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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.

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.

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.

Entrepreneurship, strategy, family business.

MBA, PhD (Mgmt)

DK Dies and Tools was set up initially as a tool room by its founder Krishna Verma. It manufactured machine parts, sheet metal tools, jigs and fixtures, plastic/rubber moulds and metal fabrications. The firm came to be known as DK Exports (henceforth DKX) when it was professionalized in the year 2003 for merchant exporting. Lately, after the founder's demise, professionalization had become a dire need when the firm faced with loss of customers, the market share was taken over by the Chinese, workers' expectations had risen, poor internal communications, search for dynamic capabilities and finally a need to diversify had arisen. Unexpected death of the founder had pushed the firm into doldrums. It was because of the founder's relationship and reputation in the market that the business prospered. Unfortunately, the tacit knowledge he possessed could not be handed over to his son Kunal, which led to complexes in business. Hence, there arose a need for internationalization for finding new customers and markets. Entrepreneurial orientation needed a change. The new Chairman, Kunal, had expertise in operations management, with his wife, Priyanka, looking after development via overseas collaborations. The firm had been struggling to create a two-tier top-level management to decide on operational issues, besides search for newer destinations for increasing the scale of operations.

To understand how multilevel entrepreneurship happens and the importance of translating tacit knowledge to explicit knowledge, especially at times when the founder has to pass the baton to the second generation.

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

In the fall of 2014, Chad Kartchner, senior manager of marketing and product management at Honeywell Aerospace (HA), pondered how technology could transform the way aircraft were maintained. He had heard a lot of buzz about cognitive analytics, an artificial intelligence term referring to the use of computer models and algorithms to simulate human thought through self-learning systems, data mining, pattern recognition, and natural language processing. The sheer volume of parts and the time-sensitive nature of repairs in the aviation industry made it complicated to identify problems and address them quickly.

Kartchner contemplated the options for updating HA's ground-based maintenance system. Should he emulate HA's state-of-the-art on-board system for an entire aircraft or try something new? Emulating the on-board system, which HA developed internally, would be an easy sell to leadership given internal buy-in and satisfaction with the on-board system, but he contemplated new approaches because he did not want to overlook rapidly emerging technologies. The latter could include crowdsourced features that leveraged the abundance of knowledge among HA's customers' technicians or a cognitive analytics approach. Even if he could persuade leadership to try a new cognitive analytics approach, should HA partner with an established entity or work with a relatively unproven startup who promised lower cost, better features, and quicker turnaround to develop a new system?

Students will step into the shoes of Kartchner as he leads the internal discussion on whether and how to tap into the benefits of cognitive analytic solutions for Honeywell Aerospace and its customers.

This case is meant for MBA students as a part of their leadership/information technology and system curriculum. It is suitable for classes in both offline and online mode.

Human resources management/information technology and systems.

The case discusses how Poppy Gustafsson (Gustafsson) (she), Cofounder and Chief Executive Officer of Darktrace plc, one of the world’s largest cyber-AI companies, is reinventing enterprise security by using artificial intelligence (AI) to detect and respond to cyberthreats to businesses and protect the public. Darktrace’s technology leverages the principles of the human immune system to autonomously defend organizations from cyberattacks, insider threats and AI warfare. In addition to leading a cutting-edge cybersecurity company, Gustafsson evangelizes gender diversity at Darktrace where 40% of employees and four C-level executives are women, a number nearly unheard of in the tech sector.The case chronicles the journey of Gustafsson and how she led the company to growth and success. Under her leadership, Darktrace has grown into a market leader in the AI cybersecurity space serving 5,600 customers in 100 countries, as of June 2021. Gustafsson not only redefined the cybersecurity space but also inspired women to pursue a career in the field of cybersecurity. She also collaborated with a social enterprise called WISE to encourage more girls to consider STEM careers.However, along the way, she faced several challenges including growing competition, procuring funds from investors, cybersecurity talent shortage and training personnel. Going forward, some of the challenges before Gustafsson would be to meet the changing cyber protection demands of customers; hire, train and retain highly skilled cybersecurity personnel; beat the competition in a saturated cybersecurity services space; sustain revenue growth; and post profits as Darktrace had incurred losses every year since its inception.

This case is designed to enable students to: understand the issues and challenges women face in the field of cybersecurity; understand the qualities required for a woman leader to lead a technology firm; study the leadership and management style of Gustafsson; understand the importance of transformational leadership in management; understand the role of Gustafsson in Darktrace’s growth and success; analyze the traits that Gustafsson possesses as a tech leader in an emerging cybersecurity space; understand the importance of gender diversity in cybersecurity; and analyze the challenges faced by Gustafsson going forward and explore ways in which she can overcome them.

CSS: 11 Strategy.

With the development of inclusive financial business in China in recent years, this case describes the credit risk control of “mobile credit”, a smart online credit platform launched by Shanghai Mobanker Co. Ltd. (referred to as “Mobanker”, previously named as “Shanghai Mobanker Financial Information Service Co., Ltd.”) which provides technical services for inclusive finance industry.

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.

The learning outcomes are as follows:

1. enable students to appreciate how a platform company can navigate through diminishing network effects;

2. enable students to foresee the downside of scaling up a platform business;

3. enable students to appreciate the trade-off between an efficiency-centric and a novelty-centric business model for platform businesses; and

4. enable students to create a platform business model canvas for a company.

The teaching case discusses the dilemma of Akshay Chaturvedi, the founder of Leverage Edu, an artificial intelligence-enabled platform for students seeking admission to foreign universities. It had received nearly US$9.6m in funding until December 2021.

Chaturvedi wanted to make the best use of his funds, but was torn between turning Leverage Edu into an “efficient platform” and transforming it into a “novelty-centric platform”. The teaching note attempts to resolve Chaturvedi’s dilemma by analyzing competitors using the platform canvas model and determining how Chaturvedi could create and use network effects to Leverage Edu’s advantage. The case is based on secondary data that is freely available in the public domain.

This case is intended for MBA Entrepreneurship students taking a platform business elective. It can also be used in faculty and management development programs under the banner “Technology and Platform Businesses”.

Teaching notes are available for educators only.

CSS 3: Entrepreneurship.