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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.

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 asks students to step into the role of Adalberto Flores, co-founder and CEO of Kueski, one of the first companies to develop a proprietary algorithm for online loan approval in Mexico. Mexico lacks a standardized credit scoring system, making it difficult for many Mexicans to get approved for a loan or credit card. This, together with the fact that Mexicans generally do not trust traditional banks, makes Mexico an attractive opportunity for fintech companies. Growth, however, could require fintech companies to partner with traditional banks. Students assume the role of Flores to think about the benefits and risks associated with a partnership between Kueski and traditional banks. Students are also challenged to compare the structure of U.S. financial services markets with the Mexican structure and consider the implications on the sustainability of fintech companies in the two markets. The teaching note analyzes the Mexican financial market and the benefits and threats it holds for fintech companies, and outlines a framework for evaluating the risk associated with partnerships.

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

This case reviews the development of Dianping. After seeing Zagat's unique business model in the United States, founder Zhang Tao found that he could bring it to China and bring about local innovation. At the beginning of its establishment, the collection and promotion of comment content was the major challenge for Dianping. At the same time, Dianping faced legal issues. To solve these problems, the review mechanism of Dianping was designed to a certain extent to ensure the fairness of the review. With the advent of the mobile Internet era, Dianping began to develop a new business model. Relying on its high-quality “word-of-mouth” content and mass basis, Dianping launched group buying, online restaurant ordering, and other businesses. Dianping has always been open to strategic partners. Since 2015, Dianping has undergone historical changes, merging with Meituan. Since then, Dianping has continuously adjusted its business and organizational structure to maintain its competitiveness. Gradually, Dianping has changed from an independent business entity into a business unit of Meituan.

UrbanClap was setup in October 2014 to address the opportunity of bringing the workforce from the unorganised sector into the mainstream using the power of technology. It was an on-demand marketplace for services available through a mobile app. In the initial years, UrbanClap, developed as horizontal marketplace, saw intense competition from existing and new players who were operating in the hyperlocal services space. It competed in the on-demand service marketplace by categorising its services into a lead generation business (where it connected customers with the service provider and charged a fee for matchmaking) and a fulfilment business (where UrbanClap took end-to-end responsibility for quality of service delivery). After three and half years of operations, the three co-founders wondered if it was time they moved out of lead generation and instead focussed on the fulfilment business.

“Originally as a business providing community life services since its founding in 2017, Dingdong (Cayman) has transformed itself into a fresh e-commerce company. After making adjustments to its business model and operating strategy for three times, Dingdong (Cayman) has completed the strategic transition from grocery surrogate shopping to comprehensive self-operation, and built its own commercial fortress. In 2019, the total revenue of the company was five billion yuan. Upon the outbreak of COVID-19, its monthly revenue exceeded 1.2 billion yuan in February 2020, and the year's total revenue was expected to hit 15∼18 billion yuan. To date, Dingdong (Cayman) has formed a supply chain fully based on digital operation and built a commercial fortress in the fresh e-commerce industry. Despite this, its future prospect is not free from challenge. This case mainly deals with the following questions: How about the strategic positioning and core competitiveness of Dingdong (Cayman) in its early days? In the process of rapid expansion, what are the advantages and problems in its business model? How can the digitally operated supply chain support its continuous expansion in the future?”

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.

This case has four major learning outcomes using hands-on spreadsheet tool. First is to introduce and apply the Clarke and Wright’s Savings algorithm. Second is to conduct a cost-benefit analysis in transportation set up. Third is to find out the optimal route to be taken to fulfill given demand while satisfying time and capacity constraints. Finally, one has to optimize the number of vehicles required for daily operations.

Dhruvam, the protagonist of the case, working at ZeNXL, a third-party logistics service provider, was assigned the task of reducing the operational cost of the company as part of the new service offering called “Route optimization.” This new offering would help optimize vehicle delivery routes to meet daily customer demand. The launch of the new service offering was due in the next 10 days with client LG Electronics to be the first beneficiary.

MBA-1 (Logistics Management), MBA-2 (Route Optimization). This case provides an opportunity for instructors to introduce vehicle routing and scheduling as part of logistics management. Students are expected to use the data given in the case and exhibits to develop the optimal routes (using Clarke and Wright’s Savings algorithm) and conduct cost-benefit analysis. This case also provides insights on the challenges associated with start-up operations.

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

CSS 9: Operations and Logistics

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.