Empathy between human and robot?

Jo, Doori; Han, Jooyun; Chung, Kyungmi; Lee, Sukhan

doi:10.1109/hri.2013.6483546

Cited by 7 publications

(2 citation statements)

References 3 publications

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“…In turn, a lack of empathy often leads to undesirable outcomes such as misunderstandings, hostility, and frustration (Preece, 1999). Analogously, (genuine) robot laughter discourages a range of negative affective responses among users (Jo et al, 2013), and the associated emotional competencies help customers to cope with any negative episode during the service encounter (e.g., a small delay and a rude fellow customer). In summary, customers likely appraise more intense levels of feeling AI as a desirable outcome, such that they experience more positive and less negative emotions.…”

Section: Conceptual Developmentmentioning

confidence: 99%

How Smart Should a Service Robot Be?

Schepers

Belanche

Casaló

et al. 2022

Journal of Service Research

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Service robots are taking over the frontline. They can possess three types of artificial intelligence (AI): mechanical, thinking, and feeling AI. Although these intelligences determine how service robots can help customers, not much is known about how customers respond to robots of different intelligence. This paper addresses this gap, builds on the appraisal theory of emotions, and employs three online experiments and one field study to demonstrate that customers have different emotional responses to the three types of AI. Particularly, the influence of AI on positive emotions becomes stronger as the AI type becomes more sophisticated. That is, feeling AI relates more strongly to positive emotions than mechanical AI. Also, feeling AI and thinking AI increase spending and loyalty intention through customers’ positive emotions. We also identify important contingency effects of service tiers: mechanical AI is more suitable for low-cost firms, whereas feeling AI mainly benefits full-service providers. Remarkably, none of the three intelligences are directly related to negative emotions; perceived robot autonomy is an important mediator in these relationships. The findings yield concrete managerial guidance as to how smart a service robot should be by pinpointing the right type of AI given the market segment of the service provider.

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Section: Conceptual Developmentmentioning

confidence: 99%

How Smart Should a Service Robot Be?

Schepers

Belanche

Casaló

et al. 2022

Journal of Service Research

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“…Some proposed optimal algorithms such as best proximity points [30] and particle swarm optimization [31] were applied to evade the non-convex difficulties. However, there still exists an enormous gap between human physical simulation and computational simulation like emotions [32]. e state caused by human action is discretely jumpy rather than the inflexible inference from a fixed step to another.…”

Section: 3mentioning

confidence: 99%

On Cognitive Searching Optimization in Semi‐Markov Jump Decision Using Multistep Transition and Mental Rehearsal

Ren

Yin

2021

Complexity

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Cognitive searching optimization is a subconscious mental phenomenon in decision making. Aroused by exploiting accessible human action, alleviating inefficient decision and shrinking searching space remain challenges for optimizing the solution space. Multiple decision estimation and the jumpy decision transition interval are two of the cross-impact factors resulting in variation of decision paths. To optimize the searching process of decision solution space, we propose a semi-Markov jump cognitive decision method in which a searching contraction index bridges correlation from the time dimension and depth dimension. With the change state and transition interval, the semi-Markov property can obtain the action by limiting the decision solution to the specified range. From the decision depth, bootstrap re-sampling utilizes mental rehearsal iteration to update the transition probability. In addition, dynamical decision boundary by the interaction process limits the admissible decisions. Through the flight simulation, we show that proposed index and reward vary with the transition decision steps and mental rehearsal frequencies. In conclusion, this decision-making method integrates the multistep transition and mental rehearsal on semi-Markov jump decision process, opening a route to the multiple dimension optimization of cognitive interaction.

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Recreating a therapeutic blue urban space through the architectural restoration of the Triton Fountain in Valletta, Malta

Satariano

2021

Cities & Health

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Empathy between human and robot?

Cited by 7 publications

References 3 publications

How Smart Should a Service Robot Be?

How Smart Should a Service Robot Be?

On Cognitive Searching Optimization in Semi‐Markov Jump Decision Using Multistep Transition and Mental Rehearsal

Recreating a therapeutic blue urban space through the architectural restoration of the Triton Fountain in Valletta, Malta

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