Trust in artificial intelligence within production management – an exploration of antecedents

Figures & data

Table 1. Guiding questions for the expert interviews.

Decisions and AI

How are decisions in production management made: based on evaluations or experiences or group discussions? Is the quality of the decisions reviewed? If it is possible, how? Imagine that in 30 to 40 years, AI will be able to make decisions that are currently reserved for humans. Which decisions in production management could be affected first? Can you imagine that an AI is not only subordinate (assistant) to humans, but equal (colleague)?

Trust

In your opinion, how important is trust in production management? In which decisions do you think greater trust is required and in which lower trust? What does this depend on? If the colleague is an AI, are there other/further antecedents on which trust could depend? What other enablers or barriers to trust in AI in production management might exist?

Figure 1. First potential antecedents for trust in AI within production management.

Organisational chart of the six antecedents divided into the three characteristic groups, first the AI group consisting of the two antecedents perceived capability and perceived understandability, second the human group consisting of the expert status and the digital affinity, and third the decision situation group consisting of the predictability and the error costs.

Figure 2. Model of hypotheses under investigation.

Illustration of the six antecedents and trust in the SOR model, as described above. In addition, there is an arrow going from each antecedent to the trust, with the label of the respective hypothesis. Here, the arrows for hypotheses H5 and H6 are drawn in dashed lines to indicate the assumed mediated effect.

Table 2. Measurements to operationalise the variables.

Variable	No. of items	Scale	Source
Trust	3	5-stage Likert	Beierlein et al. (2014)
Perceived ability	3	5-stage Likert	Schyns and Collani (2014)
Perceived comprehensibility	2	5-stage Likert	self-developed
Digital affinity	4	5-stage Likert	self-developed with Karrer, Glaser, and Clemens (2009) and Neyer, Felber, and Gebhardt (2016)
Expert status	1	5-level branching	self-developed with Benner (2017)
Predictability	1	3 scenarios	self-developed
Error costs	1	3 scenarios	self-developed

Table 3. Evaluation of measurement model.

Variable	Item	Loading	Tau-equivalent reliability	Roh A	Congeneric reliability	AVE
Evaluation step		one	two	two	two	three
Trust	Trust_1	0.775	0.632	0.731	0.838	0.723
	Trust_2	0.919
Perceived ability	Abi_1	0.848	0.728	0.782	0.840	0.638
	Abi_2	0.788
	Abi_3	0.757
Perceived comprehensibility	Comp_1	0.894	0.532	0.604	0.805	0.677
	Comp_2	0.742
Digital affinity	Digi_1	0.886	0.646	0.728	0.861	0.757
	Digi_2	0.852
Expert status	Exp_1	1.000	1.000	1.000	1.000	1.000
Predictability	Pred_1	1.000	1.000	1.000	1.000	1.000
Error costs	Cost_1	1.000	1.000	1.000	1.000	1.000

Table 4. Evaluation of Fornell-Larcker criterion.

Variable	Trust	Perc. ability	Perc. comp.	Digital affinity	Expert status	Predictability	Error costs
Trust	0.850
Perceived ability	0.578	0.799
Perceived comprehensibility	0.418	0.505	0.823
Digital affinity	0.198	0.120	0.063	0.872
Expert status	−0.122	−0.091	0.012	−0.371	1.000
Predictability	−0.167	−0.266	−0.271	0.000	0.000	1.000
Error costs	0.121	0.241	0.178	0.000	−0.000	−0.455	1.000

Table 5. Evaluation of cross-loadings.

Item	Trust	Perc. ability	Perc. comp.	Digital affinity	Expert status	Predictability	Error costs
Trust_1	0.775	0.359	0.261	0.128	−0.116	−0.112	0.064
Trust_2	0.919	0.587	0.423	0.197	−0.099	−0.164	0.130
Abi_1	0.564	0.848	0.529	0.077	−0.024	−0.328	0.270
Abi_2	0.382	0.789	0.348	0.107	−0.088	−0.166	0.151
Abi_3	0.396	0.758	0.271	0.118	−0.140	−0.079	0.118
Comp_1	0.405	0.477	0.895	0.072	0.028	−0.261	0.149
Comp_2	0.264	0.338	0.744	0.023	−0.017	−0.174	0.147
Digi_1	0.182	0.090	0.078	0.888	−0.316	−0.000	0.000
Digi_2	0.162	0.120	0.029	0.856	−0.333	−0.000	0.000
Exp	−0.122	−0.091	0.012	−0.371	1.000	−0.000	−0.000
Pred	−0.167	−0.266	−0.271	0.000	−0.000	1.000	−0.455
Cost	0.121	0.241	0.178	0.000	−0.000	−0.455	1.000

Table 6. Evaluation of heterotrait-monotrait ratio (HTMT).

Variable	Trust	Perc. ability	Perc. comp.	Digital affinity	Expert status	Predictability	Error costs
Trust	–
Perceived ability	0.777	–
Perceived comprehensibility	0.663	0.736	–
Digital affinity	0.287	0.179	0.137	–
Expert status	0.157	0.122	0.037	0.450	–
Predictability	0.203	0.278	0.359	0.000	0.000	–
Error costs	0.142	0.261	0.245	0.000	0.000	0.455	–

Table 7. Evaluation of structural model with trust as independent variable.

Variable	VIF	Path coefficient with p-value		Indirect effect with p value		f²	R²	Q²
Perceived ability	1.423	0.480	0.000***			0.264	0.843 (adj. 0.377)	0.249
Perceived comprehensibility	1.387	0.174	0.000***			0.037
Digital affinity	1.172	0.117	0.002**			0.021
Expert status	1.170	−0.037	0.271			0.003
Predictability	1.334	−0.006	0.881	−0.137	0.000***	0.002
Error costs	1.286	−0.028	0.501	0.085	0.001**	0.003

**p ≤ 0.01: very significant; ***p ≤ 0.001: highly significant.

Figure 3. Coefficients of the structural model.

Illustration of the six antecedents and trust with an arrow going from each antecedent to trust labelled with the respective effect size and significance. Significant effects on trust with their respective effect size are digital affinity (0.117), perceived ability (0.480), perceived comprehensibility (0.174), predictability as indirect effect (−0.137), and error costs also as indirect effect (0.085).

Table 8. Results of hypothesis testing.

Hypothesis	Statement	Significant
H1	Perceived ability of AI in production management increases trust.	Yes
H2	Perceived comprehensibility of AI in production management increases trust.	Yes
H3	The expert status of humans decreases his/her trust in AI.	No
H4	The digital affinity of humans increases his/her trust in AI.	Yes
H5	The predictability of the situation in production management indirectly decreases trust in AI, mediated through perceived ability and/or perceived comprehensibility.	Yes
H5a	Mediation only through perceived ability	Yes
H5b	Mediation only through perceived comprehensibility	Yes
H6	The error costs of the situation in production management indirectly increase trust in AI, mediated through perceived ability and/or perceived comprehensibility.	Yes
H6a	Mediation only through perceived ability	Yes
H6b	Mediation only through perceived comprehensibility	No

Table

Variable	Item	Scale	Source
Trust	I am convinced that the AI has good intentions.	totally true rather true neither nor rather not true not true at all	Beierlein et al. (2014)
	You cannot rely on the AI. (-)
	In general, the AI can be trusted.
Perceived ability	The AI is up to the needs of the situation.	totally true rather true neither nor rather not true not true at all	Schyns and Collani (2014)
	If the AI is confronted with a new situation, it knows how to deal with it.
	For every problem the AI has one or more ideas for solutions.
Perceived comprehen-ibility	I do not understand the decision of the AI. (-)	totally true rather true neither nor rather not true not true at all	self-developed
	The explanation helped me to understand the AI.
Digital affinity	I am very interested in the latest digital developments, even though I have no intention of buying.	totally true rather true neither nor rather not true not true at all	self-developed with Karrer, Glaser, and Clemens (2009) and Neyer, Felber, and Gebhardt (2016)
	I love to use and own the latest electronic devices.
	For me, dealing with new PC programs or apps is usually too much of a challenge. (-)
	It is easy for me to learn how to use new devices, programs and apps.
Expert status	In your career to date, have you gained professional experience in the field of production?	Branching questions with yes/no or always/never answers	self-developed with Benner (2017)
Predictability	Stages of low, mean and high predictability within the scenarios	Different Scenarios: Layout optimisation (mean predictability and mean error costs) Order acceptance (high predictability and high error costs) Job scheduling (high predictability and low error costs) Production extension (low predictability and high error costs)	self-developed descriptions of different decision situations
Error costs	Stages of low, mean and high error costs within the scenarios

Table

Scenario	Introduction	Description
Scenario 1 layout optimisation	For the tasks and decisions concerning material flow optimisation in your production, you use the artificial intelligence FLOW.	FLOW recommends you to reposition several machines in your production shop, which would require an investment of 80,000 €. FLOW justifies the decision by saying “The new machine arrangement enables a more efficient and flow-oriented logistics between the machines. Stocks on the machines can be reduced and thus the overall throughput time can be decreased”.
Scenario 2 order acceptance	The artificial intelligence DOOR helps you with the tasks and decisions to accept orders for your production.	DOOR recommends you not to accept a new order worth 800,000 €. DOOR justifies the decision with “The production is already overloaded for the next five months, which is balanced by overtime and working weekends. This new order should be ready in four months, which is not achievable”.
Scenario 3 job scheduling	The artificial intelligence PRIO assists you with the tasks and decisions for planning the job sequence.	PRIO recommends the rescheduling of an order from machine 6 to machine 2. PRIO justifies the decision by stating “Machine 2 has free capacity due to delivery delays so it can relieve machine 6. In total, orders can thus be accelerated”.
Scenario 4 production expansion	In the tasks and decisions that affect the entire production site, you are supported by the artificial intelligence SITE.	SITE recommends that you invest in the purchase of a metal 3 D printer and the necessary extension of the production shop for a total of 800000 €. SITE justifies its decision with “To enter a strategic market niche, it is necessary to invest in this new production technology”.

Beierlein, C., C. J. Kemper, A. Kovaleva, and B. Rammstedt. 2014. Interpersonales Vertrauen (KUSIV3): Zusammenstellung sozialwissenschaftlicher Items und Skalen. Leibnitz, Germany: GESIS Leibniz Institute for the Social Sciences.

 Google Scholar

Schyns, B., and G. V. Collani. 2014. Berufliche Selbstwirksamkeitserwartung: Zusammenstallung sozialwissenschaftlicher Items und Skalen. Leibniz, Germany: GESIS Leibniz Institute for the Social Sciences.

 Google Scholar

Karrer, K., C. Glaser, and C. Clemens. 2009. “Technikaffinität Erfassen - Der Fragebogen TA-EG.” In Der Mensch im Mittelpunkt Technischer Systeme: 8. Berliner Werkstatt Mensch-Maschine-Systeme, edited by A. Lichtenstein, C. Stößel, and C. Clemens, 196–201. Düsseldorf: VDI Verlag.

 Google Scholar

Neyer, F. J., J. Felber, and C. Gebhardt. 2016. Kurzskala Zur Erfassung Von Technikbereitschaft (Technology Commitment). Zusammenstellung sozialwissenschaftlicher Items und Skalen. Leibniz, Germany: GESIS Leibniz Institute for the Social Sciences.

 Google Scholar

Benner, P. E. 2017. Stufen zur Pflegekompetenz: From novice to expert. 3rd ed. Bern: Hogrefe.

 Google Scholar