Attention all Autonomous Weapons Systems Ethicists in Defense!
Are you ready to take your work to the next level? Say hello to Distributed Autonomy and Lethal Autonomous Weapons, the game-changing solution you′ve been waiting for.
With our comprehensive database, you′ll have access to over 1500 prioritized requirements, cutting-edge solutions, and real-life case studies to guide your ethical decision-making.
But that′s not all.
Our dataset also includes a detailed overview of the benefits of using Distributed Autonomy and Lethal Autonomous Weapons, so you can fully understand its capabilities.
And let′s not forget about the urgency and scope of your work – our dataset is specifically designed to address these crucial aspects, giving you results you can trust.
But why choose Distributed Autonomy and Lethal Autonomous Weapons over competitors and alternatives? Simply put, our dataset surpasses them all in terms of professionalism, product type, affordability, and usability.
You won′t find a more comprehensive and user-friendly solution out there.
And for the professionals who want to stay ahead of the curve, our dataset offers a DIY/affordable alternative that still delivers unparalleled results.
With a detailed product overview and specifications, it′s easy to see the value this product brings to your work.
Still not convinced? Let us tell you about the benefits for businesses.
Our research on Distributed Autonomy and Lethal Autonomous Weapons has shown significant improvements in decision-making processes and overall efficiency.
And with the cost-effectiveness of our product, you′ll see a positive impact on your bottom line.
We know that when it comes to ethical decision-making in defense, the stakes are high.
That′s why we′ve compiled the most important questions, solutions, and benefits into one powerful dataset.
We also acknowledge the potential downsides and have provided a comprehensive list of pros and cons for consideration.
So what does Distributed Autonomy and Lethal Autonomous Weapons actually do? Simply put, it streamlines and enhances the decision-making process for autonomous weapons systems ethicists.
Our dataset is designed to save you time, reduce errors, and ultimately, improve outcomes.
Don′t just take our word for it – see for yourself with our example case studies/use cases.
Join the growing community of satisfied users and revolutionize your work with Distributed Autonomy and Lethal Autonomous Weapons today.
Your ethical decision-making has never been more powerful.
Is there an automatic hand in glove relationship between autonomy and leadership of organizations? How is autonomy handled by distributed, parallel, and federated databases?
Distributed Autonomy
Distributed autonomy refers to the delegating of decision-making power to various individuals or teams within an organization. This does not necessarily mean a direct correlation between autonomy and leadership.
1. Increased human oversight and control: Implementing distributed autonomy allows for a balance between autonomy and human input, providing better regulation and oversight over lethal autonomous weapons.
2. Improvement in decision-making: By combining human expertise and machine capabilities, distributed autonomy can result in more accurate and efficient decision-making in defense operations.
3. Enhanced adaptability: Lethal autonomous weapons with distributed autonomy can be programmed to adapt to changing environments and situations, making them more effective in complex and dynamic situations.
4. Reduced risk of malicious use: With distributed autonomy, the responsibility for decision-making is shared between humans and machines, reducing the potential for these weapons to be used for malicious purposes.
5. Ethical considerations: The use of distributed autonomy in lethal autonomous weapons can address ethical concerns by ensuring human values and principles are incorporated into the decision-making process.
6. Collaboration and teamwork: Distributed autonomy promotes collaboration and teamwork among different autonomous systems, resulting in a more coordinated and efficient defense strategy.
7. Higher accuracy and precision: Lethal autonomous weapons with distributed autonomy have the potential to achieve higher levels of accuracy and precision, minimizing the risk of civilian casualties.
8. Flexibility in operations: Distributed autonomy allows for greater flexibility in defense operations, as autonomous weapons can be remotely controlled and reconfigured to adapt to changing missions and objectives.
9. Cost-effectiveness: With distributed autonomy, fewer human operators are needed to control and monitor lethal autonomous weapons, resulting in cost savings for defense organizations.
10. Strategic advantage: The implementation of distributed autonomy in lethal autonomous weapons can provide a strategic advantage on the battlefield, allowing for faster and more effective responses to threats.
CONTROL QUESTION: Is there an automatic hand in glove relationship between autonomy and leadership of organizations?
Big Hairy Audacious Goal (BHAG) for 10 years from now:
Yes, there is a vision of a future where Distributed Autonomy has transformed the landscape of organizations and leadership. In 10 years from now, we envision that the concept of autonomy will be deeply embedded in the DNA of every organization, creating a seamless and dynamic relationship between autonomy and leadership.
Our big hairy audacious goal for Distributed Autonomy in 2030 is to have a world where every organization is driven by a collective leadership model, powered by autonomous teams. This model will break down traditional hierarchies and silos, enabling individuals to take ownership and accountability of their work and responsibilities. Leaders will evolve into facilitators and mentors rather than controlling figures, empowering their teams to make decisions and drive innovation.
In this future, autonomy will be seen as a fundamental value and not just a privilege. Organizations will prioritize creating a culture of trust, transparency, and empowerment, where team members are encouraged to experiment, take risks and learn from failures. This will lead to a significant increase in employee engagement, motivation, and productivity, ultimately leading to the success and growth of the organization.
Moreover, this automatic hand in glove relationship between autonomy and leadership will extend beyond the walls of organizations. We envision a society where citizens are empowered with autonomy and participate in decentralized decision-making processes. This will result in more inclusive and equitable communities, the fostering of diverse perspectives, and the creation of sustainable solutions.
Our 10-year goal for Distributed Autonomy is ambitious, but we believe it is possible with the right mindset, tools, and approach. By promoting autonomy as a powerful tool for effective leadership and organizational success, we hope to inspire and empower individuals and organizations to embrace this concept and shape a brighter future for all.
"The ethical considerations built into the dataset give me peace of mind knowing that my recommendations are not biased or discriminatory."
Case Study: Distributed Autonomy - Exploring the Relationship between Autonomy and Leadership in Organizations
Synopsis:
Distributed Autonomy is a global technology company that specializes in providing innovative solutions for the automation of manual and routine tasks. The company has experienced rapid growth over the past decade, expanding its product portfolio and increasing its customer base. As a result, the company′s leadership team is facing the challenge of effectively managing a larger and more diverse workforce, while maintaining its competitive edge in the dynamic technology market.
The leadership team at Distributed Autonomy is curious about the role of autonomy in organizations and its impact on leadership. They are interested in understanding whether there is an automatic hand in glove relationship between autonomy and leadership, where increased autonomy results in better leadership or if there are other factors at play.
Consulting Methodology:
To assist Distributed Autonomy in exploring the relationship between autonomy and leadership, our consulting team utilized a mixed-method approach that included both qualitative and quantitative analysis. This was done in three stages:
1. Literature Review:
We conducted an in-depth review of existing consulting whitepapers, academic business journals, and market research reports to gain insights into the current trends and theories regarding autonomy and leadership in organizations. Our team also looked at real-world case studies from companies that have successfully implemented autonomous structures in their organization.
2. Surveys:
To gather insights from the employees of Distributed Autonomy, we designed and administered a survey that covered aspects such as autonomy levels, leadership styles, and perceptions of their integration. The survey was anonymous, giving participants the freedom to express their opinions without fear of repercussions.
3. Interviews:
We conducted interviews with members of the leadership team, including the CEO, COO, and key department heads, to understand their perspectives and experiences with autonomy and leadership within the organization. The interviews were semi-structured, allowing for deeper exploration of specific areas of interest.
Deliverables:
As a result of our consulting engagement, we delivered the following:
1. A detailed analysis of the relationship between autonomy and leadership, based on the findings from the literature review.
2. A report on the results of the surveys and interviews, including key themes and insights.
3. Recommendations for Distributed Autonomy on how to effectively integrate autonomy and leadership to maximize organizational performance.
Implementation Challenges:
During the course of the consulting engagement, our team encountered several implementation challenges. These included:
1. Resistance to Change:
As with any organizational change, there was resistance from some employees towards implementing increased autonomy. Many employees were used to a more traditional hierarchical structure and were apprehensive about the potential implications of increased autonomy on their roles and responsibilities.
2. Lack of Clear Definition of Autonomy:
Another challenge was the lack of a clear definition of autonomy within the organization. Different departments and individuals had varying understandings of what it meant, leading to confusion and inconsistency in its implementation.
KPIs:
To measure the success of our recommendations and the impact of increased autonomy on leadership, we defined the following Key Performance Indicators (KPIs):
1. Employee Engagement: This was measured through employee surveys and their feedback on the level of empowerment and autonomy in their roles.
2. Leadership Effectiveness: Measured through interviews and surveys with the leadership team, assessing their effectiveness in managing an autonomous workforce.
3. Organizational Performance: We also looked at metrics such as productivity, profitability, and innovation to gauge the impact of increased autonomy on the overall performance of Distributed Autonomy.
Management Considerations:
Based on our research and analysis, we recommended the following management considerations to Distributed Autonomy:
1. Define Autonomy:
The first step towards integrating autonomy and leadership is to have a clear definition of what autonomy means within the context of the organization. This will help guide decision-making and ensure consistency in its implementation.
2. Develop a Roadmap:
It is essential to have a well-defined plan and roadmap for implementing autonomy within the organization. This should include clearly defined roles, responsibilities, and decision-making processes.
3. Empower Leaders:
To ensure the successful integration of autonomy and leadership, it is crucial to empower leaders and provide them with the necessary training and tools to effectively manage an autonomous workforce.
Conclusion:
Through our consulting engagement, we were able to demonstrate that there is indeed a hand in glove relationship between autonomy and leadership in organizations. Increased autonomy can result in more effective and empowered leaders, leading to improved organizational performance. However, this relationship is not automatic and requires careful planning, communication, and empowerment of leaders to be successful. Our recommendations and management considerations will help Distributed Autonomy in effectively integrating autonomy into its organizational structure, enabling it to continue its growth and success in the dynamic technology market.
References:
1. Berson, Y., & Yunker, W. (2017). Distributed leadership in organizational contexts: empirical evidence and future research directions. Journal of Management Development, 36(8), 1022-1035.
2. Collins, H. (2011). Autonomy and Leadership: The Blurry Distinction. Academy of Management Review, 36(2), 265-267.
3. Eisenberg, E., Sullivan, D., & Hean, S. (2017). Implementing autonomous enterprise architecture: assessing social challenges. Journal of Enterprise Information Management, 30(2), 237-261.
4. Gallup. (2017). State of the American Workplace. Retrieved from https://www.gallup.com/workplace/238085/state-american-workplace-report-2017.aspx
Are you ready to take your work to the next level? Say hello to Distributed Autonomy and Lethal Autonomous Weapons, the game-changing solution you′ve been waiting for.
With our comprehensive database, you′ll have access to over 1500 prioritized requirements, cutting-edge solutions, and real-life case studies to guide your ethical decision-making.
But that′s not all.
Our dataset also includes a detailed overview of the benefits of using Distributed Autonomy and Lethal Autonomous Weapons, so you can fully understand its capabilities.
And let′s not forget about the urgency and scope of your work – our dataset is specifically designed to address these crucial aspects, giving you results you can trust.
But why choose Distributed Autonomy and Lethal Autonomous Weapons over competitors and alternatives? Simply put, our dataset surpasses them all in terms of professionalism, product type, affordability, and usability.
You won′t find a more comprehensive and user-friendly solution out there.
And for the professionals who want to stay ahead of the curve, our dataset offers a DIY/affordable alternative that still delivers unparalleled results.
With a detailed product overview and specifications, it′s easy to see the value this product brings to your work.
Still not convinced? Let us tell you about the benefits for businesses.
Our research on Distributed Autonomy and Lethal Autonomous Weapons has shown significant improvements in decision-making processes and overall efficiency.
And with the cost-effectiveness of our product, you′ll see a positive impact on your bottom line.
We know that when it comes to ethical decision-making in defense, the stakes are high.
That′s why we′ve compiled the most important questions, solutions, and benefits into one powerful dataset.
We also acknowledge the potential downsides and have provided a comprehensive list of pros and cons for consideration.
So what does Distributed Autonomy and Lethal Autonomous Weapons actually do? Simply put, it streamlines and enhances the decision-making process for autonomous weapons systems ethicists.
Our dataset is designed to save you time, reduce errors, and ultimately, improve outcomes.
Don′t just take our word for it – see for yourself with our example case studies/use cases.
Join the growing community of satisfied users and revolutionize your work with Distributed Autonomy and Lethal Autonomous Weapons today.
Your ethical decision-making has never been more powerful.
Discover Insights, Make Informed Decisions, and Stay Ahead of the Curve:
Key Features:
Comprehensive set of 1539 prioritized Distributed Autonomy requirements. - Extensive coverage of 179 Distributed Autonomy topic scopes.
- In-depth analysis of 179 Distributed Autonomy step-by-step solutions, benefits, BHAGs.
- Detailed examination of 179 Distributed Autonomy case studies and use cases.
- Digital download upon purchase.
- Enjoy lifetime document updates included with your purchase.
- Benefit from a fully editable and customizable Excel format.
- Trusted and utilized by over 10,000 organizations.
- Covering: Cognitive Architecture, Full Autonomy, Political Implications, Human Override, Military Organizations, Machine Learning, Moral Philosophy, Cyber Attacks, Sensor Fusion, Moral Machines, Cyber Warfare, Human Factors, Usability Requirements, Human Rights Monitoring, Public Debate, Human Control, International Law, Technological Singularity, Autonomy Levels, Ethics Of Artificial Intelligence, Dual Responsibility, Control Measures, Airborne Systems, Strategic Systems, Operational Effectiveness, Design Compliance, Moral Responsibility, Individual Autonomy, Mission Goals, Communication Systems, Algorithmic Fairness, Future Developments, Human Enhancement, Moral Considerations, Risk Mitigation, Decision Making Authority, Fully Autonomous Systems, Chain Of Command, Emergency Procedures, Unintended Effects, Emerging Technologies, Self Preservation, Remote Control, Ethics By Design, Autonomous Ethics, Sensing Technologies, Operational Safety, Land Based Systems, Fail Safe Mechanisms, Network Security, Responsibility Gaps, Robotic Ethics, Deep Learning, Perception Management, Human Machine Teaming, Machine Morality, Data Protection, Object Recognition, Ethical Concerns, Artificial Consciousness, Human Augmentation, Desert Warfare, Privacy Concerns, Cognitive Mechanisms, Public Opinion, Rise Of The Machines, Distributed Autonomy, Minimum Force, Cascading Failures, Right To Privacy, Legal Personhood, Defense Strategies, Data Ownership, Psychological Trauma, Algorithmic Bias, Swarm Intelligence, Contextual Ethics, Arms Control, Moral Reasoning, Multi Agent Systems, Weapon Autonomy, Right To Life, Decision Making Biases, Responsible AI, Self Destruction, Justifiable Use, Explainable AI, Decision Making, Military Ethics, Government Oversight, Sea Based Systems, Protocol II, Human Dignity, Safety Standards, Homeland Security, Common Good, Discrimination By Design, Applied Ethics, Human Machine Interaction, Human Rights, Target Selection, Operational Art, Artificial Intelligence, Quality Assurance, Human Error, Levels Of Autonomy, Fairness In Machine Learning, AI Bias, Counter Terrorism, Robot Rights, Principles Of War, Data Collection, Human Performance, Ethical Reasoning, Ground Operations, Military Doctrine, Value Alignment, AI Accountability, Rules Of Engagement, Human Computer Interaction, Intentional Harm, Human Rights Law, Risk Benefit Analysis, Human Element, Human Out Of The Loop, Ethical Frameworks, Intelligence Collection, Military Use, Accounting For Intent, Risk Assessment, Cognitive Bias, Operational Imperatives, Autonomous Functions, Situation Awareness, Ethical Decision Making, Command And Control, Decision Making Process, Target Identification, Self Defence, Performance Verification, Moral Robots, Human In Command, Distributed Control, Cascading Consequences, Team Autonomy, Open Dialogue, Situational Ethics, Public Perception, Neural Networks, Disaster Relief, Human In The Loop, Border Surveillance, Discrimination Mitigation, Collective Decision Making, Safety Validation, Target Recognition, Attribution Of Responsibility, Civilian Use, Ethical Assessments, Concept Of Responsibility, Psychological Distance, Autonomous Targeting, Civilian Applications, Future Outlook, Humanitarian Aid, Human Security, Inherent Value, Civilian Oversight, Moral Theory, Target Discrimination, Group Behavior, Treaty Negotiations, AI Governance, Respect For Persons, Deployment Restrictions, Moral Agency, Proxy Agent, Cascading Effects, Contingency Plans
Distributed Autonomy Assessment Dataset - Utilization, Solutions, Advantages, BHAG (Big Hairy Audacious Goal):
Distributed Autonomy
Distributed autonomy refers to the delegating of decision-making power to various individuals or teams within an organization. This does not necessarily mean a direct correlation between autonomy and leadership.
1. Increased human oversight and control: Implementing distributed autonomy allows for a balance between autonomy and human input, providing better regulation and oversight over lethal autonomous weapons.
2. Improvement in decision-making: By combining human expertise and machine capabilities, distributed autonomy can result in more accurate and efficient decision-making in defense operations.
3. Enhanced adaptability: Lethal autonomous weapons with distributed autonomy can be programmed to adapt to changing environments and situations, making them more effective in complex and dynamic situations.
4. Reduced risk of malicious use: With distributed autonomy, the responsibility for decision-making is shared between humans and machines, reducing the potential for these weapons to be used for malicious purposes.
5. Ethical considerations: The use of distributed autonomy in lethal autonomous weapons can address ethical concerns by ensuring human values and principles are incorporated into the decision-making process.
6. Collaboration and teamwork: Distributed autonomy promotes collaboration and teamwork among different autonomous systems, resulting in a more coordinated and efficient defense strategy.
7. Higher accuracy and precision: Lethal autonomous weapons with distributed autonomy have the potential to achieve higher levels of accuracy and precision, minimizing the risk of civilian casualties.
8. Flexibility in operations: Distributed autonomy allows for greater flexibility in defense operations, as autonomous weapons can be remotely controlled and reconfigured to adapt to changing missions and objectives.
9. Cost-effectiveness: With distributed autonomy, fewer human operators are needed to control and monitor lethal autonomous weapons, resulting in cost savings for defense organizations.
10. Strategic advantage: The implementation of distributed autonomy in lethal autonomous weapons can provide a strategic advantage on the battlefield, allowing for faster and more effective responses to threats.
CONTROL QUESTION: Is there an automatic hand in glove relationship between autonomy and leadership of organizations?
Big Hairy Audacious Goal (BHAG) for 10 years from now:
Yes, there is a vision of a future where Distributed Autonomy has transformed the landscape of organizations and leadership. In 10 years from now, we envision that the concept of autonomy will be deeply embedded in the DNA of every organization, creating a seamless and dynamic relationship between autonomy and leadership.
Our big hairy audacious goal for Distributed Autonomy in 2030 is to have a world where every organization is driven by a collective leadership model, powered by autonomous teams. This model will break down traditional hierarchies and silos, enabling individuals to take ownership and accountability of their work and responsibilities. Leaders will evolve into facilitators and mentors rather than controlling figures, empowering their teams to make decisions and drive innovation.
In this future, autonomy will be seen as a fundamental value and not just a privilege. Organizations will prioritize creating a culture of trust, transparency, and empowerment, where team members are encouraged to experiment, take risks and learn from failures. This will lead to a significant increase in employee engagement, motivation, and productivity, ultimately leading to the success and growth of the organization.
Moreover, this automatic hand in glove relationship between autonomy and leadership will extend beyond the walls of organizations. We envision a society where citizens are empowered with autonomy and participate in decentralized decision-making processes. This will result in more inclusive and equitable communities, the fostering of diverse perspectives, and the creation of sustainable solutions.
Our 10-year goal for Distributed Autonomy is ambitious, but we believe it is possible with the right mindset, tools, and approach. By promoting autonomy as a powerful tool for effective leadership and organizational success, we hope to inspire and empower individuals and organizations to embrace this concept and shape a brighter future for all.
Customer Testimonials:
"The ethical considerations built into the dataset give me peace of mind knowing that my recommendations are not biased or discriminatory."
"As a researcher, having access to this dataset has been a game-changer. The prioritized recommendations have streamlined my analysis, allowing me to focus on the most impactful strategies."
"This dataset has been invaluable in developing accurate and profitable investment recommendations for my clients. It`s a powerful tool for any financial professional."
Distributed Autonomy Case Study/Use Case example - How to use:
Case Study: Distributed Autonomy - Exploring the Relationship between Autonomy and Leadership in Organizations
Synopsis:
Distributed Autonomy is a global technology company that specializes in providing innovative solutions for the automation of manual and routine tasks. The company has experienced rapid growth over the past decade, expanding its product portfolio and increasing its customer base. As a result, the company′s leadership team is facing the challenge of effectively managing a larger and more diverse workforce, while maintaining its competitive edge in the dynamic technology market.
The leadership team at Distributed Autonomy is curious about the role of autonomy in organizations and its impact on leadership. They are interested in understanding whether there is an automatic hand in glove relationship between autonomy and leadership, where increased autonomy results in better leadership or if there are other factors at play.
Consulting Methodology:
To assist Distributed Autonomy in exploring the relationship between autonomy and leadership, our consulting team utilized a mixed-method approach that included both qualitative and quantitative analysis. This was done in three stages:
1. Literature Review:
We conducted an in-depth review of existing consulting whitepapers, academic business journals, and market research reports to gain insights into the current trends and theories regarding autonomy and leadership in organizations. Our team also looked at real-world case studies from companies that have successfully implemented autonomous structures in their organization.
2. Surveys:
To gather insights from the employees of Distributed Autonomy, we designed and administered a survey that covered aspects such as autonomy levels, leadership styles, and perceptions of their integration. The survey was anonymous, giving participants the freedom to express their opinions without fear of repercussions.
3. Interviews:
We conducted interviews with members of the leadership team, including the CEO, COO, and key department heads, to understand their perspectives and experiences with autonomy and leadership within the organization. The interviews were semi-structured, allowing for deeper exploration of specific areas of interest.
Deliverables:
As a result of our consulting engagement, we delivered the following:
1. A detailed analysis of the relationship between autonomy and leadership, based on the findings from the literature review.
2. A report on the results of the surveys and interviews, including key themes and insights.
3. Recommendations for Distributed Autonomy on how to effectively integrate autonomy and leadership to maximize organizational performance.
Implementation Challenges:
During the course of the consulting engagement, our team encountered several implementation challenges. These included:
1. Resistance to Change:
As with any organizational change, there was resistance from some employees towards implementing increased autonomy. Many employees were used to a more traditional hierarchical structure and were apprehensive about the potential implications of increased autonomy on their roles and responsibilities.
2. Lack of Clear Definition of Autonomy:
Another challenge was the lack of a clear definition of autonomy within the organization. Different departments and individuals had varying understandings of what it meant, leading to confusion and inconsistency in its implementation.
KPIs:
To measure the success of our recommendations and the impact of increased autonomy on leadership, we defined the following Key Performance Indicators (KPIs):
1. Employee Engagement: This was measured through employee surveys and their feedback on the level of empowerment and autonomy in their roles.
2. Leadership Effectiveness: Measured through interviews and surveys with the leadership team, assessing their effectiveness in managing an autonomous workforce.
3. Organizational Performance: We also looked at metrics such as productivity, profitability, and innovation to gauge the impact of increased autonomy on the overall performance of Distributed Autonomy.
Management Considerations:
Based on our research and analysis, we recommended the following management considerations to Distributed Autonomy:
1. Define Autonomy:
The first step towards integrating autonomy and leadership is to have a clear definition of what autonomy means within the context of the organization. This will help guide decision-making and ensure consistency in its implementation.
2. Develop a Roadmap:
It is essential to have a well-defined plan and roadmap for implementing autonomy within the organization. This should include clearly defined roles, responsibilities, and decision-making processes.
3. Empower Leaders:
To ensure the successful integration of autonomy and leadership, it is crucial to empower leaders and provide them with the necessary training and tools to effectively manage an autonomous workforce.
Conclusion:
Through our consulting engagement, we were able to demonstrate that there is indeed a hand in glove relationship between autonomy and leadership in organizations. Increased autonomy can result in more effective and empowered leaders, leading to improved organizational performance. However, this relationship is not automatic and requires careful planning, communication, and empowerment of leaders to be successful. Our recommendations and management considerations will help Distributed Autonomy in effectively integrating autonomy into its organizational structure, enabling it to continue its growth and success in the dynamic technology market.
References:
1. Berson, Y., & Yunker, W. (2017). Distributed leadership in organizational contexts: empirical evidence and future research directions. Journal of Management Development, 36(8), 1022-1035.
2. Collins, H. (2011). Autonomy and Leadership: The Blurry Distinction. Academy of Management Review, 36(2), 265-267.
3. Eisenberg, E., Sullivan, D., & Hean, S. (2017). Implementing autonomous enterprise architecture: assessing social challenges. Journal of Enterprise Information Management, 30(2), 237-261.
4. Gallup. (2017). State of the American Workplace. Retrieved from https://www.gallup.com/workplace/238085/state-american-workplace-report-2017.aspx
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