Research ArticleConstruction of an Early Risk Warning Model of OrganizationalResilience An Empirical Study Based on Samples of RampD Teams
Si-hua Chen
School of Information Technology Jiangxi University of Finance and Economics No 169 East Shuanggang RoadChangbei Nanchang Jiangxi 330013 China
Correspondence should be addressed to Si-hua Chen doriancshfoxmailcom
Received 30 November 2015 Accepted 3 May 2016
Academic Editor Lu Zhen
Copyright copy 2016 Si-hua ChenThis is an open access article distributed under the Creative Commons Attribution License whichpermits unrestricted use distribution and reproduction in any medium provided the original work is properly cited
Facing fierce competition it is critical for organizations to keep advantages either actively or passively Organizational resilienceis the ability of an organization to anticipate prepare for respond to and adapt to incremental change and sudden disruptions inorder to survive and prosper It is of particular importance for enterprises to apprehend the intensity of organizational resilience andthereby judge their abilities to withstand pressure By conducting an exploratory factor analysis and a confirmatory factor analysisthis paper clarifies a five-factor model for organizational resilience of RampD teams Moreover based on it this paper applies fuzzyintegrated evaluation method to build an early risk warning model for organizational resilience of RampD teams The application ofthe model to a company shows that the model can adequately evaluate the intensity of organizational resilience of RampD teamsTheresults are also supposed to contribute to applied early risk warning theory
1 Introduction
The market complexity and competition intensity put for-ward higher requirements for enterprises It is quite challeng-ing for enterprises to pursue long-term success in the cur-rent intense competitive environment A strategy enterprisescan take is to achieve consecutive transient advantages bycontinuous innovations whereas organizational consistencyand stability may damage their abilities to transform fromone advantage to another Rigidity can easily result in highercost for enterprises when they adjust business strategies andthereby prevent enterprises from changing and weaken theirabilities to tackle the changes As a result enterprises haveto explore a series of resilience abilities which are mainlyderived from a wide range of key resources the organiza-tional structure the diversified culture and the leadershipability Under this background the concept of organizationalresilience is proposed which brings brand new perspectivefor strategic management Organizational resilience empha-sizes increasing resilience of enterprises reducing the costof switching strategies and improving enterprises ability torespond to environmental dynamics Sutcliffe andVogus heldthe view that the resilient organizations can actively adjust
in hard conditions [1] Once they have conquered the severechallenges the resilient enterprises can develop better It iswell recognized that the studies on organizational resilienceprovide train of thought for the high growth of enterprises indynamic environment
2 Literature Review
In terms of the definition of organizational resilience gen-erally there are two different views The first view regardsorganizational resilience as simply an ability to rebound fromunexpected stressful adverse situations and to pick upwherethey left off [2ndash5]This point of view is similar to the physicaldefinition of elasticity namely the ability of an object torecover to its original shape after deformation when it issubjected to extrusion Scholars with this view often focuson the intensity of organizational resilience that is to whatextent the organization can be restored to its original stateafter being subjected to negative pressure The second pointof view deems the organizational resilience to be the ability ofan organization to be restored to its original state and becomeeven stronger under negative pressure [6ndash8]
Hindawi Publishing CorporationDiscrete Dynamics in Nature and SocietyVolume 2016 Article ID 4602870 9 pageshttpdxdoiorg10115520164602870
2 Discrete Dynamics in Nature and Society
This view understands organizational resilience as a prop-erty of creatures Not like the physical substances creaturesare not passively subjected to external force and recover after-wards They can vigorously adapt themselves and make self-adjustment What is more organizational resilience is closelyrelated to resilience of individual person in an organizationNevertheless it is not the simple aggregation of individualresilience Organizational resilience pertains to organiza-tional structure and interactions of individuals in an orga-nization [9] Until now many studies on organizationalresilience are descriptive [10ndash12] But basically they agreedthat organizational resilience was the embodiment of indi-vidual organizational capability It is argued that these orga-nizational capabilities and routines in turn are derivedfrom a combination of individual level knowledge skillsabilities and other attributes (KSAOs) that are systematicallydeveloped and integrated through a firmrsquos human resourcemanagement system [13]
In the early times scholars defined the structural dimen-sions of organizational resilience more from individual qual-ities perspective They emphasized the basis and the internalembodiment of resilience For example based on motivationtheories London divided organizational resilience into threedimensions self-efficacy risk bearing and independence[14] These studies from individual qualities perspective tostudy organizational resilience are of theoretical significancewhich laid solid foundation for the later research Howeverthis perspective did no good to exploring the mechanism ofresilience nor to designing the intervention plans [15] Theindividual qualities perspective understands resilience as astable quality [16] but it ignores the possibility of dynamicdevelopment In addition if we understand organizationalresilience as individual quality a possible deduction is thatmaybe a person does not possess such quality Thereforethe attention shifted to individual behaviors in organizationalresilience studies appears very essential The organizationalresilience related behaviors refer to some individual behav-iors presented in an organization to show individualsrsquo con-fidence or elimination of confusion [17] Noe et al designed13 behavioral dimensions such as ldquoto establish and maintainfriendship with individuals of different departmentsrdquo ldquoask ifthere is a problem when others are having a difficultyrdquo andldquogreet to others actively when seeing themrdquo [18] Some studiespointed out that the behavioral dimensions of resilienceincluded decisional behaviors behaviors to seek job andcompetitionThis behavior-oriented dimension indicates thatresilience should incorporate the idea of ldquodynamic develop-ment processrdquo [19]
Fourie and Van Vuuren made a relatively comprehen-sive study The factor structure of resilience they exploredincluded 45 items and 4 second-order factors And theyare belief in oneself disregard for traditional sources ofcareer success self-reliance and receptivity to change [20]In addition based on the studies of London and Noe et alLiu discussed a structure with 20 items and 5 factorsThe fivefactors are general properties such as change and risk takingadaptability autonomy and network building employabilityand active learning and self-confidence [21] These com-prehensive studies integrated the cognitive dimension the
contextual dimension and behavioral dimension [22] Forinstance in Fourie andVanVuuren study the belief in oneselfbelongs to cognitive dimension and disregard for traditionalsources of career success and receptivity to change belongto emotional dimension self-reliance belongs to behavioraldimension In fact cognition emotion and behavior arethe three elements of altitude [23] According to this vieworganizational resilience is individual altitude to job Basedon previous studies Obrist et al proposed that it was ofimportance to interpret organizational resilience based oncognitive emotional and behavioral dimensions This ideacoincides with that of Cynthia [24]
Early riskwarning is thewarning on the possible risksTherisks can be detected in advance and warned about becausethe happening of any risk needs certain conditions andcauses The outbreak of risk is bound to experience a periodof formation generation evolution manifestation and func-tion In other words from risk-taking effect to producingsubstantial damage and causing being out of control it is aprocessTherefore as long as there is effective early risk warn-ing risks can be effectively detected and evaluated and thentransferred diversified controlled and managed In this waywe can stop the further development of risks or deviate themfrom the target before the outbreak of risks or before theyare out of control [25] Generally organizational resiliencehas a boundary Once the pressure organizations withstandis beyond the boundary organizations can hardly be restoredto the former state and even be paralyzedTherefore buildingan early risk warning system of organizational resilience isof extreme importance However there are very few studiesdiscussing this topic
Based on analysis of samples this paper tries to addressthis problem by establishing an early risk warning modelfor organizational resilience Firstly this paper discusses thefactor structure of organizational resilience Based on itby conducting hierarchical analysis and fuzzy integratedevaluation this paper builds an early risk warning modelAnd by applying the model to a company this paper teststhe effectiveness of the model
3 Empirical Study on the OrganizationalResilience of RampD Teams
RampD team is an interactive discussion group which is usedto produce knowledge and assumptions [26] As far as allkinds of teams with different purposes are concerned theimportance of RampD teams outperforms the rest becauseRampD teams are the main subjects for enterprises to createknowledge [27] Recently ldquoRampD teamsrdquo gradually becomevery popular vocabulary for new product development RampDteam refers to a group of people who are temporarily assem-bled and are responsible for executing or completing someRampD programs Mainly by knowledge creation activities thisgroup contributes to the appreciation of knowledge capital forenterprises And its final purpose is to exploit and apply newproducts and new services In this paper we define ldquoteamrdquoas a group between departments and individuals and ldquoRampDteamrdquo as a formal group formed by the RampD personnel fromthe RampD programs
Discrete Dynamics in Nature and Society 3
Literatureanalysis
Face to faceinterview
Analysis andsorting out
Initialquestionnaires
Revisionquestionnaires
Formalquestionnaires
Figure 1 Questionnaire process
Different from other teams RampD teams are knowledgeteams All the members are ldquoknowledge workersrdquo Generallyspeaking knowledge workers are those people who havethe ability to produce create expand and apply knowledgeand take knowledge work as professions [28ndash31] Oftenknowledge capital of an enterprise is appreciated becauseof its knowledge work Comparing with the traditionaloperational workers knowledge workers more focus on andpursue the realization of their ownvaluesTheyhave relativelyhigh independence and would like to take the challengingwork with pleasure But partly because of such features theirturnover rate is high [32]
The above characteristics of RampD teams differentiatethem from other teams and departments in organizationsThe operation of other teams or departments more dependson rigid institutions while due to the particularity of its worknature RampD teams can not wholly depend on institutionRampD teams should have flexibility Therefore the study onthe resilience of RampD team is meaningful and has greatpractical significance However we can hardly see thesestudies currently
31 Study Design First of all based on the analysis ofliterature on organizational resilience we give definition toorganizational resilience and decide the range of items Thenby interview and open questionnaire we collect all the itemsrelated to organizational resilience to form the questionnairesof organizational resilience Finally by applying question-naires and multivariate statistical analysis we explore thefactor structure of organizational resilience For the flowchartof questionnaire establishment see Figure 1
32 Exploratory Factor Analysis Based on both domestic andabroad studies on the dimensions of organizational resilience[18 20 21] by literature review this paper designs open ques-tionnaires and implements structural interview Accordingto Song the structural interview objects need to be 10ndash15homogenousmembers Based on it [33] this paper chooses 15RampD team leaders andRampDengineers to interviewThe inter-view for each person lasts about 20minutes To guarantee thereliability and validity of these interviews we mainly do twothings firstly during the discussion getting the permissionof the interviewees we record the important talks points ofview and terms secondlywe sort out the notes and recordingWe take notes where there is a question and if it is necessarywe pay a return visit to the interviewees by telephone Bycontent analysis this paper sorts out the initial statementsand deletes the statements of organizational resilience with
semantic ambiguityThen wemake preliminary classificationof the remaining statements and combine the statementswith similar content and the statements only being expressedin different ways
The objects of pretest are MBA members of JiangxiUniversity of Finance and Economics who are the engineersin RampD teams Altogether we send 300 questionnaires andreceive 203 questionnaires SPSS 220 is used in processingand analyzing Based on the factor analysis and item analysiswe find that the factor structure of the initial questionnaireis not clear and it has multiple loading items So the ini-tial questionnaires are not effective ones and need furtherrevision The principles for questionnaire revision are asfollows firstly referring to the results of factor analysis wechoose the items with high communality and high factorloading and delete the items with low communality and lowfactor loading Secondly also referring to the results of factoranalysis we delete items with significant double loadings (thedifference between the two biggest values of the factor loadingis less than 02) and items whose maximum value of factorloading is less than 04 Then we adjust and delete the itemswith ambiguous meanings Thirdly based on the scores ofitems we remove the items with low internal consistencyreliability After the revision we choose 18 items from theinitial questionnaire to form the formal questionnaire
A factor analysis on the 18 items is conducted andthe principal component analysis is adopted with varimaxrotation The statistical results confirm the presence of fiveunique factors of organizational resilience of RampD teamsThevalue of KMO is 0907 and the value of Bartlettrsquos ball test is1245034 with probability less than 0001 The total varianceexplanation rate is 62010 Based on the understanding ofeach item we define the five factors as shared vision willing-ness to learn adaptation ability cooperative awareness andwork enthusiasm
33 Confirmatory Factor Analysis The results of the abovesurvey show that organizational resilience has five dimen-sions with 18 items But the factor structure only passes theexploratory factor analysis Whether the theoretical model isbetter than the single dimensional model or other possiblemodels still needs to be tested In other words we need toapply the confirmatory factor analysis to test the superiorityof the theoretical model Next we use the questionnairesof organizational resilience formed in the above survey torecollect the data to test the correctness of the questionnairesof organizational resilience
In the confirmatory factor analysis the survey objectsare the 422 RampD teams of 279 enterprises in 11 provinces inChina To ensure the independency of the data and to avoidpossible interferences this study differentiates the personsparticipating in CFA test and those in EFA test Because weneed to test the organizational resilience of RampD teams wechoose 2-3 members from each team [34] The investigationtime span is from January 2015 to June 2015 We send 1000questionnaires and collect 509 questionnaires The rate ofeffective questionnaires is 509 We use the organizationalresilience questionnaires with 18 items and apply the Likert5-point scale to evaluate each item specific questionnaire was
4 Discrete Dynamics in Nature and Society
Sharedvision
Willingnessto learn
Adaptationability
Cooperativeawareness
Workenthusiasm
Figure 2 The hypothesized model of organizational resilience
081 078 066
068 073 054
083 051
057
067 Sharedvision
Willingnessto learn
Adaptationability
Cooperativeawareness
Workenthusiasm
Figure 3 The structure model of organizational resilience
Table 1 Fit indexes of organizational resilience model
Index 1205942 GFI AGFI IFI CFI RMSEA
Value 27068 092 090 093 092 005
shown in the Appendix in Supplementary Material availableonline at httpdxdoiorg10115520164602870 In this partwe hypothesize that the factor structure of organizationalresilience is a five-factor model See Figure 2
We test the model by applying the structural equationmodel method We fit the model with the observed values ofa sample of 509 RampD teams and get the factor structure oforganizational resilience See Figure 3
To evaluate whether a model is acceptable or not wemainly check the different fit indexes For themain fit indexessee Table 1 The full name of each fit index is as followsGFI goodness-of-fit index AGFI adjusted goodness-of-fitindex IFI incremental fit index CFI comparative fit indexRMSEA Root Mean Square Error of Approximation
From Table 1 we can find that all the fit indexes fall intothe acceptable range which indicates that the observed datawell support the model The exploratory factor analysis isconfirmed
34 Analysis Comparison with Other Possible Models Tofurther test the factor structure this paper compares the five-factor model with other competitivemodelsThe competitivemodels include the single factor model two-factor modelthree-factor model and four-factor modelThere are 18 itemsin the questionnaires Therefore there is only one possibilityfor the single factor model there are 153 possibilities forthe two-factor model there are 816 possibilities for three-factor model and 3060 possibilities for four-factor model Itis impossible for us to compare all of them So the strategywe take is to find out the most reasonable model for eachfactor model and compare them Based on the literaturereview in Section 2 [18ndash23] in the two-factor model wecombine shared vision and cooperative awareness as onefactor We combine willingness to learn adaptation ability
Table 2 Comparison of fit indexes of models
Index 1205942 GFI AGFI IFI CFI RMSEA
Five-factor model 27068 092 090 093 092 005Four-factor model 15936 088 078 082 067 0161Three-factor model 18863 079 081 075 077 0059Two-factor model 21342 057 062 075 089 0231Single factor model 23034 093 088 089 090 0025
and work enthusiasm as another factor in the three-factormodel we combine shared vision and cooperative awarenessas one factor willingness to learn and adaptation ability as thesecond factor and work enthusiasm as the third factor in thefour-factormodel we combine shared vision and cooperativeawareness as one factor and the three other factors remain asthey are We use AMOS 180 to analyze the five models andcheck their fitnessWith the same data based on the question-naire of Section 33 we can have the fitness of the fivemodels
CFI and IFI are the relative fit indexes AGFI GFI andRMSEA are the absolute fit indexes From Table 2 we can seethat the RMSEA and GFI of single factor model are betterthan those of five-factor model But the AGFI IFI and CFIof single factor model are all smaller than those of five-factormodel RMSEA indicates the gap between the theoreticalmodel and the saturated model Its value is smaller than 005which indicates that themodel has very good fitness Amodelcan be accepted only when the values of GFI AGFI IFIand CFI are bigger than 09 The AGFI and IFI of the singlefactor model are smaller than 09 the values of fit indicatorsof the two-factor model three-factor model and four-factormodel are all unacceptable while all the fit indicators of thefive-factor model canmeet the requirementsTheAGFI GFIIFI and CFI of five-factor model are all bigger than 09 andRMSEA is 005 which shows good fitness Considering thefitness of these models we deem the best structure to be thefive-factor model
Discrete Dynamics in Nature and Society 5
StandardizationDivide warning
Datacollection
evaluationModel Model
establishmentResult
analysis
Decideobject
Warningresponse
Figure 4 Steps to build the early risk warning model of risk
Table 3 Reliability analysis of measurement variables
35 Reliability and Validity Analysis In this paper a test ofinternal consistency reliability using Cronbachrsquos coefficientalphas was performed As shown in Table 3 Cronbachrsquos alphacoefficients fall between 0798 and 0902 which are all biggerthan 07 It shows that the questionnaires have high internalconsistency reliability and are acceptable
Validity refers to the effectiveness and correctness ofquestionnaires that is the extent to which the questionnairecan measure the characteristics of the construct It is animportant criterion to evaluate the quality of questionnairesGenerally it includes the content validity and constructvalidity Content validity refers to the extent to which ameasure represents all facets of a given construct In termsof content validity parts of the items come from the currentpapers which are used by many scholars to measure similarvariables Other parts of the items are designed based onliterature review What is more these questionnaires arefurther revised based on the interviews and pretest resultsTherefore the content of the questionnaires matches well theobject and has good content validity
Construct validity is used to test the degree to which atest measures what it claims It mainly uses factor analysisto test the construct validity The main indexes are value ofKMO and value of the Bartlett ball test The value of KMOis 06 which indicates that construct validity is moderate 07indicates that the construct validity is good and 08 indicatesit is very good In terms of the value of the Bartlett ball test thesmaller the better It performs well when 119901 lt 00001 Table 4shows the value of KMO and value of the Bartlett ball test
From the analysis shown in Table 4 we can see that thevalue of KMO of each latent variable is bigger than 07 andthe value of 119901 is 0 the Bartlett ball tests show concentrationAll indicate that the construct validity is good Consideringboth content validity and construct validity we can draw theconclusion that the indicator system and the questionnairesof this paper have high validity
Table 4 Validity analysis of measurement variables
4 Construction of the Early RiskWarning Model of OrganizationalResilience of RampD Team
41 Steps for Construction of Early Risk Warning ModelThese are steps for enterprises to construct an early riskwarning model firstly we need to decide the objects andselect an early risk warning indicator system guided by theearly risk warning principles secondly we divide the riskwarning levels and the warning line thirdly we collect thedata and process the data and normalize the data fourthly weconstruct the mathematical model and evaluate the model byempirical analysis fifthly we analyze the results and judge therelationship between the results and thewarning line and giveearly risk warning response Please see Figure 4
In this study the object of the early risk warning model isorganizational resilience Based on the analysis of Section 3the indicator system of the early risk warning model canbe divided into 2 levels the first level is the five factors oforganizational resilience and the second level is the items ofeach factor The risk warning levels can be divided into 5levels and they are without warning light warning mediumwarning heavy warning and dangerous warning
This paper uses fuzzy integrated evaluation method [35]mainly due to the following reasons firstly it is difficult toprecisely quantify the evaluation of enterprise risk warningThere is ambiguity Based on fuzzy sets and by using variousindicators the fuzzy integrated evaluation method can givecomprehensive evaluation of the membership degree of theevaluated objects By dividing the intervals on the one handit considers the levels of the objects and reflects the ambiguityof the evaluation standards On the other hand it takes theadvantages of peoplersquos experiences which makes the resultsmore objective and adaptable to the reality By combiningboth qualitative and quantitative factors fuzzy integrated
6 Discrete Dynamics in Nature and Society
evaluationmethod improves the quality of evaluation and thereliability of results
42 Determine the Weights To reduce the randomness ofjudgment and increase the reliability of result this paperadopts the fuzzy evaluation method which combines fuzzyset theory and analytical hierarchy process119880 = (119880
1 1198802 1198803 1198804 1198805) where119880
119894denotes one dimension
of organizational resilience 119876 = (1199021 1199022 119902
13) which are
the criterion of the above five aspects 119881 = (V1 V2 V3 V4 V5)
where V1 V2 V3 V4 V5
respectively denotes the ldquostrongrdquoldquogoodrdquo ldquogeneralrdquo ldquofairly weakrdquo and ldquoweakrdquo comment ofeach criterion
This paper adopts analytical hierarchy process methodto decide the weights of indicators Users of the AHP firstdecompose their decision problem into a hierarchy of moreeasily comprehended subproblems Once the hierarchy isbuilt the decision makers will use their judgments about theelementsrsquo relative meaning and importance to evaluate theseelements by comparing them to one another at a time It isrecognized to be practical systematic and concise [36]
Based on the analysis of Section 3 we establish prioritiesamong the elements of the hierarchy by making a series ofjudgments based on pairwise comparisons of the elementsWe can synthesize these judgments to yield a set of overallpriorities for the hierarchy
The priorities of criteria 119880119894are 1198861 1198862 1198863 1198864 1198865and 119860 =
(1198861 1198862 1198863 1198864 1198865) The main steps are as follows
(1) According to scaling theory we construct pairwisecomparison judgment matrix 119860
(5) According to 119860119908 = 120582max119908 we can calculate thelargest eigenvalue and its eigenvector
(6) Consistency test by calculating the consistency indexCI = (120582max minus119899)(119899 minus 1) we can find the correspond-ing average random consistency index RI Then wecan calculate the consistency ratio CR = CIRIWhen CR lt 01 we accept the result Otherwise weneed to rectify matrix 119860 appropriately
43 Establishment of Qualitative Indexes MembershipAlthoughwe can get definite comments on each criterion theldquoboundaryrdquo is relatively ambiguousTherefore when calcula-ting the membership degree of each criterion to the evalua-tion set we need to grade each criterion based on specialistconsultancy and questionnaire analysis We can get themembership vector 119877
3 4 5) where oplus is the operator and its definition is 119887119894=
sum119899
119894=1119908119894119903119894119895 where 119887
119894is the membership vector of each kind
of organizational resilience Normalizing 119887119894we can get 119861 =
(1198611 1198612 1198613 1198614 1198615)119879
(2) Final evaluation vector of the overall goal suppose119862 = 119860 oplus 119861 We add the first two items together If thesum is bigger than 05 (ie the percentage of ldquostrongrdquo andldquogoodrdquo is bigger than 50) it indicates that the organizationalresilience is strong The more approaching 1 the sum of firsttwo items the stronger the organizational resilience
5 Case Analysis
This paper combines both empirical study and case study tosystematically study the factors which influence sustainableinnovation The main conclusions are as follows
Jiangling Motors Co Ltd (JMC in abbreviation here-inafter) a key player in China automotive industry with com-mercial vehicle as its core competitiveness has been ranked asone of ChinaTop 100 ListedCompanies for consecutive yearsIn 2014 JMChit record highs in its business indexeswith salesrevenue reaching 255 billion RMB and volume over 276000units JMC who has established international standardsthat complied with operating systems and mechanisms thatintegrate RampD logistics MSampS and financing supports hasbeen regarded as a model of successful Sinoforeign cooper-ation The company has set up a strong marketing networkthroughout China Its products include Transit commercialvehicle Kaiyun light truck Baodian pickup and YushengSUV which have become models of fuel saving practicalityand environment friendliness In recent years JMC has beeninvesting heavily in new product development to enrich itsproduct line We analyze and evaluate the organizationalresilience of RampD team of JMC company based on themethod of Section 4 The specific steps are as follows
51 Calculate Weights We apply analytical hierarchical pro-cess method to decide weights Based on the pairwise com-parison of importance of criteria we use 1ndash9 scaling methodto get 119860
119894and they are
1198601= (0341 0572 0195 0432 0273)
1198602= (0438 0581 0249 0621 0275)
Discrete Dynamics in Nature and Society 7
1198603= (0417 0359 0721 0346 0512)
1198604= (0438 0519 0434 0419 0351)
1198605= (0523 0475 0354 0464 0765)
(5)
Normalizing them we can get119908119894 We calculate the largest
eigenvalue and its eigenvector and make consistency testThen we can get CR = CIRI = 0043 lt 01 It indicatesthat the consistency of judgment matrix is acceptable
52 Establishment of Membership Matrix of Fuzzy EvaluationBased on the above analysis we select a group of 33 expertsThey mainly come from two sources 17 of them are leadersof departments and senior engineers of enterprises and 16 ofthem are college professors in human resource managementThe alternative answers include ldquoextreme important veryimportant important a little important and not importantrdquoand we give each of them from 5 to 1 respectively We sendthem the questionnaires by email and make sure they do notknow each otherrsquos answer Then we can get the membershipmatrix
1198771=((
(
0456 0812 0654 0142 0461
0451 0641 0247 0541 0622
0751 0712 0574 0341 0346
0235 0632 0341 0723 0348
0432 0543 0355 0541 0156
))
)
1198772=((
(
0634 0247 0541 0346 0316
0453 0621 0421 0261 0423
0431 0354 0354 0156 0761
0312 0317 0641 0341 0345
0231 0394 0521 0141 0432
))
)
1198773=((
(
0345 0384 0347 0512 0712
0311 0371 0621 0315 0731
0421 0381 0274 0311 0623
0235 0267 0646 0328 0512
0512 0461 0346 0612 0641
))
)
1198774=((
(
0421 0379 0513 0812 0385
0356 0812 0346 0346 0541
0841 0644 0311 0461 0197
0345 0547 0345 0856 0634
0314 0284 0346 0654 0461
))
)
1198775=((
(
0765 0698 0611 0341 0341
0395 0541 0851 0206 0261
0584 0621 0511 0509 0345
0347 0574 0341 0451 0433
0614 0354 0317 0394 0542
))
)
(6)
53 Calculating the Comprehensive Evaluation Vector of EachSubgoal From 119861
119894= 119908119894oplus 119877119894 we have
1198611=((
(
0201
0210
0177
0207
0207
))
)
119879
oplus((
(
0456 0812 0654 0142 0461
0451 0641 0247 0541 0622
0751 0712 0574 0341 0346
0235 0632 0341 0723 0348
0432 0543 0355 0541 0156
))
)
1198611= (0457 0667 0429 0646 0389)
1198612=((
(
0272
0245
0133
0215
0165
))
)
119879
oplus((
(
0634 0247 0541 0346 0316
0453 0621 0421 0261 0423
0431 0354 0354 0156 0761
0312 0317 0641 0341 0345
0231 0394 0521 0141 0432
))
)
1198612= (0446 0400 0521 0275 0436)
1198613=((
(
0127
0132
0337
0227
0155
))
)
119879
oplus((
(
0345 0384 0347 0512 0712
0311 0371 0621 0315 0731
0421 0381 0274 0311 0623
0235 0267 0646 0328 0512
0512 0461 0346 0612 0641
))
)
1198613= (0359 0358 0419 0381 0612)
1198614=((
(
0241
0282
0138
0187
0174
))
)
119879
8 Discrete Dynamics in Nature and Society
oplus((
(
0421 0379 0513 0812 0385
0356 0812 0346 0346 0541
0841 0644 0311 0461 0197
0345 0547 0345 0856 0634
0314 0284 0346 0654 0461
))
)
1198614= (0437 0561 0389 0631 0471)
1198615=((
(
0159
0131
0215
0164
0300
))
)
119879
oplus((
(
0765 0698 0611 0341 0341
0395 0541 0851 0206 0261
0584 0621 0511 0509 0345
0347 0574 0341 0451 0433
0614 0354 0317 0394 0542
))
)
1198615= (0540 0516 0470 0383 0396)
(7)
Based on 119861 = (1198611 1198612 1198613 1198614 1198615)119879 normalizing it and from
119862 = 119860 oplus 119861 we have
119862 =((
(
0195
0226
0176
0206
0196
))
)
119879
oplus((
(
0204 0267 0193 0218 0169
0199 0160 0234 0129 0189
0160 0143 0188 0178 0266
0195 0224 0175 0296 0204
0241 0206 0211 0179 0172
))
)
119862 = (0302 0200 0201 0149 0148)
(8)
The sum of first two items of 119862 is 0502 The sum offirst three items of 119862 is 0703 It indicates the light warningand shows the RampD team has relatively high organizationalresilience
6 Conclusions
Based on the structural interviews this paper exploresand confirms the structural dimensions of organizationalresilience Based on it this paper constructs an early riskwarning model of organizational resilience and applies it
to the RampD team of JMC company The conclusions are asfollows
(1) Based on literature review face to face interviewsand open questionnaires this paper applies the exploratoryfactor analysis method to discuss the factor structure oforganizational resilience of RampD teams The results showthat the factor structure of organizational resilience of RampDteams includes five dimensions shared vision willingnessto learn adaptation ability cooperative awareness and workenthusiasm Then this paper compares the five-factor modelwith other competitivemodels to further test the effectivenessof the five-factor model The results show that the five-factormodel is the best What is more the validity and reliabilityof the questionnaires of organizational resilience are provedto meet the requirements of psychometrics The model issupported
(2) Based on the factor structure of organizationalresilience this study constructs an early risk warning modelof organizational resilience of RampD teams It divides the riskwarning levels into five levels By applying fuzzy integratedevaluationmethod and based on the five-factor structure thispaper constructs a hierarchical analysis structure model Bymaking a series of judgments based on pairwise comparisonsof the elements we can get the judgment matrix and therebydecide the weight of each factor of organizational resilienceThen by using Delphi method we can get the member-ship matrix Lastly by calculating the judgment matrix andmembership matrix we can know the risk warning level oforganizational resilience of the RampD teamWe hope the resultwill provide references for the company decision
(3) This study applies the early risk warning model toRampD team of JMC company The results show that the teamhas relatively high organizational resilience These resultsmatch the work performance work experiences and leadersrsquoremarks on the team It also matches the self-evaluation ofmembers of the team All these show that the method isoperational and feasible
Competing Interests
The author declares no competing interests The author hasno financial and personal relationships with other people ororganizations that can inappropriately influence the work
Acknowledgments
This work is supported by the NSFC (71361013 7146200971273122 and 71463020) China Postdoctoral Science Founda-tion under Grant no 2013M541867 Jiangxi Province ScienceFoundation of China under Grants nos 20151BAB207059and 20142BA217018 and China Scholarship Council Fundingunder Grant no 201409805006
References
[1] K M Sutcliffe and J T Vogus ldquoOrganizing for resiliencerdquoin Positive Organizational Scholarship Foundations of a NewDiscipline pp 94ndash110 2003
Discrete Dynamics in Nature and Society 9
[2] J H Gittell K Cameron S Lim and V Rivas ldquoRelationshipslayoffs and organizational resilience airline industry responsesto September 11rdquo Journal of Applied Behavioral Science vol 42no 3 pp 300ndash329 2006
[3] J W Rudolph and N P Repenning ldquoDisaster dynamicsunderstanding the role of quantity in organizational collapserdquoAdministrative Science Quarterly vol 47 no 1 pp 1ndash30 2002
[4] J E Dutton P J Frost M C Worline J M Lilius and J MKanov ldquoLeading in times of traumardquo Harvard Business Reviewvol 80 no 1 pp 54ndash61 2002
[5] R Balu ldquoHow to bounce back from setbacksrdquo Fast Companyvol 45 pp 148ndash156 2001
[6] K EWeick ldquoEnacted sensemaking in crisis situationsrdquo Journalof Management Studies vol 25 no 4 pp 305ndash317 1988
[7] D L Coutu ldquoHow resilience worksrdquo Harvard Business Reviewvol 80 no 5 pp 46ndash55 2002
[8] S F Freeman M Maltz and L Hirschhorn ldquoThe power ofmoral purpose Sandler OrsquoNeill amp partners in the aftermath ofSeptember 11th 2001rdquo Organization Development Journal vol22 no 4 pp 69ndash82 2004
[9] F PMorgeson andD A Hofmann ldquoThe structure and functionof collective constructs implications formultilevel research andtheory developmentrdquo Academy of Management Review vol 24no 2 pp 249ndash265 1999
[10] J F I Horne ldquoThe coming of age of organizational resiliencerdquoBusiness Forum vol 22 no 2-3 pp 24ndash28 1997
[11] L A Mallak ldquoMeasuring resilience in health care providerorganizationsrdquoHealth Manpower Management vol 24 no 4-5pp 148ndash152 1998
[12] L A Mallak ldquoPutting organizational resilience to workrdquo Indus-trial Management vol 40 no 6 pp 8ndash13 1998
[13] C A Lengnick-Hall T E Beck and M L Lengnick-HallldquoDeveloping a capacity for organizational resilience throughstrategic human resourcemanagementrdquoHuman ResourceMan-agement Review vol 21 no 3 pp 243ndash255 2011
[14] M London ldquoToward a theory of career motivationrdquo Academyof Management Review vol 8 no 4 pp 620ndash630 1983
[15] Y Xiao-nan and Z Jian-xin ldquoResilience the psychologicalmechanism for recovery and growthrdquoAdvances in PsychologicalScience vol 5 no 5 pp 658ndash665 2005
[16] X Ju-Zhe S Biao and Z Zhi-Hong ldquoThe research on resilience its evolution and directionrdquo Psychological Science vol 31 no4 pp 995ndash998 2008
[17] E Grotberg ldquoResilience for tomorrowrdquo Trabajo presentado enla International Council of Psychologists Convention Foz doIguacu Brazil Extraıdo de 2005 httpswwwhitpagescomdoc62572445722214401
[18] R A Noe A W Noe and J A Bachhuber ldquoAn investigationof the correlates of career motivationrdquo Journal of VocationalBehavior vol 37 no 3 pp 340ndash356 1990
[19] F Luthans J B Avey R Clapp-Smith and W Li ldquoMore evi-dence on the value of Chinese workersrsquo psychological capital apotentially unlimited competitive resourcerdquo The InternationalJournal of Human Resource Management vol 19 no 5 pp 818ndash827 2008
[20] C Fourie and L J Van Vuuren ldquoDefining andmeasuring careerresiliencerdquo SA Journal of Industrial Psychology vol 24 no 3 pp52ndash59 1998
[21] Y C Liu Relationships between Career Resilience and CareerBeliefs of Employees in Taiwan Texas AampM University 2003
[22] B Obrist C Pfeiffer and R Henley ldquoMulti-layered socialresilience a new approach in mitigation researchrdquo Progress inDevelopment Studies vol 10 no 4 pp 283ndash293 2010
[23] S J Breckler ldquoEmpirical validation of affect behavior and cog-nition as distinct components of attituderdquo Journal of Personalityamp Social Psychology vol 47 no 6 pp 1191ndash1205 1984
[24] C A Lietz andM Strength ldquoStories of successful reunificationa narrative study of family resilience in child welfarerdquo Familiesin Society The Journal of Contemporary Social Services vol 92no 2 pp 203ndash210 2011
[25] X Zhao and Z-G Xin ldquoResearch review on models ofenterprise risk forewarning managementrdquo Journal of BeijingUniversity of Posts and Telecommunications (Social SciencesEdition) vol 12 no 1 pp 93ndash97 2010
[26] E F Fern ldquoThe use of focus groups for idea generationthe effects of group size acquaintanceship and moderator onresponse quantity and qualityrdquo Journal of Marketing Researchvol 19 no 1 pp 1ndash13 1982
[27] S-H Chen and W He ldquoStudy on knowledge propagationin complex networks based on preferences taking wechat asexamplerdquo Abstract and Applied Analysis vol 2014 Article ID543734 11 pages 2014
[28] W He ldquoAn inventory controlled supply chain model based onimproved BP neural networkrdquoDiscrete Dynamics in Nature andSociety vol 2013 Article ID 537675 7 pages 2013
[29] S-H Chen ldquoA novel culture algorithm and itrsquos application inknowledge integrationrdquo Information vol 15 no 11 B pp 4847ndash4853 2012
[30] W He and S-H Chen ldquoGame analysis of determinants ofstability of semiconductor modular production networksrdquo Sus-tainability vol 6 no 8 pp 4772ndash4794 2014
[31] S-H Chen ldquoThe influencing factors of enterprise sustainableinnovation an empirical studyrdquo Sustainability vol 8 no 5article 425 17 pages 2016
[32] S-H Chen ldquoEmpirical research on knowledge integrationimproving innovation ability of IT enterprisemdashbased on struc-tural equation modelrdquo Information vol 14 no 3 pp 753ndash7582011
[33] G-X Song ldquoStudy on construct and its dimensions of careerresilience based on Chinese indigenous culturerdquo EconomicManagement vol 33 no 11 pp 184ndash193 2011
[34] J-L Ke J-M Sun J-T Shi and Q-X Gu ldquoEmpirical studyon relationship between social capital of RampD team and teampotencyrdquoManagement World vol 3 pp 89ndash101 2007
[35] H Shouzhong and G Jianqin ldquoFuzzy integrated evaluation andits applicationrdquo Journal of China Textile University vol 21 no 1pp 74ndash80 1995
[36] A Jebreen and A Husain ldquoUtility-based approach for deter-mining the weights of participants in virtual organizationrdquoApplied Mathematical Sciences vol 6 no 96 pp 4773ndash47862012
This view understands organizational resilience as a prop-erty of creatures Not like the physical substances creaturesare not passively subjected to external force and recover after-wards They can vigorously adapt themselves and make self-adjustment What is more organizational resilience is closelyrelated to resilience of individual person in an organizationNevertheless it is not the simple aggregation of individualresilience Organizational resilience pertains to organiza-tional structure and interactions of individuals in an orga-nization [9] Until now many studies on organizationalresilience are descriptive [10ndash12] But basically they agreedthat organizational resilience was the embodiment of indi-vidual organizational capability It is argued that these orga-nizational capabilities and routines in turn are derivedfrom a combination of individual level knowledge skillsabilities and other attributes (KSAOs) that are systematicallydeveloped and integrated through a firmrsquos human resourcemanagement system [13]
In the early times scholars defined the structural dimen-sions of organizational resilience more from individual qual-ities perspective They emphasized the basis and the internalembodiment of resilience For example based on motivationtheories London divided organizational resilience into threedimensions self-efficacy risk bearing and independence[14] These studies from individual qualities perspective tostudy organizational resilience are of theoretical significancewhich laid solid foundation for the later research Howeverthis perspective did no good to exploring the mechanism ofresilience nor to designing the intervention plans [15] Theindividual qualities perspective understands resilience as astable quality [16] but it ignores the possibility of dynamicdevelopment In addition if we understand organizationalresilience as individual quality a possible deduction is thatmaybe a person does not possess such quality Thereforethe attention shifted to individual behaviors in organizationalresilience studies appears very essential The organizationalresilience related behaviors refer to some individual behav-iors presented in an organization to show individualsrsquo con-fidence or elimination of confusion [17] Noe et al designed13 behavioral dimensions such as ldquoto establish and maintainfriendship with individuals of different departmentsrdquo ldquoask ifthere is a problem when others are having a difficultyrdquo andldquogreet to others actively when seeing themrdquo [18] Some studiespointed out that the behavioral dimensions of resilienceincluded decisional behaviors behaviors to seek job andcompetitionThis behavior-oriented dimension indicates thatresilience should incorporate the idea of ldquodynamic develop-ment processrdquo [19]
Fourie and Van Vuuren made a relatively comprehen-sive study The factor structure of resilience they exploredincluded 45 items and 4 second-order factors And theyare belief in oneself disregard for traditional sources ofcareer success self-reliance and receptivity to change [20]In addition based on the studies of London and Noe et alLiu discussed a structure with 20 items and 5 factorsThe fivefactors are general properties such as change and risk takingadaptability autonomy and network building employabilityand active learning and self-confidence [21] These com-prehensive studies integrated the cognitive dimension the
contextual dimension and behavioral dimension [22] Forinstance in Fourie andVanVuuren study the belief in oneselfbelongs to cognitive dimension and disregard for traditionalsources of career success and receptivity to change belongto emotional dimension self-reliance belongs to behavioraldimension In fact cognition emotion and behavior arethe three elements of altitude [23] According to this vieworganizational resilience is individual altitude to job Basedon previous studies Obrist et al proposed that it was ofimportance to interpret organizational resilience based oncognitive emotional and behavioral dimensions This ideacoincides with that of Cynthia [24]
Early riskwarning is thewarning on the possible risksTherisks can be detected in advance and warned about becausethe happening of any risk needs certain conditions andcauses The outbreak of risk is bound to experience a periodof formation generation evolution manifestation and func-tion In other words from risk-taking effect to producingsubstantial damage and causing being out of control it is aprocessTherefore as long as there is effective early risk warn-ing risks can be effectively detected and evaluated and thentransferred diversified controlled and managed In this waywe can stop the further development of risks or deviate themfrom the target before the outbreak of risks or before theyare out of control [25] Generally organizational resiliencehas a boundary Once the pressure organizations withstandis beyond the boundary organizations can hardly be restoredto the former state and even be paralyzedTherefore buildingan early risk warning system of organizational resilience isof extreme importance However there are very few studiesdiscussing this topic
Based on analysis of samples this paper tries to addressthis problem by establishing an early risk warning modelfor organizational resilience Firstly this paper discusses thefactor structure of organizational resilience Based on itby conducting hierarchical analysis and fuzzy integratedevaluation this paper builds an early risk warning modelAnd by applying the model to a company this paper teststhe effectiveness of the model
3 Empirical Study on the OrganizationalResilience of RampD Teams
RampD team is an interactive discussion group which is usedto produce knowledge and assumptions [26] As far as allkinds of teams with different purposes are concerned theimportance of RampD teams outperforms the rest becauseRampD teams are the main subjects for enterprises to createknowledge [27] Recently ldquoRampD teamsrdquo gradually becomevery popular vocabulary for new product development RampDteam refers to a group of people who are temporarily assem-bled and are responsible for executing or completing someRampD programs Mainly by knowledge creation activities thisgroup contributes to the appreciation of knowledge capital forenterprises And its final purpose is to exploit and apply newproducts and new services In this paper we define ldquoteamrdquoas a group between departments and individuals and ldquoRampDteamrdquo as a formal group formed by the RampD personnel fromthe RampD programs
Discrete Dynamics in Nature and Society 3
Literatureanalysis
Face to faceinterview
Analysis andsorting out
Initialquestionnaires
Revisionquestionnaires
Formalquestionnaires
Figure 1 Questionnaire process
Different from other teams RampD teams are knowledgeteams All the members are ldquoknowledge workersrdquo Generallyspeaking knowledge workers are those people who havethe ability to produce create expand and apply knowledgeand take knowledge work as professions [28ndash31] Oftenknowledge capital of an enterprise is appreciated becauseof its knowledge work Comparing with the traditionaloperational workers knowledge workers more focus on andpursue the realization of their ownvaluesTheyhave relativelyhigh independence and would like to take the challengingwork with pleasure But partly because of such features theirturnover rate is high [32]
The above characteristics of RampD teams differentiatethem from other teams and departments in organizationsThe operation of other teams or departments more dependson rigid institutions while due to the particularity of its worknature RampD teams can not wholly depend on institutionRampD teams should have flexibility Therefore the study onthe resilience of RampD team is meaningful and has greatpractical significance However we can hardly see thesestudies currently
31 Study Design First of all based on the analysis ofliterature on organizational resilience we give definition toorganizational resilience and decide the range of items Thenby interview and open questionnaire we collect all the itemsrelated to organizational resilience to form the questionnairesof organizational resilience Finally by applying question-naires and multivariate statistical analysis we explore thefactor structure of organizational resilience For the flowchartof questionnaire establishment see Figure 1
32 Exploratory Factor Analysis Based on both domestic andabroad studies on the dimensions of organizational resilience[18 20 21] by literature review this paper designs open ques-tionnaires and implements structural interview Accordingto Song the structural interview objects need to be 10ndash15homogenousmembers Based on it [33] this paper chooses 15RampD team leaders andRampDengineers to interviewThe inter-view for each person lasts about 20minutes To guarantee thereliability and validity of these interviews we mainly do twothings firstly during the discussion getting the permissionof the interviewees we record the important talks points ofview and terms secondlywe sort out the notes and recordingWe take notes where there is a question and if it is necessarywe pay a return visit to the interviewees by telephone Bycontent analysis this paper sorts out the initial statementsand deletes the statements of organizational resilience with
semantic ambiguityThen wemake preliminary classificationof the remaining statements and combine the statementswith similar content and the statements only being expressedin different ways
The objects of pretest are MBA members of JiangxiUniversity of Finance and Economics who are the engineersin RampD teams Altogether we send 300 questionnaires andreceive 203 questionnaires SPSS 220 is used in processingand analyzing Based on the factor analysis and item analysiswe find that the factor structure of the initial questionnaireis not clear and it has multiple loading items So the ini-tial questionnaires are not effective ones and need furtherrevision The principles for questionnaire revision are asfollows firstly referring to the results of factor analysis wechoose the items with high communality and high factorloading and delete the items with low communality and lowfactor loading Secondly also referring to the results of factoranalysis we delete items with significant double loadings (thedifference between the two biggest values of the factor loadingis less than 02) and items whose maximum value of factorloading is less than 04 Then we adjust and delete the itemswith ambiguous meanings Thirdly based on the scores ofitems we remove the items with low internal consistencyreliability After the revision we choose 18 items from theinitial questionnaire to form the formal questionnaire
A factor analysis on the 18 items is conducted andthe principal component analysis is adopted with varimaxrotation The statistical results confirm the presence of fiveunique factors of organizational resilience of RampD teamsThevalue of KMO is 0907 and the value of Bartlettrsquos ball test is1245034 with probability less than 0001 The total varianceexplanation rate is 62010 Based on the understanding ofeach item we define the five factors as shared vision willing-ness to learn adaptation ability cooperative awareness andwork enthusiasm
33 Confirmatory Factor Analysis The results of the abovesurvey show that organizational resilience has five dimen-sions with 18 items But the factor structure only passes theexploratory factor analysis Whether the theoretical model isbetter than the single dimensional model or other possiblemodels still needs to be tested In other words we need toapply the confirmatory factor analysis to test the superiorityof the theoretical model Next we use the questionnairesof organizational resilience formed in the above survey torecollect the data to test the correctness of the questionnairesof organizational resilience
In the confirmatory factor analysis the survey objectsare the 422 RampD teams of 279 enterprises in 11 provinces inChina To ensure the independency of the data and to avoidpossible interferences this study differentiates the personsparticipating in CFA test and those in EFA test Because weneed to test the organizational resilience of RampD teams wechoose 2-3 members from each team [34] The investigationtime span is from January 2015 to June 2015 We send 1000questionnaires and collect 509 questionnaires The rate ofeffective questionnaires is 509 We use the organizationalresilience questionnaires with 18 items and apply the Likert5-point scale to evaluate each item specific questionnaire was
4 Discrete Dynamics in Nature and Society
Sharedvision
Willingnessto learn
Adaptationability
Cooperativeawareness
Workenthusiasm
Figure 2 The hypothesized model of organizational resilience
081 078 066
068 073 054
083 051
057
067 Sharedvision
Willingnessto learn
Adaptationability
Cooperativeawareness
Workenthusiasm
Figure 3 The structure model of organizational resilience
Table 1 Fit indexes of organizational resilience model
Index 1205942 GFI AGFI IFI CFI RMSEA
Value 27068 092 090 093 092 005
shown in the Appendix in Supplementary Material availableonline at httpdxdoiorg10115520164602870 In this partwe hypothesize that the factor structure of organizationalresilience is a five-factor model See Figure 2
We test the model by applying the structural equationmodel method We fit the model with the observed values ofa sample of 509 RampD teams and get the factor structure oforganizational resilience See Figure 3
To evaluate whether a model is acceptable or not wemainly check the different fit indexes For themain fit indexessee Table 1 The full name of each fit index is as followsGFI goodness-of-fit index AGFI adjusted goodness-of-fitindex IFI incremental fit index CFI comparative fit indexRMSEA Root Mean Square Error of Approximation
From Table 1 we can find that all the fit indexes fall intothe acceptable range which indicates that the observed datawell support the model The exploratory factor analysis isconfirmed
34 Analysis Comparison with Other Possible Models Tofurther test the factor structure this paper compares the five-factor model with other competitivemodelsThe competitivemodels include the single factor model two-factor modelthree-factor model and four-factor modelThere are 18 itemsin the questionnaires Therefore there is only one possibilityfor the single factor model there are 153 possibilities forthe two-factor model there are 816 possibilities for three-factor model and 3060 possibilities for four-factor model Itis impossible for us to compare all of them So the strategywe take is to find out the most reasonable model for eachfactor model and compare them Based on the literaturereview in Section 2 [18ndash23] in the two-factor model wecombine shared vision and cooperative awareness as onefactor We combine willingness to learn adaptation ability
Table 2 Comparison of fit indexes of models
Index 1205942 GFI AGFI IFI CFI RMSEA
Five-factor model 27068 092 090 093 092 005Four-factor model 15936 088 078 082 067 0161Three-factor model 18863 079 081 075 077 0059Two-factor model 21342 057 062 075 089 0231Single factor model 23034 093 088 089 090 0025
and work enthusiasm as another factor in the three-factormodel we combine shared vision and cooperative awarenessas one factor willingness to learn and adaptation ability as thesecond factor and work enthusiasm as the third factor in thefour-factormodel we combine shared vision and cooperativeawareness as one factor and the three other factors remain asthey are We use AMOS 180 to analyze the five models andcheck their fitnessWith the same data based on the question-naire of Section 33 we can have the fitness of the fivemodels
CFI and IFI are the relative fit indexes AGFI GFI andRMSEA are the absolute fit indexes From Table 2 we can seethat the RMSEA and GFI of single factor model are betterthan those of five-factor model But the AGFI IFI and CFIof single factor model are all smaller than those of five-factormodel RMSEA indicates the gap between the theoreticalmodel and the saturated model Its value is smaller than 005which indicates that themodel has very good fitness Amodelcan be accepted only when the values of GFI AGFI IFIand CFI are bigger than 09 The AGFI and IFI of the singlefactor model are smaller than 09 the values of fit indicatorsof the two-factor model three-factor model and four-factormodel are all unacceptable while all the fit indicators of thefive-factor model canmeet the requirementsTheAGFI GFIIFI and CFI of five-factor model are all bigger than 09 andRMSEA is 005 which shows good fitness Considering thefitness of these models we deem the best structure to be thefive-factor model
Discrete Dynamics in Nature and Society 5
StandardizationDivide warning
Datacollection
evaluationModel Model
establishmentResult
analysis
Decideobject
Warningresponse
Figure 4 Steps to build the early risk warning model of risk
Table 3 Reliability analysis of measurement variables
35 Reliability and Validity Analysis In this paper a test ofinternal consistency reliability using Cronbachrsquos coefficientalphas was performed As shown in Table 3 Cronbachrsquos alphacoefficients fall between 0798 and 0902 which are all biggerthan 07 It shows that the questionnaires have high internalconsistency reliability and are acceptable
Validity refers to the effectiveness and correctness ofquestionnaires that is the extent to which the questionnairecan measure the characteristics of the construct It is animportant criterion to evaluate the quality of questionnairesGenerally it includes the content validity and constructvalidity Content validity refers to the extent to which ameasure represents all facets of a given construct In termsof content validity parts of the items come from the currentpapers which are used by many scholars to measure similarvariables Other parts of the items are designed based onliterature review What is more these questionnaires arefurther revised based on the interviews and pretest resultsTherefore the content of the questionnaires matches well theobject and has good content validity
Construct validity is used to test the degree to which atest measures what it claims It mainly uses factor analysisto test the construct validity The main indexes are value ofKMO and value of the Bartlett ball test The value of KMOis 06 which indicates that construct validity is moderate 07indicates that the construct validity is good and 08 indicatesit is very good In terms of the value of the Bartlett ball test thesmaller the better It performs well when 119901 lt 00001 Table 4shows the value of KMO and value of the Bartlett ball test
From the analysis shown in Table 4 we can see that thevalue of KMO of each latent variable is bigger than 07 andthe value of 119901 is 0 the Bartlett ball tests show concentrationAll indicate that the construct validity is good Consideringboth content validity and construct validity we can draw theconclusion that the indicator system and the questionnairesof this paper have high validity
Table 4 Validity analysis of measurement variables
4 Construction of the Early RiskWarning Model of OrganizationalResilience of RampD Team
41 Steps for Construction of Early Risk Warning ModelThese are steps for enterprises to construct an early riskwarning model firstly we need to decide the objects andselect an early risk warning indicator system guided by theearly risk warning principles secondly we divide the riskwarning levels and the warning line thirdly we collect thedata and process the data and normalize the data fourthly weconstruct the mathematical model and evaluate the model byempirical analysis fifthly we analyze the results and judge therelationship between the results and thewarning line and giveearly risk warning response Please see Figure 4
In this study the object of the early risk warning model isorganizational resilience Based on the analysis of Section 3the indicator system of the early risk warning model canbe divided into 2 levels the first level is the five factors oforganizational resilience and the second level is the items ofeach factor The risk warning levels can be divided into 5levels and they are without warning light warning mediumwarning heavy warning and dangerous warning
This paper uses fuzzy integrated evaluation method [35]mainly due to the following reasons firstly it is difficult toprecisely quantify the evaluation of enterprise risk warningThere is ambiguity Based on fuzzy sets and by using variousindicators the fuzzy integrated evaluation method can givecomprehensive evaluation of the membership degree of theevaluated objects By dividing the intervals on the one handit considers the levels of the objects and reflects the ambiguityof the evaluation standards On the other hand it takes theadvantages of peoplersquos experiences which makes the resultsmore objective and adaptable to the reality By combiningboth qualitative and quantitative factors fuzzy integrated
6 Discrete Dynamics in Nature and Society
evaluationmethod improves the quality of evaluation and thereliability of results
42 Determine the Weights To reduce the randomness ofjudgment and increase the reliability of result this paperadopts the fuzzy evaluation method which combines fuzzyset theory and analytical hierarchy process119880 = (119880
1 1198802 1198803 1198804 1198805) where119880
119894denotes one dimension
of organizational resilience 119876 = (1199021 1199022 119902
13) which are
the criterion of the above five aspects 119881 = (V1 V2 V3 V4 V5)
where V1 V2 V3 V4 V5
respectively denotes the ldquostrongrdquoldquogoodrdquo ldquogeneralrdquo ldquofairly weakrdquo and ldquoweakrdquo comment ofeach criterion
This paper adopts analytical hierarchy process methodto decide the weights of indicators Users of the AHP firstdecompose their decision problem into a hierarchy of moreeasily comprehended subproblems Once the hierarchy isbuilt the decision makers will use their judgments about theelementsrsquo relative meaning and importance to evaluate theseelements by comparing them to one another at a time It isrecognized to be practical systematic and concise [36]
Based on the analysis of Section 3 we establish prioritiesamong the elements of the hierarchy by making a series ofjudgments based on pairwise comparisons of the elementsWe can synthesize these judgments to yield a set of overallpriorities for the hierarchy
The priorities of criteria 119880119894are 1198861 1198862 1198863 1198864 1198865and 119860 =
(1198861 1198862 1198863 1198864 1198865) The main steps are as follows
(1) According to scaling theory we construct pairwisecomparison judgment matrix 119860
(5) According to 119860119908 = 120582max119908 we can calculate thelargest eigenvalue and its eigenvector
(6) Consistency test by calculating the consistency indexCI = (120582max minus119899)(119899 minus 1) we can find the correspond-ing average random consistency index RI Then wecan calculate the consistency ratio CR = CIRIWhen CR lt 01 we accept the result Otherwise weneed to rectify matrix 119860 appropriately
43 Establishment of Qualitative Indexes MembershipAlthoughwe can get definite comments on each criterion theldquoboundaryrdquo is relatively ambiguousTherefore when calcula-ting the membership degree of each criterion to the evalua-tion set we need to grade each criterion based on specialistconsultancy and questionnaire analysis We can get themembership vector 119877
3 4 5) where oplus is the operator and its definition is 119887119894=
sum119899
119894=1119908119894119903119894119895 where 119887
119894is the membership vector of each kind
of organizational resilience Normalizing 119887119894we can get 119861 =
(1198611 1198612 1198613 1198614 1198615)119879
(2) Final evaluation vector of the overall goal suppose119862 = 119860 oplus 119861 We add the first two items together If thesum is bigger than 05 (ie the percentage of ldquostrongrdquo andldquogoodrdquo is bigger than 50) it indicates that the organizationalresilience is strong The more approaching 1 the sum of firsttwo items the stronger the organizational resilience
5 Case Analysis
This paper combines both empirical study and case study tosystematically study the factors which influence sustainableinnovation The main conclusions are as follows
Jiangling Motors Co Ltd (JMC in abbreviation here-inafter) a key player in China automotive industry with com-mercial vehicle as its core competitiveness has been ranked asone of ChinaTop 100 ListedCompanies for consecutive yearsIn 2014 JMChit record highs in its business indexeswith salesrevenue reaching 255 billion RMB and volume over 276000units JMC who has established international standardsthat complied with operating systems and mechanisms thatintegrate RampD logistics MSampS and financing supports hasbeen regarded as a model of successful Sinoforeign cooper-ation The company has set up a strong marketing networkthroughout China Its products include Transit commercialvehicle Kaiyun light truck Baodian pickup and YushengSUV which have become models of fuel saving practicalityand environment friendliness In recent years JMC has beeninvesting heavily in new product development to enrich itsproduct line We analyze and evaluate the organizationalresilience of RampD team of JMC company based on themethod of Section 4 The specific steps are as follows
51 Calculate Weights We apply analytical hierarchical pro-cess method to decide weights Based on the pairwise com-parison of importance of criteria we use 1ndash9 scaling methodto get 119860
119894and they are
1198601= (0341 0572 0195 0432 0273)
1198602= (0438 0581 0249 0621 0275)
Discrete Dynamics in Nature and Society 7
1198603= (0417 0359 0721 0346 0512)
1198604= (0438 0519 0434 0419 0351)
1198605= (0523 0475 0354 0464 0765)
(5)
Normalizing them we can get119908119894 We calculate the largest
eigenvalue and its eigenvector and make consistency testThen we can get CR = CIRI = 0043 lt 01 It indicatesthat the consistency of judgment matrix is acceptable
52 Establishment of Membership Matrix of Fuzzy EvaluationBased on the above analysis we select a group of 33 expertsThey mainly come from two sources 17 of them are leadersof departments and senior engineers of enterprises and 16 ofthem are college professors in human resource managementThe alternative answers include ldquoextreme important veryimportant important a little important and not importantrdquoand we give each of them from 5 to 1 respectively We sendthem the questionnaires by email and make sure they do notknow each otherrsquos answer Then we can get the membershipmatrix
1198771=((
(
0456 0812 0654 0142 0461
0451 0641 0247 0541 0622
0751 0712 0574 0341 0346
0235 0632 0341 0723 0348
0432 0543 0355 0541 0156
))
)
1198772=((
(
0634 0247 0541 0346 0316
0453 0621 0421 0261 0423
0431 0354 0354 0156 0761
0312 0317 0641 0341 0345
0231 0394 0521 0141 0432
))
)
1198773=((
(
0345 0384 0347 0512 0712
0311 0371 0621 0315 0731
0421 0381 0274 0311 0623
0235 0267 0646 0328 0512
0512 0461 0346 0612 0641
))
)
1198774=((
(
0421 0379 0513 0812 0385
0356 0812 0346 0346 0541
0841 0644 0311 0461 0197
0345 0547 0345 0856 0634
0314 0284 0346 0654 0461
))
)
1198775=((
(
0765 0698 0611 0341 0341
0395 0541 0851 0206 0261
0584 0621 0511 0509 0345
0347 0574 0341 0451 0433
0614 0354 0317 0394 0542
))
)
(6)
53 Calculating the Comprehensive Evaluation Vector of EachSubgoal From 119861
119894= 119908119894oplus 119877119894 we have
1198611=((
(
0201
0210
0177
0207
0207
))
)
119879
oplus((
(
0456 0812 0654 0142 0461
0451 0641 0247 0541 0622
0751 0712 0574 0341 0346
0235 0632 0341 0723 0348
0432 0543 0355 0541 0156
))
)
1198611= (0457 0667 0429 0646 0389)
1198612=((
(
0272
0245
0133
0215
0165
))
)
119879
oplus((
(
0634 0247 0541 0346 0316
0453 0621 0421 0261 0423
0431 0354 0354 0156 0761
0312 0317 0641 0341 0345
0231 0394 0521 0141 0432
))
)
1198612= (0446 0400 0521 0275 0436)
1198613=((
(
0127
0132
0337
0227
0155
))
)
119879
oplus((
(
0345 0384 0347 0512 0712
0311 0371 0621 0315 0731
0421 0381 0274 0311 0623
0235 0267 0646 0328 0512
0512 0461 0346 0612 0641
))
)
1198613= (0359 0358 0419 0381 0612)
1198614=((
(
0241
0282
0138
0187
0174
))
)
119879
8 Discrete Dynamics in Nature and Society
oplus((
(
0421 0379 0513 0812 0385
0356 0812 0346 0346 0541
0841 0644 0311 0461 0197
0345 0547 0345 0856 0634
0314 0284 0346 0654 0461
))
)
1198614= (0437 0561 0389 0631 0471)
1198615=((
(
0159
0131
0215
0164
0300
))
)
119879
oplus((
(
0765 0698 0611 0341 0341
0395 0541 0851 0206 0261
0584 0621 0511 0509 0345
0347 0574 0341 0451 0433
0614 0354 0317 0394 0542
))
)
1198615= (0540 0516 0470 0383 0396)
(7)
Based on 119861 = (1198611 1198612 1198613 1198614 1198615)119879 normalizing it and from
119862 = 119860 oplus 119861 we have
119862 =((
(
0195
0226
0176
0206
0196
))
)
119879
oplus((
(
0204 0267 0193 0218 0169
0199 0160 0234 0129 0189
0160 0143 0188 0178 0266
0195 0224 0175 0296 0204
0241 0206 0211 0179 0172
))
)
119862 = (0302 0200 0201 0149 0148)
(8)
The sum of first two items of 119862 is 0502 The sum offirst three items of 119862 is 0703 It indicates the light warningand shows the RampD team has relatively high organizationalresilience
6 Conclusions
Based on the structural interviews this paper exploresand confirms the structural dimensions of organizationalresilience Based on it this paper constructs an early riskwarning model of organizational resilience and applies it
to the RampD team of JMC company The conclusions are asfollows
(1) Based on literature review face to face interviewsand open questionnaires this paper applies the exploratoryfactor analysis method to discuss the factor structure oforganizational resilience of RampD teams The results showthat the factor structure of organizational resilience of RampDteams includes five dimensions shared vision willingnessto learn adaptation ability cooperative awareness and workenthusiasm Then this paper compares the five-factor modelwith other competitivemodels to further test the effectivenessof the five-factor model The results show that the five-factormodel is the best What is more the validity and reliabilityof the questionnaires of organizational resilience are provedto meet the requirements of psychometrics The model issupported
(2) Based on the factor structure of organizationalresilience this study constructs an early risk warning modelof organizational resilience of RampD teams It divides the riskwarning levels into five levels By applying fuzzy integratedevaluationmethod and based on the five-factor structure thispaper constructs a hierarchical analysis structure model Bymaking a series of judgments based on pairwise comparisonsof the elements we can get the judgment matrix and therebydecide the weight of each factor of organizational resilienceThen by using Delphi method we can get the member-ship matrix Lastly by calculating the judgment matrix andmembership matrix we can know the risk warning level oforganizational resilience of the RampD teamWe hope the resultwill provide references for the company decision
(3) This study applies the early risk warning model toRampD team of JMC company The results show that the teamhas relatively high organizational resilience These resultsmatch the work performance work experiences and leadersrsquoremarks on the team It also matches the self-evaluation ofmembers of the team All these show that the method isoperational and feasible
Competing Interests
The author declares no competing interests The author hasno financial and personal relationships with other people ororganizations that can inappropriately influence the work
Acknowledgments
This work is supported by the NSFC (71361013 7146200971273122 and 71463020) China Postdoctoral Science Founda-tion under Grant no 2013M541867 Jiangxi Province ScienceFoundation of China under Grants nos 20151BAB207059and 20142BA217018 and China Scholarship Council Fundingunder Grant no 201409805006
References
[1] K M Sutcliffe and J T Vogus ldquoOrganizing for resiliencerdquoin Positive Organizational Scholarship Foundations of a NewDiscipline pp 94ndash110 2003
Discrete Dynamics in Nature and Society 9
[2] J H Gittell K Cameron S Lim and V Rivas ldquoRelationshipslayoffs and organizational resilience airline industry responsesto September 11rdquo Journal of Applied Behavioral Science vol 42no 3 pp 300ndash329 2006
[3] J W Rudolph and N P Repenning ldquoDisaster dynamicsunderstanding the role of quantity in organizational collapserdquoAdministrative Science Quarterly vol 47 no 1 pp 1ndash30 2002
[4] J E Dutton P J Frost M C Worline J M Lilius and J MKanov ldquoLeading in times of traumardquo Harvard Business Reviewvol 80 no 1 pp 54ndash61 2002
[5] R Balu ldquoHow to bounce back from setbacksrdquo Fast Companyvol 45 pp 148ndash156 2001
[6] K EWeick ldquoEnacted sensemaking in crisis situationsrdquo Journalof Management Studies vol 25 no 4 pp 305ndash317 1988
[7] D L Coutu ldquoHow resilience worksrdquo Harvard Business Reviewvol 80 no 5 pp 46ndash55 2002
[8] S F Freeman M Maltz and L Hirschhorn ldquoThe power ofmoral purpose Sandler OrsquoNeill amp partners in the aftermath ofSeptember 11th 2001rdquo Organization Development Journal vol22 no 4 pp 69ndash82 2004
[9] F PMorgeson andD A Hofmann ldquoThe structure and functionof collective constructs implications formultilevel research andtheory developmentrdquo Academy of Management Review vol 24no 2 pp 249ndash265 1999
[10] J F I Horne ldquoThe coming of age of organizational resiliencerdquoBusiness Forum vol 22 no 2-3 pp 24ndash28 1997
[11] L A Mallak ldquoMeasuring resilience in health care providerorganizationsrdquoHealth Manpower Management vol 24 no 4-5pp 148ndash152 1998
[12] L A Mallak ldquoPutting organizational resilience to workrdquo Indus-trial Management vol 40 no 6 pp 8ndash13 1998
[13] C A Lengnick-Hall T E Beck and M L Lengnick-HallldquoDeveloping a capacity for organizational resilience throughstrategic human resourcemanagementrdquoHuman ResourceMan-agement Review vol 21 no 3 pp 243ndash255 2011
[14] M London ldquoToward a theory of career motivationrdquo Academyof Management Review vol 8 no 4 pp 620ndash630 1983
[15] Y Xiao-nan and Z Jian-xin ldquoResilience the psychologicalmechanism for recovery and growthrdquoAdvances in PsychologicalScience vol 5 no 5 pp 658ndash665 2005
[16] X Ju-Zhe S Biao and Z Zhi-Hong ldquoThe research on resilience its evolution and directionrdquo Psychological Science vol 31 no4 pp 995ndash998 2008
[17] E Grotberg ldquoResilience for tomorrowrdquo Trabajo presentado enla International Council of Psychologists Convention Foz doIguacu Brazil Extraıdo de 2005 httpswwwhitpagescomdoc62572445722214401
[18] R A Noe A W Noe and J A Bachhuber ldquoAn investigationof the correlates of career motivationrdquo Journal of VocationalBehavior vol 37 no 3 pp 340ndash356 1990
[19] F Luthans J B Avey R Clapp-Smith and W Li ldquoMore evi-dence on the value of Chinese workersrsquo psychological capital apotentially unlimited competitive resourcerdquo The InternationalJournal of Human Resource Management vol 19 no 5 pp 818ndash827 2008
[20] C Fourie and L J Van Vuuren ldquoDefining andmeasuring careerresiliencerdquo SA Journal of Industrial Psychology vol 24 no 3 pp52ndash59 1998
[21] Y C Liu Relationships between Career Resilience and CareerBeliefs of Employees in Taiwan Texas AampM University 2003
[22] B Obrist C Pfeiffer and R Henley ldquoMulti-layered socialresilience a new approach in mitigation researchrdquo Progress inDevelopment Studies vol 10 no 4 pp 283ndash293 2010
[23] S J Breckler ldquoEmpirical validation of affect behavior and cog-nition as distinct components of attituderdquo Journal of Personalityamp Social Psychology vol 47 no 6 pp 1191ndash1205 1984
[24] C A Lietz andM Strength ldquoStories of successful reunificationa narrative study of family resilience in child welfarerdquo Familiesin Society The Journal of Contemporary Social Services vol 92no 2 pp 203ndash210 2011
[25] X Zhao and Z-G Xin ldquoResearch review on models ofenterprise risk forewarning managementrdquo Journal of BeijingUniversity of Posts and Telecommunications (Social SciencesEdition) vol 12 no 1 pp 93ndash97 2010
[26] E F Fern ldquoThe use of focus groups for idea generationthe effects of group size acquaintanceship and moderator onresponse quantity and qualityrdquo Journal of Marketing Researchvol 19 no 1 pp 1ndash13 1982
[27] S-H Chen and W He ldquoStudy on knowledge propagationin complex networks based on preferences taking wechat asexamplerdquo Abstract and Applied Analysis vol 2014 Article ID543734 11 pages 2014
[28] W He ldquoAn inventory controlled supply chain model based onimproved BP neural networkrdquoDiscrete Dynamics in Nature andSociety vol 2013 Article ID 537675 7 pages 2013
[29] S-H Chen ldquoA novel culture algorithm and itrsquos application inknowledge integrationrdquo Information vol 15 no 11 B pp 4847ndash4853 2012
[30] W He and S-H Chen ldquoGame analysis of determinants ofstability of semiconductor modular production networksrdquo Sus-tainability vol 6 no 8 pp 4772ndash4794 2014
[31] S-H Chen ldquoThe influencing factors of enterprise sustainableinnovation an empirical studyrdquo Sustainability vol 8 no 5article 425 17 pages 2016
[32] S-H Chen ldquoEmpirical research on knowledge integrationimproving innovation ability of IT enterprisemdashbased on struc-tural equation modelrdquo Information vol 14 no 3 pp 753ndash7582011
[33] G-X Song ldquoStudy on construct and its dimensions of careerresilience based on Chinese indigenous culturerdquo EconomicManagement vol 33 no 11 pp 184ndash193 2011
[34] J-L Ke J-M Sun J-T Shi and Q-X Gu ldquoEmpirical studyon relationship between social capital of RampD team and teampotencyrdquoManagement World vol 3 pp 89ndash101 2007
[35] H Shouzhong and G Jianqin ldquoFuzzy integrated evaluation andits applicationrdquo Journal of China Textile University vol 21 no 1pp 74ndash80 1995
[36] A Jebreen and A Husain ldquoUtility-based approach for deter-mining the weights of participants in virtual organizationrdquoApplied Mathematical Sciences vol 6 no 96 pp 4773ndash47862012
Different from other teams RampD teams are knowledgeteams All the members are ldquoknowledge workersrdquo Generallyspeaking knowledge workers are those people who havethe ability to produce create expand and apply knowledgeand take knowledge work as professions [28ndash31] Oftenknowledge capital of an enterprise is appreciated becauseof its knowledge work Comparing with the traditionaloperational workers knowledge workers more focus on andpursue the realization of their ownvaluesTheyhave relativelyhigh independence and would like to take the challengingwork with pleasure But partly because of such features theirturnover rate is high [32]
The above characteristics of RampD teams differentiatethem from other teams and departments in organizationsThe operation of other teams or departments more dependson rigid institutions while due to the particularity of its worknature RampD teams can not wholly depend on institutionRampD teams should have flexibility Therefore the study onthe resilience of RampD team is meaningful and has greatpractical significance However we can hardly see thesestudies currently
31 Study Design First of all based on the analysis ofliterature on organizational resilience we give definition toorganizational resilience and decide the range of items Thenby interview and open questionnaire we collect all the itemsrelated to organizational resilience to form the questionnairesof organizational resilience Finally by applying question-naires and multivariate statistical analysis we explore thefactor structure of organizational resilience For the flowchartof questionnaire establishment see Figure 1
32 Exploratory Factor Analysis Based on both domestic andabroad studies on the dimensions of organizational resilience[18 20 21] by literature review this paper designs open ques-tionnaires and implements structural interview Accordingto Song the structural interview objects need to be 10ndash15homogenousmembers Based on it [33] this paper chooses 15RampD team leaders andRampDengineers to interviewThe inter-view for each person lasts about 20minutes To guarantee thereliability and validity of these interviews we mainly do twothings firstly during the discussion getting the permissionof the interviewees we record the important talks points ofview and terms secondlywe sort out the notes and recordingWe take notes where there is a question and if it is necessarywe pay a return visit to the interviewees by telephone Bycontent analysis this paper sorts out the initial statementsand deletes the statements of organizational resilience with
semantic ambiguityThen wemake preliminary classificationof the remaining statements and combine the statementswith similar content and the statements only being expressedin different ways
The objects of pretest are MBA members of JiangxiUniversity of Finance and Economics who are the engineersin RampD teams Altogether we send 300 questionnaires andreceive 203 questionnaires SPSS 220 is used in processingand analyzing Based on the factor analysis and item analysiswe find that the factor structure of the initial questionnaireis not clear and it has multiple loading items So the ini-tial questionnaires are not effective ones and need furtherrevision The principles for questionnaire revision are asfollows firstly referring to the results of factor analysis wechoose the items with high communality and high factorloading and delete the items with low communality and lowfactor loading Secondly also referring to the results of factoranalysis we delete items with significant double loadings (thedifference between the two biggest values of the factor loadingis less than 02) and items whose maximum value of factorloading is less than 04 Then we adjust and delete the itemswith ambiguous meanings Thirdly based on the scores ofitems we remove the items with low internal consistencyreliability After the revision we choose 18 items from theinitial questionnaire to form the formal questionnaire
A factor analysis on the 18 items is conducted andthe principal component analysis is adopted with varimaxrotation The statistical results confirm the presence of fiveunique factors of organizational resilience of RampD teamsThevalue of KMO is 0907 and the value of Bartlettrsquos ball test is1245034 with probability less than 0001 The total varianceexplanation rate is 62010 Based on the understanding ofeach item we define the five factors as shared vision willing-ness to learn adaptation ability cooperative awareness andwork enthusiasm
33 Confirmatory Factor Analysis The results of the abovesurvey show that organizational resilience has five dimen-sions with 18 items But the factor structure only passes theexploratory factor analysis Whether the theoretical model isbetter than the single dimensional model or other possiblemodels still needs to be tested In other words we need toapply the confirmatory factor analysis to test the superiorityof the theoretical model Next we use the questionnairesof organizational resilience formed in the above survey torecollect the data to test the correctness of the questionnairesof organizational resilience
In the confirmatory factor analysis the survey objectsare the 422 RampD teams of 279 enterprises in 11 provinces inChina To ensure the independency of the data and to avoidpossible interferences this study differentiates the personsparticipating in CFA test and those in EFA test Because weneed to test the organizational resilience of RampD teams wechoose 2-3 members from each team [34] The investigationtime span is from January 2015 to June 2015 We send 1000questionnaires and collect 509 questionnaires The rate ofeffective questionnaires is 509 We use the organizationalresilience questionnaires with 18 items and apply the Likert5-point scale to evaluate each item specific questionnaire was
4 Discrete Dynamics in Nature and Society
Sharedvision
Willingnessto learn
Adaptationability
Cooperativeawareness
Workenthusiasm
Figure 2 The hypothesized model of organizational resilience
081 078 066
068 073 054
083 051
057
067 Sharedvision
Willingnessto learn
Adaptationability
Cooperativeawareness
Workenthusiasm
Figure 3 The structure model of organizational resilience
Table 1 Fit indexes of organizational resilience model
Index 1205942 GFI AGFI IFI CFI RMSEA
Value 27068 092 090 093 092 005
shown in the Appendix in Supplementary Material availableonline at httpdxdoiorg10115520164602870 In this partwe hypothesize that the factor structure of organizationalresilience is a five-factor model See Figure 2
We test the model by applying the structural equationmodel method We fit the model with the observed values ofa sample of 509 RampD teams and get the factor structure oforganizational resilience See Figure 3
To evaluate whether a model is acceptable or not wemainly check the different fit indexes For themain fit indexessee Table 1 The full name of each fit index is as followsGFI goodness-of-fit index AGFI adjusted goodness-of-fitindex IFI incremental fit index CFI comparative fit indexRMSEA Root Mean Square Error of Approximation
From Table 1 we can find that all the fit indexes fall intothe acceptable range which indicates that the observed datawell support the model The exploratory factor analysis isconfirmed
34 Analysis Comparison with Other Possible Models Tofurther test the factor structure this paper compares the five-factor model with other competitivemodelsThe competitivemodels include the single factor model two-factor modelthree-factor model and four-factor modelThere are 18 itemsin the questionnaires Therefore there is only one possibilityfor the single factor model there are 153 possibilities forthe two-factor model there are 816 possibilities for three-factor model and 3060 possibilities for four-factor model Itis impossible for us to compare all of them So the strategywe take is to find out the most reasonable model for eachfactor model and compare them Based on the literaturereview in Section 2 [18ndash23] in the two-factor model wecombine shared vision and cooperative awareness as onefactor We combine willingness to learn adaptation ability
Table 2 Comparison of fit indexes of models
Index 1205942 GFI AGFI IFI CFI RMSEA
Five-factor model 27068 092 090 093 092 005Four-factor model 15936 088 078 082 067 0161Three-factor model 18863 079 081 075 077 0059Two-factor model 21342 057 062 075 089 0231Single factor model 23034 093 088 089 090 0025
and work enthusiasm as another factor in the three-factormodel we combine shared vision and cooperative awarenessas one factor willingness to learn and adaptation ability as thesecond factor and work enthusiasm as the third factor in thefour-factormodel we combine shared vision and cooperativeawareness as one factor and the three other factors remain asthey are We use AMOS 180 to analyze the five models andcheck their fitnessWith the same data based on the question-naire of Section 33 we can have the fitness of the fivemodels
CFI and IFI are the relative fit indexes AGFI GFI andRMSEA are the absolute fit indexes From Table 2 we can seethat the RMSEA and GFI of single factor model are betterthan those of five-factor model But the AGFI IFI and CFIof single factor model are all smaller than those of five-factormodel RMSEA indicates the gap between the theoreticalmodel and the saturated model Its value is smaller than 005which indicates that themodel has very good fitness Amodelcan be accepted only when the values of GFI AGFI IFIand CFI are bigger than 09 The AGFI and IFI of the singlefactor model are smaller than 09 the values of fit indicatorsof the two-factor model three-factor model and four-factormodel are all unacceptable while all the fit indicators of thefive-factor model canmeet the requirementsTheAGFI GFIIFI and CFI of five-factor model are all bigger than 09 andRMSEA is 005 which shows good fitness Considering thefitness of these models we deem the best structure to be thefive-factor model
Discrete Dynamics in Nature and Society 5
StandardizationDivide warning
Datacollection
evaluationModel Model
establishmentResult
analysis
Decideobject
Warningresponse
Figure 4 Steps to build the early risk warning model of risk
Table 3 Reliability analysis of measurement variables
35 Reliability and Validity Analysis In this paper a test ofinternal consistency reliability using Cronbachrsquos coefficientalphas was performed As shown in Table 3 Cronbachrsquos alphacoefficients fall between 0798 and 0902 which are all biggerthan 07 It shows that the questionnaires have high internalconsistency reliability and are acceptable
Validity refers to the effectiveness and correctness ofquestionnaires that is the extent to which the questionnairecan measure the characteristics of the construct It is animportant criterion to evaluate the quality of questionnairesGenerally it includes the content validity and constructvalidity Content validity refers to the extent to which ameasure represents all facets of a given construct In termsof content validity parts of the items come from the currentpapers which are used by many scholars to measure similarvariables Other parts of the items are designed based onliterature review What is more these questionnaires arefurther revised based on the interviews and pretest resultsTherefore the content of the questionnaires matches well theobject and has good content validity
Construct validity is used to test the degree to which atest measures what it claims It mainly uses factor analysisto test the construct validity The main indexes are value ofKMO and value of the Bartlett ball test The value of KMOis 06 which indicates that construct validity is moderate 07indicates that the construct validity is good and 08 indicatesit is very good In terms of the value of the Bartlett ball test thesmaller the better It performs well when 119901 lt 00001 Table 4shows the value of KMO and value of the Bartlett ball test
From the analysis shown in Table 4 we can see that thevalue of KMO of each latent variable is bigger than 07 andthe value of 119901 is 0 the Bartlett ball tests show concentrationAll indicate that the construct validity is good Consideringboth content validity and construct validity we can draw theconclusion that the indicator system and the questionnairesof this paper have high validity
Table 4 Validity analysis of measurement variables
4 Construction of the Early RiskWarning Model of OrganizationalResilience of RampD Team
41 Steps for Construction of Early Risk Warning ModelThese are steps for enterprises to construct an early riskwarning model firstly we need to decide the objects andselect an early risk warning indicator system guided by theearly risk warning principles secondly we divide the riskwarning levels and the warning line thirdly we collect thedata and process the data and normalize the data fourthly weconstruct the mathematical model and evaluate the model byempirical analysis fifthly we analyze the results and judge therelationship between the results and thewarning line and giveearly risk warning response Please see Figure 4
In this study the object of the early risk warning model isorganizational resilience Based on the analysis of Section 3the indicator system of the early risk warning model canbe divided into 2 levels the first level is the five factors oforganizational resilience and the second level is the items ofeach factor The risk warning levels can be divided into 5levels and they are without warning light warning mediumwarning heavy warning and dangerous warning
This paper uses fuzzy integrated evaluation method [35]mainly due to the following reasons firstly it is difficult toprecisely quantify the evaluation of enterprise risk warningThere is ambiguity Based on fuzzy sets and by using variousindicators the fuzzy integrated evaluation method can givecomprehensive evaluation of the membership degree of theevaluated objects By dividing the intervals on the one handit considers the levels of the objects and reflects the ambiguityof the evaluation standards On the other hand it takes theadvantages of peoplersquos experiences which makes the resultsmore objective and adaptable to the reality By combiningboth qualitative and quantitative factors fuzzy integrated
6 Discrete Dynamics in Nature and Society
evaluationmethod improves the quality of evaluation and thereliability of results
42 Determine the Weights To reduce the randomness ofjudgment and increase the reliability of result this paperadopts the fuzzy evaluation method which combines fuzzyset theory and analytical hierarchy process119880 = (119880
1 1198802 1198803 1198804 1198805) where119880
119894denotes one dimension
of organizational resilience 119876 = (1199021 1199022 119902
13) which are
the criterion of the above five aspects 119881 = (V1 V2 V3 V4 V5)
where V1 V2 V3 V4 V5
respectively denotes the ldquostrongrdquoldquogoodrdquo ldquogeneralrdquo ldquofairly weakrdquo and ldquoweakrdquo comment ofeach criterion
This paper adopts analytical hierarchy process methodto decide the weights of indicators Users of the AHP firstdecompose their decision problem into a hierarchy of moreeasily comprehended subproblems Once the hierarchy isbuilt the decision makers will use their judgments about theelementsrsquo relative meaning and importance to evaluate theseelements by comparing them to one another at a time It isrecognized to be practical systematic and concise [36]
Based on the analysis of Section 3 we establish prioritiesamong the elements of the hierarchy by making a series ofjudgments based on pairwise comparisons of the elementsWe can synthesize these judgments to yield a set of overallpriorities for the hierarchy
The priorities of criteria 119880119894are 1198861 1198862 1198863 1198864 1198865and 119860 =
(1198861 1198862 1198863 1198864 1198865) The main steps are as follows
(1) According to scaling theory we construct pairwisecomparison judgment matrix 119860
(5) According to 119860119908 = 120582max119908 we can calculate thelargest eigenvalue and its eigenvector
(6) Consistency test by calculating the consistency indexCI = (120582max minus119899)(119899 minus 1) we can find the correspond-ing average random consistency index RI Then wecan calculate the consistency ratio CR = CIRIWhen CR lt 01 we accept the result Otherwise weneed to rectify matrix 119860 appropriately
43 Establishment of Qualitative Indexes MembershipAlthoughwe can get definite comments on each criterion theldquoboundaryrdquo is relatively ambiguousTherefore when calcula-ting the membership degree of each criterion to the evalua-tion set we need to grade each criterion based on specialistconsultancy and questionnaire analysis We can get themembership vector 119877
3 4 5) where oplus is the operator and its definition is 119887119894=
sum119899
119894=1119908119894119903119894119895 where 119887
119894is the membership vector of each kind
of organizational resilience Normalizing 119887119894we can get 119861 =
(1198611 1198612 1198613 1198614 1198615)119879
(2) Final evaluation vector of the overall goal suppose119862 = 119860 oplus 119861 We add the first two items together If thesum is bigger than 05 (ie the percentage of ldquostrongrdquo andldquogoodrdquo is bigger than 50) it indicates that the organizationalresilience is strong The more approaching 1 the sum of firsttwo items the stronger the organizational resilience
5 Case Analysis
This paper combines both empirical study and case study tosystematically study the factors which influence sustainableinnovation The main conclusions are as follows
Jiangling Motors Co Ltd (JMC in abbreviation here-inafter) a key player in China automotive industry with com-mercial vehicle as its core competitiveness has been ranked asone of ChinaTop 100 ListedCompanies for consecutive yearsIn 2014 JMChit record highs in its business indexeswith salesrevenue reaching 255 billion RMB and volume over 276000units JMC who has established international standardsthat complied with operating systems and mechanisms thatintegrate RampD logistics MSampS and financing supports hasbeen regarded as a model of successful Sinoforeign cooper-ation The company has set up a strong marketing networkthroughout China Its products include Transit commercialvehicle Kaiyun light truck Baodian pickup and YushengSUV which have become models of fuel saving practicalityand environment friendliness In recent years JMC has beeninvesting heavily in new product development to enrich itsproduct line We analyze and evaluate the organizationalresilience of RampD team of JMC company based on themethod of Section 4 The specific steps are as follows
51 Calculate Weights We apply analytical hierarchical pro-cess method to decide weights Based on the pairwise com-parison of importance of criteria we use 1ndash9 scaling methodto get 119860
119894and they are
1198601= (0341 0572 0195 0432 0273)
1198602= (0438 0581 0249 0621 0275)
Discrete Dynamics in Nature and Society 7
1198603= (0417 0359 0721 0346 0512)
1198604= (0438 0519 0434 0419 0351)
1198605= (0523 0475 0354 0464 0765)
(5)
Normalizing them we can get119908119894 We calculate the largest
eigenvalue and its eigenvector and make consistency testThen we can get CR = CIRI = 0043 lt 01 It indicatesthat the consistency of judgment matrix is acceptable
52 Establishment of Membership Matrix of Fuzzy EvaluationBased on the above analysis we select a group of 33 expertsThey mainly come from two sources 17 of them are leadersof departments and senior engineers of enterprises and 16 ofthem are college professors in human resource managementThe alternative answers include ldquoextreme important veryimportant important a little important and not importantrdquoand we give each of them from 5 to 1 respectively We sendthem the questionnaires by email and make sure they do notknow each otherrsquos answer Then we can get the membershipmatrix
1198771=((
(
0456 0812 0654 0142 0461
0451 0641 0247 0541 0622
0751 0712 0574 0341 0346
0235 0632 0341 0723 0348
0432 0543 0355 0541 0156
))
)
1198772=((
(
0634 0247 0541 0346 0316
0453 0621 0421 0261 0423
0431 0354 0354 0156 0761
0312 0317 0641 0341 0345
0231 0394 0521 0141 0432
))
)
1198773=((
(
0345 0384 0347 0512 0712
0311 0371 0621 0315 0731
0421 0381 0274 0311 0623
0235 0267 0646 0328 0512
0512 0461 0346 0612 0641
))
)
1198774=((
(
0421 0379 0513 0812 0385
0356 0812 0346 0346 0541
0841 0644 0311 0461 0197
0345 0547 0345 0856 0634
0314 0284 0346 0654 0461
))
)
1198775=((
(
0765 0698 0611 0341 0341
0395 0541 0851 0206 0261
0584 0621 0511 0509 0345
0347 0574 0341 0451 0433
0614 0354 0317 0394 0542
))
)
(6)
53 Calculating the Comprehensive Evaluation Vector of EachSubgoal From 119861
119894= 119908119894oplus 119877119894 we have
1198611=((
(
0201
0210
0177
0207
0207
))
)
119879
oplus((
(
0456 0812 0654 0142 0461
0451 0641 0247 0541 0622
0751 0712 0574 0341 0346
0235 0632 0341 0723 0348
0432 0543 0355 0541 0156
))
)
1198611= (0457 0667 0429 0646 0389)
1198612=((
(
0272
0245
0133
0215
0165
))
)
119879
oplus((
(
0634 0247 0541 0346 0316
0453 0621 0421 0261 0423
0431 0354 0354 0156 0761
0312 0317 0641 0341 0345
0231 0394 0521 0141 0432
))
)
1198612= (0446 0400 0521 0275 0436)
1198613=((
(
0127
0132
0337
0227
0155
))
)
119879
oplus((
(
0345 0384 0347 0512 0712
0311 0371 0621 0315 0731
0421 0381 0274 0311 0623
0235 0267 0646 0328 0512
0512 0461 0346 0612 0641
))
)
1198613= (0359 0358 0419 0381 0612)
1198614=((
(
0241
0282
0138
0187
0174
))
)
119879
8 Discrete Dynamics in Nature and Society
oplus((
(
0421 0379 0513 0812 0385
0356 0812 0346 0346 0541
0841 0644 0311 0461 0197
0345 0547 0345 0856 0634
0314 0284 0346 0654 0461
))
)
1198614= (0437 0561 0389 0631 0471)
1198615=((
(
0159
0131
0215
0164
0300
))
)
119879
oplus((
(
0765 0698 0611 0341 0341
0395 0541 0851 0206 0261
0584 0621 0511 0509 0345
0347 0574 0341 0451 0433
0614 0354 0317 0394 0542
))
)
1198615= (0540 0516 0470 0383 0396)
(7)
Based on 119861 = (1198611 1198612 1198613 1198614 1198615)119879 normalizing it and from
119862 = 119860 oplus 119861 we have
119862 =((
(
0195
0226
0176
0206
0196
))
)
119879
oplus((
(
0204 0267 0193 0218 0169
0199 0160 0234 0129 0189
0160 0143 0188 0178 0266
0195 0224 0175 0296 0204
0241 0206 0211 0179 0172
))
)
119862 = (0302 0200 0201 0149 0148)
(8)
The sum of first two items of 119862 is 0502 The sum offirst three items of 119862 is 0703 It indicates the light warningand shows the RampD team has relatively high organizationalresilience
6 Conclusions
Based on the structural interviews this paper exploresand confirms the structural dimensions of organizationalresilience Based on it this paper constructs an early riskwarning model of organizational resilience and applies it
to the RampD team of JMC company The conclusions are asfollows
(1) Based on literature review face to face interviewsand open questionnaires this paper applies the exploratoryfactor analysis method to discuss the factor structure oforganizational resilience of RampD teams The results showthat the factor structure of organizational resilience of RampDteams includes five dimensions shared vision willingnessto learn adaptation ability cooperative awareness and workenthusiasm Then this paper compares the five-factor modelwith other competitivemodels to further test the effectivenessof the five-factor model The results show that the five-factormodel is the best What is more the validity and reliabilityof the questionnaires of organizational resilience are provedto meet the requirements of psychometrics The model issupported
(2) Based on the factor structure of organizationalresilience this study constructs an early risk warning modelof organizational resilience of RampD teams It divides the riskwarning levels into five levels By applying fuzzy integratedevaluationmethod and based on the five-factor structure thispaper constructs a hierarchical analysis structure model Bymaking a series of judgments based on pairwise comparisonsof the elements we can get the judgment matrix and therebydecide the weight of each factor of organizational resilienceThen by using Delphi method we can get the member-ship matrix Lastly by calculating the judgment matrix andmembership matrix we can know the risk warning level oforganizational resilience of the RampD teamWe hope the resultwill provide references for the company decision
(3) This study applies the early risk warning model toRampD team of JMC company The results show that the teamhas relatively high organizational resilience These resultsmatch the work performance work experiences and leadersrsquoremarks on the team It also matches the self-evaluation ofmembers of the team All these show that the method isoperational and feasible
Competing Interests
The author declares no competing interests The author hasno financial and personal relationships with other people ororganizations that can inappropriately influence the work
Acknowledgments
This work is supported by the NSFC (71361013 7146200971273122 and 71463020) China Postdoctoral Science Founda-tion under Grant no 2013M541867 Jiangxi Province ScienceFoundation of China under Grants nos 20151BAB207059and 20142BA217018 and China Scholarship Council Fundingunder Grant no 201409805006
References
[1] K M Sutcliffe and J T Vogus ldquoOrganizing for resiliencerdquoin Positive Organizational Scholarship Foundations of a NewDiscipline pp 94ndash110 2003
Discrete Dynamics in Nature and Society 9
[2] J H Gittell K Cameron S Lim and V Rivas ldquoRelationshipslayoffs and organizational resilience airline industry responsesto September 11rdquo Journal of Applied Behavioral Science vol 42no 3 pp 300ndash329 2006
[3] J W Rudolph and N P Repenning ldquoDisaster dynamicsunderstanding the role of quantity in organizational collapserdquoAdministrative Science Quarterly vol 47 no 1 pp 1ndash30 2002
[4] J E Dutton P J Frost M C Worline J M Lilius and J MKanov ldquoLeading in times of traumardquo Harvard Business Reviewvol 80 no 1 pp 54ndash61 2002
[5] R Balu ldquoHow to bounce back from setbacksrdquo Fast Companyvol 45 pp 148ndash156 2001
[6] K EWeick ldquoEnacted sensemaking in crisis situationsrdquo Journalof Management Studies vol 25 no 4 pp 305ndash317 1988
[7] D L Coutu ldquoHow resilience worksrdquo Harvard Business Reviewvol 80 no 5 pp 46ndash55 2002
[8] S F Freeman M Maltz and L Hirschhorn ldquoThe power ofmoral purpose Sandler OrsquoNeill amp partners in the aftermath ofSeptember 11th 2001rdquo Organization Development Journal vol22 no 4 pp 69ndash82 2004
[9] F PMorgeson andD A Hofmann ldquoThe structure and functionof collective constructs implications formultilevel research andtheory developmentrdquo Academy of Management Review vol 24no 2 pp 249ndash265 1999
[10] J F I Horne ldquoThe coming of age of organizational resiliencerdquoBusiness Forum vol 22 no 2-3 pp 24ndash28 1997
[11] L A Mallak ldquoMeasuring resilience in health care providerorganizationsrdquoHealth Manpower Management vol 24 no 4-5pp 148ndash152 1998
[12] L A Mallak ldquoPutting organizational resilience to workrdquo Indus-trial Management vol 40 no 6 pp 8ndash13 1998
[13] C A Lengnick-Hall T E Beck and M L Lengnick-HallldquoDeveloping a capacity for organizational resilience throughstrategic human resourcemanagementrdquoHuman ResourceMan-agement Review vol 21 no 3 pp 243ndash255 2011
[14] M London ldquoToward a theory of career motivationrdquo Academyof Management Review vol 8 no 4 pp 620ndash630 1983
[15] Y Xiao-nan and Z Jian-xin ldquoResilience the psychologicalmechanism for recovery and growthrdquoAdvances in PsychologicalScience vol 5 no 5 pp 658ndash665 2005
[16] X Ju-Zhe S Biao and Z Zhi-Hong ldquoThe research on resilience its evolution and directionrdquo Psychological Science vol 31 no4 pp 995ndash998 2008
[17] E Grotberg ldquoResilience for tomorrowrdquo Trabajo presentado enla International Council of Psychologists Convention Foz doIguacu Brazil Extraıdo de 2005 httpswwwhitpagescomdoc62572445722214401
[18] R A Noe A W Noe and J A Bachhuber ldquoAn investigationof the correlates of career motivationrdquo Journal of VocationalBehavior vol 37 no 3 pp 340ndash356 1990
[19] F Luthans J B Avey R Clapp-Smith and W Li ldquoMore evi-dence on the value of Chinese workersrsquo psychological capital apotentially unlimited competitive resourcerdquo The InternationalJournal of Human Resource Management vol 19 no 5 pp 818ndash827 2008
[20] C Fourie and L J Van Vuuren ldquoDefining andmeasuring careerresiliencerdquo SA Journal of Industrial Psychology vol 24 no 3 pp52ndash59 1998
[21] Y C Liu Relationships between Career Resilience and CareerBeliefs of Employees in Taiwan Texas AampM University 2003
[22] B Obrist C Pfeiffer and R Henley ldquoMulti-layered socialresilience a new approach in mitigation researchrdquo Progress inDevelopment Studies vol 10 no 4 pp 283ndash293 2010
[23] S J Breckler ldquoEmpirical validation of affect behavior and cog-nition as distinct components of attituderdquo Journal of Personalityamp Social Psychology vol 47 no 6 pp 1191ndash1205 1984
[24] C A Lietz andM Strength ldquoStories of successful reunificationa narrative study of family resilience in child welfarerdquo Familiesin Society The Journal of Contemporary Social Services vol 92no 2 pp 203ndash210 2011
[25] X Zhao and Z-G Xin ldquoResearch review on models ofenterprise risk forewarning managementrdquo Journal of BeijingUniversity of Posts and Telecommunications (Social SciencesEdition) vol 12 no 1 pp 93ndash97 2010
[26] E F Fern ldquoThe use of focus groups for idea generationthe effects of group size acquaintanceship and moderator onresponse quantity and qualityrdquo Journal of Marketing Researchvol 19 no 1 pp 1ndash13 1982
[27] S-H Chen and W He ldquoStudy on knowledge propagationin complex networks based on preferences taking wechat asexamplerdquo Abstract and Applied Analysis vol 2014 Article ID543734 11 pages 2014
[28] W He ldquoAn inventory controlled supply chain model based onimproved BP neural networkrdquoDiscrete Dynamics in Nature andSociety vol 2013 Article ID 537675 7 pages 2013
[29] S-H Chen ldquoA novel culture algorithm and itrsquos application inknowledge integrationrdquo Information vol 15 no 11 B pp 4847ndash4853 2012
[30] W He and S-H Chen ldquoGame analysis of determinants ofstability of semiconductor modular production networksrdquo Sus-tainability vol 6 no 8 pp 4772ndash4794 2014
[31] S-H Chen ldquoThe influencing factors of enterprise sustainableinnovation an empirical studyrdquo Sustainability vol 8 no 5article 425 17 pages 2016
[32] S-H Chen ldquoEmpirical research on knowledge integrationimproving innovation ability of IT enterprisemdashbased on struc-tural equation modelrdquo Information vol 14 no 3 pp 753ndash7582011
[33] G-X Song ldquoStudy on construct and its dimensions of careerresilience based on Chinese indigenous culturerdquo EconomicManagement vol 33 no 11 pp 184ndash193 2011
[34] J-L Ke J-M Sun J-T Shi and Q-X Gu ldquoEmpirical studyon relationship between social capital of RampD team and teampotencyrdquoManagement World vol 3 pp 89ndash101 2007
[35] H Shouzhong and G Jianqin ldquoFuzzy integrated evaluation andits applicationrdquo Journal of China Textile University vol 21 no 1pp 74ndash80 1995
[36] A Jebreen and A Husain ldquoUtility-based approach for deter-mining the weights of participants in virtual organizationrdquoApplied Mathematical Sciences vol 6 no 96 pp 4773ndash47862012
Figure 2 The hypothesized model of organizational resilience
081 078 066
068 073 054
083 051
057
067 Sharedvision
Willingnessto learn
Adaptationability
Cooperativeawareness
Workenthusiasm
Figure 3 The structure model of organizational resilience
Table 1 Fit indexes of organizational resilience model
Index 1205942 GFI AGFI IFI CFI RMSEA
Value 27068 092 090 093 092 005
shown in the Appendix in Supplementary Material availableonline at httpdxdoiorg10115520164602870 In this partwe hypothesize that the factor structure of organizationalresilience is a five-factor model See Figure 2
We test the model by applying the structural equationmodel method We fit the model with the observed values ofa sample of 509 RampD teams and get the factor structure oforganizational resilience See Figure 3
To evaluate whether a model is acceptable or not wemainly check the different fit indexes For themain fit indexessee Table 1 The full name of each fit index is as followsGFI goodness-of-fit index AGFI adjusted goodness-of-fitindex IFI incremental fit index CFI comparative fit indexRMSEA Root Mean Square Error of Approximation
From Table 1 we can find that all the fit indexes fall intothe acceptable range which indicates that the observed datawell support the model The exploratory factor analysis isconfirmed
34 Analysis Comparison with Other Possible Models Tofurther test the factor structure this paper compares the five-factor model with other competitivemodelsThe competitivemodels include the single factor model two-factor modelthree-factor model and four-factor modelThere are 18 itemsin the questionnaires Therefore there is only one possibilityfor the single factor model there are 153 possibilities forthe two-factor model there are 816 possibilities for three-factor model and 3060 possibilities for four-factor model Itis impossible for us to compare all of them So the strategywe take is to find out the most reasonable model for eachfactor model and compare them Based on the literaturereview in Section 2 [18ndash23] in the two-factor model wecombine shared vision and cooperative awareness as onefactor We combine willingness to learn adaptation ability
Table 2 Comparison of fit indexes of models
Index 1205942 GFI AGFI IFI CFI RMSEA
Five-factor model 27068 092 090 093 092 005Four-factor model 15936 088 078 082 067 0161Three-factor model 18863 079 081 075 077 0059Two-factor model 21342 057 062 075 089 0231Single factor model 23034 093 088 089 090 0025
and work enthusiasm as another factor in the three-factormodel we combine shared vision and cooperative awarenessas one factor willingness to learn and adaptation ability as thesecond factor and work enthusiasm as the third factor in thefour-factormodel we combine shared vision and cooperativeawareness as one factor and the three other factors remain asthey are We use AMOS 180 to analyze the five models andcheck their fitnessWith the same data based on the question-naire of Section 33 we can have the fitness of the fivemodels
CFI and IFI are the relative fit indexes AGFI GFI andRMSEA are the absolute fit indexes From Table 2 we can seethat the RMSEA and GFI of single factor model are betterthan those of five-factor model But the AGFI IFI and CFIof single factor model are all smaller than those of five-factormodel RMSEA indicates the gap between the theoreticalmodel and the saturated model Its value is smaller than 005which indicates that themodel has very good fitness Amodelcan be accepted only when the values of GFI AGFI IFIand CFI are bigger than 09 The AGFI and IFI of the singlefactor model are smaller than 09 the values of fit indicatorsof the two-factor model three-factor model and four-factormodel are all unacceptable while all the fit indicators of thefive-factor model canmeet the requirementsTheAGFI GFIIFI and CFI of five-factor model are all bigger than 09 andRMSEA is 005 which shows good fitness Considering thefitness of these models we deem the best structure to be thefive-factor model
Discrete Dynamics in Nature and Society 5
StandardizationDivide warning
Datacollection
evaluationModel Model
establishmentResult
analysis
Decideobject
Warningresponse
Figure 4 Steps to build the early risk warning model of risk
Table 3 Reliability analysis of measurement variables
35 Reliability and Validity Analysis In this paper a test ofinternal consistency reliability using Cronbachrsquos coefficientalphas was performed As shown in Table 3 Cronbachrsquos alphacoefficients fall between 0798 and 0902 which are all biggerthan 07 It shows that the questionnaires have high internalconsistency reliability and are acceptable
Validity refers to the effectiveness and correctness ofquestionnaires that is the extent to which the questionnairecan measure the characteristics of the construct It is animportant criterion to evaluate the quality of questionnairesGenerally it includes the content validity and constructvalidity Content validity refers to the extent to which ameasure represents all facets of a given construct In termsof content validity parts of the items come from the currentpapers which are used by many scholars to measure similarvariables Other parts of the items are designed based onliterature review What is more these questionnaires arefurther revised based on the interviews and pretest resultsTherefore the content of the questionnaires matches well theobject and has good content validity
Construct validity is used to test the degree to which atest measures what it claims It mainly uses factor analysisto test the construct validity The main indexes are value ofKMO and value of the Bartlett ball test The value of KMOis 06 which indicates that construct validity is moderate 07indicates that the construct validity is good and 08 indicatesit is very good In terms of the value of the Bartlett ball test thesmaller the better It performs well when 119901 lt 00001 Table 4shows the value of KMO and value of the Bartlett ball test
From the analysis shown in Table 4 we can see that thevalue of KMO of each latent variable is bigger than 07 andthe value of 119901 is 0 the Bartlett ball tests show concentrationAll indicate that the construct validity is good Consideringboth content validity and construct validity we can draw theconclusion that the indicator system and the questionnairesof this paper have high validity
Table 4 Validity analysis of measurement variables
4 Construction of the Early RiskWarning Model of OrganizationalResilience of RampD Team
41 Steps for Construction of Early Risk Warning ModelThese are steps for enterprises to construct an early riskwarning model firstly we need to decide the objects andselect an early risk warning indicator system guided by theearly risk warning principles secondly we divide the riskwarning levels and the warning line thirdly we collect thedata and process the data and normalize the data fourthly weconstruct the mathematical model and evaluate the model byempirical analysis fifthly we analyze the results and judge therelationship between the results and thewarning line and giveearly risk warning response Please see Figure 4
In this study the object of the early risk warning model isorganizational resilience Based on the analysis of Section 3the indicator system of the early risk warning model canbe divided into 2 levels the first level is the five factors oforganizational resilience and the second level is the items ofeach factor The risk warning levels can be divided into 5levels and they are without warning light warning mediumwarning heavy warning and dangerous warning
This paper uses fuzzy integrated evaluation method [35]mainly due to the following reasons firstly it is difficult toprecisely quantify the evaluation of enterprise risk warningThere is ambiguity Based on fuzzy sets and by using variousindicators the fuzzy integrated evaluation method can givecomprehensive evaluation of the membership degree of theevaluated objects By dividing the intervals on the one handit considers the levels of the objects and reflects the ambiguityof the evaluation standards On the other hand it takes theadvantages of peoplersquos experiences which makes the resultsmore objective and adaptable to the reality By combiningboth qualitative and quantitative factors fuzzy integrated
6 Discrete Dynamics in Nature and Society
evaluationmethod improves the quality of evaluation and thereliability of results
42 Determine the Weights To reduce the randomness ofjudgment and increase the reliability of result this paperadopts the fuzzy evaluation method which combines fuzzyset theory and analytical hierarchy process119880 = (119880
1 1198802 1198803 1198804 1198805) where119880
119894denotes one dimension
of organizational resilience 119876 = (1199021 1199022 119902
13) which are
the criterion of the above five aspects 119881 = (V1 V2 V3 V4 V5)
where V1 V2 V3 V4 V5
respectively denotes the ldquostrongrdquoldquogoodrdquo ldquogeneralrdquo ldquofairly weakrdquo and ldquoweakrdquo comment ofeach criterion
This paper adopts analytical hierarchy process methodto decide the weights of indicators Users of the AHP firstdecompose their decision problem into a hierarchy of moreeasily comprehended subproblems Once the hierarchy isbuilt the decision makers will use their judgments about theelementsrsquo relative meaning and importance to evaluate theseelements by comparing them to one another at a time It isrecognized to be practical systematic and concise [36]
Based on the analysis of Section 3 we establish prioritiesamong the elements of the hierarchy by making a series ofjudgments based on pairwise comparisons of the elementsWe can synthesize these judgments to yield a set of overallpriorities for the hierarchy
The priorities of criteria 119880119894are 1198861 1198862 1198863 1198864 1198865and 119860 =
(1198861 1198862 1198863 1198864 1198865) The main steps are as follows
(1) According to scaling theory we construct pairwisecomparison judgment matrix 119860
(5) According to 119860119908 = 120582max119908 we can calculate thelargest eigenvalue and its eigenvector
(6) Consistency test by calculating the consistency indexCI = (120582max minus119899)(119899 minus 1) we can find the correspond-ing average random consistency index RI Then wecan calculate the consistency ratio CR = CIRIWhen CR lt 01 we accept the result Otherwise weneed to rectify matrix 119860 appropriately
43 Establishment of Qualitative Indexes MembershipAlthoughwe can get definite comments on each criterion theldquoboundaryrdquo is relatively ambiguousTherefore when calcula-ting the membership degree of each criterion to the evalua-tion set we need to grade each criterion based on specialistconsultancy and questionnaire analysis We can get themembership vector 119877
3 4 5) where oplus is the operator and its definition is 119887119894=
sum119899
119894=1119908119894119903119894119895 where 119887
119894is the membership vector of each kind
of organizational resilience Normalizing 119887119894we can get 119861 =
(1198611 1198612 1198613 1198614 1198615)119879
(2) Final evaluation vector of the overall goal suppose119862 = 119860 oplus 119861 We add the first two items together If thesum is bigger than 05 (ie the percentage of ldquostrongrdquo andldquogoodrdquo is bigger than 50) it indicates that the organizationalresilience is strong The more approaching 1 the sum of firsttwo items the stronger the organizational resilience
5 Case Analysis
This paper combines both empirical study and case study tosystematically study the factors which influence sustainableinnovation The main conclusions are as follows
Jiangling Motors Co Ltd (JMC in abbreviation here-inafter) a key player in China automotive industry with com-mercial vehicle as its core competitiveness has been ranked asone of ChinaTop 100 ListedCompanies for consecutive yearsIn 2014 JMChit record highs in its business indexeswith salesrevenue reaching 255 billion RMB and volume over 276000units JMC who has established international standardsthat complied with operating systems and mechanisms thatintegrate RampD logistics MSampS and financing supports hasbeen regarded as a model of successful Sinoforeign cooper-ation The company has set up a strong marketing networkthroughout China Its products include Transit commercialvehicle Kaiyun light truck Baodian pickup and YushengSUV which have become models of fuel saving practicalityand environment friendliness In recent years JMC has beeninvesting heavily in new product development to enrich itsproduct line We analyze and evaluate the organizationalresilience of RampD team of JMC company based on themethod of Section 4 The specific steps are as follows
51 Calculate Weights We apply analytical hierarchical pro-cess method to decide weights Based on the pairwise com-parison of importance of criteria we use 1ndash9 scaling methodto get 119860
119894and they are
1198601= (0341 0572 0195 0432 0273)
1198602= (0438 0581 0249 0621 0275)
Discrete Dynamics in Nature and Society 7
1198603= (0417 0359 0721 0346 0512)
1198604= (0438 0519 0434 0419 0351)
1198605= (0523 0475 0354 0464 0765)
(5)
Normalizing them we can get119908119894 We calculate the largest
eigenvalue and its eigenvector and make consistency testThen we can get CR = CIRI = 0043 lt 01 It indicatesthat the consistency of judgment matrix is acceptable
52 Establishment of Membership Matrix of Fuzzy EvaluationBased on the above analysis we select a group of 33 expertsThey mainly come from two sources 17 of them are leadersof departments and senior engineers of enterprises and 16 ofthem are college professors in human resource managementThe alternative answers include ldquoextreme important veryimportant important a little important and not importantrdquoand we give each of them from 5 to 1 respectively We sendthem the questionnaires by email and make sure they do notknow each otherrsquos answer Then we can get the membershipmatrix
1198771=((
(
0456 0812 0654 0142 0461
0451 0641 0247 0541 0622
0751 0712 0574 0341 0346
0235 0632 0341 0723 0348
0432 0543 0355 0541 0156
))
)
1198772=((
(
0634 0247 0541 0346 0316
0453 0621 0421 0261 0423
0431 0354 0354 0156 0761
0312 0317 0641 0341 0345
0231 0394 0521 0141 0432
))
)
1198773=((
(
0345 0384 0347 0512 0712
0311 0371 0621 0315 0731
0421 0381 0274 0311 0623
0235 0267 0646 0328 0512
0512 0461 0346 0612 0641
))
)
1198774=((
(
0421 0379 0513 0812 0385
0356 0812 0346 0346 0541
0841 0644 0311 0461 0197
0345 0547 0345 0856 0634
0314 0284 0346 0654 0461
))
)
1198775=((
(
0765 0698 0611 0341 0341
0395 0541 0851 0206 0261
0584 0621 0511 0509 0345
0347 0574 0341 0451 0433
0614 0354 0317 0394 0542
))
)
(6)
53 Calculating the Comprehensive Evaluation Vector of EachSubgoal From 119861
119894= 119908119894oplus 119877119894 we have
1198611=((
(
0201
0210
0177
0207
0207
))
)
119879
oplus((
(
0456 0812 0654 0142 0461
0451 0641 0247 0541 0622
0751 0712 0574 0341 0346
0235 0632 0341 0723 0348
0432 0543 0355 0541 0156
))
)
1198611= (0457 0667 0429 0646 0389)
1198612=((
(
0272
0245
0133
0215
0165
))
)
119879
oplus((
(
0634 0247 0541 0346 0316
0453 0621 0421 0261 0423
0431 0354 0354 0156 0761
0312 0317 0641 0341 0345
0231 0394 0521 0141 0432
))
)
1198612= (0446 0400 0521 0275 0436)
1198613=((
(
0127
0132
0337
0227
0155
))
)
119879
oplus((
(
0345 0384 0347 0512 0712
0311 0371 0621 0315 0731
0421 0381 0274 0311 0623
0235 0267 0646 0328 0512
0512 0461 0346 0612 0641
))
)
1198613= (0359 0358 0419 0381 0612)
1198614=((
(
0241
0282
0138
0187
0174
))
)
119879
8 Discrete Dynamics in Nature and Society
oplus((
(
0421 0379 0513 0812 0385
0356 0812 0346 0346 0541
0841 0644 0311 0461 0197
0345 0547 0345 0856 0634
0314 0284 0346 0654 0461
))
)
1198614= (0437 0561 0389 0631 0471)
1198615=((
(
0159
0131
0215
0164
0300
))
)
119879
oplus((
(
0765 0698 0611 0341 0341
0395 0541 0851 0206 0261
0584 0621 0511 0509 0345
0347 0574 0341 0451 0433
0614 0354 0317 0394 0542
))
)
1198615= (0540 0516 0470 0383 0396)
(7)
Based on 119861 = (1198611 1198612 1198613 1198614 1198615)119879 normalizing it and from
119862 = 119860 oplus 119861 we have
119862 =((
(
0195
0226
0176
0206
0196
))
)
119879
oplus((
(
0204 0267 0193 0218 0169
0199 0160 0234 0129 0189
0160 0143 0188 0178 0266
0195 0224 0175 0296 0204
0241 0206 0211 0179 0172
))
)
119862 = (0302 0200 0201 0149 0148)
(8)
The sum of first two items of 119862 is 0502 The sum offirst three items of 119862 is 0703 It indicates the light warningand shows the RampD team has relatively high organizationalresilience
6 Conclusions
Based on the structural interviews this paper exploresand confirms the structural dimensions of organizationalresilience Based on it this paper constructs an early riskwarning model of organizational resilience and applies it
to the RampD team of JMC company The conclusions are asfollows
(1) Based on literature review face to face interviewsand open questionnaires this paper applies the exploratoryfactor analysis method to discuss the factor structure oforganizational resilience of RampD teams The results showthat the factor structure of organizational resilience of RampDteams includes five dimensions shared vision willingnessto learn adaptation ability cooperative awareness and workenthusiasm Then this paper compares the five-factor modelwith other competitivemodels to further test the effectivenessof the five-factor model The results show that the five-factormodel is the best What is more the validity and reliabilityof the questionnaires of organizational resilience are provedto meet the requirements of psychometrics The model issupported
(2) Based on the factor structure of organizationalresilience this study constructs an early risk warning modelof organizational resilience of RampD teams It divides the riskwarning levels into five levels By applying fuzzy integratedevaluationmethod and based on the five-factor structure thispaper constructs a hierarchical analysis structure model Bymaking a series of judgments based on pairwise comparisonsof the elements we can get the judgment matrix and therebydecide the weight of each factor of organizational resilienceThen by using Delphi method we can get the member-ship matrix Lastly by calculating the judgment matrix andmembership matrix we can know the risk warning level oforganizational resilience of the RampD teamWe hope the resultwill provide references for the company decision
(3) This study applies the early risk warning model toRampD team of JMC company The results show that the teamhas relatively high organizational resilience These resultsmatch the work performance work experiences and leadersrsquoremarks on the team It also matches the self-evaluation ofmembers of the team All these show that the method isoperational and feasible
Competing Interests
The author declares no competing interests The author hasno financial and personal relationships with other people ororganizations that can inappropriately influence the work
Acknowledgments
This work is supported by the NSFC (71361013 7146200971273122 and 71463020) China Postdoctoral Science Founda-tion under Grant no 2013M541867 Jiangxi Province ScienceFoundation of China under Grants nos 20151BAB207059and 20142BA217018 and China Scholarship Council Fundingunder Grant no 201409805006
References
[1] K M Sutcliffe and J T Vogus ldquoOrganizing for resiliencerdquoin Positive Organizational Scholarship Foundations of a NewDiscipline pp 94ndash110 2003
Discrete Dynamics in Nature and Society 9
[2] J H Gittell K Cameron S Lim and V Rivas ldquoRelationshipslayoffs and organizational resilience airline industry responsesto September 11rdquo Journal of Applied Behavioral Science vol 42no 3 pp 300ndash329 2006
[3] J W Rudolph and N P Repenning ldquoDisaster dynamicsunderstanding the role of quantity in organizational collapserdquoAdministrative Science Quarterly vol 47 no 1 pp 1ndash30 2002
[4] J E Dutton P J Frost M C Worline J M Lilius and J MKanov ldquoLeading in times of traumardquo Harvard Business Reviewvol 80 no 1 pp 54ndash61 2002
[5] R Balu ldquoHow to bounce back from setbacksrdquo Fast Companyvol 45 pp 148ndash156 2001
[6] K EWeick ldquoEnacted sensemaking in crisis situationsrdquo Journalof Management Studies vol 25 no 4 pp 305ndash317 1988
[7] D L Coutu ldquoHow resilience worksrdquo Harvard Business Reviewvol 80 no 5 pp 46ndash55 2002
[8] S F Freeman M Maltz and L Hirschhorn ldquoThe power ofmoral purpose Sandler OrsquoNeill amp partners in the aftermath ofSeptember 11th 2001rdquo Organization Development Journal vol22 no 4 pp 69ndash82 2004
[9] F PMorgeson andD A Hofmann ldquoThe structure and functionof collective constructs implications formultilevel research andtheory developmentrdquo Academy of Management Review vol 24no 2 pp 249ndash265 1999
[10] J F I Horne ldquoThe coming of age of organizational resiliencerdquoBusiness Forum vol 22 no 2-3 pp 24ndash28 1997
[11] L A Mallak ldquoMeasuring resilience in health care providerorganizationsrdquoHealth Manpower Management vol 24 no 4-5pp 148ndash152 1998
[12] L A Mallak ldquoPutting organizational resilience to workrdquo Indus-trial Management vol 40 no 6 pp 8ndash13 1998
[13] C A Lengnick-Hall T E Beck and M L Lengnick-HallldquoDeveloping a capacity for organizational resilience throughstrategic human resourcemanagementrdquoHuman ResourceMan-agement Review vol 21 no 3 pp 243ndash255 2011
[14] M London ldquoToward a theory of career motivationrdquo Academyof Management Review vol 8 no 4 pp 620ndash630 1983
[15] Y Xiao-nan and Z Jian-xin ldquoResilience the psychologicalmechanism for recovery and growthrdquoAdvances in PsychologicalScience vol 5 no 5 pp 658ndash665 2005
[16] X Ju-Zhe S Biao and Z Zhi-Hong ldquoThe research on resilience its evolution and directionrdquo Psychological Science vol 31 no4 pp 995ndash998 2008
[17] E Grotberg ldquoResilience for tomorrowrdquo Trabajo presentado enla International Council of Psychologists Convention Foz doIguacu Brazil Extraıdo de 2005 httpswwwhitpagescomdoc62572445722214401
[18] R A Noe A W Noe and J A Bachhuber ldquoAn investigationof the correlates of career motivationrdquo Journal of VocationalBehavior vol 37 no 3 pp 340ndash356 1990
[19] F Luthans J B Avey R Clapp-Smith and W Li ldquoMore evi-dence on the value of Chinese workersrsquo psychological capital apotentially unlimited competitive resourcerdquo The InternationalJournal of Human Resource Management vol 19 no 5 pp 818ndash827 2008
[20] C Fourie and L J Van Vuuren ldquoDefining andmeasuring careerresiliencerdquo SA Journal of Industrial Psychology vol 24 no 3 pp52ndash59 1998
[21] Y C Liu Relationships between Career Resilience and CareerBeliefs of Employees in Taiwan Texas AampM University 2003
[22] B Obrist C Pfeiffer and R Henley ldquoMulti-layered socialresilience a new approach in mitigation researchrdquo Progress inDevelopment Studies vol 10 no 4 pp 283ndash293 2010
[23] S J Breckler ldquoEmpirical validation of affect behavior and cog-nition as distinct components of attituderdquo Journal of Personalityamp Social Psychology vol 47 no 6 pp 1191ndash1205 1984
[24] C A Lietz andM Strength ldquoStories of successful reunificationa narrative study of family resilience in child welfarerdquo Familiesin Society The Journal of Contemporary Social Services vol 92no 2 pp 203ndash210 2011
[25] X Zhao and Z-G Xin ldquoResearch review on models ofenterprise risk forewarning managementrdquo Journal of BeijingUniversity of Posts and Telecommunications (Social SciencesEdition) vol 12 no 1 pp 93ndash97 2010
[26] E F Fern ldquoThe use of focus groups for idea generationthe effects of group size acquaintanceship and moderator onresponse quantity and qualityrdquo Journal of Marketing Researchvol 19 no 1 pp 1ndash13 1982
[27] S-H Chen and W He ldquoStudy on knowledge propagationin complex networks based on preferences taking wechat asexamplerdquo Abstract and Applied Analysis vol 2014 Article ID543734 11 pages 2014
[28] W He ldquoAn inventory controlled supply chain model based onimproved BP neural networkrdquoDiscrete Dynamics in Nature andSociety vol 2013 Article ID 537675 7 pages 2013
[29] S-H Chen ldquoA novel culture algorithm and itrsquos application inknowledge integrationrdquo Information vol 15 no 11 B pp 4847ndash4853 2012
[30] W He and S-H Chen ldquoGame analysis of determinants ofstability of semiconductor modular production networksrdquo Sus-tainability vol 6 no 8 pp 4772ndash4794 2014
[31] S-H Chen ldquoThe influencing factors of enterprise sustainableinnovation an empirical studyrdquo Sustainability vol 8 no 5article 425 17 pages 2016
[32] S-H Chen ldquoEmpirical research on knowledge integrationimproving innovation ability of IT enterprisemdashbased on struc-tural equation modelrdquo Information vol 14 no 3 pp 753ndash7582011
[33] G-X Song ldquoStudy on construct and its dimensions of careerresilience based on Chinese indigenous culturerdquo EconomicManagement vol 33 no 11 pp 184ndash193 2011
[34] J-L Ke J-M Sun J-T Shi and Q-X Gu ldquoEmpirical studyon relationship between social capital of RampD team and teampotencyrdquoManagement World vol 3 pp 89ndash101 2007
[35] H Shouzhong and G Jianqin ldquoFuzzy integrated evaluation andits applicationrdquo Journal of China Textile University vol 21 no 1pp 74ndash80 1995
[36] A Jebreen and A Husain ldquoUtility-based approach for deter-mining the weights of participants in virtual organizationrdquoApplied Mathematical Sciences vol 6 no 96 pp 4773ndash47862012
35 Reliability and Validity Analysis In this paper a test ofinternal consistency reliability using Cronbachrsquos coefficientalphas was performed As shown in Table 3 Cronbachrsquos alphacoefficients fall between 0798 and 0902 which are all biggerthan 07 It shows that the questionnaires have high internalconsistency reliability and are acceptable
Validity refers to the effectiveness and correctness ofquestionnaires that is the extent to which the questionnairecan measure the characteristics of the construct It is animportant criterion to evaluate the quality of questionnairesGenerally it includes the content validity and constructvalidity Content validity refers to the extent to which ameasure represents all facets of a given construct In termsof content validity parts of the items come from the currentpapers which are used by many scholars to measure similarvariables Other parts of the items are designed based onliterature review What is more these questionnaires arefurther revised based on the interviews and pretest resultsTherefore the content of the questionnaires matches well theobject and has good content validity
Construct validity is used to test the degree to which atest measures what it claims It mainly uses factor analysisto test the construct validity The main indexes are value ofKMO and value of the Bartlett ball test The value of KMOis 06 which indicates that construct validity is moderate 07indicates that the construct validity is good and 08 indicatesit is very good In terms of the value of the Bartlett ball test thesmaller the better It performs well when 119901 lt 00001 Table 4shows the value of KMO and value of the Bartlett ball test
From the analysis shown in Table 4 we can see that thevalue of KMO of each latent variable is bigger than 07 andthe value of 119901 is 0 the Bartlett ball tests show concentrationAll indicate that the construct validity is good Consideringboth content validity and construct validity we can draw theconclusion that the indicator system and the questionnairesof this paper have high validity
Table 4 Validity analysis of measurement variables
4 Construction of the Early RiskWarning Model of OrganizationalResilience of RampD Team
41 Steps for Construction of Early Risk Warning ModelThese are steps for enterprises to construct an early riskwarning model firstly we need to decide the objects andselect an early risk warning indicator system guided by theearly risk warning principles secondly we divide the riskwarning levels and the warning line thirdly we collect thedata and process the data and normalize the data fourthly weconstruct the mathematical model and evaluate the model byempirical analysis fifthly we analyze the results and judge therelationship between the results and thewarning line and giveearly risk warning response Please see Figure 4
In this study the object of the early risk warning model isorganizational resilience Based on the analysis of Section 3the indicator system of the early risk warning model canbe divided into 2 levels the first level is the five factors oforganizational resilience and the second level is the items ofeach factor The risk warning levels can be divided into 5levels and they are without warning light warning mediumwarning heavy warning and dangerous warning
This paper uses fuzzy integrated evaluation method [35]mainly due to the following reasons firstly it is difficult toprecisely quantify the evaluation of enterprise risk warningThere is ambiguity Based on fuzzy sets and by using variousindicators the fuzzy integrated evaluation method can givecomprehensive evaluation of the membership degree of theevaluated objects By dividing the intervals on the one handit considers the levels of the objects and reflects the ambiguityof the evaluation standards On the other hand it takes theadvantages of peoplersquos experiences which makes the resultsmore objective and adaptable to the reality By combiningboth qualitative and quantitative factors fuzzy integrated
6 Discrete Dynamics in Nature and Society
evaluationmethod improves the quality of evaluation and thereliability of results
42 Determine the Weights To reduce the randomness ofjudgment and increase the reliability of result this paperadopts the fuzzy evaluation method which combines fuzzyset theory and analytical hierarchy process119880 = (119880
1 1198802 1198803 1198804 1198805) where119880
119894denotes one dimension
of organizational resilience 119876 = (1199021 1199022 119902
13) which are
the criterion of the above five aspects 119881 = (V1 V2 V3 V4 V5)
where V1 V2 V3 V4 V5
respectively denotes the ldquostrongrdquoldquogoodrdquo ldquogeneralrdquo ldquofairly weakrdquo and ldquoweakrdquo comment ofeach criterion
This paper adopts analytical hierarchy process methodto decide the weights of indicators Users of the AHP firstdecompose their decision problem into a hierarchy of moreeasily comprehended subproblems Once the hierarchy isbuilt the decision makers will use their judgments about theelementsrsquo relative meaning and importance to evaluate theseelements by comparing them to one another at a time It isrecognized to be practical systematic and concise [36]
Based on the analysis of Section 3 we establish prioritiesamong the elements of the hierarchy by making a series ofjudgments based on pairwise comparisons of the elementsWe can synthesize these judgments to yield a set of overallpriorities for the hierarchy
The priorities of criteria 119880119894are 1198861 1198862 1198863 1198864 1198865and 119860 =
(1198861 1198862 1198863 1198864 1198865) The main steps are as follows
(1) According to scaling theory we construct pairwisecomparison judgment matrix 119860
(5) According to 119860119908 = 120582max119908 we can calculate thelargest eigenvalue and its eigenvector
(6) Consistency test by calculating the consistency indexCI = (120582max minus119899)(119899 minus 1) we can find the correspond-ing average random consistency index RI Then wecan calculate the consistency ratio CR = CIRIWhen CR lt 01 we accept the result Otherwise weneed to rectify matrix 119860 appropriately
43 Establishment of Qualitative Indexes MembershipAlthoughwe can get definite comments on each criterion theldquoboundaryrdquo is relatively ambiguousTherefore when calcula-ting the membership degree of each criterion to the evalua-tion set we need to grade each criterion based on specialistconsultancy and questionnaire analysis We can get themembership vector 119877
3 4 5) where oplus is the operator and its definition is 119887119894=
sum119899
119894=1119908119894119903119894119895 where 119887
119894is the membership vector of each kind
of organizational resilience Normalizing 119887119894we can get 119861 =
(1198611 1198612 1198613 1198614 1198615)119879
(2) Final evaluation vector of the overall goal suppose119862 = 119860 oplus 119861 We add the first two items together If thesum is bigger than 05 (ie the percentage of ldquostrongrdquo andldquogoodrdquo is bigger than 50) it indicates that the organizationalresilience is strong The more approaching 1 the sum of firsttwo items the stronger the organizational resilience
5 Case Analysis
This paper combines both empirical study and case study tosystematically study the factors which influence sustainableinnovation The main conclusions are as follows
Jiangling Motors Co Ltd (JMC in abbreviation here-inafter) a key player in China automotive industry with com-mercial vehicle as its core competitiveness has been ranked asone of ChinaTop 100 ListedCompanies for consecutive yearsIn 2014 JMChit record highs in its business indexeswith salesrevenue reaching 255 billion RMB and volume over 276000units JMC who has established international standardsthat complied with operating systems and mechanisms thatintegrate RampD logistics MSampS and financing supports hasbeen regarded as a model of successful Sinoforeign cooper-ation The company has set up a strong marketing networkthroughout China Its products include Transit commercialvehicle Kaiyun light truck Baodian pickup and YushengSUV which have become models of fuel saving practicalityand environment friendliness In recent years JMC has beeninvesting heavily in new product development to enrich itsproduct line We analyze and evaluate the organizationalresilience of RampD team of JMC company based on themethod of Section 4 The specific steps are as follows
51 Calculate Weights We apply analytical hierarchical pro-cess method to decide weights Based on the pairwise com-parison of importance of criteria we use 1ndash9 scaling methodto get 119860
119894and they are
1198601= (0341 0572 0195 0432 0273)
1198602= (0438 0581 0249 0621 0275)
Discrete Dynamics in Nature and Society 7
1198603= (0417 0359 0721 0346 0512)
1198604= (0438 0519 0434 0419 0351)
1198605= (0523 0475 0354 0464 0765)
(5)
Normalizing them we can get119908119894 We calculate the largest
eigenvalue and its eigenvector and make consistency testThen we can get CR = CIRI = 0043 lt 01 It indicatesthat the consistency of judgment matrix is acceptable
52 Establishment of Membership Matrix of Fuzzy EvaluationBased on the above analysis we select a group of 33 expertsThey mainly come from two sources 17 of them are leadersof departments and senior engineers of enterprises and 16 ofthem are college professors in human resource managementThe alternative answers include ldquoextreme important veryimportant important a little important and not importantrdquoand we give each of them from 5 to 1 respectively We sendthem the questionnaires by email and make sure they do notknow each otherrsquos answer Then we can get the membershipmatrix
1198771=((
(
0456 0812 0654 0142 0461
0451 0641 0247 0541 0622
0751 0712 0574 0341 0346
0235 0632 0341 0723 0348
0432 0543 0355 0541 0156
))
)
1198772=((
(
0634 0247 0541 0346 0316
0453 0621 0421 0261 0423
0431 0354 0354 0156 0761
0312 0317 0641 0341 0345
0231 0394 0521 0141 0432
))
)
1198773=((
(
0345 0384 0347 0512 0712
0311 0371 0621 0315 0731
0421 0381 0274 0311 0623
0235 0267 0646 0328 0512
0512 0461 0346 0612 0641
))
)
1198774=((
(
0421 0379 0513 0812 0385
0356 0812 0346 0346 0541
0841 0644 0311 0461 0197
0345 0547 0345 0856 0634
0314 0284 0346 0654 0461
))
)
1198775=((
(
0765 0698 0611 0341 0341
0395 0541 0851 0206 0261
0584 0621 0511 0509 0345
0347 0574 0341 0451 0433
0614 0354 0317 0394 0542
))
)
(6)
53 Calculating the Comprehensive Evaluation Vector of EachSubgoal From 119861
119894= 119908119894oplus 119877119894 we have
1198611=((
(
0201
0210
0177
0207
0207
))
)
119879
oplus((
(
0456 0812 0654 0142 0461
0451 0641 0247 0541 0622
0751 0712 0574 0341 0346
0235 0632 0341 0723 0348
0432 0543 0355 0541 0156
))
)
1198611= (0457 0667 0429 0646 0389)
1198612=((
(
0272
0245
0133
0215
0165
))
)
119879
oplus((
(
0634 0247 0541 0346 0316
0453 0621 0421 0261 0423
0431 0354 0354 0156 0761
0312 0317 0641 0341 0345
0231 0394 0521 0141 0432
))
)
1198612= (0446 0400 0521 0275 0436)
1198613=((
(
0127
0132
0337
0227
0155
))
)
119879
oplus((
(
0345 0384 0347 0512 0712
0311 0371 0621 0315 0731
0421 0381 0274 0311 0623
0235 0267 0646 0328 0512
0512 0461 0346 0612 0641
))
)
1198613= (0359 0358 0419 0381 0612)
1198614=((
(
0241
0282
0138
0187
0174
))
)
119879
8 Discrete Dynamics in Nature and Society
oplus((
(
0421 0379 0513 0812 0385
0356 0812 0346 0346 0541
0841 0644 0311 0461 0197
0345 0547 0345 0856 0634
0314 0284 0346 0654 0461
))
)
1198614= (0437 0561 0389 0631 0471)
1198615=((
(
0159
0131
0215
0164
0300
))
)
119879
oplus((
(
0765 0698 0611 0341 0341
0395 0541 0851 0206 0261
0584 0621 0511 0509 0345
0347 0574 0341 0451 0433
0614 0354 0317 0394 0542
))
)
1198615= (0540 0516 0470 0383 0396)
(7)
Based on 119861 = (1198611 1198612 1198613 1198614 1198615)119879 normalizing it and from
119862 = 119860 oplus 119861 we have
119862 =((
(
0195
0226
0176
0206
0196
))
)
119879
oplus((
(
0204 0267 0193 0218 0169
0199 0160 0234 0129 0189
0160 0143 0188 0178 0266
0195 0224 0175 0296 0204
0241 0206 0211 0179 0172
))
)
119862 = (0302 0200 0201 0149 0148)
(8)
The sum of first two items of 119862 is 0502 The sum offirst three items of 119862 is 0703 It indicates the light warningand shows the RampD team has relatively high organizationalresilience
6 Conclusions
Based on the structural interviews this paper exploresand confirms the structural dimensions of organizationalresilience Based on it this paper constructs an early riskwarning model of organizational resilience and applies it
to the RampD team of JMC company The conclusions are asfollows
(1) Based on literature review face to face interviewsand open questionnaires this paper applies the exploratoryfactor analysis method to discuss the factor structure oforganizational resilience of RampD teams The results showthat the factor structure of organizational resilience of RampDteams includes five dimensions shared vision willingnessto learn adaptation ability cooperative awareness and workenthusiasm Then this paper compares the five-factor modelwith other competitivemodels to further test the effectivenessof the five-factor model The results show that the five-factormodel is the best What is more the validity and reliabilityof the questionnaires of organizational resilience are provedto meet the requirements of psychometrics The model issupported
(2) Based on the factor structure of organizationalresilience this study constructs an early risk warning modelof organizational resilience of RampD teams It divides the riskwarning levels into five levels By applying fuzzy integratedevaluationmethod and based on the five-factor structure thispaper constructs a hierarchical analysis structure model Bymaking a series of judgments based on pairwise comparisonsof the elements we can get the judgment matrix and therebydecide the weight of each factor of organizational resilienceThen by using Delphi method we can get the member-ship matrix Lastly by calculating the judgment matrix andmembership matrix we can know the risk warning level oforganizational resilience of the RampD teamWe hope the resultwill provide references for the company decision
(3) This study applies the early risk warning model toRampD team of JMC company The results show that the teamhas relatively high organizational resilience These resultsmatch the work performance work experiences and leadersrsquoremarks on the team It also matches the self-evaluation ofmembers of the team All these show that the method isoperational and feasible
Competing Interests
The author declares no competing interests The author hasno financial and personal relationships with other people ororganizations that can inappropriately influence the work
Acknowledgments
This work is supported by the NSFC (71361013 7146200971273122 and 71463020) China Postdoctoral Science Founda-tion under Grant no 2013M541867 Jiangxi Province ScienceFoundation of China under Grants nos 20151BAB207059and 20142BA217018 and China Scholarship Council Fundingunder Grant no 201409805006
References
[1] K M Sutcliffe and J T Vogus ldquoOrganizing for resiliencerdquoin Positive Organizational Scholarship Foundations of a NewDiscipline pp 94ndash110 2003
Discrete Dynamics in Nature and Society 9
[2] J H Gittell K Cameron S Lim and V Rivas ldquoRelationshipslayoffs and organizational resilience airline industry responsesto September 11rdquo Journal of Applied Behavioral Science vol 42no 3 pp 300ndash329 2006
[3] J W Rudolph and N P Repenning ldquoDisaster dynamicsunderstanding the role of quantity in organizational collapserdquoAdministrative Science Quarterly vol 47 no 1 pp 1ndash30 2002
[4] J E Dutton P J Frost M C Worline J M Lilius and J MKanov ldquoLeading in times of traumardquo Harvard Business Reviewvol 80 no 1 pp 54ndash61 2002
[5] R Balu ldquoHow to bounce back from setbacksrdquo Fast Companyvol 45 pp 148ndash156 2001
[6] K EWeick ldquoEnacted sensemaking in crisis situationsrdquo Journalof Management Studies vol 25 no 4 pp 305ndash317 1988
[7] D L Coutu ldquoHow resilience worksrdquo Harvard Business Reviewvol 80 no 5 pp 46ndash55 2002
[8] S F Freeman M Maltz and L Hirschhorn ldquoThe power ofmoral purpose Sandler OrsquoNeill amp partners in the aftermath ofSeptember 11th 2001rdquo Organization Development Journal vol22 no 4 pp 69ndash82 2004
[9] F PMorgeson andD A Hofmann ldquoThe structure and functionof collective constructs implications formultilevel research andtheory developmentrdquo Academy of Management Review vol 24no 2 pp 249ndash265 1999
[10] J F I Horne ldquoThe coming of age of organizational resiliencerdquoBusiness Forum vol 22 no 2-3 pp 24ndash28 1997
[11] L A Mallak ldquoMeasuring resilience in health care providerorganizationsrdquoHealth Manpower Management vol 24 no 4-5pp 148ndash152 1998
[12] L A Mallak ldquoPutting organizational resilience to workrdquo Indus-trial Management vol 40 no 6 pp 8ndash13 1998
[13] C A Lengnick-Hall T E Beck and M L Lengnick-HallldquoDeveloping a capacity for organizational resilience throughstrategic human resourcemanagementrdquoHuman ResourceMan-agement Review vol 21 no 3 pp 243ndash255 2011
[14] M London ldquoToward a theory of career motivationrdquo Academyof Management Review vol 8 no 4 pp 620ndash630 1983
[15] Y Xiao-nan and Z Jian-xin ldquoResilience the psychologicalmechanism for recovery and growthrdquoAdvances in PsychologicalScience vol 5 no 5 pp 658ndash665 2005
[16] X Ju-Zhe S Biao and Z Zhi-Hong ldquoThe research on resilience its evolution and directionrdquo Psychological Science vol 31 no4 pp 995ndash998 2008
[17] E Grotberg ldquoResilience for tomorrowrdquo Trabajo presentado enla International Council of Psychologists Convention Foz doIguacu Brazil Extraıdo de 2005 httpswwwhitpagescomdoc62572445722214401
[18] R A Noe A W Noe and J A Bachhuber ldquoAn investigationof the correlates of career motivationrdquo Journal of VocationalBehavior vol 37 no 3 pp 340ndash356 1990
[19] F Luthans J B Avey R Clapp-Smith and W Li ldquoMore evi-dence on the value of Chinese workersrsquo psychological capital apotentially unlimited competitive resourcerdquo The InternationalJournal of Human Resource Management vol 19 no 5 pp 818ndash827 2008
[20] C Fourie and L J Van Vuuren ldquoDefining andmeasuring careerresiliencerdquo SA Journal of Industrial Psychology vol 24 no 3 pp52ndash59 1998
[21] Y C Liu Relationships between Career Resilience and CareerBeliefs of Employees in Taiwan Texas AampM University 2003
[22] B Obrist C Pfeiffer and R Henley ldquoMulti-layered socialresilience a new approach in mitigation researchrdquo Progress inDevelopment Studies vol 10 no 4 pp 283ndash293 2010
[23] S J Breckler ldquoEmpirical validation of affect behavior and cog-nition as distinct components of attituderdquo Journal of Personalityamp Social Psychology vol 47 no 6 pp 1191ndash1205 1984
[24] C A Lietz andM Strength ldquoStories of successful reunificationa narrative study of family resilience in child welfarerdquo Familiesin Society The Journal of Contemporary Social Services vol 92no 2 pp 203ndash210 2011
[25] X Zhao and Z-G Xin ldquoResearch review on models ofenterprise risk forewarning managementrdquo Journal of BeijingUniversity of Posts and Telecommunications (Social SciencesEdition) vol 12 no 1 pp 93ndash97 2010
[26] E F Fern ldquoThe use of focus groups for idea generationthe effects of group size acquaintanceship and moderator onresponse quantity and qualityrdquo Journal of Marketing Researchvol 19 no 1 pp 1ndash13 1982
[27] S-H Chen and W He ldquoStudy on knowledge propagationin complex networks based on preferences taking wechat asexamplerdquo Abstract and Applied Analysis vol 2014 Article ID543734 11 pages 2014
[28] W He ldquoAn inventory controlled supply chain model based onimproved BP neural networkrdquoDiscrete Dynamics in Nature andSociety vol 2013 Article ID 537675 7 pages 2013
[29] S-H Chen ldquoA novel culture algorithm and itrsquos application inknowledge integrationrdquo Information vol 15 no 11 B pp 4847ndash4853 2012
[30] W He and S-H Chen ldquoGame analysis of determinants ofstability of semiconductor modular production networksrdquo Sus-tainability vol 6 no 8 pp 4772ndash4794 2014
[31] S-H Chen ldquoThe influencing factors of enterprise sustainableinnovation an empirical studyrdquo Sustainability vol 8 no 5article 425 17 pages 2016
[32] S-H Chen ldquoEmpirical research on knowledge integrationimproving innovation ability of IT enterprisemdashbased on struc-tural equation modelrdquo Information vol 14 no 3 pp 753ndash7582011
[33] G-X Song ldquoStudy on construct and its dimensions of careerresilience based on Chinese indigenous culturerdquo EconomicManagement vol 33 no 11 pp 184ndash193 2011
[34] J-L Ke J-M Sun J-T Shi and Q-X Gu ldquoEmpirical studyon relationship between social capital of RampD team and teampotencyrdquoManagement World vol 3 pp 89ndash101 2007
[35] H Shouzhong and G Jianqin ldquoFuzzy integrated evaluation andits applicationrdquo Journal of China Textile University vol 21 no 1pp 74ndash80 1995
[36] A Jebreen and A Husain ldquoUtility-based approach for deter-mining the weights of participants in virtual organizationrdquoApplied Mathematical Sciences vol 6 no 96 pp 4773ndash47862012
evaluationmethod improves the quality of evaluation and thereliability of results
42 Determine the Weights To reduce the randomness ofjudgment and increase the reliability of result this paperadopts the fuzzy evaluation method which combines fuzzyset theory and analytical hierarchy process119880 = (119880
1 1198802 1198803 1198804 1198805) where119880
119894denotes one dimension
of organizational resilience 119876 = (1199021 1199022 119902
13) which are
the criterion of the above five aspects 119881 = (V1 V2 V3 V4 V5)
where V1 V2 V3 V4 V5
respectively denotes the ldquostrongrdquoldquogoodrdquo ldquogeneralrdquo ldquofairly weakrdquo and ldquoweakrdquo comment ofeach criterion
This paper adopts analytical hierarchy process methodto decide the weights of indicators Users of the AHP firstdecompose their decision problem into a hierarchy of moreeasily comprehended subproblems Once the hierarchy isbuilt the decision makers will use their judgments about theelementsrsquo relative meaning and importance to evaluate theseelements by comparing them to one another at a time It isrecognized to be practical systematic and concise [36]
Based on the analysis of Section 3 we establish prioritiesamong the elements of the hierarchy by making a series ofjudgments based on pairwise comparisons of the elementsWe can synthesize these judgments to yield a set of overallpriorities for the hierarchy
The priorities of criteria 119880119894are 1198861 1198862 1198863 1198864 1198865and 119860 =
(1198861 1198862 1198863 1198864 1198865) The main steps are as follows
(1) According to scaling theory we construct pairwisecomparison judgment matrix 119860
(5) According to 119860119908 = 120582max119908 we can calculate thelargest eigenvalue and its eigenvector
(6) Consistency test by calculating the consistency indexCI = (120582max minus119899)(119899 minus 1) we can find the correspond-ing average random consistency index RI Then wecan calculate the consistency ratio CR = CIRIWhen CR lt 01 we accept the result Otherwise weneed to rectify matrix 119860 appropriately
43 Establishment of Qualitative Indexes MembershipAlthoughwe can get definite comments on each criterion theldquoboundaryrdquo is relatively ambiguousTherefore when calcula-ting the membership degree of each criterion to the evalua-tion set we need to grade each criterion based on specialistconsultancy and questionnaire analysis We can get themembership vector 119877
3 4 5) where oplus is the operator and its definition is 119887119894=
sum119899
119894=1119908119894119903119894119895 where 119887
119894is the membership vector of each kind
of organizational resilience Normalizing 119887119894we can get 119861 =
(1198611 1198612 1198613 1198614 1198615)119879
(2) Final evaluation vector of the overall goal suppose119862 = 119860 oplus 119861 We add the first two items together If thesum is bigger than 05 (ie the percentage of ldquostrongrdquo andldquogoodrdquo is bigger than 50) it indicates that the organizationalresilience is strong The more approaching 1 the sum of firsttwo items the stronger the organizational resilience
5 Case Analysis
This paper combines both empirical study and case study tosystematically study the factors which influence sustainableinnovation The main conclusions are as follows
Jiangling Motors Co Ltd (JMC in abbreviation here-inafter) a key player in China automotive industry with com-mercial vehicle as its core competitiveness has been ranked asone of ChinaTop 100 ListedCompanies for consecutive yearsIn 2014 JMChit record highs in its business indexeswith salesrevenue reaching 255 billion RMB and volume over 276000units JMC who has established international standardsthat complied with operating systems and mechanisms thatintegrate RampD logistics MSampS and financing supports hasbeen regarded as a model of successful Sinoforeign cooper-ation The company has set up a strong marketing networkthroughout China Its products include Transit commercialvehicle Kaiyun light truck Baodian pickup and YushengSUV which have become models of fuel saving practicalityand environment friendliness In recent years JMC has beeninvesting heavily in new product development to enrich itsproduct line We analyze and evaluate the organizationalresilience of RampD team of JMC company based on themethod of Section 4 The specific steps are as follows
51 Calculate Weights We apply analytical hierarchical pro-cess method to decide weights Based on the pairwise com-parison of importance of criteria we use 1ndash9 scaling methodto get 119860
119894and they are
1198601= (0341 0572 0195 0432 0273)
1198602= (0438 0581 0249 0621 0275)
Discrete Dynamics in Nature and Society 7
1198603= (0417 0359 0721 0346 0512)
1198604= (0438 0519 0434 0419 0351)
1198605= (0523 0475 0354 0464 0765)
(5)
Normalizing them we can get119908119894 We calculate the largest
eigenvalue and its eigenvector and make consistency testThen we can get CR = CIRI = 0043 lt 01 It indicatesthat the consistency of judgment matrix is acceptable
52 Establishment of Membership Matrix of Fuzzy EvaluationBased on the above analysis we select a group of 33 expertsThey mainly come from two sources 17 of them are leadersof departments and senior engineers of enterprises and 16 ofthem are college professors in human resource managementThe alternative answers include ldquoextreme important veryimportant important a little important and not importantrdquoand we give each of them from 5 to 1 respectively We sendthem the questionnaires by email and make sure they do notknow each otherrsquos answer Then we can get the membershipmatrix
1198771=((
(
0456 0812 0654 0142 0461
0451 0641 0247 0541 0622
0751 0712 0574 0341 0346
0235 0632 0341 0723 0348
0432 0543 0355 0541 0156
))
)
1198772=((
(
0634 0247 0541 0346 0316
0453 0621 0421 0261 0423
0431 0354 0354 0156 0761
0312 0317 0641 0341 0345
0231 0394 0521 0141 0432
))
)
1198773=((
(
0345 0384 0347 0512 0712
0311 0371 0621 0315 0731
0421 0381 0274 0311 0623
0235 0267 0646 0328 0512
0512 0461 0346 0612 0641
))
)
1198774=((
(
0421 0379 0513 0812 0385
0356 0812 0346 0346 0541
0841 0644 0311 0461 0197
0345 0547 0345 0856 0634
0314 0284 0346 0654 0461
))
)
1198775=((
(
0765 0698 0611 0341 0341
0395 0541 0851 0206 0261
0584 0621 0511 0509 0345
0347 0574 0341 0451 0433
0614 0354 0317 0394 0542
))
)
(6)
53 Calculating the Comprehensive Evaluation Vector of EachSubgoal From 119861
119894= 119908119894oplus 119877119894 we have
1198611=((
(
0201
0210
0177
0207
0207
))
)
119879
oplus((
(
0456 0812 0654 0142 0461
0451 0641 0247 0541 0622
0751 0712 0574 0341 0346
0235 0632 0341 0723 0348
0432 0543 0355 0541 0156
))
)
1198611= (0457 0667 0429 0646 0389)
1198612=((
(
0272
0245
0133
0215
0165
))
)
119879
oplus((
(
0634 0247 0541 0346 0316
0453 0621 0421 0261 0423
0431 0354 0354 0156 0761
0312 0317 0641 0341 0345
0231 0394 0521 0141 0432
))
)
1198612= (0446 0400 0521 0275 0436)
1198613=((
(
0127
0132
0337
0227
0155
))
)
119879
oplus((
(
0345 0384 0347 0512 0712
0311 0371 0621 0315 0731
0421 0381 0274 0311 0623
0235 0267 0646 0328 0512
0512 0461 0346 0612 0641
))
)
1198613= (0359 0358 0419 0381 0612)
1198614=((
(
0241
0282
0138
0187
0174
))
)
119879
8 Discrete Dynamics in Nature and Society
oplus((
(
0421 0379 0513 0812 0385
0356 0812 0346 0346 0541
0841 0644 0311 0461 0197
0345 0547 0345 0856 0634
0314 0284 0346 0654 0461
))
)
1198614= (0437 0561 0389 0631 0471)
1198615=((
(
0159
0131
0215
0164
0300
))
)
119879
oplus((
(
0765 0698 0611 0341 0341
0395 0541 0851 0206 0261
0584 0621 0511 0509 0345
0347 0574 0341 0451 0433
0614 0354 0317 0394 0542
))
)
1198615= (0540 0516 0470 0383 0396)
(7)
Based on 119861 = (1198611 1198612 1198613 1198614 1198615)119879 normalizing it and from
119862 = 119860 oplus 119861 we have
119862 =((
(
0195
0226
0176
0206
0196
))
)
119879
oplus((
(
0204 0267 0193 0218 0169
0199 0160 0234 0129 0189
0160 0143 0188 0178 0266
0195 0224 0175 0296 0204
0241 0206 0211 0179 0172
))
)
119862 = (0302 0200 0201 0149 0148)
(8)
The sum of first two items of 119862 is 0502 The sum offirst three items of 119862 is 0703 It indicates the light warningand shows the RampD team has relatively high organizationalresilience
6 Conclusions
Based on the structural interviews this paper exploresand confirms the structural dimensions of organizationalresilience Based on it this paper constructs an early riskwarning model of organizational resilience and applies it
to the RampD team of JMC company The conclusions are asfollows
(1) Based on literature review face to face interviewsand open questionnaires this paper applies the exploratoryfactor analysis method to discuss the factor structure oforganizational resilience of RampD teams The results showthat the factor structure of organizational resilience of RampDteams includes five dimensions shared vision willingnessto learn adaptation ability cooperative awareness and workenthusiasm Then this paper compares the five-factor modelwith other competitivemodels to further test the effectivenessof the five-factor model The results show that the five-factormodel is the best What is more the validity and reliabilityof the questionnaires of organizational resilience are provedto meet the requirements of psychometrics The model issupported
(2) Based on the factor structure of organizationalresilience this study constructs an early risk warning modelof organizational resilience of RampD teams It divides the riskwarning levels into five levels By applying fuzzy integratedevaluationmethod and based on the five-factor structure thispaper constructs a hierarchical analysis structure model Bymaking a series of judgments based on pairwise comparisonsof the elements we can get the judgment matrix and therebydecide the weight of each factor of organizational resilienceThen by using Delphi method we can get the member-ship matrix Lastly by calculating the judgment matrix andmembership matrix we can know the risk warning level oforganizational resilience of the RampD teamWe hope the resultwill provide references for the company decision
(3) This study applies the early risk warning model toRampD team of JMC company The results show that the teamhas relatively high organizational resilience These resultsmatch the work performance work experiences and leadersrsquoremarks on the team It also matches the self-evaluation ofmembers of the team All these show that the method isoperational and feasible
Competing Interests
The author declares no competing interests The author hasno financial and personal relationships with other people ororganizations that can inappropriately influence the work
Acknowledgments
This work is supported by the NSFC (71361013 7146200971273122 and 71463020) China Postdoctoral Science Founda-tion under Grant no 2013M541867 Jiangxi Province ScienceFoundation of China under Grants nos 20151BAB207059and 20142BA217018 and China Scholarship Council Fundingunder Grant no 201409805006
References
[1] K M Sutcliffe and J T Vogus ldquoOrganizing for resiliencerdquoin Positive Organizational Scholarship Foundations of a NewDiscipline pp 94ndash110 2003
Discrete Dynamics in Nature and Society 9
[2] J H Gittell K Cameron S Lim and V Rivas ldquoRelationshipslayoffs and organizational resilience airline industry responsesto September 11rdquo Journal of Applied Behavioral Science vol 42no 3 pp 300ndash329 2006
[3] J W Rudolph and N P Repenning ldquoDisaster dynamicsunderstanding the role of quantity in organizational collapserdquoAdministrative Science Quarterly vol 47 no 1 pp 1ndash30 2002
[4] J E Dutton P J Frost M C Worline J M Lilius and J MKanov ldquoLeading in times of traumardquo Harvard Business Reviewvol 80 no 1 pp 54ndash61 2002
[5] R Balu ldquoHow to bounce back from setbacksrdquo Fast Companyvol 45 pp 148ndash156 2001
[6] K EWeick ldquoEnacted sensemaking in crisis situationsrdquo Journalof Management Studies vol 25 no 4 pp 305ndash317 1988
[7] D L Coutu ldquoHow resilience worksrdquo Harvard Business Reviewvol 80 no 5 pp 46ndash55 2002
[8] S F Freeman M Maltz and L Hirschhorn ldquoThe power ofmoral purpose Sandler OrsquoNeill amp partners in the aftermath ofSeptember 11th 2001rdquo Organization Development Journal vol22 no 4 pp 69ndash82 2004
[9] F PMorgeson andD A Hofmann ldquoThe structure and functionof collective constructs implications formultilevel research andtheory developmentrdquo Academy of Management Review vol 24no 2 pp 249ndash265 1999
[10] J F I Horne ldquoThe coming of age of organizational resiliencerdquoBusiness Forum vol 22 no 2-3 pp 24ndash28 1997
[11] L A Mallak ldquoMeasuring resilience in health care providerorganizationsrdquoHealth Manpower Management vol 24 no 4-5pp 148ndash152 1998
[12] L A Mallak ldquoPutting organizational resilience to workrdquo Indus-trial Management vol 40 no 6 pp 8ndash13 1998
[13] C A Lengnick-Hall T E Beck and M L Lengnick-HallldquoDeveloping a capacity for organizational resilience throughstrategic human resourcemanagementrdquoHuman ResourceMan-agement Review vol 21 no 3 pp 243ndash255 2011
[14] M London ldquoToward a theory of career motivationrdquo Academyof Management Review vol 8 no 4 pp 620ndash630 1983
[15] Y Xiao-nan and Z Jian-xin ldquoResilience the psychologicalmechanism for recovery and growthrdquoAdvances in PsychologicalScience vol 5 no 5 pp 658ndash665 2005
[16] X Ju-Zhe S Biao and Z Zhi-Hong ldquoThe research on resilience its evolution and directionrdquo Psychological Science vol 31 no4 pp 995ndash998 2008
[17] E Grotberg ldquoResilience for tomorrowrdquo Trabajo presentado enla International Council of Psychologists Convention Foz doIguacu Brazil Extraıdo de 2005 httpswwwhitpagescomdoc62572445722214401
[18] R A Noe A W Noe and J A Bachhuber ldquoAn investigationof the correlates of career motivationrdquo Journal of VocationalBehavior vol 37 no 3 pp 340ndash356 1990
[19] F Luthans J B Avey R Clapp-Smith and W Li ldquoMore evi-dence on the value of Chinese workersrsquo psychological capital apotentially unlimited competitive resourcerdquo The InternationalJournal of Human Resource Management vol 19 no 5 pp 818ndash827 2008
[20] C Fourie and L J Van Vuuren ldquoDefining andmeasuring careerresiliencerdquo SA Journal of Industrial Psychology vol 24 no 3 pp52ndash59 1998
[21] Y C Liu Relationships between Career Resilience and CareerBeliefs of Employees in Taiwan Texas AampM University 2003
[22] B Obrist C Pfeiffer and R Henley ldquoMulti-layered socialresilience a new approach in mitigation researchrdquo Progress inDevelopment Studies vol 10 no 4 pp 283ndash293 2010
[23] S J Breckler ldquoEmpirical validation of affect behavior and cog-nition as distinct components of attituderdquo Journal of Personalityamp Social Psychology vol 47 no 6 pp 1191ndash1205 1984
[24] C A Lietz andM Strength ldquoStories of successful reunificationa narrative study of family resilience in child welfarerdquo Familiesin Society The Journal of Contemporary Social Services vol 92no 2 pp 203ndash210 2011
[25] X Zhao and Z-G Xin ldquoResearch review on models ofenterprise risk forewarning managementrdquo Journal of BeijingUniversity of Posts and Telecommunications (Social SciencesEdition) vol 12 no 1 pp 93ndash97 2010
[26] E F Fern ldquoThe use of focus groups for idea generationthe effects of group size acquaintanceship and moderator onresponse quantity and qualityrdquo Journal of Marketing Researchvol 19 no 1 pp 1ndash13 1982
[27] S-H Chen and W He ldquoStudy on knowledge propagationin complex networks based on preferences taking wechat asexamplerdquo Abstract and Applied Analysis vol 2014 Article ID543734 11 pages 2014
[28] W He ldquoAn inventory controlled supply chain model based onimproved BP neural networkrdquoDiscrete Dynamics in Nature andSociety vol 2013 Article ID 537675 7 pages 2013
[29] S-H Chen ldquoA novel culture algorithm and itrsquos application inknowledge integrationrdquo Information vol 15 no 11 B pp 4847ndash4853 2012
[30] W He and S-H Chen ldquoGame analysis of determinants ofstability of semiconductor modular production networksrdquo Sus-tainability vol 6 no 8 pp 4772ndash4794 2014
[31] S-H Chen ldquoThe influencing factors of enterprise sustainableinnovation an empirical studyrdquo Sustainability vol 8 no 5article 425 17 pages 2016
[32] S-H Chen ldquoEmpirical research on knowledge integrationimproving innovation ability of IT enterprisemdashbased on struc-tural equation modelrdquo Information vol 14 no 3 pp 753ndash7582011
[33] G-X Song ldquoStudy on construct and its dimensions of careerresilience based on Chinese indigenous culturerdquo EconomicManagement vol 33 no 11 pp 184ndash193 2011
[34] J-L Ke J-M Sun J-T Shi and Q-X Gu ldquoEmpirical studyon relationship between social capital of RampD team and teampotencyrdquoManagement World vol 3 pp 89ndash101 2007
[35] H Shouzhong and G Jianqin ldquoFuzzy integrated evaluation andits applicationrdquo Journal of China Textile University vol 21 no 1pp 74ndash80 1995
[36] A Jebreen and A Husain ldquoUtility-based approach for deter-mining the weights of participants in virtual organizationrdquoApplied Mathematical Sciences vol 6 no 96 pp 4773ndash47862012
Normalizing them we can get119908119894 We calculate the largest
eigenvalue and its eigenvector and make consistency testThen we can get CR = CIRI = 0043 lt 01 It indicatesthat the consistency of judgment matrix is acceptable
52 Establishment of Membership Matrix of Fuzzy EvaluationBased on the above analysis we select a group of 33 expertsThey mainly come from two sources 17 of them are leadersof departments and senior engineers of enterprises and 16 ofthem are college professors in human resource managementThe alternative answers include ldquoextreme important veryimportant important a little important and not importantrdquoand we give each of them from 5 to 1 respectively We sendthem the questionnaires by email and make sure they do notknow each otherrsquos answer Then we can get the membershipmatrix
1198771=((
(
0456 0812 0654 0142 0461
0451 0641 0247 0541 0622
0751 0712 0574 0341 0346
0235 0632 0341 0723 0348
0432 0543 0355 0541 0156
))
)
1198772=((
(
0634 0247 0541 0346 0316
0453 0621 0421 0261 0423
0431 0354 0354 0156 0761
0312 0317 0641 0341 0345
0231 0394 0521 0141 0432
))
)
1198773=((
(
0345 0384 0347 0512 0712
0311 0371 0621 0315 0731
0421 0381 0274 0311 0623
0235 0267 0646 0328 0512
0512 0461 0346 0612 0641
))
)
1198774=((
(
0421 0379 0513 0812 0385
0356 0812 0346 0346 0541
0841 0644 0311 0461 0197
0345 0547 0345 0856 0634
0314 0284 0346 0654 0461
))
)
1198775=((
(
0765 0698 0611 0341 0341
0395 0541 0851 0206 0261
0584 0621 0511 0509 0345
0347 0574 0341 0451 0433
0614 0354 0317 0394 0542
))
)
(6)
53 Calculating the Comprehensive Evaluation Vector of EachSubgoal From 119861
119894= 119908119894oplus 119877119894 we have
1198611=((
(
0201
0210
0177
0207
0207
))
)
119879
oplus((
(
0456 0812 0654 0142 0461
0451 0641 0247 0541 0622
0751 0712 0574 0341 0346
0235 0632 0341 0723 0348
0432 0543 0355 0541 0156
))
)
1198611= (0457 0667 0429 0646 0389)
1198612=((
(
0272
0245
0133
0215
0165
))
)
119879
oplus((
(
0634 0247 0541 0346 0316
0453 0621 0421 0261 0423
0431 0354 0354 0156 0761
0312 0317 0641 0341 0345
0231 0394 0521 0141 0432
))
)
1198612= (0446 0400 0521 0275 0436)
1198613=((
(
0127
0132
0337
0227
0155
))
)
119879
oplus((
(
0345 0384 0347 0512 0712
0311 0371 0621 0315 0731
0421 0381 0274 0311 0623
0235 0267 0646 0328 0512
0512 0461 0346 0612 0641
))
)
1198613= (0359 0358 0419 0381 0612)
1198614=((
(
0241
0282
0138
0187
0174
))
)
119879
8 Discrete Dynamics in Nature and Society
oplus((
(
0421 0379 0513 0812 0385
0356 0812 0346 0346 0541
0841 0644 0311 0461 0197
0345 0547 0345 0856 0634
0314 0284 0346 0654 0461
))
)
1198614= (0437 0561 0389 0631 0471)
1198615=((
(
0159
0131
0215
0164
0300
))
)
119879
oplus((
(
0765 0698 0611 0341 0341
0395 0541 0851 0206 0261
0584 0621 0511 0509 0345
0347 0574 0341 0451 0433
0614 0354 0317 0394 0542
))
)
1198615= (0540 0516 0470 0383 0396)
(7)
Based on 119861 = (1198611 1198612 1198613 1198614 1198615)119879 normalizing it and from
119862 = 119860 oplus 119861 we have
119862 =((
(
0195
0226
0176
0206
0196
))
)
119879
oplus((
(
0204 0267 0193 0218 0169
0199 0160 0234 0129 0189
0160 0143 0188 0178 0266
0195 0224 0175 0296 0204
0241 0206 0211 0179 0172
))
)
119862 = (0302 0200 0201 0149 0148)
(8)
The sum of first two items of 119862 is 0502 The sum offirst three items of 119862 is 0703 It indicates the light warningand shows the RampD team has relatively high organizationalresilience
6 Conclusions
Based on the structural interviews this paper exploresand confirms the structural dimensions of organizationalresilience Based on it this paper constructs an early riskwarning model of organizational resilience and applies it
to the RampD team of JMC company The conclusions are asfollows
(1) Based on literature review face to face interviewsand open questionnaires this paper applies the exploratoryfactor analysis method to discuss the factor structure oforganizational resilience of RampD teams The results showthat the factor structure of organizational resilience of RampDteams includes five dimensions shared vision willingnessto learn adaptation ability cooperative awareness and workenthusiasm Then this paper compares the five-factor modelwith other competitivemodels to further test the effectivenessof the five-factor model The results show that the five-factormodel is the best What is more the validity and reliabilityof the questionnaires of organizational resilience are provedto meet the requirements of psychometrics The model issupported
(2) Based on the factor structure of organizationalresilience this study constructs an early risk warning modelof organizational resilience of RampD teams It divides the riskwarning levels into five levels By applying fuzzy integratedevaluationmethod and based on the five-factor structure thispaper constructs a hierarchical analysis structure model Bymaking a series of judgments based on pairwise comparisonsof the elements we can get the judgment matrix and therebydecide the weight of each factor of organizational resilienceThen by using Delphi method we can get the member-ship matrix Lastly by calculating the judgment matrix andmembership matrix we can know the risk warning level oforganizational resilience of the RampD teamWe hope the resultwill provide references for the company decision
(3) This study applies the early risk warning model toRampD team of JMC company The results show that the teamhas relatively high organizational resilience These resultsmatch the work performance work experiences and leadersrsquoremarks on the team It also matches the self-evaluation ofmembers of the team All these show that the method isoperational and feasible
Competing Interests
The author declares no competing interests The author hasno financial and personal relationships with other people ororganizations that can inappropriately influence the work
Acknowledgments
This work is supported by the NSFC (71361013 7146200971273122 and 71463020) China Postdoctoral Science Founda-tion under Grant no 2013M541867 Jiangxi Province ScienceFoundation of China under Grants nos 20151BAB207059and 20142BA217018 and China Scholarship Council Fundingunder Grant no 201409805006
References
[1] K M Sutcliffe and J T Vogus ldquoOrganizing for resiliencerdquoin Positive Organizational Scholarship Foundations of a NewDiscipline pp 94ndash110 2003
Discrete Dynamics in Nature and Society 9
[2] J H Gittell K Cameron S Lim and V Rivas ldquoRelationshipslayoffs and organizational resilience airline industry responsesto September 11rdquo Journal of Applied Behavioral Science vol 42no 3 pp 300ndash329 2006
[3] J W Rudolph and N P Repenning ldquoDisaster dynamicsunderstanding the role of quantity in organizational collapserdquoAdministrative Science Quarterly vol 47 no 1 pp 1ndash30 2002
[4] J E Dutton P J Frost M C Worline J M Lilius and J MKanov ldquoLeading in times of traumardquo Harvard Business Reviewvol 80 no 1 pp 54ndash61 2002
[5] R Balu ldquoHow to bounce back from setbacksrdquo Fast Companyvol 45 pp 148ndash156 2001
[6] K EWeick ldquoEnacted sensemaking in crisis situationsrdquo Journalof Management Studies vol 25 no 4 pp 305ndash317 1988
[7] D L Coutu ldquoHow resilience worksrdquo Harvard Business Reviewvol 80 no 5 pp 46ndash55 2002
[8] S F Freeman M Maltz and L Hirschhorn ldquoThe power ofmoral purpose Sandler OrsquoNeill amp partners in the aftermath ofSeptember 11th 2001rdquo Organization Development Journal vol22 no 4 pp 69ndash82 2004
[9] F PMorgeson andD A Hofmann ldquoThe structure and functionof collective constructs implications formultilevel research andtheory developmentrdquo Academy of Management Review vol 24no 2 pp 249ndash265 1999
[10] J F I Horne ldquoThe coming of age of organizational resiliencerdquoBusiness Forum vol 22 no 2-3 pp 24ndash28 1997
[11] L A Mallak ldquoMeasuring resilience in health care providerorganizationsrdquoHealth Manpower Management vol 24 no 4-5pp 148ndash152 1998
[12] L A Mallak ldquoPutting organizational resilience to workrdquo Indus-trial Management vol 40 no 6 pp 8ndash13 1998
[13] C A Lengnick-Hall T E Beck and M L Lengnick-HallldquoDeveloping a capacity for organizational resilience throughstrategic human resourcemanagementrdquoHuman ResourceMan-agement Review vol 21 no 3 pp 243ndash255 2011
[14] M London ldquoToward a theory of career motivationrdquo Academyof Management Review vol 8 no 4 pp 620ndash630 1983
[15] Y Xiao-nan and Z Jian-xin ldquoResilience the psychologicalmechanism for recovery and growthrdquoAdvances in PsychologicalScience vol 5 no 5 pp 658ndash665 2005
[16] X Ju-Zhe S Biao and Z Zhi-Hong ldquoThe research on resilience its evolution and directionrdquo Psychological Science vol 31 no4 pp 995ndash998 2008
[17] E Grotberg ldquoResilience for tomorrowrdquo Trabajo presentado enla International Council of Psychologists Convention Foz doIguacu Brazil Extraıdo de 2005 httpswwwhitpagescomdoc62572445722214401
[18] R A Noe A W Noe and J A Bachhuber ldquoAn investigationof the correlates of career motivationrdquo Journal of VocationalBehavior vol 37 no 3 pp 340ndash356 1990
[19] F Luthans J B Avey R Clapp-Smith and W Li ldquoMore evi-dence on the value of Chinese workersrsquo psychological capital apotentially unlimited competitive resourcerdquo The InternationalJournal of Human Resource Management vol 19 no 5 pp 818ndash827 2008
[20] C Fourie and L J Van Vuuren ldquoDefining andmeasuring careerresiliencerdquo SA Journal of Industrial Psychology vol 24 no 3 pp52ndash59 1998
[21] Y C Liu Relationships between Career Resilience and CareerBeliefs of Employees in Taiwan Texas AampM University 2003
[22] B Obrist C Pfeiffer and R Henley ldquoMulti-layered socialresilience a new approach in mitigation researchrdquo Progress inDevelopment Studies vol 10 no 4 pp 283ndash293 2010
[23] S J Breckler ldquoEmpirical validation of affect behavior and cog-nition as distinct components of attituderdquo Journal of Personalityamp Social Psychology vol 47 no 6 pp 1191ndash1205 1984
[24] C A Lietz andM Strength ldquoStories of successful reunificationa narrative study of family resilience in child welfarerdquo Familiesin Society The Journal of Contemporary Social Services vol 92no 2 pp 203ndash210 2011
[25] X Zhao and Z-G Xin ldquoResearch review on models ofenterprise risk forewarning managementrdquo Journal of BeijingUniversity of Posts and Telecommunications (Social SciencesEdition) vol 12 no 1 pp 93ndash97 2010
[26] E F Fern ldquoThe use of focus groups for idea generationthe effects of group size acquaintanceship and moderator onresponse quantity and qualityrdquo Journal of Marketing Researchvol 19 no 1 pp 1ndash13 1982
[27] S-H Chen and W He ldquoStudy on knowledge propagationin complex networks based on preferences taking wechat asexamplerdquo Abstract and Applied Analysis vol 2014 Article ID543734 11 pages 2014
[28] W He ldquoAn inventory controlled supply chain model based onimproved BP neural networkrdquoDiscrete Dynamics in Nature andSociety vol 2013 Article ID 537675 7 pages 2013
[29] S-H Chen ldquoA novel culture algorithm and itrsquos application inknowledge integrationrdquo Information vol 15 no 11 B pp 4847ndash4853 2012
[30] W He and S-H Chen ldquoGame analysis of determinants ofstability of semiconductor modular production networksrdquo Sus-tainability vol 6 no 8 pp 4772ndash4794 2014
[31] S-H Chen ldquoThe influencing factors of enterprise sustainableinnovation an empirical studyrdquo Sustainability vol 8 no 5article 425 17 pages 2016
[32] S-H Chen ldquoEmpirical research on knowledge integrationimproving innovation ability of IT enterprisemdashbased on struc-tural equation modelrdquo Information vol 14 no 3 pp 753ndash7582011
[33] G-X Song ldquoStudy on construct and its dimensions of careerresilience based on Chinese indigenous culturerdquo EconomicManagement vol 33 no 11 pp 184ndash193 2011
[34] J-L Ke J-M Sun J-T Shi and Q-X Gu ldquoEmpirical studyon relationship between social capital of RampD team and teampotencyrdquoManagement World vol 3 pp 89ndash101 2007
[35] H Shouzhong and G Jianqin ldquoFuzzy integrated evaluation andits applicationrdquo Journal of China Textile University vol 21 no 1pp 74ndash80 1995
[36] A Jebreen and A Husain ldquoUtility-based approach for deter-mining the weights of participants in virtual organizationrdquoApplied Mathematical Sciences vol 6 no 96 pp 4773ndash47862012
Based on 119861 = (1198611 1198612 1198613 1198614 1198615)119879 normalizing it and from
119862 = 119860 oplus 119861 we have
119862 =((
(
0195
0226
0176
0206
0196
))
)
119879
oplus((
(
0204 0267 0193 0218 0169
0199 0160 0234 0129 0189
0160 0143 0188 0178 0266
0195 0224 0175 0296 0204
0241 0206 0211 0179 0172
))
)
119862 = (0302 0200 0201 0149 0148)
(8)
The sum of first two items of 119862 is 0502 The sum offirst three items of 119862 is 0703 It indicates the light warningand shows the RampD team has relatively high organizationalresilience
6 Conclusions
Based on the structural interviews this paper exploresand confirms the structural dimensions of organizationalresilience Based on it this paper constructs an early riskwarning model of organizational resilience and applies it
to the RampD team of JMC company The conclusions are asfollows
(1) Based on literature review face to face interviewsand open questionnaires this paper applies the exploratoryfactor analysis method to discuss the factor structure oforganizational resilience of RampD teams The results showthat the factor structure of organizational resilience of RampDteams includes five dimensions shared vision willingnessto learn adaptation ability cooperative awareness and workenthusiasm Then this paper compares the five-factor modelwith other competitivemodels to further test the effectivenessof the five-factor model The results show that the five-factormodel is the best What is more the validity and reliabilityof the questionnaires of organizational resilience are provedto meet the requirements of psychometrics The model issupported
(2) Based on the factor structure of organizationalresilience this study constructs an early risk warning modelof organizational resilience of RampD teams It divides the riskwarning levels into five levels By applying fuzzy integratedevaluationmethod and based on the five-factor structure thispaper constructs a hierarchical analysis structure model Bymaking a series of judgments based on pairwise comparisonsof the elements we can get the judgment matrix and therebydecide the weight of each factor of organizational resilienceThen by using Delphi method we can get the member-ship matrix Lastly by calculating the judgment matrix andmembership matrix we can know the risk warning level oforganizational resilience of the RampD teamWe hope the resultwill provide references for the company decision
(3) This study applies the early risk warning model toRampD team of JMC company The results show that the teamhas relatively high organizational resilience These resultsmatch the work performance work experiences and leadersrsquoremarks on the team It also matches the self-evaluation ofmembers of the team All these show that the method isoperational and feasible
Competing Interests
The author declares no competing interests The author hasno financial and personal relationships with other people ororganizations that can inappropriately influence the work
Acknowledgments
This work is supported by the NSFC (71361013 7146200971273122 and 71463020) China Postdoctoral Science Founda-tion under Grant no 2013M541867 Jiangxi Province ScienceFoundation of China under Grants nos 20151BAB207059and 20142BA217018 and China Scholarship Council Fundingunder Grant no 201409805006
References
[1] K M Sutcliffe and J T Vogus ldquoOrganizing for resiliencerdquoin Positive Organizational Scholarship Foundations of a NewDiscipline pp 94ndash110 2003
Discrete Dynamics in Nature and Society 9
[2] J H Gittell K Cameron S Lim and V Rivas ldquoRelationshipslayoffs and organizational resilience airline industry responsesto September 11rdquo Journal of Applied Behavioral Science vol 42no 3 pp 300ndash329 2006
[3] J W Rudolph and N P Repenning ldquoDisaster dynamicsunderstanding the role of quantity in organizational collapserdquoAdministrative Science Quarterly vol 47 no 1 pp 1ndash30 2002
[4] J E Dutton P J Frost M C Worline J M Lilius and J MKanov ldquoLeading in times of traumardquo Harvard Business Reviewvol 80 no 1 pp 54ndash61 2002
[5] R Balu ldquoHow to bounce back from setbacksrdquo Fast Companyvol 45 pp 148ndash156 2001
[6] K EWeick ldquoEnacted sensemaking in crisis situationsrdquo Journalof Management Studies vol 25 no 4 pp 305ndash317 1988
[7] D L Coutu ldquoHow resilience worksrdquo Harvard Business Reviewvol 80 no 5 pp 46ndash55 2002
[8] S F Freeman M Maltz and L Hirschhorn ldquoThe power ofmoral purpose Sandler OrsquoNeill amp partners in the aftermath ofSeptember 11th 2001rdquo Organization Development Journal vol22 no 4 pp 69ndash82 2004
[9] F PMorgeson andD A Hofmann ldquoThe structure and functionof collective constructs implications formultilevel research andtheory developmentrdquo Academy of Management Review vol 24no 2 pp 249ndash265 1999
[10] J F I Horne ldquoThe coming of age of organizational resiliencerdquoBusiness Forum vol 22 no 2-3 pp 24ndash28 1997
[11] L A Mallak ldquoMeasuring resilience in health care providerorganizationsrdquoHealth Manpower Management vol 24 no 4-5pp 148ndash152 1998
[12] L A Mallak ldquoPutting organizational resilience to workrdquo Indus-trial Management vol 40 no 6 pp 8ndash13 1998
[13] C A Lengnick-Hall T E Beck and M L Lengnick-HallldquoDeveloping a capacity for organizational resilience throughstrategic human resourcemanagementrdquoHuman ResourceMan-agement Review vol 21 no 3 pp 243ndash255 2011
[14] M London ldquoToward a theory of career motivationrdquo Academyof Management Review vol 8 no 4 pp 620ndash630 1983
[15] Y Xiao-nan and Z Jian-xin ldquoResilience the psychologicalmechanism for recovery and growthrdquoAdvances in PsychologicalScience vol 5 no 5 pp 658ndash665 2005
[16] X Ju-Zhe S Biao and Z Zhi-Hong ldquoThe research on resilience its evolution and directionrdquo Psychological Science vol 31 no4 pp 995ndash998 2008
[17] E Grotberg ldquoResilience for tomorrowrdquo Trabajo presentado enla International Council of Psychologists Convention Foz doIguacu Brazil Extraıdo de 2005 httpswwwhitpagescomdoc62572445722214401
[18] R A Noe A W Noe and J A Bachhuber ldquoAn investigationof the correlates of career motivationrdquo Journal of VocationalBehavior vol 37 no 3 pp 340ndash356 1990
[19] F Luthans J B Avey R Clapp-Smith and W Li ldquoMore evi-dence on the value of Chinese workersrsquo psychological capital apotentially unlimited competitive resourcerdquo The InternationalJournal of Human Resource Management vol 19 no 5 pp 818ndash827 2008
[20] C Fourie and L J Van Vuuren ldquoDefining andmeasuring careerresiliencerdquo SA Journal of Industrial Psychology vol 24 no 3 pp52ndash59 1998
[21] Y C Liu Relationships between Career Resilience and CareerBeliefs of Employees in Taiwan Texas AampM University 2003
[22] B Obrist C Pfeiffer and R Henley ldquoMulti-layered socialresilience a new approach in mitigation researchrdquo Progress inDevelopment Studies vol 10 no 4 pp 283ndash293 2010
[23] S J Breckler ldquoEmpirical validation of affect behavior and cog-nition as distinct components of attituderdquo Journal of Personalityamp Social Psychology vol 47 no 6 pp 1191ndash1205 1984
[24] C A Lietz andM Strength ldquoStories of successful reunificationa narrative study of family resilience in child welfarerdquo Familiesin Society The Journal of Contemporary Social Services vol 92no 2 pp 203ndash210 2011
[25] X Zhao and Z-G Xin ldquoResearch review on models ofenterprise risk forewarning managementrdquo Journal of BeijingUniversity of Posts and Telecommunications (Social SciencesEdition) vol 12 no 1 pp 93ndash97 2010
[26] E F Fern ldquoThe use of focus groups for idea generationthe effects of group size acquaintanceship and moderator onresponse quantity and qualityrdquo Journal of Marketing Researchvol 19 no 1 pp 1ndash13 1982
[27] S-H Chen and W He ldquoStudy on knowledge propagationin complex networks based on preferences taking wechat asexamplerdquo Abstract and Applied Analysis vol 2014 Article ID543734 11 pages 2014
[28] W He ldquoAn inventory controlled supply chain model based onimproved BP neural networkrdquoDiscrete Dynamics in Nature andSociety vol 2013 Article ID 537675 7 pages 2013
[29] S-H Chen ldquoA novel culture algorithm and itrsquos application inknowledge integrationrdquo Information vol 15 no 11 B pp 4847ndash4853 2012
[30] W He and S-H Chen ldquoGame analysis of determinants ofstability of semiconductor modular production networksrdquo Sus-tainability vol 6 no 8 pp 4772ndash4794 2014
[31] S-H Chen ldquoThe influencing factors of enterprise sustainableinnovation an empirical studyrdquo Sustainability vol 8 no 5article 425 17 pages 2016
[32] S-H Chen ldquoEmpirical research on knowledge integrationimproving innovation ability of IT enterprisemdashbased on struc-tural equation modelrdquo Information vol 14 no 3 pp 753ndash7582011
[33] G-X Song ldquoStudy on construct and its dimensions of careerresilience based on Chinese indigenous culturerdquo EconomicManagement vol 33 no 11 pp 184ndash193 2011
[34] J-L Ke J-M Sun J-T Shi and Q-X Gu ldquoEmpirical studyon relationship between social capital of RampD team and teampotencyrdquoManagement World vol 3 pp 89ndash101 2007
[35] H Shouzhong and G Jianqin ldquoFuzzy integrated evaluation andits applicationrdquo Journal of China Textile University vol 21 no 1pp 74ndash80 1995
[36] A Jebreen and A Husain ldquoUtility-based approach for deter-mining the weights of participants in virtual organizationrdquoApplied Mathematical Sciences vol 6 no 96 pp 4773ndash47862012
[2] J H Gittell K Cameron S Lim and V Rivas ldquoRelationshipslayoffs and organizational resilience airline industry responsesto September 11rdquo Journal of Applied Behavioral Science vol 42no 3 pp 300ndash329 2006
[3] J W Rudolph and N P Repenning ldquoDisaster dynamicsunderstanding the role of quantity in organizational collapserdquoAdministrative Science Quarterly vol 47 no 1 pp 1ndash30 2002
[4] J E Dutton P J Frost M C Worline J M Lilius and J MKanov ldquoLeading in times of traumardquo Harvard Business Reviewvol 80 no 1 pp 54ndash61 2002
[5] R Balu ldquoHow to bounce back from setbacksrdquo Fast Companyvol 45 pp 148ndash156 2001
[6] K EWeick ldquoEnacted sensemaking in crisis situationsrdquo Journalof Management Studies vol 25 no 4 pp 305ndash317 1988
[7] D L Coutu ldquoHow resilience worksrdquo Harvard Business Reviewvol 80 no 5 pp 46ndash55 2002
[8] S F Freeman M Maltz and L Hirschhorn ldquoThe power ofmoral purpose Sandler OrsquoNeill amp partners in the aftermath ofSeptember 11th 2001rdquo Organization Development Journal vol22 no 4 pp 69ndash82 2004
[9] F PMorgeson andD A Hofmann ldquoThe structure and functionof collective constructs implications formultilevel research andtheory developmentrdquo Academy of Management Review vol 24no 2 pp 249ndash265 1999
[10] J F I Horne ldquoThe coming of age of organizational resiliencerdquoBusiness Forum vol 22 no 2-3 pp 24ndash28 1997
[11] L A Mallak ldquoMeasuring resilience in health care providerorganizationsrdquoHealth Manpower Management vol 24 no 4-5pp 148ndash152 1998
[12] L A Mallak ldquoPutting organizational resilience to workrdquo Indus-trial Management vol 40 no 6 pp 8ndash13 1998
[13] C A Lengnick-Hall T E Beck and M L Lengnick-HallldquoDeveloping a capacity for organizational resilience throughstrategic human resourcemanagementrdquoHuman ResourceMan-agement Review vol 21 no 3 pp 243ndash255 2011
[14] M London ldquoToward a theory of career motivationrdquo Academyof Management Review vol 8 no 4 pp 620ndash630 1983
[15] Y Xiao-nan and Z Jian-xin ldquoResilience the psychologicalmechanism for recovery and growthrdquoAdvances in PsychologicalScience vol 5 no 5 pp 658ndash665 2005
[16] X Ju-Zhe S Biao and Z Zhi-Hong ldquoThe research on resilience its evolution and directionrdquo Psychological Science vol 31 no4 pp 995ndash998 2008
[17] E Grotberg ldquoResilience for tomorrowrdquo Trabajo presentado enla International Council of Psychologists Convention Foz doIguacu Brazil Extraıdo de 2005 httpswwwhitpagescomdoc62572445722214401
[18] R A Noe A W Noe and J A Bachhuber ldquoAn investigationof the correlates of career motivationrdquo Journal of VocationalBehavior vol 37 no 3 pp 340ndash356 1990
[19] F Luthans J B Avey R Clapp-Smith and W Li ldquoMore evi-dence on the value of Chinese workersrsquo psychological capital apotentially unlimited competitive resourcerdquo The InternationalJournal of Human Resource Management vol 19 no 5 pp 818ndash827 2008
[20] C Fourie and L J Van Vuuren ldquoDefining andmeasuring careerresiliencerdquo SA Journal of Industrial Psychology vol 24 no 3 pp52ndash59 1998
[21] Y C Liu Relationships between Career Resilience and CareerBeliefs of Employees in Taiwan Texas AampM University 2003
[22] B Obrist C Pfeiffer and R Henley ldquoMulti-layered socialresilience a new approach in mitigation researchrdquo Progress inDevelopment Studies vol 10 no 4 pp 283ndash293 2010
[23] S J Breckler ldquoEmpirical validation of affect behavior and cog-nition as distinct components of attituderdquo Journal of Personalityamp Social Psychology vol 47 no 6 pp 1191ndash1205 1984
[24] C A Lietz andM Strength ldquoStories of successful reunificationa narrative study of family resilience in child welfarerdquo Familiesin Society The Journal of Contemporary Social Services vol 92no 2 pp 203ndash210 2011
[25] X Zhao and Z-G Xin ldquoResearch review on models ofenterprise risk forewarning managementrdquo Journal of BeijingUniversity of Posts and Telecommunications (Social SciencesEdition) vol 12 no 1 pp 93ndash97 2010
[26] E F Fern ldquoThe use of focus groups for idea generationthe effects of group size acquaintanceship and moderator onresponse quantity and qualityrdquo Journal of Marketing Researchvol 19 no 1 pp 1ndash13 1982
[27] S-H Chen and W He ldquoStudy on knowledge propagationin complex networks based on preferences taking wechat asexamplerdquo Abstract and Applied Analysis vol 2014 Article ID543734 11 pages 2014
[28] W He ldquoAn inventory controlled supply chain model based onimproved BP neural networkrdquoDiscrete Dynamics in Nature andSociety vol 2013 Article ID 537675 7 pages 2013
[29] S-H Chen ldquoA novel culture algorithm and itrsquos application inknowledge integrationrdquo Information vol 15 no 11 B pp 4847ndash4853 2012
[30] W He and S-H Chen ldquoGame analysis of determinants ofstability of semiconductor modular production networksrdquo Sus-tainability vol 6 no 8 pp 4772ndash4794 2014
[31] S-H Chen ldquoThe influencing factors of enterprise sustainableinnovation an empirical studyrdquo Sustainability vol 8 no 5article 425 17 pages 2016
[32] S-H Chen ldquoEmpirical research on knowledge integrationimproving innovation ability of IT enterprisemdashbased on struc-tural equation modelrdquo Information vol 14 no 3 pp 753ndash7582011
[33] G-X Song ldquoStudy on construct and its dimensions of careerresilience based on Chinese indigenous culturerdquo EconomicManagement vol 33 no 11 pp 184ndash193 2011
[34] J-L Ke J-M Sun J-T Shi and Q-X Gu ldquoEmpirical studyon relationship between social capital of RampD team and teampotencyrdquoManagement World vol 3 pp 89ndash101 2007
[35] H Shouzhong and G Jianqin ldquoFuzzy integrated evaluation andits applicationrdquo Journal of China Textile University vol 21 no 1pp 74ndash80 1995
[36] A Jebreen and A Husain ldquoUtility-based approach for deter-mining the weights of participants in virtual organizationrdquoApplied Mathematical Sciences vol 6 no 96 pp 4773ndash47862012
