Competing Models

As discussed, the proposed CPBI model was tested against two alternative models. While the literature does not provide a consensus on what would be a competing model (Byrne, 2010; Kline, 2011), the present thesis tests for two competing models which seems reasonable from the perspectives of both SEM practice and theoretical meaningfulness. Following Morgan and Hunt (1994) the proposed CPBI model was compared with its competing models using four criteria: (1) overall fit of the model measured by CFI; (2) percentage of the model’s significant hypothesised relationships; (3) ability to explain variance in the DVs measured by squared multiple correlations and; (4) parsimony as measured by TLI (Tucker and Lewis index) which rewards for model parsimony/penalizes for model complexity (Baggozi & Yi, 2012).

Competing model 1. A nonparsimonious competing model could be one positing only

direct paths from all of the IVs to the outcomes (Byrne, 2010; Morgan & Hunt, 1994). This model is presented in Figure 4.9. In this competing model CPPI (consumer perceived product innovativeness) and post-CPBI are nomologically similar to technological newness and pre-CPBI. In other words, the competing model allows no indirect effects. Although this competing model has not been theorised before, previous studies have suggested product innovations as a potential driver

of brand attitude (Aaker, 2007; Aaker & Jacobson, 2001) and purchase intention (Alexander et al., 2008; Moreau et al., 2001).

Figure 4.9 Competing model 1

Notes: CPPI = Consumer perceived product innovativeness; Pre-CPBI = Consumer perceived brand innovativeness prior to introduction of the innovation; Post-CPBI = Consumer perceived brand innovativeness after introduction of the innovation.

The results of model fit and structural paths assessments are presented in Table 4.11.

Table 4.11 Competing model 1 results

Hypothesised relationships _{Estimates (T-value)} Standardised Path p = 0.05

Technological newness Brand Attitude -0.11 (-1.907) Non-significant Technological newness Purchase Intention -0.21(-2.262) Significant,

Non-significant at p < 0.01

Pre-CPBI Brand Attitude 0.15 (3.184) Significant Pre-CPBI Purchase Intention -0.29 (-4.099) Significant CPPI Brand Attitude 0.15 (2.410) Significant,

Non-significant at p < 0.01

CPPI Purchase Intention 0.00 (-0.008) Non-significant Post-CPBI Brand Attitude 0.70 (13.533) Significant

Post-CPBI Purchase Intention 0.78 (10.146) Significant

χ2 _{[542] = 1564.827, Bollen-Stine bootstrap p > 0.001, TLI = 0.961, CFI = 0.965, RMSEA = 0.055}

Notes: p = 0.05, CPBI = Consumer perceived brand innovativeness; CPPI = Consumer perceived product innovativeness. Technological Newness Pre – CPBI CPPI   Post – CPBI Brand Attitude  Purchase Intention 

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Chapter 4 Testing the CPBI Processing Model: Laboratory Study

Although the CFI for the competing model was negligibly higher (CFI = 0.963 versus 0.965; ∆ CFI = 0.002), six of eight (75%) of its hypothesised relationships were supported at the p < 0.05 level (including only four of eight (50%) supported at p < 0.01). In contrast, all of the in hypothesised relationships in the proposed CPBI model were supported at the p < 0.001 level. Importantly, all of the significant direct effects in the competing model are significant direct or indirect effects in the proposed CPBI model. Moreover, little, if any, additional explanatory power was gained from the additional two paths. The competing’s SMCs were brand attitude = 0.71, and purchase intention = 0.26. While the SMC for the purchase intention indicated a 5% improvement (0.26 versus 0.21), there was no improvement in the explanatory power of the brand attitude (0.71 versus 0.71).

As Figures 4.8 and 4.9 show that there is a difference in parsimony between the proposed CPBI model and the competing model 1 (6 versus 8 paths). Because CFI does not account for parsimony differences (Arbuckle, 2012), the two models were compared using TLI (Morgan & Hunt, 1994). The competing model 1’s TLI of 0.961 is not meaningfully different from the proposed CPBI model’s TLI of 0.960. The proposed model accomplished an improvement in parsimony (from 8 paths to 6 paths) by sacrificing 0% in CFI which worth making for the sake of parsimony (Byrne, 2010; Morgan & Hunt, 1994). In general, compared to the proposed CPBI model, the direct competing model was not found superior. In other words, competing model 1, which could be called a ‘null theory model’ in this context, provides no fit benefits, and also no benefit of providing a theory structure to the variables.

Competing model 2. The positive effect of brand attitude on purchase intention has been

well established in the literature (Farley & Ring, 1970; Howard & Sheth, 1969). Thus, the proposed CPBI model was tested against another competing model which included the structural path from brand attitude to purchase intention. This model is presented in Figure 4.10.

Figure 4.10 Competing model 2

Notes: CPPI = Consumer perceived product innovativeness; Pre-CPBI = Consumer perceived brand innovativeness prior to introduction of the innovation; Post-CPBI = Consumer perceived brand innovativeness after introduction of the innovation.

Technological Newness

Pre – CPBI 

CPPI   Post – CPBI

Brand Attitude 

Purchase Intention 

The results of model fit and structural paths assessments are presented in Table 4.12.

Table 4.12 Competing model 2 results

Hypothesised relationships Standardised Path

Estimates (T-value) p = 0.05

Technological newness CPPI 0.86 (22.060) Significant Pre-CPBI CPPI 0.10 (3.982) Significant Pre-CPBI Post-CPBI 0.77 (23.869) Significant CPPI Post-CPBI 0.24 (9.640) Significant Post-CPBI Brand Attitude 0.84 (27.495) Significant Post-CPBI Purchase Intention 0.35 (4.885) Significant Brand Attitude Purchase Intention 0.13 (1.749) Non-significant

χ2 _{[548] = 1612.783, Bollen-Stine bootstrap p > 0.001, TLI = 0.960, CFI = 0.963, RMSEA = 0.056}

Notes: p = 0.05, CPBI = Consumer perceived brand innovativeness; CPPI = Consumer perceived product innovativeness.

The CFI values were identical for the proposed CPBI and the competing model 2 (CFI = 0.963) and the additional path did not result in a better model fit. Also, the hypothesised relationship between brand attitude and purchase intention was non-significant (β = 0.13, p > 0.05), thereby six of seven (85%) of the hypothesised relationships were supported at the p < 0.001 level. In contrast, all of the in hypothesised relationships in the proposed CPBI model were supported at the p < 0.001 level. Moreover, little, if any, additional explanatory power was gained from the additional path. The competing’s SMCs were brand attitude = 0.71, and purchase intention = 0.22. While the SMC for the purchase intention indicated a 1% improvement (0.22 versus 0.21), there was no improvement in the explanatory power of the brand attitude (0.71 versus 0.71).

The TLI values were identical for the proposed CPBI and the competing model 2 (TLI = 0.960). The proposed model accomplished an improvement in parsimony (from 7 paths to 6 paths) by sacrificing 0% in CFI which worth making for the sake of parsimony (Byrne, 2010; Morgan & Hunt, 1994). In general, compared to the proposed CPBI model, the direct competing model was not found superior. Moreover, the focus of the present thesis was on the outcomes of consumer perceived brand innovativeness rather than the causes of purchase intention. The effect of brand attitude on purchase intention was not found to add to the contributions of the thesis. Hence, it was concluded that the proposed CPBI model incorporated sufficient number of parameters that adequately represent the data (Byrne, 2010; Kline, 2011).

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Chapter 4 Testing the CPBI Processing Model: Laboratory Study

4.6 Conclusion

This chapter reported the methods, analyses, and findings of testing the proposed CPBI processing model (developed in Chapter 3) in a laboratory study. The purpose was to address research questions (3) do firms’ efforts to launch product innovations lead to CPBI and if so, how does exposure to the innovation affect consumer evaluations of the brand’s innovativeness? and (4) what are the consequences of CPBI? The model was tested using a 2 (pre-CPBI: high vs. low) × 2 (technological newness: high vs. low) pretest-posttest mixed randomised factorial design. Respondents were 617 Australian adults. The CPBI scale developed through Studies A1 to B3 was used in this study. The data were subject to SEM and analysed through several MGA. The proposed CPBI model was also tested against two competing models. In sum, the CPBI processing model proposed in this thesis was generally supported. Five out of six hypotheses were fully supported across pooled data and the four experimental conditions and one hypothesis was partially supported. The proposed CPBI model explained a substantial variance in the consumer perceived brand innovativeness construct.

The result indicates that when a brand introduces a product innovation, consumers’ perceptions of the product innovation’s technological newness directly and positively impact consumers’ perceptions of the product innovativeness (CPPI). The resulting CPPI positively affects consumers’ perceptions of the brand innovativeness (CPBI). Also, CPBI does positively shapes consumer attitudinal and behavioural responses (purchase intention) to the innovative activities of the brand such as launching an innovation. The cumulative evidence of this laboratory model testing study and previous studies (A1 to B3, reported in Chapter 2) provides evidence for nomological validity of the proposed CPBI scale and shows that the CPBI scale performs well for both Australian student and adult respondents. These findings further confirm the construct validity of the CPBI scale developed in the thesis.

A major limitation associated with the proposed CPBI model is that the model examines the effect of a narrow selection of antecedents such as consumer perceived product innovativeness (CPPI) and technological newness (TN) on consumer perceived brand innovativeness (CPBI), thus, remaining other possible antecedents, such as consumer perceived firm innovativeness (CPFI), and consumer innovativeness as exogenous. Future studies are invited to extend findings of this study by examining a variety of factors that may contribute to CPBI formation. Results indicated that a new product (regardless of featuring a high or low technological newness) leads to an enhancement of post-consumer perceived brand innovativeness and this enhancement is larger for low innovative brands than for highly innovative brands. Although possible explanations for this interesting finding

were provided in details (see Section 4.4 Laboratory Study Discussion), future research is invited to further investigate the dynamics of this effect.

Furthermore, the phenomenon under study, CPBI, and consumer response to it is complex, and the laboratory study is therefore limited by focusing upon one product category and fictitious product innovations. The logical step for future research is to replicate and extend the findings of this study by examining real product innovations and different product categories. The CPBI scale has been repeatedly validated in Chapter 2 and in this chapter, but exclusively in Australia, thus, leaving open the question of scale’s applicability to non-Australian consumers. These limitations are addressed in a field study by testing the CPBI processing model for two real product innovations from the tablet category, using an American sample. Therefore, (a) to test for the validity, replicability, and generalisability of the proposed CPBI processing model across different populations, (b) to examine the application of results of the laboratory study to real world situations, and (c) to further establish construct validity for the CPBI scale, a field study was conducted using an American consumer panel and real product innovations. This study is reported in the next chapter.

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