Decision, Operations & Information Technologies
Permanent URI for this communityhttp://hdl.handle.net/1903/2230
Prior to January 4, 2009, this unit was named Decision & Information Technologies.
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Item PRICING AND EMPLOYMENT ISSUES IN THE PROVISION OF RIDE SERVICES(2022) Cao, Ziwei; Ball, Michael; Kannan, P.K.; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Recent years have witnessed the emergence and dramatic growth of platform businesses. This dissertation addresses two challenges of significance to ride service companies: 1) it investigates promotion effects on two-sided platforms; and 2) it models platform pricing and staffing strategies under a hybrid employment mode.In the first chapter, I broadly discuss the new challenges faced by the ride service platforms in recent years and provide perspectives on related research questions. In the second chapter, I study the network effects of different promotion methods in two-sided markets. Using data from a transportation-service platform, I specify a structural model that quantifies the respective promotional effects for price discounts and service upgrades. The results show that the primary effect of price discounts is to increase demand within the same service tier, whereas upgrades have stronger stickiness effects and spillover effects. Based on the estimates, I calculate the return on investment (ROI) and find that the ROI for upgrades is higher than that for discounts. Our counterfactual analyses show that as the platform matures, the importance of upgrades increases while the importance of price discounts decreases. These results provide important managerial implications for platforms on how to design optimal promotions to grow their business. In the third chapter, I model an on-demand platform that adopts a hybrid employment mode. This work is motivated by the recent public debate over the status of drivers for the major ride- hailing platforms as contract workers. My hybrid employment environment includes both contractors and full-time employees who receive a benefits package. In the hybrid model with driver control, drivers have the flexibility to decide how long to work and consequently whether to be an employee or a Contractor. Those who work over a certain number of hours will be classified as employees and receive a benefits package. The platform is a profit-maximizer and decides the optimal price based on the required benefit amount. As the benefit amount increases, the platform's profit decreases, which is consistent with strong gig company opposition to providing benefits. Moreover, I show that higher benefits make consumers and full-time drivers better off but decrease part-time drivers' welfare as well as overall social welfare. I consider a second model to better balance the platform's profitability and drivers' welfare. Under the hybrid model with platform control, the platform hires a limited number of full-time employees while guaranteeing that a minimum proportion of all work will be fulfilled by those employees. In this way, the profit loss due to the required benefits is capped, making this alternative policy a potentially viable solution from the platform’s perspective.Item ESSAYS ON CUSTOMER ANALYTICS IN MOBLIE ECOSYSTEMS(2017) Lee, Dongwon; Gopal, Anandasivam; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation focuses on understanding the value of customer analytics in the mobile channel through three essays. Specifically, I study customer behaviors and technology use in mobile ecosystems. The first essay of this dissertation examines the difference in the effects of recommendation systems across the PC and mobile channels on customer-level decision outcomes and market. I conduct two randomized field experiments and find that the impact of the recommendation systems is higher for the mobile channel than the PC channel on customer-level decision outcomes. With respect to the market, I observe no direct effect of recommendation systems on sales diversity but I find that diversity of both product sales and views are higher on the mobile channels compared to the PC channel. In the second essay, I study the composite effect of mobile push notifications and recommendation systems on views and sales in the context of mobile retailing. While the direct effect of such notifications on the pushed product is to be expected, I find interestingly that the effect of the notification is significantly higher for recommended products, suggesting a complementarity between push notifications and recommendation systems that has not yet been addressed in the literature. Finally, I broaden the scope of my studies in my third essay by studying a context that which has received little attention within the mobile context – charitable giving and cause marketing. I study how mobile devices may be used to encourage charitable giving through cause marketing campaign by conducting a large-scale randomized field experiment, focusing on the influence of push notifications, monetary subsidies, and intertemporal choices of subsidy in mobile cause marketing context. Results of the experiment demonstrate that push notifications have a remarkably high effect on donation outcomes. Contrary to previous findings from offline contexts, I find that donation decision and donation amount are significantly higher with rebate subsidies, compared to matching subsidies. Taken as a whole, this dissertation contributes to a better understanding of customer behavior and the role of the technology use in the mobile ecosystem.Item ASSESSING QUALITY IN HIGH-UNCERTAINTY MARKETS: ONLINE REVIEWS OF CREDENCE SERVICES(2016) Lantzy, Shannon; Stewart, Katherine; Viswanathan, Siva; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In economics of information theory, credence products are those whose quality is difficult or impossible for consumers to assess, even after they have consumed the product (Darby & Karni, 1973). This dissertation is focused on the content, consumer perception, and power of online reviews for credence services. Economics of information theory has long assumed, without empirical confirmation, that consumers will discount the credibility of claims about credence quality attributes. The same theories predict that because credence services are by definition obscure to the consumer, reviews of credence services are incapable of signaling quality. Our research aims to question these assumptions. In the first essay we examine how the content and structure of online reviews of credence services systematically differ from the content and structure of reviews of experience services and how consumers judge these differences. We have found that online reviews of credence services have either less important or less credible content than reviews of experience services and that consumers do discount the credibility of credence claims. However, while consumers rationally discount the credibility of simple credence claims in a review, more complex argument structure and the inclusion of evidence attenuate this effect. In the second essay we ask, “Can online reviews predict the worst doctors?” We examine the power of online reviews to detect low quality, as measured by state medical board sanctions. We find that online reviews are somewhat predictive of a doctor’s suitability to practice medicine; however, not all the data are useful. Numerical or star ratings provide the strongest quality signal; user-submitted text provides some signal but is subsumed almost completely by ratings. Of the ratings variables in our dataset, we find that punctuality, rather than knowledge, is the strongest predictor of medical board sanctions. These results challenge the definition of credence products, which is a long-standing construct in economics of information theory. Our results also have implications for online review users, review platforms, and for the use of predictive modeling in the context of information systems research.Item Integrating Social Network Effects in Product Design and Diffusion(2012) Gunnec, Dilek; Raghavan, Subramanian; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Connectivities among people are amplified with recent advancements in internet technology increasing the number of communication channels. Information spread over these networks strengthen the social influence among individuals and affect their purchasing decisions. In this thesis, we study three problems in the product design and diffusion context by integrating such social network effects where influence takes place over neighborhood relationship ties among the users of the product. We consider the setting where peer influence plays a significant role in a consumer's product choice or there is a tangible benefit from using the same product as the rest of one's social network. Building upon the well-known Share-of-Choice problem, we model an influence structure and define the Share-of-Choice problem with Network Effects. It is an NP-Hard combinatorial optimization problem which we solve using a Genetic Algorithm. Using simulated data we show that ignoring social network effects in the design phase of a product results in a significantly lower market share for a product. Our genetic algorithm obtains near-optimal solutions and is very robust in terms of its running time, scalability, and ability to adapt to additional constraints/variations of the model. In this setting, we introduce a product diffusion problem, the Least Cost Influence Problem, which increases the market share of a product by intervening the natural diffusion of it over the social network. This intervention is in the form of incentive supply to a group of people in a least costly way while maximizing the spread of the product. We generalize the Least Cost Influence Problem by moving away from the marketing setting and by treating the previous product as any piece of "information" that can spread over a social network by adoption. We show that this problem is polynomially solvable over tree networks under some conditions. We provide a Dynamic Programming algorithm to solve this problem and show that it can be interpreted as a greedy algorithm that gives incentives starting with the people that are least influenced by their neighbors, albeit the definition of susceptibility to influence from neighbors is updated throughout the algorithm. We introduce a two dimensional influence model and extend our modeling and solution methods for the product line design problem which involves designing multiple products within the same product line with the objective of appealing to the heterogeneous structure of the market. The first dimension of influence is the affection of individuals from using the same product, and the second dimension is the influence of using a similar product from the same product line which has a lower intensity of influence. We reexamine the Least Cost Influence Problem in the product line setting.Item Essays on Issues in New Product Introduction: Product Rollovers, Information Provision, and Return Policies(2011) Koca, Eylem; Souza, Gilvan C; Xu, Yi; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In this dissertation we study several key issues faced by firms while introducing new products to market. The first essay looks at product rollovers: introduction of a new product generation while phasing out the old one. We study the strategic decision of dual vs. single roll jointly with operational decisions of inventory and pricing during this transitional period. Our results confirm previous findings and uncover the role and interaction of several parameters that were not examined before. In the second essay, we investigate the role of information provision and return policies in the consumer purchasing behavior and on the overall market outcome. We build a novel model of consumer learning, and we attain significant analytical findings without making any distributional assumptions. We then fully study the joint optimization problem analytically under uniform valuations. In the third essay, we study competition in the framework described in the second essay and we identify the potential Nash equilibria and associated conditions. Our findings demonstrate the effect of competition on return policy and information provision decisions and provide insight on some real-life observations.