Confirmatory Factor Analysis

Given the existence of a priori theory that was presented in Jamal et al.’s (2006) model, confirmatory factor analysis (CFA) was conducted to identify how well this model fits the current research data regarding shopping motivations. To achieve this end, a measurement model was conceptualised with ten latent variables and 35 original items as observed variables. Data were analysed using the AMOS19 statistical software package. The measurement model was specified as congeneric, with all indicators and error residuals restricted to load on a single factor (i.e. no cross-loading). The maximum likelihood estimation (ML) default in AMOS 19 was used.

Both the overall goodness model fit and the criteria for construct validity were considered. Indeed, there are several measurements of overall model fit including absolute fit index and incremental fit indices as well as cut off criteria for fit indices. However, Hair, et al. (2010, p. 672) argued that “at least one increment index and one absolute index, in addition to the chi square test (2) value and the associated degrees of freedom should be reported to provide adequate evidence of model fit”. Regarding the cut-off criterion, Hu and Bentler (1999, p. 27) recommend thresholds for particular measures that result in lower Type II error rates (with acceptable costs of Type-I error rates).

The Chi square (2) statistic was used as the most fundamental measure of differences between the observed and estimated covariance matrices. Root Mean Square Error of Approximation (RMSEA) and Comparative Fit Index (CFI) are the most widely used absolute fit indices. Increment indices were also assessed (Hu and Bentler, 1999). Table 6-8 detailed the selected fit statistics from the CFA output as well as guidelines for goodness of fit.

The overall 2 was 823.62 with 515 degrees of freedom. The p-value associated with this result was 0.000. This highly significant result indicated that a significant amount of observed covariance between items remains unexplained by the original model (Milošević et al., 2012). The value for RMSEA was 0.040. This value appears quite low and is below commonly accepted limits for a model with a sample size of 371. Therefore, the RMSEA indicated an unacceptable fit for the Jamal et al. (2006) model. Regarding the incremental fit indices, CFI is the most widely used measure. CFI was estimated to be 0.93, which provided additional support for the notion of inadequate model fit (see Table 6-8).

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Table 6-8: Confirmatory Factor Analysis (Goodness of Fit Statistics)

Fit index Threshold Value

Chi-square - 823.62

Degrees of freedom - 515

Probability level Sign p- value expected with large sample

0.000

RMSEA Close to 0.6 0.040

PClose > 0.05 0.999

CFI Close to 0.95 0.92

Overall, the goodness fit criteria results indicated that Jamal’s et al. (2006) model provided an unacceptable level of goodness of fit. However, to provide greater understanding of goodness of fit, further tests of reliability and validity were undertaken. To evaluate the convergent validity, the standardized factor loading was first examined. As can be seen however, given the sample size of 371, it is not unexpected that all of the loadings are statistically significant; as a result some other criteria should be used.

Calculating both the Construct Reliabilities (CR) and Average Variance Estimated (AVE) for all constructs, revealed that for some items scores were less than the suggested thresholds of 0.7 for CR and 0.5 for AVE (Hair et al., 2010) (see Table 6-10). These convergent validity issues indicate that the latent factors are not well explained by the observed variables. Therefore, this is further evidence to suggest that Jamal et al.’s (2006) model is not a good fit for the research data.

To conclude, the CFA results indicated that Jamal et al.’s (2006) model is inappropriate for the Libyan case and as a result, exploratory factor analysis (EFA) was justified to better understand Libyan shopping behaviour and to answer the research questions.

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Table 6-9: Standardized Factor Loading

Item Standardized

loading

Factor 1—Gratification seeking

When I am in down mood, I go shopping to make me feel better 0.815

To me shopping is a way to relieve stress 0.908

I go to shopping when I want to treat myself to something special 0.4681

While shopping I can normally forget my problems 0.476

Factor 2—Social shopping

I like shopping with my friends or family to socialize 0.713

I enjoy socializing with others when I shop 0.841

Shopping with others is a bonding experience 0.912

Factor 3—High quality seeking

When it comes to purchasing products, I try to get the very best or perfect choice 0.792

In general I usually try to buy the best overall quality 0.895

I make special effort to choose the very best quality products 0.497

My standards and expectations for the products that I buy are high 0.479 Factor 4—Confused by choice

There are so many brands to choose that often I feel confused 0.534

Sometimes it’s hard to choose which stores to shop at 0.553

The more I learn about products, the harder it seems to choose the best 0.899

All the information I get on different products confuses me 0.856

Factor 5—Value shopping

For the most parts, I go shopping when there are sales 0.611

I enjoy looking for discounts when I shop 0.769

I enjoy hunting for bargains when I shop 0.720

Factor 6—Brand loyal/habitual

I have favourite brands I buy over and over 0.829

Once I find a product or brand I like, I stick with it 0.674

I go to the same store each time I shop 0.325

I like to buy the same brand 0.533

Factor 7—Brand conscious

The more expensive brands are usually my choice 0.724

The higher the price of the product, the better is its quality 0.743 Nice department and specialty stores offer me the best products 0.475

The most advertised brands are usually the very good choices 0.320

Factor 8—Utilitarian

I make shopping trips fast 0.420

While shopping, I try to accomplish just what I want to as soon as possible 0.662 While shopping I try to find just the items that I am looking for 0.666

Factor 9—Hedonic shopping

Going shopping is one of the enjoyable activities for me 0.781

I enjoy shopping just for the fun of it 0.794

I enjoy shopping more than most people do 0.765

I love to go shopping when I can find time 0.476

Factor 10—Role playing

I like shopping for others because when they feel good, I feel good 0.793

I enjoy shopping for my family and friends 0.886

1

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Table 6-10: CR and AVE Estimates

Factor _{CR AVE} Role playing 0.83 0.71 Hedonic shopping 0.83 0.55 Gratification seeking 0.77 0.48 Utilitarian 0.61 0.35 Brand conscious 0.66 0.35 Brand loyalty 0.69 0.38 Value shopping 0.74 0.49 Confused by choice 0.81 0.53

High quality seeking 0.77 0.48

Social shopping 0.86 0.68