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The case is presented as descriptive in nature and primarily involves exploratory research.

Ashraf, a young graduate from Bangalore, India, started a chain of lifestyle shops, his family business in Khartoum, Sudan. To modernize the shops, Ashraf approached a small finance bank for financial assistance. However, after submitting the required documents and with a good credit score, he was denied a loan. The bank officials had mentioned that the loan automation software did not approve the application. Hence, the bank personnel said that they could not do anything further. Disappointed, Ashraf sought the help of his professor, John, to understand why the software rejected his application. Professor John explained to Ashraf the advantages and disadvantages of automation. In the process, Ashraf understood the significance and compelling need to address “Algorithm Bias,” a situation in which specific attributes of an algorithm cause unfair outcomes. The case place students in Ashraf’s position to help them understand the advantages and issues of applying automation through artificial intelligence.

The case suits graduate-level courses like business analytics, financial analytics and business intelligence.

Through the case, the students will be able to: Understand the role of algorithms in business and society. Understand the causes, effects and methods of reducing algorithm bias. Demonstrate the ability to detect algorithm bias. Define policies to mitigate algorithm bias.

The case is designed to be used with the digital business model framework developed by Peter Weill and Stephanie Woerner of Massachusetts Institute of Technology (MIT) (Weill and Woerner, 2015) and is referred to as the W & W framework. This approach provides a useful structure for thinking through the strategic options facing environments ripe for digital transformation.

Research for this case was conducted through face-to-face interviews with the protagonist, as well as through a review of their business planning documents and other data and documentation provided by the founder. Some of the market and industry data were obtained using secondary research and industry reports. Interviews were digitally recorded and transcribed to ensure accuracy.

The case follows the story of Kurt Waltenbaugh, a Minnesota entrepreneur who shared the dream of using data analytics to reduce costs within the US health-care system. In early 2014, Waltenbaugh and a physician colleague founded Carrot Health to bring together their personal experience and expertise in both consumer data analytics and health care. From the beginning, they focused on how to use data analytics to help identify high-risk/high-cost patients who had not yet sought medical treatment. They believed that they could use these insights to encourage early medical interventions and, as a result, lower the long-term cost of care.

Carrot’s initial success found them in a consultative role, working on behalf of insurance companies. Through this work, they honed their capabilities by helping their clients combine existing claims data with external consumer behavioral data to identify new potential customers. These initial consulting contracts gave Carrot the opportunity to develop its analytic tools, business model and, importantly, to earn some much-needed cash flow during the start-up phase. However, they also learned that, while insurance companies were willing to purchase data insights for one-off market expansion projects, it was much more difficult to motivate them to use data proactively to eliminate costs on an ongoing basis. Waltenbaugh believed that Carrot’s greatest potential lay in their ability to develop predictive models of health outcomes, and this case explores Carrot’s journey through strategic decisions and company transformation.

This case is intended for either an undergraduate or graduate course on entrepreneurial strategy. It provides an effective introduction to the unique structure and constraints which apply to an innovative start-up within the health-care industry. The case also serves as a platform to explore the critical criteria to be considered when developing a digital transformation strategy and exposing students to the digital business model developed by Weill and Woerner (2015) at MIT (referred to in this instructor’s manual as the W&W framework). The case was written to be used in an advanced strategy Master of Business Administration (MBA) class, an undergraduate specialty health-care course or as part of a health-care concentration in a regular MBA, Master of Health Care Administration (MHA) or Master of Public Health (MPH). It may be taught toward the end of a course on business strategy when students are building on generic strategy frameworks and adapting their strategic thinking to the characteristics of specific industries or sectors. However, the case can also be taught as part of a course on health-care innovation in which case it also serves well as an introduction to the health-care payments and insurance system in the USA. Finally, the case can be used in a specialized course on digital transformation strategy in which case it serves as an introduction to the MIT W&W framework.

The case is particularly well-suited to students who are familiar with traditional frameworks for business strategy and business models. The analysis builds on this knowledge and introduces students interested in learning about the opportunities and challenges of digital strategy. Equally, the case works well for students with clinical backgrounds, who are interested in how business strategy can influence changes within the health-care sphere. Finally, an important aspect of the case design was to develop students’ analytical confidence by encouraging them to “get their hands dirty” and to carry out some basic exploratory data analytics themselves. As such, the case requires students to combine and correlate data and to experience the potentially powerful combination of clinical and consumer data. Instructors should find that the insights from these activities give students unique insights into the potential for of data analytics to move health care from a reactive/treatment ethos to a proactive/intervention ethos. This experience can be particularly revealing for students with clinical backgrounds who may initially be resistant to the use of clinical data by commercial organizations.

In May 2016, Sarita Digumarti, Chief Operating Officer of Jigsaw Academy in Bengaluru, India, faced a challenging situation. Jigsaw Academy provided online courses in data analytics and Big Data at the beginner, intermediate and advanced levels for students as well as working professionals. It was perceived that plenty of students from premier institutions in India had a high level of theoretical knowledge about the process involved in number crunching and data analysis; however, the hands-on experience on actual business problems or actual data sets was a major limitation with these students. Given the rapid growth of the analytics sector and the limited number of academic institutions offering analytics courses, there was a lack of availability of the right skills in the analytics market. Jigsaw Academy seized this opportunity and started offering relevant courses. All efforts were made to enhance the number of students enrolling for the courses, which in turn resulted in improving its customer base. Realizing the demand of industries for employees skilled in the analytics sector, Jigsaw Academy wanted to grow its brand equity and to achieve this through business to business (B2B) collaborations and/or alliances. However, expansion through B2B has its own challenges. Given the competitive landscape of analytics market, Jigsaw Academy was wondering whether they should opt for B2B channel, and if yes, the question was related to the process of choosing potential B2B partners.

The authors have collected the data from primary sources as well as secondary sources. Primary sources include field visits and audio-recorded interviews conducted with key departmental heads in the organization. Secondary sources include data retrieved from the company website and the relevant information available about the industry with the assistance of the internet. Except the founder’s name, all other names are disguised to protect the individual’s privacy as per instructions from the founders of Jigsaw Academy.

This case can be used at the graduate or MBA level in courses such as entrepreneurship, sales and distribution management, strategic alliances and mergers.

This compact case provides a relatively large data set that students explore using visualization and a Tableau dynamic dashboard that they create. Students were asked to describe what the data set contained in relation to employee attrition experience of Baca Beverage Distributors (BBD). The application and managerial questions are set in human resources and a company that is facing high attrition during the pandemic.

BBD shared their data and problem scenario for this compact case. The protagonist, Morgan Matthews, was the authors’ contact and provided significant clarification and guidance about the data. Both the company and the protagonist have been disguised. Some of the job positions have been rephrased. All names of employees, supervisors and managers have been replaced with codes.

During the 2020–2022 pandemic years, BBD experienced, like many companies, a higher than usual employee turnover rate and Morgan Matthews, Director of People, was concerned. Not only was it time-consuming, expensive and disruptive but the company had prided itself on being a good place to work. Were they hiring the right people, people that fit the company culture and people that fit the positions for which they were hired? The company had been using the Predictive Index [1] when on-boarding employees. In addition, there were results from self-reviews and manager reviews that could be used. Morgan wondered if data visualization and visual analytics would be useful in describing their employees and whether it would reveal any opportunities to improve the turnover rate. Before seeking a solution for the high turnover, it was important to step back and learn what the data said about who was leaving and the reasons they gave for leaving.

This compact case can be used in courses that include visualization using Tableau and dashboards. As it is a compact case, it requires less preparation time from the students and less class time for discussion. The case is for students who have been recently introduced to business analytics, specifically visualization and data storytelling with Tableau. For this reason, significant guidance has been provided in the case assignment. The level of the case can be adjusted by the amount of guidance provided in the case assignment. Courses include introduction to business analytics, descriptive analytics and visualization, communication through data storytelling. The case can be used for all modalities – in person, hybrid, online. The authors use it here for visualization and dynamic dashboards but using the same data set and compact case description, exploratory data analysis could be assigned.

Supplementary material for this article can be found online.

This case exposes students to predictive analytics as applied to discrete events with logistic regression. The VP of customer services for a successful start-up wants to proactively identify customers most likely to cancel services or “churn.” He assigns the task to one of his associates and provides him with data on customer behavior and his intuition about what drives churn. The associate must generate a list of the customers most likely to churn and the top three reasons for that likelihood.

Josh Williams is a Student at the NAU who has driven buses on campus and wants to improve the transportation on campus. He is convinced that purchasing a new type of bus that is more fuel efficient, has larger capacity, better designed for boarding, and has a longer life is worth the higher purchase cost. He sets out to prove it by creating a discounted cash flow (DCF) analysis. Since many of the estimates for the DCF analysis are uncertain, he decides to perform a Monte Carlo simulation (MCS) analysis. Students are asked to step into Josh’s role and perform the analysis.

Josh Williams was a Student in the authors’ MBA program. Both authors teach in this program and one author was the Advisor for Net Impact and worked with Josh to present his idea to the university administration. The authors have changed a name or two but otherwise, the case describes a real situation in a real organization without disguise.

The authors have used this case in a first semester MBA-Applied Management course, Decision Modeling and Simulation. Students already have experience with DCF analysis and have been introduced to MCS. With this case, students apply MCS at the conclusion of a three-week module on predictive analytics. Students have run at least two MCS models and have become comfortable with the software. The case would also be appropriate for a senior-level undergraduate course such as business analytics or management science. It might also be useful for other courses that include the MCS modeling technique learning objectives such as project management.

This case provides an opportunity for students to perform an MCS analysis. MCS is useful when many of the inputs to a DCF analysis (or any model) have been estimated and the modeler is concerned that the estimates are uncertain and could perhaps be a range of values. MCS can be used to understand the effect of this uncertainty on NPV which in turn may affect the decision. The case could also be used without MCS focusing just on the DCF analysis with deterministic sensitivity analysis.

Resource-based view (RBV) theory (Barney, 1991; Barney and Mackey, 2016; Nagano, 2020) states that a firm’s tangible and intangible resources can represent a sustainable competitive advantage (SCA), a long-term competitive advantage that is extremely difficult to duplicate by another firm, when it meets four criteria (i.e. not imitable, are rare, valuable and not substitutable). In the context of this case, we believe there are three sources of SCA to be discussed using RBV – the major league soccer (MLS) team player roster, the use of artificial intelligence (AI) technologies to exploit this roster and the league’s single-entity structure: • MLS players: it has been widely acknowledged that a firm’s human resource talent, which includes professional soccer players (Omondi-Ochieng, 2019), can be a source of SCA. For example, from an RBV perspective, a player on the Los Angeles Galaxy roster: > cannot play for any other team in any other league at the same time (not imitable and are rare), > would already be a competitive player, as he is acquired to play in the highest professional league in the country (valuable) and > it would be almost impossible to find a clone player matching his exact talent characteristic (not substitutable) anywhere else. Of course, the roster mix of players must be managed by a capable coach who is able to exploit these resources and win championships (Szymanski et al., 2019). Therefore, it is the strategic human resource or talent management strategies of the professional soccer team roster that will enable a team to have the potential for an SCA (Maqueira et al., 2019). • Technology: technology can also be considered a source of SCA. However, this has been a source of contention. The argument is that technology is accessible to any firm that can afford to purchase it. Logically, any MLS team (or for that matter any professional soccer team) can acquire or build an AI system. For many observers, the only obvious constraint is financial resources. As we discuss in other parts of the case study, there is a fan-based assumption that what transpired in major league baseball (MLB) may repeat in the MLS. The movie Moneyball promoted the use of sabermetrics in baseball when making talent selection (as opposed to relying exclusively on scouts), which has now evolved into the norm of using technology-centered sports analytics across all MLB teams. In short, where is the advantage when every team uses technology for talent management? However, if that is the case, why are the MLB teams continuing to use AI and now the National Basketball Association (NBA), National Football League (NFL) and National Hockey League are following suit? We believe RBV theorists have already provided early insights: > “the exploitation of physical technology in a firm often involves the use of socially complex firm resources. Several firms may all possess the same physical technology, but only one of these firms may possess the social relations, cultural traditions, etc., to fully exploit this technology to implementing strategies…. and obtain a sustained competitive advantage from exploiting their physical technology more completely than other firms” (Barney, 1991, p. 110). • MLS League Single-Entity Structure: In contrast to other professional soccer leagues, the MLS has one distinct in-built edge – its ownership structure as a single entity, that is as one legal organization. All of the MLS teams are owned by the MLS, but with franchise operators. The centralization of operations provides the MLS with formidable economies of scale such as when investing in AI technologies for teams. Additionally, this ownership structure accords it leverage in negotiations for its inputs such as for player contracts. The MLS is the single employer of all its players, fully paying all salaries except those of the three marquees “designated players.” Collectively, this edge offers the MLS unparalleled fluidity and speed as a league when implementing changes, securing stakeholder buy-ins and adjusting for tailwinds. The “socially complex firm resources” is the unique talent composition of the professional soccer team and most critically its single entity structure. While every team can theoretically purchase an AI technology talent management system, its application entails use across 30 teams with a very different, complex and unique set of player talents. The MLS single-entity structure though is the resource that supplies the stability required for this human-machine (technology) symbioses to be fully accepted by stakeholders such as players and implemented with precision and speed across the entire league. So, there exists the potential for each MLS team (and the MLS as a league) to acquire SCA even when using “generic” AI technology, as long as other complex firm factors come into play.

This case relied on information that was widely reported within media, press interviews by MLS officials, announcements by various organizations, journal articles and publicly available information on MLS. All of the names and positions, in this case, are actual persons.

MLS started as a story of dreaming large and of quixotic adventure. Back in 1990, the founders of the MLS “sold” the league in exchange for the biggest prize in world soccer – the rights to host the 1994 Fédération Internationale de Football Association World Cup before they even wrote up the business plan. Today, the MLS is the highest-level professional men’s soccer league competition in the USA. That is a major achievement in just over 25-years, as the US hosts a large professional sports market. However, MLS has been unable to attract higher broadcasting value for its matches and break into the highest tier of international professional soccer. The key reason is that MLS matches are not deemed high quality content by broadcasters. To achieve higher quality matches requires many inputs such as soccer specific stadiums, growing the fan base, attracting key investors, league integrity and strong governance, all of which MLS has successfully achieved since its inception. However, attracting high quality playing talent is a critical input the MLS does not have because the league has repeatedly cautioned that it cannot afford them yet to ensure long-term financial sustainability. In fact, to guarantee this trade-off, the MLS is one of the only professional soccer leagues with an annual salary cap. So, the question is: how does MLS increase the quality of its matches (content) using relatively low cost (low quality) talent and still be able to demand higher broadcast revenues? One strategy is for the MLS to use AI playing technology to extract higher quality playing performance from its existing talent like other sports leagues have demonstrated, such as the NFL and NBA. To implement such a radical technology-centric strategy with its players requires the MLS to navigate associated issues such as human-machine symbioses, risking fan acceptance and even altering brand valuation.

The case is written and designed for a graduate-level (MBA) class or an upper-level undergraduate class in areas such as contemporary issues in management, human resource management, talent management, strategic management, sports management and sports marketing. The case is suitable for courses that discuss strategy, talent management, human resource management and brand strategy.

Case can be taught at the undergraduate or postgraduate level, including executive Master of Business Administration programs.

This case is intended for courses in strategic management, entrepreneurship and innovation at the undergraduate or postgraduate level.

The case is about challenges faced by Linda Portnoff, the Co-founder and Chief Executive Officer of Riteband, a Sweden-based fintech startup. In March 2020, Portnoff was conducting beta testing of Riteband’s app, which experts considered the world’s first stock exchange for music trading. After completing a PhD, Portnoff who was working as a Research Analyst, left her job to pursue entrepreneurship. Through Riteband, Portnoff helped to resolve pain points of artists who were forced to give the copyright of their music tracks or albums to distributors, in lieu of funds or promotional campaigns that distributors arranged for them. Portnoff invested in developing a patent-pending machine learning-based algorithm that based on several parameters could predict the likelihood of a music track or an album to become a success. Based on this prediction and royalty that artists were interested in sharing with fans, shares were issued to investors, who were also fans of the artists. As Portnoff identified an innovative business opportunity to trade music on a stock exchange based on Riteband’s machine learning algorithm, competition in Riteband’s strategic group was also becoming intense. Consequently, Portnoff was facing challenges of establishing competitive advantage of Riteband. Furthermore, as women in general faced challenges in raising funds for their startups, and even though Portnoff obtained some funding for Riteband, but overall, funding was a challenge for her as well. Moreover, as machine learning was a technical aspect for artists and potential investors, Portnoff also faced challenges to monetize on its machine learning algorithm.

By the end of the case study discussion, students should be able to: understand the principles of cross-industry innovation and explain the creation of new business opportunities based on cross-industry innovation; differentiate between direct and indirect competitors through strategic group analysis and further critically analyze the competitive advantage of business over other direct competitors; determine ways of reducing gender biases in venture capital funding; describe how machine learning works and further formulate ways to monetize a business through machine learning; and demonstrate the application of the value proposition canvas and business model canvas.

CSS 3: Entrepreneurship; CSS 11: Strategy.

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.

In 2010, Salil Pande founded VMock, an online product that helped MBA students prepare for job interviews. Students could upload their video interviews and get feedback from mentors and peers. Four years later, VMock pivoted from an interview feedback product to a “Smart Resume” product that focused on improving resumes. The pivot was based on the insight that job candidates first needed help fixing their resumes before they could obtain and prepare for interviews. Further, the interview feedback product was difficult to scale as it relied on human feedback. The Smart Resume product, on the other hand, was powered by machine learning and artificial intelligence technology, making it more scalable and allowing VMock to evolve its offering from a product to a platform for managing careers. VMock had forged strong relationships with top business schools in the United States and Europe and its Smart Resume platform had been well received by the market.

Now Salil and his wife (and head of product development), Kiran, had to determine the next step in the company's evolution. They realized that the time had come to take their business to the next level. But they were faced with several options on how to go about scaling VMock. Should they market directly to consumers or should they use partners to scale their user base? Should they create a solution for employers to help them recruit and manage talent? What revenue streams should they focus on to maximize growth and profitability? These strategic decisions would be key to the survival and growth of VMock.