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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.

This case allows students to engage with classical marketing tenets of branding, media and communications decisions and content marketing within a management framework.

This case is appropriate for an undergraduate or graduate-level programme in marketing management.

Suzanne Stevens was part of a group of four former senior employees of a large life insurance firm that decided to establish a new and innovative South African insurance company, BrightRock. They identified a gap in a large and highly competitive (albeit generic and opaque) insurance market and developed a distinctive positioning within the market. There was low consumer understanding of the technical aspects of life insurance products, and no existing life insurance product provided an individualized offering. Stevens developed the company’s brand and marketing strategy by drawing on reputation drivers, traditional advertising and a content marketing approach. BrightRock focused on change moments in consumers’ lives, including getting married, having children or getting a new job, and changed the standard insurance product model by launching an individualized flexible product that could adapt with the consumer through their various life stages. The case study documents the first three years of BrightRock’s operations, with a strong focus on brand and product development, distribution and communication. The case dilemma involves choices Stevens faced at the beginning of 2015 about marketing investments across paid, earned and owned media.

This study enables to critique the development of a services brand; integrate paid, owned and earned media to increase communication effectiveness and efficiency; and critique a content marketing strategy.

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 8: Marketing.

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?”

In this case, a senior business analyst at the online travel agency Trek-ation struggles with the decision of whether to pursue a potentially lucrative idea. Her innovation team had proposed revising the online pricing algorithm in order to use the cookies and other information from customers’ web browser to customize pricing for flights and hotels. Although she wanted to increase revenue for the company and meet her targets, she was also concerned not only about the backlash if this tactic was revealed to the public but also, more importantly, about both the fairness of this practice and the violation of customer privacy norms.

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

Rivigo, a new entrant in the trucking business in India, believed that a new paradigm in the trucking/logistics industry could be brought about that would not only improve the quality of service dramatically, but also upgrade a truck driver's lifestyle. While the industry faced driver shortage largely due to long stays away from home, Rivigo hoped to attract drivers by offering them roles which would bring them back home in 24 hours. Drivers would be part of a relay, handing over the truck at pit stops. Further, they leveraged an IT-enabled IoT platform on a fleet of owned trucks. All this revolutionized most of the traditions then followed in the industry. The entrepreneur and his core team comprised professionals from premium institutes of the country, with experience in professional organizations in related domains. By offering services like assured delivery at half the time and full shipment visibility, Rivigo had to charge a premium to market segments that would value this. The case raises the question of sustainability in the future.

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

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.