Table 4. Logistic regression analysis... | Emerald Publishing

Table 4.

Logistic regression analysis

Variable	Beta	Std. error	z ratio	p-value	Odds
Constant	−0.6886	0.4750	−1.4500	0.1471	0.5023
Task_B: Identification of legal references in context	−0.0661	0.3640	−0.1820	0.8558	0.9360
Task_C: Case-based calculation	−11.1680	0.3840	−2.9100	0.0036	0.3273
Task_D: Case-based decision-making	0.8807	0.3740	2.3530	0.0186	2.4126
Topic_Inventories	0.3347	0.4100	0.8160	0.4143	1.3975
Topic_Provisions	13.6710	0.4280	3.1950	0.0014	3.9239
Topic_Tangible assets	0.4177	0.4100	1.0190	0.3084	1.5185
Topic_Trade receivables	−0.7129	0.4270	−1.6710	0.0946	0.4902
GPT_Model_GPT 4.0	11.3300	0.3870	2.9260	0.0034	3.1051
GPT_Model_GPT 4o	14.5950	0.3900	3.7420	0.0002	4.3040
GPT_Model_GPT o1 preview	21.3250	0.4050	5.2640	0.0000	8.4360
GAAP_Local GAAP	−14.1340	0.2750	−5.1320	0.0000	0.2433
Prompt_zero-shot	−0.2799	0.2650	−1.0560	0.2912	0.7558
LL-Null					−221,41
Log-Likelihood					−170,25
Model x²					102,32
p					0,000
Pseudo R²					0,2311
n					320

Variable	Beta	Std. error	z ratio	p-value	Odds
Constant	−0.6886	0.4750	−1.4500	0.1471	0.5023
Task_B: Identification of legal references in context	−0.0661	0.3640	−0.1820	0.8558	0.9360
Task_C: Case-based calculation	−11.1680	0.3840	−2.9100	0.0036	0.3273
Task_D: Case-based decision-making	0.8807	0.3740	2.3530	0.0186	2.4126
Topic_Inventories	0.3347	0.4100	0.8160	0.4143	1.3975
Topic_Provisions	13.6710	0.4280	3.1950	0.0014	3.9239
Topic_Tangible assets	0.4177	0.4100	1.0190	0.3084	1.5185
Topic_Trade receivables	−0.7129	0.4270	−1.6710	0.0946	0.4902
GPT_Model_GPT 4.0	11.3300	0.3870	2.9260	0.0034	3.1051
GPT_Model_GPT 4o	14.5950	0.3900	3.7420	0.0002	4.3040
GPT_Model_GPT o1 preview	21.3250	0.4050	5.2640	0.0000	8.4360
GAAP_Local GAAP	−14.1340	0.2750	−5.1320	0.0000	0.2433
Prompt_zero-shot	−0.2799	0.2650	−1.0560	0.2912	0.7558
LL-Null					−221,41
Log-Likelihood					−170,25
Model x²					102,32
p					0,000
Pseudo R²					0,2311
n					320

Source(s): Authors’ own work