Extracting factors to investigate Consumer attitudes Using SPSS

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Extracting factors to investigate Consumer attitudes Using SPSS

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Extracting factors to investigate Consumer attitudes Using SPSS

  1. 1. Extracting factors to investigate Consumer attitudes Using SPSS Dr. Kalyan Sengupta*, Prof Atish Chattopadhyay*IntroductionA consumer survey was carried out to measure relative importanceof attributes theconsumers attach to various attitudinal variables, while purchasing branded bakeryproducts from bakery chains of the city of Kolkata. A sample of 287 consumers werecontacted at purchase points and asked about nine different attributes influencing thechoice of specific brand. The factors listed on the questionnaire were: convenience,décor and ambience of shop, service, food quality and taste, availability of a wide variety,brand reputation, value for money, freshness and portion size.It was interesting to assess how much weight the consumers put onto each of these ninefactors in a five point Likert Scale measuring 1 for absolutely no value and 5 forextremely high value. The respondents constituted a good representation of buyers withdifferent age, income, and gender groups.Initially a number of Chi-Square tests were performed to examine dependency of eachattitude factors with respect to different demographic variables, namely age, income andgender using SPSS program. It was found out from the test attitudes data that the factorswere independent of the said demographic variables. This may be interpreted as themeasured attributes influence consumers of different groups more or less in the samemanner.Factor Analysis for further understanding of attitudesFactor Analysis is a Statistical Model which uncovers the latent structure of a set ofvariables, by reducing the number of variables and clubbing them into a smaller number* Faculty Members, ICFAI Business School, Kolkata
  2. 2. of factors. SPSS has several methods or models for extracting factors from a set ofvariables. Principal Component Analysis (PCA) method was chosen for the purpose.This method seeks a liner combination of variables in a way to explain maximumvariance. Once one factor is extracted, the model searches for another linear combinationto explain maximum amount of remaining variance and the iterative process continuesuntil some tolerance limit is come across.Use of SPSSIn our case of attitude data, SPSS extracted four factor components, explaining a total of60.5 percent variance in the variables, after a factor rotation (varimax) treatment. Therotated component matrix is illustrated in the figure 1. From the correlation matrix, itwas possible to interpret those four dimensions in the following way:• Factor Component 1: Reflects onto some trendy attitude of the consumers, including the attitude variables like shop décor, brand name, etc.• Factor Component 2: Reflects onto some economic value related attitude related to the variables indicating good value for money, portion size, freshness of the product.• Factors Component 3: Reflects onto service and quality related attitudes.• Factor Component 4 reflects onto convenience (low access time) related attitude variable.Figure.1: Rotated Component Matrix for Factor Analysis Component 1 2 3 4Factors-convenient location 0.295 0.103 0.136 0.748Factors-shop décor & 0.801 -6.286E-02 8.847E-02 7.352E-02ambienceFactors-service 0.170 0.147 0.748 8.180E-02Factors-food quality/taste -6.252E-02 -3.095E-02 0.836 -4.723E-02Factors-availability of a 0.375 0.122 0.101 -0.697wide variety
  3. 3. Factors-brand 0.712 0.166 -5.643E-03 -5.091E-02name/reputationFactors-value for money 4.099E-02 0.691 -0.137 0.223Factors-freshness -9.536E-02 0.574 0.144 -0.199Factors-portion size 0.238 0.758 0.129 -1.696E-02Extraction Method: Principal Component Analysis.Rotation Method: Varimax with Kaiser Normalization.The components may be viewed from the factor component plots to locate their positionsin the multidimensional space. Such an example plot is illustrated in the figure 2. Component Plot in Rotated Space 1.0 faporsiz favalmon fafresh 0.5 Component 2 faavail fabrname faserv faconloc 0.0 fafoodqu fadecamb -0.5 -1.0 -1.0 -1.0 -0.5 -0.5 0.0 0.0 0.5 0.5 1.0 1.0 nt 3 Compone nt 1 pone Com Figure 2
  4. 4. The component score coefficient matrix, which could be used to compute factor scores ofthe individual respondent from the original variables, is displayed in figure 3. Theseindividual scores may be automatically computed by SPSS and saved in the spread sheet.Figure 3: Component Score Coefficient Matrix Component 1 2 3 4Factors-convenient location 0.176 0.014 0.070 0.646Factors-shop décor & 0.581 -0.172 -0.009 0.055ambienceFactors-service 0.023 0.024 0.546 0.071Factors-food quality/taste -0.127 -0.077 0.651 -0.036Factors-availability of a 0.252 0.032 0.026 -0.610wide varietyFactors-brand 0.494 0.016 -0.091 -0.054name/reputationFactors-value for money -0.061 0.505 -0.162 0.186Factors-freshness -0.167 0.423 0.076 -0.177Factors-portion size 0.048 0.507 0.016 -0.024Extraction Method: Principal Component Analysis . Rotation Method: Varimax withKaiser Normalization. Component Scores.The individual factor scores were used for further causal analyses of the data. Thusfactoranalysis is mainly a data reduction technique with which the analyst can reduce thevariable space, which can ease up the computations forcomplex analytical models. Key to the variable names in the Factor AnalysisAttribute Name VariableFactors-convenient location faconlocFactors-shop décor & ambience fadecambFactors-service faservFactors-food quality/taste fafoodquFactors-availability of a wide variety favail
  5. 5. Factors-brand name/reputation fabrnameFactors-value for money favalmonFactors-freshness fafreshFactors-portion size faporsiz

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