Age Group Analysis 150-

The age group profile of the respondents among women in London is presented in Table 8.1. The population covered in this research are aged 16 years old or over in 2010. The results suggest that the age group of the population is fairly represented by the sample used in this research, although, women aged over 26 to 33 years old were over-represented the sample in general, while women over the age of 66 were under-represented. This result was not unexpected, as it can be assumed that women aged over 66 are would not be willing to participate in research, and may not take part in grooming practices such as manicures. The majority of women aged 16 to 33 were presumed to be more familiar with the format of personal surveys as a research instrument and were therefore more applicable to take part.

151 Table 8.1 - Age Profile of Women in London

Frequency Percent Valid Percent Cumulative Percent

Valid 16 2 .6 .6 .6 17 2 .6 .6 1.1 18 8 2.3 2.3 3.4 19 13 3.7 3.7 7.1 20 16 4.5 4.5 11.6 21 9 2.5 2.5 14.2 22 14 4.0 4.0 18.1 23 8 2.3 2.3 20.4 24 9 2.5 2.5 22.9 25 7 2.0 2.0 24.9 26 4 1.1 1.1 26.1 27 14 4.0 4.0 30.0 28 9 2.5 2.5 32.6 29 17 4.8 4.8 37.4 30 21 5.9 5.9 43.3 31 10 2.8 2.8 46.2 32 5 1.4 1.4 47.6 33 18 5.1 5.1 52.7 34 2 .6 .6 53.3 35 8 2.3 2.3 55.5 36 1 .3 .3 55.8 37 12 3.4 3.4 59.2 38 9 2.5 2.5 61.8 39 14 4.0 4.0 65.7 40 16 4.5 4.5 70.3 41 3 .8 .8 71.1 42 4 1.1 1.1 72.2 43 3 .8 .8 73.1 44 6 1.7 1.7 74.8 45 8 2.3 2.3 77.1 47 3 .8 .8 77.9 48 2 .6 .6 78.5 49 4 1.1 1.1 79.6 50 12 3.4 3.4 83.0 51 3 .8 .8 83.9 52 6 1.7 1.7 85.6 53 2 .6 .6 86.1 55 11 3.1 3.1 89.2 56 2 .6 .6 89.8 57 6 1.7 1.7 91.5 58 3 .8 .8 92.4 59 1 .3 .3 92.6 60 5 1.4 1.4 94.1 61 5 1.4 1.4 95.5 62 3 .8 .8 96.3 63 3 .8 .8 97.2 64 5 1.4 1.4 98.6 65 1 .3 .3 98.9 66 2 .6 .6 99.4 67 1 .3 .3 99.7 70 1 .3 .3 100.0 Total 353 100.0 100.0 8.2.2. Education Analysis

The educational levels of women in London are presented in Table 8.2. The information from www.statistics.gov.uk (accessed March 2010) of educational breakdown was utilised as a

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reference point in categorising educational brackets. Therefore, the use of the educational breakdown for women in London was considered acceptable.

Table 8.2 Educational Breakdown of Women in London

Frequency Percent Valid Percent Cumulative Percent Valid G.C.S.Es 32 9.1 9.1 9.1 A-Levels 97 27.5 27.5 36.5 University Degree 112 31.7 31.7 68.3 Postgraduate Degree 52 14.7 14.7 83.0 Doctorate (PhD) 27 7.6 7.6 90.7 no qualifications (life experience) 22 6.2 6.2 96.9 Other 11 3.1 3.1 100.0 Total 353 100.0 100.0

According to the result, the educational level of the population is not adequately represented by the samples used in this research. Women with ‗GCSEs‘; ‗Doctorate (Phd)‘; ‗no qualifications (life experience); and ‗Other‘ are under-represented in general, while women with ‗A-Levels‘; ‗University Degree‘; and ‗Postgraduate Degree‘ are over-represented. This result is not unexpected, as it was assumed individuals with higher education levels would be more likely to take part in the research, while individuals with lower educational levels are less inclined to partake in survey research, as they may find difficulty in completing the questionnaire due to problems with understanding. As a result, individuals with GCSEs only are under-represented. Even though the results are not ideal, the percentages of the seven educational categories range from 3.1% to 31.7%.

8.2.3 Occupation Analysis

The occupations of women in London are presented in Table 8.3. The information from www.statistics.gov.uk (accessed March 2010) of occupational breakdown was utilised as a reference point in categorising educational brackets. Therefore, the use of the occupational breakdown for women in London was considered acceptable.

153 Table 8.3 Occupational Breakdown of Women in London

Frequency Percent

Valid Percent

Cumulative Percent Valid Managers and senior officials

20 5.7 5.7 5.7

Skilled traders 23 6.5 6.5 12.2

Professionals 146 41.4 41.4 53.5

Associate professional and

technical 26 7.4 7.4 60.9

Sales and customer service _{13 3.7 3.7 64.6}

Administrative and secretarial

22 6.2 6.2 70.8

Student 68 19.3 19.3 90.1

Unemployed 11 3.1 3.1 93.2

Retired 24 6.8 6.8 100.0

Total 353 100.0 100.0

According to the results, the occupational breakdown of women in London is not adequately represented by the sample used in this research. Occupational categories of ‗unemployed‘ and, ‗sales and customer service‘ are under-represented in general, while women falling into the categories of ‗professionals‘ and ‗student‘ are over-represented. This result is not unexpected, as it was assumed individuals with professional occupations would be more likely to take part in research, while individuals who are unemployed or have low skilled jobs would be disinclined to take part in survey research.

8.2.4 Last Handbag Purchase Analysis

This research focuses on why women in London purchase luxury designer handbags and counterfeit handbag versions; as a result a breakdown on the purchasing behaviours of women is of vital importance. The number of respondents per handbag category is not representative. According to the results in Table 8.4, 68% of women in London purchased a luxury designer handbag as their last handbag purchase, while 32% of women in London purchased a counterfeit handbag version as their last handbag purchase. Even though the numbers of respondents per handbag category are not equal it has been recommended that a sample size of 300 is respectable (Comrey and Lee 1992), therefore a sample size of 353 was considered acceptable.

154 Table 8.4 – Breakdown of Last Handbag Purchase among Women in London

Frequency Percent

Valid Percent

Cumulative Percent

Valid genuine luxury

designer handbag brand 241 68.3 68.3 68.3 counterfeit/fake designer handbag brand 112 31.7 31.7 100.0 Total 353 100.0 100.0 8.3 Reliability of Measures

The reliability of a measure relates to its consistency. In essence, it refers to the degree to which a scale constructs reliable results if repeated measurements are made (Fazio et al. 1989). Thus, a reliable measure will produce the same finding on recurring occasions if the phenomenon under investigation has not changed (Burns and Harrison 1979).

As a result, Cronbach's Alpha is employed to observe the internal consistency of the multiple- item scales: Individual factors, social consumption factors, attitudinal factors and consumption related emotions. The standard rule of thumb is that the correlation coefficient should be 0.8 or above (Bryman and Cramer 1999), although Hinkin (1995) suggested a less restrictive acceptable level of at least 0.70. This rule of thumb is applied to the ‗Attitudinal factor‘ scale which investigates attitudes towards luxury designer handbags and counterfeit versions. Kaplan and Saccuzzo (1997) suggest that if a correlation coefficient is lower than 0.8 items that reduce the reliability should be deleted from the scale. The dropping of items is practiced as it improves scale reliability. Prior to carrying out the reliability analysis, all scores of negative statements are reversed to ensure that all scores are absolute values of those items. The reason behind this is as stated by Field (2005, p. 674):

“failing to reverse-score items that have been phrased oppositely to other items on the scale will mess up your reliability analysis.”

Table 8.5 demonstrates the Cronbach's Coefficient Alpha values that were estimated to observe the internal consistency of the measurement scales. Cronbach's Alpha differed between 0.70 (attitudinal factors towards luxury designer handbags versus counterfeit handbag versions construct) and 0.93 (post-consumption related emotions construct). Brand meaning and social consumption motivation (relating to luxury designer handbags) construct

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and post-consumption related emotions construct possessed a reliability value above 0.90. Cronbach's Alpha of five constructs possessed values above 0.8. The construct investigating attitudes towards luxury designer handbags versus counterfeit handbag versions had a Cronbach‘s Alpha value of .70. High Cronbach‘s Alpha values suggest that constructs are internally consistent. This implies that all items of each construct are measuring the same content. A high Cronbach‘s Alpha value suggests a higher reliability.

Table 8.5 - Reliability of Measures Relating to Factor Analysis

Construct N Number of

items

Cronbach’s Alpha

Type

Attitudinal Factors and Individual Factors (Luxury designer handbag)

241 23 .842 High

reliability Attitudinal Factors and Individual

Factors (Counterfeit handbag versions)

112 23 .852 High

reliability Brand meaning and social consumption

motivation (Luxury designer handbag)

241

20

.925 Excellent

reliability Brand meaning and social consumption

motivation (Counterfeit handbag

version)

112 16 .886 High

reliability Attitudinal factors towards luxury

designer handbags versus counterfeit handbag versions

353 7 .702 Good

reliability Post-consumption related emotions

towards luxury designer handbags and counterfeit handbag versions

353 39 .903 Excellent

Reliability

8.4 Factor Analysis

A great deal has been written about adequate sample sizes for factor analysis. Hulin et al. (2001) claimed that a there should be a 15:1 ratio of respondents to the number of items; some researchers suggest a much lower ratio and a more detailed sample. For instance, Tabachnick and Fidell (2001) and Comrey and Lee (1992), concur that five cases for each item is sufficient in most cases, 300 is a respectable sample size, 100 is weak and 1000 is exceptional. Recently, several empirical studies have been carried out to examine the influence of sample size on factor solutions. Arrindell and van der Ende (1985) proved that variations in the ratio of respondents to items made minor discrepancies to the stability of factor solutions. While some empirical investigation (e.g. Guadagnoli and Velicer 1988; MacCallum et al., 1999) encourages the 300 rule. Therefore, the sample size of this research (353) is adequate to perform factor analysis.

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Additionally, the Kaiser-Meyer-Olkin (KMO) (Kaiser 1970) measure of sampling adequacy was employed. The KMO can be determined for single and multiple variables and signifies the ratio of the squared correlation between variables to the squared partial correlation between variables. The KMO values are detailed in Table 8.5; KMO values larger than .08, are ranked as ‗great‘ (Kaiser 1974). High KMO values suggest that the items will construct specific factors (Hutcheson and Sofroniou 1999) and the data sets are suitable for the appliance of factor analysis.

8.4.1 Objective for Using Factor Analysis

The application of factor analysis endeavours to attain two objectives. First, by compressing the highlighted dimensions into smaller groups of new combination dimensions, information is made manageable. Second, to investigate whether the measures used to measure the constructs across luxury handbags and counterfeit versions fall into the equivalent factor(s). If scale items load on the identical factor(s), and they have comparable factor loading (s), then content validity can be assumed (Bryman and Cramer 1999). This technique has been broadly applied in past cross-cultural research examinations (e.g. Poortinga 1989; Singh 1995). This research uses factor analysis to test if a construct loads similarly or differently for purchasers of luxury designer handbags and purchasers of counterfeit handbag versions.