Robot Acceptance at Work
A Multilevel Analysis Based on 27 EU Countries
Bibliographic Data
| ID | 21199525 |
|---|---|
| Authors | Tuuli Turja (0000-0001-7815-9511, Tampere University, corresponding author), Atte Oksanen (0000-0003-4143-5580, Tampere University) |
| Year | 2019 |
| Volume | 11 |
| Issue | 4 |
| Pages | 679-689 |
| Publication date | 2019-08-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Social Robotics (JOURNAL) |
| Journal identifiers | ISSN: 1875-4791 • E-ISSN: 1875-4805 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s12369-019-00526-x |
| OpenAlex | W2914842996 |
| Language | EN |
| Citations received | 33 |
| References cited | 34 |
Robots are increasingly being used to assist with various tasks ranging from industrial manufacturing to welfare services. This study analysed how robot acceptance at work (RAW) varies between individual and national attributes in EU 27. Eurobarometer surveys collected in 2012 ( n = 26,751) and 2014 ( n = 27,801) were used as data. Background factors also included country-specific data drawn from the World Bank DataBank. The study is guided by the technology acceptance model and change readiness perspective explaining robot acceptance in terms of individual and cultural attributes. Multilevel studies analysing cultural differences in technological change are exceptionally rare. The multilevel analysis of RAW performed herein accounted for individual and national factors using fixed and random intercepts in a nested data structure. Individual-level factors explained RAW better than national-level factors. Particularly, personal experiences with robots at work or elsewhere were associated with higher acceptance. At a national level, the technology orientation of the country explained RAW better than the relative risk of jobs being automated. Despite the countries’ differences, personal characteristics and experiences with robots are decisive for RAW. Experiences, however, are better enabled in countries open to innovations. The findings are discussed in terms of possible mechanisms through which the technological orientation and social acceptance of robots may be related
Business · Eurobarometer · European union · Knowledge management · Machine learning · Multilevel model · Raw data · Robot · Robotics · Applied Psychology · Computer Science · Cultural Differences and Values · Digital Marketing and Social Media · Engineering · Psychology · Technology Adoption and User Behaviour · Artificial Intelligence · Marketing
Translation, Adaptation, and Validation in Portuguese of an Acceptance Scale for Human–Robot Interaction in an Industrial Context
How Linguistic Framing Affects Factory Workers' Initial Trust in Collaborative Robots
Drivers and necessary conditions of human robot collaboration acceptance in the Industry 5.0 setting
Consumer acceptance of robotic surgeons in health services
Looking towards an automated future
Automation Anxieties
Facing with Collaborative Robots
“It's not Paul, it's a robot”
Using Machine Learning to Learn Machines
Self-determination and attitudes toward artificial intelligence
Introducing digital technologies in the factory
Human-in-the-loop by default? Demography, ideology, and civic commitment in the acceptance of autonomous security robots across risk scenarios
Media effects on the perceptions of robots
Attitudes Toward Attributed Agency
Facets of Trust and Distrust in Collaborative Robots at the Workplace
A Multidimensional Analysis of Robotic Deployment in Thai Hotels
Familiarity Breeds Affinity – How Personal Experiences Change Employees’ Attitudes Towards a Social Robot
Exploring Robot Acceptance Across Domains Considering Trust and Social Aspects
Using Structural Equation Modeling to Explore Patients’ and Healthcare Professionals’ Expectations and Attitudes Towards Socially Assistive Humanoid Robots in Nursing and Care Routine
Diversity and Culture in Social Robotics
Affective Attitudes Toward Robots at Work
Home‐care robots – Attitudes and perceptions among older people, carers and care professionals in Ireland
Attitudes of European older workers towards digitalisation from the ecological perspective
A Bourdieusian theory on communicating an opinion about AI governance
Decision-makers’ attitudes toward the use of care robots in welfare services
Digital Innovation Hubs and portfolio of their services across European economies
Who's afraid of automation? Examining determinants of fear of automation in six European countries
Embracing artificial intelligence (AI) with job crafting
Retrospective of interdisciplinary research on robot services (1954–2023)
Exploring factors influencing technology adoption rate at the macro level
Public Perceptions of Artificial Intelligence in 20 Countries
Investigating robot acceptance in UK agriculture
AI as an Artist? A Two-Wave Survey Study on Attitudes Toward Using Artificial Intelligence in Art
Changes in Unemployment and Wage Inequality
Model of Adoption of Technology in Households
Technology acceptance model
A Meta-Analysis of Factors Affecting Trust in Human-Robot Interaction
Exploring influencing variables for the acceptance of social robots
A meta-analysis of the technology acceptance model
From Intentions to Actions
A Theoretical Extension of the Technology Acceptance Model
The Skill Content of Recent Technological Change
User Acceptance of Computer Technology
Technical Change, Inequality, and the Labor Market
Toward the Human–Robot Co-Existence Society
Robot Shift from Industrial Production to Social Reproduction
Job Polarization in Europe? Changes in the Employment Structure and Job Quality, 1995-2007
Beyond acceptable risk
Experimental investigation into influence of negative attitudes toward robots on human–robot interaction
The influence of people’s culture and prior experiences with Aibo on their attitude towards robots
A Cross-cultural Study
Modernization, Cultural Change, and the Persistence of Traditional Values
| Unique citing works | 33 |
|---|---|
| Citations per year | 5,5 |
| Citation span | 2020 - 2026 (7) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 33 |