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MUST ANSWER ALL DISCUSSIONS QUESTIONS. I HAVE PASTED THE INFO FROM CHAPTER 3 BUT CAN GIVE YOU LOG IN INFO FOR THE BOOK IF NEEDED
WHAT TO SUBMIT:
Respond to the case study questions below related to the Uber case study. Your submission should be 2 to 3 pages, double spaced, and submitted as a Word document. Also, 2 to 3 resources are required and must be appropriately cited using APA style. These resources can include the textbooks and resources from previous modules, as well as original resources you have consulted. Please write out the questions in your document.
Your responses should be in complete paragraphs and should contain the following:
Answer all of the questions thoroughly and completely.
Write out the questions in your document.
Make direct connections between the issues identified in the case study and the concepts covered in the provided resources in Modules One and Two.
Support your answers with appropriate examples and facts drawn from the case study.
Use correct grammar, sentence structure, and spelling, and demonstrate an understanding of audience and purpose.
Discussion Questions:
1-Considering Uber’s mission statement and business model, in what ways do the mission and business model align with the decision to use TMC?
2-Uber is faced with the monumental challenge of managing and motivating millions of drivers who are important to its business, but who aren’t full-time employees. How effective do you think Uber’s “automated manager” is as a managerial control system for Uber drivers? Please explain.
3-What are the benefits to Uber of using TMC through its mobile app? What are the downsides?
4-What impact, if any, do you think Uber’s use of TMC has on its organizational culture? How does the fact that most of Uber’s employees are remote contractors influence the culture?
5-
How might differences in national cultures influence the response to TMC? How might Uber change or modify TMC to make it more effective?
Overview
In this module’s readings, you learned about how information technology interacts with and influences organizational structure and work design. Chapter 3 of Managing and Using Information Systems discusses how “Management control at the individual level is concerned with monitoring (i.e., data collection), evaluating, providing feedback, compensating, and rewarding. It is the job of the firm’s leaders to ensure that the proper control mechanisms are in place, and the interactions between the organization and the IS do not undermine the managerial objectives and worker performance” (p. 75).
In this activity, you will read about how Uber is using technology to monitor its drivers’ behaviors. The use of this technology is known as technology-mediated control (TMC), which refers to an organization’s use of digital technologies to influence workers to behave in a way that is consistent with its strategic and tactical objectives.
For this week’s activity:
Read the case study below, adapted from the Chapter 3 case study, “Uber’s Use of Technology‐Mediated Control.”
Consider ways in which TMC influences organization structure, culture, and work design.
Respond to the provided discussion questions below.
Prompt
Uber Technologies is a ride‐hailing company that uses the cars and time of millions of drivers who are independent contractors. The company operates in many countries around the world. One recent estimate is that Uber drivers globally spend 8.5 million hours on the road every day. Uber’s mission statement is “Transportation as reliable as running water, everywhere for everyone.” Uber also employs a dual strategy that aims to deliver value to drivers and riders alike by appealing to each group’s different incentives by creating a mutually beneficial relationship between the two.
Uber wants to control how these drivers behave and exerts this control not through human supervisors, but through a system of algorithms that serves as an automated virtual manager. Drivers’ work experiences are entirely mediated through Uber’s mobile app. Hence, Uber has been accused of using TMC to exert “soft control” over its drivers.
This app is constantly collecting data on drivers. It nudges the behavior of the drivers in such a way that in reality they aren’t as much their own boss as they might like to be. For example, while they can work when they want, Uber’s surge fare structure of charging riders more during high‐volume periods motivates drivers to work during times that they might not want to work. The app even sends push notifications based on sophisticated algorithms. For example, “Are you sure you want to go offline? Demand is very high in your area. Make more money, don’t stop now!”
The mobile app also employs interventions to encourage various driver behaviors. For example, the Uber app will let drivers know that they are close to achieving an income target when they attempt to log off and end their work. Uber also sends drivers their next fare opportunity before their current ride is over. New drivers are enticed with signing bonuses when they meet initial ride targets.
To motivate drivers to complete enough rides to earn bonuses, they occasionally receive words of encouragement through the app. The app also watches their rides to ensure that they accept a minimum percentage of ride requests, are available for a minimum period of time in order to qualify to earn profitable hourly rates during specified periods, and complete a minimum number of trips.
How is all this monitoring influencing Uber’s drivers? Uber’s turnover for drivers is high—reportedly closing in on 50% within the first year.
CASE STUDY FROM BOOK
Case Study 3‐1 Uber’s Use of Technology‐Mediated Control
Uber Technologies, founded in 2009, is a ride‐hailing company that leverages the cars and time of millions of drivers who are independent contractors in countries around the globe. One recent estimate by Uber Group Manager, Yuhki Yamashita, is that Uber drivers globally spend 8.5 million hours on the road—daily. As independent contractors, Uber tells its drivers “you can be your own boss” and set your own hours. Yet, Uber wants to control how they behave. Uber exerts this control not through human managers, but through a “ride‐hail platform on a system of algorithms that serves as a virtual ‘automated manager.’” Drivers’ work experiences are entirely mediated through a mobile app.
Uber’s mobile app collects data and guides the behavior of the drivers in such a way that in reality they aren’t as much their own boss as they might like to be. For example, while they can work when they want, Uber’s surge fare structure of charging riders more during high‐volume periods motivates them to work during times that they might not otherwise choose. The app even sends algorithmically derived push notifications like: “Are you sure you want to go offline? Demand is very high in your area. Make more money, don’t stop now!” Hence, Uber uses technology to exert “soft control” over its drivers.
Uber employs a host of social scientists and data scientists to devise ways to encourage the drivers to work longer and harder, even when it isn’t financially beneficial for them to do so. Using its mobile app, it has experimented with video game techniques, graphics and badges and other noncash rewards of little monetary value. The mobile app employs psychologically influenced interventions to encourage various driver behaviors. For example, the mobile app will alert drivers that they are close to achieving an algorithmically generated income target when they try to log off. Like Netflix does when it automatically loads the next program in order to encourage binge‐watching, Uber sends drivers their next fare opportunity before their current ride is over. New drivers are enticed with signing bonuses when they meet initial ride targets (e.g., completing 25 rides). To motivate drivers to complete enough rides to earn bonuses, the app periodically sends them words of encouragement (“You’re almost halfway there, congratulations!”). The mobile app also monitors their rides to ensure that they accept a minimum percentage of ride requests, complete a minimum number of trips, and are available for a minimum period of time in order to qualify to earn profitable hourly rates during specified periods. Uber has a blind acceptance rate policy, where drivers do not get information about the destination and pay rate for calls until after they accept them. This can mean that drivers might end up accepting rates that are unprofitable for them. On the other hand, drivers risk being “deactivated” (i.e., be suspended or removed permanently from the system) should they cancel unprofitable fares. The system keeps track of the routes taken to ensure that the driver selected the most efficient route.
The mobile app also captures passenger ratings of the driver on a scale of one to five stars. Since the drivers don’t have human managers per se, the passenger satisfaction ratings serve as their most significant performance metric, along with various “excellent‐service” and “great‐conversation” badges. But how satisfied are the drivers themselves? Uber’s driver turnover rate is high—reportedly closing in on 50% within the first year that the drivers sign up. One senior Uber official said: “We’ve underinvested in the driver experience. We are now re‐examining everything we do in order to rebuild that love.”
Discussion Questions
Uber is faced with the monumental challenge of controlling and motivating millions of drivers who are important to its business, but who aren’t on its payroll. How effective do you think Uber’s “automated manager” is as a managerial control system for Uber drivers? Please explain.
What are the benefits to Uber of using technology‐mediated control through its mobile app? What are the downsides?
What impact, if any, do you think Uber’s use of technology‐mediated control has on its organizational culture?
Do you think the Uber digital business model is a sustainable one? Please provide a rationale for your response.
Sources: JC, “How Many Uber Drivers Are There?” Ridester, January 29, 2019, https://www.ridester.com/how‐many‐uber‐drivers‐are‐there/ (accessed February 18, 2019); Wiener and Cram AMCIS 2017 and Cram and Wiener 2019 Communications of the Association for Information Systems (forthcoming); IBID and N. Scheiber, “How Uber Uses Psychological Tricks to Push Its Drivers’ Buttons,” New York Times, 2017, https://www.nytimes.com/interactive/2017/04/02/technology/uber‐drivers‐psychological‐tricks.html (accessed February 18, 2019); and A. Rosenblat, Uberland: How Algorithms Are Rewriting the Rules of Work (Oakland, CA: University of California Press, 2018).
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