Innovative capacity-based approach to blue ocean strategies of family firms: An IT2 fuzzy hybrid decision-making analysis for potential investors

Abstract

The study aims to measure the weights of innovative capacity and rank the blue ocean strategies for the family firms under the fuzzy environment. A hybrid decision making approach is proposed by considering IT2 (interval type 2) fuzzy DEMATEL (decision making trial and evaluation laboratory) for weighting the criteria and IT2 fuzzy VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) to rank different alternatives. In this study, family firms, their innovative tendencies and one of the most challenging marketing strategies they can use in combatting with fierce competition, namely, blue ocean strategy have been discussed. Through blue ocean strategies family firms can create undiscovered markets, creates their own demand and give an end to the value/cost trade-off. This strategy is envisaged to make the company attractive for potential investors after the point they start to grow and decide to expand their partnership structure to external players.

Keywords

Interval type-2 fuzzy sets innovation blue ocean strategy family firms investment decision making

1 Introduction

Small and medium-sized enterprises are essential catalyzers of economic development. And innovation can be considered as one of the most important factors giving birth to their success [1, 42]. But, since in small and medium enterprises (SMEs) most of the innovative activities are informal, their capacity to innovate is difficult to describe. According to [2 , 9], in small scale companies, the expense of innovative activities and high levels of financial risks makes innovation appear unattractive for them. Family firms are also mostly categorized as small–medium enterprises, with their distinct properties, stemming from their family owned endowments, closer departmental interactions, shorter communication lines, intimacy among members, strong organizational culture, and identified members [3]. They are the kind of organizations wherein a certain family holds an important part of ownership and several employees from this certain family are involved in critical managerial processes in the business [39]. Owing to their overlapping ownership, managerial, and administrative issues, they are conceived as belonging to the most complicated kinds of organizational forms and excessive family control and long-term orientedness, they tend to act differently compared to nonfamily firms [27, 37].

Sustainability of these firms is highly correlated to alignment of family and business realities. Family firms’ socioemotional wealth, shared backgrounds and family norms align the interests of family members with values and strategies of the business. And this makes them load greater importance to their business and behave less risk aversive and more courageous regarding innovation [31, 40]. In fact, a significant amount of studies focuses on innovation in family firms [25]. And, family firm researchers are convicted that family firms show different orientations considering innovativeness [12]. But, consensus on their innovative inclinations do not exist owing to a fragmented literature. Some of the results indicate positive effects of family ownership on innovativeness whereas others show negative effects [25].

The term of blue ocean strategy is firstly used by W. Chan Kim and R. Mauborgne in 2004 to reach new market areas by using organizational capacity systematically [22]. The strategy recommends to put aside competition with competitors and exploit the gaps of the existing market and thus create new markets. In this way, companies can protect themselves against the negative effects of competition. In addition, the number of players available in the market is very small, as market limits are re-established. These issues have positive influence on the profitability of the companies.

Accordingly, [21] investigated about 108 new business launches by including 150 strategic tactics in 30 different industries, stretching back to more than 100 years. They found out that 86% of these launches were merely line extensions and only 14% were targeted to construct new markets. However, despite the fact that these line extensions compose about 62% of total revenues, they form merely 39% of total profits [21, 22]. And this confirms the significance and effectiveness of blue ocean strategies in increasing profitability.

Due to family firm’s smaller scale and lower financial power compared to large corporations, regarding their innovative preferences, practical and efficient methods of making innovation seems more profitable and meaningful. In this point, blue ocean strategies can be considered as a meaningful alternative for family firms as an innovation method. Blue oceans are the markets that have not defined yet [15]. They search for uncontested markets and avoid zero sum game. Since a successful blue ocean strategy cannot be imitated easily [6] and offers a shortcut method to combat with fierce competition [15], in this study it is suggested that for family firms creating blue oceans markets can be a more preferable strategy for being successful despite fierce competition.

Decision making trial and evaluation laboratory (DEMATEL) is one of well-known method to measure the impact and relationship among the criteria. Thus, DEMATEL could give more comprehensive results in factor evaluation process in comparison with other similar methods. Nowadays, VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) becomes also one of the most popular multicriteria decision making method with some extensions in the literature. Similarly, VIKOR has also some advantages by using consensus of the decision makers in decision making process. Accordingly, the proposed hybrid model of the study aims to provide the more accurate analysis results under the fuzzy environment. The main motivation of the study is to provide a novel approach to innovative capacity and blue ocean strategy in uncertain conditions of business market. For this purpose, interval type-2 (IT2) fuzzy DEMATEL and VIKOR methods have been firstly used to measure the innovative capacity-based blue ocean strategies and so, it could be eased to evaluate the family firms for the potential investors in the integrated manner.

The following section continues with the literature review of innovative capacity. Section 3 presents an application of the hybrid decision making model for understanding the blue ocean strategies based on innovative capacity of the family firms comprehensively. In this context, the methodology of hybrid modelling including IT2 fuzzy DEMATEL and VIKOR is defined. Section 4 discusses the results and section 5 provides the recommendations for the future studies.

2 Literature review

Innovation is a very popular subject in the literature of business. Because of this issue, there are lots of studies considering this factor in various fields. According to [32], innovation is strictly related to the abilities of organizations to obtain and use knowledge since it is the most knowledge-intensive business process requiring continuous revitalization of knowledge [20] and can be considered as organization’s capability to take advantage of market opportunities. [13] claims that innovation explains both “how” and “what” firms innovate, that is to say it is both a process and an outcome. Most of the extant literature on innovation is related to technological innovations and innovations regarding novel products and business processes [26]. Highly innovative organizations have internal processes fostering generating and capturing new ideas. And innovative organizations view their customers and suppliers as sources of innovative ideas. They have in-house idea generation capability and they mostly have enough resources such as multi-skilled workforce, experience and technical knowledge necessary for innovation [30].

A related concept, innovation capacity, is helpful in understanding innovation inclinations of companies. Innovation capacity of a company can be described as the potential of the firm to create innovative output with the aim of meeting market expectations [5 , 30]. Innovation capacity gives organizations the opportunity to detect and get use of innovation opportunities [30]. Thus, balances the organization’s capacity to seek and find external knowledge, and its capacity to exploit this new knowledge [29]. And the capability to reach this external knowledge requires significant levels of internal integration [18]. Companies having greater capacities to innovate are often inclined to be more successful in reacting to environmental factors and creating novel methods that helps them in gaining competitive advantage [43]. And it is substantially related to firm performance. In a related study explaining the relationship between SMEs innovative capacity and performance, [4, 26] confirmed that, process innovation and investment in R&D are the most two important variables explaining firms’ improved performance.

On the one hand, some important factors should be considered in mearing innovative capacities of companies. In the extant literature, overlapping suggestions regarding dimensions of innovative capacity attract attention. For example; [41] claims that innovative capacity can be understood by 4 main dimensions including; organizational culture’s openness to entrepreneurship and commitment to innovation, experienced and multi-skilled workforce, in-house idea development competencies, and having informal and formal networks helpful for innovation. [24] also focused on the determinants of innovative capacity claiming that internal technological environment, idea generation and technology acquisition and exploitation are basic determinants of it. Moreover, [26] used product and process innovations; level of R&D investments; and the use of new distribution channels for measuring intensity of innovative capacity. Furthermore, [30] suggested that organizational culture, internal processes and resources, competence and external environment can be considered as the basic determinants of innovative capacity. They suggested that innovative companies mostly have strong cultures with a clear mission and strategy and inclination of continuous improvement. [5] also made a good summary of innovative capacity items that encompasses capability to allocate resource, to understand other players’ strategy and to meet customer demands by new product development, having the ability to foresee technological advancements, to effectively react market changes, and to show learning organization inclinations.

And there are also evidences in the related literature showing that family businesses effectuate a considerable number of the world’s most innovative companies [16]. Although they engage in innovative activities less often compared to their nonfamily counterparts [10], surprisingly they are good at attaining higher innovation performance [14]. This situation is named as the “family innovation dilemma” [16]. In fact, in family firms, resource orchestration, namely the deployment of resources and precious firms’ specific tacit knowledge is higher compared to other kinds of organizations [16]. Their strong social capital resources and high levels of communication with exterior shareholders can explain their more external orientation in innovation. Their inclination to engage in external cooperation increases exposure to different perspectives contributing to higher flexibility and innovativeness [11]. With the help of innovation, they can enjoy cost saving and better integration to larger markets. And in family firms, close social relations and trustworthy relationships among organizational members, and the shared background helpful in internalizing and using their expertise and experience in order to develop new products and processes [36].

As a result of literature review, it is demonstrated that the studies are extremely limited by combining the innovative capacity and blue ocean strategies at the same time. Moreover, it is also concluded that there is no study in the literature which uses IT2 fuzzy DEMATEL and IT2 fuzzy VIKOR together for this topic. Therefore, it is believed that this study makes significant contribution to the literature by using this proposed hybrid method.

3 Application on family firms

3.1 Methodology

A hybrid MCDM based on IT2 fuzzy sets is applied to measure the strategy priorities. The generalized form of IT1 is called as the IT2 fuzzy sets and formulized in Equation (1) [28].

$\begin{matrix} \tilde{A} & = & {((x, u), μ \tilde{A} (x, u) | \forall_{u} \in J_{x} \subseteq [0, 1], or \\ \tilde{A} & = & \int x \in X \int u \in j_{x} μ_{\tilde{A}} (x, u) / (x, u) J_{x} \subseteq [0, 1], \end{matrix}$ (1) where $\tilde{A}$ is type-2 fuzzy set, $μ_{\tilde{A} (x, u)}$ represents the type-2 membership function and ∫ ∫ is the union of all x and u. When it is equal to 1, the IT2 fuzzy set is demonstrated as Equation (2). $\tilde{A} = \int_{x \in X} \int_{u \in J_{x}} 1 / (x, u) J_{x} \subseteq [0, 1]$ (2) the upper and lower trapezoidal membership function is presented with the formula (3).

$\begin{matrix} {\tilde{A}}_{i} = ({\tilde{A}}_{i}^{U}, {\tilde{A}}_{i}^{L}) = \\ (\begin{matrix} (a_{i 1}^{U}, a_{i 2}^{U}, a_{i 3}^{U}, a_{i 4}^{U}; H_{1} ({\tilde{A}}_{i 1}^{U}), H_{2} ({\tilde{A}}_{i 1}^{U})), \\ (a_{i 1}^{L}, a_{i 2}^{L}, a_{i 3}^{L}, a_{i 4}^{L}; H_{1} ({\tilde{A}}_{i 1}^{L}), H_{2} ({\tilde{A}}_{i 1}^{L})) \end{matrix}) \end{matrix}$ (3) where, ${\tilde{A}}_{i 1}^{U}$ is the upper and ${\tilde{A}}_{i 1}^{L}$ is the lower membership function. $a_{i 1}^{U} . . . . a_{i 4}^{L}$ is the reference values. $H_{j} ({\tilde{A}}_{i}^{U})$ and $H_{j} ({\tilde{A}}_{i}^{L})$ are the membership values.

IT2 Fuzzy DEMATEL: DEMATEL method is used for analysing the mutual dependencies between factors. In the literature, there are some extensions of this method, however one of the recent extensions is IT2 based method of the DEMATEL. The methodology of IT2 fuzzy DEMATEL is demonstrated in five steps as seen below:

Step 1: Collect linguistic evaluations for each expert. Linguistic evaluations are collected and converted into the fuzzy numbers. Table 1 presents the linguistic evaluations and fuzzy numbers for the dimensions and criteria.

Table 1

Linguistic Evaluations and Fuzzy Numbers for the Factors

Linguistic Scales	Interval Type 2 Fuzzy Numbers
Very very low (VVL)	((0,0.1,0.1,0.2;1,1), (0.05,0.1,0.1,0.15;0.9,0.9))
Very low (VL)	((0.1,0.2,0.2,0.35;1,1), (0.15,0.2,0.2,0.3;0.9,0.9))
Low (L)	((0.2,0.35,0.35,0.5;1,1), (0.25,0.35,0.35,0.45;0.9,0.9))
Medium (M)	((0.35,0.5,0.5,0.65;1,1), (0.4,0.5,0.5,0.6;0.9,0.9))
High (H)	((0.5,0.65,0.65,0.8;1,1), (0.55,0.65,0.65,0.75;0.9,0.9))
Very high (B)	((0.65,0.8,0.8,0.9;1,1), (0.7,0.8,0.8,0.85;0.9,0.9))
Very very high (VB)	((0.8,0.9,0.9,1;1,1), (0.85,0.9,0.9,0.95;0.9,0.9))

Source: [48]

Step 2: Construct the initial direct relation matrix (DRM) based on IT2 fuzzy numbers. The averaged fuzzy assessments of the experts are used for generating the matrix with the Equations (4)-(5). $\tilde{Z} = [\begin{matrix} \begin{matrix} {\tilde{x}}_{11} & {\tilde{x}}_{12} & \dots & \dots {\tilde{x}}_{1 n} \\ {\tilde{x}}_{22} & 0 & \dots & \dots {\tilde{z}}_{2 n} \\ ⋮ & ⋮ & ⋱ & \dots \dots \\ ⋮ & ⋮ & ⋮ & ⋱ ⋮ \end{matrix} \\ {\tilde{z}}_{n 1} {\tilde{z}}_{n 2} \dots \dots {\tilde{x}}_{nn} \end{matrix}]$ (4) $\tilde{Z} = \frac{{\tilde{z}}^{1} + {\tilde{z}}^{2} + {\tilde{z}}^{3} + \dots {\tilde{z}}^{n}}{n}$ (5)

Step 3: Normalize the fuzzy matrix. Normalization procedure is applied by the formulas (6)-(8). $\tilde{X} = [\begin{matrix} \begin{matrix} {\tilde{x}}_{11} & {\tilde{x}}_{12} & \dots & \dots {\tilde{x}}_{1 n} \\ {\tilde{x}}_{21} & {\tilde{x}}_{22} & \dots & \dots {\tilde{x}}_{2 n} \\ ⋮ & ⋮ & ⋱ & \dots \dots \\ ⋮ & ⋮ & ⋮ & ⋱ ⋮ \end{matrix} \\ {\tilde{x}}_{n 1} {\tilde{x}}_{n 2} \dots \dots {\tilde{x}}_{nn} \end{matrix}]$ (6)

$\begin{matrix} {\tilde{x}}_{ij} = \frac{{\tilde{z}}_{a^{'} 1 j}}{r} = (\frac{{\tilde{z}}_{a_{1 j}^{'}}}{r}, \frac{{\tilde{z}}_{b_{1 j}^{'}}}{r}, \frac{{\tilde{z}}_{c_{1 j}^{'}}}{r}, \frac{{\tilde{z}}_{d_{1 j}^{'}}}{r}; H_{1} (z_{ij}^{U}), H_{2} (z_{ij}^{U}), \\ (\frac{{\tilde{z}}_{e_{1 j}^{'}}}{r}, \frac{{\tilde{z}}_{f_{1 j}^{'}}}{r}, \frac{{\tilde{z}}_{g_{1 j}^{'}}}{r}, \frac{{\tilde{z}}_{h_{1 j}^{'}}}{r}; H_{1} (z_{ij}^{L}), H_{2} (z_{ij}^{L})) \end{matrix}$ (7) $r = max (max_{1 ⩽ i ⩽ n} \sum_{j = 1}^{n} Z_{d_{ij}}, max_{1 ⩽ i ⩽ n} \sum_{j = 1}^{n} Z_{d_{ij}^{'}})$ (8)

Step 4: Generate the total influence fuzzy matrix. With the formulas (9)-(13), the final fuzzy matrix is constructed before the defuzzification process.

$\begin{matrix} X_{a^{'}} & = & [\begin{matrix} \begin{matrix} 0 & a_{12}^{'} & \dots & \dots a_{1 n}^{'} \\ a_{21}^{'} & 0 & \dots & \dots a_{2 n}^{'} \\ ⋮ & ⋮ & ⋱ & \dots \dots \\ ⋮ & ⋮ & ⋮ & ⋱ ⋮ \end{matrix} \\ a_{n 1}^{'} a_{n 2}^{'} \dots \dots 0 \end{matrix}] \dots, \\ \tilde{X} & = & [\begin{matrix} \begin{matrix} 0 & □_{12}^{'} & \dots & \dots □_{1 n}^{'} \\ □_{21}^{'} & 0 & \dots & \dots □_{2 n}^{'} \\ ⋮ & ⋮ & ⋱ & \dots \dots \\ ⋮ & ⋮ & ⋮ & ⋱ ⋮ \end{matrix} \\ □_{n 1}^{'} □_{n 2}^{'} \dots \dots 0 \end{matrix}] \end{matrix}$ (9) $\tilde{T} = lim_{x \to \infty} \tilde{X} + {\tilde{X}}^{2} + \dots + {\tilde{X}}^{k}$ (10)

$\tilde{T} = [\begin{matrix} \begin{matrix} {\tilde{t}}_{11} & {\tilde{t}}_{12} & \dots & \begin{matrix} \dots & {\tilde{t}}_{1 n} \end{matrix} \end{matrix} \\ \begin{matrix} {\tilde{t}}_{21} & {\tilde{t}}_{22} & \dots & \begin{matrix} \dots & {\tilde{t}}_{2 n} \end{matrix} \end{matrix} \\ \begin{matrix} ⋮ & ⋮ & ⋱ & \begin{matrix} \dots & \dots \end{matrix} \end{matrix} \\ \begin{matrix} ⋮ & ⋮ & ⋮ & \begin{matrix} ⋱ & ⋮ \end{matrix} \end{matrix} \\ \begin{matrix} {\tilde{t}}_{n 1} & {\tilde{t}}_{n 2} & \dots & \begin{matrix} \dots & {\tilde{t}}_{nn} \end{matrix} \end{matrix} \end{matrix}]$ (11)

$\begin{matrix} {\tilde{t}}_{ij} & = & (a_{ij}^{″}, b_{ij}^{″}, c_{ij}^{″}, d_{ij}^{″}; H_{1} ({\tilde{t}}_{ij}^{U}), H_{2} ({\tilde{t}}_{ij}^{U})), \\ (e_{ij}^{″}, f_{ij}^{″}, g_{ij}^{″}, h_{ij}^{″},; H_{1} ({\tilde{t}}_{ij}^{L}), H_{2} ({\tilde{t}}_{ij}^{L})) \end{matrix}$ (12)

$\begin{matrix} = & X_{a^{'}} \times (I - X_{a^{'}})^{- 1}, . . . ., [h_{ij}^{″}] \\ = & X_{h^{'}} \times (I - X_{h^{'}})^{- 1} \end{matrix}$ (13)

Step 5: Calculate the defuzzified values of the total fuzzy influence matrix. The formulas (14)-(17) are considered for the defuzzification process. ${Def}_{T} = \frac{\frac{(u_{U} - l_{U}) + (β_{U} \times m_{1 U} - l_{U}) + (α_{U} \times m_{2 U} - l_{U})}{4} + l_{U} + [\frac{(u_{L} - l_{L}) + (β_{L} \times m_{1 L} - l_{L}) + (α_{L} \times m_{2 L} - l_{L})}{4}]}{2}$ (14) ${Def}_{T} = T = [t_{ij}]_{n \times n}, i, j = 1, 2, . . ., n$ (15) ${\tilde{D}}_{i}^{def} = r = [\sum_{j = 1}^{n} t_{ij}]_{n \times 1} = (r_{i})_{n \times 1} = (r_{1}, . . ., r_{i}, . . ., r_{n})$ (16) ${\tilde{R}}_{i}^{def} = y = [\sum_{i = 1}^{n} t_{ij}]_{1 \times n}^{'} = (y_{j})_{1 \times n}^{'} = (y_{1}, . . ., y_{i}, . . ., y_{n})$ (17) where, ${\tilde{D}}_{i}^{def}$ is the the sum of all defuzzified vector rows and ${\tilde{R}}_{i}^{def}$ is the sum of all defuzzified vector columns. The value of $({\tilde{D}}_{i} + {\tilde{R}}_{i})^{def}$ indicates the importance degrees of factors and the value of $({\tilde{D}}_{i} - {\tilde{R}}_{i})^{def}$ is the relationship degrees among them.

IT2 Fuzzy VIKOR: VIKOR is one of the well-known methods for the multicriteria decision making approach introduced by [33]. The closest one to the ideal solution is calculated to find the optimal results. IT2 fuzzy VIKOR is a novel method to calculate the multicriteria decision problem more accurately. The method is defined in six steps:

Step 1: Collect the expert choices based on linguistic evaluations. The matrix is defined by converting the linguistic choices into the IT2 fuzzy numbers. Table 2 shows the linguistic evaluations and fuzzy numbers for the alternatives.

Table 2

Linguistic Evaluations and Fuzzy Numbers for the Alternatives

Linguistic Scales	Interval Type 2 Fuzzy Numbers
Very Poor (VP)	((0,0,0,0.1;1,1), (0,0,0,0.05;0.9,0.9))
Poor (P)	((0,0.1,0.1,0.3;1,1), (0.05,0.1,0.1,0.2;0.9,0.9))
Medium Poor (MP)	((0.1,0.3,0.3,0.5;1,1), (0.2,0.3,0.3,0.4;0.9,0.9))
Fair (F)	((0.3,0.5,0.5,0.7;1,1), (0.4,0.5,0.5,0.6;0.9,0.9))
Good (G)	((0.5,0.7,0.7,0.9;1,1), (0.6,0.7,0.7,0.8;0.9,0.9))
Very Good (VG)	((0.7,0.9,0.9,1;1,1), (0.8,0.9,0.9,0.95;0.9,0.9))
Best (B)	((0.9,1,1,1;1,1), (0.95,1,1,1;0.9,0.9))

Source: [48]

Step 2: Construct the fuzzy decision matrix. The fuzzy numbers from the experts are averaged to develop the decision matrix. The process is defined in the Equations (18) and (19). $C_{1} C_{2} C_{3} . . . C_{n}$ $X_{ij} = \begin{matrix} A_{1} \\ A_{2} \\ A_{3} \\ \begin{matrix} ⋮ \\ A_{m} \end{matrix} \end{matrix} [\begin{matrix} \begin{matrix} x_{11} & x_{12} & x_{13} & \begin{matrix} \dots & x_{1 n} \end{matrix} \end{matrix} \\ \begin{matrix} x_{21} & x_{22} & x_{23} & \begin{matrix} \dots & x_{2 n} \end{matrix} \end{matrix} \\ \begin{matrix} x_{31} & x_{32} & x_{33} & \begin{matrix} \dots & x_{3 n} \end{matrix} \end{matrix} \\ \begin{matrix} ⋮ & ⋮ & ⋱ & \begin{matrix} \dots & ⋮ \end{matrix} \end{matrix} \\ \begin{matrix} x_{m 1} & x_{m 2} & x_{m 3} & \begin{matrix} \dots & x_{mn} \end{matrix} \end{matrix} \end{matrix}]$ (18) $X_{ij} = \frac{1}{k} [\sum_{e = 1}^{n} X_{ij}^{e}], i = 1, 2, 3, . . ., m$ (19) where, X_ij defines the aggregated fuzzy ratings and k is the number of decision makers.

Step 3: Defuzzified values are calculated. The trapezoidal IT2 fuzzy sets are defuzzified by using the ranking method of [8]. The process is illustrated with the formulas (20)-(23).

$\begin{matrix} Def (x_{ij}) = Rank ({\tilde{X}}_{ij})_{m \times n} = M_{1} ({\tilde{A}}_{i}^{U}) \\ + M_{1} ({\tilde{A}}_{i}^{L}) + M_{2} ({\tilde{A}}_{i}^{U}) + M_{2} ({\tilde{A}}_{i}^{L}) + M_{3} ({\tilde{A}}_{i}^{U}) \\ + M_{3} ({\tilde{A}}_{i}^{L}) - \frac{1}{4} (S_{1} ({\tilde{A}}_{i}^{U}) + S_{1} ({\tilde{A}}_{i}^{L}) + S_{2} ({\tilde{A}}_{i}^{U}) \\ + S_{2} ({\tilde{A}}_{i}^{L}) + S_{3} ({\tilde{A}}_{i}^{U}) + S_{3} ({\tilde{A}}_{i}^{L}) + S_{4} ({\tilde{A}}_{i}^{U}) \\ + S_{4} ({\tilde{A}}_{i}^{L}) + H_{1} ({\tilde{A}}_{i}^{U}) + H_{1} ({\tilde{A}}_{i}^{L}) \\ + H_{2} ({\tilde{A}}_{i}^{U}) + H_{2} ({\tilde{A}}_{i}^{L})) \end{matrix}$ (20) $M_{p} ({\tilde{A}}_{i}^{j}) = (a_{ip}^{j} + a_{i (p + 1)}^{j}) / 2$ (21)

Where $M_{p} ({\tilde{A}}_{i}^{j})$ is the average of the elements $a_{ip}^{j}$ and $a_{i (p + 1)}^{j}$ , 1 ⩽ p ⩽ 3, $S_{q} ({\tilde{A}}_{i}^{j}) = \sqrt{\frac{1}{2} \sum_{k = q}^{q + 1} {(a_{ik}^{j} - \frac{1}{2} \sum_{k = q}^{q + 1} a_{ik}^{j})}^{2}}$ (22)

Where $S_{q} ({\tilde{A}}_{i}^{j})$ gives information about the standard deviation of the elements $a_{iq}^{j}$ and $a_{i (q + 1)}^{j}$ , 1 ⩽ q ⩽ 3, $S_{4} ({\tilde{A}}_{i}^{j}) = \sqrt{\frac{1}{4} \sum_{k = 1}^{4} {(a_{ik}^{j} - \frac{1}{4} \sum_{k = 1}^{4} a_{ik}^{j})}^{2}}$ (23)

Step 4: Define the best and worst values of the matrix. The best value (fj*) and worst value (fj-) are computed by the formula (24). $f_{j}^{*} =_{i}^{max} x_{ij} and f_{j}^{-} =_{i}^{min} x_{ij}$ (24)

Step 5: Employ the values of S_i and R_i . The values are constructed by the formulas (25)-(26) $S_{i} = \sum_{i = 1}^{n} w_{j} \frac{(| f_{j}^{*} - x_{ij} |)}{(| f_{j}^{*} - f_{j}^{-} |)}$ (25) $R_{i} = max j [w_{j} \frac{(| f_{j}^{*} - x_{ij} |)}{(| f_{j}^{*} - f_{j}^{-} |)}]$ (26) where, w_j is the weight of the criteria.

Step 6: Calculate the values of Qi. The final ranking results are obtained by the values of Qi using the Equation (27). $Q_{i} = \frac{v (S_{i} - S^{*})}{(S^{-} - S^{*})} + (1 - v) \frac{(R_{i} - R^{*})}{(R^{-} - R^{*})}$ (27) where, S^* and R^* are the minimum values and S^- and R^- are the maximum values of the results indicated in the formulas (25)-(26). The value of v is considered as 0.5 and the value of (1-v) is the weight of individual regret. Two conditions are defined for checking the results of Qi. The first condition is the acceptable advantage by using the Equation (28) and the second condition is the acceptable stability presented in the formula (29). $Q (A^{(2)}) - Q (A^{(1)}) ⩾ \frac{1}{(j - 1)}$ (28) $Q (A^{(M)}) - Q (A^{(1)}) < \frac{1}{(j - 1)}$ (29) where, M indicates the number of different alternatives. On the other side, j refers to the number of criteria. So, the alternative with the best ranking result of Qi has also the best value in one of Si and Ri values at least for providing the condition.

3.2 Analysis model

The analysis is applied for the family firms listed in the stock exchange. In the analysis process, IT2 fuzzy DEMATEL method is considered to weight the innovative capacity. On the other side, blue ocean strategies are ranked by using IT2 fuzzy VIKOR. DEMATEL approach evaluates impact relation map which is the main advantage of this model by comparing with others. On the other side, VIKOR has also some advantages by using consensus of the decision makers in decision making process Hence, the main novelty of the study is that these two significant approaches are firstly considered in this study for the evaluation of the family firms.

Initially, the multicriteria decision making problem has been defined for the innovative capacity of the family firms and their priorities based on blue ocean strategies. For this aim, 4 dimensions and 16 criteria are identified as in Table 3.

Table 3
Proposed criteria of innovative capacity

Dimensions Criteria Literature

Culture (D1) Openness of organizational culture (C1) [41, 30]

Flexibility (C2) [41]

Organizational Dynamics (C3) [41]

Rewarding successful ideas (C4) [30]

Resources (D2) Multi-skilled workforce (C5) [41]

Resource exploitation capacity (C6) [26]

Internal processes (C7) [41]

Technological abilities (C8) [41], [30]

Organizational competency (D3) R&D capacity (C9) [26], [30]

Marketing capacity (C10) [5]

Manufacturing capacity (C11) [26]

Process innovation (C12) [26]

Networking (D4) Formal and informal linkages for innovation (C13) [41]

New distribution channels (C14) [30]

Availability of business partners (C15) [30], [41]

Role of local institutions (C16) [41]

Dimensions	Criteria	Literature
Culture (D1)	Openness of organizational culture (C1)	[41, 30]
	Flexibility (C2)	[41]
	Organizational Dynamics (C3)	[41]
	Rewarding successful ideas (C4)	[30]
Resources (D2)	Multi-skilled workforce (C5)	[41]
	Resource exploitation capacity (C6)	[26]
	Internal processes (C7)	[41]
	Technological abilities (C8)	[41], [30]
Organizational competency (D3)	R&D capacity (C9)	[26], [30]
	Marketing capacity (C10)	[5]
	Manufacturing capacity (C11)	[26]
	Process innovation (C12)	[26]
Networking (D4)	Formal and informal linkages for innovation (C13)	[41]
	New distribution channels (C14)	[30]
	Availability of business partners (C15)	[30], [41]
	Role of local institutions (C16)	[41]

A set of alternatives has been also defined to construct the decision-making problem properly. Five essential blue ocean-based strategies have been generated by considering the related literature. Table 4 shows the blue ocean strategies for the family firms.

Table 4

Selected blue ocean strategies for the family firms

Strategies	Literature
Create uncontested market space (Strategy 1)	[23], [34], [21], [15]
Focus on irrelevant/strong differentiation (Strategy 2)	[23], [34], [15]
Capture new demand (Strategy 3)	[21], [22], [34]
Increase costs and raise value (Strategy 4)	[23]
Differentiate with low cost (Strategy 5)	[22], [34]

The analysis continues by selecting the decision makers and collecting their linguistic evaluations for the criteria and alternatives. In this circumstance, IT2 fuzzy numbers are considered. Three people are assigned as experts to give their linguistic choices for the criteria and alternatives. They are significant academicians in the field of business and finance. Tables 5 –9 provide the linguistic selections of these experts.

Table 5

Linguistic Choices of Experts for the Dimensions

	D1			D2			D3			D4
	E1	E2	E3	E1	E2	E3	E1	E2	E3	E1	E2	E3
D1	–	–	–	M	M	H	M	H	H	H	M	H
D2	H	M	M	–	–	–	B	H	H	H	B	M
D3	H	B	H	M	M	H	–	–	–	H	H	M
D4	M	M	H	M	L	M	M	M	H	–	–	–

Table 6

Linguistic Choices of Experts for the Criteria of D1

	C1			C2			C3			C4
	E1	E2	E3	E1	E2	E3	E1	E2	E3	E1	E2	E3
C1	–	–	–	B	BH	H	M	M	H	L	M	M
C2	H	H	M	–	–	–	M	M	H	L	M	M
C3	M	M	H	M	M	H	–	–	–	L	L	M
C4	M	M	L	M	M	L	L	L	M	–	–	–

Table 7

Linguistic Choices of Experts for the Criteria of D2

	C5			C6			C7			C8
	E1	E2	E3	E1	E2	E3	E1	E2	E3	E1	E2	E3
C5	–	–	–	H	M	M	L	VL	L	VB	B	H
C6	H	B	M	–	–	–	L	M	L	H	M	M
C7	L	VL	L	M	L	L	–	–	–	M	M	M
C8	M	M	L	H	H	M	M	M	L	–	–	–

Table 8

Linguistic Choices of Experts for the Criteria of D3

	C9			C10			C11			C12
	E1	E2	E3	E1	E2	E3	E1	E2	E3	E1	E2	E3
C9	–	–	–	B	H	H	B	VB	B	B	B	H
C10	M	M	M	–	–	–	M	M	M	M	M	H
C11	M	M	H	L	VL	M	–	–	–	B	H	VB
C12	H	B	H	M	H	H	H	M	H	–	–	–

Table 9

Linguistic Choices of Experts for the Criteria of D4

	C13			C14			C15			C16
	E1	E2	E3	E1	E2	E3	E1	E2	E3	E1	E2	E3
C13	–	–	–	B	H	H	H	B	B	B	B	H
C14	H	M	M	–	–	–	H	H	H	H	H	M
C15	H	H	H	H	M	H	–	–	–	H	H	H
C16	M	M	H	M	M	H	M	H	H	–	–	–

Table 10 represents the linguistic choices of decision makers for each blue ocean strategy on the criteria of innovative capacity.

Table 10

Linguistic Choices of Experts for the Blue Ocean Strategies

	Strategy 1 (A1)			Strategy 2 (A2)			Strategy 3 (A3)			Strategy 4 (A4)			Strategy 5 (A5)
	E1	E2	E3	E1	E2	E3	E1	E2	E3	E1	E2	E3	E1	E2	E3
C1	VG	G	VG	VG	G	VG	VG	G	G	MP	F	G	VG	G	G
C2	VG	VG	VG	VG	G	G	B	VG	G	VG	G	G	VG	G	G
C3	F	F	G	VG	VG	VG	B	VG	VG	VG	G	F	F	VG	G
C4	VG	B	G	VG	VG	VG	G	G	G	VG	VG	G	VG	VG	G
C5	G	G	F	G	G	F	G	G	G	G	G	G	F	F	F
C6	MP	F	F	G	F	G	VG	G	G	G	G	VG	B	VG	G
C7	F	F	G	G	VG	VG	F	F	G	G	G	VG	G	G	G
C8	B	VG	VG	VG	VG	G	F	F	G	G	F	G	VG	F	G
C9	B	B	VG	VG	VG	VG	VG	G	G	F	F	G	G	VG	G
C10	VG	G	G	VG	B	B	B	B	B	B	VG	G	F	VG	VG
C11	B	VG	VG	VG	B	VG	G	G	VG	G	F	G	G	VG	VG
C12	VG	B	VG	B	VG	G	VG	G	G	F	F	G	VG	G	F
C13	F	MP	F	F	F	F	G	VG	VG	MP	F	MP	MP	F	F
C14	B	VG	G	VG	VG	G	F	MP	G	MP	F	F	MP	MP	F
C15	F	MP	F	MP	F	F	F	G	VG	MP	G	F	F	F	MP
C16	MP	F	F	F	F	F	G	VG	G	G	MP	F	MP	F	F

IT2-based hybrid decision making analysis with two phases is proposed and the linguistic evaluations of decision makers for each criterion and alternative between Tables 7 –12 are applied for the integrated analysis model. The first phase of the analysis is to provide the weight of dimensions and criteria for the innovative capacity of family firms. For this purpose, IT2 fuzzy DEMATEL is used for weighting the dimensions and criteria with the data of Tables 5 –9. Following phase consists of the ranking process for the blue ocean strategies. For that, IT2 fuzzy VIKOR is considered to rank the strategy alternatives with the evaluations in Table 12. Analysis results are detailed in the following section respectively.

Table 11

DRM for the Dimensions

	D1	D2	D3	D4
D1	((0,0,0,0;1,1), (0,0,0,0;.90,.90))	((.40,.55,.55,.70;1,1), (.45,.55,.55,.65;.90,.90))	((.45,.60,.60,.75;1,1), (.50,.60,.60,.70;.90,.90))	((0.45,0.60,0.60,0.75;1,1), (0.50,0.60,0.60,0.70;0.90,0.90))
D2	((0.40,0.55,0.55,0.70;1,1), (0.45,0.55,0.55,0.65;0.90,0.90))	((0,0,0,0;1,1), (0,0,0,0;0.90,0.90))	((0.55,0.70,0.70,0.83;1,1), (0.60,0.70,0.70,0.78;0.90,0.90))	((0.50,0.65,0.65,0.78;1,1), (0.55,0.65,0.65,0.73;0.90,0.90))
D3	((0.55,0.70,0.70,0.83;1,1), (0.60,0.70,0.70,0.78;0.90,0.90))	((0.40,0.55,0.55,0.70;1,1), (0.45,0.55,0.55,0.65;0.90,0.90))	((0,0,0,0;1,1), (0,0,0,0;0.90,0.90))	((0.45,0.60,0.60,0.75;1,1), (0.50,0.60,0.60,0.70;0.90,0.90))
D4	((0.40,0.55,0.55,0.70;1,1), (0.45,0.55,0.55,0.65;0.90,0.90))	((0.30,0.45,0.45,0.60;1,1), (0.35,0.45,0.45,0.55;0.90,0.90))	((0.40,0.55,0.55,0.70;1,1), (0.45,0.55,0.55,0.65;0.90,0.90))	((0,0,0,0;1,1), (0,0,0,0;0.90,0.90))

Table 12

Defuzzified values and weights for the Dimensions

	D1	D2	D3	D4	r	y	r + y	r - y	Weights
D1	1.15	1.23	1.37	1.37	5.11	5.21	10.32	–0.10	0.252
D2	1.40	1.09	1.45	1.44	5.39	4.69	10.08	0.69	0.246
D3	1.42	1.26	1.21	1.41	5.30	5.29	10.59	0.01	0.258
D4	1.24	1.11	1.26	1.07	4.69	5.30	9.99	–0.61	0.244

3.3 Analysis results

Phase 1: Weight the dimensions and criteria of innovative capacity. DRM is calculated by the Equations (4) and (5). The results of the dimensions are illustrated in Table 11.

After that, the normalized values have been computed by the formulas (6)-(8). By using the normalized values, the total influence matrix has been constructed with the Equations (9)–(13). The equations between (14) and (17) are used for the defuzzification process and the impact and influence degrees of the dimensions are illustrated in Table 12.

According to the results, resources (D2) is the most influencing factor among the dimensions while networking (D4) is the most influenced. However, organizational competency (D3) has the relatively most importance in the dimension set whereas networking (D4) has the weakest factor. Similar procedure has been also applied for the sub dimensions defining the criteria of innovative capacity dimensions and the overall weighting results are given on Table 12.

The results demonstrate that the criteria with the highest importance in the dimensions are listed as openness of organizational culture (C1) and flexibility (C2) for culture (D1), technological abilities (C8), for resources (D2), R&D capacity (C9) and process innovation (C12) for organizational competency (D3), formal and informal linkages (C13) for networking (D4) respectively. According to the overall weighting results, R&D capacity (C9) is the most significant criterion in the dimension set while internal processes (C7) has the lowest importance among the criteria.

Phase 2: Rank the blue ocean strategies for family firms. The linguistic opinions of the experts have been collected and the decision matrix based on IT2 fuzzy sets has been constructed by the converting fuzzy numbers of the evaluations with the formulas (18) and (19). The following step continues with the aggregated ratings have been defuzzified by the Equations (20)–(23). Table 14 defines these values.

Table 13
Weights for the criteria and dimensions of innovative capacity

Dimensions Weights of Dimensions Criteria Weights of Criteria Overall Weights

D1 0.252 C1 0.258 0.065

C2 0.258 0.065

C3 0.255 0.064

C4 0.230 0.058

D2 0.246 C5 0.255 0.063

C6 0.262 0.065

C7 0.212 0.052

C8 0.270 0.066

D3 0.258 C9 0.265 0.069

C10 0.224 0.058

C11 0.246 0.064

C12 0.265 0.068

D4 0.244 C13 0.256 0.063

C14 0.243 0.059

C15 0.255 0.062

C16 0.245 0.060

Dimensions	Weights of Dimensions	Criteria	Weights of Criteria	Overall Weights
D1	0.252	C1	0.258	0.065
		C2	0.258	0.065
		C3	0.255	0.064
		C4	0.230	0.058
D2	0.246	C5	0.255	0.063
		C6	0.262	0.065
		C7	0.212	0.052
		C8	0.270	0.066
D3	0.258	C9	0.265	0.069
		C10	0.224	0.058
		C11	0.246	0.064
		C12	0.265	0.068
D4	0.244	C13	0.256	0.063
		C14	0.243	0.059
		C15	0.255	0.062
		C16	0.245	0.060

Table 14

Defuzzified values of decision matrix

	A1	A2	A3	A4	A5
C1	8.64	8.64	8.26	6.67	8.26
C2	9.03	8.26	8.86	8.26	8.26
C3	7.07	9.03	9.25	7.86	7.86
C4	8.86	9.03	7.87	8.64	8.64
C5	7.47	7.47	7.87	7.87	6.67
C6	6.27	7.47	8.26	8.26	8.86
C7	7.07	8.64	7.07	8.26	7.87
C8	9.25	8.64	7.07	7.47	7.86
C9	9.47	9.03	8.26	7.07	8.26
C10	8.26	9.47	9.69	8.86	8.24
C11	9.25	9.25	8.26	7.47	8.64
C12	9.25	8.86	8.26	7.07	7.86
C13	6.27	6.67	8.64	5.87	6.27
C14	8.86	8.64	6.67	6.27	5.87
C15	6.27	6.27	7.86	6.67	6.27
C16	6.27	6.67	8.26	6.67	6.27

The best and worst values of the matrix have been calculated with the equation (24) and the values of Si, Ri, and Qi have been computed by using the Equations (25) and (29). The results obtained from IT2 fuzzy DEMATEL have been used for the weights of the criteria in the equations. The values and final ranking scores are provided in Table 15.

Table 15

The values of Si, Ri, and Qi and ranking scores for the blue ocean strategies

	Si	Ri	Qi	Ranking
Strategy 1 (A1)	0.443	0.065	0.140	2
Strategy 2 (A2)	0.338	0.065	0.048	1
Strategy 3 (A3)	0.363	0.066	0.269	3
Strategy 4 (A4)	0.714	0.069	1.00	5
Strategy 5 (A5)	0.661	0.065	0.478	4

According to Table 15, focus on irrelevant/strong differentiation (A2) is the best alternative for the blue ocean strategies while the strategy of increase costs and raise value (A4) has the worst performance result for strategy selection of the family firms.

4 Discussions

Family firms’ less hierarchical and bureaucratic structure contributes to quicker decision-making processes that are important for innovativeness. However, their innovativeness is sometimes impeded by unsystematic and uncalculated decision-making processes [38]. Extant literature, on family firm innovativeness are composed of studies with contrasting results. According to the first stream of thought, due to their unique characteristics family firms do not encompass a proper ground for innovation. Family firms are obsessed about the protecting the previous generations’ legacy and property. But these inclinations have the risk of making the organization risk aversive and therefore may decrease their R&D spending [7]. Supporting this view there are considerable number of many empirical studies that found lower levels of R&D spending in family firms [10]. And, tendency to invest in innovation is mostly restricted by family business founders’ socioemotional wealth and their orientation to sustain their control over the organization [19].

Compared to large companies, most SMEs operate on limited that necessitates identifying specific needs of a relatively small group of customers that hinders them from getting use of economies of scale [35]. That is why, they should prefer alternative strategies to escape from fierce competition. And creating uncontested market space through “Blue Ocean” strategy is a good alternative in order to combat with large companies and other SMEs [21]. In this study it is proposed that when family firms decide to scale up, they may decide to open their shares to foreign investors. And during these processes potential investors are expected to give importance to the probability of success by creating value driven, uncontested markets and low costs through blue ocean strategies.

5 Conclusions

According to the analysis results, organizational competency is the most important dimension in the innovative capacity of family firms and, R&D and process innovation are main pillars of the internal competency for the blue ocean-based strategic success of family firms. Additionally, flexible and open behaviours in the organizational culture could provide more effectiveness than the conventional business environment. Moreover, efficiency of technology in use of resource and the multidimensional linkages among the comprehensive business priorities could mostly influence the blue ocean strategies. Focusing on irrelevant/strong differentiation is the best choice in the blue ocean strategies and the family firms should provide competitive pricing policies together with high quality for building a successful blue ocean strategy. So, potential investors can provide an alternative method to select the best family firms applying the innovative capacity-based strategies.

The main novelty of this study is to construct a hybrid decision making method with IT2 fuzzy DEMATEL and IT2 fuzzy VIKOR and propose a set of factors for innovative capacity as well as alternatives for blue ocean strategies. Alternatively, the study could be extended by using other multicriteria decision making approaches such as MOORA and TOPSIS, and also considering hesitant or intuitionistic fuzzy information [44 –49]. In addition to this situation, a set of alternatives could be widened by considering the cross-country analysis for further researches.

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	D1			D2			D3			D4
	E1	E2	E3	E1	E2	E3	E1	E2	E3	E1	E2	E3
D1	–	–	–	M	M	H	M	H	H	H	M	H
D2	H	M	M	–	–	–	B	H	H	H	B	M
D3	H	B	H	M	M	H	–	–	–	H	H	M
D4	M	M	H	M	L	M	M	M	H	–	–	–

	C1			C2			C3			C4
	E1	E2	E3	E1	E2	E3	E1	E2	E3	E1	E2	E3
C1	–	–	–	B	BH	H	M	M	H	L	M	M
C2	H	H	M	–	–	–	M	M	H	L	M	M
C3	M	M	H	M	M	H	–	–	–	L	L	M
C4	M	M	L	M	M	L	L	L	M	–	–	–

	C5			C6			C7			C8
	E1	E2	E3	E1	E2	E3	E1	E2	E3	E1	E2	E3
C5	–	–	–	H	M	M	L	VL	L	VB	B	H
C6	H	B	M	–	–	–	L	M	L	H	M	M
C7	L	VL	L	M	L	L	–	–	–	M	M	M
C8	M	M	L	H	H	M	M	M	L	–	–	–

	C9			C10			C11			C12
	E1	E2	E3	E1	E2	E3	E1	E2	E3	E1	E2	E3
C9	–	–	–	B	H	H	B	VB	B	B	B	H
C10	M	M	M	–	–	–	M	M	M	M	M	H
C11	M	M	H	L	VL	M	–	–	–	B	H	VB
C12	H	B	H	M	H	H	H	M	H	–	–	–

	C13			C14			C15			C16
	E1	E2	E3	E1	E2	E3	E1	E2	E3	E1	E2	E3
C13	–	–	–	B	H	H	H	B	B	B	B	H
C14	H	M	M	–	–	–	H	H	H	H	H	M
C15	H	H	H	H	M	H	–	–	–	H	H	H
C16	M	M	H	M	M	H	M	H	H	–	–	–

	D1			D2			D3			D4
	E1	E2	E3	E1	E2	E3	E1	E2	E3	E1	E2	E3
D1	–	–	–	M	M	H	M	H	H	H	M	H
D2	H	M	M	–	–	–	B	H	H	H	B	M
D3	H	B	H	M	M	H	–	–	–	H	H	M
D4	M	M	H	M	L	M	M	M	H	–	–	–

	C1			C2			C3			C4
	E1	E2	E3	E1	E2	E3	E1	E2	E3	E1	E2	E3
C1	–	–	–	B	BH	H	M	M	H	L	M	M
C2	H	H	M	–	–	–	M	M	H	L	M	M
C3	M	M	H	M	M	H	–	–	–	L	L	M
C4	M	M	L	M	M	L	L	L	M	–	–	–

	C5			C6			C7			C8
	E1	E2	E3	E1	E2	E3	E1	E2	E3	E1	E2	E3
C5	–	–	–	H	M	M	L	VL	L	VB	B	H
C6	H	B	M	–	–	–	L	M	L	H	M	M
C7	L	VL	L	M	L	L	–	–	–	M	M	M
C8	M	M	L	H	H	M	M	M	L	–	–	–

	C9			C10			C11			C12
	E1	E2	E3	E1	E2	E3	E1	E2	E3	E1	E2	E3
C9	–	–	–	B	H	H	B	VB	B	B	B	H
C10	M	M	M	–	–	–	M	M	M	M	M	H
C11	M	M	H	L	VL	M	–	–	–	B	H	VB
C12	H	B	H	M	H	H	H	M	H	–	–	–

	C13			C14			C15			C16
	E1	E2	E3	E1	E2	E3	E1	E2	E3	E1	E2	E3
C13	–	–	–	B	H	H	H	B	B	B	B	H
C14	H	M	M	–	–	–	H	H	H	H	H	M
C15	H	H	H	H	M	H	–	–	–	H	H	H
C16	M	M	H	M	M	H	M	H	H	–	–	–

	D1			D2			D3			D4
	E1	E2	E3	E1	E2	E3	E1	E2	E3	E1	E2	E3
D1	–	–	–	M	M	H	M	H	H	H	M	H
D2	H	M	M	–	–	–	B	H	H	H	B	M
D3	H	B	H	M	M	H	–	–	–	H	H	M
D4	M	M	H	M	L	M	M	M	H	–	–	–

	C1			C2			C3			C4
	E1	E2	E3	E1	E2	E3	E1	E2	E3	E1	E2	E3
C1	–	–	–	B	BH	H	M	M	H	L	M	M
C2	H	H	M	–	–	–	M	M	H	L	M	M
C3	M	M	H	M	M	H	–	–	–	L	L	M
C4	M	M	L	M	M	L	L	L	M	–	–	–

	C5			C6			C7			C8
	E1	E2	E3	E1	E2	E3	E1	E2	E3	E1	E2	E3
C5	–	–	–	H	M	M	L	VL	L	VB	B	H
C6	H	B	M	–	–	–	L	M	L	H	M	M
C7	L	VL	L	M	L	L	–	–	–	M	M	M
C8	M	M	L	H	H	M	M	M	L	–	–	–

	C9			C10			C11			C12
	E1	E2	E3	E1	E2	E3	E1	E2	E3	E1	E2	E3
C9	–	–	–	B	H	H	B	VB	B	B	B	H
C10	M	M	M	–	–	–	M	M	M	M	M	H
C11	M	M	H	L	VL	M	–	–	–	B	H	VB
C12	H	B	H	M	H	H	H	M	H	–	–	–

	C13			C14			C15			C16
	E1	E2	E3	E1	E2	E3	E1	E2	E3	E1	E2	E3
C13	–	–	–	B	H	H	H	B	B	B	B	H
C14	H	M	M	–	–	–	H	H	H	H	H	M
C15	H	H	H	H	M	H	–	–	–	H	H	H
C16	M	M	H	M	M	H	M	H	H	–	–	–