The Independent and Combined Effects of External Endorsements on Equity Investments: Evidence from a Randomized Field Experiment

Informations Systems and Operations Management

Intervenant : Sofia BAPNA
PhD candidate Strategic Management and Entrepreneurship , Carlson School of Management

27 novembre 2015 - HEC - Campus Jouy-en-Josas Bâtiment S Salle 210 - De 15h00 à 16h00

This study employs a randomized field experiment to causally identify which signals of external endorsement are most important to equity investors in early stage firms. The three external endorsements examined are: product certification by expert intermediaries; affiliation with prominent others; and social proof, that is, others’ interest in investing in a particular venture. The study, in the context of equity crowdfunding, randomly assigns who is able to view these endorsements and their combinations to make causal inferences about their impact. I find that experienced investors who were able to view the combined product certification and prominent affiliate signals have a 72% higher likelihood of indicating an interest in investing, than those who received no signal. Similarly, experienced investors who were able to view the combined product certification and social proof signals have a 65% higher likelihood of indicating an interest in investing. This suggests that experienced investors follow others (the crowd or high status others) when they have a concrete signal of quality. In contrast, for inexperienced investors, I find that the three endorsements and their combinations are not significantly associated with interest in investing, suggesting a lack of agreement in this group about what factors identify a high potential venture.

Pricing and Capacity Allocation for Shared Services

Informations Systems and Operations Management

Intervenant : Vasiliki Kostami
Assistant Professor , London Business School

12 novembre 2015 - HEC Paris - Campus Jouy en Josas - Bâtiment V - Salle Bernard Ramanantsoa - De 11h30 à 12h30

We study the pricing and capacity allocation problem of a service provider who serves two distinct customer classes. Customers within each class are inherently heterogeneous in their willingness to pay for service, but their utilities are also affected by the presence of other customers in the system. Specifically, customer utilities depend on how many customers are in the system at the time of service as well as who these other customers are. If the service provider can price discriminate between customer classes, pricing out a class, i.e., operating an exclusive system, can sometimes be optimal and that depends only on classes’ perceptions about each other. If the provider must charge a single price, an exclusive system is even more likely. We extend our analysis to a service provider who can prevent class interaction by allocating separate capacity segments to the two customer classes. Under price discrimination, allocating capacity is optimal if our measure of net appreciation between classes is negative. However, under a single–price policy, allocating capacity can be optimal even if this measure is positive. In fact, we show that the nature of asymmetry eventually determines the optimal strategy.

Joint work with Dimitris Kostamis and Serhan Ziya

Short Bio:

Vasiliki Kostami joined the faculty of LBS as an Assistant Professor at the Management Science and Operations Department in 2010 after completing her PhD in Operations Management at Marshall School of Business, USC. Her research interests mainly focus on the management of service operations. She works on the modelling of service systems, such as entertainment facilities, call centers and health care facilities under uncertainty. Specifically, she has looked at queue management problems for amusement parks such as Disneyland, quality management problems for healthcare and optimal inventory management in manufacturing sector. Her research articles have appeared in leading academic journals like M&SOM. She teaches on the full time and executive MBA programmes as well as the PhD programme.

Readmission analytics - Care transformation through information technology

Informations Systems and Operations Management

Intervenant : Mohan Tanniru, Ph.D
Professeur , Université d'Oakland

16 juin 2015 - Campus HEC Jouy-en-Josas Campus - Bâtiment V Salle du Conseil - De 14h30 à 16h00

Health care providers are facing multiple challenges such as improving patient satisfaction, operating with reduced reimbursements, and reducing frequent readmissions. Care providers who address these challenges independently often miss out on opportunities that surface when patient care is viewed within a system, influenced by two environments: clinical environment within the hospital and social environment of patients post-discharge. While hospitals strive for greater efficiencies within the clinical environment, they often find coordination post-discharge to reduce readmissions a major challenge. By viewing the system of patient care through the readmission lens and applying some of the templates discussed under Systematic Inventive Thinking: SIT2 (Inside the Box), this presentation looks at several innovative approaches that can help address patient care both inside and outside the hospital walls by leveraging advances in information technology. Several on-going research projects of care transformation through IT will be highlighted including on-going work of patient care at St Joseph Mercy Hospital in Pontiac, MI and peri-operative care in Stanford Medical School (inside the hospitals), and patient empowerment studies at dialysis centers (DaVita) and medication reconciliation/patient follow-up at nursing homes (outside the hospital).

Corn or Soybean: Dynamic Farmland Allocation under Uncertainty

Informations Systems and Operations Management

Intervenant : Onur BOYABATLI
Professeur Assistant , Singapore Management University

5 juin 2015 - HEC Paris - Campus Jouy en Josas - Bâtiment S - Salle 227 - De 10h30 à 12h00

This paper studies the farmland allocation decision of a farmer between two crops in a multi-period framework. In each growing period, the farmer chooses the allocation in the presence of revenue uncertainty, and crop rotation benefits across periods, i.e. revenue is stochastically larger when a crop is planted in a rotated land (where the other crop was planted in the previous period). We identify two strategies, monoculture, i.e. fully allocate the farmland to one of the crops, and rotate, i.e. plant each crop in the rotated farmland, which characterize the optimal allocation decision in each period. Our analysis provides rules of thumb for the impact of revenue uncertainty: The farmer benefits from a lower revenue correlation between the two crops. Interestingly, the farmer benefits from a higher revenue volatility only when this volatility is sufficiently high; otherwise, a lower revenue volatility increases the profitability. We propose a heuristic allocation policy which we characterize in closed form. Using a calibration based on a representative farmer planting corn and soybean in Iowa, we show that the proposed policy is near-optimal, and significantly outperforms the commonly used heuristic allocation policies in practice (such as the myopic policy, always-rotate policy and monoculture policy).

Measuring the effectiveness of mobile marketing

Informations Systems and Operations Management

Intervenant : Anindya Ghose
Professor of Information, Operations and Management Sciences and Professor of Marketing , New York University's Leonard N. Stern School of Business

3 juin 2015 - HEC - Campus Jouy-en-Josas Bâtiment V - Salle du Conseil - De 11h00 à 12h00

The explosive growth of smartphone and location-based services (LBS) has contributed to the rise of mobile advertising. In this talk, we will present results from multiple studies in Europe and Asia that are designed to measure the effectiveness of mobile marketing promotions. In the first randomized field experiment, using data from a location-based app for smartphones, we measure the effectiveness of mobile coupons. The aim is to analyze the causal impact of geographical distance between a user and retail store, the display rank, and coupon discounts on consumers’ response to mobile coupons. In a second large scale field study where we exploit a quasi-natural experiment we examine the role of contextual crowdedness on the redemption rates of mobile coupons. We find that people become increasingly engaged with their mobile devices as trains get more crowded, and in turn become more likely to respond to targeted mobile messages. The study results were consistent across peak and off-peak times, and on weekdays and weekends. The change in behavior can be accounted for by the phenomenon of “mobile immersion”: to psychologically cope with the loss of personal space in crowded trains and to avoid accidental gazes, commuters can escape into their personal mobile space. In turn, they become more involved with targeted mobile messages they receive, and, consequently, are more likely to make a purchase in crowded trains. These studies causally show that mobile advertisements based on real-time static geographical location and contextual information can significantly increase consumers’ likelihood of redeeming a geo-targeted mobile coupon. However, beyond the location and contextual information, the overall mobile trajectory of each individual consumer can provide even richer information about consumer preferences. In the third study, we propose a new mobile advertising strategy that leverages full information on consumers’ offline moving trajectories. To examine the effectiveness of this new mobile trajectory-based advertising strategy, we designed a large-scale randomized field experiment in one of the largest shopping malls in the world. We find that mobile trajectory-based advertising can lead to highest redemption probability, fastest redemption behavior, and highest transaction amount from customers at the focal advertising store as well as in the shopping mall. Our studies help firms better understand the question of which kinds of mobile advertising are most effective and how machine learning techniques can be combined with statistical models and field experiments to offer the right product to the right audience at the right time on the right channel.


Informations Systems and Operations Management

Campus HEC Paris
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Jean-Philippe COLLIN

Informations Systems and Operations Management

Voir le CV