酒店管理專業留學生dissertation范文-Findings and analysis

 

In this study, to resort to using factor analysis, factor analysis method originally developed by British psychologist C.Spearman made in 1904 . The purpose of the method is the condensed Data Indicator , through multivariate correlation can be used imaginary few variables to represent the original primary information variables , which variables the few integrated multiple variables can reflect the original most of the information . For researchers, by grasping these main factors in favor of complex economic issues for analysis and interpretation . Moreover, by factor analysis, we can not only find the basic structure of variables that can simplify the data and continue the regression analysis , cluster analysis and discriminant analysis. Factor analysis of the mathematical model are:

 

The formula , m <k

* Is the common factor (Common Factors), among them are pairwise orthogonal ;

* Is a special factor (Unique Factors), only the corresponding function ;

* Load is the common factor (Factor Loadings), is the i-th variable in the j-th factor of the load , the equivalent of the standard multiple regression regression coefficients .

 

In this study, factor analysis of principal component analysis (Principal component Analysis), principal component analysis is a factor analysis method , also known as principal component analysis , is a means to simplify the data dimension reduction method to extract eigenvalues (Eigenvalue) is greater than a common factor (Common Factor), and with maximum variance method (Varimaxmetheod) for orthogonal rotation , which will factor loadings greater than 0.6 shall be deemed a significant load to explore the food and beverage industry customers loyalty factors Affecting the main ingredients. This can be clearly catering loyalty factors in several variables which can replace the integrated response of many variables all variables.

 

Factor analysis of this study will be rational and emotional factors influencing factors separately .

 

rational factors factor analysis

Factor analysis is only rational factors influencing factors on the rational part of 16 specific factors were analyzed . First for KMO measure and Bartlett 's test of sphericity , to test whether the data is suitable for factor analysis . KMO is the Kaiser-Meyer-Olkin sampling adequacy coefficient greater when KMO value , it means that the more common factors among the variables , the more suitable for factor analysis . KMO above 0.9 is generally considered very suitable for factor analysis ; between 0.8-0.9 , it is suitable for factor analysis ; 0.7-0.8 , which is suitable for factor analysis ; between 0.6-0.7 , not suitable ; 0.5-0.6 of room , very reluctantly ; below 0.5 is not suitable for factor analysis . In this study, we can see KMO value 0.888 , indicating that the data set is suitable for factor analysis . Specific structure as shown in Table 3.5.#p#分頁標題#e#

 

Meanwhile, seen from the table , the Bartley sphere test * Statistical significance probability value is 0.000 , less than 1 %, indicating that the data correlation matrix is not unit matrix , relevant , also shows statistics are very suitable for factor analysis . Through factor analysis rotated factor loadings matrix as shown in Table 3.6.

 

As can be seenfrom Table3.6, V15 (you can easily use non-cashsettlementsuch as credit cards, etc.) This isafactorineach of the maincomponentsof thefactorloadingsdid not reachabove 0.5, so you should put thismeasure eliminates(estimated thisLoudifinancial services systemis imperfect, Currently LoudiCity has notvigorously promotenon-cashconsumption,the servicepeopledid notgive too muchattention), and again the factor analysis. KMOmeasure andBartlett's test of sphericitytest resultsare shown in Table3.7:

 

KMO value of0.827, indicating that the data setis also verysuitable for factoranalysis.Bartleyitsspherex ^ 2teststatisticsignificance probabilityvalueis0.000, less than 1%, indicating that the datacorrelation matrixis notunit matrix, relevant,statistical dataalso shows thatit is appropriatefor factoranalysis.

 

Throughfactoranalysis resultsare shown in 

 

As can be seenfrom Table3.8, catering customerloyaltyrationalfactors includethethreefactors, and through factor analysisofthesethreefactorsobtainedeigenvaluesvalue and thecumulativevarianceasshown

 

shows, the first threefactorsasthe main component ofa total of71.138%of the total varianceexplained, therefore, with thesethree factorsrationalfactorscan replacethe 16variablescan be summedoriginal variablescontainedmore than 70%of the information, whichcan initiallyfelt that the threefactorscan explainmost of thevariables, summarizemost of theinformation.

 

And fromthe same timebe seenin Table 3.8, the indexvariablefactor loadingsareabove 0.5, Table 3.9showsthe eigenvaluesof each factorinonemore, whichdescribes thevariouscomponentsof the originalindicatorswere significantly associated withand representative. Consideringthe characteristicsof each variable, the author will be namedas: "perceived quality factor", "perceived environmental factors", "perceived value factor."

 

Emotionalfactorsfactor analysis

FirstforKMOmeasure andBartlett's test of sphericity, to test whether the data issuitable for factoranalysis. The resultsare shown in Table3.10

 

SeenfromTable 3.10, KMO value of0.760, indicating that the data setissuitable for factoranalysis.Bartleyitsspherex ^ 2teststatisticsignificance probabilityvalueis0.000, less than 1%, indicating that the datacorrelation matrixis notunit matrix, relevant,statistical dataalso shows thatit is appropriatefor factoranalysis. Throughfactoranalysisresultsare shown in Table3.11,

 

Table 3.11showsemotionalfactorsincludedtwofactors. Table 3.12shows that itsvalue and thecumulativevariancecharacteristic rootsreach61.273%, indicating that they coverthe originalindexcontainsmost of the information, and the factor loadingsof each indicatorvariableis relatively high, more than 0.5, indicating that the variouscomponentsof the originalindicators aresignificantly correlated. Fortwofactors, according to its contentand consumerbehavior, the author willbe named:"brandimage to attractFactor" and "emotional satisfaction factor."