Browsing by Author "Zheng, Zhiqiang"
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Item Essays on Pricing Strategy and Decentralization Mechanism of Two-sided Sharing Platforms(2022-08-01T05:00:00.000Z) Zhang, Haozhao; Raghunathan, Srinivasan; Zhang, Zhe; Li, Jun; Cavusoglu, Huseyin; Zheng, Zhiqiang; Jacob, VargheseIn recent years, two-sided sharing platforms have emerged and thrived in many industries. For example, Uber and Lyft are two of the largest platforms in the transportation sharing market. Different from a traditional firm, a two-sided sharing platform has indirect control on its supply side, which consists of independent self-schedule agents. In contrast, a traditional firm has direct contract-based employment relationship with its suppliers. Although the difference in the business model, both the two-sided sharing platform and traditional firm face many of the same strategic and operational challenges. In my first two chapters, we investigate the pricing strategies of a two-sided sharing platform when some of the challenges are present. My third chapter is motivated by the emerging blockchain technology, which enables transactions and data storage in a decentralized system. We propose a new mechanism to build a decentralized system for sharing platforms. In Chapter 2, we examine a sharing platform’s quality differentiation strategy through priority matching to serve consumers with heterogeneous willingness-to-pay for match quality. The platforms also differentiate wages to providers that are heterogeneous in their costs and offerings. Different from a traditional firm who only faces strategic behavior from consumers, a platform faces strategic behavior from both consumers and providers. On the demand side, the strategic behavior from consumers enables demand-side cannibalization, which discourages both the platform and the traditional seller to offer priority matching. On the supply side, we find that if providers are non-strategic in accepting service requests, it is optimal for the platform to differentiate wages as well if it offers priority matching and vice versa. However, if providers behave strategically then the platform faces potential supply-side cannibalization, a risk that a traditional seller does not face. In such cases, it is not necessarily optimal for the platform to differentiate wages when it offers priority matching. We find that an increase in the degree of supply-side cannibalization discourages the platform from offering differentiated services (wages) to consumers (providers). On the other hand, an increase in the degree of demand-side cannibalization could encourage the platform to offer differentiated wages despite supply-side cannibalization. In Chapter 3, we study a sharing platform’s decision to offer subscription payment option to consumers, when it faces consumers and providers with uncertainty and heterogeneous frequencies to visit the platform. We find that the sharing economy platform’s incentive to offer subscription payment option is driven by the provider-to-consumer coverage ratio in a market. The platform chooses not to offer subscription option when there are fewer providers and more consumers but offer subscription option when there are more providers and fewer consumers. We also identify the effects of demand and supply variability on the platform’s incentive to offer subscription payment option; when the demand potential variability is greater, the platform prefers to offer a subscription payment option to induce only frequent consumers to subscribe. In contrast, when the supply potential variability is greater, the platform prefers to offer a subscription payment option with fewer number of subscribers or no subscription payment option at all. In addition, compared to when subscription payment option is not offered, when subscription payment option is offered, the infrequent consumers are worse off while the frequent consumers can be better off. On the other hand, providers are always better off and the social welfare is improved. In Chapter 4, we propose a new blockchain mechanism called Proof-of-Merit (PoM) and demonstrate our design in the context of ridesharing. Many businesses such as online retailing platforms, airline companies, and sharing economy platforms need to solve complex problems to determine how their business transactions are to be generated. However, the current popular blockchain mechanisms like Proof-of-Work (PoW) and Proof-of-Stake (PoS) can only be applied to the situations where transactions already exist, but not in the situations where the transactions need to be generated. To address this problem, we develop PoM mechanism that finds the block owner who solves complex problems that generate transactions. We replace the notion of miners in PoW with matchers who provide matching solutions that match riders to drivers in each period. In PoM, a winner is selected from a group of matchers to create the next block based on the quality (merit) of their matching solutions. We demonstrate the viability and nuances of the approach using agent-based simulation. We show how the intrinsic tradeoff between two performance aspects of PoM, efficiency and equity, is affected by a key design parameter called DCP and how PoM is able to achieve a desirable tradeoff by controlling this parameter.Item The Impact of Health Information Sharing on Duplicate Testing(University of Minnesota, 2018-05-30) Ayabakan, S.; Bardhan, Indranil R.; Zheng, Zhiqiang; Kirksey, K.; Bardhan, Indranil R.; Zheng, ZhiqiangRecent healthcare reform has focused on reducing excessive waste in the U.S. healthcare system, with duplicate testing being one of the main culprits. We explore the factors associated with duplicate tests when patients utilize healthcare services from multiple providers, and yet information sharing across these providers is fragmented. We hypothesize that implementation of health information sharing technologies will reduce the duplication rate more for radiology tests compared to laboratory tests, especially when health information sharing technologies are implemented across disparate provider organizations. We utilize a unique panel data set consisting of 39,600 patient visits from 2005 to 2012, across outpatient clinics of 68 hospitals, to test our hypotheses. We apply a quasi-experimental approach to investigate the impact of health information sharing technologies on the duplicate testing rate. Our results indicate that usage of information sharing technologies across health organizations is associated with lower duplication rates, and the extent of reduction in duplicate tests is more pronounced among radiology tests compared to laboratory tests. Our results support the need for implementation of health information exchanges as a potential solution to reduce the incidence of duplicate tests.Item Latent Growth Modeling for Information Systems: Theoretical Extensions and Practical ApplicationsZheng, Zhiqiang; Pavlou, Paul A.; Gu, Bin; 80691095 (Zheng, Z)This paper presents and extends Latent Growth Modeling (LGM) as a complementary method for analyzing longitudinal data, modeling the process of change over time, testing time-centric hypotheses, and building longitudinal theories. We first describe the basic tenets of LGM and offer guidelines for applying LGM to Information Systems (IS) research, specifically how to pose research questions that focus on change over time and how to implement LGM models to test time-centric hypotheses. Second and more important, we theoretically extend LGM by proposing a model validation criterion, namely "d-separation," to evaluate why and when LGM works and test its fundamental properties and assumptions. Our d-separation criterion does not rely on any distributional assumptions of the data; it is grounded in the fundamental assumption of the theory of conditional independence. Third, we conduct extensive simulations to examine a multitude of factors that affect LGM performance. Finally, as a practical application, we apply LGM to model the relationship between word-of-mouth communication (online product reviews) and book sales over time with longitudinal 26-week data from Amazon. The paper concludes by discussing the implications of LGM for helping IS researchers develop and test longitudinal theories.