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The perfect uberpool a case study on trade- offs

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In the company launched uberPOOL to make it easy for riders to share their trip with others heading in the same direction. Fundamental to the mechanics of uberPOOL is the intelligence that matches riders for a trip, which can introduce various uncertainties into the user experience. This case study argues that, for a reduced fare and a more direct route, riders are willing to forego the convenience of getting picked up at their door in exchange for waiting and walking a set amount to meet their driver. This case study explores the integration of qualitative and quantitative research to understand user trade-offs.
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7 Steps to Creating a Spectacular UX Case Study

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Conjoint analysis - introduction and overview | mantelzorg.info - Market research

Please explain why it is true based on the science of operations: Problem 2 Multiple choice 2 points each 44 points 1. If the variability of a process increases and no other changes are made with the capacity and inventory buffers for the process, the cycle time will: a. Increase b. Decrease c. Stay the same d. Could go either way 2.
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Conjoint analysis - introduction and overview

A couple of months ago, I came across an amazing nudge database of research papers made by Mark Egan at the Stirling Behavioural Science Centre. I spent some time converting it into a searchable Notion database to help other researchers benefit from it. However, one thing that struck me was that almost all research papers only had successful nudging interventions. The researchers aimed to create a behavior change and were able to achieve it using a specific combination of nudges.
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Ride-hailing platforms such as Uber, Lyft and DiDi have achieved explosive growth and reshaped urban transportation. The theory and technologies behind these platforms have become one of the most active research areas in the fields of economics, operations research, computer science, and transportation engineering. In particular, advanced matching and dynamic pricing algorithms — the two key levers in ride-hailing — have received tremendous attention from the research community and are continuously being designed and implemented at industrial scales by ride-hailing platforms. We first review matching and dynamic pricing techniques in ride-hailing, and show that these are critical for providing an experience with low waiting time for both riders and drivers.
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