms in the selection of 

the best practicable decommissioning method. In their study, the effects and ranking of 

key factors on the decommissioning planning process were numerically computed. 

Castrogiovanni et al. (2016) determined the sources of knowledge that have the greatest 

effect on financial entities’ knowledge acquisition and management using the Analytic 

Hierarchy Process (AHP). The results showed that human resources and new technology 

adoption are the most effective sources of knowledge acquisition and management. Patil 

and Kant (2014) prioritized the solutions of KM adoption in an Indian hydraulic valve 

manufacturing organization supply chain to overcome its barriers. They proposed a 

framework based on fuzzy AHP and fuzzy TOPSIS to identify and rank the solutions for 

KM adoption in the supply chain and overcome its barriers. The AHP was used to 

determine the weights of the barriers as criteria, and the fuzzy TOPSIS method was used 

to obtain the final ranking of the solutions of KM adoption in supply chain. 

 

Fan et al. (2009) proposed a fuzzy linguistic method based on a 2-tuple linguistic 

representative model with seven attributes. They stated that knowledge creation, 

knowledge acquisition, knowledge storage, security, and knowledge utilization have a 

strong effect on KM capability (Wen, 2009). Also, the capability of KM depends on two 

other factors: infrastructure capability and process capability (Fan et al., 2009; Chuang, 

2004; Gold et al., 2001). Cui et al. (2005) also mentioned that KM capabilities consist of 

the following three interrelated processes: knowledge acquisition, knowledge conversion, 

and knowledge application (Liao & Wu, 2010). Some important studies in KM 

effectiveness measurement with their proposed criteria are mentioned in Table 1. 
 



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Table 1 

Research on KM effectiveness measurement 
 

Source Proposed Criteria 

Tseng (2010) 

Technology; Structure; Culture; Acquisition; Conversion; Application; Security; 

Market share; New green product competitiveness; Monitoring market forces; 

Specialized market units; Export percentage; Success rate of R&D green products; 

Self-generated innovative products; Number of patents; R&D intensity; Percentage of 

researchers to overall employees; Degree of innovation of R&D green products; 

Intensity of collaboration with others; R&D knowledge sharing ability; Forecasting 

and evaluation of technological innovation; Entrepreneurial innovation initiatives. 

Tan and Nasurdin, 

(2011) 
Acquisition; Sharing; Application; Innovation. 

Fan et al. (2009) 

Technology; Structure; Culture; Acquisition (obtaining knowledge); Conversion 

(Make existing knowledge useful); Application (Actual use of the knowledge); 

Security. 

Chang and Wang (2009) 
Strategy; Employee traits; Superintendent traits; Audit and assessment; Organizational 

culture; Operation procedure; Information technology. 

Hsieh et al. (2009) 

KM strategy; KM promotion; KM assessment; Intellectual capital; Knowledge 

identification and classification; Knowledge sharing; Knowledge capture; Knowledge 

store; Knowledge application; Knowledge creation and innovation; Knowledge 

protection; Knowledge learning and training; Best practices; Communities of practice 

(CoPs); IT infrastructure; KM system. 

Wen (2009) Human resources; Information; Data; Knowledge; Wisdom. 

Oltra (2005) 
Motivation; Participation; Human resources; Integration IT infrastructure; Technical 

customization; Past experiences. 

Smits and De Moor 

(2004) 

Use of knowledge resources; Development of knowledge resources; Level of KM; 

Level of maintenance KM. 

Hoy and Miskel (2001) 
Knowledge adaptation effectiveness; Knowledge achievement effectiveness; 

Knowledge integration effectiveness; Knowledge potential effectiveness 

Mehdibeigi et al. (2016) Customer KM; Organizational agility; Organizational effectiveness 

Adaileh et al. (2020) 
Knowledge capturing; Knowledge sharing; Firm performance; Knowledge acquisition; 

Knowledge application 

Kavalić et al. (2021) 

Organizational culture; T-shaped skills: Human resources; Information technology; 

Knowledge acquisition process; Knowledge conversion process; Process of applying 

knowledge; Knowledge protection process; Competition advantage; Organizational 

culture 

Darvishi and Darvishi 

(2019) 

Knowledge creation; Knowledge accumulation; Knowledge sharing; Knowledge 

utilization; Knowledge internalization 

 
2.3 Knowledge management in research centers (RCs) 

In the most common classification of knowledge, the concept is divided into two 

categories, tacit and explicit (Li & Chandra, 2007). Since encouraging employees to 

share their tacit knowledge is crucial in a research environment, the role of KM would be 



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more obvious in these types of organizations (Joseph et al., 2005; Okemwa, 2006; Smits 

and De Moor, 2004; Astrid, 2003). RCs face varied and challenging projects which 

require knowledgeable and experienced staff to manage. In most cases, the greater 

portion of knowledge and experience that staff gain during their work and research is 

tacit not explicit. Due to the strategic role of knowledge in RCs, measuring the 

effectiveness of KM in these centers has been recognized by academics and practitioners 

(Chin et al., 2010). 

 

Tacit knowledge is a valuable resource for future projects and highly depends on the 

presence of staff. If researchers leave the RC, the tacit knowledge will disappear; this 

issue is the most important concern of RCs. Indeed, this situation illustrates the critical 

role of effective KM. Undoubtedly, to have effective KM criteria must be customized to 

measure the KM effectiveness of RCs (Okemwa, 2006). Because of the importance of 

RCs in producing and developing knowledge, researchers focus on different aspects of 

them. For instance, Adachi, (2009) attempted to answer the four following questions in 

his studies about RCs: 

 

 How is knowledge transferred to each task in the planning stage? 
 What are the problems in the planning stage and how do you eliminate them? 
 How does KM work in the planning stage of a public research organization? 
 What kinds of knowledge are created, shared, utilized, and accumulated in the 

planning stage of a public research organization? 

 

Smits and De Moor (2004) asked “when is KM effective?” and “are measurements 

necessary to realize the effectiveness of KM?” The answers to these questions were “it 

depends on the level of KM” and “for a successful KM, sufficient attention must be paid 

to the selection of key aspects, instead of trying to measure everything”, respectively. 

Joseph et al. (2005) studied the need for and impact of a KM system in a research 

organization and how it would help the researcher’s work/output and institutional values. 

Moreover, research was done by the International Atomic Energy Agency (IAEA) of 

Australia (2006) to determine the essential criteria for an effective KM system. Agostino 

et al. (2012) presented a system for performance measurement of the R&D activities in 

public research centers in Italy. Due to the multiple stakeholders with different needs, 

they concluded that managers should balance the multiple goals and integrate their 

different performance indicators. Several researchers also studied the main performance 

indicators for assessing R&D performance in public sector research centers (Leitner & 

Warden, 2004; Chu et al., 2006; Secundo et al., 2010; Agostino et al., 2012). This 

research stream is based on intellectual capital (IC) reporting models. 

  

Wang et al. (2016) found that the consistency of the three dimensions of IC (i.e. human 

capital, structural capital, and relational capital) and KM strategies facilitates both 

operational and financial performance of high-tech firms. Similarly, knowledge strategy 

was found to moderate the relationship between IC and organizational performance 

(Asiaei et al., 2018). There are also two research streams in the private sector; one 

focuses on the choice of performance indicators and assessment of the R&D department 

(Bremser & Barsky, 2004; Schiuma & Neely, 2004; Mettänen, 2005) and the other 

concentrates on the design of the performance measurement system in R&D firms and 

their characteristics, objectives and contextual variables (Chiesa & Frattini, 2007). Based 

on these considerations, some key performance indicators were defined to measure the 



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effectiveness of the RCs. The effectiveness indicator makes it possible to evaluate the 

RCs’ outputs based on the level of achievement of their research objectives (Agostino et 

al., 2012). As Steiner and Nixon (1997) mentioned, this aspect is related to a key issue of 

“what is the output of a research center” and “how do you measure it”. 

 

According to Okemwa (2006)’s study of the International Livestock Research Institute 

(ILRI), establishing KM for a research organization is necessary to determine how 

knowledge should be generated, shared, transferred and integrated into their day-to-day 

operations. Although different studies have been done in the field of KM in RCs, the 

studies did not pay enough attention to determining and prioritizing criteria of KM 

effectiveness. Therefore, this article fills this gap in the literature.  Many RCs exist that 

are very important for the production of knowledge in various fields of science. KM 

helps improve performance and processes in organizations by discovering, collecting, and 

using technical knowledge. In this research, considering the importance and role of KM 

in RCs, the influence factors of KM were evaluated using the fuzzy AHP method. By 

evaluating the influence factors of KM in RCs, the gap in knowledge production in this 

sector is filled. 

 

 

3.  Research methodology 

The present study was conducted in three main phases. The first stage included an 

exploratory literature review on KM effectiveness criteria through the study of relevant 

articles in scholarly journals. The results are summarized in Table 1. These criteria were 

investigated by conducting interviews with KM experts and senior managers of nine 

research centers in Iran (phase 1). In this phase, the experts were asked to judge the main 

criteria and sub-criteria of the hierarchical structure until a consensus was reached. The 

second phase involved collecting data through a pairwise comparison questionnaire, 

which was analyzed by the AHP using simple additive weighting techniques and fuzzy 

AHP (Chang's method). Finally, in the third phase, the validity of the proposed 

framework was tested by measuring the KM effectiveness of nine RCs (Research 

Institute of Petroleum Industry, Iranian Research Institute for Information Science and 

Technology, Center of Strategic Research, Institute for Cognitive Science Studies, IPM 

Institute for Research In Fundamental Sciences, Institute for Management and Planning 

Studies, Iran Standard and Industrial Research Institute, Chemistry & Chemical 

Engineering Research Center of Iran, and Iran Management & Productivity Study Centre 

(IMPSC)). Although the population of the study included all of Tehran's RCs, only nine 

agreed to take part in the research. It should be noted that in each RC, the manager of the 

R&D department or the assistant director was selected as the organization's 

representative. MADM methods, especially the AHP/fuzzy AHP, do not have any 

limitation on the number of experts, according to the consistency rate of each expert's 

responses. 

 



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Figure 1 Research phases 
 

As the analytical part of the research, a Multiple Attribute Decision Making (MADM) 

method was applied. Most MADM techniques resort to either the ordinal scale or the 

interval scale as an easy way to transform qualitative criteria into the numerical scale 

(Hatefi, 2021). There are several techniques for dealing with MADM problems in the 

literature. A comprehensive literature review was conducted by Toloie-Eshlaghy and 

Homayonfar (2011) who reviewed MCDM methodologies and applications from 1999 to 

2009. Based on their study, the AHP is the most widely used and popular method among 

all of the MADM methods. The AHP method, which was first introduced by Saaty 

(1980), divides a complex system into a hierarchical structure, where decision elements 

are placed at the final (bottom) level, decision criteria and sub-criteria are placed at 

intermediate levels, and the goal is placed at the first level (Ozan, 2008; He et al., 2022).  

According to Lee et al. (2008), the AHP has six fundamental steps: 

 

1. Identify the problem and determine the objectives and outcomes. 
2. Divide the compound problem into a hierarchical structure with decision elements 

(criteria, subcriteria, and alternatives). 

3. Conduct pairwise comparisons among decision elements and form comparison 
matrices. 

4. Use the eigenvalue method to calculate the relative weights of the decision elements 
approximately. 

5. Check the consistency of the matrices to ensure that the judgments of the decision-
makers are consistent. 

6. Aggregate the relative weights of the decision elements to gain an overall rating of 
the alternatives. 

 

In order to solve the ambiguity and subjectivity of human judgments in the decision-

making process and to express linguistic variables, fuzzy numbers are replaced with 

linguistic values in fuzzy AHP, as the second method of data analysis. Using the fuzzy 

AHP method reduces vague judgments and increases accuracy in the calculations (Liu et 

al., 2020). Many fuzzy AHP methods have been proposed by researchers such as van 

Laarhoven and Pedrycz, (1983); Buckley (1985); Boender et al. (1989); Chang (1996). In 

this study, Chang’s EA method (Chang, 1996) is preferred because its steps are relatively 

easier than the other fuzzy AHP approaches and are similar to the conventional AHP 

(Bozbura et al., 2007). 

 

Phase 2 

Prioritizing KM 

effectiveness criteria 

using: 

- AHP 

- Fuzzy AHP 

and testing validity using: 

- Consistency rate (CR) 

- Wen (2009) approach 

Phase 1 

Determining main criteria 

and sub-criteria of KM 

effectiveness in RCs using: 

- Literature review 

- Interviews with experts 

of IT and KM system and 

senior managers of RCs 

Phase 3 

Evaluating the validity of the 

proposed framework using: 

- Case study  

 



IJAHP Article: Nahavandi, Homayounfar, Daneshvar/A Fuzzy Analytical Hierarchy Process for 

evaluation of knowledge management effectiveness in research centers 

 

 International Journal of the 
Analytic Hierarchy Process 

11 Vol  15 Issue 1 2023 

ISSN 1936-6744 

https://doi.org/10.13033/ijahp.v15i1.978 

Chang’s extent analysis method can be represented as below (Rezaee Kelidbari et al., 

2016): 

 

Let X = {x1, x2, . . ., xn} be an object set, and G={g1, g2, …. , gn} be a goal set. Then, the 

fuzzy synthesis extent values for each object are as the following: 

 

M
1
gi , M

2
gi , …, M

m
gi        ;           i =1, 2, …, n (1) 

 

In the above equation, M
j
gi (j = 1, 2 . . . m) are triangular fuzzy numbers (TFNs) whose 

parameters are l, m, and n, which show pessimistic, the most likely, and optimistic 

values, respectively. The steps to measure Chang’s fuzzy synthesis extent can be given as 

follows: 

 

Step 1. The value of fuzzy synthetic extent with respect to the i
th
 object is defined as: 

 

Si =∑Mgi
j

m

j=1

⊗[∑∑Mgi
j

m

j=1

n

i=1

]

−1

 (2) 

 

To obtain∑ Mgi
jm

j=i , the fuzzy addition operation of m extent analysis values for a 

particular matrix is performed such that 

 

∑ Mgi
jm

j=i =(∑ lj
m
j=1 ,∑ mj

m
j=1 ,∑ uj

m
j=1 )          i =1, 2, …, n    (3) 

 

To obtain[∑ ∑ Mgi
jm

j=1
n
i=1 ], the fuzzy addition operation of Mjgi (j = 1, 2, ... , m) values is 

performed such that 

 

∑∑Mgi
j

m

j=1

n

i=1

=(∑lj

m

j=i

,∑mj

m

j=i

,∑uj

m

j=i

) (4) 

  

 

Then, the inverse of the vector in Equation 4 is calculated such that 

 

[∑∑Mgi
j

m

j=1

n

i=1

]

−1

=(
1

∑ ui
n
i=1

,
1

∑ mi
n
i=1

,
1

∑ li
n
i=1

)  

 

(5) 

 

Step 2. The possibility degree of M2 = (l2, m2, u2) ≥ M1 = (l1, m1, u1) is defined as 

 

V(M2 ≥M1 =sup⌊min⁡(μM1(x),μM2(x)⌋ (6) 

 

and can be equivalently expressed as follows: 

 

V(M2 ≥M1)=hgt(M1 ⊗M2)=μM2(d)= (7) 



IJAHP Article: Nahavandi, Homayounfar, Daneshvar/A Fuzzy Analytical Hierarchy Process for 

evaluation of knowledge management effectiveness in research centers 

 

 International Journal of the 
Analytic Hierarchy Process 

12 Vol  15 Issue 1 2023 

ISSN 1936-6744 

https://doi.org/10.13033/ijahp.v15i1.978 

{

1⁡⁡ ⁡⁡⁡⁡if⁡M2 ≥M1
0⁡ ⁡if⁡⁡l1 ≥u2
l1−u2

(m2−u2)−(m1−l1)
⁡⁡otherwise

  

 

where d is the ordinate of the highest intersection point, D, between μM1 and μM2 (see 

Figure 2). To compare M1 and M2, we need both the values of V (M1 ≥ M2) and V (M2 

≥ M1). 

 

 

 

 

 

 
 

 

 

Figure 2 Intersection between �̃�1 and  �̃�2 
 

Step 3. The possibility degree for a convex fuzzy number to be greater than k convex 

fuzzy numbers Mi (i = 1, 2 . . . k) can be defined by 

 

𝑉(𝑀 ≥𝑀1,𝑀2,…,𝑀𝑘)= 
𝑉[(𝑀 ≥𝑀1)⁡𝑎𝑛𝑑⁡(𝑀 ≥𝑀2)𝑎𝑛𝑑…𝑎𝑛𝑑(𝑀 ≥𝑀𝑘) 

=min𝑉(𝑀 ≥𝑀𝑖)                              i= 1, 2, 3, …, k 
(8) 

 

assuming that 

 

�́�(𝐴𝑖)=𝑚𝑖𝑛𝑉(𝑆𝑖 ≥ 𝑆𝑘) (9) 

 

For k= 1, 2, …, n; k≠i then the weight vector is given by 

 

𝑊 =́ (�́�(𝐴1́ ), �́�(𝐴2́ ),…, �́�(𝐴𝑛́ ))
𝑇 (10) 

 

where Ai (i = 1, 2 . . . n) are n elements. 

 

Step 4. The normalized weight vectors are 

 

𝑊 =(𝑑(𝐴1),𝑑(𝐴2),…,𝑑(𝐴𝑛))
𝑇 (11) 

 

where W is a non-fuzzy number. 

 

Measuring consistency in the fuzzy AHP method is the same as in the traditional AHP; 

however, it consists of two parallel procedures. According to the Gogus and Boucher 

(1998), each fuzzy matrix should be divided into two matrices: (1) the matrix formed 

from the geometric mean of the upper (optimistic) and lower (pessimistic) values (A
g
), 

and (2) the matrix formed from the most likely value (A
m
). Then, the consistency ratio of 

both of them must be calculated based on Saaty’s method. The steps for calculating the 

consistency ratio are as follows: 

l2 d u1 m2 
0 

M 

V (M2≥M1) 

𝑀1  𝑀2  

1 

l1 m

1 

u2 

𝜇�̃� 

https://www.sciencedirect.com/topics/mathematics/geometric-mean


IJAHP Article: Nahavandi, Homayounfar, Daneshvar/A Fuzzy Analytical Hierarchy Process for 

evaluation of knowledge management effectiveness in research centers 

 

 International Journal of the 
Analytic Hierarchy Process 

13 Vol  15 Issue 1 2023 

ISSN 1936-6744 

https://doi.org/10.13033/ijahp.v15i1.978 

 

Step 1: As mentioned above, in the first step, the fuzzy triangular matrix was divided into 

two matrices A
m
 and A

g
. Suppose the fuzzy triangular number as almu , then 

 

𝐴𝑚 =⁡[𝑎𝑖𝑗𝑚]⁡⁡⁡;⁡⁡⁡⁡𝐴
𝑔 =√𝑎𝑖𝑗𝑢.𝑎𝑖𝑗𝑙 (12) 

 

Step 2: The weight vector of each matrix should be calculated using Equation 13: 

 

𝑤𝑖
𝑚 =⁡

1

𝑛
⁡∑ ⁡⁡

𝑎𝑖𝑗𝑚
∑ 𝑎𝑖𝑗𝑚
𝑛
𝑖=1

𝑛

𝑗=1
⁡𝑤𝑚 =⁡[𝑤𝑖

𝑚] 

𝑤𝑖
𝑔
=⁡
1

𝑛
⁡∑ ⁡⁡

√𝑎𝑖𝑗𝑢.𝑎𝑖𝑗𝑙

∑ √𝑎𝑖𝑗𝑢.𝑎𝑖𝑗𝑙
𝑛
𝑖=1

𝑛

𝑗=1
⁡𝑤𝑔 =⁡[𝑤𝑖

𝑔
] 

(13) 

 

Step 3: In the next step, the highest eigen value for each matrix was calculated using 

Equation 14: 

 

𝜆𝑚𝑎𝑥
𝑚 =⁡

1

𝑛
⁡∑ ⁡⁡∑ ⁡⁡𝑎𝑖𝑗𝑚

𝑛

𝑗=1

𝑛

𝑖=1
⁡(
𝑤𝑗
𝑚

𝑤𝑖
𝑚) 

𝜆𝑚𝑎𝑥
𝑔
=⁡
1

𝑛
⁡∑ ⁡⁡∑ ⁡⁡√𝑎𝑖𝑗𝑢.𝑎𝑖𝑗𝑙

𝑛

𝑗=1

𝑛

𝑖=1
⁡(
𝑤𝑗
𝑔

𝑤
𝑖
𝑔) 

(14) 

 

Step 4: Then, the consistency ratio was calculated using Equation 15: 

 

 

𝐶𝐼𝑚 =
(𝜆𝑚𝑎𝑥
𝑚 −𝑛)

(𝑛−1)
⁡⁡⁡⁡;⁡⁡⁡⁡𝐶𝐼𝑔 =

(𝜆𝑚𝑎𝑥
𝑔
−𝑛)

(𝑛−1)
 

(15) 

 

Step 5: The consistency index (CI) was divided by the random index (RI) to calculate the 

consistency rate (CR) using Equation 16. 

 

𝐶𝑅𝑚 =
𝐶𝐼𝑚

𝑅𝐼𝑚
⁡⁡⁡⁡;⁡⁡⁡⁡𝐶𝑅𝑔 =

𝐶𝐼𝑔

𝑅𝐼𝑔
⁡ (16) 

 

If the CR index is lower than 0.1, the matrix is considered consistent and the experts’ 

judgments are reliable. In fuzzy AHP, since the numerical values of fuzzy comparisons 

are not always integers, and the geometric mean generally converts them to non-integer 

numbers even if the 1 to 9 scale was used, the RI table provided by Saaty is not 

applicable. Table 2 shows the random index of both fuzzy AHP and AHP. 

 

  

https://www.sciencedirect.com/topics/computer-science/triangular-matrix
https://www.sciencedirect.com/topics/computer-science/triangular-matrix


IJAHP Article: Nahavandi, Homayounfar, Daneshvar/A Fuzzy Analytical Hierarchy Process for 

evaluation of knowledge management effectiveness in research centers 

 

 International Journal of the 
Analytic Hierarchy Process 

14 Vol  15 Issue 1 2023 

ISSN 1936-6744 

https://doi.org/10.13033/ijahp.v15i1.978 

Table 2 

Consistency ratio in AHP and Fuzzy AHP 
 

Size of the Matrix RI
n
 RI

o
 RI 

1 0 0 0 

2 0 0 0 

3 0.4890 0.1796 0.58 

4 0.7937 0.2627 0.90 

5 1.0720 0.3597 1.12 

6 1.1996 0.3818 1.24 

7 1.2874 0.4090 1.32 

8 1.3410 0.4164 1.41 

9 1.3793 0.4348 1.45 

10 1.4095 0.4455 1.49 

11 1.4181 0.4536 1.51 

12 1.4462 0.4776 1.54 

13 1.4555 0.4691 1.56 

14 1.4913 0.4804 1.57 

15 1.4986 0.4880 1.58 

 

The process of measuring the consistency ratio in this method is the same as the in classic 

AHP method, with the only difference being that two rates of consistency, namely, 

consistency ratio of the matrix of mean values (CRn) and the consistency ratio of the 

matrix of geometric means of lower and upper bounds (CRo) should be measured for each 

fuzzy pairwise comparison matrix. The matrix is consistent if both CR values are 0.1 or 

less than 0.1. 

 

 

4. KM effectiveness evaluation framework 

4.1. Hierarchical structure to prioritize KM effectiveness measures in RCs 

As mentioned in the previous section, in phase 1 of the research 34 sub-criteria were 

finalized for measuring the KM effectiveness in RCs through a literature review and 

interviews with experts. These subcriteria were then divided into six main criteria, as 

follows: human resources, leadership and center structure, knowledge creation and 

acquisition, knowledge sharing, knowledge storage and security, and knowledge 

utilization and updating. It should be noted that, by investigating the technical documents 

of the RCs, some subcriteria were added to this list. Figure 3 illustrates the decision tree 

of the extracted factors. 



IJAHP Article: Nahavandi, Homayounfar, Daneshvar/A Fuzzy Analytical Hierarchy Process for evaluation of knowledge management effectiveness in research centers 

 

 International Journal of the 
Analytic Hierarchy Process 

15 Vol  15 Issue 1 2023 

ISSN 1936-6744 

https://doi.org/10.13033/ijahp.v15i1.978 

 
Figure 3 Decision making hierarchy

Human resources 
Leadership and  

center structure 

Knowledge creation  

and acquisition 

Knowledge 

 sharing 

Knowledge utilization and 

updating 

 

Knowledge storage  

and Security 

Knowledge Management 

Effectiveness Evaluation 

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IJAHP Article: Nahavandi, Homayounfar, Daneshvar/A Fuzzy Analytical Hierarchy Process for 

evaluation of knowledge management effectiveness in research centers 

 

 International Journal of the 
Analytic Hierarchy Process 

16 Vol  15 Issue 1 2023 

ISSN 1936-6744 

https://doi.org/10.13033/ijahp.v15i1.978 

Table 3 

KM effectiveness measures for research centers 

 
Sym. Sub-Criteria ‍Criteria 

H1 Number of researchers in the RC- (Researchers/total staff ratio) 

Human 

resources 

H2 Number of IT experts in the RC- (IT experts/total staff ratio) 

H3 
Number of personnel who are directly involved in KMS - 

(KM personnel/total staff ratio) 

H4 Creative capability of HR (Creative human resources) 

H5 
Number of employees’ proposals for improvement – 

(improving/non improving proposals ratio) 

L1 Research budget of RC (Research budget/total budget ratio) 

Leadership 

and center 

structure 

L2 Top manager support for KM 

L3 Compatible structure with KMS 

L4 Helpful and creative atmosphere in RC 

L5 Participation in decision making 

L6 Learning atmosphere in RC (Incentives for motivating personnel to learn more) 

C1 Number of domestic patents (Per capita domestic patents) 

Knowledge 

creation 

and 

acquisition 

C2 Number of international patents (Per capita international patents) 

C3 
Number of papers published in scientific journals and conferences 

(Per capita papers) 

C4 Number of scientific published journals 

C5 Number of scientific published books 

C6 Number of scientific web sites of which RC is a member 

C7 
Comprehensive data and information and various and qualitative information 

resources 

S1 IT budget of RC (IT budget/total budget ratio) 
Knowledge 

storage 

and Security 

S2 Qualitative and helpful IT infrastructures 

S3 Database for personnel's knowledge and ideas 

S4 Degree of standardization in documentation 

T1 Training budget of RC (Training budget/total budget ratio) 

Knowledge 

sharing 

 

T2 General training ratio (General training hours/total training hours) 

T3 
Professional training hours (Professional training hours/total training hours 

ratio) 

T4 
Amount of data and information that personnel are allowed to use via the web 

(Intranet, extranet and internet). 

T5 
Number of tasks and duties that personnel complete via computers 

(E-tasks and duties) 

T6 Integrated internal information 

U1 Degree to which personnel use the latest (the newest) knowledge 
Knowledge 

utilization 

and 

Updating 

 

U2 Time to answer customers’ complains 

U3 Establishing a customer database (CRM) 

U4 Number of professional certificates and scientific awards gained by personnel 

U5 Organized interval evaluation 

U6 Number of executed ideas (Executed ideas/Total ideas ratio) 

 

In past studies about prioritizing KM effectiveness criteria, the same weights were 

attributed to all criteria; however, in this study, the criteria do not have the same weights. 

Weights can be derived by pairwise comparison of the criteria based on the AHP 

methodology. In this research, decision-makers used linguistic terms to demonstrate the 

significant weights of the factors given in Table 4. 

 

  



IJAHP Article: Nahavandi, Homayounfar, Daneshvar/A Fuzzy Analytical Hierarchy Process for 

evaluation of knowledge management effectiveness in research centers 

 

 International Journal of the 
Analytic Hierarchy Process 

17 Vol  15 Issue 1 2023 

ISSN 1936-6744 

https://doi.org/10.13033/ijahp.v15i1.978 

Table 4 

Linguistic terms for importance weights of factors 
 

Triangular fuzzy reciprocal 

scale 
Triangular fuzzy scale Linguistic scale for importance 

(1,1,1) (1,1,1) Just equal 

(2/3,1,2) (1/2,1,3/2) Equally important 

(1/2,2/3,1) (1,3/2,2) Weakly important 

(2/5,1/2,2/3) (3/2,2,5/2) Strongly more important 

(1/3,2/5,1/2) (2,5/2,3) Very strongly more important 

(2/7,1/3,2/5) (5/2,3,7/2) Absolutely more important 

 

To achieve the results, the experts (managers of R&D department or their assistant 

directors) were asked to fill in the AHP pairwise comparisons matrix based on the 

linguistic values in Table 4. Then, the nine resulting individual matrices were fuzzified 

and merged based on the arithmetic mean. Table 5 illustrates the resulting group matrix 

for the main criteria.  

 

Table 5 

Group pairwise comparisons matrix of KM effectiveness criteria 
 

Knowledge 

utilization 

and updating 

Knowledge 

storage 

and Security 

Knowledge 

sharing 

 

Knowledge 

creation and 

acquisition 

Leadership 

and center 

Structure 

Human 

resources 

KM 

effectiveness 

(1.26,1.84,2.38) (1.78,2.62,2.8) (0.72,1.26,1.76) (1.96,2.47,2.97) (1.36,1.96,2.51) (1,1,1) Human resources 

(0.79,1.36,1.89) (1.82,2.32,2.82) (1,1.55,2.08) (1.84,2.38,2.9) (1,1,1) (0.4,0.51,0.74) 
Leadership and center 

Structure 

(1,1.36,1.82) (0.64,0.79,1) (0.56,0.69,0.87) (1,1,1) (0.42,0.54,0.34) (0.34,0.41,0.51) 
Knowledge creation/ 

acquisition 

(1.96,2.47,2.97) (2.32,2.82,3.32) (1,1,1) (1.14,1.44,1.8) (0.48,0.64,1) (0.56,0.79,1.39) Knowledge sharing 

(1.19,0.67,0.87) (1,1,1) (0.3,0.35,0.43) (1,1.26,1.55) (0.35,0.43,0.55) (0.36,0.38,0.56) 
Knowledge storage 

and Security 

(1,1,1) (0.84,1.14,1.49) (0.34,0.41,0.51) (0.55,0.74,1) (0.53,0.74,1.26) (0.42,0.54,0.79) 
Knowledge utilization 

and updating 

 

The results of the implementation of the extent analysis with respect to the main criteria 

were calculated and  summarized. The value of fuzzy synthetic extent was calculated for 

each criterion using Equation 2. 

 

[∑ ∑ 𝑀𝑔𝑖
𝑗𝑚

𝑗=𝑖
𝑛
𝑖=1 ]

−1
= (33.63, 42.96, 53.4)

-1
= (0.0187, 0.0232, 0.0297) 

 

Then: 

S1= (8.08, 11.15, 13.42) × (0.0187, 0.0232, 0.0297) = (0.151, 0.258, 0.398)  

S2= (6.85, 9.12, 11.43) × (0.0187, 0.0232, 0.0297) = (0.128, 0.211, 0.339) 

S3= (3.88, 4.67, 5.74) × (0.0187, 0.0232, 0.0297) = (0.0725, 0.108, 0.170)  

S4= (7.46, 9.16, 11.48) × (0.0187, 0.0232, 0.0297) = (0.139, 0.212, 0.340)  

S5= (3.68, 4.29, 5.28) × (0.0187, 0.0232, 0.0297) = (0.068, 0.099, 0.156) 

S6= (3.68, 4.57, 6.05) × (0.0187, 0.0232, 0.0297) = (0.068, 0.106, 0.179) 

 



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Next, the degree of possibility for each criterion was calculated. For each pairwise 

comparison, the minimum degree of possibility was as follows using the Equation 8: 

 

V (S1 ≥ Si) = 1; V (S2 ≥ Si) = 0.801; V (S3 ≥ Si) = 0.114; V (S4 ≥ Si) = 0.914; V (S5 ≥ Si) 

= 0.035, V (S6 ≥ Si) = 0.155 

 

After computing the primary weight vector of the main criteria (Equations 9-10) and 

normalizing them, the final weights of the main criteria (Wi) were calculated using 

Equation 11.  

 

W = (0.34, 0.27, 0.04, 0.28, 0.02, 0.05) 

 

Since the use of the weight values directly depended on the consistency of fuzzy pairwise 

comparison matrix, the Gogus and Boucher (1998) method was used to examine the 

validity of the results. For the fuzzy comparison matrix of the main criteria, the CRn and 

CRo indexes were calculated as 0.03 and 0.05, respectively which indicates the 

consistency of the fuzzy pairwise comparison matrix of the main criteria. For each of the 

fuzzy pairwise comparison matrices of the subcriteria for which this ratio was computed, 

the consistency ratio was equal to or less than 0.1. 

 

To conduct the classic AHP, the pairwise comparisons matrices which were previously 

filled in by the experts using the linguistic values of Table 4, were quantified based on 

Saaty’s scale and merged based on the arithmetic mean. Due to the simplicity of the AHP 

method, its steps are not discussed in detail; however, the weights of the criteria and 

inconsistency ratio were calculated using the Super Decision software and illustrated in 

Figure 4.  

 

 
 

Figure 4 Results of AHP method 

 

Table 6 shows the results of the classic and fuzzy AHP methods based on the weights of 

the six main criteria. The findings indicate that the weights of all criteria are relatively 

close in the two methods. Figure 5 also illustrates the RADAR chart for weights of the 

main criteria.  

 

  



IJAHP Article: Nahavandi, Homayounfar, Daneshvar/A Fuzzy Analytical Hierarchy Process for 

evaluation of knowledge management effectiveness in research centers 

 

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Table 6  

Importance weights of the six criteria with using AHP and fuzzy AHP 

 

Fuzzy AHP AHP Criteria 

0.34 0.31 Human resources 

0.27 0.26 Knowledge sharing 

0.04 0.06 Leadership and center structure 

0.28 0.25 Knowledge utilization and updating 

0.02 0.06 Knowledge creation and acquisition 

0.05 0.07 Knowledge storage and security 

 

 

 
 

Figure 5 RADAR chart for weights of the six criteria based on AHP and fuzzy AHP 

methods 

 

The weights of the subcriteria in both the AHP and fuzzy AHP were calculated and are 

summarized in Table 6. 

 

  

Human
resources

Knowledge
sharing

Leadership and
center

structure

Knowledge
utilization and

updating

Knowledge
creation

acquisition

Knowledge
storage and

security

AHP  weights

FAHP weights



IJAHP Article: Nahavandi, Homayounfar, Daneshvar/A Fuzzy Analytical Hierarchy Process for 

evaluation of knowledge management effectiveness in research centers 

 

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Table 6  

Importance weights of subcriteria using AHP and fuzzy AHP 
 

Fuzzy AHP AHP Subcriteria No. 

 

Fuzzy AHP AHP Subcriteria No. 

0.12 0.10 C7 18 0.14 0.22 H1 1 

0.12 0.03 T1 19 0.13 0.17 H2 2 

0.20 0.16 T2 20 0.24 0.18 H3 3 

0.15 0.27 T3 21 0.23 0.18 H4 4 

0.16 0.16 T4 22 0.26 0.25 H5 5 

0.21 0.22 T5 23 0.13 0.17 L1 6 

0.16 0.16 T6 24 0.19 0.29 L2 7 

0.21 0.29 S1 25 0.19 0.14 L3 8 

0.23 0.23 S2 26 0.12 0.17 L4 9 

0.26 0.21 S3 27 0.14 0.09 L5 10 

0.29 0.27 S4 28 0.23 0.14 L6 11 

0.14 0.17 U1 29 0.19 0.18 C1 12 

0.12 0.19 U2 30 0.20 0.18 C2 13 

0.20 0.10 U3 31 0.14 0.17 C3 14 

0.17 0.28 U4 32 0.16 0.16 C4 15 

0.20 0.12 U5 33 0.09 0.11 C5 16 

0.16 0.14 U6 34 0.09 0.09 C6 17 

 
4.2 Measuring the RCs’ KM effectiveness 

An approach presented by Wen (2009) was used to acquire scores of KM effectiveness. 

According to this approach, after weighting 34 sub-criteria to obtain the standard Z value 

(which is between 0 and 1), we normalized the weights. Standardization makes it possible 

to cope with problems that arise from the fact that the measurement units of these 34 

criteria are different from each other. The standard Z is used to normalize the weights. 

The sum of each indicator multiplied by the related weight was the standardized value of 

the respective criterion. The sum of each criterion multiplied by the related weight 

created the final score of KM effectiveness. To acquire the score of KM effectiveness in 

the mentioned method, the results of the fuzzy AHP were combined with the SAW 

method. Therefore, by using Equation 12, the score of the influence evaluation model of 

KM was calculated. 

 

Ai =∑Wij ×⁡Zij (12) 

 

Where Zij is the normalized value of the i
th
 criterion and the j

th
 index; Wij is the relative 

weight of the i
th
 criterion and the j

th
 index, and Ai is the score of the i

th
 criterion (Equation 

13). 

 

∑Wij

5

i=1

×Ai (13) 

 

Where Ai is the standardized value of the i
th
 criterion; Wij is the relative weight of the i

th
 

criterion, and E is the total score of KM effectiveness of Iran’s RCs. To execute the 

introduced framework, we have attempted to rank and compare nine Iranian RCs. The 

final results are summarized in Table 7. 

 

 



IJAHP Article: Nahavandi, Homayounfar, Daneshvar/A Fuzzy Analytical Hierarchy Process for 

evaluation of knowledge management effectiveness in research centers 

 

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Analytic Hierarchy Process 

21 Vol  15 Issue 1 2023 

ISSN 1936-6744 

https://doi.org/10.13033/ijahp.v15i1.978 

Table 7 

Results of ranking nine RCs in Iran 
 

Code of RCs Effectiveness score Rank 

RC2 0.007226 1 

RC5 0.006598 2 

RC7 0.006446 3 

RC1 0.006435 4 

RC6 0.006429 5 

RC4 0.006236 6 

RC3 0.005710 7 

RC8 0.005341 8 

RC9 0.005264 9 

 

 

5.  Conclusion 

Although KM effectiveness leads to an increase in efficiency in organizational 

performance and gives a competitive advantage, in order to manage it successfully, 

effectiveness measures and indicators must be identified and defined. Few systematic 

studies have been done regarding KM effectiveness in RCs. The AHP is the most 

appropriate method for this problem because of the subjective and intangible nature of 

the attributes that are used in the evaluation. 

 

The opinions of experts and specialists in areas like IT and at different management 

levels (KM researchers, etc.) should be used to present a comprehensive model for 

assessing KM effectiveness in RCs. Since experts prefer natural language expressions 

rather than sharp numerical values in their assessments, in the present research triangle 

fuzzy numbers (TFNs) were used for comparison in the form of the extent analysis 

method. This article presents a framework with six criteria and 34 sub-criteria to evaluate 

the KM effectiveness in RCs. 

 

With respect to the results derived from the fuzzy AHP, the criterion human resources 

(0.34) ranked first. The other five criteria in descending order of importance are 

knowledge sharing (0.28), leadership and center structure (0.27), knowledge utilization 

and updating (0.05), knowledge creation and acquisition (0.04) and knowledge storage 

and security (0.01). Furthermore, the number of employees’ proposals for improvement 

subcriterion (0.26) within the criterion human resources and the learning atmosphere in 

RC subcriterion (0.23) within the criterion leadership and center structure, the number of 

international patents subcriterion (0.20) within the criterion knowledge creation and 

acquisition, the amount of tasks and duties that personnel complete via computers 

subcriterion (0.21) within the criterion knowledge sharing, the degree of standardization 

in documentation subcriterion (0.29) within the criterion knowledge storage and security, 

the establishing customers' database (0.20) and organized interval evaluation subcriteria 

(0.20) within the criterion knowledge utilization and updating received the highest level 

of importance among all of the subcriteria. 

 

This framework can be used in any country; however, the obtained results reflect the 

situation of KM in Iranian RCs. Moreover, such a model could be used for a wide variety 

of research organizations in future research. Finally, we suggest developing this type of 



IJAHP Article: Nahavandi, Homayounfar, Daneshvar/A Fuzzy Analytical Hierarchy Process for 

evaluation of knowledge management effectiveness in research centers 

 

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framework in the future using a combination of fuzzy methods and other MADM 

methods. 

 

The results of the research provide some implications to consider. From a practical point 

of view, this research contributes to theory by providing a framework for measuring KM 

effectiveness in Iranian RCs. Despite the considerable attention to the evaluation of KM 

effectiveness, little is known about the importance of a systematic and comprehensive 

criteria determination and professionals’ judgments, particularly in RCs. This study 

suggests the suitable criteria of both a qualitative and quantitative nature which promotes 

the validation of the KM effectiveness measurement. The applied classic and fuzzy AHP 

methods make it possible to analyze the sensitivity of the results and make better 

assessments.  

 

From the theoretical point of view, the main criteria to measure RCs effectiveness are 

human resources, leadership and center structure, knowledge creation and acquisition, 

knowledge storage and security and knowledge sharing. Organizations that produce 

knowledge, especially RCs, need to measure KM performance at different stages in their 

life cycle. The effectiveness of the KM system strongly relies on these criteria which 

create synergy in conducting research projects and optimal use of the resources. 

Therefore, in this study an attempt was made to evaluate the influence factors of KM in 

RCs by presenting a model to improve the performance of this organization. 

 

Concerning limitations, we propose conducting further analysis to identify additional 

variables regarding the effectiveness of the RCs performance. In addition, it may be 

interesting to determine RCs performance through alternative variables (e.g., number of 

research projects) or even study the performance as a latent variable related to multiple 

measured variables. For future research, we recommend defining a framework for 

structuring and evaluating RCs’ activities according to the new approaches.  

 

  



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