How Should AI Systems Talk to Users when Collecting their Personal Information? Effects of Role Framing and Self-Referencing on Human-AI Interaction

Liao, Mengqi; Sundar, S. Shyam

doi:10.1145/3411764.3445415

Cited by 13 publications

(12 citation statements)

References 36 publications

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“…In this case, management can support, develop and motivate employees (Khan and Noor, 2020). Recent studies have found that customers and employees perceive the use of robots differently but are self-motivated to use robots (Berezina et al , 2019; Liao and Sundar, 2021). However, the results of our study show that HRM departments do not actively motivate or inspire employees.…”

Section: Discussionmentioning

confidence: 99%

“…System-related metrics are diverse and include safety, effectiveness, efficiency and team productivity. Ultimately, advances in HRC can provide JHTT 13,2 broad perspectives on the proper use of technology in the hospitality industry (Bröhl et al, 2019;Libert et al, 2020). Based on TAM (Davis, 1989;Venkatesh and Davis, 2000), actual usage can be realized with behavioral intention as a predictor.…”

Section: Human-robot Collaborationmentioning

confidence: 99%

“…Robots face technical difficulties due to installed algorithms. In addition, technological implementations in organizations can pose significant challenges to the workforce (Libert et al, 2020). Consequently, human resources should strategically engage by dedicating themselves to training and developing the workforce to meet the challenges ahead and maintain the robots.…”

Section: Strategic Human Resource Management Involvementmentioning

confidence: 99%

“…Existing robotics research in the travel, tourism and hospitality (TTH) field shows that researchers generally focus on the definition, classification, involvement and impact of robots (Dautenhahn et al, 2005;Brougham and Haar, 2018;Wirtz et al, 2018;Van Pinxteren et al, 2019;Berezina et al, 2019;Turja et al, 2020;Sinha et al, 2020;Zhu and Chang, 2020;Reis et al, 2020;Samala et al, 2020;Lee et al, 2019;Choi et al, 2020;. In addition, some researchers focus on customers' perceptions of robots (Tussyadiah and Park, 2018;Chan and Tung, 2019;Christou et al, 2020), employees' perceptions of collaborating and working with robots (Qureshi and Syed, 2014;Ivanov et al, 2018;Yu, 2020;Vatan and Dogan, 2021;Bhargava et al, 2021;Del Giudice et al, 2021a and HRC (Djuric et al, 2016;Tung and Law, 2017;Hoffman, 2019;Molitor, 2020;Henschel et al, 2020;Libert et al, 2020;Wiese et al, 2021). The results of these human-robot studies mentioned above indicate diverse feelings toward using robots.…”

Section: Introductionmentioning

confidence: 99%

“…The results of these human–robot studies mentioned above indicate diverse feelings toward using robots. Some researchers indicate that employees’ experiences with robots are self-motivated (Przegalinska et al , 2020; Liao and Sundar, 2021). Weber (2018) and Vatan and Dogan (2021) have shown that morality, empathy, trust, dependency, economic and social impact, management actions and strategic development are underway for robot adoption.…”

Section: Introductionmentioning

confidence: 99%

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Antecedents of human-robot collaboration: theoretical extension of the technology acceptance model

Parvez

Araslı

Öztüren

et al. 2022

JHTT

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Purpose This study aims to extend the technology acceptance model (TAM) to examine whether the introduction of robots influences employees’ behavioral intentions to use robots and awareness of robots to promote human–robot collaboration (HRC). Besides, the role of strategic human resource management (HRM) involvement as a moderator in the perception of robots as a team member was investigated. Design/methodology/approach Data were collected from 500 respondents via the Amazon Mechanical Turk platform. After data cleaning, 329 valid responses were analyzed. Partial least squares structural equation modeling was applied using Smart PLS Ver. 3.0 to test the study’s measurement and proposed research model. Findings The study results show that robots’ perceived usefulness and ease of use positively influence employees’ behavioral intentions to use robots. In addition, the advantages and disadvantages of robots have a positive impact on robot awareness. Employees’ behavioral intentions and awareness contribute positively to HRC. On the other hand, the moderating role of strategic human resources (HR’s) involvement in the relationships was insignificant. Research limitations/implications This study provides an exclusively applied understanding of robot presence and embodiment relevant to real-world HRC. In the travel, tourism and hospitality (TTH) industry, employees’ intention to use robots and robot awareness are significant factors. However, HRM involvement without the acceptance of robots could not enhance HRC. Originality/value Based on the literature review, to the best of the authors’ knowledge, this study is one of the first on this topic and extends TAM with new antecedents related to robot use, robot awareness and HRC in the TTH industry. In addition, this model attempts to determine the factors that favor HRC in the industry. This study also assessed the moderating role of strategic HR’s involvement in the behavioral intention of robot use, robot awareness and HRC.

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Section: Discussionmentioning

confidence: 99%

Section: Human-robot Collaborationmentioning

confidence: 99%

Section: Strategic Human Resource Management Involvementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Antecedents of human-robot collaboration: theoretical extension of the technology acceptance model

Parvez

Araslı

Öztüren

et al. 2022

JHTT

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Multichannel Consumer Service Integration of Recommendation Systems

Wadhwani,

Jain

2023

Customer Centric Support Services in the Digital Age

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Towards Ethical Argumentative Persuasive Chatbots

Al Anaissy,

Vesic,

Nevejans

2023

Lecture Notes in Computer Science

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Argumentative persuasive technologies are technologies that use argumentation in order to persuade the persuadee to believe in something or not, which can later lead the persuadee to perform an action or not. The use of such tools opens numerous ethical considerations. In this paper, we survey the literature on persuasion that might be useful for argumentative persuasive chatbots, we cover the existing legal framework and ethical principles and we critically analyze the new proposal for a regulation on artificial intelligence of the European Commission. We also show how to use argumentation to enhance explainability and transparency of the persuasion systems. We propose to show the graphical representation of the arguments used during the persuasion to the user at the end of the dialogue, containing the relations between the arguments (attacks, supports), their origin (source), who uttered them (i.e. the machine or the human participant) and the persuasive methods employed. Our approach has several benefits. Namely, it makes the system more transparent and enhances the human understanding of the system, which is a benefit per se. Furthermore, the fact that the system is transparent increases the trust of the user, which (apart from being one of the goals of AI in general) can increase the chance that the user is persuaded by the system. Finally, the user can give a feedback on the presented arguments (e.g. how much they believe the arguments are ethical), which can be later used to improve the persuasion system.

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How Should AI Systems Talk to Users when Collecting their Personal Information? Effects of Role Framing and Self-Referencing on Human-AI Interaction

Cited by 13 publications

References 36 publications

Antecedents of human-robot collaboration: theoretical extension of the technology acceptance model

Antecedents of human-robot collaboration: theoretical extension of the technology acceptance model

Multichannel Consumer Service Integration of Recommendation Systems

Towards Ethical Argumentative Persuasive Chatbots

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