Factor  analysis  -­‐  independent  variables

The initial factor analysis was constrained to include twelve factors in order to control whether the variables would load on twelve factors as assumed, by using the a priori criterion (Hair et al., 2010). This constraint acts as a form of confirmation of the model. The result showed that a few variables should be deleted, as they did not satisfy the criteria of values equal or larger than 0,5 (Hair et al., 2010). The variables were deleted stepwise. The variable Oppl_9 was first deleted with a value of 0,334. The question has low discriminant and convergent validity because it loads on a different factor than other experience questions, and it has a low value. Questions loading on the same factor refer to the size of the fair. There is no direct link between the questions, however the number of exhibitors may have an impact on how entertaining the fair is. Oppl_9 is phrased differently than the two other questions measuring experience by focusing on the entertainment part of experiences.

This may have led to its inconsistency with the two other variables.

The analysis was run again, and in the next step Mark_23 was deleted as it loaded on two factors and loadings were less than 0,5. This variable had low discriminant validity as it loaded on two factors, and the difference between the values was less than 0,2. It can therefore be questioned what this variable actually measures. It is interesting to look at the factors the variable loads on. Even though the highest loading (0,408) is below the cut-off criteria of 0,5 it is worthy to note that it loads on the same factor as peer influence variables.

There can be a connection between marketing and the influence and initiative of peers as they both involves the aspect of influencing people, however through different channels. Had Mark_23 been of a higher value, it should possibly be included together with influence as one factor. When looking at the phrasing of the question it is of similar construction as Mark_22, and quite different from the last two questions that measure marketing. The different phrasings are likely the reason why the two first and the two last questions measuring the same construct do not load on the same factor.

Mark_22 has a value of 0,338 after the removal of Mark_23 and should be deleted as it fails to satisfy the criteria of loadings equal or larger than 0,5. The question does not load on the same factor as the other variables measuring marketing, but rather on the factor where all purchase variables are gathered. It is not directly obvious that awareness and purchase load on the same factor, however consumers do need to be aware of the fair in order to make

purchases there. Thus, these questions may have something in common. As a result of the low loading, the variable Mark_22 is deleted.

The analysis can no longer produce twelve factors, and therefore the criteria is changed to follow the latent roots criterion with eigenvalues over 1 (Churchill and Iacobucci, 2005).

Values under 0,45 are excluded from the output as this reflects the expected value of a significant factor loading (Field, 2009). The value of RF_13 is below 0,5 (0,459) and is deleted. It loads on the same factor as other variables measuring relative advantage, thus discriminant validity is good. The question is phrased differently than the two remaining questions, which is likely to be the reason why it does not load as strongly as the other two.

The analysis is run once more and creates an output of ten factors. Judging by the total variance explained diagram, the eleventh component has an eigenvalue of 0,995, and should not be ignored, thus the analysis is run again constrained to eleven factors. The solution shows that Str_5, Str_6, Sos_18, Tid_29, Tid_30 and Tid_31 no longer load on any of the factors. Looking at the their communalities, they are all of low levels (below 0,3) and communalities below 0,5 does not have sufficient explanation (Hair et al., 2010). These variables will therefore be deleted from the dataset.

The factors “time available” and “number of exhibitors” have disappeared from the analysis and should thus not be part of further analysis using this dataset. The variables measuring these constructs did not load on any of the factors. This may be a result of inaccurate measurements, which will be discussed in more detail in the section about limitations and further research. Sos_18 investigates a different aspect of the fair as a social event than the two other variables measuring this construct, and it has low convergent validity. Because of the phrasing of the question, it does not load strongly on the same factor as the other variables load on, and thus disappears from the analysis. The analysis is run with the constraint of eleven factors and excluding the variables mentioned above. This yields the final solution as presented below.

Pattern Matrix^a

Table: 2 Extraction Method: Maximum Likelihood, Rotation Method: Oblimin with Kaiser Normalization. Values of less than 0,45 are excluded.

After the removal of questions with low discriminant and convergent validity, there no longer exist variables that load on more than one factor. All except one variable have values above the cut-off level of 0,5. SN_10 loads on the same factor as the two other variables measuring subjective norm, however its value is just below 0,5 (0,498). This means that convergent validity is somewhat low. A choice is made to not delete the variable as this is exploratory research and SN_10 is only 0,002 from the cut-off level. All constructs are

consistent because variables that measure the same load on the same factor, and thus the criteria of convergent validity is satisfied. Several of the loadings are also extremely high (above 0,8).

The output clearly shows the factor structure that will be used for further analysis. Two of the variables in the research model are deleted, however the remaining factors have high convergent validity and reliability as will be shown in the next section. Several of the factors only have two variables loading on them as a result of only two measures for these components, or the removal of one question such as for “relative advantage” and “the fair as a social event”. This could mean that the component’s reliability decreases (Stevens, 2009), however reliability is high for all factors. The solution shows that factor 1 measures past behaviour regarding jewellery, factor 2; price of entry ticket, factor 3; location and accessibility, factor 4; marketing, factor 5; the fair as a social event, factor 6; experience, factor 7; peer influence, factor 8; purchase, factor 9; past behaviour regarding consumer fairs, factor 10; subjective norm and factor 11 measures relative advantage.

The eleven factors explains 87,66% of variance which is considered as very good (Stevens, 2009), however the last three factors have eigenvalues below 1. Rust et al. (2004) employ a 0,5 cut-off level rather than eigenvalues of at least 1, and they argue that the cut-off must be meaningful with respect to the results. A meaningful solution in this study should have variables spread on eleven factors, since past behaviour loads on two factors. The research is exploratory, and factors should be meaningful and in accordance with the research model, which justifies the decision to keep a solution that no longer follows the latent roots criterion. Judging from the scree-plot, a logic cut-off level in the number of factors is at ten or twelve factors, although there is a fairly steep curve from the eleventh to the twelfth factor. As a result of exploratory research with measures that have not been applied in past research, the solution with eleven factors is accepted as it yields decent loadings and reliability is high.

Factor 1 measuring past behaviour regarding jewellery explains the greatest part of the variance, with a total of 26,9%. Factor 11, relative advantage, explains the least of the variance with 2,8%, which is fairly low. Of the original twelve factors in the research model, only ten remain after the factor analysis. The factor “past behaviour” is spread on two factors.

Past behaviour was assumed to be one factor, however past behaviour is comprised of two different areas of consumer behaviour. Visiting a consumer fair and purchasing jewellery are two separated actions. It can be assumed that very few (if any) of the respondents have visited a consumer fair for jewellery in the past, and considering the scarcity of consumer fairs in Norway it may be reasonable to believe that respondents have purchased a great deal more jewellery during the past three years than they have visited consumer fairs. Both variables measuring jewellery purchase, Erfar_40 and Erfar_42 load strongly on factor 1, whilst both variables measuring visits to consumer fairs, Erfar_39 and Erfar_41, load strongly on factor 9. The solution where past behaviour is divided on two factors is accepted because it is logical, and no other solution provided a different outcome.