Development of a Chinese Human-Automation Trust Scale: Phases and Insights

핵심 개념

Human-automation trust assessment requires different dimensions for initial and post-task trust, with implications for cognition-based and affect-based trust.

초록

The development of a Chinese Human-Automation Trust Scale involved three phases. The study aimed to create a reliable tool based on Lee and See (2004)’s model. Initial trust had two dimensions, while post-task trust had three. The scale showed good item discrimination, construct validity, and reliability across various automation systems.
Directory:

Introduction

Importance of human-automation trust.

Theoretical Models of Trust

Discussion on models by Muir & Moray (1996), Hoff & Bashir (2015), and Lee & See (2004).

Previous Trust Scales Based on Lee & See (2004)

Limitations of scales by Chien et al. (2014), Chancey et al. (2017), and Korber (2019).

Lack of Dynamic Trust Scale in China

Need for culturally specific trust scales.

Purpose of the Study

Development and assessment phases outlined.

Phase 1: Initial Version of C-HATS

Item generation process and exploratory factor analysis results.

Phase 2: Dimension of C-HATS for Initial and Post-Task Trust

EFA results for different automation systems.

Phase 3: Reliability and Validity of C-HATS

Item discrimination, construct validity, and reliability assessments.

Discussions on Application of C-HATS

Different dimensions for initial and post-task trust, implications for consumer automation products.

통계

After three phases of assessments including exploratory factor analysis, different dimensions were considered for initial and post-task human-automation trust.
The final scale had 14 items but only two dimensions for initial trust.
For post-task trust, the scale had three dimensions with 11 items reflecting Lee & See (2004)’s model.

인용구

"The level of trust can affect human-automation interaction."
"Human-automation trust at different development stages should be respectively considered."

핵심 통찰 요약

Development of a Chinese Human-Automation Trust Scale

by Zixin Cui,Xi... 게시일 arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.16406.pdf

더 깊은 질문

How does cultural background influence human-automation trust?

Cultural background plays a significant role in influencing human-automation trust. Different cultures have varying attitudes towards technology, which can impact how individuals perceive and interact with automation systems. For example, research has shown that people from different cultural backgrounds may have differing levels of trust in robots or automated systems. Cultural values, beliefs, and norms can shape individuals' expectations and perceptions of automation technologies.
In the context of the study provided, the development of a Chinese Human-Automation Trust Scale (C-HATS) takes into account the cultural nuances specific to China. The scale aims to effectively assess human-automation trust within the Chinese context by considering cultural factors that may influence trust dynamics.

What are the implications of cognition-based versus affect-based trust in automation?

Cognition-based and affect-based trust play distinct roles in shaping human-automation interactions:

Cognition-Based Trust: This type of trust is grounded in evidence related to the perceived "trustworthiness" or reliability of an automation system based on prior knowledge or experiences. Cognition-based trust involves rational assessments and judgments about an automation system's capabilities, predictability, dependability, etc.

Affect-Based Trust: Affect-based trust relies on emotional attributions concerning the motives or intentions behind an automation system's actions. It involves feelings such as confidence, comfort, security, or even anthropomorphizing machines by attributing emotions to them.

In real-world applications of automation technologies like autonomous vehicles or smart home devices:

Cognition-based trust might be crucial for users assessing safety features and performance metrics.
Affect-based trust could come into play when users develop emotional connections with AI assistants or robotic devices.
Understanding both types of trusts is essential for designing effective human-machine interfaces that cater to users' cognitive evaluations as well as emotional responses towards automated systems.

How can the findings from this study be applied to real-world consumer automation products?

The findings from this study provide valuable insights for enhancing user experience and building trustworthy relationships between consumers and various consumer automation products:

Design Considerations: Designers can use these findings to tailor user interfaces based on different stages of human-automation interaction (initial vs post-task). Understanding how users perceive performance attributes (predictability), process characteristics (dependability), purpose elements (faith), etc., can guide design decisions.

Training & Education: Companies developing consumer products should consider providing clear information about product functionalities upfront to build initial cognitive-based trusts among consumers before interaction.

Marketing & Communication: Communicating transparently about system intents (purpose) while also appealing emotionally through marketing strategies could help foster affective bonds with consumers over time.

Continuous Improvement: Regular feedback collection using validated scales like C-HATS across diverse consumer products allows companies to monitor changes in user perception over time and make necessary adjustments for improved user satisfaction.