Impact of human-AI trust on collaborative decision-making quality: mediating role of human-AI shared mental model

doi:10.16265/j.cnki.issn1003-3033.2026.02.1791

Abstract

Abstract:

In order to enhance human-artificial intelligence(AI) collaborative decision-making quality in complex industrial environments, mitigate the deficiency of human trust in AI, and bridge cognitive gaps in human-AI collaboration, based on the theory of mind, the study constructed a model of the relationship between human-AI trust, Human-AI SMM and human-AI collaborative decision-making quality, and task complexity was introduced as a moderating variable. First, hypotheses were proposed based on the theoretical relationships among the variables, and a questionnaire was designed by integrating the human-AI trust scale, Human-AI SMM scale, the human-AI collaborative decision-making quality scale, and the task complexity scale. Then, the questionnaires were distributed to frontline employees of AI-using companies nationwide, and 493 valid samples were collected. Finally, SPSS 26.0, AMOS 24.0 and Process 4.0 were used for data analysis and hypothesis testing on the collected valid samples. The results of the study show that human-AI trust significantly and positively affects human-AI collaborative decision-making quality. Human-AI SMM mediates the relationship between human-AI trust and human-AI collaborative decision-making quality. Task complexity positively moderates the relationship between human-AI trust and Human-AI SMM.

Key words: human-AI trust, human-AI collaborative decision-making quality, human-AI shared mental model (Human-AI SMM), theory of mind, mediating role, task complexity

CLC Number:

X915.2安全社会学

NIU Lixia, LIN Yanhong. Impact of human-AI trust on collaborative decision-making quality: mediating role of human-AI shared mental model[J]. China Safety Science Journal, 2026, 36(2): 244-252.

Figures/Tables 8

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模型	χ²	df	χ²/df	CFI	TLI	RMSEA	SRMR
4因子模型(A、B、C、D)	878.694	489	1.797	0.948	0.944	0.040	0.044
3因子模型(A、B+C、D)	2427.923	492	4.935	0.741	0.723	0.089	0.103
2因子模型(A+B+C、D)	3647.496	494	7.384	0.579	0.550	0.114	0.137
单因子模型(A+B+C+D)	4878.705	495	9.856	0.415	0.376	0.134	0.158
加入共同方法因子	706.726	457	1.546	0.967	0.961	0.033	0.040

变量	均值	标准差	1	2	3	4	5	6	7	8	9
性别	1.37	0.482	1	—	—	—	—	—	—	—	—
年龄	1.86	0.774	-0.006	1	—	—	—	—	—	—	—
学历	2.16	0.674	-0.078	-0.143^**	1	—	—	—	—	—	—
所属行业	3.65	1.809	0.065	-0.047	-0.011	1	—	—	—	—	—
.工作年限	1.69	0.920	-0.002	0.310^**	0.015	-0.021	1	—	—	—	—
人机信任	2.965	0.87807	-0.065	0.038	-0.052	0.030	0.166^**	1	—	—	—
Human-AI SMM	3.014	1.113	0.114^*	-0.055	-0.032	0.068	0.096^*	0.286^**	1	—	—
任务复杂性	2.694	1.051	0.001	0.100^*	-0.024	0.054	0.044	-0.172^**	-0.166^**	1	—
人机协同决策质量	3.041	1.122	0.024	-0.026	-0.008	-0.084	0.030	0.189^**	0.218^**	-0.212^**	1

类别		Human-AI SMM		人机协同决策质量
类别		模型1	模型2	模型3	模型4	模型5	模型6
控制变量	性别	0.247^*	0.294^**	0.066	0.099	0.011	0.045
	年龄	-0.139^*	-0.129	-0.066	-0.059	-0.035	-0.036
	学历	-0.064	-0.035	-0.022	-0.002	-0.008	0.004
	所属行业	0.036	0.030	-0.054	-0.058^*	-0.062^*	-0.064^*
	工作年限	0.155^**	0.095	0.052	0.010	0.017	-0.007
自变量	人机信任	—	0.357^***	—	0.249^***	—	0.184^**
中介变量	Human-AI SMM	—	—	—	—	0.223^***	0.183^***
R²		0.035	0.112	0.011	0.047	0.048	0.076
ΔR²		0.035	0.077	0.011	0.037	0.047	0.029
F		3.529^**	10.171^***	1.037	4.008^**	4.984^***	5.725^***
ΔF		3.529^**	41.898^***	1.037	18.671^***	24.470^***	15.318^***

效应	效应值	标准误差	95%置信区间
中介效应	0.065	0.020	[0.028, 0.106]
直接效应	0.184	0.059	[0.068, 0.300]
总效应	0.249	0.058	[0.136, 0.362]

类别		Human-AI SMM
类别		模型7	模型8	模型9
控制变量	性别	0.247^*	0.290^**	0.278^**
	年龄	-0.139^*	-0.113	-0.111
	学历	-0.064	-0.039	-0.041
	所属行业	0.036	0.035	0.036
	工作年限	0.155^**	0.102	0.096
主效应	人机信任	—	0.329^***	0.340^***
主效应	任务复杂性	—	-0.128^**	-0.115^*
调节效应	人机信任×任务复杂性	—	—	0.111^*
R²		0.035	0.125	0.135
ΔR²		0.035	0.090	0.009
F		3.529^**	9.938^***	9.437^***