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Purpose

Drawing on psychological capital theory, the purpose of this paper is to investigate the effect of cybernetic avatars (human-operated, remotely controlled robots that help physically disabled individuals participate in hospitality jobs) on their labor market participation and well-being.

Design/methodology/approach

Using a two-study, mixed-methods approach, the authors examine the adoption of cybernetic avatars in Japanese cafés by physically disabled employees (i.e. cybernetic avatar pilots). Study 1 conducts ten in-depth interviews to uncover key themes characterizing these employees’ job experiences with cybernetic avatars. Using the findings of Study 1, Study 2 develops and tests a model using survey data (n = 252).

Findings

The findings of this study show that employees’ psychological capital (their positive developmental state as characterized by self-efficacy, optimism, hope and resilience) mediates the association of perceived discrimination and social support on their intention to adopt cybernetic avatars. Overall, the results of this study support the value of these avatars in fostering these employees’ social inclusion.

Practical implications

The findings show that training disabled workers to use cybernetic avatars boosts these individuals’ well-being and social inclusion, while also raising customer loyalty along with the organization’s reputation. The results indicate the key role of these employees’ psychological capital, requiring integrated staff training.

Originality/value

While cybernetic avatars are currently used in Japan, they are not yet adopted in many other countries (e.g. the USA). This research is, thus, on the forefront of the cybernetic avatar adoption curve, which is predicted to also feature elsewhere in the coming years. Specifically, this study offers empirical support for the role of cybernetic avatars in advancing the workplace participation, social inclusion and well-being of people suffering from a disability, contributing to SDG #8 (Decent work and economic growth) and #10 (Reduced inequalities).

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