Influence of cultural factors in dynamic trust in automation

Chien, Shih‐Yi; Lewis, Michael; Sycara, Katia; Liu, Jyi-Shane; Kumru, Asiye

doi:10.1109/smc.2016.7844677

Cited by 27 publications

(21 citation statements)

References 14 publications

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“…To date, only a handful of studies consider cultural factors and potential differences in the context of trust in automation, with [99,125] and [22] being exceptions. As the use of automation gets increasingly globalized, it is imperative that we gain an understanding on how trust in automation is conceptualized across cultures and how it influences operator reliance and use of automation, and overall human-system performance.…”

Section: Properties Of the Operatormentioning

confidence: 99%

The Role of Trust in Human-Robot Interaction

Lewis

Sycara

Walker

2018

Studies in Systems, Decision and Control

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187

140

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Section: Properties Of the Operatormentioning

confidence: 99%

The Role of Trust in Human-Robot Interaction

Lewis

Sycara

Walker

2018

Studies in Systems, Decision and Control

Self Cite

187

140

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“…To measure trust across cultures more accurately, we [22][23][24][25] have developed a new measure of trust in automation validated across large samples in three diverse cultures, U.S., Taiwan. and Turkey, as representative of Dignity, Face, and Honor cultures [26].…”

Section: :3mentioning

confidence: 99%

“…All data and analyses involved in the development of the instrument are archived in Reference [24] at OpenICPSR to provide open data for conducting additional validation testing, assembling specialized scales based on item data or testing hypotheses related to the scales. English, Chinese, and Turkish versions of the scales are available in Reference [24]. An English version of scale is provided in Appendix A.…”

Section: :3mentioning

confidence: 99%

“…Thus people in high power distance societies will engage in less vigilance and monitoring for possible violations by authority figures. In contrast to the interpersonal trust literature, to date, only a handful of studies [66][67][68] consider cultural factors and potential differences in the context of trust in automation. The work described in this article is an additional step towards filling this gap.…”

Section: Operator Characteristicsmentioning

confidence: 99%

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The Effect of Culture on Trust in Automation

Chien

Lewis

Sycara

et al. 2018

ACM Trans. Interact. Intell. Syst.

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Trust in automation has become a topic of intensive study since the late 1990s and is of increasing importance with the advent of intelligent interacting systems. While the earliest trust experiments involved human interventions to correct failures/errors in automated control systems, a majority of subsequent studies have investigated information acquisition and analysis decision aiding tasks such as target detection for which automation reliability is more easily manipulated. Despite the high level of international dependence on automation in industry, almost all current studies have employed Western samples primarily from the U.S. The present study addresses these gaps by running a large sample experiment in three (U.S., Taiwan, and Turkey) diverse cultures using a "trust sensitive task" consisting of both automated control and target detection subtasks. This article presents results for the target detection subtask for which reliability and task load were manipulated. The current experiments allow us to determine whether reported effects are universal or specific to Western culture, vary in baseline or magnitude, or differ across cultures. Results generally confirm consistent effects of manipulations across the three cultures as well as cultural differences in initial trust and variation in effects of manipulations consistent with 10 cultural hypotheses based on Hofstede's Cultural Dimensions and Leung and Cohen's theory of Cultural Syndromes. These results provide critical implications and insights for correct trust calibration and to enhance human trust in intelligent automation systems across cultures. Additionally, our results would be useful in designing intelligent systems for users of different cultures. Our article presents the following contributions: First, to the best of our knowledge, this is the first set of studies that deal with cultural factors across all the cultural syndromes identified in the literature by comparing trust in the Honor, Face, Dignity cultures. Second, this is the first set of studies that uses a validated cross-cultural trust measure for measuring trust in automation. Third, our experiments are the first to study the dynamics of trust across cultures.

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“…In HRI fields, researchers employ two primary methods to investigate the supervisory processes. The first approach examines the overall system performance, such as the number of targets detected (Chen, 2009;Chien et al, 2012b), area explored (Scerri et al, 2011), or vehicles' damage levels (Chien et al, 2016;Imbert et al, 2014;Miller and Parasuraman, 2007). The other approach characterizes operators' attention allocation, such as the response rate in answering the robots' requests (Crandall and Cummings, 2007;Mekdeci and Cummings, 2009;Mercado et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Attention allocation for human multi-robot control: Cognitive analysis based on behavior data and hidden states

Chien

Lin

Lee

et al. 2018

International Journal of Human-Computer Studies

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Human multi-robot interaction exploits both the human operator's high-level decision-making skills and the robotic agents' vigorous computing and motion abilities. While controlling multi-robot teams, an operator's attention must constantly shift between individual robots to maintain sufficient situation awareness. To conserve an operator's attentional resources, a robot with self-reflect capability on its abnormal status can help an operator focus her attention on emergent tasks rather than unneeded routine checks. With the proposing self-reflect aids, the human-robot interaction becomes a queuing framework, where the robots act as the clients to request for interaction and an operator acts as the server to respond these job requests. This paper examined two types of queuing schemes, the self-paced Open-queue identifying all robots' normal/abnormal conditions, whereas the forced-paced shortest-job-first (SJF) queue showing a single robot's request at one time by following the SJF approach. As a robot may miscarry its experienced failures in various situations, the effects of imperfect automation were also investigated in this paper. The results suggest that the SJF attentional scheduling approach can provide stable performance in both primary (locate potential targets) and secondary (resolve robots' failures) tasks, regardless of the system's reliability levels. However, the conventional results (e.g., number of targets marked) only present little information about users' underlying cognitive strategies and may fail to reflect the user's true intent. As understanding users' intentions is critical to providing appropriate cognitive aids to enhance task performance, a Hidden Markov Model (HMM) is used to examine operators' underlying cognitive intent and identify the unobservable cognitive states. The HMM results demonstrate fundamental differences among the queuing mechanisms and reliability conditions. The findings suggest that HMM can be helpful in investigating the use of human cognitive resources under multitasking environments.

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Influence of cultural factors in dynamic trust in automation

Cited by 27 publications

References 14 publications

The Role of Trust in Human-Robot Interaction

The Role of Trust in Human-Robot Interaction

The Effect of Culture on Trust in Automation

Attention allocation for human multi-robot control: Cognitive analysis based on behavior data and hidden states

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