Recruiting Future with Matt Alder - What's Next For Talent Acquisition, HR & Hiring? Ep 682: What Can Talent Acquisition Learn From Network Science?
What if everything we thought about how organizations function was wrong? For decades, businesses have been structured around hierarchies and rigid processes, assuming this was the best way to manage talent, collaboration, and leadership. But what if the real key to high performance and better talent acquisition lies not in org charts but in networks? The emerging disciple of Network Science has quietly been reshaping everything from social media to healthcare, so what happens when we apply it to how we work, lead, and hire?
Organizations are complex systems, yet most companies are still managed using outdated, linear models. What if network science, a discipline that has revolutionized everything from the internet to disease control, could also transform the way we think about leadership, collaboration, and hiring
So, how do networks really shape the way organizations function, and could a deeper understanding of them change how we hire, engage, and retain talent?
My guest this week is Jeffrey Beeson, a leadership strategist and expert in organizational networks. Jeffrey’s work focuses on how businesses can optimize collaboration, unlock hidden influencers, and rethink traditional hierarchies using the principles of network science. We discuss how understanding an organization’s internal networks can improve hiring decisions, enhance onboarding, and increase retention by fostering stronger workplace connections.
In the interview, we discuss:
- What is network science, and how does it apply to business?
- How all complex systems are based on network patterns and laws
- Three degrees of influence
- How emotions travel through networks
- The best-connected individuals have the most influence.
- Identifying influencers
- Retention, onboarding, and talent management
- The application of network science to talent acquisition
- Applying network solutions to network problems
- The future of work
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