Restricted accessResearch articleFirst published online 2020
Empirical analysis of organizational quality defect management enabling factors identification based on SMT,interval-valued hesitant fuzzy set ELECTRE and QRA methods
Quality is of great significance in enterprises, strengthening and identifying enabling factors of quality defect management are one of core goals of organizational operation management. In this study, sense-making theory is adopted to identify organizational quality defect management enabling factors, and interval-valued hesitant fuzzy set ELECTRE for multi-attribute decision-making method and quantile regression analysis method are used to empirically identify and verify organizational quality defect management enabling factors, revealing influence size and scale, influence degree and influence significance level of enabling factors on organizational quality defect management. Empirical analysis results indicate that combination methods of sense-making theory, interval-valued hesitant fuzzy set ELECTRE for multi-attribute decision-making method and quantile regression analysis method can effectively identify organizational quality defect management enabling factors, which will have great promise in the field of organizational quality management and operation management based on the rationality, feasibility and applicability.
GioiaD.A. and ThomasJ.B., Identity, image, and issue interpretation: Sensemaking during strategic change in academia, Administrative Science Quarterly41(5) (1996), 370–403.
2.
YaoF., The process of sense-making of transformation from university researchers to entrepreneurs on multi-case Study, Nankai Business Review16(1) (2013), 138–148.
3.
WeiG.W., LinR. and WangH.J., Distance and similarity measures for hesitant interval-valued fuzzy sets, Journal of Intelligent & Fuzzy Systems27(1) (2014), 19–36.
4.
KayhanH., MohammadzadehB. and AtmacaS., The use of fables in science laboratory, Eurasia Journal of Mathematics Science and Technology Education13(8) (2017), 4471–4480.
5.
QiaoJ. and LiZ.X., Quantile regression theory explanation and instance analysis, Statistics and Decision (19) (2012), 104–107.
6.
SchoettliJ., Political economy of reforms in India, Asian Journal of Social Science45(3) (2017), 374–376.
7.
IsabellaL.A., Evolving interpretations as change unfolds: How managers construct key organizational events, Academy of Management Journal33(1) (1990), 7–41.
8.
ZadehL.A., Fuzzy sets, Information and control8(3) (1965), 338–353.
9.
ShiL.P., LiuQ. and TengY., Quality performance upgrading paths based on OSI-PP-Enter: Theoretical framework and empirical analysis, Journal of Industrial Engineering29(3) (2015), 152–163.
10.
ShiL.P., LiuQ., WuK.J. and DuZ.W., Function mechanisms of organizational quality specific immunity elements consistency on quality performance-Empirical analysis based on PP, fit and hierarchy regression analysis method, Industrial Engineering and Management18(3) (2013), 84–91.
11.
ShiL.P., LiuQ., JiaY.N. and YuX.Q., Quality performance upgrading paths optimization based on projection pursuit, RAGA, NK and GERT: explaining framework of setting organizational quality specific immunity and product life cycle as main logic, Operations Research and Management Science24(4) (2015), 188–197.
12.
ShiL.P., LiuQ., JiaY.N. and YuX.Q., Study on relationships among network relationship strength, TQM practice and organizational learning: Extending potential path of triggering organizational learning, Management Review26(5) (2014), 48–60.
13.
MoosM. and BothaM., How do age and location affect a business? Evaluating the objectives, outputs and outcomes of small business policy, South African Journal of Economic and Management Sciences19(3) (2016), 344–357.
14.
PetrovicM., BojkovicN., AnicI., StamenkovicM. and TarleS.P., An ELECTRE-based decision aid tool for stepwise benchmarking: an application over EU digital agenda targets, Decision Support System59 (2014), 230–241.
15.
VanM., EastinJ. and ScheinE.H., Toward a Theory of Organizational Socialization. In B. M. Staw (Ed.), research in organizational behavior, Greenwich, CTL JAI Press, (1979), 209–264.
16.
ChenN., XuZ.S. and XiaM.M., Interval-valued hesitant preference relations and their applications to group decision making, Knowledge-Based Systems37 (2013), 528–540.
17.
LiuQ., ShiL.P., ChenW. and SuY., Team quality defect management influence factors identification and fuzzy rules extraction based on SYT-FR-CA: Theoretical framework of main logic of medical immune and evidence, Industrial Engineering and Management21(3) (2016), 110–117.
18.
YuQ., HouF.J., ZhaiY.B. and DuY.Q., ELECTRE-based measure for multi-Attribute decision making using interval-valued hesitant fuzzy set, Operations Research and Management Science4(6) (2015), 16–24.
19.
KoenkerR. and BassettJ.G., Regression quantile, Econometrica46(1) (1978), 33–50.
20.
DaftR.L. and WeickK.E., Toward a model of organization as interpretation system, Academy of Management Review9(2) (1984), 284–295.
21.
LokeshaV., ShrutiR. and DeepikaT., Reckoning of the dissimilar topological indices of human liver, Applied Mathematics & Nonlinear Sciences3(1) (2018), 265–276.
22.
TorraV., Hesitant fuzzy sets, International Journal of Intelligent Systems25(6) (2010), 529–539.
23.
GaoW. and WangF., New isolated toughness condition for fractional (g, f, n)-critical graphs, Colloquium Mathematicum147(1) (2017), 55–66.
24.
GaoW. and WangW.F., The fifth geometric-arithmetic index of bridge graph and carbon nanocones, Journal of Difference Equations and Applications23(1-2SI) (2017), 100–109.
25.
GaoW., ZhuL., GuoY. and WangK., Ontology learning algorithm for similarity measuring and ontology mapping using linear programming, Journal of Intelligent & Fuzzy Systems33(5) (2017), 3153–3163.
26.
XiongW. and FengX.B., An empirical investigation into the relationship between QMP and performance firm characteristics as a variant, Journal of Zhejiang University (Humanities and Social Science)42(1) (2012), 188–200.
27.
YaoW.H., An empirical analysis on the aggregation of urban service industry and its determinants based on quantile regression method, Hunan: Master Degree Thesis of Hunan University, 2011.
28.
QinY., LouY., ZhaoY. and ZhangJ., Research on relationship between tourism income and economic growth based on meta-analysis, Applied Mathematics & Nonlinear Sciences3(2018), 105–114.
29.
XuZ.S. and DaQ.L., The uncertain OWA operator, International Journal of Intelligent Systems17(6) (2002), 569–575.
