Stanford Seminar - The Rise of the Robot Waiter
Skills:
Agent Foundations90%Tool Use & Function Calling80%Multi-Agent Systems70%Autonomous Workflows60%
November 1, 2024
Lionel Robert, University of Michigan
The Rise of the Robot Waiter: Extending the Job Characteristics Model in the Age of Automation
The global service robot market is projected to grow significantly, with robots promising cost savings, productivity gains, and enhanced customer satisfaction. However, their adoption also raises concerns about employee apprehension and the need for new theoretical frameworks. Utilizing the Job Characteristics Model (JCM), this research examines how robots alter work experiences, bridging the gap between established theories and contemporary realities. Two mixed-method studies were conducted: Study 1 (exploratory survey) Surveyed 220 restaurant employees, identifying themes like 'perceptions of robots being useful with employees' (PUwE) and 'perceptions of robots being useful without employees' (PUwoE). Study 2 (confirmatory study): Validated the JCM's relevance in this context and confirmed the significance of PUwE and PUwoE with a sample of 346 employees. This research expands the JCM by revealing new dimensions introduced by robots, such as altered skill variety and task identity, and explores their psychological impact on job satisfaction and performance. It lays the groundwork for future studies on job design and technology, offering a methodological framework to examine the influence of AI and emerging technologies on workplace dynamics.
About the speaker:
Dr. Robert is a Professor of Information and Robotics at the University of Michigan, affiliated with the School of Information and the College of Engineering Robotics Department. His research focuses on collaboration through and with technology. He holds the titles of ACM Distinguished Member, AIS Distinguished Member 'Cum Laude,' and Senior Member of INFORMS and IEEE. Dr. Robert directs the Michigan Autonomous Vehicle Research Intergroup Collaboration (MAVRIC), part of the university's Robotics Institute and the National Center for Institutional Diversity,
Watch on YouTube ↗
(saves to browser)
Sign in to unlock AI tutor explanation · ⚡30
Playlist
Uploads from Stanford Online · Stanford Online · 0 of 60
← Previous
Next →
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
Statistical Learning: 13.2 Introduction to Multiple Testing and Family Wise Error Rate
Stanford Online
Statistical Learning: 13.1 Introduction to Hypothesis Testing II
Stanford Online
Statistical Learning: 12.R.3 Hierarchical Clustering
Stanford Online
Statistical Learning: 12.R.2 K means Clustering
Stanford Online
Statistical Learning: 12.R.1 Principal Components
Stanford Online
Statistical Learning: 13.R.1 Bonferroni and Holm II
Stanford Online
Statistical Learning: 12.6 Breast Cancer Example
Stanford Online
Statistical Learning: 12.5 Matrix Completion
Stanford Online
Statistical Learning: 12.4 Hierarchical Clustering
Stanford Online
Statistical Learning: 12.3 k means Clustering
Stanford Online
Statistical Learning: 13.1 Introduction to Hypothesis Testing
Stanford Online
Stanford Seminar - Introduction to Web3
Stanford Online
Stanford Seminar - Designing Equitable Online Experiences
Stanford Online
Stanford CS330: Deep Multi-Task & Meta Learning I 2021 I Lecture 1
Stanford Online
Stanford Seminar - Perceiving, Understanding, and Interacting through Touch
Stanford Online
Stanford CS330: Deep Multi-task & Meta Learning I 2021 I Lecture 2
Stanford Online
Stanford CS330: Deep Multi-task & Meta Learning I 2021 I Lecture 3
Stanford Online
Stanford CS330: Deep Multi-Task & Meta Learning I 2021 I Lecture 4
Stanford Online
Stanford CS330: Deep Multi-task & Meta Learning I 2021 I Lecture 5
Stanford Online
Stanford Seminar - Evolution of a Web3 Company
Stanford Online
Stanford CS330: Deep Multi-task & Meta Learning I 2021 I Lecture 6
Stanford Online
Stanford CS330: Deep Multi-task & Meta Learning I 2021 I Lecture 7
Stanford Online
Stanford CS330: Deep Multi-task & Meta Learning I 2021 I Lecture 8
Stanford Online
Stanford Seminar - Designing Human-Centered AI Systems for Human-AI Collaboration
Stanford Online
The Sh*tFixers: Bob Sutton Interviews David Kelley, Design Thinking Superstar
Stanford Online
Stanford CS330: Deep Multi-task & Meta Learning I 2021 I Lecture 9
Stanford Online
Women Rise: Sheri Sheppard
Stanford Online
Stanford CS330: Deep Multi-task & Meta Learning I 2021 I Lecture 10
Stanford Online
Stanford CS330: Deep Multi-task & Meta Learning I 2021 I Lecture 11
Stanford Online
Stanford CS330: Deep Multi-task & Meta Learning I 2021 I Lecture 12
Stanford Online
Stanford CS330: Deep Multi-task & Meta Learning I 2021 I Lecture 13
Stanford Online
Stanford CS330: Deep Multi-task & Meta Learning I 2021 I Lecture 14
Stanford Online
Stanford Webinar - Cloud Computing: What’s on the Horizon with Dr. Timothy Chou
Stanford Online
Stanford CS330: Deep Multi-task & Meta Learning I 2021 I Lecture 15
Stanford Online
Stanford Seminar - Multi-Sensory Neural Objects: Modeling, Inference, and Applications in Robotics
Stanford Online
Stanford CS330: Deep Multi-task & Meta Learning I 2021 I Lecture 16
Stanford Online
Stanford Seminar - Toward Better Human-AI Group Decisions
Stanford Online
Stanford CS330: Deep Multi-Task & Meta Learning I 2021 I Lecture 17
Stanford Online
Stanford CS330: Deep Multi-Task & Meta Learning I 2021 I Lecture 18
Stanford Online
Stanford Webinar - Web3 Considered: Possible Futures for Decentralization and Digital Ownership
Stanford Online
Stanford Seminar - Ethics Governance-in-the-Making: Bridging Ethics Work & Governance Menlo Report
Stanford Online
Stanford Seminar - Towards Generalizable Autonomy: Duality of Discovery & Bias
Stanford Online
Stanford Seminar - ML Explainability Part 1 I Overview and Motivation for Explainability
Stanford Online
Stanford Seminar - ML Explainability Part 2 I Inherently Interpretable Models
Stanford Online
Stanford Seminar - ML Explainability Part 3 I Post hoc Explanation Methods
Stanford Online
Kratika Gupta talks about Stanford's Product Management Program
Stanford Online
Stanford Seminar - Making Teamwork an Objective Discipline - Sid Sijbrandij CEO & Chairman of GitLab
Stanford Online
Stanford Seminar - ML Explainability Part 4 I Evaluating Model Interpretations/Explanations
Stanford Online
Stanford Seminar - Adaptable Robotic Manipulation Using Tactile Sensors
Stanford Online
Stanford Seminar - ML Explainability Part 5 I Future of Model Understanding
Stanford Online
Meet Joe Lapin, Innovation and Entrepreneurship Program Completer
Stanford Online
Stanford Seminar: Social Media Scrutiny of Frontline Professionals & Implications for Accountability
Stanford Online
Stanford Seminar - Alphy and Alphy Reflect: creating a reflective mirror to advance women
Stanford Online
Stanford Webinar - The Digital Future of Health
Stanford Online
Stanford CS229M - Lecture 1: Overview, supervised learning, empirical risk minimization
Stanford Online
Stanford CS229M - Lecture 2: Asymptotic analysis, uniform convergence, Hoeffding inequality
Stanford Online
Stanford CS229M - Lecture 3: Finite hypothesis class, discretizing infinite hypothesis space
Stanford Online
Stanford Seminar - Decentralized Finance (DeFi)
Stanford Online
Stanford CS229M - Lecture 4: Advanced concentration inequalities
Stanford Online
Stanford Seminar - Bridging AI & HCI: Incorporating Human Values into the Development of AI Tech
Stanford Online
More on: Agent Foundations
View skill →Related AI Lessons
⚡
⚡
⚡
⚡
Operational continuity is not governability.
Medium · AI
I Asked an AI to Read My Blood Test. What It Told Me That My Doctor Didn’t Have Time To.
Medium · AI
Cursor SDK, Composer 2 e a nova economia dos agentes de código
Dev.to · Moprius
The AI Bridge Problem: Why Enterprise AI Integration Is an Architecture Challenge, Not an AI Challenge
Dev.to AI
🎓
Tutor Explanation
DeepCamp AI