PP

Group Case #1 Team Presentation

Weeks 2 and 3:

The teams for the group case assignments were set in the Introduction Discussion in Week 1. 

Each team will use collaborative tools to analyze their assigned question in association with the group case. The team members will work together collaboratively to create and post a PowerPoint slide deck (and an accompanying podcast) explaining your team’s analysis.

Each team will post their Group Case #1 presentation by Sunday, end of Week 2.  The rest of the class will have the opportunity to view and respond to the team presentations in a separate assignment.

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Refer to the following cases from the "Managing & Using Information Systems" textbook (eighth edition). Note that each case includes several questions for analysis, but only one question is assigned to each team. 

Team  5, : Uber (pages 76-77)

The teams for the group case assignments were set in the Introduction Discussion in Week 1. 

For each of the group case assignments, each team will use collaborative tools to analyze their assigned question in association with the group case. The team members will work together collaboratively to create and post a PowerPoint slide deck (and an accompanying podcast: use PowerPoint - Insert - Audio) explaining your team’s analysis. One team member will then post the completed presentation (and podcast) on the appropriate Group Case discussion board. 

·  Group Case 2 - due at the end of Week 5

Part 1: PowerPoint Presentation (6-10 slides)

1.  Background: Identify the symptoms, critical factors and the current state described in the case as relevant to the particular question you are analyzing.  It is not required to give a background about the entire case study.  Be careful to identify the real problem and not the symptoms of the problem.

2.  Analysis: Apply the concepts/models/frameworks from the course content and outside research effectively and completely to respond to your specific question.  If necessary explain the model briefly to clarify your position.  Refer to external references (at least one) to strengthen your analysis.  If applicable, logically discuss options, implications and tradeoffs.

3.  Conclusions: These should be your conclusions and if applicable, recommendations regarding how the organization should deal with the problem; they should be fully supported by the previous sections.

4.  Presentation format: The presentation should be well structured, professional and organized.  The communication should be clear, logical, and concise.  Do not use videos from external sources in your presentation!

5.  Appropriate references:  You must use 1 or 2 external sources, not including textbooks. These sources can be company websites, industry sources, journal articles, periodicals, such as the Wall Street Journal, Business Week, and so on, and governmental sources such as the SEC. Wikipedia and other similar sources are not to be used in this course.  Citations must be referenced according to APA style.

: Post Presentation to Canvas 

On the course Discussion Board, post both your PowerPoint (and your Podcast) as “reply” under the assigned Group case. The Subject Box should contain your team number and the respective name of the case (e.g. Group Case #1 FBI), the assigned question should be written out in the Message Box and within the Message Box the podcast (optional) should be embedded as a file or media.  Finally, include the PowerPoint file as an attachment.

Group Case Rubric (Presentation)

Performance Criteria

Requirements for Exemplary Performance

1. Background (1 points)

Effectively and completely identifies symptoms, critical factors and current state in Background discussion as relevant to the main assigned question.  

2. Analysis (4 points)

Completely and effectively applies IS models, course content, and outside research to support the analysis.  If applicable, logically discusses options, implications and tradeoffs.  The section flows smoothly from the background section and to the next section.

Uses external primary research sources (at least 1 excluding the textbooks) and correct APA format.

3. Conclusions (1 points)

Completely and effectively discusses conclusions logically based on the analysis. Fully supports position with research. If applicable, flows smoothly into relevant and practical recommendations.

4. Organization                    (4 points)

Teamwork; presentation flows as a whole; clarity and relevance of slides; compelling argument to the target audience (management team)

Total (out of 10)

For Team 5, your assigned question from the Managing & Using Information Systems Uber case (pages 76–77) is:

Question 3:
“How has Uber’s implementation of information systems affected the way drivers are managed and monitored? Do you think these systems are beneficial or harmful to drivers? Explain your answer.”

Book:

Case Study 3-2 Uber’s Use of Algorithmic Management

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 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 ‘algorithmic boss.’” Drivers’ work experiences are entirely mediated through a mobile app and drivers are constantly under surveillance.

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 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 system knows what they are doing, but the drivers don’t know how the system that is directing them works.

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? Well, the drivers have negative feelings about customer ratings being used to calculate their earnings or allocate rides to them. And their turnover rates suggest that the Uber drivers aren’t all that happy. 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.”

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 2020 Communications of the Association for Information Systems, 46 no. 1 (2020); Möhlmann, Mareike, Lior Zalmanson, Ola Henfridsson, and Robert Wayne Gregory. “Algorithmic Management of Work on Online Labor Platforms: When Matching Meets Control,” MIS Quarterly 45, no. 4 (2021); 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).

Discussion Questions

1. 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 “algorithmic boss” is as a managerial control system for Uber drivers? Please explain.
2. What are the benefits to Uber of using algorithmic control through its mobile app? What are the downsides?
3. What impact, if any, do you think Uber’s use of algorithmic control has on its organizational culture?
4. Do you think the Uber digital business model is a sustainable one? Please provide a rationale for your response.

Notes

1. 1   Avin Kline, “What Every Company Can Learn From Google’s Company Culture,” Success Agency, January 19, 2023.
2. 2   Lori Li, “10 Reasons Why Google’s Company Culture Works,” Tiny Pulse, April 21, 2020.
3. 3   Barr Seitz, “Learning from Google’s Digital Culture,” McKinsey, June 1, 2015, https://www.mckinsey.com/~/media/McKinsey/Industries/Technology%20Media%20and%20Telecommunications/High%20Tech/Our%20Insights/Learning%20from%20Googles%20digital%20culture/Learning%20from%20Googles%20digital%20culture.ashx (accessed March 28, 2023).
4. 4   Ryan Clancy, “Google Lays Off Largest Number of Workers in Company History via Email,” March 21, 2023, https://www.msn.com/en-us/money/companies/google-lays-off-largest-number-of-workers-in-company-history-via-email/ar-AA18UbbZ (accessed March 22, 2023).
5. 5   David Radcliffe, Bay View is open—the first campus built by Google, Life at Google website, May 17, 2022, https://blog.google/inside-google/life-at-google/bay-view-campus-grand-opening/ (accessed August 2, 2023). If you would like to see what the Bay View campus looks like, see the Washington Post video: Google aims to reimagine the office with new Bay View campus (washingtonpost.com) (accessed August 2, 2023).
6. 6   Martin Roll, “The Secret of Zara’s Success: A Culture of Customer Co-Creation,” March 2018, https://martinroll.com/resources/articles/strategy/the-secret-of-zaras-success-a-culture-of-customer-co-creation/ (accessed February 17, 2019).
7. 7   Frances Cairncross, The Company of the Future (London: Profile Books, 2002).
8. 8   Inditex, “A New 170,000 m2 Building to House the Zara Sales and Design Teams Within Inditex’s Complex in Arteixo,” December 21, 2021, News Detail https://www.inditex.com/itxcomweb/en/press/news-detail?contentId=d6a11054-f905-4f0f-8593-96097bc21f37 (accessed March 23, 2023).
9. 9   Cognizant Computer Goods Technology, “Creating a Culture of Innovation: 10 Steps to Transform the Consumer Goods Enterprise,” October 6, 2009.
10. 10 Cognizant website, August 4, 2015, https://www.glassdoor.com/Reviews/Employee-Review-Cognizant-Technology-Solutions-RVW7459726.htm (accessed February 18, 2019).
11. 11 Nathaniel Smithson, “Google’s Organizational Structure & Its Characteristics (An analysis),” September 8, 2018, https://panmore.com/google-organizational-structure-characteristics-analysis (accessed September 19, 2023).
12. 12 For more information on zero-time organizations, see R. Yeh, K. Pearlson, and G. Kozmetsky, Zero Time: Providing Instant Customer Value Every Time, All the Time (Hoboken, NJ: John Wiley, 2000).
13. 13 ET Now Digital, “TCS Shifts to a New Operational Structure, Creates 4 Distinct Business Groups,” March 1, 2022, https://www.timesnownews.com/business-economy/companies/tcs-shifts-to-a-new-operational-structure-creates-4-distinct-business-groups-article-89921721 (accessed March 23, 2023).
14. 14 Tata Consultancy Services, https://www.tata.com/business/tcs (accessed March 23, 2023).
15. 15 T. S. H. Teo, R. Nishant, M. Goh, and S. Agarwal, “Leveraging Collaborative Technologies to Build a Knowledge Sharing Culture at HP Analytics,” MIS Quarterly Executive 10, no. 1 (March 2011): 1–18.
16. 16 N. B. Ellison and D. Boyd, “Sociality Through Social Network Sites,” in The Oxford Handbook of Internet Studies, ed. W. H. Dutton (Oxford, UK: Oxford University Press, 2013), 158.
17. 17 Schafheitle, Simon, Antoinette Weibel, Isabel Ebert, Gabriel Kasper, Christoph Schank, and Ulrich Leicht-Deobald, “No Stone Left Unturned? Toward a Framework for the Impact of Datafication Technologies on Organizational Control,” Academy of Management Discoveries 6, no. 3 (2020): 455–487.
18. 18 Möhlmann, Mareike, Lior Zalmanson, Ola Henfridsson, and Robert Wayne Gregory, “Algorithmic Management of Work on Online Labor Platforms: When Matching Meets Control,” MIS Quarterly 45, no. 4 (2021): pg. 2006; And for TMC: W. A. Cram and M. Wiener, “Technology-Mediated Control: Case Examples and Research Directions for the Future of Organizational Control,” Communications of the Association for Information Systems, 46, no. 1 (2020): 4.
19. 19 Schafheitle et al. (2020), “No Stone Left Unturned.”
20. 20 Möhlmann et al. (2021), “Algorithmic Management of Work on Online Platforms,” pg. 2005.
21. 21 Katherine C. Kellogg, Melissa A. Valentine, and Angele Christin. “Algorithms at Work: The New Contested Terrain of Control.” Academy of Management Annals 14, no. 1 (2020): 366–410.
22. 22 D. Galletta and R. Grant, “Silicon Supervisors and Stress: Merging New Evidence from the Field,” Accounting, Management and Information Technology 5, no. 3 (1995): 163–183.
23. 23 Google, “Ten Things We Know To Be True,” https://about.google/philosophy/ (accessed August 2, 2023).
24. 24 Martin Wiener and Carol Saunders, “Forced Coopetition in IT Multi-Sourcing,” Journal of Strategic Information Systems 23, no. 3 (2014): 210–25.