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Empirical Findings
Data Extraction
The primary data has been collected from the 23rd to the 30th of April 2016, by the use of the online survey software of Qualitrics. The sampling mainly occurred online via social media. Out of 178 respondents 123 usable individual responses were collected online. In order to obtain the desired sample size the survey has been spread in a printed version as well. 70 respondents, predominant Swedish, were collected around the Jönköping University. Within the sampling process the respondents are somewhat selected to receive a good division by gender, nationality and education level. Further, only participants who are considered generation Y were involved.
Demographics and Sample Description
The usable sample consists of 202 high educated Swedes and Germans, ranging between 16 and 36 years of age. The motivation behind the selection of the sample is that this particular target group is at the moment the most valuable group within the e-commerce (Dijst et al., 2007; Valentine et al., 2013). In total the primary data collection retrieved 202 filled in surveys, in which six were only filled out partially. The German and the Swedish samples are similar in terms of demographics. The total sample consists of 54% female and 46% male respondents. The distribution between the countries is 54% Swedish and 46% German. German men respondents, 41 in number, were the toughest group to collect. Further 57 Swedish women responded and 52 Swedish men and 52 German women. The age distribution of the respondents is for >70% distributed within 21-26 A total of 199 survey participants reported to use internet for online shopping at least “sometimes”. The amount of people who perceive online shopping as positive or as moderate is similar to the Germans (89.1%) as to Swedes (87%). The Swedes answered more often extremely positive (32.9%), compared to Germans (22.8%). However, the amount of people who answered to be at least somewhat positive towards online shopping is higher with the Germans (66.3%), than Swedes (54.1%). The question “Do you sometimes shop online” has been answered 199 times positively. Only three respondents responded never to shop online. They were referred to one last question regarding their shopping perceptions, to indicate why they prefer to shop offline instead of online. All three respondents agree with a 5/5 on the question regarding that the experience of shopping in a retail store is more valuable than shopping online. The second highly agreed statement is the ability to see, feel or try the products, with a mean of 4.46 and variance of 0.2. The respondents who sometimes shop online, have been asked which statement suits them better whenever they purchase goods in a retail store instead of online, again the statements are in a five-point scale, 1 is strongly disagree – 5 is strongly agree. The most agreed statement is the ability to see, feel or try a product (mean 3.76, variance 0.96). The most disagreed statement is the need for a personal connection with a sales person (mean 2.11, variance 1.04). The participants were asked about their frequency of online shopping and how often they search for product information online, as shown in figure 6 and figure 7. Figure 7 the perceived frequency of online shopping and online product information search is way higher than how often they shop online. The frequency was indicated in a five-point Likert scale. The frequency of online shopping is indicated as moderate (33.2%) with a mean of 3.2. Figure 6 displays the distribution of online shopping frequency indicated by the participants. Swedes (mean 3.1) tend to indicate their online shopping behaviour less frequent. Germans (mean 3.3), perceive their online shopping behaviour as moderate. The frequencies of online shopping and information search is based on the own perception of the behaviour, without framework to compare their behaviour with. Further, the participants have been asked in which category they shop more frequent offline (1) or online (5). The five main product categories are: electronics, cosmetics, clothes, books and food. Figure 8 shows the distribution per category. The category books (mean 3.72) seems to be the most bought online compared to the other product categories, followed by electronics (mean 2.88), clothes (mean 2.66), cosmetics (mean 2.04). Food is least likely to be bought by online with a mean of 1.22. 83% responded to buy food only at retail stores.
Factors and Reliability
With the use of SPSS, several tests were performed. To be able to run tests in SPSS, the data was first coded (appendix 2) and some of the statements included reversed scales, these were transformed in order to be able to do further analysis. As mentioned in the measurements, first the internal consistency and reliability of the scales have been tested using the factor analysis and the Cronbach’s Alpha. After the reliability and internal consistency were determined, the independent- sample t-test was performed in order to find significant differences between Germans and Swedes.
Factor analysis
The factor analysis is performed in order to find relationships among possible related variables (Pallant, 2005). First, the sample size and the strength of the relationships are assessed to determine if the particular data is suitable for the factor analysis (Pallant, 2005). The larger the sample size the better, because the correlation coefficient would be more reliable with a large sample size. According to Pallant (2005) a sample size above 150 should include at least five cases of each variable. Since all cases contain more than five variables and the sample size of this research is 202, the first assumption of the sample size suits the requirements for a factor analysis. Secondly, the correlation matrix, Bartlett’s test and Kaiser-Meyer-Olkin is examined. To be able to do the factor analysis the variables need to consist correlations of at least r=.3, the Bartlett’s test of sphericity p<.05 and the Kaiser-Meyer-Olkin at least 0.6 (Pallant, 2005). The Barlett’s test and Kaiser-Meyer-Olkin test show that the second assumption that factor analysis is appropriate to be suggested, since correlations are found. the Kaiser-Meyer-Olkin test is .744 and the Barlett’s test is significant (sig.000), shown in Appendix 3, factor analysis. Since the factor analysis is appropriate the rest of the data is analysed. The components with an Eigenvalue of at least one, can be considered to be a factors. Ten components meet this criteria and explain 64% of the variance. The scree plot (appendix 3, factor analysis) shows that only the first five components (46% of the variance) are “in the shape of the plot” (Pallant, 2005), these five count as more relevant factors. To interpret the factor analysis the Varimax rotation is used (Pallant, 2005). By rotating the components of the factor analysis the following factors have loaded the strongest relations. The strongest component 1 is based on the technology acceptance, with all statements (TAM 1-7) included. The factor analysis also includes the need for experience (EXP 1, 2 and 5) as opposite factor, which include the “the experience in a retail store is more valuable compared to shopping online”, “I want to be able to experience the products” and “purchasing online contains more risks then shopping online”. Component 2 is based on the importance of appealing design (IAD 1-5 and 7), and shows a relation to the need of experience: EXP 4, “I like to experience products in a retail store, before I buy online” and EXP 2, “I want to be able to experience products”. Component 3 shows the risk avoidance by the trustworthiness of online shop characteristics (RA.b 7-10). Component 4 shows the risk avoidance within usage of an online shop (RA.a 1-5). Component 5 factorize the information search online for the best supplier (IOS 1-3). In other words, all beforehand made scales are showed within the five strongest components within the factor analysis. This suggests that the relations made in-between are valid.
Internal consistency factors
In order to determine the internal consistency regarding the factorized relations the Cronbach’s Alpha test is conducted. An internal consistency with the Cronbach’s Alpha is found whenever the tested score is higher than .7 (Pallant, 2005). Whenever the scores are rounded to one digit behind the comma, internal consistencies are found in all groups with a .7 or .8 as shown in table 3. To be able to talk about an internal consistency some items were deleted and rearranged when they influenced the internal consistency of the scale.
Differences in online shopping behaviour of Germans and Swedes
Since the factors and internal consistencies were suggested to be reliable the independent-sample t-test was conducted to find out if there is a significant difference in the mean scores between the Germans and the Swedes. Within the independent-sample t-test firstly the Leven’s test was executed. Secondly, the significant difference between the two groups by each factor was determent. The smaller the t-test score the lower the chance of coincidental difference. The alpha of 0.05 is traditional used to retain an item (Pallant, 2005). Though, Stevens (1996) suggest, that the alpha level may need to be adjusted to 0.1 or 0.15, when the group size of the sample is relatively small. The extracted results are further explained by each factor. Further, a closer look to the individual statements has been taken to find significant difference. Individualism is linked to the technological acceptance (TAM), both Germans (M=3.96/5) and Swedes (M=3.98/5) show a technological acceptance. The magnitude of the differences are very small Germans (M=29.90; SD=4.557) and Swedes (M=27.73; SD=4.047). Within the independent t-test the score of (p=0.784) confirms that no significant difference has been found between Swedes and Germans. This indicate the technological acceptance between Germans and Swedes is similar. The differences of masculinity are expressed in the EXP and IAD. In the factor analysis, the relation between EXP and IAD is found. The factor EXP, explains the need for stimuli experience and has been indicated fairly moderate by Swedes (M=3.35/5) and Germans (M=3.16/5). The independent significance t-test shows a significance level of p=0.062 between Germans (M=15.80; SD=3.177) and Swedes (M=16.69; SD=3.330). Within the factor IAD, importance to an appealing web design, Swedes (M=4.14/5) and Germans (M=4/5) display a high agreement. The independent t-test shows a slight difference between Swedes (M=33.12; SD=4.176) and Germans (M=31.99; SD=4.520), with a significance level of p=0.076, which is since the cut-off of significance is P=0.05 not significant. The difference of uncertainty avoidance are expressed in risk avoidance and information search. The factor (RA.a) risk avoidance by the usage of a website does not show a significant differences (p= 0.424) between Germans (M=20.81; SD=3.651) and Swedes (M=20.39; SD=3.569). Although, there is no significance found between the Germans and Swedes on risk avoidance. While examining the indicated statements separate from each other, Germans shows a significant difference on two statements which focus particular on the usage of a narrow sales channels. A significant difference of p= 0.003 is found between Swedes (M=2.25; SD=0.936) and Germans (M=2.68; SD=1.026) within the statement: “I would not buy on an online shop I do not have previous experience with”. A significance of p= 0.016 is found between Germans (M=3.34; SD=1.117) and Swedes (M=3.01; SD=1.048) with the statement: “I always use the same online shops”. These statement assume Germans to be more likely to use a known online supplier and thus a narrower sales channel compared with Swedes. Within the factor RA.b, which shows the trustworthiness in an online shop, no significant difference has been found (p=.144). Although, Swedes (M=17.25; SD=2.494) show a higher agreement than Germans (M= 16.25; SD=2.181). The factor IOS, online information search, displays a significant difference (p=0.03) between the Germans (M=10.27; SD=2.616) and Swedes (M=11.35; SD= 2.283). The mean of the Swedes (M=3.78/5) is higher compared to the Germans (M=3.42/5), which suggests that Swedes are more likely to spend time searching and comparing products. Further, all statements individually are found to have a significance as well.
1. Introduction
1.1 Background
1.2 Problem discussion
1.3 Research purpose
1.4 Delimitations
1.5 Definitions
2. Theoretical Background
2.1 Influencing factors on online decision-making process
2.2 Cultural dimensions in relation to online shopping behaviour
2.3 Interpretation of related market figures
3. Methodology
3.1 Research Philosophy
3.2 Research Approach
3.3 Research Design
3.4 Data Collection Method
3.5 Questionnaire
3.6 Measurements 3
4. Empirical Findings
4.1 Data Extraction
4.2 Demographics and Sample Description
4.3 Factors and Reliability
4.4 Differences in online shopping behaviour of Germans and Swedes
5. Interpretation Analysis
5.1 Findings in relation to individualism
5.2 Findings in relation to masculinity
5.3 Findings in relation to uncertainty avoidance
5.4 Analysis Summary
6 Conclusion & Discussion
6.1 Managerial Implications
6.2 Limitations
6.3 Further Research
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A quantitative study based on the online shopping behaviour of generation Y in relation to the cultural influences of Germans and Swedes
