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Proper function of landing gear plays a crucial role in the safe operation of an airplane. Traditional landing gear control system utilizes centralized control technology. The relatively heavy wire harness and low reliability accompanied with this technology make it logical to transfer from traditional control to real‐time distributed control. This paper aims to look into a new landing gear control system based on time‐triggered architecture (TTA).

In this paper, a new landing gear control system based on TTA is proposed. The reliability of the proposed system is investigated using a combination of Markov analysis and MIL‐HDBK‐217 methods.

The results show that by integration of TTP/C and TTP/A technologies, the advantages of both are achieved. A very high level of reliability is obtained. This increases the confidence when adopting distributed landing gear control technology.

The paper presents a new landing gear control system based on TTA, the reliability of which is very high.

This study aims to investigate the differences in consumers’ perceptions of trust, performance expectancy and intention to hire between human financial advisors with high/low expertise and robo-advisors.

Three experiments were conducted. The respondents were randomly assigned to human advisors with high/low expertise or a robo-advisor. Data were analyzed using MANCOVA.

The results suggest that consumers prefer human financial advisors with high expertise to robo-advisors. There are no significant differences between robo-advisors and novice financial advisors regarding performance expectancy and intention to hire.

This pioneering study extends the self-service technology adoption theory to examine adoption of robo-advisors vs human financial advisors with different expertise levels. To the best of the authors’ knowledge, it is among the first studies to address multi-dimensionality of trust in the context of artificial intelligence-based self-service technologies.