Algorithmic Control in Non-Platform Organizations – Workers' Legitimacy Judgments and the Impact of Individual Character Traits
Felix Hirsch
This study investigates how employees in traditional, non-platform companies perceive algorithmic control (AC) systems that manage their work. Using fuzzy-set Qualitative Comparative Analysis (fsQCA), it specifically examines how a worker's individual competitiveness influences whether they judge these systems as legitimate in terms of fairness, autonomy, and professional development.
Problem
While the use of algorithms to manage workers is expanding from the platform economy to traditional organizations, little is known about why employees react so differently to it. Existing research has focused on organizational factors, largely neglecting how individual personality traits impact workers' acceptance and judgment of these new management systems.
Outcome
- A worker's personality, specifically their competitiveness, is a major factor in how they perceive algorithmic management. - Competitive workers generally judge algorithmic control positively, particularly in relation to fairness, autonomy, and competence development. - Non-competitive workers tend to have negative judgments towards algorithmic systems, often rejecting them as unhelpful for their professional growth. - The findings show a clear distinction: competitive workers see AC as fair, especially rating systems, while non-competitive workers view it as unfair.
Host: Welcome to A.I.S. Insights, the podcast at the intersection of business and technology, powered by Living Knowledge. I’m your host, Anna Ivy Summers. Host: Today, we’re looking at a fascinating shift in the workplace. We all know about algorithms managing gig workers, but what happens when this A.I. boss shows up in a traditional office or warehouse? Host: We’re diving into a study titled "Algorithmic Control in Non-Platform Organizations – Workers' Legitimacy Judgments and the Impact of Individual Character Traits." It explores how employees in traditional companies perceive these systems and, crucially, how their personality affects whether they see this new form of management as legitimate. Host: To help us unpack this, we have our expert analyst, Alex Ian Sutherland. Welcome, Alex. Expert: Great to be here, Anna. Host: So, Alex, set the scene for us. What's the big problem this study is trying to solve? Expert: The problem is that as algorithmic management expands beyond the Ubers and Lyfts of the world into logistics, retail, and even professional services, we're seeing very different reactions from employees. Some embrace it, some resist it. Expert: Businesses are left wondering why a system that boosts productivity in one team causes morale to plummet in another. Most of the focus has been on the technology itself, but this study points out that we've been neglecting a huge piece of the puzzle: the individual worker. Host: You mean their personality? Expert: Exactly. The study argues that who the employee is as a person—specifically, how competitive they are—is a critical factor in whether they accept or reject being managed by an algorithm. Host: That’s a really interesting angle. So how did the researchers actually study this connection? Expert: They surveyed 92 workers from logistics and warehousing centers, which are prime examples of where these algorithmic systems are already in heavy use. Expert: They used a sophisticated method that goes beyond simple correlation to identify complex patterns. It essentially allowed them to see which specific combinations of algorithmic control—like monitoring, rating, or recommending tasks—and worker competitiveness lead to a positive judgment on things like fairness and autonomy. Host: And what were those key findings? Is there a specific type of person who thrives under an A.I. manager? Expert: There absolutely is. The clearest finding is that a worker’s personality, particularly their competitiveness, is a major predictor of how they perceive algorithmic management. Host: Let me guess, competitive people love it? Expert: You've got it. Competitive workers generally judge these systems very positively. They tend to see algorithmic rating systems, like leaderboards, as fair. They feel it gives them more autonomy and helps them develop their skills by providing clear feedback and recommendations for improvement. Host: And what about their less competitive colleagues? Expert: It’s the polar opposite. Non-competitive workers tend to have negative judgments. They often reject the systems, especially in relation to their own professional growth. They don't see the algorithm as a helpful coach; they see it as an unfair judge. That same rating system a competitive person finds motivating, they perceive as deeply unfair. Host: That’s a stark difference. So, Alex, this brings us to the most important question for our listeners. What does this all mean for business leaders? Why does this matter? Expert: It matters immensely. The biggest takeaway is that there is no 'one-size-fits-all' solution when it comes to algorithmic management. A company can't just buy a piece of software and expect it to work for everyone. Host: So what should they be doing instead? Expert: First, they need to think about system design. The study suggests that just as human managers adapt their style to different employees, algorithmic systems need to be designed with that same flexibility. Expert: For a sales team full of competitive people, a public leaderboard might be fantastic. But for a collaborative, creative team, the system should probably focus more on providing helpful recommendations rather than constant ratings. Host: That makes sense. Are there any hidden risks leaders should be aware of? Expert: Yes, a big one. The study warns that if your system only rewards and promotes competitive behavior, you risk creating a self-reinforcing cycle. Non-competitive workers may become disengaged or even leave. Over time, you could unintentionally build a hyper-competitive, high-turnover culture and lose a diversity of thought and work styles. Host: It sounds like the human manager isn't obsolete just yet. Expert: Far from it. Their role becomes even more critical. They need to be the bridge between the algorithm and the employee, understanding who needs encouragement and who thrives on the data-driven competition the system provides. Host: Fantastic insights. Let’s quickly summarize. Algorithmic management is making its way into traditional companies, but its success isn't guaranteed. Host: Employee acceptance depends heavily on individual personality, especially competitiveness. Competitive workers tend to see these systems as fair and helpful, while non-competitive workers often see them as the opposite. Host: For businesses, this means ditching the one-size-fits-all approach and designing flexible systems that account for the diverse nature of their workforce. Host: Alex Ian Sutherland, thank you so much for breaking down this complex topic for us. Expert: My pleasure, Anna. Host: And thanks to all of you for tuning in to A.I.S. Insights. Join us next time as we continue to explore the latest in business and technology.
