On December 30, 2013, an account manager in the sales department of UIT India (UIT) filed a complaint with the company’s Ethics & Compliance Cell (ECC). UIT was the Indian branch of a leading U.S. multinational corporation. In his complaint to the ECC, the account manager alleged that he had suffered discrimination by his superior. He was called to a meeting with the ECC where he learned that his superior had received complaints from other employees and was already under investigation. That information made him feel hopeful that his complaint would be resolved quickly and favourably. However, his hopes were shattered when he was placed on a performance improvement plan the next day by the company’s human resource department. According to the plan, he would be terminated if he failed to deliver the set targets within two months. The account manager was furious and felt he was being treated unfairly. However, he was required to accept or refuse the improvement plan within 48 hours. His decision would not only determine his survival at UIT, but also impact his career prospects in the industry.

Manpreet Singh, chief executive officer of the Bosch multi-brand car service provider Motorcafe, wanted to scale up his business, which he established in 2010 in Mohali, India. The multi-brand car service sector promised huge market potential for Singh, but he faced some challenges: Bosch had failed to provide the expected support; the brand was being diluted by poor services offered by other service providers in neighbouring areas; competition from other multi-brand players in the Indian market was ever-growing; original equipment manufacturer workshops were renewing their competitive efforts; and customer needs were rapidly changing. How should Singh scale up his venture in the face of changing market dynamics and implement Motorcafe’s innovative value proposition for Indian car owners?

Financial holding company Gentera S.A.B. de C.V., located in Mexico City and operating in Mexico, Guatemala, and Peru, offered microlending, financial payment, insurance, and money transfer services to individuals at the bottom of the financial pyramid. The company was founded and operated on strong social values—reduce financial exclusion, focus on human and social values (including self-actualization), and enhance economic value—which pervaded the decisions made by the board and senior management. By 2016, the company had evolved from its origins as a non-governmental organization to become a private bank and, more recently, a public company listed on the Mexican Stock Exchange. The company faced questions about its governance structure and processes: should it reduce the number of board committees and alter their function in order to accomplish a stronger strategic focus? It also faced organizational challenges related to growth and the need to both adapt its current business model to encompass financial technology and invest in information technology. How should it allow extensive developments while simultaneously updating systems required to meet current needs?

Manpreet Singh, chief executive officer of the Bosch multi-brand car service provider Motorcafe, wanted to scale up his business, which he established in 2010 in Mohali, India. The multi-brand car service sector promised huge market potential for Singh, but he faced some challenges: Bosch had failed to provide the expected support; the brand was being diluted by poor services offered by other service providers in neighbouring areas; competition from other multi-brand players in the Indian market was ever-growing; original equipment manufacturer workshops were renewing their competitive efforts; and customer needs were rapidly changing. How should Singh scale up his venture in the face of changing market dynamics and implement Motorcafe's innovative value proposition for Indian car owners?

Financial holding company Gentera S.A.B. de C.V., located in Mexico City and operating in Mexico, Guatemala, and Peru, offered microlending, financial payment, insurance, and money transfer services to individuals at the bottom of the financial pyramid. The company was founded and operated on strong social values-reduce financial exclusion, focus on human and social values (including self-actualization), and enhance economic value-which pervaded the decisions made by the board and senior management. By 2016, the company had evolved from its origins as a non-governmental organization to become a private bank and, more recently, a public company listed on the Mexican Stock Exchange. The company faced questions about its governance structure and processes: should it reduce the number of board committees and alter their function in order to accomplish a stronger strategic focus? It also faced organizational challenges related to growth and the need to both adapt its current business model to encompass financial technology and invest in information technology. How should it allow extensive developments while simultaneously updating systems required to meet current needs?

On December 30, 2013, an account manager in the sales department of UIT India (UIT) filed a complaint with the company's Ethics & Compliance Cell (ECC). UIT was the Indian branch of a leading U.S. multinational corporation. In his complaint to the ECC, the account manager alleged that he had suffered discrimination by his superior. He was called to a meeting with the ECC where he learned that his superior had received complaints from other employees and was already under investigation. That information made him feel hopeful that his complaint would be resolved quickly and favourably. However, his hopes were shattered when he was placed on a performance improvement plan the next day by the company's human resource department. According to the plan, he would be terminated if he failed to deliver the set targets within two months. The account manager was furious and felt he was being treated unfairly. However, he was required to accept or refuse the improvement plan within 48 hours. His decision would not only determine his survival at UIT, but also impact his career prospects in the industry.

Duke University had grown from an one room school house in rural North Carolina in 1859 to one of the leading research universities in the U.S. and the world. Since the late 1950s, Duke's leaders had consciously used the process of strategic planning to guide the development of the university as a whole, building up programs in interdisciplinarity and initiating ambitious projects in internationalization. Under the leadership of President Richard Brodhead, Duke first built a medical school in Singapore in collaboration with the National University of Singapore, then embarked on a journey to build a university on American standards in the People's Republic of China with its own four year liberal arts curriculum. Will Duke realize its many "outrageous ambitions" as it transitions to a new president?

The Electronic Works Amberg (EWA) was Siemens' most advanced factory. The plant built industrial automation equipment using state-of-the-art manufacturing techniques. Siemens was considering creating a consulting business for its manufacturing clients. Given the EWA's well-known expertise, the facility would be heavily involved in the consulting business. What was the best way for Siemens to operate its consulting business? What else could be done by executives at the EWA to improve their time-to-market and market share?

The case depicts the first few weeks of the 2014 Ebola outbreak in West Africa and describes how two organizations, the World Health Organization and Doctors Without Borders, assessed the seriousness of the outbreak. Both organizations have expertise and experience in containing epidemics, and past Ebola outbreaks in particular. These organizations nonetheless reached radically opposite conclusions. The case explores the possible reasons for these differences in predicting the outbreak's evolution.

This case describes the renewed growth of Chinese smartphone maker Xiaomi in 2017, after suffering bruising setbacks at the hands of local competitors during the past two years. In 2017Q2, the company rejoined the top five rankings of the global smartphone market share. It also matched Samsung's market share in India market in 2017Q3. The case highlights the company's strategic adaptation in the two-year recalibration, with regards to global expansion, R&D accumulation and online business model redefinition. By releasing its own in-house developed chip, the case shows how Xiaomi attempts to fortify its intellectual property position and increase freedom of action in anticipation of entering new geographies, product markets, or technology areas. Besides, the case also presents the company's combination of the online and offline channels, by opening more than 200 Mi Home stores both in domestic and overseas. As Xiaomi embraced a new strategy to fuel growth, this case emphasizes Xiaomi's ecosystem strategy that seeks and invests partners in different niche markets with sizable market and strong user demand. In the end, the case questions the sustainability of the strategy and the internal organizational challenges facing Xiaomi.

In March 2016, Ontario-based Cara Operations, a company that operates several restaurant chains, including Swiss Chalet (present throughout Canada, except in Quebec) - acquired Groupe St-Hubert, one of Quebec's largest restaurant chains. With the acquisition of St-Hubert, Cara became the proud owner not only of its restaurants but also of its manufacturing plants and distribution centres. The purchase of Quebec's iconic rotisserie chicken chain thus opened many doors for Cara. The case describes the situation immediately following the acquisition, when CEO Bill Gregson is wondering how to optimize its impact on his company. It ends with the five options that Gregson plans to present to his board of directors: 1) allow St-Hubert to operate as an independent division at the national level, 2) use St-Hubert to introduce Cara-branded products to Quebec, 3) have Cara and St-Hubert combine locations, 4) adopt St-Hubert's procurement process for Cara brands, or 5) simply cut costs by implementing Cara's best practices and know-how.

In early 2013, the founder of Shandong Aspop Clothing Apparel Group Co. Ltd. faced the annual “post-holiday recruitment dilemma.” His challenge was typical of the growth challenges that many labour-intensive original equipment manufacturer clothing enterprises in China encountered. Facing rising domestic labour costs, a change in attitudes among workers, the gradual shift of labour preferences from manufacturing industries to service industries, and a market environment in which international orders had shifted to Southeast Asian countries, the founder needed to make a decision. Should he strive to attract and retain more skilled front-line workers, replace personnel via automation, or set up a factory in a neighbouring country to transfer part of the enterprise’s production capacity?

In 2012, a pilot study undertaken by the data services team of the Dow Chemical Company in the polymer division of the multinational company’s Midland, Michigan, plant had revealed an uncanny trend on the company’s shop floor. Plant engineers were working for the data; the data was not working for them. The data services director saw an opportunity to reverse the trend through the deployment of big data capabilities and, more specifically, enterprise manufacturing intelligence (EMI), a subset of big data. How should he gain user acceptance of the proposed EMI?

In early 2013, the founder of Shandong Aspop Clothing Apparel Group Co. Ltd. faced the annual "post-holiday recruitment dilemma." His challenge was typical of the growth challenges that many labour-intensive original equipment manufacturer clothing enterprises in China encountered. Facing rising domestic labour costs, a change in attitudes among workers, the gradual shift of labour preferences from manufacturing industries to service industries, and a market environment in which international orders had shifted to Southeast Asian countries, the founder needed to make a decision. Should he strive to attract and retain more skilled front-line workers, replace personnel via automation, or set up a factory in a neighbouring country to transfer part of the enterprise's production capacity?

In 2012, a pilot study undertaken by the data services team of the Dow Chemical Company in the polymer division of the multinational company's Midland, Michigan, plant had revealed an uncanny trend on the company's shop floor. Plant engineers were working for the data; the data was not working for them. The data services director saw an opportunity to reverse the trend through the deployment of big data capabilities and, more specifically, enterprise manufacturing intelligence (EMI), a subset of big data. How should he gain user acceptance of the proposed EMI?

This case, along with its B case (UVA-QA-0865), is an effective vehicle for introducing students to the use of machine-learning techniques for classification. The specific context is predicting customer retention based on a wide range of customer attributes/features. The specific techniques could include (but are not limited to): regressions (linear and logistic), variable selection (forward/backward and stepwise), regularizations (e.g., LASSO), classification and regression trees (CART), random forests, graduate boosted trees (xgboost), neural networks, and support vector machines (SVM). The case is suitable for an advanced data analysis (data science, machine learning, and artificial intelligence) class at all levels: upper-level business undergraduate, MBA, EMBA, as well as specialized graduate or undergraduate programs in analytics (e.g., masters of science in business analytics [MSBA] and masters of management analytics [MMA]) and/or in management (e.g., masters of science in management [MScM] and masters in management [MiM, MM]). The teaching note for the case contains the pedagogy and the analyses, alongside the detailed explanations of the various techniques and their implementations in R (code provided in Exhibits and supplementary files). Python code, as well as the spreadsheet implementation in XLMiner, are available upon request.

Spreadsheet supplement to case UV7579.

This case, along with its A case (UVA-QA-0864), is an effective vehicle for introducing students to the use of machine learning techniques for classification. The specific context is predicting customer retention based on a wide range of customer attributes/features. The specific techniques could include (but are not limited to): regressions (linear and logistic), variable selection (forward/backward and stepwise), regularizations (e.g., LASSO), classification and regression trees (CART), random forests, graduate boosted trees (xgboost), neural networks, and support vector machines (SVM). The case is suitable for an advanced data analysis (data science, machine learning, and artificial intelligence) class at all levels: upper-level business undergraduate, MBA, EMBA, as well as specialized graduate or undergraduate programs in analytics (e.g., masters of science in business analytics [MSBA] and masters of management analytics [MMA]) and/or in management (e.g., masters of science in management [MScM] and masters in management [MiM, MM]). The teaching note for the case contains the pedagogy and the analyses, alongside the detailed explanations of the various techniques and their implementations in R (code provided in Exhibits and supplementary files). Python code, as well as the spreadsheet implementation in XLMiner, are available upon request.

Spreadsheet supplement to case UV7582.

Traditional establishment leadership of Miami is increasingly challenged by a rising millennial generation that is more diverse and brings more innovative and entrepreneurial "outside-the-building" approaches, including impatience for change. Leadership succession is already underway, with examples of increased activism and cross-sector collaboration, as well as a new millennial mayor of the central city who declares entrepreneurship to be Miami's strength and national advantage. But there is a consensus that Miami is only a short way along its path to transformation to become the city of future opportunity. Is it essential that networks become more open and inclusive, to find new sources of leadership talent?