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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.

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.

This case is designed to enable students to understand the role of women in artificial intelligence (AI); understand the importance of ethics and diversity in the AI field; discuss the ethical issues of AI; study the implications of unethical AI; examine the dark side of corporate-backed AI research and the difficult relationship between corporate interests and AI ethics research; understand the role played by Gebru in promoting diversity and ethics in AI; and explore how Gebru can attract more women researchers in AI and lead the movement toward inclusive and equitable technology.

The case discusses how Timnit Gebru (She), a prominent AI researcher and former co-lead of the Ethical AI research team at Google, is leading the way in promoting diversity, inclusion and ethics in AI. Gebru, one of the most high-profile black women researchers, is an influential voice in the emerging field of ethical AI, which identifies issues based on bias, fairness, and responsibility. Gebru was fired from Google in December 2020 after the company asked her to retract a research paper she had co-authored about the pitfalls of large language models and embedded racial and gender bias in AI. While Google maintained that Gebru had resigned, she said she had been fired from her job after she had raised issues of discrimination in the workplace and drawn attention to bias in AI. In early December 2021, a year after being ousted from Google, Gebru launched an independent community-driven AI research organization called Distributed Artificial Intelligence Research (DAIR) to develop ethical AI, counter the influence of Big Tech in research and development of AI and increase the presence and inclusion of black researchers in the field of AI. The case discusses Gebru’s journey in creating DAIR, the goals of the organization and some of the challenges she could face along the way. As Gebru seeks to increase diversity in the field of AI and reduce the negative impacts of bias in the training data used in AI models, the challenges before her would be to develop a sustainable revenue model for DAIR, influence AI policies and practices inside Big Tech companies from the outside, inspire and encourage more women to enter the AI field and build a decentralized base of AI expertise.

This case is meant for MBA students.

Teaching Notes are available for educators only.

CCS 11: Strategy

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.

After a successful transition from a projects-based IT business services company to a platform-driven analytics company, Saama's core leadership team gathered in 2017 to brainstorm the next phase of its growth. The year before, the team had decided to narrow its target market to the life sciences vertical. Saama now had to decide how to execute on this focused strategy by choosing a growth pathway within the life sciences vertical. Saama's leadership team was considering three alternatives: acquiring new customer accounts, developing existing customer accounts, or developing new products by harnessing artificial intelligence (AI) and blockchain technologies. The team had to evaluate these growth pathways in terms of both short- and long-term revenue potential, as well as their potential for sustaining Saama's competitive advantage.

This case study aims to be taught at an MBA level. Specifically, those students who are majoring in supply chain would benefit the most from this case study. This case study has elements of supply chain management, supply chain strategy, warehousing and logistics, and a digital supply chain for Industry 4.0. The learning outcome of this case study could be seen if the students are able to identify the challenges and opportunities of a digital supply chain for Industry 4.0 and how it could be implemented methodically. Teaching Objective 1: Students should be able to identify what challenges organizations face if they implement a digital supply chain for Industry 4.0. Teaching Objective 2: Students should be able to identify what opportunities can be tapped if Big Data Analytics are used in a supply chain teaching. Objective 3: Students should layout a methodical plan of how an analogue company can gradually achieve the objective of implementing a digital supply chain for Industry 4.0 in procurement function.

Based in the Lahore region of Pakistan, Xarasoft is a footwear manufacturing company which has undertaken a decision to transcend to a digital supply chain for Industry 4.0 by 2027. Asif, who is the Head of the Department of Supply Chain, has to come up with a plan to present in the next meeting with the CEO. Xarasoft is a company that preferred to work in an analogue routine. The company set production targets and sold goods through marketing. With no forecast or exact demand, the company had decided to procure 140 million units of raw material and carrying a huge inventory, a percentage of which had to be thrown away as it started to degrade. While the company did have machinery on the production floor, they were operated manually and were a generation behind. Asif faced the question of what challenges he would face and exactly how would a digital supply chain for Industry 4.0 be implemented in the company.

Masters level supply chain courses

Teaching notes are available for educators only.

CSS 9: Operations and Logistics.

The case focuses on Thivra Info Solutions Pvt Ltd, an entrepreneurial organization incubated by Prasannan (she/her) in 2017. The organization started with a mission to provide technology-based learning solutions for children diagnosed with autism spectrum disorder (ASD). Thivra Info Solutions Pvt Ltd had developed multiple offerings, including gamified learning, targeted to ASD and general ed-tech users. The firm also launched “Dwani,” the communicative-based learning app for ASD children. The initial feedback by users, parents and teachers had been encouraging. Prasannan was exploring avenues to scale the business when the Covid-19 pandemic affected all the operations.The case presents the multiple dilemmas entrepreneurial firms face in managing resources, finances, growth and product and customer focus. Students are encouraged to debate the organization strategy, product and consumer target segments and solutions to scale the business while managing frugal resources.

This case study can be used in entrepreneurship, leadership, crisis management, business development, organizational behavior and technology.

The case study describes the navigation of Thivra from a Generic Gamified App to its niche of catering for ASD students. The case presents the challenges presented to leadership to manage the crisis and try to grow their entrepreneurial venture. This case has been designed for use in business-to-consumer marketing or entrepreneurship, gender entrepreneurship, ed-tech-based startups, in MBA, executive MBA or executive education programs in the field. The case is suitable for those doing business in Asia, for post-graduate and under-graduate students studying business innovation, entrepreneurship, strategy and marketing. It is also appropriate for courses on gender entrepreneurship; women and crisis management; and product management. The case aims at facilitating classroom discussion on the extension of Indian-based ed-tech startups to ASD children.

Students will also be able to explore the following issues: to study the role played by a business model that withstands the competition over a long period and adopting sustainability; to describe the concept and implications of paradoxical leadership, thereby drawing its impact on business decisions; to analyze how a leader acts in terms of crisis from a startup point of view; to draw the phases and constraints of the enterprise development and compare and contrast it based on gender; to demonstrate the value to different constituents (ASD students, parents, teachers and ASD counselors) by understanding their differentiated needs and developing powerful value propositions for each. Articulating and demonstrating this value is key to gaining the buy-in of the various decision-making units; to understand how, having gained traction in one market segment (in this case, tractions with parents of ASD children), a company can develop new market segments; to study the issues and problems faced by startups in developing economies, especially the tech-based ones; and to understand the application of gamification on education and communication for ASD children.

Teaching notes are available for educators only.

CSS 3: Entrepreneurship

This case is designed to provide learners with strategic decision-making skills. It also introduces the various nuances of establishing a technopreneurial venture and creating and sustaining a competitive advantage. The setting allows learners to comprehend the significance of assessing the business environment and the advantage of being a first mover in any business sector. The case allows a rare glimpse into the strategic decision framework of a scale-wise relatively much small tech firm that is competing with global giants and creating waves, and winning accolades for its performance. The case also allows learners to understand the analysis and decision-making parameters studied by a company while selecting a product. After working through and discussing this case, learners should be able to identify the strategic decision framework in which Aeron undertakes a product selection decision; analyze the conditions in the operating environment that made Aeron possess a competitive advantage; and design the product choice strategy for Aeron.

This case is about the journey of two friends, Abhijeet Bokil and Ashwani Shukla, who started a company together in Pune, India, in 2008 and achieved great success. The case discusses their tryst with different products and industrial sectors. It unfolds the journey of their startups and their finding the desired product category. It also discusses the various hurdles they face while establishing the business. The journey was tough as it occurred in the unpredictable background of the dynamic international technology and policy environment. The common challenges to entrepreneurship were present throughout their journey. The team encountered various financial and technological trials during these years. The case explains how excellent strategic choices made the team overcome those challenges. The biggest dilemma faced by the team was to select between two of their products. The one who gave them visibility and success or the new upcoming one with more entry barriers. The case deals with the issue of product selection in the sectors of telematics and weather monitoring.

The case is appropriate for courses in Business management, Strategy management programs & innovation and Entrepreneurship management courses. The case would suit BBA and MBA students learning various management models.

Teaching notes are available for educators only.

CSS 3: Entrepreneurship.

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.