Uber’s Algorithmic Control and Organizational Culture: How to Analyze This Case
A practical guide for business and IS students writing a group case report on how Uber’s reliance on algorithms shapes — and strains — its organizational culture. Know what to argue, what models to use, and what the rubric actually wants.
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Algorithmic control refers to the use of software-driven systems — rather than human managers — to direct, monitor, evaluate, and discipline workers. At Uber, this means the app itself decides which drivers get rides, how they’re rated, when they’re deactivated, and how much they earn — all without a supervisor in the traditional sense. The algorithm is the manager.
Uber doesn’t have regional managers calling drivers to check in. It doesn’t have HR reps reviewing performance reviews with drivers. Instead, it runs on a system where every driver interaction is logged, scored, and fed into a decision engine that determines whether that driver keeps working. That’s algorithmic control in its most visible form.
But it goes further than just drivers. Internally, Uber’s culture — at the corporate level — was shaped by its own obsession with data-driven decision-making, speed over process, and a growth-at-all-costs mindset that the algorithms enabled. The Uber case forces you to look at two layers: how the algorithm controls the gig workforce, and what that reliance on algorithmic logic does to the internal organizational culture as a whole.
Getting this distinction clear in your head before you write a single word of your report is not optional. Your rubric rewards analysis, not description. And you can’t analyze something you haven’t precisely defined.
Identifying the Real Problem — Not the Symptoms
Your instructions explicitly say: “Be careful to identify the real problem and not the symptoms of the problem.” This is where most groups lose points. They write about Uber’s scandals, driver complaints, or media coverage — all symptoms — and never get to the structural issue underneath.
Here’s the distinction made concrete:
❌ These Are Symptoms
- Drivers complaining about unfair deactivations
- Scandals about Uber’s toxic internal culture (Fowler memo)
- High driver turnover rates
- Bad press about surge pricing
- Lawsuits over worker classification
- Travis Kalanick’s resignation
✓ The Real Problem
- Algorithmic control substitutes accountability structures with opacity
- Workers have no recourse, voice, or relationship with the organization
- Internally, data-driven culture eroded ethical guardrails
- Speed and scale were prioritized over people management
- Organizational culture was never deliberately designed — it emerged from system logic
- No feedback loop between worker experience and system design
The real problem isn’t that a driver got deactivated. The real problem is that Uber built an organizational model where accountability flows only one way — from worker to algorithm — with no mechanism for the organization to be accountable back to its workers. And internally, that same logic — move fast, trust the data, don’t slow down for process — produced a corporate culture where harassment went unreported and HR existed to protect the company, not employees.
Don’t Make This Mistake in Your Background Section
Describing what Uber does (ride-hailing app, founded 2009, operates in X countries) is not what the Background section is for. The rubric asks you to identify symptoms, critical factors, and the current state as relevant to the specific question — not to give a company overview. Stay focused on the algorithmic control issue from line one.
How to Write the Background Section (1 Point)
The Background section is worth 1 point — but it sets up everything that follows. A weak background makes your Analysis section feel disconnected and hard to follow. A tight background makes your analysis feel inevitable.
You have roughly half a page. That’s not much. Use it to establish three things: what Uber’s algorithmic control system looks like in practice, what symptoms have surfaced as a result, and what the critical tension is that your question asks you to analyze.
Frameworks and Models to Apply in Your Analysis
This is where you earn the 4 points for Analysis. The rubric says you must apply IS models, course content, and outside research — and do so “completely and effectively.” That means you need to name the frameworks, explain them briefly if they’re not obvious, and then actually use them to interpret Uber’s situation. Don’t just describe the model. Use it as a lens.
Here are the most applicable frameworks for this question:
Organizational Culture Frameworks (Schein / Hofstede)
Edgar Schein’s three-layer culture model — artifacts, espoused values, and underlying assumptions — gives you a structured way to analyze Uber’s culture at multiple depths. Uber’s artifacts were the algorithmic interface and its growth metrics. Its espoused values were “hustle,” efficiency, and disruption. But the underlying assumption — baked into how the system was built — was that people (drivers and employees) were inputs to be optimized, not stakeholders to be respected. That assumption produced the culture. Apply this model to show that the problem wasn’t bad people at Uber. It was a system built on a dehumanizing assumption about the relationship between the organization and its workforce.
Algorithmic Management Theory (Rosenblat / Kellogg et al.)
Alex Rosenblat’s research on Uber — detailed in her book Uberland and in peer-reviewed work — documents how algorithmic management creates an asymmetric power relationship: the system knows everything about the worker, and the worker knows almost nothing about how the system works or why it makes the decisions it does. This opacity isn’t accidental. It’s structural. Kellogg, Valentine, and Christin’s 2020 taxonomy of algorithmic management in the Academy of Management Annals is a solid academic source for this framework, and it’s directly relevant to the cultural impact question because it shows how algorithmic direction replaces trust, discretion, and relationship — the foundations of organizational culture.
Psychological Safety (Edmondson)
Amy Edmondson’s concept of psychological safety — the belief that one can speak up, raise concerns, or admit mistakes without fear of punishment — is directly relevant to both Uber’s internal culture and its driver workforce. Algorithmic control eliminates psychological safety by design. Drivers can’t question decisions without risking their ratings. Employees under Kalanick couldn’t raise HR complaints without retaliation (as Fowler documented). Apply this framework to argue that Uber’s algorithmic logic, when internalized as an organizational value, produced a culture structurally hostile to voice, dissent, and ethical feedback — with predictable consequences.
Information Systems Control Theory (Ouchi)
William Ouchi’s framework distinguishes between three types of organizational control: market control (price mechanisms), bureaucratic control (rules and hierarchy), and clan control (shared norms and values). Uber replaced bureaucratic and clan control with a fourth type — algorithmic control — that combines market efficiency with real-time behavioral monitoring. The cultural consequence is significant: clan control, which builds organizational identity and loyalty, is almost entirely absent. Workers have no shared norms, no community, no sense of belonging to the organization. The culture that results is transactional at best, adversarial at worst. This is a strong framework for explaining why Uber’s organizational culture developed the way it did.
Stakeholder Theory (Freeman)
R. Edward Freeman’s stakeholder theory argues that organizations create sustainable value by considering the interests of all stakeholders — not just shareholders. Uber’s algorithmic architecture is designed primarily to optimize shareholder and rider value. Driver interests are an afterthought at best, a liability at worst. Apply this as a normative framework in your conclusions: the cultural problems Uber experienced were, in part, the result of a stakeholder map that systematically excluded or minimized the people doing the actual work. This gives you a principled basis for your recommendations.
You Don’t Need All Five — Pick Two or Three and Go Deep
The rubric rewards complete and effective application — not a list of every framework you can think of. Pick two to three that fit your argument, explain each in one to two sentences, then spend the bulk of your Analysis showing how Uber’s actual situation maps onto the framework. Shallow coverage of five models will score lower than deep coverage of two.
Building Your Analysis Section (4 Points)
The analysis is where most of your points live — and where most reports fall short. The rubric wants you to “completely and effectively apply IS models, course content, and outside research” and to “logically discuss options, implications and tradeoffs.” That last part matters. You need to go beyond just applying a framework and actually weigh what different approaches to algorithmic control might mean.
Structure your analysis around the question: what impact does algorithmic control have on organizational culture? That means your analysis needs to connect the mechanism (the algorithm) to the cultural outcome (what the organization actually believes, does, and tolerates).
| Algorithmic Control Feature | Cultural Mechanism | Observable Cultural Outcome |
|---|---|---|
| Automated performance scoring (driver ratings) | Workers are evaluated by a system they can’t interrogate or appeal to | Culture of low trust, no voice, and adversarial worker relationship |
| Algorithmic wage-setting (surge pricing, incentive structures) | Pay feels arbitrary and manipulative — research shows drivers feel “tricked” | Disengagement, resistance strategies, perception of exploitation |
| Opacity of decision logic | Workers can’t learn, adapt, or improve — they just react | Learned helplessness; absence of development or growth culture |
| Internal data-driven culture (OKRs, growth metrics above all) | Process and ethics slow down velocity — they get deprioritized | HR dysfunction, harassment ignored, compliance treated as optional |
| Contractor classification (not employees) | Organization has no formal duty of care to its workforce | No organizational identity, no loyalty, no shared culture with workers |
The Tradeoffs You Should Discuss
Your rubric specifically asks you to discuss tradeoffs where applicable. Here are the real ones in the Uber case:
Efficiency vs. Accountability
Algorithmic control is extraordinarily efficient. It scales without adding management headcount. But it removes accountability structures that cultures need to function ethically.
Data Precision vs. Human Nuance
Ratings are measurable and consistent. They’re also blunt instruments that can’t distinguish a rude rider from a genuinely poor driver. The culture that results treats workers as data points, not people.
Speed vs. Deliberation
Uber’s growth velocity was enabled by moving fast and not building governance structures. The cultural cost was an organization that didn’t have the infrastructure to handle its own misconduct.
Scale vs. Relationship
You can’t build an organizational culture with 5 million contractors who have no relationship with the company. Uber’s reach is its competitive advantage and its cultural Achilles heel simultaneously.
Algorithmic management doesn’t eliminate the manager. It makes the algorithm the manager — one that can’t be reasoned with, argued with, or held accountable. That’s not a neutral technical choice. It’s a cultural statement about the value of the people being managed.
— Adapted from Rosenblat, A. (2018). Uberland: How Algorithms Are Rewriting the Rules of Work. University of California Press.How Algorithmic Control Shapes Organizational Culture — The Specific Impacts
This is the heart of your report’s answer. Don’t hedge here. The question asks whether there is an impact. Yes, there is. Substantial, documented, and multidirectional. Your job is to explain what kind of impact, how it operates, and what evidence supports it.
It Created a Culture of Mistrust Between Uber and Its Workforce
Organizational culture depends on some degree of mutual trust between the organization and the people who work within it. Algorithmic control breaks that reciprocity. Uber’s system monitors drivers constantly but explains itself rarely. Drivers have tried to game the rating system, refuse certain trips, or organize collectively — all because the relationship with the organization feels adversarial rather than mutual. Rosenblat’s ethnographic research with Uber drivers found systematic evidence that workers feel deceived by the app’s incentive communications and powerless to contest its decisions. A culture built on that relationship is not a healthy one — it’s transactional at best and coercive at worst.
It Normalized Opacity as an Organizational Value
When an organization’s primary management tool — the algorithm — is deliberately opaque to the people it governs, opacity becomes normalized as an organizational value. Internally, this played out as a culture where HR processes were opaque (Fowler’s account describes being told her harasser’s performance was “excellent” with no transparency about the review), where executive decision-making was inscrutable, and where employees who raised concerns were managed out rather than responded to. The algorithmic logic bled upward into the corporate culture: information asymmetry was a feature, not a bug.
It Suppressed the Voice Mechanisms That Healthy Cultures Require
Voice — the ability to raise concerns, challenge decisions, and participate in shaping the conditions of your work — is a foundational requirement for organizational culture to develop and self-correct. Uber’s algorithmic architecture made voice structurally impossible for drivers: there was no one to talk to. Internally, voice was suppressed through a culture of retaliation that mirrored the same power asymmetry. What emerged was an organization unable to learn from its own mistakes, because the feedback loops that would allow learning were deliberately or accidentally disabled.
It Accelerated a Growth Culture That Treated Ethics as a Speed Bump
The same data-driven, algorithm-trusting mentality that governed the driver-facing product shaped the internal culture. If metrics said growth was up, the organization was succeeding — regardless of how that growth was achieved. This produced a culture documented by Fowler and later by an independent investigation led by former Attorney General Eric Holder: one where sexual harassment went unaddressed, where HR protected senior performers rather than complainants, and where the cultural value was velocity, not integrity. The algorithm didn’t cause Kalanick’s behavior. But it created an organizational logic in which the human cost of decisions was systematically discounted.
Post-2017: Algorithmic Control Persists Despite Cultural Reform Attempts
This is worth noting in your analysis, because it speaks to how structural the issue is. After Kalanick left and Dara Khosrowshahi became CEO, Uber made significant internal cultural reforms — adding HR structures, publishing diversity reports, changing executive behavior norms. But the algorithmic control of the driver workforce changed relatively little. Drivers are still rated by the same opaque system, still face deactivation without meaningful appeal, and still have no formal voice in the platform’s rules. This suggests that organizational culture reform efforts that don’t address the underlying management architecture are incomplete. The system shapes the culture more than the values posters on the wall.
Writing Your Conclusions Section (1 Point)
The rubric says conclusions should be “your conclusions” — supported by the previous sections — and should flow into “relevant and practical recommendations.” This is not a summary of what you already said. It’s where you tell the reader what it means and what should happen.
Your conclusion should answer two things: what does this analysis tell us about the relationship between algorithmic control and organizational culture, and what should Uber — or organizations like it — actually do about it?
Practical Recommendations Worth Including
- Algorithm transparency: Uber should publish clear, human-readable explanations of how rating decisions and deactivation thresholds work — reducing the opacity that fuels mistrust.
- Appeals mechanisms: Introduce a structured, human-reviewed appeals process for deactivations. This restores voice and signals that the organization is accountable to its workforce, not just vice versa.
- Participatory system design: Involve driver representatives in shaping algorithm updates. Organizations where affected parties have input in the rules they’re governed by show significantly higher trust and lower turnover.
- Cultural audit tied to system audit: Any internal culture reform effort should simultaneously examine the technology systems that enforce organizational norms. Changing values without changing systems produces rhetoric, not culture.
One more thing: be confident in your position. Your conclusions should say something. Not “Uber might consider reviewing its algorithmic systems” — that’s hedging. Say what the analysis shows and what the organization should do. The rubric explicitly rewards fully supported positions, not diplomatic non-commitments.
What Sources to Use (APA Format Required)
Your instructions require one to two external sources, not textbooks, cited in APA format. No Wikipedia. The sources need to be credible — industry publications, peer-reviewed journals, reputable news outlets, or government sources.
Here are strong choices for this specific question:
| Source | Why It’s Relevant | Where to Find It |
|---|---|---|
| Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at work: The new contested terrain of control. Academy of Management Annals, 14(1), 366–410. | This is the peer-reviewed academic foundation for algorithmic management theory. Directly applicable to your analysis section. | Google Scholar, your university library database |
| Fowler, S. (2017, February 19). Reflecting on one very, very strange year at Uber. Susan Fowler (personal blog). | Primary source documenting Uber’s internal cultural dysfunction — the most-cited account of the 2017 crisis. | susanjfowler.com (archived) |
| Rosenblat, A. (2018). Uberland: How algorithms are rewriting the rules of work. University of California Press. | Ethnographic research on Uber drivers. Authoritative source on algorithmic control’s workforce effects. | University library, Google Books |
| Isaac, M. (2019). Super pumped: The battle for Uber. W. W. Norton. | Detailed account of Uber’s cultural development under Kalanick. Strong for background and cultural analysis. | University library |
| Harvard Business Review — search “algorithmic management” or “Uber culture” | Multiple practitioner-facing articles on gig economy culture, algorithmic management tradeoffs. | hbr.org (some free, some subscription) |
A Verified External Source Worth Citing
The Kellogg, Valentine, and Christin (2020) article in the Academy of Management Annals is peer-reviewed, directly relevant, and widely cited in the algorithmic management literature. It establishes a clear taxonomy of how algorithms direct, evaluate, discipline, and reward workers — giving you an academic framework you can map directly onto Uber’s practices. Find it through your institution’s library access to AOM journals.
Quick APA Format Reminders
For journal articles: Author, A. A., & Author, B. B. (Year). Title of article. Title of Journal, volume(issue), pages. For books: Author, A. A. (Year). Title of work: Capital letter also for subtitle. Publisher. In-text citations use (Author, Year) or (Author, Year, p. X) for direct quotes.
Mistakes That Cost Points — And How to Avoid Them
Treating This as a Technology Question, Not a Management Question
This is a question about organizational culture, not about how Uber’s app is built. Students who spend their analysis explaining how the rating system technically works — rather than what it does to the culture — miss the point. The algorithm is the mechanism. Culture is the outcome. Keep your focus on the outcome.
Presenting Only One Side
Algorithmic control has real benefits — consistency, scalability, reduced bias in some contexts, operational efficiency. A credible analysis acknowledges these before arguing that the cultural costs outweigh them. If you only write about the negatives, your analysis reads as advocacy, not analysis. Name the tradeoffs and then make your case.
Naming Frameworks Without Using Them
“Schein’s culture model has three layers: artifacts, espoused values, and underlying assumptions” — fine. But if you stop there and don’t apply it to Uber, you haven’t done analysis. The rubric requires effective application. Show how Uber’s artifacts, espoused values, and underlying assumptions actually look when run through the framework.
Exceeding 3 Pages or Leaving Blank Lines
Your instructions are specific: up to 3 pages, Times New Roman 12pt, 1.5 spaced, 1-inch margins, no blank lines between sections or paragraphs. Points are explicitly deducted for formatting violations. Run a final check before submitting.
Weak or Missing Conclusions
Conclusions that just restate the analysis are worth very little. Your conclusion should synthesize what the analysis means and commit to a recommendation. If you’ve argued that algorithmic control systematically erodes organizational culture, say what Uber should do — specifically. “Uber should consider reviewing its practices” is not a recommendation. It’s a placeholder.