Model Estimation and Comparison

The structural equation modelling was conducted using the maximum likelihood

estimation method in LISREL 8.2. The “best” model was determined through a

combined use of model comparison and model development. In this manner, the

operational model was first subjected to a rigorous test that compared the model with a

set of alternative models. The surviving model, in case of an inadequate model fit, was

then respecified through modifications in accordance with the underlying theory.

In model comparison, the operational model was compared with a null model and

four competing models by way of a nested models analysis (cf. Anderson and Gerbing

1988). The specifications of the operational, null, and competing models are illustrated in

Table 6.10. The better-fitting model was determined according to evaluation of the

goodness-of-fit statistics between the focal model and the other five models (Bentler and

Bonnet, 1980; James, Mulaik, and Brett, 1982). The null model proposed no causal

Table 6.10

____________ Descriptions of the Operational and Alternative Models____________ Model_________________________Structural Specification_____________________

OM The operational model.

CM1 Paths from the brand/product and market characteristics to the preannouncing

effectiveness are restricted to zero.

CM2 Paths from the situational factors to the new product preannouncing

behaviours are restricted to zero.

CM3 Paths from the new product preannouncing behaviours to the preannouncing

effectiveness were restricted to zero.

CM4 Paths from the market characteristics to preannouncing effectiveness are

freely estimated.

NM_____ The null model.____________________________________________________ Note; OM: The Operational Model; CM1: Competing Model 1; CM2: Competing Model 2; CM3: Competing Model 3; CM4: Competing Model 4; NM: The Null Model.

relationships among respective constructs. The first three competing models were nested

within the operational model, which was in turn nested within the fourth competing

model (cf. Anderson and Gerbing 1988).

The first competing model, as illustrated in Figure 6.2, tested the hypotheses

predicting that the characteristics of brand/product and firm directly influence the

effectiveness of new product preannouncement. This competing model argued that the

influences of the brand/product and market characteristics on new product preannouncing

effectiveness are completely mediated by the strategic behaviours of new product

preannouncement. The underlying rationale comes from the coalignment principle,

which advocates the environment —> firm behaviour —> performance paradigm (Cavusgil

and Zou 1994; Li and Calantone 1998). As such, the first competing model differed from

the operational model in that the paths from the brand/product and firm characteristics to

the preannouncing effectiveness were specified at zero.

The second competing model tested the hypotheses that the characteristics of

brand/product, firm, and market have direct impacts on the strategic behaviours of new

product preannouncement. As shown in Figure 6.3, this competing model examined the

relevance of these situational factors to new product preannouncing behaviours. It is

possible that the situational factors may not sufficiently account for the behaviours

involved in preannouncing new products. In a similar vein, the paths from the situational

factors to the strategic behaviours were restricted to zero in the competing model.

The third competing model tested the hypotheses depicting the link between the

strategic behaviours and effectiveness of new product preannouncement. In this third

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that the effectiveness may be somewhat independent of the executions of new product

preannouncements or other exclusive behavioural constructs may have more substantial

influence on the effectiveness than do the currently used constructs (cf. Govindarajan

1988). The parameters of the paths from preannouncing behaviours to the effectiveness

were constrained to zero. Figure 6.4 illustrates the third competing model.

Finally, the fourth competing model, as shown in Figure 6.5, examined the impacts

of market characteristics on the preannouncing effectiveness. It represented the next most

likely unconstrained alternative to the operational model (Anderson and Gerbing 1988).

This competing model argued for the existence of direct causal paths linking external

environmental factors to preannouncing effectiveness (cf. Green, Barclay, and Ryan

1995; Szymaski, Bhardwaj and Varadarajan 1993). In the model, the three constructs of

market characteristics, network externality, competitive hostility, and technological

turbulence, impose direct influences on preannouncing effectiveness.

A series of pairwise comparisons between the operational model and the null and

competing models were conducted to determine which model better accounts for the

observed data. As the operational and other five alternative models were nested, the

models were compared on the basis of Ax2 statistics (Hoyle 1995). Table 6.11

demonstrates the results of model comparisons.

The operational model shows a mediocre fit to the empirical data, indicating that

model modifications were necessary. The chi-square statistic, 95.80 with 39 degrees of

freedom, is highly significant (p < .001). The comparative fit index (CFI) is .88,

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normed fit index (NFI) is .84, non-normed fit index (NNFI) is .58, the root mean square

error of approximation (RMSEA) is .09, and root mean square residual (RMR) is .063.

Except the GFI, the other goodness-of-fit statistics are somewhat below the acceptable

levels for these indices.

Although the operational model has only moderate fit to the sample data, the

comparisons between it and the null and competing models demonstrate the relative

advantages of the operational model over the other models. Model comparisons were

conducted using the chi-square difference between pairwise models as an evaluation

criterion. The chi-square difference itself is also a chi-square statistic (Bentler 1980).

The chi-square difference statistics between the operational model and the constrained

models, i.e., the null model and the first, second, and third competing models, are all

significant at either .01 or .001 levels. The results suggest that the operational model is

superior to the null model and the first three competing models in terms of overall model

fit.

In contrast, the chi-square difference statistic between the operational model and

the fourth competing model indicates no significant difference between these two models

(p > .1). The difference statistic indicates that the two comparing models are not

significantly different in model fit, given that the unconstrained model (the fourth

competing model) loses 4 degrees of freedom. Joreskog and Sorbom (1993) recommend

that, when comparing a set of models, model parsimony should also be taken into

account. The parsimony goodness-of-fit index (PGFI) values of the operational model

parsimony without the loss of model fit. That is, the operational model has a better fit per

estimated coefficient. Although the second and third models show slightly better model

parsimony, the parsimony was minor compared with the loss of model fit. The first

competing model and the null model gain substantial model parsimony since they are two

more constrained models. However, the two models lose their model fit to a large extent.

In conclusion, all the evidence shows that, compared with the other five models, the

operational model is a better-fitting model.