Making Artificial Intelligence Work at Work: The Role of Human Resource Practices and Personal Attitudes in Fostering Meaningful Work with Artificial Intelligence
How can employees help shape an AI rollout they are expected to trust?
Parenting Journal · Work & family · Volume 1 · Issue 3
Trust
Reliable support, transparent decisions and relationships where people can speak honestly.
20 articles · Page 1 of 1
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Governing algorithms, empowering people: how ethical AI oversight shapes trust, technostress, and employee autonomy in AI-enabled HRM
Who can explain or review an AI decision that affects my work?
How Does AI Trust Foster Innovative Performance Under Paternalistic Leadership? The Roles of AI Crafting and Leader’s AI Opportunity Perception
When does trust in AI become confidence to reshape my work?
Competitive organizational climate and artificial intelligence (AI) acceptance: the moderating role of leaders’ power construal
Can responsible leadership make AI adoption feel less like a race?
Exploring how AI adoption in the workplace affects employees: a bibliometric and systematic review
What evidence should an organization examine before promising a better working life with AI?
Navigating the Human Side of AI: A Socio-Technical Model of Employee Attitudes Toward Algorithmic Recruitment
What would make an AI hiring process understandable to the people it affects?
The impact of artificial intelligence application on employees' job insecurity: the moderating roles of self-efficacy and transformational leadership
What can leaders communicate honestly about AI and job security?
Employees’ Appraisals and Trust of Artificial Intelligences’ Transparency and Opacity
What information would help me judge an AI decision instead of simply accepting it?
Worker Perspectives on Incorporating Artificial Intelligence into Office Workspaces: Implications for the Future of Office Work
Would an AI-adjusted office feel helpful, intrusive, or both?
Health Care Employees’ Perceptions of the Use of Artificial Intelligence Applications: Survey Study
How much do employees know about the AI they are being asked to trust?
From doubt to trust: how AI safety voice bridges the gap between employees and AI teammates through perceived capability and benevolence
Will an employee’s AI safety concern be heard and acted on?
Applying ethics to AI in the workplace: the design of a scorecard for Australian workplace health and safety
What should an employer be able to show before calling a workplace AI system safe?
Ethics training competencies and leadership enable responsible AI in hospitality and tourism
How can an organization turn AI ethics training into accountable everyday practice?
From Tool to Social Actor: A Systematic Review of the Psychological Mechanisms Through Which Conversational AI Reshapes the Employee Experience
When does a helpful AI tool start to feel like a colleague or a supervisor?
When algorithmic managers fail to fulfill their promises: The role of anthropomorphism in shaping justice perceptions
Who is accountable when an algorithmic manager fails to keep a promise?
The Impact of Perceived Algorithmic Control on Gig Workers’ Turnover Intention: Mediating Roles of Perceived Usefulness and Psychological Contract Breach
What promises do workers believe a platform has made to them?
Time-Based Stress and Procedural Justice: Can Transparency Mitigate the Effects of Algorithmic Compensation in Gig Work?
Can I understand and question how an algorithm calculates my pay?
Attitudinal openness to AI as supervisor or colleague/subordinate in Japan: the role of advanced digital technology familiarity
Would I feel differently about AI as a colleague than as my supervisor?
Insights into the relationship between usability and willingness to use a robot in the future workplaces: Studying the mediating role of trust and the moderating roles of age and STARA
What makes an industrial robot a colleague people are willing to work beside?
How Smart Technology Affects the Well-Being and Supportive Learning Performance of Logistics Employees?
What happens to trust in an employer when new technology changes how people learn?
Study designs and age labels describe the source. Read each study’s findings and limitations before applying them to your family.