Intellectual capital evaluation using fuzzy cognitive maps: A scenario-based development planning

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Intellectual capital evaluation using fuzzy cognitive maps: A scenario-based development planning Meysam Arvan a,∗, Aschkan Omidvar b, Reza Ghodsi c

Q1

a

The University of Sydney Business School, The University of Sydney, NSW 2006, Sydney, Australia Department of Industrial and Manufacturing Engineering, College of Engineering, Florida State University, Tallahassee, FL 32310, USA c Engineering Department, Central Connecticut State University, New Britain, CT 06050, USA b

a r t i c l e

i n f o

Keywords: Knowledge management Intellectual capital Fuzzy cognitive maps Intellectual capital development Strategic development planning

a b s t r a c t Evaluation of Intellectual Capital (IC) is a vital phenomenon for various organizations in order to determine the value of the organization, to improve the control system and to assist strategic planning and decision-making. This study presents a new model in which the interactions between the IC components are considered in the evaluation and development planning process. This, in turn, would enhance the accuracy in evaluating IC and would aid managers in establishing a development plan for IC. In other words, this study tackles the common problem of the inner correlation between IC components by using fuzzy logic, which bears concrete results. The procedure starts with studying numerous criteria proposed for measuring IC drawn from the literature and selecting the most frequently used ones. Then, the selected criteria are refined through a questionnaire based on their relevance to the organization in which we want to evaluate IC. Interactions among IC criteria are captured with the help of Fuzzy cognitive maps (FCMs). The present study is the first that uses FCMs method for evaluating IC. After the influence of each criterion over the others is identified, several scenarios are developed and analyzed in order to realize their efficiency and effectiveness for IC development. The results reveal that improving IC criteria with maximum influence over others, does not necessarily lead to the development of IC and investigations are required to establish the development plan. © 2016 Elsevier Ltd. All rights reserved.

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1. Introduction

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From the early practices of industrialization to the early 1950s, it had been assumed that the main reason of underdevelopment in the developing countries is the lack of financial and physical capital. In order to eliminate this plight many societies have squandered a considerable amount of financial capital. However, it is now accepted that injecting a large amount of budget does not necessarily lead to development and progress. In fact, in the societies where specialized human resources are available, physical capital and budgets are consumed more efficiently, consequently noticeable development could be observed. In addition, in the knowledge-based economy, knowledge has gained salient attention compared to other assets such as land, budget, and machines (Darabi, Rad, & Heidaribali, 2012). Economy knowledge is the most important factor in production and the main strength of an

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∗

Corresponding author. Tel.: +61470103045. E-mail addresses: [email protected], [email protected] (M. Arvan), [email protected] (A. Omidvar), [email protected] (R. Ghodsi).

organization in a competitive market (Bontis, Dragonetti, Jacobsen, & Roos, 1999; Cricelli & Grimaldi, 2008; Kujansivu & Lönnqvist, 2007; Seetharaman, Sooria, & Saravanan, 2002). Therefore, the survival of an organization in a market has a direct relationship with non-financial subjects (Stewart & Ruckdeschel, 1998). On the other hand, with the evolution of technology and Information Technology (IT) since the 1990s, the formation of global economics has drastically changed. The rapid changes of IT during the last two decades has fundamentally affected all aspects of human lives and their activities leading to the emergence of a new era entitled ‘knowledge era and farewell to the industrial epoch’ (Uhl-Bien, Marion, & McKelvey, 2007). During industrialization (i.e., 1890s) firms demanded mass production and distribution; nonetheless, in the knowledge epoch, knowledge is the key to success in business and industry. Therefore, knowledge is the indispensable component of intangible assets, which needs to be managed deliberately. In general, the assets of an organization can be divided into two categories (Feiwel, 1975): 1. Tangible and concrete assets: which function under the principles of ‘economy of scarcity’. In other words, the more they are utilized, the more they depreciate.

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2. Intangible assets: these assets could be further divided into two sub-categories: a. Those that are subjected to legal and juristic support and, in fact, are assumed as intellectual properties. Examples of these assets are copyrights, franchise, logos, and trademarks. b. Those that are categorized as intellectual capital and function under the principles of ‘economy of abundance’. To put it differently, no matter how much they are used, no depreciation and diminution in the worth of the assets occurs. Human and workforce in a company lie in this category.

This study focuses on the latter. The term ‘Intellectual Capital’ (IC) first was referred by John Kenneth Galbraith at 1969 (Galbraith, 2007). Before that, Drucker (1995) had used the term ‘knowledge workers’. By selecting the term ‘Intellectual Capital’ instead of all the synonyms, researchers mainly refer to all intellectual and intangible assets which have the potential to create economic value for the organizations (e.g., knowledge, merit, brand, and so forth). Several definitions have been proposed for IC (Ross, Ross, Edvinsson, & Dragonetti, 1997; Seetharaman et al., 2002; Stewart & Ruckdeschel, 1998). This term could be defined as the difference between the market value of an organization and the replacement costs of the assets in the same organization (Edvinsson & Malone, 1997). This capital contains knowledge, copyright, experience, and other intellectual assets. In another definition, IC is known as all processes, procedures, and capitals that do not exist usually on the balance sheet (Bontis, 1998). In this study, Bontis model is selected for the evaluation phase (Bontis, Chua Chong Keow, & Richardson, 2000). In this model, IC is divided into three major components, namely structural capital, human capital and relational capital (Bontis et al., 2000). In this paper, first, the most suitable criteria for IC evaluation in the under studied organization are determined by using Questionnaire No. 1. Following the precise selection of the criteria, Questionnaire No. 2 is used to measure the value of the criteria and examine their correlations. Subsequently, by the aid of FCMs, the influence of the IC criteria over each other is determined. Then, a number of scenarios for developing IC in the studied organization are built based on the results from Questionnaire No. 2. Finally, these scenarios are analyzed and examined in order to select the best plan for developing IC. The presented model is applied to a real case in order to examine and prove its applicability. The studied enterprise is a holding organization with more than half a century experience in macro and micro economic activities in Iran. The activities of this organization encompass managing several production plants in the fields of husbandry, textile, olive oil, detergents, sugar, meat products, as well as financial and commercial activities. This company has established a retail chain of Fast-Moving Consumer Goods (FMCG) products using self-owned chain stores. This organization is regarded as one the largest and most reliable trading organizations in Iran. The organization owns and manages more than 500 physical stores nationwide, in addition to an online shopping platform. More than 140 staff including top managers, market analysts and IT professionals work in the headquarter building, where is under the analysis in this study. The rest of the paper is organized as follows: At first the literature related to IC and the methodology used are reviewed. Afterwards, in Section 3, the methodology used in the paper is investigated in details in which the chosen IC components are discussed and the data collection process, and FCMs methodology are explained. Then, Section 4 deals with the results of implementing the model on the holding organization. Section 5 provides managerial insights as well as a discussion regarding the results of the

study. Finally, the paper is concluded in the sixth section presenting a number of directions for future research.

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2. Literature review

104

The most recent literature reviews on IC have been conducted by El Tawy and Tollington (2012), and Aisenberg Ferenhof, Durst, Zaniboni Bialecki, and Selig (2015). Earlier, Petty and Guthrie (2000) provided an extensive overview of IC. The first theoretical work in the field of intangible assets was published in the early 1990s by Itami and Roehl (1991). Following the discussions of knowledge assets, Bontis (1998) suggested a comprehensive framework, as well as a pilot study that investigated components of IC from both theoretic and practical perspectives. In this research and subsequent studies, IC was divided into three components including human capital, structural capital and customer capital. Later, customer capital was replaced by relational capital because of its more general definition. It has already proven that IC has a positive effect on business performance (Bontis et al., 2000; Sharabati, Jawad, & Bontis, 2010). It also has a considerable influence over the financial performance of an industry (Rudez & Mihalic, 2007). In addition, Marr, Gupta, Pike, and Roos (2003) identified five reasons to shed light on the significance of measuring IC: (1) it helps organizations to plan their strategy; (2) acts as an aid for implementing their strategies; (3) facilitates development decisions; (4) IC measurement results can be used as a base for supporting services; (5) it can inform stakeholders about the status of the organization. Considering all the arguments, the importance of IC development can be justified. The interaction and correlation of IC components have already been proven in several studies regardless of the type of industry (Alizadeh, Jafari, & Hooshmand, 2008; Bollen, 2005; Bontis, 1998; Bontis et al., 2000; Edvinsson & Malone, 1997). Bontis (1998), in a research carried out in Canada, noted that there are direct relationships among the IC components. Bontis et al. (2000) investigated the interrelationships among IC components and concluded that regardless of the industry sector, customer capital has a significant influence over structural capital. Another research conducted in Taiwan confirmed the results and arguments provided by previous studies (Bollen, 2005). Most of IC factors and items are particularly intangible; therefore, it is difficult, and in some cases impossible, to quantify them by using traditional crisp values (Lev, 2003; Sveiby, 2001–2010; Tai & Chen, 2009). For this reason, practitioners and managers are mostly inclined to use multi-criteria methods and fuzzy linguistic variables in the IC evaluation process (Calabrese, Costa, & Menichini, 2013; Bozbura, Beskese, & Kahraman, 2007; Lee, 2010). Furthermore, a large number of studies that have used Likert scale in their questionnaires for measuring IC assumed that the distance between two levels of a Likert scale are approximately equally divided (Cummins & Gullone, 2000; Davey, Barratt, Butow, & Deeks, 2007). This assumption interferes with the reflection of respondents’ opinion in answering the questions while they do not have any choice in between two scales, for instance if the respondents’ are asked to select among integer values ranging from 1 to 5, they would not be able to select non-integer values. Considering these arguments, and in order to boost the accuracy and efficiency in measuring IC and to take the correlations and the effects of each IC component and criterion on the other ones into consideration, Fuzzy cognitive maps (FCMs) method is utilized in this study. Using FCMs, which is a mixture of qualitative and quantitative approaches, mitigates the limitations of transforming implicit assumptions (or mental models) from experts to explicit ones and has the potential to tackle the issue discussed earlier in this paragraph (Jetter & Kok, 2014).

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Initially introduced by Zadeh (1965), ‘fuzzy set theory’ is exploited in order to deal with the intrinsic uncertainty originated from imprecision or vagueness. Moreover, introduced by Axelrod (1976), the concept of cognitive maps (CMs) demonstrates the cause and effect opinions of an expert or experts on a particular subject, and is used for analyzing the effects of the alternatives, such as policies or decisions on business, in order to achieve a predefined goal (Asher, 1983). Later, Kosko (1986) introduced FCMs, which is the integration of fuzzy set theory and CMs. FCMs is in fact a suitable tool to fill the gap between qualitative and quantitative approaches (Jetter & Kok, 2014). Several studies have used FCMs for various purposes. The applications of these maps range from simulation of enterprise strategies, modeling strategic problems, decision analysis, establishment of data banks, analysis of failure and breakdown modes, characteristic of systems, relations management, organizational systems enhancement, business management, and learning management to knowledge discovery in medicine and so forth (Rodriguez-Repiso, 2005; Lee & Lee, 2015; Azadeh et al., 2015; Razmi, Arvan, & Omidvar, 2014; Dias, Hadjileontiadou, Hadjileontiadis, & Diniz, 2015; Nápoles, Grau, Bello, & Grau, 2014). For comprehensive reviews on FCMs method, readers may refer to Jetter and Kok (2014) and Papageorgiou and Salmeron (2013). Therefore, this paper contributes to the existing literature by presenting a model, which is capable of evaluating IC in different organizations with different business settings. The diversity among business environments is captured by selecting criteria corresponding to the organizations’ characteristics. To tackle the interrelationship existing between IC components, the presented model evaluate IC by using FCMs method. The model can assist policy-makers to allocate investments for developing IC in organizations; an issue, which has been neglected in many of the past studies. This has been accomplished by using a number of scenarios, which represent investment on a number of IC criteria. Then, by assessing the influence of each scenario’s criteria over other criteria, which are not included in the scenario, the best development plan can be identified. This way, more efficient budget allocations for developing IC in an organization can be planned by managers. 3. Material and methods 3.1. The conceptual framework Inasmuch as having various definitions, there is still debate on the components that constitute IC. However, a general agreement has been reached on the fact that it is constructed from some components making it a multidimensional concept (Mention & Bontis, 2013). Altogether, a consensus is emerging in the literature regarding the three main components of IC (Sveiby, 1997; Bontis, 2001; Adekunle Suraj & Bontis, 2012, Mention & Bontis, 2013). These components are defined as: 1. Human capital: It is recognized as the individual knowledge that provides the majority of valuable assets (Stewart & Ruckdeschel, 1998). Professional skills, experience, and creativity employees possess are the instances for this knowledge (Lee, 2010). 2. Structural capital: It consists of all processes, infrastructures, systems, strategies, databases, and IT infrastructures that enable and support human to function properly in the organization (Edvinsson & Malone, 1997). 3. Relational capital: It mainly refers to the value created from the relationship of an organization with customers, partners, suppliers, employees, and other important constituencies. It includes reputation, brands, customer loyalty, long-term customer relationships, commercial name, shop sign, distribution channels and so on (Fernández, Montes, & Vázquez, 2000).

3

In some cases, the term “customer capital” is used instead of “relational capital”. However, the majority of recent studies have used relational capital (Bozbura et al., 2007; Lee, 2010; Calabrese et al., 2013). This might be due to the broader meaning of relational capital, and also the fact that it comprises more procedures within an organization compared to customer capital. As discussed, we seek to evaluate IC using some carefully selected criteria and by the aid of the FCMs method in order to establish a development plan. As shown in the flowchart in Fig. 1, the criteria used in this study are selected in a way that they are proportional to the organizations’ structure. To do so, Questionnaire No. 1 is designed to ensure the adequacy and suitability of the proposed criteria for IC evaluation in the organizations. The initial 48 criteria are discovered and processed for our case according to their suitability, relevance and usage frequency from the body of literature (Petty & Guthrie, 2000; Han & Han, 2004, Bozbura et al., 2007; Shih, Liu, Jones, & Lin, 2010; El Tawy & Tollington, 2012). In this questionnaire, the organization’s staff who are familiar with the basics of Knowledge Management (KM) and IC, respond to several questions including the selected criteria and their score based on Likert scale, which seek to extract the significant criteria that reflect the organization’s characteristics adequately. After gathering the data from Questionnaire No. 1, a hypothesis test is performed to select the most appropriate criteria. Then, Questionnaire No. 2 is designed, which enables us to measure the selected criteria and to examine the possible correlation between IC components. The IC evaluation model in this research is formed as Fig. 2, in which three main components of IC are considered to have interactions with one another. Table 1 lists the criteria selected by Questionnaire No. 1 after performing a hypothesis test on the results of the questionnaire. The hypothesis test examines the criteria’ means in order to evaluates their suitability for the organization. Based on this table, 23 criteria are selected from the suggested 48, meaning that 25 were assumed irrelevant or out-of-scope of the case of this study. It is worth noting that performing the same procedure on other cases may yield completely different results, due to the uniqueness of each organization. Subsequent to finalizing the criteria and before distributing Questionnaire No. 2, in order to ensure that the questions are measuring the right factor (i.e., criterion), Content Validity Ratio (CVR) is employed. Firstly introduced by Lawshe (1975), CVR verifies the validity of the content in a questionnaire assuring the questions measure the right factor. CVR procedure starts with choosing a panel of experts who review the questionnaire in order to evaluate how the questions reflect the factors. To do so, a poll is designed in order to ask the opinion of experts on the factors. Here, simply, the experts identify if the questions are necessary or not. The experts’ panel consists of researchers and professors in the field of IC and have an in-depth understanding of the topic. To quantify the opinions of the panel Eq. (1) is utilized.

CV R =

ne −

n 2

n 2

229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279

(1)

where n represents the total number of experts in the panel and ne stands for the number of experts who have agreed on a question’s validity. Following the calculation of CVR index, Table 2 is used for interpreting the CVR index, which can result in acceptance or rejection of a question. Based on CVR results some questions would be removed from the initial draft and some would be revised. The panel used in this study consisted of 11 experts, so every question with a CVR index under 0.59 has to go under revision or should be removed from the questionnaire.

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IC evaluation & development

Determine IC model and components

Literature review

Determine primary criteria

Refine the criteria based on the organization’s characteristics (By using Questionnaire No. 1)

Design Questionnaire No. 2

Calculate Content Validity Ratio (CVR) No

Is the CVR ok?

Yes Measure the proposed criteria

Determine the correlation coefficients for IC components

Interpret the evaluation No Improve the weak points or enhance the strengths of IC in the organization

Are the coefficients significant?

Yes Calculate the causal influence between IC criteria through FCMs

Generate development scenarios

Perform sensitivity analysis on scenarios

Establish strategic development plans for IC

Fig. 1. The general framework of the proposed model for establishing IC strategic development plans.

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Table 3 Sample size parameters based on Cochran equation for proportional sample selection method. N

t

p

q

d

148

1.64

0.5

0.5

0.05

the population is classified into different segments and the samples are randomly selected among all segments. This method is very similar to the regular sample selection; nevertheless, the segments are chosen based on organizational positions of the staff to cover the entire organization. The selected segments are managers, master experts, senior experts, and experts. The proportional method, in which the sample size corresponds to the population size in a particular segment, follows as:

nh = Fig. 2. Schematic topology of the proposed FCMs model to assess IC (Cr = criterion; HC = Human Capital; RC = Relational Capital; SC = Structural Capital).

291

3.2. Data collection

292

As discussed earlier, the data-sets used in this study are collected from two sets of questionnaires filled out by the holding organization’s staff members and KM experts, who are native to the IC topic. As discussed earlier, the first questionnaire is employed to confirm and select the adequate criteria for assessing IC in the understudied organization. Subsequent to the precise selection of the criteria, Questionnaire No. 2 is used to assess the three components that construct IC and to test their correlation. Additionally, in FCMs phase, some parts need the opinion of experts, which are explained in following wherever necessary.

293 294 295 296 297 298 299 300 301 302

303 304 305 306 307 308 309 310

Nh N

Nt 2 pq Nd2 + t 2 pq

314 315 316 317 318

319 320 321 322

(3)

where t indicates the rate of critical (yielding) limit, p refers to the occurrence probability, q equals to 1-p in sampling which is percentage of picking a choice expressed as decimal, d indicates the allowed error, and N and n represent the total population and sample size, respectively. The values of these parameters in this study are shown in Table 3. The number of staff in the under studied organization’s headquarter is 148 persons. The rate of critical limit in this survey is 1.64 that is the corresponding value of the 0.975 area under the curve of the t-student probability density function. Finally, the allowed error is five percent. Running the sample selection with the mentioned values for the parameters, the sample size of 95 will be obtained. Using the proportional sample selection method, the sample size in each segment is as Table 4. Plainly, since the number of the managers in the organization’s headquarter is more than other staff, the number of managers in the sample size outnumber staff in other positions.

3.2.1. Sample selection In terms of selecting the sample size, it is noteworthy that there are three frequently used approaches in sample selection: (1) equal method, (2) proportional method and (3) optimum (i.e., Neyman) method. Here, in order to avoid any bias and outlier data in the results, while considering the effects of different segments of the population in a fair manner, the sample selection process is accomplished using the proportional sampling method. In this method,

313

(2)

where Nh is the sample size taken from segment (h) and N is the total population size. Eq. (3) known as Cochran formula is formulated in order to obtain the sample size for this research.

n=

311 312

Table 1 IC criteria after selection by Questionnaire No.1. Selected IC criteria Relational capital

Structural capital

Human capital

The org. relationship with customers The org. relationship with suppliers The org. reputation and image Customer satisfaction Customer service Marketing ability Distribution channels

Knowledge management in the org. Investment in IT Organizational culture Working ambience Enough authorization to employees Internet access Org. learning

Employees tendency to higher education Loyalty & commitment of employees Attention to invention and new ideas Employees satisfaction Working condition Employees IT knowledge Human resource development Employees cooperate in teams Performance assessment

Table 2 Minimum CVR vs. number of experts in the panel (Lawshe, 1975). Number of experts in the panel

5

6

7

8

9

10

11

12

13

14

15

20

25

30

35

40

Minimum CVR to accept the question

0.99

0.99

0.99

0.75

0.78

0.62

0.59

0.56

0.54

0.51

0.49

0.42

0.37

0.33

0.31

0.29

Please cite this article as: M. Arvan et al., Intellectual capital evaluation using fuzzy cognitive maps: A scenario-based development planning, Expert Systems With Applications (2016), http://dx.doi.org/10.1016/j.eswa.2015.12.044

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W12

Managers sample size

Master experts sample size

Senior experts sample size

Experts sample size

27

18

24

26

C1

C2

C6 W56 W65

W23

Table 5 Cronbach Alpha for questionnaire No 2. Cronbach’s Alpha

N of Items

0.815

46

W16

C3

C5

W53

W43 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355

356 357 358 359 360 361 362 363

3.2.2. Questionnaires structure Both questionnaires in this research are based on Likert scale with seven scales from strongly disagree to strongly agree. Numbers 1–7 represent the Likert scale strongly disagree, disagree, relatively disagree, no opinion or neutral, strongly agree, relatively agree, agree and strongly agree, respectively. Note that it is assumed in Likert scale that distances on each item are approximately equally spaced (Cummins & Gullone, 2000; Davey et al., 2007). This assumption could be violated in many cases; therefore, fuzzy logic is used in the next section to cope with this deficiency. Questionnaire No. 1 simply asks about the indicators suitability to the organization. The structure of this questionnaire is as simple of having an indicator and assessing it based on Likert scale. However, Questionnaire No. 2, which can be found in the Appendix section measures the selected indicators, resulted from Questionnaire No. 1.

3.2.3. Reliability and consistency of data In order to measure the reliability of the questionnaire, Cronbach Alpha, known as one of the most commonly used measure for this purpose, is utilized. Based on Table 5, the Cronbach Alpha is 0.815 for this survey and based on many studies a Cronbach Alpha more than 0.75 leads to high reliability. Therefore, Table 5 indicates that the second questionnaire’s reliability and consistency of data is well-satisfied.

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3.3. Fuzzy cognitive maps (FCMs)

365

Kosko (1986) introduced FCMs based on a directed weighted graph consisting of nodes and weighted arcs. In fact, FCMs is a general model for causes and effects, which is derived from CMs. These maps are a soft computing technique, and are the result of integration and combination of fuzzy logic and neural network, which takes the experts’ opinions as input and generates the maps (Dickerson & Kosko, 1993). In fact, a fuzzy cognitive map describes the behavior of a system by the inputs, variables and several other factors (Xirogiannis & Glykas, 2004). Nodes of this graph, which can be variables, factors, or elements, describe the characteristics of the entire system while the arcs demonstrate the relationship between the nodes. Fig. 3 illustrates a schematic form of FCMs. The main objective of FCMs is to detect the correlations among nodes and to understand the characteristics and dynamics of a system. FCM receives the survey results and constructs an integrated system with cause and effect relationships among the elements of the system (Alizadeh et al., 2008). Next section discusses the adopted FCMs method for this study in detail.

366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383

W34

W54

C4

Fig. 3. A schematic example for a small scale FCM with six nodes as the main criteria or variables.

3.3.1. FCMs modeling methodology In 2007, Rodriguez-Repiso, Setchi, and Salmeron (2007) proposed an augmented methodology for FCMs. This study adopted their approach to find the results of FCMs. The adopted version of this method consists of four matrixes including initial influence matrix (IIM), fuzzified influence matrix (FZIM), Strength of influence relationships matrix (SIRM), and final matrix of influence (FMI) that are employed for implementing the FCMs. This methodology is based on the automatic FCMs of Schneider, Shnaider, Kandel, and Chew (1998). Although the methodology proposed by Rodriguez-Repiso et al. (2007) is recommended for evaluating the indicators of success in IT projects, considering the Schneider et al. (1998) basic model, it can also be developed for other purposes. 3.3.1.1. The initial influence matrix (IIM). In this step, the importance of factors in the system is determined according to experts’ opinions. These experts must be in a close interaction with the system. Designers, executive managers, chief executive officers, or other beneficiaries that are of this class. These people know the system properly and are able to determine the behavior of each factor or variable. This matrix is a [n × m] matrix, where n is the number of key elements (e.g., customer service, internet access, etc.), and m is the number of survey data collected from experts. Each array in this matrix (Oi j ) shows the importance of the criterion i to the system based on expert j perspective. Arrays Oi1 , Oi2 , ...., Oim are represented by vector Vi , and show the opinion of an expert about different criteria. 3.3.1.2. Fuzzified influence matrix (FZIM). In this step, each array of the initial matrix will be transformed to a fuzzy set. In other words, the dependency level of each array is determined. This dependency level is expressed by the membership function of fuzzy set in the form of a real number in [0,1] interval. This process is calculated as: The maximum value of Vi is found and Xi = 1 is assigned to it.

384 385 386 387 388 389 390 391 392 393 394 395 396

397 398 399 400 401 402 403 404 405 406 407 408 409

410 411 412 413 414 415 416 417

Max(Oiq ) → Xi (Oiq ) = 1

(4)

The minimum value of Vi is found and Xi = 0 is assigned to it.

418 419

Min(Oiq ) → Xi (Oiq ) = 0

(5)

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420 421

422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441

Oi j − Min(Oip ) Max(Oip ) − Min(Oip )

443

3.3.1.3. Strength of influence relationships matrix (SIRM). This [n×n] matrix, in which columns and rows both are the effective elements of a system (i.e., key factors), demonstrates three possible conditions between the variables as discussed earlier. Each array Si j represents the correlation between the factors i and j and takes a value from −1 to +1. For each pairs of vectors with a direct and positive correlation, or negative and indirect correlation, different calculations are required. If vectors V1 and V2 have a direct correlation, the closest correlation for each j : ( j = 1, ...., m ) is whenX1 (V j ) = X2 (V j ). The distance among the elements (j) of V1 and V2 is:





445

  d j 

j=1

m

447 448 449 450 451

452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471

(9)

Where S=1 is the complete similarity, and S=0 denotes maximum dissimilarity. However, if vectors V1 and V2 have an inverse relationship, the calculations are slightly different. In such a condition, the distance formulation between elements is the opposite of the distance between V1 andV2 .

  d j = X1 (V j ) − (1 − X2 (V j ))

N Mean Mode Std. deviation Minimum Maximum

HC

SC

CC

95 4.6531 4.00 1.19984 2.00 7.00

95 4.7755 5.00 1.15948 3.00 7.00

95 4.6531 4.00 1.29986 2.00 7.00

Table 7 Correlation coefficients of IC components.

HC

Pearson correlation Sig. (2-tailed) Pearson correlation Sig. (2-tailed) Pearson correlation Sig. (2-tailed)

SC CC ∗

HC

SC

CC

1 – .766∗ .000 .789∗ .000

.766∗ .000 1 – .763∗ .000

.789∗ .000 .763∗ .000 1 –

Correlation is significant at the 0.01 level (2-tailed).

3.3.1.5. Designing FCM graph. Finally, the final matrix of influence is designed in the form of a graph (FCM), which is used for demonstrating the relationships among nodes (i.e., key factors). Detailed discussion on the maps and analysis result are provided in the next section. The characteristics and the shape of these graphs are discussed earlier. 4. Results

(8)

Therefore, the closeness value between two vectors represented by S will be:

S = 1 − AD 446

(7)

Then, AD, which is the average distance between V1 and V2 is calculated as:

AD = 444

(6)

Following the above procedure, we convert the numerical vectors into fuzzy sets. Moreover, to consider a possible deviation of ± 20% between the interviewed experts, threshold values equal to 80 and 20 are introduced (Schneider et al., 1998). In other words, if an expert considers the significance of a criteria to be more than 80, then it will be transformed to 1 in the FZIM matrix. Likewise, if it is under 20, it will be 0 in this matrix. These thresholds will facilitate representing the membership grades in a more sensible way (Schneider et al., 1998).

d j = X1 (V j ) − X2 (V j ) 442

Table 6 Descriptive analysis of the survey conducted in the questionnaire No. 2 to measure IC criteria status in the understudied organization.

Other components are assigned into [0,1] interval proportionally.

Xi (Oi j ) =

7

(10)

The rest of the calculations remain the same. To obtain the SIRM the calculations are conducted for both inverse and direct relationships, and then the maximum S is reflected in SIRM (Schneider et al., 1998). By calculating the SIRM, three types of correlations can be obtained (Azadeh, Salehi, Arvan, & Dolatkhah, 2014): 1. S>0 which indicates the value of c j increases when ci increases and vice versa. The amount of change depends on the scale of S. 2. S<0 which denotes the value of c j increases when ci decreases and vice versa. 3. S=0 that refers to no meaningful correlation between two sets of nodes. 3.3.1.4. Final matrix of influence (FMI). Data sets from the previous matrix could be misleading. Sometimes, there is no meaningful cause and effect correlation between some variables, whereas, the matrix projects a particular correlation. In order to analyze the data and transform the SIRM to the FMI, experts’ opinion is required. Therefore, in this step, these misleading correlations are removed from SIRM resulting in the final matrix of influence.

  N

473 474 475 476 477 478

The proposed model is applied to a real case and an analysis is performed on the criteria to establish a development strategy for the intellectual capital of the organization. The results and descriptive analysis for the second questionnaire are listed in Table 6. Table 6 indicates that the IC components in this organization are slightly more than the neutral point, which is not pleasing. However, to decide on the condition of IC in this organization, careful investigations are required and the mean does not accurately reflect the real condition. ‘Correlation Coefficient’ is employed to assess the meaningfulness of the relationship between two variables. This amount is calculated based on Eq. (11), where N still represents the population size, and x and y indicate the independent and dependent variables, respectively.

r=

472



  xy − ( x )( y )    2  2 x2 − ( x ) N y2 − ( y ) N

481 482 483 484 485 486 487 488 489 490 491 492

(11)

Hence, the correlations between the IC components will be obtained as listed in Table 7. Considering the results of the test, the hypothesis that IC components are correlated cannot be rejected in 0.995 significance level. Therefore, by ensuring the existence of correlation between IC components, we can start the FCMs for calculating the degree of this correlation. Based on the introduced methodology in the previous section, the first step in implementing FCMs method is determining IIM. This matrix is obtained based on IC experts’ opinions. Sixteen experts were interviewed, and the results are collected in Table 8. Each element of the IIM matrix indicates the significance of that element to IC in the perspective of one expert. The matrix elements scale from 0 that means the criterion contribution to the organization IC is trivial to 100 that implies remarkable contribution to the organization IC. In this table, the rows are the main components of IC (i.e., human capital; structure capital; and relational capital), and the

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479 480

493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509

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Table 8 The initial influence matrix (IIM) results collected from the survey for IC components. IC components criteria

Experts opinion 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

RC

The org. relationship with customers The org. relationship with suppliers The org. reputation and image Customer satisfaction Customer service Marketing ability Distribution channels

5 10 65 20 55 15 10

65 40 60 50 40 20 15

30 15 30 45 20 30 35

25 25 80 50 50 80 40

20 30 70 40 40 80 35

20 20 70 45 45 70 25

95 85 90 90 80 70 70

15 15 60 20 25 35 25

80 80 90 85 80 60 60

80 65 60 70 65 85 50

70 65 85 90 60 60 70

50 35 40 45 30 0 5

80 70 80 85 85 90 80

45 45 35 40 40 55 45

0 0 80 30 10 15 5

70 65 80 65 65 10 0

SC

Knowledge management in the org. Investment in IT Organizational culture Working ambience Enough authorization to employees Internet access Org. learning

95 40 70 85 50 90 80

100 60 30 75 80 50 100

80 45 80 50 65 70 70

70 65 70 85 80 100 80

75 75 70 85 80 90 85

70 70 75 70 75 80 80

80 60 80 65 60 30 80

70 40 45 55 45 65 100

100 90 95 95 100 95 100

80 50 45 45 60 65 80

75 40 35 30 70 40 65

100 75 50 30 55 75 60

90 85 95 80 90 90 90

65 30 10 50 15 55 70

100 60 45 50 70 65 85

90 70 80 70 20 65 90

HC

Employees tendency to higher education Loyalty & commitment in employees Attention to invention and new ideas Employees satisfaction Working condition Employees IT knowledge Human resource development Employees cooperate in teams Performance assessment

100 60 100 40 35 75 90 75 40

70 80 85 70 20 85 100 85 60

80 30 90 50 40 80 85 60 30

70 40 90 20 70 70 100 25 40

70 50 100 75 90 60 100 50 55

75 55 90 60 75 65 100 10 15

30 70 55 75 65 65 70 80 75

80 35 90 35 75 45 85 45 25

95 80 100 65 70 80 80 90 90

65 70 60 75 60 50 55 70 80

50 75 40 80 50 45 80 75 50

85 15 75 30 20 75 80 40 75

75 90 70 90 85 70 75 85 85

80 30 80 90 15 65 85 55 35

85 40 95 50 85 100 100 75 5

95 50 100 30 40 80 100 90 80

Table 9 The Fuzzified Matrix of Criteria (FZIM) based on the experts’ opinions. IC components criteria

510 511 512 513 514 515 516 517 518 519 520 521 522 523

Experts opinion 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

RC

The org. relationship with customers The org. relationship with suppliers The org. reputation and image Customer satisfaction Customer service Marketing ability Distribution channels

0.0 0.0 0.6 0.0 0.6 0.0 0.0

0.1 0.1 0.6 0.4 0.6 0.0 0.0

0.7 0.0 0.5 0.4 0.0 0.2 0.2

0.3 0.2 1.0 0.4 0.1 1.0 0.4

0.0 0.3 0.8 0.3 0.5 1.0 0.5

0.0 0.0 0.7 0.4 0.4 0.9 0.4

1.0 1.0 1.0 1.0 1.0 0.8 0.3

0.0 0.0 1.0 0.0 0.9 0.8 0.9

1.0 1.0 1.0 1.0 1.0 0.4 0.3

1.0 0.9 1.0 0.7 0.9 1.0 0.8

0.8 0.8 1.0 1.0 0.7 0.9 0.6

0.7 0.8 0.9 0.4 0.7 0.0 0.0

1.0 0.4 1.0 1.0 1.0 1.0 1.0

0.8 0.8 0.8 0.3 1.0 1.0 1.0

0.0 0.0 1.0 0.1 0.0 0.0 0.0

0.0 0.0 1.0 0.6 0.0 0.0 0.0

SC

Knowledge management in the org. Investment in IT Organizational culture Working ambience Enough authorization to employees Internet access Org. learning

1.0 0.2 0.7 1.0 0.4 1.0 1.0

1.0 0.2 0.7 0.8 1.0 0.9 1.0

1.0 0.5 1.0 0.7 0.8 0.3 1.0

0.4 0.3 0.8 1.0 1.0 1.0 1.0

0.1 0.6 0.7 1.0 1.0 1.0 1.0

0.3 0.8 0.7 0.8 0.8 1.0 1.0

1.0 0.7 1.0 0.6 0.7 0.7 1.0

0.4 0.5 0.8 0.5 0.5 0.0 1.0

1.0 1.0 1.0 1.0 1.0 1.0 1.0

1.0 1.0 1.0 1.0 1.0 0.9 1.0

0.4 0.3 0.4 0.2 0.5 0.5 0.5

1.0 0.2 0.3 0.0 0.6 0.1 0.1

1.0 1.0 1.0 1.0 1.0 1.0 1.0

0.7 0.9 0.0 0.8 0.0 0.9 0.8

1.0 0.0 0.0 0.3 0.0 0.4 1.0

1.0 0.5 1.0 0.3 0.0 0.5 1.0

HC

Employees tendency to higher education Loyalty & commitment in employees Attention to invention and new ideas Employees satisfaction Working condition Employees IT knowledge Human resource development Employees cooperate in teams Performance assessment

1.0 0.6 1.0 0.3 0.3 0.5 1.0 0.8 0.4

1.0 1.0 1.0 0.7 0.0 1.0 1.0 1.0 0.4

1.0 0.9 1.0 0.4 0.1 1.0 1.0 0.9 0.6

0.7 0.2 1.0 0.0 0.3 0.6 1.0 0.6 0.3

0.6 0.3 1.0 0.8 1.0 0.5 1.0 0.2 0.4

0.6 0.5 1.0 0.6 1.0 0.3 1.0 0.0 0.0

0.6 0.5 0.8 0.8 0.8 0.4 1.0 1.0 0.1

1.0 0.7 1.0 0.2 0.7 0.4 1.0 0.9 0.8

1.0 1.0 1.0 0.6 0.8 1.0 1.0 1.0 1.0

0.9 0.9 1.0 0.8 0.7 0.6 0.6 1.0 1.0

0.5 0.7 0.3 1.0 0.6 0.1 1.0 0.8 0.9

1.0 0.0 0.0 0.1 0.0 0.0 1.0 0.8 0.5

0.8 1.0 0.6 1.0 1.0 0.5 0.6 1.0 1.0

1.0 1.0 1.0 1.0 0.0 0.5 1.0 0.9 0.9

1.0 0.2 1.0 0.4 1.0 1.0 1.0 0.6 0.0

1.0 0.3 1.0 0.1 0.9 1.0 1.0 1.0 1.0

columns are the experts’ opinion indicating the impact of each criterion. Table 9, which is the fuzzified matrix of the experts’ opinions in Table 8, is calculated based on Eqs. (4)–(6). Therefore, based on Eq. (7)–(10), SIRM matrix is calculated as Table 10. Based on Schneider et al. (1998), vectors do not always represent a logical relationship and the mathematical calculations could be misleading. Therefore, by using a committee of decision-makers from managerial positions directly related to knowledge management and IC evaluation in the case organization, the coincidental relationships between concepts are discovered and removed from the SIRM, which results in formation of Table 11. It can be inferred from Table 11 that most of IC criteria have positive influence over one another. However, not all of them have

an interconnection. It seems the criteria related to human capital are more under the influence of others. Using the graphical form of the FMI facilitates its interpretation. The graphical presentation of FCMs for the IC criteria is illustrated in Fig. 4. The dark colored lines (blue) in the figure represent the positive causality while the light colored lines (orange) are used for negative causality. Moreover, the thickness of the arrows corresponds to the strength of the relationship. It can be inferred from Fig. 4 that the structural component does not interact with the other two, except for the influence of ‘reputation and image of the org.’ criterion over ‘loyalty and commitment of employees’. Furthermore, human capital and relational capital components are strongly connected to each other through several criteria. Not surprisingly, most of the criteria have a positive influence over each other and just three

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524 525 526 527 528 529 530 531 532 533 534 535 536 537

JID: ESWA

Performance assessment

Employees cooperate in teams

Human resource development

Employees IT knowledge

Working condition

Employees satisfaction

Attention to invention and new ideas

Loyalty & commitment of employees

Employees tendency to higher education

Org. learning

Internet access

Enough authorization to employees

Working ambience

Organizational culture

Investment in IT

Knowledge management in the org.

Distribution channels

Marketing ability

Customer service

Customer satisfaction

Reputation and image of the org.

0.90

0.60

0.86

0.80

0.65

0.73

0.67

0.67

0.60

−0.58

0.58

−0.71

−0.56

−0.66

0.73

−0.69

0.67

−0.62

0.59

−0.66

0.74

0.84

-

0.66

0.88

0.86

0.65

0.76

0.61

0.68

0.62

0.57

0.57

−0.69

−0.55

−0.74

0.74

−0.73

0.67

−0.58

−0.62

−0.66

0.75

0.81

-

0.71

0.78

0.62

0.62

0.61

0.72

0.77

0.71

0.73

0.66

0.75

0.60

0.74

0.57

0.65

0.73

0.59

0.59

0.73

0.67

-

0.86 -

0.68 0.69 -

0.81 0.77 0.84 -

0.63 0.63 −0.68 −0.62

0.74 0.73 0.62 0.63

0.68 0.74 0.61 0.62

0.64 0.70 0.68 0.61

0.64 0.66 0.71 0.65

−0.68 0.63 0.63 −0.65

0.54 0.62 0.60 −0.61

−0.75 −0.73 −0.66 −0.77

0.79 0.83 0.67 0.75

−0.70 −0.62 −0.60 −0.72

0.73 0.70 0.73 0.76

0.62 0.64 0.77 0.71

0.62 −0.67 −0.72 −0.71

−0.60 −0.58 −0.63 −0.67

0.72 0.74 −0.63 0.65

0.78 0.81 0.65 0.69

-

0.63

0.71

0.55

0.59

0.54

0.66

0.67

0.65

0.57

−0.53

−0.58

0.73

0.55

0.79

0.72

-

0.78 -

0.77 0.72

0.76 0.73 0.75 -

0.75 0.75 0.80 0.75

0.61 0.67 0.76 0.66

0.63 0.68 0.59 −0.58

0.73 0.69 0.75 0.75

0.57 0.64 0.67 0.62

0.66 −0.65 0.63 0.69

0.72 0.70 0.70 0.75

0.68 0.67 0.63 0.62

−0.53 0.57 0.58 0.60

−0.63 0.71 0.62 0.67

0.74 0.69 0.65 0.64

0.70

0.69 0.61

0.62 0.67

0.72 0.74

-0.67 0.53

0.68 0.70

0.59 0.56

0.63 0.65

−0.67 0.67

0.60 0.61

−0.68

0.82

−0.63

−0.59

0.69

0.79

0.62

−0.60

−0.59

0.78

0.69

0.63

−0.56

0.78

0.76

−0.62

0.55

0.72

0.88

0.58

−0.58

0.66

−0.66 −0.64 -

−0.55 −0.54 0.65

0.69 −0.60 0.68

0.67 −0.63 0.63

0.57

−0.57

-

-

-

-

-

-

-

0.76 -

9

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Employees tendency to higher education Loyalty & commitment of employees Attention to invention and new ideas Employees satisfaction Working condition Employees IT knowledge Human resource development Employees cooperate in teams Performance assessment

The org. relationship with suppliers

The org. relationship with customers Knowledge management in the org. Investment in IT Organizational culture Working ambience Enough authorization to employees Internet access Org. learning

-

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The org. relationship with customers The org. relationship with suppliers Reputation and image of the org. Customer satisfaction Customer service Marketing ability Distribution channels

M. Arvan et al. / Expert Systems With Applications xxx (2016) xxx–xxx

Please cite this article as: M. Arvan et al., Intellectual capital evaluation using fuzzy cognitive maps: A scenario-based development

planning, Expert Systems With Applications (2016), http://dx.doi.org/10.1016/j.eswa.2015.12.044

Table 10 Strength of influence relationships matrix (SIRM) showing the causal influences of criteria of the IC components over one another.

-

0.71

0.80

Performance assessment

Employees cooperate in teams

Human resource development

Employees IT knowledge

Working condition

Employees satisfaction

Attention to invention and new ideas

Loyalty & commitment of employees

Employees tendency to higher education

Org. learning

Internet access

Enough authorization to employees

Working ambience

Organizational culture

Investment in IT

Knowledge management in the org.

Distribution channels

Marketing ability

Customer service

Customer satisfaction

The org. relationship with suppliers

The org. relationship with customers

Reputation and image of the org.

0.86

0.65

-

-

0.62 0.86 -

0.68 0.69 -

0.74

0.84 -

0.71

0.66

-

0.75 -

0.55

0.61

0.72 -

0.68 0.68

0.64

-

0.71 0.63 0.69

0.62 -

0.70 -

0.70

0.58 0.60 −0.67

0.59 0.74 -

−0.68

−0.63

-

0.78

0.76

-

0.67 -

0.63 -

0.76 -

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Employees tendency to higher education Loyalty & commitment of employees Attention to invention and new ideas Employees satisfaction Working condition Employees IT knowledge Human resource development Employees cooperate in teams Performance assessment

0.60

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-

M. Arvan et al. / Expert Systems With Applications xxx (2016) xxx–xxx

The org. relationship with customers The org. relationship with suppliers Reputation and image of the org. Customer satisfaction Customer service Marketing ability Distribution channels

JID: ESWA

10

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Table 11 The Final matrix of influence (FMI) showing the refined causal influences of IC components criteria over one another.

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Fig. 4. Graphical illustration of FCMs for the IC criteria.

538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570

negative causality can be seen in this figure. Considering all the influences over human capital from other components, the results denote that human capital is the most important of all three components. This has already been echoed by Bozbura et al. (2007). In the last step of the model, several scenarios are designed and assessed using the FCMs results above. Scenarios are created using managers’ comments as well as the results of the first questionnaire. In fact, the information from Questionnaire No. 2 will aid the model to identify the weakness and strength of each IC criterion. The unveiled capabilities of the organization will be a starting point for planning IC development. Each scenario contains five criteria that the organization plans to invest on and develop. The number of criteria chosen to be developed correlates to the assigned budget to IC development plan. The scenarios in Table 12 follow two dominant procedure. They have been selected either based on an individual focus on each component or based on the influence intensity of each criteria. To reflect the influence intensity of each criteria in the decisionmaking procedure, outdegree and indegree factors have been considered. Outdegree factor represents the number of connection from a criterion toward outside. While, indegree factor indicates the number of connections coming to a criterion. For instance, the first scenario emphasizes on developing structural capital component while in the third scenario the organization wants to develop the human capital component. On the other hand, scenarios No. 4 and 5 are selected based on outdegree and indegree factors. It should be noted that in both the mentioned procedures, the weakness and strength of the organization, which was identified by the second questionnaire, are also taken into account. The scenarios are compared with each other in Fig. 5. Mental Modeler software has been employed to compute the effectiveness of each scenario by plotting the relative changes each scenario causes to other criteria. Mental Modeler software was devel-

oped by Gray, Gray, Cox, and Henly-Shepard (2013) as a tool for modeling fuzzy-logic cognitive mapping in adaptive environmental management. However, it was later used in different disciplines due to its user-friendly platform and for having the capability of analyzing improvement scenarios (Nyaki, Gray, Lepczyk, Skibins, & Rentsch, 2014; Henly-Shepard, Gray, & Cox, 2015). Based on Fig. 5, the best scenario for developing IC in the case organization is scenario No. 5, because it positively influences most of the other criteria resulting in a more likely successful IC development. Even though improving each criterion of IC individually will improve the total IC, due to the interactions between criteria that might be negative, improving more than one criteria does not necessarily result in IC development. Therefore, appropriate actions should be taken in order to identify the interrelationship between the criteria. In fact, this study has attempted to present a set of actions to prevent incorrect decisions on IC development and to shed light on the importance of interactions between IC components. This issue can be seen in scenario No. 4 in which some of the criteria have declined by enhancing others. 5. Discussion and managerial implications Intellectual Capital is simply all the processes and assets that are not usually reflected in the balance sheet. As mentioned earlier, today’s competitive business environment is more unpredictable than ever. Consequently, to sustain and achieve business goals in this uncertain environment, companies inevitably utilize excellence approaches in developing their competencies and capabilities and IC can considerably contribute to this issue. Considering above, the presented model offers guidelines for managers to evaluate and more importantly develop IC in organizations considering the interactions among the IC components. The model prescribes first, considering IC components interactions

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571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589

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M. Arvan et al. / Expert Systems With Applications xxx (2016) xxx–xxx Table 12 The selected scenarios for IC development plan in the understudied organization. No.

Scenario description

1 2 3 4 5

Customer satisfaction; Marketing ability; Customer service; Organizational culture; Human resource development Marketing ability; Org. learning-Internet access; Working ambience; Performance assessment Reputation and image of the org.; Performance assessment; Human resource development; Employees satisfaction; Enough authorization to employees Marketing ability; Org. learning; Employees satisfaction; Human resource development; Performance assessment Knowledge management in the org.; Investment in IT; Internet access; Working ambience; Employees tendency to higher education

Fig. 5. Comparing the scenarios’ effectiveness based on their impact on the criteria, which are not considered in the scenarios.

602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623

in the evaluation, since the outcome might be significantly different, and second analyzing development plans before executing them. The results of this study indicate that what managers might perceive about the IC development is not necessarily true. For instance, improving one positive criteria such as internet access, which seemingly contributes to enhancing staff knowledge resulting in IC development; does not necessarily guarantee developing IC as a whole. Besides, the proposed model can be used to evaluate IC by comparing the results of Questionnaire No. 2 to the FMI or even IIM, which determine the criteria influence over each other and their significance for IC, respectively. Similar to every developing concept, IC has confronted obstacles and issues to achieve its excellence. One of these issues would be measuring IC and planning its development. A scant number of papers have addressed IC development in a practical form. Besides, considering interactions between the components of IC affects its evaluation. Although, there is ample evidence in the literature for the existence of these interactions (Edvinsson & Malone, 1997; Bontis, 1998), very few have taken appropriate actions to overcome this issue. Therefore, addressing these interactions in IC evaluation and development planning is a substantial step toward maturing the subject.

Furthermore, most of the studies that conducted IC evaluation, have analyzed the concept, based on the assumption that the distances between Likert scale options are equal (Hsu & Sabherwal, 2012; Khalique, Bontis, Abdul Nassir bin Shaari, & Hassan Md. Isa, 2015; Fan & Lee 2012). However, this assumption is frequently violated; in fact, there is no ‘term’ that can interpret the respondents’ opinion in an optimal manner. For instance, a crisp number (e.g., 5) cannot be allocated to ‘relatively acceptable’ scale and then next number with an equal distance to other levels. In other words, the method is weak in reflecting the respondents’ perception of the factor being measured. Therefore, different analysis procedures should be conducted on the data to resolve the abovementioned issue. Fuzzy logic used in this study can amend to this problem by making the responses more flexible. Although several studies have considered IC interrelationship, most of them focused on a case study (Bontis et al., 2000; Sharabati et al., 2010). Hence, generalization of the idea to all business environments may not be valid. However, the presented model overcomes this issue by giving the opportunity to modify the system components and create a procedure compatible to each case. In fact, the criteria introduced in the literature might not measure the same element in all organizations, that’s why the first

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questionnaire in this study refines the necessary and potentially significant factors. There are some limitations and drawbacks faced in this study. Firstly, this study uses data pertaining to a particular time period. Thus, if organizations are eager to be aware of the process of development in their IC over time they have to reevaluate their IC frequently over time. This however would be inefficient and time consuming. Therefore, future research can find more dynamic methods to deal with the alteration of IC during time, and to establish the development plans for IC in a way robust to time passing. Moreover, similar to most of the studies in the field, this work relies on subjective opinions of respondents, which might bear lower validity. Nevertheless, as long as the sample size is large enough, this downside can be ignored because the alternative approaches that use accounting and finance data, demand a tedious work of gathering those data. In addition, it is likely that these data are unable to properly reflect IC in an organization, since the purpose of gathering them is commonly different. However, a close attention should be paid while using the presented model, especially on designing questionnaires and interviewing the experts and committee of decision makers. Finally, it should be noted that linear dependency is discovered through FCM, but nonlinear is not. In fact, a possible nonlinear relationship between IC components cannot be identified and observed by FCMs. This could be a ground for future research to find methodologies that are able to identify the nonlinear relationships between IC components. To conclude, some considerations need to be borne in mind using the presented model. First, to refine the SIRM matrix, experts’ opinion is required. This part of the model could be tricky and experts of the domain with deep knowledge about the structure of the organization are needed to refine the results. In addition, the number of criteria chosen to develop the IC model is substantially related to the assigned budget for IC development plan. Different criteria need different portions of the available budget to enhance. This area opens a gap in the literature for research on the budget allocation for developing IC components and criteria. As of now, there is no scale to compare the budget required for developing each IC component or criteria.

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6. Conclusion and directions for future research

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In this study, a new model to evaluate and develop organizations IC is presented, which is able to consider the interactions between IC components. The model of this paper consists of identification and evaluation of IC components; determining the appropriate indicators proportional to organizational structure; determining the influence of each indicator over IC, and analyzing different development scenarios, while considering the interactions between IC criteria. First, by probing the literature, the commonly used criteria for the three main IC components were identified. Secondly, using a

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questionnaire, their relevance to the organization under studied were evaluated and the criteria were refined in a way to adequately reflect the IC in the case organization. Thirdly, another questionnaire were designed in order to measure the value of the refined criteria and to examine the possible correlation between IC components. Fourthly, FCMs was utilized to calculate the influence of the IC criteria over each other. Finally, using several carefully selected scenarios, which were made of five criteria, a development plan for IC was selected as the top plan, considering the effects each development plan can have on all IC criteria. The scenarios are selected based on the results of the second questionnaire to improve the weak points of the organization in IC, as well as to enhance its strengths. Other measures, such as diversity of scenarios and attention to each criteria intensity were also considered in developing the scenarios. The proposed model can aid managers to make decisions on the IC development plans and to evaluate the IC in the organizations. Future research can apply different methodologies such as interpretive structural modelling (ISM) (Malone, 1975), artificial neural networks (ANNs) (Yegnanarayana, 2009) or Fuzzy Decision Making Trial and Evaluation Laboratory (Fuzzy-DEMATEL) (Abdollahi, Arvan, & Razmi, 2015) to deal with the interactions between IC components and to compare the results with those in this study. Quality of Interaction (QoI) (Dias et al., 2015) can also be studied regarding to IC components or criteria. In addition, little has been done in the literature to address the issue of IC development. Therefore, future studies can propose different methodologies and models to look at different angles of this problem and to compare the results with those of this study. For instance, this study has not explored the financial issues regarding budget allocation for IC development. A good area for future research could be estimating the development expenses for each IC component or criteria. Besides, weights can be allocated to each criterion in order to represent their development expenses. However, these weights need to be carefully selected. Furthermore, the current model does not guarantee the optimum scenario for development; therefore, more efficient methods should be examined for developing the scenarios. Finally, a more dynamic methodology could be proposed in order to tackle the volatility and variability IC during the time.

697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733

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The authors sincerely convey their heartfelt thanks to the anonymous reviewers whose constructive comments on this paper were utterly helpful.

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Questionnaire No. 2 for intellectual capital evaluation.

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Employees tendency to higher education

The org. pays enough attention to the employees’ education. The org. facilitates conditions for continuing education. The org. encourages employees to continue their education. IT Knowledge level of employees is important for the org.

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Please fill out the questions as accurately and honestly as possible. Please check the appropriate box as applied to the studied organization.

Human capital

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(continued) Please fill out the questions as accurately and honestly as possible. Please check the appropriate box as applied to the studied organization. Measured factor

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Loyalty & commitment of employees

Attention to invention and new ideas

Employees satisfaction

Working condition Performance assessment Employees cooperate in teams

Human resource development

Structural capital

Knowledge management in the org. Investment in IT

Organizational culture Working ambience

Enough authorization to employees Internet access Org. learning

Relational capital

The org. relationship with customers

The org. relationship with suppliers

Customer satisfaction

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Employees are friend with technology Employees fulfill their duties properly and efficiently. Managerial board of the org. are concerned about the org’s success and achievements. Employees are honored and proud of working in the org. Policy makers in the org. put the ideas in practice properly. Development of the org. relies on novel ideas. Managers welcome employee’s ideas and suggestions. Employees are satisfied with their Job in the org. Employees are happy/satisfied with their salary. Employees are happy with the working condition in the org. Incentive policies are set proportional to employee’s output. Teamwork is praised in the org.

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Teams in the org. are dynamic and objective-oriented Managers have plans for workforce development and progress. The org. hosts periodic educational events. Knowledge is suitably processed, managed and saved in the org.

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Databases in the org. facilitates access to data. Technology plays an important role in the organizational tasks. Information Technology is effectively implemented in the org. The org. benefits from an integrated intranet / LAN system. Organizational culture is viable in the org. There is an encouraging and cooperative ambience in the org. Working atmosphere in the org. is not stressful. Employees are given enough authority for decision-making. Employees have access to internet in the org. Creation and usage of knowledge banks is satisfactory in the org. There are procedures to spread experience among employees. The org. has close interactions with the customers / clients. The relationships with customers are long-term. The org. has an efficient connection with the suppliers. The relationships with suppliers are long-term. Customers are loyal to the org. Customer satisfaction rate is high in the org. The org. is capable of responding to the customers’ needs immediately.

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(continued) Please fill out the questions as accurately and honestly as possible. Please check the appropriate box as applied to the studied organization. Measured factor

Questions

Strongly agree

Agree

Neutral Relatively agree

The org. reputation and image Marketing ability

The brand (the org. name) is popular among buyers. Market share for the org. has continuously increased (compare to rival orgs.). Market condition and demand conveys the direction of the org. Sales department is flourishing continuously. Customer service Customer database is effectively used in the org. The org. considers customers’ feedbacks. Distribution channels The number of contracts and collaborations with other orgs. increases permanently. Enough distribution channels are considered in the org. Note that only the two last columns of above table were revealed to respondents. Gender Age Education Job experience in the org. Position Employment Marital status

740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 Q2 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780

Male < 30 High-school Deg. Less than 5 years Managers Employee Married

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Female 30–40 Undergraduate Deg. 5–12 years Master experts Intern Single

References

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40–50 Graduate Deg. 13–20 years Senior experts Adjunct Other

50 < Other Over 20 years Experts Other

o Decline to answer o Decline to answer o Decline to answer o Decline to answer o Decline to answer o Decline to answer o Decline to answer

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