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The purpose of this paper is to demonstrate how a business idea can be successful in the long run in a rapidly changing environment. Students could learn about the carsharing market and the world of start-ups. During the lesson, students could practice business modelling based on “Value proposition Canvas”. With this model, they can understand the real needs of the customers and the services, with which companies can provide gains for the clients and decrease users’ pain. Beside business modelling, the case provides the opportunity to learn about the concept of First Mover Advantage, which describes the possible advantages of being first on a market. Three different sources can provide first mover advantage: technological leadership; pre-emption of scarce assets; and customer loyalty. Start-ups should systematically think about acquiring some of the above to sustain their advantage.

This case is about a carsharing start-up GreenGo, which was the first company introducing the concept of carsharing in Hungary. GreenGo was founded in November 2016 in Budapest. Until today, it has approximately 170 cars and could establish a solid customer base with 6,000 subscribers. After one year of monopoly, GreenGo got a competitor, when MOL (one of the largest companies of the Central European region) entered the market with its new carsharing service: MOL Limo (Limitless Mobility). MOL Limo is using the same business model and marketing mix as GreenGo and started to operate with 300 cars. The case describes the urban transportation of Budapest, the business model and value proposition of GreenGo and MOL Limo in depth. It also presents some possible options for GreenGo to react to the new market situation.

Master in management, MBA.

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CSS: Strategy, Case study organisation: GreenGo.

thyssenkrupp Steel Europe, a major European steel company, operates a so-called push-pickling line (PPL) in Bochum, Germany. The PPL produces a particular type of steel strips that are sold to B2B customers, mainly in the automotive industry. In spring 2014, a senior vice president of thyssenkrupp Steel's production operations and one of his production managers notice that over the span of ten years the production facility regularly did not meet its planned production volumes. They set out to determine the drivers for the deviations from planned production figures with the ultimate goal to improve the production planning process at the Bochum PPL. Students will step into the shoes of Markus Schulze a production manager at thyssenkrupp Steel as he searches for performance drivers at the Bochum PPL and analyzes recent production data to build a forecasting model for production planning.

In 2013, ‘Fashion Destination’, a well-established clothing retailer considered setting up a clothing and accessories rental service. They hired a market research agency ‘Wright & Company’ to conduct a research on the sustainability and profitability of such a business model. The consultants collected primary data and did an extensive analysis for Fashion Destination. Based on the secondary research, expert interviews, extensive qualitative and quantitative research the consultants recommended the management to start a clothes and accessories rental service but suggested that the product offering be limited to formal clothes only and offer accessories. Vishal had doubts despite of the go-ahead signal from consultancy. He wondered what recommendations should he accept and which needed further verification.

Why do some individuals become entrepreneurs while others do not? This fundamental question has vexed entrepreneurship scholars for some time, giving rise to various schools of thought. Traditional economic motivation theories, for example, suggest that entrepreneurial opportunities arise from changes in either supply (Shane, 2000) or demand (Dew et al., 2004) and emphasize personal economic gain as a motivator for pursuing entrepreneurship. Both neoclassical equilibrium and psychological theories take a person-centric perspective whereby stable, enduring differences among people’s characteristics, determines who becomes an entrepreneur. Opportunity recognition may be enhanced through perspective taking (i.e. putting oneself in the shoes of another person) (Prandelli et al., 2016).

The founders of the company provided us with access to the inner workings of the company, their mentors and advisors and themselves. This study used archival research and interviews when preparing this case. Interviews allow for the development of uncensored, real-life insights into the entrepreneur’s business experience. The authors first conducted a 90-min interview with Rajia and Ray at DifferenceMaker^® Central on the UMass Lowell campus. DifferenceMaker^® provided us with pictures and timelines from when Rajia and Ray participated in the various DifferenceMaker^® activities and competitions. A follow-on 90-min interview was conducted with the founders four months later at the UMass Lowell Innovation Hub to delve deeper into their transition from engineers to entrepreneurs.

In the summer of 2016, the future of invisaWear suddenly became uncertain. Rajia Abdelaziz and Ray Hamilton had conceived an idea for smart jewelry that they felt solved a real problem. But it was one thing to have a great idea and quite another to turn that idea into a real business. As engineering students, they were accustomed to solving problems, but if they truly wanted to make an impact, they had to learn to solve problems as entrepreneurs. With the help of DifferenceMaker^® and their mentors, they developed an entrepreneurial mind-set and decided to build invisaWear into a company.

This case is suitable for an undergraduate course in business or entrepreneurship. This case is intended to illustrate to both business and non-business students how individuals with different backgrounds can become entrepreneurs. Presented as a real-life example of how engineering students can make the transition to an entrepreneurial mind-set, the case can also be used to hone in on select topics including opportunity recognition, problem-solving, ideation and the business model canvas.

During one of the most nerve-wracking football matches of the 2012–2013 Bundesliga season, life-long friends Franz Dully and Max Vogel begin arguing about whether the wealth of a football club determines its success during the season. In order to disprove Vogel's claim that “money scores goals,” Dully must analyze the Bundesliga's current market values, points earned, and mid-season leader data.

After analyzing the case, students will be able to compute prediction intervals, develop regression models, and interpret data. The development of the regression models asks students to choose the relevant set of independent variables, as well as determine an appropriate functional form for the regression equation. The models derived have to be evaluated as well as compared to one another. Further, the students have to interpret the quantitative findings in the context of the application.

This case details the history of Southwest Airlines from its inception in 1971 until 2004. The case provides details of Southwest's business model and reasons for its success. It ends with a description of the company's competitive pressures in 2004. The case can be used for a course in service operations or strategy.

It was early 2015 and executives in iShares' Factor Strategies Group were considering the launch of a new class of exchange-traded funds (ETFs) called smart beta funds. Specifically, the group was considering smart beta multifactor ETFs that would provide investors with simultaneous exposure to four fundamental factors that had shown themselves historically to be significant in driving stock returns: the stock market value of a firm, the relative value of a firm's financial position, the quality of a firm's financial position, and the momentum of a firm's stock price. The executives at iShares were unsure whether there would be demand in the marketplace for such multifactor ETFs, since their value added from an investor's portfolio perspective was unknown. Students will act as researchers for iShares' Factor Strategies Group and conduct detailed analysis of Fama and French's five-factor model and the momentum effect, smart beta ETFs including multifactor ETFs, and factor investing with smart beta ETFs to help iShares make its decision.

The financial success of dairy farms depends critically on the price of their main output, milk. Large volatility in the price of milk poses a considerable business risk to dairy farms. This is particularly true for family-run dairy farms. The question then arises: how can a farm owner hedge the milk price risk? The standard approach to establish a price floor for a commodity such as milk is to purchase put options on commodity futures. At the Chicago Mercantile Exchange, farmers can buy put options on the price of a variety of milk products. However, the price a farm receives for its milk depends on many factors and is unique to the farm. Thus, a farmer cannot directly buy put options on the price he receives for the milk his farm produces. Instead the farmer needs to determine which of the options available for trade at the Chicago Mercantile Exchange offer the best hedge for his own milk price. The assignment in this case is to examine historical data on several prices of milk products and the milk price received by a family-run dairy farm in California. Students need to find the price that is most closely correlated to the farm's milk price and to then choose options with the appropriate strike price that serve as the best hedge for the farm's price risk.

The objective is to expose students to an interesting but simple finance application of linear regression analysis. To solve the case, students must run several simple linear regressions, then use the best regression model they find to make a prediction for the dependent price variable and analyze the prediction interval in order to achieve the desired objective outlined in the case. By completing the case, students will acquire a good understanding of their regression model and its usefulness.

Greg Mills describes his search for the perfect engagement ring which includes an analysis of the prices of 6,000 diamonds. An engineer, Greg hopes to impress Sarah Staggers by using regression to find an underpriced diamond. Students are asked to either select one of the 6,000 diamonds or provide point forecasts for prices of 3,142 diamonds in a hold-out sample. The instructor can use the actual prices of the held-out diamonds to evaluate student pricing models.

EuroPet S.A. was a multinational company operating gas stations in many European countries. There was a growing propensity for supermarkets to attach gas stations to their retail operations, which was developing into a major threat to EuroPet. As a result, in the mid-1990s, the company began to develop and brand its own convenience stores co-located with its gas stations. However, the company was spending much more on advertising the convenience stores than its competitors did. Management now had to decide if the increase in sales attributed to advertising efforts justified the advertising spend by analyzing the market data from one large metropolitan area: Marseille, France.

Students will learn: how to use cross-tabs and other marketing research tools to identify segmentation descriptors; how to analyze data and interpret results; and how these research results could guide new product development and positioning strategies in order to effectively target relevant customer segments.