Quantitative data collection and sample size assessment

The quantitative data are collected from responses to the questionnaire, which was distributed to Polish immigrants residing in the East Anglia region, which according to the Office for National Statistics (www.statistics.gov.uk), represents overall migration trend at the time the study was conducted (see Table 13 and Table 14).

109

Table 13. Minority groups in the UK (Source: Office for National Statistics)

Table 14. Minority groups in the East of the UK (Source: Office for National Statistics)

The questionnaires were distributed in two ways. The field version of the questionnaire was delivered in workplaces employing Polish immigrants. The online version (see Appendix C) was posted on Polish online forums and sent through the social media website www.nasza-klasa.pl. Both distribution channels were used to ensure an adequate dataset, in order to address the study objectives in full.

Estimated population of overseas nationals resident in the United Kingdom, by nationality (January 2010 to December 2010). Five most common nationalities

Country Estimate CI +/

1. Poland 555 32

2. Republic of Ireland 353 26

3. India 327 25

4. Pakistan 157 17

5. United States of America 147 17

Estimated population of overseas nationals resident in the East of the United Kingdom, by nationality (January 2010 to December 2010). Five most common nationalities

Country Estimate CI +/

1. Poland 46 11

2. Republic of Ireland 28 9

3. United States of America 27 9

4. India 21 8

110

The initial data collection process resulted in 158 questionnaires, of which 155 were identified as usable through data screening process. Forty-five field versions of the questionnaire and 113 online versions were collected. Three questionnaires were invalid, as respondents had failed to provide some demographic information.

After the initial data collection, an appropriate sample size was determined. Clegg (1990) suggests this may be assessed through a statistical method such as power analysis and sample sizes considered appropriate in the same research field.

According to Guo (2009), previous research employing quantitative research methods to investigate online shopping behaviour used samples of between 100 and 300 responses. Consequently, our sample size of 155 responses appears to be adequate in order to investigate the possible effect of acculturation process on consumers’ e- commerce acceptance. Additionally, following Clegg’s (1990) suggestion, power analysis was calculated in order to evaluate the strength of the correlation between the variables and to ensure that the study would have appropriate power to investigate a possible change in consumers’ attitudes towards e-commerce acceptance. Consequently, the power on the basis of 155 responses was calculated using SPSS Statistics 19 (see Table 15). The power of most tested variables is greater than 0.80, which is considered to be statistically significant. The power of PEOU (σ = 0.77) and SN (σ = 0.60) from the native culture perspective does not reach the recommended power level; however, as the average power of all items tested from the perspective of native culture is 0.86 and the average power of all items tested from the perspective of non-native culture is 0.98, the null hypothesis can be rejected at 86% and 98% confidence every time the study will be run.

111

Table 15. Effect size and power analysis (155 responses)

Item Effect size Power (σ) Power at two tailed test

α = 0.05 Native culture PU 1.2253 7.548 0.99 PEOU 0.4742 2.92 0.77 SN 0.6153 3.79 0.94 ATB 0.7053 4.34 0.97 PBC 0.5910 3.64 0.92 IM 0.3923 2.41 0.60 Non-native culture PU 1.3222 8.14 0.99 PEOU 1.1397 7.02 0.99 SN 0.6441 3.96 0.96 ATB 0.9317 5.73 0.99 PBC 0.8371 5.15 0.99 IM 0.7948 4.89 0.99

Furthermore, according to MacCallum et al. (1996) the power analysis can be effectively calculated on the basis of df and a sample size. Hence, the power calculated on the basis of df = 153 and the sample of 155 respondents confirms adequate sample size (see Table 16).

112

Table 16. Power analysis according to MacCallum et al. (1996) (155 responses)

Df

N

Power

Close Not close

153 155 >0.0650 but <0.955 >0.426 but <0.870

It follows that the power calculated on the basis of 155 responses confirms the sample size is sufficient; hence, it will allow effective investigation of consumers’ attitudes towards e-commerce acceptance. However, as the test was run at α = 0.05 and two items (PEOU and SN tested from the perspective of native culture) did not reach the recommended level of 0.80 (PEOU = 0.77 and SN = 0.60), it was decided to send follow-up messages in order to increase the response rate and improve the power of the measured items.

During the follow-up data collection stage 163 questionnaires were collected, 150 of which appeared to be valid. Fourteen field versions of the questionnaire were collected, seven of which turned out to be completed in full. Of 151 online versions of the questionnaire, 147 were found to be usable. Four responses were found to be invalid, as respondents identified themselves as Polish/German, Polish/British or British; hence, they failed to meet the criterion of unit of analysis selection.

Consequently, a total of 321 respondents participated in the study, of which 305 provided usable responses. In order to investigate whether the power of the measured items improved, the test on the sample of 305 responses was run. The results are presented in Table 17 below.

113

Table 17. Effect size and power analysis (305 responses)

Item Effect size Power (σ) Power at two tailed test

α = 0.01 Native culture PU 1.189 14.682 0.99 PEOU 0.537 6.640 0.99 SN 0.692 8.551 0.99 ATB 0.806 9.956 0.99 PBC 0.658 8.131 0.99 IM 0.604 7.467 0.99 Non-native culture PU 1.168 14.432 0.99 PEOU 0.952 11.762 0.99 SN 0.556 6.870 0.99 ATB 0.997 12.315 0.99 PBC 0.928 11.467 0.99 IM 0.805 9.950 0.99

On the basis of power analysis of the full dataset (305 responses), it can be confirmed that the power improved. The analysis run at α = 0.01 gives 99% confidence that the null hypothesis will be rejected every time the study is run, as the power of all tested variables exceeds the recommended level of 0.80, which is considered to be statistically significant.

Furthermore, the power calculated on the basis of df and a sample size, as suggested by MacCallum et al. (1996) confirms the above results (see Table 18).

114

Table 18. Power analysis according to MacCallum et al. (1996) (305 responses)

Df N Power

Close Not close Exact

303 305 0.997 0.990 0.993

As the data was collected over two time periods, it is necessary to investigate whether there is any significant difference between the two groups of responses (Armstrong and Overton, 1997; Lambert and Harrington, 1990). Thus, the possible non- response bias must be assessed before the sample can be generalised to the population. This has been effectively done while employing Ferber’s (1948-1949) approach as well as statistical tests such as Chi-square test for independence, independent sample t-test and ANOVA. The results of non-response bias assessment are presented in Appendix C. After confirming that non-response bias does not exist in the study, the two samples were merged and a full dataset was analysed. Our results are discussed below.