5.8 Relationship between Packaging and Brand Equity
5.8.2 Structural Equation Modelling
Here, the hypothesized relationship between packaging, brand equity dimensions and overall brand equity were analysed by using structural equation modelling. As recommended in the literature, the measurement model was analysed before the structural model (Byrne, 2016;
Anderson & Gerbing, 1988).
5.8.2.1 Results of Confirmatory Factor Analysis
The measurement model was carried out through the confirmatory factor analysis (CFA) to confirm the relationship between the unobserved factors and the observed factors provided by the exploratory factor (EFA). The latent variables are overall brand equity, brand awareness, packaging, brand association, brand loyalty and perceived quality with their associated indicators are depicted in the Table 5.27 and Table 5.28.
In this analysis, packaging formatively-measured construct and in order to conduct an analysis that involves formatively-measured construct through the use covariance-based structural equation model, the model must be identified to produce consistent parameter estimates and interpretable fit indices (Diamantopoulos, 2011). For this purpose, the test items of packaging were identified by constraining the residual error variance for packaging to be equal to one. In addition, to resolve the indeterminacy associated with the construct level error term, four reflectively-measured constructs (brand awareness, association, loyalty and perceived quality) were specified as outcomes of the packaging (Jarvis, Mackenzie & Podsakoff, 2003;
Diamantopoulos, 2011; MacCallum & Browne, 1993).
In order to purify the model to achieve construct validity and better model fit, standardized residual values of pairs of measured items above 2.58 were deleted from the model as they reflect greater level of error in measurement specification (Hair et al., 2010; Byrne, 2016).
Moreover, each observed variable with factor loadings below 0.50 were dropped from the model with the view to achieving convergent validity (Hair et al., 2010). Hence, the following test responses were discarded: PAS3 of brand association, PE1 and PE2 of overall brand equity and P1, P3 and P6 of packaging.
The findings of the confirmatory factor analysis in Table 5.28 show that 16 indicators loaded on their purported latent constructs in this model. The statistical significance of the relationship between packaging, overall brand equity, brand association, brand loyalty, brand awareness and their observed variables is determined at the two-tailed significance level of p < 0.001 and
C.R ≥ 2.58 (Hair et al., 2010). Furthermore, although the findings in Table 5.27 display both standardized and unstandardized estimates, a standardized regression estimate was used to present the results in this sample.
The results in Table 5.27 and Table 5.28 reveal that three indicators loaded on brand awareness, three on packaging, three on brand loyalty, three on overall brand equity, two on brand association and two on perceived quality and were all statistically significant at p < 0.001 level.
These results are in line with past studies (Pappu et al., 2005; Gil et al., 2007) where a minimum of two indicators were used to perform a CFA.
This shows that the measurement model is satisfactory in explaining the relationship between the six constructs and their observed variables in the model. The results also provide support for convergent and discriminate validity of the latent factors as each test item loaded on its constructs and their factor loadings are statistically significant (Anderson & Gerbing, 1988;
Hair et al., 2010; Kline, 2005). Furthermore, all the individual standardized factor loadings were higher and above 0.50, ranging between 0.575 and 0.893, which also established a test of convergent validity (Kline, 2005; Hair et al., 2010).
Table 5. 27: Results of Confirmatory Factor Analysis of Packaging and Brand Equity Structural Relations Unstandardized
Estimate
Standardized Estimate
S.E. C.R. P-value
A3 <--- Brand awareness 1.000 .670
A4 <--- Brand awareness 1.088 .719 .104 10.429 ***
A5 <--- Brand awareness 1.263 .787 .114 11.085 ***
PE5 <--- Brand equity 1.000 .746
PE4 <--- Brand equity 1.093 .827 .079 13.861 ***
PE3 <--- Brand equity .913 .766 .071 12.892 ***
P5 <--- Packaging 1.000 .840
P4 <--- Packaging .918 .712 .082 11.215 ***
P2 <--- Packaging .590 .575 .064 9.266 ***
PL2 <--- Brand loyalty 1.000 .740
PL4 <--- Brand loyalty 1.152 .768 .092 12.570 ***
PL5 <--- Brand loyalty 1.153 .783 .090 12.794 ***
PAS1 <--- Brand association 1.000 .698
PAS2 <--- Brand association .970 .745 .103 9.459 ***
PQ1 <--- Perceived quality 1.000 .893
PQ2 <--- Perceived quality .854 .828 .057 14.970 ***
***Means p < 0.001 at two-tailed significance level. All factor loadings are significant at p < 0.001 level
Source: Primary Data
Table 5. 28: Packaging and Brand Equity Constructs and their Indicators Packaging
P2 The packaging preserves the contents of this brand
P4 The packaging of this brand makes this brand environmentally-friendly P5 The packaging of this brand makes this brand convenient to use Brand Awareness
A3 Some characteristics of this brand`s packaging come to my mind quickly
A4 When I think about this brand, the first thing that comes to my mind is its packaging A5 I am aware of the packaging of this brand
Perceived Quality
PQ1 The packaging of this brand makes this brand function well PQ2 The packaging of this brand makes this brand very reliable Brand Association
PAS1 This brand`s packaging makes this brand offer good value for money PAS2 This brand`s packaging makes me like the image of this brand Brand Loyalty
PL2 The packaging of this brand would make me recommend this brand to my friends PL4 The packaging of this brand makes me loyal to this brand
PL5 I will keep on buying this brand as long as I am satisfied with the packaging of this brand Brand Equity
PE3 The packaging of this brand makes this brand more than a product to me
PE4 This brand`s packaging makes it seems smarter to purchase this brand, if another brand is not different from this brand in any way
PE5 The packaging of this brand makes me prefer this brand, even if there is another brand as good as this brand
Source: Primary Data
5.8.2.2 Measurement Model Path Diagram
The path diagram is a visual diagram that presents the relationship between latent factors and their underlying observed items, including the relationships among the latent factors.
Figure 5.1 below show the path diagram of the measurement model of packaging and overall brand equity. In the path diagram, the indicator items are shown by rectangles and the common factors are depicted by ellipse, whilst the letter “e” indicates the measurement error. The structural relationship between unobservable variables and indicators are shown by single
headed arrows whilst the correlations among the latent constructs are exhibited by double headed arrows.
The path diagram in Figure 5.1 below also shows the standardized regression weights of the structural relationships among the indicators and latent variables, and the correlations among the common factors. In addition, the values close to the rectangles depict the squared multiple correlations of the indicator items.
Figure 5. 1: Path Diagram of Measurement Model of Packaging and Brand Equity
Source: Developed by the Researcher
5.8.2.3 Model Fit of Measurement Model of Packaging and Brand Equity
Floyd and Widaman (1995) highlighted that the measurement model must be investigated to ascertain the plausibility of the model through goodness-of-fit indices. Specifically, goodness- of-fit indexes are employed to evaluate the acceptability or otherwise of a model. In this analysis, the goodness-of-fit statistics and fit indexes were employed to evaluate the measurement model (Hu & Bentler, 1999).
Table 5.29 below shows the summarized estimates of goodness-of-fit indices of the measurement model of the analysis. The results indicate that the Chi-square test (X2) is significant (X2 = 200.075, df = 89) at p < 0.001 level, showing that the measurement model does not exactly represent the sample data. However, the Chi-square statistic has been recognised to have severe limitations for determining the overall goodness-of-fit of a model. It has been asserted that the Chi-square statistics is directly related to sample size and hence, any model with sample size large enough is virtually rejected as a false model (Kline, 2005; Hair et al., 2010). Consequently, alternative model fit indexes have been suggested to evaluate this model (ibid).
Table 5.30 shows the suggested model fit indices for evaluating the model fit of confirmatory factor analysis in this sample. The findings reveal that all the fit indices are greater than the proposed criteria to evaluate the six-factor measurement model and hence, the model is said to be satisfactory.
The model has a Normed Chi-square (CMIN/DF) value of 2.248 which is less than three (3) showing a good model fit (Hair et al., 2010; Kline, 2005). The Goodness - of -Fit Index (GFI), Adjusted Goodness-of-Fit Index (AGFI) and Root Mean Residual (RMR) have values of 0.925, 0.886 and 0.044 respectively, which show a satisfactory fit of the model (Hair et al., 2010).
The Standard Root Mean Residual (SRMR) and Root Mean Square Error of Approximation (RMSEA) are 0.039 and 0.064, which are both less than 0.08 (Brown & Cudeck, 1993; Hu &
Bentler, 1999).
Furthermore, the Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Normed Fit Index (NFI) and Incremental Fit Index (IFI) are 0.952, 0.935, 0.917 and 0.952 respectively. These estimates are higher than the acceptable value of 0.90 or are close to 0.95 (Hu & Bentler, 1999;
Hair et al., 2010). These findings show that the measurement model fitted well with the data and therefore provides an admissible solution to the structural model.
Table 5. 29: Model Fit Summary of Confirmatory Factor Analysis CMIN
Model NPAR CMIN DF P CMIN/DF
Default model 47 200.075 89 .000 2.248
Saturated model 136 .000 0
Independence model 16 2413.404 120 .000 20.112 RMR, GFI
Model RMR GFI AGFI PGFI
Default model .044 .925 .886 .606
Saturated model .000 1.000
Independence model .449 .283 .187 .250
Baseline Comparisons
Model NFI
Delta1
RFI rho1
IFI Delta2
TLI rho2 CFI
Default model .917 .888 .952 .935 .952
Saturated model 1.000 1.000 1.000
Independence model .000 .000 .000 .000 .000
Parsimony-Adjusted Measures
Model PRATIO PNFI PCFI
Default model .742 .680 .706
Saturated model .000 .000 .000
Independence model 1.000 .000 .000
RMSEA
Model RMSEA LO 90 HI 90 PCLOSE
Default model .064 .052 .076 .028
Independence model .250 .241 .259 .000
Source: Primary Data
Table 5. 30: Criteria Used to Assess the Measurement Model Fit Goodness-of-Fit Indices Acronym Estimates of
Fit Indices
Suggested Value
of Good Fit References Goodness-of-Fit Statistics
Chi-Square Degree of Freedom Ratio/Normed Chi- Square
X2/df
(CMIN/DF)
2.248 1 to 3
1 to 5
Hair et al.(2010), Kline (2005), Hooper et al. (2008) Absolute Fit Indices
Root Mean Square Error of Approximation
RMSEA
0.064 < .60
< .50 < 0.08
Hu and Bentler (1999), Brown and Cudeck (1993)
Standardized Root Mean
Residual SRMR
0.039
< .08
Hu and Bentler (1999)
Goodness-of-Fit Index
GFI 0.925 > .90
Hair et al. (2010), Hooper et al. (2008) Incremental/Relative Fit
Indices
Comparative Fit Index CFI 0.952 > .90 or .95 Hu and Bentler (1999) Tucker-Lewis Index TLI 0.935 > .90 or 0.95 Hu and Bentler (1999) Normed Fit Index NFI 0.917 > .90 or 0.95 Hu and Bentler (1999)
Incremental Fit Index IFI 0.952 >.90 Hair et al. (2010)
Source: Compiled by the Researcher