if the model is bad (fails to predict outcomes better than a constant, and/or predicts very little of the variation in the data) we should not look at the regression coefficients. In this case, there would be no evidence that any of the coefficients have other than a zero value. What can you conclude about the suitability of the data for continuing on and looking at regression coefficients? is it worth bothering to even look at the coefficients for Model 5's two analyses?

Finland 138 4.24(16,121) *** 0.36 0.27 -0.467 ** -0.1976 * (0.107) -0.4595 *** (0.106 -0.0155 (0.101) -0.0606 (0.138) -0.0710 (0.075) -0 584 *** Figure 2-Coefficient table for RQ2-H1 Model 5-Dep. Var.: Problemformulation Perf US/China 191 F (dfModel, dfResid.) 6.24 (19,171) *** R-squared 0.40 Adj. R-squared 0.34 Coeff. (Std. Err.) DominanceBias (0.081) SolutnJumpBias -0.237 *** (0.070) InfoDistortionBias 0.072 (0.068) PerceptualBias InformalIP -0.184 (0.131) HybridIP FormalIP -0.138 (0.102) KnowledgeTacitness -0.036 (0.095) SearchBreadth -0.061 (0.056) Position -0.015 (0.049) FirmAge (0.002) NumberOfEmployees 0.000 (0.000) CountryChinaUS 0.051 (0.156) Manufacturing 107 (0.187) Service -0.040 (0.166) Info Tech -0.215 (0.206) Education -0.374 (0.285) Electronics -0.246 (0.255) Medical -0.019 (0.199) RealEstate -0.057 (0.765) Trade -0.074 (0.268) Consumer GoodsMfg -0.002 -0.0037 (0.003) -0.0001 + (0.000) -0.0179 (0.385) -0.1795 (0.441) -0.0075 (0.456) -0.5513 (0.463) -0.4279 (0.447) 0.1087 (0.426) -0.3020 (0.396) -0.3212 (0.517) 0.1503 (0.508) 0.3539 (0.379) OtherMfgRepairInstall TransportStorage Constant -1.432 *** (0.402) *p<.05 finland figure table for rq2-h1 model var.: problemformulation perf us f dfresid. r-squared adj. coeff. err. dominancebias solutnjumpbias infodistortionbias perceptualbias informalip hybridip formalip knowledgetacitness searchbreadth position firmage numberofemployees countrychinaus manufacturing service info tech education electronics medical realestate trade consumer goodsmfg othermfgrepairinstall transportstorage constant>