Evaluate A Company's Recent Actions Within The Last Y 289925

Evaluate a company's recent (within the last year) actions dealing with risk and uncertainty

Select a company of your choice, any company but Southwest Airlines, and write a six to eight (6-8) page paper in which you:

Evaluate a company's recent (within the last year) actions dealing with risk and uncertainty.

Offer advice for improving risk management.

For the company you selected, examine an adverse selection problem and recommend how it should minimize its negative impact on transactions.

For the company, determine the ways it is dealing with the moral hazard problem and suggest best practices used in the industry to address it.

Identify a principal-agent problem relevant to the company and evaluate the tools it uses to align incentives and improve profitability.

Examine the organizational structure of the company and suggest ways it can be changed to improve overall profitability.

Use at least five (5) quality academic resources in this assignment.

Paper For Above instruction

In recent years, Uber has become a dominant force in the transportation industry, renowned for its innovative ride-hailing platform that connects drivers with passengers via a mobile app. Its rapid expansion and operational strategies offer a compelling case study in risk management, adverse selection, moral hazard, principal-agent problems, and organizational structure. This paper critically examines Uber’s recent actions in addressing these issues, proposes strategies for risk management, and offers recommendations to enhance overall profitability and efficiency.

Uber's recent initiatives to mitigate risk and uncertainty include investments in technological infrastructure, enhanced driver screening processes, and diversification of service offerings such as Uber Eats and freight logistics. These actions exemplify proactive risk management strategies that respond to the inherent uncertainties of a platform-based business model. For instance, Uber continuously updates its safety protocols, including real-time ride tracking and driver background checks, to mitigate risks associated with passenger safety and legal liabilities. Their focus on data analytics and predictive algorithms also aids in operational risk mitigation, optimizing driver deployment and reducing wait times.

However, despite these efforts, Uber faces ongoing challenges related to risk management. One key area for improvement involves enhancing transparency and accountability in driver screening processes and rider feedback mechanisms. Implementation of more rigorous verification and continuous monitoring could further reduce operational risks and improve customer trust. In addition, developing comprehensive contingency plans for regulatory changes and market disruptions can help Uber buffer against future uncertainties.

Adverse selection remains a significant problem for Uber, particularly related to driver quality and passenger safety. Adverse selection occurs when Uber is unable to accurately screen drivers, leading to the potential hiring of unsafe drivers and consequently, a decrease in rider trust. To minimize this negative impact, Uber should leverage advanced machine learning algorithms to better analyze driver backgrounds and driving histories. Additionally, incentivizing high-quality drivers through premium pay rates, loyalty programs, and recognition can attract better drivers, reducing the adverse selection problem.

Uber also contends with moral hazard issues, notably with drivers misreporting hours, neglecting safety protocols, or engaging in unprofessional conduct. The company deals with these risks through real-time GPS tracking, driver ratings, and incident reporting systems. Nevertheless, industry best practices suggest that Uber could strengthen its moral hazard mitigation by implementing more frequent audits, offering ethical training courses, and providing performance-based incentives that align driver interests more closely with Uber’s safety and service standards.

The principal-agent problem in Uber's context primarily relates to the misalignment of incentives between the company (principal) and its drivers (agents). To address this, Uber employs various incentive mechanisms, such as dynamic pricing, driver ratings, and bonus schemes tied to performance metrics. These tools help align driver behavior with Uber’s profitability goals by rewarding good conduct and ensuring customer satisfaction. However, ongoing refinements—such as implementing transparent earning reports and providing opportunities for driver feedback—could further enhance motivation and reduce agency costs.

Uber’s organizational structure is characterized by a decentralized model that emphasizes rapid innovation and scaling. However, this structure can create inefficiencies, such as communication gaps and lack of centralized oversight. To improve profitability, Uber could adopt a more integrated organizational approach, establishing clearer hierarchical processes and dedicated regulatory compliance units. Streamlining decision-making and fostering a culture of accountability across operational divisions would also contribute to better resource allocation and growth management.

In conclusion, Uber has demonstrated significant efforts to manage risks and adapt to the dynamic transportation landscape. Nonetheless, continuous improvement in driver screening, transparency, incentive alignment, and organizational design remains essential. By adopting industry best practices and innovative technological solutions, Uber can further mitigate risks, reduce adverse selection and moral hazard, and enhance organizational efficiency—ultimately leading to sustained growth and profitability.

References

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