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The Nokia case provides an opportunity to explore financing alternatives in a situation of broad strategic change. The case emphasizes the difficulties of managing the financial resources of technology-based companies when they fall behind in product innovation. Nokia, the world's leading producer of mobile phones, had recently seen its market share and profits eroded by rival products such as Apple's iPhone and phones featuring Google's Android operating system. In February 2011, Nokia CEO Stephen Elop announced a strategic plan and partnership with Microsoft to have Windows serve as its primary OS for smartphones. Since that announcement, Nokia reported a net loss in earnings, followed by a downgrade of its credit rating in the summer of 2012.

Analysts regard the next two years as a period of great uncertainty for the company. In January 2012, the CFO of Nokia estimates that the firm might require up to EUR4.3 billion in funding over the next two years to implement the plan under a representative downside scenario. Students are asked to evaluate the tradeoffs of raising the funds by issuing long-term debt, issuing equity, cutting dividends, or reducing cash. Given the firm's recent competitive struggles, none of the options is particularly appealing, which forces careful consideration of tradeoffs.

The Nokia is appropriate for use in upper-level undergraduate and graduate courses covering topics in capital raising, capital structure, corporate finance, and the costs of financing. A spreadsheet file of case exhibits to facilitate student preparation, teaching note, and instructional spreadsheet file are available for the case.

The learning outcomes are as follows: understand the challenges and opportunities of an unorganized business/informal economy; compare and contrast the applications of customer engagement frameworks between small and big businesses; outline the steps in product design in a services context; discuss the services marketing mix as a part of the marketing strategy; and understand the need of scaling up the business operations in wake of new opportunity.

Sukhpal Dairy Farm (SDF) is a case of unorganized milk marketing in the Indian Emerging Market. Milk was sold as a commodity with a fragmented set of suppliers to only a small population. Changes in consumer demand, technology and supply chain presented huge opportunities to the small dairy farmer. But it was also a threat to him. The large corporater, players backed by strong logistics and supply chain operations support posed a big challenge to the small farmer who lacked scale and reach. Sukhpal, owner of SDF, struggled while considering the options to grow his business. He did not want to change the success factors of his existing business model that was based on word of mouth and customer engagement.

MBA students.

Teaching Notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes.

CSS: 8: Marketing.

Uber had pioneered the growth and delivery of modern ridesharing services by leveraging the explosive growth of technology, GPS navigation, and smartphones. Ridesharing services had expanded across the world, growing rapidly in the United States, China, India, Europe, and Southeast Asia. Even as these services expanded and gained popularity, however, the pickup experience for drivers and riders did not always meet the expectations of either party. Pickups were complicated by traffic congestion, faulty GPS signals, and crowded pickup venues. Flawed pickups resulted in rider dissatisfaction and in lost revenues for drivers. Uber had identified the pickup experience as a top strategic priority, and a team at Uber, led by group product manager Birju Shah, was tasked with designing an automated solution to improve the pickup experience. This involved three steps. First, the team needed to analyze the pickup experience for various rider personas to identify problems at different stages in the pickup process. Next, it needed to create a model for predicting the best rider location for a pickup. The team also needed to develop a quantitative metric that would determine the quality of the pickup experience. These models and metrics would be used as inputs for a machine learning.