Research Highlight
AI-Driven Job Insecurity
Artificial Intelligence (AI) is revolutionizing various industries by performing tasks traditionally done by humans, such as learning, interacting, and problem-solving. While initially used for automating routine tasks in operations and logistics, AI advancements now enable its application in managerial tasks. Major companies like Amazon, IBM, Google, Microsoft, and Tesla leverage AI for supply chain optimization, predictive maintenance, targeted advertising, cybersecurity, and autonomous driving.
The promise of AI includes increased efficiency and productivity, from diagnosing diseases accurately to transforming retail operations. However, the rapid integration of AI in workplaces has raised significant concerns about job security. The fear of widespread job displacement looms large as AI continues to automate tasks previously performed by humans.
Studies have shown the potential impact of AI on job security. Frey and Osborne estimate that nearly 47% of US jobs are at risk due to computerization. Acemoglu and Restrepo found that increased use of industrial robots negatively affects employment and wages in US labor markets. Similarly, Brougham and Haar predict that a third of current jobs could be automated by 2025, causing significant job insecurity among employees.
A survey by Qualtrics revealed that over two-thirds of employees believe some jobs are at risk due to AI, with 23% fearing for their own positions. This anxiety is reflected across various sectors, especially as AI technologies become more prevalent. The rise of AI has also highlighted a growing skills gap, contributing to increased employee stress and job insecurity.
The American Psychological Association found that nearly 38% of US workers worry that AI might render their job duties obsolete in the future. The World Economic Forum predicts significant changes in global employment within the next five years, including the creation of 69 million new jobs and the elimination of 83 million jobs, particularly in clerical and secretarial roles.
The implications of AI for job security go beyond automation and direct job replacements. AI is expected to create interconnected organizations, intensify global competition, and significantly shift how goods and services are produced and consumed. However, this transformation also brings risks such as increased unemployment and greater wealth inequalities, posing significant challenges for employees.
In conclusion, while AI holds the potential to significantly enhance efficiency and productivity, it also raises substantial concerns about job security. Understanding these dynamics is crucial for navigating the future of work in an AI-driven world. My current research examines how trust dynamics between employees and the organization can mitigate AI-driven job insecurity. This approach promotes employee well-being and enhances performance, ultimately benefiting both employees and the organization.
References
• Acemoglu, D., & Restrepo, P. (2020). Robots and jobs: Evidence from US labor markets. Journal of Political Economy, 128(6), 2188-2244.
• Allgor, R., Cezik, T., & Chen, D. (2023). Algorithm for robotic picking in Amazon fulfillment centers enables humans and robots to work together effectively. INFORMS Journal on Applied Analytics, 53(4), 266-282.
• Brougham, D., & Haar, J. (2018). Smart technology, artificial intelligence, robotics, and algorithms (STARA): Employees’ perceptions of our future workplace. Journal of Management & Organization, 24(2), 239-257.
• Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. WW Norton & Company.
• Brynjolfsson, E., & Mcafee, A. (2017). Artificial intelligence, for real. Harvard Business Review, 1, 1-31.
• Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254-280.
• Gao, B., Wang, Y., Xie, H., Hu, Y., & Hu, Y. (2023). Artificial intelligence in advertising: advancements, challenges, and ethical considerations in targeting, personalization, content creation, and ad optimization. Sage Open, 13(4), 21582440231210759.
• Huang, M.-H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155-172.
• Law, M. (2023). The Top 10 predictive maintenance companies using AI. AI Magazine.
• Makridakis, S. (2017). The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures, 90, 46-60.
Teaching Highlight
Experiential Learning in Business Education
Experiential learning is a cornerstone of effective business education, providing students with the opportunity to apply theoretical concepts to real-world scenarios. This hands-on approach to learning bridges the gap between classroom instruction and practical application, ensuring that students gain the skills and confidence needed to succeed in their careers.
In my classes, experiential learning is integrated through a variety of methods. Case studies are used extensively to analyze real business challenges, allowing students to explore multiple perspectives and develop strategic solutions. These case studies are often drawn from actual business situations, providing a realistic context for students to practice their problem-solving skills.
Another key component of experiential learning in my courses is the use of simulations. Business simulations replicate the complexities of running a company, giving students the chance to make decisions in a risk-free environment. These simulations cover various aspects of business, from marketing and finance to operations and human resources, offering a comprehensive learning experience.
Team-based projects are also a fundamental part of my teaching methodology. Working in groups, students tackle business problems that require collaboration, critical thinking, and effective communication. These projects not only enhance their technical skills but also build essential soft skills such as leadership, teamwork, and adaptability.
Guest lectures and industry partnerships further enrich the experiential learning process. By inviting business leaders and professionals to share their insights and experiences, students gain valuable exposure to current industry practices and trends. These interactions help to contextualize their learning and provide networking opportunities that can be beneficial for their future careers.
Moreover, experiential learning fosters a deeper understanding of course material by encouraging active engagement and reflection. Students are not passive recipients of information; instead, they actively participate in their learning journey, making connections between theory and practice. This active involvement leads to greater retention of knowledge and a more meaningful educational experience.
In conclusion, experiential learning is an essential element of business education, transforming theoretical knowledge into practical skills. By incorporating case studies, simulations, team projects, and industry interactions into my teaching, I strive to create a dynamic and impactful learning environment that prepares students for the complexities of the business world.
Further resources:
• Dewey, J. (1938). Experience and Education. New York: Macmillan.
• Kolb, D. A. (1984). Experiential Learning: Experience as the Source of Learning and Development. Englewood Cliffs, NJ: Prentice-Hall.
• Kolb, D. A., Boyatzis, R. E., & Mainemelis, C. (2014). Experiential learning theory: Previous research and new directions. In Perspectives on thinking, learning, and cognitive styles (pp. 227-247). Routledge.
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