Organizational Quality Speciﬁc Immune Maturity Evaluation Based on Continuous Interval Number Medium Operator

: This study sets immune theory as breakthrough points, introduces immune theory into organizational quality management scope with a view to evaluating and enhancing organizational quality speciﬁc immune maturity symmetrically and permanently. This study establishes an evaluation index system of organizational quality speciﬁc immune maturity and constructs a quality speciﬁc immune maturity evaluation and decision-making model based on an immune perspective. Further we used symmetrical methods of a continuous interval number median operator and multiple attribute decision making to carry out empirical analysis. The median idea is introduced, and the points in the 1 / 2 position of the basic interval monotone function are selected in evaluation and decision-making calculation by continuous interval number medium operator method. Empirical analysis research can avoid the inﬂuence of extreme value, provide new ideas and methods for e ﬀ ective evaluation and decision making of organizational quality speciﬁc immune maturity. The outcomes of this study have important practical signiﬁcance for enhancing organizational quality speciﬁc immune maturity of equipment manufacturing enterprises.


Introduction
Quality is the life of enterprise. Organizational quality specific immune refers to the process of embedding immune theory into the organizational quality management system. Organizational quality specific immune puts emphasis on obtaining and activating the organizational quality management risk prevention function through the dynamic immune response mechanism and eliminating various threat factors. Organizational quality specific immune is the core classification of organizational quality immune, and organizational quality immune is the core systematization of organizational immune. The combination of immune theory with organizational quality management can enable enterprises to find problems such as deviations in quality, production, and operation. Using the immune recognition function can determine the crux of quality problems and carry out immune memory and self-stability to develop immune. Immune theory can fully exert positive effects in the process of ensuring quality and safety [1]. In the study of management problems, the theory of biological immune was first applied to the mechanism to explain organizational responses to environmental changes. Subba Narasimha [2] employed the ability of the immune system to recognize external unknown antigens to organizational dynamics and defined dynamic capabilities as a property that can produce antibodies. Wang et al. [3] applied immunology theory to organizations for the first time and proposed the concept adaptive immune response. After comparing the connotations, process, and mechanisms of the two types of responses, enterprises can further solve the problem of how to effectively improve the immune response capability. Li et al. [7,8] and Shi et al. [12][13][14][15][16] all assert that organizational quality immune can be divided into two major classifications: organizational quality specific immune (core classification) and organizational quality non-specific immune. The former one exerts immune functions and conducts quality management functions with the main nutrition, acquired and non-congenital components of organizational quality monitoring, defense (core component), and memory. Li et al. [7,8] suggest that the construction dimensions of organizational quality defense consist of genetic replication, crossovers and mutations, variations, adaptations, selection, coordinating corrections, and preventing bottlenecks. On the contrary, Shi et al. [12][13][14][15][16] take construction dimensions of organization quality defense into account which are involved in organizational quality defense soft elements (basic practices) and organizational quality defense hard elements (core practices).
At present, research outcomes on organizational quality specific immune are still insufficient, and the independent innovation and empirical analysis results associated with organizational quality specific immune are relatively lacking [19,20]. Organizational quality specific immune maturity can reflect the development level, grade, stage, degree, and situation of organizational quality specific immune. In view of the importance of organizational quality specific immune maturity, it is necessary to further develop and validate how to effectively evaluate and execute decision making in organizational quality specific immune maturity Therefore, this study constructed an evaluation index system from the perspectives of biological immune, developing and demonstrating an evaluation and decision-making model based on a continuous interval number medium operator. Empirical studies on organizational quality specific immune maturity are of theoretical and practical significance.
The dimensions corresponded to diverse scales and evaluation indexes. The organizational quality specific immune maturity evaluation index system can be seen in Table 1. Genetic replication was composed of an evaluation index of a11, a21, a31, a41, a51, a61, and a71. Crossovers and mutations consisted of an evaluation index of b11 and b21, respectively. Variations were composed of an evaluation index of c11 and c21. Adaptations consisted of an evaluation index of d11, d21, and d31. Selection was composed of an evaluation index of e11, e12, e13, and e14. Coordinating corrections consisted of an evaluation index of f11, f12, f13, f14, f15, f16, f17, and f18. Table 1. Organizational quality specific immune maturity evaluation index system.

Evaluation Target Dimension Evaluation Index
Organizational quality specific immune maturity

Genetic replication
Organization quality monitoring frequency a11 Organizational quality monitoring efficiency a21 Organizational quality monitoring intensity (strength) a31 Organizational quality monitoring width (broadness) a41 Organizational quality monitoring depth Organizational quality monitoring mobility (richness) a51 Organizational quality monitoring granularity(refinement) a61 Organizational quality monitoring agency and measure ratio a71 Crossovers and mutations (Organizational quality defense soft elements) Leadership commitment and support (emphasis on quality objectives and guiding principles, quality policies, quality strategies, quality plans) b11 Employee participation, employee authorization, employee education and training b21 Variations (Organizational quality defense basic practices) Customer orientation c11 Supplier relationships management c21 Adaptations (Organizational quality defense hard elements) Product design d11 Statistical control and feedback (quality control methods, techniques and tools) d21 Processes management d31 Selection (Organizational quality defense core practices)

Benchmarking e11 Continuous improvement and innovation e12
Technical skills e13 Information and measurement analysis e14 Coordinating corrections (preventing bottlenecks) Organization quality memory frequency f11 Organizational quality memory efficiency f12 Organizational quality memory intensity (strength) f13 Organizational quality memory width (broadness) f14 Organizational quality memory depth f15 Organizational quality memory mobility (richness) f16 Organizational quality memory granularity (refinement) f17 Organizational quality memory agency and measure ratio f18

Evaluation Method and Model Based on a Continuous Interval Number Median Operator
An ordered weighted average (OWA) operator is characterized by firstly re-arranging the data in order of largest to smallest and then weighting the set of values [21][22][23]. The OWA operator does not consider the size of weight value, an arranges the position where the value is located. In recent years, the ordered weighted average operator has been widely used in evaluation, management decision making, artificial intelligence, and other fields. However, decision information in real decision-making problems often corresponds to a certain complexity and ambiguity, and decision information is usually in the form of an interval number, and the evaluator cannot give accurate information [24][25][26]. Therefore, the OWA operator is extended to a continuous interval, and scholars have proposed the continuous order weighted average (COWA) operator. Subsequently, other scholars have proposed a series of transformations. Although there are a large number of expansion forms, it is essentially a sum style and way of weighted average; that is, the interval number is represented by the mean [27,28]. In this case, when the interval number distribution is highly skewed, the mean calculation will have different effects on the calculation results [29]. For the characteristics of non-uniform distribution, the median method is more conducive to save a large amount of original information, and the result is more accurate. Namely, the continuous interval number median (CINM) operator is less susceptible to the risk preference extreme value of the decision maker than the OWA operator. Therefore, in the case of attitude preference of the evaluator, this study introduces the median idea and selects the points in the one-half position of the basic interval monotonic function to participate in the decision calculation. The solution channels and calculation measures can avoid the influence of extreme values and make effective evaluation decisions. The CINM operator provides new ideas and methods for evaluating and decision making and has important practical guiding significance for organizational quality specific immune maturity evaluation and decision making. f ω (a 1 , a 2 , · · · , a n ) = n j=1 Among of them, ω = (ω 1 , ω 2 , · · · , ω n ) T , ω j ∈ [0, 1], n j=1 ω j = 1, and the weight represented by ω is only related to the position of a i . Arranging the data (a 1 , a 2 , · · · , a n ) in descending order, b j represents the elements of the jth position after the arrangement, and the function f ω is the OWA operator.

COWA operator
Theorem 2 [23,29]: is the interval number), and The function Q satisfies the following conditions: • The value of the definition field and the value field are respectively [0, 1];

CINM operator
Theorem 3 [29]: Assuming L Q ([a, b]), and Setting L Q ([a, b]) as the continuous interval number median operator [29] Where Q(x) is the basic interval monotonic function, the Q(x) function determination depends on the preference of decision maker.

Model Principles and Construction Steps
The model principles and model construction steps are as follows [21][22][23][24][29][30][31][32][33]: Setting the decision maker set as D = {d 1 , d 2 , · · · d t }, the weight vector corresponding to the decision maker is The attribute value under each scenario is u j ( j = 1, 2, · · · , m), the attribute weight vector is W = (w 1 , w 2 , · · · w m ) T , W j ∈ [0, 1], m j=1 W j = 1. Setting the program set as X = {x 1 , . . . , x n }, decision-maker d k ∈ D, for each scenario x i ∈ X, the evaluation value given under different attributes is a Step 1: The decision matrix A is normalized to eliminate the impact on the decision results due to the different dimensions. The CINM operator is used to aggregate the attribute values under the decision information, and the decision matrix after normalization is recorded as R, R = (r Step 2: The weighted integration of attribute values r (l) ij (i = 1, 2, · · · , n; j = 1, 2, · · · , m) under attribute u j under different schemes given by the decision maker is carried out. Thus, the population attribute value h (l) ij (i = 1, 2, · · · , n; j = 1, 2, · · · , m) of the scheme x i under the attribute is obtained and In the equation, G = (g 1 , g 2 , · · · , g t ) T is the weight vector corresponding to the decision maker, further obtaining the group decision matrix H = (h ij ) n×m with the help of L Q (r (l) ij )(i = 1, 2, · · · , n; j = 1, 2, · · · , m; l = 1, 2, · · · t).
Step 3: The attribute value of the i row in the group decision matrix H = (h ij ) n×m obtained in the above step according to the weight vector of different attributes is weighted and integrated, and the group's comprehensive attribute value Z i (w)(i = 1, 2, · · · , n) under the scheme x i is obtained.

Data Analysis and Results
The effective combination of immune theory and organizational quality management can efficiently discover problems in the quality management, production, and operation of enterprises, especially for equipment manufacturing enterprises. Quality is the life of every enterprise. As the core link of the supply chain of the equipment manufacturing industry, quality management maturity is the key factor in determining the core competitiveness of equipment manufacturing enterprise. The challenge of how to evaluate and conduct decision making in organizational quality specific immune maturity of equipment manufacturing enterprises in quality management scope and range has become the core appeal.
One large-sized equipment manufacturing enterprise seeks for long-term and close partnerships. The typical and representative enterprise will determine the sorting orders of partnerships in the field of quality management, production, and operation management in the supply chain of the equipment manufacturing industry. The enterprise lies in the eastern region (established in year 2002), passes the quality management system certification, and belongs to the directory of top 100 Chinese equipment manufacturing enterprises.
This study selected five large-sized equipment manufacturing enterprises in the eastern and western regions as target objects, schemes, and sample. The selected enterprises are good at constructing immune mechanisms to deal with quality risk events with quality management capabilities and levels and quality performances that were representative. The corresponding corporate assets, profits, sizes, scales, market shares, and reputations were typical. The corporate ages of five enterprises were all more than fifteen years, and they all belonged to the directory of the top 100 Chinese equipment manufacturing enterprises. Through the continuous interval number median operator method, the organizational quality specific immune maturity of the five enterprises (decision schemes) x i (i = 1, 2, · · · , 5) were evaluated, ranked, and selected. We invited four experts (D t (t = 1, 2, 3, 4)) by distributing questionnaires in the field, and we conducted field and spot interviews to evaluate each enterprise separately, and the weight G t of each expert was 0.4, 0.2, 0.3, and 0.1 respectively. The four experts were middle and senior managers of four large-sized equipment manufacturing enterprises. The four large-sized equipment manufacturing enterprises were all at the high-quality management level and performance, had excellent corporate operation management experience in the eastern and western regions. The four large-sized equipment manufacturing enterprises where the experts worked had typical corporate assets, profits, sizes, scales, market shares, and reputations. The corporate ages of the four enterprises where the experts were located were all more than fifteen years old and were attached to the directory of the top 100 Chinese equipment manufacturing enterprises. The four experts had undergraduate and master's degrees, and all had working experience of more than 15 years.
Combined with the immune response process, the six aspects and construction dimensions of genetic replication (c 1 ), crossovers and mutations (organizational quality defense soft elements (c 2 )), variations (organizational quality defense basic practice (c 3 )), adaptations (organizational quality defense hard elements (c 4 )), selection (organizational quality defense core practice (c 5 )), coordinating corrections (preventing bottlenecks (c 6 )) were used as evaluation indicators (attributes) of the five enterprises by the four experts to evaluate organizational quality specific immune maturity and to conduct decision making. The attribute weight (W) of each evaluation index was 0.1, 0.     Step 1: The basic interval monotonic function in this study is made Q(y) = y r , r = 1. When evaluating the expert risk neutrality, we chose λ = 0.5. This study calculated the attribute value matrix of each evaluation expert according to Formula (3): Step 2: The comprehensive attribute value of the evaluation expert for each enterprise x i was calculated according to Formula (4): Step 3: According to Formula (5), the group comprehensive attribute values of each enterprise x i was obtained: Z 1 (w) = 81.57, Z 2 (w) = 81.38, Z 3 (w) = 78.09, Z 4 (w) = 80.96, Z 5 (w) = 79.5 Step 4: The population comprehensive attribute values and the empirical analysis results were sorted as follows: In the comprehensive evaluation results of organizational quality specific immune maturity of the five equipment manufacturing enterprises, the equipment manufacturing enterprise 1 had the highest comprehensive score. Followed by the equipment manufacturing enterprise 2, the equipment manufacturing enterprise 4, and the equipment manufacturing enterprise 5, successively. The last one was the equipment manufacturing enterprise 3.

Conclusions and Discussion
The immune system is embedded in organizational quality management. This study focused on organizational quality specific immune according to the organizational specific immune response process. Organizational quality specific immune maturity evaluation index systems with diverse dimensions were constructed from the perspectives of immune, organization immune, and organizational quality immune, successively. The continuous interval median operator method was used to construct an organizational quality specific immune maturity evaluation model and conduct decision making. Scientific and dynamic evaluation of immune system maturity is conducive to improving quality management processes and determining the level, grade, and situational stage of organizational quality specific immune. Due to the fact that the organizational quality specific immune system is complex, it is a top priority to be able to scientifically and effectively evaluate maturity. Moreover, experts tend to use the interval method when giving scores to indicators that can reflect the organizational quality specific immune maturity. The phenomenon will make the relative importance of the attributes of the objects and schemes to be evaluated difficult to quantify and susceptible to extreme values of the interval. The continuous interval number median operator can effectively avoid the non-uniform distribution of interval numbers and reduce the error caused by extreme values. The method not only improves the traditional evaluation and decision making, but also has wide applicability in dealing with complex uncertainty. It can effectively identify the defects of the organizational quality specific immune maturity and thus improve the quality management level.
In this study, followed by the maturity evaluation index system and model, the continuous interval number median operator aggregation method was applied to carry out maturity evaluation by specific sample, empirical objects, and schemes. The applicability, effectiveness, and efficiency were further expanded to the theoretical, practical, and applied aspects. The research outcomes can provide new ideas for solving the interval number evaluation problem and render important practical guiding significance for enterprises to determine and enhance organizational quality specific immune maturity and quality performance. However, this study was only a preliminary exploration of the usage of the continuous interval number median operator method to evaluate the organizational quality specific immune maturity of enterprises. The model provides a reference value for self-evaluation within the equipment manufacturing enterprises, and it cannot be used as an organizational quality specific immune maturity evaluation standard for all manufacturing enterprises. The challenge of how to apply the continuous interval number median operator evaluation method to more fields is the focus of future research.
Author Contributions: Q.L. contributed to the motivation, the interpretation of the methods, the data analysis and results, and provided the draft versions and references. H.H. provided the data analysis and results, the references. Y.G. provided the related concepts and minor recommendations, extracted the conclusions and discussion. All authors have read and agreed to the published version of the manuscript.
Funding: This research was funded by the National Social Science Fund Project (17CGL020).

Conflicts of Interest:
The authors declare no conflict of interest.