Research Article
A Network-Based Approach to Modeling and Predicting Product Coconsideration Relations
Table 4
Estimated coefficients and odds ratios of the dyadic model and ERGM.
| Input variables | Dyadic Model | ERGM | Est. coef. | Odds | Est. coef. | Odds |
| Network configurations of product interdependence | Edge/Intercept | | 0.00 | | 0.00 | Star effect (inverse measure) | | | | 7.20 | Triangle effect | | | | 2.01 |
| Baseline effects of vehicle attributes | Import | | 1.45 | | 1.11 | Price () | | 0.98 | | 1.01 | Power () | | 1.97 | | 1.42 | Fuel consumption (per 100 BHP) | | 1.21 | | 1.13 |
| Homophily effects of vehicle attribute matching and difference | Market segment matching | | 3.98 | | 1.94 | Make origin matching | | 3.60 | | 1.69 | Price difference () | | 0.17 | | 0.45 | Power difference () | 0.08 | 1.09 | 0.13 | 1.14 | Fuel consumption difference | | 0.92 | | 0.93 |
| Homophily effects of customer association | Distance of customers’ perceived characteristics. | | 0.66 | | 0.74 | Distance of customers’ demographics | | 0.56 | | 0.69 |
| Model performance | Null deviance | 104,618 | Bayesian Information Criterion (BIC) | 16,005 | 14,021 |
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Note. -value < 0.01, -value < 0.001. |