Measurement Model Evaluation

It is important to note the different type of indicators used in this analysis. They are either formative or reflective. All constructs except for recall memory were treated as reflective while the recall memory constructs were treated as formative for the reason that they constitute different aspects of memory. These differences required different procedures for confirming the validity and reliability of the measurement model.

Therefore, the following section explains the respective procedures followed to ensure the validity and reliability of each construct.

5.9.1.1 Reflective Constructs

As opposed to formative constructs, it was important to substantiate the validity and reliability of the reflective constructs prior to further analysis. The validity of the items was tested with both discriminant and convergent criteria (Hair, Ringle, & Sarstedt, 2011). According to Chin (2010), this can be achieved if each item in a construct demonstrates a strong connection with that construct without a stronger connection with any other construct in the model. Therefore, as shown in Table 5-47, the loading and the cross loading scores were examined with each construct represented in columns

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for determining convergent and discriminant validity respectively. Finally, to test whether a construct has more variance associated with its own indicators than with the other constructs, Fornell and Larcker (1981) criteria were adopted. Accordingly, Average Variance Extracted (AVE) values of a given construct should be greater than the highest squared values of the correlation of that construct with any other construct in the model.

Table 5-47 - Outer Model Loading and Cross Loading of Reflective Constructs

CRCE CPLX PDS CLD ATT

The value of each item in the matrix was examined against the values along each row and column (except for the values of the same construct) to detect any violation of discriminant validity. Accordingly, if the value of an item in other cells (except for those of its own construct) was greater than the value corresponding to its construct, the item was deemed to be cross loaded (Hair, Hult, Ringle, & Sarstedt, 2014). The result of this procedure revealed that some items used in the model had mainly discriminant validity issues. However, this was not surprising considering that the same items were identified as problematic in similar analyses carried out previously. As a corrective measure, two items (DF4 and DF5) from Perceived Complexity Scale, two items (CL4 and CL5) from Cognitive Load Scale, and one item each from Music Congruence

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(FT4) as well as Attitude (FT2) scales were eliminated. The analyses obtained after such corrections confirmed the presence of item level discriminant validity.

Nonetheless, the above procedure was considered somewhat liberal in nature (see Hair et al., 2014) and therefore, all the constructs were further tested for previously mentioned Fornell and Larcker criteria. For calculation purposes, instead of squaring correlation values (𝑅), the square root of 𝐴𝑉𝐸 was compared against the 𝑅 value of each construct. This test (see Table 5-48) indicated that every reflective construct met the criteria. i.e., the √𝐴𝑉𝐸 was the highest of the values listed in each corresponding column of a construct. Hence, it was safe to claim that the discriminant validity of the measurements had been met.

Table 5-48 - Fornell-Larcher Criterion Analysis

Construct CRCE CPLX PDS CLD ATT

Music Congruence (CRCE) 0.964

Message Complexity (CPLX) -0.443 0.867

Psychological Discomfort (PDS) -0.603 0.509 0.920

Cognitive Load (CLD) -0.577 0.776 0.658 0.869

Attitude towards Ad (ATT) 0.775 -0.547 -0.753 -0.657 0.969 Immediate Recall (IRCL) 0.292 -0.259 -0.353 -0.286 0.373 Immediate Recognition (IREC) 0.034 -0.248 -0.172 -0.251 0.097 Delayed Recall (DRCL) 0.161 -0.307 -0.309 -0.316 0.245 Delayed Recognition (DREC) 0.063 -0.167 -0.143 -0.148 0.074

Values in bold and italic: √𝐴𝑉𝐸 of the respective construct

Reliability was determined by examining three indicators of each reflective construct -item loadings, composite reliability, and AVE. It is normally considered that the correlation between an item and the corresponding construct should be not less than 0.4, while it is preferable to have values above 0.7 (Hair et al., 2011). AVE value being greater than 0.5 is preferable as it indicates that all the items of that construct explain more than 50% of the variance. The results of these analyses after eliminating the validity issues are shown in Table 5-49. Accordingly, all the indicators appear to be well above the recommended loading level (0.7). Further, Chin (2010) suggested that the closer the indicator loadings, the better does it explain the construct. The loadings as a result of this analysis also accord with that suggestion. Additionally, the composite

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reliability scores associated with all the constructs also had values above the accepted level (0.6) and the AVE values were well above the standard level of 0.5. Therefore, based on this evidence, it was concluded that all the indicators of the reflective constructs used in this study were reliable.

Table 5-49 - Reflective Constructs Outer Model Reliability

Construct / Item Loading

Formative constructs are multidimensional in nature and they do not necessarily highly correlate (Chin, 2010; Hair et al., 2014). Accordingly, there were two formative constructs used in this study, Immediate Recall and Delayed Recall. Both of these constructs consisted of respective Brand Recall, Category Recall, and Message Recall indicators.

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Since the indicators in a formative construct are not interchangeable, high correlation among such is not expected (Hair et al., 2014). Thus, it is important to assess the existence of any collinearity among the indicators of each formative construct. For this purpose, tolerance value and Variance Inflated Factor (VIF) were considered.

According to Hair et al. (2011), a VIF value greater than or equal to 5.00 indicates that the formative indicator has a multicollinearity issue, and a tolerance value less than 0.2 also indicates the same.

VIF values were calculated using Linear Regression in SPSS. Accordingly, all the items were considered as independent variables and collinearity statistics were obtained. The results of this analysis are presented in Table 5-50.

Table 5-50 - Collinearity Assessment of Formative Indicators

Formative Indicators Tolerance VIF

Immediate Recall

Category Recall .69 1.45

Brand Recall .69 1.46

Message Recall .67 1.50

Delayed Recall

Category Recall .71 1.42

Brand Recall .70 1.43

Message Recall .68 1.47

VIF and tolerance values of each indicator revealed that they were well within the accepted level and hence, it could be said that the formative indicators of this model did not suffer from multicollinearity issues.

The collinearity assessment was followed by an evaluation of the outer weights and their significance associated with each manifest variable. As shown in Table 5-51, Message Recall indicated a significant contribution to both Immediate and Delayed Recall constructs (𝑡_𝐼𝑀𝑅(283) = 6.15, 𝑝 < .01. ; 𝑡_𝐷𝑀𝑅(283) = 11.74, 𝑝 < .01).

However, none of the other indicators appeared to be significant in terms of outer weights. This finding was not surprising considering findings in the previous analysis where Brand and Category Recall were not significantly different between the groups (see Section 5.8).

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However, it was decided to retain these items in the further analysis in order to make both the analyses consistent in terms of their indicators. It should also be noted that the literature highlights that the theoretical rationale plays a significant role over its empirical counterpart when considering the indicators of a formative construct (Hair et al., 2011).

Table 5-51 - Outer Weights Significance Testing for Formative Construct

Outer Weights

(Outer Loadings)

t Value Sig.

Immediate Recall

Category Recall 0.071 (0.270) 0.368 0.71

Brand Recall 0.298 (0.589) 1.481 0.14

Message Recall 0.842 (0.957) 6.152 0.00

Delayed Recall

Category Recall -0.219 (-0.065) 1.317 0.19

Brand Recall 0.030 (0.332) 0.164 0.87

Message Recall 1.000 (0.976) 11.740 0.00