Search results

This case is designed to facilitate students to comprehend the challenges an e-commerce firm faces when it attempts to monetize data network effects. The challenges faced by Zomato are ideal for in-class debate and discussion. The following learning objectives can be fulfilled through this case: understanding the promises and issues raised by data network effects; comprehending the problems an e-commerce firm faces in re-configuration; illustrating the responsibility of an established e-commerce firm towards its stakeholders; and discussing how a firm should navigate its relationship with its stakeholders.

Zomato.com, the largest Indian food aggregator and delivery platform, was contemplating the launch of Zomato Instant, a 10-min food delivery. Currently, the company’s delivery model pivoted around delivering food within 30 min. Recently, Zomato acquired Blinkit, an online grocery shopping app that was positioned to deliver groceries in 10 min. Deepinder Goyal of Zomato felt that customers would soon be more discriminant in demanding quicker services, as they might not be comfortable with 30-min deliveries. Hence, Zomato’s business model must also be re-configured to provide 10-min deliveries. Armed with access to customer data, Goyal predicted items that could be prepared and delivered within 10 min from its dark stores and automated kitchens. Although the model seemed promising and the company was upbeat about it, Zomato Instant faced challenges on several fronts. From the human angle, the decision was criticized on social media, mainly around the violation of road regulations, road safety issues and pressure on the delivery personnel to perform. Many delivery personnel had fled this gig work to join their pre-COVID jobs. Even the Competition Commission of India had established an inquiry into Zomato’s anti-competitive practices using customer data.

This case is best taught as part of a curriculum in management programmes at the post-graduate level, in courses such as e-commerce, e-retailing, business models for electronic commerce and online entrepreneurship/new age entrepreneurship. In terms of the positioning in the course, this case could be used to demonstrate the challenges of re-configuration of an online platform.

Teaching notes are available for educators only.

CSS 3: Entrepreneurship.

Advances in material agency driven by artificial intelligence (AI) have facilitated breakthroughs in material adaptivity enabling smart objects to autonomously provide individualized smart services, which makes smart objects act as social actors embedded in the real world. However, little is known about how material adaptivity fosters the infusion use of smart objects to maximize the value of smart services in customers' lives. This study examines the underlying mechanism of material adaptivity (task and social adaptivity) on AI infusion use, drawing on the theoretical lens of social embeddedness.

This study adopted partial least squares structural equation modeling (PLS-SEM), mediating tests, path comparison tests and polynomial modeling to analyze the proposed research model and hypotheses.

The results supported the proposed research model and hypotheses, except for the hypothesis of the comparative effects on infusion use. Besides, the results of mediating tests suggested the different roles of social embeddedness in the impacts of task and social adaptivity on infusion use. The post hoc analysis based on polynomial modeling provided a possible explanation for the unsupported hypothesis, suggesting the nonlinear differences in the underlying influencing mechanisms of instrumental and relational embeddedness on infusion use.

The formation mechanisms of AI infusion use based on material adaptivity and social embeddedness help to develop the business strategies that enable smart objects as social actors to exert a key role in users' daily lives, in turn realizing the social and economic value of AI.

This study advances the theoretical research on material adaptivity, updates the information system (IS) research on infusion use and identifies the bridging role of social embeddedness of smart objects as agentic social actors in the AI context.

Algorithms are widely used to manage various activities in the gig economy. Online car-hailing platforms, such as Uber and Lyft, are exemplary embodiments of such algorithmic management, where drivers are managed by algorithms for task allocation, work monitoring and performance evaluation. Despite employing substantially, the platforms face the challenge of maintaining and fostering drivers' work engagement. Thus, this study aims to examine how the algorithmic management of online car-hailing platforms affects drivers' work engagement.

Drawing on the transactional theory of stress, the authors examined the effects of algorithmic monitoring and fairness on online car-hailing drivers' work engagement and revealed the mediation effects of challenge-hindrance appraisals. Based on survey data collected from 364 drivers, the authors' hypotheses were examined using partial least squares structural equation modeling (PLS-SEM). The authors also applied path comparison analyses to further compare the effects of algorithmic monitoring and fairness on the two types of appraisals.

This study finds that online car-hailing drivers' challenge-hindrance appraisals mediate the relationship between algorithmic management characteristics and work engagement. Algorithmic monitoring positively affects both challenge and hindrance appraisals in online car-hailing drivers. However, algorithmic fairness promotes challenge appraisal and reduces hindrance appraisal. Consequently, challenge and hindrance appraisals lead to higher and lower work engagement, respectively. Further, the additional path comparison analysis showed that the hindering effect of algorithmic monitoring exceeds its challenging effect, and the challenge-promoting effect of algorithmic fairness is greater than the algorithm's hindrance-reducing effect.

This paper reveals the underlying mechanisms concerning how algorithmic monitoring and fairness affect online car-hailing drivers' work engagement and fills the gap in the research on algorithmic management in the context of online car-hailing platforms. The authors' findings also provide practical guidance for online car-hailing platforms on how to improve the platforms' algorithmic management systems.