Table 4

Model fit statistics

M1M2M3M4
MetricProduct and process developmentCommercialisation of academic inventionAccess to student talentEfficiency increase of business operations
−2 Log Likelihood2575.532114.912502.082570.84
AIC (smaller is better)2627.532164.912552.082620.84
AICC (smaller is better)2627.912165.282552.442621.2
BIC (smaller is better)2604.092142.372529.552598.31
CAIC (smaller is better)2630.092167.372554.552623.31
HQIC (smaller is better)2580.422119.612506.792575.54
Fit statistics for conditional distribution
−2 log L (Contribution the target| r. effects)2570.092114.912502.082570.84
Pearson Chi-Square3135.293269.323187.443053.13
Pearson Chi-Square/DF0.850.930.860.83

Note(s): Across the four specifications, Model 2 (Invention) provides the best empirical fit: it attains the lowest −2 log likelihood and the smallest values on AIC, AICC, BIC, CAIC and HQIC, indicating the most favourable balance of fit and parsimony. All models show Pearson χ2/DF between 0.83 and 0.93, which implies only mild under dispersion rather than gross misfit. Overall, the four models capture meaningful structure in the data (information criteria and likelihood improve markedly from some alternatives to Model 2), but none exhibits signs of severe lack of fit; substantive inference should proceed after inspection of coefficients and standard diagnostics

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